Search Results (396)

Against the backdrop of global climate change, drought–flood abrupt alternation (DFAA) has become a major compound climate extreme threatening ecosystem stability and sustainable watershed management. This study investigated the spatiotemporal characteristics and ecological responses of DFAA in the Jialing River Basin, China, using meteorological and hydrological observations from 1971 to 2020. DFAA events were identified using the Standardized Weighted Average Precipitation Index (SWAP) and run theory, and their spatiotemporal heterogeneity was characterized using spatial autocorrelation analysis. The Long-duration DFAA Index (LDFAI) was derived using the WEP-L distributed hydrological model. Ecological responses during 2000–2020 were evaluated by integrating the Remote Sensing Ecological Index (RSEI), grey relational analysis, and a Copula-based conditional probability model. The results showed that drought-to-flood events exhibited stronger spatial clustering than flood-to-drought events. Ecosystem responses showed significant lag effects, averaging 6.9 months for spring–summer events and 5 months for summer–autumn events, with greater sensitivity during the summer–autumn period. Under DTF events, the probability of maintaining relatively high ecological quality was significantly higher than under FTD events, whereas FTD events were associated with a higher probability of ecological degradation. Under compound scenarios, consecutive same-type events were more conducive to ecosystem stability, while alternating sequences of different event types significantly amplified negative ecological stress and represented high-risk scenarios for ecological degradation. These findings provide scientific support for adaptive watershed management, ecological restoration, and climate change adaptation in drought–flood-prone regions. Full article

An ethnographic study employing 72 qualitative interviews with agricultural producers and associated water decision-makers concerning farm climate change adaptation in Saskatchewan, Canada concludes that farm size matters. Inductively we established a distinction of 2025 hectares as the differentiation between small and large agricultural farms; very small farms (less than 810 hectares) and one very large farm (51,030 hectares) were most vulnerable. Our research deviates from prior research that has concluded either large or small farms are more adaptive. In Saskatchewan, large farms mitigate climate impact risk of drought or flood over a bigger area, and have institutional and financial capacity to undertake adaptations. However, small farmers embrace more adaptation through environmental farm practices. Unlike other areas of the world where farms are becoming smaller and fragmenting, the trend is toward larger farms. Farmers thought of their farms as businesses; however, their multi-generational connection to the land was strong, and the concept of sustainability was rejected as farmers wanted to leave the land in a better state for future generations. The most important adaptive strategy identified, regardless of size, was ensuring the financial health of a farm through cashflow and the ability to pay expenses and debt. Full article

Sudan faces riverine flooding along the Blue Nile and chronic drought across Darfur–Kordofan, yet no national assessment integrates both hazards with social vulnerability to support sustainable and climate-resilient development. This study develops a Societal Impact Index (SII) for Sudan’s eighteen states using a terrain-based flood-susceptibility surface, a drought-frequency indicator (SPEI-12), and thirteen social-vulnerability indicators. These are combined into four weighted pillars following the Intergovernmental Panel on Climate Change (IPCC) risk architecture and validated against independent humanitarian-needs assessments, with convergent checks based on displacement and malnutrition. An unsupervised machine-learning audit, combining k-means clustering with principal component analysis, tests whether the data’s structure supports the composite ranking. The audit shows that the five High-impact states follow two distinct pathways: hazard-and-exposure dominance in Al Qadarif and Al Jazirah, and sensitivity dominance in the remaining three Darfur states. This distinction enables risk-reduction and infrastructure measures to be tailored to the dominant pathway in each state. The first two principal components correlate only weakly with the SII ($r=0.02$ and $r=0.36$), indicating that the ranking reflects the assigned weights as well as the data structure. Each score is exactly decomposed into pillar contributions, improving transparency, while a prototype scenario tool illustrates practical use. Flood-exposed population increased by 7.4 percent between 2017 and 2020, highlighting the need for continuous updating. The reproducible, open-data framework can support equitable resource allocation, sustainable infrastructure planning, long-term vulnerability reduction, and future disaster-resilience digital twins in data-scarce Sahelian settings. Full article

