Floods and droughts, also known as hydro-hazards, are phenomena that generally involve detrimental consequences to society and environment. Traditional practices for risk assessment consider flood and drought independently. However, they are two opposite extremes of the same hydrological cycle. Omitting their interaction might lead to an under- or overestimation of the current and future risks associated with such natural hazards. In history, a number of drought-flood interactions have been observed in various parts of the world. Research to these drought to flood interactions is still in its infancy. Therefore, this research explores the concept of consecutive dry and wet (CDW) events in the Netherlands. The aim of this research is two sided. First, the Consecutive Events Graph (CEG) is introduced. This is a radar chart type of graph used to quantify spatial and temporal changes in dry and wet indicators in consecutive seasons. These can be used to identify hot-spots prone to opposite extremes. Second, a fully probabilistic framework based on Non-parametric Bayesian Network is developed to model the dependence between dry and wet indicators. Such model can be used to infer expected wet conditions in a given region when dry conditions are known.
For the CEG and probabilistic model a number of settings were introduced to quantify meteorological dry and wet extremes and to couple them spatial-temporally. First, a number of indicators were selected to quantify both type of extremes. Second, the dry period is defined in summer (June, July and August) and consecutive wet period in fall (September, October and November). Third, the Netherlands was subdivided in five homogeneous regions such that both wet and dry indicators were characterized on a regional scale. Maximum values of the indicators in its corresponding period were calculated for each single region and for every single year between 1965 to 2020.
This resulted in a dataset consisting of quantities for dry extremes in summer and wet extremes in fall for 5 unique regions over 56 years. Application to the CEG shows potential to identify and quantify CDW extremes. Region-to-region and year-to-year comparison is possible to quantify changes between years or regions. Application to the NPBNs disclosed limited interdependencies across the dry and wet indicators. Using the NPBNs for precise forecasting of expected wet conditions is deemed unsuitable as of low precision. Making inference of wet indicators based on hypothetical mild to extremely dry indicators revealed multiple trends of those wet indicators. These trends are increasing for short term precipitation indicators (R1D, R3D, and R5D) and simple precipitation intensity index (SDII) and are mildly decreasing for the total precipitation (Ptot).
Extreme dry events, extreme wet events and consecutive occurrences of these events are inevitable. It is expected that these phenomena will occur more frequently and become more severe due to a changing climate. A number of recommendations for future research is proposed. Findings from this thesis will help to smooth the path towards better understanding of the identification, quantification and interaction of CDW events or multi-hazard events in general.