Land Surface Model Simulations Research Articles

Anthropogenic coal mining reducing groundwater storage in the Yellow River Basin.

Anthropogenic coal mining and water consumption affect groundwater storage (GWS) and impose substantial pressure on water resources. However, the responses and mechanisms of GWS to these activities and climate change are not well understood. In this study, the impacts of anthropogenic coal mining, water consumption, vegetation restoration and climate change on GWS in the Yellow River Basin (YRB) of China were quantitatively assessed, based on the Gravity Recovery and Climate Experiment (GRACE) satellite data, land surface model simulations, Random Forest (RF) model, normalized vegetation index data, the statistical information in yearbooks and bulletins, and in situ observations. The results revealed a significant decline at a rate of -7.52mm⋅year-1 in GWS from 2003 to 2020, with the most pronounced decreasing trends in the middle and lower reaches of the YRB. Seventy-five percent of the regions with a gain in GWS showed an increasing trend in the net water flux (precipitation minus evapotranspiration). The RF model results show that water consumption by coal mining is the main factor influencing changes in GWS in the eastern and northern coal mining areas of the basin, with importance scores of 50-53. Significant vegetation restoration increased groundwater drought in the YRB, especially in the growing season. This study provides valuable scientific insights for formulating water resource management policies in the YRB.

Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations

AbstractOffline 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.

An Evaluation of Evapotranspiration Products over the Tibetan Plateau

Abstract In situ observations from 13 sites located over the Tibetan Plateau (TP) are used to evaluate evapotranspiration (ET) products, including remote sensing–based, land surface–modeled, and reanalysis products. It is found that the Global Land Surface Satellite (GLASS) product, the Global Land Evaporation Amsterdam Model (GLEAM) product, and the simulations by the Community Land Model–dynamic global vegetation model [biogeochemical dynamic vegetation (CLM–BGCDV)] are the top-ranked products measured by the percentage bias, root-mean-square error, and correlation coefficient against in situ observations. The evaluated data are then used to examine the consistency in spatial and temporal variability of summer ET and its controlling factors on the TP and the three-river source region (TRSR). All products show consistently that precipitation in the central semiarid part of TP is the dominant factor influencing summer ET, while air temperature plays a certain role in the southeastern and eastern TP. Uncertainties exist in the western TP, possibly due to the lack of observations or gaps in the satellite data. Some suggestions for improving ET product development based on models and satellite retrievals, particularly for the cold and complex surface of the TP, are also given. Significance Statement Evapotranspiration (ET) is a crucial water cycle component, but accurately assessing it remains difficult. In situ measurements are scarce, while other products have large uncertainties, such as satellite remote sensing retrievals, land surface model simulations, and reanalyses. This is particularly a problem for the Tibetan Plateau (TP). With some considerable effort, ET fluxes from 13 sites and reasonable length in the TP have become available. These in situ data are used to evaluate gridded ET products. Based on an overall score, they are ranked as the Global Land Surface Satellite (GLASS) product, the Global Land Evaporation Amsterdam Model (GLEAM) product, and the model simulation by the Community Land Model–dynamic global vegetation model [biogeochemical dynamic vegetation (CLM–BGCDV)]. Then, we used these top-ranked products to detect the spatial and trend consistencies and the environmental impacts to further discuss the product conformance. It was shown that all products show precipitation dominated the summer ET variation in the semiarid region in the central TP, while air temperature had more impact on the relatively humid region in the east and southeast of the TP. Uncertainties exist in the west TP possibly due to the lack of observations in the assimilation product or missing gaps in the satellite product. Specifically, we conclude that dynamic vegetation could play an increasing role under the background of climate warming, which should be considered in the ET model–based product generation. The suggestions for ET generation based on models and satellite retrievals, particularly for the special cold and complex surface of the TP, are also provided.

