Journal of Hydrologic Engineering

The concept and estimation of sediment transport capacity of overland flows are pivotal to soil erosion, sediment transport, and deposition modeling. There is a limited understanding of the effect of high sediment concentration on the transport capacity of overland flow, although sediments in suspension are known to affect turbulent mixing and settling velocity in rivers. A new functional relationship between a dimensionless parameter involving stream power and settling velocity and the volumetric concentration at the transport limit was developed using a set of flume experiments with slope up to 46.6%, unit discharge up to $50{\mathrm{cm}}^2·{\mathrm{s}}^{-1}$, median particle size of 0.326 mm, and sediment concentration up to $\mathrm{1,140}\mathrm{kg}·{\mathrm{m}}^{-3}$. The new relationship has two theoretical limits on sediment concentration at the transport limit. Under low flow conditions, the sediment concentration is limited by the available stream power. At high stream power, the sediment concentration is limited by the space available in flow to accommodate sediments in motion. As a predictor of the sediment concentration at the transport limit, the new relationship worked very well with the Nash-Sutcliffe coefficient of efficiency of 0.95 and was shown to be superior to empirical relationships based on stream power and other commonly used predictors of the transport capacity for rivers. The paper also shows that formulas for the transport capacity which have been validated and widely used for rivers with high sediment concentrations are inaccurate and should not be used to predict the transport capacity of overland flow.

This research estimates sediment yield contributed from surface mining soils, stream banks, and forest soils using sediment fingerprinting with isotope tracers coupled with soil erosion and sediment yield modeling in mixed land use watersheds. New $\delta {}^{13}\mathrm{C}$ and $\delta {}^{15}\mathrm{N}$ tracer results from sediment sources and transported sediments are collected and explained; sediment fingerprinting, including uncertainty analyses, is performed; and soil erosion and sediment yield modeling is applied through calibration with sediment fingerprinting as well as sediment concentration measurements. The $\delta {}^{13}\mathrm{C}$ and $\delta {}^{15}\mathrm{N}$ tracers successfully separated the forest, reclaimed mine, and stream bank sources in Island Branch and Whitaker Branch watersheds located in the Appalachian region of Kentucky, and the results expand the literature database for the usefulness of $\delta {}^{13}\mathrm{C}$ and $\delta {}^{15}\mathrm{N}$ tracers to separate end-member sources. A significant difference was measured for the values of $\delta {}^{13}\mathrm{C}$ and $\delta {}^{15}\mathrm{N}$ of the forest sediment source when comparing data results collected in 2007 and 2009/2010, highlighting the nonconstant nature of the source. The results from the sediment fingerprinting allowed for further calibration of the transport capacity coefficient, sediment delivery ratio for reclaimed mining soils, and stream bank erosion parameters. Sediment yield from reclaimed surface mining sites showed a decrease over time including 4.4, 2.1, and $0.8\mathrm{t}·{\mathrm{year}}^{-1}·{\mathrm{ha}}^{-1}$ for 1½, 4, and 6½, respectively, years after mining. A severe ice storm and rainstorms increased forest sediment production by 3.5 times from 2007 to 2009/2010, and sediment yield increased from 0.08 to $0.29\mathrm{t}·{\mathrm{year}}^{-1}·{\mathrm{ha}}^{-1}$. Change in the stream bank sediment yield over time was not pronounced. Whitaker Branch bank sediment production was five times higher than Island Branch, which was attributable to the larger susceptible bank area and driving logging trucks across the stream. It is expected that soil erosion and sediment yield modeling in mixed land use watersheds will be coupled with sediment fingerprinting in future studies.

Land use determines the erodibility and hydrological structure of land units. Grid-based modeling is an effective approach for handling the spatial heterogeneity of basin characteristics, such as land use, soil, rainfall, and topographical information. The reliability of simulation results from erosion models is circumscribed by considerable spatial variation of numerous parameters. In this study, a grid-based distributed soil erosion and sediment transport model was used in conjunction with a laboratory rainfall simulation experiment to determine the impact of land-use changes on soil erosion and sediment yield in a river basin during individual storms. Land-use changes in Lushi Basin, primarily in Henan, China, were analyzed by comparing historical land use maps from 1990 and 1995. By comparing the responses of four selected storms that were simulated under conditions indicated by these two land-use maps, the average erosion rates were found to increase from 1989 to 1996. The increase in the average erosion rates followed land-use changes, particularly the transformation of forest to farmland. The results indicated that land-use change has a significant effect on regional soil erosion and sediment supply.

