Global Water-Related Risk Indicators: Meta-Analysis of Indicator Requirements
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(2) Vink, K. et al.. Table 1. Definitions of risk-related terms. Term (General) Disaster Indicator. Indicators. indices and their links to policy impacts, the results of which can ultimately be used to evaluate existing waterrelated disaster risk indices and indicators and act as a guideline for the construction of future indices and indicators. While there are many studies that lament the known issues with disaster risk indices, very few propose practical alternatives or solutions [2, 8]. With this study’s comprehensive overview of disaster risk indicator requirements and proposed evaluation matrices on data quality, index composition, and links to potential policy impacts and transformation, existing disaster risk indicator data and indices can be screened and ameliorated by applying scoring parameters. This allows for a meta-analysis of indicator data requirements. Table 1 contains explanatory definitions of the risk-related terms used in this study. A previous study [2] employed a scoring method for six requirements, which were unsorted but which covered both data quality and index composition, namely theoretical understanding, data, empiricism, method, validation, and feasibility. However, it did not provide any guidance for evaluating links to policy or governance impacts. Another example of a study employing a scoring method to evaluate indices is the Water Security Index (WSI) [9, 10], which applies the DPSIR framework (driving forces, pressures, states, impacts, responses) and SMART indicator requirements (specific, measurable, assignable, realistic, and time-related) and results in a five-point scale to interpret the index. The WSI is a work in progress and does not include any links to policy impacts as of yet. This is identical to the findings of Beccari [8] on disaster risk, vulnerability, and resilience composite indicators. Beccari’s results show that of the 106 methods investigated, including [2], the indicator selection process of the majority of the methods was influenced by data availability, and there was no consistent method of applying weights, index aggregation, or sensitivity and uncertainty analysis, all of which made it difficult to apply any such indices to help achieve the goals of the Sendai Framework [1] or the SDGs [3].. Outcome indicator [20] Output indicator [20]. Input indicator [20] Proxy. Index. Pedigree Matrix [2]. 1.1. Study Scope: Water-Related Hazards This study examines indicator requirements linked to DRR with a focus on water-related hazards. When developing hazard related indicators, ideally all hazard types are taken into account, which is a massive undertaking. Comparisons between existing all-hazard databases such as the World Risk Report [11], Global Adaptation Index [12], Human Insecurity Index [13], Local Vulnerability Index [14], and water-related indices such as the Flood Vulnerability Index [15] and the Water-Related Disaster Resilience Index [16], show that the chosen indicators vary significantly for water-related hazards, suggesting that the requirements for disaster risk indices concerning water-related hazards might differ as well. By focusing on one hazard type initially, we can further illustrate which types of indicator requirements are unique to water-related hazards when indicator requirements are later integrated with the specific requirements 356. Metric. Definition “A variable which is an operational representation of a characteristic or quality of a system able to provide information regarding the susceptibility, coping capacity and resilience of a system” [17]. Post disaster damages and losses, or modelled losses based on probabilities Resilience capabilities. Disaster reduction actions undertaken by the government, communities, sectors, firms, and households A figure that can be used to represent the value of something in a calculation. A composite of multiple quantitative indicators that often via some formula delivers a single numerical result. A description of elements used to e.g. build an index. Based on a scoring system, the subject is ranked from poor to excellent according to its performance. Component of a pedigree matrix that can be scored for evaluation.. Examples % of buildings compliant with building codes, % of policies including disaster resilience.. Mortality, financial damages to infrastructure or income % of assets or population exposed, % of buildings compliant with building codes, % of population with access to social safety nets, % of policies including disaster resilience % of government expenditure for DRR investments Mobile phone ownership, age, education level. World Risk Index [18], Flood Vulnerability Index [15], Disaster Risk Index [20]. Pedigree matrix for social vulnerability studies. Theoretical understanding, data, empirical aspects, method, validation, and feasibility [2]. for other hazard types. The selection of water-related hazards was finally influenced by the assumed potential of human influence: the onsets and impacts of most floods can be more easily predicted, anticipated, and influenced by human actions than can those of hazards such as earthquakes, volcanic eruptions, or landslides. After developing evaluation criteria for individual hazard types, these should be integrated into a wider development scope to address common impacts. Journal of Disaster Research Vol.12 No.2, 2017.
