Analysis of the QUESTOR water quality model using a Fourier amplitude sensitivity test (FAST) for two UK rivers

Deflandre, A.; Williams, Richard J.; Elorza, Francisco Javier; Mira, José; Boorman, David

doi:10.1016/j.scitotenv.2005.08.041

Cited by 22 publications

(12 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The network of site locations in the first column (relating Table 2 to Figure 1) provided the spatial framework for calibration. The calibration process is very necessary as a sensitivity analysis of previous QUESTOR applications in the Ouse network (Deflandre et al, 2006) suggested transformations to be at least as important as other elements of model structure revealed to be fundamental, namely a sound description of flow routing constants defining velocity (already optimised: see Boorman(2003c)) and a rigorous characterisation of tributary inputs. As well as being net sources of SRP, most reaches showed net nitrate sources (nitrification predominant) although some reaches in the middle Swale and upper Ure appeared to be net sinks (denitrification dominant).…”

Section: In-river Transformations and Hydrochemical Responsementioning

confidence: 99%

Which offers more scope to suppress river phytoplankton blooms: Reducing nutrient pollution or riparian shading?

Hutchins

Johnson

Deflandre-Vlandas

et al. 2010

Science of The Total Environment

View full text Add to dashboard Cite

River flow and quality data, including chlorophyll-a as a surrogate for river phytoplankton biomass, were collated for the River Ouse catchment in NE England, which according to established criteria is a largely unpolluted network. Against these data, a daily river quality model (QUESTOR) was setup and successfully tested. Following a review, a river quality classification scheme based on phytoplankton biomass was proposed. Based on climate change predictions the model indicated that a shift from present day oligotrophic/mesotrophic conditions to a mesotrophic/eutrophic system could occur by 2080. Management options were evaluated to mitigate against this predicted decline in quality. Reducing nutrient pollution was found to be less effective at suppressing phytoplankton growth than the less costly option of establishing riparian shading. In the Swale tributary, ongoing efforts to reduce phosphorus loads in sewage treatment works will only reduce peak (95 th percentile) phytoplankton by 11%, whereas a reduction of 44% is possible if riparian tree cover is also implemented. Likewise, in the Ure, whilst reducing nitrate loads by curtailing agriculture in the headwaters may bring about a 10% reduction, riparian shading would instead reduce levels by 47%. Such modelling studies are somewhat limited by insufficient field data but offer a potentially very valuable tool to assess the most cost-effective methods of tackling effects of eutrophication.

show abstract

Section: In-river Transformations and Hydrochemical Responsementioning

confidence: 99%

Which offers more scope to suppress river phytoplankton blooms: Reducing nutrient pollution or riparian shading?

Hutchins

Johnson

Deflandre-Vlandas

et al. 2010

Science of The Total Environment

View full text Add to dashboard Cite

show abstract

“…Cotter et al (2003), also working with SWAT, suggest there is unlikely to be significant benefit in using increasingly detailed soils data at spatial resolutions finer than 1 km 2 . The performance of Aire Models A (least complex) and B (most complex) appeared very similar in terms of N. In this respect, the influence of point source discharges, abstractions and in-stream processes dominates over the effects of the strikingly different levels of input data complexity used, as corroborated by Deflandre et al (2006). Although in terms of E bench (flow), Aire Model A outperformed Aire Model B, the latter model performed better at low flows (see Fig.…”

Section: Impact Of Model Input Data Complexitymentioning

confidence: 56%

Issues of diffuse pollution model complexity arising from performance benchmarking

Hutchins¹,

Dilks²,

Davies³

et al. 2007

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…S2, S3 and S4 cover only 9000 population equivalents although there are also two military bases included in these representations. More details on the influence of STWs on the river Swale are provided by Lewis et al (1997), Deflandre et al (2006); Neal et al (2008).…”

Section: Field Data For Sub‐daily Resolution Modelling (Admodel)mentioning

confidence: 99%

“…Less modelling effort has been made to study pollutant transport in short river stretches. A sensitivity analysis of QUESTOR (Deflandre et al ., 2006) included the Swale and highlighted suitability for modelling large rivers rather than detailed studies of short river stretches. An example of the latter would be plume dispersion studies which are carried out using small spatial (meters) and temporal steps (minutes or seconds).…”

Section: Introductionmentioning

confidence: 99%

Mathematical model to identify nitrogen variability in large rivers

Ani

Hutchins

Krasławski

et al. 2011

River Research & Apps

View full text Add to dashboard Cite

The river Swale in Yorkshire, northern England has been the subject of many studies concerning water quality. This paper builds on existing data resources and previous 1D river water quality modelling applications at daily resolution (using QUESTOR) to provide a different perspective on understanding pollution, through simulation of the short-term dynamics of nutrient transport along the river. The two main objectives are (1) building, calibration and evaluation of a detailed mathematical model (Advection-Dispersion Model: ADModel), for nutrient transport under unsteady flow conditions and (2) the development of methods for estimating key parameters characterizing pollutant transport (velocity, dispersion coefficient and transformation rates) as functions of hydrological parameters and/or seasonality.The study of ammonium and nitrate has highlighted temporal variability in processes, with maximum nitrification and denitrification rates during autumn. Results show that ADModel is able to predict the main trend of measured concentration with reasonable accuracy and accounts for temporal changes in water flow and pollutant load along the river. Prediction accuracy could be improved through more detailed modelling of transformation processes by taking into account the variability of factors for which existing data were insufficient to allow representation. For example, modelling indicates that interactions with bed sediment may provide an additional source of nutrients during high spring flows.

show abstract

Analysis of the QUESTOR water quality model using a Fourier amplitude sensitivity test (FAST) for two UK rivers

Cited by 22 publications

References 31 publications

Which offers more scope to suppress river phytoplankton blooms: Reducing nutrient pollution or riparian shading?

Which offers more scope to suppress river phytoplankton blooms: Reducing nutrient pollution or riparian shading?

Issues of diffuse pollution model complexity arising from performance benchmarking

Mathematical model to identify nitrogen variability in large rivers

Contact Info

Product

Resources

About