A statistical approach to using remote sensing data to discern streamflow variable influence in the snow melt dominated Upper Rio Grande Basin

Authors: IFEKHARUL ISLAM, KHANDAKER - New Mexico State University; Elias, Emile; BROWN, CHRISTOPHER - New Mexico State University; James, Darren; HEIMEL, SIERRA - New Mexico State University

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/28/2022
Publication Date: 11/30/2022
Citation: Ifekharul Islam, K., Elias, E.H., Brown, C.P., James, D.K., Heimel, S. 2022. A statistical approach to using remote sensing data to discern streamflow variable influence in the snow melt dominated Upper Rio Grande Basin. Remote Sensing. 14(23). Article 6076. https://doi.org/10.3390/rs14236076.
DOI: https://doi.org/10.3390/rs14236076

Interpretive Summary: The key variables that influence naturalized streamflow across the basin change not only by watershed and mountain range but also by month or season. The strong influence of precipitation on streamflow was observed in each season for all sub-watersheds. Minimum temperature is negatively correlated with streamflow in the runoff season for all sub-watersheds, but it varies for different watersheds in the base flow season.This research on surface water hydrology on the URG basin revealed the relative importance of the parameters, which are the main drivers in changing the surface water supplies.

Technical Abstract: Warming influences measured snowpack characteristics such snow cover, Snow Water Equivalent (SWE), which can affect runoff quantity and timing in snowmeltrunoff- dominated river systems in the Upper Rio Grande region. The objective of the work was to explore components of the hydrologic cycle in multiple sub watersheds to examine mechanisms, which have been contributing to runoff dynamics around the past 40 years. By studying a number of prospective factors such as precipitation, temperature, albedo, soil moisture, SWE, snow cover, snow depth and sublimation, we identified important variables that affect runoff volume in different sub-watersheds. For each sub-watershed, we conducted analyses at two different temporal scales (monthly and annual base flow/runoff periods), and we employed two different analysis strategies: 1) Pearson correlation analysis and 2) Linear regression with multimodel inference based on the second-order Akaike’s information criterion (AICc). We found that the variables influencing naturalized streamflow change not only by month or season, but also by watershed and mountain range. For instance, minimum temperature is negatively correlated with streamflow in the runoff season, but it varies for different watersheds in the base flow season. The strong influence of precipitation on streamflow was consistent in each season for all sub-watersheds. The study explores variables fundamental to streamflow generation with sub basin water balances, leading to a variety of local water management implications for the Upper Rio Grande.