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• A paired-watershed experiment provides more understanding via state-controlled test. • Runoff change after deforestation increases for higher rainfall intensity. • Higher antecedent rainfall state decreases the deforestation impact on runoff. Understanding watershed processes is critical to predicting and mitigating flood and drought risk. Over the past century, the capability to isolate the influence of individual factors on hydrologic responses has been enhanced through the introduction and application of paired-watershed experiments. However, the high variability of paired experiment outcomes has been noted to limit conclusive reasoning from the results. Thus, in an effort to resolve this issue, this study assumes that the high variability in paired-watershed experiments is due to the changes in the watershed system states (e.g., soil moisture and groundwater states). A state-controlled hypothesis test is proposed for the paired-watershed experiment to test treatment impact with the control of watershed system states. The application of the state-controlled test to one pre-existing paired-watershed experiments in southeast Australia showed that the variability in the test results can be reduced and provide further understanding about the nature of changes that occur. [ABSTRACT FROM AUTHOR]

This study was designed to characterize and quantify the influence of surface soil moisture assimilation on estimated runoff (surface flow and baseflow) and hydraulically-routed streamflow across three large river basins in South Asia that are at risk of impending water stress. Soil Moisture Active Passive (SMAP) surface soil moisture retrievals were assimilated into the Noah-MP land surface model. The gridded runoff was hydraulically routed to obtain volumetric streamflow values for a river network using a runoff routing module (Hydrological Modeling and Analysis Platform, HyMAP). The open loop (OL, model-only) and data assimilated (DA, includes soil moisture retrieval assimilation) Noah-MP runoff estimates highlighted the improvements in estimated total runoff across irrigated areas. Soil moisture assimilation impacted baseflow more relative to surface runoff. The HyMAP-based OL and DA streamflow generally underestimated the streamflow at upstream stations and overestimated the streamflow at downstream stations within the Indus basin due to missing physics related to reservoir operations. For stations located in the Ganges–Brahmaputra basins, the OL and DA estimation performance varied. The OL and DA results showed that the assimilation of soil moisture retrievals improves gridded runoff and volumetric streamflow across irrigated areas. Considerable relative change in streamflow (> 70% increase in magnitude relative to OL) is noted after assimilation across the highly irrigated lower Indus basin and high precipitation regions in Bangladesh. Improving the modeling system's representativeness of ground conditions via inclusion of water management information could improve large-scale streamflow modeling across South Asia. • SMAP soil moisture assimilation generally leads to an increase in total runoff and volumetric streamflow across irrigated areas. • Relative to surface runoff, baseflow is generally more impacted by SMAP soil moisture assimilation. • Relative change in assimilated streamflow is considerable across irrigated areas but limited along river tributaries with high streamflow. [ABSTRACT FROM AUTHOR]

• The potential of model-independent methods for hydrological interpretations is examined. • Dominance of rain, snow, and their combination in catchment hydrology is inferred. • Lagged climate variables often fail to accurately represent current hydrologic states. Machine learning (ML) is increasingly perceived as a futuristic, superior data-driven approach to scientific discovery. It has already demonstrated remarkable performance in forecasting and prediction, yet its role in enhancing our understanding of hydrological processes remains underexplored. Traditional hydrological interpretations have relied heavily on model-dependent interpretation methods, focusing on the predictive accuracy of ML model predictions. Since hydrological models are built on a collection of assumptions and simplifications, model-dependent approaches might suffer from limited model realism, adequacy, accuracy, and equifinality issues. To address this gap, this study provides an ML approach that works in a model-independent context, working directly on hydroclimatic data collected through monitoring systems. We apply our model-independent interpretation approach to a carefully designed set of hydrologic data collected across the contiguous United States to address the following questions: (1) What are the primary controls of runoff-generation mechanisms, and how can such controls be attributed to catchment properties? (2) How and under what circumstances can the history of climate variables, such as precipitation, be a surrogate for present-time state variables, such as soil moisture and snowpack? We show that the ML approach aids in distinguishing catchments characterized by strong overland flow, interflow, or baseflow components and those primarily driven by rainfall, snowmelt, or a mix thereof. We further show that typical surrogate variables used in hydrology may come short in representing the dynamics of catchments that exhibit a complex interplay of rain and snow. [ABSTRACT FROM AUTHOR]

