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The Horn of Africa Drylands are increasingly experiencing severe droughts, which impose a threat on traditional livelihood strategies. Understanding adaptation behavior in rural communities is key to helping reduce the impact of these droughts. We investigate adaptation behavior by assessing four established economic and social psychological theories on decision making under risk: expected utility theory (EUT), rank dependent utility theory (RDU), protection motivation theory (PMT), and theory of planned behavior (PMT). To measure adaptation behavior and the theory constructs, we conducted a household survey in Kenya (N = 502). Regression analysis shows that the economic theories (EUT and RDU) have the best fit for our data. Risk and time preferences are found to play an important role in adaptation decisions. An analysis of differences in decision making for distinct types of adaptation measures shows that risk averse (agro‐)pastoralists are more likely to implement adaptation measures that are adjustments to their current livelihood practices, and less willing to invest in adaptation measures that require a shift to other livelihood activities. Moreover, we find significant effects for elements of the social psychological theories (PMT and TPB). A person's belief in their own ability to implement an adaptation measure (perceived self‐efficacy) and adaptation by family and friends are important factors in explaining adaptation decisions. Finally, we find that the type of adaptation measures that people implement is influenced by, among others, gender, education level, access to financial resources, and access to government support or aid. Our analysis gives insights into the drivers of individual adaptation decisions, which can enhance policies promoting adaptation of dryland communities. [ABSTRACT FROM AUTHOR]

This study synthesizes the current understanding of the hydrological, impact, and adaptation processes underlying drought‐to‐flood events (i.e., consecutive drought and flood events), and how they interact. Based on an analysis of literature and a global assessment of historic cases, we show how drought can affect flood risk and assess under which circumstances drought‐to‐flood interactions can lead to increased or decreased risk. We make a distinction between hydrological, socio‐economic and adaptation processes. Hydrological processes include storage and runoff processes, which both seem to mostly play a role when the drought is a multiyear event and when the flood occurs during the drought. However, which process is dominant when and where, and how this is influenced by human intervention needs further research. Processes related to socio‐economic impacts have been studied less than hydrological processes, but in general, changes in vulnerability seem to play an important role in increasing or decreasing drought‐to‐flood impacts. Additionally, there is evidence of increased water quality problems due to drought‐to‐flood events, when compared to drought or flood events by themselves. Adaptation affects both hydrological (e.g., through groundwater extraction) or socio‐economic (e.g., influencing vulnerability) processes. There are many examples of adaptation, but there is limited evidence of when and where certain processes occur and why. Overall, research on drought‐to‐flood events is scarce. To increase our understanding of drought‐to‐flood events we need more comprehensive studies on the underlying hydrological, socio‐economic, and adaptation processes and their interactions, as well as the circumstances that lead to the dominance of certain processes. This article is categorized under:Science of Water > Hydrological ProcessesScience of Water > Water Extremes [ABSTRACT FROM AUTHOR]

Advances in the field of extreme event attribution allow to estimate how anthropogenic global warming affects the odds of individual climate disasters, such as river floods. Extreme event attribution typically uses precipitation as proxy for flooding. However, hydrological processes and antecedent conditions make the relation between precipitation and floods highly nonlinear. In addition, hydrology acknowledges that changes in floods can be strongly driven by changes in land‐cover and by other human interventions in the hydrological system, such as irrigation and construction of dams. These drivers can either amplify, dampen or outweigh the effect of climate change on local flood occurrence. Neglecting these processes and drivers can lead to incorrect flood attribution. Including flooding explicitly, that is, using data and models of hydrology and hydrodynamics that can represent the relevant hydrological processes, will lead to more robust event attribution, and will account for the role of other drivers beyond climate change. Existing attempts are incomplete. We argue that the existing probabilistic framework for extreme event attribution can be extended to explicitly include floods for near‐natural cases, where flood occurrence was unlikely to be influenced by land‐cover change and human hydrological interventions. However, for the many cases where this assumption is not valid, a multi‐driver framework for conditional event attribution needs to be established. Explicit flood attribution will have to grapple with uncertainties from lack of observations and compounding from the many processes involved. Further, it requires collaboration between climatologists and hydrologists, and promises to better address the needs of flood risk management. This article is categorized under:Paleoclimates and Current Trends > Modern Climate ChangePaleoclimates and Current Trends > Detection and AttributionAssessing Impacts of Climate Change > Observed Impacts of Climate Change [ABSTRACT FROM AUTHOR]

