Quantifying and Modelling Post-Wildfire Sediment Production in Waterton Lakes National Park

High-severity wildfires can increase sediment mobility and erosion rates in burned landscapes which increase the delivery of fine sediment to receiving streams. The downstream propagation of these pyrogenic materials can have significant implications for ecosystem and human health implications. The identification of areas prone to differing levels of sediment erosion is necessary for watershed managers to prioritize critical areas that may require best management practices to reduce sediment transfer from hillslopes to receiving streams. Knowledge of sediment erodibility and runoff rates at the site scale and incorporation of these data in watershed-scale sediment erosion models such as the Revised Universal Soil Loss Equation (RUSLE) is critical for landscape managers to mitigate the effects of soil erosion. The objectives of this study are to 1) quantify runoff and sediment erosion rates for dominant soil textures using a rainfall simulator at the plot scale to provide estimates of sediment erodibility and yield, 2) model post-wildfire erosion at the watershed scale to identify critical areas of sediment erosion and 3) identify priority management zones in Waterton Lakes National Park and recommend management options for the implementation of best management practices. Runoff and sediment erosion rates of various soil textures were measured using a rainfall simulator using an I10 rainfall intensity. The rainfall simulation data were used in RUSLE to determine watershed-scale sediment yields and to identify priority management areas. In the present study, a low rainfall intensity (33 mm hr-1) produced runoff and sediment erosion over a range of soil textures following a wildfire. Finer soil textures produced higher runoff rates and sediment yields compared to coarse soil textures on burned soils. RUSLE provided first-order sediment erosion estimates following wildfire and has the potential to identify areas of varying erosion rates at the watershed-scale, in a GIS environment, for use by land managers that may want to reduce sediment from potentially entering nearby streams

The optimisation of water quality monitoring schemes

The monitoring of water quality is necessary to ensure that the health of catchments is maintained. Water quality monitoring is often undertaken by government agencies to identify trends, assess management strategies and the state of catchments. Many water quality studies attempt to identify the quantity and timing of nutrients exported from a catchment. The accuracy of the monitoring scheme is largely controlled by the sampling scheme. Financial constraints are one of the limiting factors and under this constraint, sampling schemes often combine limited sample sizes with estimation methods. The use of statistical methods allows catchment managers to improve the information on expensive water quality properties based on the relationship with low cost properties. Many water quality monitoring programs have access to limited historical data, therefore there is a requirement for methods which can use this limited data to improve water quality monitoring schemes. This thesis aims at examining the effect of event-based sampling, using historical data to improve sample designs and the use of model-based geostatistical methods to improve the quantification of nutrient exports

The optimisation of water quality monitoring schemes

The monitoring of water quality is necessary to ensure that the health of catchments is maintained. Water quality monitoring is often undertaken by government agencies to identify trends, assess management strategies and the state of catchments. Many water quality studies attempt to identify the quantity and timing of nutrients exported from a catchment. The accuracy of the monitoring scheme is largely controlled by the sampling scheme. Financial constraints are one of the limiting factors and under this constraint, sampling schemes often combine limited sample sizes with estimation methods. The use of statistical methods allows catchment managers to improve the information on expensive water quality properties based on the relationship with low cost properties. Many water quality monitoring programs have access to limited historical data, therefore there is a requirement for methods which can use this limited data to improve water quality monitoring schemes. This thesis aims at examining the effect of event-based sampling, using historical data to improve sample designs and the use of model-based geostatistical methods to improve the quantification of nutrient exports