An integrated and quantitative vulnerability assessment for proactive hazard response and sustainability a case study on the chan may lang co gulf area, central vietnam

Sustain Sci DOI 10.1007/s11625-013-0221-9 CASE REPORT An integrated and quantitative vulnerability assessment for proactive hazard response and sustainability: a case study on the Chan May-Lang Co Gulf area, Central Vietnam Mai Trong Nhuan • Le Thi Thu Hien • Nguyen Thi Hoang Ha • Nguyen Thi Hong Hue Tran Dang Quy • Received: December 2012 / Accepted: 27 May 2013 Ó Springer Japan 2013 Abstract A natural factors-based approach was developed to examine proactive responses to hazards and improving sustainability on the Chan May-Lang Co Gulf area, Central Vietnam The approach was based on a weight-of-evidence method within an integrated and quantitative vulnerability assessment in which the spatial relationship between a set of evidential factors (lithology, distance to the coastline, altitude, slope, aspect, drainage, wind speed during storms, and land use and cover) and a set of hazard locations was combined with the prior probability (total vulnerability) to obtain the posterior probability of hazard occurrence The result showed that 44.3 % of the study area had high to very high total vulnerability, due to the high density of vulnerable objects and frequency of severe damage from typhoons, floods, landslides, and erosion The result also demonstrated that the contribution of natural factors was directly proportional to total vulnerability in approximately 75 % of the study area, indicating a high dependence of vulnerability on natural factors In the remaining areas, low contributions were found in the high and very high vulnerability areas dominated by high anthropogenic activities In contrast, natural Handled by Soontak Lee, Yeungnam University, Korea M T Nhuan (&) Á N T H Ha Á T D Quy Department of Geo-environment, VNU University of Science, 334 Nguyen Trai, Thanh Xuan, Hanoi, Vietnam e-mail: nhuanmt@vnu.edu.vn L T T Hien Institute of Geography, Vietnam Academy of Science and Technology, Hanoi, Vietnam N T H Hue VNU Sea and Islands Research Centre, Vietnam National University, Hanoi, Vietnam factors were important contributors to total vulnerability in areas characterized by dense vegetation, consolidated rocks, and altitude greater than 300 m, reflecting high natural resilience The present study demonstrated that a proactive approach may provide appropriate measures to mitigate hazards and to increase the sustainability of the study area Keywords Chan May-Lang Co gulf area Á Hazard Á Proactive response Á Sustainability Á Vulnerability assessment Á Weight of evidence Introduction The Vietnam coastal zone plays an important role in socioeconomic development, territorial sovereignty protection, and maintenance of biodiversity in Vietnam However, this region is vulnerable to natural hazards (e.g typhoons, floods, coastal erosion, salinity intrusion, and landslides) and anthropogenic impacts (e.g population growth, excessive aquaculture, and overfishing) These threats have the potential to limit sustainable development in the Vietnam coastal zone, through severe and widespread damage to human life and property as well as degradation of natural resources and the environment (Nhuan et al 2011a) Vulnerability and sustainability are two contrasting aspects of a system, in which local vulnerability can affect the system sustainability in a resilience framework (Eakin and Wehbe 2009) Vulnerability is one of the central elements of dialogue in science, decision-making, and sustainability research (Turner et al 2003) Appropriate adaptive and preparedness planning, and mitigation measures implemented at an appropriate time help to reduce vulnerability and the risk from potential hazards, thus 123 Sustain Sci increasing the sustainability of a system (Winograd 2007) Appropriate adaptation and effective mitigation of hazard effects requires a detailed knowledge of the vulnerability of an area to potential hazards (Cutter et al 2000) A number of vulnerability assessment methods have been suggested for