Search Results (364)

Southern India and Sri Lanka (SISL) rainfall during January–April (JFMA) exhibits strong interannual variability and is influenced by the El Niño–Southern Oscillation (ENSO), yet the long-term evolution of this relationship and its physical drivers remain unclear. Based on multiple precipitation datasets and atmospheric reanalysis products for 1950–2024, this study reveals a pronounced strengthening of the negative correlation between JFMA-mean SISL rainfall and the Niño 3.4 index, shifting from a statistically insignificant relationship prior to the late 1970s to a more coherent association after the 1980s. This transition is accompanied by intensified ENSO-related circulation anomalies. The strengthened and westward-extended Northwest Pacific Anticyclone (NWPAC) plays a dominant role, whereas an enhanced cross-equatorial temperature gradient in the Indian Ocean contributes to a lesser extent. Composite analyses further indicate that, on average, Eastern Pacific (EP) ENSO events tend to produce stronger rainfall anomalies over SISL than Central Pacific (CP) events; however, the differences between EP and CP composites are not statistically significant, reflecting pronounced event-to-event variability, especially for CP events. These results highlight the complexity of ENSO–SISL teleconnections and underscore the importance of NWPAC as a key bridge linking Pacific SST variability to regional rainfall responses. Full article

Wetlands are vital ecosystems that regulate water, store carbon and support biodiversity, but they are highly vulnerable to climate variability and human pressures. In semi-arid South Africa, montane wetlands remain understudied despite their ecological and socioeconomic importance. The study analyzed 1996–2023 climate variability and vegetation response across the Waterberg Mountain Complex (WMC) using station temperature/precipitation, Rainfall Anomaly Index (RAI), 6-month wet-season Standardized Precipitation Index (SPI) and site-level Normalized Difference Vegetation Index (NDVI) for 11 wetlands. Maximum temperatures increased at all stations, led by Warmbath (0.009 °C/month). No statistically significant changes in minimum temperature were detected. Precipitation trajectories diverged, Mokopane exhibited a statistically significant wetting trend whereas Lephalale and Marken experienced progressive drying. ENSO-driven droughts (2002/2003, 2015/2016 and 2019/2020) intensified hydroclimatic stress and shortened wetland hydroperiods. NDVI trends revealed strong coupling with rainfall variability, with high-altitude wetlands demonstrating greater resilience, while lowland systems declined in greenness. These findings highlight topography as a determinant of wetland vulnerability, positioning upland wetlands as potential climate refugia. Site-specific adaptation and conservation strategies are essential to safeguard ecosystem services and biodiversity, contributing to global sustainability goals (SDGs 6, 13 and 15). Full article

The boreal summer intraseasonal oscillation (BSISO) over the western North Pacific (WNP) exhibits significant phase asymmetry, but a systematic classification of its asymmetric modes and their regional climatic impacts remains insufficiently explored. This study introduces an objective index to quantify the asymmetry in BSISO wet phase evolution. Combined with event life cycle duration, we classify WNP BSISO events into three distinct types: a short-lived Symmetric Pattern that resembles the canonical northwestward-propagating high-frequency BSISO, and two long-lived asymmetric patterns—Asymmetric Pattern I (rapid development/slow decay) and Asymmetric Pattern II (slow development/rapid decay). Both asymmetric patterns are dominated by the low-frequency BSISO component and propagate northward; their contrasting asymmetries arise from differences in the coupling timing of a transient high-frequency signal. These BSISO types exert distinct impacts on summer precipitation over eastern China. The Symmetric Pattern causes brief, alternating anomalies. However, asymmetric modes lead to longer-lasting precipitation issues. Pattern I triggers sudden drought-to-flood shifts that pose high risks, while Pattern II moves through phases more gradually. Our objective classification of asymmetric BSISO modes and revelation of their distinct rainfall impacts together provide a physical framework for refining subseasonal forecasts over East Asia. Full article

