Acta Geophysica最新文献

{"title":"Assessment of drought dynamics and vegetation response across Moroccan climatic zones using SPEI-12 and MODIS NDVI (2010–2024): trends, frequencies, and correlations","authors":"Mohammed Mourjane","doi":"10.1007/s11600-026-01968-4","DOIUrl":"10.1007/s11600-026-01968-4","url":null,"abstract":"<div><p>Drought is one of the most critical climate-related hazards affecting North Africa, particularly Morocco, where water resources are under increasing pressure from both climatic and anthropogenic drivers. This study investigates the spatiotemporal dynamics of drought and vegetation response across three contrasting climatic zones in Morocco, namely Tensift-Haouz, Moulouya-Oriental, and Moyen Atlas, over the period 2010–2024. The Standardized Precipitation Evapotranspiration Index at the twelve-month scale (SPEI-12) was used to characterize drought conditions, while the Normalized Difference Vegetation Index (NDVI) derived from MODIS MOD13A3 monthly imagery was employed to assess vegetation response. Both datasets were extracted and processed using Google Earth Engine, and statistical analyses were conducted in Python. Results indicate persistent drought conditions across all three zones throughout the study period. Moulouya-Oriental experienced the most severe cumulative water deficit (mean SPEI-12 =  − 0.95), with monthly values frequently falling below the moderate drought threshold (SPEI &lt;  − 1.0) and extreme drought episodes (SPEI &lt;  − 2.0) recorded during 2019–2022, followed by Moyen Atlas (mean SPEI-12 =  − 0.81) and Tensift-Haouz (mean SPEI-12 =  − 0.67). The frequency of months classified as moderate to extreme drought (SPEI ≤  − 1.0) exceeded 40% across all zones, with Moulouya-Oriental recording the highest frequency at over 50%, indicating that drought was not an episodic phenomenon but rather the predominant hydroclimatic state throughout the observation period. NDVI analysis revealed a clear spatial gradient consistent with the hydroclimatic conditions of each zone, with Moyen Atlas recording the highest mean vegetation density (mean NDVI = 0.233) and Moulouya-Oriental the lowest (mean NDVI = 0.163). Statistically significant positive correlations between SPEI-12 and NDVI were identified across all three zones (<i>r</i> = 0.256–0.503, <i>p</i> &lt; 0.001), confirming the strong sensitivity of vegetation dynamics to drought variability in these water-limited environments. These findings provide a scientifically robust and reproducible framework for drought monitoring and sustainable land management in Morocco under ongoing climate change.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148614239","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":"Q estimation based on multi-trace and corresponding travel time spectral ratio method","authors":"Zhiwei Li,&nbsp;Ying Shi,&nbsp;Ning Wang,&nbsp;Siyuan Chen","doi":"10.1007/s11600-026-01944-y","DOIUrl":"10.1007/s11600-026-01944-y","url":null,"abstract":"<div><p>The attenuation of amplitude, phase, and frequency caused by stratigraphic absorption can be denoted by quality factor (Q). To address the sensitivity of Q estimation to waveform coupling in thin layers, we propose Q estimation based on multi-trace and corresponding travel time spectral ratio (M-CTSR) method. The spectral ratio (SR) method estimates Q values by quantifying the amplitude attenuation at different frequency bands. Given this, the amplitude-equalized data obtained from sub-spectrum balancing serve as the reference data. Using shaping regularization, we then estimate the effective Q at corresponding travel time, thereby reducing the influence of reflectivity on the amplitude spectrum. The instability of Q value fitting algorithm introduces wild amplitude noise. To quantify the resultant error, the discrepancy between the amplitude curve derived from spectrum division and its linear fitting line is computed. This yields an uncertainty matrix (or vector), which serves as an error parameter for assessing the accuracy of the estimated Q values. With the error parameter and the predefined normal range of Q values, anomalous Q values are identified. Then the estimation of Q for multiple traces, which is ultimately aimed at constructing Q-field, is formulated as irregular-sampled seismic data reconstruction. This is carried out by first modifying the sampling matrix to remove anomalous Q values, then applying spatial smoothing, which ultimately yields the Q‑field of the post‑stack seismic data. The synthetic models and the field data tests prove that the algorithm can, to some extent, reduce the errors of Q estimation caused by waveform coupling.