Using SWAT to Model Streamflow in Two River Basins With Ground and Satellite Precipitation Data<sup>1</sup>

Tobin, Kenneth J.; Bennett, Marvin E.

doi:10.1111/j.1752-1688.2008.00276.x

Cited by 88 publications

(54 citation statements)

References 53 publications

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“…The NEXRAD data set led to an overestimated BFI by up to 23%, which was surprising considering that the level III NEXRAD data have been shown to produce more accurate hydrologic model performance compared to satellite-derived precipitation products, and a better choice for MAP inputs for hydrologic model performance than point gauges [56][57][58]. For example, Tobin and Bennett (2009) [58] used level III NEXRAD data for input into the SWAT model and the results showed that the three day average stream flow was associated with NSE values ranging from 0.60 to 0.88, while the TRMM 3B42 data set yielded more variable results with NSE values ranging from 0.38 to 0.94. However, in the current work, the NEXRAD precipitation data set was associated with the greatest overestimation in annual total precipitation (MAE = 152 mm) which indicated potential radar accuracy issues.…”

Section: Calibrated Model Performancementioning

confidence: 99%

An Assessment of Mean Areal Precipitation Methods on Simulated Stream Flow: A SWAT Model Performance Assessment

Zeiger

Hubbart

2017

Water

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Accurate mean areal precipitation (MAP) estimates are essential input forcings for hydrologic models. However, the selection of the most accurate method to estimate MAP can be daunting because there are numerous methods to choose from (e.g., proximate gauge, direct weighted average, surface-fitting, and remotely sensed methods). Multiple methods (n = 19) were used to estimate MAP with precipitation data from 11 distributed monitoring sites, and 4 remotely sensed data sets. Each method was validated against the hydrologic model simulated stream flow using the Soil and Water Assessment Tool (SWAT). SWAT was validated using a split-site method and the observed stream flow data from five nested-scale gauging sites in a mixed-land-use watershed of the central USA. Cross-validation results showed the error associated with surface-fitting and remotely sensed methods ranging from −4.5% to −5.1%, and −9.8% to −14.7%, respectively. Split-site validation results showed the percent bias (PBIAS) values that ranged from −4.5% to −160%. Second order polynomial functions especially overestimated precipitation and subsequent stream flow simulations (PBIAS = −160) in the headwaters. The results indicated that using an inverse-distance weighted, linear polynomial interpolation or multiquadric function method to estimate MAP may improve SWAT model simulations. Collectively, the results highlight the importance of spatially distributed observed hydroclimate data for precipitation and subsequent steam flow estimations. The MAP methods demonstrated in the current work can be used to reduce hydrologic model uncertainty caused by watershed physiographic differences.

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Section: Calibrated Model Performancementioning

confidence: 99%

An Assessment of Mean Areal Precipitation Methods on Simulated Stream Flow: A SWAT Model Performance Assessment

Zeiger

Hubbart

2017

Water

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“…Soil and water assessment tool (SWAT) is a physical, semi-distributed hydrological model that has a few advantages in predicting climate change effects on water-related and hydrological processes over a continuous time [17]. The performance of this model relies on precipitation input parameters, namely accuracy and spatial distribution [18]. So, many researchers have selected grid precipitation to drive hydrological models.…”

Section: Introductionmentioning

confidence: 99%

Runoff Simulation by SWAT Model Using High-Resolution Gridded Precipitation in the Upper Heihe River Basin, Northeastern Tibetan Plateau

