Author’s Accepted Manuscript Understanding the effect of spatial patterns on the vulnerability of urban areas to flooding Yousef Sakieh
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To appear in: International Journal of Disaster Risk Reduction Received date: 1 February 2017 Revised date: 25 August 2017 Accepted date: 1 September 2017 Cite this article as: Yousef Sakieh, Understanding the effect of spatial patterns on the vulnerability of urban areas to flooding, International Journal of Disaster Risk Reduction, http://dx.doi.org/10.1016/j.ijdrr.2017.09.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Understanding the effect of spatial patterns on the vulnerability of urban areas to flooding
Yousef Sakieh* College of the Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Basij square, Gorgan, Golestan Provice, Iran
[email protected]
*Corresponding Author: Yousef Sakieh College of the Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Golestan Provice, Iran email:
[email protected]
Conflict of Interest: The author declares that he has no conflict of interest. 1
Understanding the effect of spatial patterns on the vulnerability of urban areas to flooding Abstract This study adopts an integrated spatial-statistical approach to explicitly analyze the relationships between landscape structure (composition and configuration) of human settlements and their exposure to different levels of flood hazard. In this regard, the flood hazard surface of the research location was firstly produced using the MCE (Multi Criteria Evaluation) method. Then, 2013 urban land-use map of the study area was overlaid with flood hazard layer and average hazard values were extracted from hierarchical buffer rings (500m, 1,000m, 1,500m and 2,000m) around urban and rural centers (dependent variables). In addition, landscape indices of the corresponding centers were also quantified to serve as independent variables. Having the data-set generated, scatterplots between different metrics and average flood hazard values across multiple buffer rings were drawn to study whether there are distance-dependent relationships between morphology of human settlements and their exposure to different levels of flood hazard. The regression models were then developed for each buffer area and those landscape metrics with better explanatory power were selected based on the step-wise approach. According to the results, majority of the metrics clearly demonstrated distance-dependent behaviors and developed regression models also confirmed this matter. Policy implications derived from this study indicated more connected and aggregated patterns of human settlements in rural areas can significantly increase resistance of such environments against natural hazards. Landscape ecology approach is a potential framework for studying urban sustainability at regional scales and an innovative paradigm for environmental hazard management and informed decision making. Keywords: Flood hazard, Landscape ecology, Landscape metrics, Linear regression, Iran.
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1. Introduction Protection of human settlements and natural resources against natural hazards such as flooding is becoming increasingly challenging (IPCC 2012). Due to factors such as consistent expansion of human-made elements and the their consequent vulnerability in floodplains, protection of residents’ properties along water resources (i.e. rivers, coastal areas) and maintaining economic and hydrological integrities are becoming more and more difficult (Fuchs 2009; Fuchs 2015). These challenges necessitate enhanced solutions for flood management that go beyond conventional practices such as establishing dikes. Mitigation plans and flood protection practices improve residents safety, increase property values and facilitate different activities (Johann and Leismann 2014; Tempels and Hartman 2014; Hartman and Spit 2015). In this case, there are various methodologies to study different levels of flood hazard and to devise management plans. Correia et al. (1998a) indicate that understanding of how people realize and respond to natural hazards in an urban-dominated landscape and integration of such knowledge into planning efforts are very important for improving participatory approaches in flood hazard management. Therefore, they conducted comprehensive study in Setúbal, Portugal with extensive interview program to analyze and enhance decision-making processes on flood management. Correia et al. (1998b) report that Geographic Information Systems (GIS) is a powerful tool for comprehensive floodplain management and predicating the impact of different scenarios in terms of flooded areas and associated risk. They highlight that such integrated studies might be an efficient way of 3
overcoming challenges and obtaining acceptable results for engineering practices. In a further study, Correia et al. (1999) indicate most critical flood problems occur in urban environments where risk values are greater and damages are consequently heavier. In this regard, growing urban areas might face very specific problems because of the unstable condition of these regions in terms of urban land-use planning. Therefore, a realistic approach to flood management necessitates inclusion of urban growth patterns and simulation of the consequent flood situations. Dass et al. (2011) and Bormudoi and Nagai (2016) followed a bioengineering approach that is a combination of mechanical, biological and ecological concepts for identifying erosion-prone areas (Gray and Sotir 1992). de Walque et al. (2017) developed a muddy flood hazard prediction model to evaluate the occurrence probability of muddy floods at a given location. The logistic regression model was applied and a collection of explanatory variables (geomorphology, landuse, sediment production and sediment connectivity) was used to predict flooded and nonflooded sites. They concluded that developed statistical models are of potential to prioritize sites at risk of muddy floods and to conduct mitigation plans. Accordingly, there are few researches in the literature that have adopted a landscape ecology perspective for analyzing and modeling the flood hazard phenomenon in a spatiallyexplicit manner. In contrast, this methodology has been widely implemented to study spatiotemporal urban growth patterns (Sakieh et al. 2015a, 2015b, 2015c) and to analyze the relationships between spatial patterns of built-up areas and green covers (Zhou et al. 2011; Asgarian et al. 2015), landscape aesthetics values (Sakieh et al. 2016b), fragmentation of protected areas and rangeland loss (Sakieh and Salmanmahiny 2016). Specifically, compared to 4
other methods for flood hazard management mentioned above, the distinctive feature of the landscape ecology science is its morphologic approach toward a spatial problem. Based on landscape ecology science, the modeler attempts to achieve a holistic perspective regarding exposure of built-up areas to natural hazards. Under such conditions, morphological attributes such as total area, connectivity, compactness, aggregation, isolation, fragmentation, distance from the nearest neighbor and distribution patterns become in the center of the attention. Unlike conventional methods that analyze and compare different flood hazard mapping methods and mitigation plans by considering additional environmental parameters or by employing computationally-complex algorithms (Pradhan et al. 2013; Tehrany et al. 2014; Bui et al. 2016; Pradhan et al. 2016), the landscape ecology approach enables the modeler to evaluate current exposure of urban spatial patterns to different levels of flood hazard. In other words, landscape ecology approach reaches beyond the primary purpose of spatial mapping methods. In this context, the planner not only conducts a mapping process, but also this map is used to design a safe urban environment with lower vulnerability to natural hazards. For achieving such purpose, morphological attributes and spatial configuration characteristics of human settlements play an important role to provide an informed set of solutions and designing implications. This is the unique function of the landscape ecology perspective for addressing the influence of both environmental parameters (i.e. slope, vegetation characteristics and soil properties) and morphological attributes on vulnerability of urban areas to flood hazard.