This study develops a practical framework for forecasting long-term drought conditions in Karaman Province, a semi-arid region of Turkey, where accurate climate information is vital for water planning and agriculture. Since the area has limited rainfall records and strong year-to-year fluctuations, traditional modeling approaches often fall short. To better capture local conditions, drought intensity was defined using a simple monthly wetness anomaly measure based directly on precipitation; here, positive values indicate wetter months and negative values indicate drier ones. This makes the method suitable for regions where detailed hydrological data are scarce. Rainfall observations from 1965 to 2011 were expanded using a combination of kernel density estimation and Cholesky-based correlation reconstruction. These steps preserved the main statistical and temporal patterns of the original data while increasing sample diversity. The enriched dataset was then used to train artificial neural networks to predict both precipitation and drought intensity. The models reached R² values of 0.76 and 0.72, with mean absolute errors of 12.8 mm and 28.4%, which represents an improvement of roughly 10–15% over traditional statistical methods. They were also able to capture the seasonal and year-to-year variability that strongly affects drought conditions in the region. To understand what drives the predictions, the model was examined with LIME, which consistently highlighted lagged rainfall and seasonal indicators as the most influential inputs. A walk-forward validation approach was also used to mimic real forecasting conditions and demonstrated that the model remains stable when projecting into the future. Overall, the proposed framework offers a reliable and practical basis for early-warning efforts and drought-management strategies in semi-arid regions like Karaman. Full article

A hybrid machine learning framework has been developed in this study to estimate Terrestrial Water Storage Anomalies (TWSA) in Morocco’s Casablanca–Settat region, which faces serious groundwater stress due to rapid urbanization, intensive agriculture, and climate variability. In this study, TWSA is used as an integrated proxy for groundwater-related storage changes, while acknowledging that it also includes contributions from soil moisture and surface water. The approach combines satellite-based observations from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) with key environmental indicators such as rainfall, evapotranspiration, and land use data to track changes in groundwater availability with improved spatial detail. After preprocessing the data through feature selection, normalization, and outlier handling, the model applies six base learners, i.e., Huber regressor, automatic relevance determination regression, kernel ridge, long short-term memory, k-nearest neighbors, and gradient boosting. Their predictions are aggregated using a random forest meta-learner to improve accuracy and stability. The ensemble achieved strong results, with a root mean square error of 0.13, a mean absolute error of 0.108, and a determination coefficient of 0.97—far better than single-model baselines—based on a temporally independent train-test split. Spatial analysis highlighted clear patterns of groundwater depletion linked to land cover and usage. These results can guide targeted aquifer recharge efforts, drought response planning, and smarter irrigation management. The model also aligns with national goals under Morocco’s water sustainability initiatives and can be adapted for use in other regions with similar environmental challenges. Full article

Against the backdrop of the increasing frequency of extreme hydrological events and persistent over-extraction of groundwater, the North China Plain (NCP) is facing significant land subsidence. This study systematically analyzed the surface subsidence response patterns and mechanisms of the NCP during extreme rainfall events by integrating Gravity Recovery and Climate Experiment (GRACE) data, Global Navigation Satellite System (GNSS) observations, environmental load models, well data, and precipitation records. The main findings are as follows: (1) From 2002 to 2020, the groundwater storage change (GWSC) in most of the study area declined at an average rate of trend about 5 cm/yr, while from 2021 to 2024, influenced by heavy rainfall recharge, GWSC recovered with a mean rate of trend about 7 cm/yr; (2) During the extreme rainfall event from 1 July to 31 August 2023, the environmental loading model effectively captured the vertical deformation caused by hydrological loading, showing general consistency with GNSS monitoring results in spatial distribution. Most GNSS stations experienced rapid subsidence during the event (GNSS: 5 mm, model: 2 mm), followed by a gradual rebound after the extreme rainfall, consistent with elastic theory; (3) The deformation at the TJBH station exhibited anomalies attributable to porous elastic effects; (4) Integrated well data confirmed that rainfall recharge primarily influences shallow groundwater. This study reveals the multiple mechanisms underlying extreme hydrological induced land subsidence in the NCP. Full article

