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使用GRACE数据和机器学习评估伊朗地下水干旱情况

Ali Kashani¹, Hamid R Safavi²

¹Department of Civil Engineering, Isfahan University of Technology, Isfahan, Iran.

Scientific reports

|April 26, 2025

概括

伊朗的地下水平均每年耗尽29厘米,里海盆地受到影响最大. 卫星数据和机器学习为更好的资源管理提供可靠的地下水干旱监测.

科学领域:

水文和远程传感技术
环境科学和水资源管理
气候变化影响评估气候变化影响评估

背景情况:

地下水是重要的淡水资源,对生态系统和人类需求至关重要,但易受干旱影响的影响,如充电减少和土地沉降.
传统的地下水监测方法成本高昂,缺乏全面的空间和时间覆盖.
像GRACE这样的卫星任务提供了大规模的储水数据,但局部研究由于分辨率和数据缺口而面临挑战.

研究的目的:

从2002年到2023年,调查伊朗六个主要河流流域的地下水状况和趋势.
评估地下水枯竭率和干旱传播动态.
评估卫星数据和机器学习对数据稀缺地区地下水干旱监测的有效性.

主要方法:

利用GRACE和GLDAS数据进行陆地储水异常 (TWSA) 分析.
使用极端梯度提升 (XGBoost) 将TWSA缩小到0.25°分辨率.
应用季节性自回归集成移动平均 (SARIMA) 来填补数据缺口,并使用SSP2场景预测预测地下水储存异常 (GWSA).

主要成果:

与观测数据相比,在缩小TWSA (R=0.99) 和估计GWSA (R=0.93) 中获得了高精度.
揭示了伊朗各地地下水平均耗尽29厘米/年,里海盆地显示最显著的下降.
确定了从气象 (SPI) 到地下水 (GGDI) 的干旱传播延迟8个月,并强调了远程连接指数对干旱的影响.

结论:

通过机器学习增强的卫星数据为监测地下水干旱提供了可靠的方法,特别是在地面观测有限的地区.
该研究强调了伊朗地下水的严重枯竭,需要战略性水资源管理和规划.
研究结果支持使用综合卫星和建模方法,以有效地评估地下水资源和制定政策.

关键词:

可以ESM5 缩小规模缩小规模缩小规模关于GGDI的研究格雷斯 (GRACE) 是一个恩典.地下水干旱地下水干旱机器学习是机器学习.电信连接电信连接电信连接电信连接在XGBoost中使用.

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