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On this basis, this study follows a landscape ecology perspective to quantify, analyze and compare the sensitivity of different spatial patterns of urban areas to flood hazard in a highly flood-prone area. Specifically, this research attempts to answer the following questions: 1. Is there any association between morphology of human settlements and their exposure to different flood hazard levels? 2. In case of any relationship, does this linkage demonstrate a distance-dependent behavior across multiple buffer rings surrounding these settlements? 3. By answering questions 1 and 2, how local policies could be regulated to protect human settlements against natural hazards? The remaining parts of the paper are arranged as follows. Within the Section 2, descriptions regarding study area are given and technical details for flood hazard mapping and landscape metrics calculation are provided. In Section 3, statistical analyses are described and the results are illustrated. In Section 4, the main findings of the study and their contribution to our study area are discussed and designing implications for other locations with similar issues are provided. Ultimately, in Section 5, future applications of the method and research directions are outlined.
2. Materials and methods 2.1 Study area 6
The research location includes Gorgan and Alia-Abad townships, Golestan Province, northeastern Iran, collectively named as Goragn Township (total area of ~980 sq.km). This region is located in south eastern coast of the Caspian Sea (Fig. 1). The min utility of urban and rural regions in the study area is residential use, while other activities such as industrial and commercial also exist with lower intensities. The main occupation of rural residents is related to farmland activities and agricultural fields and rangelands form the dominant matrix of the targeted landscape. The study site in the south is heavily covered by lush Hyrcanian forests; while in the north landscape of the area is dominated by farmlands and rangelands. During the recent years and after the designation of the area as a new province, increasing rates of population growth and urban development have resulted in a collection of notorious ecological consequences such as farmland and green cover loss, shortage of freshwater and water pollution (Mahiny and Clarke 2012, 2013; Sakieh et al. 2016a). According to several incidents of natural hazards in the Golestan Province such as flood, wildfire and landslides, the area is highly sensitive to a variety of natural hazards (Golestan Province Land-use Planning Report 2013). In this case, the targeted location was invaded by heavy flood streams in 2001 such that high acreage of the province was inundated (Fazel-Rastegar 2002) accompanied by profound damages to farmlands, residents’ properties, transportation network and human lives. The inundation process was initiated by heavy precipitation during a short period of time, which led to massive flood streams, soil erosion and landslides with substantial damages to built-up structures in rural areas. Rural regions were the most sensitive locations to flood streams since these areas were of low physical size and low-density distribution across the 7
study area. Supporting actions were not effectively performant since these locations were scattered with low connectivity either to transportation network or major urban centers in the area. Such conditions alarmed decision makers to devise sustainable development programs for supporting rural areas in the Golestan Province. Compared to major urban cores in the area, rural areas are found in higher frequencies and the cumulative behavior of their effects on landscape structure and processes is totally significant (Sakieh et al. 2015c). Therefore, according the Golestan Province Land-use Planning Report (2013), informed decision making for future urban constructions and rural development plans in the area should benefit from risk-based land-use planning studies. The results of such analyses can lead to designing a human-dominated landscape with lower levels of exposure to natural hazards.
2.2 Flood hazard mapping The MCE (Multi Criteria Evaluation) methodology was employed for flood hazard mapping in the targeted location. MCE is a grid-based computation method on a collection of environmental parameters that affect land suitability (or sensitivity) for a particular utility. During MCE, multiple layers of environmental elements are employed and these layers contain values in different ranges according to various levels of measurement. Thus, by applying the theory of fuzzy sets (Zadeh, 1965), the map layers could be normalized (standardized). Based on such fuzzification attempts, the modeler can recognize and include the inherent uncertainty of the database and also address different viewpoints on the relative importance of the adopted criteria. 8
AHP (Analytic Hierarchy Process) is another method used through the MCE process (Saaty 1980). AHP substantiates a basis for pair-wise comparisons among several criteria maps and assigns a relative weight to each criterion based on matrix calculations. Based on data availability limitations and according to similar researches in the research location, a series of six layers including Topographic Wetness Index (TWI), ordered rivers network (derived from the Strahler technique), elevation, distance to rivers, streams density and maximum 24-hour precipitation were considered for flood hazard mapping in the research area (Golestan Province Land-use Planning Report 2013; Sakieh et al. 2015c; Sakieh and Salmanmahiny 2016). AHP-derived weights were obtained from the Golestan Province Land-use Planning Report (2013) in which several experts and local stockholder were interviewed to assign the relative weights of importance for each layer (Table 1). These layers were obtained from the Gorgan University of Agricultural Sciences and Natural Resources, which is the leading institute for conducting land-use studies in the Golestan Province. The spatial resolution of the data layers was 30-m and these maps have been implemented as part of information for undertaking the comprehensive land-use planning study of the Gorgan Township in 2013 (Golestan Province Land-use Planning Report 2013). The TWI is an indicator of the steady state wetness. It is conventionally applied to study the effect of topographic control over hydrological processes (Sørensen et al. 2006). The TWI is the resultant behavior from both the slope and the upstream influential area per unit width 9
orthogonal to the flow direction. This metric is formulated for hillslope catenas. The TWI is greatly related to soil characteristics such as depth of horizon, percentage of silt content, phosphorus and the amount of organic matter. The TWI can be applied for a variety of purposes such as investigating the scaling effect on hydrological processes, detecting hydrological streams for geochemical modeling and identifying biological processes. This layer was linearly standardized through a monotonically increasing function. Applying the Strahler method (Strahler 1957), the ordered rivers network layer was produced. According to this method, each segment of a stream or a river within a river network is regarded as a node in a tree, with the immediate next segment as its parent. When two firstorder streams intersect each other, they produce a second-order branch. When two second-order streams join together, they yield a third-order stream. Those streams possessing lower order and crossing a higher order stream do not modify the order number of the higher stream. Consequently, if a stream with first order intersects a second-order stream, it persists as a second-order stream. This layer was standardized based on a monotonically increasing fuzzy function. Applying a user-defined fuzzification scheme with a decreasing trend, the elevation map layer was fuzzified. In addition, using a J-shaped fuzzification method with a decreasing trend, the layer of distance to rivers was standardized. Streams density layer was prepared applying the moving window analysis with a filter size of 5 × 5 pixels. Accordingly, this layer was also
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fuzzified such that locations with higher densities of streams were more sensitive to flood hazard. Based on a point data collection related to maximum 24-hour precipitation in our study area, first a continuous surface of precipitation was generated using the Inverse Distance Weighting (IDW) interpolation method. At the next step, the layer was fuzzified through a monotonically increasing function. Ultimately, the standardized layers were integrated based on the Weighted Linear Combination (WLC) map overlay procedure. The WLC equation for flood hazard mapping is as follows:
n MCE for flood hazard mapping = W i X i i 1
C i
(1)
where Wi is the AHP-derived relative weight of importance for the map layer i, Xi stands for the fuzzified (normalized or standardized) map layer i, refers to the multiplication operator and Ci is the constraint i. Accordingly, greater scores derived from this equation indicate higher levels of flood hazard.
