Enhancing the capabilities of the Chao Phraya forecasting system through the integration of pre-processed numerical weather forecasts
Study region: This study focuses on the Chao Phraya (CPY) Basin, Thailand. Study focus: This study aims to improve the skill of the CPY flood forecasting system, developed by Hydro-Informatics Institute (HII) and DHI A/S since 2012. It introduces two pre-processing workflows that are applied to the...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-04-01
|
| Series: | Journal of Hydrology: Regional Studies |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581824000855 |
| _version_ | 1797246825803022336 |
|---|---|
| author | Theerapol Charoensuk Jakob Luchner Nicola Balbarini Piyamarn Sisomphon Peter Bauer-Gottwein |
| author_facet | Theerapol Charoensuk Jakob Luchner Nicola Balbarini Piyamarn Sisomphon Peter Bauer-Gottwein |
| author_sort | Theerapol Charoensuk |
| collection | DOAJ |
| description | Study region: This study focuses on the Chao Phraya (CPY) Basin, Thailand. Study focus: This study aims to improve the skill of the CPY flood forecasting system, developed by Hydro-Informatics Institute (HII) and DHI A/S since 2012. It introduces two pre-processing workflows that are applied to the raw numerical weather prediction (NWP) provided by Weather Research and Forecasting model (WRF): quantile mapping bias correction (QM) and random forest regression (RF). Rainfall forecasts, updated with two pre-processing methods, WRF-QM and WRF-RF, were evaluated against daily rainfall measurements from HII’s stations in each subcatchment. Six hydrological re-forecasting experiments were conducted using a hydrological model to compare runoff forecasts with and without preprocessing method as well as with in-situ rainfall, climatology and persistence benchmark. We assessed rainfall and runoff predictions during training (2016–2019) and testing periods (2020–2021). New Hydrological Insights for the Region: Utilizing pre-processing methods in rainfall prediction enhances the accuracy for raw rainfall and runoff predictions. The WRF-QM and WRF-RF methods improved rainfall prediction by 12% and 18% in RMSE’s terms during testing period, respectively. Overall performance results indicate runoff forecasting with WRF-QM and WRF-RF pre-processing reduces RMSE by 34% and 40%, respectively, compared to Raw WRF. Wilcoxon signed-test confirmed significant improvement with pre-processing methods. Our study demonstrates the potential of pre-processed NWP to enhance the skill of hydrologic forecasting systems. Pre-processing methods boost flood forecasting reliability, addressing challenges caused by more frequent and severe hydrologic extremes. |
| first_indexed | 2024-04-24T19:48:57Z |
| format | Article |
| id | doaj.art-dba03044626a43b2a75e6085bd15e220 |
| institution | Directory Open Access Journal |
| issn | 2214-5818 |
| language | English |
| last_indexed | 2024-04-24T19:48:57Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj.art-dba03044626a43b2a75e6085bd15e2202024-03-25T04:17:46ZengElsevierJournal of Hydrology: Regional Studies2214-58182024-04-0152101737Enhancing the capabilities of the Chao Phraya forecasting system through the integration of pre-processed numerical weather forecastsTheerapol Charoensuk0Jakob Luchner1Nicola Balbarini2Piyamarn Sisomphon3Peter Bauer-Gottwein4Department of Environmental and Resource Engineering, Technical University of Denmark, Lyngby 2800 Kgs, Denmark; DHI A/S, Hørsholm 2970, Denmark; Hydro-informatics Institute, Bangkok, Thailand; Corresponding author at: Department of Environmental and Resource Engineering, Technical University of Denmark, Lyngby 2800 Kgs, Denmark.DHI A/S, Hørsholm 2970, DenmarkDHI A/S, Hørsholm 2970, DenmarkHydro-informatics Institute, Bangkok, ThailandDepartment of Environmental and Resource Engineering, Technical University of Denmark, Lyngby 2800 Kgs, DenmarkStudy region: This study focuses on the Chao Phraya (CPY) Basin, Thailand. Study