Taguibo Hydrological Modeling

Taguibo Hydrologicl Modeling Page 1

Taguibo Hydrological Modeling

(by Engr. Glenn B. Batincila, NEDA Caraga)

Chapter I Introduction

I-A Background of the Study

Climatic variability combined with human-induced emission of green-house gases

result in an increase in global average air and ocean temperatures, widespread melting of

snow and ice and rising global average sea level. National Oceanic and Atmospheric

Administration (NOAA) reported that global surface temperatures have increased at a rate

of 0.13 C 0.03 C per decade for the past 50 years, a rate nearly double that of the past

100 years. These large-scale factors are expected to affect the key hydrological

components in a river basin as watershed systems are directly influenced by the amount,

form, seasonality, and event characteristics of precipitation, as well as air temperature,

solar radiation, and wind that affect evaporative loss.

This paper contributes to the scientic understanding of the hydrology in Taguibo

watershed and offers baseline information to develop measures for mitigating potential

negative impacts. Understanding changes in spatial and temporal variations of runoff is

important for water resource management. Quantification of the major components of the

hydrologic balance such as surface runoff is extremely important for decision making.

Many previous studies have assessed the impact of land use changes on hydrology and the

studies found a signicant impact of land use changes on hydrology, especially those

caused by urbanization and conversion of forestland to agricultural land.

I-B Objectives of the Study

Prediction of probable impacts on water yield from various catchments land-use

change is extremely important for environmental and economic decisions and strategies.

This study has two (2) major objectives, namely: (1) To build a hydrologic model for the

Taguibo watershed; and (3) To provide tools and information to help policy makers,

planners and water managers assess and manage the impacts of land use change.

Taguibo Hydrological Modeling by Engr. Glenn B. Batincila Page 2

In addition, the specific objectives of the study are as follows:

1. To delineate the watershed boundary of the Taguibo watershed and determine

its projected area;

2. To generate streamflow network;

3. To create the land cover and land use map of the Taguibo watershed for

hydrological simulation and to create future land use change scenario;

4. To determine the variability of precipitation intensity within the Taguibo

watershed and its surrounding regions as meteorological input data; and

5. To create the hydrological model of the Taguibo watershed and define the

hydrological streamflow characteristics of the main Taguibo river and its

tributary streams;

I-C Methodology

Data needs for hydrological simulation is extensive. At a minimum, continuous

rainfall records are required to drive the runoff model and additional records of

evapotranspiration, temperature, and solar intensity are desirable. The essential geographic

information required for simulation are the watershed boundary, the delineated sub-basins,

the stream network, the land uses and the ground surface elevations. This information is

supplied to the model through the GIS.

The long-term hydrologic data series are structured for between CY1999 and

CY2012 in which El Nio (EN) and La Nia (LN) events were registered and reliable

observed data from the Tropical Rainfall Measuring Mission (TRMM) satellite are

available. Precipitation data from TRMM with grid increments at 0.25 degrees (27 km) and

observed ground-based temperature data are used as atmospheric forcing.

Evapotranspiration data are computed based on daily observed maximum and minimum

observed temperature from Malaybalay City PAGASA Station (Philippine Atmospheric,

Geophysical and Astronomical Services Administration) since Malaybalay has the

relatively the same elevation as that in Taguibo. The digital elevation model (DEM) is

taken from the United States Geological Survey Hydrological data and maps based on

Shuttle Elevation Derivatives (USGS HydroSHEDS) which is based on high-resolution

elevation data obtained during a space shuttle ight of NASAs Shuttle Radar Topography

Mission (SRTM). ArcGIS V10 is then used to delineate the watershed and to define the

river network. The precipitation data and temperature data are then compiled in watershed


data management (WDM) format using WDMUtil (Watershed Data Management Utility)

software (WDMUtil, 2001). From the daily temperature, hourly potential

evapotranspiration is computed using the WDMUtil. Using the US Environmental

Protection Agency (EPA) Better Assessment Science Integrating Point and Nonpoint

Sources (BASINS) 4 software, watershed file (WSD) is generated by intersecting the

meteorological data with land use, DEM and stream networks. The Hydrologic Simulation

Program-Fortran (HSPF) is then run using the BASINS 4 WinHSPF program to create the

users control input file (UCI) by intersecting the WSD file with the WDM file.