[Display omitted] • The Gumbel-type and Guassian-type Copula perform well in constructing SPERSI on multiple time scales. • SPERSI can reflect the onset and end of multiple types of drought of atmosphere, land surface and hydrology. • SPERSI detects integrated drought events in the YRB with POD>0.75, FAR<0.04 and CSI>0.73. • Drought in the upper YRB is relieving, while the middle and lower reaches are intensifying. The current univariate and multivariate drought indices cannot fully reflect the multiple processes of meteorological-hydrological-agricultural drought. So it is important to construct a comprehensive drought index for drought monitoring in a changing environment from the perspective of the whole process of the natural water cycle "atmosphere-land surface-hydrology". In this study, based on the standardized indices (SCI) framework and trivariate Copula functions, a multi-scale comprehensive drought index (SPERSI) integrating rainfall, temperature, runoff and soil moisture is proposed, which can synthetically reflect meteorological, hydrological and agricultural droughts. The spatial and temporal variations of drought in the Yellow River Basin (YRB) from 1961 to 2019 were investigated, and then the links between SPERSI and some remotely correlated factors were revealed by using the multiple cross wavelet transform technique. Results show that: (1) the SPERSI constructed by fusing rainfall-temperature-runoff-soil moisture information can sensitively and effectively capture the onset, persistence and termination of drought; (2) the SPERSI exhibits good performance in detecting integrated drought events in the YRB with probability of detection (POD) > 0.75, false alarm rate (FAR) < 0.04 and critical success index (CSI) > 0.73; (3) From 1961 to 2019, drought in different time scales of above Longyangxia (AL) and Longyangxia to Lanzhou (LL) zones in the YRB both show a slight mitigation trend, while the drought in remaining subzones show a trend of intensification; (4) Multiple cross-wavelet results indicate that AO is the dominant factor of drought in the Yellow River Basin, followed by ENSO. In general, SPERSI results are reliable and can provide basis for drought decision-making. [ABSTRACT FROM AUTHOR]

• Water use strategy was assessed by plant water uptake and physiological responses. • R. pseudoacacia use an opportunistic water use strategy in 11- and 25-yr plantations. • R. pseudoacacia use a conservative water use strategy in 33- and 43-yr plantations. • R. pseudoacacia exhibited similar stem embolism resistant ability in four plantations. Understanding water use strategy is essential to evaluate plant adaptability in water-limited regions, but whether this strategy changes with plant growth needs further investigation. Robinia pseudoacacia is an exotic pioneer plantation species widely planted in the semiarid Loess Plateau of China to restore degraded ecosystems. Plant water uptake proportion from different soil layers and the water uptake pattern calculated through soil water uptake niche breadth (B index) were determined for R. pseudoacacia in 11-, 25-, 33–, and 43-yr plantations. Leaf physiological parameters and carbon stable isotopic composition (δ13C) were measured concurrently to calculate short-term and long-time integrated water use efficiency (WUE). Then, the water use strategy for R. pseudoacacia at different plantation ages was assessed through water uptake patterns, stomatal conductance (g s), and WUE variation under varied soil water conditions. Results showed that R. pseudoacacia maintained high g s and low short-term WUE and δ13C through a relative equal water uptake pattern and absorbed water within 0 − 400 cm soil depths in 11- and 25-yr plantations. However, this species exhibited an exclusive water uptake pattern and absorbed water mainly from 50 − 400 cm soil depth, adopted low g s and high short-term WUE and δ13C in 33– and 43-yr plantations. Therefore, R. pseudoacacia transited from an opportunistic to a conservative water use strategy with plant growth. The short-term WUE and δ13C were mainly and significantly influenced by g s and specific leaf area, respectively. Furthermore, the significant influence of B index on g s , δ18O Δ , short-term WUE , and δ13C indicated that R. pseudoacacia coordinated both water uptake pattern and water use behavior to contend with varied soil water conditions. This study suggested that changes in age-related water use strategy were essential for the adaptability of R. pseudoacacia in water-limited conditions. [ABSTRACT FROM AUTHOR]