focus: This study aims to improve the skill of the CPY flood forecasting system, developed by Hydro-Informatics Institute (HII) and DHI A/S since 2012. It introduces two pre-processing workflows that are applied to the raw numerical weather prediction (NWP) provided by Weather Research and Forecasting model (WRF): quantile mapping bias correction (QM) and random forest regression (RF). Rainfall forecasts, updated with two pre-processing methods, WRF-QM and WRF-RF, were evaluated against daily rainfall measurements from HII’s stations in each subcatchment. Six hydrological re-forecasting experiments were conducted using a hydrological model to compare runoff forecasts with and without preprocessing method as well as with in-situ rainfall, climatology and persistence benchmark. We assessed rainfall and runoff predictions during training (2016–2019) and testing periods (2020–2021). New Hydrological Insights for the Region: Utilizing pre-processing methods in rainfall prediction enhances the accuracy for raw rainfall and runoff predictions. The WRF-QM and WRF-RF methods improved rainfall prediction by 12% and 18% in RMSE’s terms during testing period, respectively. Overall performance results indicate runoff forecasting with WRF-QM and WRF-RF pre-processing reduces RMSE by 34% and 40%, respectively, compared to Raw WRF. Wilcoxon signed-test confirmed significant improvement with pre-processing methods. Our study demonstrates the potential of pre-processed NWP to enhance the skill of hydrologic forecasting systems. Pre-processing methods boost flood forecasting reliability, addressing challenges caused by more frequent and severe hydrologic extremes.http://www.sciencedirect.com/science/article/pii/S2214581824000855Chao PhrayaFlood forecastingPerformanceUncertaintyHydrologic modelThailand |
| spellingShingle | Theerapol Charoensuk Jakob Luchner Nicola Balbarini Piyamarn Sisomphon Peter Bauer-Gottwein Enhancing the capabilities of the Chao Phraya forecasting system through the integration of pre-processed numerical weather forecasts Journal of Hydrology: Regional Studies Chao Phraya Flood forecasting Performance Uncertainty Hydrologic model Thailand |
| title | Enhancing the capabilities of the Chao Phraya forecasting system through the integration of pre-processed numerical weather forecasts |
| title_full | Enhancing the capabilities of the Chao Phraya forecasting system through the integration of pre-processed numerical weather forecasts |
| title_fullStr | Enhancing the capabilities of the Chao Phraya forecasting system through the integration of pre-processed numerical weather forecasts |
| title_full_unstemmed | Enhancing the capabilities of the Chao Phraya forecasting system through the integration of pre-processed numerical weather forecasts |
| title_short | Enhancing the capabilities of the Chao Phraya forecasting system through the integration of pre-processed numerical weather forecasts |
| title_sort | enhancing the capabilities of the chao phraya forecasting system through the integration of pre processed numerical weather forecasts |
| topic | Chao Phraya Flood forecasting Performance Uncertainty Hydrologic model Thailand |
| url | http://www.sciencedirect.com/science/article/pii/S2214581824000855 |
| work_keys_str_mv | AT theerapolcharoensuk enhancingthecapabilitiesofthechaophrayaforecastingsystemthroughtheintegrationofpreprocessednumericalweatherforecasts AT jakobluchner enhancingthecapabilitiesofthechaophrayaforecastingsystemthroughtheintegrationofpreprocessednumericalweatherforecasts AT nicolabalbarini enhancingthecapabilitiesofthechaophrayaforecastingsystemthroughtheintegrationofpreprocessednumericalweatherforecasts AT piyamarnsisomphon enhancingthecapabilitiesofthechaophrayaforecastingsystemthroughtheintegrationofpreprocessednumericalweatherforecasts AT peterbauergottwein enhancingthecapabilitiesofthechaophrayaforecastingsystemthroughtheintegrationofpreprocessednumericalweatherforecasts |