Search Results (39)

Human activities and economic development require large amounts of water and energy. The analysis of the nexus between water and energy flows can improve the understanding of the quantitative relationship between the two resources and guide actions and policies to obtain better results with lower risks. This article aimed to analyse and evaluate the use of rainwater in urban environments and its relationship with the water–energy nexus through a literature review. The PRISMA guidelines were used to structure the research, and the RStudio programme was used for the bibliometric analysis. A total of 118 articles published between 2013 and 2023 were identified in the Scopus and Web of Science databases, of which 30 met the eligibility criteria and were included in the review. The risk of bias in the studies included was assessed by two independent reviewers, and disagreements were resolved by consensus. The results were synthesized in a narrative and descriptive way, and organized in a table containing the authors, year, country, and main findings. The studies were grouped according to the theme addressed and the results related to the use of rainwater and the water–energy nexus were compared. The results indicate that the main use of rainwater is for non-drinkable purposes, to reduce the demand for potable water, lessen the pressure on water resources and contribute to environmental sustainability. Climate change can affect rainfall regimes and, consequently, the feasibility of systems. By decentralizing water supply services, the use of rainwater can save drinking water. When assessing energy savings, the use of rainwater is not always the best option, as system configurations and pump specifications are determining factors. Regarding the environmental impacts, all stages of the urban water cycle consume energy for their operation, and the environmental impact is directly related to the energy source used. Policies and regulations focused on rational use, water conservation, demand reduction, and tax incentives for the installation of rainwater harvesting systems, together with awareness campaigns, are necessary for the widespread adoption of rainwater harvesting systems. Finally, there is consensus regarding saving drinking water, but there is still a lack of studies and specifications regarding energy savings. The findings highlight the need for future longitudinal and simulation-based studies to strengthen knowledge of water–energy nexus dynamics in buildings. Full article

The impact of climate change on rainfall extremes has become increasingly obvious in many climatic regions including arid regions where extreme precipitation events are thought to have augmented or at least intensified. Driven by global factors such as greenhouse gas emissions, deforestation, and industrialization, climate change has augmented hydrological variability, thus making traditional stationary models inadequate for the estimation of extreme rainfall at various return periods. Extreme value analyses, which were traditionally derived under the assumption of stationarity (i.e., constant statistical properties over time) and typically do not account for temporal variability or external climatic drivers (e.g., temperature or large-scale climate indices), may lead to inaccurate estimation of rainfall quantiles under changing climate conditions. This paper presents a structured review of applied methodologies for quantifying the influence of climate change on extreme rainfall events, with special attention to how non-stationarity is addressed in arid regions applications, which was not a major focus in previous review papers. Relevant statistical techniques, extreme value theory, machine learning models, and high-resolution climate simulations are reviewed. From an initial pool of over 340 studies, 91 were selected based on their relevance to quantify rainfall extremes induced by climate change in arid regions. Based on the reviewed studies, the analysis revealed a strong reliance on trend analysis of downscaled Global Climate Models (GCMs) and Regional Climate Models (RCMs) within a stationary framework, with limited integration of covariates, other than time, in non-stationary frequency analysis to estimate the climate change-related value. This review identifies the research gaps in the scientific literature related to climate change impact assessment on extreme rainfall in arid regions. It emphasizes the necessity for adopting more robust hybrid approaches, adopting statistical distributions more suitable to arid conditions, careful treatment of outliers, conducting regional analyses to better understand the overall climate behavior of the region, addressing the impact on short-duration rainfall, integrating key climatic drivers through the incorporation of additional climate covariates and the impact of climate change on sub-daily rainfall patterns. Full article