Although heavy rainfall occurs infrequently during summer (June–August, JJA) in the Mu Us Sandy Land (MUSL), it has almost the same contribution to summer precipitation as light rainfall. However, it remains unclear on forcing mechanism of heavy rain events and their differences with moderate and light rainfall events from the perspective of moisture sources. In this paper, based on the Dynamical Recycling Model (DRM), we analyze moisture source and atmospheric circulation differences for summer rainfall in different intensity classes over MUSL. The results show that the moisture of summer precipitation in MUSL comes primarily from external terrestrial moisture supplies from the west and southwest directions. As the precipitation intensity increases, moisture contributions from the southwest direction increase significantly, especially for the northeastern part of the Tibet Plateau (defined as Key Region), which accounts for about 39.3% of all moisture sources for heavy rainfall events. Further analysis reveals that anomalous atmospheric circulations, such as the cyclonic circulation anomaly at lower troposphere and anomaly wave train at middle level, also favor the occurrences of different precipitation intensities. Based on these findings, our paper possibly contributes to the conservation of this fragile ecosystem and the prevention of damage caused by precipitation extremes. Full article

Landslides remain a critical geohazard in mountainous regions, where intensified extreme rainfall and rapid land-use changes exacerbate slope instability, challenging the reliability of traditional single-sensor susceptibility assessments. To overcome the limitations of data heterogeneity and noise, this study presents a decision-level fusion strategy integrating Permanent Scatterer InSAR (PS-InSAR) deformation dynamics with multi-source optical remote sensing indicators via a Wavelet Transform (WT) enhanced Multi-source Additive Model Based on Bayesian Analysis (MAMBA). San Martín del Castañar (Spain), a region characterized by rugged terrain and active deformation, served as the study area. We utilized Sentinel-1A C-band datasets (January 2020–February 2025) as the primary source for continuous monitoring, complemented by L-band ALOS-2 observations to ensure coherence in vegetated zones, yielding 24,102 high-quality persistent scatterers. The WT-based multi-scale enhancement improved the signal-to-noise ratio by 23.5% and increased deformation anomaly detection by 18.7% across 24,102 validated persistent scatterers. Bayesian fusion within MAMBA produced high-resolution susceptibility maps, indicating that very-high and high susceptibility zones occupy 24.0% of the study area while capturing 84.5% of the inventoried landslides. Quantitative validation against 1247 landslide events (2020–2025) achieved an AUC of 0.912, an overall accuracy of 87.3%, and a recall of 84.5%, outperforming Random Forest, Logistic Regression, and Frequency Ratio models by 6.8%, 10.8%, and 14.3%, respectively (p < 0.001). Statistical analysis further demonstrates a strong geo-ecological coupling, with landslide susceptibility significantly correlated with ecological vulnerability (r = 0.72, p < 0.01), while SHapley Additive exPlanations identify land-use type, rainfall, and slope as the dominant controlling factors. Full article

Aerosol microphysical and optical properties play a crucial role in cloud microphysics, precipitation physics, and flood formation over areas characterized by complex monsoon regimes. This research presents a multi-source data integration approach to analyzing the spatio-temporal interaction between precipitation, aerosols, and flooding in the state of Kerala, incorporating an air mass trajectory analysis to examine its potential contribution to flooding. The results show that the Aerosol Optical Depth (AOD) values were high in the coastal districts (>0.8) in the La Niña year (2021) but low in the El Niño year (2015). On the precipitation side, 2018 and 2021 were both years with a high degree of anomalies, resulting in heavy rainfall that led to widespread flooding in the Thrissur district, among others. The trajectory analysis revealed that the Indian Ocean controls the precipitation during the southwest monsoon and the pre-monsoon. The post-monsoon precipitation is mainly sourced from the Arabian Peninsula and Arabian Sea, transferring marine aerosols along with desert aerosols. The overall study shows that the variability in aerosols and precipitation is more subject to change by the meteorological dynamics, as well as influenced by the regional changes in land use and land cover, causing fluxes in the land–atmosphere interactions. In conclusion, the present study highlights the possible interactive functions of atmospheric dynamics and anthropogenic land use modifications in generating a flood hazard. It provides essential information for land management policies and disaster risk reduction. Full article

