Quantifying the Spatiotemporal Variation of Evapotranspiration of Different Land Cover Types and the Contribution of Its Associated Factors in the Xiliao River Plain

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-01, Vol.14 (2), p.252
• Main Authors: Lin, Nan, Jiang, Ranzhe, Liu, Qiang, Yang, Hang, Liu, Hanlin, Yang, Qian
• Format: Article
• Language: English
• Subjects: Agricultural land; Atmosphere; Climate change; Energy; Evaporation; Evapotranspiration; Groundwater; Heat; Hydrologic cycle; Hydrology; Land cover; Precipitation; Regions; Regression models; Remote sensing; ridge regression model; Rivers; Spatial analysis; spatiotemporal; Surface runoff; Surface water; Trends; Vegetation; Water resources; Water resources management; Xiliao River Plain
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14020252

Evapotranspiration (ET) is a vital constituent of the hydrologic cycle. Researching changes in ET is necessary for understanding variability in the hydrologic cycle. Although some studies have clarified the changes and influencing factors of ET on a regional or global scale, these variables are still unclear for different land cover types due to the range of possible water evaporation mechanisms and conditions. In this study, we first investigated spatiotemporal trends of ET in different land cover types in the Xiliao River Plain from 2000 to 2019. The correlation between meteorological, NDVI, groundwater depth, and topographic factors and ET was compared through spatial superposition analysis. We then applied the ridge regression model to calculate the contribution rate of each influencing factor to ET for different land cover types. The results revealed that ET in the Xiliao River Plain has shown a continuously increasing trend, most significantly in cropland (CRO). The correlation between ET and influencing factors differed considerably for different land cover types, even showing an opposite result between regions with and without vegetation. Only precipitation (PRCP) and NDVI had a positive impact on ET in all land cover types. In addition, we found that vegetation can deepen the limited depth of land absorbing groundwater, and the influence of topographic conditions may be mainly reflected in the water condition difference caused by surface runoff. The ridge regression model eliminates multicollinearity among influencing factors; R2 in all land cover types was over 0.6, indicating that it could be used to effectively quantify the contribution of various influencing factors to ET. According to the results of our model calculations, NDVI had the greatest impact on ET in grass (GRA), cropland (CRO), paddy (PAD), forest (FOR), and swamp (SWA), while PRCP was the main influencing factor in bare land (BAR) and sand (SAN). These findings imply that we should apply targeted measures for water resources management in different land cover types. This study emphasizes the importance of comprehensively considering differences among various hydrologic cycles according to land cover type in order to assess the contributions of influencing factors to ET.