Introduction

Climate change is a global and urgent concern that impacts our planet. The impact of climate change is already being felt by different countries across the world. Since the earth’s creation, climate has been changing, which makes it an inevitable phenomenon. On the other hand, climate change occurs in nature at a fairly low pace, giving species ample time to adapt to the changing environment. According to Ayanlade et al. if anthropogenic activities accelerate the change, it will happen more rapidly and species won’t be able to adapt to such rapid changes. As a result, a society has a negative impact on patterns of living, survival, and development; in developing nations, this is particularly true in the poor rural areas [1].

More frequent, intense, and extreme weather and climate-related events including warmer temperatures, sea-level rise, high rainfall, humidity, floods, damage property, critical infrastructure, impact human health, agricultural, forestry, fisheries productivity, and tourism [2]. Climate change also causes loss of ecosystems, cultural heritages and communication systems [3].

Human activity has been linked to climate change, particularly the rise in greenhouse gas emissions that occurred after the middle of the 20th century. Extreme events and other calamities are occurring more frequently, which is one of the many hazards brought on by climate change. Since AR5 (Fifth Assessment Report), there has been more evidence of observed increases in extremes such as extreme precipitation, heatwaves, tropical cyclones, and droughts, and, in particular, the attribution of these extremes to human activity. The infrastructure of society is become increasingly susceptible to extreme weather events, which are made worse by climate change [4].

It is expected that future climate change would further disrupt many areas of life, aggravating the existing challenges to prosperity posed by aged and deteriorating infrastructure, stressed ecosystems, and economic inequality. Both developed and developing countries are significantly affected by different climate hazards. Water stress and flooding vulnerability are predicted to increase due to climate change during this century [3]. Due to the rising temperature and erratic rainfall trend the resulting unfavorable floods and droughts are reinforcing higher threats to livelihood of locals [5].

South Asia is the most vulnerable region of the natural disasters in the world [6]. As a developing country, Pakistan is one of the most disaster-prone countries with flooding, earthquakes, landslides, cyclones, and other natural disasters. According to a survey more than 40% of Pakistan’s landmasses are vulnerable to flooding cyclone susceptibility, earthquakes to flooding is 60 percent and to drought is 25 percent [7]. The area has suffered enormous losses in terms of both human life and economic resources. It is predicted that in the upcoming years, the intensity, duration, and frequency of the climate change-related disasters will rise [8]. Pakistan is one of the countries with the biggest number of people directly affected by flooding each year which mainly occurs as a result of storm systems that originate in the Bengal Bay during the months of July and September. Storms from the Bay of Bengal move across lower Central India and Rajputana reaching Pakistan and continuing north via Kashmir. The mountain ranges in Pakistan’s far north provide a seasonal source of river inflow. Punjab and Sindh have been hit by floods while hill torrents look to be affecting Khyber Pakhtunkhwa’s hilly districts, Balochistan and Gilgit Baltistan. Sindh, Kabul and Swat are rivers which are prone to hazards. Pakistan continues to face flooding every year because of its climate and ecosystem. Nonetheless, once river levels rise above the average amount of flooding, these floods become dangerous. Previous floods have resulted in several deaths and considerable damage to the nation’s infrastructure [9]. The long-term temperature trends analysis shows a tendency of increase in temperature in the area. According to future forecasts, Punjab’s average annual temperature is expected to rise above the global average by the year 2100 [10]. The data in literature shows that the north of Pakistan is experiencing a more noticeable increase in temperature than the south. Rising temperatures in these northern regions have caused the Himalayan, Hindu Kush, and Karakoram glaciers to melt, creating more than 3,000 glacial lakes. 33 of them are thought to be vulnerable to floods caused by glacial lake outbursts [11].

Vulnerability assessment is the prior tool to take appropriate disaster management program in natural disaster-prone areas. The effects of climate vulnerability and related factors differ depending on the communities’ socioeconomic status, geographical location, and time frame. Accordingly, vulnerability assessment aids in determining a community’s degree of adaptability, which is the first stage in creating plans using adaptive strategies to lower the risks related to the dangers brought on by climate change [12].

