Land Model Research Articles

Predicting post-fire hydrological and erosive catchment response during rainfall events. A comparison of OpenLISEM and MOHID Land models

Wildfires are a source of instability for the natural water cycle in forested watersheds, endangering the water quantity and quality reaching downstream water bodies. The faster hydrological response of a burned area leads to increased runoff and transport of sediment and ash particles during and after rainfall events. Therefore, the use of an adequate spatiotemporal resolution in hydrological models is necessary to properly estimate post-fire impacts. Especially when addressing hydrological events such as flash floods and debris flows, which are highly unpredictable and are characterized by short duration and high impact outside the burned area.This study aims to compare the ability of two hydrological models to simulate the hydrological response and sediment transport during the first year after a fire to ultimately understand which one would best serve as a post-fire hydrological predicting tool at event scale. To achieve this goal, OpenLISEM, an event-based hydrological model, and MOHID Land, a continuous model with variable timestep, were compared. Driven by several limitations identified in previous modeling exercises at this scale during the calibration phase, this work performed a parametrization through the variation in boundary conditions characterizing each event.OpenLISEM and MOHID Land models exhibited similar capabilities in simulating runoff during the first post-fire year. However, the larger erosion input parameters required by MOHID Land increase the complexity of erosion prediction and increase equifinality. In addition, MOHID Land limited capacity to perform sensitivity and uncertainty analyses emerged as a major disadvantage, hindering the assessment of the reliability of the model's predictions. Despite its limitations for not integrating subsurface flow and base flow, OpenLISEM is the most suitable model for assessing post-fire impacts on runoff and sediment production at the event scale, because of its ease of implementation and its reduced computational requirements.

Global 1 km land surface parameters for kilometer-scale Earth system modeling

Abstract. Earth system models (ESMs) are progressively advancing towards the kilometer scale (“k-scale”). However, the surface parameters for land surface models (LSMs) within ESMs running at the k-scale are typically derived from coarse-resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1 km land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31 % to 54 % of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSM and ESM modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. The 1 km land surface parameters are publicly available at https://doi.org/10.5281/zenodo.10815170 (Li et al., 2024).

Studying Urbanization Pattern in Sambalpur City during 1992-2042 using CA-ANN, and Markov-Chain Model

Models of land use/land cover (LULC) are crucial for assessing changes in LULC, forecasting land use needs, and providing guidance for sensible land use planning and management especially in urban areas. Urban sprawl is one of the main causes of the erratic variation in LULC around the globe. In this study, we used the CA-ANN and Markov-Chain models to analyse the LULC simulation based on LULC patterns of previous decades as well as the directional changes of urban expansion in Sambalpur city. We used the random forest (RF) model and Landsat imageries to prepare LULC maps for the years 1992, 2002, 2012 and 2022, for better classification accuracy. The result showed that the overall accuracy and kappa value were 94.24%, 89%, 94%, and 90% and 0.92, 0.80, 0.90 and 0.85, respectively for the selective years. Based on the transition matrix model (1992-2012), the LULC map of the year 2022 was obtained, and validated, and subsequently, LULC maps for the years 2032 and 2042 were predicted with a Kappa value of 94.97% and 93.24% respectively. The results revealed that the maximum amount of bare land converted in the settlement area, and the maximum sprawl occur in NW and 4km buffer zones. These findings define current and future patterns in LULC and offer vital information for planning and sustainable land use management in Sambalpur city.

Evaluating the impact of peat soils and snow schemes on simulated active layer thickness at pan-Arctic permafrost sites