2.3 Distance-dependent statistical modeling between urban morphology and propagation of flood hazard values Distance-dependent relationships between morphological features of human settlements and their corresponding flood hazard values across a series of hierarchical buffer zones (buffer sizes of 500m, 1,000m, 1,500m and 2,000m around urban patches) were considered for statistical 11
modeling. In other words, average flood hazard values (dependent variable) around human settlements and across multiple buffer rings were extracted and landscape configuration and composition metrics of the corresponding settlements (independent variables) were also quantified. Under such conditions, a distance-dependent analysis can be undertaken that reveals associations between landscape features of built-up areas and their exposure to different levels of flood hazard. Referring to the main objectives of the current research, it has been decided to investigate only those man-made surfaces that are of medium physical size in the study area (between 0.1 and 0.95 sq.km). This premise is in accordance with the historical development patterns in the research location (Mahiny and Clarke 2012, 2013; Sakieh et al. 2015c; Sakieh et al. 2016b). In this regard, the Gorgan Township has experienced a growth pattern with dispersed and scattered spacing of impervious surfaces across the landscape. This pattern of sprawl has resulted in frequent and remote built-up areas that are disconnected to either each other and or to the major urban center in the area (the Gorgan City). These settlements are distributed across the landscape with low density, low connectivity and diffusive patterns (Sakieh et al. 2015c; Sakieh et al. 2016b) and this morphology is projected to persist through the forthcoming years (Mahiny and Clarke 2013, 2013; Sakieh et al. 2015c). In addition, new settlements with huge physical size are nearly impossible to be suddenly constructed. Built-up clusters of insignificant physical size (very small) were also removed from further statistical analyses because these surfaces lack a meaningful interactivity with their vicinities (Sakieh et al. 2016b). Regarding the importance of built-up centers with medium physical size, it is noteworthy that these patches possess higher frequency compared to other urban cores of bigger or smaller 12
size. In addition, these surfaces are important causes of landscape fragmentation since they are distributed throughout the study location (Sakieh et al. 2015c). Compared to main and big urban centers, medium-sized human settlements have growth rates of higher pace and they are home to considerably large populations. Due to factors such as synergism, cumulative effects, non-linear feedbacks and feedback loops, their interactivity with their surroundings and the entire of the study area is fully significant (Wu 2014). The concept of interactivity in this context indicates a sequence of feedbacks and feedback loops between built-up structures and their relevant natural hazard parameters. Namely, vulnerability of built-up structures (composition plus configuration) to natural hazards could be defined as a function of their morphological attributes. On this basis, isolated and scattered built-up areas with medium physical size have various functions in response to different environmental parameters and natural hazards. Such areas play a critical role in landscape fragmentation of the area that makes ecologically-valuable lands to be downsized and loose their ecological functions. In addition, they are exposed to higher levels of natural hazards since they are not connected to the main urban network in the area (which can provide support in case of an emergency situation). Under such conditions, a deep understanding of urban morphology in the area could be very informative and insightful for adopting appropriate mitigation and preventive plans against possible natural hazards. According to the previous study in the targeted location (Sakieh et al. 2016b), effective territory of each settlement with medium physical size is reported to be 1,000m. This threshold was expanded to lesser and higher distances (500m, 1,500m and 2,000m), which allows distance13
dependent analysis of the relationships between urban morphological features and propagation of flood hazard values. Besides, the size of buffer zones permitted exploration of the entire of the study area in a hierarchical manner. The results of such analysis can finally provide detailed implications for designing an urban landscape with lower exposure to natural hazards. Therefore, average flood hazard values within multiple buffer rings around 45 settlements were extracted (dependent variables). Afterwards, a number of widely-implemented landscape indices demonstrating fundamental and important ecological properties of the area were also computed for all of 45 clusters of human settlements (Table 2). These indices were employed as predictive parameters of flood hazard values and were selected according to the following reasons: 1. These measures reveal principal aspects of the landscape composition and configuration (areal extent, physical size, connectedness and isolation of human settlements); 2. These indices are simply comprehended by different users and easily interpreted by different modelers; and 3. They could generate reliable outputs when these metrics are jointly employed to characterize composition and configuration attributes of a landscape structure in terms of characteristics such as patch spatial heterogeneity and connectedness. The Fragstats computer program version 4.2 (McGarigal et al. 2012) was implemented to compute landscape indices. 14
Fig. 2 shows the overall steps undertaken in the present research.
3. Results 3.1 flood hazard mapping results According to the MCE procedure and the WLC map integration method, the resultant flood hazard layer has been generated (Fig. 3). Referring to Table 1, layers including TWI, distance to rivers and ordered network of rivers received higher relative weights of importance indicating that these layers are important parameters of the landscape for its sensitivity to flood hazard. Accordingly, as depicted in Fig. 3, closer locations to rivers network are highly correlated with higher hazard values. It should be noted that the main urban core and urban satellite centers are greatly threatened by flood hazard in the area and in case of any emergency situation they would undergo serious damages. There are also a series of separated and distant settlements across the entire of the study area. In this regard, it is important to mention that these isolated locations are not connected to the main urban center and this matter can reduce the supporting function of major urban cores for smaller and scattered settlements. Specifically, authorities should put special attention to both spatial distribution of flood hazard values in the region and sensitivity of human settlements and their corresponding spatial patterns to flood 15
hazard levels. Therefore, informed management strategies should be devised to prevent sever and irreversible damages to local residents in urban and rural areas.
