Environmental Earth Sciences最新文献

{"title":"Evaluation of UAV-based 3D modeling methods for landslides under different route plans","authors":"Yunchuan Wang,&nbsp;Jia Li,&nbsp;Long Sun,&nbsp;Ping Duan,&nbsp;Rui Wang","doi":"10.1007/s12665-025-12285-2","DOIUrl":"10.1007/s12665-025-12285-2","url":null,"abstract":"<div><p>Currently, aerial photographs are acquired by unmanned aerial vehicles (UAVs) along preplanned flight routes, and a 3D model is constructed to investigate and measure the features of geological disasters. In this work, the effectiveness and quality of three kinds of UAV routes used for landslide investigations are explored on the basis of a survey of a landslide in Luquan County, Kunming city, Yunnan Province. The advantages and disadvantages of 3D models based on the \"horizontal round‒trip route (HRr)\", \"terrain follow route (TFr)\" and \"inclined route (Ir)\" methods are studied and compared through quality evaluations of the final products. The results show that the 3D models based on HRr and TFr have different degrees of distortion and blurring, whereas the 3D model based on Ir has a finer surface resolution. Ir offers the best performance among the three routes and can more accurately capture the true surface of the survey features. Correspondingly, the checkpoint accuracies of the HRr, TFr, and Ir methods are 0.155, 0.064 and 0.033 m, respectively, indicating that the 3D model based on Ir is the most accurate. This research indicates that inclined routes provide better applicability in the investigation and monitoring of landslides.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 11","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Groundwater sustainability in the Varuna river basin: impacts of climate change and population growth","authors":"Ranveer Kumar,&nbsp;Rajarshi Bhattacharjee,&nbsp;Shishir Gaur,&nbsp;Anurag Ohri","doi":"10.1007/s12665-025-12213-4","DOIUrl":"10.1007/s12665-025-12213-4","url":null,"abstract":"<div><p>This study uses the SWAT model combined with bias-corrected climate projections across four SSP scenarios to assess the impacts of climate change and population-driven water demand on groundwater sustainability in the Varuna river basin (VRB). Climate anomaly analysis revealed a significant decrease in precipitation and an increase in temperature under higher emission scenarios (SSP370 and SSP585), intensifying recharge drought conditions and evapotranspiration rates. Using Anselin’s Local Moran’s I method, we identified distinct spatial patterns of groundwater recharge. The low-emission scenario showed a stable recharge distribution (SSP126), whereas higher-emission pathways revealed extensive clusters of recharge hotspots and coldspots, indicating regional disparities in recharge. Additionally, the analysis of the groundwater sustainability ratio (GSR) dynamics showed intensified over-exploitation risks in SSP585, which were driven by reduced recharge and higher atmospheric water demand. These findings highlight the necessity for adaptive water management strategies to address climate-driven recharge disparities and enhance groundwater sustainability in the VRB through artificial recharge.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 11","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation on water yield service in Nansihu River Basin of China during the recent 20 years","authors":"Wei Zhang,&nbsp;Difei Zhao,&nbsp;Dandan Liu","doi":"10.1007/s12665-025-12305-1","DOIUrl":"10.1007/s12665-025-12305-1","url":null,"abstract":"<div><p>Assessing the water yield function of a river basin is crucial for hydrological ecosystem protection. This research took the Nansihu River Basin as a case and evaluated its water yield from 2000 to 2022. The results showed that: (1) Water yield decreased 33.51% over the 20 years, from 369.91 mm to 220.06 mm, with the peak occurring in 2005. Both precipitation and evapotranspiration were positively correlated with water yield and exhibited similar trends and spatial distributions. (2) Water yield declined from the eastern mountainous areas to the western plains, with significant reductions ranging from − 200 to -100 mm in the western plains, while the central area remained stable. (3) Climatic factors significantly impacted water yield, and LULC determined its distribution. Non-natural areas produce three times the water yield of natural areas. These results are instrumental in guiding water resource management strategies within the basin.