Research papersClimate and land-use change impact on faecal indicator bacteria in a temperate maritime catchment (the River Conwy, Wales)
Introduction
Anthropogenic activities such as farming and livestock management or sewage discharges can cause pollution by faecal bacteria and pathogenic viruses in rivers (Malham et al., 2014). This type of pollution can pose both serious health threats and affect the use of water by humans, such as commercial activities (for example, shellfish growth in river estuaries) or recreational activities, with severe social and economic consequences. This has already occurred in many regions of the world, such as the decline of marine water quality in Western Africa (Bouvy et al., 2008), the degradation of coral reef in the Caribbean (Hernández-Delgado et al., 2008) and the contamination of drinking water in Brazilian shanty towns (Copeland et al., 2009) with global impacts on the recreational use of water (WHO, 2003). Faecal Indicator Bacteria (FIB), such as total coliforms or faecal coliforms, are used to detect and measure faecal contamination in water and sediment, and are a widespread indicator of the presence of pathogen organisms in freshwater or brackish water.
Whilst there is a growing body of research outcomes concerning the sources, sinks and transport of FIB in freshwater and estuarine environments (Perkins et al., 2016), in river sediment (Perkins et al., 2014), in sewage water (Kay et al., 2008) and groundwater (Kay et al., 2007), these approaches might be limited for the analysis of the dynamic response of FIB to changes in climate and land use/land management (Whitehead et al., 2016). To understand land-use, climate change, and population growth impacts on FIB, mathematical models are required to describe point and diffuse FIB sources and transport at the catchment scale. Models can be used to develop informed health risk assessments and evaluate policy reforms and land-use change options (Whitehead et al., 2016), taking into account the joint impact of climate and land-use change (Bussi et al., 2016a).
In particular, physically-based models, or mechanistic modes (de Brauwere et al., 2014), combined with direct FIB measurements, can provide daily estimates of FIB in rivers in response to climatic events. An example is the INCA-Pathogens model (Whitehead et al., 2016), which belongs to the INCA family of models (Whitehead et al., 1998b), and can be used to simulate the cycle of FIB in a catchment, taking into account both diffuse and point sources and the decay from sources to sinks. However, the use of physically-based models is limited by the lack of data. Usually, FIB data are intermittent, with low collection frequencies (weeks to months) and short time coverage. Therefore, robust modelling approaches are needed to tackle this problem, taking into account the different sources of uncertainty (data, model structure, model parameters, etc.).
In this study, the INCA-Pathogens model was used to understand the impacts of upland faecal pollution on the downstream concentration of FIB under a changing climate and under several scenarios of land use and land management. The model was calibrated against observed FIB data (Emmett et al., 2016) using a Monte Carlo procedure, and then coupled with climatic projections (Prudhomme et al., 2012) and land-use scenarios (Prosser et al., 2014). The model parametric uncertainty of the climatic model and the pathogens model were also taken into account. The model results were used to estimate the consequences of upland pollution on the flux of FIB into the river estuary and to gain knowledge regarding the possible measures to mitigate it by acting on the upland landscape and land cover. In particular, the objectives of this study are: (i) to assess the fate of pathogen contamination from upland livestock activity in terms of river water quality; (ii) to investigate and quantify the impact of landscape management on the mitigation of faecal pollution; and (iii) to provide a methodological framework to analyse the impact of faecal pollution under a changing climate and land use.
Section snippets
Study area
The River Conwy catchment is located in the north-west of Wales (UK) and is the third largest river discharging into the Irish Sea along the North Wales coast. It drains a catchment of 678 km2, with the main drainage channel covering a distance of 55 km (Fig. 1). The upper reaches of the river cross upland blanket bog and moorland (Smith et al., 2007), passing through improved grazing land and conifer plantations via steep gorges to the town of Betws-y-Coed (reach 4 in Fig. 1). The lower Conwy,
Faecal indicator bacteria data
Freshwater samples were collected from several locations throughout the River Conwy catchment (Fig. 1). As detailed in Perkins et al., 2014, Emmett et al., 2016, samples were mixed and homogenised by shaking. Briefly water samples were processed within 4 h of collection in accordance with the Revised Bathing Water Directive (2006/7/8EC) (Perkins et al., 2014). Bacteria were enumerated following vacuum-filtration of the water sample through a 0.2 µm cellulose acetate membrane filter. The membranes
Model calibration
The results of the selected behavioural models from the Monte Carlo General Sensitivity Analysis are shown in Fig. 5 for Reach 4 (Cwn Llanerch). It can be seen that the model performance in reproducing the observed flow is very good. On the other hand, the spread of the model results for FIB in water and FIB in sediment is quite wide, although the observed values (black dots) are predominantly contained within the model result area, indicating that, given the observations available, the model
Discussion
Several sources of uncertainty affect the results of the INCA-Pathogens model. First of all, the FIB model used in this study was calibrated based on instantaneous intermittent data. These data are certainly affected by measurement uncertainty and by the natural sub-daily variability, which is shown in Fig. 2. This is likely to affect the model ability to reproduce the catchment processes. Thus, it is important to highlight that more data is needed to understand FIBs movement in rivers and
Conclusion
This paper shows how faecal pollution might vary due to natural and anthropogenic stressors in a temperate catchment where the local economy has strong ties with the quality of water in the river mouth. In particular:
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Climate change is expected to decrease slightly the pathogens concentration and load in the river due to increased die-off rates caused by warming, and to lower flows.
