The topographic wetness index (TWI, ln(a/tanβ)), which combines local upslope contributing area and slope, is commonly used to quantify topographic control on hydrological processes. Methods of computing this index differ primarily in the way the upslope contributing area is calculated. In this study we compared a number of calculation methods for TWI and evaluated them in terms of their correlation with the following measured variables: vascular plant species richness, soil pH, groundwater level, soil moisture, and a constructed wetness degree. The TWI was calculated by varying six parameters affecting the distribution of accumulated area among downslope cells and by varying the way the slope was calculated. All possible combinations of these parameters were calculated for two separate boreal forest sites in northern Sweden. We did not find a calculation method that performed best for all measured variables; rather the best methods seemed to be variable and site specific. However, we were able to identify some general characteristics of the best methods for different groups of measured variables. The results provide guiding principles for choosing the best method for estimating species richness, soil pH, groundwater level, and soil moisture by the TWI derived from digital elevation models.

The age, or residence time, of water is a fundamental descriptor of catchment hydrology, revealing information about the storage, flow pathways, and source of water in a single integrated measure. While there has been tremendous recent interest in residence time estimation to characterize watersheds, there are relatively few studies that have quantified residence time at the watershed scale, and fewer still that have extended those results beyond single catchments to larger landscape scales. We examined topographic controls on residence time for seven catchments (0.085–62.4 km²) that represent diverse geologic and geomorphic conditions in the western Cascade Mountains of Oregon. Our primary objective was to determine the dominant physical controls on catchment‐scale water residence time and specifically test the hypothesis that residence time is related to the size of the basin. Residence times were estimated by simple convolution models that described the transfer of precipitation isotopic composition to the stream network. We found that base flow mean residence times for exponential distributions ranged from 0.8 to 3.3 years. Mean residence time showed no correlation to basin area (r² < 0.01) but instead was correlated (r² = 0.91) to catchment terrain indices representing the flow path distance and flow path gradient to the stream network. These results illustrate that landscape organization (i.e., topography) rather than basin area controls catchment‐scale transport. Results from this study may provide a framework for describing scale‐invariant transport across climatic and geologic conditions, whereby the internal form and structure of the basin defines the first‐order control on base flow residence time.

Computer models, especially conceptual models, are frequently used for catchment hydrology studies. Teaching hydrological modeling, however, is challenging, since students have to both understand general model concepts and be able to use particular computer programs when learning to apply computer models. Here we present a new version of the HBV (Hydrologiska Byråns Vattenavdelning) model. This software provides a user-friendly version that is especially useful for education. Different functionalities, such as an automatic calibration using a genetic algorithm or a Monte Carlo approach, as well as the possibility to perform batch runs with predefined model parameters make the software interesting especially for teaching in more advanced classes and research projects. Different teaching goals related to hydrological modeling are discussed and a series of exercises is suggested to reach these goals.

The dialog between experimentalist and modeler in catchment hydrology has been minimal to date. The experimentalist often has a highly detailed yet highly qualitative understanding of dominant runoff processes; thus there is often much more information content on the catchment than we use for calibration of a model. While modelers often appreciate the need for “hard data” for the model calibration process, there has been little thought given to how modelers might access this “soft” or process knowledge. We present a new method where soft data (i.e., qualitative knowledge from the experimentalist that cannot be used directly as exact numbers) are made useful through fuzzy measures of model simulation and parameter value acceptability. We developed a three‐box lumped conceptual model for the Maimai catchment in New Zealand, a particularly well‐studied process‐hydrological research catchment. The boxes represent the key hydrological reservoirs that are known to have distinct groundwater dynamics, isotopic composition, and solute chemistry. The model was calibrated against hard data (runoff and groundwater levels) as well as a number of criteria derived from the soft data (e.g., percent new water, reservoir volume, etc.). We achieved very good fits for the three‐box model when optimizing the parameter values with only runoff (R_eff = 0.93). However, parameter sets obtained in this way showed in general a poor goodness of fit for other criteria such as the simulated new water contributions to peak runoff. Inclusion of soft data criteria in the model calibration process resulted in lower R_eff values (around 0.84 when including all criteria) but led to better overall performance, as interpreted by the experimentalist's view of catchment runoff dynamics. The model performance with respect to soft data (like, for instance, the new water ratio) increased significantly, and parameter uncertainty was reduced by 60% on average with the introduction of the soft data multicriteria calibration. We argue that accepting lower model efficiencies for runoff is “worth it” if one can develop a more “real” model of catchment behavior. The use of soft data is an approach to formalize this exchange between experimentalist and modeler and to more fully utilize the information content from experimental catchments.

In hydrological climate-change impact studies, regional climate models (RCMs) are commonly used to transfer large-scale global climate model (GCM) data to smaller scales and to provide more detailed regional information. Due to systematic and random model errors, however, RCM simulations often show considerable deviations from observations. This has led to the development of a number of correction approaches that rely on the assumption that RCM errors do not change over time. It is in principle not possible to test whether this underlying assumption of error stationarity is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well correction methods perform for conditions different from those used for calibration with the relatively simple differential split-sample test. For five Swedish catchments, precipitation and temperature simulations from 15 different RCMs driven by ERA40 (the 40 yr reanalysis product of the European Centre for Medium-Range Weather Forecasts (ECMWF)) were corrected with different commonly used bias correction methods. We then performed differential split-sample tests by dividing the data series into cold and warm respective dry and wet years. This enabled us to cross-evaluate the performance of different correction procedures under systematically varying climate conditions. The differential split-sample test identified major differences in the ability of the applied correction methods to reduce model errors and to cope with non-stationary biases. More advanced correction methods performed better, whereas large deviations remained for climate model simulations corrected with simpler approaches. Therefore, we question the use of simple correction methods such as the widely used delta-change approach and linear transformation for RCM-based climate-change impact studies. Instead, we recommend using higher-skill correction methods such as distribution mapping.