Prediction and uncertainty source analysis of the spatial and temporal disturbance from off-road vehicular traffic in a complex ecosystem

Vehicle use during military training activities results in soil disturbance and vegetation loss. The capacity of lands to sustain training is a function of the sensitivity of lands to vehicle use and the pattern of land use. The sensitivity of land to vehicle use has been extensively studied. Less well understood are the spatial patterns of vehicle disturbance. Since disturbance from off-road vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) was used to predict the pattern of vehicle disturbance across a training facility. An uncertainty analysis of the model predictions assessed the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty.For the most part, this analysis showed that mapping and modeling process errors contributed more than 95% of the total uncertainty of predicted disturbance, while satellite imagery error contributed less than 5% of the uncertainty. When the total uncertainty was larger than a threshold, modeling error contributed 60% to 90% of the prediction uncertainty. Otherwise, mapping error contributed about 10% to 50% of the total uncertainty. These uncertainty sources were further partitioned spatially based on other sources of uncertainties associated with vehicle moment, landscape characterization, satellite imagery, etc.     

The effect of model structure and data in modeling land conditions in disturbed complex ecosystems

Off-road vehicles increase soil erosion by reducing vegetation cover and other types of ground cover, and by changing the structure of soil. The investigation of the relationship between disturbance from off-road vehicles and the intensity of the activities that involve use of vehicles is essential for water and soil conservation and facility management. Models have been developed in a previous study to predict disturbance caused by off-road vehicles. However, the effect of data on model quality and model performance, and the appropriate structure of models have not been previously investigated. In order to improve the quality and performance of disturbance models, this study was designed to investigate the effects of model structure and data. The experiment considered and tested: (1) two measures of disturbance based on the Vegetation Cover Factor (C Factor) of the Revised Universal Soil Loss Equation (RUSLE) and Disturbance Intensity; (2) model structure using two modeling approaches; and (3) three subsets of data. The adjusted R-square and residuals from validation data are used to represent model quality and performance, respectively. Analysis of variance (ANOVA) is used to identify factors which have significant effects on model quality and performance. The results of the ANOVA show that subsets of data have significant effects on both model quality and performance for both measures of disturbance. The ANOVA also detected that the C Factor models have higher quality and performance than the Disturbance models. Although modeling approaches are not a significant factor based on the ANOVA tests, models containing interaction terms can increase the adjusted R-squares for nearly all tested conditions and the maximum improvement can reach 31%.     

Suitability of different erosivity models used in RUSLE2 for the South West Indian region

RUSLE2 is the most used soil erosion model in practice. The rainfall-erosivity factor (R) is one of the six factors that is taken into consideration while estimating soil loss at a hill slope profile. R is determined using rainfall data collected from any region making use of basic rainstorm kinetic energy versus rainfall intensity relationships, which are variable for different geographic regions. Indian researchers used a specific erosivity model for building an iso-erosivity map for India. Many other erosivity models around the world are now available. However, it is not clear whether one can replace RUSLE2 recommended model by the ones derived in other geographic regions for using in Indian soil erosion studies. This has been examined here on south-western Indian data. Various models derived in diverse places were analyzed and compared with the RUSLE2 recommended relationship; and found that, a few could very well replace the usual RUSLE2 recommended expression.     

Determination of Soil Erosion Risk in the Mustafakemalpasa River Basin, Turkey, Using the Revised Universal Soil Loss Equation, Geographic Information System, and Remote Sensing

Sediment transport from steep slopes and agricultural lands into the Uluabat Lake (a RAMSAR site) by the Mustafakemalpasa (MKP) River is a serious problem within the river basin. Predictive erosion models are useful tools for evaluating soil erosion and establishing soil erosion management plans. The Revised Universal Soil Loss Equation (RUSLE) function is a commonly used erosion model for this purpose in Turkey and the rest of the world. This research integrates the RUSLE within a geographic information system environment to investigate the spatial distribution of annual soil loss potential in the MKP River Basin. The rainfall erosivity factor was developed from local annual precipitation data using a modified Fournier index: The topographic factor was developed from a digital elevation model; the K factor was determined from a combination of the soil map and the geological map; and the land cover factor was generated from Landsat-7 Enhanced Thematic Mapper (ETM) images. According to the model, the total soil loss potential of the MKP River Basin from erosion by water was 11,296,063?Mg?year(-1) with an average soil loss of 11.2?Mg?year(-1). The RUSLE produces only local erosion values and cannot be used to estimate the sediment yield for a watershed. To estimate the sediment yield, sediment-delivery ratio equations were used and compared with the sediment-monitoring reports of the Dolluk stream gauging station on the MKP River, which collected data for >41?years (1964-2005). This station observes the overall efficiency of the sediment yield coming from the Orhaneli and Emet Rivers. The measured sediment in the Emet and Orhaneli sub-basins is 1,082,010?Mg?year(-1) and was estimated to be 1,640,947?Mg?year(-1) for the same two sub-basins. The measured sediment yield of the gauge station is 127.6?Mg?km(-2)?year(-1) but was estimated to be 170.2?Mg?km(-2) year(-1). The close match between the sediment amounts estimated using the RUSLE-geographic information system (GIS) combination and the measured values from the Dolluk sediment gauge station shows that the potential soil erosion risk of the MKP River Basin can be estimated correctly and reliably using the RUSLE function generated in a GIS environment.     

