Acta Geophysica最新文献

{"title":"L-moments based regional flood frequency analysis of the Firat (Euphrates) and Dicle (Tigris) basins in Türkiye","authors":"Recep Yurtal,&nbsp;Mohammad Wes","doi":"10.1007/s11600-026-01927-z","DOIUrl":"10.1007/s11600-026-01927-z","url":null,"abstract":"<div><p>Floods are a major hydrological hazard in Türkiye, and robust design-flood estimation remains essential for hydraulic infrastructure and risk management in the transboundary Firat (Euphrates) and Dicle (Tigris) basins. This study applies an L-moments based regional flood frequency analysis (RFFA) to annual maximum flow data from 59 gauging stations. Homogeneous regions were identified using L-moment discordancy and heterogeneity measures. Six candidate distributions (GEV, GNO, GPA, PE3, GLO, and WAK) were fitted and evaluated using the L-moment Z-statistic. The Dicle Basin was found to be acceptably homogeneous and was best represented by the generalized normal (GNO) distribution, whereas the Firat Basin was heterogeneous and was subdivided into two homogeneous sub-regions, both best fitted by the generalized extreme value (GEV) distribution. Empirical power-law relationships between drainage area and index flood (mean annual flood) were also developed to support flood estimation at ungauged sites. Monte Carlo simulations were used to quantify uncertainty, indicating reliable performance for moderate return periods and widening uncertainty bounds for very large return periods. These uncertainties should be considered when applying the regional models to high-consequence hydraulic designs.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11600-026-01927-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping flood susceptibility with a stacking ensemble approach: an exemplary case study in Yemen","authors":"Mustafa Ghaleb,&nbsp;Ahmed M. Al-Areeq,&nbsp;Radhwan A. Saleh,&nbsp;Nabil M. Al‑Areeq,&nbsp;Atef Q. Kawara,&nbsp;Sani I. Abba","doi":"10.1007/s11600-026-01915-3","DOIUrl":"10.1007/s11600-026-01915-3","url":null,"abstract":"<div><p>Flood susceptibility mapping (FSM) provides a practical basis for land use planning and disaster risk reduction in data-scarce and flood-prone regions such as the Qaa’Jahran watershed in Dhamar, Yemen. Many FSM studies still rely on single learners and rarely pair rigorous ensemble validation with transparent inventory verification (e.g., using SAR observations to check reported inundation extents), leaving a methodological gap for arid and semi-arid basins. This study introduces a stacking ensemble machine learning approach for flood susceptibility mapping in the Qaa’Jahran watershed, addressing the need for more accurate flood prediction in data-scarce regions. A flood inventory was compiled from documented historical flood information and refined using SAR imagery as a supplementary verification layer, and 15 conditioning factors were compiled in a GIS environment. Five base classifiers (Artificial Neural Networks (ANN), Random Forest (RF), K-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Logistic Regression (LR)) were combined with a LR metamodel, and the resulting historical flood inventory (verified using SAR imagery) was used for model training. The stacking ensemble outperformed all individual models (AUC = 0.92–0.97) and two current benchmarks (TPOT and ABRBF) in discrimination (AUC = 0.98) and classification (accuracy = 98.75%). Event-based comparison against the 2022 flood event indicates strong spatial agreement between observed inundation and mapped high-susceptibility zones. The proposed workflow is transferable to other data-scarce basins and provides a reproducible decision support tool for prioritizing flood mitigation and preparedness under evolving hydroclimatic conditions.