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• 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]

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]

Summary: In this paper we extend the usefulness of the TOPKAPI model by adding a Green-Ampt infiltration module and make the model and source code freely available on the internet as PyTOPKAPI. Then, we investigate the sensitivity of the PyTOPKAPI hydrological model to systematic bias in the variables rainfall and evapotranspiration, as well as the physically based soil properties that describe the model behaviour. The model sensitivity is assessed in terms of relative changes in the Soil Saturation Index (SSI), which is defined as the percentage of soil pore space filled by water. The volumetric soil moisture content, can be calculated from SSI using location dependent soil properties, if required. The model sensitivity is calculated at 7200 sites in South Africa, for a 2.5 year simulation period with a time-step of three hours. This large spatial extent gives results for a wide array of climates and land properties. Overall, the sensitivity of the model turns out to be a closely linear function of, and the same order of magnitude as (or less than), the forcing/parameter bias. This indicates that the model is robust to errors in forcing/parameters. The results also show that the best estimates of soil water can be obtained by improving estimates of the storage parameters and rainfall forcing. However, the storage parameters must be obtained from static soil property data-sets and we show that there is value in making improvements to the rainfall forcing (in this case TRMM 3B42RT) for places where it is biased relative to observed rainfall. This work is particularly relevant for model application in ungauged basins, where the quality of forcing variables and physical parameters cannot be calibrated. [Copyright &y& Elsevier]

Abstract: Rainfall is regarded as the most important input for the hydrology and nonpoint source (H/NPS) models and uncertainty related to rainfall is generally recognized as a major challenge in watershed modeling. In this paper, we focus on the impact of spatial rainfall variability on H/NPS modeling of a large watershed. The uncertainty introduced by spatial rainfall variability was determined using a number of commonly-used interpolation methods: (1) the Centroid method; (2) the Thiessen Polygon method; (3) the Inverse Distance Weighted (IDW) method; (4) the Dis-Kriging method; and (5) the Co-Kriging method. The Soil and Water Assessment tool (SWAT) was used to quantify the effect of rainfall spatial variability on watershed H/NPS modeling of the Daning watershed in China. Results indicated that these interpolation methods could contribute significant uncertainty in spatial rainfall variability and the carry-magnify effect caused even larger uncertainty in the H/NPS modeling. This uncertainty was magnified from hydrology modeling (stream flow) into NPS modeling (sediment, TP, organic nitrogen (N) and dissolved N). This study further suggested that H/NPS prediction uncertainty relating to spatial rainfall variability was scale-dependent due to the averaging effect of spatial heterogeneity. From a practical point of view, a global interpolation method, such as IDW and Kriging, as well as elevation data derived from a digital elevation model (DEM), should be included into the H/NPS models for reliable predictions in larger watersheds. [Copyright &y& Elsevier]

Summary: Knowledge of soil moisture is critical to understanding many of the hydrological processes that are of interest in soil hydrology, meteorology, and ecology research. In the present study, we used rainfall and soil moisture data from 2003 to 2008 measured at grassland and meadow sites in the Qilian Mountains of northwestern China to analyze the response of soil moisture to rainfall event size during the growing season. The responses of soil moisture at the grassland and meadow sites to rainfall event size were similar, although total rainfall, the frequency of large rainfall events, and the vegetation types were different. Soil moisture at depths of 20 and 40cm increased significantly after rainfall events larger than 15 and 20mm, respectively, but the magnitude of these changes varied in response to differences in the duration of the dry interval preceding the rain. Soil moisture at depths from 60 to 80cm in the grassland increased obviously with rainfall events larger than 40mm or after two consecutive large rainfall events, but was not significantly correlated with rainfall event size in the meadow. Soil moisture at depths from 120 to 160cm did not change significantly during the growing season at either site. The soil water storage at depths from 20 to 80cm at both sites increased obviously after rainfall events larger than 20mm. The present results suggest that large rainfall events (>20mm) play a key role in increasing soil water storage in the grassland and meadow ecosystems of these semi-arid mountains. [ABSTRACT FROM AUTHOR]

