Evaluation of the Water Resources of the Central Luzon Basin, Philippines

Evaluation of the water resources of the Central Luzon Basin, Philippines.

Item Type Dissertation-Reproduction (electronic); text

Authors Galvez, Jose Alfonso,1943-

Publisher The University of Arizona.

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Link to Item http://hdl.handle.net/10150/191032 EVALUATION OF THE WATER RESOURCES OF THE CENTRAL LUZON BASIN, PHILIPPINES

by Jose Alfonso Galvez

A Dissertation Submitted to the Faculty of the

DEPARTMENT OF HYDROLOGY AND WATER RESOURCES

In Partial Fulfillment of the Requirements For the Degree of

DOCTOR OF PHILOSOPHY WITH A MAJOR IN WATER RESOURCES ADMINISTRATION In the Graduate College THE UNIVERSITY OF ARIZONA

1976 THE UNIVERSITY OF ARIZONA

GRADUATE COLLEGE

I hereby recommend that this dissertation prepared under my direction by Jose Alfonso Galvez entitled Evaluation of the Water Resources of the Central Luzon Basin, Philippines be accepted as fulfilling the dissertation requirement for the degree of Doctor of Philosophy

AN101-- kmode,-- 10 /9 76 Dissertation,Director- Date Dissertation CO-DireCtor Date As members of the Final Examination Committee, we certify

that we have read this dissertation and agree that it may be

presented for final defense.

1'910 ‘7

tf-/

Final approval and acceptance of this dissertation is contingent on the candidate's adequate performance and defense thereof at the final oral examination. STATEMENT BY AUTHOR

This dissertation has been submitted in partial fulfillment of requirements for an advanced degree at The University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library.

Brief quotations from this dissertation are allowable without special permission, provided that accurate acknowledgment of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author.

SIGNED: Dedicated

Thelma, Mark, and Jan ACKNOWLEDGMENTS

The author wishes to extend his heartfelt gratitude to Dr. Simon

Lice whose guidance, suggestions, and assistance were generously given

during the course of this work. The writer is greatly indebted to Prof.

Theodore G. Roefs who suggested the major idea in this study and showed major interest in its completion. To the members of his degree

committee, Dr. Michael D. Bradley, Dr. John W. Harshbarger, Dr. William

G. Matlock, and Dr. Delmar D. Fangmeier, who showed concern and reviewed

the final draft, the author expresses his appreciation and gratitude.

The writer conveys his appreciation and thanks to Prof. Alfredo

L. Juinio, the National Irrigation Administration (NIA) Administrator,

who made the NIA facilities available for this study. The office space

provided by the National Water Resources Council facilitated the work.

The unselfish assistance of the various divisions of the Upper Pampanga

River Project is acknowledged with gratitude. Special thanks are due the

Agricultural Development Division and Project Development Division for

their major contributions in the collection of data and drafting. The National Irrigation Administration, the Bureau of Public

Works, the National Power Corporation, the National Water Resources

Council, the National Pollution Control Commission, the Bureau of Mines,

the Philippine Atmospheric, Geophysical and Seismological Administration,

and the National Census and Statistics Office are among the government

iv agencies which provided the data base and major Philippine references for this work.

To his colleagues and co-fellows in the Civil Decision Quantifi- cation Program, Lino Aldovino, Danny Franco, Leony Liongson, and Pat

Ongkingco, who worked with enthusiasm with the author as a team, the writer wishes to convey his appreciation and admiration. The study was made possible by the computer programming skill of Mr. Liongson, who developed the computer program for the statistical analysis and augmentation procedures. To him, the author is greatly indebted.

The program which culminated in the completion of this study was financially supported by the Ford Foundation through The University of

Arizona. Additional support was extended by the National Irrigation

Administration during the final year. The author expresses his profound gratitude to these agencies.

To other government organizations and numerous friends who, in one way or another, contributed to the completion of this work, the author conveys his appreciation and thanks. TABLE OF CONTENTS

Page LIST OF TABLES

LIST OF ILLUSTRATIONS xv

ABSTRACT xviii

1. INTRODUCTION 1

1.1 Importance of the Work 1 1.2 Scope and Objectives of the Work 3

2. DESCRIPTION OF THE AREA 5

2.1 Location and Extent 5 2.2 Physiography 5 2.3 Geology 9 2.3.1 Description of Geologic Units 9 2.4 Climate 13 2.5 Soils and Land Use 14 2.6 Population 20 2.7 Water Resources Development 23 2.7.1 Hydroelectric Power 23 2.7.2 Irrigation 25 2.7.3 The Upper Pampanga River Project (UPRP) 29 2.7.4 Water Supply and Sewerage 31 2.7.5 Flood Control and Drainage 33 2.7.6 Flood Control Projects 33

3. REVIEW OF PREVIOUS WORK 35

3.1 Central Luzon Water Resources 35 3.2 Augmentation of Hydrologic Data 38

4. THEORETICAL CONSIDERATION AND METHODOLOGY: STREAMTLOW DATA AUGMENTATION 41

4.1 Introduction 41 4.2 Augmentation Procedures 45 4.2.1 Percent Deviation Method 45 4.2.2 Matalas-Jacobs Method 48 4.2.3 YOR Method 53 4.2.4 HEC-4 Method 63

vi vii

TABLE OF CONTENTS--Continued

Page

5. THEORETICAL CONSIDERATION AND METHODOLOGY: HYDROLOGIC BUDGET . 68

5.1 Water Balance 68 5.2 Rainfall 71 5.3 Runoff 73 5.4 Evapotranspiration 74 5.4.1 Empirical Method 74 5.4.2 Mass Transfer Methods 76 5.4.3 Energy Budget Method 77 5.4.4 Penman's Equation 78 5.4.5 Other Methods of Estimating ET 79 5.5 Groundwater 80 5.5.1 Equations of Groundwater Flow 82 5.5.2 Well Hydraulics 83 5.5.3 Groundwater Flow 85 5.5.4 Estimation of Recharge 85 5.5.5 Recharge Estimation by the Meyboom Method 88

6. DATA 91

6.1 Data Collection 91 6.1.1 Rainfall and Streamflow Data 93 6.1.2 Other Climatological Data 93 6.2 Data Preparation 105 6.2.1 Rainfall Data 105 6.2.2 Streamflow Data 108

7. COMPARISON OF STREAMFLOW AUGMENTATION METHODS 116

7.1 Description of Gaging Stations 116 7.2 Comparison Criteria 122 7.2.1 Annual Flows 122 7.2.2 Monthly Flows 124 7.3 Scoring System 125 7.4 Results and Discussion 126 7.4.1 Annual Flows 126 7.4.2 Double Mass Analysis 126 7.4.3 Mean Annual Flows 137 7.4.4 Annual Deviations 143 7.4.5 Standard Deviations 150 7.4.6 Monthly Flows 153 7.4.7 Annual Hydrographs 153 7.4.8 Monthly Means 163 7.4.9 Standard Deviations 166 viii

TABLE OF CONTENTS--Continued

Page

7.5 Summary and Findings 171 7.6 Application of the Augmentation Method 178 7.7 Consistency of Augmented Data 180

8. THE HYDROLOGIC BUDGET 186

8.1 Period of Analysis 186 8.2 Description of Gaging Stations 188 8.2.1 Rainfall 189 8.2.2 Streamflow 189 8.2.3 Groundwater 195 8.2.4 Evaporation 198 8.3 The Water Budget 205 8.4 Recharge Estimation 215 8.5 Summary and Findings 218

9 THE BASIN WATER RESOURCES 220

9.1 Surface Water 220 9.1.1 Rainfall 220 9.1.2 Streamflow 223 9.1.3 Surface Storage 225 9.1.4 Surface Water Quality 226 9.1.5 Linkage of Surface and Sub-Surface Water 230 9.2 Groundwater 231 9.2.1 Chemical Quality of Groundwater 234 9.2.2 Groundwater Potential Productivity . 238 9.3 Water Requirement in the Basin 240

10. SUMMARY AND CONCLUSIONS 245

APPENDIX A: HYDROGEOLOGY OF CENTRAL LUZON BASIN, PHILIPPINES 254

APPENDIX B: MEAN ANNUAL RAINFALL OF CENTRAL LUZON BASIN, PHILIPPINES 256

APPENDIX C: MONTHLY PERCENTAGE OF DAYTIME HOURS (p) OF THE YEAR FOR LATITUDES 0 TO 65 ° NORTH OF THE EQUATOR 258

APPENDIX D: INVENTORY OF SHALLOW OBSERVATION WELLS, CENTRAL LUZON BASIN, PHILIPPINES 261

APPENDIX E: SAMPLE COMPUTATION OF FULL NATURAL FLOW 268 i x TABLE OF CONTENTS--Continued

Page APPENDIX F: CORRELATION COEFFICIENTS OF SELECTED PAIRS OF STREAMFLOW GAGING STATIONS 283 APPENDIX G: HISTORICAL DATA AND AUGMENTATION ESTIMATES AT VARIOUS STREAMFLOW GAGING STATIONS, CENTRAL LUZON BASIN, PHILIPPINES 286

SELECTED BIBLIOGRAPHY 314

LIST OF TABLES

Table Page

2.1 Average Monthly Rainfall at Selected Stations, Central Luzon Basin, Philippines 15

2.2 Mean Monthly Temperature at Selected Stations, Central Luzon Basin, Philippines 17

2.3 Average Monthly Relative Humidity at Selected Stations, Central Luzon Basin, Philippines 18

2.4 Average Monthly Pan Evaporation at Selected Stations, Central Luzon Basin, Philippines 19

2.5 Land Utilization by Province, Central Luzon Basin, Philippines 21

2.6 Area Planted to Rice and Corn in 1970-1971, Central Luzon Basin, Philippines 22

2.7 Population by Province, 1903 to 1970, Central Luzon Basin, Philippines 24

2.8 Existing Major Hydroelectric Power Projects, Central Luzon Basin, Philippines 26

2.9 Irrigable Area of Existing Irrigation Projects, Central Luzon Basin, Philippines 28

2.10 Actual Irrigated Areas from Typical Irrigation Systems, Central Luzon Basin, Philippines 30

2.11 Existing Water Supply Systems Established by the Government, Central Luzon Basin, Philippines 32

6.1 Rainfall Stations, Central Luzon Basin, Philippines 94

6.2 Streamflow Gaging Stations, Central Luzon Basin, Philippines 100

6.3 Rainfall Average Based on Two Methods 107

7.1 Stations Used in the Comparison of Augmentation Procedures 118

7.2 Augmentation Strategy for Test of Methods 120 xi

LIST OF TABLES--Continued

Table Page

7.3 Critical Minimum Values of Correlation Coefficient for the Mean 121

7.4 Historical and Augmented Annual Flow of Station Q03, Pampanga River 127

7.5 Historical and Augmented Annual Flow of Station Q07, Coronel River 128

7.6 Historical and Augmented Annual Flow of Station Q22, Talavera River 129

7.7 Historical and Augmented Annual Flow , of Station QSL, Angat River 130

7.8 Historical and Augmented Annual Flow of Station Q78, Tarlac River 131

7.9 Scores Obtained by Various Methods in the Point System, Double Mass Curve Comparison 138

7.10 Percentages Obtained by Various Methods in the Percentage Scoring System, Double Mass Curve Comparison 139

7.11 Historical dnd Augmented Annual Mean Flows of All Stations . 140

7.12 Scores Obtained by Various Methods in the Point System, Comparison of Mean Annual Flow 141

7.13 Percentages Obtained by Various Methods in the Percentage Scoring System, Comparison of Mean Annual Flow 141

7.14 Scores Obtained by Various Methods in the Point System, Comparison of Cumulative Deviations from Historical Data . 149

7.15 Average Percentages Obtained by Various Methods in the Percentage Scoring System, Comparison of Deviation from Historical Annual Flow 151

7.16 Standard Deviation of Historical and Augmented Annual Flows of All Stations 151

7.17 Scores Obtained by Various Methods in the Point System, Comparison of Standard Deviation of Annual Flows 152 xii

LIST OF TABLES--Continued

Table Page

7.18 Percentages Obtained by Various Methods in the Percentage Scoring System, Comparison of Standard Deviations of Annual Flows 152

7.19 Peak Flow and Month of Occurrence of Historical and Augmented Annual Hydrograph 159

7.20 Minimum Flow and Month of Occurrence of Historical and Augmented Annual Hydrograph 160

7.21 Scores Obtained by Various Methods in the Point System, Comparison of Peak Flow of Annual Hydrographs 162

7.22 Scores Obtained by Various Methods in the Point System, Comparison of Minimum Flow of Annual Hydrographs 162

7.23 Percentages Obtained by Various Methods in the Percentage Scoring System, Comparison of Peak Flow of Annual Hydrographs 164

7.24 Percentages Obtained by Various Methods in the Percentage Scoring System, Comparison of Minimum Flow of Annual Hydrographs 165

7.25 Mean Monthly Flow of Historical Record and Augmentation Estimates 167

7.26 Average Scores Obtained by Various Methods in the Point System, Comparison of Monthly Mean 168

7.27 Average Percentages Obtained by Various Methods in the Percentage Scoring System, Comparison of Monthly Mean . . 168

7.28 Standard Deviations of Historical and Augmented Monthly Flows 169

7.29 Average Scores Obtained by Various Methods in the Point Scoring System, Comparison of Standard Deviations of Monthly Flows 170

7.30 Average Percentages Obtained by Various Methods in the Percentage Scoring System, Comparison of Standard Deviations of Monthly Flows 170

7.31 Summary of Average Scores Obtained by Various Methods in the Point Scoring System (Station Q22 Included) 172 LIST OF TABLES--Continued

Table Page

7.32 Summary of Average Percentages Obtained by Various Methods in the Percentage Scoring System (Station Q22 Included) . 173

7.33 Summary of Average Scores Obtained by Various Methods in the Point Scoring System (Station Q22 Not Included) . . 176

7.34 Summary of Average Percentages Obtained by Various Methods in the Percentage Scoring System (Station Q22 Not Included) 177

7.35 Augmentation Strategy for Streamflow Gaging Stations, Central Luzon Basin, Philippines 179

7.36 Statistics of Annual Streamflows Before and After Augmentation by Percent Deviation Method 181

8.1 Basin Average Rainfall for the Three Selected Water Years . 187

8.2 Average Rainfall and Streamflow in Three Selected Water Years, Central Luzon Basin, Philippines 193

8.3 Changes in Water Table Levels, Central Luzon Basin, Philippines 197

8.4 Monthly Rainfall and Pan Evaporation, Central Luzon Basin, Philippines 201

8.5 Monthly Adjustment Coefficients for Various Cases 206

8.6 Adjusted Monthly Pan Evaporation and Corresponding Percentages, Water Year 1959-1960 207

8.7 Adjusted Monthly Pan Evaporation and Corresponding Percentages, Water Year 1960-1961 208

8.8 Adjusted Monthly Pan Evaporation and Corresponding Percentages, Water Year 1963-1964 209

8.9 Annual Water Budget Analysis, Central Luzon Basin, Philippines 210

8.10 Actual Monthly Evapotranspiration, Water Year 1959-1960 . . 211

8.11 Actual Monthly Evapotranspiration, Water Year 1960-1961 . . 212 xiv

LIST OF TABLES--Continued

Table Page

8.12 Actual Monthly Evapotranspiration, Water Year 1963-1964 . . . 213

8.13 Water Budget Analysis for the Period June to November, Central Luzon Basin, Philippines 214

8.14 Estimated Annual Recharge for Three Water Years, Central Luzon Basin, Philippines 217

9.1 Water Quality of Central Luzon Rivers 227

9.2 Chemical Quality of Surface Water, Dry and Wet Seasons . . 229

9.3 Coefficients of T and S from Selected Wells 236

9.4 Chemical Quality of Groundwater from'Selected Wells 237

9.5 Projected Water Requirements of the Central Luzon Basin . . . 243

E.1 Estimated Monthly Irrigation Diversion 274

E.2 Estimated Monthly Full Natural Flow, Pampanga River Streamflow Gaging Stations 280

LIST OF ILLUSTRATIONS

Figure Page

2.1 Location Map of Central Luzon Basin, Philippines 6

2.2 Average Monthly Rainfall in the Central Luzon Basin, Philippines 16

4.1 Actual Data Files of Streamflow Gaging Stations along the Agno and Pampanga Rivers 42

4.2 Typical Record Length in a River Basin 55

4.3 Fill-In Path of YOR Method 59

4.4 Relationship of Existing End Points in a Gap and Predicted Values 60

4.5 Diagram of Data Matrix 64

5.1 Components of a Hydrologic Budget 69

6.1 Rainfall Stations, Central Luzon Basin, Philippines 92

6.2 A Typical Gaging Station Affected by Reservoir Regulation and Irrigation Diversion 110

7.1 Double Mass Curve of Annual Flows for Station Q03, Pampanga River 132

7.2 Double Mass Curve of Annual Flows for Station Q07, Coronel River 133

7.3 Double Mass Curve of Annual Flows for Station Q22, Talavera River 134

7.4 Double Mass Curve of Annual Flows for Station QSL, Angat River 135

7.5 Double Mass Curve of Annual Flows for Station Q78, Tarlac River 136

7.6 Annual Means of All Stations for Various Augmentation Methods 142

XV xvi

LIST OF ILLUSTRATIONS--Continued

Figure Page

7.7 Cumulative Annual Deviation of Augmentation Estimates from Historical Values, Station Q03 144

7.8 Cumulative Annual Deviation of Augmentation Estimates from Historical Values, Station Q07 145

7.9 Cumulative Annual Deviation of Augmentation Estimates from Historical Values, Station Q22 146

7.10 Cumulative Annual Deviation of Augmentation Estimates from Historical Values, Station QSL 147

7.11 Cumulative Annual Deviation of Augmentation Estimates from Historical Values, Station Q78 148

7.12 Annual Hydrograph for Station Q03 154

7.13 Annual Hydrograph for Station Q07 155

7.14 Annual Hydrograph for Station Q22 156

7.15 Annual Hydrograph for Station QSL 157

7.16 Annual Hydrograph for Station Q78 158

8.1 Isohyetal Map of Central Luzon Basin, 1959-1960 190

8.2 Isohyetal Map of Central Luzon Basin, 1960-1961 191

8.3 Isohyetal Map of Central Luzon Basin, 1963-1964 192

8.4 Streamflow Gaging Stations Used in the Water Budget Analysis 194

8.5 Shallow Observations Wells 196

8.6 Monthly Rainfall and Pan Evaporation in Three Selected Water Years 202

8.7 Relationship between AE/PE and Soil Moisture Content . . . . 203

8.8 Mean Monthly Groundwater Stage in Three Selected Water Years 216 xvii

LIST OF ILLUSTRATIONS--Continued

Figure Page

9.1 Monthly Rainfall at Three Selected Gaging Stations 221

9.2 Monthly Runoff at Two Selected Gaging Stations 222

9.3 Estimated Annual Runoff, Central Luzon Basin, Philippines . 224 9.4 Central Luzon Aquifers 232

9.5 Central Luzon Geological Cross-Sections 233

9.6 Transmissivity Map of Central Luzon 235

9.7 Frequency Curves of Specific-Capacity Data 241 10.1 Drainage Areas of Streamflow Gaging Stations in Tandem . . . 249

A.1 Hydrogeologic Map of the Central Luzon Basin, Philippines . 255

B.1 Mean Annual Rainfall, Central Luzon Basin, Philippines . . . 257

E.1 Streamflow Gaging Stations and Irrigation Diversion Points . 269 E.2 Relationship of Irrigation Diversion and Upstream Gaged Flow, PRIS versus Q03 270

E.3 Relationship of Irrigation Diversion and Upstream Gaged Flow, PenRIS versus Q16 271

E.4 Relationship of Irrigation Diversion and Upstream Gaged Flow, TRIS versus Q22 272

E.5 Relationship of Irrigation Diversion and Upstream Gaged Flow, LTRIS versus Q22 273 ABSTRACT

The study aims to provide a framework for the quantitative evaluation of the water resources on a regional basis. The work involves hydrologic data augmentation and analysis of the water budget of the

Central Luzon Basin, a humid, tropical region.

Four hydrologic data augmentation methodologies -- percent deviation, HEC-4, YOR, and Matalas-Jacobs -- were analyzed and compared.

Streamflow data of five gaging stations in the Central Luzon Basin were divided into early-half and late-half series. Augmentation estimates based on the late-half series were compared with the corresponding historical early-half series. The methods were assessed based on eight comparison items and two scoring systems. The comparison items considered were double mass analysis, mean, cumulative annual deviations, and standard deviations, for the annual flows; and maximum, minimum, mean, and standard deviations, for the monthly flows. The percent deviation method excelled in both scoring systems and was selected as the best method for the hydrological characteristics and type of available data in the region.

Application of the percent deviation method in augmenting the record of selected streamflow gaging stations within the Central Luzon

Basin pointed out some limitations of the method. These limitations may be remedied by proper selection of the station on which the augmentation estimates are based. Strong statistical relationship between the xviii xix dependent and base stations, in addition to other hydrological factors, should be satisfied for the derivation of more reliable estimates in the application of the percent deviation to streamflow data augmentation.

The water budget analysis revealed that about 54 percent of the annual precipitation in the basin is measured as streamflow. The balance of 46 percent is shared by evapotranspiration and change in basin storage. An average annual evapotranspiration of about 1,070 mm was found. This is about 0.5 of the average Class A pan evaporation data from two measurement points.

Surface water resources remain the most important source of water for the basin requirements. No serious water quality problems exist and surface water in the basin is suitable for both domestic and agricultural purposes. Groundwater could be a promising alternative resource for both domestic and agricultural uses. High recharge during the rainy season, about 5,000 to 7,000 MCM, and relatively shallow aquifers are among the significant features of the groundwater basin. CHAPTER 1

INTRODUCTION

1.1 Importance of the Work

Inventory of the water resources of the Philippines was started long ago. Rainfall data collection must have started in the later years of the nineteenth century, while streamflow measurements began in the early part of the twentieth century (Bureau of Public Works, 1959). For various reasons, however, mainly due to inadequate funds and lack of coordination among agencies involved in the water resources development

(United Nations, 1968), collection of basic data and subsequent pro- cessing and publication have been fragmentary, resulting in discontinuous records for a more or less random network of gaging stations.

Appraisal and planning for the development of the country's water resources have to be based on short and fragmentary data on many locations, and long but discontinuous records in a few stations. Sites important to feasibility studies of development plans commonly have no, or only newly established, gaging stations for hydrological and meteorological data collection. A methdology for extending the data of stations having short records, based on other stations of longer record periods, would greatly facilitate the work of planners.

1 2

Assessment of the hydrologic budget of a river basin has been the subject of numerous research studies'. Unfortunately, all of these works were conducted in countries other than the Philippines. Yet, an impressive amount of hydrologic, as well as geologic, data have been available in the country, especially in the Central Luzon Basin (United

Nations, 1968).

Water balance studies in the Philippines were confined to small farm areas like those studied by Kampen (1970), Wickham (1971), and the

National Irrigation Administration in 1975, which were conducted to provide basic information in irrigation water management for rice production. Other studies in Central Luzon consider only one or two components of the hydrologic budget, e.g., Bureau of Public Works (1959), Sandoval and Mamaril (1970). Although the Central Luzon has been the subject of 2 several water resources studies , none addressed itself to the assessment of the basin water resources based on the solution of the total hydrologic budget equation.

1. To cite a few research studies concerning the hydrologic budget, there are the work of Rasmussen and Andreasen (1959), Meyboom (1961), Pittams (1968), Caldwell (1968), Rasmussen (1970), Walton (1970), and Toebes (1972).

2. Among the comprehensive studies and reports are the Bureau of Public Works (1959) report on the surface water supply of the Central Luzon, the Water Resources Planning and Development Committee (1961) report on the water resources of Central Luzon, the U. S. Bureau of Reclamation (1966) report on the Central Luzon Basin, the United Nations (1968) Water Resources Development Task Force report, and the report by Sandoval and Mamaril (1970) on hydrogeology of Central Luzon. Highlights of these reports will be discussed in Chapter 3. 3

Complete information on the total water resources of a basin would provide a baseline for the formulation of development plans. And information on the disposition of the water resources to the various components of the hydrologic budget, on the other hand, would give a quantitative picture essential to the allocation of the water resources to various sectors of development and to the economic comparison of development alternatives such as utilization of local groundwater resources versus a transbasin scheme. Furthermore, reduction of water losses due to non-beneficial evapotranspiration, for instance, may be assessed and then justified on the grounds of inadequacy of the water resources to meet the present and projected demands.

1.2 Scope and Objectives of the Work

This study deals with the assessment of the water resources of

the Central Luzon Basin based on the identification and quantification of

the various components of the basin water budget. Basic hydrological and

geological data essential to the analysis were obtained from data files

of various government and private agencies.

Four streamflow augmentation methods were used in extending the

records of selected stations of the Agno and Pampanga Rivers, the major

rivers draining the Central Luzon Basin. Comparison of the methods was made. The method best suited to existing data files and to the hydro-

logical characteristics of Philippine river basins was selected and used

to augment data of key stations for the evaluation of the surface water

resources of the basin. 4

Estimation of the groundwater annual recharge was made based on water-table fluctuations and gravity yield estimates. The recharge amount relates the linkage between the surface and subsurface waters of the basin. It also provides information on the "safe yield" of the groundwater resources and the permeability of the basins' groundwater- bearing formations.

It is hoped that this study will provide a framework for the quantitative evaluation of the water resources on a regional basis when sufficient physical and hydrological information is available. This would be the first step toward the formulation of alternative plans for the development of the water resources of the region. CHAPTER 2

DESCRIPTION OF THE AREA

2.1 Location and Extent

The Central Luzon Basin lies between latitudes 15 and 17 °N, and longitudes 120 and 122 ° E. It is located in the central part of Luzon, the largest island in the Philippines. It is bounded on the east by the

Sierra Madre Mountains, on the north by the Caraballo Mountains, on the west by the Zambales Mountains, on the south by Manila Bay, and on the northwest by the Lingayen Gulf (Figure 2.1).

The Central Luzon Basin, as used in this study, includes the basins of two major river systems, the Agno River and the Pampanga River.

It primarily covers the provinces of Bulacan, Nueva Ecija, Pampanga,

Pangasinan, and Tarlac, although portions of Zambales, Quezon, Mountain

Province, Nueva Vizcaya, La Union, and Rizal are within the basin. The 2 2 basin area is about 18,000 km (6,944 mi ) or about 17 percent of the

Island of Luzon. The valley floor, which is about 40 percent of the basin, is known as the Central Luzon Plain.

2.2 Physiography

Two major river systems drain the basin -- the Agno River system, 2 2 carrying the runoff from about 5,749 km (2,218 mi ) and emptying north toward the Lingayen Gulf; and the Pampanga River system, draining about 2 2 8,912 km (3,438 mi ) and discharging toward the Manila Bay. Smaller 5 6

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- o o o o 7 rivers carrying runoff from small portions of the drainage area are: the 2 Dagupan and Patalan Rivers to Lingayen Gulf (1,603 km ) and Gomain,

Porac, and Caulaman Rivers to Manila Bay (1,734 km2 ). The two river systems are separated by a low, poorly defined, topographic ridge extending generally northeast from Tarlac.

Mount Arayat, an extinct volcano, is a distinct physical feature in the south-central part of the area. It rises about 1,000 meters above the surrounding area. To the east of Mount Arayat are depressions known generally as the San Antonio and Candaba Swamps. During the wet season, flood waters from the Pampanga River overflow into these depressions and inundate several thousand hectares of land. When the water surface recedes in the dry season, a substantial area of the swampland is cultivated.

The Pampanga River, which is the main drainage channel for the eastern and southern portions of the basin, is formed by the Carranglan and Pantabangan Rivers in the Caraballo Mountains. It flows in a general southerly direction to its mouth in Manila Bay. Steep slopes of the stream channel in the upper reaches are very pronounced. Downstream of Rizal, Nueva Ecija, where the river emerges on the plain, the stream gradient becomes flatter as it progressively cuts through agricultural lands in Nueva Ecija, through extensive swamplands near Mount Arayat, and finally into commercial fishponds in Bulacan where the main channel divides into several smaller channels, forming tidal streams which eventually reach Manila Bay. The river length is about 284 km (176 mi). 8

Among the major tributaries of the Pampanga River are: the

Pantabangan, Digmala, Coronel, Penaranda, Bulu, and Angat Rivers on the east side of the basin and the Carranglan, Talavera, hog Baliwag, and

Rio Chico Rivers on the west side.

The Agno River heads from the southern slope of the Cordillera

Mountains in the Mountain Province. It flows in a generally southerly direction from the source to Tayug, Pangasinan, where it changes course to the southwest through Rosales and into the Poponto Swamp until it finally reaches Lingayen Gulf. The length of the river from the source to the mouth is about 270 km (168 mi). Among the major tributaries of the Agno River are: the Ambayoan, Totonoguen, Tarlac, Camiling, and Pila

Rivers.

At the mouth of the rivers in Lingayen Gulf and Manila Bay, swamps reduce the velocity of the flow of water and induce deposition of sediments. The land surface around the deltas is only 5-10 km (16-33 ft) above the mean sea level.

Three more swamp areas within the basin form natural surface storages, thus retarding the flow of the Agno and Pampanga Rivers. The 2 Poponto Swamp, with an approximate area of 80 km ,i s formed at the confluence of the Tarlac and Agno Rivers. San Antonio and Candaba Swamps, near Mount Arayat in the Pampanga River Basin, cover an aggregate area of about 240 km2 . Due to the low elevation of the swamps, they become discharge points of groundwater in the basin. 9

2.3 Geology

The Central Luzon Basin is covered by alluvial materials and a thick sequence of sedimentary and pyroclastic rocks with intercalated volcanic rocks beneath the alluvium. The core of the Zambales Mountains

is composed of ultramafic rocks. The Sierra Madre and Southern

Cordillera Mountains are underlain by Mesozoic to Early Paleocene volcanic and metamorphic rocks.

The Central Luzon Plain is part of the geosynclinal trough that

extends from Manila Bay to the Ilocos Region. Deposition of a thick

series of continental, brackish and marine sediments associated with vol-

canic rocks on the gently subsiding basin occurred from Eocene to

Pliocene time. Fissure eruptions at the flank of the geosynclinal basin

produced volcanic rocks intercalated with sedimentary sequence. Fault

action in the Plio-Pleistocene time caused the sudden uplift of the moun-

tain ranges east of the basin. The uplifted area became the source of

very coarse materials which formed the Alat conglomerate and its equiva-

lent time-rock units. Continuous sedimentation raised the level of the

basin, resulting in the gradual recession of the sea. The hydrogeologic

map of the Central Luzon Basin is shown in Appendix A.

2.3.1 Description of Geologic Units Only rock units younger than Oligocene are included because of

their bearing on the groundwater resources of the basin.

Pre-Miocene. The oldest rocks forming the core of the mountain

ranges surrounding the basin consist of metamorphic, basic, and ultra-

basic rocks. These rocks were probably formed during the Mesozoic era. 10

Lower Miocene. The oldest Miocene rocks in the region are represented by the equivalent Angat and Moriones formations. Angat formation consists of thin-bedded calcareous shale and clayey sandstone. The formation was deposited in shallow to moderate depths. Fine-grained limestone, tuffaceous sandstone, and coralline limestone with calcareous siltstone and sandstone form the Moriones formation which is about 1,500 meters thick from measured sections.

Middle Miocene. This is represented by the Malinta and Madlum formations which were deposited over the older Moriones and Angat formations. The Madlum formation is composed of clastic, volcanic, and limestone members. The clastic member, which consists of alternating shale and sandstone beds, occupies the lowest section of the Madlum formation.

The middle volcanic member is composed of rocks which are generally massive and of little importance as groundwater sources. The limestone member consists of bedded and massive limestone units. Solution cavities and caverns make the units porous. Combined thickness of the limestone units has been reported to be about 150 meters.

