Explicating the Causes of Frequent Floods in Pakistan: A Structural Modeling and Analysis

Aim of study is explicating the causes of frequent floods in Pakistan. Overall design of the study comprises of relevant literature review, primary data collection and structural modelling & analysis of the phenomena. The method of modelling is ISM (Interpretive Structural Modeling) and method of analysis is MICMAC (cross impact matrix multiplication applied to classification). The population under study comprises the folk stakeholders of the phenomenon. The sampling design is purposive (i.e. a focus group consisting of a panel of experts) and the sample size is eleven experts (a medium-sized panel). Results of modeling show that causes namely: changes in land use, poor waste management, slums along rivers, erosion and sedimentation, improper flood control systems, river physiography, high rainfall, inadequate river capacity, water structures, land subsidence, damage to flood control structures, poor drainage system fall at Level I (the top level), therefore, are least critical. The causes namely: effects of high tides, lack of discipline among people, glacial melt fall at Level II (middle level) therefore are moderate critical. The cause namely: deforestation falls at Level III (the bottom level) therefore is the most critical. The scale-centric MICMAC analysis shows that all the causes are categorized in the linkage quadrant and the independent, dependent, and autonomous quadrants are empty. The data-centric MICMAC analysis shows that the effects of high tides, glacial melt, and deforestation fall in the independent quadrant. The erosion sedimentation and river physiography fall in the dependent quadrant. The changes in land use, poor waste management, improper flood control systems, slums along rivers, high rainfall, land subsidence, inadequate river capacity, water structures, lack of discipline among people, damage to flood control structures, and poor drainage system categorized in the linkage fall in the linkage quadrant, whereas, the autonomous quadrant is empty. The results of MICMAC analysis implicitly corroborate the results of modeling. It is an original valuable study because it is based on first-hand real experimental data collected by authors who have hands on job of data collection for decades. It also uses unique and different methodologies to collect data, perform modeling and analysis. This methodology is simple, unique, and understandable by a wide range of stakeholders. Its results are also logical and realistic that correspond to ground realities.