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 11","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12665-025-12305-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The genetic mechanism of Hongjiang geothermal system in Jiangxi, Southeast China: insight from the evidence of hydrochemistry, multiple isotopes, and inverse geochemical models","authors":"Junliang Sun,&nbsp;Kai Liu,&nbsp;Shouchuan Zhang,&nbsp;Qingcheng He,&nbsp;Wuhui Jia,&nbsp;Luyao Wang,&nbsp;Tingxi Yu","doi":"10.1007/s12665-025-12314-0","DOIUrl":"10.1007/s12665-025-12314-0","url":null,"abstract":"<div><p>Investigating the hydrochemical evolution characteristics, circulation processes, and formation mechanisms of geothermal systems provides critical insights for geothermal resources development. This study employs hydrochemistry, multiple isotopes (δ¹⁸O, δ²H, ⁸⁷Sr/⁸⁶Sr), silica-enthalpy mixing model and hydrogeochemical inverse models determine the key hydrogeochemical process. The results demonstrate that all geothermal waters belong to Na-HCO₃ type. Silicate minerals dissolution, cation exchange and mixing take place during the geothermal fluid circulation. The geothermal fluid is originated from precipitation, with a recharge elevation of 813 ~ 1012 m. The reservoir temperature is 111 ~ 121 ℃, determined by SiO₂ geothermometer and multimineral equilibrium method. The geothermal circulation depth with an average of varies from 2641 to 2919 m. Under the effect of hydraulic pressure, the deep geothermal groundwater upwells mixed with shallow cold groundwater with a proportion of 73 ~ 93%. The amount of mineral transfer in the different flow paths is calculated by inverse geochemical simulation. The study indicates that the key reactions in geothermal circulation include albite, quartz and CO₂ (g) dissolution, kaolinite precipitation, and cation exchange interaction. Finally, Conceptual model for genesis of Hongjiang geothermal system has been developed.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 11","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144091007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of two groundwater sustainability indicators in semi-arid aquifers using machine learning","authors":"Susan Hayeri Yazdi,&nbsp;Maryam Robati,&nbsp;Saeideh Samani,&nbsp;Fariba Zamani Hargalani","doi":"10.1007/s12665-025-12253-w","DOIUrl":"10.1007/s12665-025-12253-w","url":null,"abstract":"<div><p>Groundwater serves as a crucial freshwater resource, especially in arid and semi-arid regions, making accurate predictions essential for sustainable management. This research evaluates and contrasts the effectiveness of four machine learning (ML) techniques in forecasting two key indicators of groundwater sustainability: the groundwater level index (GWLI) and the drought index (DI). The investigated models include Artificial Neural Network (ANN), Adaptive Network-based Fuzzy Inference System (ANFIS), Group Method of Data Handling (GMDH), and Least Squares Support Vector Machine (LSSVM). These models were implemented for the Houmand Absard aquifer in Iran. Historical time-series data were split into training (70%) and testing (30%) sets, with input variables consisting of past groundwater levels (m), precipitation (mm), temperature (°C), and evaporation (m) across six unique configurations. Among the evaluated models, GMDH demonstrated the highest predictive accuracy, exhibiting superior correlation coefficients (R), reduced root mean square error (RMSE) and mean absolute error (MAE), and higher Nash–Sutcliffe efficiency (NSE) in both training and testing phases. The GMDH model achieved an average R value of 0.9763 for GWLI and 0.9719 for DI, highlighting its strong predictive capability. These findings underscore the effectiveness of GMDH for short-term groundwater sustainability forecasting and its potential to improve water resource management strategies in the region. Furthermore, results suggest that GMDH slightly outperforms DI in predicting GWLI across all six examined scenarios.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 11","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144091008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The recharge and infiltration of soil water in the subalpine shrub zone of the eastern Qilian Mountains in China","authors":"Zhi Wang,&nbsp;Wenxiong Jia,&nbsp;Yue Zhang,&nbsp;Xin Lan,&nbsp;Zhijie Yu,&nbsp;Huifang Luo,&nbsp;Lifang Chou","doi":"10.1007/s12665-025-12312-2","DOIUrl":"10.1007/s12665-025-12312-2","url":null,"abstract":"<div><p>The Qilian Mountains are an important ecological security barrier in the northwest China and a major water supply area for the Hexi Corridor. It is of great significance to study their hydrological processes. Based on the stable isotope values of precipitation, soil water and groundwater in the subalpine shrub zone of the eastern Qilian Mountains from May to October 2019, their stable isotope characteristics as well as