Application 1721 A2 2016 – Long-term changes in river discharge in central Europe 1869-2016: effects of ice cover change, lake formation and the dynamic hydrological cycle
Long-term changes in river drainage in central Europe 1869-2016: impact of changing ice cover, lake formation and the strong hydrological cycle Long-term changes in river drainage in central Europe 1869-2016: impact of glacial adaptation … Erwin Rottler et al . already
Application 1721 A2 2016
Invited presentation by Erwin Rottler, recipient of Climate: EGU Outstanding Student Poster, PICO Present and Future Awards 2018.
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Recent climate changes have the potential to significantly alter river flows, especially in ice-laden rivers. The impacts of ice cover changes are particularly high with changes in precipitation and anthropogenic modification of water networks and rivers. In an effort to identify and detect the long-term effects of different methods, we use a set of analytical tools to produce high-resolution long-term changes in river flow. We combine quantitative sampling with dynamic movement statistics and empirical method decomposition and use these tools to extract recorded data from rivers with nival, pluvial and mixed flow regimes and temperature and precipitation data covering a frame period 1869-2016. Focusing on central Europe, we investigate the long-term effects of snow cover and precipitation changes and their interaction with lake formation.
Our results show that the seasonal flow of ice-filled rivers is decreasing. Flow increases in winter and spring, while runoff decreases in summer and early fall. This redistribution of annual runoff is largely due to the formation of alpine fringe lakes. In the last century, large parts of Alpine rivers were drained for hydropower. In recent decades, changes in water flow caused by dam construction seem to outpace changes in ice cover. We suggest that Alpine signals move downstream and influence the flow well beyond the Alpine zone in river regions with mixed flow regimes. Furthermore, our results mean more (heavy) precipitation in the last decade. The observed increase in peak discharge can be traced back to corresponding changes in precipitation.
Rottler, E., Francke, T., Bürger, G., and Bronstert, A. Earth Syst. Sci., 24, 1721–1740, https://doi.org/10.5194/-24-1721-2020, 2020.
In many regions of the world, rivers are important lifelines and the basis of human life. However, recent climate change may significantly affect the hydrological cycle and endanger the functional diversity of river systems. The worst changes will probably occur in the frozen rivers. In a warmer world, the properties of snow cover and how snowmelt contributes to river flow will change dramatically. Rising temperatures are expected to result in less winter precipitation such as snow and earlier melting of existing snowpack in spring (Barnett et al., 2005; Simpkins, 2018; Kormann et al., 2015; Birsan et al., 2005). Recent studies suggest that the amount of rain and the number of heavy rain events increase due to the warming of the air that holds more water and better evaporation (Lehmann et al., 2015; Coumou and Rahmstorf, 2012; Mueller and Pfister, 2011) . Examining changes in snowpack characteristics and snowmelt in large mountain areas, Stewart (2009) summarizes that “both temperature and precipitation increases have so far affected mountain snowpack” already. In the Rhine, one of the most important rivers in Europe, Stahl et al. (2016) show that “the impact of climate change can be seen mainly in the temporal changes of the sub-season and the extent of the hydrological regime of snow and glacier melt in full alpine runs.”
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In addition to changes in snowpack and precipitation, anthropogenic changes in the land surface, underground structures and river network change the river flow. In the 20th century, more than 45 000 large dams were built worldwide (Word Commission on Dams, 2000). Also in the Rhine basin, human activities change water flow in terms of volume, its temporal distribution and water quality (Wildenhahn and Klaholz, 1996; Belz et al., 2007; Wildi et al., 2004).
