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• 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]

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

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

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

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

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

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

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

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

Soil moisture dynamics plays an active role in ecological and hydrological processes, and it depends on a large number of environmental factors, such as topographic attributes, soil properties, land use types, and precipitation. However, studies must still clarify the relative significance of these environmental factors at different soil depths and at different spatial scales. This study aimed: (1) to characterize temporal and spatial variations in soil moisture content (SMC) at four soil layers (0–40, 40–100, 100–200, and 200–500 cm) and three spatial scales (plot, hillslope, and region); and (2) to determine their dominant controls in diverse soil layers at different spatial scales over semiarid and semi-humid areas of the Loess Plateau, China. Given the high co-dependence of environmental factors, partial least squares regression (PLSR) was used to detect relative significance among 15 selected environmental factors that affect SMC. Temporal variation in SMC decreased with increasing soil depth, and vertical changes in the 0–500 cm soil profile were divided into a fast-changing layer (0–40 cm), an active layer (40–100 cm), a sub-active layer (100–200 cm), and a relatively stable layer (200–500 cm). PLSR models simulated SMC accurately in diverse soil layers at different scales; almost all values for variation in response (R 2 ) and goodness of prediction (Q 2 ) were >0.5 and >0.0975, respectively. Upper and lower layer SMCs were the two most important factors that influenced diverse soil layers at three scales, and these SMC variables exhibited the highest importance in projection (VIP) values. The 7-day antecedent precipitation and 7-day antecedent potential evapotranspiration contributed significantly to SMC only at the 0–40 cm soil layer. VIP of soil properties, especially sand and silt content, which influenced SMC strongly, increased significantly after increasing the measured scale. Mean annual precipitation and potential evapotranspiration also influenced SMC at the regional scale significantly. Overall, this study indicated that dominant controls of SMC varied among three spatial scales on the Loess Plateau, and VIP was a function of spatial scale and soil depth. [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]

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]

Snowmelt-runoff processes on frozen ground are poorly understood at the hillslope scale. This is especially true for hillslopes on the northern Great Plains of North America where long periods of snow-covered frozen ground with very shallow slopes mask any spatial patterns and process controls on connectivity and hillslope runoff generation. This study examines a 4.66 ha (46,600 m 2 ) hillslope on the northern Great Plains during the 2014 spring snowmelt season to explore hillslope runoff processes. Specifically, we explore the spatial patterns of runoff production source areas and examine how surface topography and patterns of snow cover, snow water equivalent, soil water content, and thawed layer depth – which we measured on a 10 m grid across our 46,600 m 2 hillslope – affect melt water partitioning and runoff connectivity. A key question was whether or not the controls on connectivity are consistent with the fill and spill mechanism found in rain-dominated and unfrozen soil domains. The contrast between the slow infiltration rates into frozen soil and the relatively fast rates of snowmelt delivery to the soil surface resulted in water accumulation in small depressions under the snowpack. Consequently, infiltration was minimal over the 12 day melt period. Instead, nested filling of micro- and meso-depressions was followed by macro-scale, whole-slope spilling. This spilling occurred when large patches of ponded water exceeded the storage capacity behind downslope micro barriers in the surface topography, and flows from them coalesced to drive a rapid increase in runoff at the hillslope outlet. These observations of ponded water and flowpaths followed mapable fill and spill locations based on 2 m resolution digital topographic analysis. Interestingly, while surface topography is relatively unimportant under unfrozen conditions at our site because of low relief and high infiltrability, surface topography shows episodically critical importance for connectivity and runoff generation when the ground is frozen. [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]

• DHI-GHM provides valuable hydrological information for data scarce areas. • DHI-GHM allows for hourly simulations, as often as new forecasts are available. • Physical based parameter estimation gives informed start for calibration activities. • DHI-GHM produces comparable results in world's main basins to other GHMs. • DHI-GHM is an operational system ideal to support decision-making. An operational global hydrological model (DHI-GHM) has been developed that provides historical, real-time, 10-day and seasonal forecast simulations at the global scale. DHI-GHM is comprised of a distributed gridded rainfall-runoff model with a spatial resolution of 0.1° and an agile kinematic routing model that moves water between model grid cells and sub-catchments and in the river system. Model output includes gridded hydrological variables, such as soil moisture content and different runoff components, and discharge at more than 1 million river points globally. DHI-GHM uses a novel parameterization scheme, where global data products of land surface physical characteristics are used to establish relations with model parameters, and thus DHI-GHM does not rely on local discharge data for model calibration. Multiple forcing datasets were tested, and the combination of ERA5 precipitation and Priestley-Taylor potential evaporation yielded the overall best results with a median KGE of 0.51 and average water balance error of 0.78% based on data from 1667 discharge stations worldwide. The model results and the operational system are particularly relevant for organizations with national, regional, or global scope who need information on large areas at once and for data scarce areas or projects without resources for local scale models. [ABSTRACT FROM AUTHOR]