Climate change education has expanded in curricular, institutional and media arenas, yet it still faces a central challenge: turning scientific knowledge into educational practices that transform perceptions, decisions and participation. This article argues that the challenge cannot be addressed by merely adding content about the climate system, because the climate crisis is experienced in specific territories, affects communities unequally and activates emotions that can either enable or inhibit action. Through a critical integrative review and research-reflection approach, the article connects three bodies of scholarship that are commonly developed in parallel: territorial and place-based learning, climate justice, and emotional literacy. Its conceptual novelty lies not in claiming that these components are individually new, but in specifying territorial memory as the mediating mechanism through which scientific evidence, unequal vulnerability, affective experience and collective action are combined in teacher education. On this basis, it presents the TEJA Model—territorialization, experience, justice and action—as a heuristic framework for designing, analyzing and evaluating teacher-education sequences. Its practical contribution is a five-phase process and an operational matrix that translate nearby risks such as floods, droughts and wildfires into inquiry, deliberation, emotional care and feasible collective action. The model is presented as a testable conceptual proposal rather than a validated intervention. The article concludes that relevant climate education must be scientific, human, territorial and political: it should recognize vulnerability, begin from lived places and address unequal responsibilities and collective capacities for action. Full article

Based on daily precipitation observations from six national meteorological stations in the Zhangye region from 1960 to 2023, this study examines the spatiotemporal evolution and possible driving factors of extreme precipitation using four ETCCDI-recommended indices (SDII, R10mm, R95p, and RX1day). Trends were evaluated using linear regression and the Mann–Kendall test, with Sen’s slope estimation. The results reveal significant (p < 0.05) increasing trends in both the frequency and intensity of extreme precipitation, with a shift from low-intensity, low-frequency to high-intensity, high-variability modes. Temporally, all indices exhibited a step-like surge around 2000, entering a period of high-level oscillation, with extreme characteristics amplified during strong El Niño years. Spatially, a distinct “higher in the south, lower in the north” pattern prevails among the six stations, with the southern Qilian Mountains being the primary contributor and the central plains showing a bimodal distribution, reflecting joint modulation by westerly troughs and local strong convection. The intensification is linked to enhanced atmospheric water vapor, northward penetration of the East Asian summer monsoon, and topographically forced lifting. While alleviating drought stress, this trend substantially elevates the risk of flash floods and debris flows in mountainous areas. It should be noted that the spatial patterns are derived from six stations and should be interpreted as inter-station comparisons rather than continuous spatial fields. Full article

Hydroclimatic evidence is often carried too directly from global-scale attribution studies into local water-resources decisions, despite important differences among variables, methods, and spatial scales. This critical narrative review examines how climate variability, statistical trends, detection, attribution, non-stationarity, and risk should be distinguished when interpreting changes in precipitation, drought, streamflow, floods, groundwater, and water availability. The review compares global assessments, Mediterranean studies, and selected local examples to clarify what each line of evidence can—and cannot—support in adaptation planning. Human influence on global warming is unequivocal, and increases in atmospheric evaporative demand are well supported across many regions; anthropogenic influence has also been detected in several large-scale water-cycle responses. Historical changes in precipitation, river flooding, groundwater, and local drought remain spatially heterogeneous because internal variability interacts with circulation, storage, landscape properties, abstraction, infrastructure, and demand. Statistically significant trends do not by themselves establish hydrological importance or causation, while non-significant local trends do not imply an absence of operational risk. On this basis, the review proposes a scale-aware way of matching hydroclimatic evidence with system vulnerability and the degree of commitment involved in adaptation. Low-regret and adjustable measures can address current vulnerabilities under uncertainty, whereas costly, long-lived, or difficult-to-reverse interventions require stronger local evidence and stress testing across plausible futures. Full article

