Extreme Temperature (ET) and Extreme Precipitation (EP) variations impose critically influence agriculture, ecosystems, and water resource security in the Middle and Lower Yangtze River (MLYR). However, the response of the ET and EP in the region to changes in atmospheric circulation and human activities, and the potential driving mechanisms remains unclear, raising difficulties in forecasting the ET and EP. This study first used TheilSen Median, Mann-Kendall test, and Detrended Fluctuation Analysis (DFA) to analyze the spatio-temporal variations of the ET and EP indices within the MLYR during 1963-2019. Then, the Slip correlation analysis and Ftest were applied to calculate the lagged correlations between typical climate factors with the ET and EP. Finally, the potential driving mechanisms of these changes were trying to be identified from the perspectives of atmospheric circulation and human activities. Results demonstrated that increasing (decreasing) warm (cold) ET indices, and EP exhibit rising frequency and intensity but shorter duration for historical and future periods, especially intensified in mountainous areas. Multiple climatic factors were correlated with ET and EP indices with different lag times. Among these, the Arctic Oscillation (AO) was negatively correlated to frost days (FD0) at lag times of 0, 1, and 8 a. ET increases are driven by enhancing surface heat flux, reducing daytime cloud cover, stronger winter southeasterlies (SE), and rising geopotential height, while the EP rises stem from intensifying anomalies anticyclones over the western Pacific and Mongolia, which strengthen northeasterlies, suppress the northward advance of the East Asian Summer Monsoon, and extend the rainy season. Moreover, Urbanization further suppresses cold ET, enhances warm ET, and increases EP, contributing -17.9%, 19.3%, and 42.9%, respectively. These findings provide clearer evidence for regional disaster prevention and prediction in the responses to climate change.

Accurate quantification of ecohydrological processes is essential for effective water and carbon management in terrestrial ecosystems. Traditional simulations mainly rely on mechanistic models, yet their accuracy is often limited by inconsistencies in representing physical processes and uncertainties in parameterization. Integrating remote sensing signals offers a promising way to reduce these uncertainties and enhance model applicability. In this study, in-situ observations from a wheat cropland in the Guanzhong Plain were used to simulate gross primary productivity (GPP) and latent heat flux (LE) by comparing a forward model (STEMMUS-SCOPE) with a remote sensing-driven inverse model (STEMMUS-MLR). We further examined the role of solar-induced chlorophyll fluorescence (SIF), an emerging proxy for photosynthesis, as an input to improve mechanistic modeling of GPP and LE. Results show that STEMMUS-MLR outperformed STEMMUS-SCOPE in estimating water and carbon fluxes, demonstrating that incorporating SIF effectively reduces bias associated with uncertainties in parameters and forcing data. The contribution of SIF was quantified using Random Forest regression and Shapley additive explanations (SHAP), revealing that SIF markedly reduced the dependence of GPP and LE simulations on shortwave radiation (SW), air temperature (Ta), and leaf area index (LAI). These findings highlight the critical role of SIF in ecohydrological modeling of semi-arid cropland ecosystems and provide a scientific basis for advancing process understanding and improving the precision management of water and carbon budgets in terrestrial ecosystems.

Global warming increases the rainfall intensity and frequency in most regions, intensifying the urgency of investigating the influence of various factors on soil erosion. However, existing studies mainly investigate this issue on the monthly or annual scale. There remains a research gap regarding how these factors influence soil erosion on the event scale. In this study, the Min River Watershed (MWR), located in the eastern Tibetan Plateau, was chosen as the study region. Based on the daily measured hydro-meteorological and underlying data, the effects of precipitation, temperature, Land Cover (LC), connection, and runoff on soil erosion have been decoupled on the event and monthly scales using the Partial Least Squares-Structural Equation Model (PLS-SEM). Results show that the annual runoff, Suspended Sediment Concentration (SSC), and Sediment Flux (SF) were increased significantly (P < 0.05) from 2007 to 2020. On the event scale, the SF variation was more obvious than that of other variables, which may be caused by the inconsistent changes of rainfall amount and intensity. The parameters of Sediment Rating Curves (SRC) varied on different scales, indicating that the SF is governed by soil supply on the event scale but by runoff erosive power on the monthly scale. PLS-SEM results exhibit that SF is predominated by SSC on the monthly scale, but shifts to an equivalent balance between supply and transport capacity on the event scale, and connection significantly modulates SSC on the event scale. Precipitation remains a universal driver; event-based sediment dynamics are uniquely governed by multi-path cascading effects of temperature and vegetation on connection and runoff, reflecting a transition from simple hydrological forcing to complex biophysical interactions on the event scale. Results could enhance the understanding of the influence paths of various factors on soil erosion on multiple scales and support future soil erosion prevention under global warming.

