Aeolian Research最新文献_第2页

Machine learning-based variable importance analysis of soil factors influencing wind erodibility and threshold wind velocity in Central Iran 伊朗中部影响风可蚀性和阈值风速的土壤因子的机器学习变重要度分析

IF 3.4 3区地球科学 Q2 GEOGRAPHY, PHYSICAL

Aeolian Research

Pub Date : 2025-12-16 DOI: 10.1016/j.aeolia.2025.101026

Nilofar Vakili , Shuai Zhao , Shamsollah Ayoubi , Ana Maria Tarquis Alfonso

Wind erosion is a major driver of land degradation in arid and semi-arid regions, posing significant threats to soil fertility, ecosystem health, and agricultural productivity. This study aimed to identify the most influential soil properties governing wind erodibility and threshold wind velocity in Central Iran, using a combination of wind tunnel experiments, statistical analyses, and machine learning models. Forty surface soil samples were collected and characterized for physical and chemical attributes, including texture, organic matter, gypsum, EC, sodium absorption ratio (SAR), and mean weight diameter (MWD). Wind erosion parameters were quantified through controlled wind tunnel simulations. Multiple linear regression (MLR) and Random Forest (RF) models were developed to predict sediment yield and threshold Wind velocity. Results revealed that gypsum content, SAR, EC, and MWD were dominant in explaining sediment yield, while clay content, CaCO₃, and shear strength significantly influenced the threshold wind velocity. RF outperformed MLR for threshold velocity prediction (R² = 0.83), whereas MLR provided higher accuracy for sediment yield estimation (R² = 0.81). These findings demonstrate the potential of machine learning in modeling complex soil–wind interactions and provide valuable insights for targeted soil conservation strategies in vulnerable landscapes.

风蚀是干旱和半干旱地区土地退化的主要驱动因素，对土壤肥力、生态系统健康和农业生产力构成重大威胁。本研究旨在通过风洞实验、统计分析和机器学习模型的结合，确定伊朗中部控制风可蚀性和阈值风速的最具影响力的土壤特性。采集了40个表层土壤样品，并对其理化性质进行了表征，包括质地、有机质、石膏、EC、钠吸收比（SAR）和平均重径（MWD）。通过可控风洞模拟，对风蚀参数进行了量化。建立了多元线性回归（MLR）和随机森林（RF）模型来预测产沙量和阈值风速。结果表明，石膏含量、SAR、EC和MWD是影响产沙量的主要因素，而粘土含量、CaCO3和抗剪强度对阈值风速有显著影响。RF在阈值速度预测上优于MLR (R2 = 0.83)，而MLR在产沙量估计上具有更高的准确性（R2 = 0.81）。这些发现证明了机器学习在模拟复杂土壤-风相互作用方面的潜力，并为脆弱景观中有针对性的土壤保持策略提供了有价值的见解。

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引用次数: 0

An updated Irwin sensor for measurement of surface shear velocity 用于测量表面剪切速度的更新的欧文传感器

IF 3.4 3区地球科学 Q2 GEOGRAPHY, PHYSICAL

Aeolian Research

Pub Date : 2025-12-11 DOI: 10.1016/j.aeolia.2025.101024

Nancy P. Ziegler , Nicholas P. Webb , Brandon L. Edwards , George Nikolich , John A. Gillies , Sahil G. Patel , Brandi Wheeler , Pei Zhang , Justin W. Van Zee , Sandra L. LeGrand , Andrew C. Trautz

Accurate and efficient collection of field data related to aeolian processes is critical for improving wind erosion predictions and related management decisions. The Irwin sensor (Irwin, 1981) has been used in numerous wind tunnel and field studies to indicate surface shear velocity. However, the sensitivity of the sensor makes them difficult to maintain in a range of environmental conditions (e.g., moisture/high sand flux). This study presents a new generation of Irwin sensor incorporating updated electronics, battery operation, wireless data transmission, and streamlined field deployment and removal. A total of 20 sensors were manufactured and calibrated in a wind tunnel at the Engineer Research and Development Center (ERDC). A subset of the sensors was calibrated using a PI-SWERL, which confirmed the two calibration methods converge on similar values for flat smooth test surfaces. The updated sensors were installed around a mesquite shrub at the Jornada Experimental Range, New Mexico, USA from February to July 2023. We found that initial data from the sensors accurately captured spatial patterns of surface shear velocity surrounding the shrub. The improvements to the sensor reduced workload for both deployment and maintenance, and reduced disturbance at the field site. We discuss potential opportunities to use the improved sensor network in a range of geomorphological research areas including quantifying aeolian sediment transport, building and parameterizing wind erosion models that incorporate spatial dependencies, and improving predictive tools for landform change.

