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• Divergent shift of normal alpine meadows altered the soil and meadow properties. • Ks of topsoil significantly reduced by normal meadow transition into severely degraded meadow. • Ks of sub topsoil significantly reduced by normal meadow transition into shrub meadow. • Divergent shift towards shrub and degraded meadows both decreased water retention and storage. Soil saturated hydraulic conductivity (Ks) is a critical ecohydrological parameter for assessing the capacity of soil layers in grassland ecosystems to respond to precipitation efficiency, recharge groundwater and supply freshwater to rivers, which is significantly impacted by shifts in vegetation composition. Nevertheless, few studies focused on the effects of the shifts in vegetation composition on Ks of alpine grassland ecosystems. This study examined the effects of normal alpine meadow transition into shrub and severely degraded meadow stages on Ks and further qualified the driving processes. The results showed that the divergent shift of normal alpine meadows altered the soil and meadow properties. Soil water retention and storage capacity of the topsoil was reduced due to the divergent shift of the normal meadow transition into shrub meadow or severely degraded meadow. Ks and field moisture capacity (FMC) reduced by 73.1% and 64.9% in the 0–10 cm soil layer during the transition from normal to degraded meadow, while their variation in the 0–10 cm soil layer were not significant but water consumption increased during the shift from normal to shrub meadow. At the state of normal alpine meadow shift toward severely degraded meadow, the dominant factors affecting Ks were meadow cover (MC), root mass density (RMD), clay content and total porosity, with total effect coefficients of 0.48, 0.42, 0.39 and 0.69, respectively. Meanwhile, when normal alpine meadow shift toward shrub meadow, dominant factors affecting Ks were MC , non-capillary porosity, clay and sand contents, with total effect coefficients of 0.47, −0.86, −0.54 and −0.50, respectively. This study helps understanding the impacts of the shifts in vegetation composition on the ecohydrological processes in alpine meadow ecosystems in response to climate change and overgrazing. [ABSTRACT FROM AUTHOR]

• The first study to comprehensively analyze and compare the long-term variations of global surface soil moisture among multiple datasets. • Pervasive surface soil moisture drying is found across the globe, especially in humid-arid transitional regions, in most datasets. • Warming plays a primary role in global surface soil moisture drying in most datasets, especially GLEAM. Accurate soil moisture datasets are essential to understand the impacts of climate change. However, few studies have evaluated the consistency and drivers of long-term trends in soil moisture among different dataset types (satellite, assimilation, reanalysis, and climate model) at the global scale. Here we analyze the spatio-temporal variations of global surface soil moisture and associated climate dynamics over 1980–2020 using multiple soil moisture datasets, i.e., multi-satellite assimilated remote sensing datasets (ESA CCI), simulated soil moisture based on LSMs (GLDAS, GLEAM, CMIP6), and reanalysis (ECMWF ERA5, MERRA2, CRA-Land). Most of these datasets indicate pervasive drying of global surface soil moisture over the last four decades. Prominent soil moisture drying is detected in North America, Europe, northeastern Asia, North Africa, and the Arabian Peninsula. The cross-correlations among the five synthetic soil moisture datasets are the highest between GLEAM and the reanalysis datasets. Using the Aridity Index (AI, the ratio between annual total precipitation and potential evapotranspiration), we find that soil moisture drying is the most intensive in the humid-arid transitional regions with AI ranging 0.8–1.2. Surface soil moisture drying is primarily driven by increases in temperature, followed by ENSO, as indicated by Maximum Covariance Analysis (MCA). However, the significance of the impact of ENSO on soil moisture variability is sensitive to the choice of soil moisture dataset used in the MCA. [ABSTRACT FROM AUTHOR]

• Model quantifying hydrological effects of moss and fallen leaves was presented. • Moss and pine needles were considered as mulch rather than part of vadose zone. • CoreKarHydro performed well for simulating the soil water content curves. • CoreKarHydro is useful to understand effects of moss on soil evaporation. Due to recent vegetation restoration projects in karst mountainous areas of China, the living moss and litter layer play important roles in controlling soil water dynamics in karst forests. However, the underlying mechanisms of the effects of moss and the litter layer on soil water dynamics are still controversial. To explore the underlying mechanisms, we employ the CoreKarHydro model, capturing soil water content dynamics driven by evapotranspiration and influenced by living moss and fallen leaves. This model integrates surface changes' impact on evapotranspiration, categorizing the domain into: no cover (S), moss (SM), pine needles (SP), and both (SMP). Model validation employs the MOCOM-UA method, using diverse metrics (RMSE , R2 , ACC , ED , KGE and SA) against lysimeter data with 4 moss biomass levels and 3 pine needle biomass levels. The results indicated: 1) The CoreKarHydro model exhibited reliable performance in simulating the soil water content curves, with an RMSE ranging from 1.49% to 4.21% and an R2 value ranging from 0.60 to 0.77. 2) Among the factors that influenced evaporation processes, soil hydraulic properties emerged as the most sensitive parameters, whereas the properties of the moss and pine needles played a relatively lesser role. 3) The impact of moss cover on evaporation processes varied depending on the soil texture and the stage of evaporation. Moss cover reduced evaporation loss in 8 out of 12 soil textures (loamy sand, loam, silt, silt loam, sandy clay loam, silty clay loam, sandy clay, and silty clay). On the other hand, moss cover increased evaporation loss in 4 out of 12 soil textures (sand, sandy loam, clay loam, and clay). This study contributes to the understanding of the mechanisms of coupled soil-moss(litter)-hydrological processes in karst areas. [ABSTRACT FROM AUTHOR]

• Established a precipitation manipulation experiment under a plantation (∼ 4000 m2). • Investigated DSLs response to precipitation gradients in the 21-m soil profiles. • Rainfall accession alleviated mean DSLFD (1.8 m compared with interception's 1 m). • Mean DSL-SWC responsed greater under the PAT (0.2%–0.9%) than under the PIT (0.1%–0.3%). • Increased rainfall in turn exacerbated DSLs during the transition period. Ongoing climate change alters the amount and frequency of precipitation, deteriorating the water status of soils and the ecosystem services that they provide. The decline of soil water formed the dried soil layers (DSLs), which is particularly relevant for dryland ecosystems like the forests planted on the Chinese Loess Plateau (CLP), because there is an increased water demand for vegetation transpiration. To assess the impacts of precipitation changes on DSLs, we conducted a large throughfall manipulation experiment comprising seven precipitation gradients, ranging from 30%, 50%, and 65% precipitation interception treatments (PIT) to the ambient condition and then to 30%, 50% and 65% precipitation accession treatments (PAT), under a Robinia pseudoacacia planting forest on the CLP. We measured changes in soil water content (SWC) and examined the development of DSLs for over a year. We found that: (1) mean SWC in the 0–2100 cm soil profile and in DSLs decreased by 0.44%–0.77% and 0.13%–0.36% under the PIT, respectively, while increasing by 0.13%–0.36% and 0.16%–0.92% under the PAT, respectively; (2) DSLs were observed c. 100 cm and c. 100–180 cm below the soil surface under the PIT and PAT, respectively; (3) DSLs varied seasonally, with the largest reduction occurring during the transition period between the dry and rainy seasons, while the reduction developed more rapidly under the PAT than the PIT; (4) the incremental rate of SWC in DSLs increased linearly (p < 0.05) with increasing precipitation, while the increase was faster when precipitation was above (PAT) than below the ambient conditions (PIT), indicating greater precipitation sensitivity of DSLs under enriched than limited water supply. These changes in DSLs provide experimental evidence for the effects of climate change on soil water, and may have strong effect on the sustainable development management of drylands. [ABSTRACT FROM AUTHOR]

Hillslopes are the fundamental building blocks of catchments, however, fully disentangling their hydrological response remains an outstanding challenge. In natural settings, hillslope hydrology is challenged by the interplay of several factors, such as, heterogeneous morphology, vegetation, and complex soils. Herein, we attempt to clarify the mechanisms that control the rainfall-runoff relationship by investigating the response of an artificial outdoor slope setup with uniform geometry, soil properties, topography, and in the absence of vegetation located in a Mediterranean climate. Based on the analysis of more than 100 storms across six consecutive hydrological years, our results demonstrate that either pre-event soil water content or rainfall conditions alone are not sufficient to explain the slope response. Seasonality played a major role through soil moisture conditions, and pre-event soil moisture, peak rainfall intensity and rainfall duration influenced the slope runoff response. Observations suggest that in fall and winter subsurface flow was the prevalent runoff mechanism, while infiltration excess overland flow mostly occurred in the dry season. Our findings show that continuous measurement of runoff could be beneficial to understand the peak rainfall-peak runoff relationship. • Rainfall-runoff studied in artificial slope through more than 100 storms. • Peak rainfall intensity, rain distribution and soil moisture influence runoff. • Occurrence of runoff before peak rainfall beneficial to predict peak runoff. [ABSTRACT FROM AUTHOR]

• MASCP considers the effect of ponding by introducing a developing saturated zone. • MASCP reveals scaling relations of infiltration for sloping and horizontal surfaces. • New numerical algorithm can model slope infiltration with non-uniform initial SWC. Slope rainfall infiltration is a critical component of the Earth's hydrologic processes. Although various infiltration models have been proposed to simulate rainfall infiltration, simple and accurate ones are still scarce. This work developed a non-integral infiltration model for sloping surfaces, called modified approximate solutions under constant pressure conditions (MASCP). New expressions were proposed to improve slope infiltration simulation by introducing a developing saturated zone in soil water content (SWC) profile to quantify the role of ponding water during infiltration. The MASCP was then extended to simulate the infiltration with uniform and non-uniform initial SWC distributions under complex rainfall patterns. The model performance was evaluated by comparing with Hydrus-1D and the sloping Green-Ampt model for four distinct textured soils. The results demonstrated that the ponding time and infiltration rate simulated by the MASCP agreed well with those by Hydrus-1D at all given conditions. Besides, the MASCP yielded higher simulation accuracy than the sloping Green-Ampt model, especially for coarse-textured soils, low rainfall intensity, and long rainfall hiatus. In conclusion, the MASCP is a simple and accurate computation tool for slope rainfall infiltration modeling. [ABSTRACT FROM AUTHOR]

