Spatial-Scale Prediction of the SVAT Soil Hydraulic Variables Characterizing Stratified Soils on the Tibetan Plateau from an EnKF Analysis of SAR Soil Moisture

摘要: In highly stratified soils as on the Tibetan Plateau, uncertainty associated with a vertical profile of soil and hydraulic properties largely restricts performance Soil Vegetation Atmosphere Transfer (SVAT) model. lieu commonly used pedotransfer functions (PTFs) or artificial neural networks (ANNs), in this study were inverted from an Ensemble Kalman filter (EnKF) analysis Synthetic Aperture Radar (SAR) surface moisture. The calibrated SVAT scheme using variables C 1 θ geq was better matched situ field measurements than uncalibrated maps–based PTFs local point scale. It shown that inverse calibration two solved forecast bias (underestimation) moisture due to assumption homogeneity site-specificity empirical PTFs. Additionally, at SAR spatial scale, appropriately captured high gradient between subsurface moisture, while could not. This suggests it is possible infer are main error source data assimilation analysis.