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• Determine amount, frequency, and duration of dew/hoar frost using weighing lysimeters. • Compare characteristics of dew/hoar frost between Artemisia ordosica and bare soil. • Dew provides 30% of water for Artemisia ordosica during extreme drought periods. Dew and hoar frost are important water resources in arid and semi-arid regions. However, knowledge of the characteristics of dew/hoar frost formation in the typical shrub canopy of Northwestern China and its importance for vegetation survival is still not clear as it is difficult to observe. In this study, we investigated dew/hoar frost formation characteristics under both Artemisia ordosica and bare soil conditions based on weighing lysimeters in Mu Us Sandy Land, Northwest of China. The results show that dew and hoar frost occurred 214 days for Artemisia ordosica and 162 days for bare soil. The hoar frost formation mainly occurs from October to March, while dew formation occurs every month. The amount of hoar frost was 19.6 mm under Artemisia ordosica condition and 17.4 mm under bare soil condition, and the amount of dew was 27.5 mm of Artemisia ordosica and 18.4 mm of bare soil. The most common daily amount of dew/hoar frost is between 0 and 0.2 mm, which accounts for 60.7 % and 52.5 % of dew/hoar frost formation days for the Artemisia ordosica and bare soil conditions, respectively. The results from boosted regression tree analysis indicated that relative humidity had the greatest influence on dew/hoar frost formation. During soil drought periods (July 15 to August 18, 2021), the soil water content of Artemisia ordosica kept constant at 0.061 cm3/cm3, while the soil water content of bare soil was dropped at the rate of 0.00135 (cm3/cm3)/d. The ratio of dew amount of Artemisia ordosica to rainfall amount was 42.1 %. Despite the small amount of dew, it serves as a frequent and important supplementary water source for the ecosystem. This study provided valuable insights into dew/hoar frost formation, especially its importance for vegetation survival. [ABSTRACT FROM AUTHOR]

• Monitoring hydro-evolution of an artificial rainfall-induced loess landslide by tl-3D-ERT. • Slope experienced a movement process of initiation, acceleration, and deceleration. • Slope wetting showed substantial spatiotemporal heterogeneity. • Variability of 3D hydro-evolution is an essential indicator of slope movement trend. Rainfall can increase the moisture content of a slope, which changes its mechanical properties and thus acts as an important mechanism to trigger landslides. However, it is unclear how moisture contents vary in space and time during rainfall-induced slope movements, and which soil-wetting patterns precede landslide events. Here, we used point sensors and time-lapse 3D electrical resistivity tomography (tl-3D-ERT) technology to monitor the spatiotemporal evolution of the hydrology and movement within a rainfall-induced loess landslide. We observed that movement of the slope involved a semi-continuous process of initiation, acceleration, and deceleration to stabilization. The slope hydrology evolution suggested that initial saturation, dominant flow, and changes in slope recharge and drainage owing to internal seepage and erosion are important factors that affect moisture changes. The movement accelerated when the average saturation value and spatial variation in moisture distribution within the slope increased; however, the movement decelerated when both parameters did not change significantly with time. The accumulation and dissipation of pore water pressure within the slope owing to uneven humidification may be the underlying cause of changes in landslide movement. Our study demonstrates the potential of tl-3D-ERT for monitoring the spatiotemporal variability of moisture evolution within rainfall-induced landslides to determine landslide deformation trends and develop a landslide early warning system. [ABSTRACT FROM AUTHOR]

[Display omitted] • The role of the soil mantle on groundwater recharge of karst aquifers was investigated. • A characterization of soil and land cover was performed by field and laboratory analysis. • Soil Water Balance code was applied to evaluate the variability of the recharge. • Groundwater recharge rates depend on land cover types and soil thickness. Karst aquifers represent the fundamental groundwater resources in Italy and in many other countries because supplying urban areas of drinking water and sustaining industrial and agricultural activities. Accordingly, advancing the assessment of groundwater recharge of these aquifers is a major scientific challenge, specifically if considering the hydrological role exerted by soil covering and the related land cover types. In such a framework, the soil water balance model is an effective tool for providing accurate estimates of groundwater recharge depending on precise field and laboratory characterizations of the soil covering. Considering the features of karst aquifers of southern Italy and the ubiquitous occurrence of a soil mantle, mainly of ash-fall pyroclastic origin, a methodology based on field and laboratory characterizations, as well as modeling approaches, is proposed in this work to advance the knowledge on groundwater recharge processes. The experimental approach was applied to the Soprano-Vesole-Chianello Mts. karst aquifer (Campania region, southern Italy) which is considered representative of karst aquifers of southern Italy and Mediterranean areas as well. A comprehensive characterization of the soil mantle was developed, considering different land cover classes, measurements and stochastic spatial modeling of soil thickness. The unsaturated hydraulic properties of undisturbed soil samples were analyzed by laboratory tests resulting in the determination of Soil Water Retention Curves (SWRCs). Moreover, a field monitoring of soil water content was carried out by multi-profile soil moisture sensors, allowing the assessment of the hydrological response of the soil covering and of the regime of groundwater recharge process as well. Among the principal outcomes is the recognition, for each land cover class, of different values of the depth to which the evapotranspiration effect is extended. Based on a consistent spatial modeling of soil thickness and soil hydrologic properties, the Soil Water Balance code (SWB 1.2) was applied to evaluate the spatial and temporal variability of the groundwater recharge of the karst aquifer. As a principal outcome, groundwater recharge rates were found to be dependent on land cover class and soil thickness as well, showing lower values for wooded areas and higher for denudated ones, respectively due to the higher and lower evapotranspiration rates. [ABSTRACT FROM AUTHOR]

• Spatial and temporal SM anomalies decomposed using EOF analysis in two paired catchments. • Low temporal dynamics in the gully area of the treated catchment was due to the storing function. • High temporal dynamics in the gully area of the untreated catchment was due to the discharge effect. • Land use was the dominant spatiotemporal SM variation control at a catchment scale. • Land restoration projects altered spatiotemporal SM variation at a catchment scale. Soil moisture (SM) is a critical factor for material circulation and energy exchange in Earth's critical zone, but it is spatially and temporally variable. This study aimed to provide new insight into spatiotemporal SM variability and its associative controls in areas where land restoration projects have been implemented. Specifically, we compared environmental controls on spatiotemporal SM variability in deep soil layers (0–500 cm) of two paired catchments, one of which was treated by the Gully Land Consolidation (GLC) project and the other which was not, where the slopes of both catchments had initially been impacted by the Grain for Green (GFG) program. For this purpose, we used the empirical orthogonal function (EOF) to decompose spatial and temporal SM anomalies. Two dominant spatial and temporal EOF anomalies (EOF1 and EOF2) were obtained from six soil layers in both catchments, with an explained variance of 86%–97% and 32%–53%, respectively. These spatial anomalies that derived from spatial structures exhibited similar characteristics and controls in both catchments, while underlying temporal SM dynamic mechanisms differed. Temporal dynamics of the treated catchment were lower in the gully area (a relative flat region), while the opposite was true for the gully area (at a lower elevation) in the untreated catchment. The GLC and GFG programs both altered catchment scale temporal SM dynamic forcings. Results obtained from this study are helpful for understanding spatiotemporal SM variability with associated the hydrological processes in areas where the intensive land restoration projects have been implemented at a catchment scale. [ABSTRACT FROM AUTHOR]

• The water release capacity of BSCs was related to their types and water suction. • BSCs maintained lower water release capacity compared to bare soil under higher water suction. • Lower water release capacity of BSCs under higher suction was attributed to the increase of > 0.25 mm aggregates and SOM. • BSCs exhibited a better WRC due to their lower water release capacity under relatively dry conditions. • Lower water release capacity of BSCs is favorable for dryland ecosystem restoration. Water retention capacity (WRC) of soil is the ability of soil to hold and retain water, which reflects soil water availability and soil drought resistance. Biological soil crusts (biocrusts), the ubiquitous living cover in drylands, regulate water holding capacity (WHC) by changing surface soil physico-chemical properties. However, to date, the water release capacity of biocrusts remains unclear, which hinders a deep understanding of the WRC of biocrusts. Therefore, we examined the WRC by analyzing the WHC and water release capacity of five types of biocrusts at different successional stages: light cyanobacteria (LC), dark cyanobacteria (DC), cyanobacteria mixed with a few mosses (CM), moss mixed with small patches of cyanobacteria (MC), and moss (MS) biocrust. Bare soil (CK) was used as the control. The results showed that there was no significant difference in WHC between CK and the five types of biocrusts. However, biocrusts displayed different water release capacities at different soil water suctions. Biocrusts maintained a higher water release capacity when the soil water suction ranged from 200 cm H 2 O to 800 cm H 2 O (the soil volumetric water content (θ v) ranged from 32% to 22%); furthermore, there were no significant differences in the water release capacity of different types of biocrusts. The biocrusts exerted a lower water release capacity compared to bare soil when the soil water suction ranged from 800 cm H 2 O to 3000 cm H 2 O (θ v ranged from 22% to 11%), which was due to the increases in the > 0.25 mm aggregate content and the soil organic matter (SOM) contents. Biocrusts exhibited a better WRC due to their lower water release capacity under relatively dry conditions (11% < θ v < 22%), which enables biocrusts to maintain longer wet duration and benefits their development. Hence, good development of biocrusts is considered beneficial for the restoration, stability and sustainability of drylands with limited water resources are limited. [ABSTRACT FROM AUTHOR]

• WUE-water source relation was determined in the hot-dry valleys with a reservoir. • A source contributing score was proposed to integrate source contribution changes. • Larger WUE is associated with greater reliance on deep water sources. • Soil moisture d-excess explained more variation in WUE than soil moisture content. • Soil and microclimate explain more than 89.8% of the variations in tree water use. Identifying the carbon–water relationship of plants helps to understand their adaptive water use strategies. The relationship between WUE and the water source remains uncertain in the hot-dry valleys with reservoirs. The leaf δ13C and soil and xylem water isotopes (δ2H and δ18O) were determined to identify the WUE and water source contributions, respectively, in Leucaena leucocephala (Lam.) de Wit communities. We propose a source contributing score (SCS), calculated as a value for the contribution of water from different soil layers (a higher SCS value indicates plants use deeper water sources). The positive relationship between leaf δ13C and SCS (R2 = 0.16, P = 0.022) and the negative relationship between leaf δ13C and topsoil contribution (R2 = 0.21, P = 0.009) confirmed the close association of larger WUE with less reliance on shallow soil water. The redundancy analysis suggested a stronger connection between leaf δ13C and d-excess than leaf δ13C and topsoil moisture content, which emphasized the key role of soil moisture d-excess. Variation partitioning analysis (VPA) suggested soil properties and microclimate explain more than 89.8 % of the variations in tree water use. VPA also suggested different roles of microclimate and soil properties in explaining tree water use strategies. Findings broadened the understanding of the relationship between WUE and water sources and emphasized the key role of soil moisture d-excess in hot-dry valleys with reservoirs. [ABSTRACT FROM AUTHOR]

