Satellite-Based Evapotranspiration in Hydrological Model Calibration
Hydrological models are usually calibrated against observed streamflow (Q<sub>obs</sub>), which is not applicable for ungauged river basins. A few studies have exploited remotely sensed evapotranspiration (ET<sub>RS</sub>) for model calibration but their effectiveness on stre...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/3/428 |
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| author | Lulu Jiang Huan Wu Jing Tao John S. Kimball Lorenzo Alfieri Xiuwan Chen |
| author_facet | Lulu Jiang Huan Wu Jing Tao John S. Kimball Lorenzo Alfieri Xiuwan Chen |
| author_sort | Lulu Jiang |
| collection | DOAJ |
| description | Hydrological models are usually calibrated against observed streamflow (Q<sub>obs</sub>), which is not applicable for ungauged river basins. A few studies have exploited remotely sensed evapotranspiration (ET<sub>RS</sub>) for model calibration but their effectiveness on streamflow simulation remains uncertain. This paper investigates the use of ET<sub>RS</sub> in the hydrological calibration of a widely used land surface model coupled with a source−sink routing scheme and global optimization algorithm for 28 natural river basins. A baseline simulation is a setup based on the latest model developments and inputs. Sensitive parameters are determined for Q<sub>obs</sub> and ET<sub>RS</sub>-based model calibrations, respectively, through comprehensive sensitivity tests. The ET<sub>RS</sub>-based model calibration results in a mean Kling−Gupta Efficiency (KGE) value of 0.54 for streamflow simulation; 61% of the river basins have KGE > 0.5 in the validation period, which is consistent with the calibration period and provides a significant improvement over the baseline. Compared to Q<sub>obs</sub>, the ET<sub>RS</sub> calibration produces better or similar streamflow simulations in 29% of the basins, while further significant improvements are achieved when either better ET or precipitation observations are used. Furthermore, the model results show better or similar performance in 68% of the basins and outperform the baseline simulations in 90% of the river basins using model parameters from the best ET<sub>RS</sub> calibration runs. This study confirms that with reasonable precipitation input, the ET<sub>RS</sub>-based spatially distributed calibration can efficiently tune parameters for better ET and streamflow simulations. The application of ET<sub>RS</sub> for global scale hydrological model calibration promises even better streamflow accuracy as the satellite-based ET<sub>RS</sub> observations continue to improve. |
| first_indexed | 2024-04-11T18:43:51Z |
| format | Article |
| id | doaj.art-1cf273e84a024a39aa9470507ba3cc38 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T18:43:51Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1cf273e84a024a39aa9470507ba3cc382022-12-22T04:08:52ZengMDPI AGRemote Sensing2072-42922020-01-0112342810.3390/rs12030428rs12030428Satellite-Based Evapotranspiration in Hydrological Model CalibrationLulu Jiang0Huan Wu1Jing Tao2John S. Kimball3Lorenzo Alfieri4Xiuwan Chen5Institute of Remote Sensing and Geographical Information System, Peking University, Beijing 100871, ChinaGuangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, ChinaClimate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USANumerical Terradynamic Simulation Group, W.A. Franke College of Forestry & Conservation, The University of Montana, Missoula, MT 59812, USAEuropean Commission, Joint Research Centre, 21027 Ispra, ItalyInstitute of Remote Sensing and Geographical Information System, Peking University, Beijing 100871, ChinaHydrological models are usually calibrated against observed streamflow (Q<sub>obs</sub>), which is not applicable for ungauged river basins. A few studies have exploited remotely sensed evapotranspiration (ET<sub>RS</sub>) for model calibration but their effectiveness on streamflow simulation remains uncertain. This paper investigates the use of ET<sub>RS</sub> in the hydrological calibration of a widely used land surface model coupled with a source−sink routing scheme and global optimization algorithm for 28 natural river basins. A baseline simulation is a setup based on the latest model developments and inputs. Sensitive parameters are determined for Q<sub>obs</sub> and ET<sub>RS</sub>-based model calibrations, respectively, through comprehensive sensitivity tests. The ET<sub>RS</sub>-based model calibration results in a mean Kling−Gupta Efficiency (KGE) value of 0.54 for streamflow simulation; 61% of the river basins have KGE > 0.5 in the validation period, which is consistent with the calibration period and provides a significant improvement over the baseline. Compared to Q<sub>obs</sub>, the ET<sub>RS</sub> calibration produces better or similar streamflow simulations in 29% of the basins, while further significant improvements are achieved when either better ET or precipitation observations are used. Furthermore, the model results show better or similar performance in 68% of the basins and outperform the baseline simulations in 90% of the river basins using model parameters from the best ET<sub>RS</sub> calibration runs. This study confirms that with reasonable precipitation input, the ET<sub>RS</sub>-based spatially distributed calibration can efficiently tune parameters for better ET and streamflow simulations. The application of ET<sub>RS</sub> for global scale hydrological model calibration promises even better streamflow accuracy as the satellite-based ET<sub>RS</sub> observations continue to improve.https://www.mdpi.com/2072-4292/12/3/428hydrological modelcalibrationremote sensingevapotranspirationungauged river basins |
| spellingShingle | Lulu Jiang Huan Wu Jing Tao John S. Kimball Lorenzo Alfieri Xiuwan Chen Satellite-Based Evapotranspiration in Hydrological Model Calibration Remote Sensing hydrological model calibration remote sensing evapotranspiration ungauged river basins |
| title | Satellite-Based Evapotranspiration in Hydrological Model Calibration |
| title_full | Satellite-Based Evapotranspiration in Hydrological Model Calibration |
| title_fullStr | Satellite-Based Evapotranspiration in Hydrological Model Calibration |
| title_full_unstemmed | Satellite-Based Evapotranspiration in Hydrological Model Calibration |
| title_short | Satellite-Based Evapotranspiration in Hydrological Model Calibration |
| title_sort | satellite based evapotranspiration in hydrological model calibration |
| topic | hydrological model calibration remote sensing evapotranspiration ungauged river basins |
| url | https://www.mdpi.com/2072-4292/12/3/428 |
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