• Greater variation observed during wet periods in low-VWC catchments, and vice versa in high-VWC catchments. • One most time-stable location was adequate for mean VWC at all depths in high-VWC catchments. • Location of mean VWC tends to align with mid-level elevation or EC a at a specific scale. • The number of required samples can be ranked based on VWC spatial variation. Characterizing the temporal variation across a forest catchment soil volumetric water content (VWC) with different environmental conditions at different depths for different seasons requires a robust point-scale sampling strategy that balances monitoring cost with predicting accuracy. In the study, the most time-stable locations (MTSLs) using the time-stable concept and the minimum number of required samples (NRS) based on random selection method were studied to explore the optimal sampling design for catchment-mean VWC with an acceptable error (the relative bias (RBIAS) less than 10 %). VWC monitoring was conducted at approximately 25 plots throughout the year in five forest catchments with varying areas and VWC status. Results revealed that the number of MTSLs for mean-VWC estimation with RBIAS within 10 % was directly related to the spatial variability of VWC. Specifically, one MTSL was feasible for accurately estimating mean VWC at different depths in high-VWC catchments with area around or less than 100 ha; one MTSL was adequate for each depth in the catchment with moderate-level VWC; whereas more than one MTSL were necessary for each depth in low-VWC catchment with a larger are of 1800 ha. To identify the MTSL for each catchment, the location with mean VWC was typically situated at the mid-elevation or soil apparent electrical conductivity (EC a) at a certain spatial scale. The NRS for catchment-mean VWC could be determined based on the combination of VWC status, its temporal stability, and potential catchment size, and could be ranked based on the spatial variation of each catchment. If the MTSL cannot be guaranteed for a catchment, the NRS might be a better alternative for optimum sampling design. These finding could be useful in providing guidelines for optimizing VWC monitoring in forest catchments by sampling at a few selected locations representative of the catchment-scale behavior. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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]

• 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]

• 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]

• 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 • 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 • 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 • 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]

• A surrogate model for distributed hydrological models is developed using deep networks. • The surrogate model can handle high-dimensional input–output maps of VIC. • The surrogate model can capture the dynamic relationship between the time-varying input and output. • The surrogate model outperforms LSTM in approximating spatial patterns of VIC output. Surrogate modelling methods have been used to estimate spatially distributed parameters of land surface hydrological models due to their computational efficiency. However, traditional surrogate techniques have trouble in modelling high-dimensional input–output maps. In addition, their applications to a distributed model over large spatial and temporal domains are very unlikely to produce consistent spatial pattern and dynamic relationship of model input and output, leading to unbalanced model performance across different spatiotemporal domains. To address these issues, we employ a deep autoregressive neural network to construct an accurate and reliable surrogate system of distributed dynamic model outputs. The network uses a deep convolutional encoder-decoder architecture to take full advantage of the deep convolutional layers in high-dimensional image processing and spatial feature extraction. Moreover, the autoregressive strategy makes the network good at handling dynamic relationship between the time-varying model input and output. We apply this deep learning (DL) network to building a surrogate model of the Variable Infiltration Capacity (VIC) model simulated soil moisture over the Huaihe river basin of China. The results show that the adopted deep autoregressive neural network can provide an accurate surrogate model of the high-dimensional dynamic relationship between 15 input fields and 1 output field, each covering 408 grids, over multiple years. This surrogate model significantly outperforms the popular long short-term memory (LSTM) model, achieving an average mean squared error (MSE) of 0.26 and an average R 2 of 0.76 using the test datasets. The surrogate's MSE and R 2 performance represents an average improvement of 43% and 33%, respectively, compared to the LSTM model. In addition, it shows better spatial performance than the LSTM model, demonstrating its superior performance in approximating spatial patterns of VIC simulated soil moisture. The accuracy of this surrogate method and its ability to handle high-dimensional data is promising for advancing parameter uncertainty quantification of distributed land surface hydrological models, which usually have high dimensionality due to the large number of variables involved, including distributed parameters, input forcing variables, and output variables. [ABSTRACT FROM AUTHOR]

