Sensitivity of Remotely Sensed Vegetation to Hydrologic Predictors across the Colorado River Basin, 2001–2019. (31st October 2021)

• Record Type: Journal Article
• Authors: Saby, Linnea; McKee, Kevin L.; Kansara, Prakrut; Goodall, Jonathan L.; Band, Lawrence E.; Lakshmi, Venkataraman
• Is Part Of: Journal of the American Water Resources Association. Volume 58:Number 6(2022)
• Page Start: 1017
• Page End: 1029
• Publication Date: 2021-10-31
• Subjects: vegetation response -- remote sensing -- ecohydrology -- soil moisture; Water-supply -- Periodicals; Hydrology -- Periodicals; Water resources development -- Periodicals; Water resources development -- Environmental aspects -- Periodicals; 333.9100973
• DOI: 10.1111/1752-1688.12965
• Languages: English
• ISSNs: 1093-474X

Abstract

Abstract: In water‐limited regions, it is important to understand the response of vegetation to hydrologic predictors for fire management and water resource planning. We present a novel spatially distributed analysis of ecohydrological interactions in the semi‐arid Colorado River Basin (CRB) over 18 years, 2001–2019. The hydrologic predictors used include precipitation from the Integrated Multi‐satellitE Retrievals for GPM, 0‐ to 10‐cm‐depth surface soil moisture (SSM) estimations from the National Land Data Assimilation System, and newly available 0‐ to 40‐cm depth Soil MERGE root zone soil moisture (RZSM) estimations. These are evaluated using time‐lagged correlations with the Enhanced Vegetation Index (EVI) from MODerate Resolution Imaging Spectroradiometer (MODIS). We demonstrate that EVI response is strongest and most immediate to the hydrologic predictors in the hot and dry southwestern CRB, with lag times of 0–32 days. RZSM was expected to provide the best predictor of EVI, but we found SSM to be a superior predictor of EVI with interquartile range correlations of 0.20–0.37 across the CRB. RZSM had slightly lower interquartile range correlations of 0.15–0.35, and precipitation was the least effective predictor of EVI with interquartile range correlations of 0.11–0.26. Plotting these cross‐correlations provides a spatially explicit overview of temporal dependence between SSM, RZSM, precipitation, and EVI with publicly available datasets.