Flooding is one of the most widespread and destructive natural disasters, causing immense economic losses and far-reaching consequences globally. In our 15 years installing… In the Hunza-Nagar valley of Northern Pakistan, communities face acute vulnerability to floods, posing significant hurdles to sustainable development. To address this critical challenge, this study aims to comprehensively evaluate flood risk in the region by integrating big climate data records with a GIS-based Multi-Criteria Decision Analysis (MCDA) approach.
Now, this might seem counterintuitive…
By employing a robust flood risk assessment model, the research team generated a detailed flood hazard map considering nine influential factors: rainfall, regional temperature variation, distance to the river, elevation, slope, Normalized Difference Vegetation Index (NDVI), Topographic Wetness Index (TWI), land use/land cover (LULC), curvature, and soil type. The Analytical Hierarchy Process (AHP) was utilized to assign weights to each factor, which were then integrated with geospatial data using Geographic Information Systems (GIS) to produce comprehensive flood risk maps.
The study assigned higher significance to rainfall, distance to the river, elevation, and slope compared to NDVI, TWI, LULC, curvature, and soil type. The weighted overlay flood risk map identified 6% of the total area as very high, 36% as high, 41% as moderate, 16% as low, and 1% as very low flood risk. The accuracy of the flood risk model was validated through the Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) analysis, yielding a commendable prediction accuracy of 0.773.
This MCDA approach offers an efficient and direct means of flood risk modeling, utilizing fundamental GIS data. The model serves as a valuable tool for decision-makers, enhancing flood risk awareness and providing vital insights for disaster management authorities in the Hunza-Nagar Valley. As future developments unfold, this study remains an indispensable resource for disaster preparedness and management in the Hunza-Nagar Valley region.
Flood Risk Assessment and Mapping
Floods are an inherent component of the hydrological cycle, and their occurrence can lead to loss of life, population displacement, and environmental devastation, significantly jeopardizing economic progress. Certain human activities, such as irresponsible and negligent actions within the basin, can diminish the natural retention and transformation capacities, leading to a surge in economic losses and heightened flooding risks.
To mitigate flood damage, the application of conventional flood protection measures remains crucial. However, equal emphasis should be placed on flood prevention and flood risk management, as their roles are progressively becoming more significant. Floods are one of the most destructive natural disasters on Earth, impacting around a billion people who live in areas that could flood.
Climate change projections indicate a significant increase in extreme hydrological events and flood risks in the future. Both industrialized and developing nations face growing concerns over natural hazards, with natural disasters accounting for 40% of global socioeconomic losses. Global warming alters rainfall patterns, leading to intensified and extreme rainfalls, and causing severe floods and extensive damage.
These floods result in significant socioeconomic losses, loss of lives, and property destruction. During the summer monsoon season, South Asian countries experience significant impacts from floods, resulting in extensive devastation to both human lives and livelihoods. The frequency of extreme floods is increasing in South Asian countries, and future projections suggest that these events are likely to occur even more frequently due to the warming climate.
Flood Risk in the Hunza-Nagar Valley
Pakistan is a country that experiences considerable vulnerability to natural hazards, with floods becoming a significant and urgent concern, resulting in considerable damage to livelihoods and human life. The catastrophic flood that struck Pakistan in August 2022, preceded by a severe heat wave in May, has disproportionately affected the southern provinces of the country.
The Hunza-Nagar Valley in Pakistan has a significant historical record of flood events, with widespread consequences for the region. Located in the northern part of the country, the valley is susceptible to flooding during the summer monsoon season when intense rainfall occurs in the river catchments. The combination of melting snow from alpine regions and the monsoonal rains can lead to substantial floods, posing risks to human lives, infrastructure, and agricultural lands.
Over the years, the valley has witnessed several devastating floods that have caused extensive destruction and forced communities to be displaced. Particularly, flash floods triggered by intense localized rainfall in hilly and semi-hilly areas have been extremely destructive, resulting in loss of lives and property damage. The unpredictability and severity of these floods highlight the urgency to implement effective flood risk management and mitigation strategies to protect the valley’s population and infrastructure.
Integrating Satellite Data and GIS
Flood risk, also known as susceptibility, is the probability that a certain location may be impacted by a flood event. Flood vulnerability evaluates the possible degree of destruction and adverse outcomes that could occur following a flood. This assessment takes into account a number of variables, including the likelihood of flooding, the susceptibility of populations and infrastructure to flooding, and the possible effects on the environment, the economy, and society.
Flood risk is a comprehensive evaluation of the potential for loss and damage due to flooding and goes beyond simple vulnerability. It considers hydrodynamic, ecological, social, economic, and physio-climatic variables. Analyzing flood risk can be a complex and challenging task, mainly due to limitations in data availability. To overcome these challenges, numerical modeling and index-based analyses have gained popularity as effective alternatives for flood risk assessments.
The multi-criteria decision analysis model, specifically using the Analytical Hierarchy Process (AHP) as an empirical approach, holds significant recognition and widespread adoption in flood risk assessment. AHP involves a weighted evaluation process based on pairwise comparisons of each parameter, leading to the identification of optimal solutions for specific problems. This method has proven successful in flood analysis and mapping of flood-prone areas, particularly when integrated with Geographic Information Systems (GIS) and remote sensing.
