Hydraulic redistribution is the process of soil water transport through the low‐resistance pathway provided by plant roots. It has been observed in field studies and proposed to be one of the processes that enable the Amazon rainforest to resist periodical dry spells without experiencing water limitations. How and to what extent hydraulic redistribution may increase vegetation resistance to longer or more severe droughts than seasonal dryness have not been investigated yet, which is the focus of this study. The artificially prolonged drought produced by the rainfall exclusion experiment is used as an example of long drought, and the 2005 drought is used as a severe drought. The parameterization of hydraulic redistribution proposed by Ryel et al. (2002) was incorporated into the Community Land Model version 4 (CLM4). Three paired numerical experiments were conducted, one set using the default model (CTL) and the other using the model with considerations of hydraulic redistribution (HR). Results show that the vegetation response (including evapotranspiration, biomass, and leaf area index (LAI)) to dryness of all the three types is better captured with hydraulic redistribution incorporated. Plants are more resistant to dryness when hydraulic redistribution increases plant water availability and thus facilitates their growth. When a drought is long lasting, the vegetation response is delayed by hydraulic redistribution. Therefore, if a drought ends earlier than permanent damage is made, the magnitude of vegetation response will be lowered by this mechanism, i.e., the vegetation will be more resistant to dryness.

Accurately quantifying the vegetation rainfall interception at a high resolution is critical for rainfall-runoff modeling and flood forecasting, and is also essential for understanding its further impact on local, regional, and even global water cycle dynamics. In this study, the Remote Sensing-based Gash model (RS-Gash model) is developed based on a modified Gash model for interception loss estimation using remote sensing observations at the regional scale, and has been applied and validated in the upper reach of the Heihe River Basin of China for different types of vegetation. To eliminate the scale error and the effect of mixed pixels, the RS-Gash model is applied at a fine scale of 30 m with the high resolution vegetation area index retrieved by using the unified model of bidirectional reflectance distribution function (BRDF-U) for the vegetation canopy. Field validation shows that the RMSE and R² of the interception ratio are 3.7% and 0.9, respectively, indicating the model’s strong stability and reliability at fine scale. The temporal variation of vegetation rainfall interception and its relationship with precipitation are further investigated. In summary, the RS-Gash model has demonstrated its effectiveness and reliability in estimating vegetation rainfall interception. When compared to the coarse resolution results, the application of this model at 30-m fine resolution is necessary to resolve the scaling issues as shown in this study.