Rainfall-runoff models are extensively utilized in hydrology to investigate hydrological processes and play a crucial role in water resources management, encompassing flood prediction and drought analysis. These models have been developed for diverse applications, ranging from small catchments to global scales.
The primary input to the rainfall-runoff models is past forcing data (such as precipitation, temperature), constant regional attributions (such as land use land cover (LULC), topography, and others) and the output is the future runoff or streamflow. Improving process-based rainfall-runoff models requires progress on several fundamental research challenges: (1) building appropriate methods to forecast the runoff for the different time scales within the model domain (Short-term Forecasts and Long-term Forecasts); (2) representing the variability of hydrologic processes across a hierarchy of spatial scales (Spatial Variability); (3) verifying model reliability (Bias Reduction and Model Reliability); (4) testing a model across the different model subdomains or ungauged basins (Missing Data and Ungauged Basins); (5) estimating input data and model parameters (Parameterization); and (6) characterizing model uncertainty (Uncertainty Estimation in Rainfall-runoff Forecast).
