Evaluation of Gridded Precipitation Data for Driving SWAT Model in Area Upstream of Three Gorges Reservoir

Yang, Yan; Wang, Guoqiang; Wang, Lijing; Yu, Jingshan; Xu, Zongxue

doi:10.1371/journal.pone.0112725

Cited by 79 publications

(44 citation statements)

References 40 publications

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“…Other studies using CFSR data not in the Ethiopian context (De Almeida Bressiani et al, 2015;Alemayehu et al, 2015) and with large to very large catchments (13 750 to 73 000 km 2 ) concluded that CFSR data yielded good to very good results and the SWAT model responded reasonably to the data set. One CFSR application in China (Yang et al, 2014) with meso-scale watersheds (366 to 1098 km 2 ) concluded that CFSR data were significantly different and that the CFSR data spatial distribution might be the cause for the weak performance.…”

Section: Roth and T Lemann: Comparing Cfsr And Conventional Weathmentioning

confidence: 99%

Comparing CFSR and conventional weather data for discharge and soil loss modelling with SWAT in small catchments in the Ethiopian Highlands

Roth

Lemann

2016

Hydrol. Earth Syst. Sci.

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Abstract. Accurate rainfall data are the key input parameter for modelling river discharge and soil loss. Remote areas of Ethiopia often lack adequate precipitation data and where these data are available, there might be substantial temporal or spatial gaps. To counter this challenge, the Climate Forecast System Reanalysis (CFSR) of the National Centers for Environmental Prediction (NCEP) readily provides weather data for any geographic location on earth between 1979 and 2014. This study assesses the applicability of CFSR weather data to three watersheds in the Blue Nile Basin in Ethiopia. To this end, the Soil and Water Assessment Tool (SWAT) was set up to simulate discharge and soil loss, using CFSR and conventional weather data, in three small-scale watersheds ranging from 112 to 477 ha. Calibrated simulation results were compared to observed river discharge and observed soil loss over a period of 32 years. The conventional weather data resulted in very good discharge outputs for all three watersheds, while the CFSR weather data resulted in unsatisfactory discharge outputs for all of the three gauging stations. Soil loss simulation with conventional weather inputs yielded satisfactory outputs for two of three watersheds, while the CFSR weather input resulted in three unsatisfactory results. Overall, the simulations with the conventional data resulted in far better results for discharge and soil loss than simulations with CFSR data. The simulations with CFSR data were unable to adequately represent the specific regional climate for the three watersheds, performing even worse in climatic areas with two rainy seasons. Hence, CFSR data should not be used lightly in remote areas with no conventional weather data where no prior analysis is possible.

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Section: Roth and T Lemann: Comparing Cfsr And Conventional Weathmentioning

confidence: 99%

Comparing CFSR and conventional weather data for discharge and soil loss modelling with SWAT in small catchments in the Ethiopian Highlands

Roth

Lemann

2016

Hydrol. Earth Syst. Sci.

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“…Twenty sensitive parameters were selected according to previous studies [5,47,66,67] and tested in the SWAT-CUP to perform a sensitivity analysis. Fourteen sensitive parameters were selected according to their performances in the sensitivity analysis, and manual calibration and auto-calibration were performed using a Sequential Uncertainty Fitting (SUFI-2) algorithm to achieve acceptable performance [68][69][70][71][72][73].…”

Section: Model Calibration and Validationmentioning

confidence: 99%

Investigating Alternative Climate Data Sources for Hydrological Simulations in the Upstream of the Amu Darya River

