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• Central Asia drought propagation time were longer early and late in the growing season. • Drought thresholds in Central and Western Central Asia were often below 30 mm during 1948–2022. • Soil moisture drought intensity was positively correlated with drought propagation characteristics. • VPD and AI are the main drivers of accelerated drought propagation. Drought propagation time (PT) and threshold (TR) play crucial roles in real-time monitoring of drought progression and severity under climate change, for which there is currently no unified method or standard for quantitative characterization. Central Asia (CA), a prototypical arid region, is frequently plagued by droughts that profoundly impact its ecology and socioeconomic framework, leaving a lack of comprehension about the transition from meteorological to soil moisture drought as for the complex impact of ground-atmosphere interaction. Therefore, this study revealed high-resolution PT and TR across CA based on precipitation and soil moisture datasets by employing a Bayesian causal framework for drought propagation modeling, in which the sliding window with the random forest method was integrated to investigate the dynamic changes and driving factors of drought propagation over recent decades. The conclusions drawn are as follows: (1) in the southeastern hilly and eastern plateau, a prolonged PT (i.e., longer than 100 days) was experienced during the extremities of the growing season, while most areas had a shorter mid-season PT (i.e., less than 40 days), in which the soil moisture drought intensity was positively correlated with longer PTs; (2) from 1948 to 2022, the central and western regions in CA generally displayed lower TR values than those in the northeast, seldom exceeding 30 mm, reflecting the regional precipitation gradient and the positive relationship between TR and severe soil moisture droughts; and (3) the increase in vapor pressure deficit (VPD), the decrease in aridity index (AI) for the reduced precipitation, and the considerable impact of vegetation transpiration on soil moisture consumption contributed to accelerated drought propagation. Overall, this study reveals the complex dynamics and mechanisms of drought propagation amid environmental changes, laying the foundation for innovative drought early warning systems and emergency management approaches in CA. [ABSTRACT FROM AUTHOR]

[Display omitted] • The soil water movement of dip/anti-dip karst slopes is revealed by water isotopes. • Precipitation drives the seasonal variations of soil water isotopes. • Preferential flow is dominant in the upper soil layers on dip and anti-dip slopes. • Preferential flow is deeper on anti-dip versus dip karst slopes. • Soil texture and structure regulate the spatial movement of soil water. Exploring temporal and spatial variations in soil water processes is crucial for identifying the ecological water supply capacity of soil and understanding hydrologic cycles within karst catchments. However, the existing body of knowledge related to soil water dynamics across dip and anti-dip slopes in karst landscapes remains limited. To address this gap, this study investigated soil water dynamics in a karst trough valley (KTV) in southwest China, using stable isotopes (δD and δ18O) from eight sampling sites and precipitation data from April 2017 to November 2018. Our findings suggest that seasonal variations in soil water isotopes are primarily influenced by climatic factors, with soil water lagging behind rainwater isotopes by one to four months due to differences in the residence time of precipitation in different soil layers. Spatially, our results reveal the coexistence of diffuse and preferential flows on both dip and anti-dip slopes. Specifically, on the dip slope, preferential flow dominates above 20 cm, while diffuse flow prevails below this depth. In contrast, on the anti-dip slope, preferential flow is prominent above 40 cm depth, while diffuse flow dominates beneath this threshold. These spatial patterns not only impact the ecological water and nutrient supply capacity of soil but they also contribute to distinct patterns in vegetative growth on both slopes. In turn, vegetation growth facilitates preferential flow pathways, establishing a positive feedback loop between soil water movement and vegetative cover. These findings may lead to a deeper understanding of soil water processes on the dip and anti-dip slopes of KTVs and provide crucial insights into the relationship between soil water and vegetative growth in similar karst landforms. [ABSTRACT FROM AUTHOR]

[Display omitted] • Surface conductance is affected by environmental variable interactions nonlinearly. • Surface conductance response to environmental variables varies by vegetation types. • Mixed generalized additive model determines the interaction between environmental variables. • Machine Learning model improves surface conductance simulation. Surface conductance (G s) is a key factor in the Penman-Monteith (PM) equation; the interaction between environmental variables such as CO 2 concentration, air temperature (TA), vapor pressure deficit (VPD), soil water content (SWC), and net radiation (R) affects G s , evapotranspiration and thus impacts the hydrological cycle. These interactions are highly nonlinear and vary among different vegetation types. However, conventional G s simulation models use fixed interactions between environmental variables in their equations for all vegetation types. Moreover, the characterisation and parameterisation of conventional G s models is highly uncertain due to the high spatiotemporal variability in key environmental variables and plant parameters, which inhibits their generalisation. This study investigates whether G s could be estimated more accurately by nonlinear statistical techniques that capture the multiple interactions between the environmental variables that affect G s for each vegetation type. We compare mixed generalized additive model (MGAM) for G s simulation with semi-empirical and empirical models at 20 eddy covariance flux tower sites with four different vegetation types at daily and monthly timescales. The results show that the Nash-Sutcliffe Efficiency (NSE) in G s simulation increased by up to 50% in MGAM model in comparison to the semi-empirical and empirical models. The MGAM model highlighted the interactive effects of CO 2 , VPD, and SWC for crops and grasses. The interactive effects of CO 2 , VPD, and TA were important for trees and grasses. The results from this study expand our understanding of the ability of G s simulation models to identify and include the interactive effects of crucial environmental variables on plant transpiration and hydrological processes. [ABSTRACT FROM AUTHOR]

• The CMIP6 SM among three SSPs from the same model display similar performance. • Most CMIP6 SM could reasonably depict the pattern of truth value, however obvious bias is found in cold zones and barrens. • Visible accuracy deficiency of CMIP6 SM exists when compared to SMAP and ERA5-Land SM products. The Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) provides ample soil moisture (SM) products during 2015–2100, which are widely used in investigating future hydrological pattern. However, there remains limited understanding regarding the accuracy of multi-model CMIP6 SM future simulations following different Shared Socioeconomic Pathways (SSP). This study assesses the spatial–temporal accuracy of 22 CMIP6 model-derived SM products using 670 ground observation stations globally during 2015–2023. Each CMIP6 model encompasses SM estimations from SSP1-2.6 (The sustainable pathway), SSP2-4.5 (The medium pathway), and SSP5-8.5 (Fossil-fueled development). The latest Soil Moisture Active Passive (SMAP) and European Centre for Medium-Range Weather Forecast Reanalysis v5-Land (ERA5-Land) SM products serve as reference benchmarks. The SM among three SSPs within the same model exhibits similar performance in in situ validation, the extended triple collocation assessment, and the classical seasonal decomposition trend analysis. This similarity may suggest a subtle variation in the influence of different SSPs on SM in the early stages of the future scenario. The majority of CMIP6 model-derived SM could reasonably capture the spatial distribution pattern and temporal evolution trend of the truth value. Meanwhile, noticeable discrepancies in accuracy, particularly regarding goodness-of-fit, are evident when compared to SMAP and ERA5-Land SM products. The CMIP6 models could provide better SM estimation in temperate and tropical zones than cold zones. In terms of differences between the land cover types, CMIP6 model-derived SM products perform well over various vegetated regions except for barren, where there are sequential overestimation and relatively low fitting degree. Extensive accuracy discrepancies exist among the 22 CMIP6 model-derived SM products due to variations in model parameterization methods and atmospheric variables. [ABSTRACT FROM AUTHOR]

• Soil moisture dynamics were compared between four typical types of land use and land cover in a humid mountainous watershed. • The bamboo forest had the largest variability in soil moisture content. • The cumulative infiltration increased but the infiltration rate decreased with the rainfall grade. • The runoff coefficient during representative rainfall events varied with the LULC. Identifying soil moisture dynamics is critical for understanding watershed hydrological processes. Soil moisture responses to rainfall vary with land use and land covers (LULCs) and thus influence rainfall-runoff processes, however, the knowledge about how different LULCs affect such processes was less revealed, especially in humid areas. In this study, we investigated the characteristics of soil moisture content (SMC) and rainfall-related soil moisture responses under four typical LULCs, i.e., waxberry forest, farmland, sloping farmland, and bamboo forest, based on in-situ observations in a humid mountainous watershed. We then used the HYDRUS-1D model to analyze the patterns of the rainfall-infiltration processes and quantified corresponding runoff generation to reveal the partitioning between infiltration and runoff at the event scale. We found significant differences of the average SMC at multiple depths for four LULCs. The deep soil (80 cm) had the largest SMC for the waxberry forest, farmland, and sloping farmland, while the surface soil (10 cm) was the wettest soil layer for the bamboo forest. During the representative rainstorms, although soil moisture responses exhibited depth gradients, the earlier responses at some deep layers indicated the occurrence of preferential flow. Generally, the cumulative infiltration increased while the infiltration rate decreased with the rainfall grade. Farmland and bamboo forest showed the most and the least infiltration, respectively. These results suggested that both the rainfall properties and LULCs had a strong impact on the soil moisture responses. Meanwhile, the estimated average runoff coefficients of four typical LULCs increased with rainfall grade, and the sloping farmland generated the most runoff during the representative rainfall events. Our findings provide new insights into soil moisture dynamics and infiltration regimes under different LULCs in humid areas, which can contribute to hydrological modeling at the field-scale and regional water resource management. [ABSTRACT FROM AUTHOR]

• Rare examples of highly instrumented observatories in African drylands. • Large water surplus during monsoon not represented by annual scale of aridity index. • Overland flow is rare and root zone achieves field capacity across hillslopes. • Stable isotopes indicate recharge season timing, not a link to heavy rainfall. • Recharge is diffuse in semi-arid Africa validating soil moisture balance model use. We use two comprehensively instrumented field observatories to understand groundwater recharge processes in African drylands. The observatories are located on crystalline basement geology in semi-arid parts of Ghana and Burkina Faso, aridity indices 0.43 and 0.29, respectively, and we report 2017–2019 observations. Groundwater recharge was quantified by inverse water table fluctuation models using specific yield estimates derived from magnetic resonance soundings. Evidence for recharge drivers and mechanisms comes from high resolution meteorological observations, soil moisture (logged hourly and weekly along hillslope transects), overland flow plots, river stage, and stable isotopes of O and H in rainfall events and groundwater. Groundwater recharge varied between 87 and 175 mm/y, i.e. 7–15 % of annual rainfall. Rainfall was twice the volume of water lost via actual evapotranspiration across the four peak months (Jun-Sep) of the monsoon. This seasonal water surplus of ∼ 350 mm/y is not characterised by the annual scale of the aridity index. Overland flow was rare and soil moisture deficits were overcome at all monitoring locations. Large rainfall events only produced appreciable recharge when the antecedent soil moisture was close to field capacity, yet always produced large responses in river stage. Stable isotopes of O and H in groundwater indicate no evidence of evapotranspiration prior to infiltration and their composition is akin to depleted isotopic rainfall at the monsoon peak. Stable isotopes indicate recharge season timing and not a relationship between intense rainfall and groundwater recharge. We contend that the mechanism for groundwater recharge is predominantly diffuse in these semi-arid African settings. [ABSTRACT FROM AUTHOR]