准确和有效地收集与风蚀过程有关的现场数据对于改进风蚀预测和相关管理决策至关重要。Irwin传感器（Irwin, 1981）已在许多风洞和现场研究中使用，以指示表面剪切速度。然而，传感器的灵敏度使得它们难以在一系列环境条件下保持（例如，潮湿/高砂通量）。该研究提出了新一代Irwin传感器，该传感器集成了更新的电子设备、电池操作、无线数据传输以及简化的现场部署和拆卸。在工程师研究与发展中心（ERDC）的风洞中，总共制造和校准了20个传感器。使用PI-SWERL对传感器子集进行了校准，这证实了两种校准方法在平坦光滑测试表面上收敛到相似的值。更新后的传感器于2023年2月至7月安装在美国新墨西哥州Jornada实验靶场的豆科灌木周围。我们发现传感器的初始数据准确地捕获了灌木周围表面剪切速度的空间格局。传感器的改进减少了部署和维护的工作量，并减少了现场的干扰。我们讨论了在一系列地貌研究领域中使用改进的传感器网络的潜在机会，包括量化风沙输运，建立和参数化包含空间依赖性的风蚀模型，以及改进地形变化的预测工具。

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引用次数: 0

Electrical charges play a role in the dust-lifting and deposition processes in cultivated peatlands 电荷在泥炭地扬尘和沉积过程中起着重要作用

IF 3.4 3区地球科学 Q2 GEOGRAPHY, PHYSICAL

Aeolian Research

Pub Date : 2025-12-02 DOI: 10.1016/j.aeolia.2025.101014

Charles Frenette-Vallières , Jean Caron , Nicholas Lefebvre , Steeve Pepin , Alain N. Rousseau

Organic soils are often used to cultivate high-value field vegetables because of their high organic matter content and fertility. However, these productive soils are subject to many degradation processes that jeopardize their sustainability. In southern Quebec, wind erosion alone has been shown to provoke losses of peat depth ranging from 1 to 4 cm every year. Organic soils are known to be very erodible when compared to mineral soils, partly due to their low density and lack of structure. Understanding the physical erosive mechanisms is important as the degradation of cultivated histosols is a major concern worldwide. The physical mechanisms of wind erosion are well documented in general, with saltation, creep and suspension being the main processes. Yet, recent field works over cultivated peatlands in southern Quebec revealed the presence of high concentrations of aerosols above the surface even under quiescent wind conditions. Since the traditional wind erosion models could not explain this phenomenon, this study aims to explore potential explanations of such observations. The role of electrical charges was considered in this paper as a potential dust-lifting force. We introduce a series of laboratory experiments performed on organic soil samples that revealed a significant increase in suspended organic soil particles (i.e., soil losses) through the presence of electrical charges. This could explain the presence of aerosols observed above organic soil fields, which highlights the necessity to further investigate the role of electrical charges in wind erosion processes of organic soils.

有机土壤有机质含量高，肥力好，常用于栽培高价值蔬菜。然而，这些生产性土壤受到许多退化过程的影响，危及其可持续性。在魁北克南部，仅风蚀一项就显示每年造成泥炭深度1至4厘米的损失。众所周知，与矿物土壤相比，有机土壤非常容易被侵蚀，部分原因是它们的密度低，缺乏结构。了解物理侵蚀机制是重要的，因为培养组织溶胶的降解是全世界关注的主要问题。一般来说，风蚀的物理机制有很好的文献记载，其中跳跃、蠕变和悬浮是主要的过程。然而，最近在魁北克南部开垦的泥炭地进行的实地工作显示，即使在静风条件下，地表以上也存在高浓度的气溶胶。由于传统的风蚀模型无法解释这一现象，本研究旨在探索这种观测结果的潜在解释。本文认为电荷的作用是一种潜在的扬尘力。我们介绍了在有机土壤样品上进行的一系列实验室实验，这些实验显示，由于电荷的存在，悬浮有机土壤颗粒（即土壤流失）显著增加。这可以解释在有机土壤上观测到的气溶胶的存在，这突出了进一步研究电荷在有机土壤风蚀过程中的作用的必要性。

