Highlights: What are the main findings? Multitemporal surface reflectance shows trends in oxbow lake area and water types. Oxbow lake connectivity peaks during the flooding period under natural conditions. What are the implications of the main findings? Connectivity-based groups of oxbow lakes represent the availability of fish habitat. Oxbow lake diversity and connectivity are essential for river ecological integrity. In meandering river floodplain systems, remote sensing is a valuable tool for assessing connectivity processes relevant to fish ecological functions. This study used the Google Earth Engine platform and multispectral Landsat 7 imagery. A random forest classifier was used to evaluate water types and area changes in oxbow lakes of the Beni River in Bolivia. Water type dynamics were mainly associated with lake age and distance from the main channel. Seasonal variations highlighted the role of wind-driven sediment resuspension and overflow during high discharge conditions. Long-term lake area changes reflected typical oxbow lake evolution as well as alterations caused by the main channel. Multiannual changes showed a notable area decrease during years of low discharge. Relationships between discharge and lake area dynamics allowed the classification of three lake groups with different levels of connectivity and overbank flow influence. The ecological relevance of these groups was evaluated based on fish habitat preferences and migration patterns. Results emphasize the importance of preserving natural hydrologic variability, with flooding associated with increased habitat availability. Overall, this study demonstrates the usefulness of satellite remote sensing for detecting ecohydrological processes and offers insights to preserve ecological functions in data-scarce regions.

The natural flow regimes of Andean-Amazon tributaries play a vital role in sustaining their rich biodiversity and productive local fisheries, but ongoing and proposed alteration of river flow regimes by large dams threatens to negatively impact river ecosystems. Despite its importance, our understanding of how hydrologic variability influences ecological functions in the Andean Amazon is limited, particularly in regions with scarce data. In these regions, growing research highlights the value of fishers' local ecological knowledge in addressing these gaps. This study focused on increasing our knowledge of ecohydrological relationships in the Beni River of Bolivia through the analysis of fishers' knowledge through 28 individual semi-structured interviews. Results indicate how key species rely on hydrologic variability, connectivity, and flooding dynamics to carry out their life stages of reproductive migration and access different habitats in the floodplains. Fishers mentioned using hydrologic indicators at multiple scales to guide their fishing activity. For instance, flooding extent and duration help anticipate fish abundance in the next years; connectivity between the main channel and oxbow lakes indicates fish migration; and within-site observations of water level on the bank, water depth, flow direction, flow velocity, and backwater effects are used to select a fishing location. In addition, the fishers described characteristics of habitat such as substrate, vegetation, and turbidity, as well as fish feeding habits and sequential migration patterns that represent valuable observations about fish ecology. The comparison with scientific information not only confirmed existing insights but also extended our understanding of ecohydrological relationships and helped explain possible causes of relevant long-term trends. In conclusion, our findings highlight the vital role of the flooding dynamics in the fishing practice and draw attention to the potential negative impacts of hydrologic alteration on the livelihoods of fishers.

Optimising rainwater harvesting (RWH) systems’ design involves sizing the storage and catchment areas to enhance cost-effectiveness, self-sufficiency, and water quality indicators. This paper considers the design of RWH systems under long-term uncertainty in precipitation and demands. In this work, we formulate and solve a multi-objective stochastic optimisation problem that allows explicit trade-offs under uncertainty, maximising system efficiency and minimising deployment cost. We use the yield after spillage (YAS) approach to incorporate the physical and operational constraints and the big-M method to reformulate the nonlinear min\max rules of this approach as a mixed-integer linear programming (MILP) problem. By posing a risk averseness measure on efficiency as a conditional value at risk (CVaR) formulation, we guarantee the designer against the highest demand and driest weather conditions. We then exploit the lexicographic method to effectively solve the multi-objective stochastic problem as a sequence of equivalent single-objective problems. A detailed case study of a botanical garden in Amsterdam demonstrates the framework's practical application; we show significant improvements in system efficiency of up to 15.5% and 28.9% in the driest scenarios under risk-neutral and risk-averse conditions, respectively, compared to deterministic approaches. The findings highlight the importance of taking into account multiple objectives and uncertainties when designing RWH systems, allowing designers to optimise efficiency and costs based on their specific requirements without extensive parameterisation.

East Africa relies heavily on satellite-based rainfall estimates due to the lack of in situ data. However, satellite rainfall products often perform poorly in this region. In this study, data from the Trans-African Hydrometeorological Observatory (TAHMO) were used to build a regional rainfall product in East Africa based on the Soil Moisture to Rain (SM2Rain) algorithm. Subsequently, this regional product was merged with a reanalysis product (ERA5) and two microwave (MW)/infrared (IR)-based rainfall products (IMERG and CHIRPS) based on the Statistical Uncertainty Analysis-Based Precipitation Merging (SUPER) framework. Within this framework, merging weights are derived from the error variances of the rainfall products determined from quadruple collocation on a pixel-to-pixel basis. The merged and individual products are evaluated using data from individual TAHMO stations. We tested SUPER with various interproduct dependency assumptions and found that, in the best-performing configuration, IMERG contributed the most to the merged product, followed by CHIRPS, ERA5, and SM2Rain. SM2Rain showed performance comparable to other rainfall products but is more useful for detecting the offset of the rainy season in drier climates and less reliable under wet conditions. The findings indicated that the merged product outperforms the individual products in most performance metrics. Additionally, we demonstrated the importance of comparing satellite and ground-measured precipitation time series, alongside evaluating performance metrics. The ultimate goal of this study is to develop a workflow to enhance the accuracy of rainfall measurements in East Africa by leveraging information from TAHMO data and different existing products, contributing to the improvement of satellite-based rainfall estimates in East Africa.

