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• 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]

• Spatial model assessment best in catchments with greatest soil depth variability. • Modified Budyko framework captures 10 × 10 m2 grid-scale hydrologic variability. • Topography and soil depth control partitioning of precipitation within a field. • Hydrologic management zones respect drivers of crop yield and nutrient loss. Crop yield patterns at the field level in dryland agricultural systems often trace back to the spatial structure of water storage and subsurface flow paths. These lateral flow paths create zones of saturation that promote nutrient loss from runoff and leaching beneath the root zone. Despite its clear importance, a spatially explicit representation of landscape hydrology is frequently missing from site-specific crop and fertilizer management plans. This work uses the grid-based water balance model, SMR (soil moisture routing), to adapt an established method of hydrologic classification, the Budyko framework, for small agricultural watersheds. The model was evaluated with spatially distributed soil moisture data and surface runoff measurements in four annually cropped fields located across the eastern Palouse region of the inland Pacific Northwest. The Budyko framework, modified to include change in root zone water storage and lateral flow, was combined with a separate indicator of yield potential to form a dual-threshold classification scheme for variable-rate fertilizer management that balances agronomic benefit with environmental impact. Each field was divided into zones based on crop yield and nitrogen loss potentials, which link to the hydrologic functioning of the landscape through soil water storage and subsurface redistribution, respectively. Results showed large variability in the annual water balance assessed within individual fields and between fields. The greatest variability was observed in the two eastern-most fields where the wettest zone of each had water supply 172% and 137% that of the dry zone. Depth to a hydraulic restrictive layer and topography were identified as primary landscape controls on the water supply (precipitation plus net lateral flow minus change in water storage) in these fields. Moreover, the steep climate gradient in the region created field-to-field variability in water balance with the ratio of evapotranspiration to precipitation ranging from 91% in the western-most field to approximately 70% in each of the fields to the east. The clearest choice is for growers to apply more fertilizer in areas with high water storage capacity and high crop yield and where fertilizer loss through leaching and runoff is minimal. Less fertilizer should be applied in areas with low water storage capacity that frequently saturate. Areas with high storage capacity and frequent saturation involve a tradeoff between yields and minimizing fertilizer loss that may be best served by splitting fertilizer application to avoid excessive over-winter leaching losses. Even though remote sensing and variable-rate applicators are at the frontiers of precision agriculture, landscape hydrology underlies all dryland crop production and should accordingly be looked to for inspiration in the design of cropping systems. [ABSTRACT FROM AUTHOR]

• Hydrologic impacts of climate change assessed in Manning River basin using XAJ model. • XAJ model performs satisfactorily with R2 and NSE more than 0.94 and 0.92. • Runoff is projected to increase in summer and decrease in winter. • Projected changes in water availability are largely dominated by change in rainfall. Hydrological responses of catchments to climate change require detailed examination to ensure sustainable management of both water resources and natural ecosystems. This study evaluated the impacts of climate change on water resource availability of a catchment in eastern Australia (i.e. the Manning River catchment) and analyzed climate-hydrology relationships. For this evaluation, the Xinanjiang (XAJ) model was used and validated to simulate monthly rainfall-runoff relationships of the catchment. Statistically downscaled climate data based on 28 global climate models (GCMs) under RCP8.5 scenarios were used to assess the impacts of climate changes on the Manning River catchment. Our results showed that the XAJ model was able to reproduce observed monthly rainfall-runoff relationships with an R2 ≥ 0.94 and a Nash-Sutcliffe Efficiency ≥ 0.92. The median estimates from the ensemble of downscaled GCM projections showed a slight decrease in annual rainfall and runoff for the period 2021–2060 and an increase for the period 2061–2100. Annual actual evapotranspiration was projected to increase slightly, while annual soil moisture content was predicted to decrease in the future. Our results also demonstrated that future changes in seasonal and annual runoff, actual evapotranspiration and soil moisture are largely dominated by changes in rainfall, with a smaller influence arising from changes in temperature. An increase in the values of high runoffs and a decrease in the values of low runoffs predicted from the ensemble of the 28 GCMs suggest increased variability of water resources at monthly and seasonal time-scales in the future. A trend of decreasing values in winter runoff and soil moisture content in the future is likely to aggravate possible future reductions in water availability in eastern Australia. These results contribute to the development of adaptive strategies and future policy options for the sustainable management of water resources in eastern Australia. [ABSTRACT FROM AUTHOR]

• A Bayesian approach has been used for merging multiple satellite rainfall products. • We created a superior product that can be efficiently run in near-real time. • Soil moisture can provide useful information for improving satellite rainfall. This work investigates the potential of using the Bayesian-based Model Conditional Processor (MCP) for complementing satellite precipitation products with a rainfall dataset derived from satellite soil moisture observations. MCP – which is a Bayesian Inversion approach – was originally developed for predictive uncertainty estimates of water level and discharge to support real-time flood forecasting. It is applied here for the first time to precipitation to provide its probability distribution conditional on multiple satellite precipitation estimates derived from TRMM Multi-Satellite Precipitation Analysis real-time product v.7.0 (3B42RT) and the soil moisture-based rainfall product SM2RAIN-CCI. In MCP, 3B42RT and SM2RAIN-CCI represent a priori information (predictors) about the "true" precipitation (predictand) and are used to provide its real-time a posteriori probabilistic estimate by means of the Bayes theorem. MCP is tested across Italy during a 6-year period (2010–2015) at daily/0.25 deg temporal/spatial scale. Results demonstrate that the proposed methodology provides rainfall estimates that are superior to both 3B42RT (as well as its successor IMERG-early run) and SM2RAIN-CCI in terms of both median bias, random errors and categorical scores. The study confirms that satellite soil moisture-derived rainfall can provide valuable information for improving state-of-the-art satellite precipitation products, thus making them more attractive for water resource management and large scale flood forecasting applications. [ABSTRACT FROM AUTHOR]

• Streamflow is correlated more strongly with snowfall than with rainfall. • Warming has caused peak flow to occur earlier. • The effects of snowmelt on streamflow is subject to rainfall regulation on soil moisture condition. • Spring rainfall and snowmelt play a dominant role in spring and summer flow generation. • The mechanisms for flow generation in autumn and winter are complex and vary across space. Mountain watersheds serve as important water sources for socioeconomic activities in semiarid and arid regions. This study examined the streamflow response to snow regime shifts associated with climate variability in the US Great Basin. To this end, the coupled hydro-ecological simulation system (CHESS), a process-based and distributed model, was applied to four mountain watersheds in the US Great Basin. Historical weather records for the period of 1961–1990 were used to spin-up model simulations so that the soil water and vegetation reached the equilibrium state under long-term climate conditions; the period of 1991–2015 was selected as the study period. The model evaluation suggested that CHESS was able to simulate major hydrological processes in the four mountain watersheds. In addition, it was found that annual streamflow was more strongly correlated with snowfall than with rainfall during 1991–2015 and increased by 0.62–0.76 mm as snowfall increased by 1 mm in the study regions. Recent regional warming advanced the timing of the peak flow and shifts in the snow regime also affected the intra-annual pattern of the soil moisture content. Seasonally, spring rainfall and snowmelt were dominant factors in generating spring streamflow and summer baseflow. The mechanisms for flow generation were more complex and more variable in the four watersheds in autumn and winter than in spring and summer. On an annual basis, shifts in snowmelt resulting from climate variability accounted for more than 60% of the variability in the annual streamflow, surpassing the contribution of rainfall in the four watersheds. Overall, our results suggest that shifts in the snow regime due to climate variability have important implications for the intra-annual distribution and availability of water resources in semiarid and arid regions of the US Great Basin. [ABSTRACT FROM AUTHOR]

• Effects of interpolation methods on rainfall data scarcity were compared. • Impacts of of data scarcity on model uncertainty were investigated. • The effects of the different imputed data sets on H/NPS results were quantified. • The EMB algorithm was proved to be superior to the traditional methods. • The threshold effect and monitoring during high-flow periods were highlighted. Rainfall data scarcity has caused enormous problems in hydrologic and non-point pollution (H/NPS) predictions, as rainfall data represent the key input to watershed models. In this study, the effects of different imputation methods such as the data augmentation (DA) and the expectation maximization with bootstrap (EMB) algorithms on rainfall data scarcity were compared. The effects of different data scarcity rates and periods on model performance and prediction uncertainty were then quantified. Finally, the effects of different imputed data sets on H/NPS results were evaluated with the soil and water assessment tool (SWAT). A real case study in the Daning River watershed, Three Gorges Reservoir Region, China, was evaluated. The results indicated that rainfall data scarcity during low flow periods would result in poorer model performance and larger prediction uncertainty, especially to some minimum values, and the time when the maximum values are more susceptible is the rainfall data scarcity during high flow periods. The repair of rainfall data and the H/NPS model performance obtained by the EMB algorithm are superior to the traditional DA and weather generator performances. This advantage of the EMB algorithm would be more definitive if a specific threshold of data scarcity is reached. It is noted that even if the best algorithm is used, the imputed value is always lower than the peak observed value. This paper reports important implications for the choice of imputation methods and the use of H/NPS models for solving data scarcity problems for watershed studies. [ABSTRACT FROM AUTHOR]

• The SMAP and SMOS soil moisture products were compared against in-situ observations. • Satellite soil moisture products were downscaled using thermal inertia theory. • A regression tree was developed for downscaling, based on in-situ soil moisture data. • Downscaled SMAP and SMOS products showed ubRMSEs of 0.07 and 0.05 cm3/cm3. • Downscaled airborne soil moisture retrievals showed an accuracy of 0.07 cm3/cm3. High spatial resolution soil moisture information is important for hydrological, climatic and agricultural applications. The lack of high resolution soil moisture data over large areas at the required accuracy is a major impediment for such applications. This study investigates the feasibility of downscaling satellite soil moisture products to 1 km resolution. This study was undertaken in the semi-arid Goulburn River Catchment, located in south-eastern Australia. The Soil Moisture Active Passive (SMAP)-Enhanced 9 km (L3SMP-E) and Soil Moisture and Ocean Salinity (SMOS) 25 km gridded (SMOS CATDS L3 SM 3-DAY) radiometric products were compared with in-situ soil moisture observations and a regression tree model was developed for downscaling based on thermal inertia theory. Observations from a long-term soil moisture monitoring network were employed to develop a regression tree model between the diurnal temperature difference and the daily mean soil moisture for soils with different clay content and vegetation greenness. Moderate-resolution Imaging Spectroradiometer (MODIS) land surface temperatures were used to estimate the soil moisture at high spatial resolution by disaggregating the satellite soil moisture products through the regression model. The downscaled SMAP-Enhanced 9 km and SMOS 25 km gridded soil moisture products showed unbiased root mean square errors (ubRMSE) of 0.07 and 0.05 cm3/cm3, respectively, against the in-situ data. These ubRMSEs include errors caused by measuring instrument and the scale mismatch between downscaled products and in-situ data. An RMSE of 0.07 cm3/cm3 was observed when comparing the downscaled soil moisture against the passive airborne L-band retrievals. The findings here auger well for the use of satellite remote sensing for the assessment of high resolution soil moisture. [ABSTRACT FROM AUTHOR]

