Stochastic Ecohydrological Perspective on Semi-Distributed Rainfall-Runoff Dynamics

Abstract

Quantifying watershed process variability consistently with climate change and ecohydrological dynamics remains a central challenge in hydrology. Stochastic ecohydrology characterizes hydrologic variability through probability distributions that link climate, hydrology, and ecology. However, these approaches are often limited to small spatial scales (e.g., point or plot level) or focus on specific fluxes (e.g., streamflow), without accounting for the entire water balance at the basin scale. While semi-distributed models account for spatial heterogeneity and upscaled hydrologic fluxes, they lack the analytical simplicity of stochastic ecohydrology or the SCS-CN method and, perhaps more importantly, do not directly characterize probability distributions that integrate the effects of past random variability in hydroclimatic conditions. This hinders an efficient characterization of hydrological statistics at the watershed scale. To overcome these limitations, we merge stochastic ecohydrology, the spatial upscaling of semi-distributed modeling, and the SCS-CN rainfall-runoff partitioning. The resulting unified model analytically characterizes watershed ecohydrological and hydrological statistics using probability density functions (PDFs) that are functions of climate and watershed model parameters (e.g., baseflow coefficient)—something unattainable with the Monte Carlo methods of traditional stochastic hydrology. Calibrated across 81 watersheds in Florida and southern Louisiana, the model PDFs precisely capture the long-term average water balance and runoff variance, as well as the runoff quantiles with a median Nash–Sutcliffe efficiency of 0.98. These results also advance the SCS-CN method by providing an analytical PDF for the Curve Number (CN), explicitly linked to climate variables, baseflow, and the long-term water balance partitioning described by the Budyko curve.