Spatial Analysis of Climate Risk in the West Bank, Palestine

Abstract

In the developing countries (e.g., Palestine) a reliable assessment of climate vulnerability, exposure, and consequently risk is a key step in developing successful adaptation and mitigation plans. This study aims to examine the spatial distribution of climate risk across the different governorates of the West Bank (Palestine) by assessing climate-risk exposure. A GIS-based Multi-Criteria Decision Analysis approach was employed to estimate climate exposure across the West Bank governorates. Additionally, sensitivity analysis is used to explore the impact of indicator weight on the final climate-risk map. The climate-risk map was subsequently developed based on the exposure map, classifying the governorates into five risk categories: very high, high, moderate, low, and very low. This analysis revealed that 42% of the West Bank population resides in areas classified as having high to very high climate exposure, which corresponds to approximately 39% of the total land area. Conversely, about 21% of the West Bank area is categorized under low to very low risk conditions. By measuring risk based on this exposure, and considering vulnerability, it was determined that 82% of the population lives within areas identified as high to very high zones, underscoring the significant climate risk of populated regions. This study offers the first spatially explicit climate-risk assessment for the West Bank, applying a widely accepted approach that integrates vulnerability and exposure components. The results provide critical insights to inform targeted adaptation and mitigation efforts, supporting decision-makers in enhancing climate resilience across the region.

1. Introduction

Worldwide, climate change is considered one of the most serious challenges facing the international community [1]. Its adverse impacts can no longer be ignored [2,3]. The growing concern over climate change stems from its role in accelerating the occurrence of extreme weather events [4,5], with their intensity, frequency, and severity expected to increase in the coming years [6]. Worldwide, climate change has disrupted ecosystems and human livelihoods, affecting people, flora, and fauna alike [7,8]. Managing its impacts poses a significant challenge, particularly for low- and middle-income countries, such as Palestine [9]. Climate change affects a wide range of sectors, including agriculture [10,11], food security [12], land cover [13], forestry [14], hydrology [15,16], economics [17,18], biodiversity [19], and the supply of renewable energy [20].

Over the past two decades, approximately 11,000 extreme weather events have been recorded globally, affecting around 94.9 million people and resulting in 475,000 fatalities [21]. Since the mid-19th century, the Earth has entered a prolonged warming phase, marked by rising global temperatures and an increased frequency of extreme events such as floods and droughts. Global temperatures have already risen by 0.6 °C since the beginning of the Industrial Revolution [22]. Projections suggest that by the end of this century, global temperatures could increase by 1.8 °C to 4 °C [23], which could lead to widespread water and food shortages, potentially affecting billions of people in the coming decades [24].

The Middle East (e.g., Palestine) is highly vulnerable area to the climate change [25]. It is predicted that the temperature will increase by 2–3 °C by 2050. Additionally, it is expected to increase by 3–5 C by 2100. The spatial and temporal distribution of rainfall will change significantly and is expected to be more intense and less frequent [26]. Accordingly, it is classified as a climate change hotspot in future climate change projections [27].

Climate change is expected to intersect with and influence all sectors and scales, particularly in the context of disaster risk (IPCC, 20). Due to complex interdependencies among environmental, socio-economic, and technological systems, risks may shift from one domain to another, leading to the emergence of new threats or the amplification of existing ones [28,29,30].

The concept of “risk” itself lacks a universally accepted definition among scholars [31]. It is inherently dynamic and evolves over time [32,33], generally referring to the potential for adverse impacts on ecological systems or human well-being [34]. A basic definition of risk is “the probability that a substance or situation will produce harm under specified conditions” [35]. It is commonly understood to comprise two core components: the probability of occurrence and the severity of consequences [35]. The IPCC defines risk as “the potential for consequences where something of human value (including humans themselves) is at stake and where the outcome is uncertain” [5]. The IPCC’s Fifth Assessment Report defines the climate risk as a “potentially severe adverse consequences for humans and social-ecological systems resulting from the interaction of climate-related hazards with vulnerabilities of societies and systems exposed” [36].

The impacts of climate change have already begun to affect human populations, and the consequences of extreme events are expected to intensify in the future [37,38]. Assessing these impacts relies largely on understanding and measuring both vulnerability and climate risk, concepts that vary depending on the methodological and disciplinary lens applied. These assessments may be framed from social, political, economic, biophysical, or hybrid perspectives [39]. Climate change can be conceptualized as an environmental risk in two primary ways: first, through its direct effects on exposed natural systems; and second, through the indirect risks it poses to human activities as a result of environmental degradation [40].

