Flood Vulnerability Assessment through Different Methodological Approaches in the Context of North-West Khyber Pakhtunkhwa, Pakistan

Abstract

There are several approaches to assess flood vulnerability as a proactive measure to reduce the risk of flooding. The indicator-based approach is primarily practiced from a policy point of view through the use of composite indicators. Composite indicators can be built from very easy to very complex and sophisticated methods. However, there are two complications that arise with this issue. On the one hand, the flood vulnerability index should be fairly simple, taking into account the interdisciplinary nature of various stakeholders involved in flood risk management. While on the other hand, addressing the issue of subjectivity or prejudice should be scientifically defensible. As there is no a single universally “best” methodological approach for the construction of composite indicator due to its data-specific nature for each individual study. The aim of this study is therefore to construct such an index of flood vulnerability that is not only intuitive to a variety of stakeholders, but also scientifically justified in the context of Khyber Pakhtunkhwa, Pakistan. Therefore, the current study demonstrated a detailed procedure to construct the flood vulnerability indices through different methodological approaches of data rescaling, weighting, and aggregation schemes, along with a fairly simple approach for robustness. For this purpose, data was collected through different (official) portals for the nine highly flood-prone districts of the Khyber Pakhtunkhwa. It was found that the weighting schemes had a greater influence on the flood vulnerability ranking of the selected districts compared to data rescaling and aggregation schemes. The simple model, which is the frequently using approach of building composite indicators in scientific community, was found to be appropriate for the selected data. The methodology adopted in the study can provide decision-makers and relevant authorities with a practical tool to identify and prioritize certain vulnerable areas and measures to mitigate current flood vulnerabilities while preparing for future flood risk mitigation in the province through a fairly simple and methodologically defensible approach.

Review article: A systematic review and future prospects of flood vulnerability indices

“…The high use of PCA can be attributed to the pioneering work by Cutter et al (2003), who recommended the use of a factor analytic approach. Other less common statistical methods include dividing the indicator values by the total or maximum value (Okazawa et al, 2011), hot spot analysis (Kubal et al, 2009), and the unequal weighting method (Kablan et al, 2017).…”

Effects of Different Normalization, Aggregation, and Classification Methods on the Construction of Flood Vulnerability Indexes

“…Even though ranking normalization with liner aggregation brought a high Spearman's correlations coefficient (0.92 to 0.93), there were small spatial changes in the flood vulnerability class in comparison with the other normalization methods with linear aggregation. High Spearman's correlation coefficients (>0.98) for the min-max and z-scores were also found in the vulnerability indexes elaborated by other authors [12,34]. However, for other application areas (e.g., the agricultural sustainability index), significant differences were found according to the different normalization methods [39].…”

“…By variating the normalization, aggregation, and classification methods, we verified how these disturbances affected the results when all the other parameters remained constant [49]. The similarity of the outputs when considering these changes was measured by conducting a correlation analysis using Spearman's rank correlation [12,34,39]. This nonparametric correlation allows measuring the strength of the association between two variables [50].…”

“…Despite the advantages of index-based approaches, there are some limitations as each step of the index construction carries uncertainty [11,12]. Such uncertainties are mainly related to the normalization of indicators, criteria weighting, and the aggregation method, as well as the quality and availability of data, natural variability, and human judgment [13][14][15][16].…”

“…Most of the published articles deal only with the uncertainty associated to the weighting process [24][25][26][27][28][29] or deal with understanding the uncertainties in hazard models [30][31][32][33]. However, other steps that are also relevant, such as the uncertainty introduced by the normalization and aggregation [12,34], and the classification of the results into different vulnerability classes are understudied.…”