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数据同化策略的好处和注意事项:建模地下水充电的一个例子
Allen M Shapiro1, Frederick D Day-Lewis2,3
1Hydrogeologist, Great Falls, VA, USA.
Ground water
|August 28, 2023
概括
整合最近的数据可以增强地下水充电模型. 过和固定滞后平滑 (FLS) 比预测更好地减少不确定性,尽管预测保持了质量保存.
科学领域:
- 水文学的水文学
- 水文地质学 水文地质学
- 环境科学 环境科学
背景情况:
- 地下水模型对于评估水资源至关重要.
- 实时地下水评估从纳入最近的观测数据中受益.
- 在断裂岩层含水层中估计时间变化的充电存在挑战.
研究的目的:
- 为了比较不同数据同化策略在改善地下水充电模型方面的有效性.
- 评估预测,过和固定滞后平滑 (FLS) 方法,以估计含水层状态和充电.
- 评估模型准确性,不确定性降低和质量保存之间的权衡.
主要方法:
- 将卡尔曼波器应用于数据驱动的机械充电模型.
- 实施并比较了三个数据同化方法:预测,过和固定延迟平滑 (FLS).
- 分析了随时间变化的水平面高度 (h) 和充电估计,包括它们的误差共变率.
主要成果:
- 预测产生了很高的错误共变率,表明存在很大的不确定性.
- 过和FLS通过同化最近的观察结果,产生了与地下水平面动态更好的补充估计,与预测相比,减少了不确定性.
- 虽然过和FLS降低了不确定性,但与预测不同,它们不能保证质量保存.
结论:
- 数据同化策略,特别是过和FLS,通过减少不确定性,显著改善实时地下水状况估计.
- 在数据同化方法之间进行选择需要平衡改进状态估计与质量保存的潜在损失.
- 这些发现适用于对系统输入和外部数据敏感的各种地下水过程模型.
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