Exploiting satellite data for total direct runoff prediction using CN-based MSME model

Andrzej Wałęga; Jakub Wojkowski; Mariusz Sojka; Devendra Amatya; Dariusz Młyński; Sudhanshu Panda; Peter Caldvell

doi:10.1016/j.scitotenv.2023.168391

Exploiting satellite data for total direct runoff prediction using CN-based MSME model

Sci Total Environ. 2024 Jan 15:908:168391. doi: 10.1016/j.scitotenv.2023.168391. Epub 2023 Nov 11.

Authors

Andrzej Wałęga¹, Jakub Wojkowski¹, Mariusz Sojka², Devendra Amatya³, Dariusz Młyński⁴, Sudhanshu Panda⁵, Peter Caldvell⁶

Affiliations

¹ University of Agriculture in Krakow, Poland, Faculty of Environmental Engineering and Land Surveying, al. Mickiewicza 21, 31-120 Krakow, Poland.
² Poznań University of Life Sciences, Department of Land Improvement, Environmental Development and Spatial Management, Piątkowska 94E, 60-649 Poznań, Poland.
³ Center for Forest Watershed Research, Southern Research Station, USDA Forest Service, 3734 Highway 402, Cordesville, SC 29434, USA.
⁴ University of Agriculture in Krakow, Poland, Faculty of Environmental Engineering and Land Surveying, al. Mickiewicza 21, 31-120 Krakow, Poland. Electronic address: dariusz.mlynski@urk.edu.pl.
⁵ Institute of Environmental Spatial Analysis, University of North Georgia, 3820 Mundy Mill Road, Oakwood, GA 30566, USA.
⁶ Center for Forest Watershed Research, Southern Research Station, USDA Forest Service, 3160 Coweeta Lab Rd, Otto, NC 28763, USA.

PMID: 37956841
DOI: 10.1016/j.scitotenv.2023.168391

Abstract

This paper explores the potential to enhance the functionality of the modified Sahu-Mishra-Eldho model (MSME-CN) using indirect soil moisture measurements derived from satellite data. The current version of the MSME-CN model is not applicable in ungauged watersheds due to the necessity of calibrating the crucial parameter α, which reflects soil saturation, based on measured rainfall-runoff events. We hypothesize that the Normalized Difference Vegetation Index (NDVI) can serve as an indirect indicator of soil moisture to assess the soil saturation parameter α in the MSME model. This hypothesis was tested across five different watersheds, three located in the southeastern USA and two in southern Poland. The NDVI product, developed from data obtained from the Advanced Very High-Resolution Radiometer (AVHRR), was utilized in this study. Results indicate that NDVI is a robust indicator of soil moisture for representing the α parameter in the MSME model. The correlation coefficient between α and NDVI a day prior to a rainfall event was around 0.80 for the WS80 and Kamienica watersheds and nearly 0.60 for the other watersheds. The analysis corroborates the hypothesis that NDVI can serve as an indirect parameter of soil moisture to assess the soil saturation parameter α in the MSME-CN model. Based on Nash-Sutcliffe Efficiency (NSE) statistics, the total direct runoff predicted by the MSME-CN model, with the α parameter updated using NDVI, was rated 'very good' for the WS80 and AC11 watersheds, 'good' for the Kamienica watershed, 'satisfactory' for Stobnica, and 'unsatisfactory' for the high forest density WS14 watershed, potentially highlighting the model's limitation in such watersheds.

Keywords: Direct runoff; Forested waterbeds; NDVI; Overland flow; Soil saturation; Subsurface saturated runoff.