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Extreme rainfall events pose a severe challenge to soil and water conservation, even in areas with high vegetation cover on the Loess Plateau. In this study, the artificial extreme rainfalls with cumulative rainfall of 270 mm and intensity of 60 mm · hr−1 were conducted on in‐situ experimental plots (20 × 2.5 m) on a loess gully–slope with gradients of 35°–40° that were treated with different grass coverage: (0%, 30%–40%, 70%–80%, >90%). The ephemeral gully/rill and shallow landslide occurred in plots were analyzed. Revegetation changed the erosion type on gully–slope, reducing gully erosion but promoting shallow landslide due to the change from infiltration–excess runoff to saturation–excess runoff. Under grass coverage of >90%, over 95% of rainfall seeped into the soil, and subsurface flow was generated due to the lower saturated hydraulic conductivity of underlying soil, which increased the possibility of landslides. The average erosion rate (0.36–3.29 g · m−2 min−1; no obvious erosion) in plots with 70%–80% coverage was 95.5% lower than that in bare land plots (27.8–47.5 g · m−2 min−1; ephemeral gully erosion), while due to landslides the average erosion rate in plots with >90% coverage (135.1–184.3 g · m−2 min−1) was 86.5 times higher than that in plots with 70%–80%. For grass, a coverage of 70%–80% was most effective in preventing soil erosion, controlling gully erosion and preventing landslides under extreme rainfall. These results deepen the understanding of the complex relationship between vegetation, gully erosion, and landslides in ecologically sensitive areas. Key Points: Vegetation changed the erosion type on slope from water erosion to gravity erosionHigh‐coverage vegetation promoted shallow landslides under extreme rainfallFor grass cover, a coverage of 70%–80% was most effective in preventing soil erosion [ABSTRACT FROM AUTHOR]

Because tension infiltrometers apply water through a disk of finite size, the infiltrated water moves laterally as well as downward. Only the vertical component of this flow is indicative of the hydraulic conductivity K, so the algorithm for computing K must include a way of isolating that component from the total flow. Some commonly used formulas correct for the multidimensional effects by subtracting an estimate of the laterally spreading flow. For disks smaller than about 200 mm in diameter, however, lateral spreading constitutes so much of the total flow that these subtractive formulas lose considerable accuracy, and sometimes overcorrect so severely as to produce a negative number for K. Other methods rely on empiricisms that are not completely consistent with unsaturated‐flow theory and that require prior knowledge of certain soil properties. We developed a new formula that uses a multiplicative factor instead of a subtracted term to achieve the needed correction. For testing we conducted numerical experiments with synthetic data produced by solving the Richardson‐Richards equation using the code VS2DRTI, for diverse media and a range of disk sizes, including the widely used 45‐mm diameter. We compared K values calculated from our formula to the actual K used to generate the simulated data, as well as to results from other published formulas. This comparison shows that our method provides an algorithm based in unsaturated‐flow theory that produces more reliable values for small disks without requiring prior knowledge of soil properties. Plain Language Summary: Tension infiltrometers are much‐used devices for field measurement of the hydraulic conductivity of soil. Hydraulic conductivity is a measure of how easily water flows in the soil. If it is high, water at the soil surface can cause more infiltration and less runoff, and can move faster within the soil. Tension infiltrometers apply water over a circular area of the soil surface and measure the flow rate of the water being drawn into the soil. For small infiltrometers, a large fraction of the water spreads laterally within the soil, rather than straight down. Consequently, the amount of water flow measured by the infiltrometer is much greater than just the downward‐flowing portion needed to indicate hydraulic conductivity. This laterally flowing water must be corrected for in the formulas that calculate hydraulic conductivity from the measured data. If the diameter is less than about 200 mm, as it is for the great majority of infiltrometers, the needed correction is so large that large errors result from the approximate corrections applied by commonly used formulas. Our new method uses an estimate of the distance the water spreads laterally to give more accurate results. Key Points: Measurements from small diameter tension infiltrometers require a large correction to account for the lateral spreading of infiltrated waterThe use of a multiplicative, rather than subtractive, correction method provides greater accuracy when the correction is large, as needed for small disksOur proposed method provides a direct estimate of how much flow is going into lateral versus vertical components, thereby yielding additional information for interpretation as well as more accurate hydraulic conductivity results [ABSTRACT FROM AUTHOR]