These days, research on climate change places a special emphasis on how households perceive the phenomenon in order to comprehend it better. In this regard, primary data on the social, economic, and demographic profile (age, sex, occupation, income, food consumption, health, etc.) of various households are gathered. Following this, structured questionnaire surveys and focus groups are used to identify people’s perceptions of climatic fluctuations (temperature changes, rainfall, and the intensity of natural hazards). A number of techniques are also taken into consideration for assessing climate vulnerability, which looks at vulnerability across time and space and offers an opportunity to develop community-based mitigation and adaptation strategies for the future resilience [12].

Several frameworks have been proposed for assessing vulnerability, each with its own set of indicators and methods. Notable frameworks include: Sustainable Livelihood Framework (SLF): This framework provides a comprehensive set of indicators that unify the often inconsistent approaches to vulnerability assessment. It focuses on the assets, capabilities, and strategies that communities use to cope with stresses such as environmental hazards [13]. Composite Vulnerability Index (CVI): The CVI measures vulnerability through a set of weighted indicators that consider both external economic factors and environmental hazards. This approach enables a more nuanced understanding of vulnerability, factoring in both social and ecological components. Social Vulnerability Index (SoVI): Developed to assess hazard vulnerability in U.S. counties, the SoVI evaluates a range of socioeconomic and demographic variables (e.g., income, education, race, and age) to measure a community’s ability to respond to and recover from natural disasters [14]. This index highlights how social structures can influence vulnerability to climate-related risks.

In addition to these frameworks, indicator-based numerical approaches such as the Livelihood Vulnerability Index (LVI), developed by Hahn et al. [8], and the IPCC Vulnerability Index [9], which incorporates the three key dimensions of vulnerability—exposure, sensitivity, and adaptive capacity—are widely used in the literature to assess the vulnerability of communities to climate hazards. These indices are instrumental in assessing the vulnerability of a region and its adaptive capacity in the face of natural disasters. The LVI, for instance, evaluates how various factors, including economic, social, and environmental components, interact to determine a community’s vulnerability to climate-induced stressors [15].

Remote sensing technologies have also become crucial in monitoring climate-related hazards. Satellite observations have revolutionized our ability to track and manage natural disasters, including droughts, floods, and food shortages. By providing real-time data on land use and land cover changes (LULC), remote sensing tools help in mapping the vulnerability of affected areas. Techniques like the Standardized Precipitation Index (SPI) are increasingly used to identify patterns of wet and dry seasons, helping in the development of early warning systems for communities at risk of climate-induced disasters (National Drought Monitoring Centre, 2018) [16].

While exposure to hazards may be similar across different communities, the impacts of these hazards often depend on each community’s ability to cope with and manage these risks. Vulnerability assessments, therefore, play a critical role in understanding how different communities are affected by natural hazards and in designing targeted interventions to reduce their vulnerability [17–19].

Understanding the relationship between climate projections, societal structures, political systems, and health outcomes is crucial to addressing the complexities of climate change. Many vulnerability frameworks in the literature have focused on sensitivity and adaptive capacity, providing valuable insights into how communities may fare in the face of changing climate patterns [20–24].Previous vulnerability assessment framework includes sustainable livelihood framework, composite vulnerability index and social vulnerability index. The sustainable livelihoods framework provides a unified framework of indicators for the current inconsistent classification of vulnerability assessment index [13]. Composite Vulnerability Index evaluates external economic factors and environmental hazards to measure vulnerability by means of a set of weighted indicators previously selected. Hazard vulnerability in US counties is examined by the Social Vulnerability Index (SoVI) through the assessment of 11 variables related to socio-economic and demographic dimensions [14].

In this background, the present study aimed to evaluate the vulnerability level in study area, identify the weak aspects of the community and people’s livelihood assets to adapt with the natural hazards using different indices and natural capitals and to assess which factors contribute more towards the vulnerability of households in the face of changing climate. The goal of the study was to gain a better understanding of people’s livelihoods and vulnerability in flood-prone areas and to analyze how socioeconomic and environmental changes affect the livelihood, susceptibility, and adaptive capacity of different social classes. The current study investigated the flood’s social and economic vulnerabilities, as well as the effects it had on people and their belongings. For this purpose, focus group discussions along with other qualitative techniques were used carried out. There are few studies undertaken in Mianwali that provided a foundation for understanding how people’s livelihoods were affected make individuals more vulnerable.