Using the entropy theory, this paper derives an instantaneous unit sediment graph (IUSG or USG) to determine sediment discharge and the relation between sediment yield and runoff volume. The derivation of IUSG requires an expression of the effective sediment erosion intensity whose relation with rainfall is revisited. The entropy theory provides an efficient way to estimate the parameters involved in the derivations. Sediment discharge is also computed using the instantaneous unit hydrograph (IUH), which can also be derived using the entropy theory. This method works as well as the IUSG method, especially when the peak sediment discharge and peak runoff occur at the same time. The entropy theory yields the probability distribution of sediment yield and of sediment discharge, which can then be used to estimate uncertainty in sediment yield prediction.

Soil erosion has become a serious environmental problem in southern Italy, where annual soil loss associated with extreme rainfall events can reach $100–150\mathrm{t}{\mathrm{ha}}^{-1}$. In order to predict rates of soil loss and sediment yields, to inform the development of effective erosion and sediment control strategies in these areas, several prediction models have been utilized in recent years. Most of these models are based on the universal soil loss equation (USLE) or revised universal soil loss equation (RUSLE). However, they require calibration and validation if they are to be used to provide reliable estimates of soil erosion and sediment yield. The use of fallout ${}^{137}\mathrm{Cs}$ measurements affords a useful means of assembling spatially distributed information on soil redistribution rates, which can be used to calibrate and validate sediment yield models. This paper reports a study in which ${}^{137}\mathrm{Cs}$ measurements have been used to calibrate the sediment delivery component of the sediment delivery distributed (SEDD) model for four catchments of contrasting size, slope, and land use, located in southern Italy. The estimates of net soil loss for the individual catchments and subcatchments derived from the ${}^{137}\mathrm{Cs}$ measurements have been used to calibrate the model specific parameter, $\beta$, which showed a strong dependence on the sediment delivery ratio (SDR) values for individual subcatchments. The SEDD model was then successfully validated at the annual timescale using values of annual specific sediment yield derived from measurements of the suspended sediment loads at the catchment outlets.

The study of the detachment of soil particles due to rainfall erosivity requires knowledge of the energetic characteristics of the precipitation. In this paper, following a review of both the drop-size distribution (DSD) proposed by one researcher and the corresponding relationship for evaluating the kinetic power of rainfall, the reliability of that researcher’s DSD using the size distributions of raindrops detected by an optical disdrometer installed at Palermo (Sicily) is experimentally tested. Finally, an experimental verification of both the relationship proposed by two separate research teams for evaluating the specific and unit rainfall kinetic energy is carried out.

Prediction of the sediment generated within a catchment basin is a crucial input in the management and design of water resources projects. Due to the unavailability and complexity of the precipitation and hydrological process, reliable sediment concentration is hardly predicted by applying linear and nonlinear regression methods. In the present study, an attempt has been made to explore the use of Tropical Rainfall Measuring Mission (TRMM-3B42) dataset for modeling suspended sediment using neural networks (NNs) with different training functions, i.e., Levenberg-Marquardt (LM), scaled conjugated gradient (SCG), and Bayesian regulation (BR) for the Kopili River basin, India. The input vector to the various models using different algorithms were derived considering the statistical properties such as autocorrelation function, partial autocorrelation function, and cross-correlation function of the time series. The daily rainfall data from 2000 to 2010 (4,018 days) were considered for the training (70%) and validation (30%) of the models. The model ANNLM6 performed better than other models during calibration ($E_{\mathrm{NS}}=99.49\%$, $\mathrm{CORR}=0.997$, $\mathrm{RSR}=0.049$) and validation ($E_{\mathrm{NS}}=98.11\%$, $\mathrm{CORR}=0.990$, $\mathrm{RSR}=0.085$), and the optimum structure of the artificial neural network (ANN) model was found to be six neurons in the hidden layer. Moreover, the analysis also concludes that ANN model using the LM algorithm is able to predict suspended sediment at the outlet fairly well. The overall analysis reveals that TRMM rainfall is promising for suspended sediment prediction in the absence of ground-based measurements of rainfall. It is inferred that, the TRMM-3B42 rainfall estimates can be used for the conceptual rainfall-runoff-sediment modeling where the gauges are sparsely distributed and the radar measurements are rarely available.

