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Browning of Scottish surface water sources exposed to climate change [1]
['Ståle Haaland', 'Norwegian Institute Of Bioeconomy Research', 'Nibio', 'Ås', 'Faculty Of Environmental Sciences', 'Natural Resource Management', 'Norwegian University Of Life Sciences', 'Nmbu', 'Bjørnar Eikebrokk', 'Drikkevannskonsult']
Date: 2023-09
Abstract Levels of dissolved natural organic matter (DNOM) are increasing in our boreal watercourses. This is manifested by an apparent increase in its yellow to brown colour of the water, i.e., browning. Sound predictions of future changes in colour of our freshwaters is a prerequisite for predicting effects on aquatic fauna and a sustainable operation of drinking water facilities using surface waters as raw water sources. A model for the effect of climate on colour (mg Pt L-1) has been developed for two surface raw water sources in Scotland, i.e., at Bracadale and Port Charlotte. Both sites are situated far out on the Scottish west coast, without major impact of acid rain, with limited amounts of frost, and with limited recent land-use changes. The model was fitted to 15 years long data-series on colour measurements, provided by Scottish Water, at the two sites. Meteorological data were provided by UK Met. The models perform well for both sites in simulating the variation in monthly measured colour, explaining 89 and 90% of the variation at Bracadale and Port Charlotte, respectively. These well fitted models were used to predict future changes in colour due to changes in temperature and precipitation based on median climate data from a high emission climate RCP8.5 scenario from the HadCM3 climate model (UKCP18). The model predicted an increase in monthly average colour during growing season at both sites from about 150 mg Pt L-1 to about 200 mg Pt L-1 in 2050–2079. Temperature is found to be the most important positively driver for colour development at both sites.
Citation: Haaland S, Eikebrokk B, Riise G, Vogt RD (2023) Browning of Scottish surface water sources exposed to climate change. PLOS Water 2(9): e0000172.
https://doi.org/10.1371/journal.pwat.0000172 Editor: Bimlesh Kumar, Indian Institute of Technology Guwahati, INDIA Received: April 19, 2023; Accepted: August 7, 2023; Published: September 7, 2023 Copyright: © 2023 Haaland et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: Raw data used can be found at: DOI 10.17605/OSF.IO/96R3F Public databases are linked to in the manuscript. Funding: Thanks to Norwegian Water and waterworks in Norway, Sweden, Finland and Scotland for their financial support to the NOMiNOR project. NOMiNOR had a role in the study design, data collection and analysis. We also acknowledge the support and input from the TAČR KAPPA project No. 2020TO01000202, funded by the Norway Grants. TAČR KAPPA have also a focus on the study on DNOM and climate change. Competing interests: The authors have declared that no competing interests exist.
Introduction Many boreal surface water sources have had a distinct increase in colour throughout the past few decades [1–3]. In the past this increase was mainly due to the decline in anthropogenic acid rain deposition [4, 5]. At present the changes in climate (i.e., increase in growing season) and land-use (e.g., less outfield grazing), resulting in increased biomass (i.e., catchment Greening), are strong drivers for the ongoing Browning [6–9]. The link is that incomplete decomposition of the increased biomass leads to increased input of allochthonous natural dissolved organic matter (DNOM), often in complexation with increased levels of iron [10–13]. Both DNOM and ferric iron species compounds have strong abilities to absorb light in the blue PAR-area [14–16], and Fe-DNOM complexes have been shown to enhance [17–19], but also to suppress [20] the DNOM absorbance. Increased colour and influx of DNOM has multiple effects on aquatic biota and represent a considerable challenge for drinking water treatment plants using surface waters as their raw water sources. An increase in DNOM concentrations may potentially require upgrading of waterwork facility, representing a substantial investment cost [21]. The large spatial and temporal variations in soils and the amount and quality of dissolved organic matter, governed by complex interaction with hydrological and microbial responses, is challenging to model. Here, a simple conceptually based but empirically fitted model-approach was chosen to link the main climatic driving factors to the fluctuations in colour. This study presents a model for predicting near future changes in colour due to changes in climate at two small raw water sources; i.e., Bracadale and Port Charlotte. Both sites are situated far out on the west coast of Scotland. Bracadale is a pure lotic water system, whereas Port Charlotte also comprises a small water reservoir (Fig 1). Both sites are dominated by organic moorland [22]. PPT PowerPoint slide
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TIFF original image Download: Fig 1. Raw water sources of Bracadale and Port Charlotte, located in the west of Scotland. Locations of meteorological stations (UK Met Office; historic station data) are shown with numbers 1–5 (mid figure); 1-Stornoway Airport, 2-Tiree, 3-Dunstaffnage, 4-Ballypatrick Forest (located in Northern Ireland), 5-Paisley. Maps are created in QGIS using shapefiles from Natural Earth (CC-BY 4.0;
http://www.naturalearthdata.com). Stream networks for Bracadale and Port Charlotte (lower left; arrows indicate flow direction) are sketches made using satellite images.
