Search Results (1,473)

Nuclear Factor Y, subunit A (NFYA) constitutes a family of transcription factors that play critical roles in plant growth, development and abiotic stress responses. Taxodium hybrid ‘Zhongshanshan’ (T. mucronatum × T. distichum) is a fast-growing tree species with high industrial value and remarkable flooding tolerance. However, the systematic characteristics and abiotic stress response patterns of the ThNFYA gene family remain unclear. In this study, a total of 11 ThNFYA genes were identified. The encoded proteins ranged from 67 to 372 amino acids in length, with predicted molecular weights between 16.84 and 40.12 kDa. Phylogenetic analysis classified plant NFYAs into four clades, with all ThNFYAs falling into clades I and IV. Expression profiling revealed tissue-specific patterns, with six members showing the highest transcript levels in the cambium. Multiple cis-acting elements associated with stress and hormone responses were detected in the promoter regions of ThNFYAs. Most ThNFYAs were differentially regulated under salt, drought, and flooding stresses. Notably, most clade IV members (ThNFYA3, ThNFYA4, and ThNFYA6-ThNFYA8) were downregulated in the wood under partial submergence. This indicates their potential role in modifying wood properties in response to flooding. Co-expression network analysis identified ThNFYA1 and ThNFYA8 as central hub genes in leaves under partial submergence. Overall, these results suggest that the ThNFYA family may serve as candidate regulators of development and stress adaptation in T. hybrid ‘Zhongshanshan’. This study provides valuable insights for further functional verification of ThNFYAs and lays a foundation for marker-assisted breeding of stress-tolerant varieties. Full article

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

Anthropogenic climate change has disrupted the global hydrological cycle, increasing compound extreme events like Dry–Wet Abrupt Alternations (DWAAs). Regarding the Mediterranean Basin, Greece is highly susceptible to these abrupt hydroclimatic shifts, which frequently overwhelm reactive disaster management. This study quantifies the spatiotemporal dynamics of DWAA events across Greece from 1990 to 2024. Using the 1-month Standardized Precipitation Evapotranspiration Index (SPEI-1) from ERA5 reanalysis, transitions were classified into dry-to-wet (DW) and wet-to-dry (WD) across moderate (±1.0), severe (±1.5), and extreme (±2.0) thresholds. Core physical metrics (duration, severity, and intensity) were evaluated using Anselin Local Moran’s I (LISA) and Mann–Kendall tests to identify spatial hotspots and temporal trends. Results revealed a spatially decoupled hazard regime dictated by topography and atmospheric mechanics. Severe DW transitions primarily manifest as intense autumn flash floods (62.7%) concentrated in western and southern districts. Conversely, severe WD transitions emerge as high-magnitude summer agricultural flash droughts (52.5%) clustered in central and northern continental plains. Crucially, while the magnitudes of these events demonstrate historical temporal stationarity, their decadal frequency doubled in the 2020s. This increase validates the idea that global warming accelerates systemic climate extremes, necessitating an urgent shift toward proactive, highly localized adaptation strategies. 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

It is becoming harder to feel at home. As wildfires, floods, drought, and heat become recurrent rather than exceptional, and as everyday life fills with weather alerts, evacuation warnings, and preparedness drills, many people experience a particular kind of distress: the pain of remaining in a place that is still physically present but no longer feels safe, familiar, or trustworthy. This paper examines that experience through the concept of solastalgia—homesickness felt while still at home—and argues that it is not a private mood or a distortion of thinking, but a meaningful disturbance in the very conditions of dwelling. Bringing together phenomenology, emotional geography, and emotion-regulation theory, it shows how a pervasive “culture of emergency” reshapes the way we inhabit space, time, our bodies, and our relationships with others, narrowing the room we feel we have to live and act. Rather than treating climate-related anxiety as something to be corrected, the paper understands it as an honest response to a world whose stability has genuinely been compromised. It then asks how home might become livable again, arguing that repair must happen at several levels at once—personal, communal, and institutional—and that a workable sense of belonging can be rebuilt without denying what has been lost. Full article

