将来の環境流量に対する気候変動の影響評価

评估气候变化对未来环境流的影响

• 批准号: 17F17372
• 负责人: 鼎 信次郎
• 金额: $ 1.41万
• 依托单位: Tokyo Institute of Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for JSPS Fellows
• 财政年份: 2017
• 资助国家: 日本
• 起止时间: 2017-11-10 至 2020-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-17F17372/
• 关键词: Environmental Flow; Climate Change; climate change; environmental flows; statistical modelling; climate change impact; downscaling; environmental flow

In the previous fiscal year, it was found that models developed with Parallel Genetic Programming (PGP) algorithm (introduced in this research) show better generalisation skills and produce fewer unphysically large outliers compared to that of models developed with the traditional single deme GP. However, both conventional GP and PGP algorithms were not able to capture the extremes in the observed time series accurately. In a preliminary investigation, it was found that a monthly downscaling approach coupled with a monthly to daily disaggregation method has a better potential to simulate extremes, but the overall performance of the model is not as good as that of a daily downscaling model. Also, disaggregation methods are known to perform poorly when months with similar totals/averages with significantly different intra-monthly distributions are present in observations. In this year, a comprehensive investigation on the improvement to the simulation of extremes in precipitation, temperature and streamflows was conducted focussing on the use of disaggregation. Potential methods to combine the daily simulations with monthly to daily disaggregated simulations were also investigated. Streamflows were projected into the future with the improved methodology, availability of water to satisfy the needs of the environment was investigated. Results were summarized into manuscripts and submitted as papers to journals based on the finding of these new analyses.

在上一个财政年度，发现使用平行遗传编程（PGP）算法开发的模型（在本研究中引入）显示出更好的概括技巧，并且与传统的单个DEME GP开发的模型相比，毫无疑问的异常值较少。但是，常规的GP和PGP算法都无法准确捕获观察到的时间序列中的极端。在初步的研究中，发现每月的缩小方法以及每月至每日分类方法具有更大的模拟极端潜力，但是该模型的整体性能不如每日降尺度模型的总体表现良好。同样，当观察值中存在具有相似总数/平均分布明显不同的数月/平均分布明显不同时，分解方法的性能很差。在今年，对降水，温度和流汇中极端模拟的改善的全面研究集中在使用分解。还研究了将每日模拟与每月至每日分类模拟相结合的潜在方法。通过改进的方法，将溪流投影到未来，并研究了满足环境需求的水的可用性。根据这些新分析的发现，将结果汇总到手稿中，并将其作为论文提交给期刊。