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Terrestrial water storage (TWS) is an integrative hydrological state that is key for our understanding of the global water cycle. The TWS observation from the GRACE missions has, therefore, been instrumental in the calibration and validation of hydrological models and understanding the variations in the hydrological storage. The models, however, still show significant uncertainties in reproducing observed TWS variations, especially for the interannual variability (IAV) at the global scale. Here, we diagnose the regions dominating the variance in globally integrated TWS IAV and the sources of the errors in two data-driven hydrological models that were calibrated against global TWS, snow water equivalent, evapotranspiration, and runoff data. We used (1) a parsimonious process-based hydrological model, the Strategies to INtegrate Data and BiogeochemicAl moDels (SINDBAD) framework and (2) a machine learning, physically based hybrid hydrological model (H2M) that combines a dynamic neural network with a water balance concept. While both models agree with the Gravity Recovery and Climate Experiment (GRACE) that global TWS IAV is largely driven by the semi-arid regions of southern Africa, the Indian subcontinent and northern Australia, and the humid regions of northern South America and the Mekong River basin, the models still show errors such as the overestimation of the observed magnitude of TWS IAV at the global scale. Our analysis identifies modeling error hotspots of the global TWS IAV, mostly in the tropical regions including the Amazon, sub-Saharan regions, and Southeast Asia, indicating that the regions that dominate global TWS IAV are not necessarily the same as those that dominate the error in global TWS IAV. Excluding those error hotspot regions in the global integration yields large improvements in the simulated global TWS IAV, which implies that model improvements can focus on improving processes in these hotspot regions. Further analysis indicates that error hotspot regions are associated with lateral flow dynamics, including both sub-pixel moisture convergence and across-pixel lateral river flow, or with interactions between surface processes and groundwater. The association of model deficiencies with land processes that delay the TWS variation could, in part, explain why the models cannot represent the observed lagged response of TWS IAV to precipitation IAV in hotspot regions that manifest as errors in global TWS IAV. Our approach presents a general avenue to better diagnose model simulation errors for global data streams to guide efficient and focused model development for regions and processes that matter the most.

Different disturbance events lead to varied response patterns in terrestrial biomass, while regulating the terrestrial ecosystems' short- and long-term carbon cycle dynamics. Quantifying the disturbance regimes is essential for understanding and reducing the uncertainty of climate factors affecting vegetation mortality and its responses on the carbon cycle. Based on model-based exercise, we built a machine learning model to predict three disturbance regime parameters, μ (probability scale), α (clustering degree), β (intensity slope) using the spatial pattern of emergent biomass. Here, relying on the model relationships, we utilize Earth observation data of high-resolution biomass, the GlobBiomass with a spatial resolution of 25m, to infer regional disturbance regime statistics.We first conduct a series of comparison exercises to test whether the current framework is robust for retrieving realistic disturbance regimes, including varying factors controlling: (i) the impacts of disturbance shape setting; (ii) the recovery pattern after perturbation; and (iii) the downsampling process for the biomass simulation. It was found that different model settings mainly lead to the inconsistency of texture features, and the disturbance regime prediction accuracy was maintained with different shape settings, or even higher after downsampling with a mean of 0.98 for Nash-Sutcliffe model efficiency coefficient (NSE).Given the robustness in the framework for retrieving disturbance regimes statistics from modelled biomass results we contrasted these spatial patterns with local GlobBiomass patterns across the world. The comparison between model and observations show data aggregation needs that provide information on aspects of scale and spatial resolution required for simulations. Finally, we provide a sparse, but globally distributed, characterization of disturbance regimes based on remote sensing observations and discuss potential climate links, and mechanisms behind, the spatially continuous distribution of disturbance regime.Given the novelty of the assessment of disturbance regimes with high-resolution biomass data, our study provides opportunities to evaluate and improve the representation of disturbance dynamics in dynamic vegetation and Earth System models.

Earth’s climate is strongly influenced by global biogeochemical cycles. Atmospheric carbon dioxide (CO2) is the main component of one of the most crucial biogeochemical cycles, i.e., the carbon cycle. Terrestrial ecosystems can regulate the atmospheric CO2 concentration, and absorb a substantial fraction of anthropogenic emissions via photosynthesis. Quantifying and understanding the rate of carbon assimilation through photosynthesis, or gross primary production (GPP), at different spatial and temporal scales, is of utmost importance.In this direction, the development of an eco-evolutionary optimality (EEO) perspective on ecophysiological plant parameters has been proposed as a robust theoretical framework to capture GPP dynamics. Its translation into modelling frameworks has been proposed by combining the least-cost and coordination principles and implemented in big leaf light use efficiency models. These models have been contrasted against in situ observations of carbon fluxes from FLUXNET at daily and sub-daily scales, distinguishing between instantaneous (fast) and acclimated (long-time) biochemical and stomatal response of leaf for the latter case. A fundamental assumption and difference to many other modelling approaches is the constant parameterization across all plant-functional-types (PFT) in this case. We further investigate the applicability of the approach in this study.We simulate half-hourly GPP across 191 FLUXNET sites, representing a wide variety of vegetation and climatic zones to evaluate the extent of observational support to a global EEO approach as implemented in the P-model (Mengoli et al., 2022). Model assessment metrics such as Nash-Sutcliffe efficiency (NSE), root mean squared error (RMSE), and coefficient of determination (R2) were calculated between observed and simulated GPP at various timescales for this purpose. The analysis is performed globally, but also within PFT and bioclimatic regimes. Furthermore, we relax the acclimation time-periods, gradually from 1 to 80 days, to identify potential changes in acclimation windows between sites and how it varies across PFTs.Negative values of NSE, suggesting poor model performance, were found for almost 50% of sites at half-hourly, daily, weekly, and monthly timescales. Specifically, inter-annual variability of GPP cannot be reproduced for most of the sites when sub-daily GPP was aggregated to annual values. It was observed that the model performs better in croplands, followed by mixed and deciduous broadleaf forests, and then grasslands and savannas. The model can perform better at boreal and temperate sites than at tropical and arid sites. Moreover, we found different time-period of acclimation across different PFT, for example, croplands have an average timescale of 10-15 days for acclimation whereas it is 75-80 days for evergreen needle leaf forests.One known limiting aspect in the current implementation is the lack of the effects of soil water limitation on photosynthesis. As a consequence, we found that model performance was positively correlated with the aridity index of sites. Specifically, model fails to capture annual GPP at arid sites. We further analyse and discuss how exploring soil moisture effects on photosynthesis parameters may lend support to this framework although model performance across some of the energy demand driven sites suggests a cautionary remark on the applicability of the approach.

