Spatialised flood resilience measurement in rapidly urbanized coastal areas with complex semi-arid environment in Northern Morocco

1. Abstract. Enhancing resilience is critical for coastal urban systems to cope with and minimize flood disaster risks. The global increase in the frequency of floods is a significant concern for many areas in Africa. With this regard, urban planners 10 need accurate approaches to set up a standard for measuring the resilience to floods. In Morocco, this issue is still not fully covered by the scientific community, despite the obvious need for a new approach adapted to local conditions. This study applied a composite index and geographic information system approach to measure and map resilience to floods in three northern coastal municipalities. The approach is also based on a linear ranking of resilience parameters, offering a more optimal classification of spatial resilience variation. The findings allowed to identify specific areas with different resilience 15 levels and revealed the relationship between urban dimensions and the flood resilience degree. This approach provides an efficient decision support tool to facilitate flood risk management, especially in terms of prioritization of protective actions.

to organize itself, to learn from past disasters,and to improve risk reduction measures (UNISDR, 2015). Some previous work (Meerow et al., 2016,) linked the concept to the temporal and spatial scales, considering resilience as the ability of urban 65 system components (ecological and socio-technical) to maintain or rapidly return to desired functions, adapting to change in the face of disturbance and quickly transform systems that limit current or future adaptive capacity. Resilience has a systemic property and implies greater consideration of the time variable (Reghezza, 2015). Furthermore, some works (Chen N, & Graham P. 2011;Colding J., & Barthel S, 2013) describe the resilience of the system as the ability of short-term absorbing, self-organizing, and long-term learning and adaptation. The abundance of definitions makes it difficult to have a common 70 concept (Carpenter et al., 2001). Therefore, set a resilience definition concept could be an appropriate basis.
In this work, the resilience of the urban system to floods is the capacity of urban-flooded areas to maintain the activities during and after floods. Simply put, a resilient coastal urban area will be able to absorb the disaster (at an acceptable level) and adapt to the changes. Besides, urban resilience is a complex and multidimensional concept (Sharifi, 2016). The resilience of the urban system to floods includes several dimensions of an urban system. Social, economic, physical, natural, and institutional 75 dimensions equally important (Batica, 2015;Qasim et al., 2016). The social dimension explores flexibility, health status, knowledge, while the economic dimension is related to the economic capacities, income resources, and connections devices within the community. The physical dimension may include urban density, building materials, and infrastructure (Qasim et al., 2016) or quantified based on physical indicators such as flood depth or flood duration extracted from flood simulation data (Mugume et al.,2015;Chen and Leandro, 2019 ). Areas located at low elevations or near rivers are more sensitive to flood 80 disasters, which constitutes the natural component of resilience (Hung et al., 2016). Thus,, institutions efforts aiming at coping with disasters through better planning, awareness programs prefer integrated approachesby considering all these dimensions in the evaluation of resilience. This may help to have a global perspective , which will lead to create suitable management tools that can be very useful in the decision-making process (Bertilsson et al., 2019). Supporting this decision-making process on strategies, actions, and measures to be taken, planning for the long-, medium-and short-terms and assessing the progress, 85 start with the assessment of the current and expected future status of resilience, to know where urban resilient areas are, and to identify their strengths and weaknesses (Cardoso et al.,2020).
Because of its multidimensional aspect, it remains challenging to quantify resilience (Bertilsson et al., 2019). Many works have shown the need to have some metrics allowing to have some measure of resilience. Yet there is no consensus about a single metric of evaluation, and the literature (e.g. Meerow et al., 2016;Asadzadeh et al., 2017;Rus et al., 2018) refers to the 90 need for measures. Furthermore, making resilience tangible and practical for cities, through a transition from theory to practice is challenging (Kontokosta & Malik, 2018;Meerow et al., 2016). Quantitative approaches through composites indicators provide a synthetic measurement of a complex, and ) multidimensional phenomena (Changdeok et al., 2019. Those indicators are developed based on the aggregation of multiple individual indicators (OECD, 2008). The choice of method to construct composite indices dependent upon the type of problem, the nature of the data, and the goals (Nardo et al., 2005). Several 95 composite indices to assess resilience of urban areas and to compare their resilience levels within specific geographical regions have been proposed in recent years (e.g. Sharifi et al., 2016;Asadzadeh et al., 2017). For example, the work of Cutter et al. (2014) using BRIC (Baseline Resilience Indicators for Communities) as the first attempt to the operationalized version of the conceptual framework "DROP model (Cutter et al., 2008). Within a socio-ecological approach, BRIC was calculated for multihazard context. Among other analysts, Joerin et al., (2014) state CDRI (Climate Disaster Resilience Index) gauges the different 100 capabilities needed for communities in an urban system to regain an equilibrium state after climate-related disasters such as cyclones, droughts, floods, and heatwaves. Following the same holistic spirit, this index was adopted in Climatic Hazard Resilience Indicators for Localities (CHRIL) (Hung et al., 2016). (Mayunga.2007) also proposed a Community Disaster Resilience Index (CDRi). All of those previous indicators were applied to quantify community resilience to multi natural hazards. (Qassim et al.2016) determined community resilience to a particular hazard "floods", and specific community "urban 105 areas", as recently proposed also by Cariolet et al., (2019). Although, many particular indicators were developed for a specific case of urban resilience to a specific hazard like floods.
Based on time-dependent characteristics, FResI was constructed to assess future resilience responses relative to the present situation (Miguez and Verol. 2016). Further, Chen and Leandro (2019) quantified the flood resilience of households in urban areas by FRI (Flood Resilience Index) as a time-dependent method. More examples of specific indicators are available (Kotzee 110 and Reyers, 2016) using Geographic Information Systems (GIS) and stressing the need to move towards diversifying resilience assessement approaches.
Regardless of several challenges associated with data quality and availability constraint (Moghadas et al., 2019;Cai et al., 2018), and standard procedure for composite indicator development (Asadzadeh et al., 2017), particular attention has been paid to composite indicators (Heinzlef et al., 2019a), regarding their ability to analyze the urban, social and technical resilience 115 of a city. However, a lack of resilience measurement tools developed by local authorities and organizations in the developing countries was reported in a critical review by Sharifi.A., & Yamagata, Y. (2016).
The Mediterranean region will suffer from severe impact due to climate change (Tuel and Eltahir,2020). Particularly Morocco is mentioned as a hotspot for climate change in several works (Born et al. ,2008;Driouech et al. ,2009;Ouhamdouch & Bahir, 2017). Moreover, the seasonal distribution of the precipitation influence strongly the Mediterranean river hydrology (Thornes 120 et al., 2009). Assessing the intensity of the impact, Regional Climate Models (RCM) simulations over this area, all agree that Morocco might experience an increase in temperature and a decrease in precipitation (Driouech et al., 2010). Which will have a severe impact on water (Bahir et al., 2020), and natural hazards (Satta et al., 2016), among others. Consequently, increasing resilience against flooding is, therefore, of utmost importance to achieve sustainability (Snoussi et al., 2008). However, a knowledge gap for a better understanding of resilience has been identified at national and local levels (Price, R.A. 2017)