Interbedded sequences of sandstone, shale, conglomerate, and tuff compose the Malinta formation. The lower section consists of well- cemented and massive volcanic rocks, and lithic tuff. The tuff is composed of fine to coarse volcanic fragments cemented with volcanic ash.

Clastic rocks of this formation are poor prospects for groundwater development since they are poorly sorted, compact, and well-cemented.

Upper Miocene. Among the rock units formed during this period are the Makapilapil formation, Lambak shale, Tarlac formation, and the 11

Pantabangan formation. The Tarlac formation is of particular significance to groundwater studies.

The Tarlac formation, which consists of interbedded sequences of tuffaceous sandstone, shale, and some conglomerate, form low rolling hills extending from Capas, Tarlac, to Urdaneta, Pangasinan. The sandstone, which is composed of medium to coarse sand grains loosely cemented with tuffaceous materials, is friable and porous. The formation, which is correlative with Makapilapil and Lambak formations in eastern Bulacan and Nueva Ecija, is reported to be about 1,200 meters thick.

The Lambak shale and Makapilapil formation cover small adjoining areas near the Nueva Ecija-Bulacan border. The Lambak shale is composed of hard, massive, and compact but highly fractured shale and occasional conglomeratic arkosic sandstone. Estimated thickness of the Lambak shale is about 100 meters. The Makapilapil formation, which outcrops only in

Nueva Ecija, is composed of flatbedded sequences of sandstone, shale, and lenses of conglomerate and limestone. The sandstone is formed of medium to coarse sand grains cemented with tuffaceous and calcareous materials.

The Pantabangan formation, which occupies the southwestern foothills of the Cordillera Mountains east and south of San Jose, Nueva

Ecija, is primarily composed of massive conglomerate with thin lenses of coarse grained sandstone. Rounded cobbles and boulders of diorite, volcanic rocks, and limestone tightly cemented with clay and sand form the conglomerate. The formation rests on Cretaceous to Paleocene volcanic rocks. 12

Pliocene. Tartaro formation was deposited during this period in addition to other sedimentary units that accumulated during the upper

Miocene time. This formation is essentially composed of thick-bedded siltstone and clayey sandstone. The loosely cemented and poorly con- solidated sandstone occupies the top and bottom section of the formation.

The sandstone is composed of medium to coarse grains of volcanic rocks and limestone. Maximum thickness of the formation is estimated at 150 meters.

Pleistocene. Alat and Guadalupe formations represent the

Pleistocene units. The Alat formation outcrops at the Rizal-Bulacan area toward Manila Bay. There are indications, however, that the formation extends as far as Cabanatuan City in Nueva Ecija beneath the alluvial deposits. It is primarily composed of massive conglomerate over weathered siltstone and tuffaceous sandstone.

The Guadalupe formation, which generally overlies the Alat formation, is found outside the basin being considered. Sandy and conglomerate tuff, which forms part of the formation, supplies considerable amounts of water in urban areas surrounding Manila and Quezon City.

Recent. Abrupt changes in gradient at the foothills of the mountain ranges resulted in thick and extensive alluvial deposits in the plains of Central Luzon. The composition and thickness of the alluvium varies along the length of Central Luzon.

Rounded cobbles and boulders of igneous and sedimentary rocks predominate in the floodplains of the Agno and Pampanga Rivers. Fine to medium sand with considerable amount of clay and seashells form the 13 deltaic deposits around Manila Bay and Lingayen Gulf. Alluvium surrounding the volcanic centers consists of considerable amounts of clay.

Thin and numerous sand layers make up the water-bearing zones.

Along the Agno River, the thickness of the alluvium varies from

about 75 meters near Asingan, Pangasinan, to about 20 meters near Rosales

and San Manuel, Pangasinan. Across the plain, the thickness of the

alluvium varies from about 75 meters near the center at the Tarlac-Nueva

Ecija border between Victoria, Tarlac, and Guimba, Nueva Ecija, to about

30 meters near the foothills of the Zambales Mountains in Tarlac. The

deltaic deposits at Dagupan and Manila areas are estimated to be about

200 meters thick.

2.4 Climate

The Central Luzon Basin has two distinct seasons -- wet, from May

to October, and dry, from November to April. The mean annual rainfall

varies from less than 2,000 mm (80 in) in the central part, to more than

4,000 mm (160 in) in the northwestern part. Appendix B presents the mean

annual rainfall in the basin. The rainfall variation may be attributed

primarily to the shielding effect of the topographic relief surrounding

the basin upon the water-bearing seasonal winds. The Caraballo Mountains

on the north and northeast shield the basin from the northeast monsoon from October to January. The Sierra Madre Mountains on the east shield

the basin from the east trade winds from February to April. The Zambales

Mountains on the south shield the basin from the southwest monsoon from

May to October. 14

About 90 percent of the annual precipitation generally occurs during the southwest monsoon, with the heaviest precipitation usually occurring in August. Table 2.1 shows the average monthly rainfall and the percentage of May to October rainfall over the annual total at selected stations in the basin. Figure 2.2 illustrates the mean monthly rainfall based on the arithmetic average of 44 stations. Fairly uniform temperature is experienced within the basin. As shown in Table 2.2, a mean temperature of about 27 ° C is common in the Central Plain. Average relative humidity may range from 65 to 85 percent in the lowlands and from 80 to 90 percent in the mountain areas. Average monthly relative humidity is shown in Table 2.3 for Baguio, Dagupan, and

Cab anatuan.

Observed pan evaporation within the basin is given in Table 2.4.

Annual pan evaporation generally exceeds 1,800 mm, with the monthly maximum occurring in April or March.

2.5 Soils and Land Use

Fertile alluvial soils derived from the mountain ranges flanking the plains make up the soils of the Central Luzon Plains. This consists mainly of coarse sand, pebbles, clays, and shells. The area is the most extensively cultivated part of the Philippines and has been known as the

"rice bowl" of the country. Poorly drained soils, usually clayey, are found in the lowlands, on level ridge tops, in valleys, and in depressions in the hills and mountains. These lands are largely utilized as rice fields. The sub- soils, 30 to 60 am deep, are compacted clay pan of very low permeability.

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600

500 —

400 —

300 —

100 —

APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR

MONTH

Figure 2.2 Average Monthly Rainfall in the Central Luzon Basin, Philippines. 17

Table 2.2 Mean Monthly Temperature at Selected Stations, Central Luzon Basin, Philippines. -- Tempera- ture in °C.

Station Month Cabanatuan Dagupan MRRTC*

Apr 28.8 29.3 28.4

May 29.7 29.7 28.7

Jun 28.3 28.8 28.1

Jul 27.9 28.1 27.3

Aug 27.6 27.8 27.1

Sep 27.7 27.8 27.1

Oct 27.6 28.2 26.8

Nov 27.1 27.4 25.6

Dec 26.2 26.2 25.3

Jan 25.9 26.1 25.2

Feb 26.3 26.7 25.8

Mar 27.6 28.1 27.0

*Maligaya Rice Research and Training Center, Munoz, Nueva Ecija. 18

Table 2.3 Average Monthly Relative Humidity at Selected Stations, Central Luzon Basin, Philippines. -- Humidity in percent.

Station Month Baguio Dagupan Cabanatuan MRRTC*

Apr 81.3 63.8 70.4 71.6

May 85.0 70.3 74.5 81.0

Jun 88.2 80.5 80.6 86.3

Jul 90.1 83.5 83.7 88.2

Aug 91.8 85.7 84.9 88.8

Sep 91.0 85.2 84.2 89.3

Oct 86.7 80.1 29.4 83.0

Nov 82.7 78.0 76.9 80.6

Dec 80.2 74.0 75.6 77.0

Jan 79.8 71.1 73.6 73.4

Feb 78.5 68.4 72.4 72.3

Mar 79.4 64.8 70.9 71.3

*Maligaya Rice Research and Training Center, Munoz, Nueva Ecija. 19

Table 2.4 Average Monthly Pan Evaporation at Selected Stations, Central Luzon Basin, Philippines. -- Evaporation in mm.

Station Sto. Tomas* Tibagan Hda. Luisita San Manuel ' Reservoir Month (Bulacan) (Tarlac) Pangasinan Benguet

Apr 245.9 226.2 260.0 93.7

May 226.8 177.1 236.6 93.5

Jun 145.7 135.5 148.2 77.5

Jul 134.3 116.1 140.7 83.3

Aug 124.8 98.4 134.7 76.7

Sep 115.0 111.0 121.0 71.5

O ct 143.4 134.5 144.3 94.1

Nov 126.8 132.2 141.9 103.9

Dec 143.3 136.9 161.5 103.4

Jan 158.4 153.2 184.8 92.5

Feb 183.1 167.3 203.3 94.1

Mar 236.5 219.4 252.9 96.6

Total 1984.0 1807.8 2129.9 1080.8

*Floating pan. 20

This property considerably reduces the natural drainage capability of the overlying soil and makes it suitable for rice crops.

Gently sloping upland areas adjacent to the lowlands are devoted to other crops such as sugar cane, coconut, and vegetables. These areas have more or less friable, well-drained soils. Such soils, when used for lowland irrigated rice, will generally have higher water requirements than soils with natural water-retarding clay pan.

The Central Luzon Plain is an agricultural area with a potential

land resource of about 1,800,000 ha. It represents one of the most extensively cultivated areas in the Philippines. Virtually all land suitable for cultivation is now being farmed, although a substantial part of the agricultural production occurs only during the wet season. Nearly

40 percent of this farmed area is devoted to rice and corn. Table 2.5 presents the land utilization in the area and the percentage of various

dispositions; and Table 2.6 gives the area devoted to rice and corn in

1970-1971.

About 1,660,000 ha of the basin lands have been covered by soil

surveys. About 825,000 ha (nearly 50%) of these surveyed lands are

classed as suitable for irrigation. This total area suitable for irriga-

tion consists of 508,000 ha without limitation, 208,000 ha less suitable

due to soils limitation, and 109,000 ha less suitable due to topographi-

cal limitations.

2.6 Population

The total population in the provinces of Bulacan, Nueva Ecija,

Pampanga, Pangasinan, and Tarlac, based on the latest census in 1970, is -

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Table 2.6 Area Planted to Rice and Corn in 1970-1971, Central Luzon Basin, Philippines. -- Area in ha.

Province Rice Corn Total

Bulacan 88,990 400 89,390

Nueva Ecija 34,840 - 34,840

Pampanga 88,820 990 89,810

Pangasinan 150,190 4,040 154,230

Tarlac 15,920 - 15,920

Total 378,760 5,430 384,190 23

4,537,126 -- an increase of 1,204,895 (36.26%) over the 1960 census.

Table 2.7 summarizes the population data in the five provinces for the period 1903 to 1970.

2.7 Water Resources Development

Various government agencies are engaged in the assessment and development of the water resources of the Philippines. Collection of basic hydrological data and flood control activities are undertaken by the Bureau of Public Works (BPW), but the National Power Corporation

(NPC) and the National Irrigation Administration (NIA) also operate rainfall and streamflow gaging stations. Agencies dealing with the groundwater are the BPW, the NIA, the Metropolitan Waterworks and Sewerage

Services (MWSS), formerly the National Waterworks and Sewerage Authority

(NWSA), the Bureau of Mines and Geology (BMG), and many private companies and farmers. Development of hydroelectric power is under the jurisdic- tion of the National Power Corporation. Since 1973, upon the integration of the Irrigation Service Unit (ISU) with the NIA, the NIA became the only major implementing government agency for irrigation.

2.7.1 Hydroelectric Power

There are 19 completed hydroelectric projects in the Philippines, and only five are classified as large, exceeding 36,000 kilowatts capacity. Three of these major hydroelectric power plants are within the Central Luzon Basin -- the Ambuklao (75 MW) and Binga (100 MW) along the

Agno River, and the Angat Multi-Purpose project (218 MW) in the Pampanga

River basin. The two other projects are the Caliraya (36 MW) and the

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Maria Cristina (150 MW), which are located in Laguna Province and Lanao

Province, respectively. The Ambuklao, Binga, Angat, and Caliraya Pro- jects are connected to the Central Luzon transmission grid providing power for the Central Luzon area and Metropolitan Manila.

A sixth large project which is under construction is the Upper Pampanga River Project (UPRP) in Nueva Ecija Province. The multi-purpose project, when completed, has an installed capacity of 100,000 kilowatts.

The power phase of the UPRP is envisioned to be completed in 1977. The power output of the UPRP will also be connected to the Central Luzon transmission grid.

Table 2.8 gives the existing major hydroelectric power projects in Central Luzon. The total power capability is 393,000 kilowatts and the yearly available energy is about 1.566 billion kilowatt hours.

2.7.2 Irrigation

Irrigation projects in the Philippines are classified into three categories: national, communal, and private. National irrigation projects include gravity irrigation projects which are constructed, operated, and maintained by the government through the NIA, and small pump projects which are developed by the government through either the NIA or the

Agricultural Productivity Commission (APC). The communal irrigation projects are self-help projects which are given assistance by the government through either the NIA or the Department of Local Government and Community Development (DLGCD), formerly the Presidential Arm on Community

Development (PACD). Private irrigation projects are those constructed and operated by individuals or groups of farmers without any assistance 26

Table 2.8 Existing Major Hydroelectric Power Projects, Central Luzon Basin, Philippines.

Power Annual Energy Power Source or Name Capability Available Province of Project (Kw) (Kw-HT)

Benguet Agno River

Atbuklao Hydroelectric Project 75,000 437,000,000

Binga Hydroelectric Project 100,000 516,000,000

Nueva Ecija Pampanga River

Upper Pampanga River Project 100,000 Under Construction

Bulacan Angat Multi-Purpose Project 218,000 613,000,000

Total* 393,000 1,566,000,000

*Upper Pampanga River Project not included. 27 from the government. About 1.25 million hectares of cultivated land are under irrigation in the Philippines. Some 225,000 hectares are irrigated through private systems.

A ten-year irrigation development program for the period 1975 to

1985 (National Irrigation Administration, 1974) aims to increase the total irrigable area of government constructed and assisted projects from the present 1 million hectares to 2.35 million hectares, an average annual increase of 135,000 hectares. A planning horizon longer than ten years was not attempted due to lack of reliable data on water resources

(National Irrigation Administration, 1974).

In the Central Luzon Basin, irrigation systems in the five provinces primarily comprising the basin have a total irrigable area of more than 390,000 hectares. Table 2.9 presents the irrigable area by province. This does not include private irrigation systems due to lack of complete records on this category.

Most national irrigation projects are the run-of-the-river type wherein a diversion dam is constructed across a stream to raise the water level, eventually making it flow by gravity through conveyance and distribution network of canals to the farm lands. Except for the Angat

River Irrigation System in Bulacan and UPRP in Nueva Ecija, and areas irrigated by pumps, essentially all irrigation projects have no storage and regulation facilities, so that water supply is fully dependent on the available flow of the rivers. And because of wide variations of streamflow throughout the year, all irrigable areas could not be adequately served by the irrigation system. Some systems supply irrigation water

•

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2.7.3 The Upper Pampanga River Project (UPRP)

The UPRP is the largest single item of infrastructure in the

Philippines. It is a multiple-purpose water resource development project

that will harness the irregular flows of the Pampanga River for irriga-

tion, power generation, domestic water supply, recreation, and fish con-

servation. The main objective of the project is irrigation.

The project, when fully operational, will provide irrigation water to some 83,000 hectares during the wet season and 79,000 hectares during the dry season. Annual palay production in the area is expected

to reach more than 650,000 metric tons. The project's salient features are:

1 The Pantabangan Dam, a zoned earthfill dam at Pantabangan across

the Pampanga River just downstream of the confluence of the

Pantabangan and Carranglan Rivers to create a storage reservoir

with a capacity of about 3,000 million cubic meters. This was

completed in August, 1974.

2. Irrigation service facilities involving the upgrading, rehabili-

tation, and improvement of some 46,000 ha of existing irrigation

systems and construction of additional facilities to irrigate

some 37,000 hectares of new area.

3. Hydroelectric plant with an installed capacity of 100,000 kilo-

watts at the downstream toe of Pantabangan Dam and associated 30

Table 2.10 Actual Irrigated Areas from Typical Irrigation Systems, Central Luzon Basin, Philippines.*

Irrigable Area Percent Irrigated** Province (ha) Wet Season Dry Season

Bulacan 33,200 83 51

Nueva Ecija 73,150 86 19

Pampanga 5,800 94 71

Pangasinan 22,700 80 24

Tarlac 29,140 76 14

*Source: Water Resources Situation Report, Vol. IX, 1973. **Average for 6 years, 1965-1970. 31 transmission lines to feed the generated power to the NPC Luzon grid.

In the 1975 annual report of the NIA, the overall status of the

UPRP is about 90 percent complete. Completion of the project is expected by the end of 1976.

2.7.4 Water Supply and Sewerage

In the Philippines, development of water supply for domestic use is undertaken by the Metropolitan Waterworks and Sewerage Services

(MWSS), the Bureau of Public Works (BPW), and, to some extent, by the Department of Local Government and Community Development (DLGCD).

Sources of water tapped for domestic use are streams, springs, and groundwater aquifers. Families not served by the waterworks established by the government, generally in the rural areas, put up their own shallow wells where hand-driven pumps are installed, In 1972, less than 45 percent

(only about 17 million) of the total Philippine population were served by water supply systems developed by the government through the BPW and

MWSS. The rest of the population depended on their individually installed systems.

In the Central Luzon Basin, some 1.6 million persons were served by water supply systems in 1972. There were 3,497 supply systems composed of 73 waterworks, 3,402 wells, and 22 developed springs. Table

2.11 gives the number of the water supply systems and corresponding population served in the Central Luzon Basin. ▪• ▪▪ ▪• •

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71- rl C*4 33 2.7.5 Flood Control and Drainage

Intense and prolonged rainfall, triggered by typhoons and the

southwest monsoon, cause major rivers to overflow their banks and cause

damaging floods. The severity of floods in the floodplains of major

rivers is further intensified by bad channel characteristics of

Philippine rivers: steep channel slopes at the upper reaches, abrupt

changes of channel slopes from very steep to very flat as the rivers emerge in the plains, and excessive meandering at the lower reaches.

During major floods, about 299,000 hectares of residential and

agricultural land in the provinces of Nueva Ecija, Pampanga, and Bulacan

are inundated for long periods by flood flows from the Pampanga River and

its tributaries. Similarly, about 189,000 hectares are inundated in the provinces of Pangasinan and Tarlac by overflows from the Agno River and its tributaries. The estimated average annual flood damages in the

Pampanga and Agno Rivers watersheds are about t36 million and t2.9 million, respectively. Damages in August of 1960, July of 1972, and May of 1976 are much above these figures, however. The flood of May, 1976, inundated some 360,000 hectares and caused an estimated crop damage of approximately t85 million in the provinces within the Central Luzon

Basin (Bulletin Today, 1976).

2.7.6 Flood Control Projects

The Pampanga River Control Project and the Agno River Control

Project are two main projects initiated to mitigate floods along the

Pampanga and Agno Rivers. 34

Initial plans for the Pampanga River Control Project were prepared by the BPW in 1938 which were later expanded and improved into a final scheme in 1962. The final scheme, which was reviewed and favorably endorsed by experts provided by the United States Agency for International

Development (AID), is composed of a system of earth dikes and river walls, cut-off or diversion channels, drainage and flood gates. The project has so far completed: 166,350 lineal meters of earth dikes and river walls,

19,600 lineal meters of cut-off channels, and installation of 55 units of drainage gates and 7 units of flood gates.

The Agno River Control Project will reduce floods along the Agno

River and its tributaries. The project is composed of a system of levees and cut-off channels, and utilization of the Poponto Swamp as a retarding basin. The system of levees is about 180 kilometers long with an average height of 5 meters and top width of 6 meters. Some 122 kilometers of earth dikes and river walls are already completed. CHAPTER 3

REVIEW OF PREVIOUS WORK

3.1 Central Luzon Water Resources

The earliest work on the water resources of the Central Luzon Basin was conducted by the Bureau of Public Works (BPW).

The Hydrographic Section of the BPW put out a report on the surface water supply of the basin in 1959. Highlights of the work were: a) delineation of drainage areas and lengths of the different rivers draining the basin; b) comprehensive description of the major rivers and their tributaries; c) water utilization in the basin (irrigation, power, water supply for domestic and industrial use, and flood control); d) flow duration curves of selected gaging stations; e) isohyetal maps of storms causing notable floods in Central Luzon; and f) discussion of measurement and analysis of sediment loads. The report described basic hydrologic techniques and their application to the existing hydrologic data in the Central Luzon Basin. Discussion of data augmentation methods was limited to the graphical correlation procedure only.

In 1961, a report on the basin water resources was prepared by the Water Resources Planning and Development Committee (WRPDC). For the first time, both the surface and subsurface water resources were discussed. Watershed management, which is an important aspect of water resources development, was also included. Identification and quantification of groundwater recharge was not attempted. 35 36

Another study on the Central Luzon was done by the U. S. Bureau of Reclamation (1966). The work was conducted to formulate a basin-wide plan for water resources development. The report provides an assessment of the basin water resources and presents interrelated but separate projects designed to regulate streamflow. Six of these projects are in the

Agno River Basin and five in the Pampanga River Basin. One of these formulated projects, the Upper Pampanga River Project, was pursued with some modifications, after a more detailed study was conducted. All other projects are under consideration for further study.

In 1968, a report prepared by the Water Resources Development

Task Force of the United Nations reviewed the Philippine programs for water resources development. The task force studied the state of irrigation and made proposals for conjunctive utilization of surface and groundwater resources for agricultural production. They strongly advised the unification of administration in water resources development, especially in irrigation. This report might have been the turning point of Philippine water resources organization. The integration of the Irrigation Service Unit (ISU) and the National Irrigation Administration

(NIA) in 1973, and the creation of the National Water Resources Council

(NWRC) in 1974 could be attributed to the recommendations of this task force.

One of the most comprehensive works in Central Luzon's groundwater resources is, perhaps, the report made by Sandoval and Mamaril

(1970). The work deals mainly with the geologic characteristics of the groundwater basin. Detailed description of various geologic units which 37

are important water-bearing formations are given. An attempt to provide

a comprehensive picture of groundwater occurrence in the basin, based on

combined geologic and hydrologic information, was the highlight of the work. The most complete hydrogeologic map of Central Luzon ever published is found in this report. Groundwater quality is also discussed. 1 A joint effort of several agencies produced a situation report

of the Philippine water resources development in 1973. The report gives an inventory of existing and proposed water resources projects for: hydroelectric power; irrigation, water supply and sewerage; flood control

and drainage; dams and reservoirs; and lake development. The informative report also enumerates various water resources development projects proposed and under study by the different government sectors.

The Development Academy of the Philippines (1975) estimated the projected regional water requirements in the Philippines for the period

1980 to 2000. Population projections made by the National Census and

Statistics Office (NCSO) were the bases for the estimates. The water requirement projections were determined for three sectors of users, namely, agriculture, domestic, and industry. An attempt to assess the adequacy of the water resources in the eleven regions of the country was made.

1. Various agencies responsible for the report are the National Economic and Development Authority (NEDA); Department of Public Works, Transportation and Communications (DPWTC); Institute of Planning, University of the Philippines (UPIP); and the United Nations Development Program (UNDP). 38

3.2 Augmentation of Hydrologic Data

Streamflow data files in the Central Luzon Basin are charac- terized by few long records and numerous fragmentary records. Utility of these records to water resources evaluation and subsequent formulation of development plans requires extension of the historical files. Although numerous augmentation methodologies have been established (Young, Orlob, and Roesner, 1970; Matalas and Jacobs, 1964; Fiering, 1962; U. S. Army

Corps of Engineers, 1971), there is a need to identify a methodology which is best suited to the hydrologic characteristics and type of data files existing in basins of the Philippines.

Augmentation of hydrologic data has the main objective of increasing the number of elements of a given data series without making additional observations in time which is not always operationally or economically feasible. Established augmentation schemes may be grouped into two broad categories, namely, the regional estimation procedures and the statistical methods. The regional estimation methods make use of the physical characteristics of the gaging stations rather than the statistical properties of the data which is utilized in statistical methods.

In streamflow augmentation, for instance, regional estimation methods commonly incorporate the drainage area of the gaging station, the mean rainfall over the watershed, the mean runoff depth, slope of main channel, forested area, and surface storage area. The statistical procedures, on the other hand, commonly utilize the correlation analysis between two stations or among several stations of the same or closely related hydrologic phenomena (rainfall and runoff, for example). 39

In the Angat Dam study of the National Power Corporation (1972),

drainage area proportion was utilized to derive portion of the flow

series at the dam site from a gaging station downstream. This procedure

assumes that the two watersheds of both gaging stations have the same

runoff coefficients and rainfall depths, making the flow a function of

the drainage area only. In the Angat study, this assumption may, for all practical purposes, be considered sufficiently reliable, since the two

gaging stations are within the same river basin and the difference in

drainage areas of the two stations is less than 10 percent of the smaller

drainage area. Besides, the drainage area of the base station is composed of the drainage area of the augmented station and the small area between the two stations.

Engineering Consultants, Incorporated (ECI), in their Maria-

Aurora Transbasin Feasibility Study (1973) and Casecnan Transbasin Scheme

Reconnaisance Study (1971), used a slightly different and better method.

Instead of drainage area alone, a basin factor was used to establish flows of one station from another. The basin factor, as defined in these reports, is the product of the drainage area of the station and the mean seasonal rainfall over its watershed. The generating equation is expressed as:

Q1 = (BF 1/BF2 )Q2 (3.1) where 1 and Q are the flows at Stations 1 and 2, respectively, and BF Q 2 1 and BF two stations 2 are the basin factors of the corresponding to one season. 40

One advantage of the regional estimation technique is its

applicability in cases of extremely short records at the station being

augmented because the estimates depend on the physical characteristics of

the basin or watershed. In fact, in cases whereby no observed data are

available in a station of interest, regional estimation may work well,

provided appropriate factors are included in the estimates.

Statistical augmentation makes use of the concurrent records of

stations to augment a short sequence. Correlation analysis is commonly

applied. In the bivariate case, for example, a prediction equation is

established using the concurrent record period of the two stations and

the least squares method. The short sequence is lengthened using two

types of regression equations, one with noise and the other without noise. The noise term is a random component corresponding to the non- determination of the estimate.

In Central Luzon, the statistical methods of extending hydrologic data for water resources studies have not been utilized. In the UPRP studies, only regional estimation techniques were used. CHAPTER 4

THEORETICAL CONSIDERATION AND METHODOLOGY: STREAMFLOW DATA AUGMENTATION

4.1 Introduction

Data files for a representative river basin are typified by one or two long records and then a succession of shorter records, decreasing down to essentially zero length corresponding to newly established gages

(Young et al., 1970). In the Central Luzon Basin, the situation is somewhat different. The data matrix is composed of several long but intermittent records and a number of short and fragmentary records. A schematic diagram of the streamflow data files of Agno and Pampanga Rivers is shown in Figure 4.1.

In planning a water resources development, hydrologic input data such as streamflow, rainfall, or evaporation, are required from many sites with long periods of record. A long record is desired since the variances of the mean and variance are inversely related to record length (Young et al., 1970). Unfortunat ely, long records for many sites in a given river basin rarely exist in historical files. Consequently, water resources development planners are often faced with difficulty in pre- paring long-term development programs.

Schemes of augmenting hydrologic records were developed to lessen the burden of the already difficult task of plan formulation. Although established augmentation procedures are numerous, these may be classified 41 42 43

into two main categories as discussed earlier. Methods that obtain the

estimates by way of making relationships based on physical character-

istics of the gaging stations may be grouped into the regional estimation

techniques and those methods which utilize the statistical properties of

the historical data may be classified into the statistical methods.

In the statistical methods, correlation analysis is commonly

applied. The short sequence is lengthened using two types of regression

equations. The first type is the regression equation without noise

(Fiering, 1962) and the other applies the same equation but a noise term

is added (Matalas and Jacobs, 1964; Young et al., 1970; U. S. Army Corps

of Engineers, 1971). The noise term is a random component corresponding

to the non-determination of the estimate.

The correlation technique, however, should not be relied upon

indiscriminately but should be evaluated prior to use. Fiering (1962)

demonstrated that the use of the regression does not always yield better

estimates of population parameters. He then established criteria to

assess whether regression will improve the estimate of the population

mean or not. In 1964, Matalas and Jacobs added a noise term to the

linear regression estimates and derived an unbiased estimate of the variance for the bivariate case. Gilroy (1970) extended the Matalas-

Jacobs scheme and derived the multivariate generalization of the regression with noise term.

Four of the statistical method -- percent deviation, YOR (Young et al., 1970), HEC-4 (Hydrologic Engineering Center) and Matalas-Jacobs -- were selected, analyzed, and compared. The comparison aims to identify a 44

method best suited to the hydrologic characteristics existing in the

Central Luzon Basin, in particular, and to types of hydrologic records

available in the Philippines, in general. The desirability of the method

should reflect the requirements of the intended application of the extended record.

In this study, streamflow data are important in the hydrologic

budget analysis. Reliability of the result of a water budget analysis

largely depends on the accuracy of data inputs and time period considered

in the analysis. A method of augmentation that will generate annual and

monthly streamflow data, whose magnitude and sequence conform closely

with the historical records, is aimed at.

To pursue the comparison of the four selected methods, representa-

tive streamflow gaging stations in the Central Luzon Basin were used.

Five stations in the Pampanga River system and two stations in the Agno

River system were chosen. In each river system, the selection of the

stations was based on the length of available records and similarity of

physical features of the drainage areas of the stations. The five

stations in the Pampanga River system consist of one station in the upper

reaches of the main channel of the Pampanga River and four stations in

the tributaries. In the Agno River system, one station is at the main

channel and the other is in the Tarlac River, a tributary. Detailed description of the gaging stations used for comparison is given in

Chapter 7.

A base station was chosen for each river system and the remaining stations were designated as dependent stations. Records in the dependent 45 stations were divided into two parts, early and late series. Using the full sequence of the base station and the late-series records, data of the dependent stations were augmented by the four selected methods to obtain estimates corresponding to the early-series historical records.

At the end of the augmentation process, five sets of early-series data were available in each dependent station, which became the basis for comparison. These are the historical set and augmentation estimates (one set for each method).

The comparison criteria were established based on annual and monthly flows. The augmentation methods were given scores according to the closeness of their estimated values with the historical record. The full description of the criteria and mode of comparison is given in

Chapter 7.

4.2 Augmentation Procedures

The four selected methods are presented separately. For clarity, notations for each method were defined separately and were made independently in each method.

4.2.1 Percent Deviation Method

The method which uses the linear regression analysis on annual events was established and used by the California Water Resources Depart- ment (Roefs, 1974, 1975; personal communication). Apparently, there were no publications describing the method. The procedure that follows was based on the author's personal communications with Professor T. Roefs who worked with the department for some time. 46 Let observed annual events from two gaging stations, A and B, be represented by X and Y, respectively, and arrayed as follows:

X1 , X2, XN1' XN1+1' XN1+N2

Y Y .. Y 1 , 2' • ' N1

where N1 represents the length of the short sequence and the concurrent period of observations, and Nl+N2 denotes the length of the long

sequence. The observed monthly events are likewise represented as x and

y. The augmentation of the shorter record at station B is undertaken as follows:

Step 1. Using the simultaneous records with length Ni, establish a linear regression equation for the annual series:

= a + bXk (4.1)

where Y the regressed annual flow at station B for year k, Xk is the

recorded annual flow at station A for year k outside the concurrent

period, and a and b are regression coefficients derived from simultaneous records of the two stations.

Equation 4.1 is used to extend the record of station B for the

years outside the concurrent period of records. This gives annual

events. Monthly flows are derived by the succeeding steps.

Step 2. For the available years of records, compute the per-

centage of monthly flow for each month and station:

A P.. = (x. ./X.) 100 (4.2) 13 13 1

and 47

P. = (y../Y.) 100 13 13 1 (4.3)

A where P.. and P.. are the 13 13 percentage flow of month j in year i at stations A and B, respectively. The terms x.. and y.. are the 13 13 monthly flows, and Xi and Yi are the annual flows.

