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Soil erosion has long been recognised as a significant process of soil destruction, affecting millions of hectares of land worldwide, resulting in loss of fertility and biodiversity, decreased stability of marine and terrestrial ecosystems and enhanced exposure to climate change. In semi-arid zones, the highest rate of deforestation occurred in wooded grassland, bushland and shrubland systems, while the lowest rate occurred in woodland. Satellite remote sensing technology for tracking and modelling soil erosion has exploded in popularity worldwide over the last decade. More precisely, renewed emphasis has been placed on recent advances in remote sensing technologies and the availability of these data at various resolutions, as well as on the critical need for up-to-date knowledge on soil loss levels, soil erosion monitoring and modelling, in particular, to ensure that viable agricultural fields are available to ensure food security. GIS research delivers adequate results when developing erosion surveys and risk maps using GIS data layers such as DEM, slope, aspect and land use. The Revised Universal Soil Loss Equation (RUSLE), the Water Erosion Prediction Project (WEPP) and Environmental Information Coordination are the most widely used scientific erosion prediction models that are combined with remote sensing and GIS (CORINE). Remote sensing techniques and the universal soil loss equation were established as the primary tools for mapping and tracking soil erosion in this chapter. It consists of four components: baseline sheet and rill erosion mapping, real-time rill and gully erosion monitoring, future sheet and rill erosion change forecast and long-term pattern determination.

Almost 20 years of observed 1-minute weather data from the most complete and longest operational NOAA Automated Surface Observation Station (ASOS) network station were used to prepare seven climate inputs (the raw, unadjusted, quality checked, and gap-filled gauge data and six temporal aggregations of the same data) to the Water Erosion Prediction Project (WEPP) model. The nearest stations from both the old (1995) and new (2015) CLIGEN databases were also used to compare climate inputs and corresponding soil loss predictions from WEPP as it would typically be applied in the field for research or soil conservation planning. The results showed that WEPP simulations driven by CLIGEN closely approximated the vigor (erosivity) of breakpoint quality climate inputs despite using only 15-minute precipitation data for CLIGEN input file parameterization. Erosion prediction models and corresponding theory were developed initially based on breakpoint rainfall data, so climate inputs that more closely approximate that kind of data should be considered ideal for soil erosion modeling efforts. These results call into question the use of coarser resolution climate inputs such as 15-minute precipitation data, which are most common, without applying sufficient adjustments and/or corrections to better approximate breakpoint data. Our findings also suggest that intensity corrections of 4% used by Hollinger et al. (2002), USDA-ARS (2008, 2013), and McGehee and Srivastava (2018) for the Revised Universal Soil Loss Equation (RUSLE2) erosivity inputs may not be sufficient for at least the climate near these stations in the Northeastern United States. A national study of precipitation intensity and erosivity based on high resolution climate inputs like those available from the ASOS network may be warranted.

A soil aggregate represents a key soil structural unit that influences several physical soil properties such as water infiltration, runoff and erosion. The relationships between soil aggregate stability and interrill and rill erodibility are critical to process-based erosion prediction models yet remain unclear, likely due to the difficulty of distinguishing between interrill and rill-eroded sediment during the erosion process. This study was designed to partition interrill and rill erosion rates and relate them to the aggregate stability of Ultisols in subtropical China. Six kinds of rare earth elements (REEs) were applied as tracers mixed with two cultivated soils developed over Quaternary red clay or shale at six slope positions. Soil aggregate stability was determined by the Le Bissonnais (LB)-method. Simulated rainfall of three intensities (60, 90 and 120 mm h − 1 ) was applied to a soil plot (2.25 m long, 0.5 m wide, 0.2 m deep) at three slope gradients (10°, 20° and 30°) for a duration of 30 min after runoff initiation. The results indicated that rill and interrill erosion rates in the soil developed over shale were considerably greater than those in the soil developed over Quaternary red clay. Equations using an aggregate stability index A s to replace the erodibility factor of interrill and rill erosion in the Water Erosion Prediction Project (WEPP) model were constructed after analysing the relationships between estimated and measured rill and interrill erosion data. The results show that these equations based on A s have the potential to improve methods for assessing interrill and rill erosion erodibility synchronously for subtropical Ultisols by using an REE tracing method.

Soil erosion is one of the serious issues threatening the environment. It is a growing problem especially in areas of agricultural activity where soil erosion not only leads to de-creased agricultural productivity but also reduces water availability. This leads to drastic degradation of the agricultural lands. So there is a need to take up conservation and management measures which can be applied to check further soil erosion. Universal Soil Loss Equation (USLE) is the most popular empirically based model used globally for erosion prediction and control. Remote sensing and GIS techniques have become valuable tools for the digitization of the input data and genereation of maps. In the present study, RUSLE model has been adopted to estimate the soil erosion in the Khajuri watershed of Uttar Pradesh, India. This model involves calculation of parameters including runoff-rainfall erosivity factor (R), soil erodability Factor (K), topographic factor (LS), cropping management factor (C), and support practice factor (P). Layer wise thematic maps of each of these factors were generated using GIS platform using various data sources and data preparation methods. The results of the study indicate that the annual average soil loss within the watershed is about t/ha/yr (metric ton per hectare per year).

Correct estimation of soil loss at catchment level helps the land and water resources planners to identify priority areas for soil conservation measures. Soil erosion is one of the major hazards affected by the climate change, particularly the increasing intensity of rainfall resulted in increasing erosion, apart from other factors like landuse change. Changes in climate have an adverse effect with increasing rainfall. It has caused increasing concern for modeling the future rainfall and projecting future soil erosion. In the present study, future rainfall has been generated with the downscaling of GCM (Global Circulation Model) data of Mandakini river basin, a hilly catchment in the state of Uttarakhand, India, to obtain future impact on soil erosion within the basin. The USLE is an erosion prediction model designed to predict the long-term average annual soil loss from specific field slopes in specified landuse and management systems (i.e., crops, rangeland, and recreational areas) using remote sensing and GIS technologies. Future soil erosion has shown increasing trend due to increasing rainfall which has been generated from the statistical-based downscaling method.

Summary Increases in wildfire occurrence and severity under an altered climate can substantially impact terrestrial ecosystems through enhancing runoff erosion. Improved prediction tools that provide high resolution spatial information are necessary for location-specific soil conservation and watershed management. However, quantifying the magnitude of soil erosion and its interactions with climate, hydrological processes, and fire occurrences across a large region (>10,000 km2) is challenging because of the large computational requirements needed to capture the fine-scale complexities of the land surface that govern erosion. We apply the physically-based coupled Variable Capacity Infiltration–Water Erosion Prediction Project (VIC–WEPP) model to study how wildfire occurrences can enhance soil erosion in a future climate over a representative watershed in the northern Rocky Mountains – the Salmon River Basin (SRB) in central Idaho. While the VIC model simulates hydrologic processes at larger scales, the WEPP model simulates erosion at the hillslope scale by sampling representative hillslopes. VIC–WEPP model results indicate that SRB streamflow will have an earlier shift in peak flow by one to two months under future climate scenarios in response to a declining snowpack under warming temperatures. The magnitude of peak flow increases with each higher severity fire scenario; and under the highest fire severity, the peak flow is shifted even earlier, exacerbating the effects of climate change. Similarly, sediment yield also increases with higher fire severities for both historical and future climates. Sediment yield is more sensitive to fire occurrence than to climate change by one to two orders of magnitude, which is not unexpected given that our fire scenarios were applied basin wide as worst case scenarios. In reality, fires only occur over portions of the basin in any given year and subsequent years’ vegetation regrowth reduces erosion. However, the effects of climate change on sediment yield result in greater spatial heterogeneities, primarily because of the spatial differences in precipitation projections, while fire conditions were uniformly applied. The combined effects of climate change and a possible continuation of increasing fire frequency and severity will compound excess sediment issues that already exist in this region of the intermountain West.