Risk assessment, whether quantitative or qualitative, along with spatial analysis, has become essential for managing and mitigating climate-related risks [41]. This process typically involves three key steps: risk identification, analysis, and evaluation [42]. Effective risk assessment plays a crucial role in reducing the probability of adverse outcomes or expected losses, including human casualties, property damage, economic disruption, and harm to livelihoods or ecosystems [43]. Although climate change significantly influences the spatial and temporal patterns of natural hazards, it does not directly cause disaster losses. Rather, such losses emerge from the interaction between physical exposure and the vulnerability of the affected system [44].

Climate-risk assessments are now being applied across all critical infrastructure and service sectors [45,46]. Reflecting this trend, the climate change discourse has increasingly shifted from a focus on vulnerability alone toward a more integrated understanding of risk [47]. Spatial visualization of current and future climate conditions is a key component in the assessment of related impacts and risks in a given region [48]. Researchers have explored climate risk at various spatial scales, including local, regional, and national levels [49,50,51]. In this study, spatial distribution of climate risk is assessed at the governorate level within the West Bank.

There is no universally accepted framework for climate-risk assessment [31]. While some researchers emphasize vulnerability, others incorporate exposure explicitly [52,53,54,55]. Because traditional definitions of vulnerability often omit exposure, the latter is now typically included within broader definitions of risk [23]. In the IPCC framework, exposure is treated as a spatial concept, highlighting the geographical distribution of at-risk systems [56].

According to the IPCC, vulnerability and exposure are the core components of climate risk [23]. The relationship between these two elements is direct: as vulnerability increases, so does overall risk, assuming all other variables remain constant [57]. Importantly, if vulnerability is reduced to zero, the system is considered free from risk, even if other risk components are present. However, the relationship between vulnerability and risk is not reciprocal; reducing vulnerability lowers risk, but reducing risk does not necessarily reduce vulnerability [58]. In this study, both vulnerability and exposure are used to assess climate risk across governorates in the West Bank.

The primary aim of this study is to estimate climate risk across the different governorates of the West Bank, based on an analysis of vulnerability and exposure. To assess the climate risk, the spatial distribution of the climate exposure through the different West Bank governorates was analyzed to classify them into different classes based on their exposure to climate risk. Finally, climate-risk and climate-exposure maps were developed to categorize the West Bank areas into five categories depending on their risk and exposure level (very low, low, moderate, high and very high). To the authors’ knowledge, this is the first study to systematically examine the spatial distribution of climate risk in this region. The findings are intended to serve as a foundation for future climate-risk management efforts in the West Bank. Additionally, the results of this study are presented in easily understandable maps. These maps can be used by the decision-makers identify areas with a high level of exposure and climate risk, and target specific mitigation and adaptation measures.

2. Materials and Methods

2.1. Study Area

This study assesses climate risk in the West Bank, Palestine, an area identified as highly vulnerable to the impacts of climate change [59]. The region’s population is particularly at risk due to pre-existing socio-environmental vulnerabilities. In addition, this is a scarce data context that can serve to the development of a robust spatially explicit climate-risk assessment approach that can be transferred at other similar contexts. [59] report that approximately 60% of the West Bank is classified as having high to very high vulnerability to climate change, with 76% of the population residing in these areas. This highlights the urgent need for comprehensive climate-risk assessment in the region.

The West Bank encompasses a total area of 5660 km² and is divided into 11 governorates: Hebron, Jenin, Tubas, Jerusalem, Nablus, Jericho and Al-Aghwar, Qalqiliya, Tulkarm, Bethlehem, Ramallah and Al-Bireh, and Salfit [60], as illustrated in Figure 1. As of the end of 2024, the total population was approximately 3.36 million [61]. Hebron is the most populous governorate, accounting for about 25% of the total population, whereas Jericho and Al-Aghwar are the least populated, with less than 2% of the population [61].

The West Bank experiences a Mediterranean climate, characterized by hot, dry summers and cool, wet winters [62]. The rainy season typically spans from October to May [63], with a long-term average annual rainfall of approximately 420 mm [64]. Climatically, the region is divided into six zones: hyper-arid, extremely arid, arid, semi-arid, semi-humid, and humid [65], with the majority of the area classified as semi-arid.

Soil types in the West Bank are predominantly clay and clay loam, constituting approximately 47% and 31% of the total area, respectively. Other soil types include loamy and sandy loam soils [66].