Ruan

Zou

Yang

et al. 2017

Water

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Abstract:The scarcity and uneven distribution of precipitation stations in the inland river basins of the Northeastern Tibetan Plateau restrict the application of the distributed hydrological model and spatial analysis of water balance component characteristics. This study used the upper Heihe River Basin as a case study, and daily gridded precipitation data with 3 km resolution based on the spatial interpolation of gauged stations and a regional climate model were used to construct a soil and water assessment tool (SWAT) model. The aim was to validate the precision of high-resolution gridded precipitation for hydrological simulation in data-scarce regions; a scale transformation method was proposed by building virtual stations and calculating the lapse rate to overcome the defects of the SWAT model using traditional precipitation station data. The gridded precipitation was upscaled from the grid to the sub-basin scale to accurately represent sub-basin precipitation input data. A satisfactory runoff simulation was achieved, and the spatial variability of water balance components was analysed. Results show that the precipitation lapse rate ranges from 40 mm/km to 235 mm/km and decreases from the southeastern to the northwestern areas. The SWAT model achieves monthly runoff simulation compared with gauged runoff from 2000 to 2014; the determination coefficients are higher than 0.71, the Nash-Sutcliffe efficiencies are higher than 0.76, and the percentage bias is controlled within ±15%. Meadow and sparse vegetation are the major water yield landscapes, and the elevation band from 3500 m to 4500 m is the major water yield area. Precipitation and evapotranspiration present a slightly increasing trend, whereas water yield and soil water content present a slightly decreasing trend. This finding indicates that the high-resolution gridded precipitation data fully depict its spatial heterogeneity, and scale transformation significantly promotes the application of the distributed hydrological model in inland river basins. The spatial variability of water balance components can be quantified to provide references for the integrated assessment and management of basin water resources in data-scarce regions.

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“…Simulated bacterial concentrations were determined using the SWAT model. Tobin and Bennett [1] previously modeled the middle Rio Grande Basin, which overlaps with the study area, and this work provided information that facilitated model calibration. The precipitation dataset input into the model was the National Weather Service Multisensor Estimator product (MPE).…”

Section: Methodsmentioning

confidence: 99%

Bacterial loading during flooding: a case study from 2010 on the Bi-national Rio Grande

Torres¹,

Tobin²,

Wilson³

2012

Water Pollution XI

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Escherichia coli (E. coli) bacteria loadings were tracked during the summer 2010 major flood event of the Rio Grande using the Soil and Water Assessment Tool (SWAT) hydrologic model. This bi-national watershed spanning Texas and Mexico has loading from urban point source sewage outfalls and non-point source loadings from rangeland and agricultural areas located upstream of a urban centre (Laredo/Nuevo Laredo). Flood events represent a high risk for human health since bacterial loadings increased during the flood. Sampling during flood conditions is dangerous and therefore there are comparatively few studies that have examined bacterial values in rivers during flood events. Therefore, the safest way to obtain bacterial concentrations during a flood event is through hydrologic modelling. Significantly, the initial assumption of constant bacterial loading within the basin is not valid. As demonstrated by previous research there is a strong linear correlation between streamflow and E. coli values. The outcomes of this study will provide guidelines for users of the SWAT model to predict bacteria fate and transport during future flood events and will therefore assist communities in improving management of their water supplies. Finally, use of satellite precipitation data as primary input for the SWAT model potentially extends the findings of this study to areas of the developing world that lack ground monitoring of precipitation.

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Using SWAT to Model Streamflow in Two River Basins With Ground and Satellite Precipitation Data¹

Cited by 88 publications

References 53 publications

An Assessment of Mean Areal Precipitation Methods on Simulated Stream Flow: A SWAT Model Performance Assessment

An Assessment of Mean Areal Precipitation Methods on Simulated Stream Flow: A SWAT Model Performance Assessment

Runoff Simulation by SWAT Model Using High-Resolution Gridded Precipitation in the Upper Heihe River Basin, Northeastern Tibetan Plateau

Bacterial loading during flooding: a case study from 2010 on the Bi-national Rio Grande

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Using SWAT to Model Streamflow in Two River Basins With Ground and Satellite Precipitation Data1

Cited by 88 publications

References 53 publications

An Assessment of Mean Areal Precipitation Methods on Simulated Stream Flow: A SWAT Model Performance Assessment

An Assessment of Mean Areal Precipitation Methods on Simulated Stream Flow: A SWAT Model Performance Assessment

Runoff Simulation by SWAT Model Using High-Resolution Gridded Precipitation in the Upper Heihe River Basin, Northeastern Tibetan Plateau

Bacterial loading during flooding: a case study from 2010 on the Bi-national Rio Grande

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Using SWAT to Model Streamflow in Two River Basins With Ground and Satellite Precipitation Data¹