The mechanism of the interrill erosion process is still unclear under complex conditions. Spatio-temporal variations of the near-surface hydraulic gradient are a common occurrence; however, few attempts have been made to characterize the near-surface hydraulic gradient for erosion prediction. Therefore, the objective of this study is to determine the influence of exogenic erosional forces (rainfall, overland flow, and seepage) on interrill erosion processes by considering the impact of the near-surface hydraulic gradient. Five near-surface hydraulic gradients (70 % of field capacity, field capacity, saturation, artesian seepage at 0.20 and 0.40 m of the hydrostatic pressure head) were applied in clay loam soil at two representative slope gradients of 8.7 % and 17.6 % under three rainfall intensities of 30, 60, and 90 mm h−1. The results showed that the near-surface hydraulic gradient was the dominant factor in the interrill erosion process in addition to rainfall intensity (I), runoff (Q), and slope gradient (S). There was a significant improvement in the prediction accuracy of the interrill erosion rate when the factor of near-surface hydraulic gradient was introduced into the interrill erosion prediction equation based on the Water Erosion Prediction Project (WEPP) concept (R2 = 0.92, Nash-Sutcliffe simulation efficiency (NSE) = 0.92). The R2 and NSE values were 22.4 %–210.0 % higher than those of existing empirical equations (main parameters: I, I&S, I&Q, I&S&Q). The correlation matrix results indicated that the flow velocity was a key hydraulic parameter for predicting the interrill erosion rate. The interrill erosion rate was predicted well by a simple power function of the flow velocity (R2 = 0.88, NSE = 0.88), although this relationship lacks clear physical meaning. We also found that the interrill erosion rate increased as a power function with the runoff depth, rainfall intensity, hydrostatic pressure head, and slope gradient (R2 = 0.88, NSE = 0.88). Considering the integrated effect of the exogenic erosional dynamics on interrill erosion, a power function that included the physical description of the hydrodynamic parameters, rainfall intensity, and hydrostatic pressure head was used to predict the interrill erosion rate (R2 ≥ 0.85, NSE ≥ 0.86). The results of this research provide new insights into developing process-based and mechanistic models for interrill erosion processes.

Made available in DSpace on 2018-11-26T15:47:45Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-01-01. Added 1 bitstream(s) on 2019-10-09T18:25:23Z : No. of bitstreams: 1 S0100-69162018000100149.pdf: 1268234 bytes, checksum: bd53a7e08b246abd0e7a891c549bbea5 (MD5) Phosphorus losses in runoff water and eroded sediment may increase the risk of eutrophication. Erosion prediction models can be used to assess and quantify nutrient losses and transport in different soil management scenarios. This study aimed to assess the performance of models to estimate the losses of available phosphorus in eroded sediments and assess their spatial variability pattern. The experiment was installed on a eutrophic Red Ultisol located in Tabapua, northwestern Sao Paulo State, Sao Domingos Stream Watershed. Aiming at estimating and validating the models, measurements were made from 2007 to 2015 at 17 observation points (slopes), determining soil phosphorus content (P-soil), eroded sediments (P-sed), and enrichment rate (ERsed). The models applied to estimate the enrichment rates of phosphorus in the eroded sediment and runoff are efficient when in use with the predictions of the model Water Erosion Prediction Project (WEPP). The enrichment rates obtained presented a Nash-Sutcliffe (NS) coefficient close to 1. Losses of soluble phosphorus in runoff above 0.02 mg L-1, a critical value for eutrophication, can be obtained in 81% of the watershed area with a probability higher than 75%. Univ Estadual Paulista, Jaboticabal, SP, Brazil Univ Estadual Paulista, Jaboticabal, SP, Brazil

Soil erosion prediction technology began over 70 years ago when Austin Zingg published a relationship between soil erosion (by water) and land slope and length, followed shortly by a relationship by Dwight Smith that expanded this equation to include conservation practices. But, it was nearly 20 years before this work's expansion resulted in the Universal Soil Loss Equation (USLE), perhaps the foremost achievement in soil erosion prediction in the last century. The USLE has increased in application and complexity, and its usefulness and limitations have led to the development of additional technologies and new science in soil erosion research and prediction. Main among these new technologies is the Water Erosion Prediction Project (WEPP) model, which has helped to overcome many of the shortcomings of the USLE, and increased the scale over which erosion by water can be predicted. Areas of application of erosion prediction include almost all land types: urban, rural, cropland, forests, rangeland, and construction sites. Specialty applications of WEPP include prediction of radioactive material movement with soils at a superfund cleanup site, and near real-time daily estimation of soil erosion for the entire state of Iowa.

Modeling soil erosion contributes to the understanding of the erosion processes and needs to predict accurately the erosion rates under different environmental conditions. Few studies have investigated the WEPP's applicability for arid and semi-arid conditions that differ from those where the model was developed. This research was carried out to evaluate and improve the WEPP model for arid and semiarid regions for interrill erosion using a rainfall simulator at plot scale. The results showed that measured interrill erosion rates ranged from 9.3 × 10− 6 to 89.6 × 10− 6 kg m− 2 s− 1. In comparison, the WEPP-interrill erosion prediction values were on average 14.5 times lower than the measured ones, which can be ascribed to a systematic error. To calibrate the model, two regression equations were presented to estimate baseline interrill erodibility (Kib) and adjusted interrill erodibility (Kiadj). Accordingly, the baseline interrill erodibility was influenced by surface rock cover much more than any other soil property. Also, Kiadj was functioned as sediment concentration and sand percent. Model validation showed that after calibrating Kib and Kiadj, the coefficient of efficiency enhanced to 0.31 and 0.90, respectively. However, the model tended to overestimate the lower erosion rates and to underestimate the higher erosion rates. To modify interrill erodibility and to achieve a process-based parameter, a simple equation was developed based on stream power. The coefficient of efficiency of 0.61 was obtained for the proposed approach. In general, the model predictions enhanced after calibration, but the model tendency to underestimate high erosion rates was observed. One of the reasons for low accuracy of the WEPP was its dependence on soil empirical parameters used in predicting the sub-model components. The model prediction was found to be improved through process-based calibration of soil parameters.

