http://dx.doi.org/10.2139/ssrn.4853626

The North American prairie region is characterized by numerous land depressions, resulting in spatiotemporally variable Non-Contributing Areas (NCAs) that impact runoff translation into streamflow. Current hydrological models address temporal changes in NCA but neglect spatial distribution and geolocation. The only spatial NCA maps available for the prairies were derived by the Prairie Farm and Rehabilitation Association (PFRA) from paper-based contour maps using subjective interpretation of a 2-year rainfall event. PFRA maps are therefore static and inadequately represent the dynamic nature of NCAs across different return periods. To address this gap, this study introduces NCAmapper, a GIS-based model relying on Digital Elevation Models (DEMs) to map NCAs dynamically for different runoff events in prairie and arctic regions. Evaluation of NCAmapper demonstrates its capabilities in dynamically representing the spatiotemporal variability in NCAs corresponding to different rainfall events over multiple prairie basins. NCAmapper additionally enhances hydrological model parameterization, aiding practitioners in quantifying effective drainage areas and evaluation of flood vulnerability.

This is a preprint article, it offers immediate access but has not been peer reviewed.

This is a preprint article, it offers immediate access but has not been peer reviewed.

“We thank John Pomeroy and the Global Water Futures program for providing the LiDAR DEM and water extent observations for the Smith Creek Research Basin (SCRB). The model source code, inputs, and outputs can be down[1]loaded from https://zenodo.org/records/1463584”

https://ssrn.com/abstract=4853626