Spatial and Temporal Assessment of Cumulative Disturbance Impacts Due to Military Training,Burning, Haying,and Their Interactions on Land Condition of Fort Riley

The effects of military training activities on the land condition of Army installations vary spatially and temporally. Training activities observably degrade land condition while also increasing biodiversity and stabilizing ecosystems. Moreover, other anthropogenic activities regularly occur on military lands such as prescribed burns and agricultural haying—adding to the dynamics of land condition. Thus, spatially and temporally assessing the impacts of military training, prescribed burning, agricultural haying, and their interactions is critical to the management of military lands. In this study, the spatial distributions and patterns of military training-induced disturbance frequency were derived using plot observation and point observation-based method, at Fort Riley, Kansas from 1989 to 2001. Moreover, spatial and variance analysis of cumulative impacts due to military training, burning, haying, and their interactions on the land condition of Fort Riley were conducted. The results showed that: (1) low disturbance intensity dominated the majority of the study area with exception of concentrated training within centralized areas; (2) high and low values of disturbance frequency were spatially clustered and had spatial patterns that differed significantly from a random distribution; and (3) interactions between prescribed burning and agricultural haying were not significant in terms of either soil erosion or disturbance intensity although their means and variances differed significantly between the burned and non-burned areas and between the hayed and non-hayed areas.     

US army land condition-trend analysis (LCTA) program

The US Army Land Condition-Trend Analysis (LCTA) program is a standardized method of data collection, analysis, and reporting designed to meet multiple goals and objectives. The method utilizes vascular plant inventories, permanent field plot data, and wildlife inventories. Vascular plant inventories are used for environmental documentation, training of personnel, species identification during LCTA implementation, and as a survey for state and federal endangered or threatened species. The permanent field plot data documents the vegetational, edaphic, topographic, and disturbance characteristics of the installation. Inventory plots are allocated in a stratified random fashion across the installation utilizing a geographic information system that integrates satellite imagery and soil survey information. Ground cover, canopy cover, woody plant density, slope length, slope gradient, soil information, and disturbance data are collected at each plot. Plot data are used to: (1) describe plant communities, (2) characterize wildlife and threatened and endangered species habitat, (3) document amount and kind of military and nonmilitary disturbance, (4) determine the impact of military training on vegetation and soil resources, (5) estimate soil erosion potential, (6) classify land as to the kind and amount of use it can support, (7) determine allowable use estimates for tracked vehicle training, (8) document concealment resources, (9) identify lands that require restoration and evaluate the effectiveness of restorative techniques, and (10) evaluate potential acquisition property. Wildlife inventories survey small and midsize mammals, birds, bats, amphibians, and reptiles. Data from these surveys can be used for environmental documentation, to identify state and federal endangered and threatened species, and to evaluate the impact of military activities on wildlife populations. Short- and long-term monitoring of permanent field plots is used to evaluate and adjust land management decisions.     

Spatially explicit sensitivity and uncertainty analysis for multicriteria-based vulnerability assessment