Climate change poses a significant threat to water resources and agricultural sustainability, particularly in semi-arid and socio-economically vulnerable regions such as South Africa. This review synthesizes empirical, modelling, and policy-based evidence on the impacts of climate change on water availability, crop production, and adaptation strategies in the country, drawing on approximately 162 peer-reviewed studies and institutional reports published between 2010 and 2025. The findings indicate that rising temperatures, shifting rainfall patterns, and an increasing frequency of extreme events, such as droughts and floods, are intensifying water stress and disrupting agricultural systems. Hydrological models consistently project declines in runoff, soil moisture, and streamflow, while crop simulation models predict reductions in the yields of major staple crops, including maize, wheat, and sorghum, particularly under high-emission scenarios. Although localized improvements in water availability and crop productivity may occur, these tend to be limited and highly context-specific. In response, South Africa has implemented a range of adaptation strategies, including climate-smart agriculture, water-efficient irrigation, ecosystem-based approaches, and policy-driven interventions. However, their effectiveness remains constrained by institutional fragmentation, limited financial capacity, and persistent socio-economic inequalities, particularly among smallholder farmers. The review underscores the need for integrated, inclusive, and context-specific adaptation strategies that strengthen governance, enhance the science–policy interface, and improve access to climate finance. The insights provided offer valuable guidance for advancing climate resilience in South Africa and other vulnerable regions across the Global South. Full article

Across Africa, drought seldom occurs alone. Rainfall deficits often coincide with heat, rapid soil moisture loss and reduced streamflow, producing compound events whose impacts exceed those of any single driver. This review synthesises station observations, satellite and reanalysis products, and climate model simulations to clarify where such events are most common, how they form, how they affect societies and ecosystems, and how risks are changing. A practical tiered definition tailored to African conditions is outlined and applied to identify five recurrent hotspots: the Sahel, the Greater Horn of Africa, southern Africa, the margins of the Congo Basin and the Guinea Coast. The review sets out a physically consistent sequence that links basin-scale sea surface temperature anomalies to shifts in monsoon circulation, and then to land processes that amplify and prolong heat and dryness through reduced evapotranspiration and soil-moisture memory. Documented impacts include lower crop and pasture productivity, pressure on rivers, reservoirs and groundwater, stress on hydropower and wider consequences for food and energy security. Compound drought frequency across these hotspots has risen by 18–55% since 1980, with the probability of the most severe events roughly doubling at 1.5 °C of global warming and tripling at 3 °C. The review highlights near-term priorities, including compound-aware monitoring, sub-seasonal-to-seasonal early warning and conjunctive water management. Full article

Understanding landform evolution is essential for assessing how terrain responds to geomorphic drivers such as weathering, fluvial erosion, hillslope processes, and tectonic uplift. This is particularly important in applications such as constructed post-mining landform rehabilitation, where predicting long-term erosional stability is vital for sustainable closure planning. In addition to long-term average erosion rates, the spatial patterns of gullies, rills, and channels are critical for assessing landform stability. This review examines Digital Elevation Model (DEM)—driven, process-based Landform Evolution Models (LEMs), with a primary focus on SIBERIA, CAESAR-Lisflood, and SSSPAM, which are widely used to evaluate the erosional behaviour of constructed post-mining landforms, each with distinct characteristics. These models are systematically compared in terms of input requirements, process representations, parameterisation, and predictive capabilities. Recent advances in high-spatial resolution DEMs (e.g., LiDAR, SRTM), along with digital soil and rainfall databases and satellite-derived vegetation indices, have improved the parameterisation of erosion, hydrological, and sediment-transport processes of the LEMs. A brief comparative case study is presented to demonstrate how these LEMs simulate 1000-year erosional behaviour along a linear hillslope. This review synthesises the current capabilities and limitations of DEM-driven LEMs, providing guidance for researchers, land managers, and practitioners in selecting appropriate models to support sustainable post-mining landform management, as well as outlining potential future advancements. Full article