Flood-prone area mapping necessitates the integration of diverse datasets, encompassing topographic characteristics extracted from Digital Elevation Models (DEMs), hydrological data like rainfall and river discharge, and geomorphological information. These vital datasets are commonly obtained through remote sensing techniques and subsequently analyzed using Geographic Information Systems (GIS) to create comprehensive flood-prone area maps.
Methodology and Data
The flood risk assessment for the proposed site incorporates various essential factors, including precipitation, distance to the river, elevation, slope, topographic wetness index (TWI), normalized difference vegetation index (NDVI), land use land cover (LULC), curvature, and soil type. These criteria and their respective significance have been carefully determined through a thorough analysis of existing research and relevant studies.
The Hunza-Nagar Valley in Pakistan is located within the geographical coordinates of approximately 35°45′15′′to 37°00′N latitude and 74°00′00′′to 76°05′25′′E longitude, and is nestled amidst the breathtaking landscapes of the northern region, surrounded by the majestic Himalayas. The valley encompasses an estimated area of 14,440 square kilometers, with elevations ranging from approximately 1,640 to 7,896 m above sea level.
The methodology employed in this research is structured into five main sections: 1) data collection, 2) identification of factors contributing to flood risk, 3) data processing using GIS to obtain reclassified maps for these contributing factors, 4) overlaying the reclassified maps using the AHP method to produce the flood risk map, and 5) validation of the achieved result.
The data acquisition process played a crucial role in evaluating urban flood risk for the current study area. Reputable sources were utilized to obtain data related to key factors influencing flood risk, including the Digital Elevation Model (DEM) from Aster Global Digital Elevation Model, soil data from the World Digital Soil Map, High-Resolution Land Use/Land Cover Map from Sentinel-2, Level-1 Satellite Imagery (Landsat 8–9) for vegetation analysis, and Monthly High-Resolution Precipitation Data (CRU TS) for rainfall data.
Flood Risk Mapping
The analytical hierarchy process (AHP) was applied for a weighted overlay analysis, resulting in the final flood risk map. The AHP is a highly effective method used in a variety of decision-making situations, taking into account the preferences of individuals or groups and facilitating the evaluation of both quantitative and qualitative criteria.
In this study, a total of nine factors influencing floods were identified, and Saaty’s scale of preference ranging from 1 to 9 was utilized to establish the relative weight of each layer. A pairwise comparison matrix table was constructed to facilitate the assessment process, with each aspect systematically compared to determine their respective importance.
The pairwise comparison matrix obtained for the current study had a consistency ratio (CR) equal to 3.5% < 10%, which is considered of viable consistency. The percentage weights assigned to each criterion placed greater emphasis on the first four criteria: rainfall, distance to rivers, slope, and elevation, compared to the remaining five criteria.
The flood risk map was created by integrating the reclassified maps of the nine parameters using the weighted overlay analysis in ArcGIS. The resulting map shows the various levels of flood risk in the research area, with 16% classified as low risk, 1% as very low risk, 36% as high risk, 41% as moderate risk, and 6% as very high risk.
Validation and Implications
To validate the flood risk model, the Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) analysis was conducted by comparing the flood risk map raster layer with flooded and non-flooded point features. The AUC value for the current model is 0.773 (77.3%), placing it in the “good” category and nearly approaching the “very good” range, demonstrating the model’s effective performance in generating the flood risk map.
The flood risk map generated in this study holds significant implications for policymakers and disaster management authorities in the Hunza-Nagar Valley. By effectively identifying areas prone to flooding, authorities can implement preemptive measures to minimize potential damage and protect lives and assets during flood events. The Multi-Criteria Decision Analysis (MCDA) framework integrated within this study aids in formulating nuanced flood vulnerability assessments that encompass various dimensions such as economics, environment, and societal wellbeing.
This research contributes to informed disaster planning, bolstering community resilience, and facilitating disaster response mechanisms in flood-prone regions. It furnishes a valuable tool for disaster management and planning entities to strategically allocate resources and establish preemptive measures in areas predisposed to flooding. As future developments unfold, this study remains an indispensable resource for disaster preparedness and management in the Hunza-Nagar Valley region.
Conclusion
This study utilized the GIS-based AHP technique to create a comprehensive flood risk map for Pakistan’s Hunza-Nagar Valley. By analyzing important flood-triggering elements such as rainfall, river distance, elevation, slope, curvature, NDVI, TWI, LULC, and soil type, and integrating them using the AHP approach, the research team generated a detailed flood risk map for the study area.
The generated map classifies the flood risk in the Hunza-Nagar Valley into five distinct categories: 16% low risk, 1% very low risk, 36% high risk, 41% moderate risk, and 6% very high risk. This flood risk map serves as a valuable tool for policymakers and planners, aiding in informed decision-making regarding climate change adaptation, land use planning, and flood risk management.
The study emphasizes the significance of combining diverse geographic information sources to improve the accuracy of risk assessment. Regions classified as high- and extremely high-risk should be examined more thoroughly using higher-resolution satellite images. This research underlines the potential of geospatial approaches in disaster analysis and the vital significance of precise data integration.
In the context of an increasingly vulnerable world, the flood risk map contributes to better risk mitigation strategies, ensuring the safety of communities and their assets. As future developments unfold, this study remains an indispensable resource for disaster preparedness and management in the Hunza-Nagar Valley region.
Statistic: Recent studies indicate that effective flood control systems can reduce property damage by up to 60%