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Morocco. It is highly recommended to provide policymakers with a simple approach and ways to enhance resilience to floods in the local area (OCDE 2016).
The present study is the first attempt to provide a methodological way to measure flood resilience for Northern coastal municipalities in Morocco: Martil, M'diq and Fnideq. In this work, flood resilience refers to the resilience of these coastal urban areas (Martil, M'diq and Fnideq) to floods. These areas were selected because they show18 hot spots highly exposed to 130 floods (ABHL 2016). Moreover, the area is particularly highly vulnerable to multi-hazards types including floods (Karrouchi et al., 2016;Taouri et al., 2017), sea-level rise (Niazi, 2007;Snoussi et al., 2010), and coastal erosion (Satta et al., 2016;Nachite, 2009). However, the littoral is nowadays very urbanized, and tourist activities are the main economic resources in the area (Anfuso et al., 2010).

Fnideq, M'diq, and Martil municipalities
Related to M'diq-Fnideq prefecture, Fnideq, M'diq and Martil municipalities have a population of "984hab/km²" (RGPH 2014). Precipitation regime characterized by seasonality, annual average rainfall of 679 mm (ABHL 2016). Rainfall variability is based on altitude and the geographic situation (Karrouchi et al., 2016). Rivers flowing into the Mediterranean Sea (Martil, Mellah, Smir, Negro and Fnideq) drain slowly during the rainy months and highly in a short time during flash floods (Niazi, 140 2007). While, the frequency of flood events and related damages increased gradually over time (e.g. on 26 December 2000, Martil Floods have invaded more than 2400 ha in the Martil plain) (Fig.1). Urbanization is concentrated in coastal zones and puts pressure on coastal ecosystems with high touristic value (Snoussi et al.,2010). It is pitiable that municipalities are also vulnerable to multiple climate and non-climate hazards such as erosion and morphological changes (Satta et al., 2016).