• An individualized application of water management was proposed. • Pinus koraiensis with strong plasticity showed different water use patterns in different habitats. • Sand content was the key factor for Pinus koraiensis water use pattern in Changbai Montain. Plant water uptake plays a crucial role in regulating the water balance for the stability of ecosystems. Previous studies on plant water uptake, focused on arid and semi-arid regions due to water scarcity in these areas, leaving gaps in understanding the strategies and factors affecting plant water uptake in temperate semi-humid regions, where there is a high sensitivity to climate change To assess water uptake patterns through stable isotopes (δD and δ18O), we chose Pinus koraiensis , a dominant species in the broadleaf Korean pine forest, poplar–birch forest and spruce–fir forest of the Changbai Mountain. Our findings indicated that soil water served as the direct water source for Pinus koraiensis , while precipitation, river water, and groundwater indirectly contributed via replenishing soil moisture and subsequently supporting plant water uptake. Within the broadleaf Korean pine forest and poplar–birch forest, Pinus koraiensis primarily extracted soil water from depths of 0–10 cm, accounting for approximately 49.6 % and 54.5 % of its total water uptake, respectively. In contrast, within the spruce–fir forest, Pinus koraiensis primarily extracted soil water from depths 40–50 cm, representing approximately 31.9 % of its total water uptake. In addition, through the partial correlations and variation partitioning analyses, the results revealed that sand content was the driver of the plant water uptake pattern, which affected the fine root distribution by determining the field capacity and bulk density to drive the plant water uptake. Future forest conservation management endeavours will primarily concentrate on protecting soil integrity, reducing soil erosion, maintaining regional water balance, and preserving plant diversity in the Changbai Mountain region. [ABSTRACT FROM AUTHOR]

• The abandoned pastures are increasingly important ecosystems in the Alps. • The model Hydrus 1D with double vegetation (DV, grassland and shrubland) fits the observed actual evapotranspiration (ETa). • The occurring shrub encroachment of grassland increased ETa at the site, if compared to simulations with only grassland. • Vapor pressure deficit drives ETa deviations (observations - DV simulations and between single vegetation simulations). • Wider-scale hydrological models should include information about the isohydric or anisohydric behavior of vegetation. This study analyzes the effect of shrub encroachment on actual evapotranspiration (ETa), a still poorly studied phenomenon in the Alps. The effect of shrub encroachment is investigated on an Alpine grassland in Western Italy using both data and a soil hydrological model (Hydrus 1D), which is used to model three different land covers: grassland, shrubland, and a mixture of the two land covers with a novel double vegetation approach recently introduced. Four growing seasons of eddy covariance measurements are used as an approximate reference for the interpretation and consistency of the model outputs. Also, the impact of meteorological inter-annual variability and of different environmental conditions on both modeled and measured evapotranspiration is analyzed. The modeling results show that the model is able to capture the inter-annual variability of ETa. The double vegetation approach suggests that the percentage of total transpiration flux assigned to the shrubland is between 20 and 60 %. Single-vegetation simulations show that shrubs lead to an enhancement of ETa equal to + 27.1 %, +26.0 %, +26.8 %, and + 23.9 % (range 2014–2017) compared to grassland, which could lead to an alteration of the hydrological cycle. Moreover, chambers measurements of shrubs transpiration show a good agreement with the eddy covariance measurement, suggesting that the ecosystem's behavior is already close to a shrubland, which yields an increased ETa if compared to grassland. The evaporative index from the modeled shrubland is higher (range +24–27 %) than the case of a modeled grassland. Finally, ETa and the evaporative fraction (EF) are in the energy-limited regime in most cases. This result was obtained from the analysis of the relationship between ETa (and EF) and either meteorological variables or soil water content including the simulated one in the 0–100 cm horizon. The following analysis, more focused on micrometeorological variables, namely vapor pressure deficit, net radiation, wind speed, air temperature, and ground heat flux, indicates that ETa is mostly affected by the vapor pressure deficit. [ABSTRACT FROM AUTHOR]

• A karst cave monitoring work about δ18O with the designment of precipitation-soil water-drip water in Southern China. • Deep soil water is mainly supplied by the rainy season precipitation with light δ18O value. • The condition inside cave and overlying soil could affect the drip δ18O signature. Understanding the modern transport processes of δ18O between different waters in cave systems can provide a basis for the paleoenvironmental interpretation of records of stalagmite δ18O. We conducted monthly in-situ monitoring of local precipitation, soil water at three different soil depths, five drip water sites and environmental parameters in Remi cave, Western Hunan Province, Central China, from December 2020 to January 2024. During the monitoring period, due to differences in geological structure and ventilation effect at different locations within the cave, cave air temperature and relative humidity gradually remained constant from the cave entrance to the deep parts of the cave. On intra-annual timescales, precipitation δ18O p were depleted in the wet season and enriched in the dry season, which were mainly controlled by changes in moisture sources, upstream convective activity, and rainout effects. With the increasing thickness of the soil layer above the cave, soil water δ18O soil gradually became negative and leveled off, possibly because shallow soil water responded sensitively to local year-round precipitation, accompanied by surface evaporation. While, deep soil water with more negative δ18O, may be mainly replenished by heavy precipitation during the wet season, but the effect of water retained in the soil from preceding years couldn't be excluded and would be needed to verify by conducting more monitoring studies on the variation of δ18O soil covering the long period. The mean δ18O values of drip water at sites D1 and D2 near the cave entrance were heavier than that of shallow soil water and precipitation, possibly due to evaporation from the thin overlying soil layer, together with ventilation effect. There were seasonal variations of δ18O d values at site D5 in the deep parts of the cave, which may be associated with the seasonal variation of drip rate at this site. Due to the smoothing effect of soil layer and epikarst zone, there were no seasonality in δ18O d values at sites D3 and D4, in the middle and deep parts of the cave. The mean δ18O d at these two sites were lighter than amount-weighted annual mean δ18O p , and close to the deep δ18O soil and the wet-season amount-weighted mean δ18O p values. This suggests that the thick soil layer overlying the cave raises the precipitation threshold, causing these sites to be mainly recharged by heavy rainfall in the wet season. Therefore, the δ18O of stalagmites growing under these drip sites may mainly inherit the wet-season δ18O p signal. The short monitoring period of this study (three years) and the lack of modern calcite δ18O data, mean that caution is need in interpreting stalagmite δ18O on a timescale longer than that of this study. [ABSTRACT FROM AUTHOR]

• Global vegetation water use efficiency will increase under four future climate scenarios. • Coupled soil-atmosphere droughts in the future usually occur during the warm season. • About 47–59% of global vegetated lands is dominated by soil – atmospheric coupling factor. • Soil-atmosphere coupling factor dominated areas in croplands and grasslands are expanding. The response of vegetation water use efficiency (WUE) to soil or atmospheric moisture conditions has been widely identified in the past decades, however, the relationships between soil-atmospheric coupling and global vegetation WUE remain unclear under different CO 2 emission scenarios at the end of the 21st century. In this study, we investigated the evolution and characteristics of global soil-atmospheric coupling by the atmospheric vapor pressure deficit (VPD) and soil moisture (SM), and vegetation WUE for both the baseline period (1982–2014) and future periods (2015–2100) under four representative pathways using Coupled Model Intercomparison Project Phase 6 data, and then quantified the impacts of soil-atmospheric coupling on global WUE. Results indicate that the global average vegetation WUE will increase significantly in the future, and the SSP5-8.5 is observed at the fastest growth rate at 0.0902 gC kg−1 H 2 O/decade. Global soil and atmospheric are projected to become drier, especially in the Amazon and Patagonia plateaus, the Middle and Lower Yangtze River Plains; however, VPD remains relatively stable after 2050 under the SSP1-2.6 scenario, highlighting the effectiveness of active restoration policies on atmospheric drought elimination. It is revealed that over 82.13% of vegetated land exhibits a negative correlation between SM and VPD, suggesting vegetation is more susceptible to coupled soil-atmosphere, and SM-VPD coupling tend to occur during the warm season. Approximately 47–59% of global vegetated land is dominated by SM-VPD under four climate scenarios, mainly in the 20°–60°N and 20°–40°S. The affected area expands under moderate and high emission scenarios, particularly in croplands and grasslands. As climate warms, low-altitude regions will become more susceptible to VPD, and some temperature vegetation may transfer from reliance on SM-VPD to dependence on SM. These findings enrich the understanding of global vegetation WUE trends and provide deeper insights into soil-atmosphere coupling and WUE interactions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Swales can be surprisingly dry despite conventional expectations of their wetness. • Soil and vegetation characteristics strongly influence soil moisture variations. • Over 50% of soil moisture variations are explained by non-topographic factors. • A new index that integrates topographic and non-topographic information is proposed. • This index advances predictability of catchment-scale soil moisture variation. Concave hillslopes, also known as swales, play a pivotal role in facilitating the transfer of water and nutrients from hillslopes to riparian zones in headwater catchments by serving as pathways for surface and subsurface flow. Swales have long been recognized as hydrologic wet spots that contribute significantly to catchment runoff. However, long-term in situ observations have rarely confirmed this conventional understanding. In this study, we present a comprehensive analysis of three years of daily soil moisture measurements taken at multiple depths across 33 sites within a forested catchment to delineate key determinants of soil moisture variation at the catchment scale. Contrary to conventional understanding, our findings indicate that swales may represent areas of lower soil moisture, with those located on sunny hillslopes experiencing a higher frequency of dry conditions, amounting to 13.31% of the time during the study period, when compared to other topographical features such as slopes, valleys, and ridges. Topography alone accounted for less than half of the variation of soil moisture across the catchment. Whereas non-topographic factors (such as vegetation, soil porosity, soil temperature, and soil depth) strongly influenced soil moisture distribution in time and space. To better capture the nuances of soil moisture dynamics, we propose an enhancement to the Topographic Wetness Index (TWI) through the incorporation of non-topographic variables, which improves the model's representation of soil moisture variations. Our findings elucidate the complex interactions between topographic and non-topographic factors in shaping soil moisture dynamics at the catchment scale, offering valuable insights for refining process-based hydrological models and contributing to a deeper understanding of the spatial and temporal variability of soil moisture in headwater catchments. [ABSTRACT FROM AUTHOR]

• The impact of freeze − thaw cycles and initial soil moisture content on soil detachment capacity (D c) was quantified. • Response of D c to hydrodynamic parameters in overland flow was analyzed. • The dominated variable contribution rate to D c was evaluated. • The general flow intensity index proved effective in estimating D c. • A D c prediction model was established for the compound action of freeze–thaw and overland flow. During the thawing period in seasonal freeze–thaw regions, soil detachment, which is an initial process in soil erosion, is an intricate erosion process involving interaction between freeze–thaw cycles (FTC) and runoff. Quantifying soil detachment capacity (D c) is essential for constructing process–based erosion prediction models that elucidate soil erosion intensity. However, systematic investigations of compound freeze–thaw and hydrodynamic action on D c are still limited. In this study, typical soils from seasonal freeze–thaw zones were used as test soils. Through artificial freeze–thaw and indoor simulated scouring tests, the response relationships between D c and FTC, initial soil moisture content (SMC), flow intensity indices, and soil characteristic parameters were explored. The results revealed that XJ aeolian sandy soil exhibited the highest D c , and its mean D c was 1.47 and 1.80 times higher than that of SM sandy loess and KS black soil, respectively. This indicated a significant difference in D c among different soil textures (P < 0.01). Under diverse SMC conditions, after alternating FTC, the D c of XJ aeolian sand soil, SM sandy loss, and KS black soil was increased by an average of 1.39, 1.54, and 1.88 times compared to non–freeze–thaw treatment, respectively, suggesting a vigorous destructive effect of FTC on D c in these soils. Additionally, during the alternate freeze–thaw process, for the XJ aeolian sand soil, D c steadily increased with increasing SMC, whereas the D c of the SM sandy loess and KS black soil exhibited a decreasing–increasing trend with the variation in SMC, illustrating that the FTC deterioration impact on the soil was related to the SMC. Dimensionless effective stream power (ω eff ∗ ) was identified as the optimal flow intensity index to describe D c. The contribution rate of ω eff ∗ to D c was the highest at 45.05 %, while those of FTC and SMC were 13.72 % and 6.71 %, respectively, indicating that the freeze–thaw effect on D c may be concealed by hydrodynamic characteristics. A D c predictive model was established using a general flow intensity index with high R 2 (0.96) and NSE (0.95) values, whereas RRMSE was lower at 0.18. However, this model requires further validation when applied beyond its scope of development. This research provides a new understanding for elucidating the soil detachment mechanism when subjected to freeze–thaw and hydrodynamics effects. [ABSTRACT FROM AUTHOR]

