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.
In many Austrian catchments in recent decades an increase in the mean annual air temperature and precipitation has been observed, but only a small change in the mean annual runoff. The main objective of this paper is (1) to analyze alterations in the performance of a conceptual hydrological model when applied in changing climate conditions and (2) to assess the factors and model parameters that control these changes. A conceptual rainfall-runoff model (the TUW model) was calibrated and validated in 213 Austrian basins from 1981–2010. The changes in the runoff model’s efficiency have been compared with changes in the mean annual precipitation and air temperature and stratified for basins with dominant snowmelt and soil moisture processes. The results indicate that while the model’s efficiency in the calibration period has not changed over the decades, the values of the model’s parameters and hence the model’s performance (i.e., the volume error and the runoff model’s efficiency) in the validation period have changed. The changes in the model’s performance are greater in basins with a dominant soil moisture regime. For these basins, the average volume error which was not used in calibration has increased from 0% (in the calibration periods 1981–1990 or 2001–2010) to 9% (validation period 2001–2010) or –8% (validation period 1981–1990), respectively. In the snow-dominated basins, the model tends to slightly underestimate runoff volumes during its calibration (average volume error = –4%), but the changes in the validation periods are very small (i.e., the changes in the volume error are typically less than 1–2%). The model calibrated in a colder decade (e.g., 1981–1990) tends to overestimate the runoff in a warmer and wetter decade (e.g., 2001–2010), particularly in flatland basins. The opposite case (i.e., the use of parameters calibrated in a warmer decade for a colder, drier decade) indicates a tendency to underestimate runoff. A multidimensional analysis by regression trees showed that the change in the simulated runoff volume is clearly related to the change in precipitation, but the relationship is not linear in flatland basins. The main controlling factor of changes in simulated runoff volumes is the magnitude of the change in precipitation for both groups of basins. For basins with a dominant snowmelt runoff regime, the controlling factors are also the wetness of the basins and the mean annual precipitation. For basins with a soil moisture regime, landcover (forest) plays an important role.
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.