Materials and methodology

Study area

In the North West of the Punjab province, District Mianwali is located representing the western plains of the salt-ranges close to the Sakesar hill [25]. Geographically district Mianwali lies between 32° 30′ and 33° 14′ N. and 71° 7′ and 71° 44′ E., covering an area of 5840 square kilometers (Fig 1A) . There are three sub-districts in Mianwali: the northern and western part of the district is occupied by Isa Khel, Mianwali lies in the east, and Piplan covers the south [26]. Climate of the district is severe with cold winters with little rain and extended hot summer. June and July are the hottest months with maximum temperature of 48° C whereas December and January are the coldest months with minimum temperature of 4-5 ⁰C. During the monsoon season, this district is traditionally vulnerable to floods and overflow. According to PPA, Mianwali is a poor district ranking 29^th out of 36 districts on the Multi-Dimensional Poverty Index [26].

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Fig 1 . A: Map of the study area. B: Research methodology.

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Data collection

Baseline data such as socio-economic indicators, flood history in the district and potential hazards of the district was collected from District Disaster Management Authority (DDMA). Three phases of the study process were carried out, one for each of the objectives, in order to accomplish them. In order to collect the data, the basic stages were the establishment of direct communication with the community and its residents and then evaluate official sources of information. In order to categorize the community into well-being groups, a total of 200 households were randomly surveyed to fill the questionnaire as followed by Shahzad [25]. Data was collected in April 2021 and was accessed in December 2021. As Urdu was the language widely spoken in the local community, interview questions were asked in Urdu language and questionnaires were translated in Urdu as well. For the collection of data the risk analysis data provided in the Table 3 was taken into consideration.

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Table 3. Characteristics of population indicating well-being categories.

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The following aspects have been considered for participant confidentiality and ethical consideration in this research: First, anonymity is ensured by using unique identifiers instead of names. Second, data protection measures are implemented, including encrypted files and limited access for authorized personnel. Third, informed consent is prioritized, with participants clearly informed about data usage and storage, allowing them the option to withdraw at any time. Additionally, data will be securely disposed of after the study, with digital files deleted and physical documents shredded. Finally, results will be reported in a way that prevents the identification of individual participants, such as through aggregation or generalization. These measures uphold ethical standards and create a safe environment for all participants.

Community ranking based on well-being status

A participatory approach of community livelihood based on their wellbeing status was used to rank the community on criteria of wealth and poverty. This approach was developed by Pakistan Participatory Assessment for the assessment of the level of relative wealth and poverty among the locals. To classify the surveyed population into wellbeing categories, the basic characteristics of population were asked. Respondents were questioned regarding their possession of land as owning land was considered a sign of richness and stability in that area. In addition, respondents were questioned whether their children attend private or government schools. Access to food and availability of local resource were also asked. They were also asked about their health status as to whether people had chronic disease (more than 3 months) or acute disease (a week or more) and migration patterns were also asked.

Local community was categorized into four well-being groups named as well-off, better off, poor and very poor people based on the data collected. For empirical validity, scholars have broadly used well-being categorization based on socio-economic status [27–29].

Vulnerability capacity assessment of local community.

