Coupling soil moisture and precipitation observations for predicting hourly runoff at small catchment scale

摘要: Summary The importance of soil moisture is recognized in rainfall–runoff processes. This study quantitatively investigates the use measured at 10, 20, and 40 cm depths along with rainfall predicting runoff. For this purpose, two small sub-catchments Tiber River Basin, Italy, were instrumented during periods October 2002–March 2003 January–April 2004. Colorso Basin about 13 km 2 Niccone basin 137 km . Rainfall plus formed input vector while discharge was target output model generalized regression neural network (GRNN). for each calibrated tested using data. GRNN then employed to predict runoff period performance found be satisfactory determination coefficient, R , equal 0.87 Nash–Sutcliffe efficiency, NS, 0.86 validation phase both catchments. investigation effects on prediction revealed that addition data, rainfall, tremendously improves model. sensitivity analysis indicated data different allows preserve memory system thus having a similar effect employing past values but improved performance.