This paper outlines the design and experimentation of an Empirical Multistructure Framework (EMF) for lumped conceptual hydrological modeling. This concept is inspired from modular frameworks, empirical model development, and multimodel applications, and encompasses the overproduce and select paradigm. The EMF concept aims to reduce subjectivity in conceptual hydrological modeling practice and includes model selection in the optimisation steps, reducing initial assumptions on the prior perception of the dominant rainfall-runoff transformation processes. EMF generates thousands of new modeling options from, for now, twelve parent models that share their functional components and parameters. Optimisation resorts to ensemble calibration, ranking and selection of individual child time series based on optimal bias and reliability trade-offs, as well as accuracy and sharpness improvement of the ensemble. Results on 37 snow-dominated Canadian catchments and 20 climatically-diversified American catchments reveal the excellent potential of the EMF in generating new individual model alternatives, with high respective performance values, that may be pooled efficiently into ensembles of seven to sixty constitutive members, with low bias and high accuracy, sharpness, and reliability. A group of 1446 new models is highlighted to offer good potential on other catchments or applications, based on their individual and collective interests. An analysis of the preferred functional components reveals the importance of the production and total flow elements. Overall, results from this research confirm the added value of ensemble and flexible approaches for hydrological applications, especially in uncertain contexts, and open up new modeling possibilities. [ABSTRACT FROM AUTHOR]

Urbanisation alters the hydrological response of catchments to storm events and spatial measures of urban extent and imperviousness are routinely used in hydrological modelling and attribution of runoff response to land use changes. This study evaluates whether a measure of catchment urban extent can account for differences in runoff generation from storm events along an rural-urban gradient. We employed a high-resolution monitoring network across 8 catchments in the south of the UK - ranging from predominantly rural to heavily urbanised - over a four year period, and from this selected 336 storm events. Hydrological response was compared using volume- and scaled time-based hydrograph metrics within a statistical framework that considered the effect of antecedent soil moisture. Clear differences were found between rural and urban catchments, however above a certain threshold of urban extent runoff volume was relatively unaffected by changes and runoff response times were highly variable between catchments due to additional hydraulic controls. Results indicate a spatial measure of urbanisation can generally explain differences in the hydrological response between rural and urban catchments but is insufficient to explain differences between urban catchments along an urban gradient. Antecedent soil moisture alters the volume and timing of runoff generated in catchments with large rural areas, but was not found to affect the runoff response where developed areas are much greater. The results of this study suggest some generalised relationships between urbanisation and storm runoff are not represented in observed storm events and point to limitations in using a simplified representations of the urban environment for attribution of storm runoff in small urban catchments. The study points to the need for enhanced hydrologically relevant catchment descriptors specific to small urban catchments and more focused research on the role of urban soils and soil moisture in storm runoff generation in mixed land-use catchments. [ABSTRACT FROM AUTHOR]

The Gravity Recovery and Climate Experiment (GRACE) has been in operation since 2002. Water storage estimates are calculated from gravity anomalies detected by the operating satellites and although not the true resolution, can be presented as 100 km × 100 km data cells if appropriate scaling functions are applied. Estimating total water storage has shown to be highly useful in detecting hydrological variations and trends. However, a limitation is that GRACE does not provide information as to where the water is stored in the vertical profile. We aim to partition the total water storage from GRACE into water storage components. We use a wavelet filter to decompose the GRACE data and partition it into various water storage components including soil water and groundwater. Storage components from the Australian Water Resources Assessment (AWRA) model are used as a reference for the decompositions of total storage data across Australia. Results show a clear improvement in using decomposed GRACE data instead of raw GRACE data when compared against total water storage outputs from the AWRA model. The method has potential to improve GRACE applications including a means to test various large scale hydrological models as well as helping to analyse floods, droughts and other hydrological conditions. [ABSTRACT FROM AUTHOR]

In this study, we evaluated the performance of community land surface model (CLM4.5) to simulate the hydrologic fluxes, such as, soil moisture (SM), evapotranspiration (ET) and runoff with (without) remote sensing data assimilation. The Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR_E) daily SM (both ascending and descending) are incorporated into the CLM4.5 model using data assimilation (DA) technique. The GLDAS data is used to validate the AMSR_E SM data and evaluate the performance of CLM4.5 simulations. The AMSR_E SM data are rescaled to meet the resolution of CLM4.5 model. By assimilating the AMSR_E SM data into the CLM4.5 model can improve the SM simulations, especially over the climate transition zones in Africa, East Australia, South South America, Southeast Asia, and East North America in summer season. The Local Ensemble Kalman Filter (LEnKF) technique improves the performance of CLM4.5 model compared to the directly substituted method. The improvement in ET and surface runoff simulations from CLM4.5 model assimilated with AMSR_E SM data shares similar spatial patterns with SM. [ABSTRACT FROM AUTHOR]

In recent years, nitrogen (N) loss from upland fields has become one of the most important sources for agricultural nonpoint source (NPS) pollution. Understanding the relationships between soil hydrological processes and N loss in NPS pollution is vital for controlling the agricultural NPS pollution in upland fields. The objective of this study was to analyze the interaction of N loss with different moisture conditions in the freeze-thaw zone. The semi-distributed hydrologic model Soil and Water Assessment Tool (SWAT) was used in this study to simulate runoff and different forms of N loss, which provided a basis for analyzing characteristics of N loss in the study region. Results showed that the soil moisture content was an important factor affecting N loss in the study region. Different forms of N loss were also analyzed and it was found that N loss occurred primarily in the form of organic-N, which is likely due to the dominant role of erosion-induced pollution. This study provides useful information for preventing NPS pollution within the study region. [ABSTRACT FROM AUTHOR]