Abstract Permafrost stability is significantly influenced by the thermal buffering effects of snow and active-layer peat soils. In the warm season, peat soils act as a barrier to downward heat transfer mainly due to their low thermal conductivity. In the cold season, the snowpack serves as a thermal insulator, retarding the release of heat from the soil to the atmosphere. Currently, many global land models overestimate permafrost soil temperature and active layer thickness (ALT), partially due to inaccurate representations of soil organic matter (SOM) density profiles and snow thermal insulation. In this study, we evaluated the impacts of SOM and snow schemes on ALT simulations at pan-Arctic permafrost sites using the Energy Exascale Earth System Model (E3SM) land model (ELM). We conducted simulations at the Circumpolar Active Layer Monitoring (CALM) sites across the pan-Arctic domain. We improved ELM-simulated site-level ALT using a knowledge-based hierarchical optimization procedure and examined the effects of precipitation-phase partitioning methods (PPMs), snow compaction schemes, and snow thermal conductivity schemes on simulated snow depth, soil temperature, ALT, and CO2 fluxes. Results showed that the optimized ELM significantly improved agreement with observed ALT (e.g. RMSE decreased from 0.83 m to 0.15 m). Our sensitivity analysis revealed that snow-related schemes significantly impact simulated snow thermal insulation levels, soil temperature, and ALT. For example, one of the commonly used snow thermal conductivity schemes (quadratic Sturm or SturmQua) generally produced warmer soil temperatures and larger ALT compared to the other two tested schemes. The SturmQua scheme also amplified the model’s sensitivity to PPMs and predicted deeper ALTs than the other two snow schemes under both current and future climates. The study highlights the importance of accurately representing snow-related processes and peat soils in land models to enhance permafrost dynamics simulations.

Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)

Abstract. Understanding carbon exchange processes between land reservoirs and the atmosphere is essential for predicting carbon–climate feedbacks. Still, considerable uncertainty remains in the representation of the terrestrial carbon cycle in Earth system models. An emerging strategy to constrain these uncertainties is to include the role of different microbial groups explicitly. Following this approach, we extend the framework of the MIcrobial-MIneral Carbon Stabilization (MIMICS) model with additional mycorrhizal groups and a nitrogen cycle that includes a novel representation of inorganic nitrogen sorption to particles via a Langmuir isotherm. MIMICS+ v1.0 is designed to capture and quantify relationships between soil microorganisms and their environment, with a particular emphasis on boreal ecosystems. We evaluated MIMICS+ against podzolic soil profiles in Norwegian forests as well as the conventional Community Land Model (CLM). MIMICS+ matched observed carbon stocks better than CLM and gave a broader range of C:N ratios, more in line with observations. This is mainly explained by a higher directly plant-derived fraction into the soil organic matter (SOM) pools. The model produces microbial biomass estimates in line with numbers reported in the literature. MIMICS+ also showed better representation of climate gradients than CLM, especially in terms of temperature. To investigate responses to changes in nutrient availability, we performed an N enrichment experiment and found that nitrogen sorbed to particles through the sorption algorithm served as a long-term storage of nutrients for the microbes. Furthermore, although the microbial groups responded considerably to the nitrogen enrichment, we only saw minor responses for carbon storage and respiration. Together, our results present MIMICS+ as an attractive tool for further investigations of interactions between microbial functioning and their (changing) environment.

Optimizing Parameters in the Common Land Model by Using Gravity Recovery and Climate Experiment Satellite Observations

Terrestrial water storage (TWS) is pivotal in understanding environmental dynamics, climate change, and human impacts. Despite the utility of land surface models, uncertainties persist in their parameterization schemes. This study employs GRACE (Gravity Recovery and Climate Experiment) satellite data to optimize the runoff parameterization scheme within the Common Land Model by a data assimilation and parameter optimization method. The optimization algorithm sets an adjustment factor that varies with time and space for runoff simulation and updates it along with the running of the land surface model. The evaluation reveals that there are improved correlation coefficients and reduced root mean square errors compared to GRACE observations. Independent assessments by using in situ river discharge observations demonstrate enhanced model performance, particularly in mountainous regions such as western North America. This study underscores the efficacy of integrating GRACE data to improve land surface model parameterization, offering more accurate predictions of TWS changes.

The Respective Effects of Vapor Pressure Deficit and Soil Moisture on Ecosystem Productivity in Southwest China

This study aims to examine the individual and combined effects of soil moisture (SM) and vapor pressure deficit (VPD) on ecosystem productivity in Southwest China. Utilizing the community land model (CLM) to simulate the regional soil moisture and vapor pressure deficit, we analyzed their impacts on ecosystem productivity through a data binning approach and employed sun-induced chlorophyll fluorescence yield (SIFyield) as a productivity indicator. Our findings highlight a significant coupling effect between SM and VPD, which diminishes with finer temporal data resolution. The data binning analysis indicates that VPD has a predominant influence on SIFyield across 70% of the study area, whereas SM is more influential in the remaining 30%. Notably, the correlation between SIFyield and SM, modulated by VPD, is stronger in forest and shrubland ecosystems, whereas in grasslands, the influence pattern is reversed, with VPD having a more significant impact. The study concludes that in Southwest China, ecosystem productivity is more significantly affected by VPD than by SM.