Step 3. Determine the average percentage for each month in each station:

Nl+N2 —A A P. = ( E P..)/(N1+N2) . (4.4) i=1 13

Ni --B B P..:( EP..)/N1 . ij (4.5) J 1=1

—A —B where and P. are the average percentages of flow in month j at stations A and B, respectively.

Step 4. Compute the ratio of average monthly percentages of station B to station A, R.:

—B —A R. = P./P. (4.6) J 33 At the end of this step, twelve ratios should have been found, one for each month.

Step 5. Find the percentages of monthly flows for each month for the period between N1+1 and Nl+N2 for station B:

A P . = R jP . kJ kJ (4.7)

A where P Pki are flow percentages of month j at stations A and B, respectively, for year k = N1+1, N1+2, ..., Nl+N2. 48 Step 6. Adjust each monthly flow percentage at station B, deter- 12 B IninedfromSt eP 5 above , suchthatEP.=100, using: j=1

B B 12 P' . = P . (100/ kJ kJ P) (4.8) j=E-.1 inwhichP.IsB . kJ the adjusted percentage flow of station B for month j and year k.

Step 7. Compute the monthly flow for each month of station B for years k = N1+1, N1+2, ..., Nl+N2:

v* Y* Pf 'kj k kj (4.9) where yfe(i is the flow at station B in month j and year k, and Y* is the derived annual flow from the linear regression equation 4.1.

It has to be noted that, with this method, the base station should have no gaps for the period being considered. When the base station has intermittent records, filling up the gaps from other stations within the basin may have to be done first if complete series are sought.

The percent deviation method may be used to fill up the gaps. The base station becomes temporarily the dependent station in this case and another station is selected as the basis for the estimates.

4.2.2 Matalas-Jacobs Method

The method uses linear regression with a noise term to extend the short sequence of hydrologic phenomena. The noise is a random variable with zero mean and variance proportional to the variance of the observation for the short sequence about the line of regression. The addition 49 of noise does not affect the reliability of the estimate of the mean, instead it leads to an unbiased estimate of the variance for the

lengthened sequence, provided that there is sufficiently strong relationship between the concurrent events of the short and long sequences (Matalas and Jacobs, 1964).

Matalas and Jacobs derived the generating equation and the equations relating the reliability of estimates of the mean and variance of the lengthened sequence with the assumptions that:

1. The events are independently distributed in time.

2. The concurrent events for two sequences have a joint normal distribution.

3. The relationship between the concurrent events is defined by a linear regression.

4. No changes occur in the hydrologic regimes with which the sequences are associated.

These assumptions are necessary for the mathematical tractability of the procedure. Under the assumption of normality, for instance, only the mean and variance are considered because these two parameters uniquely define a normal probability distribution. The skewness is zero for this distribution. Linear relationship is much more desirable because the regression is easily extended beyond the concurrent period of records.

Although this method may be used for other hydrologic events, in this study the application was limited to annual streamflow. Let the 50

observed annual events for the long and short sequences of a pair of

streamflow gaging stations be represented as:

X1 , X2 , ..., XN .., X_ i N1+1 , 1+N2

Y Y 1 , Y 2' •••, N1

N1 is the length of the short sequence and, at the same time, the con-

current period of observation; Nl+N2 denotes the length of the long

sequence. In practice, the Ni observations for the short sequence may

not correspond to the first N1 observations of the long sequence, nor may

the N1 concurrent observations of X and Y occur consecutively. There is

no loss of generality if the two sequences are represented as above,

however. The following steps describe the augmentation procedure.

Step 1. Perform a Gaussian transformation. Simple transforma-

tions such as logarithms and square roots may be used. In this study,

logarithms were used which yield a better normalizing transformation than

the square root, as suggested in the 1976 study in progress by Liongson, using the data sets found in Central Luzon.

Step 2. Using the transformed variables, compute the statistics of the two sequences. The following equations are used:

Ni Y = ( E Y.)/N1 (4.10) 1 i=l

Ni X = ( E X.)/N1 1 (4.11) i=

Ni +N2 X2 = ( E Xk)/N2 (4.12) k=N1+1 51 Nl+N2 = ( E Xk)/(N1+N2) (4.13) k=1

Ni VARY ( E (Y. - )2)/ (N1-1) Y 1 1.1 1 1 (4.14)

Ni 2 VAR = ( E (X. - ) )/(N1-1) X 1 1 (4.15) 1 1=1

Nl+N2 2 VARv = ( E (Xk - R2 ) )/(N2-1) (4.16) 2‘2 k=N1+1

Nl+N2 2 VARx = ( E (Xk - R) )/(Nl+N2-1) (4.17) k=1

1/2 SD = (VAR) (4.18)

Ni Ni b = ( E Y.(X. E (X. - 2 ) (4.19) 1 - 1 fl/(i=1 1 ) 1=1 1

r = bSD /SD X Y (4.20) 1 l where ? and R are the means of the corresponding variables. The subscripts 1 and 2 refer to the record length Ni and N2, respectively. The absence of the subscript denotes that the whole length (i1+N2) is represented. VAR is the variance of the variable defined by the corresponding subscript. SD is the standard deviation, b is the coefficient of regression, and r is the correlation coefficient which measures the strength of the relationship between X and Y.

Step 3. Extend the short sequence for the period N1+1 to

Nl+N2: 52

= 7 / + b(Xk - + A(1 _ r2 ) 1/2 sp t k (4.21) 1 where Yikc denotes the estimate outside the concurrent period, i.e., for the values of Y tk is a N1+1' YN1+N2; random normal variate with zero mean and unit variance; and A is a coefficient used to improve the expected value of VARy and obtained from:

A = N2(N1-4)(N1-1) (N2-1)(N1-3)(N1-2) (4.22)

Step 4. Inverse the transformation done in Step 1 for all elements of Y. At the end of this step, data set of station B is now composed of historical records for year i=1 to Ni and augmentation estimates for year k = N1+1 to Nl+N2.

To judge the reliability of an estimate, Matalas and Jacobs (1964) considered two properties, the expected value and the variance of the estimate of both the population mean and variance. They deduced that the regression is useful, i.e., the estimate of the population parameters is improved by lengthening the short sequence, when the measure of the strength of correlation, r, exceeds a critical minimum value. For 7 to be a better estimate of than 7 for instance, the 'y l' correlation coefficient, r, must be greater than (1/VN1-2). This relationship of r to the reliability of the estimate of the mean is similar to that derived by

Fiering (1962), although he used the regression equation without noise.

Likewise, Matalas and Jacobs (1964) also derived an expression relating r as an indicator in determining whether the regression can improve the estimate of the population variance. 53

Hydrologic data are not usually independently distributed in time. In streamflow events, high flows are generally followed by high

flows and low flows by low flows. This dependency is attributed to

storage processes in the hydrologic regime with which the given sequence

is associated. Increasing the time interval between observations (annual

instead of monthly, for instance), however, decreases the dependency between successive events. This augmentation procedure was suggested by

the developers (Matalas and Jacobs, 1964) to be restricted to annual

sequences where the carryover factors are a minimum.

4.2.3 YOR Method

Young et al. (1970) developed a means of augmenting monthly

hydrologic data derived from numerical analysis and variance reduction

considerations. For convenience, the scheme is referred to in this study

as the YOR method.

The method basically uses an overall monthly multiple regression

analysis for a number of interrelated stations. This approach differs

from that of the HEC-4 method, although both schemes were established to

handle monthly hydrologic data. In this procedure, a single generating

equation is established for each station based on the overall monthly

data and used to obtain the estimate for all months. The following steps

describe the scheme.

Step 1. Arrange the data set according to decreasing record

length. The gaging station with the longest record is placed in the top

row, the next longest following next, and so on, until the station with

the least record occupies the bottom row. This arrangement concentrates 54

and ranks the information measured by the length of record from top to bottom in the row data matrix (Figure 4.2).

Step 2. Perform a Gaussian transformation on each observation

(X ..). In this study, the log transform was used such that:

X = log X .. slj sij (4.22)

where X is the observed value at station s, year i, and month j.

Step 3. Compute the monthly mean, R si , standard deviation, SD

and skewness coefficient, g., for sj each station using the transformed data, x ... Voids are excluded. sij Step 4. Standardize each nonvoid value of x. using the

appropriate monthly mean and standard deviation:

x' = (x- c .)/SD . (4.23) sij sij sj sj

where x' is the standardized value at station s, year i, and month j.

Step 5. Normalize each standardized element x' .. using the

Wilson-Hilferty transformation. This step further reduces the skewness

coefficient of the standardized elements in Step 4 and makes the

statistical portion of this method and that of the HEC-4 method similar

in terms of transformations used:

1/3 y .. = 61g .(((g ../2) + 1) - 1) + g ./6 (4.24) sij sj sj Si) sj

where ysii is the normalized value at station s, year i, and month j.

This transformation was not utilized by the developers of the YOR method but was added as a modification in this study.

At this step, the ij notations for the year and month are also

translated into a single-letter subscript corresponding to the time 55

• r""1

7 cd C

bA • g 0 .4= 0 CO • 4.• ;-4 TC 0

G W

— j t)'3e cn

4-d ci•- ci ci ci Gf) CO Ca 56 frame, f. This makes the subsequent presentation and formulation more convenient. Thus, any normalized element y is expressed as y in sf' which f is defined as:

f = 12(i - 1) + j (4.25)

The illustration below describes the time frame under the ij and f notations:

i: 1 2

j: 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 ...

f: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 ...

Step 6. Establish linear prediction equations for each site.

Use the normalized data and employ the least squares regression. The prediction equations are used for filling in voids. Since existing information is concentrated in the upper rightmost portions of the data matrix (Figure 4.2), the dependent variable at the time step f is taken

as function of other f period variates and f+1 period variates. The

equation for station 1 is:

y = a y + e (4.26) lf 11 1f+1 lf with corresponding correlation coefficient of R1 ; for station 2: (4.27) Y2f = a21Y 1f+1 a22Y2f+1 b 21Y 1f e2f with corresponding correlation coefficient of R 2 ; for station 3, + b y r3f = a31Y1f+1 a32Y2f+1 a33Y3f+1 31 lf

+ b 32y2f + e3f (4.28) with corresponding correlation coefficient of R3 ; and in an analogous

fashion for station s: 57

s-1 Ysf = askYkf+1 E b skykf + esf (4.29) k=1 k=1

with correlation coefficient R ; s where the a and b terms are coefficients found using least squares regression, and e is the noise term.

Step 7. Fill in data voids using predictor equations with noise

term. Voids are filled in moving from right to left and top to bottom,

i.e., voids in the top row (station 1) are estimated first using:

t1f(1 R2)1/2 Y lf = a11z1f+1 (4.30) 2 where Ri is the coefficient of determination, t if is an independent

random normal deviate, y* is the estimated l f value, and z is the independent variable, interpreted as:

z 1f+1 = Y1f+1 (4.31) if an observed datum exists, or

z lf+1 = Ytf+1 (4.32) if a previous value was filled in.

After filling in the first station, the second station voids are estimated, moving right to left. The equation used is:

2 1/2 y * a z + a z + b z + t (1 - R ) 2f = 21 lf+1 22 2f+1 21 lf 2f 2 (4.33)

2 in which R is determination, a and b 2 the coefficient of are regression coefficients, z is existing normalized values or estimated values as in equations 4.31 or 4.32, and t2f is an independent normal random deviate.

In analogous fashion, voids in station s are filled in using: 58 s-1 2 1/2 Y's'f = askzkf+1 + E b k zkf + t sf (1 - Rs ) (4.34) k=1 k=1 s

The fill-in procedure is continued until all required stations are completed.

Figure 4.3 shows the recursive nature of the fill-in procedure

from right to left and from the nature of the arrangement of the data set

(Figure 4.2), the backward motion moves away from the existing informa-

tion and exploits the existing data in the prediction. Furthermore, the

fill-in procedure proceeds toward no known end point, except when filling record gaps where splicing problems occur.

Step 8. Adjust fill-in values falling within a gap having two

existing end data points. The adjustment is done on the value obtained

from the least squares predictors (equation 4.34) immediately after the value is found. The adjustment is made to avoid abrupt transitions which could occur when fill-in values butt up against existing data at the edge of the gap.

Single-row (station) adjustment is performed and each row is considered independent of the others. Consider a particular row and let p be the lag-one serial correlation coefficient (Markov correlation) of the normalized series ysf • Let yf and yo be the right- and left-hand existing values, respectively, at the edge of the gap (Figure 4.4). The station subscript may be dropped since the adjustment procedure is performed in a single row (station). There are f-1 points within the gap.

It is desired to adjust q_ 1 such that the influence of yo is introduced into the estimate. 59

ON= r-1

CNJ vr d -ci ci ci • • • ci is)- 'a co." coi 60

4— o

I 14m. CL. 4—) 11 cu

rts

10 0

tu) 61

Referring to Figure 4.4, let P be the predictor computed from the linear regression equation with noise, equation 4.34, or the value of

nf-1 . Assuming further that P derives from the single-site Markov model, since each station is considered independently for adjustment purposes,

P = py 2 1/2 f + t(1 - p ) (4.35) where t is a random normal deviate with zero mean and unit variance as before. This assumption was made to evaluate the values of the smoothing coefficients which will be shown later.

An adjusted value of P is defined as:

w = aP + 8y0 (4.36) to introduce the value of y in the estimate. The final value, *y* o f-1' which is entered as the final estimate, is set equal to w, i.e.: *v* = w f-1 (4.37)

The final value, *y„ in effect bears with the right-hand existing value yf and left-hand value yo . The terms a and 8 are smoothing coefficients employed to make the final fill-in estimate, *y,f-1 conform with the known end points.

Since each row had been normalized, the expectation of w2 or 2 E(w ) is unity and to force the Markov property the correlation of w with 2 yf is set at p. This establishes two conditions, E(w ) = 1 and E(wy f) p, which can be utilized to set a and 8.

Squaring equation 4.36 and taking the expected value one obtains:

2 2 2 f+1 E(w ) = a + 18 + 2a8p (4.38) 62 Similarly, if equation 4.36 is multiplied by yf and the expected value is determined, one arrives at:

E(wy i ) f = cp + (4.39)

Substituting 2 the two conditions E(w ) = 1 and E(wyf) = p into equations 4.38 and 4.39 and solving them simultaneously gives:

f-1 Œ = 1 - F3p (4.40) and

f-1 f+1 = 2 (p - p ) 2f-2 2f (4.41) 1 + p - 2p

After adjustment of y f_ i , it then assumes the role of yf and the process is repeated, going right to left across the gap.

Step 9. Perform the inverse of the normalizing transform in

Step 5:

3 X'.. = (((g ./6)(y - g5 /6) + 1) - 1)2/g sljsj sj (4.42)

Step 10. Perform the inverse of the standardization in Step 4

using the same values of j . and SD . as used in si si equation 4.23. The inverse is:

x = SD .x'..+ j . su j sj slj sj (4.43) where x' .. is either the existing value or a fill-in s ij estimate. Step 11. Perform the inverse of the Gaussian transformation in

Step 2. For this study, the inverse is:

X.. = antilog x (4.44) su j su j

At the end of Step 11, a data set of both observed and estimated values will exist. 63 4.2.4 HEC-4 Method

This method was adopted from the generalized computer program

"HEC-4 monthly streamflow simulation" prepared in the Hydrologic Engi-

neering Center of the U. S. Army Corps of Engineers. This is essentially

the reconstitution portion of the whole program.

The program was established to analyze monthly streamflows at a

number of interrelated stations. It determines the stations' statistical

properties and generates a sequence of synthetic streamflows of any desired length retaining these statistical properties.

The program could be divided into two major components: a) the

statistical and reconstitution part, and b) the synthetic streamflow

generation part. Only the first part, statistical and reconstitution of missing values, is herein discussed.

The statistical portion of this augmentation procedure is somewhat similar to that of the YOR method, except that no pre-arrangement of

the data matrix is necessary. Let the data matrix be represented as in Figure 4.5.

Step 1. Add a small increment to each monthly existing value for each station to avoid infinite negative logarithm in case there are monthly streamflows equal to zero:

Xsij = Qsij q (4.45) where X .. is the adjusted flow at station s in year i and month j, Q si2. j is the observed flow at station s in year i and month j, and q is the increment. The original HEC-4 program sets q equal to 1 percent of the monthly average. 64

• •

•

• •

•

III . "1-7-7-1-1-1%- 0 E d ,ci ci ci ci (. 7) .4. ). . co (• • • • • . 7) • • eem• . 0 65 Step 2. Perform a Gaussian transformation:

xsij = log Xsii (4.46)

Step 3 -Computethemean,R. sj ' standard deviation, SD., and skewness coefficient, gsj , for each month, j, in each station, s:

E x ..)/n . =( sij (4.47) sj i=1

- SD = (( E (x - .)2)/(n - 1)) 1/2 (4.48) S)j si) x sj i=1

Ti - 3 3 g . = n E (x - x .) /((n - 1)(n - 2) SD .) (4.49) sj si) sj sj i=1

where n is the number of years and all other terms are as defined

previously.

Step 4. Standardize each value x. using appropriate monthly

mean and standard deviation:

X'.. = (x- j .)/SD . (4.50) Si) Si) sj sj

in which standardized value. x'..Si) is the Step 5. Normalize the standardized elements x'.. using the

Wilson-Hilferty transformation:

.(((g ../2) +1) 1/3 4- 1 6 (4.51) sij 6./gsj sj Si) gsj

where v is the normal standard variate. 'sij Step 6. Monthly streamflows missing from the records of the

various stations are estimated for all stations for each month. In recon-

stituting the missing value, a regression equation in terms of normal 66 standard variates is computed for that month. The regression equation, with a random component, is used to compute the missing value.

The regression equation for the missing value is established based on current month records or reconstituted flows in other stations; or, if no current month record is found, the preceding month is used (U. S. Army Corps of Engineers, 1971, p. 6):

In reconstituting missing data, a search is made for each month of record starting with the first for stations that have no record during that month. When one is found, a search of all other stations is made to determine whether recorded or previously reconstituted flows exist for the current month or, if not, for the preceding month. If one is found, it will con- stitute an independent variable for estimating the missing value, and its value and pertinent correlation coefficients are stored in new arrays for computation purposes. The correlation coefficients with the dependent variable is temporarily stored to assure that coefficients relating independent variables have sufficient array space.... If no independent variables with data are found, as can happen in the first month of record, a correlation is made with the preceding value for the same station and that preceding value is arbitrarily set at the average of the month. The regression equation and determination coefficient are then computed.... The missing value is then computed by use of the regression equation and adding a random component normally distributed with zero mean and with variance equal to the error variance of the regression equation.

Step 7. Inverse the normalizing transform, the standardization, and the logarithms transformation: 3 X , .. = (((g ./6)(y - g ./6) + 1) - 1)2/g sj (4.52) sli si sli si

x = x + x', .SD (4.53) sij sj su i si

Xsij = antilog xsij (4.54)

Step 8. Subtract the small increment added: (4.55) Qsij = Xsij 67

At the end of Step 8, a data set of both observed and estimated values will exist.

Although both the YOR and HEC-4 methods were established to handle monthly events using linear regression with noise, their similarity ends here. In the YOR method, a single prediction equation is established for each dependent station. The regression equation is established based on an overall monthly correlation analysis. The HEC-4 method, on the other hand, utilizes a separate monthly regression, i.e., 12 equations, one for each month, are established for every dependent station.

Each approach has its own advantages and disadvantages. In the

YOR approach, more points become the basis of determining the regression coefficients (12 points for every year of record is available), but the monthly variability of the events may not be reflected by the computed regression coefficients. In the HEC-4 method, since the calendar months are considered separately, the regression coefficients differ from month to month. This approach, however, would require much longer record than that required in the YOR method in order to establish the separate monthly regression equations.

In the percent deviation method, no random component is added to the regression equation for the annual events. The derivation of the monthly events, however, incorporate the monthly variations in the base station. This approach seems to preserve the randomness of the monthly events in the base station which is injected into the monthly estimates in the dependent station. CHAPTER 5

THEORETICAL CONSIDERATION AND METHODOLOGY:

HYDROLOGIC BUDGET

5.1 Water Balance

A water balance is a quantitative accounting of the total water gains and losses of a basin, for a specified period of time. The accounting considers all waters, surface and sub-surface, entering and leaving or stored, within a basin.

The general water balance equation for a basin for a specified period is given as:

I = 0 + dS (5.1) where I is the amount of water entering the basin (gains), 0 is the amount of water leaving the basin (losses), and dS is the change in basin storage.

With reference to Figure 5.1, the water gains are P, precipita-

tion; RI, streamflow entering the area; and GWI, groundwater underf low

into the area. The water leaving the area is composed of ET, evapo-

transpiration, both from the surface and sub-surface of the area; RO,

streamflow out of the area; and GWO, groundwater underf low from the area.

The total water budget equation for the area for a given period of time

is then written as:

P + RI + GWI = RO + ET + GWO + dS (5.2)

68 69

RI RO Ground Surface

Water Table

1/41\r\J--> GWI GWO

//// // Impermeable Zone / // // /// , Boundary of Basin // // /// / /

Figure 5.1 Components of a Hydrologic Budget.

Most drainage basins are contiguous to headwater reaches of

streams. In the Central Luzon Basin, the whole drainage areas of the

rivers were considered; thus, streamflow entering the area, RI, is zero.

Also, the groundwater and topographic divides reasonably conform in the

Central Luzon Basin boundaries, except in the vicinity of streamflow

gaging stations. Under this condition, the groundwater underflow

entering the area, GWI, from adjacent river basins may be neglected.

Hence, there is no surface or sub-surface flow into the basin being con-

sidered and the terms RI and GWI may be eliminated from the water budget

equation.

The annual changes in surface storages in the Central Luzon Basin,

ponds and natural depressions, are relatively very small. The average

annual change in storage in the Central Luzon Basin swamps (Candaba, San

Antonio, and Poponto Swamps) was estimated to be about 0.16 percent only

of the average annual streamflow of the Pampanga and Agno Rivers. The

estimate was based on the average annual water level fluctuation and area

of the swamps. Withdrawal from wells is usually for domestic and small

irrigation systems' use and is mostly negligible. Therefore, several

items of the total water budget for the Central Luzon Basin could be

eliminated because they do not significantly affect the balance between water gains and losses.

Under these specified conditions, precipitation is the only water

gain considered in the water budget. Water outflows or losses include

streamflow, evapotranspiration, and sub-surface underflow at the vicinity

of streamflow gaging stations. Basin water storages are provided by the 71

soil and groundwater reservoirs. Changes in soil moisture content

reflect the changes in storage of water in the soil and changes in water

levels in wells indicate changes in storage of the groundwater reservoir.

Equation 5.2 could be written as:

P = RO + ET + GWO + dSs + dSg (5.3) where dSs is change in soil moisture, dSg is change in groundwater

storage, and all other terms are as previously defined. All quantities

are volumes during the period for which the balance is made.

Soil moisture assessments are not generally done. Such lack of

measurements limits the application of Equation 5.3 to periods within

which change in soil moisture is generally small and could be neglected

(Walton, 1970). Weekly or monthly water budget analysis is not possible

since the changes in soil moisture for these periods may be large and

significantly affect the accounting procedure. Average soil moisture

changes during the period between the same date in two succeeding water

years (April 1, 1959, to April 1, 1960, for example) could be suffi-

ciently small to be negligible. Thus, Equation 5.3 can be rewritten for

an annual inventory period for the Central Luzon Basin as:

P = RO + ET + GWO + dSg (5.4)

5.2 Rainfall

Precipitation, P, includes all forms of measurable amounts of

water reaching the ground surface from the atmosphere. In the

Philippines, a tropical country, rainfall is the only significant form of

precipitation. 72

The three procedures generally used for estimating the volume of rainfall over the basin are the Thiessen, isohyetal, and arithmetic mean.

The analysis of the same set of data in three different ways results in insignificant differences between the volume amounts (Hershfield, 1965).

Apparently, the average rainfall over the basin could be estimated using any of the three procedures without introducing significant effects on the water budget.

The density of a network of raingages depends on the uses for which the rainfall data are intended. A relatively sparse network of gages would suffice to estimate annual averages over large areas of level terrain and a very dense network would be required to determine the rainfall pattern in thunderstorms.

Huff (1970) investigated sampling errors in the measurement of areal mean precipitation from networks of various densities in Central and Southern Illinois. Sampling errors tend to increase with increasing areal mean precipitation and to decrease with increasing gage density, rainfall duration, and size of area. Thus, a given network yields greater average errors for storm precipitation than for monthly or seasonal rainfall. Huff reported, for example, that for seasonal rain- 2 fall and relatively sparse network density of 200 square miles (518 km ) per station, the average sampling error was less than 5%, but for a 6-hour rainfall with mean of 0.5 in (13 mm) the average error was 14%.

Linsley, Kohler, and Paulhus (1975) presented the recommendations of the World Meteorological Organization (WMO) for the minimum densities of precipitation networks as follows: 73 2 2 1. 600 to 900 km (230 to 350 mi ) per station for flat regions of

temperate, mediterranean, and tropical zones. 2 2 2. 100 to 250 km (40 to 100 mi ) per station for mountainous

regions of temperate, mediterranean, and tropical zones. 2 2 3. 25 km (10 mi ) per station for small mountainous islands with irregular precipitation. 2 4. 1,500 to 10,000 km (600 to 4,000 mi2 ) per station for arid and

polar zones.

Hershfield (1965) investigated 15 watersheds with a total of 400 raingages to obtain relationships fundamental to the spacing of raingages for hydrologic investigation. He plotted iso-correlation lines around a key station in each watershed and arbitrarily selected a correlation coefficient of 0.9 in obtaining distance between gages. Johanson (1971), in his investigation of precipitation network requirements for streamflow estimation, found that the number of gages needed for simulation work is independent of watershed area. 'He pointed out that a network of gages could be reasonably designed to simulate various sizes of watershed with similar levels of accuracy.

5.3 Runoff

Streamflow measured at gaging stations includes two components, surface runoff and groundwater runoff. Surface runoff reaches streams more rapidly and is discharged from the basins within a matter of days.

Groundwater percolates slowly toward, and discharges into streams gradually. In many basins, a few days after precipitation ceases, 74 streamflow is sustained entirely from groundwater runoff. In tropical zones, streams continue to flow even after prolonged dry periods because of groundwater runoff. In the Central Luzon Basin, although 80-90 percent of the annual rainfall occurs during the May-October period, sub-

stantial streamflow is measured during the other months of the year.

5.4 Evapotranspiration

The amount of water actually returned to the atmosphere is

referred to as evapotranspiration, ET. It includes evaporation from

water surfaces and soils, and transpiration by vegetation within the basin.

Evapotranspiration depends primarily upon meteorological factors, available soil moisture and groundwater, and soil and vegetation types (Walton, 1970). Important meteorological factors acting on evapotran-

spiration are air temperature, wind velocity, vapor pressure, and solar radiation (Linsley et al., 1975).

Methods used in estimating evapotranspiration may be grouped into

three broad categories (Levine, 1959) as follows: 1. Empirical method.

2. Theoretical methods based on mass transfer.

3. Theoretical methods based on energy budget.

5.4.1 Empirical Method

Many empirical formulas have been derived and are discussed and presented by Linsley et al. (1975), Levine (1959), and Hounam (1971). 75 These methods are based on the Dalton approach and are empirical in that experimental constants are derived and incorporated in the formula. The general form of the Dalton equation for free water surface is:

E = (e s - ed) f(u) (5.5)

where E is the evaporation in unit time, es is the vapor pressure at the evaporating surface, e is d the vapor pressure at some height above the surface, and f(u) is a function of the horizontal wind velocity. Although numerous formulas have been derived empirically, only the equations developed by Blaney and Criddle in 1950 and Thornthwaite (1948) are included here.

The Blaney-Criddle formula is expressed mathematically as:

U = KF = Ekf (5.6) in which U is the evapotranspiration (or consumptive use) of a crop, in inches for a given period; f is the monthly consumptive use factor; k is the empirical, consumptive use, crop coefficient for a month (varies by crops); F is the sum of the monthly consumptive use coefficients for the period; and K is the empirical, consumptive use, crop coefficient for the growing season (also varies by crops). The monthly consumptive use factor, f, is computed from:

f = tp/100 (5.7) where t is the mean monthly temperature in ° F, and p is the monthly percentage of daylight hours in the year. Values of p for latitudes 0 to

65 ° north of the equate''r are given in Appendix C. 76 Thornthwaite (1948) introduced the concept of potential evapotranspiration to circumvent the difficulty of estimating the decreasing amount of evapotranspiration due to soil moisture depletion. He defined

potential evapotranspiration as the evapotranspiration that would occur were there an adequate supply of moisture at all times. The formula which he developed is expressed by the following general equation: a e = ct (5.8) where e is the monthly evaporation in cm, t is the mean monthly temperature in ° C, and a and c are coefficients which vary from locality to locality.

The term a is evaluated from:

3 2 a = 0.000000675 I - 0.0000771 I + 0.01792

+ 0.49239 (5.9) where T is an annual heat index and is equal to the sum of monthly heat indices, i, which is determined by:

(t/5) 1.514 (5.10)

The coefficient c varies inversely with I. From these relations, the equation for potential evapotranspiration was developed as:

e = 1.6 (10 t/) 2 (5.11) where a has the value determined by Equation 5.9.

5.4.2 Mass Transfer Methods

An example of a formula based upon the theory of vapor diffusion

(mass transfer) was developed by Thornthwaite and Holzman (Levine, 1959) and is presented as: 77 2 E = pk (q - q )(U - U )/(ln Z /Z ) 2 1 2 2 1 2 1 (5.12) where E is the rate of evaporation; q and U are specific humidity and mean wind velocity, respectively, at two heights, Z l and Z2 ; k is the von

Karman constant (equal to 0.4); and p is air density. Extreme precision in the measurement of the variables in Equation 5.12 is required, which imposes considerable restriction upon its practical application.

5.4.3 Energy Budget Method

The energy balance approach employs the principle of conservation of energy. When applied to lake or reservoir evaporation, the energy budget may be expressed (Linsley et al., 1975) for a specified period as:

Qo = Qnr Qe Qh Qv (5.13) where go is the change in stored energy by the water, 0 is the net radiation received by the water body, Q is the energy used for evapora- e tion, Qh is the sensible heat transfer (conduction) to the atmosphere,

and Qv is the net energy content of inflowing and outflowing water

(advected energy), all in calories per square centimeter.

The primary difficulty in the application of Equation 5.13 is the determination of the sensible heat transfer, Qh , which is not readily observed or computed. The Bowen ratio, R, which is the ratio of thê heat

loss by conduction to the heat loss by evaporation, was conceived to eliminate Q from the energy budget equation. Thus, Equation 5.13 h becomes:

Q0 = Qnr - Qe (1 R) + Qv (5.14) 78

Application of the energy budget requires an approximate water budget of the lake or reservoir being considered since the inflow, out- flow, and storage of water in conjunction with the respective tempera- tures represent energy values which must be considered in the evaluation Q of the Qv and o terms. Because of the extensive data requirements, the energy budget approach has not been attempted in studies in the Central Luzon Basin.

5.4.4 Penman's Equation

Using a combination of the energy balance and vapor diffusion theories, Penman (1948) derived the equation:

E = (AH + E ay)/(A + y) (5.15) where E is the evaporation from a free water surface, in mm/day; y is the constant of wet and dry bulb hydrometer, in ° F and mm Hg; y = 0.27; A is the slope of saturated vapor pressure ed versus temperature Ta ; E a is the measure of the diffusion of vapor, in mm/day; and H is the daily heat budget, in mm, of water.