[Display omitted] • An LSTM model outperforms a physics-based SWMM model in predicting recession rates. • A novel hydrological database provides continuous data for these models. • LSTM models can be powerful tools for GSI performance and maintenance needs forecasting. The expected performance of Green Stormwater Infrastructure (GSI) is typically quantified through numerical models based on hydrologic parameters and physics-based equations. With numerical models, the choice of a spatio-temporal discretization scheme for the computational domain is a strenuous task that requires extensive calibration and potentially lab-based parameters and experimentation. The performance of GSI has high temporal dynamics due to natural, anthropogenic, and climatic processes that are not well represented by the traditional physics-based hydrologic models, which are calibrated against only a few historical observations and have a user-defined and constrained set of computational outcomes. Deep learning-based predictive models, such as Long Short-Term Memory (LSTM) neural networks, offer an exciting opportunity to quantify GSI performance, accounting for its highly dynamic and constantly evolving nature by leveraging advancements in observational data. A LSTM regression can overcome some of the limitations associated with traditional hydrological models to aid the development of a fully data-informed GSI performance predictor. To demonstrate the LSTM and traditional model outcomes, both methods were applied to a rain garden in Villanova, PA, USA. Specifically, an LSTM model was used to predict the recession of ponded water depth in the rain garden using five years of observed data. A set of eight predictors (i.e., precipitation, air temperature, soil temperature, soil moisture content at a depth of 10 cm, 35 cm, 65 cm and 91 cm, water depth) and a target variable (i.e., recession rate) were considered for training/testing the LSTM model. A comparative study with the USEPA Storm Water Management Model (SWMM) was performed to observe the performance of a physics-based model and a LSTM model for continuous recession rate time series and specific storms. The LSTM model had a performance score, Root Mean Square Error (RMSE), of 0.081 for the continuous time series, outperforming the SWMM performance with a score of 2.173 when compared to observed data. In the case of storm-specific prediction, LSTM also outperformed SWMM simulation for four storms with lower RMSE values when compared to the observed data. The application of the LSTM model in predicting GSI performance is a crucial stride towards efficient real-time forecasting. [ABSTRACT FROM AUTHOR]

• Contrasting saturated hydraulic conductivity led to soil stratification at 30–50 cm. • Soil moisture was more correlated within layers above and below the interface. • Above the interface, preferential flow and soil moisture dynamics were more active. • Threshold was identified in runoff generation, controlled by soil moisture storage. Soil moisture is essential in runoff generation, chemical transport, vegetation growth, and geological hazards like landslides in mountainous regions. However, due to its difficulty of measurement and its significant spatial heterogeneity and temporal variability, the dynamics and influence of soil moisture in the rainfall-runoff process are still not fully understood. In this study, precipitation, runoff, and soil moisture at multiple depths were measured and analyzed in a small, post-seismic mountainous catchment. Our results suggest that the dynamics of soil moisture could be explained by the characteristics of soil. The drastic change and the anisotropy of saturated hydraulic connectivity in the upper soil of the loose deposits in our catchment would lead to soil stratification at depths between 30 cm and 50 cm. The temporal variability of soil moisture was more highly correlated within the soil layers above and beneath the interface. Above the interface, besides vertical infiltration, occurrences of preferential flow were also frequent, transient perched water table may emerge during the wet period, making it a more active runoff contributor than the soil layers below the interface. When the level of rainfall reached the threshold, the correlation between rainfall and runoff became linear. This threshold response was more significant at soil layers above the interface, especially at the site with large topographic gradient. High antecedent soil moisture could skew the partitioning of precipitation into discharge, leading to an earlier flow peak. Our findings provide guidance on observation spot selection and the soil properties needed for the interpretation of future measurements, and may help develop flood early warnings system in small ungauged mountainous catchments with high flood risk. [ABSTRACT FROM AUTHOR]

• Funneling ratio of live C. korshinskii branches was 1.8 times higher than dead ones. • Funneling ratio of dead S. psammophila branches was 1.6 times higher than live ones. • Rainfall intensity significantly affected stemflow intensity and time lags to rain. Shrub mortality due to self-thinning and climate change is a natural consequence of the long residence times of standing dead branches in unmanaged shrub plantations. Stemflow (SF) represents a key point source of input water and nutrients into the soil in shrub ecosystems. However, little information is available about how shrub mortality affects SF variables. In this study, we measured the SF volume (SFV), funneling ratio (FR), SF intensity, time lag to rain, and rainfall characteristics for live and standing dead branches of two major shrub species comprising Caragana korshinskii and Salix psammophila during the rainy season in 2022 on the Chinese Loess Plateau. The results demonstrated that the SF variables and temporal dynamic processes differed between standing live and dead branches of the two shrubs. For C. korshinskii , the mean SFV and FR were 1.9 and 1.8 times higher for live branches than dead branches, respectively, whereas for S. psammophila , the mean SFV and FR were 1.3 and 1.6 times higher for dead branches than live branches. These contradictory results can be explained by differences in the canopy structures of the species, such as the surface area that contributes to the generation of SF in the upper canopy, branch angles, and the degree of branch bark decomposition and decay. In addition, the SF variables were significantly temporally dependent on the rainfall characteristics. SFV, SFI, and SFD were positively and linearly correlated with the rainfall amount, intensity, and duration at the inter-event scale (P < 0.001). The influence of rainfall intensity on SF time lag to rain were not significant at the intra-event scale (P > 0.05). Considering the enrichment ratio due to canopy dry deposition and bark decomposition, the periodic flattening of dead S. psammophila branches to restore their growth is less significant than allowing them to die and renew naturally. These findings may serve as a reference for developing management practices for shrub species in areas with soil water and nutrient deficiencies. [ABSTRACT FROM AUTHOR]

• Rainfall amount, duration, and intensity significantly affect Ec from diurnal to seasonal timescales. • Rainfall timing has a considerable impact on the magnitude, timing, and intra-daily distribution of Ec. • The responses of Ec to rainfall vary among canopy phenology periods. • The driving mechanisms of biophysical variables to Ec are more complex in days without rainfall events. Rainfall patterns affect vegetation water use through canopy transpiration (Ec) and are becoming increasingly variable due to global warming. However, Ec and its controlling mechanisms in boreal forests have rarely been investigated under various rainfall patterns. In this study, we measured the sap flow using thermal dissipation method in a boreal larch forest (Larix gmelinii) and observed biophysical variables simultaneously (e.g., air temperature, solar radiation, relative humidity, vapor pressure deficit, wind speed, soil water content, rainfall, and leaf area index) over two consecutive growing seasons (May–September) in 2021 and 2022. The responses of Ec to rainfall patterns were evaluated among three rainfall categories (classified based on rainfall amount, duration, and intensity), five types of rainfall timing, and two phenology periods (i.e., leaf expansion and defoliation periods). The results showed that daytime Ec significantly decreased, while nighttime Ec was less variable with increasing rainfall amount, duration, and intensity. The responses of Ec to rainfall categories with different rainfall amount, duration, and intensity were inconsistent at the monthly scale. The magnitude and diurnal dynamics of Ec were greatly influenced by the rainfall timing, and these effects got more considerable with increasing rainfall amount, duration, and intensity. Surprisingly, Ec showed opposite responses to rainfall in leaf expansion and defoliation periods, suggesting an enhanced impact in the leaf expansion period while a diminished impact in the leaf defoliation period. The significantly affected biophysical factors of Ec varied dramatically among rainfall categories. The interactions between biophysical factors and Ec were more complex and diverse in days without rainfall events than days with rainfall days. These findings highlight the importance of rainfall patterns in affecting Ec, provide new insight into the interactions between rainfall patterns, Ec, and biophysical factors, and are important supports for improving evapotranspiration simulations in the context of climate change. [ABSTRACT FROM AUTHOR]

• RF-SM framework outperforms the four coarse-scale SM products. • RF-SM framework is a robust ML algorithm for SM retrieval and downscaling. • The spatial pattern of RF-SM results has high spatial heterogeneity at the field scale. As a key variable used to characterize the climate process between the land surface and atmosphere, the surface soil moisture (SM) plays an irreplaceable role in the fields of hydrology, meteorology and agriculture. However, the relatively coarse spatial resolution of SM products currently limit the application in water resource management at the field scale. In this study, we proposed a high-spatial-resolution SM retrieval framework based on the random forest algorithm (RF-SM) to integrate in situ SM dataset from in the International Soil Moisture Network (ISMN), Landsat 8 optical and thermal observations, soil properties from SoilGrids V2.0, meteorological variables from the fifth generation of the European ReAnalysis (ERA5) dataset and four coarse-scale SM products including the Soil Moisture Active/Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), European Space Agency Climate Change Initiative (ESA CCI) and U.S. National Climate Assessment Land Data Assimilation System (NCA-LDAS). Compared to three other machine learning (ML) algorithms [extremely randomized trees (Extra-Trees), gradient boosting regression tree (GBRT) and extreme gradient boosting (XGBoost)], the random forest (RF) algorithm exhibited the best performance against a subset of 100 validation sites with a Kling–Gupta efficiency (KGE) of 0.69 and root-mean-square-error (RMSE) of 0.063 m3/m3. In terms of different land cover types and typical sites, RF-SM also showed a better accuracy than any of the individual SM product. Finally, the retrieval framework was applied to map the 30-m resolution SM spatial distributions in five substudy areas in the U.S. The results suggest that it is feasible to retrieve accurate SM at a 30-m spatial resolution from multiple satellite datasets based on the RF algorithm, which has important practical significance for agricultural drought monitoring at the field scale. [ABSTRACT FROM AUTHOR]

• Soil moisture, GWSC, and TWSC in the YRB increased during 2003 to 2019. • A new weighted GRACE drought standardisation index (WGDSI) was reconstructed. • The interannual variability was beneficial for detecting drought in the YRB. • The recovery times were 30 and 39 months in 2006 and 2011 droughts in the YRB. • Different signal spectrums components of TWSC are useful for drought monitoring. With the intensification of global climate change, droughts have frequently been occurring in the Yangtze River Basin (YRB), significantly affecting the production patterns, lives, and socioeconomic development of humans. Drought characteristics must be comprehensively identified and quantified to reduce the damage caused by droughts in the YRB. The spatiotemporal variability of precipitation, runoff, soil moisture, groundwater change (GWSC) and terrestrial water storage change (TWSC) in the YRB was comprehensively estimated using the Gravity Recovery and Climate Experiment (GRACE), hydrological and in situ observations by decomposing the variabilities into seasonal, sub-seasonal, trend, and interannual. The new weighted GRACE drought standardized index (WGDSI) and weighted groundwater standardized index (WGWSI) were reconstructed by weighing the seasonal, trend, interannual and sub-seasonal of TWSC and GWSC. They were compared with the standardized soil moisture index (SSI06), standardized precipitation index (SPI06), and standardized runoff index (SRI06). Additionally, the drought characteristics identified based on the observations of the water storage deficit, severity, peak, duration, and recovery time in the YRB were also quantified using the WGDSI/WGWSI over the YRB. The results indicated that soil moisture, TWSC, and GWSC in the YRB increased from 2003 to 2019, mainly based on seasonal and interannual signals. The correlation coefficients between the WGDSI and the SSI06, SPI06, and SRI06 at the monthly were 0.84, 0.56, and 0.75, respectively, representing increases of 9 %, 14 %, and 21 % compared to that with the unweighted GRACE drought standardized index, respectively. The interannual variability of the hydrologic variables was more consistent with the drought events in the YRB, which was beneficial for detecting droughts. Two severe serious droughts occurred in the YRB from 2003 to 2019. In 2006, a continuous seven-month-long drought occurred, with a peak at − 28.974 km3, a severity of − 174.767 km3∙month, an average drought recovery rate of 0.83 km3/month, and a recovery time of 30 months, while in 2011, a continuous five-month-long drought occurred, with a peak at − 18.384 km3, a severity of − 78.106 km3∙month, an average drought recovery rate of 0.40 km3/month, and a recovery time of 39 months. The results indicated that the WGDSI could effectively identify drought events and measure their severity accurately, offering a more holistic understanding of drought conditions compared to other available indices. The index proposed in this study can be applied to generate new datasets and methods for quantifying and detecting droughts on a global scale. [ABSTRACT FROM AUTHOR]

