Since the beginning of hydrological research hydrologists have developed models that reflect their perception about how the catchments work and make use of the available information in the most efficient way. In this paper we develop hydrologic models based on field-mapped runoff generation mechanisms as identified by a geologist. For four different catchments in Austria, we identify four different lumped model structures and constrain their parameters based on the field-mapped information. In order to understand the usefulness of geologic information, we test their capability to predict river discharge in different cases: (i) without calibration and (ii) using the standard split-sample calibration/ validation procedure. All models are compared against each other. Results show that, when no calibration is involved, using the right model structure for the catchment of interest is valuable. A-priori information on model parameters does not always improve the results but allows for more realistic model parameters. When all parameters are calibrated to the discharge data, the different model structures do not matter, i.e., the differences can largely be compensated by the choice of parameters. When parameters are constrained based on field-mapped runoff generation mechanisms, the results are not better but more consistent between different calibration periods. Models selected by runoff generation mechanisms are expected to be more robust and more suitable for extrapolation to conditions outside the calibration range than models that are purely based on parameter calibration to runoff data.
The occurrence of river floods is strongly related to specific climatic conditions that favor extreme precipitation events leading to catchment saturation. Although the impact of precipitation and temperature patterns on river flows is a well discussed topic in hydrology, few studies have focused on the relationship between peak discharges and standard Climate Change Indices (ETCCDI) of precipitation and temperature, widely used in climate research. It is of interest to evaluate whether these indices are relevant for characterizing and predicting floods in the Alpine area. In this study, a correlation analysis of the ETCCDI indices annual time series and annual maximum flows is presented for the Piedmont Region, in North-Western Italy. Spearman’s rank correlation is used to determine which ETCCDI indices are temporally correlated with maximum discharges, allowing to hypothesize which climate drivers better explain the interannual variability of floods. Moreover, the influence of climate (decadal) variability on the tendency of annual maximum discharges is examined by spatially correlating temporal trends of climate indices with temporal trends of the discharge series in the last twenty years, calculated using the Theil-Sen slope estimator. Results highlight that, while extreme precipitation indices are highly correlated with extreme discharges at the annual timescale, with different indices that are consistent with catchment size, the decadal tendencies of extreme discharges may be better explained by the decadal tendencies of the total annual precipitation over the study area. This suggests that future projections of the annual precipitation available from climate models simulations, whose reliability is higher compared to precipitation extremes, may be used as covariates for non-stationary flood frequency analysis.
Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union–EGU in Vienna in April 2017. This event was a follow-up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process, and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the above journals.
This study evaluates MODIS snow cover characteristics for large number of snowmelt runoff events in 145 catchments from 9 countries in Europe. The analysis is based on open discharge daily time series from the Global Runoff Data Center database and daily MODIS snow cover data. Runoff events are identified by a base flow separation approach. The MODIS snow cover characteristics are derived from Terra 500 m observations (MOD10A1 dataset, V005) in the period 2000–2015 and include snow cover area, cloud coverage, regional snowline elevation (RSLE) and its changes during the snowmelt runoff events. The snowmelt events are identified by using estimated RSLE changes during a runoff event. The results indicate that in the majority of catchments there are between 3 and 6 snowmelt runoff events per year. The mean duration between the start and peak of snowmelt runoff events is about 3 days and the proportion of snowmelt events in all runoff events tends to increase with the maximum elevation of catchments. Clouds limit the estimation of snow cover area and RSLE, particularly for dates of runoff peaks. In most of the catchments, the median of cloud coverage during runoff peaks is larger than 80%. The mean minimum RSLE, which represents the conditions at the beginning of snowmelt events, is situated approximately at the mean catchment elevation. It means that snowmelt events do not start only during maximum snow cover conditions, but also after this maximum. The mean RSLE during snowmelt peaks is on average 170 m lower than at the start of the snowmelt events, but there is a large regional variability.
Substantial evidence shows that the frequency of hydrological extremes has been changing and is likely to continue to change in the near future. Non-stationary models for flood frequency analyses are one method of accounting for these changes in estimating design values. The objective of the present study is to compare four models in terms of goodness of fit, their uncertainties, the parameter estimation methods and the implications for estimating flood quantiles. Stationary and non-stationary models using the GEV distribution were considered, with parameters dependent on time and on annual precipitation. Furthermore, in order to study the influence of the parameter estimation approach on the results, the maximum likelihood (MLE) and Bayesian Monte Carlo Markov chain (MCMC) methods were compared. The methods were tested for two gauging stations in Slovenia that exhibit significantly increasing trends in annual maximum (AM) discharge series. The comparison of the models suggests that the stationary model tends to underestimate flood quantiles relative to the non-stationary models in recent years. The model with annual precipitation as a covariate exhibits the best goodness-of-fit performance. For a 10% increase in annual precipitation, the 10-year flood increases by 8%. Use of the model for design purposes requires scenarios of future annual precipitation. It is argued that these may be obtained more reliably than scenarios of extreme event precipitation which makes the proposed model more practically useful than alternative models.
Direct interpolation of daily runoff observations to ungauged sites is an alternative to hydrological model regionalisation. Such estimation is particularly important in small headwater basins characterized by sparse hydrological and climate observations, but often large spatial variability. The main objective of this study is to evaluate predictive accuracy of top-kriging interpolation driven by different number of stations (i.e. station densities) in an input dataset. The idea is to interpolate daily runoff for different station densities in Austria and to evaluate the minimum number of stations needed for accurate runoff predictions. Top-kriging efficiency is tested for ten different random samples in ten different stations densities. The predictive accuracy is evaluated by ordinary cross-validation and full-sample crossvalidations. The methodology is tested by using 555 gauges with daily observations in the period 1987-1997. The results of the cross-validation indicate that, in Austria, top-kriging interpolation is superior to hydrological model regionalisation if station density exceeds approximately 2 stations per 1000 km2 (175 stations in Austria). The average median of Nash-Sutcliffe cross-validation efficiency is larger than 0.7 for densities above 2.4 stations/1000 km2 . For such densities, the variability of runoff efficiency is very small over ten random samples. Lower runoff efficiency is found for low station densities (less than 1 station/1000 km2 ) and in some smaller headwater basins.