Despite its importance for hydrological and ecological soil functioning, characterizing, and quantifying soil structure in the field remains a challenge. Traditional characterization of soil structure often relies on point measurements, more recently, we advanced the use of minimally invasive geophysical methods that operate at plot‐field scales and provide information under natural conditions. In this study, we expand the application using geoelectrical and time‐domain reflectometry (TDR) monitoring of soil water dynamics to infer impacts of compaction on soil structure and function. We developed a modeling scheme combining a new pedophysical model of soil electrical conductivity and a soil‐structure‐informed one‐dimensional water flow and heat‐transfer model. The model was used to interpret Direct Current (DC)‐resistivity and TDR monitoring data in compacted soils at the Soil Structure Observatory (SSO) located in the vicinity of Zürich, Switzerland. We find that (1) soil compaction leads to a persistent decrease in soil electrical resistivity and (2) that compacted soils are typically drier than non‐compacted soils during long drying events. The main decrease in electrical resistivity is attributed to decreasing macroporosity and increasing connectivity of soil aggregates due to compaction. Higher water losses in compacted soils are explained in terms of enhanced evaporation. Our work advances characterization of soil structure at the field scale with electrical methods by offering a physically based explanation of the impact of soil compaction on electrical properties and by interpreting DC‐resistivity data in terms of soil water dynamics. Key Points: Effects of soil compaction on soil hydraulic properties are reflected in soil water dynamics as seen by geoelectrical measurementsPedophysical model predictions linking soil structural traits with electrical resistivity are consistent with soil hydraulic modelsSoil compaction reduced both electrical resistivity and hydraulic conductivity and enhanced surface evaporation relative to uncompacted soil [ABSTRACT FROM AUTHOR]

This study proposes a hydrological drought index, the standardized wetness index (SWI), by combining the structure of the Standardized Precipitation-Evapotranspiration Index and actual-evaporation-based residual water-energy ratio, in which actual evaporation is estimated using the Budyko hypothesis. The SWI requires three parameters-precipitation, potential evaporation, and parameter [ABSTRACT FROM AUTHOR]

Fire is known to impact soil properties and hydrological flow paths. However, the impact of prescribed vegetation burning on blanket peatland hydrology is poorly understood. We studied 10 blanket peat headwater catchments. Five were subject to prescribed burning, while five were unburnt controls. Within the burnt catchments, we studied plots where the last burn occurred ∼2 (B2), 4 (B4), 7 (B7), or greater than 10 years (B10+) prior to the start of measurements. These were compared with plots at similar topographic wetness index locations in the control catchments. Plots subject to prescribed vegetation burning had significantly deeper water tables (difference in means = 5.3 cm) and greater water table variability than unburnt plots. Water table depths were significantly different between burn age classes (B2 > B4 > B7 > B10+) while B10+ water tables were not significantly different to the unburnt controls. Overland flow was less common on burnt peat than on unburnt peat, recorded in 9% and 17% of all runoff trap visits, respectively. Storm lag times and hydrograph recession limb periods were significantly greater (by ∼1 and 13 h on average, respectively) in the burnt catchments overall, but for the largest 20% of storms sampled, there was no significant difference in storm lag times between burnt and unburnt catchments. For the largest 20% of storms, the hydrograph intensity of burnt catchments was significantly greater than those of unburnt catchments (means of 4.2 × 10−5 and 3.4 × 10−5 s−1, respectively), thereby indicating a nonlinear streamflow response to prescribed burning. Together, these results from plots to whole river catchments indicate that prescribed vegetation burning has important effects on blanket peatland hydrology at a range of spatial scales. [ABSTRACT FROM AUTHOR]

A lysimeter experiment was conducted in southeastern Australia to quantify the deep percolation response under irrigated pasture to different soil types, water table depths, and ponding times during surface irrigation. Deep percolation was governed by the final infiltration rate of the subsoil, the ponding time, the water table depth, and the amount of water stored in the rootzone between saturation and field capacity. These key variables were used to characterize both steady- and nonsteady-state percolation in a conceptual model of deep percolation. The conceptual model was found to provide an effective representation of deep percolation for both the lysimeter and field-scale water balance data. Steady-state percolation during irrigation was the dominant process contributing to deep percolation on most of the studied soils. Nonsteady-state percolation (redistribution) was very important for the sandiest soil type. The conceptual model provided better prediction of deep percolation than both data-based model (artificial neural network) and process-based modeling approach (1-D Richards' equation model). [ABSTRACT FROM AUTHOR]

