Accurate simulation of energy, water, and carbon fluxes exchanging between the land surface and the atmosphere is beneficial for improving terrestrial ecohydrological and climate predictions. We systematically assessed the Noah land surface model (LSM) with mutiparameterization options (Noah‐MP) in simulating these fluxes and associated variations in terrestrial water storage (TWS) and snow cover fraction (SCF) against various reference products over 18 United States Geological Survey two‐digital hydrological unit code regions of the continental United States (CONUS). In general, Noah‐MP captures better the observed seasonal and interregional variability of net radiation, SCF, and runoff than other variables. With a dynamic vegetation model, it overestimates gross primary productivity by 40% and evapotranspiration (ET) by 22% over the whole CONUS domain; however, with a prescribed climatology of leaf area index, it greatly improves ET simulation with relative bias dropping to 4%. It accurately simulates regional TWS dynamics in most regions except those with large lakes or severely affected by irrigation and/or impoundments. Incorporating the lake water storage variations into the modeled TWS variations largely reduces the TWS simulation bias more obviously over the Great Lakes with model efficiency increasing from 0.18 to 0.76. Noah‐MP simulates runoff well in most regions except an obvious overestimation (underestimation) in the Rio Grande and Lower Colorado (New England). Compared with North American Land Data Assimilation System Phase 2 (NLDAS‐2) LSMs, Noah‐MP shows a better ability to simulate runoff and a comparable skill in simulating R_n but a worse skill in simulating ET over most regions. This study suggests that future model developments should focus on improving the representations of vegetation dynamics, lake water storage dynamics, and human activities including irrigation and impoundments.

Accumulation of snowfall during winter and snowmelt in the subsequent spring or earlier summer provides a dominant water source in alpine regions. Most land surface and hydrological models use near‐surface air temperature (T_a) thresholds to partition precipitation into snow and rain, underestimating snowfall over drier regions. We developed a snow‐rain partitioning scheme using the wet‐bulb temperature (T_w), which is closer to the surface temperature of a falling hydrometeor than T_a. T_w becomes more depressed in drier environments as derived from T_w depression equation using T_a and surface air humidity, resulting in a greater fraction of snowfall. We implemented this new T_w scheme in the Noah‐MP land surface model and evaluated the model against a high‐quality ground‐based snow product over the contiguous United States. The results suggest that the new T_w scheme substantially improves the model skill in simulating snow depth and snow water equivalent over most snow‐covered grids, especially the higher and drier continental mountain ranges in the Western United States, while it retains the modeling accuracy over the more humid Eastern United States.

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.

This study examines the overall performance of the Noah with multiparameterization (Noah‐MP) land surface model in simulating key land‐atmosphere variables at a global scale and explores the feasibility of running Noah‐MP with regionally different combinations of parameterization schemes. We conducted Noah‐MP ensemble simulations and evaluated the annual means and seasonal cycles of the simulated latent heat flux, net radiation (RN), runoff, soil moisture, snow water equivalent, land surface temperature (LST), leaf area index (LAI), and gross primary productivity (GPP) against a wide variety of global products. The results show that the global patterns of the modeled annual means of these variables generally agree with those of the reference data sets. By evaluating the best simulations in the ensemble, we show that Noah‐MP performs very well in simulating global LST and RN but produces biases in annual mean LAI and GPP by more than 40% in most herbaceous regions. Overall, the main disagreements between Noah‐MP and the reference data sets occurred in the tropical, polar, high‐altitude, and hyperarid regions. This study also highlights the potential of land‐cover‐specific combinations of parameterization schemes to produce optimal modeling results over different land‐cover types. In addition, we strongly suggest the use of multi‐objective optimization of the key parameterizations and parameters to further improve the Noah‐MP's overall performance.

Most land surface models (LSMs) used in Earth System Models produce a lower ratio of transpiration (T) to evapotranspiration (ET) than field observations, degrading the credibility of Earth System Model‐projected ecosystem responses and feedbacks to climate change. To interpret this model deficiency, we conducted a pair of model experiments using a three‐dimensional, process‐based ecohydrological model in a subhumid, mountainous catchment. One experiment (CTRL) describes lateral water flow, topographic shading, leaf dynamics, and water vapor diffusion in the soil, while the other (LSM like) does not explicitly describe these processes to mimic a conventional LSM using artificially flattened terrain. Averaged over the catchment, CTRL produced a higher T/ET ratio (72%) than LSM like (55%) and agreed better with an independent estimate (79.79 ± 27%) based on rainfall and stream water isotopes. To discern the exact causes, we conducted additional model experiments, each reverting only one process described in CTRL to that of LSM like. These experiments revealed that the enhanced T/ET ratio was mostly caused by lateral water flow and water vapor diffusion within the soil. In particular, terrain‐driven lateral water flows spread out soil moisture to a wider range along hillslopes with an optimum subrange from the middle to upper slopes, where evaporation (E) was more suppressed by the drier surface than T due to plant uptake of deep soil water, thereby enhancing T/ET. A more elaborate representation of water vapor diffusion from a dynamically changing evaporating surface to the height of the surface roughness length reduced E and increased the T/ET ratio.