A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT Model
The spatial and temporal scale of rainfall datasets is crucial in modeling hydrological processes. Recently, open-access satellite precipitation products with improved resolution have evolved as a potential alternative to sparsely distributed ground-based observations, which sometimes fail to captur...
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| Format: | Article |
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MDPI AG
2020-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/18/3088 |
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| author | Yeganantham Dhanesh V. M. Bindhu Javier Senent-Aparicio Tássia Mattos Brighenti Essayas Ayana P. S. Smitha Chengcheng Fei Raghavan Srinivasan |
| author_facet | Yeganantham Dhanesh V. M. Bindhu Javier Senent-Aparicio Tássia Mattos Brighenti Essayas Ayana P. S. Smitha Chengcheng Fei Raghavan Srinivasan |
| author_sort | Yeganantham Dhanesh |
| collection | DOAJ |
| description | The spatial and temporal scale of rainfall datasets is crucial in modeling hydrological processes. Recently, open-access satellite precipitation products with improved resolution have evolved as a potential alternative to sparsely distributed ground-based observations, which sometimes fail to capture the spatial variability of rainfall. However, the reliability and accuracy of the satellite precipitation products in simulating streamflow need to be verified. In this context, the objective of the current study is to assess the performance of three rainfall datasets in the prediction of daily and monthly streamflow using Soil and Water Assessment Tool (SWAT). We used rainfall data from three different sources: Climate Hazards Group InfraRed Rainfall with Station data (CHIRPS), Climate Forecast System Reanalysis (CFSR) and observed rain gauge data. Daily and monthly rainfall measurements from CHIRPS and CFSR were validated using widely accepted statistical measures, namely, correlation coefficient (CC), root mean squared error (RMSE), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The results showed that CHIRPS was in better agreement with ground-based rainfall at daily and monthly scale, with high rainfall detection ability, in comparison with the CFSR product. Streamflow prediction across multiple watersheds was also evaluated using Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE) and Percent BIAS (PBIAS). Irrespective of the climatic characteristics, the hydrologic simulations of CHIRPS showed better agreement with the observed at the monthly scale with the majority of the NSE values ranging between 0.40 and 0.78, and KGE values ranging between 0.62 and 0.82. Overall, CHIRPS outperformed the CFSR rainfall product in driving SWAT for streamflow simulations across the multiple watersheds selected for the study. The results from the current study demonstrate the potential of CHIRPS as an alternate open access rainfall input to the hydrologic model. |
| first_indexed | 2024-03-10T16:10:58Z |
| format | Article |
| id | doaj.art-bd0e7df8e5004638a2f34054685a8786 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T16:10:58Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-bd0e7df8e5004638a2f34054685a87862023-11-20T14:29:18ZengMDPI AGRemote Sensing2072-42922020-09-011218308810.3390/rs12183088A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT ModelYeganantham Dhanesh0V. M. Bindhu1Javier Senent-Aparicio2Tássia Mattos Brighenti3Essayas Ayana4P. S. Smitha5Chengcheng Fei6Raghavan Srinivasan7Department of Civil Engineering, Texas A&M University, 534 John Kimbrough Blvd., College Station, TX 77843-2120, USADepartment of Civil Engineering, Indian Institute of Technology Madras, Chennai 600036, IndiaDepartment of Civil Engineering, Catholic University of San Antonio, Campus de Los Jeronimos s/n, 30107 Murcia, SpainGraduate Program in Environmental Engineering, Federal University of Santa Catarina, Florianopolis 88040-900, BrazilFormation Environmental, 1631 Alhambra Blvd, Sacramento, CA 95816, USADepartment of Civil Engineering, Indian Institute of Technology Madras, Chennai 600036, IndiaDepartment of Agricultural Economics, Texas A&M University, 600 John Kimbrough Blvd., College Station, TX 77843-2120, USADepartment of Ecology and Conservation Biology, Texas A&M University, 600 John Kimbrough Blvd., College Station, TX 77843-2120, USAThe spatial and temporal scale of rainfall datasets is crucial in modeling hydrological processes. Recently, open-access satellite precipitation products with improved resolution have evolved as a potential alternative to sparsely distributed ground-based observations, which sometimes fail to capture the spatial variability of rainfall. However, the reliability and accuracy of the satellite precipitation products in simulating streamflow need to be verified. In this context, the objective of the current study is to assess the performance of three rainfall datasets in the prediction of daily and monthly streamflow using Soil and Water Assessment Tool (SWAT). We used rainfall data from three different sources: Climate Hazards Group InfraRed Rainfall with Station data (CHIRPS), Climate Forecast System Reanalysis (CFSR) and observed rain gauge data. Daily and monthly rainfall measurements from CHIRPS and CFSR were validated using widely accepted statistical measures, namely, correlation coefficient (CC), root mean squared error (RMSE), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The results showed that CHIRPS was in better agreement with ground-based rainfall at daily and monthly scale, with high rainfall detection ability, in comparison with the CFSR product. Streamflow prediction across multiple watersheds was also evaluated using Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE) and Percent BIAS (PBIAS). Irrespective of the climatic characteristics, the hydrologic simulations of CHIRPS showed better agreement with the observed at the monthly scale with the majority of the NSE values ranging between 0.40 and 0.78, and KGE values ranging between 0.62 and 0.82. Overall, CHIRPS outperformed the CFSR rainfall product in driving SWAT for streamflow simulations across the multiple watersheds selected for the study. The results from the current study demonstrate the potential of CHIRPS as an alternate open access rainfall input to the hydrologic model.https://www.mdpi.com/2072-4292/12/18/3088SWATHAWQSrainfall comparisonCHIRPSCFSR |
| spellingShingle | Yeganantham Dhanesh V. M. Bindhu Javier Senent-Aparicio Tássia Mattos Brighenti Essayas Ayana P. S. Smitha Chengcheng Fei Raghavan Srinivasan A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT Model Remote Sensing SWAT HAWQS rainfall comparison CHIRPS CFSR |
| title | A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT Model |
| title_full | A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT Model |
| title_fullStr | A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT Model |
| title_full_unstemmed | A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT Model |
| title_short | A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT Model |
| title_sort | comparative evaluation of the performance of chirps and cfsr data for different climate zones using the swat model |
| topic | SWAT HAWQS rainfall comparison CHIRPS CFSR |
| url | https://www.mdpi.com/2072-4292/12/18/3088 |
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