2.2. Methodological Approach

This study aimed to assess the spatial distribution of climate risk across the various governorates of the West Bank. It is important to note that climate change alone does not inherently generate risk. Rather, risk emerges from the interaction between physical exposure to climate-related hazards and the vulnerability of the exposed elements [44,67,68,69]. Based on this understanding, the assessment in this study focused on two primary components: exposure and vulnerability.

To evaluate climate risk, spatially explicit maps of exposure and vulnerability were developed. These maps were then integrated to produce a composite climate-risk map, which served as the basis for analyzing both overall and spatial variations in climate risk across the West Bank (see Figure 2). The methodological framework for constructing the exposure, vulnerability, and risk indicators is described in detail in the following sections.

2.2.1. Data Collection for Exposure Calculation

As a foundational step in evaluating climate risk, this study assessed the physical exposure of the West Bank to climate-related hazards. A climate-exposure map was developed to classify the West Bank governorates into five exposure categories: very low, low, medium, high, and very high. The selection of appropriate and representative exposure indicators is critical to ensuring the reliability and validity of such assessments [70].

Based on an extensive review of the literature, 17 indicators were selected to represent climate exposure. These indicators span multiple climatic and environmental dimensions, including temperature [15,71,72,73,74,75,76,77], rainfall [15,71,72,74,75,76,78], relative humidity [79], soil type [80], and wind speed [80,81,82]. A summary of these indicators is provided in

Table 1. Climate-risk exposure indicators.

Group	Exposure Indicator	Abbreviations	Description	Source
Temperature	Maximum Temperature	Tma	Maximum temperatures	PMD [83]
	Minimum Temperature	Tmi	Minimum temperatures
	Maximum Temperature Days	D35	Days with maximum temperature above 35 °C
	Minimum of Minimum Temperature	mi Tmi	Annual minimum value of daily minimum temperature
	Mean Temperature	Tme	Mean temperature
	Maximum of Maximum Temperature	ma Tma	Maximum of maximum temperature
	Frost Days	FD	Number of annual frost days
	Cold Days	CD	Percentage of days when TX < 10th percentile
	Warm Days	WD	Percentage of days when TX > 90th percentile
Rainfall	Heavy Precipitation Days	HP	Days when precipitation exceeds 20 mm	PMD [83]
	Rainy Days	RD	Number of rainfall days
	Annual Rainfall	AR	Annual rainfall quantity
	Flood River	FR	Risk of flood river	World Bank [84]
	Water Scarcity	WS	Water scarcity
Overall	Wind Speed	WS	Mean wind speed	PMD [83]
	Relative Humidity	RH	Relative humidity
	Soil Type	ST	Percentage of clay and clay loamy from total governorate area	MOA [66]

PMD = Palestinian Metrological Department; MOA = Ministry of Agriculture.

.

Data for the selected indicators were sourced from several institutions, including the Palestinian Meteorological Department [83], the Palestinian Ministry of Agriculture [66], and relevant World Bank reports [84]. Each indicator reflects the long-term average over a 25-year period (1997–2021) to ensure consistency and account for interannual variability.

2.2.2. Calculation of the Climate Risk and Exposure Index

The climate-exposure index serves as an effective tool for analyzing climate exposure in a structured and reliable manner [85]. In this study, the index was constructed using the Analytical Hierarchy Process (AHP), a widely used Multi-Criteria Decision Analysis (MCDA) technique that facilitates the determination of relative weights for multiple indicators [86]. The weighting system plays a critical role in the overall analysis, as it can significantly influence the prioritization of indicators [87].

The AHP methodology, originally developed by [88], was adopted to assign weights to the selected exposure indicators. This approach is one of the most established techniques for implementing multi-criteria decision-making, allowing for the quantitative comparison, integration, and ranking of various indicators [89]. It has been used in many different fields over the last forty years [88] and can handle both quantitative and qualitative data effectively [90]. It helps to break down complicated decisions into simpler, more manageable ones [91]. Each indicator was compared pairwise with the others using the Saaty scale, where scores range from 1 (equal importance) to 9 (extreme importance) [92]. The pairwise comparison matrix and a relative importance rating scale were employed to estimate the weight of each indicator [93]. In each comparison, one indicator served as the reference, and the importance of the other was evaluated relative to it. An example of this process is presented in

Table 2. AHP matrix for climate-risk exposure.

