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

- The calibrated and tested GRNN model successfully predicts runoff hydrographs.
- The addition of soil moisture data, along with rainfall, tremendously improves the performance of the model.
- The use of soil moisture data at different depths allows to preserve the memory of the system.
- Replacing past values of rainfall with soil moisture data is effective in GRNN modeling.

SummaryThe importance of soil moisture is recognized in rainfall-runoff processes. This study quantitatively investigates the use of soil moisture measured at 10, 20, and 40 cm soil depths along with rainfall in predicting runoff. For this purpose, two small sub-catchments of Tiber River Basin, in Italy, were instrumented during periods of October 2002-March 2003 and January-April 2004. Colorso Basin is about 13 km2 and Niccone basin 137 km2. Rainfall plus soil moisture at 10, 20, and 40 cm formed the input vector while the discharge was the target output in the model of generalized regression neural network (GRNN). The model for each basin was calibrated and tested using October 2002-March 2003 data. The calibrated and tested GRNN was then employed to predict runoff for each basin for the period of January-April 2004. The model performance was found to be satisfactory with determination coefficient, R2, equal to 0.87 and Nash-Sutcliffe efficiency, NS, equal to 0.86 in the validation phase for both catchments. The investigation of effects of soil moisture on runoff prediction revealed that the addition of soil moisture data, along with rainfall, tremendously improves the performance of the model. The sensitivity analysis indicated that the use of soil moisture data at different depths allows to preserve the memory of the system thus having a similar effect of employing the past values of rainfall, but with improved GRNN performance.