This research analyses the application of spatially explicit sensitivity and uncertainty analysis for GIS (Geographic Information System) multicriteria decision analysis (MCDA) within a multi-dimensional vulnerability assessment regarding flooding in the Salzach river catchment in Austria. The research methodology is based on a spatially explicit sensitivity and uncertainty analysis of GIS-CDA for an assessment of the social, economic, and environmental dimensions of vulnerability. The main objective of this research is to demonstrate how a unified approach of uncertainty and sensitivity analysis can be applied to minimise the associated uncertainty within each dimension of the vulnerability assessment. The methodology proposed for achieving this objective is composed of four main steps. The first step is computing criteria weights using the analytic hierarchy process (AHP). In the second step, Monte Carlo simulation is applied to calculate the uncertainties associated with AHP weights. In the third step, the global sensitivity analysis (GSA) is employed in the form of a model-independent method of output variance decomposition, in which the variability of the different vulnerability assessments is apportioned to every criterion weight, generating one first-order (S) and one total effect (ST) sensitivity index map per criterion weight. Finally, in the fourth step, an ordered weighted averaging method is applied to model the final vulnerability maps. The results of this research demonstrate the robustness of spatially explicit GSA for minimising the uncertainty associated with GIS-MCDA models. Based on these results, we conclude that applying the variance-based GSA enables assessment of the importance of each input factor for the results of the GIS-MCDA method, both spatially and statistically, thus allowing us to introduce and recommend GIS-based GSA as a useful methodology for minimising the uncertainty of GIS-MCDA.     

Optimal spatial resolution for collection of ground data and multi-sensor image mapping of a soil erosion cover factor

Military training activities disturb ground and vegetation cover of landscapes and increases potential soil erosion. To monitor the dynamics of soil erosion, there is an important need for an optimal sampling design in which determining the optimal spatial resolutions in terms of size of sample plots used for the collection of ground data and the size of pixels for mapping. Given a sample size, an optimal spatial resolution should be cost-efficient in both sampling costs and map accuracy. This study presents a spatial variability-based method for that purpose and compared it with the traditional methods in a study area in which a soil erosion cover factor was sampled and mapped with multiple plot sizes and multi-sensor images. The results showed that the optimal spatial resolutions obtained using the spatial variability-based method were 12 and 20m for years 1999 and 2000, respectively, and were consistent with those using the traditional methods. Moreover, the most appropriate spatial resolutions using the high-resolution images were also consistent with those using ground sample data, which provides a potential to use the high-resolution images instead of ground data to determine the optimal spatial resolutions before sampling. The most appropriate spatial resolutions above were then verified in terms of cost-efficiency which was defined as the product of sampling cost and map error using ordinary kriging without images and sequential Gaussian co-simulation with images to generate maps.     

Grassland Plant Composition Alters Vehicular Disturbance Effects in Kansas,USA

Many “natural” areas are exposed to military or recreational off-road vehicles. The interactive effects of different types of vehicular disturbance on vegetation have rarely been examined, and it has been proposed that some vegetation types are less susceptible to vehicular disturbance than others. At Fort Riley, Kansas, we experimentally tested how different plant community types changed after disturbance from an M1A1 Abrams tank driven at different speeds and turning angles during different seasons. The greatest vegetation change was observed because of driving in the spring in wet soils and the interaction of turning while driving fast (vegetation change was measured with Bray-Curtis dissimilarity). We found that less vegetation change occurred in communities with high amounts of native prairie vegetation than in communities with high amounts of introduced C₃ grasses, which is the first experimental evidence we are aware of that suggests plant communities dominated by introduced C₃ grasses changed more because of vehicular disturbance than communities dominated by native prairie grasses. We also found that vegetation changed linearly with vehicular disturbance intensity, suggesting that at least initially there was no catastrophic shift in vegetation beyond a certain disturbance intensity threshold. Overall, the intensity of vehicular disturbance appeared to play the greatest role in vegetation change, but the plant community type also played a strong role and this should be considered in land use planning. The reasons for greater vegetation change in introduced C₃ grass dominated areas deserve further study.     

An erosion-based land classification system for military installations

The universal soil loss equation (USLE) has been integrated with a geographic information system known as the geographical resources analysis support system (GRASS) to create a land classification system for use by military trainers and land managers to minimize the environmental impacts of military training activities. The USLE provides an estimate of current average annual sheet and rill erosion based upon factors representing climate, soil erodibility, topography, cover, and conservation support practices. The erosion estimate is compared to erosion tolerance values to produce an expression of the current erosion status. An index of inherent site erodibility is also achieved through manipulation of the USLE. Based on published soil surveys, satellite imagery, and ground-truth vegetation transects, data layers are created within GRASS for each of the component factors of the USLE. Appropriate mathematical operations are performed with the data layers, and color-coded maps are produced that represent the erosion status and erodibility index for each 50-m × 50-m area of soil surface. These maps aid military trainers and land managers in scheduling appropriate kinds and intensities of military training activities.     