E and H are evaluated by the following equations: a

Ea = 0.35(e a - ed)(1 + 0.0098 U2 ) (5.16)

4 H = R (0.18 + 0.55 n/N)(1 - r) - T a (0.56 - 0.092 e ) A d

- ŒT4 (0.56 - 0.0927e- )(0.10 + 0.90 n/N) (5.17) where e vapor pressure at mean air temperature, in mm a is the saturation Hg; ed is the saturation vapor pressure at mean dew point (i.e., actual vapor pressure in the air), in mm Hg; U2 is the wind speed at 2 meters 79

above ground surface, in mi/day; RA is the mean monthly extraterrestrial radiation, in mm of water evaporated per day; n/N is the ratio of the actual to possible sunshine hours; r is the estimated percentage of reflecting surface; a is the Stefan Boltzman constant, 2.10 x 10 -9 mm/day; and Ta is the absolute air temperature, in degrees Kelvin.

Using the above relations, evaporation from free water surface

can be estimated. With the use of reduction coefficients, the free water

surface evaporation is correlated to potential evapotranspiration.

Values deduced by Penman (1948) for southern England are 0.80 for summer,

0.60 for winter, 0.70 for the equinoctial months, and 0.75 for the whole year. For the equatorial regions, the reduction coefficient suggested by

the World Meterological Organization (Hounam, 1971) is 0.70.

5.4.5 Other Methods of Estimating ET

Aside from derived formulas discussed earlier, evapotranspiration

could also be based on water budget analysis and field determination.

Assuming all items in a water budget equation for a basin except evapotranspiration can be measured, the amount of water required to balance the continuity equation represents evapotranspiration. The reliability of a water budget computation largely depends on the time increments considered in the analysis. Normal annual evapotranspiration can be reliably computed (Linsley et al., 1975).

Field determinations of evapotranspiration are mostly made on the field plots, tanks, evapotranspirometers, and lysimeters. In the field plot determination, percolation and soil moisture measurements tend to become the principal sources of error. Small evapotranspirometers, on 80 the other hand, provide only an index to potential evapotranspiration, like evaporation pans (Linsley et al., 1975). Limitation on the tank and lysimeters is the difficulty of maintaining comparable soil moisture and vegetal cover in and adjacent to the instruments.

In this study, annual evapotranspiration over the basin was estimated using a simplified version of Equation 5.4. All other methods discussed previously present application difficulties due to data requirements and lack of established reduction coefficients to transform potential ET to actual ET, under Philippine conditions.

Annual changes in the groundwater storage in the basin being investigated is not appreciable and generally negligible. This observation is reflected from the data on water table level measurements on shallow wells within the Central Luzon Basin. Water table level fluctuations between April 1 and March 31 from individual observation wells range from 0 to 0.5 m and the average of all wells is about 0.01 to 0.1 m.

Equation 5.4 can, therefore, be evaluated on an annual basis if the

change in groundwater is neglected; thus:

P = RO + ET + GWO (5.18)

5.5 Groundwater

The fundamental relationship for the quantitative evaluation of

groundwater resources is Darcy's law (Walton, 1970). General flow equations of groundwater are derived based on the principle of conservation of mass and Darcy's law. Darcy's law states that the velocity of flow through porous media is proportional to the hydraulic gradient. This direct (linear) relationship between velocity and gradient implies that 81 the specific discharge (the discharge per unit area normal to the flow) is also proportional to the hydraulic gradient, which can be expressed as:

q= - KI (5.19) where q is the specific discharge; K is the hydraulic conductivity of the porous medium, which has a dimension of velocity; I, the hydraulic gradient, is the change in head ( h) per unit length (1) along the flow path; and the negative sign indicates that the flow is toward the direction of decreasing head.

The discharge, Q, through a given cross-section of water-bearing material, normal to the flow, is the product of the specific discharge, q, and cross-sectional area, A. Thus:

Q = KIA (5.20)

The hydraulic conductivity, K, depends on the properties of the fluid and the medium, and is expressed as:

2 K = Cd (y/u) = k(y/u) (5.21) in which k is the intrinsic permeability, being characteristic of the medium alone; y is the specific weight of the fluid; u is the dynamic viscosity of the fluid; C is a dimensionless constant or shape factor involving effects of packing, grain arrangement, size distribution, porosity, and stratification; and d is the average pore size of the porous medium. Walton (1970) gave the definition of the mean grain diameter as the diameter of sand grain such that 10 percent of the aquifer material (by weight) is of smaller grains and 90 percent is of 82 larger grains. This corresponds to the effective grain size, the sieve size that retains 90 percent of the aquifer materials (d 90 ).

Oftentimes, it is convenient to express Equation 5.20 in terms of the transmissivity, T, which is defined (Lohman and others, 1972) as the rate at which water of the prevailing kinematic viscosity is transmitted through a unit width of the aquifer under a unit hydraulic gradient; hence:

Q = TIL (5.22) in which L is the width of the aquifer.

5.5.1 Equations of Groundwater Flow

The widely used general equations governing groundwater flow derived by Jacob in 1950 (Walton, 1970) are as follows:

2 2 2 3 h a h 3 h _ Ss ah (5.23) 2 2 2 K at ax ay az

2 2 2 2 a h a h 3 h S a h (5.24) 2 2 2 T 3t ax ay az

S = p8y(1 + a/p8) (5.25) s

S = p8ym(1 + a/p8) = Sm (5.26)

T = Km (5.27) where S is specific storage and is defined as the volume of water s the which a unit volume of the aquifer releases from storage because of expansion of water and compression of the aquifer under a unit decline in average head within the unit volume of the aquifer, S is the coefficient of storage, h is the piezometric head, t is the time of observation, p is 83 the porosity, m is the saturated thickness of the aquifer, is the reciprocal of the bulk modulus of elasticity of water, a is the reciprocal of the modulus of elasticity of the aquifer skeleton, and x,y,z are rectangular coordinates.

Equation 5.23 is the differential equation governing the unsteady-state flow of groundwater in a homogeneous and isotropic aquifer. Equation 5.24 is for the special case of an unsteady horizontal flow in a confined aquifer of uniform thickness. The aquifer constants, S and T, in Equation 5.24 are generally determined from pumping tests. In water

table aquifers, the coefficient of storage is equal to the specific yield

for all practical purposes (Walton, 1970).

The specific yield of a rock or soil is the ratio of the volume of water which the rock or soil, after being saturated, will yield by gravity to the volume of the rock or soil. It is generally observed as

the change that occurs in the amount of water in storage per unit area of unconfined aquifer as the result of a unit change in head. It is equal

to porosity minus specific retention, the volume of water a rock or soil,

after being saturated, will retain against the pull of gravity divided by the volume of the rock or soil.

5.5.2 Well Hydraulics Application of the groundwater flow to well hydraulics requires

analytical solution of the governing differential equations. Theis

(1935) presented a formula based on the heat flow analogy and applicable

to an ideal aquifer of infinite areal extent. The ideal aquifer is homogeneous, isotropic, non-leaky (i.e., the discharge is provided 84 entirely from release of stored water) and of the same thickness throughout. Additional assumptions of fully penetrating wells and constant discharge conditions were also required. The Theis solution, known as the non-equilibrium equation, is:

co -u Q r e , s= J - uu 4T u (5.28) u where s is the drawdown observed in a well r distance from the pumping well at a time t, Q is the constant discharge imposed upon the pumping 2 well, T is the transmissivity, and u = r 5/4Tt. The exponential integral in Equation 5.28 is expressed symbolically as W(u) and is called the well function, that is:

-u If(u) = I du u u

2 u u3 u4 = (- 0.5772 - ln u + u - 2.21 3.3! 4.4! ..) (5.29)

Values of W(u), in terms of the practical range of u, are given in standard books on hydrology and groundwater (Walton, 1970; Davis and

DeWiest, 1966; Linsley et al., 1975, 1958).

Equation 5.28 may be rewritten for the gallon-ft-day units as:

s . 114.6 Q w(u) (5.30) and

2 1.87 S r u - (5.31) T t where s is the drawdown in feet at a well r distance from the pumped well,

Q is the discharge in gpm, T is the transmissivity in gallons per day per 85

foot (gpd/ft), S is the coefficient of storage in decimal number, r is

the distance between pumped and observation well in feet, and t is the time after pumping started in days.

Equations 5.30 and 5.31 have been extensively used in analyzing

aquifer tests to determine the aquifer characteristics S and T. Two methods of solving the above equations for S and T are commonly used, namely: Theis' graphical method of superposition and Jacob's simplified

version. The latter, however, should be restricted to values of u less

than about 0.01 (Davis and DeWiest, 1966).

5.5.3 Groundwater Flow

Groundwater underflow, one of the unknown items in the hydrologic

budget equation, may be computed using Darcy's law, in the form of

Equation 5.22. Most water resource evaluation studies utilize this rela-

tionship to estimate the amount of groundwater flowing into or out of the

basin under consideration (Olmsted, Loeltz, and Irelan, 1973; Walton,

1970) with much success. When the hydraulic gradient and transmissivity

of the formation are very small, underflow is often neglected since it is

comparatively insignificant with respect to other budget components.

5.5.4 Estimation of Recharge

In the Central Luzon Basin, the rainy season starts in May and

the dry season begins in November, so that, in most years, the soil is at

field capacity on June 1 and on November 30. Therefore, it can be safely

assumed that, during the period between these dates, the actual evapo-

transpiration from the basin approaches the potential evapotranspiration. 86

Mather (1959, P. 38) said, "... evaporation from a moist soil, at first, goes on at nearly the potential rate from all soils, but by the time 1 in of water has been removed, the rates from different soils, holding different amounts of water at field capacity, begin to differ." Using derived formulas for potential evapotranspiration, one could estimate the basin evapotranspiration during this period.

Applying Equation 5.4 for the period between June 1 and November

30, therefore, one could solve for the dSg, the change in storage of the groundwater. Rainfall, P, is determined from the raingage network, runoff, RO, is taken from streamflow measurements, groundwater underflow,

GWO, is obtained using Darcy's law, and ET is estimated using

Thornthwaite's method and mean temperature measurements or some other evaporation formula. Thus:

dSg = P - RO - ET - GWO (5.32) but

dSg = Gy-dH (5.33) in which Gy is the average gravity yield of the basin and dH is the mean fluctuation in groundwater stage.

Rasmussen and Andreasen (1959) defined the the gravity yield of a rock or soil as the ratio of (1) the volume of water it will yield by gravity, after saturation or partial saturation, to (2) its own volume, during the period of groundwater recession. The term gravity yield differs from the specific yield in that the definition of the former, the length of drainage time, has been included. In effect, gravity yield is a function of time as well as of the characteristics of the rock or soil. 87

Specific yield, on the other hand, is the upper limit of the gravity yield which may be reached when complete drainage is attained. Because

of the length of time required for complete draining, specific yield is seldom attained under field conditions.

Substituting Equation 5.33 into Equation 5.32, an expression for gravity yield is obtained:

P - RO - ET - GWO Gy = dH (5.34)

This equation, when applied during the period between June 1 and November 30, yields the value of Gy.

Groundwater recharge, a hydrologic component of interest in any

water resource evaluation study, may be determined from water table level observations. Rasmussen and Andreasen (1959) described a method by

which recharge is related to mean groundwater stage of the basin.

The method requires construction of the hydrograph of the mean

groundwater stage. The mean groundwater stage rises sharply in response

to each significant rainfall. The amount of recharge is nearly equal to

the sum of individual rises multiplied by the gravity yield. This

amount, however, is less than the true recharge by the amount of ground-

water drainage occurring during the rise. To account for this portion of

recharge, the hydrograph prior to the rise is extended to the date on which the peak of the rise occurred. Such projection or antecedent hydro-

graph represents the groundwater recession had there been no recharge

that took place. The difference between the peak stage and recession

stage on the day of the peak of the rise, multiplied by the gravity yield,

is equal to the groundwater recharge. 88

5.5.5 Recharge Estimation by the Meyboom Method

The recession curve of a stream hydrograph represents withdrawal of water from storage within the basin. The recession curve may be described by a characteristic depletion equation (Barnes, 1939):

(5.35) Qt = Qokt where Q is the discharge t time units after Q , and k is the depletion t o factor.

If one plots the discharge on a logarithmic scale and the time on an arithmetic scale, a linear relationship between the two variables may be obtained. The best straight line is generally for the groundwater recession (Meyboom, 1961).

Meyboom used this relationship in solving the basic groundwater balance of the Calgary area, Alberta, Canada. By integrating the area under the recession curve, he estimated the amount of groundwater actually discharged from the basin and, relating this to the potential groundwater discharge, he obtained the amount of groundwater recharge.

The total potential groundwater discharge, as defined by Meyboom

(1961), is the total volume of base flow that would be discharged during the entire groundwater recession, if complete depletion were to take place without interruption. This volume represents the total available base flow at the beginning of a given base flow recession and is obtained by (4eyboom, 1961):

Q = k k /2.3 (5.36) tp 1 2 89 in which Q is tp the total potential groundwater discharge in cubic meters, 1( 1 is the groundwater discharge at the beginning of the base flow recession in cubic meters per day, and k 2 is the time increment corresponding to one log cycle change in Q in days.

The actual groundwater discharge refers to the actual volume of base flow that leaves the basin during the duration of the recession.

The groundwater recession ceases as soon as any base flow component becomes affected by recharge. The first component to be affected is the bank storage; thus, the first storm in May generally reflects the end of the recession.

The volume of groundwater actually discharged is determined from:

t 2/k 2 Qgw = k k /2.3 - (k k /2.3)/(10 ) 1 2 1 2 (5.37) where Q is the groundwater actually discharged in cubic meters, and t gw 2 is the number of days between the beginning and ending of a base flow recession.

When Q is subtracted from Q , the difference, is called gw tp Q,rp the "remaining potential groundwater discharge." This difference represents the amount of groundwater discharge left in aquifer storage after a given recession.

If several years of streamflow data are plotted semi- logarithmically, corresponding recession curves are identified. For each recession curve, and Q may be calculated. The values of Q,tp gwQ, rp total groundwater recharge during the period between the end of one base flow recession and the beginning of the next is determined as the 90 difference of Q of the next recession minus Q of the preceding tp rp recession, or:

R = (Qtp) n (5.38) (Qrp) n-1 where R is the recharge during the period between n and n-1. CHAPTER 6

DATA

6.1 Data Collection

This study utilized the data gathered and compiled by various government and private agencies. Hydrological, climatological, geological, and social data were obtained directly from agencies conducting the field surveys and data collection programs. This portion of the study was found to be most onerous, since most of the data needed had to be copied directly from the office files of these agencies. Duplicate copies of the data files are non-existent. This situation prolonged the period required for the compilation and preparation of the data for analysis.

As discussed in Chapter 2, aspects of water resources development in the Philippines are handled by several independent agencies, and it seems there was a gross lack of coordination among these agencies (United

Nations, 1968). The overlapping functions of the agencies led to dupli- cation of some activities. In the collection of hydrological data alone, at least three agencies maintained rainfall gaging stations at one point in time. This resulted in randomly established networks of gages in important river basins, leading to a very dense network in some areas, while in other areas there were very few rain gages or none at all

(Figure 6.1). Furthermore, data collected was generally discontinuous except for key stations established by the Weather Bureau (WB).

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”-4 93 6.1.1 Rainfall and Streamflow Data

Within the Central Luzon Basin there are 121 rain gages and 86 streamflow gaging stations. Tables 6.1 and 6.2 describe the stations.

The rainfall stations were established and maintained by the Weather

Bureau (KB), the National Power Corporation (NPC), and the Bureau of

Public Works (BPW). The streamflow gaging stations were solely established and maintained by the BPW.

Initially, 67 rainfall stations were selected to represent the basin precipitation. The selection was primarily based on the available years of record and the areal distribution of the stations. The selection was aimed at uniform areal distribution covering the whole basin.

Later, however, after compiling the available data for each selected station, only 56 stations were utilized to determine the mean annual basin rainfall. Some of the stations were deleted because their records could not be located or only a year or two were available.

Streamflow gaging stations were selected to provide a maximum number of years of records of representative river systems within the basin. These selected stations are described in Chapters 7 and 8.

6.1.2 Other Climatological Data

Evaporation data were obtained from 6 sites. One of these sites is in Laguna, outside of the basin being considered, but included only for comparison purposes. Temperature, relative humidity, solar radiation, and wind speed are available in 7, 6, 3, and 3 stations, respectively.

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Groundwater levels were observed from shallow observation wells established and maintained by the BPW. About 70 observation wells have been established since 1958. An inventory of the conditions of the wells as of December 1974 is presented in Appendix D. Water levels are observed once a month and data accumulated since 1958 are in the files of the Groundwater Section of the BPW. Wells with longer records (more than

2 years) were selected and available data were copied.

6.2 Data Preparation

The bulk of the available data consists of rainfall and streamflow measurements.

6.2.1 Rainfall Data

Monthly rainfall (in mm) for each station was coded and punched in computer cards. The data were arranged to conform to the April-March water year. The selection of the water year was intended to minimize carry-over characteristics of hydrologic phenomena, especially streamflow. April and March are the middle of the dry season. Single-station statistics, mean, standard deviation, skewness coefficient, and monthly percentage distribution were computed. The mean annual rainfall of each station was utilized to construct the mean annual isohyetal map and to determine the mean annual rainfall on the basin.

Since the water budget analysis will be pursued for three years with below, near, and above average rainfall, selection of these years was made using the arithmetic average of the stations within the basin. 106

The result obtained by arithmetic average, although it does not differ significantly from that of the isohyetal method, was not used in the water budget analysis, however. That is, for the three selected years, the basin mean rainfall considered in the water budget was determined by the isohyetal method. The comparison of the results obtained by the arithmetic mean and isohyetal method for these three years is given in Table 6.3.

Stations with one, two, or three month gaps were augmented by proportion, and those stations whose annual totals were missing because

of more than three months gap were augmented based on three surrounding

stations.

The augmentation by proportion was done by means of the

relationship: PP . si Al' . = 100 (6.1) S]. n E PD . j=1 53

where AP si. = annual precipitation for station s at year i,

PP . = rainfall total from months with record, si PD = monthly average percentage distribution of months with

record, and

n = number of months with record (9 < n < 12).

This method was not applied when the months with missing values occurred

in July or August, where the second method was considered to give a more

reasonable estimate. 107

Table 6.3 Rainfall Average Based on Two Methods. -- Rainfall in mm.

Correlation coefficient: r = 0.992

Regression Equation: Isohyetal = 0.9979 (arithmetic)

- 162.917

Water Year Period Isohyetal Arithmetic

1959-60 Annual 2049.0 2228.3

June-November 1527.4 1711.6

1960-61 Annual 2760.9 2887.4

June-November 2361.3 2391.7

1963-64 Annual 2445.5 2563.6

June-November 2069.5 2236.3

Using Annual Mean of all Stations 2381.8 2651.5

Average 2213.6 2381.5 108

When a particular station in a given water year had no record for

more than three months, the annual rainfall for that particular station

was reconstituted, based on three surrounding stations. The equation used is:

AP. AP,,, AP. AP s ( ai ci (6.2) si 3 5-Cb

where 5-C is the annual average from existing records; a, b, and c are

stations surrounding the station with missing data; and AP is the annual

precipitation. Monthly rainfall at station s is then obtained by

distributing the annual precipitation according to the average monthly

percentage of the corresponding station, i.e.:

X .. = AP .(PD .) (6.3) sii sl si

where X.. is the precipitation at station s in year i and month j.

6.2.2 Streamflow Data

The augmentation procedures for streamflow discussed in Chapter 4

require that the streamflow data represent the full natural flow or the

"virgin flow" of the river. Since most of the gaging stations within the

Central Luzon Basin are either affected by diversions and/or regulation

reservoirs, the gaged flow must be adjusted to account for these

activities. The objective of the adjustment is to determine as nearly as

possible the natural flow for conditions existing during the period

selected. Corrections were made for all diversions above a gaging

station for irrigation, water supply, and change in reservoir storage. 109

In mathematical form, the adjustment is presented as (refer to Figure 6.2):

FNF = Qg + Qd - gr + dSR + E R - Gr (6.4) in which FNF is the full natural flow, Q is the observed or gaged flow,

Q is the diverted flow, Q is the return flow from is d irrigation, dSR the change in reservoir storage, E R is the evaporation from the reservoir, and G is the return flow from groundwater pumpings.

Of the adjustment items in Equation 6.4, Qd and dSR are, perhaps, the most important in order of magnitude. Existing national irrigation systems alone within Central Luzon have an aggregate potential irrigable area of 179,720 ha. Applying the design rate used by the NIA of 1.5

1/sec/ha, a total diversion requirement of 270 cms is needed to irrigate an area this size. Unfortunately, the river flows are not sufficient to satisfy irrigation requirements during the dry season; and because the irrigation systems are run-of-the-river type, much of the potential area is not irrigated in the dry season. It is then apparent that the amount of diversions of each irrigation system within the basin could be estimated based on records of irrigated area whenever diversion amounts are not available. Records of individual systems, however, were found to be fragmentary for both water diverted and irrigated areas. Because of this situation, a method was devised to extend the fragmentary diversion data.

It was observed that most irrigation systems are equipped with a streamflow gaging station upstream of the diversion point. Records of these upstream gaging stations were found to have more or less the same 110

Reservoir dSR

Diversion Dam

II ms SSSSSS linsIgna MUM MUM. HIM" Gr UMW UM" Irrigated mum=MUM' ps• .1111 Areas

Gaging Station

/Qg

Figure 6.2 A Typical Gaging Station Affected by Reservoir Regulation and Irrigation Diversion. 111 length as the gaging station of interest downstream, for which adjust- ments had to be made. If it were possible to establish a relationship between the flow at the upstream gaging station and the diversion, the diversion record could be extended. Physically, the amount of the diversion is dependent on the amount available (upstream gage) although the dependency may not be absolute because of other factors, such as crop growth stage, farm activities, rainfall, etc., which play an equally important role in the determination of diversion requirements.

A graphical correlation technique was applied to establish a relationship between the upstream gaging station and diverted flow. Simultaneous monthly records of diversion and gaged streamflow upstream of diversion dam were plotted on log-log paper. A straight line or curve was fitted on the scatter diagram by eyeballing. Using the curve as a rating curve, and the available monthly streamflow record at the upstream gaging station, the monthly record of water diversions was extended Where the graphical correlation method could not be applied because of the absence of an appropriate gaging station, the monthly average of diversions was used. In both cases, several values of monthly diversions were estimated from the reported irrigated area of the system for the corresponding month. The irrigated area was transformed to diverted flow by applying the design discharge of 1.5 1/sec/ha used by the NIA.

two reservoirs affecting the Along the Ag-no River, there are gaging stations. The Ambuklao and Binga dams constructed mainly for hydroelectric power generation are located at the upper reaches of the 112

Agno River. Fortunately, complete records of the operation of these

reservoirs were available from the National Power Corporation (NPC). In

the application of the change in reservoir storage to Equation 6.4, it

was assumed that all gaging stations downstream of the reservoirs fully felt the effects of the regulations.

Return flow from irrigation systems generally may be comparatively

small for systems with upland crops. For rice irrigation, however, return flow may be significant.

Kampen (1970) reported that as much as 70% of the water diverted

to rice fields is wasted. He estimated that the overall efficiency of Philippine irrigation systems is about 30-45%. He also pointed out that

of the design discharge of 1.5 1/sec/ha (13 mm/day) only about 4 to 6

mm/day is consumed by plants and the rest, 7 to 9 mm/day, is lost to conveyance, leakage, seepage, percolation, and direct drainage.

From this point of view, the total available return flow would

range from 55 to 70 percent of the diverted amount. Not all of the

potential amount finds its way back to the streams, however, because part

of it is lost to direct evaporation and deep percolation. In effect, return flows will be primarily composed of farm losses due to leakage,

lateral seepage, and direct drainage minus water utilized by farms with

lower elevation. Cerdan (1973) reported that about 68% of the total water supply

was directly drained by farmers during the rainy season of 1972. Forty-

six percent of this amount, however, was utilized by the adjacent lower 113 farms. These findings were based on a water balance study in a 30-ha, well-bounded area in one pilot area of the Upper Pampanga River Project (UPRP).

A subsequent study in the same area reported that drained water was 50.5% of the total supply and 74% of this was re-used.

Lucero (1976) reported that about 53% of irrigation demand is the

total available return flow in the UPRP service area for the dry season operation of the project in 1975. Only 16% of this, however, was considered re-used. He reported further that the project-wide efficiency was 46.5%, a value somewhat higher than that estimated by Kampen (1970).

The systems studied by Kampen were unrehabilitated. UPRP, on the other hand, was rehabilitated and employs improved water management on the

farm level as pointed out by Cerdan (1973).

Summarizing the results of water balance studies in the UPRP area

(UPRP, 1974), mean farm level efficiencies i of 34.1 and 80.6 percent for

the wet and dry seasons, respectively, were obtained. Assuming a conveyance efficiency of 70 percent (Kampen, 1970) and water re-use of 40 percent, the return flow as percentage of total diversion could be computed for both the wet and dry seasons. The result of the computation is as follows:

1. For the wet season (May to October):

Irrigation efficiency = 70 (0.341)

= 24

1. Farm level efficiency was defined in these studies as the amount of water beneficially used (evapotranspiration by the crop) divided by the total supply of water. 114

or available return flow is (100 - 24) = 76.0 percent of total

diversion. But water re-use is 40 percent, therefore, the amount

of return flow that may be expected to reach the stream is on the

average 76 (0.60) = 45.6 percent.

2. For the dry season (November to April):

Irrigation efficiency = 70 (0.806)

= 56.0

or available return flow is (100 - 56.0) = 44.0 percent of total

diversion and the expected return flow, on the average, is

44 (0.60) = 26.4 percent.

Evaporation from a reservoir may be estimated from evaporation observations using lake evaporimeters; from an energy budget analysis; from a water budget analysis; from empirical formulas; or from correlation with observation from class A pan evaporation. Most of these methods were discussed in Chapter 5.

Fortunately, the reservoir operation records took into account the lake evaporation in the computation of the reservoir changes in storage. The monthly storage changes were actually obtained from initial and final reservoir volumes; thus, evaporation losses were already included in the difference. Return flows from groundwater pumping were found to be generally negligible. Groundwater withdrawals from the basin aquifers are mostly confined to domestic water supply and a few small irrigation systems.

The shallow aquifer is the main source of domestic water supply with- drawn by hand-driven lift pumps. It was observed that the majority of 115 the communities within the basin do not have a common source of water supply. Instead, each family unit puts up its own shallow well pump.

Such installations result in the withdrawal of just enough water for household requirements, leading to a very small amount of waste water. Of the few pump irrigation systems using groundwater, high water use efficiency (80-90%) had been observed (Kampen, 1970). Kampen also noted that there was no direct drainage from farms utilizing pumped groundwater. Even in pump irrigation systems, therefore, return flow may be insignificant and sufficiently small to be neglected.

From the above considerations, the final equations used for streamflow adjustment were:

FNFD = Qg + Qd - 0.264 Qd + dSR

= Qg + 0.736 Qd + dSR (6.5) for the dry season (November to April); and

FNFw = Qg + Qd - 0.456 Qd + dS R

= Qg + 0.544 Qd + dSR (6.6) for the wet season (May to October). However, the full effect of the the diversion is for water diverted amount, Qd' is applied whenever supply or when the gaging station is not downstream of the irrigated area. Sample computation of FNF is given in Appendix E. CHAPTER 7

COMPARISON OF STREAMFLOW AUGMENTATION METHODS

7.1 Description of Gaging Stations

A total of seven gaging stations were selected, five in the

Pampanga River and two in the Agno River. The selection was based on length of record and similarity of the stations' physical, as well as hydrological, properties.

Length of record was the more important criterion. The comparison methodology dictates that a long record be used, since only one-half of the historical data would be utilized for the augmentation procedure.

The early-half series of the record were removed and the later-half series were assumed to be the only record available. Based on this truncated series, new sets of data were established for each station using all four methods: percent deviation, YOR (Young, Orlob, and

Roesner), Matalas-Jacobs, and HEC-4. The augmented flows were then compared with the historical data.

At the end of the augmentation, there were five sets of data for each station. These were the historical set and the sets produced by each of the fous methods. Separating the data of the period corresponding to the period of the early-half series of the historical record, the results of the four methods for each station were compared with the observed data set. 116 117

Table 7.1 gives the description of the stations used and Table

7.2 presents the augmentation strategy for each station. In Table 7.2, it is shown that the base station and the dependent station have poor to good correlation coefficients. This situation was actually preferable in testing the adaptability of the methods to real conditions in the

Philippines. It was noted that streamflow records at one station seldom correlate strongly with another station, even for the same river basin.

This could be attributed to factors like elevation of the watersheds, size of drainage areas, rainfall variation within the basin, and climatic factors affecting the stations. In the cross-correlation study that was made to identify pairs of stations suitable for augmentation by regression, a number of very low correlation coefficients were obtained.

Appendix F gives correlation coefficients for selected pairs of gaging stations within the Central Luzon Basin.

Fiering (1962), and Matalas and Jacobs (1964) emphasized that correlation techniques in augmenting hydrologic data should be used with caution. Indiscriminate application of the technique may lead to more unreliable estimates of population parameters of the variates based on lengthened record than that of the short series. Evaluation prior to use must be employed to make sure that augmenting the record by correlation techniques will increase the reliability of the parameter estimates.

Investigation of the strength of relationship between the stations provides the information.

118

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Table 7.3 gives critical minimum values of correlation coefficients for corresponding numbers of simultaneous record of the two stations. The elements in the table were computed from:

r = 1/in 1 - 2 (7.1) where r is the minimum correlation coefficient and n number of 1 is the simultaneous records. When the tabulated r is exceeded by the correlation coefficient of the stations used, the mean of the lengthened record is a better estimate of the population mean than the mean of the short unaugmented record.

Table 7.3 Critical Minimum Values of Correlation Coefficient for the Mean. n1 5 6 7 8 9 10 15 20 25 30 r 0.58 0.50 0.45 0.41 0.38 0.35 0.28 0.24 0.21 0.19

Values of the correlation coefficients between the dependent station and base station, when compared to Table 7.3, were found to be slightly higher, except in the case of Q22 which was much below the critical minimum value. Nevertheless, Station Q22 was still included to find out the behaviour of the methods in extreme conditions, i.e., very short available record and poor correlation between the dependent and the base stations.

The poor correlation between the dependent station, Q22, and the base station, Q16, could be attributed to different climatic regimes 122 affecting the two stations. Station Q22, which is located near the northern boundary of the basin, is strongly affected by the northeast monsoon from October to January. Station Q16, on the other hand, which is located near the eastern boundary of the basin, is affected by the east trade winds from February to April. This situation probably made the streamflow characteristics of the two stations nearly independent.

7.2 Comparison Criteria

7.2.1 Annual Flows

Four comparison items were included in the test for annual flows: the double mass curve of annual flows, the annual mean, the summation of deviations from historical annual flow, and the standard deviations of annual flows.

The double mass curve is a test of consistency with the historical record. For each station, cumulative annual flows of the historical data were plotted against cumulative annual flows of the augmented data.

The methods were judged according to their consistency (defining the best straight line) and their nearness to the 45-degree line. The method generating a double mass curve nearest the 45-degree line yields annual values that are nearly equal to the annual values of the historical record. A double mass curve that is almost a 45-degree straight line indicates that the generated annual flow nearly coincides with the historical record.

The expected annual flow of a given stream is its annual mean flow when this is determined over a long period of record. In water 123 resources evaluation, it is referred to as the available annual runoff over a given watershed or drainage area subject to variations caused by variations of precipitation, soil moisture content, and fluctuations in groundwater storage. Furthermore, it is one of the most important components in the annual hydrologic budget. The best augmentation method, therefore, is one that will yield an annual mean as close as possible to the observed or recorded annual mean should there have been measurements.

Deviations from the historical annual flows should be compen- sating; hence, the summation of the deviations must be nearly equal to zero. The summation of annual deviations were plotted against year for each method in each station. The methods were graded according to their position with respect to the zero line.