particular hazards, such as sea level rise (Torresan et al 2008), storms (Bosom and Jimenez 2011), floods (FAO 2004; Snoussi et al 2008), erosion (Boruff et al 2005), and landslides (Szlafsztein and Sterr 2007; Uzielli et al 2008) Recently, the importance of a multihazard approach to risk management has been emphasized (Kappes et al 2011) However, few studies have presented an integrated approach to multi-hazard assessment (Cutter et al 2000; Kappes et al 2011; Kumar et al 2010; Mahendra et al 2011; Nhuan et al 2009, 2011a, b; NOAA 1999; Pratt et al 2005) Vulnerability has been assessed by qualitative, semiquantitative, and quantitative methods Quantitative methods involve statistical, geotechnical, and artificial neural network methods that reduce subjectivity and are more easily reproduced One quantitative method, a weight of evidence model, uses evidence from previous events to predict the probability of hazards occurring in the study area The relative importance of each line of evidence is estimated by a statistical method, based on the available data (Mathew et al 2007) However, this model is used primarily for vulnerability assessment of landsides, rather than for multi-hazard environments (Barbieri and Cambuli 2009; Mathew et al 2007) An object-related approach creates a clear separation between the biophysical or natural dimension and the socio-economic dimension when assessing vulnerability (Adger 1999; Cutter et al 2000; Nhuan et al 2009, 2011a, b) Almost all studies using such an approach have performed a vulnerability assessment subsequent to hazard occurrence Although such studies provide some useful results, their ability to assess the adaptability of a system and the timeliness of the response to hazards is limited Natural factors such as geology, geography, hydrology and meteorology are important components that influence the vulnerability of a region (Birkmann 2006; Furlan et al 2011; Marchand 2009; Nhuan et al 2009, 2011a, b) Determining the contribution of natural factors to vulnerability by applying the weight-of-evidence method provides a reliable base for assessing and forecasting the vulnerability of a region This proactive, prediction-based approach is a fundamental requirement for outlining appropriate strategies for community response to hazards (Mimura 2008), hazard adaptation, and hazard mitigation The prospect of a proactive approach highlights the need to conduct appropriate research on which this approach is based 123 The objective of the present study was to propose a new approach for assessing and forecasting vulnerability based on natural factors and evidence that can create proactive responses to hazards and thus enhance sustainability Subsequently, the approach developed was applied to determine the contribution of natural factors to the total vulnerability of the Chan May-Lang Co Gulf area, Central Vietnam Proposed measures for hazard mitigation and improvement of sustainability are also discussed Study area The Chan May-Lang Co Gulf area is located in Central Vietnam (Fig 1) It is approximately 711 km2 in area, and is surrounded by 18 communes There are two lagoons (Cau Hai and An Cuu) and two gulfs (Chan May and Lang Co), which are the most popular and important wetlands of the Central Vietnam coastal zone In addition, the study area is a key economic zone in Central Vietnam, as it is on shipping routes to northern and southern Asia Land use in the study area is divided between scattered forest (48.8 %), anthropogenic construction (17.0 %), agriculture (12.1 %), aquaculture (17.4 %), and others (4.7 %) (PLPC 2010) The major igneous rocks are biotite granite, two-mica granite, aplite, pegmatite, and granite (Nhuan and Tien 1993, 2011b) There are four main types of sedimentary materials: marine–river sediments (maQ32), lagoon sediments (bmQ32), and two types of marine sediments (mQ1–2 and mQ32) The sediments are composed primarily of sand, sand–mud, mud–sand, mud, mud–clay, and clay The geomorphology is typically characterized by erosion– denudation relief in the mountain area, and mixed depositional relief of alluvium, deluvium, and