The hydrological model is simulated on a daily time step basis for the simulation

period taken from January 2000 to April 2012. This twelve-year period ensures that the

calibration encompasses the full ranges for hydrologic conditions and ow regimes like

several high ows, low flows, and El Nio and La Nia conditions. Due to the absence of

gauging stations in the Taguibo Watershed, the model is calibrated using the established

parameters values in the Agusan River Basin Hydrological Model (Batincila, 2012).

Finally, significant outputs from the model system such as hydrograph, flow duration

curves, seasonal flow frequency output, and highest and lowest flow values for a return

period of 1, 5, 10, 25, 50, and 100 years are generated with graphical visualization.

I-D The Study Area

The Taguibo Watershed is located in the eastern side of Butuan City and lies at 12536' E

to 12543' E and from 857' N to 96' N (Fig. 1). The Taguibo watershed is situated within

the Sibagat-Wawa Forest Reserve covered by Proclamation No. 308 dated September 3,

1954. It is also one of the critical watersheds in Caraga that was declared as Forest

Reserve by virtue of Presidential Proclamation No. 1076 issued by then President Fidel V.

Ramos on September 4, 1997. Covering a total area of about 4,300 hectares for protection

and conservation under the public domain of the City of Butuan and Municipalities of RTR

and Cabadbaran in the province of Agusan del Norte, the watershed serves as the major

source of potable water and irrigation for over 300,000 people in Butuan City.


Fig. 1 Location of the Taguibo Watershed

Chapter II Data Sources

II-A PAGASA (the Philippine Meteorological Agency)

The Philippine Atmospheric, Geophysical and Astronomical Services Administration

(PAGASA) is the Philippine national institution dedicated to provide flood and typhoon

warnings, public weather forecasts and advisories, meteorological, astronomical,

climatological, and other specialized information and services primarily for the protection

of life and property and in support of economic, productivity and sustainable development.

PAGASA has maintained an excellent set of meteorological records surrounding the

Agusan River Basin (ARB). PAGASA has four (4) synoptic weather stations surrounding

the ARB as shown in Fig. 2.


Based on the data collected from the four stations

humidity ranges from 85% to 98% and temperature

ranges from 25 to 28 degree Celsius while pressure

ranges from 1008 to 1010 millibars (MBS). Precipitation

data are available daily as early as CY 1951 with some

missing data in Hinatuan station and as early as CY 1982

in Butuan City.

Precipitation is the key input for

hydro-meteorological modelling and applications.

Reliable quantication of precipitation data is crucial. In

the Philippines, reliable estimation of rainfall

distribution poses a great challenge, not only due to

undulating surface terrain and complex relationships

between land elevation and precipitation, but also due to

the lack of a sufcient number of rainfall measurement points. Since the nearest PAGASA

station is about 20 kilometers from the Taguibo watershed, reliance on satellite-based

precipitation data is a necessity.

Several satellite-based precipitation products have emerged that provide

uninterrupted precipitation time series. These satellite-based precipitation products provide

an unprecedented opportunity for hydro-meteorological applications and climate studies.

To remedy the problem, the Tropical Rainfall Measuring Mission (TRMM) precipitation

dataset is considered as precipitation input to drive the hydrological model. TRMM is the

first meteorological satellite specially used to gauge tropical and subtropical precipitation.

II-B The Tropical Rainfall Measuring Mission Satellite

The National Aeronautics and Space Administration (NASA), in cooperation with

the Japan Aerospace Exploration Agency (JAXA), launched the Tropical Rainfall

Measuring Mission (TRMM) on November 27, 1997 from the Tanegashima Space Center

in Tanegashima, Japan (Kummerow et al., 1998). Designed as a minimum three-year

mission with the goal of five years duration, TRMM has been collecting data since 1997.

Although initially intended as a purely research-oriented mission, TRMM is now used in

operational applications such as hurricane forecasting because data from its suite of

complementary sensors are unique and available in near real time. In the United States,

Fig. 2 Location of PAGASA stations

near the ARB Watershed


TRMM data are used operationally by the Joint Typhoon Warning Center (JTWC), the National

Center for Environmental Prediction (NCEP), and the National Hurricane Center (NHC), among

others. Internationally the data are used operationally by entities such as JAXA, the European

Centre for Medium-Range Weather Forecasts (ECMWF), and the World Meteorological

Organization (WMO) tropical cyclone warning centers.