Climate-resilient land-use planning increasingly requires methods that can translate heterogeneous climate-risk knowledge into actionable territorial recommendations. This study develops the Hazard–Sector Translation Framework, a planning-oriented method for linking regional hazard pathways with affected territorial systems, land-use domains, recommendation families, planning instruments, and resilience functions. The framework was developed through a qualitative cross-regional synthesis of five Southern European regions: Sicily, Costa del Sol, Osijek-Baranja County, Central Greece, and the Troodos Mountain Range. The analysis identifies how diverse hazard pathways, including heat, drought, water scarcity, pluvial flooding, wildfire, hail, frost risk, and tourism climate-suitability pressures, become actionable through recurring land-use domains. Results show that heterogeneous regional risks converge around five main land-use resilience domains: buildings, agriculture, blue-green infrastructure, transportation, and protected-area conservation, while governance and capacity are treated separately as cross-cutting implementation conditions. The operational matrix demonstrates how region-specific hazard pathways can be converted into traceable recommendation structures without reducing local complexity. The framework does not replace detailed hazard modeling or climate-risk assessment; rather, it provides an intermediate methodological bridge between climate-risk evidence and land-use planning action. The approach is transferable to other regions seeking to organize stakeholder-derived needs and adaptation recommendations into coherent, sector-specific planning responses. Full article

Satellite altimetry has become an important tool for monitoring inland water dynamics and has been widely used for water-level retrieval, hydrological simulation, and flood–drought risk assessment. To review research progress and development trends over the past three decades, this study combines bibliometric analysis with a traditional review approach. A total of 4764 publications indexed in the Web of Science Core Collection from 1991 to 2025 were analyzed. Using VOSviewer_1.6.20 and CiteSpace_6.4, we constructed knowledge maps of publication trends, disciplinary intersections, author collaboration, keyword clustering, and burst evolution. Representative studies were further synthesized qualitatively. The results show that remote sensing, geology, and imaging science form the core disciplinary framework of this field. Research hotspots have shifted from single water-level observation to multi-parameter retrieval and integration with hydrological models. New missions, including Surface Water and Ocean Topography (SWOT) and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), have improved spatial resolution and coverage, accelerating the development of this field. However, agricultural water management and the integration of artificial intelligence with hydrological models remain limited. Key challenges include monitoring small and complex water bodies, multi-source data fusion, uncertainty quantification, physics-informed artificial intelligence, and operational applications. Full article

The Mississippi River Delta region is of national and international relevance in terms of agriculture, energy, river navigation, and fisheries. Being one of the most engineered rivers in the world, the Mississippi River has been intensively altered over the past 150 years. The river alterations included the construction of dams, levees, diversions, channelization, spillway flood control systems, and many others. These engineering practices have significantly modified the natural hydrology and sediment dynamics of the river and its deltaic region. While these interventions have provided critical benefits such as flood protection, improved navigation, and economic development, they have also led to profound environmental and ecological consequences. The reduction in sediment delivery to the Mississippi River Delta has accelerated land loss, contributing to the disappearance of coastal wetlands at an alarming rate. The land loss has diminished critical habitats for wildlife, reduced storm surge protection for coastal communities, and disrupted the delta’s natural ability to adapt to fast subsidence. The long-term sustainability of the delta is further threatened by the compounding effects of climate change, including rising sea levels, increased storm intensity, and extreme precipitation and drought conditions. This paper examines the effects, consequences, and future risks of the major river engineering practices on the Mississippi River Delta and provides strategic recommendations that balance human needs with changing natural conditions to ensure sustainability. Specific recommendations include river diversion upstream of New Orleans, better strategies to deal with floods and droughts, strategic maintenance or removal of portions of levees, hybrid coastal-inland human migration, improved transportation connections between coast and inland, and better preparation for future ecosystem shifts. This review is needed because river engineering has made the Mississippi River Delta economically vital yet increasingly vulnerable to sediment loss, wetland collapse, saltwater intrusion, flooding, and population decline. By synthesizing these linked natural and human consequences, it provides a timely framework for rethinking delta sustainability under climate change. Full article