proluvium in the coastal plain The study area is located within a distinct monsoon climate zone, with a rainy season from August to January, and a dry season from February to July Annual average rainfall level is 2,800 mm and the annual average temperature is 25 °C The average annual wind speed and maximum wind speed are 1.5 and approximately 40 m/s, respectively The prevailing wind directions are northwest in winter (14–34 %) and south–southwest in summer (10–17 %) Analysis of historical data shows that typhoons, landslides, floods, and erosion are the most frequently occurring hazards and cause the most severe damage (MONRE 2008) Annually, there are 4–5 typhoons and tropical lowpressure storms, causing severe damage to property and loss of human lives (MONRE 2008) For example, Typhoon Tilda struck the Lang Co region on 22 September 1964 with wind speeds of 38 m/s and a storm surge of 1.7 m (MONRE 2008) In addition, the Bach Ma mountain chain in the southwest of the study area affects the regional Sustain Sci Fig Map showing the study area rainfall regime, intensifying the occurrence of hazards For example, in November 1999, severe rains caused a flood and landslides which resulted in property damage in the 3,000 m2 mountain area of the L Tien and L Vinh communes (MONRE 2008) The flood and landslide hazards threatened 50 households, and destroyed roads and infrastructure The study area is representative of the Central Vietnam coastal zone, which is characterised by a contrast between flat lagoons and river plains, and adjacent mountains ranges The Central Vietnam coastal zone is experiencing rapid economic development while facing increasing natural hazards Therefore, an integrated quantitative vulnerability assessment for proactive responses to hazards is crucial to the continued development of the study area and the Central Vietnam coastal zone Methodology Proactive approach Previous vulnerability assessments and reduction measures have used two major approaches: (1) post-event or (2) preevent A number of vulnerability assessments have focussed on the former (e.g Adger 1999; Snoussi et al 2008; Uzielli et al 2008) This approach, shown in Fig 2, is largely considered a passive response, as damage from the event has already occurred In contrast, a proactive approach would provide more effective and active responses prior to any event occurring (Fig 2) Analysis of natural factors in a region can provide evidence for the probability of a hazard occurring (Birkmann 2006; Furlan et al 2011; Marchand 2009) Natural factors that may generate, intensify, or mitigate natural hazards include the geology, geography, hydrology, oceanography, meteorology, and land cover in the region For example, landslides can often be attributed to the local geology, geomorphology, land cover, and drainage (Mathew et al 2007) Similarly, mean tidal range, coastal slope, rate of relative sea level rise, shoreline erosion or accretion rates, and mean wave height, are key indicators of erosion vulnerability (Boruff et al 2005) In addition, Furlan et al (2011) revealed that the geomorphology, geology, pedology, and vegetation are important criteria in assessing natural vulnerability Vulnerability assessments of multi-hazards based on all natural factors are extremely complex Therefore, evidence and damage of major hazards in the study area needs to be assessed and weighted, thus enabling less important factors to be disregarded A deficiency of reliable data and information, which is a problem in developing countries such as Vietnam, can also restrict the assessment of all natural factors In addition, some attribute parameters (e.g., rainfall, geodynamic features) are also disregarded due to the paucity of spatial differentiation in the small study area Therefore, in this study, several major parameters have been selected to assess the natural component of hazard vulnerability (Table 1) This selection was based on evidence from field surveys, existing data, further data analysis, and spatial differentiation of parameters As shown in Fig 2, the contribution of natural parameters to