Theoretical comprehensiveness for composite index development 145
To produce an aggregate measure of resilience, through manipulation of individual variables, constructing a "Composite indicator" is often applied. It is a mathematical combination of thematic sets of variables that represent different dimensions of a concept that cannot be fully captured by any individual indicator alone (Nardo et al., 2008). An indicator is a quantitative or qualitative measure derived from observed facts revealing the relative position of the phenomena being measured. "It can illustrate the magnitude of change (a little or a lot) as well as the direction of change over time (up or down; increasing or 150 decreasing)" (Cutter et al., 2010). Moreover, considerable attention is increasingly given to composite indices as useful tools for decision-making and public communication. They simplify and communicate easily the reality of complex situations (Freudenberg, 2003) and convey more precise information (Saisana et al.,2005). However, through different geographical contexts and scales, these indices may encompass many theoretical perspectives (Cutter et al.,2008). Also,the quality of the framework, the data, and the used methodology can significantly influence the qualities of a composite index and the soundness 155 of the messages that convey.
Flood Resilience Index is explored and calculated differently in several works. Kotzee and Reyers (2016) used PCA as a method to construct this index and define its component weights. For Batica (2015) It has been built taking into account different spatial scales and focusing on urban functions. Using time series indicators (event phase and recovery phase), Chen and Leandro(2019) computed FRI at time t as the product of the recovery factor and the FRI at the previous time step t-1. By defining resilience as time-dependent, based on two phases (event and recovery), Leandro et al., (2020) shown also the ability of the developed FRI for assessing climate change adaptation. Despite the already existing studies on flood resilience assessment, there is still a need to develop methods for a specific case of study, where data availability remains a challenge, and the need for a tangible and simple way to better understand resilience is increasing.

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In this study, we adopt the specific Flood Resilience Index to quantify the resilience of coastal urban areas to floods. FRI was divided into four sub-indicators: Social, Physical, Economic, and Natural sub-indexes , so as to enable sufficient flexibility to include or not each sub-index in global composite index (Fig.2). Three indicators were chosen for each sub-index (Tab1) based on data availability and their contribution to persistence, recovery, or adaptative capacity (the main components of the adopted resilience definition): Households Density (HD), Illiteracy Rate (IR), and Vulnerable Individuals Indicator (VII) were 170 taken into consideration as the mean indicators that affect the social resilience negatively and construct the social sub-index.

Selecting indicators: Scoring and classification
Based on their relevance, analytical, representativeness, and accessibility, 16 indicators (variables) were selected (Tab.1). The data used was mainly drawn from the National Population and Housing Census (RGPH, 2014). The Arc Hydro and Line 180 Density modules of ArcGIS© were used to generate a stream network density from an ASTER digital elevation model (30 meters of spatial resolution), while Google high-resolution satellite imagery was used for digitizing the building area. This was converted firstly into points, and then their density was calculated using the ArcGIS© Point Density module.

Normalisation
Indicators integration into sub-indicators needs data normalization. Respecting the theoretical framework and the data 185 characteristics, a Min-Max normalization was applied. Subsequently, each variable was normalized from 0 to 100 according to the following equations (1) and (2): (1) Equation (1) was applied for variables that positively influence resilience while Eq.
(2) was applied to those that are negatively 190 correlated with resilience. When the scores are attributed, each of these indicators was gridded and then a geodatabase was created to calculate the sub-indexes by using the GIS. Each sub-index is the mean value of all correspondent indicators.