the recharge and infiltration of soil water were analyzed by the lc-excess method and the lc-excess balance equation. The results showed that stable isotopes of precipitation showed significant enrichment in spring and depletion in summer and autumn, characterized by large fluctuations. Stable isotopes of soil water were enriched in summer and depleted in spring, appearing a tendency of gradual decrease with the increase of soil layer depth. However, stable isotopes of groundwater had the smallest fluctuations. Soil water was obviously recharged by precipitation in summer, but lower recharge was observed in spring and autumn. The main contributors to soil water recharge were precipitation events with the intensities of 10–20 mm/day and 20–30 mm/day. In subalpine shrub zone, both piston flow and preferential flow patterns coexisted in the infiltration of soil water, with a relative contribution rate of 76% from plug flow and 24% from preferential flow to groundwater recharge. The results are of theoretical value and practical significance for understanding hydrological processes and evaluating groundwater quantity in the subalpine shrub zone.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 11","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing machine learning and geospatial technologies for precise soil erodibility mapping and prediction","authors":"Wudu Abiye,&nbsp;Endalamaw Dessie Alebachew,&nbsp;Orhan Dengiz","doi":"10.1007/s12665-025-12270-9","DOIUrl":"10.1007/s12665-025-12270-9","url":null,"abstract":"<div><p>Soil erosion threatens fertility and sustainability, with soil erodibility influencing erosion rates based on physical and chemical properties. This study aimed to estimate soil erodibility for various land uses using the K-factor from the Wischmeier equation, assess indicators such as the structural stability index, clay ratio, and dispersion ratio, and develop a predictive model for erosion risk using artificial neural networks (ANN) and geospatial technologies. High-resolution spatial maps of erosion risk were created to inform land management and conservation efforts. An ANN model in MATLAB R2024a predicted soil erodibility as well as indicators such as the dispersion ratio, crust formation, and clay ratio. Statistical analyses, including principal component analysis (PCA) and correlation assessment, were performed with OriginPro 2021b to explore relationships between soil properties. Spatial maps of observed and predicted erodibility were created using ArcGIS 10.7.1. Results showed that erodibility values ranged from 0.023 to 0.152 t·ha·hr·MJ^-1·mm^-1 for the observed data and 0.026 to 0.148 t·ha·hr·MJ^-1·mm^-1 for the predicted values. For different land uses, it included 0.09513t·ha·hr·MJ^-1·mm ¹ for cultivated land, 0.060796 t·ha· hr·MJ ¹ · mm ¹ for forest land, and 0.092685 t·ha·hr·MJ^-1·mm^-1 for pasture land. The ANN model demonstrated high accuracy, with R-values of 0.999 for soil erodibility, 0.996 for the structural stability index (SSI), 0.995 for the clay ratio (CR), and 0.904 for the dispersion ratio (DR). This study effectively combines machine learning and geospatial technologies to predict and map soil erodibility, providing insights for erosion control and sustainable land management.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 11","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12665-025-12270-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of paleo-rainfall events in the South Atlantic Convergence Zone (SACZ) and human pressures since ~ 1950 in southeastern Brazil: Paraty and Saco de Mamanguá","authors":"Vahideh Farhangi,&nbsp;Maria Virgínia Alves Martins,&nbsp;Rubens Cesar Lopes Figueira,&nbsp;Paulo Alves de Lima Ferreira,&nbsp;Egberto Pereira,&nbsp;Denise Lara,&nbsp;Caroline Adolphsson do Nascimento,&nbsp;Johann Hohenegger,&nbsp;Murilo Barros Saibro,&nbsp;Josefa Varela Guerra,&nbsp;Renata Cardia Rebouças,&nbsp;Cleverson Guizan Silva,&nbsp;André Luiz Carvalho da Silva,&nbsp;Fabio Ferreira Dias,&nbsp;Rodolfo Dino,&nbsp;Heloisa Helena Gomes Coe,&nbsp;Fernando Rocha","doi":"10.1007/s12665-025-12248-7","DOIUrl":"10.1007/s12665-025-12248-7","url":null,"abstract":"<div><p>Protected coastal areas, such as bays, estuaries, and coastal lagoons, are generally highly populated and impacted by anthropogenic activities. These regions are also vulnerable to the effects of climate change and sea level fluctuations. This work aims to study the records of temporal changes induced by shifts in rainfall and human factors in the western region of Ilha Grande Bay (BIG; Rio de Janeiro State, SE Brazil). The study compares textural, mineralogical, and geochemical data in two cores, BIG01 and BIG02, collected in Paraty Harbor