Explore the vast range of titles available.

Increased human water use combined with climate change have aggravated water scarcity from the regional to global scales. However, the lack of spatially detailed datasets limits our understanding of the historical water use trend and its key drivers. Here, we present a survey-based reconstruction of China’s sectoral water use in 341 prefectures during 1965 to 2013. The data indicate that water use has doubled during the entire study period, yet with a widespread slowdown of the growth rates from 10.66 km³·y−2 before 1975 to 6.23 km³·y−2 in 1975 to 1992, and further down to 3.59 km³·y−2 afterward. These decelerations were attributed to reduced water use intensities of irrigation and industry, which partly offset the increase driven by pronounced socioeconomic development (i.e., economic growth, population growth, and structural transitions) by 55% in 1975 to 1992 and 83% after 1992. Adoptions for highly efficient irrigation and industrial water recycling technologies explained most of the observed reduction of water use intensities across China. These findings challenge conventional views about an acceleration in water use in China and highlight the opposing roles of different drivers for water use projections.

The rate at which land surface soils dry following rain events is an important feature of terrestrial models. It determines, for example, the water availability for vegetation, the occurrences of droughts, and the surface heat exchanges. As such, surface soil moisture (SSM) “drydowns,” i.e., the SSM temporal dynamics following a significant rainfall event, are of particular interest when evaluating and calibrating land surface models (LSMs). By investigating drydowns, characterized by an exponential decay time scale τ, we aim to improve the representation of SSM in the ORCHIDEE global LSM. We consider τ calculated over 18 International Soil Moisture Network sites found within the footprint of FLUXNET towers, covering different vegetation types and climates. Using the ORCHIDEE LSM, we compare τ from the modeled SSM time series to values computed from in situ SSM measurements. We then assess the potential of using τ observations to constrain some water, carbon, and energy parameters of ORCHIDEE, selected using a sensitivity analysis, through a standard Bayesian optimization procedure. The impact of the SSM optimization is evaluated using FLUXNET evapotranspiration and gross primary production (GPP) data. We find that the relative drydowns of SSM can be well calibrated using observation-based τ estimates, when there is no need to match the absolute observed and modeled SSM values. When evaluated using independent data, τ-calibration parameters were able to improve drydowns for 73% of the sites. Furthermore, the fit of the model to independent fluxes was only minutely changed. We conclude by considering the potential of global satellite products to scale up the experiment to a global-scale optimization.

The freshwater 1-D FLake lake model was coupled to the ORCHIDEE land surface model to simulate lake energy balance at the global scale. A multi-tile approach has been chosen to allow the modeling of various types of lakes within the ORCHIDEE grid cell. Thus, three different lake tiles have been defined according to lake depth which is the most influential parameter of FLake, but other properties could be considered in the future. Several depth parameterization strategies have been compared, differing by the way to aggregate the depth of the subgrid lakes, i.e., arithmetical, geometrical, harmonical mean and median. Five atmospheric reanalysis datasets available at 0.5∘ or 0.25∘ resolution have been used to force the model and assess model systematic errors. Simulations have been performed, evaluated and intercompared against observations of lake water surface temperatures provided by the GloboLakes database over about 1000 lakes and ice phenology derived from the Global Lake and River Ice Phenology database.The results highlighted the large impact of the atmospheric forcing on the lake energy budget simulations and the improvements brought by the highest resolution products (ERA5 and E2OFD). The median of the root square mean errors (RMSEs) calculated at global scale ranges between 3.2 and 2.7 ∘C among the forcings, CRUJRA and ERA5 leading respectively to the worst and best results. The depth parameterization strategy appeared to be less influential, with RMSE differences less than 0.1 ∘C for the four aggregation scenarios tested.The simulation of ice phenology presented systematic errors whatever the forcing and the depth parameterization used. Large systematic errors were highlighted such as negative biases on the onset and positive biases on the offset. Freezing onset was shown to be the less sensitive to atmospheric forcing with the median of the errors ranging between 10 and 14 d. Larger errors up to 25 d were observed on the simulation of the end of the freezing period. Such errors, already highlighted in previous works, could be explained by scale effects and deficiencies in the modeling of snow–ice processes not accounting for partial ice cover. Various pathways are drawn to improve the model results, including the use of remote sensing data to better constrain the lake radiative parameters (albedo and extinction coefficient) as well as the lake depth thanks to the recent and forthcoming high-resolution satellite missions.