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interpretable stacking ensemble model for simulating ice thickness in shallow lakes: Lake Ulansuhai of Central Asia","authors":"Cheng Zhang,&nbsp;Senlin Zhu,&nbsp;Jun Qian,&nbsp;Francesco Granata","doi":"10.1007/s11600-026-01929-x","DOIUrl":"10.1007/s11600-026-01929-x","url":null,"abstract":"<div><p>Central Asia lies in the interior of the Eurasian continent, dominated by a temperate continental climate with long and cold winters. As a typical lake type in this region, shallow lakes are characterized by shallow water depth and low heat capacity, making them highly sensitive to atmospheric forcing. The distribution and thickness evolution of lake ice cover not only exert significant impacts on the structure and function of lake ecosystems but also serve as crucial sensitive indicators to regional climate change. Therefore, accurately and efficiently simulating lake ice thickness and investigating the driving mechanism of its changes have become increasingly important. Using in situ measurements collected by the Floating Remote Observation System during the complete ice season of 2022–2023 in Lake Ulansuhai, Central Asia, this study proposed a stacking ensemble model (XRF-Stacking) that fuses XGBoost and Random Forest, with RidgeCV as the meta-model, integrated through tenfold cross-validation. Four benchmark models, including XGBoost, Random Forest, SVR, and LightGBM, were selected for comparison, and model performance was evaluated using three metrics: R², RMSE, and MAE. Results show that the XRF-Stacking model substantially outperforms all benchmark models, with an R² of 0.995, RMSE of 0.010 m, and MAE of 0.007 m, indicating high consistency between predicted and measured values. Further model interpretation based on SHAP reveals that water temperature, net shortwave radiation, and net longwave radiation were the three most influential variables on ice-thickness variation. This study can provide methodological references for the modeling of ice thickness in shallow lakes.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of rapid urbanization on the urban heat island and its spatial characteristics in Delhi, India","authors":"Ahmed Ali Bindajam,&nbsp;Javed Mallick,&nbsp;Mohd Waseem Naikoo,&nbsp;Atiqur Rahman,&nbsp;Hoang Thi Hang","doi":"10.1007/s11600-026-01908-2","DOIUrl":"10.1007/s11600-026-01908-2","url":null,"abstract":"<div><p>Rapid urbanization and changes in land use and land cover have led to uncontrolled and unsustainable expansion of Indian cities. This growth has brought with it several environmental challenges, including a significant increase in the intensity of urban heat islands (UHIs) in these urban areas. This study investigates the profound impact of rapid urbanization on changes in land use and land cover (LULC), land surface temperature (LST), and urban heat island (UHI) intensity in the National Capital Territory (NCT) of Delhi, India. Using Landsat satellite images from 1990, 2000, and 2020, we conducted a comprehensive analysis using random forest (RF) algorithms for LULC classification and the Mono-Window algorithm for LST detection. Additionally, a mixed linear modeling (MLM) approach was employed to analyze the statistical relationship between LULC types and LST dynamics across three time periods (2001, 2011, and 2021), accounting for inter-annual variability and fixed effects of land cover categories. The results show a significant expansion of built-up areas from 479.66 km² (32.32%) in 2001 to 719.64 km² (48.49%) in 2021, with a corresponding decrease in open areas by over 17% and water bodies from 16.09 km² (1.08%) to 12.34 km² (0.83%). The mixed linear model revealed built-up areas as the primary contributors to rising LST, with an annual increase of 0.262 °C/year, while water bodies exhibited the strongest cooling effect, reducing LST by 5.87 °C relative to dense vegetation. Year-specific random effects showed a net increase of 3.81 °C in 2021 compared to 2001. This urban expansion was directly related to an increase in LST, where the average temperature increased by about 3–5 °C, and a significant amplification of UHI effects, with urban areas warming by up to 7 °C compared to rural areas. Spatial analysis of UHI morphology using landscape metrics demonstrates increasing fragmentation and spatial expansion of heat-affected areas over time, with the number of SUHI patches rising by approximately 50% during the study period. These findings emphasize the escalating thermal stress in rapidly urbanizing areas and highlight the urgent need for climate-sensitive urban planning and targeted green infrastructure interventions to mitigate adverse heat impacts.