Abstract Low flow conditions are governed by short-to-medium term weather conditions or long term climate conditions. This prompts the question: given climate scenarios, is it possible to assess future extreme low flow conditions from climate data indices (CDIs)? Or should we rely on the conventional approach of using outputs of climate models as inputs to a hydrological model? Several CDIs were computed using 42 climate scenarios over the years 1961–2100 for two watersheds located in Quebec, Canada. The relationship between the CDIs and hydrological data indices (HDIs; 7- and 30-day low flows for two hydrological seasons) were examined through correlation analysis to identify the indices governing low flows. Results of the Mann-Kendall test, with a modification for autocorrelated data, clearly identified trends. A partial correlation analysis allowed attributing the observed trends in HDIs to trends in specific CDIs. Furthermore, results showed that, even during the spatial validation process, the methodological framework was able to assess trends in low flow series from: (i) trends in the effective drought index (EDI) computed from rainfall plus snowmelt minus PET amounts over ten to twelve months of the hydrological snow cover season or (ii) the cumulative difference between rainfall and potential evapotranspiration over five months of the snow free season. For 80% of the climate scenarios, trends in HDIs were successfully attributed to trends in CDIs. Overall, this paper introduces an efficient methodological framework to assess future trends in low flows given climate scenarios. The outcome may prove useful to municipalities concerned with source water management under changing climate conditions.

中文翻译：

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开发一种仅使用气候数据评估流域尺度低流量未来趋势的方法

摘要 低流量条件受中短期天气条件或长期气候条件的控制。这就提出了一个问题：在给定气候情景的情况下，是否有可能通过气候数据指数 (CDI) 来评估未来的极端低流量条件？还是我们应该依赖使用气候模型输出作为水文模型输入的传统方法？使用 1961-2100 年间的 42 个气候情景为位于加拿大魁北克的两个流域计算了几个 CDI。CDI 与水文数据指数（HDI；两个水文季节的 7 天和 30 天低流量）之间的关系通过相关性分析来确定控制低流量的指数。Mann-Kendall 检验的结果，对自相关数据进行了修改，清楚地识别了趋势。偏相关分析允许将观察到的 HDI 趋势归因于特定 CDI 的趋势。此外，结果表明，即使在空间验证过程中，该方法框架也能够从以下方面评估低流量系列的趋势：(i) 有效干旱指数 (EDI) 的趋势由降雨加融雪减去 PET 计算得出，超过 10 到水文积雪季节的十二个月或 (ii) 无雪季节五个月的降雨量和潜在蒸发量之间的累积差异。对于 80% 的气候情景，HDI 的趋势成功归因于 CDI 的趋势。总体而言，本文介绍了一种有效的方法框架，以评估给定气候情景下低流量的未来趋势。