Identification of critical watersheds prone to soil erosion has been performed by using a hydrological model in data scarce Damodar River catchment, located in Jharkhand state of India. Model is calibrated and validated for two watersheds, i.e., (1) Nagwan, $94.43{\mathrm{km}}^2$; and (2) Banikdih, $80.06{\mathrm{km}}^2$, nested within the catchment. The achieved $R^2$ values of predicted monthly runoff and sediment yield varies, respectively, 0.65–0.89 and 0.78–0.84, for both the watersheds during calibration and validation period. Calibration and validation results revealed that model is predicting monthly runoff and sediment yield satisfactory for the two watersheds of the Damodar River catchment. The validated model parameters were then up-scaled to the whole catchment and model was run from 1993–2001 to identify the critical watersheds. Model was successfully used for prioritization of 406 watersheds delineated using the computer software model within the catchment. In delineation process, the boundaries of 175 watersheds matched exactly with the watersheds delineated manually by Damodar Valley Corporation. Out of these 175 watersheds, erosion classes of 157 exactly matched with the manually described erosion class. For remaining 18 watersheds, priority of 10 watersheds was either one class higher or lower, whereas eight watersheds showed complete mismatch. Overall results showed that the hydrological model used in this paper may be helpful in prioritization of management strategies to manage the resources where availability of data is a big concern. Such approach for managing resources is particularly needed in developing countries for better utilization of limited resources.

Ephemeral gully erosion seriously degrades agricultural soils, but few conservation planning tools adequately account for this form of erosion. To address this deficiency, this paper describes a spatially distributed adaptation of version 2 of the Revised Universal Soil Loss Equation and a new ephemeral gully erosion estimator. The modeled results were compared to runoff and sediment yield measured from 1975 to 1991 on a 6.3-ha instrumented watershed near Treynor, Iowa, managed with conventional tillage corn and containing a grassed waterway. Using a 3-m rectangular grid, this investigation determined surface drainage patterns and delineated concentrated flow channels where contributing areas exceeded $600{\mathrm{m}}^2$. Computed gully evolution based on soil properties, runoff, and sediment transport contributed approximately one-fourth of the total erosion, with the rest contributed by sheet and rill erosion. More than half of the eroded sediment was deposited within the grassed waterway. Without local calibration, simulated runoff of $67\mathrm{mm}{\mathrm{year}}^{-1}$ was 34% larger than the observed $50\mathrm{mm}{\mathrm{year}}^{-1}$, and simulated sediment yield of $17.5\mathrm{Mg}{\mathrm{ha}}^{-1}{\mathrm{year}}^{-1}$ was 20% larger than the measured $14.6\mathrm{Mg}{\mathrm{ha}}^{-1}{\mathrm{year}}^{-1}$.

The effects of soil erosion and sedimentation are important for natural resources conservation planning. However, although tremendous resources have been invested in developing more erosion models, the prevailing modeling studies are relying on a single model due to various reasons. The Agricultural Policy Environmental Extender (APEX) provides multiple water erosion equations. This study tests and evaluates the Bayesian model averaging (BMA) scheme on sediment predictions based on four water erosion methods in the APEX model using data from two watersheds. The APEX hydrology and soil erosion and sedimentation components were calibrated simultaneously using the APEX autocalibration tool APEX-CUTE. The BMA scheme is employed to obtain consensus predictions by weighing individual predictions based on their probabilistic likelihood measures. Simulated monthly flow was satisfactory for both the calibration and validation periods, with BMA resulting in Nash–Sutcliffe efficiency (NSE) values from 0.56 to 0.87 and percent bias (PBIAS) within $\pm 17\%$. No individual soil erosion method in APEX is consistently outperformed other method over the entire sediment yield regimes at both watersheds. The BMA scheme led to improved predictions compared with individual methods and resulted in satisfactory performance statistics (NSE from 0.55 to 0.88) for both the calibration and validation periods based on the optimal solutions. BMA also led to relatively higher Brier scores and narrower prediction bounds than individual methods. The methodology is unrestrictedly general and can be applied to any other combination of models in the environmental sciences.

