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04 University of Plymouth Research Theses 01 Research Theses Main Collection
2004 DEVELOPMENT OF WATER RESOURCES IN BAHRAIN: A COMBINED APPROACH OF SUPPLY-DEMAND ANALYSIS
AL-NOAIMI, MUBARAK AMAN http://hdl.handle.net/10026.1/1951
University of Plymouth
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APPROACH OF SUPPLY - DEMAND ANALYSIS
VOLUME TWO
by
MUBARAK AMAN AL-NOAIMl
A thesis submitted to the University of Plymouth in partial fulfillment for the degree of
DOCTOR OF PHU.OSOPHY
School of Earth, Ocean, and Environmental Sciences Faculty of Science
2004 University of Plymouth Library Item no. MOo-i fc,te ferT2Lj+. TABLE OF CONTENTS
VOLUME TWO
CHAPTER SIX 379 WATER USE ANALYSIS
6.1 Data Sources and Types 379 6.1.1 Water Use Data 380 6.1.2 Demographic Data 381 6.1.3 Other Related Information 386 6.2 Trends in Water Use 389 6.3 Analysis ofWater Use Patterns 400 6.3.1 Municipal Water Use 401 6.3.2 Agricultural Water Use 425 6.3.3 Industrial Water Use 435
CHAPTER SEVEN 443 WATER USE SURVEYS
7.1 Research Questions 443 7.2 Sampling Design and Procedures 447 7 .3 Methods of Data Collection 463 7.4 Analysis of Surveys Data 481 7.4.! Municipal Water Use Survey 481 7.4.2 Agricultural Water Use Survey 501 7.4.3 Industrial Water Use Survey 508
CHAPTER EIGHT 523 WATER DEMAND MODELLING
8.1 Municipal Water Demand 526 8.1.1 Residential Water Demand 527 8.1.2 Non-residential Water Demand 545 8.1.3 Aggregate Municipal Water Demand 552 8.2 Agricultural Water Demand 567 8.3 Industrial Water Demand 576 CHAPTER NINE 586 SUPPLY-DEMAND ANALYSIS AND POLICY OPTIONS
9.1 Supply-Demand Analysis 586 9.1.1 Water Supply 586 9.1.2 Water Demand 588 9.2 Water Balance and Policy Options 606 9.2.1 Water Balance 607 9.2.2 Alternative Policy Scenarios 609
CONCLUSIONS AND RECOMMENDATIONS 616
APPENDICES
Appendix A Meteorological Data 631 Appendix B Hydrogeological Data 640 Appendix C Hydrochemical Data 648 Appendix D Pre-sampling Data and Survey Questionnaires 675 Appendix E Residual Plots 700
REFERENCES 709 LIST OF TABLES
VOLUME TWO
Table
6.1 Growth in population, housing units, buildings, establishments, and —382 population growth rates 1941 - 2001 6.2 Actual population for 2001 and population forecasts 2002 - 2010 384 6.3 Historical records on groundwater pumpage by aquifers and user sectors 391 1952-2000 6.4 Discharge history of land and submarine springs 1924 - 2000 392 6.5 Estimates for treated sewage efiQuent utilisation 1999 - 2000 394 6.6 Water use by categories of use, source and their percentage shares 1990 396 6.7 Water use by categories of use, source and their percentage shares 2000 397 6.8 Percentage contributions of total water use by source of supply in 1990 and 399 2000 6.9 Annual total water sales of municipal water 1993 - 2001 403 6.10 Percentage distribution of the municipal water uses by source 2001 406 6.11 Pre-1992 water tariff for domestic and non-domestic water uses 407 6.12 The current water tariff for domestic and non-domestic water uses 407 6.13 Components of municipal water demand and daily per capita consumption 415 6.14 Average daily water consumption over the months and their corresponding 426 peak factors 1985 - 2000 6.15 Agricultural water consumption by source and their percentage distribution 430 2001 6.16 Trends in total cultivated areas and irrigation water use 1953 - 2000 431 6.17 Irrigated areas in hectares under different crop patterns and their percentage 434 distribution 1974 - 2000 6.18 The industrial estates along with their total areas 437 6.19 Industrial employment based on population censuses of 1981, 1991 and 439 2001 6.20 A breakdown of industrial water use by source and percentage distribution 441 2001
ni 7.1 Descriptive statistics on raw and log transformed consumption variables of 451 a random sample of municipal water customers, with n = 4,006, together with raw sample data of agricultural water users, with /i = 143 7.2 Variable definitions and their percentage missing values from the municipal 475 survey returns 7.3 Variable definitions and their percentage missing values from the 478 agricultural survey returns 7.4 Variable definitions and their percentage missing values from the industrial 479 survey returns 7.5 Summary statistics on average water use and consumption variability for 484 the residential and aggregate municipal water uses 7.6 Wilcoxon signed-ranks lest for differences between summer and winter 486 averages residential and municipal water consumptions 7.7 Sununary statistics for the in-door water use activities 487 7.8 Summary statistics for the out-door water use activities 489 7.9 A breakdown of components of the residential daily per capita water use 491 7.10 Summary statistics on variables related to employment, number of public 493 users, and number of customers within the large consumers group 7.11 Average and per capita water uses wathin the large consumers group 495 7.12 Summary statistics on the socio-economic variables and households fixtures 497 7.13 Statistics on ownership of water-using appliances and swrimming pools, and 500 connection to sewers 7.14 Frequencies on the consumption and farm variables, and irrigation water 502 requirements 7.15 Wilcoxon signed-ranks test for differences between summer and winter 503 irrigation demands 7.16 The distribution of the cultivated area according to the cropping patterns 505 7.17 Summary statistics on irrigation frequencies, number of pumping hours, and 507 pump capacities 7.18 Distribution of areas under different methods of irrigation and their 509 percentage of total irrigated area 7.19 Summary statistics of the key variables from the industrial water use survey 511 7.20 Distribution of the industrial firms surveyed according to their industrial 514 activities based on the Standard Industrial Classification (SIC) 7.21 Distribution of average water use, per employee water use, water use per 515 unit of output, and water use per factory floor area by industrial activity 7.22 Average water uses by internal type of use in manufacturing and their 518 percentage distribution
IV 8.1 Correlation matrices showing the simple linear relationship between 528 average monthly consumption and variables influencing residential water use as well as the simple correlations between each per of these variables 8.2 Ordinary least squares estimations of residential water demand functions 532 8.3 Average monthly residential water consumption by household size and 535 family monthly income 8.4 Average monthly consumption by type of dweUing, household size, and 536 garden area 8.5 Correlation matrices showing the signs and strengths of the simple linear 538 relationship between per capita residential water use and variables potentially influencing this use as well as correlations between each pair of these variables 8.6 Per capita daily residential water use by household size and per capita 541 income 8.7 Ordinary least squares estimations of summer and winter residential water 543 demand functions 8.8 Correlation matrix showing the simple linear correlations between each pair 547 of variables of non-residential water use 8.9 Cross tabulation showing the simple correlations between average monthly 553 consumption and variables influencing aggregate municipal water use as well asthe cqrre!at[ons betw^een each pair of these variables 8.10 Derived demand functions of aggregate municipal water use 555 8.11 Cross tabulation showing the simple correlations between per capita 558 municipal water consumption and potential explanatory variables along with the correlations between each pair of these variables 8.12 Derived demand functions of aggregate municipal water use with the per 559 capita daily consumption as dependent variable 8.13 Derived demand functions for summer and winter municipal water uses 564 8.14 Cross tabulations of average agricultural water use and potential farm and 568 irrigation explanatory variables along with their means and standard deviation values 8 .15 Derived log-linear estimating equations of agricultural water use 572 8.16 Derived summer and winter demand models of agricultural water use 575 8.17 Intercorrelation for average water consumption against three potential 577 predictors for industrial water use - raw data 8.18 Intercorrelation for average water consumption against three potential 578 predictors for industrial water use - log transformed data 8.19 Derived linear and log-linear demand estimating equations of industrial 580 water use 8.20 Muhiple regression of average industrial water use, number of persons 585 employed and SIC Categories
9.1 Current and potentially available water supplies 2001 - 2010 587 9.2 Population forecast, daily per capita water use, and residential water use 590 forecast 2001 -2010 9.3 Residential water use forecasts adjusted for VIP unbilled consumption 2001 592 -2010 9.4 EsUmates for aggregate municipal water demand 2001 - 2010 595 9.5 Number of connections, water use per connection, population per 596 connection, and municipal water use forecasts per connection 2001 - 2010 9.6 Comparison of municipal water use forecast with other forecast 2001 - 2005 597 9.7 Agricultural water demand 2001 - 2010 599 9.8 Industrial water demand 2001 - 2010 602 9.9 Results of water use forecasts from analytical and improved trend 604 procedures for the year 2001 compared with the actual water use 9.10 Water supply and demand and water balance for years 2001, 2005, and 607 2010 9.11 Projected water budgets based on three alternative development scenarios 611 2001 -2010 9.12 Water supply and demand by source and category of use for three 615 alternative scenarios for years 2005 and 2010
VI LIST OF FIGURES
VOLUME TWO
Figure
6.1 Actuar population for census years 1971, I98I, 1991, and 2001, and 385 population forecasts 2002 - 2011 6.2 Trend in number of building permits 1981 - 2001 387 6.3 Annual production of desalinated water 1980 - 2001 393 6.4 Total number of connections to the distribution network, actual 1980 - 404 2001, projected 2002-2010 6.5 Growth in average daily per capita consumption related to population 409 growth 1980-2001 6.6 Desalinated water production and groundwater withdrawal 1980 - 2001 412 6.7 Annual variations in the unaccounted-for water 1993 - 2001 413 6.8 Average month-wise daily water consumption 1985 - 2001 417 6.9 Average, maximum, and minimum daily consumption 1985 - 2001 418 6. J 0 Average daily consumption during the months of January and July 2001 420 6.11 Average monthly consumption (1985 - 2001) with the corresponding 421 monthly averages temperature and relative humidity, and total monthly rainfall (1986-2001) 6.12 Peak and average demand components of the municipal water use 1985 - 423 2001 6.13 Monthly average peak factors 1985 - 2001 424
7.1 Conceptual model showing factors hypothesised to influence water use in 444 the major water use activities 7.2 Histogram of frequency distribution of random sample data of municipal 450 consumption - raw veuiable 7.3 Histogram of frequency distribution on logged consumption variable of a 453 random sample of municipal water users 7.4 Frequency distribution of agricultural water use sample data 459 7.5 Number of occupants per household unit based on data from census years 498 7.6 Percentage of industrial water use by SIC categories of users 516
9.1 Projected sectoral and total water demand 2001 - 2010 603
vn 9.2 Water supply and demand and net annual deficit for 2001, 2005, and 2010 608 9.3 Projected deficits and surpluses for three alternative scenarios 2001 - 2010 612
VIII LIST OF APPENDICES
Appendix
Appendix A Meteorological Data
Table A-1 Monthly and means rainfall at Bahrain International Airport 1902 - 632 1997 Table A-2 Monthly means relative humidity at Bahrain International Airport 1963 635 -1997 Table A-3 Monthly means wind speed at Bahrain International Airport 1977 - 637 1997 Table A-4 Mean monthly sunshine hours at Bahrain International Airport 1968 - 638 1997
Appendix B Hydrogeological Data
Text B-1 Information contained in the hydrogeological database 641 Table B-1 Piezometric level data of the Alat Limestone aquifer for years 1987 642 and 1997 and water level difference Table B-2 Piezomelric level data of the Khobar aquifer for years 1987 and 1997 644 and water level difference Table B-3 Piezometric level data of the Rus - Umm Er Radhuma aquifer for years 646 1987 and 1997 and water level difference
Appendix C Hydrochemical Data
Table C-1 Chemical analysis data of groundwater fi-om the Alat Limestone 649 aquifer 1998/99 Table C-2 Chemical analysis data of groundwater from the Khobar aquifer 650 1999/2000 Table C-3 Salinity changes of groundwater from the Khobar aquifer 1991/92 - 658 1999/00 Table C-4 Chemical analysis data of groundwater from the Rus - Umm Er 662 Radhuma aquifer 1983 - 2000 Table C-5 Saturation indices of water samples from the Alat Limestone aquifer 664 1998/99 Table C-6 Saturation indices of randomly selected water samples from the 665 Khobar aquifer 1999/2000 Table C-7 Saturation indices of water samples from the Rus - Umm Er Raduma 668 aquifer 1983 -2000 Table C-8 Chemical analysis data of water from Bahrain land springs 1998/99 671 ix Figure C-1 Dammam aquifers sampling points 672 Figure C-2 Bahrain land springs sampling points 673 Figure C-3 The Rus - Umm Er Radhuma aquifer sampling points 674
Appendix D Pre-samplingData and Survey Questionnaires
Table D-1 Municipal consumption of random sample data 676 Table D-2 Agricultural consumption data of sample farms 684 Text D-1 Survey questionnaire for. municipal water, use 689 Text D-2 Survey questionnaire for agricultural water use 693 Text D-3 Survey questionnaire for industrial water use 696 Text D-4 Model covering letter for respondents of the survey questionnaires 699
Appendix E Residual Plots
Figure E-1 Normal P-P and scatter plots of standarised residuals for model 2A 701 Figure E-2 Normal P-P and scatter plots of standarised residuals for per capita 702 residential water demand model Figure E-3 Normal P-P and scatter plots of standarised residuals for mode! 2B 703 Figure E-4 Normal P-P and scatter plots of standarised residuals for model 2C 704 Figure E-5 Normal P-P and scatter plots of standarised residuals for an improved 705 version of Model 2C Figure E-6 Normal P-P and scatter plots of standarised residuals for agriculture 706 Model 1 Figure E-7 Normal P-P and scatter plots of standarised residuals for industrial 707 Model 3 Figure E-8 Normal P-P and scatter plots of standarised residuals for industrial 708 Model 4 CHAPTER SIX WATER USE ANALYSIS
The analysis of water use patterns is a prerequisite for water demand forecasting. Without a clear understanding of the current and historical trends of water use patterns, it will be difBcult to produce reliable estimates of future water demands. In essence, the primary objectives of analyses of current and past trends of water use are to estimate the average water use and to determine variations in water consumption levels, both seasonal and non- seasonal, over time. This chapter briefly evaluates the various factors and variables that might have influence on the water uses, and assesses at some length the trends in water use. It concludes by providing a critical macro-component analysis of the water use patterns.
6.1 Data Sources and Types
Data and information needed for assessing current water use patterns and forecasting of future water demand are normally of difiFerent types and quality, and can be obtained from a variety of sources. Admittedly. sufBcient and reliable data on water use and other related variables are essential for water use analysis and demand forecasting studies; as
Boland ei al. (1983) state, the more accurate and complete the data used, the more reliable the resulting analysis and forecast. These data include current and historical trends of water use, per capita consumption, per employee water use, and per unit agricultural area consumptive use. Other important variables related to water use and demand forecasting include, but are not limited to, demographic, socio-economic, technological, and weather variables. Examples of such variables are current and projected population, personal income, current and future levels of employment, industrial production, levels of
379 commercial and industrial aaivities, temperature and amount of rainfall. In addition, some potentially usefiil variables embrace the number of connections to the distribution system and the number of building permits.
6.1.1 Water Use Data
Most of the^data on water use in the study area can be obtained from various governmental- agencies responsible for water resources supply and management. Water use data commonly are not available from individual utilities and private water consumers, except for BAPCO and possibly other large private utilities. Estimates on past trends of groundwater abstraction, including spring discharges, are available from several unpublished reports. The Bahrain Water Resources Directorate (WRD) has a reasonable computerised database for groundwater withdrawal data storage and retrieval in the form of annual totals since 1985. These data are classified by categories of use and aquifer source, and are supplemented by groundwater withdrawal estimates from the public supply pumping stations and BAPCO municipal and industrial wells to form total groundwater pumpage.
Sufficient historical data on production of desalinated water, groundwater pumpage from public supply wells, and blended water made available for distribution are readily obtainable on a monthly and annual basis from the statistical books and water statistic summaries, produced approximately every three years by the Water Transmission
Directorate (WTD) and Water Distribution Directorate (WDD) of the Ministry of
Electricity and Water (MEW). More importantly, the Consumer Affairs Directorate
(CAD) of the same ministry has a fijlly computerised database on water sales and customer billing records, normally produced on an annual basis or at desired intervals and
380 formats, although this billing system has not proved to be ideal for analysis of water consumption. Information on distribution losses and unaccounted-for water is compiled by the WDD on a monthly basis and totalled over each year as a percentage from the total piped supply. The sewerage and agriculture authorities maintain records on amounts of
TSE re-used for agriculture in governmental and private farms for the last 17 years.
Estimates of treated wastewater used for landscape irrigation could be obtained from the
CMC or be estimated from the total amount of treated wastewater re-used for agriculture.
Measured estimates for agricultural drainage water discharge and the amount of this source re-used for irrigation are available for year 1990; but less reliable estimates are made for the purpose of this research for years 2000 and 2001.
6.1.2 Demographic Data
The only source of population statistics in the country is the general census of population prepared by the Central Statistics Organisation (CSO). This census is conducted approximately every ten years; the latest being completed in May 2001. The last two censuses (1991 and 2001) include demographic information on the number of buildings and establishments, whilst the earlier censuses were limited to population characteristics and number of housing units. The results of these censuses along with their associated population growth rates are summarised in Table 6.1.
The CSO population figures show an increase in the total population from 89,970 inhabitants in 1941 to 650,604 during 2001, an increase by more than seven-fold. The population growth rate was 2.2% from 1941 to 1950, increased to 3.0% between 1950 and
1959, and to 4.3% from 1959 to 1965. This increasing trend had been reversed between
1965 and 1971; a period in which a population growth rate of 2.8% was reported. Between
381 Table 6,1 Growth in population, housing units, buildings, establishments, and population growth rates 1941-2001
Census 1941 1950 1959 1965 1971 1981 1991 2001
Population 89,970 109,650 143,135 182,203 216,078 350,798 508,037 650,604
Housing units 14,382 16,274 22,030 26,300 31,045 52,810 , 83.470 105.686
Buildings* — — — — — — .81,552 105,603
Establishments* — — — — — — 20,449 26,923
Population growth 2.2 ^ 3.0 4.3 2.8 5.0 3.6 2.7 rate**
Source of data, CSO (2002). • Buildings and establishment census began in 1991 •* In percentage between census years.
382 1971 and 1981. however, the Non-Bahraini population grew by 11.46%, causing the overall rate of population growth to increase considerably to 5.0%. This reflects a very
high economic growth over the period 1976 - 1984, as a result of the oil boom that led to
more jobs and an influx of expatriates. During the same decade, the growth rate for the
Bahraini population was 2.95% (CSO, 2002). The annual percentage increase in
population was 3.6% during the decade 1981 - 1991, declined to 2.7 % during the 1991 -
2001 decade. The overall annual average population rate of growth for the study area
between 1941 and 2001 is computed at 3.9 %.
The CSO also publishes statistical abstracts on a yearly basis that provide population
projections and demographic charaaeristics for the years between consecutive censuses.
These projections are continually revised and relatively different sets of projections are
prepared in each statistical abstract. Population census results for 2001 proved that the
CSO population projections were seriously inaccurate. Projections for the decade after the
2001 census are yet to be published. Consequentiy, for the purpose of this study, three
fresh population projections have been prepared, with the CSO census results and
considerations remaining the basis for these projections. Assumptions used for preparing
these projections are based on three scenarios: population low growth, population medium
growth, and population high growth.
The low projection assumes a constant growth rate of 1.5% per annum to 2005, and a
lower growth rate of 1.1% throughout the remaining forecasting period. The medium
projection has been derived assuming that the annual growth rate of 2.7% based on the
2001 census results remains constant from 2002 to 2005. while a lower growth rate of
2.5% is assumed from 2006 up to the end of the plaiming period. The assumptions
383 underlying the high projection imply a continuing growth rate of 4.3% from 2002 to 2005 and a lower growth rate of 3 .9% thereafter.
Population projections through 2010 are listed in Table 6.2 and are shown graphed in
Figure 6.1 along with the past years' censuses results. These projections are calculated on the basis of the above assumptions using the geometric growth model: Pt+ti = Ft "j^^" where:
is the population at time (t+n)
Pt is the population at the present time
r is the rate of growth, and
n is the length of time for which the projection is to be made.
Tabic 6.2 Actual population for 2001 and population forecasts 2002 - 2010
Population forecasts Year Low growth Medium growth High growth 2001 650,604*
2002 660.364 668,170 678,580
2003 670,296 686,211 707,759
2004 680,323 704,739 738.193
2005 690,528 723,767 769,935
2006 698,124 741.862 799,962
2007 705,804 760,408 831,161
2008 713,567 779,418 863,576
2009 721,416 798,903 897.256
2010 729,352 818,876 932,249
* Based on the actual 2001 census. Population forecast figures are rounded to the nearest decimal points.
384 Figure 6.1 Actual population for census years 1971, 1981, 1991, and 2001 and population forecasts 2001 -2011
1.200.000
1.000.000 Low growth
' Medium growth
800,000 Hi^ growth
g 600.000 1
400.000
200.000
1971 1981 1991 2001 2011
Years
385 Demographic trends of the medium forecast show the study area population increasing
from 650,604 in 200! to 723,767 in 2005, and to 818.876 by 2010, representing rises of
11% and 26%. The low and high growths project the total population to increase from
2001 to 2005 by 6% and 18%, respectively, to 690,528 and 769,935 inhabitants. The
population for the low and high forecasts in 2010 is projected to be 729,352 and 932,249,
respectively, which are equivalent to growths by -12% and 43% over the base year
population.
Table 6.1 also presents demographic data on the number of housing units and
establishments. The data show that the number of housing units gradually increased from
14,382 units in the 1941 census to 105,686 units during 2001, a 635% increase; reflecting
a direct response to population growth. The average domestic water consumption per
household for_2001 (derived by dividing domestic water use by the number of household
units) was 815.9 m^. Building units were 81,552 in 1991 increased to 105,603 in 2001, a
30% increase. The trend in the number of building permits issued from 1981 to 2001 is
shown diagrammatically in Figure 6.2. In 1981, the number of building permits issued
was 6,292 increased to 6,907 in 1990 and to 7,280 in the year 2001, but showed no
discernible trend during the intervening period. The same table also shows that the
number of establishments rose by 32% from 20,449 in 1991 to 26,923 in 2001.
6.1.3 Other Related Information
As mentioned earlier, other information that can be useful in the analysis of water use and
predicting water demand includes, amongst others: socio-economic, weather, and
technological and legislative variables. Data on levels of economic growth, gross
domestic product (GDP), and value added to the economy by the various economic sectors
386 Figure 6.2 Trend in the number of building permits 1981 - 2001
10000
9000 ^
8000
I 7000
S: 6000 H
3 5000
4000
3000
2000 Source of data: Ministry of Municipalities and Agriculture 1000
0 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 199? 2000 2001
Years
387 are available from the Directorate of Economic Planning of the Ministry of Finance and
National Economy (MFNE). The MEW provides valuable information on water distribution system management and tariff structures. Time series data on the number of connections to the distribution system are available from the same authority. Information regarding family income, type of consumers, and technological variables including household amenities are - derived-from the questionnaire-survey returns that will be- discussed in the chapter that follows. Statistics on employment trends and manpower characteristics are normally obtainable from the statistical abstracts. Unfortunately, however, information on structural changes, the degree of industrial development at sub-
sector level, and the direction of technological progress in industry was not made available to this study. In Bahrain, such information, if available, is commonly not accessible to an individual researcher and, if given, is scant and in most cases its reliability is doubtful. In addition, there are also insufficient systematic statistics on the future manufacturing employment.
Climatic parameters are useful indicators for explaining seasonal variability in water use.
Changes in climatological conditions, particularly in temperature and precipitation are
normally responsible for fluctuations in water usage. Detailed discussion of the climate characteristics in the study area is presented in Chapter Two. The main source of data on total agricultural areas, both irrigated and non-irrigated, the number of farm units, agricultural output and crop patterns is the series of statistical reports published yearly by the Agricultural Economic Section (AES) of the Ministry of Municipalities and
Agriculture (MMA). Information on land use planning and zoning is limited, and is normally furnished by the Natural Plarming Directorate of the MMA. It should be emphasised here that a proper national land use and zoning policy hardly exists in Bahrain
388 , except for some areas designated for industry and land set aside for military purposes and agricultural development. Section 2.5 of Chapter Two provided a full discussion of the changes in land use patterns from 1956 to 1988 as well as land use projections for 2001.
An update on land use variation and zoning policy is yet to be undertaken.
6.2 Trends in Water Use
Time series data generally covering the period from 1980 to 2001 were made available for trend analysis, although the available records on groundwater pumpage and spring discharges date back to 1952 and 1924, respectively. Total water use in the study area represents total pumpage from the aquifer systems for agriculture (including livestock and poultry), municipal, commercial and industrial uses, and water flows or withdrawn from natural springs for agricultural and rural uses. It is also made up of blended water delivered from the public supply facilities for municipal purposes, TSE supplied to agricultural areas and landscaping, and agricultural drainage water re-used for irrigation.
The total amount of groundwater withdrawn during 2000 was about 262.8 Mm^. Of that total about 218.8 Mm^ (almost 83.3%) was withdrawn from the Dammam - Neogene aquifer, and 44 Mm^ or 16.7% from the Rus - Umm Er Radhuma aquifer. Agriculture was by far the largest consumer of groundwater from the Dammam - Neogene aquifer at
73.1% (159.9 Mm"*). The municipal sector was the next largest user, claiming about
24.1%, or 52.7 Mm^. Industrial needs had the smallest share of 6.2 Mm^ (2.8%). The greatest volume of groundwater withdrawn from the Rus - Umm Er Radhuma aquifer was for domestic purposes at 31 Mm^, or nearly 70.4% of the total groundwater pumped from this aquifer. About 16.6% of this total (7.3 Mm"*) was used to irrigate the unproductive agriculture: football fields, golf courses, and racecourses. Groundwater from the Rus -
389 Umm Er Radhuma aquifer was the source of about 13 % of the self-supplied industry, accounting for 5.7 Mm"'.
The 2000 groundwater withdrawal estimate was about 303% more than the 65.2 Mm^ withdrawn in 1952. Total groundwater withdrawn for all uses in 2000 was 43.3 Mm^ mprejhan in 1990, representing a net increase of just under 20%, but was about 25.1 Mm^ or 8.7% less than during 1997. To a large extent, the latter result reflects the decline in municipal abstraction from the Dammam aquifer from 65.2 Mm^ to 52.7 Mm^ in response to the increase in desalination capacity following the commissioning of the Hidd Power and Desalination Plant during the third quarter of 2000. Historic trends of annual groundwater withdrawal by user sectors and aquifer source for the period 1952 - 2000 are given in Table 6.3. The discharge history of the land and oflfshore springs is discussed in some detail in Chapter One, pages from 34 to 37, and is presented here as Table 6.4. The table shows that the total springs discharge was 54.8 Mm"* in 1952, steadily decreased to
5 Mm^ in 1990, and to about 1.2 Mm^ in 2000 (estimated by the author).
The annual growth in production from the desalination plants during the period 1980 -
2001 is illustrated in Figure 6.3. The installed capacity of desalination increased dramatically from 3.3 Mm^ during 1980 to 93.9 Mm"' during 2001, an increase by more than 27 times. By 1985, the total desalination capacity reached about 42.9 Mm^. Between
1990 and 2000, more desalinated water continued to be produced from 54.2 to 89.2 Mm^, an increase of more than 64%. The fluctuations in desalination output during the 1990s were closely related to the unstable production from the Ad Dur Desalination Plant.
The total amount of TSE supplied for agriculture and landscape irrigation rose by 87%
390 Table 6.3 Historical records on groundwater pumpage by aquifers and user sectors 1952 - 2000
Groundwater abstraction in million cubic metres (Mm )
Aquifer system/categories 1952 1966 1971 1979 1985 1990 1993 1997 2000
Dammam - Neogene aquifer
Agricultural uses 48.0 89.0 96.0 90 0 99.9 124.7 139.0 178.3 159.9
Municipal uses 5.1 16.5 209 41.1 47.4 56.8 61.1 65.2 52.7
Industrial uses 101 8.2 8.2 7.0 4.9 5.9 6.0 7.4 6.2
Rus - Umm Er Radhuma • aquifer
Agricultural uses — — — 1.3* 2.5 5.0 6.1 6.4 7.3
Municipal uses — — — — 25.7 23.1 27.3 26.9 31.0
Industrial uses 2.0 2.0 2.0 8.0 4.6 4.0 3.5 3.7 5.7
Total withdrawal 65.2 115.7 127.1 147.4 185.0 219.5 243.0 287.9 262.8 1 Notes: (1 ] 1952 data are after Bapco, 1966 after Sutcliffe, 1971 from Italconsult, 1979 are after GDC. 1985 to 2000 are data obtained from WRD files, data on agriculture and industrial abstraclion from the Rus Umm Er Radhuma aquifer for 2000 are estimates by the present study. ' [2] 'Estimated by Al-Noaimi 1993. [3] A dash indicates zero withdrawal.
391 Table 6.4 Discharge history of land and submarine springs 1924 - 2000
History of spring discharges in million cubic metres (Mm ) i Spring category 1924 1952 1966 1971* 1979 1990 2000
Land springs 70.0 42.0 28.0 '23.0 8.1 1.5 0.4
Submarine springs 13.0 12.8 9.6 6.0 6.6 ^3.5 0.8
Total discharge 83.0 54.8 37.6 29.0 14.7 5.0 1.2
Notes: [ 1 ] 1924 data ofter Heim; 1952 date based on Bapco surveys; 1966 data are after Suicliffe; 1971 figures are from llalconsult, 1979 after GDC, 1990 based on Al-Noaimi prediction, and 1997 figures are recent rough estimates. I [2] 1924 land springs discharge estimates after correction by Sutcliffe. [3] 1971 land springs discharge based on GDC adjusUnent. [4] 1924 submarine springs discharge based on Sutcliffe back extrapolation and correction by GDC. ^ [5] 1952 submarine springs discharge is based on GDC adjustment for diffused discharge.
392 Figure 6.3 Annual production of desalinated water 1980 - 2001
n n
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Years
393 from 8 Mm^ in 1990 to 15 Mm^ in 2000. During 1997, this amount was estimated at
12.8 Mm^, out of which 10.5 Mm^ was for agriculture and 2.3 Mm^ for landscaping. The vast majority (nearly 75%) or 11.2 Mm^ of TSE used in 2000 was for irrigating crops and the remaining quantity of 3.8 Mm^ or 25%, went for landscape irrigation. Table 6.5 gives
estimates for TSE utilisation for the period 1990 - 2000.
Table 6.5 Estimates for treated sewage effluent utilisation (Mm^) 1990 - 2000
Year Agriculture Landscaping Total
1990 6^9 n 8!0
1997 10.5 2.3 12.8
2000 11.2 3.8 15.0
Notes: (Ij 1990 estimates are after Al-Noaimi (1993). 121 Figures are rounded to the nearest decimal point.
Available estimates indicated that less agricultural drainage water was re-used for
irrigation during 2000 than during 1990; falling from 3 Mm^ in 1990 to 0.3 Mm^ in 2000,
a 92% decrease. Although this should be viewed as a rough estimate, it is consistent with
the further deterioration in the agricultural drainage water salinity as clearly demonstrated
by Table 5 .14 of Chapter Five.
The amount of water consumed during 1990 for the three principal categories of use
totalled about 270.1 Mm^; of which 146.9 Mm^ (54.4%) was used for agriculture. About
86.5 % (127.1 Mm^) of the water used for agriculture was groundwater from the Dammam
aquifers, and only 5.4% was from TSE. Total water use for municipal purposes in 1990
amounted to 112 Mm**, a share of 41.5% of the total amount used during that year. The
quantity provided from the desalination plants accounted for 54.2 Mm"* of this total, or
394 ahnost 48.4%, while 57.8 Mm^, or 51.6% derived from the groundwater sources. This calculation is based on the assumption that the 6.5 Mm^ of blended water disaggregated into industrial and agricultural uses is deducted from the total municipal use to avoid double calculation; no adjustment has been made to the amount of groundwater withdrawn from the Dammam aquifers for private municipal use since these figures were provided as total groundwaterabstraction with no information on plant availabilities.
Industrial users in 1990 received about 11.2 Mm^, a percentage share of 4.1% from total water used. Of that quantity, 8.5 Mm^, or nearly 75.9% came from groundwater sources, either desalinated, or directly used. Industrial demand on metered public supply amounted
to 2.7 Mm^ or 24.1%, on the assumption that blended water used for industrial and
commercial purposes (non-domestic use) in 1990 represents about 8% of the total piped
supplies (amounting to 85 Mm*' in that year). The resultant figure of 6.8 Mm^ is
distributed at 40% (2.7 Mm^) for industrial use, and 60% (4.1 Mm^) for commercial use.
No disaggregation for the commercial use is made. The water uses by categories of use
and source, and their percentage shares during 1990 are summarised in Table 6.6.
In 2000, a total of 347.4 Mm^ was used for agriculture, municipal, and industrial purposes
(Table 6.7). Just over 53.5 % of this quantity, or 186 Mm^ was used for agriculture. Self-
supplied agriculture consumed about 158.9 Mm^; the corresponding percentage is 85.4%.
Reclaimed sewage wastewater accounted for 15 Mm"*, or a share of almost 8.1%.
Municipal water used during 2000 was estimated at 144.3 Mm^, or 41.5% of the total
water use. About 61.8% or 89.2 Mm^ of the total amount used for municipal purposes
was from the desalination plants. Groundwater withdrawals for blending, direct use, and
desalination at Awali accounted for 30.8%, or 44.4 Mm^ (after deducting 10.5 Mm"* from
395 Table 6.6 Water use by categories of use, source and their percentage shares 1990 (Modified from Al-Noaimi, 1993)
Sectors consumption in million cubic metres (Mm) Water source Agriculture Municipal Industrial
Dammam groundwater 127.1 18.9 2.1
Rus - Umm Er Radhuma groundwater 106^ 05
Sea water desaUnation 41.8 —
Desalinated groundwater from Dammam 4.0 2.4
Desalinated groundwater from Rus - UER 5.0 12.4 3.5
Dammam groundwater used for blending 308
Blended water 3.8 (6.5) 2.7
Treated sewage effluent 8.0 —-
Agricultural drainage water 3.0
Total (Mm^) 146.9 112 11.2
• Grand total (Mm^) 2701
Percentage sector share (%) 54.4 41.5 4.1
Notes: [I] A dash indicates no use from a particular source. [2] Bracketed figure represents blended water used for the agriculture and industrial purposes, and is deducted from the total municipal use to avoid double calculation. [3] Represents the quantity of the reject water at Ras Abu-Jarjur; the balance of the total abstraction from the aquifer is the quantity desalinated (12.4 Mm^).
396 Table 6.7 Water use by categories of use, source and their percentage shares 2000
Sectors consumption in milhon cubic metres; (Mm ) Water source Agriculture Municipal Industrial
Dammam groundwater 158.9 13.3 2.0
Rus - Umm Er Radhuma groundwater 107^ 1.3
Seawater desalination 68.9
Desalinated groundwater from Dammam 3.7 3.4
Desalinated groundwater from Rus - UER 7.3 203 4.4
Dammam groundwater used for blending 37.9
Blended water 4.5 (10.5) 6.0
Treated sewage effluent 15.0 ---- —
Agricultural drainage water 03
Total (Mm^) 186.0 144.3 17.1
Grand total (Mn?) 347.4
Percentage sector share (%) 53.5 41.5 4.9 1 Notes; [ 1 ] A dash indicates no use from a particular source. [2] Bracketed figureTcpresenls blended water used for the agricultural and industrial purposes, and is deducted from the total municipal use to avoid double calculation. [3] Represents the reject water; the balance of the total abstraclion from the aquifer is the quantity desalinated and actually used (equal 20.3 Mm^). [4] Percentage may not add to totals because of rounding.
397 the total blended water). The balance of 7.4%, or 10.7 Mm"* represents, the reject groundwater after desalination at Ras Abu-Jarjur Reverse Osmosis Desalination Plant.
Once again, the assumption here is that the 10.5 Mm^ of blended water used for industrial and agricultural purposes is deducted from the total municipal use to avoid double counting, and no adjustment or separation has been made for the amounts of groundwater withdrawn for private desalination purposes.
Industrial users in 2000 consumed about 17.1 Mm^, or just over 4.9% of the total water use. Total withdrawals for self-supplied industry in 2000 were about 11.1 Mm^, representing 64.9% of the total industrial demand. Industrial demand through service connection from the public water system made the balance of 35.1%, or 6 Mm^, assuming that blended water used for industrial and commercial purposes (non-domestic use) in the year 2000 constituted about 10.5% of the total blended supply of 127.1 Mm^, and that the derived figure of 13.3 Mm^ is distributed at 45% (6 Mm^) and 55% (7.3 Mm^) between industrial and commercial uses, respectively. As previously stated, the amount of water used for commercial purposes is included in the municipal category.
A comparative analysis of the figures presented in Tables 6.6 and 6.7 suggests that although more water was used in 2000 than in 1990 by 28.6% from 270.1 Mm^ to
347.4 Mm^, the percentage sectoral shares for municipal and agricultural uses remained almost constant. Only the industrial sector showed a noticeable increase in its share of slightly more than 19.5%.
The percentage contributions of total use by source of supply in 1990 and 2000 are tabulated in Table 6.8. The total volume of groundwater withdrawn in 1990 (assuming
398 Table 6.8 Percentage contributions of total water use by source of supply in 1990 and 2000
Quantity (Mm^) Percentage contribution (%) Water source 1990 2000 1990 2000
Groundwater 204.9 242.9 75.9 69.9
Desalinated water 54.2 89.2 20.1 25.7
Treated sewage effluent 8.0 15.0 3.0 4.3
Agricultural drainage 3.0 0.3 1.1 0.09 water
Total water use 270.1 347.4 100.0 100.0
Notes: [ 1] The differences benveen total groundwater withdrawals presented in this table and those given in Table 6.3 are made up by the total abstraction from Ras Abu-Jaijur wellfield for municipal purposes which were distributed between groundwater and desalinated water. The slight difTerences are due to the accumulated rounding. [2] Percentage distribution may not add to totals because of rounding.
399 that the quantity of blended water used for agriculture and industrial uses (6.5 Mm"*) is distributed equally between the groundwater and desalinated water) was 204.9 Mm"* or nearly 75.9% of the total water use. Non-conventional water resources contributed to the total water uses in that year by 65.2 Mm^, or 24.1%, distributed amongst the desalinated water, TSE, and agricultural drainage water at 20.1% (54.2 Mm^), 3% (8 Mm^), and 1.1%, or 3 Mm\ respectively.
A decade later, in 2000, total groundwater withdrawal dropped by 6% to 242.9 Mm^, or
69.9% of the total water use (the above assumption is also valid here, with the amount of blended water used for agriculture and industry equalling 10.5 Mm^). Non-conventional water sources supplied the user sectors with a total of 104.5 Mm"*, indicating a 30.1% share, or an increase by 6% over that reported for 1990. Water from desalination plants contributed to the total ^yater use in that year by 89.2 Mm^, or 25.7%, while a smaller share of 4.3% (15 Mm**) came from TSE. The agricultural drainage water contributes only a fraction of just under 0.1%. One of the main conclusions that can be drawn from this analysis is that in spite of the noticeable increase in the total output of non- conventional water resources during the last decade, reliance on groundwater remained high.
6.3 Analysis of Water Use Patterns
The latest readily obtainable data on groundwater withdrawals from private sources, amounts of blended water and groundwater put into the public supply network, and treated sewage effluent re-used for irrigation are primarily those for year 2001. Well-defined time series data on the daily, monthly, and yearly distillate and blended water supplies, per capita consumption, water sales, and amount of unaccounted-for water up to the year 2001
400 expressed as a percentage of the total consumption were made available for this study.
This permits analysis of patterns of water use in the municipal sector to be made in some detail. However, attempts to analyse the monthly and annual consumptions from the billing system records for the 22 distribution areas failed to produce reliable results because of the inconsistency and unreliability of these records. Also gathered are statistics on irrigated hectares, agricultural consumptive use, and annual pimipage for indi^^^om self-supplied sources, as well as data on industrial employment and total industrial areas.
The latter set of statistics, although very scant, provided a framework for a brief analysis of water use characteristics in the agricultural and industrial sectors. The following rather detailed analysis of the water use patterns considers each major sector of use separately, and focuses on the water use data set for year 2001.
6.3.1 Municipal Water Use
Municipal water use in the study area is broken down into domestic and non-domestic water uses. The domestic category can be further divided into residential and non• residential water uses. Residential use represents water use in household units, while the non-residential sub-category includes water use in public buildings and institutional establishments. The non-domestic category primarily constitutes the commercial and industrial water users. This categorisation is solely based on an economic criterion; generally when water is used to produce income it is classified under non-domestic use.
Some agricultural farms and small public gardens are, however, receiving metered blended water from the piped supplies; most of those users are arbitrarily included within the domestic category. The dividing line here is unclear because some of these farms gain income torn crops and livestock production. In addition, water used to irrigate public gardens cannot be considered as purely domestic.
401 Mott MacDonald (1997) found that many commercial enterprises and hotels are included within the domestic category. Moreover, based on information received from the municipal questionnaire survey, some commercial and industrial customers are charged at the domestic rates. This rather poor disaggregation tends to make the analysis of municipal water use data fairly complicated. Of particular importance in this respect is that the official per capita figures (because they^inclu^ cpmmercial an industrial.uses) should be viewed with some caution, particularly when the per capita consumption in
Bahrain is compared with that in other parts of the world. This simply means that the quoted per capita consumption tends to be higher than when calculated for separate household uses. The per capita issue is discussed latter in this chapter.
Table 6.9 presents data on water sales fi*om 1993 - 2001 classified into domestic, non- domestic, and annual total water sales. Understandably, domestic consumers are always the major users of municipal water. Non-domestic users accounted for between 8 - 12% of the total billed consumption with a pronounced increasing trend in recent years. In
1993, non-domestic users consumed almost eight percent or 5.7 Mm"' of the total publicly supplied water; this has increased to about 9.8 Mm^ in 2000, a percentage share of 11.7%.
This is in line with the recent growth rates in industrial and commercial activities supported by information obtained from the industrial questionnaire returns, which revealed that the piped supply is gradually gaining interest among industrial users most probably due to quality considerations (with the gradual improvement in blended water quality and the instability of groundwater quality, piped water seems to be a reasonable solution). The drop in the non-domestic share to about 10.3% in 2001 is most probably due to the abandonment of the supply restriction policy from August 2000, which primarily benefited domestic users.
402 Table 6.9 Annual total water sales of municipal water 1993 - 2001
Water sales in cubic metres (m^) Year Total sale Domestic Non-domestic 1993 71,629,808 5,722,555 77,352,363
1994 73,263,085 6.394.554 79,657,639
1995 72,142,136 6,404,159 78,546,295
1996 72,137,233 6~553,254 78,690,487
1997 71.259,654 8,114,085 79,373,739
1998 80,552,066 9,221,337 89,773,403
1999 85,896,259 9,740,592 95,636,851
2000 83,322,644 9,763,109 93,085,753
2001 86,224,341 9,866,673 96,091,014
SOURCE: Ministry of Electricity and Water
The MEW is responsible for the provision and management of water supply services for the whole country; the exception being the town of Awali, which has its own piped water supply administered by Awali Services Department functioning under BAPCO authority.
According to the 2001 census, the town of Awali had a population of 810 inhabitants
(Sheikh Ahmed bin Attiyat Allah Al-Khalifa, Director of Statistics, written communication), which was about 0.12% of the total population. Virtually all the water consumers in the study area are connected to the public water supply system and have access to safe municipal water facilities. In Figure 6.4, the total number of consumers connected to the piped system from 1980 - 2001 is illustrated along with the projected growth up to 2010. The total number of connections to the distribution network has increased from 61,979 in 1980 to 126,840 in the year 2001, an increase of slightly more than 104%. The average rate of growth per annum during this period is calculated at
403 Figure 6.4 Total number of connections to the distribution network, actual 1980 - 2001, projected 2002 -2010
180000
160000
140000
120000
100000
80000
60000
40000
20000 -1
T 1 r T-p-T 1 ^ r T 1 1 1 1 5- *P op T 1 ( 1 r 00 ov o «0000 00 00 00 00 00 00WO\O^O\O^C^g^g^5^5^5^QQQ©QOQQ^ (N O 0^0^0^0^0^CT^0^0^0^0^0\0^0^0^0^0^0^0^0^0^O0OOOOOOOOO
Years
404 3.4%. This annual average growth rate was used to project the total number of connections up to year 2010. The projections indicate that by year 2005, the total number of connections will be 144,990, an increase of 14.3% over that reported for year 2001.
A total of 171,372 customers are projected to be connected to the distribution network by year 2010, accounting for a growth of 35.1% over the 2001 figure. Water use per connection (total municipal water output in a given year divided by number of connections to the distribution system) was 85 m^ per connection per month in 1990, increased to
90 m^ per connection per month in 2001, equivalent to a growth rate of almost 5.9%.
Municipal water use is met by metered and billed blended water and brackish groundwater pumped into the public supply distribution network, and by metered but unbilled self- supplied groundwater or desalinated groundwater, particularly in private housing compounds and the Awali community. Most of the self-supplied municipal users use their privately owned wells as an additional supply primarily for swimming pools and other out• door use activities. The percentage distribution of the municipal water uses by source for
2001 is given below in Table 6.10. From this table we can see that about 59.1%, or
93.8 Mm'' of the total piped supply in the year 2001 was from the desalination plants.
Dammam groundwater used for blending contributed to the total municipal water used during that year by 24.3%. Raw Dammam groundwater used for municipal purposes totalled about 11.1 Mm^, out of which 6.3 Mm"* was used in private housing compounds and 4.8 Mra^ was put directly into the distribution network during water shortages.
Desalinated groundwater from the Dammam aquifer amounted to 4.4 Mm^, of this quantity
3.6 Mm^ was withdrawn to meet the municipal demand of Awali and the remaining
0.8 Mm^ was for hotels use.
405 Table 6.10 Percentage distribution of municipal water uses by source 2001
Quantity % Water source (Mm^) Distribution Dammam - Neogene groundwater 11.1 7.0
Rus - Umm Er Radhuma groundwater 10.8 6.8
Seawater desalination 74.1 46.7
Desalinated groundwater from Dammam 4.4 2.8
Desalinated groundwater from the Rus - UER 19.7 12.4
Dammam groundwater used for blending 38.6 24.3
Total 58.7 100.0
rejected after desalination; the balance of the total withdrawal from the aquifer to feed the Ras Abu-Jaijur RO Desalination Plant, which represents the quantity desalinated and actually used was 19.7 Mra^ as indicated in row 5.
Metered water users obtaining water from the public supply system are charged according to a rising block pricing structure imposed in 1990 to promote water conservation, with different rates set for domestic and non-domestic customers, and blended and brackish groundwater supplies (Table 6.11). This tariff structure has been modified and significantly reduced since May 1992, as shown in Table 6.12. The adopted water prices are substantially smaller than the production costs (charges do not reflect actual cost, especially with respect to desalinated water). To illustrate, according to (OFWAT, 2004), the average unit cost in England and Wales during 2002 - 2003 was £ 0.69/m^, or
BD 0.442/m^ (at a conversion rate of 0.640), while the average unit cost in Bahrain is
BD 0. in/m"*. In addition, in most cases, the metered water customers in England and
Wales pay a standing charge of £ 24.00 per annum (Twort ei al, 2001). Municipal water
406 Table 6.11 Pre-1992 water tariff for domestic and non-domestic water uses
Water use and type Water usage (m /month) Tariff (BD/m')
Domestic water uses
Blended water 1 - 50 0045 51 - 100 Olio 101 and above O200
Brackish groundwater 0-50 Minimum charge of
BD 1.500 51 - 100 0.035 Above 100 O085
Non-domestic water uses
Bloided water 1 -450 0300 451 and above 0.400
Table 6.12 The currsit water tariff for domestic and non-domestic water uses
Water use and type Water usage (m^/month) Tariff (BDW)
Domestic water uses
Bloided water 0-60 0.025
61 - 100 0.080
101 and above 0.200
Brackish groundwater 0-60 O030
61 - 100 0.025
Above 100 0085
Non-domestic water uses
BlCTided water 1 -450 0.300
451 and above O400
407 customers in the study area do not pay sewerage and/or discharge charges. A seasonal orpeak-load pricing policy (variation in water charge by season) is not applied either.
Mott MacDonald (1997) postulated that within this erratically defined municipal water use, three separate components should be considered. These are: (1) legitimate billed and unbilled domestic and non-domestic water consumption, including meters__under registration; (2) unaccounted-for water; and (3) other measured raw water and transmission losses. The legitimate unbilled demand represents water delivered to VIP residences both metered and unmetered. Meter under-registration may be defined as the difference between the quantity of water billed and that actually consumed by consumers due to low flow rate and/or malfunctions of water meters. The unaccounted-for water may be defined as the difference between the amount of water supplied into the distribution network and the water actually used at consumption outlets. This component includes leakage from distribution mains, water used through illegal connections, and other unmeasured elements such as fire fighting, streets cleaning and mains and sewers flushing.
Other measured uses embrace the amount of groundwater pumped directly to some agricultural farms from public supply stations.
Daily Per Capita Consumption
In the graph (Figure 6.5) the variations in average daily per capita consumption (bars) and population forecasts (line) for the period 1980 - 2001 are compared. The data indicate that the average per capita water use has shown a steady growth from 359.2 Ipcd
(79.1 gpcd) in 1980 to 584.6 Ipcd (128.8 gpcd) in 1989, but generally exhibited a decreasing pattern between 1990 and 1992, falling by 4.2%. This declining trend can mostly be explained by the introduction of the water pricing policy in 1990 by which a
408 Figure 6.5 Growth in average daily per capita consumption related to population gro>\lh 1980 - 2001
140 n T 800000
-f 700000
ci.
^01)()()(I
1(10(100
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Years
409 block rate tariff structure was imposed as indicated earlier. In 1993, however, there was a significant increase in the per capita water use, most probably in response to the considerable reduction in this tariff beginning from May 1992 (see Tables 6.11 and 6.12).
During 1994, the amount of water consumed per person per day dropped again to
543.2 Ipcd (119.6 gpcd) on average; this was only to be expected in view of the supply restriction policy, with which a daily maximum supply ceiling of 70 mgd (116.1 Mm"*) was set in May 1994. As a consequence, from 1994 to 1999, daily average per capita water use again showed a shghtly falling trend. The commissioning of the Hidd
Desalination Plant from August 2000 allowed cession of this policy, resulting in higher average daily per capita consumptions of 503 Ipcd (110.8 gpcd) and 517.1 Ipcd
(113.9 gpcd) in the years 2000 and 2001. respectively, than the 479 Ipcd (105.5 gpcd) reported for the year 1999.
Figure 6.5 suggests that before the implementation of the metering policy in 1986, the increase in rates of per capita use by far exceeded the population growth rates. For example, between 1981 and 1982, the rate of per capita use increased at about
9.4% per year, against an annual population growth of 3.5%. Per capita consumption almost outpaced population growth by the same rate from 1984 to 1985. This indicates that the increase in per capita consumption was in excess of what could be explained by an increase in population. Another noticeable trend in this figure is that by late 1980s, the per capita water use appears to have approached a saturation level, and has since gradually declined with the annual percentage increase in population generally exceeding the rates of daily per capita water use.
410 It is important to remember that the daily per capita water use figures given in Figure 6.5 were derived by dividing the total supply throughput in each year by the estimated population served in the respective year (population figures used are taken from the CSO population forecasts between 1981 - 1991 and 1991 - 2001 censuses). As has already been demonstrated, total municipal water supplied through the distribution network contains several unaccounted-for water and other urmieasured components. This implies that the actual per capita water use is expected to be lower than the official figures, and in order to arrive at an average daily per capita consumption (excluding system losses), it would be necessary to quantify these components separately and eliminate them from the total piped supply.
Figure 6.6, showing the total water production from the desalination plants and groundwater withdrawal put into the public supply system for municipal purposes during
1980 - 2001, indicates that in the year 2001 a total of 139.1 Mm^ was distributed through the public network, out of which desalinated water accounted for 93 .9 Mm"* or 67.5%, and
45.2 Mm^ (32.5%) was drawn from groundwater sources.
Annual variations in the unaccounted-for water from 1993 - 2001 are illustrated in
Figure 6.7. Unaccounted-for water varies significantly from about 18% in 1999 to 35.7% in 1996. In 1993, the level of unaccounted-for water was estimated at 31.6%, substantially reduced to 23.1% in the year 2001, accounting for 32.1 Mm^ of the total piped water used in that year. Leakage from the distribution network alone represents about 18.4% of the total unaccounted-for water, or almost 25.6 Mm^. The remaining part is for illegal connections and other miscellaneous unbilled consumptions.
411 Figure 6.6 Desalinated water production and groundwater withdrawal 1980 - 2001
^ 140
120
100
80
40 5 I •I J 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
• Deulinated water • Oroundwaier Years
412 Figure 6.7 Annual variations in the unaccounted-for water 1993 - 2001
1993 1994 1995 1996 1997 1998 1999 2000 2001
Years 413 The VIP unbilled and urmietered consumption was estimated by Mott Macdonald (1997) for 1993 at about 6.0 Mm"*, and was predicted to increase to 7.0 Mm^ from 1996 up to
2005. Detailed analysis based on disaggregation of the total municipal water use data for the period 1993 to 2002 into domestic, non-domestic water sales, unaccounted-for water, and VIP unbilled consumption indicates that the amount of water used by the VIP unbilled users in 2001 accounted for almost^ 10% of the total pip^d^ supply, or 13^ Mni^- _Part of the VCR unbilled consumption is, however, actually used for domestic purposes. On the assumption that this quantity represents 25% of the total unbilled, or nearly 3.5 Mm"*, the balance of 10.4 Mm^ was more likely to have been used for irrigation in palaces and private gardens, and should therefore, for the purpose of this analysis, be deducted from the total supply.
The amount of groundwater directly supplied through the public supply system to irrigate private farms in the Buri area accounted for 1.8 Mm^. Blended water used to irrigate public and private gardens in 2001 was estimated at 4.7 Mm^ (see Table 6.15). Meter under-registration, including transmission losses, are difficult to quantify; however, considering meter ageing and a gradual increase in inaccuracies, a figure of 2.5% of the total throughput (estimated at 1.5% by the Mott MacDonald study) was considered reasonable to represent this component. This places the total quantity of this element of supply at around 3.5 Mm^. By definition, meter under-registration should not be subtracted from the total supply; but this is not the case for transmission losses. Meter under-registration was assumed to represent 55% of this component or about 1.9 Mm"*; the remaining portion (1.6 Mm^) was considered to be transmission losses. The latest census, conducted in 2001, showed the Bahrain population to be 650,604 inhabitants
(see Table 6.1).
414 As Table 6.13 shows, given these adjustments, this allows us to arrive at a more representative average daily per capita water use of 372.3 Ipcd (82 gpcd). An interesting finding of this re-adjustment exercise is that re-adding the quantity of blended water used for irrigation (4.7 Mm"*), since it represents part of the water sales, puts the total municipal consumption (excluding system leakage) at around 93.2 Mm^. This is in an acceptable agreement with the total water sales for year 2001 (see Table 6^9). The difference of about
2.9 Mm^ can be attributed to underestimation/overestimation of some components, notably the VIP unbilled consumption, since this component will always be questionable and difficult to estimate.
Table 6.13 Components of municipal water demand and daily per capita consumption
Component Quantity (Mm^)
Total supply 139.1
Unaccounted-for water (32.1)
VIP unbilled and unmetered consumption (10.4)
Raw groundwater from Hamala wells (18)
Blended water for irrigation in private and public geu'dens (^-7)
Transmission losses (1.6)
Gross supply 88.5
Population in 2001 650,604
Actual daily per capita consumption (Ipcd) 372.3
Further adjustments to the per capita consumption can be made if Awali municipal use, which amounts to about 3.56 Mm"' (represents total groundwater abstraction from the
Zailaq wells (3.60 Mm^), minus 0.04 Mm"*, quantity supplied from the same source to the
415 Bahrain National Gas Company (BANGAS) and Bahrain Centre for Studies and Research
(BCSR)), is added to the total piped supply, since Awali consumers are included in the
total population. This quantity is totally used for municipal purposes including lawn
watering with raw groundwater (Mr Jaffar Tuq, Awali Services Department, personal
communication), meaning that the gross average daily per capita consumption in the study
area, including Awali domestic use, amounts to almost 387.3 Ipcd, or 85.3 gp^d.
Consumption Variability
Seasonal variations in water use are normally related to changes in weather variables and
other variables that are seasonal in nature, whilst non-seasonal variations are those due to
the influence of other factors. Statistics on the daily and monthly water consumption for
the period 1985 - 2001 are readily obtained from the WDD of the MEW. Measurements
on average, maximum and minimum daily water consumption are commonly produced
monthly. These data permit estimates to be made for consumption variability.
Unfortunately, however, data on maximum and minimum hourly water use were not made
available to this study to facilitate variability analysis for daily water use.
Time series data for the monthly-wise average daily water consumption covering the
period 1985 to 2001 is shown in Figure 6.8. Examination of these data indicates that the
daily average water consumption has been steadily increasing from 48.2 mgd
(219,090 m^ day'*) in 1985 to 81.2 mgd (369,136 m^ day ") in 2001, slightly more than a
68% increase. Variations on average, maximum, and minimum daily water consumption over the same period are shown in Figure 6.9. Since the data in this figure are daily actual consumption, no smoothing to actually represent the water use per a given calendar month is made. The figure indicates that maximum daily water use ranged from 70.3 mgd
416 Figure 6.8 Average month-wise daily water consumption (mgd) 1985 - 2001
^^0
SO
70 rn
5(1 1
40 -a
.^0 T
20
10
oL .J 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 19% 1997 1998 1999 2000 2001
Years
417 Figure 6.9 Average, maximum, and minimum monthly consumption 1985 - 2001
100
90
80
70
60
50
40
30
20 Average Maximum — • — - Minimum 10
Jan Feb Mar Apr May Jun ' Jul Aug Sep Oct Nov Dec
Months
418 (319,727 m' day'') to 88.1 mgd (400,227 m'^ day *). Average daily consumption varied from 56.2 mgd (255,636 day'') to 67.8 mgd (308,227 day"'). The minimum values varied between 42 mgd (190,909m^ day') and 52.8 mgd (240,000 m^ day'). The month of July had the highest maximum and average daily water consumption, whilst the lowest values were reported for January. This can be further demonstrated by Figure 6.10, which compares the average daily variations in water use between thesejponths for year 200L
The average monthly consumption figures from 1985 - 2001 are plotted against the corresponding monthly average temperature, total average monthly rainfall, and monthly average relative humidity as illustrated in Figure 6.11. The figure reveals high relationships between these climatic parameters and water use. Less water is being used for human consumption in the winter months than in summer months, indicating a strong relationship between temperature and water use. Total monthly rainfall and average monthly relative humidity appear to have inverse relationships with the average monthly water consumption. Apparently, rainfall reduces the need to irrigate lawns and gardens, resulting in less water consumption when precipitation is high. In the case of Bahrain, the negative correlation between relative humidity and water use cannot be reasonably explained in terms of consumption variability, but possibly reflects less evaporation, and hence less out-door water use when humidity is high.