• A modified permeability testing device is developed to test the permeability of subgrade soil under the overlying stress. • The hydraulic characteristics model, considering the influence of stress and wetting-drying cycles, is established. • A method, repaid obtaining the water migration law of subgrade under stress and wetting-drying cycles, is developed. The wetting–drying cycles and overlying stresses experienced by unsaturated subgrade alter soil pore structure, resulting in variations in its hydraulic characteristics, ultimately affecting subgrade water migration and moisture distribution. However, the combined effects of these two phenomena on key subgrade hydraulic characteristics including the soil–water characteristics and the permeability property remain unclear. This paper aimed to experimentally measure soil–water characteristic curve (SWCC) and saturated permeability of subgrade soil under various overlying stresses and wetting–drying cycles. A customized SWCC measurement system and a modified permeability testing device were developed to conduct the test program on compacted silty clay soil specimens. Subsequently, the influence of overlying stress and wetting–drying cycles on the hydraulic characteristics of the subgrade was analyzed, with a focus on the SWCC and saturation permeability coefficient, and the corresponding fitting model is established. Finally, the water transfer behavior of subgrade considering the effect of wetting–drying cycles and overlying stresses was analyzed using finite element method (FEM). The results indicated that an increased number of wetting–drying cycles and higher overlying stress led to a decrease in the saturated moisture content of subgrade soil, an increase in the air-entry value, and a flatter SWCC. The saturation permeability coefficient increased with the number of wetting–drying cycles, but decreased with increasing overlying stress. Furthermore, a high correlation was observed between the SWCC and permeability coefficient predictive model, considering both the number of wetting–drying cycles and overlying stress. Lastly, when considering the stress state and wetting–drying cycles, significant differences in the evolution of matric suction of the subgrade during rainfall were observed. The overlying stress amplifies the decrease in matric suction of the soil above the subgrade, while wetting–drying cycles reduce this decrease. [ABSTRACT FROM AUTHOR]

• Bidirectional dependency between 4 water resource types and vegetation was evaluated globally. • Bidirectional dependency between forest and water resources stronger than farmland/grassland. • 46.8 % area of the world existing bidirectional dependency between vegetation and water resources. • Climate types affecting bidirectional dependency between water resources and vegetation. Water resources and vegetation jointly affect almost every aspect of nature and society, such as geomorphology, biodiversity, local climate, settlement, and so on. However, our understanding of their dependency relation is incomplete, which is merely on the unidirectional impact of a single water resource on vegetation. Inversely, vegetation productivity could feed back water resources by directly affecting evapotranspiration and indirectly regulating precipitation. The bidirectional influencing mechanism between global water resources and vegetation productivity is still unknown. This study focused on revealing the bidirectional dependency relation between water resources and vegetation productivity using multi-source data and causality modeling. Our analysis showed that the unidirectional dependence of vegetation on water resources, the unidirectional dependence of water resources on vegetation, and their bidirectional dependence at the global scale accounted for 40.9 %, 12.4 %, and 46.8 %, respectively. Moreover, different types of water resources and vegetation perform varying degrees of bidirectional dependency. For instance, precipitation influences 64.9 % of the vegetation in a unidirectional way, while soil moisture, surface runoff, and groundwater interact with vegetation at the level of 74.6 %, 44.8 %, and 45.6 %, respectively. Similarly, the bidirectional dependency between forest (54.5 %) and water resources was usually higher than that of cultivated land (45.9 %) and grassland (44.5 %), due to different transpiration and water interception abilities. It has great significance on water resources management, vegetation greening, and understanding of terrestrial ecological water circulation systems in the context of global climate change by quantifying the interaction mechanism between various water resources and vegetation productivity in this study. [ABSTRACT FROM AUTHOR]

[Display omitted] • A Cluster-Based Local soil moisture Modeling (CBLM) Paradigm was proposed. • CBLM was combined with deep learning methods for soil moisture modeling. • CBLM showed NRMSE of less than 12%. • The performance of global and local modeling paradigms were compared. • Robust clustering algorithms were tested to identify homogenous sub-areas. Producing precise soil moisture maps through soil moisture modeling is highly valued for a variety of purposes, such as agricultural productivity, water resource management, climate modeling, environmental monitoring, and disaster management. However, the severe spatial variability of soil moisture makes a single model insufficient for accurately estimating soil moisture in an area. In this study, a deep learning-based soil moisture estimation approach using the Cluster-Based Local Modeling (CBLM) paradigm is presented. The approach involves the utilization of Sentinel-2 imagery and ancillary data such as soil maps, geology maps, land use/cover maps as well as topographic and spectral indices to create a database for modeling. The optical trapezoid model (OPTRAM) was also employed to estimate relative soil moisture content as another ancillary data for soil moisture modeling. Some robust clustering algorithms i.e. K-means, genetic algorithm, particle swarm optimization, and density-based spatial clustering were applied to divide the study area into homogenous sub-areas. Two powerful deep learning methods, i.e., Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), were then trained on the dataset to make accurate soil moisture estimations. Promising results were obtained through the proposed approach in accurately modeling soil moisture. According to the findings, the global modeling approach using both LSTM and CNN models exhibited moderate performance. Isolated pixels with unreasonably high soil moisture values were produced by the LSTM model which may be related to modeling process errors. Significantly enhanced results were observed in both deep learning models with the use of the local modeling approach. The cluster-based local modeling paradigm was able to capture local variations in soil moisture and produce more interpretable results by developing separate models for each cluster. Moreover, the CBLM-based CNN-CLTM hybrid model outperformed both local CNN and LSTM models. Generalization of the results was also confirmed by applying the best model for a different year and different season. Overall, the approach shows promise in improving our understanding of soil moisture dynamics and identifying areas that are vulnerable to drought or flooding, which could help inform targeted interventions. [ABSTRACT FROM AUTHOR]

To minimize uncertainty related to soil processes in extreme events, we need accurate soil hydraulic properties across the entire range of soil water content. However, conventional methods are time-consuming and limited to specific ranges. To estimate soil hydraulic properties throughout the entire range, we conducted inverse modeling using upward infiltration experiments, where a shortwave infrared imaging camera was used to obtain high-resolution soil moisture data in space and time. Because the commonly used van Genuchten–Mualemmodel is unsuitable for describing soil hydraulic properties for dry conditions, we tested an alternative model, the Peters-Durner-Iden model, which considers both capillary and film water. The inverse modeling successfully estimated soil hydraulic properties for sandy loam and loam soils, and we demonstrated that the Peters-Durner-Iden model captured soil moisture dynamics better than the van Genuchten–Mualemmodel for dry conditions. However, both models could not adequately describe the soil moisture data for the other soils. The direct observation of the water flow via shortwave infrared images clarified that the reduced success was because of violating the one-dimensional flow assumption for coarse-textured soils and the micro-heterogeneity in soil hydraulic properties for soils with fine silt and clay materials. • Soil hydraulic properties were estimated from shortwave infrared images. • Peters-Durner-Iden model captured soil moisture dynamics for dry conditions. • The framework was most suitable for sandy loam and loam soils. • Micro-heterogeneity of soils prevented the success of the framework. [ABSTRACT FROM AUTHOR]

• Deuterium labeling was used to monitor tree and wheat water uptakes in agroforestry. • Young trees and wheat took up water mainly from the upper soil layer. • Wheat in alley-cropping took up more water at 30 cm depth than in sole-crop plot. • Intercropped wild cherry and alder may exploit deep soil water during dry events. Despite numerous studies investigating competition and/or facilitation for soil water resources in alley-cropping systems (AC), the share of water at the early stage of tree establishment in AC has been poorly examined. This work aimed to explore the water share between crops and trees after four years of tree establishment in AC using deuterium labeling tracer. The investigations were carried out at the Ramecourt AC experimental site, in northern France, according to a randomized block design with three replications. In mid-spring, we injected 300 mL of 10 % deuterated water at 30, 50 and 100 cm soil depths at a 1.5 m distance from a reference tree (Alnus glutinosa or alder, Carpinus betulus or hornbeam, Prunus avium or wild cherry) in an AC, in a pure-forest control plot with ryegrass (FC) and in a randomly chosen area in monocrop wheat control (CC) plot. The tracer uptake was monitored by collecting tree leaves and wheat and ryegrass (RG) whole-plant samples every two weeks in labeled and unlabeled areas. For deuterium natural abundance analyses, the global mean δ2H was significantly lower for wheat (-44 ± 4 ‰) than for RG (-27 ± 6 ‰) and trees (-20 ± 3 ‰). The plant species, the block and the plant water content (PWC) accounted for 38 % of the δ2H variability (p < 0.01). This result indicated that the most active sites of plant water uptake were different between species. The mean wheat δ2H was 2481 ± 523 ‰, 715 ± 218 ‰, and 133 ± 68 ‰ at the 30, 50 and 100 cm labeling depths, respectively. The value was significantly higher in the AC (2883 ± 585 ‰) than in the CC (1131 ± 274 ‰) only at the 30 cm labeling depth. For trees, the δ2H of labeled samples remained negative, unlike wheat. Particularly in the AC, alder and wild cherry presented significantly a higher δ2H at 8 and 28 days after labeling, respectively, from 50 and 100 cm labeling depths, compared to the 2H natural abundance. We concluded that trees and wheat take up their water in the upper soil layer, but in the AC, trees favor wheat water uptake in the topsoil and can flexibly shift their water source to the deep layer in the case of low water availability in the upper soil layer. [ABSTRACT FROM AUTHOR]

• We develop a fast physically-guided emulator of MATSIRO land surface model. • We evaluate the emulator performance at simulating key land surface quantities. • The emulator makes a tremendous improvement in computational speed. Current land surface models are useful tools to study the effects of global climate change on hydrological variables. However, these models tend to be relatively computationally expensive to run. This can hinder their widespread application in large ensemble modelling or uncertainty quantification studies. Therefore, it is necessary to build fast reduced-order emulators that approximate the dynamics of the original models. Here we build a fast physically based emulator that approximates the MATSIRO land surface model. The emulator equations are chosen based on review of the original MATSIRO equations, and on preliminary data analysis. The emulator uses minimal atmospheric input, and models snow water equivalent, wetland water storage, upper layer soil moisture, and total runoff on the daily time scale at 0.5° spatial resolution. Emulator parameters are optimized by fitting the emulator output to the original MATSIRO model. Parallelization allows lightning-fast computation of land surface evolution on the decadal scale. The emulator can work on the range of spatial scales, from local to regional to global. We validate simulated output with respect to the original MATSIRO model, and discuss global maps of parameter estimates. The emulator achieves excellent performance for snow water equivalent and wetland water storage. There are more challenges in simulating upper layer soil moisture and runoff, including systematic runoff underestimation over northern extratropics. We find that 25 years of training data are sufficient for reasonably fitting the emulator. The emulator can be further refined and coupled with river routing and/or economic models in future studies. [ABSTRACT FROM AUTHOR]

