Assessing the Impact of Areal Precipitation Input on Streamflow Simulations Using the SWAT Model1

Masih Ilyas, Shreedhar Maskey, Stefan Uhlenbrook, and Vladimir Smakhtin, 2011. Assessing the Impact of Areal Precipitation Input on Streamflow Simulations Using the SWAT Model. Journal of the American Water Resources Association (JAWRA) 47(1):179‐195. DOI: 10.1111/j.1752‐1688.2010.00502.x Abstract: Reduction of input uncertainty is a challenge in hydrological modeling. The widely used model Soil Water Assessment Tool (SWAT) uses the data of a precipitation gauge nearest to the centroid of each subcatchment as an input for that subcatchment. This may not represent overall catchment precipitation conditions well. This paper suggests an alternative – using areal precipitation obtained through interpolation. The effectiveness of this alternative is evaluated by comparing its simulations with those based on the standard SWAT precipitation input procedure. The model is applied to mountainous semiarid catchments in the Karkheh River basin, Iran. The model performance is evaluated at daily, monthly, and annual scales by using a number of performance indicators at 15 streamflow gauging stations each draining an area in the range of 590‐42,620 km². The comparison suggests that the use of areal precipitation improves model performance particularly in small subcatchments in the range of 600‐1,600 km². The modified areal precipitation input results in increased reliability of simulated streamflows in the areas of low rain gauge density. Both precipitation input methods result in reasonably good simulations for larger catchments (over 5,000 km²). The use of areal precipitation input improves the accuracy of simulated streamflows with spatial resolution and density of rain gauges having significant impact on results.     

增江流域非点源污染模拟研究

构建增江流域非点源污染数据库,包括DEM、土地利用,土壤类型,气象数据等,应用分布式流域水文模型SWAT(Soil andWater Assessment Tool,swat 2009版)对增江流域的非点源污染进行模拟。模型运行阶段为2000-2003年,分别应用2000-2001年和2002-2003年的实测月均流量及硝酸盐氮监测数据对模型的参数率定和验证,采用决定系数R2和Nash-Suttcliffe系数对模拟结果进行评定。其中水文模拟的R2均>0.9,Nash-Suttcliffe模型效率系数均>0.8;硝酸盐氮模拟的R2均>0.7,Nash-Suttcliffe模型效率系数均>0.6,表明SWAT模型在增江流域具有较好的适用性。     

PRISM vs. CFSR Precipitation Data Effects on Calibration and Validation of SWAT Models

Precipitation is one of the most important drivers in watershed models. Our objective was to compare two sources of interpolated precipitation data in terms of their effect on calibration and validation of two Soil and Water Assessment Tool (SWAT) models. One model was a suburban watershed in metropolitan Atlanta, Georgia. The precipitation sources were Parameter‐elevation Relationships on Independent Slopes Model (PRISM) data on a 4‐km grid and climate forecast system reanalysis (CFSR) data on a 38‐km grid. The PRISM data resulted in a better fit to the calibration data (Nash Sutcliffe efficiency [NSE] = 0.64, Kling‐Gupta efficiency [KGE] = 0.74, p‐factor = 0.84, and r‐factor = 0.43) than the CFSR data (NSE = 0.47, KGE = 0.53, p‐factor = 0.67, and r‐factor = 0.39). Validation results were similar. Sensitive parameters were similar in both the PRISM and CFSR models, but fitted values indicated more rapid groundwater flow to the streams with the PRISM data. The same comparison was made in the Big Creek watershed located approximately 1,000 km away, in central Louisiana. Results were similar with a more responsive groundwater system indicating PRISM data may produce better predictions of streamflow because of a more accurate estimate of rainfall within a watershed or because of a denser grid. Our study implies PRISM is providing a better estimate than CFSR of precipitation within a watershed when rain gauge data are not available, resulting in more accurate simulations of streamflows at the watershed outlet. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Streamflow Changes in the South Atlantic,United States During the Mid‐ and Late 20th Century1

Abstract: Repeated severe droughts over the last decade in the South Atlantic have raised concern that streamflow may be systematically decreasing, possibly due to climate variability. We examined the monthly and annual trends of streamflow, precipitation, and temperature in the South Atlantic for the time periods: 1934‐2005, 1934‐1969, and 1970‐2005. Streamflow and climate (temperature and precipitation) trends transitioned ca. 1970. From 1934 to 1969, streamflow and precipitation increased in southern regions and decreased in northern regions; temperature decreased throughout the South Atlantic. From 1970 to 2005, streamflow decreased, precipitation decreased, and temperature increased throughout the South Atlantic. It is unclear whether these will be continuing trends or simply part of a long‐term climatic oscillation. Whether these streamflow trends have been driven by climatic or anthropogenic changes, water resources management faces challenging prospects to adapt to decadal‐scale persistently wet and dry hydrologic conditions.     