Sidike

Chen

et al. 2016

Water

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Abstract:The main objective of this study is to investigate alternative climate data sources for long-term hydrological modeling. To accomplish this goal, one weather station data set (WSD) and three grid-based data sets including three types of precipitation data and two types of temperature data were selected according to their spatial and temporal details. An accuracy assessment of the grid-based data sets was performed using WSD. Then, the performances of corrected data combination and non-corrected grid-based precipitation and temperature data combinations from multiple sources on simulating river flow in the upstream portion of the Amu Darya River Basin (ADRB) were analyzed using a Soil and Water Assessment Tool (SWAT) model. The results of the accuracy assessments indicated that all the grid-based data sets underestimated precipitation. The Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE) precipitation data provided the highest accuracy (correlation coefficients (CF) > 0.89, root mean square error (RMSE) < 41.6 mm), followed by the CRUNCEP reanalysis data (a combination of the CRU TS.3.2 data and the National Centers for Environmental Prediction (NCEP) reanalysis data) (CF > 0.5, RMSE < 58.1 mm) and Princeton's Global Meteorological Forcing Dataset (PGMFD) precipitation data (CF > 0.46, RMSE < 62.8 mm). The PGMFD temperature data exhibited a higher accuracy (CF > 0.98, RMSE < 7.1 • C) than the CRUNCEP temperature data (CF > 0.97, RMSE < 4.9 • C). In terms of the simulation performance, the corrected APHRODITE precipitation and PGMFD temperature data provided the best performance. The CF and Nash-Sutcliffe (NSE) coefficients in the calibration and validation periods were 0.96 and 0.92 and 0.93 and 0.83, respectively. In addition, the combinations of PGMFD temperature data and APHRODITE, PGMFD and CRUNCEP precipitation data produced good results, with NSE ≥ 0.70 and CF ≥ 0.89. The combination of CRUNCEP temperature data and APHRODITE precipitation produced a satisfactory result, with NSE = 0.58 and CF = 0.82. The combinations of CRUNCEP temperature data and PGMFD and CRUNCEP precipitation data produced poor results.

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“…In addition, in basins upstream of the Three Gorges Reservoir in China, Yang et al [8] compared precipitation datasets derived from land-surface models, reanalysis datasets, and climatology models with in situ station data. They discovered that, in a relatively flat basin, gridded precipitation datasets estimated runoff better than the in situ station data within the SWAT model.…”

Section: Introductionmentioning

confidence: 99%

Increasing the Accuracy of Runoff and Streamflow Simulation in the Nzoia Basin, Western Kenya, through the Incorporation of Satellite-Derived CHIRPS Data

Pricope

2017

Water

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Hydrologic models will be an increasingly important tool for water resource managers as water availability dwindles and water security concerns become more pertinent in data-scarce regions. Fortunately, newly available satellite remote sensing technology provides an opportunity for improving the spatial resolution and quality of input data to hydrologic models in such regions. In particular, the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) dataset provides quasi-global high resolution precipitation information derived from a blend of in situ and active and passive remote sensing data sources. We piloted the incorporation of the CHIRPS dataset into the Soil and Water Assessment Tool (SWAT), a hydrologic model. Comparisons of results between estimation of streamflow using in situ rainfall gauge station data, the Climate Forecast System Reanalysis (CFSR) dataset, and the CHIRPS dataset in the data-scarce Nzoia Basin in western Kenya over the temporal range 1990-2000 were reported. Simulated streamflow estimates were poor with rainfall gauge station data but improved significantly with the CFSR and CHIRPS datasets. However, the use of the CHIRPS dataset in comparison with the CFSR dataset provided an improved statistical performance following model calibration with the exception of one streamflow gauge station in higher elevation regions. Overall, the use of the CHIRPS dataset had the greatest linear correlation, relative variability, and normalized bias despite overall average Nash-Sutcliffe Efficiency (NSE) and R 2 values.

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Evaluation of Gridded Precipitation Data for Driving SWAT Model in Area Upstream of Three Gorges Reservoir

Cited by 79 publications

References 40 publications

Comparing CFSR and conventional weather data for discharge and soil loss modelling with SWAT in small catchments in the Ethiopian Highlands

Comparing CFSR and conventional weather data for discharge and soil loss modelling with SWAT in small catchments in the Ethiopian Highlands

Investigating Alternative Climate Data Sources for Hydrological Simulations in the Upstream of the Amu Darya River

Increasing the Accuracy of Runoff and Streamflow Simulation in the Nzoia Basin, Western Kenya, through the Incorporation of Satellite-Derived CHIRPS Data

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