total vulnerability was calculated using the 123 Sustain Sci Fig Formal and proactive approaches in vulnerability assessment Table Parameters used to assess natural dimension vulnerability Natural factors Parameters Hazard/resilience to hazards Calculated methods Geology Lithology Landslide, erosion Classification of rock types based on consolidated levels Geography Distance to coastline Typhoon, erosion Calculation for each cell the Euclidean distance to the closest coastline Altitude Slope Flood Landslide, flood Rank of the terrain elevation 3D analyst: interpolation of slope Aspect Erosion, flood, landslide 3D analyst: interpolation of aspect Hydrology Drainage Flood, landslide Drainage density (km/km2): length of the stream channels per calculated unit area Meteorology Wind speed during storms Typhoon, flood, landslide Classification of wind speed levels corresponding to the wind in the storm Land use and cover Land use and cover Flood, landslide, erosion Classification of land cover and land use patterns weight-of-evidence method The relative contribution of natural parameters is assumed to be constant and is used to estimate the total vulnerability of an area when natural parameters change Total vulnerability assessment Vulnerability is considered as the potential for loss or damage to objects and systems from hazards (Cutter 1996; Cutter et al 2000; Mitchell 1989; Nhuan and Tien 2011b) The vulnerability of natural and social systems has been assessed using three components: danger level of hazards, density of vulnerable objects, and resilience (Nhuan et al 2009, 2011a) It is noteworthy that the level of probability caused by a hazard depends on both the danger of the hazard and the resilience of the system For example, given a particular probability hazard, a region of low resilience 123 will experience more damage than a region of high resilience Damage caused by an event is considered to be a practical and reliable method for weighting evidence in vulnerability assessments In this study, the total vulnerability of the Chan MayLang Co Gulf area was evaluated using the following components: proportion of people evacuated per year, total economic losses, and density of vulnerable objects Each component was then divided into five levels based on the damage caused by hazards (for evacuations and economic losses) or the level of vulnerability (for density of vulnerable objects; Table 2) The number of people evacuated and the economic losses for the period 2004–2010 were determined from existing data and from field surveys conducted in 2010 Vulnerable objects included humans, natural resources, economic assets (agriculture, aquaculture, and tourism), and infrastructure (construction, roads, Sustain Sci Table Classification of vulnerability criteria on the Chan May-Lang Co Gulf area Proportion of people evacuated per year Value Total economic losses (million VND/person) Value Density of vulnerable objects Value \1.03 1 Very low 0–1 1.04–7.32 1–7 Low 1–2 7.33–10.96 7–11 Medium 2–3 10.97–23.68 11–23 High 3–4 23.69–36.83 23–38 Very high 4–5 and houses) Analysis and calculation of total vulnerability, as well as the contribution of natural factors, were performed using ArcGIS 10 Weight of evidence The weight of evidence was based on a log-linear Bayesian model using the prior and posterior probabilities (Jeffreys 1998) The method has been used for mineral potential mapping (Agterberg et al 1990; Bonham-Carter et al 1989) and landslide hazard mapping (Barbieri and Cambuli 2009; Hien 2010; Mathew et al 2007) This approach uses the prior probability of an occurred hazard to find the posterior probability based on the relative contribution of the subject by evidence Prior and posterior probabilities of a hazard (S), given the presence or absence of any binary pattern (Bi or Bi ), are calculated using Eqs and 2: PPrior ẳ PfSg ẳ Npix Hazardị Npix Totalị 1ị and, PfSjBi g ¼ PfS \ Bi g Npix fS \ Bi g ẳ Npix fBi g PfBi g 2ị where Npix (Hazard) and Npix (Total) are the number of pixels affected by the hazard and the total number of pixels in the study area, respectively À