Accurate simulation of multi-frequency brightness temperature (TBp) observations over frozen ground remains challenging using current forward models. This study presents the first attempt to simultaneously reproduce dual-polarized TBp observations at multiple frequencies from different satellite sensors under both frozen and thawed soil conditions, using a unified modeling framework that augments the community microwave emission model (CMEM) and couples it with an optimization algorithm. The proposed framework is developed by (i) evaluating whether a unified set of parameterization options can simultaneously reproduce SMAP (L-band) and AMSR2 (C-, X-, and K-band) TBp observations on the Tibetan Plateau, (ii) employing parameter optimization strategies to improve model performance and explore the frequency- or sensor-dependence of model parameters, and (iii) assessing the transferability of optimized parameters across other sensors. Results show that a unified combination of parameterization options performs comparably to frequencyspecific combinations, demonstrating its feasibility for reproducing both SMAP and AMSR2 observations in the Tibetan desert and meadow ecosystems. The remaining simulation deviations are largely reduced via implementing parameter optimization, with sensor-dependent parameter values achieving similar performance to frequency-dependent ones (RMSE differences across frequencies <3 K). Parameters optimized using AMSR2 observations also perform well for FY-3B and WindSat, confirming the transferability of model parameters to sensors with similar configurations. Furthermore, re-optimizing these sensor-dependent parameters effectively captures the seasonal dynamics observed by SMAP and AMSR2 in the boreal forest zone, highlighting the model's applicability across diverse landscapes and climate conditions. These findings provide a valuable basis for improving the modeling of multi-frequency TBp observations over frozen ground, thereby facilitating the monitoring of freeze/thaw processes and addressing spatiotemporal discontinuities of soil moisture retrievals in cold regions.

Study region: The source region of the Yangtze River, a core part of the “Asian Water Tower,” has sparse gauges, cold high elevation, and complex relief. Together with strong climatic variability, these factors cause large precipitation-estimation errors that undermine hydrological modeling and meteorological assessments. Study focus: We introduce the Season–Land-Use Heterogeneity Bayesian Three-Cornered Hat (SLH-BTCH), an enhancement of BTCH. Data are grouped by season–land-use strata; within each group we estimate error covariance and then fuse products by weighted averaging, using only multi-source precipitation fields—no in-situ priors. Using CHIRPS, CMFD, TPHiPr, and CHM-PRE, we assess daily performance of the originals, BTCH, and SLH-BTCH against ground observations, and include an equal-weight average (EWA) baseline to gauge the benefit of grouped error modeling. New hydrological insights for the region: Compared with the original products, BTCH and equal-weight averaging, SLH-BTCH yields event timing and seasonal precipitation more consistent with gauges while reducing storm-intensity bias and day-to-day noise across contrasting land-surface types. Around key headwater stations (Tuotuohe, Wudaoliang, Zhiduo) this sharpens damaging-storm signals and reduces false alarms, providing tighter basin water-balance closure and more reliable flood simulation, routing and dry-season water-availability estimates in ungauged, data-sparse sectors of the Tibetan Plateau.