• Performance of ERA5, ECV, FLDAS, and GLDAS SM products was assessed in Central Asia. • ERA5 SM product has the highest correlation and lowest RMSE with in-situ stations. • GLDAS has the lowest uncertainty followed by FLDAS and ERA5 SM products. • GLDAS performs better than the other three SM products in capturing drought events. Reanalysis and remotely sensed soil moisture (SM) products are important for monitoring hydrology and the terrestrial carbon cycle, especially in water-limited arid regions. However, it is essential to assess the reliability of SM products before they are used. In this study, surface SM from the European Reanalysis-Interim v5 (ERA5), Essential Climate Variables (ECV), the Famine Early Warning Systems Network (FEWS) Land Data Assimilation System (FLDAS), and the Global Land Data Assimilation System (GLDAS) were assessed based on in-situ data and the Three-Cornered Hat (TCH) approach in a typical arid region, Central Asia (CA). The validation based on in-situ data indicated that all three products underestimated the observed surface SM, except for ERA5. The ERA5 SM was closest to the in-situ observations, averaging a higher correlation coefficient of 0.59, followed by the GLDAS, ECV, and FLDAS. The TCH method revealed low uncertainties (<0.05 m3/m3) for all four SM products, and high uncertainties were found at high altitudes. For ERA5 and ECV SM products, the uncertainty is high in the shrublands and forests, respectively. FLDAS and GLDAS SM products performed better in terms of uncertainty compared to ERA5 and ECV, with an average uncertainty below 0.03 m3/m3, except for forests. The above assessment results indicate that, compared with the other three SM products, ERA5 has a better performance and more potential for application in CA. There was an upward trend in surface SM from 1982 to 2018 in CA. The relationship between the four SM products and aridity index and drought events showed that GLDAS could capture the hydrothermal conditions and drought events, followed by FLDAS, ERA5, and ECV. Our research is intended to provide some reference for the application of gridded SM products to ecosystem and water resources monitoring in arid regions. [ABSTRACT FROM AUTHOR]

Summary In addition to the soil saturated hydraulic conductivity, K s , the initial soil moisture content, θ i , is the quantity commonly incorporated in rainfall infiltration models for simulation of surface runoff hydrographs. Previous studies on the effect of the spatial heterogeneity of initial soil water content in the generation of surface runoff were generally not conclusive, and provided no guidance on designing networks for soil moisture measurements. In this study, the role of the spatial variability of θ i at the small watershed scale is examined through the use of a simulation model and measurements of θ i . The model combines two existing components of infiltration and surface runoff to model the flow discharge at the watershed outlet. The observed values of soil moisture in three experimental plots are combined to determine seven different distributions of θ i , each used to compute the hydrographs produced by four different rainfall patterns for two initial conditions classified as “dry” soil and “wet” soil. For rainfalls events typically associated with floods, the spatial variability of θ i at the watershed scale does not cause significant variations in surface runoff for initially dry or wet soils. Furthermore, when the main objective is to represent flood events a single ground point measurement of θ i in each area with the same land use may suffice to obtain adequate outflow hydrographs at the outlet. [ABSTRACT FROM AUTHOR]

Summary Process-based hydrological models have a long history dating back to the 1960s. Criticized by some as over-parameterized, overly complex, and difficult to use, a more nuanced view is that these tools are necessary in many situations and, in a certain class of problems, they are the most appropriate type of hydrological model. This is especially the case in situations where knowledge of flow paths or distributed state variables and/or preservation of physical constraints is important. Examples of this include: spatiotemporal variability of soil moisture, groundwater flow and runoff generation, sediment and contaminant transport, or when feedbacks among various Earth’s system processes or understanding the impacts of climate non-stationarity are of primary concern. These are situations where process-based models excel and other models are unverifiable. This article presents this pragmatic view in the context of existing literature to justify the approach where applicable and necessary. We review how improvements in data availability, computational resources and algorithms have made detailed hydrological simulations a reality. Avenues for the future of process-based hydrological models are presented suggesting their use as virtual laboratories, for design purposes, and with a powerful treatment of uncertainty. [ABSTRACT FROM AUTHOR]

Summary Extensive vegetation restoration practices have been implemented to control soil erosion on the Loess Plateau, China. However, no strict guidelines are available to determine the most suitable plant species for vegetation restoration within a given area. The objective of this study was to quantify the changes of each component (soil water storage, surface runoff, and actual evapotranspiration) of a water balance model and soil loss over time under eight different vegetation types, and to further determine the optimal vegetation type for soil and water conservation and sustainable ecological restoration on the steep slopes (>25°) on the Loess Plateau. The results indicated that vegetation type substantially affected soil water storage and that the greatest soil water storage in both the shallow (0–2 m) and the deep soil layers (2–5 m) occurred under Bothriochloa ischaemum L. (BOI). Vegetation type also affected surface runoff and soil losses. The most effective vegetation types for reducing soil erosion were BOI and Sea-buckthorn ( Hippophae rhamnoides L.), while Chinese pine ( Pinus tabulaeformis Carr . ) and Chinese pine + Black locust ( Robinia pseudoacacia L . ) were the most ineffective types. Soil water dynamics and evapotranspiration varied considerably among the different vegetation types. A soil water surplus was only found under BOI, while insufficient water replenishment existed under the other seven vegetation types. The higher water consumption rates of the seven vegetation types could result in soil desiccation, which could lead to severe water stresses that would adversely affect plant growth. This study suggested that both vegetation type and its effect on controlling soil erosion should be considered when implementing vegetation restoration and that BOI should be highly recommended for vegetation restoration on the steep slopes of the Loess Plateau. A similar approach to the one used in this study could be applied to other regions of the world confronted by the same problems of water scarcity along with the need for vegetation restoration. [ABSTRACT FROM AUTHOR]