HYDROCLIMATIC VARIABILITY IN THE ROCKY MOUNTAINS1

ABSTRACT: The spatial and temporal variability of hydroclimatic elements were investigated in the central and northern Rocky Mountains (Colorado, Idaho, Montana, Utah, and Wyoming) during the 1951–1985 period. The three hydroclimatic elements studied were total water-year (October 1-September 30) streamflow (ST), winter (October 1-March 31) accumulated precipitation (PR), and April 1 snowpack (SN). An analysis of 14 virgin watersheds showed wide spatial djfferences in the temporal variability of SN, PR, and ST, and these were found to be caused largely by basin exposure to moist air flows. The more stable (low variability) basins were those exposed to prevailing northerly to westerly flow, while unstable (high variability) basins were exposed to occasional southwesterly to southeasterly moist flow. Snowpack was the better indicator of ST in 11 of the 14 watersheds, explaining 37 to 87 percent of the ST variance. Analysis of the spatial variability, based on all SN and PR data from across the study area, revealed 11 discrete climatic regions. Both SN and PR exhibited coherent regions of stable and unstable temporal variability. The average variability between stable and unstable regions differed by a factor of two, and the differences were best explained by the exposure of the mountain barrier to moist air flows.     

Spatial Calibration and Temporal Validation of Flow for Regional Scale Hydrologic Modeling1

Abstract: Physically based regional scale hydrologic modeling is gaining importance for planning and management of water resources. Calibration and validation of such regional scale model is necessary before applying it for scenario assessment. However, in most regional scale hydrologic modeling, flow validation is performed at the river basin outlet without accounting for spatial variations in hydrological parameters within the subunits. In this study, we calibrated the model to capture the spatial variations in runoff at subwatershed level to assure local water balance, and validated the streamflow at key gaging stations along the river to assure temporal variability. Ohio and Arkansas‐White‐Red River Basins of the United States were modeled using Soil and Water Assessment Tool (SWAT) for the period from 1961 to 1990. R² values of average annual runoff at subwatersheds were 0.78 and 0.99 for the Ohio and Arkansas Basins. Observed and simulated annual and monthly streamflow from 1961 to 1990 is used for temporal validation at the gages. R² values estimated were greater than 0.6. In summary, spatially distributed calibration at subwatersheds and temporal validation at the stream gages accounted for the spatial and temporal hydrological patterns reasonably well in the two river basins. This study highlights the importance of spatially distributed calibration and validation in large river basins.     

Estimation of Evapotranspiration Across the Conterminous United States Using a Regression With Climate and Land‐Cover Data1

Sanford, Ward E. and David L. Selnick, 2012. Estimation of Evapotranspiration Across the Conterminous United States Using a Regression with Climate and Land‐Cover Data. Journal of the American Water Resources Association (JAWRA) 1‐14. DOI: 10.1111/jawr.12010 Abstract: Evapotranspiration (ET) is an important quantity for water resource managers to know because it often represents the largest sink for precipitation (P) arriving at the land surface. In order to estimate actual ET across the conterminous United States (U.S.) in this study, a water‐balance method was combined with a climate and land‐cover regression equation. Precipitation and streamflow records were compiled for 838 watersheds for 1971‐2000 across the U.S. to obtain long‐term estimates of actual ET. A regression equation was developed that related the ratio ET/P to climate and land‐cover variables within those watersheds. Precipitation and temperatures were used from the PRISM climate dataset, and land‐cover data were used from the USGS National Land Cover Dataset. Results indicate that ET can be predicted relatively well at a watershed or county scale with readily available climate variables alone, and that land‐cover data can also improve those predictions. Using the climate and land‐cover data at an 800‐m scale and then averaging to the county scale, maps were produced showing estimates of ET and ET/P for the entire conterminous U.S. Using the regression equation, such maps could also be made for more detailed state coverages, or for other areas of the world where climate and land‐cover data are plentiful.     

