Search Results (102)

Understanding spatio-temporal rainfall variability is critical for water resource management, especially for climate-sensitive river basins. This study examines rainfall trends and variability in the Baitarani River Basin (eastern India) using high-resolution gridded data for 1979–2020. Rainfall trends were investigated using non-parametric Mann–Kendall test (MK test) and Sen’s slope estimator (SSE). The shift point was detected using multiple homogeneity tests [Pettitt test, Standard Normal Homogeneity Test (SNHT), and Buishand test], while rainfall variability was quantified using an entropy-based Marginal Disorder Index (MDI). The analyses were performed at annual and seasonal scales. MK Z-statistic indicates the increasing or decreasing nature of a series, whereas Sen’s β slope provides the rate of change in that particular series. The MK test and SSE were applied again to examine trends before and after the identified change point. Finally, maps illustrating spatial trends and percentage changes were produced using ArcGIS 10.6. Over the 42-year period, the MK test revealed significant increasing annual trends in both districts, Keonjhar (Z = +2.4, β = 0.7 mm/year), with a percentage change of around +21.8%, and Mayurbhunj (Z = +2.4, β = 0.7 mm/year), with a percentage change of around +19.2%. During 1979–2020 post-monsoon rainfall showed the highest increase (62–70%) while, post 2001, monsoon rainfall declined substantially (1.7–3.3 mm/year) across all districts, with Balasore showing the largest decrease (−3.3 mm/year). The earlier period (1979–2001) had stable monsoon rainfall but greater variability in retreating monsoon, especially in northern regions. Entropy-based variability analysis indicated the Bhadrak and Balasore districts as having maximum variability with an MDI value of 1.44 and 1.35, respectively, for monsoon and annual rainfall series. These findings underscore the importance of incorporating changing seasonal dynamics into water-resource planning and flood-risk management for the Baitarani River Basin in the context of climate change. Full article

Vegetation phenology refers to the cyclical growth patterns of vegetation in nature, which are influenced by climatic conditions, human activities, and genetic factors. It plays an irreplaceable role in regulating carbon cycling and energy flow within natural ecosystems. However, the combination of a cloudy and rainy climate with a landscape characterized by the interplay of land and water and fragmented patches has long posed challenges for remote sensing phenological monitoring data, including a scarcity of valid observations, frequent temporal gaps, and spectral distortion in mixed pixels. These issues make it difficult to reliably support the needs of wetland phenological inversion and mapping. To address this issue, this study uses vegetation inversion in the Poyang Lake wetlands as a case study and reconstructs high-spatiotemporal-resolution time-series kNDVI data based on multi-source remote sensing data. Methodologically, we propose an improved and enhanced spatiotemporal adaptive reflectance fusion model, IESTARFM. This model enhances the homogeneity of similar pixel selection through adaptive matching windows and land cover constraints. Additionally, it explicitly incorporates cloud probability and time-lag factors into the weighting structure to systematically downweight unreliable observations, and further employs quadratic term corrections to account for the nonlinear growth response of kNDVI. Using the reconstructed dataset, key phenological information is extracted by combining third-order harmonic analysis with a dynamic thresholding method, thereby enhancing the robust characterization of seasonal trajectories under conditions of missing data and noise. Accuracy evaluation results show that the 10m/8d high-frequency kNDVI dataset reconstructed by IESTARFM achieves at least a 12.61% improvement in fusion accuracy compared to classical methods such as ESTARFM, STARFM, and FSDAF, with a maximum reduction in RMSE of 0.026, and effectively restores details in areas with thin cloud cover. The reconstructed kNDVI series achieved a coefficient of determination $R^2$ = 0.875 and RMSE = 0.066 relative to Sentinel-2 observations, indicating that the reconstructed series closely reproduces the reference imagery in both amplitude and spatial structure. The phenological parameters derived from kNDVI exhibit an RMSE of 4.81 days compared to field observations, demonstrating that the reconstructed time series reliably captures the timing of key phenological events. It should be noted that the proposed approach is designed for post-event time-series reconstruction and is not intended for real-time forecasting. In summary, this study collaboratively enhanced the reliability of high-resolution index time-series reconstruction and phenological identification in cloudy and rainy wetlands through three key aspects: cloud noise suppression, heterogeneous boundary preservation, and nonlinear growth characterization. It provides a generalizable technical foundation for dynamic monitoring of wetland vegetation, ecological restoration assessment, and refined management in regions with frequent cloud and rainfall. Full article

