UNIVERSITY of CALGARY Flash Flood Modelling in Oman Wadis by Ghazi Ali Abdullah Al-Rawas a THESIS SUBMITTED to the FACULTY of GR

UNIVERSITY OF CALGARY

Flash Flood Modelling in Oman Wadis

Ghazi Ali Abdullah Al-Rawas

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF DOCTOR OF PHILOSOPHY

DEPARTMENT OF CIVIL ENGINEERING

CALGARY, ALBERTA

DECEMBER, 2009

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Abstract

Oman is one of several countries located in an arid zone that is subject to flash flooding.

Records show that major flash floods occurred in Oman in 1989, 1997, 2002, 2003, 2005, and 2007. Few flash flood studies in the literature have focused on the issue of flash flooding in an arid environment. Consequently, flash flooding in arid regions affecting

Wadis like Oman, which is the focus of this research, is poorly understood. A review of the research gaps demonstrates that rainstorm and watershed characteristics are the most influencing factors on urban flash flood studies for an arid environment like Oman. The main objective of this research is to improve flash flood prediction by providing new knowledge and better understanding of the hydrological processes governing flash floods in arid regions like Oman. This includes developing rainstorm time distribution curves that are unique for this type of study region; and analyze, investigate, and develop a relationship between arid watershed characteristics (including urbanization) and wadi flow flood frequency in Oman.

Data from 2042 rainstorm events in the Rustaq watershed were used to develop heavy rainfall temporal distribution curves characteristic of arid climates. Orographic effects on rainfall were also investigated by separating the data into two regions, mountainous and coastal, and hyetographs were developed for both regions. The curves for both regions are similar and display a very high intensity at the beginning of the storm, which is known to be a characteristic of storms in arid regions. The new distributions were compared to other standard established distributions derived for regions in Canada and

ii the United States. In addition, curves were developed for an area of a similar climate in southern Alberta. The greatest similarity was found between the Oman and Calgary curves but there was significantly higher intensity earlier in the storm in the Oman curves.

Relationships between 12 watershed characteristics and mean wadi flood-peaks in northern Oman are investigated. Drainage area (DA), wadi slope (WS), watershed mean elevation (BE), and agricultural/farm area (FR) were found to be the key variables affecting flood flows, with DA having the strongest relationship. Bigger watersheds with high wadi slope, low altitudes, and less farms tend to have higher mean peak flow discharge (QMPF). Unlike past literature, this research shows that DA is positively related to flood peak discharge rates. A new approach is introduced by including FR impacts on runoff in Oman. The approach showed that FR improved the variance explanation by

11% over models using only traditional variables such as DA and BE.

Rapid urban expansion in the Wadi Aday watershed in Oman and its impact on wadi peak flow generation and flood frequency is investigated. New hydrologic curve number

(CN) and runoff coefficient (C) tables were created specifically for this type of region and for its unique residential characteristics and arid soils. The effect of urbanization on wadi peak flow between 1960 and 2003 shows that the urbanized area increased by 92%; the average simulated wadi peak flows increased by 68%, time to peak decreased by 22.2 min; the weighted C increased from 0.302 to 0.417, and the weighted CN increased from

76 to 79.

iii Acknowledgements

First and foremost I want to thank my God Almighty for allowing me to complete this work successfully. This work has been funded by Sultan Qaboos University, Oman. I would like to express my deep and sincere gratitude to my supervisor, Dr. Caterina

Valeo, for her encouragement, help, support, and guidance throughout this thesis. I wish to express my warm and sincere thanks to the members of my supervision committee,

Prof. Naser El-Sheimy, Dr. Quazi Hassan, Dr. Stefania Bertazzon, and Dr. Reza

Ardakanian. During this research I have collaborated with many colleagues for whom I have great regard, and I wish to extend my warmest thanks to the Ministry of Regional

Municipalities & Water Resources (MRMWR) in Oman, particularly Eng. Aisha Al-

Khatri, and Eng. Faiza Al-Wehaibi.

I owe my loving thanks to my wife, Ameerah, my kids Faisal, Fatima, and Sarah, for their patience during the years of the thesis preparation. My special gratitude is due to my brother, Prof. Amer Ali Al-Rawas for his continued support, advice, and words of encouragement. My loving thanks are due to my parent, brothers, and sisters in Oman.

Lastly, I offer my regards to all of those who supported me in any respect during the completion of this thesis.

iv Table of Contents

Abstract...... ii Acknowledgements...... iv Table of Contents...... v List of Tables ...... vii List of Figures and Illustrations ...... viii List of Symbols, Abbreviations and Nomenclature...... x Epigraph...... xii

CHAPTER ONE: INTRODUCTION...... 1 1.1 Flash Floods in Oman...... 2 1.2 Problem Statement...... 5 1.3 Research Objectives and Methodology ...... 8 1.3.1 Objectives ...... 8 1.3.2 Recognizing Challenges in This Research ...... 8 1.4 Research Contributions...... 10 1.5 Thesis Outline...... 11

CHAPTER TWO: LITERATURE REVIEW...... 13 2.1 Characterizing and Modelling Heavy Rainfall ...... 13 2.2 Characterizing the Watershed – A Hydrological Perspective ...... 16 2.2.1 Black Box Models and the Use of GIS & Remote Sensing for Watershed Assessment and Analysis...... 17 2.2.2 Rainfall-Runoff Modelling...... 21 2.3 Mitigation, Planning, Warning and Management Systems ...... 23 2.4 Gaps Relating to Wadi Flash Flooding...... 25 2.4.1 Rainfall Characteristics ...... 26 2.4.2 Watershed Characteristics ...... 27

CHAPTER THREE: CHARACTERISTICS OF RAINSTORM TEMPORAL DISTRIBUTIONS IN OMAN MOUNTAINOUS AND COASTAL REGIONS* ..29 3.1 Introduction...... 29 3.2 Methodology and Analysis ...... 33 3.2.1 Study Areas and Rainfall Characteristics ...... 33 3.2.1.1 Oman Rainfall Processes ...... 33 3.2.1.2 Southern Alberta...... 39 3.2.2 Analysis of Rainfall Data ...... 41 3.3 Results and Discussion ...... 42 3.3.1 Arid Zone Storm Distributions Derived in Oman ...... 42 3.3.2 Comparison with Other Climates ...... 46 3.3.3 Data Validation...... 50

CHAPTER FOUR: RELATIONSHIP BETWEEN WADI DRAINAGE CHARACTERISTICS AND PEAK FLOOD FLOWS IN OMAN* ...... 53 4.1 Introduction...... 53 4.2 Methodology...... 58

v 4.2.1 Study Area...... 58 4.2.2 Wadi Flood-Peak Flow Analysis...... 61 4.2.2.1 Trend Analysis: Mann-Kendall Test...... 63 4.2.3 Extraction of Watershed Characteristics ...... 65 4.2.4 Correlation and Regression Analysis ...... 69 4.2.5 Flood-peak frequency estimation from watershed characteristics ...... 73 4.2.6 Model Validation...... 74 4.3 Results and Discussion ...... 74 4.3.1 Method A...... 79 4.3.2 Method B: Effect of urbanization...... 83 4.3.3 Validation and comparison with MRMWR method ...... 91 4.3.4 Comparison with different climate, Salalah ...... 96

CHAPTER FIVE: URBANIZATION EFFECTS ON WADI FLOOD FLOW FREQUENCY ANALYSIS IN OMAN ...... 98 5.1 Introduction...... 98 5.1.1 Modeling Urbanization Impacts on Runoff...... 100 5.1.2 Changing Urban Forms in Oman ...... 103 5.1.3 Objectives ...... 107 5.2 Study Area and Methodology...... 107 5.2.1 Randomly Generated Rain Storms ...... 110 5.2.2 Soil Map ...... 113 5.2.3 Hydrological Curve Number (CN)...... 117 5.2.4 Wadi Peak Flows...... 118 5.2.5 Effective Drainage Area ...... 120 5.3 Results and Discussion ...... 123

CHAPTER SIX: CONCLUSIONS AND RECOMMENDATIONS ...... 136

REFERENCES ...... 142

vi List of Tables

Table 1-1 Source:2006 Dartmouth Flood Observatory, www.dartmouth.edu/~floods...... 3

Table 3-1 Monthly rainfall summary (1983-2003)...... 37

Table 3-2 Six hour storms at station EL772223AF...... 43

Table 3-3 The 50th percentile difference curves...... 51

Table 4-1 Statistics of wadis log flood-peak (QMPF) records...... 60

Table 4-2 Watershed characteristics for 12 Wadi-gauging stations in Oman...... 69

Table 4-3 Pearson correlation coefficients (r) ...... 71

Table 4-4 Calibration accuracy assessment ...... 73

Table 4-5 Selection of regression models for Method A...... 81

Table 4-6 Effect of urbanization U & FR...... 85

Table 4-7 Coefficients of Method B...... 86

Table 4-8 Validation watershed’s characteristics...... 91

Table 4-9 Accuracy assessment...... 93

Table 4-10 Accuracy assessment for Salalah...... 97

Table 5-1 Rainfall statistics...... 111

Table 5-2 Goodness of Fit Test...... 112

Table 5-3 Soil Classification...... 115

Table 5-4 Arid C values...... 126

Table 5-5 Watershed’s weighted C values...... 128

Table 5-6 Arid Curve Number (CN) values...... 130

Table 5-7 Watershed’s weighted Curve Number (CN)...... 131

Table 5-8 Wadi peak flow for different return periods using Log-Pearson Type III. .... 133

vii List of Figures and Illustrations

Figure 1.1 Map of major centers in Oman. This map is not an authority on international borders...... 4

Figure 1.2 A new building under construction located on the main wadi bed...... 7

Figure 1.3 Current flash flood zones map Ref. MRMEWR...... 7

Figure 2.1 Characteristics of a watershed...... 21

Figure 3.1 Digital elevation map of Rustaq watershed with gauging stations’ IDs (arrows in inset denote the predominate cold north-west and dry north-east winds in the winter season). This map is not an authority on international borders...... 36

Figure 3.2 Orographic effect on mean annual rainfall...... 39

Figure 3.3 Temporal rain distribution of gauge EL772223AF...... 44

Figure 3.4 Coastal region probability curves for (a) 2 hr and (b) 6 hr...... 46

Figure 3.5 Mountainous region probability curves for (a) 2hr and (b) 6hr...... 46

Figure 3.6 Comparison of Huff, Hogg, SCS, and Hershfield curves...... 48

Figure 3.7 Calgary, Alberta probability curves...... 49

Figure 3.8 Comparison of (a) coastal and mountainous probability curves and (b) total rainfall depths for validation and characterized curves...... 52

Figure 4.1 Study area. This map is not an authority on international borders...... 59

Figure 4.2 Scatter plot between QMPF and (a) DA, (b) BE, (c) SF, (d) DD, (e) WS, & (f) FR...... 78

Figure 4.3 Effect of DA, WS, & FR on QT...... 90

Figure 4.4 Comparison of the three models...... 95

Figure 4.5 Scatterplot of relationships of QMPF...... 95

Figure 5.1 Land use change in Muscat...... 106

Figure 5.2 Study area. This map is not an authority on international borders...... 109

Figure 5.3 CDF of exponential (2P) probability distribution...... 112

Figure 5.4 Soil and slope map of Aday watershed...... 114

viii Figure 5.5 Residential area in Oman vs. Canada...... 117

Figure 5.6 Wadi Aday effective area (dotted area)...... 122

Figure 5.7 Aday watershed’s urban expansions from 1960 to 2003...... 124

Figure 5.8 Change in urbanized area from 1960-2003...... 125

Figure 5.9 Change in C values...... 127

Figure 5.10 Fitted distributions to 2003 data generated (Rational Method)...... 133

Figure 5.11 Flow distribution curves for the years 1960 and 2003 for (a) Rational method and (b) SCS model...... 134

ix List of Symbols, Abbreviations and Nomenclature

%E Percent error A Effective drainage area ALERT Automated Local Evaluation in real Time AMC Antecedent Moisture Condition ASCE American Society of Civil Engineers ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer BE Mean basin elevation BL Basin length BP Basin perimeter BS Basin slope BW Basin width C Runoff coefficient CDF Cumulative Distribution Function CN Hydrologic Curve Number Cw Weighted runoff coefficient D Storm duration DA Drainage area DD Drainage density DEM Digital Elevation Model E Nash–Sutcliffe model efficiency coefficient E0.85WL The elevation at 85% of the distance along WL Egauge The elevation at the gauge station EIA Effective impervious area EMA Emergency Management Agency ER Elongation ratio ff Final infiltration rates during the storm event FFG Flash Flood Guidance FFGIT Flash Flood Guidance Improvement Team FFMP Flash Flood Monitoring and Prediction fi Initial infiltration rates during the storm event FR Percent of agricultural/farm area GIS Geographic Information Systems HEC-GeoHMS The Hydrologic Engineering Centers-Geospatial Hydrologic Modeling Extension HEC-HMS The Hydrologic Engineering Centers- Hydrologic Modeling System HEC-RAS The Hydrologic Engineering Centers-River Analysis System i Rainfall intensity I Accumulated infiltration depth k Infiltration coefficient L Hydraulic length of watershed MAF Mean peak flood

x MAR Mean annual rainfall MNE Ministry of National Economy, Oman MRMEWR Ministry of Regional Municipalities, Environment, & Water Resources, Oman MRMWR Ministry of Regional Municipalities & Water Resources, Oman MWR Ministry of Water Resources NM Proportion of non-mountain watershed PDF Probability Density Function PDO Petroleum Development Oman Q Storm runoff Qave Average simulated flow QMPF Mean wadi peaks flows Qpeak Wadi peak flow r Pearson's correlation coefficient R2 Coefficient of determination RMSE Root Mean Square Error S Average potential maximum retention SCS The Soil Conservation Services SF Basin shape factor SL Stream length SP Slope proportion SR Slope ratio Symbol Definition TIA Total impervious area tlag Lag time Tp, tpeak Time to peak U Percent of urbanized area UNDP/FAO United Nations Development Programme and Food and Agriculture Organization of the United Nations US The United States USA The United States of America USGS U.S. Geological Survey WFO Weather Forecast Offices WL Wadi length WS Wadi slope Y Average land slope

xi Epigraph

xii 1

Chapter One: Introduction

There are many definitions of a flash flood. One is a rapid rise of water in streams, creeks and storm drains that poses a threat to life and property. Flash floods are usually caused by excessive rainfall but ice jams and dam or levee failures can also cause flash flooding

(Emergency Management Agency EMA, 2002). Another definition is a flood which is caused by heavy or excessive rainfall in a short period of time (National Weather Service,

1995). Generally, flash floods generated in less than 6 hours can lead to rapid water level rises and falls. The term may also be used to alert the public of non-life-threatening flooding of small streams, streets, storm drains, and low lying urban areas. Flash floods are one of the most dangerous weather-related natural disasters in the world, and can create hazardous situations for people and cause extensive damage to property.

Measures to prepare for and mitigate flash floods are of primary importance for urban planning and agricultural land development projects within flash flood prone areas. Flash floods in arid environments are in fact common, but their occurrence is also poorly understood. Flash floods essentially occur when precipitation overcomes the drainage capacity of the basin resulting in an exceptionally high discharge in a short amount of time. They are the result of a meteorological event in combination with the basin’s condition (the hydrological condition) at the time of the meteorological event. The main hydrological factors contributing to flash flooding are the size, shape, topography, and land cover of the watershed. Flash flood hazard zones in arid regions are often

2 characterized by infrequent precipitation in the form of intense thunderstorms, steep slope topography and a lack of dense vegetation.

With regard to mitigation and preparation, flash floods in arid zones can occur within less than one-hour of an advanced warning. The problem lies in not only predicting the occurrence of the storm event itself but the amount of the precipitation in the storm that can threaten life in that basin. Rainfall runoff modeling is a primary tool used in flash flood studies; however, the literature has shown that this modeling is inadequate for prediction, mitigation or management for a variety of reasons, particularly in arid regions.

Arid and semi arid areas are characterized by a high variability and diversity in watershed characteristics. The wide diversity in some characteristics may require different parameter values and possibly, different approaches in different regions (Pilgrim, et al.,

1988). GIS based methods can aid in defining characteristics for differing regions and they can also provide a way to address the environmental problems arising from flash flooding in arid regions. However, further work is required in this area.

1.1 Flash Floods in Oman

Oman is one of the countries located in an arid zone that is subject to flash flooding.

Catastrophic floods and prolonged periods of drought are the main ‘water-related’ challenges facing Oman (Al-Ismaily, and Probert, 1998). A map of the region is shown in

Figure 1.1. Records show that major flash floods occurred in Oman in 1989, 1997, 2002,

2003, and 2005. Wadi Aday, Wadi Kabir, and Wadi Samail are the main wadis (dry river

3 channels incised in the mountains) that have experienced flash floods with major damage to people and property. Flash floods also carry trees, boulders and other large debris along wadi beds to the alluvial fan (as in Wadi Samail, Al-Khodh and Wadi Aday,

Qurum) and finally out to the sea. High slopes and sparse vegetation are also very important factors in increasing flash flood flow generation during heavy rainfall

(Schmittner and Giresse, 1996). Table 1-1 shows some historic records about flash floods in Oman. The first study on floods conducted in Oman was in 1987 by the Council of

Environment Protection & Water Resources (MRMEWR 2005). Based on this study, floods were divided into three categories; areas of high, low, and medium hazards.

Table 1-1 Source:2006 Dartmouth Flood Observatory, www.dartmouth.edu/~floods

Duration Displa- Area Year Locations Began Ended Dead (days) ced (km2)

1989 Muscat 14 Sep 16 Sep 3 2 72,160 Dibba 1997 21 Jun 23 Jun 3 4 21,980 Al-Baih

2002 Salalah 10 May 12 May 3 9 100 9,460

2003 Nizwa Muscat 14 Apr 19 Apr 6 30 23,060 Muscat, Dhofar 2005 Batinah Nizwa 01 Mar 23 Mar 23 7 700 489,000 Musandam

2007 Muscat 06 Jul 12 Jul 7 61 60,000 373,000

Figure 1.1 Map of major centers in Oman. This map is not an authority on international borders.

A report on the hydrology and hydrogeology of Samail, Oman (MWR, 1996), states that major rainfall events took place during 1977-78, 1981-83, 1986-88, 1989-90, and 1991-

92. Mean annual runoff at the wadi gauge site between 1984 and 1992 ranges from 0

Mm3/y (Wadi Al-Bahayis) to 4.0 Mm3/y (Al-Khawd).

Two distinct seasons, of summer (May–October) and winter (November–April), occur in

Oman. These seasons are associated with different hydrological processes occurring in various regions of Oman. The recorded annual flows for the main channel of Wadi

Dhayqa have ranged from 3x106 to 192x106 m3. The average annual flow is 45x106 m3 for the 15-year recorded period from 1979 to 1994.

1.2 Problem Statement

Omani villages whose settlement has been determined by water availability, exist mainly along the wadis. Based on this principle, the urban areas are located very close to, if not directly on the wadis. The capital, Muscat, is the fastest growing city in Oman. Due to the geographic location of the capital, which is surrounded by mountains, and the horizontal expansion of the urban areas, most of the wadis and the main channels have been occupied by urban development. The literature review shows a direct relationship between the urban growth and the increase in water runoff (Montz, et al., 2002). In addition, this could be more significant in Oman due to the lack of sewer and storm drainage systems in this area.

There is a huge potential for studying flash floods in Oman. Few reports and studies that are related to flash floods have been conducted. Greater details into this lack of study on flash flooding is provided in Al-Rawas & Valeo (2007) and in Chapter 2. Due to the rapid growth of population, industry, and tourism, studying flash floods has become very essential and inevitable. Because of insufficient information about flash flood hazard zones, some of the new government projects are located on the wadi bed as shown in

Figure 1.2. Current maps for flooding in Oman are very traditional and are created manually. In addition, they are general and lack details. Figure 1.3 below shows an example of these maps for the capital, Muscat, where the yellow color represents low hazard and the blue and red represent the medium and high hazard areas respectively.

Past studies on flash floods in Oman did not include any information on the amount of rainfall and flow which lead to flash floods, and the extent of the area that could be affected by these floods for different return periods. This kind of accurate information is very important for many purposes, such as dam design and development planning.

Therefore, the general objective of this research is to conduct a comprehensive GIS-based study that will improve flash flood prediction by providing new knowledge and tools specifically for arid regions and regions like Oman.

Figure 1.2 A new building under construction located on the main wadi bed.

Figure 1.3 Current flash flood zones map Ref. MRMEWR.