(3) Global Water-Related Risk Indicators: Meta-Analysis of Indicator Requirements. 2.2. Literature Review of Existing Indicator Requirements First, an overview was made of known indicator requirements. The data gathering method developed by Johnson et al. [22] and applied by Vink et al. [23] and Ahsan et al. [24] was used to gather existing studies covering indicator requirements (see Table 2). This method consists of three steps: 1) using keywords to search major academic databases, 2) refining the search results by adding parameters, and 3) removing duplicates and evaluating selected abstracts for relevancy. The search was performed in January 2016. The databases searched here were the following: Scopus (A), Web of Science (B), and the Natural Hazards Center Online Library Catalog (C), each of which has its own search term descriptors and limitations. In the first two databases, irrelevant scientific fields, such as those related to health or mineralogy, were excluded. Initially, the chosen search parameters were “indicat*” or “prox*” and “requir*,” in combination with “risk,” “disast*,” “hazard*,” “vulnerab*,” or “resilien*.” (The use of the asterisk (*) indicates that during a search all the text that matches the text before the asterisk is returned, meaning that for a search using “disast*,” phrases using “disaster” or “disastrous” are returned, but “disassemble” is not.) For brevity, the slash (/) is used to indicate connecting Journal of Disaster Research Vol.12 No.2, 2017. A. B. C. indicat* / prox* AND require*AND risk / disast* / hazard* / vulnerab* / resilien* indicator / proxy AND requirement AND risk / disaster / hazard / vulnerability / resilience indicator and requirement. 6,144 flood inundat* storm. flood inundation storm 10,469 521 2,207 1,430 910 29 n.a.. Results. 3) Abstracts, duplicates. Results. 2) Refining. Results. 1) Search criteria. 2.1. Output and Input Indicators The UNDP [20] has described different types of indicators measuring resilience, paraphrased as follows. On the one hand, the term “outcome indicators” refers to postdisaster damages and losses or to modeled losses based on probabilities, including mortality, financial damages to infrastructure or income, etc. As a response to the Sendai Framework [1], a global measurement for outcome indicators is currently being developed by an intergovernmental expert work group [21]. On the other hand, the term “output indicators” refers to (pre-disaster) resilience capabilities and includes examples such as the percentage of assets or population exposed, the percentage of buildings compliant with building codes, the percentage of the population with access to social safety nets, and the percentage of policies including disaster resilience. The term “input indicators” refers to disaster reduction actions being taken by different actors, such as the government, communities, business sectors, firms, and households. An example would be the percentage of government expenditure for DRR investments. Both outcome and output indicators are required to anticipate and measure the (potential) impacts of disasters and where to most rationally invest disaster-related funds. Given the objectives of this study and the problems with linking indicators to mortality [2], the focus is on both output and input indicators as they both address predisaster resilience and provide a link to the capabilities of different actors.. Table 2. Literature review search results (for a detailed explanation of the methodology, please refer to the text).. Source. 2. Methods. 479 95 272. 743 104 269. 1,172 ▼ 28 (442 documents pending evaluation). 29. 6. Total documents selected. 34. alternatives used during searches; i.e. the first search using Scopus consisted of indicat* AND require* AND risk. “Requirement” or “required (noun)” is the shortest word describing the term in English. It was assumed that authors describing this term would use the shortest description in the abstracts of their works, as these are commonly constricted by word limitations. The accepted document types were journal articles, reviews, books and book chapters, and conference proceedings, in English. Although the end goal is to establish criteria for the meta-analysis of water-related risk indicators, to achieve this, the initial focus used a top down approach from general indicator requirements and disaster indicator requirements, as well as more specific indicator requirements for water-related hazards, thus excluding other specific hazard indicator requirements. This first step resulted in 21,710 documents. In the second step, the search results were refined by the keywords “flood,” “inundat*,” or “storm,” which resulted in 1,991 documents. In the third step, duplicate results, i.e., documents that were found in more than one database, were removed, which led to 1,172 documents from Scopus and Web of Science being combined through the use of Endnote software. The results from the Natural Hazards Center Online Library Catalog were not among these results. The abstracts of the resulting documents were examined to confirm that the topics addressed covered water-related disaster indicators or risk indicator development. Unrelated topics were removed. 62%, or 730, of these documents were judged in order to determine common indicator requirements. Several publications from the UNISDR and other UN agencies were added to this review.. 357.