Enhanced relationship between seasonal soil moisture droughts and vegetation under climate change over China

Climate change could intensify seasonal soil moisture droughts and subsequently degrade vegetation, but it can also facilitate vegetation growth with the rising temperature and CO2 concentrations. Therefore, the change of the vegetation-drought relationship under climate change remains elusive. Here, we investigate the effects of vegetation greening (i.e., increasing leaf area index (LAI)) on seasonal soil moisture drought trends by performing a set of land surface model simulations with the Conjunctive Surface-Subsurface Process model version 2 (CSSPv2), as well as the impact of seasonal soil moisture drought change on vegetation productivity via a statistical fitting model. Our results indicate that both the impact of increasing LAI on exacerbating seasonal droughts and the impact of increasing seasonal droughts on reducing vegetation productivity have been enhanced over China during 1961–2018. Specifically, increased LAI accounts for 48 % of the rising drought frequency through escalated evapotranspiration losses, with more pronounced effects in northern semiarid regions. The decrease in vegetation stomatal conductance caused by rising CO2 concentrations limits transpiration and alleviates 11 % of the increase in drought frequency, but it cannot fully offset the drought aggravation induced by increased vegetation water consumption under climate warming. Meanwhile, increased drought frequency and intensity have reduced the upward trend in vegetation productivity by 5 % over China, and by 8 % in water-limited northern regions. Our study shows the relationship between seasonal soil moisture droughts and vegetation over China has strengthened, indicating that vegetation greening will lead to increased water shortages and further impact vegetation growth and carbon sequestration. This challenges water resources management and environmental sustainability, especially in semiarid regions.

Assessing drought conditions in Northeast Brazil: A comparative analysis of soil moisture, groundwater, and total water storage

Study regionNortheast Brazil (NEB). Study focusOver the past few decades, there has been a noticeable surge in the applications of drought indices centered around Total Water Storage (TWS) and its components. This study aims to evaluate the dynamics of continental water storage and its components in Northeast Brazil (NEB), utilizing drought indices based on soil moisture (SMI), groundwater (GWI) and TWS (WSDI) for the period 2003–2022. Four types of data were employed: remote sensing (ESA CCI SM and GRACE), in-situ data (SM and GW storage), land surface model simulations, and global Standardized Precipitation Evapotranspiration Index (SPEI). The first two served as the reference for comparison with the SM, GW, and TWS simulated by the Catchment Land Surface Model (CLSM).New hydrological insights for the region: Drought indices based on CLSM data from the Global Land Data Assimilation System (GLDAS) can be considered reliable after comparison with SPEI and analysis of their patterns using continuous wavelet transformation (CWT). The SMI is more correlated with the SPEI-3 (3-month time scale), and the GWI presented better correlations with the time scales between 12 and 36 months of the SPEI. Changes in the trend of the indices were detected in the hydrographic regions localized in the south of the study area, indicating an increase in drought occurrence. The calculation of the TWS (doing the sum of SM and GW from the CLSM) fitted well with the GRACE data.

Warming-induced soil moisture stress threatens food security in India.

Soil moisture (SM) interconnects various components of the Earth system and drives the land-atmosphere feedbacks and food production. However, around 40% of global vegetated land experiences SM drying. India is one of the global hotspots of land-atmosphere interactions and an extensively agrarian economy, but underexplored in terms of SM dynamics and its ramifications on food security. Here, we examine the mechanism of SM drying and its implications on cropland productivity in India based on remote sensing measurements and land surface model simulations in recent decades (2000-2019) and future projection of the 21st century. We find SM reduction predominantly in monsoon (4.5%) and winter (3%) seasons that areinthemajor agricultural seasonsofKharif and Rabi, respectively. Machine learning (ML)-based random forest (RF) reveals that temperature (T, 30.76%) is the dominant driver of SM variability, and then precipitation (P, 26.34%), evapotranspiration (ET, 26.08%) and surface greenness (16.82%). Concurrently, India experiences severe warming in terms of land (0.59 ℃/dec), soil (0.48 ℃/dec) and soil heat flux (SHF, 0.16 W/m2/dec) during 2000-2019. Partial correlation analysis between SM and T limiting the influence of P reveals a strong negative (> - 0.5) relationship in the agriculture intensive regions of Indo-Gangetic Plain (IGP) and South India (SI). Drying owing to warming and increased SHF, termed as warming-induced moisture stress, reduces gross primary productivity (GPP) (i.e. browning) and yield of major food crops of wheat, rain-fed rice, maize and soyabean, predominantly in SI and eastern IGP. Granger Causality shows that warming-induced soil moisture stress has a maximum temporal lag of 1month. In a warming world, the ever-growing population demands more food, and therefore, the warming-induced soil moisture stress is a serious threat to food security in India and similar agro-climatic regions of the world. This calls for climate-resilient agriculture, better agronomic management, improved irrigation and adoption of water-efficient crops.