This study presents an innovative approach to risk-aware decision-making in water resource management. We focus on a case study in the Netherlands, where risk awareness is key to water system design and policy-making. Recognizing the limitations of deterministic methods in the face of weather, energy system, and market uncertainties, we propose a scalable stochastic Model Predictive Control (MPC) framework that integrates probabilistic forecasting, scenario generation, and stochastic optimal control. We utilize Combined Quantile Regression Deep Neural Networks and Non-parametric Bayesian Networks to generate probabilistic scenarios that capture realistic temporal dependencies. The energy distance metric is applied to optimize scenario selection and generate scenario trees, ensuring computational feasibility without compromising decision quality. A key feature of our approach is the introduction of Exceedance Risk (ER) constraints, inspired by Conditional-Value-at-Risk (CVaR), to enable more nuanced and risk-aware decision-making while maintaining computational efficiency. In this work, we enable the Noordzeekanaal–Amsterdam-Rijnkanaal (NZK-ARK) system to participate in Demand Response (DR) services by dynamically scheduling pumps to align with low hourly electricity prices on the Day Ahead and Intraday markets. Through historical simulations using real water system and electricity price data, we demonstrate that incorporating uncertainty can significantly reduce operational costs—by up to 44 percentage points compared to a deterministic approach—while maintaining safe water levels. The modular nature of the framework also makes it adaptable to a wide range of applications, including hydropower and battery storage systems.

Decarbonisation of the built environment is needed to abate the use of fossil fuels and greenhouse gas emissions. In the city of Amsterdam, multiple bottom-up initiatives have been initiated to reach these goals. In this paper, we explore how energy justice is reshaped by these initiatives on an urban scale. This is done by a case study on a platform that aims to connect, support and inform community energy initiatives. Based on ethnographic fieldwork performed between 2019 and 2022 on the heat transition in Amsterdam, we describe how relations between governmental bodies, businesses and urban residents are contested through this platform. Additionally, we describe how the platform shapes the access of citizens to decision-making spaces, financial tools and information to foster new forms of local autonomy, physical heating infrastructures and decision-making procedures. By analysing the motivations and activities for increasing users’ influence and ownership of resources with the notion of ‘commoning practices’, we show how activities of the platform do not only shape physical heating infrastructures, but also the decision-making processes for achieving low-carbon and renewable heating systems in Amsterdam. We, therefore, propose that the notion of ‘commoning practices’ can be used in future research to contribute to a dynamic understanding of how energy justice concerns are expressed and shaped in practice.

The dependency structure between hydrological variables is of critical importance to hydrological modelling and forecasting. When a copula capturing that dependence is fitted to a sample, information on the uncertainty of the fit is needed for subsequent hydrological calculations and reasoning. A new method is proposed to report inferential uncertainty in a copula parameter. The method is based on confidence curves constructed with the use of a pseudo maximum likelihood estimator for the copula parameter. The method was tested on synthetic data and then used as a tool in two hydrological examples. The first examines the probability of major floods in two locations on the Rhine River and its tributaries in the same calendar year. In the second example, rainfall–runoff from a karst region in Tunisia was analysed to determine a confidence interval for the delay between precipitation and runoff.

Global warming impacts the hydrological cycle, affecting the seasonality and timing of extreme precipitation. Understanding historical changes in extreme precipitation occurrence is crucial for assessing their impacts. This study uses relative entropy to analyze historical changes in seasonality and timing of extreme daily precipitation occurrences on the global domain for 63 years of fifth generation of the European Reanalysis reanalysis data. Our analysis reveals distinct regional patterns of change. During the second half of the 20th century, Africa and Asia experienced high clustering of precipitation extremes. Over the past 60 years, clustering increased in Africa while becoming more spread out in Asia. North America and Australia had initially lower clustering and showed slight increases over time. Extreme events in extra-tropical land regions mainly occurred in summer, with modest shifts in timing. These findings have implications for risk assessments of natural hazard like flash floods and landslides, emphasizing the necessity for region-specific adaptation strategies.

Quantifying the magnitude and frequency of extreme precipitation events is key in translating climate observations to planning and engineering design. Past efforts have mostly focused on the estimation of daily extremes using gauge observations. Recent development of high-resolution global precipitation products, now allow estimation of global extremes. This research aims to quantitatively characterize the spatiotemporal behavior of precipitation extremes, by calculating extreme precipitation return levels for multiple durations on the global domain using the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset. Both classical and novel extreme value distributions are used to provide insight into the spatial patterns of precipitation extremes. Our results show that the traditional Generalized Extreme Value (GEV) distribution and Peak-Over-Threshold (POT) methods, which only use the largest events to estimate precipitation extremes, are not spatially coherent. The recently developed Metastatistical Extreme Value (MEV) distribution, that includes all precipitation events, leads to smoother spatial patterns of local extremes. For durations of 5 and 10 days, however, there are less events per year to fit the distribution (37 and 22 on average, respectively), leading to larger inter-annual variability and possible overestimation of the extremes. While the GEV and POT methods predict a consistent shift from heavy to thin tails with increasing duration, the MEV method predicts a relatively constant heaviness of the tail for any precipitation duration, opening up an important research question on what is the ‘correct’ tail behavior of extreme precipitation for different durations. The generated extreme precipitation return levels and corresponding parameters are provided as the Global Precipitation EXtremes (GPEX) dataset. These data can be useful for studying the underlying physical processes causing the spatiotemporal variations of the heaviness of extreme precipitation distributions.