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• W3044291704 abstract "ABSTRACT The authors explore persistence in streamflow forecasting based on the real-time streamflow observations. They use 15-min streamflow observations from the years 2002 to 2018 at 140 U.S. Geological Survey (USGS) streamflow gauges monitoring the streams and rivers throughout Iowa. The spatial scale of the basins ranges from about 7 to 37 000 km 2 . Motivated by the need for evaluating the skill of real-time streamflow forecasting systems, the authors perform quantitative skill assessment of persistence schemes across spatial scales and lead times. They show that skill in temporal persistence forecasting has a strong dependence on basin size, and a weaker dependence on geometric properties of the river networks. Building on results from this temporal persistence, they extend the streamflow persistence forecasting to space through flow-connected river networks. The approach simply assumes that streamflow at a station in space will persist to another station which is flow connected; these are referred to as pure spatial persistence forecasts (PSPF). The authors show that skill of PSPF of streamflow is strongly dependent on the monitored versus predicted basin area ratio and lead times, and weakly related to the downstream flow distance between stations. River network topology shows some effect on the hydrograph timing and timing of the peaks, depending on the stream gauge configuration. The study shows that the skill depicted in terms of Kling–Gupta efficiency (KGE) > 0.5 can be achieved for basin area ratio > 0.6 and lead time up to 3 days. The authors discuss the implications of their findings for assessment and improvements of rainfall–runoff models, data assimilation schemes, and stream gauging network design." @default.
• W3044291704 created "2020-07-29" @default.
• W3044291704 creator A5007094465 @default.
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• W3044291704 date "2020-08-01" @default.
• W3044291704 modified "2023-10-11" @default.
• W3044291704 title "Streamflow Forecasting without Models" @default.
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• W3044291704 doi "https://doi.org/10.1175/jhm-d-19-0292.1" @default.
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