Journal of Atmospheric and Solar-Terrestrial Physics最新文献_第8页

Physicochemical analysis and source apportionment of PM1.0 and PM2.5 in Harbin 哈尔滨市PM1.0和PM2.5的理化分析及来源解析

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-01-01 Epub Date: 2025-12-27 DOI: 10.1016/j.jastp.2025.106711

Likun Huang , Zhouyu Guo , Yan Wang , Guangzhi Wang , Dongdong Wang , Jingyi Zhang , Xinyu Feng

Fine particulate matter (PM_1.0 and PM_2.5) warrants concern due to its health impacts and atmospheric reactivity, yet their source contributions and seasonal variations remain unclear. To investigate their characteristics, sources, and interrelationships, we collected concurrent PM_1.0 and PM_2.5 samples at an urban site in Harbin from November 2014 to November 2015. The samples were analyzed for mass concentrations, water-soluble ions via ion chromatography, inorganic elements using ICP-OES/ICP-MS, particle morphology by scanning electron microscopy (SEM), and source apportionment using the Positive Matrix Factorization (PMF) model. The results showed that PM_1.0 and PM_2.5 exhibited similar seasonal trends, with the highest concentrations in winter and the lowest in summer. PM_1.0 accounted for 60–90 % of PM_2.5, indicating that most pollutants were concentrated in the finer fraction. Major water-soluble ions included SO₄²⁻, NO₃⁻, and NH₄⁺, suggesting significant secondary aerosol formation. Inorganic element analysis revealed elevated concentrations of crustal elements such as Al, Ca, Fe, K, Mg, Na, and Si, pointing to soil dust as a major contributor. SEM observations showed that particles were predominantly irregular mineral grains and spherical fly ash, indicating contributions from both soil dust and coal combustion. PMF source apportionment further revealed distinct seasonal patterns: soil dust and industrial emissions were dominant in spring and summer, while biomass burning, vehicle exhaust, and coal combustion were the primary sources in autumn and winter. This study provides scientific evidence and technical support for targeted fine particulate pollution control and regional air quality management in Harbin and other similar cold-climate cities.

细颗粒物（PM1.0和PM2.5）由于其健康影响和大气反应性值得关注，但其来源贡献和季节变化尚不清楚。为了研究它们的特征、来源和相互关系，我们于2014年11月至2015年11月在哈尔滨的一个城市站点同时采集了PM1.0和PM2.5样本。通过离子色谱分析样品的质量浓度、水溶性离子、ICP-OES/ICP-MS分析样品的无机元素、扫描电子显微镜（SEM）分析样品的颗粒形态，并使用正矩阵分解（PMF）模型分析样品的来源。结果表明：PM1.0和PM2.5具有相似的季节变化趋势，冬季浓度最高，夏季浓度最低；PM1.0占PM2.5的60 - 90%，说明大部分污染物集中在细颗粒物中。主要的水溶性离子包括SO42−、NO3−和NH4+，表明有重要的二次气溶胶形成。无机元素分析显示，地壳元素如Al、Ca、Fe、K、Mg、Na和Si的浓度升高，表明土壤粉尘是主要因素。扫描电镜观察表明，颗粒主要为不规则矿物颗粒和球形粉煤灰，表明土壤粉尘和煤炭燃烧都有贡献。春季和夏季以土壤扬尘和工业排放为主，秋季和冬季以生物质燃烧、机动车尾气和燃煤为主。本研究为哈尔滨市及类似寒冷气候城市细颗粒物污染定向控制和区域空气质量管理提供了科学依据和技术支撑。

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引用次数: 0

Framework for the lightning risk assessment over India – a case study over a peninsular state 印度雷电风险评估框架——对一个半岛国家的案例研究

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-12-01 Epub Date: 2025-11-15 DOI: 10.1016/j.jastp.2025.106680

Nambi Manavalan Rajan , Alok Taori , Degala Venkatesh , M. Mallikarjun , Sameer Saran , Rajiv Kumar , Dhiroj Kumar Behra , Goru Srinivasa Rao , Prakash Chauhan