• A process-based model is used to simulate groundwater dynamics in the Mekong. • Groundwater flow exhibits high spatial heterogeneity driven by climate and geology. • Groundwater in the Mekong is tightly coupled with surface water. • Irrigation-induced groundwater depletion is minimal in the long-term but notable in dry years. Groundwater depletion is a worldwide concern and an emerging issue in regions such as the Mekong River Basin (MRB). However, even the natural dynamics of groundwater in the MRB is yet to be fully explored, making the quantification of groundwater response to climate variability and anthropogenic activities a major scientific challenge. Here, we examine various groundwater mechanisms in the MRB, focusing on groundwater flow processes that are modulated by climate variability, physiographic features, and key drivers of groundwater-surface water interactions. We further quantify the influence of anthropogenic activities on groundwater dynamics. We use the Community Land Model version 5 (CLM5) at 0.05° (∼5km) spatial resolution with an improved representation of groundwater processes, including lateral groundwater flow and aquifer pumping for irrigation. Results indicate high spatial heterogeneity in groundwater recharge and discharge across the basin governed by climate and subsurface characteristics. A pronounced seasonality is found in groundwater recharge due to precipitation; ∼52% of wet season precipitation recharges groundwater, with substantial carryover to the consecutive dry season that alleviates soil moisture. Importantly, groundwater discharge is a dominant source of streamflow all year round, which suggests a strong surface water-groundwater coupling in the MRB. Finally, our results indicate that irrigation pumping is directly altering groundwater flows and storages; climate variability smoothens pumping effects over long times, but the model simulates region-wide groundwater depletion (up to 1 m/year) in the Mekong Delta during dry years. Our study provides key insights on the evolving groundwater systems in the MRB, also advancing process-based groundwater modeling capabilities. [ABSTRACT FROM AUTHOR]

[Display omitted] • Daily streamflow in data-scarce watersheds is reproduced using coupled SWAT-LSTM. • Precipitation contributes the most to simulated streamflow. • SWAT-LSTM without retraining performs satisfactory in ungauged watersheds. • Transfer learning using few observations optimizes model in poorly gauged watersheds. • Consider both spatial proximity and physical similarity for robustness of source selection. There is a scarcity of streamflow data owing to the limited availability of gauge networks or delayed gauging in most parts of the world. To overcome this challenge and reproduce long-duration daily streamflow in both ungauged and poorly gauged watersheds, we proposed a novel approach that couples the process-based model Soil and Water Assessment Tool (SWAT) and the interpretable machine learning (ML) model long short-term memory (LSTM). The watershed process features generated by SWAT were combined with meteorological features as inputs for LSTM. The coupled SWAT-LSTM approach was first developed in a data-rich coastal watershed in Fujian Province, China. During the testing period, the obtained Nash-Sutcliffe efficiency coefficient (NSE) of SWAT-LSTM is 0.885, which outperformed other SWAT-MLs (e.g., backward propagation neural network, NSE = 0.843; random forest, NSE = 0.838) and calibrated SWAT (NSE = 0.706) used as comparators. Precipitation is considered the most important feature to local streamflow from a ML perspective. The pre-trained SWAT-LSTM presented satisfactory performances over 30 years of simulations in 24 hypothesized data-scarce watersheds. In ungauged watersheds, the NSE ranged from 0.474 to 0.898, with a mean of 0.685. In poorly gauged watersheds, the pre-trained SWAT-LSTM was optimized using limited local observations by introducing the transfer learning technique, and the NSE ranged from 0.591 to 0.918, with a mean of 0.760, which was markedly more accurate than the new trained models locally. Spatial proximity and physical similarity should be considered simultaneously when selecting the optimal source for data-scarce watersheds, as better performance can be achieved in less time than with tandem trained the observations of all sources. This study demonstrates that coupling SWAT with interpretable LSTM enhances the modeling confidence and provides a potential shortcut to achieving long-duration streamflow simulations in both ungauged and poorly gauged watersheds. [ABSTRACT FROM AUTHOR]

