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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]

Disaster Risk Management (DRM) is complex due to interacting climate risks from interacting hazards and sectors. We develop a synthetic multi‐risk test case to explore the effects of these interactions on decision‐making under deep uncertainty. The test case accounts for changes in hazard impacts and occurrence frequency due to interactions between floods and droughts. Interactions between the shipping, housing and agricultural sectors are considered as well. We use this test case to explore the utility of the Dynamic Adaptive Policy Pathways for Multi‐Risk (DAPP‐MR) framework. DAPP‐MR has been introduced to assess DRM policies' effectiveness under deep uncertainties and to develop integrated adaptive strategies considering interactions across hazards, sectors and time. With the test case, we show that the stage‐wise approach of DAPP‐MR, which gradually increases the complexity of the analysis, can facilitate the evaluation process. Earlier stages of the analysis can be used to establish the direct cause‐effect relations, later stages allow us to identify whether additional interacting factors have a significant effect on the direct cause‐effect relations. As a result, decision‐makers can gain insights into dependencies and their relevance for developing short‐to long‐term strategies under deep uncertainty. We show that multi‐risk interactions can lead to non‐linear effects that influence the outcome of the policy analysis, for example, the indirect influence of a decision in one sector on another. Future work could investigate further improving the operationalization of this staged approach as well as extending the set of uncertainties, dynamics and decision‐rules accounted for in the multi‐risk test case. Plain Language Summary: This paper addresses the complex challenge that many regions face when dealing with multiple interconnected disasters, like floods, droughts and earthquakes. Traditional Disaster Risk Management methods often fall short, particularly in long‐term planning that considers factors such as climate change. We apply a step‐by‐step approach known as "Dynamic Adaptive Policy Pathways for Multi‐Risk (DAPP‐MR)" to assist decision‐makers in better understanding and planning for these intertwined risks over time. This paper employs a hypothetical test case where different sectors (e.g., agricultural sector) seek effective ways to prepare for and respond to flood and drought risks. The DAPP‐MR method facilitates this by breaking down the process into smaller, more manageable steps. This approach allows for an easier understanding of the direct effects of disasters and gradually incorporates more complex interactions between different risks. Our findings suggest that this step‐by‐step method can aid decision‐makers in grasping the connections between various factors and their significance in formulating both short‐ and long‐term strategies. The study also reveals that interactions between different risks can produce unexpected, nonlinear effects, adding complexity to the interpretation of policy outcomes. Key Points: A multi‐risk modeling case with interacting hazards and sectors is created to explore effects for decision‐making under deep uncertaintyDynamic Adaptive Policy Pathways for Multi‐Risk has proven effective for pathway analysis to grasp interactions and their relevance through a structured, stage‐by‐stage approachMulti‐risk interactions can alter the decision‐making outcome because of changes in performance or timings of potential pathways [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]

Infragravitywavesmay contribute significantly to coastal flooding, especially during stormconditions. However, inmany national and continental to global assessments of coastal flood risk, their contribution is not accounted for, mostly because of the high computational expense of traditionalwave-resolving numericalmodels. In this study, we present an efficient stationary wave energy solver to estimate the evolution of incident and infragravity waves from offshore to the nearshore for large spatial scales. This solver can be subsequently used to provide nearshore wave boundary conditions for overland flood models. The new wave solver builds upon the stationary wave energy balance for incident wave energy and extends it to include the infragravity wave energy balance. To describe the energy transfer from incident to infragravity waves, an infragravity wave source term is introduced. This termacts as a sink term for incident waves and as a complementary source term for infragravity waves. The source term is simplified using a parameterized infragravity wave shoaling parameter. An empirical relation is derived using observed values of the shoaling parameter from a synthetic dataset of XBeach simulations, covering a wide range of wave conditions and beach profiles. The wave shoaling parameter is related to the local bed slope and relative wave height. As validation, we show for a rangeofcases fromsyntheticbeachprofiles tolaboratory tests that infragravitywave transformation can be estimated using this wave solver with reasonable to good accuracy. Additionally, the validity in real-world conditions is verifiedsuccessfully for DELILAHfield caseobservations atDuck, NC, USA. We demonstrate thewave solver for a large-scale application of the fullOuter Banks coastline in theUS, covering 450 km of coastline, from deep water up to the coast. For this model, consisting of 4.5 milliongridcells, thewavesolver canestimatethestationary incidentandinfragravity wave field in a matter of seconds for the entire domain on a regular laptop PC. This computational efficiency cannot be provided by existing process-based waveresolving models. Using the presented method, infragravity wave-driven flooding can be incorporated into large-scale coastal compound flood models and risk assessments. [ABSTRACT FROM AUTHOR]