Background Wildfires represent an important element in the bio-geophysical cycles of various ecosystems across the globe and are particularly related to land transformation in tropical and subtropical regions. In this study, we analyzed the links between fires, land use (LU), and meteorological variables in the South American Chaco (1.1 million km 2 ), a global deforestation hotspot and fire-exposed region that has recently attracted greater attention as the largest and one of the last tropical dry forests in the world. Results We found that the Dry Chaco (73% of the total area of Chaco) exhibits a unimodal fire seasonality (winter-spring), and the Wet Chaco (the remaining 23%) displays a bimodal seasonality (summer-autumn and winter-spring). While most of the burnt area (BA) was found in the Wet Chaco (113,859 km 2 ; 55% of the entire BA), the Dry Chaco showed the largest fraction of forest loss (93,261 km 2 ; 88% of the entire forest loss). Between 2001 and 2019, 26% of the entire Chaco’s forest loss occurred in areas with BA detections, and this percentage varies regionally and across countries, revealing potential connections to LU and policy. Argentina lost 51,409 km 2 of its Chaco tree cover, surpassing the forest losses of Paraguay and Bolivia, and 40% of this loss was related to fire detections. The effect of meteorological fluctuations on fuel production and flammability varies with land cover (LC), which emerged as the principal factor behind BA. While wet areas covered with herbaceous vegetation showed negative correlations between BA and precipitation, some dry regions below 800 mm/year, and mostly covered by shrublands, showed positive correlations. These results reveal the two different roles of precipitation in (a) moisture content and flammability and (b) production of biomass fuel. Conclusions As fires and deforestation keep expanding in the South American Chaco, our study represents a step forward to understanding their drivers and effects. BA is dependent on LC types, which explains the discrepancies in fire frequency and seasonality between the Wet and Dry Chaco subregions. The links between fires and deforestation also vary between regions and between countries, exposing the role of anthropic forcing, land management, and policy. To better understand the interactions between these drivers, further studies at regional scale combining environmental sciences with social sciences are needed. Such research should help policy makers take action to preserve and protect the remaining forests and wetlands of the Chaco.

Land surface temperature (LST) plays an essential role in water and energy exchanges between the Earth's surface and atmosphere. Recent advancements in high-quality satellite-derived LST data and land data assimilation systems present a unique opportunity to bridge the gap between global observational data and land surface models (LSMs) to better constrain the water and energy budgets in a changing climate. In this vein, this study focuses on the assimilation of the ESA CCI-LST product into the ORCHIDEE LSM (the continental part of the Institut Pierre-Simon Laplace Earth system model) with the aim of optimizing key parameters to improve the simulation of LST and surface energy fluxes. We use the land data assimilation system for the ORCHIDEE model (ORCHIDAS) to conduct a series of synthetic twin data assimilation experiments accounting for actual data availability and uncertainty from ESA CCI-LST to find an optimal strategy for assimilating LST. Here, we test different strategies of assimilation, notably investigating (i) two optimization methods (a random search technique and a gradient-based technique) and (ii) different ways to assimilate LST using the only raw data and/or different characteristics of the LST diurnal cycle (e.g. mean daily, daily amplitude, maximum and minimum temperatures, and morning and afternoon gradients). Upon identifying the optimal approach, we use ORCHIDAS to assimilate ESA CCI-LST data across 34 European sites provided by the Warm Winter database. Our results demonstrate the effectiveness of assimilating 3 h CCI-LST data in ORCHIDEE over a single year in 2018, thereby improving the accuracy of simulated LST and fluxes. This improvement, assessed against CCI-LST and in situ observations, reaches up to a 60 % reduction in the root-mean-square deviation, with a median decrease of 20 % over the entire validation period (2009–2020). Furthermore, we evaluate the effectiveness of optimized parameters for application at larger scales using the median of optimized parameters per vegetation type across sites. Notably, the performance for both LST and fluxes exhibits consistent stability over the years, comparable to using site-specific parameters, and indicates a significant improvement in the modelled fluxes. Future work will be focused on refining the utilization of the observation uncertainties provided by the ESA CCI-LST product (e.g. decomposed uncertainties and spatio-temporal variability) in the assimilation process.

The FLake lake model embedded in the ORCHIDEE land surface model was recently updated to better represent winter ice cover. MODIS albedo data and the Great Lakes ice cover fraction dataset over the Laurentian Great Lakes were used to calibrate and validate a new parameterization of the lake albedo accounting for a partial ice cover fraction. The developments were validated for the phenology of the ice cover of 200 lakes of various sizes reported in the Global Lake and River Phenology Database. The results are in better agreement with the observations for all lake size categories, with the largest and deepest lakes showing more significant error reductions in the duration of the ice cover period up to 18 d. This study highlights the importance of considering partial ice cover to correctly model lake albedo in cold regions and thus to simulate realistic mass and energy exchanges at the land–atmosphere interface.