Soil loss by erosion is a major threat to European soil resources. It is linked to most other threats to soils and is estimated to incur substantial costs to society. To monitor and evaluate soil erosion comprehensively, measurements and modelled estimates of soil loss by erosion need to integrate water, wind and tillage erosion, as these are common throughout Europe. Tolerable rates of soil erosion (thresholds) in Europe can be set equal to estimated soil formation rates, that is, 1 t ha 1 yr 1, but defining this threshold will require further research in the context of soil functions. There is a strong need for improving the knowledge base of current erosion rates, especially to develop and validate reliable erosion prediction models. This should include depositional environments (e.g. flood plains) where greater erosion rates than those on hill slopes may be tolerable, depending on the consequent effects on all relevant ecosystem goods and services provided by soil. Moreover, in considering erosion at catchment (watershed) level, the distance between erosion sites and areas of deposition should be taken into account. published

Interest in improving the performance of water and wind erosion prediction models, such as the Revised Universal Soil Loss Equation (RUSLE), the Water Erosion Prediction Project (WEPP), and the Wind Erosion Prediction System (WEPS), led to this study of the relationship between the mass of crop residue and crop yield produced on nonirrigated cropland of the Inland Pacific Northwest United States, consisting of eastern Washington, northern Idaho, and northcentral Oregon. Retaining postharvest crop residues as soil surface cover is a primary method for controlling wind and water erosion; accordingly, erosion prediction models are highly sensitive to the amount of surface residue retained as soil cover. Traditionally, crop biomass calculations and erosion prediction models used expected or modeled crop yields and a fixed residue/grain index (R/G Index) value to determine residue quantity. Literature search indicated that cereal breeding efforts that emphasize yield have reduced the amount of residue for each unit of grain produced. In order to fulfill our objective of improving the relationship between grain yield and residue production, we assembled and examined a large set of regional crop yield and residue production data collected in research plot studies, on-farm field studies, and in available literature from eastern Washington and northcentral Oregon. Results of the study indicated that the R/G Index varies with yield. We also found that residue production versus grain yield for major nonirrigated crops of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) cereals, as well as for annual legumes and brassicas, can be fit with a linear relationship with a positive intercept and that the slope and intercept of the line are crop specific. Parameter values for specific crops are given. Using the historical fixed R/G Index can result in overestimating residue production of high-yielding winter wheat by as much as 35% and underestimating residue production for low spring wheat yields by as much as 66%. The results provide improved residue-to-grain yield relationships for use in water and wind erosion prediction models applied to the conditions of the Inland Pacific Northwest and adjacent areas. They provide a basis for estimating crop residue production in the region, and in conjunction with carbon sequestration models, a basis for determining if and where residues can be harvested for biomass in the region.

Soil erosion is a growing problem especially in areas of agricultural activity where soil erosion not only leads to decreased agricultural productivity but also reduces water availability. Universal Soil Loss Equation (USLE) is the most popular empirically based model used globally for erosion prediction and control. Remote sensing and GIS techniques have become valuable tools specially when assessing erosion at larger scales due to the amount of data needed and the greater area coverage. The present study area is a part of Chotanagpur plateau with undulating topography, with a very high risk of soil erosion. In the present study an attempt has been made to assess the annual soil loss in Upper South Koel basin using Universal Soil Loss Equation (USLE) in GIS framework. Such information can be of immense help in identifying priority areas for implementation of erosion control measures. The soil erosion rate was determined as a function of land topography, soil texture, land use/land cover, rainfall erosivity, and crop management and practice in the watershed using the Universal Soil Loss Equation (for Indian conditions), remote sensing imagery, and GIS techniques. The rainfall erosivity R-factor of USLE was found as 546 MJ mm/ha/hr/yr and the soil erodibility K-factor varied from 0.23 - 0.37. Slopes in the catchment varied between 0% and 42% having LS factor values ranging from 0 - 21. The C factor was computed from NDVI (Normalized Difference Vegetative Index) values derived from Landsat-TM data. The P value was computed from existing cropping patterns in the catchment. The annual soil loss estimated in the watershed using USLE is 12.2 ton/ha/yr.

Forest roads can be a source of accelerated erosion, which can be detrimental to aquatic habitat, fish, and other aquatic biota. Erosion models are increasingly used to quantify sediment production from forest roads. This project evaluated the efficacy of these models to predict erosion from forest roads. Sediment production was measured from 44 road segments in the humid, temperate rain forests of Oregon and California. Sediment production from these road segments was estimated with four contemporary erosion models: the Washington Road Surface Erosion Model (WARSEM); Sediment Model 2 (SEDMODL2); WEPP:Road, an interface for the Water Erosion Prediction Project Model; and the revised universal soil loss equation (RUSLE). The erosion models consistently overestimated the amount of sediment produced by the road segments by 2 to 8 times. The results were highly variable, and there were considerable differences in erosion estimated by the four models, even for the same road segment. Further, the erosion models could not consistently identify the road segments that were the top sediment producers. It is hypothesized that the regionalized parameters used as inputs for the models do not adequately characterize the hydrology of the individual road segments. In the humid, temperate rain forests of the Pacific Northwest, surface erosion from forest roads is best predicted by the amount of runoff from the road during storms. Thus, research that will better quantify the hydrology of forest roads will provide better information to predict surface erosion from forest roads.

Accelerated soil erosion due to extreme climate change, such as increased rainfall intensity, and human-induced environmental changes, is a widely recognized problem. Existing soil erosion models are generally based on the gross erosion concept to compute annual upland soil loss in tons per acre per year. However, such models are not suitable for event-based simulations of erosion and deposition in time and space. Recent advances in computer geographic information system (GIS) technologies have allowed hydrologists to develop physically based models, and the trend in erosion prediction is towards process-based models, instead of conceptually lumped models. This study aims to propose an effective and robust distributed rainfall-sediment yield-runoff model consisting of basic element modules: a rainfall-runoff module based on the kinematic wave method for subsurface and surface flow, and a runoff-sediment yield-runoff model based on the unit stream power method. The model was tested on the Cheoncheon catchment, upstream of the Yongdam dam using hydrological data for three extreme flood events due to typhoons. The model provided acceptable simulation results with respect to both discharge and sediment discharge even though the simulated sedigraphs were underestimated, compared to observations. The spatial distribution of erosion and deposition demonstrated that eroded sediment loads were deposited in the cells along the channel network, which have a short overland flow length and a gentle local slope while the erosion rate increased as rainfall became larger. Additionally, spatially heterogeneous rainfall intensity, dependant on Thiessen polygons, led to spatially-distinct erosion and deposition patterns.

Growing awareness of the wider environmental significance of fine sediment transport by rivers and associated sediment problems linked to sediment–water quality interactions, nutrient and contaminant transfer, and the degradation of aquatic habitats has resulted in the need for an improved understanding of the mobilization and transfer of sediment in catchments to support the development of effective sediment management strategies. The sediment budget provides a key integrating concept for assembling information on the internal functioning of a catchment in terms of its sediment dynamics by providing information on the mobilization, transfer, storage and output of sediment. One key feature of a catchment sediment budget is the relationship between the sediment yield at the catchment outlet and rates of sediment mobilization and transfer within the catchment, which is commonly represented by the sediment delivery ratio. To date, most attempts to derive estimates of this ratio have been based on a comparison of the measured sediment yield from a catchment with an estimate of the erosion occurring within the catchment, derived from an erosion prediction procedure, such as the Universal Soil Loss Equation (USLE) or its revised version, RUSLE. There is a need to obtain more direct and spatially distributed evidence of the erosion rates occurring within a catchment and to characterize the links between sediment mobilization, transfer, storage and output more explicitly. In this context, fallout radionuclides have proved particularly useful as sediment tracers. This paper reports the results of a study aimed at exploring the use of caesium-137 (137Cs) measurements to establish sediment budgets for three catchments of different sizes and contrasting land use located in Calabria, southern Italy. Long-term measurements of sediment output were available for the catchments, and, by using the estimates of gross and net rates of soil loss within the catchments provided by 137Cs measurements, it was possible to establish the key components of the sediment budget for each catchment. By documenting the sediment budgets of three catchments of different sizes, the study provides a basis for exploring the effects of scale on catchment sediment budgets and, in particular, the increasing importance of catchment storage as the size of the catchment increases. The results of this study demonstrate a reduction in the sediment delivery ratio from 98 to 2% as catchment area increases from 1·47 ha to 31·2 km2. Copyright © 2010 John Wiley & Sons, Ltd.

