Benchmarking Real-Time Streamflow Forecast Skill in the Himalayan Region
Improving decision-making in various areas of water policy and management (e.g., flood and drought preparedness, reservoir operation and hydropower generation) requires skillful streamflow forecasts. Despite the recent advances in hydrometeorological prediction, real-time streamflow forecasting over...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Forecasting |
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| Online Access: | https://www.mdpi.com/2571-9394/2/3/13 |
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| author | Ganesh R. Ghimire Sanjib Sharma Jeeban Panthi Rocky Talchabhadel Binod Parajuli Piyush Dahal Rupesh Baniya |
| author_facet | Ganesh R. Ghimire Sanjib Sharma Jeeban Panthi Rocky Talchabhadel Binod Parajuli Piyush Dahal Rupesh Baniya |
| author_sort | Ganesh R. Ghimire |
| collection | DOAJ |
| description | Improving decision-making in various areas of water policy and management (e.g., flood and drought preparedness, reservoir operation and hydropower generation) requires skillful streamflow forecasts. Despite the recent advances in hydrometeorological prediction, real-time streamflow forecasting over the Himalayas remains a critical issue and challenge, especially with complex basin physiography, shifting weather patterns and sparse and biased in-situ hydrometeorological monitoring data. In this study, we demonstrate the utility of low-complexity data-driven persistence-based approaches for skillful streamflow forecasting in the Himalayan country Nepal. The selected approaches are: (1) simple persistence, (2) streamflow climatology and (3) anomaly persistence. We generated the streamflow forecasts for 65 stream gauge stations across Nepal for short-to-medium range forecast lead times (1 to 12 days). The selected gauge stations were monitored by the Department of Hydrology and Meteorology (DHM) Nepal, and they represent a wide range of basin size, from ~17 to ~54,100 km<sup>2</sup>. We find that the performance of persistence-based forecasting approaches depends highly upon the lead time, flow threshold, basin size and flow regime. Overall, the persistence-based forecast results demonstrate higher forecast skill in snow-fed rivers over intermittent ones, moderate flows over extreme ones and larger basins over smaller ones. The streamflow forecast skill obtained in this study can serve as a benchmark (reference) for the evaluation of many operational forecasting systems over the Himalayas. |
| first_indexed | 2024-03-10T18:36:24Z |
| format | Article |
| id | doaj.art-41634c243c6f48fe890d604d6a6e5bd5 |
| institution | Directory Open Access Journal |
| issn | 2571-9394 |
| language | English |
| last_indexed | 2024-03-10T18:36:24Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj.art-41634c243c6f48fe890d604d6a6e5bd52023-11-20T06:10:07ZengMDPI AGForecasting2571-93942020-07-012323024710.3390/forecast2030013Benchmarking Real-Time Streamflow Forecast Skill in the Himalayan RegionGanesh R. Ghimire0Sanjib Sharma1Jeeban Panthi2Rocky Talchabhadel3Binod Parajuli4Piyush Dahal5Rupesh Baniya6IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, IA 52242, USAEarth and Environmental Systems Institute, The Pennsylvania State University, University Park, PA 16801, USADepartment of Geosciences, University of Rhode Island, Kingston, RI 02881, USADisaster Prevention Research Institute, Kyoto University, Fushimi-ku, Kyoto 612-8235, JapanDepartment of Hydrology and Meteorology, Ministry of Energy, Water Resources and Irrigation, Kathmandu 44600, NepalThe Small Earth Nepal, Kathmandu 44600, NepalInstitute of Engineering, Pulchowk Campus, Tribhuvan University, Lalitpur 44700, NepalImproving decision-making in various areas of water policy and management (e.g., flood and drought preparedness, reservoir operation and hydropower generation) requires skillful streamflow forecasts. Despite the recent advances in hydrometeorological prediction, real-time streamflow forecasting over the Himalayas remains a critical issue and challenge, especially with complex basin physiography, shifting weather patterns and sparse and biased in-situ hydrometeorological monitoring data. In this study, we demonstrate the utility of low-complexity data-driven persistence-based approaches for skillful streamflow forecasting in the Himalayan country Nepal. The selected approaches are: (1) simple persistence, (2) streamflow climatology and (3) anomaly persistence. We generated the streamflow forecasts for 65 stream gauge stations across Nepal for short-to-medium range forecast lead times (1 to 12 days). The selected gauge stations were monitored by the Department of Hydrology and Meteorology (DHM) Nepal, and they represent a wide range of basin size, from ~17 to ~54,100 km<sup>2</sup>. We find that the performance of persistence-based forecasting approaches depends highly upon the lead time, flow threshold, basin size and flow regime. Overall, the persistence-based forecast results demonstrate higher forecast skill in snow-fed rivers over intermittent ones, moderate flows over extreme ones and larger basins over smaller ones. The streamflow forecast skill obtained in this study can serve as a benchmark (reference) for the evaluation of many operational forecasting systems over the Himalayas.https://www.mdpi.com/2571-9394/2/3/13Himalayan regionstreamflow forecast verificationpersistencesnow-fed riversintermittent rivers |
| spellingShingle | Ganesh R. Ghimire Sanjib Sharma Jeeban Panthi Rocky Talchabhadel Binod Parajuli Piyush Dahal Rupesh Baniya Benchmarking Real-Time Streamflow Forecast Skill in the Himalayan Region Forecasting Himalayan region streamflow forecast verification persistence snow-fed rivers intermittent rivers |
| title | Benchmarking Real-Time Streamflow Forecast Skill in the Himalayan Region |
| title_full | Benchmarking Real-Time Streamflow Forecast Skill in the Himalayan Region |
| title_fullStr | Benchmarking Real-Time Streamflow Forecast Skill in the Himalayan Region |
| title_full_unstemmed | Benchmarking Real-Time Streamflow Forecast Skill in the Himalayan Region |
| title_short | Benchmarking Real-Time Streamflow Forecast Skill in the Himalayan Region |
| title_sort | benchmarking real time streamflow forecast skill in the himalayan region |
| topic | Himalayan region streamflow forecast verification persistence snow-fed rivers intermittent rivers |
| url | https://www.mdpi.com/2571-9394/2/3/13 |
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