The standard deviation of a number of sampled variates defines the average dispersion of the variates about the mean. It gives a measure of the variability of the variates. In streamflow, it refers to the dependability of average supply from the river flow. A stream whose data are defined by a small standard deviation is more dependable than one with a large standard deviation. Thus, standard deviation of streamflow data is an important consideration in water resources planning since it provides a measure of the dependability of the expected flow of a given stream. The best augmentation method, therefore, is one that will retain the standard deviation of the observed or recorded flow should there have been measurements. 124 7.2.2 Monthly Flows

Annual hydrographs for each station were constructed using the mean monthly flow derived from each method, together with the mean monthly historical flows. Similarity of the hydrographs of augmented flows to the historical hydrographs were studied, and minimum and peak flows were compared. Foremost to the comparison of minimum and peak flows is the time of their occurrence. The magnitude of flow is used in the comparison when the time of occurrence failed to distinguish the ranks of the methods; that is, when the time of occurrence of the augmented and historical flows coincide, or when the augmented flows have the same time of occurrence.

Monthly means obtained by each method were compared with the corresponding monthly mean of the historical data for each station. The methods were ranked according to the magnitude of their difference from the corresponding monthly historical flow.

Standard deviations of monthly flows derived by each method were compared with the corresponding standard deviation of the historical data of each station. The methods were ranked according to the magnitude of their difference from the corresponding standard deviation of the monthly historical flows.

In summary, the augmented flows of each method were compared to the corresponding historical flow following the eight items of comparison: a) double mass analysis of annual flows, b) mean annual flows, c) cumulative deviation from historical annual flows, d) standard deviation of annual flows, e) peak flow of annual hydrographs, 125

f) minimum flow of annual hydrograph, g) mean monthly flow, and

h) standard deviations of monthly flows. The average score of each

method from each station for each item of comparison was then computed

and, in turn, the overall average score of each method for all items of

comparison was determined. The method whose overall average score

exceeds that of the other methods and excelled in the majority of the comparison items was judged as the best.

7.3 Scoring System

To assess the performance of each method in each category of

comparison against the historical flow, two scoring schemes were devised.

These are the point and percentage scoring schemes.

In the point scoring scheme, scores of 4, 3, 2, and 1 were

assigned to the best, second best, third, and fourth method, respectively,

in each comparison item for each station. Average scores obtained by

each method in all items of comparison were given equal weights. Varying

weights may have been desirable, but the difficulty of deciding which of

the items are more important, not to mention the uncertainty of deter-

mining the corresponding weights without introducing bias in the compari-

son, are enough reasons to drop the idea. Although this scoring system

does not provide a scale of closeness of the estimates to the historical

data, it distinguishes among the methods relative to each other. The method which obtained the highest average score was judged as the best.

In the percentage scoring scheme, the absolute difference between the augmented and historical data was expressed as the percentage of the historical. In this manner, the augmentation methods were judged not 126 only relative to each other but also in terms of the closeness of their estimates to the historical record. The greater percentage a method obtains, the farther away are its estimates from the historical data; thus, the method which obtained the lowest average percentage was judged as the best.

It may be pointed out that the two scoring schemes may lead to two different conclusions and that the choice of scoring system is somewhat arbitrary. Furthermore, the selection of comparison items is sub- jective and depends largely on the intended application of augmented data.

7.4 Results and Discussion

7.4.1 Annual Flows

Tables 7.4. through 7.8 show the annual flows in each station.

Historical as well as augmentation estimates are given. It can be noted that values obtained by the YOR (Young, Orlob, and Roesner) and percent deviation methods consistently exhibited the highest and lowest variability, respectively. The low variability of values obtained from the percent deviation method could be attributed to the omission of the random component in the prediction equation of the method; this property, however, differentiated the percent deviation method from the Matalas-

Jacobs scheme.

7.4.2 Double Mass Analysis

Figures 7.1 through 7.5 present the double mass analysis for each station. The scores obtained by each method in all the stations 127

Table 7.4 Historical and Augmented Annual Flow of Station Q03, Pampanga River. -- Flow in million cubic meters.

Historical Water (Early-half Percent Matalas- Year series) Deviation HEC-4 YOR Jacobs

1959-60 1097.48 1050.48 1156.12 874.99 953.71

1960-61 1928.55 1544.10 1494.70 1835.58 1398.07

1961-62 1294.56 1584.86 1912.46 3030.97 1458.89

1962-63 1319.17 1045.34 1236.04 1987.01 965.35

1963-64 1378.63 1298.67 735.33 1300.51 1387.35

1964-65 1359.62 1183.36 1226.45 970.89 1343.55

Mean 1396.34 1284.47 1293.52 1666.66 1251.15

Standard deviation 279.48 236.64 390.34 804.66 228.90

Coefficient of variation 0.200 0.184 0.302 0.483 0.183

Skewness coefficient 1.683 0.387 0.344 0.976 -0.856 128

Table 7.5 Historical and Augmented Annual Flow of Station Q07, Coronel River. -- Flow in million cubic meters.

Water Percent Matalas- Year Historical Deviation HEC-4 YOR Jacobs

1960-61 1823.13 1748.78 1735.71 1770.23 1946.26

1961-62 1640.88 1785.76 2183.73 3140.10 2325.06

1962-63 1340.69 1296.26 1632.31 1049.92 1539.13

1963-64 1144.55 1526.11 976.83 1587.04 1673.51

1964-65 1914.84 1421.49 1511.25 1583.31 1734.67

Mean 1572.82 1555.68 1607.97 1826.12 1843.73

Standard deviation 324.64 210.02 782.35 434.77 306.53

Coefficient of variation 0.206 0.135 0.270 0.428 0.166

Skewness coefficient -0.427 -0.023 -0.301 1.543 2.713 129

Table 7.6 Historical and Augmented Annual Flow of Station Q22, Talavera River. -- Flow in million cubic meters.

Water Percent Matalas- Year Historical Deviation HEC-4 YOR Jacobs

1957-58 399.86 485.15 255.72 524.87 401.47

1958-59 297.15 489.42 561.94 377.88 365.63

1959-60 210.34 492.94 395.87 892.21 370.81

1960-61 497.45 502.25 564.49 549.86 510.88

1961-62 279.53 503.02 604.78 419.12 542.45

1962-63 365.53 492.84 518.62 453.56 382.31

1963-64 490.10 497.62 389.15 538.97 470.01

Mean 362.82 494.75 470.08 536.64 434.79

Standard deviation 108.17 6.57 126.40 169.64 72.30

Coefficient of variation 0.298 0.013 0.269 0.316 0.166

Skewness coefficient 0.626 -0.037 -0.764 1.852 0.591 130

Table 7.7 Historical and Augmented Annual Flow of Station QSL, Angat River. -- Flow in million cubic meters.

Water Percent Matalas- Year Historical Deviation HEC-4 YOR Jacobs

1946-47 1728.52 1929.39 1488.75 1062.44 1021.63

1947-48 2721.05 2449.38 2514.18 1650.70 1455.38

1948-49 2543.75 2627.72 1749.32 2591.42 2072.42

1949-50 1807.69 2228.91 2442.90 1894.05 1385.60

1950-51 2464.55 2135.25 2029.94 2954.02 2074.45

1951-52 2750.95 2069.20 2197.89 2494.21 2963.53

1952-53 2312.56 2001.29 1648.50 1740.70 1719.96

1953-54 2636.08 2089.57 2844.38 1676.00 2408.29

1954-55 1708.09 1898.71 2002.24 910.28 1776.68

1955-56 1652.39 1970.56 1442.52 1693.58 2330.15

1956-57 2682.91 2319.79 1785.75 2132.52 1392.57

1957-58 3873.67 1674.23 1577.05 1471.22 1274.33

1958-59 1950.66 1873.56 2112.17 2122.19 1283.58

1959-60 1800.63 2037.96 2236.03 2720.07 2243.75

Mean 2330.96 2093.25 2005.12 1936.67 1814.45

Standard deviation 613.94 247.99 417.39 604.12 551.97

Coefficient of variation 0.263 0.118 0.208 0.312 0.304

Skewness coefficient 1.074 0.666 0.445 0.036 0.518 131

Table 7.8 Historical and Augmented Annual Flow of Station Q78, Tarlac River. -- Flow in million cubic meters.

Water Percent Matalas- Year Historical Deviation HEC-4 YOR Jacobs

1946-47 1879.17 1726.61 2111.04 765.22 952.61

1947-48 1716.66 2003.46 947.56 1449.47 1148.38

1948-49 1659.07 1979.80 1031.07 1278.54 1523.70

1949-50 836.57 1565.06 1070.57 1882.18 946.40

1950-51 1812.15 1855.19 1130.26 5234.43 1895.24

1951-52 1623.42 1692.14 1981.54 888.65 2652.11

1952-53 1040.38 1531.83 1273.87 1283.53 1369.55

1953-54 1438.21 1928.26 1864.08 890.07 2394.15

1954-55 939.91 1559.16 1180.07 1094.51 1582.43

Mean 1438.39 1760.17 1398.89 1640.73 1607.17

Standard deviation 397.41 187.44 453.53 1390.25 605.58

Coefficient of variation 0.276 0.106 0.324 0.847 0.377

Skewness coefficient -0.585 0.068 0.776 2.670 0.704 132

0 Percent Deviation

A HEC -4

Y YOR

+ Matalas-Jacobs

1 1 1 1 f °moo 2.000 4.000 6.000 6.000 10.000 12.000 CUMULATIVE AUGMENTED ANNUAL FLOW, MCM(X10 3 I

Figure 7.1 Double Mass Curve of Annual Flows for Station Q03, Pampanga River. 133

0 Percent Deviation

2S HEC-4

Y YOR

+ Matalas-Jacobs

ao 1 60.000 2.000 4.000 6.000 8.000 10.000 12.000 CUMULATIVE AUGMENTED ANNUAL FLOW, MCM(X10 3 3

Figure 7.2 Double Mass Curve of Annual Flows for Station Q07, Coranel River. 134

O Percent Deviation

REC-4

Y YOR

▪ Matalas-Jacobs

=Moo 7.000 14.000 21.000 28.000 35.000 42.000 CUMULRTIVE RUGMENTED RNNURL FLOW, MCM(X10 2 1

Figure 7.3 Double Mass Curve of Annual Flows for Station Q22, Talavera River. 135

Q Q Q °MOO 6.000 12.000 18.000 24.000 30.000 36.000 CUMULATIVE AUGMENTED ANNUAL FLOW, MCN(X10 3 1

Figure 7.4 Double Mass Curve of Annual Flows for Station QSL, Angat River. 136

45° Line

0 Percent Deviation LN HEC-4

Y YOR

+ Matalas-Jacobs

o o o 1=0.000 3.000 6.000 9.000 12.000 15.000 18.000 CUNULRTIVE RUGNENTEO RNNURL FLOW, MCN(X10 3 3

Figure 7.5 Double Mass Curve of Annual Flows for Station Q78, Tarlac River. 137

considered are given in Table 7.9 for the point system and in Table 7.10 for the percentage scoring system.

In the point system, the highest average score of 3.4 was obtained

by the percent deviation method. Following in the order of decreasing

scores was HEC-4 with 3.2, Matalas-Jacobs with 2.2, and YOR with 1.2. In

the percentage scoring system, the lowest average percentage of 7.64 was

obtained by the percent deviation method. This was followed by HEC-4

with 10.22 percent, Matalas-Jacobs with 20.06 percent, and YOR with 23.61

percent. The ranking of the methods was identical for both scoring

systems.

Highly consistent values obtained from the percent deviation

method resulted in almost straight line, double mass curves, as seen in

Figures 7.1 through 7.5. High variability of values given by the YOR

method, on the other hand, plotted as a very inconsistent double mass

curve. HEC-4 offered a high competition to the percent deviation method

for the highest average score. The consistency and nearness of HEC-4

values to the 45-degree line made the selection of the best method for

each station very difficult.

7.4.3 Mean Annual Flows

Table 7.11 presents the mean annual flows of all stations, both

historical and augmented. The comparison of the methods for all stations

is given in Tables 7.12 and 7.13 and graphically presented in Figure 7.6.

rn the point system, HEC-4 obtained the highest average score of 3.4,

followed by the percent deviation with 2.8, Matalas-Jacobs with 2.2, and

YOR with 1.6. Under the percentage scoring system, the methods were 138

Table 7.9 Scores Obtained by Various Methods in the Point System, Double Mass Curve Comparison.

Percent Matalas- Station Deviation HEC-4 YOR Jacobs

Q03 3.0 4.0 1.0 2.0

Q07 4.0 3.0 1.0 2.0

Q22 2.0 3.0 1.0 4.0

QSL 4.0 3.0 2.0 1.0

Q78 4.0 3.0 1.0 2.0

Total 17.0 16.0 6.0 11.0

Average 3.4 3.2 1.2 2.2 139

Table 7.10 Percentages Obtained by Various Methods in the Percentage Scoring System, Double Mass Curve Comparison.

Percent Matalas- Station Deviation HEC-4 YOR Jacobs

Q03 7.37 6.74 24.77 14.18

Q07 2.92 9.09 22.33 18.80

Q22 41.58 32.33 56.37 18.92

QSL 6.11 12.18 19.90 26.23

Q78 14.14 12.86 27.42 21.01

Total 72.12 73.20 150.79 99.14

Average 14.42 14.64 30.16 19.83 140

Table 7.11 Historical and Augmented Annual Mean Flows of All Stations. -- Flows in million cubic meters.

Percent Matalas- Station Historical Deviation HEC-4 YOR Jacobs

Q03 1396.34 1284.47 1293.52 1666.66 1251.15

Q07 1572.84 1555.75 1607.97 1826.12 1843.73

Q22 362.85 494.74 470.08 536.64 434.79

QSL 2330.96 2093.12 2005.12 2936.67 2824.45

Q78 1438.39 2760.17 1398.89 1640.73 1607.17 141

Table 7.12 Scores Obtained by Various Methods in the Point System, Comparison of Mean Annual Flow.

Percent Matalas- Station Deviation HEC-4 YOR Jacobs

Q03 3.0 4.0 1.0 2.0

Q07 4.0 3.0 2.0 1.0

Q22 2.0 3.0 1.0 4.0

QSL 4.0 3.0 2.0 1.0

Q78 1.0 4.0 2.0 3.0

Total 14.0 17.0 8.0 11.0

Average 2.8 3.4 1.6 2.2

Table 7.13 Percentages Obtained by Various Methods in the Percentage Scoring System, Comparison of Mean Annual Flow.

Percent Matalas- Station Deviation HEC-4 YOR Jacobs

Q03 8.01 7.36 19.36 10.40

Q07 1.09 2.23 16.10 17.22

Q22 36.35 29.55 47.90 19.83

QSL 10.20 13.98 16.92 22.16

Q78 22.37 2.75 14.07 11.73

Total 78.02 55.87 114.35 81.34

Average 15.60 11.17 22.87 16.27 142

1 Q03 +4 /

/A5/:3 Line

Percent Deviation HEC-4 Y YOR

Q22 + Matalas-Jacobs /*Y

C a C r -I r I 1 cb .000 4.000 8.000 12.000 16.000 20.000 24.00C PLIGNENTED RNNURL. MERN , KM( X10 2 3

Figure 7.6 Annual Means of All Stations for Various Augmentation Methods. 143 ranked in the same order as in the point system. HEC-4 registered the lowest percentage of 11.17%, percent deviation method was second with

15.60%, Matalas-Jacobs was third with 16.28%, and YOR was fourth with

22.87%.

The Matalas-Jacobs method seems to work well with stations having very weak correlation coefficients. Station Q22, which has very low correlation coefficient with the base station, seems to have been consistently tracked down by the Matalas-Jacobs scheme. The method obtained the highest score for this station in both the double mass curve and the annual mean comparison. The YOR method, on the other hand, consistently yielded the lowest score for Station Q22 for both categories.

7.4.4 Annual Deviations

The annual deviations of the augmented flows from the historical flows were taken as the difference of the annual historical flow minus the annual augmented flow. Cumulative annual deviations were plotted against the water year. Figures 7.7 through 7.11 illustrate graphically the cumulative annual deviations of the different methods in each station. Considerable deviations of the YOR method were noticeable in all stations.- Close competition between HEC-4 and percent deviation was again noticeable, although the Matalas-Jacobs scheme was a close competi- tor as well.

In the point scoring system, the methods were given scores based on Figures 7.7 through 7.11. By visual comparison, the methods were graded according to their departure from the zero axis. Table 7.14 presents the scores obtained by the methods in each station. In the 144

,1n11,

.;.> Percent Deviation

REC-4

Y. YOR

+ Matalas-Jacoba

19.590 s 19.600 19.610 19.620 19.630 19.640

WRTER YERR STRRTING WITH RPRIL(X102 )

Figure 7.7 Cumulative Annual Deviation of Augmentation Estimates from Historical Values, Station Q03. 145

Ow,

CD Ce L.L. _

F•••4 CC I••n1 LL1 cp R co — 0 percent -Deviation CC HEC-4 Z CC Y YOR

+ Matalaa- Jacobs CC co

19 690 19.600 19.610 19.620 19.630 19.640 19.660 WATER YERR STARTING WITH RPRIL(X10 2

Figure 7.8 Cumulative Annual Deviation of Augmentation Estimates from Historical Values, Station Q07. 146

j11.1.11,

<> Percent Deviation

A HEC-4

Y YOH

+ Matalas-Jacoba

19.570 19.585 19.600 19.615 19.630 19.645 WATER YERR STRRTING WITH RPRIL(X10 2

Figure 7.9 Cumulative Annual Deviation of Augmentation Estimates from Historical Values, Station Q22. 147

c 9 CD cr) r- >< 2: <> Percent Deviation C.) 2: o REC-4 . 0 _J u; Y YOR CC + Matalas-Jacobs 0 CD CID

19.470 19.500 19.530 19.560 19.590 19.620 WATER YERR STRRTING WITH RPRIL(X10 2

Figure 7.10 Cumulative Annual Deviation of Augmentation Estimates from Historical Values, Station QSL. 148

<> Percent Deviation

A HEC-4

Y YOR

+ Matalas-Jacobs

19. 1 65 19.100 19.495 19.510 • 19.525 • 19.540

WRIER. YERR STRRTING WITH RPRILJX10 2 1

Figure 7.11 Cumulative Annual Deviation of Augmentation Estimates from Historical Values, Station Q78. 149

Table 7.14 Scores Obtained by Various Methods in the Point System, Comparison of Cumulative Deviations from Historical Data.

Percent Matalas- Station Deviation HEC-4 YOR Jacobs

Q03 3.0 4.0 1.0 2.0

Q07 4.0 2.0 1.0 3.0

Q22 2.0 3.0 1.0 4.0

QSL 4.0 3.0 2.0 1.0

Q78 2.0 4.0 1.0 3.0

Total 15.0 16.0 6.0 13.0

Average 3.0 3.2 1.2 2.6 150 percentage scoring system, the absolute difference between the historical and augmented annual flow was expressed as percentage of the corresponding annual flow in the historical record. The average percentages obtained by the methods in each station are given in Table 7.15.

In this comparison category, the two scoring systems gave different results. HEC-4 obtained the highest average score of 3.2, followed by the percent deviation method with 3.0, Matalas-Jacobs with 2.6, and

YOR with 1.2 in the point system. Under the percentage scoring system, however, the best method was the percent deviation with 25.27%, followed closely by the Matalas-Jacobs with 26.59%. HEC-4 was third with 30.34% and the YOR was fourth with 45.77%.

In both scoring systems, the Matalas-Jacobs scheme excelled in

Station 22 as in the previous two comparison items.

7.4.5 Standard Deviations

Table 7.16 gives the standard deviations of historical and augmented annual flows of all stations. The comparison of the methods in both scoring systems is presented in Tables 7.17 and 7.18. The ranking of the methods was again slightly different in the two scoring systems, although the HEC-4 method was the best in both systems for Station Q22.

In the point system, the highest average score of 3.2 was obtained decreasing order of scores by the Ma -talas -Jacobs method. Following in a were HEC-4 with 2.6, YOR with 2.2, and percent deviation with 2.0. The

Matalas-Jacobs and HEC-4 methods retained their ranks in the percentage scoring system but the percent deviation method was better than the YOR in this scoring system. 151

Table 7.15 Average Percentages Obtained by Various Methods in the Percentage Scoring System, Comparison of Deviation from Historical Annual Flow.

Percent Matalas- Station Deviation HEC-4 YOR Jacobs

Q03 14.36 23.07 40.69 13.67

Q07 15.06 19.07 34.39 23.78

Q22 48.24 57.96 68.16 29.31

QSL 17.03 22.66 26.17 28.63

Q78 31.67 28.92 59.42 37.57

Total 126.36 151.68 228.83 132.96

Average 25.27 30.34 45.77 26.59

Table 7.16 Standard Deviation of Historical and Augmented Annual Flows of All Stations. -- Flows in million cubic meters.

Percent Matalas- Station Historical Deviation HEC-4 YOR Jacobs

228.90 Q03 279.48 236.64 390.34 804.66 306.53 Q07 324.64 210.02 782.35 434.77 72.30 Q22 108.17 6.57 126.40 169.64 551.97 QSL 613.94 247.99 417.39 604.12 605.58 Q78 397.41 187.44 453.53 1390.25 152

Table 7.17 Scores Obtained by Various Methods in the Point System, Comparison of Standard Deviation of Annual Flows.

Percent Matalas- Station Deviation HEC-4 YOR Jacobs

Q03 4.0 2.0 1.0 3.0

Q07 2.0 1.0 3.0 4.0

Q22 1.0 4.0 2.0 3.0

QSL 1.0 2.0 4.0 3.0

Q78 2.0 4.0 1.0 3.0

Total 10.0 13.0 11.0 16.0

Average 2.0 2.6 2.2 3.2

Table 7.18 Percentages Obtained by Various Methods in the Percentage Scoring System, Comparison of Standard Deviations of Annual Flows.

Percent Matalas- Station Deviation HEC-4 YOR Jacobs

Q03 15.33 39.67 187.91 18.10

Q07 35.31 140.99 33.92 5.58

Q22 93.93 16.85 56.83 33.16

QSL 59.61 32.01 1.60 10.09

Q78 52.83 14.12 249.83 52.38

Total 257.01 243.64 530.09 119.31

Average 51.40 48.73 106.02 23.86 153 7.4.6 Monthly Flows

Comparison among methods of augmentation for the monthly flows was based on the monthly means and standard deviations of the historical record and augmented flows. Annual hydrographs and mean monthly deviations from the historical records were utilized.

The same two scoring systems were used as for the annual flows.

However, only three of the methods were available for comparison, since the Matalas-Jacobs scheme was limited to the generation of annual flow series, as explained in Chapter 4. The point scoring system, therefore, consisted of 4, 3, and 2 for the best, second, and third ranking, respectively. In the percentage scoring system, the same ranking holds, i.e., the method with the lowest average percentage was considered the best.

7.4.7 Annual Hydrographs

Annual hydrographs based on mean monthly flows of both historical and augmented data were constructed for each station. The comparison was based on the similarity of the hydrograph of the augmented flows to that of the historical. Two separate comparison items were applied: 1) peak flow, its time of occurrence and magnitude; and 2) minimum flow, its time of occurrence and magnitude. Figures 7.12 through 7.16 illustrate the hydrographs for Stations Q03, Q07, Q22, QSL, and Q78, respectively.

Tables 7.19 and 7.20 summarize the peak and minimum flows, respectively.

Considerable deviations of the hydrographs obtained from the YOR method in terms of the peak flows were prominent, both in time of occurrence and magnitude. In Stations Q03, Q07, Q22, and Q78, the 154

<5 Percent Deviation

HEC-4

Y YOR

Matalas-Jacoba

2.000 4.300 6.000 8.000 10.000 12.000 MONTH

Figure 7.12 Annual Hydrograph for Station Q03. 155

01•••11,

— u;

C 1 cb.oco 2.000 4.000 6.000 8.000 10.000 12.000 MONTH

Figure 7.13 Annual Hydrograph for Station Q07. 156

O Percent Deviation A Erec-4

Y YOR

+ Historical

=0.000 2.000 4.000 6.000 8.000 10.000 12.000 MONTH

Figure 7.14 Annual Hydrograph for Station Q22. 157

cl,

P Percent Deviation HEC-4

Y YOR

+ Historical

o 0

`30 .000 2.000 4.0U0 6.000 8.000 10.000 12.000 MONTH

Figure 7.15 Annual Hydrograph for Station QSL. 158

o cr,

0 Percent Deviation

A HEC-4

Y YOR

+ Historical

o o o

cb .000 2.000 4.000 6.000 8.000 10.000 12.000 MONTH

Figure 7.16 Annual Hydrograph for Station Q78.

159

Table 7.19 Peak Flow and Month of Occurrence of Historical and Augmented Annual Hydrograph. -- Flow in million cubic meters.

Percent Station Historical Deviation HEC-4 YOR

Q03 384.04 360.87 273.11 332.75 August September September July

341.11 September

Q07 277.97 352.52 169.66 276.04 August September June July

297.74 309.51 September November

Q22 91.98 110.15 90.67 73.38 September September August May

103.54 August

QSL 319.99 264.34 300.21 254.75 August August August August

378.08 333.88 339.61 310.91 November November November October

Q78 440.74 472.69 498.00 259.87 August September ,August June

343.93 September 160

Table 7.20 Minimum Flow and Month of Occurrence of Historical and Augmented Annual Hydrograph. -- Flow in million cubic meters.

Percent Station Historical Deviation HEC-4 YOR

Q03 5.67 7.81 11.34 9.77 April April April April

Q07 23.58 15.48 24.04 23.25 April April March April

Q22 5.56 10.12 9.14 10.10 April April February April

QSL 49.84 42.33 39.41 41.12 May May April April

Q78 11.06 13.91 11.07 14.91 April April March March 161 hydrographs obtained from the YOR monthly means were all double-peaked, while those of the historical were single-peaked. For the minimum flow, however, the method produced flows surprisingly similar to the historical record. This tends to single out the strong point of the YOR method, which is in the generation of reliable estimates for the dry season.

Hydrographs obtained from HEC-4 provided generally lesser deviations from the historical in magnitude, but failed to conform to the time of occurrence of both the peak and minimum flows, but more strongly so for the minimum flow where the method obtained the lowest average score in the point scoring system.

The percent deviation method displayed remarkable consistency for both the peak and minimum flows and obtained the highest average score in both categories under the point system. The similarity of the time of occurrence and the nearly equal magnitude of the flow compared to the historical record were distinctly noticeable. Tables 7.21 and 7.22 summarize the comparison for the peak and minimum flows following the point scoring system.

The determination of the percentages in the percentage scoring system was rather difficult and, thus, became somewhat arbitrary since not only the magnitude but also the time of occurrence were examined.

The comparison of the flow magnitude was straightforward. In the comparison of the time of occurrence, however, separate scales of obtaining the percentage were devised for the peak and minimum flows.

For the peak flow comparison, the months were numbered from 1 to

12, starting in April, which is the end of the dry season and start of 162

Table 7.21 Scores Obtained by Various Methods in the Point System, Comparison of Peak Flow of Annual Hydrographs.

Percent Station Deviation HEC-4 YOR

Q03 4.0 3.0 2.0

Q07 4.0 3.0 2.0

Q22 4.0 3.0 2.0

QSL 3.0 4.0 2.0

Q78 3.0 4.0 2.0

Total 18.0 17.0 10.0

Average 3.6 3.4 2.0

Table 7.22 Scores Obtained by Various Methods in the Point System, Comparison of Minimum Flow of Annual Hydrographs.

Percent Station Deviation HEC-4 YOR

Q03 4.0 2.0 3.0

Q07 3.0 2.0 4.0

Q22 3.0 2.0 4.0

QSL 4.0 2.0 3.0

Q78 4.0 3.0 2.0

Total 18.0 11.0 16.0

Average 3.6 2.2 3.2 163 the water year under consideration. The month numbers of the historical and augmented peak flows were then determined. The absolute difference of the two numbers divided by the historical month number expressed in percent was considered to be the percentage of the time of occurrence.

If the augmented hydrograph was double-peaked while the historical hydrograph was single-peaked, or vice versa, 100 percent was assigned as percentage for the time of occurrence and the higher peak was used in the comparison of magnitude.

For the minimum flow comparison, the months were numbered from 1 to 12, starting in October, which is the end of the wet season. The percentage for each method in each station was determined in a similar manner as that in the peak flow comparison.

Tables 7.23 and 7.24 summarize the comparison of the annual hydrographs using the percentage scoring system. In both the peak and minimum flow comparison, the percent deviation method obtained the lowest average percentage. The HEC-4 method was better than the YOR in the peak flow comparison but fell behind the YOR method in the minimum flow comparison. The two scoring systems gave identical ranking of the methods in these two items of comparison.

7.4.8 Monthly Means

Monthly means obtained by each augmentation method were compared with the corresponding monthly mean of the historical data for each station. Again the two scoring systems were applied. The augmentation procedure generating means nearly equal to the historical mean was given the score of 4, i.e., the method producing monthly means with the least 164

Table 7.23 Percentages Obtained by Various Methods in the Percentage Scoring System, Comparison of Peak Flow of Annual Hydro graphs.

Station Item Deviation HEC-4 YOR

Q03 Time 20.00 20.00 100.00 Magnitude 6.03 28.88 11.18 Average 13.02 24.44 55.59

Q07 Time 20.00 100.00 100.00 Magnitude 26.82 7.11 11.35 Average 23.41 53.56 55.68

Q22 Time 0.00 16.67 100.00 Magnitude 19.75 1.42 12.57 Average 9.88 9.05 56.29

QSL Time 0.00; 0.00 0.00; 0.00 0.00; 12.50 Magnitude 17.39; 11.69 6.18; 10.18 20.39; 17.77 Average 7.27 4.09 12.67

Q78 Time 20.00 0.00 100.00 Magnitude 7.25 12.99 21.97 Average 13.63 6.50 60.99

Total of Averages 67.21 97.64 241.22

Average 13.44 19.53 48.24 165

Table 7.24 Percentages Obtained by Various Methods in the Percentage Scoring System, Comparison of Minimum Flow of Annual Hydrographs.

Percent Station Item Deviation HEC-4 YOR

Q03 Time 0.00 0.00 0.00 Magnitude 37.74 100.00 72.31 Average 18.87 50.00 36.16

Q07 Time 0.00 14.29 0.00 Magnitude 34.35 1.95 1.40 Average 17.18 8.12 0.70

Q22 Time 0.00 28.57 0.00 Magnitude 82.01 64.39 81.65 Average 41.01 46.48 40.83

QSL Time 0.00 12.50 12.50 Magnitude 15.07 20.93 17.50 Average 7.54 16.72 15.00

Q78 Time 0.00 14.29 14.29 Magnitude 25.77 0.09 34.81 Average 12.89 7.19 24.55

Total of Averages 97.49 128.51 117.24

Average 19.50 25.70 23.45 166 deviations from the historical was taken as the best method in the point scoring system. In the percentage scoring system, the method with the lowest percentage was judged as the best.

Table 7.25 gives the monthly mean of the historical data and the estimates of the augmentation methods. Tables 7.26 and 7.27 summarize the scores and percentages obtained by the methods in each station. In the point system, the percent deviation method is slightly better than the HEC-4, while in the percentage scoring system the HEC-4 method has a slight edge over the percent deviation. The YOR method ranks third in both scoring systems.

7.4.9 Standard Deviations

The standard deviations of historical and augmented monthly flows of all stations are given in Table 7.28. The comparison of the methods using both scoring systems is presented in Tables 7.29 and 7.30. The ranking of the methods derived from the two scoring systems is again slightly different, although the percent deviation method is the best in both systems.

In the point scoring system, the highest average score of 3.05 is obtained by the percent deviation method. The YOR method follows with

2.98 points, which is slightly higher than the HEC-4 method with 2.96 points. In the percentage scoring system, the percent deviation method is still the best method, but HEC-4 is much better than the YOR method.

The percent deviation obtains an average of 45.88%, the HEC-4 an average of 72.04%, and the YOR an average of 81.76%.