Climate changes may have great impacts on the fragile agro-ecosystems of the Loess Plateau of China, which is one of the most severely eroded regions in the world. We assessed the site-specific impacts of climate change during 2010–2039 on hydrology, soil loss and crop yields in Changwu tableland region in the Loess Plateau of China. Projections of four climate models (CCSR/NIES, CGCM2, CSIRO-Mk2 and HadCM3) under three emission scenarios (A2, B2 and GGa) were used. A simple spatiotemporal statistical method was used to downscale GCMs monthly grid outputs to station daily weather series. The WEPP (Water and Erosion Prediction Project) model was employed to simulate the responses of agro-ecosystems. Compared with the present climate, GCMs projected a −2.6 to 17.4% change for precipitation, 0.6 to 2.6°C and 0.6 to 1.7°C rises for maximum and minimum temperature, respectively. Under conventional tillage, WEPP predicted a change of 10 to 130% for runoff, −5 to 195% for soil loss, −17 to 25% for wheat yield, −2 to 39% for maize yield, −14 to 18% for plant transpiration, −8 to 13% for soil evaporation, and −6 to 9% for soil water reserve at two slopes during 2010–2039. However, compared with conventional tillage under the present climate, conservation tillage would change runoff by −34 to 71%, and decrease soil loss by 26 to 77% during 2010–2039, with other output variables being affected slightly. Overall, climate change would have significant impacts on agro-ecosystems, and adoption of conservation tillage has great potential to reduce the adverse effects of future climate changes on runoff and soil loss in this region.

Erosion models play an important role in assessing the influence of human activities on the environment. For cold areas, adequate frost simulation is crucial for predicting surface runoff and water erosion. The Water Erosion Prediction Project (WEPP) model is a physically based erosion prediction software program developed by the USDA. One of the major components of WEPP is the simulation of winter processes, which include snow accumulation and melt as well as soil freeze and thaw. WEPP is successfully used in the evaluation of important natural resource issues throughout the U.S. and in a number of other countries. However, previous studies revealed problems in the winter component of the WEPP model, especially the routine for frost simulation. The main purpose of this study was to improve the WEPP model (v2006.5) by changing the soil profile discretization and computation of key thermal and hydraulic parameters in the frost simulation routines so that the model can adequately simulate soil freeze-thaw and winter runoff and erosion. WEPP v2006.5 and the modified version (v2010.1) were applied to experimental plots in Pullman, Washington, and Morris, Minnesota. The simulated snow and frost depths as well as runoff and sediment yield were contrasted and compared with field observations; the results from v2010.1 showed substantial improvement compared to those from v2006.5.

Rill erosion can be a serious problem on cultivated slopes. Understanding the mechanisms of the detachment process in eroding rills is important in developing and improving process-based erosion prediction models. The detachment rate, defined as the sediment amount detached from a unit area in a unit time, is a crucial parameter in most physical erosion prediction models. Most current studies on rill detachment rates use spatially averaged soil erosion data, which provides little information about soil erosion dynamics due to the lack of spatially distributed, contemporaneous rill erosion data. The objective of this study was to estimate the net detachment rate in eroding rills using a rare earth element (REE) tracing method. Flume experiments were conducted with ten REE oxides (Ho2O3, Tb4O7, Eu2O3, Yb2O3, Dy2O3, Sm2O3, La2O3, Tm2O3, CeO2, and Nd2O3) as tracers mixed with soil in different sections of five slopes (8.74%, 17.62%, 26.78%, 36.38%, and 46.6%) exposed to three water flow rates (2, 4, and 8 L min− 1). Regulated water flow was introduced to the upper end of flumes, 8 m long and 0.1 m wide, which were divided into 10 equal segments, each containing a different REE–soil mixture. The net detachment rate from each rill segment was calculated from the REE concentrations in the eroded and transported material. The detachment rates from different segments along the rills, at different slope gradients and flow rates, were compared with those estimated directly from previous studies, and were found to increase with increasing slope gradient and flow rate. Results also showed that the net rill detachment rates decreased linearly with an increase in sediment concentration, and decreased exponentially with an increase in rill length. The REE-derived detachment data and their relationship with sediment concentrations are consistent with the detachment function used in the WEPP model, and are helpful in understanding the processes of rill erosion. Using REEs to trace detachment rates in continuous rills is a valid and advantageous technique for estimating the spatial distribution of the rill detachment rates.

Unpaved forest roads are known as the major source of sediment delivered to streams in forested watershed. There is a need for improved forest road network planning systems that estimate and consider the environmental costs associated with this erosion in the planning process. This study incorporates the environmental impact of forest roads into an economic analysis for resource transportation planning. The USDA Forest Service WEPP:Road, a road erosion prediction model, was used to estimate road erosion rates and sediment delivery to streams. Based on the estimated sediment delivery, NETWORK2000, a forest transportation planning model, produced alternative road systems that simultaneously minimized transportation costs and overall sediment delivery. The methodology was applied to the Mica Creek watershed in northern Idaho, where eleven alternative road networks were developed. The results indicate that incorporating environmental impacts into transportation planning can generate alternative road networks that reduce a large amount of estimated sediment delivery at the expense of a relatively small increase in transportation costs.

Soil erosion is one of the key concerns in land use management for the Loess Plateau of the Yellow River, where serious soil loss is the root cause of environmental and ecological degradation of the basin. In this paper, a physically-based, distributed-parameter, and continuous erosion prediction model at the river basin scale was developed with the aim of assisting in developing better land use management strategies. The framework, the major supporting techniques, and the typical erosion processes are described. The physical processes of sediment yield and transport in the Loess Plateau are divided into three sub-processes, including the runoff and sediment yield on hillslopes, gravitational erosion in gullies, and hyperconcentrated flow routing in channels. For each sub-process, a physically-based simulation model was developed and embedded into the whole model system. The model system was applied to simulate the runoff and sediment yield in several typical years in the coarse sediment source area of the Loess Plateau, and the simulated results were in reasonably good agreement with the measured values. � 2007 International Association for Hydraulic Engineering and Research, Asia Pacific Division. Published by Elsevier B.V. All rights reserved.