In this study a combination of various participatory and qualitative methods was used to gather data from residents as community representatives, NGO employees and government organizations. The technique of Participatory rural appraisal (PRA) was used as this technique was used in many previous studies [25]. The participatory tools used in this study include Focus Group discussions (ranking of hazard severity, livelihood and seasonal mapping, institutional strength) and Transect Walk [30,31]. In the community of Mianwali, a transect walk was done to determine resource distribution and location. Because it provided good description of land usage, resource forms and uses, vegetation, everyday supply of water, and living conditions etc. Per walk, 10 people were questioned and the data obtained during the walk was stated on sheets of paper. Focus Group Discussions (FDGs) were conducted with ten people for each discussion, ensuring diversity in terms of age, gender, religion, race, and caste. The FGDs took place in nearby small rest houses, with a total of four discussions involving 40 participants (10 females and 30 males). The FGDs aimed to acquaint stakeholders with the different indicators used to evaluate vulnerability in the VCA method, to collect the data available from the community and to familiarize stakeholders with significant vulnerabilities and approaches (solutions) to mitigate the vulnerabilities. The respondents were asked about the impacts of flooding on their livelihoods, including its effects on food availability, water quality, access, and agricultural production. The study also investigated the current availability and seasonal variations of resources, identifying any resources that were previously present but no longer available. The participants were questioned about their flood-protection systems and the natural resources they rely on for their livelihoods and daily activities. Informants were asked about their methods of storing food and water, and community mobility was explored in relation to migration patterns from Mianwali district to larger cities for better livelihood opportunities or other facilities. The severity of the hazards that significantly affected their livelihoods was ranked on a scale of 0–5, with 5 indicating the highest level of hazard. Each household representative was also asked about their coping strategies. Finally, the vulnerability status of different social classes was determined based on the available livelihood options in the region. This comprehensive data collection process allowed for the creation of livelihood and seasonal mapping, as well as the ranking of hazard severity.

Livelihood vulnerability quantification.

An index namely Livelihood Vulnerability Index was used for the quantification of livelihood vulnerability from floods in the study area. The idea of using an index for vulnerability assessment was derived from the basic requirement of vulnerability by Intergovernmental Panel for Climate Change (IPCC). Two approaches used for this purpose were: LVI and LVI-IPCC. Livelihood Vulnerability Index or LVI was developed by Hahn [8], Madhuri [32], Simane [33] and Richardson [34]. The Livelihood Vulnerability Index was based on the descriptive information gathered from the field survey which was based on 41 sub-components [19]. These subcomponents were combined into 8 major components and three vulnerability factors. The detail of components and subcomponents of vulnerability is shown in Table 1:

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Table 1. Key Components and Sub-Components of the Livelihood Vulnerability Index (LVI) used to assess Vulnerability.

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Approach 1-Livelihood Vulnerability Index (LVI) calculation.

• Based on the field survey questions, 8 major components were queried. These major components were calculated at household level namely socio-demographic profile (SDP), Livelihood strategies (LS), Social network (SN), health (H), food (F), water (W), natural disaster (ND) and climatic variability (CV). These components were inquired using 35 sub-components.
• Following four steps were used to measure each major component of LVI

1. Calculate subcomponent data from the questionnaire and convert it into percentage and index
2. Unit standardization of all transformed data was performed for each subcomponent. It was done to balance the weights based on Sullivan [35].
3. To get the final value for each main component, compute the average of each standardized value
4. The final step includes the weighted averages of all major components to get the LVI value.

This procedure affirms the equivalent contribution of major components in generating the overall LVI. As described by Hahn [19], the LVI scale calculated from this approach ranges from 0 (minimum) to 0.5 (maximum). The min-max normalization technique [36] was adopted to normalize variables.

The normalization method, which is frequently employed in the literature on climate change susceptibility, was utilized to calculate the vulnerability index [37]. We used the following formula to all indications.

(1)

The formula followed is

(2)

Where, S_s =  original sub-components for each of the site;

Smin = the minimum value for each sub-component,

S_max =  the maximum value for each sub-component.

• The average of each sub-component was determined using the formula after obtaining the values of the sub-components

(3)

M_S = one of the eight major components for study site

= subcomponent, n=number of sub-components in each major component.

The above values were calculated for each of the eight major components and LVI was calculated using the following formula

(4)

Where SDP denote socio-demographic profile, LS refers to livelihood strategies, SN stand for social network, F represent food, W correspond to water, H symbolize health, CV represent climatic variability and ND stand for natural disaster. w =  weighting factor for each component.

Approach 2-IPCC framework for calculating LVI.