(4) Vink, K. et al.. ation of the risk formula being a combination of these base categories. The data and methods of current indices related to risk and water-related disasters were examined for evaluation potential, and four disaster risk indices were found to mention a sufficient number of source data and index construction as suitable: Fig. 1. General concept of pedigree matrix scores.. 2.3. Development of Pedigree Matrices and Future Metric Scores for Evaluation After indicator requirements (either general, disaster, or water related) were gathered from different sources, as described in section 2.2, different categories of requirements related to disaster risk emerged from the results. Therefore, the results were divided into three tiers: 1) requirements of indicator data quality (technical), 2) requirements of index composition (methodological/epistemological), and 3) impact requirements (policy and actors of change). Recurring requirements of the tier “requirements of indicator data quality” were transposed into a pedigree matrix with corresponding scores for each of the requirements. This method was chosen as a pedigree matrix that “covers qualitative dimensions concerning the theoretical understanding, data quality, empirical reliability, methodology, validity, and feasibility” of indices (Gall [2]). Additionally, the purpose of a pedigree matrix is not to rank indices by a final set of scores. Instead, it can point out trends and gaps in data quality or index construction, and it can serve as an analytical tool to communicate qualitative and quantitative results [2]. Performing a meta-analysis of indicator requirements with such a scoring method can reduce arbitrary indicator selection based on mere data availability. The general concept of the pedigree matrix scores is set out in Fig. 1. As a preliminary test, the scoring was set to 0 if the data quality was thought to lead to an unreliable risk index; it was set to 5 for ideal data quality. The individual score parameters are based on the expected increased rates of change in the global population [25], land use [26], urbanization of areas exposed to water-related disasters [27], and increase in the frequency and intensity of floods and other water-related disasters [28]. Future studies should further test how each requirement of the tiers “requirements of index composition” and “impact requirements” can be represented by a metric with a series of performance scores, as was done for the first tier of “requirements of indicator data quality” in this study. 2.4. Testing the Pedigree Matrix For the initial testing of the example evaluation scoring system, a selection from the existing disaster risk indicator databases was made focusing on the most pivotal and most commonly used information among existing indices. An additional effort was made to ensure that information pertaining to hazard, exposure, vulnerability, and/or resilience was evaluated, as most indices subscribe to a vari358. 1. 2. 3. 4.. Disaster Resilience Index (DRI), Cutter [19] INFORM, EU [29] World Risk Index (WRI), UNU-EHS [18] Flood Vulnerability Index (FVI), UNESCOIHE [15]. The Disaster Resilience Index (DRI) was evaluated using the pedigree matrix for the requirements of indicator data quality. This index had the most complete description of data sources, and most original data values (21 out of 36 total indicators) could be found using the sources mentioned in the DRI itself. These were scored in accordance with preliminary example scores per metric. The results were combined into five indicator categories (social resilience, economic resilience, institutional resilience, infrastructure resilience, and community capital) as described by the DRI itself.. 3. Results 3.1. Requirements of Indicator Data Quality For the first tier of requirements, seven objective data quality requirement metrics were developed, based on the results seen in Table 3. As risk, resilience, vulnerability, exposure, and hazards are all dynamic and subject to changes within society and climate, it is important that the input data of water-related risk indices reflect this. Therefore, three of the seven requirement metrics address the capability of the data to show the rate of change and time period the data is available for, namely capability recentness, update frequency, and history. Recentness is specifically needed to verify things such as local changes in population and land use as well as how this affects the accuracy of the risk formula. Accuracy is a second indicator data quality requirement that occurs frequently in the literature examined, and it is reflected by two requirement metrics: scale (what is the scale/resolution of the data?) and reliability of methods. The coverage metric reflects the area of the world covered by the data. In order to hold responsible governments and institutions accountable, it is important that data be openly available, as promoted by the World Bank open data initiative [30]. This is reflected in the accessibility metric. Another indicator requirement mentioned by several sources but not taken into account in this matrix is cost (the price of data used as indicator [17, 31]). The main reason for this exclusion is that, ideally, data is openly accessible, as is reflected in the accessibility metric. Other reasons include varying prices over time, region, and data provider, as well as the increased level of subjectivity Journal of Disaster Research Vol.12 No.2, 2017.
(5) Global Water-Related Risk Indicators: Meta-Analysis of Indicator Requirements. Table 3. Requirements of indicator data quality. Metrics Recentness Update frequency. History. Accessibility. Reliability methods. Scale/ resolution. Coverage. J. Requirements as described by various sources • Recentness [2] • Monitored over time [35] • Sensitive/responsive [36], sensitive enough to capture changes over time, time-bound when a change is expected [37] • Periodic (how often the data is updated) [38], time bound, timely evaluated—attached to a time frame, states when it will be measured [39] • Vulnerability is dynamic; every year updated data is required [40] • Measurable in time and space [41] • Repeated measurement to determine trends and be iterative, adaptive, and responsive to change and uncertainty because systems are complex and change frequently [42] • Data readily available across space and time [2] • Data should remain the same over time [33] • Adopt a time horizon long enough to capture both human and ecosystem time scales, thus responding to the needs of future generations as well as needs current to short term decision-making; build on historic and current conditions to anticipate future conditions [42] • Data availability [17, 31, 33, 35], existing data [43] • Data readily available across space and time [2] • Indicators that can be used within the present limitations of data availability worldwide [41] • Openness: make the data that are used accessible to all [42] • Validity, data quality, quantitative [35] • Reliable, quantitative [36], reliable [44], data: reliable and representative, reproducibility [45] • Measurable [31], measurable, reproducible [17], measurable, verifiable, can be checked [37], measurable: has the capacity to be counted, observed, analyzed, tested, or challenged [39] • Available from a reliable source, use credible data and be verifiable, credible and reproducible; must have classifications that can be explored further by other methods, such as surveys, statistics, and data analysis, and by other researchers; must be measurable via a readily understood method; clear methods of measurement