Groundwater level fluctuations play a critical role in shaping hydrological extremes in flat sedimentary landscapes, where shallow water table depth (WTD) and strong surface-subsurface connectivity modulate the impacts of floods and droughts. The Western Pampean Plain (Argentina) exemplifies these dynamics; however, accurate modeling is often hindered by the lack of continuous in situ monitoring. In this context, manual WTD measurements collected by local farmers represent an underexploited source of information for modeling. In this study, we developed a Random Forest framework integrating farmer-operated observations with climatic and satellite-derived data and evaluated its ability to reconstruct and predict WTD. We tested seven modeling strategies, integrating: (i) climatic variables (including effects up to 15 months); (ii) high-resolution satellite-derived Surface Water Cover Index (SWCI) from Landsat; and (iii) coarse-resolution Terrestrial Water Storage Anomalies (TWSA) from GRACE. The best-performing model integrated climatic variables and SWCI, yielding strong reconstruction (R² = 0.861, RMSE = 0.266 m) and robust prediction (R² = 0.752, RMSE = 0.344 m) performances under cross-validation and rolling-origin validation, respectively. Model interpretation revealed SWCI as the dominant predictor, reflecting the strong surface-subsurface connectivity that characterizes this environment. This study provides a practical framework that integrates farmer-operated groundwater monitoring with freely available satellite observations to support agricultural decision-making in flood and drought risk management across flat sedimentary landscapes. Full article

The probability estimation of agrometeorological damage risk is the core technical support for consolidating the agricultural disaster prevention and reduction system and ensuring the sustainable development of national agriculture. It has been widely applied in agricultural production and agricultural insurance. Based on the crop planting area data, as well as the damaged crop area data (damage-affected, damage-stricken, and dead harvest) of 31 provinces and municipalities from 1980 to 2018, this study creatively builds a comprehensive damage strength index. After that, this study obtains accurate risk probability estimation results of five meteorological damage types by using the parameters of the nonparametric normal information diffusion model. The results show that the risk probability of comprehensive meteorological damage is the largest, followed by drought, flood, wind and hail, and freezing. Flood in Hubei, drought in NeiMenggol, windstorm and hailstorm in Qinghai, freezing damage in Hainan, and comprehensive meteorological damage in NeiMenggol have the highest risk probability. The regions where various meteorological damage occur show different distribution characteristics, which is closely related to the latitude and longitude and topography of China. These findings indicate that it is necessary to understand the overall patterns of agrometeorological damage risks and consider their internal heterogeneity, in order to take targeted prevention and control measures to avoid systemic risks in agricultural production and safeguard sustainable and high-quality agricultural development. Full article

Climate change is intensifying hydrological extremes, yet most frameworks assess drought and flood hazards independently, limiting integrated risk management. This study proposes a two-dimensional analytical framework to characterize the drought-flood continuum, moving beyond single-index approaches. We introduce the Hydro-Hazard Index (HHI) as a directionality metric (HHI = Flood Severity − Drought Severity) to classify the dominant hazard type, and the Total Severity Index (TSI = Flood Severity + Drought Severity) as a complementary metric to quantify overall hazard magnitude. Analyzing multi-temporal data from 115 hexagonal units (2018–2024), we employed dynamic features (trends, changes, volatility) and four machine learning models to classify areas as “flood-prone” based on validated flood records. Our results show HHI values ranging from −2.44 to 8.81, with 20.9% of areas classified as Flood-Dominated (mean HHI = 4.58) and 79.1% as Normal (mean HHI = 0.76). Crucially, the two-dimensional analysis revealed that areas with identical HHI values can have vastly different TSI values, under scoring the importance of our dual-index approach. Random Forest achieved the highest performance in predicting flood-prone status (Accuracy = 0.913, AUC = 0.967, Recall = 1.00), with flood_volatility as the most important predictor (24.2%). Spatial autocorrelation confirmed strong clustering of high-risk areas (Moran’s I = 0.716, p < 0.001). By analyzing flood and drought as distinct but interacting dimensions, this framework provides a more robust and nuanced tool for integrated risk assessment. While acknowledging limitations related to data availability and the need for further independent validation, the proposed framework supports sustainable water resource management and climate adaptation planning under increasing hydrological uncertainty. Full article