Spring snow cover extent (SCE) in the Northern Hemisphere has decreased in the last four decades but with significant interannual variability. Investigations of the mechanisms that control SCE variations were almost exclusively focused on the year-to-year variability of forcing variables and SCE integrated over a certain period of the year (e.g. season). Here, we use state-of-the-art climate reanalysis dataset to analyze the contribution of different surface energy fluxes to the inception and development of below-normal spring SCE from an intraseasonal perspective. During years identified with lower-than-average SCE by the end of spring, higher-than-average net longwave radiation and sensible heat that is greater than the decrease of net shortwave radiation in the early spring snowmelt season induces the initial SCE deficit. This can be mainly explained by the finding that the increase of downwelling longwave radiation because of increased water vapor significantly exceeds the attenuation of downwelling short-wave radiation due to increased cloudiness. When a SCE deficit has been incepted in early spring, net shortwave radiation in late spring gradually becomes higher than average through snow albedo feedback, which further accelerates snowmelt. This suggests that short-wave radiation is not responsible for the initiation of negative SCE anomaly by the end of spring but acts as an amplifying feedback once the snow melt is started.

The ratio of plant transpiration to total terrestrial evapotranspiration (T/ET) captures the role of vegetation in surface–atmosphere interactions. However, its magnitude remains highly uncertain at the global scale. Here we apply an emergent constraint approach that integrates CMIP5 Earth system models (ESMs) with 33 field T/ET measurements to re-estimate the global T/ET value. Our observational constraint strongly increases the original ESM estimates (0.41 ± 0.11) and greatly alleviates intermodel discrepancy, which leads to a new global T/ET estimate of 0.62 ± 0.06. For all the ESMs, the leaf area index is identified as the primary driver of spatial variations of T/ET, but to correct its bias generates a larger T/ET underestimation than the original ESM output. We present evidence that the ESM underestimation of T/ET is, instead, attributable to inaccurate representation of canopy light use, interception loss and root water uptake processes in the ESMs. These processes should be prioritized to reduce model uncertainties in the global hydrological cycle.

Evapotranspiration (ET) is critical in linking global water, carbon and energy cycles. However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed the basic theory and state-of-the-art approaches for estimating global terrestrial ET, including remote-sensing-based physical models, machine-learning algorithms and land surface models (LSMs). We then utilized 4 remote-sensing-based physical models, 2 machine-learning algorithms and 14 LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the ensemble means of annual global terrestrial ET estimated by these three categories of approaches agreed well, with values ranging from 589.6 mm/yr (6.56×10^4 cu.km/yr) to 617.1 mm/yr (6.87×10^4 cu.km/yr). For the period from 1982 to 2011, both the ensembles of remote-sensing-based physical models and machine-learning algorithms suggested increasing trends in global terrestrial ET (0.62 mm/sq.yr with a significance level of p<0.05 and 0.38 mm yr−2 with a significance level of p<0.05, respectively). In contrast, the ensemble mean of the LSMs showed no statistically significant change (0.23 mm/sq.yr, p>0.05), although many of the individual LSMs reproduced an increasing trend. Nevertheless, all 20 models used in this study showed that anthropogenic Earth greening had a positive role in increasing terrestrial ET. The concurrent small interannual variability, i.e., relative stability, found in all estimates of global terrestrial ET, suggests that a potential planetary boundary exists in regulating global terrestrial ET, with the value of this boundary being around 600 mm/yr. Uncertainties among approaches were identified in specific regions, particularly in the Amazon Basin and arid/semiarid regions. Improvements in parameterizing water stress and canopy dynamics, the utilization of new available satellite retrievals and deep-learning methods, and model–data fusion will advance our predictive understanding of global terrestrial ET.

The existing medium-resolution land cover time series produced under the European Space Agency's Climate Change Initiative provides 29 years (1992-2020) of annual land cover maps at 300m resolution, allowing for a detailed study of land change dynamics over the contemporary era. Because models need two-dimensional parameters rather than two-dimensional land cover information, the land cover classes must be converted into model-appropriate plant functional types (PFTs) to apply this time series to Earth system and land surface models. The first-generation cross-walking table that was presented with the land cover product prescribed pixel-level PFT fractional compositions that varied by land cover class but that lacked spatial variability. Here we describe a new ready-to-use data product for climate modelling: spatially explicit annual maps of PFT fractional composition at 300m resolution for 1992-2020, created by fusing the 300m medium-resolution land cover product with several existing high-resolution datasets using a globally consistent method. In the resulting data product, which has 14 layers for each of the 29 years, pixel values at 300m resolution indicate the percentage cover (0%-100%) for each of 14 PFTs, with pixel-level PFT composition exhibiting significant intra-class spatial variability at the global scale. We additionally present an updated version of the user tool that allows users to modify the baseline product (e.g. re-mapping, re-projection, PFT conversion, and spatial sub-setting) to meet individual needs. Finally, these new PFT maps have been used in two land surface models - Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) and the Joint UK Land Environment Simulator (JULES) - to demonstrate their benefit over the conventional maps based on a generic cross-walking table. Regional changes in the fractions of trees, short vegetation, and bare-soil cover induce changes in surface properties, such as the albedo, leading to significant changes in surface turbulent fluxes, temperature, and vegetation carbon stocks. The dataset is accessible at 10.5285/26a0f46c95ee4c29b5c650b129aab788 (Harper et al., 2023).