Most studies show that comparison between the TRMM 3B42 V6 rainfall data and

observed data on total monthly values has high correlativity. Data differences can be

explained by considering that the satellite and the gauges measure precipitation in very

different ways. While TRMM instruments make essentially instantaneous area-averaged

measurements over a 27 km by 27 kilometer grid area, rain gauges from meteorological

stations provide good time sampling but poor spatial sampling as it is basically point

measurements.

In this study, satellite-based precipitation product from TRMM 3B42 Version 6

dataset is used as forcing data for streamow simulations at hourly time scale for about

twelve year period from CY2000 to CY2012.

II-C TRMM Dataset for the Taguibo Watershed

The hydrological model is simulated on a

daily time step basis for the simulation period

(January 2000 to May 2012). This twelve-year

period ensures that the calibration encompassed El

Nio and La Nia conditions. The coordinates of

TRMM dataset to be used for the simulation are in

Barangay Sinaca with 9.08 N latitude and 125.71

E longitude and in Brgy. Anticala with 8.98 N

latitude and 125.66 E longitude (Fig. 3). A total of

two (2) precipitation datasets at 0.25 interval are

assigned to the sub-basins as precipitation input for

the simulation. Each of the sub-basin is linked to the precipitation data from TRMM and

temperature data from PAGASA based on the spatial proximity of the meteorological

station to the centroid of the catchment. Figs. 3-b and 3-c shows the hourly precipitation

data from TRMM dataset as input for the model.

Fig. 3-a. Location of TRMM Dataset


Fig. 3-b. Hourly Satellite-generated Precipitation Data near Barangay Anticala (unit inches)

Fig. 3-c. Hourly Satellite-generated Precipitation Data near Barangay Sinaca (unit-inches)


II-D The Digital Elevation Model, Sub-basins and Stream Network

In this study, the DEM is extracted from the United States Geological Survey

Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales

(USGS HydroSHEDS) using ArcGIS. HydroSHEDS provides hydrographic

information in a consistent and comprehensive format for regional and global-scale

applications (Lehner et al., 2006).

HydroSHEDS has been developed by the Conservation Science Program of World

Wildlife Fund (WWF), in partnership with the U.S. Geological Survey (USGS), the

International Centre for Tropical Agriculture (CIAT), The Nature Conservancy (TNC), and

the Center for Environmental Systems Research (CESR) of the University of Kassel,

Germany.

The DEM from HydroSHEDS is extracted and processed using ArcGIS hydrology

tools and ArcHydro tools. The watershed boundary is first delineated using the hydrology

tool. The pouring point of the watershed is selected at coordinates 125.60E and 8.99N

approximately ten (10) kilometres upstream from the shoreline boundary so that tidal

influence does not affect the model simulation. The stream network determination is then

processed using ArcHydro tools with one percent (1%) of the maximum flow accumulation,

considered as the river threshold or the minimum drainage area. Fig. 4-a and 4-b shows the

digital elevation model, and the sub-basins and stream networks of the Taguibo watershed,

respectively.

Fig. 4-a Digital Elevation of the Taguibo Watershed Fig. 4-b. Location of 17 Sub-watersheds


The DEM shows that the highest elevation of the watershed reaches as high as 1,882

m (6,175 ft) above mean sea level. These mountainous areas can be found at the

northeastern side of the watershed. For model, the watershed is divided into seventeen (17)

sub-basins (Fig. 4-b). Six (6) streams namely, Reaches 1, 10, 17, 2, 3, and 7 in the

sub-watersheds are selected in the simulation as the hydrological information are necessary

to help policy makers, planners and water managers. Reach 1 serves as the outlet of the

Taguibo watershed at approximately 10 kilometers from the shoreline. Reach 10 serves as

the stream where the infiltration gallery of the Butuan City Water District (BCWD) is

located. Reach 17 serves a major tributary stream of the Taguibo river downstream, while

Reaches 2, 3, and 7 serve as the major tributary streams upstream.

II-E. The Taguibo Watershed Land Cover and Land Use Data

Land cover is the physical material at the surface of the earth. Land cover

information derived from satellite imagery provides a convenient illustration of the way

information is subsequently treated by users (Comber et al., 2005). In this study, the land

cover data in the format of GIS shapefile is obtained from the European Space Agency

(ESA) GlobCover 2005 project.