Soil erosion in many parts of the developing world poses a threat to rural livelihoods, to the sustainbility of the agricultural sector, and to the environment. Most erosion prediction models are mechanistic and unsuited to quantify the severity of soil erosion in a data-limited developing world context. The model developed in this paper for Negros Island, in the central Philippines, is based on the Revised Universal Soil Loss Equation, but contains important innovations such as the movement of eroded soil over the landscape, simulating deposition on lower slopes and in waterways. It also includes a term describing farmer strategies to reduce soil erosion, which are typically ignored in erosion prediction models. A two-sample t-test found that model-predicted sediment loading values were not significantly different from field-measured sediment loading values when corrected for watershed size (P = 0.857). The model predicts an annual loss of 2.7 million cubic meters of sediment to waterways such that by 2050 more than 416,000 ha of agricultural land will be rendered unproductive due to erosion. Farmer behavior conserves soil, but on the steepest slopes soil conservation practices are not adequate to prevent erosion. Of two proposed strategies to control soil erosion in the rural Philippines, the model suggests that a complete switch to tree crops would conserve more soil than universal terrace adoption. However, even under these conservation scenarios, erosion threatens the areal extent of upland agriculture on Negros, and hence the sustainability of the island’s food supply.

Soil erosion on construction sites can be many times greater than on agricultural fields, yet there has been little modeling done for construction conditions. The objective of our study was to calibrate management and soil parameters in the agriculturally based model Water Erosion Prediction Project (WEPP) for construction and post-construction site conditions. Data from a 4 ha watershed at various stages of construction in Wake County, North Carolina, were used to compare model results with measured runoff volume and sediment yields. Model simulations were performed in GeoWEPP, a geospatial interface designed for WEPP that operates within ArcView GIS. Model parameters were adjusted from WEPP default parameters as supported by the literature and site observations. Predicted values were regressed against field data for Nash-Sutcliffe model efficiency (NSE), with NSE > 0.50 regarded as satisfactory performance. We were able to generate runoff and sediment yields comparable to observed values by replacing soil surface properties with subsoil properties, in conjunction with the cutslope management parameter file in WEPP. We found a similar agreement between predicted and observed values for stabilized conditions by increasing critical shear stress from 0.3 to 10 Pa for the soil input layer. In addition, changes to the model source code to reduce the lower limit of effective hydraulic conductivity (Kef) for impermeable surfaces resulted in improved runoff NSE, but consequently increased sediment yield on areas with higher Kef values. WEPP has great potential for modeling applications on construction sites; however, more validation studies are needed to confirm and expand upon our findings.

The Water Erosion Prediction Project (WEPP) was initiated in August 1985 to develop new-generation water erosion prediction technology for use by federal action agencies involved in soil and water conservation and environmental planning and assessment. Developed by the USDA-ARS as a replacement for empirically based erosion prediction technologies, the WEPP model simulates many of the physical processes important in soil erosion, including infiltration, runoff, raindrop and flow detachment, sediment transport, deposition, plant growth, and residue decomposition. The WEPP project included an extensive field experimental program conducted on cropland, rangeland, and disturbed forest sites to obtain data required to parameterize and test the model. A large team effort at numerous research locations, ARS laboratories, and cooperating land-grant universities was needed to develop this state-of-the-art simulation model. WEPP project participants met frequently to coordinate their efforts. The WEPP model can be used for common hillslope applications or on small watersheds. Because it is physically based, the model has been successfully used in the evaluation of important natural resources issues throughout the U.S. and in many other countries. Upgrades to the modeling system since the 1995 DOS-based release include Microsoft Windows operating system graphical interfaces, web-based interfaces, and integration with Geographic Information Systems. Improvements have been made to the watershed channel and impoundment components, the CLIGEN weather generator, the daily water balance and evapotranspiration routines, and the prediction of subsurface lateral flow along low-permeability soil layers. A combined wind and water erosion prediction system with easily accessible databases and a common interface is planned for the future.

Modifications are made to the revised Morgan–Morgan–Finney erosion prediction model to enable the effects of vegetation cover to be expressed through measurable plant parameters. Given the potential role of vegetation in controlling water pollution by trapping clay particles in the landscape, changes are also made to the way the model deals with sediment deposition and to allow the model to incorporate particle-size selectivity in the processes of erosion, transport and deposition. Vegetation effects are described in relation to percentage canopy cover, percentage ground cover, plant height, effective hydrological depth, density of plant stems and stem diameter. Deposition is modelled through a particle fall number, which takes account of particle settling velocity, flow velocity, flow depth and slope length. The detachment, transport and deposition of soil particles are simulated separately for clay, silt and sand. Average linear sensitivity analysis shows that the revised model behaves rationally. For bare soil conditions soil loss predictions are most sensitive to changes in rainfall and soil parameters, but with a vegetation cover plant parameters become more important than soil parameters. Tests with the model using field measurements under a range of slope, soil and crop covers from Bedfordshire and Cambridgeshire, UK, give good predictions of mean annual soil loss. Regression analysis of predicted against observed values yields an intercept value close to zero and a line slope close to 1·0, with a coefficient of efficiency of 0·81 over a range of values from zero to 38·6 t ha−1. Copyright © 2007 John Wiley & Sons, Ltd.

Forest roads are obliterated to lower the risks of surface erosion and mass failures. One purpose of the road obliteration is to return the compacted forest roads to productive pre-road conditions, i.e., a forest floor with high infiltration capacity, low interrill erodibility, and high vegetation ground cover. It is important to know how these characteristics recover following road obliteration. Infiltration capacity, interrill erodibility, and vegetation ground cover are essential parameters for modeling erosion from obliterated roads for erosion prediction models such as the Water Erosion Prediction Project (WEPP). We chose three sites located on the Payette National Forest, Idaho. Rainfall simulations were conducted on 1 × 1 m plots with three replications in two consecutive years. Three 30 min storm events with an intensity of 89 mm h-1 were applied to each plot. Photos were taken to determine vegetation ground cover. Infiltration capacity and interrill erodibility in this study were determined as 9.0 mm h-1 for saturated hydraulic conductivity and 3.2 × 106 kg·s m-4 for interrill erodibility. This study postulated a history of saturated hydraulic conductivity on a forest road from prior to road building to years after obliteration. The low elevation (1400 m) site had vegetation ground cover of 27% after three years following road obliteration, while the other high elevation (1800 m and 2200 m) sites had 8% after four years. We conclude that four years was not sufficient time for obliterated roads to return to the pre-road (forest floor) conditions, especially for infiltration capacity.

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 ev...

Sediment budget analyses require watershed scale evaluation of road erosion and delivery. The Watershed Erosion Prediction Project (WEPP) model, as developed by the USDA Forest Service, simulates sediment detachment and delivery for a road, fill, and buffer system. Time and budget constraints typically prevent a comprehensive sediment loading analysis using WEPP throughout a watershed. We present an automated approach to run the hillslope version of WEPP to simulate sediment detachment and delivery for a large road network. Road attributes are acquired from global positioning system-assisted road surveys and mapped in a geographic information system (GIS). After data manipulation in GIS and Excel, the required input files for WEPP are built. The approach can be applied to multiple road designs and climate regimes, with unique attributes for each road segment. We applied the automated approach to the 3,040 km² South Fork Clearwater River watershed for 1,017 km of road divided into 6,955 road segments. The availability of analysis capabilities of the WEPP results from large road networks within GIS provides a spatially explicit tool for the management and evaluation of sediment production throughout large road networks.