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148614480","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":"Enhancing forest soil water retention curve prediction accuracy and interpretability through CatBoost, artificial ecosystem optimization, and SHAP analysis","authors":"Hassan Ojaghlou,&nbsp;Masoud Karbasi","doi":"10.1007/s11600-026-01953-x","DOIUrl":"10.1007/s11600-026-01953-x","url":null,"abstract":"<div><p>Soil water retention curve, or SWRC, is a vital tool in the assessment of soil–water relations, but the determination of SWRC is a very tedious and costly process. To develop an easier approach, this study aims to improve the accuracy of SWRC prediction through the use of advanced machine learning algorithms. Four models, Random Forest (RF), K-Nearest Neighbors (KNN), Kernel Ridge Regression (KRR), and CatBoost with Artificial Ecosystem Optimization (CatBoost-AEO) were used to predict the SWRC using datasets from 108 soil samples collected in central Finland. The Boruta algorithm was applied to the feature selection process, and three input sets were obtained. Three different input scenarios were developed based on different soil characteristics. Quantitative measures that were used to assess the model's performance include the correlation coefficient (R), Root Mean Square Error (RMSE), and Mean Absolute Percent Error (MAPE). Thus, the findings of the current study revealed that the proposed CatBoost-AEO hybrid model performed better than other models. By using optimal inputs, CatBoost-AEO produced an R score of 0.9996 with an RMSE of 0.4400 and MAPE of 2.54% for training sets and attained an R-value of 0.9876 with an RMSE of 2.6515 and MAPE of 17.02% for testing data. SHAP (SHapley Additive exPlanations) values demonstrated that soil suction stood out as the key variable, with a SHAP mean nearly four times greater than the porosity. This study shows that using AEO and SHAP analysis with CatBoost enhances SWRC prediction accuracy.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148613868","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":"Assessment of non-stationarity in meteorological drought across India under changing climate","authors":"S. P. Swarna Latshmi,&nbsp;Degavath Vinod,&nbsp;Amai Mahesha","doi":"10.1007/s11600-026-01939-9","DOIUrl":"10.1007/s11600-026-01939-9","url":null,"abstract":"<div><p>The Standardised Precipitation Index (SPI) was considered in this work for evaluating non-stationarity in India's meteorological drought using the Generalised Additive Model in Location, Scale, and Shape (GAMLSS) structure for the period 1989–2023, with time and climate covariates. The non-stationary model's performance is evaluated in a comparative research study on time scales of 0.5, 1, 3, 6, 12, 24, and 48 months. The Akaike Information Criteria (AIC) is used to select the best models. Using the Kernel Density Estimate (KDE), the characteristics of drought, specifically Drought Duration (DD) and Drought Severity (DS), are examined. Furthermore, significant variations are observed when drought characteristics are taken into account. The significant explanatory covariates for each of the 4641 grid locations across all time scales are highlighted. The findings showed that, for various drought scales, non-stationarity is prevalent in India's meteorological drought. The Diurnal Temperature Range (DTR) is the most significant of the chosen climatic covariates. Spatial variations in precipitation and temperature indicate that non-stationary location scale (NS–LS) parameters outperform those with only location non-stationarity (NS–L) across India. A comparative analysis at Rajkot and Ramanadapuram reveals that Non-Stationary (NS) models outperform stationary ones, with DTR as the dominant covariate across all time scales. Compared with KDE plots, the Stationary (S) model plot varies with drought characteristics in the absence of external influences, whereas the NS model plot shows a different behaviour. During the intense 2016 drought, NS models accurately depicted the spatial distribution and intensity of drought across India, underscoring the importance of climatic covariates. Deficient monsoonal rains caused decreased agricultural output and further strained water supplies. Sustainable mitigation and adaptation methods that account for non-stationary trends in climatic data are necessary to effectively address the evolving nature of drought. In a changing environment, this innovative drought analysis method yields consistent results across the research area. The findings carry considerable consequences for climate risk assessment, drought assessment, and policy formulation. Spatial mapping of dominant covariates across India enables region-specific drought early warning systems, where DTR-sensitive regions can be prioritised for temperature-based mitigation measures. The methodology can also be applied to other climate indices and regions to examine non-stationary drought dynamics. For policymakers and planners, this approach provides a quantitative basis for improving irrigation scheduling, water allocation, and agricultural resilience planning in response to changing drought risks.