Repeated sowing of winter cereals represents one of the adaptive dryland approaches to make more sustainable the rainfed agriculture activities in southern Kazakhstan. This study conducted a multi-year reconstruction of crop transitions using Sentinel-2 imagery for 2018–2025, based on the combined analysis of Normalized Difference Vegetation Index (NDVI) temporal profiles and the Plowed Land Index (PLI), enabling the creation of a field-level harmonized classification set. The transition “spring crop → winter crop” was used as a formal indicator of repeated winter sowing, from which annual repeat layers and an integrated metric, the R-index, were derived. The results revealed a pronounced spatial concentration of repeated sowing in foothill landscapes, where terrain heterogeneity and locally elevated moisture availability promote the recurrent return of winter cereals. Comparison of NDVI composites for the peak spring biomass period (1–20 May) showed a systematic decline in NDVI with increasing R-index, indicating the cumulative effect of repeated soil exploitation and the sensitivity of winter crops to climatic constraints. Precipitation analysis for 2017–2024 confirmed the strong influence of autumn moisture conditions on repetition phases, particularly in years with extreme rainfall anomalies. These findings demonstrate the importance of integrating multi-year satellite observations with climatic indicators for monitoring the resilience of agricultural systems. The identified patterns highlight the necessity of implementing nature-based solutions, including contour–strip land management and the development of protective shelterbelts, to enhance soil moisture retention and improve the stability of regional agricultural landscapes. Full article

This study examines the hydroclimatic controls on reservoir storage dynamics in the Sinni River basin (southern Italy), with a specific focus on the Monte Cotugno dam—the largest earth-fill reservoir in Europe. Using monthly precipitation data (2000–2024) from eight gauges and standardized indicators (SPI at multiple timescales and SRI for storage), we apply robust trend, correlation, autocorrelation, and causality analyses, supported by advanced preprocessing (TFPW), to disentangle climatic influences from anthropogenic pressures. Results show a statistically significant and persistent decline in the SRI series, indicating progressive storage depletion, despite stationary or slightly positive trends in precipitation at annual and hydrologically relevant timescales. These findings highlight the dominant role of cumulative operational losses and systemic inefficiencies—rather than sustained climatic drying—as primary drivers of reservoir decline. Granger causality and lagged-correlation analyses reveal that multi-month to annual precipitation anomalies (SPI-3, SPI-6, SPI-12) exert the strongest influence on storage variations, yet the basin’s ability to convert rainfall into effective reservoir supply is severely constrained by infrastructural and management limitations. The study underscores the urgent need to integrate climate-based monitoring with infrastructural modernization and governance reforms to address the combined climatic and anthropogenic pressures increasingly affecting Mediterranean water systems. Full article

Explosive volcanic eruptions are key drivers of climate variability, yet their hemispheric-dependent impacts remain uncertain. Using multi-model ensembles from Coupled Model Intercomparison Project Phase 6 (CMIP6) historical data and Decadal Climate Prediction Project (DCPP) simulations, this study examines how the spatial distribution of volcanic aerosols modulates climate responses to Northern Hemisphere (NH), Tropical (TR), and Southern Hemisphere (SH) eruptions. The CMIP6 ensemble captures observed temperature and precipitation patterns, providing a robust basis for assessing volcanic effects. The results show that the hemispheric distribution of aerosols strongly controls radiative forcing, surface air temperature, and hydrological responses. TR eruptions cause nearly symmetric cooling and widespread tropical rainfall reduction, while NH and SH eruptions produce asymmetric temperature anomalies and clear Intertropical Convergence Zone (ITCZ) displacements away from the perturbed hemisphere. The vertical temperature structure, characterized by stratospheric warming and tropospheric cooling, further amplifies hemispheric contrasts through enhanced cross-equatorial energy transport and shifts in the Hadley circulation. ENSO-like responses depend on eruption latitude, TR and NH eruptions favor El Niño–like warming through westerly wind anomalies and Bjerknes feedback, and SH eruptions induce La Niña–like cooling. The DCPP experiments confirm that these signals primarily arise from volcanic forcing rather than internal variability. These findings highlight the critical role of aerosol asymmetry and vertical temperature structure in shaping post-eruption climate patterns and advancing the understanding of volcanic–climate interactions. Full article

