Landslides pose a significant global threat, causing extensive loss of life, economic damage and environmental degradation. Despite advancements in landslide susceptibility mapping, existing methods often lack global-scale applicability and fail to incorporate robust optimization strategies for improved predictive accuracy. This study addresses these gaps by developing an optimized framework using support vector regression (SVR) enhanced with meta-heuristic algorithms (grey wolf optimizer [GWO] and bat algorithm) to refine model hyper-parameters. It integrates a globally representative data set of 37,984 landslide and non-landslide locations, ensuring broader applicability and generalizability. The information gain ratio method assessed the relative importance of 12 geo-environmental factors influencing landslide. The results indicated that all models achieved good predictive performance during the testing phase, as evidenced by an area under the receiver operating characteristic curve (AUC) value exceeding 0.8, but the SVR-GWO model exhibited the highest prediction accuracy (AUC = 0.92), making it suitable for large-scale hazard assessment. Plan curvature emerged as the most influential factor, surpassing slope, land use, and rainfall that are dominant at regional or local scales. The five countries with the highest landslide-prone areas were Russia, Canada, USA, China, and Brazil. The results support policymakers and urban planners in developing efficient strategies to minimize landslide risks.

Undertaking systemic risk assessments of critical infrastructures (CIs) is necessary to improve understanding, mitigate impacts, and increase resilience to cascading effects of intensifying hydrometeorological hazards. This paper presents a novel quantitative approach with stakholder participation for simulating local physical interdependencies between multiple infrastructure sectors that may be disrupted by floods. The model comprised power, water, telecommunications, emergency, and transport systems. Local (node-edge) resilience metrics were computed to identify critical, vulnerable, and non-redundant CIs in the network. For infrastructures located in areas under risk of floods, global resilience metrics (for whole-network degradation) evaluated failure propagation. The approach was tested in a case study of Halmstad Municipality, Sweden, with a history of extreme hydrometeorological events. Results identified key power, water, and communication infrastructures with high disruption potential under flood exposure, as well as specific residential and industrial areas near hazard zones being the most vulnerable due to their extensive dependencies.

Climate change is known to worsen conflicts, but its combination with other factors affecting water-related conflicts remains less explored. Using a scoping review, this study examined research in the climate–water–conflict nexus. Using semi-automatic text mining approaches, key research gaps and differences in conflict factors and themes across different regions and conflict types were analyzed. Studies focused on Asia and Africa, with few exploring other regions. Governance and livelihoods emerged as significant factors in water-related conflict responses worldwide, with differences across regions. For instance, farmer–herder conflicts were common in Africa, while agriculture was more related to governance and water management in Asia. Research priorities forward should diversify the range of water-related conflict subjects and regions and give special focus to regions vulnerable to hydroclimatic change. More focus on cooperation and non-violent conflicts is also vital for understanding and being able to project and mitigate future water-related conflict responses to climate change.

In this paper we present a framework to aid in the selection of optimal environmental indicators for detecting and mapping extreme events and analyzing trends in heatwaves, meteorological and hydrological droughts, floods, and their compound occurrence. The framework uses temperature, precipitation, river discharge, and derived climate indices to characterize the spatial distribution of hazard intensity, frequency, duration, co-occurrence, and dependence. The relevant climate indices applied are Standardized Precipitation Index, Standardized Precipitation and Evapotranspiration Index (SPEI), Standardized Streamflow Index, heatwave indices based on fixed (HWIS) and anomalous temperatures (HWIE), and Daily Flood Index (DFI). We selected suitable environmental indicators and corresponding thresholds for each hazard based on estimated extreme event detection performance using receiver operating characteristics (ROC), area under curve (AUC), and accuracy, which is defined as the proportion of correct detections. We assessed compound hazard dependence using a Likelihood Multiplication Factor (LMF). We tested the framework for the case of Sweden, using daily data for the period 1922–2021. The ROC results showed that HWIS, SPEI12 and DFI are suitable indices for representing heatwaves, droughts, and floods, respectively (AUC > 0.83). Application of these indices revealed increasing heatwave and flood occurrence in large areas of Sweden, but no significant change trend for droughts. Hotspots with LMF > 1, mostly concentrated in Northern Sweden from June to August, indicated that compound drought-heatwave and drought-flood events are positively correlated in those areas, which can exacerbate their impacts. The novel framework presented here adds to existing hydroclimatic hazard research by (1) using local data and historical records of extremes to validate indicator-based hazard hotspots, (2) evaluating compound hazards at regional scale, (3) being transferable and streamlined, (4) attaining satisfactory performance for indicator-based hazard detection as demonstrated by the ROC method, and (5) being generalizable to various hazard types.

Soil erosion is a major degradation process affecting many regions worldwide and impairing the ability of soil to provide ecosystem services. This chapter provides a brief overview of the soil erosion processes and their susceptibility in different land uses, including agricultural, forest, and urban land, as well as different soil management practices that have been implemented to mitigate the problem. Additionally, this chapter synthesizes information regarding the use of remote sensing to support soil erosion assessments, providing examples of remote sensing data used in different studies to support soil erosion modeling.