Severe soil erosion in the Loess Plateau region and associated large sediment discharge downstream in China have been of great concern for years. A physically based distributed model, the Digital Yellow River model (DYRIM), was developed in 2007 for land management at the basin scale in this region. In DYRIM, an explicit sediment yield equation was derived based on simple assumptions where sediment discharge would increase nonlinearly with the slope length, leading to unrealistic results as the spatial scale increases. The main aims of this study were to reformulate the module of DYRIM in comparison with other process-based erosion models; to develop a generic framework for erosion prediction and simulation of erosion and sediment delivery processes within storm events; to validate the new hillslope erosion module for improved performance across a range of spatial scales; and to evaluate parameter consistency among different runoff events. Observed flow and sediment discharge data for 16 runoff events and three runoff plots with different slope lengths (20, 40, and 60 m) for each runoff event from the Tuanshangou experimental site in the Loess Plateau region were used to calibrate the new module for a 20 m slope and the calibrated parameter values were then used to predict the sediment discharge for slope lengths of 40 and 60 m at the same site. For calibration, the Nash-Sutcliffe coefficient of efficiency (NSE) was 0.92 on average for the 20 m plot. For prediction, the average NSE values were 0.87 for 40 m plot and 0.90 for the 60 m plot. There is a marked improvement over the existing module, for prediction with the existing module, the average NSE values were only $-0.86$ for the 40 m plot and $-6.3$ for the 60 m plot. Whereas the calibrated parameter values vary from event to event, results show that using a common set of parameter values for all runoff events to predict sediment discharge, the overall NSE values using the combined data set are as high as 0.89 for the 40 m plot and 0.90 for the 60 m plot. This research shows that it is absolutely imperative to include a control term involving the sediment transport capacity, even for the highly erodible region of the Loess Plateau in China.

There is a great reliance on the Universal Soil Loss Equation (USLE) and the Revised Universal Soil Loss Equation (RUSLE) to predict long-term rainfall erosion in relation to land management in both the United States and the rest of the world. In the USLE/RUSLE modeling approach, geographic variations in soil loss associated with climate and soil are taken into account through variations in the rainfall-runoff erosivity factor ($R$) and the soil erodibility factor ($K$) and equations developed in the United States to predict the values for these factors are often used outside the United States without validation in the area that they are applied. Data on rainfall kinetic energy required to calculate the event erosivity index, $E{I}_{30}$, are seldom directly available and consequently, $E{I}_{30}$ values are often calculated using rainfall intensity-kinetic energy relationships that may produce erroneous results because the rainforming systems at a location differ to a great extent from those occurring at the location where the drop-size data were obtained. Also, failure to consider runoff as an independent factor in producing rainfall erosion in the USLE/RUSLE leads to spatial variations in $K$ that cannot be determined simply from measured soil properties. Erosivity indices that include runoff as a factor may lead to soil erodibility factors that are better related to measured soil properties but that potential has yet be realized. Meanwhile, models like RUSLE2 take account of the runoff effect on soil loss through temporal variations in $K$ that are empirically related to rainfall and temperature in the United States but the method is untested elsewhere. The modeling approaches adopted USLE/RUSLE and RUSLE2 are extremely useful in determining soil loss for land management purposes despite any real or perceived lack of ability to predict event soil loss accurately.

