Physics and Chemistry of the Earth最新文献

Geotechnical and microstructural assessment of gas condensate–contaminated clayey gravel 凝析气污染粘土砾石的岩土力学与微观结构评价

IF 4.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2026-02-07 DOI: 10.1016/j.pce.2026.104340

Amin Falamaki , Abdosalam Dashti , Aghileh Khajeh , Amir Hossein Vakili , Amir Hossein Karimi

This study investigates the detrimental effects of gas condensate contamination on the geotechnical properties of clayey gravel (GC) soil, addressing a pressing environmental and geotechnical engineering challenge. Soil samples were systematically contaminated with gas condensate at concentrations of 0, 2, 4, 6, and 8% by dry weight and subjected to comprehensive geotechnical testing, including Atterberg limits, modified Proctor compaction, direct shear, unconfined compressive strength (UCS), and California bearing ratio (CBR). Testing of Atterberg limits revealed progressive reductions in soil consistency, with the liquid limit decreasing by 15.2% (from 30.9% to 26.2%) and the plastic limit by 13.5% (from 21.0% to 18.16%) at maximum contamination. Modified Proctor compaction tests identified a critical threshold at 4% contamination, where maximum dry density initially increased by 1.2% before declining by 4.5% at higher concentrations, while optimum moisture content decreased by 28.6%. Strength characterization showed severe degradation, with UCS experiencing a 68.8% reduction (from 938.49 to 293.07 kPa) and CBR values decreasing by 52.3% at 100% relative density. Direct shear tests demonstrated substantial weakening of shear strength parameters, with cohesion declining by 53% and friction angle by 25%. These findings underscore the severe implications of gas condensate contamination for soil stability and highlight the urgency of implementing mitigation measures to safeguard infrastructure and environmental integrity at gas condensate storage sites.

本研究探讨了凝析油污染对粘性砾石（GC）土岩土性能的不利影响，解决了一个紧迫的环境和岩土工程挑战。土壤样品系统地受到干重浓度为0、2、4、6和8%的凝析气污染，并进行综合岩土测试，包括阿特伯格极限、改良普罗克特压实、直接剪切、无侧限抗压强度（UCS）和加州承载比（CBR）。Atterberg极限测试显示土壤稠度逐渐降低，在最大污染下，液体极限下降15.2%（从30.9%降至26.2%），塑料极限下降13.5%（从21.0%降至18.16%）。改良的Proctor压实测试确定了4%污染时的临界阈值，其中最大干密度最初增加1.2%，然后在更高浓度下下降4.5%，而最佳水分含量下降28.6%。强度表征显示出严重的退化，在100%相对密度下，UCS降低了68.8%（从938.49 kPa降至293.07 kPa）， CBR值降低了52.3%。直剪试验表明，抗剪强度参数明显减弱，黏聚力下降53%，摩擦角下降25%。这些发现强调了凝析油污染对土壤稳定性的严重影响，并强调了在凝析油储存地点实施缓解措施以保护基础设施和环境完整性的紧迫性。

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引用次数: 0

Generative AI-enabled forecasting and green supply chain sustainability assessment: Evidence from China's palm oil trade with ASEAN 生成式人工智能预测和绿色供应链可持续性评估：来自中国与东盟棕榈油贸易的证据

IF 4.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2026-02-06 DOI: 10.1016/j.pce.2026.104341

Yuhan Liu , Xiangzheng Deng , Yunxiao Gao

Food systems and agri-commodity supply chains face increasing pressures from climate change, trade volatility, and environmental degradation, palm oil positioned at the center of sustainability debates. China, as a major importer, and Indonesia, the leading producer, play crucial roles in influencing the environmental footprint and resilience of cross-border palm-oil trade. This study integrates generative AI-based demand forecasting with spatial sustainability assessment to evaluate the alignment between China's future palm-oil demand and Indonesia's sustainability-compliant supply. Using Time-series Generative Adversarial Networks (TimeGAN), we generate scenario-rich forecasts of China's palm-oil imports through 2030. A province-level Green Supply Chain Sustainability Index (GSCI) for Indonesia, incorporating deforestation intensity, land-use efficiency, zero-deforestation commitments, and RSPO certification, supports traceability-based allocation. Results show that more than 60% of China's projected imports can be met by high-GSCI provinces, indicating strong potential for deforestation-free procurement without undermining supply security. The results demonstrate how AI-enabled forecasting combined with spatial sustainability indicators can inform environmentally responsible sourcing strategies and enhance resilience in cross-border palm-oil supply systems.