1.3 Research Objectives and Methodology

1.3.1 Objectives

The general objective of this research is to conduct a GIS-based study that will improve flash flood prediction by providing new knowledge and tools specifically for arid regions like Oman.

The specific objectives of this research are to:

1. Develop time-distributions of heavy storms that are applicable in some arid

regions but specifically to Oman. Because of the high variability of rainfall in the

arid areas, regional tables for rainfall depths of different return periods for every

basin could be created and would be better to use than the current tables for the

whole Muscat area.

2. Analyze the wadi flood flow data and develop a relationship between Oman

watershed characteristics and wadi flow for different return periods in Oman.

3. Observe changes in urbanization in the wadi watersheds and investigate the

impact of this change in urbanization on wadi peak flow and flood frequency.

1.3.2 Recognizing Challenges in This Research

In attempting to achieve these objectives, the author encountered several challenges either directly or indirectly related to hydrological engineering and flash flooding that are

9 particular to Oman (the study area for the author). The challenges could be divided into three categories; watershed analysis, rainfall-runoff modeling, and management issues.

Watershed analysis challenges:

A need for high resolution DEM, especially in the urban areas.

Rainfall Analysis and Rainfall-runoff modeling challenges:

• Lack of an operational model that can predict flash flood prone wadis.

• The Short length of available rainfall and runoff records. In some gauges, no

hourly rainfall data are available in the Ministry of Regional Municipalities,

Environment & Water Resources (MRMEWR) before 1998. It is acknowledged

that daily records do not reveal the characteristics of rainstorms of short duration;

however, they are believed to indicate the occurrence and the total rainfall depth

of the events adequately (Bogardi, et al., 1988).

• A lack of sufficient number of flow gauges in urban areas.

• Varying types of projections in spatial data.

• An absence of a detailed soils map for the Sultanate.

• Mismatching temporal and spatial scales.

• A lack of survey data for wadis such as cross-sectional data along the main wadis.

Management challenges:

• A lack of a national database that could serve researchers.

• Scattered data among different governmental organizations. There are many

governmental organizations that possess data required for flash flood studies. The

following is a list of some of these organizations:

a) Ministry of Regional Municipalities, Environment & Water Resources

b) Ministry of Defence

c) Ministry of National Economy

d) Directorate General of Civil Aviation and Meteorology, Ministry of

Transportation & Communications

e) Ministry of Electricity & Water

f) Muscat Municipality

g) Petroleum Development Oman (PDO)

1.4 Research Contributions

This work is a comprehensive study that merges modeling of heavy rainfall storms and uses GIS & Remote Sensing for watershed assessment and analysis. Huff (1986) found that variations in time-distribution models significantly affect the runoff computations in design models for urban areas. Therefore, providing accurate time distribution models on a regional basis is essential.

Studying rainstorm characteristics in an arid region like Oman and developing rainstorm time distributions models (cumulative-rainfall hyetographs) using procedures similar to

11 those given by Huff (1967, 1990) or by Hogg (1980) for this region is the first research contribution of this dissertation.

The application of a new approach developed in this dissertation combines GIS &

Remote Sensing for watershed analysis also improves the study of flash floods and it is an addition to flash flood studies especially for Oman. New relationships between Oman watershed characteristics and wadi peak flow are developed. Where most of the previous studies used common physical characteristics such as watershed area, slope, and stream length, this research uses a new characteristic: the urbanization factor. In addition, new tables for runoff coefficients (C) and curve numbers (CN) were developed for arid regions but specifically for Oman.

1.5 Thesis Outline

A literature review of flash floods and methods is described in Chapter 2. A detail review of past studies done on flash floods using different approaches and topics for flash flood modelling, assessment, analysis, mitigation, planning & management, emergency, and warning systems is also covered in this Chapter. Then, the research gaps to be examined in the thesis are provided in this Chapter. At the end of this chapter, recent research works on the rainstorm characteristics in arid areas and their effect on flash flood studies.

Chapter 3 of the thesis covers a detailed rainfall analysis of the relevant rain-gages to reach the typical design storm in Oman and achieve specific Objective 1. Rainfall

12 analysis is described in Chapter 3, this chapter includes rainstorm characterization and

Huff and Hogg diagrams. Chapters 4 and 5 cover the methodology for achieving

Objectives 2 and 3, respectively. A DEM, GIS, and Remote Sensing are used for delineating the watershed zones and calculating the hydrological and physical properties needed to generate the design floods for different design return periods. Chapter 4 covers

Oman arid watershed analysis that includes the wadi peak flow analysis and development of equations of the relationship between wadi peak flow and watershed characteristics.

Investigation of the effect of urbanization on wadi flood flow in Oman, and the simulation of wadi flows using new tables for hydrological coefficients (C & CN) developed specifically for Oman are described in Chapter 5. Chapter 6 contains the conclusions and future research recommendations of the thesis. Finally, Chapter 7 lists all the references and appendices used in this research.

Chapter Two: Literature Review*

There are few studies on flash flooding in the literature and fewer still that focus on flash flooding in arid regions. The studies also vary widely in their focus with some focusing on predicting and analyzing different types of heavy rainfall storms that can generate flash floods for use in warning and weather forecast systems. Other, hydrologically focused studies, deal with the analysis and assessment of watershed factors that effect flash flooding such as geometric properties of the watershed, water channel flow, etc.

One additional type of study is that dealing specifically with decision support systems and are concerned with the technical (and possibly mechanical or electrical) aspects of an existing type of warning system and how this system is working. A brief review of these studies is provided here.

2.1 Characterizing and Modelling Heavy Rainfall

Modelling meteorology and heavy rainfall events is a logical starting point for many studies whose ultimate goal is the management of flash floods. A few examples of this type of work include Bechtold and Bazile (2001) who used three different meteorological modeling frameworks (ARPEGE, ALADIN, and Meso-NH) for the 12–13 November

1999 Flash Flood in Southern France. The main objective of that study was to investigate how well these models could realistically represent the spatial and temporal rainfall

* Some material in this chapter can be found in Al-Rawas, G and Valeo, C. 2007. “Factors affecting flash flood modeling in arid regions’ wadis.” 4th Int. Conf. on Wadi Hydrology, Muscat, Dec. 2-4, 13 pp.

14 distribution creating the flash flood event. The analysis was performed from an atmospheric perspective that solely focused on the storm. The primary conclusion of the study was that the rainfall showed significant sensitivity to atmospheric moisture analysis, sea surface temperature forcing, and formulations of model physics.

In a paper on the evaluation of a warm season severe eastern Kentucky flash floods,

Henry and Mahmood (2005) examined the atmospheric conditions associated with one of eastern Kentucky's major flash flood events that took place August 3-4, 2001. The author tracked a flash-flood event in this area and compared it to simulated radar images. While model simulations of the rainfall event leading to flash flooding proved to provide satisfactory results, the author reported that extreme moist conditions coupled with topographic effects resulted in heavy precipitation and flash flooding.

Detailed studies of the types of rainfall leading to flash flooding are no more common in the literature than studies attempting to model these events. A few examples include a water resources evaluation of rainfall enhancement in Oman in which Yates & Bruitjes

(2004) addressed the feasibility of rainfall enhancement for man-made increases in groundwater recharge. Average rainfall figures for annual, summer, and winter seasons illustrate the clear spatial variability in rainfall across the area and the strong relationship between rainfall and topography. Slightly higher annual average rainfall has been noted in the northern part of the Oman Mountains, while a second maximum was located further to the southeast in more mountainous terrain. The author recommended that modern weather radar be used for longer-term rain storm evaluation.

A study that focused both on characterizing rainfall and the watershed characteristics leading to flash floods was Jackson et al.’s (2005) study that related meteorological data with flash flood hazard maps by observing and predicting the expected rainfall for certain basins and comparing them to the associated hazard maps. This study’s characterization of the watershed focused mainly on soil moisture which was used to determine a flash flood index. Other important hydrological factors of the basin physiographic characteristics such as shape factor were not considered.

Another example of a cross-cutting study is that of Davis (2004). Davis used weather radar in conjunction with rain gauges to show that more rainfall was associated with tropical storms than convective storms. This study also concluded that the watershed’s hydrologic response to the heavy rainfall is a key factor and extremely important to the correct issuance of flash flood warnings. Similarly, Schmittner & Giresse (1996) observed that kinetic energy rather than rainfall depth or rainfall intensity was more closely linked with rapid runoff.

The distribution of rainfall with time is very useful for understanding many hydrologic problems, such as the design of urban storm-sewer systems. This knowledge is also useful for studying the flood potential of various types of rainfall events, as well as advancing the general understanding of the physics of the atmosphere with regard to precipitation processes (Huff, 1967).

Watershed and urban runoff models are becoming more sophisticated and require temporal inputs and a definition of the time distribution characteristics of rainfall during heavy storms to drive the modeled hydrologic processes (Bonta, 2004; Huff, 1990). A method for synthesizing short-time increment rainfall modelling is by use of Huff curves

- statistical characterizations of rainfall intensities within storms. These curves are developed from 15-mi or hourly data, and can vary depending upon factors that affect their development, such as sample size, storm size, season of year, etc (Bonta and

Shahalam, 2003).

Other studies have been involved in the development of time-distribution relations such as the Soil Conservation Service (1986). Pani & Haragan (1981) used Huffs (1967) methodology to develop relations for Texas, Bonta & Rao (1987) investigated application of the Huff curves in Ohio (Huff, 1990).

2.2 Characterizing the Watershed – A Hydrological Perspective

The types of studies that take a hydrological perspective attempt to use watershed characteristics to predict or understand the mechanisms leading to flash floods; and thus often use some kind of model. How the studies vary is in the approach used in the modeling process. Some studies take a black-box simplistic approach that will develop flash flood indices using a Geographic Information System and information on the watershed’s topography, landuse and rainfall characteristics possibly derived from remote

17 sensing. Other more intensive efforts will use more physically-based rainfall-runoff models.

2.2.1 Black Box Models and the Use of GIS & Remote Sensing for Watershed Assessment and Analysis

Jackson et al.’s (2005) study developed a flash flood index based on soil moisture but did not consider other important hydrological factors. The study did use a 90 meter resolution

Digital Elevation Model (DEM) to derive slopes, and vegetation characteristics were derived from 30 meter Landsat images. Flash flood prediction is often a small scale phenomenon affecting urban areas with heterogeneity in the order of less than 10 meters.

This can have repercussions to the resolution required for DEMs and land use/cover maps.

Davis’s (2004) study looking at the rainfall depths associated with tropical storms and convective storms also concluded that the watershed’s hydrologic response to heavy rainfall is a key factor and extremely important to the correct issuance of flash flood warnings. By using GIS, the author also concluded that watershed factors such as hydrologic connectivity to contributing upstream watershed areas and channel routing should be considered.

Predicting flows in semi-arid watersheds using GIS technologies is an ongoing project conducted in San Diego state university. The goal of this project is to use geographic

18 information system (GIS) technologies to develop a simple stream discharge model for predicting flows through the Tijuana river watershed (Wright, 2002).

Schmittner and Giresse (1996) developed models for Mediterranean Roussillon area, SE-

France, depending on geomorphic-environmental predictors, to calculate flash-flood flow in nearly natural and human influenced environments. The author pointed out that the effect of steep slopes, high kinetic energy and less vegetation cover in the headwater areas help to generate the flash-flood flow during the heavy rainfall. They found that these geomorphic impacts are more important in flash flood generation than rainfall specific intensity of 1 to 6 hours. In general, this paper presented the effect of urban expansion, slope, soil, and vegetation on flash floods. With the increase of urbanization and building constructions along or very close to the river channel, the channels tend to become more canalized (narrower) and will increase the amount flow rate and runoff in the channels.

A paper on modeling urban growth effects on surface runoff with the integration of remote sensing and GIS by Weng (2001) used Landsat imagery to detect urban landcover changes. GIS analysis was then conducted to determine the magnitude of urban growth rates within administrative units which was in turn used to examine the changing spatial patterns of urban growth. In this study, GIS was used to derive two key parameters: rainfall and hydrological soil groups. Integration of remote sensing and GIS was applied to automate the estimation of surface runoff based on the SCS model through map algebra and overlay functions of GIS. This paper examined the impacts of urban growth

19 on surface runoff and the rainfall–runoff relationship by linking the two modeling results with spatial analysis techniques. This study proved that urban growth played a critical role in changing the relationship between rainfall and surface runoff. Where there is more urban growth, the greater the potential for increased surface runoff. In addition, the methodology developed in this paper provided an alternative to traditional empirical observations and analysis using in situ (field) data for environmental studies. The author recommended that future research efforts should validate these results. He also recommended that an investigation of the possibility and feasibility into the integration of remote sensing and GIS should be applied in a regional and global context.

Laben (2002) talked about the importance and the capability of remote sensing and GIS in natural disaster management and different types of satellite and radar imagery that could be used to mitigate and manage natural disaster. He pointed that a GIS capability will not only be effective in the response and recovery phases of disaster management, but should be used in the mitigation and preparation phases as well. The author made some recommendations that might result in a smooth-flowing operation and an efficient use of time and resources in a disaster situation. For example, remotely sensed and other

GIS data should be stored with consistent formats, datum, and projection.

A DEM can be used to automatically extract topographic variables of the watershed from raster data on elevation (Jain et al., 2004). To delineate the physical properties of the watersheds from the DEM, a specifically customized GIS hydrology model, ArcHydro, and Remote Sensing imagery are used. Basins could be determined using DEM along

20 with satellite images. The physical parameters could be calculated for each subwatershed within the basin. Watersheds and their geomorphic properties could be delineated in a faster and more accurate way giving reliable measurements than traditional techniques based on manual calculations using hard copies of maps. Figure 2.1 shows some of these characteristics. These parameters include:

1. Watershed boundary

2. Flow Direction

3. Flow accumulation

4. Shape factor

5. Mean land and channel slope

6. Mean rainfall

7. Drainage density

8. Vegetation density

Figure 2.1 Characteristics of a watershed.

2.2.2 Rainfall-Runoff Modelling

Other more intensive studies that go beyond the development of a simple flash flood index or GIS-based characterization of the watershed will often use sophisticated rainfall- runoff models. Reed et al. (2004) used a statistically-distributed modeling approach for flash flood prediction. Their model was used to develop and simulate floods on small basins (ungauged locations) and account for hydrologic modeling uncertainty of a wide range of scales using radar precipitation data. The problem in this approach is that the minimum basin size that can be used is around 130 km2 using radar-rainfall data available at 4 km. Basins less than this size can have a very high amount of uncertainty introduced by their model. This kind of study requires very detailed rainfall-runoff data to compare it with radar rainfall data. The author concluded that the ‘statistical’ part of the approach addressed the expected uncertainties at smaller scales and ungauged locations where

22 calibration was not possible; whereas the ‘distributed’ part addresses the scale issues. In other words, a statistical-distributed approach provides more spatially explicit and physically consistent estimates of relative flood severity compared to what is currently provided.

Foody, et al. (2004) predicted the locations sensitive to flash flooding in an arid environment but the study area of this paper had a lack of hydrological information. Most of these hydrological properties were derived indirectly from a land cover classification of satellite remotely sensed imagery data. The objective of this study was to predict sites at risk from large peak flows associated with flash flooding in a wadi located in the

Eastern Desert of Egypt. The main indicator of flash flooding in this study was the hydrograph’s peak discharge predicted using the Hydrologic Modeling System (HEC-

HMS). Thus, the relative magnitude of predicted peak discharges, limited field data of soil texture and infiltration properties, and hydrological properties such as curve number derived indirectly through geological land cover classification of satellite remote sensing were used as indicators for flash flood potential sites.

Julien, et al. (1998) developed the first version of the model CASC2D at Colorado State

University to simulate surface runoff from flash floods caused by intense thunderstorms moving across watershed areas. CASC2D model has the capability to model flash floods from localized storms in semi-arid areas like Macks Creek, Idaho. This model could calculate infiltration depth and the surface flow depth on each pixel at each time step, and the flow hydrograph can be plotted for any selected point on the watershed. The

23 advantage of this model is that the effect of storm motion on surface runoff hydrographs can be quantified with radar data and two-dimensional CASC2D runoff simulations.

2.3 Mitigation, Planning, Warning and Management Systems

These types of studies go beyond the meteorological and hydrological perspective to often include policy implementation or development, decision support system development, or even sociological elements that can assist in the mitigation of flash floods. An example of the latter is a study on human activity and urban growth (Montz, et al., 2002). Montz, et al. (2002) pointed out that human activity is expanding into more hazardous areas, particularly in or next to mountainous regions (e.g. recreation places).

Examples of the relevant sociological elements that should be researched/included are: individual action, perception of risk to flash flooding, and raising the awareness in the public about the risk of flash floods. This paper concluded that the relationship between hydrometeorology and social science is critical to advancing our abilities to cope with flash floods.

Flash Flood Guidance (FFG) (Sweeney & Baumgardner, 1999) is the amount of rainfall needed in a specified period of time to initiate flooding in small streams. FFG is computed for small, ungaged streams (in grids, zones, and counties) and for gauged streams (primarily headwaters) for different time durations. In FFG, the rainfall-runoff model uses the current moisture state and runoff as input data to provide rainfall. The resulting rainfall is the Flash Flood Guidance. A unit hydrograph is required to calculate

24 the threshold runoff (the runoff needed to initiate flooding) as input data for FFG. In the case of an ungaged basin, the unit hydrograph peak must be determined empirically using the physical characteristics of that basin. Depending on the availability of precipitation data, the forecast system can update soil moisture conditions via rainfall-runoff curves every six hours and thus, the FFG can be computed every six hours.

In areas of the USA where soil moisture influences the occurrence of flash flooding more than land characteristics and rainfall intensity (FFGIT, 2003), Weather Forecast Offices

(WFO) use zone/county and headwater FFG as criteria for issuing flash flood watches and warnings. For example, the WFO application Flash Flood Monitoring and Prediction system compares gridded FFG and rainfall estimates from the radar stations. If the radar estimated rainfall equals or exceeds the FFG for a grid, the grid would be depicted as red on a graphical display and the WFO would issue a flash flood warning for the flood prone area represented by the grids.

Flash Flood Monitoring and Prediction (FFMP) software was used in a study by Davis

(2003) to detect flash floods. Average basin rainfall based on rainfall estimates from the weather surveillance radar was used with FFMP. An interesting technique here is using

FFMP in flash flood analysis by reversing the sequence of events; that is, to specify the location of damage caused by a flash flood event first and then go backwards to the stream segment that suffered from the damage and then use the average basin rainfall for that segment (and upstream) as the causal event for a flash flood.

Platt and Cahail (1987) focused on a system known as Automated Local Evaluation in real Time (ALERT) that employs stream and gauges equipped with self-activating radio transmitters communicating with a central microcomputer. This paper presented the financial, economical, and technical aspect of this system. Based on the experiences of 32 system users, the survey found a high level of satisfaction with ALERT. However, few have been tested on actual flash floods.

2.4 Gaps Relating to Wadi Flash Flooding

What the literature demonstrates is that there is currently an incomplete understanding of the Wadi (dry river channels incised in the mountains) channel hydrological processes in arid and semi-arid environments. The gaps in knowledge and issues to be addressed for this type of area include:

1. Inadequate representation of the high variability of precipitation in space and time

leading to a very high variability of runoff;

2. Lack of detailed field measurements or insufficient length of historical records

that further exacerbate the problem;

3. Each area prone to flash flood modeling will exhibit unique problems on top of

these general problems;

4. The literature review shows that study of rainstorm characteristics for arid regions

is very limited, and most of the literature is based on a humid climate. Huff

(1986) pointed out that the time-distribution relations will vary between regions

of the country having different climate regimes. Rainstorm characteristics such as

the time distribution of the precipitation corresponding to the design return period

and duration is very important and can have a significant effect on the runoff

computations for flood studies (Nouh 1985; Huff 1990). Studying rainstorm

characteristics such as temporal distribution and its effect on flash flooding for

arid regions is a big gap that needs to be investigated.

5. Improved watershed characteristics delineation using Remote Sensing and GIS,

and their relationship with wadi flood flow for an arid area could be an important

factor in flash floods studies.

From the above list, the gaps that have the largest influence on urban flash flooding that need to be investigated for an arid environment like Oman are: (i) rainfall characteristics in Oman; (ii) the influence of Oman arid watershed characteristics on wadi peak flow; and (iii) the impact of urbanization on wadi flood peak flow.

