Benchmarking Real-Time Streamflow Forecast Skill in the Himalayan Region

Ganesh R. Ghimire, Sanjib Sharma, Jeeban Panthi, Rocky Talchabhadel, Binod Parajuli, Piyush Dahal and Rupesh Baniya
Additional contact information
Ganesh R. Ghimire: IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, IA 52242, USA
Sanjib Sharma: Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, PA 16801, USA
Jeeban Panthi: Department of Geosciences, University of Rhode Island, Kingston, RI 02881, USA
Rocky Talchabhadel: Disaster Prevention Research Institute, Kyoto University, Fushimi-ku, Kyoto 612-8235, Japan
Binod Parajuli: Department of Hydrology and Meteorology, Ministry of Energy, Water Resources and Irrigation, Kathmandu 44600, Nepal
Piyush Dahal: The Small Earth Nepal, Kathmandu 44600, Nepal
Rupesh Baniya: Institute of Engineering, Pulchowk Campus, Tribhuvan University, Lalitpur 44700, Nepal

Forecasting, 2020, vol. 2, issue 3, 1-18

Abstract: 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 2 . 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.

Keywords: Himalayan region; streamflow forecast verification; persistence; snow-fed rivers; intermittent rivers (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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