	Wind Speed	Soil Type	Rainfall	Temperature	Relative Humidity	Final Weight
Wind Speed	1.0	2.0	0.3	0.3	4.0	0.18
Soil Type	0.5	1.0	0.3	0.3	0.5	0.06
Rainfall	4.0	4.0	1.0	2.0	5.0	0.39
Temperature	3.0	3.0	0.5	1.0	4.0	0.28
Relative Humidity	0.3	2.0	0.2	0.3	1.0	0.09
Sum	8.8	12.0	2.2	3.9	14.5	1.00

.

To ensure the consistency of the assigned weights, the Consistency Ratio (CR) was calculated. A CR value of ≤0.1 is considered acceptable, indicating that the comparisons made are consistent and reliable [94]. The CR was computed using the following formula, as defined by [88]:

$$\mathrm{CR} ={\displaystyle \frac{CI}{RI}}$$

(1)

$$\mathrm{CI}={\displaystyle \frac{\lambda -n}{n-1}}$$

(2)

where CI is the consistency index, RI is a random consistency index depending on the number of indicators as calculated by Saaty, λ is the maximum eigenvector of the matrix, and n is the number of indicators. In accordance with the number of indicator groups (Table 1) three matrices were developed; the first for temperature, the second for rainfall and the last for overall exposure (Table 2).

2.2.3. Climate Risk and Exposure Mapping

The spatial assessment of climate exposure in the West Bank was conducted using GIS, specifically ArcMap 10.2.2, and was based on the final indicator weights derived from the AHP matrices. Exposure was calculated using both numerical and categorical approaches, following methodologies outlined in previous studies [95,96].

Indicator data, initially aggregated at the governorate level, were converted into raster format using GIS tools to facilitate spatial analysis. Each indicator was further subdivided into seven sub-indicator classes, which were assigned scores ranging from 1 (lowest contribution to exposure) to 9 (highest contribution to exposure) based on their relative impact (see

Table 3. Final score for the climate-risk exposure indicators.

Indicator #	Indicator	Sub-Indicator	Score
1	Wind Speed	<4.731579	2
		4.731579–5.717391	3
		5.717391–6.417017	4
		6.417017–6.776147	5
		6.776147–7.298941	6
		7.298941–8.234783	8
		≥8.234783	9
2	Soil Type	<0.506949	2
		0.506949–0.608942	3
		0.608942–0.716993	4
		0.716993–0.770676	5
		0.770676–0.864419	6
		0.864419–0.984142	8
		≥0.984142	9
3	Rainfall	<2.66	2
		2.66–3.91	4
		3.91–4.92	5
		4.92–5.5	6
		5.5–6.11	7
		6.11–6.84	8
		≥6.84	9
4	Temperature	<4.54	2
		4.54–4.65	3
		4.65–4.84	4
		4.84–5.31	6
		5.31–5.38	7
		5.38–5.83	8
		≥5.83	9
5	Relative Humidity	<49.47826	2
		49.47826–57.57143	4
		57.57143–62.099998	5
		62.099998–63.573357	6
		63.573357–64.15374	7
		64.15374–65.434784	8
		≥65.434784	9

).

To integrate the various indicators into a single exposure map, the Weighted Overlay Summation tool in ArcMap was employed. This tool multiplies the raster score of each indicator by its corresponding weight and sums the results to produce a composite exposure score. The final exposure value for each spatial unit was computed using the following equation:

$$Climat eexpousre for each component=\sum _{i=1}^{n}Wi\times Sij$$

(3)

where Wi is a normalized weight for each indicator (ΣWi = 1), Sij is the score of the ith cell for the jth raster, and n is the number of cells in each jth layer. Afterwards, the results of the index for each exposure components were used in the estimation of the overall index. In the overall index, the Wi is the weight for each component and Sij is the score value for each developed component in the different layers. An overview of the entire methodological process for developing the climate-exposure map is illustrated in Figure 2, which outlines the steps from indicator selection and weighting to spatial analysis and map generation.

2.2.4. Risk Assessment of Climate Change in the West Bank

Climate risk is not solely determined by the severity or probability of a hazard; it also critically depends on the level of exposure and the vulnerability of the affected system [32,67,68,69]. These two components, exposure and vulnerability, are widely recognized as the most significant determinants of climate risk [43].

In this study, climate risk in the West Bank was assessed using a compound function of exposure and vulnerability, expressed as follows [97,98]:

$$Risk=Vulnerability\times Exposure$$

(4)

This formulation implies that climate risk only exists when both exposure and vulnerability are present. If either component equals zero, the overall risk is considered null [99]. The vulnerability assessment was a critical step in this analysis. The vulnerability map developed by [59] in a previous study was adopted for this research. The detailed approach for vulnerability assessment is described in Section 2.2.5.