The aim of this study is to analyze the perceptions of students in Primary Education (5th and 6th grades; n = 260; Valencian Community, Spain) regarding natural hazards, based on their personal experiences, memories, school-based instruction and preventive measures. Methodologically, the study is based on the administration of a mixed-type questionnaire. The high proportion of students reporting strong awareness (68.5%) suggests that school-based instruction is already contributing to a foundational level of risk perception. Floods associated with torrential rainfall are the most frequently recalled hazard, influenced by the cut-off low event of 29 October 2024 (Valencia) (72.3%). Furthermore, no significant differences were found between students who experienced or remembered a flood and those who did not, in terms of preventive measures received, indicating a consistent and homogeneous instruction. In conclusion, this study highlights that students hold a strong awareness of natural hazards, particularly floods, with uniform school-based training. The cut-off low 2024 event in Valencia stood out among other disasters, reinforcing the educational importance of water-related hazards in high-risk regions and underscoring the need to strengthen hydrological awareness as a key component for enhancing socio-territorial risk perception. Full article

The El Niño–Southern Oscillation (ENSO) is the main driver of interannual climate variability, strongly influencing precipitation, temperature, and extreme events worldwide. In South America, its impacts are well documented. However, studies examining different ENSO types—Eastern Pacific (EP), Central Pacific (CP), and Mixed (MX), defined according to the location of sea surface temperature (SST) anomalies in the tropical Pacific—remain limited, particularly for the Brazilian subtropical climate. This study investigates rainfall variability in the Brazilian subtropical region associated with different ENSO types. Composite analyses of precipitation, wind, and SST anomalies were performed, and monthly rainfall data from 703 stations were used to identify homogeneous regions. The results show the intensity and spatial coherence of rainfall signals vary according to El Niño type, with EP events favoring widespread wet conditions and CP events producing more heterogeneous or locally negative anomalies. For La Niña, the intensity and seasonal distribution of negative rainfall anomalies vary by ENSO type: stronger impacts occur in summer (EP), spring (MX), and autumn (CP). These findings improve the understanding of ENSO-related rainfall variability in the Brazilian subtropical region and provide valuable insights for the management of climate-related risks in an area frequently affected by rainfall extremes. Full article

Tropical coastal regions exhibit pronounced spatial and temporal variability in rainfall driven by seasonal atmospheric circulation and coastal–orographic interactions. Accurate representation of the temporal distribution of rainfall is essential for hydrologic modeling and infrastructure design. This study develops regionalized Huff curves for the Department of Magdalena, Colombia, addressing a critical gap in the characterization of rainfall temporal patterns in tropical coastal regions. A total of 270 short-duration (5–6 h) rainfall events from automatic stations were converted into normalized cumulative mass curves. The resulting curves were grouped into homogeneous temporal patterns using clustering algorithms. Three dominant storm types were identified: early-peak (Curve 1), intermediate (Curve 2), and uniform (Curve 3), reflecting the region’s coastal, lowland, and orographic influences. Probability envelopes and representative design hyetographs were derived to quantify intra-event variability. Rainfall–runoff simulations for a 100-km² watershed showed peak-flow differences of up to 132% between storm types, highlighting the sensitivity of hydrologic response to rainfall temporal distributions. The resulting regionalized Huff curves provide a practical and transferable framework for hydrologic modeling, flood-risk assessment, and infrastructure planning in tropical regions with limited high-resolution rainfall data. Full article

Accurate prediction of landslide run-out distance is fundamental to hazard mapping, emergency planning, and risk-informed engineering design. However, many data-driven studies implicitly treat landslides as a homogeneous population and provide limited, physically interpretable insights into how geomorphic factors govern run-out behavior. To address these limitations, we propose a cluster-aware and explainable modeling framework to predict run-out distance L using four source-region and slope descriptors: crown–toe relief H, source area A, source volume V, and mean source-slope inclination $\theta$. The dataset consists of 10,159 rainfall-induced landslides compiled from official inventories and peer-reviewed literature. After standardizing predictors, the optimal number of clusters is determined using information criteria (AIC/BIC), followed by k-means clustering to identify distinct landslide regimes. We first benchmark Random Forest, eXtreme Gradient Boosting, CatBoost, and LightGBM on identical data splits without hyperparameter tuning, using $R^2$, RMSE, and MAE as performance metrics. LightGBM consistently outperforms the alternatives and is therefore selected as the base learner. Within each cluster, LightGBM is further optimized using the Alpha Evolution (AE) algorithm, with Particle Swarm Optimization and Bayesian Optimization serving as benchmarks. The resulting AE-LightGBM model achieves the highest predictive accuracy across clusters. Model interpretability is achieved using TreeSHAP, which decomposes predictions into cluster-specific baselines and additive contributions from H, A, V, and $\theta$. By integrating regime-sensitive learning with robust explainability, the proposed framework improves run-out distance prediction while providing transparent, physically meaningful insights to support scenario analysis and engineering decision-making. Full article