Summary: Under future climate scenarios, rainfall patterns and species composition in California grasslands are predicted to change, potentially impacting soil-moisture dynamics and ecosystem function. The primary objective of this study was to assess the impact of altered rainfall on soil-moisture dynamics in three annual grassland vegetation types. We monitored seasonal changes in soil moisture under three different rainfall regimes in mesocosms planted with: (1) a mixed forb-grass community, (2) an Avena barbata monoculture, and (3) an Erodium botrys monoculture. We applied watering treatments in pulses, followed by dry periods that are representative of natural rainfall patterns in California annual grasslands. While rainfall was the dominant treatment, its impact on hydrological processes varied over the growing season. Surprisingly, there were only small differences in the hydrologic response among the three vegetation types. We found significant temporal variability in evapotranspiration, seepage, and soil-moisture content. Both Water Use Efficiency (WUE) and Rain Use Efficiency (RUE) decreased as annual precipitation totals increased. Results from this investigation suggest that both precipitation and vegetation have a significant interactive effect on soil-moisture dynamics. When combined, seasonal precipitation and grassland vegetation influence near-surface hydrology in ways that cannot be predicted from manipulation of a single variable. [Copyright &y& Elsevier]

Summary: Information on soil moisture is vital to describe various hydrological processes. Soil moisture parameters are normally measured using buried sensors in the soil. Alternatively, spatial and temporal characteristics of surface soil moisture are estimated through satellites. Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) is one of such satellites that estimate surface soil moisture in an operational context. These estimates need validation prior to use in various hydrological and water management applications. Such validations are normally carried out using field measurements of soil moisture. This is not technically feasible in vast river basins such as the Indus Basin and for pixel sizes of 25km×25km with non-homogeneous soils and land use. Therefore, AMSR-E data interpreted with Njoku model and posted by the National Snow and Ice Data Center (NSIDC) for the Indus Basin is evaluated by comparing it against auxiliary spatial data. The auxiliary data exists of (i) land use, (ii) rainfall from the Tropical Rainfall Measuring Mission (TRMM) satellite, (iii) seasonality of vegetation from SPOT-Vegetation and (iv) saturated water content (θsat ) inferred from soil maps. A strong relationship was observed between rainfall and surface soil moisture in the land use class “rainfed”. Spearman’s rank correlation coefficient (rs ) between the soil moisture and rainfall ranged from 0.14 to 0.55 with a mean of 0.36. For irrigated land uses, rs ranged from −0.04 to 0.52 with a mean of 0.29 due to control of soil moisture by irrigation water supply. The temporal analysis of soil moisture data with vegetation time series showed resemblance with growth phenology. Higher Pearson’s correlation coefficient (r) between the soil moisture and vegetation development was found for time lags of a few weeks. The daily maximum values estimated by AMSR-E ranged from 0.08 to 0.38cm3 cm−3. The maximum values were near, but below θsat limits for different soil types. AMSR-E captured the flooding processes during July and August 2010 by showing the soil moisture values to approximate the saturated soil moisture content for areas that are reported to be flooded. This suggests that the absolute AMSR-E soil moisture data from NSIDC are accurate in the upper range of land wetness. It is concluded that AMSR-E surface soil moisture data exhibits spatio-temporal behavior, and the trends agree with auxiliary spatial data sets. [Copyright &y& Elsevier]