2.4.1 Rainfall Characteristics

Many hydrological and water resources engineering designs (e.g. urban drainage systems) are heavily dependent on the temporal distribution (hyetograph) of total rainfall during a storm event. Therefore, understanding the accurate estimation of rainstorm characteristics is essential. The rainfall characteristics model should help the hydrologist in design problems or other applications in which time distribution is an important factor, which is the case for flash flooding in Wadis. Many studies (Huff, 1967; Bogardi, et al.,

1988; Graef & Haigis, 2001; Mays, 2001; Gheith & Sultan, 2002; Goodrich, et al., 1990;

Ferreira, 1990) have addressed the development of rainfall time distribution curves and their applications and impact on water resources design problems. A complete literature review on rainfall characteristics is described in Chapter 3.

2.4.2 Watershed Characteristics

The land surface can be divided into watersheds based on the drainage system of water where surface flow within a watershed drains to the same point (outlet). Surface flow paths are commonly derived from raster DEMs and several algorithms exist to determine the direction of water flow from cell to cell based on terrain analysis and DEMs (Martz and Garbrecht, 1992). The most convenient and widely used method is the D8 algorithm

(Maidment, 1993), where the direction of flow out of each cell corresponds to the direction of steepest descent to one of the eight surrounding cells.

Knebl et al. (2005) developed a framework for regional scale flood modeling that integrates NEXRAD rainfall and GIS with the HEC-HMS rainfall–runoff model and the

HEC-RAS river hydraulic model. They used an extension of GIS called HECGeoHMS for terrain preprocessing, implying the utilization of the surface topography (DEM) as the origin of the stream network. HEC-GeoHMS was also used to delineate subbasins from the network and local topography; for calibration purposes. The locations of USGS stream gages were designated as subbasin outlets. The author noted the importance of using an accurate, high-resolution DEM for terrain preprocessing steps in hydrologic modeling. If the DEM used is not sufficiently accurate, simulated rivers may follow very

28 different paths from their actual pathways, and consequently watersheds will be delineated incorrectly (Knebl et al. 2005). Chapters 4 and 5 investigate the influence of

Oman arid watershed characteristics including urbanization impacts on wadis peak flows.

A better understanding of the relationship between arid watershed characteristics and wadi peak flow, together with the rainfall characteristics in Oman could improve flash flood prediction.

Chapter Three: Characteristics of Rainstorm Temporal Distributions in Oman Mountainous and Coastal Regions*

3.1 Introduction

The accurate estimation of rainstorm characteristics, specifically the temporal distribution

(hyetograph) of total rainfall during a storm event, is essential for the solution of many engineering and hydrologic problems including the design of urban storm-sewer systems.

In addition, detailed information on the hyetograph is useful in the study of soil erosion, the flood potential of various types of rainfall events, as well as advancing the general understanding of precipitation processes. The selection of the hyetograph is known to have a significant impact on the runoff hydrograph (Nouh 1985). For example, Knapp and Terstriep (1981) found significant differences when computing peak runoff from probable maximum precipitation and 100-year storm events using a variety of standard hyetographs including Huff’s Illinois distributions, SCS Type-II distribution and the

Corps of Engineers distribution. Several standard hyetographs for severe rainstorms have been developed in the past (Pilgrim and Cardery 1975; Huff 1967; and Hogg 1980) but the detailed analysis of the temporal distribution of rainstorms essentially began in 1967 when the Illinois State Water Survey published data collected from heavy storms sampled during a twelve year period from 1955 to 1966.

* Some material in this chapter is presented in the Journal of Hydrology, 376 (1-2): 318-326, 2009 with some modifications.

Huff (1967) developed hyetographs for heavy storms which were presented in probability terms to provide quantitative measures of both inter-storm variability and general characteristics of the temporal sequence of precipitation in storms. Huff represented the distributions in terms of the percentage of storm rainfall to the percentage of total storm time and grouped the data by quartiles. The study found that storm duration and areal mean rainfall explain only a small portion of the variance in the statistical analysis when the temporal distributions are classified by quartile and expressed as percentages of total storm duration and rainfall. Numerous factors contribute to the storm variance; among these are the stage of development of the storm, the size and complexity of the storm system, rainfall type, synoptic storm type, the location of the sampling area with respect to the storm center, and the movement of the storm system across the sampling region.

Huff (1967) recommended that in many cases a median hyetograph will be most useful.

However, in some cases an extreme type of storm distribution may be desired in which the runoff is most likely to maximize at a particular time during a storm.

Following this, the Soil Conservation Services (SCS) published average hyetographs in

1972 that became widely used for design purposes; however, these hyetographs were derived from a specific climate. There are numerous other examples of published hyetographs specific to various regions; however, most of the published literature available on rainstorm characteristics is derived in humid climates with relatively little focus on arid climates. This creates a serious problem when designing for arid regions.

Bogardi et al. (1988) presented an event-based approach adapted to semi-arid climates that is applicable when the data available are considered unreliable. The study found a weak relationship between event duration and rainfall depth per event and no significant pair-wise correlations were detected between inter-event time and duration, nor rainfall depth of the preceding and succeeding events. Graef and Haigis (2001) studied the spatial and temporal rainfall variability in semi-arid Niger. They showed that rainfall can vary considerably within a distance of a few kilometers and with varying time scales. Survey scale rainfall data for southwest Niger showed that annual differences of 200-300 mm may occur within a radius of 100 km.

Mays (2001) asserted that the main feature of precipitation in arid and semi-arid regions is the high variability in time and space of the small amount of precipitation received.

Mays stated that flash floods in these areas are caused by high intensity, short duration storms with a high degree of spatial variability and that the runoff hydrographs typically exhibit very short rise times, even for large catchments. Thus the temporal characteristics of the rain events are keys to developing standard hyetographs.

Ferreira (1990) discussed the temporal characteristics of arid land rainfall events. The results showed a strong sensitivity of runoff predictions to the time interval of input rainfall data. The Goodrich et al.’s (1990) study in arid and semi-and areas of the Arizona concluded that the appropriate rainfall sampling interval for arid land watersheds depends on many factors including the temporal pattern of the rainfall intensity, watershed response time, and infiltration characteristics. They recommended that data sampled at

32 uniform time increments be used for watersheds with equilibrium times smaller than 15 minutes, but a maximum interval of 5 minutes should be used for more slowly responding basins.

Sabol and Stevens (1990) indicated in their study comparing design rainfall criteria for the arid region of US southwest that some of the more commonly used design rainfall criteria may not adequately represent rainfall characteristics of the southwest. Therefore, specific design rainfall criteria should be developed based on historic storms when data are available.

Zeller (1990) stated in a research study of precipitation on arid and semi-arid regions of the southwestern United States that the proper characterization of the amount and spatial/temporal distribution is the key element of rainfall-runoff modelling in arid and semi-arid regions. Because the precipitation in arid and semi-arid regions has unique characteristics and extremely violent nature when it does occur, Zeller suggested that the

“conventional” methods used in eastern or midwestern US is not generally appropriate for use by engineers and hydrologists.

Duffy et al. (1990) studied the orographic (altitude) relationship with spectral analysis of annual time series of mountain precipitation in northern Utah, in western US. They indicated a substantial orographic effect on the precipitation mean and variance. Many arid regions, and particularly in the case of developing countries, have a very limited number of gauging stations. This is suspected to be the main reason behind the lack of

33 historical data, information, and rainfall studies in arid regions. Despite these obstacles, this study is focused on developing and analyzing rainstorm hyetographs using data from an arid region. Because of the lack of data, very few areas present viable cases from which to develop design criteria in arid regions. However, this work focuses on an arid region in the Middle East (Oman) with another arid region in Canada (southern Alberta) as a comparison. It is anticipated that the hyetographs are unique to arid regions of this type and would be appropriate for use in a similar context. The study also compares arid region rainstorm behaviour with the standard hyetographs developed for other climates.

3.2 Methodology and Analysis

3.2.1 Study Areas and Rainfall Characteristics

3.2.1.1 Oman Rainfall Processes

The Rustaq watershed in Oman was selected for the current study because of availability of rainfall data. The watershed is located in northern Oman and covers an area of 2720 km2. Figure 3.1 is a digital elevation map of the Rustaq watershed and surrounding area.

Mean annual rainfall throughout most regions in Oman is relatively low, less than 100 mm, in the coastal regions, but reaching as much as 350 mm in the mountainous regions

(MRMEW 2005). The total average annual amount of rain falling on Oman is estimated to be about 19,250 Mm3. The majority of which evaporates, leaving less than 20% as

34 effective rainfall generating runoff and direct infiltration to the groundwater reservoirs

(MRMEWR 2005).

The Water Resources Facilities and Management Strategy for Oman (Al-Ismaily and

Probert 1998) noted that the earliest rainfall data recorded for Muscat began in 1893. The rainfall data indicate significant annual variations with evidence of the occurrence of both severe floods and long dry periods. Most of the precipitation in Oman occurs as rainfall.

However, isolated hail storms occur, with snow on rare occasions in the mountains of

Jabal Al Akhdar. The mid-Asian high-pressure zone and the low-pressure area of the

Indian Ocean have major influences upon the winter climatic conditions in Oman.

Wheater and Bell (1983) used all available rainfall data for Oman for rainfall frequency analysis and a comparison with data from other parts of the Arabian Peninsula. The resulting work took the form of rainfall intensity curves for storms with durations ranging from 15 minutes to 24 hours and with return periods ranging from 2 to 500 years. In

1997 the work was revised and updated to include data from stations built after 1983

(MWR, 1997).

Two distinct seasons occur in Oman, summer (from May to October) and winter (from

November to April). The seasons are associated with different hydrological processes occurring in various regions of Oman. There are four principal mechanisms that cause rainfall in Oman; convective rain storms which can develop any time of the year but mostly during the summer months; cold frontal troughs, which are common during the

35 winter and early spring; On-shore monsoon currents which occur from June to September that bring a complex regional circulation and result in frequent drizzle along Dhofar, the southern part of Oman; and Tropical cyclones which move in from the Arabian Sea and occur on average about once in 5 years in Dhofar and once in 10 years in Muscat.

In winter, Oman is affected by the cold north-west wind that blows from the high pressure belt of the northern part of the Arabian Peninsula and dry north-east wind that blows from the high pressure belt over central Asia as shown in Figure 3.1. At this time, a low pressure area is developed on the Gulf of Oman. The dry north-east winds that carry some moisture from the Gulf of Oman meets with the colder north-west winds and creates local troughs resulting in rain (MRMEWR, 2005; Babikir, 1985). In the summer time, a low pressure belt is developed over Oman and the Arabian Peninsula. This region is affected by the pressure from both the Indian Ocean from the south and the north-east.

In this season (especially June to August) rainfall mostly occurs in the southern part of

Oman due to the south-west monsoon moist winds.

Available daily rainfall data from 20 stations in the same area (five of them are not included in the temporal distribution analysis) are analyzed. The time period of these stations ranges from 1983 to 2003 (20 years). Table 3-1 shows the average of: mean annual rainfall in mm, standard deviation, maximum recorded rainfall, and the number of rain days. A seasonal trend is obvious in this table where March (wet season) has the maximum mean rainfall of 15.4 mm. The monthly mean rainfall for July is usually less than the reported value of 13.7 mm. This is due to the high amount of rainfall (up to 300

37 mm) that the Hajar Mountains in northern Oman received in July, 1995 due to an uncommon monsoon depression (Membery, 2001). Excluding this event from the analysis, July mean rainfall drops to 9.6 mm instead of 13.7 mm. Usually at this time of the year, only the southern part of Oman is affected by the monsoon winds with the exception of some rare monsoon tropical cyclones that could reach the Arabia and northern Oman.

Table 3-1 Monthly rainfall summary (1983-2003)

Mean (mm) St. Dev. (mm) Max. (mm) Rain days

Jan. 7.0 11.9 39.9 0.9

Feb. 9.5 16.1 58.7 1.2

Mar. 15.4 30.4 123.4 1.5

Apr. 11.9 20.2 65.8 1.0

May 2.9 5.6 21.4 0.4

Jun. 4.2 7.0 25.4 0.5

Jul. 13.7 22 82.0 7.0

Aug. 8.5 13.1 46.4 0.9

Sept. 4.2 6.8 22.8 0.5

Oct. 5.0 11.3 43.4 0.5

Nov. 4.2 10.9 42.6 0.5

Dec. 7.3 16.7 63.9 0.8

Annual Total 93.8 15.7

Orographic effects strongly control the pattern of rainfall in Oman and there is an often great difference in the amount of rainfall over short distances. Babikir (1985) on his study on the distribution of rainfall over the Sultanate of Oman stated that heavier amounts of rainfall are generally associated with higher altitudes. Although the distance between stations in Figure 3.1 is less than 15 km, the significant variability in the mean annual rainfall is evident. Generally, arid regions are characterized by a high spatial and temporal variability in rainfall and consistent rainfall-elevation relationships are sometimes difficult to observe. However, Figure 3.2 shows a good trend in positive relationships between mean annual rainfall and elevation.

It is known that the temporal distribution of rainstorms can vary between regions with different precipitation climate regimes (Huff 1986). For this reason the study area was divided into two main regions; mountainous and coastal areas. The costal area appears as light gray and areas considered mountainous are identified with dark gray in Figure 3.1.

In the study temporal probability distributions were created for each of the two regions, mountainous and coastal, by merging the data from stations within each region. The mountainous region has more gauging stations, with a total of 13. Seven stations are located within the coastal region. Each of the 20 stations used in the study had 20 years of rainfall data available for the period from 1983 to 2003. As shown in Figure 3.1, five of the stations included in the study are not actually located within the Rustaq watershed.

The five stations from the neighboring watershed have all been included as additional data for the mountainous region. In addition to having more stations, the stations in the mountainous region typically have more data than the coastal region. The total number

39 of storms recorded in the mountainous region was higher 1631, whereas the total number of storms in the coastal region was 411. The difference is due to a combination of effects. There is a lower frequency of rainfall in the coastal areas and there are fewer gauging stations in that region.

300

250 R² = 0.864

200

150

100 Mean annualMeanrainfall, mm

0 0 100 200 300 400 500 600 700 800 900 1000 1100 Elevation, m

Figure 3.2 Orographic effect on mean annual rainfall.

3.2.1.2 Southern Alberta

For comparative purposes, data were collected from two rain gauges in Calgary (southern

Alberta, Canada) and were analyzed in a similar manner. These two stations are approx.

30 km apart with the McKenzie Lake station residing in the south of Calgary and the

Country Hills station in the north of Calgary. The total number of storms that occurred from 1995 to 2006 for the Calgary region was 619 for both stations. Alberta has a continental climate and features a wide variation in temperature, cloud, and precipitation between summer and winter and from day to day in every season. Southern Alberta is distinguished for its dry climate, rainfall variability, and prolonged droughts

(Environment Canada, 1990).

The average precipitation in Calgary, for example, is a modest 430 mm a year, with about

150 mm usually falling as snow. Most of the heavy rainfall events are generally associated with convective activity from late spring through the summer season which starts from June to August and is considered the most important period for thunderstorm activity in southern Alberta (Khandekar, 2002). June is the normally the wettest month in the summer season, where the average rainfall is 89.4 mm. July and August average rainfall 65.4 and 55.4 mm respectively (Klivokiotis and Thomson, 1986). Although the prevailing winds are from the northwest, warm westerly winds frequently blow through the mountain passes. This phenomenon is called "Chinook", causing temperature to rise, snow to melt (if occurring in the winter) and a decrease in humidity.

There are two main weather systems that bring the majority of precipitation to southern

Alberta. The dominant source of precipitation is westerly winds that bring precipitation in the fall and winter. In spring and summer, precipitation is caused by winds that swing north around a high pressure system in Idaho. Although both systems derive precipitation

41 from the Pacific Ocean, the moisture in the westerlies originates from higher latitudes. In the winter months the Rockies have a strong rain shadow effect on the westerly flow, creating drier conditions from west to east. In the spring and summer this affect is reversed, as air systems from Idaho rise orographically against the eastern dopes of the

Rocky Mountains, creating drier conditions from east to West (Rehalt, 1970).

3.2.2 Analysis of Rainfall Data

The analysis used in the current study is similar to Huff (1967) and Hogg (1980) with the exception that the time increments are not equal due to insufficient data. The rainfall for each storm is expressed as a cumulative percentage of the total rainfall of the storm. The percentages are used to calculate separate temporal distributions for percentiles ranging from the 10th percentile to the 90th percentile in increments of 10 from the total number of storms for a specified duration. The different hyetographs permit the selection of a temporal distribution that is most appropriate for a particular application. As mentioned previously, in some cases the 50th percentile distribution will be the most useful and in other cases more extreme distributions may be desirable. The range of distributions provides quantitative measures of both the inter-storm variability and the general characteristics of the temporal sequence of rainfall (Hogg 1980).

3.3 Results and Discussion

3.3.1 Arid Zone Storm Distributions Derived in Oman

If the gauging stations are considered independently, the total number of storms recorded for both regions is 2042. For the temporal distribution analysis, storms of 2, 6, 24, and 48 hour durations were selected. Most of these events represent convective thunderstorms, especially those of durations of 2 and 6 hours. Of the total, 610 storms (30%) had a duration less than or equal to 2 hours; 394 (19%) storms had a duration of 2.1 to 6 hours;

514 (25%) had a duration of 6.1 to 24 hours; and 524 (26%) had a duration of 24.1 to 48 hours. All original data were extracted from the recording charts provided by the

Ministry of Regional Municipalities and Water Resources in the Sultanate of Oman.

Table 3-2 is an example tabulation for 6 hour storms for the EL772223AF station in the

Rustaq watershed (the selection of this station was completely arbitrary for this one example tabulation). In Table 3-2 rainfall is represented as a percentage of the total storm. Similar data were available for each of the stations in the study. Some storms had an actual duration slightly shorter than the nominal duration of 2, 6, 24, and 48 hours. In that case a rainfall of 0 mm is used to complete the storm to the nominal duration.

Examples of this type of correction can be seen in Table 3-2. Storms in the first seven rows of Table 3-2 reach 100% of the cumulative rainfall at three hours.

Table 3-2 Six hour storms at station EL772223AF.

0-min 15-min 30-min 1h 2h 3hr 6hr

0 27 36 45 64 100 100

0 42 74 79 79 100 100

0 47 53 88 94 100 100

0 31 62 69 77 100 100

0 67 94 97 97 100 100

0 76 76 76 76 100 100

0 51 60 71 97 100 100

0 45 45 45 91 91 100

0 27 36 45 64 100 100

0 42 74 79 79 100 100

0 53 53 53 60 93 100

0 31 63 63 69 75 100

0 47 53 88 94 100 100

0 60 91 91 91 94 100

0 31 62 69 77 100 100

0 17 33 50 83 92 100

0 12 24 45 64 73 100

0 71 71 71 71 71 100

0 23 38 62 85 92 100

0 67 94 97 97 100 100

0 71 86 95 95 95 100

0 76 76 76 76 100 100

0 33 48 57 71 95 100

0 56 81 95 95 96 100

0 51 60 71 97 100 100

Figure 3.3 represent the temporal distributions produced for the station EL772223AF.

This figure indicates that 10% of the storms at the station deliver 71% or more of the total rainfall within the first 15 minutes of the storm. At the other extreme, within the first 15 minutes 10 % of the storms have delivered 25% or less of the total rainfall.

100 10% 90

80 50%

70 90% 60 10% 50 20% 30% 40 40%

% of %total depth 50% 30 60% 20 70% 80% 10 90% 0 0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30 5:00 5:30 6:00 Time (hour)

Figure 3.3 Temporal rain distribution of gauge EL772223AF.

For 2 and 6 hour rain storm duration distributions, individual storms demonstrated high variability especially in the first 40 minutes of the 2 hours storm and the first 2 hours of the 6 hours storm in both the coastal and mountainous regions as shown in Figures 3.4 and 3.5. The results also confirm that most of the rain falls at the beginning of the storm, which is typical of high intensity rainfall in arid regions. The hyetographs for the two regions do not exhibit any large differences in storm behaviour. However, minor differences do exist. In the case of the mountainous region, more rainfall is deposited during the first half of the storm, whereas the curves for the coastal region rise more gradually. For 6 hours storm, the 70th percentile curve for the coastal region shown in

Figure 3.4 indicates about 59% of the rainfall occurs during the first hour (17%) of the storm and 86% of rainfall occurs during the first half (50%) of the storm duration. For similar storm duration, the 70th percentile curve in the case of the mountainous region in

Figure 3.5 about 65% of the rainfall occurs during the first hour (17%) of the storm duration and 96% of rainfall occurs during the first half (50%) of the storm duration.