Soil erosion by water is a serious problem in southern Italy, particularly in Sicily which is one of the Italian administrative regions prone to desertification. Soil erosion not only affects soil quality, in terms of agricultural productivity, but also reduces the availability of water in reservoirs. This study was conducted in the Comunelli catchment in south-central Sicily, to predict potential annual soil loss using the revised universal soil loss equation (RUSLE) and to test the reliability of this methodology to predict reservoirs siltation. The RUSLE factors were calculated for the catchment using survey data and rain gauge measurement data. The R-factor was calculated from daily, monthly and annual precipitation data. The K-factor was calculated from soil samples collected in May and November 2004. The LS topographic factor was calculated from a 20 m digital elevation model. The C- and P-factors, in absence of detailed data, were set to 1. The results were compared with those obtained from another soil loss estimation method based on 137Cs and with the soil loss estimated from the sediment volume stored in the Comunelli reservoir between 1968 and 2004.

The occurrence of snow and soil frost influences hydrology and, in turn, the mechanisms of soil erosion processes in cold regions. For these regions, reliably modeling the dynamics of snow accumulation and melt, and soil frost formation and melt, is necessary prior to accurately predicting runoff and erosion. Only then will methods for predicting the rates and amounts of soil erosion by water be established on a firm hydrological footing. This article examines the potential of an energy budget approach to simulate the magnitude and variations of snow and soil frost depths. It is assumed that the net sum of all energy components in the environment is consumed or compensated by water phase change occurring near or under the ground surface, such as snow melting or soil freezing and thawing. Testing indicates that this energy budget approach demonstrates promise to simulate winter hydrology and to be adapted to erosion prediction models.

Soil erosion, associated with environmental impacts and crop productivity loss, is usually considered the most impacting of surface hydrology processes. Runoff plays a major role in the erosion process, but it is also important by itself as it directly influences several surface hydrologic processes. In this paper, a computer interface (Erosion Database Interface, EDI) is described that allows processing the surface hydrology output database of the Water Erosion Prediction Project (WEPP) erosion prediction model, resulting in a georeferenced estimation of runoff. WEPP output contains non-georeferenced daily information about estimated runoff at the lower end of each Overland Flow Element. EDI, when running with WEPP, allows extracting WEPP-calculated runoff values, transforming them into annual means and relocating them in a georeferenced database readable by Geographic Information Systems (GIS). EDI was applied to a 1990ha watershed in southeast Brazil, with vegetation of mainly sugarcane, forest, and pasture. A 100-year climate simulation was used as input to WEPP, and erosion values were calculated at about six points per hectare and interpolated to a raster format. EDI was successful in preparing the database for automatic calculation of erosion and hydrologic parameters with WEPP and to restore georeferences to mean annual accumulated runoff data that were imported in the GIS as a vector database. Of all the resulting maps, the runoff map is the one that integrates all of the input parameters required for WEPP simulation, thus reflecting not only the physical environment but also crop growth and management and tillage operations. A very small correlation between runoff and erosion shows them to behave independently. Moreover, it is concluded that on analyzing runoff related to agricultural management, georeferenced runoff studies are especially important. In this context, EDI may be a useful tool to assess the effect of tillage and crop management on runoff production.

Erosion caused by concentrated flows in agricultural areas is responsible for important soil losses, and rapid sediment transfer through the channel network. The main factors controlling concentrated flow erosion rates include the erodibility of soil materials, soil use and management, climate and watershed topography. In this paper, two topographic indices, closely related to mathematical expressions suggested by different authors, are used to characterize the influence of watershed topography on gully erosion. The AS1 index is defined as the product of the watershed area and the partial area-weighted average slope. The AS2 index is similar to the AS1 but uses the swale slope as the weighting factor. Formally, AS2 is the product of the watershed area and the length-weighted average swale slope. From studies made using different ephemeral gully erosion databases, it is shown that a high correlation consistently exists between the topographic indices and the volume of eroded soil. The resulting relationships are therefore useful to assess soil losses from gully erosion, to identify the most susceptible watersheds within large areas, and to compare the susceptibility to gully erosion among different catchments. This information is also important in studying the response of natural drainage network systems to different rainfall inputs. Copyright © 2005 John Wiley & Sons, Ltd.

CLIGEN is the only weather generator that generates internal storm patterns, which are required by many agricultural system models such as the Water Erosion Prediction Project (WEPP) model. The lack of correlation between CLIGEN-generated storm variables may limit those models' abilities to predict surface runoff and soil erosion. The objectives of this study were to: (1) test a distribution-free method for inducing desired rank correlation between generated storm variables, and (2) compare WEPP-predicted runoff and soil loss using measured vs. variously generated storm patterns on eight U.S. sites. Four climate files containing four storm patterns (measured, original uncorrelated CLIGEN output, correlated CLIGEN output, and correlated output with exponentially generated storm durations), along with measured soil, slope, and crop management on each site, were used as input to WEPP. The distribution-free approach was simple to use and capable of inducing desired rank correlation between storm depth and duration and consequently between storm depth and relative peak intensity. Original CLIGEN output after inducing desired correlation considerably improved WEPP runoff and soil loss predictions on most sites where strong correlation between storm depth and duration existed. On average, the relative errors averaged over all sites were reduced from 15.0% to 4.6% for runoff prediction and from 11.1% to 1.5% for soil loss prediction. The use of exponentially distributed storm duration, compared with the original CLIGEN output, doubled the overall relative error for soil loss prediction due to the undesirable alteration of relative peak intensity estimates. Overall results indicate that for better runoff and soil loss prediction, correlated CLIGEN output should be used on sites where strong correlation between storm depth and duration exists.

Erosion prediction utilizing digital elevation models (DEMs) is a logical advancement for automating the simula- tion process for models such as the Water Erosion Prediction Project (WEPP). The effects of using different DEM resolutions on watershed simulations and the ability to accurately predict sediment yield and runoff from different rainfall event sizes were studied using three application methods and data from six research watersheds. Simulating watersheds with a range of resolutions can help address the problem of deciding what topographic DEM resolution is ideal for model simulations of the watershed outlet, the end of each hillslope, and along the slope profiles. The three application methods studied here were: (1) HillslopeChanleng, (2) HillslopeCalcleng, and (3) Flowpath. The two Hillslope methods use a representative slope profile to represent each hillslope in the watershed, and the Flowpath method uses all of the individual flowpaths as model input for WEPP simulations. Results show that the Hillslope methods were not significantly influenced by DEM resolutions; however, there was an observable interaction between resolutions and the Flowpath method. Large rainfall events were pre- dicted better than small events, but fine DEM resolutions did not improve predictions of either large or small rainfall events. Using coarse DEM resolutions for the topographic input will not decrease the accuracy of erosion prediction using the WEPP model and the Hillslope methods, unless the coarseness of the DEM compromises the delineation of the watershed or hill- slopes.

The Water Erosion Prediction Project (WEPP) is a physically based erosion model for applications to dryland and irri- gated agriculture, rangeland, and forests. U.S. Forest Service (USFS) experience showed that WEPP was not being adapted because of the difficulty in building files describing the input condi- tions in the existing interfaces. To address this difficulty, a suite of Internet interfaces with a database was developed to more easily predict soil erosion for a wide range of climatic and forest condi- tions, including roads, fires, and timber harvest. The database included a much larger climate database than was previously avail- able for applications in remote forest and rangeland areas. Valida- tion results showed reasonable agreement between erosion values reported in the literature and values predicted by the interfaces to the WEPP model. (KEY TERMS: erosion; sedimentation; forest hydrology; modeling; forest roads.)