To address the challenges of high cost, complex deployment, and difficulties in real-time early warning for small landslides near residential areas, a portable and low-cost high-frequency monitoring device based on Micro-Electro-Mechanical Systems (MEMSs) was developed, and an advanced warning algorithm based on anomaly intensity factors was constructed. The device is easy to install and can collect and transmit data to the cloud in real time. Equipped with edge computing capabilities, it can independently analyze data in the absence of network connectivity and transmit real-time early warning information to terminals within a range of 5 km. To verify the performance of the system, a large-scale outdoor landslide simulation test site was constructed, where slope failure was induced through artificial rainfall to obtain the complete process data from deformation to failure. The experimental results demonstrate that the proposed early warning algorithm effectively identified different stability levels, providing warnings up to 13 s prior to catastrophic failure, confirming the high sensitivity and reliability of the device. This study offers a cost-effective and efficient approach to landslide monitoring and early warning, with notable prospects for broader implementation in practice. Full article

Climate change is increasingly threatening the sustainability of coffee farming in northern Peru, particularly in the Amazonas region, where coffee cooperatives serve as vital socioeconomic hubs for thousands of families. This study analyzed historical climate data from 1979 to 2024 to project trends up to 2030, integrating local perceptions from coffee producers to identify trends, anomalies, and future scenarios within four coffee cooperatives in northern Peru. We examined variables such as precipitation, temperature, evapotranspiration, and wind speed using nonparametric statistical analyses and SARIMA time-series models. The findings indicate a steady increase in maximum and average temperatures, alongside greater irregularity in precipitation. Specifically, the Bagua Grande and COOPARM cooperatives are experiencing precipitation deficits, while the Alta Montaña and Ocumal cooperatives are facing excess rainfall. Additionally, we project an increase in evapotranspiration by 2030. Surveys conducted with coffee growers reveal a consensus regarding irregular rainfall patterns; however, there is less recognition of the rising temperature trends. This discrepancy emphasizes the importance of combining scientific data with local knowledge to develop more effective adaptation strategies at the cooperative level. We conclude that enhancing climate training and cooperative management is essential for improving the resilience of regional coffee farming. Full article

Intensity–Frequency–Duration Curves (IDF curves) are a tool applied in hydraulic and hydrology engineering to design infrastructure for rainfall management. They express how precipitation, with a defined duration (D) and intensity (I), is frequent in a certain area. They are built from past recorded rainfall series, applying the extreme value statistics, and they are considered invariant in time. However, the current climate change projections are showing a detectable positive trend in temperatures, which, according to Clausius–Clapeyron, is expected to intensify extreme precipitation (higher temperatures bring more water vapour available for precipitation). According to the IPCC (Intergovernmental Panel on Climate Change) reports, rainfall events are projected to intensify their magnitude and frequency, becoming more extreme, especially across “climatic hot-spot” areas such as the Mediterranean basin. Therefore, a sensible modification of IDF curves is expected, posing some challenges for future hydraulic infrastructure design (i.e., sewage networks), which may experience damage and failure due to extreme intensification. In this paper, a methodology for reconstructing IDF curves by analysing the EURO-CORDEX climate model outputs is presented. The methodology consists of the analysis of climatic rainfall series (that cover a future period up to 2100) using GEV (Generalised Extreme Value) techniques. The future anomalies of rainfall height (H) and their return period (RP) have been evaluated and then compared to the currently adopted IDF curves. The study is applied in Lombardy (Italy), a region characterised by strong orographic precipitation gradients due to the influence of Alpine complex orography. The future anomalies of H evaluated in the study show an increase of 20–30 mm (2071–2100 ensemble median, RCP 8.5) in rainfall depth. Conversely, a significant reduction in the return period by 40–60% (i.e., the current 100-year event becomes a ≈40–60-year event by 2071–2100 under RCP 8.5) is reported, leading to an intensification of extreme events. The former have been considered to correct the currently adopted IDF curves, taking into account climate change drivers. A series of applications in the field of hydraulic infrastructure (a stormwater retention tank and a sewage pipe) have demonstrated how the influence of IDF curve modification may change their design. The latter have shown how future RP modification (i.e., reduction) of the design rainfall may lead to systematic under-design and increased flood risk if not addressed properly. Full article