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基于有限的水下调查点的数据稀缺的水文地区的水资源评估
Chen Wang1, Bo Zhang2, Rui Zhu2
1School of Water Conservancy and Environment, University of Jinan, Jinan, 250022, China.
Environmental monitoring and assessment
|September 25, 2025
概括
传统的空间插值方法比机器学习更有效,用于在数据稀缺地区创建湖泊数字海拔模型 (DEM). 这项研究监测了宁夏的湖泊动态,估计了水储量,并揭示了湖泊的相对稳定性.
科学领域:
- 水文学和地质形态学
- 遥感 遥感 遥感 遥感
- 水资源管理 水资源管理
背景情况:
- 湖泊地形对于水资源监测和水文研究至关重要.
- 由于卫星的限制和深度估计的不确定性,监测数据稀缺地区的湖泊动态具有挑战性.
- 有效的湖泊数字海拔模型 (DEM) 建设对于准确的水资源评估至关重要.
研究的目的:
- 为了比较传统的空间插值方法与机器学习 (XGBoost) 在数据稀缺地区的湖泊DEM建设.
- 通过将DEM分析与遥感图像集成,开发一种有效的监测湖水动态的方法.
- 为数据稀缺地区的水资源管理提供科学和实际支持.
主要方法:
- 使用无人水面船收集浴度数据.
- 应用空间插值方法和机器学习 (XGBoost) 进行DEM生成.
- 整合DEM衍生关系曲线 (水域,水位,储量) 与遥感数据.
主要成果:
- 传统的空间插值方法被证明比机器学习更有利于在数据稀缺的地区进行DEM建设,这些地区具有显著的人类诱导的地形变化.
- 平均每年的储水量估计为336.249 × 10^4 m3.
- 湖泊储量从2016年的最低307.246 × 10^4立方米到2024年的最高411.802 × 10^4立方米不等,水位波动一般低于1米.
结论:
- 建议在机器学习上进行空间插值,以在数据有限的环境中与修改的景观中创建湖泊DEM.
- 综合方法有效地促进了湖水动态的监测.
- 沙波图干旱地区的湖泊在水位和储存方面表现出相对稳定.
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