使用缩小规模的GRACE Mascon数据来评估密西西比河流平原水源层的总储水量
Zahra Ghaffari1,2, Greg Easson1,2, Lance D Yarbrough1
1Department of Geology & Geological Engineering, University of Mississippi, University, MS 38677, USA.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究使用随机森林模型将粗卫星总储水 (TWS) 数据缩小到约5公里的分辨率. 这为密西西比三角洲等地区的可持续地下水管理提供了关键的高分辨率水文数据.
科学领域:
- 水文学的水文学
- 遥感 遥感 遥感 遥感
- 地理空间分析是什么
背景情况:
- 高分辨率的水文数据对于有效的水资源管理至关重要.
- 基于卫星的总水储量 (TWS) 数据,就像来自GRACE一样,提供了广泛的覆盖范围,但往往缺乏当地规模研究所需的分辨率.
- 使用粗的卫星数据监测较小地区的TWS变化存在重大挑战.
研究的目的:
- 为了空间缩小,重力恢复和气候实验 (GRACE) 的数据从0.5°到大约5公里的分辨率.
- 评估随机森林模型 (RFM) 的适用性,以提高TWS数据的空间分辨率.
- 为改善密西西比河流平原地下水管理提供高分辨率的水文数据.
主要方法:
- 使用随机森林模型 (RFM) 来缩小GRACE的mascon图像.
- 选择并纳入四个重要的物理和水文参数作为RFM的输入变量.
- 将RFM应用于2020年4月份的GRACEmascon数据,用于密西西比河流平原.
主要成果:
- 与原来的0.5°GRACE数据相比,实现了大约5公里的空间分辨率.
- 随机森林模型表现出高精度,验证R2为0.88.
- 缩小规模的TWS数据与密西西比河三角洲中部已知的水资源枯竭模式一致.
结论:
- 随机森林模型有效地缩小了GRACE mascon数据,提高了空间分辨率.
- 这种方法提供了有价值的,高分辨率的水文洞察力,适合当地规模的应用.
- 缩小规模的数据可以显著帮助分县级的地下水管理活动,特别是在密西西比三角洲地区.
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