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引用次数: 0

Corrigendum to “Off-season wind-induced soil erosion from potato fields under varying bedding preparations”. [Aeolian Res. 74 (2025) 101000] “不同垫层处理下马铃薯田非季节风致土壤侵蚀”的勘误表。[风沙法令74 (2025)101000]

IF 3.4 3区地球科学 Q2 GEOGRAPHY, PHYSICAL

Aeolian Research

Pub Date : 2025-12-01 DOI: 10.1016/j.aeolia.2025.101013

Matt Ball , Guillermo Hernandez-Ramirez , Willemijn Appels , Sheng Li , Rezvan Karimi Dehkordi

引用次数: 0

Quantifying the impact of fire events on dust emission potential from partially vegetated dunes in the southwest Kalahari 量化火灾事件对喀拉哈里西南部部分植被沙丘粉尘排放潜力的影响

IF 3.4 3区地球科学 Q2 GEOGRAPHY, PHYSICAL

Aeolian Research

Pub Date : 2025-12-01 DOI: 10.1016/j.aeolia.2025.101012

Rosemary A. Huck , Giles F.S. Wiggs , David S.G. Thomas , Natasha S. Wallum

The removal of stabilising vegetation from sand dunes by fire has been widely linked to increases in aeolian sediment transport and dune movement. However, substantial gaps exist in our knowledge of whether burned dunes in arid environments have the potential to emit dust. To explore relationships between fire and dust emission on partially vegetated sand dunes in the Namibian Kalahari Desert, 180 measurements of wind erosion threshold and dust flux were carried out using a portable wind tunnel (Portable In-Situ Wind Erosion Laboratory or PI-SWERL). Data were analysed to compare erosion thresholds and dust emission flux on adjacent burned and unburned sites. The data suggest that both burned and unburned dune crests, flanks, and interdunes have a low potential for dust emission. Whilst there was no significant difference in dust emission flux between burned and unburned control surfaces (Kruskal-Wallis, p > 0.05), there was evidence of significantly higher erosion thresholds on burned surfaces (T-test, p < 0.01). Where the surface had been disturbed, resulting in the removal of the typically present biological soil crusts (biocrust), our data suggest that dust emission fluxes are, on average, 8–13 times higher those of undisturbed surfaces. The analysis reveals that even when burned and devoid of vegetation, the Kalahari linear dune system is sediment-availability limited. This finding indicates the importance of ground surface characteristics, such as biocrusts, in preventing dust emission from the Kalahari dune field.

人们普遍认为，火灾对沙丘稳定植被的破坏与风成沉积物运输和沙丘运动的增加有关。然而，我们对干旱环境中被烧毁的沙丘是否有可能排放灰尘的了解还存在很大的空白。为了探讨纳米比亚喀拉哈里沙漠部分植被沙丘上的火灾与沙尘排放的关系，利用便携式风洞（便携式原位风蚀实验室，PI-SWERL）对180个风蚀阈值和沙尘通量进行了测量。对数据进行了分析，比较了相邻燃烧点和未燃烧点的侵蚀阈值和粉尘排放通量。这些数据表明，燃烧和未燃烧的沙丘顶部、侧翼和沙丘间的粉尘排放潜力都很低。虽然燃烧面和未燃烧面之间的粉尘排放通量没有显著差异（Kruskal-Wallis, p > 0.05），但有证据表明，燃烧面的侵蚀阈值明显更高（t检验，p < 0.01）。在地表受到干扰，导致典型的生物土壤结皮（生物结皮）消失的地方，我们的数据表明，尘埃排放通量平均比未受干扰的地表高8-13倍。分析表明，即使在燃烧和缺乏植被的情况下，喀拉哈里线性沙丘系统的沉积物可用性是有限的。这一发现表明地表特征（如生物结皮）在防止喀拉哈里沙丘场的粉尘排放方面的重要性。