Global climate change significantly alters environmental and health dynamics, posing a major 21st-century public health threat. Diseases commonly transmitted through water and/or food (diarrhea, cholera, dysentery) are particularly sensitive to these changes. While numerous studies have investigated the climate impacts on waterborne diseases, a critical assessment of the applied methodologies and the consistencies of their outcomes is lacking. This systematic review synthesizes the impact of climate change on waterborne diseases, focusing on analytical methods and predictive models used to explain disease presence or abundance. We further evaluate the strengths and limitations of each approach, identifying key gaps for future research. We analyzed 78 indexed research articles published between 1996 and 2023. Our findings demonstrate that diverse climate-related extreme events, including precipitation, flooding, and drought, substantially impact waterborne disease incidence. Additionally, temperature, salinity, humidity, sunshine duration, wind patterns, and specific water quality parameters play significant roles, with regional variations observed. Sensitivity analysis methods and mathematical models are employed to assess sensitivities, quantify correlations, examine impacts, and predict risks. Based on these findings, we urge future research to prioritize elucidating mechanisms and developing models that leverage climate data to predict disease presence or abundance accurately and quantitatively. Full article

This study addresses the dual challenges of seasonal water scarcity and urban flooding in the Garzan River basin, a region with a semi-arid climate. We propose and analyze an integrated water management system designed to mitigate these risks and promote both ecological and economic sustainability. Our methodology began with a comprehensive analysis of meteorological data from 2000 to 2024, which quantified the significant seasonal irregularity in the annual rainfall regime. The findings revealed that the bulk of the average 800 mm of rainfall occurs between January and May, while the summer months experience near-drought conditions. Based on this, we calculated the potential of various water conservation strategies. The system combines rainwater harvesting from a 1000 m² roof and a 500 m² parking lot, projected to collect 1020 m³ annually, with greywater reclamation and low-flow fixtures, which add a combined 400 m³ of annual savings. The total annual water savings of 1420 m³ were found to provide a gross annual economic benefit of $3550. Considering the installation and maintenance costs, the project’s payback period is estimated to be around 32 years. We also developed an annual precipitation prediction model providing a locally applicable early warning mechanism that forecasts total rainfall based on spring data. The use of proactive hydrometeorological data can improve the feasibility of long-term infrastructure projects to a certain extent. Finally, the proposed system’s design was confirmed to be eligible for multiple LEED certification credits, demonstrating its alignment with international sustainability standards. In conclusion, this research provides a comprehensive and viable solution that addresses local water issues and offers a valuable model for other regions facing similar challenges. Full article

Extreme meteorological and climate events, such as floods and prolonged droughts, cause socio-environmental disasters in several regions of the world, including the Amazon. The Amazon Delta-Estuary, which is shaped by coastal and river environments, has constantly changing land use patterns, making it vulnerable to such events. This study aimed to evaluate rainfall regimes and flood scenarios in the tributaries of the Amazon Delta-Estuary, known as the Ottocoded River sub-basins, which are located in the municipalities of Abaetetuba and Barcarena in the Brazilian state of Pará. The Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset was used to analyze rainfall dynamics. The Height Above the Nearest Drainage (HAND) model was employed for flood modeling. The geomorphological, altimetric, bathymetric, and hydrodynamic components of the water bodies were also analyzed. Flood susceptibility was zoned as follows: Very High, High, Medium, Low and Very Low. Of the surveyed buildings, 28.8% were classified as ‘Very High’, 29.1% as ‘High’, 20.2% as ‘Medium’, 13.4% as ‘Low’, and 8.5% as ‘Very Low’. Many of these buildings are located in flood-prone areas, posing a significant risk of socio-environmental disasters. This research could inform public policies aimed at managing the risks associated with environmental disasters and extreme events. Full article