Highlights • Modification of equivalent cross-section approach for catchment modelling. • Comparison of semi-distributed and lumped conceptual modelling. • Reduction of computational units in distributed hydrological modelling. • Analysis of impact of climate and land use on soil moisture dynamics. Abstract Physically based distributed hydrological models are useful for simulating the spatial distribution of hydrologic fluxes across the catchment under various climate and land cover change scenarios. However, complexities associated with their implementation at large scales make their applications limited. Previously, an equivalent cross-section (ECS) based distributed hydrological modelling approach was developed for first order sub-basins to reduce the computational time/effort. Here, the ECS approach is modified for semi-distributed hydrological modelling at the catchment scale. The modelling approach is implemented for a 314 km2 McLaughlin catchment located in south-eastern New South Wales (NSW), Australia that consists of 822 first order sub-basins. A 26 year long streamflow record simulated using an ECS based modelling approach are compared against daily observed streamflow and four calibrated lumped conceptual hydrologic models, and found to be consistent. Further, the simulated actual evapotranspiration and soil moisture from the ECS approach are compared against the Australian Water Availability Project (AWAP) model simulations and results found to be consistent. In addition, the temporal dynamics of simulated soil moisture from the ECS approach is consistent with the satellite derived European Space Agency Climate Change Initiative (ESA CCI) surface soil moisture data. In the ECS based semi-distributed modelling, all parameters are derived from the actual topographic and physiographic information of the catchment and none of the parameters is calibrated. Therefore, this approach has the advantage of simulating streamflow in ungauged catchments compared to lumped conceptual models. The impact of spatially distributed climatic forcing and land cover on soil moisture is investigated across four landforms (upslope, midslope, footslope and alluvial-flats) and at various soil depths. Our results show increase of mean soil moisture in shallow layers of upslope toward alluvial-flats. However, mean soil moisture in deeper horizons remained almost constant across all landforms. The variability of daily soil moisture at surface soil layers is higher than the deeper soil layers for all landforms. Our results illustrated that disaggregation of a catchment to a series of ECS at the scale of first order sub-basins, captures dynamics of soil moisture and actual evapotranspiration across the landscape and results are consistent with the climatology, land cover type, topography and soil hydraulic properties. Further, the use of ECS approach in the McLaughlin catchment reduced the number of computational units by 40 times in comparison to 3-d grid based distributed modelling setup. [ABSTRACT FROM AUTHOR]

The goal of this study was to determine if assimilating a combination of various derived data products can help circumvent some of the difficulties associated with urban watershed modeling. Combinations of the SNODAS (Snow Data Assimilation System) snow water equivalent data, the SMOS (Soil Moisture and Ocean Salinity) L2 soil moisture, and streamflow observations were used for the data assimilation schemes. Combinations of these observation data sets were assimilated into lumped conceptual rainfall-runoff models of the highly-urbanized Don River basin (in southern Ontario) to determine if assimilation of geophysical variables will have a significant impact on simulations and forecasting in an urbanized watershed. The Ensemble Kalman Filter (EnKF) data assimilation method was used for these analyses, with various rainfall-runoff models that include GR4J, HyMod, MAC-HBV, and SAC-SMA models. The best data assimilation scheme for hydrologic modeling involved using a combination of streamflow, soil moisture, and snow water equivalent while performing both state and parameter updating. These results suggest that using a combination of soil moisture and snow water equivalent from the SMOS and SNODAS data products can improve simulations and ensemble forecasts in an urban basin. [ABSTRACT FROM AUTHOR]

Soil water availability has a strong role on vegetation conservation and biodiversity, particularly at vulnerable environments such as archipelagos, subject to significant temporal changes of climate and rainfall, which influence soil weathering and erosion processes. Few field studies on hydrology of volcanic islands in the Atlantic Ocean have been developed focusing on soil and water temporal dynamics and conservation, mainly because such studies require data with high temporal resolution, and also due to the financial costs involved with field data acquisition at such remote places. Considering the importance of soil moisture content to several hydrological processes and islands environmental sustainability, this study aimed to investigate the soil water temporal dynamics in relation to different rainfall patterns and also the rainfall erosivity and its temporal variation, and the resulting infiltration depths, during a typical hydrological period, in an experimental plot at the main watershed of Fernando de Noronha, in the Brazilian Atlantic Coast. Rainfall was recorded every 5 min, and soil moisture was intensely monitored by 16 moisture probes, installed at 10–40 cm and 40–70 cm layers, during three years, representing the typical local hydrological regime and seasons. Rainfall bursts pattern were identified and described. Experimental data allowed a hydrological model to be calibrated and validated, providing estimates of infiltration depths, which can be valuable for water resources planning and management. Hydrological patterns were determined for each erosive rainfall, depending on the precipitation peak position, as advanced, intermediate and delayed. The soil water effectiveness was also evaluated and related to the different rainfall patterns. Soil moisture is highest from April to June, close to saturation; whilst for the other months the values were close to the residual value. The rainfall erosivity in the archipelago was identified as moderate to strong, and the most frequent rainfall pattern was the advanced. Fast wetting-drying cycles during the rainfall events were observed, due to rainfall intermittency, evapotranspiration, and to soil hydraulic conductivity. It was observed the prevalence of high moisture indexes, with percentages higher than 40% (extremely effective level) for both investigated depths within the first 70 cm of soil, essential for plant water uptake. The observed rainfall erosivity highlights the importance of defining local strategies for soil and water conservation aimed at water security in the island. Based on a simulation model, which was successfully calibrated (Nash-Sutcliffe Efficiency Index NS = 0.76) and validated (NS = 0.81) for periods in the wet season, high infiltration depths have been identified, which contributes to canopy conservation in the archipelago. [ABSTRACT FROM AUTHOR]