Enabling Advanced Snow Physics Within Land Surface Models Through an Interoperable Model‐Physics Coupling Framework

AbstractAccurate estimation of snow accumulation and melt is a critical part of decision‐making in snow‐dominated watersheds. In this study, we demonstrate a flexible methodology to couple a detailed snow model, Crocus, separately to two different land surface models (LSMs), Noah‐MP and Noah. The original LSMs and the coupled models (Noah‐MP‐Crocus and Noah‐Crocus) are used to simulate snow depth, snow water equivalent, and other water and energy states and fluxes. The results of simulations are compared against a wide range of independent gridded and point scale reference data sets. Our results show that coupling the detailed snow model, Crocus, with the LSMs improves the snow depth and snow water equivalent relative to independent observations. Overall, larger improvements are obtained with coupling Crocus to the Noah LSM, with the coupled Noah‐Crocus configuration reducing the RMSE and bias of snow depth from 2% to 12% and 57% to 75%, respectively, relative to Snow Data Assimilation System (SNODAS) and snow product from the University of Arizona. On the other hand, smaller improvements are obtained by coupling Crocus with Noah‐MP. The Coupled Noah‐MP‐Crocus reduces the snow depth bias but slightly degrades the RMSE of snow depth and snow water equivalent. The corresponding impacts in other water budget terms such as evapotranspiration, soil moisture, and streamflow, however, are mixed, pointing to the significant need to improve the coupling assumptions of these processes within land models. Overall, the interoperable coupling framework demonstrated here offers the opportunity to include more detailed snow physics and processes, and to advance data assimilation systems through improved exploitation of information from snow remote sensing instruments.

Integrating Tide‐Driven Wetland Soil Redox and Biogeochemical Interactions Into a Land Surface Model

AbstractRedox processes, aqueous and solid‐phase chemistry, and pH dynamics are key drivers of subsurface biogeochemical cycling and methanogenesis in terrestrial and wetland ecosystems but are typically not included in terrestrial carbon cycle models. These omissions may introduce errors when simulating systems where redox interactions and pH fluctuations are important, such as wetlands where saturation of soils can produce anoxic conditions and coastal systems where sulfate inputs from seawater can influence biogeochemistry. Integrating cycling of redox‐sensitive elements could therefore allow models to better represent key elements of carbon cycling and greenhouse gas production. We describe a model framework that couples the Energy Exascale Earth System Model (E3SM) Land Model (ELM) with PFLOTRAN biogeochemistry, allowing geochemical processes and redox interactions to be integrated with land surface model simulations. We implemented a reaction network including aerobic decomposition, fermentation, sulfate reduction, sulfide oxidation, methanogenesis, and methanotrophy as well as pH dynamics along with iron oxide and iron sulfide mineral precipitation and dissolution. We simulated biogeochemical cycling in tidal wetlands subject to either saltwater or freshwater inputs driven by tidal hydrological dynamics. In simulations with saltwater tidal inputs, sulfate reduction led to accumulation of sulfide, higher dissolved inorganic carbon concentrations, lower dissolved organic carbon concentrations, and lower methane emissions than simulations with freshwater tidal inputs. Model simulations compared well with measured porewater concentrations and surface gas emissions from coastal wetlands in the Northeastern United States. These results demonstrate how simulating geochemical reaction networks can improve land surface model simulations of subsurface biogeochemistry and carbon cycling.