Daily weather data are required as climate input to many models that continuously simulate natural resources systems. CLIGEN is a stochastic weather generator to produce 10 daily weather variables to meet this need. CLIGEN has primarily been used to provide climate input for the process-based runoff and soil erosion prediction model WEPP (water erosion prediction project). Runoff and erosion prediction is particularly sensitive to the four precipitation variables generated by CLIGEN. Weather data for 43 sites representing all major climate zones around Australia were used to prepare input parameter files for CLIGEN to generate 100 years climate data for each of these sites. The quality of generated precipitation variables was assessed in terms of (1) simulated runoff and soil loss for each of three soils under bare fallow conditions with WEPP, (2) climate inputs for the revised universal soil loss equation (RUSLE), and (3) a comparison with published rainfall intensity maps for fixed average recurrence interval and duration. This paper shows that uncalibrated CLIGEN can generate the required climate data for WEPP for these sites. Model efficiency between predicted runoff and soil loss using CLIGEN-generated and observed precipitation data is in excess of 0.95. Generated rainfall erosivity for RUSLE is systematically higher than (about 50% for the R-factor and 25% for the 10-year storm erosivity index) and closely related to the measured erosivity for these sites (r2=0.88–0.94), a trend consistent with what is observed for sites in the United States. CLIGEN can also be used to predict the seasonal distribution of rainfall erosivity quite well for all climate zones in Australia. Detailed analysis of the observed 6 min intensity data for these sites shows that over-prediction of rainfall erosivity and rainfall intensity at short time scales in general is an outcome of the particular storm pattern adopted in CLIGEN for WEPP, not an intrinsic deficiency of CLIGEN per se.

Rainfall simulation experiments are widely used to study erosion and contaminant transport in overland flow. We investigated the use of two rainfall simulators designed to rain on 2-m-long (2-m2) and 10.7-m-long (32.6-m2) plots to estimate overland flow and phosphorus (P) transport in comparison with watershed-scale data. Simulated rainfall (75 mm h(-1)) generated more overland flow from 2-m-long (20 L m2) than from 10.7-m-long (10 L m2) plots established in grass, no-till corn (Zea mays L.), and recently tilled fields, because a relatively greater area of the smaller plots became saturated (75% of area) during rainfall compared with large plots (75% area). Although average concentrations of dissolved reactive phosphorus (DRP) in overland flow were greater from 2-m-long (0.50 mg L(-1)) than 10.7-m-long (0.35 mg L(-1)) plots, the relationship between DRP and Mehlich-3 soil P (as defined by regression slope) was similar for both plots and for published watershed data (0.0022 for grassed, 0.0036 for no-till, and 0.0112 for tilled sites). Conversely, sediment, particulate phosphorus (PP), and total phosphorus (TP) concentrations and selective transport of soil fines (2 microm) were significantly lower from 2- than 10.7-m-long plots. However, slopes of the logarithmic regression between P enrichment ratio and sediment discharge were similar (0.281-0.301) for 2- and 10.7-m-long plots, and published watershed data. While concentrations and loads of P change with plot scales, processes governing DRP and PP transport in overland flow are consistent, supporting the limited use of small plots and rainfall simulators to assess the relationship between soil P and overland flow P as a function of soil type and management.

The multidisciplinary approach of soil erosion research often requires erosion to be treated as spatial georeferenced information. This condition is essential so as to be compatible with information analyzed via Geographic Information Systems (GIS). The original versions of important soil erosion prediction models such as the Universal Soil Loss Equation (USLE) and Water Erosion Prediction Project (WEPP) do not operate on a georeferenced basis. The Erosion Database Interface (EDI) is a computer program for georeferenced application of USLE and WEPP. EDI uses, as input, a text format database with points defined by coordinates (x, y and z) representing hillslopes, each point associated to soil type and land use. Such input data can be provided by different methods. Exclusive field work with ordinary topographic equipment and GIS procedures are examples of methods that can be used for this purpose. Flexibility in the methods adopted for providing input data is an important prerequisite for erosion prediction in tropical and developing regions, where soil erosion is a major concern and the availability of digital data is usually restricted. Hillslopes for EDI were defined as straight line segments beginning at the upper slope and ending down at runoff output. This restricts EDI as a complete erosion-prediction method for areas where runoff deflecting features predominate or where channel or gully erosion is to be considered. As output, EDI provides georeferenced soil erosion values in another text format database. This database can be used directly for statistical or geostatistical analysis or imported into a GIS for further processing. A practical example representative of a sugarcane-growing area located at the southeastern part of Brazil is used to illustrate EDI's performance. In this example, soil erosion maps were produced from GIS data using EDI as interface for erosion calculations for WEPP and USLE.

Soil erosion in agricultural watersheds is a systemic problem that has plagued mankind ever since the practice of agriculture began some 9000 years ago. It is a worldwide problem, the severity of which varies from location to location depending on weather, soil type, topography, cropping practices, and control methods. Research to address and predict soil loss from agricultural land and in watersheds began in earnest in the 1930s following the events of the Dust Bowl. Early research primarily consisted of monitoring of soil loss from natural runoff plots and small watersheds. Gradually and over time, the focus shifted toward the development of prediction equations based on the acquired soil loss database. With computer technology, modeling watershed erosion and sedimentation processes became routine. Also, fundamental research was conducted to acquire a better understanding of the complex aspects of soil erosion and sediment transport processes and to fill in knowledge gaps in cases where data were not readily available. In recent years, most soil loss from upland areas occurs as gully erosion. This chapter presents a background of the knowledge that was systematically acquired in predicting soil erosion from upland areas and the technology that was developed and is used today. This chapter does not address all the aspects of upland soil erosion, but focuses primarily on the erodibility (K-factor) and hydrological aspects (R-factor) of the most widely used erosion prediction equations: the revised universal soil loss equation, version 2 (RUSLE2) and water erosion prediction project model (WEPP) models-based formulae. This chapter also includes a presentation of the Chinese approach of adapting gully erosion predictions according to the universal soil loss equation (USLE) format. Finally, ongoing research and technology development using light detection and ranging (LiDAR) and photogrammetry in gully erosion predictions is discussed.

Sediment delivery from small watersheds or eroMost existing soil loss data are spatially-averaged, though various sion plots has been well monitored. These data are tracing techniques have been used for obtaining spatially-distributed extremely useful in developing erosion prediction moddata. Spatially-distributed soil erosion data are needed for validating els, understanding erosion principles, assessing the onphysically-based erosion prediction models and for better understand- site and off-site impacts, and deriving best management ing soil erosion dynamics. The objectives of this study were to evaluate practices to combat soil erosion. However, these data do the feasibility of using rare earth element (REE) oxides directly as not contain information on soil redistribution patterns tracers for soil erosion studies by examining their binding ability with within a watershed or an erosion plot. Spatially-distribsoil materials, and also to test a quick acid-extraction procedure. Five uted erosion data are needed for validating physicallyREE oxide powders were directly mixed with a Miami silt loam soil based erosion models and for understanding soil erosion (fine-loamy, mixed, mesic Typic Hapludalfs) and then leached with deionized water in a soil box to evaluate the mobility of REEs. Follow- dynamics at a process level. Since erosion processes ing leaching, soil samples were sectioned in 25-mm increments and change in time and space, physically-based models such analyzed for REEs. The REE-tagged soil was wet sieved to obtain as the Water Erosion Prediction Project model have REE concentrations in each aggregate size group. A simple acid- been developed to predict soil erosion at any given leaching method was used to extract REEs from all soil samples. The time and location. Process-based models can only be extracts were analyzed by Inductively Coupled Plasma-Mass Spec- thoroughly validated with spatially-distributed erosion trometry (ICP-MS) techniques. The data indicated that the maximum data. coefficient of variation of the proposed procedure was 10% for To obtain spatially-distributed data, various types of all REEs. The REE oxides were uniformly incorporated into soil tracers have been developed and used. Most of them aggregates of different sizes (53 m) and were bound with silt-size involve atomic bomb fallout radionuclide

