Explore the vast range of titles available.

Numerous flood hazard assessment and risk management studies depend on hydrodynamic flood models, which require detailed inputs. However, these models face challenges when assessing flood hazards and risks at national scales due to the unavailability of input data and high computational demands. Recent advancements in global flood models (GFMs) have emerged as promising solutions. These widely adopted GFMs, capable of producing flood characteristics, require runoff input typically derived from land surface models (LSMs) or global hydrological models (GHMs), which are prone to inherit cascading uncertainties. Moreover, the utilization of a single runoff input into a GFM can produce biased and misinterpreted flood hazards due to underestimation or overestimation of GFM outputs. To highlight these implications, the present study examines GFM simulations forced with eight state-of-the-art model runoff datasets, including LSMs, GHMs, and reanalysis observations, uncovering unsafe inter-model flood depth variation (IMDV). Focusing on the flood-prone Mahanadi River Basin (MRB) of India, the study observes that IMDV surpasses the self-help range of humans (0.2 m) for 65% of the MRB region, and exceeds human and vehicle safety thresholds (2 m) for 15% of the region, based on four past flood events from the Dartmouth Flood Observatory. These regions exhibiting high IMDV overlap with densely populated areas, potentially affecting 1.66–3.65 million people. Thus, the injudicious use of runoff in GFM for flood disaster planning can lead to inaccurate flood hazard and risk assessments, significantly affecting populous regions. An alternative approach is recommended, advocating for the use of multiple simulations incorporating diverse runoff datasets. This approach would generate conservative and optimistic flood scenarios, leveraging each model’s strengths. Such comprehensive hazard scenarios would enhance flood management and decision-making for policymakers by addressing the uncertainty and providing possible impacts through risk assessments.

Floodplains are highly complex and dynamic systems in terms of their hydrology. Thus, they harbor highly specialized floodplain plant species depending on different inundation characteristics. Climate change will most likely alter those characteristics. This study investigates the potential impact of climate change on the inundation characteristics of a floodplain of the Rhine River in Hesse, Germany. We report on the cascading uncertainty introduced through climate projections, climate model structure, and parameter uncertainty. The established modeling framework integrates projections of two general circulation models (GCMs), three emission scenarios, a rainfall–runoff model, and a coupled surface water–groundwater model. Our results indicate large spatial and quantitative uncertainties in the simulated inundation characteristics, which are mainly attributed to the GCMs. Overall, a shift in the inundation pattern, possible in both directions, and an increase in inundation extent are simulated. This can cause significant changes in the habitats of species adapted to these highly-endangered ecosystems.

Available transfer capability (ATC) provides important information for power marketers and planning sectors in restructured power systems. The stochastic nature of power system behaviour, however, made ATC determination a difficult and complicated task. A stochastic framework has been established for ATC calculation with implication of uncertainties in transmission failures, hourly peak loads and system cascading collapse. The proposed ATC calculation framework is based on parametric bootstrap technique which enables generating random samples of system operating condition with uncertainty at a predefined confidence interval. The IEEE RTS-96 network is employed to demonstrate the effectiveness of the proposed framework. The results presented indicate the significant impact of uncertainties on ATC value.

Climate change affects inequalities between countries in two ways. On the one hand, rising temperatures from greenhouse gas accumulation cause impacts that fall more heavily on low-income countries. On the other hand, the costs of mitigating climate change through reduced emissions could slow down the economic catch-up of poor countries. Whether, and how much the recent decline in between-country inequalities will continue in the twenty-first century is uncertain, and the existing projections rarely account for climate factors. In this study, we build scenarios that account for the joint effects of mitigation costs and climate damages on inequality. We compute the evolution of country-by-country GDP, considering uncertainty in socioeconomic assumptions, emission pathways, mitigation costs, temperature response, and climate damages. We analyze the resulting 3408 scenarios using exploratory analysis tools. We show that the uncertainties associated with socioeconomic assumptions and damage estimates are the main drivers of future inequalities. We investigate under which conditions the cascading effects of these uncertainties can counterbalance the projected convergence of countries’ incomes. We also compare inequality levels across emission pathways and analyze when the effect of climate damages on inequality outweigh that of mitigation costs. We stress the divide between IAM- and econometrics-based damage functions in terms of their effect on inequality. If climate damages are as regressive as the latter suggest, climate mitigation policies are key to limit the rise of future inequalities between countries.