• A new evolutionary paradigm predicts suspended sediment load. • An in-depth analysis is conducted on the proposed prediction methodology. • The proposed methodology presents a reliable predictive model for sediment load. The estimation of Suspended Sediment Load (SSL) is challenging due to its complex nature within the field of hydrology. The selection and reduction of input feature dimensions, along with the non-uniformity of the sediment and estimation accuracy, pose challenges when estimating suspended sediment load. By combining support vector regression models with MOGWO, MOPSO, and MOOTLBO, this study aimed to predict suspended sediment load. Hybrid models are constructed with three key objectives: enhancing accuracy, minimizing feature count, and optimizing sediment classification. In order to accomplish this, two scenarios have been formulated. The first scenario prioritizes performance accuracy, whereas the second scenario assigns equal importance to three objectives. The study focuses on the Kosar Dam watershed in southwest Iran. The CHIRPS precipitation product and the GLDAS soil moisture product are considered to be predictors. The extraction of input features is carried out utilizing Principal Component Analysis (PCA). The results from both scenarios indicated that the optimal sediment classification consists of 10 categories, demonstrating superior performance across all three integrated models. Incorporating feature selection enhances the model's performance and decreases the number of features. In the first scenario, 10 features are selected from a set of 30 input feature vectors, while in the second scenario, 14 features are chosen. Consequently, hybrid models prove to be effective in reducing input features, optimizing classification of SSL, and enhancing prediction accuracy. In general, the SVR-MOOTLBO model exhibits superior performance when compared to other models. The performance indices (R, RMSE, MAE, PBIAS, and MD) exhibit variations ranging from 0.02% to 0.75% between the first and second scenarios in SVR-MOOTLBO, while the RPIQ index shows a relatively modest difference of 6.2%. In both scenarios, the SVR parameters are well-tuned, and the search agents of the MOOTLBO algorithm exhibit effective functionality. [ABSTRACT FROM AUTHOR]

• Adjusting water head in Moistube can regulate tube discharge and soil water dynamics. • HYDRUS well simulates water dynamics under varying WPH by treating Moistube as clay. • Raising or lowering WPH accelerates or decelerates wetting front advance. • Scenario of dynamically regulated WPH maintains fit SWC and reduces deep percolation. • Moistube discharge characteristic self-adapts to rainfall and crop evapotranspiration. Moistube irrigation (MTI) accurately delivers moisture to root zone through subsurface nanotube irrigators. How to dynamically adjust the working pressure head (WPH) considering crop evapotranspiration remains unstudied. Here, the Moistube discharge characteristics, wetting front (WF) morphology, and soil water content (SWC) distribution under adjusted WPH (0 → 1, 0 → 2, 1 → 2, 1 → 0, 2 → 0 and 2 → 1 m) were experimentally investigated. HYDRUS 2D model was employed to reproduce the soil water dynamics and validated against the experimental data. Maize growth scenarios under regulated WPH for three representative regions (Nanjing, Hami, and Siping in China) with typical hydrological years were performed. The relatively long-term Moistube discharge, SWC, and deep percolation were analyzed. Results demonstrated that flexible WPH adjustment regulated Moistube discharge, the advance velocity of WF, and SWC within the wetting patterns. With increased (or decreased) WPH, the Moistube discharge rate precipitously raised (or descended) to values positively correlated with WPHs. WPHs of 0, 1, and 2 m corresponded to discharge rates of 0.63–0.76, 2.15–3.34, and 5.53–6.41 cm2 h−1, respectively. WF also noticeably accelerated (or decelerated) as WPH, meanwhile SWC within the wetting pattern increased (or decreased). Upon WPH increase, the elevated SWC intensified the deviation (δ) of wetting body centroid from Moistube. HYDRUS 2D model accurately reproduced the Moistube discharge rate and WF advance (R 2 ≥ 0.90, NSE ≥ 0.70, RSR close to 0), corroborating the feasibility of treating Moistube as a heavy clay. Surprisingly, the Moistube discharge rate obeyed exponential decreasing trend over time after WPH increased but abnormally displayed restrained trends followed by rebounding after WPH decreased. SWC and the deviation of wetting body centroid from Moistube exhibited discrepancies in absolute values but consistencies in trends between the observed and simulated data. Scenario analyses of maize cultivation revealed that regulating WPH accurately replenished crop evapotranspiration and maintained appropriate SWC environments. Regulated WPH in MTI was efficacious in reducing deep percolation and suitable for arid regions. These results could contribute to intelligent MTI by automatically maintaining a balance between Moistube discharge and crop water requirements. [ABSTRACT FROM AUTHOR]

• A new hybrid Land Surface Temperature (LST) reconstruction method is developed. • Performance indicators of the developed method are R2 = 0.94 and RMSE = 1.84°K. • Gapless LST data is used to downscale Surface Soil Moisture with Random Forest. • SMAP SSM L4 downscaled to 1 km accuracy with mean R (0.82) and ubRMSE (0.06 cm3/cm3). • Using antecedent precipitation as variable increased training performance (R2) from 0.76 to 0.93. Downscaling of soil moisture data is important for high resolution hydrological modeling. Most downscaling studies in the literature have used spatially discontinuous land surface temperature (LST) maps as the main auxiliary parameter, which limits the creation of continuous soil moisture maps. The number of studies on soil moisture downscaling with machine learning that use gapless LST maps is limited. With this motivation, a hybrid reconstruction method has been proposed in this study to practically obtain continuous LST maps, which are then used to produce high resolution surface soil moisture (SSM) datasets. The proposed method is shown to have high mean performance with R2 and RMSE values of 0.94 and 1.84°K, respectively, for the period between 2019 and 2022. The developed reconstructed LST maps were then used to downscale original 9 km spatial resolution soil moisture datasets of SMAP L3 and SMAP L4 with Random Forest (RF) machine learning algorithm. The RF model were run with four different rainfall datasets, and the MSWEP rainfall dataset was found to produce the best results. The use of antecedent rainfall values as input variables in machine learning models has been shown to improve the performance of the models R2 0.76 to 0.93. The accuracy of the downscaled data was later evaluated for Western Anatolia Basins (WAB) in Türkiye with 31 in-situ stations. The downscaled SMAP L4 had good average statistical indicators R (0.815 ± 0.1), RMSE (0.09 ± 0.047 cm3/cm3), and ubRMSE (0.058 ± 0.025 cm3/cm3). Downscaled SMAP L3 was also validated with in-situ observations with satisfactory R (0.79 ± 0.074), RMSE (0.09 ± 0.043 cm3/cm3), and ubRMSE (0.06 ± 0.026 cm3/cm3) statistics. Furthermore, the performance of the downscaled SMAP L3 was also cross validated with SMAP + Sentinel 1 (L2) dataset between 2019 and 2022. The mean statistics of R (0.761 ± 0.11) and Root Mean Squared Difference (RMSD) (0.05 ± 0.014 cm3/cm3) between downscaled SMAP L3 and L2 data revealed that the new reconstruction method of LST used in the RF model for downscaling of soil moisture performed well to obtain high resolution soil moisture datasets. The proposed technique also overcame the difficulties associated with coastal regions where data was masked for quality considerations, by not only enhancing overall spatial resolution but also filling these data gaps and giving a complete SSM coverage. [ABSTRACT FROM AUTHOR]

[Display omitted] • Coupling PT-JPL into DTVGM improved the hydrological simulation accuracy in UHRB. • Except for Pr, which had a high positive correlation with BW, other climatic factors correlated relatively weakly with BW and GW. • Vegetation showed a significant greening trend in UHRB, facilitating the transformation of BW to GW. • Vegetation greening will alter the original correlation between climatic factors and hydrological processes. The characteristics of water fluctuations within the basin have remained ambiguous in recent years due to climate and vegetation changes, posing a challenge to the planning and utilization of regional water resources. This study coupled the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model, which can consider vegetation dynamics information and evapotranspiration physical mechanisms, into the Distributed Time Variant Gain Model (DTVGM) to improve the hydrological simulation accuracy under changing environments. Based on different simulation scenarios of the coupled model, the effects of climate and vegetation changes on hydrological processes were fully investigated using blue and green water (BW and GW) as assessment metrics. The upper Han River Basin (UHRB), as water source for the Central Line Project of China's South-North Water Diversion (CSNWD), was selected as a typical basin for the study, results indicated that: (1) The DTVGM coupled with PT-JPL could better reflect the spatiotemporal patterns of major hydrological processes (runoff, evapotranspiration, and soil moisture) in UHRB from 2001 to 2019; (2) The BW in UHRB exhibited decreasing trend from 2001 to 2019, while the GW on the contrary. Surface runoff (Rs), vegetation transpiration (Et), and soil moisture (W) contributed most in the components of BW and GW; (3) Vegetation change dominated by greening in UHRB, which facilitated the transformation of BW to GW in the basin, and it increased the proportion of GW mainly by promoting Et. BW was mainly influenced by precipitation, but vegetation change altered the original correlation between climatic factors and hydrological processes. This study can support the management of water resources in the basin under changing environments. [ABSTRACT FROM AUTHOR]

• 36 km SMAP brightness temperature (TB) are disaggregated via GWR model. • Disaggregated TB are converted into 1 km soil moisture based on RTM model. • 1 km SM is validated and compared with in-situ measurements and other SM products. Passive microwave soil moisture (SM) products hold tremendous potential for both scientific research and practical applications. However, due to the limitations imposed by brightness temperature (TB), passive microwave SM suffer from lower spatial resolution, which significantly restricts their application in regional scale like crop growth monitoring, water resource management, runoff forecasting, and others. To address this challenge, this study proposed a feasible methodology to disaggregate 36 km Soil Moisture Active Passive (SMAP) SM in the ShanDian River Basin via coupling geographically weighted regression (GWR) and radiative transfer model (RTM). Firstly, we employed GWR to disaggregate 36 km SMAP TB to 1 km via MODIS LST, NDVI and DEM data. Subsequently, in accordance with the microwave radiative transfer model, the disaggregated TB was converted to 1 km SM using a single-channel algorithm (SCA) in V polarization. The disaggregated SM, derived from the disaggregated TB, was validated against in-situ SM from the SMN-SDR network and compared with various SMAP SM products at different spatial resolutions. The results show the disaggregated SM effectively inherits the performance of SMAP official products, while offering enhanced spatial resolution and enabling more accurate applications. This study effectively enhances the performance of coarse-resolution passive microwave brightness temperature data at fine scales. [ABSTRACT FROM AUTHOR]

• The THREW-T model produced good simulation on streamflow, snow, soil moisture, and isotope simultaneously in two basins. • Calibration toward isotope improved the simulations of internal streamflow and soil moisture's spatiotemporal variation. • Calibration toward isotope yielded the most reasonable runoff source apportionment, and reduced the uncertainty. Multiple-objective calibration helps constrain the parameter uncertainties and improve the performances of hydrological models. Previous studies have indicated that calibration toward soil moisture data could improve the streamflow simulation, but its influence on the runoff source apportionment quantification still needs to be analyzed. Meanwhile, although isotope calibration has proved to improve the representation of internal hydrological processes, the value of isotope on the simulation of internal state variables such as soil moisture has yet to be examined. This study utilized the tracer-aided hydrological model THREW-T (Tsinghua Representative Elementary Watershed – Tracer-aided version) in two mountainous basins on the Tibetan Plateau (The Upper Brahmaputra and Upper Yangtze basins) to evaluate the value of soil moisture and isotope data on model calibration. The result shows that: (1) The THREW-T model produced good simulation on streamflow, snow cover area, soil moisture, and stream water isotope simultaneously in the two study areas. Calibration toward soil moisture and isotope caused slight (∼0.03) but statistically significant (p < 0.01) decrease on the Nash-Sutcliffe coefficient of streamflow simulation compared to the baseline calibration variant only toward streamflow. (2) Calibration toward soil moisture brought no improvement to streamflow simulation for the validation period and stations in both basins, only improving soil moisture simulation. However, calibration toward the isotope improved the simulations of internal streamflow and soil moisture's spatiotemporal variation. (3) Different calibration variants resulted in different estimations of the runoff source apportionment, and independent evidence indicated that the results obtained by isotope calibration were most reasonable. Calibrations toward streamflow and soil moisture underestimated and overestimated the contributions from subsurface runoff, respectively. Isotope was the most sensitive objective to the runoff source apportionment and significantly reduced the uncertainty. Our study found a lower value of soil moisture data than the isotope on model calibration. However, we believe that the full potential of soil moisture data was not utilized due to the current limitations in soil moisture simulation and measurement methods, and the development of relevant technologies will make the soil moisture data more valuable for model calibration. [ABSTRACT FROM AUTHOR]