167

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V4 e2 N N CD C4 168 Table 7.26 Average Scores Obtained by Various Methods in the Point System, Comparison of Monthly Mean.

Percent Station Deviation HEC-4 YOR

Q03 3.08 3.17 2.75

Q07 3.08 2.83 3.08

Q22 3.00 2.83 3.17

QSL 3.25 3.33 2.42

Q78 3.08 3.00 2.92

Total 15.49 15.16 14.34

Average 3.10 3.03 2.87

Table 7.27 Average Percentages Obtained by Various Methods in the Percentage Scoring System, Comparison of Monthly Mean.

Percent Station Deviation HEC-4 YOR

Q03 35.92 31.23 50.08

Q07 24.16 37.91 44.83

Q22 66.66 52.29 150.20

QSL 13.46 15.02 21.05

Q78 43.91 25.26 44.23

Total 184.11 161.71 310.39

Average 36.82 32.34 62.08

169

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Table 7.29 Average Scores Obtained by Various Methods in the Point Scoring System, Comparison of Standard Deviations of Monthly Flows.

Percent Station Deviation HEC-4 YOR

Q03 3.17 2.83 3.00

Q07 3.08 3.08 2.83

Q22 3.00 3.08 2.92

QSL 2.92 2.83 3.25

Q78 3.08 3.00 2.92

Total 15.25 14.82 14.92

Average 3.05 2.96 2.98

Table 7.30 Average Percentages Obtained by Various Methods in the Percentage Scoring System, Comparison of Standard Deviations of Monthly Flows.

Percent Station Deviation HEC-4 YOR

Q03 57.57 123.02 100.76

Q07 57.20 72.39 88.57

Q22 133.66 120.15 854.46

QSL 28.33 38.92 38.97

Q78 40.41 53.84 98.74

Total 317.17 408.32 1181.50

Average 63.43 81.66 236.30 171

7.5 Summary and Findings

The comparison presented above aims at providing an unbiased assessment of the performance of the selected augmentation procedures.

The selection of the items of comparison and scoring schemes, however, affects the resulting conclusion from the comparison. Since this choice is somewhat arbitrary and depends largely on the objectives of the analysis, the intended application of the augmented data could provide a guideline in determining the importance of the chosen items of comparison and scoring systems.

Tables 7.31 and 7.32 summarize the average scores and percentages obtained by the various methods in the different comparison items. In the point scoring system (Table 7.31), the percent deviation method excelled in five out of eight items of comparison and obtained the highest general average score of 3.07. The HEC-4 method was the best in two out of eight items of comparison, placing second and third in the other comparison items. The HEC-4 method also competed very well for the highest general average, but fell slightly behind the percent deviation method with 3.00 because of poor performance in the monthly flow items.

The YOR method consistently displayed the lowest average scores in the annual flow comparison, but performed very well in tracking down the minimum flow of the annual hydrograph. The general average score obtained was 2.16. The Matalas-Jacobs method, which was only used for the annual flow comparison, performed better than the YOR method and obtained an average score of 2.55. 172

Table 7.31 Summary of Average Scores Obtained by Various Methods in the Point Scoring System (Station Q22 Included). -- Underlined value refers to the best in that item of comparison.

Percent Matalas- Item of Comparison Deviation HEC-4 YOR Jacobs

Double mass curve of annual flow 3.40 3.20 1.20 2.20

Annual mean 2.80 3.40 1.60 2.20

Deviation from historical annual flow 3.00 3.20 1.20 2.60

Standard deviation of annual flows 2.00 2.60 2.20 3.20

Peak flow of annual hydrograph 3.60 3.40 2.00

Minimum flow of annual hydrograph 3.60 2.20 3.20

Mean monthly flow 3.10 3.03 2.87

Standard deviation of monthly flows 3.05 2.96 2.98

Total 24.55 23.99 17.25 10.20 Average 3.07 3.00 2.16 2.55*

*Based on four items only. 173

Table 7.32 Summary of Average Percentages Obtained by Various Methods in the Percentage Scoring System (Station Q22 Included). -- Underlined value refers to the best method in that item of comparison.

Percent Matalas- Item of Comparison Deviation HEC-4 YOR Jacobs

Double mass curve of annual flow 14.41 14.64 30.16 19.83

Annual mean 15.60 11.17 22.87 16.27

Deviation from historical annual flow 25.27 30.34 45.77 26.59

Standard deviation of annual flows 51.40 48.73 106.02 23.86

Peak flow of annual hydrograph 13.44 19.53 48.24

Minimum flow of annual hydrograph 19.50 25.70 23.45

Mean monthly flow 36.82 32.34 62.08

Standard deviation of monthly flows 63.43 81.66 236.30

Total 239.88 264.11 574.89 86.55

Average 29.99 33.01 71.86 21.64*

*Based on four items only. 174

In the percentage scoring system (Table 7.32), the percent deviation method performed better than either the HEC-4 method or the YOR method in the overall comparison of annual and monthly flows. Again, the percent deviation method excelled in five out of eight items of comparison; two and three in the annual and monthly flow categories, respectively. The HEC-4 method was the best in two items out of eight; one in the annual flow category and the other in the monthly flow category. The

YOR method again consistently displayed the highest deviation from the historical data except in the minimum flow of the annual hydrograph. The

Matalas-Jacobs method got the lowest average percentage, although it only excelled in one of the four items of annual flow comparison. This tends to point out that looking at the overall average percentage alone may lead to a different conclusion. The performance of the methods should also be assessed in each item of comparison separately. Thus, the selection of the best method should be based not only on the overall average percentage but also on the number of comparison items in which the method performed well.

From the foregoing discussion, therefore, and considering the information provided by Tables 7.31 and 7.32, the two scoring systems revealed that the percent deviation method gave the best augmentation estimates based on the overall comparison.

Looking at the annual flow comparison only, the HEC-4 method obtained the highest average score in the point scoring system, but placed second in the percentage scoring system where the Matalas-Jacobs method ranked the first. The percent deviation method in this case 175 placed second in the point scoring system and third in the percentage system. The YOR method ranked fourth in both the scoring systems.

Isolating the monthly flow comparison, on the other hand, the two scoring systems gave the same ranking of the augmentation methods. In both systems, the percent deviation method was the best, the HEC-4 method second, and the YOR method third.

From this point of view, the best method to be chosen will depend on the question of which is more important, the annual flow or the monthly flow. In most water resources planning, however, both the annual

and monthly flows are essential. Hence, the most suitable method is one that will generate both monthly and annual flows as close to the histori-

cal record as possible.

As discussed earlier, Station Q22 does not correlate strongly with the base station, Q16, from which the generated estimates were

derived. Eliminating Station Q22 and looking back at the comparison items based on the remaining four stations, the results are of interest.

Tables 7.33 and 7.34 give the summary of the average scores and percentages obtained by the various methods when Station Q22 was excluded.

In both scoring systems, the ranking of the methods did not change based on the overall comparison, but the averages of the percent deviation were improved in both scoring systems while the averages of the other methods were better in the percentage scoring system only. Con- sidering the individual comparison items, however, minor changes were recorded in the point scoring system. In two items, deviation from historical annual flow and peak flow of annual hydrograph, the percent 176

Table 7.33 Summary of Average Scores Obtained by Various Methods in the Point Scoring System (Station Q22 Not Included). -- Underlined value refers to the best method in that item of comparison.

Percent Matalas- Item of Comparison Deviation HEC-4 YOR Jacobs

Double mass curve of annual flow 3.75 3.25 1.25 1.75

Annual mean 3.00 3.50 1.75 1.75 Deviation from historical annual flow 3.25 3.25 1.25 2.25

Standard deviation of annual flows 2.25 2.25 2.25 3.25

Peak flow of annual- hydrograph 3.50 3.50 2.00

Minimum flow of annual hydrograph 3.75 2.25 3.00

Mean monthly flow 3.12 3.08 2.79

Standard deviation of monthly flows 3.06 2.94 3.00 9.00 Total 25.68 24.02 17.29 2.16 2.25* Average 3.21 3.00

*Based on four items only. 177

Table 7.34 Summary of Average Percentages Obtained by Various Methods in the Percentage Scoring System (Station Q22 Not Included). -- Underlined value refers to the best method in that item of comparison.

Percent Matalas- Item of Comparison Deviation HEC-4 YOR Jacobs

Double mass curve of annual flow 7.64 10.22 23.61 20.06

Annual mean 10.42 6.58 16.61 15.38

Deviation from historical annual flow 19.53 23.43 40.17 25.91

Standard deviation of annual flows 40.77 56.70 118.32 21.54

Peak flow of annual hydrograph 11.47 17.72 36.99

Minimum flow of annual hydro graph 14.12 20.51 19.10

Mean monthly flow 29.39 27.36 40.05

Standard deviation of monthly flows 45.88 72.04 81.76

Total 179.22 234.56 376.61 82.89

Average 22.40 29.32 47.08 20.72*

*Based on four items only. 178 deviation and HEC-4 methods registered equal point scores. This seems to show that the augmentation methods become more reliable as the relationships of the stations become stronger, i.e., as the correlation coefficients approach unity.

7.6 Application of the Augmentation Method

The best method (percent deviation method) selected from the comparison made earlier was applied to extend the records of selected stations within the Central Luzon Basin. The results of these augmentation efforts, which were used in the evaluation of the surface water resources of the basin, are given in Appendix G. The hydrologic budget analysis was also based on some of these augmented data.

The selection of the base station for the augmentation was done using the cross-correlation coefficient, drainage area, and similarity of physical features with the station to be augmented. The augmentation strategy that was followed is given in Table 7.35.

In the application of the percent deviation method, whenever a missing monthly runoff was detected in the dependent station record, an annual runoff for that water year was derived from the regression equation established between the base and dependent stations. The proper percent deviations were computed and monthly filler values were obtained as the product of the corresponding monthly percent deviation and computed annual runoff. This procedure was followed even if some months in the water year had records. The existing values were, however, retained and the filler just appended to them. Inasmuch as the information contained in the partial year was not utilized to generate the 179

Table 7.35 Augmentation Strategy for Streamflow Gaging Stations, Central Luzon Basin, Philippines.

Augmented Station Base Station Correlation Simultaneous D. A.* D. A. Coefficient Years of Record 2 2 Code (km ) Code (km ) Before After Before After

Pampanga River Basin

Q17 573 Q16 518 0.9335 0.9642 22 27 Q29 6487 Q16 518 0.7210 0.6969 23 27 Q38 150 Q16 518 0.4197 0.4266 23 27 Q21 208 Q16 518 0.6172 0.8055 10 27 Q26 148 Q16 518 0.4794 0.7861 6 27 Q20 284 Q17 573 0.5340 0.4078 13 27 Q31 204 Q38 150 0.3445 0.5246 15 27 Q32 256 Q38 150 0.9249 0.8270 13 27 Q55 118 Q20 284 0.8252 0.5233 11 27 QSL 568 Q20 284 0.6730 0.6212 22 27 Q53 128 Q20 284 0.6632 0.6050 24 27 Q19 3467 Q29 6487 0.7886 0.9468 6 27 Q08 2015 Q19 3467 0.3823 0.5765 15 27 Q24 1177 Q31 104 0.4334 0.4289 8 27 Q54 111 Q32 256 0.5017 0.6554 24 28

Agno River Basin

Q70 1225 Q72 2209 0.8404 0.8501 25 28 Q78 872 Q72 2209 0.7033 0.8331 9 28 Q82 280 Q72 2209 0.8709 0.9638 8 28 Q85 5134 Q72 2209 0.7331 0.8733 11 28 Q71 281 Q82 280 0.7536 0.8143 14 28 Q84 126 Q82 280 0.8940 0.9130 7 28 Q77 405 Q70 1225 0.7355 0.9379 10 28 Q60 53 Q71 281 0.6914 0.7248 13 21 Q61 180 Q71 281 0.7235 0.7692 15 28 Q76 240 Q71 281 0.9119 0.8789 4 28

*Drainage area. 180

filler value, unreasonably high or low derived filler values could be

expected to occur. The next section provides further discussion on this aspect.

Table 7.36 gives a comparison of the statistics before and after

augmentation of the streamflow data in each gaging station. No substan-

tial change of the annual mean was noticed. Several stations, however,

have noticeable variations in standard deviations and skewness coeffi-

cients between the historical and blended 1 annual series. This implies

that these statistics are quite unstable and difficult to retain.

Slight variations of the correlation coefficients were observed.

Most of the changes are toward larger correlation coefficients, but a

number of cases have smaller correlation coefficients after the

augmentation.

7.7 Consistency of Augmented Data

One method of assessing whether the augmented streamflow data are 2 reasonable or not is a consistency test on tandem stations . In a river

system, VRO successive gaging stations along the same river channel

should provide streamflow record consistent with each other, i.e., the

downstream station should register higher flows than the upstream station

although there are exceptions to such generalization. When the flow at

the downstream station becomes significantly lower than that recorded at

1. Blended series refers to the combined historical data and augmentation estimates.

2. Tandem stations are stations in a river system in which one of the stations is downstream of the rest. 181 Table 7.36 Statistics* of Annual Streamflows Before and After Augmentation by Percent Deviation Method.

Skewness Mean (MCN) Station Standard Deviation Coefficient Code Before After Before After Before After

Pampanga River Basin

Q08 2521.61 2556.86 730.43 660.68 -0.056 0.471 Q17 1097.60 1149.06 300.96 365.86 0.356 1.219 Q19 4675.38 4394.25 670.95 1116.28 -0.530 0.092 Q20 227.17 222.72 67.43 53.39 0.418 0.602 Q21 297.52 286.80 163.81 141.84 0.880 0.833 Q24 1747.46 1699.16 452.03 294.74 1.399 1.062 Q26 234.90 222.93 88.49 95.50 -0.049 1.030 Q29 7928.11 8260.27 2011.95 2130.32 0.099 0.037 Q31 188.55 188.74 73.91 60.49 0.052 0.271 Q32 412.06 381.52 206.18 174.48 1.782 1.693 Q38 258.34 266.81 109.41 144.58 0.590 1.678 Q53 284.78 285.54 93.08 87.72 0.528 0.526 Q54 165.49 165.80 67.10 72.11 -0.163 0.224 Q55 96.16 105.10 67.73 83.22 0.509 2.470 Q56 2319.06 2361.74 623.13 643.01 0.411 0.206

Agno River Basin

Q60 281.54 231.34 390.29 317.04 3.215 3.815 Q61 629.49 611.85 110.16 85.71 -0.600 -0.097 Q70 3060.33 2963.81 1498.14 1480.36 2.319 2.217 Q71 687.61 614.10 220.45 190.22 0.548 0.987 Q76 231.86 242.34 25.79 73.24 0.558 3.590 Q77 872.93 835.92 205.34 356.50 0.210 0.226 Q78 1484.31 1647.26 649.36 542.02 1.214 0.925 Q82 609.61 646.32 194.18 199.71 0.891 0.796 Q84 243.94 255.17 78.11 76.34 -0.921 0.007 Q85 10002.56 8519.95 2677.05 2349.07 -1.309 0.091 *Statistics were computed from the historical data alone for the "before augmentation" case and from the combined historical values and augmentation estimates for the "after augmentation" case. 182 the upstream station, i.e., when the anomaly could not be reasonably explained by probably measurement errors, something else must be happening along the river channel between the two stations. One probable explanation to such a case is the presence of a groundwater recharge area between the two stations in question. This possibility is not remote in an alluvial basin such as the Central Luzon Basin. In order to investi- gate this question, tandem stations along the Agno and Pampanga Rivers were scrutinized.

In the Agno River system, Stations Q70, Q71, and Q72 were considered. Both Q70 and Q72 are in the main channel of the Agno River, while Q71 is in a tributary channel. Station Q70 is at San Roque, San

Manuel, Pangasinan (upstream), and Q72 is at Carmen, Rosales, Pangasinan

(downstream). Station Q71 is at Ambayoan River, a tributary of the main channel upstream of Q72 but downstream of Q70. The distance between Q70 and Q72 is about 40 km. Drainage areas at these stations are 1,225, 281, 2 and 2,209 km for Q70, Q71, and Q72, respectively. The difference of the drainage area of the downstream station (Q72) and the sum of the drainage 2 areas of the two upstream stations is 703 km .

In the Pampanga River system, Stations Q19 and Q29 were studied.

Both stations are along the main channel of the Pampanga River. Station

Q19 is at San Vicente, Cabiao, Nueva Ecija (upstream) and Q29 is at San

Agustin, Arayat, Pampanga (downstream). The distance between the two 2 stations is about 12 km. The drainage areas are 3,467 and 6,487 km for

Q19 and Q29, respectively, or a difference of 3,020 km2 . The large difference in drainage areas between the two stations is brought about by 183 a tributary (Rio Chico), which joins the main channel about 7 km upstream

of Station Q29. The drainage area of Rio Chico at its junction with the main channel of Pampanga River is about 3,000 km 2 .

To test the consistency of the augmented data in the Agno River system, the flow of Station Q72 was compared with the sum of the runoff

of Stations Q70 and Q71. In the Pampanga River, a direct comparison of

Stations Q19 and Q29 was made. In the comparison of annual flows, the downstream stations in both river systems registered consistent records with the upstream stations except in one water year (1972-73) in the Agno River. Looking back at the unaugmented record, however, it was found

that this anomalous record is the historical value itself. Studying the

rainfall records further, it was found that 1972-73 is one of the wettest years, recording more than three standard deviations above the mean

annual rainfall in most stations within the basin. In fact, the flood in

July of 1972 was among the greatest in the Central Luzon Basin, lasting

for more than one month. Isolating the water year 1972-73 and comparing

the monthly flows of the upstream stations and downstream station, the

inconsistent records were found in July and August, the height of the

1972 flood. It is conceivable that the flows at Station Q72 during these

months were underestimated because the floodwater overflowed the river

banks, or scoured the river channel; the stage-discharge relationship

would then indicate only a portion of the actual discharge.

The monthly flows of Stations Q19 and Q29 registered inconsistent

records in only three months of a 27-year blended record. The anomalous

months were found in April and May of 1967-68 and November of 1968-69. 184

Station Q29 had a partial record in these particular years, with the missing values in April and May of 1967-68, and October and November of

1968-69. These inconsistent values could have been caused by low estimates of the annual flows in the water years in question which led to underestimation of the filler values for the months with missing record.

This particular case, as discussed earlier, points out one limitation of the percent deviation method; unreasonably high or low estimates can be obtained for water years with partial year data. Evaluation of the consistency of the estimated values with the existing data and subsequent adjustment in such cases is, therefore, warranted before use of the augmented data for the intended purpose.

In the Agno River system, a greater number of monthly runoffs were found inconsistent. The sum of the upstream stations (Q70 and Q71) in several months was larger than the corresponding runoff at the downstream station (Q72). In the case of these stations, only data of Q70 and Q71 were augmented as Q72 had a complete record without gaps between

November 1945 and October 1974. For Q70, only November and December of

1960-61, January and February of 1966-67, and December to March of

1973-74 were filler values. In Station Q71, augmentation estimates were data within the periods April 1946 to May 1958, and January to March

1974. Not one of the inconsistent monthly runoffs fell in the months where Q70 was augmented. Thus, the first suspicion was that the filler values in Station Q71 caused the anomalies. However, the runoff magnitude recorded in Station Q71 was hardly comparable to those recorded in

Q70 as the difference in drainage areas of the two stations is enormous 185

(944 km2 ). Besides, the monthly filler values in Q71 consistently follow the annual hydrographs of its historical record. This gave rise to a strong suspicion that something could be wrong with the historical record itself. Comparison of the simultaneous historical data of Q70 and Q72 revealed several months of higher runoff record in Station Q70 than in

Station Q72. There were also cases where the Q70 runoff is nearly equal to the Q72 value; hence, the addition of the Q71 runoff to Q70 increased the number of anomalous months.

This situation is hardly expected to happen in tandem streamflow stations without any hydrological or geological reasons, or both. A possible explanation to this phenomenon is that the stream channel between the upstream and downstream stations acts as a losing stream;

thus, the stream discharge is significantly reduced as it reaches the downstream station. During the flood months, however, the downstream

station, which is lower in elevation and situated in the plain, possibly underestimates the river discharge as a portion of the runoff spreads out on the flood plain and leaves the river main channel, bypassing the

gaging station completely. These possibilities are not remote and probably caused the inconsistent historical records of Stations Q70 and Q72. CHAPTER 8

THE HYDROLOGIC BUDGET

8.1 Period of Analysis

The basin hydrologic budget was analyzed for three water years of above-, near-, and below-average precipitation. The years considered were selected using the arithmetic average of 28 selected rainfall stations within the basin. Water years 1959-60, 1960-61, and 1963-64 were found to represent below-, above-, and near-average precipitation years, respectively.

Isohyetal maps for these water years were constructed and the average rainfall depth corresponding to each year was determined. Each value was compared with the mean basin rainfall obtained from the isohyetal map constructed using annual average precipitation of available rainfall stations. Table 8.1 presents the average precipitation depth

(by isohyetal method) over the basin corresponding to each water year selected.

Two components of the hydrologic budget are generally unknown.

These are the changes in basin storage and evapotranspiration. Changes in basin storage are reflected in fluctuations of the groundwater stages and soil moisture contents. Evapotranspiration is not completely unknown since observed pan evaporation may give an index to the basin potential evapotranspiration. 186

187

Table 8.1 Basin Average Rainfall for the Three Selected Water Years. -- Rainfall in mm.

Water Year Annual Rainfall

1959-60 2049

1960-61 2761

1963-64 2346

Mean for the basin* 2382

*Mean rainfall for the basin was determined by the isohyetal method using annual means of rainfall stations within the basin. 188

Since no soil moisture assessment was done, weekly or monthly budgets could not be made. Annual soil moisture fluctuation is generally

small and could be neglected without affecting the hydrologic budget significantly. Fluctuations of the groundwater stages between the start and

end of the water year (April 1 and March 31, respectively) of individual observation wells are small and mean changes of groundwater stage between

these dates range from 0.01 to 0.1 meter. Hence, neglecting the changes

In storage of the basin would not significantly affect the accounting of

water gains and losses in an annual hydrologic budget analysis. Annual

evapotranspiration was estimated through Equation 5.18. Monthly evapo-

transpiration was obtained by distributing this annual ET, based on

monthly percentage distribution of adjusted pan evaporation in the corre-

sponding water year.

For the period between June 1 and November 30, Equation 5.32 was

solved for the change in groundwater storage, dSg, using the evapotran-

spiration estimates in Equation 5.18. Gravity yield of the basin was

computed by means of Equation 5.33. Average gravity yield obtained in

the three water years was used in the estimates of basin recharge.

8.2 Description of Gaging Stations

In this section, the gaging and observation stations for rain-

fall, streamflow, water table levels, and evaporation used in the water

budget analysis are described. The methods used in determining the

values in the budget are also presented. 189 8.2.1 Rainfall

Some 121 rainfall stations were established in the Central Luzon

Basin by various government agencies. Fifty-six stations were used to determine the mean annual basin rainfall. Only about 50 percent of the stations were utilized because the other stations have either one or two years of record only or the data could not be located. The number of stations was further reduced corresponding to the number of stations with actual or augmented data for the rainfall analysis of the three selected water years. Some 33 stations were utilized to construct the isohyetal maps of these three water years in determining the annual and June to

November basin rainfalls. Rainfall averages for these two periods were evaluated over the whole basin and sub-basins corresponding to the drainage areas of streamflow gaging stations considered. The isohyetal maps are presented in Figures 8.1 through 8.3 and rainfall averages are given in Table 8.2.

8.2.2 Streamflow

The water budget analysis was made for the Central Luzon Basin using streamflow gaging stations Q85 for the Agno River and Q29 for the

Pampanga River. These two stations are the most downstream stations in the two major rivers. Figure 8.4 gives the approximate locations of the stations with respect to the basin. The streamflow records for the three selected water years are shown in Table 8.2.

Station Q85 at the Agno River has a drainage area of 5134 km2 .

The length of record is 12 years, covering the period 1963-64. The flows during the water years 1959-60 and 1960-61 were estimated using the

190

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Figure 8.4 Streamflow Gaging Stations Used in the Water Budget Analysis. 195

percent deviation method, as discussed in Chapter 7. The mean annual

flow based on the historical record is 10,002 million cubic meters.

Station Q29 at the Pampanga River has a drainage area of 6487 2 km . The length of record is 27 years, covering the period 1946-72. The

mean annual flow, based on the historical record, is 7,928 million cubic meters.

8.2.3 Groundwater

Changes in groundwater storage are reflected by mean groundwater

stage fluctuations. Some 70 shallow observation wells have been

installed by the Bureau of Public Works since 1958 ( Figure 8.5). Data

from 33 sites are available during the three water years being considered.

Water table level observations in these wells (Table 8.3) were used in

determining the average annual and June-November groundwater storage

changes.

It is noticed in Table 8.3 that the mean change in water table

levels during the above- and near-average years (1960-61 and 1963-64,

respectively) is less than that in the below-average year (1959-60).

This is probably due to the different monthly rainfall distribution in

these three years. The percentage of the annual rainfall during the

period June to October is about 81 percent in 1960-61, 86 percent in

1963-64, and 66 percent in 1959-60. It is conceivable that the infiltra-

tion rate of the soils in the basin was greatly exceeded by the rainfall

intensities during the June to October period in the selected above- and near-average years. This phenomenon induced a greater percentage of the

196

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Table 8.3 Changes in Water Table Levels, Central Luzon Basin, Philippines. -- Levels in meters.

1959-1960 Well 1960-1961 1963-1964 No. Annual Jun-Nov Annual Jun-Nov Annual Jun-Nov

P-3 0.03 -0.21 -0.11 0.16 -0.33 -0.56 P-4 -0.13 0.95 0.72 0.87 -0.03 0.11 P-5 0.10 0.51 -0.14 0.67 -0.21 0.14 P-6 0.54 1.24 -0.50 0.74 -0.18 0.27 P-7 0.00 0.51 -0.18 0.12 0.01 -0.61 P-8 -0.84 0.42 0.21 0.70 0.18 0.56 P-9 0.16 2.47 -0.10 1.80 0.01 0.76 P-10 -0.08 0.56 0.62 -0.02 -0.05 0.06

NJ-1 0.42 0.88 -0.41 0.75 0.23 1.09 NJ-2 -0.52 -0.48 -0.32 3.06 1.43 3.08 NJ-5 1.53 0.87 0.07 -0.01 -0.53 -0.14 NJ-9 -0.16 0.05 -0.20 0.23 -0.23 2.84 NJ-10 -0.49 0.88 -0.06 0.81 0.50 1.59 NJ-11 1.16 1.69 0.01 2.22 0.39 2.23 NJ-12 -0.67 2.03 1.00 2.48 -0.97 1.35 NJ-13 -0.14 1.43 -0.05 1.74 -0.07 0.10

B-1 0.01 1.53 0.77 0.06 -0.46 0.15 B-2 -0.25 0.20 0.16 0.32 0.00 0.02 B-4 -0.05 0.76 -0.18 0.19 0.74 0.85 B-5 -0.77 4.12 -0.11 1.26 -0.12 0.67 B-6 -0.14 1.17 -0.81 1.43 -0.05 1.55 B-7 0.27 1.71 -0.08 1.59 -0.10 -0.11 B-8 -0.20 0.94 -0.08 0.51 -0.08 0.01

Pa-1 0.12 0.78 0.30 -0.06 -0.39 0.50 Pa-2 1.00 0.95 -0.26 0.74 -0.20 -0.24 Pa-4 0.90 1.88 -0.05 1.91 -0.59 0.24 Pa-5 1.89 2.02 -0.33 1.42 0.37 1.52 Pa-6 -1.76 0.66 0.22 0.26 3.65 1.89 Pa-8 -0.60 1.02 -0.16 1.94 0.24 2.08 Pa-10 0.74 0.51 0.03 -0.19 0.12 0.43 Pa-11 0.19 0.42 0.01 0.14 0.00 1.07 Pa-12 0.18 -0.17 0.01 -0.21 -0.37 0.13 Pa-13 1.26 3.39 -0.02 3.53 0.05 2.78 26.41 Total 3.70 35.69 -0.02 31.16 2.96 0.09 0.800 Mean 0.112 1.081 -0.0006 0.944 198 rainfall to run off before the water could enter the sub-surface storage.

The streamflow records in the three water years seem to confirm this situation.

Groundwater underflow refers to the groundwater migration into or out of the basin under consideration. In the Central Luzon Basin, the groundwater and topographic divides reasonably conform except in the vicinity of streamflow gaging stations. The net groundwater underflow along the basin boundaries under this condition could, therefore, be neglected. In the vicinity of the streamflow gaging stations, however, the underflow could be significant.

Groundwater underflow near streamflow gaging stations was estimated using the Darcy equation. Mean groundwater stage contour map was constructed using the average water levels of observation wells. Ground- water hydraulic gradient and length of groundwater flow section were estimated from the contour map. Transmissivities were obtained from the average transmissivities of tested wells in the vicinity of the gaging stations. The computed annual groundwater underflows were 5.5 and 3.7 million cubic meters for Stations Q85 and Q29, respectively. These values are only about 0.05 percent of the average annual streamflow measured at the corresponding stations; hence, the effect of the underflow was neglected in the water budget analysis.

8.2.4 Evaporation

Pan evaporation (Class A) data for the three selected water years are available only in two locations: one at San Roque, San Manuel, 199

Pangasinan; and the other at Tibagan, Bustos, Bulacan. The average of

the two stations was considered to be representative of the basin. This

assumption was made after the monthly means of these two stations were

compared with the monthly means of four other stations within the basin.

All of these stations seem to agree in the magnitude of the monthly mean.

The pan evaporation data were used to distribute the annual evapotranspiration obtained from the annual water budget analysis. Since

the pan evaporation reflects the potential evapotranspiration and the

result of the annual water budget analysis reflects the actual evapotranspiration, adjustment of the former was necessary prior to its applica-

tion, especially during the dry season months when the evaporative demand of the atmosphere is high but actual evapotranspiration rate becomes less than the potential rate.

The rate of evaporation from soil is affected by the evaporative demand of the atmosphere, nature of the evaporating surfaces, and the moisture content of the soil (Holmes and Robertson, 1963). At first, when the soil moisture is plentiful, the rate of evapotranspiration is nearly the potential rate and depends largely on the amount of energy available for evaporation. According to Hounam (1971), the water loss from a catchment in which soil moisture is not a limiting factor is at the potential rate and areal variations across the catchment would result from differences in meteorological conditions, including advective effects. When drying of the soils begins, however, the actual evapotranspiration rate falls below the potential rate and factors such as available soil moisture and vegetation characteristics become more important 200

than the meteorological factors. Thus, the pan evaporation data do not

describe the actual monthly evapotranspiration trend but rather provide

an idea of the atmospheric evaporative demand.

From the observed monthly pan evaporation, Table 8.4 and Figure

8.6, it can be seen that, during the dry season months (November to

April), the atmospheric evaporative demand is greater than that of the wet season months Nay to October). This does not mean, however, that

the actual evapotranspiration rate during the dry season exceeds that of

the wet season. During the dry season, although the amount of energy

available to evaporate water is large, the actual evapotranspiration rate

would probably be lower than the potential rate because soil moisture may

be a limiting factor. The evapotranspiration rate during these months is

governed largely by the amount of water available for evaporation.

Adjustment of the monthly pan evaporation data prior to use in the deter-

mination of the actual monthly evapotranspiration in the basin is, there-

fore, necessary.

Holmes and Robertson (1963) presented the concept of the rela-

tionships between actual evapotranspiration (AE) and potential evapotran-

spiration (PE) based on the work of Lemon. The concept is illustrated in

Figure 8.7. According to Holmes and Robertson (1963), actual evapotran-

spiration proceeds at the potential rate so long as moisture is conducted

to the surface fast enough to meet the atmospheric deficit. When the moisture transport system breaks down, there is a sharp reduction in the

actual rate of evapotranspiration and the shape of the drying curve from

this point assumes an exponential form characteristic of many tension vs.