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148613749","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":"Predicting shear-wave velocity from few well logs plus derivatives and volatilities optimized by dual-objective feature-selection machine learning","authors":"David A. Wood","doi":"10.1007/s11600-026-01951-z","DOIUrl":"10.1007/s11600-026-01951-z","url":null,"abstract":"<div><p>Well-log attributes incorporating derivatives and volatility calculated for just two or three recorded well logs can be used effectively to predict shear-wave velocity (<i>Vs</i>) in sparsely logged wellbores. Sensitivity analysis is used to configure the well-log attribute calculations to suit specific datasets. Recorded gamma ray (<i>GR</i>), bulk density (<i>PB</i>), and compressional wave velocity (<i>Vp</i>) data from the Bakken Formation (North Dakota, USA) type-well logs (1000 data records) are used to demonstrate the feasibility of the method. Data-matching machine-learning algorithms K-nearest neighbor (KNN) and transparent-open-box (TOB) outperform linear regression least absolute shrinkage and selection operator (LASSO) and extreme gradient boosting (XGBoost) in <i>Vs</i> prediction with this dataset. The comparative methods used to assess <i>Vs</i> prediction performance are statistical measures derived from multi-K-fold cross-validation analysis (3-, 4-, 5-, 10-, and 15-fold) that facility uncertainty analysis. Dual-objective optimized feature selection with a KNN–sine–cosine optimizer identifies the best performing recorded log and attribute combinations for <i>Vs</i> prediction. An eight-variable combination including <i>GR</i>, <i>PB</i>, and <i>Vp</i> recorded well logs with five computed attributes enabled the TOB model to predict <i>Vs</i> with a mean absolute error (MAE) of ~6 m/s and root mean square error (RMSE) of ~19 m/s. A 7-variable combination including <i>PB</i> and <i>Vp</i> recorded well logs with five computed attributes enabled the KNN model to predict <i>Vs</i> with MAE of ~8 m/s and RMSE of ~28 m/s. Data mining of these datasets with the TOB model revealed that just a few outlying predictions associated with the stratigraphic member transition zones were responsible for the relatively high RMSE values. As there are many sparsely logged wellbores, particularly in shale plays and in reservoir development wells, the well-log attribute technique described and evaluated offers potential to enhance the spatial distribution of reliable <i>Vs</i> values.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148613906","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":"High-resolution non-invasive mapping of vertical heterogeneity in sandy soils of the Oak Openings Region, Ohio, USA, using electromagnetic induction and ground penetrating radar methods","authors":"Obinna Urom,&nbsp;Ahzegbobor P. Aizebeokhai,&nbsp;Kennedy O. Doro","doi":"10.1007/s11600-026-01950-0","DOIUrl":"10.1007/s11600-026-01950-0","url":null,"abstract":"<div><p>Geophysical methods provide a non-invasive approach to rapidly characterize soil physicochemical properties distribution. Currently, gaps exist in the systematic application and validation of non-contact-based geophysical methods, such as ground penetrating radar (GPR) and electromagnetic induction (EMI), for characterizing the vertical variation of soil properties and quantitatively linking geophysical responses to independently measured soil parameters. This study assesses the use of EMI and GPR for quantifying the vertical variation of soil moisture content (SMC), soil organic matter (SOM), and soil texture. Co-located EMI and GPR surveys were conducted at the Stranahan Arboretum research site in Toledo, which is within the Oak Openings Region in the state of Ohio, USA. Soil samples collected from nine locations along the transects were analysed for SMC, SOM, and soil texture. Apparent electrical conductivity (<span>({sigma}_{text{a}})</span>) datasets from EMI survey were inverted to obtain lateral and vertical variations of soil electrical conductivity (<span>(sigma)</span>), which captures three major lithostratigraphic units—sandy, silt loam, and silt soils—found in soil cores within the top 2.0 m. Soil electrical conductivity correlates with measured soil properties (SMC, SOM and soil texture), with coefficient of determination (<i>R</i>²) ranging from 0.6 and 0.9. The GPR radargrams show structural boundaries, with reflectors consistent in delineating sandy unit but unable to distinguish between the silt loam and silt. These results validate the effectiveness of combining EMI with GPR to delineate vertical variation of soil properties and characterize stratigraphic heterogeneity, expanding the possibilities for non-invasive three-dimensional soil properties mapping.