Cloud-to-ground lightning is recognized as a major weather hazard in India, with mortality and losses persisting. A reproducible lightning-risk framework for India is developed and demonstrated for the peninsular state of Andhra Pradesh, using lightning-occurrence data together with topography from CartoDEM, land cover from NRSC's LULC, and socio-economic and infrastructure indicators derived from SECC-2011 and OpenStreetMap. Guided by the UNDRR hazard–exposure–vulnerability concept and FEMA's National Risk Index factorization, the study combines a Lightning Hazard Index (LHI) and a six-factor Lightning Vulnerability Index (LVI) to generate seasonal Lightning Risk Index (LRI) maps. Hazard mapping reveals a monsoon concentration along the north-coastal corridor, a post-monsoon southward shift, and minimal winter risk, while vulnerability peaks along the urban–industrial chain and within the Krishna–Godavari deltas. These season-resolved, decision-ready LRI maps are expected to be highly useful for targeted lightning protection, early-warning placement, and community preparedness. The proposed framework offers a transferable model for lightning risk mapping across India, supporting climate-aware disaster mitigation strategies.

云对地闪电被认为是印度主要的天气灾害，造成的死亡和损失持续存在。本文针对印度安得拉邦半岛开发并演示了一个可复制的雷电风险框架，该框架使用了雷电发生数据、来自CartoDEM的地形数据、来自NRSC的LULC的土地覆盖数据以及来自SECC-2011和OpenStreetMap的社会经济和基础设施指标。该研究以联合国减灾中心的灾害暴露脆弱性概念和联邦应急管理局的国家风险指数分解为指导，结合了闪电危害指数（LHI）和六因素闪电脆弱性指数（LVI），生成了季节性闪电风险指数（LRI）地图。灾害地图显示，季风集中在北部沿海走廊，季风后向南移动，冬季风险最小，而脆弱性在城市-产业链和克里希纳-戈达瓦里三角洲地区达到峰值。这些随季节变化、可随时做出决策的LRI地图预计将对有针对性的雷电防护、早期预警安置和社区准备非常有用。拟议的框架为整个印度的闪电风险测绘提供了一个可转移的模型，支持具有气候意识的减灾战略。

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引用次数: 0

An analysis of the impacts of meteorological factors on ozone concentration using generalized additive model in Tianjin, China 利用广义加性模式分析气象因子对天津地区臭氧浓度的影响

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-12-01 Epub Date: 2025-10-24 DOI: 10.1016/j.jastp.2025.106669

Xu Zhang , Chang Liu , Yixin Liu , Xumei Yuan

Ozone (O₃) pollution in China is an increasingly serious problem and Tianjin experiences O₃ pollution as its predominant air quality challenge. An analysis of data from 2018 to 2022 indicated that the level of O₃ pollution in Tianjin showed a trend of first decreasing and then increasing. The monthly distribution of O₃ concentration and the number of O₃ exceeding the standard day in Tianjin showed a single peak trend in June. Based on the ground meteorological data and air quality data in Tianjin from 2018 to 2022, the study developed a time-phased generalized additive model (GAM) of meteorological factors (temperature, relative humidity, sunshine duration, pressure, precipitation and wind speed) in Tianjin to analyze their specific effects on O₃ concentration. The results showed that the GAM had a high quality and effectively captured the complex nonlinear relationship between O₃ and meteorological factors. Seasonal differences were identified in the relationship between O₃ concentration and different meteorological factors in Tianjin. Notably, temperature was the dominant meteorological factor affecting O₃ concentration change in Tianjin. The interaction of high temperature and medium relative humidity was highly correlated with O₃ concentration in Tianjin in summer. The research results are helpful to clarify the influence of meteorological conditions in different seasons on O₃ concentration change in Tianjin. It is of great significance for the accurate prediction of O₃ pollution and the formulation of pollution prevention and control policies according to local conditions.

臭氧（O3）污染在中国日益严重，天津的主要空气质量挑战是O3污染。2018 - 2022年数据分析表明，天津市O3污染水平呈现先下降后上升的趋势。6月天津市O3浓度和O3超标日数的月分布呈单峰趋势。基于天津市2018 - 2022年地面气象资料和空气质量数据，建立了天津市气温、相对湿度、日照时数、气压、降水、风速等气象因子的时间阶段广义加性模型（GAM），分析其对O3浓度的具体影响。结果表明，GAM具有较高的质量，能有效地捕捉到O3与气象因子之间复杂的非线性关系。天津市不同气象因子对O3浓度的影响存在季节差异。气温是影响天津市O3浓度变化的主要气象因子。天津夏季高温和中等相对湿度的交互作用与O3浓度高度相关。研究结果有助于阐明不同季节气象条件对天津市O3浓度变化的影响。对准确预测O3污染，因地制宜制定污染防治政策具有重要意义。

