41 research outputs found
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)Â 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 (1976–1986, 1987–1997, 1998–2008), 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.
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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><sub>95</sub> low-flow projections in the
future period. In Austria, the range of simulated <i>Q</i><sub>95</sub> 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><sub>95</sub> may result in a
range of up to 60 % depending on the decade used for calibration.
<br><br>
The low-flow projections of Q<sub>95</sub> 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 −5 to −20 % for the
climate change projected for the future period 2021–2050, relative the reference
period 1978–2007. 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><sub>95</sub> projections is
the largest in basins with a winter low-flow regime and, in some basins the
range of <i>Q</i><sub>95</sub> 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
Climate driven trends in historical extreme low streamflows on four continents
Understanding temporal trends in low streamflows is important for water management and ecosystems. This work focuses on trends in the occurrence rate of extreme low-flow events (5- to 100-year return periods) for pooled groups of stations. We use data from 1,184 minimally altered catchments in Europe, North and South America, and Australia to discern historical climate-driven trends in extreme low flows (1976–2015 and 1946–2015). The understanding of low streamflows is complicated by different hydrological regimes in cold, transitional, and warm regions. We use a novel classification to define low-flow regimes using air temperature and monthly low-flow frequency. Trends in the annual occurrence rate of extreme low-flow events (proportion of pooled stations each year) were assessed for each regime. Most regimes on multiple continents did not have significant (p < 0.05) trends in the occurrence rate of extreme low streamflows from 1976 to 2015; however, occurrence rates for the cold-season low-flow regime in North America were found to be significantly decreasing for low return-period events. In contrast, there were statistically significant increases for this period in warm regions of NA which were associated with the variation in the Pacific Decadal Oscillation. Significant decreases in extreme low-flow occurrence rates were dominant from 1946 to 2015 in Europe and NA for both cold- and warm-season low-flow regimes; there were also some non-significant trends. The difference in the results between the shorter (40-year) and longer (70-year) records and between low-flow regimes highlights the complexities of low-flow response to changing climatic conditions
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 NiederwasserabflĂĽssen <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