The feasibility of simulating the hydrological response of a grass swale to runoff inflows was examined using the hydrological model Mike SHE and the available input data from 12 irrigation events mimicking runoff from block rainfalls. The test swale channel had a trapezoidal cross-section, bottom slope of 1.5%, length of 30 m, and was built in loamy fine sand. The irrigation events consisted in releasing two equal constant inflows to the swale: a concentrated longitudinal flow at the upstream end and a distributed lateral inflow along the swale side slope adjacent to the contributing drainage area. The total inflows approximated runoff from two events with return periods of 2 months and 3 years, respectively, for durations of 30 min. Irrigation experiments were done for two states of the initial soil moisture, dry or wet antecedent moisture conditions (AMC). Mike SHE has been extensively used on catchments of various sizes, but rarely for small stormwater management facilities and their detailed topography investigated in this study. The latter application required high spatial and temporal resolutions, with computational cells of 0.2 × 0.2 m and time steps as short as 0.6 s to avoid computational instabilities. For dominant hydrological processes, the following computational options in Mike SHE were chosen: Soil infiltration – the van Genuchten equation, unsaturated zone flow – the one-dimensional Richards equation, and overland flow – the diffusive wave approximation of the St. Venant equations. For study purposes, the model was calibrated for single events representing one of four combinations of low and high inflows, and dry and wet AMC, and then applied to the remaining 11 events. This was complemented by calibration for two events, representing high inflow on wet AMC and low inflow in dry AMC. The goodness of fit was statistically assessed for observed and simulated peak flows, hydrograph volumes, Nash-Sutcliffe model efficiencies (NSE), and soil water content (SWC) in swale soil layers. The best fit (NSE > 0.8) was obtained for high inflows and wet AMC (i.e., when the primary swale function is flow conveyance); the least fit was noted for low inflows and dry AMC, when the primary swale function is flow attenuation. Furthermore, this observation indicates the overall importance of correct modelling of the soil infiltration. The effects of spatial variation of SWC on the swale discharge hydrograph could not be confirmed from simulation results, but high topographical accuracy was beneficial for reproducing well the locations of the observed water ponding. No significant increases in simulated SWC at 0.3 m or greater depths were noted, which agreed with field observations. Overall, the results indicated that Mike SHE was effective in process-oriented small-scale modelling of grass swale flow hydrographs. [ABSTRACT FROM AUTHOR]

Real time hydrologic forecasting requires near accurate initial condition of soil moisture; however, continuous monitoring of soil moisture is not operational in many regions, such as, in Ganga basin, extended in Nepal, India and Bangladesh. Here, we examine the impacts of perturbation/error in the initial soil moisture conditions on simulated soil moisture and streamflow in Ganga basin and its propagation, during the summer monsoon season (June to September). This provides information regarding the required minimum duration of model simulation for attaining the model stability. We use the Variable Infiltration Capacity model for hydrological simulations after validation. Multiple hydrologic simulations are performed, each of 21 days, initialized on every 5th day of the monsoon season for deficit, surplus and normal monsoon years. Each of these simulations is performed with the initial soil moisture condition obtained from long term runs along with positive and negative perturbations. The time required for the convergence of initial errors is obtained for all the cases. We find a quick convergence for the year with high rainfall as well as for the wet spells within a season. We further find high spatial variations in the time required for convergence; the region with high precipitation such as Lower Ganga basin attains convergence at a faster rate. Furthermore, deeper soil layers need more time for convergence. Our analysis is the first attempt on understanding the sensitivity of hydrological simulations of Ganga basin on initial soil moisture conditions. The results obtained here may be useful in understanding the spin-up requirements for operational hydrologic forecasts. [ABSTRACT FROM AUTHOR]

