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Plants are able to adapt to changing environments and thus survive droughts. However, most land surface models produce unrealistically low ecosystem resiliency to droughts, degrading the credibility of the model‐predicted ecohydrological responses to climate change. We aim to enhance the Noah‐MP modeled ecosystem resilience to droughts with an explicit representation of plant water storage supplied by dynamic root water uptake through hydrotropic root growth to meet the transpiration demand. The new model represents plant stomatal water stress factor as a function of the plant water storage and relates the rate of root water uptake to the profile of model‐predicted root surface area. Through optimization of major leaf, root, and soil parameters, the new model improves the prediction of leaf area index, ecosystem productivity, evapotranspiration, and terrestrial water storage variations over most basins in the contiguous United States. Sensitivity experiments suggest that both dynamic root water uptake and groundwater capillary rise extend the plants' "memory" of antecedent rainfall. The modeled plants enhance their efficiency to use antecedent rain water stored in shallow soils mainly through more efficient root water uptake over the U.S. Southwest drylands while use that stored in deep soils and aquifers with the aid of groundwater capillary rise in the Central United States. Future plant hydraulic models should not ignore soil water retention model uncertainties and the soil macropore effects on soil water potential and infiltration. Plain Language Summary: Plants are able to adapt to changing environments and thus survive droughts. However, the plants represented in current computer models do not well survive droughts for lacking a representation of adaptation mechanisms. This study develops explicit representations of plant water storage and plant water availability, which are enhanced by root water uptake that is linked to the predicted vertical distribution of fine root biomass in response to soil water content. The new model enhances ecosystem productivity and transpiration under droughts in most large river basins in the contiguous United States. Virtual experiments reveal two "pumping" mechanisms for plants under droughts to use antecedent rain water. The plants tend to more efficiently use antecedent rain water stored in shallow soils through more efficient root water uptake over the U.S. Southwest drylands and that stored in deeper soils or aquifers with the aid of groundwater capillary rise in the Central U.S. basins. Soil water pressure becomes critically important for pushing the soil water into plant tissues and up to the leaves in the new model. Therefore, uncertainties in soil water retention models and the effects of soil macropores on soil water potential and infiltration should be well treated in future models. Key Points: We developed a model of plant water storage supplied by dynamic root water uptake through hydrotropic growth in Noah‐MPIt enhances plants' efficiency to use antecedent rain water stored in shallow soils and that in aquifers with the aid of capillary riseFuture plant hydraulic models should consider soil water retention model uncertainties and soil macropore effects on water retention [ABSTRACT FROM AUTHOR]

Soil moisture influences precipitation mainly through its impact on land–atmosphere interactions. Understanding and correctly modeling soil moisture–precipitation (SM–P) coupling is crucial for improving weather forecasting and subseasonal to seasonal climate predictions, especially when predicting the persistence and magnitude of drought. However, the sign and spatial structure of SM–P feedback are still being debated in the climate research community, mainly due to the difficulty in establishing causal relationships and the high degree of nonlinearity in land–atmosphere processes. To this end, we developed a causal inference model based on the Granger causality analysis and a nonlinear machine learning model. This model includes three steps: nonlinear anomaly decomposition, nonlinear Granger causality analysis, and evaluation of the quality of SM–P feedback, which eliminates the nonlinear response of interannual and seasonal variability and the memory effects of climatic factors and isolates the causal relationship of local SM–P feedback. We applied this model by using National Climate Assessment–Land Data Assimilation System (NCA-LDAS) datasets over the United States. The results highlight the importance of nonlinear atmosphere responses in land–atmosphere interactions. In addition, the strong feedback over the southwestern United States and the Great Plains both highlight the impacts of topographic factors rather than only the sensitivity of evapotranspiration to soil moisture. Furthermore, the SM–P index defined by our framework is used to benchmark Earth system models (ESMs), which provides a new metric for efficiently identifying potential model biases in modeling local land–atmosphere interactions and may help the development of ESMs in improving simulations of water cycle variability and extremes. [ABSTRACT FROM AUTHOR]

We investigated eleven particle-size distribution (PSD) models to determine the appropriate models for describing the PSDs of 16349 Chinese soil samples. These data are based on three soil texture classification schemes, including one ISSS (International Society of Soil Science) scheme with four data points and two Katschinski's schemes with five and six data points, respectively. The adjusted coefficient of determination r (2), Akaike's information criterion (AIC), and geometric mean error ratio (GMER) were used to evaluate the model performance. The soil data were converted to the USDA (United States Department of Agriculture) standard using PSD models and the fractal concept. The performance of PSD models was affected by soil texture and classification of fraction schemes. The performance of PSD models also varied with clay content of soils. The Anderson, Fredlund, modified logistic growth, Skaggs, and Weilbull models were the best.