• Non-destructive approach to predict soil water content (SWC) over larger areas. • Electromagnetic Induction (EMI) and Ground penetrating radar (GPR) to estimate SWC. • Prediction models were developed for SWC estimation with integrated GPR-EMI. • Replace tedious & destructive point-scale measurements with electromagnetic methods. Ground-penetrating radar (GPR) and electromagnetic induction (EMI) are used to determine and map soil water content (SWC) in the agricultural landscape. While GPR provides a straightforward estimation of SWC, EMI requires site-specific calibrations mainly based on point-scale measurements such as time domain reflectometry (TDR). However, there is a significant difference in the measurement volumes between EMI and point-scale TDR measurements. This study aimed to enhance the calibration of EMI for estimating SWC in the agricultural landscape by leveraging the larger sampling volumes provided by GPR. Apparent electrical conductivity (EC a) from a multi-coil EMI sensor and dielectric constant from GPR with two center frequencies (500 MHz and 250 MHz) were collected as soil proxies along with TDR measurements. Calibration models were developed under irrigated conditions and the model evaluation was conducted under natural moisture conditions. Correlations were assessed between the proxies from GPR and EMI with TDR-derived SWCs. Simple linear regression (SLR) models were developed between EMI and GPR data to predict SWC. Strong positive correlations (r ≥ 0.80) were observed between the proxies (GPR and the shorter inter-coil spacing (0.32 m) of EMI) and TDR-measured SWC. EC a data from vertical and horizontal coil orientations of the shorter inter-coil spacings were selected to develop SLR models with GPR. The SLR models with the GPR 500 MHz frequency showed a higher coefficient of determination (R2 > 0.70) for both coil orientations of EMI. Results showed the potential of using GPR to calibrate EMI for shallow SWC estimation (below 0.5 m). Nevertheless, the EMI estimated SWCs were not effectively verified with GPR-estimated SWCs, which could be attributed to specific site conditions in the study area. Further research must focus on improving the calibration and model evaluation by incorporating the variability of other soil parameters (soil porosity, pore-water conductivity, and soil salinity) that may affect the SWC–EC a relationship. [ABSTRACT FROM AUTHOR]

Infiltration is one of the key components of rainfall-runoff processes at small catchment scale and is therefore a vital component of hydrological models. However, small catchments typically lack direct infiltration measurements and associated soil hydraulic characteristics, which are being substituted with soil databases and pedotransfer functions built upon these databases. We utilized two sets of pedotransfer functions Rosetta 3 and EUPTFv2 derived from two well known soil databases: UNSODA, based on US and global soil data, and EU-HYDI, based on EU soil data. Our study evaluates the impact of choosing one of these pedotransfer functions on modeled infiltration in relation to variable initial moisture content- It investigates the behavior of the simplified infiltration models of Green-Ampt and Philip under different initial conditions, in comparison with Richards' complex solution, which is used as a benchmark. Our evaluation was conducted using soil property data available in the Czech Republic, covering ten USDA soil textures that are prevalent within the European Union. The applied initial moisture conditions were derived for specific suction pressure values that correspond to hydrological initial conditions ranging from near saturation to near the permanent wilting point. Variation in the derived parameters for the Green-Ampt and Philip models across the pedotransfer function sources results in considerable variation in the modeled infiltration. Our results imply different sensitivities of the simplified infiltration models to the initial moisture for different pedotransfer functions. The Green-Ampt and Philip infiltration models demonstrate better alignment with Richards' complex solution with a low and consistent RMSE when using the Rosetta 3 pedotransfer function. By contrast, the EUPTFv2 model scenarios, show a clear trend of increasing RMSE towards dry initial conditions. As the European soil database is not an open data source, we only hypothesize that the distributions of soil characteristics in the European soil database differ systematically from those in UNSODA, and that this would cause different results of pedotransfer functions for different initial moisture conditions. The findings of this study are important for selecting appropriate infiltration models and, appropriate pedotransfer functions for estimating model parameters, and for assessing uncertainties connected with varying initial conditions. [ABSTRACT FROM AUTHOR]

• ML algorithms efficiently connect soil thermal conductivity with water content and matric potential. • SMOTE can efficiently pair data with an unequal number of measurements. • Datasets have significant influence on selection of ML algorithms. Properly understanding the strongly coupled thermal and hydraulic properties of porous media is crucial to interests in different fields, e.g., agriculture, building physics and meteorology. Previous studies generally consider the thermal and hydraulic properties independently, specifically, the soil thermal conductivity curve and the soil water retention characteristics curve as methods to connect such process are unavailable or untested. In an effort to test methods based on machine learning (ML) to effectively integrate the soil thermal conductivity (λ) , matric potential (ψ) and water content (θ), 2328 measurements from 43 soils were compiled using ML algorithms. Five ML algorithms, including random forest-RF, gradient boosting decision tree-GBDT, support vector machine-SVM, multiple linear regression-LR and backpropagation neural network-BPNN, based on the balanced dataset with synthetic minority over-sampling technique were trained, validated and tested to establish the λ (ψ) and λ (θ) relationships. Results demonstrate that, ML algorithms perform better than empirically based methods to optimize the thermal and hydraulic relationship. The selection of ML algorithms has a significant influence on the coupling relationship and should be taken into account. GBDT performed superior for small soil hydrothermal datasets (Testing set: Nash-Sutcliff efficiency-NSE = 0.9999 and Root Mean Square Error-RMSE = 0.0144 W m−1 K−1; independent verification: NSE = 0.7122 and RMSE = 0.2726 W m−1 K−1), while BPNN is limited by the amount of data with NSE = 0.9970, RMSE = 0.0790 W m−1 K−1 of Testing set. [ABSTRACT FROM AUTHOR]

• The influence of equifinality on the hybrid model accuracy was assessed. • The hybrid model was developed by coupling SWAT with LSTM. • The two hybrid models constrained by RS-ET or not were compared. • The hybrid models showed better predictions than standalone SWAT and LSTM. • Among the hybrid models, those with RS-ET indicated enhanced performances. Hybrid models coupling process-based models with deep learning models have been widely used for estimating water quantity and quality. However, the impacts of uncertainty within process-based models on the performance of hybrid models remained largely unknown. This study focused on assessing the impact of uncertainties on a hybrid model that combines Soil and Water Assessment Tool (SWAT) with Long Short-Term Memory (LSTM) for estimating streamflow and suspended solids (SS). This study applied the output of SWAT as input for LSTM to make a hybrid model. By incorporating an additional constraint, remotely sensed evapotranspiration (RS-ET), the performance of hybrid models with and without considering RS-ET was evaluated at various temporal and spatial scales. Furthermore, various input settings (default and calibrated SWAT and inclusion/exclusion of precipitation) were considered. The results showed that the hybrid models tended to provide accurate estimations of monthly streamflow and SS compared to standalone SWAT and LSTM. The addition of precipitation to hybrid models did not make noticeable improvements for streamflow and SS. The hybrid models constrained by RS-ET tended to have improved estimations on streamflow and SS at daily and monthly scales compared to those unconstrained by RS-ET. Similar results were also observed at the sub-watershed level. These insights highlighted the potential of hybrid models to enhance hydrological estimations and underscore the importance of incorporating additional constraints to reduce uncertainties. Such advancements have practical implications for water resource management and decision-making processes, enabling more reliable and accurate estimations in applications of hybrid models. [ABSTRACT FROM AUTHOR]

• Integrated Hydrological Models (IHMs) are trustworthy but data-demanding. • Remote sensing (RS) and hydrogeophysics open exciting prospects for IHMs. • Combining RS and in-situ data for bias correction yields optimal climate forcings. • RS provides IHM input of surface/soil while hydrogeophysics, also of subsurface. • Evapotranspiration, soil moisture and stages are most used to constrain IHMs. Integrated Hydrological Models (IHMs) dynamically couple surface and groundwater processes across the unsaturated zone domain. IHMs are data intensive and computationally demanding but can provide physically realistic output, particularly if sufficient input data of high quality is available. In-situ observations often have a small footprint and are time and cost-demanding. Satellite remote sensing observations, with their long time series archives and spatially semi-continuous gridded format, as well as hydrogeophysical observations with their flexible, 'on-demand' high-resolution data coverage, perfectly complement in-situ observations. We review the contribution of various satellite remote sensing products for IHM: (1) climate forcings, (2) parameters, (3) boundary conditions and (4) observations for constraining model calibration and data assimilation. Our review of hydrogeophysics focuses on the four mentioned IHM contributions, but we analyze them per data acquisition platform, i.e., surface, drone-borne and airborne hydrogeophysics. Finally, the review includes a discussion on the optimal use of satellite remote sensing and hydrogeophysical data in IHMs, as well as a vision for further improvements of data-driven, integrated hydrological modelling. [ABSTRACT FROM AUTHOR]

• Ground cover management (GCM) overall increased soil moisture content (SMC) by 9.2% in orchards. • Effects of GCM on SMC change varied with management and environmental factors. • Climate and tree age were the dominant drivers of SMC responses to GCM. • High soil bulk density (greater than 1.4 g cm−3), full cover, and mowing could constrain the GCM-induced positive effect. Ground cover management (GCM) is an important practice for sustainable agricultural development, which has been garnering popularity in numerous orchards worldwide. It can effectively improve soil health, reduce soil erosion, and enhance carbon sequestration. However, the effects of GCM on soil moisture content (SMC) dynamics vary with management and environmental factors, and it remains unclear how these factors affect GCM-induced SMC. In addition, it remains unknown how SMC and influencing factors such as climatic, edaphic, and agronomic factors are inter-related in responding to mulching practices. Therefore, we performed a meta-analysis to identify the effect of these factors on SMC accumulation and elucidate the potential mechanisms involved under different environmental and management conditions. Results revealed that GCM practices significantly enhanced SMC (9.2%) compared with the effects of traditional clean tillage. The maximum benefits of SMC were observed when the average annual temperature was smaller than 8 ℃, initial soil organic carbon was 20 g kg−1, and tree age was greater than 20 yr. Further analysis using a random forest model revealed that sampling time, tree age, and soil depth were the predominant drivers of SMC change. Overall, these findings highlight the critical importance of GCM in increasing SMC in orchards worldwide. In addition, the results show that the corresponding changes are controlled by specific climatic, soil, and management factors. These findings provide insights and guidance that can assist practitioners and policy-makers to establish and manage site-specific GCM practices to maximize SMC benefits and promote sustainable agricultural development. [ABSTRACT FROM AUTHOR]

• Pulse reserve paradigm observed across biomes over India. • Pulse reserve is distinctly visible from both X and L-band satellite data. • Spatio-temporally and seasonally varying Pulse reserve characteristics. • Pulse reserve characteristics sensitive to pulse soil moisture and EVI. • These characteristics less sensitive to clay fraction opposed to global traits. The pulse-reserve paradigm associated with the plant's increased water uptake following surface soil moisture pulses is not well understood in India. India has a very high spatio-temporal climate and ecological variability. Such ecological and climatological diversity makes the country-scale analysis of the pulse-reserve paradigm challenging. Here, we present the first analysis of this phenomenon using 9 years of space based passive microwave estimates of surface soil moisture and vegetation water content from both X-band and L-band. We observed from both datasets that the pulse-reserve paradigm is widespread in India across seasons and biomes. The seasonal climate in India drives the precipitation characteristics, thus uphold the pulse-reserve paradigm. The estimated Soil Moisture Threshold (SMT), at which the vegetation water content drops, has substantial spatio-temporal variations, unlike other regions worldwide. The comparisons between X-band and L-band products show significant differences in the magnitudes of soil moisture and vegetation optical depths due to inherent properties. However, most of the findings related to the pulse-reserve paradigm, specifically the sensitivity of processes on different factors, remain similar. We found a strong dependence of SMT on seasonal climate, vegetation greenness, and pulse soil moisture. The association of SMT with soil characteristics, such as clay fraction, is weak in India, contrary to global traits. Our analysis provides new insights into the plant water uptake mechanisms in India to improve the parameterization schemes for better representation of the soil–plant continuum structural and functional behavior. [ABSTRACT FROM AUTHOR]