The GlobCover products have been processed by ESA and by the Universit

catholique de Louvain. They are made available to the public by ESA. In 2008, the

ESA-GlobCover 2005 project delivered to the international community the very first 300

m global land cover map for 2005 as well as bimonthly and annual MERIS Fine

Resolution Full Swath (FRS) surface reflectance mosaics.

In this study, the spatial analysis

module embedded in the ArcView

system is applied for computing the

land use and land cover statistics

corresponding to each drainage

sub-basin. Fig. 5 shows the classied

land cover pattern in the watershed.

Five (5) land cover patterns are

classied which include a) Rainfed

Fig. 5 Land Cover Map of the Taguibo Watershed


croplands 2,370 hectares, b) Mosaic croplands 2,237 hectares, c) Mosaic Vegetation

2,600 hectares, d) Closed to open forest 2,274 hectares, and i) Closed to open

shrubland 2,510 hectares.

The dominant land-cover types are composed by evergreen forest land

(approximately 35% of the Taguibo watershed), rainfed croplands (30%), and mosaic

cropland (28%). The forested areas are distributed in the mountain ranges in the

northeastern side of the watershed while cultivated land are evenly distributed on the

low-lying southeastern side (Brgys Anticala, Pianing and Taguibo).

II-F. Daily Observed Temperature Data from PAGASA

A series of daily observed temperature from Malaybalay City and Butuan City

meteorological stations are taken from year 2000 to 2012. This served as input for

computing the hourly potential evapotranspiration atmospheric forcing for the model. This

temperature data is assigned to each of the sub-basin and selection is decided according to

the elevation of the sub-watershed. All heavily forested and highly elevated sub-basins are

assigned with temperature data from Malaybalay City while for the other sub-basins,

temperature data from Butuan City PAGASA station is used.

Fig. 6 Daily observed temperature data from PAGASA

A. Malaybalay City Station B. Butuan City Station


II-G. Potential Evapotranspiration Dataset

Potential evapotranspiration (PET) is an important index of hydrologic budgets at

different spatial scales and is a critical variable for estimating actual evapotranspiration

in rainfall-runoff and ecosystem modelling (Lu, J. et al., 2005).

Given the daily temperature and the geographic location of the meteorological

stations with respect to the earth equator, the potential evapotranspiration is computed

using Hamons Equation (Hamon, W.R., 1960, 1961) and is automatically computed using

the EPA BASINS 4 WDMUtil software. The Hamon equation, provides an approximation

of the potential evapotranspiration from knowledge of the mean daily temperature and

tabular values of the day length normalized to a 12-hour day.

Fig. 7 shows the graphical representation of the daily potential evapotranspiration

computed from WDMUtil. The annual average potential evapotranspiration from the two

meteorological stations is computed at 50 inches per year.

Fig. 7 Daily potential evapotranspiration

A. Malaybalay City (ET= 44 in/year) B. Butuan City (ET= 55 in/year)

Chapter III The Hydrological Model

3.2 General Description of the The HSPF Model

Hydrological Simulation Program - FORTRAN (HSPF) is a lumped parameter,

modular-structured comprehensive model for simulation of watershed surface and

subsurface hydrologic and water quality processes for both conventional and toxic organic


pollutants (Bicknell et al., 1997, 2001, 2005, Donigian et al. 1984). It is a physically based

model that incorporates GIS-data (Bicknell et al., 2001) and has a friendly computational

environment for easy scenarios testing and simulation of large-scale watersheds. It is an analytical

tool which has application in the planning, design, and operation of water resources systems.

3.4 The HSPF Model Concept

The model consists of three primary modules, PERLND for pervious land,

IMPLND for impervious land, and RCHRES for stream reaches, to simulate ow, water

quality, and sediment transport in pervious land, impervious land, and streams. The

PERLND simulates snow accumulation and melt, the water budget based on interactions

among various storages, sediment produced by erosion, and water quality constituents in

the dissolved as well as particulate phase. The IMPLAND accounts for snow accumulate

and melt, surface runoff, surface detention storage, water evaporation, and buildup/washoff

of sediment and water quality constituents. Water, solids, and various pollutants ow from

the segments by moving laterally to a downslope segment or to a stream or lake. The

RCHRES module simulates the processes that occur in a single reach of open or closed

channel or a completely mixed lake. Flow through a RCHRES is assumed unidirectional.

