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

Pooja Preetha,Ashraf Al-Hamdan

doi:10.3390/rs14020400

Abstract

(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 R2 (PR2): 0.60–0.70, R2: 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.

Highlights

Soil erosion is the process by which land surface is washed away by hydrogeological factors, meteorological agents, and human interference
Winyah Bay watershed (WBW) was developed with the land cover, soil type, and climate condition for the year 2009 with 31 sub-basins, 2118 hydrologic response unit (HRU) within 31 sub-basins, 15 land cover classes, and 32 soil categories
Various conceptual frameworks of sediment yield assessments have been developed in recent years for enhanced soil erosion modeling

Summary

Introduction

Soil erosion is the process by which land surface is washed away by hydrogeological factors, meteorological agents, and human interference. It leads to long-term changes in the environment and results in reduced water quality, which affects environmental and human health. Universal Soil Loss Equation (MUSLE) Model [14], USLE-M [15], dUSLE [16], Water and Tillage Erosion Model/Sediment Delivery Model [17,18], and the Agricultural Non-Point. USLE is the well-known and most used empirical model for estimating long-term average annual soil loss globally. USLE estimates long-term annual soil loss and guides proper cropping, management, and conservation strategies [24,25].

Objectives

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Results

Conclusion