will encourage wider indicator acceptance and limit the bias or subjectivity of the data collator; a well explained method of quantitative measurement will provide clarity and can be comprehended by decision makers [33] • Validity as well as sensitivity, feasibility, and reliability; best available practice in well-established discipline [2] • Data collection and source (databases used); comments and limitations (anything affecting data quality) [38], Openness: make the methods that are used accessible to all; make explicit all judgments, assumptions, and uncertainties in data and interpretations [42] • Representative, measurable, and reliable; the methodology should be flexible and easily adjustable [41] • Discriminating [36], Disaggregation (whether and how the data should be diversified in smaller categories) [38] • Adequate, providing enough relevant information, direct, closely tracking results [37] • Scale at which processes operate, unit of investigation/unit at threat [45] • Scale; health discrepancies manifest themselves directly in mortality disparities between groups of different race/ethnicity, age, socioeconomic status, education and so forth [2] • Specific, it must be able to be translated into operational terms and made visible [39] • End-point indicators (cover conditions and processes that only operate well if the causes leading up to them are also operating well) [46] • General indicators and indices should be downscaled to site-specific indices to render them functional [47] • Accurate [44] • The utility of national-level indicators in lower-resolution studies [48] • Holistic perspective: include review of the whole system as well as its parts; scope: define the space of study large enough to include not only local but also long distance impacts on people and ecosystems [42] • Globally applicable [49] • [This is a goal of the Sendai Framework [1]]. l f Di. R. h. b i. d. involved in determining an ideal and reasonable price, which may vary depending on states of development and other priorities.. 3.2. Requirements of Index Composition The second tier of requirements covers the index composition and addresses both methodological and epistemological questions, which were transformed into requirements as based on the results described in Table 4. While the debate on how to precisely select indicators remains undecided, many studies argue the need for either deductive or inductive reasoning to select indicators [see also 8]. After examining these motivations, the authors have come to the opinion that both deductive and Journal of Disaster Research Vol.12 No.2, 2017. 5. inductive methods should ideally be included, as statistical proof alone cannot always account for the logic behind indicator selection. Correlation does not necessarily imply causation, and this requires the potentially subjective choice to be argued for through deductive reasoning. We need to both examine the existing theories of resilience against new data, e.g., people living in poverty are affected worse by disasters but have a shorter recovery time than people not living in poverty, as well as test existing data, e.g., historical statistics on age, mortality, income, housing quality, etc., to determine their relationship to resilience and generate robust theories. In addition, certain social effects are not measurable within short timeframes, and this necessitates a wider context to prove the rela-. 359.
(6) Vink, K. et al.. Table 4. Requirements of index composition. Metrics Deductive (reasoning). Inductive (statistical proof). Completeness (all included/ none left out). Transformation Weights. Disaggregation (global and local scale). 360. Requirements as described by various sources • Propose relationships derived from theory or conceptual framework and select indicators on the basis of these relationships [45], Definition, rationale (reason for using this indicator) [38] • Be coherent [50] • Be objective [35] • Have a clear and conceptual basis [51], Develop with theoretical model [43], Use a well-defined framework with scientifically acceptable methods [52] • Support concept: the most important aspect of indicator development is to ensure the indicators selected serve the needs of the research question, accurately represent concepts expressed in model, and are acknowledged as valid substitutes for concepts [33] • Be specific and reflect things the project intends to control [37] • View indicators as tools used to articulate a concept [31] • Select indicators rooted in theoretical framework; theoretical understanding; Past vulnerability research provides plenty of evidence of the importance of the following dimensions: age, gender, race/ethnicity, income, dependence on social services, residential property, infrastructure, occupation, social networks, education, risk culture, urban-rural dichotomy, population growth, special needs population, commercial/industrial development, built environment, as well as institutions and governance. inequality, health status, broader economic conditions, civil liberties, political rights, and environmental stewardship [2] • Be guided by a clear vision of sustainable development and goals that define that vision [42] • Be indicative, significant [36], Method of computation (values used to represent the indicator) [38] • Have a fixed set of tested indicators [43] • Limit bias or subjectivity of data collator [33] • Be scientifically valid [31], Validity, accuracy, data comparability, analytically and statistically sound [17], Deliver risk information in a scientifically acceptable way [49] • Use systematical risk assessment [52] • Verify: Evaluate validity and plausible outcome, compare with findings of relevant studies, comparable past event, case study, explaining relationships, significant statistical relationships [45] • Have empirical proof of validity and/or refinement of the methodology are needed; the selection of indicators must be guided by correlations with proxy measures and not by a theoretical framework or expert opinion; compared with independent measurements of same variable; many weaknesses of social vulnerability indices can be eliminated with a sound index design that adheres to basic statistical rules and that incorporates findings from disciplines such as environmental economics; the performed analyses underscore the need for conducting uncertainty and sensitivity analyses; by doing so, initially subjective design choices can be corrected, modified, and ultimately justified, which in turn increases the reliability of the index [2] • Be realistic and relevant; should be a valid measure of the result/outcome and be linked through research and professional expertise; there is no reason to create an indicator which does not relate to the larger outcome; the indicator should be meaningful and important to the outcome to certify that the results are actually showing a related impact; broad outcomes/results can and should have numerous specific and applicable indicators through which progress can be assessed; an indicator is relevant to the extent that it captures or measures a facet of the outcome that it is intended to measure; the best way to think about relevance is to ensure that there is a relationship between what the indicator measures and the theories that help create the outcomes for the client, program, or system [39] • Be direct [35] • Have a narrow range [51], Have an appropriate scope, only measure important key-elements instead of trying to indicate all aspects [17] • Use indicators that have already been recognized by researchers as important contributors to social