Average annual water use may be envisaged as the sum of two components: the average demand component and the peak demand component. The average demand component is the normal demand over the calendar months for each year; whilst the peak demand component is the demand above the normal (average) demand attributed to the seasonal variations in water use. Based on Boland et al. (1983), the average demand component
419 Figure 6.10 Average daily consumption during the months of January and July 2001
100
90
80
1c 70 .2 January 60
s 50
40 -i
30 ! 20 10
0 I I I I I I I I I I I 1 I I I I I I t I I 1 I 1 I I I 1 I I 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28129 30 31
Days
420 Figure 6.11 Average monthly water consumption (1985 - 2001) with the corresponding monthly averages temperature, relative humidity, and total monthly rainfall (1986 - 2001) |
80
70
60
50
40 I 2
S S 30
20
10 H i <
T r Jon Feb Mar Apr May Jiin Jul Aug Sep Oct Nov ' Dec
t Months
' Average monthly (daily-wise) water consumption (mgd) • Monthly avearagc tcn^JOBiure (C) — - — -Monthly total rainfall (mm) — - • — Monthly average relative humidity (%)
421 may be calculated as the average water use for winter months (December - March) multiplied by 12 to account for the average demand over the whole year (i.e. the winter monthly demand calculated on an annual basis), while the corresponding peak demand component is the excess of total average annual water use over the average demand component. The annual average water uses for the period 1985 - 2001 were analysed in order to allow separation of the peak and average water demand components as illustrated in Figure 6.12. Average annual water use was found to vary considerably from
17,593 million gallons per year (mg/year) in 1985 (of which 15,796.75 mg/year is for the average demand component and 1,796.25 mg/year for the peak demand component) to
29,641.65 mg/year in 2001, distributed as 27,365.17 mg/year for the average demand component and 2276.48 mg/year for the peak demand component, with an almost steady increase over the past decade.
The analysis shows that the average demand component ranges from about 88% to 95% of the average total annual water use, leaving nearly 5% to 12% for the peak demand component. This suggests slight seasonal variations in water use. In fact, this is quite an acceptable result because sprinkling demand of seasonal effects is not a common practice in most of the dwelling units, although recent years show upward trends of this consumption element. In addition, the prevailing warm climate in the country tends to keep seasonal variations in the in-house water use relatively low.
As shown in Figure 6.13, analysis of the long-term (1985 - 2001) monthly peak trend offers some evidence of an inconsistent pattern. Peak factor is the ratio of the maximum daily water use to average daily water use. Peak factors are found to range from 1.25 to
1.35, with an average peak faaor of 1.28. The year-to-year peak variations also showed
422 Figure 6.12 Peak and average demand components of the municipal water use 1985 - 2001 Average Peak Average annual Year demand demand vsaler use 1985 1579675 1796.25 17593.00 1986 16800.51 1887.49 18688.00 1987 18268.46 1149.54 19418.00 1988 18243.05 1795.45 20038.50 19070.87 1953.13 21024.00 1990 20432.05 2070.20 22502.25 1991 20202.02 1862.23 22064.25 1992 20395.97 2752.33 23148.30 1993 21768.28 2792.57 24560.85 1994 23184.22 1161.28 24345.50 1995 2153592 1951 83 23487.75 21982.02 1976.58 23958.60 I 15000 1997 23017.87 1404.28 24422.15 1998 23115.13 171582 24830.95 ^ 10000 - 1999 23604,95 1627.50 25232.45 2000 25135 75 2356.05 27491.80 2001 27365.17 227648 29641 65
Average annual water use for each year equals the sum of the average demand component and the peak demand component Data arc given in million gallons per year 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 (mg'year). Years
Average demand component is calculated as the average monthly water use for December - March consumption times 12 Peak demand component is the average annual water use minus the average demand component.
423 Figure 6.13 Monthly average peak factors 1985 - 2001
1.36
1.34
1.32
1.3
1.28
1.26 n
1.24
1.22
1.2 Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec
Months
424 inconsistent trends, probably partially attributed to some policy intervention. The results of this analysis appear as Table 6.14. The column labelled "peak factor" shows slight evidence of increasing annual peak Actors in 1992 and 1993. The lower figures reported fi-om 1994 - 1999 seem to reflect the supply restriction policy imposed since 1994.
Axmual variability in average water use is relatively small. The annual peak factors over the 17-year time series water use datajang^ from 1.05 Jq 1J1, and_averaged_at ,1.08.
This conforms well to the percentage figures accounting for the peak demand component
(see Figure 6.12), confirming that climate is the main determinant of seasonal variations.
Given the absence of data on houriy peak variations, it was impossible to compute daily and weekly peak variations. According to the MWPW (1993), however, analysis of long- term (1977 - 1992) houriy water use data indicated a daily peak factor of 1.50.
6.3.2 Agricultural Water Use
Historical evidence suggests that agriculture in Bahrain was very prosperous. Present agriculture, however, has a limited potential where it suffers from unfavourable climatic conditions, limited natural resources, poor cultivation and farming practices, small farm holdings, land tenure system problems, soil salinisation and drainage problems, and lack of financial incentives (FAO, 2002). In addition, bad management and the absence of appropriate planning within the agricultural authority further contribute in complicating this problem. Farmlands are located according to the availability of suitable groundwater supplies. Thus, agricultural activities are confined to a narrow relatively fertile strip along the northern and western coasts.
Arable land is estimated at about 11,000 ha, or 15.5% of the country's total area. In 1953, the total cultivated area was estimated at about 6,460 ha, with the area actually under
425 Table 6.14 Average daily water consumption over the months (mgd) and the corresponding peak factors 1985 - 2001
Years Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec Aver. Peak Factor 1985 42.00 43.40 43.50 45.80 49.00 52.30 50.90 52.50 52.80 51.60 48.50 46.00 48.20 TTo" 1986 45.30 45.00 46.50 48.00 52.90 55.30 55.00 55.80 55.80 54.50 53.00 47.30 51.20 1.09 1987 48.60 50.30 50.30 52.50 55.90 55.50 56.50 56.40 54.90 53.80 52.70 51.00 53.20 1.06 1988 48.60 47.70 49.90 53.40 54.50 57.90 58.90 59.30 59.50 59.10 56.00 53.60 54.90 1.08 1989 49.70 51.90 54.00 58.70 61.10 61.30 60.20 61.50 61.10 59.70 58.40 53.30 57.60 1.07 1990 53.63 54.16 57.67 59.88 64.26 65.87 66.77 67.12 66.34 64.76 60.91 58:46 61.65 1.09 1991 54.75 55.26 56.09 60.49 63.18 63.84 64.84 64.21 64.44 62.60 60.42 55I27 60.45 1.07 1992 54.30 54.47 56.70 60.79 66.41 70.60 70.20 69.22 68.78 66.53 63.36 57'.36 63.42 1.11 1993 54.67 56.16 63.83 64.90 69.60 73.42 74.64 74.59 72.74 71.00 68.07 63191 67.29 1.11 1994 62.89 65.40 63.78 68.80 68.83 70.29 70.13 68.97 68.73 66.21 64.53 62".06 66.70 1.05 1995 59.69 61.64 60.20 64.04 68.35 69.64 68.78 67.95 68.03 66.56 62.82 541.47 64.35 1.08 1996 56.78 61.46 59.53 64.01 67.16 70.69 70.86 70.43 69.64 68.30 65.69 63111 65.64 1.08 1997 63.33 63.69 61.58 65.83 68.66 70.38 71.26 70.49 70.90 6968 63.49 63!63 66.91 1.07 1998 62.00 61.34 63.44 67.03 70.49 71.47 71.79 71.53 71.73 70.64 68.36 66i53 68.03 1.06 1999 64.03 61.62 64.08 68.77 70.96 71.68 72,42 72.44 72.46 71.61 70.59 68194 69.13 1.05 2000 65.53 65.92 68.10 73.64 76.64 81.13 81.64 82.43 80.44 77.37 75.08 75189 75.32 1.09 2001 70.34 72.71 75.52 78.97 81.81 84.55 88.05 86.54 85.86 84.46 84.42 81.'33 81.21 1.08 Average annual peak factor 1.08
Note: Peak factors and and overage annual peak factor values are rounded to the nearest decimal points.
426 irrigation totalling 3,224 ha. Soil salinisation resulting from deterioration in the quality of groundwater, as well as the increase in urban development and desertification over the last four decades has led to a general reduction of the cultivated lands. In the year 2000, the total cultivated area was significantly reduced to about 4,766 ha, or 6.7% of the country's total area. This gives a per capita share of cultivable land of 0.0073 ha. Out of the total cultivated area, about _4,199 ha, or slightly _mpre_than 88% was fully^irrigated_ and. maintained. About 3,871.16 ha (92%) of the total cuhivated area are privately owned farmlands and almost 8%, or 3,27.84 ha, are under government control.
In spite of the measures that are being undertaken to restore agriculture through government subsidies for machinery, irrigation equipment, greenhouses and pesticides, subsidised credits to farmers, legislative action to protect agricultural lands and to conserve irrigation water, encouragement of private investment in agriculture, as well as promotion of TSE utilisation for irrigation, Bahrain has no clear agricultural policy. Areas classified as agricultural land (the green belt) are now gradually disappearing due to deliberate negligence and abandonment by some owners, urban and industrial expansion and a lack of enforced legislation.
Currently (2001) there are about 1,093 farms ranging in size fi-om 0.0016 ha to 91.11 ha, with an average holding of 4.10 ha. Both traditional open field cultivation and protected agriculture in greenhouses are being practised. Mechanised agriculture is only possible in the large-sized farm holdings operated by the government, and a limited number of privately owned farms. Chief crops grown are perennial date palms and fodder crops, and seasonal vegetables, whilst minor crops include fiiiit trees. Owing to the harsh climatic conditions, a single crop system is the main cropping pattern; although inter-cropping is
427 common. Poultry production commonly exists in separate farms, whereas a few farm owners and tenants raise some form of livestock beside their normal agricultural activity.
Food for the entire population of the country is mostly imported with only a small amount
(about 6%) of total food needs being produced locally. The value of the local agriculture and fishing products, at cujTent__prices„ js jabguLBB_^2L67,m jn 200J,_of vyhich agriculture accounts for BD 13.82 million and fishing for BD 7.85 million (MFNE, 2002).
This corresponds to only 11.3% of the total import value of the agriculture and fishery goods during the same period (estimated at BD 192.2 million - Mr AbduUa Al-Bashir,
Agricultural Affairs, personal communication). Vegetable production reaches about
12 thousand tonnes in the winter season and covers about 14% of the total local consumption. The total production of dates is about 16 thousand tonnes; this represents about 80% of the country's needs. Self-sufficiency from green fodder crops (mainly alfalfa) is between 50 - 60%.
In 1980, egg production accounted for 90.5 million eggs, representing 93% of self- sufficiency, but due to increased imports from neighbouring countries in recent years, production has declined to 53 .5 million eggs in 2000, covering only 35% of the national needs (FAO, 2002). It follows that the agriculture sector is the smallest sector of the economy in terms of contribution to the GDP, and also has the smallest share in the total labour force. The contribution of agriculture (including fishing) to the GDP is limited to only 0.73% (MFNE, 2002); and it only employs about 1.54% of the total workforce (CIO,
2003), representing a decline of 35.8% from that reported in the 1991 census.
Irrigated agriculture is the only form of agriculture in the country; rainfed irrigation is
428 absent due the scarcity and irregularity of rainfall. Recalling the groundwater abstraction figures presented earlier in Table 6.3, it is apparent that agriculture is the major groundwater consumer in the study area. Agricultural water use includes water applied in the irrigation of crops and landscaping, and water used for dairy, poultry and other livestock raising and production. Agriculture in the study area obtains water fi^om five sources: groundwater withdrawal from mainly metered but unbjUed privately owned wells, metered but not charged for TSE, metered and billed blended piped water, desalinated brackish water from self-supplied sources, and agricultural drainage water.
Water meters are installed for most of the privately owned irrigation wells and at farm connections for farm using TSE; charges for excess water usage are proposed but are yet to be imposed.
Table 6.15 presents the amounts of water used for agriculture during 2001, broken down by sources and their percentage distribution. The total water consumed by agriculture amounts to 164.5 Mm^, of which 83% comes from the Dammam - Neogene aquifer, while
15.4 Mm^, or 9.4% comes from TSE. Desalinated groundwater from the Rus - Umm
Er Radhuma aquifer supplied agriculture (non-productive agriculture) with 7.5 Mm"* or
4.6%. The remaining quantity is distributed among blended water and agricultural drainage water sources.
Irrigation Efficiency
The eflBciency of water use in irrigation is determined by the irrigation water required to be applied per unit of irrigated area to produce optimum crop yields. The irrigation water requirements include the amount of water applied for consumptive use of the crops and leaching needs to remove salts that normally accumulate in the root zone of plants. In the
429 Table 6.15 Agricultural water consumption by source and their percentage distribution 2001
Water source (Quantity (Mm^) % distribution
Dammam - Neogene groundwater 136.6 83.0
Desalinated groundwater from Rus - UER 7.5 4.6
Blended water 4.7 2.9
Treated wastewater 15.4 9.4
Agricultural drainage water 0.3 0.2
Total agricultural water use 164.5 100.0
Note: Percentage distribution may not add to totals because of rounding.
study area where the irrigation water is of high salinity, adequate leaching of excessive salts is of particular importance, as farmers tend to increase their application rates to combat building up of salts.
Table 6.16 summarises the trends in total irrigated areas and the irrigation water requirements per unit irrigated area from 1953 to 2000. The actual irrigated area increased from 3,224 ha in 1953 to 3,793 ha in 1966, but gradually decreased thereafter to reach
1,750 ha in 1979. The loss in agricultural lands in the 1970s is closely related to the urban expansion and soil salinisation due to irrigation water quality deterioration (Zubari et al.,
1996). The irrigated area totalled about 4,199 ha in the year 2000 - an increase of almost
44.3% over the 1985 figure of 2,910 ha. The substantial growth in cultivated lands in the
1980s was due to the increase of investment in the agriculture sector in response to the financial incentives provided by the government as part of the income diversification policy adopted following the decline in oil prices.
430 Table 6.16 Trends in total cultivated areas and irrigation water use 1953- 2000 (modified and updated after Al-Noaimi, 1993)
Year Cultivated areas (ha) * Groundwater withdrawal Treated sewage Total irrigation Irrigation water for agriculture (Mm^ effluent (Mir?) water use (Mm^) requirements Irrigated areas Abandoned areas (m /ha/year) 1953 3224 3236 90.0 — 90.0 27916 i 1966 3793 2049 117.0 — 117.0 30846
1971 2220 3950 119.0 — 119.0 53604
1979 1750 1788 99.4 ™ 99.4 56800
1985 2910 909 105.92 — 105.'92 36399 1 1990 3007 1041 130.64 6.18^* 136.82 45500 1 1 1997 3390 1373 178.94 10.44* 189.38 55864
2000 4199^ 567^ 159.90 11.22' 171.52 40848
(1) Earlier data on cultivated areas are taken from different sources; data from 1985 are based on the annual agricultural statistical reports. (2) 2000 cultivated areas estimates are after adjustment by FAO, 2002. I (3) Including discharge from natural springs. (4) Represent TSE amounts re-used for productive agriculture.
431 The table indicates that the irrigation efficiency in Bahrain is very low. The size of the cultivated area cannot justify the huge amounts of water applied at the farm level (FAO,
2002). The annual water requirement per hectare of irrigated land has increased from
27,916 m^ in 1953 to 40,848 m^ in the year 2000, an increase of 46.3%. The significant decline in irrigation efficiency from 30,846 m^/ha/year in 1966 to 56,800 m^/ha/year in
1979 is difficult to interpret since^oundwater v^thdrawal in the same period declined considerably. As possible partial explanation, this inconsistency is probably attributed to inaccuracy of data on cultivated areas, particularly with respect to the abandoned ones.
Irrigation efficiency improved in the early 1980s owing to the introduction of modem irrigation techniques, and the provision of an agricultural incentive programme as part of the Agricultural Development Plan 1981 - 1985. Notably, the deterioration in irrigation water quality during the 1990s led to another decline in irrigation efficiency because of the excessive requirements to leach out salts present in the soil.
Destane and Ayub (1979) calculated the actual irrigation requirements per hectare for different crop patterns, taking into consideration the local agricultural conditions. They found that in 1978, a total of 78.4 Mm"* was used to irrigate about 1,622 ha of cultivated land. The actual irrigation requirements (consumptive use plus leaching needs) of this area were calculated at 43.4 Mm^, indicating that about 35 Mm^, or just under 45% were irrigation losses. This suggests an average actual irrigation requirement per hectare of cultivated land per annum of about 26,758 m^ and an irrigation efficiency of 55%. In
2000, about 171.52 Mm^ was used to irrigate some 4,199 ha, indicating an irrigation requirement of 40,848 m'*/ha/year (Table 6.16). Applying Destane and Ayub figures, this area only requires 112.4 Mm"*, and indicates irrigation losses of 35% or an overall irrigation efficiency of about 65%. This cleariy reflects primitive and inefficient irrigation
432 practices, although it represents an improvement of 10% over that reported for 1978. The average leaching ratio for 1978 (computed by the author based on the published data of
Destane and Ayub, 1979) was found to be in the order of 61%. The leaching requirement based on recent salinity data has been calculated from the equation (Richards, 1969):
In which ECiw is the electrical conductivity of the irrigation water, and ECdw is the electrical conductivity of the drainage water. ECiw is taken as the average electrical conductivity of the normal irrigation water derived from Table C-2 of Appendix C (equals
8,086 (imohs/cm). ECdw is obtained from Table 5.14 (the average TDS of agricultural drainage water, 9,225 mg I * divided by a factor of 0.64, refer to the discussion in Chapter
Four, page 292). The resulting leaching ratio of 56% is almost comparable with the average leaching requirement given earlier, which gives some reliability to our findings.
While it is true that the deterioration in irrigation water quality tends to increase the amount of water required for leaching, the main reason for the decrease in the leaching ratio is most likely to be the widespread adoption of modem irrigation methods since the early 1980s. This factor may also be responsible for the 10% improvement in irrigation efficiency referred to earlier.
At present, about 2,990 ha, or 71% of the total irrigated hectares, mostly grown with perennial date palms and alfalfa, are under traditional and wastefril irrigation practices
(FAO, 2002). The total area under modem irrigation methods is estimated at 1,045 ha, or about 25% of the total irrigated area. The area under mixed or improved irrigation
433 practices makes up the balance of 164 ha, or approximately 4%. Although areas under modem irrigation systems have considerably increased during the last two decades, field observations indicate that such systems are poorly operated and managed, with design and installation problems and an absence of irrigation scheduling and proper field management.
Crop Patterns
The irrigated areas under different crop patterns and their percentage distributions fi-om
1974 to 2000 are given in Table 6.17. The crop structure indicates that date palms
(including fruit trees) is the major crop, generally representing more than 55% of the irrigated area. Vegetable crops come second with slightly more than 28%, and fodder crops (mainly alfalfa) come last with a percentage share of almost 17%. Between 1974 and 2000, areas covered by irrigated daje palms and ftuit trees increased by around 176% fi-om 880 ha to 2,425 ha. Areas under vegetable and fodder crops increased during the same period by 191% and 153%, respectively.
Table 6.17 Irrigated areas in hectares under different crop patterns and their percentage distribution 1974 - 2000
Year Date palms and fiuits Vegetable crops Fodder crops 1974 880(57.5) 340(22.2) 310(20.3) 1978 780(51.7) 410(27.1) 320(21.2) 1982 — 488 316 1986 983(46.2) 835(39.2) 312(14.6) 1991 1805(59.1) 820(26.8) 431(14.1) 1995 2181(59.0) 1111(30.1) 404(10.9) 2000 2425(57.7) 989(23.6) 785(18.7) Modified and updated from AI-Noaimi (1993), originally data are obtained from the Annual Statistical Reports (several years) and various sources. Data of 2000 are alter modification by FAO (2002). Bracketed figures are the percentage distribution.
434 In the year 2000, the total cultivated area of 4,766 ha is planted with about 2,592 ha of date palms, 785 ha fodder crops, vegetables 989 ha, and 400 ha of fruit trees (FAO, 2002).
Non-irrigated date palms cover an area of about 567 ha. Out of the 785 ha of fodder crops, about 585 ha is planted with alfalfa. Vegetable crops grown in open fields cover 925 ha; and those under protected cultivation occupy the remaining area of 64 ha.
6.3.3 Industrial Water Use
Until the late 1930s, Bahrain depended basically on pearl fishing, agriculture, and fishery for its economic well-being, with pearl fishing representing the major economic activity
(AI-Noaimi, 1993). The discovery of oil in 1932 has largely transformed the economy of the country; oil has since become the major contributor to the country's gross national product (UNEP, 1988). This has been enhanced by the construction of the Bahrain
Refinery in-1936 with an oil refining capacity of 10,000 barrels per day ~(bpd)
(1,590 m^ day'^) based on a crude oil supply imported fi^om Saudi Arabia. This has been increased to a current capacity of 250,000 bpd (3,9747 m"' day'*). Since then the impact of the oil industry on the economy of Bahrain has been considerable, particularly during the period of the oil boom (1973 - 1983). Over the period (1990 - 2000) the share of the oil sector to the gross domestic product (GDP) increased from 15% in 1990 to 17.83% in
2000 (constant prices), a growth rate of 18.86%. Currently (2001) the percentage contribution of oil to the GDP is 17.15% and 24.53% at constant and current prices respectively (MFNE, 2002).
The establishment of the Bahrain Aluminium Company (ALBA) and the Arab
Shipbuilding and Repairing Yard Company (ASRY) in 1968 and 1974, respectively, has formed the economic base of the heavy industry in the study area and has been a major
435 feature of its industrial development. Ever since, several mostly oil-based and aluminium related medium and small-scale industries have been developed and Bahrain industry has undergone major organisational changes. Developments in industrial activities have steadily increased following the decline in oil prices since the mid 1980s have constrained the government to adopt a new industrial policy aimed at diversifying the economic base by strengthening the manufacturing, ^ankmg and services, and tourism economic activities. So far, however, the country has no significant industrial base. MOH and
UNCHS (1988) summarise the constraints facing the industrial development in Bahrain in the following: small market size which inhibits fijll use of economies of scale, lack of local supply of raw materials other than oil and gas, absence of broad engineering base apart from ASRY, and lack of identified investment opportunities. More importantly, the country suffers from the absence of synthesised industrial policy and lacks of a proper industrial planning.
The manufacturing (conversion industries) sector in the study area is dominated by aluminium, aluminium related, petrochemical, plastic products, iron and steel, food and beverages, and constmction material industries. Other important manufacturers include chemicals, paper and pulps, garments, metals industries, and marine services. Data on national accounts indicate that the manufacturing sector is the third largest economic sector in terms of its contribution to the national economy. During the period from 1990 to 1995, the contribution of the manufacturing sector to the GDP has grown from 10.9% in
1990 to 17.5% in 1995 (current prices), but has then exhibited a negative growth up to the year 2000. According to the MFNE report cited earlier, the percentage share of manufacturing to the GDP was 11.44%, or BD 342.8 million in 2000 increased to 12.0%, or BD 358.1 million in 2001 at current prices, accounting for a growth rate of about 4.9%.
436 Manufacturing activities are organised in a number of industrial estates covering an area of approximately 4.1 million m^ (excluding the Sulmabad industrial area), the most important of which is the North Sitra and Mina Sulman industrial areas, which account for nearly
34.8% of the total industrial areas. The data are presented in Table 6.18. From the distribution of the industrial areas it can be noted that most of the industrial activities are sited in places where there is no oMittle_groundwater erf sujtablejquality Xor direct use.
Plans for fijture industrial expansion are in progress through the development of the South
Hidd industrial area that includes a new port. The project will be implemented in three phases with an ultimate total area of 6.5 million square metres, of which phase one is scheduled to cover 2.4 million square metres (MOI, 2002).
Table 6.18 The industrial estates along with their total areas
Industrial area Total area (m^) Percentage oftotal area (%)
North Sitra 713,490 17.5 Mina Sulman 708,540 17.3 Maameer 566,265 13.9 South Alba 34,063 0.8 South Refinery 299,467 7.3 Sitra Roundabout 110,075 2.7 Southwestern Hidd 723,199 17.7 West of Asry 450,000 11.0 Other fragmental industrial areas* 478,837 11.7 Total 4,083,936 100.0
SOURCE: Modified and updated fromGIC O (2000) and MOI (2002). Notes: * Including Arad, Muharraq causeway, and crusher plants (Hafeera), but excluding Sulmabad industrial area. Percentage figures may not add to totals due to rounding.
Ln the year 2001, manpower engaged in the oil and manufacturing sectors totalled about
52,759 (CIO, 2003); the manufacturing sector was the major employer with
437 49,979 employees, a percentage contribution to the total industrial employment of 94.7%.
The balance of 2,780 employees were engaged in the oil and mining industries.
Employment shares of manufacturing, and oil and mining sectors for the last three censuses are presented in Table 6.19. As can be seen, the industrial labour force in
Bahrain, particularly in the manufacturing sector, has growm rapidly over the last two decades. Employment in the industrial sector has risen from 16,126 in 1981 to 52,75.9 in
2001, equivalent to an increase of just over 227%. Between 1991 and 2001, the employed industrial labour force increased by nearly 74.4% from 30,256 to 52,759 employees. The percentage participation of the manufacturing sector to the total industrial employment in the 1981 and 1991 censuses were 70.4% and 88%, respectively. Manufacturing employment has grovm by almost 88% from 1991 to 2001. It is expected that with the
South Hidd industrial area project in progress, the manufacturing sector will grow at even higher rates, both in terms of development in industrial activities (contribution to the GDP) and employment opportunities. This should have a significant effect on the estimation of future industrial water demand.
Industry requires water for a variety of uses including processing purposes, cooling, product cleaning, and the sanitary needs of employees. Quality and quantity requirements for manufacturing water use vary wadely, not only from one individual industrial activity to another, but also v^thin the same industrial facilities. Industrial water in the study area constitutes the blended water supply, self-supplied groundwater pumpage, and water produced from on-site seawater desalination plants. In addition, some industrial establishments now rely, solely or partly, on better quality desalinated water purchased from private companies to meet their needs. The blended water source is normally used to supply small to medium-scale industries, while water-intensive industries such as oil-
438 Table 6.19 Industrial employment based on the population censuses of 1981, 1991 and 2001
Economic activity' Censuses
1981 1991 2001
Manufacturing sector 11.354 26,618 49,979
Oil and mining sector (including quarrying) 4,772 3,638 2,780
Total industrial employment 16,126 30,256 52,759
SOURCE: Prepared based on data from CSO (2002)
439 refining and aluminium-related industries, beverage manufacturers, including ice making and water bottling factories, and construction materials manufacturers normally develop their own sources of supply.
Since groundwater in the study area is of generally poor quality, private industrial users normally desalinate their groundwater to meet the quality r«iuirements of specific industrial activities, although raw groundwater is, in rare cases, the source for some industrial activities. Because of the latest improvement in blended water quality, however, industrial firms, which relied heavily on groundwater drawn from private wells in the past are now switching to the publicly supplied water or using it as a back-up source of water, but this tendency appears to be highly governed by the reliability of this supply. Re• cycling and water re-use are not common water use practices in Bahrain industry and are only applied in some large industries; notably the petrochemical industries. Industrial water users do not pay a discharge (polluter) charge, but effluent control is being observed by certain industries in accordance with some environmental restrictions.
Past trends of water use, as set out in Tables 6.5, 6.6, and 6.7, indicate that industry is the smallest user of water in the study area and has never represented a burden on the total water consumption; the only exception was in the early 1950s and late 1960s when the groundwater abstractions at the Bahrain Refinery were at their peak and groundwater was the only available source of water. The recent trends, however, highlight a fairly steady growth in industrial water demand. In Table 6.20, a breakdov^ of the industrial water use for year 2001 is presented by sources along with their percentage distribution. Total water consumption for the manufacturing industry in 2001 amounted to 19.6 Mm^. Withdrawals from the Rus - Umm Er Radhuma and Dammam aquifers by large industrial consumers
440 amounted to some 8.0 Mm^ and 4.5 Mm^, respectively. Together, these account for nearly
63.8% of the total water consumed by industry in that year, while the metered piped demand made up the remaining share of 36.2%, or 7.1 Mm^, indicating that industrial users continue to be more interested in the publicly supplied water.
Table 6.20 A breakdown of industrial water use by source and percentage distribution 2001
Water source Quantity (Mm^) % distribution
Blended water 7.1* 36.2
Desalinated groundwater from Rus - UER 6.2 31.6
Desalinated groundwater from Dammam 3.2 16.3
Groundwater from Dammam aquifer 1.3 6.6
Groundwater from Rus - UER aquifer 1.8 9.2
Total 19.6 100.0
* Represents 10.3% (non-domestic uses) of the total piped supply, equals 137.3 Mm^ (139.1 - 1.8 (raw groundwater pumped from the public supply stations for agricultural use)| divided evenly between the commercial and indusUial uses. Percentage figures may not add (o totals due to rounding.
The present research is the first attempt to systematically analyse water use patterns in the industrial sector in the study area. Al-Noaimi (1993) and Mon MacDonald (1997) provided brief discussions on the subject, though none of these studies are based on sampling surveys. Data on water use characteristics at sub-sector levels, technological progress and re-cycling advancement are absent. In addition, information on the industrial water use at sector level is insufficient to permit a thorough water use analysis. However, statistics made available on employment and total industrial areas enable a brief analysis of the industrial water use to be made. The results reported in Table 6.18 indicate that the
441 total area of the present industrial estates is about 4.1 million m^. Making due allowance of 10% to account for the Sulmabad industrial area and other small fragmental industries outside the industrial estates, the total area under industry as of 2001 would be around
4.5 million m^.
The^otaJ industrial water u^ for 2001 is^stimated at 19.6 Mm^ (Table 6.20). This gives an annual average industrial water use of 4.35 m^ per m^ of factory floor area
(0.0119 m^/dayW of factory floor area). This area, however, does not include the large industrial firms outside the state-controlled industrial settlements (i.e. Bahrain Refinery,
ALBA, ASRY, Gulf Investment Industries Company (GDC), Gulf Petrochemical
Industries Company (GPIC), and BANAGAS), which occupy together an area of approximately another 4.5 million m^ (calculated from data received from the industrial questionnaire survey returns). This suggests an estimated annual industrial demand figure of 2.177 m^ per m^ of factory floor area (0.00596 m^/day/m^ of factory floor area).
By linking the industrial water consumption in 2001 to the workforce size (Tables 6.18 and 6.20), an average per employee water use of about 1.486 m^ day"' or 1,486 litres per employee per day (Iped) is obtained based on 250 working days per year. Data on per employee output and unit production are not available for possible conversion to consumption values such as water use per employee output or water use per unit of production. However, estimate on the later consumption indicator is made possible from the analysis of the industrial questionnaire survey as will be discussed in the next chapter.
442 CHAPTER SEVEN WATER USE SURVEYS
In this research, cross-sectional water use surveys were conducted to investigate the water demand characteristics and to develop water demand forecasting models for the major water use activities. Cross-sectional surveys for water users, as noticed by Billings and
Jones (1996), are frequently the basis of research on analysis and forecasting of water demands. They found that when cross-sectional data are based on random samples of water users and data collection focused on their water use characteristics; they strongly support developing of multivariate or structural water demand forecasting models. Boland et al. (1983) also noticed that cross-sectional data usually exhibit much greater variance than do time-series data, permitting more statistically reliable estimates of model coefficients. Ziegler and Bell (1984), suggested that the survey data necessary to make the demand estimates tend to be more available in cross section form than in time-series form.
This chapter begins by outlining the research questions to be addressed and hypothesis to be tested, and continues by considering in some detail the sampling design and procedures, and methods of data collection employed to conduct the surveys. Then, it continues by providing a brief descriptive statistical analysis of the surveys data.
7.1 Research Questions
The research questions are concerned with the factors that could have influence on the water demand characteristics in the major water use activities. The theoretical bases of these factors are reviewed in Chapter One, while Figure 7.1 presents a conceptual framework of the proposed water demand models. The following hypothesis v^ll be tested separately for each water use activity.
443 Figure 7.1 Conceptual model showing factors hypothesised to influence water use in the major water use activities
Water Demand Models
IXnix'praphic I aclop.
Factory floor area
Household size Appb Type of dwelling Per capiu Production capacity ownenhip conumpiion
ImgMion Family income lIouKholdarea Number at rcqurment. Ownership of Population cmplwee swimming (iardcnsrea Price of water Croppincrm pod Number of InduNtehal categoiy sludenti. Number of ImgAlion mcthoik Connoction lo employee, RedrcuiHion uae bathrooms wofshipera,
Irngation etc L'le of desalination frequency Number of taps Number of Comectionto einployee aewen Number of cars
wrier lource
444 The research question for the municipal water use is formulated as follows: there is a significant relationship between the annual average water use or the per capita daily water use as dependent variables and the presumed independent variables: number of bathrooms,
number of taps, number of cars, household size (number of occupants per household),
monthly family income, household area, garden area, appliances ownership, connection to
sewers, and ov^nership of swinuning pools. Therefore, the survey was conducte:d_to test
the null hypothesis (Ho) of no significant relationship between the average water use and
the given set of independent variables. The hypothesised structural relationship can be
represented in the following form:
Q =f{Nt, Nu Nc, Hs, F, Ha. C T)
where
Q annual average water use in Mm^ year"', or average daily per capita consumption
in Icpd; / denotes the function of; A'i, number of bathrooms; M number of taps; Nc
number of cars; //, household size (number of occupants); F, family income in
BD per month; Ha household area in m^; Go garden area in m^; and T representing
the various technology variables.
Consumer's willingness to pay for increase in water prices or their perception of water as a
commodity were not considered in this research as explained in the introduction to this
thesis. Other factors not considered in this survey include effects of conservation variables,
religious practices, education levels, and cultural origin on water consumption. Variables
such as number of rooms, household composition, and age of property were also not
considered in this analysis. Questions related to cooking and drinking were also not
445 addressed here because, owing to quality considerations, these uses are normally met by purchasing water of better quality (bottled water) from outside sources.
With respect to the agricultural water use, the research sought to investigate whether there
is a significant positive/negative effect of variables explaining cultivated area, average
irrigation requirements per hectare, crop patterns, imgation frequency, and irrigation
methods on the dependent variable (average agricultural water use). Such a research
question would allow testing the null hypothesis of no significant relationship between the
dependent variable and the presumed set of independent variables, and that any association
between these variable is likely to arise by chance. Given the time and budgetary
constraints, the potential effects of the weather variables, soil types, agricultural inputs on
the agricultural water demand were not considered in this research (for further details
regarding this issue, refer to the limitations section in the introduction to this thesis).
The industrial questionnaire survey addresses the following question: is there a
combination of explanatory variables such as factory floor area, production capacity,
industrial category, number of employee, water source, number of shif\, and use of
recycling equipment that predicts the levels of water use in industry better than any one
alone and, if so, what is the best combination that can be formed from these factors? If the
altemative hypothesis Hj (i.e. there will be a significant association between the
consumption level and those potential explanatory variables) is found to be true, then we
reject the null hypothesis Ho (there will be no significant relationship between the variables
of interest, and conclude that there is a significant relationships between these variables.
The theoretical equation of the hypothesised relationship can be written as follows;
446 Q =f(Fa, Pc. Ic, Ne, Ns, Ur^ where
Q = daily water use in industry, per day; Fa = factory floor area in m^; Pc =
production capacity in tonnes per year; Ic = industrial activity; A'^ = number of
employee; M = number of shift; and, Urd = use of recycling equipment.
7.2 Sampling Designs and Procedures
The sampling design and procedures is referred to the detailed plan that involves choosing of an appropriate sample selection technique and data collection methods, and collecting of a representative sample size from which a valid inference or generalisation from sample findings to the whole population under study can be drawn. It also involves a clear specification of the population of interest (target population), the units or elements to be sampled and analysed (the sampling units),_and the list of units from which the sample is going to be drawn (the sampling frame). The sampling design and procedures should also include a statement of the precision desired; this is the amount of error that a researcher is willing to tolerate. This tolerable error must be specified along with a degree of confidence for estimating the population parameters.
DziegielewskJ e/ al. (1996) noted that sampling has many advantages over a complete enumeration (or inventory) of the population under study. These advantages include reduced cost, greater speed of obtaining information, and a greater scope of information that can be obtained. The discussion of the types and characteristics of these sampling designs is beyond the scope of this research. For further elaboration on these issues see, for example, Fowler (2002); Henry (1990); Kalton (1983); and Cochran (1977).
447 Sample Sizes and Selection Procedures
The determination of sample size is a crucial issue in any sampling design. Bryman (2001) states that decisions about sample size depend on a number of considerations. Although
Dziegielewski et al. (1996) suggest that the size of the sample depends on the required
accuracy (error that can be tolerated), which indicates the deviations of the sample
measurements from the true values in the populatjon, and^the confidence level that is_
needed as captured by the /-value, Henry (1990) argues that sample size is a product of
cost and time rather than an estimate of the tolerable error or power required. Fowler
(2002) also warns against a simple acceptance of the desired level of precision as a single
criterion for sample size determination. In his view, this implies that sampling error is the
only or main source of error in a survey estimate.
It has also been argued (Kish, 1965; quoted in Henry, 1990) that calculations of the
sample size have been devised that allow the researcher to minimise the sample size for a
fixed cost or minimise the cost for a fixed level of precision. Bryman (2001) advocates
this view by stating that, decisions about sample size invariably represents a compromise
between the constraints of time and cost, the need for precision, and a variety of fijrther
considerations. This indicates that, while these arguments do not diminish the importance
of the level of precision in the sample size determination, they underline the need to
consider other important factors.
The target population for the municipal water survey is specified as the total number of
customers connected to the metered public water system at a given time. The total number
of these customers as of the end of December 2001 was 126,840 (A^ = 126,840). The
customer billings record, which contains practically the complete listing of the sampling
448 units, is obviously considered to be the sampling frame for this sector.
Designing a good sampling plan requires collection and analysing preliminary water use
data, either through a pilot study, or by obtaining existing published/unpublished data from
water agencies. Such an analysis often produces useful estimates of the population
parameters that enable calculation of the required sample size to be made. Efforts were
made to obtain the fiill list of customers in an appropriate form, including their
consumption figures, mailing addresses, and/or their account numbers from the
computerised billing records of the water utility. Unfortunately, obtaining such data
proved to be impossible because of political and legal reasons. In addition, interviews with
officials from the customer affairs indicate that the existing billings facility does not allow
fiimishing these records on electronic form, which renders them inconvenient to handle.
As an alternative to obtaining the complete sampling frame, a random selection of 4,006
customers (including all types of customers served by the publicly supplied water) was
made available for analysis. It is important at this stage to mention that even this dataset
was obtained with a great deal of difficulty (released after seven months of making the
initial request), which significantly affected the progress of the sampling programme. The
consumption data of this random selection customers were examined and carefiiUy
analysed and their monthly consumption were calculated to produce estimates for the
population parameters. The derived consumption figures in m^ per month of these sample
customers are presented in Table D-1 of Appendix D, and their frequency distribution is
shown in Figure 7.2.
Table 7.1 presents descriptive statistics of these sample data. It shows a mean monthly
449 Figure 7.2 Histogram of frequency distribution of random sample data of municipal consumption - raw variable
•a 400 I -
100
04W s £ s 5 s s « 5 ? s 5 £ s 3 s s e £ g i s £ » s i M 1 i n § § 3 i § i § § ^
Water consumption in m /consumer/month
450 Table 7.1 Descriptive statistics on raw and log transformed consumption data of a random sample of municipal water customers, with n = 4,006, together with raw sample data of agricultural water users, with w = 143
Statistical parameters Variable type Mean SE. Median Mode SD. Skew. SEskew. Min. Max.
Raw variable of municipal 44.45 1.19 30.25 2.75 75.50 11.74 0.039 1.08 1682.00 water users
Log transformed variable 1.44 0.0068 1.48 0.44 0.43 •0.40 0.039, 0.03 3.32 of municipal water users
Agricultural water users 89.86 - 4.55 69.73 27.43* 54.46 0.896 0.203 24.12 240.93
Consumption values for municipal water use are in m'*/customer/month, while those for agricultural water use are in m /ho/day. • Multiple modes exist. The smallest value is shown.
451 consumption of about 44.45 m^ per customer per month (m^/c/month), with a standard deviation of 75.50. The skewness statistic of 11.74 and the frequency distribution in
Fig:ure 7.2 exhibit a marked positive skewmess, which characterises many types of
economic-related variables. Boland et a/., (1983) and Billings and Jones (1996) observe
that this kind of skewness is typical distribution of individual water use. This is because
high consumption often seems to be_due to relatively few_custpiners using extremely large_
quantities of water. The main feature of this type of variable is that the standard deviation
is larger than the mean, indicating a wide variation around the mean score. A second
feature is that the mean is larger than the median, and the former could be a biased
estimator for the central tendency of a given variable.
One way of coping with such problem is by transforming the variable. A commonly used
transformation for removing positive skew is the log transformation (Miles and Shevlin,
2001). This is normally done by transforming the raw scores of the variable to logarithmic
values, and re-calculating the mean by taking the antilog of the resultant logged value.
This corrected mean (the one which has not been influenced by the skew) is then used as a
population parameter estimator for calculating the sample size. The distribution of the
logged variable of the sample data is shown in Figure 7.3. Both the skewness statistic
(Table 7.1) and the graph in Figure 7.3 clearly demostrate that the transformed distribution
of the variable closely approximates the normal distribution. The derived corrected mean
is close to the median, probably indicating that the median is a more appropriate measure
of the central tendency of this variable. This may seem to suggest that the sample is
slightly over-representing the small customers. However, repeated random selections at
different percentages of this sample produce variances in the mean values of no more than
10% from the obtained value. For this reason, and given the insufficient time and financial
452 Figure 7.3 Histogram of frequcncv'distributio n on logged cx)nsumption variable of a random sample of municipal water users
160 T
140 4
100 -
4
2(1
ljia4iiL L,iii,ij.ij.i. ,1, , — >o t*> "9 »r\ ^ o ^ s z ^. M
453 resources available for this research, the corrected mean value was considered to be reasonable for calculating the sample size.
Since the greater the variation in the variable scores, the larger a sample will require to be, the so-called stratified random sampling is the most appropriate sampling technique for reducing the sampling variability, thus obtaining a niore representative sample with a minimum sample size. The possibility of obtaining a homogeneous stratified random sample for municipal water users was explored by grouping the sample data into different customer groups based on the level of consumption, with the existing tariff structure (see
Table 6.12) being a baseline criterion for this stratification. First, the sample data were classified into three strata: low, medium, and high consumption water users. In the second attempt, the stratification process was based on five strata; these are users with a very low, low, medium, high, and very high consumption. Both attempts failed to produce practical sample sizes.
Inevitably, the estimated parameters from these consumption data were, therefore, used to calculate the first approximation of the sample size for a random simple sampling (every member of the population under investigation has an equal opportunity of being included in the sample) based on the formula (Dziegielewski et a/., 1996):
(1) VrY) where no is the first approximation of sample size, / is the t-value for the desired confidence probability. This value is 1.64, 1.96, and 2.58 for confidence levels of 90, 95, and 99 percent, respectively. S is the population standard deviation estimate, Y is the estimated
454 population mean, and r is the tolerable error (relative error of the parameters being measured).
If nJN is not negligible, we calculate the finite sample size by correcting the first approximation of sample size rio using the following formula:
"=T^ (2)
. N)
here, n is the sample size with the finite population correction, and N is the defined study population (the total number of customers served by the public supply system).
Substituting the sample data estimates into equation (1), assuming a 95% level of confidence of estimating the average monthly consumption within a 10% of the true value, we obtain:
96)(75.50)' = 1106 1(0- 10)(44.45)^
In practice, the finite population correction can be ignored whenever the sampling fi-action does not exceed 5%, and for many puiposes even it it is as high as 10% (Cochran, 1977).
This is because in such a case the finite population correction is close to unity. It is further suggested by Mendenhall et al. (1971) that if (A^ - no)IN > 0.95, the finite population correction can be ignored. For our sample, the sampling fi-action Wf/W equals 0.87% and,
(N-noYN =(126840- 1106)/126840 = 0.99
455 This indicates that the finite population correction can not be applied, and the first approximation had, therefore, to be accepted as the required sample size. In statistical terms, this means that if the average monthly water use per customer is unknown, to be
95% confident of estimating it by sampling customer billing records with an error of 10%, a sample of 1,106 customers would be required.
Although it is obvious that the assumed tolerable error of 10% may be considered very high, which might affect the reliability of the results, asking for an increased precision was not possible as this would result in a substantial increase in the sample size. As a general rule, to halve the sampling error we have to quadruple the sample size (de Vaus, 2002).
For example, decreasing the desired tolerance of the estimate to 5% would raise the sample size to 4,443 customers. Evidently, collecting a sample of this size would be a task beyond the budgetary ^d tinie resources made available for this researc]^. In gener^, as Cochran
(1977) states, the error limit is determined in the light of the uses to which the sample results are to be put, and in terms of consequences of errors in the decision. In such a case, as suggested by Henry (1990), trade-offs between cost/time and level of precision must be considered essential. Therefore, for the purpose of this research, the 10% error was considered to be a reasonable compromise.
As stated earlier, the complete listing of the sampling units from the consumer billing records (the sampling frame) was not made accessible to this research. Besides, the list of sample data acquired from the water utility did not provide an ideal semi-sampling frame because it lacks important information such as mailing addressess, customer categories, premise codes or account numbers, and telephone numbers. Since the major task in sampling is to select a sample from the defined population by an appropriate technique that
456 ensures the sample is representative of the population and as far as possible fi'ee from selection bias (Bums, 2000), the absence of a complete sampling frame has highlighted considerable difiBculties on the proposed random selection process. Henry, (1990), however, emphasised that the choice of a sampling frame or using an alternative technique that does not require a frame (i.e. cluster or multisatage sampling) must be made corLsidering_the^ ramifications for data collection meth^s, _cost, constraints on other choices, and total error.
Realizing this limitation, the only feasible alternative, taking into account the limited resources available for this study, was carefiilly to design and perform a sampling plan capable, as much as possible, of reducing the nonsampling bias that is likely to arise.
Such non-probability sampling procedure is highly concerned with representativeness, and is frequently described as the representative sampling technique. Accordingly, a representative sample of municipal water users of size n = 1,106 was chosen with steps being taken to ensure that all the consumer categories, types of properties, and social grouping are fairly represented in the sample in order to minimise possible selection bias.
Consideration was also given during the course of sampling for guaranteeing complete but, obviously, unequal geographical reflection of the sampling units.
The target population for the agricultural water survey is defined as the entire active and semi-active farm plots in the study area. From the statistical farms record, readily obtained from the Statistical Department of the Agricultural Authority, a total number of 1,093 farm plots is recorded up to the end of December 2002 (A^ = 1,093). This record comprises a complete listing of these farms, including information on plot number, plot size in dunum, location, farm owner, and, in many cases, farm tenant. Another list that contains the farm
457 plots currently being supplied by TSE was also acquired from the same authority. These records form a very useful sampling frame that contains practically the entire individual farm plots in the area of study (farm plots using TSE are contained in the original list provided by the Agricultural Authority; thus, with these farms being considered N is still
1,093).
Fortunately, as already stated in the preceding chapter, most of the irrigation wells in
Bahrain are metered to monitor the groundwater abstraction. This is also the case for farm connections at farm plots supplied with TSE. These data have yielded information of considerable value for estimating the population parameters. Metered consumptions for agricultural for around 220 farms for year 2000 were made readily available for analysis.
Out of which a final selection of 143 farms was made on the basis of the quality and
reliability of the available abstraction data. These data were analysed to produce estimates of the population parameters. This was achieved by matching the well numbers with the
plot numbers and calculating the average daily consumption for each plot. These average
consumption figures were then simply divided by the plot sizes to produce the irrigation
water requirements for each farm in cubic metre per hectare per day (m^/ha/day).
Table D-2 of Appendix D presents the consumption at these farms, and Figure 7.4 provides
an illustration of their frequency distribution.
The descriptive statistics of these data are also given earlier in Table 7.1. It shows that the
sample data has a mean consumption of 89.86 ra^/ha/day, a standard deviation of
54.46 m^'/ha/day, and a skewness of 0.896. Figure 7.4 indicates that the distribution of this
data set displays some positive skewness. For the purpose of this survey, however, the
distribution of the variable was assumed to be approximately close to normality, and the
458 standard deviation estimate was used to calculate the sample size from the following formula (Johnson, 1996; Cochran, 1977):
a_ iN-n
in which, E is the maximum tolerable error (taken here at 10 deviation units), Zaa is the standardised /-statistics at the desired confidence level, a is the standard deviation estimate, n is the sample size, and N is the population size (here represents the total number of farms).
Figure 7,4 Frequency distribution of agricultural water use sample data
12 T
10 +
e o I
HH- l"l I I 1"!
Consumption (m /ha/day)
459 Further manipulation:
n \E\N'\y7Jal^a^\=Z'a/2a^N
Solving for n.
n =
This gives: ^= (1.96)^54.46)^(1093) _ (lO)' (l093-l) + (l.96)'(54.46)'
Statistically speaking, this means that since the standard deviation of the population is unknown, to be 95% confident of estimating its true value within an error of 10 deviation units, a sample of 104 farms is needed. This sample size represents approximately 10% of the total target population. The complete sampling frame was entered into the computer, and a random sample of 111 farms was selected, with sample size set at approximately
10% of all cases and a 95% level of confidence. The process was repeated several times, and the group of cases that produces the minimum absolute standard error was chosen as the cases (farm plots) to be included in the survey. After the sample had been seleaed, a list of these farms containing the plot number, location, owner and/or tenant was generated.
The total number of industrial firms in the study area is identified as the target population for the industrial water use survey, which equals 355 industrial firms according to the latest
(up to September 2001) industrial register made accessible to this study. This industrial register provides valuable information on the firms commercial names, their addresses, telephone and fax numbers, as well as number of employees classified into Bahrainis and
460 Non-Bahrainis, and is considered to be the sampling frame for this sector. Each individual
industrial firm in this register represents the unit or element of analysis that might be
selected in the sample.
As a means to select a probability sample, the original sampling frame had to be modified.
For one things some qfthe industrial^rms^Imve been U^^ severaL times,. pxejumably_in
compliance v^th the industrial registration procedures (each industrial category should
have a separate license), though many of these are occupying the same factory floor area,
operating under a unified management and accounting system, and, more importantly,
using a common source of water supply. For another, it was found that some of the listed
firms are more in the commercial and/or marketing sides. This is particularly the case with
some aluminum-related and food supply activities. Finally, the list contains industrial
firms that are no longer exist or investors have already left the country. This was
recognised by carefiilly studying the sampling frame, making a number of telephone calls
and site visits for personal checks, and arranging several meetings with officieils from the
Ministry of Industry for discussions. The modification process yielded a shorter list of
305 industrial firms that has been considered to be a more accurate sampling frame.
Data on industrial water abstraction from private groundwater sources are available or
could easily be made obtainable for most of the large industrial users. On the contrary,
consumption data of industrial water users served by piped supply or those meeting their
demand by purchasing water from outside sources are unavailable. Ideally, a complete
sampling frame of the municipal water users, with consumption data specified by
categories of use (i.e. domestic, conmiercial, and industrial) would have been, together
with consumption figures from private industrial users, highly desirable for analysis and
parameter estimates purposes.
461 In the absence of such data, however, it was decided to conduct a pilot study of a fragment of the population in order to provide estimates for the population parameters. This is achieved by choosing a probability sample of 5% (/; = 15) of the industrial water users from the modified sampling frame through a random selection process. Not surprisingly, the results of this pilot survey exhibited a v^de variation of the average water consumption.
Such a_variation is a function oHhe broad classification of ^t^^^^ possible influence of a few major users. This has placed considerable constraints on the determination of a practical sample size, since the derived population parameter estimates were totally inadequate for this purpose.
Increasing the number of pilot cases to 31 to ensure better representation did not solve the problem. Prompting the pilot survey to the so-called step sampling approach was also attempted. This approach, according to Cochran (1977), involves t^en saniple in two steps with normally wide range of error is considered to give a good initial estimate of the required sample size, and a correction factor (correction for bias) is computed and applied for the final estimate. Unfortunately, considerable difficulty was experienced in applying this approach most probably due to the nature of the target population and the complexity involved in the calculation procedures.
Owing to these constraints, no further attempt was made to obtain estimates of the population parameters. Therefore, despite the presence of a reasonable sampling frame, a representative sample that included examples of all types and levels of industrial activities in the study area was selected. This was considered to be a better sampling option in order to study the water use characteristics and demand patterns in the industrial sector, considering the nature, size, and characteristics of the target population. A sampling
462 fraction of 45% of the modified sampling frame was arbitrarily specified for sample size determination. This indicated a sample of size n= 137. It was assumed that such a large sample size would probably compensate for the bias that would be apparent because of the inability to estimate the population parameters and the failure to obtain a simple random
sample.
7.3 Methods of Data Collection
While this chapter considers the issues of sampling design and procedures and methods of
data collection separately, they are in fact interrelated and form together part of the
standard concept of survey research design. The placement of the data collection methods
under separate subheading is merely for convenience.
The method of data collection is normally guided by the nature of the research problem to
be addressed, the objective of the research, characteristics of sample and population, and
the resources available to the researcher in terms of finance and time. Once the appropriate
sample selection technique has been chosen, various methods of data collection would be
available to the investigator. These data collection methods entail, but are not limited to,
personal structured or semi-structured interviews, telephone interviews, self-administered
or interview-administered questionnaires, and Internet surveys. For fiiller discussion of the
characteristics of the various methods of data collection, their advantages and
disadvantages see for example. Fowler (2002); Bryman (2001); Babbie (1998); Nachmias
and Nachmias (2000).
The primary sources of data for this research are survey questionnaires specifically
designed and administered for collecting data on the water use patterns and demand
463 characteristics in the municipal, agricultura], and industrial sectors using cross-sectional surveys. The combination of the survey questionnaires as data collection instrument, and the cross-sectional approach as survey design was considered to be the most appropriate methodology to achieve the objective of this research. Babbie (1998) demonstrated that such a survey design can be used not only for purposes of description but also for the deterrrunation of relationship between vari^les, and malMg inferences d)^^ of change over time. Oppenheim (1992) makes the point that questionnaires are a way of collecting certain types of information quickly and relatively cheaply as long as the researcher is sufficiently disciplined to abandon questions that are superfluous to the main task. Among other advantages, Denscombe (1999). claimed that questionnaires are at their most productive when what is required tends to be fairly straightforward information, relatively brief and uncontroversial - perhaps this is the case with the present research.
Questionnaire Designs
Developing an appropriate set of question is one of the critical phases of obtaining information from water users (Billings and Jones, 1996). The questionnaires were designed based on the consultation of previous Uterature on the subject, the researcher field experience, and his previous involvement in designing questiormaires on water use patterns in Bahrain for the Bahrain Centre for Studies and Research (see BCSR, 1990). These provided a reasonable theoretical justification for inclusion particular questions, and deciding on the overall questionnaire design process.
Considerations were given from the early stages to design very simple, yet effective, as short as possible with no excessive elaborations, consistence, and easy to analyse questionnaires. Therefore, the majority of the questions asked were formulated using
464 close-ended and pre-coded questions, with which the respondent is presented with a set of fixed alternative answers (categories) from which he/she has to choose the appropriate ones. In some cases, respondents are specifically asked to choose firom dichotomous
choice questions where they only required to tick either "Yes" or '^^^o", or to answer
questions that require entering only numeric values. The exception is the final question in
the municipal questionnaire, which is an open-end^ type intended ^o allow respondents,
should they wish, to comment and to fi-eely express their views on various issues of the
subject matter. The questionnaires are divided into groups of questions, each of which is
structured to obtain specific information on certain variables pertinent to the research
hypotheses. The response categories (alternatives) were carefully selected, mainly based
on the researcher's field experience, and as dictated by the research objectives.
The questionnaires were originally written in the English language; however, both the
municipal and agricultural questionnaires were later translated into the Arabic language to
be suitable for most of the population who have no access to the English language. The
Arabic versions of these questionnaires were written in a very simple, to the point, and
easy to understand language level. The industrial questionnaire, on the contrary, was not
subjected to translation into the Arabic language, on the assumption that the entire
potential respondents in this sector are likely to have an easy access to the English
language.
Before the final administration, the questionnaires were subjected to small-scales pilot
testing. The piloting phase was intended to improve the questionnaire contents through the
respondents comments and interpretation of the questions, remove ambiguity fi-om
questionnaire items, deal vAih confusion surrounding some questions, discover potential
465 problems with coding and/or item sequences, record time required to complete the questionnaires, measure the reaction of the respondents towards the questionnaires, and finally to train field workers.
The pilot tests involve 32, 12, and 7 of potential survey respondents for the municipal, agricultural, and industrial questionnaires, respectively. These tests were Jound^ to ^e particularly usefiil in revising the questionnaires and modifying (re-wording) parts of their contents, and in identifying areas where potential problems with analysis might arise. The earlier drafts of the questionnaires were also passed to selected experts on questionnaire designs for feedbacks and examining the reliability and validity of the questionnaires as data collection instrument. They were invited to comment on the contents of the questionnaires, their clarity, arrangement of their items, and language. Their comments were proved to be valuable in improving the questionnaires coding, ordering, and re-phrasing of some questions.
The final versions of the municipal, agricultural, and industrial questionnaires are included in Appendix D as Texts fi'om D-1 to D-3, respectively. As can be seen, because of their apparent simplicity, introductory instructions to help respondents answering the questionnaire items were considered unnecessary. However, in the event that respondents were sceptical about the survey, a covering letter explaining the objectives and importance of the research and encouraging respondents to participate in this survey is attached to the questionnaires. This is shown as a model covering letter written on a personal heading paper (Text D-4, Appendix D). As illustrated, the letter provides sufiBcient background information about the research, and it assures the respondents fiill confidentiality by explicitly stating that their responses will be treated in absolute confidence, and their
466 names will be kept completely anonymous. It also specifies the researcher's telephone/fax numbers and e-mail address for easy contact should the informant requires further clarifications regarding the survey or wishes to send a completed questionnaire. Further, owing to the wide diversity of the municipal questionnaires, the non-residential water users were supplied with instruction notes to help them answering the applicable questions.
Data Collection
The study involved a questionnaire survey of 1,354 water users, distributed as 1,106, 111, and 137 samples for the municipal, agricultural, and industrial sectors, respectively.
A total of 1,300 questionnaires (almost 18% larger than the required sample size) was distributed to the municipal water users. T\us was to account for possible non-responses and unusable returns (questionnaires completed in unsatisfactory manner). Because the sampling units in the agricultural survey were selected through a random selection process with known probability, questiormaires in this sector were distributed according to the determined sample size. With respect to the industrial survey, about 160 questionnaires
(almost 17% more than the required sample size) were distributed to the users. This is again to account for possible non-responses and unusable returns.
The questionnaires as data collection instruments were administered by a combination of
self-completion and interview-administered strategies in order to obtain an adequate level
of response and, at the same time, limiting administration costs. The municipal water use
survey was primarily conducted using the face-to-face approach. The agricultural water
use data were collected through interview-administered strategy using trained personnel.
The industrial water users were asked to complete the questionnaires on their own, because
it was felt that demands on busy people normally result in high rates of refusals. Some of
467 the questionnaires in this survey were, however, filled using the face-to-face approach.
The self-completed questionnaires were distributed by means of home delivery and direct personal contacts during which the objectives and importance of the study were fully explained.
Regarding the municipal questionnaire survey, data were collected on. the consumption, variables including indoor and outdoor water uses, household variables, frequency of water usages, water-using installations, socio-economic variables, appliances ownership, as well as information related to the consumption characteristics within the large customers group.
The agricultural survey seeks to elicit information on potential determinants of irrigation water use, including agricultural holdings, areas actually cultivated and actually irrigated, water source variables, irrigation variables, consumptive use, and crop patterns. Farmers are also requested to give perspectives on their plans Jor fliture development, if any. The industrial questionnaire was designed to obtain responses on varies variables such as the industrial category, factory floor area, number of employees, and production capacity. It also compiles data on water sources and consumption patterns, water recycling and desalination. Industrial firms expected economic outlooks with respect to future employment and increase in factory floor areas were also considered among the questionnaire items.
