ASSESSMENT OF FLOOD HAZARD SUSCEPTIBILITY IN SOUTH SUDAN’S UPPER NILE STATE USING GIS-BASED MULTICRITERIA ANALYSIS

openLe alluvioni sono tra i rischi naturali più rovinosi. I loro effetti avversi comprendono danni alle strutture fisiche, sociali ed economiche, ed un deterioramento dei mezzi di sussistenza. Allo stesso tempo, attribuito alle variazioni climatiche ed eventi estremi causati dal cambiamento climatico, è stato registrato un incremento nella frequenza di alluvioni a livello globale, aumentando la necessita di comprendere gli aspetti spazio-temporali di questi fenomeni. Questo studio esamina la dimensione spaziale del rischio di inondazione nell’Alto Nilo, Sudan del Sud, regione con una riconosciuta vulnerabilità verso le inondazioni, causata principalmente dal suo posizionamento geografico all’interno di una pianura alluvionale caratterizzata da una notevole variabilità della portata di piena. L’obiettivo di questa indagine è quello di mappare la potenziale estensione spaziale degli allagamenti all’interno dell’area di studio in uno scenario di inondazione. La mappa del rischio di inondazioni, fondata su diversi indici, è stata sviluppata utilizzando una decisione d’analisi multicriteriale (MCDA) basata su GIS, ed il analytical hierarchy process (AHP). Gli otto fattori d’influenza per le alluvioni utilizzati per lo studio sono: distanza da fiumi, indice di umidità topografica, densità di drenaggio, copertura del suolo (LULC), precipitazioni medie annue, pendenza, altitudine, e tipo di suolo. La mappa del rischio di inondazione sviluppata per l’area di studio è composta da cinque zone di suscettibilità: molto alta, alta, moderata, bassa, molto bassa. Queste zone coprono rispettivamente il 12%, 26%, 29%, 22%, e 9% dell’area di studio. La mappa è stata ulteriormente validata tramite un confronto con la mappa satellitare dello storico delle inondazioni, ed è risultata soddisfacente nello stimare la probabile estensione spaziale degli allagamenti. Il modello della mappa è potrà risultare strumentale per le misure di preparazione alle inondazioni, e come guida per future indagini specifiche nella dimensione spazio-temporale di eventi alluvionali nella regione dell’Alto Nilo.Floods are among the most ruinous of all natural hazards. Its adverse effects include damages to the physical, social, and economic structures, and disruption of livelihoods. contemporary, attributed to climate change-induced climate variations and extreme weather events, the frequency of flood occurrence has increased all around the globe. This has therefore, augmented the necessity to comprehend the spatial and temporal dimension of flood phenomena. The current study examines the spatial dimension of flood hazard in the Upper Nile state, South Sudan, a region acknowledged to be highly vulnerable to inundation, mainly due to is geographical position within a flood plain characterized by a notable variability in discharge. The objective of this investigation is to map the potential spatial extent of floodwater within the boundaries of study area under flood scenarios. The index-based flood hazard map was developed using GIS-based multicriteria decision analysis (MCDA), and the analytical hierarchy process (AHP). Eight flood influencing factors were used in this study, namely; distance to rivers, topographic wetness index, drainage density, land-coverage (LULC), annual average rainfall, slope, elevation, and soil types. The flood hazard map developed for study area consist of five flood hazard susceptibility zones: very high, high, moderate, low, and very low. These zones encompass proportions of 12%, 26%, 29%, 22%, and 9% of the study area, respectively. The flood hazard map was further validated using satellite historical inundation map and determined to be satisfactory in depicting the probabilistic spatial extent of inundation. The flood hazard model developed is anticipated to be instrumental in pre-flood preparedness measures as well as a guide for future detailed investigations on the spatial–temporal dimension of flood incidents in the Upper Nile state

GIS-based multi-criteria decision making for delineation of potential groundwater recharge zones for sustainable resource management in the Eastern Mediterranean: a case study

