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Offline land surface models (LSMs) require atmospheric forcing data sets for simulating water, energy, and biogeochemical fluxes. However, available forcing data sets remain highly uncertain and can introduce additional differences in LSM simulations. This study explored the impact of various forcing data sets, ranging from widely used to newly developed, on hydrological simulations using the Common Land Model 2024 (CoLM2024). We conducted 12 global experiments using different forcing data sets to force CoLM2024. We evaluated the model's performance against plot‐scale observations and globally gridded reference data. We examined the uncertainties in forcings and their impact on output variables such as latent heat, sensible heat, net radiation, and total runoff. Globally, precipitation has the highest degree of uncertainty at 4.4%. The forcing uncertainties propagate to the model simulations and cause significant differences in simulated variables. Runoff uncertainty is about 15.7% globally, with a greater impact in low latitudes. Our evaluation shows that the newly developed data sets, such as CRUJRA and ERA5LAND, generally outperform the others. However, the optimal forcing data set varies depending on the variable of interest and the targeted region. Partial Least Squares Regression analysis reveals that different simulated variables are associated with dominant forcing variables, highlighting the importance of selecting forcing data sets for specific applications and regions. This study confirms the importance of improving the quality and consistency of meteorological data. This would help reduce simulation biases and guide the improvement of the model structure and parameterization of CoLM2024. Plain Language Summary: Atmospheric forcing data sets, including precipitation, air temperature, humidity, radiation, wind speed, and air pressure, are necessary for driving offline land surface models, for simulating water, energy, and carbon fluxes. However, atmospheric forcings deviate from each other and can lead to varying results in land surface model (LSM) simulations. Here we investigated to what extent the forcing data set can affect the simulation using the CoLM2024. To achieve this, we compared 12 different forcing data sets, from widely used to newly developed. Our results confirm that the uncertainties among these data sets, particularly in precipitation, can lead to significant differences in simulations such as runoff and surface fluxes. Our results show that the uncertainty in runoff is about 15.7% globally, with more pronounced in tropical regions. We find that using CRUJRA and ERA5LAND data sets improved CoLM2024 simulations compared to traditional ones. Our analysis reveals that different output variables are sensitive to different forcing variables, and the best data set varies depending on the region and the simulated variable. For example, runoff is mainly influenced by precipitation, while net radiation is more sensitive to shortwave radiation. This study underscores the need for improved meteorological data quality to enhance simulation accuracy, guiding CoLM2024 refinement and reducing biases. Key Points: 12 forcing data sets show large uncertainties and great effects on land surface model simulationsNewly data sets for CoLM2024 simulations generally outperformed others, but optimal choice variesPartial Least Squares Regression revealed the mechanism of uncertainty propagation [ABSTRACT FROM AUTHOR]

Understanding urban vertical structures, particularly building heights, is essential for examining the intricate interaction between humans and their environment. Such datasets are indispensable for a variety of applications, including climate modeling, energy consumption analysis, and socioeconomic activities. Despite the importance of this information, previous studies have primarily focused on estimating building heights regionally at the grid scale, often resulting in datasets with limited coverage or spatial resolution. This limitation hampers comprehensive global analysis and the ability to generate actionable insights at finer scales. In this study, we developed a global building height map at the building footprint scale by leveraging Earth Observation (EO) datasets and advanced machine learning techniques. Our approach integrated multisource remote-sensing features and building morphology features to develop height estimation models using the extreme gradient boosting (XGBoost) regression method across diverse global regions. This methodology allowed us to estimate the heights of individual buildings worldwide, culminating in the creation of the three-dimensional (3D) Global Building Footprints (3D-GloBFP) dataset for the year 2020. Our evaluation results show that the height estimation models perform exceptionally well at a global scale, with R2 values ranging from 0.66 to 0.96 and root-mean-square errors (RMSEs) ranging from 1.9 to 14.6 m across 33 subregions. Comparisons with other datasets demonstrate that 3D-GloBFP closely matches the distribution and spatial pattern of reference heights. Our derived 3D global building footprint map