粮食系统和农产品供应链面临来自气候变化、贸易波动和环境退化的越来越大的压力，棕榈油处于可持续性辩论的中心。作为主要进口国的中国和主要生产国的印度尼西亚，在影响跨境棕榈油贸易的环境足迹和弹性方面发挥着至关重要的作用。本研究将基于生成式人工智能的需求预测与空间可持续性评估相结合，以评估中国未来棕榈油需求与印尼符合可持续性的供应之间的一致性。利用时间序列生成对抗网络（TimeGAN），我们对中国到2030年的棕榈油进口进行了场景丰富的预测。印度尼西亚的省级绿色供应链可持续发展指数（GSCI）综合了毁林强度、土地利用效率、零毁林承诺和RSPO认证，支持基于可追溯性的分配。结果显示，中国60%以上的预计进口量可以由高gsci省份满足，这表明在不破坏供应安全的情况下实现无砍伐采购的巨大潜力。研究结果表明，人工智能预测与空间可持续性指标相结合，可以为环境负责任的采购策略提供信息，并增强跨境棕榈油供应系统的复原力。

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引用次数: 0

Uncertainty quantification in the estimation of Probable Maximum Precipitation (PMP) in Iran: A comprehensive analysis 伊朗可能最大降水（PMP）估算中的不确定性量化：综合分析

IF 4.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2026-02-05 DOI: 10.1016/j.pce.2026.104331

Mahdi Delghandi, Behnam Abdollah-Abadi

Probable Maximum Precipitation (PMP) is a fundamental input for estimating the Probable Maximum Flood (PMF), and therefore plays a key role in the safe and cost-effective design of hydraulic structures. However, the estimation of PMP is inherently associated with various sources of uncertainty. This study investigates uncertainties in PMP estimation in Iran, focusing on calculation methods (Hershfield method (H), the Site-Specific (SS) method, and a newly proposed Developed Site-Specific (DSS) approach), rain-gauge station density, and climatic conditions. Monte Carlo simulation and Sobol’ global sensitivity analysis were employed to quantify contribution of frequency factor curve parameters (α, c and LR) to the uncertainty in PMP estimates. Results revealed that H approach produced the highest PMP values (135-1175 mm), while DSS, most compatible with Iran's climate, yielded lower values (110-632 mm). Application of DSS reduced the weighted mean PMP from 309 mm to 230 mm, which can significantly reduce design costs of high-hazard hydraulic structures. Uncertainty analysis demonstrated that climatic conditions accounted for the largest share of total uncertainty (41.2%), followed by the number of stations (36.2%) and the PMP estimation method (22.6%), respectively. Regardless of the specific contribution of each source of uncertainty, it is evident that all three factors have a significant impact on PMP estimation. Sobol’ analysis demonstrated that parameter c is the dominant contributor to PMP uncertainty. These findings highlight the need for an uncertainty-aware framework to support cost-efficient and safe hydraulic infrastructure planning.