Estimates of net primary productivity and actual evapotranspiration over the Tibetan Plateau from the Community Land Model version 4.5 with four atmospheric forcing datasets

Abstract The accuracy of the simulation of carbon and water processes largely relies on the selection of atmospheric forcing datasets when driving land surface models (LSM). Particularly in high-altitude regions, choosing appropriate atmospheric forcing datasets can effectively reduce uncertainties in the LSM simulations. Therefore, this study conducted four offline LSM simulations over the Tibetan Plateau (TP) using the Community Land Model version 4.5 (CLM4.5) driven by four state-of-the-art atmospheric forcing datasets. The performances of CRUNCEP (CLM4.5 model default) and three other reanalysis-based atmospheric forcing datasets (i.e. ITPCAS, GSWP3 and WFDEI) in simulating the net primary productivity (NPP) and actual evapotranspiration (ET) were evaluated based on in situ and gridded reference datasets. Compared with in situ observations, simulated results exhibited determination coefficients (R2) ranging from 0.58 to 0.84 and 0.59 to 0.87 for observed NPP and ET, respectively, among which GSWP3 and ITPCAS showed superior performance. At the plateau level, CRUNCEP-based simulations displayed the largest bias compared with the reference NPP and ET. GSWP3-based simulations demonstrated the best performance when comprehensively considering both the magnitudes and change trends of TP-averaged NPP and ET. The simulated ET increase over the TP during 1982–2010 based on ITPCAS was significantly greater than in the other three simulations and reference ET, suggesting that ITPCAS may not be appropriate for studying long-term ET changes over the TP. These results suggest that GSWP3 is recommended for driving CLM4.5 in conducting long-term carbon and water processes simulations over the TP. This study contributes to enhancing the accuracy of LSM in water–carbon simulations over alpine regions.

Quantifying the Hydrological Impacts of Irrigation on a Mediterranean Agricultural Context Through Explicit Satellite‐Derived Irrigation Estimates

AbstractWater budget over anthropized basins is profoundly altered by human interventions, among which irrigation prevails. This study investigates how such water use affects hydrological flux and state variables by comparing two SURFEX/ISBA (SURFace EXternalisée/Interaction Sol Biosphère Atmosphère) Land Surface Model simulations performed over a portion of the Ebro basin (Spain) that includes a heavily irrigated area. The first simulation is forced by atmospheric input only, while the second one is fed with atmospheric forcing plus remote sensing‐derived irrigation amounts added to the liquid precipitation. Four main hydrological output variables are compared under the two configurations: surface soil moisture (SM), total evaporative flux (i.e., the sum of components from bare soil and vegetation) (E), drainage, and runoff. Results indicate that SM and E are the variables mostly impacted during the main watering season. Considering mean daily rates per each month, a maximum increase of +30% and of +220% is found over the irrigation districts in July for SM and E, respectively. The reliability of the results is supported by comparisons with satellite evapotranspiration data from MODIS (MODerate‐resolution Imaging Spectroradiometer). The inclusion of irrigation reduces the RMSE between monthly anomalies of modeled E and of MODIS reference data by 41% during the main watering season. Further validation through wavelet coherence analysis is also proposed. This study sheds light on the potential of explicit satellite‐derived irrigation water use data that, when integrated into modeling platforms, allow us to track and quantify anthropogenic impacts on water resources.

Evaluating Noah‐MP Simulated Runoff and Snowpack in Heavily Burned Pacific‐Northwest Snow‐Dominated Catchments