Figure 3.5 also shows that, in the mountainous region, the 10th percentile distribution of 6 hours storm indicates that on the average, one storm out of every ten will have at least

89% of its rainfall in the first 30 minutes of the storm duration. Similarly, the 50th percentile (median) curve indicates that on the average, five storms out of every ten will have at least 85% and 92% of the total rainfall in the first two hours for the coastal and mountainous regions, respectively.

100 100

90 40% 90 10%

80 80 50% 90% 70 70 10% 60 60 20% 90% 10% 50 30% 50 20% 30% 40% 40 40 40%

50% of total% depth 50% 30 60% 30

% of total depthtotalof % 60% 20 70% 20 70% 80% 80% 10 10 90% 90% 0 0 0:00 0:20 0:40 1:00 1:20 1:40 2:00 0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30 5:00 5:30 6:00 Time (hour) Time (hour)

(a) (b)

Figure 3.4 Coastal region probability curves for (a) 2 hr and (b) 6 hr.

100 100 10% 10% 90 90 50% 50% 80 80 90% 70 70 10% 90% 60 60 20% 10% 50 30% 50 20% 30% 40 40% 40 40%

50% of total% depth 30 30 50% 60% % of total depthtotalof % 60% 20 70% 20 70% 80% 80% 10 10 90% 90% 0 0 0:00 0:20 0:40 1:00 1:20 1:40 2:00 0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30 5:00 5:30 6:00 Time (hour) Time (hour)

(a) (b)

Figure 3.5 Mountainous region probability curves for (a) 2hr and (b) 6hr.

3.3.2 Comparison with Other Climates

Figure 3.6 compares the results of the current study with other established temporal distribution curves, Hogg (1980), Hershfield (1962), Huff (1967), and the Soil

Conservation Service (SCS). The standard distributions are derived from different geographical locations in Canada and the United States for different storm durations

Figure 3.6 shows the 50th percentile curves for six hour duration storms for both coastal and mountainous regions. These curves explain the difference between the two regions in terms of shape and magnitude of the distribution. In both cases the rise to the peak rainfall is very rapid. The 50th percentile curve for the coastal region continues to rise gradually after the third hour. The difference between the two curves comprises less than

10% of the total rainfall. The proximity of the two regions might be responsible for the similarity. The distance between the centers of the two regions is less than 30 km.

Hogg (1980) rain distributions were developed for different parts of Canada. The temporal distribution for the 70th percentile developed by Hogg (1980) for the Canadian prairies is also displayed in Figure 3.6. Hershfield (1962) created temporal distribution curves from hourly data from 50 widely separated stations. The curve included in Figure

3.6 is based on the average Hershfield (1962) distribution which is assumed to be valid for storms of 6 hour duration. Huff (1967) developed distributions from data for 261 large storms that occurred in central Illinois. The storms used in the Huff (1967) study were grouped into four quartiles. Huff (1990) stated that storms with durations of 6 hours or less are associated more often with the first quartile group. The second quartile group represents storms of duration of 6.1-12 hours. The curve included in Figure 3.6 is based on the first quartile group.

The SCS-24 hour curves, types I, IA, II, and III, were developed by the US Department of Agriculture Soil Conservation Service (SCS 1986) for different regions in the United

States. The distributions are applicable for both small and large watersheds, where design storms for shorter duration are included in these 24-hour distributions (ASCE

1996). Figure 3.6 displays the normalized SCS-6 hour curve.

100

90 Mountinous

70 Coastal

50 Calgary Calgary 40 Hogg, Prairies Hershfield-6h 30 SCS II-6hr % of% total depth 20 Huff 1st Quartile Coastal Region 10 Mountainous Region

0 0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30 5:00 5:30 6:00 Time (hour)

Figure 3.6 Comparison of Huff, Hogg, SCS, and Hershfield curves.

100

90 10% 80

60 50% 10% 90% 50 20% 30% 40 40%

% of %total depth 50% 30 60% 20 70% 80% 10 90%

0 0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30 5:00 5:30 6:00 Time (hour)

Figure 3.7 Calgary, Alberta probability curves.

Data from the two rain gauges selected in Calgary were analyzed in a similar manner.

Total number of storms was 619 for both stations. Figure 3.7 represent the temporal distributions produced for Calgary, Canada. This figure indicates that 10% of the storms at the station deliver at least 67% of the total rainfall within the first 15 minutes of the storm, whereas 71% of the total rainfall was delivered in the first 15 minutes in the Oman case. Figure 3.7 shows also that Calgary’s 6 h storm behavior is similar to that of the

Oman mountainous region; however, the Calgary curves show less intensity than the

Oman curve.

All the standard temporal distribution curves mentioned previously and the median percentile curves obtained for Oman included in Figure 3.6 exhibit major discrepancies.

The curves obtained for the mountainous and coastal regions display early intensity and a very rapid rise in cumulative rainfall compared to the other distributions. Differences between the Oman distributions and the standard distributions involve more than 60% of the total cumulative rainfall. Figure 3.6 indicates the storms in an arid region like Oman behave in a manner that is entirely different from those in geographical locations such as

Canada and the United States. The conclusion implies that standard distributions are not appropriate for regions such as Oman. The Huff (1967) first quartile curve exhibits the greatest similarity to the Oman data. Recall that Huff (1967) stated that in the first quartile the greatest percentage of rainfall occurs in the first quarter of the storm period.

More than 50% of the storms occurring in mountainous and coastal regions of Oman show a distribution trend similar to Huff’s first quartile, with a significant difference in the magnitude of rainfall. The shape of the hyetograph curve can indicate the type of storm. It is often found that there are unique signatures associated with different rainfall generating mechanisms, such as the difference between thunderstorms and tropical cyclones. The curves obtained for coastal and mountainous regions of Oman follow the temporal pattern associated with thunderstorms (for more information, see Eagleson

(1970)).

3.3.3 Data Validation

For validation purposes and due to the limited available data, only ten storm events (five for mountainous and another five for coastal) were excluded from the analysis and used as set of validation data for these distributions. Every event in each set was selected

randomly from five different locations in order to include spatial variation to the

validation data set. The average curve of each five event set for each region is used in the

validation with the 50% curves of the characterized distribution. These curves are highly

coincident as shown in Figure 3.8a and 3.8b. Table 3-3 shows the percent difference and

coefficient of determination R2 between rainfall depth from the validated and

characterized distribution for both the mountainous and coastal regions. The predicted

and observed values are very similar and well matched, R2 is 99%. Table 3-3 shows that

the maximum difference between the observed and predicted curves for different time

periods is ± 5% of the total rainfall.

Table 3-3 The 50th percentile difference curves.

Time period 0 0:15:00 0:30:00 1:00:00 2:00:00 4:00:00 6:00:00 R2

Mountainous Region (%)

Available data 0 44 62 78 92 100 100

Validation data 0 43 58 83 87 100 100 0.99

Difference % 0 1 4 -5 5 0 0

Coastal Region (%)

Available data 0 47 58 73 85 94 100

Validation data 0 52 60 70 85 91 100 0.99

Difference % 0 -5 -2 3 0 3 0

100

% of%total depth Mountainous Region 20 Validation, Mountinous Coastal Region 10 Validation, Coastal 0 0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30 5:00 5:30 6:00 Time (hour) (a)

100

30 Mountainous

Validation rainfallValidationdepth, % R² = 0.993 20 Coastal Mountainous R² = 0.994 10 Coastal

0 0 20 40 60 80 100 (b) Characterized distribution rainfall depth, %

Figure 3.8 Comparison of (a) coastal and mountainous probability curves and (b) total rainfall depths for validation and characterized curves.

Chapter Four: Relationship between Wadi Drainage Characteristics and Peak Flood Flows in Oman*

4.1 Introduction

Watershed characteristics related to topography, hydrology, land use, and climate

(Paybins, 2008) have long been used to develop flood-peak flow equations. While not a great deal is known of the mechanisms generating wadi flood peaks, they are suspected to be influenced by these same watershed characteristics, and thus, developing equations relating wadi flood-peak flow to known watershed characteristics are possible and also useful in estimating flows in ungauged Oman watersheds.

Several studies have related watershed characteristics to stream flow, but very few have been conducted in arid regions. In addition, when it comes to which characteristics should be considered, nothing is definitely known (Mwakalila et al., 2002) for arid regions. The main advantage of runoff prediction based on geomorphologic watershed characteristics is that it does not require historic flood-peak records and it has been proven to be reasonably accurate especially in some small size humid catchments

(Rodriguez-Iturbe & Valdes, 1979). An evaluation of the majority of indirect regional methods for humid climates indicates that none of the methods provide accurate prediction of maximum flood-peak flow rates in arid catchments (Nouh, 1990).

* Some material in this chapter has been accepted for publication in the Hydrological Sciences Journal with some modifications.

Mason, et al. (1999) concluded that for estimating the magnitude and frequency of flood- peak discharge in Alabama, drainage area is the only statistically significant explanatory variable. Others like Farquharson, et al. (1992) in their study on regional flood frequency analysis in arid and semi-arid areas implemented regression to estimate the mean peak flood (MAF) for each catchment from both basin area and mean annual rainfall (MAR).

Their study covered arid and semi-arid regions with 162 stations across Africa, Iran,

Jordan, Saudi Arabia, Botswana and South Africa, Australia, southwest USA and Russia.

They suggested such areas world-wide could be treated as a single homogeneous region excluding catchments dominated by factors such as snowmelt. All arid basins used in their study exhibited a positive relationship between MAF and drainage area with some variation in the slope of the regression line. They found that the MAR was not a significant predictor, and MAF could be estimated from regression equations using the combined flood frequency curve for all similar arid and semi-arid regions. Arid and semi- arid regions produced strongly similar flood frequency curves and were different from those found for the more humid climate of the UK. They concluded that the similarity of these regional curves suggests that the storm magnitude/extent/frequency relationships are similar. Mimikou and Gordios (1989) in their study on data from 11 gauged stations of the five major rivers in northwestern and western Greece found that stream frequency, drainage area, and the intensity of the 1-day rainfall of a 5-year return period were the variables that had the greatest effect on the mean instantaneous flood. Pitlick (1994) noted that the basin area and the mean annual precipitation were the most important independent variables for five basins in the western USA.

Nouh (2006) estimated the mean annual flood flow using a relationship between floods and drainage watershed characteristics in the Arabian Gulf states. He did not recommend the inclusion of a large number of variables in the relationship. He showed that the size of the basin and the mean elevation of the drainage basin are sufficient to explain the variance of the relationship. Kokkonen et al. (2003) explained that catchments with higher elevations and steeper slopes have a flashier response and smaller quick flow times than those with lower elevations and gentler slopes.

Enzel et al. (1993) in their study on drainage within arid and semiarid parts of the

Colorado River Basin in Arizona and southern Utah, stated that after drainage area, average basin elevation and mean annual precipitation slightly improved the prediction of flood-peak magnitudes (from regression equations) for Arizona. Asquith and Slade

(1999) found that watershed drainage area, shape factor and stream slope are the most significant characteristics influencing flood flow in natural basins in Texas. Nouh (1990) investigated the effect of various catchment and rainstorm characteristics on the performance of a geomorphologic rainfall-runoff model in simulating observed flash flood hydrographs in 32 extremely arid catchments using real rainfall-runoff data. Three main characteristics: size, followed by the rate of infiltration, and the slope of land, were found to be the most effective catchment characteristics affecting the accuracy of the model.

Pilgrim et al. (1988) mentioned that there was no direct relationship between rainfall on

the watershed’s surface and downstream runoff, except in headwater area. Driver &

Troutman (1989) found that the most significant explanatory variables used in estimating storm-runoff throughout the United States are the total storm rainfall (R2 = 0.35) and total contributing drainage area (R2 = 0.95) for 34 regions.

For flash flood studies, understanding the relationship between the watershed characteristics and flood-peak discharge is also a key issue, specifically in arid regions, where flash floods are characterized by infrequent precipitation in the form of intense thunderstorms, steep slope topography and a lack of dense vegetation. Furthermore, arid regions are characterized by a high variability and diversity in watershed characteristics.

Some studies on semi-arid and arid areas include Patton & Baker (1976) and Costa

(1987) who have suggested that small, steep, and highly dissected basins that receive very intense precipitation are more likely to have flash floods. Examples of these regions are central Texas, southern California, and north central Utah (Patton & Baker, 1976).

Costa (1987) also concluded that the discharges of very rare floods were determined largely by precipitation intensity rather than basin characteristics. Al-Rawas & Valeo

(2007) found that watershed characteristics along with rainstorm are the most influencing factors on urban flash flood studies for an arid environment like Oman. Many other studies (Jackson et al., 2005; Davis, 2004; Wright, 2002; Schmittner & Giresse, 1996;

Weng, 2001; Laben, 2002) as mentioned in the literature review in chapter 2 used

Geographic Information Systems (GIS) to extract watershed characteristics and relate

them to flood flows or to predict/understand the mechanisms leading to flash floods.

Investigating the relationship between watershed characteristics and flood-peak discharge

in arid regions is still a critical knowledge gap to better understanding the hydrological

57 processes in this region. This improved understanding will lead to improved rainfall- runoff modeling, management, and planning strategies.

The main objective of this study is to analyze the wadi flow data and examine the relationship and influence of watershed characteristics on wadi flow for different return periods in Oman. Previous studies have not focused on land use as a watershed characteristic for arid regions but many arid regions such as Oman are experiencing rapid increases in urbanization and farming – primarily occurring in the wadi flood plain. This study investigates the relative role of these watershed characteristics for predicting flash floods, in particular, the type of urbanization and farming occurring in arid, developing countries. Real data from 12 catchments were used to accomplish this study. Wadis flow data records were collected by the Ministry of Regional Municipalities and Water

Resources (MRMWR), which is responsible for all hydrological activities in Oman. In this study, watershed climate characteristics such as rainfall are not included due to a lack of available data. This study focused on mean peak flow (QMPF) and 5 to 100 return period wadi flood-peak flows (Q5, Q10, Q20, Q50, and Q100). Numerous watershed characteristics including land use are investigated to develop robust relationships. These relationships potentially lead to a better understanding of the mechanisms leading to flash flooding in wadi and estimate wadi flows in ungauged Oman catchments.

4.2 Methodology

4.2.1 Study Area

Oman is located in the southeastern part of the Arabian Peninsula, bounded in the northeast by the Gulf of Oman, in the southeast by the Arabian Sea, in the northwest by the United Arab Emirates, on the west by Saudi Arabia, and on the southwest by Yemen

(Figure 4.1). Oman covers an area of approximately 309,500 km2 with the northern part of Oman being mostly occupied by the Jabal Al-Hajar mountains that range parallel to the coast and separate the fertile Al-Batina plain from the interior region. Jabal Al-Hajar altitudes reach more than 3000 m in Jabal Shams. In the north, Al-Batina coastal plains comprise the agricultural and industrial regions. In the south, the Dhofar mountain range extends up to 1800 m high and separates the Salalah plain which has a unique monsoon climate. The northern front, where the study area is, is drained northward by a series of deeply incised wadis transforming coastward into gravel terraces, and finally the coastal plain, which consists of alluvial deposits, scattered sabkhas, strips of fertile soils and coastal sand dunes (MRMEWR, 2005).

Twelve watersheds representing mountainous and plain regions of northern Oman were selected as calibration watersheds for estimating the mean peak flood of Oman wadis in ungauged watersheds based on the relationship between a watershed’s physical characteristics and the mean peak flow (QMPF). These watersheds represent the major wadis in northern Oman that experience flash floods regularly including Wadi Dayqah,

Wadi Aday, and Wadi Samail. Figure 4.1 shows the location of these watersheds along with the gauges used in the study. Two criteria were implemented in selecting these watersheds, first is the variability of the watershed characteristics (example, the size and elevation), and second is the availability of the gauge flow records. The area of the watersheds ranged in size from 64 km2 in Wadi Lansab to 1,730 km2 in Wadi Dayqah at

Mazara village (Figure 4.1 and Table 4-1).

Figure 4.1 Study area. This map is not an authority on international borders.

Table 4-1 Statistics of wadis log flood-peak (QMPF) records.

Period Log (Q) Number Mann-Kendall Wadi Station ID of St. of Trend at 0.05 Name Mean records Dev. events level

FA395799AD Lansab 97 - 07 1.34 0.61 20 No trend

Downward FA777631AD Hayfadh 97 - 07 1.10 0.75 15 trend

FA160968AD Al 'Uqq 97 - 07 1.74 0.34 19 No trend

FA585595AD Manzariyah 96 - 07 1.62 0.48 27 No trend

EL574613AD Awabi 97 - 06 1.41 0.46 36 Upward trend

FB505467AD Aday 96 - 07 1.81 0.87 18 No trend

GA057335AD Arabiyin 97 - 07 2.11 0.67 20 No trend

FA596055AD Mayh 96 - 07 1.62 0.59 35 No trend

EL895407AD Afi 97 - 07 1.55 0.55 34 No trend

FA877343AD Miglas 97 - 07 1.70 0.90 13 No trend

FB104840AD Al Khawd 98 - 07 1.60 0.87 19 Upward trend

FA950420AD Dayqah 04 - 07 2.35 0.78 14 Upward trend

Total 270

The methodology of this study consists of four stages: (i) Wadi flood flow analysis is

conducted in which wadi flood-peak records of events were used to derive QMPF; (ii) the physical watershed characteristics were extracted from a Digital Elevation Model (DEM) with the use of a Geographic Information System (GIS) and satellite imagery in some instances for verification; (iii) development of a relationship between QMPF and

61 watershed characteristics by performing a correlation analysis and multiple regression analysis; and finally (iv) validation of this relationship by estimating the QMPF at different watersheds.

4.2.2 Wadi Flood-Peak Flow Analysis

The selection of each station’s location and associated events are based on the availability of the flood-peak record. Gauged records were used regardless of the station length of observation. Twelve wadis flow gauges were used in this study and most of these stations have 10 year records of flood (most station records range from 1997 to 2007 except for

Wadi Dayqah). The total number of events used in this study was 270. Table 4-1 shows the wadi name and number of available events for each station. Most of these stations are located along wadis in the coastal plain regions as illustrated in Figure 4.1. The mean peak flow (QMPF) was expressed in this study as the arithmetic mean value of peak wadi flows for the period of record. This study takes into account the 2007 floods that resulted from tropical cyclone Gonu. The QMPF is defined as:

1 n = (1) QMPF ∑Qi n i=1

where Qi denotes the flood-peak series with n values. Table 4-1 shows that the standard

deviation of the logarithms of the QMPF is very high. The standard deviation of QMPF can

sometimes be interpreted as a measure of flash flooding potential (Enzel et al., 1993;

Patton & Baker, 1976; Beard, 1975) with higher standard deviations indicating a larger potential for flash floods.

The analysis of these flood events shows that the hydrograph behavior of the cyclone event that took place in the period of July 6, 2007 for all stations was different than the other recorded events. It shows a smooth rising limb, where the rest of events show very steep rising hydrographs. However, the peak discharge was very high and reaches more than 2000 m3/s in many stations across the study area.

In most of the cases, baseflow is present but is seasonally dependent. Excluding “Gonu” events in June 2007, data shows that baseflow is present in the months of March and

April. Other studies (McIntyre et al., 2007; Kwarteng et al., 2008) mentioned that the largest events of rainfall in this region of study were in the months of January, February, and March. In most of the cases, baseflow is very small compared to peak flows (less than 5%) in all Wadis except Wadi Dayqah where the baseflow exists for almost the entire year. Usually, in most cases, baseflow has a minor influence and constitution on the total storm runoff (ASCE, 1996). Therefore, inaccuracies in baseflow separation may not be very important (Viessman & Lewis, 2002). In this work, a straight line method of baseflow separation was used. To ensure quality control in baseflow separation, a comparison to a nearby watershed was made in order to check the consistency of flow in event periods. For inter-event periods, separation of peaks was conducted when there was no rainfall for more than three hours.

4.2.2.1 Trend Analysis: Mann-Kendall Test

Trend analysis of hydrological time series is important for global climate change effects and water resources management and planning. A common non-parametric trend detection used in hydrological time series data is the Mann–Kendall test (Mann, 1945;

Kendall, 1975) for trend. Many studies (e.g., Déry and Wood, 2005; Hamed, 2008;

Kwarteng et al., 2008; Onoz and Bayazit, 2003) used the Mann–Kendall test for trend detection in hydrological time series data, specifically peak streamflow records.