• Rainfall losses are a key input to event-based flood models widely used in practice. • Relationship between rainfall losses and antecedent soil moisture are quantified. • Climate change projections of soil moisture are used to project rainfall losses. • Both the magnitude and variance of future rainfall losses are projected to increase. • Climate change will affect rainfall losses and hence flood responses. Climate change is expected to impact the severity and frequency of floods, which has implications for flood risk management. Design floods and derived flood frequency curves obtained using event-based rainfall-runoff models are widely used in industry to assess flood risks for planning and design purposes. For these approaches it is necessary to have (a) rainfall inputs, and (b) rainfall losses specified, the latter representing the amount of rainfall that is either intercepted, stored on the surface, or infiltrated into the soil and does not contribute to the flood hydrograph. There is extensive research on changes in flooding under climate change that focus on projections of rainfall. However, there is little research into projections of rainfall losses under climate change, despite the knowledge that their changes will modulate the flood response. Here, we present one of the first studies seeking to quantify how rainfall losses, as represented by estimates of initial and continuing losses used in event-based models, are projected to change under climate change. We identify dependencies between rainfall losses and antecedent soil moisture in around half (over 200) of the largely unregulated catchments (i.e. watersheds) in Australia analysed in this study and use these relationships to project rainfall losses under climate change. Near universal increases in both the mean and variance of both initial losses and continuing losses are projected in these catchments, suggesting that increased rainfall losses could offset the impact of increased rainfalls for frequently occurring floods and result in an increased variance in flood responses. [ABSTRACT FROM AUTHOR]

• A Soil-Water Conservation parameterization scheme was developed based on the GBEHM. • It revealed the spatiotemporal hydrological changes in the YRB during 1982–2019. • Impacts of the climate change and SWC measure on the runoff change were quantified. Intensive Soil and Water Conservation (SWC) has been conducted in the Yellow River basin (YRB) since the 1950s, which has greatly altered the eco-hydrological processes and particularly led to the runoff reduction. However, previous hydrological models performed in the YRB rarely simulated the SWC explicitly to quantify its spatiotemporal impacts. This study developed a SWC parameterization scheme based on the distributed physically-based model named Geomorphology-Based Ecohydrological Model (GBEHM). The hillslope SWC (HSWC) was parameterized as an additional surface storage capacity and simulated together with hillslope hydrological processes. The check dams along the river networks (i.e., river-networks SWC, RSWC) were parameterized as reservoirs and simulated together with the flow routing. The enhanced model (GBEHM-SWC) was first calibrated and validated using natural streamflow and remote sensing-based evapotranspiration. Then it was applied to analyze the hydrological changes from 1982 to 2019 in the YRB. Our simulation showed that, compared with the first period (1982–2000), the climatic factors (especially an increase of annual precipitation by 27.3 mm) would cause an increase of annual natural runoff by 2.9 mm (2.17 billion m3) in the second period (2001–2019). But in fact, the observed annual natural runoff decreased by 4.3 mm in the second period, which was caused by the reduction effects of the HSWC and RSWC by 3.8 and 3.7 mm (2.84 and 2.74 billion m3), respectively. Regarding the spatial distribution, the simulated natural runoff increased above Lanzhou and decreased on the Loess Plateau. The SWC impacts showed high spatial heterogeneity over the YRB, which offered useful information to evaluate the SWC measures. [ABSTRACT FROM AUTHOR]

Large region surface soil moisture estimates are important for both hydrologic modeling and remote sensing applications. For soil moisture monitoring, gravimetric soil moisture sampling is reliable; however, it requires a significant effort to gather and process samples. Portable impedance probes serve as a valuable alternative to destructive gravimetric sampling. These probes measure the dielectric properties of the soil–water–air mixture from which we can infer the volumetric soil moisture. As part of recent large-scale experiments in the summers of 2002 and 2003, three different methods for calibrating impedance probes were investigated with the support of coincident gravimetric samples. Field specific calibration improved the accuracy of the probe from greater than ±5% volumetric soil moisture (using the generalized calibration) to less than ±4%. In addition, a significant amount of bias (∼2%) was eliminated. It was also determined that field specific calibration removes a bias due to bulk density variations. Based upon these results it was concluded that the generalized calibration is adequate for estimation of diverse conditions. For studies with more stringent accuracy requirements, field specific calibration is necessary because of reduced bias and error.

Ground-based measurement of the spatial distribution of soil moisture can be difficult because sampling is essentially made at a point and the choice of both sample depth and sample spacing affects the interpretation of the measurements. Hydrological interest has generally been in soil moisture of the root zone. Microwave Remote Sensing methods are now available that allow the interpretation of spatial distributions of soil moisture, however, their signals respond to moisture in the upper few centimetres of soil. These instruments are still being developed, but one of the questions surrounding their application is how to interpret the surface moisture in a hydrological context. In this study we compare measurements of soil moisture in 0-30 cm of soil with those in 0-6 cm to examine how representative this surface measure is with regard to the root zone. Detailed spatial measurements of soil moisture were conducted at three pasture sites in the 50 km2 Mahurangi River catchment of northern New Zealand as part of a comprehensive hydrology project; MARVEX (MAhurangi River Variability EXperiment). In three field sites, on each of three occasions, field measurements were made using both 30 and 6 cm dielectric-based instruments. Spatial grids of several hundred moisture measurements were collected over 0-30 cm and compared with those collected simultaneously over 0-6 cm. Results indicate that temporal and spatial issues interfere with correlation of the two sets of series. Rapid wetting of 0-6 cm compared with 0-30 cm is seen following storm activity. Some evidence of the decoupling of moisture content response is also evident when sites are measured on days following a storm. Rapid, but not unrealistic, response to intense rainfall was also observed. Implications are that detailed and accurate knowledge of local soil conditions and a sound model of soil water redistribution are required before surface soil moisture measurements can be used to infer root zone behaviour. Such knowledge was not available in this study, from either published data or field observation. In this study, without suitable a priori knowledge, soil property information was found via calibration. © 2003 Elsevier Science B.V. All rights reserved.

Ground-based measurement of the spatial distribution of soil moisture can be difficult because sampling is essentially made at a point and the choice of both sample depth and sample spacing affects the interpretation of the measurements. Hydrological interest has generally been in soil moisture of the root zone. Microwave Remote Sensing methods are now available that allow the interpretation of spatial distributions of soil moisture, however, their signals respond to moisture in the upper few centimetres of soil. These instruments are still being developed, but one of the questions surrounding their application is how to interpret the surface moisture in a hydrological context. In this study we compare measurements of soil moisture in 0-30 cm of soil with those in 0-6 cm to examine how representative this surface measure is with regard to the root zone. Detailed spatial measurements of soil moisture were conducted at three pasture sites in the 50 km2 Mahurangi River catchment of northern New Zealand as part of a comprehensive hydrology project; MARVEX (MAhurangi River Variability EXperiment). In three field sites, on each of three occasions, field measurements were made using both 30 and 6 cm dielectric-based instruments. Spatial grids of several hundred moisture measurements were collected over 0-30 cm and compared with those collected simultaneously over 0-6 cm. Results indicate that temporal and spatial issues interfere with correlation of the two sets of series. Rapid wetting of 0-6 cm compared with 0-30 cm is seen following storm activity. Some evidence of the decoupling of moisture content response is also evident when sites are measured on days following a storm. Rapid, but not unrealistic, response to intense rainfall was also observed. Implications are that detailed and accurate knowledge of local soil conditions and a sound model of soil water redistribution are required before surface soil moisture measurements can be used to infer root zone behaviour. Such knowledge was not available in this study, from either published data or field observation. In this study, without suitable a priori knowledge, soil property information was found via calibration. © 2003 Elsevier Science B.V. All rights reserved.

Groundwater storage in the High Plains aquifer has been steadily decreasing for 50 or more years due to withdrawals for irrigation. This trend has been documented in annually published United States Geological Survey reports of water level changes in the High Plains aquifer, but assessments of groundwater storage changes in other parts of the world are incomplete. NASA's gravity recovery and climate experiment (GRACE) soon may provide an alternative means for monitoring groundwater changes, via satellite remote sensing. That terrestrial water storage changes are likely to be detectable by GRACE satellites has been demonstrated by prior studies. This investigation builds on those studies by evaluating the potential for isolating changes in the groundwater component of terrestrial water storage. In the High Plains, the magnitude of annual groundwater storage changes averaged 19.8 mm between 1987 and 1998. Uncertainty in deriving estimates of High Plains aquifer storage changes from GRACE observations will arise mainly from the removal, via land surface modeling, of the effects of soil moisture changes from the gravity signal. Total uncertainty is predicted to be about 8.7 mm. © 2002 Elsevier Science B.V. All rights reserved.

• Soil moisture and isotopes revealed percolation and groundwater recharge mechanisms. • Water flow was preferential after extreme rainfall and mainly translatory otherwise. • Bypass flow after extreme rainfall produced stream water and groundwater recharge. Soil water storage and movement are highly heterogeneous across landscapes and their response to spatiotemporal variations in meteorological forcing is complex. While different pools of soil water (including bound and mobile water) are observed, the mechanisms of soil water movement in semi-arid and sub-humid regions are not well understood due to high variation in soil water storage conditions. The Taihang Mountain is a headwater region that recharges both groundwater and surface water systems of the North China Plain, where groundwater levels have been declining and water storage loss is serious. Increasing land cultivation in the Taihang Mountain areas has increased evapotranspiration and reduced both surface runoff and groundwater recharge. Although extreme precipitation is critical for groundwater recharge in the headwater regions, the response mechanism of soil water movement and groundwater recharge remains unclear. In this study, soil water movement and groundwater recharge mechanisms in a cultivated farmland (FL) and land under natural vegetation (NV) were determined for a normal and an extreme precipitation year through the combined use of soil water content and stable isotopes of water (18O and 2H). Soil water got enriched in δ18O and δ2H (δ18O changed from −11.2 to −7.0‰ at NV and from −11.1 to −4.4‰ at FL; δ2H changed from −71 to −49‰ at NV and from −73 to −30% at FL) with increasing soil depth during the growing season suggesting that winter precipitation was generally transported via advection dispersion flow mechanism. However, this process was accompanied by the mixing of previously enriched soil water after large rain events (20–50 mm/day) during the rainy season in a normal precipitation year. Water movement changed from translatory flow to preferential flow after extreme precipitation in a wet precipitation year. Cultivation intensified water evaporation in the top soil layer (upper 10–20 cm), and induced preferential flow down to 50 cm soil depth under FL relative to land under NV. Thus, cropping significantly reduced groundwater recharge. Excessive storm during a wet year produced bypass flow after the first rainstorm, which rapidly recharged deep soil layers (50–100 cm depth). Bypass flow induced by excessive precipitation and contributed the most to groundwater in FL. The observed rapid response of soil water and groundwater to extreme precipitation events is critical for soil and water management to mitigate problems such as nitrate leaching and groundwater contamination in headwater regions of semi-arid and sub-humid areas. [ABSTRACT FROM AUTHOR]