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-physics-informed PINNs-Prod: a mathematical model framework for interpretable productivity forecasting and fracturing optimization in tight gas reservoirs","authors":"Huohai Yang,&nbsp;Xinwei Luo,&nbsp;Yi Li,&nbsp;Xiufa Yuan,&nbsp;Zhiwei Yu,&nbsp;Ting Liu,&nbsp;Kaizhong Huang,&nbsp;Renze Li","doi":"10.1007/s11600-026-01912-6","DOIUrl":"10.1007/s11600-026-01912-6","url":null,"abstract":"<div><p>Tight gas reservoirs are characterized by low porosity and permeability, complex pore–throat architectures, and strong heterogeneity, which collectively lead to highly nonlinear gas-flow behavior. However, conventional methods for production forecasting and fracturing parameter optimization often suffer from high computational costs and limited flexibility. This study proposes an intelligent optimization framework that integrates data-driven learning with physics-informed modeling to address these challenges. The framework operates across three layers—data, model, and physical constraints—to enable accurate productivity forecasting and effective fracturing parameter optimization in tight-gas wells. Using field data from the Sulige Gas Field, correlation analysis and causal-effect estimation were conducted to identify 12 key controlling factors (e.g., reservoir thickness and permeability). A gas well production forecasting model (CWOA–PINN) is developed by coupling physics-informed neural networks (PINNs) with the chaotic whale optimization algorithm (CWOA), and the proposed model substantially outperforms conventional data-driven baselines in predictive accuracy. The Newton–Raphson-based optimization (NRBO) algorithm is then employed to solve the inverse problem of fracturing parameter design using the trained CWOA–PINN model as the forward predictor. The resulting optimal design is consistent with a “liquid-control and proppant-increase” strategy and increases the average single-well production by 0.89 × 10⁴ m³/d. These results provide practical guidance for development planning and fracturing optimization in the Sulige Gas Field.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148173150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thresholding in preconditioned full waveform inversion: depth resolution and convergence analysis using the Marmousi model","authors":"Mehmet Ali Uge","doi":"10.1007/s11600-026-01895-4","DOIUrl":"10.1007/s11600-026-01895-4","url":null,"abstract":"<div><p>The performance of full waveform inversion (FWI), a high-resolution seismic imaging technique, depends not only on the choice of numerical solvers, but also on effective parameterization strategies that guide convergence and improve depth resolution. Among these, optimization methods, particularly those employing preconditioners, play a central role in stabilizing updates and enhancing the efficiency of the inversion process. This study examines how threshold scheduling within a diagonal pseudo-Hessian-based preconditioner affects inversion outcomes in frequency-domain acoustic FWI. Using the synthetic Marmousi model, two strategies are compared under an identical cumulative frequency-continuation schedule: a constant threshold (fixed θ across all multiscale steps), and a variable-threshold strategy in which θ decreases from 10⁻¹ to 10⁻⁴ from Step 1 to Step 4. This framework enables a controlled investigation of how depth sensitivity and convergence behavior are shaped by threshold selection within a consistent preconditioner formulation. The results indicate that variable thresholding leads to more balanced gradient updates and improved imaging of deep velocity structures, as confirmed by depth-binned RMS/NRMS error analysis, while constant thresholding can bias updates toward shallow zones at larger θ or yield less stable deep behavior at the smallest θ. These results support threshold scheduling as a practical control on depth-dependent conditioning for the Marmousi benchmark under the tested acquisition and frequency band and demonstrate that FWI results can benefit from threshold scheduling and motivate further testing to identify effective schedules for a given dataset and acquisition setting.