Soil classification and prediction model for critical land on the slopes of Mount Raung in Indonesia

Indonesia is in the ring of fire, which has an impact on the characteristics of volcanoes, one of which is Mount Raung. Mount Raung, part of the Iyang-Argopura mountains, has a role in influencing the development of soil types and the environment; it was recorded in 1586, 1987, 1597, and 1638. Soil types and environmental parameters have an influence on critical land conditions. Remote sensing technology has been used in various fields, one of which is land evaluation. This study examined the distribution of soil characteristics and the mapping of critical lands through remote sensing approaches. The method used to identify the morphological characteristics of soil classification and the potential for critical land was a descriptive-exploratory method. The results of the research on soil types are divided into three orders, namely Andisols, Inceptisols, and Alfisols. The three land orders are divided into nine great groups, with Lithic Eutrudepts having the highest area of 38.02%, followed by Typic Hapludalfs (21.70%), Typic Eutrudepts (9.79%), Typic Epiaquepts (7.84%), Aquic Eutrudepts (7.71%), Aquic Eutrudepts (5.64%), Fluventic Epiaquepts (5.30%), Typic Udivitrands (2.16%), and Vitric Hapludands (1.83%). Critical land based on the analysis of five factors of erosion, land use, slope, rainfall, and soil canopy density, is divided into four criteria i.e., critical area of 895.88 ha, medium critical 9,027.69 ha, and lightly critical of 14,096.89 ha. Land use, slope, and plant canopy density play a major role in the potential for critical land with a strong-very-strong level of closeness (0.350-0.610).

Enhancing Urban Climate‐Energy Modeling in the Community Earth System Model (CESM) Through Explicit Representation of Urban Air‐Conditioning Adoption

AbstractImproved representation of urban processes in Earth System Models (ESMs) is a pressing need for climate modeling and climate‐driven urban energy studies. Despite recent improvements to its fully coupled Building Energy Model (BEM), the current Community Land Model Urban (CLMU) in the Community Earth System Model (CESM) lacks the infrastructure to model air‐conditioning (AC) adoption explicitly. This undermines CESM's fidelity in modeling urban climate and energy use, and limits its use in climate and energy risk assessments. Here, we establish a new parameterization scheme in CESM that represents AC adoption explicitly through an AC adoption rate parameter in the BEM of CLMU, and build a present‐day, global, survey‐based, and spatially explicit AC adoption rate data set at country and sub‐country level that is integrated within CESM. The new data set can be leveraged for other ESMs or global‐scale models and analyses. The explicit AC adoption scheme and the AC adoption rate data set significantly improve the accuracy of anthropogenic heat modeling due to AC in CESM. The new parameterization scheme makes it possible to evaluate the effects of changing AC adoption on global urban energy and climate using CESM. These developments enhance CESM in its use for climate impact assessments under future climate and socioeconomic development scenarios, and represent continued efforts in better representing urban processes and coupled human‐urban‐Earth dynamics in ESMs.

Экологическая типизация сельскохозяйственных земель и моделирование устойчивых агроландшафтов на локальном уровне

The purpose of the study: carry out an ecological typification of the territory of agricultural land use and model the sustainability of agricultural landscapes by assessing agro-ecological factors. Research methods: general scientific methods were used: observation, description, comparative, historical, modeling. When studying the structure, system analysis was used, analysis and synthesis methods were used to identify agro-ecologically homogeneous areas and territories of the same type, cartographic methods were used to zoning and compile cartographic material, and information analysis was used to model agricultural landscapes. Research results. A study of the agricultural lands of an economic entity with an area of more than 87 km2 showed that it consists of five landscape areas that are universally susceptible to degradation processes. As a result of a comprehensive analysis of agricultural landscapes, factors limiting the productivity of agricultural crops were identified. The agro-ecological assessment of landscapes made it possible to typify agricultural land and zone the territory. For each agro-ecotype of land, a complex of agro-, phyto-, reclamation measures has been developed aimed at increasing the sustainability of agricultural landscapes, as well as land use models that allow predicting the use of land resources for a multi-term perspective. Conclusions. 1. The scientific article presents the results of a study of the agro-ecological state of agricultural land use through agro-ecological typification of land, carried out on the basis of an analysis of limiting factors and conditions affecting the adaptive potential of agricultural plants. 2. It has been established that the agricultural landscapes of the study area are actively used in agricultural production (mainly crop production) and are everywhere subject to degradation processes. It was determined that land use includes five landscape areas that differ significantly in area and morphological characteristics. The ecological classification of lands at the local level (level of an economic entity) is shown, highlighting groups, subgroups, classes, subclasses, genera and subgenera. Limiting agro-ecological factors and conditions inherent in the study area were identified and its zoning was carried out. It was found that the agro-ecological state of the territory (assessed using more than ten indicators) differs significantly among agricultural groups. The assessment of the agro-ecological state made it possible to draw up a unique map-scheme of land agro-ecotypes and develop a set of agro-, phyto-, and reclamation measures aimed at optimizing agricultural land use. Three models of agricultural land use aimed at increasing the sustainability of agricultural landscapes are presented, with calculated forecast characteristics. 3. The results of scientific research can be used in the organization, monitoring, protection of agricultural lands and management of the sustainability of agricultural landscapes at the local level.