• Preferential flow in unsaturated saprock (SPF) was revealed in forested hillslopes. • GPR, controlled infiltration, and subsurface moisture monitoring were combined. • SPF is controlled by bedding structure and gravitational force along slope gradient. • SPF laterally directs water from planar and convex hillslopes to concave hillslopes. • The 3D fill-and-spill behavior of SPF is crucial to understand hillslope hydrology. The role of preferential flow through unsaturated saprock (fractured bedrock with weathering restricted to fracture margins) in hillslope hydrology remains inadequately described. To address this issue, ground-penetrating radar (GPR), controlled infiltration, and high-frequency subsurface moisture monitoring were integrated to characterize saprock preferential flow (SPF) in the Susquehanna Shale Hills Critical Zone Observatory in Pennsylvania, U.S.A. In a planar hillslope with shallow fractured shale bedrock (starting at 0.1–0.3 m below ground), two pulses of water (79.5 L in total) were released followed by time-lapse GPR surveys. Differentiating GPR images collected before and after infiltration revealed lateral SPF in the direction of bedding plane fractures near the infiltration trench but with limited development of SPF down gradient along the hillslope. This was confirmed by soil and saprock moisture monitoring at a soil pit (0.2 m downslope of the GPR grid) where only one out of fifteen probes responded to the controlled infiltration. Lateral SPF frequently occurred at the GPR grid during a 24-day period with ten rainfall events, especially under the wet initial conditions. Additional infiltration experiments in a convex hillslope and a nearby bare slope with exposed saprock demonstrated the impact of fracture patterns on the routing of SPF. Three types of SPF in hillslope hydrology were identified, including (1) vertical percolation, (2) exfiltration from saprock to soil, and (3) stormwater transported downslope from planar and convex hillslopes to concave hillslopes. A 3D fill-and-spill model is proposed for the study site and similar areas that recognizes the importance of subsurface flow networks, with shallow saprock and concave hillslopes as essential controls of hillslope subsurface flow. [ABSTRACT FROM AUTHOR]

• JMS can be used to analyze relationship among more than two variates. • JMS method effectively proved the nonlinear relationship between soil temperature and soil moisture. • JMS method could diagrammatically illustrate the frozen soil process with soil data. Joint multifractal spectrum analysis (JMS) is an effective approach for investigation of relationship among hydrological variables and is regarded as one potential way to reveal nonlinear relationship in hydrology. In this study, the joint multifractal spectrum analysis was for the first time used to analyze soil moisture-soil temperature-precipitation (SM-ST-P) relationship. Pearson's linear correlation coefficient considering lag effects and scale effects was also applied for the aims of comparison. Soil data were collected at four sites in Tibet and three depths for each site (10 cm, 30 cm and 60 cm) at 2-hourly frequency from November 2016 to November 2017. Correlation analysis showed that the coefficients varied at every measurement point but failed to provide general and comprehensive analysis of SM-ST-P relationship. On the contrary, multifractal spectra of SM-ST-P confirmed that the relationship was strong for all measurement points. Furthermore, JMS analysis provided holistic results including verification of multifractal of SM-ST-P relationship, relationship evaluation for each part of variables separately (high value, middle value, low value), intuitive diagrammatic comparison of SM-ST and SM-P relationships, etc. Seasonal JMS analysis was adopted to investigate the freezing-thawing process in the study area as well. The multifractal spectra of frozen soil site and no frozen soil site are totally different for dry cold period but similar for rainy warm period. The difference in multifractal of SM-ST-P relationship with and without frozen soil presented the latent application of JMS to quantitatively detect the process of frozen soil. This study implied the capability of JMS analysis in hydrology, especially for multivariable relationship like SM-ST-P relationship. [ABSTRACT FROM AUTHOR]

• Proposed model captures range of observed soil moisture (SM) in time. • SMAP estimes are drier than modeled & observed SM during summer. • SM model was able to generate observed SM spatial statistics. • Observed and modeled average SM has highest variability at intermediate range. • SM skewness is positive and negative for dry and wet average SM, respectively. This study focused on the utility of coarse surface soil moisture observations for applications that require high resolution surface soil moisture information. This was accomplished by quantifying the information content of average soil moisture for three different spatial scales of 81 km2, 790 km2, and 4400 km2. In situ point observations of soil moisture from 31 stations in Iowa were used to develop a spatial stochastic model that assumes hillslope-scale model parameters are independent. Soil moisture dry-downs and wetting regimes were analyzed using rain gauge and soil moisture sensor data. The statistical nature of dry-downs were parameterized using a power-law decay, and soil moisture increases due to rainfall were parameterized using a non-dimensional logistic curve that is a function of soil moisture deficit. The resulting stochastic model is used to quantify the magnitude of the standard deviation, σ (θ) , and skewness G (θ) as a function of the areal average. We show that the greatest information content (small spatial standard deviation) of average observation corresponded to values near the minimum or the maximum soil moisture with σ (θ) < 5 % , while average observations for intermediate soil moisture values had the lowest information content with σ (θ) > 20 %. The differences in information content as a function of the areal average were consistent with the statistical nature of soil moisture that can be interpreted as small range bounded variable. However, this study provides quantitative estimates for the magnitude of the sub-grid and basin scale variability, documenting the utility for applications that require high resolution information. These results form the basis for the investigation of spatial runoff production in response to rainfall and to inform plot scale agriculture applications. [ABSTRACT FROM AUTHOR]

• BEST methods to determine soil hydraulic properties are illustrated. • Interest of the scientific community for the methodology is analyzed. • Infiltration prediction and use of BEST in real soils are discussed critically. • Developments of BEST methods for use in dual-permeability soils are presented. • Practical recommendations and theoretical and experimental perspectives are provided. Theories and models used in soil hydrological sciences rely on the knowledge of the spatially and temporally variable soil hydraulic properties, i.e. the water retention and hydraulic conductivity curves. Obtaining reliable experimental information on these curves for an area of interest is not easy and it still remains a challenge for soil scientists. In 2006, a simple and physically based methodology was proposed to completely characterize the soil using a single ring infiltration experiment in the field and determination of soil particle size distribution, initial and final soil water content and dry soil bulk density. This methodology, named BEST – Beerkan Estimation of Soil Transfer parameters, has received great attention and interest from the worldwide scientific community and it shows promise for further developments. This review paper aims to take the reader into the BEST world. Initially, the BEST experiment is described and the different algorithms that can be applied to analyze the infiltration data are illustrated. Then, an overview of the use that has been made of BEST up to now in the world is provided. Subsequently, infiltration prediction by BEST equations is discussed from both a theoretical and a practical point of view, i.e. with reference to the shape and geometric parameters of the assumed infiltration model and to the effects of the available information for the transient and steady-state phases of the process. Then, the application of BEST is presented with reference to different types of heterogeneous soils. Finally, practical recommendations and theoretical and experimental perspectives are provided. [ABSTRACT FROM AUTHOR]

• Three GRACE mascon solutions are fused to a unified solution in North China Plain. • Unified GRACE data reveal an accelerated groundwater depletion in 2013–2016. • Water table changes agree with the accelerated groundwater depletion. • Land subsidence in North China Plain was accelerated and expanded in 2013–2016. • This acceleration was caused by overexploitation in drought 2013–2016. As one of the most important agricultural bases in China, the North China Plain (NCP) has experienced serious groundwater depletion. However, estimates of groundwater depletion rates in NCP differ considerably from one another due to different datasets and methods used in the estimation. To get a better estimate of the groundwater depletion and to reveal the recent groundwater changes in NCP, we first merge Gravity Recovery and Climate Experiment (GRACE) mass concentration (mascon) solutions from different organizations and soil moisture from different land surface models to yield unified mascon solutions and soil moisture estimates. We then estimate the groundwater changes by subtracting the unified soil moisture and surface water storages in the major reservoirs in NCP from the unified mascon solutions. Results show that the groundwater storage in NCP depleted at a rate of 1.7 ± 0.1 cm/yr from 2004 to mid-2016 but this depletion accelerated to 3.8 ± 0.1 cm/yr from mid-2013 to mid-2016. Ground-based water table data show a consistent groundwater depletion rate (−3.9 ± 0.3 cm/yr from mid-2013 to mid-2016) with the GRACE results. We also analyze the land subsidence in NCP by using 116 Global Positioning System (GPS) sites and depict the accelerated land subsidence and expanded subsidence funnels since 2013, which is consistent with the groundwater depletion. All these point to a strong acceleration in groundwater depletion in NCP since mid-2013. Land surface model outputs suggest a precipitation decline from 2013 to 2016, which caused a decline in soil moisture content and surface water storage. This drought forced people to exploit more groundwater to compensate the surface water shortage and thereby led to the accelerated groundwater depletion. [ABSTRACT FROM AUTHOR]

• A MLR model built in cropland using Sentinel-1 and soil moisture probes. • The model used temporal statistics of Sentinel-1 data and had a RMSE of 0.02 m3 m−3. • Soil water dynamics were mapped at 30-m across one field in the cropping season. • Location-specific and time-dependent interaction between water and control factors. • The method can be applied elsewhere to improve irrigation under climate change. Advances in sensor technologies provide the potential for real-time monitoring of soil moisture status. Little work has been conducted to study the spatial and temporal variations of soil water content at the field scale and the interactions between soil water content and environmental controlling factors in cropped fields during the growing season. In this study, we assessed the spatial and temporal variations of surface soil water content (0–0.1 m) using seven in-situ soil moisture probes and Sentinel-1 satellite data over the 2016 and 2017 cropping seasons in irrigated fields of the Central Sands Plains, Wisconsin, USA. An empirical multiple linear regression model was developed between the soil water content with Sentinel-1 backscatter data. The model was evaluated with leave-one-group-out cross-validation, which showed an R2 of 0.60, mean error of –0.00 m3 m−3, root mean square error of 0.02 m3 m−3, and Lin's concordance correlation coefficient of 0.75. Soil volumetric water content (VWC) and soil water deficit (SWD) were mapped at 30-m resolution every 6–12 days across one field (DU field at the Wallendal farm). Using two statistical metrics ("first metric" and "last metric") and a bootstrapping approach, the location-specific and time-dependent interactions between estimated soil water content and environmental controlling factors across the DU field during the cropping season were evaluated, and it was found that such interactions were affected by initial soil water content and plant stress status. The robustness of the model needs to be improved in the future by collecting more VWC measurements in different soil and crop conditions. The method has the potential to provide a baseline soil moisture map and can be coupled with mechanistic models and subsoil VWC measurements for improved irrigation management and water use efficiency under a changing climate. [ABSTRACT FROM AUTHOR]