An alternative approach was also used to measure the LVI which integrates the IPCC definition of vulnerability. The LVI–IPCC framework organizes the eight major components that were used in the first approach. Table 1 demonstrates the organization of those components into the LVI-IPCC framework. According to the framework [36,38], Exposure is symbolized by e and it is measured by the number of natural disasters that have happened over the past 5 years, while the climate variability is measured by the average standard deviation of the maximum and minimum monthly temperatures and monthly precipitation over a span of 9 years. Adaptive capacity is assessed by the district’s socio-demographic profile, the livelihood strategies used and the power of social networks. Moreover, vulnerability is determined by assessing the existing condition of food and water safety and health status in a region.

(5)

where LVI–IPCC_s is the LVI for district s represented by using the IPCC vulnerability framework, e_s is the computed exposure score for district s (equivalent to the Natural Disaster and Climate Variability major component), a_s is the calculated score of adaptive capacity for district s (weighted average of the Socio-Demographic, Livelihood Strategies, and Social Networks major components), and s_s is the calculated score of sensitivity for district s (weighted average of the Heath, Food, and Water major components). The scale of LVI–IPCC ranges from -1 (least vulnerable) to + 1 (most vulnerable).

Results

Baseline data of study area

Socio-economic indicators such as population, number of Tehsils and Union Council, number of Revenue estates, population density, family size, population growth rate, poverty rate, population/doctor ratio, infant mortality ratio, maternal mortality ratio, literacy rate, Global Acute Malnutrition (GAM) rate, Severe Acute Malnutrition (SAM) rate, population having access to clean water, annual crime rate of Mianwali has been summarized in the Table 2 below;

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Table 2. Socio-economic indicators of Mianwali district.

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Livelihood vulnerability index

Vulnerability was measured at household level and findings are described in Table 4. Based on the data obtained and observed Mianwali was found to be vulnerable to repeated floods with calculated numerical value of 0.4. This high value shows that the community residing along riversides was more vulnerable. The component health issue had highest scores (0.58) than the other major components. The score of food availability component was 0.49. Most of the households were dependent on the self- farmed food. For water availability in households, Mianwali had an index value of 0.255. Some of the households were more reliant on natural sources of water to fulfill their everyday water needs. Socio-demographic profile had an index value of 0.54. Most of the households had T.V and radio in their homes. Many households had a male head and their qualification was middle or matric. The local population was more vulnerable to the flooding, because the natural hazard exposure component had a score of 0.34. Also, the climate variability score (0.127) was determined using the Mianwali meteorological data. The value of livelihood strategy index is 0.38. This indicates that family dependent on the agricultural sector is more vulnerable to flooding than those families who rely not just on the agriculture industry. Based on the level of vulnerability classification conducted by Hahn et al., (2009), the scale of the LVI value is set between 0 (not vulnerable) and 0.5 (very vulnerable). Generally, the local population of Mianwali was found to be vulnerable as the aggregated value of LVI for Mianwali district is 0.4.

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Table 4. LVI score for Mianwali.

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Approach 1

After calculating LVI scores for the district Mianwali, a spider diagram was created as shown in Fig 2 to show the scores of major components contributing to the livelihood vulnerability index. In the study area, health issues had comparatively higher scores than those of the other components.

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Fig 2. Spider diagram showing scores of major components contributing to the livelihood vulnerability index (LVI).

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Calculation of adaptive capacity through LVI-IPCC

The three major components (Adaptive capacity, Exposure and Sensitivity) that contribute to the LVI-IPCC were calculated from eight major components of LVI (Fig 3). Fig 3 shows the three major components of LVI-IPCC i.e., Adaptive capacity, exposure and sensitivity of Mianwali. The vulnerability factor “Adaptive capacity” comprised of socio-demographic profile, livelihood strategies and social network of local community. Exposure factors consist of climatic variability and natural disasters. In sensitivity index, health, food and water related issues were considered. There are three categories for LVI-IPCC as given by IPCC [17] to check the vulnerability status. The categories of LVI-IPCC overall analysis is given in Table-5 showed that the Mianwali district had very low adaptive capacity. High exposure was attributed to the recurring flooding in the study area. Results of LVI-IPCC showed that the exposure value for Mianwali community was high (0.424). This high value implied a high vulnerability to flooding and climatic variability for the community. The final IPCC weighted LVI scores range between (−1) is not vulnerable and (1) is the highly vulnerable. On the whole, LVI and LVI-IPCC for Mianwali scored 0.4 and 0.0305 respectively. LVI-IPCC results given in Table 5 indicates that the Mianwali community is vulnerable to floods.

