Reply to comment by K. Beven et al. on "Pursuing the method of multiple working hypotheses for hydrological modelling"

Martyn P. Clark, Dmitri Kavetski, and Fabrizio Fenici

Methods comparison for detecting trends in herbicide monitoring time-series in streams

An inadvertent consequence of pesticide use is aquatic pesticide pollution, which has prompted the implementation of mitigation measures in many countries. Water quality monitoring programs are an important tool to evaluate the efficacy of these mitigation measures. However, large interannual variability of pesticide losses makes it challenging to detect significant improvements in water quality and to attribute these improvements to the application of specific mitigation measures. Thus, there is a gap in the literature that informs researchers and authorities regarding the number of years of aquatic pesticide monitoring or the effect size (e.g., loss reduction) that is required to detect significant trends in water quality. Our research addresses this issue by combining two exceptional empirical data sets with modelling to explore the relationships between the achieved pesticide reduction levels due to mitigation measures and the length of the observation period for establishing statistically significant trends. Our study includes both a large (Rhine at Basel, ∼36,300 km2) and small catchment (Eschibach, 1.2 km2), which represent spatial scales at either end of the spectrum that would be realistic for monitoring programs designed to assess water quality. Our results highlight several requirements in a monitoring program to allow for trend detection. Firstly, sufficient baseline monitoring is required before implementing mitigation measures. Secondly, the availability of pesticide use data helps account for the interannual variability and temporal trends, but such data are usually lacking. Finally, the timing and magnitude of hydrological events relative to pesticide application can obscure the observable effects of mitigation measures (especially in small catchments). Our results indicate that a strong reduction (i.e., 70–90 %) is needed to detect a change within 10 years of monitoring data. The trade-off in applying a more sensitive method for change detection is that it may be more prone to false-positives. Our results suggest that it is important to consider the trade-off between the sensitivity of trend detection and the risk of false positives when selecting an appropriate method and that applying more than one method can provide more confidence in trend detection

HESS Opinions: Are soils overrated in hydrology?

Traditional hydrological theories are based on the assumption that soil is key in determining water's fate in the hydrological cycle. According to these theories, soil hydraulic properties determine water movement in both saturated and unsaturated zones, described by matrix flow formulas such as the Darcy–Richards equations. They also determine plant-available moisture and thereby control transpiration. Here we argue that these theories are founded on a wrong assumption. Instead, we advocate the reverse: the terrestrial ecosystem manipulates the soil to satisfy specific water management strategies, which are primarily controlled by the ecosystem's reaction to climatic drivers and by prescribed boundary conditions such as topography and lithology. According to this assumption, soil hydraulic properties are an effect rather than a cause of water movement. We further argue that the integrated hydrological behaviour of an ecosystem can be inferred from considerations about ecosystem survival and growth without relying on internal-process descriptions. An important and favourable consequence of this climate- and ecosystem-driven approach is that it provides a physical justification for catchment models that do not rely on soil information and on the complexity associated with the description of soil water dynamics. Another consequence is that modelling water movement in the soil, if required, can benefit from the constraints that are imposed by the embedding ecosystem. Here we illustrate our ecosystem perspective of hydrological processes and the arguments that support it. We suggest that advancing our understanding of ecosystem water management strategies is key to building more realistic hydrological theories and catchment models that are predictive in the context of environmental change.</p