Terrestrial carbon and water cycles are intricately related across spatial (leaf to global) and temporal (instantaneous to multi-annual) scales through multitude of coupled biogeochemical processes that govern the land water and carbon states and fluxes and their feedback to climate. Yet, there are clear discrepancies in modeling key carbon-water processes that lead to large uncertainties in model simulations that often divert away the observations. In fact, the terrestrial biogeochemical models used to represent vegetation-water-carbon interactions vary in complexity and parameterization that are often underconstrained. The current era of rapid growth of satellite Earth Observation, observational networks, and well as observation-based estimate, therefore, provides unprecedented opportunities to improve the models. Unfortunately, most terrestrial biogeochemical models often contain too rigid model structures, and are too demanding to carry out model-data-fusion experiments that leverage the strengths of observational data constraints.In this study, we present a newly developed terrestrial/ecosystem model-data-integration (MDI) framework, the SINDBAD, that allows for seamless integration of diverse observational data to constrain terrestrial models of varying complexity. The SINDBAD provides a modular framework to create different combinations of terrestrial processes to realize a terrestrial model structure, which can be driven by observed data of climate and/or land characteristic and optimized against provided observation constraints using different cost metrics and parameter optimization methods. To demonstrate the capabilities, we present three MDI experiments of SINDBAD: setup E1 - a global scale model focused on vegetation's role on water cycle; setup E2 - a regional scale model with physiological coupling of water and carbon cycle focused on role of interannual variability of vegetation fraction; and setup E3 - an ecosystem scale model with a prognostic carbon cycle that is used to evaluate the values of using data for ecosystem carbon states.In the simplest E1 setup, where vegetation only has structural influence on the water cycle, we find that the spatial information of vegetation using satellite-based vegetation index as model input shows clear improvement in the simulation of monthly runoff, as well as interannual variability of terrestrial water storage in arid regions. In setup E2, use of vegetation fraction data from geostationary satellite to drive a physiologically coupled model of water-carbon relations shows a clear improvement in the simulations of interannual variability of gross primary productivity only when the data includes the year-to-year variability of vegetation fraction. In fact, we find that the using mean seasonal cycle of vegetation fraction is able to reproduce the monthly variation but not the interannual variability. Lastly, in setup E3, which includes fully coupled water-carbon model with prognostic evolution of carbon pools with dynamic allocation scheme (with competition for light and water) reveals that the remote sensing observation of carbon states provides better constraints for the carbon cycle compared to the experiment where only eddy covariance measurements are used. The results also indicate that even coarser remote sensing data have a potential to complement ecosystem scale measurements of water and carbon fluxes to improve the prediction of carbon-water coupling at the ecosystem scale.

The study of plant trait variability is critical for understanding ecosystem dynamics and predicting the response of vegetation to varying climatic conditions. Understanding the factors controlling the spatial and temporal variability in vegetation traits is key for addressing the ecosystem responses and feedbacks to changes in climate. In this study, we used the adaptive dynamic global vegetation model version 2 (aDGVM2) to simulate the temporal evolution and spatial distribution of plant traits across a wide range in edapho-climatic conditions. For such, we select locations of existing different ecosystem types and where in situ meteorological and eddy covariance flux measurements are taken.We forced the aDGVM2 with FAO soil and flux site climate data, extended until 2020 and gap-filled with ERA5 data. To ensure that the simulated vegetation had sufficient time to adapt to prevailing local environmental conditions we conducted simulations for 500 years, split into a 400-year spin-up phase and a 100-year transient phase. For the spin-up phase, we randomly sampled years of the first 30 years of daily climate. Stochasticity in the selection-driven assembly of plant communities within the model can lead to multiple potential state; therefore, 10 replicate runs were conducted for each site with same model configuration.We examine the differences in the 25 simulated trait values across sites, replicates and time via an analysis of variance (ANOVA). The analysis shows significant differences in trait values between sites, with some traits showing higher variability than others. In particular, we find that traits related to plant structural support (height, stem counts) were highly variable across sites, while traits related to resource acquisition (e.g., specific leaf area, leaf nitrogen content) are more stable. These results provide important insights into the factors that influence trait variability in space, and will be valuable for predicting the response of terrestrial ecosystems to environmental change. Further understanding the factors driving trait variability is of essential value in the design of mitigation and adaptation strategies and guide conservation efforts in the face of a rapidly changing world.

The forest age can be defined as the time since the last stand-replacement event. Within the local context, it determines the forest successional stage, a fundamental variable for diagnosing the net carbon fluxes in terrestrial ecosystems. To accurately quantify the carbon sink-source strength in these ecosystems and inform adaptation-mitigation strategies, it is essential to be able to infer forest age at high resolution.This study presents an updated version of the MPI-BGC forest age product, featuring global distributions of forest age for 2010, 2017, 2018, and 2020 at 100m spatial resolution. We employed two machine learning approaches, XGBoost and a multi-layer perceptron model, to create data-driven estimates of forest age based on over 40,000 forest inventory plots, biomass, remote-sensing, and climate data. One key innovation of our approach is the incorporation of Landsat-based disturbance history metrics as input variables. Our updated estimates show better precision in identifying old-growth forests and reduce overestimation biases in young forests and underestimation biases in old forests, but not completely. Additionally, we found substantial regional variations related to changes in covariate strength and improvement in the model. Also, we discussed the uncertainty layers, created using model ensembles, that materialize the quantification of methodological uncertainty in the forest age estimates.An analysis of the global distribution of forest age reveals significant variations across the years studied. We also quantified the changes in forest age in regions with high deforestation or forest degradation rates, where younger stands are becoming more prevalent. We discuss the challenges and limitations of using regression-based mapping approaches, including the choice of machine learning algorithm, spatial cross-validation techniques, and the caveats of extrapolation, given data limitations. Our research highlights the complementary biomass-based approaches for determining forest age and underscores the importance of detailed global estimates at high spatial resolutions. Overall, this study advances our understanding of forest age, a key variable for understanding the carbon cycle in terrestrial ecosystems.

Carbon use efficiency (CUE) of vegetation is a property emerging from physiological processes, a key parameter to determine vegetation growth, which ultimately reflects the relative potential of terrestrial ecosystems to store atmospheric carbon in biomass. Large uncertainties were found in global CUE estimated by remote sensing data models or process-based models, especially across plant functional types (PFTs) and regarding seasonal variations. This study explores the specific effects of climate and vegetation state on CUE based on a model-data integration approach by analyzing outputs from a terrestrial ecosystem model driven with local meteorological variables and constrained by in situ observations of vegetation biomass, as well as carbon and water fluxes from eddy covariance measurements. We leverage on a modular model-data-integration framework – SINDBAD – that allows for an integrated model selection, parameterization and evaluation approach based on in situ observations. In addition, various other simulations based on global observations and global modeling approaches, including MODIS, GLASS, Trendy, MsTMIP, and CMIP6, are further explored with the aim of examining spatial and temporal patterns of CUE to better understand the modelled biological and climate controls of CUE. The range of global annual CUE values for the 50 models between 2001 and 2010 is 0.3083 to 0.5920. Our study shows that adding constraints on modelled vegetation biomass, in general, brings slight deterioration to the simulation of carbon fluxes but significantly changes the patterns of CUE. In general, biomass constraints decrease the emerging CUE estimates, even in non-forested sites in the Northern Hemisphere. The vegetation state constraints increase 14.42% of sites distributed at 0.2-0.4 median CUE yearly values and decrease 14.92% of sites distributed at 0.4-0.6. Constraints on vegetation carbon stocks result in changes in modelled autotrophic respiration, which change more significantly across sites than gross primary productivity. The information provided by the vegetation state variables results generally in lower wood and slow litter turnover rate, and an increased sensitivity of CUE to moisture and adaptation to temperature. Ultimately, here we provide model-based results for investigating the mechanisms behind the spatial and temporal variability of CUE, potentially contributing to a better quantified CUE variation globally.