Summary Despite the importance of groundwater recharge ( GR ), its accurate estimation still remains one of the most challenging tasks in the field of hydrology. In this study, with the help of inverse modeling, long-term (6 years) soil moisture data at 34 sites from the Automated Weather Data Network (AWDN) were used to estimate the spatial distribution of GR across Nebraska, USA, where significant spatial variability exists in soil properties and precipitation ( P ). To ensure the generality of this study and its potential broad applications, data from public domains and literature were used to parameterize the standard Hydrus-1D model. Although observed soil moisture differed significantly across the AWDN sites mainly due to the variations in P and soil properties, the simulations were able to capture the dynamics of observed soil moisture under different climatic and soil conditions. The inferred mean annual GR from the calibrated models varied over three orders of magnitude across the study area. To assess the uncertainties of the approach, estimates of GR and actual evapotranspiration ( ET a ) from the calibrated models were compared to the GR and ET a obtained from other techniques in the study area (e.g., remote sensing, tracers, and regional water balance). Comparison clearly demonstrated the feasibility of inverse modeling and large-scale (>10 4 km 2 ) soil moisture monitoring networks for estimating GR . In addition, the model results were used to further examine the impacts of climate and soil on GR . The data showed that both P and soil properties had significant impacts on GR in the study area with coarser soils generating higher GR ; however, different relationships between GR and P emerged at the AWDN sites, defined by local climatic and soil conditions. In general, positive correlations existed between annual GR and P for the sites with coarser-textured soils or under wetter climatic conditions. With the rapidly expanding soil moisture monitoring networks around the globe, this study may have important applications in aiding water resources management in different regions. [ABSTRACT FROM AUTHOR]

Summary Knowing the amount of soil water storage (SWS) in agricultural soil profiles is important for understanding physical, chemical, and biological soil processes. However, measuring the SWS in deep soil layers is more expensive and time consuming than in shallower layers. Whether deep SWS can be predicted from shallow-layer measurements through temporal stability analysis (TSA) remains unclear. To address this issue, the soil water content was measured at depths of 0–1.6 m (0.2-m depth intervals) at 79 locations along an agricultural slope on 28 occasions between July 2013 and October 2014. SWSs values were then calculated for the 0–0.4, 0.4–0.8, 0.8–1.2, 1.2–1.6, and 0–1.6 m soil layers. The SWS exhibited strong temporal stability, with mean Spearman’s ranking coefficients ( r s ) of 0.83, 0.92, 0.83, and 0.79 in the 0–0.4, 0.4–0.8, 0.8–1.2, and 1.2–1.6 m soil layers, respectively. As expected, the most temporally stable location (MTSL1) accurately predicted the average SWS of the corresponding soil layer, and the values of absolute bias relative to mean (ARB) were lower than 3% for all of the investigated soil layers. Using TSA, deep-layer SWS information could be predicted using a single-location measurement in the 0–0.4 m soil layer. The mean ARB values between the observed and predicted mean SWS values were 2.9%, 4.3%, 3.9%, and 2.7% in the 0.4–0.8, 0.8–1.2, 1.2–1.6, and 0–1.6 m soil layers, respectively. The prediction accuracy of the spatial distribution generally decreased with increasing depth, with linear determination coefficients ( R 2 ) of 0.93, 0.79, 0.72, and 0.84 for the four soil layers, respectively. The proposed method could further expand the application of the temporal stability technique in the estimation of SWS. [ABSTRACT FROM AUTHOR]

Summary Recently, the characterization of soil moisture spatiotemporal variability is recommended to consider temporal soil moisture anomalies because of their distinctive behaviors with absolute soil moisture and their importance in hydrological applications. Here we characterized soil moisture spatiotemporal variability in the Yuanzegou catchment (0.58 km 2 ) on the Loess Plateau of China, considering both absolute soil moisture and temporal anomalies. The dataset contained soil moisture observations in the 0–80 cm between 2009 and 2011 at 78 sampling locations. The spatial variance of time-invariant temporal means was shown to be the primary contributor (61.7–76.2%) to the total variance but the magnitude of this contribution was much lower than observed in large-scale studies. The seasonal variation in contribution can be attributed into differences in soil wetness conditions; lower contribution was found at intermediate wetness for spatial variances of temporal mean and temporal anomalies. Furthermore, the upward-convex relationship between spatial variance and spatial means of absolute soil moisture was mainly characterized by the covariance of temporal mean and temporal anomalies. Time stability of absolute soil moisture and its components were analyzed by using both the “accuracy” metric mean relative difference (MRD) and the “precision” metric variance of relative difference (VRD). As MRD was considered, time stability of absolute soil moisture primarily characterized time-invariant patterns. However, as VRD was used, the time stability of absolute soil moisture characterized only a small part of time-invariant or -variant pattern. [ABSTRACT FROM AUTHOR]

Summary This study explores the performance of soil moisture data from the global European Centre for Medium Range Weather Forecasts (ECMWF) ERA interim reanalysis datasets using the Weather Research and Forecasting (WRF) mesoscale numerical weather model coupled with the Noah Land surface model for hydrological applications. For evaluating the performance of WRF for soil moisture estimation, three domains are taken into account. The domain with best performance is used for estimating the soil moisture deficit (SMD). Further, several approaches are presented in this study to evaluate the efficiency of WRF simulated soil moisture for SMD estimation and compared against Soil Moisture and Ocean Salinity (SMOS) downscaled and non-downscaled soil moisture. In this study, the first approach is based on the empirical relationship between WRF soil moisture and the SMD on a continuous time series basis, while the second approach is focused on the vegetation cover impact on SMD retrieval, depicted in terms of growing and non-growing seasons. The linear growing and non-growing seasonal model in combination performs well with the NSE = 0.79, RMSE = 0.011 m; Bias = 0.24 m, in comparison to linear model (NSE = 0.70, RMSE = 0.013 m; Bias = 0.01 m). The performance obtained using WRF soil moisture is comparable to SMOS level 2 product but lower than the downscaled SMOS datasets. The results indicate that methodologies could be useful for modelers working in the field of soil moisture information system and SMD estimation at a catchment scale. The study could be useful for ungauged basins that pose a challenge to hydrological modeling due to unavailability of datasets for proper model calibration and validation. [ABSTRACT FROM AUTHOR]

