A three-pillar approach to assessing climate impacts on low flows

The objective of this paper is to present a framework for assessing climate impacts on future low flows that combines different sources of information, termed pillars. To illustrate the framework three pillars are chosen: (a) extrapolation of observed low-flow trends into the future, (b) rainfall–runoff projections based on climate scenarios and (c) extrapolation of changing stochastic rainfall characteristics into the future combined with rainfall–runoff modelling. Alternative pillars could be included in the overall framework. The three pillars are combined by expert judgement based on a synoptic view of data, model outputs and process reasoning. The consistency/inconsistency between the pillars is considered an indicator of the certainty/uncertainty of the projections. The viability of the framework is illustrated for four example catchments from Austria that represent typical climate conditions in central Europe. In the Alpine region where winter low flows dominate, trend projections and climate scenarios yield consistently increasing low flows, although of different magnitudes. In the region north of the Alps, consistently small changes are projected by all methods. In the regions in the south and south-east, more pronounced and mostly decreasing trends are projected but there is disagreement in the magnitudes of the projected changes. The process reasons for the consistencies/inconsistencies are discussed. For an Alpine region such as Austria the key to understanding low flows is whether they are controlled by freezing and snowmelt processes, or by the summer moisture deficit associated with evaporation. It is argued that the three-pillar approach offers a systematic framework of combining different sources of information aimed at more robust projections than that obtained from each pillar alone

Uncertainty contributions to low-flow projections in Austria

The main objective of the paper is to understand the contributions to the uncertainty in low-flow projections resulting from hydrological model uncertainty and climate projection uncertainty. Model uncertainty is quantified by different parameterisations of a conceptual semi-distributed hydrologic model (TUWmodel) using 11 objective functions in three different decades (1976&ndash;1986, 1987&ndash;1997, 1998&ndash;2008), which allows for disentangling the effect of the objective function-related uncertainty and temporal stability of model parameters. Climate projection uncertainty is quantified by four future climate scenarios (ECHAM5-A1B, A2, B1 and HADCM3-A1B) using a delta change approach. The approach is tested for 262 basins in Austria. <br><br> The results indicate that the seasonality of the low-flow regime is an important factor affecting the performance of model calibration in the reference period and the uncertainty of <i>Q</i>₉₅ low-flow projections in the future period. In Austria, the range of simulated <i>Q</i>₉₅ in the reference period is larger in basins with a summer low-flow regime than in basins with a winter low-flow regime. The accuracy of simulated <i>Q</i>₉₅ may result in a range of up to 60 % depending on the decade used for calibration. <br><br> The low-flow projections of Q₉₅ show an increase of low flows in the Alps, typically in the range of 10–30 % and a decrease in the south-eastern part of Austria mostly in the range &minus;5 to &minus;20 % for the climate change projected for the future period 2021&ndash;2050, relative the reference period 1978&ndash;2007. The change in seasonality varies between scenarios, but there is a tendency for earlier low flows in the northern Alps and later low flows in eastern Austria. The total uncertainty of <i>Q</i>₉₅ projections is the largest in basins with a winter low-flow regime and, in some basins the range of <i>Q</i>₉₅ projections exceeds 60 %. In basins with summer low flows, the total uncertainty is mostly less than 20 %. The ANOVA assessment of the relative contribution of the three main variance components (i.e. climate scenario, decade used for model calibration and calibration variant representing different objective function) to the low-flow projection uncertainty shows that in basins with summer low flows climate scenarios contribute more than 75 % to the total projection uncertainty. In basins with a winter low-flow regime, the median contribution of climate scenario, decade and objective function is 29, 13 and 13 %, respectively. The implications of the uncertainties identified in this paper for water resource management are discussed

Flächenhafte Bestimmung von Hochwasserspenden

Der Projektabschlussbericht befasst sich mit der Bestimmung von Hochwasserscheitelabflüssen mit zugeordneter Jährlichkeit HQT an unbeobachteten Gewässerquerschnitten in Sachsen. Grundlage sind beobachtete Hochwasserscheitel an 113 Pegeln und hydrologische Überlegungen zur räumlichen Variabilität von Hochwassern. Zur Anwendung kommen Index-Flood-Verfahren, Top-Kriging und Georegression. Die Bewertung der Ergebnisse erfolgt mit einem Jack-Knife-Vergleich für die durch Pegel beobachteten Einzugsgebiete. Zur Bestimmung von Hochwasserspenden wird für Sachsen eine Kombination aller drei Verfahren empfohlen. Die Ergebnisse sollen Planern und Wasserbehörden für die Bemessung wasserbaulicher Anlagen zur Verfügung stehen