Water and other constituents that are input from other RCHRES and local sources enter the

RCHRES through a single gate. Outows may leave the RCHRES through one of several

gates or exits. Precipitation, evaporation, and other uxes also inuence the processes that

occur in the RCHRES module, but do not pass through the exits.

HSPF routes water (which the HSPF manual terms moisture) in pervious and

impervious areas in somewhat different ways. Impervious areas (IMPLND unit in HSPF)

principally generate surface runoff, whereas pervious areas (PERLND) contribute actively

to all three major compartments (surface runoff, interow, and groundwater) (Fig. 8).

Precipitation intercepted by tree canopies and roof tops is represented by

interception storage. When the precipitation rate satises interception and surface

depression storage, it results in surface runoff and inltration. The ratio between the

potential direct runoff and inltration is determined by factors such as surface vegetation,

slopes, soil permeability, and soil moisture content. The moisture inltrating in the soil

prole moves towards the deeper part by gravity and capillary forces and enters the

so-called lower zone. Water from the lower zone undergoes evapotranspiration (ET),


moves deeper into the groundwater, or becomes interow. From the groundwater zone,

water can move deeper into an inactive groundwater zone where it is considered lost from

the system, or it can move as active groundwater along the hydraulic gradient and emerge

to a stream. Each of the possible pathways can be controlled by means of parameter

variables describing the portion of ow that enters each pathway. Water budget allocations

between surface ow, interow, baseow, storage, interception, detention and evaporation

are controlled within the PWATER (pervious areas) and IWATER (impervious areas) units

of HSPF. Flow routing in reaches or reservoirs is performed using a hydraulic function

table FTABLE that represents the functional relationship between water depth, surface

area, water volume, and outow in the segment (Bicknell et al., 2001). Contributions from

precipitation and evaporation are also considered for RCHRES.

Fig. 8 Schematic diagram of the HSPF model

Data needs for HSPF can be extensive. HSPF is a continuous simulation

program and requires continuous data to drive the simulations. The model uses time series

data of rainfall, temperature and solar radiation; information of land surface characteristics

such as land-use patterns; and land management practices to simulate the hydrologic


processes that occur in a watershed. The simulation results produce a time history of the

runoff flow rate, sediment load, and nutrient and pesticide concentrations, along with a

time history of water quantity and quality at any point in a watershed.

Chapter IV Results and Discussion

In this study, the watershed analysis is comprised of two major components, the

spatial analysis and hydrological analysis. A spatial analysis is performed to process the

digital elevation model and land use/land cover (LULC) in the study area. Categories of

LULC, from the U.S. Geological Survey Land Use and Land Cover Classification System

(Anderson et al., 1976), are used to classify the land cover. Spatial data layers used for the

analysis are integrated into a geographic information system (GIS) using ArcGIS V10

software and projected at World Geodetic System (WGS) 1984 datum Universal

Transverse Mercator (UTM) geographic coordinate system Zone 51N. The hydrological

analysis is performed using the EPA BASINS 4 WinHSPF Program.

Values of various parameters for HSPF calibration are selected based on the

Agusan River Basin Hydrological Model. The values of the principal hydrologic

calibration parameters for HSPF are typically determined based on land use, agricultural

activity, literature values, and slope and soil characteristics. They are then varied within

recommended limits to obtain a calibration.

4.4 Graphical Visualization of the Model

A hydrograph is a time series plot of predicted and measured flow throughout the

calibration and validation periods. Hydrographs help identify model bias (ASCE, 1993)

and can identify differences in timing and magnitude of peak flows and the shape of

recession curves. On the other hand, percent exceedance probability curves, which often

are daily flow duration curves, can illustrate how well the model reproduces the frequency

of measured daily flows throughout the calibration and validation periods (Van Liew et al.,

2007). Fig. 9 shows the simulated hydrographs for the six identified reaches.