Vegetation plays a fundamental role in modulating the exchange of water, energy, and carbon fluxes between the land and the atmosphere. These exchanges are modelled by Land Surface Models (LSMs), which are an essential part of numerical weather prediction and data assimilation. However, most current LSMs implemented specifically in weather forecasting systems use climatological vegetation indices, and land use/land cover datasets in these models are often outdated. In this study, we update land surface data in the ECMWF land surface modelling system ECLand using Earth observation-based time varying leaf area index and land use/land cover data, and evaluate the impact of vegetation dynamics on model performance. The performance of the simulated latent heat flux and soil moisture is then evaluated against global gridded observation-based datasets. Updating the vegetation information does not always yield better model performances because the model’s parameters are adapted to the previously employed land surface information. Therefore we recalibrate key soil and vegetation-related parameters at individual grid cells to adjust the model parameterizations to the new land surface information. This substantially improves model performance and demonstrates the benefits of updated vegetation information. Interestingly, we find that a regional parameter calibration outperforms a globally uniform adjustment of parameters, indicating that parameters should sufficiently reflect spatial variability in the land surface. Our results highlight that newly available Earth-observation products of vegetation dynamics and land cover changes can improve land surface model performances, which in turn can contribute to more accurate weather forecasts.

Funding Information: The article processing charges for this openaccess publication were covered by the Max Planck Society. Publisher Copyright: © 2022 Tina Trautmann et al. So far, various studies have aimed at decomposing the integrated terrestrial water storage variations observed by satellite gravimetry (GRACE, GRACE-FO) with the help of large-scale hydrological models. While the results of the storage decomposition depend on model structure, little attention has been given to the impact of the way that vegetation is represented in these models. Although vegetation structure and activity represent the crucial link between water, carbon, and energy cycles, their representation in large-scale hydrological models remains a major source of uncertainty. At the same time, the increasing availability and quality of Earth-observation-based vegetation data provide valuable information with good prospects for improving model simulations and gaining better insights into the role of vegetation within the global water cycle. In this study, we use observation-based vegetation information such as vegetation indices and rooting depths for spatializing the parameters of a simple global hydrological model to define infiltration, root water uptake, and transpiration processes. The parameters are further constrained by considering observations of terrestrial water storage anomalies (TWS), soil moisture, evapotranspiration (ET) and gridded runoff (Q) estimates in a multi-criteria calibration approach. We assess the implications of including varying vegetation characteristics on the simulation results, with a particular focus on the partitioning between water storage components. To isolate the effect of vegetation, we compare a model experiment in which vegetation parameters vary in space and time to a baseline experiment in which all parameters are calibrated as static, globally uniform values. Both experiments show good overall performance, but explicitly including varying vegetation data leads to even better performance and more physically plausible parameter values. The largest improvements regarding TWS and ET are seen in supply-limited (semi-arid) regions and in the tropics, whereas Q simulations improve mainly in northern latitudes. While the total fluxes and storages are similar, accounting for vegetation substantially changes the contributions of different soil water storage components to the TWS variations. This suggests an important role of the representation of vegetation in hydrological models for interpreting TWS variations. Our simulations further indicate a major effect of deeper moisture storages and groundwater-soil moisture-vegetation interactions as a key to understanding TWS variations. We highlight the need for further observations to identify the adequate model structure rather than only model parameters for a reasonable representation and interpretation of vegetation-water interactions. publishersversion published

Vegetation turnover time (τ) is a central ecosystem property to quantify the global vegetation carbon dynamics. However, our understanding of vegetation dynamics is hampered by the lack of long-term observations of the changes in vegetation biomass. Here we challenge the steady state assumption of τ by using annual changes in vegetation biomass that derived from remote-sensing observations. We evaluate the changes in magnitude, spatial patterns, and uncertainties in vegetation carbon turnover times from 1992 to 2016. We found that the forest ecosystem is close to a steady state at global scale, contrasting with the larger differences between τ under steady state and τ under non-steady state at the grid cell level. The observation that terrestrial ecosystems are not in a steady state locally is deemed crucial when studying vegetation dynamics and the potential response of biomass to disturbance and climatic changes.

Terrestrial ecosystems respond to changes in environmental conditions, mainly via key climatic controls of precipitation and temperature on vegetation activities and decomposition processes (Taylor et al., 2017). Yet, the relationship between climate and the overall spatiotemporal dynamics and uncertainties of the global carbon cycle, i.e., gross primary productivity (GPP), effective ecosystem carbon turnover times (τ), and consequently the total ecosystem carbon stock (Ctotal), are unclear (Anav et al., 2013; Friedlingstein et al., 2014; Friend et al., 2014; Jones et al., 2013). Using a global observation-based synthesis, we first show that the apparent partial spatial climate sensitivities of GPP and τ are associated with relative availability of precipitation and temperature, and are therefore modulated by aridity. The apparent sensitivity of GPP to temperature increases from arid to humid climatic regions. In contrast, its sensitivity to precipitation is invariant throughout different climatic regions. Simultaneously, the τ-precipitation response is strongly non-linear resulting in ~2 times longer τ in arid regions compared to humid regions for a given temperature. Compared with these observed patterns, the offline carbon cycle simulations of seven European Earth System Models (ESMs), that participated in CMIP6, perform relatively better for climate sensitivities of GPP than those of τ. This leads to a large spread and bias in Ctotal in both warm and cold semi-arid and arid regions where only a few models capture the observed τ-precipitation relationship. The emergence of the hydrological controls, modulated by aridity, on global carbon cycle implies that the changes in precipitation may moderate the temperature-driven climate feedback of the global carbon cycle under climate change.

Terrestrial water storage (TWS) is an integrative hydrological state that is key for our understanding of the global water cycle. The TWS observation from GRACE missions has, therefore, been instrumental in calibration and validation of hydrological models and understanding the variations of the hydrological storages. The models, however, still show significant uncertainties in reproducing observed TWS variations, especially for the interannual variability (IAV) at the global scale. Here, we diagnose the regions dominating the variance of globally integrated TWS IAV, and sources of the errors in two data-driven hydrological models that were calibrated against global TWS, snow water equivalent, evapotranspiration, and runoff data: 1) a parsimonious process-based hydrological model, the Strategies to INtegrate Data and BiogeochemicAl moDels (SINDBAD) framework, and 2) a machine learning-physically based hybrid hydrological model (H2M) that combines a dynamic neural network with a water balance concept. While both models agree with GRACE that global TWS IAV is largely driven by the semi-arid regions of southern Africa, Indian subcontinent and northern Australia, and the humid regions of northern South America and Mekong River Basin, the models still show errors such as overestimation of the observed magnitude of TWS IAV at the global scale. Our analysis identifies modeling error hotspots of the global TWS IAV mostly in the tropical regions including Amazon, sub–Saharan regions, and Southeast Asia, indicating that the regions that dominate global TWS IAV are not necessarily the same as those that dominate the error in global TWS IAV. Excluding those error hotspot regions in the global integration yields large improvements of simulated global TWS IAV, which implies that model improvements can focus on improving processes in these hotspot regions. Further analysis indicates that error hotspot regions are associated with lateral flow dynamics, including both sub-pixel moisture convergence and across pixel lateral river flow, or with interactions between surface processes and groundwater. The association of model deficiencies with land processes that delay the TWS variation could, in part, explain why the models cannot represent the observed lagged response of TWS IAV to precipitation IAV in hotspot regions that manifest to errors in global TWS IAV. Our approach presents a general avenue to better diagnose model simulation errors for global data streams to guide efficient and focused model development for regions and processes that matter the most.

