Search Results (2,937)

The rainfed paddy farming system faces profound vulnerabilities due to daily climate non-stationarity. This mixed-methods study in Central Java analyses daily climate signals, total rice production, and household adaptation over 25 years (2001–2025). Moving beyond simple correlation, a Principal Component Regression model integrating five climate variables and three agronomic confounders reveals a profound climate–production decoupling. The composite climate index explains only 7.9% of total production variation, while non-climate factors account for 92.1%. Physical stability is maintained through asymmetric temporal scheduling and a distinct hierarchy of responses, employing active, planned adaptations alongside passive, reactive coping. However, quantitative household evaluation reveals this tonnage stability incurs severe hidden costs; the titip gabah post-harvest system maintains a high Yield Stability Index (0.93) but yields a negative Return on Storage (−7.15%), functioning as a risk-mitigation buffer rather than a profit-maximising tool. Furthermore, climate anomalies drive the progressive alienation of traditional ethnoclimatological knowledge, forcing a cognitive shift toward hybridised decision-making. To prevent passive coping from evolving into systemic maladaptation, we propose a stratified policy framework ranging from village-level knowledge integration and Subdistrict daily risk warnings to regency-level subsidies targeted at smallholders (<0.5 ha). Full article

Drought is becoming more frequent and severe in many regions, particularly in Mediterranean climates, where water demand is increased by warming and changes in precipitation regimes. A long-term assessment of meteorological drought at the Lisbon climatological station is provided in this study using the Standardized Precipitation Index (SPI) and the Reconnaissance Drought Index (RDI) over the period 1864–2021. Monthly precipitation and temperature data are used to compute SPI and RDI at 3-, 6-, and 12-month time scales, so that short-, mid-, and long-term droughts and their temporal evolution can be characterized. RDI is evaluated with three widely used empirical potential evapotranspiration (PET) formulations—Hargreaves, Thornthwaite, and Blaney–Criddle—in order to examine how PET estimations influence drought classification. Given the absence of a physically based reference PET—such as FAO-56 Penman–Monteith—for this station, the focus is on the internal consistency of the PET methods. Furthermore, the Hargreaves formulation is retained as a representative empirical PET for subsequent SPI–RDI comparison. The results show broadly consistent standardized RDI behavior across PET methods; it is indicated that drought conditions are captured more comprehensively by RDI than by SPI because both precipitation deficits and enhanced evaporative demand are included. At the Lisbon station, the estimated average return periods for short-, mid-, and long-term droughts are 3.79, 7.31, and 7.92 years according to RDI, compared with 3.86, 5.69 and 10.88 years from SPI. Several severe drought episodes are identified, including the years 1907, 1922–1923, 1944–1945, 1976, 1981, 1992–1993, 2005, and 2018. While no formal attribution analysis is performed, the drought characteristics are interpreted in the context of observed long-term warming and documented rainfall variability in Lisbon. The findings provide a single-station benchmark of historical drought behavior, by which local water-resources management can be supported and which can serve as a basis for future multi-station and climate-projection-based studies in Portugal. Full article

This study examines how climate change interacts with social, ecological, and policy factors to shape food security among Indigenous tribal communities in Jharkhand, focusing on Saraikela Kharsawan district. It combines a scoping review, policy analysis, and a climate–agriculture case study of Saraikela Kharsawan to identify vulnerabilities and pathways for more resilient Indigenous food systems. The research is qualitative, using a scoping review of 28 studies on Indigenous food security and climate impacts in Jharkhand, thematic analysis of nine national and state policies, and a district-level case study using land use, climate trends/projections, and crop statistics for Saraikela Kharsawan. Additionally, findings from participant observation were integrated into how tribal communities in Saraikela Kharsawan experience and respond to climate variability and its implications for local food systems and nutrition. The study identifies a nutrition paradox, where Indigenous communities experience micronutrient deficiencies and anaemia despite rich biodiversity and Indigenous knowledge. This is accompanied by a decrease in the consumption of nutrient-dense Indigenous foods and a predominance of rainfed monoculture rice cultivation. Marked by rising temperatures and erratic rainfall, climate variability is destabilising agroforestry systems, narrowing dietary options and reducing adaptive capacity. Additionally, policy and institutional gaps reveal fragmented support—strong rights laws and calorie-focused welfare schemes but weak integration of Indigenous foods, agroforestry, and traditional ecological knowledge into nutrition and climate programmes. The paper argues that climate change acts as a threat multiplier on already fragile Indigenous food systems and calls for nutrition-sensitive safety nets, community-based agroforestry, gender-inclusive Indigenous knowledge governance, and cross-sectoral policy alignment to support resilient, culturally appropriate food systems in Jharkhand. Full article