vulnerability [33] • Be dynamic: Multiple pressures, processes affecting factors of vulnerability [45] • Avoid conditions such as the presence of multi-collinearity and eliminate redundant variables from a statistical and index design standpoint; the incorporation of a multitude of indicators though—an attempt to increase the dimensionality of an index—makes an index highly prone to uncertainties; with a large number of indicators, the complexity of the aggregation structure often increases as well; indices suffer from redundant indicators and in most cases the latter is rarely suited to balance indicator representation [2] • Realize that interrelationships between indicators might be an ignored factor in the analysis [48] • [Not specified but of major influence on indicator comparison and final index scores] • Ensure equal dimensional representation; large weight discrepancies and a multitude of aggregation levels accelerate imbalanced indicator representation; test if their assignment of weights, indeed, generates the anticipated outcome [2] • Have uniformity of datasets for all countries [52] • Provide global standards, common criteria that all countries need to observe [44] • Realize that, due to variation in biophysical and socio-economic conditions, indicators used in one country are not necessarily applicable to other countries [53] • Disaggregate to relevant social level [50] • Realize that it is challenging to identify a shared set of indicators that is equally true in the United States of America as it is in Bangladesh [2] • Analyze hazards under the same severity condition (e.g. 50 year flood) [49]. 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(7) Global Water-Related Risk Indicators: Meta-Analysis of Indicator Requirements. Table 5. Impact requirements. Metrics Related legal documents. Governance models. Responsibility. Accountability. Resource access. Requirements as described by various sources • Develop a policy-informing tool [2], Policy-relevant [17], Assess policy effectiveness in flood mitigation, indicators with which the members can assess their flood situation and policies [54], Assess policy effectiveness toward the achievement of national and international goals (reduce by 50% the proportion of economic losses due to water-related hazards by 2015) [41], Trend of progress in policies (e.g., legislation and budget allocation for water-related disasters) [55] • Not only identify areas at risk but also enlighten stakeholders on proper development strategies (social welfare policies, deforestation) [34] • Select indicators that influence policy goals [55]. • Provide clarity comprehended by decision makers [33] • Rank countries in regard to targets, thresholds, and goals [2] • Make models attainable: achievable if the performance target accurately specifies the amount or level of what is to be measured in order to meet the result/outcome; the indicator should be achievable both as a result of the program and as a measure of realism; the target attached to the indicator should be achievable [39] • Identify and quantify the current state of the effect of adopted response policies in improving social, cultural, environmental, ecological, and economic developments [55] • Measure progress toward the mitigation of water-related risk, enable the measurement of the progress in policies and propose sound action to ensure sustainability toward the target under the acknowledgement of data scarcity [41] • Make governance participatory, consensus oriented, accountable, transparent, responsive, effective, efficient, equitable, and inclusive. Make it also follow the rule of law, which is very important for planning, implementing, allocating budgets, and managing water-related disaster mitigation infrastructures, emergency situations, and manpower [47] • Encourage the solving of regional water problems [52] • Shed light on proper development strategies, weak governance, and the corruption perception index [44] • Offer political insights about why certain countries, particularly some of the less developed countries, will have difficulties in attaining an acceptable level of risk and why they need to receive global support today to enhance their coping capacities [34] • Improve understanding of the relationship between development and disaster risk, (identify) the development factors and underlying processes that configure disaster risks. If disaster risks are to be managed and reduced, change in development policy and planning is required at the national level. Future work should also seek to incorporate an assessment of the extent to which national policy has included risk reduction and the impacts of such policy on disaster risk [2, 21]) • Realize that “how” and “where” the “who” is doing the “what” is important to include in the indicator, as it provides the action for the intervention [39] • Allow an analysis of the driving forces and pressures increasing flood threat to be carried out with the final aim of defining corrective policies, such as budget allocation, to mitigate flood vulnerability [54] • Include disaster management awareness [52] • Be meaningful for day-to-day decision making [34] • Stimulate various stakeholders toward international cooperation in the field of DRR [49] • Promote knowledge transfer between stakeholders [2] • Include trends of driving forces and pressures (natural & human-made features) [55] • Provide reliable risk information about areas with the highest priority in implementing DRR actions [52] • Include accountability, more direct proxies useful to understanding the reasons why flood fatalities disproportionally occur in less developed countries [44] • Identify investment priorities and risk management; accurate global indices are thus crucial in ensuring resource deployment to eligible countries as well as benchmarking and monitoring the countries’ performance; without calibrated measures of social vulnerability, vulnerability cannot be captured or managed; thus mitigation cannot be effective and it will be increasingly difficult to reduce future losses; evaluate indices in terms of their implications for decision-making, resource allocation, etc. [2] • Justify need for DRR and water-related disaster management investment [52] • Human capacity building programs; understand what investments and programs are beneficial to mitigate their vulnerability [44]. tionship between socioeconomic conditions and mortality patterns, etc. [2]. Regarding completeness, Gall [2] proved that many indices are designed in such a way that, in the end, only two indicators determined the final index score. This could mean that many indicators are redundant, their significance is overshadowed due to the inherent structure of the index, or both. Transformation refers to the choice of minimum and maximum values an indicator can have and how the data that are found are transformed along this range. Weights and global/local disaggregation are imporJournal of Disaster Research Vol.12 No.2, 2017. tant to capturing cultural differences affecting resilience. While making a uniform set of indicators is desired by the UN, even for all hazard types combined, such a system may not take into account the local characteristics influencing vulnerability if this means the proxies also need to be comparable in all countries [32]. Another indicator requirement was mentioned by several sources but not taken into account in this matrix, namely simplicity or understandability, meaning the ease of data collection or transparency of the indicator [2, 17, 33, 34]. The main reason for this exclusion is that, while 361.