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引用次数: 0

Research on hourly precipitation prediction along railways based on ERA5 reanalysis and post-processing correction 基于ERA5再分析及后处理校正的铁路沿线逐时降水预报研究

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-12-01 Epub Date: 2025-10-25 DOI: 10.1016/j.jastp.2025.106666

Xiangshun Meng , Yong Wang , Yunlong Zhang , Chengwu Yang , Chen Chang , Haozhe Chi , Yanping Liu

Global climate change has intensified extreme precipitation events, highlighting the urgent need for high-precision short-term rainfall forecasts to ensure railway transportation safety. However, existing meteorological monitoring remains limited by sparse station distribution, observational blind spots, and data inaccuracies. Global reanalysis datasets are hindered by low spatial resolution and precipitation underestimation, while numerical weather prediction models, typically with spatial resolutions exceeding 10 km, cannot satisfy the kilometer-scale disaster prevention demands along railway corridors. To address these limitations, we propose an “FFT–LSTM + post-processing correction” framework, which combines Fast Fourier Transform (FFT) and Long Short-Term Memory (LSTM) networks to extract nonlinear temporal characteristics of precipitation evolution from multivariate meteorological variables. The model further refines precipitation predictions through post-processing correction methods, including Simple Linear Regression (SLR), enhanced Piecewise Linear (PL), and Quantile Mapping (QM). FFT is initially employed to identify the best common period (143 h) among the inputs, guiding the optimal LSTM input window length. Subsequently, tailored correction strategies are applied according to rainfall intensity levels to improve prediction accuracy. Validation based on Meiyu-season data from four representative stations along the Guangzhou–Zhanjiang railway confirms that the proposed approach significantly enhances prediction skill. In hourly predictions, the Probability of Detection (POD) for moderate, heavy, and torrential rainfall reaches 0.562, 0.625, and 0.500, respectively; the Critical Success Index (CSI) for torrential rainfall peaks at 1.0, and the False Alarm Rate (FAR) is reduced to 0.000—indicating substantial gains over baseline models such as ARIMA and XGBoost (CSI <0.08). This study effectively integrates deep learning and statistical correction techniques to overcome key limitations of reanalysis data, providing high-precision support for short-term precipitation forecasting along railways and thereby supporting meteorological disaster mitigation and transportation safety decision-making.

全球气候变化加剧了极端降水事件，凸显了对高精度短期降水预报的迫切需求，以确保铁路运输安全。然而，现有的气象监测仍然受到站点分布稀疏、观测盲点和数据不准确的限制。全球再分析数据集受到低空间分辨率和降水低估的阻碍，而空间分辨率通常超过10 km的数值天气预报模式无法满足铁路走廊千米尺度的防灾需求。为了解决这些问题，我们提出了一个“FFT - LSTM +后处理校正”框架，该框架结合了快速傅里叶变换（FFT）和长短期记忆（LSTM）网络，从多元气象变量中提取降水演变的非线性时间特征。该模型通过简单线性回归（SLR）、增强分段线性（PL）和分位映射（QM）等后处理校正方法进一步细化降水预测。最初采用FFT识别输入间的最佳共同周期（143 h），指导LSTM输入窗口的最佳长度。随后，根据降雨强度等级，采用有针对性的校正策略，提高预报精度。基于广湛铁路沿线4个代表性站点梅雨季节数据的验证表明，该方法显著提高了预测能力。逐时预报中、强、暴雨的探测概率（POD）分别为0.562、0.625和0.500；暴雨的关键成功指数（CSI）达到峰值1.0，虚警率（FAR）降至0.000，这表明与ARIMA和XGBoost （CSI <0.08）等基线模型相比有了实质性的进步。本研究有效结合深度学习和统计校正技术，克服再分析数据的关键局限性，为铁路沿线短期降水预报提供高精度支持，从而为气象减灾和交通安全决策提供支持。

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引用次数: 0

Variability in the thermospheric neutral mass density: A multiple model comparison 热层中性质量密度的变率：多模式比较

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-12-01 Epub Date: 2025-09-12 DOI: 10.1016/j.jastp.2025.106630