Summary: Variation in soil moisture content throughout soil profiles during several sequential rainfalls represents the internal hydrological response on a hillslope scale. A multiplex TDR system has been operating on a mountainous hillslope to obtain the time series of soil moisture along two transects in the study area. The soil moisture modeling conducted in this study highlights our understanding of the inter-relationships between soil moistures at identical spatial locations, but at different depths. A sequential procedure was used for the time series modeling to delineate an appropriate model for application to all monitoring points. The feedback relationship of soil wetness between two different depths was expressed with the proposed vector autoregressive model. Based on the successful modeling of 31 coupled soil water histories, the vertical distributions of the stochastic model throughout the study area were obtained. The distribution of the delineated models implied a spatial distribution of the hydrological processes, such as vertical infiltration for the upper soil layers and some of the lower soil layers (38 out of 62 models), lateral redistribution and subsurface flow over bedrock mostly for the lower soil layers (24 out of 62 models) on the steep hillslope. With the use of the resultant models, applications were proposed to improve the data acquisition system, i.e. gap filling for missing data and limited prediction for an ungauged location. [ABSTRACT FROM AUTHOR]

Summary: A simple conceptual model for local infiltration into a two-layered soil profile with the upper layer much more permeable than the subsoil is proposed. It is a reformulation of a conceptual model of [J. Hydrol. 273, 58–73] involving coupled ordinary differential equations that was developed for infiltration and soil water content distribution in a two-layered soil under any rainfall pattern. The simpler model framework proposed here is restricted to the estimate of infiltration rate alone in cases where the upper layer is more permeable than the lower one. First, the maximum value, K 1 h , of the saturated hydraulic conductivity of the upper soil, K 1 s , that yields the time to ponding while the dynamic wetting front is entirely within this homogeneous layer is defined by a conceptual approach. Then, parameterized forms for the time when the wetting front reaches the interface, for time to ponding associated with K 1 s ⩾ K 1 h , and for the suction head at the interface as a function of time are determined. With these functional forms, the current model utilizes algebraic equations for ponding time and soil infiltration capacity, thus providing substantial savings in computational effort for describing infiltration in two-layered soils. When compared to the detailed conceptual model, the proposed model represents the infiltration rate accurately, with less than 10% average error on the dynamic cumulative infiltration depth. This simplified model offers potential for upscaling to field-scale infiltration over layered soils exhibiting spatial heterogeneity. [Copyright &y& Elsevier]

Summary: Soil infiltrability, or infiltration capacity, is the rate at which water penetrates into the soil at a rate directly controlled by soil factors alone. The infiltrability is of great importance in understanding and managing hydrological processes, crop water supply, irrigation, and soil erosion. The infiltration dynamics measured with the run off-on-out (ROOO) method follows the changes in infiltrability during the infiltration process under rainfall conditions. In this study, the ROOO method was used to quantitatively measure the soil infiltrability under three rainfall intensities (20, 40 and 60mm/h) and three antecedent soil moisture contents (2.6%, 10.4% and 19.5%, equivalent to 7.5% (air-dry), 30% and 60% of field capacity, respectively, of a clay loam soil. The soil infiltrability determined by the ROOO method decreased with increase in initial soil water content, due to the lower hydraulic gradient at the wetting front. Surface seal formation due to raindrop impact had a significant influence on soil infiltrability and was related to the soil water content. The faster wetting rate of drier soil under higher rainfall intensities, or when using the double-ring infiltrometer for comparison, caused severe aggregate breakdown to promote surface sealing, and significant decrease in soil infiltrability. Cumulative infiltration increased rapidly at the beginning of the infiltration process and then increased approximately linearly with time as the infiltration rate approached to constant. The rate of increase in cumulative infiltration was less under higher initial soil water contents, especially in the initial rainfall stage. Moreover, lower rainfall intensity resulted in higher infiltration rates and greater cumulative infiltration. The soil infiltrability processes measured with the ROOO method were fitted better by Kostiakov, Horton and Philip infiltration models than those measured by the double-ring infiltrometer method. The ROOO method provided reliable data for the entire infiltration process without the limitations of conventional rainfall simulation during the initial phase or of the drawbacks of the double-ring infiltrometer method. The results will have introductory meanings to further studies along this line. [Copyright &y& Elsevier]