A fundamental limitation of maximum likelihood and Bayesian methods under model misspecification is that the asymptotic covariance matrix of the pseudo-true parameter vector ${\mathsf{\theta }}_{*}$ is not the inverse of the Fisher information, but rather the sandwich covariance matrix ${\displaystyle \frac{1}{n}}{\mathbf{A}}_{*}^{-1}{\mathbf{B}}_{*}^{\phantom{-1}}{\mathbf{A}}_{*}^{-1}$, where ${\mathbf{A}}_{*}$ and ${\mathbf{B}}_{*}$ are the sensitivity and variability matrices, respectively, evaluated at ${\mathsf{\theta }}_{*}$ for training data record ${\omega }_1,\dots ,{\omega }_n$. This paper makes three contributions. First, we review existing approaches to robust posterior sampling, including the open-faced sandwich adjustment and magnitude- and curvature-adjusted Markov chain Monte Carlo (MCMC) simulation. Second, we introduce a new sandwich-adjusted MCMC method. Unlike existing approaches that rely on arbitrary matrix square roots, eigendecompositions or a single scaling factor applied uniformly across the parameter space, our method employs a parameter-dependent learning rate $\lambda \left(\mathsf{\theta }\right)$ that enables direction-specific tempering of the likelihood. This allows the sampler to capture directional asymmetries in the sandwich distribution, particularly under model misspecification or in small-sample regimes, and yields credible regions that remain valid when standard Bayesian inference underestimates uncertainty. Third, we propose information-theoretic diagnostics for quantifying model misspecification, including a strictly proper divergence score and scalar summaries based on the Frobenius norm, Earth mover’s distance, and the Herfindahl index. These principled diagnostics complement residual-based metrics for model evaluation by directly assessing the degree of misalignment between the sensitivity and variability matrices, ${\mathbf{A}}_{*}$ and ${\mathbf{B}}_{*}$. Applications to two parametric distributions and a rainfall-runoff case study with the Xinanjiang watershed model show that conventional Bayesian methods systematically underestimate uncertainty, while the proposed method yields asymptotically valid and robust uncertainty estimates. Together, these findings advocate for sandwich-based adjustments in Bayesian practice and workflows. Full article

Climate change refers to a significant and measurable alteration in the climate’s state, evident through shifts in the average and variability of key climate factors. Although the onset of climate change spans several decades, recent studies reveal a concerning intensification that is increasingly driven by anthropogenic activities, with the construction sector emerging as a significant contributor. The present paper investigates climate-conscious innovations within Romania’s construction industry, with a specific focus on the implementation of adaptive strategies. Through a narrative review methodology, this study synthesizes diverse sources, including scientific literature, technical reports, urban policy documents and relevant websites, to map the integration of sustainable construction practices in response to climate pressures. The findings highlight a range of local approaches, including passive design, green infrastructure, and reversible architecture, reflecting Romania’s gradual alignment with broader European environmental objectives. Despite Romania’s relatively low green contribution on a global scale, the country faces significant climate risks, including heatwaves, intense rainfall, and droughts. This evolving climate context necessitates a comprehensive adaptation of architectural practices, construction processes, material selection, and design strategies to mitigate environmental impact and enhance resilience. However, the narrative review approach has inherent limitations, including the potential for selection bias and limited replicability, which constrain the generalizability of the findings. Future research should employ quantitative and empirical methods to validate the effectiveness of climate-adaptive measures in structural engineering. Key areas include the integration of climate-resilient materials, structural performance under climate-induced stressors, and lifecycle carbon assessments of building components. Additionally, further investigation is needed into the development of predictive simulation models that assess the long-term structural impacts of evolving climate scenarios specific to Romania’s geographic and climatic conditions. Full article

Malaria is a persistent public health problem, particularly in sub-Saharan Africa where its transmission is intricately linked to climatic factors. Climate change threatens malaria elimination efforts in limited resource settings, such as in the Mberengwa district. However, the role of climate change in malaria transmission and management has not been adequately quantified to inform interventions. This protocol employs a multi-method quantitative study design in four steps, starting with a scoping review of the literature, followed by a multi-method quantitative approach using geospatial analysis, a quantitative survey, and the development of a predictive Susceptible-Exposed-Infected-Recovered-Susceptible-Geographic Information System model to explore the link between climate change and malaria transmission in the Mberengwa district. Geospatial overlay, Getis–Ord Gi* spatial autocorrelation, and spatial linear regression will be applied to climate (temperature, rainfall, and humidity), environmental (Land Use–Land Cover, elevations, proximity to water bodies, and Normalised Difference Vegetation Index), and socio-economic (Poverty Levels and Population Density) data to provide a comprehensive understanding of the spatial distribution of malaria in Mberengwa District. The predictive model will utilise historical data from two decades (2003–2023) to simulate near- and mid-century malaria transmission patterns. The findings of this study will be used to inform policies and optimise the management of malaria in the context of climate change. Full article