• Pore-water salinity in marshes is influenced by tides, evaporation and macropores. • Macropores induce salinity difference from the macropore wall to adjacent soil. • Macropores' impact on pore-water salinity varies with distances from tidal creeks. • Macropore density is the main factor affecting salinity distribution. Salt marshes, a type of coastal wetlands, are influenced by various hydrogeological processes. These processes affect water and salt transport and play a critical role in determining the ecosystem services of salt marshes. Responses of pore-water flow and soil moisture to individual factors (e.g., macropores, evaporation and semidiurnal tides) have been well examined. However, how macropores interact with spring-neap tides, evaporation and rainfall and affect pore-water salinity in salt marshes remains unclear. This study investigated this question by analyzing water exchange across the marsh surface and the distribution of pore-water salinity in marsh sediment, based on a three-dimensional marsh-creek model. The results reveal high spatial heterogeneity of pore-water salinity in the presence of macropores. Due to the lateral recharge of macropore water, a negative salinity gradient is formed from macropore walls toward adjacent sediment. Additionally, macropores' impact on water exchange and salt transport varies with distance from creeks. This results in two mechanisms by which macropores affect pore-water salinity, flushing and recharging. In well-drained near-creek areas, macropores reduce salt accumulation (flushing) compared with salt marshes without macropores. Whereas in the poorly-drained marsh interior, the recharge from macropores inhibits rainfall infiltration. This, together with enhanced evaporation, increases pore-water salinity (recharging) in the shallow soil layer. Moreover, the macropores' impacts on pore-water salinity vary remarkably with macropore density. This study improves the understanding of pore-water salinity distribution in coastal wetlands and has potential implications for related biogeochemical processes. [ABSTRACT FROM AUTHOR]

• Four plant species segregate along a niche axis defined by water source and WUEi. • Hydrological niche segregation is dynamic in response to changing water patterns. • Dynamic hydrological niche segregation facilitates efficient soil water use. Hydrological niche segregation (HNS), specifically the variation in root water uptake depth among coexisting species, is an understudied area of research. This is especially the case in semi-arid ecosystems, such as China's Loess Plateau (CLP), where seasonal aridity necessitates adaptive water use strategies among plant species. In this study, we conducted a two-year investigation to understand the water sources and intrinsic water use efficiency (WUEi) of four coexisting plant species: Populus simonii (tree), Caragana korshinskii and Salix psammophila (shrubs), and Artemisia ordosica (semi-shrub). We analyzed the isotopic compositions of xylem and soil water (δ2H and δ18O) and leaf δ13C to identify the water sources and WUEi, respectively, of each species. We then used the nicheROVER model to quantify HNS based on the variations in xylem water δ2H and δ18O. Our results show that the four co-existing species occupied distinct positions on a hydrological niche axis, delineated by their respective water sources and disparate WUEi. The tree P. simonii exhibited a preference for deep soil water and demonstrated a high WUEi. Both shrubs, S. psammophila and C. korshinskii , utilized intermediate and deep soil water, respectively, and with comparable WUEi. Conversely, the semi-shrub A. ordosica relied on shallow soil water and displayed a low WUEi. These differences in water sources and WUEi led to HNS between A. ordosica and the other three species in a relatively wet year. However, in a relatively dry year, HNS between A. ordosica and the other three species contracted and WUEi increased as species increased the use of deep soil water. Overall, these results demonstrate that HNS is a dynamic phenomenon that varies on at least an annual basis. It expands and contracts as plants regulate their water uptake and loss in response to changing soil moisture conditions. [ABSTRACT FROM AUTHOR]

• A climate-adaptive transfer learning framework for soil moisture estimation is proposed. • The framework mainly uses ERA5-Land data, ISMN data, and global Köppen climate classification data. • The framework is designed for data-scarce region and performed well on the Qinghai-Tibet Plateau. • The framework can contribute to historical soil moisture data reconstruction. • A long-term (1960–2019) soil moisture dataset with accuracy improvement is produced. Soil moisture (SM) plays essential roles in revealing complex interaction mechanisms among air–soil-water-plant processes. In the Qinghai-Tibet Plateau (QTP), the in-situ SM data is sparse and limited, satellite-based SM data has short period, while reanalysis SM data has advantages on long-term and high spatiotemporal resolution but has relatively high error. In this study, to improve soil moisture estimation in the QTP, we aim to propose a Climate-Adaptive Transfer Learning (CATL) framework for data scarce region based on reanalysis data (ERA5-LAND dataset) and the in-situ data (International Soil Moisture Network (ISMN) data). Specifically, regarding the QTP as the target region, selecting the areas with similar climate types with QTP as the source region, we train the CNN-LSTM fusion model in the source region and then transfer it to the target region via fine-tuning strategy. Results indicate that the produced soil moisture data based on CATL framework achieves CC of 0.755 and ubRMSE of 0.042, which has better quality than SMAPL3 during 2015–2019. Additionally, the CATL framework also produced the historical SM data reconstruction during 1960–2010, with CC increased by 11.3 % and ubRMSE reduced by 1.5 % compared with the original ERA5-Land reanalysis data. Furthermore, compared to the direct fine-tuning strategy (without climate adaptive), the CATL framework showed an increase of CC with 2.6 %, and decreases in RMSE, MAE, and ubRMSE of 5.3 %, 4.2 %, and 7.5 %, respectively. Finally, an improved soil moisture dataset (daily, 0.05°) ranging from 1960 to 2019 is produced for the QTP. This study provides a new tool for soil moisture estimation improvement in data-scarce region which will also benefit basin hydrology and water resources management. [ABSTRACT FROM AUTHOR]

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

• Seasonal water uptake differed among different tree species. • Deep-rooted tree species had lower growth resistance to drought. • Shallow-rooted tree species was more adaptative to seasonal precipitation areas. • The root structure and deep soil water availability were the key factors influencing the drought resistance of trees. Understanding the water use patterns of different tree species and their adaptability to drought is urgently needed due to global warming. In this study, we investigated the seasonal water uptake patterns of Pinus tabulaeformis and Picea wilsonii on the eastern Tibetan Plateau using stable isotope methods. In addition, tree ring width data was used to evaluate the radial growth resistance of the two species to drought in 2022. The focus was on identifying species differences in water uptake patterns during drought spells and radial growth resistance to drought. The results showed that although Pinus tabulaeformis could increase water uptake from deeper soil layers during drought spells, the radial growth resistance to drought was significantly lower (p < 0.001) than that of Picea wilsonii due to the limited water content in the deep soil layers. In contrast, Picea wilsonii had higher resistance and adaptations to drought based on rapid access to water resources from seasonal precipitation with denser fine roots. Overall, shifting water sources to deeper soils during drought spells was not always effective in alleviating drought stress, but the root structure and deep soil water availability were the key factors influencing the drought resistance of trees. The findings had significant implications for decision-making on the species suitability of plantation projects under future climatic change. [ABSTRACT FROM AUTHOR]

• A significant water storage loss of −158 ± 22 km3 occurred in August 2022 concerning July 2022 in the Yangtze River Basin. • The precipitation deficit and prolonged high temperature caused this extreme drought. • The hydrological drought identified by gravity satellite responds fast to meteorological drought. In 2022, the Yangtze River Basin (YRB) experienced its most extreme drought event since 1961. Conducting a comprehensive assessment of this severe drought is crucial for understanding the entire drought process. GRACE and GRACE-FO gravity satellites have the capability to monitor global mass transport, making them suitable for large-scale drought monitoring. In this study, we investigated the changes in water storage caused by the drought using GRACE/GRACE-FO data and multi-source observations. Our evaluation incorporated meteorological drought indices, including the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), to explore the drought propagation process. Additionally, we examined the effects of trends in terrestrial water storage in the YRB, resulting from reservoir impoundment and climatic factors, on the drought index calculation. The results indicate that the extreme drought in the YRB began in August 2022, resulting in a water storage loss of approximately −158 ± 22 km3 compared to July 2022. There was almost no delay in the response of soil moisture drought and hydrological drought to the meteorological drought at a monthly scale. This extreme drought had its most severe impact on the middle and lower reaches of the YRB, especially in the Dongting Lake Basin, where the GRACE drought index value remained below −2.5 until December 2022. This study demonstrates the significant water storage deficit during the extreme drought in the YRB in 2022, providing a comprehensive understanding of this historic drought. [ABSTRACT FROM AUTHOR]

• Most of the Euro-Mediterranean region affected by drought conditions during 2021/22. • Recursive high-pressure systems & a poleward jet shift explain rainfall deficits. • Most severe drought since at least 1891 when considering evaporative demand (AED) • Enhanced AED caused unprecedented low soil moisture & runoff since at least 1965. • Potential worst-case droughts if historic rainfall deficits occur with current AED. The Euro-Mediterranean region experienced a remarkable drought during the hydrological year 2021/22. Substantial and widespread impacts on water supply systems, agricultural crops, and the production of hydroelectric power were observed. This assessment characterises the drought from a long-term perspective using a multi-index approach and analyses the associated atmospheric circulation at the annual and monthly time scales. The main dynamical forcing of the drought was the unusual recurrence of high-pressure systems over western Europe, at least partly due to an anomalous southward shift in blocking activity and a remarkable occurrence of low-latitude blocks. This led to record-breaking positive geopotential height anomalies over western Europe and a poleward displacement of the North Atlantic eddy-driven jet. Although most of the region was affected by mild drought conditions, the 2021/22 event was not unprecedented in terms of precipitation deficits since other periods of the 20th century (e.g., in the 1920s, 1940s and 1970s) displayed moderate and severe drought conditions over larger areas. However, the 2021/22 drought has been the most intense since at least 1891 because of high atmospheric evaporative demand (AED) values associated with extreme temperatures, especially during the summer of 2022. This enhanced AED also contributed to depleting soil moisture and reducing runoff generation, leading to unprecedented deficits since at least 1965. Finally, we find important differences in the 2021/22 event as compared to other major historical droughts over the Euro-Mediterranean region. In particular, the contrasting effect of AED evidences its increasing role over the last decades and warns about the current risk of experiencing unprecedented droughts. [ABSTRACT FROM AUTHOR]

• We proposed a new index for assessing the water conservation function (WCI). • WCI can well reflect water retention, water yield stability, and vegetation quality. • We compared the spatiotemporal dynamics of WC in a watershed using different methods. The water conservation (WC) function is an important indicator for ecosystem health. However, the commonly-used technical term like water retention (WR) and its computation methods could not well reflect the concept of WC because WR focused on the amount of water retained in vegetation canopy and soil, whereas WC includes WR and water supply as well. This study is to propose a new index for assessing the WC function (WCI) by integrating ecological and hydrological factors including WR, water yield, and vegetation production. We further developed a new framework for calculating spatiotemporal dynamics of WCI using ecohydrological modeling of Soil and Water Assessment Tool (SWAT) and remote sensing data. We then used the framework to assess the spatiotemporal dynamics of WC function in the Weihe River Basin (WHRB), the largest tributary of the Yellow River. Compared to the traditional method, our newly-developed approach is more reasonable because it could reflect water retention amount, temporal stability of water yield, and vegetation quality. Further, the case study can tell it is our new index (with forest being the highest, followed by grassland and cropland), instead of WR (with grassland being the highest and forest being the least), that could reflect the definition of WC and meet our cognition. Spatially, the high WCI (0.4 ∼ 0.6) areas are mainly located in the southern and central-eastern parts of the WHRB, characterized by relatively higher forest coverage (e.g., the Qinling Mountains in the southern part of the study area in southern part and representative 'Grain-For-Green' in central-eastern part). In brief, this study enhances understanding of the WC function and also provides a method for assessing the dynamics of WC that can be applicable to other regions. [ABSTRACT FROM AUTHOR]