(8) Vink, K. et al.. Table 6. Metrics and preliminary example individual scoring conditions for the indicator requirement matrix.. 362. Journal of Disaster Research Vol.12 No.2, 2017.
(9) Global Water-Related Risk Indicators: Meta-Analysis of Indicator Requirements. the final data should be understandable and collectable, a preference for simplicity does not mean that more complex but more accurate methods should be shunned in the index itself.. 3.3. Impact Requirements The third tier of requirements describes impact requirements (on policies/governance), referring to policy changes and actors of change, as based on the results described in Table 5. 3.4. Requirements of Indicator Data Quality – Pedigree Matrix Examples of individual scoring parameters of the seven metrics for the requirements of indicator data quality are presented in Table 6. For all metrics, higher scores indicate higher desirability. The value 0 indicates that the data is thought to lead to unreliable results, and the value 5 indicates the ideal attainable state of data. 3.5. Requirements of Indicator Data Quality – Evaluation The Disaster Risk Index [19] was evaluated using the first pedigree matrix for the requirements of indicator data quality (see Fig. 2). Out of the water-related risk indices that were considered, this index had the most metadata available for evaluation. Two overall low-scoring values of the DRI can be easily explained. The database was constructed in 2010 and uses the U.S. Census from 2000 for a majority of the indicators (16). As the maximum recentness score is set to 5 years or more, this automatically leads to a score of 0 for the indicators found using that source. The other low scoring value is coverage, which currently focuses on the global scale. The DRI is applied only in a part of the USA, and all data sources used contain information of the scale of the USA only at a maximum. Therefore, all indicators receive a score of 0 for coverage. Another low scoring metric is history, especially for indicators in the category “social resilience.” This is inherent to the index construction, as all seven indicators used in that category rely on the US Census of the year 2000, and thereby the history of data collection is set to one year only, as the censuses of previous years used different data gathering methodologies and were therefore not included. It should be noted that for one category, “institutional resilience,” only one of the indicator source data was found, thus drastically skewing the results for that category. For the other categories, either six out of seven or four out of seven indicator source data were found.. 4. Discussion This study has led to a compilation of three different types of requirements related to the construction and evaluation of indices concerning water-related hazards. It is Journal of Disaster Research Vol.12 No.2, 2017. Fig. 2. Metric scores of indicator requirements per indicator category of the DRI.. comprehensive in the sense that it covers a majority of the requirements found in the available literature from multiple fields of study. It provides a clear distinction into separate important tiers of index creation given the commonly found purposes of indices: indicator, index, and impact. The preliminary example metric scores generated for the first matrix of indicator requirements allow for an evaluation of data quality, following the perspective that high scores represent ideal data and not necessarily available data. This provides an entrance for comparing available data to ideal data, as well as a standard for investments in database construction and maintenance. By evaluating more databases and indices, it can become apparent whether or not this ideal data standard is achievable. As guidance for future research, these example pedigree matrix scores should be converted into clear evaluation scores in an objective manner. This should include a statistical analysis, pointing out what values lead to actual unreliable indices, for each data quality requirement. It should also include an in-depth discussion, e.g., with the intergovernmental expert working group [21], on what ideal and obtainable data is, after which the individual scoring values per requirement can be derived. As of yet, there is no international consensus on, for instance, how old population data is allowed to be before it becomes obsolete. This may vary from location to location per dataset, depending on other factors, such as, for this example, urbanization rate, aging, and immigration. While one of the requirements of indicator data quality is to have annually updated data in the most ideal scenario, the general indicator requirements prescribe a globally applicable, uniform method of data gathering and storing, one which must be applicable in developing countries as well. The same may hold true for the metrics of recentness, history, and coverage. Developing countries in general tend to have fewer resources available for gathering and storing data related to disaster resilience or may have 363.
