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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

JAX-CanVeg: A Differentiable Land Surface Model

Land surface models consider the exchange of water, energy, and carbon along the soil-canopy-atmosphere continuum, which is challenging to model due to their complex interdependency and associated challenges in representing and parameterizing them. Differentiable modeling provides a new opportunity to capture these complex interactions by seamlessly hybridizing process-based models with deep neural networks (DNNs), benefiting both worlds, that is, the physical interpretation of process-based models and the learning power of DNNs. Here, we developed a differentiable land model, JAX-CanVeg. The new model builds on the legacy CanVeg by incorporating advanced functionalities through JAX in the graphic processing unit support, automatic differentiation, and integration with DNNs. We demonstrated JAX-CanVeg's hybrid modeling capability by applying the model at four flux tower sites with varying aridity. To this end, we developed a hybrid version of the Ball-Berry equation that emulates the water stress impact on stomatal closure to explore the capability of the hybrid model in (a) improving the simulations of latent heat fluxes (LE) and net ecosystem exchange (NEE), (b) improving the optimization trade-off when learning observations of both LE and NEE, and (c) benefiting a multi-layer canopy model setup. Our results show that the proposed hybrid model improved the simulations of LE and NEE at all sites, with an improved optimization trade-off over the process-based model. Additionally, the multi-layer canopy set benefited hybrid modeling at some sites. Anchored in differentiable modeling, our study provides a new avenue for modeling land-atmosphere interactions by leveraging the benefits of both data-driven learning and process-based modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A Framework for Parametric and Predictive Uncertainty Quantification in the E3SM Land Model: Assessing Site and Observable Generalizability

Quantifying parametric uncertainty using observations from individual sites provides a critical foundation for Earth system modeling, serving as a necessary first step before scaling up to regional or global applications. This study introduces a novel computational framework designed to enhance model predictability by reducing parametric uncertainty and assessing site and observable generalizability using various observational constraints. The framework integrates five components: Model Simulation, Statistical Emulation, Global Sensitivity Analysis (GSA), Model Calibration, and Model Prediction. Using the E3SM land model, we simulated site-level land-atmosphere carbon and energy fluxes from 2003 to 2007 across five evergreen needleleaf FLUXNET sites, perturbing 26 vegetation-related model parameters. Gaussian process emulators were employed to expedite GSA and model calibration. Four critical parameters that strongly influence selected land-atmosphere fluxes were identified by GSA. Bayesian approaches were used to infer parameter probability distributions leveraging synthetic data and FLUXNET observations. The results reveal that posterior parameter distributions vary significantly across different sites and observables within the same plant functional type. Probabilistic predictions indicate that parameters calibrated at one site can enhance predictive accuracy at other sites, although site heterogeneity may sometimes outweigh parametric uncertainty. Additionally, the probabilistic predictions demonstrate that calibration for one variable can also improve predictability for other variables, thereby maximizing predictive capabilities with limited observations. This framework provides a powerful approach for reducing parametric uncertainty in Earth system models and deepening our understanding of carbon dynamics and energy cycles. Its adaptability makes it a valuable tool for broader applications in Earth system modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Development of High Resolution Land Surface Parameters for the Community Land Model