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11600-026-01950-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466354","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":"Machine learning-based model for evaluating multi-perforation hydraulic fracturing and analysis of fracturing mechanisms","authors":"Da Zhao,&nbsp;Zewen Gu,&nbsp;Xiangqing Kong,&nbsp;Sen Wang,&nbsp;Jianlin Liu","doi":"10.1007/s11600-026-01957-7","DOIUrl":"10.1007/s11600-026-01957-7","url":null,"abstract":"<div><p>Given that substantial hydrocarbons remain locked within source rock formations such as shale, unrecoverable by conventional extraction methods, hydraulic fracturing has become a pivotal technology for exploiting these unconventional resources. Although optimal perforation design is essential to improving hydraulic fracturing efficiency, the complex interactions among multiple perforations during the fracturing process led to markedly different outcomes depending on the perforation distribution pattern. This study leverages an integrated framework of discrete element method (DEM) simulations and deep learning (DL) to classify and evaluate perforation layouts for improved fracturing effectiveness. First, a theoretical model for a single perforation, based on the Kirsch solution from elasticity theory, was developed to elucidate the mechanism by which injection pressure alters the surrounding stress field prior to fracture initiation. Next, a numerical model for hydraulic fracturing was constructed using DEM, wherein the material parameters of the shale were calibrated with machine learning techniques. The consistency observed between the theoretical predictions, numerical results, and experimental data validates the adopted methodology. Further comparative analysis through DEM simulations of single and multi-perforation hydraulic fracturing revealed that reasonable multi-perforation configurations can induce a more favorable stress environment for fracture propagation, promoting the formation of complex fracture networks. On this basis, a multi-perforation DEM model was developed to simulate fracture initiation and propagation from randomly distributed perforations. The simulated dataset was used to train the developed (deep neural networks) DNN classification model for predicting fracturing effects of different multi-perforation configurations. Its performance was validated by confusion matrix analysis and ROC curve evaluation, which demonstrates its high predictive accuracy. The proposed model accurately captures the intrinsic relationship between perforation parameters and fracture morphology, thereby elucidating the underlying mechanisms. The fracture–perforation interaction analysis framework established in this study provides a theoretical basis and practical guidance for optimizing perforation design in large-scale hydraulic fracturing operations.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466228","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":"Deep learning and multi-view image fusion for identification of karst voids and areal porosity quantification in deep carbonate reservoirs: a case study of the Lungu-7 block, Tarim Oilfield, China","authors":"Zhuolin Li,&nbsp;Guoyin Zhang,&nbsp;Jianli Lin,&nbsp;Xin Zhang,&nbsp;Jinqiang Tian","doi":"10.1007/s11600-026-01964-8","DOIUrl":"10.1007/s11600-026-01964-8","url":null,"abstract":"<div><p>Deep carbonate reservoirs represent one of the most important types of hydrocarbon reservoirs, and accurate identification and characterization of their reservoir space structures remain among the key technical challenges for efficient exploration and development in the petroleum industry. To address the low accuracy and limited classification capability of conventional void recognition methods in electrical image logs of deep carbonate formations, this study proposes an integrated technical workflow that combines data preprocessing, deep-learning-based recognition, and quantitative parameter characterization. First, multiple image restoration algorithms were compared, and the radial basis function (RBF) interpolation algorithm was selected to eliminate blank stripes in electrical image logs. A joint annotation method was established to achieve precise multi-type labeling of voids. By integrating dynamic and static imaging data, a dual-channel dataset was constructed to effectively fuse complementary dynamic and static image information. Subsequently, an adaptive dynamic fusion module (EC-Gate) was designed and embedded into cross-layer connection structures. A deep-learning semantic segmentation model, GateNet, was developed for multi-type cavity recognition. Comparative experiments demonstrated that GateNet exhibited good generalization performance within the study area and high recognition accuracy, outperforming multiple classical models in both recognition precision and completeness, achieving the highest evaluation metrics (mean intersection over union [MIoU] = 90.89%, pixel accuracy [PA] = 94.24%). Finally, an integrated workflow of “intelligent recognition – morphological optimization – parameter characterization” was established and successfully applied to cavity identification and areal porosity extraction in a blind well section. This study provides a novel technical approach for refined void characterization in carbonate reservoirs and may offer potential application value for oil and gas exploration and development.