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引用次数: 0

Analyzing the development of two regional dust storms on Mars using MRO-MCS and Perseverance observations 利用MRO-MCS和毅力号观测分析火星上两次区域性沙尘暴的发展

IF 3.4 3区地球科学 Q2 GEOGRAPHY, PHYSICAL

Aeolian Research

Pub Date : 2025-11-20 DOI: 10.1016/j.aeolia.2025.101010

Anirban Mandal, Jagabandhu Panda

Dust storms on Mars are important weather phenomena that are intricately linked to the dynamics and thermodynamics of the atmosphere. This study utilized the observations from Mars Climate Sounder (MCS) instrument onboard the Mars Reconnaissance Orbiter (MRO) and Mars Environmental Dynamics Analyzer (MEDA) onboard the Perseverance rover, to analyze the growth and development of two regional dust storms occurred in the Martian Year (MY) 36. The said dust storms were observed around L_S = 153°–156° (RDS-A) and 310°–330° (RDS-B). MCS observations realized unstable layers in the latitude range 40°S–90°N for RDS-A, and 90°S–40°N for RDS-B, mostly up to 25 km altitude. During the storms’ extension phase, a stable layer with a bridge-like structure forms at 40–60 km for RDS-A and 20–40 km for RDS-B. The mixing ratio values indicated higher availability of dust particles at 30–50 km in 40°S–40°N region for RDS-A and at 25–60 km in 90°S-40°N region for RDS-B. MEDA observations displayed a decrease in daytime ground and air temperature, but an increase during the night. The overall pressure increased during the storm, but the minimum pressure was found to decrease. Pressure variation exhibited the diurnal behavior to the presence of four components. The detection of higher number of vortices during the storms indicated the possibility of the contribution of storm-induced turbulence. The current study provided some insights on the intricate dust-lifting mechanisms, advancing the understanding of the Martian atmosphere.

火星上的沙尘暴是重要的天气现象，它与大气的动力学和热力学有着复杂的联系。本研究利用火星勘测轨道器（MRO）上的火星气候探测器（MCS）和毅力号火星车上的火星环境动力学分析仪（MEDA）的观测数据，分析了火星年（MY） 36发生的两次区域性沙尘暴的增长和发展。在LS = 153°-156°（RDS-A）和310°-330°（RDS-B）附近观测到上述沙尘暴。RDS-A和RDS-B分别在40°~ 90°N和90°~ 40°N的纬度范围内实现了MCS观测的不稳定层，高度大多在25 km以内。在风暴扩展阶段，RDS-A和RDS-B分别在40-60 km和20-40 km处形成一个具有桥状结构的稳定层。混合比值表明，RDS-A在40°S-40°N区域30 ~ 50 km和RDS-B在90°S-40°N区域25 ~ 60 km的有效度较高。MEDA观测显示白天地面和空气温度下降，但夜间升高。风暴期间总气压增加，但最小气压减小。压力变化对这四种成分的存在表现出日变化特征。在风暴期间检测到的较高数量的涡旋表明可能是风暴引起的湍流的贡献。目前的研究为复杂的尘埃提升机制提供了一些见解，促进了对火星大气的理解。

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引用次数: 0

Dust emission forecasting using time series spatial data and machine learning algorithms 利用时间序列空间数据和机器学习算法进行粉尘排放预测

IF 3.4 3区地球科学 Q2 GEOGRAPHY, PHYSICAL

Aeolian Research

Pub Date : 2025-11-15 DOI: 10.1016/j.aeolia.2025.101011

Maryam Sanisales , Zahed Rahmati , Ali Darvishi Boloorani

The objective of our research is to use remote sensing, reanalyzed time series data, and a machine learning algorithm to forecast dust emissions from sources in the Tigris-Euphrates basin. This basin is one of the most significant global dust source areas and includes more than 10,000 dust hotspots of dust emission sources. To our knowledge, this is the first attempt to predict the emission of dust hotspots one year in advance. The methodology was developed based on an analysis of the spatiotemporal behavior of environmental parameters and the use of machine learning algorithm, including support vector machine, random forest, multinomial Naïve Bayes, and