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Figure 8.7 Relationship between AE/PE and Soil Moisture Content. -- After Holmes and Robertson (1963). 204 moisture curves. Holmes and Robertson then verified the concept by a growth chamber experiment using three soil types subjected to conditions of high and low evaporation, cropped and uncropped. The general shape of the curves obtained from these experiments was similar to that in Figure 8.7.

Holmes and Robertson (1963, p. 66) summarize their findings from the growth chamber experiments as follows:

... under low evaporation conditions a well-defined plateau, or region where AE equals PE exists until moisture transport to and through the drying surface and/or to roots breaks down. Then AE sharply decreases, relative to PE, in exponential fashion. How- ever, under high evaporative conditions the moisture content transport system may break down very early regardless of moisture content. The curve that results is more shallow in slope with a lack of sharp breaks. It is not difficult to imagine a nearly linear relationship under some conditions ....

The conditions during dry season months in the Central Luzon Basin, as observed from pan evaporation data, fall under the high evaporative demand situation. This seems to point out that during these months a linear relationship for the adjustment of monthly evaporation is applicable. At the start of the dry season in November, the soils in the basin may be assumed at field capacity; thus, the soil moisture depletion starts from this month and continues until April, when it reaches the minimum. In May, the beginning of the rainy months, the early May precipitation will start to replenish the depleted soil moisture and start to build up the soil moisture storage.

In view of the above, and the assumptions of a linear AE/PE relationship, various reduction coefficients were applied to the pan evaporation of each month. Five cases were studied. In Case I, the pan 205 evaporation was left unaltered for all months. This assumes that the soil moisture content does not affect the actual evapotranspiration. In

Cases II through V, pan evaporation data in the dry season were multiplied by coefficients which are less than one. This approach takes into account the reduction of the actual evapotranspiration as the soil moisture content is depleted. Table 8.5 summarizes the reduction coefficients in each case. Table 8.6 through 8.8 give the monthly evaporation percentage in the three water years.

8.3 The Water Budget

Stations Q85 and Q29 (in the Agno and Pampanga Rivers, respectively), were considered in the water budget analysis of the Central

Luzon Basin. These are the most downstream stations in the two river 2 systems. The total drainage area above these stations is 11,621 km

(4,483 mi 2 ).

Table 8.9 summarizes the annual water budget of the basin for the three selected water years. Reasonable values of annual ET were obtained.

The largest amount of ET was obtained in the water year of below-average precipitation. Monthly evapotranspiration values are given in Tables

8.10 through 8.12 for the three water years and various cases of distributing the actual annual evapotranspiration. The differences in the estimated monthly ET are reflected in the estimation of the gravity yield and eventually in the estimates of the basin recharge.

Table 8.13 presents the water budget of the basin for the period from 19.0 between June 1 and November 30. Values of gravity yield range to 39.5 percent for the five cases considered. These values are 206

Table 8.5 Monthly Adjustment Coefficients for Various Cases.

Month Case I Case II Case III Case IV Case V

Apr 1.0 0.5 0.60 0.70 0.6

May 1.0 0.9 0.92 0.94 0.8

Jun 1.0 1.0 1.00 1.00 1.0

Jul 1.0 1.0 1.00 1.00 1.0

Aug 1.0 1.0 1.00 1.00 1.0

Sep 1.0 1.0 1.00 1.00 1.0

Oct 1.0 1.0 1.00 1.00 1.0

Nov 1.0 1.0 1.00 1.00 1.0

Dec 1.0 0.9 0.92 0.94 0.8

Jan 1.0 0.8 0.84 0.88 0.8

Feb 1.0 0.7 0.76 0.82 0.7

Mar 1.0 0.6 0.68 0.76 0.6 - • ▪ •

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Table 8.9 Annual Water Budget Analysis, Central Luzon Basin, Philippines. -- Precipitation was determined by isohyetal method. Runoff is the sum of streamflow measured at Q85, Agno River, and Q29, Pampanga River. Total drainage area of the two stations is 11,621 sq. km. Groundwater underflow was estimated from Darcy's equation. This item was neglected in the analysis because the computed amount was small enough to be significant. Changes in soil and groundwater storages were neglected.

Item 1959-1960 1960-1961 1963-1964 Average

23,559Precipitation, P, MCM 31,215 26,221 26,998

Runoff, RO, MCM 8,402 18,488 16,725 14,538

Groundwater underflow, GWO - (neglected) - -

Evapotranspiration, ET:

12,727MCM 15,157 9,496 12,460

min 1,304 1,095 817 1,072

mm/day 3.6 3.0 2.3 2.9 211

Table 8.10 Actual Monthly Evapotranspiration, Water Year 1959-1960. -- Evapotranspiration in mm.

Month Case I Case II Case III Case IV Case V

Apr 168 101 117 131 122

May 140 152 149 147 135

Jun 119 143 137 132 143

Jul 87 104 100 96 105

Aug 75 90 86 83 90

Sep 80 97 93 90 97

Oct 92 111 107 103 112

Nov 84 101 97 93 102

Dec 97 105 103 101 94

Jan 104 100 101 102 94

Feb 115 97 101 105 98

Mar 143 103 113 121 112

Annual 1,304 1,304 1,304 1,304 1,304

Jun-Nov 537 646 620 597 649 212

Table 8.11 Actual Monthly Evapotranspiration, Water Year 1960-1961. -- Evapotranspiration in mm.

Month Case I Case II Case III Case IV Case V

Apr 111 67 77 86 80

May 109 117 115 113 105

Jun 79 94 91 87 95

Jul 83 99 95 92 100

Aug 59 70 68 65 71

Sep 63 75 72 70 76

Oct 74 89 86 83 90

Nov 87 104 100 97 105

Dec 88 95 93 92 85

Jan 109 105 106 107 99 90 Feb 107 89 93 97 99 Mar 126 91 99 106 1,095 1,095 Annual 1,095 1,095 1,095 494 537 Jun-Nov 445 531 512 213

Table 8.12 Actual Monthly Evapotranspiration, Water Year 1963-1964. -- Evapotranspiration in mm.

Month Case I Case II Case III Case IV Case V

Apr 106 65 74 83 78

May 109 119 117 114 106

Jun 48 58 56 54 59

Jul 46 56 54 52 57

Aug 47 57 55 52 58

Sep 39 48 45 44 48

Oct 59 72 69 67 73

Nov 59 72 68 66 72

Dec 57 63 62 60 56

Jan 66 64 65 65 61

Feb 84 72 75 77 72

Mar 97 71 77 83 77

Annual 817 817 817 817 817

Jun-Nov 298 363 347 335 367

▪

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8.4 Recharge Estimation

Monthly mean basin groundwater stages were determined from 33 shallow observation wells for the three selected water years. Monthly mean groundwater stages were plotted together with mean monthly basin rainfall, as shown in Figure 8.8. Increases in groundwater stage due to recharge were determined as the difference of the antecedent hydrograph and peak stage at the end of the recharge period. In this case, the groundwater discharge occurring during the recharge period was taken into account. Recharge was computed as the product of the average gravity yield and the groundwater stage rise. Table 8.14 gives the estimated recharge for the three water years.

All five cases gave similar trends of annual recharge, i.e., highest during the wet year and lowest during the dry year. Case I and

Case V gave the highest and lowest estimates, respectively, for all years. The difference of these two cases was about 27 percent of the estimate of Case I. Considering the estimation method and the scarcity of data on pan evaporation and groundwater observation, this difference seems reasonable and within the limitations of the data.

Designating the estimates obtained in Case I and Case V as the upper and lower limits, respectively, it may be inferred that the annual recharge in the basin is in the range of 4,400 to 6,000 MCM for the dry year, 5,900 to 8,100 MCM for the wet year, and 4,800 to 6,600 MCM for the 216

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Table 8.14 Estimated Annual Recharge for Three Water Years, Central Luzon Basin, Philippines.

Item 1959-1960 1960-1961 1963-1964 Average

Average groundwater stage, m above MSL: Peak 34.09 34.06 34.15 34.10 Minimum 32.61 32.07 32.53 32.40 Difference 1.48 1.99 1.62 1.70

Recharge (lc x Gy), mm: Case I 521 700 570 598 Case II 386 519 423 444 Case III 417 561 457 479 Case IV 444 597 486 510 Case V 379 509 415 435

Recharge, MCM: - Case I 6,054 8,135 6,624 6,949 Case II 4,485 6,031 4,916 5,160 Case III 4,846 6,519 5,311 5,566 Case IV 5,160 6,938 5,648 5,927 Case V 4,404 5,915 4,823 5,055

Recharge as percentage of annual rainfall: Case 25.7 26.1 25.3 25.7 Case II 19.0 19.3 18.7 19.0 Case III 20.6 20.9 20.2 20.6 Case IV 21.9 22.2 21.5 21.9 Case V 18.7 18.9 18.4 18.7 218 average year. The U. S. Bureau of Reclamation (1966) estimated the average yearly recharge in the Central Luzon Basin as 5,550 MCM.

8.5 Summary and Findings

The three water years representing below-, near-, and above- average precipitation were identified based on the arithmetic average of

28 rainfall stations. The precipitation component used in the water budget analysis was determined by the isohyetal method. The water budget analysis was pursued for two periods: the annual budget was made to estimate the annual actual evapotranspiration, ET; and the period between

June and November budget provided estimates of the basin gravity yield.

The annual water budget revealed an average evapotranspiration of about 1,072 mm for the three Water years. This value gave an average evapotranspiration-pan evaporation ratio (ET/E 0 ) of 0.51, which is somewhat lower than those previously reported. Hargreaves (1966) and Gray

(1970), for example, reported class A pan coefficient ranges of 0.55 to

1.00 and 0.50 to 0.68, respectively, to estimate seasonal consumptive use of various crops. These values were determined for specific crops, however, and not for a basin canopy composed of a wide variety of vegetation. Kampen (1970), for instance, reported ET and E 0 relationships for rice crop at various growth stages determined in the Philippines with coefficients much higher than 0.51. Rice crop, on the other hand, is grown in an environment where water is not a limiting factor, which is not likely to happen under a basin-wide consideration, especially during the dry season when rainfall is much less than the evaporative demand of atmosphere. It is, therefore, conceivable that the ET/E 0 ratio is less 219 than the 0.51 average during the dry season and higher during the wet season for the Central Luzon conditions.

The annual hydrologic budget for the three selected water years seems to point out that, on the average, about 54 percent of the rainfall within the Central Luzon Basin is measured as streamflow. The balance of

46 percent is shared by evapotranspiration and change in storage, with the bulk of it going to evapotranspiration. Essentially, the increase in the basin storage during the wet season months is eventually evapotran- spired and discharged to streams during the dry season months. Increased sub-surface recharge, to some extent, may occur during the rainy months when the groundwater is depleted more than the natural groundwater discharge (evapotranspiration and base flow). Additional groundwater pumping during the dry season would dewater a greater portion of the aquifer than at the present rate. In the rainy months, a larger volume of the aquifer would take in recharge water. However, this presumption is based on purely speculative fashion and should merit further investigation.

The method used in the estimation of recharge is highly dependent on the accuracy of available data, particularly on the water table observations. Although the limited number of shallow observation wells used in the study may be questioned in the light of whether they are truly representative of the basin or not, the resulting estimates seem to be reasonable and within the range, at least in the order of magnitude, of previously reported annual recharge. The U. S. Bureau of Reclamation

(1966) reported an annual recharge of 5,550 MCM. In this study, annual recharge in the range of 5,000 to 6,900 MCM was determined. CHAPTER 9

THE BASIN WATER RESOURCES

9.1 Surface Water

Surface water resources of the basin include river runoff; natural surface storages such as lakes, swamps, and ponds; springs; and

estuarine areas. Although all of these waters originate mainly from precipitation, separate discussion of the various items is attempted to provide as complete a picture of the surface water potentials of the

Central Luzon Basin as possible.

9.1.1 Rainfall

Water returning from the atmosphere in the form of rainfall remains the major source of surface water in the Central Luzon Basin, which is characteristic for all humid tropical areas. The average annual rainfall in the basin is about 2,380 mm (94 in). More than 85 percent of the annual rainfall occurs during the period between May and October, with a monthly maximum of about 21 percent occurring in August. The remaining 15 percent occurs during November through April, with a minimum of less than two percent occurring during each of the months January, February, March, and April. Figure 9.1 illustrates the monthly variation of rainfall in three selected locations. The pattern of river runoff is similar to the pattern of rainfall (Figure 9.2).

220 221

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Figure 9.2 Monthly Runoff at Two Selected Gaging Stations. 223 9.1.2 Streamflow

All basin runoff originates from rainfall or groundwater storage.

To illustrate the monthly variation of the basin runoff, records from the

streamflow gaging stations on the Agno River at San Roque (Q70) and

Pampanga River at San Agustin (Q29) were used. These records, for the

period 1946 through 1974, show that the annual total runoff varied from

a maximum of 8,720 MCM in 1972-73 to a minimum of 1,143 MCM in 1959-60 at

Q70. At Station Q29, the records show that the annual runoff varied from

a maximum of 12,428 MCM in 1971-72 to a minimum of 4,015 MCM in 1955-56.

The average annual runoffs in the two gaging stations are 3,060 and 7,928

MCM in Q70 and Q29, respectively. Figure 9.2 presents the monthly runoffs

at the two stations.

The estimated average annual runoff of the Central Luzon Basin is

shown in Figure 9.3. More than 25,000 MCM is discharged to the sea by

the river systems in Central Luzon. About 50 percent of this comes from

the Agno River system, 45 percent from the Pampanga River system, and the

remaining 5 percent is discharged from minor rivers such as the Dagupan

and Patalan Rivers to the Lingayen Gulf, and the Porac, Gomain, and

Caulaman Rivers to the Manila Bay.

The runoff values in Figure 9.3 were determined from the derived

full natural flow (FNF) of gaging stations. In the Agno River, for

example, adjusted gage flow of Station Q85 was utilized to obtain the

estimated discharge near the mouth of the river. The mean annual runoff

at Q8 5 was proportioned to the drainage areas at the station and at the

mouth in Lingayen Gulf and multiplied by the ratio of the average 224

121° 00'

Figure 9.3 Estimated Annual Runoff, Central Luzon Basin, Philippines. -- Runoff in million cubic meters. 225 precipitation at the watersheds of the two points. The average precipitation ratio was determined from the isohyetal method used in the water budget analysis. Thus, the estimates do not only consider the difference in drainage areas but also the rainfall variation in the watershed.

9.1.3 Surface Storage

Distinct natural depressions that function as natural surface storages are the Poponto Swamp formed at the confluence of the Tarlac and

Agno Rivers, and the San Antonio and Candaba Swamps formed near the foot of Mount Arayat in the Pampanga River basin. The aggregate area of these 2 swamps is about 320 km , less than 2 percent of the basin area.

In the rainy season (May to October), the swamps act as retarding basins in the respective river systems. The change in storage of the swamps between the period of April (the end of the dry season) to

October (the end of the wet season), was estimated at about 900 MCM. The estimate was based on the average swamp area and the difference in water levels at the swamps between these periods. The annual change in storage, however, was relatively small and estimated to be less than two percent of average annual streamflow from the Pampanga and Agno Rivers.

Other surface storages are man-made reservoirs. In the Agno

River, the Ambuklao and Binga Dams have average reservoir areas of 400 ha and 200 ha, respectively. The storage capacities of these dams are 258 MCM for the Ambuklao Dam and 48 MCM for the Binga Dam. The Angat Dam, in the Pampanga River basin, has a reservoir area and storage capacity of about 2,400 ha and 1,100 MCM, respectively. The newly constructed 226

Pantabangan Dam, in the upper reaches of the Pampanga River, has a reservoir area of about 8,420 ha and storage capacity of about 3,000 MCM.

9.1.4 Surface Water Quality

The quality of surface water in the Central Luzon Basin was assessed from the water quality data obtained from the Bureau of Public

Works. The BPW samples were obtained from streamflow gaging points during the period between 1962 and 1973. There were varying numbers of analyzed samples. There were even cases where only one sample was obtained for analysis. Table 9.1 presents the ranges of water quality parameters at different sampling points.

Total dissolved solids (TDS) commonly range from less than 100 ppm to about 250 ppm. Some samples, however, registered more than 600 ppm, such as those obtained from Q70 (630 ppm) and Q57 (647 ppm). Hard- ness expressed as calcium carbonate generally ranges from about 70 to 300 ppm. Calcium and chloride contents are commonly less than 100 ppm. The maximum carbonates and bicarbonates contents are generally less than 500 ppm. The alkalinity expressed as calcium carbonate is commonly in the range of about SO to 500 ppm. The pH of the water samples normally ranges from about 6 to 9, with several stations recording in the order of 10.

The surface water in the Central Luzon Basin could pass the water standards for domestic supply of the National Pollution Control

Commission (NPCC) and the Metropolitan Waterworks and Sewerage Services

(MSS). In fact, the NPCC classified most of the Central Luzon rivers

227

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The author, in conjunction with Mr. L. Q. Liongson, a team member in the University of Arizona graduate degree program, also made water sampling at selected points along the Agno and Pampanga Rivers. The objective of the sampling was to provide a picture of the variations of the water quality parameters in the dry and wet seasons. The water sampling was done in two periods: one from April 26 to May 1, 1976, for the dry season; and the other from June 28 to July 3, 1976, for the wet season. The wet season sampling was made two weeks after the heavy rains in May. Table 9.2 illustrates the comparison of the water quality parameters in the two sampling periods.

Table 9.2 shows that pH value is relatively consistent in the two sampling periods. The pH ranges from 7.7 to 8.5. Total dissolved solids

(TDS), hardness, and alkalinity are generally lower in the rainy season samples than in the dry season. The cations (Na, K, Ca, and Mg) concen- trations are also lower in the wet season than in the dry season samples.

(SO CO and Cl) are generally more diluted in the Likewise, the anions 4' 3' June-July sampling than in the April-May sampling. Turbidity and suspended residue values in the wet season sampling are much greater than the dry season values. The relative values of the dominant ions did not change with the season. The basic alkaline-bicarbonate type of the water was retained. Two seasonal influences were, however, detected; one is in the dilution effect on the TDS by the high wet month flows and the other

229

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9.1.5 Linkage of Surface and Sub-Surface Water

The interconnection between the surface water and groundwater could be described by the amount of groundwater recharge and discharge.

Recharge to the groundwater may be achieved in various ways. Significant factors are: infiltration of stream water into permeable stream beds, infiltration of rain water through permeable alluvial deposits, deep percolation from irrigated areas, and infiltration through fault surfaces.

Sandoval and Mamaril (1970) estimated that the direct rainfall recharge through the permeable alluvial deposits, reworked portions of the pyroclastic rocks, Alat, and Tarlac formations is about 200 MCM annually. The recharge through these rocks reaches the aquifers which yield freely flowing water in some wells. Deep percolation through irrigated rice lands is about 1.5 mm/day (Kampen, 1970) on the average. Con- sidering the large area covered by irrigated rice, the recharge by this process could amount to about 1,000 MCM annually. No estimates have been reported on the amounts of recharge accomplished through infiltration through river beds and fault surfaces.

The total annual recharge in the Central Luzon Basin was estimated to be in the order of 5,000 to 6,900 MCM from the water budget analysis. This large amount of annual recharge indicates a high degree of surface and groundwater linkage. 231

Groundwater discharge probably occurs largely as river base flow during the dry season. All major rivers in the basin continue to flow even during the prolonged dry periods, indicating their function as groundwater outlets. The swamps within the region are also possible groundwater discharge areas due to their relatively low elevations, although they may act as recharge points also during months of high water stage in the swamps. These natural depressions lose water through evaporation and transpiration. The groundwater movement toward the Lingayen

Gulf and Manila Bay could be another process of groundwater discharge.

9.2 Groundwater

The groundwater area in the Central Luzon Basin covers some 9,200 2 2 km , of which about 8,600 km correspond to the water table aquifer

(United Nations, 1968). Figure 9.4 illustrates the Central Luzon aquifers and Figure 9.5 gives geological cross-sections.

The recent alluvial deposits contain the water table aquifer with estimated thickness of about 30 to 50 m (98 to 164 ft). The maximum thickness is found approximately in the center of the basin and thins out toward the basin flanks. The water table depth varies from 1 to 8 m below ground level. Storage coefficient of the shallow aquifer is probably in the order of 0.01 to 0.1. Results of pump tests on the shallow aquifer alone are not available. Both deep and shallow aquifers are commonly pumped simultaneously in the observation wells during pumping tests.

Artesian aquifers are found in both Pleistocene and Pliocene formations. The aquifer is confined by a clay layer encountered at 232

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I CONFINED AQUIFER RECHARGE AREA A— - —8 GEOLOGICAL CROSS SECTIONS

0 143 20 30

Figure 9.4 Central Luzon Aquifers. -- After United Nations (1968). 233

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E I • 234 depths of about 50 to 100 m (164 to 328 ft). Transmissivities of the aquifer range from 62 to 1,243 m3/day/m (5,000 to 100,000 gpd/ft).

Storage coefficients are in the order of 10 -4 .

Figure 9.6 presents the variation in transmissivity in the region.

The aquifers were classified according to their transmissivities as: a) poor, less than 62 m3 /day/m (5,000 pd/ft); b) fair, 62 to 310 3 3 m /day/m (5,000 to 25,000 d/ft); c) good, 310 to 620 m /day/m (25,000 to 50,000 gpd/ft); d) very good, 620 to 1,240 m3/day/m (50,000 to 100,000 gpd/ft); and e) excellent, greater than 1,240 m3 /day/m (100,000 d/ft).

Highest transmissivity values were reported to have been encountered from limestone aquifers in Sibul, Bulacan. Table 9.3 gives the T and S values from selected wells in Central Luzon.

9.2.1 Chemical Quality of Groundwater

Groundwater in the Central Luzon Basin is generally of good quality. Table 9.4 presents chemical quality of groundwater from selected wells. Most wells yield water suitable for both domestic and

agricultural uses without prior treatment. Average total dissolved solids (TDS) of groundwater in the area has been reported to be about 500 ppm. Several wells, especially those near the coastal areas, yield water with TDS as high as 3,000 ppm, however.

Most wells in the Central Luzon Basin yield groundwater with is hardness from 100 to 250 ppm as calcium carbonate. Chloride content 50 ppm. usually below 100 ppm and has been reported to average about

Shallow aquifers found near the coast, however, could yield water with those in Lingayen, chloride content as high as 1,000 ppm, such as 235 236 Table 9.3 Coefficients of T and S from Selected Wells. -- After Sandoval and Mamaril (1970).

Depth Location (ft) (gpd/ft) Bulacan -3 Marilao, Nagbalan 730 5,000 1.6 x 10 -3 Bulacan, Pitpitan 450 30,000 1.2 x 10 -7 Gui guinto, Balubad 500 15,000 9.7 x 10 -3 Meycauayan, Bangkal 650 13,500 3.7 x 10 -3 Sta. Maria, Partida 903 4,400 8.9 x 10 Nueva Ecija -6 Aliaga 500 25,200 7.0 x 10 -1 Bongabon, Malate 425 1,340 2.6 x 10 -5 Cabanatuan City 504 32,050 2.6 x 10 -1 Penaranda, San Josef 637 3,440 1.1 x 10 -3 San Antonio 500 30,470 4.9 x 10 Pampanga -12 Angeles City 500 72,300 8.8 x 10 -7 Apalit, Sulipan 500 95,070 9.8 x 10 -12 Candaba, Bahay Pare 500 46,400 1.0 x 10 -4 Floridablanca, San Jose 490 7,870 4.2 x 10 -9 San Fernando, San Felipe 700 27,620 6.7 x 10 Pangasinana -10 Rosales, Carmen 506 33,650 1.6 x 10 -1 Umingan, La Paz 504 16,410 1.6 x 10 -3 Urbiztondo, Central School 500 13,000 1.6 x 10 -6 Urdaneta 500 31,150 2.7 x 10

237

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Several wells in Angat, Bulacan, have been reported to contain marsh gas in the water. It is believed that the presence of this gas is due to isolated patches of swamp deposits. Such deposits are usually high in organic matter. Anaerobic decomposition of organic matter results in formation of march gas.

9.2.2 Groundwater Potential Productivity

The shallow aquifer in the region covers about 8,600 km2 , with an average storage coefficient of about 0.05. The estimated average thickness of the aquifer is about 40 m. These figures indicate that the

aquifer may contain about 17,200 MCM (14 million ac-ft) of water in the

upper 50 m.

The artesian aquifer is confined by a clay layer of 5 to 20 in

(16 to 65 ft) thickness. The aquifer is usually encountered at depths

ranging from 50 to 100 m (164 to 328 ft). Transmissivity values range 3 from about 62 to 1,240 m /day/m and average storage coefficient is about -4 10 The extent of the area covered by the confined aquifer is not yet

well defined; thus, estimates of the potential available water in the

formation in unknown. Existing deep wells in the area, however, yield as

much as 90 to 100 1/sec (1,425 to 1,584 gpm). 239 Hydrogeologic properties of an aquifer could be estimated based on well log, water level, and specific capacity data (Walton, 1970). Specific capacity of a well has been defined (Lohman and others, 1972) as the rate of discharge of water from the well divided by the drawdown of water level within the well. High specific capacities are generally associated with high aquifer transmissivity, and low specific capacities with low transmissivity. Adverse factors such as improper well construction and development, well loss, and hydrogeologic boundaries, however, affect the specific capacity of a well; thus, coefficient of transmissivity computed from specific capacity data could be less than the actual coefficient of transmissivity. Nevertheless, even rough estimates of the coefficient of transmissivity could be valuable, especially in reconnaissance groundwater investigation.

Frequency analysis of specific capacity data of wells penetrating one or several units of a multiunit aquifer could be useful in estimating the range in productivity and relative consistency in productivity of the various units (Walton, 1970). Specific capacity data of 179 deep wells

(depth greater than 50 m) and 228 shallow wells (depth less than 50 m) were subjected to such analysis.

The objectives of the analysis are to obtain a measure of the probable yield of wells in the basin and to compare the capacities of the shallow and deep aquifers. Frequency curves of specific capacity per unit depth of penetration were constructed for the two aquifers. Wells

With available specific capacity data were grouped into the shallow aquifer and deep aquifer categories. Specific capacity values were 240 the divided by corresponding well depth. The resulting data of specific capacity per unit depth of well penetration were arranged and ranked in decreasing order in each group. The probability of each value was computed by P = m/(n + 1), where P is the probability, m is the rank, and n is the number of wells considered. The specific capacity per unit depth was plotted against the corresponding probability on logarithmic probability paper. A straight line was fitted in each group of data.

Figure 9.7 illustrates the frequency curves of the shallow and deep aquifers.

The information depicted by Figure 9.7 indicates that wells in the upper 50 m of the alluvial deposits yield more consistent specific capacities than those sunk at greater depths. The frequency analysis also tends to show that the shallow aquifer is probably more productive than the deep aquifer, i.e., as the well depth increases, the specific capacity per meter of penetration decreases. Taking the specific capacities at 50 percent probability, for example, the shallow aquifer would yield about 1.2 liters per min/m drawdown/m penetration, whereas the deep aquifer would only yield about 0.4 liter per min/m drawdown/m penetration. Thus, it seems more advisable to develop the shallow aquifer than the deeper formation for both domestic and agricultural purposes. Consideration of the groundwater quality, especially in the coastal areas, should not be overlooked, however.

9.3 Water Requirement in the Basin

Determination of water requirement includes only those sectors industry. Which are consumptive users, e.g., agriculture and 241

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Data. Figure 9.7 Frequency Curves of Specific-CaPacity 242

Hydroelectric power generation is not regarded as consumptive use since any water utilized to drive the turbines to produce power is brought back to the stream for other users. The increased evaporation as a conseqeunce of the reservoir is the only amount that could be regarded as consumed by power generation projects. Hydroelectric power plants may, however, interfere with other water-interested sectors because of the resulting river flow modification due to their operations. This conflict could be remedied through coordination among the resource users. The region's water requirement is, thus, largely dependent on the population and agricultural and industrial activities. The population reflects the domestic water requirement. Agricultural activity and land area devoted to crops provide estimates for agricultural water requirements. Extent of industrial development and types of industrial activities determine the water requirement for this sector.

The Development Academy of the Philippines (1975) estimated the projected regional water requirements in the Philippines for the period

1980 to 2000. The estimates were based on the population projections made by the National Census and Statistics Office (NCSO) derived from the 1970 population census. The DAP water requirement determination considered three sectors of users: agriculture, domestic supply, and industry. Domestic requirements were computed based on an average per agricultural capita consumption of 700 liters per day (185 gpd). The consumptive water requirements were determined from the cropped area and vegetables, use of corresponding crops. Seven major crops (rice, corn, coconut) were considered. Industrial fruits, sugar cane, tobacco, and 243

water requirements were based on manufacturing industries. Eleven types

of industries (food, beverages, textile, petroleum and coal, chemical

products, electrical machinery, industrial and agricultural machinery,

transport equipment, basic metal and metal products, and wood cane and

cork) were included. The estimates for the Central Luzon region are

given in Table 9.5

Table 9.5 Projected Water Requirements of the Central Luzon Basin. -- Requirements in MCM/day (Development Academy of the Philippines (1975).

Year Sector 1980 1990 2000

Agriculture 55.6 55.7 67.3 (20,300)* (20,330) (24,570)

Domestic 3.7 5.1 6.6 (1,350) (1,860) (2,410)

Industry 1.1 2.4 4.9 (400) (880) (1,790)

60.4 63.2 78.8 Total (22,050) (23,070) (28,770)

*Number in parentheses is in MCM per year.

the If one assumes that all the annual river discharges from annual water Central Luzon Basin could be stored and utilized, the could be satisfied from surface requirements for the years 1980 and 1990 year 2000, however, this water resources alone (see Figure 9.3). For the would not be sufficient. Since, economically and technically, it may not 244 be possible to build storage reservoirs in the plains and delta areas, reliance on surface water sources alone is not advisable. In fact, even now (1976), water shortages are being experienced, particularly during the dry season. This is brought about by the concentration of rainfall in the wet season months. Most national irrigation systems, for example, could only irrigate about 20 percent of their potential area during the dry months. Parched, idle, agricultural land is not an uncommon sight in

February, March, and April. Insufficient domestic supply in some big cities like Manila during prolonged dry spells has also been experienced. CHAPTER 10

SUMMARY AND CONCLUSIONS

The first portion of the work dealt with the problem of stream-

flow data augmentation. Four augmentation methodologies -- percent

deviation, HEC-4, YOR, and Matalas-Jacobs -- were selected and compared.

The comparison was made based on 5 streamflow gaging stations within the

Central Luzon Basin. Historical records of the 5 stations were divided

into the early-half and late-half series. The late-half series was used

to obtain estimates corresponding to the early-half series for each sta-

tion and augmentation procedure. The estimates were determined by cross-

correlation with a base station. The comparison was made on the early-

half historical data and corresponding augmentation estimates. Eight

items of comparison were considered -- four items for the annual flow and

another four for the monthly flow. Two scoring systems, the point and

the percentage scoring systems, were applied. that the most The results of the two scoring systems tend to show study is the suitable method for the type of available data used in the other methods in five percent deviation method. The method outscored the flow comparison out of the eight items of comparison. When the annual stations obtained items were isolated, however, the average score from all

by the percent deviation method was outranked by the HEC-4 in the point percentage scoring scoring system and by the Matalas-Jacobs in the annual regression without noise System. This seems to prove that the 245 246 term being used in the percent deviation to derive annual values for the dependent station is not capable of tracking down the variability of the flows. In the monthly flow items, nevertheless, the percent deviation method ranked first in both scoring systems. This tends to isolate the strong points of the percent deviation method for the generation of monthly flow estimates. When the dependent station having the poorest correlation coefficient with its base station was eliminated, both scoring systems showed that the percent deviation outranked all other methods in both the annual and monthly flow comparison items. This indicates that the relationship between the base and dependent stations largely affects the augmentation estimates, i.e., the higher the cross- correlation coefficients, the more reliable are the generated estimates.

