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 km2. 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.
Highlights
Skillful streamflow forecasts are critically important in improving decision-making in water-related policy and management
This paper explores the following questions: (1) what is the utility of data-driven persistence-based approaches for skillful streamflow forecasting in the Himalayan region? (2) Which forecast conditions, such as lead time, flow threshold, basin size and flow regime, benefit potential increase in forecast skill? We organize the paper as follows: In Section 2, we discuss the materials and methods used in this study
The stronger relationship between the Kling–Gupta efficiency (KGE) and basin size emerges with the increasing forecast lead times
Summary
Skillful streamflow forecasts are critically important in improving decision-making in water-related policy and management (e.g., flood and drought preparedness, reservoir operation and hydropower generation). Khadka et al [5] showed, in the simulation study of Tamakoshi basin in eastern Nepal for the years 2000–2009, that snowmelt contributes about 18% of the annual runoff. Dhami et al [9] used the Soil and Water Assessment Tool and snow-melt runoff model in the Karnali river basin in western Nepal to simulate components of water balance. They reported that about 12% of annual runoff is contributed by the snowmelt, while about 29% by the groundwater base flow
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