3.2 Statistical analyses 3.2.1 Distance-dependent statistical analysis between urban morphological attributes and propagation of flood hazard values in the Gorgan Township area Scatterplots portraying the type (positive or negative) and the strength (coefficient of determination or R2) of relationships between flood hazard scores and landscape indices were firstly analyzed to illustrate the separate influence of each metric on the propagation of flood hazard values across multiple buffer zones (Fig. 4). Accordingly, the positive correlation for an explanatory parameter indicates that parameter has a direct effect on flood hazard values, or that land sensitivity to flood hazard increases with the surge of values for that variable; whereas the negative correlation implies flood hazard values decrease with the rise of the value for that parameter. These plots provide an informative vision and an improved understanding regarding the sensitivity of current urban spatial patterns to flood hazard in the Gorgan Township area. Generally, majority of the metrics clearly depict a distance-dependent behavior across multiple buffer rings. In other words, as distance from human settlements becomes further, the effect of urban morphology on propagation of flood hazard values varies.
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In case of the Class Area metric (Fig. 4), there are negative correlations between the size of settlements and their exposure to flood hazard values. Namely, as settlements become bigger in their physical size, they become less exposed to high values of flood hazard. According to R2 values across quadruplet buffer zones, the association between the Class Area metric and flood hazard values becomes weaker as distance from these areas becomes further. Based on this distance-decay behavior, it can be drawn that the interactivity between physical size of human settlements and their exposure to flood hazard values is more meaningful in closer distances and as distance from built-up clusters becomes further, this negative association losses its strength (R2 values across four buffer areas show the trend of 0.18, 0.16, 0.12 and 0.10). A negative relationship and a distance-decay performance were also detected, when considering the association between the Clumpiness Index and the distribution of flood hazard values across multiple buffer rings (Fig. 4). The clumpiness characteristic is computed from the matrix of adjacency that depicts the number with which various pairs of patch categories are located in immediate neighborhood of each other on the map. Accordingly, R2 values decrease as distance form settlements becomes further (0.18, 0.17, 0.16 and 0.15), which indicates urban and rural centers of higher clumpiness are less sensitive to flood hazard in the area. Specifically, the supporting influence of clumpiness metric on the protection of urban clusters against flood hazard is more meaningful in closer areas to built-up locations. The physical connectedness of a given patch type at class level is calculated through the Patch Cohesion Index. In this regard, the association between this metric and flood hazard values 17
across buffer zones first increases and then decreases (R2 values of 0.16, 0.19, 0.17 and 0.16) (Fig. 4). Simply put, as physical connectedness of settlements increases, they become more resistant against flood hazard in the area and this relationship is stronger at 1,000m buffer radius (R2 = 0.19) and across other buffer areas it remains relatively stable (R2 values of 0.16 and 0.17). These patterns indicate physical connectedness of urban and rural centers can make them to be less vulnerable against flood hazard in the area and this is more meaningful, when considering the 1,000m buffer area. Similar to the Class Area and the Clumpiness Index, the Effective Mesh Size metric clearly demonstrates a negative and a distance-decay relationship with flood hazard values (R2 values of 0.18, 0.14, 0.10 and 0.08 across quadruplet buffer rings) (Fig. 4). The Effective Mesh Size metric reveals the likelihood of two nodes in a landscape to be connected. The likelihood value could be transformed into the size of a given area, which is referred to as Effective Mesh Size. According to this metric, as settlements become closer to each other, they become more resistant against flood hazard in the Gorgan Township area. In case of the Aggregation Index, there are also negative and distance-decay relationships between urban morphology and propagation of flood hazard values across several buffer zones (R2 vales of 0.18, 0.17, 0.16 and 0.15) (Fig. 4). Therefore, as settlements become more aggregated, they establish a less vulnerable pattern to flood hazard; however, this association spatially decreases across buffer rings.
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Finally, in case of the Splitting Index, there are totally different results compared to the above-mentioned set of the metrics (Fig. 4). This metric mirrors isolation of human settlements from each other and as urban and rural centers become more distant and less connected to each other, the value for the Splitting Index increases. In this regard, there are positive associations between this metric and exposure of settlements to higher levels of flood hazard in the area. In other words, as urban clusters become more isolated and dispersed with low-density patterns across the landscape, they become more sensitive to flood hazard values in the area. This sensitivity is more meaningful across 1,000m and 1,500m buffer rings (R2 values of 0.19 and 0.18, respectively) since cumulative behavior of flood hazard values can threaten urban and rural centers in a more significant manner. In contrast, there are R2 values of 0.13 and 0.16 across buffer areas of 500m and 2,000m, which indicate these locations are less invasive against isolated urban areas throughout the landscape. 3.2.2 Linear regression analyses between urban morphology and flood hazard in the Gorgan Township area Table 3 represents the data-set used for the regression analyses across quadruplet buffer zones. Accordingly, average flood hazard values around 45 human settlements and across multiple buffer rings were extracted and landscape metrics of the corresponding built-up surfaces were also quantified. Prior to modeling, all dependent and independent variables were evaluated for normality assumption employing the Kolmogorov-Smirnov analysis. At the next step, the 19
Spearman correlation analysis was implemented to study whether there are bivariate associations between average flood hazard values and landscape metrics. Besides, the interrelationships between independent parameters were also investigated to prevent generation of redundant data and magnified outputs. In developed regression models that are based on step-wise approach, the t test was applied to detect meaningful variations in independent parameters’ coefficients. Plus, as a validation method, models’ residuals were examined for their normal distribution through the Kolmogorov-Smirnov test. Linear regression models were finally fitted to the employed variables to study the associations between flood hazard values and landscape indices across multiple buffer areas. Applying the ANOVA (p > 0.01) analysis, the significance of the regression models between dependent and predictive parameters was assessed. The performance of the regression models (model-goodness-of-fit) was also examined employing scatterplots and calculating simple linear regression coefficients on the actual versus predicted scores of flood hazard. The database was evaluated to investigate whether it is consistent with the premise of normal distribution. According to Table 4, majority of dependent and independent parameters possess normal distribution. According to the Spearman correlation analysis results (Table 5), all landscape metrics implemented in this study are significantly correlated with flood hazard values across buffer zones, with some metrics having more powerful linkages than others. For instance, variables including the Aggregation and the Clumpiness indices have stronger linkages to the 20
dependent variable for 500m buffer size, while the Splitting Index demonstrates stronger relevancy to flood hazard values within 2,000m buffer area. In addition, all independent variables across all buffer zones are negatively correlated with flood hazard values. In this regard, the Splitting Index is an exception which is positively related to the dependent variable across buffer rings (Table 5). The results derived from the Spearman correlation analysis between landscape indices are also given in Table 5. Accordingly, majority of the metrics are found to be correlated with each other, however, it has been decided to include all metrics in regression analyses according to the following reasons: 1. The collinearity between landscape metrics was measured by calculating Variance Inflation Factor (VIF) statistic, which prevents data redundancy effect and generation of exaggerated results; 2. These metrics have interpretive value and provides additional knowledge when analyzing their results together; and 3. The standardized beta coefficients (β relative weights) of the metrics were also calculated, and therefore, the modeler can understand in what distances, which type of the metrics (connectivity or isolation) are more performant in predicting flood hazard values.