Fig. 9 Simulated hydrographs

a. Precipitation (in inches) and Flow (cubic feet per second) at Reach 01

Monthly Flow Attributes (cubic feet per second)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Max 2,950 3,593 1,847 916 399 258 285 188 228 561 1,990 4,466

Mean 418 332 212 115 93 76 72 60 61 75 161 281

Min 78 62 42 34 27 34 42 35 30 32 30 48

High Flow Values and Number of Days per Return Period

Probability Return Period/Days 1 10 30 60 90 183

100% 1.0001 793 352 347 268 208 129

20% 5 2578 1148 598 463 395 257

10% 10 3325 1363 684 559 484 307

4% 25 4512 1658 805 712 625 382

2% 50 5603 1894 906 852 754 448

1% 100 6900 2146 1015 1016 907 522

Low Flow Values and Number of Days per Return Period


0.9999 1.0001 73 83 138 172 179 258

0.2 5 32 35 39 42 44 55

0.1 10 30 32 36 39 41 51

0.04 25 27 29 34 37 38 48

0.02 50 25 27 33 36 37 46

0.01 100 24 26 33 36 35 45


b. Precipitation (in inches) and Flow (cubic feet per second) at Reach 17



Max 331 379 162 84 73 50 57 40 78 54 204 397

Mean 66 57 36 20 19 16 16 13 14 16 26 43

Min 16 13 8 6 5 7 9 7 6 8 6 11



100% 1.0001 60 41 49 39 33 22

20% 5 277 172 96 77 66 44

10% 10 341 200 110 93 81 53

4% 25 432 236 129 120 105 66

2% 50 507 263 144 143 127 77

1% 100 589 291 160 171 154 90



0.9999 1.0001 16 20 35 51 52 52

0.2 5 6 7 8 9 9 12

0.1 10 6 6 7 8 9 11

0.04 25 5 5 7 8 9 10

0.02 50 5 5 6 8 8 10

0.01 100 4 5 6 8 8 9


c. Precipitation (in inches) and Flow (cubic feet per second) at Reach 10



Max 2,320 2,763 1,590 697 268 190 229 116 171 422 1,643 3,638

Mean 276 210 136 73 54 42 38 32 31 41 105 189

Min 39 34 26 20 17 19 23 19 15 16 17 23



100% 1.0001 612 254 232 177 136 80

20% 5 2092 795 401 302 255 163

10% 10 2680 948 461 363 312 194

4% 25 3599 1162 546 460 403 242

2% 50 4431 1336 617 548 485 283

1% 100 5407 1523 694 652 583 330


Probability Return Period 1 10 30 60 90 183

0.9999 1.0001 36 42 54 74 81 157

0.2 5 18 19 20 22 24 28

0.1 10 16 17 19 20 22 26

0.04 25 15 16 17 18 20 25

0.02 50 14 15 16 18 19 24

0.01 100 14 15 16 17 18 23


d. Precipitation (in inches) and Flow (cubic feet per second) at Reach 02



Max 1,147 1,353 801 344 129 92 115 54 83 208 824 1,825

Mean 111 82 53 28 20 16 14 12 12 16 43 77

Min 11 7 9 8 7 7 8 7 6 6 7 8



100% 1.0001 291 101 86 70 55 33

20% 5 1060 353 169 122 102 64

10% 10 1346 423 195 147 125 77

4% 25 1784 521 232 187 162 96

2% 50 2172 600 263 223 196 113

1% 100 2619 684 296 265 238 133



0.9999 1.0001 13 14 20 27 29 57

0.2 5 7 8 8 8 9 11

0.1 10 7 7 7 8 8 10

0.04 25 6 6 7 7 8 9

0.02 50 6 6 6 7 7 8

0.01 100 5 6 6 6 7 8


e. Precipitation (in inches) and Flow (cubic feet per second) at Reach 03



Max 355 420 249 107 40 29 36 17 26 65 255 566

Mean 36 27 18 9 7 5 5 4 4 5 14 25

Min 4 3 3 3 2 2 3 2 2 2 2 3



100% 1.0001 91 33 29 23 18 11

20% 5 328 112 55 40 33 21

10% 10 417 135 63 48 41 25

4% 25 554 166 75 61 53 32

2% 50 676 191 85 73 64 37

1% 100 817 218 96 87 77 43



0.9999 1.0001 4 5 7 9 10 19

0.2 5 2 2 3 3 3 3

0.1 10 2 2 2 3 3 3

0.04 25 2 2 2 2 3 3

0.02 50 2 2 2 2 2 3

0.01 100 2 2 2 2 2 3


f. Precipitation (in inches) and Flow (cubic feet per second) at Reach 07



Max 135 163 99 42 19 13 14 8 10 25 97 218

Mean 27 21 14 7 5 4 4 3 3 4 10 18

Min 5 4 2 2 1 2 2 1 1 1 1 2



100% 1.0001 47 24 24 16 13 8

20% 5 121 67 36 29 25 16

10% 10 156 79 41 35 30 19

4% 25 213 97 48 43 38 23

2% 50 267 111 54 51 45 27

1% 100 334 127 60 60 54 31


Probability

Return

Period/Days 1 10 30 60 90 183

0.9999 1.0001 3 3 4 6 6 13

0.2 5 1 1 2 2 2 3

0.1 10 1 1 1 2 2 2

0.04 25 1 1 1 1 2 2

0.02 50 1 1 1 1 1 2

0.01 100 1 1 1 1 1 2


Estimates of flood flows having given recurrence intervals or probabilities of

exceedance are needed for design of hydraulic structures and floodplain management

(Flynn, K.M. et al., 2006). The frequency grid statistics are computed according to

