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.

The wheat production in midland China is under serious threat by frequent Dry-Hot Wind (DHW) episodes with high temperature, low moisture and specific wind as well as intensive heat transfer and evapotranspiration. The numerical simulations of these episodes are important for monitoring grain yield and estimating agricultural water demand. However, uncertainties still remain despite that enormous experiments and modeling studies have been conducted concerning this issue, due to either inaccurate synoptic situation derived from mesoscale weather models or unrealistic parameterizations of stomatal physiology in land surface models. Hereby, we investigated the synoptic characteristics of DHW with widely-used mesoscale model Weather Research and Forecasting (WRF) and the effects of leaf physiology on surface evapotranspiration by comparing two land surface models: The Noah land surface model, and Peking University Land Model (PKULM) with stomata processes included. Results show that the WRF model could well replicate the synoptic situations of DHW. Two types of DHW were identified: (1) prevailing heated dry wind stream forces the formation of DHW along with intense sensible heating and (2) dry adiabatic processes overflowing mountains. Under both situations, the PKULM can reasonably model the stomatal closure phenomena, which significantly decreases both evapotranspiration and net ecosystem exchange of canopy, while these phenomena cannot be resolved in the Noah simulations. Therefore, our findings suggest that the WRF-PKULM coupled method may be a more reliable tool to investigate and forecast DHW as well as be instructive to crop models.

The IMERG V06 hourly rainfall product at daily and hourly scales was evaluated during the warm season (May to September) from 2015 to 2020 using 651 high-quality rain-gauge stations over the Tibetan Plateau (TP). Based on hourly observed rain-gauge precipitation, four categories were classified: light rainfall (0–12 mm·d⁻¹), moderate rainfall (12–20.1 mm·d⁻¹), torrential rainfall (20.1–32.2 mm·d⁻¹), and extreme torrential rainfall (>32.2 mm·d⁻¹). Precipitation frequency and intensity were calculated to further validate the accuracy and suitability of the IMERG estimated-precipitation product. At the daily scale, IMERG underestimated the number of days with less than moderate rainfall, but overestimated the frequency of torrential and extreme torrential rainfall. IMERG estimated the main characteristics of precipitation frequency at different daily precipitation amount levels better than the precipitation intensity, but its best estimate was at the moderate rainfall level, with the highest correlation coefficient (0.69) and the lowest root mean square error (0.17). At the hourly scale, IMERG underestimated the hourly precipitation amount mainly between the early morning and midday (the average deviation was 0.019 mm·h⁻¹) and overestimated it between the afternoon and late night (the average deviation was 0.047 mm·h⁻¹). IMERG overestimated precipitation frequency and underestimated precipitation intensity between the afternoon and the evening, which means that this analysis shows that IMERG estimated more precipitation hours than the observation and underestimated precipitation intensity. These results further our understanding of the suitability of the IMERG precipitation products over the TP and further improve the IMERG retrieval algorithm to better apply the corresponding precipitation product to light and extreme rainfall over regions with complicated topography.

We develop a novel framework for rigorously evaluating land surface models (LSMs) against observations by recognizing the asymmetry between verification‐ and falsification‐oriented approaches. The former approach cannot completely verify LSMs even though it exhausts every case of consistency between the model predictions and observations, whereas the latter only requires a single case of inconsistency to reveal that there must be something wrong. We argue that it is such an inconsistency that stimulates further development of the models and enhancement of the observations. We therefore propose a falsification‐oriented signature‐based evaluation framework to identify cases of inconsistency between model predictions and observations by extracting signatures based on a set of key assumptions. We apply this framework to evaluate an ensemble of simulations from the Noah‐MP LSM against observations over the continental United States under the three assumptions of water mass conservation, no lateral water flow, and a sufficiently long period of time. Regions showing inconsistencies between the Noah‐MP ensemble simulations and the observations are located in the western mountainous areas, the Yellowstone river basin, the lower Floridan aquifer, the Niobrara river basin at the north tip of the Ogallala aquifer, and the basins downstream of the Balcones fault zones in Texas. These regions coincide with the sites where both advances in theoretical modeling and new observational data (e.g., from the Critical Zone Observatories) have emerged.

As the scale of renewable energy sources (RESs) expands, it is essential to optimize the configuration of wind, solar, and storage resources across different areas. Nevertheless, the unavoidable uncertainties associated with both energy supply and demand present significant challenges for planners. This study aims to address the challenge of coordinated planning for multiarea wind-solar-energy storage systems considering multiple uncertainties. First, uncertainties related to future peak demand, thermal generation output boundaries, demand variability, and stochastic unit production are analyzed and modeled on the basis of robust optimization and stochastic programming techniques. Then, a hierarchical coordinated planning model that incorporates both system-wide (SW) and local area (LA) planning models is proposed. The SW planning model is designed to manage the optimal capacity configuration of RESs and energy storage systems (ESSs) within each LA, as well as the operational boundary of LAs. The LA planning models aim to further optimize the capacities of RESs and ESSs and minimize the economic cost within each LA on the basis of local resource characteristics. To achieve the optimal solution, the analytical target cascading (ATC) algorithm is integrated with the column-and-constraint generation (C&CG) algorithm. The simulation results validate the effectiveness and reasonableness of the proposed coordinated planning model, which not only outperforms independent planning approaches but also effectively manages the uncertainties.

Terrestrial water budget (TWB) data over large domains are of high interest for various hydrological applications. Spatiotemporally continuous and physically consistent estimations of TWB rely on land surface models (LSMs). As an augmentation of the operational North American Land Data Assimilation System Phase 2 (NLDAS-2) four-LSM ensemble, this paper describes a dataset simulated from an ensemble of 48 physics configurations of the Noah LSM with multi-physics options (Noah-MP). The 48 Noah-MP physics configurations are selected to give a representative cross-section of commonly used LSMs for parameterizing runoff, atmospheric surface layer turbulence, soil moisture limitation on photosynthesis, and stomatal conductance. The dataset spans from 1980 to 2015 over the conterminous United States (CONUS) at a monthly temporal resolution and a $\mathrm{1}/\mathrm{8}$ ^∘ spatial resolution. The dataset variables include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The dataset is available at https://doi.org/10.5281/zenodo.7109816 (Zheng et al., 2022). Evaluations carried out in this study and previous investigations show that the ensemble performs well in reproducing the observed terrestrial water storage, snow water equivalent, soil moisture, and runoff. Noah-MP complements the NLDAS models well, and adding Noah-MP consistently improves the NLDAS estimations of the above variables in most areas of CONUS. Besides, the perturbed-physics ensemble facilitates the identification of model deficiencies. The parameterizations of shallow snow, spatially varying groundwater dynamics, and near-surface atmospheric turbulence should be improved in future model versions.