Critical infrastructure (CI) is exposed to natural hazards that may lead to the devastation of these infrastructures and burden society with the indirect consequences that stem from this. The vulnerability is a key determinant for understanding, assessing and reducing natural hazard-induced risks to these infrastructures. To date, however, essential vulnerability information for CI is distributed across literature instead of being accessible through a centralized dataset. This study, through a systematic literature review, synthesises the state-of-the-art of fragility and vulnerability curves for CI assets of energy, transport, water, waste, telecoms, health and education in context of natural hazards and offers a unique physical vulnerability database. The publicly available centralized database that contains over 1,250 curves can directly be used as input for risk assessment studies that evaluate the expected or potential damages to assets due to flooding, earthquakes, windstorms and landslides. The literature review highlights that vulnerability development has mainly focused on earthquake curves for a wide range of infrastructure types. Windstorms have the second largest share in the database, but are especially limited to energy curves. While all CI systems require more vulnerability research, additional efforts are needed for telecommunication which is largely underrepresented in our database. [ABSTRACT FROM AUTHOR]

Coastal flooding and sea level rise (SLR) will affect farmers in coastal areas, as increasing salinity levels will reduce crop yields, leading to a loss of net annual income for farming communities. In response, farmers can take various actions. In order to assess such a response under SLR, we applied an agent-based model (ABM) to simulate the adaptation and migration decisions of farmers in coastal Mozambique. The ABM is coupled with a salinization module to simulate the relationship between soil salinity and SLR. The decision rules in the model (DYNAMO-M) are based on the economic theory of subjective expected utility. This theory posits that households can maximize their welfare by deciding whether to (a) stay and face losses from salinization and flooding, (b) stay and adapt (switching to salt-tolerant crops and enhancing physical resilience such as elevating houses), or (c) migrate to safer inland areas. The results show that coastal farmers in Mozambique face total losses of up to US$12.5 million per year from salt intrusion and up to US$800 million per year from flooding of buildings (RCP8.5 in the year 2080). Sorghum farmers may experience little damage from salt intrusion, while rice farmers may experience losses of up to US$15,000 per year. We show that medium-sized farmers (1–20 ha) are most at risk. This is because their farm size means that adaptation costs are substantial, while their incomes are too low to cover these costs. The number of households adapting varies between different districts (6 %–50 %), with salt adaptation being the most common, as costs are lowest. Despite adaptation measures, about 13 %–20 % of the total 300,000 farmers in coastal flood zones will migrate to safer areas under different settings of adaptive behaviour and different climatic and socioeconomic scenarios. [ABSTRACT FROM AUTHOR]

Coastal flood risk is expected to increase as a result of climate change effects, such as sea level rise, and socioeconomic growth. To support policymakers in making adaptation decisions, accurate flood risk assessments that account for the influence of complex adaptation processes on the developments of risks are essential. In this study, we integrate the dynamic adaptive behavior of homeowners within a flood risk modeling framework. Focusing on building‐level adaptation and flood insurance, the agent‐based model (DYNAMO) is benchmarked with empirical data for New York City, USA. The model simulates the National Flood Insurance Program (NFIP) and frequently proposed reforms to evaluate their effectiveness. The model is applied to a case study of Jamaica Bay, NY. Our results indicate that risk‐based premiums can improve insurance penetration rates and the affordability of insurance compared to the baseline NFIP market structure. While a premium discount for disaster risk reduction incentivizes more homeowners to invest in dry‐floodproofing measures, it does not significantly improve affordability. A low interest rate loan for financing risk‐mitigation investments improves the uptake and affordability of dry‐floodproofing measures. The benchmark and sensitivity analyses demonstrate how the behavioral component of our model matches empirical data and provides insights into the underlying theories and choices that autonomous agents make. [ABSTRACT FROM AUTHOR]