This study presents the development and validation of a consistent rainfall database for Maranhão State, Brazil, covering historical records from 1987 to 2023 obtained from 100 rainfall stations (90 from ANA and 10 from INMET). A total of 314 missing records across 74 stations were corrected using the Regional Weighting method, restricted to stations within the same Homogeneous Precipitation Region (HPR). The consistency of the reconstructed series was verified using the Double Mass method, which yielded coefficients of determination (R²) above 0.97 for all stations, confirming the robustness of the procedure. Statistical analyses with the Mann–Kendall test and Sen’s Slope estimator did not identify significant long-term trends, although weak positive slopes were detected in some regions (e.g., HPR3: +9.98 mm/year; HPR6: +3.70 mm/year), while HPR10 showed a negative slope (−0.99 mm/year). The novelty of this work lies in consolidating the first homogeneous and validated rainfall database for Maranhão, providing a reliable foundation for assessing regional climate variability. The results provide a solid foundation for future applications, including drought monitoring, agricultural planning, water resource management, and adaptation strategies under climate change scenarios. Full article

Groundwater modeling in semi-arid regions presents significant challenges due to complex aquifer dynamics, limited data availability, and heterogeneous hydrogeological conditions. This study presents a comprehensive comparative analysis of three fuzzy expert system strategies for monthly groundwater level forecasting in the Al-Hsain Basin, Syria: localized models based on hydrogeographical grouping, a unified basin-wide approach, and an innovative behavioral clustering methodology. Using synchronized rainfall and temperature data from 35 monitoring wells over four years (2020–2024), we developed and evaluated fuzzy inference systems’ directional classification accuracy as the primary performance metric, categorizing groundwater level changes into rise, stable, and decline states rather than predicting continuous values. This choice reflects the qualitative nature of fuzzy expert systems and their suitability for groundwater management under data-limited conditions. The behavioral clustering approach achieved excellent overall performance with a mean accuracy of 0.74, outperforming localized models (0.71) and unified models (0.67). Behavioral clustering demonstrated effectiveness in 66% of wells, with individual accuracy improvements reaching up to 0.23, while reducing model complexity from five group-specific systems to three behaviorally coherent clusters. Localized models achieved optimal performance in 29% of wells where hydrogeological conditions aligned with spatial assumptions, whereas unified models provided consistent moderate performance across 89% of locations. The incorporation of lagged variables and seasonal indices in behavioral clustering models proved essential for capturing temporal complexity in semi-arid groundwater responses. Statistical analysis revealed lower intra-group variability in behavioral clusters (standard deviation 0.06–0.09) than in geographical groupings (0.08–0.14), confirming improved functional homogeneity through response-based organization. These findings indicate that fuzzy modeling strategy selection should be context-dependent, with behavioral clustering offering an effective balance between accuracy, interpretability, and generalization for regional groundwater management applications. The novelty of this work lies in isolating the effect of fuzzy system organization logic (localized, unified, and behavioral) on forecasting performance, robustness, and transferability, evaluated under an identical inference and time-series validation framework. Full article

Accurate characterization of precipitation in complex terrain is essential for hydrological modeling and climate studies. This study uses daily observations from 156 rain gauges in Sichuan Province (2015–2020) to evaluate two high-resolution satellite products (GSMaP-GNRT and IMERG-Early) and to develop a Transformer-based fusion framework at the gauge scale. All three datasets reproduce the regional seasonal cycle with more rainfall in summer and less in winter. At the daily scale, the fused product attains correlation comparable to GSMaP, while GSMaP and the fusion slightly overestimate precipitation (Bias = 6.24% and 5.21%), and IMERG shows stronger underestimation (Bias = −11.46%). At the monthly scale, the fused dataset achieves the best overall performance in terms of correlation, bias and RMSE. Spatially, the fusion reduces bias and RMSE and yields more homogeneous patterns over Sichuan’s complex terrain. Detection metrics indicate that the fused product increases the probability of detection and slightly improves the critical success index, while the false alarm ratio remains relatively high and comparable to the original products. This implies a gain in event sensitivity and spatial consistency rather than substantially reduced false alarms. Overall, the Transformer-based fusion provides a useful compromise between GSMaP and IMERG, adding value particularly for bias reduction, monthly statistics and event detection. The fused dataset offers a promising input for precipitation monitoring, hydrological simulation and disaster-risk analysis in Sichuan and similar mountainous regions. Full article