shows a distinct spatial pattern of building heights across regions, countries, and cities, with building heights gradually decreasing from the city center to the surrounding rural areas. Furthermore, our findings indicate disparities in built-up infrastructure (i.e., building volume) across different countries and cities. China is the country with the most intensive total built-up infrastructure (5.28×1011 m3 , accounting for 23.9 % of the global total), followed by the USA (3.90×1011 m3 , accounting for 17.6 % of the global total). Shanghai has the largest volume of built-up infrastructure (2.1×1010 m3) of all representative cities. The derived building-footprint-scale height map (3D-GloBFP) reveals the significant heterogeneity in urban built-up environments, providing valuable insights for studies on urban socioeconomic dynamics and climatology. The 3D-GloBFP dataset is available at 10.5281/zenodo.11319912 (Building height of the Americas, Africa, and Oceania in 3D-GloBFP; Che et al., 2024c), 10.5281/zenodo.11397014 (Building height of Asia in 3D-GloBFP; Che et al., 2024a), and 10.5281/zenodo.11391076 (Building height of Europe in 3D-GloBFP; Che et al., 2024b). [ABSTRACT FROM AUTHOR]

Hyper‐resolution land surface modeling provides an unprecedented opportunity to simulate locally relevant water and energy cycles. However, the available meteorological forcing data is often insufficient to fulfill the requirements of hyper‐resolution modeling. Here, we developed a comprehensive downscaling framework based on topography‐adjusted methods and automated machine learning (AutoML). With this framework, a 90 m and hourly atmospheric forcing data set was developed from ERA5 data at a 0.25° resolution, and the Common Land Model (CoLM) was then forced with the developed forcing data over two complex terrain regions (the Heihe River Basin and Upper Colorado River Basin). We systematically evaluated the downscaled forcing and the CoLM outputs against both in situ observations and gridded data. The ground‐based validation results suggested consistent improvements for all downscaled forcing variables with mean RMSE improved by 6.362%–95.86%. The downscaled forcings, which incorporated detailed topographic features, offered improved magnitude estimates, achieving a comparable level of performance to that of regional reanalysis forcing data. The downscaled forcing driving the CoLM model showed comparable or better skills in simulating water and energy fluxes, as verified by in situ validations. The hyper‐resolution simulations provided a detailed and more reasonable description of land surface processes and attained similar spatial patterns and magnitudes with high‐resolution land surface data, especially over highly elevated areas. Additionally, this study highlighted the benefits of using mountain radiation theory‐based shortwave radiation downscaling models and AutoML‐assisted precipitation downscaling models. These findings emphasized the significance of integrating topography‐based downscaling methods for hillslope‐scale simulations. Plain Language Summary: High‐resolution atmospheric forcing data is required for accurate hyper‐resolution land surface modeling. However, the currently available forcing data is not sufficient due to its coarse spatial resolution. In this study, we developed a downscaling framework that employs physical realism equations and machine learning to account for the impact of topography on atmospheric conditions. We tested the framework using the Heihe River Basin and Upper Colorado River Basin as examples and found that the downscaled forcing provided more realistic representations than those from the coarse‐resolution data. The downscaled forcing‐based hyper‐resolution simulation captured topographic heterogeneity and improved water and energy variable simulations. These findings emphasize the efficacy of the proposed topographic‐based downscaling framework and offer a valuable way to enhance predictions over regions with complex terrain. Key Points: A downscaling framework for atmospheric forcing based on topography‐adjusted methods and automated machine learning is developedThe downscaled forcing offers better estimates of atmospheric conditions in terms of accuracy, magnitude, and spatial detailed featuresThe CoLM driven by the downscaled forcing significantly improved the simulation of water and energy variables over complex terrain areas [ABSTRACT FROM AUTHOR]