In light of population growth and climate change, groundwater is one of the most important water resources globally. Groundwater is crucial for sustaining many vital sectors in Syria, including industrial and agricultural sectors. However, groundwater exploitation has significantly escalated to meet different water needs especially in the post-war period and the earthquake disaster. Therefore, the goal was this study delineation of the groundwater potential zones (GPZs) by integrating the analytic hierarchy process (AHP) method in a geographic information systems (GIS) within the AlAlqerdaha river basin in western Syria. In this study, ten criteria were used to map the spatial distribution of GPZs, including slope, geomorphology, drainage density, land use/land cover (LU/LC), lineament density, lithology, rainfall, soil, curvature and topographic wetness index (TWI). GPZs map was validated by using the location of 74 wells and the Receiver Operating Characteristic Curve (ROC). The findings suggest that the study area is divided into five GPZs: very low, 21.39 km2 (10.87%); low, 52.45 km2 (26.65%); moderate, 65.64 km2 (33.35%); high, 40.45 km2 (20.55%) and very high, 16.90 km2 (8.58%). High and very high zones mainly corresponded to the western regions of the study area. The conducted spatial modeling indicated that the AHP-based GPZs map showed a remarkably acceptable correlation with wells locations (AUC = 87.7%, n = 74), demonstrating the precision of the AHP–GIS as a rating method. The results of this study provide objective and constructive outputs that can help decision-makers to optimally manage groundwater resources in the post-war phase in Syria

Landslides Hazard Mapping in Rwanda Using Bivariate Statistical Index Method

Landslides hazard mapping (LHM) is essential in delineating hazard prone areas and optimizing low cost mitigation measures. This study applied the Geographic Information System and statistical index method in LHM in Rwanda. Field surveys identified 336 points that were employed to construct a landslides inventory map. Ten landslides predicting factors were analyzed: normalized difference vegetation index, elevation, slope, aspects, lithology, soil texture, distance to rivers, distance to roads, rainfall, and land use. The factor variables were converted into categorized variables according to the percentile divisions of seed cells. Then, values of each factor’s class weight were calculated and summed to create landslides hazard map. The estimated hazard map was split into five hazard classes (very low, low, moderate, high, and very high). The results indicated that the northern, western, and southern provinces are largely exposed to landslides hazard. The major landslides hazard influencing factors are elevation, slope, rainfall, and poor land management. Overall, this LHM would help policy makers to recognize each area’s hazard extent, key triggering factors, and the required hazard mitigation measures. These measures include planting trees to enhance vegetation cover and reduce the runoff, and construction of buildings on low steep slope areas to reduce people’s hazard exposure; while agroforestry and bench terraces would reduce sediments that take out the exposed soil (erosion) and pollute water quality

Decision-analytic frameworks for multi-hazard mitigation and adaptation

Developing effective decision-support for multiple hazards needs to build on a foundation of existing research into best practices for the management of single hazards analysis. This comes from the hazards literature, recent and ongoing EU research projects, and from the climate vulnerability literature, in which the theoretical focus on multiple drivers of vulnerability is already well established. The first part of this task will rely on a desk study of established management practices and decision-analytic methods. The latter include several standard methods for conducting sound formal decision-analysis, including cost-benefit analysis, risk- benefit analysis, and multi-criteria analysis. Each of these has its strengths, weaknesses, and set to best practices in particular contexts. The second part of this task will identify these in the case of multiple hazards, and appraise how they may differ in their application and appropriateness from the single-hazard case. It will rely on an application of these modeling methods to the simulated city case study