Fatalities caused by natural hazards are driven not only by population exposure, but also by their vulnerability to these events, determined by intersecting characteristics such as education, age and income. Empirical evidence of the drivers of social vulnerability, however, is limited due to a lack of relevant data, in particular on a global scale. Consequently, existing global‐scale risk assessments rarely account for social vulnerability. To address this gap, we estimate regression models that predict fatalities caused by past flooding events (n = 913) based on potential social vulnerability drivers. Analyzing 47 variables calculated from publicly available spatial data sets, we establish five statistically significant vulnerability variables: mean years of schooling; share of elderly; gender income gap; rural settlements; and walking time to nearest healthcare facility. We use the regression coefficients as weights to calculate the "Global‐Empirical Social Vulnerability Index (GlobE‐SoVI)" at a spatial resolution of ∼1 km. We find distinct spatial patterns of vulnerability within and across countries, with low GlobE‐SoVI scores (i.e., 1–2) in for example, Northern America, northern Europe, and Australia; and high scores (i.e., 9–10) in for example, northern Africa, the Middle East, and southern Asia. Globally, education has the highest relative contribution to vulnerability (roughly 58%), acting as a driver that reduces vulnerability; all other drivers increase vulnerability, with the gender income gap contributing ∼24% and the elderly another 11%. Due to its empirical foundation, the GlobE‐SoVI advances our understanding of social vulnerability drivers at global scale and can be used for global (flood) risk assessments. Plain Language Summary: Social vulnerability is rarely accounted for in global‐scale risk assessments. We develop an empirical social vulnerability map ("GlobE‐SoVI") based on five key drivers of social vulnerability to flooding, that is, education, elderly, income inequality, rural settlements and travel time to healthcare, which we establish based on flood fatalities caused by past flooding events. Globally, we find education to have a high and reducing effect on social vulnerability, while all other drivers increase vulnerability. Integrating social vulnerability in global‐scale (flood) risk assessments can help inform global policy frameworks that aim to reduce risks posed by natural hazards and climate change as well as to foster more equitable development globally. Key Points: We develop a global map of social vulnerability at ∼1 km spatial resolution based on five key vulnerability drivers ("GlobE‐SoVI")We establish vulnerability drivers empirically based on their contribution to predicting fatalities caused by past flooding eventsAccounting for social vulnerability in global‐scale (flood) risk assessments can inform global policy frameworks that aim to reduce risk [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]

In the coming decades, coastal flooding will become more frequent due to sea‐level rise and potential changes in storms. To produce global storm surge projections from 1950 to 2050, we force the Global Tide and Surge Model with a ∼25‐km resolution climate model ensemble from the Coupled Model Intercomparison Project Phase 6 High Resolution Model Intercomparison Project (HighResMIP). This is the first time that such a high‐resolution ensemble is used to assess changes in future storm surges across the globe. We validate the present epoch (1985–2014) against the ERA5 climate reanalysis, which shows a good overall agreement. However, there is a clear spatial bias with generally a positive bias in coastal areas along semi‐enclosed seas and negative bias in equatorial regions. Comparing the future epoch (2021–2050) against the historical epoch (1951–1980), we project ensemble‐median changes up to 0.1 (or 20%) in the 1 in 10‐year storm surge levels. These changes are not uniform across the globe with decreases along the coast of Mediterranean and northern Africa and southern Australia and increases along the south coast of Australia and Alaska. There are also increases along (parts) of the coasts of northern Caribbean, eastern Africa, China and the Korean peninsula, but with less agreement among the HighResMIP ensemble. Information resulting from this study can be used to inform broad‐scale assessment of coastal impacts under future climate change. Plain Language Summary: In the next few decades, coastal flooding is expected to become more frequent due to rising sea levels and changes in storms. To understand and prepare for these changes, we use a global hydrodynamic and multiple climate models to investigate how storm surges (a temporarily rise in water level during a storm) will respond to a warmer climate. These are the first global projections of storm surges based on highly detailed climate models (25–50 km). We compare the model output for recent years (1985–2014) with real‐world weather data, known as reanalysis data, and find a good overall agreement. However, the comparison also shows relatively large differences with larger or smaller values in certain areas. For the future (2021–2050), results show storm surge changes up to 20%. These changes vary around the world. Some areas (Mediterranean, northern Africa, and southern Australia) might experience lower storm surges, whereas other areas (South Australia, Alaska, the northern Caribbean, eastern Africa, China, and the Korean Peninsula) might experience higher storm surges. By using advanced computer models as done here, we are able to better understand how climate change could impact coastal area and thus make informed decisions for the future. Key Points: Storm surge projections from 1950 to 2050 based on the Global Tide and Surge Model and a ∼25 km‐resolution High Resolution Model Intercomparison Project climate model ensembleValidation against ERA5 reanalysis (1985–2014) shows that the model performs well globally, but also reveals a clear spatial biasThe median‐ensemble change of the 1 in 10‐year storm surge levels from 2021–2050 compared to 1951–1980 shows changes up to 0.1 m or 20% [ABSTRACT FROM AUTHOR]