In this paper, the contributions of rill and interrill components to total soil erosion monitored at event scale at the Sparacia experimental area, southern Italy, were first compared and the dominance of the rill component was detected. Then, the reliability of two empirical relationships between the rill length and its eroded volume and among the morphological variables (length, width, depth, and volume) describing the channelized process was tested using both direct measurements of rills, carried out for the October 3, 2011, event, and those of ephemeral gullies surveyed on February 2010 at Sparacia. The measurements of rills detected by the direct field relief were compared with those obtained using a survey that allowed the generation of a digital elevation model of the rilled area. Finally, plot rill erodibility values were estimated and their spatial and time variability were analyzed. Spatial variability of rill erodibility was negligible whereas a temporal variability was detected.

In this study, the Morgan and Duzant version of the modified Morgan-Morgan-Finney (MMF) model coupled with geographical information system (GIS) is used for sediment yield estimation from the Gamber watershed, Satluj basin, Himachal Pradesh, India. The model incorporates particle size selectivity in the process of erosion, transport, and deposition, i.e., it simulates these processes for clay, silt, and sand separately. This modified MMF model also allows for surface runoff and sediment routing, which improves sediment yield estimation accuracy. It also determines the watershed contributions to the total sediment yield at the basin outlet. The present research fetches the MMF model in a geospatial environment and develops a system that can be used to estimate the actual sediment yield. Generally, it has been observed that the detachment of soil is greatly affected by raindrop impact. Therefore, the estimation of kinetic energy of erosive rainfall is a very important factor. Moreover, it has been noticed that the major contribution to kinetic energy of rainfall comes from direct throughfall, as compared to leaf drainage. Therefore, the relationship to estimate kinetic energy of direct throughfall developed for Indian conditions has been used rather than the traditional relationship developed for the United Kingdom. The model parameters were calibrated for the years 1998 and 2002. The results were validated for the years 1995 and 1999. The efficiency coefficient the model could achieve was 0.91. It was concluded that the model can estimate sediment yield from a catchment with reasonable accuracy.

Soil erosion is one of the major hazards affected by the climate change, particularly the changed precipitation trend. The present paper has generated future precipitation by downscaling general circulation model (GCM, HADCM3) data of A2 scenario in a part of the Narmada River Basin in Madhya Pradesh, India, to obtain future impact of climate change on soil erosion. Least-square support vector machine (LS-SVM) and statistical downscaling model (SDSM) models were used for downscaling, and the universal soil loss equation (USLE) model was used for estimating soil loss. The results were analyzed with different slope, land use, and soil category. Outcome showed an increase in future precipitation with the resultant increase in soil erosion, with a positive change of 18.09 and 58.9% in years 2050s and 2080s respectively in LS-SVM, while it is decreasing in the year 2020s ($-5.47\%$). Rate of change of soil erosion with SDSM is 15.52 and 105.80% in years 2050s and 2080s respectively, and decrease in the 2020s ($-8.51\%$). The soil erosion rate is more in steep slopes ($73.04\mathrm{t}/\mathrm{h}\mathrm{a}/\mathrm{year}$ current rate) than in less slope ($3.01\mathrm{t}/\mathrm{h}\mathrm{a}/\mathrm{year}$ at current rate) regions. Waste land type of land use ($15.15\mathrm{t}/\mathrm{h}\mathrm{a}/\mathrm{year}$ at current rate), and sandy loam ($25.45\mathrm{t}/\mathrm{h}\mathrm{a}/\mathrm{year}$ at current rate) and sandy clay loam ($23.03\mathrm{t}/\mathrm{h}\mathrm{a}/\mathrm{year}$ at current rate) soil types, give a higher rate of erosion than other land use and soil types.

The spatial distribution of land management practices (LMPs), such as the use of vegetated filters, may have a strong impact on their efficiency in trapping sediments and pollutants. Distributed water erosion models help managers, planners, and policymakers optimize the efficiency of these LMPs regarding their location relative to water and sediment pathways. In this work, the authors analyzed the impact of the spatial distribution of LMPs using an existing distributed model and sensitivity analysis procedures. The distributed model that was used is a distributed single-event physically based water erosion model developed to calculate erosion rates and sediment flow for small (less than $10{\mathrm{km}}^2$) agricultural catchments. To measure the impact of the spatial distribution of LMPs, the authors developed a stochastic model that generates LMP locations over the entire catchment. The stochastic model has three input parameters: the density of LMPs, their downslope/upslope location probability, and the probability density function shape controller. Because of its ability to account for the cross effects between parameters, the variance-based Sobol method was used to calculate the sensitivity of the soil loss ratio of a typical Mediterranean agricultural catchment (Roujan, southern France) to the LMP location model parameters. Three measurement points (two subcatchment outlets and the main outlet) were used to examine the spatially distributed effects of the LMP locations. The simulation results indicated that 70% of the variation of the net erosion is explained by variations in LMP density for the main outlet catchment, making LMP density the most sensitive parameter. However, the total Sobol sensitivity indices indicate a strong interaction among the three parameters when the density values are low (few LMPs are applied). Thus, although the density of the LMPs is the most sensitive parameter, their location may influence their global trapping efficiency in (real) cases where few LMPs are applied.

