通过考虑红河洪水路线的融雪效应来开发分布式非线性Muskingum模型
Vida Atashi1, Reza Barati2, Yeo Howe Lim3
1Department of Civil Engineering, University of North Dakota, Upson II Rm. 260K, 243 Centennial Dr. Stop 8115, Grand Forks, ND, 58202-8115, USA. vida.atashi@und.edu.
Scientific reports
|December 4, 2023
概括
一个新的非线性Muskingum模型准确地路由洪水,即使有横向流入. 将河流分为分流,使用Salp Swarm算法可以提高洪水预测的准确性.
科学领域:
- 水文与水资源工程 水文与水资源工程
- 计算流体动力学的流体动力学.
- 环境建模环境建模
背景情况:
- 精确的洪水路由对于有效的水资源管理和防灾准备至关重要.
- 传统的马斯金库姆模型在处理复杂的河流动态方面存在局限性,包括侧流入和不同的河床条件.
- 北方的红河对洪水路线提出了独特的挑战,因为它的斜坡和易受各种洪水事件的影响.
研究的目的:
- 开发和验证一个非线性Muskingum模型,用于精确的河流流域的洪水路线.
- 为了研究侧流入条件对洪水路线准确性的影响.
- 为了评估将河流分成多个子范围的有效性,以提高模型性能.
主要方法:
- 开发一种非线性马斯金库姆模型,包括横向流入.
- 通过将河流流域分为多个子流域,应用分布式马斯金库姆方法.
- 校准和验证使用来自北方红河USGS站的十四个洪水水文仪对.
- 使用Salp Swarm算法优化模型参数.
主要成果:
- 开发的非线性Muskingum模型证明了河床斜率 (0.0002-0.0003) 轻微的河流的准确洪水路由能力.
- 洪水事件成功地被分为由融雪驱动的,由降雨引发的和混合类型,影响模型性能.
- 部分范围的数量显著影响了参数估计和性能评估标准 (PEC),最佳配置在不同类型的洪水事件中有所不同.
结论:
- 非线性马斯金库姆模型,尤其是在利用具有最佳次数的分布式方法时,提供了准确的洪水路由.
- 萨尔普集群算法有效地帮助开发模型的参数估计.
- 特定地点的分析和选择适当数量的子范围对于优化洪水路线中的峰值排放预测至关重要.
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