Yongliang Zhang, Patrick B. Dandenault, Larry J. Paxton, Robert Schaefer, Clayton Cantrall, Hyosub Kil, Rafael Mesquita, Matthew E. Zuber

Variability in the thermospheric neutral mass density in LEO/VLEO altitudes has been investigated using outputs from five models (MSIS2.0, HASDM, WACCM-X, TIEGCM, and WAM-IPE) under different geophysical conditions: geomagnetically quiet, moderate storm and super storm. These models are selected to represent empirical, assimilation, and physics-based methods. We compared the global neutral mass density distribution and the time variations in the densities using equatorial and polar orbits at three fixed LEO/VLEO altitudes (100, 200, and 300 km) from the five models. Our key findings from the analyses are: (1) there are significant systematic biases among the model results; (2) WACCM-X, TIEGCM and HASDM peak densities are roughly consistent with each other during a super storm. However, their UT differences are up to a half day; (3) WAM-IPE and MSIS-2.0 models tend to give lower densities than other models; (4) the geomagnetic activity impact on neutral mass densities increases with altitude and it is negligible at 100 km altitude, becomes evident at 150 km, and is significant at 200 km; (5) geomagnetic storms tend to reduce the biases among the model densities The systematic biases among models are likely due to the different parameterizations, drivers and boundary conditions used in the models. A systematic evaluation of the models using multiple and cross-calibrated ground truth data sets is needed to fully address the biases and offer the insight required to improve the models.

利用5种模式（MSIS2.0、HASDM、WACCM-X、TIEGCM和WAM-IPE）在不同地球物理条件下（地磁安静、中风暴和超级风暴）的输出，研究了低空/超低空热层中性质量密度的变化。选择这些模型来代表经验、同化和基于物理的方法。我们比较了5种模式在3个固定的LEO/VLEO高度（100、200和300 km）使用赤道和极地轨道的全球中性质量密度分布和密度的时间变化。分析的主要发现有：(1)模型结果存在显著的系统偏差；(2)超级风暴期间WACCM-X、TIEGCM和HASDM的峰值密度基本一致。然而，他们的UT差异高达半天；(3) WAM-IPE和MSIS-2.0模型给出的密度较其他模型低；(4)地磁活动对中性质量密度的影响随海拔高度增大而增大，在100 km高度可忽略，在150 km高度显著，在200 km高度显著；(5)地磁风暴会减小模型密度间的偏差，而模型间的系统偏差可能是由于模型所采用的参数化、驱动因素和边界条件不同造成的。需要使用多个和交叉校准的地面真值数据集对模型进行系统评估，以充分解决偏差并提供改进模型所需的洞察力。

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引用次数: 0

Quantization of natural energy pathways in space 空间自然能量路径的量子化

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-12-01 Epub Date: 2025-10-30 DOI: 10.1016/j.jastp.2025.106672

Andrei Moldavanov

A phenomenon of energy transfer in space through a natural energy infrastructure shaped by confinement of the parameters of energy exchange is considered. The infrastructure consists of two interconnected energy spectra intrinsically stemming from the suggested model of an open thermodynamic system. The spectra are based on innate limitation imposed on the efficiency of energy exchange (primary spectrum) and the net passing energy (secondary spectrum). In this context, the primary spectrum creates the quantitative basis for realization of solution in the points of equilibrium, whereas the secondary spectrum forms the guiding energy tubes (GET) for bidirectional transfer between the distant energy points. According to the discussing theory, the family of GETs may be taken as the pathways for energy transfer with the observable signatures of spontaneous shaping, folding, and invariancy. Fundamentally, the conducted simulation for the event of energy transfer in the magnetosphere reveals the existence of connections between GET and the well-known magnetic flux tube, with the major advantage of GET in the more universal character of the underlying theory.