k

-nearest neighbors. Additionally, the ensemble model Dempster–Shafer theory was used to merge the results from all machine learning algorithm to obtain dust source emission forecasting. Time series data were used to identify the primary dust drivers, including vegetation cover, precipitation, soil moisture, wind speed, temperature, soil texture, soil thickness, elevation, and slope. The location and temporal behavior (January 2000 to December 2020) of 10,422 dust events, obtained from a previous study conducted by the authors, along with data from January 2021 to December 2021, were utilized as the basis for the analysis. The training data set encompasses the period from January 2000 to December 2020, with January 2021 to December 2021 serving as the forecasting test data. The accuracy of the algorithms for prediction dust source emissions forecasting was evaluated using the AUC-ROC curve, with the random forest model achieving the highest performance of about 69%. Using the Dempster–Shafer model, we combined four machine learning models, and the accuracy reached about 73%, which improved the forecasting accuracy of dust emissions by 4.7%.

我们的研究目的是利用遥感、重新分析的时间序列数据和机器学习算法来预测底格里斯河-幼发拉底河流域污染源的粉尘排放。该盆地是全球最重要的沙尘源区之一，包括1万多个沙尘排放源的沙尘热点。据我们所知，这是首次尝试提前一年预测尘埃热点的排放。该方法是基于对环境参数时空行为的分析和机器学习算法的使用，包括支持向量机、随机森林、多项式Naïve贝叶斯和k近邻。此外，采用集成模型Dempster-Shafer理论对所有机器学习算法的结果进行合并，得到尘源排放预测结果。时间序列数据包括植被覆盖、降水、土壤湿度、风速、温度、土壤质地、土壤厚度、高程和坡度等。作者先前进行的一项研究获得了10422次沙尘事件的位置和时间行为（2000年1月至2020年12月），以及2021年1月至2021年12月的数据，这些数据被用作分析的基础。训练数据集的时间为2000年1月至2020年12月，预测测试数据为2021年1月至2021年12月。采用AUC-ROC曲线对算法的预测精度进行了评价，其中随机森林模型的预测精度最高，约为69%。使用Dempster-Shafer模型，我们结合了4种机器学习模型，准确率达到73%左右，将粉尘排放的预测精度提高了4.7%。

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引用次数: 0

Land cover, drought, and dust emission in arid Succulent Karoo shrublands 干旱多肉的卡鲁灌木地的土地覆盖、干旱和粉尘排放

IF 3.4 3区地球科学 Q2 GEOGRAPHY, PHYSICAL

Aeolian Research

Pub Date : 2025-11-14 DOI: 10.1016/j.aeolia.2025.101006

Heleen C. Vos , Johanna R. von Holdt , Helga van der Merwe , Igshaan Samuels , Ute Schmiedel , Norbert Jürgens , Juliane Krenz , Mmoto L. Masubelele , Bavisha Koovarjee , Wolfgang Fister , Frank D. Eckardt , Clement Cupido , Nikolaus J. Kuhn , Susanne Fietz

Sand and dust storms are natural environmental processes that occur at various scales and they can have significant ecological and social impacts. Globally, patterns of aeolian activities are changing, and on South Africa’s west coast a marked increase in dust emission was observed during a prolonged drought lasting from 2015 to 2022. This region, dominated by Succulent Karoo shrublands, experienced a decline in vegetation cover and shifts in vegetation states due to the combined effects of drought, grazing, mining activities, and wind erosion. This review examines the transition in vegetation cover, the environmental and anthropogenic processes driving these changes, and their relationship to increased wind erosion and dust emission. Key influencing factors include rainfall variability, grazing intensity, and the degrading effects of wind erosion, shaped by broader forces such as climate change and land-use practices like mining and limited crop farming. To synthesise these dynamics, we present a conceptual State-and-Transition Model (STM) that captures vegetation responses in arid ecosystems and integrates the feedback between vegetation loss, wind erosion, and dust emission. This research furthermore summarises the broader consequences of dust events, including soil degradation, public health risks, and off-site effects on ocean chemistry and marine ecosystems. Understanding the drivers of vegetation change and the role of human and climatic pressures is crucial for anticipating future atmospheric dust loads and managing the resilience of arid landscapes.

