Evaluation of WRF model rainfall forecast using citizen science in a data-scarce urban catchment: Addis Ababa, Ethiopia - Tedla, H. Z.; Taye, E. F.; Walker, D. W.; Haile, Alemseged Tamiru (Elsevier, 2022)
Study region: The Akaki catchment is found in the Upper Awash River Basin in Ethiopia. Study focus: Understanding the accuracy of rainfall forecasts in the data-scarce urban catchment has a multitude of benefits given the increased urban flood risk caused by climate change and urbanization. In this study, accuracy of the weather research and forecasting (WRF) model rainfall forecast was evaluated using citizen science data. Categorical and continuous accuracy evaluation metrics were used beside gauge representativeness effect. New hydrological insights for the region: The rainfall forecasts performance accuracy is high for 1–3- days lead-time but deteriorates for 4–5-days lead-time. The WRF model captured the temporal dynamics and the rainfall amount according to the estimated KGE values. The model has relatively higher detection performance for no rain and light rain events (< 6 mm/day), but it has lower performance for moderate and heavy rain events (> 6 mm/day). Use of data from a single rain gauge misrepresents the accuracy level of the rainfall forecast in the study area. The gauge representativeness error contributed a variance of 28.08–83.33 % to the variance of WRF-gauge rainfall difference. Thus, the use of citizen science rainfall monitoring program is an essential alternative source of information where in-situ rainfall monitoring is limited that can be used to understand the “true” accuracy of WRF rainfall forecasts.