Summary: This study conducts a broad sensitivity analysis, taking into account the influence of initial soil moisture content in two soil layers, layer depths, event properties, and two infiltration models. A distributed hydrology and soil erosion model (LISEM) is used. Using the terrain data from the Catsop research catchment and two different rainfall events, the sensitivity of discharge is investigated for a range of pre-event soil moisture contents (0.1–0.40cm3 cm−3) in two-layers for a two-layer Green–Ampt as well as Richards infiltration model. The sensitivity of the predicted discharge to the initial condition of soil moisture appears to depend highly on all factors: infiltration model, event properties, topsoil/subsoil depth configuration and the level of the initial condition itself. There are interaction effects between all the factors. However, the effect of the different infiltration models is most pronounced. The Green–Ampt model shows less sensitivity to moisture content variation of both top and subsoil. Top/subsoil depth configuration rarely influences the results of the Green–Ampt model. The Richards model shows a highly variable discharge – initial soil moisture relation with changing rainfall intensity and topsoil/subsoil depth configurations. Two methods of sensitivity analysis, relative sensitivity and One factor-At-a Time sensitivity, have been used. The two methods gave comparable results. Depending on the other parameter values, 1% changes in topsoil moisture content resulted into 0.8–1.81% and 0.03–3.5% changes in total discharge predicted by the Green–Ampt and Richards models, respectively. [ABSTRACT FROM AUTHOR]

Summary: Hillslope threshold response to storm rainfall is poorly understood. Basic questions regarding the type, location, and flow dynamics of lateral, subsurface flow remain unanswered, even at our most intensively studied field sites. Here we apply a forensic approach where we combined irrigation and excavation experiments at the well studied Maimai hillslope to determine the typology and morphology of the primary lateral subsurface flowpaths, and the control of bedrock permeability and topography on these flowpaths. The experiments showed that downslope flow is concentrated at the soil bedrock interface, with flowpath locations controlled by small features in the bedrock topography. Lateral subsurface flow is characterized by high velocities, several orders of magnitude greater than predicted by Darcy’s Law using measured hydraulic conductivities at the site. We found the bedrock to be moderately permeable, and showed that vertical percolation of water into the bedrock is a potentially large component of the hillslope water balance. Our results suggest that it is the properties of the bedrock (topography and permeability) that control subsurface flow at Maimai, and the soil profile plays a less significant role than previously thought. A companion paper incorporates these findings into a conceptual model of hydrological processes at the site to explore the generalities of whole-hillslope threshold response to storm rainfall. [ABSTRACT FROM AUTHOR]

Summary: The Gourma site in Mali is one of the three instrumented meso-scale sites deployed in West-Africa as part of the African Monsoon Multi-disciplinary Analysis (AMMA) project. Located both in the Sahelian zone sensu stricto, and in the Saharo–Sahelian transition zone, the Gourma meso-scale window is the northernmost site of the AMMA-CATCH observatory reached by the West African Monsoon. The experimental strategy includes deployment of a variety of instruments, from local to meso-scale, dedicated to monitoring and documentation of the major variables characterizing the climate forcing, and the spatio-temporal variability of surface processes and state variables such as vegetation mass, leaf area index (LAI), soil moisture and surface fluxes. This paper describes the Gourma site, its associated instrumental network and the research activities that have been carried out since 1984. In the AMMA project, emphasis is put on the relations between climate, vegetation and surface fluxes. However, the Gourma site is also important for development and validation of satellite products, mainly due to the existence of large and relatively homogeneous surfaces. The social dimension of the water resource uses and governance is also briefly analyzed, relying on field enquiry and interviews. The climate of the Gourma region is semi-arid, daytime air temperatures are always high and annual rainfall amounts exhibit strong inter-annual and seasonal variations. Measurements sites organized along a north–south transect reveal sharp gradients in surface albedo, net radiation, vegetation production, and distribution of plant functional types. However, at any point along the gradient, surface energy budget, soil moisture and vegetation growth contrast between two main types of soil surfaces and hydrologic systems. On the one hand, sandy soils with high water infiltration rates and limited run-off support almost continuous herbaceous vegetation with scattered woody plants. On the other hand, water infiltration is poor on shallow soils, and vegetation is sparse and discontinuous, with more concentrated run-off that ends in pools or low lands within structured endorheic watersheds. Land surface in the Gourma is characterized by rapid response to climate variability, strong intra-seasonal, seasonal and inter-annual variations in vegetation growth, soil moisture and energy balance. Despite the multi-decadal drought, which still persists, ponds and lakes have increased, the grass cover has largely recovered, and there are signs of increased tree cover at least in the low lands. [Copyright &y& Elsevier]

