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Modelling biocide and herbicide concentrations in catchments of the Rhine basin

Abstract. 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 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 (31–35 899 km2)
and for herbicides for the entire Rhine basin upstream of the Dutch–German
border (160 000 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.