The surveys were carried out over a 14-month period from April 2002 to May 2003 by the researcher and a small number of trained field workers. Responses to the municipal questionnaire items are intended to be representative of the dwelling units not the individual; thus, information on personal characteristics and qualification levels was not considered. However, respondents in the households are assumed to be the heads of the
468 family (either husband or wife) or, in exceptional cases, adults of at least 20 years of age.
In the case of non-residential users, respondents were primarily administrative officers, or chief engineers. With respect to the agricultural water use survey, questionnaires were to be completed by the farm owners or tenants. After initial meetings with plant officials in each industrial firm surveyed, they were requested to answer the questionnaires or passing them to the most appropriate _person; often public relations„officers,_ production/plant managers or administrative officers.
Considerable difficulties were faced during the data collection stage. In general, the non• residential water users have shown the lowest degree of co-operation. A number of reminder calls and re-visits seemed to be of little help in further improving the rate of responses within this consumer group. The major problem experienced during the agricultural water use survey was the unavailability of farm owners or tenants during the planned survey visit, which required intensive follow-up effi3rts, including re-visits and calling for appointments at different times of the day. In the industrial questionnaire, extensive follow-up telephone calls, several visits and re-visits, which in many cases involved providing replacement questionnaires for lost or misplaced ones, were necessary to improve the response rate. Overall, these difficulties have greatly increased the time needed to complete the surveys.
Of the 1,300 questionnaires distributed to the households, non-residential, and non- domestic water users. 1,132 questionnaires were completed and returned, representing a response rate of 87%. As mentioned above, the majority of the survey non-returns were among the non-residential water users. However, obtaining a response rate higher than the desired sample size, highlights the advantages of drawing an initial sample that is larger
469 than needed. The poorer responses (26 questionnaires) were excluded from the analysis.
Responses from the agricultural water users were most encouraging, with a response rate of 100%, all usable.
A satisfactory rate of response of 80.6% (out of the 160 questionnaires distributed,
129 questionnaires were_ oompleted _and . returned) was._alsp_ achieved .from the„industrial water use survey. Five of these returns were, however, poorly completed and were considered to be unusable returns, and had to be excluded from the analysis. Nevertheless, the usable returns of 124 questionnaires represent a satisfactory return of 90.5% of the intended sample size (/i = 37), or nearly 41% of the total target population. This again highlights the advantages of drawing an initial sample that is larger than required. As indicated earlier, however, the great proportion of the firms who refused to take part in the survey or those who declined to return their questionnaires is concentrated within those consuming less water, indicating that it is highly likely that the sample over-represents the large customers.
Once completed and returned, each questionnaire was assigned an identification (serial) number to make it ready for computer data entry. The survey data were then entered into the computer and data files were created for each sector survey.
Consumption Variables
At early stages of this research, it was questionable whether the surveys respondents would be able reliably to answer questions related to water consumption. Respondents having problem with these questions were simply allowed to skip them, since attempts were made at the design stage to obtain the required consumption data from the concerned authorities.
470 Data on municipal consumption were obtained fi'om the water authority in the form of annual account summaries (actual amount of metered water use) for year 2001 using the addresses given by the respondents as codes. From these accounts (lagged one month), the average monthly summer (June to November) and winter (December to May) consumptions were computed, and the average consumption (measured in cubic metre per customer per month) was then derived by simply dividing the sum of these_ayerag^ by Z
For most of the agricultural farms sampled, the average water use for irrigation (measured in cubic metre per month) was computed by matching the well numbers with the plot numbers. This was achieved by calculating the averages sunmier and winter consumptions fi-om groundwater abstraction records maintained by the WRD, then simply computing the average consumption for each farm by dividing the sum of summer and winter averages by 2. The classification of summer and winter consumptions is based on the officially adopted growing seasons, where the summer season is from March to August, and the winter season continues from September until February. Likewise, the average monthly consumptions (also measured in cubic metre per month) for the farms supplied by TSE were calculated from consumption figures made available from the concerned authority by matching the plot number with the farm connection code, which is primarily based on the farm number/owner or tenant.
Industrial water users were generally able to provide sensible consumption figures.
However, in order to ensure that the reported consumption figures provided by the surveyed self-supplied industries were reasonably accurate, they were checked against the actual consumption data obtained from the WRD. Since most of these figures are actual metred consumptions, only a few adjustments were found to be necessary. Consumption
471 data for some industries served by the public supply were obtained fi-om the annual account summaries.
Creation of New Variables
New variables were created by either arithmetic transformation or a combination of two existing varia^es in order to further facilitate statistical analysis. The average domestic per capita daily consumption (measured in litres per person per day) was simply derived fi-om the average consumption in litre per customer per day divided by the number of occupants per household. A variable representing the per capita income was created fi-om the family monthly income and household occupancy. The number of person per day working, visiting, studying, worshipping, etc. were used to calculate the municipal daily per capita consumption. The average number of showers taken per day, average floor washings per month, average car washing per week, and average number of guests per day, were also created as either potential independent variables or useful ones for descriptive analysis, by simply summing up their summer and winter average values and dividing them by 2. The average irrigation fi-equency per week is similarly derived from the summer and winter average irrigation frequencies. A set of dummy variables such as dummy connection to sewers, dummy swimming pool and dummy type of dwelling was also included in the analysis.
An added variable representing the average irrigation requirements per hectare of cultivated land (measured in cubic metre per hectare per month) was also computed by simply dividing the average consumption per month by the gross cultivated area in hectare.
The average summer and winter irrigation frequencies were summed up and divided by 2 to create a variable reflecting the average irrigation frequency. Likewise, a variable
472 representing the average pumping hours was created. The created dummy variables for agricultural water use include dummies for crop patterns and irrigation methods. Industrial dummy variables constitute dummies for industrial activity, number of shifts, connection to sewers, water source, water re-cycling, and use of desalination plants.
Preparing Variables for Analysis „ _ _ _
Preparing variables in suitable form before nmning the main analysis is a crucial step between data collection and data analysis. The questionnaires coding and variable definitions are planned before questionnaires distribution, though a re-coding process has been continued until later stages based on the initial examination of survey results.
Distinctive codes (different fi-om those of valid responses) were also assigned beforehand to the missing values and non-applicable questions.
During the data entry stage, questions were carefully checked for possible misunderstanding or confusion. Accordingly, those particular respondents who have provided some unclear information were contacted and/or re-visited to correct and refine their responses. Initially, the data were explored by obtaining fi'equency tables and getting an overview of shape of distributions of each variable. This has allowed checking for miskeying, coding errors, changing codes, adding/deleting categories if necessary, and rearranging the order of categories, though the later was found to be of less importance for our continuous variables.
Of particular importance in this respect is the way of dealing with and handling of missing values and non-responses as well as transformation of variables if proved necessary.
Missing values are a problem because they reduce the number of cases available for
473 analysis, particularly when the research question involves bivariate and multivariate analysis (de Vaus, 2002). Tabachnick and Fidell (2001) demonstrate that the seriousness of missing values primarily depends on their amount and patterns of distribution. They suggest that deletion of cases having missing values is a reasonable choice if the pattern appears random and if only a very few cases have missing data, and those cases are missing on different variables; deletion of variables with a lot_ of missing data is also acceptable as long as that variable is not critical to the analysis. Despite the fact that there are no firm guidelines for how much missing data can be tolerated for a sample of a given size. Tabachnick and Fidell (2001), suggest that if only a few data points (5%) are missing in a random pattern from a large data set. the problems are less serious and almost any procedure for handling missing values yields similar results.
Table 7.2 presents the percentages of missing values on the variables of the municipal water use survey along with their definitions. As the table suggests, the great proportion of missing data is related to the consumption levels (i.e. variables qa3agics, qaSagicw, and avercons). This is because a relatively large proportion of respondents did not supply their addresses. Also, some addresses were not recognised in the billing system most probably due to the distinction between the given address and the billing mailing address. The customer account number would have been a better option to reduce the missing values in this variable. Although this option was considered at an early stage of the questionnaire design, it was felt that asking for customer account might raise response problems.
Besides, it is highly likely that the great majority of the customers do not keep their paid bills nor memorise their account numbers.
The percentage of missing values on the variable avercons (monthly average consumption)
474 Table 7.2 Variable definitions and their percentage missing values from the municipal survey returns
Variable Variable definition % Missing Variable Variable definition % Missing values i values qa3agics Summer average consumption (mVc/month) 33.4 qclaiuss Number of shower taken in summer/day 0.7 qa3agicw Winter average consumption (m^/c/month) 34.5 qclbiusw Number of shower taken in ;winter/day 0.9 avercons Average consumption (mVc/month) 29.5 qc2iuntf Number of toilet flushing/occupant/day 2.8 qa5gicss Connection to sewers 1.0 qc3aiuwh Number of clothes washing ^by hand/week 2.3 qaSgiswp Ownership of swimming pool 0.0 qc3biuwm Number of clothes washing by machVweek 2.1 qalOginb Number of bathrooms per dwelling 1,4 qc4aiudh Number of dishwashing by hand/day 2.2 qallginf Number of flush toilets per dwelling 1.4 qc4biudw Number of dishwashing by dishwasher/day 0.0 qal2gint Number of taps per dwelling 2.0 qc5iunhw Number of hand wash per occupant/day 2.6 qa13ginc Number of cars per dwelling 2.0 qdlougar Garden area in m ' 5.6 qblhvnoc Number of occupants per dwelling 0.4 qd2ouimd Irrigation method 0.4 qb2hvhar Household area in nf 8.5 qd3oumxd Mixed irrigation methods 1.1 qb3hvnih Family monthly income in BD 14.2 qd4aouis Number of irrigation in summer time/week 0.2 qb4hvnsh Number of showers per dwelling 1.2 qd4bouiw Number of irrigation in winter time/week 0.4 qbShvnwm Number of washing machines per household 0.3 qd5oucpn Crop patterns 3.8 qb6hvndw Number of dishwashers per household 0.2 qd6aoucs Floor washing in summer time/month 12.6 qd6boucw Floor washing in winter time/month 13.5 qe4alcrw Aver, number of guests per day in winter 0.0 qd7aoubs Car washing by bucket in summer per week 4.3 qe51cmw Average number of worshipers per day 0.0 qd7boubw Car washing by bucket in winter per week 4.7 qe61chnp Average number of in-patients per month 0.0 qd7couhs Car washing by hose in summer per week 4.2 qe7Icsns Average number of students and staff 0.0 qd7douhw Car washing by hose in winter per week 4.0 qeSlcms Average number of cuslome • per day (Res) 0.0 qellcgne Number of employee in government est. 11.1 qe91cfiis Number of employee in the factory 0.0 qe31ccne Number of employee in commercial est. 14.3 qelOlcns Number of shift 0.0 qe4alcrs Aver, number of guests per day in summer 0.0
475 is 29.5%. Since this variable is the presumed dependent variable, such a.reduction on the valid cases would likely to degrade the final results of the models. Variable qb3hvnih
(family monthly income) also recorded a relatively large proportion of missing value
(14.2%), indicating that some respondents were reluctant to give details of their earnings.
Following the guideline procedures suggested by Tabachnick and_FidelL(2001) for^dealing with the missing values, when the percentage of missing values for each key variable are less than 5% they were replaced by the mean values. Missing values were retained when their proportions are more than 5% on key variables on the ground that, in such cases, substitution of the missing values with the means would likely to result in distortions of the sample data. This assertion was also based on running repeated analysis with and without the missing data. Variables with missing values of fewer than 5% but are of less importance to the research questions were also retained^ _
From Table 7.2, substitution of missing values by the mean was avoided on the variables qa3agics (summer average consumption), qa3agicw (winter average consumption), and avercons (average monthly consumption). This was also the case with the key variables qb2hvhar (household area), qb3hvnih (monthly family income), qellcgne (number of employee in government establishment), and qe31ccne (number of employee in commercial establishment), and the less important variables qd6aoucs (summer floor washing) and qd6boucw (winter floor washing). For the rest of the key variables, the missing values were replaced by the mean using the so-called group mean approach with the type of dwelling being used as a background (group) variable. Indeed, the group mean approach is superior to the overall mean approach when the sample is of a highly dispersed nature. This approach involves obtaining the mean value for the missing data within each
476 category of the selected background variable. Missing values were retained for the less important variables (e.g. qd4aouiw - irrigation in winter), although their missing values are less than 5%. Obviously, missing values for the dichotomous and polytomous categorical variables were not substituted by their mean values.
The^percentages of the misang_yjdues mi the^^^ of the agricultural water use survey together v^ath their definitions are given in Table 7.3, The table shows that only three variables have missing values of less than 5%. The missing values of variables qdlaivfs
(irrigation frequency in sunmier) and qdlbivfw (irrigation frequency in winter) were replaced by the overall mean values (group mean approach was not applicable in this survey), while the missing values for the third variable qc3awsps (pumping hours in summer) were retained because it was not considered as an important variable. Six variables recorded high missing values of generally more than 20%; thus, their missing values were not substituted by their means. All but four of the farmers failed to provide information regarding the anticipated scopes of future agricultural development, recording missing values of around 96%. This is quite understandable in view of the unsecured tenure system being practiced in the study area. These variables (represented by the questions from 5B to 8B in the agricultural questionnaire survey) were, therefore, completely eliminated from the analysis.
It can be seen from Table 7.4, which demonstrates the percentages of the missing values on the variables of the industrial water use survey and their definitions, that five variables have missing values of less than 5%. These are qalfvfia (factory floor area), qa5fvnem
(number of employee), qc2awuvp (quantity of water used for production), qc2bwuvs
(quantity of water used for staff hygiene), and qc2cwuvo (quantity of water used for other
477 Table 7.3 Variable definitions and their percentage missing values from the agricultural water use'survey returns
Variable Variable definition % Missing Variable Variable definition % Missing values ' values qalagdcs Average consumption in summer in m^/day 22.5 1 qclwsws Water source 0.0 1 qal bgccw Average consumption in winter in m'^/day 23.4 qc2wsmxd N4ixed water source 0.0 avercons Average farm consumption in mVmonth 0.0 qc3awsps Number of pumping hours-in summer 4.6 qblfVpla Plot area in hectare 0.0 qc3bwspw Number of pumping hours lin winter 5.6 qb2fVcua Cultivated area in hectare 0.0 qc4wspcy Pump capacity in horsepower 8.3 qbSfvira Irrigated area in hectare 0.0 qcSawses Electricity consumption in summer (KWh) 37.0 qb4fVpaa Area under protected agriculture in hectare 0.0 qcSawsew Electricity consumption in winter (KWh) 46.3 qb9fvcrp Crop patterns 0.0 qc6wssn Average monthly consumption (pip. sup.) 8.0 1 qblOfVmv Area under vegetable crops in hectare 0.0 qc7wster Average monthly consumption (TSE) 0.0 qblOfvmp Area under palm trees in hectare 0.0 qcSwsanp Water used for another purpose 0.0 qblOfvmf Area under fruit trees in hectare 0.0 qc9wsquo Quantity used for this purpose 66.0 qblOfvma Area under alfalfa in hectare 0.0 q 1 Owssp Ownership of swimming pool 0.0 qblOfvmo Area under ornamental trees in hectare 0.0 qcl Iwspv Volume of swimming pool in m^ 0.0 qblOfvmt Area under poultry in hectare 0.0 qcl2aw5s Number of changing water in pool summer 31.0 1 qblOfvml Area under livestock in hectare 0.0 qcl2bwsw Number of changing water in pool winter 48.3 qcl3wssc Source for changing water in the pool 0.0 qd2ivirm Irrigation method 0.0 qdlaivfs Summer irrigation frequency(Time/day ) 1.8 qd3ivmxd Mixed irrigation method 0.0 qdlbivfw Winter irrigation frequency(Time/day ) 3.6 i
478 Table 7.4 Variable definitions and their percentage missing values from the industrial water use survey returns
Variable Variable definition % Missing Variable Variable defiriition % Missing values ! values qalfvfla Factory floor area in 0.8 qc3wuvwr Use of water re-cycling equipment 0.0 qa2fvinc Industrial category 0.0 j qc4wuvqr Quantity of water re-cycled 6.3 qa4fvpca Production capacity in tonnes per day 11.9 qcSwuvdw Use of desalination plant 0.0 qaSfVnem Number of employees 0.8 qc6awuvt Plant type 0.0 qa6fVnsh Number of shifts 0.0 qc6bwuvc Plant edacity 0.0 qa7fvswr Connection to sewers 0.0 qc6cwuva Plant availability 7.9 qaSfvrej Waste disposal point 0.0 qdlfdvfd Plans for future development 0.0 qbl wsvws Source of water 0.0 qd2fdvfa Type pf proposed future actmty 0.0 qb2wsvmx Mixed source 0.0 , qd3fdvot Other industrial activity 0.0 qbSwsvaq Aquifer source if water well or mixed 0.0 qd4fdvws Proposed source of water for the new plans 0.0 qb4wsvac Average cost of water if piped supply 13.0 qdSafdeS Expected increase in emplo>^ment in 2005 0.0 qclwuvac Average water consumption in mVday 0.0 qdSbfdeO Expected increase in employment in 2010 0.0 qc2a\vuvp Percentage of water used for production 3.2 qd6afda5 Expected increase in factory^ floor area 2005 0.0 qc2bvvuvs Percentage of water used for stafif hygiene 3.2 qd6bfda0 Expected increase in factory floor area 2010 0.0 qc2cwuvo Percentage of water used for other purposes 3.2
479 purposes). These variables are important to determine the distribution of water use within each industrial firm. Their missing values were, therefore, substituted by the means using the group means approach, with the industrial category used as a background variable.
Despite the assurance of full confidentiality, some of the industrial water users were reluctant to disclose their production capacity (missing data of 11.9%), claiming that this is against their management policy. The maximum niissing values (13%) is, however, in the variable qb4wsvac (average monthly cost of water bills if water source is supply network and/or purchase fi-om outside), though this has been somewhat reduced fi-om the original missing data by obtaining data on monthly average cost fi-om the annual account summaries. The missing values for these two variables have been retained.
The problem of non-response has been encountered in the municipal and industrial water use surveys. With regard to the municipal survey, the non-response is adequately dealt with at the outset by drawing an initial sample that is larger than needed. Although the achieved response rate is considered satisfactory in the industrial water use survey, its inadequate random distribution of these non-responses may lead to sampling bias.
However, apart fi'om the extensive follow-up efforts, explained eariier in this chapter, no allowances or adjustments have been made to account for the effect of possible non- response bias.
Variable transformations were found to be necessary during the model building process.
This was attempted to achieve several objectives: building more accurate models, eliminating the problem of multicollinearity, dealing with outliers, and meeting the normality assumption.
480 7.4 Analysis of Surveys Data
The analysis of the surveys data was performed using the computer software Statistical
Package for Social Sciences (SPSS) for Windows Version 9.0. Although, the questionnaire designs were not specifically intended to produce a thorough descriptive analysis, a brief micro-component analysis (within household, dwelling unit, farm, industrial firm) is considered here for_completeness.„ln.other_words,_the systematic-analysis of the demand characteristics presented in this chapter is complementary to the macro-component analysis of Chapter Six. The following discussion will address the surveys analysis for each water use separately.
7.4.1 Municipal Water Use Survey
As previously stated, the water use in the municipal sector is broken down into domestic and non-domestic water-use categories;-the former, in tums,-is a combination of residential and non-residential uses. The residential water use is a combination of common indoor uses such as toilet flushing, showering, and washing and some outdoor uses such as watering lawns and gardens, and car washing, and it can be divided into use by single- family units, extended households, and multifamily units. The single-family units are those consisting of parents and a number of dependants varying fi-om 2 to 6 persons. The extended household units may be defined as a household consisting of persons related by blood or affinity who are sharing food (CSO, 2002), with a high occupancy (sometimes reaching up to 25 persons per household). These sorts of families normally live in lower income areas.
A special form of extended family unit is popular in rural areas, where multi-marriage and nuclear family traditions are common. Multifamily-units are those living in multiple
481 premises dwellings (apartment buildings, flat apartments or housing compounds) and consisting of husband and wife and normally a smaller number of dependants than in single-families. The main characteristic of this type is that most of them are not metered and billed individually (i.e. a single bill is normally paid by the building owner). Another form of multi&mily is when part of the house is rented to another family or families, again
\yith a conrmipn meter._ _ . _
Naturally, these types of unit have different patterns of water use. For instance, on both a per housing unit and a per capita basis, water use in the multifamily units tends to be lower than in the single-family units, because of different household composition and the fact that multifamily residents have less opportunity for out-door water use practices
(Dziegielewsld ei al., 1996). Further, because of the economies of scale, per capita daily consumption tends to be lower in the extended household units than in both single-family and multifamily units.
Non-residential water uses in government offices, institutional premises and military establishments are also a combination of traditional in-door and out-door uses, but differs markedly in pattern and characteristics from those of residential users. Although not adequately defined, the non-domestic water use in the study area refers to the demand on piped water by the commercial and industrial users (referred to in the questionnaire, together with the non-residential users, as the large customers group). Commercial uses constitute a highly heterogeneous group of activities ranging from small retail shops and restaurants to hotels and apartments, and big service-oriented businesses, implying that water use in this category is of great variability. Water in the commercial sector is also used for conunon domestic needs including in-house uses and watering of gardens and
482 lawns. Industries served by the municipal supply are mostly those of small to medium- scale conversion industries; water in these industries is used for conventional industrial uses, including plant processes and staff hygiene, normally v^thout further treatment.
Table 7.5 presents information on the average water use and consumption variability for the residential^ and aggregate municipal uses. The average jwateii use pCT house^^ month is computed at 53.8 m^ (645.6 m\ear''). This suggests almost a 27% less use per household than the average of 68.0 per month (815.9 ra^year"*) estimated earlier from the demographic data for the year 2001. Despite the fact that water requirements per household or per capita tend to vary depending upon the prevailing climate and social and economic conditions, this figure is much higher, for example, than the domestic water uses per household of 397.2 and 314.6 m"'year'* reported by Martin (1999) for Jordan and Syria, respectively. As also illustrated in Akintola and Areola (1980). evidence from Nigeria shows an average domestic consumption (piped supply) of 236.1 m^ per household per year.
The average monthly municipal consumption per dwelling unit is found to be 73.7 m^
(884.4 m^year"'). As Table 7.5 shows, the survey data yielded average daily per capita residential water consumption of 282.3 Ipcd (62.2 gcpd), and an average daily municipal water consumption of 268.3 Ipcd (59.1 gpcd). Allowing 23.1% for system leakage, the average daily residential and municipal per capita consumption will be 347.5 Ipcd
(76.5 gpcd) and 330.3 Ipcd (72.8 gpcd), respectively. Clearly, the derived residential and municipal per capita daily consumptions are much lower than both the official and the modified per capita figures previously obtained from the analysis of macro-data. The significance of this result is that it confirms the observation made earlier that a great
483 Table 7.5 Summary statistics on average water use and consumption variability for the residential and aggregate municipal water uses
Variable Residential water use Municipal water use
Valid N Mean SD Sum Valid N Mean SD Sum 1 Per household/dwelling water use (mVmonlh) 736 53.8 31.6 39622.7 781 73.7 135.8 575795 (1.164) (4.858)
Per capita consumption (Ipcd) 736 282.3 1891 207788.9 781 268.29 192.80 209538.8 (6.969) (6.899)
Average summer consumption (mVmonth) 704 56.9 34.0 40098.7 737 74.7 1397 55060.4 (1.282) (5.144)
Average winter consumption (m^/month) 698 52.8 , 31.4 36888.3 731 67.2 118.3 49107.5 (1.190) (4.375)
Figures in brackets arc the standard errors of mean.
484 proportion of the water distributed through the public supply network, burdened on the per capita consumption, is actually used by the VIP unbilled and extremely large customers.
Of course, considering the uncertainties inherent in using relatively small sample size compared to the total population, and the fact that the obtained figures seem to be affected by the supply ceiling policy, more data are needed to verify this conclusion.
Table 7.5 goes further to suggest that, on average, a common household consumes
56.9 mVmonth in summer, and 52.8 m^/month in winter. The average dwelling uses about
74.7 and 67.2 mVmonth in summer and winter months, respectively. As Table 7.6 illustrates, both results indicate high statistically significant differences between average summer and winter consumptions (Wilcoxon, Z = -7.620, p = 0.001, two-tailed) for the domestic water use, and (Wilcoxon Z = - 8.013, p = 0.001. two-tailed) for the municipal water use. This can be translated into domestic and municipal seasonal variability of 7.8% and 11.2%, respectively, suggesting that consumption tends to increase by nearly between
7 - 12% during peak demands. It is of interest to note that this finding is in good agreement with the seasonal consumption variability and peak ratios obtained from the analysis of macro-data (see the discussion of this issue in Chapter Six, pages 422 and 425).
Frequency of use is defined as the number of times the listed activity occurs per unit of time in a household (Thackray, et a/., 1978). Table 7.7 gives a summary statistic of the frequency of use for each in-house water use activity. Analysis of the results showed that frequency of showering varied considerably over the surveyed households. On average, a person in the study area takes a shower 10.6 times a week (approximately 1.5 times/day), with summer showering frequency (13.04 times per week) exceeding the winter shower taken frequency (8.14 times per week) by more than 60%. A slightly less than four toilet
485 Table 7.6 Wilcoxon signed - ranks test for difference between summer and winter averages residential and municipal water consumptions
Domestic consumption Municipal consumption
Valid N Mean ranks Sum of ranks Valid N Mean ranks Sum of ranks Wilcoxon signed - ranks test Average consumption Negative ranks 433' 365.46 158245.50 456" 383.70 174968.00 winter Average consumption Positive ranks 255' 308.90 78770.50 265^ 321.94 85313.00 summer Ties 10^ 10^
Total 698 731
a Average consumption winter < average consumption summer b. Average consumption winter > average consumption summer c. Average consumption summer = average consumption winter
Test statistics average consumption on winter - average consumption on summer •8.013" -7.620" Assym sig. (2-tailed) 0.000 0.000 Q. Based on positive ranks. b. Wilcoxon Signed - Ranks Test.
486 Table 7.7 Summary statistics for the in-door water use activities
Std. Variable Valid N Mean Median Std. dev. Min. Max. error 1 Frequency of summer showering (time/person/week) 1060 13.04 0.153 14.00 4.98 2.0 28
Frequency of winter showering (time/person/week) 1060 8.14 0.105 7.00 3.40 1.0 21 1 Average showering (time/person/week) 1060 10.6 0.113 10.50 3.69 1.5 24.5
Frequency of toilet flushing (time/person/day) 1059 3.65 0.058 3.00 1.89 1.0 24
Frequency of washing by machine (time/week) 1048 4.88 0.107 4.00 3.46 0.0 28
Frequency of washing by hand (time/week) 1060 1.90 0.092 0.00 1.36 0.0 22
Dishwashing by machine (time/day) 24 1.19 0.240 1.00 1.19 0.0 5
Frequency of dishwashing by hand (time/day) 1060 2.89 0.041 3.00 3.01 0.0 21
Frequency of hand wash (time/person/day) 1060 7.94 0.112 8.00 3.64 1.0 36
Note: Because some data are not normally distributed, both the means and medians are reported.
487 flushes are made on average per person per day. Washing by machine averages at
4.88 times per week, whilst the average frequency of washing by hand is 2.89 times per household per week. Dishwashing usage frequency with and without dishwashers averages at 1.19 and 1.90 times per day, respectively. The table also shows that each person in the study area washes his/her hand 7.94 times/day. In general, the data demonstrate little diflferences between the mean and median values, which is normal for such regular .water use activities.
The results of the frequency of the external water use components are shown in Table 7.8.
The data in the table indicates that the average irrigation frequency per dwelling unit per week is 4.27, with the summer and winter averages being 5.50 and 3.01 times per week, respectively. The component of courtyards and floors washing records an average frequency of 2.69 per month; the difference between summer and winter frequency of floors washing is 0.72 times, or almost 31%. The mean frequency of car washing is just over one wash per week; but car washing using a bucket occurs 2 times per week on average. Contrary to what has been expected, car washing by hose in summer and winter show negligible difference on their mean values. Again, as it the case vnth the in-door water use activities, no appreciable differences between the mean and median frequency values within the outside water use activities have been observed.
Responses to the nominal questions related to the out-door water uses (i.e. irrigation methods and crop patterns variables) indicate that almost 38.4% (183 respondents) of those who maintain residential lawns use flood irrigation to irrigate their lawns; those who use drip irrigation come second with 24.7%, or 118 respondents. Sprinkling irrigation is only used at 86 dwelling units, or 18% of the total dwelling with private lawns. Up to 18.9%
488 Table 7.8 Summary statistics for the out-door water use activities
Std. Variable Valid N Mean Median Std. dev. Min. Max. error Irrigation frequency in summer (time/week) 481 5.50 0.135 6.00 2.96; 0.0 21
Irrigation frequency in winter (time/week) 484 3.00 0.093 3.00 2.06 0.0 21
Average irrigation frequency (time/week) 479 4.27 0.104 4.00 2.28 , 0.5 21
Summer floor washing (wash/dwelling/month) 1013 3.05 0.137 2.00 4.36 0.0 30
Winter floor washing (wash/dwelling/month) 1013 2.33 0.121 1.00 3.86 0.0 30
Average floor washing (wash/dwelling/month) 1013 2.69 0.125 1.85 1.85 0.0 30
Car washing by bucket in summer (wash/week) 1041 2.02 0.057 2.00 1.84 0.0 20
Car washing by bucket in winter (wash/week) 1042 1.98 0.053 2.00 1.72 0.0 15
Car washing by hose in summer (wash/week) 1044 0.20 0.073 0.00 2.36 0.0 75
Car washing by hose in winter (wash/week) 1043 0.19 0.050 0.00 1.64 0.0 50
Average car washing by bucket (wash/week) 1042 2.00 0.053 1.74 1.73 0.0 15
Average car washing by hose (wash/week) 1043 0.19 0.061 0.00 1.99 0.0 62.5
Average car washing (wash/week) 1040 1.09 0.039 1.00 1.27 0.0 31.3
Note: Because some data are not nomially distributed, both the means and medians are reported.
489 (91 respondents) irrigate their gardens with mixed irrigation. Those who apply both drip and sprinkling irrigation represent 52.7%; those who use a combination of flood and drip irrigation were 29, or 31.9%. Nine respondents (9.9%) depend on both flood and sprinkling irrigation. Those who combine flood, sprinkling, and dip irrigation techniques were only five, accounting for 5. 5%. A high proportion (77.9%) of those who have lawns grew mixed crops; followed by 19.7%, or 91 respondents planting date palms; and those, who cultivate vegetables account for 2.2% (10 respondents). Only one reply representing
0.2% is groAving alfalfa.
The frequencies of the in-house and out-door water usage activities were used to estimate the daily per capita consumption by multiplying the average frequency of use for each water use activity by a known average volume per use. A breakdown of the average per capita in-door and out-door uses is presented in Table 7.9. Demand for in-house uses in a typical family breaks down as follows: toilet flushing 27.9%, washing clothes uses 20.3%, dishwashing 7.1%, and miscellaneous uses (assumed to be 6% of the total in-door uses), including cleaning of vegetables, mopping of floors, etc. take up 5.5%, or 15.8 Ipcd. The bulk quantity (36.5%, or 105 Ipcd) is devoted to showering and other personal hygiene uses. The actual quantity for showering alone is estimated to be about 90 Ipcd (31.3%); the remaining 15 Ipcd is broken down as follows: 7.9 Ipcd for hand wash assuming one litre per hand wash and an average frequency of 7.9 hand wash per person; the rest 7.1 Ipcd is for bmshing teeth, shaving, washing before praying, and other in-toilet cleaning.
The fact that showering demand exceeds toilet flushing is to be expected. This is unlike the situation in colder climates where toilet flushing often appears to be the major water use activity (see for example Thackray et a/., 1978; Hall el a/., 1988. Twort et ai., 2001;
490 Table 7.9 A breakdown of components of the residential daily per capita water use
Households without Households with Activity sprinkling demand % of total sprinkling demand % of total (l/pers6n/day) In-door water usage 1
Toilet flushing* 80.3 27.9 80.3 25.9 Showering and personal hygiene** 105.0 36.5 105.0 33.8 Washing clothes i
- by hand^ 16.4 5.7 16.4 5.3 - by washing machine** 42.0 14.6 42.0 13.5 Dishwashing^ 20.4 7.1 20.4 6.6 Miscellaneous uses (6%)*^ 15.8 5.5 15.8 5.1 Out-door water usage 1 1 Car and floor washings^ 7.6 2.7 716 2.4 Gardens watering and lawns sprinkling (8%)^ — — 23.0 7.4 i Total per capita water use 287.5 100 310.5 100
Based on traditional 22 I/flush toilets, and 3.65 toilet flushing frequency. ° Based on average of 6 minutes showering, U^dilional 101/minute/shower, and an average of 1.5 showering frequency per person per day. ^ Based on 40 I/wash, and an average frequency of 0.41 wash/day. ''Based im 60 1/load. and an average of 0.70 load per day (assuming an average of load per wash). " Based on a dishwashing frequency of 2,04 per day, and a 10 litre per dishwashing. Wludirvg cleaning of vegetables, mopping of floors, washing before praying etc. * Car washing is based on 0.16 wash per day and lo 1/wash, with allowance to account for floor washing and car washing by hose. '*Taken as 8% of the total in-door wntcr use on the basis of our analysis of the seasonal variability. Cooking and drinking components were not considered because due to quality considerations, consumers in the study area tend to purchase water from outside sources to meet the demand of these components. i
491 Evans and Cook, 1979; Lofting and Davis, 1977). The straightforward explanation for this discrepancy is that frequency of showering in hotter climates such as in the study area tends to be higher than in temperate climates. To quote some figures from elsewhere for comparison, Thackray et al. (1978) reported daily showering frequencies of 0.162 and
0.109, respectively, for Malvern and Mansfield towns in England, while the present study has shown that each person in Bahrain takes shower 1.5 times a day.
The sprinkling demand is estimated at 23 Ipcd (7.4% of the total per capita use for households v^th gardens watering and lawns sprinkling), on the assumption that sprinkling demand represents 8% of the total in-door water use. This assumption is based on our detailed analysis of water use on both macro and micro-component levels. The table gives a per capita daily consumption figure for all domestic uses of 287.5 Ipcd for households without gardens and lawns, and an extra of^3_lpcd (total 310.5 Ipcd) for those having gardens and lawns. Although these daily per capita figures are much less than those released annually by the concerned water authority, they are almost twice as big than the average per capita consumption estimated, for example, for South East England (see
Herrington, 1998). With an average of 6 persons per household (taking the median value - see Table 7.12), the average monthly consumption per household would be 51.8 m*' for households without sprinkling demand, and 55.9 m^ for households with sprinkling demand.
An examination of frequencies of the independent variables in the large customers group
(Table 7.10) indicates substantially large standard errors of means. At extreme, the variable number of worshippers and visitors per day has a standard error of about 55% of the estimated mean. The obvious reason for this large variability is the small number of cases
492 Table 7.10 Summary statistics on variables related to employment, number of public users, and number of customers within the large consumers group
Std. error Std.: Valid N Mean Median Minimum Maximum Variable of mean deviation Number of employees government establishment 3 126.33 37.65 103.00 65.21 76 200
Number of employees commercial establishment 5 43.60 16.12 38.00 36.05 7 100
Number of guests in summer per day 10 85.80 18.18 70.00 57.48 30 200
Number of guests in winter per day 10 91.30 23.53 62.50 74.42 20 250
Average number of guests per day 10 88.55 20.62 67.50 65.22 25 225
Number of worshippers and visitors per day 6 246.33 119.57 175.00 292.90 6 800
Number of patients per month* 1 150 — 150 — 150 150
Number of students including staff (schools) 8 945.63 100.68 877.00 284.75 535 1400
Aver, number of customer per day (restaurants) 3 66.67 8.33 75.00 14.43 50 75 1 Number of employees (factory) 6 199.17 86.23 135.00 211.22 45 600
Number of members and staff (clubs) 4 73.75 13.75 80.00 36.37 35 100
Note: Because some data are not normally distributed, both the means and medians are reported. • Based on one observation; consequently, no firm conclusion can be drawn.
I
I 493 ! 1 for each sub-category. What is of particular importance, however, is the average water use and daily per capita consumption in each utility. These are illustrated in Table 7.11. Not surprisingly, the average water use writhin the large customers group shows large variability from 49.11 mVmonth in mosques and other religious establishments to
790.48 m'^/month in hotels and flat apartments. The mean water use per utility per month is estimated to be_about 399 m^._ ._ _
The average person within the large customers group consumes between a high of
117.66 litres per day in the commercial sector to a low of 0.22 litres per day in the religious establishments. The latter figure probably reflects the intensive demand management campaign being particularly exercised in this utility since the early 1990s, including fitting of flow regulators and other water-conservation appliances. Because of such little evidence, no explanation is readily available for _the high consumption in the_commercial sector, except that it is possible that the data appear to be largely affected by one major user. The average per employee water use in factories is found to be 69.60 Ipcd.
Quite expectedly, the economies of scale appear to operate within the large customers group in terms of the overall per capita consumption; the average person in this group consumes only 38.9 litres per day. This means that, on average, the average daily per capita water use in commercial, industrial, and public utilities = (0.14) average daily per capita residential water use. This factor is by far lower than those obtained from analysis of water use data elsewhere, possibly reflecting the lower state of development in these sectors in the study area. For example, Linaweaver et al. (1967), reported in Joseph
(1982), calculated a factor of 0.28 for Western USA.
494 Table 7.11 Average and per capita water uses within the large customers group
Average water use Perjcapita daily consumption Utihty Valid N (m /month) (litres/capita/day) Government estabhshments' 3 87.30 31.41 1 Commercial establishments 4 159.07 117.66
Hotels and flats apartment 10 790.48 9.80
Religious establishments 6 49.11 0.22
Schools and educational institutes^ 8 534.06 25.67
Factories or industrial firms'' 6 360.43 69.60
Social and health clubs 4 321.37 4.84
Restaurants 3 214.50 3.57
Hospitals and health centres'^ 1 495.00 110.00
Overall average consumption 399.00 38.9
' Based on 5 studying days a week; no adjustments for holidays are made. ' ''Based on 6 working days a week; no adjustments for holida>'s are made. ^ Based on one observation; may be unreliable. Omitting this observation would indicate on overall average consumption of 396.85 mVmonlh, and a per capita daily consumption of 37.30 litres per day.
495 Table 7.12 provides statistics for the socio-economic variables and household water- usingfixtures. The table indicates that the average number of occupants (household size) in the study area is 6.98 person per household unit. As displayed in Figure 7.5, household sizes derived by dividing the population in each census year by the number of housing units in the respective year based on the available demographic data (see Table 6.1 -
Chapter Six), fall within the range from 6.08 to 6.96 persons per_household, with a discernible declining tendency in recent years. The demographic data give an average household size of 6.53, which correlates very well with the average household size of 6.98
(median value 6 persons per household) obtained from the analysis of the survey returns.
This average household occupancy is considered to be very high when compared with published averages from other countries. To illustrate, average household occupancy in
England and Wales, for instance, varies from 2.40 to 2.75, and averaged 2.65 (Twort et a/.,
2001). although household occupancy in other less developed countries is often slightly higher.
The analysis demonstrates that households in Bahrain ranges in area from 11.2 to
6,000 m^, and averaged 299.72 m^ with a median value of 241.00 m^. The average area of private gardens is 48.14 m^, which is considerably different from the median value of
12.00 m^, reflecting great dispersion. The average earning of the surveyed families is found to be BD 730.810 a month. However, owing to the wide variability in the family monthly income, the median value of BD 600.000 may be regarded more representative.
The ownership level of cars records an average of 2.15 cars per dwelling unit. The household fixtures variables: number of bathrooms, number of taps, number of showers, and number of flush toilets per dwelling as well as number of cars are analysed here for all the dwelling units (i.e. including the large customers) because they are considered as part
496 Table 7.12 Summary statistics on the socio-economic variables and households fixtures
Std. error Std. Variable Valid N Mean Median Minimum Maximum of mean deviation j Number of occupants (person/ household) 1061 6.98 0.108 6.00 3.513 ' 1 29
Household area (m^) 969 299.72 7.216 241.00 224.63 . 11.2 6000
Garden area (m^) 442 48.14 6.977 12.00 146.68 , 0.9 2200
Family income (BD/month) 910 730.81 18.602 600.00 561.15 ' 60 6000
Number of bathrooms per household 1105 4.73 0.239 4.00 7.94 , 1 125
Number of taps per household 1105 12.65 0.560 9.00 18.60 . 2 270
Number of showers per household 1060 3.38 0.051 3.00 1.66 0 16
Number of flush toilets per household 1105 4.85 0.280 3.00 9.29 1 134
Number of washing machines 1060 1.25 0.019 1.00 0.65 ; 0 5
Number of dishwashers per household 1060 0.024 0.004 0.00 0.16 ' 0 2
Number of cars per household 1090 2.15 0.081 2.00 2.68 ; 0 60 1 Notes: Because some data are not nomially distributed, both the means and medians are reported. 1 Variables number of bathrooms, number of taps, number of showers, number of flush toilets, and number of cais include the large consumers group.
497 Figure 7.5 Number of occupants per household based on data from census years
7.2 1
6.8
6.6
6.4
6.2
5.8
5.6 1941 1950 1959 1%5 1971 1981 1991 2001
Years
498 of the general variables. As expected, the average numbers of bathrooms and flush toilets are closely related, with just less than five bathrooms and flush toilets per dwelling unit
(medians are four bathrooms and three flush toilets per dwelling unit). On average, each dwelling unit has 12.65 taps and 3.38 showers, with median values of nine taps and three showers.
The respondents were asked to supply information on their owmership of water-using appliances (washing machine and dishwashers), ownership of swimming pool, and to indicate whether they are conneaed to the sewage system. The results of their responses are shown in Table 7.13. The vast majority of the respondents (98.6%) own a washing machine (assumed to be automatic) with a mean value of 1.25 (see also Table 7.12). Out of those who have zmswered this question positively, the great majority (851 respondents), or 81.4% have one washing machine; 12.2% or 127 households have two machines; and those with three, 5% (52 respondents). Eleven families representing 1.1% have four washing machines; and those with five (four households) made up the balance of 0.4%.
Those who have dishwashers represent only a small proportion of 2.4%, or 25 households of the total sample, of which 24 households, or 96% have one dishwasher, and only one household (4%) has two dishwashers. Of the 1,106 dwelling units surveyed, only 2.6% (29 respondents) indicated that they own swimming pool. Fourteen replies (48.3%) of those who responded positively to the question regarding the ownership of swimming pool, fill their pools using water fi-om outside sources; nine respondents (31%) rely on the publicly supplied water; the remaining two respondents, or 6.9% depend on both publicly supplied and outside sources. Four of these respondents failed to indicate the source for changing water in the pool. The mean fi-equency of changing water in the pools is 2.20 times per
499 Table 7,13 Statistics on ownership of water-using appliances and swimming pools, and connection to sewers i Variable Valid N Mean Yes % of total No % of total 1 Ownership of water- using appliances ) - Washing machines 1060 1.25 1045 98.6 15 1.4
- Dishwashers 1060 0.024 25 2.4 1035 97.6
Ownership of swimming pool 1106 1.97 29 2.6 1077 97.4
Connection to sewer 1095 1.30 769 70.2 326 29.8
500 annum, with the minimum and maximum being one and five times per annum, respectively. A substantial proportion of the respondents (70.2%) said that they are connected to the public sewer, compared to a 29.8% not yet connected. It is interesting to recall that the later finding is in line with OUT estimate of the percentage of population already covered by the sewer system (see the discussion in Chapter Five, page 346).
7.4.2 Agricultural Water Use Survey
The agricultural water use survey covers the active and semi-active farms that use groundwater, treated sewage effluent, public supply water, or a combination of two or more of these sources as an irrigation water source. Table 7.14 summarises the results of the average consumptions, farm variables, and irrigation water requirements for the farms included in the survey. The average summer (March - August) irrigation demand is estimated at. 5,846.03_m*' per.farm per month (m-/farm/month), with-a median-value of
5,011.10 mVfarm/month. The average irrigation demand during the winter growing season
September - February is found to be around 5,050.75 mVfarm/m, with a median value of
4,024.70 m'*/farm/m. This suggests a seasonal variability of almost 16%. A Wiicoxon test was conducted to evaluate whether this difference is statistically significant. Table 7.15 documents that there is a high statistically significant difference between sunmier and winter monthly average consumptions at Z = -5.019, p = 0.000, two-tailed. The overall monthly average consumption (Table 7.15) indicates that each farm uses about
5,461.80 m^ per month, with a median value of 4,565.65 mVfarm/month, and a standard deviation of 4,180.60. The survey results also show that the average monthly irrigation demand varies widely from farm to farm, ranging from a minimum of 286.2 m^ to a maximum of 23,869 m^.
501 Table 7.14 Frequencies on the consumption and farm variables, and irrigation water requirements
Std. Variable Valid N Mean Median Std. dev. Min. Max. error 1 Average consumption (m^/farm/month) 100 5461.80 418.06 4565.65 4180.60 286.20 23869.00 i Aver. Summer consumption (m'*/farm/month) 86 5846.03 511.70 5011.10 4745.37 307.60 26471.20
Aver. Winter consumption (mVfarm/month) 85 5050.75 486.53 4024.70 4485.59 295.50 23914.60
Plot area (ha) 111 3.13 0.308 2.20 3.24| 0.20 18.10
Gross cultivated area (ha) 111 2.62 0.280 1.61 2.95, 0.16 18.00
Gross irrigated area (ha) 111 2.19 0.246 1.28 2.59 0.05 14.48
Area under protected agriculture (ha) 107 0.26 0.054 0.00 0.55 0.00 2.88
Irrigation requirements (m'^/ha/month) 100 3263.99 280.53 2346.10 2805.38 109.32 14017.20
Requirements from TSE (mVfarm/month) 7 5107.99 1199.41 4200.00 3173.34 2100.00 9000.00
Requirements from public supply (mVfarm/month) 24 ;82.74 4.58 75.00 22.42 60.00 165.00
Notes: Because most of the variables are not normally distributed, both the means and medians are reported. Average monthly consumption, and summer and winter average monthly consumptions are in mVmonth. Plot area, cultivated area, area under irrigation, and area under protected agriculture are in hectares (ha). Irrigation requirements are in m^/ha/month. Requirements from TSE and public supply network arc in m^ and it should be noted that they do not represent irrigation requirements.
502 Table 7.15 Wilcoxon signed - ranks test for diflferehce between summer and winter irrigation demands
Valid N Mean ranks Sum of ranks
Wilcoxon signed - ranks test
Winter average consumption Negative Ranks 66' 45.05 2973.00
Summer average consumption Positive Ranks 19b 35.89 682.00
Ties
Total 85
a Winter average consumption < summer average consumption ; b. Winter average consumption > summer average consumption ' c. Summer average consumption = winter average consumption
Test statistics winter average consumption - summer average consumption Z -5.019" Assym Sig. (2-taiied) 0.000 Q. Based on positive ranks. b. Wilcoxon Signed • Ranks Test.
503 Based on the sample data, the proportion of the area under cultivation (291.31 ha) to the total agricultural area (347.71 ha) is almost 84%. The table indicates that the average plot area is 3.13 ha, and the average cultivated area is 2.62 ha. Area under protected agriculture
(green houses) represents 9.9% of the total cultivated area, or 0.26 ha. The irrigation water requirement per hectare of cultivated land (a product of the average monthly consumption divided by the average^ cultivated .hectares) is found to be 3,263.99.m^/ha/month;.but hasa wide range from a high of 14,017.20 to a low of 09.32 m^/ha/month; with the median value being 2,346.10 m^/ha/month. Using the mean and median values, this suggests irrigation water requirements of 39,168 m^/ha/year and 28,153 m^/ha/year, respectively. Because the data are not highly skewed (skewness statistic is 1.66), the figure derived based on the mean value is probably reasonable, especially that this figure is in reasonable agreement with the figure adopted for irrigation planning and sizing of the irrigation systems, particularly for irrigation with TSE. The obtained irrigation requirement figures are much lower than the 70,000 m^/ha/year, commonly claimed by the Agricultural Authority officials to represent the irrigation water requirements in the study area.
The analysis indicates that seven of the total farms surveyed (6.3%) are using TSE to meet their irrigation demands (supplemented in few cases with groundwater abstraction), with an average monthly consumption of 5,108 m'^/farm. Twenty-four farms of those included in the survey are connected to the blended water supply in order to supplement their main groundwater abstraction. In most of these farms, the blended water is normally used for domestic purposes and livestock requirements, with an average demand of
82.74 mVfarm/month.
Table 7.16 shows the distribution of the cultivated area according to the cropping patterns.
504 Table 7.16 The distribution of the cultivated area according to the cropping patterns
Std. error of Std. Total area under % of total Variable Valid N Mean mean deviation a given crop (ha) cuhivated area
Vegetable crops 111 1.16 0.159 1.67 129.53 ! 44.46 1 Date palms 111 0.86 0.159 1.67 96.11 33.00
Alfalfa 111 0.16 0.030 0.31 18.29 6.28
Fruit crops 111 0.24 0.040 0.43 27.19 9.33
Ornamental 111 0.02 0.008 0.09 2.42 0.83
Poultry 111 0.02 0.007 0.08 2.45 0.84
Livestock 111 0.13 0.028 0.29 15.32 5.26 1
Total 291.31 100
505 Over than 44 % of the total cultivated area of the farms sampled is reported to have been cultivated vnth vegetable crops. About 96. II ha, or 33% of the total area surveyed is covered with perennial date palms. Area planted with alfalfa accounts for 6.3%, or
18.30 ha of the total area of the sampled farms. Fruit crops alone covers an area of
27.20 ha, or 9.3%, with the areas under ornamental trees, poultry and livestock raising, accounting.together.for_the balance of 6.9%, or-20.20-ha. - - - -
As illustrated in Table 7.17, the survey results showed that the average irrigation frequency is 1.34 times/day. The summer irrigation frequency of 1.50 times/day exceeds the irrigation frequency in winter (1.17 times/day) by a little more than 28%. Number of pumping hours in summer varies considerably, primarily depending upon total cultivated area of each farm, from 0.50 hour/day to 12 hours/day. The mean value is estimated at
4.21 hours/day. Farmers report that they_operateJheir.pumps 2.91 hours/day-on.average during the wanter months, but this includes a wide range from a minimum of 0.50 hour/day to a maximum of 10 hours/day. The mean pump capacity in the farms with pump installations is found to be 6.62 horsepower (hp), with a median capacity of 5 hp
(3,730 watts).
Of those farms sampled, 45% or 50 farms are still irrigated using the traditional flood irrigation method; only four farms or 3.6% use drip irrigation, and the majority of 51.4%
(57 farms) irrigate with mixed irrigation methods. The replies from the later group suggested a bulk proportion (96.5%), or 55 farmers combine flood and drip irrigation; one farm, representing 1.8%, uses drip irrigation v^th lined channel techniques; and another one farmer applies a combination of flood, drip, and lined channel irrigation methods.
These responses are translated into areas under different irrigation methods and proportion
506 Table 7.17 Summary statistics on irrigation frequencies, number of pumping hours, and pump capacities
Std. error i Valid N Mean Median Std. dev. ' Min. Max. Variable of mean Irrigation frequency in summer (time/day) 111 1.50 0.066 1.00 0.69 0.30 5.0
Irrigation frequency in winter (time/day) 111 1.17 0.065 1.00 0.68 0.50 7.0
Average irrigation frequency (time/day) 111 1.34 0.057 1.00 0.61 ' 0.50 6,0
Pumping hours in summer (hour/day) 108 4.21 0.256 4.00 2.65 0.50 12.0
Pumping hours in winter (hour/day) 108 2.91 0.213 2.00 2.21 ; 0.50 10.0
Pump capacity (hp) 99 6.62 0.289 5.00 2.87 2.0 15.0
507 of each method to the total irrigated area as shown in Table 7.18.
Twenty seven percent (30 respondents) reported that they maintain swimming pool in their farms, with an average swimming volume of 161.9 m^. Of those, almost 96.7%, or
29 farmers change their pool water using the water source, and only one farmer uses an outside source to change his pool water. The fi-equendes ofj:hanging_water„in pools.range from 1 - 2 times per day in summer, and 1 - 3 times per day in winter; with mean values of
1.41 and 1.13 times per day in summer and winter, respectively.
7.4.3 Industrial Water Use Survey
This survey covers considerably different mixes of small to medium manufacturing industries, including petrochemical industries, plastic and aluminium products, food and beverages manufacturing, construction materials, and oil-based industriesrlt also includes some industrial activities, which may be referred to as service industries such as ship repairs. Analysis of the survey data shows that industries requiring large quantities of water include the petrochemicals, petroleum refining, construction activities and, to a lesser extent, the food and beverages industry. In the case of the aluminium industry, only the large aluminium plants such as ALBA and the Gulf Aluminium Rolling Mill Company
(GARMCO) were found to consume large quantities of water.
Most of the water-intensive industries rely primarily on their own supplies desalinated either from private groundwater sources or seawater, while publicly supplied water is the main source for the small industrial customers. In many cases, however, some of the large water customers are connected to the piped supply, or they purchase water from outside sources to provide backup supplies, primarily for their staff hygiene and catering purposes.
508 Table 7.18 Distribution of areas under different methods of irrigation and their percentage of total irrigated area I Method of irrigation Area under particular % of total irrigated method of irrigation (ha) area "1 Flood irrigation 69.09 28.37
Drip irrigation 4.67 1.92
Mixed irrigation 169.76 69.71
Total 243.52 100.00
Areas under different type of mixed irrigation and their percentages of total
- Flood and drip irrigation 167.88 98.89
- Drip irrigation and lined channels 1.02 0.60
- Flood, drip, and lined channels irrigation 0.86 0.51
Total area under mixed irrigation 169.76 100.00
509 The survey also indicates that only a few firms are using water re-cycling systems and/or practising water conservation measures. Many of the industrial users surveyed claimed that they were not satisfied with the water supply situation, particularly in terms of unavailability of adequate and reliable supply of water during peak demand. A large proportion of the industrial firms included in the sample indicated that they had plans for fijture_developments_inJerms_of increases in.employment and expansion Jn.factory_floor areas.
Table 7.19 presents summary statistics for four key variables in this survey: the average water consumption, production capacity, factory floor area, and number of employees.
The table shows that the average water use within the industrial firms surveyed varied widely from a minimum of 0.7 m^ day ' to a maximum of 13,909.1 m^ day *. The mean and median values are 315.1 m"* day'Vand 25,5 m^ day *, respectively. The wide band of variation around the mean value is reflected in the standard deviation of 1,306.8, and the
notable skewed distribution (skewness statistic 9.457). This is to be expected given the
heterogenity among the industrial water users referred to earlier, and that no consumption
or employment limits were set during the sample selection process.
The lowest and highest production levels were found to be 0.20 and 39,747 tonnes day'*,
respectively, with an average value of 1,012.3 tonnes day '. The variable factory floor area
has also a wide range from 93 m^ to 1,950,767 m^, with a mean value of 50,252.3 m^, and
a standard deviation of 201,749.91 m^. The average number of employees was found to be
165.72 employees, but this variable also exhibits large variations from a minimum of
4 employees to a maximum of 2,600 employees. The median number of employees is
62.50 and the standard deviation is 348.52 employees.
510 Table 7.19 Summary statistics of the key variables from the industrial water use survey
Std. error Std. Variable Valid N Mean Median Minimum Maximum Sum of mean deviation Average water consumption (m"* day*') 124 315.07 117.355 25.50 1306.81 0.7 13909.1 39068.70 1 (9.457) 1 Production level (tonnes day'*) 105 1012.29 417.683 63.36 4279.97 0.20 39747.00 106291.28 1 (7.774) 1 Factory floor area (m^) 124 50252.34 18117.680 6226.50 201749.91 93.0' 1950767 6231290 (7.773) Number of employees 124 165.72 31.300 62.50 348.52 4.0 2600 20549 (4.926)
Figures in brackets are skewness statistics.
511 The statistics presented in Table 7.19 were of considerable value for estimating the water use per employee, per unit of output, and per factory floor area. The average consumption per employee in manufacturing is calculated at 2.775 m^ per employee per day (2,775 Iped) based on 250 working days. The figure quoted is derived by dividing the total volume of water used by the firms surveyed by the total number of employee. When compared with the figures .estimated , for elsewhere, this average is, for ^example, , much higher, than .the average consumption per employee of 2,065 Iped (also based on 250 working days) obtained by Thackray and Archibald (1981) for UK industry. Despite the fact that such comparisons are always questionable and their uncertainties increase with the degree of variations in type of industry, size of employment, and plant processes within a country's industry and fi-om country to country, the estimated per employee water use is, by any standard, extremely high. Although the resultant figure seems to be highly affected by the influence of certain industries as \y\\\ be discussed later, it may suggest that efforts toward improving the efficiency of water use in manufacturing through re-cycling schemes and water saving plans should be highly encouraged.
The total number of employees included in this survey (20,549) represents almost 39% of the total workforce in manufacturing, and oil and mining industries (see Table 6.19,
Chapter Six). In addition, the total water use in the manufacturing firms surveyed accounts for 59.4% of total industrial water use for the year 2001 (including metered blended use, which is assumed to represent 45% of the non-domestic water use - refer to the discussion in Chapter Six, pages 440 and 441). This gives an idea of the comprehensiveness of this survey, which perhaps adds to the reliability of its findings.
The average volume of water used per unit of output is derived by dividing the total
512 volume of water used by the total output; again this calculation is based on 250 working days. The average water use per unit of output is estimated at 0.536 per tonne
(536 litres per tonne). The analysis of the relationship between average water use and factory floor area gives an estimate of 2.288 m^ per m^ of factory floor area
(0.00626 m^/dayW of factory floor area). It is interesting to see that the generated average-is in- close agreement-with the 2.177- m^ per m^ of factory floor-^area
(0.00596 rr?/dayfrT? of factory floor area) obtained earlier fi-om the analysis of the water use patterns. The total factory floor area of the industrial firms sampled (6,231,290 m^) represents 69.2% of the estimated total area under manufacturing, which again reflects the extent of the coverage of this survey, and substantially enhances the credibility of its findings.
For planning purposes and possibly for making a more meaningfiji comparison with estimates fi-om elsewhere, if a reasonable match can be made, it would be more useful to provide estimates of water use per these indicator variables based on type of industry, since each industrial category and also individual firms within the same industry differ in terms of water use. To illustrate this, subsets of the sampled industrial firms are categorised according to the latest edition (MOl, 2003) of the two-digit Standard Industrial
Classification (SIC). This standard classifies industrial users into nine industrial categories as shown in Table 7.20. The table also gives the corresponding case (firm) serial numbers from the questionnaire returns under each SIC category.