To compute the climate-risk map, the exposure and vulnerability layers were multiplied using GIS tools (ArcMap 10.2.2). The raster multiplication technique integrated the spatially distributed exposure and vulnerability values, resulting in a comprehensive climate-risk map. Based on the final risk scores, the West Bank governorates were categorized into different climate-risk classes, enabling the identification of areas most at risk from the impacts of climate change.

2.2.5. Vulnerability Index Construction

In previous research, ref. [59] estimated the climate change vulnerability index for the West Bank. Their methodology, which was aligned with the approach described in Section 2.2.2, involved using 50 indicators to evaluate and classify the spatial distribution of climate change vulnerability across West Bank governorates. These indicators were organized into six main components: Health, Housing, Socio-demographic, Agriculture, Service, and Economic. Each one includes different indicators, as presented in

Table 4. Climate change vulnerability indictors.

Vulnerability Components	Vulnerability Indicators	Abbreviations	Description
Health	Infant Mortality Rate	IM	Infant mortality rate (per 1000 live births)
	Daily Necessities	DN	Percentage of children under weight (12 months)
	Recent Death	RD	Death rate (per 1000 population)
	Health Center	HC	Population per primary healthcare center
	Healthcare Visit	HV	Rate of visits for physician per person in year (PHC)
	Nursing & Midwife	NM	Nurses and midwives (per 10,000 population)
	Diabetes	Di	Rate of diabetes (per 100,000 of population)
	Cancer	Ca	Incidence rate (per 100,000 population)
	Hospital Beds	HB	Number of hospital beds (per 10,000 population)
	Chronic Diseases	CD	Number of populations diagnosed with chronic diseases (per 10,000 population)
	Health Insurance	HI	Number of health insured persons (per 10,000 population)
	Under-Five Mortality Rate	UM	Under-five mortality rate (per 1000 live births)
	Health Vulnerability	Vu	COVID vulnerability index
Socio demographic	Adult Literacy	AL	Percentage of population (15 years and above) that can read and write (excluding the population with educational attainment equating to elementary, preparatory, secondary, associate diploma, bachelor, and above)
	Older Persons	OP	Number of population more than 65 years old (per 10,000 population)
	Education	Ed	Percentage of population (15 years and above) who have elementary educational attainment
	Female	Fe	Number of females (per 10,000 population)
	Young Person	YP	Persons (0–17) years old (per 10,000 population)
	Disabled People	DP	Number of disabled people (per 10,000 population)
	Suicide Rates	SR	Suicide attempt incident rate
	Homicide Crime	HC	Number of victims in homicide crime (per 10,000 population)
Economic	Financial Services	FS	Capita per bank branch
	Population in the Workforce	PW	Labor force participation rate of persons aged 15 years and above
	Dependency Ratio	DR	Unemployment rate of population aged 15 years and above
	Household Received Assistance	HA	Percentage of household that received assistance
	Loans	Lo	Percentage of households that received loans/advanced payments/debts (during the past 12 months)
	Industrial Development	ID	Number of operating industrial enterprises in Palestine (5 employed persons and more)
Agriculture	Plant Area	PA	Percentage of cultivated area of field crops, vegetables and tree horticulture from total governorate area
	Main Income From Agriculture	MI	Percentage of agricultural holders worked in agriculture as their main job from the total number of agricultural holders
	Irrigated Land	IL	Percentage of irrigated field crops area
	Bovine	Bo	Number of cattle
	Chemical Fertilizer	CF	Percentage of agricultural holdings that use chemical fertilizer from total area of agricultural holdings
	Land Suitability	LS	Percentage of high agricultural land value from total governorate area
	Agriculture Water Poverty Index	WP	Agriculture water poverty index
	Farm Organization	FO	Percentage of agricultural holders by receiving an agricultural training/education from total agricultural holders
	Aquaculture Area	AA	Percentage of area used for aquaculture from total area of agriculture holding
Housing	Household Sizes	HS	Average household size
	Built Up Area	BA	Percentage of residential built-up land from total governorate area
	Water Consumption	WC	Daily consumption rate per capita per day
	Housing Density	HD	Average housing density (person per room)
	Renter Population	RP	Percentage of rented household (furnished and unfurnished) from total household
	Population Density	PD	Capita per km2
Services	Electricity	El	Percentage of occupied housing units which connected to electricity public net work from total household
	Toilet Facility	TF	Percentage of population with access to improved sanitation
	Internet Access	IA	Percentage of households that connected to the internet
	Landfills	La	Number of landfill
	Road Length	RL	Road network length
	Access to Water	AW	Percentage of population using safely managed drinking water services
	Forest-based Energy	FE	Percentage of household used wood for cooking from total household
	Number of Vehicle	NV	Number of licensed road vehicles (per 10,000 ) population

Source: [59].