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Table 5. LVI and LVI-IPCC based on contributing factors and vulnerability scores for Mianwali.

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A Vulnerability triangular diagram was developed using the scores of LVI-IPCC calculated from LVI. It ranges from 0 to 0.5 demonstrating that Mianwali has a high level of exposure with low adaptive capacity. Mianwali district is geographically prone to repeated floods. People have faced many floods and consequently some adaptations have been learned by them.

Risk analysis: potential hazards of the district

DDMA data regarding flood history in the study area depicts that in 2010, a total of 461,472 people were affected by floods, resulting in 12 deaths. Additionally, 126 revenue estates were affected, with 163,218 houses being partially damaged and 107,190 houses being fully damaged.

In 2011, there is no data available for the number of people affected, deaths, revenue estates affected, or houses damaged, indicating that there were no significant flood events during that year. Similarly, in 2012, 2014, 2016, 2017, 2018, 2019, 2020, and 2021, there were no reported flood events, as indicated by the absence of data in the respective columns.

In 2013, 64,124 people were affected by floods, but there were no reported deaths. Ten revenue estates were affected, with 12,885 houses being partially damaged and 3,206 houses being fully damaged. In 2015, 15,420 people were affected by floods, resulting in four deaths. Additionally, 95 revenue estates were affected, with 56 houses being partially damaged and 1,299 houses being fully damaged.

The historical flood data presented in the above paragraph clearly demonstrates that flood events pose a significant risk. The varying number of people affected, deaths, and damage to properties in different years highlights the unpredictable nature of floods and their potential consequences. While some years may experience low flood occurrences or even no reported incidents, other years show a substantial impact on communities, resulting in a high number of affected individuals, loss of life, and extensive damage to revenue estates and houses. This pattern underscores the importance of recognizing and preparing for the potential risks associated with floods, as they can have devastating effects on both human lives and infrastructure. Understanding the flood history serves as a reminder of the need for proactive measures, such as effective disaster management strategies and infrastructure planning, to mitigate the risks and protect vulnerable communities from future flood events.

Fig 4 illustrates a comprehensive assessment of the risk associated with various hazards.

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Fig 3. Three major components of LVI-IPCC i.e., adaptive capacity, exposure, and sensitivity regarding Mianwali.

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Fig 4. Potential hazards of the district Mianwali.

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Among the hazards shown in Fig 3, floods, hill torrents, and environmental hazards were identified as high-risk (Supporting Information Tables 1–2 in S1 and S2 Files). Floods have a likelihood score of 4 and an impact score of 3, resulting in a risk score of 15–25. Similarly, hill torrents and environmental hazards also fall within the same risk score range, indicating a significant level of risk (Fig 4).

Urban flooding, flash floods, and other major accidents are considered as medium risk category. Urban flooding has a likelihood score of 3 and an impact score of 2, resulting in a risk score of 8–14. On the other hand, landslides, tornadoes, earthquakes, droughts, epidemics, fire incidents, and landslides are classified as low-risk hazards. These hazards have a risk score range of 1–7, indicating a relatively lower level of risk compared to the high and medium-risk hazards.

Overall, this assessment provides valuable insights into the varying levels of risk associated with different hazards. Such information is crucial for stakeholders to prioritize and allocate resources effectively to mitigate and manage these risks efficiently.

Discussion

In this study, the results from the household study, the FGDs and key informant interviews on the impact of floods on people’s livelihoods are addressed with people’s perceptions of flooding as a threat to their livelihoods.

Climate extremes not only adversely affect livelihoods in the short term (in the form of yield losses, loss of human lives and productive assets, and disease spread), but can contribute to a series of long-term impacts such as increasing food prices and loss of agricultural productivity contributing to food shortages over the years [39].