• A new composite index is proposed for seasonal drought detection. • The index is derived from precipitation, soil-moisture, and SIF datasets. • Model is developed under nonparametric joint probabilistic framework. • Seasonal drought events occurred from Indian during monsoon periods. • Spatial severity, duration or intensity helps to identify drought susceptible area. Effective drought monitoring is crucial for mitigation efforts and the implementation of early warning systems. Due to the intricate nature of drought phenomena, its impact cannot be accurately characterized solely through the use of univariate indicators. This study introduces a composite version of the modified multivariate standardized drought index (MMSDI) for the Indo-Gangetic Plains (IGP) in India. The MMSDI integrates precipitation, soil moisture, and solar-induced chlorophyll fluorescence datasets from 2001 to 2020, utilizing a nonparametric joint probabilistic framework. It combines three consolidated drought indices: standardized precipitation index (SPI), standardized soil moisture index (SSI), and standardized solar-induced chlorophyll fluorescence Index (SSIFI). The drought patterns among SPI, SSI, SSIFI, and MMSDI indices are compared for short, and mid-term time scales (herein 1, 3 and 6 month) over IGP. The performance of MMSDI is evaluated through quantitative metrics e.g. probability of detection (POD), false alarm ration (FAR) and critical success index (CSI) at various time scales. Results reveal that MMSDI provides a reliable estimation of the severity and spatial coverage of major drought events over IGP at various time scales. The MMSDI demonstrates superior effectiveness in identifying and characterizing the spatial severity of drought, i.e. the visibility of the drought hotspots. The MMSDI has the capability to assess agrometeorological drought, indicating its potential as a valuable tool for identifying regions vulnerable to drought. Overall, this modified drought index derived from multi-sensor datasets produced comprehensive insights for policymakers in implementing effective agricultural drought management practices to understand intricate drought phenomena over IGP region in India. [ABSTRACT FROM AUTHOR]

• Soil moisture dynamics were compared between artificial and natural recovery. • The soil moisture content in planted forest was the lowest at various locations. • Planted forest exhibited the slowest response speed of soil moisture to storm events. • The occurrence frequency of preferential flow in planted forest was the lowest. The global trend of vegetation "greening" in the context of ecological restoration necessitates an urgent assessment of ecosystem services. As essential components of ecosystem services, the hydraulic functions of soil in infiltrating and retaining water following vegetation restoration remain unclear, especially in subtropical mountainous and hilly areas with complex topographies. From 2018 to 2021, soil moisture data collected at five-minute intervals were monitored for three restoration strategies in a hilly catchment of China's Three Gorges Reservoir area. The restoration strategies included planted forest (PF) and natural restoration (naturally regenerated forest, NF; deforested pasture, DP). The soil moisture response to rainfall under these strategies was evaluated using several metrics, including the time difference between peak rainfall intensity and peak soil moisture response (T p2p), cumulative infiltration, and occurrence frequency of preferential flow (PRF). The results showed that the average soil moisture content (SMC) of PF was significantly (p < 0.05) lower than that of NF and DP in both dry and wet seasons, regardless of upslope or downslope location. However, the topographic position affected the difference in average SMC between DP and NF. At the downslope location, the average SMC of DP (0.39 cm3 cm−3) was significantly higher than that of NF (0.33 cm3 cm−3). Conversely, at the upslope location, the average SMC of DP (0.27 cm3 cm−3) was lower than that of NF (0.30 cm3 cm−3). These findings suggested that PF had a lower amount of soil water storage than NF and DP, which was supported by the lowest cumulative infiltration in PF during storm events. The response of soil moisture to storms in PF (T p2p = 3.1 h) was slower than that in NF (T p2p = 1.9 h) and DP (T p2p = 2.5 h). This was consistent with the lower occurrence frequency of preferential flow in PF (PRF = 19.2 %) than NF (PRF = 39.2 %) and DP (PRF = 32.9 %). Therefore, longer response time and less preferential flow indicated that the PF had a relatively poor soil moisture responsiveness to storm events. Accordingly, this study highlights the insufficiency of afforestation in soil water infiltration and retention compared to natural restoration, meriting consideration when assessing soil hydraulic functions in vegetation restoration areas. [ABSTRACT FROM AUTHOR]

• Hydrological reliability (HR) of 12 SBP/GBPs is compared over >200 basins in China. • The GBPs and IR-MW-merged SBPs show higher HR than the IR-only SBPs significantly. • HR is more related to number of gauge used to produce SBP/GBP than spatial resolution. • Regional GBP called CN051_CLDAS performs best, followed by IMERG-F V6 and GPCC V2022. • Snow depth and runoff are more sensitive to precipitation products than ET and SM. Numerous satellite- and gauge-based precipitation products (SBPs/GBPs) have emerged in recent decades, providing unprecedented opportunities for process-based hydrological modeling. However, their reliability in high-resolution hydrological modeling at continental scales, especially for variables other than streamflow, remains to be investigated. Factors influencing their hydrological performance (e.g., catchment sizes, precipitation products' spatial resolution and raw observation information) are also under debate. Here, we provide a comprehensive analysis of the hydrological reliability of 12 state-of-the-art precipitation datasets over China using a process-based land surface model at 0.125° resolution. Simulations driven by different precipitation products are evaluated across ∼ 200 basins with different catchment sizes. Results show that all simulations well reproduce the streamflow, soil moisture, evapotranspiration, and snow-depth at monthly timescale. None of the precipitation products provides best results for all variables, but the gauge-based and infrared-microwave-merged satellite products are superior to the infrared satellite products. The gauge-based regional precipitation product based on the 0.25° China precipitation dataset and the 6-km China Land Data Assimilation System (CN051_CLDAS) provides the best performance generally, followed by two global precipitation products from the Global Precipitation Climatology Centre (GPCC V2022) and the Global Precipitation Measurement Integrated Multi-satellite Retrievals (IMERGE-F V6). However, the advantages of CLDAS_CN051 and GPCC V2022 against satellite-based precipitation products are scale-dependent and tend to disappear over large-scale catchments (>106 km2). The spatial resolutions of precipitation products show no relationship with their hydrological performance, but the number of stations used to generate/bias-correct the precipitation is positively correlated with the performance of streamflow and soil moisture simulations. Differences among the climatology of terrestrial water-cycle components caused by using different precipitation products are notable and tend to increase over the dry and cold regions. Snow depth is the variable most sensitive to precipitation datasets, followed by runoff, ET, and soil moisture. This study suggests that using more station observations is more important than increasing spatial resolutions to enhance the applications of precipitation products in high-resolution hydrological modeling. [ABSTRACT FROM AUTHOR]

• A conditional reactive nitrogen transport model was developed in Hydrus 1D-PHREEQC. • Nitrogen reactions are conditional to water content, temperature, and oxygen content. • Nitrate transport is simulated with dual-porosity, mobile-immobile approach. • Residual soil nitrate is best simulated with physical nonequilibrium transport approach. • The conditional kinetic, mobile-immobile model sufficiently estimates nitrate leaching across multiple soil textures. In agricultural ecosystems, nitrate (NO 3 –) leaching is the most widespread loss pathway and non-point source of nitrogen (N) to surface water and groundwater. NO 3 – leaching in rain-fed and irrigated agricultural systems has been extensively modeled using different numerical approaches of varying complexity. Most numerical modeling studies use simplified zero-order or first-order kinetics when optimizing N budgets. Few studies consider the impacts of temperature, soil moisture and other environmental conditions on biogeochemical reaction rates. In this study, we simulate NO 3 – leaching and biogeochemical processes in two soil columns under large water application events (groundwater recharge events) using a 1D non-equilibrium (e.g. mobile-immobile) reactive transport HP1 (HYDRUS-1D and PHREEQC) model. We compare this calibrated model to several other calibrated models representing simpler HYDRUS nitrate leaching modeling approaches (uniform flow, non-reactive, zero- and first-order kinetics). Results show that the incorporation of conditional environmental factors such as temperature, soil moisture, and a proxy for oxic/anoxic conditions (percent pore-space filled) results in superior model performance representing the timing and magnitude of important biogeochemical processes when estimating cumulative NO 3 – leached from the shallow vadose zone. In addition, using a physical non-equilibrium (i.e. dual-porosity type, mobile-immobile solute transport) approach improves model performance when estimating residual NO 3 – in the soil profile after water application events. [ABSTRACT FROM AUTHOR]

• Grassland had a greater soil water gain and loss than abandoned crop and shrubland. • Hot spots with great deep soil water gain and loss were in layered soils and grassland. • Silt and SOC content primarily controlled the spatial pattern of soil water dynamics. • Plant and soil profile properties secondly controlled the spatial pattern of soil water dynamics. The increase in the extreme drought trend, reduction in soil erosion, and acceleration of deep soil desiccation are renewing interests in soil water dynamics and its environmental controls after long-term (>30 years) grass and shrub revegetation on the Chinese Loess Plateau (CLP). However, inconsistent findings have been reported on soil water dynamics in these typical land uses due to the multi-dependent environmental controls. The objectives of this study are to identify the spatial variability of soil water dynamics and its controls in a small watershed. For this purpose, spatiotemporal soil water storage in 1 m interval to a depth of 5 m was decomposed into a temporal mean (i.e., time-stable pattern) and temporal anomaly (A tn ), which varies over time and is related to soil water dynamics. A tn was then decomposed into a space-invariant temporal anomaly (i.e., spatial mean of A tn ) and a space-variant temporal anomaly (R tn ) , which reflects the spatial variability of soil water dynamics. The R tn was further decomposed using the empirical orthogonal function (E O F) to identify its major controls. Results showed that soil water dynamics (gain and loss) decreased from grassland to abandoned cropland followed by shrubland after long-term revegetation. The hot spots in deep soil water dynamics (2–5 m) were identified in layered soils with an overlying sand layer and underlying silt layer, and grassland with the coexistence of tree-shrub-grass and high deep soil organic carbon (SOC) content. 50–70% of the total variations of R tn were explained by the first two spatial patterns ( E O F s). Silt and SOC content controlled E O F 1 of R tn at 0–2 and 2–5 m, respectively. Plant canopy and soil profile properties (e.g., layered soils with an overlying sand layer and underlying silt layer) controlled E O F 2 of R tn at 0–1 and 2–5 m, respectively. The E O F analysis suggests that silt and SOC dominated soil water storage capacity, plant canopy regulated water flux, and variations of hydraulic properties in soil profiles may jointly drive spatial variability of soil water dynamics. [ABSTRACT FROM AUTHOR]