Summary Soil moisture is a variable of paramount importance for a number of natural processes and requires the capacity to be routinely measured at different spatial and temporal scales (e.g., hillslope and/or small catchment scale). The electromagnetic induction (EMI) method is one of the geophysical techniques potentially useful in this regard. Indeed, it does not require contact with the ground, it allows a relatively fast survey of hillslope, it gives information related to soil depth greater than few centimetres and it can also be used in wooded areas. In this study, apparent electrical conductivity (EC a ) and soil moisture (SM) measurements were jointly carried out by using a multi-frequency EMI sensor (GEM-300) and Time Domain Reflectometry (TDR) probes, respectively. The aim was to retrieve SM variations at the hillslope scale over four sites, characterized by different land-soil units, located in a small mountainous catchment in southern Italy. Repeated measurements of EC a carried out over a fixed point showed that the signal variability of the GEM-300 sensor (Std. Err. ∼[0.02–0.1 mS/m]) was negligible. The correlation estimated between point EC a and SM, measured with both portable and buried TDR probes, varied between 0.24 and 0.58, depending on the site considered. In order to reduce the effect of small-scale variability, a spatial smoothing filter was applied which allowed the estimation of linear relationships with higher coefficient of correlation ( r ∼ 0.46–0.8). The accuracy obtained in the estimation of the temporal trend of the soil moisture spatial averages was in the range ∼4.5–7.8% v/v and up to the ∼70% of the point soil moisture variance was explained by the EC a signal. The obtained results highlighted the potential of EMI to provide, in a short time, sufficiently accurate estimate of soil moisture over large areas that are highly needed for hydrological and remote sensing applications. [ABSTRACT FROM AUTHOR]

Summary Modeling of soil water content (SWC) is one of the most studied topics in hydrology due to its essential application to water resources management. In this study, an adaptive neuro fuzzy inference system (ANFIS) method is used to simulate SWC in the extreme arid area. In-situ SWC datasets for soil layers, with depths of 40 cm (layer 1), 60 cm (layer 2) below surface was taken for the present study. The models analyzed different combinations of antecedent SWC values and the appropriate input vector has been selected based on the analysis of residuals. The performance of the ANFIS models in training and validation sets are compared with the observed data. In layer 1, the model which consists of six antecedent values of SWC, has been selected as the best fit model for SWC modeling. On the other hand, which includes two antecedent values of SWC, has been selected as the best fit model for SWC modeling at layer 2. In order to assess the ability of ANFIS model relative to that of the ANN model, the best fit of ANFIS model of layer 1 and layer 2 structures are also tested by two artificial neural networks (ANN), namely, Levenberg–Marquardt feedforward neural network (ANN-1) and Bayesian regularization feedforward neural network (ANN-2). The comparison was made according to the various statistical measures. A detailed comparison of the overall performance indicated that the ANFIS model performed better than both the ANN-1 and ANN-2 in SWC modeling for the validation data sets in this study. [ABSTRACT FROM AUTHOR]

Summary The appropriate grassland management practices play an important role for sustainable use of grassland. Rest grazing is beneficial to maintain higher grassland productivity and species diversity. However, little knowledge exists about the effects of rest grazing on soil water and carbon storages in arid regions. In the current study, we investigated the above- and below-ground community characteristics of the three-paired rest-grazing and grazing grasslands in an arid region of northern-west China. An 11-year rest grazing grassland and a continuous grazing grassland were studied to understand soil water and carbon storages. The results revealed that soil water content and carbon storage significantly increased after rest grazing, which was mainly attributable to increasing below-ground biomass density. At the 30–50 cm soil layer depth of the continuously grazing grassland, bulk density was higher and below-ground biomass was lower than the rest of the grazing grassland. This layer significantly affected the water cycle by blocking water exchange between the upper and lower soil layers. Soil carbon content did not significantly increase after rest grazing. The results indicated that rest grazing has a great potential for the recovery of soil water storage, and is an effective way to enhance grassland restoration in the arid area. [ABSTRACT FROM AUTHOR]

Summary Using the Green–Ampt model for layered soils, a theoretical method is proposed to estimate the layers’ hydraulic parameters based on infiltration test data. To evaluate the proposed method and to investigate the effect of the layers’ permeability on infiltration, constant head infiltration tests were conducted on six different two-layered soil columns. When the upper layer was more permeable than the lower layer, piston flow was observed in both layers, and the air-filled volume fraction of the lower layer was nearly equal to the observed values in homogenous soils. In this situation, the layers’ hydraulic parameters can be estimated from the proposed method. When the lower layer was more permeable than the upper layer, piston flow with a large air-filled volume fraction was observed in the lower layer. When the lower layer was significantly more permeable with an approximately zero wetting front matric head, preferential flows or instability in the wetting front was observed in the lower layer. In two-layered soils with a more permeable layer below a low permeable layer, the hydraulic conductivity of the lower layer cannot be estimated based on infiltration test data. [ABSTRACT FROM AUTHOR]