Mann (1945) first suggested using the test for significance of Kendall's test where the (x1, x2, . . , xn) variable is time as a test for trend. The Mann-Kendall test is based on the statistic Kendall's S statistic. The Mann–Kendall test statistic is defined as

(2)

Each pair of observed values xi and xj (i >j) is inspected to find out whether xi > xj (P, concordant) or xi < xj (M, discordant). Then Kendall’s S statistic is defined as

(3)

Assuming that the data are independent and identically distributed, the mean and variance of the S statistic above are given by (Kendall, 1975)

(4)

(5)

where n is the number of observations. Kendall (1975) stated that the distribution of S tends to normality as the number of observations becomes large. Therefore, for n > 10, the significance of S can be tested by comparing the standardized variable Z which follows the standard normal distribution where

(6)

The null hypothesis is H0: there is no trend, and the alternative hypothesis is H1: there is a trend. The H0 is rejected when the computed Z value is insignificant (p-value > 0,05).

4.2.3 Extraction of Watershed Characteristics

An accurate, 40 m Digital Elevation Model (DEM) was used to extract 14 watershed characteristics representing the geomorphologic properties of the twelve watersheds that were investigated in this study. All watershed boundaries were generated using the Hydro extension in ArcMap 9.1. In order to verify the accuracy of the generated watershed boundaries, they were checked with respect to the same watershed boundaries generated from topographic maps by the Ministry of Regional Municipalities and Water Resources

(MRMWR) in Oman. Additionally, high resolution satellite imagery IKONOS for the same areas were employed to double check the boundaries and wadi channel path. In some cases, drainage areas were edited, e.g. Afi and Al Khawd watersheds, and some other gauge stations were slightly re-located, e.g. Awabi gauge station. Also, DEM reconditioning using GIS ArcMap was implemented, where the system adjusts the surface elevation of the DEM to be consistent with the vector coverage of drainage streams provided by the MRMWR. The DEM reconditioning produced a large difference in extracted watershed characteristics.

Seven watershed characteristics were generated directly by the GIS and the rest are calculated from these seven. All the variables were transformed later by taking the natural logarithms for the regression analysis.

Drainage area (DA km2) is assumed to be the same area of bounded watershed that contributes to surface runoff. DA was delineated from the DEM by computing the flow

66 direction. Nouh (1990) found that the accuracy of runoff prediction decreases as the size of the catchment increases. Basin length (BL km) is defined using ARCHydro’s Basin

Length function in its Watershed Processing routine. A “cost” at each grid cell is determined using the square of the inverse of the Euclidian distance to each boundary cell. The line along which basin length is measured is positioned where the cost is a minimum delineated by using the geometry to travel through the approximated centroid of the basin. Wadi length (WL km) is computed as the longest flow path (including flow in the main channel) in the watershed. Mean basin elevation (BE m) is measured in m above sea level and is the mean watershed altitude. Basin slope (BS %) is the mean basin slope and is measured by calculating the maximum rate of change between each cell and its eight neighbors. It provides an indication of the steepness of the drainage area. Costa

(1987) found that basins producing flash floods were not particularly steep, nor were they characterized by a very high drainage density. Nouh (1990) claims that as the slope decreases, catchment soils become more permeable, and thus the effect of infiltration becomes more significant. Basin perimeter (BP km) is the distance measured around a basin boundary.

Basin shape factor (SF no units) (Patra, 2001) is expressed as the ratio of the square of the valley length to the watershed area. Because of high variability of arid watershed characteristics, every basin is expected to have a unique shape. In this study, SF was computed by dividing the squared wadi length by basin area:

WL2 SF = (7) DA

The SF is expected to be < 1 if the watershed is long and narrow, and SF = 1 if the

watershed is square (Patra, 2001). Some studies including Newson (1994) stated that

studies made in many regions of the world have shown that basin shape is less reliable as

a flood indicator than basin size and slope. Nouh (1990) noted that the catchment’s shape

affect on the accuracy of runoff prediction is insignificant.

Drainage density (DD km/km2) is computed by dividing the total summation of all

streams (SL km) in the basin by the drainage area:

∑ SL DD = (8) DA

Wadi slope (WS m/km) was calculated by determining the elevation at the gauge station

and 85 percent of the distance along WL and then dividing the difference in elevation

between these two points by the length of that wadi channel connecting the two points:

− EE WS = WL85.0 gauge (9) 0.85WL

Elongation ratio (ER no units), originally proposed by Schumm (1956), is the ratio of the diameter of a circle with the same area to the maximum length of the valley.

 DA4  5.0  1  5.0 ER =   = 13.1   (10)  πWL2   SF 

Basin width (BW km) is computed by dividing the drainage area by the wadi length.

DA BW = (11) WL

Slope ratio (SR no units) is computed by dividing the wadi slope by the basin slope.

WS SR = (12) BS

Slope proportion (SP no units) is computed by dividing the wadi length by the wadi slope.

WL SP = (13) WS 5.0

The percent of urbanized area (U) and the percent of greenness/farm area (FR) were determined manually by using the watershed boundaries and high resolution satellite imagery (IKONOS and ASTER). The statistical descriptions of these characteristics’ are shown in Table 4-2.

4.2.4 Correlation and Regression Analysis

Flood-peak flow (Q) is expressed as a function of the independent variables mentioned above. For a good estimation of Q, the relationship should ideally contain independent variables and be statistically significant and physically sensible. To derive the relationship between Q and independent variables, inspection and correlation analysis, through to stepwise regression and finishing with multiple regression steps are implemented.

Table 4-2 Watershed characteristics for 12 Wadi-gauging stations in Oman.

Range Watershed's Characteristic Mean St. Dev. Min Max

Wadi Flow, Q (m3/s) 240.06 264.17 60.50 1061.76

Drainage Area, DA (km2) 532.77 549.62 46.00 1730.00

Basin Length, BL (km) 34.44 16.68 9.69 69.29

Wadi Length, WL (Km) 46.03 24.13 15.50 93.50

Shape Factor, SF (1) 5.03 0.91 3.06 6.42

Basin Elevation, BE (m) 757.66 319.70 311.43 1502.22

Basin Slope, BS (%) 29.91 8.60 17.28 49.32

Drainage Density, DD (km/km2) 2.25 0.43 1.45 3.15

Basin Perimeter, BP (km) 164.88 97.29 46.24 366.16

Wadi Slope, WS (m/km) 14.56 10.24 4.50 36.22

Elongation Ratio, ER (1) 0.51 0.05 0.45 0.65

Basin Width, BW (km) 9.25 4.77 2.97 18.50

Slope Ratio, SR (1) 0.48 0.29 0.16 1.21

Slope Proportion, SP (1) 14.97 10.20 3.09 35.35

Urbanized area, U (%) 0.48 0.38 0.08 1.45

Farms, FR (%) 0.26 0.19 0.05 0.66

In statistical analyses of water resources applications, unique basin characteristics are assumed to be independent variables, but in fact, there are some significant relations or multicollinearity among these variables, which may be problematic. Thus, correlation analysis within variables of watershed characteristics and with wadi flood-peak flow Q was examined. Furthermore, graphical scatter plots were evaluated to identify possible interdependencies. A Pearson correlation coefficient (r) analysis was used to compare the basin characteristic data. Correlations were considered high where the absolute value of

Pearson’s r was greater than or equal to 0.80 (Table 4-3). This was somewhat arbitrary because although the selection of this particular threshold has not been specified in the literature, some other communities (e.g. Geography) prefer to use 0.7 as an unwritten rule of thumb for a threshold in order to reduce the risk of near-multicollinearity. Greater discussion on the decision behind using 0.8 is given later in the discussion section.

Table 4-3 Pearson correlation coefficients (r)

Q DA BL WL SF BE BS DD BP WS ER BW SR SP U FR

Q 1 DA .726** 1 BL .598* .903** 1 WL .713** .962** .969** 1 SF .143 .041 .256 .242 1 BE .121 .112 .167 .202 .479 1 BS -.052 -.035 -.038 .022 .433 .821** 1 DD -.132 -.109 -.323 -.301 -.512 -.432 -.264 1 BP .730** .979** .935** .966** .019 .106 -.051 -.184 1 WS -.152 -.397 -.284 -.322 .523 .672* .578* -.086 -.403 1 ER -.176 -.069 -.251 -.247 -.984** -.406 -.362 .459 -.034 -.482 1 BW .664* .960** .899** .931** -.114 .054 -.100 -.137 .979** -.491 .117 1 SR -.103 -.430 -.264 -.353 .431 .348 .135 .037 -.423 .876** -.413 -.504 1 SP .660* .965** .880** .934** -.060 -.056 -.166 -.136 .960** -.595* .039 .971** -.600* 1 U -.124 .141 .267 .181 .117 -.502 -.474 .043 .098 -.370 -.132 .140 .214 -.106 1 FR -.107 .398 .472 .460 .422 .379 .402 -.071 .311 .022 -.383 .314 .344 -.158 .341 1 ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed).

Backward stepwise multiple linear regression analysis was performed on watershed

characteristics with mean peak flow (QMPF), where the weakest predictor (highest sig.-p

value) variable was removed in each step and the regression re-calculated until only

useful predictor variables remain in the model. Statistical significance was evaluated for

each model and in the independent variable. In this study’s multiple regression analysis,

the research hypotheses is that for Oman the watershed characteristics that influence

flood-peak flow are different than in non-arid regions.

The regression equation takes the following form:

1 2 bbb 3 bi MAF 0 1 2 3 ××××= ...... AAAAbQ i (14)

3 where QMPF is the estimated mean peak flow (m /s); A1 through to Ai are the explanatory variables; b1 through to bi are the regression coefficients; and b0 is a constant. This form was chosen to be consistent with other regression studies found in the literature.

Two methods for independent variable selection were implemented in this study. The first method uses only the delineated and computed watershed’s physical characteristics based on the DEM. The variables used in Method A are: DA, SF, BE, DD, and WS. In

Method B, the effects of urbanization were investigated by adding the variables of the percent of urbanized area (U) and the percent of greenness/farm area (FR) of each watershed. Previous studies on wadi hydrology show that there is a lack of urbanization and land-use impact on runoff, the importance of this research gap has been pointed out by Singh (2009); Pilgrim et al., (1988); and Cordery et al., (1983).

A comparison of both the regression’s coefficient of determination (R2) and the p-value observed between the two Methods A & B helped to select a model that most accurately predicts the flood-peak for 16 new watersheds in the validation process. Table 4-4 shows the calibration accuracy assessment for both Methods A & B.

Table 4-4 Calibration accuracy assessment

% E E RMSE Method A 32.02 0.39 205.51 Method B 22.23 0.61 165.29

4.2.5 Flood-peak frequency estimation from watershed characteristics

In recent years Oman has experienced floods almost every year in the form of flash

floods. There are many reasons for the accelerating frequency of flooding experienced in

Oman specifically in the last 10 years. Fast urban growth may be one of the key

contributors to this problem. The relationships between watershed characteristics and

various flood-peak return periods, Q5, Q10, Q20, Q50, and Q100, were analyzed in the same manner as the relationship between the watershed characteristics and QMAF. Farquharson, et al. (1992), indexed floods in Saudi Arabia by the 5-year flood return period. In that study, watershed characteristics were also related to flood-peak frequency values derived from flood-peak frequency curves available for the same selected calibration watersheds.

4.2.6 Model Validation

One of the main benefits of deriving a relationship for QMPF in terms of watershed characteristics is to estimate QMPF in ungauged watersheds; thus, model validation is essential. For this propose, QMPF are estimated (predicted QMPF) using the derived models and compared with the original values (observed QMPF). Additionally, QMPF for 16 other watersheds are used to validate the relationships (these were not used to develop the derived relationships). Several accuracy tests are used to assess the relationship’s ability to predict QMPF. These include average percent error (%E), Nash–Sutcliffe model efficiency coefficient (E), root mean square error (RMSE), and the coefficient of determination (R2).

4.3 Results and Discussion

All 12 watersheds used in this study exhibit high variability in flood-peak discharges as

was shown in Table 4-1, where 50% of stations’ standard deviation was more than 35%

of the mean. Wadi Dayqah (station ID FA950420AD) scored the highest QMPF. The

Mann–Kendall test showed that most of the wadis showed no significant trend except

Wadi Hayfadh (station ID FA777631AD), Wadi Awabi (station ID EL574613AD), Wadi

Al Khawd (station ID FB104840AD) and Wadi Dayqah (station ID FA950420AD) as shown in Table 4-1. Wadi Hayfadh was a borderline case, where the flood-peak discharge showed a significantly declining trend over time (p-value is 0.046). Wadi Al

Khawd and Dayqah showed a significantly increasing trend in flood-peak discharges with

75 p-values of 0.009 and 0.011, respectively. The latter two stations are located in urban areas, specifically Al Khawd watershed where many residential areas are bounded by this watershed. Kotwicki et al. (2007) also found similar upward trend in Wadi Dayqah. Fast urban growth in this region could be one of the most likely reasons behind the increase in flood-peak discharge. Kwarteng et al. (2008) in their analysis of 27-year Oman rainfall data stated that no significant trend was found in rainfall for the same region in Oman.

Hamed (2008) investigated a group of 57 worldwide total annual river flow time series.

His results indicated that highly significant increasing trends seem to be more common than negative ones.

Table 4-2 shows the watershed characteristics for the calibration watersheds used in this study. This table also shows high variability in all of the watershed properties as a common feature in arid regions watersheds. This high variability and range in the data is expected to provide robustness to the model because the data is comprised of real observations representing as much of the population of drainage characteristics as possible. However, given the low number of watersheds involved in the analysis, it is suspected that the model’s translatability in time or space maybe be reduced. The SF in all the watersheds used in this study is > 1 which means they do not have an elongated shape. Singh (1997) stated that the influence of storm movement was more pronounced on elongated watersheds than on bulbous-shaped ones. Because the shape of watersheds in this region is more circular than elongated, the time to peak Tp in this region is expected to be shorter with a steeper rise in flow hydrograph when the storm is moving downstream. Therefore, this region tends to have a greater potential for flash floods.

Using Pearson's (linear) correlations, highly correlated watershed characteristics were determined when the absolute value of the correlation coefficient, r ≥ 0.80. Using a threshold of 0.8 was partially based on the fact that DA should be included as an explanatory variable in the modelling and a lower value such as 0.7 on at least one occasion suggested the removal of DA as an explanatory variable which is not reasonable nor sensible in hydrology. Other than this particular issue, Table 4-3 reveals that there is no significant consequence to multi-collinearity in using a threshold of 0.8 over 0.7.

First, DA is found to be significantly positively correlated with BL, WL, BP, BW, and

SP. In this case, only DA can be used as an independent variable representing this group of watershed characteristics in multiple regression analysis. Second, SF is significantly negatively correlated with ER (r = -0.98); third, BE also significantly positively correlated with BS; and finally, WS is significantly positively correlated with SR.

Drainage density DD does not exhibit any significant correlation with any of the basin characteristics. The percent of urbanized area (U) and the percent of greenness/farm area

(FR) are positively but insignificantly correlated (r = 0.34). Both U and FR are not significantly correlated to any of the other variables. Given this, only 7 main variables were selected as independent variables to avoid information redundancy or multicollinearity problems in the multiple regression analysis: DA, SF, BE, DD, WS, U, and FR. Figure 4.2 shows a scatter plot between QMPF and these variables (except U).

Not surprisingly, the wadi mean peak flow (QMPF) is highly positively correlated with drainage density DA (Figure 4.2a). This means that larger watersheds are expected to have a higher mean peak flow. A comparative study done for similar arid regions by

Nouh (2006) on wadi flow in the Arabian Gulf States, found a similar result. He found that the effect of drainage basin size on predicting mean peak flows is positively related and larger than that of drainage basin altitude.

10000 10000

1000 1000 /s /s 3 3 , m , 100 m , 100 MAF MAF Q Q

10 10 10 100 1000 10000 100 1000 10000 (a) DA, km2 (b) BE, m

10000 10000

1000 1000 /s 3 /s 3 , m , , m ,

MAF 100 100 Q MAF Q

10 10 1 10 0 2 4 (c) SF, km/km (d) DD, m/m

10000 10000

1000 1000 /s /s 3 3 , m , m , 100 100 MAF MAF Q Q

10 10 1 10 100 0.00 0.50 1.00 (e) WS, m/km (f) FR, %

Figure 4.2 Scatter plot between QMPF and (a) DA, (b) BE, (c) SF, (d) DD, (e) WS, & (f) FR.

4.3.1 Method A

Table 4-5 shows the results for Method A that includes variables: of DA, SF, BE, DD,

and WS. This table shows that adjusted R2 decreases with a decrease in the number of watershed characteristics and including more watershed characteristics improves the predictive power of the regression equation in terms of R2. Table 4-5 also assesses the overall significance of our model and when p < 0.05 the model is considered significant.

In this table, model 1, which included all five variables accounted for 73% of the variance (adjusted R2 = 50%). The removal of SF (model 2) which was the least significant variable (data not provided) from model 1, resulted in a decrease of 1% in R2

(or 1% decrease in the variance being explained), but adjusted R2 increased by 7%.

Removing DD from model 2 accounted for 72% of the variance (Adjusted R2 = 0.61) in model 3. In model 3, adjusted R2 increased by 4%. In addition, model 3 became more significant with a p-value of 0.014 (<0.05). Removing further variables like BE in model

4, results in an adjusted R2 decreasing by 7%.

Because of the multicolinearity in watershed characteristics mentioned earlier, several choices were made as to which variable to include or exclude when correlated with another. Recall the correlation between SF and ER. Since the definition of shape factor in this study gives an indication of a watershed’s elongation (when SF<1), the author preferred to select SF (r = 0.14) in the regression analysis rather than ER (r = -0.18). The

DA, WS, and BE, contain much of the information presented by SF and DD. These results show that removing DD improves the adjusted R2, thus, implying that DD is not a

80 significant variable in QMPF prediction. Drainage density DD is always higher on impermeable rocks and clays, and less in permeable rocks and sands. Due to the lack of information on the type and distribution of rocks and soil in this region, this may justify the removal of DD as insignificant variable, although the regression results show an expected positive relationship between DD and flood-peak discharge. Therefore, QMPF can well be estimated from model 3 in Table 4-5 where adjusted R2 has the highest value and the model p-value is significant (<0.05), the multiple regression equation is:

670.0863.0 − 851.0 QMPF = 77.56 BEWSDA (15)

This indicates that QMPF is affected by these watershed characteristics and that some watershed characteristics are more useful than others in QMPF prediction. Equation (15) describes a new relationship between QMPF and watershed characteristics for this arid region that differs from previous studies for humid regions (e.g. Kokkonen et al., 2003).

For example, BE in Equation (15) is the least powerful explanatory variable in the regression and negatively related to QMPF. This implies lower elevated catchments have higher peak flow, where for example Kokkonen et al., 2003 in their study in North

Carolina found that elevation was the most powerful explanatory variable in the regressions and higher elevated catchments have quicker responses and higher yields than catchments at lower elevations. Furthermore, Equation (15) also differs from previous studies for arid and semi-arid regions (e.g. Patton & Baker, 1976; Costa, 1987; Nouh,

2006). For example, Nouh (2006) found that only two variables the drainage area and elevation were sufficient to estimate the mean peak flood flow, and both were positively

81 related to the mean peak flood flow. Equation (15) has WS in addition to DA and BE, and shows a negative relationship between QMPF and BE. In a similar manner, the relationships between flood-peak frequency discharge and watershed characteristics were determined. For Q5 in Table 4-5, Model 4 with only two variables of DA and WS was selected as the best model to represent Q5 estimation.

Table 4-5 Selection of regression models for Method A.