https://doi.org/10.1371/journal.pwat.0000172.g001
Material and methods Bracadale and Port Charlotte drinking water treatment plants are two small waterworks with highly coloured raw water sources, commonly reaching 150–200 mg Pt L-1 during the summer season (Fig 2). Variations in colour intensity are mainly due to annual fluctuations in concentrations of DNOM and to a lesser degree iron (Fig 2). Annual precipitation amounts are high with more than 2 000 mm yr-1 (Fig 3). Located on the western coast of Scotland, the catchments in this region have received lower levels of acid deposition in comparison to eastern regions of the UK [23, 24]. Minor long term annual changes in the electric conductivity (EC) of precipitation in the area are furthermore not expected to have led to any particular changes in soil DNOM concentration (low DOC prop ) [25]. Nevertheless, there has been a significant percentage decrease in the overall deposition of sulphur over the area from year 1986 to 2005 [24]. Conductivity of the raw water has been measured frequently at Port Charlotte from year 2000–2004 and is maintaining relative stable within the range of 100–150 μS/cm without observable indications of a downward trend. Furthermore, pH levels of the raw water have been measured at both sites throughout the entire duration of the studied period (year 2001–2016) and is mainly ranging between 6.5 and 7.5 at both sites with no observable indications of an upward trend. Due to the stable coastal climate air temperatures only rarely drop below 0°C (Fig 3). From that, winter hydrology is more or less absent, and runoff would insignificantly be affected by freezing/thawing, snowmelt and ice-cover. The area has a significant history of land use, such as peatland ditching and sheep and goat farming. However, the land-use in the studied catchment sites would be restricted due to their designation as Drinking Water Protected Areas (DWPA) [26]. The Water Framework Directive ensures that no activities within DWPA areas lead to the degradation of water quality. PPT PowerPoint slide
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TIFF original image Download: Fig 2. Long term data series on colour (mg Pt l-1) (2001–2015), mainly on a weekly to bi-weekly temporal scale, sampled at Bracadale and Port Charlotte. Data provided by Scottish Water.
https://doi.org/10.1371/journal.pwat.0000172.g002 PPT PowerPoint slide
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TIFF original image Download: Fig 3. XY-plots for colour vs concentrations of iron (Fe) and total organic carbon (TOC, a proxy for DNOM) at Bracadale (open circles) and Port Charlotte (filled circles) for the period with accessible data; year 2000–2015. TOC (n = 68 at Bracadale and n = 106 at Port Charlotte) is here a stronger explanatory factor for colour than iron (n = 161 at Bracadale and n = 277 at Port Charlotte). Linear trend regressions: r2 = 0.80–0.95 for colour vs TOC; r2 = 0.25–0.30 for colour vs Fe.