Quantifying the long-term changes of terrestrial water storage and their driving factors

Global warming is expected to cause changes in terrestrial water storage (TWS) across the land surface, with widespread impacts on ecosystems and society. Although extensive research has been performed to analyze TWS changes and possible drivers during the post-2000 period, longer-term evolution of TWS and associated environmental forcings remain relatively unexplored. In this study, we evaluated the performance of the Energy Exascale Earth System model (E3SM) land model ELM version 1 (ELM v1) in simulating global TWS, and used factorial simulations of ELMv1 to quantify global TWS changes and their drivers during 1948–2012. We found that ELM’s agreed best with existing satellites and reconstruction datasets in temperate regions unaffected by irrigation. Biome- and climate zone-averaged TWS mainly increased at rates between 0 and 10 mm/year over 1948–2012, but the second half of that period saw smaller positive trends than the first half or even negative trends. Climate change explained >80 % of the TWS trends across most biomes and climate zones, followed by land use and land cover change. The physiological and phenological effects of CO2 primarily induced noticeable TWS trends in the more humid biomes and climate zones across different latitudes. In contrast, nitrogen deposition and aerosol deposition generally had smaller and negative impacts across the biomes and climate regions. Among the meteorological drivers analyzed, the long-term average imbalance between precipitation (P), evapotranspiration (E), and runoff (Q) contributed >50 % of the TWS trends in most biomes and climate zones, with nonlinearity being induced by spatially heterogenous changes in E/P and Q/P ratios. The accumulated detrended anomalies in P, E, and Q also often contributed substantially, while the trends difference between P, E, and Q contributed little. Together, these findings unveiled an intensification of the global TWS and its diverse patterns of climate change and different non-withdrawal human-induced alterations, contributing to a more comprehensive understanding and projection of the global water cycle.

An optimal ensemble of the CoLM for simulating the carbon and water fluxes over typical forests in China

Stomatal conductance (gs) and compensatory water uptake (CWU) are crucial processes in land surface models, as they directly influence the exchange of carbon and water fluxes between terrestrial ecosystems and the atmosphere. In this study, we integrated a new stomatal scheme derived from optimal stomatal theory (Medlyn's gs model), and an empirical CWU scheme into the Common Land Model (CoLM). Assessing the impacts on modeling gross primary productivity (GPP) and latent flux (LE) through observations obtained from eddy covariance (EC) measurements at three forest sites in China. Our results show that replacing the Ball-Berry's gs model (termed BB) with Medlyn's gs model (termed MED) did not bring about significant changes (had neutral impacts) in the performance of CoLM simulations at three forest sites. Considering the climate factors of annual mean precipitation to optimize key fitting parameters in gs exhibited improvement in model simulations. The average coefficient of determination (R2) achieved to 0.65 for GPP and LE at three sites, and the normalized root mean squared error (NRMSE) decreased from 0.83 to 0.77 at those sites. Besides, incorporating CWU into the model improved its performance. The R2 increased to 0.84 and RMSE decreased to 4.84 μmol m−2 s−1 for GPP, and the R2 increased to 0.62 and RMSE decreased to 55.64 W m−2 for LE. Therefore, modifying the model process of both contributed more to enhancing the model simulations than relying solely on one of these functions. Our study highlights that the response of plant functional types (PFTs) to water stress can be effectively represented in gs models when coupled with biochemical capacity to quantify carbon and water fluxes in forest ecosystems or other ecosystems.