• Conditional Bias-Penalized EnKF (CBEnKF) has been developed. • CBEnKF significantly outperforms EnKF for high flow events. • CRPSS of CBEnKF in reference to EnKF is larger for larger flows. We present a novel ensemble extension of the conditional bias-penalized Kalman filter, referred to herein as the conditional bias-penalized ensemble Kalman filter (CBEnKF), and apply it to flood forecasting. The CBEnKF differs from most data assimilation (DA) techniques in that it minimizes a weighted sum of the error variance and the expected value of the Type-II conditional bias squared for improved estimation and prediction of extremes. To assess the ability of the CBEnKF to improve flood prediction, we carried out real-world DA experiments in which the CBEnKF and the EnKF were applied under identical conditions for assimilation of hourly streamflow observations into the lumped Sacramento soil moisture accounting and unit hydrograph models. Ten headwater basins in Texas, whose drainage areas and times-to-peak range from 137 to 1037 km2 and from 3 to 21 hrs, respectively, were used in the twin experiments. All events in a 10-yr period with peak flow exceeding 100 m3/s were used. Verification of the ensemble mean predictions indicates that the CBEnKF improves the multi-basin mean of the mean square error skill score over the EnKF by about 0.15 over lead times of up to the time-to-peak of the basin. Verification of the ensemble predictions indicates that the CBEnKF improves the mean continuous ranked probability skill score by about 0.2 on average over the EnKF for all ranges of flow within the significant events, and by about 0.3 for flows exceeding the 95th percentile in those events. That the gain in skill is larger for larger flows makes the CBEnKF very appealing despite the significantly higher computational cost. [ABSTRACT FROM AUTHOR]

• Rainfall isotopic signature dependence with air temperature and rainfall amount. • Development of a Local Meteoric Water Line for Vallcebre Research Catchments, Spain • Use of backward trajectories relating moisture source and isotopic signature. • Case studies analysis of moisture origin and intra-event isotopic variability. This study investigates moisture origin, characteristics and intra-event scale variability of the isotopic signature of rainfall in a Mediterranean mountain area, based on data from the Vallcebre Research Catchments, NE Spain. Weekly data and samples were used to analyse the general dynamics of the isotopic composition of precipitation and its relationship with meteorological variables at the sampling site. Then, based on the data and samples available at the intra-event scale, the variability of the isotopic signature during rainfall events was characterised and an explanation of what caused the observed variability was attempted. Results at the weekly time scale show that the maximum relative deviation of the heavy isotopic content with respect to Standard Mean Ocean Water (δ 18 O) of the precipitation signature occurred during summer months and, in contrast, the minimum during winter months. The intra-annual trend of rainfall δ 18 O roughly followed the mean monthly air temperature. Using intra-event information, three main types of δ 18 O isotopic trends were found during rainfall events: most of the events had a V-shaped isotopic trend, followed by L-shaped and constant trend events. Changes in moisture sources 'diagnosed using a Lagrangian approach and a previously existing contribution algorithm' were one of the causes of the isotopic variability and changes at the intra-event scale, but these may also have been influenced by sub-cloud processes that modified the isotopic signature. Moisture sources for analysed events showed the main influence of the Mediterranean area, but also an important influence of the Atlantic Basin. [ABSTRACT FROM AUTHOR]

• Changes in both soil moisture and extreme rainfall influence historical flood trends. • The influence of soil moisture and extreme rainfall depends on flood severity. • The more extreme a flood event is, the less dependent it is on soil moisture changes. • Changes in soil moisture need to be considered in projections of flooding. Despite the expectation that increases in rainfall with climatic change will result in increases in pluvial flooding, there is more historical evidence for decreases in flood magnitude. In Australia, as in many other parts of the world, flood magnitudes are mostly decreasing, despite increasing rainfall extremes. Here, we show how changes in soil moisture have led to decreasing flood magnitudes while rainfall extremes have been increasing. Using gauged streamflow, catchment average rainfall, and modelled soil moisture data across Australia we confirm that streamflow peaks (or floods) that occur at least once a year are strongly related to both the peak rainfall causing the flood and the antecedent soil moisture conditions preceding the storm event. Regions where the magnitude of the peak flow has decreased are visually and statistically correlated to regions of decreasing soil moisture. Flood magnitudes are more likely to increase only for the rarest events, with increases in rainfall offset by decreases in soil moisture for more frequent events. A recurrence interval dependent tipping point is identified beyond which trends in catchment rainfall outweigh those of soil moisture trends and dominate the flood response. For regions where soil moisture has decreased, this tipping point occurs at an average recurrence interval of ten years or more. As most studies investigating trends in flooding generally use annual maxima, changes in soil moisture are likely to be the dominant mechanism behind observed flood trends. Hence changes in soil moisture conditions need to be considered when predicting catchment flood response due to climatic change. [ABSTRACT FROM AUTHOR]

• A modified SWAT model for representation of two-way groundwater-surface water interactions. • Impacts of evapotranspiration on groundwater tables in the saturated and unsaturated zone. • The model test against measured groundwater table height at Lethbridge and Barons, Alberta, Canada. • A good agreement between simulated dynamics of water tables and the measured data. • A useful tool for prediction of groundwater table height and groundwater storage. Groundwater is a priceless resource in Alberta and therefore, estimating groundwater are crucial to identifying and promoting holistic and integrated management of groundwater-surface water. However, it is a challenge to simulate groundwater storage due to the current rudimentary representation of two-way groundwater-surface water exchange in current hydrologic models, such as Soil and Water Assessment Tool (SWAT), which, in turn, limits our ability to predict land-atmosphere processes and groundwater storage. In this study, we modified the SWAT model to improve module of evapotranspiration in two-way groundwater-surface water exchange. The modified SWAT was calibrated and validated against the groundwater table height and evapotranspiration from 2008 to 2011 period at two location (Lethbridge and Barons) Alberta, Canada. The results showed that the modified SWAT model predicts the groundwater table height very well at both locations. The modified model predicted the daily groundwater table height with R2 values of 0.86 and 0.89 in the calibration period (2008–2009), 0.81 and 0.83 for the validation period (2010–2011) at Lethbridge and Barons, respectively. The Nash-Sutcliffe model efficiency (NSE) for daily groundwater table height was 0.69 and 0.71 during calibration periods (2008–2009) while the model gives lower values of NSE 0.65 and 0.67 for validation periods (2010–2011) at Lethbridge and Barons, respectively. Similarly, the model estimates evapotranspiration well with correlation coefficient (R2) of 0.77 during the calibration period and 0.81 for validation period. Our result showed that the modified SWAT model did improve estimates to dynamic groundwater table heights. [ABSTRACT FROM AUTHOR]

• Peak value of evapotranspiration appeared at 3600–3700 m in the Qinghai Lake watershed. • Evapotranspiration was dominantly controlled by water conditions below 3650 m, and by energy conditions above 3650 m. • Energy conditions were more influential factors limiting the evapotranspiration. Evapotranspiration (ET) and its dominant controls show obvious elevational pattern on the Qinghai-Tibet Plateau, due to heterogeneous environmental conditions regulated by altitude. To understand better how and why ET varies at different altitudes in the Qinghai Lake watershed, this study measured and retrieved soil moisture, solar radiation, and ET data between 3300 m and 4700 m, and further investigated the influence of water and energy conditions on ET. Results showed that, the mean annual terrestrial ET was 343 and 360 mm in this watershed in 2014 and 2015, respectively. The higher ET mainly appeared in the midstream of Shaliu River and on the southern region of Qinghai Lake, while the sand land on the east of Qinghai Lake and the bare rock in the northwest of the watershed had the lowest values. The ET increased firstly and then decreased with the increase of altitude, and its peak value appeared at 3600–3700 m. The dominant controls for ET switched from water conditions to energy conditions with the increase of elevation. The maximum limited fraction [(predicted value – actual value)/predicted value × 100] of ET by water condition (soil moisture content) achieved 11.2% and 10.1% at 3300–3350 m in 2014 and 2015, and the corresponding mean area-weighted values were 7.3% and 5.4% at 3300–3650 m, respectively. The maximum limited fraction of ET by energy condition (shortwave radiation and air temperature) achieved 24.4% and 29.8% at 4650–4700 m in 2014 and 2015, and the corresponding mean area-weighted values were 10.8% and 11.9% at 3650–4700 m, respectively. Overall, the energy conditions were more influential factors limiting the ET in the entire watershed, and this conclusion is critical for the management of regional terrestrial ecosystem with future climate change. [ABSTRACT FROM AUTHOR]

• A newly developed hybrid intelligent model is proposed for soil moisture prediction. • The proposed model is validated against well-established machine learning models. • Several related morphological variables are used to build the predictive models. • The designed predictive models are evaluated and assessed comprehensively. • The ANFIS-GWO is showed a robust and reliable model for soil moisture prediction. Accurate estimation of soil moisture content is necessary for optimal management of water and soil resources. Soil moisture is an important variable in the hydrologic cycle, which plays an important role in the global water and energy balance due to its impact on hydrological, ecological, and meteorological processes. The purpose of the present study was to explore a newly developed hybrid intelligent model for simulating soil moisture content. A hybrid adaptive neuro fuzzy inference system (ANFIS) model-grey wolf optimization (GWO) algorithm was designed here and validated against the neural network (ANN), support vector regression (SVR) and standalone ANFIS models. The models input parameters were di-electric constant, soil bulk density, clay content and organic matter of 1155 soil samples. Various statistical indices were employed to evaluate the performances of the applied models. For a reliable ranking of models, the Global Performance Indicator (GPI) was utilized, which is a 5-agent index. The results evidenced the feasibility of the developed hybrid ANFIS-GWO model with superior simulation results. At the testing stage, the MAE and SI values for the ANFIS-GWO were 1.468% and 0.098, respectively, which indicated the superiority of the ANFIS-GWO compared to the ANFIS-Fuzzy C mean (MAE = 6.427%, SI = 0.354), and ANFIS-sub clustering (MAE = 2.137%, SI = 0.141) models. Based on the GPI, the ANFIS-GWO model was ranked as the best model, followed by the standalone ANFIS and SVR models, while the worst accuracy was attained through ANN model. The ANFIS-GWO model improved the simulation accuracy by 48% and 50%, respectively, compared to the standalone ANFIS and SVR models. In addition, based on the GPI, the ANFIS-GWO model presented an enhancement of about 77 percent compared to the ANN model. The high accuracy of the ANFIS-GWO model compared to the standalone ANFIS model represents the performance of the GWO algorithm for escaping local optima, which makes the ANFIS-GWO as a powerful tool for estimating soil moisture. Overall, the explored hybrid intelligent models demonstrated a reliable pedotransfer function of soil moisture where it can contribute to several geo-sciences engineering principles. [ABSTRACT FROM AUTHOR]