Bulletin 17-B Guidelines (Interagency Advisory Committee on Water Data, 1982) and the

US EPA BASINS 4.0 Analysis tool is used to generate the output. The flow frequency grid

analysis in this study considered the highest and the lowest flow each for a recurrence

interval of 1, 5, 10, 25, 50, and 100 years. The Pearson Type III frequency distribution is fit

to the logarithms of instantaneous annual peak flows following Bulletin 17B guidelines of

the Interagency Advisory Committee on Water Data (Interagency Advisory Committee on

Water Data, 1982). The parameters of the Pearson Type III frequency curve are estimated

by the logarithmic sample moments (mean, standard deviation, and coefficient of

skewness), with adjustments for low outliers, high outliers, historic peaks, and generalized

skew.

A ow duration curve depicts ows of various magnitudes within the limits of the

watershed. The gradient of the upper and lower extremities of the ow duration curve are

of particular relevance in evaluating the extremes of stream and basin behaviour. Figure 10

shows the flow duration curve of the six (6) identified reaches in the Taguibo watershed.

Fig. 10 Flow Duration Curve (flow in cubic feet per second unit)


Chapter V Conclusion

In this study, the hydrological behavior of the Taguibo watershed is analyzed. The

study will provide insights into the challenges that climate change and land use change

could impose on the water resources management. Using GIS-derived hydrology model,

the streamflow in the basin and sub-basin level are simulated. The highest and lowest

streamflow forecast with return periods of one year, five years, ten years, 25 years, 50

years and 100 years are generated, and the maximum, mean and average seasonal runoff

are simulated. The model has the potential of providing valuable aid in developing efficient

management strategies for Taguibo watershed. These will allow planners and managers to

make decisions on assumed level of risk and to prepare for adverse situations.

Accurate inputs are crucial for hydrological models to produce sound information.

As the number of operational PAGASA meteorological stations are sparse, reliance on

alternative precipitation dataset from the Tropical Rainfall Measuring Mission (TRMM)

with grid increments at 0.25 degrees (27 km) are used as atmospheric forcing.

Evapotranspiration data are computed based on daily observed maximum and minimum

temperature from two (2) observed meteorological stations. The long-term meteorological

data series are structured for between CY2000 and CY2012 in which El Nio (EN) and La

Nia (LN) events were registered. The digital elevation model (DEM) is taken from

HydroSHEDS and processed using ArcGIS to delineate the watershed and to define the

river networks. The precipitation data and temperature data are then compiled in watershed

data management (WDM) format using WDMUtil software. From the daily temperature,

hourly potential evapotranspiration is computed using the WDMUtil. Using the US

Environmental Protection Agency (EPA) Better Assessment Science Integrating Point and

Nonpoint Sources (BASINS) 4 software, watershed file (WSD) is generated by intersecting

the meteorological data with land use, DEM and steam networks. The Hydrologic

Simulation Program-Fortran (HSPF) is then run to create the users control input file (UCI)

by intersecting the WSD file with the WDM file.

From the simulated hydrograph, areas in the upstream part of the Taguibo

watershed experience relatively high peak flow especially in the months of January and

February. As this area is vulnerable to flash flood, this calls for the proper preservation and

protection of the forest within the sub-watershed as this is needed to intercept precipitation,

to reduce the chance of flash flood occurrence, and to reduce sedimentation by soil erosion.

