Marc J. P. Vis

The value of multiple data set calibration versus model complexity for improving the performance of hydrological models in mountain catchments

The assessment of snow, glacier, and rainfall runoff contribution to discharge in mountain streams is of major importance for an adequate water resource management. Such contributions can be estimated via hydrological models, provided that the modeling adequately accounts for snow and glacier melt, as well as rainfall runoff. We present a multiple data set calibration approach to estimate runoff composition using hydrological models with three levels of complexity. For this purpose, the code of the conceptual runoff model HBV-light was enhanced to allow calibration and validation of simulations against glacier mass balances, satellite-derived snow cover area and measured discharge. Three levels of complexity of the model were applied to glacierized catchments in Switzerland, ranging from 39 to 103 km2. The results indicate that all three observational data sets are reproduced adequately by the model, allowing an accurate estimation of the runoff composition in the three mountain streams. However, calibration against only runoff leads to unrealistic snow and glacier melt rates. Based on these results, we recommend using all three observational data sets in order to constrain model parameters and compute snow, glacier, and rain contributions. Finally, based on the comparison of model performance of different complexities, we postulate that the availability and use of different data sets to calibrate hydrological models might be more important than model complexity to achieve realistic estimations of runoff composition.

Spatial distribution of lion kills determined by the water dependency of prey species

Abstract Predation risk from lions (Panthera leo) has been linked to habitat characteristics and availability and traits of prey. We separated the effects of vegetation density and the presence of drinking water by analyzing locations of lion kills in relation to rivers with dense vegetation, which offer good lion stalking opportunities, and artificial water points with low vegetation density. The spatial distribution of lion kills was studied at the Klaserie Private Nature Reserve, South Africa. The distance between 215 lion kills and the nearest water source was analyzed using generalized linear models. Lions selected medium-sized prey species. Lion kills were closer to rivers and to artificial water points than expected by random distribution of the kills. Water that attracted prey, and not the vegetation density in riverine areas, increased predation risk, with kills of buffalo (Syncerus caffer), kudu (Tragelaphus strepsiceros), and wildebeest (Connochaetes taurinus) as water-dependent prey species. Traits of prey species, including feeding type (food habits), digestion type (ruminant or nonruminant), or body size, did not explain locations of lion kills, and no seasonal patterns in lion kills were apparent. We argue that the cascading impact of lions on local mammal assemblages is spatially heterogeneous.

Upper and lower benchmarks in hydrological modelling

Whenassessing theperformanceofahydrologicalmodel, aquestionthat can be raised is, how good is really good? Despite several calls to use benchmarks (Pappenberger, Ramos, Cloke, & Fredrik, 2014; Schaefli & Gupta, 2007; Seibert, 2001), model performance in the scientific literature, conference presentations, and discussions among hydrological modellers is still often solely judged based on the value of some performancemeasure.Forinstance,amodelisratedaswell‐performingbecause model efficiency (Nash & Sutcliffe, 1970) values are above 0.7. Some authors (e.g., Moriasi et al., 2007; Ritter & Muñoz‐Carpena, 2013) even suggestperformance classes basedonmodel efficiencyvalues.Basedon our experiences with the application of hydrological models for catchmentswith largely varying characteristics,we argue that such judgments on model performance can only be made if model performances are relatedtobenchmarksthatrepresentwhatcouldandshouldbeexpected. The idea of using benchmarks is by no means new and actually the most commonly used performance measure in hydrological modelling, the model efficiency or Nash‐Sutcliffe efficiency (Nash & Sutcliffe, 1970), can be interpreted as the comparison of model simulations with a constant streamflow equal to the observed mean streamflow (lower benchmark) and a perfect fit (upper benchmark). Obviously, this lower benchmark is not too hard to beat, whereas this upper benchmark is hardly achievable in practice. To better evaluate how good model simulations are, more informative lower benchmarks have been suggested (Garrick, Cunnane, & Nash, 1978; Schaefli & Gupta, 2007; Seibert, 2001). However, the use of benchmarks that are taking into account what is possible with the data, that is, what could and should be expected, is still not common practice in hydrological modelling. In hydrological modelling, it is never possible to obtain a perfect model fit. This is partly due to the complexity of processes in nature but also due to errors in observations of the driving data and streamflow. Therefore, the upper benchmark should not be an unrealistic perfect simulation but take potential errors in the data into account. On the other hand, there is usually also a lower limit on how bad a model can be, simply because the driving data ensure that