Weighting and aggregation
The existing methods for determining weights are not always reflecting the priorities of decision-makers (Esty et al., 2005), which are subjective (Cutter et al., 2010). Equal-weighting is the most common for composite indices with several sub-195 indicators (OECD, 2008). Thus, several arguments listed by Greco et al., 2019 ("i" simplicity of construction, "ii" a lack of theoretical structure to justify a differential weighting scheme, "iii" no agreement between decision-makers, "iv" inadequate statistical and/or empirical knowledge, and, finally "v" alleged objectivity). Moreover, the weighting method selection depends on the local factors where the method is applied (Mayunga, 2007;Reisi et al., 2014) The simple method of aggregation supposed to be transparent and easy to understand, is a critical criterion for potential users 205 (Cutter et al.,2010). All individual indicators have the same measurement unit. Therefore, using linear aggregations is preferred over geometric aggregation. The linear aggregation formula of the FRI takes the following form Eq. (3).
Social Resilience Index (SRI); Physical Resilience Index (PRI); Economical Resilience Index (ERI); Natural Resilience Index (NRI); Zero is considered as low resilience level, 100 as high resilience level, and 50 medium resilience level. 210

Links to other indicators
To assess the correlation between the composite index and corresponding sub-indices/variables, a statistical analysis was performed, using the program SPSS 23. Data presented as a mean and standard deviation (st.dev) were statistically analyzed using multi-variance to confront data of natural, physical, economic, and social conditions with the Flood Resilience Index.
Furthermore, to identify which variables differ significantly between the three case-study sites. The significant differences were distinguished by posthoc Tukey's Honestly Significant Difference (HSD) test at p<0.05. The Spearman's rho coefficient was used for correlations between variables. Only significant correlation coefficients at a level of 0.05 are considered.

Visualization and validation
Particular attention has been paid to the visualization given its relevance in helping and enhancing interpretability. Maps facilitate further exploration of spatial data trends (Kotzee and Reyers, 2016). For that purpose, Geographic Information 220 Systems (GIS) was adopted to visualize FRI and sub-indicators. After visualizing the composite index results, validation was the last step. Acting like a 'quality assurance', this step will highly reduce the possibilities to convey a misleading message (Saisana et al., 2005). Unfortunately such step is often skipped for the vast majority of the composite indices (OECD.2008).
External validation has been the most used to validate several indicator results (CDRI 2009, BRIC 2012, CDRI 2013, and BRIC 2014. 225 In our case, the validation based on actual outcomes of the municipalities is possible using cross-validation type. It was performed to test and compare the reliability of FRI results using the results of other studies interested in hydro-climatic hazards as suggested by Satta et al., (2016). This is possible through the opposite correlation between risk and resilience (Cutter et al., 2014;Sherrieb et al.,2010). Seeking optimization by considering social and economic pathways, and combining flood resilience and flood risk, measures can be effective against a broader range of hazards than when considering either method 230 alone (Disse et al., 2020).

Sub-indices
Each sub-index was observed separately to get additional insights about Flood resilience Index. For the social resilience ( Fig.  235 3D), produced based on the three indicators of social resilience (Fig. 3A, B and C). The highest values of social resilience are more related to a few urban areas than rural and less developed sectors ones. In terms of mean value, the social resilience sub- Higher physical resilience scores ( Figure 4A, B, C and D) are concentrated in the urban center areas with a spatial tendency 240 towards the coastal area. Even though pockets of lower scores exist in the central area and some less developed sectors indicating low physical resilience levels. Therefore, the central area had a bit low level of physical resilience as compared to Fnideq, M'diq and Martil urban centres and the coastal zone (Fig.4 D).
Results (Fig.5D) show a concentration of the low and moderate level of economic resilience in the three urban centers.
However, this does not exclude that some coastal urban sectors showed high levels of Economic resilience sub-index. 245 The overall map of Natural Resilience Index shows a spatial variability between the lowest and the medium level of NRI in the whole study area (Fig.6 D). However, the high level of natural resilience is more prevalent in areas with high altitudes, such as Capo-Negro (Fig.6 AC).

Total Flood Resilience Index
The results reveal a marked spatial variability of resilience to floods (Fig.7). Overall, 31% of the study area varies from low 250 to very low, which equals 45 km² (Fig.8a). 43% of the studied area, which equivalent to 52 km2, was classified as moderately resilient and only 17% of the studied area (17 km2) was classified as highly resilient and the remaining 3% with very high resilience. The central area shows the lowest levels of FRI, including sensitive coastal sites such as Smir Lagoon, Kabila beach, and Restinga beach. In contrast, M'diq and the North of Martil have relatively moderate to high values in terms of resilience to floods. However, the significant disparities between rural and urban areas, especially in terms of socioeconomics, highly 255 influences the flood resilience index values.
In order to avoid any confusion related to flood management priorities between the rural and the urban areas. The resilience map corresponding to urban areas were extracted, and the index values using GIS were reclassified to have the priority areas without taking into account the rural part. Using this tool to overlay the spatial distribution of households (RGPH 2014) and FRI map, it turns out that 1151 households (around 2.4%) are in areas of very low resilience and more than 7800 households 260 (about 16%) in low-resilience areas. On the other hand, 7402 households are in a high resilience situation, and only 177 can be qualified as very high resilient (Fig.8b).