Table 7.21 shows the distribution of the average water use, per employee water use, water use per unit of output, and water use per of factory floor area by categories of users according to the SIC system. It can be seen that the average water use varied widely
513 Table 7.20 Distribution of the industrial firms surveyed according to their industrial activities based on the Standard Industrial Classification (SIC)
SIC Categories Industrial activity Corresponding case numbers
Category No. 31 Manufacture of food, beverages and tobacco 3, 4, 5,6, 9,12, 13, 19, 23, 24, 34, 38, 51, 57, 80, 103,114
Category No. 32 Textile wearing apparel & leather industries 20, 39, 62, 99, 102, 115
Category No. 33 Manufacture of wood and wood products including furniture 53, 110, 123
Category No. 34 Manufacture of paper and paper products, printing and 17, 36, 85, 96, 97 publishing Category No. 35 Manufacture of chemicals, and of chemical, petroleum, coal, 7, 15, 25, 30, 33, 37, 46, 63, 64, 72, 73, 75, 76, rubber and plastic products 77, 81,83, 84, 88,91,94,98, 100, 104, 105. 107, 112, 117, 124
Category No. 36 Manufacture of non-metallic products, except products of 1,8. 11, 14, 16,21,22, 26, 27,28, 29,32, 35,40, petroleum and coal 47. 48, 50, 54, 58, 67, 68,169, 89, 92, 93, 101, 108, 109, 111, 113, 119, 121, 122
Category No. 37 Basic metal industries 18.31,56,61,65, 70, 95
Category No. 38 Manufacture of fabricated metal products, machinery and 41. 42, 43, 44. 45, 49, 52,!55, 59, 60, 66, 71, 74, equipment 78, 79, 82, 86,87, 90, 106, 118, 120
Category No. 39 Other manufacturing industries 2, 10, 116
514 Table 7.21 Distribution of average water use, per employee water use. water use per unit of output, and water use per factory floor area by industrial activity
Water use per unit of Water use per Average consumption Per employee Industry output factory floor area (m^ day'*) water use (Iped) (mVtonne/d^) (mVmV fact, area) Manufacture of food, beverages and tobacco 463.4 8197 1.08 0.036
Textile wearing apparel & leather industries 133.4 221 9.52 0.015
Manufacture of wood and wood products including 11.8 103 7.9 0.0021 furniture
Manufacture of paper and paper products, printing 103.8 1753 7.6 0.0054 and publishing
Manufacture of chemicals, and chemical, petroleum, 610.1 4032 0.33 0.0069 coal, rubber and plastic products
Manufacture of non-metallic products, except 147.0 1569 0.13 0.0048 products of petroleum and coal
Basic metal industries 930.3 1478 0.31 0.0042
Manufacture of fabricated metal products, machinery 12.5 134 0.35 0.00077 and equipment
Other manufacturing industries 372.1 723 0.0022
A dash indicates missing values on this variable for the particular industrial activity.
515 among the SIC categories, ranging from 11.8 m^ day * in the furniture industry to
930.3 m^ day * in the basic metal industries Intensive water users include the food and beverages industry (463.4 m^ day'*) and chemicals, petroleum, and plastic industries
(610 1 m^ day"') The percentage of water use for each industrial type to the total volume of industrial water use are of great value for examining the potential for water conservation in specific manufacturing activities The bar chart in Figure 7.6 illustrates this situation.
Figure 7.6 Percentage of industrial water use by SIC categories of user
Category 39 •
Category 38 I
Category 37
g Category36 •
^ Category35 •
U Category 34 • on
Category 33 I
CaIcvor% • Category 31 I
5 10 15 20 25 30 35
Percentage of total industrial water use
It shows that SIC37 (basic metal industries) is the biggest industrial user of water This category together with SIC35 (chemicals, petroleum and plastic industries), add up to more than 55% of the total industrial water use The water requirements for SIC34 (manufacture of paper products, printing and publishing), SIC36 (manufacture of non-metallic products, except products of petroleum and coal), and SIC32 (textile wearing apparel and leather industries) are considerably lower. The SIC33 (manufacture of wood and wood products.
516 including furniture) and SIC38 (fabricated metal products and machinery equipment) categories have the lowest shares of 0.4% and 0.5%, respectively.
Per employee water use at different types of SIC also displays a great variability. The labour intensive industries, obviously, consume less water per employee than the water inteiisive industries. For example,_in the furniture industry. the_water use. per. employee is estimated at 103 Iped, while it is found to be 8,197 Iped in the food and beverages industry.
Evidently, the major water users in the latter category are the ice making and sweet water plants, which are normally run by a limited number of employees (i.e. per employee use represents a small fi-action of the total plant use). Therefore, the high per employee water use associated with this sub-category is possibly responsible for the unexpectedly high average water use per employee reported earlier for the whole manufacturing industry.
The three largest categories in terms of water use per unit of output are the textiles, furniture, and papers and pulps industries, respectively, recording 9.52, 7.9, and
7.6 m^/tonne/day. The SIC36 (manufacture of non-metallic products) has the lowest water use per unit of output of 0.13 mVtonne/day. Quantity of water consumed per of factory floor area in the SIC31 (food and beverages industry) is estimated at 0.036 m''/m^ of factory floor area, representing the largest water use per factory floor area among the SIC categories. Again, this finding can be primarily attributed to the ice making and sweet water sub-category where plants occupying small areas use large quantities of water. The
SIC38 category (fabricated metal products) uses a small fi-action of 0.00077 m"* per m^ of factory floor area.
Table 7.22 shows the distribution of the average water use in industry by its three major
517 activities: (1) water used for production, including processing, product cleaning, cooling, etc.; (2) water used for staff hygiene and catering; and (3) water used for other purposes
(e.g. lawn watering, floor and car washing, etc).
Table 7.22 Average water uses by internal type of use in manufacturing and their percentage distribution . Average volume of use . i Activity ® % of total (m' day"')
Production 265.49 S43
Staff hygiene 35.11 11.1
Other purposes 14.47 4.6
Average consumption 315.07 100
Note: re-cycling factor was not considered in these calculations.
The table indicates that the highest proportion of water used in manufacturing (84.3%) is for production and processing purposes. On average, 35.11 m^ day'* is used for staff hygiene, corresponding to 11.1%, whilst water used for other purposes has the lowest share of 4.6%, or 14.47 m"* day'*, on average. For comparison purposes, the most important statistic in this table is the average amount of water used for staff hygiene, because fi*om this the average consumption per person per day in manufacturing can be estimated and compared with the average daily residential per capita consumption. Tables 7.19 and 7.22 are used to illustrate this comparison. The average daily water use per employee in manufacturing is estimated at 0.3094 m^, or 309.4 Iped based on the normal 250 working days (211.9 iped based on 365 days). This indicates that the average water use per person per day in manufacturing is more than the daily per capita consumption obtained for the domestic users (282.3 iped). It is also higher than the daily per capita water use based on
518 activities of use (287.5 Ipcd) for households without sprinkling demand, and is almost identical to the 310.5 Ipcd obtained for households with sprinkling demand (see Table 7.9).
It is clear that this finding should have important implications fi-om the perspective of water resources planning and management.
Further analysis to the survey data showed that 68 industrial firms (54.8%) indicated that they were connected to the sewer system, while 45.2%, or 56 firms, said that they used other means for disposing of their wastes. The majority (67.9%) of those who are not connected dispose of their wastes using on-site septic tanks, 12.5%, or seven firms discharge their wastes directly to the sea, whilst another seven firms have their own sewage treatment units. Three firms (5.4%) of those not connected have private disposal wells, while only one firm claimed that its reject is disposed of by both septic tank and directly to the sea.
Of the total industrial firms surveyed, 25 firms (20.2%) depend solely on their private water wells, while the highest proportion (41.1%), or 51 firms, depend on the piped supply, confirming that with the improvement in blended water quality, reliability of supply, and relatively lower cost, industrial consumers tend to favour water distributed through the public supply network. Just above one third of the sample population meet their water requirements fi-om mixed supply sources, while three firms (2.4%) purchase water fi-om outside sources. Two firms (1.6%) get their water intake fi-om on-site seawater desalination plants, and the remaining one firm claims that its water requirements are met by re-cycling.
Of those who have access to mixed supplies, the majority (a little more than 78%) depend on both public supply and purchasing ft-om outside sources to satisfy their water needs.
519 Six firms (14.3%) use both their private wells and the public supply source. One firm uses desalinated seawater in addition to its privately developed source, while another relies on both purchasing fi-om outside sources and water re-cyciing. The remaining firm uses a combination of water well, piped water, and purchase fi-om outside to meet its industrial water demand.
Of the 33 firms indicated that they had private wells, a large proportion (13 firms), or
39.4%, have their wells opened into the Rus - Umm Er Radhuma aquifer; a similar proportion of firms said that they were abstracting water fi-om the Khobar aquifer. Two firms (6.1%) have their wells developed in both Rus and Khobar aquifers, and another two firms withdraw water fi-om the Alat Limestone aquifer. Three firms, or 9.1% of those who answered this question, have Dammam dual-penetration wells. The survey also shows that an average of BD 906.700, or approximately US$ 2,393.70 (median value BD 148.000, or
US$ 390.72) is spent every month to supply the needs of firms using piped water and/or purchasing water fi-om outside sources.
Only fifteen (12.1%) of the firms included in the survey have water re-cycling systems.
The median quantity of re-cycled water is found to be 100 m"' day * (mean quantity
349.7 m^ day"*), while the total quantity re-cycled is estimated at 10,036.7 m^ day"*, accounting for 25.7% of the total daily intake volume. However, this should not be taken as an optimistic result because 62.4%, or 6,264 m"* day'* of this quantity is re-cycled at one industrial establishment - GPIC. Thirty-nine firms (31.5%) indicated that they desalinate their intake water before use. Almost eighty percent of these firms use reverse osmosis systems, four firms (10.3%) have eiectrodialysis units, and about 7.7% (3 firms) preferred multi-stage flash units. The remaining one firm applies the multi-effect boiling technique.
520 The median plant capacity is 300 m^ day ', and the average is 1,345.3 m"* day'*, the range being from 4 to 10,000 m^ day *. Plant availabilities range from a minimum of 38% to a maximum of 100%, with median and mean availabilities of 85% and 81%, respectively.
It is quite interesting to note that questions relating to the expectations for future plans with regard to^prospects for_grovvth_in factory_floor_areas.and-fijture employment for-the-years
2005 and 2010 had 100% valid responses. These questions were designed to gather information on such plans to assist in water demand forecasting in the event that data on these economic outlooks could not be obtained fi-om official sources. The responses show that slightly over seventy percent (70.2%) indicated they did not have plans for increasing their factory floor areas by the year 2005, while 29.8%, or 37 firms claimed that they did have plans. Over half (56.7%) of those having such plans, said that they would increase their factory floor area by 10%, and 13_firms, or a little more than one third (35.1%), expected a 30% increase in their factory floor area. Very few respondents (8.1%), representing three firms, have plans to increase their factory floor areas by 20% by the year
2005.
With respect to the expected percentage increase in employment by year 2005, 56 firms
(45.2%) reported that they did not have plans to increase their current employment levels.
Those who answered this question positively account for 54.8%, or 68 firms. Two thirds
(66.2%) of those firms expected to increase their workforce by 10%, 14 firms (20.6%) expected a 20% increase, and the remaining 13.2%, or nine firms, planned to increase their workforce by thirty percent.
The long-term plarming horizon for the year 2010, of course, receives substantially less
521 positive responses, particularly with regard to the prospective growth in factory floor areas.
Only 29 firms, or 23 .4%, provided expectations for expansion in their factory floor areas for this year, of which 41.4% (12 firms) expected a 10% increase, and seven firms (24.1%) anticipated a 20% increase. Three firms (10.3%) expected a 30% expansion, and two firms, or 6.9%, foresee a 40% expansion in their factory floor area. Slightly more than
17% (5 firms) of those who had expansion plans said that they .would increase their factory- floor areas by 50%.
Those who mentioned that they had plans for increasing their manpower constitute 43.5%, or 54 firms, while the balance of 56.5% (70 firms) is made up by those firms who had no idea regarding their future employment policy for the year 2010. The 54 firms who had answered this question positively are split as follows: 50% (27 firms) expected a 10% increase in their employment, ten firms (18.5%) indicated that they would increase their workforce level by 20%, nine firms (16.7%) had plans to increase their employment by
30%, and a 40% expansion was expected by 11.1% of these firms. Finally, the remaining two firms (3.7%) indicated that they were planning to raise their employment capacity by
50% in the year 2010.
522 CHAPTER EIGHT WATER DEMAND MODELLING
Developing demand functions for water is essential for forecasting water use. Forecasting the demandsjor watCT isjhe^asis of long-terin water resources development, because it enables authorities and water planners to anticipate water problems, and to recognise those factors, which have an effect on water use (United Nations. 1988).
Forecasts of future water demand have traditionally relied on the projection of past trends using the so-called trend extrapolation approach (OECD, 1989; Gardner and Herrington,
1986; Archibald, 1983; Power e/ a/., 1981; Domokos e/ a/., 1976; Sewell and Bower,
1968). This water demand forecasting approach implies continuation of present trends to a given time in the future (i.e. the^change o^ water use oyer time is extrapolated into the future). Such an extrapolation may be accomplished by graphical or mathematical means, and the change over time may be assumed in linear, exponential, logistic, or any other functional form (Boland et a/., 1981).
Some forecasts (called the single-coefficient methods) attempt to explain water use in terms of a single explanatory variable. This implies a simple bivariate statistical model that can be expressed in linear form as follows:
Q^a + bx
where Q is the water use per unit of time, x the explanatory variable, and a and b are, respectively, the intercept and slope coefficients.
523 The most widely used form of this forecasting approach, is the so-called per capita requirements approach, which is commonly based on forming a total demand projection by multiplying the average per capita water use by the projected service area population. This relationship is often expressed mathematically in the form:
Q~Pc.Po
in which Q is the average quantity of water required, Pc is the average per capita water use, and Po is the population forecast. The per capita coefficient may be assumed fixed over time, or it may be projected to change with time (Prasiflca, 1988). Several forecasts have been presented in the literature using this method (e.g. Billings and Jones, 1996; Boland ei a/., 1983; Park, 1980; Central Water Planning Unit, 1976; 1977; and Water Resources
Board, 1966).
A distinctive approach of the single-coefficient method is the so-called per connection approach. In this forecasting approach, fijture water use may be estimated as a produa of average water use per connection and projected numbers of connections or population per connection. Boland (1978) and Boland et al. (1983) presented empirical evidence that the number of connections is superior to population as a predictor of water use. The unit use coefficient approach is another form of the single-coefficient methods, with which future water use is expressed as a fiinction of a given unit use other than population or number of connections. For example, industrial water use may be forecast as the product of total industrial employment and per employee water use (see, Boland et a/., 1983;
Dziegieiewski, 1988), or a function of the level of output and average volume of water use per unit of output (see for example, Calloway et al., 1974). Mercer and Morgan (1973)
524 also specified water use in commercial establishments as a ftinction of the number of employees and estimated water use per employee. Berry and Bonem (1974) have also shown that the average daily per capita residential water use may be related in a bivariate model to the unit per capita income. McCuen et al. (1975) used gross store area to forecast water use in shopping centres.
Although these methods may produce reliable forecasts (Makridakis ei al., 1982; Million,
1980; Domokos ei al., 1976; Mitchell and Leighton, 1977), recent development in water resources plarming, the overall increase of water demands over the available supply, growing conflicts among the various demand sectors, and the emergence of multiple-use concepts suggest that such an approach has become inadequate to solve the ever-increasing water resources plaiming and management problems. This has emphasised the need for improved and more sophisticated techniques to water demand forecasting that take into account the various demographic, economic, social, climatological, technological, and policy decisions variables that are likely to have influence on the current and future water demands. These techniques include multivariate statistical and linear programming modelling approaches.
Statistical models are widely known to be a better option for modelling municipal and residential water demands, because these models are capable to handle the wide variability in water use in these sectors, and to allow for detailed analysis of different water uses, and the factors that affect them (Hanke and de Mare 1984; Hanke, 1978). On the contrary, studies have shown (Khater, 1994b; KJndler and Russell, 1984; United Nations, 1976) that the mathematical programming technique is a plarming tool that is normally more effective in modelling water demand in the agricultural activities. Stone and Whittington (1984)
525 argued that, with certain considerations and limitations, taking into account the multidimensional phenomenon of the water use in industry, both the mathematical programming and statistical approaches could be effectively used for modelling industrial water demand relationships.
In .this_research,_multiyariate statistical water demand models. were_deyelop€d to_ forecast the water demands in the residential, municipal, agricultural, and industrial sectors. For one reason, while data on water use at sectoral levels, population, per capita consumption,
irrigated area, etc. are available for the study area in sufficient quantity and are, in most
cases, of reliable quality; specific information on cost, industrial output, subsidies,
substitutions, agricultural inputs, and other variables related to the agricultural and
industrial processes hardly exist. For another, the proposed models are intended for policy
implementation purposes, meaning that they will be presented t^ the decision makers as a
possible plarming and policy formulation tools. Thus, selection of a more flexible and
relatively less complex forecasting approach in terms of operation and output generation
may be reasonably justified. Thirdly, the small size of the nation may also justify
conducting of explanatory cross-sectional surveys to model water demands in the study
area.
8.1 Municipal Water Demand
It has already been demonstrated that municipal water use is highly heterogeneous,
covering a wide variety of water uses. Such a multiplicity of uses, as pointed out by
Archibald (1983), creates major difficulties in forecasting municipal water demand on an
aggregate basis because each use has a different potential rate of growth in demand.
Because of this multiplicity of use, it was decided to model the water demand in this sector
526 in three components: residential (domestic), non-residential (non-domestic), and aggregate total municipal water demands. Although the disaggregation of this sector into domestic and non-domestic users is rather poor, it will remain here as a basis for deriving the component demands, given the fact that the collected data do not allow for better disaggregation.
Appropriate model structures and explanatory variables were selected as properly as possible for each component to account for component differences. Both the average water demand per household/dwelling unit and average daily per capita water consumption were examined as dependent variables, again with appropriate explanatory variables for each case. Estimates of equations of best fit were also developed for summer and winter residential and aggregate municipal demands in order to investigate the seasonal effect on water use.
8.1.1 Residential Water Demand
Table 8.1 is a cross tabulation showing the sign and magnitude of the simple relationships between the variables within the residential water use. Also reported in the table are the means and standard deviations for each variable. The key factor to note in this table is the relationships represented by the entries in the second column (labelled 1), which illustrate the simple correlations between the dependent variable (average water use per household) and the presumed predictor variables. Table 8.1a indicates that, apart from the number of toilet flushes per person per day, which does not have a significant impact on domestic water use, there are highly significant moderate to substantial positive relationships between the average residential water usage and the selected independent variables. For example, the slope coefficient between the average water use and family monthly income
527 Table 8.1 Correlation matrices showing the simple linear relationship between average monthly consumption and variables influencing residential water use as well as the simple correlations between each pair of these variables
(a)
Std. 7 8 9 10 Mean Variable 1 2 3 4 5 6 dev. 1. avercons 1.000 53.84 31.57
2. qalOginb 0.397** 1.000 1 3.76 1.76
3. qallginf 0.341** 0.823** 1.000 3.60 1.73
4. qal2gint 0.324** 0.721** 0.721** 1.000 10.50 5.53
5. qal3ginc 0.379** 0.422** 0.417** 0.401** 1.000 2.01 1.14
6. qblhvoc 0.407** 0.587** 0.420** 0.421** 0.397** 1.000 6.97 3.51
7. qb2hvhar 0.271** 0.439** 0.428** 0.438** 0.334** 0.192** 1.000 299.72 224.64
8. qbShvniv 0.331** 0.305** 0.367** 0.322** 0.412** 0.076* 0.450** 1.000 730.82 561.15
9. qdlougar 0.278** 0.244** 0.222** 0.263** 0.138** b.oii 0.400** 0.329** 1.000 32.30 72.23
10. qc2iuntf 0.035 -0.080** -0.004 0.000 0.015 -0.167** 0.021 0.136** -0.003 1.000 3.66 1.90 1 avercons = average monthly consumption in m"* per household per month; qalOginb = number of bathrooms per household; qallgiiif = number of flush toilets per household; qal2gint = number of laps per household; qal3ginc = number of cars per household; qblhvnoc = number of occupantSj(persons) per household; qb2hvhar = household area measured in m^ qb3hvnih = family monthly income in BD; qdlougar = garden area expressed in m^ cic2iuntf = number of toilet flushing per person per day; ' ** Correlation is significant at the 0.01 level (one-tailed). * Correlation is significant at the 0.05 level (one-tailed).
528 (b) Std. 8 9 10 Mean Variable 1 2 3 4 5 6 7 dev. 53.84 31.57 1. avercons 1.000
2. qbShvnwm 0.287** 1.000 1.25 0.65
3. qb6hvndw 0.080* 0.023 1.000 0.025 0.16
4. qc3biuwm 0.172** 0.083* -0.010 1.000 4.88 3.46
5. qc4biudw -0.215 0.158 0.325 0.071 1.000 4.16 1.19
6. qb4hvnsh 0.396** 0.492** 0.110** 0.152** -0.232 1.000 3.39 1.66
7. avershtkn 0.003 0.013 -0.010 0.064* -0.341 0.008 1.000 10.60 3.70
8. avercarwsh 0.072* -0.010 0.162** 0.054* 0.256 0.113** 0.020 1.000 1.06 0.85
9. averflwshg 0.063* -0.064* 0.082** 0.049 -0.106 0.115** 0.074** 0.088** 1.000 2.62 3.71
10. seasonfacr 0.180** -0.018 0.146** -0.006 -0.284 0.066* 0.024 0.033 0.068* 1.000 4.16 15.39 avercons = as before; qbShvnwm = number of washing machines per household; qb6hvndw = number of dishwashers per household; qc3biuwm = number of clothes washes by washing machine per week; qc4biudw = number of dishwashes per dishwasher per day; qb4hvnsh = number of showers per household; avershtkn = average number of showers taken per person per day; avercarwsh = average car washing per week; averflwshg = average floor washing per month; seasonfacr = seasonal factor; Correlation is significant at the 0.01 level (one-tailed). • Correlation is significant at the 0.05 level (one-tailed).
529 is 0.331, suggesting that a 10% increase in family income would lead to a 3.3% increase in the average domestic water consumption.
The number of persons per household has the highest zero-order linear correlation coefficient (0.407) with the average water use. As one would expect, strong correlations exist between the njumber of bathrooms per household and the related variables representing the number of taps and the number of flush toilets per household, suggesting a possibility of multicollinearity if these variables are included as input variables. The number of bathrooms, household area, and number of occupants per household are also positively correlated. The same is true for the relationships between family income and variables relating to the economic ability such as the number of cars per household and other physical assets (household area and garden area). Household area also has a strong positive effect on garden area, as might be expected.
As Table 8.1b suggests, the average monthly residential water use is positively related to the variables number of washing machines per household and number of washing by machine (both coefficients are statistically significant at the 0.01 probability level).
However, the number of washing by machine and the number of washing machines are weakly correlated with each other. The number of showers per household also has a significant link to the average volume of water used per household per month. Despite the fact that the signs of the beta coefficients of average fi"equencies of car washing per week and floor washing per month are positive, as expected, they are weakly linked to the average monthly water use.
530 Models Specification and Development
The variables relevant to the residential water use are cleariy defined in terms of time, space and units of analysis in Table 7.2 and again in notes to Table 8.1. Examination of the above cross tabulations indicates that variations in average residential water use may be explained by a nimiber of explanatory variables: number of bathrooms, number of cars, number_ojLQcaupants, Ji^^^^ area, family monthly income, garden area, and number of washing machines.
The regression equations relating the annual average residential water use to the presumed explanatory variables are presented in Table 8.2. The initial estimating equation produced insignificant coefficient of number of bathrooms, household area, number of washing machines, and number of dishwashers. Household area and number of bathrooms are collinear variables with family income; therefore it is not surprising that they do not contribute to the explanation of the variance in the dependent variable. The insignificant coefficients of number of washing machines and number of dishwashers probably confirms the findings of Thackray et al. (1978) that the average annual water consumption is relatively insensitive to anything other than really dramatic changes in ownership of such appliances. In the case of dishwashers, the statistical results appear to be considerably affected by the limited number of observations (see Table 7.13).
Model 1A represents the results when the above variables are eliminated fi-om the analysis.
Two dummy variables are introduced to the demand fimction: a dummy sewer connection that has a value of' 1' for households with sewer connection, and '0' for those using septic tanks for sewage disposal, and a dummy swimming pool (takes on a value of * T for presence of swimming pool and '0' for absence). The literature suggests that households
531 Table 8.2 Ordinar>' least squares estimations of residential water demand functions
Regression statistics Values Independent variables Coefficients r-statistic Valid N Tolerance VIF
(/7-value) r
1 Model lA Constant 175.471 4.039(0.000) R' 0.305 Number of cars (Xi) 47.976 2.564 (0.011) 1060 0.695 1.439 Adjusted 0.296 Number of occupants (X2) 36.050 6.509 (0.000) 1060 0.833 1.201 1 F- Statistic 34.75 Family monthly income (X3) 0.123 3.360 (0.001) 910 0.747 1.338 Significance of F test 0.0000 Garden area (X4) 1.020 . 3.922 (0.000) 422 0.891 K122 Durbin-Watson Statistic 1.972 Std. error of estimate 317.81
Model 2A Constant 174.442 4.036(0.000) R' 0.314 Number of cars (Xi) 50.517 2.708 (0.007) \m 0.692 1.445 Adjusted 0.303 Number of occupants (X2) 36.470 6.614(0.000) 1060 0.832 1.203 i F- Statistic 28.955 Family monthly income (X3) 0.106 2.833 (0.005) 910 0.711 1.407 Significance of F test 0.000 Garden area (X4) 1.031 3.982(0.000) 422 0.891 1.122 Durbin-Watson Statistic 1.962 Dummy swimming pool (X5) 276.661 2.075 (0.039) 1060 0.945 1.058 1 Std. error of estimate 316.170
Dependent variable is annual average water use in m-* per household per year. Probability values are in parentheses.
532 with septic tanks are assumed to use significantly less water than those with public sewers
(see for example, Joseph, 1982). The absence of sewer charge in the study area may support this hypothesis. Conversely, ownership of swimming pools would obviously suggest more water use. The variable dunmiy swinmiing pool is significant at p < 0.05, but the variable connection to public sewer is found to be insignificant plus having the v^ong expected sigTK TJLC res^ this model are presented as_Model 2A. This model represents the equation of best fit for the residential water use in terms of both predictability power and residuals examination. The normal P-P plot and the residual scatter plot in Figure E-1 of Appendix E reveals that the assumptions of normality and linearity are reasonably met.
The model explains a small proportion of the variance in the annual average residential water use; only 30% of the variation in the dependent variable is accounted for by these variables, leaving almost 70% of the variance unexplained. Such a weak explanatory power is typical for a cross-sectional data, but possibly reflects low variability among the domestic users; data problems and/or variation due to factors not taken into account in the model (specification error) may be another possible explanation. The table shows that all the predictor variables are highly significant, with the expected positive signs (i.e. all indicative of higher consumption). The coefficient on family income gives an income elasticity of residential demand of 0.12, suggesting that the demand for residential use is income inelastic. This elasticity value is within the range of income elasticities estimated by Headley(1963).
The model data indicate that the most important individual variable in explaining the variation in average residential water use is household size. The elasticity of water usage
533 with respect to this variable is 0.39; indicating that a 10% increase in household size would likely to produce a 3.9% increase in annual average residential water use. The effect of this variable on the average monthly consumption at different family income levels is further analysed as shown in Table 8.3. The table reveals that both family income and household size have a consistent effect upon average water use; at every income level, withan increase in household jize, monthly average reskiential water use increases.
Both the double-log and semi-log functional forms were tested using the same set of variables, but their fits were found to be generally poor, although the semi-log function with natural logarithmic transformations of the independent variables slightly improves the normal P-P plot. The specified semi-logs and double-log models produced insignificant income variable.
A linear fijnction was developed to determine whether there is any effect of dwelling type on residential water use. The three types of residential units sampled (i.e. houses, villas, and flats) were entered as dummy variables with dummy flat treated as the reference category. The following regression equation (with figures in parentheses below the estimated parameters are the standard error of estimates) is derived:
Q = 34.610 + 22.929DUMHOU + 21.298DUMVILA + e (3.015) (3.491) (3.553)
where Q is the average monthly consumption per dwelling unit, DUMHOU (a dummy variable: house is represented as 1; 0 otherwise), DUMVILA (a dummy variable: taking the value of 1 if the dwelling unit is villa, and 0 otherwise), and e is the disturbance or error term. The model yielded an adjusted value of 0.056, F-Statistic of 22.757
534 Table 8.3 Average monthly residential water consumption by household size and family monthly income
Number of persons per Family income (BD per family Average monthly consumption Standard error Standard household per month) (m^ per household per month) deviation
1 - 4 persons (80) 0 - 400 (80) 32.86 (36) 3.i:67 18.999
(82) 401 - 800 (82) 31.63 (60) 3.013 23.341
(39) 801 + (39) 41.64(19) 4.057 17.683
5 - 7 persons (116) 0-400(116) 48.18(83) 2.629 23.950
(181) 401 -800(181) 50.55 (129) 2.172 24.670 1 (130) 801 + (130) ' 58.89(101) 2.817 28.310
+ 7 persons (115) 0-400(115) 53.45 (75) 2.850 24.684
(111) 401 -800(111) 66.25(80) 3.542 31.680
(84) 801 + (84) 76.08 (65) 5.508 44.410
Number of observations in parentheses.
535 (Significance of F test 0.000) with a computed Durbin-Watson of 1.957. The intercept and the regression coefficients are significant at better than 0.01 level, and examination of the residual plots revealed that the assumptions of normality and homoscedasticity were reasonably satisfied. When these variables are, however, entered to Model 2A collectively
(results not shown here), only the dummy flat was found to be significant at the 0.01 level.
The results suggest that household type is responsible for nearly 6% of the variation in water use. On average, people living in traditional houses consume about 23 m^ (close to
60%) per month more than those living in flats, and families residing in houses use nearly as much water as those residing in villas. The latter finding is difiFerent from the hypothesis that water use in villas would likely to be higher since it normally involves sprinkling demand. However, as indicated earlier and as Table 8.4 clearly suggests, household size appears to be the most important determinant of residential water demand.
Table 8.4 Average monthly consumption by type of dwelling, household size, and garden area
Type of Average monthly Average Average garden Valid dwelling consumption (m ) household size area (m^) N Houses 57.34(1.700) 7.94 26.88 146
Villas 55.96(1.706) 6.89 36.87 175
Flats 23.07(1.339) 3.87 N/A 63
Figures in parentheses are the standard errors of estimates. (N/A) Indicates not applicable.
In order to investigate the individual effect of the dummy sewer connection on the average monthly consumption, the following simple bivariate relationship was developed. The beta coefficient for this dummy is -5.646 (significant at p-value 0.027); the following equation provides a correlation coefficient of -0.082.
536 Q = 57.802 - 5.646 DUMSEWER
As an interpretation, this indicates that households wath sewer connection consume, on average, about 6 m^ (almost 10%) per month less than those using septic tanks for disposing of their domestic wastes. The negative sign on the coefficient of this variable is not as expected, and probably caused by local.factors.
The simple linear correlations between a number of predictors and average daily per capita domestic water use are shown in Table 8.5. Table 8.5a indicates that household size is the most important single variable contributing to the average quantity of water used per capita per day, and has the expected negative sign (correlation coefficient -0.340 significant at the 0.01 level). Garden area also significantly influences the per capita water use, with the expected positive sign. Understandably, income level and the number of toilet flushing per person per household correlate positively with the daily average per capita consumption. Table 8.5b shows that per capita daily water use significantly correlates with the number of washing machines, number of dishwashers, average car washing, and average floor washing.
The initial run of residential water demand fijnctions using the per capita daily water use as dependent variable indicates that the number of cars, household area, number of bathrooms, and number of washing machines are insignificant predictors. The mode! of best fit with these variables being omitted fi'om the model specification (figures in parentheses below the estimated coefficients are the p-values and standard error of estimates, respectively) is given by:
537 Table 8.5 Correlation matrices showing the signs and strengths of the simple linear relationship between per capita residential water use and variables potentially influencing this use as well as the correlations between each pair of these variables
(a)
Std. 6 7 8 9 10 Mean Variable 1 2 3 4 5 dev. 1. percavco 1.000 282.32 189.07 2. qalOginb -0.080 1.000 3.76 1.76 3. qallginf -0.019 0.823** 1.000 3.60 1.73
4. qal2gint -0.017 0.721** 0.721** 1.000 10.50 5.53 5. qal3ginc -0.024 0.422** 0.417** 0.401** 1.000 2.01 1.14
6. qblhvoc -0.340** 0.587** 0.420** 0.421** 0.397** 1000 6.97 3.51 7. qb2hvhar 0.089* 0.439** 0.428** 0.438** 0.334** 0.192** 1.000 299.72 224.64
8. qb3hvniv 0.168** 0.305** 0.367** 0.322** 0.412** 0.076* 0.450** 1.000 730.82 561.15
9. qdlougar 0.224** 0.244** 0.222** 0.263** 0.138** ,0.011 0.400** 0.329** 1.000 32.30 72.23 i 10. qc2iuntf 0.160** -0.080** -0.004 0.000 0.015 -0.167** 0.021 0.136** -0.003 1.000 3.66 1.90 pcrcavco = average daily per capita water use in litres per capita per day. The other variables arc as defined before in the notes to Table 8. la. •* Correlation is significant at the 0.01 level (one-tailed). ' • Correlation is significant at the 0.05 level (one-tailed).
538 (b)
Std. Variable 1 2 3 4 5 6 7 8 9 10 Mean dev. 1. percavcon 1.000 282.32 189.07 2. qbShvnwm -0.151** 1.000 1.25 0.65
3. qb6hvndw 0.153** 0.023 1.000 0.025 0.16 4. qc3biuwm -0.039 0.083** -0.010 1.000 4.88 3.46
5. qc4biudw -0.219 0.158 0.325 0.071 1.000 4.16 1.19
6. qb4hvnsh -0.052** 0.492** 0.110** 0.152** -0.232 1.000 3.39 1.66
7. avershlkn 0.031 0.013 -0.010 0.064* -0.341 0.008 1.000 10.60 3.70 8. avercarvvsh 0.095** -0.010 0.162** 0.054* 0.256 0.113** 0.020 1.000 1.06 0.85
9. averflwshg 0.105** -0.064* 0.082** 0.049 -0.106 0.115** 0.074** 0.088** 1.000 2.62 3.71
10. seasonfacr 0.250** -0.018 0.146** -0.006 -0.284 • 0.066* 0.024 0.033 0.068* 1.000 4.16 15.39
pcrcovcon = per capita daily municipal water use in litres per capita per day. The other variables are as defined before in the notes to Table 8.1 b. Correlation is significant at the 0.01 level (one-tailed). * Correlation is significant at the 0.05 level (one-tailed).
539 2 = 284.188- 11.402A'/ +0.416^2+ 0.488A'j+ e (0.000) (0.000) (0.002) (0.000) (26.395) (2.864) (0.130) (0.081)
Adjusted = 0.245, F- Statistic = 35.763, and Durbin-Watson Statistic = 1.957
where Xi = number of occupants per household, X2 = garden area in m^, Xs = per capita income in BD per capita per month; and e = random error term.
It should be noted that the variable per capita income is a created variable obtained by dividing the total family monthly income by the number of occupants per household to ensure consistency in dimensions with the dependent variable. Although this tends to produce a collinear variable, the obtained simple correlation between the number of occupants and per capita income (-0.399) does not seem to represent a serious collinearity problem. The binary variables connection to public sewer and ownership of a swimming pool were introduced to this model, but were found to be insignificant predictors.
The signs of the regression coefficients follow a priori expectations. The positive sign on the coefficient of per capita income yielded an income elasticity of demand of 0.22, which appears to correlate favourably with some estimates found in the literature (Headley, 1963;
Wong, 1972; Darr et a/., 1976; Katzman, 1977). The model results support the contention that per capita daily water use increases at a decreasing rate with the increase in the number of occupants per household, suggesting certain economies of scale. The elasticity with respect to this variable indicates that a 10% increase in household size will produce a
3% decrease in per capita daily water consumption. This relationship is fijrther illustrated by Table 8.6. The table shows that, apart fi-om the very slight discrepancy within the larger households (more than seven persons) and high per capita income (BD 201 and
540 Table 8.6 Per capita daily residential water use by household size and per capita income
Number of persons per Per capita income (BD per capita Average daily per capita Standard error Standard household per month) 1 consumption (Ipcd) 1 deviation 1 1 1 - 4 persons (55) 0- 100 (55) 312.57 (26) 35.299 179.992
(75) 101 - 200 (75) 331.76 (48) 34.550 239.373 j (71) 201 + (71) 464.11 (41) 49. "^25 318.395
5-7 persons (208) 0-100 (208) 258.66(146) 11.324 136.835
(178) 101 - 200(178) 305.79(137) 14.681 171.837 1 (40) 201 + (40) 338.94(29) 33.394 179.833 1 ) + 7 persons (230) 0-100 (230) 20092(159) 7.213 90.960 j (39) 101 - 200(39) 243.74 (30) 24.850 136.111
(12) 201 + (12) 34085(10) 58.32 184.439 1 Number of observotions in parentheses.
541 above), at every level of income, average daily per capita water use decreases as the household size increases. Quite understandably, among the mentioned families, it appears that water use is much less responsive to change in income primarily because of the
ineffectiveness of the adopted water tariff structure. Similar conclusion is reached by
Grima (1972). An interesting finding is that increases in income in smaller households are
assocmted with a substantial rise in per capita consumption as found elsewhere_(see_Parr
et ai, 1975; Darr et a/., 1976).
CJarden area has a significant effect upon per capita water use; with every 10% increase in
the garden area, an increase of about 4.2% in the average daily per capita consumption
would be expected. Almost 25% of the variance in the residential per capita daily water
use is accounted for by the effect of the variables in the model. The model reasonably
satisfies the basic regression assumptions as shown by the associated regression statistics,
and the normal P-P and residual scatter plots in Figure E-2 of Appendix E. Semi-logs and
double-log functional forms were also tested but produced poorer fits when compared to
the linear model. Rather surprisingly, with the double-log functional form, the normality
assumption is also substantially violated.
Separate summer and winter water demand functions were developed in order to examine
the seasonal effect on average monthly residential water use. The sample data were fitted
to equations of both linear and log-linear forms. The results of the models of best fits for
summer and winter residential demands are summarised in Table 8.7. The coeflBcients of
the Summer Model are highly statistically significant and have the correct directions. The
coefficient on income variable produces a summer elasticity of residential demand of 0.15,
slightly higher than the income elasticity of the total residential demand. The regression
542 I Table 8.7 Ordinary least squares estimations of summer and winter residential water demand funciont s
Regression statistics Values Independent variables Coefficients /-statistic Valid N Tolerance VIF (p-value) 1 1 1 Summer Model Constant 21.241 5.463 (0.000) i le 0.263 Number of occupants (Xi) 3.336 7.075 (0.000) I6A 0.989 1.011 Adjusted 0.253 Family monthly income (X2) 0.0119 3.738 (0.000) G6\ 0.846 1.182 1 F- Statistic 28.035 Garden area (X3) 0.0992 4.112(0.000) 320 0.891 1.122 1 Significance of F test 0.000 Dummy swimming pool (X4) 28.956 2.333 (0.020) 704 0.949 1.054 Durbin-Watson Statistic 1.941 Std. error of estimate 29.399
1 Winter Model Constant 14.642 3.926 (0.000) le 0.272 Number of cars (Xi) 5.226 3.251 (0.001) 698 0.695 1.439 Adjusted ^ 0.262 Number of occupants (X2) 2.817 5.922 (0.000) 698 0.833 1.201 i F- Statistic 28.916 Family monthly income (X3) 0.0086 2.769 (0.006) 595 0.747 1.338 Significance of F test 0.000 Garden area (X4) 0.0526 2.356(0.019) 315 0.891 1.122 Durbin-Watson Statistic 1.966 Std. error of estimate 27.001
Dependent variables are summer and winter monthly average water uses in per household per month. Probability NTilues are in parentheses.
543 coefficient with respect to a dummy swimming pool suggests that, holding other factors constant, average monthly consumption in summer in households with swimming pools is expected to be higher by 29 m"* than in households without swimming pools. Despite the fact that 56% of the respondents owning swimming pools reported that they fill their pools using outside sources, it appears that the need for topping up due to evaporation fi-om surface substantially contributes, to this The semi-log specification with the independent variables appearing in logarithmic forms produced ahnost identical results to those of the linear model, though the linear equation has a slightly better performance in terms of explanatory power (adjusted R^, overall model significance, and assumptions tenability. The double-log function was also tested but did not improve the model fit. The winter demand is found to be a fimcdon of number of cars, number of occupants, monthly income, and garden area. As shown in Table 8.7, all the variables are highly statistically significant. The coefficient of the income variable is positive, as expected, and the derived winter elasticity of demand is estimated at 0.14, almost comparable to that obtained for the summer demand. The positive signs of the number of cars, number of occupants, and garden area indicate that average monthly winter consumption increases as the values of these variables increase. Household size elasticity of winter demand is estiniated at 0.37. 544 The natural logarithmic transformation of the independent variables of the above linear equation yielded a shift in the intercept sign, with a slight reduction of the explanatory power, and insignificant coefficient of the family income (p-value 0.289). However, when the raw variable of family income is introduced to this model, the derived coefficient was significant with an income winter elasticity of 0.13. In general, the results of this model demonstrated the superiority of ^he Imear specification in terms of statistical fit and predictive capabilities. The double-log functional form was also tested but provided no better statistical results. The comparison between the winter and summer residential linear models suggests some interesting findings. First, as one would expect, the variable dummy swimming pool is not significant in the case of winter demand but it is highly significant in the summer model. Second, the coefficient for garden area in the summer demand model (0.0992) is about twice as much than in the winter demand model (0.0526). Third, the number of cars is significant with respect to winter residential demand, whereas it is insignificant v^th respect to the summer demand (the interesting point here is that the associated variable car washing is expected to be higher in winter than in summer, although the coefficients display slight discrepancies). Fourth, the number of occupants has a slightly more influential eflfect on water use during summer months (household size elasticity of summer demand is 0.41 against a household size elasticity of winter demand of 0.37). Lastly, when the variable number of cars is dropped fi-om the vAnter demand model, the resultant adjusted value becomes 0.240, slightly less than that of the summer model. 8.1.2 Non-Residential Water Demand The results of the cross tabulation of the relationships between variables for non- 545 residential water use are shown in Table 8.8. Because most of the variables are not normally distributed and the sample size of these users is small, both Pearson's and Spearman's rho correlation coefficients are reported. In searching for predictors, the table indicates that the variable number of bathrooms per dwelling unit could be the best single predictor of non-residential water use with the average water use. Other variables of significant„effects_inc!ude_the_number_of_cars, and garden_area^JNui^ of taps per dwelling unit and average water use also vary positively with each other. As one would anticipate, the number of taps and number of bathrooms also vary significantly with each other. In spite of the fact that the two variables have the same coefficient of variation (standard deviation/mean) of 1.12, the number of bathrooms was selected for inclusion on the grounds that respondents would likely to give more reliable answers with respect to this variable. No other independent variables in the matrix seem to be seriously mutually correlated. Models Specification and Development The variables that were considered to be of potential value in explaining variation in the non-residential water use are defined earlier in Table 7.2. Fuller definitions of these variables are also given in notes to the correlation matrix of Table 8.8. A look at the data given in this table reveals that average non-residential water use may be assumed to be a fijnction of a limited number of explanatory variables; namely, the number of bathrooms, number of cars, and garden area. Unfortunately, the derived demand fijnctions obtained for this sub-sector gave less reliable statistical results, most likely because of the small sample size (owing to the uncooperative attitude of the large customers to the survey) and lack of specific explanatory variables for each sub-category (the latter possibly reflects a major deficiency in the design of the municipal questionnaire). 546 Table 8,8 Correlation matrix showing the simple linear correlations between each pair of variables of non-residential water use Variable I 2 3 4 5 6 Valid N Mean Std. dev. 1. avercons 1.000 45 i 399.04 441.86 2. qalOginb 0.555** 1.000 45 ' 27.58 30.86 (0.577**) 3. qal2gint 0.503** 0.773** 1.000 45 64.07 71.60 (0.493**) (0.846**) 4. qal3ginc 0.332* 0.036 0.151 1.000 30 7.17 14.05 (0.557**) (0.412**) (0.296) 5. qdlougar 0.352 -0.0163 0.057 0.118 1.000 20 382.60 510.36 (0.3787) (0.132) (0.219) (0.184) 6. qblhvoc 0.43 0.017 -0.008 -0.184 -0.214 1.000 44 0.70 0.46 (0.095) (0.069) (0.187) (-0.493**) (-0.124) avercons = average consumption in m^ per month; qalOginb = number of bathrooms; qal2ginl = number of taps; qaI3ginc = number of cars; qdlougr = garden area in m^; qblhvoc = number of occupants (here represents number of employees, students and staff, club members, worshippers, etc. for the large consumers group). i Figures in brackets are Spearman's rho correlation (nonparamctric). Correlation is significant at the 0.01 level (one-tailed). • Correlation is significant at the 0.05 level (one-tailed). 