• GRACE DA improves our surface soil moisture estimates in humid regions, West Africa. • ASCAT and CLSM have slower soil drying rates during fall season than SMOS and SMAP. • The annual trends of the surface soil moisture are corrected through GRACE DA. Recent annual trends of precipitation and terrestrial water storage (TWS) in West Africa have been increasing over the past decade. Despite a significant impact of soil moisture on the TWS in West Africa, there is little research on the recent spatial and temporal behaviors of surface soil moisture (SSM) along with the hydrological trends and variability in West Africa. In this study, we assimilate TWS estimates from the Gravity Recovery and Climate Experiment (GRACE) mission into the Catchment Land Surface Model (CLSM) and evaluate its impacts on SSM simulations for the years, 2002–2017. The evaluation is performed using reference datasets: the African Monsoon Multidisciplinary Analysis (AMMA) in situ soil moisture observations, three currently available microwave satellite SSM observations from the Advanced Scatterometer (ASCAT), the Soil Moisture Ocean Salinity (SMOS), and the Soil Moisture Active Passive (SMAP) satellites and also the triple collocation analysis (TCA). Overall, modeled SSM shows good agreement with the reference datasets in terms of the anomaly SSM correlations. However, both modeled and ASCAT SSM are limited in their representation of the drying rates, as observed by ground observations, SMOS and SMAP estimates. Further, GRACE data assimilation results in improved SSM simulations in the humid regions with large annual TWS variability. This study demonstrates the utility of land data assimilation to inform hydrological conditions in West Africa, where soil moisture monitoring is necessary for water resource and livestock management. [ABSTRACT FROM AUTHOR]

• The five widely used calculation models for the unfrozen water content of freezing soils are evaluated. • The parameters in the five models have different effects on the change process of the volumetric unfrozen water content. • The five models have great potential to calculate unfrozen water content of soils in field. • This paper would provide significant reference for the selection of unfrozen water content models. Evaluation of calculation models for the unfrozen water content of freezing soil is very important for numerical simulations. Based on the published 250 laboratory data and around 5 years field observed data, this paper evaluated the five widely used unfrozen water content models, i.e., the Zhang et al. (2017a) model, the Anderson and Tice (1972) model, the Michalowski (1993) model, the Mckenzie et al. (2007) model, and the Kozlowski (2007) model. The results show that the five models can all be used to calculate the unfrozen water content of laboratory soil samples. The Anderson and Tice (1972) model, the Michalowski (1993) model, and the Kozlowski (2007) model are descripted by gravimetric unfrozen water content. However, it is difficult to directly measure the gravimetric unfrozen water content during the freezing process. The Mckenzie et al. (2007) model and the Zhang et al. (2017a) model are descripted by volumetric unfrozen water content which can be directly measured during the freezing process. Besides, the parameters of the Zhang et al. (2017a) model are relatively simple, although the model slightly overestimates the volumetric unfrozen water content for some laboratory soils. Therefore, the Zhang et al. (2017a) model is more convenient for numerical simulations. Furthermore, all the models have great potential to calculate unfrozen water content of freezing soils in field observations. This paper would provide significant reference for the selection of unfrozen water content models in the description of hydro-thermo coupling process in cold regions. [ABSTRACT FROM AUTHOR]

• Runoff was 40% higher and leaching 95% lower in compacted than uncompacted plots. • Soil compaction increased rainsplash and sheetwash, but not rill erosion. • Mulch and slash did not affect soil hydrology, but strongly reduced rainsplash. • Mulch cover, but not slash cover, reduced sheetwash and rill erosion. • Compaction and soil cover contact were drivers of hydrologic and erosive variables. Soil compaction during post-fire logging can increase runoff and erosion. Increasing surface cover is an effective way to reduce erosion, but this has not been tested on soils impacted by both fire and compaction. We measured the effects of compaction (bulk density of 0.9 or 1.1 g cm−3) and surface cover (0% or 60%) using bark mulch or logging slash (compacted only) on runoff, leaching and erosion. Four 0.5 m2 plots per treatment were filled with burned soil and inclined to 18°. Two 30-min simulated rainfall events with 72 mm h−1 intensity and one concentrated flow event were applied to each plot. The runoff from the dry and wet runs totaled 32 mm in the uncompacted bare plots and the value was not significantly different for the compacted bare plots, any of the mulch plots, or the compacted slash plots. Leaching was higher for the uncompacted bare plots (7 mm) than for the compacted bare plots (1 mm), and mulch or slash did not affect leaching. The uncompacted bare plots produced 38 g of rainsplash and 497 g m−2 of sheetwash, and these were about half the values for the compacted bare plots. Mulching strongly reduced the rainsplash and sheetwash for both the uncompacted (10 g and 184 g m−2, respectively) and compacted conditions (19 g and 431 g m−2, respectively), although the difference in sheetwash was only significant for the uncompacted plots. Slash also reduced rainsplash (19 g) relative to the compacted bare plots (74 g) but did not affect sheetwash. Concentrated flow produced 858 g m−2 of rilling from the uncompacted bare plots, which was greater than the compacted plots (237 g m−2). Mulching reduced rilling by 95% in the uncompacted plots and 78% in the compacted plots, but slash did not reduce rilling. The reductions in rainsplash erosion caused by the mulch and slash were attributed to the increase in surface cover, which reduced the energy imparted to the soil by the rainfall. The mulch, with its high degree of contact with the soil, also reduced particle detachment and transport by overland flow. In contrast, the slash did not have complete contact with the soil surface and did not affect sheetwash or rilling. Compaction and the type of material used to increase surface cover are important factors to consider when assessing potential impacts of logging on runoff and sediment delivery from burned soils. [ABSTRACT FROM AUTHOR]

• Hydrological processes were explored through soil moisture wavelet analysis. • Vegetation and topography controlled hydrological processes in the downslope area. • Soil moisture coherence indicates distinct controls and seasonality upslope and downslope. Spatial and temporal distributions of soil moisture are important for explaining hydrological processes at the hillslope scale. The fate of various hillslope flow paths can vary seasonally and their relationship with rainfall events can be nonstationary. We collected high resolution in situ soil moisture datasets at 30 points (10 locations in three depths) between March and November in a steep mountainous hillslope. The wavelet coherence analysis of eco-hydrometeorological factors and soil moistures indicated distinctive controls of rainfall and evapotranspiration over soil moisture at time scales of under a day. The vertical flow in upslope areas can be explained by significant wavelet coherence in soil moisture profiles. The appearance of lateral flow with vertical flow in the downslope is associated with even more significant wavelet coherence between identical depths. The wavelet coherence analysis between two soil moisture time series both along hillslope layers and depth profiles provided good spatial and temporal representations of hillslope hydrological processes in terms of their timing and scale. The distributions of soil moisture coherences showed distinctive characteristics for three different seasons. The seasonally distinctive role of vegetation to infiltration was apparent for the upslope area, but the combined impact of vegetation and topography controlled more complicated hydrological processes in the downslope area. The phase angle analysis was introduced to show the profile difference of hydrological processes on various time scales. [ABSTRACT FROM AUTHOR]

• Enough water to the sites despite urban surroundings and different water regimes. • Groundwater contributes 30–40% of the total transpiration during the driest periods. • Transpiration is 3–16% of total daily transpiration during night-time heat stress. During the expansion of urban areas, small natural reserves have often been left intact within the built environment as central elements of biodiversity conservation, ecological connectivity, landscape sustainability, and quality of life of urban dwellers. Consequently, the surrounding urbanized landscape may impact the environmental conditions of these reserves (e.g., high temperatures, low moisture conditions), resulting in the need for extensive maintenance. This study presents an estimation of the water balance over two years (2017–2018) in three small urban reserves (between 2 and 30 ha) within the Greater Melbourne metropolitan area in Australia, for the purpose of understanding tree water use. Measurements of micrometeorological variables, soil moisture content profiles, water-table levels, sap flow velocities, and stem diameter variations were used to quantify the water sources of tree transpiration in these reserves. Results revealed that, despite the urban surroundings and the climate variations, these reserves have enough water to sustain tree transpiration. In two of the three reserves, groundwater was pivotal in sustaining transpiration rates; specifically, groundwater was estimated to contribute about 30–40% of the total transpiration amount during the driest periods of the year. Groundwater also played an essential role during nights with temperatures above 25 °C, helping trees to maintain night-time water use from 3 to 16% of the daily water use. In the third reserve, the presence of a shallow layer of heavy clay supplied water to the trees, which were able to maintain relatively constant transpiration rates throughout the year. These results demonstrate the importance of understanding the water regime of each urban reserve in order to support government authorities in preserving these ecosystems. [ABSTRACT FROM AUTHOR]

• A change detection algorithm was applied to disaggregate PALS soil moisture retrievals by using UAVSAR radar data. • The algorithm was developed on the linear relationship between change in soil moisture and radar backscatter coefficient. • The disaggregated results show good agreement validated using ground measurements collected in SMAPVEX12 campaign. The SMAPVEX12 (Soil Moisture Active Passive (SMAP) Validation Experiment 2012) experiment was conducted during June-July 2012 in Manitoba, Canada with the goal of collecting remote sensing data and ground measurements for the development and testing of soil moisture retrieval algorithms under varying vegetation and soil conditions for the SMAP satellite. The aircraft based soil moisture data provided by the passive/active microwave sensor PALS (Passive and Active L-band System) has a nominal spatial resolution of 1600 m. However, this resolution is not compatible with agricultural, meteorological and hydrological studies that require high spatial resolutions and this issue can be solved by soil moisture disaggregation. The soil moisture disaggregation algorithm integrates radiometer soil moisture retrievals and high-resolution radar observations and it can provide soil moisture estimates at a finer scale than the radiometer data alone. In this study, a change detection algorithm was used for disaggregation of coarse resolution passive microwave soil moisture retrievals with radar backscatter coefficients obtained from the higher spatial resolution UAVSAR (Unmanned Air Vehicle Synthetic Aperture Radar) at crop field scale. The accuracy of the disaggregated change in soil moisture was evaluated using ground based soil moisture measurements collected during SMAPVEX12 campaign. The results showed that soil moisture spatial variabilities were better characterized by the disaggregated change in soil moisture estimates at 5 m/800 m resolution as well as a good agreement between disaggregated estimates and in situ measurements. It also showed that VWC (Vegetation Water Content) did not have a big impact on disaggregation algorithm performance, with R2 of the disaggregated results ranging 0.628–0.794. The 5 m and 800 m resolution disaggregated soil moisture did no show significant difference in statistical performance variables. [ABSTRACT FROM AUTHOR]

• GRACE-based TWSA shows a downward trend at multiple timescales. • AO and ENSO exert stronger influences than PDO and sunspot activities on the TWSA in the YP. • Teleconnection factors have stronger linkages with precipitation-concentration degree than precipitation magnitude. Changes in terrestrial water storage anomalies (TWSA) are not only influenced by local climate conditions but also by teleconnection factors. To date, how teleconnection factors including sunspot activities could affect TWSA has been under-examined. In this study, we investigate the TWSA dynamics in the Yunnan Province (YP), China, and assess their relationships with teleconnection factors at multiple timescales. Results show that: (1) GRACE data is able to reflect TWSA dynamics in the study region, as demonstrated by its high correlations with soil moisture, precipitation, and groundwater anomalies; (2) GRACE-based TWSA shows a downward trend of varying degrees at the monthly, seasonal, annual and multi-year scales, with obvious periodic changes of 11 and around 60 months; (3) precipitation magnitude and its distribution characteristics in humid areas are the main driving factors of TWSA, and teleconnection factors have stronger association with precipitation-concentration degree (PCD) than precipitation magnitude; (4) the 2009 extreme drought has caused a sharp decrease in TWSA, and the correlations between TWSA with Arctic Oscillation (AO) and El-Niño Southern Oscillation (ENSO) were 0.88 (a = 0.01) and −0.58 (a = 0.01). This study sheds new insights into how teleconnection factors could TWSA dynamics in a changing environment. [ABSTRACT FROM AUTHOR]

