塔克拉玛干沙漠表层沉积物粒度综合数据库。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Huiliang Li, Xin Gao, Yongcheng Zhao, Jie Zhou, Zihao Hu, Zhuo Chen, Zuowei Yang, Shengyu Li
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引用次数: 0

摘要

本研究通过系统的野外采样和激光衍射分析,编制了目前最全面的开放获取的整个塔克拉玛干沙漠地表沉积物粒度数据库(n = 596个样本)。它为认识沙漠的形成、演化、沙源和风沙环境的恢复提供了重要的数据。通过分析主要泥沙参数(平均粒度、分选、偏度、峰度)和颗粒组成,揭示了泥沙输运动力学和沉积过程,这对理解沙漠形成、沙源和风成环境重建至关重要。沉积物结构和分选机制的定量表征为研究区域沙尘排放、风蚀模式和输沙能力提供了基础数据。虽然主要关注塔克拉玛干沙漠,但方法和数据集适用于其他干旱地区,使其成为比较沙漠研究的宝贵资源。它是研究沙漠景观和解决与沙漠化和风成过程有关的环境挑战的研究人员不可或缺的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A comprehensive grain-size database of surface sediments from the Taklamakan Desert.

This study compiles the most comprehensive open-access surface sediment grain-size database (n = 596 samples) spanning the entire Taklamakan Desert, obtained through systematic field sampling and laser diffraction analysis. It provides essential data for understanding the desert formation, evolution, sand sources, and the restoration of aeolian environments. By analyzing key sediment parameters (mean grain size, sorting, skewness, kurtosis) and particle compositions, the dataset reveals sediment transport dynamics and depositional processes critical for understanding desert formation, sand provenance, and aeolian environmental reconstruction. The quantitative characterization of sediment texture and sorting mechanisms provides foundational data for investigating regional dust emissions, wind erosion patterns, and sediment transport capacities. While the primary focus is on the Taklamakan Desert, the methodology and dataset apply to other arid regions, making it a valuable resource for comparative desert studies. It is an indispensable tool for researchers investigating desert landscapes and addressing environmental challenges related to desertification and aeolian processes.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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