将来の環境流量に対する気候変動の影響評価

在上一财年，研究发现，与使用传统单deme GP开发的模型相比，使用并行遗传规划（PGP）算法开发的模型（在本研究中引入）显示出更好的泛化技能，并且产生更少的非物理大异常值。然而，传统的GP和PGP算法都不能准确地捕捉到观测时间序列中的极值。初步研究发现，逐月降尺度方法与逐月至逐日分解方法相结合具有更好的极值模拟潜力，但模型的整体性能不如逐日降尺度模型。此外，已知当观测中存在具有相似总数/平均值但月内分布显著不同的月份时，分解方法表现不佳。在这一年中，对降水、温度和水流极端模拟的改进进行了全面调查，重点是使用分解。研究了将日模拟与月-日分解模拟相结合的可能方法。用改进的方法预测了未来的河流流量，调查了满足环境需要的水的可用性。结果被总结成手稿，并根据这些新的分析结果作为论文提交给期刊。

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.