Surface water runoff estimation: a review of methods incorporating terrain shape

Abstract

Surface runoff is a key variable in the water balance, representing excess water that exceeds soil infiltration capacity and is not absorbed by drains. Accurate estimation of surface runoff is crucial for flood prevention, optimizing agricultural water use, and detecting irregular water capture. Various computational approaches, including machine learning, deep learning, statistical models, and hydraulic simulations, have been developed to estimate runoff. However, despite extensive research, many models overlook the influence of terrain characteristics—such as topography, slope, and surface roughness—leading to potential inaccuracies in runoff prediction. This study conducts a comprehensive bibliographic review of state-of-the-art research on surface runoff estimation, with a focus on methods that integrate digital terrain models (DTMs), remote sensing, and computational modeling techniques. Through this analysis, a specific research niche was identified and verified, highlighting the need for terrain-sensitive runoff models that better incorporate topographic variables into hydrological modeling. By evaluating and comparing existing methodologies, this review provides insights into the most effective approaches for runoff estimation and offers recommendations for selecting the appropriate models based on landscape and hydrological conditions. It also presents potential solutions that may pave the way for a better understanding of runoff processes.