Summary Increasing the accuracy of pedotransfer functions (PTFs), an indirect method for predicting non-readily available soil features such as soil water retention characteristics (SWRC), is of crucial importance for large scale agro-hydrological modeling. Adding significant predictors (i.e., soil structure), and implementing more flexible regression algorithms are among the main strategies of PTFs improvement. The aim of this study was to investigate whether the improved effect of categorical soil structure information on estimating soil-water content at various matric potentials, which has been reported in literature, could be enduringly captured by regression techniques other than the usually applied linear regression. Two data mining techniques, i.e., Support Vector Machines (SVM), and k-Nearest Neighbors (kNN), which have been recently introduced as promising tools for PTF development, were utilized to test if the incorporation of soil structure will improve PTF’s accuracy under a context of rather limited training data. The results show that incorporating descriptive soil structure information, i.e., massive, structured and structureless, as grouping criterion can improve the accuracy of PTFs derived by SVM approach in the range of matric potential of −6 to −33 kPa (average RMSE decreased up to 0.005 m 3 m − 3 after grouping, depending on matric potentials). The improvement was primarily attributed to the outperformance of SVM-PTFs calibrated on structureless soils. No improvement was obtained with kNN technique, at least not in our study in which the data set became limited in size after grouping. Since there is an impact of regression techniques on the improved effect of incorporating qualitative soil structure information, selecting a proper technique will help to maximize the combined influence of flexible regression algorithms and soil structure information on PTF accuracy. [ABSTRACT FROM AUTHOR]

Summary Cosmic-Ray neutron sensing (CRS) is a unique approach to measure soil moisture at field scale filling the gap of current methodologies. However, CRS signal is affected by all the hydrogen pools on the land surface and understanding their relative importance plays an important role for the application of the method e.g., validation of remote sensing products and data assimilation. In this study, a soil moisture scaling approach is proposed to estimate directly the correct CRS soil moisture based on the soil moisture profile measured at least in one position within the field. The approach has the advantage to avoid the need to introduce one correction for each hydrogen contribution and to estimate indirectly all the related time-varying hydrogen pools. Based on the data collected in three crop seasons, the scaling approach shows its ability to identify and to quantify the seasonal biomass water equivalent. Additionally, the analysis conducted at sub-daily time resolution is able to quantify the daily vertical redistribution of the water biomass and the rainfall interception, showing promising applications of the CRS method also for these types of measurements. Overall, the study underlines how not only soil moisture but all the specific hydrological processes in the soil-plant-atmosphere continuum should be considered for a proper evaluation of the CRS signal. For this scope, the scaling approach reveals to be a simple and pragmatic analysis that can be easily extended to other experimental sites. [ABSTRACT FROM AUTHOR]

Summary The features of water regimes and nitrogen distribution in hillslope soil could provide information on the design suitability of hillslope shapes near water bodies and knowledge about the interception of nutrients before entering water bodies. Hillslope shape effects on overland flow and subsurface flow have been demonstrated by many previous studies, particularly in arid and semiarid regions. We present results from a 1-year monitoring study of two naturally formed hillslopes (convex and concave) with average gradients of 41% in a humid area (eastern China) to better understand the soil water content (SWC) variations and nitrogen concentration distributions. The SWC at the upper position of the convex hillslope (CVS) varied widely with relatively high coefficients of variance, while those at the lower position of the concave hillslope (CCS) maintained continuous, relatively high values during the study period. Comparatively, the low segment soil profile (top 80 cm) of the CCS maintained the highest soil water storage, followed by the up segment (top 80 cm) of the CCS and the low segment of the CVS, and finally the up segment of the CVS. Lateral subsurface flow discharged out from the 30–80 cm soil layer at the mid positions of the two hillslopes and the lower position of CVS; while the lateral subsurface flow from the upper hillslope recharged into the 30–80 cm soil layer at the upper and lower positions of the CCS and the upper position of CVS. Most of the soil nitrogen infiltrated the hillslope with water. Nitrogen concentrations at 30 cm depth on both the CVS and CCS varied temporally, responding strongly with rainfall events, while nitrogen concentrations at 80 cm depth changed slightly throughout the study period and maintained relatively low values. Nitrogen concentrations remained relatively low at the upper position on the CCS compared with those at the mid and lower positions. Inversely, on the CVS, the upper position had relatively higher or proximate nitrogen concentrations compared to its mid and lower positions. Nitrogen storage in the low segment soil profile of the CCS was higher than its up segment, but they were similar on the CVS. The nitrogen redistributions on the hillslopes were dominantly from water regimes, in particular, from the lateral subsurface flow. Due to the diversity of water regimes in different shaped hillslopes, the interception of lateral subsurface flow and its nitrogen pollution should receive more attention in a humid region. [ABSTRACT FROM AUTHOR]