最大可能降水量（PMP）是估计最大可能洪水（PMF）的基本输入，因此在水工建筑物的安全和经济设计中起着关键作用。然而，PMP的估计本质上与各种不确定性来源有关。本研究调查了伊朗PMP估算中的不确定性，重点研究了计算方法(赫什菲尔德方法（H)、特定站点方法（SS）和新提出的特定站点方法（DSS））、雨量站密度和气候条件。采用蒙特卡罗模拟和Sobol全局敏感性分析来量化频率因子曲线参数（α， c和LR）对PMP估计不确定性的贡献。结果表明，H方法的PMP值最高（135 ~ 1175 mm），而与伊朗气候最相容的DSS方法的PMP值较低（110 ~ 632 mm）。DSS的应用将加权平均PMP从309 mm降低到230 mm，可以显著降低高危水工结构的设计成本。不确定性分析表明，气候条件在总不确定性中所占比例最大（41.2%），其次是台站数（36.2%）和PMP估算方法（22.6%）。不考虑每个不确定性来源的具体贡献，很明显，所有三个因素对PMP估计都有重大影响。Sobol的分析表明，参数c是PMP不确定性的主要贡献者。这些发现强调了需要一个具有不确定性意识的框架来支持经济高效和安全的水利基础设施规划。

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引用次数: 0

Microplastic contamination in urban agriculture: Pathways, crop uptake, human exposure and policy interventions 都市农业中的微塑料污染：途径、作物吸收、人类接触和政策干预

IF 4.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2026-02-05 DOI: 10.1016/j.pce.2026.104337

Veeramalai Gopal , Karuppasamy Manikanda Bharath , Ramamoorthy Ayyamperumal

Microplastic contamination has emerged as a significant threat to urban food systems. Growing evidence demonstrates the presence of microplastics in agricultural soils, irrigation water, compost, and through atmospheric deposition within cities. This article synthesizes current knowledge on the pathways through which microplastics enter urban and peri-urban agriculture, including rooftop farms, community gardens, and wastewater-irrigated systems. Particular emphasis is placed on plant uptake mechanisms, translocation, and accumulation of microplastics in edible tissues, along with the co-transport of toxic additives, heavy metals, and pathogenic microorganisms that may elevate human health risks. Drawing on interdisciplinary studies and case examples from urban areas with varying socioeconomic contexts, potential human exposure routes are examined through dietary intake of contaminated crops, inhalation of airborne particles, and dermal contact. Urban populations are highlighted as being particularly vulnerable to these exposure pathways. The review further evaluates existing monitoring approaches, identifies regulatory gaps, and discusses key uncertainties in current risk assessment frameworks, including challenges in detecting and quantifying microplastics in urban agroecosystems. While complete removal of microplastics from urban agricultural systems is unlikely, long-term reductions are achievable through integrated strategies such as improved waste segregation, compost certification, decentralized wastewater treatment, and the adoption of plastic-free or biodegradable agricultural inputs. Strengthening regulatory frameworks and incorporating microplastic monitoring into educational and extension programs are also recommended.

微塑料污染已成为城市食品系统的重大威胁。越来越多的证据表明，微塑料存在于农业土壤、灌溉水、堆肥以及城市内的大气沉降中。本文综合了目前关于微塑料进入城市和城郊农业的途径的知识，包括屋顶农场、社区花园和废水灌溉系统。特别强调植物吸收机制、微塑料在可食用组织中的易位和积累，以及可能增加人类健康风险的有毒添加剂、重金属和致病微生物的共同运输。根据跨学科研究和来自不同社会经济背景的城市地区的案例，通过饮食摄入受污染的作物、吸入空气中的颗粒和皮肤接触来检查潜在的人类接触途径。城市人口特别容易受到这些接触途径的影响。该综述进一步评估了现有的监测方法，确定了监管缺口，并讨论了当前风险评估框架中的主要不确定性，包括在检测和量化城市农业生态系统中的微塑料方面面临的挑战。虽然不太可能从城市农业系统中完全消除微塑料，但通过综合战略，如改进废物分类、堆肥认证、分散废水处理以及采用无塑料或可生物降解的农业投入物，可以实现长期减少。还建议加强监管框架，并将微塑料监测纳入教育和推广计划。

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引用次数: 0

Bridging data scarcity in groundwater quality studies: A systematic evaluation of statistical and deep learning-based generators 弥合地下水质量研究中的数据短缺：基于统计和深度学习的生成器的系统评估