AbstractTerrestrial hydrology is altered by fires, particularly in snow‐dominated catchments. However, fire impacts on catchment hydrology are often neglected from land surface model (LSM) simulations. Western U.S. wildfire activity has been increasing in recent decades and is projected to continue increasing over at least the next three decades, and thus it is important to evaluate if neglecting fire impacts in operational land surface models (LSMs) is a significant error source that has a noticeable signal among other sources of uncertainty. We evaluate a widely used state‐of‐the‐art LSM (Noah‐MP) in runoff and snowpack simulations at two representative fire‐affected snow‐dominated catchments in the Pacific Northwest: Andrew's Creek in Washington and Johnson Creek in Idaho. These two catchments are selected across all western U.S. fire‐affected catchments because they are snow‐dominated and experienced more than 50% burning in a single fire event with minimal burning outside of this event, which allows analyses of distinct pre‐ and post‐fire periods. There are statistically significant shifts in model skills from pre‐to post‐fire years in simulating runoff and snowpack. At both study catchments, simulations miss enhancements in early‐spring runoff and annual runoff efficiency during post‐fire years, resulting in persistent underestimates of annual runoff anomalies throughout the 12‐year post‐fire analysis periods. Enhanced post‐fire snow accumulation and melt contributes to observed but unmodeled increases of spring runoff and annual runoff efficiency at these catchments. Informing simulations with satellite observed land cover classifications, leaf area index, and green fraction do not consistently improve the model ability to simulate hydrologic responses to fire disturbances.

Snow water equivalent retrieved from X- and dual Ku-band scatterometer measurements at Sodankylä using the Markov Chain Monte Carlo method

Abstract. Radar at high frequency is a promising technique for fine-resolution snow water equivalent (SWE) mapping. In this paper, we extend the Bayesian-based Algorithm for SWE Estimation (BASE) from passive to active microwave (AM) application and test it using ground-based backscattering measurements at three frequencies (X and dual Ku bands; 10.2, 13.3, and 16.7 GHz), with VV polarization obtained at a 50° incidence angle from the Nordic Snow Radar Experiment (NoSREx) in Sodankylä, Finland. We assumed only an uninformative prior for snow microstructure, in contrast with an accurate prior required in previous studies. Starting from a biased monthly SWE prior from land surface model simulation, two-layer snow state variables and single-layer soil variables were iterated until their posterior distribution could stably reproduce the observed microwave signals. The observation model is the Microwave Emission Model of Layered Snowpacks 3 and Active (MEMLS3&a) based on the improved Born approximation. Results show that BASE-AM achieved an RMSE of ∼ 10 cm for snow depth and less than 30 mm for SWE, compared with the RMSE of ∼ 20 cm snow depth and ∼ 50 mm SWE from priors. Retrieval errors are significantly larger when BASE-AM is run using a single snow layer. The results support the potential of X- and Ku-band radar for SWE retrieval and show that the role of a precise snow microstructure prior in SWE retrieval may be substituted by an SWE prior from exterior sources.

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.

Increased atmospheric water stress on gross primary productivity during flash droughts over China from 1961 to 2022

Flash droughts threaten ecosystems substantially because of the fast onset and low predictability. Soil and atmospheric water stress are two main factors reducing ecosystem productivity during flash droughts. However, the long-term trends in the soil and atmospheric water stress on vegetation during flash droughts are unclear. By conducting long-term land surface model simulations, this study investigated the impact of atmospheric and soil water stress on gross primary productivity (GPP) during flash droughts and hot periods of flash droughts, as well as the long-term changes in water stress from 1961 to 2022 over China. The areas dominated by soil and atmospheric stress were 65.2% and 19.9% during flash droughts, respectively. During the hot periods of flash droughts, the areas dominated by atmospheric water stress were raised to 39.4%, and the areas dominated by soil water stress were reduced to 48.7%. During 1961–2022, the frequency, intensity, and duration of flash droughts all showed significant upward trends (p < 0.05) over China. Meanwhile, soil water stress on GPP decreased significantly (p < 0.05), but the atmospheric water stress increased significantly (p < 0.05). Correspondingly, the areas dominated by soil water stress decreased at 0.8%/decade, while the areas dominated by atmospheric water stress rose at 1.6%/decade during hot periods of flash droughts. With sensitivity simulations, we found that the water stress was weakened in the North China plain under irrigated conditions, but the trend was consistent with non-irrigated conditions over China. Our study indicated the importance of atmospheric moisture stress on vegetation productivity during flash droughts under climate warming.

