Abstract δ18O in precipitation at station Liptovský Mikuláš (about 8.5 km south from the outlet of the Jalovecký Creek catchment) remains constantly higher since 2014 that might be related to greater evaporation in the region of origin of the air masses bringing precipitation to the studied part of central Europe. Increased δ18O values are reflected also in the Jalovecký Creek catchment runoff. Seasonality of δ18O in the Jalovecký Creek became less pronounced since 2014. The most significant trends found in annual hydrological data series from the catchment in the study period 1989–2018 have the correlation coefficients 0.4 to 0.7. These trends are found in the number of flow reversals (change from increasing to decreasing discharge and vice versa), June low flow, number of simple runoff events in summer months (June to September) and the flashiness index. The attribution analysis suggests that drivers responsible for the changes in these data series include the number of periods with precipitation six and more days long, total precipitation amount in February to June, number of days with precipitation in June to September and total precipitation in May on days with daily totals 10 mm and more, respectively. The coefficients of determination show that linear regressions between the drivers and supposedly changed data series explain only about 31% to 36% of the variability. Most of the change points detected in the time series by the Wild Binary Segmentation method occur in the second and third decades of the study period. Both hydrometric and isotopic data indicate that hydrological cycle in the catchment after 2014 became different than before.

中文翻译：

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原始山区流域水文循环变化分析[J]. 2. 同位素数据、趋势和归因分析

摘要 2014 年以来，Liptovský Mikuláš 站（距 Jalovecký Creek 流域出口以南约 8.5 公里）降水中的 δ18O 一直保持在较高水平，这可能与气团起源地区的更大蒸发有关，将降水带到研究的部分欧洲中部。δ18O 值的增加也反映在 Jalovecký Creek 流域径流中。自 2014 年以来，Jalovecký Creek 中 δ18O 的季节性变得不那么明显。在 1989-2018 研究期间的流域年度水文数据系列中发现的最显着趋势的相关系数为 0.4 至 0.7。这些趋势体现在逆流次数（从增加流量变为减少流量，反之亦然）、6 月低流量、夏季月份（6 月至 9 月）的简单径流事件数量和闪光指数。归因分析表明，导致这些数据系列变化的驱动因素包括降水时间为 6 天及以上的时段数、2-6 月的总降水量、6-9 月的降水天数和 5 月的总降水量。天数分别为 10 毫米或更多。决定系数表明，驱动因素和假定发生变化的数据系列之间的线性回归仅解释了大约 31% 到 36% 的可变性。Wild Binary Segmentation 方法在时间序列中检测到的大多数变化点发生在研究期间的第二个和第三个十年。