The event- and physics-based KINEROS2 runoff/erosion model for predicting overland flow generation and sediment production was applied to unpaved mountain roads. Field rainfall simulations conducted in northern Thailand provided independent data for model calibration and validation. Validation shows that KINEROS2 can be parameterized to simulate total discharge, sediment transport and sediment concentration on small-scale road plots, for a range of slopes, during simulated rainfall events. The KINEROS2 model, however, did not accurately predict time-dependent changes in sediment output and concentration. In particular, early flush peaks and the temporal decay in sediment output were not predicted, owing to the inability of KINEROS2 to model removal of a surface sediment layer of finite depth. After 15–20 min, sediment transport declines as the supply of loose superficial material becomes depleted. Modelled erosion response was improved by allowing road erodibility to vary during an event. Changing the model values of erosion detachment parameters in response to changes in surface sediment availability improved model accuracy of predicted sediment transport by 30–40%. A predictive relationship between road erodibility ‘states’ and road surface sediment depth is presented. This relationship allows implementation of the dynamic erodibility (DE) method to events where pre-storm sediment depth can be estimated (e.g., from traffic usage variables). Copyright © 2001 John Wiley & Sons, Ltd.

The USDA-Water Erosion Prediction Project (WEPP) demonstrates a new generation of water erosion prediction technology for use in soil and water conservation planning and assessment. The WEPP computer model is based on various interacting natural processes in hydrology, plant sciences, soil physics, and erosion mechanics. The model offers several advantages over existing erosion prediction technology and has the capability of accommodating spatial and temporal variability in topography, soil properties, cropping and management, and sediment detachment and deposition. The model has a wider range of applicability as it accounts for most of the variables affecting runoff and erosion processes. However, considerable validation is required to assess the reliability of predictions obtained from the model. Sixteen-hundred plot years of natural runoff plot data were used for verification and validation of WEPP, including most of data used to develop Universal Soil Loss Equation (USLE). WEPP predictions of soil loss from natural runoff plots at 20 different locations were compared to measured data and existing technology (i.e., USLE and RUSLE). WEPP recorded a model efficiency of 0.71 compared to 0.80 and 0.72 for the USLE and RUSLE respectively. While the USLE and RUSLE did exhibit better model efficiency than WEPP this could be attributed to availability of more refined and site specific input parameters for the empirical models.

This study compared simulated values of surface runoff and erosion from two current erosion prediction technologies (WEPP and GLEAMS) to observed data for two grazing pressures on three pasture vegetation types (switchgrass, bermudagrass, and tall fescue) in the Tennessee Valley region of Alabama. Based on the results of statistical analysis for each forage type and grazing pressure, GLEAMS performed better than WEPP, although either model did not predict sediment loss within an acceptable level. Because it was determined that grazing pressure had no significant impact on runoff and erosion from the experiment plots, the data for each forage were pooled over the same grazing pressure and the correlation between observed and simulated data were analyzed. This analysis showed that overall, predicted runoff values for both WEPP and GLEAMS had linear relationship to observed data. When sediment loss was analyzed, GLEAMS predicted value showed significant correlation with observed data, while WEPP pre...

Newly developed erosion prediction models require detailed input parameters including those describing cropping systems. Each model exhibits a different level of complexity for crop growth modeling, and many plant parameters for these models are difficult to obtain. Furthermore, numerous parameters require interpretation from other measurable plant characteristics. We developed a database containing measurable plant characteristics to supply users of these new erosion prediction technologies with the necessary parameters. The new database supports the Revised Universal Soil Loss Equation (RUSLE) and the Water Erosion Prediction Project (WEPP) erosion prediction models. The Crop Parameter Intelligent Database System (CPIDS) was developed to assist crop database builders and users of RUSLE and WEPP in interpretation of plant characteristics and selection of plant parameters. CPIDS is a Microsoft® Windows™-based program, currently running on a personal computer (PC) under Windows 3.1™, Windows 95™ and Windows NT 4.0™.

Evaluation of various soil erosion models with large data sets have consistently shown that these models tend to over-predict soil erosion for small measured values, and under-predict soil erosion for larger measured values. This trend appears to be consistent regardless of whether the soil erosion value of interest is for individual storms, annual totals, or average annual soil losses, and regardless of whether the model is empirical or physically based. The hypothesis presented herein is that this phenomenon is not necessarily associated with bias in model predictions as a function of treatment, but rather with limitations in representing the random component of the measured data within treatments (i.e., between replicates) with a deterministic model. A simple example is presented, showing how even a `perfect' deterministic soil erosion model exhibits bias relative to small and large measured erosion rates. The concept is further tested and verified on a set of 3007 measured soil erosion data pairs from storms on natural rainfall and run-off plots using the best possible, unbiased, real-world model, i.e., the physical model represented by replicated plots. The results of this study indicate that the commonly observed bias, in erosion prediction models relative to over-prediction of small and under-prediction of large measured erosion rates on individual data points, is normal and expected if the model is accurately predicting erosion rates as a function of environmental conditions, i.e., treatments.

Conservation Reserve Program (CRP) areas that are returned to crop production will initially be much less erodible than fields which were farmed using conventional practices. In this study, a rainfall simulator was used to measure runoff and erosion from former CRP areas in Mississippi, Nebraska and South Dakota over approximately a two year period. Soil loss rates measured immediately following tillage on each of the three sites were similar to values obtained on the undisturbed CRP treatments. However, when left in a fallow condition, the erosion-reducing effectiveness of the sod appeared to have lasted less than one year. The rapid increase in soil erodibility following tillage was attributed to a reduction in surface cover and organic material. The WEPP and RUSLE models are currently used extensively in conservation planning and assessment. The experimental data collected in this study were used to derive selected parameter values for use in these models.

Current soil-erosion prediction technology addresses processes of rainsplash, overland-flow sediment transport, and rill erosion in small watersheds. The effects of factors determining sediment yield from larger-scale drainage basins, in which sediment movement is controlled by the combined small-scale processes and a complex set of channel and other basin-scale sediment-delivery processes, such as soil creep, bioturbation, and accelerated erosion due to denudation of vegetation, have been poorly evaluated. General suggestions are provided for the development of erosion-prediction technology at the geomorphic or drainage-basin scale based on the separation of sediment-yield data for channel and geomorphic processes from those of field-scale soil loss. An emerging technology must consider: (1) the effects on sediment yield of climate, geology and soils, topography, biotic interactions with other soil processes, and land-use practices; (2) all processes of sediment delivery to a channel system; and (3) the general tendency in most drainage basins for progressively greater sediment storage in the downstream direction.