Over the last decade Terrestrial Water Storage (TWS) variations from GRACE and GRACE-FO satellite gravimetry have provided valuable observations for validation and calibration of hydrological models, and for data assimilation. While GRACE estimates represent the vertically integrated variations of all water storages, previous studies have shown the regional relevance of surface water and flood plain storage for TWS variations, and the inability to reproduce observed TWS by global hydrological models is often attributed to neglecting processes of river routing and floodplain dynamics. However, it is unclear if these processes need to be considered by computationally expensive river routing schemes in hydrologic model calibration and validation at large to global scales. In this exploratory analysis, we assess the effect of river water storage that is included in the vertically integrated GRACE TWS variations on the calibration of a global hydrological model, and its relevance for model validation. For this purpose, we first determine an observation-based estimate of river storage by applying a routing scheme on GRUN runoff data, considering different effective flow velocities. Obtained river storage is then removed from GRACE TWS, and the TWS variations either with or without river storage are used along with other observational based data of evapotranspiration, soil moisture and runoff to constrain parameters of a simple global hydrological model that does not encompass a river routing module in a multi-criteria calibration approach. While the removal of river storage changes the TWS constraint itself, especially its amplitude, at regional and global scale, we do not find a significant influence on calibrated parameters and thus model simulations. Instead, issues related to data uncertainty and inconsistency, as well as hydrological processes neglected by the model impose greater limitations than the rather local to regional relevance of river water storage. Furthermore, additional constraints from other data streams seem to not allow for adjustment to the changed TWS constraint in the calibration approach. However, simulating and adding river storage to modelled TWS after model calibration improves model validation relative to GRACE TWS globally and regional. Largest improvement is obtained in tropics and Northern low- and wetlands, where a substantial volume of water accumulates in major rivers, highlighting the importance of considering river water in these regions. Difficulties to reproduce TWS variations are mainly apparent in semi-arid regions where a generally lower volume of water is stored on land surface and neglected processes (e.g. evaporation and percolation from the flow channel) play a role. While it’s arguable that the presented results are quite specific to the used data and model structure, the key issues are shared among global hydrological modelling studies. Therefore, our findings suggest that omitting routing for large-scale model calibration against GRACE TWS is a valid option, considering limited computational and temporal resources. Nonetheless, the findings encourage the inclusion of river storage dynamics for validation of large-scale hydrological studies.

Terrestrial water storage (TWS) is a socially (e.g., floods and drought) and scientifically (e.g., water and carbon cycles) important variable. Hydrological models have been extensively used to study variations in TWS but the models are showing significant uncertainties, especially for the interannual variability (IAV). It is therefore essential for TWS IAV studies to further improve model accuracy, calling for a better understanding of the TWS IAV simulation error. We conducted a covariance matrix analysis to spatially attribute the contributions to global TWS IAV and its simulation error by two hydrological models: 1) a parsimonious process-based one, implemented in the Strategies to INtegrate Data and BiogeochemicAl moDels (SINDBAD) framework, and 2) a hybrid one, the hybrid hydrological model (H2M), which combines a dynamic neural network and a water balance concept. Both models were calibrated against observation-based data streams for evapotranspiration, snow water equivalent, and runoff, as well as against the Gravity Recovery and Climate Experiment (GRACE) satellite observations of TWS. Both models indicate that the global TWS IAV is largely driven by some regions such as Amazon, Zambezi, Mekong basins, and India. The analysis also identified hotspots of the global TWS IAV error from river basins (e.g., Amazon, Paraná, Congo, and Mekong basins) and inland water bodies (e.g., the Laurentian Great Lakes). Excluding those hotspots in the global integration, the 12-month running mean of the global TWS IAV showed a large improvement: R2 of the global TWS IAV time series was improved from 0.62 to 0.82 for SINDBAD and from 0.62 to 0.88 for H2M. Therefore, the model simulation of the global TWS can efficiently be improved by focusing on correcting the hotspot regions. Comparing GRACE and modelled TWS IAV time series revealed various potential sources of errors, including anthropogenic factors (e.g., reservoir management) and model structure (e.g., insufficient storage capacity and missing storage processes), while the latter was prevalent over many regions. A further comparison to surface water data could characterize the hotspots as areas of 1) more dynamic surface water, and 2) wetlands (i.e., near-inland water bodies). These characteristics of hotspots imply that surface water processes (e.g., seasonal inundation) are relevant for understanding global TWS IAV and are underestimated in the two tested models, calling for further improvement in that respect. Our approach presents a general avenue to identify model simulation errors for global data streams and can guide efficient model development.

Results: Soil temperature is a crucial variable in Land Surface Models (LSMs) because it affects the fractions of frozen and unfrozen water content in the soil. For example, getting the coupling between below-ground heat- and water transfer correct in LSMs is very important in permafrost regions because these are particularly sensitive to climate change. Poor predictions of the energy- and water balance in these regions will lead to large uncertainties in predicted carbon fluxes, and related land-atmosphere feedbacks. Also, simulated near-surface soil temperatures can be used to diagnose and explain model differences in skin temperatures and soil heat fluxes, both of which are pivotal in the prediction of the surface energy balance.Soil temperature is generally under-researched as part of LSM intercomparisons. Here we present an analysis of the spatial distribution (including the vertical distribution along the soil profile) and seasonal evolution of soil temperature simulated by eight LSMs as part of the Soil Parameter Model Intercomparison Project (SP-MIP). We found large inter-model differences in key metrics of the annual soil temperature wave, including the amplitude, phase shift and damping depth, which were partly attributed to diversity in hydraulic as well as thermal soil properties. Soil layer discretisation also played a role.Methods: Via manipulation of model soil hydraulic properties, and the soil texture inputs required to calculate these properties, controlled multi-model experiments have been conducted as part of SP-MIP, this MIP was originally proposed at the GEWEX-SoilWat workshop held in Leipzig (June 2016).The model experiments closely followed the LS3MIP protocol (van den Hurk et al. 2016). Eight land models (CLM5, ISBA, JSBACH, JULES, MATSIRO, MATSIRO-GW, NOAH-MP and ORCHIDEE) were run globally on 0.5° with GSWP3 forcing, from 1980-2010, for vertically homogeneous soil columns. There were 4 model experiments, leading to 7 model runs: Experiment 1. Global soil hydraulic parameter maps provided by SP-MIP; Experiment 2. Soil-hydraulic parameters derived from common soil textural properties, provided by SP-MIP, using model-specific pedotransfer functions (PTFs); Experiment 3. Reference run with all models applying their default soil hydraulic settings (including their own soil maps to derive the parameters); Experiment 4: four runs using spatially uniform soil hydraulic parameters for the whole globe (loamy sand, loam, clay and silt) provided by SP-MIP.Differences between the model experiments will allow the assessment of the inter-model variability that is introduced by the different stages of preparing model parameters. Soil parameters for Experiments 1 and soil textures for Experiment 2 at 0.5° resolution were prepared from dominant soil classes of the 0-5 cm layer of SoilGrids (Hengl et al. 2014) at 5 km resolution. Brooks and Corey hydraulic parameters come from Table 2 of Clapp and Hornberger (1978), Mualem-Van Genuchten hydraulic parameters are ROSETTA class average hydraulic parameters (Schaap et al. 2001), and soil textures are from Table 2 of Cosby et al. (1984). Experiments 4 a-d use the USDA soil classes, using the same PTFs for Brooks and Corey and Mualem-van Genuchten parameters as in Experiment 1.