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According to Table 6 and based on the regression model developed within 500m buffer area, 0.251 of the variation in flood hazard values is jointly explained by the two parameters of the Class Area (p < 0.05) and the Aggregation Index (p < 0.05). Regarding the standardized coefficients (β coefficient) of the metrics in this buffer area, both variables have equal performance (-0.334) in terms of predicting their associated flood hazard values. Regression model within 1,000m buffer zone is formulated through two landscape metrics including the Patch Cohesion Index of (p < 0.05) and the Aggregation Index (p < 0.05), which reflect connectivity and aggregation of human settlements (Table 6). Compared to previous model, this regression model is less successful in explaining flood hazard values around settlements referring to 0.247 of adjusted r2 value. Based on β coefficients of independent parameters in this regression equation, the Patch Cohesion Index has a better explanatory performance compared to the Aggregation Index (-0.339 versus -0.315). Regression model constructed within 1,500m buffer zone (Table 6) is even less successful in predicting flood hazard values compared to the previous models. Accordingly, 0.223 of variation in flood hazard values is jointly described through the Splitting Index (p < 0.05) and the Aggregation Index (p < 0.05). In addition, based on β relative weights of the variables, the Splitting Index is more powerful in predicting flood hazard values compared to the Aggregation Index (0.333 versus -0.297). Finally, regression model formulated within buffer area with 2,000m radius is leastsuccessful in explaining associated flood hazard values (Table 6, adjusted r2 value of 0.203). 22
Similar to the previous model, two parameters of the Splitting Index (p < 0.05) and the Aggregation Index (p < 0.05) were also selected as more influential variables in explaining flood hazard values. In addition, considering β coefficients in this model, the Splitting Index is more performant compared to the Aggregation Index for predicting the variations of the dependent variable (0.316 versus -0.291). For all constructed regression models, the values of VIF reveal that there is no multicollinearity between independent parameters. The performances of all four models were confirmed by assessing models’ residuals for normal distribution. The Kolmogorov-Smirnov analysis illustrated that all models’ residuals possess the normal distribution characteristic (Table 6, p ≤ 0.01). Ultimately, performances of the linear regression models were examined by applying scatterplots (predicted against actual scores of flood hazard) and calculating simple linear regressions (Fig. 5).
4. Discussion Urban growth patterns in the Gorgan Township area, as a growing region, are now dominated by horizontal expansion of human settlements with low-density and scattered patterns across the landscape. Under such conditions, urban areas possess lower levels of connectivity and compactness and isolated and dispersed built-up clusters with medium physical size (not too 23
big and not too small) can be found throughout the landscape. Such patterns can be challenging in terms of the following reasons: 1. These patterns are of low manageability since more connected and compacted arrangements of urban clusters are more desired by city planners for establishing urban infrastructures; 2. Under such patterns, natural lands and ecologically-valuable systems become heavily fragmented and downsized and ecosystems loose their ecological functions to support biodiversity and provide ecosystem services; and 3. According to the results of this study, such patterns of urban landscape are more exposed to natural hazards (e.g. flood hazard) since more connected and aggregated network of urban structures is less sensitive to severe impacts of environmental hazards. Therefore, this study adopted an integrated spatial-statistical approach to explicitly answer the questions mentioned in the Introduction of this research. According to the findings of the present study, there are distance-dependent relationships between morphology and spatial patterns of human settlements and their exposure to different levels of flood hazard in the area. Since landscape structure of the Gorgan Township area is covered by high frequency of built-up regions with medium physical size and low levels of connectivity, informed and sustainable planning perspectives are critically needed to further enhance planning of urban areas and prevent adverse consequences resulted from natural hazards. In this case, urban morphology studies in a spatially-explicit fashion can supply a holistic perspective through which planners 24
can concurrently study, interpret, monitor, model and examine the results of decisions they might adopt. Hence, the landscape ecology approach implemented in this study can improve urban development plans and provide an informed framework for conducting risk-based studies of future circumstances (Sakieh et al. 2016b). There is a high number of rives and surface streams in the Golestan Province and majority of human settlements are located in the vicinity of such resources. In addition, during the last two decades, urban construction activities have been accelerated and riparian and forest ecosystems are now encroached by unplanned and uncontrolled development of urban and rural centers (Sakieh et al. 2015c). Therefore, such heterogeneous pattern of growth has exposed transportation network, urban and rural areas and human lives to a variety of natural hazards. In this regard, the province has experienced a drastic flash flooding event in 2017 that caused huge damage to the transportation system and human properties in rural areas. Isolated and separated rural centers were highly vulnerable to such flash flood event, which was started in very short period of time. Factors such as lack of efficient access to remote areas prevented appropriate support for such locations. In addition, as reported by Sharifi et al. (2012), in August 2001, the worst flash flooding event of the Caspian Sea regions during the past two centuries occurred in the Golestan Province and took 300 lives after a weekend of heavy precipitation and caused a calamity in the province. Majority of damages to human lives were reported to be in suburbs and rural areas that were more exposed to massive flood streams. In contrast, major damages in big cities were mainly related to people properties. In this regard, Sharifi et al. (2012) analyzed the basic drivers of the frequent floods and debris flow occurrence in the area and evaluated the main 25
runoff mechanism of such events. They compared the maximum observed 24-hour precipitation depth and maximum peak discharge at current gauges with their corresponding values in 2001. The results indicated rainfall depth exceeds the historical records. Such factors plus existence of barren lands in the province, steep slopes, heavy precipitation periods, removal of green covers (i.e. pastures and forests) and climate change make the area highly prone to flash flooding events (the flood height in 2001 was between 10 and 15m, while passing through the Golestan National Park). Therefore, according to historical events in the province, flood management policies should be regulated in a way that encourages immediate support for remote rural areas in case of any emergency situation. According to Fig. 4, some important implications can be elicited. Majority of human settlements analyzed in this study have areas (Class Area metric) between 0.11 and 0.50 sq.km (43 patches) and there are only a limited number of urban clusters beyond this threshold (only two patches). It should be noted that the majority group (43 patches) has stronger correlations with high flood hazard values and the minority group (2 patches) is significantly linked to lower levels of flood hazard. On this basis, an area threshold might be detected such that smaller settlements should meet that area threshold to prevent dramatic exposures to natural hazards. In case of this study, urban locations with areas lower than 0.3-0.5 sq.km suddenly become highly sensitive to flood hazard in the area and bigger areas demonstrate higher resistance against flood hazard.