Summary Planning of semi-urban developments is often hindered by a lack of knowledge on how changes in land-use affect catchment hydrological response. The temporal and spatial patterns of overland flow source areas and their connectivity in the landscape, particularly in a seasonal climate, remain comparatively poorly understood. This study investigates seasonal variations in factors influencing runoff response to rainfall in a peri-urban catchment in Portugal characterized by a mosaic of landscape units and a humid Mediterranean climate. Variations in surface soil moisture, hydrophobicity and infiltration capacity were measured in six different landscape units (defined by land-use on either sandstone or limestone) in nine monitoring campaigns at key times over a one-year period. Spatiotemporal patterns in overland flow mechanisms were found. Infiltration-excess overland flow was generated in rainfalls during the dry summer season in woodland on both sandstone and limestone and on agricultural soils on limestone due probably in large part to soil hydrophobicity. In wet periods, saturation overland flow occurred on urban and agricultural soils located in valley bottoms and on shallow soils upslope. Topography, water table rise and soil depth determined the location and extent of saturated areas. Overland flow generated in upslope source areas potentially can infiltrate in other landscape units downslope where infiltration capacity exceeds rainfall intensity. Hydrophilic urban and agricultural-sandstone soils were characterized by increased infiltration capacity during dry periods, while forest soils provided potential sinks for overland flow when hydrophilic in the winter wet season. Identifying the spatial and temporal variability of overland flow sources and sinks is an important step in understanding and modeling flow connectivity and catchment hydrologic response. Such information is important for land managers in order to improve urban planning to minimize flood risk. [ABSTRACT FROM AUTHOR]

Summary A combination of driving forces are increasing pressure on local, national, and regional water supplies needed for irrigation, energy production, industrial uses, domestic purposes, and the environment. In many parts of Europe groundwater quantity, and in particular quality, have come under sever degradation and water levels have decreased resulting in negative environmental impacts. Rapid improvements in the economy of the eastern European block of countries and uncertainties with regard to freshwater availability create challenges for water managers. At the same time, climate change adds a new level of uncertainty with regard to freshwater supplies. In this research we build and calibrate an integrated hydrological model of Europe using the Soil and Water Assessment Tool (SWAT) program. Different components of water resources are simulated and crop yield and water quality are considered at the Hydrological Response Unit (HRU) level. The water resources are quantified at subbasin level with monthly time intervals. Leaching of nitrate into groundwater is also simulated at a finer spatial level (HRU). The use of large-scale, high-resolution water resources models enables consistent and comprehensive examination of integrated system behavior through physically-based, data-driven simulation. In this article we discuss issues with data availability, calibration of large-scale distributed models, and outline procedures for model calibration and uncertainty analysis. The calibrated model and results provide information support to the European Water Framework Directive and lay the basis for further assessment of the impact of climate change on water availability and quality. The approach and methods developed are general and can be applied to any large region around the world. [ABSTRACT FROM AUTHOR]

Summary Soil moisture is a key variable in hydrology, meteorology and agriculture. It is influenced by many factors, such as topography, soil properties, vegetation type, management, and meteorological conditions. The role of these factors in controlling the spatial patterns and temporal dynamics is often not well known. The aim of the current study is to analyze spatio-temporal soil moisture patterns acquired across a variety of land use types, on different spatial scales (plot to meso-scale catchment) and with different methods (point measurements, remote sensing, and modeling). We apply a uniform set of tools to determine method specific effects, as well as site and scale specific controlling factors. Spatial patterns of soil moisture and their temporal development were analyzed using nine different datasets from the Rur catchment in Western Germany. For all datasets we found negative linear relationships between the coefficient of variation and the mean soil moisture, indicating lower spatial variability at higher mean soil moisture. For a forest sub-catchment compared to cropped areas, the offset of this relationship was larger, with generally larger variability at similar mean soil moisture values. Using a geostatistical analysis of the soil moisture patterns we identified three groups of datasets with similar values for sill and range of the theoretical variogram: (i) modeled and measured datasets from the forest sub-catchment (patterns mainly influenced by soil properties and topography), (ii) remotely sensed datasets from the cropped part of the Rur catchment (patterns mainly influenced by the land-use structure of the cropped area), and (iii) modeled datasets from the cropped part of the Rur catchment (patterns mainly influenced by large scale variability of soil properties). A fractal analysis revealed that all analyzed soil moisture patterns showed a multifractal behavior, with at least one scale break and generally high fractal dimensions. Corresponding scale breaks were found between different datasets. The factors causing these scale breaks are consistent with the findings of the geostatistical analysis. Furthermore, the joined analysis of the different datasets showed that small differences in soil moisture dynamics, especially at the upper and lower bounds of soil moisture (at maximum porosity and wilting point of the soils) can have a large influence on the soil moisture patterns and their autocorrelation structure. Depending on the prevalent type of land use and the time of year, vegetation causes a decrease or an increase of spatial variability in the soil moisture pattern. [ABSTRACT FROM AUTHOR]

Summary Assessment of uncertainty in land surface models is complex, mainly because of the numerous sources of error from model parameters, initial states, input forcing data, and the model structure. These sources of uncertainty interact together to impact the simulated output generated from the land surface model. To account for these uncertainties, the goal in diagnostic model evaluation has been to determine the erroneous/inadequate components of the model structure that need improvement. However, the specification of inaccurate model components is not straightforward, requiring crucial steps to determine the uncertainty contributions from individual error sources. Also, the interaction between the uncertainty sources makes it difficult to assess the impact of the individual error sources on the simulated output. The approach undertaken in this study was to quantify the specific uncertainties linked with model parameters, states, input forcing variables, and spatial variations in landscape properties, such that the remaining model uncertainty is equivalent to the inadequacy/inaccuracy associated with the model structure. This study employed the Evolutionary Data Assimilation, together with multi-dimensional clustering, to quantify these individual uncertainties for the Community Atmosphere Biosphere Land Exchange (CABLE) model, in terms of soil moisture estimation for the Yanco area in south-east Australia. The findings showed that the updated soil moisture was more accurate than both the open loop and calibrated estimates. The minimum uncertainty for model components was found to reduce the original and updated bounds by 68% and 62% respectively. The estimated model pathway has accurately reproduced the updated estimates with less than 0.02 m 3 / m 3 error, and was found to be more accurate than both the calibrated and the updated estimates when evaluated against in-situ soil moisture. [ABSTRACT FROM AUTHOR]

