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混合深度学习缩小GCMs用于气候影响评估和未来预测在阿曼

Erfan Zarei¹, Mohammad Reza Nikoo², Ghazi Al-Rawas²

¹Department of Water Engineering, Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran.

Journal of environmental management

|February 14, 2025

概括

这项研究开发了一种新的混合深度学习模型,用于准确的气候缩小,优于现有方法. 该模型预测降水和溪流的显著变化,强调了适应性水资源管理战略的必要性.

科学领域:

气候科学气候科学
水文学的水文学
机器学习机器学习

背景情况:

准确的气候变化影响评估和水资源管理依赖于缩小的全球循环模型 (GCM).
评估多个GCM对于确定区域气候研究中表现最好的模型至关重要.

研究的目的:

开发和验证一种新的混合深度学习模型,用于缩小 GCM 输出.
根据各种共享社会经济途径 (SSP) 评估未来气候变化对瓦迪戴卡盆地的影响.

主要方法:

使用泰勒图对14个GCM的评估,IITM-ESM被确定为表现最佳的模型.
开发一种混合深度学习模型,将序列对序列架构与时间卷积网络 (TCN) 和变压器组件相结合.
拟议模型与量子映射,随机森林,LSTM和TCN进行比较,并使用粒子群优化 (PSO) 进行优化.
为Wadi Dayqah流域构建HEC-HMS水文模型,用历史数据进行校准和验证.

主要成果:

新的混合深度学习模型以0.907的NSE,2.10的RMSE,0.63的BIAS和21.96%的相对误差实现了卓越的性能.
下调的预测表明,在SSP126和SSP245下降雨量显著下降,在SSP585.5下变化增加.
未来的温度上升预计在SSP126下适度,但在SSP245和SSP585.5下显著.
预计在SSP126和SSP245下,最大年度流量将减少,而SSP585预测极端峰值流量高达历史平均水平的7倍.

结论:

开发的混合深度学习模型为气候缩小和影响评估提供了强大的方法.
未来的气候情景预测,Wadi Dayqah盆地将发生重大水文变化,需要适应性水资源管理.
积极的水资源战略对于减轻预计气候变化影响的不利影响至关重要.

关键词:

气候变化气候变化气候变化缩小规模缩小规模缩小规模水文建模水文建模时间卷积网络 (TCN)

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