Current knowledge of how climate change and river basin change affect river flow comes largely from records of hydroclimatic variables, particularly temperature, precipitation and flow. Birsan et al. (2005). the problem of interpreting trends in data flow. “Furthermore, the quality and length of the recorded time series are often insufficient to identify and distinguish the effects of different processes. Sufficient time span is important, among other things, to distinguish between natural climate variability and symptoms of climate change. Large-scale atmospheric flow variability on annual to multi-decadal scales, for example, can cause changes in hydroclimatic data, which can contradict or amplify signals of long-term changes (Hanson et al., 2006; Frei et al., 2000; Kerr, 2000; Scherrer et al. , 2016).Research that prepares and analyzes long-term time series of high quality is very important and underlines our current understanding of the characteristics and extent of recent climate changes (eg Vincent et al., 2002; Begert et al., 2005; Schmidli and Frei, 2005; Moberg et al., 2006; Scherrer et al., 2016).In general, simple linear regression methods are used to estimate climate change factors. One frequently used analytical tool in this context is the non-parametric Mann-Kendall robust trend test (Kendall, 1975; Theil, 1950; Sen, 1968). However, it is difficult to limit the test to confirm only linear trends. The potential for more detailed analyzes regarding seasons, movement time windows (e.g., Kormann et al., 2015 ) or target variable values has not been addressed. To integrate and extend the findings obtained so far, new sets of analytical tools are needed to extract, develop, determine and apply to climate and hydrological records.
Our research aims to better understand long-term changes in river flows and identify driving mechanisms, by analyzing hydro-climatic time series of daily changes recorded in central Europe between 1869 and 2016. We investigate long-term changes in a very smooth way . method by combining quantitative sampling, trend statistics and empirical method decomposition. The two main research questions we want to answer are as follows.
We study the discharge time series at four gauge stations (Figure 1 and Table 1). The presented gases stand out for the specific length of their records and represent different types of flow regimes: nival, pluvial and complex. The Wasserburg Gauge is located on the river Inn in Upper Bavaria, Germany. The river Inn is a separate tributary of the Danube. The source of the river is in the Swiss Alps and most of its drainage area has a high alpine character (1.20 × 104 km2 up to the Wasserburg diameter). Three other research centers, namely Basel, Würzburg and Cologne, are located in the Rhine basin. The Rhine is one of the largest rivers in Europe. It is the waterway and the most used way of life in the area. At the base of Basel, the flow of the river is controlled by snow and precipitation flow from the Alps. Gauge Würzburg is located on the Great River in northern Bavaria, Germany. The Grutte Rivier is its own tributary of the Rhine. The catchment area up to the Würzburg benchmark is 1.40 × 104 km2. The city of Cologne is the largest city on the Rhine and is located in the Lower Rhine region after the confluence of the main tributaries. Up to Cologne, the Rhine covers an area of 1.44 × 105 km2. For all selected gauges, resolution data at daily corrections are available since at least 1869. For the Basel gauge, a statistical test of the daily means by Pfister et al. (2006) show that the estimated emissions have been the same since 1869 (the digital part of the time series); that is, the values do not have anthropogenic effects such as instrument changes, daily recording frequency changes or river bed lowering (Pfister et al., 2006). Some of the investigated measuring stations are part of the hydrometric observation network of the water bodies in Germany. Records are regularly checked to ensure high quality and reliability. Runoff time series were obtained from the Global Runoff Data Center (GRDC). Data from the GRDC were used as is without further manipulation. Elevation distribution and monthly Pardé coefficients for the studied rivers are presented in the Appendix (Fig. A1).
Jane M. Horgan
Table 1 Analyzed database: station name, relative river, location (WSG 84), altitude (m), time series of daily corrections analyzed for temperature (T), humidity (P) and discharge (D), catchment area, mean discharge (MQ) ) and data source by the Global Runoff Data Center (GRDC) and the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss).
In addition, we analyze the daily temperature and precipitation data provided by the Swiss Federal Office for Meteorology and Climate (MeteoSwiss). In MeteoSwiss, a standard homogenization procedure is applied to a set of monthly temperature and precipitation time series (Begert et al., 2005). Through this homogenization process, monthly correction values were also applied to the daily release data. Homogenization of long-term climate time series is necessary to correct for non-climatological factors that affect the data. Currently, daily temperature/precipitation data is available from stations 28 – 73. In the following, we focus on weather stations where both temperature and precipitation data are available since
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