• Compiled a multi-year, multi-layer daily soil moisture record across Anhui Province. • Soil moisture generally increases with depth in Anhui Province. • Soil moisture in the region has large seasonal fluctuations, decaying with depth. • Effects of antecedent accumulated rainfall on soil moisture varies over depth. Little measurement-based soil moisture information is available for the humid to semi-humid transitional zones in China. Anhui Province is a representative area of these zones and spans several hydroclimatic and geomorphological zones. To overcome shortage of observational soil moisture information in this area, we collected and compiled a multi-layer daily soil moisture record from 2012 to 2016 across Anhui Province based on the observations of a soil moisture-observing network composed of 85 sites. We then analyzed the vertical and spatiotemporal variability of soil moisture across this region. Primary findings are: (a) soil moisture in this region has apparent vertical gradients and increases with soil depth; (b) substantial soil moisture spatial variability appears in the shallow soil layers (depth ≤40 cm) with the highest values in the middle part of this region, but soil moisture in the deep layers (depth >40 cm) shows low spatial variability; (c) seasonality of soil moisture across this region is complicated and has fluctuations with July as the wettest month in most of the province; (d) inter-monthly fluctuation of soil moisture is larger in the upper soil layers than in the lower layers; and (e) antecedent accumulated rainfall is a major factor influencing soil moisture throughout this region with soil moisture in the upper layers more sensitive to immediate weather conditions and soil moisture in the lower layers showing lagged responses to the weather conditions. This study can not only reveal the spatiotemporal and vertical characteristics of soil moisture across this region, but also provide a useful reference dataset for the other soil moisture modeling and retrieval studies. [ABSTRACT FROM AUTHOR]

• Probabilistic threshold integrates antecedent soil moisture with rainfall threshold. • Bayes' theorem is applied to estimate the probability of landslide occurrences. • Soil moisture is estimated by the hydrological simulations. Various methods have been proposed to define the rainfall thresholds for the landslide prediction. Once the threshold is determined, it remains the same regardless of the antecedent soil moisture conditions. However, given the important role of the antecedent soil moisture in the initiation of landslides, it is considered if the rainfall threshold level varies according to the antecedent soil moisture conditions, the prediction performance will be improved. Therefore, in this study we propose a probabilistic threshold to integrate antecedent soil moisture conditions with rainfall thresholds. In order to take into account the conditions with landslides and without landslides, the Bayesian analysis is applied to estimate the landslide occurrence probability given the various combinations of two factors: the antecedent soil moisture and the severity of the recent rainfall event. These combinations are then divided into conditions that are likely to trigger landslides and those unlikely to trigger landslides by comparing their probabilities with a critical value. In this way, the probabilistic threshold is determined. Here the soil moisture is estimated using the distributed hydrological model, and the severity of the rainfall event is characterized by the cumulated event rainfall-rainfall duration (ED) thresholds with different exceedance probabilities. The proposed approach was applied to a sub-region of the Emilia-Romagna region in northern Italy. The results show that the probabilistic threshold has a better prediction performance than the ED rainfall threshold, especially in terms of reducing false alarms. This study provides an effective approach to improve the prediction capability of the ED rainfall threshold, benefiting its application in the landslide prediction. [ABSTRACT FROM AUTHOR]

• Two-component mixture distributions (TCMD) are used to model annual maximum floods. • Usage of flood timescale (FT) to classify distinct flood generating mechanisms. • Snowmelt-induced floods and rainfall-induced floods are identified in Norway. • The FT -based TCMD reduces uncertainty of design flood by 40% than traditional TCMD. The conventional flood frequency analysis typically assumes the annual maximum flood series (AMFS) result from a homogeneous flood population. However, actually AMFS are frequently generated by distinct flood generation mechanisms (FGMs), which are controlled by the interaction between different meteorological triggers (e.g., thunderstorms, typhoon, snowmelt) and properties of underlying surface (e.g., antecedent soil moisture and land-cover types). To consider the possibility of two FGMs in flood frequency analysis, researchers often use the two-component mixture distributions (TCMD) without explicitly linking each component distribution to a particular FGM. To improve the mixture distribution modeling in seasonally snow covered regions, an index called flood timescale (FT), defined as the ratio of the flood volume to peak value and chosen to reflect the relevant FGM, is employed to classify each flood into one of two types, i.e., the snowmelt-induced long-duration floods and the rainfall-induced short-duration floods, thus identifying the weighting coefficient of each component distribution beforehand. In applying the FT -based TCMD to model the AMFS of 34 watersheds in Norway, ten types of mixture distributions are considered. The design floods and associated confidence intervals are calculated using parametric bootstrap method. The results indicate that the FT -based TCMD model reduces the uncertainty in the estimation of design floods for high return periods by up to 40% with respect to the traditional TCMD. The improved predictive ability of the FT -based TCMD model is attributed to its explicit recognition of distinct generation mechanisms of floods, thereby being able to identify the weighting coefficient and FGM of each component distribution without optimization. [ABSTRACT FROM AUTHOR]

• Model-based polarimetric decomposition method for soil moisture estimation (SME). • Dihedral scattering component is neglected in bare soil and sparse vegetation areas. • SME results of C- and L-band full polarimetric data are analyzed. • SME accuracies of bare soil, sparse, and dense grassland are evaluated. Polarimetric synthetic aperture radar (PolSAR), as an advanced active sensor, can penetrate vegetation to detect ground information. The multimode observation data can provide more information to interpret different scattering mechanisms and have great application potential in soil moisture estimation (SME). In this paper, a modified method of soil moisture inversion from PolSAR data based on a two-component polarimetric decomposition model is introduced. The proposed method neglects the dihedral scattering component in the case of sparse vegetation cover and no-building areas, compensates the orientation angle by considering the influence of surface roughness and terrain slope, and calculates the dielectric constant with the hybrid X-Bragg/Fresnel model. Gaofen-3 (C-band) data from Ordos, Inner Mongolia, and Soil Moisture Experiment 2003 (SMEX03) airborne synthetic aperture radar (AIRSAR) (C- and L-band) data from Southern Oklahoma were applied in the experimental research. Furthermore, the performance and validity limits were assessed by comparing the retrieval results with in situ measurements and Soil Moisture Active Passive (SMAP) soil moisture products. The results reveal a root-mean-square error (RMSE) of 8.66% with an inversion rate of up to 61% when using the Gaofen-3 data from Ordos. For the Southern Oklahoma study area, the inversion RMSE of the AIRSAR C- and L-band data is 8.57% and 8.95%, respectively. The tendency of the inversion results is also consistent with the change trend of the site observations. In the bare soil and sparse vegetation covered areas, the retrieval error is relatively small, while in dense vegetation covered areas, soil moisture is underestimated in both the C- and L-band data, and the former is more underestimated than the latter. [ABSTRACT FROM AUTHOR]

• The increase in Ks and n could effectively reduce the peak and total flow at different stages of runoff generation. • By increasing Ks , re-vegetation could result in shift of dominant runoff generation mechanism in Loess Plateau. • By increasing n , the vegetation would slow down the flow velocity and trap the water and soil along the flow path. • Smaller erodibility coefficient helped to slow down the fragmentation of landscape in the Loess Plateau. The changes of soil properties caused by ecosystem restoration have played critical roles in controlling water and sediment loss in Loess Plateau. However, it is difficult to isolate and quantify the individual impact of each factor as the impact is hard to monitor separately. The objectives of this study are to explore how the changes in different soil properties influence the hydrologic response and sediment transport at catchment scale. We applied a coupled hydrologic-sediment transport model to a small catchment in the Loess Plateau. Sensitivity analyses were conducted on three influential soil properties: saturated hydraulic conductivity (Ks), Manning's roughness coefficient (n) and the variations of erodibility coefficient (φ). Our results indicated that the increase in near surface saturated hydraulic conductivity could effectively reduce the peak and total flow (i.e. when Ks is smaller than rainfall intensity, 22.5% increase in Ks leads to over 70% reduction in total discharge), and could lead to transition of runoff generation mechanism from Horton Overland Flow to Dunne Overland Flow. The increase in Manning's roughness coefficient would also reduce the discharge and sediment yield substantially by slowing down the flow velocity which leads to more infiltration along the flow path after the runoff is generated. The decrease in erodibility coefficient reduces the sediment rate and total sediment mass delivered (i.e. 20% decrease can reduce total sediment mass by 5%), alleviating the undercutting in upstream gullies and deposition at downstream, eventually slowing down landscape evolution. Our findings could be used to provide valuable guidance in the ecosystem restoration and land use management. [ABSTRACT FROM AUTHOR]

• The relative effect of moisture content and salinity on ERT surveys was quantified. • ER preferential paths are mostly induced by preferential flow rather than salinity. • Moisture content variations have larger effects than salinity variations on ER. • ERT scans can map preferential flow pathways in brackish water irrigated soils. The identification of preferential flow generation and the tracking of their pathways in agricultural soils are challenging tasks, in particular while keeping the soil profile undisturbed. The noninvasive electrical resistivity tomography (ERT) method seems, a priori, to be an ideal tool for these purposes, but the simultaneous dependence of soil electrical resistivity (ER) on soil moisture content and salt concentration poses a problem when high-salinity water is involved. The dependence of these variables is expressed, for example, by Archie's law. Irrigation with brackish or treated wastewater are two such complicated cases. The latter has been found to render soils water-repellent with inherent flow in preferential pathways. A method that resolves this complication is developed and applied for in-situ-measured ERT data. This method combines a model that simulates the preferential ER(t) as a series of interconnected well-mixed units (WMUs) with frequent ERT scans during soil wetting and subsequent drying. Following validation by comparison of its output to simulations using the Richards equation for flow and convection-dispersion equation for transport, the WMU model was used to simulate scenarios of simultaneous variations in moisture content, salinity, and ER(t) in a well-mixed soil volume (unit). The characteristic patterns acquired by these simulations were compared with in-situ measured ER(z,t) to determine whether the observed preferential ER pathways coincide with preferential flow or salinity pathways, or both. From the eight sections in the profile where this analysis was made for, five exhibit preferential flow, characterized by fast wetting front propagation that includes by-passing of a certain soil volume, while the wetting front velocity in the other three was moderate. We, therefore, concluded, based on the water content/salinity effect on ER, that the measured preferential ER pathways coincide with preferential flow pathways. Salinity has, for the current case, a minor, if any, effect on the ER spatial variation. Therefore, ERT is an advantageous mean of mapping of preferential flow pathways in the soil profile, and the method developed herein can be used for studying the water distribution in the soil profile. Particular irrigation design and management can be then used to ease the negative effects of preferential flow pathways on soil water availability to plant roots and the enhanced leaching of agrochemical below the active root zone. [ABSTRACT FROM AUTHOR]