Proper delineation of flood hazard and rain-induced landslide risk map is a necessity.


Effective measures should be adopted to reduce potential flood damage

particularly in the lower part of the Taguibo to reduce agricultural vulnerability. As such,

appropriate water resources policies, management and infrastructures should be adopted to

meet the challenges likely to occur in the future. In the face of uncertain future change, it is

increasingly important to prepare for flood and draught, and to balance the competing

usage of water supply, such as for irrigation, household water supply and other in-stream

flow needs. Since ood phenomena have occurred from time to time in the area of study,

the present model could become a useful tool for the prediction of ood events and for the

better management of water supplies. Essentially, proper water management should be

based on a basin-wide viewpoint and should balance considerations of the whole water

system from upstream to downstream with the incorporation of environmental

conservation and citizen participation into the planning processes, and with due

consideration to conservation of the natural environment.

References:

1. Kummerow, C., Barnes, W., Kozu, T., Shiue, J., Simpson, J., 1998. The Tropical Rainfall Measuring

Mission (TRMM) sensor package. J. Atmos. Oceanol. Technol. 15, 809817.

2. Lehner, B., Verdin, K., Jarvis, A. (2006): HydroSHEDS Technical Documentation. World Wildlife

Fund US, Washington, DC. Available at http://hydrosheds.cr.usgs.gov.

3. Comber, A., Fisher P., Wadsworth R., (2005). "What Is Land Cover?". Environment and Planning B:

Planning and Design (32): 199209.

4. Hamon, W.R. 1960. Estimating potential evapotranspiration. Massachusetts Institute of Technology

Department of Civil and Sanitary Engineering, unpublished B.S. thesis, 75 p.

5. Hamon, W.R. (1961) Estimating potential evapotranspiration, proceedings of the American Society of

Civil Engineers. J Hydraulic Div 87(HY3):107120.

6. Bicknell, B.R., Imhoff, J.C., Kittle, J.L. Jr., Donigian, A.S., Johanson, R.C., 1997. Hydrological

Simulation ProgramFORTRAN. Users Manual for Release 11. EPA-600/R-97-080, USEPA,

Athens, GA, 755 p.

7. Bicknell, B.R., Imhoff, J.S., Kittle, J.L., Jobes, T.H., Donigian, A.S., 2001. Hydrological Simulation

Program FORTRAN (HSPF): Users Manual Version 12. National Exposure Research Laboratory,


Ofce of Research and Development, US Environmental Protection Agency, Athens, Georgia, USA.

8. Bicknell, BR; Imhoff, JC; Kittle, JL, Jr; et al. (2005) Hydrological simulation program - FORTRAN

(HSPF). Users manual for release 12.2 U.S. EPA National Exposure Research Laboratory, Athens,

GA, in cooperation with U.S. Geological Survey, WRD, Reston, VA.

9. Donigian, A.S., Jr., Imhoff, J.C., Bicknell, Brian, Kittle, J.L., Jr., 1984, Application guide for

Hydrological Simulation Program--Fortran (HSPF): U.S. Environmental Protection Agency,

Environmental Research Laboratory, Athens, Ga., EPA-600/3-84-065, 177 p.

10. ASCE. 1993. Criteria for evaluation of watershed models. J. Irrigation Drainage Eng. 119(3): 429-442.

11. Van Liew, M. W., T. L. Veith, D. D. Bosch, and J. G. Arnold. 2007. Suitability of SWAT for the

conservation effects assessment project: A comparison on USDA-ARS experimental watersheds. J.

Hydrologic Eng. 12(2): 173-189.

12. Flynn, K.M., Kirby, W.H., and Hummel, P.R., 2006, Users Manual for Program PeakFQ Annual

Flood-Frequency Analysis Using Bulletin 17B Guidelines: U.S. Geological Survey, Techniques and

Methods Book 4, Chapter B4; 42 pgs.

13. Interagency Advisory Committee on Water Data, 1982, Guidelines for determining flood-flow

frequency: Bulletin 17B of the Hydrology Subcommittee, Office of Water Data Coordination, U.S.

Geological Survey, Reston, Va., 183 p., http://water.usgs.gov/osw/bulletin17b/bulletin_17B.html.

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Taguibo Hydrological Modeling

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