School of Agriculture, Food and Ecosystem Sciences - Theses
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ItemAridity as a predictor of the hydrogeomorphic response of burnt landscapes( 2016)Wildfire is an important disturbance in natural systems which can lead to changes in runoff and erosion processes. Increased runoff and erosion, including extreme erosion events such as debris flows, pose a hazard to soil and water resources, habitats, infrastructure, and lives. Natural resource managers require information about potential post-fire response in order to plan prevention, mitigation, or recovery activities which deal with the impacts of runoff and erosion events. Changes in hydrogeomorphic processes due to fire may also have important implications for long-term sediment budgets and landscape development. However, post-fire responses vary widely due to the landscape, fire, and post-fire rainfall properties, making it important to identify and understand mechanisms which control response variation. Previous studies, anecdotal evidence, and soil development theory suggest that moisture availability may influence post-fire runoff and erosion response. Therefore, this study aimed to investigate and quantify the relationship between moisture availability (characterised by an aridity index) and hydrogeomorphic response following high severity wildfire in the upper reaches of forested water catchments in central Victoria, Australia. This aim was addressed by examining the relationships between aridity index (AI) and soil infiltration capacity, runoff generation, and debris flow occurrence. In the first year following fire, infiltration rates and patterns, soil moisture, and soil water repellency were measured in the field. Saturated hydraulic conductivity (Ksat), soil porosity, texture, and density as well as water repellency of air-dried soils and the change in repellency with soil moisture were measured in the laboratory. To quantify the relationship between AI and surface runoff, total (all events) and real-time (within events) surface runoff and rainfall were monitored over 10 months. Aerial photography and spatial datasets were used to model (logistic regression) the relationship between the AI and the probability of post-fire debris flow occurrence. Overall the results suggest aridity exerts control over both long-term (decades to centuries) and short-term (daily to annual) system properties which result in a strong, quantifiable relationship between AI and post-fire soil infiltration capacity, runoff generation, and debris flow occurrence in the forested catchments of Victorian uplands. Increased AI resulted in reduced saturated hydraulic conductivity, suggesting a long-term control of aridity on soil structure. This, coupled with long- and short-term control of aridity on soil water repellency, led to field infiltration rates three times higher and over twice the proportion of the soil actively contributing to infiltration at the lowest AI site (AI 1.1) compared with the highest AI site (AI 2.4). Higher AI sites were consistently drier (had lower soil moisture content) leading to increased actual water repellency (measured in situ), as well as having an increased level of potential (air dry) water repellency. The reduction in infiltration capacity resulted in higher AI sites producing a greater percentage of runoff and higher peak discharge, for longer timeframes than lower AI sites. Average runoff ratio of the highest AI site (33.6%) was an order of magnitude higher than the lowest AI site (0.3%). Peak discharge during rainfall events also increased with increasing AI, with up to a thousand fold difference observed during one event. Undergrowth on the lower AI sites (AI 1.2 and 1.4) recovered more quickly (> 30% projected foliage cover within the first 6 months) than higher AI sites (AI 1.9 and 2.4) (< 5%), suggesting increased AI increases the window of disturbance. At the single headwater catchment scale, increased AI was empirically related to increased probability of post-fire debris flows. Post-fire debris flow producing catchments had significantly higher minimum, mean, and maximum AI values than non-debris flow producing catchments on average. Results indicated debris flows only occurred in catchments which contained pixels with an AI of 2.2 or higher. Logistic regression modelling results showed the probability of debris flows was highly sensitive to changes in maximum AI (sensitivity 0.95 and elasticity 2.05). This study supports the theory that aridity is a dominant first order control of hydrogeomorphic sensitivity to wildfire in this environment. Over geomorphic timescales, aridity-driven variation in runoff and erosion could have significant implications for landscape and ecosystem development. The effect of aridity is likely to become even more pronounced as climate change alters current rainfall regimes, and as the frequency and intensity of fires subsequently increases. This is the first study to quantify the relationship between AI and runoff and erosion processes following wildfire in this environment. The results of the study are a first step in using AI to predict and map soil hydrologic properties, runoff potential, and debris flow risk across burnt landscapes. As these properties and post-fire processes are resource intensive to determine in the field, AI provides an alternative spatial predictor variable which can be used to estimate these factors. Results of the study suggest AI could be a useful environmental indicator for management, capable of identifying areas of post-fire runoff and erosion risk in S.E. Australia.