While deep learning models are capable of representing complex temporal processes in a data-adaptive way, they lack physical consistency and interpretability. Thus, the combination of machine learning and physically-based approaches in so-called hybrid modeling has been proposed recently [1]. Gathering insights into complex Earth system processes in a data-driven way has, arguably, a large potential for hydrology. This is, on the one hand, due to the richness of Earth observations of hydrological quantities, such as terrestrial water storage, runoff, snow cover, or evapotranspiration. On the other hand, the large uncertainties in current global hydrological models across spatial and temporal scales motivate the exploration of alternative, complementary approaches.In this work [2], we evaluate an experimental approach for the global, data-driven decomposition of terrestrial water storage. Therefore, we developed a dynamic hybrid model which represents the main terrestrial water storage components of groundwater, soil moisture, and snowpack. The model consists of a recurrent neural network that estimates spatiotemporally varying and physically interpretable quantities, which are used as coefficients in a set of hydrological balance equations. The hybrid model is fed with meteorological variables and gridcell-level landscape properties and is optimized end-to-end using gridded evapotranspiration, runoff, terrestrial water storage, and snow water equivalent.By outsourcing the estimation of coefficients to a neural network, we achieve improved local data adaptivity. The simulated fluxes and storage components are realistic and plausible overall, and our approach yields a larger contribution of soil moisture to the terrestrial water storage variations compared to physically-based hydrological models, especially in tropical savanna regions. The presented approach is a proof of concept of the hybrid modeling approach for the global terrestrial water cycle and we acknowledge uncertainties due to data and physical constraints that can be further improved. The presented work is a first step toward the data-driven yet physically constrained estimation of global water storage components and could find broad application in the Earth sciences.[1] Reichstein et al. (2019, Nature) https://www.nature.com/articles/s41586-019-0912-1[2] Kraft et al. (2021, HESSD) https://hess.copernicus.org/preprints/hess-2021-211/

The ecosystem carbon turnover time (τ) is an emergent ecosystem property that partly determines the feedback between the terrestrial carbon cycle and climate which is strongly controlled by temperature1–3. However, it remains uncertain to what extent hydrometeorological conditions may influence the apparent temperature sensitivity of τ, defined as the change in the carbon turnover times with a 10ºC rise in temperature (Q10). Here, we show that Q10 is unlikely temperature dependent and that it converges to 1.6 ± 0.1 globally once hydrometeorological conditions are taken into account. By investigating the responses of τ to temperature and hydrometeorological factors using an ensemble of observation-based global datasets and a global compilation of in-situ measurements, we show that temperature and hydrometeorology are almost equally important in shaping the spatial pattern of τ, explaining 60% and 40% of its global variability, respectively. Our findings reveal a strong constraint on Q10 values with significant reduction in magnitude and uncertainties, by accounting for the hydrometeorological effects on spatial variability of τ. These findings suggest that hydrometeorological conditions modulate the apparent temperature sensitivity of terrestrial carbon turnover times, confounding the role of temperature in quantifying the response of carbon cycle to climate change.

The role of soil moisture for organic matter decomposition rates remains poorly understood and underrepresented in Earth System Models (ESMs). We apply the Dual Arrhenius Michaelis‐Menten (DAMM) model to a selection of ESM soil temperature and moisture outputs to investigate their effects on decomposition rates, at different soil depths, for a historical period and a future climate period. Our key finding is that the inclusion of soil moisture controls has diverging effects on both the speed and direction of projected decomposition rates (up to ±20%), compared to a temperature‐only approach. In the top soil, the majority of these changes is driven by substrate availability. In deeper soil layers, oxygen availability plays a relatively stronger role. Owing to these different moisture controls along the soil depth, our study highlights the need for depth‐resolved inclusion of soil moisture effects on decomposition rates within ESMs. This is particularly important for C‐rich soils in regions which may be subject to strong future warming and vertically opposing moisture changes, such as the peat soils at northern high latitudes., Plain Language Summary: Soils contain a lot of carbon (C). Earth System Models (ESMs) predict that the amount of C released from soils into the atmosphere as CO2 will increase in response to increased warming and microbial activity. Soil moisture also controls microbial C decomposition, but most ESMs do not yet describe this process very well. In this study we apply a simple equation to different ESMs, to see how both temperature and soil moisture change microbial decomposition under future climate. First, we show that the speed of C released into the atmosphere changes when we include soil moisture changes, compared to what is expected due to warming alone. Second, we found that the future speed at which carbon that can be decomposed in the topsoil mainly depends on how much carbon microbes have access to, but that in the deeper soil this process becomes much more affected by the absence/presence of oxygen. Including these soil moisture interactions in ESMs for different soil depths is important to predict whether soils will store more or less C in the future. Our findings are particularly relevant for high latitude soils which store large amounts of C, will warm fast, and experience frequent (re)wetting and drying., Key Points: Considering soil moisture effects can change modeled decomposition rates by up to ±20% compared to considering only temperature effects. The majority of these changes are driven by substrate availability, in particular in the top soil. In the subsoil, oxygen availability becomes an increasingly important factor., Norwegian Research Council, https://doi.org/10.5281/zenodo.5654554

Terrestrial ecosystems are essential for food and water security and CO2 uptake. Ecosystem function is dependent on the availability of soil moisture, yet it is unclear how climate change will alter soil moisture limitation on vegetation. Here we use an ecosystem index that distinguishes energy and water limitations in Earth system model simulations to show a widespread regime shift from energy to water limitation between 1980 and 2100. This shift is found in both space and time. While this is mainly related to a reduction in energy-limited regions associated with increasing surface net radiation, the largest shift towards water limitation is found in regions where net radiation increases are accompanied by soil moisture decreases. We therefore demonstrate a widespread regime shift in ecosystem function that is stronger than implied by individual trends in net radiation, soil moisture and terrestrial evaporation, with important implications for future ecosystem services.

Funding Information: The present work has received funding from Sino-German (CSC-DAAD) Postdoc Scholarship Program (57460082) and the European Union's Horizon 2020 Research and Innovation Program under grant agreement No. 101004188. This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The FLUXNET eddy covariance data processing and harmonization was carried out by the ICOS Ecosystem Thematic Center, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of CDIAC, and the OzFlux, ChinaFlux and AsiaFlux offices. Qian Zhang gratefully acknowledges the support of K. C. Wong Education Foundation and DAAD. Funding Information: The present work has received funding from Sino-German (CSC-DAAD) Postdoc Scholarship Program ( 57460082 ) and the European Union's Horizon 2020 Research and Innovation Program under grant agreement No. 101004188 . This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The FLUXNET eddy covariance data processing and harmonization was carried out by the ICOS Ecosystem Thematic Center, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of CDIAC, and the OzFlux, ChinaFlux and AsiaFlux offices. Qian Zhang gratefully acknowledges the support of K. C. Wong Education Foundation and DAAD. Publisher Copyright: © 2022 This study aims to (1) investigate whether two-big-leaf light use efficiency (LUE) models (TL) outperform big-leaf LUE models (BL) by incorporating different gross primary productivity (GPP) responses in sunlit and shaded leaves; (2) explore the robustness of using the leaf area index (LAI), clumping index (Ω) and spherical leaf angle distribution to partition canopies into sunlit and shaded leaves across canopy architectures; (3) identify optimal light response forms in LUE models. To exclude influences of drivers of GPP other than radiation, we collected various formulations of GPP response functions to temperature, vapor pressure deficit, CO2, soil water supply, light intensity and cloudiness index to construct 5600 BLs and 1120 TLs. These models were evaluated at 196 globally-distributed eddy covariance sites from the FLUXNET observational network using the Nash-Sutcliffe model efficiency (NSE), root mean squared error and Bayesian information criterion. Across all sites, the best big-leaf model (BL*; NSE=0.82) was statistically equal to the best TL (TL*; NSE=0.84). However, daily dynamics in GPP under hot and dry conditions were best described using TL* in 17% of sites, highlighting the local importance in separating sunlit and shaded leaves. Across approaches to represent effective LAI, the best approach relies on using normalized difference vegetation index with a spherical or flexible leaf angle distribution across sites rather than satellite LAI and Ω. We also observed similar performance between non-rectangular hyperbola and reciprocal light response functions across TLs. Models degrade when the maximum LUE is not differentiated between sunlit and shaded leaves, but not when light saturation levels are the same. Despite functional differences, the best five TLs agree in a larger contribution of shaded leaf area to total GPP, resulting from higher LAI and LUE. Overall, these results suggest marginal but robust selection of TL compared to BL. publishersversion published

