A Statistical Analysis on The Hazard of Drought

The Quantication of Hydrological Drought in California, United States of America

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Drought is a recurrent extreme climate event that strongly affects every spectre of the natural environment and human lives (Madadgar andMoradkhani, 2014). There were numerous drought episodes recorded in MidwesternUSA, particularly in California. It has affected many aspects, including water shortages, groundwater overdraft, critically low stream flow (Diffenbaugh et al., 2015) and economical loss. In contrast, California as the third most extensive area in the U.S., is occupied most of the land use for agricultural purposes. In order to capture the trend of hydrological drought in California, understanding towards the risk of hydrological drought in comprehensive way should be conducted. In a broader sense, the quantification of hazard of hydrological drought will be a preliminary step mitigating the drought event, as an integral part of risk management.

This research is aimed (i) to understand the characteristic of hydrological drought using statistical tool; (ii) to build a model to estimate the low stream-flow using the relationship between hydro-meteorological and hydrological drought; and (iii) to quantify the risk ratio of the hydrological drought duration using a statistical method. In general, the thesis will be divided into two big parts, hydrological drought severity and hydrological drought duration. The former part will be analysed using Bayesian Network to estimate the mean monthly discharge in California. The latter part is evaluated using Generalised Linear Model to study the risk of having the expected number of days without discharge in California under different cases of scenarios. Besides gaining information from modelling hydrological drought, this thesis will also try to answer the formulated research questions. The research questions focus upon the statistical perspective towards hydrological drought, by explaining the input data for the model and the advantage(s) and disadvantage(s) of such models.