Quantifying surface soil moisture (SSM) is essential for understanding hydrological cycles, land-atmosphere exchanges, and field-scale water management. However, satellite-based and land surface model products, typically available at kilometer-scale resolution, cannot resolve the sub-field heterogeneity required for small-catchment or precision agriculture applications. This study develops and evaluates a two-step Random Forest (RF) downscaling framework that links a regional 1 km SSM product to a 30 m product and then tests a UAS-supported 16 cm super-resolution mapping experiment.The framework was implemented at the Monteforte Cilento experimental sub-catchment in southern Italy. Model I used 1 km SSM and land surface predictors to generate a five-year 30 m SSM product (SSM30m). Model II used the 30 m product and scale-consistent predictors to test whether Uncrewed Aerial Systems (UAS)-derived 16 cm features, including land surface temperature, diurnal temperature difference, vegetation index, and micro-topographic variables, can further disaggregate the 30 m patterns for a single UAS campaign day.Internal model testing yielded R2 values of 0.805 for Model I and 0.837 for Model II. For independent field evaluation: Dynamics of SSM30m closely tracked cosmic-ray neutron sensor references, whereas the single-day 16 cm resolution surface soil moisture map (SSM16cm) achieved a Pearson correlation coefficient of 0.71 and an unbiased root mean squared error (ubRMSE) of 0.0473 cm3 cm−3 against 20 time-domain reflectometry (TDR) measurements. From Model I to Model II, predictor importance shifted from regional LST at coarser scales to diurnal thermal contrast and micro-topography at sub-meter scale. Furthermore, an analyse of spatial variance revealed strong spatial coherence across all resolutions, from 1 km to 16 cm. This research demonstrates a scalable framework from regional monitoring to “single plant” scale water management, providing a vital tool for sustainable environmental and agricultural decision-making.

Vegetation–heat flux feedbacks have a great influence on ecosystems, but the interaction between them is still unclear. This is particularly critical in ecologically fragile areas, where plant growth is especially sensitive to land–atmosphere interactions that help plants withstand environmental pressures. To the causal relationship between vegetation and heat flux under different topographies on the Tibetan Plateau, we improved the Granger causality model to handle nonstationary scenarios, enabling us to uncover previously unknown interaction patterns between unstable vegetation change and heat fluxes. Further sensitivity analysis was performed to assess the strength of causal influences. The results showed that the sensible heat (SH) and latent heat (LH) fluxes were increasing at rates of 0.28 W·m−2·decade−1 and 0.105 W·m−2·decade−1, respectively. The interaction between them on vegetation change depends on terrains, at low elevations below 3000 m and high elevations of 5000–6000 m, SH and LH jointly regulate vegetation growth of shady and gentle to moderate slopes, predominantly involving dense grasslands, but the influence of SH is stronger. While at middle elevations of 3000–5000 m and on steep slopes, LH and vegetation of all types interact to form an intensive local energy cycle. Conversely, vegetation change also influences heat flux. Below 6000 m (excluding the 2000–3000 m), vegetation only regulates LH, and this influence appears largely independent of terrain, contributing to energy redistribution and water cycle maintenance in these regions. These interactions suggest that vegetation plays a central role in shaping energy distribution on the plateau, maintaining the water cycle, and regulating climate in alpine regions by regulating heat flux.

Plant photosynthesis and transpiration are basic processes in water and carbon cyclings in ecosystems. Accurate simulation of the net CO2 assimilation rate (Am-c) and transpiration rate (Em-c) of citrus tree canopy under high temperature stress can help to obtain rational planting and irrigation schemes to cope with the impact of environmental stress on the citrus trees. In this study, Am-c and Em-c were measured by the wrapped Stem Flow Meter and Portable Photosynthesis System to analyze the daytime variation characteristics of Am-c and Em-c combined with the observed meteorological data. Then, the Synthetic Model of Photosynthesis-Transpiration based on the Stomatal Behavior model (SMPTSB) into the Photosynthetic-Transpiration coupling Model based on three Temperatures (PTM-3T) by modifying the calculation method of aerodynamic resistance (ra) and canopy resistance (rc) using imitation leaf method and temperature difference method, respectively, to improve the simulation accuracy of Am-c and Em-c under high-temperature condition. The results indicate that: (1) Under high temperature stress condition, there was an "asynchronous" phenomenon where the daytime peak of Am-c appeared earlier than that of Em-c. (2) Compared with the SMPTSB model, the PTM-3T model has higher simulation accuracy when citrus trees are affected by high temperature, where the R2 of the Am-c is increased from 0.79 to 0.82, and the RMSE is reduced from 0.87 to 0.77 mu mol m-2 s-1, the R2 of the Em-c is increased from 0.17 to 0.76, and the RMSE is reduced from 0.68 to 0.27 mmol m-2 s-1, respectively. This study can provide a way to more accurately simulate the Am-c and Em-c under environmental stress conditions and can obtain a better understanding of the mass transport and energy exchange of the soil-plant-atmosphere system.