Large-scale hydrologic modeling at national scale is an increasing important effort worldwide to tackle ecohydrologic issues induced by global water scarcity. In this study, a surface water-groundwater integrated hydrologic modeling platform was built using ParFlow, covering the entire continental China with a resolution of 30 arcsec. This model, CONCN 1.0, has a full treatment of 3D variably saturated groundwater by solving Richards' equation, along with the shallow water equation at the ground surface. The performance of CONCN 1.0 was rigorously evaluated using both global data products and observations. RSR values show good to excellent performance in streamflow, yet the streamflow is lower in the Endorheic, Hai, and Liao Rivers due to uncertainties in potential recharge. RSR values also indicate good performance in water table depth of the CONCN model. This is an intermediate performance compared to two global groundwater models, highlighting the uncertainties that persist in current large-scale groundwater modeling. Our modeling work is also a comprehensive evaluation of the current workflow for continental-scale hydrologic modeling using ParFlow and could be a good starting point for the modeling in other regions worldwide, even when using different modeling systems. More specifically, the vast arid and semi-arid regions in China with substantial sinks (i.e., the end points of endorheic rivers) and the large uncertainties in potential recharge pose challenges for the numerical solution and model performance, respectively. Incompatibilities between data and model, such as the mismatch of spatial resolutions between model and products and the shorter, less frequent observation records, require further refinement of the workflow to enable fast modeling. This work not only establishes the first integrated hydrologic modeling platform in China for efficient water resources management, but it will also benefit the improvement of next generation models worldwide. [ABSTRACT FROM AUTHOR]

Terrestrial evapotranspiration (ET) is a vital process regulating the terrestrial water balance. However, significant uncertainties persist in global ET estimates. Focusing on the area between 60°, we performed an intercomparison of 90 state‐of‐the‐art ET products from 1980 to 2014. These products were obtained from various sources or methods and were grouped into six categories: remote sensing, reanalysis, land surface models, climate models, machine learning methods, and ensemble estimates. It is shown that global ET magnitudes of categories differ considerably, with averages ranging from 518.4 to 706.3 mm yr−1. Spatial patterns are generally consistent but with significant divergence in tropical rainforests. Global trends are mildly positive or negative (−0.10 to 0.37 mm yr−2) depending on categories but with distinct spatial variability. Evaluation against site measurements reveals various performances across land cover types; the ideal point error values range from 0.45 to 0.83, with wetlands performing the worst and open shrublands the best. Using the three‐cornered hat method, there are spatial differences in ET uncertainty, with lower uncertainty for ensemble estimates, showing less than 15% relative uncertainty in most areas. The best global ET data set varies depending on the intended use and study region. Distinct spatial patterns of controlling factors across categories have been identified, with precipitation driving arid and semi‐arid regions and leaf area index dominating tropical regions. It is suggested to include advancing precipitation inputs, incorporate vegetation dynamics, and employ hybrid modeling in future ET estimates. Constraining estimates using complementary data and robust theoretical frameworks can enhance credibility in ET estimation. Plain Language Summary: Evapotranspiration (ET) is an essential process where water transfers from the land to the atmosphere through evaporation and plant transpiration. However, substantial uncertainties exist across available global ET estimates produced by various models and methods. To date, we created the most comprehensive compilation of over 90 state‐of‐the‐art ET data products, including satellite‐based, land surface model, climate model, reanalysis, machine learning, and ensemble‐based estimates, covering 1980–2014 and ranging between 60°N and 60°S. These estimates show large differences in global ET magnitude, trends, and spatial patterns. We evaluated ET estimates against in situ measurements and found diverging performance across land cover types. We used statistical methods to quantify uncertainties in ET data products and found discrepancies in uncertainty distributions across methods. We also identified key factors driving spatial ET variability in each product category. Our analysis underscores the importance and opportunities for reducing the uncertainty in terrestrial ET and provides valuable insights for improving hydroclimatic predictions. Key Points: Global ET estimates show large discrepancies in magnitude, trends and spatial patterns across 90 state‐of‐the‐art data setsThere is no single best global ET data set suitable for all applications and locations, the choice depends on the intention and region of studyDominant controls on ET differ across categories spatially; constraining estimates via Budyko and novel hybrid modeling can enhance confidence [ABSTRACT FROM AUTHOR]