• A model coupled evaporative demand and water supply on transpiration was developed. • The increasing rate of transpiration with soil water was quantified. • The independent contribution of soil water on transpiration was quantified. • Reference canopy-level stomatal conductance increased with increasing soil water. Transpiration (Tr) is influenced by environmental factors, vegetation properties, and anthropogenic management. The effect of environmental factors on Tr are taking place from two aspects: evaporative demand (i.e., potential evapotranspiration, PET) and water supply (i.e., soil water). Soil water is one of the most important factors that limit plant transpiration in terrestrial ecosystems, especially in semi-arid and arid regions. Investigating the relationship between Tr and soil water is crucial for an improved understanding of plant survival strategies and predicting hydrological cycles and water resources under climate change. Although the relationships of soil water and Tr have been widely studied, the independent effects of soil water on Tr are difficult to separate because soil water and PET occur concurrently under natural conditions. This study carried out field observations of sapflow density, meteorological factors and soil water in a black locust (Robinia pseudoacacia) plantation in the semi-arid Loess Plateau of China. This information was used to develop a model integrating the daily PET and relative extractable soil water (REW, an index that represents the available soil water): Tr = (0.27 × PET − 0.02 × PET2 − 0.32) × (1 − e−5.70×REW). The model fitted the measured data well (R2 = 0.65 and RMSE = 0.06 mm day−1). We found that the daily Tr increased as the REW increased under varying PET levels. Additionally, the independent effects of soil water on Tr were analysed using the factorial experiment analysis method. The REW was manipulated and PET varied naturally during the measurements to separate the independent effects of the REW on Tr. The results showed that Tr increased with the REW during the study period at a rate of 0.53 mm day−1 per 0.1 REW when the REW < 0.4 and 0.09 mm day−1 per 0.1 REW beyond the threshold (REW = 0.4). Tr increased by 27.3 mm (43.7%) compared to the controlled Tr (observed PET and lowest REW) due to the effects of increasing REW (ranging from 0.14 ∼ 0.81) during the study period (growing seasons of 2015 and 2016). Furthermore, canopy-level stomatal conductance was calculated using a simplified inverted Penman-Monteith equation. The reference canopy-level stomatal conductance (Gsref), which represents the Gs capacity and its sensitivity to soil water, showed an increasing response with increasing REW, which is the possible reason for the Tr dynamics with the REW. Our study provided an improved understanding of the soil water-Tr relationship and predicted effects of climate change on tree water use. Although the method required abundant field observation data, we have provided a feasible method of accurately quantifying the independent contribution of soil water on forest Tr. [ABSTRACT FROM AUTHOR]

• Impact-based flash flood probabilistic forecasting system is designed and described. • Sensitivity to high resolution weather prediction and soil moisture is assessed. • Benefit of using a multi-meteomodel approach is investigated. • Forecasts are expressed in terms of streamflow, damage and affected people. • A warning decision making process based on forecasted impacts is proposed. In recent years, continuous improvements have been made in weather forecasting and flood prediction with great benefit from Early Warning Systems (EWSs). Despite the continuous quest for innovation from the scientific and user communities, EWSs remain based mostly on hazard forecast, and the information on possible consequences and potential impacts is generally missing. In this work, a methodology for quantitative real-time impact assessment for flash floods is presented. The methodology uses a multi-model ensemble approach and considers soil moisture uncertainty. Moreover, the flood forecasting chain, which normally provides only the discharge probability of exceeding a given threshold, is extended to include a fully 2D hydraulic model and a damage estimation model to quantitatively assess impacts in terms of economic losses and the people involved. The procedure was tested on recent flood events occurring in Genoa in northwestern Italy. This paper discusses the potential challenges and opportunities offered by this approach in the decision-making workflow in an operational context. [ABSTRACT FROM AUTHOR]

• Enhanced hydrological model (SWAT-AG) is proposed based on SWAT framework. • Close interactions of soil water, groundwater and plant growth are well described. • Hydrological processes in watershed with shallow aquifer are reasonably simulated. • SWAT-AG improves the model performance in agro-watershed compared with SWAT. This article introduces the SWAT-AG (a modified SWAT for agro-hydrological modeling) as an enhanced hydrological model for simulating the hydrological processes in agricultural watershed with shallow water tables. The model is developed based on the framework of the SWAT with significant improvements on describing the interactions of the vadose zone water, groundwater, salt and plant growth. The main features of the SWAT-AG include that it can simulate the close interactions of soil water and shallow groundwater with considering the capillary rise effects; it provides functions to simulate the soil salt movement and salt-stress on plant growth; and it incorporates a balance-based module on a sub-basin scale for shallow aquifer with involving canal seepage and ditch drainage calculation. Model testing and application were conducted in Jiyuan Irrigation System located in the Hetao of upper Yellow River basin, northwest China. The comparison between simulated and observed data showed that the SWAT-AG performed well and obviously better than the original SWAT in simulating the soil water and salt dynamics, groundwater depth fluctuations, and plant growth and water consumption. The results showed that the NSE averagely increased from −17.57 to 0.57 and from 0.71 to 0.79 for soil water storage and leaf area index, respectively, in typical croplands and natural patches. In addition, the effects of shallow groundwater in sustaining the agro-ecosystems were reasonably revealed with the spatial analysis of evapotranspiration, capillary rise and salt accumulation. The test case proved that the proposed SWAT-AG could efficiently enhance the functionality and practicality of hydrological models for agricultural watersheds with shallow groundwater. [ABSTRACT FROM AUTHOR]

Highlights • Deep soil water infiltration was seasonally controlled by soil frost processes. • Stronger connection between deep soil water infiltration and volumetric soil moisture. • There was no correlation between rainfall and deep soil water infiltration. • Root system, soil organic matter and Mattic Epipedon should be considered in hydrological processes modeling. Abstract Deep soil water infiltration is a key hydrological process in seasonally frozen regions, and displays a distinct behavior from that in other unfrozen regions; however, the seasonal dynamics and key controls of deep soil water infiltration in the seasonally frozen region of the Qinghai-Tibet plateau (QTP) are still poorly understood. In this study, we examine the seasonal dynamics of deep soil water infiltration, and attempt to quantitatively assess the relative influences of several driving factors by applying a random forests statistical analysis. The following key results were obtained. Deep soil water infiltration shows marked seasonal variation. Rainfall intensity exerts little effect on the seasonal dynamics of deep soil water infiltration as almost all rainfall returns to the atmosphere via evapotranspiration. No infiltration occurred when the depth of lower layer frozen soil reached 29 cm, whereas deep infiltration increased during the thawing of soil frost, suggesting that deep infiltration is impeded by soil freezing. Overall, the observed seasonal dynamics of deep soil water infiltration and volumetric soil moisture content were similar during the freeze-thaw process. Furthermore, deep soil water infiltration was more strongly influenced by deep volumetric soil moisture than by shallower volumetric soil moisture. Our results suggest that the topsoil Mattic Epipedon, soil organic matter content, and root systems' role in vertical moisture movement should be taken into consideration when modeling hydrological processes in alpine meadows. [ABSTRACT FROM AUTHOR]

Highlights • Two commonly used methods for assessing the ecohydrological separation show inconsistent results. • A new method based on root zone isotope mass balance supports the ecohydrological separation at the studied humid site. • The ecohydrological connection occurs during snowmelt at the site. Abstract The ecohydrological separation between soil water sources for plant water uptake and groundwater recharge has been recently examined in various climate zones primarily based on isotopic composition of water. The existence of the ecohydrological separation has profound implications for mechanistic ecohydrological modeling and water resource management. However, it is still unclear when and where the ecohydrological separation occurs, especially in humid regions. In this study, high frequency sampling of precipitation, bulk soil water, groundwater and twig xylem water for hydrogen and oxygen isotope composition measurement was conducted in a humid subtropical site in the central southern China from March 2017 to April 2018. We examined evidence of the ecohydrological separation with three methods (dual-isotope space, line-conditioned excess (lc-excess), and the piecewise isotope balance (PIB) method). The results show that the isotopic composition of plant xylem and bulk soil water are not distinguishable from those of precipitation water on the dual-isotope space due to a weak evaporation effect at the study site, indicating that there is no evidence of the ecohydrological separation. However, the other two methods support the ecohydrological separation in this humid area, with the results from the PIB method revealing more temporal details. The present study suggests that the ecohydrological separation can happen in subtropical humid climate. It is more likely to occur in spring and winter at the study site when plant-accessible water pool has been replenished by antecedent precipitation, while ecohydrological connection seems to occur during winter snowmelt. With the limitations of three methods, the caution should be taken when only one method is applied in examining the ecohydrological separation in such an environment. [ABSTRACT FROM AUTHOR]