Successive major landslides during October and November 2018 in Baige village, eastern Tibet, dammed the Jinsha River on two occasions, and the subsequent dam breaches instigated a multi-hazard chain that flooded many towns downstream. Analysis of high-resolution aerial images and field investigations unveiled three potentially unstable rock mass clusters in the source area of the landslides, suggesting possible future failures with potential for river-damming and flooding. In order to evaluate and understand the disaster chain effect linked to the potentially unstable rock mass, we systematically studied the multi-hazard scenarios through an integrated numerical modelling approach. Our model begins with an evaluation of the probability of landslide failure, including runout and river damming, and then addresses the dam breach and resultant flood—hence simulating and visualising an entire disaster chain. The model parameters were calibrated using empirical data from the two Baige landslides. Then, we predict the future cascading hazards via seven scenarios according to all possible combinations of potential rock mass failure. For each scenario, the landslide runouts, dam-breaching, and flooding are numerically simulated with full consideration of uncertainties among the model input parameters. The maximum dam breach flood extent, depth, velocity, and peak arrival time are predicted at sequential sites downstream. As a first attempt to simulate the full spectrum of a landslide-induced multi-hazard chain, our study provides insights and substantiates the value provided by multi-hazard modelling. The integrated approach described here can be applied to similar landslide-induced chains of hazards in other regions.

The Greater Himalayas hold the largest mass of ice outside polar regions and are the source of the 10 largest rivers in Asia. Rapid reduction in the volume of Himalayan glaciers due to climate change is occurring. The cascading effects of rising temperatures and loss of ice and snow in the region are affecting, for example, water availability (amounts, seasonality), biodiversity (endemic species, predator-prey relations), ecosystem boundary shifts (tree-line movements, high-elevation ecosystem changes), and global feedbacks (monsoonal shifts, loss of soil carbon). Climate change will also have environmental and social impacts that will likely increase uncertainty in water supplies and agricultural production for human populations across Asia. A common understanding of climate change needs to be developed through regional and local-scale research so that mitigation and adaptation strategies can be identified and implemented. The challenges brought about by climate change in the Greater Himalayas can only be addressed through increased regional collaboration in scientific research and policy making.

Climate change loss and damage (L&D) presents an existential threat to the Fiji Islands. This case study examines how rural Indo-Fijian sugarcane farming communities face challenges in minimising, averting, and addressing L&D from cyclones. In-depth semi-structured interviews (n = 68) were conducted with 40 sugarcane farmers in two Indo-Fijian sugarcane communities, Barotu and Toko settlements in Western Viti Levu, Fiji, and with 28 key stakeholders from government ministries, academia, and climate change response services. Despite implementing climate change adaptation measures, Fiji’s sugar industry has faced devastating L&D from frequent and severe cyclones. Much of the climate change L&D to crops, property, and income was irreversible and unavoidable. Non-economic loss and damage (NELD) was found insurmountable in both field sites, including the loss of homes and places of worship, cascading and flow-on effects as well as the heightening of uncertainty, fear, and trauma. Evidence suggests that L&D, including NELD, is highly context specific, and UNFCCC’s broad NELD categories do not fully capture L&D at the local level. The systematic documentation of L&D within vulnerable communities would improve understanding of L&D, including NELD, and assist to facilitate the mobilisation of immediate support and action to address L&D in countries that lack the capacities to respond independently. This paper recommends crucial policy interventions such as livelihood diversification, integration of disaster risk reduction and climate change adaptation, land tenure policy reforms, and the operationalisation of the Santiago Network for Loss and Damage.

Scenario planning (SP) has been increasingly utilized by water managers and planners in the 21st century as climate and other uncertainties have challenged traditional planning approaches. This paper discusses the potential for scenario planning processes in the Colorado River Basin in the southwestern United States to build collective understanding of compound and cascading risks, and to identify possible solutions at multiple scales. Under the Colorado River Conversations Project, we convened a series of conferences and scenario planning workshops over the past 3 years to explore the potential to enhance the use of social and physical sciences in river management, and to broaden the community of people and entities engaged in discussions about managing the Colorado River. Working with a group of thirty water managers and other interested parties representing all 7 basin states, several Tribes, NGO’s and Mexico, we used a participatory, mixed-methods approach to scenario planning that identified multiple drivers of change and developed eight science-based storylines from the intersection of these drivers. The development of the storylines and the subsequent conversations with participants about impacts and solutions resulted in a framework for understanding low probability-high consequence climate and other risks across the Colorado River Basin. We highlight three lessons that speak to the value and role of SP for fostering collaboration and creativity. These lessons include: (1) the importance of process in SP in fostering deliberate community building across sectors and geographies; (2) identifying challenges with engaging with uncertainty, complexity, and risk; and (3) determining what these findings mean for future SP in the Colorado River Basin and beyond.