26
Taking the results of the present study into account, it is important to differentiate between growth patterns of urban areas with enormous physical size and those urban regions with medium size. On this basis, compact and infilling growth patterns are recommended for major urban cores in the area that passed the area threshold mentioned above. In contrary, in case of urban areas with medium physical size, it is important to enable theses patches to experience linear and outward growth patterns. Under such conditions, human settlements can grow additional edges and those built-up areas located in closer distances to each other have an opportunity to merge together and increase their connectedness and aggregation. Consequently, by joining isolated urban clusters to each other, an opportunity is provided to decrease their sensitivity against natural hazards. This is because remote settlements under such patterns have improved accessibility to major urban centers that provide support in case of a flash flooding event. In this regard, the findings of this study are in accordance with those of derived from Sakieh et al. (2015c), Sakieh et al. (2016b) and Sakieh and Salmanmahiny (2016) that conducted previous researches in the area. They mentioned management of medium-sized human settlements in terms of their composition attributes (e.g. area) and configuration properties (e.g. connectedness and isolation) through the upcoming decades can result in an urban landscape with higher urbanization suitability index, improved landscape aesthetics and enhanced protection of agricultural fields and natural resources. According to regression models developed across hierarchical buffer zones, it was revealed that both composition (e.g. Class Area) and configuration (e.g. Aggregation Index and Splitting Index) of human settlements can influence their vulnerability to flood hazard. In this 27
case, the Class Area, as the only composition metric used in this study, is the more important metric in closer distances to urban and rural centers (buffer area 500m) compared to other configuration-based indices. Simply put, in closer distances to built-up areas it is the structure and physical size of the patches that influence their exposure to natural hazards and as distance from settlements becomes further, configuration metrics become more important and explain resistance of built-up locations against flood hazard. Specifically, within 500m and 1,000m buffer areas, those configuration metrics indicating connectivity and aggregation of built-up centers demonstrate higher predictive power (e.g. Aggregation Index). In contrast, as distance from settlements becomes further, configuration metrics reflecting isolation and fragmentation of urban regions (e.g. Splitting Index) illustrate higher explanatory power for prediction of flood hazard values. This is an important implication since it reveals how propagation of flood hazard values and their cumulative behavior can impact human settlements. As a recommended strategy for urban and flood management, integrated urban-rural land-use planning system is an appropriate alternative in the Gorgan Township area (Sakieh et al. 2015c). Under such policy, major urban cores in the area that already have violated their carrying capacities in terms of spatial extent, population size and ecological footprints should highly decrease their outward growth cycles and their future development trajectories should be inward and under the concept of compact growth. In contrast, built-up patches of medium spatial extents in rural areas can receive higher pressures from urbanization process. These centers have higher rates of expansion and they are less interactive with their vicinities. This matter implies that these regions have lower ecological footprint and they are anticipated to receive major population 28
growth during the upcoming decades (Sakieh et al. 2015c). Therefore, building a new countryside under the policy of polycentric urban management is recommended for the Gorgan Township area. Spontaneous expansion of rural lands is permitted to take place but their spatial extents should be monitored to make them less sensitive to natural hazards. Therefore, formation of a polycentric rural land growth is a proven and sustainable way, which might contribute to an urban dominated landscape with lower exposure to natural hazards. Demonstration projects for adopting the polycentric planning system in rural areas with large populations is suggested to evaluate the functionality of the results derived from this study (Xi et al. 2012). Legislation related to rural land development plans with strategy implementation should also be considered. For example, low-density and scattered dispersion of rural areas with very small physical size are major barriers against farmland protection, nature conservation and effective management natural hazards. Thus, a framework for land law enforcement could be devised and employed in the Gorgan Township area.
5. Conclusions In this research an integrated spatial-statistical approach was adopted to understand the effect of spatial patterns on the vulnerability of urban areas to flooding. Flood hazard layer of the study area was firstly generated using the MCE method. Then, urban land-use map of the study area was overlaid with the flood hazard surface and the relationships between spatial patterns of human settlements and their exposure to flood hazard were analyzed. Multiple linear regression models across multiple buffer zones were developed to model the linkages between composition 29
and configuration attributes of the settlements and their adjacent flood hazard values. According to the results, both composition and configuration of human settlements have meaningful influence on the vulnerability of urban areas to flood hazard and such relationships tend to follow a distance-dependent pattern. In other words, as urban areas become bigger in size and more connected and compacted in pattern, they become less vulnerable to flood hazard in the area. Such findings provide valuable planning implications for informed flood management and urban land-use planning at a regional scale and offer innovative alternatives for sustainable development of a growing urban landscape. Management of the Gorgan Township landscape under the strategy of rural development and integrated urban-rural land management is a recommended policy for flood hazard management. These findings of this study could be regarded as cognitive viewpoint that connect the modelling process with practical planning attempts. Since urban environments in Iran are now undergoing heterogeneous and complex growth patterns, there is a crucial need for realistic and practical solutions based on local characteristics and growth patterns of a location. Improved performance of planners and governmental authorities, who normally determine the future urban growth directions in Iran, is largely dependent on leading development profiles in those directions with lower exposure to different natural hazards. Finally, it is recommended to evaluate the designing implications outlined in this paper in a real-world planning process and investigate how these planning efforts could support sustainability of an urban-dominated landscape at different spatiotemporal scales.
30
Acknowledgments The author highly appreciates the support by the staff of the Golestan Provincial office and Dr. Abdolrassoul Salmanmahiny in providing him with the required data.