Highlights • A merging framework was developed for improving field-scale RZSM with the CRNP. • Ancillary RZSM provided by a time stable location was coupled into CRNP measurement. • Local soil and vegetation affecting RZSM can be interpreted in framework procedure. • A superior performance of field-scale RZSM was achieved using the merging framework. • The merging framework performance depends on the variation of CRNP effective depth. Abstract Accurate estimation of field-scale root-zone soil moisture (RZSM) is essential for hydro-meteorological and agricultural management. The cosmic-ray neutron probe (CRNP) is an innovative technique for field-scale soil moisture observation. However, it is limited at deeper depths, and requires suitable methods for scaling the CRNP effective depth to represent the root-zone layer (up to 100 cm depth). A merging framework was developed in this study for improving field-scale RZSM with the CRNP via coupling the representative ancillary RZSM information with cosmic-ray soil moisture. By using ancillary RZSM retrieved at the most time stable location, this approach alleviates the problems associated with lack of independent datasets, while maintaining scale representativeness. A linear autoregressive model was adopted to forecast the errors between two input datasets (the cosmic-ray soil moisture and ancillary RZSM) and a reference product, which was computed by spatially weighting the deepest in-situ soil moisture measurements apart from the most time stable location. The variances of estimated errors were then used to compute a suitable weight for each single product by following the original linear combination of forecasts, and subsequently merging the ancillary RZSM and cosmic-ray soil moisture. Performance of the merged RZSM in comparison to input datasets and exponential filter-based RZSM over three different environments was evaluated against the reference RZSM product. The results indicated that differences in vegetation coverages related to total aboveground and belowground biomass accumulation, root water uptake rate and canopy density are the major factors controlling the temporal variation in merged RZSM, and they can be partly interpreted via the framework procedure. Superior performance achieved by the merging framework demonstrated its robustness in improving field-scale RZSM measurement compared to other products. This study also underlines the strong relationship between input data quality and the performance of the selected merging method, with respect to variations of CRNP effective depths. Overall, the merging framework is simple to apply, enables unrestricted-use in different environments, and is flexible to combine further standalone data sources. [ABSTRACT FROM AUTHOR]

Highlights • There exists a contrasting trend in water content response between seasons. • Water content in vegetated soil is higher than in a bare soil during autumn. • Low soil temperature could inhibit root water uptake ability. • Root-water uptake is overestimated when soil temperature is ignored. Abstract Presence of vegetation in urban landscape influences soil temperature and soil moisture. Some studies in literature have focused on development of correlations between the above two parameters for agricultural fields and forest regions. These correlations could be dissimilar due to the differences in soil conditions (density and type), vegetation species and temperature between agricultural and urban landscape. This paper investigates the effects of vegetation on soil temperature and soil moisture by an integrated approach of field monitoring and numerical modelling. A simple hydrological model is developed to capture seasonal variations of surface soil temperature and its effect on root water uptake. Hypothesis of effects of soil temperature on root water uptake was investigated using field measurements of soil water content. Field site in urban landscape containing bare, grass and tree species were selected for investigation. There existed a contrasting trend in water content response between summer and autumn for both bare and vegetated soils. Due to higher evapotranspiration in summer, water content in vegetated soils were lower by up to 50%. However, water content in vegetated soil was found to be up to 70% higher than in a bare soil during autumn. It could be due to the lower soil temperature that inhibits root water uptake ability. This was verified using a series of numerical simulations that consider effects of soil temperature on root water uptake. Without considering soil temperature effects on root water uptake, there was an overestimation of reduction of soil water content by up to 50% in autumn period. [ABSTRACT FROM AUTHOR]

Highlights • Variabilities in initial moistures and rainfalls bring nonnegligible uncertainties. • Evolutions of slope stability uncertainties are driven by mean flow field. • Situations in which uncertainties impact reliability of slope stability are presented. • Rainfall induced localized low-reliability zone leads to possible slope failure. • Monitoring is recommended to location of low-reliability zone to reduce uncertainty. Abstract A first-order moment analysis is developed to investigate the temporal and spatial propagation of uncertainty of slope stability during rainfall, considering spatial variabilities in initial soil water pressure and soil hydraulic properties, and temporal variability of rainfall. Results of the analysis indicate that the uncertainties resulting from variabilities in initial soil pore water pressure distributions and rainfalls are comparable with that from the variability in soil hydraulic properties. Further, the evolution of slope stability uncertainty is driven by the mean flow field, and a localized large-uncertainty zone along the slope profile could form, leading to a localized low-reliability zone, which may lead to the failure of the slope. In particular, when the slope is close to saturation, the reliability of the stability analysis of any elevation of the slope is low even at early rainfall times. On the other hand, when the slope is unsaturated and heavy rainfalls occur, the low-reliability zone exists at shallow parts of the slope at early times. The results also show that greater unreliability exists at shallow depths at early times when the rainfall has a descending trend in comparison with uniform and increasing trend. Lastly, the low-reliability zone is always near the impermeable bedrock if rainfall persists. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • An integrated method is proposed for studying the parameter scaling effect. • Sensitivity parameters of the SWAT were explored at different scales. • Key parameters of hydrological/NPS prediction at varying scales were compared. • Temporal and spatial scaling effects of parameter sensitivity were explored. • Suggestions were given for model construction and NPS management. Abstract Scale represents an important concept in all scientific disciplines, but the scaling effect related to non-point source (NPS) pollution simulation and sensitivity parameters has not yet been reported. In this study, the sliding window, the Fourier amplitude sensitivity test and the nested watershed idea were used with the Soil and Water Assessment Tool (SWAT) model to explore the temporal and spatial scaling effects of parameter sensitivity in a typical watershed of the Three Gorges Reservoir Region, China. The results indicated that a great scaling effect could be observed at varying spatio-temporal scales, while the scaling effect would be transferred and amplified from the hydrological modelling to the NPS simulation. Soil properties such as SOL_K and SOL_BD were identified as key parameters under smaller spatial and temporal scales, while channel-related parameters in terms of ALPHA_BF , CH_K and CH_N showed greater sensitivity at larger scales. Specifically, some parameters, such as CH_N , USLE_K , USLE_P and ERORGP , were always identified as key sensitive parameters for the sediment and NPS-TP simulations, but some parameters, such as CH_K , showed sensitivity only above a specific spatial scale (778 km2 in this study). These results could be used as a reference for studying the scaling effect of model parameter sensitivity and provide important information for model construction and the management of NPS pollution at different scales. [ABSTRACT FROM AUTHOR]

Abstract Accurate long-term estimates of precipitation at fine spatiotemporal resolution are vital for several applications ranging from hydrometeorology to climatology. The availability of a good network of rain gauges, and high precipitation variability associated with two annual monsoon systems and complex topography make India a suitable test-bed to assess the performance of any satellite-based precipitation product. This study assesses the performance of latest versions of four multi-satellite precipitation products: (i) Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), (ii) Multi-Source Weighted-Ensemble Precipitation (MSWEP), (iii) SM2RAIN-Climate Change Initiative (SM2RAIN-CCI), and (iv) TRMM Multisatellite Precipitation Analysis (TMPA) across India using gauge-based observations for the period of 1998–2015 at monthly scale. These four multi-satellite precipitation products are essentially based on different algorithms and input data sets. Among these multi-satellite precipitation products, SM2RAIN-CCI is the only product that does not use rain gauge observations for bias adjustment. Results indicate that CHIRPS and TMPA are comparable to gauge-based precipitation estimates at all-India and sub-regional scales followed by MSWEP estimates. However, SM2RAIN-CCI largely underestimates precipitation across the country as compared to gauge-based observations. The systematic error component in SM2RAIN-CCI dominates as compared to random error component, which suggests the need of a suitable bias correction to SM2RAIN-CCI before integrating it in any application. The overall results indicate that CHIRPS data set could be used for long-term precipitation analyses with rather higher confidence. [ABSTRACT FROM AUTHOR]

Highlights • Three methods are compared in obtaining soil moisture in the freeze–thaw area. • Data supply rate of SMAP product was 27.1% in FT period and 57.5% in NFT period. • The root mean square error of the three data sets were 0.03, 0.08, and 0.05. Abstract Soil moisture is a core ecohydrological element in the middle–high latitude frozen area, which occupies approximately 30% of the global land area. The Sanjiang Plain, an important commercial grain production area, is the largest swampy low plain in a middle-high latitude frozen area in China. However, no published studies on this region have clarified the accuracy of soil moisture data from in-situ measurements; the performance of ecohydrological models are also unknown. In this study, we compared the ability of the SMAP product, a RS-DTVGM model, and two self-erected soil moisture monitoring stations to capture soil moisture information in the Sanjiang Plain by using the Triple Collocation method. Soil moisture data were collected for 642 continuous days, and the data period was divided into three frozen periods (FT) and two non-frozen (NFT) periods. Valid data supply rates of the three methods were also calculated and compared. From the Triple Collocation method, the root mean square error (RMSE) of the SMAP, model and in-situ monitoring data sets were 0.03, 0.08, and 0.05. The valid data supply rates of the SMAP product in the FT and NFT periods were 27.1% and 57.5%, respectively. The Pearson correlation coefficient between the SMAP product and the in-situ monitoring dataset was 0.301 for the FT period and 0.557 for the NFT period. The RS-DTVGM model had a data supply rate of 100%. The Pearson correlation coefficient for this model was 0.101 for the FT period, but reached 0.726 for the NFT period. For the three soil moisture data sets, the accuracy ranking in this middle-high latitude area was SMAP > in-situ monitoring > model simulation. Although neither SMAP nor the RS-DTVGM model could capture the real soil moisture information for the FT period, both could accurately simulate the soil moisture in the NFT period. The results from the RS-DTVGM model demonstrated a closer relationship with in-situ soil moisture measurement data relative to the SMAP method. The in-situ method provided accurate soil moisture data, but its application is limited by high cost. In the future, the data from the three methods should be assimilated together to measure soil moisture data with high accuracy and high spatial and temporal resolution in this important area. [ABSTRACT FROM AUTHOR]

Highlights • Unsaturated soil hydraulic conductivity USHC control rainfall infiltration and slope stability. • We quantified the effect of USHC anisotropy on the hillslope hydrology/stability. • We applied a 3D hydrological model coupled with infinite slope stability model in two test cases. • Results confirm the control of USHC anisotropy on failure time and size of unstable area. Abstract Rainfall-induced shallow landslides cause significant damage involving loss of life and property. Many hydrological processes such as rainfall infiltration, soil water dynamics, and slope stability are controlled by unsaturated soil properties, such as unsaturated hydraulic conductivity. Natural soils often exhibit a certain degree of anisotropy in hydraulic conductivity due to stratification and compaction mechanisms in soil formation processes. In this paper we investigate the effect of soil hydraulic conductivity anisotropy (SHCA) on hillslope hydrology and stability using a three-dimensional hydrological model coupled with a probabilistic infinite slope stability model. The model is applied in two independent case studies. The first aims to quantify the combined effect of different anisotropy ratios (lateral/normal saturated hydraulic conductivity) and hillslope morphologies (convex, concave, and planar) on slope stability. Anisotropy ratios are assumed in this case higher than one (1, 2, 10). Results show that increasing the anisotropy ratio (from 1 to 10) anticipates the failure time (from 12 to 9 h after the start of rainfall) and that in concave morphologies the unstable area tends to be wider than planar and convex. The second application aims to simulate the soil moisture dynamic and the probability of failure at different depths (100, 500, and 900 mm) of a stratified volcanic soil, making leverage on the model flexibility to accommodate SHCA. No assumptions are made on the anisotropy ratio in this case study. Our results, based on model parameter calibration and verification against in-situ soil moisture measurements during the year 2009, showed good model performance in simulating the soil moisture dynamic (Kling-Gupta Efficiency higher than 0.78) and confirmed no failure for the simulated year. The promising results support the aspiration that the physically based hydrological model can complement and improve the current predictions of landslide early warning systems. [ABSTRACT FROM AUTHOR]