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11600-026-01895-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148172503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rupture characteristics and tectonic mechanisms of the 2025 Dingri Mw6.8 earthquake in southern Tibet","authors":"Zhangdi Xie","doi":"10.1007/s11600-026-01900-w","DOIUrl":"10.1007/s11600-026-01900-w","url":null,"abstract":"<div><p>The ongoing Cenozoic collision between the Indian plate and the Eurasian plate has induced intense tectonism across the Tibetan Plateau, producing a suite of N–S-trending extensional normal faults. Internally, the plateau accommodates east–west rift systems under the combined effects of compression-driven uplift and gravitational collapse, with earthquakes dominated by extensional mechanisms. Along the Himalayan orogenic front, thrust faults governed by compressional stresses prevail, and large earthquakes are frequent. The 2025 <i>M</i>_w6.8 Dingri earthquake nucleated on the Dingmucuo fault in the southern segment of the Xainza–Dinggye Rift System at a focal depth of ~ 10 km. The focal mechanism indicates normal faulting with a minor left-lateral strike-slip component. Field surveys and InSAR data reveal a ~ 30–50 km-long surface rupture with a maximum vertical offset of 3.0 m. Kinematic inversion reveals unilateral rupture propagation with a peak slip of 4 m and dip angles of 50–60°. Using high-precision aftershock relocations and cluster analysis, we divide the seismogenic fault into three segments striking 140.4°, 193.7°, and 224.9°; the dip angle decreases progressively along strike, which is consistent with the trend of the Dingmucuo fault. Variations in fault geometry may have altered the rupture process, generating high-frequency radiation that intensified near-surface damage. Dynamic stress-drop analysis indicates that stress release is concentrated within the 0–10 km depth range, whereas the 5–15 km depth range retains unrelieved stress and hosts abundant aftershocks. Directivity effects render ground motions on the northern segment markedly greater than those on the southern segment, with stronger shaking on the hanging wall than on the footwall; the simulated maximum intensity reaches VIII. The Dingri event disrupted the stress equilibrium between the Himalayan thrust belt and the intraplateau rifts. Although the late Pleistocene slip rate of the Dingmucuo fault is &lt; 1 mm/a, the magnitude of the actual earthquake matches its potential, underscoring that faults with low activity rates remain capable of generating significant future earthquakes.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148172642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deformation due to non-planar fault movement in fractional Maxwell medium","authors":"Pabita Mahato,&nbsp;Seema Sarkar Mondal,&nbsp;Subhash Chandra Mondal","doi":"10.1007/s11600-026-01901-9","DOIUrl":"10.1007/s11600-026-01901-9","url":null,"abstract":"<div><p>In earthquake-prone regions, the accumulation of geophysical stress during the aseismic period plays a critical role in determining which faults are more likely to be reactivated in future seismic events. However, a clear understanding of how non-planar fault geometry influences aseismic stress buildup is still lacking. This study examines how non-planar fault affect displacement, stress, and strain evolution in a viscoelastic medium during the aseismic period. We model an infinite non-planar fault composed of three interconnected planar segments embedded in a viscoelastic half-space represented by a fractional Maxwell medium. The problem is formulated as a two-dimensional boundary value problem and solved numerically using a Laplace transformation, fractional derivative, correspondence principle and Green’s function technique. The outcomes are demonstrated graphically using appropriate model parameters. The computational findings highlight the significant influence of fault motion and geometry in shaping the displacement, stress and strain fields in the vicinity of the fault zone. A comparative analysis with existing theoretical models is also performed to validate the results and highlight the impact of fractional derivatives on the response. These results can provide insights into subsurface deformation and its impact on fault movement, which may contribute to the study of earthquake activity.