Abiotic stresses—drought, salinity, extreme temperature, flooding, and heavy-metal toxicity—constrain rice (Oryza sativa L.) yield worldwide, and the cellular programmes underlying them are unevenly distributed across cell types that bulk-tissue assays average together. This review examines, from a methodological standpoint, what single-cell and spatial omics technologies can and cannot establish about rice abiotic stress biology. We first define the modality space: single-cell omics measures RNA, chromatin accessibility, DNA methylation, protein, or metabolite features at the resolution of individual cells or nuclei, whereas spatial omics measures such features while retaining tissue coordinates; the two are complementary rather than interchangeable. We then treat each platform class—droplet-based scRNA-seq, combinatorial-indexing approaches including SPLiT-seq, nuclei-based snRNA-seq and multiome, sequencing-based and imaging-based spatial transcriptomics—under a common template covering measurement principle, the questions each can answer, applicability to rice tissues, dominant biases, and the inferences each cannot support. To make evidence strength comparable across a heterogeneous literature, we apply a four-tier scheme throughout: Tier A, direct rice cell-resolved or spatial evidence with functional or field validation; Tier B, robust rice functional and localization evidence without single-cell data; Tier C, cell-resolved evidence without causal validation; and Tier D, cross-species analogy or reasoned proposal. Applying this scheme shows that the genes with genuine breeding traction in rice—SUB1A, OsHKT1;5, OsHMA3, OsNRAMP5, DRO1—rest on Tier B evidence from classical genetics and field testing, whereas the most cell-resolved rice evidence concentrates in root outer layers and barrier formation at Tier C, and heat and cold stress, despite dominating yield loss, lack rice cell-resolved data almost entirely. We extend the discussion beyond transcriptomics to single-cell DNA methylome profiling, spatial proteomics and metabolomics, and three-dimensional analysis of thick plant tissues, in each case distinguishing demonstrated plant capability from mammalian-only capability, and we assess the expanding role of artificial intelligence in annotation, segmentation, batch correction, integration, and perturbation prediction alongside its documented failure modes. Rice, maize, and wheat are compared to identify transferable methodology. Cell-resolved omics has to date improved biological interpretation and candidate prioritization; demonstrating an incremental breeding advantage from it remains an unmet requirement. Full article

Cities are confronted with heat, drought, and pluvial flooding, highlighting the need for renovation strategies that enhance climate resilience while minimizing greenhouse gas emissions. This research developed an integrated assessment framework to quantify the mitigation potential of urban and building surface components using five area-based key performance indicators (KPIs): Surface Heat Contribution (SHC), Flood Mitigation Factor (FMF), Water Storage Capacity (WSC), Evaporation Volume (EVA), and Global Warming Potential (GWP). The framework combines simplified life-cycle assessment with biophysical models for heat, water storage, and evaporation, designed as an accessible complement to 3D microclimate tools. Applied to an exemplary residential area in Daegu, South Korea, the method benchmarks existing surfaces and evaluates renovation scenarios targeting heat reduction, flood mitigation, and drought resilience. Results show that surface renovation measures improve mitigation potential. The performance varies across KPIs, involving trade-offs with embodied emissions. Optimal outcomes arise from balanced hybrid strategies integrating complementary measures. Selective greening combined with low heat capacity, high-conductivity materials (e.g., metal façades) effectively reduces heat stress but increases embodied GWP and structural demands. Permeable and greened surfaces improve WSC and EVA, supporting short-term flood mitigation, yet reveal limitations under prolonged rainfall. The proposed framework supports transparent and low-carbon climate-resilient renovation decision-making. Full article

Surface soil moisture (SM) is the keystone variable of terrestrial ecohydrology. Yet, the rapid diversification and development of deep learning architectures for satellite SM estimation have outpaced practitioners’ capacity to select among them. This review synthesizes 37 deep learning studies from the SMAP era (2015–2026) across five architecture families (MLP and physics-informed neural networks [MLP/PINN], long short-term memory [LSTM] and gated recurrent unit [GRU] networks, convolutional neural networks [CNN], convolutional LSTM and graph neural networks [GNN], and Transformer-based models) to establish an architecture–task-matching framework that links each family to its dominant estimation niche. The analysis reveals consistent specializations: MLP/PINN models achieve competitive surface SM retrieval from satellite inputs; recurrent networks extend SMAP temporally (RMSE ≤ 0.035  ${\mathrm{m}}^3\hspace{0.17em}{\mathrm{m}}^{-3}$); CNN disaggregates SMAP to 1 km (reported unbiased root-mean-square error (ubRMSE) approaching 0.04  ${\mathrm{m}}^3\hspace{0.17em}{\mathrm{m}}^{-3}$); ConvLSTM and GNN address spatiotemporal gap-filling (low reported ubRMSE 0.022  ${\mathrm{m}}^3\hspace{0.17em}{\mathrm{m}}^{-3}$); and Transformers enable global multi-source fusion and decadal climate-scenario projection. Across all families, four physics-DL integration modes (hard architectural constraints, soft loss-function penalties, physics-as-input feature engineering, and physics-ML hybrid output fusion) consistently yield RMSE reductions of 8–50% relative to data-driven baselines. These findings provide a practitioner-oriented framework that is applicable to ecohydrological monitoring of plant water stress, agricultural drought, early flood warnings, and land–atmosphere coupling. Full article