• Combined altimeter satellites can accurately measure water levels. • Reservoir landslides exhibit periodic displacement characteristics. • Water level fluctuation triggers a step-like creep landslide deformation. Effective utilization of reservoirs facilitates the distribution of water resources in both time and space, providing strong support for the sustainable growth of the economy and society. However, the periodic water level fluctuations of a reservoir during its operation may lead to geological hazards such as landslides. Here, we conducted a comparative analysis of the deformation processes of reservoir and non-reservoir landslides in the Jilintai area between 2017 and 2022 using Interferometric Synthetic Aperture Radar (InSAR) technology and wavelet analysis. Results show that more unstable locations were found on shaded slopes with a soil moisture of 6–10% than on sunny ones with a soil moisture of 15–19%. Both reservoir and non-reservoir landslides showed continuous and prolonged creep displacement over time. The deformation curve of the reservoir landslides displayed a step-like trend, whereas it was a linear trend without the discernible acceleration period for the non-reservoir landslides. Furthermore, the wavelet analysis revealed that the deformation of reservoir landslides follows a one-year period, almost synchrony with reservoir level changes. The results from this work deepen our understanding of kinematic processes of reservoir and non-reservoir landslides, which is crucial for reasonable and effective landslide monitoring and prevention. [ABSTRACT FROM AUTHOR]

• Spatial distribution of drought onset time decreased with an increase in regional wetness in China. • Drought onset speed was higher in the southern humid region than that in the northern region. • Drought onset was accelerated after 1994, particularly in regions with precipitation range from 300 mm to 600 mm. • The greater the warming magnitude, the wetter the region, the faster the drought onset. • Increased water deficiency was the main reason for the accelerating drought onset. This study investigated the effects of warming on the drought onset in China using meteorological data and soil moisture data from 1980 to 2020. The variation characteristics of drought onset time and onset speed were analyzed systematically. The differences in drought onset time and onset speed during the period 1995–2020 and the period 1980–1994 were compared, and possible impact factors were analyzed. Results indicated that the spatial distribution of drought onset time decreased with an increase in regional wetness in China. In arid regions, onset time at 73 % of sites was over 35 days, and about 13 % was among 60–90 days. In semi-arid and sub-humid regions, the onset time was 25–40 days, and in humid regions, it was 20–30 days. After 1994, namely the significant temperature rise period, the onset time has remarkably shortened in parts of the southern Northeast, North China, the eastern Northwest, the western Southwest, the middle of Tibet, the lower reaches of the Yangtze River, and the coastal region of South China. The shortening was most obvious in sub-humid regions. The greater the warming magnitude, the wetter the region, the faster the drought onset speed. Drought onset speed (γ) was higher in the southern humid region than that in the northern region, and γ for southern Northeast China, northern North China, and eastern Northwest China was higher than that for the rest of North China. After 1994, drought onset speed was accelerated in most regions, especially in the semi-arid and sub-humid regions with precipitation in the range of 300–600 mm. Under the combined effect of increased evapotranspiration and reduced precipitation, the region has been the hot spot with increased drought speed after obvious warming in China. [ABSTRACT FROM AUTHOR]

• A comprehensive evaluation of the global soil moisture drought development and recovery characteristics is conducted. • Drought development speed statistically increased and drought duration statistically decreased from 1960 to 2020. • The timing of the occurrence of extreme meteorological conditions shows more significant effects on the propagation of drought than average meteorological conditions. Soil moisture drought is one of the driving forces behind agricultural drought and disastrousness for food production, but the understanding of internal drought propagation is limited, making it difficult to track and predict drought propagation. Based on the trend analysis, Spearman correlation analyses, and the random forest model simulations, we analyzed the spatial and temporal distribution characteristics of soil moisture drought propagation globally from 1960 to 2020 and evaluated the contributions of various meteorological factors on soil moisture drought propagation. The results show that the mean drought instantaneous recovery speed (IRS) is 1.52 times greater than the instantaneous development speed (IDS) globally from 1960 to 2020. IDS (IRS) in arid region is lower (higher) by 24.9 % (11.7 %) than in humid areas. The drought IDS statistically increased 0.012 % pentad−1 year−1 and drought duration statistically decreased 0.020 pentad year−1 from 1960 to 2020, especially in the mid- and high-latitudes of the Northern Hemisphere (45–67.5°N). The timing of the occurrence of extreme meteorological conditions shows more significant effects on the propagation of drought than average meteorological conditions within the drought period. Since evapotranspiration (precipitation) exerts greater influences on drought development (recovery) than other meteorological variables, intensified hydrological cycle under a warming climate could possibly accelerate the drought development and recovery. [ABSTRACT FROM AUTHOR]

• Scarcity of atmospheric Cl deposition data challenges variability assessment and Cl tracer techniques. • Examined inverse Cl mass balance (ICMB) and inverse Cl accumulation (ICA) methods for Cl deposition reconstruction. • ICA method is effective in reconstructing Cl deposition rates under natural conditions. • ICMB method may be an option for reconstructing Cl deposition rates in areas with anthropogenic Cl inputs. The scarcity of chloride (Cl) deposition rate data is a serious impediment to assess the variability of atmospheric Cl deposition, develop Cl tracer techniques and improve the understanding of hydrological process. In this study, we use collocated tritium tracer data in the unsaturated zone (USZ) to attempt the reconstruction of Cl deposition rates. Sixteen data sets of atmospheric Cl deposition and soil profile observations from different locations were analyzed, with an aim to evaluate the feasibility of reconstructing Cl deposition rate based on tritium information in the deep USZ. Reconstructed models for Cl deposition rates include the inverse Cl mass balance method (ICMB) and the inverse Cl accumulation method (ICA). The two methods were basically consistent with the field observations. But the Cl deposition rate reconstruction by the ICA method (R2 = 0.96, RMSE*= 0.29, and MEC = 0.92) is more accurate than the ICMB method (R2 = 0.78, RMSE*= 0.48, and MEC = 0.82) under natural conditions. The ICMB method resulted in a larger uncertainty of reconstructions due to uncertainties from the tritium peak method and soil water chloride concentration. In contrast, the bias from the ICA method reconstruction results from the anthropogenic input of chloride sources in the USZ. This study offers valuable and complementary tools for determining Cl deposition rates and can aid in promoting the development of global Cl deposition databases or networks, enhancing their accuracy and providing essential information for relevant research and applications. [ABSTRACT FROM AUTHOR]

• Multifractal method is suitable for predicting drying SWCC of loess. • Building a matrix for the drying path, makes the water content changes visualizes. • Estimate the hysteresis factors in wetting path and forecast the wetting SWCC. • This study can predict the complete SWCC of loess only by grain size distribution. Soil and water characteristic curve (SWCC) is an important soil parameter which is used to estimate various parameters to describe unsaturated soil hydrodynamic properties. Due to the large span of soil matric suction, this procedure is time-consuming and difficult to measure it in the laboratory. However it is of great significance to find a scientifically efficient and economically viable method to predict SWCC of loess soil. Based on the measurement of soil–water characteristic curves in the laboratory, this study predicted the drying curve (DC) based on the soil particle size distribution (PSD) fractal theory. It also used the domain model to predict the wetting curve (WC) based on the pore distribution, and finally obtained the complete soil–water characteristic curve of loess. These procedures improved the research status which using the drying curve to instead of the wetting curve, and this replace usually leads to a large calculation error and underestimates the safety of the slope. The method proposed in this study can quickly obtain the hysteretic soil–water characteristic curve of loess based on only the soil particle size distribution, improve the correlative calculation accuracy of unsaturated loess, and play an important role in predicting the hydraulic parameters of loess soil. [ABSTRACT FROM AUTHOR]

[Display omitted] • Ten years of stable water isotope data collected at a forest site were analyzed. • A numerical model was used to simulate soil water and isotope dynamics. • The model successfully located the root uptake of pre-event water in the soil profile. • Isotopic differences became apparent when flux-type weighting was taken into account. • Mechanisms for the existence of isotopically distinct subsurface waters were explored. The availability of long-term data series of natural stable isotopes of water (18O and 2H) observed in a small mountainous headwater catchment provides an opportunity to study soil water partitioning and runoff generation processes. In this study, the analysis of the isotopic composition of rainwater, soil water, subsurface stormflow, groundwater, and streamflow was carried out over a 10-year period. A one-dimensional vertical dual-continuum model was used to analyze the preferential flow of water and the transport of 18O in a hillslope. The model, previously validated against observed hillslope discharge and isotope transport data dynamics, provided a framework for evaluating seasonal and episodal runoff regimes. For episodal simulations, the partitioning of soil water into pre-event and event water was done using virtual tracer concentrations instead of the observed 18O contents – to maximize the contrast between pre-event and event water signatures. Simulated seasonal isotopic variations of 18O in stormflow and soil water compared well with the observed 18O contents. When evaluated as long-term median values, the observed isotopic composition did not suggest significantly different water in the different discharge components. However, when the flux-type weighting of isotopic signatures in hillslope stormflow, deep percolation, and root water uptake was applied, isotopically distinct waters became evident. For the selected episodes, approximately 62 % of stormflow and water transpired by plants was associated with pre-event water. The results suggested that isotopic differences between water associated with different discharge mechanisms prevail despite intense pre-event and event water mixing above the soil–bedrock interface. The modeling approach helped to explore the potential mechanisms behind the existence of isotopically distinct subsurface waters. [ABSTRACT FROM AUTHOR]

[Display omitted] • A stochastic simulation-based chance-constrained programming (SSCP) is developed. • SWAT is used to estimate the NPS loads after the implementation of BMPs. • A joint GLUE-CCP approach captures the propagation of SWAT parameter uncertainty. • GA with parallel sampling technique can save computational time of SSCP. • SSCP is used to seek the layout of BMPs to control NH 3 -N in a basin under uncertainty. In this study, a stochastic simulation-based chance-constrained programming (SSCP) model is developed by integrating soil and water assessment tool (SWAT), generalized likelihood uncertainty estimation (GLUE), chance-constrained programming (CCP) and genetic algorithm (GA) for managing non-point source (NPS) pollution under uncertainty. The SSCP model improves upon conventional simulation-based optimization methods by effectively (i) reflecting the propagation of stochastic uncertainties from the SWAT to the optimization framework by the joint GLUE and CCP (GLUE-CCP) approach, and (ii) identifying the optimal implementation levels of best management practices (BMPs) under different constraint-violation risk levels. The SSCP is solved using a GA with parallel sampling technique after conversion into the corresponding deterministic version. The model is applied to determine the optimal types, sizes and locations of BMPs for NPS pollution control in the Hanjiang River basin, China. The results indicate that, under low-risk conditions, grassed waterways and filter strips should be implemented in the northern part of the basin and larger detention ponds of about 637.8 ∼ 850.4 ha should be installed in the northern and eastern parts of the basin; whereas under high-risk conditions, filter strips should be installed in the northeastern part of the basin and larger detention ponds of about 562.1 ∼ 749.5 ha should be installed in the northern and southwestern parts of the basin. Moreover, as the risk level decreases, stricter control of constraint violations leads to higher BMP implementation costs in order to mitigate the risk of excessive NPS pollution loads. Thus, the proposed model provides valuable insights into the selection of BMP placement schemes at various risk levels, facilitating the reduction of NPS pollution at the outlet of the Hanjiang River basin to acceptable levels while considering the tradeoff between system cost and risk. [ABSTRACT FROM AUTHOR]