A diagram illustrating distribution and evolution model of soil water isotopic differences in the studied catchment. [Display omitted] • The immobile soil water showed high deviation from the LMWL in summer than in spring. • The mobile soil water was controlled by the precipitation input and less influenced by evaporation fractionation. • The mobile/immobile soil waters showed large isotopic differences and different mobility in summer. • Soil thawing provided important soil water sources and influenced the subsurface mixing processes. Recently studies postulate that there are two distinct soil water pools, i.e., mobile and immobile soil water (MSW and IMSW), and different soil waters have different mobility to generate runoff. Evaluating the influences of soil thawing on the subsurface mixing between different soil waters is important for understanding subsurface hydrological processes. In this study, the isotopic differences between different soil waters were evaluated in a permafrost-underlain headwater catchment by comparing the variations of stable isotopes and seasonal origin of MSW and bulk soil water (BSW), including both IMSW and MSW. BSW was sampled using soil augering and cryogenic vacuum distillation in thawed and frozen soil layers, and MSW was collected using a zero-tension lysimeter in thawed soil layers. The results show the following: (1) The average δ2H values of MSW, thawed BSW, and frozen BSW were similar during the spring snowmelt period (SSP), this can be attributed to weak evaporation and their similar origin, which is supported by the high line-conditioned excess values and their similar seasonal origin index values. (2) The enriched MSW collected during the SRP compared to the spring MSW and was associated with enriched summer precipitation, and the IMSW experienced strong evaporation during the period from the SSP to the SRP, both leading to enriched summer BSW compared to the spring BSW. (3) The frozen and thawed BSW had very similar isotopic compositions and seasonal origin, indicating the close hydrological connection between them and the important recharge of the melting frozen soil to soil water or runoff. The different freeze–thaw stages and soil moisture conditions controlled the stable isotope variations and mixing of different soil waters, this results in the different mobility of the mobile/immobile soil waters involved in the runoff generation processes during the SRP and the SSP, and therefore these features are useful to verify the isotopes-aided precipitation–runoff models applied in such catchments. [ABSTRACT FROM AUTHOR]

• Water supply and pit level are simulated dynamically to understand subirrigation. • Geohydrological settings determine the hydrological propagation of subirrigation. • Subtle differences in geohydrology define if subirrigation improves crop yield. Controlled drainage with subirrigation (CD-SI) could be a viable measure to cope with the imbalance in water demand and supply in groundwater dependent regions. CD-SI has the potential to i) retain, ii) recharge and iii) discharge groundwater, i.e. anticipate both dry and wet weather extremes. The aim of this paper is to specify the hydrological consequences of CD-SI at field scale. Four field CD-SI pilots with varying geohydrological conditions in Dutch sandy Pleistocene uplands were monitored (minimum 5 years) to study the effects on groundwater level, soil moisture content and soil water potential. Dynamic modelling using the SWAP-model, calibrated with PEST, was used to quantify all water balance components for the field pilots, needed for responsible implementation of CD-SI. Using SWAP-PEST, measured groundwater levels and soil moisture conditions were reproduced sufficiently accurate. Both the water supply rates and the water and crest levels in the control pit of CD-SI systems could be simulated dynamically, which is an important improvement to earlier modelling approaches. Model calculations were used to study the hydrological responses of CD-SI for meteorological dry to wet years and for each of the field sites. Simulations show that transpiration increases with 38 to 206 mm in dry years for sites with respectively low and higher hydraulic resistances in the subsoil. Simulations also show that the required water volume can be large (664 – 728 mm, respectively). CD-SI could thus improve hydrological conditions for crop growth, but the success depends on subtle differences in geohydrologic characteristics. [ABSTRACT FROM AUTHOR]

• A proportionality coefficient is proposed to approximate the GRACE TWS by the SMS. • Using multi-source SMS products can efficiently reconstruct continuous TWSA. Since 2002, the terrestrial water storage anomalies (TWSA) derived from the Gravity Recovery and Climate Experiment (GRACE) and its successor, GRACE Follow-on (GRACE-FO), have been widely used in water storage studies. Interannual and subseasonal variabilities in terrestrial water storage are critical for understanding the change of water cycle and interpretation of GRACE error. In this study, we evaluate the interannual plus subseasonal terms of global TWSA from GRACE satellite data and soil moisture storage estimated from multi-source soil moisture data. Results show that soil moisture change dominated total water storage change in most regions, especially in relatively arid regions. We further reconstruct the continuous TWSA by combing the trend and seasonal terms from the GRACE(-FO) TWSA data with the interannual and subseasonal variabilities of multi-source soil moisture products on a global scale from April 2002 to December 2021. Our reconstruction result is further compared with the TWSA from GRACE(-FO) and two recent reconstruction products. The comparisons show that the reconstruction result has great consistency with GRACE(-FO), with 82% of the grids showing Nash-Sutcliffe efficiency coefficient values greater than 0.6 based on JPL Mascon solution. This study emphasizes that the information from soil moisture can provide an efficient approach for tracking the interannual and subseasonal variabilities of long-term terrestrial water storage. [ABSTRACT FROM AUTHOR]

• A time-varying copula-based approach was proposed to uncover the potential regimes between antecedent drought and hot extremes. • Drier antecedent soil moisture condition would trigger a higher risk of ET. • Incorporation of trend-caused nonstationarity and tail monotonicity helps identify the temporal variations of driving mechanism. Hot extremes may adversely impact human health and agricultural production. Owing to anthropogenic and climate changes, the close and dynamic interaction between drought and hot extremes in most areas of China need to be revisited from the perspective of nonstationarity. This study therefore proposes a time-varying Copula-based model to describe the nonstationary dependence structure of summer extreme temperature (SET) and antecedent soil moisture condition to quantify the dynamic risk of hot extremes conditioned on dry/wet condition. A general statistical inspection procedure which was composed of maximum likelihood (ML)-based estimation, nonstationary Goodness-of-fit (GOF) tests, the log likelihood ratio (LR) tests and minimum corrected Akaike information criterion-based selection was promoted to select the best-fitted nonstationary models efficiently. This study proposed a new approach to identify the soil moisture driving law over extreme temperature from the point view of tail monotonicity and nonstationary risk assessment. Owing to the LTI-RTD (left tail increasing and right tail decreasing) tail monotonicity for dependence structure of these two extremes derived from most areas, two kinds of the driving laws of soil moisture over SET were detected. Because of the spatiotemporal divergence of sensitivity index derived from tail monotonicity (SITM), we can conclude that the spatial and temporal heterogeneity of response degree of ET over the variations of antecedent dry/wet conditions is evident. Incorporation of nonstationarity and tail monotonicity helps identify the changes of driving mechanism (laws) between soil moisture and hot extremes. [ABSTRACT FROM AUTHOR]

• DISPATCH performed better than RF approach in downscaling microwave-based SSM. • DISPATCH was inapplicable in pixels with weak link between SSM and evapotranspiration. • Combining DISPATCH and RF with seasonality considered achieved improved accuracy. Microwave remote sensing has great potential for capturing surface soil moisture (SSM) but suffers from its coarse spatial resolution. In this study, we combined the disaggregation based on physical and theoretical change (DISPATCH) and random forest (RF) approaches to downscale microwave-based SSM in the Yangtze River Delta region, China. Results showed that downscaled SSM by DISPATCH approach was superior to that by RF approach with Pearson correlation coefficient (R), unbiased root mean square error (ubRMSE), and the mean bias (Bias) of 0.778, 0.022 m3 m-3 and 0.061 m3 m-3, respectively. However, the DISPATCH approach was inapplicable in pixels with weak link between SSM and evapotranspiration, while the RF approach did not have such restriction. Therefore, the DISPATCH approach was supplemented with the RF approach to downscale the SSM for the entire study area. In addition, this downscaling process was conducted for the four different seasons and for the entire study period to test whether considering seasonality was necessary in the downscaling. The downscaled SSM was the best in autumn with R = 0.843, ubRMSE = 0.022 m3 m-3, and Bias = 0.048 m3 m-3, followed by summer and spring, and poorest in winter. However, the seasonally downscaled SSM performed better than that downscaled for the entire period. Moreover, over the entire study period, land surface temperature, land cover heterogeneity, soil texture, and water area percentage exerted significant influences on errors. However, contribution order of these environmental factors on errors varied with seasons. This study would be helpful to widen DISPATCH applicability in the semi-humid/humid region and maximize the potential of various data in generating high accurate and spatiotemporally continuous SSM estimates. [ABSTRACT FROM AUTHOR]

• The coexistence of ice, water, and vapor in unsaturated frozen soil is investigated. • Four stages of soil water transfer process in seasonally frozen soils are identified. • Thermally driven vapor flow affects water transfer under freezing and dry conditions. • The proposed model can clarify the effects of soil water flow on vegetation growth. In seasonally frozen soils, liquid water and water vapor movement are critical in numerous environmental and engineering areas. However, the process of soil water movement becomes complicated due to the coexistence of liquid water, water vapor, and ice, and the underlying mechanisms are not yet fully understood. Based on the theory of coupled water, vapor, and heat transport, the modified HYDRUS-1D freezing module was used to investigate the mechanisms of soil water transfer in a cold region. Compared with its previous version, the newly developed model overcomes the numerical instability caused by the phase transformation between liquid water and ice. The verification results showed that the modified model was numerically stable and highly accurate, indicating its suitability for the study region. The variations in soil temperature, water content, water storage, evaporation, and drainage displayed typical seasonal patterns during the analyzed period, reflecting the effects of freeze–thaw cycles and rainfall-infiltration processes. Liquid flow, driven by a water potential gradient, played a major role under most environmental conditions, except when the soil was frozen when it became negligible. In comparison, vapor flow, driven by a temperature gradient, dominated soil water flow when ice was present and accounted for a considerable proportion of the total water flux in dry conditions, proving its significance for affecting soil water transport. These results clarified the mechanisms of soil liquid water and water vapor transfers in seasonally frozen soils and provided more insights for deepening the hydrological cycle theory. [ABSTRACT FROM AUTHOR]

• Heatwaves in CA are becoming earlier, more frequent, more intense, more persistent. • Heatwaves are more frequently observed in the west but less in the central CA. • Soil moisture depletion is the main factor in formulating heatwaves in CA. Heatwaves pose increasing threats on water resources for ecosystems and human consumption especially in arid region, such as Central Asia (CA). As the hinterland of Eurasia, water scarcity and land degradation in CA have increased its vulnerability to heatwaves. While the frequency of heatwaves has been examined, more heatwave metrics such as timing are less known, possible influences of soil moisture on heatwave changes in this region are not fully understood. Using reanalysis and observational datasets, here we examine the spatial–temporal changes of heatwaves in CA during 1981–2020 and the role of various associated factors including soil moisture. The results show that the extreme heatwaves occurred more frequently in northwestern Kazakhstan. Temporally, heatwaves have been intensifying with significant increases in frequency (0.10 events/year), intensity (0.81 °C/year), and duration (0.08 days/year) since the 1980 s, and these trends have accelerated since 1997. The first yearly heatwave event tended to occur earlier in more recent decades, indicating an advanced heatwave season. We also found that the heatwave occurrences were significantly correlated with hydro-meteorological factors. In northwestern of CA, extreme heatwaves are exacerbated by a rapid decrease in soil moisture resulting from reduced precipitation and increased solar net radiation in this area. Our findings provide a scientific basis for understanding the mechanisms and predictions of heatwave in arid regions from the climate change perspective. [ABSTRACT FROM AUTHOR]