Modelling biocide and herbicide concentrations in catchments of the Rhine basin

Impairment of water quality by organic micropollutants such as pesticides, pharmaceuticals or household chemicals is a problem in many catchments worldwide. These chemicals originate from different urban and agricultural usages and are transferred to surface waters from point or diffuse sources by a number of transport pathways. The quantification of this form of pollution in streams is challenging and especially demanding for diffuse pollution due to the high spatio-temporal concentration dynamics, which require large sampling and analytical efforts to obtain representative data on the actual water quality.Models can also be used to predict to what degree streams are affected by these pollutants. However, spatially distributed modelling of water quality is challenging for a number of reasons. Key issues are the lack of such models that incorporate both urban and agricultural sources of organic micropollutants, the large number of parameters to be estimated for many available water quality models, and the difficulty to transfer parameter estimates from calibration sites to areas where predictions are needed.To overcome these difficulties, we used the parsimonious iWaQa model that simulates herbicide transport from agricultural fields and diffuse biocide losses from urban areas (mainly façades and roof materials) and tested its predictive capabilities in the Rhine River basin. The model only requires between one and eight global model parameters per compound that need to be calibrated. Most of the data requirements relate to spatially distributed land use and comprehensive time series of precipitation, air temperature and spatial data on discharge. For larger catchments, routing was explicitly considered by coupling the iWaQa to the AQUASIM model.The model was calibrated with datasets from three different small catchments (0.5–24.6&thinsp;km2) for three agricultural herbicides (isoproturon, S-metolachlor, terbuthylazine) and two urban biocides (carbendazim, diuron). Subsequently, it was validated for herbicides and biocides in Switzerland for different years on 12 catchments of much larger size (31–35&thinsp;899&thinsp;km2) and for herbicides for the entire Rhine basin upstream of the Dutch–German border (160&thinsp;000&thinsp;km2) without any modification. For most compound–catchment combinations, the model predictions revealed a satisfactory correlation (median r2: 0.5) with the observations. The peak concentrations were mostly predicted within a factor of 2 to 4 (median: 2.1 fold difference for herbicides and 3.2 for biocides respectively). The seasonality of the peak concentration was also well simulated; the predictions of the actual timing of peak concentrations, however, was generally poor.Limited spatio-temporal data, first on the use of the selected pesticides and second on their concentrations in the river network, restrict the possibilities to scrutinize model performance. Nevertheless, the results strongly suggest that input data and model structure are major sources of predictive uncertainty. The latter is for example seen in background concentrations that are systematically overestimated in certain regions, which is most probably linked to the modelled coupling of background concentrations to land use intensity.Despite these limitations the findings indicate that key drivers and processes are reasonably well approximated by the model and that such a simple model that includes land use as a proxy for compound use, weather data for the timing of herbicide applications and discharge or precipitation as drivers for transport is sufficient to predict the timing and level of peak concentrations within a factor of 2 to 3 in a spatially distributed manner at the scale of large river basins.</p

Identification of Novel Linear Megaplasmids Carrying a ß-Lactamase Gene in Neurotoxigenic Clostridium butyricum Type E Strains

Since the first isolation of type E botulinum toxin-producing Clostridium butyricum from two infant botulism cases in Italy in 1984, this peculiar microorganism has been implicated in different forms of botulism worldwide. By applying particular pulsed-field gel electrophoresis run conditions, we were able to show for the first time that ten neurotoxigenic C. butyricum type E strains originated from Italy and China have linear megaplasmids in their genomes. At least four different megaplasmid sizes were identified among the ten neurotoxigenic C. butyricum type E strains. Each isolate displayed a single sized megaplasmid that was shown to possess a linear structure by ATP-dependent exonuclease digestion. Some of the neurotoxigenic C. butyricum type E strains possessed additional smaller circular plasmids. In order to investigate the genetic content of the newly identified megaplasmids, selected gene probes were designed and used in Southern hybridization experiments. Our results revealed that the type E botulinum neurotoxin gene was chromosome-located in all neurotoxigenic C. butyricum type E strains. Similar results were obtained with the 16S rRNA, the tetracycline tet(P) and the lincomycin resistance protein lmrB gene probes. A specific mobA gene probe only hybridized to the smaller plasmids of the Italian C. butyricum type E strains. Of note, a ß-lactamase gene probe hybridized to the megaplasmids of eight neurotoxigenic C. butyricum type E strains, of which seven from clinical sources and the remaining one from a food implicated in foodborne botulism, whereas this ß-lactam antibiotic resistance gene was absent form the megaplasmids of the two soil strains examined. The widespread occurrence among C. butyricum type E strains associated to human disease of linear megaplasmids harboring an antibiotic resistance gene strongly suggests that the megaplasmids could have played an important role in the emergence of C. butyricum type E as a human pathogen

Clostridium difficile is not associated with outbreaks of viral gastroenteritis in the elderly in the Netherlands

The coincidental increase in norovirus outbreaks and Clostridium difficile infection (CDI) raised the question of whether these events could be related, e.g. by enhancing spread by diarrhoeal disease outbreaks. Therefore, we studied the prevalence of C. difficile in outbreaks of viral gastroenteritis in nursing homes for the elderly and characterised enzyme immunoassay (EIA)-positive stool samples. Stool samples from nursing home residents (n = 752) in 137 outbreaks of viral aetiology were investigated by EIA for the presence of C. difficile toxins. Positive samples were further tested by a cell neutralisation cytotoxicity test, a second EIA and culture. Cultured isolates were tested for the presence of toxin genes, the production of toxins and characterised by 16S rRNA polymerase chain reaction (PCR) and sequencing. Twenty-four samples (3.2%) tested positive in the EIA. Of these 24 positive samples, only two were positive by cytotoxicity and three by a second EIA. Bacterial culture of 21 available stool samples yielded a toxinogenic C. difficile PCR ribotype 001 in one patient sample only. In conclusion, we found no evidence in this retrospective study for an association between viral gastroenteritis outbreaks and C. difficile. The high rate of false-positive EIA samples emphasises the need for second confirmation tests to diagnose CDI