Summary: This paper describes a study developed to estimate available surface water resources in twelve basins in the north of Spain by means of numerical tools. These basins are characterized by their small surface area, short length, steep slope, and scarce hydrological gauging. The available information has conditioned the use of a lumped continuous simulation hydrological model. The HEC-HMS program with continuous soil moisture accounting (SMA) model was chosen. The model development is described in detail, focusing on the methodology used to fill the incomplete daily rainfall series and to establish the daily uniform precipitation over the sub-basins taking advantage of the results provided by a previous study with different time scale. Several techniques were used to calibrate and validate the hydrologic model, since few stream gauges were available. Comparison between observed flow data and results provided by simulation was in good agreement. The model application allowed assessment of the daily flow series at different main stream locations during a continuous time period up to thirty-three water years. These series have been used as input data for instream flows studies following the guidelines of the European Water Framework Directive. The processing of results allowed for analysis of the hydrological behaviour of the selected basins focusing in low water periods. [Copyright &y& Elsevier]

• Antecedent SMAP soil moisture (SM) has a significant relationship with runoff ratio. • Probability of higher runoff ratios increases as event total rainfall increase. • SM-deficit-normalized rainfall has a better predictive power than SM and rainfall. • Vegetation has significant effect on runoff in an agricultural region. Following results by Crow et al. (2017) [Geophys. Res. Lett. 44, 5495–5503] on the impact of antecedent soil moisture on runoff production, we investigate total runoff production during individual rainfall-runoff events in agricultural landscapes as a function of antecedent soil moisture, total rainfall, and vegetation cover for catchments with drainage areas ranging from 80 to 1000 km 2 in the state of Iowa, USA. For our study, we use Enhanced SMAP soil moisture estimates, the MODIS enhanced vegetation index (EVI), gauge-corrected Stage IV radar rainfall, and USGS streamflow data. We analyze the event runoff ratio as a function of event-scale rainfall, antecedent SMAP soil moisture and soil-moisture-deficit-normalized rainfall for the events in a period from March 31, 2015 to October 31, 2018. Our goal is to confirm the relationships identified by Crow et al. (2017) in heavily managed agricultural landscapes and to refine some of their methodological steps to quantify the role of additional variables controlling runoff production. To this end, we define three different strategies to identify rainfall-runoff events and add a baseflow separation step to better insulate event scale stormflow runoff. We test the effects of antecedent soil moisture, rainfall, and vegetation on the event-scale runoff ratio. The antecedent SMAP soil moisture and event-scale rainfall are found to have significant predictive power in estimating event runoff ratio. Soil moisture deficit-normalized rainfall, introduced as the ratio of event-scale rainfall to available space in top soil before initiation of the event, exhibited a more distinct relationship with runoff ratio. The long-term analysis of runoff ratio, rainfall, and MODIS EVI indicated that, in an agricultural region, vegetation plays a significant role in determining event-scale runoff ratios. The methodology and outcome of our study have direct implications on real-time flood forecasting and long-term hydrologic assessments. [ABSTRACT FROM AUTHOR]