The locals had shared their experiences that agriculture was a subsistence livelihood practice and it had become vulnerable due to dependence on water and temperature. Results of this study revealed that 52.5 percent of the households surveyed had no direct water source to irrigate the crops which may be count as a factor for their high vulnerability and sensitivity. It is extremely significant as agriculture is heavily dependent on water but infrastructure is either inadequate or unavailable to poor farmers [40].

As the pattern of rainfall has changed as told by local residents in FGD’s, the overall agricultural shift and productivity is thus disturbed. Local people noticed that as the rainfall was unpredictable, crop production was influenced by changes in the seeding and harvesting time [41]. Changing climate patterns have also influenced the livestock rearing, collection and selling of wood. Similar results were found in the Jumla district of Nepal where crop growth management activities were usual in both the front and back yard of homes [26].

Seasonal variation showed significant influence on their livelihood strategies particularly those related to the farming and fishing. It was found that 65% of the surveyed households had agriculture as the main livelihood. Most of the households with agriculture as their main earning source reported low agricultural productivity after the flood of 2010 which destroyed their land and damaged their crops. Floods and heavy rainfall wash away the growing field, which also contributes to a decline in crop production [42].

FGD participants had suggested that loss of crop and livestock is the most economically significant effect of flood. This is particularly alarming considering that the increasing frequency and severity of these disasters erode small landholders’ livelihoods, reducing their agricultural assets such as grains, seeds, and soil productivity, and magnifying their vulnerabilities. Moreover, because small farmers primarily meet their household food demand from crops, crop losses will intensify food insecurity [43].

The bulk of the households surveyed in that community was low to medium income and employed in the agriculture sector. Nearly half of the households surveyed were residing in highly vulnerable floodplains and some had also established houses near embankments. Such homes were unlawfully constructed within the floodplains when the floods had receded last time. The design and location of the residential buildings are known as the key factors in the vulnerability analysis [44]. During FGD citizens said their houses were constructed in a conventional fashion and ignored engineered steps. In the area of study there were three types of houses, i.e., i.e., Muddy, Partial Concrete, and Concrete. The research showed that most of the buildings were Muddy while others were Partially Concrete and very few were Concrete homes.

The results of this study revealed that, owing to flood damage, 47 per cent of families moved to other areas. As reported by Najam-u-Din [45], seven million residents around the country were prompted by the 2010 flooding to evacuate their homes at least temporarily. Hoq et al [46] reported similar findings that households in Sunamganj district were more vulnerable to flood hazard and natural disaster in terms of food, water, and health because of lowest adaptive capacity and highest sensitivity and exposure [46].People were confronted with problems due to the loss of houses and communication, health and education facilities, as well as reduced livelihood opportunities, at least in the short term, as the floods had massively destroyed crops and livestock [45]. Focus group discussion members and primary informants acknowledged that flooding has impacted livestock with elevated water quantity.

Well-off and medium-income respondents identified flood-affected farmlands and communal lands, such as forests and highways. Local residents also indicated that the trees were eradicated by the flood which leads to decreased supply of Non-Timber Forest Products (NTFPs). Several families engaged in collecting and marketing medicinal plants suffered when forestland was ruined by flood. Similar findings were found in Nepal when local people collecting fuel wood from nearby woods resulted in a reduced forest size [29].

Food and water vulnerabilities within the Mianwali district, as assessed by the LVI-IPCC, highlight critical challenges faced by the community. Food vulnerability is largely driven by the community’s dependence on agriculture, making it highly sensitive to climatic changes such as frequent flooding, which disrupts crop production and leads to food shortages. Economic factors, including low income and limited financial resources, hinder access to food during these crises, exacerbating food insecurity. Additionally, the resilience of local food systems plays a crucial role; when markets are disrupted, access to food diminishes, increasing vulnerability. Nutritional knowledge also affects dietary diversity and food choices, further contributing to malnutrition.

Water vulnerability focuses on the community’s ability to access clean and safe water, which is influenced by the management of water resources and the state of infrastructure. Ineffective management can lead to scarcity, especially during floods that overwhelm water systems, compromising water quality. Poor infrastructure exacerbates these issues, making it difficult for the community to access safe drinking water. Furthermore, changes in precipitation patterns can affect water availability, and flooding can lead to waterborne diseases when clean sources are contaminated.

