This study elucidates drought characteristics in China during 1980–2015 using two commonly used meteorological drought indices: standardized precipitation index (SPI) and standardized precipitation–evapotranspiration index (SPEI). The results show that SPEI characterizes an overall increase in drought severity, area, and frequency during 1998–2015 compared with those during 1980–97, mainly due to the increasing potential evapotranspiration. By contrast, SPI does not reveal this phenomenon since precipitation does not exhibit a significant change overall. We further identify individual drought events using the three-dimensional (i.e., longitude, latitude, and time) clustering algorithm and apply the severity–area–duration (SAD) method to examine the drought spatiotemporal dynamics. Compared to SPI, SPEI identifies a lower drought frequency but with larger total drought areas overall. Additionally, SPEI identifies a greater number of severe drought events but a smaller number of slight drought events than the SPI. Approximately 30% of SPI-detected drought grids are not identified as drought by SPEI, and 40% of SPEI-detected drought grids are not recognized as drought by SPI. Both indices can roughly capture the major drought events, but SPEI-detected drought events are overall more severe than SPI. From the SAD analysis, SPI tends to identify drought as more severe over small areas within 1 million km² and short durations less than 2 months, whereas SPEI tends to delineate drought as more severe across expansive areas larger than 3 million km² and periods longer than 3 months. Given the fact that potential evapotranspiration increases in a warming climate, this study suggests SPEI may be more suitable than SPI in monitoring droughts under climate change.

Plants are expected to face increasing water stress under future climate change. Most land surface models, including Noah‐MP, employ an idealized “big‐leaf” concept to regulate water and carbon fluxes in response to soil moisture stress through empirical soil hydraulics schemes (SHSs). However, such schemes have been shown to cause significant uncertainties in carbon and water simulations. In this paper, we present a novel plant hydraulics scheme (PHS) for Noah‐MP (hereafter, Noah‐MP‐PHS), which employs a big‐tree rather than big‐leaf concept, wherein the whole‐plant hydraulic strategy is considered, including root‐level soil water acquisition, stem‐level hydraulic conductance and capacitance, and leaf‐level anisohydricity and hydraulic capacitance. Evaluated against plot‐level observations from a mature, mixed hardwood forest at the University of Michigan Biological Station and compared with the default Noah‐MP, Noah‐MP‐PHS better represents plant water stress and improves water and carbon simulations, especially during periods of dry soil conditions. Noah‐MP‐PHS also improves the asymmetrical diel simulation of gross primary production under low soil moisture conditions. Noah‐MP‐PHS is able to reproduce different patterns of transpiration, stem water storage and root water uptake during a 2‐week dry‐down period for two species with contrasting plant hydraulic behaviors, i.e., the “cavitation risk‐averse” red maple and the “cavitation risk‐prone” red oak. Sensitivity experiments with plant hydraulic capacitance show that the stem water storage enables nocturnal plant water recharge, affects plant water use efficiency, and provides an important buffer to relieve xylem hydraulic stress during dry soil conditions.

The precipitation partitioning between evapotranspiration (ET) and runoff (R) at the land surface is controlled by atmospheric boundary layer and terrestrial hydrological processes. These processes in land surface models are manifested primarily as stomatal conductance, soil moisture limitation factor to transpiration (β‐factor), turbulence, and runoff generation. What are the sensitivities of precipitation partitioning to the parameterizations of these processes? To address this overarching question, the annual and seasonal means of ET and R over the conterminous United States were simulated using 48 configurations of the Noah land surface model with multiparameterization options (Noah‐MP). The Sobol' total sensitive index was used to quantify the sensitivity of ET and R to the parameterizations of the four processes mentioned above. Results show that the sensitivities of the annual means depend on climatic conditions and the interplay between ET and R plays an important role. In humid regions, precipitation is mostly partitioned into R, whereas the simulations can be more sensitive to ET's parameterizations. In arid regions, ET accounts for the major partition, whereas the simulations can be more sensitive to the runoff parameterization. Seasonal means exhibit different sensitivities from the annual means. The seasonal mean ET is more sensitive to ET's parameterizations, and R is more sensitive to the runoff parameterization. The β‐factor, which is neglectable for the annual means, is important for summer‐time ET. Mediated by the terrestrial water storage memories, ET interplays R across seasons. The winter‐time R is still sensitive to the stomatal conductance that only modulates growing‐season ET.

Snow cover affects the thermal conditions of the Tibetan Plateau through snow–albedo feedback and snowmelt, which, in turn, modulates the Asian summer monsoon climate. An accurate estimation of the snow condition on the Tibetan Plateau is therefore of great importance in both seasonal forecasts and climate studies. Estimation of snow water equivalent (SWE) over the Tibetan Plateau is challenging due to the high altitude, complex terrain, and insufficient in situ observations. Multiple SWE products derived from satellite estimates, reanalyses, regional climate model simulations, and land data assimilations are intercompared in terms of daily, seasonal, and annual variations and are then evaluated against in situ SWE observations. The results show a relatively consistent seasonal to interannual variability of the SWE estimates among the products. The discrepancies in magnitude are large, however, especially in winter and spring. Evaluation against in situ SWE observations indicates that none of these products is capable of accurately characterizing both the spatial pattern and temporal variations.

Hydrological simulations play an important role in estimating terrestrial water budgets and monitoring extreme events such as floods. This study investigates how these simulations are affected by soil‐type datasets and characterizes how these effects vary with climate. We study the differences between two ensemble simulations in China with the Noah‐MP land surface model using two soil datasets from the Food and Agriculture Organization and Beijing Normal University. The differences in ensemble means are analyzed over a 10 year period from 2003 to 2012 with respect to estimated soil moisture, the partition of precipitation between evapotranspiration and runoff, and a flood magnitude index. Results show that the hydrological simulations using sandier soil types result in lower soil moisture, lower evapotranspiration, and higher subsurface runoff. Each of these effects varies uniquely with aridity. The changes in soil moisture decrease with increasing aridity, while the changes in water balance components (evapotranspiration and runoff) peak in the transitional zone between humid and arid regions. The flood magnitude, expressed as the maximum daily flow normalized by annual flow, is also substantially influenced by the input soil type. Soil types with more clay and less sand content yield significantly bigger floods, especially in arid regions.