Positive and negative weights (wỵ i and wi ) are developed from these conditional probabilities as defined by Eqs and 4: PfBi jSg ẫ wỵ i ẳ loge ẩ P Bi jS 3ị and, wÀ i È É P Bi jS É ¼ loge È P Bi jS ð4Þ The difference between the positive and negative weights is termed the contrast (Cw) for each parameter class and is calculated to reflect the spatial combination between the evidence of vulnerability and the occurrence of the hazard, as shown in Eq (Barbieri and Cambuli 2009): Cw ẳ wỵ i wi 5ị In addition, Cw/S(Cw), where S(Cw) is the standard deviation, provides an indication of the reliability of the relationship calculated between the hazard parameters A higher Cw/S(Cw) value reflects a closer relationship between the hazard and the parameters used in the calculation (Barbieri and Cambuli 2009) In the present study, the spatial relationship between a set of evidential themes and a set of hazard locations is combined with the prior probability (total vulnerability) to derive the posterior probability of hazard occurrence This enables the contribution of natural factors to total vulnerability to be calculated Results and discussion Total vulnerability assessment A total of 755 billion Vietnamese Dong (US $36 million) was lost in the period from 2004 to 2010 as a result of natural hazards in the study area (Table 3; PLPC 2009) The damage from the hazards was scattered throughout the study area The highest economic losses occurred in several communes in the northwest of the Chan May-Lang Co Gulf area (L Bon, L Son, and L Dien communes) However, more than 90 % of the populations in the L Tri, L Tien, and L Co communes were affected by the hazards (Table 3) More than 20 % of the populations of the L Binh, L Vinh, V Hien, and V Hai communes were evacuated each year (Table 3) Total vulnerability is shown in Fig The vulnerability level is classified into levels: very high (4–5), high (3–4), medium (2–3), low (1–2), and very low (0–1) These classes account for 10.0, 34.3, 12.8, 23.8, and 19.0 % of the Chan May-Lang Co Gulf, respectively The result showed that approximately 44.3 % of the study area has high to very high vulnerability levels, encompassing the coastal and the northwestern communes of the Chan May-Lang Co Gulf region (Fig 3) These regions have a high density of vulnerable objects and frequently suffer severe damage from typhoons (L Vinh and V Hai communes), floods 123 Sustain Sci Table Population, affected and evacuated people, and economic loss on the ChanLang Co Gulf area due to natural hazards in the period from 2004 to 2010 Source: PLPC (2009) a US dollar is equal to 20,850 VND (2012) Population (people) Population density (people/km2) Proportion of people affected per year (%) Proportion of people evacuated per year (%) Economic losses (million VND)a No Commune L Bon 14,022 431 6.3 1.05 67,030 L Son 7,665 401 24.5 4.11 71,138 X Loc 2,554 58 82.6 14.15 52,713 L An 13,731 508 51.5 8.68 41,517 L Dien 16,015 139 33.4 5.55 65,056 L Hoa 2,804 86 44.8 7.55 48,964 P Loc L Tri 11,372 8,894 418 141 66.2 92.7 11.09 15.36 44,666 51,867 L Binh 2,650 97 14.3 23.59 33,760 10 L Thuy 13,167 187 35.6 5.73 53,910 11 L Tien 9,051 158 90.5 15.13 46,754 12 L Vinh 26,569 13 L Co 14 6,872 199 22.1 38.04 12,026 114 95.5 15.95 26,704 V Hien 9,145 403 17.9 30.15 22,925 15 V Hai 2,668 433 12.8 21.37 17,343 16 V Giang 5,114 273 61.7 9.95 29,896 17 V My 6,330 779 25.8 4.30 18,115 18 V Hung 8,365 521 31.1 5.16 – – Total 152,445 – 36,234 755,161 Fig Map of the total vulnerability in the period from 2004 to 2010 on the Chan MayLang Co Gulf area (L Vinh and L Tien communes), landslides (L Tien, L Son, X Loc, and L Vinh communes), and erosion (L Vinh and V Hai communes) Conversely, the regions with very low and low vulnerability levels corresponded to areas that have a medium density of vulnerable objects, but have suffered little damage from natural hazards 123 Contribution of natural factors to total vulnerability The weight of evidence is shown in Tables 4, 5, 6, 7, 8, 9, 10, 11 The weight of evidence was calculated for various parameter classes used in the study (Table 1) Sustain