沙尘暴是发生在不同尺度上的自然环境过程，可产生重大的生态和社会影响。在全球范围内，风沙活动的模式正在发生变化，在2015年至2022年的长期干旱期间，南非西海岸的尘埃排放量显著增加。由于干旱、放牧、采矿活动和风蚀的综合影响，该地区以多肉的卡鲁灌丛为主，植被覆盖减少，植被状态发生变化。本文综述了植被覆盖的变化、驱动这些变化的环境和人为过程，以及它们与风蚀和沙尘排放增加的关系。关键的影响因素包括降雨变异性、放牧强度和风蚀的退化效应，这些因素受到气候变化以及采矿和有限作物种植等土地利用做法等更广泛力量的影响。为了综合这些动态，我们提出了一个概念性的状态-过渡模型（STM），该模型捕捉了干旱生态系统中植被的响应，并整合了植被损失、风蚀和粉尘排放之间的反馈。这项研究进一步总结了沙尘事件的更广泛后果，包括土壤退化、公共健康风险以及对海洋化学和海洋生态系统的非现场影响。了解植被变化的驱动因素以及人类和气候压力的作用对于预测未来大气粉尘负荷和管理干旱景观的恢复能力至关重要。

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引用次数: 0

On the chronology and development of Europe’s tallest aeolian landform: the Sarykum dune complex in the North Caspian region 关于欧洲最高的风成地貌的年代学和发展：北里海地区的Sarykum沙丘群

IF 3.4 3区地球科学 Q2 GEOGRAPHY, PHYSICAL

Aeolian Research

Pub Date : 2025-11-10 DOI: 10.1016/j.aeolia.2025.101007

Dmitry Zastrozhnov , Andrey Zastrozhnov , Sergei Medvedev , Idris Idrisov , Viktor Spiridonov , Dmitry V. Nazarov , Anna A. Cherezova

The tallest aeolian landform in Europe (∼170 m above the base), the Sarykum dune complex, is located in the southwestern Caspian Depression near the Caucasus Mountains. Its morphology and evolution remain poorly studied. In this study we investigate the dune complex through a synthesis of geomorphological, geological, and geochronological methods. Our findings link its development to major regression phases of the Caspian Sea driven by climatic aridifications during the Late Pleistocene–Holocene. Optical stimulated luminescence dating indicates that dune formation began at least 60 ka during the Atelian regression, with reactivation around 11–6 ka during the Mangyshlakian regression. By the mid-Holocene, the complex had nearly reached its modern height. The Shura-Ozen’ River, which divides the dune complex in two major segments, played a significant role in the complex’s evolution, influencing aeolian sediment transport and trapping material on its banks, particularly on the left bank, where the highest Central Massif is located. Extensive vegetation now stabilizes most of the complex, except for active ridges in the Central Massif. A numerical analysis of wind potential for dune migration, based on recent meteorological data, shows that sand movement is primarily driven by self-sustaining NW–SE wind fluctuations. Paleowind analysis suggests that similar long-term wind patterns have persisted since the onset of aeolian deposition during the Atelian phase. Our study sheds new light on the Sarykum dune complex’s history and highlights its significance as a dynamic archive of climatic and geomorphic processes in the Caspian region.

欧洲最高的风成地貌（海拔约170米），Sarykum沙丘群，位于高加索山脉附近的里海洼地西南部。其形态和进化的研究仍然很少。在这项研究中，我们通过综合地貌学、地质学和地质年代学的方法来研究沙丘复合体。我们的发现将其发展与里海在晚更新世-全新世期间由气候干旱化驱动的主要回归阶段联系起来。光学激发发光测年表明，沙丘形成至少始于阿特利期60 ka，在曼吉斯拉克期11-6 ka左右重新激活。到全新世中期，这个建筑群几乎达到了现代的高度。舒拉-奥岑河将沙丘群分成两个主要部分，在沙丘群的演变中发挥了重要作用，影响了风成沉积物的运输和河岸上的物质捕获，特别是在最高的中央地块所在的左岸。除了中部地块活跃的山脊外，大面积的植被现在稳定了大部分建筑群。基于近期气象资料的沙丘移动风势数值分析表明，沙丘移动主要是由自维持的西北-东南风波动驱动的。古风分析表明，自阿特利期风成沉积开始以来，类似的长期风模式一直持续存在。我们的研究揭示了Sarykum沙丘复合体的历史，并强调了它作为里海地区气候和地貌过程动态档案的重要性。

