OR/13/031 Results and discussion - Revision history

Ajhil: /* Catchment river discharge and sediment discharge events */

2021-10-19T08:18:42Z

Catchment river discharge and sediment discharge events

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	To assess the relationship between river flows and sediment flux events, the two are plotted independently in Figure 28. Whilst some high flow events are accompanied by high sediment flux rates, particularly at the start of winter there are also high discharge events which result in little sediment transport. In Figure 29, modelled sediment transport from water bodies 6 and 3 are plotted directly against observed discharge at their two nearest gauging stations in the Nene Catchment (Orton and Upton respectively). Regression analysis reveals that there is no statistically significant relationship between catchment flow rates and sediment discharge events, preventing river gauging data from providing an alternative method to project modelled flux rates back through time.		To assess the relationship between river flows and sediment flux events, the two are plotted independently in Figure 28. Whilst some high flow events are accompanied by high sediment flux rates, particularly at the start of winter there are also high discharge events which result in little sediment transport. In Figure 29, modelled sediment transport from water bodies 6 and 3 are plotted directly against observed discharge at their two nearest gauging stations in the Nene Catchment (Orton and Upton respectively). Regression analysis reveals that there is no statistically significant relationship between catchment flow rates and sediment discharge events, preventing river gauging data from providing an alternative method to project modelled flux rates back through time.

	[[Image:OR13031fig28.jpg\|thumb\|center\|~~400px~~\| '''Figure 28'''    River flow data from the Orton gauging station, and modelled sediment transport events plotted for water body 6 for the two modelling periods (1972–1982 and 1992–2002). The blue line is gauging station discharge and black discs are modelled sediment fluxes. River gauging data was only available from 1995–1997 in the second modelling period. ]]		[[Image:OR13031fig28.jpg\|thumb\|center\|600px\| '''Figure 28'''    River flow data from the Orton gauging station, and modelled sediment transport events plotted for water body 6 for the two modelling periods (1972–1982 and 1992–2002). The blue line is gauging station discharge and black discs are modelled sediment fluxes. River gauging data was only available from 1995–1997 in the second modelling period. ]]

	[[Image:OR13031fig29.jpg\|thumb\|center\|350px\| '''Figure 29'''    Plots of ten-day averaged water discharge from the Orton gauging station versus modelled sediment flux for the two modelled periods (1972–1982 and 1992–2002 respectively). ]]		[[Image:OR13031fig29.jpg\|thumb\|center\|350px\| '''Figure 29'''    Plots of ten-day averaged water discharge from the Orton gauging station versus modelled sediment flux for the two modelled periods (1972–1982 and 1992–2002 respectively). ]]

Ajhil: /* Cumulative twentieth Century sediment flux rates */

2021-10-19T08:18:28Z

Cumulative twentieth Century sediment flux rates

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	The cumulative twentieth century sediment fluxes simulated by the model are presented in Figure 27. The two red lines in each of the water body plots represent the cumulative rate from the two modelled periods that have attained an approximately linear steady-state relationship. For the period prior to 1972, the mean flux rates from the 1972–1982 simulations were assumed appropriate and projected backward to assess sediment flux over 1910 to 1972. Similarly, beyond 2002, the mean flux rates from the 1992–2002 model run were used to project cumulative sediment flux forward to 2010. For the interim period 1982–1992, the average mean flux rates from the two simulated periods were used to predict sediment flux.		The cumulative twentieth century sediment fluxes simulated by the model are presented in Figure 27. The two red lines in each of the water body plots represent the cumulative rate from the two modelled periods that have attained an approximately linear steady-state relationship. For the period prior to 1972, the mean flux rates from the 1972–1982 simulations were assumed appropriate and projected backward to assess sediment flux over 1910 to 1972. Similarly, beyond 2002, the mean flux rates from the 1992–2002 model run were used to project cumulative sediment flux forward to 2010. For the interim period 1982–1992, the average mean flux rates from the two simulated periods were used to predict sediment flux.