• Soil water content reveals the quantity and depth of soil water replenishment. • Replenishment depth was often limited to the top 40 cm from July to November. • Summer precipitation constituted the largest fractions of soil water storage. • Winter precipitation was overrepresented in soil water at 0–20 cm depth in spring. • Antecedent soil water conditions affect the pattern of soil water replenishment. Assessing the integration of precipitation and its isotopic signals in soil water is crucial for understanding the sensitivity of soil available water to precipitation variability. Here the investigations of water stable isotopes and in-situ soil water content were conducted for determining the spatiotemporal patterns of soil water replenishment and their seasonal origins in a subtropical red soil critical zone. Variations in soil water content provide valuable information regarding the quantity and depth of soil water replenishment in response to precipitation events. Soil water replenishment often penetrated to a depth of 100 cm or more from winter to early summer (from December to the next June), but the corresponding replenishment amounts were relatively low. However, most soil water replenishment events were generally limited to the upper 40 cm soil layers between midsummer and autumn (from July to November), with relatively large amounts in summer. The temporal and vertical variations in the seasonal origin index of soil water indicated that summer precipitation constituted the largest fractions (∼66 %) of soil water storage in the entire explored soil profile (thickness = 130 cm) during the study period. Winter precipitation was overrepresented in soil water only at the depth of 0–20 cm in spring, but it may vary slightly from year to year due to changes in winter precipitation patterns (amount and frequency). The findings of the current study broadly highlight how precipitation contributes to soil water storage over space and time, which has significant implications for plant water sources. [ABSTRACT FROM AUTHOR]

• Groundwater mounds resulting from nearshore recharge is identified in a sandy hillslope. • The thickness of vadose zone determines fluxes across water table along the hillslope. • Nearshore recharge temporarily changes streamlines and increases tortuosity of flowpaths. • Flowpaths near the wetland are mainly controlled by regional GW discharge and strong ET. Groundwater mounds and depressions induced by rainfall and evapotranspiration near wetlands/lakes have been widely observed, but their effects on groundwater flow field are poorly known. Here, a sandy hillslope near a wetland in the Ordos Plateau, China, which has a semi-arid climate, is selected for investigation. After identifying temporary groundwater mounds/depressions based on three water level observation wells, a HYDRUS-2D model is setup to obtain the highly transient daily flow field. The variable thickness of unsaturated zone near the wetland, which controls soil water content and unsaturated hydraulic conductivity, is found to be responsible for groundwater mounds resulting from focused recharge and groundwater depressions resulting from nonuniform evapotranspiration. On nonrainy days, evapotranspiration in the lowland is supported by flowpaths from regional groundwater discharge, and in the slope is supported by a flow cell from the upland to the upper part of the slope, which causes water table recession in the upland. On rainy days, focused recharge in the lowland leads to two temporary local flow cells, which break the regional groundwater discharge flowpaths into two temporary flow cells. The two temporary flow cells toward the upland contribute to water table rises in the uplandMoreover, we find focused recharge in rainy days does not change the overall direction of flowpaths controlled by regional groundwater discharge and strong evapotranspiration, but only increase the tortuosity of flowpaths. By coupling daily atmospheric conditions and regional groundwater discharge, this study has implications for groundwater recharge estimation and future groundwater modelling in arid regions. [ABSTRACT FROM AUTHOR]

• ET and T from several models are evaluated by eddy covariance and sap flow data. • Comparison of remote sensing ET models with process based hydrological model. • Remote sensing models tend to underestimate ET in dry season but overestimate in the rainy season. Evapotranspiration (ET) and its components, transpiration (T) and soil evaporation (Es) are crucial to understand hydrological cycle and effective water loss in water resource management. Remote sensing models and process based hydrological models are two kinds of effective methods for evaluating ET and its partitions. Although the ability of remote sensing models to simulate ET has been widely verified, there is limited verification of ET components, especially T, which is the major component of ET. This research used ET obtained from eddy covariance system and T obtained from sap flow observation in an apricot orchard of north China to evaluate the accuracy of ET and its components from three remote sensing models (PT-JPL, PML, and SWH) and a process-based hydrological model (SHAW). The results showed that after calibrations, all the four models satisfactorily simulated total ET particularly at annual level. Among them, the PML model had the best fit efficiency, with the most satisfied R 2 and NSE values. The PT-JPL estimated average ET was the most accurate, with the slope nearest to 1.0. While for the simulation of T, SHAW model had a better performance owing to the fact that SHAW model simulate plant transpiration using changes in soil water content. The average T/ET of the models for the growing season was close to the observed value of 0.70, except that the T/ET of SWH is high due to the lack of Ei calculation. Furthermore, nearly all the three remote sensing models underestimated ET in the dry spring and overestimated ET in the rainy summer. In comparison with the process-based SHAW model, it is suggested that remote sensing ET relies more heavily on the seasonal change of meteorological factors such as precipitation but less capable in reflecting the gradually delaying process of soil moisture in the dry season. Therefore, it is indicated that for future improvement of remote sensing ET models, adjustment of soil water availability in a much longer term is suggested at least for semi-humid and semi-arid ecosystems. [ABSTRACT FROM AUTHOR]

• Snowmelt and soil moisture percolation recharge groundwater in freeze–thaw areas. • Spring snowmelt mainly affects groundwater in high altitude mountainous areas. • Seasonal frozen soil thawing impacts on groundwater in plain areas. • The snow and groundwater show a downward trend, while soil moisture increases. Due to the dense vegetation coverage in mountainous areas and difficulties in field monitoring in mid-high-latitude freezing-thawing zones, satellite and remote sensing technologies can be used to monitor long-term global hydrological changes. This study uses multiple satellite data of snow water equivalent (SWE), soil moisture (SM), and groundwater storage anomaly (GWSA) to analyse their spatial–temporal changes in the seasonal freezing-thawing Tumen River Basin. Water transformation during the thawing period is determined through groundwater level fluctuations and correlation analysis methods; thus, the recharge effects of snowmelt and seasonally frozen soil thawing on soil moisture and groundwater are identified. The snow begins to melt in February, and snow water equivalent decreases gradually from upstream to downstream after reaching the peak. Furthermore, seasonally frozen soil melts gradually from March. Soil moisture downstream is higher than that in the upper and middle reaches. Additionally, the GWSA increases gradually in the thawing period, which is high in mountainous areas and low in plains. The Spearman correlation coefficient among ΔSWE, ΔSM, and ΔGWSA is more significant than 0.5 and dependent on altitude. With snow and frozen soil melting, vertical infiltration and meltwater percolation increase soil moisture and GWSA. This phenomenon is predominant at low altitudes (<600 m) and pore phreatic groundwater in the middle and lower reaches. However, snowmelt is the dominant factor in groundwater recharge at high-altitude upper reaches. For seasonal freezing-thawing zones, a close water transformation was observed between snow melting, soil water thawing, and groundwater in spring. Thus, the environmental and ecological impact of spring snowmelt and floods in freezing-thawing zones can be monitored using multi-source data. [ABSTRACT FROM AUTHOR]

• A numerical model was proposed to study soil water movement in sprinkler irrigation. • Soil tank experiments were used to validate the accuracy of the simulation model. • Soil texture and initial soil water content influenced soil wetting patterns. • Soil moisture uniformity was higher than the irrigation uniformity above ground. Water movement and its distribution uniformity in the soil are essential for the design of sprinkler irrigation systems. They are both crucial factors influencing nutrient migration and the productivity of crops. In this study, a new numerical simulation method was proposed to investigate the soil water movement in sprinkler irrigation under nonuniform infiltration boundary conditions using COMSOL software. Besides, two soil tank experiments were conducted to test the reliability of the simulation model. Finally, the model was applied to evaluate the effects of sprinkler irrigation uniformity, soil texture, and initial soil water content on soil wetting patterns and soil water content uniformity. The results showed that the COMSOL-2D predictions of the vertical wetting fronts were in good agreement with the experimental data. The soil texture and initial soil water content influenced the soil wetting pattern and the risks of surface runoff and deep percolation in sprinkler irrigation systems. When the water application rate was 11.35 mm h−1 with an irrigation duration of 10 h, it was easy to cause surface runoff in silty clay loam while deep percolation in loamy sand. Additionally, when the initial soil water content is high, it should be better to avoid irrigation or cut down the irrigation duration to prevent percolation. The water within the soil was more uniformly distributed than that applied through a sprinkler irrigation system. The uniformity coefficient of soil water content distribution (CU s) increased from 87.75 % to 95.56 %, as the uniformity coefficient of sprinkler irrigation (CU) increased from 40.74 % to 82.41 %. Although a higher initial soil water content brought a higher soil CU s value, it also led to a higher risk of percolation. The experimental and simulation results indicated that the COMSOL-2D model could be used to accurately simulate soil water movement in sprinkler irrigation and determine the suitable operation mode of low-pressure sprinklers. [ABSTRACT FROM AUTHOR]

[Display omitted] • Piston flow dominates the water flux from thick vadose zone to loess aquifer. • Sulfate isotopes are useful to identify connectivity among different aquifers. • Unconfined and confined aquifers are not vertically hydraulically connected. • Multiple tracers can advance the conceptualization of water cycle processes. Low water flux and a thick vadose zone increase the likelihood that water bearing units are hydraulically disconnected in shallow groundwater systems. Particularly for the thick loess deposit of China, hydraulic connectivity among the vadose zone, unconfined and confined aquifers is still unclear since it is controversial whether the unconfined groundwater is recharged by precipitation or confined groundwater. Taking several loess tablelands as example, we collected precipitation, soil water, unconfined and confined groundwater to measure the contents of multiple tracers (e.g., hydrochemistry, δD, δ18O water , δ17O water , δ34S, δ18O sulfate , δ13C and 14C) to evaluate hydraulic connectivity among the vadose zone, unconfined and confined aquifers. Deep soil water can be recharged by precipitation with intensities ≥ 30–40 mm/day, and then vertically discharge to unconfined water primarily through matrix flow with an infiltration velocity of 0.15 m/year. Matrix flow dominates the water flux from the vadose zone to the unconfined aquifer with the rates of 85.4 ± 39.6 mm/year, reflecting the regional hydraulic equilibrium. The unconfined and confined aquifers exhibit negligible vertical hydraulic connectivity because of different recharge periods, flow patterns, water − rock interaction, water residence time and sulfate evolution behaviors. The identified connectivity supports local precipitation as the recharge source, which conflicts with the external source hypothesis. The utilization of multiple tracers in this study can complement each other to provide more comprehensive results, and the results greatly improve the conceptualization of vertical water exchange processes in the loess deposits. [ABSTRACT FROM AUTHOR]