考虑了能量交换参数限制形成的自然能量基础设施在空间中的能量转移现象。基础结构由两个相互连接的能谱组成，这些能谱本质上源于一个开放热力学系统的建议模型。光谱是基于对能量交换效率（一次光谱）和净通过能量（二次光谱）的固有限制。在这种情况下，一次光谱为平衡点解的实现提供了定量基础，而二次光谱则形成了引导能量管（GET），用于远距离能量点之间的双向传递。根据讨论理论，get族可以作为能量传递的途径，具有可观察到的自发成形、折叠和不变性特征。从根本上说，对磁层中能量传递事件的模拟揭示了GET与众所周知的磁通管之间存在联系，而GET的主要优势在于其基础理论具有更普遍的特征。

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引用次数: 0

Spatially heterogeneous wetting and climatic drivers of precipitation variability in arid and semi-arid Northwest China since 1960 1960年以来中国西北干旱半干旱地区降水变异的空间非均质湿润和气候驱动因素

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-12-01 Epub Date: 2025-09-12 DOI: 10.1016/j.jastp.2025.106632

Hao Wu , Xinyan Li , Zuohui Cai , Yao Chen

This study analyzes the spatiotemporal evolution of precipitation across the arid and semi-arid regions of Northwest China from 1960 to 2020, focusing on long-term trends, regional disparities, and climatic drivers. Piecewise linear regression reveals a sharp wetting transition around 2000, characterized by rising precipitation frequency and intensity. However, this trend is spatially heterogeneous. Fuzzy clustering reveals four distinct change patterns that correspond with key geographic subregions. Before 2000, increases were concentrated in the arid Northern Tianshan (NT) and Tarim Basin (TB); after 2000, semi-arid Northeastern Tibetan Plateau (NETP) and Loess Plateau (LP) became dominant contributors. Precipitation become more seasonally balanced, potentially easing drought stress. Yet, extreme precipitation events have intensified, particularly in arid regions, posing escalating risks to the fragile ecosystems. Slow feature analysis isolates dominant low-varying modes, revealing that NT and NETP are primarily influenced by El Niño-Southern Oscillation, with a two-year lag in NETP. LP is modulated by the East Asian summer monsoon. TB is predominantly affected by the Eurasian wave train pattern and equatorial Indian Ocean sea surface temperature anomalies. These results highlight the complex and regionally varied hydroclimatic change across Northwest China, urgently calling for tailored adaptation and water management strategies.

本文分析了1960 - 2020年中国西北干旱半干旱区降水的时空演变特征，重点分析了长期趋势、区域差异和气候驱动因素。分段线性回归表明，2000年前后出现了急剧的湿润过渡，降水频率和强度均有所上升。然而，这种趋势在空间上是异质的。模糊聚类揭示了与关键地理分区相对应的四种不同的变化模式。降水变得更加季节性平衡，可能缓解干旱压力。然而，极端降水事件有所加剧，特别是在干旱地区，对脆弱的生态系统构成了越来越大的风险。慢特征分析分离出主要的低变化模式，揭示了NT和NETP主要受El Niño-Southern振荡的影响，NETP滞后2年。低压受东亚夏季风调制。结核主要受欧亚波列型和赤道印度洋海面温度异常的影响。这些结果凸显了中国西北地区水文气候变化的复杂性和区域性差异，迫切需要制定有针对性的适应和水管理策略。

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引用次数: 0

Drought and flood evolution characteristics during winter rapeseed season based on the Z-index in Hubei Province, China 基于z指数的湖北省冬季油菜籽季旱涝演变特征

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-12-01 Epub Date: 2025-09-22 DOI: 10.1016/j.jastp.2025.106646

Yaotian Tian, Enhao Zhang, Yongyuan Huang, Ming Huang, Haoran Shi, Hui Chen

Analyzing drought and flood characteristics is of great significance to ensure production security, disaster prevention, and mitigation. Hubei Province, as a major rapeseed-producing area in China, is crucial for the nation's edible oil supply. However, there were no studies on drought and flood characteristics during rapeseed season in Hubei Province. To study the drought and flood evolution, daily meteorological data from 28 surface weather stations in Hubei Province during the rapeseed season from 1960 to 2019 was adopted to calculate the Z-index. The Mann-Kendall trend test and wavelet analysis were employed. The results indicated that precipitation at the seedling, flowering, ripening, and whole growth stages of rapeseed generally decreased by 4.79, 2.14, 2.58, and 7.38 mm (10 yr)⁻¹, respectively. Precipitation at the budding stage of rapeseed increased by 1.74 mm (10 yr)⁻¹. Based on the Mann-Kendall trend test, the mutation of precipitation at the seedling, budding, flowering, ripening, and whole growth stages of rapeseed began in 1987, 1983, 1961, 1960, and 1968, respectively. Precipitation at the seedling, ripening, and whole growth stages decreased from south to north across Hubei Province. From southeast to northwest of Hubei Province, precipitation at the budding and flowering stages decreased. A shift from drought to flood was observed at the budding stage, whereas a shift from flood to drought occurred at the other stages. Severe drought (15.0 %) and mild drought (20 %) occurred most frequently at the flowering stage. Severe flood occurred most often at the budding stage (16.7 %), moderate flood at the ripening stage (13.3 %), and mild flood at the seedling stage (13.3 %). According to the wavelet analysis, the cycles of drought and flood disasters at the seedling, budding, flowering, ripening, and whole growth stages were 28, 23, 14, 18, and 7 years, respectively. This study provides a scientific basis for predicting drought and flood disasters and guiding rapeseed production in Hubei Province.