	[[Image:OR13031fig27.jpg\|thumb\|center\|~~500px~~\| '''Figure 27'''    Projected cumulative sediment discharge plots for the six sub-catchments. The simulated rates are given as red lines and projected rates as dashed line. The fainter dashed line represents a projection back beyond the 1947 flood event. ]]		[[Image:OR13031fig27.jpg\|thumb\|center\|600px\| '''Figure 27'''    Projected cumulative sediment discharge plots for the six sub-catchments. The simulated rates are given as red lines and projected rates as dashed line. The fainter dashed line represents a projection back beyond the 1947 flood event. ]]

	The cumulative flux rates are arbitrarily set to zero at 1910. The unknown influence of the 1947 snow melt floods on sediment erosion and deposition rates suggest greater uncertainty on the earlier sections of the plots (finer dashing). Water bodies in the upper reaches of the river (1, 2, and 3) show a varied response over the modelled periods. Water body one has a near perfect linear response, while water body 2 has differing responses when the two periods are compared, creating a non-linear overall appearance, with a more rapid rate of sediment loss observed in the 1992–2001 period of the simulation. Water body 3 has a tendency for sediment flux rates to tail-off towards the end of the simulation. The water bodies in the lower reaches of the river catchment (4, 5, and 6) exhibit a more uniform near-linear trend in sediment flux rates.		The cumulative flux rates are arbitrarily set to zero at 1910. The unknown influence of the 1947 snow melt floods on sediment erosion and deposition rates suggest greater uncertainty on the earlier sections of the plots (finer dashing). Water bodies in the upper reaches of the river (1, 2, and 3) show a varied response over the modelled periods. Water body one has a near perfect linear response, while water body 2 has differing responses when the two periods are compared, creating a non-linear overall appearance, with a more rapid rate of sediment loss observed in the 1992–2001 period of the simulation. Water body 3 has a tendency for sediment flux rates to tail-off towards the end of the simulation. The water bodies in the lower reaches of the river catchment (4, 5, and 6) exhibit a more uniform near-linear trend in sediment flux rates.

Ajhil: /* Extractable P in homogenised sediment cores */

2021-10-19T08:18:04Z

Extractable P in homogenised sediment cores

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	Concentrations of Olsen extractable P (OEP) for the 5 homogenised cores taken from each water body are shown in Figure 17 and mean values for each water body are shown in Figure 18. Results show a trend of increasing OEP concentrations from water body 1 through to water body 6. This is likely for three reasons. Firstly, the sediment distribution was slightly coarser in the headwaters, suggesting that clay and silt-sized particles that enter the channel are washed downstream by the faster flowing waters. The greater surface area provided by fine sediments will provide a greater sorption capacity for SRP from the river water. Secondly, greater deposition of sediment is found lower down the river as the cumulative catchment size increases, water currents are slower and catchment erosion rates generally increase ([[Media:OR13031tab15.jpg\|Table 15]]). Thirdly, the number of sources of SRP entering the river increase as the distance from the headwaters increase. This is demonstrated by the increase in SRP which appears to be associated with STW’s within the catchment ([[OR/13/031 Results and discussion#River Nene water chemistry \|''see'' River Nene water chemistry]]). Concentrations of OEP vary between ~17 -100 mg kg<sup>-1</sup>. There were no relationships between OEP and the typical sorption surfaces including LOI, Fe and Mn when analysing the whole dataset. However, splitting the data into water bodies 1–3 and 4–6 produced stronger linear regressions (Figure 19). No relationships were found in water bodies 1–3 between OEP and Fe, Mn and Ca. However, for water bodies 4–6, a positive linear regression with Mn and a weak negative linear regression with Ca were found. These results suggest that no specific sorption surface was dominant for OEP in water bodies 1–3, whereas in water bodies 4–6, Mn oxides appeared to assume a greater importance. It was found that OEP was <5% of TP in all instances with most samples being <2%. A positive correlation of rs = 0.72 was found between TP and OEP. However, there is no mechanistic basis for this relationship and it is likely that it is a consequence of more TP containing minerals being found as there is a gradual fining of sediment which also carries a greater sorption capacity.		Concentrations of Olsen extractable P (OEP) for the 5 homogenised cores taken from each water body are shown in Figure 17 and mean values for each water body are shown in Figure 18. Results show a trend of increasing OEP concentrations from water body 1 through to water body 6. This is likely for three reasons. Firstly, the sediment distribution was slightly coarser in the headwaters, suggesting that clay and silt-sized particles that enter the channel are washed downstream by the faster flowing waters. The greater surface area provided by fine sediments will provide a greater sorption capacity for SRP from the river water. Secondly, greater deposition of sediment is found lower down the river as the cumulative catchment size increases, water currents are slower and catchment erosion rates generally increase ([[Media:OR13031tab15.jpg\|Table 15]]). Thirdly, the number of sources of SRP entering the river increase as the distance from the headwaters increase. This is demonstrated by the increase in SRP which appears to be associated with STW’s within the catchment ([[OR/13/031 Results and discussion#River Nene water chemistry \|''see'' River Nene water chemistry]]). Concentrations of OEP vary between ~17 -100 mg kg<sup>-1</sup>. There were no relationships between OEP and the typical sorption surfaces including LOI, Fe and Mn when analysing the whole dataset. However, splitting the data into water bodies 1–3 and 4–6 produced stronger linear regressions (Figure 19). No relationships were found in water bodies 1–3 between OEP and Fe, Mn and Ca. However, for water bodies 4–6, a positive linear regression with Mn and a weak negative linear regression with Ca were found. These results suggest that no specific sorption surface was dominant for OEP in water bodies 1–3, whereas in water bodies 4–6, Mn oxides appeared to assume a greater importance. It was found that OEP was <5% of TP in all instances with most samples being <2%. A positive correlation of rs = 0.72 was found between TP and OEP. However, there is no mechanistic basis for this relationship and it is likely that it is a consequence of more TP containing minerals being found as there is a gradual fining of sediment which also carries a greater sorption capacity.