There is a growing need for high-resolution land surface parameters as land surface models are being applied at increasingly higher spatial resolution offline as well as in regional and global models. The default land surface parameters for the most recent version of the Community Land Model (i.e. CLM 4.0) are at 0.5° or coarser resolutions, released with the Community Earth System Model (CESM). Plant Functional Types (PFTs), vegetation properties such as Leaf Area Index (LAI), Stem Area Index (SAI), and non-vegetated land covers were developed using remotely sensed datasets retrieved in late 1990’s and the beginning of this century. In this study, we developed new land surface parameters for CLM 4.0, specifically PFTs, LAI, SAI and non-vegetated land cover composition, at 0.05° resolution globally based on the most recent MODIS land cover and improved MODIS LAI products. Compared to the current CLM 4.0 parameters, the new parameters produced a decreased coverage by bare soil and trees, but an increased coverage by shrub, grass, and cropland. The new parameters result in a decrease in global seasonal LAI, with the biggest decrease in boreal forests; however, the new parameters also show a large increase in LAI in tropical forest. Differences between the new and the current parameters are mainly caused by changes in the sources of remotely sensed data and the representation of land cover in the source data. Advantages and disadvantages of each dataset were discussed in order to provide guidance on the use of the data. The new high-resolution land surface parameters have been used in a coupled land-atmosphere model (WRF-CLM) applied to the western U.S. to demonstrate their use in high-resolution modeling. A remapping method from the latitude/longitude grid of the CLM data to the WRF grids with map projection was also demonstrated. Future work will include global offline CLM simulations to examine the impacts of source data resolution and subsequent land parameter changes on simulated land surface processes.

Environmental Molecular Sciences Laboratory↗
NASA NTRS2021

Southern Bhutan Ecological Forecasting III: Utilizing NASA Earth Observations to Model Land Cover Change and Elephant Wildlife Corridors in Southern Bhutan

Habitat loss of the endangered Asian elephants (Elephas maximus) accompanied by rapid urbanization has contributed to the rising human-elephant conflict (HEC) crisis in southern Bhutan. This poses a serious threat to the survival of Asian elephants, a keystone wildlife species essential for maintaining Bhutan’s forest ecosystems and rich biodiversity. With expanding urban areas, HECs present challenges to conservation efforts in the region. The team partnered with the Bhutan Foundation, the Bhutan Tiger Center, and Bhutan Ecological Society to help mitigate this issue using remote sensing technology and NASA Earth observations. The team refined Land Use and Land Cover (LULC) maps for 2010–2019 generated in previous terms and elephant corridor maps to include information on human settlements using Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) data. We generated LULC change maps and forecasted the LULC to 2030 using TerrSet Land Change Modeler, providing insights into future elephant habitat suitability in southern Bhutan. The results indicated that built-up areas increased approximately 688.9% from 2010 to 2019 and the forecasted 2030 LULC also predicted an increase in built-up areas compared to 2019. Suitable corridors in Gelephu intersect cultivated and built-up areas, indicating close proximity of elephants to humans and a need to research alternative corridor strategies. The end products from this project will aid partner organizations in decision-making processes in urban planning and future conservation strategies that include the refined placement of biological corridors to aid elephant movement and reduce the risk of HECs.

Thinley Yidzin Wangden↗
DOE OSTI2016

Divergent predictions of carbon storage between two global land models: Attribution of the causes through traceability analysis

Representations of the terrestrial carbon cycle in land models are becoming increasingly complex. It is crucial to develop approaches for critical assessment of the complex model properties in order to understand key factors contributing to models' performance. In this study, we applied a traceability analysis which decomposes carbon cycle models into traceable components, for two global land models (CABLE and CLM-CASA') to diagnose the causes of their differences in simulating ecosystem carbon storage capacity. Driven with similar forcing data, CLM-CASA' predicted – 31 % larger carbon storage capacity than CABLE. Since ecosystem carbon storage capacity is a product of net primary productivity (NPP) and ecosystem residence time ( τ E ), the predicted difference in the storage capacity between the two models results from differences in either NPP or τ E or both. Our analysis showed that CLM-CASA'simulated 37 % higher NPP than CABLE. On the other hand, τ E , which was a function of the baseline carbon residence time ( τ ' E ) and environmental effect on carbon residence time, was on average 11 years longer in CABLE than CLM-CASA'. This difference in τ E was mainly caused by longer τ ' E of woody biomass (23 vs. 14 years in CLM-CASA'), and higher proportion of NPP allocated to woody biomass (23 vs. 16 %). Differences in environmental effects on carbon residence times had smaller influences on differences in ecosystem carbon storage capacities compared to differences in NPP and τ ' E . Altogether, the traceability analysis showed that the major causes of different carbon storage estimations were found to be parameters setting related to carbon input and baseline carbon residence times between two models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Evaluation of the Community Land Model simulated carbon and water fluxes against observations over ChinaFLUX sites