Highlights • Soil moisture is characterized by high spatial and temporal variability. • A sampling scheme to optimize its monitoring over large areas is investigated. • During humid periods just one optimal measurement point can represent the catchment. • During dry periods the number of optimal measurement points become at least two. Abstract Soil moisture plays a fundamental role in the mass and energy balance between the land surface and the atmosphere, making its knowledge essential for several hydrological and climatic applications. The aim of this study is to extend the current knowledge of soil moisture spatial-temporal variability at the catchment scale (up to 500 km2). The main implication is to provide guidelines to obtain soil moisture values representative of the mean behaviour at the medium-sized river basin scale, which is useful for remote sensing validation analysis and rainfall-runoff modeling. To this end, 23 measurements campaigns were carried out during a time span of 14 months at 20 sites located within the Upper Chiascio River Basin, a catchment with a drainage area of about 460 km2 in the Umbria Region (central Italy). The data set allowed the analysis of both soil moisture temporal stability and its dynamics. On the basis of statistical and temporal stability approaches, it was investigated how factors such as climatic regime and geomorphology influence soil moisture behaviour. For the investigated area, the spatial variability of soil moisture was higher in dry periods with respect to wet periods, mainly due to the rainfall pattern characteristics during different periods of the year. Soil moisture values recorded during wet periods showed a better correlation than those recorded during dry periods. The maximum number of required samples, to obtain the mean areal soil moisture with an absolute error of 3% vol/vol, was found equal to 12. The temporal stability analysis showed that during wet periods just one "optimal" measurement point can provide values of soil moisture representative of the catchment-mean behaviour, while during dry periods the number of "optimal" measurement points became equal to two. Therefore, at the adopted spatial scale the use of a single measurement point can lead to significant errors. From the perspective of soil moisture dynamics, the decomposition of the spatial variance showed that the contribution of the time-invariant component (temporal mean of each site) was predominant on respect to the total spatial variance of absolute soil moisture data, for almost the whole observation period. Results provided guidance to optimize soil moisture sampling by performing targeted measurements at a few selected points representative of the catchment-mean behaviour. [ABSTRACT FROM AUTHOR]

Highlights • Land use scenario in 2020 is predicted based on CA-Markov. • Forestland would increase significantly for afforestation in 2020. • Future runoff during 2020–2030 may increase due to climate change. • Land use/land cover change may lead to a slightly decrease in runoff. • Simulated lateral flow accounts for a larger proportion and determines runoff change. Abstract The Luanhe River basin, the mostly afforested river basin in North China, has exhibited significant land use/land cover change (LUCC) under climate change that could jointly affect water availability of the basin in the future. This study examines both impacts of LUCC and climate change on runoff over the upper reaches of the Luanhe River basin. First, the land use in 2020 is predicted based on the Cellular Automata-Markov (CA-Markov). Second, a hydrological model (Soil Water Assessment Tools, SWAT) is set up for the baseline period 1961–1979 and driven primarily by outputs from five general circulation models (GCMs) under four representative concentration pathways (RCPs) (i.e., RCP2.6, RCP4.5, RCP6.0 and RCP8.5) for the period 2020–2030. Results show that the ensemble mean annual precipitation may increase under four RCPs for the period 2020–2030, with the maximum (470 mm/yr) and minimum (444 mm/yr) for RCP8.5 and RCP6.0, respectively, 1–7% higher than the observed mean annual precipitation (441 mm/yr) during 1961–1979. The relationship between the runoff simulations and the RCPs under the 2020 land use scenario is nonlinear, with the maximum (57 mm/yr) and minimum (50 mm/yr) mean annual runoff depths under the RCP4.5 and RCP6.0 scenarios, respectively, ∼58% and ∼39% higher than the mean annual observed runoff depth (36 mm/yr) for the baseline period. The increase in forestland (∼56%) and decrease in agriculture land (∼−30%) are remarkable for the period 1970–2020, driven primarily by afforestation implemented in the Luanhe River basin. LUCC would lead to a slight decrease in mean annual runoff, and the runoff only increases in summer but decreases in other three seasons. The decrease in surface runoff and groundwater discharge jointly results in the overall decrease in runoff due to LUCC. In general, the climate change impact will dominate runoff change for the study basin, though marked afforestation has taken place and is likely to continue in the future. [ABSTRACT FROM AUTHOR]

Highlights • An analytical model for estimation of land surface net water flux is introduced. • Near-surface soil moisture is the only input variable. • The model can be parameterized without the need for calibration with measured data. Abstract The accurate determination of land surface water fluxes at various spatiotemporal scales remains a challenge in hydrological sciences. It is intuitive that land surface net water flux (i.e., infiltration minus evapotranspiration) directly affects near-surface soil moisture. However, there exists no hydrological model suitable to calculate net water flux based on measured near-surface soil moisture data. This is a consequence of the mathematical structure of existing models that use 'boundary conditions' to determine 'internal conditions', whereas what is needed is a model amenable to use near-surface soil moisture data (an internal condition) to determine the surface water flux (a boundary condition). To pursue the idea of utilizing remotely-sensed or in situ (i.e., sensor networks) near surface soil moisture data for estimation of net water flux, we derived an analytical model via inversion of Warrick's 1975 analytical solution to the linearized Richards' equation for arbitrary time-varying surface flux boundary conditions. The applicability of the new analytical solution was evaluated based on actual water flux observations as well as HYDRUS-1D simulations for four vastly different sites in Arizona, California, Idaho, and Indiana. Our results demonstrate that the proposed model reasonably captures net water flux variations in natural settings, including layered and vegetated soils, especially at larger time scales (e.g., monthly). While the model works for a wide range of climatic conditions, the prediction accuracy is somewhat lower for extreme dry or wet conditions. A major advantage of the new model is that it does not require calibration, which provides an unprecedented opportunity for large scale estimation of land surface net water flux using remotely sensed near-surface soil moisture observations. [ABSTRACT FROM AUTHOR]

Highlights • Spatially varying Statistical Soil Moisture Profile (SSMP) model is developed. • SSMP considers Hydrologic Soil Groups (HSGs) to impart spatial transferability. • SSMP model is able to estimate soil moisture profile at ungauged locations. • Promising to assess vertical soil moisture profile over a large area. Abstract Information on vertical Soil Moisture Content (SMC) profile is important for several hydro-meteorological processes. This study borrows the idea of coupling the memory and forcing from a previous study and develops a spatially-varying Statistical Soil Moisture Profile (SSMP) model to estimate the vertical SMC profile. It uses only surface soil moisture (0–5 cm) values and Hydrological Soil Groups (HSGs) information of the location. The focus of the study is incorporation of the HSG information to ensure the spatial transferability of the proposed model by capturing the spatial variations of soil moisture profile with the change in soil hydraulic properties. The wide range of soil moisture data for model development as well as for spatial validation is obtained from 171 stations from different networks of International Soil Moisture Network (ISMN) at five different depths, i.e., 5, 10, 20, 51 and 102 cm. The HSG information at the locations are extracted from the Web Soil Survey (WSS) database. The potential of spatial transferability of the SSMP model is assessed by applying it to the new stations within the corresponding HSG. Model performances are promising for all four depth pairs (5–10, 10–20, 20–51 and 51–102 cm) of all four HSGs during both model development and spatial validation given the model complexity. Hence, the spatially-varying SSMP model is suitable at the ungauged locations by incorporating the HSG information. The potential application of the proposed model shows the future scope to assimilate the satellite based surface SMC data into the model to develop a vertical soil moisture profile map over a large area. [ABSTRACT FROM AUTHOR]

Highlights • A Monte Carlo approach is effectively used to sample weights to combine precipitation products. • Modeled soil moisture can be improved by optimally combining different precipitation products. • Combining three precipitation products is more efficient than using one or two to improve soil moisture simulations. Abstract Precipitation is a fundamental forcing variable in land surface modeling, controlling several hydrological and biogeochemical processes (e.g., runoff, carbon cycling, evaporation, transpiration, groundwater recharge, and soil moisture). However, precipitation estimates from rain gauges, ground-based radars, satellite sensors, and numerical models are affected by significant uncertainties, which can be amplified when exposed to highly non-linear land model physics. This work tests the hypothesis that precipitation data from different sources can be optimally merged to minimize the hydrologic response error in surface soil moisture simulations and maximize their correlation with ground observations (multi-objective optimization problem). This hypothesis is tested by merging three precipitation products (one satellite product, a ground-based dataset, and model-base estimates) that force a land surface model trained to minimize soil moisture anomalies. A Monte Carlo-based algorithm is developed to generate weights to linearly combine these precipitation datasets. Optimal combinations of weights are identified by minimizing the errors and maximizing the correlation between the model simulated soil moisture and the satellite-based SMOS soil moisture product. The proposed methodology has been tested over Oklahoma where high-quality, high-resolution (independent) ground-based soil moisture observations are available for validation purposes. Results show that there exist optimal combinations of these precipitation datasets that provide smaller errors and larger correlation coefficients between modeled soil moisture estimates and ground-based data with respect to forcing the land surface model with single precipitation datasets. Specifically, combining three precipitation products from different sources provides the largest correlation coefficient and the lowest root mean square error at several locations across Oklahoma. [ABSTRACT FROM AUTHOR]

