Review of Recent Developments in Hydrologic Forecast Merging Techniques
Accurate forecasting in hydrologic modeling is crucial for sustainable water resource management across various sectors, where predicting extreme flow phases holds particular significance due to their severe impact on the territory. Due to the inherent uncertainties in hydrologic forecasting, relyin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/16/2/301 |
| _version_ | 1797342392113692672 |
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| author | Md Rasel Sheikh Paulin Coulibaly |
| author_facet | Md Rasel Sheikh Paulin Coulibaly |
| author_sort | Md Rasel Sheikh |
| collection | DOAJ |
| description | Accurate forecasting in hydrologic modeling is crucial for sustainable water resource management across various sectors, where predicting extreme flow phases holds particular significance due to their severe impact on the territory. Due to the inherent uncertainties in hydrologic forecasting, relying solely on a single rainfall–runoff model may not provide reliable predictions. To address this challenge, over the years, researchers have developed and applied hydrologic forecast merging (HFM) techniques that combine multiple models or ensembles to enhance forecast accuracy, reduce uncertainty, and increase confidence in the forecast. This review summarizes the progress in HFM techniques since the early 1990s and covers developments and applications in flow simulation, uncertainty analysis, monthly and seasonal streamflow predictions, ensemble forecasts, flood forecasting, and climate change analysis. The findings indicate that while HFM techniques outperform individual models regarding forecasting efficiency, their performance across applications is not uniform. Among the different methods, Bayesian model averaging (BMA) is the most popular due to its ability to reduce uncertainty and provide accurate and reliable forecasts in deterministic and probabilistic simulations. With their application simplicity, regression techniques are also robust and efficient as they perform competitively well across different model-merging applications. While specific techniques, such as model-dependent weighted averaging and neural network methods, effectively reduce forecast uncertainty, there is still room for improving forecast accuracy across different lead times. Future research can focus on advanced HFM techniques for estimating optimal weights in time-varying domains and overcoming limitations, like simulating low flows in seasonally dry catchments. |
| first_indexed | 2024-03-08T10:32:37Z |
| format | Article |
| id | doaj.art-9477a74674d746c38fe3c05301ef6a30 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-08T10:32:37Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-9477a74674d746c38fe3c05301ef6a302024-01-26T18:51:50ZengMDPI AGWater2073-44412024-01-0116230110.3390/w16020301Review of Recent Developments in Hydrologic Forecast Merging TechniquesMd Rasel Sheikh0Paulin Coulibaly1School of Earth, Environment & Society, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4K1, CanadaSchool of Earth, Environment & Society, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4K1, CanadaAccurate forecasting in hydrologic modeling is crucial for sustainable water resource management across various sectors, where predicting extreme flow phases holds particular significance due to their severe impact on the territory. Due to the inherent uncertainties in hydrologic forecasting, relying solely on a single rainfall–runoff model may not provide reliable predictions. To address this challenge, over the years, researchers have developed and applied hydrologic forecast merging (HFM) techniques that combine multiple models or ensembles to enhance forecast accuracy, reduce uncertainty, and increase confidence in the forecast. This review summarizes the progress in HFM techniques since the early 1990s and covers developments and applications in flow simulation, uncertainty analysis, monthly and seasonal streamflow predictions, ensemble forecasts, flood forecasting, and climate change analysis. The findings indicate that while HFM techniques outperform individual models regarding forecasting efficiency, their performance across applications is not uniform. Among the different methods, Bayesian model averaging (BMA) is the most popular due to its ability to reduce uncertainty and provide accurate and reliable forecasts in deterministic and probabilistic simulations. With their application simplicity, regression techniques are also robust and efficient as they perform competitively well across different model-merging applications. While specific techniques, such as model-dependent weighted averaging and neural network methods, effectively reduce forecast uncertainty, there is still room for improving forecast accuracy across different lead times. Future research can focus on advanced HFM techniques for estimating optimal weights in time-varying domains and overcoming limitations, like simulating low flows in seasonally dry catchments.https://www.mdpi.com/2073-4441/16/2/301hydrologic modelingmodel averagingmulti-model combinationensemble combinationforecast merging techniqueforecast accuracy and reliability improvement |
| spellingShingle | Md Rasel Sheikh Paulin Coulibaly Review of Recent Developments in Hydrologic Forecast Merging Techniques Water hydrologic modeling model averaging multi-model combination ensemble combination forecast merging technique forecast accuracy and reliability improvement |
| title | Review of Recent Developments in Hydrologic Forecast Merging Techniques |
| title_full | Review of Recent Developments in Hydrologic Forecast Merging Techniques |
| title_fullStr | Review of Recent Developments in Hydrologic Forecast Merging Techniques |
| title_full_unstemmed | Review of Recent Developments in Hydrologic Forecast Merging Techniques |
| title_short | Review of Recent Developments in Hydrologic Forecast Merging Techniques |
| title_sort | review of recent developments in hydrologic forecast merging techniques |
| topic | hydrologic modeling model averaging multi-model combination ensemble combination forecast merging technique forecast accuracy and reliability improvement |
| url | https://www.mdpi.com/2073-4441/16/2/301 |
| work_keys_str_mv | AT mdraselsheikh reviewofrecentdevelopmentsinhydrologicforecastmergingtechniques AT paulincoulibaly reviewofrecentdevelopmentsinhydrologicforecastmergingtechniques |