IF 4.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2026-02-05 DOI: 10.1016/j.pce.2026.104327

C.D. Aju , Bhupendra Bahadur Singh , A.L. Achu , Madhusudan Ingale , Mangesh M. Goswami , M.C. Raicy , L. Elango

Large-scale groundwater quality assessments are often hindered by the limited availability of hydrochemical data. Synthetic data generation provides a means to augment small datasets; however, the reliability of these methods and their implications for predictive modeling remain underexplored in environmental studies, particularly in the context of groundwater sustainability. We systematically evaluated six approaches, including bootstrap sampling, Gaussian noise perturbation, Monte Carlo sampling, SMOGN, CTGAN, and TVAE, using a groundwater quality dataset from southern India. Synthetic datasets were evaluated for their similarity to real data using the Kolmogorov–Smirnov test, the Wasserstein distance, moment differences, Pearson correlation, kernel density estimation plots, and principal component analysis. The practical utility of the synthetic data was evaluated by training a Random Forest model to predict total dissolved solids (TDS) from major ions. The model performance on the real dataset was assessed using R², RMSE, and MAE. Bootstrap delivered near-perfect agreement with the real data (R² = 0.999, NSE = 0.999, RMSE = 41.5 mg L⁻¹), with SMOGN being competitive. Gaussian perturbation was acceptable, while TVAE was moderate. Monte Carlo and CTGAN performed poorly, with negative NSE indicating performance worse than predicting the mean. SHAP-based feature importance analysis confirmed that the best-performing synthetic methods preserved the dominant hydrochemical drivers. Overall, traditional resampling approaches (Bootstrap, SMOGN) outperformed complex deep generative models on small-sample groundwater datasets. This methodology can support risk assessments by improving the accuracy of water-quality predictive models, thereby facilitating effective resource management and pollution control. This study provides practical guidance for assessing and managing groundwater quality by recommending synthetic data augmentation strategies tailored to dataset characteristics, particularly in data-limited regions.

大规模地下水质量评价常常因水化学数据有限而受到阻碍。合成数据生成提供了一种增强小数据集的方法；然而，在环境研究中，特别是在地下水可持续性的背景下，这些方法的可靠性及其对预测模型的影响仍未得到充分探讨。我们系统地评估了六种方法，包括自举采样、高斯噪声扰动、蒙特卡罗采样、SMOGN、CTGAN和TVAE，使用了印度南部的地下水质量数据集。使用Kolmogorov-Smirnov检验、Wasserstein距离、矩差、Pearson相关、核密度估计图和主成分分析来评估合成数据集与真实数据的相似性。通过训练随机森林模型来预测主要离子的总溶解固体（TDS），评估了合成数据的实际效用。使用R2、RMSE和MAE评估模型在真实数据集上的性能。Bootstrap与实际数据接近完美吻合（R2 = 0.999, NSE = 0.999, RMSE = 41.5 mg L−1），SMOGN具有竞争力。高斯扰动是可以接受的，而TVAE是中等的。蒙特卡罗和CTGAN表现不佳，负NSE表明性能比预测平均值更差。基于shap的特征重要性分析证实，性能最好的合成方法保留了主要的水化学驱动因素。总体而言，传统的重采样方法（Bootstrap、SMOGN）在小样本地下水数据集上的表现优于复杂的深层生成模型。这种方法可以通过提高水质预测模型的准确性来支持风险评估，从而促进有效的资源管理和污染控制。本研究通过推荐适合数据集特征的综合数据增强策略，特别是在数据有限的地区，为评估和管理地下水质量提供了实用指导。

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引用次数: 0

Machine learning based evaluation of fluoride contaminated groundwater and health risks in the Pambar River basin, South India 基于机器学习的印度南部潘巴尔河流域氟污染地下水和健康风险评估

IF 4.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2026-02-04 DOI: 10.1016/j.pce.2026.104334

