ALBERT

Description: Investigating long range dependence of river flows, especially in connection with various climate and storage related factors, is important in order to improve stochastic models for long range dependence and in order to understand deterministic and stochastic variability in long term behaviour of streamflow. Long range dependence expressed by the Hurst coefficient H is estimated for 39 (deseasonalized) mean daily runoff time series in Europe of at least 60 years using five estimators (rescaled range, regression on periodogram, Whittle, aggregated variances, and least squares based on variance). All methods yield estimates of H  〉 0.5 for all data sets. The results from the different estimators are significantly positively correlated for all pairs of methods indicating consistency of the methods used. Correlations between H and various catchment attributes are also analysed. H is strongly positively correlated with catchment area. Apparently, increasing storage with catchment area translates into increasing long range dependence. H is also positively correlated with mean discharge and air temperature and negatively correlated with the mean specific discharge and the seasonality index (maximum Pardé coefficient). No significant correlation is found between the Hurst coefficient and the length of the analyzed time series. The correlations are interpreted in terms of snow processes and catchment wetness. Copyright © 2012 John Wiley & Sons, Ltd.

Description: 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 by regularising the point variogram over the catchment area (kriging support), which accounts for the nested nature of the catchments. We test the top-kriging method for a comprehensive Austrian data set of low stream flows. We compare it with the regional regression approach where linear regression models between low stream flow and catchment characteristics are fitted independently for sub-regions of the study area that are deemed to be homogeneous in terms of flow processes. Leave-one-out cross-validation results indicate that top-kriging outperforms the regional regression on average over the entire study domain. The coefficients of determination (cross-validation) of specific low stream flows are 0.75 and 0.68 for the top-kriging and regional regression methods, respectively. For locations without upstream data points, the performances of the two methods are similar. 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. © 2012 John Wiley & Sons, Ltd.

Description: Investigating long range dependence of river flows, especially in connection with various climate and storage related factors, is important in order to improve stochastic models for long range dependence and in order to understand deterministic and stochastic variability in long-term behaviour of streamflow. Long range dependence expressed by the Hurst coefficient H is estimated for 39 (deseasonalized) mean daily runoff time series in Europe of at least 59years using five estimators (rescaled range, regression on periodogram, Whittle, aggregated variances, and least squares based on variance). All methods yield estimates of H〉0.5 for all data sets. The results from the different estimators are significantly positively correlated for all pairs of methods indicating consistency of the methods used. Correlations between H and various catchment attributes are also analysed. H is strongly positively correlated with catchment area. Apparently, increasing storage with catchment area translates into increasing long range dependence. H is also positively correlated with mean discharge and air temperature and negatively correlated with the mean specific discharge and the seasonality index (maximum Pardé coefficient). No significant correlation is found between the Hurst coefficient and the length of the analyzed time series. The correlations are interpreted in terms of snow processes and catchment wetness. © 2012 John Wiley & Sons, Ltd.

Description: In this study we examine three seasonality indices for their potential in regionalizing low flows. The indices are seasonality histograms (SHs) that represent the monthly distribution of low flows, a cyclic seasonality index (SI) that represents the average timing of low flows within a year, and the seasonality ratio (SR), which is the ratio of summer and winter low flows. The rationale of examining these indices is the recognition that summer and winter low flows are subject to important differences in the underlying hydrological processes. We analyse specific low flow discharges q95, i.e. the specific discharge that is exceeded on 95% of all days at a particular site. Data from 325 subcatchments in Austria, ranging in catchment area from 7 to 963 km2, are used in the analysis. In a first step, three seasonality indices are compared. Their spatial patterns can be interpreted well on hydrological grounds. In a second step, the indices are used to classify the catchments into two, three, and eight regions based on different combinations of the indices. In a third step, the value of the seasonality indices for low flow regionalization is examined by comparing the crossvalidation performance of multiple regressions between low flows and catchment characteristics. The regressions make use of the three seasonality-based classifications. The results indicate that grouping the study area into two regions and three regions and separate regressions in each region gives the best performance. A global regression model yields the lowest performance and a global regression model that uses different calibration coefficients in each of the eight regions only performs slightly better. This suggests that separate regression models in each of the regions are to be preferred over a global model in order to represent differences in the way catchment characteristics are related to low flow. Copyright © 2006 John Wiley & Sons, Ltd.

Description: This is the third of a three-part paper series through which we assess the performance of runoff predictions in ungauged basins in a comparative way. Whereas the two previous papers by Parajka et al. (2013) and Salinas et al. (2013) assess the regionalisation performance of hydrographs and hydrological extremes on the basis of a comprehensive literature review of thousands of case studies around the world, in this paper we jointly assess prediction performance of a range of runoff signatures for a consistent and rich dataset. Daily runoff time series are predicted for 213 catchments in Austria by a regionalised rainfall–runoff model and by Top-kriging, a geostatistical estimation method that accounts for the river network hierarchy. From the runoff time-series, six runoff signatures are extracted: annual runoff, seasonal runoff, flow duration curves, low flows, high flows and runoff hydrographs. The predictive performance is assessed in terms of the bias, error spread and proportion of unexplained spatial variance of statistical measures of these signatures in cross-validation (blind testing) mode. Results of the comparative assessment show that, in Austria, the predictive performance increases with catchment area for both methods and for most signatures, it tends to increase with elevation for the regionalised rainfall–runoff model, while the dependence on climate characteristics is weaker. Annual and seasonal runoff can be predicted more accurately than all other signatures. The spatial variability of high flows in ungauged basins is the most difficult to estimate followed by the low flows. It also turns out that in this data-rich study in Austria, the geostatistical approach (Top-kriging) generally outperforms the regionalised rainfall–runoff model.