The percent deviation method was used to augment the record of selected streamflow gaging stations within the Central Luzon Basin. The application of the method threshed out some limitations of the method, such as:

1. Monthly filler values for water years with partial data could be

unreasonably high or low relative to the existing values.

2. Monthly and annual filler values in tandem stations could be sta- inconsistent, i.e., the derived estimates in the downstream

tion could be unreasonably lower than those derived for upstream

stations.

3. The regression equation used to derive annual estimates could dependent station generate negative values when the flow in the

is too small compared to the flow in the base station. 247

The first limitation was noticed when the estimates in Stations

Q29 and Q19 were compared. The two stations were only inconsistent in months where the filler values for Station Q29 were in the years of partial record. This situation came about because the estimates were derived without making use of the existing information provided by the partial record in the year. The shortcoming could be remedied when the estimates could be made consistent with the existing values. Thus, a modification is herein proposed. For years with partial records, the regressed annual values (derived from the regression equation established from the simultaneous records of the dependent and base stations) should be checked against the partial total before deriving the monthly fillers for the corresponding gaps. If the regressed value becomes unreasonably higher or lower than the partial total, there is a great possibility that monthly fillers butt up with the existing values; thus, the regressed annual total has to be adjusted first, or a new estimate has to be made.

Since the regression equation will only provide one value for each existing value in the base station, a new annual value has to be derived from another relationship.

Let the partial year total of existing monthly flows of a particular water year in the dependent station be T and the regressed annual flow be T with T > T . This case would be more likely to r' P r derive underestimated monthly filler values. A new annual value could be derived from the partial year total as: 12 12-k T' =( E PD. .T )/( E PD..) (10.1) r . 'J P 13 J=1 ) - I 248 where T; is the new annual value, PD is the derived monthly percent deviation for the year with gaps, and k is the number of months with the missing record. This relationship would be more useful when k is small.

In any case, there is no guarantee that this would give better results than the regression equation. This is only used when the regression equation fails to generate a reasonable value compared with the existing partial total. Monthly filler values are then obtained in the usual percent deviation fashion, retaining the existing monthly values and appending the monthly filler values to them.

The second limitation could be due to inconsistent historical values like those obtained from the comparison of Q70, Q71, and Q72. The full natural flow (FNF) used in the augmentation could also become the source of these inconsistencies. The gaged flow of the stations were adjusted based on estimated components like irrigation diversions and return flows. The derived FNF reflects the entire drainage area above the station which includes the drainage areas of upstream stations and the drainage area directly contributing to the flow of the station in stations question. Figure 10.1 illustrates the drainage areas of three in tandem. flow at Station The drainage area A contributes directly to the watersheds of Sta- Q72. Likewise, drainage areas B and C are the direct data augmentation, it tions Q71 and Q70, respectively. In the streamflow the drainage is desired to derive full natural flows reflecting only Thus, areas directly contributing to the flow measured at the stations. A only. This in this case, FNF of Q72 should reflect the drainage area 249

Agno River

Figure 10.1 Drainage Areas of Streamflow Gaging Stations in Tandem. 250 procedure eliminates the problem associated with the inconsistency of derived flows in tandem stations since the augmentation is carried out on flows derived from the net (non-overlapping) drainage areas; however, the stations in tandem should have sufficiently long simultaneous data. In this study, Q72 was used as the base station to lengthen the record of

Q70 and of other stations within the Agno River Basin (see Table 7.35).

This requires that the record of Station Q72 be the longest and without gaps in order to bring the records of the dependent stations to the desired period. Adjustment of the record of Station Q72 to represent flows reflected in its direct watershed will bring the Q72 record to a length equal to the length of the shortest record in tandem with it; hence, its qualification as base station will be lost. Thus, the FNF of

Q72 used in this study reflects the sum of the drainage areas A, B, and

C, or the entire watershed above the gaging point. Such type of data base used in augmentation would result in some unreasonable filler values. Consistency test is, therefore, necessary to check the reasonableness of the augmentation estimates. Negative annual runoff is never expected to happen. The augmentation procedure should guard against generating such meaningless runoff. In the application of the percent deviation method, some derived flows as the base for Station Q60 are negative. These were obtained with Q71 linear regression station. Negative values could be generated from the equation is nega- equation when at least one of the coefficients in the tively large. In the case of Q60 and Q71, for example, the annual flows at Q60 were derived from Q71 through: 251

Y = 1.395 Y 60 71 - 721.97 (10.2)

where Y is the regressed annual flow at Q60, and Y is observed 60 71 the annual flow at Q71. The large negative intercept and relatively small

regression coefficient for Y71 in Equation 10.2 produce negative Y60

values when the recorded Q71 flow is small. Regression equations like

that in Equation 10.2 are derived whenever the drainage area of the

dependent station is much smaller than the drainage area of the base sta-

tion, resulting in large differences between the annual flows in the sta- 2 tions. In this case, Q60 has a drainage area of 53 km , while Q71 has 2 281 km . The problem could be avoided if selection of the base station

includes, among others, compatibility of drainage areas such that the base station drainage area is comparable to that of the dependent

station.

In effect, three major points were identified for the derivation

of more reliable estimates in the application of the percent deviation method to streamflow data augmentation: 1) utilization of the informa-

tion provided by the partial year data to avoid unreasonably low monthly

filler estimates; 2) application of full natural flow corresponding to

the drainage area directly contributing to the flow of a gaging station

to eliminate the problem of inconsistent values in tandem stations; and

3) limitation of the method, as much as possible, to stations of negative estimates which comparable drainage areas to avoid generation of are both meaningless and useless. the measured The water budget analysis of the basin revealed that 46 percent is streamflow is about 54 percent of rainfall. The balance of 252 shared by evapotranspiration and change in basin storage. An average annual evapotranspiration of about 1,070 mm was derived. This is about

0.5 of the average Class A pan evaporation data from two measurement points. The evapotranspiration-pan evaporation ratio was regarded as low compared to previously reported values.

Surface water resources remain the most important source of water for the basin requirements. No serious water quality problems exist and surface water in the basin is suitable for both domestic and agricultural purposes. The major problem lies in the development and management of the basin water resources such that water shortages in the dry season could be alleviated. Storage of the high streamflows during the wet season for dry season utilization could probably provide solution to the problem. Series of catchment ponds along major rivers in the basin where they are economically and technically feasible could impound enough water during the rainy season. These ponds could also function as flood control structures by delaying the river flows and modifying the flood hydrographs to non-damaging levels. and Groundwater could be a promising resource for both domestic rela- agricultural purposes. High recharge during the rainy season and of the basin. tively shallow aquifers are among the significant features in the area of immediate use Furthermore, the availability of groundwater diversion, and would eliminate the construction of expensive storage, systems. The conveyance facilities for water distribution in irrigation surface groundwater, when properly managed and used in conjunction with 253 water, coUld provide a long-term supply of water for irrigation and other uses.

Groundwater development could enhance greater recharge and reduce natural groundwater discharge. This may have some effect in the reduction of floods due to enhanced infiltration and reduction of surface runoff. The development of the sub-surface resource, however, calls for more detailed study to better understand the characteristics of the aquifers. Sufficient design data for groundwater development in specific locations should also be provided through continuous research on areas of major groundwater potential such as the Central Luzon Basin. APPENDIX A

HYDROGEOLOGY OF CENTRAL LUZON BASIN, PHILIPPINES

254 255 APPENDIX B

MEAN ANNUAL RAINFALL OF CENTRAL LUZON BASIN, PHILIPPINES

256 257 APPENDIX C

MONTHLY PERCENTAGE OF DAYTIME HOURS (p) OF THE YEAR FOR

LATITUDES 0 TO 65 ° NORTH OF THE EQUATOR*

(1967). *After U. S. Department of Agriculture 258 259

Latitude North Jan. Feb. Mar. Apr. May June Jui Au Sept. Oct. Nov. Dec.

65 ° 3.52 5.13 7.96 9.97 12.72 14.15 13.59 11.18 8.55 6.53 4.08 2.62 64° 3.81 5.27 8.00 9.92 12.50 13.63 13.26 11.08 8.56 6.63 4.32 3.02 63 ° 4.07 5.35 8.04 9.86 12.29 13.24 12.97 10.97 8.56 6.73 4.52 3.36 62 ° 4.31 5.49 8.07 9.80 12.11 12.92 12.73 10.87 8.55 6.80 4.70 3.65 61 ° 4.51 5.58 8.09 9.74 11.94 12.66 12.51 10.77 8.55 6.88 4.86 3.91 60 0 4.70 5.67 8.11 9.69 11.78 12.41 12.31 10.68 8.54 6.95 5.02 4.14 59 ° 4.86 5.76 8.13 9.64 11.64 12.19 12.13 10.60 8.53 7.00 5.17 4.35 58 ° 5.02 5.84 8.14 9.59 11.50 12.00 11.96 10.52 8.53 7.06 5.30 4.54 57 ° 5.17 5.91 8.15 9.53 11.38 11.83 11.81 10.44 8.52 7.13 5.42 4.71 56 ° 5.31 5.98 8.17 9.48 11.26 11.68 11.67 10.36 8.52 7.18 5.52 4.87 55 ° 5.44 6.04 8.18 9.44 11.15 11.53 11.54 10.29 8.51 7.23 5.63 5.02 54 0 5.56 6.10 8.19 9.40 11.04 11.39 11.42 10.22 8.50 7.28 5.74 5.16 53 ° 5.68 6.16 8.20 9.36 10.94 11.26 11.30 10.16 8.49 7.32 5.83 5.30 52 0 5.79 6.22 8.21 9.32 10.85 11.14 11.19 10.10 8.48 7.36 5.92 5.42 51° 5.89 6.27 8.23 9.28 10.76 11.02 11.09 10.05 8.47 7.40 6.00 5.54 50° 5.99 6.32 8.24 9.24 10.68 10.92 10.99 9.99 8.46 7.44 6.08 5.65 49 0 6.08 6.36 8.25 9.20 10.60 10.82 10.90 9.94 8.46 7.48 6.16 5.75 48 ° 6.17 6.41 8.26 9.17 10.52 10.72 10.81 9.89 8.45 7.51 6.24 5.85 47° 6.25 6.45 8.27 9.14 10.45 10.63 10.73 9.84 8.44 7.54 6.31 5.95 46° 6.33 6.50 8.28 9.11 10.38 10.53 10.65 9.79 8.43 7.58 6.37 6.05 45° 6.40 6.54 8.29 9.08 10.31 10.46 10.57 9.75 8.42 7.61 6.43 6.14 44° 6.48 6.57 8.29 9.05 10.25 10.39 10.49 9.71 8.41 7.64 6.50 6.22 43° 6.55 6.61 8.30 9.02 10.19 10.31 10.42 9.66 8.40 7.67 6.56 6.31 42° 6.61 6.65 8.30 8.99 10.13 10.24 10.35 9.62 8.40 7.70 6.62 6.39 41° 6.68 6.68 8.31 8.96 10.07 10.16 10.29 9.59 8.39 7.72 6.68 6.47 6.54 40° 6.75 6.72 8.32 8.93 10.01 10.09 10.22 9.55 8.39 7.75 6.73 6.61 39° 6.81 6.75 8.33 8.91 9.95 10.03 10.16 9.51 8.38 7.78 6.78 7.80 6.83 6.68 38 0 6.87 6.79 8.33 8.89 9.90 9.96 10.11 9.47 8.37 6.88 6.74 37° 6.92 6.82 8.34 8.87 9.85 9.89 10.05 9.44 8.37 7.83 7.85 6.93 6.81 36° 6.98 6.85 8.35 8.85 9.80 9.82 9.99 9.41 8.36 7.88 6.98 6.87 35' 7.04 6.88 8.35 8.82 9.76 9.76 9.93 9.37 8.36 0 8.35 7.90 7.02 6.93 34 7.10 6.91 8.35 8.80 9.71 9.71 9.88 9.34 8.35 7.92 7.06 6.99 33° 7.15 6.94 8.36 8.77 9.67 9.65 9.83 9.31 8.34 7.95 7.11 7.05 32° 7.20 6.97 8.36 8.75 9.62 9.60 9.77 9.28 8.34 7.97 7.16 7.11 31° 7.25 6.99 8.36 8.73 9.58 9.55 9.72 9.24 8.33 7.99 7.20 7.16 30° 7.31 7.02 8.37 8.71 9.54 9.49 9.67 9.21 8.33 8.00 7.24 7.22 29° 7.35 8.69 9.50 9.44 9.62 9.19 7.05 8.37 8.32 8.02 7.28 7.27 28 ° 7.40 8.37 8.67 9.46 9.39 9.58 9.17 7.07 8.32 8.04 7.32 7.32 27° 8.66 9.41 9.34 9.53 9.14 7.44 7.10 8.38 8.32 8.06 7.36 7.37 6 9.29 9.49 9.1] 2 :' 7.49 7.12 8.38 8.64 9.37 8.08 7.40 7.42 25° 9.45 9.08 8.31 7.54 7.14 8.39 8.62 9.33 9 . 24 8.10 7.44 7.47 24° 9.30 9.19 9.40 9.06 8.31 7.58 7.16 8.39 8.60 8.30 8.12 7.47 7.51 23° 9.26 9.15 9.36 9.04 7.62 7.19 8.40 8.58 8.30 8.13 7.51 7.56 22° 9.22 9.11 9.32 9.01 7.67 7.21 8.40 8.56 8.29 8.15 7.55 7.60 21 0 9.18 9.06 9.28 8.98 7.71 7.24 8.41 8.55 8.29 8.17 7.58 7.65 20° 9.15 9.02 9.24 8.9-5 7.75 7.26 8.41 8.53 8.29 8.19 7.61 7.70 19° 9.12 8.97 9.20 8.93 7.79 7.28 8.41 8.51 8.29 8.20 7.65 7.74 0 9.16 8.90 18 7.83 7.31 8.41 8.50 9.08 8.93 260

Latitude North Jan, Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

20° 7.75 7.26 8.41 8.53 9.15 9.02 9.24 8.95 8.29 8.17 7.58 7.65 19 ° 7.79 7.28 8.41 8.51 9.12 8.97 9.20 8.93 8.29 8.19 7.61 7.70 18° 7.83 7.31 8.41 8.50 9.08 8.93 9.16 8.§0 8.29 8.20 7.65 7.74 17° 7.87 7.33 8.42 8.48 9.04 8.89 9.12 8.88 8.28 8.22 7.68 7.79 16 ° 7.91 7.35 8.42 8.47 9.01 8.85 9.08 8,85 8.23 8.23 7.72 7.83 15° 7.94 7.37 8.43 8.45 8.98 8.81 9.04 8.83 8.27 8.25 7.75 7.83 14° 7.98 7.39 8.43 8.43 8.94 8.77 9.00 8.80 8.27 8.27 7.79 7.93 13 ° 8.02 7.41 8.43 8.42 8.91 8.73 8.96 8.78 8.26 8.29 7.82 7.97 12° 8.06 7.43 8.44 8.40 8.87 8.69 8.92 8.76 8.26 8.31 7.85 8.01 11° 8.10 7.45 8.44 8.39 8.84 8.65 8.88 8.73 8.26 8.33 7.88 8.05 10° 8.14 7.47 8.45 8.37 8.81 8.61 8.85 8.71 8.25 8.34 7.91 8.09 9° 8.18 7.49 8.45 8.35 8.77 8.57 8.81 8.68 8.25 8.36 7.95 8.14 8° 8.21 7.51 8.45 8.34 8.74 8.53 8.78 8.66 8.25 8.37 7.98 8.18 7° 8.25 7.53 8.46 8.32 8.71 8.49 8.74 8.64 8.25 8.38 8.01 8.22 6° 8.28 7.55 8.46 8.31 8.68 8.45 8.71 8.62 8.24 8.40 8.04 8.26 5 ° 8.32 7.57 8.47 8.29 8.65 8.41 8.67 8.60 8.24 8.41 8.07 8.30 4 ° 8.36 7.59 8.47 8.28 8.62 8.37 8.64 8.57 8.23 8.43 8.10 8.34 3° 8.40 7.61 8.48 8.26 8.58 8.33 8.60 8.55 8.23 8.45 8.13 8.38 2° 8.43 7.63 8.49 8.25 8.55 8.29 8.57 8.53 8.22 8.46 8.16 8.42 1 0 8.47 7.65 8.49 8.23 8.52 8.25 8.53 8.51 8.22 8.48 8.19 8.46 0° 8.50 7.67 8.49 8.22 8.49 8.22 8.50 8.49 8.21 8.49 8.22 8.50 APPENDIX D

INVENTORY OF SHALLOW OBSERVATION WELLS, CENTRAL LUZON BASIN, PHILIPPINES

261 262

Ground Surface Well Location Elevation Period of No. (Barrio, Town) (m above MSL) Record Remarks

A. Pampanga River Basin Bulacan Province

B-1 Tuktukan, Guiguinto 2.53 1958-1972 Covered, April 1972 B-2 Tartaro (Dinarayat), 31.79 1958-1967 Abandoned, San Miguel 1969&1972 May 1972 B-3 Sta. Clara, Sta. 9.14 1958-1964 Closed by Maria owner, August 1964 B- 3A Patag, Sta. Maria 1968-1970 Abandoned, June 1972 B-3B San Gabriel, Sta. 1972-1975 To be leveled Maria B-4 Pob. Norzagaray 38.92 1958-1975 Abandoned, B-5 30.31 1958-1966 Binagbag, Angat July 1967 1968-1970 Abandoned, B- SA Sabang, Angat April 1970 1972-1974 To be leveled B- 5B Sulukan, Angat 10.60 1958-1965 Abandoned, B-6 San Pedro, Bustos July 1970 1964-1975 B-6A Tanawan, Bustos 9.86 10.72 1958-1965 Flowing, B-7 Maginao, San Rafael 1965-1968 1968-1972 Abandoned, May 1972 7.02 1958-1972 Covered, B-8 Balong-Bato, San April 1972 Ildefonso 1964-1975 B-9 Palazan, Polo 3.66 Valenzuela 1964-1975 B-10 Pob. Malolos 0.42 1964-1975 B-11 San Nicolas, Bulacan 8.97 8.39 1964-1968 Abandoned B-12 San Isidro, Hagonoy February 1969 263

Ground Surface Well Location Elevation Period of No. (Barrio, Town) (m above MSL) Record Remarks

B-13 Catholic Church, 8.95 1958-1960 Abandoned, Plaridel July 1960

Nueva Ecija Province

NJ-1 Pob. Cabiao 12.02 1958-1970 Abandoned, July 1970 NJ-2 Sto. Tomas, 30.31 1958-1964 Abandoned, Penaranda August 1964 NJ-2A San Josef, Penaranda 1968-1970 Abandoned, July 1970

NJ-3 Del Pilar, Zaragoza 19.35 1958-1975 NJ-4 Pob. Talavera 38.12 1958-1975 NJ-5 Pob. Laur 62.83 1958-1975 82.53 1958-1961 Abandoned, NJ-6 Pob. Rizal December 1961 32.17 1958-1960 Abandoned, NJ-7 Aduas, Cabanatuan November 1960 City 36.94 1958-1963 Abandoned, NJ-8 Maturanog, Guimba March 1963 1958-1975 NJ-9 Matindog, Cuyapo 36.53 130.88 1958-1966 Abandoned, NJ-10 Burgos St., Lupao October 1966 1958-1975 NJ-11 Pob. Quezon 23.59 13.50 1958-1968 Abandoned, NJ-12 San Francisco, San April 1968 Antonio 1968-1970 Abandoned, NJ-12A Pob. San Antonio March 1970 68.38 1958-1964 Abandoned, NJ-13 Bicus, Rizal November 1964 17.32 1964-1966 Abandoned, NJ-14 Pob. San Isidro August 1967 56.82 1968-1975 NJ-14A Malapit, San Isidro 1964-1975 NJ-15 Pob. Sta. Rosa 25.68 25.11 1965-1966 Abandoned, NJ-16 San Juan de Dios, July 1967 Aliaga 264

Ground Surface Well Location Elevation Period of No. (Barrio, Town) (m above MSL) Record Remarks

NJ-16A San Pablo, Aliaga 24.41 1968-1975 NJ-17 Abar 1st, San Jose 104.01 1964-1975 City NJ-18 Pob. Nampicuan 17.85 1965-1971 Filled up, April 1971 NJ-19 Bantog, Licab 17.26 1967-1969 Abandoned, March 1970

Pampanga Province

P-1 Pob. Macabebe 0.94 1958-1961 Covered, July 1968-1970 1970 P-2 San Jose, San Simon 2.18 1958-1961 Abandoned, February 1962

P-3 Pob. Porac 71.20 1958-1975 P-4 San Jose, 28.29 1958-1966 Covered, May Floridablanca 1968-1969 1970 P-5 Pob. Magalang 40.97 1958-1975 P-6 Mexico 23.75 1958-1972 Abandoned, Sep- Anao, tember, 1972 P-7 Sta. 6.74 1958-1972 Abandoned, San Matias, July 1972 Rita Closed by Maimpas, San 26.98 1958-1966 P-8 1968-1970 owner, April Fernando 1970 Closed, April P-9 Mapalad, Arayat 8.44 1958-1964 1964 7.19 1968-1970 Covered, May P-9A Pob. Arayat 1970 88.78 1959-1970 Covered, May P-10 San Jose, Angeles 1970 City 1968-1971 Covered, July P-11 Pob. Sta. Ana 1971

Tarlac Province 56.39 1964-1972 Covered, T-2 Talaga, Capas April 1972 265 Ground Surface Well Location Elevation Period of No. (Barrio, Town) Ou above MSL) Record Remarks

T-7 Pob. Ramos 17.30 1963-1975 T-8 San Gabriel, 22.17 1965-1967 Abandoned, Victoria February 1968 T-8A San Gavino, Victoria 72.73 1968-1972 Abandoned, May 1972 T-11 Maliwalu, Tarlac 40.47 1965-1975 T-12 Pob. Pura, 4 26.50 1965-1975 T-13 Canarem, Victoria 1969-1975 To be leveled

B. Agno River Basin

Tarlac Province

T-1A Pob. Camiling 18.18 1964-1967 Abandoned, February 1968 T-1B Surgui III, Camiling 1968-1972 To be leveled T-3 Sepung-Calzada, 40.40 1964-1966 Tarlac 1968-1975 T-4 Katagudingan, San 15.44 1965-1975 Clemente T-5 Salumagui, Paniqui 15.47 1965-1975 T-6 Abagon, Gerona 21.70 1964-1975 T-9 Pob. San Manuel 24.73 1958-1959 Closed, January 1960

T- 9A Sta. Maria, San 21.14 1964-1975 Manuel 30.03 1965-1967 Abandoned, T-10 Pob. Concepcion February 1968 To be leveled T-10A Santiago, Concepcion 1968-1974

Pangasinan Province 23.76 1958-1967 Abandoned, Pa-1 Pob. Villasis March 1968 Abandoned, Pa-lA San Nicolas, 1968-1973 July 1973 Villasis 266 Ground Surface Well Location Elevation Period of No. (Barrio, Town) Om above MSL) Record Remarks

Pa-2 Pob. Urdaneta 23.45 1958-1975 Pa-3 San Aurelio, 34.24 1958-1962 Abandoned, Balungao November 1962 Pa-4 Pob. Umingan 111.55 1958-1975 Pa-5 Pob. Natividad 73.83 1958-1968 Abandoned, 1970-1973 October 1973 Pa-6 Pob. San Manuel 61.21 1958-1974 Pa-7 Pob. Mangaldan 3.86 1958-1959 Abandoned, 1964-1967 January 1968 Pa-7A Bari, Mangaldan 8.35 1968-1975 Pa-8 Pob. Malasiqui 9.01 1958-1975 Pa-9 Pob. Bayambang 21.85 1958-1962 Abandoned, November 1962 Pa-9A Talbang, Bayambang 21.85 1965-1972 Abandoned, December 1972 Pa-10 Pob. Mangatarem 10.79 1958-1975 Pa-11 Pob. Aguilar 7.65 1958- 19 75 Pa-12 Pob. Lingayen 1.10 1958-1975 Pa-13 Alopangpang, 56.35 1958-1975 Pozorrubio Pa-14 Pob. San Carlos City 7.44 1964-1970 Abandoned, July 1970

Pa-15 Pob. Sta. Maria 4.25 1964-1975 Abandoned, Pa-16 Pob. Urbiztondo 6.36 1964-1975 January 1973

Pa-17 Pindangan, Alcala 20.19 1965-1975 1965-1975 Pa-18 Pob. Sto. Tomas 24.10 Abandoned, Pa-19 Bautista 15.54 1965-1968 Pob. March 1968 1968-1975 Pa-19A Nardacan Sur, 52.72 Bautista Covered, Pa-20 San Jacinto 9.18 1964-1970 Pob. April 1970 267

Ground Surface Well Location Elevation Period of No. (Barrio, Town) (m above MSL) Record Remarks

Pa-21 Caloocan, Manaoang 89.70 1968-1975

Pa-22 Kabangaran, San 1974 To be leveled Quintin APPENDIX E

SAMPLE COMPUTATION OF FULL NATURAL FLOW

Derivation of full natural flow (FNF) of stations in the Pampanga

River Basin is presented. Monthly gaged flows of five stations -- Q08,

Q16, Q17, Q19, and Q29 -- were adjusted by means of the relationship derived in Chapter 6. Figure E.1 shows the location of the stations and irrigation diversion points. Monthly diversions by the irrigation systems were derived by:

1. Graphical correlation with upstream or downstream streamflow

record:

a. Talavera River Irrigation System (TRIS) versus Q22.

b. Lower Talavera River Irrigation System (LTRIS) versus Q22.

c. Pampanga River Irrigation System (PRIS) versus Q03.

d. Penaranda River Irrigation System (PenRIS) versus Q16.

2. Monthly averages of available monthly diversion data:

a. Vaca Creek Irrigation System (VCIS).

b. Murcon Creek Irrigation System (MCIS). (PBRIS). c. Pampanga-Bongabon Rivers Irrigation System (BBCIS). d. Bical-Bical Creek Irrigation System

Figures E.2-E.5 illustrate the rating curves derived using graphical cor- diversion relation technique. Table E.1 presents the estimated monthly diversion for the systems for each irrigation system and the mean monthly where graphical correlation method was not used. 268 269

II ) irais c....

•*".. ) BBCIS 16

PeuRIS /

Q29

)

Figure E.1 Streamflow Gaging Stations and Irrigation Diversion Points. 270

II 1 '1 I 1 .5 1.0 Diversion at MIS; CMS

Upstream Gaged Flow, Figure E.2 Relationship of Irrigation Diversion and PRIS versus Q03. 271

o o 272

100

1 1 I I 1.1 t 1 1 1111 5 10 0.1 0.5 1.0 Diversion at TRIS, CMS

Figure E.4 Relationship of Irrigation Diversion and Upstream Gaged Flow, TRIS versus Q22. 273

100

00 50

1n•n.,

1 1 1 1.0 1 1 1 0.1 Diversion at .LTRIS, CMS

Figure E.5 Relationship of Irrigation Diversion and Upstream Gaged Flow, LTRIS versus Q22.

•▪

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Q08 = FNF Q08G + DPRIS

2. Station Q16:

Q16 = Q16 + D FNF G PenRIS

3. Station Q17:

Ql7FNF = Q i7 G + DPenRIS

4. Station Q19:

TD - TRF Q19FNF = Ql9 G + ( 19 19 )

where

TD 19 = DPRIS + DVCIS + DMCIS 4. DPBRIS + DBBCIS + DPenRIS

TRF = 0.264 TD (for the dry season, November to April) 19 19 = 0.456 TD (for the wet season, May to October) 19 Hence,

Q19 = Q19 + 0.736 TD (for the dry season) FNF G 19 and

Q19 = Q19 + 0.544 TD (for the wet season) FNF G 19

5. Station Q29:

Q29 = Q29 + - TRF ) FNF G (TD29 29

where

TD = TD + D + D 29 19 (TRIS + SAE) LTRIS

TRF = 0.264 TD (for the dry season) 29 29

= 0.456 TD (for the wet season) 29 279

Hence,

0.736 TD (for the dry season) Q 29 FNF = Q29 G 29 and

Q29 = Q29 + 0.544 TD (for the wet season) FNF G 29 where Q is the monthly flow defined by the subscripts FNF for full the natural flow and G for gaged flow, in the corresponding station; D is monthly diversion by the corresponding irrigation system; TD is the total affecting diversion affecting the station; TRF is the total return flow the station; and the coefficients 0.264 and 0.456 were derived in for the adjusted Chapter 6. Table E.2 gives the estimated monthly FNF gaging stations. River Basin was Derivation of FNF for stations within the Agno caused by the Binga and done in the same manner. River flow modification system in addition to Ambuklao Reservoirs was considered in this river irrigation diversions. 280

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,TPCT.CPPTC7,17,Cr. C.Pa.Cr.PCf.a. CPCT ,r,JcsvyrsioJr.v. rvr 0 o 0103000000 0,000 cY ca3 0 ocr0op APPENDIX F

CORRELATION COEFFICIENTS OF SELECTED PAIRS

OF STREAMFLOW GAGING STATIONS

283 284

Pair of Stations n R Pair of Stations n R

Pampanga River Basin

Q09 Q16 15 0.3248 Q21 Q24 6 -0.5776 Q17 13 0.5387 Q26 5 -0.4992 Q19 6 0.8951 QSL 6 0.3003 Q29 12 0.5707 Q24 Q31 7 0.4164 Q16 Q17 22 0.9335 Q32 5 0.5625 Q19 6 0.4733 Q38 8 0.1243 Q20 13 0.4914 Q21 10 0.6172 Q26 024 4 0.9779 Q24 8 0.1434 Q26 6 0.4794 Q29 Q31 12 0.4932 Q29 23 0.7210 Q32 10 0.7259 Q31 15 0.1386 Q38 20 0.6066 Q32 13 0.4336 QSL 20 0.4765 Q38 23 0.4197 QSL 22 0.2694 Q31 Q32 12 0.0800 Q38 12 0.4039 Q17 Q19 6 0.4306 QSL 10 0.8081 Q20 10 0.5214 Q21 9 0.8044 Q32 Q38 10 0.8303 Q24 5 -0.0253 QSL 9 0.3258 Q26 6 0.2135 Q53 24 0.2531 Q29 20 0.5510 Q54 24 0.5017 Q55 11 0.0509 Q19 Q29 6 0.7886 Q38 QSL 18 0.2909 Q20 Q21 10 0.3990 Q24 7 0.3815 Q53 Q54 23 0.4340 Q26 6 -0.1480 Q55 9 0.7231 QSL 8 0.6983 Q53* 24 0.6126 Q54 Q55 9 -0.4316 Q54* 24 0.0324 Q55* 11 0.7287

*Blended series of Q20 was used. 285

Pair of Stations . n R Pair of Stations

Agno River Basin

Q60 Q61 13 0.4941 Q72 Q77 10 0.3803 Q63 8 0.0854 Q78 9 0.7033 Q85 11 0.7331 Q61 Q63 9 -0.5601 Q76 Q60 4 0.9719 Q70 Q72 25 0.8404 Q61 4 -0.0639 Q77 9 0.8170 Q63 3 0.0308 Q78 8 0.4034 Q82 4 0.0873 Q86 2 1.0000 Q71 Q60 13 0.6914 Q61 14 0.6672 Q82 Q84 7 0.8940 Q63 9 -0.5578 Q86 5 0.3229 Q76 4 0.9119 Q82 14 0.7536 Q84 6 0.9265 Q86 5 0.5506

*Blended series of Q20 was used. APPENDIX G

HISTORICAL DATA AND AUGMENTATION ESTIMATES AT VARIOUS

STREAMFLOW GAGING STATIONS, CENTRAL LUZON

BASIN, PHILIPPINES

Legend

Boxed figures are augmentation estimates. -1.000 indicates missing value.

H indicates historical value.

286

287

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