Evaluating and Enhancing Snow Compaction Process in the Noah‐MP Land Surface Model

AbstractThe accuracy of snow density in land surface model (LSM) simulations impacts the accuracy of simulated terrestrial water and energy budgets. However, there has been little research that has focused on enhancing snow compaction in operationally used LSMs. A baseline snow simulation with the widely used Noah‐MP LSM systematically overestimates snow depth by 55 mm even after removing daily snow water equivalent (SWE) biases. To reduce uncertainties associated with snow compaction, we enhance the most sensitive Noah‐MP snow compaction parameter—the empirical parameter for compaction due to overburden (C bd)—such that C bd is calculated as a function of surface air temperature as opposed to a fixed value in the baseline simulation. This enhancement improves accuracy in simulated snow compaction across the majority of western U.S. (WUS) SNOTEL test sites (biases reduced at 88% of test sites), with modest bias reductions in cooler accumulation periods (biases reduced at 70% of test sites) and substantial improvements during warmer ablation periods (biases reduced at 99% of test sites). Relatively larger improvements during warm conditions are attributable to the default C bd value being reasonable for cold temperatures (≤−5°C). Improvements in simulated snow depth and density with observations outside of the training sites and optimization periods support that the snow compaction enhancement is transferable in space and time. Differences between enhanced and baseline gridded simulations across the total WUS support that the enhancement can have important impacts on snowpack evolution, snow albedo feedback, and snow hydrology.

The important role of reliable land surface model simulation in high-resolution multi-source soil moisture data fusion by machine learning

While machine learning (ML) is used to correct gridded soil moisture (SM) products by fusing in-situ observations, the contribution of land surface model (LSM)- and satellite-based SM products are yet to be validated over a large area, leading to imprudent adoption of SM product(s) for data fusion. In this study, single or multiple SM products from CSSPv2 LSM simulation, ERA5 and GLDASv2.1 reanalysis, and ESA-CCI satellite data with different resolutions are used to train ML models and generate daily SM estimates at 0.0625° resolution with in-situ measurements as target and relevant variables as auxiliary. Three widely used ML methods, namely Random Forest (RF), LightGBM, and XGBoost, were compared. Validations over independent in-situ stations during 2012–2017 showed the improvement of fusion products against their corresponding raw products, with KGE and CC increased by 87 % and 6 %, and RMSE decreased by 22 % for SM at surface and rootzone layers. Regionally, ML-based SM estimates improve mainly in southeast China. Merging three coarse-resolution SM datasets (i.e., ERA5, GLDASv2.1 and ESA-CCI) together with in-situ observations further increases KGE and CC by 15 % and 5 % against individual fusion products, but it still fails to outperform the individual high-resolution fusion product of ML/CSSPv2. Merging all four gridded SM products with in-situ data shows advantage against the ML/CSSPv2, with KGE and CC increased by 9 % and 7 %. The results are consistent by using different ML methods. This study suggests the importance of high-resolution LSM for SM data fusion, even with the emergence of ML approaches.

Benefit of incorporating GLASS remote sensing vegetation products in improving Noah-MP land surface temperature simulations on the Tibetan Plateau

Land Surface Temperature (LST) is important for diagnosing surface energy balance in land surface models (LSMs). However, LST simulation in current LSMs tends to show large cold biases, partially due to the reason that the model's prescribed vegetation parameters (e.g., Leaf Area Index (LAI) and Fraction of Vegetation Cover (FVC)) are misrepresented, especially in regions with complex topography and climate such as Tibetan Plateau. Recent advancements in remote sensing technologies provide a unique opportunity to improve the model's vegetation parameters at large scales. In this study, we practice two experiments to improve LST simulations in Noah-MP LSM by (1) incorporating LAI and FVC from the Global Land Surface Satellite (GLASS) remote sensing product (exp_RS); and (2) incorporating an empirical LAI and FVC parameterization scheme based on the soil temperature stress factor (exp_RL02). Results show that the effect of vegetation on simulated LST is the most significant in summer season when the model-satellite LAI and FVC differences are the largest. Compared to the default experiment that uses static LAI and FVC values from the model's look-up table (exp_CTL), the results in exp_RS and exp_RL02 show domain-wide improvement of the simulated LST. The LAI and FVC effect on LST are also well reflected in model's energy budget components (i.e., longwave emissivity, sensible and latent heat fluxes, etc). Validation of the model simulated soil temperature with in-situ observations further demonstrate the model improvements. Our study underscores the important role of vegetation in regulating surface energy transfer processes. Our study also highlights the feasibility and benefit of incorporating remote sensing data in improving land surface model simulations.