The Community Land Model (CLM) is an advanced process-based land surface model that simulates carbon, nitrogen, water vapor and energy exchanges between terrestrial ecosystems and the atmosphere at various spatial and temporal scales. We use observed carbon and water fluxes from five representative Chinese Terrestrial Ecosystem Flux Research Network (ChinaFLUX) eddy covariance tower sites to systematically evaluate the new version CLM4.5 and old version CLM4.0, and to generate insights that may inform future model developments. CLM4.5 underestimates the annual carbon sink at three forest sites and one alpine grassland site but overestimates the carbon sink of a semi-arid grassland site. The annual carbon sink underestimation for the deciduous-dominated forest site results from underestimated daytime carbon sequestration during summer and overestimated nighttime carbon emission during spring and autumn. Compared to CLM4.0, the bias of annual gross primary production (GPP) is reduced by 24% and 28% in CLM4.5 at two subtropical forest sites. However, CLM4.5 still presents a large positive bias in annual GPP. The improvement in net ecosystem exchange (NEE) is limited, although soil respiration bias decreases by 16%–43% at three forest sites. CLM4.5 simulates lower soil water content in the dry season than CLM4.0 at two grassland sites. Drier soils produce a significant drop in the leaf area index and in GPP and an increase in respiration for CLM4.5. The new fire parameterization approach in CLM4.5 causes excessive burning at the Changbaishan forest site, resulting in an unexpected underestimation of NEE, vegetation carbon, and soil organic carbon by 46%, 95%, and 87%, respectively. Altogether, our study reveals significant improvements achieved by CLM4.5 compared to CLM4.0, and suggests further developments on the parameterization of seasonal GPP and respiration, which will require a more effective representation of seasonal water conditions and the partitioning of net radiation between sensible and heat fluxes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Runoff evaluation in an Earth System Land Model for permafrost regions in Alaska

Modeling of hydrological runoff is essential for accurately capturing spatiotemporal feedbacks within the land–atmosphere system, particularly in sensitive regions such as permafrost landscapes. However, substantial uncertainties persist in the terrestrial runoff parameterization schemes used in Earth system and land surface models. This is particularly true in permafrost regions, where landscape heterogeneity is high and reliable observational data are scarce. In this study, we evaluate the performance of runoff parameterization schemes in the Energy Exascale Earth System Model (E3SM) land model (ELM). Our proposed framework leverages simulation results from the Advanced Terrestrial Simulator (ATS), which is a physics-based integrated surface/subsurface hydrologic model that has been successfully evaluated previously in Arctic tundra regions. We used ATS to simulate runoff from 22 representative hillslopes in the Sagavanirktok River basin, located on the North Slope of Alaska, then compared the output with ELM's parameterized representation of total runoff. Results show that (1) ELM's total runoff was the same order of magnitude as the ATS simulations, and both models were similarly variable over time; (2) minor adjustments to coefficients in ELM's runoff parameterization improved the match between the ATS simulation and ELM's parameterized representation of annual and seasonal total runoff; (3) overall, runoff responses in ATS and ELM are more similar in flat hillslope environments compared to steep hillslopes; and (4) shallower active layer thicknesses and higher precipitation simulations resulted in lower correlations between the two models due to greater total runoff. By incorporating the optimized runoff coefficients from the Sagavanirktok River basin into ELM, the simulated total runoff better matched the streamflow observations at a small watershed located on the Seward Peninsula of Alaska. Our findings revealed important insights into the effectiveness of runoff parameterizations in land surface models and pathways for improving runoff coefficients in typical Arctic regions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Implementation and Evaluation of Irrigation Techniques in the Community Land Model

