Abstract:This study uses field observations of the extent of saturated area over limited areas of the small Uhlirska catchment (1Ð87 km 2 ) in the Czech Republic in calibrating the parameters of a version of TOPMODEL. The field information is used within the GLUE methodology, which involves evaluating many different randomly chosen parameter sets within the chosen model structure. The different parameter sets are evaluated on performance in both discharge prediction and prediction of the observed saturated areas using appropriate likelihood measures. The results show that the saturated area information results in a strong constraint of the transmissivity parameter of the model, but that the other parameters show good fits across most of the range over which they are sampled. Quite different posterior distributions for the transmissivity parameter are found for the two different years of data used in conditioning the model. The effect on the prediction bounds for stream discharges is much less, perhaps because the transmissivity parameter combines with different values of the other parameters in the two years to capture the dominant modes of discharge response of the catchment.

[1] The distributed predictions of the original version of TOPMODEL are here compared with distributed observations of water table levels in the Uhlirska catchment in the Jizera Mountains, Czech Republic. The calibration of the model has been carried out within the GLUE framework, which allows the estimation of uncertainties in predicting the distributed patterns of the water table at different times. Many of the water table levels are predicted within the limits of uncertainty, but it is shown that the predictions could be improved by the calculation of a local effective transmissivity value (or local upslope contributing areas) at each observation site. These effective transmissivities show a similar relationship to the topographic index as found in a previous study of a small catchment in Norway. Some of the anomalies can be explained by deficiencies in the topographic analysis but this may also be an indication of possible structural deficiencies in the model. Interpretation is, however, difficult, and it remains to be seen whether these anomalies they might be avoided in more dynamic distributed models.

The formation of baseflow and stormflow was examined in the 1.18 km 2 part of the headwater catchment Uhlí rská, Jizera Mountains, Czech Republic, over the period 2007-2011, by means of run-off data and environmental tracers 18 O and SiO 2 . The baseflow, computed using the digital filter approach BFLOW, contributes 67% to total streamflow and has a mean residence time of 12.3 months. It is formed by groundwater discharge from the valley deluviofluvial granitic sediments, in combination with soil water in weathered layers on hillslopes during rainfall and snowmelt periods. The prevailing source of the groundwater is the infiltration of snowmelt water. Analysis of 20 run-off events and their hysteretic patterns demonstrated that the stormflow water has a residence time of about 4 months and is generated by preferential flow on hillslopes combined by soil matrix drainage. Because of slower flow in the soil matrix, the enrichment of pore water in SiO 2 is more pronounced. The stormflow and snowmelt water flowing via preferential pathways of upslope minerals soils pushes the pre-event groundwater through the pathways in wetlands to the stream, and the wetland can be therefore considered as groundwater supplied. This mechanism has been found to be typical for the groundwater-supplied headwater catchments of the Jizera Mountains and can be also assumed in other mountainous headwaters of the granitic massif in Central Europe. The main methodological contribution of this study are the residence time calculations stratified by baseflow and event flow, identifying run-off components of different travel times to streams and linking them with geochemical run-off sources. This achievement was possible because of a comprehensive dataset on hydrology, stable isotopes and silica hydrochemistry in all relevant run-off generation components. This concept indicates that a possible long-term change in snowmelt may affect the run-off regime of headwater catchments to climate or land-use changes. The valley is formed by the Cerná Nisa stream and has gentle convex-concave slopes. The average channel slope is 2.3%, the average length of the hillslopes is 450 m and the slope angle varies between 5% and 20% (Hrn cí r et al., 2010). The Cerná Nisa stream is a right-hand tributary of the Lu zická Nisa river, which later merges with the river 3218 M. ŠANDA ET AL.

Abstract:The study is focused on runoff formation processes at two scales: the scale of a small mountainous catchment at its outlet and the scale of an experimental plot located in a typical hillslope subregion. The heterogeneous soil profile of the catchments is formed by Cambisols developed on granite bedrocks. The surface runoff appears rarely, the subsurface flow forms a dominant part of the hydrograph. From the period 1998-2008, a set of 44 rainfall-runoff episodes was selected to analyse the rainfall-runoff relationship using multiple regression analysis. From a set of physical parameters, the initial soil water content came out as the statistically significant parameter that controls the runoff forming process at the catchment scale. The rainfall-runoff relationship at the experimental plot scale is more scattered. The dynamic thresholds of rainfall totals apparently control the ratio and the magnitude of stormflow at both scales. Up to the threshold value, the runoff strongly depends on the initial saturation conditions. Above the threshold value, the initial soil moisture conditions are less important.

Understanding and modelling the processes of flood runoff generation is still a challenge in catchment hydrology. In particular, there are issues about how best to represent the effects of the antecedent state of saturation of a catchment on runoff formation and flood hydrographs. This paper reports on the experience of mapping saturated areas using measured water table by piezometers and more qualitative assessments of the state of the moisture at soil surface or immediately under it to provide information that can usefully condition model predictions. Vegetation patterns can also provide useful indicators of runoff source areas, but integrated over much longer periods of time. In this way, it might be more likely that models will get the right predictions for the right reasons.