Highlights • Evaporation from an array of thin line sources of water between smooth plates. • Bridging 1-D diffusion from bare water surfaces and 3-D diffusion from the leaves. • Understanding evaporation from leaves and soils when water is just below the surface. • High evaporation rate is sustained due to lateral conduction of heat. • Film positioning decides evaporation rate and surface and sub-surface temperatures. Abstract We report evaporation characteristics due to higher heating from above from surfaces having an array of line sources. The line sources are created by vertically stacked rectangular plates in a box with water. Two different types of plates were used. In one case line source or film thickness was 66 µm and open area ratio was 4%, and in the second case film thickness was 28 µm and 20% was the open area ratio. Even at the 4% open area ratio the evaporation rate was ∼85% compared to a bare water surface, at the same heat flux. Lateral conduction of heat from the impervious hotter regions to the line sources and the 2-D nature of diffusion near these tiny line sources enhances the evaporative flux, owing to increase in the concentration gradient of water vapour, explains the high evaporation rate, observed in the present work. This system bridges the gap between the understanding of evaporation from bare water surfaces (1-D vapour diffusion) and leaf surfaces (3-D vapour diffusion). Evaporation rates for the fully saturated conditions are in good agreement with the theoretical predictions of Suzuki and Maeda (1968) and Schlünder (1988). The computed surface temperatures and its width-wise variation match well with the experimental values. We also propose a simple film model for the unsaturated condition of the porous medium and show that the temperature distribution obtained using this model is in reasonably good agreement with the measured values. [ABSTRACT FROM AUTHOR]

Highlights • Hydrological signatures about dynamics derived from distributed water level and soil moisture. • Information relevant to document impact of land use, rainfall type, soil moisture. • Information useful to diagnose simulated processes in distributed hydrological models. • Information useful to prioritize future model improvements or parameters estimation. • Qualitative information not sufficient to improve simulated water volume. Abstract This paper emphasizes the importance of integrating outlet discharge and observed internal variables in the evaluation of distributed hydrological models outputs. It proposes a general methodology for a diagnostic evaluation of a complex distributed hydrological model, based on discharge data at the outlet and additional distributed information such as water level and surface soil moisture data. The proposed methodology is illustrated using the PUMMA model in the Mercier sub-catchment (6.6 km2). Model parameters are specified according to field data and a previous study performed in a neighbouring catchment (Jankowfsky et al., 2014), without calibration. The distributed water level and soil moisture network of sensors were useful in the model evaluation process. Thus, model parameters are specified either using in situ information or results from previous studies. A stepwise approach is used for model evaluation. It includes standard water balance assessment as well as comparison of observed and simulated outlet discharge, whether on annual or event timescales. Soil moisture sensors are used to assess the ability of the model to simulate seasonal water storage dynamics based on a normalized index. The water level sensors network is used on two timescales: on a seasonal timescale, sensors network is used to assess the model's ability to simulate intermittency; whereas on event timescales, sensors network is used in determining the model's ability to reproduce observed reaction as well as response times. Event timescales do also focus on the correlation between hydrological response and either rainfall event or antecedent soil moisture variables. Results show that the non-calibrated model is quite effective at capturing water flow and soil water-storage dynamics, but it fails to reproduce observed runoff volume during events. There is strong indication of a deficiency in the characterization of catchment storage and upstream flowpath description. The soil water content and a network of water level sensors provide interesting information about soil moisture and river flow dynamics. They however fail to provide quantitative information about catchment storage. This study opens interesting perspectives for the evaluation of distributed hydrological models using hydrological signatures. Furthermore, it highlights the requirement of quantitative as well as qualitative signatures for improving such models. [ABSTRACT FROM AUTHOR]

Highlights • New methodology for addressing model error in Bayesian-MCMC stochastic inversions. • Construction of model-error basis using principal components analysis. • Projection of residual onto basis allows identification of the model-error component. • Identified model error is removed prior to computing the likelihood. • Application to synthetic vadose-zone infiltration problem. Abstract Proxy forward solvers are commonly used in Bayesian solutions to inverse problems in hydrology and geophysics in order to make sampling of the posterior distribution, for example using Markov-chain-Monte-Carlo (MCMC) methods, computationally tractable. However, use of these solvers introduces model error into the problem, which can lead to strongly biased and overconfident parameter estimates if left uncorrected. Focusing on the specific example of estimating unsaturated hydraulic parameters in a layered soil from time-lapse ground-penetrating radar data acquired during a synthetic infiltration experiment, we show how principal component analysis, conducted on a suite of stochastic model-error realizations, can for some problems be used to build a sparse orthogonal basis for the model error arising from known forward solver approximations and/or simplifications. Projection of the residual onto this basis during MCMC permits identification and removal of the model error before calculation of the likelihood. Our results indicate that, when combined with an informal likelihood metric based on the expected behaviour of the ℓ 2 -norm of the residual, this methodology can yield posterior parameter estimates exhibiting a marked reduction in bias and overconfidence when compared to those obtained with no model-error correction, at reasonable computational cost. [ABSTRACT FROM AUTHOR]

Highlights • Assessed impact of soil moisture on runoff in semi-arid area. • High antecedent moisture led to more runoff at plot and catchment scales. • Upscaling plot-scale parameters is difficult given high spatial variability. • Infiltration model performance depends on spatial scale. • Simplistic models may be preferable if data is limited. Abstract Antecedent soil moisture is an important factor in the generation of runoff, but guidance for modeling moisture conditions in semiarid catchments is limited and conflicting. In this study, the impact of antecedent moisture was assessed at the plot scale (2.8 m2) using a portable rainfall simulator, and at the catchment scale based on observed precipitation and discharge for a 2.8 km2 watershed in central New Mexico. Performance of three loss models commonly used for hydrologic analysis in the southwestern U.S. was tested at both scales. High initial moisture content led to substantially higher runoff ratios at both scales, confirming the importance of antecedent soil moisture for runoff predictions in semiarid drainages. Hydrologic parameters estimated based on plot experiments, however, were highly variable and cannot easily be upscaled to the catchment scale. Loss model performance was clearly scale dependent: more simplistic (one- and two parameter) loss methods out-performed a more complex (four parameter) model at the watershed scale, while the opposite held true for test plot simulations. At the catchment scale, all models performed poorly for small runoff events, but yielded acceptable results for storms causing large discharges if antecedent soil moisture was considered. Failing to account for antecedent moisture led to simulated runoff volume errors up to one order of magnitude. [ABSTRACT FROM AUTHOR]

Highlights • Temporal stability of soil water storage (SWS) in high-elevation forests are analyzed. • Temporal changes and spatial variation of SWS decreased with increasing soil depth. • One time-stable location (TSL) can be representative of the mean SWS. • These TSLs be with average LAI or relatively low canopy interception loss locations. • Temporal stability of SWS was mainly affected by soil and vegetation properties. Abstract Understanding soil water storage (SWS) dynamics in soil profiles is important for hydrological modeling and restoration of vegetation in semi-arid areas. Using the temporal stability method, we aimed to investigate the temporal stability of SWS at 0–40, 40–80, and 80–120 cm depth, and to identify representative sites for reliable estimates of the mean SWS in the permanent forest plot in the Qilian Mountains of China. Further, we wanted to identify correlations between temporal stability of SWS and soil, topography, and properties of the vegetation. Soil water content at soil depths 0–120 (for locations 1–40) and 0–70 cm (for locations 41–60) were measured using time-domain reflectometry (TDR) on 52 dates from 2016 to 2017. Results revealed that time-averaged mean SWS for the three layers differed significantly (P < 0.05), and the temporal changes and spatial variations of the mean SWS all decreased with increasing soil depth. Based on either the Spearman correlation coefficient or the standard deviation of relative difference (SDRD) index, the temporal stability of SWSs within the soil profiles under different soil layers were strong, and the number of time-stable locations increased with increasing soil depth, indicating that the SWS intended to be more temporally stable in deeper soil. One time-stable site can be representative of the mean SWS for multiple soil layers for the whole plot, and can accurately estimate the mean SWS for the three soil layers (R2 > 0.86, P < 0.001). Moreover, these time-stable locations should be those having average LAI, SBD or relatively low canopy interception loss compared to the corresponding field means. Soil bulk density, canopy interception rate, and aboveground biomass of mosses can significantly (P < 0.05) affect the stability of SWSs in this high-elevation forests. Such effects, however, differed among the different soil layers due to different conditions of soil characteristics, distribution of root biomass, and freeze–thawing processes. These results suggest that the influence of vegetation properties of the temporal stability of SWS should attract more attention and that both the relative importance of and interactions among different determining factors is helpful for better understanding the mechanistic determinants of SWS temporal stability in these areas. [ABSTRACT FROM AUTHOR]

Highlights • CRNPs were used in 3 different soil textures and compared with buried SWC sensors. • CRNPs estimate SWC in three soil textures with RMSE is 0.05, 0.10, 0.03 respectively. • Silt loam dam plot had the highest rates of change but held more water than others. Abstract The cosmic-ray neutron probe (CRNP) is a new tool for continuously monitoring mean soil–water content (SWC) on a hectometer scale, but the performance of CRNPs in highly heterogeneous distributions of SWC, vegetation and especially soil texture caused by the broken surface on the Loess Plateau in China remains uncertain. The study evaluated the application and accuracy of the method for three soil textures in a small watershed on the plateau to provide a reference and scientific basis for its application. Three CRNPs were installed in a loamy sand hills (LSH) plot, a silty loam dam (SLD) plot, and a sandy loam slope (SLS) plot, soil moisture sensors were buried at various depths in three plots. The footprints of the CRNPs for SWC observation had radii of 127–170, 126–174 and 148–174 m in SLD, SLS and LSH, respectively, and the mean measuring depth was about 20 cm for SLD and SLS and 40 cm for LSH based on general calculation equations. The accuracy of the CRNP method was high when validated using oven-dried method (RMSE = 0.068 cm3 cm−3). The mean SWCs estimated from the other types of sensors were consistent with the mean SWCs from the CRNP probes in the three plots. The coefficient of determination of the linear relationship was in the order: SLS > LSH > SLD. The coefficient of variations (CV) were higher in LSH than SLD and SLS, indicating that SWC varied most in LSH. During periods of rain, the variations were larger for SLD than SLS and LSH, with a CV of 32.2%. The CRNP for SLD had the highest rates of change during periods with and without rain for both increasing and decreasing SWC, but this plot held more water than LSH and SLS, consistent with the high clay and organic-matter contents in SLD. The rates of change were significantly correlated with the antecedent SWCs (P < 0.01), and the higher the antecedent SWC, the faster the rate of change for each plot. This study verified the application of the CRNP for monitoring field-average SWC of the broken surface on the Loess Plateau and represents a new approach for the study of the spatial distribution of SWC on the plateau. CRNP can measure SWC of the complex underlying surface accurately, so as to provide a more reliable assessment of the impacts of landscapes and vegetation changes, such as conversion of cropland to forest and grassland on the Loess Plateau on water resources. [ABSTRACT FROM AUTHOR]