Empirical upscaling of OzFlux eddy covariance for high-resolution monitoring of terrestrial carbon uptake in Australia

Abstract. We develop high-resolution (1 km) estimates of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) over the Australian continent for the period January 2003 to June 2022 by empirical upscaling of flux tower measurements. We compare our estimates with nine other products that cover the three broad categories that define current methods for estimating the terrestrial carbon cycle and assess if consiliences between datasets can point to the correct dynamics of Australia's carbon cycle. Our results indicate that regional empirical upscaling greatly improves upon the existing global empirical upscaling efforts, outperforms process-based models, and agrees much better with the dynamics of CO2 flux over Australia as estimated by two regional atmospheric inversions. Our nearly 20-year estimates of terrestrial carbon fluxes revealed that Australia is a strong net carbon sink of −0.44 PgC yr−1 (interquartile range, IQR = 0.42 PgC yr−1) on average, with an inter-annual variability of 0.18 PgC yr−1 and an average seasonal amplitude of 0.85 PgC yr−1. Annual mean carbon uptake estimated from other methods ranged considerably, while carbon flux anomalies showed much better agreement between methods. NEE anomalies were predominately driven by cumulative rainfall deficits and surpluses, resulting in larger anomalous responses from GPP than ER. In contrast, we show that the long-term average seasonal cycle is dictated more by the variability in ER than GPP, resulting in peak carbon uptake typically occurring during the cooler, drier austral autumn and winter months. This new estimate of Australia's terrestrial carbon cycle provides a benchmark for assessment against land surface model simulations and a means for monitoring of Australia's terrestrial carbon cycle at an unprecedented high resolution. We call this new estimate of Australia's terrestrial carbon cycle “AusEFlux” (Australian Empirical Fluxes).

High-resolution land use and land cover dataset for regional climate modelling: historical and future changes in Europe

Abstract. Anthropogenic land use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently being extensively investigated within the World Climate Research Program (WCRP) Coordinated Downscaling Experiment (CORDEX) Flagship Pilot Study (FPS) Land Use and Climate Across Scales (LUCAS) using an ensemble of different regional climate models (RCMs) coupled with diverse land surface models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM–LSM simulations. To account for these needs, we generated the LUCAS Land Use and land Cover change (LUC) dataset version 1.1 at 0.1∘ resolution for Europe with annual LULC maps from 1950 to 2100 (https://doi.org/10.26050/WDCC/LUC_hist_EU_v1.1, Hoffmann et al., 2022b, https://doi.org/10.26050/WDCC/LUC_future_EU_v1.1, Hoffmann et al., 2022a), which is tailored to use in state-of-the-art RCMs. The plant functional type (PFT) distribution for the year 2015 (i.e. the Modelling human LAND surface Modifications and its feedbacks on local and regional cliMATE – LANDMATE – PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details on the conversion method, cross-walking procedure, and evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (2022b). Subsequently, we applied the land use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25∘ resolution as input for Coupled Modelling Intercomparison Project Phase 6 (CMIP6) experiments, to derive LULC distributions at high spatial resolution and at annual time steps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a land use translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different shared socioeconomic pathway (SSP) and representative concentration pathway (RCP) combinations used in the framework of CMIP6. The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions worldwide.

Interannual variability of spring and summer monsoon growing season carbon exchange at a semiarid savanna over nearly two decades

Eddy covariance measurements of land-atmosphere energy, carbon, and water exchange now span multiple decades at some sites, supporting an improved understanding of flux interannual variability (IAV) and its ecophysiological and physical controls. Most eddy covariance IAV studies have focused on temperate forest ecosystems, where carbon fluxes are large and flux records are longest – but also where IAV is much lower than in dryland regions, which have been identified as an essential driver of the trend and variability in the global terrestrial carbon sink. In this study, we leveraged 19 years of continuous micrometeorological measurements at the AmeriFlux US-SRM mesquite savanna site in southern Arizona, USA to quantify the IAV, trends, and drivers of carbon fluxes during the distinct spring and summer growing seasons. We also assessed the ability of modern satellite and land surface models to capture the IAV of seasonal water and carbon fluxes. Annual net ecosystem production (NEP) was small and highly variable (23 +/- 64 gC m − 2 yr−1). Precipitation and associated measures of water availability determined most of the variability in NEP, largely through their influence on annual and seasonal gross ecosystem productivity (GEP) as opposed to ecosystem respiration (ER). Root-zone soil moisture captured between 73% (spring) and 85% (summer) of GEP variability and between 73% (spring) and 58% (summer) of ER variability. Throughout the study period, soil moisture and greenness increased with associated increases in GEP, ER and NEP. These trends were strongly influenced by very productive and wet summer growing seasons during the last two years, which were characterized by abundant understory grass cover. Typically, less than half of the variability in growing season GEP and evapotranspiration was captured by satellite-based estimates and land surface model simulations with local site forcing and calibration, highlighting the ongoing utility of long-term datasets to support careful model testing and improvement.