Geospatial methods and tools for natural risk management and communications

In the last decade, real-time access to data and the use of high-resolution spatial information have provided scientists and engineers with valuable information to help them understand risk. At the same time, there has been a rapid growth of novel and cutting-edge information and communication technologies for the collection, analysis and dissemination of data, re-inventing the way in which risk management is carried out throughout its cycle (risk identification and reduction, preparedness, disaster relief and recovery). The applications of those geospatial technologies are expected to enable better mitigation of, and adaptation to, the disastrous impact of natural hazards. The description of risks may particularly benefit from the integrated use of new algorithms and monitoring techniques. The ability of new tools to carry out intensive analyses over huge datasets makes it possible to perform future risk assessments, keeping abreast of temporal and spatial changes in hazard, exposure, and vulnerability. The present special issue aims to describe the state-of-the-art of natural risk assessment, management, and communication using new geospatial models and Earth Observation (EO)architecture. More specifically, we have collected a number of contributions dealing with: (1) applications of EO data and machine learning techniques for hazard, vulnerability and risk mapping; (2) natural hazards monitoring and forecasting geospatial systems; (3) modeling of spatiotemporal resource optimization for emergency management in the post-disaster phase; and (4) development of tools and platforms for risk projection assessment and communication of inherent uncertainties

Understanding the vulnerability of the population of Afghanistan under multiple natural and anthropogenic risks with an indicator-based analysis

The purpose of this study is to understand the vulnerability to natural and anthropogenic hazards of the population of Afghanistan and the social factors which enhance or moderate such vulnerability. While vulnerability studies are commonly conducted in the United States, as well as many other global north countries, most studies of this type utilize data collected by central government entities in the form of a census which is periodically executed and uses standardized collection methods. In the case of Afghanistan, and many other countries in the global south, such data is hard to acquire, lacks a high level of confidence, or does not exist. For these reasons, this study will focus on efficiently utilizing data which has been collected by the Central Statistics Organization of Afghanistan, as well as data compiled and made available by the Oak Ridge National Laboratory (ORNL) Geographic Information Systems and Technology (GIS&T) Group to identify the most significant indicators of vulnerability within the population of Afghanistan. The result of this study is a by district analysis of the country of Afghanistan, in which vulnerability to hazards is inferred for the population of each district and ranked based on the relative vulnerability of the population. This information can assist the Government of the Islamic Republic of Afghanistan, as well as other aid organizations, to prepare to respond to humanitarian crises more effectively

Assessing Extreme Rainfall Variation and its Relation to Flood Hazard in Sri Lanka

Climate change intensifies extreme rainfall and flood hazards, significantly impacting tropical nations like Sri Lanka. The research reveals increased extreme rainfall across the island, leading to a wetter environment in dry and intermediate zones. An increasing rainfall pattern in the Kelani River basin creates a probability of flooding, and nearly 16.4% of the lower basin is identified as highly susceptible to floods. The findings can contribute to improved disaster preparedness and mitigation efforts effectively

Hydro-meteorological risk assessment methods and management by nature-based solutions

Hydro-meteorological risk (HMR) management involves a range of methods, such as monitoring of uncertain climate, planning and prevention by technical countermeasures, risk assessment, preparedness for risk by early-warnings, spreading knowledge and awareness, response and recovery. To execute HMR management by risk assessment, many models and tools, ranging from conceptual to sophisticated/numerical methods are currently in use. However, there is still a gap in systematically classifying and documenting them in the field of disaster risk management. This paper discusses various methods used for HMR assessment and its management via potential nature-based solutions (NBS), which are actually lessons learnt from nature. We focused on three hydro-meteorological hazards (HMHs), floods, droughts and heatwaves, and their management by relevant NBS. Different methodologies related to the chosen HMHs are considered with respect to exposure, vulnerability and adaptation interaction of the elements at risk. Two widely used methods for flood risk assessment are fuzzy logic (e.g. fuzzy analytic hierarchy process) and probabilistic methodology (e.g. univariate and multivariate probability distributions). Different kinds of indices have been described in the literature to define drought risk, depending upon the type of drought and the purpose of evaluation. For heatwave risk estimation, mapping of the vulnerable property and population-based on geographical information system is a widely used methodology in addition to a number of computational, mathematical and statistical methods, such as principal component analysis, extreme value theorem, functional data analysis, the Ornstein–Uhlenbeck process and meta-analysis. NBS (blue, green and hybrid infrastructures) are promoted for HMR management. For example, marshes and wetlands in place of dams for flood and drought risk reduction, and green infrastructure for urban cooling and combating heatwaves, are potential NBS. More research is needed into risk assessment and management through NBS, to enhance its wider significance for sustainable living, building adaptations and resilience