Adaptation of RUSLE in the Eastern Part of the Mediterranean Region

Empirically based models are used worldwide to estimate soil erosion. The Revised Universal Soil Loss Equation (RUSLE) is one such model that has been intensively tested and validated under conditions in the United States. RUSLE estimates average soil loss as a function of five main factors: rainfall erosivity (R), soil erodibility (K), crop management (C), support practice (P), and topographic (LS) factors. This study investigated the application of RUSLE to Mediterranean conditions. The validation and calibration of RUSLE in the study area utilized field plots soil erosion measurements. The results found the RUSLE soil loss estimation to be three times the actual soil loss (7.8 and 2.6 Mg/ha, for RUSLE and actual measured soil loss, respectively). The difference between the RUSLE factors and the measured factors were responsible for the differences between the soil loss estimation by RUSLE and the measured soil loss. Specifically, the RUSLE K-factor showed three times the magnitude of the measured K-factor, the RUSLE C-factor underestimated the measured C-factor, and the RUSLE P-factor overestimated the measured P-factor by three times. Adjusting the RUSLE factors according to the measured ones increased the models predictability, whereas the adjusted-RUSLE soil loss estimation underestimated the measured soil loss by 14%. The adjustment of RUSLE, according to the prevailing conditions of the study area, increased the model efficiency three times (0.26 and 0.86 before and after adjustment of the mode,l respectively). For more accurate and reliable validation of the RUSLE under the Mediterranean conditions, it is advisable to conduct long-term soil loss experimentation and measurements.     

Predicting soil erosion for alternative land uses

The APEX (Agricultural Policy-Environmental eXtender) model developed in the United States was calibrated for northwestern China's conditions. The model was then used to investigate soil erosion effects associated with alternative land uses at the ZFG (Zi-Fang-Gully) watershed in northwestern China. The results indicated that the APEX model could be calibrated reasonably well (+/-15% errors) to fit those areas with >50% slope within the watershed. Factors being considered during calibration include runoff, RUSLE (Revised Universal Soil Loss Equation) slope length and steepness factor, channel capacity flow rate, floodplain saturated hydraulic conductivity, and RUSLE C factor coefficient. No changes were made in the APEX computer code. Predictions suggest that reforestation is the best practice among the eight alternative land uses (the status quo, all grass, all grain, all grazing, all forest, half tree and half grass, 70% tree and 30% grain, and construction of a reservoir) for control of water runoff and soil erosion. Construction of a reservoir is the most effective strategy for controlling sediment yield although it does nothing to control upland erosion. For every 1 Mg of crop yield, 11 Mg of soil were lost during the 30-yr simulation period, suggesting that expanding land use for food production should not be encouraged on the ZFG watershed. Grass species are less effective than trees in controlling runoff and erosion on steep slopes because trees generally have deeper and more stable root systems.     

Effects of Short- and Long-Term Disturbance Resulting from Military Maneuvers on Vegetation and Soils in a Mixed Prairie Area

Loss of grassland species resulting from activities such as off-road vehicle use increases the need for models that predict effects of anthropogenic disturbance. The relationship of disturbance by military training to plant species richness and composition on two soils (Foard and Lawton) in a mixed prairie area was investigated. Track cover (cover of vehicle disturbance to the soil) and soil organic carbon were selected as measures of short- and long-term disturbance, respectively. Soil and vegetation data, collected in 1-m² quadrats, were analyzed at three spatial scales (60, 10, and 1 m²). Plant species richness peaked at intermediate levels of soil organic carbon at the 10-m² and 1-m² spatial scales on both the Lawton and Foard soils, and at intermediate levels of track cover at all three spatial scales on the Foard soil. Species composition differed across the disturbance gradient on the Foard soil but not on the Lawton soil. Disturbance increased total plant species richness on the Foard soil. The authors conclude that disturbance up to intermediate levels can be used to maintain biodiversity by enriching the plant species pool.     

Soil erosion vulnerability under scenarios of climate land-use changes after the development of a large reservoir in a semi-arid area

Climate and land-use/cover changes (LUCC) influence soil erosion vulnerability in the semi-arid region of Alqueva, threatening the reservoir storage capacity and sustainability of the landscape. Considering the effect of these changes in the future, the purpose of this study was to investigate soil erosion scenarios using the Revised Universal Soil Loss Equation (RUSLE) model. A multi-agent system combining Markov cellular automata with multi-criteria evaluation was used to investigate LUCC scenarios according to delineated regional strategies. Forecasting scenarios indicated that the intensive agricultural area as well as the sparse and xerophytic vegetation and rainfall-runoff erosivity would increase, consequently causing the soil erosion to rise from 1.78 Mg ha^?1 to 3.65 Mg ha^?1 by 2100. A backcasting scenario was investigated by considering the application of soil conservation practices that would decrease the soil erosion considerably to an average of 2.27 Mg ha^?1. A decision support system can assist stakeholders in defining restrictive practices and developing conservation plans, contributing to control the reservoir's siltation.     