Several previous studies have highlighted the irrigation-induced impacts on the global and regional water cycle, energy budget, and near-surface climate. While land models are widely used to address this question, the implementations of irrigation in these models vary in complexity. Here, we expand the representation of irrigation in Community Land Model to enable six different irrigation methods. We find that using a combination of irrigation methods, including default, sprinkler, flood and paddy techniques performs best as determined by evaluating the simulated irrigation water withdrawals against observations, and therefore select this combination as the new irrigation scheme. Then, the impact of the new irrigation scheme on surface fluxes is evaluated and detected using single-point simulations. Finally, the global and regional irrigation-induced impacts on surface energy and water fluxes are compared using both the original and the new irrigation scheme. The new irrigation scheme substantially reduces the bias and root-mean-square error of simulated irrigation water withdrawal in the USA and other countries, but considerably overestimates withdrawals in Central China. Results of single-point experiments show that different irrigation methods have different effects on surface fluxes, while the magnitudes are small. At the global scale, the new scheme enlarges the irrigation-induced impacts on water and energy variables relative to the original scheme, with varying magnitudes across regions. Overall, our results suggest that this newly developed scheme is a better tool for simulating irrigation-induced impacts on climate, and highlight the added value of incorporating human water management in Earth system models.

Community land models↗
NASA NTRS2014

Comparison of Land Skin Temperature from a Land Model, Remote Sensing, and In-situ Measurement

Land skin temperature (Ts) is an important parameter in the energy exchange between the land surface and atmosphere. Here hourly Ts from the Community Land Model Version 4.0, MODIS satellite observations, and in-situ observations in 2003 were compared. Compared with the in-situ observations over four semi-arid stations, both MODIS and modeled Ts show negative biases, but MODIS shows an overall better performance. Global distribution of differences between MODIS and modeled Ts shows diurnal, seasonal, and spatial variations. Over sparsely vegetated areas, the model Ts is generally lower than the MODIS observed Ts during the daytime, while the situation is opposite at nighttime. The revision of roughness length for heat and the constraint of minimum friction velocity from Zeng et al. [2012] bring the modeled Ts closer to MODIS during the day, and have little effect on Ts at night. Five factors contributing to the Ts differences between the model and MODIS are identified, including the difficulty in properly accounting for cloud cover information at the appropriate temporal and spatial resolutions, and uncertainties in surface energy balance computation, atmospheric forcing data, surface emissivity, and MODIS Ts data. These findings have implications for the cross-evaluation of modeled and remotely sensed Ts, as well as the data assimilation of Ts observations into Earth system models.

Modeling↗
DOE OSTI2016

Testing a land model in ecosystem functional space via a comparison of observed and modeled ecosystem flux responses to precipitation regimes and associated stresses in a Central U.S. forest: Test Model in Ecosystem Functional Space

Testing complex land surface models has often proceeded by asking the question: does the model prediction agree with the observation? This approach has yet led to high-performance terrestrial models that meet the challenges of climate and ecological studies. Here we test the Community Land Model (CLM) by asking the question: does the model behave like an ecosystem? We pursue its answer by testing CLM in the ecosystem functional space (EFS) at the Missouri Ozark AmeriFlux (MOFLUX) forest site in the Central U.S., focusing on carbon and water flux responses to precipitation regimes and associated stresses. In the observed EFS, precipitation regimes and associated water and heat stresses controlled seasonal and interannual variations of net ecosystem exchange (NEE) of CO 2 and evapotranspiration in this deciduous forest ecosystem. Such controls were exerted more strongly by precipitation variability than by the total precipitation amount per se. A few simply constructed climate variability indices captured these controls, suggesting a high degree of potential predictability. While the interannual fluctuation in NEE was large, a net carbon sink was maintained even during an extreme drought year. Although CLM predicted seasonal and interanual variations in evapotranspiration reasonably well, its predictions of net carbon uptake were too small across the observed range of climate variability. Also, the model systematically underestimated the sensitivities of NEE and evapotranspiration to climate variability and overestimated the coupling strength between carbon and water fluxes. Its suspected that the modeled and observed trajectories of ecosystem fluxes did not overlap in the EFS and the model did not behave like the ecosystem it attempted to simulate. A definitive conclusion will require comprehensive parameter and structural sensitivity tests in a rigorous mathematical framework. We also suggest that future model improvements should focus on better representation and parameterization of process responses to environmental stresses and on more complete and robust representations of carbon-specific processes so that adequate responses to climate variability and a proper degree of coupling between carbon and water exchanges are captured.