	[[Image:OR13031fig17.jpg\|thumb\|center\|~~500px~~\| '''Figure 17'''    Changes in OEP concentration (mg kg<sup>-1</sup>) in homogenised cores with distance going down the 6 water bodies sampled of the River Nene. Error bars show the standard deviation of the 3 replicates analysed for each core. Water body 1 (cores 1–6), water body 2 (cores 7–12), water body 3 (cores 13–18), water body 4 (cores 19–24), water body 5 (cores 25–30) and water body 6 (cores 31–36). Missing values are where CoreD samples were taken. ]]		[[Image:OR13031fig17.jpg\|thumb\|center\|600px\| '''Figure 17'''    Changes in OEP concentration (mg kg<sup>-1</sup>) in homogenised cores with distance going down the 6 water bodies sampled of the River Nene. Error bars show the standard deviation of the 3 replicates analysed for each core. Water body 1 (cores 1–6), water body 2 (cores 7–12), water body 3 (cores 13–18), water body 4 (cores 19–24), water body 5 (cores 25–30) and water body 6 (cores 31–36). Missing values are where CoreD samples were taken. ]]

	[[Image:OR13031fig18.jpg\|thumb\|center\|400px\| '''Figure 18'''    Mean Olsen Extractable P concentrations for each water body sampled from the River Nene. Error bars are ±1 standard deviation. ]]		[[Image:OR13031fig18.jpg\|thumb\|center\|400px\| '''Figure 18'''    Mean Olsen Extractable P concentrations for each water body sampled from the River Nene. Error bars are ±1 standard deviation. ]]

