Development of daily bias-corrected ensemble precipitation estimates over the Upper Indus Basin of the Hindukush-Karakoram-Himalaya
Accurate precipitation estimates over space and time are critically important, particularly in data-scarce areas, for effective hydrological modeling and efficient regional water resources management. Gridded precipitation datasets are the preeminent alternative in such areas. However, gridded preci...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IWA Publishing
2023-10-01
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| Series: | Journal of Water and Climate Change |
| Subjects: | |
| Online Access: | http://jwcc.iwaponline.com/content/14/10/3517 |
| _version_ | 1827278811387920384 |
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| author | Kashif Jamal Xin Li Yingying Chen Sajjad Haider Muhammad Rizwan Shakil Ahmad |
| author_facet | Kashif Jamal Xin Li Yingying Chen Sajjad Haider Muhammad Rizwan Shakil Ahmad |
| author_sort | Kashif Jamal |
| collection | DOAJ |
| description | Accurate precipitation estimates over space and time are critically important, particularly in data-scarce areas, for effective hydrological modeling and efficient regional water resources management. Gridded precipitation datasets are the preeminent alternative in such areas. However, gridded precipitation datasets contain different kinds of uncertainties owing to the retrieval algorithms used in their development. In this study, five precipitation datasets (Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC), APHRODITE, Climate Hazards Group Infra-Red Precipitation with Station data (CHIRPS), and PERSIANN) were evaluated, and an ensemble of daily precipitation datasets from 2001 to 2017 at a resolution of 0.05 degree was created based on three ensemble approaches (Bayesian model ensemble, relative bias-based ensemble, and correlation-based ensemble) over the Upper Indus basin. To improve the accuracy of the ensemble dataset, a linear bias correction technique is applied with respect to gauging precipitation. The accuracy of the bias-corrected ensemble dataset was evaluated using statistical and novelty categorical measures. A reasonable agreement was found between the ensemble and gauge precipitation (Pearson correlation 0.83–0.89 and relative bias 1–8.7 mm/month), while large biases were noted in five precipitation datasets (1.7–53.9 mm/month). The study suggests that utilizing ensemble approaches to gridded precipitation can significantly enhance the accuracy of the estimates compared to relying on a single precipitation dataset.
HIGHLIGHTS
The study developed bias-corrected precipitation estimates using three ensemble approaches.;
The new relative bias-based ensemble approach estimates are slightly better than the existing ensemble approaches used in this study.;
A nonlinear precipitation increase/decrease trend is found with altitude.;
The direct use of gridded precipitation is not recommended due to the large biases present in each precipitation dataset.; |
| first_indexed | 2024-03-11T11:14:02Z |
| format | Article |
| id | doaj.art-027073e77b1d470289e89599f4fadcb1 |
| institution | Directory Open Access Journal |
| issn | 2040-2244 2408-9354 |
| language | English |
| last_indexed | 2024-04-24T08:07:48Z |
| publishDate | 2023-10-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Journal of Water and Climate Change |
| spelling | doaj.art-027073e77b1d470289e89599f4fadcb12024-04-17T08:35:19ZengIWA PublishingJournal of Water and Climate Change2040-22442408-93542023-10-0114103517353810.2166/wcc.2023.202202Development of daily bias-corrected ensemble precipitation estimates over the Upper Indus Basin of the Hindukush-Karakoram-HimalayaKashif Jamal0Xin Li1Yingying Chen2Sajjad Haider3Muhammad Rizwan4Shakil Ahmad5 Key Laboratory of Remote Sensing and Geospatial Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China School of Civil and Environmental Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan Department of Civil Engineering, Swedish College of Engineering and Technology, Rahim Yar Khan 64200, Pakistan School of Civil and Environmental Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan Accurate precipitation estimates over space and time are critically important, particularly in data-scarce areas, for effective hydrological modeling and efficient regional water resources management. Gridded precipitation datasets are the preeminent alternative in such areas. However, gridded precipitation datasets contain different kinds of uncertainties owing to the retrieval algorithms used in their development. In this study, five precipitation datasets (Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC), APHRODITE, Climate Hazards Group Infra-Red Precipitation with Station data (CHIRPS), and PERSIANN) were evaluated, and an ensemble of daily precipitation datasets from 2001 to 2017 at a resolution of 0.05 degree was created based on three ensemble approaches (Bayesian model ensemble, relative bias-based ensemble, and correlation-based ensemble) over the Upper Indus basin. To improve the accuracy of the ensemble dataset, a linear bias correction technique is applied with respect to gauging precipitation. The accuracy of the bias-corrected ensemble dataset was evaluated using statistical and novelty categorical measures. A reasonable agreement was found between the ensemble and gauge precipitation (Pearson correlation 0.83–0.89 and relative bias 1–8.7 mm/month), while large biases were noted in five precipitation datasets (1.7–53.9 mm/month). The study suggests that utilizing ensemble approaches to gridded precipitation can significantly enhance the accuracy of the estimates compared to relying on a single precipitation dataset. HIGHLIGHTS The study developed bias-corrected precipitation estimates using three ensemble approaches.; The new relative bias-based ensemble approach estimates are slightly better than the existing ensemble approaches used in this study.; A nonlinear precipitation increase/decrease trend is found with altitude.; The direct use of gridded precipitation is not recommended due to the large biases present in each precipitation dataset.;http://jwcc.iwaponline.com/content/14/10/3517bias correctionensemble approachesprecipitation datasetsupper indus basin |
| spellingShingle | Kashif Jamal Xin Li Yingying Chen Sajjad Haider Muhammad Rizwan Shakil Ahmad Development of daily bias-corrected ensemble precipitation estimates over the Upper Indus Basin of the Hindukush-Karakoram-Himalaya Journal of Water and Climate Change bias correction ensemble approaches precipitation datasets upper indus basin |
| title | Development of daily bias-corrected ensemble precipitation estimates over the Upper Indus Basin of the Hindukush-Karakoram-Himalaya |
| title_full | Development of daily bias-corrected ensemble precipitation estimates over the Upper Indus Basin of the Hindukush-Karakoram-Himalaya |
| title_fullStr | Development of daily bias-corrected ensemble precipitation estimates over the Upper Indus Basin of the Hindukush-Karakoram-Himalaya |
| title_full_unstemmed | Development of daily bias-corrected ensemble precipitation estimates over the Upper Indus Basin of the Hindukush-Karakoram-Himalaya |
| title_short | Development of daily bias-corrected ensemble precipitation estimates over the Upper Indus Basin of the Hindukush-Karakoram-Himalaya |
| title_sort | development of daily bias corrected ensemble precipitation estimates over the upper indus basin of the hindukush karakoram himalaya |
| topic | bias correction ensemble approaches precipitation datasets upper indus basin |
| url | http://jwcc.iwaponline.com/content/14/10/3517 |
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