Future shifts in extreme flow regimes in Alpine regions

Abstract

Abstract. Extreme low and high flows can have negative economical, societal, and ecological effects and are expected to become more severe in many regions due to climate change. Besides low and high flows, the whole flow regime is subject to changes. Knowledge on future changes in flow regimes is important since regimes contain information on both extremes and conditions prior to the dry and wet season. Changes in individual low- and high-flow characteristics as well as flow regimes under normal conditions have been thoroughly studied. In contrast, little is known about changes in extreme flow regimes. We here propose two methods for the estimation of extreme flow regimes and apply them to simulated discharge time series for future climate conditions in Switzerland. The first method relies on frequency analysis performed on annual flow duration curves. The second approach performs frequency analysis on the discharge sums of a large set of stochastically generated annual hydrographs. Both approaches were found to produce similar 100-year regime estimates when applied to a data set of 19 hydrological regions in Switzerland. Our results show that changes in both extreme low- and high-flow regimes for rainfall-dominated regions are distinct from those in melt-dominated regions. In rainfall-dominated regions, the minimum discharge of low-flow regimes decreases by up to 50 %, whilst the reduction is of 25 % for high-flow regimes. In contrast, the maximum discharge of low- and high-flow regimes increases by up to 50 %. In melt-dominated regions, the changes point into the other direction than those in rainfall-dominated regions. The minimum and maximum discharge of extreme regimes increase by up to 100 % and decrease by less than 50 %, respectively. Our findings provide guidance in water resources planning and management and the extreme regime estimates are a valuable basis for climate impact studies.

The importance of ecosystem adaptation on hydrological model predictions in response to climate change

“…4), we assume an unchanged land use and that vegetation has not adapted its root-zone storage capacity to the aridity and seasonality of the 2K climate. This scenario implies stationarity of model parameters by using S R,max,A in both the historical and 2K runs, a common assumption of many climate change impact assessment studies (Booij, 2005;de Wit et al, 2007;Prudhomme et al, 2014;Hakala et al, 2019;Brunner et al, 2019;Gao et al, 2020;Rottler et al, 2020). This is the benchmark scenario against which we compare the hydrological response considering non-stationarity of the system, as in the following three scenarios.…”

Pattern identification of flow regime variation in the source small watersheds in the water conservation zone of the Yellow River

“…地 理 科 学 进 展 第 42 卷 融水密切相关。Singh 等 [10] 发现未来气候情景下喜 马拉雅山西部 Sutlej 河的季节流量上升与降水增加 和温度升高所引起的冰川融化有关。Gray 等 [11] 预估 了未来气候变化情景下全球网格单元尺度的径流 和城市径流量级的变化趋势。Poff 等 [12] 提出河流径 流情势这一概念全面刻画了径流的综合变化特征， 主要包括 5 大信号即量级、 频率、 持续时间、 时序和 变率。此外， Richter 等 [13] 提出基于 IHA(indicators of hydrologic alteration) 的 变 化 范 围 (range of variability approach， RVA)法， 成为研究河流径流情势变 化程度及评估河流环境流量的常用方法。径流情 势不仅是河流结构和生态功能的主要驱动力， 影响 着河道的生物多样性， 还可以反映其对河流健康生 态系统稳定性和功能完整性的影响 [14][15] 。径流情势 通过直观反映河川径流的年际变化、 年内分配、 洪 水和枯水过程等极端水文事件变化特征及其空间 分异， 对评估径流过程的总体变化和改变程度有重 要作用 [16][17][18][19] 。目前， 径流情势指标已广泛应用于定 量评估气候变化和人类活动特别是水利工程的建 设对径流情势的改变程度， 以及对水生生态系统的 影响等 [20][21][22][23] 。如 Brunner 等 [24] 发现在瑞士由降雨和融 水分别主导地区的极端高、 低流量在未来气候变化 下的特征指标变化相反。Cui 等 [25] 和 Fang 等 [26] [30][31] 。 2.2.2 趋势检验 准确识别水文要素的趋势变化是科学认识变 化环境下水循环演变规律的基础， Mann-Kendall (MK)检验和 Sen 斜率估计作为稳健的非参数统计 的趋势识别方法， 广泛适用于水文气象资料时间序 列的趋势分析， 不受部分缺测数据影响 [32]…”