A STRIP MODEL APPROACH TO PARAMETERIZE A COUPLED GREEN‐AMPT KINEMATIC WAVE MODEL1

ABSTRACT: Infiltration processes at the plot scale are often described and modeled using a single effective hydraulic conductivity (Kg) value. This can lead to errors in runoff and erosion prediction. An integrated field measurement and modeling study was conducted to evaluate: (1) the relationship among rainfall intensity, spatially variable soil and vegetation characteristics, and infiltration processes; and (2) how this relationship could be modeled using Green and Ampt and a spatially distributed hydrologic model. Experiments were conducted using a newly developed variable intensity rainfall simulator on 2 m by 6 m plots in a rangeland watershed in southeastern Arizona. Rainfall application rates varied between 50 and 200 mm/hr. Results of the rainfall simulator experiments showed that the observed hydrologic response changed with changes in rainfall intensity and that the response varied with antecedent moisture condition. A distributed process based hydrologic simulation model was used to model the plots at different levels of hydrologic complexity. The measurement and simulation model results show that the rainfall runoff relationship cannot be accurately described or modeled using a single Kg value at the plot scale. Multi‐plane model configurations with infiltration parameters based on soil and plot characteristics resulted in a significant improvement over single‐plane configurations.     

Maintaining the Conservation Value of Shifting Cultivation Landscapes Requires Spatially Explicit Interventions

Fallow vegetation within landscapes dominated by shifting cultivation represents a woody species pool of critical importance with considerable potential for biodiversity conservation. Here, through the analysis of factors that influence the early stages of fallow vegetation regrowth in two contrasting forest margin landscapes in Southern Cameroon, we assessed the impact of current trends of land use intensification and expansion of the cultivated areas, upon the conservation potential of shifting cultivation landscapes. We combined the analysis of plot and landscape scale factors and identified a complex set of variables that influence fallow regrowth processes in particular the characteristics of the agricultural matrix and the distance from forest. Overall we observed a decline in the fallow species pool, with composition becoming increasingly dominated by species adapted to recurrent disturbance. It is clear that without intervention and if present intensification trends continue, the potential of fallow vegetation to contribute to biodiversity conservation declines because of a reduced capacity, (1) to recover forest vegetation with anything like its original species composition, (2) to connect less disturbed forest patches for forest dependent organisms. Strategies to combat biodiversity loss, including promotion of agroforestry practices and the increase of old secondary forest cover, will need not only to operate at a landscape scale but also to be spatially explicit, reflecting the spatial pattern of species reservoirs and dispersal strategies and human usage across landscapes.     

Sampling and Mapping a Soil Erosion Cover Factor by Integrating Stratification,Model Updating and Cokriging with Images

Cost-efficient sample designs for collection of ground data and accurate mapping of variables are required to monitor natural resources and environmental and ecological systems. In this study, a sample design and mapping method was developed by integrating stratification, model updating, and cokriging with Landsat Thematic Mapper (TM) imagery. This method is based on the spatial autocorrelation of variables and the spatial cross-correlation among them. It can lead to sample designs with variable grid spacing, where sampling distances between plots vary depending on spatial variability of the variables from location to location. This has potential cost-efficiencies in terms of sample design and mapping. This method is also applicable for mapping in the case in which no ground data can be collected in some parts of a study area because of the high cost. The method was validated in a case study in which a ground and vegetation cover factor was sampled and mapped for monitoring soil erosion. The results showed that when the sample obtained with three strata using the developed method was used for sampling and mapping the cover factor, the sampling cost was greatly decreased, although the error of the map was slightly increased compared to that without stratification; that is, the sample cost-efficiency quantified by the product of cost and error was greatly increased. The increase of cost-efficiency was more obvious when the cover factor values of the plots within the no-significant-change stratum were updated by a model developed using the previous observations instead of remeasuring them in the field.     

