Issues of diffuse pollution model complexity arising from performance benchmarking

Abstract

Flow and nitrate dynamics were simulated in two catchments, the River Aire in northern England and the River Ythan in north-east Scotland. In the case of the Aire, a diffuse pollution model was coupled with a river quality model (CASCADE-QUESTOR); in the study of the Ythan, an integrated model (SWAT) was used. In each study, model performance was evaluated for differing levels of spatial representation in input data sets (rainfall, soils and land use). In respect of nitrate concentrations, the performance of the models was compared with that of a regression model based on proportions of land cover. The overall objective was to assess the merits of spatially distributed input data sets. In both catchments, specific measures of quantitative performance showed that models using the most detailed available input data contributed, at best, only a marginal improvement over simpler implementations. Hence, the level of complexity used in input data sets has to be determined, not only on multiple criteria of quantitative performance but also on qualitative assessments, reflecting the specific context of the model application and the current and likely future needs of end-users.

Evaluation of River Water Quality Simulations at a Daily Time Step – Experience with SWAT in the Axe Catchment, UK

“…However, excessive daily variability in nitrate simulated by SWAT 2005 was also observed in other research such as that in the Grote Nete catchment in Flanders (Belgium) [56] or in the river Aire in northern England and the river Ythan in north-east Scotland [55]. Highly simulated N peaks could be ascribed to the dominance of groundwater N contribution to streamflow [53].…”

“…These uncertainties further combine with those uncertainties in the spatial and attribute soil data which will affect nitrate losses [55]. Coarse resolution of soil and land use maps may only include generalised information on soil and land cover and therefore does not reflect the true heterogeneous nature of catchments and local characteristics of certain tributary catchments which can have an important influence on overall contribution to pollution and successful implementation of environmental measures.…”

What impact might mitigation of diffuse nitrate pollution have on river water quality in a rural catchment?

“…It is accepted that for applications where predictions of greater detail are required a 60 more complex model would be more accurate and better suited. Undertaking model simplification 61 may involve aspects of process representation, spatial variability and/or temporal resolution, the 62 process often also providing benefits by reducing model uncertainty without greatly impairing 63 performance (Hutchins et al, 2007;Lindenschmidt, 2006;Snowling and Kramer, 2001). Whilst initial 64 risk assessments have been undertaken for nutrient leaching (e.g.…”

Feasibility Assessment of Data-Driven Models in Predicting Pollution Trends of Omerli Lake, Turkey

“…Since dissolved oxygen needs to be related to another set of variables, an ideal tool would be the multiple regression model (MLR; Hutchins et al 2007;Vryzas et al 2007). Multiple linear regression is the most straightforward analysis and is ideal when the relationships are linear; however, the results of this paper show that artificial neural network (ANN) seems to be the ideal model for more precise analysis.…”