An Unstructured Mesh Generation Tool for Efficient High‐Resolution Representation of Spatial Heterogeneity in Land Surface Models

AbstractThis study develops an unstructured mesh generation tool for land surface modeling using a multi‐scale hexagon discrete mesh. The tool can automatically determine the required mesh resolution for different regions based on multi‐objective criteria such as elevation, slope, land cover, and land use. The refined unstructured meshes demonstrate significant enhancement in the representation of spatial heterogeneity. The tool is coupled with the Common Land Model (CoLM) to enable land surface simulations using unstructured meshes. Evaluations focused on runoff, river discharge, and inundation indicate improved model performance compared to traditional structured mesh‐based CoLM simulations under the same computational cost constraints. This tool provides new capabilities for more efficiently capturing localized land surface processes and extreme events.

Impacts of Compound Hot–Dry Events on Vegetation Productivity over Northern East Asia

Climate extremes, such as heatwaves and droughts, significantly impact terrestrial ecosystems. This study investigates the influence of compound hot–dry (CHD) events on vegetation productivity in northern East Asia. Four of the most widespread CHD events occurring during the summer from 2003 to 2019 were selected as the focus of this research. We first verified the performance of the Community Land Model version 5 (CLM5) in the region and then conducted factor-controlled experiments using CLM5 to assess the effects of different climate factors on gross primary productivity (GPP) changes during CHD events. Our results show that vegetation productivity exhibits greater sensitivity to CHD events within the transitional climatic zone (TCZ) than in other affected areas. In grassland areas within the TCZ, precipitation deficit is the primary factor leading to the decrease in GPP (explaining 56%–90% of GPP anomalies), while high temperatures serve as a secondary detrimental factor (explaining 13%–32% of GPP anomalies). In high-latitude forests outside the TCZ, high temperature has a more significant impact on suppressing GPP, while the decrease in soil moisture has a synchronously negligible impact on GPP. There are differences in the effects of high solar radiation on grasslands and woodlands during CHD events. It was observed that high radiation benefits trees by increasing the maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax), as well as enhancing photosynthesis, but has a negligible impact on grasses. Furthermore, this study highlights the potential for compound events to impact vegetation productivity more than expected from individual events due to confounding nonlinear effects between meteorological factors. More than 10% of the negative anomalies in GPP during two CHD events in 2017 and 2010 were attributed to these nonlinear effects. These research findings are significant for understanding ecosystem responses to climate extremes and their influence on carbon cycling in terrestrial ecosystems. They can also contribute to more precisely evaluating and predicting carbon dynamics in these regions.

Integrating NDVI-Based Within-Wetland Vegetation Classification in a Land Surface Model Improves Methane Emission Estimations

Earth system models (ESMs) are a common tool for estimating local and global greenhouse gas emissions under current and projected future conditions. Efforts are underway to expand the representation of wetlands in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) by resolving the simultaneous contributions to greenhouse gas fluxes from multiple, different, sub-grid-scale patch-types, representing different eco-hydrological patches within a wetland. However, for this effort to be effective, it should be coupled with the detection and mapping of within-wetland eco-hydrological patches in real-world wetlands, providing models with corresponding information about vegetation cover. In this short communication, we describe the application of a recently developed NDVI-based method for within-wetland vegetation classification on a coastal wetland in Louisiana and the use of the resulting yearly vegetation cover as input for ELM simulations. Processed Harmonized Landsat and Sentinel-2 (HLS) datasets were used to drive the sub-grid composition of simulated wetland vegetation each year, thus tracking the spatial heterogeneity of wetlands at sufficient spatial and temporal resolutions and providing necessary input for improving the estimation of methane emissions from wetlands. Our results show that including NDVI-based classification in an ELM reduced the uncertainty in predicted methane flux by decreasing the model’s RMSE when compared to Eddy Covariance measurements, while a minimal bias was introduced due to the resampling technique involved in processing HLS data. Our study shows promising results in integrating the remote sensing-based classification of within-wetland vegetation cover into earth system models, while improving their performances toward more accurate predictions of important greenhouse gas emissions.

