IJERD – International Journal of Environmental and Rural Development (2017) 8-1

Research article
erdApplication of Soil and Water Assessment Tool (SWAT) Modelto Predict Streamflow and Sediment Yield in Wahig-Inabanga
Watershed, Bohol, Philippines
TOMAS D. REYES, JR.*
Bohol Island State University, Philippines
Email: tomseyer@gmail.com
Received 18 January 2017

Accepted 5 May 2017(*Corresponding Author)

Abstract The study applied the Soil and Water Assessment Tool (SWAT) model -- to predict
streamflow and sedimentation in Wahig-Inabanga Watershed, Bohol, Philippines. The
applicability of the SWAT model was evaluated and its output was integrated to GIS togenerate sedimentation hazard map. The result of the SWAT model performance evaluation on
stream flows was satisfactory given prediction efficiency values: NSE = 0.6578; R2 = 0.7038;
PBIAS = 14.94%; and RSR = 0.5850. Satisfactory result was also achieved in model validation
with model accuracy values on NSE, R2, PBIAS and RSR of 0.41, 0.57, 25.09%, and 0.71,
respectively. However, the model did not provide precise estimates of sediment yield in subbasins and hydrological response units (HRU) especially with corn as single land use or one of
the landuses even on flat to gently rolling terrain. Inaccuracy issue on sediment yield prediction
deferred further analysis including thesedimentation risk valuation which supposed to provide
baseline information for watershed management and land use planning.
Keywords Soil and Water Assessment Tool, SWAT, streamflow, sediment yield

INTRODUCTION
The Philippines is very vulnerable to natural disasters because of its natural setting (Palao et al., 2013;Principe, 2012; Luna, 2001), its socioeconomic, political, and environmental context, and especially its
widespread poverty (DepEd et al., 2008; Lasco et al. [undated]). Moreover, the country is prone togeologic and natural hazards mainly because of its geographic, geologic, and tectonic setting. Disasters,
whether natural or human-induced, have caused tremendous losses in the country’s national economy.
Storms, landslides, floods, and sometimes the combination of all three at once, displace as many as 8
million people every year. The National Disaster Risk Reduction and Management Council(NDRRMC) under the Office of the Civil Defense, Department of National Defense (OCD-DND) hasrecorded more than 15 major disasters to have devastated different parts of the country in the last fiveyears alone, including typhoon Haiyan (“Yolanda”), said to be the greatest typhoon ever recorded in
Philippine history. Being mountainous and rugged in topography, the Philippines is no doubt prone tomassive erosions and landslides. These two, together with floods, typhoons, and fire, account forbillions of pesos of government spending annually for disaster rehabilitation programs.
Soil erosion alone has been considered as a major threat to sustainable agriculture and to theenvironment (Paningbatan et al., 1995) especially in the developing world particularly the Philippines(Genson, 2006; Bantayan, 2006; Bantayan, 1997). According to a report from PCARRD (1999), some74 to 81 million tons of soil is lost annually, affecting 63% to 77% of the country’s total land area.
Using the Global Assessment of Soil Degradation (GLASOD) database, FAO (2000) as cited byGenson (2006) confirmed the severity of land degradation giving an estimate of about 79%. Indeed,land degradation is a major environmental and development issue in the Philippines. Its process isⒸ ISERD

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advancing at an alarming rate due to deforestation and inappropriate agricultural activities in fragileand highly sensitive mountainous environments. This is further aggravated by frequent cyclones andthunderstorms due to climate change and weather extremes which somehow explain the very highsediment yield rates throughout the country (Palao et al., 2013; Principe, 2012; PCARRD, 2008).
The integration of SWAT and GIS in streamflow and sediment yield modeling in Wahig-Inabanga
Watershed, Bohol, Philippines, is perceived to be of great advantage in terms of improved procedures
and analyses, and yield outputs both for sub-basin and hydrological response unit (HRU) scale.
Evidence of GIS and SWAT integration are the availability of several readily downloadable SWAT
add-ins compatible with selected GIS software packages. One particular SWAT add-in used in thisstudy was ArcSWAT, the SWAT geo-spatial interface in ArcGIS.
OBJECTIVESThe study aimed to apply and evaluate the performance of the Soil and Water Assessment Tool(SWAT) model in streamflow and sedimentation modeling in the Wahig-Inabanga Watershed using
ArcGIS as the platform of geo-spatial analysis.
METHODOLOGY
Study Site
The Wahig-Inabanga Watershed, also known as Wahig-Inabanga River Watershed Forest Reserve(WIRWFR), is the biggest watershed in the province of Bohol, Philippines. From the Northwest Bay of
Inabanga, the river dissects the central part of the island embracing a total land area of more than 610
km2 (Figure 1). This watershed is about 15.20% of the total land area of the province.It is
geographically located between 124o3’36” and 124o23’24” East longitude, and between 9o43’48” to10o4’48” North latitude (GCS Luzon-Philippines 1911 datum).

