在南非建模干旱:气象洞察力和预测参数
Nnaemeka Onyeuwaoma1, Venkataraman Sivakumar2,3, Mahesh Bade2
1Discipline of Physics, School of Chemistry and Physics, College of Agriculture, Engineering and Science, University of KwaZulu Natal, Westville Campus, Durban, 4001, South Africa. onyeuwaoman@ukzn.ac.za.
Environmental monitoring and assessment
|September 20, 2024
概括
南非 南非 南非
科学领域:
- 环境科学 环境科学
- 气象学 天气学
- 人工智能的人工智能
背景情况:
- 南非面临着持续的干旱挑战,造成了重大的经济影响.
- 西开普省从2015年到2018年经历了严重的干旱,导致国家灾害状态和开普敦的严格水量配给.
- 经常出现的干旱需要改善水资源管理的预测模型.
研究的目的:
- 开发和评估用于南非干旱预测的人工神经网络 (ANN) 模型.
- 在干旱模拟中评估各种气象变量和气溶参数的有效性.
- 确定影响干旱预测准确性的关键参数和特定位置因素.
主要方法:
- 利用人工神经网络 (ANN) 进行干旱预测模拟.
- 采用标准降水指数 (SPI) 作为主要干旱指标,遵循WMO的指导方针.
- 整合了21年来南非四个地点的气象数据 (相对湿度,温度,土壤湿度,蒸发,蒸发,海面温度) 和气溶光学深度.
主要成果:
- ANN模型展示了显著的预测能力,R2值因位置和SPI时间表 (SPI3和SPI6) 而异.
- 蒸发转化率 (et) 被确定为准确干旱模拟的关键参数.
- 预测准确度在不同地点有很大差异,表明没有普遍的干旱预测方案.
结论:
- 人工神经网络 (ANN) 是南非干旱研究和模拟的有效工具.
- 特定位置的因素显著影响干旱预测模型.
- 该研究为模拟南非各地未来干旱情景提供了宝贵的见解,有助于规划水资源.
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