Description: The objective of this paper is to assess the performance of methods that predict low flows and flood runoff in ungauged catchments. The aim is to learn from the similarities and differences between catchments in different places, and to interpret the differences in performance in terms of the underlying climate-landscape controls. The assessment is performed at two levels. The Level 1 assessment is a meta-analysis of 14 low flow prediction studies reported in the literature involving 3112 catchments, and 20 flood prediction studies involving 3023 catchments. The Level 2 assessment consists of a more focused and detailed analysis of individual basins from selected studies from Level 1 in terms of how the leave-one-out cross-validation performance depends on climate and catchment characteristics as well as on the regionalisation method. The results indicate that both flood and low flow predictions in ungauged catchments tend to be less accurate in arid than in humid climates and more accurate in large than in small catchments. There is also a tendency towards a somewhat lower performance of regressions than other methods in those studies that apply different methods in the same region, while geostatistical methods tend to perform better than other methods. Of the various flood regionalisation approaches, index methods show significantly lower performance in arid catchments than regression methods or geostatistical methods. For low flow regionalisation, regional regressions are generally better than global regressions.

Description: Recent studies highlight that spatial interpolation techniques of point data can be effectively applied to the problem of regionalization of hydrometric information. This study compares two innovative interpolation techniques for the prediction of low-flows in ungauged basins. The first one, named Physiographical-Space Based Interpolation (PSBI), performs the spatial interpolation of the desired streamflow index (e.g., annual streamflow, low-flow index, flood quantile, etc.) in the space of catchment descriptors. The second technique, named Topological kriging or Top-kriging, predicts the variable of interest along river networks taking both the area and nested nature of catchments into account. PSBI and Top-kriging are applied for the regionalization of Q355 (i.e., a low-flow index that indicates the streamflow that is equalled or exceeded 355 days in a year, on average) over a broad geographical region in central Italy, which contains 51 gauged catchments. The two techniques are cross-validated through a leave-one-out procedure at all available gauges and applied to a subregion to produce a continuous estimation of Q355 along the river network extracted from a 90m elevation model. The results of the study show that Top-kriging and PSBI present complementary features. Top-kriging outperforms PSBI at larger river branches while PSBI outperforms Top-kriging for headwater catchments. Overall, they have comparable performances (Nash-Sutcliffe efficiencies in cross-validation of 0.89 and 0.83, respectively). Both techniques provide plausible and accurate predictions of Q355 in ungauged basins and represent promising opportunities for regionalization of low-flows.

Description: The objective of this paper is to assess the performance of methods that predict low flows and flood runoff in ungauged catchments. The aim is to learn from the similarities and differences between catchments in different places, and to interpret the differences in performance in terms of the underlying climate-landscape controls. The assessment is performed at two levels. The Level 1 assessment is a meta-analysis of 14 low flow prediction studies reported in the literature involving 3112 catchments, and 20 flood prediction studies involving 3023 catchments. The Level 2 assessment consists of a more focused and detailed analysis of individual basins from selected studies from Level 1 in terms of how the leave-one-out cross-validation performance depends on climate and catchment characteristics as well as on the regionalisation method. The results indicate that both flood and low flow predictions in ungauged catchments tend to be less accurate in arid than in humid climates and more accurate in large than in small catchments. There is also a tendency towards a somewhat lower performance of regressions than other methods in those studies that apply different methods in the same region, while geostatistical methods tend to perform better than other methods. Of the various flood regionalisation approaches, index methods show significantly lower performances in arid catchments than regression methods or geostatistical methods. For low flow regionalisation, regional regressions are generally better than global regressions.

Description: 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 parameterizations of a conceptual semi-distributed hydrologic model (TUWmodel) using 11 objective functions in three different decades (1976–1986, 1987–1997, 1998–2008), which allows disentangling the effect of modeling 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. 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 Q95 low flow projections in the future period. In Austria, the calibration uncertainty in terms of Q95 is larger in basins with summer low flow regime than in basins with winter low flow regime. Using different calibration periods may result in a range of up to 60 % in simulated Q95 low flows. The low flow projections 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 period 2021–2050 relative the reference period 1976–2008. 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. In 85 % of the basins, the uncertainty in Q95 from model calibration is larger than the uncertainty from different climate scenarios. The total uncertainty of Q95 projections is the largest in basins with winter low flow regime and, in some basins, exceeds 60 %. In basins with summer low flows and the total uncertainty is mostly less than 20 %. While the calibration uncertainty dominates over climate projection uncertainty in terms of low flow magnitudes, the opposite is the case for low flow seasonality. The implications of the uncertainties identified in this paper for water resources management are discussed.