https://doi.org/10.1371/journal.pwat.0000172.g003 The model approach is based on a best fit parameterization using air temperature and precipitation amounts as conceptually based predictors for colour concentrations (Eq 1). A similar approach is presented and explained in Haaland et al. [6]. C t is the modelled watercolour in mg Pt L-1, while C 0 is the minimum background colour concentration (mg Pt L-1) at each catchment. k is a constant adjusting for differences in denomination (set to 1 for both sites). Precip is monthly amounts of precipitation (mm), which serves a proxy for runoff. a is a fitted catchment specific constant conceptually reflecting the impact of runoff on transport of DNOM from the soils to surface waters. Monthly weather data from five meteorological stations (Fig 1) were downloaded from the UK Met Office (Weather and climate change—Met Office). Meteorological data from the weather stations at Stornoway Airport, Tiree and Dunstaffnage were averaged and used for Bracadale, whereas averaged data from the stations at Tiree, Dunstafnage, Ballypatrick Forest (located in Northern Ireland) and Paisley were used for Port Charlotte. Precipitation amounts varied considerably between years, though there were no significant (p < 0.05) long term trends. As there are considerable spatial differences in the amount of precipitation between the weather stations the accuracy of this approach will at times be low. To adjust for absolute errors in precipitation amounts, the precipitation data were related to gridded (12 km2) data for an area close to the sampling sites from UK Met Office database portal (
https://climate-themetoffice.hub.arcgis.com/). These datasets are also for monthly average levels, though cover only the period 1991–2020. During this period the averaged weather data from the weather stations differed from the gridded data by a factor of 1,55 and 1,1 at Bracadale and Port Charlotte, respectively. These factors were therefore used to adjust the meteorological data. From this we expect that both precipitation amount and intensity have good accuracy and precision for both sites. T max and T min are maximum and minimum monthly measured air temperature (°C), respectively. Differences between the average temperature data from the Met UK meteorological stations and the Met Office database for T max and T min monthly averages at a nearby grid were small (often < 0.5 ⁰C). The temperature data were hence not adjusted for deviation in levels. A few months in which minimum air temperature dropped sub-0°C, i.e., 3 months at Port Charlotte and 1 month at Bracadale, out of a total of 180 months of data, were not used in the calibration procedure. Moreover, less days of frost are expected in the future according to the predictions from the UK Met Office [27]. The expression (T max + T min )/2 was used. b is a fitted index conceptually adjusting for differences in the temperature effect on catchment production of DNOM. Increased temperature is expected to promote both increased primary production (catchment greening) and also higher DNOM concentration in surface waters due to increased microbial degradation of organic matter in the catchment soils. The temperature part of the model is thus a bulked proxy for temperature dependent enzymatic reactions [28] and can be regarded as a very simple exponential model fitted for an Eyring–Polanyi model for temperature dependence in biology [29]. The models were calibrated for a 15-year period of monthly average colour concentrations, based on weekly to bi-weekly data series, for each of the sites provided by Scottish Water through the NOMiNOR-project [22]. Data from odd years (2001, 2003, 2005, …, 2015) were used for calibration. A best-fit approach was used for optimizing the models in response to changes in climate (air temperature and precipitation amounts). Data from even years (2002, 2004, 2006, …, 2016) were used for a validation of the models. Differences in water balance (i.e., precipitation–runoff) due to differences in evapotranspiration on a monthly scale between years were by using this approach expected to be minor between the model calibration and validation periods.
Conclusions A model for effects of climate change on water colour has been fitted for two Scottish surface raw water sources. The model explains 89 and 90% of the variation in monthly averaged colour at the two sites. Using climate data scenario based on the RCP8.5 scenario, the model predicts an increase in monthly average colour during growing season from about 150 mg Pt L-1 to about 200 mg Pt L-1 in 2050–2079. Temperature is found to be the most important positively driver for colour development at the studied sites.
Acknowledgments Thanks to Scottish Water providing us with long term raw water data, and thanks to UK Met Office for providing historic and modelled climatic data.
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