Modeling of Sediment Yield From Anjeni-Gauged Watershed,Ethiopia Using SWAT Model1

Setegn, Shimelis G., Bijan Dargahi, Ragahavan Srinivasan, and Assefa M. Melesse, 2010. Modeling of Sediment Yield From Anjeni-Gauged Watershed, Ethiopia Using SWAT Model. Journal of the American Water Resources Association (JAWRA) 46(3):514-526. DOI: 10.1111/j.1752-1688.2010.00431.x Abstract: The Soil and Water Assessment Tool (SWAT) was tested for prediction of sediment yield in Anjeni-gauged watershed, Ethiopia. Soil erosion and land degradation is a major problem on the Ethiopian highlands. The objectives of this study were to evaluate the performance and applicability of SWAT model in predicting monthly sediment yield and assess the impacts of subbasin delineation and slope discretization on the prediction of sediment yield. Ten years monthly meteorological, flow and sediment data were used for model calibration and validation. The annual average measured sediment yield was 24.6 tonnes/ha. The annual average simulated sediment yield was 27.8 and 29.5 tones/ha for calibration and validation periods, respectively. The study found that the observed values showed good agreement with the simulated sediment yield with Nash-Sutcliffe efficiency (NSE) = 0.81, percent bias (PBIAS) = 28%, RMSE-observations standard deviation ratio (RSR) = 0.23, and coefficient of determination (R²) = 0.86 for calibration and NSE = 0.79, PBIAS = 30%, RSR = 0.29, and R² = 0.84 for validation periods. The model can be used for further analysis of different management scenarios that could help different stakeholders to plan and implement appropriate soil and water conservation strategies.     

Effectiveness of Multi‐Site Weather Generator for Hydrological Modeling1

Khalili, Malika, François Brissette, and Robert Leconte, 2011. Effectiveness of Multi‐site Weather Generator for Hydrological Modeling. Journal of the American Water Resources Association (JAWRA) 1‐12. DOI: 10.1111/j.1752‐1688.2010.00514.x Abstract: A multi‐site weather generator has been developed using the concept of spatial autocorrelation. The multi‐site generation approach reproduces the spatial autocorrelations observed between a set of weather stations as well as the correlations between each pair of stations. Its performance has been assessed in two previous studies using both precipitation and temperature data. The main objective of this paper is to assess the efficiency of this multi‐site weather generator compared to a uni‐site generator with respect to hydrological modeling. A hydrological model, known as Hydrotel, was applied over the Chute du Diable watershed, located in the Canadian province of Quebec. The distributed nature of Hydrotel accounts for the spatial variations throughout the watershed, and thus allows a more in‐depth assessment of the effect of spatially dependent meteorological input on runoff generation. Simulated streamflows using both the multi‐site and uni‐site generated weather data were statistically compared to flows modeled using observed data. Overall, the hydrological modeling using the multi‐site weather generator significantly outperformed that using the uni‐site generator. This latter combined to Hydrotel resulted in a significant underestimation of extreme streamflows in all seasons.     

Improved SWAT Model Performance With Time-Dynamic Voronoi Tessellation of Climatic Input Data in Southern Africa1

Andersson, Jafet C.M., Alexander J.B. Zehnder, Bernhard Wehrli, and Hong Yang, 2012. Improved SWAT Model Performance with Time-Dynamic Voronoi Tessellation of Climatic Input Data in Southern Africa. Journal of the American Water Resources Association (JAWRA) 48(3): 480-493. DOI: 10.1111/j.1752-1688.2011.00627.x Abstract: In this study, we compared two approaches to obtain climatic time series for the Soil and Water Assessment Tool (SWAT), namely the conventional centroid method and time-dynamic Voronoi tessellation, and assessed the performance of SWAT in simulating discharge and smallholder maize yields in Southern Africa. Climatic time series were estimated with each method. The Voronoi method utilized all available precipitation and temperature data, but the centroid method used only 14.5 and 82.5%, respectively. After centroid processing, sub-basin time series were on average 42 and 63% incomplete, respectively. After Voronoi processing, all time series were complete. SWAT was fed with each climate dataset. Each model setup was independently calibrated and validated against discharge and maize yield. Similar model performance was obtained with both methods for yield. The root mean squared error during calibration was 0.26 and 0.27 t ha⁻¹ for the centroid and Voronoi methods, respectively (p-value: 0.80). However, daily discharge simulations improved significantly with the Voronoi method. The coefficient of determination increased from 0.24 to 0.39 in the calibration period (p-value: 9.6 × 10⁻¹³) and from 0.41 to 0.48 in the validation period (p-value: 3.1 × 10⁻³). The Voronoi method improved the simulation of the river flow regime. The largest improvements were obtained in data scarce situations, at high spatial and temporal resolution, and where the centroid method performed the worst.