Watershed models supported by a geographic information system (GIS) and remote sensing data have proven to be vital research tools for the planning and management of land and water resources at watershed or river basin scales. The present study was undertaken with the objective of evaluating a watershed model for the simulation of streamflow and sediment yield in the Baitarani watershed of Eastern India. The watershed model was calibrated and validated using daily rainfall and streamflow data from 1998 to 2005 and sediment yield data from 2002 to 2005. Other input parameters of the watershed model such as digital elevation model, soil map, and land use/land cover map were generated using GIS. The performance of the model was evaluated using salient statistical and graphical indicators. The model calibration and validation results revealed that the watershed model simulated streamflow in the study watershed with a higher level of accuracy at daily [Nash-Sutcliffe efficiency (NSE) = 0.86 and 0.84] and monthly ($\text{NSE}=0.95$ and 0.93) time steps during both calibration and validation periods. However, the model simulated sediment yields more accurately at the monthly time step ($\text{NSE}=0.91$ and 0.90) than the daily time step ($\text{NSE}=0.62$ and 0.59). During the entire period of sediment simulation (2002–2005), the total model-simulated sediment yield was $\mathrm{1,771}\times {10}^3\text{tons}$ against the observed sediment yield of $\mathrm{1,799}\times {10}^3\text{tons}$ with a deviation of approximately 1.6%. It is concluded that the watershed model is suitable for the simulation of streamflow and sediment transport in the study area, and it also has potential for adoption in other watersheds of the Baitarani River Basin as well as in other watersheds of India.

In this paper a frequency analysis of event soil loss measurements collected in the period 1999–2012 at the microplots and plots of the Sparacia Experimental Area in Sicily, southern Italy, was developed. The analysis was carried out using the annual maximum soil loss measurements normalized by the mean soil loss measured at a given temporal and spatial scale. The empirical frequency distribution of the normalized variable was well fitted by two Gumbel’s theoretical probability distributions discriminated by a value of the normalized variable equal to 2. This last value discriminates between the relatively low and frequent values of the normalized variable and the high and rare ones. The annual maximum soil loss was demonstrated to be representative of the total annual soil erosion at the Sparacia Experimental Area. Then, a threshold soil loss value at the annual temporal scale was calculated by multiplying the frequency factor, equal to 2, by the mean annual maximum soil loss values for each given plot length. The value of the threshold soil loss decreased as the plot length increased. Finally, an analysis of occurrence of rilling was developed with regard both to the classical soil loss tolerance and the defined threshold soil loss. The conclusion that rilling can be limited using a threshold soil loss which is greater than the classical soil loss tolerance was drawn.

Most of the hydrological models developed and used previously in sediment yield modeling are complex and lack general applicability. Moreover, the availability of sediment data for the development and calibration of such models is very scarce. Therefore, there is a need to study and develop models that can handle easily measurable parameters of a particular site, even with short length. In recent years, multidisciplinary artificial intelligence techniques—namely, artificial neural networks (ANNs)—have shown the capability to solve such complex nonlinear systems. This study investigates the suitability of an inductive group method of data handling polynomial neural network (GMDH-NN) technique in estimating sediment yield. The data on various meteorological and geomorphological features—namely, river length, watershed area, erodible area, average slope of watershed, annual average rainfall, and drainage density—from 20 subwatersheds of the Arno River Basin in Italy were used for model development. The results of this study show that the inductive GMDH-NN can efficiently capture the trend of sediment yield with a coefficient of correlation of 0.975, even with this small data set.

