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.

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 leaflevel 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 riskaverse" 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.Plain Language Summary Plants regulate transpiration dynamically through the stomatal aperture, which, in many cases, is governed by plant water status and hydraulic properties. Plant hydraulics describes the mechanics of water movement through plant vascular systems, which is the culmination of emergent phenotypical hydraulic functional traits at the leaf, stem, and root levels. Such physiological mechanisms are excluded in most land surface models, which typically represent plant water stress through empirical soil hydraulics schemes (SHSs) based on either soil water content or soil water potential. In this study, we present a novel plant hydraulics scheme (PHS) to represent plant water stress and the regulation of stomatal conductance for use in the Noah-MP land surface model. Our results show Noah-MP-PHS performs better in its water and carbon simulations than the default Noah-MP with traditional SHSs, especially under dry soil conditions. Noah-MP-PHS also successfully captures the different plant hydraulic behaviors between the "cavitation risk-averse" red maple and the "cavitation risk-prone" red oak. Sensitivity experiments also highlight the vital role played by plant water storage in water and carbon simulations in terms of buffering xylem hydraulic stress during soil moisture dry-down periods. The ...

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.

Sustainable management of water for human uses and maintaining river health requires reliable information about the future availability of water resources. We quantified the separate and combined impacts of climate and land cover changes on runoff for the historical record and for modelled future scenarios in the upper Han River and Luan River, supply and demand zones respectively of the middle route of the South to North Water Transfer Project in China, the world's largest inter-basin water transfer project. We used a precipitation-runoff model, averaged multiple climate model predictions combined with three emissions scenarios, a combined CA-Markov model to predict land cover change, and a range of statistical tests. Comparing baseline with 2050: climate change would cause an average reduction in runoff of up to 15 % in the upper Han River and up to 9 % in the Luan River catchment; a scenario involving increased forest cover would reduce runoff by up to 0.19 % in the upper Han River and up to 35 % in the Luan River; a scenario involving increased grass cover would increase runoff by up to 0.42 % in the upper Han River and up to 20 % in the Luan River. In the lower Luan River, the mean annual flow after 1998 fell to only 17 % of that of the baseline period, posing a serious threat to river health. This was explained largely by extraction of surface water and groundwater, rather than climate and land use change.Water Resour Manage

Extreme precipitation has received much attention because of its implications for hazard assessment and risk management. However, accurate precipitation information for extreme precipitation research from dense rain gauges is still difficult to obtain in developing countries or mountainous regions. Satellite-based precipitation products (SPPs) with high spatial and temporal resolution offer a new way of supplementing data from gauge-based observations. This study aims to evaluate the precision of six SPPs in detail at multiple temporal and spatial scales and explore their ability to capture the characteristics of extreme precipitation from 2009 to 2014 over a semi-arid to semi-humid climate transition area (Wei River basin) in China. The six products are TRMM 3B42RT, TRMM 3B42V7, PERSIANN, PERSIANN CDR, CMORPH RAW, and CRORPH CRT. China gauge-based daily precipitation analysis (CGDPA) provided by the China Meteorological Administration is used as the benchmark reference data. Various statistical evaluation techniques and extreme precipitation indices are used to evaluate and compare the performance of the selected products. The results show that the post real-time products (TRMM 3B42V7, PERSIANN CDR, and CMORPH CRT) agreed better with the reference data than PERSIANN and CMORPH RAW. On a daily scale, TRMM 3B42V7, PERSIANN CDR, and CMORPH CRT displayed similarly good performance. However, at the monthly or annual scale, TRMM 3B42V7 was superior to the other products. With regard to the spatial distribution of precipitation, the datasets performed better over plains and were disappointing over mountainous areas. Additionally, TRMM 3B42V7 provided higher precision and less spatial uncertainty when monitoring extreme precipitation. This study provides a basis for selecting alternative precipitation data for climate transition basins.