美国流量数据集的非线性动态
Krzysztof Raczyński1, Katarzyna Grala1, John H Cartwright1
1Mississippi State University, Geosystems Research Institute, 2 Research Blvd, 39759, Starkville, MS, USA.
Data in brief
|October 23, 2025
概括
这项研究提供了一个全面的水文数据集,介绍了美国各地的河流动态. 它包括分数和混乱指标,以帮助理解水资源的变化和开发预测模型.
科学领域:
- 水文学的水文学
- 地质物理学 地质物理学
- 数据科学数据科学数据科学
背景情况:
- 流量数据对于水资源管理和水文研究至关重要.
- 了解流动的复杂动力学,包括碎形和混乱的行为,对于准确的建模和预测至关重要.
- 现有的数据集往往缺乏对这些复杂的动态在不同的空间和时间尺度上的全面分析.
研究的目的:
- 创建一个全面的水文数据集,包括流量流程时间序列和相关的分数和混乱指标.
- 提供数据用于对流动动力学进行基准测试,评估水文模型,并支持机器学习应用程序.
- 根据动态性质,使水文行为能够进行区域分类.
主要方法:
- 从2899个美国和波多黎各测量站 (1970-2023) 编制的每日,每周,每月,每季度和每年流量时间序列.
- 计算了一套分数和混乱指标,包括赫斯特指数,DFA,多分数,WTM,样本,RQA和利亚普诺夫指数.
- 应用模糊C-means集群将测量器分为三个动态行为类别,生成成员概率并包括站点元数据.
主要成果:
- 一个丰富的数据集,包含原始和处理的流量时间序列,计算的分数和混乱指标,集群分配和地理位置元数据.
- 数据涵盖了五个时间分辨率的三个流动模式 (最大,平均,最小),并将数据插入以确保完整性.
- 根据水文复杂性确定了测量站的三个不同的动态行为组.
结论:
- 该数据集为研究人员和水资源管理人员提供了宝贵的资源,用于分析和基准流动流动力学.
- 促进对水文模型的评估和开发先进的水资源管理策略.
- 通过数据驱动的洞察力和机器学习应用程序支持水文科学的进步.
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