A decrease in soil water content (SWC) has been observed at the global scale and has led to a reduction in evapotranspiration (ET). However, the trend in SWC in eastern Asia differed from the global trend, and this may be due to the overlapping of warm/wet seasons. Because of the limitation of deep soil water content data, especially in drylands, changes in SWC and the correlation between SWC (particularly deep SWC) and ET remain poorly understood. In this study, spatial distribution of correlation coefficient (r) between SWC and ET were calculated based on reanalysis data and remote sensing data, which were validated by in situ data (2004–2005) and sampling data (2016–2017). Firstly, the correlation between SWC and ET decreased with soil depth. The depth of the sharp decrease was 10 cm based on in situ monitoring data (2005–2008) and spatial sampling data (2016–2017). Secondly, the spatial correlation between SWC and ET varied with different vegetation cover types. The correlation increased from the western typical steppe to the eastern forest. The highest values of r were in a forest-covered area, because it had the highest water uptake capacity. However, the difference between r_10 and r_100 (D_r) showed a different spatial pattern, and the lowest negative D_r value was obtained from a forest–grass transition zone in which the enhanced vegetation index showed a significant increasing trend. The significant negative regression determination coefficient (R2 = 0.9, p < 0.01) between B_ EVI (0–0.1) and D_r indicated that the narrowed D_r in the transition zone was due to the vegetation increase. This meant that more deep soil water was taken up into the atmosphere. Thus, the deep SWC could become unstable and deep soil could become drier, which would be unsustainable for the ecosystem. [ABSTRACT FROM AUTHOR]

• Water vapor in wet island of Ili Valley is mainly replenished by the westerlies. • Air temperature is the significant controlling factor for isotopes in precipitation. • Local water vapor recycling is an important part of precipitation. As a typical wet island in arid Central Asia, the Ili Valley located in northwestern China provides water resources for the oasis in the lower reaches. However, it is not clear whether its unique climatic characteristics will lead to different isotopes of precipitation and what is the major factor controlling the variation of precipitation isotopes in this region. Based on three sites spread over the Ili Valley, 297 precipitation samples were collected from May 2015 to April 2016. The δ18O and δD values range from −37.8‰ to 3.2‰ and −261.5‰ to 34.5‰, with a weighted mean −9.4‰ and −65.2‰, respectively. A clear correlation exists between mean monthly δ18O values and mean monthly temperature, with R2 = 0.68 for the mean monthly values increase of 0.91‰ per degree Celsius. A positive gradient of 0.32‰/100 m was obtained from the Ili Valley when an annual weighted average of δ18O values was used. Considering the temporal and spatial variation of hydrogen and oxygen isotopes and d-excess value of precipitation, combined with the results from the HYSPLIT4.0 air mass trajectory model, we found that moisture source in the study area is considered mainly replenished by the westerlies, and the moisture recycle from local surface water and soil water around Yining cannot be ignored. Although the data covers a year, it can still provide a good database for studying hydrological cycle in Centeral Asian. [ABSTRACT FROM AUTHOR]

• Develop a new dielectric sensor with a perforated cylinder coaxial (PCC) probe design. • Eliminate temperature effects on the circuit board and the permittivity of soil water. • Assess E-field distributions of the PCC probe and two alternative dielectric probes. • Three dielectric probes for near-surface soil water content measurement are tested. • PCC probe provides the most accurate measurement of near-surface soil water content. Near-surface soil water content (NSWC) is an important parameter for characterizing hydrological processes, water and vapor exchange at the soil-atmosphere boundary, and potential biological productivity. Here we develop a dielectric based frequency-domain (FD) impedance sensor operating at 100 MHz, with a novel coaxial probe design and a temperature sensor embedded in the circuit board. This perforated cylinder coaxial (PCC) sensor provides simultaneous measurements of volumetric soil water content (VSWC) and of temperature. The temperature measurement allows signal correction for the temperature response of the circuit board and of the permittivity of soil water. We characterize the performance of the PCC sensor and compare it with alternative 3-pin- and 2-pin-electrode FD impedance sensors each using a similarly performing circuit board. A series of theoretical analyses and experiments were conducted, including (i) sensors calibration, (ii) temperature effects and their correction, (iii) soil volume sensed by each sensor through simulation analysis and a stepwise soil removal (SSR) test and (iv) accuracy comparison of the three sensors when applied for the NSWC measurements. The calibration results verified that the PCC, 3-pin and 2-pin probes were suitable to measure VSWC (RMSE ≤ 0.0142 cm3 cm−3). The simulation analysis using High Frequency Structure Simulator software (HFSS) indicated that the novel PCC probe had a symmetrical distribution of the E-field, wholly contained within the cylinder, avoiding leakage of the E-Field into air at the soil-atmosphere interface and removing the dependency of sensed volume on VSWC. The results of the SSR test showed that the E-field distributed outside the receiving electrode of the PCC probe could be ignored whereas that of both pin-type electrodes was distributed outside of the receiving electrode to a distance that depended on VSWC (i.e. up to 8 mm for 3-pin and up to 11 mm for 2-pin designs when VSWC > 0.17 cm3 cm−3), which corresponds to the simulation analysis. Moreover, the new PCC probe provided more accurate NSWC measurement than 3-pin or 2-pin probes because the E-field was contained within the perforated cylinder, avoiding E-field leakage and fixing sensed soil volume independent of VSWC. The pin-structure probes appear to perform best when installed horizontally in the soil at least 11 mm below the surface. [ABSTRACT FROM AUTHOR]

Schematic diagram of the groundwater and soil water mixing by means of dissolved 137Cs. • Dissolved 137Cs in stream water derived mainly from soil water. • Low impact of dissolved 137Cs in groundwater was confirmed. • 137Cs stored in the shallow unsaturated area was the source of temporary increase. Numerous studies have been conducted on 137Cs transport in the environment in Fukushima, Japan, since the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) accident. Many studies have monitored river and stream water, and a declining trend in dissolved 137Cs concentrations has been reported. In addition, temporary increases in dissolved 137Cs concentrations have been observed during rainstorm events. However, the source and process of such temporary increases remain poorly understood. To address these gaps in knowledge, intense multi-point sampling and analysis of dissolved 137Cs in stream water, soil water, and groundwater were conducted in a forested headwater catchment in Yamakiya district, located 35 km northwest of the FDNPP, from July 2015 to November 2016. The average dissolved 137Cs concentration in stream water during baseflow conditions was 17 mBq/L, whereas the concentration during rainstorm events was 31 mBq/L (maximum: 170 mBq/L on July 15, 2015). The dissolved 137Cs concentration in groundwater (average: 0.93 mBq/L) was lower than that in stream water. Conversely, high concentrations of dissolved 137Cs in soil water were observed (average: 81 mBq/L), particularly at lower parts of the slope near the spring point and in wet conditions throughout summer. Consequently, the dissolved 137Cs concentrations in stream water were considered to be derived mainly from soil water and diluted by groundwater. The high dissolved 137Cs concentration in soil water may be derived from litter leachate in the shallow soil layer stored on the slope. During rainstorm events, the saturated area expands upward; therefore, the temporary increase in the dissolved 137Cs concentration in stream water observed at the beginning of a rainstorm may be caused by the release of high levels of dissolved 137Cs in soil water stored in the shallow soil layer. [ABSTRACT FROM AUTHOR]

• The efficiency of SM products to improve unmodelled effects in LSM are examined. • Assimilation of ASCAT and CCI products effectively captures the dynamics of SM. • ASCAT based SM products have high skill to improve LSM simulations. • Blended SM products are more effective to capture the daily SM variability. Realistic representation of surface states using the land surface model (LSM) is extremely challenging owing to human-induced changes and uncertainty in forcing data. In this study, we focus on two crucial objectives pertaining to hydrology namely (a) to understand the ability of different soil moisture (SM) products to improve simulation of unmodeled irrigation processes through data assimilation process, and (b) to learn the feasibility of these SM products to correct the spatial surface soil moisture artifacts caused due to error in precipitation forcing. The utility of SM products evaluated in the present study are retrieved from different satellite sensors and algorithms such as the active satellite-based the Advanced Scatterometer (ASCAT), merged SM from European Space Agency Climate Change Initiative (ESA CCI V4.2) and the latest passive microwave-based SMOS INRA-CESBIO (SMOS-IC) SM product. The results presented for three years (2010, 2011 and 2012) suggest that assimilation of ASCAT and CCI based products effectively captures the SM changes due to irrigation. Similarly, it corrects the spatial artifacts caused due to precipitation errors. However, the single sensor based products have a limitation in spatial samples per day which is critical to capture dynamic SM products over a larger area. Hence, blended products are more effective on a larger area to capture the dynamics in a more effective manner at daily temporal resolutions. [ABSTRACT FROM AUTHOR]

• A comprehensive model for three-dimensional single-ring infiltration was evaluated. • The model allows to analyze both transient and steady-state infiltration data. • The model may allow researchers to better approach heterogeneous datasets. The objective of this paper was to evaluate a recently proposed comprehensive model for three-dimensional single-ring infiltration and its suitability for estimating soil hydraulic properties. Infiltration data from four different soils with contrasting characteristics were inverted to estimate field-saturated soil hydraulic conductivity, K fs , values using a total of fourteen different scenarios. Those scenarios differed by: i) the way they constrained the macroscopic capillary length, λ, and the initial and saturated soil water contents, θ i and θ s , ii) the use of transient or steady-state data, and iii) the fitting methods applied to transient data. For comparative purposes, the SSBI method (Steady version of the Simplified method based on a Beerkan Infiltration run) was also applied. For validation purposes K fs data estimated from the different scenarios were compared with those values obtained by numerical inverse modeling with HYDRUS-2D/3D. This comparison identified Approaches 1 and 3, which respectively estimate K fs via optimization and using analytical expressions, as the most accurate methods. The steady-state scenario of Approach 4 and the SSBI method, both of which use a λ value of first approximation, appeared preferable for field campaigns aimed to sample remote or large areas, given that they do not need additional data and still provide acceptable estimates. The reliability of K fs data was also checked through a comparison with unsaturated hydraulic conductivity, K h , values measured in laboratory on extracted soil cores, in order to discriminate between theoretically possible (K fs > K h) and impossible (K fs ≤ K h) situations. Physically possible K fs values were always obtained with the exception of the crusted soil, where K fs < K h situations suggested that the crust layer reduced water flow during ponding experiments in the field. The new comprehensive model tested in this study represents a valuable tool for analyzing both transient and steady-state infiltration data, as well as experiments carried out with different depths of ponded water, ring sizes and ring insertion depths. [ABSTRACT FROM AUTHOR]