58 GEOSCIENCES↗
DOE OSTI2016

A scalable framework for the global offline community land model ensemble simulation

Current earth system models have a large range of uncertainty, owing to differences in the simulation of feedbacks and insufficient information to constrain model parameters. Parameter disturbance experiment provides a straightforward method to quantify the variation (uncertainty) outputs caused by model inputs. Owing to the software complexity and computational intensity of earth system models, a large-scale simulation framework is needed to support ensemble simulation required by parameter disturbance experiment. This paper presents a parallel framework for the community land model ensemble simulation. After a software structure review of the community land model simulation, a single factor parameter disturbance experiment of a reference computational experiment design is used to demonstrate the software design principles, computational characteristics of individual application, parallel ensemble simulation implementation, as well as the weak scalability of this simulation framework on a high-end computer. Lastly, the paper discusses some preliminary diagnostic analysis results of the single factor parameter disturbance experiments. The framework design considerations and implementation details described in this paper can be beneficial to many other research programmes involving large scale, legacy modelling system.

58 GEOSCIENCES↗
DOE OSTI2022

Towards Ultra-high-resolution E3SM Land Modeling on Exascale Computers

Here we present an ultra-high-resolution E3SM land model (uELM) for high-fidelity land simulations targeting new Exascale computers. After considering modeling infrastructure compatibility and ELM software features, we designed a parallel model for the uELM development targeting hybrid architectures of new US Exascale computers. We also described a function unit test framework to expedite the piece-wise code porting (with compiler directives), verification, and global variable management. Furthermore, in this study, we report an early uELM model development using OpenACC within a function unit test framework on a pre-Exascale computer, demonstrate the performance of a uLEM submodel with a 3.0-time speedup, and summarize the code porting experience regarding global variable handling, deepcopy, memory reduction, and parallel loop reconstruction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Global land model development: time to shift from a plant functional type to a plant functional trait approach (Final Report)

This project advanced global land models by shifting from the current plant functional type approach to one that better utilizes what is known about the importance and variability of plant traits, within a framework of simultaneously improving fundamental physiological relations that are at the core of model carbon cycling algorithms. A primary goal for Earth system modeling is to make accurate predictions of the future trajectory of the climate system, based on a mechanistic understanding of processes regulating fluxes of mass and energy among system components. Land plays an important role in modifying the Earth’s mass and energy balance, as a critical link in the global cycling of carbon, among others. Land surface models have developed to include mechanistic representations of vegetation physiology, carbon and nutrient dynamics in plants and soils, how they might respond to changing climate and chemistry, and how those changes might feedback to influence changes in atmospheric greenhouse gases themselves. Existing models represent the global distribution of vegetation types using the Plant Functional Type concept. Plant Functional Types are classes of plant species with similar evolutionary and life history with presumably similar responses to environmental conditions like CO 2 , water and nutrient availability. Fixed properties for each Plant Functional Type are specified through a collection of physiological parameters, or traits. These traits, mostly physiological in nature (e.g., leaf nitrogen and longevity) are used in model algorithms to estimate ecosystem properties and/or drive calculated process rates. In most models, 5 to 15 functional types represent terrestrial vegetation; in essence, they assume there are a total of only 5 to 15 different kinds of plants on the entire globe. This assumption of constant plant traits captured within the functional type concept has serious limitations, as a single set of traits does not reflect trait variation observed within and between species and communities. While this simplification was necessary decades past, substantial improvement is now possible. Rather than assigning a small number of constant parameter values to all grid cells in a model, we developed procedures that predict a frequency distribution of values for any given grid cell. Thus, by incorporating the mean and variance model fidelity has been improved. The trait-based approach has improved land modeling by (1) incorporating patterns and heterogeneity of traits into model parameterization, thus evolving away from a framework that considers large areas of vegetation to have near identical trait values and (2) allowing for improved treatment of physiological responses to environmental drivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Representing Fine‐Scale Topographic Effects on Surface Radiation Balance in Hyper‐Resolution Land Surface Models