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引用次数: 0

Spatiotemporal variations in aeolian sediment source fingerprinting using geochemical tracers and a Bayesian mixing model 基于地球化学示踪剂和贝叶斯混合模型的风成沉积物源指纹图谱的时空变化

IF 3.4 3区地球科学 Q2 GEOGRAPHY, PHYSICAL

Aeolian Research

Pub Date : 2025-11-09 DOI: 10.1016/j.aeolia.2025.101008

Kazem Taherinezhad-Javazm , Kazem Nosrati , Peter Fiener

Understanding spatiotemporal variations in aeolian sediment sources is vital for developing effective soil conservation strategies in arid environments. This study quantified sediment provenance in the Daranjir Playa (Kavīr-e Dar Anjīr), central Iran, using geochemical fingerprinting combined with a Bayesian mixing model. Potential sources, including old and young alluvial fans, orchards, salt pan areas, and streambanks, were identified through field surveys and wind analyses. Thirty-two source and twenty seasonal dune samples were analyzed for 25 geochemical elements (Al, As, B, Ba, Be, Ca, Co, Cr, Fe, Ga, K, La, Li, Mg, Mn, Na, Ni, P, Pb, S, Sr, Ti, V, Zn, Zr). Conservative tracers were used to select final tracers using the Kruskal–Wallis H-test and discriminant function analysis, yielding five tracers for spring (Al, B, Mg, Ni, Zn), four for summer (Al, Mg, S, Sr), and five for autumn and winter (Al, B, Mg, Na, Sr). Source contributions were quantified with the MixSIR Bayesian model and validated through virtual mixing experiments. Wind data showed strong seasonal variability, with the highest velocities in summer and dominant sand transport in winter. Model results indicated that salt pan areas were the main sediment source in spring, autumn, and winter (90.3%, 97.4%, and 90.5%), while streambanks dominated in summer (98.3%). Model validation yielded RMSE values ranging from 0.5 to 15.0%, MAE from 0.4 to 9.8%, and an index of agreement (d) between 0.13 and 1.00. This approach elucidates the seasonal dynamics of aeolian sediment sources, reflecting surface conditions and seasonal wind variations, and supports targeted land management in desert landscapes.

了解风沙来源的时空变化对于制定有效的干旱环境土壤保持策略至关重要。利用地球化学指纹图谱结合贝叶斯混合模型，对伊朗中部达兰吉尔盐湖（kavvar īr-e Dar anjj īr）沉积物物源进行了定量分析。通过实地调查和风力分析，确定了潜在的来源，包括老的和年轻的冲积扇、果园、盐田地区和河岸。分析了32个源样和20个季节沙丘样的25种地球化学元素（Al、As、B、Ba、Be、Ca、Co、Cr、Fe、Ga、K、La、Li、Mg、Mn、Na、Ni、P、Pb、S、Sr、Ti、V、Zn、Zr）。使用保守示踪剂通过Kruskal-Wallis h检验和判别函数分析选择最终示踪剂，得到5种春季示踪剂（Al， B, Mg, Ni, Zn）， 4种夏季示踪剂（Al, Mg， S, Sr）， 5种秋季和冬季示踪剂（Al， B, Mg, Na, Sr）。使用MixSIR贝叶斯模型量化源贡献，并通过虚拟混合实验进行验证。风资料表现出强烈的季节变化，夏季风速最高，冬季输沙优势明显。模型结果表明，春季、秋季和冬季盐田区是主要的沉积物来源（90.3%、97.4%和90.5%），夏季以河岸区为主（98.3%）。模型验证的RMSE值为0.5 ~ 15.0%，MAE为0.4 ~ 9.8%，一致性指数(d)在0.13 ~ 1.00之间。该方法阐明了风成沉积物来源的季节动态，反映了地表条件和季节性风的变化，并支持了沙漠景观中有针对性的土地管理。

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