Q Model R2 Adj. R2 ∆ Adj. R2 Sig. Variables used

1 0.73 0.50 0.095 DA, SF, WS, DD, BE

QMPF 2 0.72 0.57 0.07 0.039 DA, WS, DD, BE

3 0.72 0.61 0.05 0.014 DA, WS, BE

4 0.63 0.54 -0.07 0.012 DA, WS

1 0.76 0.56 0.069 DA, SF, WS, DD, BE

2 0.76 0.62 0.06 0.026 DA, WS, DD, BE

Q5 3 0.75 0.66 0.04 0.008 DA, WS, BE

4 0.74 0.68 0.02 0.002 DA, WS

5 0.70 0.67 -0.02 0.001 DA

Q10 1 0.70 0.45 0.121 DA, SF, WS, DD, BE

2 0.70 0.53 0.08 0.052 DA, WS, DD, BE

3 0.69 0.58 0.05 0.019 DA, WS, BE

4 0.67 0.60 0.02 0.007 DA, WS

5 0.64 0.61 0.01 0.002 DA

1 0.67 0.39 0.155 DA, SF, WS, DD, BE

2 0.67 0.48 0.08 0.071 DA, SF, WS, BE

Q20 3 0.66 0.53 0.05 0.029 DA, WS, BE

4 0.63 0.55 0.02 0.011 DA, WS

5 0.61 0.57 0.02 0.003 DA

1 0.65 0.35 0.184 DA, SF, WS, DD, BE

2 0.64 0.44 0.09 0.087 DA, SF, WS, BE

Q50 3 0.63 0.49 0.05 0.038 DA, WS, BE

4 0.60 0.52 0.02 0.016 DA, WS

5 0.58 0.54 0.03 0.004 DA

1 0.64 0.33 0.198 DA, SF, WS, DD, BE

2 0.63 0.42 0.09 0.096 DA, SF, WS, BE

Q100 3 0.62 0.47 0.05 0.044 DA, WS, BE

4 0.59 0.50 0.02 0.018 DA, WS

5 0.57 0.53 0.03 0.005 DA

It was noticed from Table 4-5 that there is a trend found in QMPF, Q5, Q10 and Q20, Q50,

Q100, where in the first three flood-peak flows, the absence of only shape factor (SF)

produces a significant model of p ≤ 0.05 (Model 2). In the cases of Q20, Q50, Q100, the

83 absence of both drainage density (DD) and shape factor (SF) produce a significant model of p < 0.05 (Model 3). Furthermore, for QMPF, Q5, and Q10, SF was excluded first and then DD, whereas in the case of second group (Q20, Q50, and Q100), DD was excluded first

2 and then SF. Another observation was that adjusted R in Q10, Q20, Q50, and Q100 continue to increase until one reaches Model 5. For consistency, the author decided to choose

Model 4 as a representative of Method A in Q5, Q10, Q20, Q50, and Q100. Method A regression equations that describe the relationship between watershed characteristics and different return periods flood-peak flows are:

268.0720.0 Q5 = 55.2 WSDA (16)

221.0692.0 Q10 = 28.5 WSDA (17)

202.0680.0 Q20 = 04.8 WSDA (18)

189.0673.0 Q50 = 67.11 WSDA (19)

183.0669.0 Q100 = 42.14 WSDA (20)

4.3.2 Method B: Effect of urbanization

In this Method, the effect of urbanization was investigated by adding percent of

urbanized area (U) and the percent of agricultural/farm area (FR) for each watershed. In

Oman, most of the farms are developed alongside the urban areas (with the owners) and

both are located along the wadis and water sources. This is particularly true for the

villages that are scattered in the mountainous areas. Table 4-6 shows that in all flood-

84 peak return periods, adding FR causes an increase in adjusted R2 and significance level of the model. For example, adjusted R2 has increased from 61 % in Model 1 to 74 % by adding FR. Including U decreases R2 to 72 %. In addition, Model 2 became more significant. The same observation was noticed in Q10, Q20, Q50, and Q100. This implies that including only FR may give better results in terms of the effect of urbanization.

Model 2 equations (21) through (26) were selected to estimate flood-peak discharges that include the urbanization effect (through the increase in greenness/farm area):

778.0974.0 − − 457.0608.0 QMPF = 28.2 FRBEWSDA (21)

− 349.0445.0847.0 Q5 = 45.0 FRWSDA (22)

450.0857.0 − 451.0 Q10 = 57.0 FRWSDA (23)

− 500.0456.0863.0 Q20 = 68.0 FRWSDA (24)

− 539.0463.0870.0 Q50 = 81.0 FRWSDA (25)

466.0873.0 − 558.0 Q100 = 92.0 FRWSDA (26)

Table 4-6 Effect of urbanization U & FR.

Q Model R2 Adj. R2 ∆ Adj. R2 Sig. Variables used

1 0.72 0.61 0.014 DA, WS, BE

QMPF 2 0.83 0.74 0.12 0.008 DA, WS, BE, FR

3 0.85 0.72 -0.01 0.019 DA, WS, BE, FR, U

1 0.74 0.68 0.002 DA, WS

Q5 2 0.81 0.74 0.06 0.003 DA, WS, FR

3 0.81 0.71 -0.03 0.011 DA, WS, FR, U

1 0.67 0.60 0.007 DA, WS

Q10 2 0.79 0.71 0.11 0.005 DA, WS, FR

3 0.79 0.67 -0.04 0.016 DA, WS, FR, U

1 0.63 0.55 0.011 DA, WS

Q20 2 0.77 0.69 0.13 0.006 DA, WS, FR

3 0.78 0.65 -0.04 0.020 DA, WS, FR, U

1 0.60 0.52 0.016 DA, WS

Q50 2 0.76 0.67 0.15 0.008 DA, WS, FR

3 0.76 0.63 -0.04 0.024 DA, WS, FR, U

1 0.59 0.50 0.018 DA, WS

Q100 2 0.75 0.66 0.16 0.008 DA, WS, FR

3 0.76 0.62 -0.04 0.026 DA, WS, FR, U

Table 4-7 shows the regression result for Method B, Model 2, and the unstandardized B coefficients (partial regression coefficients) which give a measure of the contribution of each variable to the model. A large value indicates that a unit change in this independent variable has a large effect on the dependent variable (the wadi flood-peak discharge

QMPF, in Q5, Q10, Q20, Q50, and Q100). The t and Sig (p) values give a rough indication of the impact of each independent variable – a big absolute t value and small p value suggest that an independent variable is having a large impact on the dependent variable. For example, the DA in all the models has the biggest significant impact on flood-peak discharge prediction. In the case of standardized coefficients, the intercept of the regression equation becomes zero and the change in the dependent variable (e.g. QMPF) is expressed in standard deviation units produced by one standard deviation in the independent variable concerned.

Table 4-7 Coefficients of Method B.

Unstandardized Standardized

Model Coefficients Coefficients t Sig.

B Std. Error Beta

(Constant) .824 2.506 .329 .752

ln(DA) .974 .174 1.249 5.594 .001

QMPF ln(WS) .778 .301 .665 2.582 .036

ln(BE) -.608 .444 -.332 -1.370 .213

ln(FR) -.457 .211 -.412 -2.163 .067

(Constant) -.789 1.455 -.542 .602

ln(DA) .847 .150 1.098 5.637 .000

Q5 ln(WS) .445 .222 .384 2.008 .080

ln(FR) -.349 .201 -.318 -1.742 .120

(Constant) -.562 1.567 -.359 .729

ln(DA) .857 .162 1.092 5.291 .001 Q10 ln(WS) .450 .239 .383 1.887 .096

ln(FR) -.451 .216 -.404 -2.086 .070

(Constant) -.388 1.651 -.235 .820

ln(DA) .863 .171 1.083 5.060 .001 Q20 ln(WS) .456 .252 .381 1.814 .107

ln(FR) -.500 .228 -.441 -2.198 .059

(Constant) -.205 1.726 -.119 .909

ln(DA) .870 .178 1.073 4.876 .001 Q50 ln(WS) .463 .263 .381 1.760 .116

ln(FR) -.539 .238 -.467 -2.264 .053

(Constant) -.085 1.763 -.048 .963

ln(DA) .873 .182 1.068 4.793 .001 Q100 ln(WS) .466 .268 .381 1.737 .121

ln(FR) -.558 .243 -.480 -2.295 .051

Standardized regression coefficients (beta weights) in a multiple regression equation are measured on the same scale with a mean of zero and a standard deviation of one. They are useful for determining which of the independent variables has a greater effect on the dependent variable in multiple regression analysis, specifically when the variables are measured in different units. Beta weights give the number of standard deviation changes on the flood-peak discharge that will be produced by a change of one standard deviation on the watershed characteristics concerned. For QMPF in Table 4-7, DA has the greatest contribution, where a change of one standard deviation in DA produces a change of 1.249 standard deviations in QMPF (the next largest is 0.665). Table 4-7 also shows that BE has the least impact on QMPF. In the case of Q10, Q20, Q50, and Q100, WS has the least impact.

However, WS has more of an impact on Q5 than FR.

In this study where both the dependent variable (e.g. QMPF) and independent variables

(e.g. DA, WS, and FR) are log-transformed, observing coefficients between 0 and 1 are typical in log-log regression. In this case, the predictor variable’s coefficient is interpreted as the percent change in the dependent variable while the predictor variable increases by one percent (Gelman & Hill, 2007). For the QMPF model, a 1% increase in

DA would yield a 0.974% increase in QMPF. Similarly, 1% increase in WS would yield a

0.778% increase, etc.. The QMPF model shows also a negative relationship with BE. As

Figure 4.1 shows, the gauge stations are located at the lower elevations, which are in turn located at the alluvial mouth (example: Al Khawd station) where more surface runoff is collected from the whole mountainous area within the same watershed. All models show a negative relationship between FR and QMPF, where in this region having very little

89 farming area results in less infiltration water use by vegetation and thus, more surface runoff. The regression results show that larger DA & WS and lower BE & FR tend to produce higher QMPF. Similarly, watersheds with higher elevations (particularly in the mountainous areas) with smaller DA, tend to have less QMPF.

Similarly, the bigger DA, and less FR tends to have higher QMPF. A good example of these type of watersheds is Wadi Dayqah (biggest watershed of DA = 1,730 km2, less FR

3 of only 0.19 %, and has the highest QMPF of 1062 m /s). Most of the literature notes that the most important factor in predicting flood-peak discharges is DA and WS. However,

Table 4-7 shows that FR plays an important rule (after DA and more than WS) in predicting flood-peak discharge especially in higher return periods (Q10, Q20, Q50, and

Q100), and this variable should not be ignored in flooding studies in this region. A similar trend in Q5, Q10, Q20, Q50, and Q100 flood-peaks was found where the impact of DA and

WS on flood-peak frequency decreases for higher flood-peak return periods, and the impact of FR on flood-peak frequency increases for higher flood-peak return periods. To compare which predictor has more of an effect on flood-peak estimation, standardized beta coefficients in Figure 4.3 show that DA has the largest impact on flood-peak estimation, followed by FR and WS respectively. A trend could be observed in that the effect of urbanization increases with increasing return period.

Drainage area, DA

1.100 1.095 1.090 1.085 1.080 1.075 Beta weightsBeta 1.070 1.065 1 10 100

Return Period (yrs)

Wadi slope, WS

.385 .384 .384 .383 .383 .382 .382 Beta weightsBeta .381 .381 .380 1 10 100

Return Period (yrs)

Agricultural/Farms, FR

.500 .480 .460 .440 .420 .400 .380 .360 Beta weightsBeta .340 .320 .300 1 10 100

Return Period (yrs)

Figure 4.3 Effect of DA, WS, & FR on QT.

4.3.3 Validation and comparison with MRMWR method

The derived regression equations of both Method A (Model 3 for QMPF and Model 4 for

Q5 to Q100) and Method B (Model 2) were used to estimate flood-peak discharges for 16 new watersheds that were not used in the calibration procedure. These new watersheds are located in the same region (northern Oman) and vary in size from 39 to 2125 km2, and altitude from 270 to 1764 m. Thus, these validation watersheds have a wider range than the calibration watersheds. Details of these validation watersheds and their derived characteristics and both observed and predicted QMPF values are shown in Table 4-8.

Table 4-8 Validation watershed’s characteristics.

Watershed's St. Range Mean Characteristic Dev. Min Max

DA 547.03 583.96 39.29 2124.80

SF 5.82 1.89 2.92 10.91

WS 21.73 22.10 4.28 94.83

DD 2.37 0.92 1.04 4.93

BE 995.53 378.65 269.93 1764.03

U 0.19 0.16 0.02 0.65

FR 0.41 0.21 0.08 0.75

Observed QMPF 242.94 149.71 58.00 703.00

Predicted QMPF 172.06 108.64 44.43 474.33

Dam affected watersheds were not included in the validation set of watersheds. The accuracy assessment statistics for the validation are shown in Table 4-9. An updated method (Equation (27)) developed by the Ministry of Regional Municipalities and Water

Resources (MRMWR) in 2007 is currently distributed by the ministry to all engineering consultants to be used to estimate design floods for watershed more than 10 km2.

20.076.0 − 15.0 Qmpf = 91.1 NMWSDA (27)

NM is the proportion of non-mountain (flat, alluvial) watershed to the total watershed area (ratio). From the above relationship, we can also see that area and slope have a positive relationship with QMPF, and NM is negatively related to QMPF. Similar to the other findings of this study, BE and NM are both negatively related to QMPF. A lower NM ratio is usually associated with basin’s altitude BE, and both tend to produce higher QMPF. For the purposes of a comparison, all models (Method A, Method B and MRMWR) were tested on the validation set to estimate flood-peak discharges as shown in Table 4-9. For

QMPF, Methods A and B produce the same percent error of 24 % and RMSE of 116, where MRMWR produces 79 % percent error and 228 RMSE. Method B has the highest

R2 of 63 % compared to 52 % and 48 % of models A and MRMWR. Method B which

2 takes into account urbanization effects was the best model in R in all cases. For Q10, Q20,

Q50, and Q100, %E, and RMSE were less in Method A, although the difference is very minor.

Table 4-9 Accuracy assessment.

% E E RMSE R2

Method A 44.59 0.39 116.45 0.52

QMPF Method B 44.94 0.40 115.83 0.63

MRMWR Model 79.48 -1.31 227.68 0.48

Method A 36.45 0.59 191.05 0.62

Q5 Method B 35.95 0.61 186.40 0.72

MRMWR Model 72.07 -0.76 396.66 0.62

Method A 34.71 0.54 341.73 0.58

Q10 Method B 34.51 0.53 348.25 0.66

MRMWR Model 68.19 -0.22 560.47 0.59

Method A 36.01 0.51 507.26 0.54

Q20 Method B 37.32 0.46 528.89 0.61

MRMWR Model 72.88 -0.14 772.95 0.56

Method A 38.75 0.48 729.52 0.52

Q50 Method B 41.15 0.43 767.44 0.57

MRMWR Model 73.99 -0.01 1017.46 0.54

Method A 40.08 0.47 901.58 0.51

Q100 Method B 43.20 0.40 953.48 0.55

MRMWR Model 74.63 0.05 1203.17 0.53

Both Methods A and B produce better results than the MRMWR model. Based on this table, Method B’s Model 2 was selected as the best model to estimate flood-peak discharge of all return periods used in this study.

Figure 4.4 shows graphical predictions QMPF using all models of Method A, Method B, and MRMWR. Method A’s model produced 4 over-estimations and 12 under-estimations of QMPF. Method B’s model produces 3 over-estimations and 13 under-estimations of

QMPF. MRMWR model produced 12 over-estimations and 4 under-estimations of QMPF.

Comparison between the observed and predicted values of QMPF for all calibration and validation stations is shown in Figure 4.5. Despite the lack of some data that may impact

QMPF estimation (e.g. climate characteristics: rainfall, soil, geologic characteristics), and the data limitation in the calibration procedure in this study, the results are encouraging.

Figure 4.5 shows that the scatter of both calibration and validation watersheds is relatively clustered along a straight line.

10000 Validation stations

1000 /s 3 m MAF, Q 100

10 EA350697AD EA479986AD EA638772AD EA144301AD EA546034AD EM304474AD EA330729AD EV394690AD EL381641AD DM578762AD DB576043AD DB496929AD DB388507AD DB554869AD FA968370AD DC237976AD Station ID Station

MRMWR Model A Observed Model B

Figure 4.4 Comparison of the three models.

10000

1000 /s 3

, m , 100 MAF

10 PredictedQ

1 1 10 100 1000 10000

3 Observed QMAF, m /s

Calibration Validation Salalah Model line

Figure 4.5 Scatterplot of relationships of QMPF.

4.3.4 Comparison with different climate, Salalah

One of the problems facing rainfall-runoff modeling in arid regions stated by Pilgrim et

al., (1988) was that the climatological differences may require modification of the models

and techniques appropriate to different regions. From this perspective, the model

equation was tested on a region that is different climatologically from Oman. Salalah, the

southern part of Oman, is more than 1000 km a way from the study area and is

climatologically different.

Salalah has a unique and moderate climate throughout the year, and different from the

rest of the Arabian Peninsula. In the summer season, the temperature ranges between 24 –

30 °C, where at the same time of the year the temperature can exceed 45 °C and 50 °C in

the coastal and interior regions of north Oman respectively. Furthermore, the Dhofar

Mountains that bound Salalah Plain indicate lower rainfall intensities than in the rest of

the Oman region (Kwarteng et al., 2008). Three watersheds from Salalah ranging in size

from 102 to 213 km2 were tested using Method B’s model (equation 21). The results show that two watersheds were underestimated with a percent error of 73% and 25%, and one overestimated with a percent error of 9% as shown in Table 4-10. Figure 4.5 shows that at least one of the Salalah points has a high error (%E = -73 %) and falls away from the 1:1 line. This implies that this model is not suitable for the Salalah region due to its different tropical climate from the rest of Oman. Another reason is that the type greenness/farm are in Salalah is totally different from that in the study area. For example, the monsoon rain and the moderate weather throughout the year help and maintain

97 natural vegetation in Salalah mountains, specifically in the Khareef season (July to

September) where all the mountains are covered by natural grass. Furthermore, unlike in the north of Oman, the urbanized areas in Salalah are concentrated in the coastal plain and not near/along the wadis in the mountainous areas. Based on this, a new model specifically for this region would be more appropriate.

The observed flow data started from 1997 to 2007 (10 years) and some go back to 1996.

This is a very short period of record to use in estimating floods with return periods of 10 years or higher. Thus, caution should be exercised when using the selected model equations for high return periods. However, the results show that the selected model performs better than the MRMWR model for all return periods.

Table 4-10 Accuracy assessment for Salalah.

3 QMPF (m /s) Station ID Location % E Observed Predicted

BD095279 Salalah, Oman 53 14 -73

AD997284 Salalah, Oman 49 37 -25

AD980966 Salalah, Oman 12 13 9

Chapter Five: Urbanization Effects on Wadi Flood Flow Frequency Analysis in Oman*

5.1 Introduction

Several studies in the literature (e.g. Huang et al., 2008; Ferguson & Suckling, 1990;

Kang et al., 1998) have related watershed characteristics to flow discharge, but very few

have focused on the impacts of urbanization on stream flow in arid regions. In addition, the effect of urbanization in stream hydrology and flood processes is different for arid and semi-arid areas (Sheng & Wilson, 2009). There are many definitions of urbanization: one is an increase in the total population that lives in urban areas (Pacione, 2009), another definition is the development of metropolitan areas for business and residential proposes

(Wigmosta & Burges, 2001). Urbanization is also defined by the United Nations as movement of people from rural to urban areas or the increase in the urban share of total population. Urbanization term in this study refers to all the definitions mentioned earlier which causes an increase of impervious area. Many arid regions such as Oman are experiencing rapid increases in urbanization – primarily occurring in the wadi flood plain. Furthermore, previous studies on arid wadi hydrology show that there is a research gap in urbanization and land-use impacts on runoff (Singh, 2009; Pilgrim et al., 1988; and Cordery et al., 1983).

* Some material in this chapter has been submitted to the Journal of Hydrologic Engineering with some modifications.

Huang et al. (2008) investigated the effect of growing watershed imperviousness on hydrograph parameters and peak discharge in Wu-Tu watershed, north of Taiwan. They found that the time to peak of flood hydrographs for various storms was diminished by approximately 5 h, whereas peak flow increased by 502 m3/s for different storm intensities. Wang et al. (2007) in his study on the effects of land use changes on hydrological processes in the an arid region in China stated that due to the continuous expansion of the cultivated land area, the downstream runoff has decreased by 27-32%.

Ferguson & Suckling (1990) found that increasing urbanization in Peachtree Creek,

Georgia leads to increase in the total annual flows in wet years and declining runoff in dry years. Others like Galster et al. (2006) studied the effects of urbanization on the discharge–drainage area relationship in east-central Pennsylvania. They found that the impervious surfaces in urban environments decrease infiltration and increase the rate and volume of water delivered to the river. Hollis (1975) developed curves describing the general effect of urbanization on floods of different recurrence interval based on similar published results in widely differing climatic environments from the semiarid parts of the

United States to Japan and western Britain. He found that the effect of urbanization to increase flood flow decreases as flood recurrence intervals increase. Kang et al. (1998) observed that peak discharge increased and the mean lag time of the On-Cheon Stream watershed in Pusan, Korea, decreased because of urbanization. As the literature shows, much of this knowledge has been accumulating in wetter, humid climates. However, Al-

Rawas and Valeo (2009) investigated the relationship between flood flows and watershed characteristics in northern Oman including farming area as an indicator of urbanization.

They found that drainage area (DA), Wadi slope (WS), and farms (FR) were the most

100 significant variables among 14 watershed characteristics in flood-peak flow estimation using regression analysis.

