CaMMP - Case Study 5

QUESTOR modelling shows that all the mitigation methods applied can reduce the amount of Chlorophyll-a predicted to occur in the river. The extent of the reduction is different depending on the data used to drive the QUESTOR model. The effect of increasing the shading (from 20-60%) declines downstream as the river becomes too wide for trees to provide shade.

We investigated the effectiveness of a range of possible measures for the mitigation of algal blooms in the River Thames using a combination of models and observed data. Three mitigation options were employed: Increased river shading, reduced diffuse inputs of P and reduced P inputs from sewage works. The QUESTOR model was used to link changes in light and river P concentrations to changed algal growth along the river Thames. SAGIS and INCA-P were used to calculate the inputs to QUESTOR for the P reduction mitigations. The QUESTOR and SAGIS models had already been calibrated for the Thames catchment in previous studies. INCA-P was a new model application and was calibrated for P species and river flow using observed data. Model outputs allowed us to investigate how sensitive the results of the mitigation methods were to the model used to implement that mitigation.

This study used models to assess the effects of interventions designed to reduce periods of unacceptable river water quality that arise from the growth of algae. A particular focus was on driving the river model (QUESTOR) with observed data and data from the models INCA-P and SAGIS to investigate the effects of such different data sources. The QUESTOR and SAGIS models had already been calibrated for the Thames catchment in previous studies. INCA-P was a new model application and was calibrated for P species and river flow using observed data

The study was carried out on the River Thames Catchment in South East UK, a river known to have high loadings of nutrients and subsequent high productivity leading to nuisance algal growth and on occasions low dissolved oxygen (DO) levels. The interventions investigated were:

Increased river shading provided by growing tree on the river bank (only feasible down to Wallingford where the river width is small enough). The shading was applied to the baseline QUESTOR calibrated model run driven by observed data, the QUESTOR model driven by INCA-P data (as calibrated) and the QUESTOR model driven by SAGIS data (as calibrated).

Changes in farm practice. A national case study has looked at sets of farm practices to reduce N and P loss from catchments. The percentage change in loss rates associated with various levels of uptake in the farming community have been calculated using the Farmscoper model. The values for the Thames basin were extracted from this assessment and applied to the SAGIS input loads. The resulting SAGIS outputs were used to drive the QUESTOR model to determine the effects on river P concentrations and algal growth relative to current practice. Two uptake levels of uptake were assessed: 100% uptake of best practice is taken up voluntary (22.6% P reduction) and all Farmscoper measures are taken up (~ 37.3% P reduction).

The effect of imposing stricter discharge consents for SRP on all sewage works irrespective of size (80% reduction in discharge loads). Catchment loads estimated from the calibrated versions of SAGIS and INCA-P were reduced by reducing the point source discharges that were included in them by 80% and then each was used to drive the QUESTOR model. The Questor model itself received direct sewage works discharges and these were also reduced by 80%.

Data from these different model runs were analysed to look at sensitivities in model output resulting from different sources of driving data and how these differences compared with differences that resulted from the effects of the mitigations themselves. 

The simulations with SAGIS and QUESTOR showed that improved on-farm practice as describe by Farmscoper had a very small impact on the Chlorophyll-a concentrations predicted for the river Thames (Figure 1). Both increased shading (driven by observed data or by outputs from SAGIS and INCA-P, Figure 2) and the 80% sewage P reduction (inputs from INCA-P, Figure 3) had a significant effect on the Chlorophyll-a concentrations. Figure 3 also shows that the 80% effluent reduction is a more effective mitigation measure along the whole river than shading. However, note that shading is only effective down to Wallingford. It is encouraging to note that the results obtained for shading with all three models are consistent for Chlorophyll-a. This is also shown in the dissolved oxygen concentrations that are predicted to change as a result of the changes in Chlorophyll-a concentration (Figure 4). QUESTOR simulated an afternoon dissolved oxygen concentration. 

Model Performance: The QUESTOR model was calibrated against observed data for the case where the model inputs are based on interpolated observed values. In this study the QUESTOR model was also driven with outputs from SAGIS and from INCA-P. SAGIS provided only P data while INCA-P provided both P and flow data. The model performance against observed flow at Caversham is poorer when the QUESTOR model uses the flow data from INCA-P (Nash-Sutcliffe = 0.81) than when the observed flows are used (Nash-Sutcliffe = 0.90, Figure 1). INCA-P flow data leads to an over prediction of summer low flows and to over prediction of some high flows in winter compared to the observed data. For the simulation of dissolved P a comparison can be made for all three sets of driving data (Figure 2). The differences between the simulations are not so clear cut. In fact the simulation with the observed data at Sonning is not particularly impressive and give the same in goodness of fit measures to the results when the model is driven by data from SAGIS (% error in means = -7.4 and RMSE =  0.083). The values for the model simulations when driven by INCA data are inferior (% error in means = 47.8 and RMSE = 0.134).

Results of mitigation measures: The locations of the sites referred to in this section are shown in Figure 3. Implementing the two Farmscoper scenarios in SAGIS to drive QUESTOR, gave a small reduction in P concentrations in the River Thames and therefore very little change in the number of days where Chlorophyll-a concentrations were over WFD limits (Tables 1 and 2). These differences are essentially negligible in the context of the likely simulation errors.

Reducing light reaching the River by shading from trees had a significant impact in the top half of the river dopwn to Wallingford - the section where it is narrow enough to have an effect. Tables 3-5 show the days where unacceptable conditions were exceeded for Chlorophyll-a and Dissolved Oxygen for 20% (present conditions) and 60% shading. The direction of change of all the model runs is the same although the magnitude of the change is different. The results from the INCA-P/QUESTOR model pair are somewhat different from the other two model runs. This is most likely due to the overestimation of the summer low flows, which are critical for algal growth in the River Thames.

An 80 percent reduction in P from all sewage treatment plants was also very effective in reducing Chlorophyll-a along all the river system (Figure 4). Figure 4 shows a comparison between the effects of shading and P reduction from sewage works for the INCA-P/ Questor model pair. This implies that a combination of upstream shading and tighter downstream sewage works discharge consents would significantly improve water quality.

QUESTOR can predict the effects of nutrients on algal growth, but cannot predict future nutrient changes resulting from the mitigation methods. We needed another model to derive the impacts of nutrient reduction mitigation options to provide revised loads from which QUESTOR could calculate changes in algal biomass.

Using one model for the assessment of mitigation options leads to one outcome that depends on the model and the data used to drive it. Using multiple models in combination provides multiple outcomes for mitigation methods allowing assessment of how sensitive were the outcomes to driving data and models.

When joining models off-line, it is very important to ensure that the model output and input points are properly paired up, which can be a slow process. The three models operate at different time steps, but this was simple to accommodate in the QUESTOR programme structure.

All models require considerable pre-processing on inputs to provide data in specific formats for the models. Outputs are extensive and available in files compatible with data analysis packages for the production of graphical output. SAGIS can provide show outputs directly in a GIS based environment.

Flow data from the National River Flow Archive

Effluent Flow Data from a number of sources

Environment Agency routine water quality monitoring (GQA) data (including Temperature) from the WIMS database

Weekly Nutrient and Chlorophyll-a data from the UKCEH Thames Catchment Initiative Sampling

Solar Radiation data from BADC

QUESTOR:

• Location of weirs from WIKIPedia
• Extent of tree shading from visual inspection of satellite data on Google Earth®

INCA – P:

• Land Cover Data
• Rainfall and soil moisture deficit across the catchment

SAGIS:

• Flow data from LowFlowEnterprise
• N and P loss data from Farmscoper Model