So far, various studies aimed at decomposing the integrated terrestrial water storage variations observed by satellite gravimetry (GRACE, GRACE-FO) with the help of large-scale hydrological models. While the results of the storage decomposition depend on model structure, little attention has been given to the impact of the way how vegetation is represented in these models. Although vegetation structure and activity represent the crucial link between water, carbon and energy cycles, their representation in large-scale hydrological models remains a major source of uncertainty. At the same time, the increasing availability and quality of Earth observation-based vegetation data provide valuable information with good prospects for improving model simulations and gaining better insights into the role of vegetation within the global water cycle. In this study, we use observation-based vegetation information such as vegetation indices and rooting depths for spatializing the parameters of a simple global hydrological model to define infiltration, root water uptake and transpiration processes. The parameters are further constrained by considering observations of terrestrial water storage anomalies (TWS), soil moisture, evapotranspiration (ET) and gridded runoff (Q) estimates in a multi-criteria calibration approach. We assess the implications of including vegetation on the simulation results, with a particular focus on the partitioning between water storage components. To isolate the effect of vegetation, we compare a model experiment with vegetation parameters varying in space and time to a baseline experiment in which all parameters are calibrated as static, globally uniform values. Both experiments show good overall performance, but including vegetation data led to even better performance and more physically plausible parameter values. Largest improvements regarding TWS and ET were seen in supply-limited (semi-arid) regions and in the tropics, whereas Q simulations improve mainly in northern latitudes. While the total fluxes and storages are similar, accounting for vegetation substantially changes the contributions of snow and different soil water storage components to the TWS variations, with the dominance of an intermediate water pool that interacts with the fast plant accessible soil moisture and the delayed water storage. The findings indicate the important role of deeper moisture storages as well as groundwater-soil moisture-vegetation interactions as a key to understanding TWS variations. We highlight the need for further observations to identify the adequate model structure rather than only model parameters for a reasonable representation and interpretation of vegetation-water interactions.

Snow is a crucial component of the hydrological cycle in the Western Himalaya, where the warming climate is already impacting precipitation and melt runoff patterns. In this study, we investigated the future evolution of snow cover and snowmelt in the Panjshir catchment (2,210 km2) of Afghanistan. Located in northern Afghanistan, the Panjshir catchment of the Kabul river basin is the westernmost catchment of the transboundary Indus river system. The climate in Panjshir catchment is characterised by warm-dry summer and cold-wet winter with a large spatial and temporal heterogeneity. Water from snowmelt is used in various sectors in downstream regions, and thus plays a critical role in securing the livelihood of millions of people. In order to analyse the future evolution of snow-related processes under climate change, a few global climate model simulations from CMIP5 climate datasets for RCP4.5 and RCP8.5 which showed reasonable performance when compared with ERA5 data for the historic period (1981-2010) were selected. The selected models were then segregated into two groups: those projecting a cold-wet climate with a 13-28% and 2.5-4.9oC increase in precipitation and temperature respectively, and those projecting a warm-dry climate with a 26-40% decrease in precipitation and a 4.3-7.8oC increase in temperature by the end of the 21st century. These GCMs were downscaled to a higher resolution using empirical statistical downscaling. To simulate the snow processes, we used the distributed cryospheric-hydrological J2000 model. Results of our analysis show that the J2000 model captures the snow cover dynamics well in the historical period (2003-2018) compared to the improved MODIS-derived snow cover with a coefficient of determination of 0.94. The model was then forced by climate projections from the selected GCMs to quantify the future changes in snow cover area, snow storage and snowmelt. A consistent decrease in decadal snow cover is projected in which the warm-dry models showed a higher decrease than cold-wet models. A 10-18 % reduction in annual snow cover is projected by the cold-wet models whereas a 22-36% reduction is expected from the warm-dry models. At the seasonal scale, across all models and scenarios, the snow cover in autumn and spring seasons are projected to reduce by as much as 25%, with an increase in winter and spring snowmelt and a decrease in summer snowmelt. The projected changes in the seasonal availability of snowmelt-driven water resources in the Panjshir region have direct implications for the water-dependent sectors in the downstream regions and highlight a need for a better understanding of current water usage and future adaptation practices in the Western Himalaya.

Quantifying the responses of forest dynamics to fluctuations in the atmosphere, hydrosphere and land surface conditions at global scales have been rather challenging. Despite the understanding that favourable environmental conditions promote forest growth, the effects have been challenging to observe across different ecosystems and climate gradients. Based on a global annual time series of aboveground biomass (AGB) from 1992 to 2018, we present forest carbon changes and provide insights on the controls of atmospheric (e.g., climate), hydrosphere (e.g., soil water availability) and land surface (e.g., changes in forest cover) conditions on forest carbon changes from local to global scales. Our findings indicate a gradient of forest gains and losses across AGB classes, with regions with carbon stocks of 50-100 MgC ha-1 depicting both the highest forest gains and losses. Furthermore, we observe that changes in forest carbon stocks were systematically positively correlated with changes in forest cover, while it was not necessarily the case with other environmental variables, such as air temperature and water availability at the uni-variate level. We also used a gradient boosted decision tree model and a variable importance metric (i.e., SHAP values) to demonstrate that atmospheric conditions largely dictate forest carbon changes followed by land surface and hydrosphere conditions. Interestingly, the observed functional relationships indicate a strong sensitivity of forest carbon changes to recent-past carbon stocks and both recent-past and concurrent atmospheric water demand. Regionally, we find evidence that carbon gains from long-term forest growth covary with long-term carbon sink inferred from atmospheric inversions at the ecosystem level. Our study quantifies the contributions from the atmosphere, hydrosphere, and land surface conditions to forest carbon changes and provides new insights into the underlying mechanisms of forest growth on the global carbon cycle.

Drylands contribute strongly to global biogeochemical cycles and their variability. While precipitation is the main driver of plant water availability, secondary water resources like shallow groundwater and lateral convergence of soil moisture may play important roles in supporting ecosystems against water limitation at the local scale. Despite their strong relevance, the effects of secondary water resources are often ignored or highly uncertain in studies over large spatial domains. Here, we aimed to quantify the degree to which land properties control secondary water resources over water-limited regions in Africa. To do so, we first detected the seasonal decay periods of Fractional Vegetation Cover (FVC) time series from the changes in FVC over time at daily temporal resolution. FVC data is provided by the EUMETSAT from the image acquisitions from the geostationary satellite MSG. We then calculated the seasonal decay rate of FVC (λ) and used it with other climate, land and vegetation properties at 5 km spatial resolution. We hypothesized that any secondary water resource should slow down vegetation decays in drylands. We used gradient boosting machine learning to model λ and constrained the model according to the hypothesis. Finally, we used Shapley additive explanations in order to quantify the effects of land properties on spatial variation of the modelled λ.Model output (NSE = 0.55) revealed that over drylands of Africa, ∼1/3 of spatial variation of λ is attributed to land properties, half of which is attributed to direct land effects while the rest is attributed to the land interactions with climate and vegetation. Though at local scales, this attribution gets much stronger over hotspots with strong secondary water resources, i.e., shallow groundwater. Spatially, land attributed variations of λ show that vegetation decays slower in regions with shallow groundwater and faster in regions where land surface is disconnected from the groundwater. Topographic complexity is another important factor, with slower vegetation decay in complex terrain, likely due to enhanced lateral moisture convergence. Moreover, these responses intensify with increasing climatological water limitation. We found strong effects of land parameters on seasonal vegetation decay rate, spatially structured but at local scales. This highlights the importance of local scale processes affecting water availability in drylands not only at local but also continental to global scales and shows the need of bridging processes across spatial scales in regional-to-global hydrological and vegetation models.