Summary Soil water is a major driving force for plant community succession in semi-arid area. Many studies have focused on the relationships of species diversity–productivity, but few studies have paid attentions to the effects of soil water content heterogeneity on the plant species diversity in the semi-arid loess regions. To determine relationship of soil water content heterogeneity and plant community structure properties a semi-arid steppe on the Loess Plateau, we conducted a gradient analysis of soil water content variation and above- and below-ground properties of plant communities. Results showed that community coverage, above- and below-ground biomass were significantly and positively related to the surface soil water contents (0–5 cm). Plant diversity (Shannon index and Richness index) were closely correlated to soil water content at the soil depth of 0–10 cm. But plant height, litter biomass and root/shoot ratio were not related to soil water content. These results showed that there is an positive interaction effects for plant diversity and soil water content in the semi-arid grassland communities. Our observations indicate that change of plant species diversity is also an important community responses to soil water content heterogeneity in the semi-arid grassland ecosystem. [ABSTRACT FROM AUTHOR]

Summary Alley cropping is an agroforestry practice whereby crops are grown between hedgerows of trees planted at wide spacings. The local climate and the physiological adaptation mechanisms of the trees are key factors in the growth and survival of the trees and intercrops, because they directly affect the soil moisture distribution. In order to evaluate the long-term hydrological impact of climate change in an alley cropping system in eastern Canada, we developed a framework that combines local soil moisture data with local projections of climate change and a model of soil water movement, root uptake and evapotranspiration. Forty-five frequency domain reflectometers (FDR) along a transect perpendicular to the tree rows generated a two-year dataset that we used for the parameterization and evaluation of the model. An impact study with simulations based on local projections of three global and one regional climate simulation suggest that the soil becomes drier overall in the period between 2041 and 2070, while the number of critically wet periods with a length of one day increases slightly with respect to the reference period between 1967 and 1996. Hydrological simulations based on a fourth climate scenario however point toward wetter conditions. In all cases the changes are minor. Although our simulations indicate that the experimental alley cropping system will possibly suffer drier conditions in response to higher temperatures and increased evaporative demand, these conditions are not necessarily critical for vegetation during the snow-free season. [ABSTRACT FROM AUTHOR]

Highlights: [•] The surface soil moisture content (SMC) dynamic of mountain grasslands is analyzed. [•] RADARSAT SAR, hydrological model outputs and ground observations are compared. [•] We evaluate RADARSAT with respect to a hydrological model in estimating SMC. [•] RADARSAT has a good ability to reproduce SMC patterns for alpine grasslands. [•] The main physical controls on SMC patterns are soil, land cover and topography. [ABSTRACT FROM AUTHOR]

Highlights: [•] There exists a potential for estimating soil hydraulic properties from soil moisture data. [•] van Genuchten’s n parameter strongly related to the dry part of soil moisture spatial variability relationship. [•] Ks variability influences the n parameter effect on soil moisture variability. [•] Climate affects maximum moisture variability but not its corresponding average. [ABSTRACT FROM AUTHOR]

Highlights: [•] Hydrological connectivity of soil moisture is employed in the modeling. [•] Representation points were identified and used to predict soil moisture. [•] A hillslope wetness index shows threshold behavior in model predictability. [Copyright &y& Elsevier]

Highlights: [•] We performed a series of three replicated field scale irrigation experiments. [•] We examined the link between irrigation, flow paths, and pesticide transport. [•] Irrigation amount and soil moisture threshold control solute transport. [•] We observed remobilization of stored pesticides occurring together with soil water mobilization. [Copyright &y& Elsevier]

Highlights: [•] First time evaluation of long-term soil moisture products in NLDAS-2 using three in situ observations. [•] Evaluation was performed for multiple models, soil layers and metrics. [•] Provide solid analysis and support for use of soil moisture in U.S. drought monitor. [Copyright &y& Elsevier]

Highlights: [•] Soil hydraulic parameter retrieval from surface moisture data is demonstrated. [•] Current soil hydraulic property information is based on soil texture maps. [•] Soil parameters from surface moisture is an improvement over published data. [Copyright &y& Elsevier]

Highlights: [•] Crusts may serve as biomarkers for surface wetness duration. [•] A close link between crust biomass and the upper 30cm soil moisture was found. [•] Crust type may serve as biomarkers for the upper 30cm soil moisture. [•] Crust typology is important for understanding ecosystem function and structure. [Copyright &y& Elsevier]

Highlights: [•] We studied the temporal stability characteristics of near-surface soil moisture. [•] The strong temporal persistence existed for near-surface soil moisture. [•] Identified representative locations well-represented the mean soil moisture content. [•] The mean soil moisture contents can be predicted by other topography and soil factors. [•] A priori identification of representative locations is presently infeasible for other fields. [ABSTRACT FROM AUTHOR]

Highlights: [•] CCGP can be used as a rapid ground truthing method for soil moisture measurements. [•] Expected accuracy of the CCGP method is mostly within ±1%. [•] CCGP can be used as stand alone method for small scale hydrological applications. [ABSTRACT FROM AUTHOR]