(10) Vink, K. et al.. a shorter history of data storage. Therefore, the ideal complete database may have a lower chance of being realized in developing countries than in developed countries. To ensure that the evaluation method is globally applicable, this difference has been reflected in the individual score parameters of the metrics. The metric “history” in the matrix of the requirements for indicator data quality can be enlarged to include future estimates of indicator data, such as population, socioeconomic developments, and hazard occurrence. This enlargement would allow for more than just a current status or historical trends of resilience; it would also point out which data could be extrapolated to produce future estimates. The separation into three matrices highlights the need to focus on more than just data quality. Combining the highest-rated indicator databases into one index does not automatically lead to a guaranteed accurate representation of resilience. This will continue to depend highly on the index itself, which can be rated by the requirements in the second matrix. The initial testing of the preliminary example metric scores shows that the current metric methods are capable of meta-analysis of indicator data quality and that the scoring parameters may be adjusted to reflect the subject and scale of the index. These results suggest that on a subnational or national level, the census data and other sources could provide indicators with sufficient data quality, but for a global level, the methods of data gathering will have to be evaluated for consistency. Censuses will also have to be conducted more often than once every 10 years if they are to capture the dynamic nature of population changes related to vulnerability. In order to determine trends, annual censuses with consistent data gathering methodologies are an important investment area. Future separate studies can use these requirements to build new indices for water-related hazards with a more robust, scientifically sound structure and that include links to impacts. Secondly, other indicator requirements based specifically on different natural hazards could be found and compared to the current results.. 5. Conclusion We have generated a clear evaluation matrix of the requirements of indicator data quality as reported by various literature sources, from indicator to index to impact tier. We have also proposed a future research setup that allows an evaluation of where available data meeting the requirements match the requirements of ideal data. This would answer the question of whether or not a reliable index could be generated with the currently available data related to DRR. This comprehensive framework covers indicator and index requirements as developed by various scientific fields. The initial testing results show that the preliminary example metric methods are capable of meta-analysis of indicator data, and that the scoring parameters may be ad364. justed to reflect the subject and scale of the index. As this study shows the potential uses of proposed evaluation tools, it is suggested that future work with the currently gathered data needs to do the following: 1. Include more data quality requirements for disaster risk indicators, depending on the remaining related documents to update the comprehensive overview of requirements into three pedigree matrices; 2. Develop and test metric scores for the index and impact matrices, based on comparable scoring principles; 3. Apply the matrices to other indices that provide sufficient information. Organizations that aim to develop new disaster risk indices for water-related hazards can apply these data quality requirements to prospective indicators in order to obtain a more robust, scientifically sound index as well as provide more direct links to policies and actors. Another focal point lies in gathering requirements for different natural hazards and comparing these to the current results. In both cases, the developed comprehensive overview of three tiers of requirements can serve as a guideline for both index evaluation and construction, which are urgently needed. References: [1] United Nations Office for Disaster Risk Reduction (UNISDR), “Sendai Framework for Disaster Risk Reduction 20152030,” Switzerland, 2015, http://www.unisdr.org/files/43291 sendaiframeworkfordrren.pdf [accessed Jun. 6, 2016] [2] M. Gall, “Indices of social vulnerability to natural hazards: a comparative evaluation,” Department of Geography, University of South Carolina, USA, 2007. 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(12) Vink, K. et al.. Name:. Name:. Karina Vink. Md. Nasif Ahsan. Affiliation:. Affiliation:. Research Specialist, International Centre for Water Hazard and Risk Management (ICHARM) under the auspices of UNESCO, Public Works Research Institute (PWRI). Professor, Economics Discipline, Khulna University. Address:. Address:. 1-6, Minamihara, Tsukuba-shi, Ibaraki-ken 305-8516, Japan. Economics Discipline, Khulna University-9208, Bangladesh. Brief Career:. Brief Career:. 2007 Junior Consultant/Permit Granter, Public Works Institute/MH Poly, Rotterdam/Zwijndrecht, Netherlands 2008 Junior Researcher, ISIS CSMR, Radboud University, Nijmegen, Netherlands 2009 Data Analyst, Rosen Europe, Oldenzaal, Netherlands 2011 Research Assistant, ICHARM and GRIPS, Tokyo, Japan 2015 Research Specialist, ICHARM, Tsukuba, Japan 2017 Postdoctoral Researcher, NIMS GREEN, Tsukuba, Japan. 