• Rainfall classification aided the understanding of deep soil moisture (DSM) recharge. • Groundwater overwhelmed the role of direct rainfall in recharging DSM. • A four-stage relationship between DSM and groundwater table was identified. • A conceptual model for DSM recharge was proposed. Deep soil moisture (DSM) plays a critical role in vadose zone hydrological processes. However, the recharge processes and their underlying mechanisms remain unclear due to the scarcity of comprehensive deep hydrological data, especially in regions characterized by active interactions between surface and subsurface hydrological processes. To address this knowledge gap, we performed an event-based study using a four-year hydrological monitoring dataset, encompassing rainfall, profile soil moisture, and groundwater along a hillslope at the Red Soil Critical Zone Observatory (CZO) in China. A total of 226 rainfall events were classified into four distinct types (I-IV) using a k -means clustering algorithm. The changes in DSM and groundwater table (GWT) were dependent on the rainfall type. The GWT (0.57–0.97) played a greater role in contributing to the DSM than the direct rainfall (0.48–0.64) based on a structural equation model, which was more pronounced from upper slope to foot slope. DSM recharge pattern can be described by a four-stage conceptual model, as indicated by the relationship between DSM and GWT: a slow and linear increase (stage 1), a small plateau (stage 2), a rapid linear rise (stage 3), and finally a great plateau (stage 4). DSM at the small and great plateau were closely related with the field capacity and saturation, respectively. Rainfall type, vadose zone features, and soil physical properties jointly shaped DSM recharge patterns. These findings can offer valuable insights into understanding the deep hydrological processes of the red soil region as well as regions with similar hydroclimatic conditions. [ABSTRACT FROM AUTHOR]

• A new method of constructing hybrid model is used to retrieve Cyclone Global Navigation Satellite System (CYGNSS) soil moisture. • Bayesian model averaging (BMA) can reduce the model uncertainty. • Novel vegetation data of Sun-induced fluorescence (SIF) achieve better performance than Normalized Difference Vegetation Index (NDVI). Using Global Navigation Satellite System-Reflectometry (GNSS-R) for soil moisture (SM) retrieval has recently gained importance due to its high temporal-spatial resolution. However, the current methods, i.e., constructing a single machine learning (ML)-based model, have large model uncertainty resulting from ML networks and input schemes. Moreover, traditional Normalized Difference Vegetation Index (NDVI) cannot capture the rapid vegetation changes well. In this paper, a new SM retrieval method of constructing a hybrid model based on Bayesian model averaging (BMA) is employed to reduce the model uncertainty. Meanwhile, novel Sun-induced fluorescence (SIF) data is used as ancillary data to represent the rapid change of vegetation. We validate the proposed method at point and regional scales using in-situ data and the Global Land Data Assimilation System (GLDAS) product. The results demonstrate that our method has high accuracy and low uncertainty in SM retrieval. At the point scale, as accuracy indices, the average R (μ R) of BMA increases from 0.90 to 0.93 and the average root-mean-square-error (μ RMSE ) decreases from 0.034 c m 3 / c m 3 to 0.029 c m 3 / c m 3 ; as indices of uncertainty, the standard deviations of R and RMSE (σ R and σ RMSE ) decrease by 32 % and 9 % compared to the single ML-based model. For the regional scale, the μ R increases from 0.79 to 0.81, the μ RMSE decreases from 0.024 c m 3 / c m 3 to 0.023 c m 3 / c m 3 , and the σ R decreases by 19 %. Moreover, we take the point-scale experiment as an example for comparison to compare the performance of SIF with that of NDVI. The μ R of BMA trained by SIF is 0.03 higher than that trained by NDVI and the μ RMSE decreases by 0.002 c m 3 / c m 3 ; σ R and σ RMSE decrease by 25 % and 6 %. Based on these results, the proposed method can reduce the uncertainty and the advantage of SIF has potential for improving the SM retrieval. [ABSTRACT FROM AUTHOR]

• Introduces a novel approach to monitor soil moisture at sub-daily scale. • ANN models excel in global soil moisture prediction using FY-3 MWRI TB and ISMN data. • 2019 Global sub-daily SM product unveiled, confirming diurnal shifts. • The FY-NN-SM, a new product, offers up to 4 daily updates on global soil moisture. Soil moisture (SM) is a critical parameter influencing hydrological cycles, evaporation, and plant transpiration, connecting land surface and atmospheric interactions. However, traditional SM inversion methods mainly offer daily resolution data, potentially overlooking diurnal fluctuations due to factors such as precipitation and human activities. This study addresses this limitation by shifting to sub-daily (four times per day) SM data, utilizing artificial neural networks (ANN) with microwave brightness temperature data obtained from Fengyun-3C and Fengyun-3D (FY-3C and FY-3D) satellites, alongside the microwave vegetation index (MVI) to correct for vegetation effects. The ANN was trained from July 2018 to December 2019 (FY-3C) and January 2019 to December 2022 (FY-3D) using the International Soil Moisture Network as the training target. The ANN method demonstrates favorable global performance, as indicated by r = 0.751–0.805, NSE = 0.56–0.64, RMSE = 0.069–0.077 m3/m3, ubRMSE = 0.066–0.071 m3/m3, and mean Bias = 0.002–0.007 m3/m3 under the cross-validation mode. It can capture significant diurnal variations in SM, especially in regions like central Asia, western Australia, and South America. This research presents the feasibility of producing sub-daily high-temporal-resolution SM products with potential applications in large-scale agricultural drought and flood disaster monitoring, thereby enhancing national disaster management and mitigation strategies. [ABSTRACT FROM AUTHOR]

• Critical rainfall for recharging flat sandy lands is 15 mm in hyper-arid desert. • Lateral gravity water flow and surface flow on sandy slopes were identified. • Water flow on sandy slope improves the recharge rate of deep soil from rainfall. • Local rainfall has the capacity to sustain the development of Badain Jaran Lake. • Extreme rainfall does not play a decisive role in the sustaining of desert lake. To elucidate the slope hydrology and their influence on the regional water balance and desert lake development, water migration and soil water conditions were studied in the megadunes of Badain Jaran Desert. Simulated rainfall of 15–60 mm with infiltration depths of ∼ 0.3–0.5 m, the lateral water migration ratios of ∼ 52.9 %-86.2 % and the differential distribution of tracers with depth in the flat sand lands indicate that 15 mm rainfall is the critical rainfall for effective recharge of deep soil water and that there is a shift from piston flow to preferential flow of water during infiltration. Soil micro-bedding and 4.2 % soil gravity water from locally wet surface soils and 6 mm of natural rainfall resulted in lateral migration of the tracer ∼ 1.2 m and 0.4 m in the slope surface and soil respectively, indicate surface flow and lateral gravity water flow resulting from differences in hydraulic conductivity and water-holding capacity of the soil micro-bedding. Soil water storage at 0–4 m depth showed that the bottom of the slope was ∼ 30 mm higher than the middle of the slope after the rainy season and ∼ 9 mm higher before the rainy season; in general the post-rainy season was ∼ 30–40 mm higher than the pre-rainy season. Surface flow and lateral gravity water flow in unsaturated soils increase the intensity of lateral water movement on the slope and improve the effective conversion rate of rainfall to deep soil water, especially below 15 mm. Annual recharge of deep soil water, assessed using soil water change and critical rainfall, will not be less than 8.5 mm and may even reach an upper limit of ∼ 30–40 mm. The megadune slopes respond strongly to local precipitation, which is sufficient to maintain the lake, but extreme precipitation does not play a decisive role. The methods and results provided in this work can advance our understanding of the desert hydrological cycle and water resources. [ABSTRACT FROM AUTHOR]

• The runoff-imperviousness relationship has a segmented linear pattern. • The runoff-imperviousness relationship can be described by the R-I sensitivity. • The R-I sensitivity can be expressed by a simple equation (RISE). • A framework helps for LID adaptation was established based on RISE. The urban RPA (Receiving Pervious Area)-DIA (Disconnected Impervious Area) system is the sub-catchment with a typical land distribution pattern where the connectivity between impervious area and drainage is disconnected by pervious area in the context of Low Impact Development (LID). The rainfall-runoff response of the RPA-DIA system is complex and plays an important role in LID management, which is not yet fully understood. In this study, to systematically learn the rainfall-runoff response of the RPA-DIA system, an indoor rainfall simulation experiment was applied to a laboratory RPA-DIA system, and an integrated surface-subsurface flow model was used to simulate the hydrological processes of actual-scale RPA-DIA systems with consideration of numerous rainfall and underlying surface conditions. Both the laboratory experiment and the numerical simulations show a segmented linear runoff-imperviousness relationship for the RPA-DIA system. A threshold of impervious area exists in the runoff-imperviousness relationship, and when the impervious area is less than the threshold, the surface runoff coefficient is zero, otherwise the surface runoff coefficient increases with impervious area in a generally linear trend. We define the slope of the linear trend as the R-I sensitivity (sensitivity of runoff to imperviousness), which is the key metric to determine the runoff-imperviousness relationship. We further explored how the R-I sensitivity changes between different rainfall and RPA conditions (e.g., different LID facilities and initial soil moistures) and found that both the laboratory experiment and the numerical simulations suggest a very simple equation to express the R-I sensitivity (RISE, Runoff-Imperviosness Sensitivity Equation) in terms of rainfall depth and intensity, including three parameters indicating the specific RPA conditions. Based on the segmented linear relationship and RISE, we have established a framework that vividly presents the rainfall control behavior of a specific RPA under different rainfall events, which can be a simple tool to provide cost-effective guidelines for LID facilities adapting to current or future rainfall characters. [ABSTRACT FROM AUTHOR]

[Display omitted] • Soil moisture can alter E c in response to meteorological variables. • The R s and SWC made the greatest contribution to E c during droughts. • The effect of T a on G c was reversed as soil drought intensified. • The E c decreased significantly by strong stomatal regulation during droughts. Soil moisture is a critical variable controlling the processes of the Earth's energy-water-carbon cycles, balances and their feedback loops, which plays an important role in climate change prediction. However, soil moisture and meteorology are interrelated and interact through different ecological processes, resulting in variable and complex biophysical regulation of tree water use in water-scarce areas. This study selected Pinus tabulaeformis Carr. as the target tree species in the semiarid area of the Chinese Loess Plateau. Canopy transpiration (E c), soil water content (SWC), and meteorological variables were continuously monitored during the growing seasons (May–September) in 2015–2020. Throughout the study period, precipitation and SWC were crucial factors affecting E c and stomatal activity, and both were positively correlated with E c and canopy conductance (G c) on an annual scale. The threshold point of soil water stress for Pinus tabulaeformis was when the value of soil relative extractable water (REW) reached 0.36. Moreover, soil moisture can alter the response of E c to meteorological variables. Under soil water stress (REW < 0.36), total solar radiation (R s) and SWC contributed the most to E c , and E c trended to decrease under high vapor pressure deficit (VPD) and air temperature (T a). At this point, VPD and T a significantly suppressed G c. In contrast, under non-water stress conditions (REW ≥ 0.36), R s and VPD contributed more to E c than other variables. As VPD , R s , and T a rose, E c increased more quickly when REW ≥ 0.36, while VPD still exerted an inhibitory effect on G c to avoid excessive water loss. Our results suggest that Pinus tabulaeformis exhibits a drought avoidance water use strategy by controlling transpiration with strict stomatal regulation under soil water stress. These findings will contribute to understanding how the changing soil moisture affects tree transpiration regulation under the background of climate change in semiarid areas. [ABSTRACT FROM AUTHOR]