• Intelligent adaptation parameter method improves the hydrological model performance. • LightGBM builds flood characteristics-parameter intelligent adaptation relationship. • The XAJ-IAP model can provide accurate and reliable flood simulation. • LightGBM analysis reveals importance of flood characteristics and parameters. Traditional hydrological modeling methods use a set of parameters to simulate flood processes with complex causes and variable intensity, which can easily lead to parameter instability. To address the problem of parameter instability, this study proposes an approach integrating the hydrological model with Intelligent Adaptation Parameters (IAP), whose intelligent adaptation relationship is established by the light gradient-boosting machine (LightGBM) based on individual calibration parameters by each flood event and flood characteristics including flood-caused rainstorm information and initial soil moisture. A widely used hydrological model, Xin 'anjiang (XAJ) model, is chosen to be integrated with IAP (XAJ-IAP) in this study, which has a relatively complex structure and a total of 15 model parameters. The obtained findings demonstrate that: (1) recalibrating the sensitive runoff concentration and separation parameters with a single flood leads to a notable enhancement in simulation accuracy, while simultaneously considering the model's physical significance; (2) the XAJ overestimates large floods and underestimates small floods. Compared with the XAJ, the XAJ-IAP has a better rain-flood response relationship and simulation accuracy for floods of different magnitudes, solving the problem of parameter instability that exists in XAJ; and (3) evaluated in terms of information gain, sensitive parameters contribute the most to the establishment of the intelligent adaptation relationship in the LightGBM compared to flood-caused rainstorm information and initial soil moisture, indicating that sensitive parameters are the most important input features of the LightGBM. It can be concluded that the intelligent adaptation system can not only solve the problem of parameter instability that exists when traditional hydrological models simulate complex and changeable floods, but also further reveal the relationship between the model and floods. [ABSTRACT FROM AUTHOR]

• In-situ soil moisture is used to characterize the spatiotemporal scales of precipitation, especially for those under 10 km. • A sliding window approach is adopted to capture the spatiotemporally varying rainfall events. • The Central TP is dominated by precipitation with relatively short durations (0–6 h) and small spatial extents (< 16 km). The spatiotemporal scale of precipitation is crucial for understanding the local hydrometeorological processes. It also plays a significant role in determining the layout of rain gauges. Convectional rainfalls are typical in the Tibetan Plateau (TP), but the spatiotemporal scales of precipitation events in the TP, particularly at scales less than 10 km, remain unclear, which is mainly due to limited in-situ precipitation observations and the relative coarse spatial resolution of satellite precipitation products. Building upon the strong response of soil moisture changes to precipitation, this study attempts to reveal the spatiotemporal scales of precipitation in the central TP during summertime (June–September) by investigating the inter-station correlation of accumulated positive soil moisture increments obtained from in-situ soil moisture measurements in a mesoscale region of the central TP. The soil moisture-implied spatiotemporal scales are further verified against precipitation from the Integrated Multi-satellitE Retrievals for GPM (IMERG) at spatial scales larger than 10 km. Results show that: 1) the variations of surface soil moisture increase and precipitation are fairly consistent, making positive increments in surface soil moisture an effective surrogate for reflecting the spatiotemporal scales of precipitation in central TP; 2) the spatial extents of precipitation as indicated by the precipitation-induced positive soil moisture increments are positively correlated with the temporal scales; 3) precipitation events with relatively short durations (0–6 h) and spatial extents of less than 16 km (and in particular, 0–3 h for scales less than 10 km) dominate the central TP. This study provides a novel approach for characterizing precipitation scales in regions with scarce in-situ precipitation observations, and is expected to inspire new way of assessing large scale soil moisture and precipitation products. [ABSTRACT FROM AUTHOR]

• An intercomparison of five RS SM products with in situ measurements was carried out. • Sixteen different European ecoregions were considered as test areas. • Product performances were evaluated in zones with diverse flora, fauna and climate. • SMAPL4 and ESA CCI provided the best performance as complementary products. • ASCAT products furnished the best results among the non-blended/modeled products. Several satellite microwave-based soil moisture (SM) products have become available in recent years, making possible to produce different datasets at continental and/or a global scale. Numerous variables, including geographic heterogeneity, weather and climate, and land cover, have an impact on these products' accuracy. In this study, we account for all these variables by using the ecoregions as the scale of analysis and conducting a thorough comparison of satellite products with ground-based SM measurements. The whole Europe has been selected as study area, considering it as the aggregation of several ecoregions, each of them classified as an homogeneous zones, in terms of climate, vegetation and potentially investigable soil cover. Selected SM satellite data includes: i) the National Aeronautics and Space Administration (NASA) SMAP L4 V5; ii) the European Space Agency (ESA) SMOS-IC V2.0; iii) the H115 and H116 SM products in time-series format generated by analyzing data collected by the Advanced Scatterometer (ASCAT) aboard MetOp satellites; iv) the CGLS SSM 1 km obtained by the radar onboard ESA's SENTINEL-1 platforms; and v) the European Space Agency's Climate Change Initiative for Soil Moisture (ESA CCI SM) "COMBINED" ESA CCI SM v06.1 product, created by merging satellite-based passive/active microwave measurements. Such a variety, in terms of technologies and main features, of publicly available online SM products (in their up-to-date version), allowed for a comprehensive intercomparison against in situ measurements of the International Soil Moisture Network (ISMN), that spreads across all of Europe within the above-mentioned terrestrial ecoregion, using four different metrics, i.e., r, bias, ubRMSE and r/r ANOM. Overall the intercomparison has underlined how SMAP L4 and ESA CCI show the best performance regardless of the considered metric. ASCAT has achieved best performances among non-modeled/blended products, while SMOS-IC has showed slightly better performance than the CGLS SSM 1 km. Using ecoregions allowed to further characterize the differences of each satellite products and identify the areas where all these products are more reliable (e.g., group (1) made by ecoregions as the Cantabrian Mixed forests or the Balkan Mixed forests; or group 2 made by ecoregions which belong to Mediterranean area) or less performing (e.g., the Central European mixed forests and the Pontic steppe). Hence, these classes have been further analyzed exploiting the ERA5-Land data, in terms of the SM, air temperature, precipitation, evaporation and Leaf area index. The research has provided helpful insights into how the performance of particular SM satellite products changes based on both the characteristics of the product under consideration and the area under investigation (i.e., one ecoregion compared to another). [ABSTRACT FROM AUTHOR]

• The two-reservoir model incorporated with SAS functions performs best in simulating streamflow δ18O. • The model reproduces temporal variations of δ18O in soil moisture and groundwater. • The SAS modeling results confirm occurrences of preferential flow in the Moso bamboo watershed. Understanding seasonal variations of the hydrological fluxes and preferential flow is crucial in exploring contaminant fate and transport processes in a watershed of interest. Here, performance of different StorAge Selection (SAS)-based rainfall-runoff models was compared for a Moso bamboo watershed under the East Asian monsoon climate. Most of these models are composed of a two-parallel-reservoir flow module and a companioned SAS module. The results show that the temporal variation in streamflow is well captured by a two-parallel-reservoir flow module. The tracer simulations from different models show that two-uniform-reservoir (SSU) model with no SAS function has the lowest NSE value (0.17), the model including an SAS in one of the two reservoir outflows (SSD(U) and SSD(L)) improves performance (NSE greater than 0.5). The performance in simulating streamflow δ18O is further improved when SAS functions are applied for outflows from both reservoirs (SSD (U, L), NSE = 0.76). Additional inclusion an SAS function for ET fluxes in the model (SSD (U, ET, L)) results in a minor improvement from SSD (U, L). The results of SSD (U, ET, L) have achieved the best transport performance (NSE = 0.77), with implication for preferential flow in the watershed. The preferential flow for deep drainage and lower reservoir outflow shows seasonal variation, with more young water in the East Asian summer monsoon period than that in the East Asian winter monsoon period. The upper reservoir storage and deep drainage rates are the main factors determining the temporal variation of preferential flow for deep drainage and lower reservoir outflow, respectively. [ABSTRACT FROM AUTHOR]

• Globally, RZSM showed a decreasing trend over 1981–2017. • RZSM over 50% of global land areas decreased. • Globally, decreased RZSM was mainly induced by the rising temperature. Root zone soil moisture (RZSM) has a direct impact on ecosystem function, vegetation growth and food security, and plays a vital role in global climate system, water and carbon cycles. However, large variations and uncertainties still exist in RZSM across the globe under the warming climate. In this study, we applied comparison map profile (CMP), Theil-Sen regression and partial correlation analysis to investigate the spatial and temporal changes of RZMS and its driving factors from 1981 to 2017 by using three soil moisture products–ERA5, GLDAS and MERRA-2. Results showed that RZSM derived from three products presented a similar spatial pattern that the highest RZSM values occurred in tropical forest and cold areas, followed by subtropical, while the relatively low RZSM values were observed in arid and semiarid regions. Globally, RZSM decreased in all of three datasets with a rate of −0.14 × 10-3 m3 m−3 yr−1 on average (p < 0.001), which was largely correlated with temperature anomalies. Spatially, the RZSM trends greatly varied, with 21–31% of global land areas experiencing a significant decreasing trend and 7–24% for an increasing trend, respectively, confirming their different sensitivities to climate change. Temperature-driven RZSM dominated 19–29% of global land areas and was primarily distributed in northern high-latitude areas. The areas dominated by evapotranspiration were mainly in arid and semiarid areas, accounting for 29–44% of global land areas. Precipitation dominates the remaining 36–45% of global land areas mainly in eastern America and Europe, suggesting variations in the dominance of environmental factors on the spatial patterns of RZSM trend. Our findings will deepen our understanding of the impacts of climate change on the long-term trend of global soil moisture, and will be greatly critical to global soil water resource protection and management under the warming climate. [ABSTRACT FROM AUTHOR]

• A new laboratory method for estimating soil water retention is introduced. • The method only requires soil water content-dependent SWIR reflectance data. • Validation for 21 vastly different soils indicates a good performance. The soil water retention curve (SWRC) is an essential soil property that relates soil water content and matric potential. It plays a crucial role in soil water dynamics and the understanding of various hydrological phenomena at the land surface, including infiltration, runoff, evaporation, and energy exchange processes. In recent years, proximal sensing methods have shown great potential for retrieving this challenging-to-measure property from spectral reflectance. However, a physically-based approach is still lacking as current methods rely on empirical data-driven algorithms. Here we propose a novel physics-based laboratory method that, for the first time, enables direct estimation of the entire SWRC from saturated to dry using soil water content/reflectance data pairs within the shortwave infrared domain. The main hypothesis underlying the new method is that soil optical properties not only vary with soil water content but also with the pore scale distribution of capillary and adsorbed soil water. For evaluation, retrieved soil water retention curves of 21 soils that vastly differ in physical and hydraulic properties were compared to direct measurements. The results suggest that the new method is a rapid and efficient alternative to established laboratory measurement methods. [ABSTRACT FROM AUTHOR]