. For the calculation of this index, data from different sources was needed, such as Ministry of Health, the Palestinian Central Bureau of Statistics, and Palestinian Water Authority. The AHP weighting approach was used to estimate the weight for each indictor, the CR value was estimated to evaluate the consistency of the indicators. Finally, the Weight Overlay Summation process in ArcMap was utilized to estimate the final vulnerability index.

2.3. Sensitivity Analysis

To evaluate the robustness of the climate-risk assessment, a sensitivity analysis was conducted by modifying the weighting schemes used in both the exposure and vulnerability components. Specifically, the analysis examined how changes in the final weights assigned to indicators in the AHP influenced the results. In this sensitivity scenario, equal weights were assigned to all exposure indicators, replacing the original differentiated weights. The resulting exposure map, based on equal indicator weights, was then combined with two versions of the vulnerability map to generate corresponding risk maps.

The key difference between the two scenarios lies in the weighting of the vulnerability components; scenario 1 (S1): vulnerability components were weighted according to their relative importance, as determined by AHP; scenario 2 (S2): All vulnerability components were assigned equal weights. In both scenarios, the same exposure map (based on equal indicator weighting) was used to isolate the impact of vulnerability weighting on the final climate-risk outcomes.

3. Results and Discussion

3.1. Spatial Analysis of Climate-Risk Exposure

In the context of climate change, disaster risk management is increasingly recognized as a vital adaptation strategy [100]. It also plays a critical role in supporting long-term sustainable development [100,101]. Realistic and data-informed planning is essential for effective disaster risk reduction [102,103]. As emphasized earlier, exposure analysis represents a foundational step in the comprehensive assessment of climate risk. This paper adopted a robust MCDA approach to estimate the spatial distribution of climate exposure over the different West Bank governorates, that could be replicable in similar contexts. Based on this, we have developed the first climate-risk research in the West Bank that uses a vulnerability and exposure indexes to develop a climate-risk map at the governorate level.

This study conducted a spatial analysis of climate-risk exposure across the West Bank, classifying its governorates into five exposure categories: very low, low, moderate, high, and very high, as illustrated in Figure 3.

The results indicate that the West Bank exhibits generally low exposure to climate risk, with approximately 46% of its area falling under the low to very low exposure classes. These include the governorates of Qalqiliya, Salfit, Jericho & Al-Aghwar, Hebron, and Bethlehem.

In contrast, 39% of the area is categorized as having high to very high exposure, and an additional 15% is classified as moderate exposure. The governorates with the highest exposure levels are Nablus, Tubas, Jerusalem, and Ramallah & Al-Bireh.

Although these high-exposure governorates cover a relatively smaller portion of the West Bank’s total area, they account for a disproportionately large share of the population—over 42%. This concentration of population in high-exposure zones significantly amplifies the potential climate risk.

The elevated exposure levels in these areas can be attributed to specific climatic stressors. For instance, Nablus and Ramallah & Al-Bireh exhibit high exposure to intense and variable rainfall, while Tubas and Jerusalem experience elevated temperature extremes, making them more susceptible to the adverse effects of climate change.

3.2. Risk Assessment of Climate Change in the West Bank

The climate-exposure map developed in the previous section (Figure 3), together with the vulnerability map, served as the foundation for assessing climate risk across the West Bank. The integration of these two components allows for a comprehensive spatial analysis of climate-risk distribution among the different governorates.

Based on the results of this combined assessment, the West Bank governorates were classified into five categories of climate risk: very low, low, moderate, high, and very high. Although the region is not uniformly exposed to climate hazards, the findings reveal that the West Bank remains under significant risk due to widespread vulnerability. As illustrated in the climate-risk map (Figure 4), approximately 64% of the West Bank’s land area is classified as being under high-risk conditions. These areas are inhabited by about 82% of the population, reflecting a substantial concentration of people in the most at-risk zones. The governorates falling under the high-risk classification include Jenin, Tulkarm, Nablus, Ramallah & Al-Bireh, Jerusalem, and Hebron.

