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In this paper, we analyze the effect of natural hazards on migration in the United States (US) and the importance of spatial dependence in such assessments. We use two measures of migration: migration rates and flows. The model for migration flows is estimated using the gravity model, whereas out- and in-migration rates are analyzed using the spatial Durbin model. Our results indicate there is a major and significant impact of economic damage caused by natural hazards on out-migration rates and outward migration flows. In the spatial Durbin model and in the gravity model, a $1,000 dollar damage per capita is associated with an increase in out-migration of 16.0% and 9.1%, respectively. However, when spatial dependence is not accounted for, the effect of natural hazards on migration is substantially overestimated: the coefficients are 1.5–2 times larger when spatial dependence is not considered.

The recently developed Plant-FATE (Plant Functional Acclimation and Trait Evolution) model is a trait-size-structured eco-evolutionary population model derived from the ‘Plant’ model. It includes a McKendrick-von Foerster partial differential equation (PDE) describing how the size distribution of each species evolves through time. The trait structure allows for modelling functional diversity and adaptations, whereas size structure allows for modelling competition for light. Plant-FATE also includes a new P-hydro model for optimal photosynthesis, the ‘perfect plasticity approximation’ for modelling optimal crown placement, and an extended version of the T-model for biomass allocation. Forced with climatic variables and soil-water availability, Plant-FATE can predict emergent species compositions, size-distributions, and ecosystem services such as leaf area, productivity, evapotranspiration, living biomass, and seed output. Plant-FATE currently predicts vegetation properties and associated ecosystem functions of areas under forest cover. To analyse the -water-biodiversity nexus, it is necessary to cover additional aspects of areas under different land-use, such as croplands, plantations, and urban areas. To that end, we have coupled PlantFATE with a Community Water Model (CWatM) that captures ground water discharge and simulates basin-wide water circulation. CWatM is an open-source model to examine how future water demand will evolve in response to socioeconomic change and how water availability will change in response to climate. As a case study, we apply this coupled model to the Bhima Basin to examine the feedback between forest management and land-use. This coupling will enable us to better represent nexus issues, such as the feedback between biodiversity and ecosystem functioning that affect vegetation carbon storage and water provisioning under future land-use and projected climate change scenarios.

‘Water circles’ are presented as flexible water cycle diagrams aggregating the flows through a system for a specific region and time period, categorized by flow type and organized by magnitude. Water circles for an entire system and separate storage components can be interpreted as water cycle speedometers and can help compare and communicate different climate and human impacts on different regions, time periods, and storage components. Water circles can facilitate comparisons between hydrological models and other methods for deriving water balances.

Drought is a persistent hazard that impacts the environment, people's livelihoods, access to education and food security. Adaptation choices made by people can influence the propagation of this drought hazard. However, few drought models incorporate adaptive behavior and feedbacks between adaptations and drought. In this research, we present a dynamic drought adaptation modeling framework, ADOPT-AP, which combines socio-hydrological and agent-based modeling approaches. This approach is applied to agropastoral communities in dryland regions in Kenya. We couple the spatially explicit hydrological Dryland Water Partitioning (DRYP) model with a behavioral model capable of simulating different bounded rational behavioral theories (ADOPT). The results demonstrate that agropastoralists respond differently to drought due to differences in (perceptions of) their hydrological environment. Downstream communities are impacted more heavily and implement more short-term adaptation measures than upstream communities in the same catchment. Additional drivers of drought adaptation concern socio-economic factors such as wealth and distance to wells. We show that the uptake of drought adaptation influences soil moisture (positively through irrigation) and groundwater (negatively through abstraction) and, thus, the drought propagation through the hydrological cycle.

This is the first release of the Data Privacy Handbook.The Data Privacy Handbook is a practical guide on handling personal data in scientific research, primarily written for Utrecht University researchers and research support staff in the Netherlands. It is an initiative of Research Data Management Support, in collaboration with privacy and data experts at Utrecht University.The Data Privacy Handbook consists of: A knowledge base which explains how the EU General Data Protection Regulation (GDPR, Dutch: Algemene Verordening Gegevensbescherming) applies to scientific research, including guidelines and good practices in carrying out GDPR-compliant scientific research; An overview of privacy-enhancing techniques & tools and practical guidance on their implementation; Use Cases in the form of research projects with privacy-related issues, for which a reusable solution (e.g., tool, workflow) is shared.