Land surface models are increasingly used to simulate land surface processes at hyper-spatial resolutions (e.g., ∼1 km). As model resolution increases, grid-scale topographic effects on surface radiation fluxes and their interactions between adjacent grids become more pronounced. However, current land surface models routinely neglect the fine-scale topographic effects on surface radiation balance. This study developed physically-based and computationally-efficient parameterizations (fineTOP) that explicitly resolve fine-scale topographic effects on downward shortwave and longwave radiation as well as land surface radiative properties. The newly developed parameterizations were implemented and tested in the Energy Exascale Earth System Model (E3SM) Land Model (ELM). Multi-decadal km-resolution ELM simulations over the California Sierra Nevada show that fine-scale topography significantly impacts the surface energy balance and snow processes across seasons. Slope determines the magnitude of topographic effects, while aspect controls their sign. For slopes larger than 30°, topography-induced change in annual surface temperature can be as large as 3.3 K. Regionally, the mean value and standard deviation of topography-induced changes in annual surface temperature are −0.22 ± 0.38 K and +0.25 ± 0.37 K over north-facing and south-facing slopes, respectively. Topography-induced changes in surface radiative properties account for 3.5% ± 13.8% of total topographic effects on annual net radiation. With fineTOP, ELM captures the aspect-dependence of snow cover fraction, snow water equivalent, and land surface temperature found in MODIS satellite observations and a snow reanalysis data set, while the default ELM fails to capture this phenomenon. The enhanced capability to represent fine-scale topographic effects on surface radiation balance can be used to advance understanding of the role of fine-scale topography in land surface processes and land-atmosphere interactions over mountainous regions.

Hao, Dalei [Pacific Northwest National Laboratory ↗
DOE OSTI2025

Data Files for Runoff Evaluation in an Earth System Land Model for Permafrost Regions

Modeling of hydrological runoff is essential for accurately capturing spatiotemporal feedbacks within the land–atmosphere system, particularly in sensitive regions such as permafrost landscapes. However, substantial uncertainties persist in the terrestrial runoff parameterization schemes used in Earth system and land surface models. This is particularly true in permafrost regions, where landscape heterogeneity is high and reliable observational data are scarce.This data set includes all files that were produced and applied in the paper Runoff Evaluation in an Earth System Land Model for Permafrost Regions [Xiang et al. in review]. The paper is in review as of July 1 2025 in Geoscientific Model Development (GMD). In this study, we evaluate the performance of runoff parameterization schemes in the Energy Exascale Earth System Model (E3SM) land model (ELM). Our proposed framework leverages simulation results from the Advanced Terrestrial Simulator (ATS), which is a physics-rich integrated surface/subsurface hydrologic model that has been successfully evaluated previously in Arctic tundra regions. We used ATS to simulate runoff from 22 representative hillslopes in the Sagavanirktok River basin, located on the North Slope of Alaska, then compared the output with ELM’s parameterized representation of total runoff. This dataset contains 2 figure image files (*.png, *jpg) that describe the study site and methods, as well as folders (Figure*.zip) that contain the associated data files (*.csv, *.dat) and python code notebooks (*.ipynb) for figures 3-7 in the paper. Jupyter notebook (*.ipynb) files that produce the figure files using the associated data files will run within a python environment configured with Jupyter Lab or Notebook packages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Impacts of Sub‐Grid Topographic Representations on Surface Energy Balance and Boundary Conditions in the E3SM Land Model: A Case Study in Sierra Nevada

