STRUCTURAL MODELING AND ANALYSIS OF SOCIAL OBSTACLES IN IMPLEMENTING FLOOD EARLY WARNING SYSTEMS IN PAKISTAN

Abstract

The study aims to theorize social obstacles in properly implementing flood early warning systems.  Overall design of the study comprises of review of relevant literature, primary data collection, structural modeling and analysis of the phenomena. The population under study consists of social and non-social beneficiary groups, and socially and non-social adversely affected groups by floods. The sampling design is purposive sampling (focus group consisting of a panel of experts) and the sample size is twenty-seven experts. The method of modeling is “Interpretive Structural Modelling (ISM)” and the method of analysis is “Cross Impact Matrix Multiplication Applied to Classification (MICMAC)”. Results of the ISM model show that social obstacles in implementing an early warning system of floods namely: lack of access to necessities, lack of awareness, lack of comprehension of warnings, lack of coordination, failure to pay to heed the early flood warning system, incomplete warning alerts, emergency plans not implemented, lack of political commitment, un-customized contingency plans, exclusion of social groups, scarce resources, unique and different type of flood, attitude to neglect, misperception of risks and deep cultural connection to ancestral lands occupy Level-I (top of the model). Social obstacle namely: lack of updated information occupies Level-II (middle of the model). Social obstacle namely: inadequate preparedness Level-III (bottom of the model). Whereas, results of scale-centric MICMAC analysis shows that all the factors fall in linkage quadrant and independent, autonomous, and dependent quadrants are empty. It happens in the cases where the system elements are agile, unstable, unsettled, and riddled. It is an original real-time primary data-based study having profound practical implications for stakeholders. It invites attention of the stakeholders to the complex, riddled and unsettled relationships among elements of the phenomenon.