Sci Table Weights and contrast values for the lithology Table Weights and contrast values for the distance to coastline Table Weights and contrast values for the altitude Table Weights and contrast values for the slope Lithology Class w? Area (square km) w- Contrast (Cw) Cw/S(Cw) a,am: loam/sandy/pebble-gravel 121 0.6104 -0.1416 0.7520 40.7750 Biotite granite/binary granite 303 -0.6266 0.3762 -1.0027 -62.3451 Gabbro/olivine gabbro/ gabbronorite 25 -0.5826 0.0189 -0.6015 -13.1919 m,bm,vm: sand/calcareous sand/ coral/peat 30 0.7074 -0.0348 0.7422 21.9666 m,m(v): sand/calcareous sand/ coral 11 35 0.7374 -0.0429 0.7802 24.8656 Sandstone/siltstone/shale/ limestone 13 56 0.2614 -0.0236 0.2850 10.8985 Shale/sandstone/conglomerate 14 38 1.7432 -0.1123 1.8556 55.9522 Distance to coastline (km) Class 7.6 to 12.6 3.6 to \7.6 1.8 to \3.6 Area (square km) w? w- Contrast (Cw) Cw/S(Cw) 57 0.3402 -0.0301 0.3704 14.8960 94 -0.3034 0.0448 -0.3482 -16.9047 206 -0.2364 0.0943 -0.3307 -21.7867 0.7 to \1.8 230 -0.0944 0.0446 -0.1390 -9.5261 to \0.7 125 0.6188 -0.1332 0.7520 41.5849 Area (square km) w? w- Contrast (Cw) Cw/S(Cw) Altitude (m) Class \-1 24 0.2189 -0.0081 0.2270 5.8332 -1 to 86 -1.7433 0.1474 -1.8907 -48.9652 to 50 243 0.6277 -0.3920 1.0197 66.7701 50 to 300 190 0.2469 -0.0958 0.3428 21.1597 [300 167 -0.9700 0.2259 -1.1960 -55.5344 Slope (°) Class Area (square km) 0–6 404 w? 0.67 w-0.29 Contrast (Cw) 0.96 Cw/S(Cw) 53.18 6–12 61 -0.10 0.01 -0.11 -3.89 12–20 103 -0.82 0.07 -0.89 -29.96 20–28 98 -2.04 0.12 -2.12 -65.08 28–57.2 66 0.09 0.02 -1.45 -15.75 Lithology Among the lithological classes, the shale–sandstone–conglomerate has the highest w? and Cw values (Table 4), indicating that landslides and erosion could occur, resulting in high vulnerability In contrast, the consolidated rocks composed of biotite granite and binary granite have the lowest w? and Cw values Distance to coastline Previous observations indicate that areas close to the coastline experience more frequent and more intense coastal erosion This parameter contributes significantly to vulnerability in areas located 0–0.7 km from the coastline (Table 5) This result is in accordance with the high frequency of typhoons and erosion and high proportion of people evacuated in the L Vinh and V Hai communes (Table 3) Altitude The w? and Cw values showed a positive correlation with vulnerability at 0–50 m altitude (accounting for approximately 34 % of the study area; Table 6) It is noteworthy that the majority of the population and infrastructure are distributed within this altitude range Therefore, 123 Sustain Sci Table Weights and contrast values for the aspect Aspect (degree according to the north direction) Class Table 11 Weights and contrast values for the land use and cover Contrast (Cw) Cw/S(Cw) 51 -1.8068 0.0861 -1.8928 -36.9889 South (157.5–202.5) 51 0.3967 -0.0332 0.4299 15.9148 Southeast (112.5–157.5) 53 0.2227 -0.0189 0.2415 8.9205 North (337.5–360) 47 0.0466 -0.0034 0.0499 1.6998 North (0–22.5) 66 -0.0462 0.0047 -0.0510 -1.9878 Northeast (22.5–67.5) 123 -0.2111 0.0421 -0.2532 -12.5114 West (247.5–292.5) 82 0.2260 -0.0313 0.2573 11.5568 Southwest (202.5–247.5) 71 0.2653 -0.0315 0.2969 12.6095 80 -0.1468 0.0183 -0.1650 -6.9072 10 81 0.2490 -0.0342 0.2831 12.6949 Northwest (292.5–337.5) Table 10 Weights and contrast values for the wind speed during storms w- Flat (-1) East (67.5–112.5) Table Weights and contrast values for the drainage w? Area (square km) Drainage (km/km2) Class Area (square km) w? 0–1 191 -0.5820 1.1–3.5 191 0.2861 3.6–6.5 112 0.5249 6.6–10 59 10.1–20.5 Wind speed during storms (m/s) Class w- Contrast (Cw) Cw/S(Cw) 0.1804 -0.7624 -41.3772 -0.1131 0.3992 24.7497 -0.1095 0.6344 33.3181 0.5685 -0.0566 0.6252 25.0766 159 -0.4227 0.1081 -0.5308 -27.8593 Area (square km) w? Contrast (Cw) Cw/S(Cw) w- 26.0–26.6 65.50 1.3925 -0.1656 1.5582 63.7094 26.6–27.3 165.01 -0.2696 0.0758 -0.3454 -18.9945 27.3–28.0 480.90 -0.1311 0.2544 -0.3855 -25.0396 w? Cw/S(Cw) 0.0780 -2.8735 -38.4400 -0.8012 0.1385 -0.9397 -46.0253 -0.1479 0.1918 -0.3398 -24.9003 113 1.3047 -0.2326 1.5374 74.0988 48 1.3077 -0.0888 1.3965 