The hydrological community often turns to widely available spatial datasets such as the NRCS Soil Survey Geographic database (SSURGO) to characterize the spatial variability of soil properties. When used to spatially characterize and parameterize watershed models, this has served as a reasonable first approximation when lacking localized or incomplete soil data. Within agriculture, soil data has been left relatively coarse when compared to numerous other data sources measured. This is because localized soil sampling is both expensive and time intense, thus a need exists in better connecting spatial datasets with ground observations. Given that hydrogeophysics is data-dense, rapid, non-invasive, and relatively easy to adopt, it is a promising technique to help dovetail localized soil sampling with spatially exhaustive datasets. In this work, we utilize two common near surface geophysical methods, cosmic-ray neutron probe and electromagnetic induction, to identify temporally stable spatial patterns of measured geophysical properties in three 65 ha agricultural fields in western Nebraska. This is achieved by repeat geophysical observations of the same study area across a range of wet to dry field conditions in order to evaluate with an empirical orthogonal function. Shallow cores were then extracted within each identified zone and water retention functions were generated in the laboratory. Using EOF patterns as a covariate, we quantify the predictive skill of estimating soil hydraulic properties in areas without measurement using a bootstrap validation analysis. Results indicate that sampling locations informed via repeat hydrogeophysical surveys, required only five cores to reduce the cross-validation root mean squared error by an average of 64% as compared to soil parameters predicted by a commonly used benchmark, SSURGO and ROSETTA. The reduction to five strategically located samples within the 65 ha fields reduces sampling efforts by up to ∼90% as compared to the common practice of soil grid sampling every 1 ha. [ABSTRACT FROM AUTHOR]

This paper advances the physical understanding of the flow duration curve (FDC) regional variation. It provides a process-based analysis of the interaction between climate and landscape properties to explain disparities in FDC shapes. We used (i) long term measured flow and precipitation data over 73 catchments from the eastern US. (ii) We calibrated the Sacramento model (SAC-SMA) to simulate soil moisture and flow components FDCs. The catchments classification based on storm characteristics pointed to the effect of catchments landscape properties on the precipitation variability and consequently on the FDC shapes. The landscape properties effect was pronounce such that low value of the slope of FDC (SFDC)—hinting at limited flow variability—were present in regions of high precipitation variability. Whereas, in regions with low precipitation variability the SFDCs were of larger values. The topographic index distribution, at the catchment scale, indicated that saturation excess overland flow mitigated the flow variability under conditions of low elevations with large soil moisture storage capacity and high infiltration rates. The SFDCs increased due to the predominant subsurface stormflow in catchments at high elevations with limited soil moisture storage capacity and low infiltration rates. Our analyses also highlighted the major role of soil infiltration rates on the FDC despite the impact of the predominant runoff generation mechanism and catchment elevation. In conditions of slow infiltration rates in soils of large moisture storage capacity (at low elevations) and predominant saturation excess, the SFDCs were of larger values. On the other hand, the SFDCs decreased in catchments of prevalent subsurface stormflow and poorly drained soils of small soil moisture storage capacity. The analysis of the flow components FDCs demonstrated that the interflow contribution to the response was the higher in catchments with large value of slope of the FDC. The surface flow FDC was the most affected by the precipitation as it tracked the precipitation duration curve (PDC). In catchments with low SFDCs, this became less applicable as surface flow FDC diverged from PDC at the upper tail (> 40% of the flow percentile). The interflow and baseflow FDCs illustrated most the filtering effect on the precipitation. The process understanding we achieved in this study is key for flow simulation and assessment in addition to future works focusing on process-based FDC predictions. [ABSTRACT FROM AUTHOR]

There is a need to model and predict the transfer of phosphorus (P) from land to water, but this is challenging because of the large number of complex physical and biogeochemical processes involved. This study presents, for the first time, a ‘limits of acceptability’ approach of the Generalized Likelihood Uncertainty Estimation (GLUE) framework to the Soil and Water Assessment Tool (SWAT), in an application to a water quality problem in the Newby Beck catchment (12.5 km 2 ), Cumbria, United Kingdom (UK). Using high frequency outlet data (discharge and P), individual evaluation criteria (limits of acceptability) were assigned to observed discharge and P loads for all evaluation time steps, identifying where the model was performing well/poorly and to infer which processes required improvement in the model structure. Initial limits of acceptability were required to be relaxed by a substantial amount (by factors of between 5.3 and 6.7 on a normalized scale depending on the evaluation criteria used) in order to gain a set of behavioral simulations (1001 and 1016, respectively out of 5,000,000). Of the 39 model parameters tested, the representation of subsurface processes and associated parameters, were consistently shown as critical to the model not meeting the evaluation criteria, irrespective of the chosen evaluation metric. It is therefore concluded that SWAT is not an appropriate model to guide P management in this catchment. This approach highlights the importance of high frequency monitoring data for setting robust model evaluation criteria. It also raises the question as to whether it is possible to have sufficient input data available to drive such models so that we can have confidence in their predictions and their ability to inform catchment management strategies to tackle the problem of diffuse pollution from agriculture. [ABSTRACT FROM AUTHOR]