Statistical analysis
In order to evaluate the contribution of the sub-dimensions (Social, Economic, Physical and natural dimensions) for the resilience analysis, the statistical relationship between the total Flood Resilience Index (FRI) and its sub-indices was estimated 265 for each municipality (Tab.2).
The SRI is positively correlated to the FRI index in the three municipalities (p<0.001), particularly in the urban areas where it is proven to be important as an FRI component. Regarding the ERI sub-index, it shows a moderate correlation at the Fnideq and Martil municipalities (p<0.01), or even a low correlation at the M'diq level (p<0.05). Unlike SRI and ERI, the correlation to the PRI sub-index is different from one municipality to another. It is strong at the level of Martil (p<0.001), weak at the 270 level of Fnideq (p<0.01) and absent at the level of M'diq. In the case of the NRI sub-index, it displays a strong correlation at the level of Fnideq and moderate at the level of Martil and M'diq.

Discussion
Within the current context of global climate change associated with an increase of flood damage, the efficient use of available data is, in most cases, the primary source of judgment control decision-making for flood risk management (Ouma et al., 2014). 275 Producing flood resilience maps has thus become a crucial issue for local flood management planners (Godschalk, 2003).
However, these products require generally detailed knowledge about all resilience components in time and space to be effective. They should be designed in such a way that can help the decision-making by using ranking and prioritization process (Chitsaz et al., 2015). Accordingly, the choice of a good methodology to assess and quantify the resilience attains its utmost importance and relevance. Indeed, the adopted methodological approach as well as the quality of the data has a significant 280 influence on the obtained results, and hence on the final decision making (Suárez et al., 2016).
In this paper, the adopted methodology is adaptable according to the study case and the available data. Moreover, the adapted ranking process is based in a linear scoring, which offers the advantage to be more sensitive to changes compared to the usual methods based on assigning scores according to intervals (e.g. Angeon et al., 2015). It also provides a more reliable and objective spatial comparison of resilience parameter values which will finally allow obtaining effective prioritization of 285 resilient areas.
It should be noted that significant components for the resilience analysis have been considered and the obtained resilience map allowed to classify the study area according to four resilience degrees to floods: very low, low, moderate, and high.
The difference in the social resilience sub-index between urban and rural areas could be explained by the fact that human development indicators are generally lower in rural and less developed areas, especially those related to school attendance and 290 people vulnerability, which affect social resilience negatively. However, the difference in SRI between municipalities may occur because of the great growth rate of Martil municipality rather than Fnideq and M'diq (HCP, 2018) The low physical resilience in the central area and the less developed sectors may exist because of the low population and urbanization (e.g. At the central area access to water infrastructure, as basic service is still low ( Figure 4C). Unlike in the case of the urban centers with high physical resilience scores. 295 Meanwhile, the high level of Economic resilience sub-index in some coastal urban sectors may be explained by tourism and economic activities. An expected result given the characteristics of the wealthy residents living there (Tempelhoff et al., 2009;Kotzee et Reyers. 2016). Unlike in the three urban centers having low and moderate economic resilience. That could be explained by the high unemployment rate "17.9 %'' (HCP, 2018)  The areas with very low and low Flood Resilience Index seem to be generally associated with the areas showing unstable socials conditions. This observation is confirmed by the statistical analysis, and studies (Godschalk, 2003;Cutter et al., 2010;Kotzee et Reyers, 2016;Moghadas et al., 2019) showing that social resilience is strongly correlated to flood resilience degree.
Moreover, the disparities highlighted between rural and urban areas revealed that rural areas display the lowest resilience to floods. 310 Economic and natural resilience which is tightly linked in the sites, is the second most statistically significant indicators linked to the total FRI. Disparities between municipalities are less significant. This means that areas having low or moderate resilience to floods need equal attention (Qasim et al., 2016).
The risk and vulnerability-oriented studies (Niazi, 2007;Snoussi et al., 2010;Nejjari, 2014;Satta et al., 2016) in the coastal area were used for validation. The results are consistent, showing that coastal sites such Restinga plain, kabila beach, Smir 315 lagoon and Martil-Alila plain having a low resilience are highly vulnerable to the flash floods and sea-level rise impacts (Snoussi et al., 2010;Niazi, 2007;Satta et al., 2016). Considering all the output, this confirms that the flood resilience index is relatively valid and can be adapted and tested in other geographical areas. Moreover, this robustness analysis makes the FRI in this case of study support the idea that areas with higher vulnerability levels examined have lower resilience levels (H.-C. Hung et al., 2016). 320 In this context, there is a need to prioritize the actions contributing to enhancing the social and economic communities' levels.
Providing support and strengthen actions promoting social and economic level in the municipalities.
Further, the statistical analysis shows a significant link between the natural characteristics and resilience degrees. In that situation, it is recommended to establish best practices and measures to avoid urban development in flooded areas and to provide more efforts to manage the risk of floods in urbanized areas, with a strong focus on the contingency plans in case of 325 power or drinking water failure in the three municipalities.
Therefore, there is a need to incorporate disaster management education in college to explain hazards adaptation. Also, educate people through communication devices, seminars and workshops involve citizens to be aware of the damages and the climate change effects.
The obtained results highlight the importance of using a multidimensional approach to assess flood resilience. Furthermore, 330 GIS is also highly recommended as a solution to complex situations and as a decision support tool that offers an interactive use and continuing improvement (Ouma et al., 2014;Mayunga, 2007).

