Projections of future deterioration in UK river quality are hampered by climatic uncertainty under extreme conditions

Abstract

A modelling study was undertaken to quantify effects that the climate likely to prevail in the 2050s might have on water quality in two contrasting UK rivers. In so doing, it pinpointed the extent to which time series of climate model output, for some variables derived following bias correction, are fit for purpose when used as a basis for projecting future water quality. Working at daily time step, the method involved linking regional climate model (HadRM3-PPE) projections, Future Flows Hydrology (rainfall-runoff modelling) and the QUESTOR river network water quality model. In the River Thames, the number of days when temperature, dissolved oxygen, biochemical oxygen demand and phytoplankton exceeded undesirable values (>25°C, <6 mg L −1 , >4 mg L −1 and >0.03 mg L −1 , respectively) was estimated to increase by 4.1-26.7 days per year. The changes do not reflect impacts of any possible change in land use or land management. In the River Ure, smaller increases in occurrence of undesirable water quality are likely to occur in the future (by 1.0-11.5 days per year) and some scenarios suggested no change. Results from 11 scenarios of the hydroclimatic inputs revealed considerable uncertainty around the levels of change, which prompted analysis of the sensitivity of the QUESTOR model to simulations of current climate and hydrology. Hydrological model errors were deemed of less significance than those associated with the derivation and downscaling of driving climatic variables (rainfall, air temperature and solar radiation). Errors associated with incomplete understanding of river water quality interactions with the aquatic ecosystem were found likely to be more substantial than those associated with hydrology, but less than those related to climate model inputs. These errors are largely a manifestation of uncertainty concerning the extent to which phytoplankton biomass is controlled by invertebrate grazers, particularly in mid-summer; and the degree to which this varies from year to year. The quality of data from climate models for generating flows and defining driving variables at the extremes of their distributions has been highlighted as the major source of uncertainty in water quality model outputs.

Weekly water quality monitoring data for the River Thames (UK) and its major tributaries (2009–2013): the Thames Initiative research platform

“…Since its commencement in March 2009, the Thames Initiative data have been used to quantify nutrient dynamics (Bowes et al, 2015a;Skeffington et al, 2015;Wade et al, 2012), nutrient sources (Bowes et al, 2014), phytoplankton dynamics (Bowes et al, 2012a(Bowes et al, , 2016Read et al, 2014) and bacterioplankton biodiversity . The resulting data sets have been extensively used as a modelling resource (Bussi et al, 2017(Bussi et al, , 2016Hutchins et al, 2016;Whitehead et al, 2015). Other studies have used the Thames Initiative monitoring sites as a framework for investigations of organic pollutants (Nakada et al, 2017;Singer et al, 2014), heavy metals (Turner et al, 2014), emerging contaminants (Horton et al, 2017) and antimicrobial resistance (Amos et al, 2015).…”

Weekly water quality monitoring data for the River Thames (UK) and its major tributaries (2009–2013): The Thames Initiative research platform

“…The resulting data sets 10 have been extensively used as a modelling resource (Bussi et al, 2017;Bussi et al, 2016;Hutchins et al, 2016;Whitehead et al, 2015). Other studies have used the Thames Initiative monitoring sites as a framework for investigations of organic pollutants (Nakada et al, 2017;Singer et al, 2014), heavy metals (Turner et al, 2014), emerging contaminants (Horton et al, 2017), and antimicrobial resistance (Amos et al, 2015).…”

During a winter of storms in a small UK catchment, hydrology and water quality responses follow a clear rural-urban gradient

“…The occurrence of similarly low levels of DO during the summer months would have been compounded with low background levels on account of increased temperatures, resulting in fish-kill across the catchment area (Villate et al, 2013 andRao et al, 2014). The relationship between dissolved oxygen and temperature is a critical driver for aquatic integrity, whereby reaeration of in-stream DO concentrations is recognised as being temperature dependant (Owens et al, 1964 andHutchins et al, 2016). This is a key factor in water quality simulations of dissolved oxygen and is commonly applied in numerical models such as QUESTOR (Hutchins et al, 2016).…”

“…The relationship between dissolved oxygen and temperature is a critical driver for aquatic integrity, whereby reaeration of in-stream DO concentrations is recognised as being temperature dependant (Owens et al, 1964 andHutchins et al, 2016). This is a key factor in water quality simulations of dissolved oxygen and is commonly applied in numerical models such as QUESTOR (Hutchins et al, 2016). …”