Abstract Sub‐grid topographic heterogeneity has large impacts on surface energy balance and land‐atmosphere interactions. However, the impacts of representing sub‐grid topographic effects in land surface models (LSMs) on surface energy balance and boundary conditions remain unclear. This study analyzed and evaluated the impacts of sub‐grid topographic representations on surface energy balance, turbulent heat flux, and scalar (co‐)variances in the Energy Exascale Earth System Model (E3SM) land model (ELM). Three sub‐grid topographic representations in ELM were compared: (a) the default sub‐grid structure (D), (b) the recently developed sub‐grid topographic structure (T), and (c) high spatial resolution (1KM). Additionally, two different solar radiation schemes in ELM were compared: (a) the default plane‐parallel radiative transfer scheme (PP) and (b) the parameterization scheme (TOP) that accounts for sub‐grid topographic effects on solar radiation. A series of offline simulations with the three grid discretization structures (D, T, and 1KM) and two schemes of solar radiation (TOP and PP) were carried out using the Sierra Nevada, California. 1KM simulations with TOP well capture the spatial heterogeneity of surface fluxes compared to Moderate Resolution Imaging Spectroradiometer remote sensing data. There are significant differences between TOP and PP in the 1‐km simulated surface energy balance, but the differences in mean values and standard deviations become small when aggregated to the grid scale (i.e., 0.5°). The T configuration better mimics the 1KM simulations with TOP than the D configuration and better captures the sub‐grid topographic effects on surface energy balance and boundary conditions. These results underline the importance of representing sub‐grid topographic heterogeneities in LSMs and motivate future research to understand the sub‐grid topographic effects on land‐atmosphere interactions over mountainous areas.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2000

A Catchment-Based Approach to Modeling Land Surface Processes in a GCM: Parameter Estimation and Model Demonstration - Part 2

The viability of a new catchment-based land surface model (LSM) developed for use with general circulation models is demonstrated. First, simple empirical functions -- tractable enough for operational use in the LSM -- are established that faithfully capture the control of topography on the subgrid variability of soil moisture and the surface water budget, as predicted by theory. Next, the full LSM is evaluated offline. Using forcing and validation datasets developed for PILPS Phase 2c, the minimally calibrated model is shown to reproduce observed evaporation and runoff fluxes successfully in the Red-Arkansas River Basin. A complementary idealized study that employs the range of topographic variability seen over North America demonstrates that the simulated surface water budget does vary strongly with topography, which can, by itself, induce variations in annual evaporation as high as 20%.

Ducharne, Agnes↗
DOE OSTI2023

Characterizing uncertainty in Community Land Model version 5 hydrological applications in the United States

Abstract Land surface models such as the Community Land Model Version 5 (CLM5) are essential tools for simulating the behavior of the terrestrial system. Despite the extensive application of CLM5, limited attention has been paid to the underlying uncertainties associated with its hydrological parameters and how these uncertainties affect water resource applications. To address this long-standing issue, we use five meteorological datasets to conduct a comprehensive hydrological parameter uncertainty characterization of CLM5 over the hydroclimatic gradients of the conterminous United States. Key datasets produced from the uncertainty characterization experiment include: a benchmark dataset of CLM5 default hydrological performance, parameter sensitivities for 28 hydrological metrics, and large-ensemble outputs for CLM5 hydrological predictions. The presented datasets will assist CLM5 calibration and support broad applications, such as evaluating drought and flood vulnerabilities. The datasets can be used to identify the hydroclimatological conditions under which parametric uncertainties demonstrate substantial effects on hydrological predictions and clarify where further investigations are needed to understand how hydrological prediction uncertainties interact with other Earth system processes.

54 ENVIRONMENTAL SCIENCES↗