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466101","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":"Experimental study on compression characteristics and resistivity response of sulfate-containing site soil","authors":"Hui Liu,&nbsp;Qiang Sun,&nbsp;Jishi Geng,&nbsp;Jingjing Nan,&nbsp;Kai Cui,&nbsp;Yuxin Zhang","doi":"10.1007/s11600-026-01949-7","DOIUrl":"10.1007/s11600-026-01949-7","url":null,"abstract":"<div><p>Soil salinization is a major cause of structural degradation, strength deterioration, and deformation intensification in earthen heritage sites across northwestern China. Therefore, investigating salt-induced damage mechanisms in such soils holds significant engineering and conservation value . In this study, uniaxial compression tests were performed on soil samples with varying sodium sulfate (Na₂SO₄) contents (0%, 2%, 4%, and 6%) and different moisture levels (10%, 13%, 16%, 19%, and 22%). Resistivity measurements were simultaneously monitored to assess the electromechanical response of site soils during compression under different water and salt conditions. The results show that: (1) water content is the dominant controlling factor: both the uniaxial compressive strength (UCS) and electrical resistivity decrease monotonically with increasing water content. However, the rate of strength reduction diminishes markedly once the water content exceeds 16%. (2) Conversely, the influence of Na₂SO₄ was non-monotonic. The UCS initially decreased as salt content increased from 0 to 2%, likely due to the weakening of interparticle bonds, but then recovered at higher concentrations (up to 6%), suggesting the onset of salt crystallization-induced cementation. (3) This dual role of salt was mirrored in the electrical response: resistivity plummeted with initial salt addition, indicating enhanced ionic conduction, and then decreased gradually beyond 2% Na₂SO₄, consistent with a transition to a crystallization-dominated regime. These findings elucidate the complex interplay governing salt damage and highlight the potential of resistivity as a nondestructive indicator for assessing the condition of saline earthen heritage.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466327","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":"Future impacts of temperature and precipitation induced by climate change over Tigris River Basin in Iraq","authors":"Bassim Mohammed Hashim,&nbsp;Zaher Mundher Yaseen,&nbsp;Zulfaqar Sa’adi,&nbsp;Ricky Anak Kemarau,&nbsp;Najeebullah Khan,&nbsp;Leonardo Goliatt,&nbsp;Sajjad Firas Abdulameer,&nbsp;Shamsuddin Shahid","doi":"10.1007/s11600-026-01954-w","DOIUrl":"10.1007/s11600-026-01954-w","url":null,"abstract":"<div><p>The Tigris River Basin (TRB) in West Asia, a vital water source in an already water-stressed region, faces increasing challenges due to climate change. The current study aims to assess the future impacts of climate change on temperature and precipitation in TRB. Historical data (1984–2022) were analyzed, and four global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were used to make future projections for three time periods (2016–2035, 2046–2065, and 2081–2100) under two emission scenarios (RCP4.5 and RCP8.5). The Mann–Kendall (MK) test and Sen’s slope estimator applied to detect and assess temporal trends and change magnitudes in temperature and precipitation throughout the basin in order to extensively investigate climatic behaviors. Based on the findings, temperatures rose to 12.7–15.4 and 12.9–17.8 °C, respectively, under RCP4.5 and RCP8.5 in the far future (2081–2100). For precipitation, the upper TRB recorded the highest values, ranging from 1987 to 2318 mm in the far period (2081–2100) under RCP4.5. Under RCP8.5, the highest precipitation was recorded in the mid-century period (2046–2065), ranging from 2857 to 3425 mm. The statistical trend analysis showed strong consensus among the models on a clear and persistent warming trend, with projected temperature increases of 2.05–4.13 °C every 20 years, particularly affecting the downstream regions of TRB in Iraq. In contrast, precipitation projections and MK trend results exhibited high variability and uncertainty. However, under the high-emissions scenario (RCP8.5), a strong inverse relationship was observed between temperature increase and precipitation decrease, suggesting that extreme warming may be accompanied by a sharp decline in precipitation. The study confirms that the TRB is heading toward a warmer future, placing significant pressure on water resources, agriculture, and ecosystems.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466414","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}