Converting Climate Change Gridded Daily Rainfall to Station Daily Rainfall—A Case Study at Zengwen Reservoir
With improvements in data quality and technology, the statistical downscaling data of General Circulation Models (GCMs) for climate change impact assessment have been refined from monthly data to daily data, which has greatly promoted the data application level. However, there are differences betwee...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/13/11/1516 |
| _version_ | 1797532275466829824 |
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| author | Tse-Yu Teng Tzu-Ming Liu Yu-Shiang Tung Ke-Sheng Cheng |
| author_facet | Tse-Yu Teng Tzu-Ming Liu Yu-Shiang Tung Ke-Sheng Cheng |
| author_sort | Tse-Yu Teng |
| collection | DOAJ |
| description | With improvements in data quality and technology, the statistical downscaling data of General Circulation Models (GCMs) for climate change impact assessment have been refined from monthly data to daily data, which has greatly promoted the data application level. However, there are differences between GCM downscaling daily data and rainfall station data. If GCM data are directly used for hydrology and water resources assessment, the differences in total amount and rainfall intensity will be revealed and may affect the estimates of the total amount of water resources and water supply capacity. This research proposes a two-stage bias correction method for GCM data and establishes a mechanism for converting grid data to station data. Five GCMs were selected from 33 GCMs, which were ranked by rainfall simulation performance from a baseline period in Taiwan. The watershed of the Zengwen Reservoir in southern Taiwan was selected as the study area for comparison of the three different bias correction methods. The results reveal that the method with the wet-day threshold optimized by objective function with observation rainfall wet days had the best result. Error was greatly reduced in the hydrology model simulation with two-stage bias correction. The results show that the two-stage bias correction method proposed in this study can be used as an advanced method of data pre-processing in climate change impact assessment, which could improve the quality and broaden the extent of GCM daily data. Additionally, GCM ranking can be used by researchers in climate change assessment to understand the suitability of each GCM in Taiwan. |
| first_indexed | 2024-03-10T10:56:47Z |
| format | Article |
| id | doaj.art-a31554a97a4043eba88b770fc148dd58 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T10:56:47Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-a31554a97a4043eba88b770fc148dd582023-11-21T21:50:23ZengMDPI AGWater2073-44412021-05-011311151610.3390/w13111516Converting Climate Change Gridded Daily Rainfall to Station Daily Rainfall—A Case Study at Zengwen ReservoirTse-Yu Teng0Tzu-Ming Liu1Yu-Shiang Tung2Ke-Sheng Cheng3National Science and Technology Center for Disaster Reduction, New Taipei City 23143, TaiwanNational Science and Technology Center for Disaster Reduction, New Taipei City 23143, TaiwanNational Science and Technology Center for Disaster Reduction, New Taipei City 23143, TaiwanDepartment of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, TaiwanWith improvements in data quality and technology, the statistical downscaling data of General Circulation Models (GCMs) for climate change impact assessment have been refined from monthly data to daily data, which has greatly promoted the data application level. However, there are differences between GCM downscaling daily data and rainfall station data. If GCM data are directly used for hydrology and water resources assessment, the differences in total amount and rainfall intensity will be revealed and may affect the estimates of the total amount of water resources and water supply capacity. This research proposes a two-stage bias correction method for GCM data and establishes a mechanism for converting grid data to station data. Five GCMs were selected from 33 GCMs, which were ranked by rainfall simulation performance from a baseline period in Taiwan. The watershed of the Zengwen Reservoir in southern Taiwan was selected as the study area for comparison of the three different bias correction methods. The results reveal that the method with the wet-day threshold optimized by objective function with observation rainfall wet days had the best result. Error was greatly reduced in the hydrology model simulation with two-stage bias correction. The results show that the two-stage bias correction method proposed in this study can be used as an advanced method of data pre-processing in climate change impact assessment, which could improve the quality and broaden the extent of GCM daily data. Additionally, GCM ranking can be used by researchers in climate change assessment to understand the suitability of each GCM in Taiwan.https://www.mdpi.com/2073-4441/13/11/1516general circulation modelGCMs rankingstatistical downscaling daily dataprobability of precipitationtwo-stage bias correction method |
| spellingShingle | Tse-Yu Teng Tzu-Ming Liu Yu-Shiang Tung Ke-Sheng Cheng Converting Climate Change Gridded Daily Rainfall to Station Daily Rainfall—A Case Study at Zengwen Reservoir Water general circulation model GCMs ranking statistical downscaling daily data probability of precipitation two-stage bias correction method |
| title | Converting Climate Change Gridded Daily Rainfall to Station Daily Rainfall—A Case Study at Zengwen Reservoir |
| title_full | Converting Climate Change Gridded Daily Rainfall to Station Daily Rainfall—A Case Study at Zengwen Reservoir |
| title_fullStr | Converting Climate Change Gridded Daily Rainfall to Station Daily Rainfall—A Case Study at Zengwen Reservoir |
| title_full_unstemmed | Converting Climate Change Gridded Daily Rainfall to Station Daily Rainfall—A Case Study at Zengwen Reservoir |
| title_short | Converting Climate Change Gridded Daily Rainfall to Station Daily Rainfall—A Case Study at Zengwen Reservoir |
| title_sort | converting climate change gridded daily rainfall to station daily rainfall a case study at zengwen reservoir |
| topic | general circulation model GCMs ranking statistical downscaling daily data probability of precipitation two-stage bias correction method |
| url | https://www.mdpi.com/2073-4441/13/11/1516 |
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