Thesis (M.Eng. (Hons.)) -- University of Western Sydney, 2003. / "Thesis submitted for the degree of Masters of Engineering (Honours) Environmental Engineering" Includes bibliography.

Thesis(M.S.)--Auburn University, 2005. / Abstract. Vita. Includes bibliographic references (p.86-89).

Thesis (M.S.)--Auburn University, 2008. / Abstract. Vita. Includes bibliographical references (p. 54-58).

Accelerated soil erosion is drawing a growing attention with the recognition that the rate of soil loss is too great to be met by soil formation rate. Weenen Game Reserve (WGR) is an area with an unfortunate history of prolonged soil erosion due to excessive overgrazing that led to severe land degradation with prominent visible scars. This problem triggered the general objective of estimating and mapping potential soil erosion in WGR. Assessing soil loss in the area objectively has important implications for the overall management plans as it is reserved for ecological recovery. The most important variables that affect soil erosion are considered as inputs in soil loss estimation models. In this study the RUSLE model, which uses rainfall, soil, topography, and cover management data, was employed. From the rainfall data, an erosivity factor was generated by using a regression equation developed by relating EI30 index and total monthly rainfall. The soil erodibility factor was calculated using the soil erodibility nomograph equation after generating the relevant data from laboratory analysis of soil samples gathered from the study area. Using exponential ordinary kriging, the point values of this factor were interpolated to fill in the non-sampled areas. The topographic effect, which is expressed as the combined impact of slope length and slope steepness, was extracted from the DEM of the study area using the flow accumulation method. For mapping of the land cover factor, in situ measurements of cover from selected sites were undertaken and assigned values from the USLE table before being related with MSAVI of Landsat 7 ETM+ image. These values were then multiplied to get the final annual soil loss map. The resulting potential soil loss values vary between 0 and 346 ton ha-1 year-l with an average of 5 ton ha-1 year-l. About 58% of the study area experiences less than 1 ton ha-1 year-1 indicating the influence of the highest values on the average value. High soil erosion rates are concentrated in the central part extending as far as the south and the north tips along the eastern escarpments and these areas are the ones with the steepest slopes. The results indicate a high variation of soil loss within the study area. Nevertheless, the majority of the area falling below the average might foresee that the soil erosion problem of the area can be minimized significantly depending largely on soil management. The most important areas for intervention are the medium and low erosion susceptible parts of WGR, which are mainly found in the flatter or gently sloping landscapes. The steepest areas are mostly covered with rocks and/or vegetation and hence less effort must be spent in managing them. Overall, the reported increasing density of the vegetation community in the area that reduces the exposure of soil from the impact of direct raindrops and surface-flowing water must be pursued further. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2004.

Thesis (M.S.) -- Mississippi State University. Department of Geosciences. / Title from title screen. Includes bibliographical references.

A method for estimating long-term average annual soil loss under different land management scenarios from the Marquis and Soufriere watersheds on St. Lucia is presented. The Revised Universal Soil Loss Equation (RUSLE) was used, and a GIS was employed to generate the required input parameters. Model execution and results were also generated within the GIS. Modelling soil loss for the different land management scenarios was based upon a land capability classification and associated conservation treatments. Soil losses under current agricultural land-use patterns were analyzed and compared to potential soil losses under conservation treatments following the criteria specified in the land capability classification. The model predicted substantial declines in soil loss where conservation treatments were assigned, as compared to soil loss under current land-use patterns. It was found that predicted soil losses from the Soufriere watershed were four times that predicted for the Marquis watershed for all the land management scenarios modelled. Of the input parameters in the model, slope steepness was most highly correlated to predicted soil loss. It is anticipated that the findings of this study will be used in the development of a decision support system for agricultural and forestry land planning on St. Lucia.

Environmental degradation and soil erosion begins when production forests are harvested. Unfortunately, logging cannot be avoided in plantation forests and since this operation can render the land more susceptible to erosion, any negative impacts need to be addressed properly. Erosion potential is predicted by evaluating the response of land cover, soil and slope to the impact of rainfall and human activities. The role of remote sensing and geographical information systems (GIS) in erosion prediction is to collect information from images and maps; combine and analyse these data so that it is possible to predict the erosion risk. The objective of this study was to produce a method to identify areas most susceptible to erosion and predict erosion risk. It is intended that the method be used particularly by forestry planners and decision makers so that they can improve forest management, especially during logging. The study area was within Kebumen and Banjarnegara districts of Central Java, Indonesia. Imagery used included a Landsat 7 satellite image (28th April 2001) and panchromatic aerial photos (5th July 1993). Other data was derived from topographical, soil, and geological maps, and 10 years of daily rainfall data from 17 rainfall stations. Predicting erosion in this study was done by combining rainfall, slope, geology, and land cover data. The erosion risk was predicted using land cover and soil type and depth. A rainfall map was generated using a thin plate spline method. A slope map was derived from a DEM which was generated by digitizing contours and spot heights from topographic maps. A geological map was derived from Landsat image classification with assistance from a 1:100000 scale geological map; and a land cover map was produced from an interpretation of the Landsat image and aerial photographs. A stratified classification technique was used to delineate land covers in the study area with an accuracy of 44%. The low accuracy could be attributed to the complexity of the area and the temporal variation in the data acquisition. The analysis of erosion risk showed that mixed forests and monotype forest experienced high and moderately high erosion risk. This condition supported the contention that harvest plans must incorporate soil conservation measures.

No description available.

<p>Current watershed-scale, nonpoint source (NPS) pollution models do not represent the processes and impacts of agricultural best management practices (BMP) on water quality with sufficient detail. To begin addressing this gap, a novel process-based, watershed-scale, water quality model (WEPP-WQ) was developed based on the Water Erosion Prediction Project (WEPP) and the Soil and Water Assessment Tool (SWAT) models. The proposed model was validated at both hillslope and watershed scales for runoff, sediment, and both soluble and particulate forms of nitrogen and phosphorus. WEPP-WQ is now one of only two models which simulates BMP impacts on water quality in ‘high’ detail, and it is the only one not based on USLE sediment predictions. Model validations indicated that particulate nutrient predictions were better than soluble nutrient predictions for both nitrogen and phosphorus. Predictions of uniform conditions outperformed nonuniform conditions, and calibrated model simulations performed better than uncalibrated model simulations. Applications of these kinds of models in real-world, historical simulations are often limited by a lack of field-scale agricultural management inputs. Therefore, a prototype tool was developed to derive management inputs for hydrologic models from remotely sensed imagery at field-scale resolution. At present, only predictions of crop, cover crop, and tillage practice inference are supported and were validated at annual and average annual time intervals based on data availability for the various management endpoints. Extraction model training and validation were substantially limited by relatively small field areas in the observed management dataset. Both of these efforts contribute to computational modeling research and applications pertaining to agricultural systems and their impacts on the environment.</p>