Coastal flooding is driven by both high tides and/or storm surge, the latter being caused by strong winds and low pressure in tropical and extratropical. The combination of storm surge and the astronomical tide is defined as the storm tide. To gain understanding into the threat imposed by coastal flooding and to identify areas that are especially at risk, now and in the future, it is crucial to accurately model coastal inundation and assess the coastal flood hazard. Most models capable of simulating coastal inundation at the global scale follow a simple planar approach, often referred to as bathtub models. The main limitations of this type of model are that they implicitly assume an infinite flood duration and do not capture relevant physical processes. In this study we develop a method to generate hydrographs called HGRAPHER, and provide a global dataset of storm tide hydrographs. These hydrographs represent the typical shape of an extreme storm tide at a certain location along the global coastline. We test the sensitivity of the HGRAPHER method with respect to two main assumptions that determine the shape of the hydrograph, namely the surge event sampling threshold and coincidence in time of the surge and tide maxima. These hydrographs can be used to move away from planar to more advanced dynamic inundation modelling techniques at large scales. [ABSTRACT FROM AUTHOR]

Humans play a large role in the hydrological system; for example, by extracting large amounts of water for irrigation, often resulting in water stress and ecosystem degradation. By implementing large-scale adaptation measures, such as the construction of irrigation reservoirs, water stress and ecosystem degradation can be reduced. Yet we know that many decisions, such as the adoption of more effective irrigation techniques or changing crop types, are made at the farm level by a heterogeneous farmer population. While these decisions are often advantageous for an individual farmer or their community, detrimental effects are frequently experienced downstream. Therefore, to fully comprehend how the humannatural water system evolves over time and space, and to explore which interventions are suitable to reduce water stress, it is important to consider human behaviour and feedbacks to the hydrological system simultaneously at the local household and large basin scales. Therefore, we present the Geographical, Environmental and Behavioural model (GEB), a coupled agentbased hydrological model that simulates the behaviour and daily bi-directional interaction of up to ~10 million individual farm households with the hydrological system on a personal laptop. GEB is dynamically linked with the spatially distributed grid-based hydrological model CWatM at 30’’ resolution (< 1km at the equator). Because many small-holder farmer fields are much smaller than 1×1 km, CWatM was specifically adapted to implement dynamically sized hydrological response units (HRUs) at the farm level, providing each agent with an independently operated hydrological environment. While the model could be applied globally, we explore its implementation in the heavily managed Krishna basin in India, which encompasses ~8% of India’s land area and ~11.1 million farmers. Here, we show how six combinations of storylines with endogenous and exogenous drivers of adaptation affect both the hydrological system and the farmer population. [ABSTRACT FROM AUTHOR]