Climate change is intensifying extreme weather events and placing increasing pressure on global water resources, particularly in regions with high climatic variability such as Mexico. However, long-term changes in precipitation patterns and their implications for water resource vulnerability remain insufficiently characterized. This study analyzes historical precipitation trends from 1950 to 2018 and evaluates their implications for water resource vulnerability in the Lerma Santiago River System, one of Mexico’s most critical hydrological systems. A longitudinal analysis of nearly seven decades of precipitation data was conducted. Data quality and homogeneity were ensured using the RHtestV4s tool, and climate extremes and trends were assessed with the RClimDex package following ETCCDI guidelines. The findings indicate a significant decline in annual precipitation, with reductions of approximately 15% in downstream areas. Consecutive dry days increased by nearly 20%, while consecutive wet days decreased by about 10%. Although rainfall intensity has increased, events are concentrated over fewer days, amplifying water-scarcity risks. These climatic pressures are further compounded by dam construction, which restricts water availability. The results highlight the high vulnerability of the Lerma Santiago River System to combined climatic and anthropogenic stresses, underscoring the urgent need for integrated, multi-scale water and climate management strategies to enhance regional resilience. Full article

The applied literature on the MENA region remains fragmented between studies focused on the economic determinants of agricultural value added and climate and agriculture analyses via food security, without jointly assessing productive and climatic factors. This article fills that gap by measuring the combined effects of temperature, precipitation, capital, labor, and arable land on agricultural value added (VAAG) across 21 MENA countries over 1990–2024. We estimate a fixed effects model with cluster-robust standard errors and verify robustness using System GMM. The results indicate that rising temperatures are associated with a significant decline in VAAG, whereas moderate and regular rainfall, as well as endowments of capital, labor, and arable land, exert positive effects. Theoretically, the study highlights, over a long horizon and a reasonably homogeneous regional scope, the differentiated roles of thermal constraints and water availability, with inference strengthened by System GMM as a robustness check. Operationally, the findings support policies for efficient irrigation, decentralized storage, and managed aquifer recharge, alongside financial incentives and training to accelerate the adoption of resilient techniques, while safeguarding arable land. Together, these measures provide concrete levers to strengthen agricultural resilience in the MENA region. Full article

This study addressed rainfall patterns and trends on São Miguel Island (Azores, Portugal). Homogeneous rainfall areas were identified using Ward’s hierarchical clustering method. The analysis was performed using monthly rainfall data from 21 rainfall stations, considering the climatological periods of 1978/79–2019/20 (full dataset), 1978/79–2009/10 (oldest sub-dataset), and 2010/11–2019/20 (latest sub-dataset). Four rainfall clusters were identified, with Mean Annual Rainfall (MAP) ranging from 835.8 mm in low-altitude areas to 2925.5 mm in regions with higher elevation. These clusters showed consistent patterns for the analysed periods, although some changes in their composition were observed when considering the oldest and latest sub-datasets. The correlation between altitude and rainfall was strong (R² up to 0.83), indicating an increase of approximately 196 mm of MAP per 100 m elevation gain. Moreover, no notable variation was observed between the island’s windward and leeward slopes. Rainfall trend analysis using Mann–Kendall and Sen’s slope tests revealed significant declines in both annual and seasonal rainfall in recent years. The strongest decreases occurred in autumn and winter, with trends as steep as −31.6 mm/year and −12.1 mm/year in autumn (both at the Fogo III station). Between the oldest and most recent periods, rainfall reductions reached up to 41% in autumn (P10) and 55% in winter (P10), particularly affecting clusters at lower and mid-altitudes. Although certain stations showed significant declines, the overall trend hints at a potential deviation from projections of global climate change models, as well as from trends identified in previous long-term rainfall studies. The predominantly positive phase of the North Atlantic Oscillation (NAO) observed in recent years has likely contributed, at least partially, to the decrease in the rainfall trend. Overall, the results provide a refined spatial and temporal characterisation of rainfall across São Miguel Island, improving the understanding of local climatic variability and offering valuable insights for regional water management and climate adaptation strategies. Full article