2005-2008 Lecturer, 2008-2012 Assistant Professor, 2012-2016 Associate Professor, Economics Discipline, Khulna University, Bangladesh 2013-2016 Research Assistant, ICHARM, Tsukuba, Japan 2016- Professor, Economics Discipline, Khulna University, Bangladesh. Selected Publications:. • M. N. Ahsan, K. Takeuchi, K. Vink, and J. Warner, “Factors affecting the evacuation decisions of coastal households during Cyclone Aila in Bangladesh. Environmental Hazards,” Vol.15, No.1, pp. 16-42, Doi: 10.1080/17477891.2015.1114912, 2016. • S. Lee and K. Vink, “Assessing the vulnerability of different age groups regarding flood fatalities: Case Study in the Philippines,” Water Policy, Vol.17, No.6, pp. 1045-1061, Doi:10.2166/wp.2015.089, 2005. • K. Vink, K. Takeuchi, and K. M. Kibler, “A quantitative estimate of vulnerable populations and evaluation of flood evacuation policy,” Journal of Disaster Research, Vol.9, No.5, pp. 887-900, Doi: 10.20965/jdr.2014.p0887, 2014. • K. Vink, “Transboundary Water Law and Vulnerable People – Legal Interpretations of the ‘Equitable and Reasonable Use’ Principle,” Water International, Vol.39, Issue 5, pp. 743-754, Doi: 10.1080/02508060.2014.951827, 2014. • K. Vink, “Vulnerable People and Flood Risk Management Policies,” PhD Thesis, National Graduate Institute for Policy Studies, Tokyo, Japan, Doi: 10.13140/RG.2.1.1860.7122, 2014. • K. Vink and K. Takeuchi, “International comparison of measures taken for vulnerable people in disaster risk management laws,” International Journal of Disaster Risk Reduction, 2013, Vol.4, pp. 63-70, Doi: 10.1016/j.ijdrr.2013.02.002, 2013.. Selected Publications:. • K. N. Ahsan, K. Takeuchi, K. Vink, and J. Warner, “Factors affecting the evacuation decisions of coastal households during Cyclone Aila in Bangladesh,” Environmental Hazards, Vol.15, No.1, pp. 16-42, doi:10.1080/17477891.2015.1114912, 2016. • A. N. Ahsan, “Effects of livelihood strategies on mangrove-forest resource: Do the consumption behaviour of households jeopardise the forest resource base?,” Management of Environmental Quality: An International Journal, Vol.25, No.6, pp. 696-711, doi:10.1108/MEQ-05-2013-0048, 2014. • M. N. Ahsan and J. Warner, “The socioeconomic vulnerability index: A pragmatic approach for assessing climate change led risks-A case study in the south-western coastal Bangladesh,” International Journal of Disaster Risk Reduction, Vol.8, pp. 32-49, doi:10.1016/j.ijdrr.2013.12.009, 2014.. Academic Societies & Scientific Organizations:. • South Asian Network for Development and Environmental Economics (SANDEE).. Name: Hisaya Sawano. Affiliation: Chief Researcher, International Centre for Water Hazard and Risk Management (ICHARM) under the auspices of UNESCO, Public Works Research Institute (PWRI). Address: 1-6 Minamihara, Tsukuba, Ibaraki, Japan. Brief Career: 2004- Professional Officer, Technical Support Unit, Associated Programme on Flood Management (APFM), World Meteorological Organization (WMO) 2007- Deputy Director (Oct. 2007) and Director (Apr. 2008), Water Resources Division, Japan Institute of Construction Engineering (JICE) 2008- General Manager, Shinano River Office, MLIT 2010- JICA Expert (Policy Adviser on Integrated Water Resources Management) for the Government of the Republic of Indonesia (Ministry of Public Works) 2013- Director of Integrated Water Resources Management Strategy Office, Water and Disaster Management Bureau, Ministry of Land Infrastructure, Transport and Tourism (MLIT), Japan 2014- Chief Researcher of ICHARM, PWRI. Selected Publications:. • H. Sawano, “Role of Public Participation in Flood Risk Management,” Publication for International Seminar on Water Related Risk Management, Jakarta, Indonesia, pp. 486-492, 2011.. Academic Societies & Scientific Organizations: • Japan Society of Civil Engineers (JSCE). 366. Journal of Disaster Research Vol.12 No.2, 2017.
(13) Global Water-Related Risk Indicators: Meta-Analysis of Indicator Requirements. Name: Miho Ohara. Affiliation: Senior Researcher, International Centre for Water Hazard and Risk Management (ICHARM) under the Auspices of UNESCO, Public Works Research Institute (PWRI). Address: 1-6 Minamihara, Tsukuba, Ibaraki, Japan. Brief Career: 2001 M.Eng., Department of Civil Engineering, University of Tokyo 2001-2003 JSPS Research Fellow, Institute of Industrial Science, The University of Tokyo 2005 Dr.Eng., Department of Civil Engineering, University of Tokyo 2003-2008 Research Associate, Institute of Industrial Science, The University of Tokyo 2008- Institute of Industrial Science, The University of Tokyo 2008- Associate Professor, Interfaculty Initiative in Information Studies, The University of Tokyo 2014- Adjunct Associate Professor, Disaster Management Program, National Graduate Institute of Policy Studies (GRIPS), Japan 2014- Senior Researcher, International Centre for Water Hazard and Risk Management (ICHARM), Public Works Research Institute(PWRI), Japan. Selected Publications:. • M. Ohara, N. Nagumo, B. B. Shrestha, and H. Sawano, “Flood Risk Assessment in Asian Flood Prone Area with Limited Local Data – Case Study in Pampanga River Basin, Philippines –,” Journal of Disaster Research, Vol.11, pp. 1150-1160, 2016.. Academic Societies & Scientific Organizations:. • Japan Society of Civil Engineers (JSCE) • Japan Society for Disaster Information Studies (JSDIS) • Institute of Social Safety Science (ISSS) • Japan Association for Earthquake Engineering (JAEE). Journal of Disaster Research Vol.12 No.2, 2017. 367.
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