31
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Table 1 Criteria raster layers and their corresponding AHP-derived weights employed to produce flood hazard surface in the Gorgan Township Area
Criteria Topographic wetness index Distance to rivers Ordered rivers network Elevation Streams density Maximum 24-hour precipitation Inconsistency Ratio
AHP-derived relative weight of importance 0.350 0.275 0.195 0.080 0.070 0.030 0.0400
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Table 2 Landscape metrics (independent variables) employed to quantify the linkages between flood hazard scores and morphological characteristics of built-up clusters (McGarigal et al. 2012) Landscape indices (acronym)
Equation
Class Area (CA)
a
n
ij
j 1
Range (unit)
1 10, 000
CA> 0, with no limit (hectares)
n
Effective Mesh Size (MESH)
a j 1
2
ij
A Patch Cohesion Index (COHESION)
n p ij j 1 1 n p ij aij j 1
Aggregation Index (AI)
g ij 100 max g ij
Clumpiness Index (CLUMPY)
Splitting Index (SPLIT)
Ratio of cell size to landscape area ≤ MESH ≤ total landscape area (hectares)
1 10, 000 1 1 1 . 100 A
g ii Given G i n g ik min e i k 1
A2
aij 2 j 1
Areal extent, dominance Subdivision of built-up patches
0 ≤ COHESION < 100 (none)
Physical connectivity of urban clusters
0 ≤ AI ≤ 100 (percent)
Aggregation of built-up patches
-1 ≤ CLUMPY ≤ 1 (none)
G i Pi P for G i Pi & Pi 5 else i G i Pi 1 P i
n
Implication
1 ≤ SPLIT ≤ frequency of cells in the landscape area squared (none)
Frequency and connectedness of built-up patches
Isolation and fragmentation of urban clusters
Table 3 Average flood hazard values derived from multiple buffer rings around urban clusters and implemented landscape metrics to quantify
38
morphological attributes of urban patches Average flood hazard values derived from multiple buffer rings (dependent variable) Patch Code 500m 1000m 1500m 2000m 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 Mean Std. Dev.
76.92 81.54 166.13 124.36 114.32 137.58 155.65 143.37 181.86 149.22 86.93 168.37 193.30 185.21 181.47 107.09 127.25 122.52 190.68 151.69 87.93 184.03 172.77 79.78 99.30 178.72 107.10 184.96 144.81 76.10 199.72 91.48 192.23 171.02 194.44 198.42 145.37 198.45 147.09 198.44 165.64 138.51 162.49 177.70 176.50 149.30 39.37
87.60 90.58 167.82 147.90 118.24 154.92 150.63 149.61 145.54 119.13 97.62 144.01 169.59 165.54 161.78 113.89 101.02 127.12 178.81 152.32 88.26 158.59 156.74 78.70 99.93 172.40 106.49 184.84 151.05 72.12 183.16 91.56 178.08 183.89 181.33 203.47 152.39 194.11 123.16 193.51 161.31 137.11 175.07 192.36 182.88 145.47 36.08
90.83 100.16 158.76 160.66 124.05 155.12 152.05 154.59 113.26 100.37 98.97 119.85 154.17 142.31 162.60 124.33 98.42 131.71 190.67 143.60 97.51 143.07 130.18 78.84 106.99 163.67 110.25 183.74 156.33 71.93 171.22 96.30 161.46 185.14 162.99 195.84 155.82 187.85 107.10 174.35 155.74 134.70 176.43 182.13 181.18 141.05 33.44
89.36 126.44 146.70 156.83 124.73 139.13 149.17 153.81 105.70 88.97 104.07 112.73 142.10 105.21 174.53 138.56 110.54 137.00 188.52 148.06 104.55 132.16 105.29 84.36 113.54 153.22 131.02 184.41 161.07 77.18 160.22 110.49 149.65 183.36 145.36 186.21 154.77 182.11 106.58 158.96 150.61 132.80 172.13 168.33 175.88 138.36 30.19
Landscape metrics as quantitative measures of urban morphology (independent variables) Patch Class Clumpiness Effective Aggregation Cohesion Splitting Index Area Index Mesh Size Index Index 95.73 0.88 97.40 0.0219 19191191.04 88.06 85.20 0.85 97.17 0.0172 24437615.17 85.25 50.70 0.72 96.25 0.0061 68415053.05 71.61 49.89 0.88 96.21 0.0059 70655980.63 87.83 47.01 0.80 96.08 0.0053 79584309.36 80.16 41.79 0.88 95.82 0.0042 100723891.5 88.01 41.25 0.90 95.79 0.0041 103380231.8 90.03 38.73 0.79 95.63 0.0036 117282049.5 78.85 35.30 0.81 95.41 0.0030 141122520.3 80.51 33.59 0.81 95.28 0.0027 155865786.1 80.76 32.87 0.93 94.91 0.0025 165470847.3 93.05 32.69 0.72 95.21 0.0025 164571719.8 72.34 31.16 0.73 94.90 0.0022 186451696.0 73.39 30.53 0.72 95.02 0.0022 188698766.6 71.61 30.17 0.70 94.99 0.0022 193231908.7 70.30 30.08 0.73 94.98 0.0022 194390718.1 72.74 28.10 0.81 94.71 0.0019 224203913.8 81.29 26.57 0.82 94.49 0.0017 252588155.9 81.98 26.48 0.87 94.50 0.0017 252596982.5 87.16 26.48 0.82 94.62 0.0017 250884480.4 81.92 26.48 0.81 94.62 0.0017 250884480.4 80.65 25.13 0.83 94.46 0.0015 278586489.8 83.02 24.86 0.81 94.43 0.0015 284675632.1 80.89 24.86 0.88 94.43 0.0015 284675632.1 88.42 23.15 0.78 93.69 0.0012 354865092.7 78.38 21.34 0.90 93.95 0.0011 386075076.1 89.62 20.98 0.81 93.90 0.0010 399444656.4 80.69 20.89 0.70 92.06 0.0007 566022419.9 69.75 20.08 0.73 93.75 0.0010 436072532.2 72.60 18.55 0.89 93.48 0.0008 511015433.9 89.30 18.19 0.87 93.41 0.0008 531454047.4 87.47 18.10 0.80 93.39 0.0008 536755301.9 80.43 17.83 0.75 90.46 0.0004 1070886467 75.48 17.83 0.82 93.18 0.0008 558759364.9 82.29 17.74 0.76 92.77 0.0007 582019135.0 76.44 17.02 0.71 93.17 0.0007 607078495.9 70.86 15.76 0.80 91.47 0.0004 937749230.3 79.57 15.58 0.74 92.84 0.0006 724563164.5 74.29 14.68 0.88 92.61 0.0005 816193720.2 88.33 13.96 0.78 92.21 0.0005 914342073.3 78.25 13.60 0.71 92.30 0.0004 951074556.0 71.48 13.51 0.79 92.28 0.0004 963797820.1 79.27 13.24 0.69 92.19 0.0004 1003537922 68.77 12.43 0.86 91.93 0.0004 1138702528 86.11 11.80 0.68 91.70 0.0003 1263647279 68.20 28.26 0.80 94.09 0.0026 429036141.53 79.94 16.96 0.07 1.57 0.004018 341771877.60 6.78