In many cases, the classification of these governorates as high-risk is driven more by their elevated levels of vulnerability than by exposure alone. For example, Nablus, Ramallah & Al-Bireh, and Jerusalem are characterized by both high exposure and high vulnerability, which explains their pronounced risk levels. In contrast, Jenin, Tulkarm, and Hebron, although not highly exposed to climate hazards, still fall into the high-risk category due to their significant vulnerability. These findings suggest that vulnerability is a more influential factor than exposure in determining overall climate risk in the West Bank. Consequently, areas experiencing high vulnerability are more likely to be at risk, even if their exposure levels are moderate or low. This underscores the importance of reducing vulnerability through socioeconomic development, infrastructure improvement, and institutional strengthening as a key strategy for climate-risk mitigation.

Supporting this interpretation, previous studies have indicated that risk conditions in several of these governorates, particularly Jenin, Nablus, and Jerusalem, are expected to worsen due to increasing variability in rainfall and rising average temperatures [104]. These climate trends are likely to exacerbate existing vulnerabilities and further intensify the impacts of climate change in these areas.

Figure 5 presents a summary of the spatial distribution of exposure, vulnerability, and risk, expressed in terms of both land area and population. The figure reveals that exposure levels are relatively low and display a more balanced distribution across geographic and demographic dimensions. In contrast, vulnerability and risk are both significantly higher and more unevenly distributed. Highly vulnerable areas tend to concentrate a larger share of the population, indicating a spatial mismatch that increases the overall severity of climate risk. This pattern highlights the need for geographically targeted policy interventions that prioritize the most vulnerable and densely populated areas to effectively address the challenges posed by climate change in the West Bank. Based on the figure, it can be seen that 39% of the West Bank areas are highly exposed to climate risk, and 42% of the West Bank populations live in these areas. Additionally, 41% of the West Bank areas are under low to very low exposure conditions (46% of the West Bank area). However, only 8% of the West Bank population live in areas with low to very low risk conditions. While, nearly 82% are at high risk conditions. More than half of the West Bank areas (64%) are classified as a climate-risk hotspot. Moreover, almost 10% of the West Bank governorates areas are classified as low to very low.

3.3. Sensitivity Anlaysis

Figure 6 presents the climate risk for S1 and S2 in the different West Bank governorates. The climate-risk level for S1 did not change in the five governorates (Tulkarm, Qalqiliya, Salfit, Ramallah & Al-Bireh and Jericho & Al-Aghwar). However, it changed by one level of risk in the other six governorates (3 increase, 3 decrease). A similar situation for S2, where the risk categorization did not change in four governorates and changed by one level in the remaining governorates (2 increase, 4 decrease). The climate-risk category remained unchanged in the Qalqiliya, Ramallah & Al-Bireh and Jericho & Al-Aghwar governorates in both scenarios.

In both scenarios, the West Bank will be under less climate-risk impact than the original estimation (Figure 7). The percentages of the low- to very-low-risk areas in the West Bank ranges from 25% to 36% in the two scenarios, while 57% of the West Bank population will be in high-risk conditions in S1, and 51% in S2.

3.4. Policy and Recommendations for Climate-Risk Adaptation

Comprehensive assessments of vulnerability and exposure are crucial to ensure that effective adaptation measures are integrated into sustainable development plans [105]. This study presents a thorough climate-risk assessment for the West Bank, explicitly considering both vulnerability and exposure dimensions.

Risk assessment is fundamental for formulating reliable and targeted adaptation strategies [23]. To reduce overall climate risk, efforts must focus on decreasing either vulnerability or exposure. In line with Palestine’s Nationally Determined Contribution (NDC), reducing vulnerability is identified as a key objective, primarily through enhancing the adaptability and resilience of public institutions and local communities [106]. This research underscores the dominant role of vulnerability in shaping climate risk, reinforcing the NDC’s emphasis on vulnerability reduction.

The spatial dimension of this risk assessment offers valuable insights for policymakers, enabling them to concentrate resources and interventions in identified climate-risk hotspots [107,108]. The findings of this study can inform the ongoing implementation of the National Adaptation Plan (NAP), emphasizing the need for prioritized support to populations in high-risk areas. Particular attention should be directed towards the vulnerable sectors highlighted in the NAP, including health, industry, water resources, energy, urban infrastructure, transportation, coastal and marine economy, agriculture, tourism, food production, terrestrial ecosystems, and waste management [109].