547 The first run with the above variables in the specification shows that only the variable number of bathrooms is significant at the 0.05% level. In addition, the overall model is insignificant (significant of F-test is 0.065), plus suffering fi-om serious positive serial correlation (Durbin-Watson Statistics = 0.993). When the jnsigmficant ijidependent^ variables are eliminated fi-om fiirther consideration, and the variable dummy swimming pool is introduced to the model specification, the model fit has been substantially improved in terms of overall significance (Significance F- test = 0.000), with a Durbin-Watson Statistics value of 1.716. The specified independent variables are highly significant and explain 44% of the variance in the average annual water use. The regression equation for this model (p-values in parentheses) is as follows: 2 = 2031.580 + 55.625A^/ + 5978.566^^2 + e (0.015) (6.018) (0.001) The inclusion of the variable dummy connection to sewer produced an insignificant coefficient, with a different sign than hypothesised. The semi-log and double-log specifications did not improve the fit of the regression equations, although examination of the residual plots revealed that the normality assumption was better satisfied in the case of the double-log model. The coefficient on the number of bathrooms suggests that average water use per annum increases by about 56% for every 1% increase in the number of bathrooms. The model indicates that, holding other things constant (number of bathrooms here), dwelling units with swimming pools tend to use more water than those not having swimming pools by 5978.6 m^ per annum (498.2 m"* a month). Attempts were made to perform bivariate analysis for the different sub-categories of use 548 on the basis of employment variable such as number of students and staff, number of worshippers, average number of guests, ... etc. The intent here was to develop water use models for sub-categories of the non-domestic water users, which are as homogenous as possible. The derived demand fijnctions indicate that, with the exception of the sub• categories representing hotels and flat apartments, and social and health clubs, the exercise was statistically, disappointing. The straightforward explanation for this is that the nurnber of the available sample points was too small to facilitate reliable statistical analysis. As a result, regression equations were only estimated for- the significant sub-categories. The regression equation for hotels and flat apartments (with the standard errors of estimate and probability values in parentheses, respectively) is: Qh = 2236.305 + 91.571A^/ + e Adjusted = 0.848 (1487.0) (13.537) F- Statistics = 45.761 (0.176) (0.000)) Durbin-Watson = 2.584 where Qh is the average annual consumption in the hotels and flat apartment sub-category, Xi is the average number of guests per day (including staff), and e is the error term. From the equation, 85% of the variation in the hotels and flat apartment water use is accounted for by this single variable. The functional relationship between water use in the social and health clubs sub-category and number of members and guests (including staff) can be given by the equation (with the standard errors of estimate and significance t-statistic in parentheses, respectively): ec= 16631.755- 173.2241^; + e (3165.441) (40.844) (0.034) (0.051) 549 Here Qc is the average annual water use in the social and health clubs sub-category, Xj is the average number of members and guests per day (including staff), and e is the error term. The estimated equation yielded a high value of 0.900, indicating that the model explains 90% of the variance in the water use in this sub-sector, although its beta coefficient is barely insignificant (p-value 0.051). The negative sign of this coefficient demonstrates a type of scale economy. _ _ _ Attempts were also made to perform analysis based on dividing the non-residential water use into two components: conmiercial use and public use. The commercial use includes water use in hotels and flat apartments, commercial establishments, restaurants, and clubs, whereas the public use constitutes water use in government buildings, schools and educational institutes, hospitals, and religious institutes. The industrial component was not considered here since the demand in industry is affected by a different set of factors; besides, industrial water demand is modelled separately in section 8.3. The water use in social and health clubs is included within the commercial use since most of the sampled clubs are generating income, although the dividing line here is not quite clear. Only limited independent variables were made available for this analysis. These are the number of people employed (including visitors, guests, customers, etc.), number of bathrooms, garden area, and the dummy variables representing connection to sewer and ownership of swimming pool. The obtained regression equation for the commercial use (with standard errors of estimate and /7-values in parenthesis below each coefficient) is: =-49.081 + 7.388;^/+ 3.994;\r2+8 (127.660) (2.177) (1.676) (0.705) (0.003) (0.028) where Qc is the average commercial water use in m^ per month, Xj is the number of 550 bathrooms, X2 is the number of customers per day (including staff), and e is the error term. About 63% of the variation in consumption in the commercial sector is explained by these independent variables. Garden area, and the dummy variables connection to sewer and ownership of swimming pool were found to be insignificant predictors. The model yielded a Durbin-Watson Statistic of 1.839, F-Statistic of 17.927 (significant at the 0.000 level), and a standard error of estimate of 312.167. _ - _ _ _ _ The estimation equation for the public water use is given by: 145.883 +0.569-Y/ + e F-Statistic = 21.230 (73.824) (0.124) Durbin-Watson = 1.478 (0.068) (0.000) where Qp\s the average.public water use in m^ per month, Xi is the garden area expressed in m^, and e is the error term. Figures in parenthesis beneath the regression coefficients are standard error of estimates and p-values, respectively. The model indicates that the garden area is usefiji as a single indicator of public water use, explaining slightly more than 57% of the variation in water demand in this sub-sector. It is surprising, however, that both the number of bathrooms and number of employees (including number of students, worshippers, patients, etc.) are not significant (the latter variable is only significant at the 10% level). Clearly, the degree of significance with respect to the variable number of employees is highly influenced by the number of worshippers in the religious establishments (refer to the discussion of this point in Chapter Seven, page 492). The insignificant coeflQcients on the variables dummy sewer connection and dummy swimming pool is not surprising. In the final analysis, it is noticeable that 551 although the commercial water use model seems to produce more reliable statistical fits, the two models suffer from the problems of small sample size (valid is 21 and 18 for the commercial and public uses, respectively) and unavailability of explanatory variables that specifically explain the variance in the dependent variable. 8.1.3 ^Aggregate.Municipal-Water Demand _ „ „ In the aggregate context, there would seem to be associations between the average municipal water use and the variables number of bathrooms, number of cars, garden area, average car washing, and number of occupants per dwelling (here represents the number of persons per household as well as the number of persons working, studying, visiting, worshipping, dining, etc., within the large customers group). Table 8.9 shows the zero- order correlations of pairs of variables representing the municipal water use. The data in the table reveal that the number of bathrooms and average municipal water use varies substantially with each other. Number of cars, number of occupants, and garden area also had considerable effects upon average water use. All the variables are indicative of more consumption. The response of average municipal water use to average car washing is also positive. It is somewhat surprising that there is a weak (0.090) correlation between the number of cars and average car washing, although significant at the p < 0.01. As an explanation, it appears that most of car washings with respect to the large customers group is taking place outside the dwellings, since the correlation coefficient between these variables for the domestic water use is 0.167 and is significant at the 0.01 level. It is interesting to note that garden area is strongly related to the seasonal factor (correlation coefficient 0.822 significant at p < 0.01). The seasonal factor is also significantly correlated with both average car washing and average floor washing, but 552 Table 8.9 Cross tabulations showing the simple correlations between average monthly consumption and variables influencing aggregate municipal water use as well as the correlations between each pair of these variables Std. Variable 1 2 3 4 5 6 7 8 9 Valid N Mean 1 dev. 1. avercons 1.000 ' 781 73.73 135.76 1 2. qalOginb 0704** 1.000 1105 4.73 7.95 3. qal3ginc 0.440** 0.238** 1.000 1090 2.15 2.69 4. qblhvnoc 0.310** 0.295** 0025 1.000 1 1105 93.27 878.79 5. qdlougar 0557** 0.212** 0205** 0.088* 1.000 ; 442 48.14 14668 6. averflwshg 0.043 -0.016 0151** 0.145** O070 , 1.000 1013 2.70 3.97 7. avercarwsh 0239** 0.006 O090** 0.085** 0510** :ao9i** 1.000 1040 1.09 1.28 8. averirigfreq 0102* 0.122** O038 0.097* O028 0107* 0.114** 1.000 479 4.28 2.29 1 9. seasonalfactr 0.428** O020 0242** 0.062* 0822** 0124** 0.426** -0.040 1.000 731 7.72 52.02 1 1 avercons = average monthly consumption in m^ per day; qalOginb = number of bathrooms per dwelling; qal3ginc = number of cars; qblhhoc = number of occupants (here represents number of persons per household as well as number of employees, students |and staff, visitors, club members, w-orshippers, etc. [for the large consumer group); qdlougar = garden area in averflwshg = average frequency of floor washing; avercarwsh = average frequency of car washing; averirigfreq = average irrigation frequency; seasonalfactr = seasonal factor rcpreseniing the difference between summer and winter consumption. •* Correlation is significant at the O.OI level (one-tailed). | * Correlation is significant at the 0.05 level (one-tailed). I I 553 seems to have insignificant correlation with average showers taken (correlation coefficient 0.024, see Table 8.1b). The poor relationship between average water use and average showers taken support the assertion that showers taken in such a warm climate would unlikely to exhibit wide variability. Models Specification and Development The variables potentially responsible for variations in aggregate municipal demand are clearly defined in space and unit of analysis in Table 7.2, and in more detail in notes to Table 8.9. The estimated aggregate municipal demand functions are given in Table 8.10. The regression equation indicated by Model IB represents the initial run afler the elimination of family income, which was found insignificant plus having the wrong expected sign. The model shows that average municipal water use is significantly and directly related to the number of bathrooms, number of cars, garden area, and number of occupants, and is significantly, but inversely, related to the household area. Overall, almost 80% of the variation in municipal water use is accounted for by the variables in the model, with the number of bathrooms, number of cars, and garden area being the most important determinants of average municipal water use. In Model 2B, two dummy variables were introduced to the specification: dummy sv^mming pool coded 1 for presence of swimming pools, and 0 for absence, and dummy sewer set equal to I for those connected to the public sewer and 0 otherwise. The coefficients on these dummies were found to be highly significant, and their inclusion raised the degree of the explanatory power of the ftinction to 83%. In spite of the fact that the coefficient on dummy sewer is negative, which is different than what has been hypothesised, the variable is retained in the model since this finding may reflect local 554 Table 8.10 Derived demand functions of aggregate municipal water use Regression statistics Values Independent variables Coefficients r-statistic Valid N Tolerance VIF (p-value) Model IB Constant 309.120 5.100 (0.000) le 0.799 Number of bathrooms (Xi) 130.123 22.457 (0.000) 781 0.749 1.335 Adjusted 0.796 Number of cars (X2) 179.357 11.277 (0.000) 765 0.868 1.152 F- Statistic 267.199 Household area (X3) -2.358 -10.871 (0.000) 679 0.669 1.495 Significance of F test 0.0000 Garden area (X4) 5.336 17.930 (0.000) 0.833 1.200 Durbin-Watson Statistic 1.684 Number of occupants (X5)* 0.252 5.293 (0.000) 781 0.904 1.106 Std. error of estimate 735.733 Constant 461.529 5.411 (0.000) Model 2B Number of bathrooms (XO 118.357 21.425 (0.000) 781 0.693 1.443 1 0.832 Number of cars (X2) 172032 11.753 (0.000) 765 0.862 1.160 Adjusted 0.829 Household area (X3) -2.344 -11.598(0.000) 679 0.647 1.544 F- Statistic 236.444 Garden area (X4) 5.138 18.656 (0.000) 342 0.818 1.223 Significance of F test 0.000 Number of occupants (X5)* 0.237 5.409 (0.000) 781 0.898 1.113 Durbin-Watson Statistic 1.772 Dummy connection to sewer (Xa) -177.065 -2.181 (0.030) 776 0.968 1.033 Std. error of estimate 674.541 Dummy swimming poo! (X?) 1909.679 7.783 (0.000) 780 0.866 1.155 • Including large customers group. 555 conditions as previously stated. As depicted by Figure E-3 (Appendix E), the model suffers from serious normality problem, but reasonably satisfies the other basic regression assumptions. It should be stressed here that in such a highly heterogeneous and vary large sample, slight deviation from the normality curve is inevitable. According to Tabachnick and Fidell (2001), with large samples (200+), the seriousness of violating the normality assumption significantly diminishes. _ _ _ _ Semi-log transformed fijnctions were tested but failed to improve the situation in terms of normality. In fact, with the semi-log specification the normality assumption is more seriously violated than with the linear fiinction. The double-log transformed fijnction as an alternative specification slightly improves the model fit with respect to normality, but significantly reduces its predictability power. In addition, this fijnction produced insignificant coefficients for household area, dummy swimming pool and dummy sewer connection. The equation obtained for the log-linear model with the insignificant variables being omitted (standard error of estimates in parentheses under each regression coefficient) is: \nQ = 4.568 + 0.3361nAO + 0.345lnA'2 + 0.144InA'j + 0.246IaY^ + e (0.114) (0.096) (0.081) (0.027) (0.040) where Q is the water use per dwelling unit in m^ averaged for the year, Xi is the number of bathrooms/dwelling unit, X2 is the number of cars/dwelling unit, X3 is the garden area in m^, X4 is the number of occupants per dwelling unit, and e is the disturbance or error term. The variables explain about 48% of the variation in average annual municipal water use. All the variables are significant at p-value < 0.01, vAih F-Statistics of 76.246 (significant 556 at better than 0.001 level). The computed Durbin-Watson Statistic is 1.732, and no serious multicollinearity problem is detected. The correlation between average daily per capita municipal water use and a set of variables potentially affecting this use are presented as correlation matrix in Table 8.11. An examination of this matrix table shows that the number of bathrooms, number of cars, number of occupants, and garden area correlate weakly and inversely vAxh the average per capita municipal water use. The negative beta coefficient with respect to the number of occupants is an a priori expected sign. The signs of the coefficients on the number of bathrooms and number of cars demonstrate a type of scale economy, most likely caused by the influence of large customers. The negative sign on garden area appears to be inconsistent with the literature findings, although Howe and Linaweaver (1967) pointed out to the instability of this variable. As also indicated in Column 1 of the table, average floor washing, average car washing, and average irrigation fi-equency do not have a significant impact on the per capita municipal water use. The seasonal factor also does not correlate significantly with the per capita municipal water use. Table 8.12 presents the derived aggregate municipal demand fiinctions with per capita daily water use as dependent variable. Model IC shows the regression results after excluding the insignificant variables, number of occupants and number of cars, fi'om the analysis. The coefficients on the number of bathrooms and garden area are indicative of lower consumption, while those of per capita income and household area suggest an increase in per capita water use as the values of these variables increase. Per capita income elasticity of municipal demand is estimated to be 0.33. This elasticity estimate appears to correlate well with some estimates found in the literature (e.g. Headley, 1963; 557 Table 8.11 Cross tabulations shovsing the simple correlations between per capita municipal water consumption and potential explanatory variables along with the correlations between each pair of these variables Std. 7 8 9 Valid N Mean Variable 1 2 3 4 5 6 dev. 1. percavco 1.000 781 268.29 192.80 2. qalOginb -0197** 1.000 1105 4.73 7.95 3. qaI3ginc -O075* 0238** 1.000 1090 2.15 2.69 4. qblhvnoc -O160** 0295** 0.025 1.000 1105 93.27 878.79 5. qdlougar -O098* 0212** 0.205** O088* 1.000 442 48.14 14668 6. averflwshg 0.056 -O016 015)** 0145** 0.070 1.000 1013 2.70 3.97 7. avercarwsh 0.016 0.006 0.090** O085** 0.510** O091** 1.000 1040 1.09 1.28 8. averirigfreq -0016 0122** 0038 0097* 0028 0107* 0114** 1.000 479 4.28 2.29 9. seasonalfactr -0.015 0.020 0242** O062* 0822** 0124** 0426** -0.040 1.000 731 7.72 52.02 percavco = per capita average daily consumption m Ipcd. For explanation of the other variables defmitions, see the notes lo Table 8.1 (a and b). Correlation is significant at the 0.01 level (one-tailed). • Correlation is significant at the 0.05 level (one-tailed). 558 Table 8.12 Derived demand functions of aggregate municipal water use with the average per capita daily consumption as dependent variable Regression statistics Values Independent variables Coefficients /-statistic Valid N Tolerance VIF (/7-value) Model IC Constant 169.745 11.073 (0.000) R' 0.273 Number of bathrooms (Xi) -4.902 -3.843 (0.000) 781 0.780 1.281 Adjusted 0.265 Household area (X2) 0.157 3.270 (0.001) 679 0.686 1.457 F- Statistic 31.677 Per capita income (X3) 0.673 9.088 (0.000) 628 0.915 1.093 Significance of F test 0.0000 Garden area (X4) -0.288 -4.272 (0.000) 342 0.818 1.223 Durbin-Watson Statistic 2.008 Std. error of estimate 165.33 Constant 4.922 77.310 (0.000) Model 2C Number of bathrooms (Xi) -0.0460 -8.774 (0.000) 781 0.745 1.342 R' 0.543 Household area (X2) 0.00222 11.038 (0.000) 679 1 0.633 1.580 Adjusted R^ 0.535 Per capita income (X3) 0.00115 3.478 (0.001) 628 0.741 1.350 1 F- Statistic 66.293 Garden area (X4) [ -0.00234 -8.575 (0.000) 342 , 0.807 1.239 Significance of F test 0.0000 Number of occupants (X5)* -0.000427 -8.931 (0.000) 781 1 0.732 1.367 1 Durbin-Watson Statistic 2.094 Number of cars (Xe) .0.0340 -2.367 (0.018) 765 0.866 1.155 Std. error of estimate 0.664 Dependent variable for Model IC: average daily per capita consumption in litres per capita per day. Dependent variable for Model 2C: natural logarithm of average daily per capita consumption in litres per capita per day. * Including large customers group. 559 Gottlieb, 1963; Howe and Linaweaver. 1967; Wong, 1972; Morgan and Smolen, 1976; Katzman, 1977; Danielson, 1979). The variables in the model explain 27 % of the variation in the daily per capita municipal water use. An average price specification based on the total bill is obtained by dividing the total annual bill, fo_r„ each customer by the . annual amount consumed. This variable-was introduced to Model IC but was found to have an improper sign. The bivariate relationship between per capita municipal water use and average price, however, shows a highly significant coefficient with the expected negative sign. Price elasticity of demand associated with this coefficient is estimated at -0.066, indicating an extremely inelastic demand, which reflects negligible price effisct. This average price specification was also introduced as an independent variable to the residential and municipal water use models, with the average water use as dependent variable. The resulting coefficients show the average price to be a collinear variable plus having the incorrect sign. Although it was significant, the model of best fit for the per capita residential water use showed an average price coefficient of a positive sign. The simple bivariate relationship between average price and the per capita residential water use produced an insignificant average price coefficient, confirming the low variation within the residential water users (i.e. the majority of these customers are within the first block of the tariff structure). The dummy variables ownership of swimming pool and connection to sewer were introduced to Model IC but were found to be insignificant. The insignificant variables in Model IC (i.e. number of cars and number of occupants) produced significant coefficients 560 in the semi-log specification with the dependent variable appearing in logarithmic form as shown by Model 2C. The coefficient on per capita income yields an income elasticity of demand of about 0.15. The explanatory power of the model substantially increased to about 54%, with the beta sign of the variable number of occupants as expected. The regression statistics indicate that the basic assumptions of multicollinearity and independence ,of_error are reasonably ,met, and examination.of the residual.pIots-(Appendix- E, Figure E-4) indicates that the normality and linearity assumptions are also fairly satisfied. Further improvement of this model is achieved both in terms of the explanatory power and satisfying of the normality assumption by inclusion of the variable dummy sewer connection. The regression equation for this model is as follows: InQ = 4.566 - 0.0499Jr/ + 0.00326A^2 - 0.00283JG - 0.000463A^^ (0.087) (0.005) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) - 0.03 54Aj + o.oooniiXe + 0.189;\r7 + e (0.013) (0.000) (0.075) (0.008) (0.008) (0.012) where \nQ is the natural logarithm of per capita daily water use, in litres per capita per day, Xj is the number of bathrooms, X2 is the household area in m^, X3 is the garden area in m^, X4 is the number of occupants per dwelling unit, X5 is the number of cars, X^ is the per capita income in BD per capita per month, X? is a dummy sewer connection, and e is the error term. The standard error of estimates and probability values are, respectively, given in parentheses below the regression coefficients. The variables in the model explain almost 61% of the variance in the daily per capita municipal water use. What is interesting to note is that the coefficient on the dummy variable connection to sewer has the hypothesised positive sign, and suggests that dwelling units with public sewer connection use less water by almost 21%. The independent variables number of occupants and per capita income 561 have also a priori expected signs. The income elasticity of demand associated with the obtained coefficient is estimated to be 0.11. The relevant regression statistics are given as follows: is 0.618, adjusted is 0.609, /'-Statistic is 75.02 (Significant at 0.000), Durbin-Watson Statistic is 1.808, and Standard error of estimate is 0.608. The situation with respect to the residual plots for this model is shown diagrammatically in Figure E-5 of Appendix_E. _ _ The semi-log function with the independent variables log transformed yielded poorer fit both in terms of the adjusted value and examination of residuals. The double-log form was also fitted to the sample data. This specification has substantially improved the explanatory power of the model (explains 97% of the variation in the dependent variable), but seriously violates the normality assumption. Because of the inconsistent sign associated with the regression coefficient of the variable per capita income, and that garden area seems to be a collinear variable, these variables were eliminated from the analysis. Rather surprisingly, the variable dummy swimming pool was also found to have no significant influence on water use and was, therefore, removed fi-om the model specification. The fit of the double-log function with these variables being excluded from the analysis is given by the following regression equation (with standard error of estimate and /?-values in parentheses): \r\Q = 3.029 - 0.187InA'/ - 0.849lnA^2 + 0.176laVi + 0.757lnAV+ O.OSSPJTj (0.140) (0.051) (0.020) (0.042) (0.029) (0.035) (0.000) (0.000) (0.000) (0.000) (0.000) (0.015) Adjusted value = 0.825, f-Statistic = 639.56, and Durbin-Watson Statistic = 1.903 where InQ is the natural logarithm of per capita daily water use, litres per capita per day, 562 X} is the number of bathrooms, X2 is the number of occupants per dwelling unit, X3 is the number of cars, X4 is the household area in m^, X5 is a binary variable representing connection to sewer, and z is the error term. The variables in the model explain about 83% of the variation in the daily municipal per capita water use. The computed overall standard error of estimate is 0.407, and examination of the residual plots suggests that the model reasonably meets the assumption of normality. _ _ _ As was the case with the residential water use, separate summer and winter demand fijnctions were developed for the municipal use in order to assess the seasonality effects. Linear and log-linear equations were fitted to the sample data for both sunmier and winter municipal demands as illustrated in Table 8.13. As shown by Summer Model 1, apart from the variable household area, which has a negative sign, ail the variables in the model contribute to higher summer average consumption. Although the estimated linear model yielded high predictability power (it almost perfectly explains the variance in the dependent variable), it appears to suffer from serious positive serial correlation, and seriously violates the normality assumption. Summer Model 2 reports the OLS estimates of the semi-log specification for summer municipal demand. The signs of the estimated coefficients agree with the a priori expectations. The logged variable of family income was found to be insignificant but the raw income variable is statistically significant at the 5% level or better. The value of the estimated income elasticity is 0.23. The coefficients on garden area, number of occupants, and dunmiy swimming pool indicate that these variables are the most significant determinants of summer municipal demand. Overall, 55% of the variation in the dependent variable is accounted for by the variables in the model. The estimate resulting from this 563 Table 8.13 Derived demand functions for summer and winter municipal water uses j Regression statistics Values Independent variables Coefficients /-statistic VaUdN Tolerance VIF (p-value) 1 Summer Model 1 Constant 37.596 33.460 (0.000) !e 0.991 Number of bathrooms (Xi) 7.935 73.744 (0.000) 737 0.749 1.335 Adjusted !^ 0.991 Number of cars (X2) 18.114 61.327 (0.000) 724 0.868 1.152 F- Statistic 7059.922 Household area (X3) -0.251 -62.438 (0.000) 653 0.669 1.495 Significance of F test 0.000 Garden area (X4) 0.629 113.887 (0.000) 333 0.833 1.200 Durbin-Watson Statistic 1.597 Number of occupants (Xj) 0.0604 68.260 (0.000) 737 0.904 1.106 Std. error of estimate 13.482 Summer Model 2 Constant -200.648 -11.376 (0.000) 0.561 Log number of cars (Xi) 62.490 4.791 (0.000) 724 0.850 1.177 Adjusted 0.554 Log garden area (X2) 16.485 3.585 (0.000) 333 0.777 1.287 F- Statistic 83.447 Log number of occupants (X3) 64.957 12.366 (0.000) 733 0.856 1.169 Significance of F test 0.0000 Dummy swimming pool (X4) 204.123 6.036 (0.000) 733 0.895 1.117 Durbin-Watson Statistic 1.748 Family monthly income (X5) 0.0239 2.271 (0.024) 601 0.746 1.340 Std. error of estimate 83.447 Dependent voriablcs for Summer Model 2: naiurnl logarithm of monthly average water use in m-* per dwelling unit. Probability values are in parentheses. 564 1 auic o.A^ ^v^uiiiuiubuy Regression statistics Values Independent variables Coefficients /-statistic VaUdiN Tolerance VIF (p-value) Winter Model 1 Constant 35.063 11.062 (0.000) 0.900 Number of cars (Xi) 13.908 16.675 (0.000) 718 0.868 1.152 Adjusted 0.898 Household area (X2) -0.202 -17.777 (0.000) 647 0.669 1.495 F- Statistic 576.804 Number of bathrooms (X3) 8.908 29.316(0.000) 731 0.749 1.335 Significance of F test 0.000 Garden area (X4) 0.318 20.381 (0.000) 328 0.833 1.200 Durbin-Watson Statistic 1.589 Number of occupants (X5) 0.0569 22.770 (0.000) 731 0.904 1.106 Std. error of estimate 37.781 Winter Model 2 Constant -153.892 -10.153 (0.000) 0.555 Log number of cars (Xi) 57.219 5.104 (0.000) 718 0.850 1.177 1 0.548 Log garden area (X2) 8.790 2.224 (0.027) 328 0.777 1.287 Adjusted ^ 1 F- Statistic 80.179 Log number of occupants (X3) 54.795 12.137 (0.000) 731 0.856 1.169 Significance of F test 0.000 Family monthly income (X4) 0.0169 1.862 (0.064) 595 0.746 1.340 Durbin-Watson Statistic 1.828 Dummy swimming pool (X3) 207.492 7.139 (0.000) 731 0.895 1.117 Std. error of estimate 79.564 Dependent variables for Winter Model 1: monthly average water use in per dwelling unit. Dependent variables for Winter Model 2: natural logarithm of monthly average water use in m' per dwelling unit. Probability values are in parentheses. 565 semi-log specification appears to be superior in terms of improving the model fit and satisfying the regression assumptions including the normality. The semi-log specification with the dependent variable appearing in natural logarithmic fonm produced very poor model fit. The results fi-om the estimated log-linear equation show that the signs of_the_ coefficients, are all as expected. The number of cars, garden area, and number of occupants are highly significant predictors, whilst the number of bathrooms is barely significant. Both the log and raw variables of family income were shown to be insignificant. The estimated equation is less satisfactory on both the statistical and assumptions grounds when compared to the linear and semi-log functions (i.e. Summer Models 1 and 2). In like maimer, separate demand estimating equations have been fitted to the winter municipal water use (Table 8.13). The comparison between the summer and winter municipal demand functions provides little evidence of seasonality influence on water use. For example, fi-om the Summer Model I and Winter Model 1, the regression coefficients of the variables garden area, number of cars, and dummy swimming pool indicate that the amount of water use appears to be slightly higher in summer months. Similarly, the elasticity of demand of the variable number of occupants is higher during the summer season. The responsiveness of winter municipal demand to change in income is insignificant. Unlike the situation with regard to the residential demand, the coefficient on dummy sv^mming pool is significant in the case of municipal winter demand; it is more likely that this reflects the use of heated swimming pools within the large customers group. In general, the estimated summer demand functions show higher adjusted values than those obtained fi-om the winter demand models. 566 8.2 Agricultural Water Demand Table 8.14 summarises the results of the cross tabulations of average water consumption in agriculture and selected farm variables (8.14a) and irrigation variables (8.14b) along with the intercorrelation among these variables and their mean and standard deviation values. Because of the relatively small sample size (w = 111) and that the majority of the variables show slight departures from normality, both the Pearson and Spearman rho correlations are reported. Table 8.14a indicates that factors such as plot area, cultivated area, area under vegetable crops, and area under fodder crops (alfalfa) have measurable effects on the average amount of water used for agricuhure; all are significant at the 0.01 level and indicative of higher consumption. As one would expect, the simple relationships between the variable gross area under cultivation and the other independent variables signifying the major crop patterns, and the intercorrelations among the latter variables yielded highly significant correlation coefficients, indicating that serious multicollinearity problems would be likely to be encountered if these variables were included together in a model specification. The entries in Column 2 of Table 8 14b show moderate simple correlations between the amount of water used for agriculture and a number of irrigation variables, including irrigated area, irrigation requirements per hectare, average pumping hours, and average irrigation frequency. The correlation coefficient of 0.255 between the average monthly agriculture water use and gross area under irrigation, for example, indicates that a 10% increase in the irrigated area will generate an increase in average water use of almost 2.6%. The variable average pumping hours has the highest zero-order correlation with the average water use. The irrigation requirement per hectare also correlates positively with the average water use. 567 Table 8.14 Cross tabulations of average agricultural water use and potential farm and irrigation expIanator>' variables along with their means and standard deviation values a) Farm variables Variable Std. 6 7 8 9 10 Mean 1 2 3 4 5 dev. 1. avercons 1.000 5461.80 4180.60 2. qblfv'pla 0.263** 1.000 3.13 3.24 (0.453**) 3. qb2fVcua 0.276** 0.984** 1.000 2.63 2.96 (0.467**) (0.963**) 4. qblOfvmv 0.332** 0.724** 0.719** 1.000 1.16 1.67 (0.404**) (0.774**) (0.791)** 5. qblOfvmp 0.074 0.759** 0.777** 0.163* 1.000 0.86 1.67 (0.677**) (0.225*) (0.683)** (0.295)** i 6. qblOfvmf 0.115 0.450** 0.470** 0.213* 0.368** 1.000 0.24 0.43 (0.193*) (0.378**) (0.429)** (0.199)* (0.332)** 7. qblOfvma 0.265** 0.336** 0.374** 0.093 0.304** 0.038 1.000 0.16 0.31 (0.149) (0.106) (0.134) (0.006) (-0.011) (0.066) 0.136 0.099 0.098 0.184* -0.046 ' 0.017 -0.006 8. qblOfv'mo 1.000 0.02 0.09 (0.169*) (0.060) (0.090) (0.150) (-0.047) (0.103) (-0.056) 9. qblOfvmt 0.082 0.132 0.089 0.107 -0.047 I -0.010 0.103 •^•^^^ 1 000 0.02 0.08 (-0.085) (-0.054) (-0.001) (0.094) (-0.105) (-0.018) (-0.040) (-0.068) ; 10. qblOfvml 0.170* 0.359** 0.383** 0.099 0.356** -0.092 0.343** -0.109 0.199* i 1.000 0.13 0.29 (0.136) (0.216*) (0.238)** (0.126) (0.097) , (-0.010) (0.133) (-0.223)** (0.341)**j avercons = average consumption in m"* per month; qbl fvplo = plot area m hectare; qb2fvcua = area under culuvation m hectare; qblOfvmv = area under vegetable cro] hectare; qblOfvmp = area under palm trees in hectare; qblOfVmf = area under fruit trees in hectare; qblOfvma = area under alfalfa in hectare; qblOfvmo = area under ornamental trees in hectare; qblOfvmt = area used for poultry production in hectare; qblOfvml = area used for raising livestock in hectare. Figures in brackets are Spearman's rho correlations (nonparametric). •* Correlation is significant at the 0.01 level (one-tailed). • Correlation is significant at the 0.05 level (one-tailed). 568 (b) Iriigation variables Std. Variable 1 2 3 4 5 ' 6 10 Mean dev. 1. avercons 1.000 I 5461.80 4180.60 2. qb3fVira 0.255** 1.000 (0.476)** 2.19 2.59 3. qb4fvpaa 0.013 0.641** 1.000 (0.112) (0.416)** 0.26 0.55 4. irrigrechtr 0.307** -0.400** -0.284** 1.000 3264.00 2805.39 (0.404)** (-0.538)** (-0.263)** 5. averirrigfreq 0.193* 0.095 0.019 -0.002 1.000 (0.152) (0.245)** (0.186)* (-0.052) 1.34 0.61 6. seasonalfctr 0.100 0.138 0.135 0.038 -0.131' 1.000 (0.233)* (0.128) (0.031) (0.038) (0.014) 860.45 2215.50 7. averpumhrs 0.340** 0.166* 0.027 0.033 0.132 -0.037 (0.389)** (0.387)** (0.084) (0.069) (0.215)* (0.002) 1.000 3.56 2.29 8. qc6wssn -0.034 0.637** 0.721** -0.268 -0.032' •0.069 (0.001) (0.403)* (0.307) (-0.210) (-0.301) (-0.153) (a220) «2.47 22.42 9. qc7wster 1.000** 0.365 0.440 0.224 0.569 1.000** (1.000)** (0.288) (0.174) (0.224) (0.517) (1.000)** 10. averchwpol 0.275 0.081 -0.161 0.182 0.368 -0.142 (0.379) (0.311) (0.042) (-0.108) (0.485): (-0.257) <::S, s -;'«»» • »» nvercons = as before; qb3fvira = gross area under irrigation in hectare; qb4fvpaa = gross area under protected agriculture in hectare; irrigrechtr = irrigation requirements in per hectare per month; averirrigfreq = average irrigation frequency in time per day;'seasonalfcU- = seasonal factor; averpumhrs = average pumping hours in hours per day; qc6wssn = average water supplied from piped water in m^ per month; qc7wsler = average water requirements from TSE in m' per month'; averchwpol = average frequency of changing water in the pool. 1 Figures in brackets are Spearman's rho correlations (nonparametric). Correlation is significant at the 0.01 level (one-tailed). * Correlation is significant at the 0.05 level (one-tailed). I 569 The perfect correlation between the average water consumption and average requirements fi-om TSE, and between the latter variable and average requirements from piped supply (see Colunm 8 of Table 8.14b) may be understandable in view of the fact that relatively large farms supplied with TSE normally have their own water wells or are connected to the public system. This means that these supply sources often serve as supplemental or backup water sources. The relatively strong association between gross area under irrigation.and area under protected agriculture (correlation coefficient 0.641) may be substantiated by the fact that most of the areas under protected agriculture are commonly fully irrigated. Economies of scale appear to operate with regard to the relationships between irrigation requirements per hectare and both irrigated area and area under protected agriculture. The data in the table also indicate that the irrigation requirement per hectare of irrigated area tends to decrease as the total irrigated area and the area under protected agriculture increase. On average, a 10% increase in both the irrigated area and the area under protected agriculture will, respectively, result in 4% and 2.8% decreases in the amount of irrigation water required per hectare. Owing to quality considerations, protected agriculture in the study area depends primarily on water fi-om the public supply source. This may reasonably explain the strong positive association between these variables as suggested by Column labelled 3 (correlation coefficient 0.721, significant at p < 0.01). Models Speciflcation and Development The variables included in the agricultural water use survey are defined in terms of time and units of analysis in Table 7.3, and more fully in the notes to Table 8.14. From the initial correlation analysis of the data in the correlation matrices in Table 8.14, it is apparent that the variables that could be good predictors for explaining the variation in average 570 agriculture water use are fairly limited. In general, it should be emphasised that the results from the agricultural survey exercise were statistically disappointing. This limits the ability to perform meaningful multiple regression analysis and to build reliable demand models for this sector that can be used in predictive mode. This may support the common contention that linear programming approach is more effective in modelling agricultural water demand; a^mpdelUng appro^ that„was not considered here.for reasons-explained earlier in this chapter. Attempts to obtain data on productivity and size of workforce per farm unit, that could have been useful predictors, failed because such data (according to Mohammed Al-Thawadi, Agricultural Statistical Section, personal communication) are not available on a per farm basis and are normally aggregated using crude assumptions on a yearly basis for the purposeof the armual statistical reports. A survey was conducted, during.2002 as part of the FAO (2002) project in which valuable data on important agricultural inputs and outputs, including crop output, fertilizers, seeds, and labour and machinery requirements were collected for each farm sampled. These data were made accessible to this research but, unfortunately, the farms surveyed were not identified by either their farm number or their owner/tenant information. This has created problems for the necessary matches between farms in the two surveys, which rendered these data unusable for the purpose of this study. To this it should be added that these data are collected on per selected crop and growing season basis rather than on per farm basis. To illustrate the results, the equations of best fit are represented by bivariate log-linear models relating the average monthly agricultural water use to single independent variables: gross cultivated area and area actually irrigated as shown by Models I and 2 of Table 8.15. 571 Table 8.15 Derived log-linear estimating equations of agricultural water use Models Regression equations F- Adjusted Std. error Durbin- ! Average Average Statistics R^ of the Walson 1 Tolerance VIF estimate Statistic , Model 1 InQ = 7.720+ 0.6331nXi+E 31.293 0.244 0.657 2.287 11 1.000 1.000 (56.115) (15.594) (O.OpO) R = 0.502 (0.000) (0.000) (0.138) (0.113) Model 2 InQ = 7.756+ 0.6701nXi+e 30.868 0.241 0.658 2.272 1.000 1.000 (58.389) (5.556) (0.000) R = 0.499 (0.000) (0.000) (0.133) (0.121) Model 3 InQ = 7.278 + 0.5401nXi + 0.3831nX2 + e 19.640 0.291 0.637 2.367 ' 0.910 1.099 (33.982) (4.629) (2.642) (0.000) (0.000) (0.000) (0.010) (0.214) (0117) (0.145) Model 4 InQ = 7.489 + 0.1021nXi + 0.4861nX2 + e 13.878 0.221 0.667 2.418 0.962 1.040 (34.679) (3.392) (3.289) (0.000) (0.000) (OOOl) (OOOl) (0.216) (0.030) (0.148) 1 In Model I: InQ is the natural logarithm of the average monthly consumption in m per month, and InX, is the natural logarithm of gross area under cultivation in hectare. In Model 2: InQ is as defined before, and InX, is the natural logarithm of gross area under irrigation in hectare. In Model 3: InQ and InX, are defined before in Model 1, and InXj is the natural logarithm of average pumping hours in hours per day. In Model 4: InQ and InX, are as defined before in Model 2, and InXj is as defined before in Model 3. In all models, c is the error term. Figures in parentheses under each coefTicient are t-statislics, p-values, and standard error of estimates, respectively. Figures in parentheses under the F-Statistic values are the significance of F -test. 572 The tested linear functions were found to be inferior to the log-linear models in terms of both explanatory power and residual exainination. The two models show a high degree of similarity, though Model 1 is preferable on the grounds that the variable gross area under cultivation is easier to forecast. Model 1 yields a simple correlation coefficient of 0.502 (adjusted = 0.244). The regression statistics in Table 8.15 and the residual plots in Figure E-6 of Appendix E_indicate.that, apart from the Durbin-Watson value,, which seems to indicate presence of serial correlation, the model reasonably satisfies the basic regression assumptions. The proportion of variance explained by the single independent variable gross irrigated area (Model 2) is 0.499; the model has an adjusted of 0.241. The variable average pumping hours was considered for inclusion to the above models, though this variable is difficult to forecast (i.e. it has little value from the forecasting point of view). As indicated by Models 3 and 4, however, feeding this variable into Models 1 and 2 did not significantly improve the explanatory power of the regression equations and aggravate the problem of autocorrelation. In fact. Model 4 resulted in less predictive capability and much higher Durbin-Watson statistic. Despite the fact that the variables representing irrigation methods (introduced to Models 1 and 3 as a set of dummy variables) had the expected negative signs (i.e. the use of modem irrigation techniques reduces the amount of water applied for irrigation), they were found to be insignificant predictors. It is evident that the degree of significance of the variable irrigation technique is highly affected by the large number of farms using mixed irrigation, which was difficult to isolate. As previously mentioned, the variables designating the crop patterns were identified as having potential multicollinearity problems. Only the crop patterns representing vegetable 573 crops and alfalfa were shown to be significant predictors of agricultural water use. The variable palm tree had the expected negative sign, but its coefficient was found to be insignificant. The multiple regression equation with only the significant variables included and values in parentheses beneath each regression coefficient being the /-statistics, probability values, and standard error of estimates, respectively, is: \nQ = 7.945 + 0.58510^7 + O.8I6I1LY2 + e Adjusted = 0.245 (78.615) (4.729) (2.563) F-Statistic = 16.258 (0.000) (0.000) (0.012) Durbin-Watson = 2.395 (0.101) (0.124) (0.319) where Q = average monthly agricultural water use in m"* per month, Xj = area under vegetable crops, X2 = area under alfalfa, and e is the error term. The empirical evidence suggests that both variables are indicative of higher consumption, as one would expect particularly with respect to alfalfa, which appears here to have more influence on the average amount of water used for agriculture. To at least partly account for the limitation caused by not taking into account the influence of climate variables upon irrigation requirements in this analysis, separate sunmier and winter agricultural demand functions were developed to demonstrate the seasonal eflfects on the irrigation water demand. The results of these demand function estimates are presented in Table 8.16. Summer Model 1 and Winter Model 1 show the gross cultivated area as a single predictor of summer and winter agricultural water demand, while Summer Model 2 and Winter Model 2 demonstrate the combined effect of this independent variable and the variables measuring the number of pumping hours in summer and winter on the agricultural water use. 574 Table 8.16 Derived summer and winter demand models of agricultural water use Models Regression equations F- Adjusted Std. error Durbin- Average Average Statistics of the Watson Tolerance VIF estimate Statistic Summer Model 1 = 7.881 + 0.54llnXi+e 17.451 0.171 0.695 2.378 1.000 1.000 (50.014) (4.177) (0.000) R = 0.425 (0.000) (0.000) (0.158) (0.130) Summer Model 2 = 7.297 + 0.428lnX, + 0.461 lnX2 + E • 14.747 0.256 0.658 2.192 0.92! 1.085 (31.090) (3.344) (3.169) (0.000) (0.000) (0.001) (0.002) 1 (0.235) (0.128) (0.146) Winter Model 1 InQw = 7.568+ 0.6261nX, +e 18.568 0.180 0.779 2.392 1.000 1.000 (42.822) (4.309) (0.000) R = 0.436 (0.000) (0.000) (0.177) (0.145) Winter Model 2 InQw = 7.024 + 0.4961nXi + 0.5491nX2+ e 15.348 0.264 0.738 2.223 0.919 1.089 (29.663) (3.456) (3.164) (0.000) (0.000) (0.001) (0.002) (0.237) (0.144) (0.173) \ \ In Summer Model 1: InQj is the natural logarithm of the average monthly summer consumption in per month, and InX, is the natural logarithm of gross area under cultivation in hectare. In Summer Model 2: InQ,ond InX, are as defmcd before in Summer Model 1, and InXiis the natural logarithm of the pumping hours in summer in hours per day. In Winter Model 1: InQ^ is the natural logarithm of the average monthly winter consumption in m-* per month, and hiXj is the natural logarithm of gross area under cultivation in hectare. In Winter Model 2; lnQ„ and InX, are as defined before in Winter Model 1. and InXj is the natural logarithm of the pumping hours in winter in hours per day, In all models, e is the error term. I Figures in parentheses under each coefficient are t-statistics, values, and standard error of estimates, respectively. Figures in parentheses under the F-Statistic values are the significance of F-test. i 575 In general, the seasonal models show that the summer and winter agricultural water usage do not vary greatly, although the winter models slightly account for larger variability than those representing summer agricultural demands. Despite that this finding appears to be somewhat surprising, it is to be expected in the case of Bahrain where the cropping density in winter growing season is much higher than in the summer growing season (in fact owing to the harsh climatic conditions^during the summer growing season,, farming,is partially or entirely halted in some farms throughout this season). 8.3 Industrial Water Demand Results of simple linear correlation relating average industrial water use with presumed key determinants are given in Table 8.17. These are factory floor area, production level, and number of employees. The correlations between these variables are given in both Pearson's and Spearman's rho correlations.—The simple"correlation"coefficiehts"of fa"ct6ry floor area and production level with average quantity of water demanded for industry are 0.884 and 0.892, respectively, which are highly statistically significant at p < 0.001. However, the variables factory floor area and production level appear to be almost perfectly inter-correlated, the correlation coefficient between these variable is 0.942. The number of employees has a substantial positive effect on the average consumption. The number of employees is also significantly related to both factory floor area and production level; correlation coefficients are 0.594 and 0.506, respectively. This clearly suggests that multicollinearity exists between these explanatory variables, and would likely to be a potential problem if they were used as input variables to a multiple regression model. The simple linear correlations of the log-transformed data of these variables are examined as shown in the matrix of Table 8.18. The results suggest that the log transformation 576 Table 8.17 Iniercorrelations for average water consumption against three potential predictors for industrial water use - raw data Variable avercons qalfvfla qa4fvpca qa5fvnem Valid N Mean Std. dev. avercons 1.000 124 315.07 1306.81 qalfv'fla 0.884** 1.000 124 50252.34 201749.91 (0.413)** qa4fvpca 0.892** 0.942** 1.000 105 1012.30 4279.98 (0.681)** (0.458)** qa5fvnem 0.653** 0.594** 0.506** 1.000 124 165.72 348.52 (0.270)** (0.270)** (0.214)* number of employees. Figures in brackets arc Spearman's rho correlations (nonparametric). Correlation is significant at the 0.01 level (one-tailed). • Correlation is significant at the 0.05 lev-cl (one-tailed). 577 Table 8.18 Intercorrelations for average consumption against three potential predictors i for industrial water use - log transformed data ' Variable logavcons logfacar logprodn lognumem Valid N Mean Std. dev. logavcons 1.000 124 3.64 2.09 logfacar 0.414 1.000 124 8.91 1.77 logprodn 0.687 0.474 1.000 105 4.10 2.70 lognumem 0.306 0.574 0.223' 1.000 124 4.22 .26 logavcons = natural logarithm of average consumption; logfacar = natural logarithm of factory floor area; logprodn = natural logarithm of production level; lognumem = natural logarithm of the number of employees. Correlation is significant at the 0.01 level (one-tailed). i * Correlation is significant at the 0.05 level (one-tailed). 578 of these variables sUghtly boosts the situation in terms of minimising the multicoUinearity effects as compared to the raw data. From Table 8.18, the simple correlation coefficients between average industrial water use and the natural logarithms of factory floor area, production level, and number of employees are 0.414, 0.687, and 0.306, respectively; all highly significant at the 0.01 probability level. The zero-order correlation between factory floor area and production level is reduced to 0.474, and the association of factory floor area with number of employees is decreased to 0.574, still suggesting a probable collinearity problem. Models Specification and Development The industrial water use variables of interest to this research are clearly defined in Table 7.4 as well as in the notes to Table 8.17. Based on the results of these correlations, average daily industrial water use was regressed on the three key explanatory variables. The fitted linear and log-linear estimating equations are presented in Table 8.19. Because of the apparent collinearity between the variables factory floor area and production level, the demand fimction is divided into two basic equations for each functional form. The double-log equations were found to provide more satisfactory residuals than both the linear and semi-log specifications (the latter is not presented in the table). More specifically. Model 3 is preferable on both statistical grounds and linearity assumption, but Model 4 yielded better residual plot with respect to normality. The scatter plots for these models are shown in Figures E-7 and E-8 of Appendix E, respectively. Model 3 relates the natural logarithm of average daily industrial water use to the natural logarithms of the variables production level, and number of employees. The empirical findings of this model suggest that the coefficient of 0.549 on production level, implies 579 Table 8.19 Derived linear and log-linear demand estimating equations of industrial water use Models Regression equations df F- Adjusted Std. error Durbin- Average Average Statistics of the Watson Tolerance VIF estimate statistic Model 1 Q =-86.332+ 0.231X,+ l.OnXj+e 2 289.616 0.847 510.602 2.128 0.744 1.344 (-1.671) (17.003) (6.083) (0.000) (0.098) (0.000) (0.000) (51.666) (0.014) (0.167) Model 2 Q -56.829 + 0.738Xi + 0.00496X2+6 2 251.508 0.803 580.188 2.086 0.647 1.546 (-0.484) (3.954) (15.403) (0.000) (0.327) (0.000) (0.000) (57.777) (0.187) (0.000) Model 3 InQ = 0.498 + 0.5491nXi + 0.2271nX2+ e 2 69.588 0.583 1.321 2.025 0.941 1.063 (1.062) (10.750) (2.104) (0.000) (0.291) (0.000) (0.038) (0.469) (0.051) (0.108) Model 4 InQ = -3.206 + 0.411 InXi + 0.5451nX2+ e 2 33.137 0.359 1.633 1.992 0.746 1.341 (-3.790) (2.757) (5.224) (0.000) (0.000) (0.007) (0.000) (0.846) (0.149) (0.104) In model 1: Q (dependent variable) is the average consumption in m^ per day, X, is the production level in tonnes per day, and is the number of employees. In model 2: Q is as defined before, X, is the number of employees, and Xj is the factory fioor area inm^ in model 3: InQ, InXi.andlnXjare the natural logarithms of the variables defined before in model 1, respectively. In model 4; InQ, InX,, and InXj are the natural logarithms of the variables defined before in model|2, respectively. In all models, e is the error term. ! Figures in parentheses under each coefficient are t-statistics, p-values, and standard error of estimates, respectively. Figures in parentheses under the F-Statistic values are the significance of F-test. 580 that, on average, for a 1% increase in production level (measured in tonnes per day), the demand for industrial use (measured in m^ per day) increases by about 0.55%. On average, the elasticity coefficient of number of employees indicates that for a 1% increase in the number of employees, the demand for industrial water increases by about 0.23 %. Overall, 58% of the variance in the daily industrial water use is shared by the combination of these variables.