The authors explore simple concepts of persistence in streamflow forecasting based on the real-time streamflow observations from the years 2002 to 2018 at 140 U.S. Geological Survey (USGS) streamflow gauges in Iowa. The spatial scale of the basins ranges from about 7 km2 to 37,000 km2. Motivated by the need for evaluating the skill of real-time streamflow forecasting systems, the authors perform quantitative skill assessment of different 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, but non-negligible, dependence on geometric properties of the river networks in the basins. 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 vs. 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 three 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.
A skill assessment of techniques for real-time diagnosis and short-term prediction of tornado intensity using the WSR-88D