Healthy soil is the foundation of our food, clean water, and a stable climate, and cutting-edge science helps us to protect it.

The Tibetan Plateau, a critical regulator of the global water cycle and climate system, represents a highly sensitive region to environmental changes, with significant implications for sustainable development. This study focuses on Nam Co Lake, the third largest lake on the Tibetan Plateau, and investigates the hydrochemical evolution of the lake and the driving mechanisms in regard to the lake–river–groundwater system within the Nam Co Basin over the last 20 years. Our findings provide critical insights for sustainable water resource management in regard to fragile alpine lake ecosystems. The hydrochemical analyses revealed distinct temporal patterns in the total dissolved solids, showing an increasing trend during the 2000s, followed by a decrease in the 2010s. Piper diagrams demonstrated a gradual change in the anion composition from the Cl type to the HCO3 type over the study period. The ion ratio analyses identified rock weathering (particularly silicate, halite, sulfate, and carbonate weathering), ion exchange, and evaporation processes as primary controlling processes, with notable differences between water bodies: while all four weathering processes contributed to the lake’s water chemistry, only halite and carbonate weathering influenced river and groundwater compositions. The comparative analysis revealed more pronounced ion exchange processes in lake water than in river and groundwater systems. Climate change impacts were manifested through two primary mechanisms: (1) enhanced evaporation, leading to elevated ion concentrations and isotopic enrichment; and (2) temperature-related effects on the water chemistry through increased dilution from precipitation and glacial meltwater. Understanding these mechanisms is essential for developing adaptive strategies to maintain water security and ecosystem sustainability. The relationships established between climate drivers and hydrochemical responses provide a scientific basis for predicting future changes and informing sustainable management practices for inland lake systems across the Tibetan Plateau.

Hight-quality and Long-term measurements of land-atmosphere fluxes are vital for climate monitoring and Land Surface models (LSMs) benchmarking. Eddy covariance provides key in-situ data for theory and LSMs evaluation, but most flux towers lack continuous soil-plant-atmosphere measurements. Here, we present a long-term global dataset of water, energy and carbon fluxes, along with the corresponding above and below-ground hydrological, photosynthetic, and radiative data derived from the STEMMUS-SCOPE model simulations at 170 sites. In-situ observed fluxes data from PLUMBER2 and soil moisture (SM) data from FLUXNET2015 are employed to validate the effectiveness of the STEMMUS-SCOPE dataset. Results demonstrate that, without site-specific model tuning or calibration, and driven solely by global parameters and forcing datasets, simulated net radiation, latent heat flux, sensible heat flux, gross primary production, net ecosystem exchange, and SM datasets consistently agree with available in-situ measurements (median KGE: −0.03 to 0.80; median R2: 0.46 to 0.97; median rRMSE: 4.09% to 29.11%). This dataset supplements the existing ecosystem flux and SM network, enhancing our understanding of ecosystem functioning.

The soil health assessment has evolved from focusing primarily on agricultural productivity to an integrated evaluation of soil biota and biotic processes that impact soil properties. Consequently, soil health assessment has shifted from a predominantly physicochemical approach to incorporating ecological, biological and molecular microbiology indicators. This shift enables a comprehensive exploration of soil microbial community properties and their responses to environmental changes arising from climate change and anthropogenic disturbances. Despite the increasing availability of soil health indicators (physical, chemical, and biological) and data, a holistic mechanistic linkage has not yet been fully established between indicators and soil functions across multiple spatiotemporal scales. This article reviews the state-of-the-art of soil health monitoring, focusing on understanding how soil-microbiome-plant processes contribute to feedback mechanisms and causes of changes in soil properties, as well as the impact these changes have on soil functions. Furthermore, we survey the opportunities afforded by the soil-plant digital twin approach, an integrative framework that amalgamates process-based models, Earth Observation data, data assimilation, and physics-informed machine learning, to achieve a nuanced comprehension of soil health. This review delineates the prospective trajectory for monitoring soil health by embracing a digital twin approach to systematically observe and model the soil-plant system. We further identify gaps and opportunities, and provide perspectives for future research for an enhanced understanding of the intricate interplay between soil properties, soil hydrological processes, soil-plant hydraulics, soil microbiome, and landscape genomics.

