Preprints
https://doi.org/10.5194/hess-2022-210
https://doi.org/10.5194/hess-2022-210
 
10 Jun 2022
10 Jun 2022
Status: this preprint is currently under review for the journal HESS.

Improved Soil Evaporation Remote Sensing Retrieval Algorithms and Associated Uncertainty Analysis on the Tibetan Plateau

Jin Feng1,2,3, Ke Zhang1,2,3,4, Huijie Zhan1, and Lijun Chao1,2,3 Jin Feng et al.
  • 1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, and College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210098, China
  • 2Yangtze Institute for Conservation and Development, Hohai University, Nanjing, Jiangsu, 210098, China
  • 3CMA-HHU Joint Laboratory for Hydro-Meteorological Studies, Hohai University, Nanjing, Jiangsu, 210098, China
  • 4Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, Jiangsu, 210098, China

Abstract. Actual evapotranspiration (ET) is the key link between water and energy cycles. However, accurate evaporation estimation in alpine barren areas remains understudied. In this study, we aimed to improve the satellite-driven Process-based Land Surface ET/Heat fluxes algorithm (P-LSH) by introducing two frameworks for quantifying moisture constraints to ET, and to test the applicability of satellite soil moisture and precipitation data for improving ET retrieval. As a result, it formed two improved P-LSH algorithms. The first framework normalizes the surface soil moisture to represent moisture stress, while the second framework takes the ratio of cumulative precipitation to cumulative equilibrium evaporation to quantify soil water stress. We systematically assessed the performances of the two improved P-LSH algorithms and six existing remote sensing ET retrieval algorithms on two barrens-dominated basins of the Tibetan Plateau using reconstructed ET estimates derived from the terrestrial water balance method as a benchmark. The two frameworks largely improved the performance of the P-LSH algorithm and showed better performance in both basins (root mean square error (RMSE) = 7.36 and 7.76 mm month-1; R2 = 0.86 and 0.87), resulting in a higher simulation accuracy than all six existing algorithms. We used five soil moisture and five precipitation datasets to investigate the impact of moisture constraint uncertainty on the improved P-LSH algorithm. The ET estimates of the improved P-LSH algorithm, driven by the GLDAS_Noah soil moisture, performed best compared with those driven by other soil moisture and precipitation datasets, while ET estimates driven by various precipitation datasets generally showed a high and stable accuracy. These results suggest that high-quality soil moisture can optimally express moisture supply to ET, and that more accessible precipitation data can serve as a substitute for soil moisture as an indicator of moisture status for its robust performance in barren evaporation.

Jin Feng et al.

Status: open (until 05 Aug 2022)

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Jin Feng et al.

Jin Feng et al.

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Short summary
Here we improved a satellite-driven evaporation algorithm by introducing two frameworks for quantifying moisture constraints to ET. The two frameworks greatly improved the evaporation estimation on two barrens-dominated basins of the Tibetan Plateau. Moisture constraint uncertainty showed that high-quality soil moisture can optimally express moisture, and more accessible precipitation data generally have robust performance in barren evaporation.