| Summary: | The aim of this thesis is to enhance some of the hydrologic analyses involved in drought risk assessment (DRA) to uncertainty-driven analyses therefore improving the accuracy and informativeness of DRA. In DRA, risk, or the expected loss from drought hazard is estimated by integrating the magnitude of hazard (i.e., drought severity) with vulnerability (i.e., susceptibility to losses from drought). Most hydrologic analyses including DRA are traditionally performed in a deterministic setting, ignoring data quality and uncertainty issues. Uncertainty can affect the accuracy of modeling results and undermine subsequent decision making. In order to handle uncertainty in DRA, this thesis uses the Dempster-Shafer theory (DST) which provides a unified platform for modeling and propagating uncertainty in the forms of variability, conflict and incompleteness. First, DST is used to model and propagate uncertainty arisen from a high degree of conflict between two datasets of a drought hazard indicator, the snow water equivalent. Four DST combination rules are used for conflict-resolution and results unanimously indicate a high possibility of drought. Second, the Standardized Precipitation Index (SPI) is used as a generic measure of hazard and is linked directly with wildfire risk in current and future climate scenarios. Using DST, modifications are introduced into SPI, enabling the integration of uncertainty analysis with SPI processes. The resulting enhanced SPI can model the effects of long-term shifts in climate normals on drought hazard while simultaneously evaluating the significance of these shifts within the range of surrounding uncertainty. Later, vulnerability to wildfire is simulated using enhanced SPI and two additional variables: evaporation and firefighting capacity. The estimated risk indicates that forests in Okanagan Basin are vulnerable to wildfires during periods of 2040-2069 and 2070-2099 unless the firefighting capacity is enhanced with a presumed rate. Through the successful implementation of DST into DRA processes, this research demonstrates the capability of DST in improving hydrologic analyses and enhancing informativeness in the water resources arena in general.
|
|---|
