Assessing the Reliability, Resilience and Sustainability of Water Resources Systems in Data-rich and Data-sparse Regions

Abstract

Uncertainty associated with the potential impact of climate change on supply availability, varied success with demand-side interventions such as water efficiency and changes in priority relating to hydrometric data collection and ownership, have resulted in challenges for water resources system management particularly in data-sparse regions.

Consequently, the aim of this thesis is to assess the reliability, resilience and sustainability of water resources systems in both data-rich and data-sparse regions with an emphasis on robust decision-making in data-sparse regions. To achieve this aim, new resilience indicators that capture water resources system failure duration and extent of failure (i.e. failure magnitude) from a social and environmental perspective were developed. These performance indicators enabled a comprehensive assessment of a number of performance enhancing interventions, which resulted in the identification of a set of intervention strategies that showed potential to improve reliability, resilience and sustainability in the case studies examined. Finally, a multi-criteria decision analysis supported trade-off decision making when the reliability, resilience and sustainability indicators were considered in combination.

Two case studies were considered in this research: Kingston and St. Andrew in Jamaica and Anyplace in the UK. The Kingston and St. Andrew case study represents the main data-sparse case study where many assumptions were introduced to fill data gaps. The intervention strategy that showed great potential to improve reliability, resilience and sustainability identified from Kingston and St. Andrew water resources assessment was the ‘Site A-east’ desalination scheme. To ameliorate uncertainty and lack of confidence associated with results, a methodology was developed that transformed a key proportion of the Anyplace water resources system from a data-rich environment to a data-sparse environment. The Anyplace water resources system was then assessed in a data-sparse environment and the performance trade-offs of the intervention strategies were analysed using four multi-criteria decision analysis (MCDA) weighting combinations. The MCDA facilitated a robust comparison of the interventions’ performances in the data-rich and data-sparse case studies. Comparisons showed consistency in the performances of the interventions across data-rich and data-sparse hydrological conditions and serve to demonstrate to decision makers a novel approach to addressing uncertainty when many assumptions have been introduced in the water resources management process due to data sparsity.