39
Table 4 Results of normality test for flood hazard values (based on different buffer sizes) and landscape metrics according to Kolmogrov-Smirnov test (df = 45) Kolmogorov-Smirnov Variable Statistic Sig. Flood Hazard (500m) 0.128 0.064** Flood Hazard (1000m) 0.128 0.061** Flood Hazard (1500m) 0.142 0.024* Flood Hazard (2000m) 0.106 0.200** Class Area 0.177 0.001 Effective Mesh Size 0.287 0.000 Patch Cohesion Index 0.120 0.111** Aggregation Index 0.101 0.200** Clumpiness Index 0.101 0.200** Splitting Index 0.197 0.000 * Normal distribution of the variable at 0.05 significance level ** Normal distribution of the variable at 0.01 significance level
40
Flood Hazard
Class Area
Effective Mesh Size
Patch Cohesion Index
Aggregation Index
Clumpiness Index
Splitting Index
Table 5 Spearman correlation analysis results based on multiple buffer sizes around urban patches to represent bivariate associations between flood hazard (dependent variable) and landscape indices (shaded rows) and to reveal interrelationships between landscape indices as independent parameters (n = 45)
Flood Hazard (500m)
1
-0.347*
-0.356*
-0.359*
-0.395**
-0.395**
0.362*
Flood Hazard (1000m)
1
-0.460**
-460**
-469**
-0.411**
-0.411**
0.474**
Flood Hazard (1500m)
1
-0.427**
-0.424**
-0.433**
-0.381**
-0.381**
0.422**
Flood Hazard (2000m)
1
-0.405**
-0.402**
-0.411**
-0.381**
-0.381**
0.418**
1
0.989**
0.967**
0.279
0.279
-0.990**
1
0.989**
0.297*
0.297*
-0.998**
1
0.285
0.285
-0.988**
1
1.000**
-0.292
1
-0.292
Class Area Effective Mesh Size Patch Cohesion Index Aggregation Index Clumpiness Index Splitting Index
1
* 0.05 statistical significance level (two-tailed) ** 0.01 statistical significance level (two-tailed)
Table 6 Descriptive results of developed linear regression models between flood hazard values (dependent variable) and landscape indices of urban clusters (independent variable) across multiple buffer rings Buff er widt h (m) 500
Statistics
Variable
KolmogorovSmirnov
Coefficien Depende nt Flood Hazard
Independent
Constant
Class Area
t (β)
326.108 -0.776 (0.334)
Adjus 2
S.E.
r
ted r2
F
Sig.
t
Sig.
61.38
0.2
0.251
8.3
0.001
5.3
0.000
3
85
79
**
13
**
-
0.018
1.0
2.4
*
85
0.315
41
VIF
Statist ics 0.097
Sig.
0.1 50
58
100
Flood
0
Hazard
Aggregation
-1.937 (-
Index
0.334)
Constant
1012.184
Patch Cohesion Index
150 0
Flood Hazard
200 0
Flood Hazard
-7.790 (0.339)
Aggregation
-1.674 (-
Index
0.315)
Constant
244.224
Splitting Index
008
2.4 56
284.9
0.2
14
81
0.247
06
**
53
**
61 0.733
2.2 82
8
58
Index
0.297)
Constant
229.790
39
2.790E008
0.000
(0.316)
Aggregation
-1.293 (-
Index
0.291)
09
0.028
1.1
*
09
**
80
**
2.3
0.021
1.0
94
*
98
0.039
1.0
*
98
0.116
0.003
4.4
0.000
91
**
03
**
2.2
0.030
1.0
40
*
98
0.046
1.0
*
98
2.0 60
0.1 30
6.5
0.628
*
20
35
6
1.1
0.000
2.1
0.203
0.018
4.2
-
0.1 50
0.002
0.686
0.2
0.091
7.3
0.000
52.18
85
0.001
2.4
0.223
*
3.5
3.165
0.2
1.0
0.001
-
57.05
0.018
8.2
(0.333) -1.466 (-
Index
0.789
3.260E-
Aggregation
Splitting
-
0.091
0.1 50
The unstandardized regression multipliers and standardized multipliers (β, given in the parentheses) are provided. β multipliers are employed to identify the relative importance of explanatory parameters. Higher scores of the β multiplier imply its more relative importance and explanatory ability in describing variations in flood hazard scores. * 0.05 statistical significance level (two-tailed) ** 0.01 statistical significance level (two-tailed)
42
Fig. 1. Gorgan Township area location across Golestan Province, northeastern Iran
43
44
Fig. 2. Overall modeling steps undertaken in this study to analyze the effect morphological attributes on urban areas susceptibility to flood hazard
Fig. 3. MCE-derived flood hazard surface in Gorgan Township area overlaid with urban land-use map of the year 2013
45
Hierarchical buffer zones computed around human settlements Buffer width 1000m
Buffer width 1500m
Buffer width 2000m
Clumpiness Index (none) Patch Cohesion Index (none) Effective Mesh Size (hectare) Aggregation Index (percent) Splitting Index (none)
Landscape metrics calculated for human settlements (unit)
Class Area (hectare)
Buffer width 500m
Fig. 4. Bivariate linkages between calculated landscape measures of built-up clusters (x axis) and average flood hazard values (y axis) derived from hierarchical buffer zones around
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human settlements (n = 45, p < 0.05)
Fig. 5. The observed against predicted scores for flood hazard based on multiple buffer sizes around human settlements in Gorgan Township area (n = 45, p < 0.05)
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