Landslides are the most common natural disasters in mountainous areas that follow major seismic events, volcanic activity, melting snow, or prolonged and intense rainfalls and cause severe disruptions to ecosystems, economies, and societies worldwide. Therefore, minimizing their negative effects through landslide-susceptibility assessment is essential. In this study, the standard support vector regression (SVR) integrated with the gray wolf optimizer (GWO) and particle swarm optimization (PSO) algorithms were used to map landslide-prone areas. The landslide inventory map was automatically generated using a pixel-based technique based on residual U-Net algorithm from the Sentinel-2 data. In total, 4900 landslide samples were identified and divided randomly into two groups, creating training (70%) and testing (30%) datasets. In addition, nine factors that affect landslides were selected to construct a model using each algorithm. Finally, the performance of the models (SVR, SVR-GWO, and SVR-PSO) were validated and compared using the area under the receiver operating characteristic curve. The findings showed that the hybrid SVR-GWO model performed better than the standard model and is recommended for landslide susceptibility assessment due to its accuracy and efficiency.

Climate sensitivity of infectious diseases is discussed in many studies. A quantitative basis for distinguishing and predicting the disease impacts of climate and other environmental and anthropogenic driver-pressure changes, however, is often lacking. To assess research effort and identify possible key gaps that can guide further research, we here apply a scoping review approach to two widespread infectious diseases: Lyme disease (LD) as a vector-borne and cryptosporidiosis as a water-borne disease. Based on the emerging publication data, we further structure and quantitatively assess the driver-pressure foci and interlinkages considered in the published research so far. This shows important research gaps for the roles of rarely investigated water-related and socioeconomic factors for LD, and land-related factors for cryptosporidiosis. For both diseases, the interactions of host and parasite communities with climate and other driver-pressure factors are understudied, as are also important world regions relative to the disease geographies; in particular, Asia and Africa emerge as main geographic gaps for LD and cryptosporidiosis research, respectively. The scoping approach developed and gaps identified in this study should be useful for further assessment and guidance of research on infectious disease sensitivity to climate and other environmental and anthropogenic changes around the world.

Coastal regions host a large portion of the global population and face complex balancing challenges for achieving economicEconomic, socialSocial, and environmental sustainabilityEnvironmental sustainability. This chapter presents an innovative participatory approachParticipatory approach to meeting these challenges, developed in the EU Horizon 2020 project “The collaborative land-sea integration platform” (COASTAL) and applied and tested in two Mediterranean coastal case studies of Mar Menor in SpainMar Menor Spainand South-Western MessiniaSouth-Western Messinia Greece in Greece. The chapter describes the COASTAL participatory approachParticipatory approach and the outcomes of its application to these two Mediterranean cases. This approach engages stakeholders from various sectors to identify key challenges and robust solution roadmaps for meeting them to achieve a sustainable coastal development. It further employs system dynamics modeling, scenario analysis, and solution robustness analysis to assess the effects of the stakeholder-prioritized solution roadmaps under uncertain future climate and socioeconomic conditions. The outcomes for the Mediterranean case studies are consistent in identifying rural-coastal management integration and synergy (such as promoting rural and coastal ecotourism and transitioning from conventional to more sustainable farming practices) as key solution components of a robust sustainable development roadmap. In both cases, a timely (even partial) implementation of the stakeholder-prioritized solution roadmaps is also found to be critical for improving local population well-being, meeting water availability and quality demands, protecting good ecological status in the coastal lagoons, and strengthening the regional resilience to uncertain external factors, such as forthcoming climate change and new future socioeconomic challenges.

Flooding is one of the most serious and frequent natural hazards affecting human life, property, and the environment. This study develops and tests a deep learning approach for large-scale spatial flood modeling, using Convolutional Neural Network (CNN) and optimized versions combined with the Gray Wolf Optimizer (GWO) or the Imperialist Competitive Algorithm (ICA). With Sweden as an application case for nation-wide flood susceptibility mapping, this modeling approach considers ten geo-environmental input factors (slope, elevation, aspect, plan curvature, length of slope, topographic wetness index, distance from river, distance from wetland, rainfall, and land use). The GWO and ICA optimization improves model prediction by 12% and 8%, respectively, compared with the standalone CNN model performance. The results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility. They also show the aspect to have the highest input factor impact on flood susceptibility prediction while, for example, rainfall ranks only seven of the total 10 considered geo-environmental input factors. In general, accurate nation-wide flood susceptibility prediction is essential for guiding flood management and mitigation efforts. This study's approach to such prediction has emerged as well-performing and cost-effective for the case of Sweden, calling for further application and testing in other world regions.

Agricultural land degradation is a global problem affecting food production and other ecosystem services worldwide such as water regulation. It is driven by unsustainable land use and management practices (e.g. intensive tillage, overuse of agrochemicals) and can be aggravated by future climate change. Land degradation is particularly problematic in arid and semi-arid areas of southern Europe, and distinct soil degradation processes impair agricultural areas in Portugal and Greece. This chapter aims to improve understanding of various degradation processes affecting agricultural land, including soil erosion, compaction, contamination, and salinity and sodicity. It summarises the scientific literature on the current status of these degradation processes in agricultural areas of Portugal and Greece and their main causes and consequences. Moreover, it provides examples of best management practices implemented to mitigate agricultural land degradation. Some degradation processes are relatively well documented (e.g. erosion), while knowledge of the spatial extent of others such as soil compaction is still limited. A better understanding of soil degradation processes and of the counter-impacts of improved agricultural management practices is critical to support decision-making and ensure long-term fertility and productivity, thereby maintaining the sustainability of agriculture.