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148172640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"L1-Norm Multi-Constraint AVO Inversion Based on N-ADMM with an adaptive penalty","authors":"Yu Wang,&nbsp;Suping Peng,&nbsp;Yongxu Lu,&nbsp;Xiaoqin Cui,&nbsp;Dong Li,&nbsp;Zihe Xu,&nbsp;Yaoquan Hu,&nbsp;Di Yao","doi":"10.1007/s11600-026-01884-7","DOIUrl":"10.1007/s11600-026-01884-7","url":null,"abstract":"<div><p>Pre-stack seismic data typically suffer from poor signal-to-noise quality, possibly leading to unstable inversion results. Traditional multi-channel laterally constrained inversion will blur the steep inclined strata, while noise undermines the reliability of simple structure constrained inversion. We present a new inversion method, L1-norm multi-constraint inversion, to overcome these limitations using the exact Zoeppritz equations method (EZMI). Built upon an L1-norm sparsity-regularized objective function, this method constrains the inversion of three parameters horizontally and vertically and the dip angle of the formation, effectively restores the sparse characteristics of inversion outcomes, protects the amplitude information of the stratum boundary, produces inversion outputs that converge better from trace to trace, and improves inversion precision. When dealing with nonlinear optimization tasks, we use the Nesterov-type accelerated alternating direction method of multipliers with adaptive penalty (N-ADMM) combined with the Levenberg–Marquardt (LM) algorithm to drive the objective toward its minimum, which accelerates convergence speed and ensures good stability of the equation. At the same time, by employing the exact Zoeppritz equation, we further cut the inversion misfit and boost overall accuracy. Synthetic data are employed to benchmark the EZMI approach against the unconstrained inversion based on the exact Zoeppritz equation (EZUI), thereby validating the proposed method. Finally, using the actual data for experimental analysis, results further indicate that EZMI delivers superior inversion quality and offers clear benefits for AVO inversion.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148172489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends and variability of extreme rainfall events in varied agroclimatic regions of Southern Karnataka","authors":"Shankarappa Sridhara,&nbsp;Mahesh Haroli,&nbsp;Hemareddy Thimmareddy,&nbsp;Santanu Kumar Bal,&nbsp;Bappa Das","doi":"10.1007/s11600-026-01899-0","DOIUrl":"10.1007/s11600-026-01899-0","url":null,"abstract":"<p>Understanding the evolution of extreme rainfall patterns due to climate change is essential for climate-resilient agricultural planning and hydrological risk management. This study examined long-term (1965–2022) daily rainfall data from 44 stations in the coastal, Western Ghats, and arid agroclimatic regions of southern Karnataka, utilizing fifteen extreme precipitation indices according to the India Meteorological Department (IMD) and Expert Team on Climate Change Detection and Indices (ETCCDI) standards. Trend detection was conducted utilizing the Mann–Kendall (MK) test and the Innovative Trend Analysis (ITA) method, facilitating the evaluation of both monotonic changes and transitions among low-, medium-, and high-intensity rainfall categories. The results indicated significant regional variability in extreme rainfall patterns. The coastal zone demonstrated considerable reductions in southwest monsoon heavy rainfall (HRE: -0.045 days/year), very heavy rainfall (VHRE: -0.031 days/year) and maximum 1-day and 5-day rainfall (RX1: -0.39 mm/year, RX5: -1.94 mm/year), alongside an increase in light (LRE: 0.136 days/year) and moderate rainfall (MRE: 0.092 days/year) occurrences and reduced wet spell durations (WN: 0.046 days/year). This indicates a fragmentation of monsoonal precipitation, probably affected by diminishing westerlies and modified moisture transport from the Arabian Sea. Conversely, the arid region witnessed substantial rises in heavy precipitation occurrences (HRE: 0.005 days/year), RX1 (0.181 mm/year), RX5 (0.269 mm/year), and the duration of wet spells, aligning with intensified convective activity attributable to land surface warming and augmented atmospheric moisture availability. The Western Ghats exhibited predominantly stable extreme rainfall indices, with only increases observed in rainy days (RD: 0.187 days/year) and light rainfall events (LRE: 0.161 days/year), indicating that orographic processes continue to mitigate intense climatic variations in the region. The integrated MK–ITA framework identified indices demonstrating monotonic trends versus those displaying non-monotonic or category-specific behavior, providing more refined insights than traditional trend tests alone. The results underscore evolving climatic changes with direct consequences for water management, agriculture, infrastructure development, and disaster risk mitigation throughout southern Karnataka.</p>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148173426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}