Producing reliable profile soil moisture and temperature (SMST) simulations for the Tibetan Plateau (TP) is challenging with current model-based products. This study examines error sources in GLDAS-2.1 Noah through numerical experiments focusing on impact of soil properties, meteorological forcing, and model physics. Profile SMST observations from the Maqu network characterized by grassland with humid climate and Shiquanhe network dominated by bare ground with arid climate serve as ground truth. The control experiment running the default Noah model with GLDAS-2.1 meteorological data and FAO soil data mirrors the GLDAS-2.1 Noah product, both of which underestimate profile SM in Maqu and overestimate them in Shiquanhe, with profile ST underestimated in both areas. Using realistic soil types from in situ samples reduces RMSD by 27% and 57% on average in simulating profile SM for Maqu and Shiquanhe, respectively. Adoption of improved meteorological forcing further alleviates remaining overestimation in Shiquanhe during warm season with RMSD reduced by 45%. Implementation of augmented model physics largely addresses remaining deficiencies, which further reduces RMSD by more than 40% in both network via improving parameterizations of soil hydraulic properties and freezing characteristics. Implementation of improved soil type and meteorological forcing shows minor impact on profile ST simulations, while the augmented model physics improving the parameterization of surface heat exchange largely reduces the RMSD by 34% and 51% for Maqu and Shiquanhe, respectively. These findings provide valuable insights for understanding and addressing the uncertainties of profile SMST simulations on the TP.

Droughts and heatwaves jeopardize terrestrial ecosystem services. The development of an open digital twin of the soil-plant system can help monitor and predict the impact of these extreme events on ecosystem functioning. We illustrate how our recently developed STEMMUS-SCOPE model-STEMMUS, Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil; SCOPE, Soil Canopy Observation of Photosynthesis and Energy fluxes-links soil-plant processes to novel satellite observables (e.g. solar-induced chlorophyll fluorescence), contributing to such a digital twin. This soil-plant digital twin allows a mechanistic window for tracking aboveand below-ground ecophysiological processes with remote sensing observations. Following Open Science and FAIR (Findable, Accessible, Interoperable, Reusable) principles, both for data and research software, we present the building blocks of the soil-plant digital twin. It emphasizes the importance of FAIR-enabling digital technologies to translate research needs and developments into reproducible and reusable data, software and knowledge.

Accurate hydrological modelling is crucial for understanding natural processes and managing water resources. However, simulation accuracy depends on the availability of field observations for calibration and validation. It is therefore critical to develop effective calibration strategies to reduce prediction uncertainties. This study applies the DREAM model to the experimental basin of Fiumarella of Corleto in Southern Italy to assess the benefits of single and multicriteria calibration approaches. The former uses total runoff; the latter optimizes total runoff, baseflow, and annual water balance. The study also compares uniform or spatially-based parameterization, including correction factors and recession constants. Parameters were optimized through automatic calibration using a genetic algorithm (GA) and the Kling-Gupta efficiency (KGE) as the objective function. Results show that spatially distributed information improves model reliability compared to a uniform parameterization set-up. The multi-objective calibration constrained on baseflow and balance allowed us to optimize the model, reducing variability compared to mono-objective calibration.

Emission and backscattering at different frequencies have varied responses to soil physical processes (e.g., moisture redistribution, freeze-thaw) and vegetation growing/senescencing. Combing the use of active and passive microwave multi-frequency signals may provide complementary information, which can be used to better retrieve soil moisture, and vegetation biomass and water content for ecological applications. To this purpose, a Community Land Active Passive Microwave Radiative Transfer Modelling Platform (CLAP) was adopted in this study to simulate both emission (TB) and backscatter (σ0), in which the CLAP is backboned by the TorVergata model for modelling vegetation scattering, and an air-to-soil transition model (ATS) (accounting for surface dielectric roughness) integrated with the Advanced Integral Equation Model (AIEM) for modelling soil surface scattering. The accuracy of CLAP was assessed by both ground-based and spaceborne measurements, and the former was from the deployed microwave radiometer/scatterometer observatory at Maqu site on an alpine meadow over the Tibetan plateau. Specifically, for the passive case, simulated TB (emissivity multiplied by effective temperature) were compared to the ground-based ELBARA-III L-band observations, as well as C-band Advanced Microwave Scanning Radiometer 2 (AMSR2) and L-band Soil Moisture Active Passive (SMAP) observations. For the active case, simulated σ0 were compared to the ground-based scatterometer C- and L-bands observations, and C-band Sentinel and L-band Phased Array type L-band Synthetic Aperture Radar 2 (PALSAR-2) observations. This study is expected to contribute to improving the soil moisture retrieval accuracy for dedicated microwave sensor configurations.

