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Are we using the right fuel to drive hydrological models? A climate impact study in the Upper Blue Nile

机译:我们是使用正确的燃料来驱动水文模型吗? 在上蓝尼罗河的气候影响研究

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摘要

Climate simulations are the fuel to drive hydrological models that are used to assess the impacts of climate change and variability on hydrological parameters, such as river discharges, soil moisture, and evapotranspiration. Unlike with cars, where we know which fuel the engine requires, we never know in advance what unexpected side effects might be caused by the fuel we feed our models with. Sometimes we increase the fuel's octane number (bias correction) to achieve better performance and find out that the model behaves differently but not always as was expected or desired. This study investigates the impacts of projected climate change on the hydrology of the Upper Blue Nile catchment using two model ensembles consisting of five global CMIP5 Earth system models and 10 regional climate models (CORDEX Africa). WATCH forcing data were used to calibrate an eco-hydrological model and to bias-correct both model ensembles using slightly differing approaches. On the one hand it was found that the bias correction methods considerably improved the performance of average rainfall characteristics in the reference period (1970-1999) in most of the cases. This also holds true for non-extreme discharge conditions between Q(20) and Q(80). On the other hand, bias-corrected simulations tend to overemphasize magnitudes of projected change signals and extremes. A general weakness of both uncorrected and bias-corrected simulations is the rather poor representation of high and low flows and their extremes, which were often deteriorated by bias correction. This inaccuracy is a crucial deficiency for regional impact studies dealing with water management issues and it is therefore important to analyse model performance and character-istics and the effect of bias correction, and eventually to exclude some climate models from the ensemble. However, the multi-model means of all ensembles project increasing average annual discharges in the Upper Blue Nile catchment and a shift in seasonal pat
机译:气候模拟是推动水文模型的燃料,用于评估气候变化和可变性对水文参数的影响,如河流流量、土壤湿度和蒸散量。与汽车不同的是,我们知道发动机需要哪些燃料,但我们永远不会事先知道我们为模型提供的燃料可能会产生什么意外的副作用。有时我们会增加燃料的辛烷值(偏差校正),以获得更好的性能,并发现模型的行为有所不同,但并不总是如预期或期望的那样。本研究使用由五个全球CMIP5地球系统模型和10个区域气候模型(CORDEX Africa)组成的两个模型集合,调查预测气候变化对上蓝尼罗河流域水文的影响。观测强迫数据用于校准生态水文模型,并使用略有不同的方法对两个模型集合进行偏差校正。一方面,人们发现,在大多数情况下,偏差校正方法显著改善了参考期(1970-1999年)平均降雨特征的表现。这也适用于Q(20)和Q(80)之间的非极端放电条件。另一方面,经过偏差校正的模拟往往过分强调预测变化信号和极端值的大小。未修正模拟和偏差修正模拟的一个普遍缺点是,高流量和低流量及其极值的表现相当差,偏差修正往往会使其恶化。这种不准确是处理水管理问题的区域影响研究的一个关键缺陷,因此,分析模型性能和特征以及偏差校正的效果,并最终将一些气候模型从集合中排除是很重要的。然而,所有集合的多模型平均值预计,青尼罗上游流域的年平均流量将增加,季节性pat将发生变化

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