C.N. Sridhar , T. Subramani , Renato Somma , T. Dineshkumar

Excess fluoride in groundwater has become a significant health issue across several regions globally, yet comprehensive studies integrating seasonal variation, human health implications, and predictive modeling remain limited. This study examines fluoride ranges and adverse health effects in groundwater of Pambar River basin, South India, employing machine-learning techniques to classify groundwater based on its aptness for consumption. Totally, 100 groundwater samples were collected during North-East monsoon 2024, Post-monsoon, Pre-Monsoon and South-West Monsoon of 2025. Hydrochemical investigation indicated predominantly alkaline water, with sodium and bicarbonate as dominant ions. The overall mean fluoride concentration, calculated from four seasonal datasets, was 1.38 mg/L, and 43% of samples exceeded the World Health Organization guideline of 1.5 mg/L. Correlation analysis revealed calcium negatively influenced fluoride levels due to Calcium-Fluoride precipitation. Principal Component Analysis explained 64.9%-68.3% of variance, highlighting geochemical processes as primary controls, with secondary influence from agricultural runoff and waste leaching. Entropy-based water quality evaluation revealed 36.75% of samples were safe for drinking, while 58.75% required treatment. Among machine-learning models, support vector machines achieved the best predictive performance, with random forest and extreme gradient boosting also performing well under limited seasonal datasets. Evaluation of Human health hazard indicated potential fluoride-related risks, particularly for children (50%), teens (45%), women (44%), and men (43%). These findings provide a baseline for future groundwater management and underscores the importance of implementing sustainable measures to mitigate fluoride pollution in Pambar's groundwater resources.

地下水中过量氟化物已成为全球多个地区的重大健康问题，但综合季节性变化、人类健康影响和预测模型的综合研究仍然有限。本研究考察了印度南部潘巴尔河流域地下水中的氟化物范围和对健康的不利影响，采用机器学习技术根据其适合消费的程度对地下水进行分类。在2024年东北季候风、后季候风、前季候风和2025年西南季候风期间共采集了100份地下水样本。水化学研究表明，水以碱性为主，钠和碳酸氢盐为主要离子。根据四个季节数据集计算得出的总体平均氟化物浓度为1.38 mg/L， 43%的样本超过了世界卫生组织1.5 mg/L的指导标准。相关分析显示，由于氟化钙沉淀，钙对氟化物水平有负相关影响。主成分分析解释了64.9%-68.3%的差异，强调地球化学过程是主要控制因素，其次是农业径流和废物浸出的影响。基于熵的水质评价结果显示，36.75%的样本可安全饮用，58.75%的样本需要处理。在机器学习模型中，支持向量机的预测性能最好，随机森林和极端梯度增强在有限的季节性数据集下也表现良好。对人类健康危害的评估表明，与氟化物有关的潜在风险，特别是对儿童（50%）、青少年（45%）、妇女（44%）和男子（43%）。这些发现为未来地下水管理提供了基准，并强调了实施可持续措施以减轻Pambar地下水资源中氟化物污染的重要性。

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引用次数: 0

A Maxout-enhanced robust deep convolutional neural network model for flood mapping using Sentinel-1 SAR data 基于maxout增强的基于Sentinel-1 SAR数据的洪水制图鲁棒深度卷积神经网络模型

IF 4.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2026-02-03 DOI: 10.1016/j.pce.2026.104316