The high exposure value (0.424) reflects significant risks from climatic events, directly impacting livelihoods, food security, and water accessibility. The overall vulnerability score (0.0305) underscores the interplay between low adaptive capacity and high exposure, indicating a pressing need for interventions to enhance resilience in food and water systems. Targeted strategies, such as improving agricultural practices, enhancing water management infrastructure, and increasing community awareness about nutrition and water safety, are essential to address these vulnerabilities effectively.

The results implied that adaptive capability for families should also be addressed explicitly, since community capacity is the secret to addressing economic, social and environmental issues. In addressing similar issues in Myanmar, Oo et al. [47] reported that lack of adaptive capability among households is a major cause of high vulnerability to the consequences of climate change and disasters.

It was reported that owing to inadequate knowledge and prevalent poverty, citizens had low adaptation capacity; as the locals’ coping mechanisms were the combination of adaptation action to adjust in changing climate. The lack of government support for the community is a big reason behind low adaptive ability. Just 6 percent of those surveyed received any form of social aid from private NGOs and 8.5 percent received assistance from government agencies. Only 39 percent of surveyed households mentioned having some form of warning the last time a flood occurred in the city.

It was also observed that the local people in research site were discontented with the health services accessible to them. Just 40 percent of the overall households surveyed were found to have basic (emergency) healthcare services. The presence of health care centers in their area was confirmed by 56.5 percent. Community members mentioned that most of the people were too far away during the floods and it was difficult for them to access the medical centers due to blockage of roads as roads and pathways were disrupted or ruined by water and the severity of the floods caused major loss of infrastructure [48].

Around 35.5 percent of the respondents claimed their family members suffered from chronic illness. In terms of sensitivity of livelihoods to flood impacts, human capital plays a fundamental role, especially if taken in terms of health. Health issues reduce labor capacities and result in a reduced number of working days [21].

Livelihood vulnerability was also analyzed according to gender of the household head. The findings of survey showed that the proportion of male- and female-headed households was 82.5% (n =  165) and 17.5% (n =  35), respectively. The findings of vulnerability study according to household head was relevant in the rural context of Nepal where number of female-headed households rapidly increased primarily due to migration of male members to the Gulf and other countries for labor and remittance purpose.

Adaptation includes steps and approaches to mitigate and combat the adverse effects of environmental disasters and climate change in the future [49]. The incidence of flood hazard impacts citizens in different ways. The individuals impacted were compelled to use coping strategies. The surveyed households adopted various preparedness and coping mechanism. 66% of the total households surveyed store food (grains, seeds) for use during or after flooding conditions. Food security improves resilience of households to external pressures like severe weather events [50–52].

Conclusion

Mianwali District’s vulnerability, primarily due to its geographical location along the Indus River, has been exacerbated by severe flood events, notably the 2010 flood. This study utilized the Participatory Rural Appraisal (PRA) technique, including Focus Group Discussions (FGDs) and Transect Walks, to assess local community vulnerability, quantified through the Livelihood Vulnerability Index (LVI), which yielded a value of 0.4. This high LVI underscores the significant vulnerability of riverside communities, particularly driven by poverty and reliance on agriculture and forest resources.

To address these challenges, it is crucial for local government and community leaders to implement targeted rural development programs that enhance coping mechanisms and invest in resilient infrastructure. Specific recommendations include constructing embankments and flood shelters to mitigate flood impacts. Additionally, developing educational programs focused on flood awareness and preparedness will empower residents to respond effectively to hazards. Policymakers should also encourage economic diversification to reduce dependence on vulnerable agricultural and forest-based livelihoods.

Finally, ongoing research and monitoring are essential for understanding community resilience and developing adaptive strategies for flood mitigation. These measures are vital for alleviating the impacts of floods on vulnerable populations in Mianwali District and for promoting sustainable development in the region.

Supporting information

Acknowledgments

Authors are thankful to all the members of the Department of Environmental Sciences, Lahore College for Women University, Lahore, for their support and help during this research.