Highlights • SWAT and DE were coupled to provide a dynamic calculation of WEC for NPS pollution. • Three different WECs were analyzed to reveal the impacts of upstream on downstream. • Seasons change had impacts on the WEC, and the smallest value occurred in wet season. • WEC of nutrients in organic species was more affected by seasons than that in inorganic. • Controlling NPS pollution and enhancing the WEC should be carried out simultaneously. Abstract The objective of this study was to advance a new method to dynamically analyze the water environmental capacity (WEC) of non-point source (NPS) pollution. A Soil and Water Assessment Tool (SWAT) model and differential evolution (DE) algorithm were coupled to automatically provide a calculation of the WEC in a daily step. The results showed that the largest ideal WECs occurred in the wet season with values up to 4500 t and 80 t for total nitrogen (TN) and total phosphorus (TP), respectively. The ideal WEC had a positive correlation with the rainfall intensity, while the opposite was true for actual WEC. The smallest value of actual WEC for TN was −150 t in June, and a negative value also occurred in June for TP. This is because NPS pollution discharge was more sensitive to rainfall and flow change than the ideal WEC. Compared to phosphorus, the WEC of nitrogen was more affected by the hydrological and NPS pollution discharge conditions. In addition, the WEC of nitrogen and phosphorus in organic species was more affected by seasonal change than that of nitrogen and phosphorus in inorganic species. Spatially, the channels in the middle and southeast of the study area had the smallest actual WEC and even negative values were observed. The results of remnant WEC showed that the smallest value occurred in July and June with values of −800 t and −140 t for TN and TP, respectively. Thus, significant attention should be paid to controlling NPS pollution during the wet season, and organic nitrogen and phosphorus should be set as the priority pollutants to control. Furthermore, controlling the NPS pollution discharge and enhancing the WEC should be carried out simultaneously. Compared to previously used methods, the calculation method employed in this study had the ability to simulate the transformation and interaction between nitrogen and phosphorus in organic and inorganic species, and better considered dynamic changes in the input conditions. [ABSTRACT FROM AUTHOR]

Highlights • GIS and factor analyses used to unravel factors causing As enrichment in the basin. • Sediment/soil As contents (0.3–13 mg/kg) showed no correlation with other elements. • As WHO limit exceeded in samples from wells (24.6%), springs (16%), and streams (10%). • As mobilization is governed by sulfide oxidation and aquifer salinization/oxygenation. • Both geogenic processes and human-induced perturbations affect groundwater As levels. Abstract The Uluova basin aquifer system (UBAS), comprised of fractured/karstic rock and multilayer basin-fill aquifers, provides water for both domestic and agricultural needs in the Elazığ province (Eastern Turkey). Although, the UBAS has been subject of large-scale hydrological modifications and controversial water management practices since late 1950s, thus far no studies have examined basinwide implications of such human perturbations in an integrated manner. In this study, GIS, geostatistics, R -mode factor analysis (R -mFA) and geochemical modeling techniques were employed to unravel the factors controlling the distribution and sources of arsenic (As) and selected potentially toxic elements (PTEs) in the stream sediment-soil and groundwater-surface water systems of the UBAS. As revealed by the results from R -mFA, three geogenic factors (F1: Clay minerals and Fe–Mn oxyhydroxides, F2: Weathering of parent materials, and F3: Sulfide oxidation in mineralized zones) govern the geochemical dynamics of the PTEs in the stream sediment/soil media. In stream sediment and soil samples, especially Ni, Cr, and Co presented significant enrichment relative to upper continental crust average composition, whereas As contents were relatively low, varying from 0.3 to 13 mg kg−1. Factors extracted from the combined water dataset (F1: Groundwater salinization and arsenic mobilization, F2: Clay minerals and Fe–Mn oxyhydroxides, F3: pH and redox conditions, and F4: Aquifer oxygenation and nitrate contamination) accounted for 72.59% of the total variance. Water-rock interaction (e.g. sulfide oxidation, carbonate dissolution, silicate hydrolysis, adsorption-desorption, ion exchange, and evaporite dissolution), dilution/mixing with fresh/saline water components, evapoconcentration, and human induced perturbations causing internal salinization and oxygenation of the UBAS were the key mechanisms controlling the chemistry of waters and mobilization of As. In the UBAS, majority of As-rich water samples are confined to central-northern half of the basin and typically display high levels of dissolved O 2 , inorganic oxyanions (HCO 3 −, SO 4 2− and of Si, B, Mo, Sb and V) and alkaline pH. Oxidation of sulfides (e.g. pyrite and arsenopyrite) found within the highly fractured Elazığ magmatics in the upland areas at north and subsequent competitive adsorption-desorption processes occurring under alkaline, oxidizing and high ionic strength aquifer conditions along the downgradient groundwater flow path play a pivotal role in the As-enrichment in the UBAS. As concentrations ranged between 0.02 and 367.2 μg L−1 in groundwater, 0.13–4842 μg L−1 in spring water, and 0.04–31.1 μg L−1 in the stream water samples, of which 20.83% exceeded the WHO provisional guideline value. In the water samples, As occurs mainly as As(V) species (HAsO 4 2− and H 2 AsO 4 −), indicated by the Eh-pH diagram and speciation calculations. The results of this study have shown that As enrichment in the UBAS can be attributed to both geogenic processes and anthropogenic activities that have modified the basin hydrology/hydrochemistry. [ABSTRACT FROM AUTHOR]

Highlights • Soil moisture and soil water tension showed seasonally different characteristics at a steep hillslope. • The distribution of soil moisture characteristics curve was different between spring and autumn seasons. • The soil moisture transfer process was seasonally evolved from spring to autumn. Abstract Understanding variations in soil moisture and soil water potential is important for describing hydrological processes at the hillslope scale. An intensive field campaign to measure soil moisture, soil water potential, and rainfall events was performed at many locations and depths to obtain seasonal and spatial distributions of hydrometric measurements along steep hillslopes. Soil moisture and soil water tension analysis showed seasonally distinctive features in statistics and probability distributions. The relationship between soil moisture and soil water tension was explored to characterize seasonal and spatial variations. The recession slopes of soil moisture at several given points of the relationship were also seasonally different. The Brooks and Corey model and the van Genuchten model were used to estimate parameters for the measured data to explain the spatial and seasonal variations. Soil water storage analysis not only indicated that lateral flow tends to be abundant at greater depths but also revealed a seasonal trend in development of subsurface flow in the downslope direction. The combined impact of topography and vegetation determined the seasonal differences in hydrometric responses along the hillslope locations. [ABSTRACT FROM AUTHOR]

Highlights • A coupled model was proposed for bed deformation and bank erosion in the JR. • The model covered morphodynamic, groundwater flow and bank stability modules. • Model performance was assessed by comparing calculations with measurements. • Seasonal changes in soil property and critical overhanging width were clarified. Abstract Recent significant channel evolution in the Jingjiang Reach has raised much attention, particularly the remarkable bank erosion. A coupled model for simulating bed deformation and bank erosion has been proposed in this study, which focuses on the erosion of the bank with a composite structure in the Lower Jingjiang Reach. In order to cover three contributing processes that may interact with each other, the proposed model integrates a one-dimensional morphodynamic module with a two-dimensional module of ground water flow and a bank erosion module for the cantilever failure of a composite riverbank. Model performance was evaluated through a detailed simulation of channel evolution along a 150.8-km subreach in the Jingjiang Reach over the 2005 hydrological year. Satisfying results were obtained from the simulation, showing relatively close agreement between the calculations and measurements in terms of hydrological data at the outlet section, bank erosion sites, longitudinal channel profile and typical cross-sectional profiles. In addition, investigations into temporal changes in bank soil properties and critical overhanging width at cantilever failure demonstrate that there was a seasonal variation in the volumetric water content of bank soil, which increased during the rising and flood periods and then decreased during the recession period, showing an impact on the occurrence timing of cantilever failures. The tensile strength and critical overhanging width had an inverse relationship with the water content, whereas the critical width sharply increased and then decreased during high flows affected by a rapid change in river stage. The temporal distribution of cantilever failure events indicates that cantilever failure primarily occurred in the flood and recession periods. The effects of bed roughness, water content variation and secondary flow on bank erosion were also discussed. [ABSTRACT FROM AUTHOR]

Highlights • Antecedent moisture conditions are shown to significantly influence flood volume. • Flood to antecedent precipitation elasticity is less than for event precipitation. • Flood to antecedent precipitation elasticity decreases as flood extremity increases. Abstract The magnitude of floods depends not only on the intensity and pattern of precipitation during the flood event (the 'flood-producing' precipitation), but also on the moisture stored in the catchment, which arises from antecedent hydrological processes over many preceding timescales. To characterise this effect, an empirical study is conducted on the influence of antecedent precipitation on significant flood events across multiple climate zones and catchment conditions, using 100 Australian catchments with hourly streamflow and precipitation. Antecedent conditions are shown to have a significant influence on flood volume, with three quarters of catchments having at least a 50% difference in flood volume depending on whether the catchment is wet or dry before the flood-producing precipitation event. The study considers the sensitivity of flow to antecedent precipitation by means of an 'elasticity' metric, which indicates the proportional change in flow for a change in antecedent precipitation or flood-producing precipitation. Flood-producing precipitation nevertheless remains the dominant flood driver across most catchments, with the elasticity of flow to antecedent precipitation typically being between 28% and 37% of the elasticity to flood-producing precipitation. Importantly, the elasticity of flow to antecedent precipitation relative to flood-producing precipitation decreases with increasing event magnitude, highlighting that conclusions of future change based on annual maximum streamflow may not be reflective of the processes that operate for more extreme floods that have the greatest impact on society. [ABSTRACT FROM AUTHOR]