Shubham Awasthi , Gopal Singh Phartiyal , Divyesh Varade , Kamal Jain

Timely and accurate flood detection is critical for minimizing loss of life and assessing disaster-induced damage. Remote sensing technologies enable consistent, near–real-time mapping of flood extents, which is essential for effective disaster response and preparedness. Among these, Synthetic Aperture Radar (SAR) data have proven particularly valuable due to their all-weather, day–night imaging capability. This study presents a robust and computationally efficient deep learning framework for flood mapping using Sentinel-1 SAR imagery. Using the Sen1Floods11 benchmark dataset, which encompasses 11 geographically and temporally diverse flood events, we propose DeeplabV3PlusMX (DB-SEN1FloodNet)—a ‘Maxout’ enhanced semantic segmentation model derived from the DeeplabV3+ architecture. The proposed model strategically integrates ‘Maxout’ activation layers at multiple stages of the encoder–decoder pipeline to reduce feature-space redundancy, enhance robustness to speckle noise, and improve generalization across heterogeneous flood scenarios. By enabling adaptive piecewise linear feature representations and synergistic regularization with dropout, the ‘Maxout’ strategy improves discrimination of flooded areas under complex scattering conditions. Experimental results demonstrate strong performance, achieving an overall accuracy of 96%, average precision of 98%, mean recall of 94%, an F1-score of 96%, and a mean Intersection over Union (mIoU) of 65%. Furthermore, the model exhibits superior transferability when evaluated on unseen geographic regions, outperforming existing approaches that are often limited to event- or region-specific training. The reduced reliance on auxiliary datasets further underscores the operational potential of the proposed framework for scalable, global flood monitoring using SAR data.

及时和准确的洪水探测对于尽量减少生命损失和评估灾害造成的损害至关重要。遥感技术能够一致地、近实时地绘制洪水范围图，这对于有效的救灾和备灾至关重要。其中，合成孔径雷达（SAR）数据因其全天候、昼夜成像能力而被证明特别有价值。本研究提出了一个强大的、计算效率高的深度学习框架，用于使用Sentinel-1 SAR图像进行洪水制图。利用Sen1Floods11基准数据集（包含11个地理和时间上不同的洪水事件），我们提出了DeeplabV3PlusMX （DB-SEN1FloodNet）——一种源自DeeplabV3+架构的“Maxout”增强语义分割模型。该模型在编码器-解码器管道的多个阶段有策略地集成了“Maxout”激活层，以减少特征空间冗余，增强对散斑噪声的鲁棒性，并提高异构洪水场景的泛化。通过自适应分段线性特征表示和带dropout的协同正则化，Maxout策略提高了在复杂散射条件下对洪水区域的识别能力。实验结果显示了较强的性能，总体准确率为96%，平均精度为98%，平均召回率为94%，f1分数为96%，平均交叉比（mIoU）为65%。此外，当在不可见的地理区域进行评估时，该模型显示出优越的可转移性，优于现有的通常限于事件或区域特定训练的方法。减少了对辅助数据集的依赖，进一步强调了使用SAR数据进行可扩展的全球洪水监测的拟议框架的操作潜力。

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引用次数: 0

Harnessing vegetation indices and remote sensing to assess the impact of Cyclone Kenneth on banana plantations: Insights from Ngazidja Island (Comoros) 利用植被指数和遥感评估“肯尼斯”气旋对香蕉种植园的影响：来自恩加济加岛（科摩罗）的见解

IF 4.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2026-02-03 DOI: 10.1016/j.pce.2026.104332

Andlia Abdoussalami , Dinesh Madhushanka , Xuesong Zhang , Qi Li , Zhenghua Hu , Abu Reza Md Towfiqul Islam

Tropical cyclones pose major risks to Small Island Developing States (SIDS) and their agriculture. Banana is one of the island's key subsistence and market crops, supporting household food security and local economies. As a vital crop, it holds significant importance both for daily consumption and economic exchange. This study provides the first island-level, crop-specific assessment of Cyclone Kenneth's impact on banana plantations across Ngazidja (Grande Comore). The Normalized Difference Vegetation Index (NDVI) is a spectral index derived from satellite imagery, which quantifies vegetation health. NDVI is calculated as the difference between near-infrared and red reflectance, providing a measure of canopy greenness. In this study, we used seasonally paired NDVI composites to reduce cloud contamination and account for short-term phenological variations between the pre- and post-cyclone imagery. Island-scale land-use analysis revealed a cropland decrease of −18.7% and a bare-land increase of +24.3%, while representative northern banana parcels (contiguous field of banana crops, delineated from high-resolution satellite imagery that is large enough to be represented accurately at a 10-m resolution) experienced severe canopy loss (ΔNDVI ≈ −0.32). Spatial regression identified proximity to the cyclone track, wind speed, and rainfall as the most significant predictors of vegetation loss, amplified in low-elevation and gentle-slope areas. The findings demonstrate the effectiveness of integrating multi-sensor remote sensing with exposure modeling for rapid post-cyclone agricultural damage assessment in data-limited island environments, supporting evidence-based recovery and resilience planning.