^ _ _ _ _ _ ^ In Model 4, the independent variable production level is replaced by the variable factory floor area (expressed in m^). The variables in the model are highly statistically significant and indicative of higher consumption. A 1% increase in the number of employees would suggest a 0.41% increase in industrial demand. The coefficient of 0.545 on factory floor area implies that, on average, for a 1 percent increase in the factory floor area, the demand for industrial water increases, by abput 0.55%. The yariables in the jnpdel explain almost 36% of the variance on the average daily industrial water use. The binary variables connection to sewer and water re-cycling were introduced to the estimated demand fijnctions, but none of them was found to be significant. Significant improvements in both the predictive power and residual statistics of Models 3 and 4 were, however, gained by examining other two sets of dummy variables. These are the dummy source of supply (coded 1 for the self-supplied firms, and 0 for publicly-supplied firms and/or those who purchase water fi-om outside sources), and the dummy use of desalination plant (coded 1 for firms having an on-site desalination plant, and 0 otherwise). These findings are to be expected since most of the firms that require large quantities of water are developing their own sources (private wells) of water supply, and due to quality considerations in the study area, water fi'om these sources is normally desalinated to satisfy 581 the required quality for a specific industrial activity. From the water resources planning and management point of view, the source of supply is an important variable for industrial water use, and because the use of desalination is closely related to this variable, only the effect of this dummy variable on the derived functions of Models 3_and 4_is_considered. The_inclusipn„of_this_yariable t(L^ModeL3 produced the following regression equation: InQ = -0.0624 + 0.428 ln;\ri + 0.400 InXj + 1.542 DUMSRCE (-0.138) (7.770) (3.717) (4.213) Adjusted = 0.645, F-Statistic = 60.402, Durbin-Watson Statistic = 2.275 where In^ is the daily average industrial water use, m^ per day, Xj and X2 are as defined before in Model 3, and DUMSRCE is the water source variable taking the dummy coding defined eariier. The numbers in parentheses are /-statistic and indicate significance at the 0.001 probability level. The model explains about 65% of the variation in the average daily industrial water use. The inclusion of the same dummy variable to Model 4 specification indicates that, despite the lower value of the adjusted multiple correlation coefficient, this model is fitted better than the previous model with respect to test of assumptions. The resulting estimation equation is: InQ = -3.263 + 0.546 btVi + 0.446 laYj + 2.319 DUMSRCE (-4.493) (4.204) (4.904) (6.427) Adjusted = 0.527, F-Statistic = 43.738, Durbin-Watson Statistic = 2.006 582 where In Q is the daily average industrial water use, m"* per day, Xj and X2 are as defined before in model 4, and DUMSRCE is as defined earlier. The numbers in parentheses are /-statistic and indicate that all the regression coefficients, including the intercept are significant at the 0.001 probability level. In general, the empirical results presented in the above demand functions indicate that, on average, daily average water use in the self supplied firms is between 3 to 9 times as much as that in publicly supplied firms. Overall, nearly 53% of the variance in the average daily industrial water use is accounted for by the effect of these explanatory variables. The dummy variables designating the number of shifts were considered for inclusion to Model 3 with firms working on a one-shift basis treated as the reference group. The analysis indicates that only the variable DUMYSHDFT 3 was found to be statistically significant, suggesting that only those firms working on a three-shift basis use more water. This means, as one might expect, that economies of scale exist with respect to this variable. However, when the average daily industrial water use is regressed on these variables alone, they were all statistically significant as shown by the following regression equation: \nQ = 2.921 + 1.090 DUMSHFT2 + 1.824 DUMSHFT3 + 3.173 DUMSHFT 4 (12.862) (2.860) (4.293) (4.516) (0.000) (0.005) (0.000) (0.000) (0.227) (0.381) (0.425) (0.703) Adjusted R^ = 0.202, standard error of estimate = 1.759, F-Statistic = 11.385, (Significance F-test 0.000), and Durbin-Watson Statistic = 1.805 583 where Q is the average daily industrial water use in and DUMSHFT 2, DUMSHFT 3, and DUMSHFT 4 are the dummy variables representing firms working on respective shift bases. The figures in parentheses below the regression coefficients denote the t-statistics, the significance level, and standard error of estimate, respectively. The sample data were usedjo relate the average daily industrial water use to the number_of_ employees based on the two-digit SIC categories according to the procedure suggested by Dziegielewski (1988). Again, the logarithmically transformed variables performed better in terms of model fit. The basic regression equation (i.e. without the dummy variables designating the SIC categories) is given by: \nQ= 1.611 +0.502lnA'7 ^ = 0.313 (2.632) (3.624) Standard error of estimate = 1.878 (0.10) (0.000) where Q = average daily industrial water use, m"* per day, and Xj = the number of employees. The /-statistics and /7-vaIues, respectively, appear in parentheses beneath the regression coefficients. The empirical result in this equation is identical to the Dziegielewski (1988) finding in that the obtained regression coefficient suggests that per employee use decreases as the number of employees increases. Table 8.20 presents the estimated coefficients for the dummy variables designating the SIC categories, and the continuous variable number of persons employed (InA^. It should be noted that only the significant SIC categories are shown in the table (i.e. the coefficients on the SIC categories 32, 36, and 37 were not significantly different from zero at the 0:05% level of probability and were, therefore, not given in the table). 584 Table 8.20 Multiple regression of average industrial water use, number of persons employed and SIC categories Regression Significance Number of Variable coefficient (p-values) establishments Constant 2.002 0.001 — \nX 0.495 0.000 — SIC 31 2.204 0.000 16 SIC 33 -1.913 0.026 3 ~ SIC 34 -1.533 0.025 5 SIC 35 -0.927 0.009 27 SIC 38 -1.838 0.000 22 Note: For categories definitions, refer to Table 7.20. Dependent variable: natural logarithm of average daily industrial waier use, per day. Adjusted = 0.442, standard error of estimate = 1.417, /^-Statistics 16.818 (sig^iilicance F-iest = 0.000), Durbin-Watson Statistic = 1.801, average Tolerance = 0.866. and average VIF= 1.160. The obtained equations are only applicable for the statistically significant industrial categories, and appear to be very useful in estimating the average water demand for various SIC categories based on the number of persons employed in each significant category. For example, the following equation could be used to develop an estimate of daily average water use in category SIC 38 on the basis of the number of persons employed: InO - 2.002 + 0.495 InA^- 1.838 (coefficient of SIC 38) 585 CHAPTER NINE SUPPLY-DEMAND ANALYSIS AND POLICY OPTIONS Chapters Three to Five presented a comprehensive quantitative and qualitative assessment of the existing conventional and non-conventional water resources, including evaluation of the various constraints facing their optimal development. Chapter Seven provided a systematic analysis of the water demand characteristics, and Chapter Eight developed predictive equations for estimating future water demands for the different types of water uses. This chapter considers the water supply and demand relationships, including water balance computations over a 10-year planning period 2001 - 2010, tests the validity of the selected water demand forecasting models as predictive tools, and develops alternative scenarios for future water resources planning and management. 9.1 Supply-Demand Analysis 9.1.1 Water Supply Table 9.1 provides estimates on the water supply for 2001 as well as the water resources expected to be made available for the years 2005 and 2010, categorised by source of supply. Total available supply was 261 Mm"* in 2001, and is projected to increase to 322.6 Mm^ in 2005, and to further increase to 400.5 Mm^ by the year 2010. In percentage terms, the total supply that would be available for use and reuse in 2010 is equivalent to increases of 53.4% and 24.1%, respectively, over those reported for 2001 and 2005. Of the amount that will be available in 2010, 200.3 Mm^, or 50% will be provided by desalination of seawater and brackish groundwater, 126.6 Mm^ or 31.6% will be from 586 groundwater, and 73 Mm^, or 18.2% will be supplied, by TSE. The agricultural drainage water will only account for a small fraction of about 0.15%. From the above percentage distribution of the available and potential water supplies by source, it is apparent that reliance upon groundwater will gradually be lessened, and that npn-cpnyentional sources_of_supply wnll. play an Jncreasing .role towards, the end„of the planning period. Table 9.1 Current and potentially available water supplies 2001 - 2010 Supply source Quantity of available supply in Mm^/year 2001 2005 2010 (Groundwater 126.6^ 126.6" 126.6=* Desalinated water 118.7** 133.6" 200.3** Treated sewage efTluent 15:4 62.2 Agricultural drainage water 0.3 0.2 0.6 Total supply 261.0 322.6 400.5 ^ased on the Dammam - Neogene aquifer safe yield estimate, uith recharge components assumed to be fixed up to the year 2010 (see Table 3.11), ^ased on the actual output from Ad-Dur Desalination Plant. "Based on the projected actual output fi-om Ad-Dur Desalination Plant and commissioning of the Alba Coke Calcining Desalination Plant. **Based on full output from Ad-Dur Desalination Plant, capacit>' expansion at Ras Abu-Jaijur from 13.5 to 16.7 mgd, and commissioning of Phase H of the Hidd Desalination Plant. *Phase 11 of the TSE Utilisation Project will be made fully operational. The above estimates represent what could be referred to as the reliable yield or dependable supply (i.e. the amount of water that was and will potentially be available for direct sustainable use, and optimal socially acceptable and environmentally sound reuse). Given the difficulties and uncertainties inherent in estimating the water budget for the Dammam - Neogene aquifer on a year-by-year basis, the share of the renewable groundwater from this 587 source is assumed to remain static at 126.6 Mm^ (i.e. the amiual safe extraction rate - see Tables 3.9 and 3.11) over the planning period. Groundwater taken fi-om the Dammam - Neogene storage and that v^thdrawn fi-om the virtually non-renewable Rus - Unun Er Radhuma aquifer were not accounted for in these estimates. However, as vAU be apparent later fi-om the discussion of the future development scenarios, adjustments will be made to ^ow for the contribution of the Rus - Umm Er Radhuma aquifer in satisfying part of the industrial uses. The projected estimates for desalinated water and TSE are based upon the planned expansions of these supplies as explained in the notes to the table. The amount of agricultural drainage water that will be available for reuse is projected to decrease to 0.2 Mm^ in 2005, but will then progressively increase to reach 0.6 Mm"* in the year 2010. This is based on the assumption that the quality of this source is expected to improve markedly in view of the anticipated improvement in TSE quality, completion of the TSE drainage network, and expansion in desalination capacity (refer to the discussion in Chapter Five, pages 377 and 378). The quoted figures for this source must, however, be regarded as rough estimates. 9.1.2 Water Demand The steadily growing demand for water by various sectors of the national economy, as illustrated in Chapter Six, is placing tremendous pressure on the available water resources. Despite the vast proposed expansion in the capacity of non-conventional supplies, future water demand is expected to be well in excess of the existing and potentially available supplies, unless the current rate of water, use is drastically reduced. Separate forecasts were developed for the residential, municipal, agricultural, and industrial water uses addressing a 10-year time horizon, with the year 2001 chosen as the 588 base year for these forecasts. These disaggregated forecasts were then aggregated to obtain the overall water demand for the study area. It should be noted that in the following discussion, the term demand has not been used in a strict economic sense where water demand necessarily implies the inclusion of the price of water as an essential demand determinant. It is also important to bear in mind that, in general, the price of water was found to be an insignificant determinant of water use. It is essential to emphasise that the forecasts presented here did not rely on rather crude estimates of past trends on per capita consumption, population growth, and other consumption indicators. Instead, they were estimated on the basis of information on consumption variables and indicators obtained fi-om the systematic analysis of the cross- sectional data, with assumptions being made, as realistically as the present information permit, to take account of the conditions that are likely to prevail over the projected period. These underlying assumptions are also primarily made on the basis of the detailed analysis of water use patterns and other relevant information. Residential Water Use Forecast Table 9.2 shows the projected future residential water needs from 2001 to 2010 derived fi-om the average daily per capita consumption multiplied by the population forecast. The average per capita daily water use (excluding system leakage) is estimated at 282.3 Ipcd (62.2 gpcd) from the analysis of the cross-sectional survey data. This figure is to be adjusted to 347.5 Ipcd (76.5 gpcd) by allowing for 23.1% system losses (see Figure 6.7). The base year per capita estimate was set equal to this figure. Because of the uncertainties inherent in using a relatively small sample size, and because this average is still slightly affected by the supply restriction policies, a daily per capita water use of 364 Ipcd 589 Table 9.2 Population forecast, daily per capita water use, and residential water use forecast 2001 - 2010 Population projections Water use forecast (Mm'^/year)** Water use per Year capita (Ipcd) Low growth Medium growih High growth Low Medium High 2001 --- 650,604* — 347.5 — 82.5 — 2002 660,364 668,170 678,580 364 87.7 88.8 902 2003 670,296 686,211 707,759 364 89.1 91.2 94.0 2004 680,323 704,739 738,193 364 904 93.6 98.1 2005 690,528 723,767 769,935 364 91.7 962 102.3 2006 698,124 741,862 799,962 386 98.4 104.5 112.7 2007 705,804 760,408 831,161 386 99.4 107.1 117.1 2008 713,567 779,418 863,576 386 100.5 109.8 121.7 2009 721,416 798,903 897,256 386 101.6 112.6 1264 2010 729,356 818,876 932,249 386 102.8 11-5.4 131.3 • Based on the actual census results. •• No adjustments ore made in these forecasts for system losses, 590 (80 gpcd) is assumed for the period 2002 to 2005. There appears to be some scope for further growth in per capita consumption in response to the anticipated expansion in the supply capacity that would be likely to result in abandonment of the supply restriction policies. For this reason it is assumed that the average daily per capita consumption will grow to 386 Ipcd (85 gpcd) from 2006 and to continue at this level up to the end of the forecast period. The base year population is taken from the published population census results (CSO, 2002). For the rest of the plamiing period, population projections are those prepared earlier as illustrated in Table 6.2 and shown diagrammatically in Figure 6.1. This projection considers three population growth rates: low growth, medium growth, and high growth; for each of which a separate residential water use forecast is developed. As can be seen, the forecast for the middle series shows the residential demand to rise from 96.2 Mm^ in 2005 to 115.4 Mm^ in 2010. The upper bound estimate projects the demand for residential water use to increase to 131.3 Mm"* in 2010; that is an additional amount of 29 Mm^ or 28% over that reported for 2005. Table 9.3 presents the per capita residential water use forecast adjusted for the VTP unbilled consumption. The amount of this component of use is estimated as a percentage of the total supply. The derived estimates were based on the analysis of water sales, unaccounted-for water and total supply for the period 1993 - 2002 as explained in Chapter Six, page 414. The actual VTP unbilled ratio to the total supply is calculated at 10% and 12% for the years 2001 and 2002, respectively. The analysis shows an increasing growth of this component at an almost fixed rate in recent years. It is, therefore, safe to assume that this pattern of the annual growth will continue up to the year 2006 (i.e. implying a 591 Table 9.3 Residential water use forecasts adjusted for VIP unbilled consumption 2001 - 2010 Water use forecasts (MmVyear) Water use forecasts adjusted for VIP unbilled Year consumption (MmVyear) Low Medium Low Medium 1 High 2001 — 82.5 — — 90.8 — 2002 87.7 88.8 90.2 98.2 99.5 101.0 2003 89.1 91.2 94.0 101.6 104.0 107.2 2004 90.4 93.6 98.1 104.9 108.6 113.8 2005 91.7 96.2 102.3 108.2 113.5 120.7 2006 98.4 104.5 112.7 118.1 125.4 ' 135.2 2007 99.4 107.1 117.1 119.3 128.5 140.5 2008 100.5 109.8 121.7 120.6 131.8 146.0 2009 101.6 112.6 126.4 121.9 135.1 151.7 2010 102.8 115.4 131.3 123.4 138.5 ^ 157.6 Note: Demand estimates ore not adjusted for system leakage. 592 continuing growth by 2% annually). From this level afterwards, the ratio of the VIP unbilled consumption to the overall supply is projected to remain constant at 20%, assuming that the provision of new supplies will primarily benefit the billed users. This assumption has also taken into consideration the possible trend change and fluctuations in this ratio (fluctuations in this ratio were observed during 1993 - 1997). Given these assumptions, compared to the base year forecast of 90.8 Mm^, the medium growth residential water demand is projected to increase to 138.5 Mm^ in 2010, an increase of about 53%. The high growth series projects the residential water demand to reach 157.6 Mm"* by the year 2010, representing an increase of nearly 31% above that reported for 2005. In general, when compared to the actual residential use of years 2001 and 2002, the results of these forecasts produce reasonable estimates for residential water use. Municipal Water Use Forecasts Because of the small sample size of the non-residential water users, the obtained average per capita municipal water use of 268.3 Ipcd (330.3 Ipcd after allowing 23.1% for system leakage) appears to be of limited value in developing meaningftil forecasts for the aggregate municipal water demand. Water use for this sector is, therefore, projected as the residential water demand adjusted for the VTP unbilled consumption plus a ratio that represents the contribution of the non-domestic water use to the total water sales, with adjustments being made to account for the poor disaggregation referred to earlier. Again this was achieved by analysing the water sales data set for 1993 - 2002. The ratio of the non-domestic water use to the total water sales was estimated at 11% in 2001; this was increased to 12% in 2002. The field industrial survey indicates that industries (the major 593 non-domestic users), which used to rely heavily on self-supplied sources are now switching to the public mains to satisfy their water needs, owing to the quality improvement of this source. Thus, this forecast assumes that there would be a pronounced switch from self-supplied abstraction to the piped supply over the coming years. Between 2003 and 2006, the non-domestic contribution to the total supply is projected to increase to 15%; further increase to 20% is foreseen for the remaining period, to account for the proposed industrial expansion as part of the South Hidd Industrial Area. The resulting projections of the total municipal water demand are summarised in Table 9.4. As can be seen, the projections are performed for the low, medium, and high population growths. The table shows the base year municipal demand for the population medium growth to be 100.8 Mm^; this is projected to increase to 130.5 Mm^ in 2005 and to 166.2 Mm"* in 2010. The upper forecast estimates the municipal demand to grow to 138.8 Mm^ and 189.1 Mm^ in 2005 and 2010, respectively. To demonstrate the ability of this forecast to approximate reality, the medium growth projection for the year 2002 indicates a municipal water demand figure of 111.4 Mm^; allowing about 21% for leakages in the distribution networks (the actual percentage of this component for 2002 is not readily available) will result in a demand estimate of 134.8 Mm^. In that year, an actual quantity of 138.1 Mm'' was supplied to satisfy the demand for municipal use. Because of the availability of reliable information on the number of connection and water use per connection, forecasts for aggregate municipal water demand are also performed on a per connection basis. This requires projection of the number of connections and water use per connection for each forecast year. The number of conneaions to the distribution system was projected earlier as shown by Figure 6.4 of Chapter Six. The average water 594 use per connection for the base year is measured from the cross-sectional analysis at 73.7 m"* per connection per month. A 5% annual growth in water use per connection is assumed for the forecasting period 2002 - 2005 in line with the growth pattern observed from 1991 - 2000, with allowances being considered to account for the effect of the supply restriction policy, which prevailed during that period. Between 2006 and 2010, the water use per cormectipn is pxojected Jojncrease at a decreasing .rate,of_3%_in resp.onsejo the_ steady decrease in population per connection. Table 9.4 Estimates for aggregate municipal water demand 2001 - 2010 Aggregate municipal water demand in Mm^/year Year Low Medium High 2001 — 100.8 — 2002 90.7 II1.4 113.1 2003 96.3 119.6 _ J 23.3 2004 99.4 124.9 130.9 2005 102.6 130.5 138.8 2006 119.9 144.2 155.5 2007 126.5 147.8 161.6 2008 127.9 158.2 175.2 2009 129.4 162.1 182.0 2010 130.8 166.2 189.1 Note: Demand estimates exclude s>'Siem leakage. The results of the per connection municipal water use forecasts are presented in Table 9.5. Population per connection is given in the table as additional information, and is calculated by dividing the population projection of the medium growth series for each year by the projected number of cormections in the respective year. It is interesting to note that the declining tendency in population per connection reveals an almost similar pattern to that of 595 the household size (see Figure 7.5). A close look at the data in the table cleariy demonstrates the superiority of the per connection approach to the modified per capita method in forecasting aggregate municipal water demand. This conclusion would appear to be consistent with that reached by Boland (1978) and Boland et al. (1983). As an illustration, if the 112.2 Mm** projected to be used in 2001 is adjusted for the system !eal«ige^p£that_yeari(estM 23.J%), a demand_figure_of 13_8.J Mmi_will be„pb^^ The actual quantity supplied in 2001 was 137.3 Mm^ (equals 139.1 Mm^ total supply minus 1.8 Mm^ raw groundwater used for agriculture in the Buri area - see Tables 6.13 and 6.20). Table 9.5 Number of connections, water use per connection, population per connection, and municipal water use forecasts per connection 2001 - 2010 Water use per Water use Year Number of Population per connection forecasts connections connection* (mv month) (MmVyear)** 2001 126,840 73.7 5.13 112.2 2002 131,152 77.4 5.10 121.8 2003 135,612 81.3 5.10 132.3 2004 140,223 85.4 5.02 143.7 2005 144,990 89.7 5.00 156.1 2006 149,620 92.4 4.96 165.9 2007 155,017 95.2 4.91 177.1 2008 160,288 98.1 4.86 188.7 2009 165,737 101.0 4.82 200.9 2010 171,372 104.0 4.78 213.9 * Based on medium population growth. *• Excluding system leakage. 596 Comparison With Other Forecasts It is of some interest to compare the present projections with those previously made by other investigators. In Table 9.6, both the present modified per capita and per connection municipal water use forecasts are compared v^ath the results of a previous municipal water use forecast made for the study area by Mott MacDonald (1997). The table indicates that the results of this study show markedly lower municipa!^ water^use forecasts^than.those given in the consultant's report. For instance, the present forecast (on a per connection basis) projects the total municipal demand to reach 156.1 Mm^ by the year 2005, compared vnth 209.6 Mm^ in the consultant's forecast. Table 9.6 Comparison of municipal water use forecast with other forecast 2001 - 2005 Present forecast (Mm^/year) Mott Year On a modified On a per connection MacDonald per capita basis* basis (Mm^/year) 2001 100.8 112.2 183.2 2002 111.4 121.8 189.7 2003 119.6 132.3 196.0 2004 124.9 143.7 203.0 2005 130.5 156.1 209.6 Note: present forecasts exclude system leakage. * Based on medium population growth. When compared to the actual water use for 2001 and 2002 (refer to the earlier discussion on this point), the present forecasts are far more reliable, especially the one expressed as a function of the number of connections and water use per connection. Apparently, the consultant's forecast suffers from serious deficiencies. This arises largely from the fact that the consumption indicators used to develop the present forecasts are, as indicated earlier, based on a detailed and systematic analysis of the water use patterns and demand 597 characteristics (the bases and underlying assumptions used to develop the other forecast are reviewed in Chapter One, pages 45 and 46). Agricultural Water Use Forecast Agricultural water demand was forecasted based on the amount of water used per unit area of oiltiyatedjajid. _ Jlie^ampunt of watex is given by the irngatLon water requirements per hectare of cultivated area, while the areas are those expected to be irrigated at a given year over the specified planning period. Agricultural areas have been decreasing over the last four years; this has been markedly observed during 1998 - 2001. Agricultural areas are projected to decrease firom the base year level up to the year 2003 at an annual rate similar to that observed over the last two years. The availability of TSE towards the end of 2004 will, however, result in an increase in the cultivated area. Between 2005 and 2010, the total area under cultivation^is estimated on the basis of a detailed calculation of areas to be supplied according to the TSE availability programme. No adjustments are made to cater for a potential decrease/increase in the existing cultivated area. From the analysis of the agricultural survey data, the irrigation water requirement per hectare of cultivated area is measured at 39,168 m^/ha/year. This figure is projected to decline to about 37.000 m"*/ha/year starting fi-om the year 2008 and to remain at this level up to the end of the forecasting period. This is a reasonable assumption given the proposed expansion in the desalination and TSE facilities, which would lead to a decrease in the leaching requirements due to the expected improvement in irrigation water quality. In addition, further scope for adopting modem irrigation techniques would be highly likely with the availability of irrigation water of better quality. 598 Table 9.7 shows the projected increase in agricultural area along with the agricultural water demand from 2001 to 2010. It indicates that the total agricultural water use will grow to 181.9 Mm^ in 2005 compared to a baseline level of 153.4 Mm"*, showing an increase of about 19%. Total agricultural demand is expected to reach 183 .1 Mm^ by the end of the projection period. This quantity will be used on approximately 4,949 ha of cultivated land. . . .. , Table 9.7 Agricultural water demand 2001 - 2010 Irrigation water Agricultural water Year Cultivated land requirements demand (ha) (m^/ha/year) (Mm^/year) 2001 3916 39168 153.4 2002 3681 39168 144.2 2003 3571 39168 139.9 2004 3924 39168 153.7 2005 4644 39168 181.9 2006 4695 39168 183.9 2007 4734 39168 185.4 2008 4802 37000 177.7 2009 4887 37000 180.8 2010 4949 37000 183.1 To verify the degree of accuracy of the above forecasts, the amount of groundwater withdrawn from the Rus - Umm Er Radhuma aquifer (equals 7.5 Mm^) should be deducted from the total agricultural water use for the year 2001 (equals 164.5 Mm"* - see Table 6.15), since it is normally used for the non-productive agriculture. The remaining 157 Mm^ was actually used in that year to irrigate about 3,916 ha of cultivated land. This 599 figure is only 2.3% higher than the projected agricultural demand for the same period (Table 9.7). An important implication of this result is that the water demand for agriculture can be predicted with high confidence using the obtained figure of the irrigation requirements per hectare of cultivated area. Industrial Water Use Forecast . . _ Future industrial water use is estimated as the total projected industrial employment multiplied by the per employee water use. Estimates for ftjture industrial employment are not readily made available fi'om the official statistical sources. The annual growth rate in total industrial employment was 8.8% during the period 1981 - 1991, decreased to 7.4% between 1991 and 2001 (see Table 6.19). The growth in total industrial employment is assumed to continue at the same rate observed during 1991 - 2001, up to the year 2007. The proposed industrial expansion associated with the South Hidd Industrial Area suggests that there is a considerable potential for growth in the total industrial employment. Therefore, an annual growth rate of 10% is assumed from 2008 up to the end of the planning period. The average water use per employee in industry is calculated fi-om the industrial survey returns at about 2.8 m^ per employee per day (2,800 Iped). As pointed out in Chapter Seven, page 517, this figure appears to be highly influenced by the SIC Category 31 (Manufacture of food, beverages and tobacco) and, by any standard, does not reflect the level of industrial development in the study area. The per employee water use of 1.486 m^ per employee per day (1,486 Iped) estimated fi'om the analysis of water use patterns (refer to the discussion of this issue in Chapter Six, page 442) was considered to be a better estimate. For the purpose of this forecast, this consumption indicator is assumed to remain 600 constant up to the year 2006, but after then to drop to 1.45 m^ per employee per day (1,450 Iped) on the assumption that opportunities for recycling and efficiency improvement through process changes and promotion of water-saving schemes are highly likely in this sector. It should be emphasised that, during the questionnaire survey, industries indicated a great willingness to adopt various demand-saving measures; in fact, many_ of_the_industrial„users_interviewed_explained_their_pIans for_embarking_on_such schemes, some of which are already being put into practice. The result of the industrial water use forecast is shown in Table 9.8. Total industrial water demand will increase from 19.7 Mm^ in the base year to 26.1 Mm^ in 2005, corresponding to an increase of about 32.5%. Total industrial employment is projected to reach 107,771 by the year 2010, for which an amount of 39.1 Mm^ will be required. These forecasts indicate that the per employee water use_appears to be a very usefuLindicator for forecasting industrial water demand. For example, the table shows that about 21.1 Mm^ would be needed in the year 2002 to meet the industrial demand, while the actual amount used in that year (estimated using the same procedure adopted for breaking down and computing the industrial water use for the year 2001 - see Table 6.20) was about 22.4 Mm^ (a forecast error of only 5 .8%). Total Water Demand The projected sectoral and total water demand for the period 2001 - 2010 are presented in Figure 9.1, with municipal water demand given on a per connection basis, since this approach offers more accurate results. In 2005, the projected water demand of 364.1 Mm^ will exceed the available supplies by about 13%, or 41.5 Mm^. By the year 2010, total water demand is forecasted to reach about 436.1 Mm^ against an expected dependable 601 supply of 400.5 Mm"*, indicating a net annual deficit of 35.6 Mm^, or about 9%. This anticipated reduction in the supply demand imbalance is attributed to the expected gradual increase in TSE availability and commissioning of the major desalination facility as part of Phase IT of the Hidd Power and Desalination Plant. Table 9.8 Industrial water demand 2001 - 2010 Per employee water Industrial water Year Total industrial use (Iped) demand employment (Mm^/year) 2001 52759 1490 19.7 2002 56663 1490 21.1 2003 60856 1490 22.7 2004 65359 1490 24.3 2005 70196 1490 26.1 2006 75391 1490 28.1 2007 80970 1450 29.4 2008 89067 1450 32.3 2009 97974 1450 35.5 2010 107771 1450 39.1 With regard to the sectoral share, the figure shows that agriculture will continue to dominate all other uses up to 2007, beyond which the municipal sector is projected to have a larger share. This appears to be quite logical considering the deteriorating condition of agriculture in Bahrain and the land use problems (i.e. strong competition exists between areas designated for agriculture and areas required for urban settlement, given the extremely small area of the nation) that will slightly offset the advantages of TSE 602 Figure 9.1 Projected sectoral and total water demand 2001-2010 500 • Municipal demand 450 • Agricultural demand • Industrial demand • l otal demand n n 1 2001 : Years 603 availability. The share of the industrial water use to the total demand will keep rising over the projection period, consistent with its increasing trend observed during the last ten years. It is most likely that, starting fi-om 2008, the municipal s^or will rely almost exclusively on desalinated water. Va!ijlity_TesU for the Analytical Models In order to test the predictive ability of the analytical models developed as part of this research, the models of best fit are validated against actual values of independent variables. Fortunately, estimates for most of the independent variables in the models for the year 2001 are available, or can be estimated. The resulting forecasts are compared with the results obtained fi-om applying improved trend forecasting procedures and the actual water use for 2001, as shovm in Table 9.9. Table 9.9 Results of water use forecasts from analytical and improved trend procedures for the year 2001 compared with the actual water use (Mm'') Demand sector Analytical forecasts Improved Actual water (mean prediction) trend forecasts use 2001 Residential water demand 71.8* 82.5' 86.2 Municipal water demand 168.5^ 100.8^ 137.3 Municipal water demand — 112.2^ 137.3 Agricultural water demand 72.1' 153.4 157.0 Industrial water demand 10.2* 19.7 19.6 ^ Based on the double-log model. ' Based on Model 1. ' Based on Model 3. ' Based on the medium per capita. ^ Based on the per connection forecast. All forecasts are excluding system losses. 604 Model lA is used to check the validity of the developed models to forecast the residential water demand. As can be seen, the model gives a demand figure of 71.8 MmVyear against an actual water use of 86.2 Mm^. The low variation explained by this model possibly imposes limitations on its accuracy. In comparing the results of the two forecasting procedures, it is evident that the improved per capita forecast allows more precision. The double-log fiinctional form (see page 556. Chapter Eight)Js selected as the bes^possible predictive too! to forecast the municipal water demand. The model forecasts the annual municipal demand to be 168.5 Mm^. The actual water use in 2001 was lower than the projected demand by almost 23%. This large margin of error can be expected in such a highly aggregated and heterogeneous demand, with reliable estimates on some independent variables being extremely difficult to make. Validity tests are also made for the agricultural Models 1 and 3 (Table 8.15). Model 1 estimates the agricultural water demand to be 72.1 MmVyear. The actual agricultural water use (excluding the abstraction fi-om the Rus - Umm Er Radhuma aquifer) in 2001 was higher than the predicted demand by as much as 54%. This is not surprising because a lot of the variance in the dependent variable is not being explained by this regression model. The lower and upper bound predictions at the 95% confidence interval are estimated at 70.8 MmVyear and 73.4 MmVyear, respectively. This small prediction interval may indicate that the independent variable (cultivated area in ha) is a good variable to predict the average water use in agriculture. The large diffisrence between the model forecast and the actual water use may have been arisen fi'om the omission of some important variables fi'om the model (specification error). Model 3 (Table 8.15) yielded an annual average agricultural demand estimate of 66.1 Mm^; this lower estimate, associated with a relatively higher explanatory power, is possibly attributable to the autocorrelation 605 between the explanatory variables in the model (Durbin-Watson statistic is 2.367). In the case of the industrial water demand. Models 3 and 4 (Table 8.19) are used for the validity test. Model 3 projects the base year industrial water demand to be 10.2 Mm^'/year (based on 250 working days - 14.9 Mm'/year based on 365 days), or 52% of the actual demand of 19.6 Mm^ (see Table 6.19), with lower and upper bounds of 7.6 MmVvear_and. 12.8 Mm^/year respectively, at the 95 percent confidence interval. The lower and upper predictions for 365 days are 12.3 MmVyear and 17.5 Mm^/year, respectively. This is quite an acceptable result, given the proportion of variance being explained by this model. Model 4, on the contrary, produces an unreliable forecast when compared to the actual water use, most likely due to the inaccuracy of data made available on the variable factory floor area. In general, the results of the validity tests indicate that, with the exception of the residential and industrial Model 3 water demand functions, which appear to produce satisfactory results, there is a need to considerably improve these models before using them as reliable predictive tools. 9.2 Water Balance and Policy Options The purpose of this section is to evaluate the water supply and demand relationships, and to investigate the possibility of bringing them into balance by developing alternative scenarios, each denoting responses to a different set of policy assumptions affecting water use. 606 9.2.1 Water Balance The total water demand in comparison with the available supply in the form of water balance for the years 2001, 2005 and 2010 is summarised in Table 9.10, and is shown graphically in Figure 9.2. The calculated water balances show that the net annual deficit between water supply and demand will be lowered progressively fi-om 2001 to 2010 owing to the anticipated.new supplies from desalination and.TSE sources. A deficit of 35.6 Mm^ is projected for the year 2010 compared to the base year deficit of 50 Mm^. In 2005, an amount of 364.1 Mm*^ will be required to meet the total water demand against an available supply of 322.6 Mm"*, indicating a net deficit of 41.5 Mm^. An important point to consider is that the calculated water budgets for the years 2005 and 2010 assume implementation of the supply augmentation projects as planned, and fiiU utilisation of the TSE, although the later assumption seems doubtfiil due to various land use problems and reluctance of some farm owners to use or to exchange groundwater for this source. Table 9.10 Water supply and demand and water balance for years 2001,2005, and 2010 Supply and demand sources Water supply and demand in Mm'' 2001 2005 2010 Water supply Groundwater 126.6 126.6 126.6 Desalinated water 118.7 133.6 200.3 Treated sewage effluent 15.4 62.2 73.0 Agricultural drainage water 0.3 0.2 0.6 Total supply 261.0 322.6 400.5 Water demand Municipal demand 139.1 156.1* 213.9* Agricultural demand 160.6 181.9 183.1 Industrial demand 11.3 26.1 39.1 Total demand 311 364.1 436.1 Net annual deficit -50 ^1.5 -35.6 * Based on per connection forecast. 607 Figure 9.2 Water supply and demand and net annual deficits for 2001, 2005, and 2010 500 1 • Water supply 450 • Water demand • Net annual deficit E 400 - 350 - a. % 5 2001 2005 2010 Years 0(,)S It is evident from the above analysis that the anticipated fijture reduction in the water budget deficit would only be possible by investing in expensive additional water supplies, and that any efforts to bring the water demand into balance with the available supply will be at the expense of the ah-eady over-drafted Dammam - Neogene groundwater resources. 9.2.2 Alternative Policy Scenarios From the water balance analysis, it is apparent that the existing and ftjture water supplies cannot meet the ever-increasing demand. Therefore, alternative water resources management scenarios for supply-demand manipulation need to be considered to investigate the future conditions, to provide options for balancing supply and demand, and to offer the decision-makers a wider range of possibilities in dealing with the water management problems in order to ensure sustainable water resources development and management. Three alternative scenarios were developed for the study area. The scenario analyses were performed with due consideration to alternative assumptions that reflect a different set of policy situations, choices, and constraints affecting water uses. The following scenarios were considered: Scenario A: this is the so-called business-as-usual scenario, which assumes the continuation of the present trend in groundwater withdrawal, and that the per capita municipal consumption will continue growing at the levels prevailing during the last ten years, with minor adjustments to reflect the situation associated with availability of an additional municipal supply. The scenario assumes that, apart from Phase II of the Hidd Power and Desalination Plant which is still at the pre-planning stage, the proposed expansion in the non-conventional water supplies will be implemented as planned. No demand management measures other than those currently being adopted are envisaged in 609 this scenario. This essentially assumes that no modification to the existing water tariflF is to be considered and that leakage reduction and pressure control are held at their 2001 level. This also implies that no further improvement in irrigation efficiency and no modification of the cropping patterns are achieved. Further, the scenario foresees no major legal enforcement and institutional reforms over the planning period. Since population control policies hardly exist in the study area. Scenario A a^well as the other scenarios apply the high growth population projection prepared as part of this study. Scenario B: this is the supply augmentation scenario. The underiying assumptions for this scenario (including the re-use option) are as in Scenario A, except that it envisages that Phase n of the Hidd Power and Desalination Plant will be operational by the year 2008. Adjustments to account for this positive condition are considered. Scenario C: this is the supply augmentation and demand management scenario, or the sustainable scenario. This scenario is characterised by controlled development of the existing water resources in conjunction with provision of additional supplies. More specifically, this scenario explores the possible impact of improving the demand management tools, including leakage control, tariffs and institutional re-structuring, legal enforcement, conservation incentives and subsidies, improving irrigation efficiency, changing crop patterns as well as augmenting the available supply, on the overall water balance. Development of additional new supplies is as in Scenario B. Table 9.11 summarises the total water demand in comparison with the available supply for the proposed alternative scenarios fi-om 2001 to 2010, while the computed water balances for each scenario are shown diagrammatically in Figure 9.3. The detailed underlying 610 Table 9.11 Projected water budgets based on three altemalive development scenarios 2001 -2010 (in Mm^) Future scenarios 2001 2002 2003 2004 1 2005 2006 2007 2008 2009 2010 1 1 Scenario A 1 Water supply 261 261.2 271.1 311.0 322.6 329.4 330.9 333.0 335.1 3507 Water demand 325.9 331.9 338.5 355.5 399.8 401.7 404.8 412.4 421.0 431.9 Net deficit/surplus -64.9 -70.7 -67.4 -44.5 . -77.2 -72.3 -74.4 -79.4 -85.9 -81.2 Scenario B 1 Water supply 261 261.2 271.1 311.0 322.6 329.4 3309 382.8 384.9 4005 Water demand 325.9 331.9 338.5 355.5 1 399.8 401.7 404.8 389.8 378.4 371.8 Net deficit/surplus -64.9 -70.7 -67.4 -44.5 -77.2 -72.3 -74.4 1 +6.5 +28.7 -7.0 Scenario C Water supply 261 261.2 271.1 311.0 322.6 329.4 330.9 382.8 384.9 40O5 Water demand 325.9 331.9 338.5 355.5 1 3998 392.7 384.7 364.8 3505 342.7 Net deficit/surplus -64.9 -70.7 -67.4 -44.5 -77.2 -63.3 -53.8 +18 +34.4 +57.8 Scenario A assumes full TSE utilisation, maximum output from Ad-Dur t)esaIination Plant, and successful implementation of Ras Abii-Jarjur upgrading. Groundwater abstraction will continue decreasing in response to the expansion in the desalination and TSE capacities. A 1% decline is assumed from 2001 to 2005, 5% from 2006 to 2007, and 8% from200 8 to 2010. Per capita consumption is projected to continue increasing at a calculated annual growth rate of 2%. No demand management measures other than those currently being implemented are envisaged. Scenario B enxisages the same supply.augmentation efforts, in addition to the conunissioning of Phase n of the Hidd Power and Desalination Plant by 2008. For this reason, 12% reduction in groundwater withdrawal is suggested starting from this year. Per capita daily water use is assumed to reach a saturation point at the 2007 level of 582.5 Ipcd. Scenario C considers development of additional non-conventional supplies as in Scenario B. reduction in groundwater withdrawal at 1% from200 1 to 2005! and at 10% from200 6 to 2007. A fxiriher reduction of 20% is envisaged from200 8 to 2010 allowing for the positive combined impact of the supply augmentation and demand management policies. Per capita consumption is assumed to continue at the same rate up to 2006 (571 Ipcd) but then to stabilise at this level due to the saturation and effect of the demand management measures. These measures are expected to promote a gradual saving in overall water demand, particularly beginning from 2006. For all scenarios, the year-by-year TSE availability is worked out on the basis of data furnished by ACE. i 611 Figure 9.3 Projected deficits and surpluses for three alternative scenarios Scenario A 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Scenario B ^ 40 S 20 I 1 Z -100 2001 2(X)2 2003 2004 2005 2006 2007 2008 2009 2010 Scenario C 80 Z -100 2(K)I 2002 2003 2004 2005 2006 2007 2008 2009 2010 612 assumptions used to develop these scenarios are given in the notes to the table. Scenario A represents the worst-case scenario in which the water balance will maintain a deficit throughout the planning period. A deficit of 81.2 Mm"' is foreseen for the year 2010, but this will vary considerably fi-om 44.5 Mm^ in 2004 to 85.9 Mm^ in 2009. The percentage of the water balance deficit to water demand ranges fi^om }2.5% in 2004 to 20.4% in 2009. This scenario suggests that even with the gradual increase in the desalination and TSE output, the current and emerging deficit will continue rising because demand is expected to grow substantially. The addition of new agricultural areas will slightly offset the advantage of the TSE contribution to the water budget. The scenario cleariy points out to the importance of the second phase of the Hidd Power and Desalination Plant as well as the inefficiency of the existing demand management practices, as will be demonstrated in the following scenarios. Closing the gap between supply and demand within this scenario impUes over-exploitation fi-om the renewable groundwater resources, which will result in ftirther groundwater depletion and quality deterioration. Under Scenario B, the percentage water deficit to the total demand will have a range from 1.8% to 21.3%, and will be lowered significantly starting from 2008 to reach 7 Mm^ This water deficit reduction primarily comes from the anticipated expansion in the desalination capacity as part of Hidd Phase II. Compared to the previous scenario, this scenario envisages net surpluses during the last two years of the planning period. A surplus of about 6.5 Mm"* is foreseen for year 2009. Water balance is projected to be on the positive side at approximately 28.7 Mm^ by the year 2010. By this year, groundwater abstraction from the Dammam - Neogene aquifer is projected to decline to about 101 Mm"*. This rale 613 of abstraction is well below the aquifer safe annual yield, which should have a positive impact on the groundwater situation in terms of piezometry and quality improvements. It is to be emphasised, however, that in this scenario, the balance gap can only be closed by investing in capital intensive water supply projects. Scenario C represents the most efficient development strategy. By augmenting the water supply and at the same time making better use of the existing supply through enhancing the various demand-management remedies, the water situation in the study area is expected to be improved significantly. According to this scenario, the water deficit will gradually decrease fi-om 77.2 Mm^ in 2005 to 53.8 Mm^ in 2007. The positive water balance will progressively increase fi-om 18 Mm"* starting fi-om 2008 to 57.8 Mm^ in the year 2010. The end of the planning period shows a positive percentage of water balance to the total demand of almost 17%. Reduction below this level can be achieved with fijrther improvement in demand management measures, particularly with respect to the pricing policies and legal enforcement. In spite of the probable high political and socio-economic cost associated with this scenario, it offers the most appropriate alternative and clearly suggests that integration of the supply-demand management policies provides the most efficient approach for achieving an optimal balance between water supply and demand. Table 9.12 presents a supply and demand analysis on the basis of source of supply and sectoral use for Scenarios A, B, and C for the years 2005 and 2010. It indicates that under the sustainable scenario (Scenario C), a substantial reduction in the amount of water required for irrigation (the major water user) will be achieved. This should have important implications fi-om the water management point of view. The increase in the municipal and industrial shares seems logical and in line with the previous findings of this research. 614 Table 9.12 Water supply and demand by source and categor>' of use for three allemative scenarios for years 2005 and 2010 (in Mm ) Future scenarios Scenario A Scenario B Scenario C 2005 2010 2005 2010 2005 2010 Water supply Groundwater 126.6 126.6 126.6 126.6 126.6 126.6 Desalinated water 133.6 150.5 133.6 20O3 133.6 20O3 Treated sewage effluent 62.2 73.0 62.2 73.0 62.2 73.0 Agricultural drainage water 02 06 0.2 0.6 0.2 0.6 Total supply 322.6 3507 322.6 40O5 322.6 400.5 Water demand Municipal demand 143.2 185.2 143.2 174.2 143.2 168.3 Agricultural demand 205.4 193.1 205.4 161.6 205.4 142.4 Industrial demand 21.1 36.2 21.1 36.0 21.1 32.0 Total demand 369.7 414.5 369.7 371.8 367.7 342.7 Net annual deficit/surplus -47.1 -63.8 -47.1 +28.7 -47.1 +57.8 Note: Apart from the allowance lo account for the contribution of the Rus - Umm Er Radhuma aquifer in meeting part of the industrial demand, groundwater from this aquifer was not considered in these water balance calculations. ' 615 CONCLUSIONS AND RECOMMENDATIONS Bahrain is an arid country and is facing serious water shortage problems. In an attempt to overcome these problems, water management strategies have emphasised supply au^nentation policies througli "the provision of costly substitutes. The demand-side solutions have always been received little attention. It is evident that this emphasis on the supply-based approach towards water planning has demonstrated its inability to provide practical solutions to these acute water shortages. In this research, a combined approach of supply-demand analysis is employed to investigate the water situation and management problems in the study area, with a prime objective of establishing a supply-demand analytical fi-amework to enable formulation of an integrated water resources management policy ~ The major findings and conclusions" of "this " research may be summarised ~aT follows: 1. Over-exploitation of the renewable groundwater resources has resulted in a drastic water level decline, leading to serious storage depletion and a severe deterioration in groundwater quality. The net annual deficit in the Dammam groundwater budget for the year 2000 is estimated at 112.6 Mm^. Groundwater has been drawn fi-om the storage of this aquifer since 1945. The total cumulative volume of groundwater abstracted fi"om the Rus - Umm Er Radhuma aquifer up to the year 2000 was about 687.3 Mm^, while that which entered the aquifer by recharge was found to be 325.2 Mm\ indicating a net deficit of 362.1 Mm^. The analysis ftirther reveals that groundwater has been taken fi-om this aquifer storage since 1986. 616 2. The spatial and temporal groundwater hydrochemistry investigations show that almost 70% of the Dammam groundwater has been lost to salinisation. Groundwater fi-om both Dammam and Rus - Umm Er Radhuma aquifers is of a sodium-chloride type, and has a predominant cation sequence of Na"^ > Ca^^ > Mg^\ and a predominant anion sequence of CI" > S04^" > HCO3", indicating old brackish groundwater typical of a discharge area. The Dammam groundwater is^ undersaturated with respect to anhydrite, dolomite, gypsum, and halite, and is oversaturated with respect to calcite and aragonite. The groundwater in the Rus - Umm Er Radhuma aquifer is undersaturated with respect to anhydrite, gypsum, and halite, but is oversaturated with respect to aragonite, calcite, and dolomite. 3. Despite the various techno-economical, environmental, social, institutional, and public health constraints facing their optimal development, the non-traditional water resources (desalinated water and TSE) are shown to be viable strategic options to satisfy the growing water demands. 4. Analysis of water use per categories of use and source for the years 1990 and 2000, indicates that although more water was used in 2000 than in 1990 by almost 28.6%, from 270.1 Mm"* to 347.4 Mm"*, the percentage sectoral shares for the municipal and agricultural uses essentially remained the same, whereas that of industry demonstrated an increase of slightly more than 19.5%. Most importantly, the analysis indicates that in spite of the noticeable increase in the total output from non-conventional supplies, reliance on groundwater remained high. 5. Analysis of municipal consumption data shows that before the implementation of the metering policy in 1986, the increase in per capita consumption outpaced the population growth, indicating that the per capita consumption was in excess of 617 what could be explained by an increase in population. The modification in the pricing structure since 1992 appears to have had a significant effect on the trend of per capita daily water consumption. Average daily per capita water use has exhibited a noticeable decreasing pattern since 1994; this was only to be expected in view of the supply restriction policies. Cessation of this policy by the end of the year 2000 resulted in a slightly rising trend. 6. Average daily per capita municipal water use, excluding system leakages and other unaccounted-for water, and VIP unbilled consumption for year 2001, is estimated at about 372.3 Ipcd (82 gpcd). 7. Analysis of consumption variability shows that the average demand component ranges fi-om 88% to 95% of the total water use, leaving 5% to 12% for the peak demand component, indicating slight seasonal variations. However, this seems to be quite an acceptable result because sprinkling demand of seasonal effect is not a common practice in the study area, although the recent years exhibit upward trends of this consumption element. In addition, the prevailing warm climate in the country tends to keep the seasonal variation in the in-house water use relatively low. 8. Peak factors (the ratio of the maximum daily water use to average daily water use) are found to range fi-om 1.25 to 1.35, with an average peak ratio of 1.28. The annual peak factors vary from 1.04 to 1.11, and average at 1.07. This conforms well to the percentage figures accounting for the peak demand component, confirming that climate is the main determinant of peak variations. 9. The contribution of agriculture (including fishing) to the GDP is limited to only 618 0.73%, and it only employs about 1.54% of the total workforce. Yet agriculture remains the largest user of the renewable groundwater resources, generally accounting for more than 70% of the total abstraction from these resources. This clearly reflects poor and inefficient irrigation practices; irrigation efficiency is calculated at 65%, meaning that 35% of the water used in agriculture represents irrigation losses. 