The dynamics of methane (CH₄), nitrous oxide (N₂O) and carbon dioxide (CO₂) in groundwater have rarely been investigated. As dissolved gases they may be transported to distant sites and, hence, to the atmosphere. Crumps Cave (CC) is located on a perched aquifer in south-central Kentucky. Water was sampled at a waterfall within the cave located 15 m below the surface, at two adjacent surface wells 15 m and 50 m deep, providing samples from the epikarst and regional aquifer, respectively. Dissolved gases and geochemistry parameters were analyzed for seasonal changes across three years of weekly monitoring (2015–2017) using Kruskal–Wallis H tests and Bonferroni-corrected pairwise comparisons. Dissolved CO₂ concentrations are mainly controlled by percolation through the epikarst, influenced by soil respiration, and vary with rainfall and seasonal temperature fluctuations. CH₄ showed a site-dependent pattern: concentrations were significantly elevated in warm seasons at the shallow and deep wells, where anaerobic conditions and agriculturally derived organic matter promote methanogenesis; no seasonal variation was detected at the cave site, where oxic conditions limit CH₄ year-round. N₂O was significantly elevated in cold seasons at all three sites, driven by cold-season denitrification of agriculturally derived nitrates. N₂O did not differ between sites, indicating seasonal temperature-driven denitrification as the primary control rather than site hydrology, with cold-season denitrification of agriculturally derived nitrates from fertilizer application. Indirect gas emissions are characteristic of karst systems and may be transported or stored in aquifers through complex interactions of groundwater recharge, microbial activity, and seasonal land-use variability. Full article

Accurate field-scale surface soil moisture (SSM) measures are important for agricultural water management. Conventional satellite SSM products remain too coarse for within-field applications. Here, we developed sequence-aware deep learning models for growing-season SSM estimation by fusing data from Sentinel-1 C-band SAR, harmonized Landsat-8/Sentinel-2 (HLS), and local ancillary datasets. We assembled a multi-source dataset on Sentinel-1 overpass time for 2016–2024 (May–September), yielding 1469 samples and 65 features per sample, including SAR and optical features, meteorological data, soil texture and bulk density, topography, crop labels, irrigation-likelihood flag, and irregular-time-step encoding. We compared long short-term memory (LSTM) and temporal convolutional neural network (TCN) architectures together with attention-augmented variants, including feature attention (FA), temporal attention (TA), and the combined feature–temporal attention (FTA). Models were trained and tested on seven years of data and were validated based on a temporal generalization using combined data of a wet year (2016) and a dry year (2023). The best model, FTA-TCN, achieved R² = 0.851, RMSE = 0.024 m³.m⁻³, and MAE = 0.020 m³.m⁻³ on the withheld validation years, outperforming the base LSTM (R² = 0.422; RMSE = 0.053 m³.m⁻³; MAE = 0.043 m³.m⁻³) and the base TCN (R² = 0.746; RMSE = 0.034 m³.m⁻³; MAE = 0.022 m³.m⁻³). Shapley additive explanations (SHAP) analysis indicated that antecedent precipitation and short-term rainfall accumulations were dominant forcings, while soil texture, elevation, incidence angle, and vegetation indices modulated SSM variability. Satellite-derived features accounted for ~28.5% of aggregated SHAP importance. Overall, the results show that dual-attention temporal convolution can capture field-scale SSM dynamics across wet and dry seasons when satellite signals are coupled with local soil-meteorological-management context. Full article

Spatial and temporal patterns of terrestrial water storage (TWS), and their relationship with groundwater levels, were investigated with the Gravity Recovery and Climate Experiment (GRACE) satellite data, the Global Land Data Assimilation System (GLDAS) land surface model results, and climate observations for the Murray–Darling Basin (MDB). The results show that: (1) TWS displays a clear temporal variability: a negative TWS anomaly with a declining trend during 2002–2009, a positive TWS anomaly with a decreasing trend during 2010–2017, and a period of mixed positive and negative TWS anomalies being accompanied by an increasing trend from 2018 to 2025; (2) five dominant cluster patterns were identified that explain the spatial variability of temporal TWS across the MDB; (3) overall, TWS temporal variability is strongly correlated with rainfall, although it is weak at certain locations; (4) TWS is also influenced by evaporation (both actual and potential evapotranspiration, AET and PET) and runoff, and a combined model significantly improves the overall performance in explaining TWS temporal variability; and (5) TWS-derived groundwater storage changes show both similarities and differences in comparison with groundwater level observation changes, reflecting complex hydrogeological processes and the influence of human activities such as groundwater extraction. These findings provide valuable insights to support improved groundwater resource management with GRACE satellite information and land surface models. Full article