Highlights • Soil moisture sensing networks are typically deployed under grassland vegetation. • Conditions under grassland can differ greatly from those under adjacent cropland. • A neural network translated grassland soil moisture into cropland soil moisture. • This efficient new approach does not rely on temporal stability or complex models. • The approach revealed the dichotomous soil moisture pattern within a SMAP grid cell. Abstract Large-scale in situ soil moisture monitoring networks are becoming increasingly valuable research tools, but existing networks feature almost exclusive deployment of stations in grassland vegetation. These grassland soil moisture observations are unlikely to adequately represent the real soil moisture patterns in landscapes with intermixed land cover types. Here we demonstrate the severity of the problem for one particular landscape and introduce a flexible new method for solving the problem. The specific objectives of this study were (i) to compare root-zone soil moisture dynamics of two dominant vegetation types across Oklahoma, grassland (observed) and winter wheat cropland (simulated); (ii) to relate the soil moisture dynamics of grassland and cropland vegetation using an artificial neural network (ANN) as an observation operator; and (iii) to use the resulting ANN to estimate the soil moisture spatial patterns for a landscape of intermixed grassland and wheat cropland. Root-zone soil moisture was represented by plant available water (PAW) in the top 0.8 m of the soil profile. PAW under grassland was calculated from 18 years of soil moisture observations at 83 stations of the Oklahoma Mesonet, whereas PAW under winter wheat was simulated for the same 83 locations using a calibrated and validated soil water balance model. Then, we trained an ANN to reproduce the simulated PAW under winter wheat using only six inputs: day of the year, latitude and longitude, measured PAW under grassland, and percent sand and clay. The resulting ANN was used, along with grassland soil moisture observations, to estimate the detailed soil moisture pattern for a 9 × 9 km2 Soil Moisture Active Passive (SMAP) grid cell. The seasonal dynamics of root-zone PAW for grassland and winter wheat were strongly asynchronous, so grassland soil moisture observations rarely reflect cropland soil moisture conditions in the region. The simple ANN approach facilitated efficient and accurate prediction of the simulated PAW under winter wheat, RMSD = 21 mm and normalized RMSD = 0.17, using observed PAW under grassland as an input. This new method for estimating soil moisture under adjacent, contrasting land covers at a relatively low computational cost could be employed for any region and land cover pairing with training data available, and it may significantly enhance the applications of existing large-scale soil moisture monitoring networks. [ABSTRACT FROM AUTHOR]

Highlights • Hydraulic redistribution (HR) widely occurs in the rainy season in the humid area. • Hydraulically redistributed water replenished >75% of the total daily water use. • Significant effect of HR on water recharge, especially in the 15–20 cm root zone. • The defined buffer capacity can be used to characterize ability of water retention. Abstract Hydraulic redistribution (HR) in sites with shallow soils and its response to random droughts during the summer rainy season in humid areas remains poorly understood. We investigated the soil moisture dynamics, HR and water buffer of shallow soils by monitoring the soil moisture content at multiple depths during a random drought event in the summer rainy season, in typical forestland and orchard in a subtropical area. Soil moisture sensors were installed at depths of 5, 10, 15, 20 and 30 cm at three sites (forest, peach1, and peach2). The total and net daily water use and water buffer capacity of the root zone were defined and calculated. During the short-term drought, the HR of soil moisture occurred at all sites, suggesting that it could be an important process in both forest and orchard land in response to short-term drought. HR occurred at both daily and multiple-day timescales in the shallow soils. The net daily water use had less difference between the sites than the total daily water use. Principal component analysis of the total and net daily water use showed only evident clustering characteristics for the total daily water use, indicating an important plant-soil interaction effect. The depth-averaged magnitude of daily HR varied from 0.385 mm (0.0077 m3 m−3) at the forest site to 0.725 mm (0.0145 m3 m−3) at the peach1 site. The redistributed water replenished over 75% of the water depleted from the shallow soil. In particular, soil moisture was better retained in 15–20 cm root zone than in other soil layers. Furthermore, the water buffer capacity of the forest site was not higher than those of the two peach sites (where land use converted from forest), indicating that land use conversion does not necessarily weaken soil water retention. The study results highlighted that HR has a significant influence on water recharge and water retention in humid area, thus benefiting plant drought tolerance and total water utilization. This study provides more insights to evaluate HR's effect on water retention and enhance our understanding of HR in shallow soils during random droughts in the summer rainy season in humid areas. [ABSTRACT FROM AUTHOR]

Highlights • A coupled water and heat model (CM) is developed for a reclamation cover. • The CM can simulate soil temperature dynamics under frozen and unfrozen conditions. • The CM cannot simulate frozen ground infiltration due to ignoring macropores. • Improved CM simulates snow melt infiltration by the macropore preferential filling. • New CM can simulate water and heat dynamics under frozen and unfrozen conditions. Abstract Numerical models of soil-water dynamics have been widely used in the design of soil cover systems for mine site reclamation; however, in most cases these models only consider water dynamics without consideration of heat dynamics. For cover systems in northern climates, such as those associated with oil sands mines in northern Alberta, Canada, freezing conditions exist for approximately 6 months of the year and snow melt comprises approximately 25% of annual precipitation. This study attempts to assess whether a fully coupled water and heat flow model (CM) provides additional insights into cover performance as compared with a water flow model (FM). The CM and FM are developed for a monitored reclamation cover constructed over oil sands shale overburden. The validated results indicated that the key limitation of the CM was its inability to simulate frozen ground infiltration. This limitation results in an overestimate of snow melt runoff and does not replicate the development of perched conditions on the shale overburden surface as a result of snow melt infiltration. The FM is also unable to simulate the observed infiltration of snow melt deep into the cover when snow melt is represented simply as surface precipitation in early spring following ground thaw. Both models are improved if snow melt infiltration is represented in the model by the preferential filling of macropores across the full depth of the cover and oxidized shale prior to ground thaw. This methodology is incorporated with the CM to produce a coupled water and heat model with enhanced infiltration (CM-EI), and with the FM to produce a flow model with enhanced infiltration (FM-EI). The CM-EI provided an improved simulation of soil temperature dynamics under frozen and unfrozen conditions, as well as soil water dynamics under unfrozen conditions, including the improved representation of the annual water balance components over each water year. Given the small difference in annual water balance components between the CM-EI and FM-EI modelling approaches (∼5 mm/year), it is concluded that a FM-EI provides the best tool with which to assess the performance of these reclamation covers. [ABSTRACT FROM AUTHOR]

Highlights • The VIC simulated and in-situ observed SM data were merged. • This was done mainly using CDF matching approach and OI technique. • The CDF was parameterized and then estimated using kriging technique. • A spatial-temporal correlation error function was proposed for OI technique. • The merged SM product was improved compared to the original data. Abstract In order to obtain an improved soil moisture (SM) dataset at large scale, an advanced SM merging methodology based on error correction methods was constructed to merge the model-based and in-situ observed SM data. The SM datasets in a 0–40 cm soil layer were derived from 10 km × 10 km Variable Infiltration Capacity (VIC) model and 797 in-situ stations, respectively. The merging methodology was conducted grid by grid, and mainly included two parts: bias correction and random error correction. Firstly, the bias correction was performed for the VIC simulations by applying the Cumulative Distribution Function (CDF) matching approach combined with the kriging technique. Secondly, the random error of the VIC simulations was corrected using an Optimal Interpolation (OI) technique based on a spatio-temporal correlation function which was proposed and constructed in this study. Through validations against in-situ observations, the merged SM was evaluated, and the merging errors in each step were analyzed and discussed. The results showed that the merged SM product was improved compared to the original SM data, both temporally and spatially. The SM merging methodology is effective and reliable in combining the accurate but sparse in-situ observations and the continuous VIC simulations. In addition, the spatial mismatch impact on the representativeness of in-situ stations was limited, and the merging errors were mainly produced in the CDF estimation process. The random error information in the spatial dimension exhibited a bigger impact on the random error correction comparing to that in the temporal dimension. This study provided strong encouragement for the efficient use of in-situ SM observations and provided valuable methods for combining multi-sources SM datasets. [ABSTRACT FROM AUTHOR]

Highlights • The SPoRT-LIS surface soil moisture estimate was validated across the U.S. Great Plains region. • Validation performance was evaluated across varying soil types, land cover, depth, slope, aspect, and pixel heterogeneity. • Results demonstrate that SPoRT-LIS surface soil moisture is suitable for water resources applications. Abstract Evaluation of surface soil moisture is necessary to understand spatiotemporal soil moisture trends and their implications on water resources management. This research evaluated a real-time instantiation of NASA's Land Information System (LIS) for water resources management applications at a higher spatial and temporal resolution than is currently available with remotely-sensed satellite estimates or in situ measurements of the same product. Managed by NASA's Short-term Prediction Research and Transition (SPoRT) Center, the "SPoRT-LIS" is an observation-driven, real-time simulation of the Noah land surface model at a 3-km resolution over the full continental United States. Surface soil moisture estimates from SPoRT-LIS (0–10 cm layer) were validated against in situ soil moisture from the International Soil Moisture Network in the Missouri and Arkansas-Red-White River Basins. Validation was conducted at in situ measurement depths of 5-cm and 10-cm, and performance was evaluated across varying soil types, land cover, depth, slope, aspect, and pixel heterogeneity to determine conditions under which SPoRT-LIS surface soil moisture had excellent estimation capability. Results demonstrate that 53% of data at a depth of 5-cm and 51% of the data at a depth of 10-cm were significantly correlated with a Spearman's ρ greater than 0.5 on a daily basis. Based upon validation results, it is evident that the SPoRT-LIS surface soil moisture estimate is satisfactory for research and operational water resources management applications. [ABSTRACT FROM AUTHOR]