45.8469 Class Dense forest 34 -2.7956 Spare forest and afforestation 117 Grass and bush 412 Agriculture/aquaculture/road Human settlement vulnerability is heightened due to the high density of vulnerable objects w- Contrast (Cw) Land use and cover Area (square km) vulnerability The difference between the two studies is attributable to the high population density in areas of slope gentler than 6° in the present study area Slope Aspect Slopes of 0°–6° were found to be a significant contributor to landslides and other hazards (Table 7) This contradicts the results reported by Mathew et al (2007) that slopes under 30° were insignificant in terms of hazard 123 The w? and Cw values are high in regions with southerly, southeasterly, westerly, southwesterly, and northwesterly aspects (Table 8) This pattern indicates that the prevailing Sustain Sci wind direction (northwest in winter and south–southwest in summer) has a major influence on hazard vulnerability Drainage Drainage significantly influences slope stability by controlling toe erosion and the saturation of slope material (Gokceoglu and Aksoy 1996; Mathew et al 2007) The efficiency of the river system also controls the extent of flooding The intensity of hazards increased in areas where drainage density ranged from 3.6 to 10.0 (Table 9), resulting in increased vulnerability This is due to the distribution of these areas within regions of complex topography The distribution of high drainage density in a relatively flat area is considered to minimize the occurrence of flash floods in that area Wind Wind speed during storms contributes significantly to hazard intensity The maximum wind speed in storms occurred most frequently in classes 1–3 The highest w? and Cw values correlated to winds of 26.0 to 26.6 m/s (Table 10), showing that high wind speeds result in high vulnerability This is due to substantial storm damage in areas of high population density and low altitude, without adjacent mountains acting as wind barriers Land use and cover Vegetation plays a crucial role in slope stability and the regulation of surface flow In the absence of other factors, areas of dense vegetation should be less susceptible to landslides and erosion than bare areas The present results showed that the agriculture, aquaculture, roads, and human settlement had the highest contrast values (Table 11), reflecting high vulnerability associated with weakly cohesive materials (Mathew et al 2007) This result was supported by the evidence of landslides observed in the northern L Tien, L Son, and L Vinh communes In addition, these land uses were also classified as vulnerable objects, consequently enhancing their vulnerability The contribution of natural factors to total vulnerability is shown in Fig in which the negative and positive values indicate the low and high contribution The result showed that the contribution of natural factors was directly proportional to total vulnerability in approximately 75 % of the study area (Figs 3, 4) This pattern reflected the fact that vulnerability is highly dependent on natural factors The result also indicated that social resilience was so low that it contributed little to resisting natural hazards in the study area Social resilience remains low as a result of an outdated forecasting system for hazards, low community awareness of hazards, and low income In contrast, social resilience is an important contributor to total vulnerability in developed countries (Boruff et al 2005; Cutter 1996; Harvey and Woodroffe 2008; NOAA 1999) In the high and very high vulnerability areas, two contrast trends of the contribution of the natural factors to total vulnerability were found The first trend showed a high contribution in the L Son, L Binh, southern L Tien, and southern L Tri communes (Figs 3, 4) Natural factors were dominant in regions characterized by dense vegetation, consolidated rocks, and altitude greater than 300 m (Fig 4) This demonstrates the role of natural factors in enhancing natural resilience In contrast, natural factors contributed little to total vulnerability in the regions Fig Contribution