>Magister Scientiae - MSc

>Magister Scientiae - MSc / The Kuils River Catchment is an urban river catchment that forms part of the larger Kuils-Eerste River system draining the eastern half of the Cape Metropolitan Authority area and Stellenbosch Municipality. Rapid urbanisation has resulted in the encroachment of residential and industrial areas into the river system through channelization and sewage disposal. This research project intends to assess the quality of surface runoff in the Kuils River catchment and determining non-point source pollutant loading rates in the catchment using GIS-based modelling. The study results show how modelled potential sources of surface runoff and NPS pollutants using desktop GIS analysis tools in a sequential process that involved different levels of software applications could explain the characteristics of the catchment. With the help of the Expected Mean Concentration (EMC) values associated with surface runoff from land use/covers, NPS pollutant loads were assessed downstream towards the Kuils River Catchment outlet using the Nonpoint Source Pollution and Erosion Comparison Tool (N-SPECT) based in ArcGIS. The outputs from this model consist of predicted annual pollutant loading (mg/mvyear) for each Kuils-Eerste River that occurs in the catchment. The results have shown clearly the spatial distribution of sources of particular pollutants in the catchment. Further or advanced processing knowhow with this model might provide far reaching insights into the problem and it is however recommended that these results produced using N-SPECT be compared to those of other hydrologic models using the same inputs.

No description available.

<p>Field-edge buffers are a promising way to address nonpoint source pollution from agricultural runoff, but concentrated runoff flow often renders standard fixed-width linear buffers ineffective. AgBufferBuilder (ABB) is a tool within ESRI ArcMap Geographic Information Systems software that designs and evaluates targeted, nonlinear buffers based on hydrologic modeling and other field-specific parameters. We tested ABB on n=45 Areas of Interest (AOIs) stratified based on estimated sediment loading across three sub-watersheds within Michigan’s Saginaw Bay watershed to evaluate the effectiveness of ABB relative to existing practices across a wide range of landscape conditions. We modeled tractor movement around ABB buffer designs to assess more realistic versions of the likely final designs. ABB regularly failed to deliver the desired 75% sediment capture rate using default 9 m x 9 m output raster resolution, with Proposed buffers capturing from 0% to 68.49% of sediment within a given AOI (mean=37.56%). Differences in sediment capture between Proposed and Existing buffers (measured as Proposed – Existing) ranged from -48% to 66.81% of sediment (mean=24.70%). Proposed buffers were estimated to capture more sediment than Existing buffers in 37 of 45 AOIs, representing potential for real improvements over Existing buffers across the wider landscape. In 13 of 45 AOIs, ABB buffers modified for tractor movement captured more sediment than Existing buffers using less total buffer area. We conducted a collaborative design process with three Saginaw Bay watershed farmers to assess their willingness to implement ABB designs. Feedback indicated farmers may prefer in-field erosion control practices like cover cropping and grassed waterways over field-edge ABB designs. More farmer input is needed to better assess farmer perspectives on ABB buffers and to identify preferred data-based design alternatives. Engineered drainage systems with raised ditch berms and upslope catch basins piped underground directly into ditches were encountered several times during site visits. ABB only models surface flow and does not recognize drain output flow entering waterways. Modified ABB functionality that models buffers around drain inlets would greatly improve its functionality on drained sites. This may be accomplishable through modification of user-entered AOI margins but requires further investigation. Unfortunately, the existing tool is built for outdated software and is not widely accessible to non-expert users. We suggest that an update of this tool with additional functionality and user accessibility would be a useful addition in the toolbox of conservation professionals in agricultural landscapes.</p>

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>