Conclusion
Building and enhancing resilience to floods becomes critical, as the urban development in a coastal area in Africa is increasingly stressed. Especially for the coastal zones situated in semi-arid threatened areas. Nevertheless, in the local contexts 335 of Morocco, where this study is the first attempt focusing on enhancing the understanding of resilience to floods highlighting the application of the tangible approach to summarize and present complex components linked to resilience to floods.
Flood resilience assessment was piloted using a composite index and a GIS. The spatial and statistical analysis gave further insights into the geographic distribution of FRI across Fnideq, M'diq and Martil municipalities. Moreover, clarify the presentation of a complex set of components linked in a reproducible way. 340 The findings indicate that different factors can vary spatial patterns of resilience to floods. The framework is flexible enough to allow the proposed index, in future work, to take into consideration the institutional component. Including the institutional component could advance our understanding of flood resilience, and provide useful results to suggest floods adaptation strategies in a coastal area. The robustness of flood resilience indicator was tested by comparing the results against additional case studies and operationalized measures of resilience. Some of the main limitation of the developed FRI are: starting with 345 tackling the main limitations from considering real/simulate flood inundation maps, to integrating climatic data (flood data or flood simulation data). Besides, for robust validation, date of resilience assessment and validation tool date should be highlighted to take the specific changes in land covers between the two periods of time. Further work will use other methodologies developing FRI in the same coastal area, to provide further insights about indicators assessments and the relationships among flood resilience and flood risk. 350

Physical (PD)
Old Buildings Rate (OBR) Is the percentage of buildings that are over 50 years old, it expresses the fragility that increases with building materials age.
Modernly Built Houses (MBH) ) Based on the building material factor (by Reinforced concrete and bricks with mortar) modernly built houses will suffer less exterior damage during floods events in the local state (Cutter et al., 2010).
Connection to Water Infrastructure (CWI) The rate of connection to the sewage system and drinking water distribution strength resilience community (Cutter et al., 2010).
A not being guaranteed access to water during and after emergency (Pagano et al., 2017) will aggravate the situation.

Economic (ED)
Unemployment Rate (UR) It expresses the decrease in the individual economic capacity.
Unemployed people are faced with difficulties related to their disability to recover or rebuild their damaged property (Cutter et al., 2010;Sherrieb et al., 2010).
Building Density (BD) It reflects the concentration of building per area. People are more concentrated in low quality urban housing, infrastructure and services the impact of natural disaster is higher (Pallard et al., 2009).It was selected based in the fact that an area with high building density is less resilient to floods.  (Cutter et al., 2010).

Natural ( ND)
Elevation (E) It was selected based on the fact that lands with low elevation, are more risked to flooding and exposed to damages compared to high elevation areas.