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评估高流量和低流量水文预测中不同的不确定性来源:土耳其伊斯坦布尔奥默利盆地的案例研究。

Assessing different sources of uncertainty in hydrological projections of high and low flows: case study for Omerli Basin, Istanbul, Turkey.

作者信息

Engin Batuhan Eren, Yücel Ismail, Yilmaz Aysen

机构信息

Earth System Science Department, Graduate School of Natural and Applied Sciences, Middle East Technical University, 06800, Ankara, Turkey.

Civil Engineering Department, Middle East Technical University, 06800, Ankara, Turkey.

出版信息

Environ Monit Assess. 2017 Jul;189(7):347. doi: 10.1007/s10661-017-6059-3. Epub 2017 Jun 21.

Abstract

This study investigates the assessment of uncertainty contribution in projected changes of high and low flows from parameterization of a hydrological model and inputs of ensemble regional climate models (RCM). An ensemble of climate projections including 15 global circulation model (GCM)/RCM combinations and two bias corrections (change factor (CF) and bias correction in mean (BC)) was used to generate streamflow series for a reference and future period using the Hydrologiska Byråns Vattenbalansavdelning (HBV) model with the 25 best-fit parameter sets based on four objective functions. The occurrence time of high flows is also assessed through seasonality index calculation. Results indicated that the inputs of hydrological model from ensemble climate models accounts for greater contribution to the uncertainty related to projected changes in high flows comparing to the contribution from hydrological model parameterization. However, the uncertainty contribution is opposite for low flows, particularly for CF method. Both CF and BC increases the total mean variance of high and low flows. The variability in the occurrence time of high flows through RCMs is greater than the variability resulted from hydrological model parameters with and without statistical downscaling. The CF provides more accurate timing than BC and it shows the most pronounced changes in flood seasonality.

摘要

本研究调查了通过水文模型参数化和集合区域气候模型(RCM)输入来评估高流量和低流量预测变化中的不确定性贡献。使用包括15种全球环流模型(GCM)/RCM组合以及两种偏差校正方法(变化因子(CF)和均值偏差校正(BC))的气候预测集合,基于四个目标函数,利用具有25个最佳拟合参数集的水文气象局水均衡部门(HBV)模型,生成参考期和未来期的流量序列。还通过季节性指数计算来评估高流量的发生时间。结果表明,与水文模型参数化的贡献相比,来自集合气候模型的水文模型输入对与高流量预测变化相关的不确定性贡献更大。然而,对于低流量,不确定性贡献则相反,特别是对于CF方法。CF和BC都增加了高流量和低流量的总平均方差。通过RCMs得到的高流量发生时间的变异性大于水文模型参数(有无统计降尺度)所导致的变异性。CF比BC提供了更准确的时间,并且它在洪水季节性方面显示出最明显的变化。

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