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Land use can alter significantly the pathogen cycle, leaving room for substantial reduction of faecal pollution by
Acknowledgements
We are grateful to the Department for Environment, Food & Rural Affairs (DEFRA) and the Natural Environment Research Council (NERC) for funding the Integrated Modelling Project (contract NEC05158) and to the MaRIUS project (Managing the Risks, Impacts and Uncertainties of droughts and water Scarcity), also funded by NERC, under the UK Droughts and Water Scarcity Programme (Grant NE/L010364/1). FIB data were collected within the framework of the Turf2Surf project funded by NERC, under the
References (59)
- et al.
Effects of afforestation on runoff and sediment load in an upland Mediterranean catchment
Sci. Total Environ.
(2016) - et al.
Modelling the future impacts of climate and land-use change on suspended sediment transport in the River Thames (UK)
J. Hydrol.
(2016) - et al.
Dynamic response of land use and river nutrient concentration to long-term climatic changes
Sci. Total Environ.
(2017) - et al.
Impacts of climate change, land-use change and phosphorus reduction on phytoplankton in the River Thames (UK)
Sci. Total Environ.
(2016) - et al.
Impacts of climate change on surface water quality in relation to drinking water production
Environ. Int.
(2009) - et al.
Survival of manure-borne E. coli in streambed sediment: effects of temperature and sediment properties
Water Res.
(2010) - et al.
Do higher data frequency and Bayesian auto-calibration lead to better model calibration? Insights from an application of INCA-P, a process-based river phosphorus model
J. Hydrol.
(2015) - et al.
Faecal indicator organism concentrations and catchment export coefficients in the UK
Water Res.
(2008) - et al.
An assessment of the fine sediment dynamics in an upland river system: INCA-Sed modifications and implications for fisheries
Sci. Total Environ.
(2010) - et al.
Fate and transport of polychlorinated biphenyls (PCBs) in the River Thames catchment – insights from a coupled multimedia fate and hydrobiogeochemical transport model
Sci. Total Environ.
(2016)
A model framework to assess the effect of dairy farms and wild fowl on microbial water quality during base-flow conditions
Water Res.
River flow forecastin through conceptual models – Part 1 – A discussion of principles
J. Hydrol.
Eutrophication in peel inlet—II. Identification of critical uncertainties via generalized sensitivity analysis
Water Res.
Modeling the relationship between land use and surface water quality
J. Environ. Manage.
Dynamic modelling of multiple phytoplankton groups in rivers with an application to the Thames river system in the UK
Environ. Model. Softw.
An INCA model for pathogens in rivers and catchments: model structure, sensitivity analysis and application to the River Thames catchment, UK
Sci. Total Environ.
A semi-distributed integrated nitrogen model for multiple source assessment in catchments (INCA): Part I—model structure and process equations
Sci. Total Environ.
A semi-distributed integrated flow and nitrogen model for multiple source assessment in catchments (INCA): Part II—application to large river basins in south Wales and eastern England
Sci. Total Environ.
Changing ideas in hydrology: the case of physically-based models
J. Hydrol.
Effects of sewage discharges on microbial components in tropical coastal waters (Senegal, West Africa)
Mar. Freshw. Res.
Chapter 27. Pathogens
Surface Water-Quality Modelling
An investigation of the laws of disinfection
J. Hyg. (Lond)
Modeling of pathogen indicator organisms in a small-scale agricultural catchment using SWAT
Hum. Ecol. Risk Assess. An Int. J.
Faecal contamination of drinking water in a Brazilian shanty town: importance of household storage and new human faecal marker testing
J. Water Health
Modelling phosphorus loading and algal blooms in a Nordic agricultural catchment-lake system under changing land-use and climate
Environ. Sci. Process. Impacts
Modeling fecal indicator bacteria concentrations in natural surface waters: a review
Crit. Rev. Environ. Sci. Technol.
Aquatic DOC export from subarctic Atlantic blanket bog in Norway is controlled by seasalt deposition, temperature and precipitation
Biogeochemistry
Simulating nitrogen budgets in complex farming systems using INCA: calibration and scenario analyses for the Kervidy catchment (W. France)
Hydrol. Earth Syst. Sci.
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