10. The analysis of the industrial water use patterns produces important figures with respect lo some key consumption indicators. Annual average water use per factory floor area is estimated at 2.177 mVm^ of factory floor area (0.00596 mVday/m^ of factory floor area). Per employee water use is estimated at 1.486 m"* per employee per day (1,486 Iped) based on 250 working days. 11. Resuhs from the cross-sectional municipal survey indicate average residential and municipal water uses of 53.8 m"* and 73.7 m"* per household and dwelling unit per month, respectively. This analysis produces an average daily per capita residential consumption (excluding system leakage) of 282.3 Ipcd (62.2 gpcd). Allowing for 23.1% system leakage, a per capita daily residential water use of 347.5 Ipcd (76.5 gpcd) is obtained. The municipal daily per capita consumption is found to be 268.3 Ipcd (59.1 gpcd); this is 330.3 Ipcd (72.8 gpcd) including system losses. Since these figures are markedly lower than the official estimates, they may confirm that great proportion of the piped water burdened on the per capita consumption is actually used by the VIP unbilled and extremely large customers. However, considering the uncertainties inherent in using a relatively small sample size compared to the total population and that the generated figures may be slightly influenced by the supply restriction policy, more data are needed to verify this finding. 619 12. The average daily per capita residential water use based on activities of use is calculated at 287.5 ipcd (63.3 gpcd) for households without sprinkling demand, and 310.5 Ipcd (68.4 gpcd) for households with sprinkling demand. The average daily per capita water use within the large consumers group is found to be 38.9 Ipcd (8.6 gpcd), demonstrating a sort of economy of scale, although this figure appears to be less reliable owing to the limited number of observations. 13. Consumption variability fi-om the cross-sectional analysis indicates that water use tends to increase by neariy 7 - 12% during peak demand. It is interesting to see that this result compares favourably with the consumption variability estimates obtained fi*om the analysis of macro-data. 14. On average, each farm uses about 5,461.8 mVmonth (median value 4,565.7 m"*/month). During the summer and winter growing seasons, each farm uses on average 5,846 and 5,050.8 m"* per month, respectively, indicating seasonal variability of neariy 16%. Average irrigation requirement per hectare of cultivated area is found to be 3,264 m"'/ha/month (39,168 m"*/ha/year). This figure is in reasonable agreement with the irrigation requirements estimate of 2,400 m^/ha/year normally adopted for irrigation planning and sizing of the irrigation systems, particularly for irrigation with TSE. 15. The cross-sectional analysis of industrial water use yields an average per employee water use of 2,775 Iped based on 250 working days. By any standard, this estimate is extremely high and does not reflect the level of the industrial development in the study area. It is probable that such an estimate is greatly affected by the influence of SIC Category 31 (Manufacture of food, beverages and tobacco). The average daily water use per employee (based on activity of use - staff hygiene needs in this 620 respect) is estimated at 309.4; Ipcd (68.1 gpcd). This figure is considered to be very high when compared to the average daily per capita residential water use. On r average, 0.536 of water is used per unit of output (tonne) also based on 250 working days. Water use per factory floor area is estimated at 2.288 m"*/m^ of factory floor area, or 0.00626 m''/day/m^ of factory floor area. This figure is in I close agreement with the 2.177 m"* per m^ of factory floor area resulting from the analysis of macro-data. 16. The empirical evidence presented in this research suggests that certain socio• economic, socio-demographic, physical, climatic, and technological factors do affect water use. Inclusion of these factors in demand forecasting models is likely to result in better demand projections. Both linear and log-linear equations are I fitted to the sample data. In general, in the case of the residential and aggregate municipal demands, the linear models produced better statistical results in terms of both model fits and explanatory powers, whilst the double-log ftinctional forms I demonstrated superiority with the agricultural and industrial demands. 17. The variables, number of occupants, family monthly income, garden area, number I of cars and dummy swimming pools are found to be important determinants of average annual residential water use. Income elasticity of demand is estimated at r 0.12; the household size elasticity estimate is 0.39. Both family income and household size are found to have a consistent effect upon water use; at every income level, with an increase in household size, average residential water use increases. 8. Per capita residential water use significantly responds to household size, per capita income, and garden area, i The coefficient of per capita income yields an income 621 elasticity of demand of 0.22, which appears to correlate favourably with some estimates found in the literature (e.g. Headley, 1963; Wong, 1972; Darr e( ai, 1976; Katzman, 1977). Per capita daily water use increases at a decreasing rate with the increase in household size, suggesting certain economies of scale consistent with the theoretical development. Apart from the very slight discrepancy within households of large size and high per ^apita_jncqnie, at_eve^^ of income, average daily per capita water use decreases as household size increases. Increases in income in smaller households are shown to be associated with a substantial rise in per capita consumption as found elsewhere (see for example, Darr ^/a/., 1975). 19. Income elasticity of summer residential demand is estimated at 0.15, slightly higher than the income elasticity of the total residential demand. The income cbefficierit on winter residential demand produces an income elasticity of 0.14. The comparison between the summer and winter residential demand functions suggests some interesting findings with respect to the seasonal variability in water use. 20. The small sample size and lack of specific explanatory variables limit the ability of developing reliable demand functions for the non-residential water use. However, the number of bathrooms and dummy swimming pool are shown to explain a significant portion of the variation in water use in this sub-sector. The attempts made to develop bivariate models for this sub-sector on the basis of the employment variable also failed to produce statistically reliable results. The exceptions are the models developed for hotels and flat apartments, and social and health clubs, which yield reliable statistical results. 622 21. There appears to be a statistically significant association between average municipal water use and the variables measuring number of bathrooms, number of cars, garden area, number of occupants, and dummy swimming pools are found to have on. All are indicative of higher consumption. Average municipal water use is also significantly, but inversely, related to the variables, household area and dummy sewer connection. Family incomejs shown to be an invalid predictor.^ 22. Per capita municipal water demand is found to be a function of the predictors: number of bathrooms, household area, garden area, and per capita income. Per capita income elasticity of municipal water demand is estimated at 0.33. This elasticity estimate appears to correlate well with some estimates found in the literature (e.g. Headley, 1963; Gottlieb, 1963; Wong, 1972; Morgan and Smolen, 1976; Katzman, 1977; Danielson, 1979). Average price based on the total bill is found to have an improper sign. The bivariate relationship between the per capita municipal water use and the specified average price, however, produces a highly significant coefficient with the expected negative sign. Price elasticity of demand associated with this coefficient is estimated at -0.066, reflecting an extremely inelastic demand, and negligible price effect, as expected. This average price specification is also introduced to the previous residential and average municipal demand models, but is found to be either of an incorrect sign or insignificant predictor. 23. Separate demand functions are also derived for the summer and winter municipal demands; generally, these provide little evidence of seasonality effect, although the estimated summer models show slightly higher predictive capabilities. 623 24. In general, the results from the agricultural water use survey exercise were statistically disappointing. This may support the common contention that the linear programming approach is more effective in modelling agricultural water demand. The equation of best fit is represented by a bivariate log-linear model relating the average monthly agricultural water use to the variable gross area under cultivation. Average pumping hours is also found to be a valid predictor; its inclusion slightly improves the explanatory power of the model. The summer and winter agricultural demand models do not vary greatly, although the winter models somewhat account for larger variability. 25. Industrial water demand is significantly and directly related to the variables, number of employees, factory floor area, and production level. Because of the apparent coLlinearity between the latter variables, the industrial demand function is divided into two basic linear £md log-linear equations. Irf spite of the fact that they account for more variation in industrial water use, the linear models are found to be seriously violating the basic regression assumptions. 26. The improved trend forecasting procedures provide encouragingly accurate results when compared to the actual water use. For example, the medium growth projection for the municipal water use indicates a demand figure of 111.4 Mm'' for the year 2002; allowing about 21% for leakages in the distribution networks will result in a demand estimate of 134.8 Mm^, against an actual demand of 138.1 Mm^; a margin of error of only 2.4%. On the other hand, the results of the validity tests for the analytical water demand forecasting models generally indicate that the developed models need to be improved before being used as reliable predictive tools. However, the fact remains that these models represent a valuable 624 framework for improving the decision-making process in the area of water demand forecasting. 27. Three alternative scenarios are developed for future water management strategies and the possibility of bringing supply and demand into balance. Scenario A represents the worst-case scenario in which the water balance will maintain a net deficit thro^ighout the planning period, reaching 81.2 Min^ in"tHe~year^20lO. Scenario B (the supply augmentation scenario) envisages the net deficit to be significantly lowered, starting from 2008 to reach 7 Mm"*. Surpluses of 6.5 Mm^ and 28.7 Mm^ are foreseen for the years 2009 and 2010, respectively. Scenario C, which combines the supply and demand management policies, is shown to be the most efiBcient alternative to sustainable development and management of the water resources in the study area. It shows that the water deficit will gradually decrease from 77.2 Mm^ in 2005 to 53.8 Mm^ in 2007, and that a positive water balance will progressively increase from 18 Mm^ starting from 2008 to 57.8 Mm' in the year 2010. This scenario analysis indicates that unless the current rate of abstraction is drastically reduced and investment in new non-conventional supplies is substantially increased, future water demand would be in excess of the existing and potentially available water supplies. In view of the foregoing conclusions, the following policy recommendations may be considered: 1. The availability of reliable information systems and databases is necessary to support the decision-making process required for eflFective water planning and management. Data on water level monitoring and well completion records are 625 available in reasonable quality. However, there is a need to upgrade this hydrogeological information system in terms of data collection, processing, retrieval, and presentation. At present, data on groundwater withdrawals are not of sufficient accuracy; in particular, the adopted estimation criteria for the unmetered abstractions need to be verified. It is surprising that, with the groundwater quality continuously deteriorating at alarming rates, there is no systematic monitoring programme for groundwater quality. There is an urgent need for establishing of an efficient and cost-effective water quality observation network. In addition, since the climatic data are essential for the hydrogeological and water resources studies, it may appear necessary to upgrade the meteorological facilities at the Sheikh Isa Air Base to include rainfall records, and to set up an appropriate climatic station in the Huwar Islands to ensure adequate coverage of the climatological control. 2. Carefijl planning and proper management," including setting of appropriate standards and quality assurance regulations that take into account the specific local conditions, is necessary to ensure a high quality and cost-effective TSE supply. In this respect, particular attention should also be paid to improving the existing analytical facilities at the TWPCC to support these efforts. Also of particular concern are the current problems of activated sludge stockpiling, handling, and utilisation. Adequate measures, including proper composting and careful checking of parasites contents, should be taken to avoid health risks associated with sewage sludge utilisation as soil conditioner. More importantly, institutional and organisational re-structuring is urgently needed to soundly manage the TSE utilisation programme. Indeed, at this advanced stage of the programme, a clear national policy on TSE re-use and the establishment of 626 an independent body to set up and implement this policy are immediately required. Privatisation of non-conventional water projects should be seriously considered as a potential economic solution to reduce the financial burdens associated with the provision of such expensive water installations. Favourable incentives and investment policies to attract the private sector's participation in financing and operating such projects are to be greatly encouraged. Meanwhile, it wi[l be necessary to support studies and research aiming at reducing desalination and wastewater treatment costs, and assessing of the environmental impacts associated with their development. 3. Intensive efforts must be made to enhance the demand management tools, including leakage reduction and pressure control, tariff* re-structuring, the launch of educational campaigns using different media to promote public awareness of water conservation, the encouragement of the use of water-saving devices and investment in recycling schemes and water-saving technologies, as well as the adoption of various conservation incentives and subsidies. All possible efforts should also be devoted to increase efficiency of water use in agriculture through fijrther expansion in using modem irrigation techniques, changing crop patterns, charging for irrigation water, and applying of new farming practices. 4. Since water pricing, if properly designed, is the most effective policy instrument for discouraging high consumption and waste, water tariff* re-structuring should have a higher priority among the aforementioned demand measures. It is quite clear that the water tariff" currently being paid is heavily subsidised. To illustrate, the average monthly water use per residential household is 53.8 m"'. The monthly bill for such use is BD 1.345, whereas its actual cost is BD 16.006 627 (assuming, based on the existing blending ratio, that 13.45 m"* of this quantity (groundwater) is purchased at BD 0.080/ m"*, and 35.86 m^ (desalinated water) is purchased at BD 0.370/m'*). This indicates that the average customer is paying about 8.4% of the actual water price. 5. From the analysis of the daily per capita consumption, it appears that the actual water use per capita per day is markedly lower than the officially-published estimates. It is clear that a great proportion of the piped supply included in the per capita consumption is actually used by the VIP unbilled users for other than normal municipal purposes. Therefore, there is a need to regulate and restrict the supply to these users. More importantly, with the expected increase in their demand, charges for their uses and transparency regarding the amount of supplies to these users should be highly considered. 6. Institutional weakness and fragmentation of responsibilities is one of the major reasons for water policy failure. Urgent steps should, therefore, be taken to introduce major institutional reforms in order to ensure more effective coordination mechanisms and integration among the competing water users. In this regard, particular consideration should be given to the possibility of creating a single water authority to be responsible for the water resources development, operation, and management. Alternatively, there is an urgent need to re-activate the High Water Council (a governmental body responsible for drawing the overall water policies). 7. Within the wider context of the institutional framework, the government is strongly encouraged to introduce legal reforms by considerably revising the existing water legislation and drafting new legislation. There is a need to enact laws and 628 resolutions for water allocation priorities and a licensing system. Most importantly, the authorities should be fully aware that establishing proper enforcement mechanisms for the water laws and regulations, without exceptions, is a pre• requisite for their effectiveness. In addition, from the institutions' point of view, it is essential to address the need for adopting efficient human resources development and capacity building programmes in a wide range of water management aspects, to keep pace with the escalating water shortage problems. 8. Other related management aspects to be considered include the necessity of undergoing a major shift in the current policies of population and land use, and ensuring an adequate stakeholder participation in decision-making concerning water issues. 9. Needless to say that regulating and restricting water consumption through demand management measures must be fully integrated with the supply augmentation policies in a holistic manner. This balanced strategy of managing both water supply and demand is a key to ensuring sustainability. 10. Assessment of available water supplies and demand is a pre-requisite for effective water policy formulation and implementation. This research has provided a thorough background document on the water resources assessment and supply- demand relationships. However, the study calls for the continuous re-assessment and updating of these resources as more adequate data and information become available. 11. This thesis opens avenues for further research in water demand modelling in the study area. Clearly, much further work is also needed on developing more reliable 629 analytical water demand forecasting models by using improved methodologies, and incorporating various factors potentially influencing water use, especially those proven by this research to be important demand determinants. 630 Appendix A Meteorological Data 631 Table A-1 Monthly and means rainfall at Bahrain International Airport (mm) 1902- 1997 Year Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec 1902 NR NR NR NR NR NR NR NR NR 3.8 2.5 — 1903 2.3 36.1 7.9 8.9 — — — — — — — 3.8 1904 6.4 4.1 21.8 2.5 6.4 — — — — — — — 1905. 2.5 11.4 9.7 _ — --- -r- — — —- . .19.1 1906 8.1 31 33.0 — 1.3 — — — — 1.0 — 1907 — 47.5 — 6.6 12.7 — — — — — 0.8 1908 18.5 2.5 2.5 3.8 — — — — — — 1.3 1909 19.1 — — 9.1 — — — — — 6.9 90.7 1910 28.7 2.5 10.9 1.0 1.5 — — — — — 74.2 1911 10.7 2.5 64.8 2.3 — — — — — 69.6 7.9 1912 16.0 13.5 6.6 27.9 --- — — — — — 46.2 1913 11.2 59.9 — — 0.3 NR NR NR NR NR NR NR 1928 NR NR NR NR NR NR NR NR NR — 70.1 19.8 1929 — 15:0 4.8 — — — — — 33.5 1930 17.3 2.0 3.3 5.3 — — — ... — — — 21.1 1931 31.5 69.3 — 13.5 — — — — — — — 1.3 1932 — — 27.7 — 7.1 — — — — — 51.1 1933 9.9 13.7 0.5 17.8 1.0 — — — — — — — 1934 — 0.8 44.2 7.1 — — — — — — 8.9 18.5 1935 ... 33.5 11.4 6.9 — — — — — — 2.5 20.6 1936 16.3 11.2 17.8 0.8 0.8 — — — — — 44.7 63.2 1937 10.4 6.3 16.5 4.3 — — — — — — 11.7 — 1938 14.5 27.4 0.3 — — — — — — — — 15.0 1939 6.3 7.9 20.6 65.0 — — — — — — — 11.7 1940 11.2 7.4 1.5 1.3 1.0 — — — — — 147 — 1941 — 9.1 19.3 10.7 — — — — — — — 1.5 1942 0.5 57.1 — 2.0 — — — — — — — 4.6 1943 12.7 8.4 10.9 0.8 — — — — — — — 20.1 1944 — — 1.3 — — — — — — — 10.7 50.3 632 Table A-1 (Continued) Year Jan Feb Mar Apr Oct Nov Dec 1945 39.1 1.8 5.8 — 1946 1.3 — 0.2 — 1948 1.3 24.2 32.8 16.7 18.4 1949 5.7 9.2 10.4 — 0.9 7.2 52.7 1950 1.3 — 19.7 — 1951 3.1 17.5 0.4 — 23.7 1952 7.2 — 0.2 5.6 2.1 7.6 1953 2.0 6.7 7.5 — 0.9 60.9 1954 0.9 85.2 4.6 0.1 13.3 0.7 1955 66.8 — 43.7 0.6 3.7 53 1956 16.6 0.1 7.5 0.1 1957 34.5 17 4.4 15.9 11.9 3.3 22.9 1958 39.8 19.9 1.6 — 1.3 13.8 1959 136 — 14.2 0.9 2.8 - - 7.3 0.0 1960 0.5 0.4 5.4 26 1961 6.4 17.6 14.1 69.9 4.3 3.7 1962 — 2.2 — 11.9 1963 — — 1.3 4.4 10.2 1.5 16.1 2.2 1964 32.4 27.2 — 0.8 354 1965 17.4 — — 6.9 1966 — 21.3 0.1 13.7 1967 1.8 8.3 5.5 6.4 3.2 ~ 3.7 ™ 1968 — 52.7 1.4 14.3 - 2.7 5.1 1965 17.4 — — 6.9 1966 — 21.3 0.1 13.7 1967 1.8 8.3 5.5 6.4 3.2 - 3.7 1968 — 52.7 1.4 14.3 2.7 5.1 1969 87.7 10.4 0.6 49.2 8.9 9.1 1970 7.4 — 1.2 — 1971 2.6 8.2 1.9 28.4 0.1 1.3 3.2 1972 19.5 9.9 42 3.1 0.1 4.0 6.1 1973 5.7 — — 0.4 63: Table A-I (Continued) Year Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec 1974 4.6 7.2 33.9 3.9 — ._- 96.2 1975 22.6 5.7 1.7 — 0.1 ._- ... 10.4 1976 21.1 73.2 70.7 51.8 10.0 1977 58.9 — 2.2 5.7 9.2 1.3 0.2 4.9 1978 2.3 6.3 3.4 — ... 7.5 ... 1979 4.7 1.8 6.1 — 7.4 1980 31.2 57.1 2.0 — ...... 3.2 1981 19.1 1.3 5.2 — 3.3 1982 4.4 41.3 71.9 3.9 — 58.8 14 1983 19.2 1.1 55.8 3.7 1984 — 10.5 29.9 — 0.8 9.4 1985 10.5 — 1.1 — 5.1 ...... 32.6 1986 9.2 2.7 8.4 10.6 24.2 1987 — 0.7 23.7 — 0.7 1988 14.7 107 1.8 2.3 — 3.0 0.6 1989 — 0.3 30.1 2.7 33.9 1990 33.9 5.3 1.1 4.7 1991 41.0 3.0 21.6 1.2 21.1 1992 26.4 5.1 18.5 1.3 ™ 1.2 77.7 1993 35.6 57 1.9 10.0 1.7 1994 — 0.5 15.9 1.4 4.2 1995 0.3 25.5 139 22.1 38.4 1996 34.2 18.4 33.8 0.8 1997 21.4 — 47.2 0.2 1.8 1.4 102 3.2 '^"""^ 15.3 15.8 15.1 7.3 1.2 ... 7.8 16.4 yMean Annual 77.9 average SOURCE: Modified after MeleorologicaJ Services Notes: [1| No records were kept from 1914 - 1927. [21 NR = no records. [3] A dash indicates nil precipitation. [41 Some decimal numbers are omitted or rounded due to insuiTicient place. 634 Table A-2 Monthly mean relative humidity at Bahrain International Airport 1963- 1997 Mean monthly relative humidity (%) Year Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec 1963 — — — — — — — — 64.9 66.9 66.3 68.6 1964 68.7 77.3 66.7 59.5 59.3 57.8 64.9 62.1 67.9 70.3 73.4 78.0 1965- 80.0 73.9 71:0 62.1 56.1 57.1 60.1 64.9 67.5 66.5 70:7 "74.1" 1966 80.4 74.2 66.0 69.3 61.5 56.6 59.4 63.5 64.0 67.1 68.0 72.8 1967 73.8 73.1 67.3 65.8 61.0 58.1 62.6 62.3 68.2 67.4 65.6 71.3 1968 70.5 71.9 64.6 62.1 62.5 57.1 58.7 59.8 65.8 70.5 70.2 75.4 1969 78.7 73.7 65.4 68.0 62.1 60.0 60.9 64.9 69.2 71.2 71.0 75.9 1970 73.2 72.6 66.5 60.2 60.5 56.7 56.1 61.9 65.4 67.7 72.3 72.9 1971 75.0 68.2 65.2 65.7 56.0 56.7 58.8 61.2 66.5 67.4 68.1 70.5 1972 77.5 76.4 — 66.2 61.4 60.1 58.9 66.3 66.8 73.0 67.7 69.4 1973 72.5 70.5 62.8 56.6 53.2 53.5 58.4 68.0 66.3 66.2 66.5 73.4 1974 73.3 74.4 71.3 64.8 59.6 62.2 58.8 61.8 66.6 65.8 68.5 75.8 1975 78.9 74.5 63.5 64.3 57.0 ~60.4 63.9 63.7 66.9 66.9 68.9 75.0 1976 79.9 74.6 75.1 68.5 58.9 58.6 58.5 68.4 66.0 66.4 68.6 76.5 1977 73.8 77.2 67.1 61.6 60.4 56.4 57.4 67.2 68.4 67.9 71.7 72.4 1978 72.6 75.6 66.0 55.2 56.2 55.6 64.3 61.3 66.5 70.8 69.8 74.7 1979 72.7 74.7 64.5 60.1 63.8 58.5 63.2 66.7 68.6 70.8 68.8 74.8 1980 75.5 77.6 65.4 60.5 58.5 59.3 62.4 64.6 67.2 70.5 69.6 72.3 1981 76.8 69.8 69.0 60.4 60.1 61.6 62.6 64.4 67.4 68.7 66.8 72.3 1982 71.6 69.7 71.2 62.6 61.0 59.7 61.1 60.0 69.8 66.6 70.3 74.9 1983 73.3 73.0 71.8 65.5 59.0 57.1 61.2 64.6 65.6 67.0 71.3 70.2 1984 74.5 74.3 67.8 56.3 55.4 55.2 60.4 57.9 65.8 65.3 70.8 72.3 1985 74.4 64.1 66.4 63.0 56.9 56.9 59.0 66.4 65.4 66.1 71.5 72.0 1986 75.5 73.7 67.3 67.4 58.0 58.4 59.3 68.3 65.9 66.4 68.4 72.3 1987 73.9 71.0 64.4 56.3 55.4 51.0 56.7 60.2 63.2 63.9 71.6 68.9 1988 70.8 73.6 66.1 60.2 52.3 51.2 58.4 60.1 63.5 63.5 66.1 70.1 1989 63.5 66.0 63.8 59.4 51.9 45.1 61.7 58.4 60.4 62.4 66.5 72.7 1990 69.2 66.3 63.0 57.4 50.9 52.5 54.0 60.3 62.2 66.2 69.3 70.5 1991 76.2 67.7 72.0 57.1 46.8 45.3 51.7 60.8 67.1 71.3 64.9 71.4 635 Table A-2 (Continued) Mean monthly relative humidity {%] Year Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec 1992 65.9 68.1 65.2 57.3 54.0 48.9 49.9 55.8 64.0 65.2 66.6 71.4 1993 70.5 69.0 59.6 60.9 53.9 48.9 55.3 62.1 64.6 62.6 66.1 73.6 1994 68.6 62.3 62.2 56.6 46.7 49.2 52.5 59.3 61.7 65.9 66.1 66.3 1995 72.1 68.7 67.3 59.0 52.7 50.7 53.7 62.9 59.0 63.8 67.0 78.5 1996 77.7 74.8 71.4 58.4 53,5 _ 54,9. _5„7,2^ 6 2^8 ,_59-2 _ _62-9^ „66.5_ .72.7 1997 71.9 65.7 72.8 59.9 44.1 51.4 47.1 55.0 61.4 66.2 73.6 74.1 A dash indicates no record. 636 Table A-3 Monthly mean wind speeds at Bahrain International Airport 1977 - 1997 Monthly mean wind speeds (Knots) Year Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec 1977 12.8 9.1 11.2 11.2 8.9 11.1 11.2 8.4 7.3 9.9 12.0 10.2 1978 ._1.1.1 9.6_ 10.2 10.7 .10.1 12.7 9.5 11.1 8.0 . 6.7 -12.8--10.7 1979 10.7 II.O 11.8 10.9 8.9 13.7 10.5 9.8 6.0 8.3 6.4 11.1 1980 11.0 10.0 10.4 11.2 10.2 10.4 8.3 9.2 7.7 9.0 8.5 10.5 1981 11.3 12.2 10.7 11.1 11.4 9.4 9.2 9.1 7.4 7.8 10.3 7.1 1982 10.1 11.1 9.9 7.7 8.9 9.8 8.6 9.0 6.6 8.0 11.7 11.2 1983 11.5 10.1 9.6 8.7 9.0 12.7 9.2 8.6 8.3 8.0 7.2 9.7 1984 9.7 10.5 10.4 10.2 9.3 11.8 8.5 11.3 6.9 7.5 7.4 10.2 1985 9.5 13.1 10.4 9.0 9.0 10.8 9.6 6.4 6.4 8.7 8.2 10.5 1986 7.7 10.3 9.2 8.2 9.4 12.8 8.0 7.1 6.0 6.3 10.0 10.5 1987 9.9 9.9 9.5 9.8 7.3 10.2 8.5 9.5 7.3 8.3 9.0 9.6 1988 10.0 10.3 11.3 8.5 11.2 11.1 1.9 8.0 7.1 6.8 9.5 9.1 1989 11.6 10.0 8.6 7.6 11.0 10.5 6.5 7.1 7.4 7.2 8.8 10.4 1990 10.9 10.0 9.9 9.6 9.3 11.0 8.2 8.2 7.8 7.9 7.9 8.0 1991 9.1 10.0 9.2 9.5 10.7 10.0 9.3 8.1 7.6 8.6 7.4 10.1 1992 10.0 10.5 9.4 8.2 8.7 10.1 9.7 7.4 6.9 6.9 8.6 10.1 1993 10.0 9.9 10.6 8.1 10.4 10.2 8.7 7.6 6.5 6.2 9.9 8.1 1994 8.5 10.9 8.8 7.9 9.3 10.7 10.4 8.2 7.3 6.8 9.3 10.9 1995 9.8 10.1 9.0 8.0 8.2 9.8 9.9 7.1 7.5 7.3 9.4 9.0 1996 8.2 9.5 9.3 9.8 6.6 9.3 7.1 6.3 8.2 7.7 9.6 6.9 1997 8.6 12.3 9.7 9.3 6.7 8.4 10.6 10.9 7.6 7.3 8.4 9.5 Monthly 10.1 10.5 10.0 9.3 9.3 10.8 9.1 8.5 7.2 7.7 9.2 9.7 average Average wind 9.3 speed SOURCE: Modified afler Meteorological Services. 637 Table A-4 Mean monthly sunshine hours at Bahrain International Airport 1968 - 1997 Mean monthly sunshine hours (hours per day) Year Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec 1968 6.8 7.9 8.6 8.9 9.7 11.9 11.2 10.4 10.9 10.3 8.9 8.2 1969 6.1 8.1 8.7 7.8 11.2 12.2 11.4 — — ... — — 1970 — — — 9.6 ll.O 11.5 11.4 10.8 10.7 10.4 9.9 7.5 1971 8.2 — — — — — — — — — — — 1972 — — — — — — 11.4 11.2 10.6 9.4 8.9 7.0 1973 8.1 7.9 8.9 8.0 9.9 no 11.0 10.8 10.4 10.1 9.2 8.0 1974 5.8 7.4 5.9 10.1 10.6 12.3 12.2 11.5 10.9 9.8 9.3 6.1 1975 6.5 7.5 8.8 8.2 10.1 11.4 11.6 10.6 10.5 10.1 9.1 6.3 1976 7.0 6.8 6.3 8.7 10.8 12.1 11.2 n.o 10.5 9.0 8.6 7.1 1977 7.2 9.4 8.5 7.9 10.0 11.4 10.9 10.9 10.3 9.1 9.4 6.8 1978 8.6 7.9 8.2 6.3 11.0 10.8 9.6 10.2 10.3 9.3 7.2 8.0 1979 6.6 8.4 4.8 10.0 9.9 10.1 9.5 10.3 10.5 8.3 8.9 6.9 1980 7.4 6.7 8.5 8.8 9.8 11.1 10.4 111 8.8 9.7 8.0 7.2 1981 6.4 7.7 7.8 8.8 9.4 11.7 11.1 10.8 10.6 9.8 8.9 8.3 1982 8.3 7.0 5.3 8.5 9.2 10.8 10.5 9.9 10.2 8.4 6.9 7.1 1983 6.9 7.9 7.8 8.7 8.1 10.5 9.6 8.9 10.4 9.8 8.7 7.9 1984 8.3 8.2 6.8 8.1 8.4 9.5 9.9 10.2 9.4 9.2 7.8 5.9 1985 7.2 8.3 6.7 9.3 9.4 11.4 9.9 10.7 10.4 10.1 9.0 6.6 1986 7.6 8.2 7.9 8.2 10.5 12.0 10.3 10.8 10.3 9.8 8.2 7.4 1987 8.0 8.2 7.1 10.3 9.5 9.6 11.1 10.2 10.0 9.7 7.3 7.1 1988 6.7 6.4 7.1 6.8 11.3 11.1 9.8 11.3 10.3 10.1 9.3 6.8 1989 8.3 7.8 7.3 6.7 11.1 11.2 11.2 11.0 10.6 10.3 8.7 7.6 1990 7.4 7.7 8.3 8.4 10.7 11.6 11.6 11.0 10.2 10.1 9.4 9.1 1991 6.0 6.5 6.1 7.7 8.1 9.8 9.0 9.5 9.5 8.2 9.4 6.4 1992 5.3 6.7 4.9 8.0 8.7 11.1 9.7 10.4 10.1 9.4 6.5 5.5 1993 6.2 7.1 7.6 8.0 9.7 11.6 11.0 11.0 10.7 9.9 8.8 7.9 1994 7.7 9.3 7.3 7.9 8.3 11.5 10.4 10.0 10.5 9.4 8.7 7.9 1995 8.8 7.9 8.1 8.9 11.2 12.0 10.6 11.3 10.8 10.2 9.3 3.7 638 Table A-4 (Continued) Mean monthly sunshine hours (hours per day) Year Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec 1996 7.3 8.0 6.8 8.4 10.4 12.1 11.1 10.8 10.3 10.4 8.3 9.2 1997 8.4 8.6 6.7 8.6 10.1 12.1 11.2 11.1 10.9 9.1 6.3 6.4 Month. 1.1 7.8 7.3 8.4 9.9 11.3 10.7 10.6 10.3 9.6 8.5 7.1 average Annual 9.1 average SOURCE: Modified ftx)m Meteorological Seivices data. A dash indicates no record. 639 Appendix B Hydrogeological Data 640 Text B-1 Information contained in the geological and hydrogeological database 1- Well No. 2- Well Inventory No. 3- Location. 4- Grid Reference (UTM). 5- _ Well.Owner._ . . _ 6- Well Use. 7- Elevation (BNLD). 8- Casing Depth (m). 9- Casing Diameter (in). 10- Aquifer Open. 11- Total Depth (m). 12- Date of Completion. 13- Water Level on Completion (m- below ground surface). 14- Salinity on Completion (ppm-TDS). 15- Top Neogene (m- below ground level). 16- Top Alat Limestone (m- below ground level). 17- Top Orange Marl (m- below ground level). 18- Top Khobar (m- below ground level). 19- Top Shark Tooth Shale (m- below ground level). 20- Top Rus (m- below ground level). 21- Top Umm Er Radhuma (m- below ground level). 22- Top Aruma (m- below ground level). 23- Top Neogene (m-BNLD). 24- Top Alat Limestone (m-BNLD). 25- Top Orange Marl (m-BNLD). 26- Top Khobar (m-BNLD). 27- Top Shark Tooth Shale (m-BNLD). 28- Top Rus (m-BNLD). 29- Top Umm Er Radhuma (m-BNLD). 30- Top Aruma (m-BNLD). 31- Transmissivity (mVday). 32- Storage Coefficient (dimensionless). 33- Permeability (m/day). 34- Specific Capacity (m^/day/m). 35- Remarks. Note: BNLD is Bahrain National Leveling Datum = mean sea level. 641 Table B-1 Piezomelric level data of the Mat Limestone aquifer for years 1987 and 1997 and water level difference Well no. Location Grid Reference (UTM) Water level 1987 Water level 1997 Water level difference (m-BNLD) (m-BNLD) , (m) North East 1102 Budaiya 99495 45320 2.42 0.44 -1.98 1132 Hidd 99725 65545 1.88 0.65 -1.23 1134 Sanabis 00770 55360 2.06 0.13 -1.93 1136 North Silra 96260 61150 1.68 0.52 -1.16 1142 Mahooz 98900 57620 2.10 0.12 -1.98 1170 Umm Nakhila 06830 62100 2.15 0.14 -2.01 1176 Silra (Wadian) 93400 61850 1.35 0.25 -1.10 1183 Sitra (Cent. Store) 90460 62800 0.94 0.083 -0.86 1185 Hamala 91040 47365 3.92 3.36 -0.56 1191 Hamala 93820 45655 1.76 0.38 -1.38 1197 Hidd (Asry) 96750 66545 1.99 0.74 -1.25 1200 Jasra 95030 48200 2.18 0.85 -1.33 1204 Qalali 04870 62620 2.11 0.65 -1.46 1219 Sitra Refinery 87880 62140 0.22 0.06 -0.16 1247 Nabuh Saleh 95010 58900 1.76 0.19 -1.60 1253 Saar 97810 48600 3.31 0.88 -2.43 642 Table B-1 (Conlinued) Well no. Location Grid Reference (UTM) Water level 1987 Water level 19S|7 Water level difference (m-BNLD) (m-BNLD) (m) North East 1263 King Fahad 96150 32850 2.44 0.32 -2.12 Causway 1216 Sehlal Al- 90970 53470 1.53 0.40 -1.13 Favvqiah 643 Table B-2 Piezometric level data of the Khobar aquifer for years 1987 and 1997 and water level difTerence Well no. Location Grid Reference (UTM) Water level 1987 Water level 1997 Water level difference (m-BNLD) (m-BNLD) (m) North East 648 A Sitra 94580 60730 1.59 0.25 1.34 648 B Sitra 94580 60730 1.35 0.14 1.21 107 Ma ameer 90275 61510 0.98 0.3 0.68 1001 Sadad 85475 48645 0.053 -1.44 1.49 1004 Saar 94875 48157 1.73 -0.63 2.36 1007 Tubli 97290 54895 1.89 0.11 1.78 1011 Wasmiyah 78330 48305 -0.14 -1.55 1.41 1015 Mumattalah 66360 52180 0.005 -0.06 0.065 1019 Saar 94760 48425 1.79 -0.66 2.45 1051 Aali 93865 52350 2.79 2.50 0.29 1100 Budaiya 99495 45300 2.93 0.17 2.76 1103 Refinery 89515 59390 1.80 1.12 0.68 1121 Ras Abu Jarjur 84115 62290 0.44 0.22 0.22 1123 Ras Hayyan 79965 63135 -0.10 -0.77 ' 0.67 1124 Hidd (Asry) 96825 66470 0.43 -0.70 1.13 1135 Sanabis 00735 55360 2.18 -0.24 • 2.42 644 Table B-2 (Continued) Well no. Location Grid Reference (UTM) Water level 1987 Water level 1997 Water level difference (m-BNLD) (m-BNLD) 1 (m) North East 1138 Umm Al-Hassam 97405 59220 1.77 -0.006 1.77 1164 Isa Town 93610 56480 2.10 1.31 0.79 1171 Umm Nakhilah 06840 62110 2.20 0.21 i 1.99 1 1181 Hamala 90960 47370 0.44 -1.85 2.28 1184 Hamala 93780 45820 1.83 -0.47 ' 2.30 1351 Dar Kulaib 82210 50550 -0.004 -1.65 1.60 1018 Saar 94720 48543 1.63 — — 1021 Sitra 90400 62780 1.03 0.13 0.90 1022 Sitra 93400 61840 1.26 0.10 1.16 1023 Sitra 95790 62470 0.92 -0.13 ' 1.05 1128 Hidd (Hyundai) 99950 65485 1.35 0.51 0.84 A dash indicates no record, 645 Table B-3 Piezometric level data of the Rus - Umm Er Radhuma aquifer for years 1987 and 1997 and water level difference Well no. Location Grid Reference (UTM) Water level 1987 Water level 1997 Water level difference (m-BNLD) (m-BNLD) 1 (m) North East 1010 Wasmiyah 78350 48305 4.28 1.96 ; 2.32 1012 Awali 83940 53550 3.80 1.37 ; 2.43 1013 Awali 83940 53560 3.80 1.07 i 2.73 1014 Mumattalah 66350 52180 3.93 1.93 2.00 — 1016 Rumaithah 71900 59040 3.69 ! 1017 Aali 93890 52420 3.82 1.38 2.44 1003 Saar 94865 48157 3.85 1.43 2.42 1005 Saar 94855 48180 3.94 1.33 2.61 1006 Saar 94855 48159 3.85 1.40 2.45 1008 Tubii 97290 54390 3.47 1.13 2.34 — 1125 Sitra 90470 62740 — 1.71 1143 South west of 80095 56905 3.79 1.27 1 2.52 Askar 1144 Ras Abu-Jarjur 82010 60800 3.53 1.05 2.48 1114 Southern Area 83085 58075 3.81 1.37 ' 2.44 1115 Sothem Area 82830 59233 3.75 1.11 2.64 1118 Sothem Area 82580 60345 3.63 1.23 1 2.40 1 646 Table B-3 (Continued) Well no. Localion Grid Reference (UTM) Water level 1987 Water level 1997 Water level (m-BNLD) (m-BNLD) difference (m) North East 1119 Sothem Area 80313 59245 3.80 1.23 , 2.57 1120 Ras Hay>'an 79825 60515 3.99 1.56 2.43 1126 Hamala 91350 45725 2.45 -0.51 ; 2.96 1207 Al-Buhair 93230 56060 3.97 1.51 2.46 A dash indictes no record. 647 Appendix C Hydrochemical Data 648 Table C-1 Chemical analysis data of groundwater from the Alat Limestone aquifer 1998/99 i l n CD D> m n CI KB Co Cf LMOlaa K na Tnr» ( Tstil Ilti*rii (•1 Taiil MkilBdIT n lO,* CO,' HCOi' Ct" tit ?ii' r r Q POrK nOrlt Rlirn po,r '»i- tut n •Cl C*COJ (nCtCOJ on lt<) wit) um na nm 11 n IM IM nl in t lU m a Ul II It • 1KI il 411 ••I fmliQ *>a 649 o o o o o o o o o o o o o o o o o o o o o ti 9 9 9 9 9 9 9 9 V 9 9 9 9 V 9 9 9 V ti V ti ti ti ti ti ti ti ti ti o o o o o o o o o oV o o o o oV oV a oV oV oV V V V V o o O9 o o o o o o o o o o a o o o o w V w 9 9 9 9 $ 9 V ti ti V V ti ti V w V V V V ti V ti V V V ti V V 8 8 8 V V 8 8 8 8V 8V 8 8 8 8 8 8 8 8 8 8V 8 8V 8 8 8 8V 8 8 o s os s o o o o o o w i-l V 9 9 9 V 9 9 9 9 9 9 V V V V ti ti ti ti ti w ti ti ti ti ti ti ti V V 8 8 8 8V 8V 8 8V 8 8V 8 8 8 8V 8 8V 8V 8V 8V 8V 8 S 0 > 8 8 8 8 8 8 8 8 8 8 a o o o o 9 9 9 9 9 9 9 9 9 9 9 9 9 9 V ti ti ti V ti V ti ti ti ti V V ti ti ti 8 8 8 8 V V 8V 8 8V 8 V 8V 8V 8V 8 8 8 8 8 8 8 8 8 8 8 8V 8V 8V 8 1-8 so s 9s 9 o o 9 o 9 9 9 9 9 9 V ti 9 9 9 9 V ti ti ti V ti ti ti V ti ti ti ti ti 9 9 o o o o o p p oV o a o p p p a oV V oV o V o oV oV o p pV V V V pV 9 9 9 9 V V V V 9 9 V 9 V 9 9 V V V 9 9 9 9 V V V 9 V V V o o o o o o o o p o p p a o o o o o p p 9 V V V V 9 9 V 9 V V V 9 V 9 9 9 9 9 9 9 V 9 9 9 9 V 9 V a o o o o o o o o o o o o o o o a o o o o o o o o o o o o o o o o o o o o o o 9 9 9 V 9 9 V V 9 V u 9 9 V V 9 V 9 V V ti ti ti V ti ti ti ti ti ti ti •S 3 o o o o oV o oV 'o o' o o o oV o o o' V oV oV o oV oV o o o o oV aV o o o e o o o o 9 9 V V 9 V V V 9 9 9 9 9 9 9 9 V 9 ti ti ti ti ti ti V ti ti ti ti « o •o o 9 9 _ V V V V V sV « 8 5 V s s s o 3 s sV S s s sV o V 5 c> ti ti ti ti o ti o- o o ti ti ti ti ti ti o o o o o ti ti ti o 8 o o o o o o o o o o o o o o o o o o o o e> o o o o o o o o ti o o o V a a o V V V 9 9 9 V V V V 9 9 9 V V 9 9 9 V 9 ti ti V ti I V V _ o o o 5 o as o o s s o o o o 5 o s s S s o s s o 9 - a 9 9 9 o o o o V o o o ti w ti ti ti ti ti V ti ti ti V ti ti ti V V V _ •B _ IB CO _ oa 3 V H • Urn 8 8 8 8 8 8 8 g 8 § i i 8 § 8 s 8 8 8 8 8 y 8 8 8 8 8 8 o o o o o o o o o o o o o o o o e o o o o rt i I ti ti ti o ti ti ti ti .£3 o o o o o o o o o ti o o o o o o o o o o 9 o o o O n _ — B s —" —' — — -— — -• — — — — —" — — — o Ok CO o lO o Ck n e o s s » n S IS » 5 3 CO s 8 B 3 = 3 3 CO 3 i Q o o s 8 3 3 o •e 5 5 a s s s 3 s s> s — o o 5 to 3 Z 5 1 s § « e s o o g o « o o o o «> -o p • o 3 3 8 o I f* ** r« jj T* " " I o O o O o o o o o o o o o o o o a o o o o o o 00 o o o o 8 o e a o o o a o 5 IB 8 3 i s 8 «p o § o o £ i •o « i § § 3 s _ „ •o •a IB S OB JO o A - Z in o o o o o O a ti s o s 00 £ ca s ft s S s S 3 s s § o 3 u 3 9 8 IS 8 8 S I I ess 1 = U I? u m 5 3 5 2 5 " 5 5 S I 3 i £ i s I ^ ^= C 5 f- m 5i ^ S 5 i i i i i 1 i 1 I s I 1 1 I 1 1 1 1 I i I 1 I I I I I S 8 S 2 5 O n p o s f i s s s » ^ s ? 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Q & S 3 S S 3 3 8 111 4 I i i ^ £ S 5 2 = I 5 ^ ^ i CO o. ± s TO O o o S i 5 o u o V- e u o 8 § 8 a o o o ooooooooooooooooooooo M o o o o o o o o « O a t at o o o i * i t £ £ * B o o 3 o s o o o £ s o s § = o CO 01 at O o o oo 8 i 8 00 S = a a s 3 s 8 i s s s = = Q o o X 3 o> e n o s s 5 2 w M 8 8 o o i o t o 5 t 1 E 3 ai to 8 2 •o r n^^i^Mu^ob^wBu — OM — 'A — uo — b oSwi-S '^So '-J ob- oooooooooooooooooooopopppodpoppoppo ooapAoooooooAOpooopo^^o^o^ogo^^p^So I I i S I S i 3 g g = S i 2 g § 2 = 8 8 I I 8 i 8 § 8 i 3 8 g 8 § 1 8 i «MOi-ooij — ;2* oyijU«»ooUti «wt*»»«i-wwi-M — un — wmi- 222i2222i222SS2iSi2222222S22£2S22222SSS2 ^&&&&&&&&&&&&&&&&&&&&&&&^^^$&&&&&&&^ 22i222222i2S2S2222222222S2222222222222 poppppPpoppp«.ppoopoP«PPP<»f2SSSS22-S2 o^o_o o_o_o_ o_o o o o 2 2 2-2 2 -2 2 2-2 2 2-2 2 2 2 — 2-2 2-2 -9 -2 9 -2- £ 2 222iS2222222222S222222222222222222222 222222222222222222222222222222222222 22222222222i5222222222222222222222222 & &&&&&&& |8888SSS83S88888|SSS888S3SS8S8SS diiiiiid^iiiiiiiiiiiiii^iiiissss^SpS 388S8S|8S383SS88888888SS8S|S88SS8 888383S8SS88S8888SSS88§888S88S8S|SS8 222222222222222iSi2222 '2 222222222222222 H CD a t 5 = or n n f. f r s 1.1 11 sj I f I- f 11 f 11 i- If 111.11111 i I f I f I n o S2SS£8SSSS 3 s „ 3 „ . s 1 1 I i i i i S S i5 oi Io i * <• £ 2$ £ ^ S § e i K o o o s i 1 1 5 i 5 i S i s s = s •o * " i I iil l i 1 i i i I i i I I I g i i I 1 i i 8 i 1 1 i i I a s 3 5 •> CO o 1 s I I 5 s i S 2 3 1 i i I i i i I " 2 i i i i O H * O i I i I 1 i i i I i I i i s i i i 1 i i 1 i i i i 1 1 i i 1 1 i i 1 I i =H 1 » ^ ^ S £ 3 £ a 3 g a 2 S S £ S Q o a o o o o o o o o o o o o o o a o o a a o o o o o o OS LA OM-'O — OMw O O 3 S O O kJ M « « « 3 Z 3 2 3 3 3 3 S -J o...* -< & & t> CO3 — — — ^ o * — ^ u * I •h^ ^ W w w A ^ H H ^ *H» 1^ * M M M A s Irf & 4.. H U PhJ U K O u 1> a ta M — u o « n 3 is^S5i2§ £ ^ e> ^ S — ~t o S 2 3 a 2 3 o i S S 2 s s 5 £ S 110 6 s 5 ^ s = I 1 i 3 § S £ 1 t § 1 1S 1 w it o £ c: S o e = s 2 a « « CO » o o o o o o o o o o p p o p oopopppo o P O o P o P o ppoppppopp O O O 9 ooooooeoooo 8 8 2 S i s i I g § g § I 8 § S 5 S C o " A A n OOP o p d fi ° ° fi fi fi o o p o o o o p p p p fi fi fi fi fi fi fi fi fi fi fi fi fi I I S fi I fi S fi fi s 2 S 2 2 2 p p p o o o o o o o p p p o a p p o a p p p p p o p p * ^ w — S S — M s - A A A A A A A A A A A A A A <0. 0 <0, 0 A <0 0 A A A A A O p O P a O O O O P O O O O P P O P O 00 > 00 > fi fi fi fi 00 > fi fi fi :00 > A A A A A A A A A A A A A A A A A A A A A A A A A "2A O O O O O O fi fi fi fi O P P O O fi O O fi fi fi o p o O fi fi O P o P a a P o O O o is io o o o o o O O O o o 13 O o o 'o o o o a a o O O o O o O O A & & A A A A A A A A A A 6 A A A & A A A A A A A O o o O •a e o O •3 •3 •3 O O io O o •3 o O is o •3 O b b os O O b b a O O A A A A A A A A A A A A A A A A A A A A A A A A A A O O O O o P o O O O P O O O O O o o P O P O P D O ofi o O afi ofi o o O O o O O o •3 o O o ofi ofi o o O bfi O O O fio b o bfi ofi ofi A A A A A A A A A A A A A A A A A A A A A A A A A A A A O p o O O O P p P P O P fi O fi p O O P fi O P P O p fi O p P fi o fi fi O 8 8 8 8 8 8 8 8 8888SSS§88888|S|88S||SSS88s S fi fi fi fi fi "fiisfiifiiiiiiii"""""""""""'"fi fi £ fi fi fi fi" fi S 8 8 8 8 8 8 8 8 8 8 8 8 8 S S 8 S8888SSS8888S 8 8 8 8 8 8 o o o fi fi fi fi fi fi p p p o p p fi fi fi fi fi fi fi fi 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 i i 8 i i 8 8 8 8 8 11 ^ fi fi fi fi fi fi fi poppppop p p p p p p o o a o o o o o o o o p p p o p p p o o a oooooooooo p P o o o p p a Pfi Pfi P oobbbbbobo o o o fio s opooopppppp O P p p oooooppp oooooooo i ! p p pi p 9 ? ? 9 o o O O P o POOP p p O O O P 1 I ! 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I I O O O p o o o O p p p p - p p p p p I I ! §§S§|8S8S8Ss8Si8S888|8i88 > \ 1 A A A A A A o o & & & P P OOP o o o o oooooooooooo I I P P o p o P P P P pop A u w » n 5 » 1 i 3 & £ £ S & & 2 2 2 !_ i. o o o o o i I I I I ! { I I i ! ! I ! I I i 1 t ! I I 1 ! I i t i ! ! : i ) I : i I till • I I ! I I I ] t : : ] > I i i I ; i { i I ! \ i I i ! I I i 1 i i I 3 3 i S = ^ 5 2 i 5 S 2 S i i S i S s ff ? f I If f I •ii n 2 3 S 3 e S g S S S 3 I use 1 1 I ? i i " 1 i ' ^ i i 5' sis i I S s a S1 i i 1 1 i o o o fil c s. » » Vr —• » = s i s ^ i i i i I ? S - s i i i i 8 5 S u o o S o S w % 1060 0 * o S £ s t s c S 8 5 a o o 3 i o o 0810 1 i s 8 OOCO l om i o o a s 1 s i a 1 s i a * » e * n M s C o o K •o Sit o s s * s - o s s i 1 3 o 1 i 3 1 .-1 IT- •J -J ^ o o •a o o IIS 5 is » a 5 o s s s 8 i I t t 1 : I i = = 2 i i i i i i i S s s i 1 i i i 1 i s i i i I i s i If { ! I I 1 I I 1 i I I i I I s 3 2 2 ; ! I i ! I i ! ! If 101 8 181 7 o at S S 1 o s 3 3 2 i 5 s 1 s o o o o i M i ; o 1 5 1 i M o e r « o o o s s o ^ _ o _3. e 9 i 8 s ON = s * * a » t to 8 >s s t » s 3 1 s M hi o * « s o s s s a •o s s o S s 3 3 3 i 3 o s sa n o 3 £ S » a a s o § t » t § i OS o 1 o 1 o s s 1 O z. o * o o o o o o a 3 s 3 o s 8 I I i I I I i i ! ! ! i i ! I 1 I 1 I • i t I I I I i I \ { I lit! ! ! i ! I i 3 ! I I { I 1 i I i I i ! I t \ ! ! I E I I i I 1 : ! ! ] i t J 1 ! ) i I : I i I ! ! I ! ! I I ! I i I r I ! I ! ! I t i I ! ! i ; i Table C-2 (Continued) CfWRrftRMtfUTM) LCwMLnd IDS pll Tnop TeUIIlinkMn Totil AlUMI; SO.' CO,' IICO, Ci" Mi" «i" I f North Etd yOj-n WOrW Wlj-N POcP P« Al"- Cu Zn ni AI-QidBTl 3I»0 1M0O 9171 1.16 3740 4609 1108 166 661 163 1114 107 WHO 49170 13100 744) 7,06 1690 iW 1611 336 669 148 1336 73 Knktn tsoao 47103 4330 16M 7J7 «5 91J 399 lid 141 81 310 11 Ittn 03190 43890 BTOO 3680 7.06 1100 1937 1311 186 143 IW 1061 10 DURURVI B9I60 46990 9110 3689 7.13 1990 30)3 1308 313 463 101 1171 IS am 61340 49103 10040 JB86 7.)B 1960 1334 Mil 330 473 183 1198 61 w Buri 91B40 491 TO MOO 391T 13 1)40 1691 1838 36) 601 116 960 48 MCWB 0114] }p:oo 39J0 3)39 7,47 033 831 314 306 313 79 463 13 83MD 40340 97 BO 3876 7.19 1B0O 1481 1134 1)4 443 160 1310 63 im «]1S0 43)33 4800 IB3B 7.3 1063 1071 394 110 170 91 148 33 11 JidWiltIn] 97U1 40630 3700 1193 7,3 mi BflS 410 1« 139 76 4)B 10 Sttbd U130 48J10 110OO 7793 7.33 1710 340) 1471 149 671 169 1647 73 tni DsKulub 831» 49113 11400 6017 60 1400 3007 1110 110 381 131 1460 70 JunM> M7BD 36790 1100 4164 10 1693 1360 960 314 4)1 111 ^*i 11 All bi millipwTi pa lin A dnh mdic (tet pmxui 657 Table C-3 Salinity changes of groundwater from the Khobar aquifer 1991/92 - 1999/2000 Well no. Location Grid Reference (UTM) Salinity 1991/92 Salinity 1999/2000 Salinity difference Percentage change % ! East North 1111 Muharraq 462140 2904970 2610 ' 4945 2335 89 1041 Muharraq 460880 2903002 2530 3163 633 25 1047 Sulmania 457590 2899530 3165 4390 1225 39 1308 Karanah 451450 2901150 4100 4121 21 0.5 1107 Quraiah 446850 2898345 2770 3347 577 21 1344 Al-Markh 447210 2900380 4180 3493 -687 -16 1110 Jasra 445940 2844915 2250 2695 445 20 1312 Jasra 448210 2894289 3110 7320 4210 135 3/6 Tubli 456600 2895500 4065 4380 315 8 13/112 Bilad Al-Qadim 455300 2897850 3005 3356 351 12 13/107 Al-Khamis 455600 2898300 3080 3756 676 22 1141 Saar 448710 2897880 4880 5209 329 7 1077 Budaiya 445685 2899820 2690 3253 563 21 1076 Janabiya 447150 2895910 2465 3547 1082 44 2/8 Sitra 460430 2893970 5520 7391 1871 34 1/3 Nuwaidrat 459250 2889960 8300 7875 -425 -5 1/1 Nuwaidrat 459400 2889830 8810 6697 -2113 -24 2/11 Sitra 460430 2894340 5570 8013 2443 44 1/13 Sanad 458670 2893660 3815 7814 3999 105 7/1 Al-Jazair 446340 2874975 4675 4987 312 1 7 1065 Hamala 445480 2893630 3695 4250 555 1 15 1062 Hamala 445535 2893585 2220 2580 360 1 16 1331 Wasmiyah 449300 2879350 7040 5639 -1401 1 -20 658 Table C-3 (Continued) Well no. Location Grid Reference (LTriVl) Salinity 1991/92 Salinity 1999/2000 Salinity difference Percentage change % East North 1232 Al-Jazair 446650 2874750 5540 5673 133 2 1112 Jasra 445220 2894975 2390 2520 130 5 1314 Qufool 456650 2899010 3435 2885 -550 -16 2/3 Sitra 460440 2893570 5075 6314 1239 24 2/12 . Sitra 461520 2895210 4000 5148 1148 29 1/14 Sanad 458600 2893360 4830 7149 2319 48 1217 Dumistan 446660 2890970 2560 2739 179 , 7 1407 Jurdab 457760 2894570 3775 5963 2188 58 6/14 Zallaq 448800 2881980 4890 5702 812 17 7/19 Wasmiyah 448860 2879770 4695 6044 1349 29 7/32 Zallaq 448950 2881610 5460. 5878 418 8 6/29 Darkulaib 449060 2882860 6100 6334 234 4 1/92 Jurdab 457190 2894160 6450 7690 1240 19 13/22 Manama 458840 2901110 2590 4655 2065 80 4/15 Bun 449400 2892240 6400 6219 -181 -3 1108 Hamala 445480 2891620 2340 2295 -45 -2 6/180 Dumistan 447240 2889450 2990 6971 3981 133 1339 Hamala 446870 2890620 3665 5644 1979 53 6/86 Sadad 449360 2885010 5550 5787 237 4 6/84 Sadad 448900 2885080 7250 6455 -795 -11 6/40 Darkulaib 450120 2883080 6120 7000 880 14 9/13 Hamala 446590 2890860 2695 4539 1844 , 68 16/129 Karzakan 448080 2887660 8680 7405 -1275 -15 659 Table C-3 (Continued) Well no. Location Grid Reference (UTM) Salinity 1991/92 Salinity 1999/2000 Salinity difference Percentage change % East North 6/113 Malikiyah 448545 2887045 10100 8718 -1382 -14 6/118 Malikiyah 448140 2886780 8790 5733 -3057 -35 6/8 Zallaq 448600 2882680 3220 4325 1105 34 6/23 Zallaq 448180 2882680 3090 3768 678 22 1296 Janusan 449410 2901280 3220 3541 321 10 1257 Budaiya 445470 2899430 2540 3281 741 29 13/161 Musalah 454800 2899490 3260 3444 184 6 1095 Adhari 454800 2897940 5480 4912 -568 -10 13/50 Manama 459230 2902525 2496 2379 -117 -5 13/16 Manama 459460 2900465 2590 4730 2140 83 12/104 Bani Jamra 446620 2898980 2810 3254 444 16 12/101 Al-Markh 447020 2899490 2705 3244 539 20 10/44 Moqabah 448790 2899700 2240 6870 4630 207 11/178 Barbar 449120 2900790 4630 3335 -1295 -28 11/90 Karanah 451140 2900760 6000 3983 -2017 -34 10/54 Shakhoora 449690 2899550 4875 11629 6754 138 10/14 Moqabah 448830 2898110 5300 6626 1326 25 10/17 Moqabah 448710 2899510 7285 6855 -430 -6 1053 Hamala 446520 2891540 2950 7720 4770 161 14/29 Qalali 465575 2905710 3105 4990 1885 61 14/115 Muharraq 460630 2903920 2580 3050 470 18 1367 Budaiya 445625 2900015 2816 3235 419 15 1409 Noaim 456310 2900800 2624 2800 176 7 660 Table C-3 (Continued) Well no. Location Grid Reference (UTM) Salinity 1991/92 Salinity 1999/2000 Salinity difference Percentage change % 1 East North 1 1 1069 Sumaheej 463380 2907170 2365 2825 460 ' 19 14/110 Muharraq 461840 2904490 2530 4610 2080 ; 82 13/111 Al-Khamis 455450 2897900 3290 1 3208 -82 -2 12/51 Janbiyah 444680 2897170 2635 ' 3259 624 ' 24 1066 Diraz 446555 2901120 2720 1 2672 -48 ; -2 10/109 Al-Qadam 451230 2899810 3315 1 9271 5956 ' 180 1343 Moqabah 449170 2899120 5515 7443 1928 ' 35 6/141 Karzakan 447105 2888080 3525 2604 -921 -26 9/91 Jasra 445890 2895190 4125 5680 1555 : 38 6/173 Dumistan 446990 2889260 3780 5688 1908 ' 50 1 6/90 Malkiyah 449105 2885540 5510 , 5886 376 7 13/49 Manama 459200 2902745 2700 2359 -341 -13 1244 Shahrakan 448540 2883940 4320 1 5876 1556 ' 36 1060 Manama 458445 2902185 3050 3628 578 19 6/32 Darkulaib 449335 2883120 7520 6827 -693 -9 11 Jiddah Island 440650 2897235 2230 2295 65 3 1372 Jurdab 456980 2895245 4416 4164 -252 -6 13/38 Manama 457260 2901540 2688 1 3107 419 16 Salinity data are as total dissolved solids in milligrams per litre (mg 1'). Negative values indicate salinity improvement. 661 Table C-4 Chemical analysis data of groundwater from the Rus - Umm Er Radhuma aquifer 1983 r 2000 Well no. Ijocatlon Grid Reference (UTM) FX TDS pH Total Hardness Total Alkalinity CI S04* C03' iico'^ Ca>* ME'* No* I East North mlcrohms/cm (pH unit) (as CaCO,) (as Ca COj) M61 Awali 455020 2886230 20500 11680 6.9 — 1 — 6617 584 0 214 655 397 3011 141 1573 Rumailhah 455807 2869050 12200 7283 8.2 1790 3811 716 0 116 429 175 2036 — 1 _ 170 1586 Awali 456755 2886720 14600 9995 7.5 — 5034 751 0 189 553 243 2603 1592 Sakhir 452360 2885770 16450 10369 7.7 2480 — 5637 750 0 183 641 214 2767 177 1S98 Sofroh 453630 2887650 11330 7190 7.4 1950 3651 861 0 103 453 199 1923 — 1605 Safrah 453676 2887470 7460 4269 7.8 1160 — 2148 484 0 106 305 97 1131 — 1628 Sitra 461800 2896350 10530 6246 7.6 1590 ' 135 3368 450 0 165 369 163 1662 69 1635 Sakhir 452405 2885720 16280 9779 7.2 ._ _ 5424 600 0 177 569 236 2662 111 1639 Awali 456500 2886560 16500 10000 7.7 2420 ' ... 5672 501 0 183 553 253 2902 — 1644 Ikur 461390 2891420 16500 11500 7.8 2425 — 5539 1054 0 165 446 319 3000 1645 S.W. of Alba 461655 2884795 16760 10507 7.0 2420 98 5850 675 0 120 569 243 2897 153 1403 AL-Buhair 456200 2891750 12000 7680 7.32 _ _ 4278 1677 0 306 476 299 2500 130 1416 Jaw 461850 2875130 6500 5000 8.1 1100 , 2198 378 0 171 257 112 1165 _ 1451 West Rifia 451625 2888010 18500 11850 7.25 150 5849 633 0 183 593 253 2340 140 2500 1 1452 WcstRiffa 451635 2887905 18500 12004 7.0 2500 — 5849 619 0 183 585 253 2720 228 1474 Ad-Dur 461550 2872800 17500 11200 7.2 5844 1397 0 367 760 270 3151 100 — _ 1476 East of Alba 46)800 2885855 17500 12000 7.2 2820 — 6452 570 0 183 641 297 3231 1488 Sh. Asa Air Base 457160 2868370 25600 16640 _ _ _ _ — — — _ _ _— — 1499 Sh. Asa Air Base 46MS0 2869900 16163 10506 ... — 1518 AL- Buhair 456225 2891900 10020 6012 6,8 — 2836 430 _. 1521 Awali 454900 2885080 11080 7323 8.3 1750 — 3244 1400 1.5 24, 465 143 1935 no 1523 AL- Buhair 456080 2892010 16380 10432 6.7 2470 5283 751 0 183 601 236 3260 118 1524 AL* Buhair 457570 2891725 17000 10182 6.6 2460 ._ 5495 586 0 159 589 241 2980 132 1552 Southern Area 454485 2866900 13000 9710 7,5 1910 4041 824 0 165 441 221 2250 _ 1558 Dcur 461280 2891470 9800 7036 7.2 1340 i _ 3545 486 0 no 409 78 1957 103 1559 Awali 454890 2885630 16800 10410 7.2 — ' 5388 641 0 201 654 607 2535 130 1381 Alba 459930 2885720 17000 12260 6.9 2850 6558 573 0 171, 621 316 3283 ._ 1382 Alba 459930 2885720 17000 11930 7.2 2850 6558 602 0 I7r 621 316 3297 — 1276 Safrah 453065 2886690 11960 7654 7.61 4367 1658 0 229' 470 257 2479 184 1274 West Riflo 453030 2887240 9400 6016 7.91 ... 2947 1316 0 2681 384 178 1771 156 1207 AL- Buhair 456060 2893230 16720 10701 7.19 — 5769 1584 0 305! 580 233 3340 156 1210 Sakhir 450365 2881960 26400 16896 7.21 — - 10650 3312 0 3051 1040 353 6440 274 662 Table C-4 (Continued) Well no. I>ocntlon Grid Reference (UTM) EC TDS pll Total Hardness Total Alkalinity CI S04' C03' HCp*^ Ca'- ME'* Na* 1 i East North mlcrohma/cm (pH unit) (as CoCOj) (as Ca COj) _ 196 1223 AL- Amur 432690 2874530 13200 8448 7.56 — 4078 1735 0 306 360 151 1113 1226 Hafecrn 58330 2879750 15400 9836 7.63 — — 3680 96 0 427 530 186 2833 156 1231 Mahooz 437460 2899570 11250 9130 7.00 3560 148 3700 2196 0 180 440 598 1890 81 _ _ _ _ 1648 Awati 436314 2886699 18000 11500 7.2 _ — — — — 1604 JufToir 461625 2899910 31000 17500 10.0 3040 8366 2146 30 1122 58 3081 — 1143 RQ3 AbU'Jaijur 460290 2883580 17800 . 11280 6.72 2338 5783 573 0 199 608 247 2940 114 lt}2 Ros Abu-JorjuT 460755 2881900 19800 12800 6.76 2812 ... 6666 584 0 194 675 273 3300 133 1159 Ros Abu'Jorjur 460830 2880160 18800 12290 6,75 2682 , 6285 579 0 192 662 250 3140 127 1106 Maanieer 461300 2890210 21186 13771 8.1 — , 8048 489 0 133 781 323 3997 „ 1109 Dcur 439620 2892125 10629 6909 7.7 — 3793 501 0 85 473 148 1909 — 1566 North Sitrt 461000 2896140 28680 17630 „. 10273 650 0 159 1082 363 5101 — 1139 Abu-Ruwaidhnh 461500 2885500 14850 9304 7.02 _ 3230 3064 0 275 505 803 2783 141 1555 Schla 433720 2898950 12000 9470 7.7 3000 3403 2183 0 146 801 228 2500 7.8 1101 Isa Town 454070 2893020 MS50 7392 11.1 _ 3651 1263 123 36 637 57 2154 147 _ ... _ _ 1096 Isa Town 454040 2892080 17900 11436 _ ... —. — ._ 1173 Sakhir 452300 2877210 13420 8389 7.64 — 3934 1213 0 247 519 203 2176 119 1161 Wadi Ali 431473 2872900 11150 7136 7.18 _ — 4881 943 0 366 519 273 2530 184 1002 Sadad 444870 2883480 12100 9760 7.4 ... — 4218 1441 0 122 631 190 2253 121 1143 Southcni Area 456905 2880095 16000 11000 6.77 2800 195 6100 588 0 238 374 330 2800 140 1114 Southern Area 458075 2883085 14600 10200 2280 135 5650 533 0 189 580 200 2900 141 1115 Southern Area 459233 2882830 15200 10800 6.9 2850 155 6000 576 0 189 694 270 2900 142 708 Alba 460900 2883400 17800 12830 _ 2800 155 7300 667 0 189 574 330 3650 170 918 Sakhir 452370 2885710 14160 10300 2600 145 5600 721 0 177 626 230 2850 133 860 UBF 438165 2880340 13340 10900 — 2600 155 6100 583 0 189 626 230 3000 145 1120 West of Ros Hayyan 460515 2879823 20300 12800 6.78 3200 ISO 7189 576 0 92 717 340 3598 196 1123 Sitra 462740 2890470 23400 17000 - 3930 120 10000 538 0 144 786 480 4700 200 1624 Qudaibiya 439630 2899550 19670 13587 9.5 3000 30 6523 2100 18 0 882 194 3715 155 ... — 1 _ _ 1381 Ra3 I^ayjan 461315 2890255 17260 11392 — — ... ~; — ._ 907 Sakhir 452420 2885640 16376 10643 6.2 2690 — 5868 769 0 122 633 270 2983 uos Maamecr 462420 2880200 24000 15154 7.1 2980 , 8367 818 0 104 561 384 4700 220 1650 East of Alba 461850 2883835 22720 14207 7.2 3270 8225 550 0 173 774 326 3993 166 All in milligram per litre (mg 1' ), unless otherwise stated, A dash indicates parameter not analysed. 663 Table C-5 Saturation indices of water samples from the Aiat Limestone aquifer 1998/99 Well no. TDS Anhydrite Aragonite Calcite Dolomite Gypsum Halite Ionic Strength Log Pco2 1093 2564 -1.061 -0.105 0.038 -0.543 -0.842 -4.931 0.05591 0.00986 -2.006 1378 3268 -0.967 0.016 0.16 -0.319 -0.748 -4.697 0.07078 0.0109 -1.963 13/1 2820 -1.051 -0.045 0.098 -0.46 -0.832 -4.818 0.06103 . 0.0101 -1.997 1104 2432 -1.035 -0.034 0.11 -0.5 -0.816 -5.001 0.05383 0.00985 -2.007 14/33 3381 -1.015 -0.236 -0.092 -0.759 -0.796 -4.639 0.07346 0.0152 -1.817 1371 3919 -0.915 -0.147 -0.003 -0.704 -0.697 -4.502 0.08334 0.0171 -1.767 1234 3111 -0.978 0.108 0.251 -0.166 -0.759 -4.746 0.06685 ' 0.00900 -2.046 1282 3283 -0.977 0.018 0.161 -0.326 -0.758 -4.685 0.07048 0.0113 -1.948 1281 3163 -0.967 0.107 0.25 -0.183 -0.749 -4.727 0.06807 ' 0.00868 -2.061 1519 3386 -0.983 -0.086 0.058 -0.496 -0.769 -4.649 0.07258 0.0139 -1.856 12/104 4132 -0.544 0.239 0.381 0.009 -0.333 -4.705 0.09588 ' 0.0118 -1.926 1415 2529 -1.029 -0.119 0.024 -0.611 -0.814 -4.967 0.05588 0.0130 -1.887 1412 5696 -0.966 0.178 0.322 0.152 -0.748 -4.176 0.11472 ; 0.00978 -2.009 1602 13796 -0.722 0.018 0.161 0.101 -0.508 -3.356 0.28647 . 0.0209 -1.68 1640 3000 -1.047 0.233 0.377 0.123 -0.829 -4.731 0.0652 0.00465 -2.332 1563 4576 -0.987 -1.181 -1.037 -2.638 -0.769 -4.306 0.09788 ' 0.149 -0.828 1608 2859 -0.922 0.105 0.248 -0.229 -0.703 -4.875 0.06449 . 