分析旱涝特征对保障生产安全、防灾减灾具有重要意义。湖北省作为中国主要的油菜籽产区，对全国的食用油供应至关重要。但目前尚无对湖北省油菜季旱涝特征的研究。利用1960 - 2019年湖北省28个地面气象站的油菜籽季逐日气象资料，计算z指数，研究旱涝演变。采用Mann-Kendall趋势检验和小波分析。结果表明：油菜籽苗期、开花期、成熟期和全生育期降水总体上分别减少4.79、2.14、2.58和7.38 mm (10 yr)−1；油菜出芽期降水量增加1.74 mm (10 yr)−1。根据Mann-Kendall趋势检验，油菜苗期、出芽期、开花期、成熟期和全生育期降水分别在1987年、1983年、1961年、1960年和1968年发生突变。苗期、成熟期和全生育期降水量由南向北递减。湖北从东南向西北，出芽期和开花期降水呈减少趋势。出芽期由干旱向洪水转变，其他阶段由洪水向干旱转变。重度干旱（15.0%）和轻度干旱（20%）发生在花期。出芽期多发生重度洪水（16.7%），成熟期多发生中度洪水（13.3%），苗期多发生轻度洪水（13.3%）。根据小波分析，苗期、出芽期、花期、成熟期和全生育期的旱涝灾害周期分别为28年、23年、14年、18年和7年。该研究为预测湖北省旱涝灾害和指导油菜籽生产提供了科学依据。

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引用次数: 0

Time series analysis of the impact of global warming on Türkiye 全球变暖对土壤影响的时间序列分析

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-12-01 Epub Date: 2025-09-22 DOI: 10.1016/j.jastp.2025.106647

Arif Ozbek, Mehmet Bilgili

According to the assessments of the Intergovernmental Panel on Climate Change (IPCC), Türkiye, located within the Mediterranean basin, is among the regions most susceptible to the adverse impacts of climate change. This heightened vulnerability is largely attributed to its geographic location, climatic characteristics, and socio-economic structure, which together amplify the risks associated with rising temperatures and increasing climate variability. In the present study, monthly mean air temperature data for Türkiye, recorded by the Turkish State Meteorological Service between 1970 and 2022 (TSMS dataset), were analyzed in combination with reanalysis-based satellite observations obtained from the ERA5 (ERA5 dataset). These historical records formed the foundation for developing temperature projections extending to the year 2050. To achieve this, two complementary time-series forecasting approaches were applied: the Long Short-Term Memory (LSTM) deep-learning model, known for its ability to capture nonlinear dependencies and long-range temporal patterns, and the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model, a classical statistical method suitable for handling seasonality and trend components in climate data. The projection results revealed that Türkiye's mean temperature anomaly relative to the 1970–1980 baseline period is expected to rise by approximately 2.52 °C when based on in-situ observational data, and by about 3.48 °C when derived from ERA5 reanalysis estimates. These findings consistently indicate a significant warming trajectory, regardless of the dataset or modeling approach applied.

根据政府间气候变化专门委员会（IPCC）的评估，位于地中海盆地内的t rkiye是最容易受到气候变化不利影响的地区之一。这种脆弱性的增加主要归因于其地理位置、气候特征和社会经济结构，这些因素共同放大了与气温上升和气候变率增加相关的风险。在本研究中，结合ERA5 （ERA5数据集）获得的基于再分析的卫星观测数据，分析了土耳其国家气象局1970 - 2022年记录的 rkiye月平均气温数据（TSMS数据集）。这些历史记录为发展到2050年的温度预测奠定了基础。为了实现这一目标，采用了两种互补的时间序列预测方法：长短期记忆（LSTM）深度学习模型，以其捕获非线性依赖关系和长期时间模式的能力而闻名，以及季节自回归综合移动平均（SARIMA）模型，这是一种经典的统计方法，适用于处理气候数据中的季节性和趋势成分。预估结果显示，与1970-1980年基线期相比，基于原位观测资料的 rkiye平均温度距平预计将上升约2.52℃，而基于ERA5再分析估计的距平预计将上升约3.48℃。无论采用何种数据集或建模方法，这些发现一致表明一个显著的变暖轨迹。