Highlights • Drought-induced anomalies of soil water isotopes used to identify of deep uptake. • Acacia erioloba obtains 37% (median) from groundwater. • Isotopic labeling experiments used to constrain uptake depths. • Combined methods to assess the deep water uptake and source partitioning are needed. • We call for a separation of lateral and deep roots in hydrological models. Abstract Knowledge on the water uptake depths of vegetation is crucial for understanding water transport processes of the soil-vegetation atmosphere continuum and relevant for many applications (e.g. the estimation of groundwater recharge, irrigation planning and the parameterization of (eco-) hydrological models). The identification and quantification of water uptake from deep soil layers and groundwater remain challenging. This study uses a combined framework based on natural abundances of stable water isotopes and isotopic labeling experiments with deuterium oxide (2H 2 O) to study root water uptake and identify uptake from deep soil in a semi-arid environment. Between 2013 and 2016, more than 1000 soil (isotope depth profiles); plant (xylem and transpiration) and water (precipitation and groundwater) samples for the analysis of isotope ratios were collected. Two experiments using isotopic labeling were carried out in order to assess root water uptake depths. Herein, we i) present series of deep soil water isotope depth profiles, interpret water transport dynamics and ecohydrological feedbacks; ii) examine the suitability of natural isotope depth profiles for identifying deep root water uptake; iii) apply the Bayesian mixing model MixSIAR to quantify deep root water uptake and iv) constrain water uptake depths using isotopic labeling experiments and derive an active root water uptake distribution. Our results show that the form of isotope depth profiles of soil in water-limited environments follows characteristic shapes for the end of the rainy and dry seasons, respectively. Isotope ratios in the upper 4 m of the soil are heavily dependent on the character of the respective rainy season. Under certain conditions – e.g. droughts or weak rainy seasons – the isotope depth profile displays an enrichment in heavy isotopes up to 4 m depth. Such pronounced anomalies provide an opportunity for studies on source water partitioning. In the present experiment, the studied individuals of Acacia erioloba were found to obtain 37% [24–52%] of their water from deep soil (>4 m) and groundwater at the end of the dry season of 2015. All other investigated trees (individuals of B. plurijuga, S. luebertii, T. sericea and C. collinum) mainly utilize water originating from 1 m to 2.5 m depth. Under "average" rainy season conditions, the similarity of isotope ratios of potential plant water sources hinders a conclusive identification of water uptake depths. Deep natural isotope depth profiles in dry environments can – under certain conditions – be used to identify and quantify access of vegetation to deep water resources. Isotopic labeling enables to determine active root distributions for the lateral root zone. Combined frameworks contribute to a better understanding of deep water uptake. A differentiated consideration of water uptake from the lateral root zone and deep, potentially groundwater-tapping roots is required in order to fully investigate ecohydrological feedbacks and for a proper parameterization of models. [ABSTRACT FROM AUTHOR]

Highlights • Areal infiltration models are crucial for studies at different spatial scales. • Saturated hydraulic conductivity greatly influences areal infiltration modeling. • Experimental evidence of the accuracy of a model for areal infiltration is given. Abstract Operative distributed/semi-distributed rainfall-runoff formulations need a mathematically tractable component for estimating the expected areal-average infiltration rate, I , while point infiltration models are usually adopted for this purpose. A model for I , suitable for hydrological applications, was proposed by Morbidelli et al. (2006, Hydrological Processes, 20: 1465–1481). The main model tenet is the use of cumulative infiltration as an independent variable and replacement of time by its expected value. While this model - as with many other models for field-scale infiltration - was tested through Monte Carlo methods, it remains untested with experimental observations. In this study, a reassessment of the model's applicability is conducted through experimental evidence from several rainfall-runoff events observed under natural conditions at the plot scale over a slight slope characterized by a silty loam soil. During each experiment rainfall rate, surface runoff at the plot outlet and soil water content vertical profiles were monitored at short time intervals. An overall analysis of our results suggests that the model reliably simulates I and surface runoff as functions of time and is also suitable for applications under time-dependent rainfall events. The process of redistribution under rainfall hiatus is not addressed by this model. [ABSTRACT FROM AUTHOR]

Highlights • Hydrological Models and GCMs were used to simulate runoff in the selected Basin. • The daily scale factors performed better than monthly scale factors. • Findings showed multiple models should be employed to best simulate actual runoff. • Multiple models are necessary for management of water resources in the basin. Abstract The Songhua River Basin (SRB) plays a vital role in supplying water resources to northeast China. This river has a substantial effect on agricultural grain producing region. Therefore, the current study was carried out to evaluate the performances of multiple hydrological models driven by bias corrected precipitation estimates from a group of General Circulation Models (GCMs). The variations in discharge obtained from multiple hydrological models were also analyzed. For this purpose, bias correction methods for GCMs and five hydrological models, lumped rainfall runoff (Nedbor Afstromnings Model (NAM), GRJ4), semi-distributed (the Water Evaluation and Planning tool (WEAP), HBV), and the distributed (Soil Water Assessment Tool (SWAT)) were used in the SRB. Before applying the bias correction method, raw GCMs were also plotted against observational data. The results revealed that none of GCM performed well against observational data. The results also showed that quantile mapping method corrects the biases of GCMs better than daily and monthly scale factors. Distributed and semi-distributed hydrological models performed better than lumped rainfall runoff models in the selected study area of SRB. By forcing hydrological models with bias corrected precipitation data, the differences in the simulated discharge by SWAT, HBV, NAM, GR4J and WEAP were −3%, −2%, −4%, −7 and +3, respectively, when compared with observed discharge. The results also show that the overall performance of SWAT, HBV, NAM, and WEAP was better than GRJ4. The results also suggest that the application of multiple hydrological models is necessary to make improvements in the planning and management of agricultural and water resources in the SRB region. [ABSTRACT FROM AUTHOR]

Highlights • New grid-based model is used to compute flood and runoff-contributing area. • New proposed IRCA index combines runoff-contributing area and rain intensity on it. • IRCA for a characteristic duration is well correlated with flood peak discharge. • Land-uses with low runoff potential become dominant in large–extreme flood events. • Pre-peak soil moisture differs between flood magnitudes while pre-event does not. Abstract During the complex dynamic interactions between rainfall and basin properties, different portions of the basin produce runoff at different moments. Capturing this spatiotemporal variability is important for flood analysis, but knowledge of this subject is limited. The presented research aims at improving the understanding of runoff-contributing areas (RCA; hillslope sections from which water flows, reaches the stream network, and consequently the basin outlet) and at examining their relationship with the magnitude of a flash flood's peak discharge. A distributed hydrological model (GB-HYDRA) that enables computing RCA and flood discharge was developed. The model was applied to four medium-size basins (18–69 km2) in a Mediterranean climate and 59 flash flood events were analyzed. The correlation between basin input flux (basin area multiplied by the basin maximal rain intensity averaged over the time of concentration) and output flux (observed peak discharge) was poor (R2 = 0.16). However, using a newly developed index, termed IRCA, to calculate the input flux accounting only for the RCA extent and rainfall intensity over it, resulted in a substantially higher correlation (R2 = 0.64) across a wide range of flood magnitudes. The highest correlation was found using a 50-min time window, which is shorter than the time of concentration. Flood events were categorized according to their magnitude and the differences of several factors among the groups were examined. Pre-storm soil moisture content was found to be similar for all event magnitudes; however, pre-peak soil moisture content was substantially different between moderate and large–extreme events. Other important properties that differed between magnitudes were: RCA extent and its averaged rain intensity and ratio of convective rainfall. Finally, areas with land-uses characterized by low runoff potential became dominant and contributed mainly during large and extreme events. Although the RCA and its extent full potential is yet to be fulfilled, it is proposed as a significant tool for understanding processes of flash flood generation at the basin scale in future research. [ABSTRACT FROM AUTHOR]

Highlights • The dependence between flow and hydroclimatic factors in Baden-Württemberg is modeled. • The probabilities of droughts and floods under various hydroclimatic scenarios is examined. • The links between flow variability and atmospheric circulation are explored. Abstract This study presents an investigation of probabilistic relationships between streamflow and hydroclimatic variables (including precipitation, temperature and soil moisture) and the potential links to large-scale atmospheric circulation over Baden-Württemberg, Southwest Germany. First, the joint dependence between seasonal streamflow and hydroclimatic variables was established by using copulas. On the basis of the joint dependence structure, we estimated the probabilities of hydrological droughts and prolonged high-streamflow events conditioned upon two different scenarios (high and low) of hydroclimatic variables for various seasons over the study area. The results indicate that both precipitation and soil moisture are positively related to the streamflow variation in Baden-Württemberg for each season and strongly impact the likelihoods of hydrological droughts and high-streamflow events. Temperature tends to have less impact on the streamflow variation, and an inverse connection between streamflow and temperature is found in spring and summer. Then, the connections between streamflow variability and large-scale atmospheric circulation in summer and winter were explored by using composite analysis. Although the atmospheric circulation patterns vary in each season, it can be found that in summer high streamflow anomalies over Baden-Württemberg are related to a cyclonic activity over central Europe while low streamflow anomalies are linked to anticyclonic patterns over western Ireland. The high streamflow anomalies in winter are strongly impacted by the westerlies that play an important role in favoring warm and moist airstreams from the Atlantic Ocean towards the study area, while an opposite atmospheric pattern is found for the years with low streamflow anomalies. The presented probabilistic methodology could also be applied in other regions worldwide. [ABSTRACT FROM AUTHOR]

Abstract Plant water use is an important component in the function of Earth's critical zone and this can be examined by decomposing isotope composition of xylem water into contributions from precipitation stored in shallow soil layers and deeper groundwater. The usual procedure for estimating the proportional use of groundwater by plants is to sample the isotope composition of soil and groundwater and determine the most probable mixing coefficients from all potential sources. Here we propose and test a novel method for achieving the same goal without sampling soil water. The method is based on analyzing variability in the stem water isotope ratios of several members of a community and the known isotope ratio of groundwater to 'triangulate' the unknown isotope ratio of stored rainwater. Using a simple water balance model, parameterized to produce the best fit between actual and estimated stem water isotope ratios, we simulated seasonal variation in the volume and isotope ratio of rainwater storage, along with species-specific groundwater use ratios. The method was applied to eight woody plant species growing on two rocky outcrops in the South China karst. Estimated average proportional groundwater use over two seasons varied between 14% and 62% and was site-dependent. For the majority of species, groundwater use increased as estimated stored rainwater volume declined. The two species with highest groundwater use were taller, deciduous or semi-deciduous trees with lower wood densities. While the new method was inspired by the inability to sample water stored in the rocky outcrops, it may have broader use in any environment where the spatial variability of soil water isotope composition is a barrier to estimating average groundwater use ratios. The broader adoption of this or equivalent methods would greatly improve the study of the Earth's critical zone. [ABSTRACT FROM AUTHOR]