Seasonal delineation represents a critical interface between the natural environment and human activities. However, the conventional climate-temperature (C–T) method, which relies solely on thermal thresholds, has limited applicability in regions with complex climatic regimes. In this study, we develop and apply a composite seasonal index (CSI) integrating temperature and precipitation, using meteorological observations from 298 stations across mainland China during 1980–2020, with CSI calculation based on 278 stations that had valid paired temperature and precipitation records, to investigate spatial patterns of seasonal variability. The results show that incorporating precipitation improves the identification of regional heterogeneity in seasonal dynamics. In northeastern and northwestern China, spring rainfall advances spring onset, while autumn rainfall delays autumn termination, producing a CSI–defined spring duration 1–2 months longer than that derived from the C–T method and an autumn duration about one month longer. In some arid regions, concentrated precipitation prolongs summer by approximately 1–2 months. An independent comparison with land surface phenology metrics during 1982–2018 further shows that CSI–derived seasonal transition dates are broadly consistent with the spatial patterns of SOS, maturity, senescence, and EOS, especially in monsoonal and hydrothermally complex regions. Differences between the CSI and C–T methods are generally small (approximately ±1 month) where precipitation and temperature vary synchronously, but increase to approximately ±2 months where precipitation exerts stronger control. Overall, the CSI preserves the structure of the traditional C–T classification while accounting for hydrological influences, thereby enhancing seasonal delineation in climatically Eastern Monsoon Region and improving the ecological interpretability of hydrothermal seasonality assessment. Full article

The Curve Number (CN) remains one of the most widely applied parameters for estimating direct surface runoff. However, its conventional application based on watershed-aggregated tabulated values conceals hydrological variability in regions with contrasting soils and steep topographic gradients. A recurring limitation of distributed CN approaches is the absence of independent hydrological validation; most machine learning models are trained and evaluated against the same SCS-USDA lookup values used to construct the training target, a circular scheme that measures statistical agreement rather than physical credibility. This study develops a reproducible geospatial workflow for distributed CN estimation across continental Ecuador, combining official MAG land use, soil surface texture natural drainage, and topographic slope layers at 1:25,000 scale with a Random Forest regression model at 10 m spatial resolution. The CN reference raster was derived from official geo-pedological layers and independently validated, not against tabulated assumptions, but against observed hydrological behaviour. Field hydraulic characterization across four dominant land cover classes in the Guamote microwatershed (Chimborazo Province), combined with HEC-HMS (US Army Corps of Engineers, Davis, CA, USA) rainfall-runoff modelling over 41 years (1981–2021), confirmed a mean annual discharge of 0.1568 m³ s⁻¹ consistent with the tabulated CN assignments. To our knowledge, this is the first nationally distributed CN map with field-anchored hydrological benchmarking for an Andean country. The Random Forest model achieved an RMSE = 10.4, an R² = 0.42, and an NSE = 0.41, a performance consistent with published field-based CN estimation studies and expected given the inherent scatter of the SCS-USDA method under real-world conditions. Zonal CN comparisons confirmed a mean absolute error below 5 CN units across the Andean highland and Amazon watersheds; the Guamote watershed showed a mean ∆CN below 4 units against the field-calibrated model. Land use and surface texture emerged as the dominant CN predictors, with natural drainage providing critical discrimination in volcanic and poorly drained soil environments. The resulting 10 m national CN map offers a physically grounded, spatially explicit parameterization layer for distributed hydrological modeling and water resources planning across data-scarce Andean and tropical territories, with direct relevance for flood risk screening, irrigation planning, watershed conservation, and climate adaptation under SDG 6, SDG 11, SDG 13 and SDG 15. Full article

This study investigated the relationship between precipitation indicators and water availability in the Muriaé River Basin (MRB), Southeast Brazil, using rainfall and streamflow series from 1961 to 2020. Monthly mean precipitation (MMP), the total annual precipitation (PRCPTOT), the Rainfall Anomaly Index (RAI), and the Q95 low flow parameter were analyzed to evaluate hydrological variability and drought conditions. Trend analyses were performed using the Mann–Kendall test and Sen’s slope estimator, and Pearson correlation analysis was applied to assess the relationship between precipitation and low flow availability. The results showed marked temporal variability in precipitation and hydrological conditions throughout the basin. Although statistically significant increasing trends in annual precipitation were identified at Carangola and Patrocínio do Muriaé, no generalized long-term reduction in precipitation was observed in the MRB. In contrast, Q95 exhibited reductions at all monitored stations, with decadal decreases ranging from approximately 31% at Carangola to 56% at Itaperuna. The RAI analysis indicated predominance of very dry and extremely dry events during the most recent decade, coinciding with reduced low flow availability. The results indicate that changes in water availability are linked to the temporal distribution and persistence of dry anomalies. These findings can influence decisions in hydrological monitoring and water resource management strategies in the basin. Full article

