How Climate Extremes Influence Conceptual Rainfall-Runoff Model Performance and Uncertainty
How Climate Extremes Influence Conceptual Rainfall-Runoff Model Performance and Uncertainty
复制标题
极端气候如何影响概念降雨径流模型的性能和不确定性
作者：
Andrew Watson;G. Midgley;Patrick L Ray;S. Kralisch;J. Helmschrot
降雨径流模型经常用于评估气候风险，通过预测由于预期的人为气候变化，气候变率和土地管理引起的径流和其他水文过程的变化。历史观测通常用于经验校准概念水文机制的性能。因此，当外推到未来情景下的新气候条件时，校准程序受到限制。本文以南非贝格河流域为例，利用JAMS/J2000模型，探讨了该模型在当前气候条件尾部分布情况下的径流模型性能和模拟流域水文过程。一个进化的多目标搜索算法被用来开发一套参数，最好的模拟“湿”和“干”的时期，提供了时间的不确定性分析方法，以确定受这些极端气候的变量的上限和下限。受影响最大的变量包括土壤水储存和时间的interflow和地下水流，出现的模拟过程线的整体阻尼。以前的模拟表明，JAMS/J2000模型提供了一个“良好”的模拟期间，年长期平均降水量不足<28%。在此阈值以上，秋季降水量减少了50%，本文表明，建议使用一组“干”参数，以提高模式的性能。这些“干”参数更好地解释了径流集中时间的变化和峰值流量的减少，这发生在干燥的冬季，将2015-2018年验证期的纳什-萨克利夫效率（NSE）从0.26提高到0.60，尽管气候数据的可用性仍然是一个潜在的因素。由于使用长期校准的参数，在“湿润”期间模型性能“良好”（NSE > 0.7），因此不建议在贝格河流域使用“湿润”参数，但在热带气候中可能发挥很大作用。这项研究的结果可能转移到其他概念降雨/径流模型，但可能会有所不同的各种气候。随着气候变率的增加，世界各地的水文变化也在加剧，未来基于水文学的水文预测需要评估有关储存和模拟水文过程的假设，以加强气候风险管理。
Rainfall-runoff models are frequently used for assessing climate risks by predicting changes in streamflow and other hydrological processes due to anticipated anthropogenic climate change, climate variability, and land management. Historical observations are commonly used to calibrate empirically the performance of conceptual hydrological mechanisms. As a result, calibration procedures are limited when extrapolated to novel climate conditions under future scenarios. In this paper, rainfall-runoff model performance and the simulated catchment hydrological processes were explored using the JAMS/J2000 model for the Berg River catchment in South Africa to evaluate the model in the tails of the current distribution of climatic conditions. An evolutionary multi-objective search algorithm was used to develop sets of parameters which best simulate “wet” and “dry” periods, providing the upper and lower bounds for a temporal uncertainty analysis approach to identify variables which are affected by these climate extremes. Variables most affected included soil-water storage and timing of interflow and groundwater flow, emerging as the overall dampening of the simulated hydrograph. Previous modeling showed that the JAMS/J2000 model provided a “good” simulation for periods where the yearly long-term mean precipitation shortfall was <28%. Above this threshold, and where autumn precipitation was reduced by 50%, this paper shows that the use of a set of “dry” parameters is recommended to improve model performance. These “dry” parameters better account for the change in streamflow timing of concentration and reduced peak flows, which occur in drier winter years, improving the Nash-Sutcliffe Efficiency (NSE) from 0.26 to 0.60 for the validation period 2015–2018, although the availability of climate data was still a potential factor. As the model performance was “good” (NSE > 0.7) during “wet” periods using parameters from a long-term calibration, “wet” parameters were not recommended for the Berg River catchment, but could play a large role in tropical climates. The results of this study are likely transferrable to other conceptual rainfall/runoff models, but may differ for various climates. As greater climate variability drives hydrological changes around the world, future empirically-based hydrological projections need to evaluate assumptions regarding storage and the simulated hydrological processes, to enhanced climate risk management.