Snow is a crucial component of the hydrological cycle in the Western Himalaya. Water from snowmelt is used in various sectors in downstream regions, thus playing a critical role in securing the livelihoods of millions of people. In this study, we investigated the future evolution of snow cover and snowmelt in the Panjshir catchment of Afghanistan, a sub-basin of the Indus, in the Western Himalaya. We applied a three-step approach to select a few global climate model (GCM) simulations from CMIP5 climate datasets for RCP4.5 and RCP8.5, which showed reasonable performance with ERA5-Land dataset for the chosen historical period (1981–2010). The selected model simulations were then segregated into two groups: those projecting a cold-wet climate and those projecting a warm-dry climate by the end of the 21st century (2071–2100). These GCMs were downscaled to a higher resolution using empirical statistical downscaling. To simulate the snow processes, we used the distributed cryospheric-hydrological J2000 model. The results indicate that the model captures well the snow cover dynamics for the historical period when compared with the daily MODIS-derived snow cover. The J2000 model was then forced by climate projections from the selected GCMs to quantify future changes in snow cover area, snow storage and snowmelt. While a 10–18% reduction in annual snow cover area is projected in the cold-wet models, a 22–36% reduction is projected in the warm-dry models. Similarly, the snow cover area is projected to decrease in all elevation bands under climate change. At the seasonal scale, across all models and scenarios, the snow cover in the autumn and spring seasons are projected to reduce by as much as 25%, with an increase in winter and spring snowmelt and a decrease in summer snowmelt. The projected changes in the seasonal availability of snowmelt-driven water resources are likely to have direct implications for water-dependent sectors in the region and call for a better understanding of water usage and future adaptation practices.

Ecosystem processes are driven both by contemporary and antecedent environmental and land surface conditions through ecological memory effects. This chapter provides an insight into the relevance of memory effects in the Earth system and presents an experimental case study to use an Recurrent Neural Network (RNN) model to emulate a physical model. In addition to introducing an experimental design suitable for such purposes, we demonstrate that an RNN is largely capable of learning the memory effects encoded in a physical model. A non-temporal fully connected model cannot reproduce such memory effects, especially during anomalous conditions (e.g. climate extremes).

The responses of forest carbon dynamics to fluctuations in environmental conditions at a global scale remain elusive. Despite the understanding that favourable environmental conditions promote forest growth, these responses have been challenging to observe across different ecosystems and climate gradients. Based on a global annual time series of aboveground biomass (AGB) estimated from radar satellites between 1992 and 2018, we present forest carbon changes and provide insights on their sensitivities to environmental conditions across scales. Our findings indicate differences in forest carbon changes across AGB classes, with regions with carbon stocks of 50-125 MgC ha-1 depict the highest forest carbon gains and losses, while regions with 125-150 MgC ha-1 have the lowest forest carbon gains and losses in absolute terms. Net forest carbon change estimates show that the arc-of-deforestation and the Congo Basin were the main hotspots of forest carbon loss, while a substantial part of European forest gained carbon during the last three decades. Furthermore, we observe that changes in forest carbon stocks were systematically positively correlated with changes in forest cover fraction. At the same time, it was not necessarily the case with other environmental variables, such as air temperature and water availability at the bivariate level. We also used a model attribution method to demonstrate that atmospheric conditions were the dominant control of forest carbon changes (56% of the total study area) followed by water-related (29% of the total study area) and vegetation (15% of the total study area) conditions. Regionally, we find evidence that carbon gains from long-term forest growth covary with long-term carbon sinks inferred from atmospheric inversions. Our results describe the contributions from the atmosphere, water-related and vegetation conditions to forest carbon changes and provide new insights into the underlying mechanisms of the coupling between forest growth and the global carbon cycle.

Publisher Copyright: © 2022 The Authors. Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union. Hydrological interactions between vegetation, soil, and topography are complex, and heterogeneous in semi-arid landscapes. This along with data scarcity poses challenges for large-scale modeling of vegetation-water interactions. Here, we exploit metrics derived from daily Meteosat data over Africa at ca. 5 km spatial resolution for ecohydrological analysis. Their spatial patterns are based on Fractional Vegetation Cover (FVC) time series and emphasize limiting conditions of the seasonal wet to dry transition: the minimum and maximum FVC of temporal record, the FVC decay rate and the FVC integral over the decay period. We investigate the relevance of these metrics for large scale ecohydrological studies by assessing their co-variation with soil moisture, and with topographic, soil, and vegetation factors. Consistent with our initial hypothesis, FVC minimum and maximum increase with soil moisture, while the FVC integral and decay rate peak at intermediate soil moisture. We find evidence for the relevance of topographic moisture variations in arid regions, which, counter-intuitively, is detectable in the maximum but not in the minimum FVC. We find no clear evidence for wide-spread occurrence of the “inverse texture effect” on FVC. The FVC integral over the decay period correlates with independent data sets of plant water storage capacity or rooting depth while correlations increase with aridity. In arid regions, the FVC decay rate decreases with canopy height and tree cover fraction as expected for ecosystems with a more conservative water-use strategy. Thus, our observation-based products have large potential for better understanding complex vegetation-water interactions from regional to continental scales. publishersversion published

The turnover time of terrestrial ecosystem carbon is an emergent ecosystem property that quantifies the strength of land surface on the global carbon cycle–climate feedback. However, observation- and modeling-based estimates of carbon turnover and its response to climate are still characterized by large uncertainties. In this study, by assessing the apparent whole ecosystem carbon turnover times (τ) as the ratio between carbon stocks and fluxes, we provide an update of this ecosystem level diagnostic and its associated uncertainties in high spatial resolution (0.083∘) using multiple, state-of-the-art, observation-based datasets of soil organic carbon stock (Csoil), vegetation biomass (Cveg) and gross primary productivity (GPP). Using this new ensemble of data, we estimated the global median τ to be 43-7+7 yr (median-difference to percentile 25+difference to percentile 75) when the full soil is considered, in contrast to limiting it to 1 m depth. Only considering the top 1 m of soil carbon in circumpolar regions (assuming maximum active layer depth is up to 1 m) yields a global median τ of 37-6+3 yr, which is longer than the previous estimates of 23-4+7 yr (Carvalhais et al., 2014). We show that the difference is mostly attributed to changes in global Csoil estimates. Csoil accounts for approximately 84 % of the total uncertainty in global τ estimates; GPP also contributes significantly (15 %), whereas Cveg contributes only marginally (less than 1 %) to the total uncertainty. The high uncertainty in Csoil is reflected in the large range across state-of-the-art data products, in which full-depth Csoil spans between 3362 and 4792 PgC. The uncertainty is especially high in circumpolar regions with an uncertainty of 50 % and a low spatial correlation between the different datasets (0.2