Bayesian methods have revolutionised hydrological modelling by providing a framework for managing uncertainty, improving model calibration, and enabling more accurate predictions. This paper reviews the evolution of Bayesian methods in hydrology, from their initial applications in flood-frequency analysis to their current use in streamflow forecasting, flood risk assessment, and climate-change adaptation. It discusses the development of key Bayesian techniques, such as Markov Chain Monte Carlo (MCMC) methods, hierarchical models, and approximate Bayesian computation (ABC), and their integration with remote sensing and big data analytics. The paper also presents simulated examples demonstrating the application of Bayesian methods to flood, drought, and rainfall data, showcasing the potential of these methods to inform water-resource management, flood risk mitigation, and drought prediction. The future of Bayesian hydrology lies in expanding the use of machine learning, improving computational efficiency, and integrating large-scale datasets from remote sensing. This review serves as a resource for hydrologists seeking to understand the evolution and future potential of Bayesian methods in addressing complex hydrological challenges. Full article

This article draws on over 40 years of the author’s experience with hydroinformatics tools for water and sustainability challenges, including flooding. It aims to spark discussion on urban flood risk and resilience rather than provide a literature review or definitive answers. Assessing urban flood risk and resilience is complex due to the spatio-temporal nature of rainfall, urban landscape features (e.g., buildings, roads, bridges and underpasses) and the interaction between aboveground and underground drainage systems. Flood simulation methods have evolved to analyse flood mitigation schemes, damage evaluation, flood risk mapping and green infrastructure impacts. Advances in terrain mapping technologies have improved flood analyses. Despite investments in flood management infrastructure, a residual flood risk remains, necessitating an understanding of the recovery and return to normality post-flood. Both risk and resilience approaches are essential for urban flood planning and management. Future challenges and opportunities include both technological and governance solutions, with artificial intelligence advancements offering the potential for digital twins to better protect urban communities and enhance the recovery from flood disasters. Full article

With the intensification of climate change and the continuous advancement of urbanization, the pressure on urban drainage systems has increased, leading to the growing prominence of urban waterlogging issues. Besides the destruction of infrastructure, urban waterlogging also affects environmental quality, economy, and residents’ daily lives. Researchers have recently analyzed the causes of urban waterlogging from multiple perspectives, including land-use changes driven by urbanization, the inadequacy of urban drainage systems, and extreme rainfall events resulting from climate change. Various strategies have been proposed to address waterlogging, including optimizing urban green spaces, establishing forecasting systems, and creating effective emergency management systems. Additionally, some scholars highlight the significance of integrated urban planning and interdepartmental collaboration, suggesting that multi-party cooperation can help mitigate the risks of waterlogging. This paper conducts a comprehensive literature review to summarize the current research status of urban waterlogging, focusing on theoretical, experimental, numerical simulation, and artificial intelligence approaches. The review aims to provide a clearer understanding of the existing knowledge, identify gaps for future research and propose ideas that combine advanced technologies and interdisciplinary approaches. Full article