and Saco de Mamanguá, respectively. The two cores have recorded sedimentary changes since 1950, as indicated by ²¹⁰Pb and ¹³⁷Cs dating. The statistically integrated results indicate higher moisture levels before ~ 2000 in this region and a general trend toward reduced rainfall since then, accompanied by an increase in the sediment accumulation rate since the 1980s, likely due to the progressive expansion of urbanization, deforestation, agriculture, and dredging activities in BIG. The Paraty region (BIG01) was most affected by recurrent landslides during concentrated rainfall in SACZ events and by anthropic impact, especially since ~ 1970. This impact has resulted in silting, eutrophication, and moderate pollution by potentially toxic elements, especially Cu, Pb, Sn, Zn, and As. The records of cores BIG01 and BIG02 highlight the influence of climate change, namely SACZ and human activities, on sedimentation and sediment quality. The recurrence of mega-events, such as SACZ paleo-events, and their impact on landslides in slope zones require further study, particularly in densely populated areas.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 11","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12665-025-12248-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed neural networks for predicting sediment transport in pressurized pipe flows","authors":"Rupesh Kumar Tipu,&nbsp;Ruchika Bhakhar,&nbsp;Kartik S. Pandya,&nbsp;Vijay R. Panchal","doi":"10.1007/s12665-025-12295-0","DOIUrl":"10.1007/s12665-025-12295-0","url":null,"abstract":"<div><p>This study presents the development of a Physics-Informed Neural Network (PINN) for predicting sediment transport rates, integrating physical laws governing sediment transport dynamics to improve prediction accuracy. The model was evaluated against traditional machine learning models, including Random Forest and Support Vector Regression (SVR), as well as empirical formulas, demonstrating superior performance with an average <span>(R^2)</span> of 0.9573 and low error metrics. SHapley Additive exPlanations (SHAP) analysis revealed that dimensionless bed shear stress (<span>(eta _b)</span>) and relative grain size (<i>Z</i>) were the most significant contributors to model predictions. A Graphical User Interface (GUI) was also developed to facilitate real-time interaction with the model, making advanced predictions accessible to hydrological engineers. The study underscores the potential of combining machine learning with physics-based constraints to enhance the predictive capabilities of sediment transport models, offering a practical tool for environmental management.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 11","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporating the Geological Strength Index (GSI) of the transmission strata into the attenuation law of ground vibration from open pit bench blasting operations: An investigative approach","authors":"Subhamoy Ghosh,&nbsp;Vivek Kumar Himanshu,&nbsp;Chandrakanta Behera,&nbsp;Manoj Kumar Mishra","doi":"10.1007/s12665-025-12303-3","DOIUrl":"10.1007/s12665-025-12303-3","url":null,"abstract":"<div><p>An effective method of quantifying the levels of ground vibration is in terms of peak particle velocity through predictive modelling using empirical formulations and analysis. Of the existing particle velocity attenuation laws, the scaled-distance concept has been widely used for estimating peak particle velocities at known distances for different explosive charge weights. Several blast design parameters have been known to influence the particle velocities. However, it is seldom that the geological characteristics of the transmission strata between the point of detonation and the monitoring point are incorporated into the attenuation laws. Consequently, this study incorporated Geological Strength Index (GSI) as the parameter representing the geological settings of the transmission strata into the particle velocity attenuation law and thereby proposed a new predictive model for blast-induced ground vibration based on the geological observations at the blasting benches and along the direction of seismic monitoring. In this process, the United States Bureau of Mines (USBM) model was modified with an addition parameter of GSI <span>(({R}^{2}=0.77))</span>. Lastly, the proposed model was validated against unused blast monitoring data <span>(({R}^{2}=0.89))</span>. Thus, this study successfully incorporated a geological parameter and modified a universal predictor for better efficiency and applicability as per the dominant geological strata characteristics.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 11","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}