Changes of terrestrial water storage during 1981–2020 over China based on dynamic-machine learning model

A reliable estimation of long-term change in terrestrial water storage (TWS) is critical for a sustainable management of freshwater resources and infrastructures. Although the Gravity Recovery and Climate Experiment (GRACE) satellites provide unprecedented accurate TWS anomaly (TWSA) observation since 2002, the limited time span and the gaps between the GRACE and GRACE-Follow On datasets hinder their climatological applications. Here we apply LightGBM, a more accurate and efficient machine learning model, to merge the land surface model simulation, human activities, land properties, and establish a dynamic-machine learning model that can reasonably depict the impacts of climate variation and human interventions on TWSA. We apply the hybrid model to reconstruct the historical TWSA during 1981–2020 over China, which shows reliable results and is consistent with other independent datasets including drought index, TWS change based on water balance, and water withdrawal dataset. The reconstructed TWSA shows significant drier (wetter) trend in North (South) China during the past 40 years, while the TWS variability has increased over 60% regions. We find the change of TWSA is mainly attributed to the climate variation in humid regions, but the human interventions dominate the decline of TWSA in arid and semiarid areas, such as the Yellow and Hai river basins (contributing about 70%). Our findings imply the potential of using artificial intelligence to represent human interventions, which is still uncertain in physical models. Our physical-machine learning model can be applied in long-term prediction and projection of TWSA and its components as well.

Evaluation of the High-Resolution MuSyQ LAI Product over Heterogeneous Land Surfaces

In recent years, the retrieval and validation of remotely-sensed leaf area index (LAI) products over complex land surfaces have received much attention due to the high-precision land surface model simulations and applications in global climate change. However, most of these related researches mainly focus on coarse resolution products. This is because few products have been specifically designed for solving the problems derived from complex land surfaces in mountain areas until now. MuSyQ LAI is a new product derived from Gaofen-1 (GF-1) satellite data. This product is characterized with a temporal resolution of 10 days and a spatial resolution of 16 m. As is well known, high-resolution products have less uncertainties because of the homogeneities of sub-pixel. Therefore, to evaluate the precision and uncertainty of MuSyQ LAI, an up-scaling strategy was employed here to validate MuSyQ LAI for three mountain regions in Southwest China. The validation strategy can be divided into three parts. First, a regression model was built by in situ LAI measured by LAI-2200 and the normalized difference vegetation index (NDVI) from unmanned aerial vehicle (UAV) images to obtain a 0.5 m resolution LAI map. Second, an up-scaled LAI map with a spatial resolution consistent with MuSyQ LAI was calculated by the pixel-averaging method from the UAV-based LAI map. Third, the MuSyQ LAI was validated by the up-scaled UAV-based LAI in pixel scale. Simultaneously, the sources of uncertainty were analyzed and compared from the view of data source, retrieval model, and scale effects. The results suggested that MuSyQ LAI in the study areas are significantly underestimated by 53.69% due to the complex terrain and heterogeneous land cover. There are three main reasons for the underestimation. The differences between GF-1 reflectance and UAV-based reflectance employed to estimate LAI are the largest factors for the validation results, even accounting for 61.47% of the total bias. Subsequently, the scale effects led to about 28.44% bias. Last but not least, the models employed to retrieve LAI contributed merely 10.09% uncertainties to the total bias. In conclusion, the accuracy of MuSyQ LAI still has a large space to be improved from the view of reflectance over complex terrain. This study is quite important for applications of MuSyQ LAI products and also provides a reference for the improvement and application of other high-resolution remotely sensed LAI products.