Soil freeze–thaw (FT) cycles impact soil functions and atmosphere–land interaction, but accurate measurements are very limited. Since surface dielectric properties and microwave emissions are sensitive to the FT state, brightness temperature (TB) measurements at L-band allow retrieval of the FT state. We have demonstrated the potential of a soil FT retrieval algorithm from Soil Moisture Active Passive (SMAP) TB measurements. This retrieval algorithm is formulated regarding Diurnal Amplitude Variation (DAV), which is defined as the difference in TB observations of ascending and descending orbits. The DAV-FT algorithm uses globally fixed parameters. However, parameters should vary regionally considering factors like land cover type, terrain, and climate regions. We introduce Overall Classification Accuracy (OA) to characterize the extraction of DAV annual variation under different parameters. Then, the parameter optimization process, akin to maximum likelihood estimation, selects a combination of parameters to extract the annual variation of the DAV optimally. The DAV-FT algorithm uses optimized parameters, and the results show that compared to using fixed parameters, (a) the area with OA > 0.7 increases from 54.43% to 89.36%; (b) consistency with ERA5-Land and SMAP data has improved in southwestern North America, the Qinghai–Tibet Plateau, and southwestern Eurasia, with regions showing over 0.7 consistency reaching 81.28% for ERA5-Land and 79.54% for SMAP-FT; and (c) in situ stations with higher accuracy outnumber those with lower accuracy (48.11% versus 22.97% for fixed parameters, 35.14% versus 33.51% for SMAP FT). Furthermore, the algorithm achieves the highest median (0.92) and median accuracy (0.88), compared to fixed parameters and SMAP.

How is climate change altering the global patterns, frequency, and intensity of drought? To investigate this, we analyzed global drought events from 2000-2020 using a composite of hydrological indices (Σ(ET-P), SSMI, and GRACE-DSI). Our results reveal that near-normal to severe droughts became increasingly widespread after 2014. Unexpectedly, extreme, 100-year return period droughts occurred not only in semi-arid zones but also in high latitude and typically wet regions. The frequency of these rare events increased globally, with durations exceeding 100 days in hotspots like Eurasia. Ecosystem case studies further showed divergent vulnerabilities in carbon and water cycling. These findings underscore an urgent need for region-specific adaptation strategies to enhance resilience against increasingly extreme droughts.

Keywords: Digital twin Earth, soil-plant-atmosphere continuum, soil-plant hydraulics, leaf water potential, cellular scale, turbulence length scale

Different canopy resistance (rc) parameterization has been used in land surface models to simulate actual evapotranspiration (ETc) and soil hydraulic variable for crop fields. However, the influence of rc parameterization on evapotranspiration (ET) partitioning and soil water dynamics has not been fully investigated with consideration of the coupled soil water and vapor physics. This study investigated the influential mechanisms of five rc methods (viz., Jarvis, Katerji-Perrier, Massman, Kelliher-Leuning, and Farias) on ET partitioning and soil water contents in an irrigated maize field under a semiarid climate through a soil water and vapor transfer model. The Jarvis method presented the best ET results (R2 = 0.86 and RMSE = 0.71 mm·d–1). Different rc parameterization mainly altered the simulated amount of soil water contents, while not changed the response of soil water dynamics to irrigation events. By the integrated analysis of the ET partitioning and root-zone water budget, different rc methods varied in the choice of the optimum irrigation water use strategies. This study identified the direct and indirect impacts of rc on the ET partitioning and emphasizes the necessity of both the ET partitioning and water supply sources in the decision-making for irrigation water management in semiarid regions.