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

In this study, we couple an integrated flood damage and agent-based model (ABM) with a gravity model of internal migration and a flood risk module (DYNAMO-M) to project household adaptation and migration decisions under increasing coastal flood risk in France. We ground the agent decision rules in a framework of subjective expected utility theory. This method addresses agent's bounded rationality related to risk perception and risk aversion and simulates the impact of push, pull, and mooring factors on migration and adaptation decisions. The agents are parameterized using subnational statistics, and the model is calibrated using a household survey on adaptation uptake. Subsequently, the model simulates household adaptation and migration based on increasing coastal flood damage from 2015 until 2080. A medium population growth scenario is used to simulate future population development, and sea level rise (SLR) is assessed for different climate scenarios. The results indicate that SLR can drive migration exceeding 8000 and 10,000 coastal inhabitants for 2080 under the Representative Concentration Pathways 4.5 and 8.5, respectively. Although household adaptation to flood risk strongly impacts projected annual flood damage, its impact on migration decisions is small and falls within the 90% confidence interval of model runs. Projections of coastal migration under SLR are most sensitive to migration costs and coastal flood protection standards, highlighting the need for better characterization of both in modeling exercises. The modeling framework demonstrated in this study can be upscaled to the global scale and function as a platform for a more integrated assessment of SLR-induced migration.

In the context of changing climate and increasing water demand, large-scale hydrological models are helpful for understanding and projecting future water resources across scales. Groundwater is a critical freshwater resource and strongly controls river flow throughout the year. It is also essential for ecosystems and contributes to evapotranspiration, resulting in climate feedback. However, groundwater systems worldwide are quite diverse, including thick multilayer aquifers and thin heterogeneous aquifers. Recently, efforts have been made to improve the representation of groundwater systems in large-scale hydrological models. The evaluation of the accuracy of these model outputs is challenging because (1) they are applied at much coarser resolutions than hillslope scale, (2) they simplify geological structures generally known at local scale, and (3) they do not adequately include local water management practices (mainly groundwater pumping). Here, we apply a large-scale hydrological model (CWatM), coupled with the groundwater flow model MODFLOW, in two different climatic, geological, and socioeconomic regions: the Seewinkel area (Austria) and the Bhima basin (India). The coupled model enables simulation of the impact of the water table on groundwater–soil and groundwater–river exchanges, groundwater recharge through leaking canals, and groundwater pumping. This regional-scale analysis enables assessment of the model's ability to simulate water tables at fine spatial resolutions (1 km for CWatM, 100–250 m for MODFLOW) and when groundwater pumping is well estimated. Evaluating large-scale models remains challenging, but the results show that the reproduction of (1) average water table fluctuations and (2) water table depths without bias can be a benchmark objective of such models. We found that grid resolution is the main factor that affects water table depth bias because it smooths river incision, while pumping affects time fluctuations. Finally, we use the model to

Neural networks have been shown to be extremely effective rainfall-runoff models, where the river discharge is predicted from meteorological inputs. However, the question remains: what have these models learned? Is it possible to extract information about the learned relationships that map inputs to outputs, and do these mappings represent known hydrological concepts? Small-scale experiments have demonstrated that the internal states of long short-term memory networks (LSTMs), a particular neural network architecture predisposed to hydrological modelling, can be interpreted. By extracting the tensors which represent the learned translation from inputs (precipitation, temperature, and potential evapotranspiration) to outputs (discharge), this research seeks to understand what information the LSTM captures about the hydrological system. We assess the hypothesis that the LSTM replicates real-world processes and that we can extract information about these processes from the internal states of the LSTM. We examine the cell-state vector, which represents the memory of the LSTM, and explore the ways in which the LSTM learns to reproduce stores of water, such as soil moisture and snow cover. We use a simple regression approach to map the LSTM state vector to our target stores (soil moisture and snow). Good correlations (R2>0.8) between the probe outputs and the target variables of interest provide evidence that the LSTM contains information that reflects known hydrological processes comparable with the concept of variable-capacity soil moisture stores. The implications of this study are threefold: (1) LSTMs reproduce known hydrological processes. (2) While conceptual models have theoretical assumptions embedded in the model a priori, the LSTM derives these from the data. These learned representations are interpretable by scientists. (3) LSTMs can be used to gain an estimate of intermediate stores of water such as soil moisture. While machine learning interpretability is sti

[Figure: see text].