The relationship between climate change and migration remains widely debated, particularly regarding the extent to which environmental factors directly drive mobility. While climate-related stressors such as droughts, floods, and environmental degradation can disrupt livelihoods, migration decisions are rarely shaped by environmental factors alone. This study argues that climate change is often overstated as a primary driver of migration and may function instead as a secondary explanatory frame for mobility shaped by deeper socioeconomic and institutional constraints. Focusing on environmentally vulnerable regions of the Global South, this study examines the interplay between ecological stressors, structural inequalities, governance frameworks, and development conditions in shaping migration outcomes. It adopts a sustainability-oriented perspective to highlight how migration is embedded within broader adaptive and developmental processes rather than operating as a direct response to environmental change. The analysis demonstrates that effective policy responses must move beyond climate-deterministic narratives and instead adopt integrated approaches that combine environmental management, socioeconomic development, and migration governance. Strengthening adaptive capacity, improving institutional resilience, and promoting inclusive urban and rural development are identified as key policy priorities. By linking climate-related migration to the Sustainable Development Goals (SDGs)—notably SDG 10 (Reduced Inequalities), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action)—the study contributes to a more holistic understanding of mobility in the context of environmental change. Overall, it calls for a re-evaluation of climate–migration dynamics in the Global South, emphasizing sustainable and context-sensitive governance approaches that reflect the complexity of migration processes. 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

The Tenagi Plains Basin periodically suffers either severe flooding or drought conditions especially due to the lack of both sufficient and proper water management infrastructure works, whilst at the same time playing a vital role for the survival and development of agriculture within its incorporated areas in Paggaion Municipality, Kavala Prefecture, Greece. Moreover, due to its limited reservoir storing capacity within its upper catchment area, some dams of relatively small capacity are suggested to be taking advantage of the great water resources regime of the entire watershed area, especially during hydrological years of increased rainfall. The present study, using hydrological modeling (“HEC-HMS” and Geographical Information Systems), explores the total water quantities of the entire watershed that would have been effectively controlled during the hydrological year 2024–2025. Full article

With global warming, both the frequency and intensity of drought–flood abrupt alternation (DFAA) have been increasing, posing a severe threat to the sustainability of crop production. This study involved a two-year rainproof-pond experiment on maize (Zea mays L.) to investigate the effects of varied DFAA combinations on leaf senescence characteristics and grain yield. The treatments consisted of well-watered conditions throughout the maize growing season (CK), maintaining soil moisture at 55–60% of field capacity (D), waterlogging for 6 days (W), and abrupt alternation between drought (D) and slight (3 days), moderate (6 days), and severe (9 days) waterlogging, denoted as D-LW, D-MW, and D-HW, respectively. The water stress was applied at the sixth leaf (V6) stage. Compared to CK, D, W, D-MW, and D-HW significantly decreased the leaf area index, soil and plant analyzer development (SPAD) value, net photosynthetic rate, superoxide dismutase, peroxidase, catalase activities, soluble protein content, shoot biomass, harvest index, ears per hectare, kernels per cob, and 1000-kernel weight, thus significantly lowering grain yield (reduced by 9.4–74.9%). D-LW, however, behaved in the opposite manner and achieved a high grain yield (reduced by 7.0%). Enhanced root growth contributed to the relatively stable grain yield under D-LW. Thus, drought–slight waterlogging abrupt alternation could sustain maize yield by stabilizing leaf size, photosynthesis, antioxidant defense system, shoot biomass, and harvest index. Full article