Global land surface and hydrologic models, used to quantify the influence of groundwater (GW) dynamics on land surface hydrology, vary in parameterizations of the GW capillary flux and depth of moisture flux exchange between GW and unsaturated soil column (interaction depth). In this study, we demonstrate that the parameterization of capillary flux significantly affects the magnitude of GW‐supported ET (±15%) and GW recharge (±25%). The largest sensitivities are associated with a strong dependence of the capillary flux parameterization on water table depth (WTD) with further controls by soil hydraulic properties. Under shallow WTD condition, the finer soils show a larger GW‐supported ET for parameterization that depends only on WTD. Further, GW‐supported ET reduces by 7–13% per meter increase in interaction depth used in simulation compared to simulation in which the interaction depth varies in space and time with WTD. The simulations of runoff, soil moisture, and WTD are even more sensitive to the difference in parameterization and interaction depth. Moreover, these sensitivities vary largely in both space and time. Evaluations against observation‐based data and previous modeling simulations reveal that simulations with capillary flux perform better than that the one without it in regions with large GW‐supported ET. We concur that capillary flux has a large and nonnegligible influence in global hydrologic simulation. But, the spatiotemporal variabilities of sensitivities of hydrological fluxes and storages demonstrate that different parameterization of GW capillary flux may also lead to a substantial uncertainty in global hydrologic simulations and subsequent water resources assessments.

Forests cover about 30% of the terrestrial surface of our planet and store a large part of the terrestrial carbon (C), indicating their fundamental role in terrestrial C dynamics. In recent years, significant advances have been made in understanding terrestrial C cycling across scales, albeit uncertainties remain about fundamental processes, such as photosynthesis, allocation, and mortality, which exert dominant controls on vegetation C dynamics. Allocation plays a critical role in forest ecosystem C cycling by partitioning the products of net photosynthesis into leaves, wood, and below-ground components but is still poorly represented mostly given limitations in process understanding as well as in both suitable and commensurate observations.Here, we explore different approaches in constraining C allocation alongside processes driving assimilation and out fluxes in a terrestrial ecosystem model based on novel forest biomass datasets. More specifically, we use a series of temporally changing above-ground biomass (AGB) data from local (i.e. in-situ forest inventory data) to global (i.e. long-term C-band satellite retrievals from 1992 to 2018) scales, in a multi-constraint approach. We explore the information contained in a novel AGB time series to diagnose the potential of using changes in vegetation C stocks, jointly with C and water fluxes, to constrain and parameterize different C allocation modeling approaches. Both at FLUXNET site level and global scale, we will: i) present these novel AGB datasets, their strengths and limitations, ii) demonstrate the relevance of constraining C allocation with such temporally changing AGB estimates, and iii) provide a comparison of different C allocation approaches (i.e. fixed versus dynamic allocation, and an hybrid modeling approach) and their implications in representing ecosystem dynamics.

Vegetation structure and activity are the crucial links between the water, carbon and energy cycles. However, their representation remains a major source of uncertainty in large-scale models. Hydrological models not only vary in the way they include vegetation and its interaction with water, but also become less tangible when their complexity increases. This poses a challenge in validating these models, as shown by several comparisons of dynamic vegetation models.In this context, the increasing availability and quality of Earth observation-based data provides a new avenue and valuable information to improve model simulations and gain insights into the role of vegetation within the global water cycle. On the one hand, such observations can be used to calibrate model parameters. On the other hand, more vegetation related data allows new approaches to describe in-model vegetation characteristics beyond the values that are traditionally defined for plant functional types.In this study, we use a simple and highly transparent global hydrological model and constrain its vegetation related parameters against diverse Earth observation-based data. We include GRACE terrestrial water storage anomalies, GlobSnow snow water equivalent, ESA CCI soil moisture as well as estimates of evapotranspiration from FLUXCOM and gridded runoff from GRUN in a multi-criteria calibration approach that considers the strengths and uncertainties of each data stream.Further, we conduct several factorial experiments to test alternative approaches for representing vegetation characteristics that influence processes like infiltration, root water uptake and transpiration. The approaches range from the simple differentiation of vegetated and non-vegetated areas over applying plant functional type-specific parameters to defining vegetation characteristics as functions of Earth observation-based data such as EVI, tree cover and estimates of plant rooting depth.For each of the experiments, the model is calibrated and the results are finally compared with each other and against observations to quantify the ability to reproduce observational patterns and to assess the effects of vegetation on simulated hydrological processes across spatio-temporal scales.

Given its simplicity, the light use efficiency (LUE) concept is widely used for the estimation of gross primary productivity (GPP) at the ecosystem scale. In the last three decades, many types of LUE models have been developed to explain the dependencies of GPP to different environmental and meteorological conditions across spatial and temporal scales. Despite the simplicity, LUE models are robust against observations from daily to seasonal scales, though entailing challenges in parameter upscaling. The main differences across LUEs resides in the presence/absence of certain meteorological drivers and in the particular formulations of different response functions to diagnose instantaneous light use efficiency (ε*). Here, we collected different algorithms for describing the meteorological constraints on ε* from literature and performed a factorial experiment by recombining the different response functions between the different models to assess model performance at site and network level. These models were forced and parameterized by remote sensing data, meteorological data and GPP for 177 eddy covariance flux sites from the FLUXNET2015 and LaThuile using a data assimilation approach. The results show that the two selected optimal LUE models had no significant differences in model performances at site-level at daily, weekly, and monthly scales. The Nash-Sutcliffe Model Efficiency Coefficient (NSE) of 50% sites were larger than 0.726 and 0.725 at daily, 0.788 and 0.783 at weekly, 0.836 and 0.834 at monthly and 0.544 and 0.510 at annual scales. Based on the selected models, we further explored the different methods to upscale the optimized parameters: a) site means and medians per plant functional type and climate class, b) random forest regression, using bioclimatic variables and corresponding vegetation index, and c) selection according to the similarity between sites, determined via the NSE in mean seasonal cycle temperature, precipitation, radiation, and vegetation indexes within the same plant functional type. The model efficiencies in cross validation for both models show that using the median parameters per plant functional type had the best performance to upscale parameters from site-level to global-level at daily, weekly and monthly scales. Since the meteorological response functions and the corresponding parameters represent the sensitivity of plant photosynthesis to the meteorological conditions we further explore the relationship between the climate sensitivities and other environmental drivers as well as biophysical plant traits using global retrievals of Sun induced fluorescence. Our results emphasize that novel Earth Observations datasets and transfer learning approaches bridge the LUE formulation tradeoffs between complexity and tractability.

This article is designed to assess the impact of real effective exchange rate (REER) on economic growth of Nepal. The study uses annual time series data for the period of 1975 to 2015. Engle- Granger residual based test and error correction model have been used to detect the impact of REER on real GDP of Nepal. The explanatory variables used in the study are real effective exchange rate, broad money supply, trade openness and gross fixed capital formation. The results of the study reveal that real effective exchange rate has positive impact on the real GDP of Nepal. Based on the findings, the study concludes that the transmission mechanism of REER through aggregate demand hold in case of Nepal and this result is compatible with the traditional approach to exchange rate. Finally, it is recommended that broad money supply continues to be relevant monetary policy for Nepal. Moreover, Nepal must use the real exchange rate as one of the macroeconomic policies. Pravaha Vol. 24, No. 1, 2018, page: 206-216