热带气旋对小岛屿发展中国家及其农业构成重大威胁。香蕉是岛上主要的生计和市场作物之一，支持家庭粮食安全和当地经济。作为一种重要的农作物，它在日常消费和经济交流中都具有重要意义。这项研究首次在岛屿层面对肯尼斯气旋对Ngazidja（大科摩罗）各地香蕉种植园的影响进行了具体的作物评估。归一化植被指数（NDVI）是一种基于卫星影像的光谱指数，用于量化植被健康状况。NDVI是用近红外反射率和红光反射率之间的差来计算的，提供了冠层绿度的度量。在这项研究中，我们使用季节配对的NDVI复合材料来减少云污染，并解释气旋前和气旋后图像之间的短期物候变化。海岛尺度的土地利用分析显示，耕地减少了−18.7%，裸地增加了+24.3%，而具有代表性的北部香蕉地块（香蕉作物的连片田，由高分辨率卫星图像描绘，大到足以以10 m分辨率精确表示）经历了严重的冠层损失（ΔNDVI≈−0.32）。空间回归发现，接近气旋路径、风速和降雨量是植被损失的最重要预测因子，在低海拔和缓坡地区被放大。研究结果表明，在数据有限的岛屿环境中，将多传感器遥感与暴露建模相结合，可用于气旋后农业灾害快速评估，支持基于证据的恢复和复原力规划。

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引用次数: 0

Chemical equilibrium of solid phases governing phosphorus solubility in intensively cultivated soils of the Indo-Gangetic Plains 印度-恒河平原密集耕作土壤中控制磷溶解度的固相化学平衡

IF 4.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2026-02-03 DOI: 10.1016/j.pce.2026.104313

K.K. Rao , Mandira Barman , S.P. Datta , Debarup Das , V.K. Sharma , Debrup Ghosh , T.K. Das

The problem of phosphorus (P) fixation into insoluble mineral forms is particularly acute in the intensively cultivated Indo-Gangetic Plains (IGP), where decades of fertilizer application have created complex P dynamics. Although earlier studies have examined P availability in different soils, the mechanistic understanding of equilibrium relationships between soil-solution phosphate species and solid P phases under varying pH across the IGP remains limited.

To address this gap, the present study quantitatively assessed the chemical equilibria between soil-solution phosphate (H₂PO₄⁻) and dominant solid P phases by modelling soil solution and solid-phase interactions using geochemical equilibrium approaches across a representative pH gradient. This methodological framework enabled identification of the controlling mineral-phases and their saturation states under different soil reactions.

Results revealed that H₂PO₄⁻ concentrations varied markedly with pH, ranging from 3901 × 10⁻⁶ to 397 × 10⁻⁶ M. Oversaturation of mineral phases such as variscite and K-taranakite was observed, particularly in soils receiving long-term applications of potassium- and ammonium-based phosphatic fertilizers. In acidic soils, P solubility was governed by iron- and aluminium-phosphate equilibria, whereas in neutral to alkaline soils, calcium phosphate phases predominated.

These findings demonstrate that soil pH and fertilizer legacy effects jointly regulate P solubility equilibria and phase transitions across the IGP. The study underscores the necessity of developing management strategies to mobilize the accumulated, less-available P pools through pH-specific interventions. Such approaches can enhance sustainable P use efficiency, reduce fertilizer dependency, and improve long-term crop productivity, contributing valuable insights to regional nutrient management and global P sustainability frameworks.