Departing from this research findings, specific adaptation measures could be proposed to minimize the climate-risk impact in the highly vulnerable areas. These measures relate to the vulnerability and exposure of these governorates to the climate risk. In this sense, the impact of climate risk can be minimized by either increasing vulnerability or decreasing climate exposure in a specific area. According to the developed exposure map, Nablus and Ramallah & Al-Bireh are highly exposed to the rainfall variability. Accordingly, the rainwater harvesting or Sustainable Drainage Systems should be adopted in these areas to minimize the risk of flooding to their populations. Meanwhile, agriculture vulnerability should be decreased to mitigate the climate impact in Jenin and Jerusalem. Additionally, improving the services sector could mitigate climate risk in Hebron. Some additional and place-specific measures for adapting to climate risk are presented in

Table 5. Suggested adaptation and mitigation measures for governorates with high climate risk in the West Bank.

Governorate	Suggested Adaptation Measures
Jenin and Jerusalem	Climate-resilient agriculture Land-use planning and management Agricultural disaster risk reduction and management plans
Nablus and Ramallah & Al-Bireh	Rainwater harvesting infrastructure Rehabilitation of resilient road infrastructure Develop and improve stormwater systems and drainage infrastructure
Hebron	Providing reliable electricity supply Providing safely managed drinking water services

.

This study represents a pioneering effort to spatially analyze climate risk across the West Bank governorates, providing a practical framework for risk management at a sub-national scale. Unlike many climate-risk assessments that are limited by specific spatial or temporal scales and consequently have restricted applicability [110], the current approach leverages commonly accessible data aggregated at the governorate level. These data are generally robust and readily available, allowing for consistent and replicable assessments. Moreover, the resultant climate-risk maps offer a clear, user-friendly visualization that facilitates communication and decision-making among diverse stakeholders.

Ultimately, this research highlights the indispensability of comprehensive climate-risk assessment as the foundation for any effective risk management strategy. Without a detailed understanding of both vulnerability and exposure, targeted and efficient climate-risk management interventions cannot be realized.

3.5. Study Limitations and Further Studies

Risk assessment inherently requires the collection and processing of large, complex, and often unstructured datasets [111,112]. In this study, data collection posed a significant challenge, as diverse datasets describing various climate-risk indicators were necessary to conduct a comprehensive assessment. Furthermore, the exposure data needed to be accurate and representative of the West Bank region. To address gaps and enhance the reliability of the assessment, the methodology incorporated the use of average indicator values over a 25-year period, as detailed in the methodology section.

To date, no prior research has examined climate risk in the West Bank explicitly within the combined framework of vulnerability and exposure. Therefore, further studies adopting alternative climate-risk assessment methodologies are warranted to compare, validate, and potentially refine the results presented here. Such comparative research could involve applying different risk assessment frameworks or employing alternative weighting systems to evaluate uncertainties inherent in the assessment process.

Additionally, incorporating a temporal dimension into climate-risk assessments would significantly advance understanding and management of climate risks in the Palestinian context. Future work focusing on the temporal dynamics of climate exposure, vulnerability, and risk could provide valuable insights for more effective adaptation planning and policy formulation.

4. Conclusions

This study represents a pioneering effort to assess climate risk in the Palestinian context, focusing on the West Bank. Using a widely applied MCDA approach, the assessment centered on two key components: vulnerability and exposure. Various indicators were selected to estimate climate-risk exposure, including rainfall, temperature, relative humidity, wind speed, and soil type. The AHP was employed to assign weights to these indicators, while GIS tools facilitated spatial analysis and mapping of exposure and risk. The resulting maps classify the West Bank governorates into five exposure and risk categories: very low, low, moderate, high, and very high. Findings reveal that 39% of the West Bank area is highly exposed to climate risk, affecting approximately 42% of the population. More notably, 64% of the territory falls under high to very high overall climate-risk conditions, highlighting significant vulnerability.

These results provide critical insights for policymakers and stakeholders to develop targeted climate-risk mitigation and adaptation strategies. Efforts should prioritize highly vulnerable populations and areas. Specific adaptation and mitigation measures, such as those proposed, should be implemented to minimize the impact of climate risks in these high risk areas. For instance, rainwater harvesting techniques are highly recommended as part of these adaptation measures to reduce flood hazard. This will reduce the impact of flooding. Additionally, climate-smart agriculture is highly recommended in highly vulnerable areas to strengthen food security. Moreover, the study advocates for the creation of a dedicated climate-risk policy brief to guide decision-makers in implementing effective risk reduction measures across the West Bank.

Finally, further climate-risk studies are recommended in the West Bank, based on different exposure and vulnerability indicators. Vulnerability, exposure, and risk assessments could use a different weighting approach (e.g., fuzzy). Additionally, forecasting the spatial distribution of climate risk in the future, could be essential for the successful development of climate-risk management plans that are effective in the longer term.