The cumulative displacement of landslides in alpine regions is jointly affected by rainfall, temperature variation, freeze–thaw cycles, and other factors, and usually exhibits nonlinear, non-stationary, and multi-scale fluctuation characteristics. To improve the accuracy of landslide displacement prediction under complex environmental conditions, this study takes the Taker Tubek Village landslide in Gongliu County, Xinjiang, China, as the study object. Cumulative displacement data from GNSS02 and GNSS03, together with daily rainfall and daily mean temperature, were used to construct a SOO-VMD-CNN-TimeXer hybrid prediction model. First, SOO was employed to adaptively optimize the VMD parameters, and the cumulative displacement series were decomposed into multiple IMF components. Then, CNN was used to extract local fluctuation features, while TimeXer was applied to model long-term temporal dependencies and the effects of exogenous variables. Finally, the predicted results of all components were reconstructed to obtain the cumulative displacement prediction. The results show that the proposed model achieved high prediction accuracy at both GNSS02 and GNSS03. The MSE, MAE, MAPE, and R² values were 0.0158, 0.0960, 0.0112, and 0.9464 for GNSS02, and 0.0483, 0.1590, 0.0203, and 0.9946 for GNSS03, respectively, outperforming LSTM, Informer, iTransformer, Crossformer, and other models. The results indicate that the SOO-VMD-CNN-TimeXer model can effectively characterize the cumulative displacement evolution of landslides in alpine regions and provide technical support for landslide deformation trend forecasting and disaster early warning. Full article

Understanding how interannual climatic variability shapes must composition is critical for predicting wine quality under warming conditions, particularly for acid-retaining cultivars such as Vitis vinifera L. cv. Furmint. This study—conducted as a continuation of a previous investigation on Furmint berry weight, total soluble solids and total dry extract—evaluated titratable acidity, pH, potassium, ammonia and tannin content across three contrasting vintages (2022–2024) in the Tokaj wine region. Using a high-resolution meteorological dataset and an extensive climatic parameter matrix, exploratory analysis was conducted to evaluate responses, and the most influential thermal, radiation-related and water-balance related climatic factors associated with each must parameter were identified. Total acidity and pH showed consistent sensitivity to climatic variability: acidity decreased with mid-season warm nights and abundant summer rainfall, while pH was inversely associated with extreme heat events but increased under higher early-season rainfall and post-véraison irradiation. Potassium content exhibited partly atypical responses, showing positive correlations with late-season warm nights and frequent summer precipitation, and negative with early heat. Ammonia displayed weak to moderate climatic dependence, while tannic acid consistently decreased with higher thermal and irradiation loads. Overall, these results imply cultivar-specific climatic responses in Furmint and suggest that temperature extremes, nighttime heat and rainfall timing are important factors shaping must composition, providing a foundation to better understand the expression of vintage effects under climate change. Full article

Mangrove forests deliver globally significant climate-mitigation and coastal-protection benefits, yet their resilience to climate extremes remains poorly quantified—a key uncertainty for sustainable coastal management. We reassess the unprecedented 2015–2016 mangrove dieback along ~1000 km of the Gulf of Carpentaria, northern Australia, to determine its driver and whether the collapse was structurally abrupt. Combining a mangrove-extent mask, an 11-year radar backscatter series, satellite precipitation, the modeled sea level, the reanalysis temperature and atmospheric dryness, and an El Niño index, we show that an apparent abrupt radar decline during the event was an artifact of non-vegetated tidal-flat and open-water pixels: once analysis was restricted to mangrove pixels, the signal remained stable throughout. Independent spaceborne lidar confirmed that canopy structure concentrates within the mapped mangrove zones, validating the mask. The dieback coincided with a strong sea-level fall, with anomalies reaching about −15 cm, under near-to-above-average rainfall and low atmospheric dryness, indicating that sea-level fall, not rainfall deficit, was the proximate stressor. These findings advance sustainable, mask-applied satellite monitoring of blue-carbon ecosystems and provide an evidence base for climate-adaptive coastal-resilience planning under intensifying climate variability. Full article