of natural factors to total vulnerability on the Chan May-Lang Co Gulf area 123 Sustain Sci dominated by high anthropogenic activities such as the northern V Hai, L Vinh, X Loc, northern L Tien, and northern L Tri communes (Fig 4) The present study clearly demonstrates that natural factors influence the resilience of both natural and socio-economic systems Mangrove and terrestrial forests, mountainous areas, consolidated rocks, and distance from the coast increase natural resilience Low elevation, unconsolidated rocks, high wind speed, and natural hazards decrease natural resilience The location of vulnerable socio-economic objects in these areas of low natural resilience results in low socio-economic resilience Based on this, appropriate measures for proactive responses to hazards can be proposed to reduce this risk of disaster, and increase the sustainability of the study area The results of the present study indicate that proposed measures should aim to increase social resilience Three groups of solutions can be implemented to achieve this, as follows: Natural vulnerability assessment and forecasting-based planning such as sustainable resource use (Adger et al 2005); implementation of sustainable livelihood solutions (e.g the Satoyama–Satoumi model, sustainable economic development models, diverse agriculture, eco-tourism, and community frameworks); locating evacuation channels, technical infrastructure, and social infrastructure in areas of low natural vulnerability; installation of early warning systems in highvulnerability areas; and ensuring that vulnerable communities have access to emergency health services, safe havens, and evacuation channels Management strategies, such as creating and implementing proactive policies for responses to natural hazards; and enhancing sustainability, adaptive management of wetlands, integrated community-based coastal zone management (Nunn and Mimura 2007) and integrated mountainous area management Hazard mitigation plans, policies, and measures based on the results of the present study such as installation of updated early warning systems, policies for proactive mitigation of hazards, afforestation and reforestation of mangrove areas, construction of coastal protection structures, and maintenance of the natural sediment balance (Winchester et al 2007) In addition, community awareness and education campaigns, regular training, and guidance materials should be implemented with reference to natural hazards, disasters, and factors contributing to vulnerability Conclusions Eight natural parameters (lithology, distance to coastline, altitude, slope, aspect, drainage, storm wind speed, and 123 land use and cover) were used to evaluate the influence of natural factors on total vulnerability The contribution of natural factors was directly proportional to total vulnerability in approximately 75 % of the study area This result indicated that the vulnerability was highly dependent on natural factors In contrast, low contribution was found in the high and very high vulnerability areas dominated by high anthropogenic activities The results of this study highlight the need for increasing resilience and sustainability of natural and socio-economic systems by implementing management practices, sustainable resource use planning, and proactive hazard mitigation measures Future research should focus on forecasting and verifying vulnerability based on natural and socio-economic factors Using a proactive approach to hazard response will help to increase the resilience and sustainability of important ecosystems such as coastal waters, marine ecosystems, and mangrove and terrestrial forests 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sustainability, adaptive management of wetlands, integrated community-based coastal zone management (Nunn and Mimura 2007) and integrated mountainous area management Hazard mitigation plans, policies, and. .. Gulf area, Central Vietnam Proposed measures for hazard mitigation and improvement of sustainability are also discussed Study area The Chan May- Lang Co Gulf area is located in Central Vietnam (Fig