Seasonality of Soil Erosion Under Mediterranean Conditions at the Alqueva Dam Watershed

The Alqueva reservoir created the largest artificial lake of Western Europe in 2010. Since then, the region has faced challenges due to land-use changes that may increase the risk of erosion and shorten the lifetime of the reservoir, increasing the need to promote land management sustainability. This paper investigates the aspect of seasonality of soil erosion using a comprehensive methodology that integrates the Revised Universal Soil Loss Equation (RUSLE) approach, geographic information systems, geostatistics, and remote-sensing. An experimental agro-silvo pastoral area (typical land-use) was used for the RUSLE factors update. The study confirmed the effect of seasonality on soil erosion rates under Mediterranean conditions. The highest rainfall erosivity values occurred during the autumn season (433.6 MJ mm ha^?1 h^?1), when vegetation cover is reduced after the long dry season. As a result, the autumn season showed the highest predicted erosion (9.9 t ha^?1), contributing 65 % of the total annual erosion. The predicted soil erosion for winter was low (1.1 t ha^?1) despite the high rainfall erosivity during that season (196.6 MJ mm ha^?1 h^?1). The predicted annual soil loss was 15.1 t ha^?1, and the sediment amount delivery was 4,314 × 10³ kg. Knowledge of seasonal variation would be essential to outline sustainable land management practices. This model will be integrated with World Overview of Conservation Approaches and Technologies methods to support decision-making in that watershed, and it will involve collaboration with both local people and governmental institutions.     

Dynamic floodplain vegetation model development for the Kootenai River, USA

The Kootenai River floodplain in Idaho, USA, is nearly disconnected from its main channel due to levee construction and the operation of Libby Dam since 1972. The decreases in flood frequency and magnitude combined with the river modification have changed the physical processes and the dynamics of floodplain vegetation. This research describes the concept, methodologies and simulated results of the rule-based dynamic floodplain vegetation model "CASiMiR-vegetation" that is used to simulate the effect of hydrological alteration on vegetation dynamics. The vegetation dynamics are simulated based on existing theory but adapted to observed field data on the Kootenai River. The model simulates the changing vegetation patterns on an annual basis from an initial condition based on spatially distributed physical parameters such as shear stress, flood duration and height-over-base flow level. The model was calibrated and the robustness of the model was analyzed. The hydrodynamic (HD) models were used to simulate relevant physical processes representing historic, pre-dam, and post-dam conditions from different representative hydrographs. The general concept of the vegetation model is that a vegetation community will be recycled if the magnitude of a relevant physical parameter is greater than the threshold value for specific vegetation; otherwise, succession will take place toward maturation stage. The overall accuracy and agreement Kappa between simulated and field observed maps were low considering individual vegetation types in both calibration and validation areas. Overall accuracy (42% and 58%) and agreement between maps (0.18 and 0.27) increased notably when individual vegetation types were merged into vegetation phases in both calibration and validation areas, respectively. The area balance approach was used to analyze the proportion of area occupied by different vegetation phases in the simulated and observed map. The result showed the impact of the river modification and hydrological alteration on the floodplain vegetation. The spatially distributed vegetation model developed in this study is a step forward in modeling riparian vegetation succession and can be used for operational loss assessment, and river and floodplain restoration projects.     

Spatial Analysis of Soil Erosion and Sediment Fluxes: A Paired Watershed Study of Two Rappahannock River Tributaries, Stafford County, Virginia

Soil erosion is a serious problem in areas with expanding construction, agricultural production, and improper storm water management. It is important to understand the major processes affecting sediment delivery to surficial water bodies in order to tailor effective mitigation and outreach activities. This study analyzes how naturally occurring and anthropogenic influences, such as urbanization and soil disturbance on steep slopes, are reflected in the amount of soil erosion and sediment delivery within sub-watershed-sized areas. In this study, two sub-watersheds of the Rappahannock River, Horsepen Run and Little Falls Run, were analyzed using the Revised Universal Soil Loss Equation (RUSLE) and a sediment delivery ratio (SDR) to estimate annual sediment flux rates. The RUSLE/SDR analyses for Horsepen Run and Little Falls Run predicted 298 Mg/y and 234 Mg/y, respectively, but nearly identical per-unit-area sediment flux rates of 0.15 Mg/ha/y and 0.18 Mg/ha/y. Suspended sediment sampling indicated greater amounts of sediment in Little Falls Run, which is most likely due to anthropogenic influences. Field analyses also suggest that all-terrain vehicle crossings represent the majority of sediment flux derived from forested areas of Horsepen Run. The combined RUSLE/SDR and field sampling data indicate that small-scale anthropogenic disturbances (ATV trails and construction sites) play a major role in overall sediment flux rates for both basins and that these sites must be properly accounted for when evaluating sediment flux rates at a sub-watershed scale.