5.1.1 Modeling Urbanization Impacts on Runoff

Despite being one of the oldest methods available, the Rational method is still considered

the simplest, comprehensive rainfall-runoff model that relates runoff peak discharge to

rainfall intensity. It is widely favored and continues to be used by engineers (Yen &

Akan, 1999; Crobeddu et al., 2007) in many areas. Debo & Reese (2002) however,

advised against using the Rational method in large semiarid and arid watersheds, where

storm cells are relatively small in areal extent but with high variability in rainfall

intensity. Crobeddu et al. (2007) improved the Rational method by proposing an

alternative to the lumped runoff coefficient by introducing infiltration and initial

abstractions for pervious areas and impervious areas, respectively. Guo (2001) derived a

new formula to compute the time of concentration for the Rational Hydrograph method

for Denver metropolitan area, Colorado.

The runoff coefficient (C) in the Rational method is dimensionless and defined as the ratio of runoff to rainfall. Generally, many reasons (including low infiltration capacities on vegetation-free surfaces and high rainfall intensities) make runoff coefficients higher in arid regions as compared to humid regions (Cooke, et al., 1993). Mahe (2006) reported that rapid changes in land-use/land-cover in the semi-arid West African Sahelian river basins in Burkina-Faso and Niger caused an increase in runoff coefficients, thus leading

101 to increased flood peaks. In addition, the variation in C for arid areas is greater than in humid areas (Longobardi et al., 2003). Young et al. (2009) found that C for rural watersheds in Kansas varied with location and recurrence interval. They found that C values computed for eastern Kansas are much higher than the commonly used values for rural watersheds found in many hydrology design books.

Although the SCS (1986) method using the curve number (CN) is widely used for its simplicity, one of its disadvantages is that it does not take into account regional qualities based on geologic and climatic settings. It was originally developed using data from the

Midwestern United States (Ponce et al., 1996). Sharma (1987) found that derived CN values for bare crust-forming sandy soils in the Indian arid zone were higher than the values provided by the Soil Conservation Service (SCS) handbook. For example, CN estimated by the SCS method for 0.5% slope is 77, where the actual optimized value was

87. Sharma (1987) stated that using a CN provided by SCS gave under-predicted generated runoff volumes by 47-68% for storms more than 100 mm, and 163-400% for storms less than 25mm. Descheemaeker et al. (2008) found in their study area of rangelands in semi-arid tropical highlands of northern Ethiopia, that hydrologic soil group was not as important an explanatory factor as land use type for explaining variability in curve numbers. They found that the CN determined for their study is a preferred alternative to the CN handbook. Because of the sensitivity of the SCS method to CN, determining CN values from local studies are more reliable than those taken from

CN estimates provided by SCS tables (Hawkins, 1993).

102

The literature shows a relatively poor understanding of hydrological processes in arid regions, and thus, the appropriate application of the SCS method using curve numbers also faces the same difficulties and challenges (Descheemaeker et al., 2008). Shi et al.

(2009) in their study in central China found that using the standard SCS method assumptions underestimates large runoff events, and overestimates some of the small events. Others (Ponce & Hawkins, 1996; Lim et al., 2006; Mishra & Singh, 1999) also found that using modified SCS values (e.g. CN) instead of the standard SCS values could improve runoff predictions. Cooley & Lane (1980) stated that optimized CN values for a

Hawaiian watershed are significantly different from the standard tabulated CN values. To use the tabulated CN values, Debo & Reese (2002) suggested that CN should be selected only after a field inspection of the watershed and a review of soil maps.

A study by Nouh (1987) on flood frequency analysis in the southwest region of Saudi

Arabia stated that the Rational method produces overestimates of peak flow results compared to the Phi-index and SCS methods when using larger sized drainage basins because of the inaccurate determination of runoff coefficients for the study region (using standard tabulated values). Others like Kang et al. (2009) in his study of design floods in

Korea pointed out that the rational method underestimated values as compared to those estimated by the SCS method.

103

Based on the above literature, it is not easy to compare the Rational method against the

SCS method. One may work better for a specific region and input assumptions. Some limitations and/or assumptions in both the Rational and SCS methods are summarized by

Debo & Reese (2002) and are summarized as follows. For the Rational method: (i) the peak flow is a maximum when the rainfall intensity equals or is longer than the time of concentration. This limits the size of the watershed applicable for use with this method, in particular, it makes it inappropriate for large watersheds; (ii) the frequency of the peak discharge becomes the same as that of the rainfall intensity which is not realistic; (iii) C is independent of rainfall intensity but its selection should depend on the storm, soil, and land-use conditions; and (iv) all losses are lumped into the runoff coefficient. With regard to the SCS peak discharge method: (i) the watershed must be hydrologically homogeneous and described by a single CN value; (ii) the watershed can have only one time of concentration derived from the main stream; (iii) the method does not consider hydrologic routing effects; and (iv) the weighted CN can only range between 40 and 98.

5.1.2 Changing Urban Forms in Oman

The Governorate of Muscat is the capital of Oman as well as being the largest city in

Oman and the most densely populated region. Located in north-east Oman, it is the

nation’s political, economic, and administrative center. Until 1970, Oman had only a few

basic unpaved roads and a small number of schools. When Sultan Qaboos came to power

in 1970, a new era had begun in Oman. Since 1970, Muscat has experienced a rapid

development in its infrastructure. According to the 2003 census conducted by the Oman

104

Ministry of National Economy, the population of Muscat increased from 549,150 in 1993 to 632,073 in 2003 (MNE, 2007).

A handful of studies have looked at flooding in Oman wadis. El-Zawahry (2007) stated that urbanization within the very small Wadi Aday sub-catchment may cause relatively high increases in peak flooding, and all urbanized areas in the Wadi Aday watershed would be fully flooded during the 100 year return period flood. Al-Awadhi et al. (2009) mentioned in their study, which modeled the urban growth of greater Muscat using the

SLEUTH model, that the constructed area had increased 840% from 1960 to 2003, and a significant change took place from 1970 to 1980. In addition, greenness is expected to decrease by 78% in the next 45 years. Al-Awadi (2005) stated that in Al-Seeb Wilayat only, the agricultural land reduced by 52.6% between 1991 and 2003. Due to the economic growth and the improvements in transportation infrastructure, some other regions in Oman have experienced an expansion in agricultural areas like the Al-Batina coastal region (Harris, 2003). After tropical cyclone Gonu hit Muscat on the 5th and 6th of

June 2007, the government of Oman decided to install dams in this watershed to protect the main urban centers of Al-Qurm area against flooding (Al-Abri & Magnan, 2007).

Due to economic reasons and booming oil prices, the government has expanded its plans for tourism and encourages foreign investments. Many agricultural lots have changed to residential and other commercial and business projects. Unfortunately, this increases the impervious area in Muscat in general and in the Aday watershed - especially in the Al-

Qurm residential and commercial area. This increase of impervious surface not only

105 increases the volume of discharge delivered, but also the rate of that delivery (Ferguson

& Suckling, 1990). Figure 5.1 is IKONOS high resolution imagery showing agricultural areas that have changed into commercial and business landuses for different parts of

Muscat.

Figure 5.1a shows agricultural land in 2000 that was converted to a commercial landuse by 2008 (Figure 5.1b). Again, former areas of agricultural land shown in 5.1c are converted to residential areas shown in 5.1d. In the previous two, the area became almost completely impervious. In addition, as shown in image 5.1d, the wadi in Al-Hail south became narrower and a new house was built on a large lot in the middle of the wadi channel. Many other similar practices have occurred, which contribute to the decrease of green lands, a narrowing of the wadi channels, and thus, an increase in the surface runoff in this region. This leads to possible explanations for the recent increases in flash floods in Oman.

106

(a) Al-Khoudh IKONOS 2000 (b) Al-Khoudh IKONOS 2008

Figure 5.1 Land use change in Muscat.

107

5.1.3 Objectives

The main objectives of this study are not to predict the wadi peak flow, but to: (i) observe

changes in an urbanization in flash flood affected areas of Oman; (ii) develop new values

for runoff coefficients and curve numbers for arid regions that may be used in Oman and

similar arid regions; and (iii) investigate the impact of this change in urbanization on

wadi peak flow and flood frequency in the Wadi Aday watershed in the time period from

1960 to 2003. Since the literature shows a research gap in urbanization and land-use

impacts on runoff in wadi hydrology, this study is one of the few applications on the

effects of urbanization for this type of area. Moreover, new tables of hydrological

coefficients are created specifically for this type of landform and arid region, which may

be used by hydrological engineers in areas with similar arid and urban characteristics.

5.2 Study Area and Methodology

The Aday watershed in Oman was selected for the current study. The watershed is

located in the Governorate of Muscat and covers an area of 357 km2 to the outlet, 320 km2 to Al-Qurm gauge station, and 268 km2 to Al-Amrat station. The Western Al Hajar

Mountains run through the watershed. Figure 5.2 is a digital elevation map of the Aday watershed. Mean annual rainfall throughout most regions in Oman is relatively low, less than 100 mm, in the coastal regions, but reaching as much as 350 mm in the mountainous regions with relatively frequency (MRMEW 2005). The total average annual amount of rain falling on Oman is estimated to be about 19,250 Mm3. Approximately 80% of the

108 total rainfall evaporates, leaving 1,400Mm3 as effective rainfall generating runoff and direct infiltration to the groundwater reservoirs (MRMEWR 2005). The study area is drained northward by wadi Aday through Al-Qurm residential and commercial areas, and finally to the coastal sand plain of Al-Qurm area (MRMEWR, 2005).

Available rainfall data ranges from 1983 to 2003 (20 years) with 384 storms of 6 h duration. These data are used to randomly generate 10,000 storm depths for the same duration. Geographical Information Systems (GIS) data of land use for the years 1960,

1970, 1980, 1990, 2000, and 2003 are also used. This data is part of an integration of historical aerial photographs and IKONOS high resolution satellite imagery. The greatest urban expansion occurred in the period from 1980 to 1990, with the total urban change from 5.783 km2 and 13.567 km2 respectively. During this period of time, most of the biggest projects in Oman including the road network were planned to be completed by the celebration of Oman’s 15th national day in 1985.

109

Figure 5.2 Study area. This map is not an authority on international borders.

110

The methodology of this study consists of: (i) randomly generating 10,000 rain storms;

(ii) creating a digital soil map for the study area; (iii) computing new runoff coefficient

(C) and new curve number (CN) tables for the study area based on the soil map; and finally (iv) applying different runoff models with the 10,000 different storms to explore the effect of urbanization on runoff peak flow frequency analysis. A Digital Elevation

Model (DEM) of 40 m along with Hydro extension in ArcMap 9.1 was used to extract the watershed characteristics representing average slope and drainage properties of the watershed.

5.2.1 Randomly Generated Rain Storms

Because of the random rainfall patterns in arid regions, their prediction becomes difficult

(Sen & Eljadid, 1999). Therefore, a suitable selection of a probability distribution is

essential to expressing the behavior of rainfall in this region. Sen (2008) recommended

the Gamma Probability Density Function (PDF) for rainfall prediction in arid regions. In

this study, a total number of 384 of 6 h duration storms were used to generate 10,000

randomly artificial storms. Table 5-1 shows the statistical summary of the rainfall storms

used in this study.

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Table 5-1 Rainfall statistics.

Statistic Value

Sample Size 384

Mean 16.23

Std. Deviation 11.88

Skewness 2.51

Min 5.40

50% (Median) 12.5

Max 78.5

Different statistical probability distributions were fitted to all 384 rainfall events. To

measure the compatibility of a random sample with a theoretical probability distribution

function, the χ-square test was used to show how well the distribution used fit the data.

The null hypothesis (H0) that the data follow the specified distribution was tested at the

0.05 significance level. The test showed that the Exponential (2 parameter) distribution provided the best fit to the rainfall data (see Table 5-2). The hypothesis was rejected if

2 2 the chi-square test statistic (χ ) was greater than the critical value (χc ). Figure 5.3 shows the observed and fitted data using this distribution. Note that the cumulative Distribution

Function (CDF) for the two-parameter Exponential function is:

= − −λ − γ))x(exp(1)x(F (1)

112

1.2

1.0

0.8

0.6

0.4

0.2

0.0 5 15 25 35 45 55 65 75

Observed Fitted

Figure 5.3 CDF of exponential (2P) probability distribution.

Table 5-2 Goodness of Fit Test.

Critical Chi-square Reject H0 at Probability distribution Mean St. Dev. 2 (χ ) 2 α = 0.05? (χc )

Normal (= 16.23, σ = 11.87) 16.04 11.73 55.414 15.507 Yes

Lognormal (= 2.599, σ = 0.582) 15.93 10.11 16.187 15.507 Yes

Exponential (2P) (λ = 0.09231, γ 16.28 10.84 4.7675 15.507 No = 5.4)

Gamma (α = 1.868, β = 8.689) 16.16 11.79 39.660 15.507 Yes

Pearson Type III (α = 0.916, β = 16.11 11.05 12.778 15.507 No 11.723, γ = 5.4)

Log Pearson Type III (α = 16.70 15.11 7.9828 15.507 No 11.767, β = 0.1699, γ = 0.60024)

113

5.2.2 Soil Map

To date, there is no digital soil data that gives detailed information on soil classification

in Oman. The only source of data available is The Oman Soil Atlas, prepared in 1992 by

the Ministry of Agriculture and Fisheries in Oman with collaboration with the United

Nations Development Programme and the Food and Agriculture Organization of the

United Nations (UNDP/FAO) through Project-OMA/87/011 Soil Survey and Land

Classification.

A GIS digitizing function was used to convert the hard copy of the soil map of the study

area into a digital format as depicted in Figure 5.4a which shows the spatial distribution

of soil type in the study area. The hydrological soil group was determined for each of the

soil classes used in this study and its percent to the total watershed’s area as shown in

Table 5-3.

The watershed was divided into different zones A1, A2, A3, .., An based on land use, with their runoff coefficients C1, C2, C3, .., Cn, and the weighted C for the whole watershed was computed as:

∑ ii )AC( C = (2) w A

114

(a) Soil classification (b) Slope percent

Figure 5.4 Soil and slope map of Aday watershed.

115

Table 5-3 Soil Classification.

Symbol Name Soil Group Area (%)

Calciorthids: loamy-skeletal, deep to moderately 5 B 2.42 deep, strongly dissected, 0 to 15 percent slopes. Calciorthids-Torriorthents: loamy and sandy-skeletal, 12 A 4.28 deep soils, moderately flooded, 0 to 3 percent slopes. Gypsiorthids: loamy-skeletal to loamy soils, gypsum 17 pan, on strongly dissected high alluvial terraces and B 0.79 fans, 0 to 15 percent slopes. Gypsiorthids: sandy to loamy-skeletal soils, on 21 moderetely disscted high alluvial terraces and fans, 0 A 0.05 to 8 percent slopes. Torriorthents: extremely gravelly sandy, deep soils 43 on young flooded alluvial terraces and fans, 0 to 5 A 3.78 percent slopes. Torriorthents and Calciorthids-Rock outcrop: loamy 46 and loamy-skeletal, shallow and moderetely deep B 3.27 soils and rock outcrop, 0 to 15 percent slopes. Torriorthents-Gypsiorthids: sandy-skeletal on young 48 A 16.27 alluvial fans and terraces, 0 to 5 percent slopes. Playas-Salorthids: Playas and clayey to sandy, deep 57 D 0.59 soils, strongly saline, 0 to 1 percent slopes. Rock Outcrop Torriorthents: mountains and strongly dissected rocky plateaus, and loamy-skeletal to sandy R C 68.54 skeletal, shallow soils, 0 to more than 100 percent slopes.

116

In this study, the type, size, and style of buildings in Oman were considered to select the proper values of runoff coefficient for the rational method and curve number for the SCS method. Residential areas in Oman are totally different typical north American residences because the houses are surrounded by interlock instead of greenness/grass (such as in the backyard). The Muscat Municipality and Ministry of Housing in Oman allows 50% of residential a land’s lot for construction, and the rest usually includes car park covered by interlock. Figure 5.5 shows a comparison between houses in Oman and Canada. These pictures show that the allowable lot’s percent of building in Oman is larger than is typical in Canada. Therefore, using the standard tables for runoff coefficient and curve number may not be valid in Oman, knowing that there is a strong dependence of runoff coefficient on urban form. This is one of the main weaknesses of the Rational Method

(Rossmiller, 1980). Thus, new values for runoff coefficient and curve number are essential specifically to regions of similar urban form.

As shown in Figure 5.5 (a), all of the houses are surrounded by an interlock surface which is very widely used in Oman. Furthermore, the houses in Oman are made of concrete and not the wood as in Canada due to the lack of natural forests and the high price of wood. Brown (2007) found that for interlock, runoff/rainfall ratio is 0.83, and the concrete roof is assumed as 0.95 (Viessman 2002). Thus the computation of the residential area runoff coefficient (C) in Oman is a result of 50% of the residential lot being concrete and the rest being 50% interlock surface. The amount of trees was neglected because not all the houses have trees in their patios.

117

Interlock surface

(a) Muscat, Oman. (b) Calgary, Canada.

Figure 5.5 Residential area in Oman vs. Canada.

5.2.3 Hydrological Curve Number (CN)

New values of CN were computed for Oman similar to the Soil Conservation Services

(SCS, 1986). For residential lot size of 1/8 acre or less (506 m2) with an average of 65% impervious surface, Soil Conservation Services (SCS, 1972) curve numbers are computed assuming the runoff from the house and driveway is directed towards the street with a minimum of roof water directed to lawns where additional infiltration may occur. The remaining previous area of 35% (lawns) is considered to be in good pasture condition. In

Oman the remaining 50% of the lot is mostly covered by interlock and not lawns. Based on Brown (2007) findings, Equation (3) was used to compute the average potential maximum retention (S) for interlock.

118

= P17.0S (mm) (3)

25400 CN += 254 (4) II S

CN2.4 CN = II − CN058.0 (5) I 10 II

5.2.4 Wadi Peak Flows

Two runoff models for peak flow were used in this study: the Rational method and the

SCS method. The Rational model is the most widely used uncalibrated equation and

3 2 relates the peak discharge (Qpeak in m /sec) to the drainage area (A in km ), the rainfall intensity (i in mm/hr), and the runoff coefficient (C).

peak ⋅⋅= AiC278.0Q (6)

Another model based on the SCS dimensionless unit hydrograph was used to determine the wadi peak flow. This model is based on the time to peak as shown in Equation (7).

This method is then compared to the Rational method to investigate the effect of urbanization on wadi peak flow.

119

QA peak = 8.2Q (7) t peak

= − IRQ (8)

where tpeak (h) is the time to peak, Q (cm) is storm runoff, and I is the accumulated infiltration depth for the storm duration of 6 h. Horton’s (1940) infiltration exponential empirical model was used to calculate the flow depth using the following equation:

− kt f −+= )e1(mfI (9)

 − ff  m =  fi  (10)  k 

where fi and ff are the initial and final infiltration rates during the storm event and k is the infiltration coefficient. Ghorbani et al. (2009) investigated the performance of different infiltration models including those by Kostiakov, Mezencev, Horton and Philip with data from 123 study sites with different soils in the Colal plain of Iran. Infiltration coefficients used in this study are the same ones obtained by Ghorbani et al. (2009) because the regions are similar climatically and geologically. Time to peak was computed using

Equation 11. Equation 12 was then used to calculate the lag time based on the SCS method (McCuen, 2005).

120

D t += t (11) peak 2 lag

7.0 8.0 1000  L  − 9  CN  t = I (12) lag 1900Y 5.0

where D is the storm duration (6 h), tlag (h) is the lag time, Y (%) is the average land slope

(see Figure 5.4b), and L (ft) is the hydraulic length of the watershed. Equation 12 accounts for the effect of urbanization on the lag time and the time of concentration.

5.2.5 Effective Drainage Area

The effective drainage area is the area that contributes to the runoff response in a

watershed. The effective drainage area is usually related to the impervious area in the

watershed. The effective impervious area is a key predictor for urbanization impacts on

runoff (Lee & Heanery, 2003; Fletcher & Deletic, 2008; Bledsoe & Watson, 2001; Booth

and Jackson, 1997). The effective impervious area that contributes to the direct runoff

could be only a part off, or the total impervious area in the watershed. Lee & Heanery

(2003) found that the directly connected impervious area for an apartment area that

covered 44% of a catchment in Miami contributed 72% of the total runoff volume over

52 years. Some studies (e.g. Booth and Jackson, 1997) suggest that using the effective

impervious area (EIA) - the areas that contribute directly to the storm runoff response -

121 instead of total impervious area (TIA) is more appropriate. However, they explain the limitations and difficulty in measuring EIA. Others (e.g. Alley and Veenhuis, 1983) used an empirical relationship between TIA and EIA from a highly urbanized portion in

Denver. Effective impervious area could result in greater peak flow than considering the entire watershed area (Debo & Reese, 2002).

