A new approach to quantifying and comparing vulnerability to drought

In this study we develop an “inference modeling” approach to compare and analyze how different disciplines (economics, political science, and behavioral science/environmental psychology) estimate vulnerability to drought. It is thought that a better understanding of these differences can lead to a synthesis of insights from the different disciplines and eventually to more comprehensive assessments of vulnerability. The new methodology consists of (1) developing inference models whose variables and assertions incorporate qualitative knowledge about vulnerability, (2) converting qualitative model variables into quantitative indicators by using fuzzy set theory, (3) collecting data on the values of the indicators from case study regions, (4) inputting the regional data to the models and computing quantitative values for susceptibility. The methodology was applied to three case study regions (in India, Portugal and Russia) having a range of socio-economic and water stress conditions. In some cases the estimates of susceptibility were surprisingly similar, in others not, depending on the factors included in the disciplinary models and their relative weights. A new approach was also taken to testing vulnerability parameters by comparing estimated water stress against a data set of drought occurrences based on media analysis. The new methodologies developed in this paper provide a consistent basis for comparing differences between disciplinary perspectives, and for identifying the importance of the differences.

Application of fuzzy models to assess susceptibility to droughts from a socio-economic perspective

By combining the concepts of environmental stress, state susceptibility and environmental crisis, “Security Diagram” (SD) provides a quantitative approach to assessing environmental change and human security. The SD is a tool that clearly presents in a diagram the security situation of a population or region affected by a particular environmental crisis. Its underlying concept emphasises that the higher the level of environmental stress and socio-economic susceptibility, the higher the probability of the occurrence of crisis. Focusing on drought, this study analyses the susceptibility of case study regions in India, Portugal, and Russia from a socio-economic perspective. A conceptual framework of socio-economic susceptibility is developed based on the economic development theories of modernisation and dependency. Fuzzy set theory is used to generate susceptibility indices from a range of national and sub-national indicators, including financial resources, agricultural dependency and infrastructure development (for economic susceptibility), and health condition, educational attainment and gender inequality (for social susceptibility). Results indicate that socio-economic susceptibility over the period 1980–1995 was highest in India, followed by Russia and (since 1989) lowest in Portugal. Globalisation is likely to contribute to changes in the level of socio-economic susceptibility over time. Moreover, specific social and economic structures unique in each country (e.g., the role of women in society in India, the socialist legacy in Russia) may explain differences in susceptibility between the case study regions.

Reducing mine water network energy requirements

Increasing energy efficiency and reducing water consumption are two of the key requirements in moving toward a more sustainable mining industry. This paper demonstrates a method of determining the lowest energy option for a mine/mill water system network. The method uses a linear programming algorithm to compare different possible combinations of supplying water to mine and mill consumers. First, a site water balance is determined and then every major water consumer and source is specified. Water sources may require pumping, cooling and/or treatment before the water meets the specifications of the water consumer. The amount of energy required for each water source to supply each consumer is calculated and then the model is solved to minimize power consumption. An example is used to illustrate the method.