Hydrology needed to manage droughts: the 2015 European case

Hydrology needed to manage droughts: the 2015 European cas

Trends in flow intermittence for European rivers

Intermittent rivers are prevalent in many countries across Europe, but little is known about the temporal evolution of intermittence and its relationship with climate variability. Trend analysis of the annual and seasonal number of zero-flow days, the maximum duration of dry spells and the mean date of the zero-flow events is performed on a database of 452 rivers with varying degrees of intermittence between 1970 and 2010. The relationships between flow intermittence and climate are investigated using the standardized precipitation evapotranspiration index (SPEI) and climate indices describing large-scale atmospheric circulation. The results indicate a strong spatial variability of the seasonal patterns of intermittence and the annual and seasonal number of zero-flow days, highlighting the controls exerted by local catchment properties. Most of the detected trends indicate an increasing number of zero-flow days, which also tend to occur earlier in the year, particularly in southern Europe. The SPEI is found to be strongly related to the annual and seasonal zero-flow day occurrence in more than half of the stations for different accumulation times between 12 and 24 months. Conversely, there is a weaker dependence of river intermittence with large-scale circulation indices. Overall, these results suggest increased water stress in intermittent rivers that may affect their biota and biochemistry and also reduce available water resources

Prozessbasierte Regionalisierung von Niederwasserabflüssen <engl.>

Zsfassung in dt. Sprache11

Extreme weather exposure identification for road networks in heterogeneous landscapes

Resilient transport infrastructure is essential to the functioning of society and economy. Ensuring network"br" functionality is particularly vital in the case of severe weather events and natural disasters, which pose serious"br" threats to both people’s health and the integrity of infrastructure elements. Thus, providing reliable estimates"br" about the frequency and intensity of extreme weather impacts on road infrastructure is of major importance for"br" road maintenance, operation and construction. However, against the background of data scarcity in terms of"br" area-covering, long-term time series, the assessment of extreme weather events is difficult, especially in areas"br" with diverse landscape properties."br" In order to account for heterogeneous small-scale topographic conditions, a hot-spot approach based on selected"br" characteristic regions is used in this study. For each region, combinations of different extreme value approaches"br" and fitting methods are compared with respect to their value for assessing the exposure of transport networks to"br" extreme precipitation and temperature impacts. Four parameter estimation methods (maximum likelihood"br" estimation, probability weighted moments, generalized maximum likelihood estimation and Bayesian parameter"br" estimation) are applied to extreme value series obtained via both the block maxima approach (annual maxima"br" series, AMS) and the threshold excess approach (partial duration series, PDS). Their relative performances are"br" compared based on the CRMSE5, i.e. the conditional root mean square error for observations with a return period"br" exceeding 5 years, which gives much weight to the most extreme events."br" The viability of the approach is demonstrated at the example of Austria by analyzing five meteorological"br" indicators related to temperature and precipitation at 26 meteorological stations. These stations have been"br" selected to represent diverse meteorological conditions and different topographic regions. Results show the"br" merits of Bayesian parameter estimation methods as compared to traditional fitting methods. Bayesian"br" estimation of generalized Pareto (GP) distributions fitted to the PDS yielded the best results in 46% of all cases,"br" followed by Bayesian estimation of Generalized Extreme Value (GEV) distributions fitted to AMS, which"br" showed the best performance in 35% of all cases. The study suggests that the concept of meteorological hot spot"br" areas offers a suitable approach for characterizing extreme weather exposure of road networks in heterogeneous"br" landscapes. The presented framework may contribute to a comprehensive climate risk assessment of"br" infrastructure networks

Spatial prediction on river networks: comparison of top-kriging with regional regression

Top-kriging is a method for estimating stream flow related variables on a river network. Top-kriging treats these variables as emerging from a two-dimensional spatially continuous process in the landscape. The Top-kriging weights are estimated from catchment area (kriging support) accounting for the nested nature of the catchments. We test the Top-kriging method for a comprehensive Austrian dataset of low stream flows. We compare it with a regional regression model where the study domain is subdivided into eight regions. Leave-one-out cross-validation results indicate that Top-kriging outperforms the regional regression on average over the entire study domain. The coefficient of determination (cross-validation) of specific low stream flows is 0.75 and 0.68 for the Top-kriging and regional regression methods, respectively. For locations without upstream data points the two methods are similar because of the external information used in the regression. For locations with upstream data points Top-kriging performs much better than regional regression as it exploits the low flow information of the neighbouring locations.JRC.H.5-Land Resources Managemen