Greater urbanization does not only mean higher concentrations of population and economic activities, but also increasing complexity and infrastructure interdependencies in the delivery of critical urban services such as energy, water, transport and communication. This paper reviews the current literature in these areas and identifies critical research and development challenges from the perspective—and for the benefit—of key stakeholders, considering their primary decision goals and context. From this vantage point, the critical evaluation framework is extended to include a classification of disruptions and extreme events and an overview of infrastructure modeling approaches and broader socioeconomic impacts assessment methods. Mapping the range of modeling and assessment methods against different decision contexts, critical gaps in knowledge and tools are identified to support the latter. Deep uncertainties characterize the challenge as each major component in the information and decision-making chain—from the frequency and intensity of a disruptive event, to assessing the first-order and immediate impacts of an infrastructure failure, to estimating the nature, extent and impact of cascading failures—multiplies the uncertainties. The emerging research challenges to deal with these interdependencies and uncertainties are discussed.

It is well known that the urban canopy (UC) layer, i.e., the layer of air corresponding to the assemblage of the buildings, roads, park, trees and other objects typical to cities, is characterized by specific meteorological conditions at city scales generally differing from those over rural surroundings. We refer to the forcing that acts on the meteorological variables over urbanized areas as the urban canopy meteorological forcing (UCMF). UCMF has multiple aspects, while one of the most studied is the generation of the urban heat island (UHI) as an excess of heat due to increased absorption and trapping of radiation in street canyons. However, enhanced drag plays important role too, reducing mean wind speeds and increasing vertical eddy mixing of pollutants. As air quality is strongly tied to meteorological conditions, the UCMF leads to modifications of air chemistry and transport of pollutants. Although it has been recognized in the last decade that the enhanced vertical mixing has a dominant role in the impact of the UCMF on air quality, very little is known about the uncertainty of vertical eddy diffusion arising from different representation in numerical models and how this uncertainty propagates to the final species concentrations as well as to the changes due to the UCMF.To bridge this knowledge gap, we set up the Regional Climate Model version 4 (RegCM4) coupled to the Comprehensive Air Quality Model with Extensions (CAMx) chemistry transport model over central Europe and designed a series of simulations to study how UC affects the vertical turbulent transport of selected pollutants through modifications of the vertical eddy diffusion coefficient (Kv) using six different methods for Kv calculation. The mean concentrations of ozone and PM2.5 in selected city canopies are analyzed. These are secondary pollutants or having secondary components, upon which turbulence acts in a much more complicated way than in the case of primary pollutants by influencing their concentrations not only directly but indirectly via precursors too. Calculations are performed over cascading domains (of 27, 9, and 3 km horizontal resolutions), which further enables to analyze the sensitivity of the numerical model to grid resolution. A number of model simulations are carried out where either urban canopies are considered or replaced by rural ones in order to isolate the UC meteorological forcing. Apart from the well-pronounced and expected impact on temperature (increases up to 2 ∘C) and wind (decreases by up to 2 ms-1), there is a strong impact on vertical eddy diffusion in all of the six Kv methods. The Kv enhancement ranges from less than 1 up to 30 m2s-1 at the surface and from 1 to 100 m2s-1 at higher levels depending on the methods. The largest impact is obtained for the turbulent kinetic energy (TKE)-based methods.The range of impact on the vertical eddy diffusion coefficient propagates to a range of ozone (O3) increase of 0.4 to 4 ppbv in both summer and winter (5 %–10 % relative change). In the case of PM2.5, we obtained decreases of up to 1 µgm-3 in summer and up to 2 µgm-3 in winter (up to 30 %–40 % relative change). Comparing these results to the “total-impact”, i.e., to the impact of all meteorological modifications due to UCMF, we can conclude that much of UCMF is explained by the enhanced vertical eddy diffusion, which counterbalances the opposing effects of other components of this forcing (temperature, humidity and wind). The results further show that this conclusion holds regardless of the resolution chosen and in both the warm and cold parts of the year.