Surface storm flow prediction on hillslopes based on topography and hydrologic connectivity

Abstract

Background: Hillslopes provide critical watershed ecosystem services such as soil erosion control and storm flow regulation through collecting, storing, and releasing rain water. During intense rainstorms, rainfall intensity and infiltration capacity on the hillslope control Hortonian runoff while the topographic attributes of the hillslope (e.g., slope, aspect, curvature) and the channel network define the structural hydraulic connectivity that determines how rapidly excess water is transferred. This paper discusses literature on the link between topographic attributes and hydrologic connectivity and demonstrates how this link can be used to define a parsimonious model for predicting surface runoff during high intensity rainfall. Main text: First, we provide a topographic characterization of the hillslope necessary to determine the structural hydrologic connectivity of surface flow based on existing literature. Subsequently, we demonstrate a hydrologic surface response model that routes the geomorphologic unit hydrograph (GIUH) through a spatial domain of representative elementary hillslopes reflecting the structural hydrologic connectivity. Topographic attributes impact flow and travel time distributions by affecting gravitational acceleration of overland flow and channel, solar irradiance, flow deceleration by vegetation, and flow divergence/convergence. Conclusions: We show with an example where we apply the GIUH-based model to hypothetical hillslopes that the spatial organization of the channel network is critical in the flow and travel time distribution, and that topographic attributes are key in obtaining simple yet accurate representations of hydrologic connectivity. Parsimonious GIUH models of surface runoff that use this hydrologic connectivity have the advantage of low data requirements, being scalable and applicable regardless of the spatial complexity of the hillslope, and have the potential to fundamentally improve flood forecasting tools used in the assessment of ecosystem services.