An analytical solution is developed for predicting two-dimensional transient seepage into ditch drainage network receiving water from a non-uniform steady ponding field from the surface of the soil under the influence of source/sink in the flow domain. The flow domain is assumed to be saturated, homogeneous and anisotropic in nature and have finite extends in horizontal and vertical directions. The drains are assumed to be standing vertical and penetrating up to impervious layer. The water levels in the drains are unequal and invariant with time. The flow field is also assumed to be under the continuous influence of time–space dependent arbitrary source/sink term. The correctness of the proposed model is checked by developing a numerical code and also with the existing analytical solution for the simplified case. The study highlights the significance of source/sink influence in the subsurface flow. With the imposition of the source and sink term in the flow domain, the pathline and travel time of water particles started deviating from their original position and above that the side and top discharge to the drains were also observed to have a strong influence of the source/sink terms. The travel time and pathline of water particles are also observed to have a dependency on the height of water in the ditches and on the location of source/sink activation area. [ABSTRACT FROM AUTHOR]

Climate and land cover are two major factors affecting the water fluxes and balance across spatiotemporal scales. These two factors and their impacts on hydrology are often interlinked. The quantification and differentiation of such impacts is important for developing sustainable land and water management strategies. Here, we calibrated the well-known Hydrus-1D model in a data-rich boreal headwater catchment in Scotland to assess the role of two dominant vegetation types (shrubs vs. trees) in regulating the soil water partitioning and balance. We also applied previously established climate projections for the area and replaced shrubs with trees to imitate current land use change proposals in the region, so as to quantify the potential impacts of climate and land cover changes on soil hydrology. Under tree cover, evapotranspiration and deep percolation to recharge groundwater was about 44% and 57% of annual precipitation, whilst they were about 10% lower and 9% higher respectively under shrub cover in this humid, low energy environment. Meanwhile, tree canopies intercepted 39% of annual precipitation in comparison to 23% by shrubs. Soils with shrub cover stored more water than tree cover. Land cover change was shown to have stronger impacts than projected climate change. With a complete replacement of shrubs with trees under future climate projections at this site, evapotranspiration is expected to increase by ∼39% while percolation to decrease by 21% relative to the current level, more pronounced than the modest changes in the two components (<8%) with climate change only. The impacts would be particularly marked in warm seasons, which may result in water stress experienced by the vegetation. The findings provide an important evidence base for adaptive management strategies of future changes in low-energy humid environments, where vegetation growth is usually restricted by radiative energy and not water availability while few studies that quantify soil water partitioning exist. [ABSTRACT FROM AUTHOR]

One of the important issues in hydrological modelling is to specify the initial conditions of the catchment since it has a major impact on the response of the model. Although this issue should be a high priority among modelers, it has remained unaddressed by the community. The typical suggested warm-up period for the hydrological models has ranged from one to several years, which may lead to an underuse of data. The model warm-up is an adjustment process for the model to reach an ‘optimal’ state, where internal stores (e.g., soil moisture) move from the estimated initial condition to an ‘optimal’ state. This study explores the warm-up period of two conceptual hydrological models, HYMOD and IHACRES, in a southwestern England catchment. A series of hydrologic simulations were performed for different initial soil moisture conditions and different rainfall amounts to evaluate the sensitivity of the warm-up period. Evaluation of the results indicates that both initial wetness and rainfall amount affect the time required for model warm up, although it depends on the structure of the hydrological model. Approximately one and a half months are required for the model to warm up in HYMOD for our study catchment and climatic conditions. In addition, it requires less time to warm up under wetter initial conditions (i.e., saturated initial conditions). On the other hand, approximately six months is required for warm-up in IHACRES, and the wet or dry initial conditions have little effect on the warm-up period. Instead, the initial values that are close to the optimal value result in less warm-up time. These findings have implications for hydrologic model development, specifically in determining soil moisture initial conditions and warm-up periods to make full use of the available data, which is very important for catchments with short hydrological records. [ABSTRACT FROM AUTHOR]