(a)
(c)

(b)

Fig.1 Location of the study area: (a) map of the Philippines showing the Central Visayas;(b) map of the Central Visayas showing Bohol; and (c) map of Bohol showing the study area
SWAT Modeling
The SWAT model, being physical-based, claims for more complex types of input data in order tobecome operationally functional. In the study, there were several types of data (database input files,GIS required and optional input layers, and the observed or acquired measured data on streamflow)Ⓒ ISERD

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obtained from different reliable sources. The database input files which were used included: 2000-2012
climatic data from Bohol Experiment Station (BES); soil physical and chemical data from the Bureauof Soil and Water Management (BSWM); and daily rainfall datafrom two rainfall stations within thewatershed, all of which need to be preprocessed and organized into a format readable by the SWAT
model. In addition, the required GIS input layers were land cover (LULC) and soil, including a digital
elevation model of the watershed in which the manual definition of slope was based. Other optional
input layers like watershed mask and stream network were also used to reduce the processing time
during watershed and sub-basins delineation. Unluckily, in the study area only streamflow dataset wasavailable and this was applied for sensitivity analysis, calibration and validation of the model.
ArcGIS was used to preprocess some of the various SWAT model parameters observed andcollected from the real watershed condition into a format that is acceptable by the model. Only themanual calibration method was applied in this study after parameterization. It was done by manually
adjusting values of identified significant parameters. This was followed by model validation which
aided in demonstrating whether a given site-specific model was capable of making sufficiently accurate
simulations or otherwise. Results of the model simulation included the estimates on streamflow andsediment yield in sub-basin and HRU levels. Acceptance of the model was based on the satisfactory
values of the model efficiency indicators such as Nash-Sutcliffe efficiency, coefficient of determination,root mean square error–observations standard deviation ratio, and percent bias. Computation, however,
was done outside the GIS interface. The ArcSWAT2009 version 488, unfortunately, does not directly
provide spatio-temporal erosion maps. However, since the SWAT simulation was run within the GIS
setting, visualization of the outputs was obtained by adding the sediment yield estimates in theattributes of sub-basin and HRU layers.
RESULTS AND DISCUSSION
Sensitivity Analysis
Auto-sensitivity analysis was performed on 20 relevant stream flow parameters for the period 19861991. The absence of sediment yield data limited the sensitivity analysis only to streamflow-relatedparameters. Based on parameter sensitivity scale rating of Lenhart et al. (2002), out of the 20parameters evaluated, half were found significant on simulated vs. observed output.
Table 1 Ranking of significant flow parameters after sensitivity analysis with observed dataParameter

Description

Rank

Sensitivity Index

CN2

Curve number for moisture condition II

10.9960**

CH_K2

Effective hydraulic conductivity in main channel (mm/hr)

20.8000**

ESCO

Soil evaporation compensation factor

30.7720**

ALPHA_BF

Baseflow alpha factor (days)

40.1920*

CH_N2

Manning’s N value for the main channel

50.1660*

BLAI

Maximum potential leaf area index

60.0693*

GWQMN

Shallow aquifer threshold water depth (mm H2O)

70.0665*

CANMX

Maximum canopy storage (mm H2O)

80.0542*

SOL_AWC

Available water capacity of the soil layer (mm H2O/mm soil)

90.0512*

Note: Sensitivity Rating (Lenhart et al., 2002): **= high; * = medium (moderate)Ⓒ ISERD106

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Table 1 shows the result of the analysis ranked according to sensitivity index values. The first
three parameters were highly sensitive based on Lenhart et al. (2002) sensitivity rating while the rests
were medium sensitive.Those parameters with less than 0.05 sensitivity index values were considerednegligible based on Lenhart et al. (2002) sensitivity rating and, thus, were not included in modelcalibration.
Stream Flow Model Calibration and Validation
SWAT stream flow model calibration was performed using the 9 significant parameters during thesame simulation period (1986-1991). Model validation, on the other hand, was done for the period
1991-1996. Each run was evaluated using model efficiency indices such as NSE (Nash-Sutcliffeefficiency coefficient), R2 (coefficient of determination), PBIAS (percent bias), and RSR (root meansquare error – observations standard deviation ratio). The results of the simulation passed the model
efficiency criteria as required by each of the pre-identified indices. Summary results of the stream flow(runoff) initial run, calibration and validation evaluation are presented in Table 2.
Table 2 Summary result of model prediction efficiency evaluation of the stream flow modelfor Wahig-Inabanga WatershedSIMULATIONPERIOD

