A Union of Dynamic Hydrological Modeling and Satellite Remotely-Sensed Data for Spatiotemporal Assessment of Sediment Yields

(1) The existing frameworks for water quality modeling overlook the connection between multiple dynamic factors affecting spatiotemporal sediment yields (SY). This study aimed to implement satellite remotely sensed data and hydrological modeling to dynamically assess the multiple factors within basin-scale hydrologic models for a realistic spatiotemporal prediction of SY in watersheds. (2) A connective algorithm was developed to incorporate dynamic models of the crop and cover management factor (C-factor) and the soil erodibility factor (K-factor) into the Soil and Water Assessment Tool (SWAT) with the aid of the Python programming language and Geographic Information Systems (GIS). The algorithm predicted the annual SY in each hydrologic response unit (HRU) of similar land cover, soil, and slope characteristics in watersheds between 2002 and 2013. (3) The modeled SY closely matched the observed SY using the connective algorithm with the inclusion of the two dynamic factors of K and C (predicted R-2 (PR2): 0.60-0.70, R-2: 0.70-0.80, Nash Sutcliffe efficiency (NS): 0.65-0.75). The findings of the study highlight the necessity of excellent spatial and temporal data in real-time hydrological modeling of catchments.

Automated summary

This study aims to use satellite remotely sensed data and hydrological modeling to predict the spatiotemporal sediment yields in watershed models. By incorporating dynamic models of crop and cover management and soil erodibility, the algorithm accurately predicts sediment yields and highlights the importance of real-time hydrological modeling with high-quality spatial and temporal data.