• SWDI is good at detecting severe/extremely droughts in humid region. • Sand are more prone to agricultural drought than clay. • Impact of groundwater on agricultural drought differ with soil textures. • 95th percentile is the best estimator of field capacity for clay and loam. • Min(maxgs) is the best estimator of field capacity for sand, loamy sand. Soil moisture is an important indicator for monitoring agricultural drought, and is closely related to soil texture. However, the role that soil textures play in agricultural drought evolution in humid climates is not well understood. In the study, we systematically assessed the feasibility of the Soil Water Deficit Index (SWDI) for agricultural drought monitoring in humid regions, examined the impact of soil texture and groundwater depth on agricultural drought evolution, and estimated the field holding capacity for different soil textures in humid areas based on a large-scale regional soil moisture monitoring network. The results show that: (1) The SWDI has reasonably good performance in detecting severe and extreme agricultural drought, but it tends to overestimate the drought intensity during non-severe/extreme drought periods in humid regions, particularly for clay soils. (2) Sand soils are more prone to agricultural drought than clay soils, which are more resistant to severe and extreme agricultural drought with high air temperatures and low precipitation. (3) During drought aggravation, clay soil showed a delay of about 0–18 weeks from the lowest SWDI to the maximum groundwater depth, while the delay was only 0–5 weeks for non-clay soil textures. (4) The 95th percentile is proposed as the best estimator of field holding capacity for clay and loam, while Min(maxgs) is the best estimator for sand, loamy sand, and sandy loam. Overall, these findings provide insights into the role of soil texture and groundwater depth in agricultural drought evolution in humid regions, and offer practical guidance for field holding capacity estimation for different soil textures. [ABSTRACT FROM AUTHOR]

• OpenKarHydro with multilayer soil water interactions was proposed. • OpenKarHydro reveals how climate and land use affect water budget and cause dry soil. • OpenKarHydro performs well in simulating the soil water content and runoff dynamics. • OpenKarHydro is a useful tool for land use optimization and controlling dry soil. In water-limited areas, soil water content (SWC) is a critical factor for plant growth and ecosystem stability. To understand the long-term effects of three different land uses (Vigna radiata : VR, Stipa bungeana : SB, and Medicago sativa : MS) and two climate scenarios (representative concentration pathway 4.5 (RCP45) and nonclimate change (Non_CC)) on water budgets and the mechanisms of dried soil layer (DSL) formation, a new complete multilayered model, OpenKarHydro, was developed. The results showed that the model adequately captured the water budget for different land uses, with root mean square errors (RMSEs) of 0.058, 0.046, and 0.046 and coefficients of determination (R2) of 0.615, 0.605, and 0.582, respectively, for SWC dynamics. This model performance was also demonstrated by the verification of runoff. Sensitivity analysis indicated that the soil porosity (n) was the most sensitive parameter in all land uses. The DSL was primarily caused by higher evapotranspiration (VR: 83.16% vs. 82.48%, SB: 80.62% vs. 77.69%, and MS: 79.93% vs. 76.26%) and canopy interception (VR: 11.92% vs. 11.99%, SB: 17.56% vs. 23.15% and MS: 22.60% vs. 26.19%) in the Non_CC climate pattern than in the RCP45 climate pattern. Climate change has caused an increase in evapotranspiration and interception for all land uses, reducing bottom leakage. Rainfall is sufficient to meet the water demand in the VR plot regardless of climate change; however, the additional water consumption caused by climate warming cannot be offset by the increased rainfall for the SB and MS plots. Compared to Non_CC, RCP45 resulted in increases of 0%, 54.19%, and 57.62% in proportion with extreme drought and decreases of 4.70%, 38.74%, and 5.24% in proportion with no drought for the soil profiles of VR, SB, and MS, respectively. For MS, RCP45 (2026) caused the DSL to penetrate the soil profile (>400 cm) 4 years earlier than in the Non_CC (2030), which should take no more than 5 and 9 years, respectively. OpenKarHydro is a useful tool for optimal water resource management and ecological restoration in water-limited areas. [ABSTRACT FROM AUTHOR]

• Proactive drought responses are crucial in a drought vulnerable future. • Antecedent conditions need to be considered in context of anticipated conditions. • We combine rainfall, soil moisture and evapotranspiration data with forecasts for early warning. • Validation efforts found promising accuracy in the drought early warning maps. • Proof-of-concept drought early warning system can aid proactive drought management. Droughts are a cyclical feature of Australia's climate that have compounding impacts on agricultural productivity and the wellbeing of drought-affected communities. Understanding anticipated drought conditions in context of antecedent observations is critical to providing early warning of drought. In this study we paired probabilistic seasonal rainfall forecasts with precipitation, soil moisture and evapotranspiration data that were objectively combined using Principal Component Analysis (PCA). The final Drought Early Warning System (DEWS) maps overlay forecasting information with the multi-variate PCA-weighted maps at 1-, 3- and 6-month timescales over the common period of overlap between all datasets (1982–2018). In this study period, the 1982–1983 Ash Wednesday tinder drought and the 1997–2001 Millennium drought were investigated. We validated PCA-weighted maps with satellite vegetation data and found performance was strongest over the Murray Darling Basin region (R = 0.63, p = 0.009) and poorest over Central interior Australia (insignificant correlations). We also validated PCA-weighted maps using agricultural commodity data from ABARES. Significant negative correlations at 95 %, 99 % and 99.9 % confidence intervals were found between %-Area in drought category and crop cultivation area; export volume/value; crop yield; and rural debt. Our findings indicate that early warning of drought can be categorised by concern – wherein dry antecedent conditions and dry forecasted conditions are of the highest concern. The developed proof-of-concept DEWS contributes to the growing body of proactive drought management research. In a drought vulnerable future, operationalising and communicating drought early warnings will be critical to reducing the harmful impacts of drought on communities, economies, and environments. [ABSTRACT FROM AUTHOR]

• Combined information from geophysics and soil cores delineated soil stratigraphic heterogeneity. • Time-lapse electrical resistivity monitoring revealed preferential infiltration paths. • Hydrological states derived from resistivity monitoring improved a surface–subsurface flow model. • The hydrogeophysical framework captured subsurface flow in response to flooding. This study presents a framework to quantify changes in soil saturation in response to flooding caused by extreme hydrologic perturbation on coastal ecosystems at the interfaces and transition between terrestrial and aquatic systems. Subsurface heterogeneity limits the use of in situ measurements to quantify subsurface flow during flooding due to the spatial discontinuity in the measured data. While geophysical methods, including time-lapse electrical resistivity imaging (ERI), are increasingly used to monitor soil hydrological processes, their abilities to parameterize flow models have been underutilized. This study combines background ERI, ground penetrating radar (GPR), time-lapse ERI, soil characterization, and a numerical flow model developed using an Advanced Terrestrial Simulator (ATS) code to quantify the infiltration pathway and describe the hydrological dynamics during a simulated flooding experiment. We assessed the use of two conceptual models developed using [1] ERI and GPR data that described the stratigraphic distribution, and time-lapse ERI that mapped permeability contrast, and [2] information from a national soil database for capturing changes in saturation. Combining the ERI and GPR results with soil core data revealed the stratigraphic heterogeneity at the site with a silty clay layer from 1 to 2 m between an overlying loamy topsoil and an underlying saturated silty sand. This silty clay layer could restrict deep infiltration. The time-lapse ERI showed up to a 35% decrease in resistivity, which correlated with soil moisture data (R2 value > 0.53) and revealed preferential infiltration zones used to inform the flow model. Numerical simulation results from both the geophysics- and soil database-informed models quantified changes in soil saturation with calculated soil moistures that agreed with field data. The geophysics-informed model captured more of the system's variability, reflective of shallow subsurface heterogeneities. The framework presented will serve as a precursor for a robust ecohydrological model that can describe the impacts of extreme events induced by climate change on coastal ecosystems. [ABSTRACT FROM AUTHOR]

• Lognormal, unsaturated hydraulic conductivity model not requiring K s as input. • K s model derived from hydraulic parameters describing bimodal soil water retention function. • The novel hydraulic conductivity model does not require the use of integrals. • The model considers the clay-water interaction without using clay content data. • Lognormal distribution function parameters were constrained for physical consistency. We developed a novel, lognormal, pore-scale, unsaturated hydraulic conductivity model, K(ψ) M odel , which does not require saturated hydraulic conductivity, K s , as an input parameter. K(ψ) M odel is derived solely from hydraulic parameters describing a bimodal, lognormal, pore-scale, soil water retention curve θ(ψ). The K(ψ) M odel is based on the Hagen-Poiseuille equation, which represents the soil as a bundle of parallel, non-intersecting capillary tubes. To improve the modelling of fine-textured soils we introduced a novel model to consider the clay-water interaction. This model assumes that clay-water interaction occurs for soils having more than 30% of clay and an effective matrix porosity greater than 35%. Compared to previously developed models, the K(ψ) M odel does not require the use of integrals and can be computed from a spreadsheet and distinguishes between macropore (non-equilibrium) and matrix (equilibrium) flows. The K(ψ) M odel gives improved results when the hydraulic parameters are dynamically constrained and when θ(ψ) describes a bimodal, lognormal distribution. [ABSTRACT FROM AUTHOR]

[Display omitted] • From spatiotemporal dimension to analyze drought propagation of SPEI to SSMI on the TP. • There is a lag time of 2–3 months from SPEI to SSMI and increases from summer to winter. • Climate, vegetation and permafrost type have an impact on drought propagation. • In winter, drought duration decreases in 45% of the TP after drought propagation, and drought severity decreases in 52%, particularly in the Inner TP. • The direction of propagation from SPEI to SSMI is predominantly northwest. Meteorological drought signals trigger different types of droughts by propagating in the water and energy cycle processes. The understanding of the propagation process from meteorological to soil moisture drought is not clear on the Tibetan Plateau (TP). We used the standardized precipitation index (SPEI) and the standardized soil moisture index (SSMI) to represent meteorological and soil moisture drought. By using Mann-Kendall (MK) trend test, wavelet analysis, run theory, Moran's I and drought migration model, we evaluated the trend of SPEI and SSMI on the TP from 1980 to 2018, analyzed the response relationship, explored the temporal (lag time (LT), propagation rate, duration (DD) and severity (DS)) and spatial (spatial autocorrelation, trajectory, direction and distance) properties of drought propagation, and discussed the potential factors. The result indicated that SPEI intensified at winter and annual scales, SSMI mitigated at all time scales. Both of them showed a consistent dry trend in the southeastern TP. In the time dimension, SSMI usually lagged behind SPEI by about 2–3 months, and the LT increased from summer to winter. The LT tended to be shorter in summer in regions characterized by humid and semi-humid climates, meadows and shrublands, and seasonally frozen ground. Propagation rates were greater in the southeast than northwest. After propagation, DD and DS became weaker in winter (45 % and 52 % of regions decreased, respectively) and stronger in summer (80 % and 76 % of regions increased, respectively). In the spatial dimension, the LT of adjacent regions is closely related, dominated by High - High values and Low-Low values. SSMI had shorter migration trajectories and slower migration rates than SPEI. In addition, the direction of drought propagation was predominantly northwest. For the first time, this study has provided insights into the spatial and temporal propagation of meteorological drought to soil moisture drought on the TP and provides a theoretical basis for understanding drought propagation and predicting soil moisture drought. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new Spatially Promoted SVM (SP-SVM) Model proposed to downscale GRACE from 0.5° to 0.25°. • The most influential fifteen variables, i.e., satellite- and gauged-based, are considered in GRACE downscaling. • The proposed SP-SVM model was compared with a commonly used statistical SVM. Satellite-based terrestrial water storage changes have been recorded using the Gravity Recovery and Climate Experiment (GRACE) satellite which causing it an important dataset in hydrology and other related fields. GRACE dataset is widely utilized in many studies, but its coarse spatial resolution is a limiting drawback. Machine-learning approaches (e.g., ANN and SVM) are commonly applied in spatially downscaling. However, their input formation, which is in vector form, is a limitation of considering neighbor relations between the gridded-based inputs, specifically in spatial downscaling. Thus, developing an appropriate, simple, fast, and novel model to spatially downscale GRACE resolution is initially necessary for its utilizations. In this study, a Spatially Promoted Support Vector Machine (SP-SVM) model is innovatively proposed for GRACE downscaling from 0.5° to 0.25°. This promotion is investigated utilizing the distances between the unknown target points (with 0.25°) and their surrounding GRACE-valued points (0.5°), called their Distance Effect Coefficient (DEC), as the SP-SVM model input. In addition, the efficiencies of different in-situ and satellite-based datasets (fifteen variables from May 2005 to August 2020) are evaluated as the inputs of the GRACE downscaling models. After finding the most influential datasets, showing the best correlation with the GRACE, their best combinations in GRACE downscaling are identified. Based on the results, the set of PERSIANN-CDR without delay, the in-situ evaporation with a 1-month delay, and the soil moisture in 0–10 cm depth with a 1-month delay show the best performance in GRACE downscaling. The results of GRACE downscaling by the SP-SVM approach are also compared with the ones based on a usual statistical SVM (S-SVM) model, consisting of an intermediate bias interpolation to improve the estimations through a bias correction step. The results show that the SP-SVM model outperforms the common statistical SVM-based. Thus, compared with the usual S-SVM approach, the proposed SP-SVM (linear) model could be used as a simpler and more accurate model for downscaling any variable in a hierarchical process. [ABSTRACT FROM AUTHOR]