在集约耕种的印度河-恒河平原（IGP），磷(P)以不溶性矿物形式固定的问题尤其严重，在那里，几十年的施肥造成了复杂的磷动态。虽然早期的研究已经检测了不同土壤的磷有效性，但在不同pH值下，对土壤溶液磷酸盐和固体磷相之间平衡关系的机制理解仍然有限。为了解决这一差距，本研究通过使用地球化学平衡方法模拟土壤溶液和固相之间的相互作用，定量评估了土壤溶液磷酸盐（H2PO4−）和主要固体P相之间的化学平衡。该方法框架能够识别控制矿物相及其在不同土壤反应下的饱和状态。结果表明，H2PO4−浓度随pH值变化显著，范围为3901 × 10−6 ~ 397 × 10−6 m。在长期施用钾基和铵基磷肥的土壤中，观察到variscite和K-taranakite等矿物相的过饱和。在酸性土壤中，磷的溶解度受铁和磷酸铝平衡控制，而在中性至碱性土壤中，磷酸钙相占主导地位。这些发现表明，土壤pH和肥料遗留效应共同调节了磷在IGP中的溶解度平衡和相变。该研究强调了制定管理战略的必要性，通过针对ph值的干预措施来调动积累的、较少可用的P池。这些方法可以提高可持续磷肥利用效率，减少对肥料的依赖，提高作物的长期生产力，为区域养分管理和全球磷肥可持续性框架提供有价值的见解。

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引用次数: 0

Investigating hydrological indices for identification of climate change tipping points in groundwater resources for the Upper Zambezi basin 研究水文指标，以确定上赞比西河流域地下水资源的气候变化临界点

IF 4.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2026-02-02 DOI: 10.1016/j.pce.2026.104318

George Z. Ndhlovu, Kawawa Banda, Christopher Mtonga

Attributes of long-term hydrological regime of a river basin are referred to as Hydrological Indices which can further be used to characterise the river flow behavior with factors including scale, occurrence, period, mastery and rate of change of flow. The focus is to evaluate hydrological indices useful for analysing changes in groundwater quantity and quality arising from impacts of climate change. A larger population in the Upper Zambezi River Basin depend on the groundwater as a source of their rural water supply. Few studies have analysed the hydrological indices from future climate change scenarios for predicting climate change tipping points in Southern Africa. This paper therefore seeks to investigate impacts on groundwater resources by applying climate model projections and hydrological indices. The results show novelty in climate change impact under Representative Concentration Pathways 8.5 scenario that will affect temporal and spatial variability of groundwater. Furthermore, spatial variability of groundwater recharge is predicted to have minimal change in the north of Upper Zambezi River Basin ranging from −12 to – 22% while a huge change is predicted in the southern direction ranging from −31 to −42%. Baseflow across the basin has shown a reduction to a considerable degree while Soil Moisture Index has shown severe dryness in Barotse and Chobe sub catchments. Hydrological indices also show that the future scenario may have reduced groundwater availability. Therefore, the novel results require strategic thinking for adaptation especially in the rural water supplies that largely depend on Groundwater resources.

流域长期水文状况的属性被称为水文指数，它可以进一步用来描述河流的流量行为，包括流量的规模、发生、周期、控制和变化率。重点是评价对分析因气候变化影响而引起的地下水数量和质量变化有用的水文指数。赞比西河上游流域的大量人口依赖地下水作为农村供水的来源。很少有研究分析了来自未来气候变化情景的水文指数，以预测南部非洲的气候变化临界点。因此，本文试图利用气候模式预估和水文指数来研究对地下水资源的影响。结果表明，代表性浓度路径8.5情景下气候变化对地下水时空变异性的影响具有新颖性。在赞比西河上游流域北部，地下水补给的空间变异性变化最小，变化幅度在- 12% ~ - 22%之间，而在南部方向变化较大，变化幅度在- 31% ~ - 42%之间。整个流域的基流显示出相当程度的减少，而土壤湿度指数显示Barotse和Chobe流域严重干旱。水文指数还表明，未来的情景可能会减少地下水的可用性。因此，新的结果需要对适应进行战略思考，特别是在很大程度上依赖地下水资源的农村供水中。

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引用次数: 0