Simulation Performance of Temperature and Precipitation in the Yangtze River by Different Cumulus and Land Surface Schemes in RegCM4

To improve the simulation performance of the RegCM4 model in climate simulations over the Yangtze River Basin (YRB), it is essential to determine the optimal cumulus convection and land surface process schemes from the numerous physical parameterization options within RegCM4. In this study, we selected five cumulus convection schemes (Kuo, Grell, Emanuel, Tiedtke, and Kain–Fritsch) and three land surface process schemes (BATS, CLM3.5, and CLM4.5) to configure 72 mixed schemes. Four years of short-term simulations (1990–1993) with a horizontal resolution of 50 km were conducted using ERA-Interim as the initial and boundary conditions for the 72 schemes. The climate simulation performance of all schemes in the YRB was comprehensively evaluated using a multi-criteria scoring approach. The results indicate that among the selected cumulus convection schemes, the Kain–Fritsch scheme, applied to both ocean and land, demonstrates optimal performance in simulating precipitation over the YRB, with spatial correlation coefficients between simulated and observed annual precipitation around 0.3. Compared to the Community Land Models (CLM3.5 and CLM4.5), BATS exhibits superior capabilities in reproducing the temperature features of the region, with spatial correlation coefficients between simulated and observed values typically exceeding 0.99 and standard deviations within 1.25 °C. Under the optimal KF scheme, the simulated soil moisture in the YRB using CLMs is notably drier, ranging from −7.79 to −8.39 kg/m2, compared to that achieved with BATS. The findings provide a localized reference for the parameterization schemes of RegCM4 in the YRB.

Nutrient Dynamics in a Coupled Terrestrial Biosphere and Land Model (ELM‐FATES‐CNP)

AbstractWe present a representation of nitrogen and phosphorus cycling in the Functionally Assembled Terrestrial Ecosystem Simulator, a demographic vegetation model within the Energy Exascale Earth System land model. This representation is modular, and designed to allow testing of multiple hypothetical approaches for carbon‐nutrient coupling in plants. Novel model hypotheses introduced in this work include, (a) the controls on plant acquisition of aqueous mineralized nutrients in the soil and (b) fairly straight forward methods of allocating nutrients to specific plant organs and their losses through live plant turnover as well as litter fluxes generated through plant mortality. This combines the new with pre‐existing hypotheses (such as nitrogen fixation and soil decomposition) into a system that can accommodate plant‐soil dynamics for a large number of size‐ and functional‐type‐resolved plant cohorts within a time‐since‐disturbance‐resolved ecosystem. Root uptake of nutrients is governed by fine root biomass, and plants vary in their fine root biomass allocation in order to balance carbon and nutrient limitations to growth. We test the sensitivity of the model to a wide range of parameter variations and structural representations, and in the context of observations at Barro Colorado Island, Panama. A key model prediction is that plants in the high‐light‐availability canopy positions allocate more carbon to fine roots than plants in low‐light understory environments, given the widely different carbon versus nutrient constraints of these two niches within a given ecosystem. This model provides a basis for exploring carbon‐nutrient coupling with vegetation demography within Earth system models.

Land Data Assimilation: Harmonizing Theory and Data in Land Surface Process Studies

AbstractData assimilation plays a dual role in advancing the “scientific” understanding and serving as an “engineering tool” for the Earth system sciences. Land data assimilation (LDA) has evolved into a distinct discipline within geophysics, facilitating the harmonization of theory and data and allowing land models and observations to complement and constrain each other. Over recent decades, substantial progress has been made in the theory, methodology, and application of LDA, necessitating a holistic and in‐depth exploration of its full spectrum. Here, we present a thorough review elucidating the theoretical and methodological developments in LDA and its distinctive features. This encompasses breakthroughs in addressing strong nonlinearities in land surface processes, exploring the potential of machine learning approaches in data assimilation, quantifying uncertainties arising from multiscale spatial correlation, and simultaneously estimating model states and parameters. LDA has proven successful in enhancing the understanding and prediction of various land surface processes (including soil moisture, snow, evapotranspiration, streamflow, groundwater, irrigation and land surface temperature), particularly within the realms of water and energy cycles. This review outlines the development of global, regional, and catchment‐scale LDA systems and software platforms, proposing grand challenges of generating land reanalysis and advancing coupled land‒atmosphere DA. We lastly highlight the opportunities to expand the applications of LDA from pure geophysical systems to coupled natural and human systems by ingesting a deluge of Earth observation and social sensing data. The paper synthesizes current LDA knowledge and provides a steppingstone for its future development, particularly in promoting dual driven theory‐data land processes studies.