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引用次数: 0

Stacked hybridization of deep learning model with grey wolf optimization for accurate and explainable reference evapotranspiration 深度学习模型与灰狼优化的叠加杂交，以获得准确和可解释的参考蒸散发

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-12-01 Epub Date: 2025-10-08 DOI: 10.1016/j.jastp.2025.106655

Truptimayee Suna , Bibhuti Bhusan Sahoo , Dipali Pawar , Nand Lal Kushwaha , Pradosh Kumar Paramaguru , P.S. Brahmanand , Himani Bisht

Accurate estimation of reference evapotranspiration (ET₀) is essential for effective irrigation scheduling and water resource management, particularly in data-scarce regions such as India, which lack advanced automatic meteorological stations. The present study developed a hybrid model (DNN-GWO) and conducted an in-depth evaluation against standalone data-driven models, including Random Forest (RF), Support Vector Machine (SVM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Deep Belief Network (DBN) for forecasting monthly ET₀ in the Upper Ganga canal command region, Uttar Pradesh, India. Three input scenarios were evaluated for their correlation to ET₀ estimation. The results revealed that the DNN model showed the best performance in all three scenarios, achieving R² = 0.958, RMSE = 0.076 mm/day, NSE = 0.954, RMSLE = 0.024, MAE = 0.055, MBE = 0.012, MSRE = 0.032, and EVS = 0.987 with solar radiation (Rs), wind speed (U), maximum temperature (Tmax), minimum temperature (Tmin), and relative humidity (RH) as inputs. The developed hybrid DNN-GWO model further improved predictive accuracy, with R² = 0.992, RMSE = 0.0317 mm/day, NSE = 0.99, RMSLE = 0.023, MAE = 0.054, MBE = 0.018, and EVS = 0.992, reducing RMSE by nearly 60 % compared to the best-performing standalone DNN. SHapley Additive explanations (SHAP) analysis revealed that temperature and solar radiation were the most influential predictors of ET₀, while the model also provided stable predictions across different input scenarios, demonstrating robustness in data-limited conditions. The developed hybrid framework, by combining deep learning, swarm intelligence, and explainability, provides a robust, accurate, and interpretable solution for agricultural water management in data-constrained environments.

准确估算参考蒸散发（ET0）对于有效的灌溉调度和水资源管理至关重要，特别是在印度等缺乏先进自动气象站的数据稀缺地区。本研究开发了一个混合模型（DNN- gwo），并对独立的数据驱动模型进行了深入评估，包括随机森林（RF）、支持向量机（SVM）、深度神经网络（DNN）、循环神经网络（RNN）和深度信念网络（DBN），用于预测印度北方邦上恒河运河指挥地区的月度ET0。评估了三种输入情景与ET0估计的相关性。结果表明，以太阳辐射（Rs）、风速(U)、最高温度（Tmax）、最低温度（Tmin）和相对湿度（RH）为输入，DNN模型在3种情景下均表现最佳，R2 = 0.958, RMSE = 0.076 mm/day, NSE = 0.954, RMSLE = 0.024, MAE = 0.055, MBE = 0.012, MSRE = 0.032, EVS = 0.987。所开发的混合DNN- gwo模型进一步提高了预测精度，R2 = 0.992, RMSE = 0.0317 mm/day, NSE = 0.99, RMSLE = 0.023, MAE = 0.054, MBE = 0.018, EVS = 0.992，与表现最好的独立DNN相比，RMSE降低了近60%。SHapley加性解释（SHAP）分析表明，温度和太阳辐射是最具影响力的ET0预测因子，而该模型在不同输入情景下也能提供稳定的预测，在数据有限的条件下表现出鲁棒性。开发的混合框架结合了深度学习、群体智能和可解释性，为数据受限环境下的农业水资源管理提供了强大、准确和可解释的解决方案。

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