The European building stock is aging and needs renovation. Holistic renovation approaches, including Vertical Forest (VF) systems, are emerging as sustainable alternatives to demolition and reconstruction. This paper reviews and defines missing reliable damage and hazard intensity measures for the holistic renovation of existing reinforced concrete (RC) buildings with VF systems. Based on an extensive literature review and preliminary studies, including empirical multiparametric system evaluation assessments, Monte Carlo simulations, and System-Theoretic Process Analysis (STPA), combined structural, non-structural, vegetation, and human comfort components are examined. Key damage indicators are identified, including interstory drift ratio, residual deformation, concrete and reinforcement strains/stresses, and energy dissipation, and their applicability to VF-integrated structures are evaluated. Green modifications are found to have higher risk profiles than traditional RC buildings (mean scores from Monte Carlo method: 9.72/15–11.41/15 vs. 9.47/15), with moisture management and structural integrity as critical concerns. The paper advances the understanding of hazard intensity measures for seismic, wind, and rainfall impacts. The importance of AI-driven vegetation monitoring systems with 80–99% detection accuracy is highlighted. It is concluded that successful VF renovation requires specialized design codes, integrated monitoring systems, standardized maintenance protocols, and enhanced control systems to ensure structural stability, environmental efficiency, and occupant safety. Full article

Many cities worldwide are increasingly threatened by compound floods resulting from the interaction of multiple flood drivers. Simultaneously, rapid urbanization in coastal areas, which increases the proportion of impervious surfaces, has made the mechanisms and simulation methods of compound flood disasters more complex. This study employs a comprehensive literature review to analyze 64 articles on compound flood risk under climate change from the Web of Science Core Collection from 2014 to 2024. The review identifies methods for quantifying the impact of climate change factors such as sea level rise, storm surges, and extreme rainfall, as well as urbanization factors like land subsidence, impervious surfaces, and drainage systems on compound floods. Four commonly used quantitative methods for studying compound floods are discussed: statistical models, numerical models, machine learning models, and coupled models. Due to the complex structure and high computational demand of three-dimensional joint probability statistical models, along with the increasing number of flood drivers complicating the grid interfaces and frameworks for coupling different numerical models, most current research focuses on the superposition of two disaster-causing factors. The joint impact of three or more climate change-driving factors on compound flood disasters is emerging as a significant future research trend. Furthermore, urbanization factors are often overlooked in compound flood studies and should be considered when establishing models. Future research should focus on exploring coupled numerical models, statistical models, and machine learning models to better simulate, predict, and understand the mechanisms, evolution processes, and disaster ranges of compound floods under climate change. Full article

Due to the significant hydrophilicity and cracking properties of expansive soils, expansive soil slopes are prone to destabilization and landslides after rainfall, seriously threatening the safety of buildings, highways, and railroads. Substantial economic losses often accompany the occurrence of expansive soil slope disasters; thus, it is of great significance to understand the slope failure mechanisms experienced by expansive soil slopes and to prevent expansive soil slope disasters. In this paper, the current research status of the landslide failure mechanism of expansive soil slopes is systematically reviewed based on three research methods: field test, model test, and numerical simulation. The failure mechanisms of expansive soil slopes and the main influencing factors are summarized. Based on the failure mechanisms, three protection principles (waterproofing and water blocking, swelling–shrinkage deformation limitation, and crack inhibition and strength enhancement) that can be followed for disaster prevention of expansive soil slopes are proposed. The research status and advantages and disadvantages of these protection methods are reviewed, and future researchable directions of the stability of expansive soil slopes and slope protection methods are explored. Based on the previous work, a new flexible ecological slope protection system with a double waterproof layer is proposed for expansive soil slopes to realize ecological, efficient, and long-term protection. This paper thus aims to provide technical reference for the prevention and control of slope engineering disasters in expansive soil areas. Full article

Event-based models focus on modelling of peak runoff from rainfall data. Conceptual models indicate simplified models that provide reasonably accurate answers despite their crude nature. Rainfall-runoff models are used to transform a rainfall event into a runoff event. This paper focuses on reviewing computational simulation of rainfall-runoff processes over a catchment. Lumped conceptual, event-based rainfall-runoff models have remained the dominant practice for design flood estimation in Australia for many years due to their simplicity, flexibility, and accuracy under certain conditions. Attempts to establish regionalization methods for prediction of design flood hydrographs in ungauged catchments have seen little success. Therefore, as well as reviewing key rainfall-runoff model components for design flood estimation with a special focus on event-based conceptual models, this paper covers the aspects of regionalization to promote their applications to ungauged catchments. Full article