The total area of the Aday watershed is 357.72 km2, and the total urbanized area in 2003 was 19.241 km2 or 5.38% of the watershed’s area. Considering the total watershed’s area, the effect of 5.38% may not give a reasonable indication of the impact of urbanization, especially in the weighted runoff coefficient (C) and the curve number (CN). Therefore, the author use the effective area to represent a 500 m buffer on both sides of the urbanized area roads’ centerline. This effective area covers all impervious surfaces in the watershed including roads, roofs, parking lots and all built up urban areas in the watersheds. Considering the size of the effective area of the watershed, the methods used in this study for calculating peak discharge may produce different values of wadi peak than using the total watershed’s area. However, using the effective area will likely provide a better estimate of the effect of urbanization on wadi peak flow. Figure 5.6 depicts the new shaded effective area of 83.66 km2 used for wadi peak flow computation.

122

Figure 5.6 Wadi Aday effective area (dotted area).

123

5.3 Results and Discussion

Because this work is investigating the impact of urbanization on wadi peak flow through runoff simulation over various degrees of impervious covering starting from 1960 to

2003, creating a new soil map for runoff hydrological parameters was necessary to compute new runoff coefficients (C) and curve numbers (CN). The soil map (Figure 5.4) shows that more than 68% of the watershed is loamy to sandy soil with high variability of percent slopes representing the hydrological soil group C. The soil type in the upstream of Wadi Aday started with loamy and loamy and sandy, and then to a sandy alluvial fan in most of the wadi, followed by extremely gravely sand in a small part of the wadi. The rest of the wadi is loamy to sandy-loam and ends with a small portion of clayey-sand in the coast line of Gulf of Oman.

Figure 5.7 shows the spatial urban expansion in the Aday watershed. Analyzing the change in urbanized area in the period from 1960 to 1970 indicates that the urbanization occurred along the wadi itself, and most of the expansion is in the downstream end close to the coast line and specifically in the existing area of Al-Qurm.

124

Figure 5.7 Aday watershed’s urban expansions from 1960 to 2003.

125

The increase in urbanized area in the study area for the years 1960, 1970, 1980, 1990,

2000, and 2003 are plotted in Figure 5.8. Figure 5.8 shows that the urbanized expansion in the Aday watershed is large and very remarkable. In 1960 the total urbanized area was

1.63 km2 and in 2003 it increased to 19.24 km2 - an increase of 1182%. In the period from 1960 to 1970, the study area didn’t experience any urban expansion in all land use classes. The change started after 1970 as mentioned and is commensurate with the economic growth in Oman and improvements in transport infrastructure. The government buildings increased from 0.358 km2 in 1980 to 1.761 km2 in 1990 (491%) followed by the residential areas increasing from 0.755 km2 in 1980 to 1.428 km2 in 1990 (288%). In contrast, a declining trend in greenness showed a decrease in agriculture lands by 86% between 1960 and 2003. In general, the most significant increase represents the impervious surfaces (e.g. residential area) in the study area.

9 8 7 6 ) 2 5 4

Area (km Area 3 2 1 0 1

Residential Commercial Industrial Governmental Roads Agricultural Recreational

Figure 5.8 Change in urbanized area from 1960-2003.

126

New weighted C values computed for Oman for each associated land use class used are listed in Table 5-4. Some values listed in this table are similar to the standard values (e.g.

Agricultural lands, roads). For example, the computed runoff coefficient (C) for residential areas in Oman is 0.83 (= 0.50*0.95 + 0.5*0.83) assuming that the 50 % of the allowable construction represents the concrete house and the remaining 50 % represents the interlock surface.

Table 5-4 Arid C values

Land use C C non-arid

Residential 0.89 0.3-0.75

Commercial 0.95 0.5-0.95

Industrial 0.80 0.5-0.90

Governmental 0.89 0.3-0.75

Roads 0.95 0.7-0.95

Agricultural 0.25 0.1-0.25

Recreational 0.35 0.1-0.35

Artificial Ponds 1.00 1.00

Figure 5.9 shows the change in C value using the whole watershed area versus the

effective area determined with a 0.5 km buffer. When using the effective area, a

significant change in C value is the result of urbanization but when using the whole

127 watershed area, only a minor difference is observed. Table 5-5 shows the percent difference in the average simulation wadi peak flow (Qpeak) of 10,000 rainfall events between the conventional C value and the non-arid C value using the effective area. The average increase of wadi peak flow increased by 7.2 m3/s between 1960 and 2003 and the flowrates computed using the arid C values are roughly 33% higher than those computed with the non-arid C values

0.420

0.400

0.380

0.360

0.340 Runoff Coefficient (C) RunoffCoefficient 0.320

0.300 1960 1970 1980 1990 2000 2003 Year

Whole Area Buffer = 0.5 km

Figure 5.9 Change in C values.

128

Table 5-5 Watershed’s weighted C values.

Arid C value Non-Arid C value Year % Difference 3 3 C Qave (m /s) C Qave (m /s)

1960 0.302 19.08 0.202 12.75 33.19

1970 0.302 19.08 0.202 12.75 33.19

1980 0.335 21.14 0.227 14.33 32.25

1990 0.387 24.44 0.262 16.54 32.34

2000 0.413 26.06 0.279 17.61 32.44

2003 0.417 26.32 0.282 17.80 32.38

The minor change in C values (0.026) between 1960 and 2003 implies that the weighted

runoff coefficient that may not be the best approach for calculating runoff coefficients of

a mixed land use watershed especially when the watershed is very large (the whole

watershed area). Therefore, to better understand the impact of urbanization on C values,

the use of effective area is more appropriate.

While not a great deal of reliable flow data exists for the Aday watershed, 18 short

duration hydrographs resulting from storms between 1996 – 2007 were made available to

the author. The average flow of each event was recorded and Grubbs test for outliers

(Grubbs, 1969) was used to eliminate 2 of these average storm flows as outliers. The

average of the remaining 16 discharges was 27.68 m3/s. Because this data is the average of average storm flows spanning a 12 year period, it cannot be deemed to provide a

129 reliable comparison to the results of the simulations shown in Table 5-5, but it is at least within range of the 2003 average simulated discharge using the arid C values.

Equations (3) through (5) have been used to compute the hydrological curve number

(CN) for interlock. The average potential maximum retention (S) for interlock as Brown

(2007) computed was defined as S = P - Pout = 100% – 83% = 17% or S = 0.17P. For

10,000 rainfall events used in this study, the average S = 2.77, and curve number for dry antecedent moisture conditions (AMC I), was computed to be CNI = 97. The new computed CN values for Oman are listed in a similar manner for the Soil Conservation

Services (SCS, 1986). The same assumption was used for various residential lot sizes used in the SCS (1986) method except that the remaining pervious area in Oman is considered to be covered by interlock. These new CN values for Oman listed in Table 5-6 are higher than the ones in SCS (1986) created for the USA. For residential lots larger than 500 m2, a fixed interlock area of 35% of the lot is considered appropriate and the rest of the lot considered is considered as agricultural area.

To better understand the impact of urbanization on wadi peak flow, Table 5-7 lists the time to wadi peak flow along with weighted CN and average Qave for 10,000 rainfall events simulated using the SCS model with the Horton infiltration model. It is found that the time to peak of the wadi flow decreased by 0.37 (22.2 min) in 2003. In addition, the wadi peak flow increases by about 2.3 m3/s between 1960 and 2003 due to the increase of

CN value from 76 in 1960 to 79 in 2003.

130

Table 5-6 Arid Curve Number (CN) values.

Arid CNI Values SCS CNI numbers

Hydrologic Soil Hydrologic Soil

Group Group

Average Land use impervious A B C D A B C D area

Urban District:

Commercial/Business 85% 89 97 98 98 77 83 87 89

Industrial 72% 90 98 98 98 64 75 81 85

Agricultural 55 72 81 86 55 72 81 86

Recreational 38 54 66 72 38 54 66 72

Open Area 63 77 85 88 49 68 79 84

Residential lot size:

1/8 acre or less (506 65% 90 98 98 98 58 70 79 83 m2)

1/4 acre (1012 m2) 38% 70 78 83 85 40 56 67 74

1/3 acre (1349 m2) 30% 65 75 81 84 36 52 64 72

1/2 acre (2023 m2) 25% 62 73 79 83 33 49 63 70

1 acre (4047 m2) 20% 59 71 78 82 30 47 61 69

2 acres (8094 m2) 12% 55 68 76 81 26 44 58 66

131

Table 5-7 Watershed’s weighted Curve Number (CN).

SCS Lag Method

Using Arid CN Using Non-Arid CN

Qave Non-Arid Qave % Year Arid CN tpeak (h) tpeak (h) (m3/s) CN (m3/s) Difference

1960 76 7.72 45.07 67 9.07 38.34 14.94

1970 76 7.72 45.07 67 9.07 38.34 14.94

1980 77 7.60 45.78 67 8.99 38.69 15.48

1990 78 7.44 46.72 68 8.92 38.97 16.59

2000 78 7.37 47.19 68 8.89 39.12 17.10

2003 79 7.34 47.37 68 8.87 39.20 17.25

The simulated discharge data generated by the application of the Rational method and the

SCS method were fitted using Normal, Lognormal, Exponential, Gumbel and Log

Pearson Type III distributions. As visual inspection of Figure 5.10 shows, the Log

Pearson Type III distribution was the best fit to the data generated by the Rational method in 2003. This was also true for 1960 and both applications of the SCS method.

Figure 5.11 (a) and (b) show the simulated peak flows for 1960 and 2003, using both

Rational and SCS models, respectively, plotted using Weibull’s plotting position. The increases of flow discharge are consistent for all return periods. However, the effect of urbanization on flow discharge using the Rational method shows much greater increase in

132 wadi peak flow values. This implies that to investigate the effect of urbanization on peak flow, the Rational method provides greater emphasis on the urban effect.

Table 5-8 lists the simulated wadi peak flow from 6 h randomly generated rainfall events for different return periods using the Log Pearson Type III distribution. This table shows that the ratio in wadi peak flow in 1960 to 2003 is the same for all return periods. For example using the rational method, the Q1960/Q2003 ratio is 0.72, and when using the SCS method, the ratio is 0.95. This result is less than other studies done on other humid climate regions (e.g. Huang et al., 2008; Wang et al. 2007; Hollis 1975). For example,

Huang et al. (2008) found that this ratio varies according to different return periods.

Huang et al. (2008) found that for 200 year return period, the ratio of natural to urban status is about 1.6. This finding does agree with Sheng & Wilson (2009) in their study on semi-arid southern California where the increase of flood magnitudes of all return periods is sensitive to urbanization. This indicates that the effect of urbanization in this arid study area may not have the same effect in other humid climates. One of the possible reasons is that the hydrological coefficients (e.g. C and CN) for pre-urbanized conditions are originally (for the natural state) higher and have less infiltration than the ones used in humid regions, therefore, changes even after urbanization remains less compared to humid regions.

133

Table 5-8 Wadi peak flow for different return periods using Log-Pearson Type III.

Rational SCS

3 3 Return Qpeak (m /s) Qpeak (m /s) period (yrs) 1960 2003 Ratio 1960 2003 Ratio

2 15.34 21.16 0.72 35.05 36.84 0.95

5 25.56 35.26 0.72 61.73 64.88 0.95

10 34.20 47.18 0.72 84.59 88.91 0.95

20 44.03 60.73 0.72 110.85 116.51 0.95

50 59.39 81.92 0.72 151.99 159.75 0.95

100 73.10 100.82 0.72 188.76 198.40 0.95

Figure 5.10 Fitted distributions to 2003 data generated (Rational Method).

134

Figure 5.11 Flow distribution curves for the years 1960 and 2003 for (a) Rational method and (b) SCS model.

135

For future efforts, the author recommends that:

1. Flash floods studies in an arid region require a different approach than most other

natural hazards; a multidisciplinary effort is required to enhance and reduce the

uncertainties in flash flood studies.

2. There is a need for high resolution DEMs, especially in urban areas to better

delineate borders and slopes of the wadis.

3. There is a need for a national database in Oman for all information related to the

environmental. This is essential and very important for flash flood studies.

4. There is a need for increasing the number of gauging sites specifically in the

urban areas and the watershed outlets in order to improve the prediction of wadi

flood flow.

5. Engineers need to be informed of the developed temporal distributions of heavy

storms that are unique for arid regions rather than the standard ones in the

literature.

6. Future research should redefine watershed characteristics using a GIS that

specifically characterizes arid regions prone to flash floods and uniquely

incorporates more watershed characteristics (e.g. infiltration and transmission

losses), etc.

7. Engineers should use the new hydrologic runoff coefficient (C) and curve number

(CN) tables that were created specifically for the study area.

8. Finally, the author recommends that immediate and serious efforts should be

implemented to stop the continuous rate of urban expansion at the expense of

greenness areas in Oman.

136

Chapter Six: Conclusions and Recommendations

As stated in Chapter 1, the general objective of this research was to conduct a GIS-based study that will improve flash flood prediction by providing new knowledge and tools specifically for arid regions like Oman. This was achieved through three specific objectives: (i) developing time-distributions of heavy storms that are applicable arid regions in Oman; (ii) analyzing wadi flood flow data and developing a relationship between Oman watershed characteristics and wadi flow for different return periods in

Oman; and finally (iii) observing changes in urbanization in the wadi watersheds and investigating the impact of this change in urbanization on wadi peak flow and flood frequency. These three objectives were each achieved through analysis and discussion in

Chapters 3, 4 and 5, respectively.

In Chapter 3, historical data for 2042 rainstorm events from the Rustaq watershed and surrounding area in Oman were used to develop temporal distributions for 2, 6, 24, and

48 hour durations suitable for use in arid climates. The data were separated into two regions, mountainous and coastal, on the basis of the location of the gauging station.

Rainstorm temporal distributions were developed for both regions. From the perspective of topographical characteristics, minor differences in rainstorm temporal distributions were observed, where the mountainous region has a higher total rainfall depth than the coastal region. In general, all storm events showed a very high intensity at the beginning of the storm.

137

This study showed that there is a significant difference between the mountainous and coastal regions in terms of rainfall amount but not in the rainstorm time distribution. In other words, the behaviour in rainstorms is shown to be very similar for both regions in this arid climate. The results indicated that there is a significant difference between the rainstorm time distributions based on the Oman data and standard established distribution such as Huff (1967), Hogg (1980), SCS (1986), and Hershfield (1962). Among the standard distributions Huff’s first quartile temporal pattern was closest to those obtained in this study. The study indicates that rainstorms in arid climate regions like Oman exhibit significantly unique temporal distribution characteristics. Curves derived for

Oman and Calgary are similar but there is a higher intensity earlier in the storm in the

Oman curves than in the Calgary curves. Currently, little is known about the effect of the temporal distribution of rainstorms on flash floods in arid regions. It is hoped that the new hyetographs could be used in conjunction with other available rainfall frequency data to enhance flash flood modeling for arid climates and specifically for Oman.

In Chapter 4, mean peak flood discharge was derived for 12 watersheds ranging from 64 to 1,730 km2 in Oman using 270 flood-peak flow events occurring over 10 years of record. Fourteen watersheds characteristics were extracted and computed using a Digital

Elevation Model and a GIS. Several basin characteristics are highly correlated, suggesting some limitations for use in multiple regression analyses.

The conventional parameters of drainage area DA, shape factor SF, wadi slope WS, watershed mean elevation BE, and drainage density DD were initially found to be the

138 best predictors of wadi flood-peak discharge. However, an even better improvement in flood peak discharge estimation was discovered when the degree of urbanization was incorporated into the equation. The percent of agricultural/farm area FR was found to be an important factor in flood-peak discharge prediction, especially for higher return period floods. Including FR improved R2 by 11% over the traditional procedure of using only the watershed’s size, slope, and mean elevation. Moreover, including FR in the model on the validation watersheds provided a better prediction of flood-peak discharge.

This study shows that urbanization and increase of land use may not necessarily lead to an increase flood-peak discharge, but may decrease the flood-peak discharge due to the increase of greenness and farms associated with urban expansion which cause greater flow resistance and greater water use by vegetation. Other studies have suggested that climate change could be another contribution to the accelerated frequency in flooding

(Bates et al., 2008; Kotwicki, 2007) and would require further investigations. Larger watersheds with higher wadi slope, lower altitude, and less agricultural/farms tend to have higher QMPF.

The derived relationship for these Oman arid watersheds was not appropriate for flood- peak flow estimation in a more humid climate like Salalah in the south of Oman. This implies that due to the diversity of arid regions’ characteristics, a derived model in one region (e.g. northern Oman), will not necessarily be applicable in another region (e.g.

Salalah, southern Oman), and derivation of flood-peak discharge equations should be regionally based instead of using the same equation for the whole country. The model

139 derived in this study shows better estimation of mean peak flow (QMPF) as compared to the currently used model in Oman. However, underestimations were found in some watersheds using this study’s selected model.

Watershed mean elevation was not an important factor for higher flood return period of

Q5, Q10, Q20, Q50, and Q100. Only DA, WS, and FR were the key variables in estimating

Q5, Q10, Q20, Q50, and Q100. Larger watersheds with higher slopes and less agricultural/farms tend to produce higher flood-peak flow in all investigated return periods. The author found a similar trend in the effect of DA, WS, and FR in estimating

Q5, Q10, Q20, Q50, and Q100. Among the explanatory variables, DA has the most significant positive impact on flood-peak frequency estimation, followed by FR with a negative impact on flood-peak discharge.

Finally in Chapter 5 which observed changes in urbanization in Oman, the author analyzed the rapid urban expansion in Oman from 1960 to 2003 and specifically in the

Wadi Aday watershed in Oman. The effect of urbanization on wadi peak flow generation and wadi flood frequency is investigated. This study showed an observed impact of urbanization expansion on wadi peak flow but the impact varies depending on the model used in the peak flow computation. The average simulated wadi peak flows of 10,000 randomly generated rainfall events increased at least by 2.3 m3/s to 7.237 m3/s as a result of urban area expansion between 1960 and 2003. Furthermore, a relationship between the urbanization and other hydrological parameters (e.g. time to peak) observed in the study area. For example, from 1960 to 2003 the time to peak decreases by 22.2 min, while the

140 urbanized area increases from 1.63 km2 in 1960 to 19.24 km2 in 2003. During this period, and when using a watershed effective area, the weighted values of runoff coefficients (C) increased from 0.302 to 0.417 and the curve number (CN) from 76 to 79. The wadi peak flow shows almost a constant increase in ratio ranging from 1.1 to 1.4 between 1960 and

2003. Weighted runoff coefficients may not be the best approach to calculate runoff coefficients of a mixed land use watershed when the percentage of urbanization is small compared to the total watershed area. Because urbanization responds differently hydrologically than non-urban areas, the manifestation of their effects in runoff paramters will be different as well. This study indicates that the effect of urbanization in this arid study area may not have the same effect as in humid climates. The standard CN values by Soil Conservation Services (SCS) are not valid for an arid region like Oman specifically for the residential areas. Thus, a new table created from this study is recommended for CN values in similar arid regions with similar urban form.

This study warns that the continuous increase of urbanized area at the expense of greenness areas would lead to higher wadi peak flow in a shorter time, and therefore more frequent flash floods. To avoid this scenario of a decrease in green lands and an increase in surface runoff in Muscat, some urgent steps are required to be implemented by the government: stop granting permissions for land use change (e.g. from agricultural to commercial); maintain a balance in the amount of urbanized versus green lands; and increase the amount of green areas in house lots by increasing public awareness to the impact of impervious surfaces on surface runoff.

141

For future research, the author recommends that:

1. Greater investigation into infiltration rates and crusting in Oman be conducted.

2. Using higher resolution data (e.g. DEM spatial resolution less than width of the

narrowest wadi) to improve the accuracy of watershed characteristics (e.g. slope).

3. Incorporation of rainfall and runoff data for the same events in order to provide a

better understanding of this region’s hydrological process.

4. Further investigation be conducted into the effect of the rainfall’s spatial

variability on runoff prediction by using remotely sensed rainfall data (e.g. Radar

satellite).

5. Detailed data on agricultural areas (e.g. Crop and soil type) be acquired to

compute new values of CN and C coefficients specifically for these arid areas.

142

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