• The drought indices SDCI, VHI and IDCI were constructed by four weighting methods. • Determination of weights was validated by comparing against scPDSI, SMCI and CY. • New classification schemes were executed by cumulative frequency curve of scPDSI. • Reliability of schemes was verified by drought affected area and frequencies. With the continuous development of remote sensing technology, an increasing number of drought indices are proposed to assess the drought process in different regions. However, most of the studies used the empirical coefficient method and standard drought classification scheme, without considering regional applicability, which may overestimate or underestimate drought intensity. Therefore, the GLDAS soil moisture dataset was firstly verified in the study area, Wei River basin, China, and then the response of precipitation accumulation to NDVI and the lagged effect were analyzed. Secondly, the Scaled Drought Condition Index (SDCI), Vegetation Health Index (VHI), and Integrated Drought Condition Index (IDCI) were constructed and the optimal weights of the drought indicators were determined against the scPDSI, SMCI and CY based on four weighting methods. Finally, the new classification schemes of the three indicators were executed based on the cumulative frequency curve of the self-calibrating Palmer Drought Severity Index (scPDSI) from June to September during the period 2000–2019, and the reliability of the new schemes was verified by comparing with drought frequency and drought affected area. Furthermore, the variabilities of drought frequency of the new classification scheme based on the three indicators were analyzed. The results showed that the correlation coefficients of 2-month accumulation of precipitation and NDVI produced the best value and no lagged effect was detected. The SDCI and VHI determined by the AHP weighting method and the IDCI determined by the entropy weighting method showed better performance. The new classified schemes of the three drought indicators demonstrated better performance than the original classified scheme with more similar frequency distributions to scPDSI and higher correlation coefficients against the drought affected area. Additionally, the three indicators based on the revised classification scheme all indicated a decreasing trend in the frequency of drought occurrence for all the different drought levels; the SDCI and VHI were better at capturing drought events than IDCI. Overall, the results of this study proved that the optimal weighting methods and new classification schemes could effectively improve the precision of drought assessment and may provide helpful information for agricultural drought disaster prevention. [ABSTRACT FROM AUTHOR]

• The propagation time in most river basins in northern China was longer than that in southern China. • More attention should be paid to moderate and above levels of meteorological or agricultural droughts. • Precipitation shortage and soil moisture deficit were not the main factors contributing to extreme groundwater drought in east-central China. Clarifying drought propagation characteristics is crucial for grasping the mechanism of drought development and evolvement and also for identifying the early warning signs of droughts. Currently, the studies examining drought propagation characteristics are primarily focused on two of the three types of droughts (meteorological, agricultural, and hydrological droughts) and are concentrated at the river basin scale; however, few studies to date have examined all three types of droughts, particularly at a large spatial scale. In this study, the Standardized Precipitation Evapotranspiration Index, Standardized Soil Moisture Index, and drought severity index of groundwater storage anomalies were used to characterize meteorological, agricultural, and groundwater droughts, respectively. Based on the copula probability model and Bayesian network analyses, time, probability, and threshold indicators were comprehensively utilized to reveal drought propagation characteristics in China. The results indicated that (1) the average propagation times from meteorological to agricultural, agricultural to groundwater, and meteorological to groundwater droughts in China were 4.1, 15.1, and 24.1 months, respectively. The propagation time in most river basins in northern China was longer than that in southern China. (2) Moderate and above meteorological droughts (agricultural drought) exhibited a much higher probability of triggering mild and moderate agricultural droughts (groundwater drought), and thus, more attention should be focused on drought early warning during this period. Statistics indicated that the average probability of extreme meteorological drought (agricultural drought) triggering mild agricultural drought (groundwater drought) was as high as 61.93 % (59.41 %), and the affected regions were primarily concentrated in the southern and northeastern areas of China. (3) When the meteorological drought level reached severe drought, it triggered mild agricultural drought in 64.3 % of the valid area. The Pearl River Basin exhibited the smallest absolute value of threshold from meteorological to agricultural drought in China, while the propagation threshold of extreme groundwater drought triggered by meteorological or agricultural drought was relatively high. Overall, the results have great implications for the framework construction of comprehensive drought monitoring. [ABSTRACT FROM AUTHOR]

• Reviewed five categories of methods for measuring unfrozen water or ice content; • Estimated the unfrozen soil water content with three empirical models; • The methods and models are evaluated, and the future development indicated. Frozen soil is a complex four-phase porous medium consisting of soil solid/rock, air, unfrozen/liquid water and ice at the subzero temperatures. Freeze-thaw cycles change the magnitude of total soil water content as well as the unfrozen water/ice ratio in frozen soil that affects soil structure and strength, infiltrability/permeability, water availability for microbial activity and chemical reactions, solute concentration and distribution, and thermodynamics. Accurate quantification of unfrozen water content is therefore critical to understand frozen soil hydrological, biogeochemical, thermal and mechanical properties and processes under climate change. Currently a variety of techniques and methods have been applied to obtain unfrozen water content in frozen soils. However, only few studies have attempted to review and synthesize these works. The objective of this study was therefore to review and collate currently available methods determining unfrozen water content in frozen soils. The principles, applications, advantages and limitations of these methods were reviewed and categorized into five categories: a pressure-based method, radioactive-methods, electromagnetic-methods, thermal-methods, and a sound-based method. Models for indirectly estimating unfrozen water content based on empirical temperature relationships, the soil water/moisture retention characteristic, and the vG-Clapeyron model, were also summarized. There is no direct method to estimate ice content but it can be indirectly calculated based on water balance (i.e., difference between total and unfrozen soil water content). The review is closed with a brief review of future needs and perspectives for simultaneous measurement of unfrozen water and ice contents in the laboratory and in the field. [ABSTRACT FROM AUTHOR]

• Complementarity of signals at different frequencies is considered. • HVCE fusion method can ensure the rationality of fusion weight determination. • The inversion model based on the HVCE fusion method has high inversion accuracy. • The method can serve as a reference for applying multi-source data fusion to hydrology. Soil moisture is a key indicator for studying the exchange of energy and matter in the soil–plant–atmosphere circulation system. With the rapid development of GNSS base station networks, the application of GNSS-IR technology in the field of soil moisture monitoring is further promoted, based on the characteristics of GNSS satellites and microwaves (L-band). The global navigation satellite system (GNSS) interferometric reflectometry (GNSS-IR) technology can realize high-precision, continuous, dynamic, and real-time monitoring of soil moisture. However, the conventional GNSS-IR monitoring technology cannot fully consider the differences and complementarity of satellite signals in different frequency bands owing to the use of single-frequency band data processing. In this study, the Helmert variance component estimation (HVCE) fusion method is proposed to perform weight fusion processing of the L 1 and L 2 frequency band characteristic parameters data of GNSS satellites. This method is based on the basic principle of posterior variance weighting, which can satisfactorily ensure the rationality of weight determination. To verify the effectiveness of the proposed method, four models, namely linear, multilayer perceptron (MLP), support vector machine (SVM), and Gaussian process regression (GPR), were used to construct the soil moisture inversion models before and after fusion. Then, the accuracies were compared with those of the entropy fusion method. The results show that the HVCE fusion method can greatly improve the correlation between characteristic parameters and in-situ soil moisture, and the amplitude and phase of nine satellites coefficient of determination (R2) increased by 53.70 % and 59.70 % on average than before fusion, which is 56.86 % higher than that of entropy method on average. Second, the HVCE fusion method can effectively improve the modeling accuracy. The GPR model achieves the highest accuracy, with its correlation coefficient (R) and root mean square error (RMSE) values improving by 14.68 % and 21.43 %, respectively. The SVM model is slightly less accurate than the GPR model, with its R and RMSE improving by 15.23 % and 15.35 %, respectively. The MLP model ranks third among the four models, whereas the linear model has the worst performance. In addition, compared with the conventional entropy fusion method, the proposed HVCE fusion method improves the R and RMSE accuracy by 19.50 % and 19.56 %, respectively. Thus, the proposed method can provide an effective solution to the problem of reasonable weighting fusion of GNSS-IR dual-frequency data. This study can be a reference for the application area of multi-source data fusion in hydrogeodesy and water resources monitoring. [ABSTRACT FROM AUTHOR]

• Reservoir drawdown zone was a hotspot of GHG emissions. • CH 4 fluxes went to zero due to dam decommissioning. • Soil volumetric moisture content and soil bulk density were the major factors contributing to GHG emissions. • The desiccation of reservoir sediments can result in loss of C from reservoir. Reservoirs are a category of artificial ecosystem that play a key role in the global carbon cycle. However, there has been limited research on the impacts of dam decommissioning (DD) on greenhouse gas (GHG) emissions over a reservoir life cycle. This study analyzed the effects of DD on CO 2 and CH 4 fluxes emitted from impounded water zone (area of inundated sediments), the drawdown zone (area of sediment exposure during DD), and the margin zone (area of long-term sediment exposure) before, during, and after the drawdown of a retired reservoir in Inner Mongolia. The margin zone encompassed ± 90 % of the total reservoir area over the study period, whereas the impounded water and drawdown zones accounted for the remainder. CO 2 emissions decreased during DD process in the margin zone (325.24 ± 313.80–128.81 ± 128.78 mg m−2h−1) and impounded water zones (286.00 ± 242.00–0.00 ± 0.00 mg m−2h−1), whereas they increased in the drawdown zone (0.00 ± 0.00–239.52 ± 104.35 mg m−2h−1). CH 4 emissions gradually declined over time after DD in the impounded water zone and this zone was the main contributor of CH 4 fluxes before DD (57.36 ± 12.77 mg m−2h−1), and finally emissions went to zero. Exposure of sediments gradually resulted in a significant increase in CO 2 emissions. The major factors affecting GHG emissions were soil volumetric moisture content (SMC) and soil bulk density (ρb). The CO 2 -eq of the reservoir ecosystem decreased after DD. This happened despite CO 2 emissions increasing because CH 4 , which has a higher global warming potential, decreased following DD. The consideration of DD in the carbon footprint of reservoirs for understanding of reservoir carbon dynamics and the global carbon balance requires further study of the long-term effects of DD on carbon fluxes. [ABSTRACT FROM AUTHOR]

• Precipitation is the dominant factor affecting soil water content. • Evapotranspiration is the dominant factor affecting vegetation growth. • The impact of meteorologic on soil moisture and vegetation decreases from temperate to tropical. • LST/Tair negatively affects soil moisture and vegetation on a small scale. Long-term meteorological drought can cause soil moisture deficiency and hinder vegetation growth, however, the dominant driving force leading to alteration of soil water content (SWC) and vegetation growth condition is still not clearly defined due to the natural coupling between meteorological elements. Here, we decoupled the relationship between precipitation, evaporation and temperature to explore the main driver of impacting SWC and solar-induced chlorophyll fluorescence (SIF) in China by means of binning, partial correlation analysis and structural equation modelling. We found that precipitation led to a positive change in SWC nationally (nonstandardized path coefficient (NSPC) = 0.75, P < 0.001), while its influence gradually decreased from temperate to tropical climate areas. Precipitation had a positive effect on SIF, except in tropical areas (NSPC = 0.7, P < 0.001). The results of the three research methods indicated that the primary factor influencing SIF was evaporation (NSPC = 0.69, P < 0.001), with a significant correlation between SIF and evaporation in approximately 75 % of the country's area. The binning analysis revealed that SIF initially increased and then decreased as evaporation increased. Partial correlation results showed that temperature had a negative impact on both SWC and SIF, while binning results showed that temperature had no significant impact on SWC, and structural equation modelling result showed temperature had relatively less impact on SWC (NSPC = -0.21, P < 0.05) and SIF (NSPC = -0.19, P < 0.01) than precipitation and evaporation. In addition, we found the dominant drivers' influence weakened from temperate to tropical climate areas, which revealed by the area percentage of partial correlation between evaporation and SIF, precipitation and SWC. The same distribution pattern can be seen from NSPC of evaporation and SIF, NSPC of precipitation and SWC. [ABSTRACT FROM AUTHOR]