This paper presents the results of statistical investigations for the evaluation of soil water retention models (SWRMs). We employed three different methods developed for model selection in the field of nonlinear regression, namely, simulation studies, analysis of nonlinearity measures, and resampling strategies such as cross validation and bootstrap methods. Using these methods together with small data sets, we evaluated the performance of three exemplarily chosen types of SWRMs with respect to their parameter properties and the reliability of model predictions. The resulting rankings of models show that the favorable models are characterized by few parameters with an almost linear estimation behavior and close to symmetric distributions. To further demonstrate the potential of the statistical methods in the field of model selection, a modification of the four-parameter van Genuchten model is proposed which shows significantly improved and robust statistical properties. [ABSTRACT FROM AUTHOR]

Environmental scientists today enjoy an ever-increasing array of geostatistical methods to analyze spatial data. Our objective was to evaluate several of these recent developments in terms of their applicability to real-world data sets of the soil field-saturated hydraulic conductivity ( Ks). The intended synthesis comprises exploratory data analyses to check for Gaussian data distribution and stationarity; evaluation of robust variogram estimation requirements; estimation of the covariance parameters by least-squares procedures and (restricted) maximum likelihood; use of the Matérn correlation function. We furthermore discuss the spatial prediction uncertainty resulting from the different methods. The log-transformed data showed Gaussian uni- and bivariate distributions, and pronounced trends. Robust estimation techniques were not required, and anisotropic variation was not evident. Restricted maximum likelihood estimation versus the method-of-moments variogram of the residuals accounted for considerable differences in covariance parameters, whereas the Matérn and standard models gave very similar results. In the framework of spatial prediction, the parameter differences were mainly reflected in the spatial connectivity of the Ks field. Ignoring the trend component and an arbitrary use of robust estimators would have the most severe consequences in this respect. Our results highlight the superior importance of a thorough exploratory data analysis and proper variogram modeling, and prompt us to encourage restricted maximum likelihood estimation, which is accurate in estimating fixed and random effects. [ABSTRACT FROM AUTHOR]

Inverse modeling is a powerful technique for identifying the hydraulic properties of unsaturated porous media. However, the selection of an appropriate parameterization of the soil water retention and hydraulic conductivity function remains a challenge. In this article, we present an improved algorithm for estimating these two relationships without assigning an a priori shape to them. The approach uses cubic Hermite interpolation and a global optimization strategy. A multilevel routine identifies the adequate number of degrees of freedom by balancing model performance, the statistical interaction of the estimated model parameters, and their number. A first-order uncertainty analysis provides a quantitative measure of how well the soil hydraulic properties can be identified in different ranges of pressure head. This offers great potential for designing optimal experimental procedures for identifying the hydraulic properties of porous media. We demonstrate the effectiveness of the algorithm for the evaluation of multistep outflow experiments by investigating synthetic data sets and real measurements. The free-form approach yields optimal model parameters that show only moderate correlation, indicating well-posed inverse problems. Since parameterization errors are almost completely avoided, the algorithm is well suited to identifying other error sources in unsaturated flow problems, e.g., limitations in the applicability of the Richards equation or problems caused by spatial heterogeneity. [ABSTRACT FROM AUTHOR]

This paper presents a vision that advocates hydropedology as an advantageous integration of pedology and hydrology for studying the intimate relationships between soil, landscape, and hydrology. Landscape water flux is suggested as a unifying precept for hydropedology, through which pedologic and hydrologic expertise can be better integrated. Landscape water flux here encompasses the source, storage, flux, pathway, residence time, availability, and spatiotemporal distribution of water in the root and deep vadose zones within the landscape. After illustrating multiple knowledge gaps that can be addressed by the synergistic integration of pedology and hydrology, we suggest five scientific hypotheses that are critical to advancing hydropedology and enhancing the prediction of landscape water flux. We then present interlinked strategies for achieving the stated vision. It is our hope that by working together, hydrologists and pedologists, along with scientists in related disciplines, can better guide data acquisition, knowledge integration, and model-based prediction so as to advance the hydrologic sciences in the next decade and beyond. [ABSTRACT FROM AUTHOR]