Flood events are expected to increase in their frequency and severity, which results in higher flood risk without additional adaptation measures. The information gained from flood risk models is essential in effective disaster risk management. However, vulnerability estimations are often a large driver of uncertainty, and flood damage is rarely estimated due to a lack of empirical damage data from flood events. This study uses a unique data set with experienced damages and the implementation of flood damage mitigation (FDM) measures on the household level, collected after the flood event in the Netherlands in 2021. Flood damage models that control for several hazard, exposure, and vulnerability indicators are estimated and allow for additional input in flood risk models. Previous estimates of the effectiveness of FDM measures are prone to a selection bias, as households that do, and do not implement FDM measures systematically differ in their risk profiles. By using an instrumental variable‐estimation, this study overcomes this selection bias and finds significant reductions in flood damage due to FDM measures. These reductions can be incorporated in multivariate flood vulnerability estimations, which indicate that FDM measures significantly reduce flood damage. Providing information on flood hazard, as well as implementing early warning systems, is crucial for ensuring effective flood risk management. Plain Language Summary: Due to climate change, we can expect more frequent and severe floods in the future. This study investigates how households are affected by flooding and explores ways to reduce potential flood damage. By understanding the impact of floods on households and identifying effective measures, we can better manage flood risks and reduce damages caused by these events. We collected survey data after the 2021 Summer Floods in the Netherlands to understand the factors that contribute to flood damage. Timely warnings before flooding play a crucial role in preparing for such events. They allow households to take emergency actions like placing sandbags or moving belongings to higher floors. These actions can reduce flood damage to buildings by almost 30% of their total value and protect nearly 40% of the value of household contents. In addition to emergency measures, households can take proactive steps to prepare for future floods. This includes using waterproof materials and elevating electrical appliances, such as power sockets or kitchen appliances when constructing new homes or making renovations. Key Points: Detailed survey data allows for the update and calibration of rarely estimated empirical vulnerability curves for buildingsFlood damage mitigation (FDM) measures have the potential to reduce flood damage to both residential buildings and household contents by halfUpdated input for flood risk models in the form of multivariate damage functions that can be adjusted for FDM measures [ABSTRACT FROM AUTHOR]

Coastal flooding is driven by the combination of (high) tide and storm surge, the latter being caused by strong winds and low pressure in tropical and extratropical cyclones. The combination of storm surge and the astronomical tide is defined as the storm tide. To gain an understanding of the threat posed 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. Most models used to simulate the coastal inundation scale follow a simple planar approach, referred to as bathtub models. The main limitations of this type of models are that they implicitly assume an infinite flood duration, and they do not capture relevant physical processes. In this study we develop a method to generate hydrographs called HGRAPHER, and we provide a global dataset of storm tide hydrographs based on time series of storm surges and tides derived from the Global Tide and Surge Model (GTSM) forced with the ERA5 reanalysis wind and pressure fields. 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 the time of the surge and tide maxima. The hydrograph dataset can be used to move away from planar inundation modelling techniques towards dynamic inundation modelling techniques across different spatial scales. [ABSTRACT FROM AUTHOR]

Humans play a large role in the hydrological system, e.g. 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 usually advantageous for an individual farmer or their community, aggregate effects of those decisions can have large effects downstream. Similarly, decisions made by other stakeholders, such as governments, often have basin-wide effects and affect each farmer differently. 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 (GEB) model, a coupled agent-based hydrological model that simulates the behaviour and daily bidirectional interaction of more than 10 million individual farm households with the hydrological system on a personal laptop. Farmers exhibit autonomous heterogeneous behaviour based on their characteristics, assets, environment, management policies, and social network. Examples of behaviour are irrigation, generation of income from selling crops, and investment in adaptation measures. Meanwhile, reservoir operators manage the amount of water available for irrigation and river discharge. All actions can be taken at a daily time step and influence the hydrological system directly or indirectly. GEB is dynamically linked with the spatially distributed grid-based hydrological model CWatM at 30 ′′ resolution (< 1 km at the Equator). Because many smallholder farm 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 anywhere globally at both large and small scales, we explore its implementation in the heavily managed Krishna basin in India, which encompasses ∼ 8 % of India's land area and ∼ 12.1 million farmers. [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]

The Last Interglacial (LIG; ca. 125 ka) is a period of interest for climate research as it is the most recent period of the Earth's history when the boreal climate was warmer than at present. Previous research, based on models and geological evidence, suggests that the LIG may have featured enhanced patterns of ocean storminess, but this remains hotly debated. Here, we apply state-of-the-art climate and hydrodynamic modeling to simulate changes in sea level extremes caused by storm surges, under LIG and pre-industrial climate forcings. Significantly higher seasonal LIG sea level extremes emerge for coastlines along northern Australia, the Indonesian archipelago, much of northern and eastern Africa, the Mediterranean Sea, the Gulf of Saint Lawrence, the Arabian Sea, the east coast of North America, and islands of the Pacific Ocean and of the Caribbean. Lower seasonal LIG sea level extremes emerge for coastlines along the North Sea, the Bay of Bengal, China, Vietnam, and parts of Central America. Most of these anomalies are associated with anomalies in seasonal sea level pressure minima and in eddy kinetic energy calculated from near-surface wind fields, and therefore seem to originate from anomalies in the meridional position and intensity of the predominant wind bands. In a qualitative comparison, LIG sea level extremes seem generally higher than those projected for future warmer climates. These results help to constrain the interpretation of coastal archives of LIG sea level indicators. [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]