Humans play a large role in the hydrological system; for example, by extracting large amounts of water for irrigation, often resulting in water stress and ecosystem degradation. By implementing large-scale adaptation measures, such as the construction of irrigation reservoirs, water stress and ecosystem degradation can be reduced. Yet we know that many decisions, such as the adoption of more effective irrigation techniques or changing crop types, are made at the farm level by a heterogeneous farmer population. While these decisions are often advantageous for an individual farmer or their community, detrimental effects are frequently experienced downstream. Therefore, to fully comprehend how the human-natural water system evolves over time and space, and to explore which interventions are suitable to reduce water stress, it is important to consider human behaviour and feedbacks to the hydrological system simultaneously at the local household and large basin scales. Therefore, we present the Geographical, Environmental and Behavioural model (GEB), a coupled agent-based hydrological model that simulates the behaviour and daily bi-directional interaction of up to ~10 million individual farm households with the hydrological system on a personal laptop. GEB is dynamically linked with the spatially distributed grid-based hydrological model CWatM at 30" resolution (< 1 km at the equator). Because many small-holder farmer fields are much smaller than 1×1 km, CWatM was specifically adapted to implement dynamically sized hydrological response units (HRUs) at the farm level, providing each agent with an independently operated hydrological environment. While the model could be applied globally, we explore its implementation in the heavily managed Krishna basin in India, which encompasses ~8 % of India's land area and ~11.1 million farmers. Here, we show how six combinations of storylines with endogenous and exogenous drivers of adaptation affect both the hydrological system and the farmer population. [ABSTRACT FROM AUTHOR]

Sea‐level rise (SLR) threatens millions of people living in coastal areas through permanent inundation and other SLR‐related hazards. Migration is one way for people to adapt to these coastal changes, but presents an enormous policy challenge given the number of people affected. Knowledge about the relationship between SLR‐related hazards and migration is therefore important to allow for anticipatory policymaking. In recent years, an increasing number of empirical studies have investigated, using survey or census data, how SLR‐related hazards including flooding, salinization, and erosion together with non‐environmental factors influence migration behavior. In this article, we provide a systematic literature review of this empirical work. Our review findings indicate that flooding is not necessarily associated with increased migration. Severe flood events even tend to decrease long‐term migration in developing countries, although more research is needed to better understand the underpinnings of this finding. Salinization and erosion do generally lead to migration, but the number of studies is sparse. Several non‐environmental factors including wealth and place attachment influence migration alongside SLR‐related hazards. Based on the review, we propose a research agenda by outlining knowledge gaps and promising avenues for future research on this topic. Promising research avenues include using behavioral experiments to investigate migration behavior under future SLR scenarios, studying migration among other adaptation strategies, and complementing empirical research with dynamic migration modeling. We conclude that more empirical research on the SLR‐migration nexus is needed to properly understand and anticipate the complex dynamics of migration under SLR, and to design adequate policy responses. This article is categorized under: Climate Economics < Aggregation Techniques for Impacts and Mitigation CostsVulnerability and Adaptation to Climate Change < Learning from Cases and AnalogiesAssessing Impacts of Climate Change < Evaluating Future Impacts of Climate Change [ABSTRACT FROM AUTHOR]

The Horn of Africa Drylands are increasingly experiencing severe droughts, which impose a threat on traditional livelihood strategies. Understanding adaptation behavior in rural communities is key to helping reduce the impact of these droughts. We investigate adaptation behavior by assessing four established economic and social psychological theories on decision making under risk: expected utility theory (EUT), rank dependent utility theory (RDU), protection motivation theory (PMT), and theory of planned behavior (PMT). To measure adaptation behavior and the theory constructs, we conducted a household survey in Kenya (N = 502). Regression analysis shows that the economic theories (EUT and RDU) have the best fit for our data. Risk and time preferences are found to play an important role in adaptation decisions. An analysis of differences in decision making for distinct types of adaptation measures shows that risk averse (agro-)pastoralists are more likely to implement adaptation measures that are adjustments to their current livelihood practices, and less willing to invest in adaptation measures that require a shift to other livelihood activities. Moreover, we find significant effects for elements of the social psychological theories (PMT and TPB). A person's belief in their own ability to implement an adaptation measure (perceived self-efficacy) and adaptation by family and friends are important factors in explaining adaptation decisions. Finally, we find that the type of adaptation measures that people implement is influenced by, among others, gender, education level, access to financial resources, and access to government support or aid. Our analysis gives insights into the drivers of individual adaptation decisions, which can enhance policies promoting adaptation of dryland communities., (© 2023 The Authors. Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis.)