Driving small-scale agricultural and hydrological models using globally available stochastic climate datasets that include point-scale precipitation

This is the authors' abstract. We don't add key points for this paper.

Stochastic weather generators (SWGs) are a portable solution for obtaining site-specific climate time series needed in many modeling applications. The SWG called CLImate GENerator (CLIGEN) provides simulated daily sequences of basic weather variables in a stationary climate, including intra- and interannual variability, and statistical information related to long-term normals. Required input parameter sets for CLIGEN are available in the form of spatial grids and ground-based networks that cover most global land area. Crucially, CLIGEN outputs contain point-scale precipitation time series that make it a compatible driver for small-scale model domains (e.g., individual fields or hillslopes). In such applications, precipitation drivers acting at a single point are more likely to accurately force runoff, erosion, and other processes than comparable grid-scale or mean areal precipitation drivers. In order to validate simulated precipitation drivers from gridded CLIGEN datasets and identify model outcomes that are sensitive to the scale of precipitation, the present analysis contrasts simulated point-scale precipitation drivers against both observed precipitation and the popular daily, grid-scale Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset. Case studies for the climate drivers involved model simulations at selected international sites using the following models: the Water Erosion Prediction Project (WEPP) model, the Rangeland Hydrology and Erosion Model (RHEM), and the Hydrus-1D model. The analysis concluded that simulated precipitation drivers produce runoff and erosion rates consistent with outcomes from observation-based drivers. The stochastic data are demonstrated to meet model requirements in a number of contexts, and given their global availability, there are a wide variety of potential applications, particularly when dealing with data limitations in small-scale modeling.