This study proposes a bivariate distribution with Exponentiated Gumbel (BEG) marginals to estimate return levels of annual maximum daily rainfall (AMDR) in Mexico. We analyze 181 gauging stations across two contrasting climates (Coahuila, Tabasco) and compare BEG against Generalized Extreme Value (GEV), Gumbel (G), and Exponentiated Gumbel (EG). Parameters are estimated by maximum likelihood. Model selection uses AICc (primary) and BIC (tie-breaker), both computed from the same maximized log-likelihood. On a per-station basis, BEG yields the lowest AICc for 70% of samples. Differences in return levels become more pronounced at high non-exceedance probabilities. Monte Carlo reliability checks show that BEG reduces bias and mean squared error (MSE) relative to univariate fits. Using L-moments to delineate homogeneous regions and fitting all BEG pairs confirms these results. A worked example (station 5001) shows that bootstrap 95% CIs for BEG are narrower than for EG, illustrating reduced marginal-quantile uncertainty under joint estimation. Together, BEG provides a robust, dependence-aware tool for regional frequency analysis of extreme rainfall. Full article

Given the climate variability of semi-arid regions, this study analysed rainfall regimes and their influence on consecutive dry days (CDDs) and cowpea (Vigna unguiculata) productivity in Ceará, Brazil. Rainfall data from 184 municipalities (1990–2019) and productivity records were used across eight homogeneous rainfall regions. Water scenarios (very dry, dry, normal, rainy, and very rainy) were defined using quantiles, and three CDD classes were considered: CDD1 (5–10 days), CDD2 (11–15 days), and CDD3 (>15 days). Statistical analyses were performed with the Kruskal–Wallis test and Spearman’s correlation, and spatial patterns were mapped with ordinary kriging. Ceará’s climate normal was 837 mm, with the Central Sertão and Inhamuns and Jaguaribana showing the lowest rainfall. A total of 39,382 CDD events were identified, with 67% as CDD1, 16% as CDD2, and 17% as CDD3. Cariri had the highest CDD1 occurrences, while Central Sertão and Inhamuns recorded the highest CDD3. Cowpea yield averaged 286 ± 85 kg ha⁻¹, with the lowest productivity in Central Sertão and Inhamuns due to reduced rainfall and frequent CDD3. Productivity correlated positively with CDD1 in one very dry scenario and negatively with CDD3 in very dry, dry, and normal conditions. The findings highlight regional vulnerabilities and the strong link between CDD and crop yield. Full article

Accurate satellite-based precipitation estimates are crucial for climate studies and water resource management, particularly in regions with sparse meteorological station coverage. This study evaluates the improvements of the Integrated Multi-satellite Retrievals for GPM (IMERG) Final Run version 07 (V07) relative to the previous version (V06). The evaluation employed gridded data from the Brazilian Daily Weather Gridded Data (BR-DWGD) product and ground observations from 58 rain gauges distributed across the Parnaíba River Basin in Northeast Brazil. The analysis comprised three main stages: (i) an intercomparison between BR-DWGD gridded data and rain gauge records using correlation, bias, and Root Mean Square Error (RMSE) metrics; (ii) a comparative assessment of the IMERG Final V06 and V07 products, evaluated with statistical metrics (correlation, bias, and RMSE) and complemented by performance indicators including the Kling-Gupta Efficiency (KGE), Probability of Detection (POD), and False Alarm Ratio (FAR); and (iii) the application of cluster analysis to identify homogeneous regions and characterize seasonal rainfall variations across the basin. The results show that the IMERG Final V07 product provides notable improvements, with lower bias, reduced RMSE, and greater accuracy in representing the spatial distribution of precipitation, particularly in the central and southern regions of the basin, which feature complex topography. IMERG V07 also demonstrated higher consistency, with reduced random errors and improved seasonal performance, reflected in higher POD and lower FAR values during the rainy season. The cluster analysis identified four homogeneous regions, within which V07 more effectively captured seasonal rainfall patterns influenced by systems such as the Intertropical Convergence Zone (ITCZ) and Amazonian moisture advection. These findings highlight the potential of the IMERG Final V07 product to enhance precipitation estimation across diverse climatic and topographic settings, supporting applications in hydrological modeling and extreme-event monitoring. Full article