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This study focuses on the prediction of event-based runoff coefficients (an important descriptor of flood events) for nested catchments up to an area of 50 km² in the Eastern Ore Mountains. The four main objectives of the study are (i) the prediction of runoff coefficients with the statistical method of generalized linear models, (ii) the comparison of the results of the linear models with estimates of a distributed conceptual model, (iii) the comparison of the dynamics of observed soil moisture and simulated saturation deficit of the hydrological model and (iv) the analysis of the relationship between runoff coefficient and observed and simulated wetness. Different predictor variables were selected to describe the runoff coefficient and were differentiated into variables describing the catchment's antecedent wetness and meteorological forcing. The best statistical model was estimated in a stepwise approach on the basis of hierarchical partitioning, an exhaustive search algorithm and model validation with jackknifing. We then applied the rainfall runoff model WaSiM ETH to predict the runoff processes for the two larger catchments. Locally measured small-scale soil moisture (acquired at a scale of four to five magnitudes smaller than the catchment) was identified as one of the key predictor variables for the estimation of the runoff coefficient with the general linear model. It was found that the relationship betweenobserved and simulated (using WaSiM ETH) wetness is strongly hysteretic. The runoff coefficients derived from the rainfall runoff simulations systematically underestimate the observed values. Copyright © 2012 John Wiley & Sons, Ltd.

Rapid flow in connected preferential flow paths is crucial for fast transport of water and solutes through soils, especially at tile-drained field sites. In the present study, we propose a spatially explicit approach to represent worm burrows as connected structures of realistic geometry, high conductivity and low retention capacity in a two-dimensional physically based model. We show that this approach allows successful prediction of a tile-drain discharge and preferential flow patterns in soil observed during the irrigation of a tile-drained hillslope in the Weiherbach catchment. However, we found a considerable equifinality in the spatial setup of the model when key parameters such as the area density of worm burrows, the maximum volumetric water flows inside these macropores and the conductivity of the tile drain were varied within the ranges of either our measurements or measurements reported in the literature. In total, we found that 67 out of 432 model runs were acceptable [Nash–Sutcliffe (NS) ≥ 0·75]. Among these, the 13 best yielded a NS coefficient of more than 0·9, which means that more than 90% of the flow variability is explained by the model. Also, the flow volumes were in good accordance and timing errors were less than or equal to 20 min. It is suggested that this uncertainty/equifinality could be reduced when more precise data on initial states of the subsurface and on the width of the control volume draining into a single drainage tube could be made available. However, such data are currently most difficult to assess even at very well investigated sites such as those studied here. We thus suggest that non-uniqueness of the spatial setup of process-based model seems to be an important factor causing predictive uncertainty at many sites where preferential flow dominates system response. Copyright © 2010 John Wiley & Sons, Ltd.

The intention of the presented study is to gain a better understanding of the mechanisms that caused the bimodal rainfall–runoff responses which occurred up to the mid-1970s regularly in the Sch¨ afertal catchment and vanished after the onset of mining activities. Understanding this process is a first step to understanding the ongoing hydrological change in this area. It is hypothesized that either subsurface stormflow, or fast displacement of groundwater could cause the second delayed peak. A top-down analysis of rainfall–runoff data, field observations as well as process modelling are combined within a rejectionistic framework. A statistical analysis is used to test whether different predictors, which characterize the forcing, near surface water content and deeper subsurface store, allow the prediction of the type of rainfall–runoff response. Regression analysis is used with generalized linear models as they can deal with non-Gaussian error distributions as well as a non-stationary variance. The analysis reveals that the dominant predictors are the pre-event discharge (proxy of state of the groundwater store) and the precipitation amount. In the field campaign, the subsurface at a representative hillslope was investigated by means of electrical resistivity tomography in order to identify possible strata as flow paths for subsurface stormflow. A low resistivity in approximately 4 m depth—either due to a less permeable layer or the groundwater surface—was detected. The former could serve as a flow path for subsurface stormflow. Finally, the physical-based hydrological model CATFLOW and the groundwater model FEFLOW are compared with respect to their ability to reproduce the bimodal runoff responses. The groundwater model is able to reproduce the observations, although it uses only an abstract representation of the hillslopes. Process model analysis as well as statistical analysis strongly suggest that fast displacement of groundwater is the dominant process underlying the bimodal runoff reactions. Copyright  2009 John Wiley & Sons, Ltd.

Understanding runoff generation processes is important for flood prediction, water management, erosion control, water quality, contaminant transport and the evaluation of impacts of land use change. However, little process research has been carried out in southern Chile. In particular the young volcanic ash soils, which are typical for this area, are not well understood in their hydrologic behaviour. To establish a ‘reference study’ which can then be used for comparison with other (disturbed) sites, this study focuses on the investigation of runoff generation processes in an undisturbed, forested catchment in the Chilean Andes. The paper reports on an investigation of these processes with different tracer methods at different spatial scales. Hydrograph separation with environmental isotopes and geochemical constituents was used on the catchment scale. Thermal energy was used as a tracer to investigate groundwater–surface water interactions at the local stream reach scale and dye tracers were used to study infiltration and percolation characteristics at the plot scale. It was found that pre-event water dominates the storm hydrograph. In the lower reaches, however, water usually exfiltrates from the stream into the adjacent aquifer. The dye tracer experiments showed that while preferential vertical flow dominates under forest, water infiltrates as a straight horizontal front in the bare volcanic ashes (no vegetation) on the catchment rim. Subsurface flow patterns in the forest differ significantly from summer to winter. All three approaches used in this study suggest an important shift in dominant processes from dry to wet season. Copyright © 2008 John Wiley & Sons, Ltd.

Have you ever been flabbergasted, after the fact, by how far your predictions were off the observed data? An underestimation of pesticide arrival time by a factor of 10, or an overestimation of flood peaks by a factor of two or more—all this with models that have apparently been well calibrated with data from the past. Of course, there is always an explanation, after the fact. The media characteristics may have been slightly different from what we initially assumed, perhaps preferential flow occurred where we did not expect it to occur, there was inaccuracy in the assumed initial conditions and, of course, in the boundary conditions as well, and the events were larger than what we thought could reasonably occur. It seems that small uncertainties can easily amplify under certain conditions and will limit predictability. In this commentary, we argue that there is a pattern to this.