NSE

R2

PBIAS

RSR

Initial Run

1986-1991

0.50350.683125.27960.7046

Calibration

1986-1991

0.65780.703814.9373

0.5850

Validation

1991-1996

0.4106

0.569925.08830.7083

Several SWAT users (Principe, 2012; Duan et al., 2009) suggested that models with NSE of morethan 0.4 and R2 greater than 0.5 for both calibration and validation can still realistically simulatebasin’s hydrological processes. Thus, based on the calibration and validation results, it was assumed
that the model captures the actual stream flow condition in the study area. According to the SWAT
user group [https://swat.tamu.edu/media/82492/swat-user-group], it is a requirement that a SWAT
model intended for soil erosion prediction through sediment yield must fulfill this condition.
Basin-Level Water Balance Tradeoff and Sediment Yield
The water balance tradeoffs and the resulting changes in sediment yield of Wahig-Inabanga Watershedas outputs of model calibration for streamflow are presented in Table 3. Results show a considerable
decreasing pattern on sediment yield as values of significant parameters were adjusted from theirdefault or assigned values. Out of the 121 sub-basins of Wahig-Inabanga Watershed, 47 exceeded thesoil tolerable loss of 11.2 tons/ha/yr (Figure 3). The tolerable soil loss is based from Hudson (1995)
and Lal (1994). It was noticed that all 47 sub-basins had CORN as a single land use or in combination
with some other land uses. This only revealed that the model may have over predicted the SYLD for
CORN, and perhaps on some other LULCs, irrespective of slope class. In this regard, a comprehensive
assessment is required in the HRU-level output of the model. Sub-basins with CORN as its single
LULC had annual averages ranging from 31.38 tons/ha to 55.21 tons/ha, while those sub-basins withseveral other LULCs in addition to CORN had annual averages of about 11.81 tons/ha to 138.58
tons/ha.

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Table 3 Basin-level water balance trade-off of Wahig-Inabanga Watershed during streamflow
model calibration

HRU-Level (LULC) Water Balance and Sediment Yield
Another important output of each simulation in SWAT is the HRU-level water balance and sedimentyield. Rearrangement of the output was purposely done to determine which among the LULC types
have been mis-predicted by the model in terms of sediment yield prediction. Table 4 presents the waterbalance and sediment yield after having adjusted all flow sensitive parameters.
Table 4 HRU-level water balance and sediment yield (ton/ha) of calaibrated flow model using
flow sensitive paramerets summarized by landuse and land cover

The spatial distribution of the predicted average annual soil erosion rates in terms of sedimentyield (tons/ha) in sub-basin and HRU-levels are presented in Figures 2 and 3. The soil erosion(sediment yield) values for the whole watershed were divided into six levels: slight (< 2 tons/ha/yr);
acceptable (2-11 tons/ha/yr); moderate (12-25 tons/ha/yr); high (26-50 tons/ha/yr); severe (51-80tons/ha/yr); and very severe (> 80 tons/ha/yr). This classification, accordingly, is based from theⒸ ISERD108

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tolerable erosion rate of 10 to 12.5 tons/ha/yr adopted from Paningbatan (1987) as cited by PCARRD(1991) and was also used by several local researchers in the Philippines.
The overall output of the model and given the limited available input data used in the simulations,
it can still be assumed that the model is able to predict stream flow. However, it is not accurate inestimating sediment yield for some of the sub-basins and HRUs, particularly those with corn as alanduse. The inaccuracy in sediment yield estimation using the SWAT model deferred its risk
quantification and valuation.

Fig. 2. Sub-basin-level sediment yield class
ratings of Wahig-Inabanga Watershed

Fig. 3. HRU-level sediment yield class
ratings of Wahig-Inabanga Watershed

CONCLUSION
The study validated the applicability of the SWAT model only in simulating flow discharge dynamics
of the watershed based on the satisfactory values of the statistical measures of model efficiency.
However, it does not provide accurate estimates of the annual sediment yield in some LULCs such as
grassland, rice, perennial cultivated crops, and especially corn, on flat to gently sloping areas in thewatershed. The model, on the other hand, somehow precisely predicted annual sediment yield for builtup, open forest, mangroves, and inland water. The absence of sediment yield data restricts thecalibration and validation only to streamflow-related parameters. The sediment yield input data,
supposedly, should have been included in the sensitivity analysis with measured data for more efficientmodel calibration in case sediment yield prediction is the intention of the study.
RECOMMENDATIONSIn order to fully appreciate the utility of the SWAT model, its application must be employed only inwatersheds with at least minimum required input data. Assumptions on the values of importantparameters, if not provided quantitatively, will just complicate the interpretation of the results.
Acquired, measured or observed data on discharges (stream flows) and sediment yields are needed
especially if the intention of using the model is for sediment yield prediction. The absence of acquired
data on sediment yield in the present study which is supposed to be included in sensitivity analysis andⒸ ISERD109

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used in calibration may have affected the results of sediment yield estimation. Therefore, subsequent orrelated studies, using the same model, are only suggested if these data are all available.
Parameters in the crop database must be studied well. The over-predicted values of sediment yieldin corn and some other LULCs demand further examination of the crop database to determine whichparameters need iteration. In addition, it is possible in the current and latest SWAT versions to delete,edit, update and add a specific crop type or LULC (e.g. agroforestry) which is not available in the crop
database as long as matching parameter values taken in the field will also be provided. This issuggested primarily to address difficulty in model parameterization.
Fine-tuning of parameters in the SWAT model is generally a stumbling block to new users of thesoftware. The task would be more tedious especially when some critical parameters need to beprecisely adjusted to minimize error and increase accuracy of the model. Therefore, auto-calibration
which was not performed in the study is suggested only for the identified sensitive parameters.
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