Appropriate methodologies for predicting erosion and sediment yield at the basin scale are of utmost importance for taking proper measures to reduce reservoir sedimentation and protect water resources. In this study, a new site-specific distributed and objective model, which was designed based on the spatially distributed scoring model (SPADS), was developed for the upstream watershed of the Miyun Reservoir (UWMR) in China. The model describes the relationships between watershed characteristics and sediment yields. The results show that vegetation, lithology, and inverse distance to the river were the main controlling factors of the specific sediment yield variation in the UWMR. On the basis of these results, a site-specific erosion and sediment yield index (ESYI) was established for erosion and sediment yield predictions at the basin scale. The ESYI-based sediment yield prediction accuracy ($R^2=0.62$, relative root $\text{mean}\text{square}\text{error}=44.7\%$) was found to be significantly lower than that from SPADS. However, because the proposed model is advantageous in terms of its low data requirements, and because it takes into consideration spatially distributed characteristics, comprehensive erosion types (sheet, rill, gully, and bank), and both on-site and off-site erosion effects, it may still be of great use in the UWMR.

Barren badlands lacking vegetation and consisting of sharp-edged ridges and v-shaped gullies are widely formed by rapid incision on silt−clay fine sediments, especially in weakly consolidated mudstones around the world. The development of these badlands has been explained in terms of the repeated drying and wetting of rocks, and high salt contents in pore water. Water contents and salt concentration were monitored, for the first time, by measuring dielectric permittivity and pore water electrical conductivity (EC) beneath a badland slope in southern Taiwan. The rock slope is composed of Plio-Pleistocene marine mudstone and has a subtropical climate with distinct dry and wet seasons. The monitoring revealed that salt water at depth migrated to the slope surface in the dry season and deposited salt in desiccation cracks. During the following rainy season, rainwater dissolved the salts, diluted pore water near the slope surface, and closed desiccation cracks, consequently slowing the downward movement of water. The combined wetting of rocks and dilution of pore water near the slope surface deteriorated the rock and dispersed rock-forming grains, ultimately leading to erosion rates as high as 9 cm during a single rainy season. Subsequent drying near the slope surface and the movement of salt water from depth led to the renewal of the cycle during the following dry season.

Extensive soil erosion has already contributed significantly to the impoverishment of land and people throughout the world. The soil lost is deposited in various reservoirs, thus reducing their capacity. Furthermore, essential plant nutrients are also lost along with the eroded topsoil. Modeling provides an approach for quantification of sediment yield. Sediment yield models ranging from empirical to distributed have been developed by many researchers. In this study, an attempt was made to use the soil and water assessment tool (SWAT) model and Geospatial techniques in an integrated way for modeling runoff and sediment yield for Khadakohol and Harsul watersheds in India. The SWAT model was calibrated for the monsoon months of a year and validated for other years. In countries like India, quantification of runoff/sediment yield is mainly constrained by the nonavailability of observed data and hence, regionalization of parameters was attempted, where the calibrated parameters from Khadakohol was used to Harsul and its applicability was assessed in an ungauged context and the results are found to be encouraging.

Rain simulators have been used extensively in order to assess the interaction between rainfall and land surface. The main objective of a rain simulator is to produce rain with characteristics similar to natural rainfall at a certain geographical location. This paper investigates the microstructural characteristics of rainfall generated by a pressurized simulation system and their impact on rainfall erosivity in comparison with natural rainfall for different environments. The simulated raindrop characteristics have been measured using two piezoelectric transducers to investigate the rainfall kinetic energy and drop size distribution. The pressurized simulation system was able to generate rain with different intensities and the relationship between the pressure and rainfall intensity was statistically significant. On the other hand, the time-based rainfall kinetic energy was increasing linearly with rainfall intensity. The pressurized rain simulator showed low volume–based kinetic energy (${\mathrm{KE}}_c$) and the trend was different from that observed under natural rainfall. The average percentage of small raindrops ($<2\mathrm{mm}$) was more than 98%, and the average median drop size was 1.4 mm.