0.0111 -1.954 1571 2419 -0.931 0.39 0.533 0.342 -0.712 -5.036 0.05524 1 0.00165 -2.784 1631 6017 -0.467 0.145 0.288 -0.136 -0.249 -4.281 0.137 0.0260 -1.584 11/283 5381 -0.522 0.19 0.334 -0.058 -0.304 -4.403 0.12323 1 0.0224 -1.65 1642 2882 -0.935 0.284 0.428 0.204 -0.716 -4.867 0.0645 0.00481 -2.318 1585 2612 -0.979 0.385 0.529 0.102 -0.76 -4.886 0.05684 0.00360 -2.444 1638 4809 -1.054 0.378 0.522 0.607 -0.836 -4.247 0.10233 0.00321 -2.494 Mean 4002 -0.915 0.037 0.180 -0.282 -0.698 -4.619 0.08678 ' 0.0173 -1.972 664 Table C-6 Saturation indices of selected water samples from the Khobar aquifer 1999/2000 Well no. TDS Anhydrite Aragonite Calcite Dolomite Gypsum Halite Ionic Strength PC02 Log Pcoi 1107 3347 -0.876 -0.133 0.275 -0.11 -0.666 -4.73 0.07381 ' 0.0118 -1.928 1050 3924 -0.645 0.099 0.241 -0.192 -0.435 -4.699 0.08952 0.0155 -1.81 12/102 3254 -0.893 0.172 0.315 0.026 -0.683 -4.758 0.07154 ' 0.00943 -2.026 1229 3116 -0.853 0.52 0.194 -0.306 -0.643 -4.83 0.06993 ' 0.0150 -1.825 12/101 3224 -0.888 0.048 0.191 -0.267 -0.678 -4.75 0.0709 1 0.0112 -1.95 11/200 3423 -0.781 -0.152 -0.01 -0.622 -0.575 -4.78 0.07785 0.00233 -1.633 11/142 3676 -0.775 0.164 0.306 -0.058 -0.589 -4.671 0.08224 0.0119 -1.925 11/136 3827 -0.75 0.204 0.346 0.044 -0.544 -4.65 0.08534 0.0128 -1.893 11/15 3112 -1.009 0.059 0.201 -0.184 -0.803 -4.705 0.06891 0.0118 -1.93 11245 3287 -0.911 -0.021 0.121 -0.349 -0.705 -4.739 0.07205 0.0143 -1.844 1308 4121 -0.663 -0.264 -0.123 -0.98 -0.457 -5.346 0.09276 0.0383 -1.416 13/236 3065 -0.951 0.104 0.246 -0.145 -0.741 -4.783 0.06603 0.00897 -2.047 1095 4912 -0.869 0.263 0.406 0.209 -0.66 -4.296 0.10455 0.00922 -2.032 13/94 3455 -0.895 0.179 0.321 -0.05 -0.685 -4.665 0.07393 0.00969 -2.014 10/19 7075 -0.457 0.148 0.29 -0.072 -0.248 -4.103 0.05796 ' 0.0198 -1.704 13/50 2379 -0.965 0.232 0.375 -0.47 -0.751 -4785 0.08067 0.00884 -2.054 13/16 4730 -0.871 0.135 0.278 -0.111 -0.667 -4.176 0.11333 , 0.0109 -1.963 3/6 4380 -0.71 -0.05 0.94 -0.555 -0.496 -4.483 0.09629 ' 0.0170 -1.768 1032 7772 -0.856 0.081 0.224 0.046 -0.643 -3.848 0.16216 [ 0.0102 -1.993 13/40 7956 -0.826 0.091 0.235 0.066 -0.614 -3.834 0.16662 0.0101 -1.995 13/60 8176 -0.773 -0.05 0.93 -0.296 -0.561 -3.813 0.17024 . 0.0171 -1.766 14/25 3737 -0.919 -0.04 0.103 -0.425 -0.705 -4.566 0.08059 . 0.0138 -1.862 14/104 2951 -0.079 -0.032 0.111 -0.372 -0.864 -4.761 0.08315 0.0108 1.965 1471 4246 -0.955 0.156 0.299 0.074 -0.74 -4.425 0.09089 0.00882 -2.055 13/17 2801 -1.048 0.043 0.186 -0.247 -0.833 -4.83 0.06058 0.00812 -2.091 665 Table C-6 (Continued) Well.no. TDS Anhydrite Aragonite Calcite Dolomite Gypsum Halite Ionic Strength Log PQOI 997 2793 -1.007 -0.089 0.054 -0.507 -0.792 -4.871 0.06123 i 0.0141 -1.85 767 2727 -1.012 -0.126 0.017 -0.602 -0.797 -4.886 0.05958 1 0.0133 -1.875 12/122 5400 -0.81 -0.0227 -0.085 -0.755 -0,601 -4.235 0.11646 ' 0.0417 -1.38 12/39 3349 -0.95 -0.331 -0.189 -0.988 -0.74 -4.694 0.07175 1 0.0301 -1.522 12/51 3155 -0.99 -0.403 -0.261 -1.133 -0.779 -4.724 0.08787 ' 0.0277 -1.558 1 10/6B 6974 -0.706 -0.141 0.001 -0.594 -0.497 -4.035 0.15685 * 0.0340 -1.469 14/101 6441 -0.885 -0.211 -0.069 -0.591 -0.676 -4.02 0.13463 j 0.0211 -1.676 1490 6671 -0.603 -0.227 -0.084 -0.748 -0.394 -4.085 0.14518 0.0256 -1.592 14/112 2883 -0.974 -0.092 0.05 -0.577 -0.763 -4.83 0.06256 I 0.0135 -1.871 1364 3216 -0.989 -0.305 -0.162 -0.866 -0.778 -4.716 0.07012 1 0.0212 -1.674 1052 6505 -0.62 0.195 0.337 0.094 -0.411 -4.106 0.14153 0.0108 -1.967 13/51 2976 -1.005 -0.091 0.051 -0.591 -0.794 -4.771 0.0635 ; 0.0139 -1.858 1467 4065 -0.97 0.204 0.346 0.16 -0.76 -4.458 0.08573 , 0.0692 -2.16 13/52 3513 -0.851 -0.009 0.133 -0.401 -0.641 -4.672 0.07739 I 0.0136 -1.867 13/3 3390 -0.837 0.071 0.213 -0.274 -0.626 -4.735 0.07603 1 0.0144 -1.842 2/9 6102 -0.958 -0.053 0.09 -0.22 -0.749 -4.056 0.12675 • 0.0138 -1.86 1447 3100 -0.986 0.01 0.152 -0.308 -0.776 -4.747 0.06686 0.0113 -1.948 1110 2695 -1.005 -0.078 0.064 -0.548 -0.798 -4.898 0.0581 i 0.0146 -1.835 1312 7320 -0.563 -0.094 0.048 -0.399 -0.359 -4.02 0.16096 1 0.0209 -1.679 4/27 5036 -0.644 -0.222 -0.08 -0.898 -0.439 -4.364 0.11102 1 0.0266 -1.576 6/187 6470 -0.301 0.321 0.463 0.231 -0.092 -4.2 0.16539 i 0.0112 -1.95 1270 3227 -0.99 0.025 0.167 -0.263 -0.779 -4.704 0.06918 1 0.0116 -1.935 6/86 5787 -0.627 0.176 0.318 0.053 -0.422 -4.232 0.1274 1 0.0104 -1.984 1399 5365 -0.388 0.21 0.352 0.035 -0.183 -4.155 0.12623 1 O.OIOI -1.995 1232 5673 -0.783 -0.351 -0.21 -0.9\4 -0.578 -4.194 0.12361 1 0.0320 -1.494 666- Table C-6 (Continued) WeU no. TDS Anhydrite Aragonite Calcite Dolomite Gypsum Halite Ionic Strength Pcoi Log Pco2 10/17 6855 -0.568 0.506 0.65 0.713 -0.351 -4.095 0.15304 0.00760 -2.119 1062 2580 -1.116 0.023 0.166 -0.272 -0.897 -4.934 0.05507 0.00111 -1.955 1383 2210 -1.138 -0.024 0.12 -0.088 -0.914 -5.25 0.05351 0.0114 -1.943 9/55 2435 -0.939 0.098 0.242 -0.513 -0.72 -5.053 0.05395 0.0133 -1.878 9/61 4850 -0.738 0.123 0.267 -0.0048 -0.52 -4.384 0.10671 0.0110 -1.958 6/215 4466 -0.73 -0.129 0.012 -0.489 -0.529 -4.484 0.0994 0.0157 -1.805 1550 6039 -0.591 0.235 0.376 0.12 0.391 -4.285 0.12678 0.00945 -2.025 1266 12338 -0.376 -0.017 0.124 -0.254 -0.179 -3.542 0.26633 0.0183 -1.737 1407 5963 -0.617 0.091 0.232 -0.028 -0.417 -4.237 0.134 0.0198 -1.708 1/1 6697 -0.48 -0.004 0.137 -0.273 -0.28 -4.18 0.151 0.0365 -1.438 1/32 6292 -0.422 0.13 0.271 -0.096 -0.221 -4.286 0.146 0.0262 -1.581 1426 9794 -0.862 -0.246 -0.106 -0.45 -0.663 -3.641 0.20335 0.0184 -1.734 1/65 5504 -0.968 -0.237 -0.096 -0.71 -0.768 -4.138 0.11317 0.0208 -1.681 2/4 7547 -0.781 -0.232 -0.091 -0.561 -0.582 -3.9 0.15762 0.0202 -1.695 6/34 8662 -0.538 0.107 0.251 -0.048 -0.321 -3.849 0.18913 0.0150 -1.825 6/8 4325 -0.808 0.083 0.227 -0.031 -0.59 -4.512 0.09782 0.0123 -1.909 1201 6084 -0.641 0.062 0.205 -0.17 -0.423 -4.181 0.13497 0.0147 -1.832 1529 4967 -0.676 0.162 0.306 0.003 -0.457 -4.385 0.11018 0.0106 -1.975 1539 6162 -0.912 0.196 0.34 0.08 -0.695 -4.059 0.12755 0.0196 -1.709 1069 2825 -0.95 0.158 0.302 -0.013 -0731 -4.915 0.0626 0.00753 -2.123 14/110 4610 -0.95 -0.019 0.125 -0.273 -0.732 -4.339 0.09693 0.00960 -2.018 1066 2672 -1.03 0.227 0.371 -0.051 -0.811 -4.892 0.05791 0.00788 -2.104 10/109 9271 -0.626 0.311 0.455 0.316 -0.41 -3.742 0.1972 0.0137 -1.863 9/91 5680 -0.457 0.194 0.338 0.045 -0.239 -4.406 0.13122 0.0196 -1.708 6/90 5886 -0.645 0.379 0.522 0.438 -0.427 -4.211 0.12926 0.00747 -2.127 Mean 4873 -0.790 0.031 0.193 -0.261 -0.583 -4.444 0.108 0.0162 -1.792 667 Table C-7 Saturation indices of water samples from the Rus - Umm Er Radhuma aquifer 1983 - 2000 Well no. TDS Anhydrite Aragonite Calcite Dolomite Gypsum Halite Ionic Strength PC02 Log Pco2 1 1561 11680 -0.972 -0.063 0.081 -0.233 -0.757 -3.474 0.24268 ] 0.0196 -1.708 1573 7283 -0.899 0.0813 0.957 1.335 -0.682 -3.85 0.15079 0.000508 -3.294 1586 9995 -0.858 0.432 0.576 0.6il -0.642 -3.641 0.19643 ' 0.00441 -2.356 1592 10369 -0.812 0.664 0.808 0.956 -0.597 -3.57 0.21083 0.00263 -2.580 1598 7190 -0.806 0.022 0.166 -0.218 -0.589 -3.893 0.15215 ' 0.00313 -2.504 1605 4269 -1.051 0.352 0.496 0.298 -0.833 -4.317 0.08942 ' 0.00133 -2.877 1628 6246 -1.11 0.378 0.522 0.50 -0.893 -3.98 0.12787 0.00319 -2.496 1635 9779 -0.942 0.126 0.27 -0.025 -0.727 -3.6 0.19951 1 0.00833 -2.080 1639 10000 -1.041 0.612 0.756 0.992 -0.826 -3.545 0.20551 . 0.00262 -2.581 1644 11500 -0.837 0.544 0.688 1.045 -0.621 -3.545 0.21745 ! 0.00178 -2.728 1645 10507 -0.909 -0.251 -0.108 -0.767 -0.694 -3.535 0.21313 0.00897 -2.047 1403 7680 -0.604 0.361 0.505 0.614 -0.388 -3.731 0.20411 ; 0.0110 -1.957 1416 5000 -1.225 0.776 0.92 1.286 -1.007 -4.293 0.08802 1 0.00104 -2.984 1451 11850 -0.906 0.204 0.348 0.144 -0.69 -3.624 0.20227 0.00766 -2.116 1452 12004 -0.935 -0.054 0.089 -0.367 -0.719 -3.562 0.21116 0.0137 -1.865 1474 11200 -0.536 0.505 0.649 0.663 -0.321 -3.509 0.2474 ; 0.0170 -1.771 1476 12000 -0.966 0.166 0.309 0.107 -0.752 -3.452 0.23371 , 0.00842 -2.075 1521 7323 -0.592 0.272 0.416 0.121 -0.375 -3.943 0.15474 0.000092 -4.034 1523 10432 -0.851 -0.35 -0.206 -r -0.635 -3.529 0.21571 0.0273 -1.564 1524 10182 -0.953 -0.508 -0.364 -1.297 -0.738 -3.549 0.20884 ' 0.0299 -1.524 1552 9710 -0.862 0.30 0.444 0.398 -0.646 -3.788 0.16594 • 0.00393 -2.405 1558 7036 -1.026 -0.149 -0.006 -0.92 -0.809 -3.89 0.13261 1 0.00547 -2.262 1559 10410 -0.957 0.201 0.345 0.479 -0.742 -3.635 0.23271 0.00891 -2.05 1381 12260 -0.982 -0.176 -0.032 -0.536 -0.768 -3.438 0.23693 , 0.0158 -1.801 1382 11930 -0.962 0.119 0.263 0.054 -0.748 -3.437 0.23786 0.00786 -2.105 668 Table C-7 (Continued) Well no. TDS Anhydrite Aragonite Calcite Dolomite Gypsum Halite Ionic Strength 1276 7654 -0.603 0.518 0.661 0.866 -0.387 -3.725 0.20078 \ 0.00419 -2.377 1274 6016 -0.691 0.839 0.983 1.434 -0.474 -4.02 0.1465 0.00248 -2.606 1207 10701 -0.581 0.299 0.443 0.301 -0.366 -3.488 0.24249 0.0147 -1.832 1210 16896 -0.215 0.455 0.599 0.548 -0.004 -2.975 0.45637 0.0133 -1.875 1223 8448 -0.605 0.506 0.65 0.719 -0.388 -4.086 0.15447 0.00667 -2.176 1226 9856 -1.744 0.917 1.061 1.491 -1.529 -3.551 0.19287 0.00733 -2.135 1231 9130 -0.571 -0.239 -0.096 -0.255 -0.355 -3.916 0.21462 0.0134 -1.874 1604 17500 -0.263 1.499 1.643 1.812 -0.05 -3.167 0.34066 3.21 E-07 -6.493 1145 11280 -0.956 -0.281 -0.137 -0.846 -0.74 -3.534 0.21341 0.0283 -1.548 1152 12800 -0.939 -0.226 -0.082 -0.735 -0.725 -3.43 0.24024 0.0248 -1.605 1159 12290 -0.935 -0.241 -0.097 -0.795 -0.72 -3.474 0.22835 0.0253 -1.597 1106 13771 -1.003 0.934 1.078 1.601 -0.79 -3.275 0.28112 0.000672 -3.173 1109 6909 -0.99 0.274 0.418 0.144 -0.774 -3.876 0.1429 0.00127 -2.895 1566 17630 -0.821 0.061 0.205 -0.229 -0.609 -3.078 0.36104 0.0111 -1.955 1139 9504 -0.474 -0.041 0.103 0.2113 -0.259 -3.621 0.2972 0.0189 -1.723 1555 9470 -0.29 0.612 0.756 0.768 -0.074 -3.832 0.20978 0.00210 -2.678 1101 7392 -0.532 2.071 2.215 3.13 -0.315 -3.851 0.16874 1.13E-8 -7.945 1173 8589 -0.65 0.655 0.799 0.998 -0.434 -3.818 0.17568 0.00423 -2.374 1161 7136 -0.797 0.366 0.51 0.555 -0.582 -3.668 0.19913 0.0181 -1.741 1002 9760 -0.506 0.196 0.34 -0.049 -0.29 -3.778 0.19087 0.00364 -2.439 1143 11000 -0.988 -0.186 -0.042 -0.505 -0.773 -3.534 0.22108 0.0300 -1.523 1114 10200 -0.986 -0.036 0.108 -0.427 -0.771 -3.547 0.20468 0.0142 -1.848 1115 10800 -0.916 -0.075 0.068 -0.453 -0.7 -3.527 0.22228 : 0.0175 -1.756 708 12850 -0.973 -0.082 0.062 -0.293 -0.759 -3.352 0.25907 0.0139 -1.858 918 10300 -0.847 -0.046 0.098 -0.385 -0.632 -3.561 0.21314 0.0132 -1.881 860 10900 -0.944 -0.018 0.126 -0.327 -0.729 -3.504 0.22107 : 0.0140 -1.854 669 Table C-7 (Continued) Well no. TDS Anhydrite Aragonite Calcite Dolomite Gypsum Halite Ionic Strength PcOi 1 1120 12800 -0.953 -0.518 -0.374 -1.247 -0.739 -3.365 0.26197 1 0.0111 -1.956 1125 17000 -1.026 -0.107 0.036 -0.309 -0.814 -3.122 0.34267 I 0.0101 -1.994 1624 13587 -0.332 1.183 1.326 1.806 -0.117 -3.399 0.28291 ' 1.88E-6 -5.725 907 10645 -0.825 -1.005 -0.862 -2.275 -0.61 -3.523 0.22079 ; 0.0574 -1.241 1105 15154 -0.945 -0.279 -0.136 -0.61 -0.732 -3.193 0.30512 0.00596 -2.225 1650 14207 -0.962 0.191 0.335 0.121 -0.749 -3.267 0.28736 ^ 0.00780 -2.108 Mean 10526 -0.833 0.237 0.381 0.193 -0.617 -3.603 0.219 0.0107 -2.400 670 Table C-8 Chemical analysis data of water from Bahrain land springs 1998/99 KOfS NOt-N Cm I* M Pk C4 Ha c* •pfkig Ntm* Ttnl Kvteni u TMilAaA*j a ICM' COl' HCO,- C." Ml*- Ni' r r • mirN r< o Eut u(CiC01) 1 i lUM Miig um Tt7l (1 iin ita lUi l» 0 in Ill »J m n 19 ai ossi It •go) Mn tTtM wn noe UK 11 lUB no not no 0 HI •31 Kl uv 4 1] Dl line trm mm 4Mn 7« » }» tjni •too 0 m IBM im I11R Ul 11 01 OQBI 11 SOI «ai eu •asi •BOI •SBI •OBI CO 001 4001 4001 4 01 4BI to* rail IIUO IMU ni> 11 n nn m tm ran 0 ni <» ti* jcn M 11 OT OBOD 11 TM nua UDD Ml} Jin T( •MB in XII »50 0 m *ll i» no U 11 B1 eseo 11 Kav tern >IMO ma Wl il • MB im >in m B » IP t*t 111} 11 11 Oi M tm nts l«t» PU *1 » tm ne 4tn ••» 0 to Tii ff tai IS 01 4031 11 4S) SSI on tw* (TKO inco nTi II n MIS in *n* IW 0 na Ul m an H 14 ei otei IJ <80l •OBI Oil •9BI •961 <«SI •OBI •ocai 4001 •0601 461 401 Ml» uds inoa IM m ninmo tnan inmiii 1* niBIOKIl n))inui imin 0 m^wi l»(H mw HBH IMtl IW4M oum SDm lllflll «BI tfll Sim U_. » line «o HH) B en KM it» DID )» II at ecei tl SSI BOI Of] j Il« «ii> tn tT H )in IB IW in B in 111 *i IM n 11 St a )1 sai SOI oil tMMCnil imin BinmM B tini 0 aniii mm muH IMIH IX n Ohfli OIOVI OOOOTTI iM»a 0 0 OIM Utto MM UM 1 H I MB lt» tut m » H9 m 111 IHO w 11 01 11 SSI SO) an U>* » IW liO no 0 SO OCA •n.'* It at BBOI nuji OBI SOI on i All IB mUifnm p 671 Figure C-1 The Dammam aquifers sampling points Muharraq Baibar Manama Budaiya Awau Ras Abu-Jaijur Wasmiyah • Aim Limestone aquifer sampling point O Khobar aquifer sampling point AJal Limestone outcrop limit Khobar outcrop limit meires Ras Al-Bar 10000 672 Figure C-2 Bahrain land springs sampling points Muharra Budaiya/ gaibar Manama ^5aTow Hamal Awali Ras Abu-Jarjur Zallaq 1 \ Wasmiyah # Sampling point of land spring Alat Limestone outcrop Limit IChobar outcrop limit metres Ras AJ-Bar 5000 10000 673 Figure C-3 The Rus - Umm Er Radhuma Aquifer sampling points Manama Budaiya Baibar Isa Town Hamala ssan Ras Abu-Jaijur Wasmiyah Sampling point I ] Rus outcrop limit metres 5000 " io5oo Ras AJ-Bar 674 Appendix D Pre-sampling Data and Survey Questionnaires 675 Table D-1 Municipal consumpliopn of random sample dala Consumption Consumption Consnmption Consmnption Consmnptlon Consmnption Consumption m*/c/m ra'/c/m mVc/m m*/c/ni m^/c/m m'/c/m 69.67 75.92 90.67 66.92 110.58 109.58 117-58 4.33 48.67 59.67 52.5 43.42 52 4933 817 49.5 4233 43 54J3 57.25 50.42 3.5 49.25 50 58 58.58 57.5 58,25 51.42 4,83 57.17 50.17 42.58 44.5 47.33 51.83 6.58 44.92 59.92 47.6? 51,67 52.5 45.75 7.58 47.08 50.75 47.08 57.25 48,67 45.33 7.08 50.58 43 53.5 460S 47.42 50 2.92 60.5 50.67 50.67 54 33 45.75 48.08 59,17 54.58 51 42.5 54,92 55,42 43.58 54.25 50.33 49.58 56.25 49 51,58 57,83 48.33 59.42 49.42 58.58 58.75 55.17 46.5 - 4 25 - -—50,67 50.08 '42 55.33 47.25 53.75 2.17 48.92 45.92 42.75 57.08 46 51.42 8.33 42 53 60,5 4383 50 25 52,25 3.92 54,25 58.58 55,33 5967 52.33 58.5 392 50.42 58,83 57.92 42.75 48.17 46,83 3 43,67 42.5 50.75 5583 43.42 49,25 3.92 42.42 45,67 56,5 44.75 43 08 4.92 6.58 57.42 47.33 4483 48 47.08 3.5 1.67 43,08 57.67 54,33 5992 51.83 6.17 3.33 42.08 57.25 53 57,67 47.08 51.08 1.58 57.58 42.17 41.75 54,17 51.83 52,92 2,83 57.83 47.92 57.17 4975 47.08 53,58 208 49.33 42.83 57.33 45 47 48,25 108.25 57.67 59.67 58.5 50,58 58.92 71.25 78.75 43 5067 43.75 55.17 57.67 45.75 2.75 47.03 57.58 4958 58 25 60 58 49,5 5.17 55.17 47.75 42.58 4958 46.58 2,08 7.17 49,67 54 50.58 54 OS 5603 3.42 292 53,42 5608 46.58 50 46.92 55 50 56,92 50,67 45.42 57,83 2.75 5242 69.16 43 83 45.75 58.25 51,67 4,42 4633 48.58 52,83 41.83 58.17 45.58 3.5 43.75 56.17 47.83 49.58 56.92 53,75 59,25 50.75 47.5 59.75 56 92 49.08 48,75 52,67_ 41.75 53.67 49.42" '43.92 55 58 42 48.92 5683 51.25 55.83 56,17 43.75 4225 5992 48.33 44.5 47.92 45,83 5608 41.92 48,25 57.92 48.83 49,92 45 43.83 4892 43.67 43.17 53.08 14 OS 45.08 53.33 47.08 52.42 5467 4625 52.42 5617 50.42 48 67 53.67 5283 45.25 50,5 42,25 42 51.75 57.67 58.75 60 56,33 46.25 47.17 57.67 55.58 59.5 52.25 44,17 44.92 45.92 55.92 4925 42.33 58,25 60 41 50,33 45.58 44,75 55.17 46 25 52.33 46.5 50,17 57.5 4825 58.67 5208 47.67 53.42 58,58 51 52.75 44.92 51.92 48,75 43,03 5367 43.75 57.17 1.25 50.42 59.75 57,83 49.25 56 58 54 46.33 46.17 59.58 60 46,58 63,83 4983 41.92 57.58 530S 43.33 55.08 42,08 51.5 45.42 54.42 57.17 54,08 57.75 75.66 47.92 5983 43,58 59 47,67 46.92 50 58 43,17 50.33 49.33 49.25 52.5 4967 88.41 48,92 55.67 45.17 49.58 49.92 51.92 495 50 53 83 57.92 47,58 41,83 48,58 2.75 4667 53.5 42,67 54.92 59.25 41.75 3.92 55.25 58.75 42.67 45.33 51.92 50.75 5 42.58 50.58 54.08 52,33 58 83 51 OS 8.0S 58.25 48.5 55.75 58,08 55.42 58,92 4 45.67 43,75 48.92 59.42 60.17 43,33 44.08 41.92 43.83 52.5 50.25 51.75 49,17 49.92 48 5983 52.42 44,42 53.08 53 83 53 57.17 4975 57.83 42,5 45.58 50 67 47.08 55.42 47.92 49.42 42.83 47 48.33 46 83 4975 4983 13.33 608 58 83 44 67 4967 51.08 55.42 45.33 I.5S 4625 41.75 47.75 52.92 46 43 67 4.92 42.83 56.17 5083 43.25 42.5 59.58 4.58 48.83 51.75 57.83 43.58 55,83 54 55.92 56.17 50.33 55.17 50.5 5942 43.92 51.17 4675 5925 42.92 52.5 44,83 53.42 49.83 4208 46,58 43.5 49.92 56,92 676 Table D-l (Continued) Consumption Consumption Consumption Consumption Consmnption Consumption Consumption m*/c/m ro'/c/m m^/c/m mVc/m m'/c/m m'/c/m m^/c/m 42.83 45.5 4S 47.17 51 48.25 49 43,42 52.33 48.67 45.83 49.17 59 49.08 51.5 47 53.58 45.58 59.17 55.58 46.25 57.25 53,92 48.75 45.17 4967 55.17 59.17 52.67 45,67 45.33 51.75 47.25 51.75 46.08 49.92 50,58 43.25 51.83 43J3 50.83 3,0S 46.17 45.58 43 44.75 48 83 48.92 46,08 44.83 42.5 57.33 53.75 55.5 55 45,5 42.17 49.5 45 53.42 42.58 51.25 44 48.03 58,92 46.58 58.67 49.17 5617 47.17 44.92 45.17 45.08 49 48.5 54.5 49.92 49.67 42.75 50.33 43.5 48.33 43.67 58.25 48.75 45 48 44,83 49.92 52.25 59.25 _45 „44,17. _ . . 58.83 58,08 --59.83- -45 83 -59.33 5675 50.17 53.42 3.33 1.83 3.17 7.33 2.25 49 42.25 7.C3 6,33 4.5 8,33 5.92 52,92 53,5 1.67 208 6,92 2.75 55.83 51.67 50.08 2.75 4.75 242 308 57.33 53.75 51.25 2.75 7.58 7.83 4,5 54 92 43.75 56.58 2.75 1.17 492 1.92 54.25 47.08 49.25 3,33 7.42 4.75 2,42 57.08 44.03 59.08 3,58 667 2,33 3.17 42.33 54,42 45.75 3.67 3,08 4.5 5483 2.75 56,08 7,83 6,08 4.75 2,58 58.58 1,08 45.5 5.17 1,33 4,58 3.17 56.25 4,83 42.75 683 4,17 4.58 2.92 46 17 4,67 5 1.08 4,17 35 5 59.42 4,17 6.92 5.25 3 2 6 83 58.58 5.08 3,17 1.33 1,33 4,17 8.17 42.83 8,08 1,92 2.75 1.17 6,33 3.92 51,67 4,92 8.17 3.67 4,5 4 2.58 53.08 7.17 2.75 1.5 4,25 4,5 5 43,5 4.42 4.08 5 2.92 3.75 2.75 49.5 50.08 5.33 3.75 2.75 3.25 1.08 42.42 44,33 4,5 2.75 2,75 4.03 4 59.67 4.5 4.5 1.33 3.33 4.42 1.67 5325 4.83 2 4.92 5 2.58 7.17 42.92 1.75 7,67 2.75 833 1.5 8.0S 45.67 1.83 6,58 3.42 3.58 3.08 7.92 57.75 47.25 1.25 7.92 7.83 4.17 5.42 49.5 56,58 7.92 2 4,53 5.08 3.92 46.92 48.92 4,03 3.5 2.75 4.5 8.33 49.25 4683 4 2.75 2,42 2.5 2.17 46 43.42 3.83 3.33 333 2.33 3,42 4.5 2.83 2.92 3.67 7.25 633 5,83 l.OS 2.75 1.83 3.42 5.5 7.92 7 2.33 2.33 3.08 4,67 8 7.83 6,75 3 4.03 2.83 1.03 5,75 7.67 8,25 8 3.33 2.92 2.75 5.92 5.42 5.17 1.17 2 4.75 2.75 65 8 6.42 3.67 2,53 2.75 3.42 7,67 7.5 7.75 2.75 467 2.92 4 42 6 6 17 7,25 2.75 2.75 2.75 2.75 625 6.17 6,08 7.67 3,17 2.75 2,75 5.42 7.5 633 383 3.25 2.83 2.75 5 42 6 5.75 3,25 1.58 5.25 4 7.67 8,17 6,08 408 1.33 4.5 2.92 7.33 667 8 3.5 4.92 3.17 4,67 7.33 6 8 383 3.42 2.75 4.67 5.08 653 6 1.92 2.75 1,75 4 7 5.5 7,42 2.75 1.33 4 2.25 6 6.17 6 1.08 4.08 1.08 408 7,83 633 5,03 1.5 4.25 3.25 567 7,83 6.5 7.33 5008 4.17 4.08 SOS 7.17 617 667 11.53 3,42 4,58 5.75 608 7,67 5.17 2.58 1,83 2,67 633 7.25 683 5.75 3.25 1.17 275 5,67 7.75 5.5 5.92 1.17 4.67 2.SS SOS 8.25 7.5 8 1,03 3 1.92 5.42 5.58 8.25 633 3.5 2 2.17 7.67 8 6,58 6,5 2,5 1.5 2,67 7.5 6.33 5,5 5,25 4 l.OS l.OS 642 5.17 7.25 7.25 42,42 303 3.92 8,08 767 7.08 6.33 3.25 l.OS 1,25 7.92 5,83 653 5.83 677 Consnmption Consumption Consumption Consoniption Consumption Consumption Consumption m'/c/m m'/c/m m'/c/m m'/c/m m^/c/m m'/c/m m'/c/m 6,25 65 24.58 34.33 9.17 12.42 33.17 7.33 7 16,17 2675 21 19.58 13.5 5.17 6.08 22.08 37.92 27.42 27 16.67 7.33 5.58 17.5 12.67 18.25 12.75 16 6 33 6.83 15.75 9.33 16,75 8,83 13.67 65 7.5 20,58 22.58 30 08 16 67 22,5 6.92 7.5 1083 22.75 9.42 17.17 9.33 5.42 6 29.17 40 25 9.75 23,67 23,58 675 8.25 29.17 11.25 903 34.17 24,83 7 6.5 12.58 14 15.75 33.58 15,5 7.83 5.5 19.75 11.42 20,42 14,42 19 8.17 5.42 13.33 18.25 23,03 20,83 13.33 5.5 675 - 28.92 30.58 8.92 11 "21:5" 8,25 6.33 12.08 . 22,67 2008 15.42 1808 6.5 6 83 18.83 34.75 20,58 30.25 17,67 7.08 7.75 3683 27.92 8.5 10 83 26.25 5,67 6,5 35.83 29,58 15.25 31,67 35.58 5,58 7.33 11 29.42 16,25 10.25 1808 633 5.5 17,83 13 16,75 32.17 18,53 6,58 5,67 27.08 26,67 15.25 12 83 13.67 608 5,08 24.08 22.17 15.92 9.5 33.42 5.75 7.08 17 33,83 15.17 15.03 39,25 7,83 19,58 3683 17.75 22.58 23.17 26 33 7.75 9,25 25,42 21.25 15.25 10 67 27.58 5.5 30 23.25 35.67 34.08 24.17 27.33 7.83 3958 17.58 16 83 17 11.25 19.33 12.03 20,33 11,08 22 16.67 10.83 20 17.08 25,58 12.33 41.5 26 9.25 18.17 19.17 19.92 21.67 10.75 28.17 21.75 27,58 11 942 17.75 16,67 22.75 31.33 11,33 18,'12 14,67 12 35,83 8.58 15.5 21 18,58 23,92 1808 16 16.67 16 42 27,08 21.33 2042 9.33 21.75 13,58 19.75 2608 1083 33,67 30,75 27.67 23.33 26 75 31 14,67 39.58 28.67 12 36,5 37.42 38.92 13.25 2092 25,25 34,75 14,83 20.33 37.92 11.42 21.17 15,33 41.67 ' 23.08 28,75 " 17,67 12 08 10 26 67 11.58 22.67 12 9.67 21.58 18.17 26,75 15,08 15.5 10 25 35.83 15.58 37.5 15.5 25.25 11.08 37.58 13.92 29,42 18,17 21.5 17,67 11.5 27.33 17.58 31.17 1617 11.83 9.83 17 11.58 30,75 25,33 18.67 13,58 21.83 25.08 17.67 29.58 20,42 16 33 19.67 9.33 16,75 1483 16,33 41.42 9.75 9.67 18,67 24.75 14,5 24.5 11 32,92 16 27.33 27.33 37.75 10,92 14,25 13 33 18,5 17.92 28 28.17 8,5 34.33 37.58 12,67 10,67 26,5 33,17 31,33 27,92 9.92 33,92 22,33 16 92 29.08 10,33 24,75 12.5 18.58 35,33 29 10 32.08 1925 29.75 17.92 30 5 16,33 2683 14 83 15 9.67 14.75 18.58 19 19.SS 37.33 21,25 16.75 17.5 17.83 25 19.25 39 32.17 29.42 11,67 14,67 10.08 16 08 18,75 14.25 1908 15,58 18,25 32 25 67 34,58 8,5 14 14.75 32.08 32.92 24.08 25.42 26.75 28,92 39,67 25.75 33.5 16.75 29 67 32,83 22.75 38,08 8.75 15 08 10,67 37 35.53 27.17 16 S8 26,33 34 35.75 1883 28,08 10,42 IS 1283 16.33 27,67 16 9.25 37.58 14,92 28,42 37.42 25.42 23.75 24,67 35.58 32,58 11.5 39.25 25,33 33.75 27.17 21,08 28.25 11.92 38.33 16,42 22 19.75 39.92 31.25 24 33 983 30,75 25 67 3975 37.17 40,75 3508 9.67 30,58 lOOS 24,67 28,25 37.92 17,25 8.5 37.17 24,83 18.25 19.33 12 83 18.42 23.17 28.83 20.33 35,92 27.92 29.53 39.33 10,75 26 83 28.5 20,92 33,08 11.25 28.25 22.53 15.83 26,67 17.5 22.17 14,17 37.83 21.83 22.83 27.42 . 21.92 13.75 27.75 17.17 23.25 20,92 25,58 22 36 40,75 23.58 10.58 15.33 8.67 26,25 21.92 28,33 13,75 40.17 40 25.92 678 Table D-1 (Conlinucd) Consnmptlon Consnmptjon Consumption Consumption Consumption Consumption Consumption m^/c/m ra^/c/m m^/c/m m'/c/m m'/c/m m^/c/m 29.17 28.25 27.92 27.42 11.33 30.33 36.17 38.25 13 92 37.75 13.58 12.92 17 14.67 10.03 13.58 32,58 37.33 9.83 35.53 36.33 29.17 34.75 23.42 37.58 37.08 10.75 19.25 34.33 38.33 31.58 13 32.92 15 36,33 16.75 9.83 31,42 33.92 11.92 30.03 27.92 19 83 31.33 16 15.08 35.17 41.25 38.25 25,83 39.33 23.42 24.33 38.25 32 10.5 17.25 13.58 15.92 16.75 3608 12.33 8.5 9.42 27.33 15.5 1083 27.25 27 25.5 23,5 11.92 17 83 20.42 32.17 16 33 38.75 14.92 21.33 27.33 33.58 26 25 26.25 30.92 27.33 36 83 15,58 40.5 10 75 10.75 32.25 .31 _ 26.5 _ . 21.83 -10.33 33.42- '33.42 -- -3808 8.42 38.67 38.92 29.17 1383 13 83 18 23,83 33.33 32.75 30 67 15.67 15 67 17.83 17,17 34.5 1683 24.67 29 08 29.08 24.92 17 27.75 20,92 37.92 11.08 11.08 36.75 10 58 20 17 32,75 27.42 9 9 19.58 26 33 32.33 20.92 11.33 17.5 17.5 31 19.83 26.42 16 83 34.75 17.75 17.75 19.83 38 40,53 20,25 15.08 12.42 12.42 13.92 28.03 37.67 11 23.75 17.33 17.33 28 16.33 37.83 36,25 33,42 22.92 22.92 40.75 36,5 31.5 27.25 25,58 36.58 36.58 39.58 23 03 14.92 24,75 28,42 21.83 21.83 21 9.08 19 27 25.75 29.17 29.17 40.17 21.5 38.5 24,67 31.83 26,58 26.53 25.5 38.33 21 41.5 37.33 34.42 34 42 32.83 28.08 10.58 21.42 16 83 18.67 18.67 32.17 36,92 27.17 20 26,58 14.92 14.92 37.03 30.33 37.5 22.75 40.67 29 08 2908 27.5 37.67 10,92 26 867 31.75 31.75 22.92 28.17 26 67 34,5 28.42 9 9 30.5 41.25 41.42 27.08 25 32.25 32,25 13.33 11.17 38,08 37.92 21.5 26.42 26.42 13,08 33.42 37 1608 3608 16 42 16.42 11.42 35.5. 37.08 29.92 26.33 15 06 15.08 -17.58- 38.75 32,92 36 27.25 33.92 33,92 18.92 1608 37 20,58 34,17 31.17 31,17 28,33 28,67 23.17 10 21.33 1908 19.03 33,08 37.5 25.42 34.17 17 9.17 22.25 19.67 39 5 22,58 3883 28.83 22.75 34 92 9,08 31.75 22.25 26.75 25.58 26.67 8.83 20.17 32.33 17.33 19.42 20.92 15 08 29.75 37.17 31.42 33.17 16.58 23.33 18.33 34.5 11 08 11.5 16.25 35.08 29.58 38.08 34.53 8.42 27.92 39.25 9.5 27.58 34 37.5 23.17 38.58 27 23.42 40.5 25.17 16 33.17 30,25 25.5 38.08 15.67 33 39.92 9.25 18.83 14.08 27.58 34.67 9,67 33.42 14.25 23.17 37.08 27.17 27.33 5883 19,5 18.5 17.25 28.08 28.25 12.67 44.33 40.25 12.5 37.75 15.58 31.58 10 58 48.17 25.83 12.17 21.58 30.92 35.92 15.42 48,83 2675 10 58 20,17 13.83 21.08 34.25 43.75 35.25 26.67 13 03 27.83 19.42 33.33 44,75 3267 16.25 20 5 30 21.92 36 83 27.75 21.53 30 31.5 39.42 23.75 33.92 39.58 9.92 25.92 31,42 28.92 29 20.75 18.5 27.92 13.75 40,17 31.42 9.08 24.83 26.25 18 11.42 25.33 11.5 36.58 42 33 28.33 22 67 29 08 15 18.17 28.17 53.67 32.83 28 22.08 17.92 36 58 41.42 44.25 31.75 9,83 32.75 26.33 8.75 29.42 53.25 21.58 24 38,25 32.17 17.92 27.25 58.67 30 67 8.67 29.92 1625 2692 23.33 32,5 17 08 37.42 9.67 35 17 27 24,5 33 29.17 19 13.03 34.92 38.5 29.17 14.33 21,67 12,92 75 93.17 6842 66 58 103.5 102 109.33 72,25 75.75 98.08 67.92 114.92 112.17 102.42 66.58 62.83 68.03 66 OS 122.33 114.5 107,67 93.53 7892 85.53 68.42 101 117.83 114 33 1000 92 117.92 136 715.58 175.08 102.42 103.16 679 Table D-l (Continued) Consumption Consumption ConsomptiDD Consumption Consumption Consumption Consumption m^/c/m m'/c/m m'/c/m mVc/m m'/c/m ra'/c/m mVc/m 9.5 28.42 34 31.17 3.33 22 32.17 S.5 3933 33.S3 40.75 23.92 31.S3 17.33 19.42 9,42 28.58 37.33 10 36 12.92 14.25 20.25 40.67 11.75 13.25 13 83 33 29.58 40,03 12.67 18.33 18.33 26.42 8.83 1292 37.5 12.33 11.92 18.42 28,5 24,67 22.33 21.75 26,75 17.83 22.S8 IS 32.03 14.92 31.17 30.5 28,83 23.5 19,42 26.67 19.75 10,75 12.42 1025 20.25 21.03 27.75 14.33 25.33 40,75 9.17 27.75 14 15 13.5 28.5 15.5 13,33 12.42 15.33 32.5 11.75 17.92 21.03 867 33.17 1225 17.25 15.08 21.58 14.83 9.92 37.42 22.42 27 19.5-- - 12.92 33 8,33 20.33 18.75" " 18 23.42 3.5 19 16.42 36.92 11.42 12,25 8.5 41.5 24,75 24.33 37.25 16.5 11.33 20,03 18.67 19.92 38.5 22.03 9.83 14.67 11.5 14 08 18.75 38.33 11.58 21.33 25.25 30.58 15.08 12.92 18.92 27.25 27,83 39.33 23.17 26.25 24,17 10.08 27.75 1008 27.5 25.5 19.33 21.58 19.17 10.25 37,03 11.83 40,17 10.58 26.25 39.25 1033 21.53 28.08 15.75 16 42 12.25 16.5 20.33 2083 24.25 21.83 12.17 17,83 13.0S 11.33 23.75 1008 19.58 13.58 22.33 17 16.42 17,58 37.17 12.25 12.92 32.92 14.17 20.17 17.17 21.25 37.42 20.17 2633 33,08 28 15.25 21.42 9.58 21.03 14.42 28,17 39.83 12.17 13.58 14.42 13.33 31.75 24.83 11.92 11.5 21.83 23.5 21,42 11,25 37.08 28 83 22.33 30 67 33.25 40 67 34.75 34,75 19.5 12.42 11 11.33 9.33 22.67 22.67 23.75 25.17 11.83 33.53 25.25 17 83 17,83 36.75 23.83 9.83 41.58 33.5 23,75 23,75 23 83 11.58 12.17 35,08 15,42 27,83 27.83 21.25 24,17 23,42 40 67 16 33.92 38.92 35.25 12 67 11.92 29.67 24,58 31.83 31.33 28 22,75 17.75 14.08 2483 17,83 17,83 1808 30.67 21.17 20.08 36.33 13.58 13,58 22.33 22.25 28.92 23,92 1008 22.75 22.75 28.75 11.33 37,33 18,03 23.67 14.5 14.5 30 25 10,42 12,92 8,92 1067 24.75 24.75 18.33 20,33 31.5 16 25 18.5 11.17 11.17 15.5 20.58 15.92 21.83 18.08 15.17 15.17 37.42 23,58 30.25 11.83 36 92 17.42 17.42 18 92 12,42 40.75 18,67 14 11.58 11.53 21.25 12,33 33.42 35.42 39.92 10,67 10 67 20.25 35.25 11.5 20,67 22,5 18,5 18.5 31,25 40.17 17,83 12,53 15,42 27.33 27.33 13.83 24,58 18,83 40.42 40.92 16 83 16 33 13,42 16 75 40.75 14,5 26 83 12,33 12.33 30.75 29,08 8,75 11.5 20.58 19 19 16 13.67 15,67 12.5 26,83 31.17 31.17 28,33 32,83 15.92 16 5 10,5 37,75 37.75 21.92 14 75 27.08 9,67 16.67 33.5 38.5 18 39.5 27,5 39.58 17.08 32.58 32.53 37 18 83 17,53 18,42 26.42 32,58 32.58 20,75 25.42 8.75 18.33 30.58 22,42 22.42 19.08 3692 11.53 24.5 24,92 25.17 25,17 12,42 23,33 16,0S 14 83 29.58 25.17 25,17 18.5 19,42 40.33 22,83 40 37.33 24,33 31.33 33.33 18,5 116 42 172.33 104 67 277.08 197.83 340.58 58.91 103.5 48 18608 143.83 136,42 102.42 52.16 123,67 101.83 104.53 604.92 105 67 114 33 53.5 102.75 1065 103.17 503.58 106.92 291.08 43.91 114 17 103,42 101,83 103.42 n 1,83 101.17 15558 122.42 105,17 101.17 131.17 185,92 109,42 75.16 123 83 111.42 526 17 117.53 122,42 115.33 41.83 103,92 106,92 392,08 107.25 65.58 123 42 81.58 121.92 109.33 224,58 113.17 37083 117,83 97 106.92 151.92 216.92 360,83 144,17 821.72 64.83 118.75 205.5 388,5 649.17 203.5S 394 56 60,41 109,75 115,33 688,17 130.17 1000.92 166 08 46.58 117.58 102.58 1181,67 191.83 117.58 129 42 44 680 Table P-l(Conlinued) Consumption Consnmption Consumption Consumption Consumption Consumption Consumption m*/c/m m^/c/m m'/c/m m'/c/m m'/c/m m*/c/m m'/c/m 22.0S 24,92 17.75 17.08 14.5 24,75 20 03 29 21.58 17.83 24,83 26.33 21.92 15,75 23.92 9.5 38.33 8.5 10.83 39.25 24,42 39 28.5 14.83 17.08 33,58 33,25 10,83 25.5 20,92 21.17 28.5 20,58 39.42 33,83 8.67 33,92 1033 33.5 21.67 26 83 31.08 29,42 16 29.58 15.92 18.42 18,67 165 13,42 11.25 15.25 15.58 11.42 21.5 10,83 24.17 21.33 22.17 10.75 18.92 26.92 16.33 11.75 9.42 12.5 22.17 16 08 23.75 27.25 24,17 9.92 20,25 20,58 17 18 16 83 25.83 27.67 10,83 11.5 3283 8,5 20.17 8.92 12.33 14,42 9.58 24,53 9,58 38.17 -1667 34.83 - --10.42 - — II — 33:53' " "24" 15.08 33,5 18 22.58 27.42 12,83 16.58 19 34 11.83 20 67 16,67 9.33 21.58 16,58 14.25 14,33 33.58 21.33 25.75 12.17 23.92 11.33 15.17 21.67 30.5 19 13.17 19 27,33 14,33 17.08 18,42 15,42 8.92 30 17.08 12.83 18.92 29,83 14 983 18.17 10.17 8.67 24.5 17.67 19.75 23.17 23 38 67 9.92 19,08 14.42 20.92 11.92 30,17 18 17.83 18,58 24 22 40.75 29.83 37.25 9.33 31 26 22.58 12.17 22.42 1617 21.17 27.42 11,33 11.25 40.92 8 58 24.92 31.42 20 83 2403 9.92 1308 10.17 9.17 11.75 13,33 19.58 15.42 IS 21.42 29,33 40.08 10.25 15 67 26 37.58 28,75 16,75 22.17 30 33 19,83 27.42 8,83 983 9.53 23.08 32.58 18 58 21.67 29 41 12,58 21.42 24.5 37,33 32.08 23,25 10.5 22.67 28,5 22 11.42 24.83 9.25 13.5 21.75 1842 13,25 25.25 21.5 36,17 13,08 24.17 32.83 11.83 18 37.83 30,83 20.17 17.42 30.5 1033 20.75 14,67 25.83 17.33 3903 15.92 19.17 883 17.58 14.92 17.5 13.58 11.58 21.75 17.42 28.25 17 39.75 17:42- '14.42 12,42 23.33 3i:75 39.33 14.75 16,92 1058 37.92 33,5 12 58 12.58 11.58 34 22.83 22.5 11,83 22.58 12.83 22,33 11.5 1683 9.75 1603 16 83 32.33 22 32.67 28 83 11.92 22 83 29.33 13,08 15,25 10.33 17.58 24,58 13.92 13.58 36,5 27.5 12.17 15.33 29.5 12,75 18,83 14 35,67 8.83 20 15 1067 15,42 19,92 24.67 12.92 27.67 13 15.5 31.5 14,67 21.08 33 17 10,42 38,33 24,33 36 83 21.08 18.5 34.67 10,42 17.58 19.92 10.75 38,67 20.25 12 58 21,83 20.92 908 27.83 9.92 867 9 83 11.25 10,33 9.17 33.17 17.92 14,67 24.92 992 17.42 9,25 16 67 15.42 18.42 17.5 37.58 20,42 13.75 20.5 27.83 11,67 36,58 37 23.17 8,92 17.08 9.58 11.25 11.67 1067 35.42 14,58 14 29,5 11,25 14.5 30 08 17.67 20.92 16,33 25.75 13.25 20.92 10 22,17 27.5 26 67 14,53 26.42 11.92 10 67 13,92 2067 28,75 16,25 11,33 15.5 40.75 26.58 23 83 12.67 37.25 25.25 19.25 23.92 16 92 34 67 38.17 25,25 18,33 22.08 9,92 12.33 28.17 26.75 16,33 14.58 33 3667 36.33 18,33 13.58 40,58 95 3375 34.42 39.58 12.92 16 75 9,42 11.75 27.5 21.42 34.17 27.83 39,42 23.67 17.08 18.42 34,92 19.17 17.42 40 3283 31.75 983 37.75 29 25.42 31,5 12.33 20,33 16.5 18.25 11.03 16,25 39.42 30 25 9.03 27.58 39,08 27.67 28.75 13.67 25.25 21.92 35.33 2667 29,17 28,17 26.58 21 13 67 18.58 18.17 35.92 12.25 20,92 34.33 29.17 30 28,75 17.58 35.92 19.5 24.5 109.42 115 1481.92 47.75 121.83 163.92 69.91 102.42 123.08 232.92 789,42 113,33 186 83 4383 100 75 I04.0S 147.67 605.67 147.92 131.33 56 58 101.17 104.5 142.92 16S2 302.42 233,83 66.16 681 Consmnption Consumption Consumption Consumption Consumption Consumption Consumption m*/c/m ra*/c/m m'/c/m m'/c/m 12.42 32.08 21.58 27.5 13.17 19.67 32.92 1967 28.25 30 25 40.5 10.42 3083 28.67 14.33 35.67 20,58 37.75 36,17 37 36,08 36.08 40,25 35,58 3975 21.17 25.83 33 67 11.67 1083 983 40.25 28,33 27.42 26,5 30,33 1083 23.58 21.58 26.08 32.33 33,25 15.58 2958 27.53 1683 32.17 29.83 18,42 11 27.75 22.25 925 30.92 34,83 36.92 14.92 31.25 35,08 22 12.75 15.33 33.03 17 13.17 29 27.25 1808 41.08 34.58 21.92 2967 942 24.08 21,58 41.17 17.75 21.83 10.17 35.42 15.58 41.5 24.17 33.53 36.67 28.5 37.5 28,67 40.33 25.42 27.42 22,67 8.92 40.83 26,42 - 34.25 18.25 - 33.33 8.42 14,25 22.33 11.83 395 35.5 26.53 13.83 14 67 28.33 32.42 23,42 38.58 74 14.5 28.75 11.58 15 67 38.67 16 08 90.33 67.75 67.42 78.58 69.03 75.17 67.75 8967 665 7267 69.25 65.83 84 25 74.5 81.75 65.5 67.33 68.33 74.17 66.75 96.42 84 63,17 75.92 87.25 6608 80.42 66 83 87.25 7308 67.83 70.67 96,92 73.75 67,83 92.92 65.42 99,33 SO 90.42 61.42 66,08 97.33 86.17 78.67 71 90 67 62.53 87.67 91.17 67.75 97.17 71.08 83 85,5 66 03 71.75 63.03 66,08 64 03 67,08 70 62.03 80.17 66.75 71.17 88.17 69.33 82.08 74,67 76,5 93.33 64.67 84.42 92,75 7967 61.08 96.17 64 94.67 94.92 77 77 99.08 66 95.92 64,17 72.5 74,5 8258 75.75 62.5 87.17 69.5 79 86 17 97,42 61.92 77.25 83.67 67.33 895 89 67 70 33 90.33 31.75 69 33 71.75 78.75 61.17 74,83 96.25 65.03 74.08 99 42 78.33 77.08 65.42 94.42 7042 61.17 70 83.75 61,92 85,5 65.75 63.5 72.17 73,33 73.17 71.33 82.92 89.83 63 67 8853 705 81.75 67,67 90.58 64 39.67 98.75 64 58- 64.33 74,42 71.17 73.17- 62.33 617 7.5 17.75 22.67 14.33 20.83 29.75 6.92 7.42 40.25 38,5 24.5 40.58 16.58 51.42 53.75 49.42 43,33 44 44.75 59.17 51.58 60.25 46 52 46 43 55.33 95 84.75 64 25 61.67 88.08 62.53 92,67 69 63.5 65 61.92 91.42 65.75 8483 99.08 91 94.83 7283 34.53 35.67 61,5 6283 75.5 72.5 65,92 93 67 75.03 99.58 69.08 64 65.5 71.5 62 67 68.33 89.33 74 67 87.92 30.33 99,25 90.5 71.92 71,42 80.33 70.58 93.25 99.25 96.75 71 79 67 67.42 97.91 105.16 44.33 7933 42.16 93 77.67 94.92 71.08 63 83 94,25 71.83 7908 64,67 82 64 58 7208 32 89.75 73.92 71.08 86.75 86,58 94 8067 91.92 63 08 85.42 61.58 61.5 92.92 87.75 88.08 70,92 87 83 64.5 82.5 7O08 67.75 66.75 77.67 98 67 65.17 87,83 63.92 34.75 76,17 64.17 69.75 91.33 88.58 88 92 83 67 81,83 71 66.42 77 73 82.25 75 64.92 98.33 69 25 69.33 69.83 78.92 63,83 93.58 82.83 65 83 7058 65.17 68.92 61 72,25 65.33 77.92 8003 99.33 62.75 74.33 84.5 80.92 88.42 71.03 94.17 87.83 71.25 92,58 7667 77.33 69,75 69.75 65.42 80.08 80.42 65.5 99 5 69.53 7067 73,42 66 67 62.17 6683 74,17 77.5 7667 75.42 68,25 30 42 63.67 97.5 91,83 69.25 65.75 85.92 7283 60.92 84,58 91.75 74,42 77 63.17 63.53 71.5 80,25 99 08 39.42 81.53 89.75 89.92 78.08 69,67 61.5 70 64 63.67 89.33 92.17 74.42 69 72.58 72.58 76 92 69.25 75 87,58 83,58 82.17 100 7953 67.5 90 94.33 61 69.42 73 67 62.5 94.58 85 99,17 64 83 67 66 17 62.92 83.08 64.33 7683 81,33 7908 71.08 73.75 82.92 84.17 682 Table D-l (Conlinucd) Consumption Consumption Consumption Consumption Consumption Consumption Coosa mptioo m^/c/m m'/c/m m'^/cJtn m'/c/m m'/c/m m'/c/m mVc/DS 115 102,25 1578.33 200.42 13083 264.92 5066 122.17 123,42 162.17 1102.03 123.83 101.17 87.41 102 119.67 107.92 820.53 122.17 129.33 64.41 121.83 101.5 103.33 1026.17 112 83 246.17 7083 113.33 107 243.33 746.75 103.92 300 92 53.66 105,5 149.33 154.33 587.33 159.92 113.92 124,16 105 67 51.58 102.25 571.33 169,17 109.33 485 78.75 68.75 91.67 62 89,5 82.33 6083 63,42 72.17 78,58 63.17 67.92 86 58 96.5 89 64.25 81.75 64.08 81.25 90,25 75 67.5 65.17 66,17 73 64 83 62.75 34.33 67.42 67.42 87.33 73.17 62.58 91.58 66.25 .64.25. - - _ 96,67 - 9083 - 72.92 69.17 - 6083- - 78.67- 81.08 86.58 84.17 73 90.33 103 83 120.33 64.25 77.08 78,5 92.17 92.42 102.33 107.25 78,42 90.42 94.75 80.58 74.75 120 149.83 73.33 74.25 82.75 88.42 7467 111.25 120.92 98.92 69,58 90.67 93.92 70.75 118.5 104,33 63,17 61,75 67.17 85.5 66 42 112 107.75 72.33 69 9333 64.75 85.58 118 83 110.42 31.17 81,67 82.17 65 71.67 105.25 113 8008 71,25 60 83 64.5 72.92 124.25 119,92 67,83 74.92 62.5 73.53 61.92 124.25 103,25 87.08 89.67 78,25 73.5 70,92 117.08 112.33 67.42 75.17 89.75 87.17 81.41 117.83 123.75 65.42 75.42 72.42 67.25 100 10083 122.33 68.58 75.67 67.42 79.25 110 124 112,67 64.42 92.33 91.42 86.5 116,92 114 101.17 89.25 93 83 75.67 81.5 107.08 103.75 103.25 68.33 100.17 96.33 98,67 154,75 110.42 124 74.92 65.58 72.83 87.67 113.33 113 67 134.67 71 83 83.33 99.75 61.33 101.42 115.5 113,17 61.42 62.25 61.5 7675 126.5 103.75 120,75 94.92 75 71.03 89.42 10642 106,25 102.25 72.75 86 33 83.53 95,25 109,25 112.83 104.75 85.67 65 71 70.5 105.5 103,42 93 83 88.83 64.83 61.17 77.17 106.83 105 67 104.67 93,25 93.83 60.92 83.33 12017 108,58 113.17 100.5 115,25 112.03 111.83 178.5 683 Table D-2 Agricultural consumption data of sample farms Plot number Plot area Location Well number Consumption (hectares) (m^/ha/day) 102 3.86 Busaiteen 14/84 117.54 103 1.98 Dair 14/81 174.14 306 3.32 Sumaheej 14/10 85.25 309 0.96 Sumaheej 14/15 67^5 401 5.09 Qalali 14/113 51.27 622 0.69 Arad 14/56 56.31 703 • 1.54 Juffair 13/41 57.62 810 0,75 Mahooz 13/79 58.28 813 7.78 Mahooz 13/76 43.99 824 3.16 Mahooz 13/69 125.36 1206 1.60 Noaim 13/6 131.75 1209,10,11 12.89 Noaim 13/141 46.73 1213 3.59 Burhama 11/5 98.57 1235 8.90 Noaim 13/130 70.64 1403A&1403B 8.20 Daih 11/28,29 &30 89.59 1502 0.92 Karbabad 11/41 61.49 1907 0.85 Jid HafiFs 13/154 136.51 1927 0.43 Jid Haffs 13/172 179.55 1934 2.65 Jid HaflFs 13/168 91.97 2615 0.72 Abu-Saiba 10/137 154.82 2704 1.93 Shakhoora 10/82 58.65 2705 1.76 Shakhoora 10/83 223.45 2716 1.87 Shakhoora 10/78 102.76 2720 1.34 Shakhoora 10/76 121.31 2302 2.03 Abu-Quwah 10/19 118.98 2401 2.89 AJ-Qadam 10/111 73.74 2503 2.35 Hajar 10/103 52.93 2505 3.48 Hajar 10/120 50.70 2614 0.75 Abu-Saiba 10/138 77.81 2710 1.01 Shakhoora 10/66 143.68 684 Table D-2 (Continued) Plot number Plot area Location Well number Consumption (hectares) (m^^a/day) 2803 1.34 Moqabah 10/51 61.38 2807 1.82 Moqabah 10/41 111.42 2809 1.60 Moqabah 10/48 115.82 2813 1.28 Moqabah 10/17 45.63 2816 4.61 Moqabah 10/78 41.82 " 3004 1.66 Murkh ' 12/95 4Y.82' 3005 4.61 Murkh 12/96 65.67 2922 1.08 Saar 12/63 92.47 2918 2.30 Saar 12/156 114.35 3706 1.07 Quraiah 12/76 128.81 3702 3.27 Quraiah 1107 184.78 3710 5.47 Quraiah 12/118 42.76 3825 3.44 Janabiyah 12/43 100.01 3817 5.20 Janabiyah 1247& 12/48 67.01 3823 2.79 Janabiyah 12/158 66.27 3821 3.80 Janabiyah 12/42 59.96 3835 1.56 Janabiyah 12/160 149.39 3839 1.72 Janabiyah 12/37 95.55 3510 6.48 Bani Jamra 12/117 60.38 4901 5.07 Jasra 9/96 56.09 4909 2.35 Jasra 9/84 57.05 4905 1.46 Jasra 9/91 69.74 5001 5.36 Jasra 9/70 42.62 4927 1.67 Jasra 9/61 47.70 5022 6.59 Hamala 9/1 62.30 5026 13.54 Hamala 9/22 62.38 5009 6.97 Hamala 9/34 130.00 4918 1.25 Jasra 9/79 77.90 5108 1.87 Du mi Stan 6/178 50.91 5116 4.34 Du mi Stan 6/173 51.84 5121 1.44 Dumistan 6/165 97.44 685 Table D-2 (Continued) Plot number Plot area Location Well number Consumption (hectares) (m^/ha/day) 5120 0.54 Duniistan 6/166 157.07 5220 2.41 Karzakan 1080 133.50 5214A&5214B 5.84 Karzakan 6/141 70.92 5221 2.14 Karzakan 9/139 52.92 5243 0.96 Karzakan 6/128 172.25 5304 " 4^4^ ' Malikiyah 6/1 ll"' 46.47 5308 0.50 Malikiyah 6/108 120.20 5411 3.01 Sadad 6/81 49.37 5420 0.75 Sadad 6/80 94.32 5417 0.88 Sadad 6/89 145.66 5608 6.49 Zallaq 6/11 98.73 5615 3.59 Zallaq 6/4 144.68 1118 2.04 Bilad AJ-Qadeem 13/107 114.11 1114 1.01 Bilad Al-Qadeem 13/128 69.31 1109 3.48 Bilad Al-Qadeem 13/112 101.45 1106 3.48 Biiad Al-Qadeem 13/119 207.69 4218 2.14 Tubli 3/15 161.75 4204 1.28 Tubli 3/4 62.10 4506 0.72 Sanad 1/77 213.59 4604 2.41 Nuwaidrat 1/3 67.48 4607 0.37 Nuwaidrat 1/30 69.73 4705 0.37 Ik-ur 1/109 131.79 4712 1.15 Ikur 1/133 70.06 4739 1.23 Ikur 1/50 79.18 4732 0.85 Ikur 1/46 121.91 3919 1.71 Buri 4/43 & 4/44 45.29 3922 1.81 Buri 4/17 49.89 2912 1.79 Saar 10/11 146.56 3825 3.44 Janabiyah 12/43 100.01 1833 4.93 Karanah 11/24 168.76 1831 1.44 Karanah 11/92 203.56 686 Table D-2 (Continued) Plot number Plot area Location Well number Consumption (hectares) (m^/ha/day) 1829 1.6 Karanah 11/100 42.84 4814 4.61 Sitra 2/10 133.81 4806 1.39 Sitra 2/3 168.44 4801 1.17 Sitra 2/12 40.80 4710 0.53 Ikur 1/109 92.31 "1622" 1.39 Moqsha 11/72 182.02 1823 2.25 Karanah 11/95 43.71 1822 1.66 Karanah 11/94 56.05 3220 2.68 Janusan 11/174 90.16 3312 1.05 Barbar 11/196 120.30 3324 9.22 Barbar 11/245 54.34 3401 26.28 Duraz 11/236 196.24 3511A&3511B 1.87 Bani Jamra 12/104 212.08 3710 5.47 Quraiah 12/118 42.76 3928 2.87 Buri 4/4 34.49 4013 1.17 Aali 4/25 26.32 4522 1.42 Sanad 1/14 31.26 5233 9.59 Karzakan 6/124 24.12 5303 11.10 Malikiyah 6/113 30.75 5505 4.98 Shahrakan 6/40 38.27 5606 11.10 Zallaq 6/27 27.43 4914 3.05 Jasra 9/65 32.80 3413 3.86 Duraz 11/249 35.82 3504 0.48 Bani Jamra 12/111 34.59 3306 6.43 Barbar 11/194 33.03 206 1.50 Dair 14/2 36.49 303 4.12 Sumaheej 14/12 40.08 618 3.53 Arad 14/76 29.58 1110 8.68 B. Al-Qadeem 13/113 32.32 1230 4.45 Noaim 13/132 37.56 1826 4.02 Karanah 11/119 35.16 687 Table D-2 (Continued) Plot number Plot area Location Well Consumption (hectares) number (m^/ha/day) 1903 2.52 Jid Haffs 13/161 217.17 1904 5.02 Jid Haffs 13/150 36.06 1924 0.43 Jid Haffs 13/160 214.05 2111 8.63 Jabalat Hibshi 10/106 39.76 2816 4.61 Moqabah 10/78 41.82 2903 1.12 Saar 12/13 240.93 2902 14.21 Saar 12/16 28.03 4813 2.41 Sitra 2/4 37.17 51I0A& 5110B 0.31 Du mi Stan 6/170 215.95 5606 11.10 Zallaq 6/27 27.43 3004 1.66 Murkh 12/95 148.24 623 2.19 Arad 14/48 27.01 1208 1.20 Noaim 11/3 109.87 1230 4.45 Noaim 13/132 37.65 1232 13.51 Noaim 13/145 37.37 5213 0.78 Karzakan 6/155 133.50 3906 1.01 Buri 4/11 42.84 1223 2.41 Mani. 13/139 49.45 826 2.62 Adliyah 13/70 34.17 2509 2.19 Hajar 10/71 47.83 688 Text D-1 Survey Questionnaire for Municipal Water Use Serial No. Address; House/FIat/Building: Road: Block: Area: Telephone: Contact person, if possible: A. General Information A. 1 Type of dwelling. [ 1 ] House [ 2 ] Villa [ 3 ] Flat [ 4 ] Building [ 5 ] Government building [ 6 ] Commercial establishment [ 7 ] Religious establishment [ 8 ] Farm [ 9 ] Factory [10] Restaurant [11] School or educational institute [12] Social or health club [13] Hospital or health centre [ 14 ] Hotel or flats apartment [15] Others. A.2 If others (please specify): A.3 Average monthly consumption per dwelling as per billing records. A3 A Summer m^ (June - November). A3B Winter"... .7. m^ (December - May). A.4 Average cost of water use BD/month. A. 5 Are you connected to the sewer system? [ I ] Yes [ 2 ] No. A.6 Do you have swimming pool? [ 1 ] Yes [ 2 ] No. A. 7 If yes, what is the volume of the pool ... A.8 How many times do you change the water in the pool time/year. A.9 What is the source you use for changing water in the pool? [ 1 ] Public supply [ 2 ] Outside source. A. 10 Number of bathrooms: A. 11 Number of flush toilets per dwelling: A. 12 Number of taps per dwelling: 689 A. 13 Number of cars in the dwelling: B. Household Variables B. 1 If household, number of occupants per household: B.2 Household area (approximate) m^/ft^. B.3 Monthly income of household occupants (BD): B.4 Number of showers per household: B.5 Number of washing machines: Do not have ( 0 ) B. 6 Number of dishwashing machines per household: Do not have (0) C. Indoor Water Use C. 1 Number of shower taken per occupant. C.IA Summer Time/day/week. C.IB Winter Time/day/week. C.2 Number of toilet flushing per occupant Time/day. C.3 Number of washing by handwash or washing machine. C.3A By hand load/week. C.3B By washing machine load/week. C.4 Number of dishwashing by hand or dishwashing machine. C.4A By hand /day. C.4B By dishwashing machine /day. C. 5 Number of hand washes per occupant Time/day D. Outdoor Water Use D. 1 Garden area (appro?cimate) m^/ft^. D.2 Irrigation method. [ 1 ] Flood irrigation [ 2 ] Sprinkling [ 3 ] Drip irrigation [ 4 ] Mixed methods. D.3 If mixed methods, which ones do you use? [ I ] Flood and sprinkling irrigation [ 2 ] Flood and drip irrigation [ 3 ] Drip and sprinkling irrigation. D.4 Number of irrigation. 690 D.4A Summer Time/week. D.4B Winter Time/week. D.5 Crop patterns [ 1 ] Vegetable [ 2 ] Date palms [ 3 ] Alfalfa [ 4 ] Mixed crops. D.6 Number of courtyards and floors washing D.6A Summer Time/month. D.6B Winter Time/month. D.7 Number of cars washing (please indicates whether by bucket or hose). By bucket: D.7 A Summer Time/week/month. D.7B Winter Time/week/month. By hose: D.7C Summer Time/week/month. D.7D Winter Time/week/month. E. Large Consumers Groups and Non-domestic Water Users (government buildings, commercial establishments, hotels, restaurants, schools, ...etc) Erl' If government building, number of employees: E.2 Number of shifts: E.3 If commercial establishment, number of employees: E .4 If hotel or apartment flats, number of guests per day including staff E.4A In summer: E.4B In winter; E.5 If religious establishment, average number of worshipers/visitors per day: E.6 If hospital or health centre, average number of in-patients per month: E.7 If school or educational institute, average number of students and staff: E.8 If restaurant, average number of customer per day: E.9 If factory, number of employee; E.IO Number of shifts: E. 11 Type of the industrial activity [ 1 ] Petroleum refining [ 2 ] Petrochemicals [ 3 ] Sand wash [ 4 ] Dairy products [ 5 ] Food and beverages [ 6 ] Ready mix and blocks [ 7 ] Aggregates [ 8 ] Textiles [ 9 ] 691 Cement [ 10 ] Ice and sweet water [ 11 ] Aluminum [ 12 ] Plastics [ 13 ] Cooling [14] Water bottling [ 15 ] Iron and Steel [ 16 ] Paper and pulp [ 17 ] Chemicals [18] Others E. 12 If farms, what is the plot area in hectare or square metre: E. 13 Irrigation method. [ 1 ] Flood irrigation [ 2 ] Sprinkling [ 3 ] Drip irrigation [ 4 ] Mixed irrigation. E.14 If mixed, whichjnethods do you use? _ __ . [ 1 ] Flood and sprinkling irrigation [ 2 ] Flood and drip irrigation [ 3 ] Drip and sprinkling irrigation. E. 15 Number of irrigation. E.15A Summer Time/week. E.15B Winter Time/week. E.16 Crop patterns. [ 1 ] Vegetable [ 2 ] Date palms [ 3 ] Alfalfa [ 4 ] Mixed crops. E.17 If social or health club, number of members per day including staflf: Your comments and amplifications, please Additional remarks by the interviewer, if any Thank you for your cooperation. Filled by: Date: Remarks: RR: Refused to response, HV: House vacant, AA: Arranged appointment. 692 Text D-2 Survey Questionnaire for Agricultural Water Use Serial no.: Farm no.: Farm owner/Tenant: Telephone: Address: Fam/Plot: Road: Block: Area: A. General Data A. 1 Average consumption of the farm from metering data, if any A.IA Summer mVday. A. IB Winter mVday. B. Farm Variables B. 1 Plot area m^/heciare. B.2 Cultivated area % of total area. B .3 Irrigated area % of cultivated area. B.4 Area under protected agriculture % of irrigated area. B.5 Expected increase in cultivated area in 2005 m^/hectare. B.6 Expected increase in cuhivated area in 2010 m^/hectare. B.7 Expected decrease in cultivated area in 2005 m^/hectare. B.8 Expected decrease in cultivated area in 2010 m^/hectare. B.9 Crop patterns [ 1 ] Vegetable [ 2 ] Date palms [ 3 ] Fruits [ 4 ] Alfalfa [ 5 ] Ornamental [ 6 ] Poultry [ 7 ] Livestock [ 8 ] Mixed crops . B.IO If mixed crops or includes poultry and livestock, indicates percentage of each crop Vegetable ....%, Date palms .... %, Fruits ...Alfalfa %, Ornamental ....%. Poultry ....%, Livestock ....%. 693 C. Water Source Variables C.I Water source [ 1 ] Water well [ 2 ] Treated effluent [ 3 ] Supply network [ 4 ] Mixed supply. C.2 If mixed supply source, indicate these sources [ 1 ] Water well and treated effluent [ 2 ] Water well and supply network [ 3 ] Treated effluent and supply network C.3 If water well, number of hours pumped C.3 A Summer hour/day. C.3B Winter hour/day. C.4 Pump capacity horse power. C. 5 Electricity consumption C.5A Summer KWh. C.5B Winter KWh. C.6 If water supply network, average consumption per month m^ (possibly from the billing records) C.7 If treated sewage effluent, average requirements per month m^ (possibly from the"Agricultural Auth"brit"y) ~~ C .8 If the water source or one of them is used for another purpose, please specify this purpose [ I ] Domestic [ 2 ] Industrial C.9 How much water is used for this purpose mVday/month. C. 10 Do you have swimming pool? [ 1 ] Yes [2] No C. 11 If yes, what is the volume of the pool m"* C. 12 How many times do you change water in the pool? C.12A Summer Day/month C.12B Winter Day/month C. 13 What is the source of water used for changing water in the pool? [ I ] The water source [ 2 ] Outside source 694 D. Irrigation Variables D. 1 Irrigation fi-equency D.IA Summer Time/day. D.IB Winter Time/day. D.2 Irrigation method [ 1 ] Flood irrigation [ 2 ] Drip irrigation [ 3 ] Lined channels [ 4 ] Sprinkler [ 5 ] Mixed methods. D.3 If mixed methods, which methods do you use? [ 1 ] Flood and sprinkler irrigation [ 2 ] Flood and drip irrigation [ 3 ] Flood and lined channels [ 4 ] Sprinkler and drip irrigation [ 5 ] Sprinkler and lined channels [ 6 ] Drip and linned channels [ 7 ] Flood, drip, and lined channels. Your comments and amplifications, please Additional remarks fi-om the interviewer, if any Thank you for your cooperation. Filled by: Date: Remarks: RR: Reftjsed to response, FV: Farm vacant, AA; Arranged another appointment 695 Text D-3 Survey Questionnaire for Industrial Water Use Serial No: Address: Building/Factory Road: Block: Area: Factory: Telephone: Contact person: .... A. 'Factbi^ Variables A. 1 Factory floor area m^ A. 2 Industrial category [ 1 ] Petroleum refining [ 2 ] Petrochemicals [ 3 ] Sand wash [ 4 ] Dairy products [ 5 ] Food and beverages [ 6 ] Ready mix and blocks [ 7 ] Aggregates [ 8 ] Textiles [ 9 ] Cement [ 10 ] Ice and sweet water [ 11 ] Aluminum [12 ] Plastic and fibreglass products [13] Cooling [14] Water bottling [ 15 ] Iron and Steel [ 16 ] Paper and pulp[ 17] Chemicals [ 18 ] Ship repairs [19] Flour [20] Asphalt [21] Precast and concrete [ 22 ] Garments [ 23 ] Rubber industry [ 24 ] Paints [ 25 ] Others A.3 If others (please specify): A.4 Production capacity tons/day/year A. 5 Number of employees: A.6 Number of shifts: A. 7 Are you connected to the sewer system? [ 1 ] Yes [ 2 ] No A. 8 If no, how do you dispose of your reject water? [ 1 ] Disposal well [ 2 ] Directly to the sea [ 3 ] Onsite septic tank [ 4 ] private treatment plant B, Water Source Variables B. 1 What is the source of your water supply? [ 1 ] Water well [ 2 ] Water supply network [ 3 ] Outside source "Purchase" [ 4 ] Recycled water [ 5 ] Onsite seawater desalination [ 6 ] Mixed supply B.2 If mixed supply, from which sources ? 696 [ 1 ] Water well and supply network [ 2 ] Supply network and purchase from outside sources [ 3 ] Water well and seawater desalination [ 4 ] Supply network and on-site seawater desalinaiton [ 5 ] Purchase from outside sources and water recycling B.3 If water well or mixed supply, from which aquifer do you obtain your water? [ 1 ] Rus - Umm Er Radhuma [ 2 ] Umm Er Radhuma [ 3 ] Rus & Khobar [ 4 ] Khobar [ 5 ] Aiat Limestone [ 6 ] AJat + Khobar B.4 If supply network or purchase from outside source, what is the a^eragejcost,of_water use 7 BD/month. C. Water Use Variables C.l Average water consumption mVday. C.2 In percentage terms, how much water is used for C.2A Production %. C.2B Staff hygiene %. C.2C Other (please specify) % C.3 Do you use water re-cycling equipment? [ 1 ] Yes [2 ] No C.4 If yes, how much quantity is re-cycled m^/day C.5 Do you desalinate water before use? [ I ] Yes [ 2 ] No C .6 If yes, please indicate C.6A Plant type [ 1 ] Electrodialyses (ionic) [ 2 ] Reverse osmosis [ 3 ] Multi Effect Boiling (MEB) [ 4 ] Multistage flash C.6B Plant capacity mVgallons/day C. 6C Plant availability % D. Future Activity and Expected Development D.l Do you have plans for future development? [ 1 ] Yes [ 2 ] No 697 D.2 If yes, please indicate type of the proposed ftiture activity [ 1 ] Petroleum refining [ 2 ] Petrochemicals [ 3 ] Sand wash [ 4 ] Dairy products [ 5 ] Food and beverages [ 6 ] Ready mix and blocks [ 7 ] Aggregates [ 8 ] Textiles [ 9 ] Cement [ 10 ] Ice and sweet water [ 11 ] Aluminum [ 12 ] Plastic and fibreglass products [ 13 ] Cooling [ 14 ] Water bottling [ 15 ] Iron and Steel [ 16 ] Paper and pulp [17] Chemicals [ 18 ] Ship repairs [19] Flour [ 20 ] Asfalt [21] Precast and concrete [ 22 ] Garments [ 23 ] Rubber industry [ 24 ] Paints [ 25 ] Others D.3 If others (please specify); D.4~ What is~the proposed water source for this activity? [ 1 ] Water well [ 2 ] Water supply network [ 3 ] Purchase from outside sources [ 4 ] Supply network and purchase from outside sources [ 5 ] Water well and supply network [ 6 ] Seawater desalination [ 7 ] Treated wastewater D.5 Expected % increases in employment D.5A In 2005 [ 1 ] 10% [ 2 ] 20% [ 3 ] 30% D.5B In 2010 [ 1 ] 10% [ 2 ] 20% [ 3 ] 30% [ 4 ] 40% [ 5 ] 50% D.6 Expected % increases in factory floor area D.6A In 2005 [ 1 ] 10% [ 2 ] 20% [ 3 ] 30% D.6B In 2010 [ I ] 10% [ 2 ] 20% [ 3 ] 30% [ 4 ] 40% [ 5 ] 50% Your comments and amplifications, please Additional remarks by the interviewer, if any Thank you for your kind cooperation Filled by: Date: RR: Refused to response, AA: Arranged another appointment. 698 MUBARAK AMAN AL- NOAIMI P O Box 29286. East Riffe State of Bahrain Telephone/Facsimile : 0973 777252 Text D-4 Model Covering Letter for Respondents of the Survey Questionnaires Development of Water Resources in Bahrain: A Combined " ' Approach oTSupply - Demand Analysis Dear Water Customers/Farmers/ Messrs/ 1 am currently undertaking a Ph D. research at the University of Plymouth of the United Kingdom. This research aims at studying the water use patterns in the municipal, agricultural, and industrial sectors in the Kingdom of Bahrain by means of questionnaire surveys. The completed questionnaires will be useful in identifying variables that might have influence on the current and future water demands in these sectors. You are randomly selected (your farm/industrial firm is randomly selected) for inclusion in this survey. I therefore seek your assistance in completing the attached questionnaire, as this will significantly contribute in achieving the objective of this study. According to my pilot exercise, taking part in this questionnaire should take a maximum of ten minutes of your time. All the questions are of close-ended type; you only need to enter number or to tick (V) as appropriate. Your response will be treated in an absolute confidence, and I assure you that the information obtained from this survey will not be used for purposes other than those of this study. For further confidentiality, you may keep your name/farm name/name of your industrial firm/company completely anonymous. Once completed, I should be most grateful if you could give me a call on: 9686486 to arrange for collection. Alternatively, you may send the completed questionnaire by fax on: 777252. If you require further information regarding this survey, please do not hesitate to contact me. Your co-operation in this matter would be very much appreciated. Yours faithfijUy, Mubarak A. Al-Noaimi malnoaimfgbatelco.combh 699 Appendix £ Residual Plots 700 Figure E-1 Normal P-P and scatter plots of standardised residuals for Model 2A 1.00 1 0.00 1 (X) ()bscrvixl cummulali\ e I Regression Standardized Predicted Value 701 Figure E-2 Normal P-P and scatter plots of standardised residuals for per capita residential water demand model I 00 ()(K) 1 (X) Observed Cummulativc Regression Standardi/rd Predicted Value 702 Figure E-3 Normal P-P and scatter plots of standardised residuals for Model 2B 1 00 ()(K) 1 (K) Observed Cummulative 6^ 1 1) 4 DC ^ C/3 8 oJ Regression Standardized Predicted Value 703 Figure E-4 Normal P-P and scatter plots of standardised residuals for Model 2C 1.00 1 00 Observed Cummulativc Standiirdi/cd Predicted Value 704 Figure E-5 Normal P-P and scatter plots of standardised residual For an improved version of Model 2C 1 (X) l.(K) Observed Cummulative Rcgrcssioa Standardized Predicted Value 705 Figure E-6 Normal P-P and scatter plots of standardised residual for agriculture Model 1 1 00 ()(K) 1 (X) C)b>scrv'cd Cuniinulative 1\ • a a 1 \ • • • Bp ^° ° ° a a r. * • D a • ° a o a a -2 \ o -3 Regression Standardized Predicted Value Figure E-7 Normal P-P and scatter plots of standardised residuals for industrial Model 3 1.00 1 .75 .50 .25 0.00 0(K) .25 .50 75 1 (K) Observed Cummulalive a DO a a r, a a a a a 0 \ o a I? D a a u o D D a w a -I a a a a -2 H -3 -2 Regression Standardized Predicted Value 707 Figure E-8 Normal P-P and scatter plots of standardised residuals for industrial Model 4 1.00 0.00 1 (K) ()bscrv'cd Cuininulativc 2 A D a a 1 J an • D a n a • 1 A ~ a a a D a a a a -3 Regression Stmidanlized Predieted Value 7()S REFERENCES Abdulgaffar, H. A., 1999, Water Challenges in Bahrain: An Economic Perspectives, Paper Presented at the Fourth Gulf Water Conference, Bahrain, 13-17 February 1999, The Arabic Volume, Water Sciences and Technology Association, (in Arabic). Abdurrahman, W. A., and Rasheeduddin, M., 1994. Future Groundwater Conditions under Long-term Water Stresses in an Arid Urban Area, Water Resources Management, 8: 245 - 264:------^ ACE (Associated Consulting Engineers), 1984, Report on Treated Effluent Sludge and Compost Utilisation, Ministry of Commerce and Agriculture and Ministry of Works, Power and Water, State of Bahrain. 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