• Remote sensing data useful for forecasting streamflow where in-situ data lacking. • Multiple linear regression produces accurate seasonal streamflow forecasts. • Incorporating soil moisture and groundwater data improves streamflow forecast accuracy. • Soil moisture, groundwater contributions to forecast accuracy vary across basins. Water managers need improved seasonal streamflow forecasts in drought-prone regions like the U.S. Great Plains, where the inter-annual variability in rainfall and streamflow is high and seasonal streamflow forecasting systems are not well developed. Recent research has demonstrated that in-situ soil moisture measurements can be used to produce accurate streamflow forecasts in the Great Plains where rainfall runoff is the predominant source of streamflow. However, an inadequate density of in-situ soil moisture monitoring locations makes producing such forecasts impossible in many locations. Therefore, our objective was to develop and quantify the accuracy of seasonal streamflow forecasts for rainfall-dominated watersheds using remotely sensed data. Precipitation and remotely sensed soil moisture and terrestrial water storage anomaly data were incorporated into a multiple linear regression (MLR) model to produce seasonal streamflow forecasts for five watersheds in the Great Plains. The best MLR models had Nash-Sutcliffe Efficiency (NSE) values ranging from 0.71 to 0.93, indicating good to very good model performance. In one watershed with nearby in-situ soil moisture data, the use of remotely sensed soil moisture data led to greater forecast accuracy than did the inclusion of the sparse in-situ data. Our results show that the use of remote sensing data in MLR models can provide accurate seasonal streamflow forecasts in rainfall-dominated regions which currently lack such information. [ABSTRACT FROM AUTHOR]

• Strategies of increasing sediment observations for SWAT calibration were evaluated. • Strategies integrating different portions of estimated sediment data were designed. • Model with different strategies performed differently for stations. • Representativeness of parameters for different strategies was analyzed. • Adopting portions of estimated data during typhoons can improve sediment simulation. Sediment yields in a watershed are usually affected by various natural disturbances and anthropogenic activities, and can be simulated by using hydrological models. However, due to the availability and type of data (i.e., continuous or discrete) of measured sediment data, model calibration and validation performances could be significantly affected and the calibrated parameters may not be representative for the area. In this study, the sediment rating curves (SRCs) were developed to increase measurements of sediments for model calibration, and five strategies of using measured and estimated sediment data on improving a hydrological model (Soil and Water Assessment Tool, SWAT) calibration and validation for multiple sediment stations were analyzed. The five strategies were: using measured sediment data (S1), using measured data and different portions of estimated sediment during typhoon events (S2 to S4), and using estimated sediment data during entire simulation period (S5). The results showed that although the S1 mostly performed better than other sediment strategies, S4 and S5 were also suitable for improving sediment simulation at the downstream stations (STN1, 2, and 3) and upstream station (STN4), respectively. Moreover, the impact of incorporating different portions of typhoon-induced sediment data (S2, S3, and S4) on the model performance showed differently at stations. In sum, the proposed analytical procedure is expected to be useful for calibrating sediment parameters of hydrological models with limited or highly unevenly distributed measured sediment data. [ABSTRACT FROM AUTHOR]

[Display omitted] • The top-down QPEs perform better over the humid and low-altitude regions. • IMERG-F (SM2RAIN-ASCAT) performs best (worst) in detecting drought occurrence. • The drought dynamic evolutions highly depend on the drought indices used. • All QPEs capture drought centroid trajectory better on smaller spatial scales. Satellite-based quantitative precipitation estimates (QPEs) have shown great potential in the precipitation-related extremes estimation. However, little is known about the capability of different QPEs to monitor the dynamic evolutions of drought on various spatio-temporal scales. Here, the drought utility of two top-down QPEs (i.e., Climate Hazards Group InfraRed Precipitation with Stations, CHIRPS; Integrated Multi-SatellitE Retrievals for Global precipitation measurement-Final run, IMERG-F) and one bottom-up QPE (i.e., Soil Moisture to Rain-Advanced SCATterometer, SM2RAIN-ASCAT), is evaluated comprehensively in mainland China, based on three commonly used drought indices (Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Composite index of meteorological drought). The results indicate that the top-down QPEs generally show better performance in detecting drought occurrence over the humid and low-altitude regions in eastern China than the arid and high-altitude regions in western China. IMERG-F performs best in detecting drought occurrence in the entire mainland China, followed by CHIRPS, and the least satisfactory by SM2RAIN-ASCAT. The trajectory of drought centroids and the temporal variability of drought characteristics are highly dependent on both the drought indices selected and QPEs used. IMERG-F and CHIRPS can better reproduce the trajectories of large-scale drought events (e.g., the locations of drought origin and termination) than SM2RAIN-ASCAT. The ability in tracking the drought centroid trajectory of all three QPEs is higher at the smaller spatial scales, also it improves from the semi-arid to humid regions. Our results highlight the superiority of the top-down QPEs in drought monitoring compared with the bottom-up QPE in mainland China, particularly over the humid regions. The application of SM2RAIN-ASCAT in drought monitoring to the large-scale arid areas should be cautious. [ABSTRACT FROM AUTHOR]

[Display omitted] • BW and GW are interlinked through dissimilar physical processes across ecoregions. • GW-BW relationship and their shift were projected under extreme warm and dry events. • In Mountains, future BW declines while GW maintains due to legacy moisture. • In natural lands, BW to GW shift is projected with a lag time and legacy moisture. • In crop lands, remarkable shortages in BW are projected with no shift to GW. As key drivers of terrestrial ecosystems and food production, green water (GW) and blue water (BW) are interlinked through natural and anthropogenic processes. Their relationship has reportedly been non-stationary, with an anticipated shift from BW to GW in the future. However, these potential shifts are poorly characterized, particularly in response to future extreme warm-dry events. To address this gap, we used a process-based agro-hydrological model to project GW-BW interlinkages and their potential shifts across three different ecohydrological regions – mountainous, crop, and natural lands – in the Nelson River Basin, the largest agricultural basin in Canada and a major breadbasket for the world. Our results indicate dissimilarity in the physical processes that link GW and BW across different ecohydrological regions, and therefore, disproportional projected changes in BW and GW. In mountainous and natural lands, with projected decreases in precipitation in extreme warm-dry years of up to 56% and 46% under SSP126 and SSP585 scenarios, BW decreases by 64% and 42% while GW decreases only by 37% and 29%. The decline in GW is expected to be less because of the availability of evaporative water supplied by legacy soil moisture and groundwater contributions from earlier seasons. This available water to supply evaporative demand represents a delayed overall shift of BW to GW. In crop lands, however, results show a significant decrease of precipitation (45%-55%), BW (up to 42%), and GW (up to 42%), with no remarkable shift of BW to GW in extreme warm-dry years in the future. The projected BW and GW declines in crop lands may impose severe water shortages on local and regional food production. Projected declines in BW from the mountains that serve as source water for downstream irrigation may exacerbate such possible shortages. [ABSTRACT FROM AUTHOR]

• Weathered bedrock thicknesses were thicker than soils in loess hilly regions. • Infiltration density was multiplier increased under thicker weathered bedrock. • Average evaporation intensity was inverse to weathered bedrock thickness. • Water distribution and storage were optimized by weathered bedrock. • Rock water storage was functionally weighty in water-limited ecosystems. Weathered bedrock ecohydrology is indispensable for terrestrial ecosystem stability; however, the influence on the hydrological processes is poorly understood in loess hilly-gully critical zone. Using simulation experiments and in-suit observations, this study evaluated the intervention of weathered bedrock particles on profile infiltration and evaporation, investigated rock core sample characteristics and detected the spatiotemporal water variation in hillslopes of woodland and shrubland. Results showed that adding weathered bedrock particles interfered with infiltration and evaporation processes. Infiltration simulation discovered that stable infiltration rates increased linearly with weathered bedrock thicknesses, which accelerated by 0.5–5.7 times more than bulk soil layers. Interestingly, due to water separation at soil-rock interface, there existed nonlinear initial and average infiltration intensity fluctuations. Evaporation simulation demonstrated that cumulative evaporation presented a linear decrease with weathered bedrock, while the initial daily evaporation intensity increased, reaching a maximum of 15.4 ± 2.2 mm d−1. Moreover, borehole investigations manifested that weathered bedrock layers presented a thickness of more than 5.0 m, and the weathering intensity gradually decreased with depth, which was related to terrains and locations in hillslopes. Furthermore, profile water movement monitoring between woodland and shrubland slopes demonstrated that weathered bedrock optimized profile hydrological allocation. The average rock moisture reached about twofold than soil moisture, with average soil and rock moisture of 0.07 ± 0.04 cm3 cm−3 and 0.15 ± 0.05 cm3 cm−3 in woodland, 0.08 ± 0.03 cm3 cm−3 and 0.15 ± 0.03 cm3 cm−3 in shrubland, respectively. Besides, the profile water storage changed dynamically in equilibrium and escalated linearly with weathering layer thickness. Fundamentally, a conceptual structure model covering soil and rock water balance, vegetation dynamics, and climate change was constructed and discussed throughout the Earth's critical zone, which was then applied in describing water circulation participating by rock moisture under relative water shortage and abundance scenarios in loess hilly-gully regions. This study would furnish insights for improving and deepening the water cycle in shallow soils, and provide new horizons for enriching water resources utilization under climate change. [ABSTRACT FROM AUTHOR]