Accurate and high-resolution spatial soil information is crucial for efficient and sustainable land use, management, and conservation. Since the establishment of digital soil mapping (DSM) and the GlobalSoilMap working group, significant advances have been made in spatial soil information globally. However, accurately predicting soil variation over large and complex areas with limited samples remains a challenge, especially for China, which has diverse soil landscapes. To address this challenge, we utilized 11,209 representative multi-source legacy soil profiles (including the Second National Soil Survey of China, World Soil Information Service, First National Soil Survey of China, and regional databases) and high-resolution soil-forming environment characterization. Using advanced Quantile Regression Forest algorithms and a high-performance parallel computing strategy, we developed comprehensive maps of 23 soil physical, chemical and fertility properties at six standard depth layers from 0 to 2 meters in China with a 90 m spatial resolution (China dataset of soil properties for land surface modeling version 2, CSDLv2). Data-splitting and independent samples validation strategies were employed to evaluate the accuracy of the predicted maps quality. The results showed that the predicted maps were significantly more accurate and detailed compared to traditional soil type linkage methods (i.e., CSDLv1, the first version of the dataset), SoilGrids 2.0, and HWSD 2.0 products, effectively representing the spatial variation of soil properties across China. The prediction accuracy of most soil properties at the 0–5 cm depth interval ranged from good to moderate, with Model Efficiency Coefficients for most soil properties ranging from 0.75 to 0.32 during data-splitting validation and from 0.88 to 0.25 during independent sample validation. The wide range between the 5 % lower and 95 % upper prediction limits may indicate substantial room for improvement in current predictions. The relative importance of environmental covariates in predictions varied with soil properties and depth, indicating the complexity of interactions among multiple factors in the soil formation processes. As the soil profiles used in this study mainly originate from the Second National Soil Survey of China during 1970s and 1980s, they could provide new perspectives of soil changes together with existing maps based on 2010s soil profiles. The findings make important contributions to the GlobalSoilMap project and can also be used for regional Earth system modeling and land surface modeling to better represent the role of soil in hydrological and biogeochemical cycles in China. This dataset is freely available and can be accessed at https://doi.org/10.11888/Terre.tpdc.301235 (Shi et al, 2024). [ABSTRACT FROM AUTHOR]

The influence of biogeochemical cycles, particularly the nitrogen cycle, on near‐surface meteorological fields is a critical yet understudied aspect of regional climate modeling. Neglecting such interactions may compromise the accurate representation of vegetation growth and hydrological processes in climate models, consequently affecting the simulated regional near‐surface climate conditions. In order to quantify such effects, we coupled the nitrogen‐augmented Noah‐MP land surface model with the Weather Research and Forecasting (WRF) model v4.1.2 (hereafter WRF‐CN) for regional climate modeling. Compared to the default WRF simulation without nitrogen dynamics, the WRF‐CN simulated net primary productivity, gross primary productivity (GPP), and leaf area index (LAI) were all higher in the study region. Because WRF underestimated the observed GPP and LAI due to the fixed nitrogen limitation of plant growth, these higher estimations improved WRF‐CN's performance in modeling GPP and LAI, which translated into improved simulations of near‐surface climate. Specifically, for the 2‐m air temperature, compared to WRF, WRF‐CN reduced the mean absolute error and root mean square error by 14.45% and 14.19%, respectively, while increased the Nash‐Sutcliffe efficiency coefficient by 7.23%, with the most pronounced improvements in the regions dominated by croplands. Our findings shed light on the crucial interactions between biogeochemical processes and near‐surface meteorological conditions, emphasizing the significance of incorporating terrestrial nitrogen dynamics in regional climate models. These insights contribute to advancing our understanding of climate system dynamics and improving the accuracy of climate predictions at the mesoscale. Plain Language Summary: Nitrogen is an indispensable nutrient for all forms of life. The terrestrial nitrogen availability modulates plant growth, regulates canopy transpiration, and affects soil moisture and land‐atmosphere interactions. Nitrogen dynamics has been included in most, if not all, global Earth system models. However, previous studies have rarely considered it in regional climate models. In this study, we coupled a nitrogen dynamics component with a regional climate model to study the effects of nitrogen cycling on regional near‐surface climate. Adding the nitrogen cycle affected vegetation productivity, the water cycle, and air temperature. The new model performed better than the original model, which indicates that nitrogen dynamics play an important role in regional climate simulations. Key Points: Integrate Noah‐MP‐CN into Weather Research and Forecasting for mesoscale climate modelingTerrestrial nitrogen dynamics largely improve the WRF performance in modeling the carbon cycle and near‐surface meteorological variablesThe nitrogen dynamics' influences are most noteworthy over croplands [ABSTRACT FROM AUTHOR]