Accurate short-term flood forecasting is vital for effective risk management and early warning systems. However, many data-driven models struggle to generalise with limited historical data and fail to consistently capture complex temporal dependencies across varying forecasting horizons. To address these challenges, this study proposes GHDFloodNet (Generalised Hybrid Data-limited Flood Prediction Network), a hybrid deep learning framework designed for robust multi-step-ahead forecasting. GHDFloodNet integrates First-Order Model-Agnostic Meta-Learning (FOMAML) with a Temporal Fusion Transformer (TFT) to enable rapid task adaptation and effectively capture long-range temporal dependencies and variable interactions. To further enhance predictive consistency, the framework incorporates a bidirectional Long Short-Term Memory (BiLSTM) network augmented with an additive attention mechanism and static feature fusion as a core learner within a meta-ensemble architecture. Bayesian hyperparameter optimisation within an AutoML framework identifies optimal model configurations, while a dedicated data handling layer with real-time augmentation improves stability under non-stationary conditions. The framework was evaluated for multi-horizon water level forecasting across four lead time ranges (1–6 h, 6–12 h, 12–24 h, and 24–48 h) using rainfall and lagged water level observations as primary inputs. Experimental results demonstrate that GHDFloodNet achieves robust, nearly invariant error distributions across the full 1–48 h forecast window, reporting an MSE of 0.53–0.55, RMSE of 0.72–0.74, and MAE of 0.35–0.36. Furthermore, the model exhibits stable goodness-of-fit, with R² and NSE values consistently ranging from 0.44 to 0.47 across all lead times, significantly outperforming conventional baselines, which typically exhibit pronounced error escalation at longer horizons. Overall, GHDFloodNet demonstrates that horizon-independent forecast reliability can be architecturally engineered, offering critical value for operational flood forecasting where consistent performance across all lead times outweighs peak short-range precision. Full article

Rainfall variability strongly influences both flood and drought hazards, especially in climatic transition zones where precipitation is highly seasonal and spatially heterogeneous. This study assessed long-term changes in rainfall patterns and compound flood–drought hazard in the Huaihe River Basin, China, using ERA5-Land-derived daily precipitation series at 174 spatial sampling locations during 1950–2025. Rainfall pattern indicators, flood-related rainfall extremes, and SPI-3-based drought indicators were calculated to characterize rainfall amount, frequency, intensity, dry–wet persistence, heavy rainfall events, and meteorological drought conditions. The Mann–Kendall test and Sen’s slope estimator were used to detect long-term trends, and a compound flood–drought hazard classification framework was developed based on a flood-related rainfall hazard index (FHI) and a drought-related hazard index (DHI). The results showed that annual total precipitation, wet days, and consecutive wet days decreased significantly, indicating reduced rainfall occurrence and wet spell persistence. Flood-related rainfall indicators generally showed decreasing tendencies, with more evident declines in persistent multi-day extremes than in single-day rainfall. In contrast, mean SPI-3 showed a significant drying tendency, although drought frequency, severe drought frequency, and drought intensity did not exhibit significant monotonic trends. Spatially, rainfall pattern, flood-related, and drought-related indicators showed clear heterogeneity across the basin. The compound hazard classification identified flood-dominated and drought-dominated areas as the two major hazard types, each accounting for 31.03% of the spatial sampling locations, while low compound hazard and compound flood–drought hazard areas each accounted for 18.97%. These findings indicate that flood- and drought-related hazards coexist but vary spatially across the Huaihe River Basin. The proposed framework provides preliminary rainfall-based information for differentiated flood–drought hazard assessment, climate-adaptive water resources planning, and the sustainable management of water resources in regions facing spatially heterogeneous hydroclimatic hazards. Full article

Water erosion is a critical degradation process that reduces fertility and agricultural sustainability, especially in semi-arid regions. The Universal Soil Loss Equation (USLE) allows for the quantification of this phenomenon using factors such as rainfall erosivity (R) and topography (length-slope, LS). In this study, both factors were estimated and analyzed in the Cañitas sub-basin, located in the semi-desert area of the state of Zacatecas, Mexico, characterized by irregular precipitation and limited data availability. The objective of this study is to estimate and analyze the R factor and LS factor to evaluate their influence on soil water erosion processes. Records from five meteorological stations (1986–2022) were used, along with the Modified Fournier Index (MFI) and Geographic Information Systems (GIS) tools, generating spatial maps of rainfall erosivity and topography. An average R factor of 81.69 MJ∙mm/ha∙h∙year was estimated, consistent with the values obtained using the MFI. The LS factor shows that the northwestern area of the study zone has the most extensive and steepest slopes (up to 20). This study analyzes the R and LS factors to identify areas vulnerable to water erosion and to understand the influence of climate and topography in a semi-arid region, which can serve as a reference for planning conservation actions and managing watersheds in semi-arid areas with high climatic variability. Full article