Ajhil: /* TP (TP) in homogenised sediment cores */

2021-10-19T08:17:50Z

TP (TP) in homogenised sediment cores

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	TP (TP) was analysed in the homogenised sediment cores and results are shown in Figure 15. The source of TP can potentially be from numerous sources including (i) soil parent material (geological), (ii) fixation and occlusion of P in oxide minerals or the precipitation of P containing minerals where P is derived from agricultural fertilisers and (iii) similar processes as (ii) occurring in the river channel. It is evident that there was an increase in TP after core 18, the end of water body 3. This roughly coincides with the change of geology to the Whitby mudstone formation and suggests that from water body 4 onwards there may have been a different geological influence on TP concentrations in the eroding soil and river bed. We assessed TP along with particle size, organic matter and other major elements commonly associated with P minerals and sorption such as Ca, Mg, Fe and Mn. Initial correlation analysis suggested no strong relationships were present between TP and these parameters. Therefore, the TP dataset was divided into (i) water body 1–3 and (ii) water body 4–6 based on the TP results (Figure 15). Improved relationships (Figure 16) were found between TP and other elements (Fe & Mn), probably partly driven by the different soil parent materials found in different parts of the catchment. Van der Perk et al. (2007)<ref name="Van der Perk 2007">Van der Perk, M, Owens, P N, Deeks, L K, Rawlins, B G, Haygarth, P M, and Bevan, K J. 2007. Controls on catchment-scale patterns of Phosphorus in soil, streambed sediment and stream water. Journal of Environmental Quality, 36, 694–708.</ref> found both soil parent material and its chemical properties to be major factors in controlling catchment scale spatial variation in TP concentrations in sediments. Both Fe and Mn oxides are known to sorb P and eventually become occluded or precipitating with these oxide minerals, so the relationships whereby P and Fe/Mn are positively correlated is expected. These minerals could be iron phosphate or combined Mn/Fe phosphate minerals such as vivianite (Fe<sub>3</sub>(PO<sub>4</sub>)<sub>2</sub>·8H<sub>2</sub>O) for example. In particular, Mn appears to show a strong correlation with P in both water bodies 1–3 and 4–6. Kawashima et al. (1986)<ref name="Kawashima 1986">Kawashima, M, Tainaka, Y, Hori, T, Koyama, M, and Takamatsu, T. 1986. Phosphate adsorption onto hydrous manganese (IV) oxide in the presence of divalent cations. Water Research, 20(4), 471–475.</ref> found that phosphate is sorbed by MnOx via the presence of divalent cations (Ba<sup>2+</sup>, Ca<sup>2+</sup>, Sr<sup>2+</sup>, Mg<sup>2+</sup>) or transition metals (Mn<sup>2+</sup>, Co<sup>2+</sup>, Ni<sup>2+</sup>). For water body 4–6, a wide range of P concentrations were found sorbed to similar concentrations of Fe, possibly suggesting that the Fe oxides contained in the sediment have further capacity to sorb phosphate from the river waters. Many reports state that Phosphorus is often found associated with Ca in river sediments, probably as apatite which can precipitate as a mineral if water chemistry is suitable, but can also be found in the shells of aquatic molluscs. From these results it would appear that water bodies 1–3 have a positive correlation whilst in water bodies 4–6 there appeared to a slight negative correlation (Figure 16). It was obvious that mollusc shells were more plentiful in water bodies 1–3 and this may be the result why a positive correlation was observed.		TP (TP) was analysed in the homogenised sediment cores and results are shown in Figure 15. The source of TP can potentially be from numerous sources including (i) soil parent material (geological), (ii) fixation and occlusion of P in oxide minerals or the precipitation of P containing minerals where P is derived from agricultural fertilisers and (iii) similar processes as (ii) occurring in the river channel. It is evident that there was an increase in TP after core 18, the end of water body 3. This roughly coincides with the change of geology to the Whitby mudstone formation and suggests that from water body 4 onwards there may have been a different geological influence on TP concentrations in the eroding soil and river bed. We assessed TP along with particle size, organic matter and other major elements commonly associated with P minerals and sorption such as Ca, Mg, Fe and Mn. Initial correlation analysis suggested no strong relationships were present between TP and these parameters. Therefore, the TP dataset was divided into (i) water body 1–3 and (ii) water body 4–6 based on the TP results (Figure 15). Improved relationships (Figure 16) were found between TP and other elements (Fe & Mn), probably partly driven by the different soil parent materials found in different parts of the catchment. Van der Perk et al. (2007)<ref name="Van der Perk 2007">Van der Perk, M, Owens, P N, Deeks, L K, Rawlins, B G, Haygarth, P M, and Bevan, K J. 2007. Controls on catchment-scale patterns of Phosphorus in soil, streambed sediment and stream water. Journal of Environmental Quality, 36, 694–708.</ref> found both soil parent material and its chemical properties to be major factors in controlling catchment scale spatial variation in TP concentrations in sediments. Both Fe and Mn oxides are known to sorb P and eventually become occluded or precipitating with these oxide minerals, so the relationships whereby P and Fe/Mn are positively correlated is expected. These minerals could be iron phosphate or combined Mn/Fe phosphate minerals such as vivianite (Fe<sub>3</sub>(PO<sub>4</sub>)<sub>2</sub>·8H<sub>2</sub>O) for example. In particular, Mn appears to show a strong correlation with P in both water bodies 1–3 and 4–6. Kawashima et al. (1986)<ref name="Kawashima 1986">Kawashima, M, Tainaka, Y, Hori, T, Koyama, M, and Takamatsu, T. 1986. Phosphate adsorption onto hydrous manganese (IV) oxide in the presence of divalent cations. Water Research, 20(4), 471–475.</ref> found that phosphate is sorbed by MnOx via the presence of divalent cations (Ba<sup>2+</sup>, Ca<sup>2+</sup>, Sr<sup>2+</sup>, Mg<sup>2+</sup>) or transition metals (Mn<sup>2+</sup>, Co<sup>2+</sup>, Ni<sup>2+</sup>). For water body 4–6, a wide range of P concentrations were found sorbed to similar concentrations of Fe, possibly suggesting that the Fe oxides contained in the sediment have further capacity to sorb phosphate from the river waters. Many reports state that Phosphorus is often found associated with Ca in river sediments, probably as apatite which can precipitate as a mineral if water chemistry is suitable, but can also be found in the shells of aquatic molluscs. From these results it would appear that water bodies 1–3 have a positive correlation whilst in water bodies 4–6 there appeared to a slight negative correlation (Figure 16). It was obvious that mollusc shells were more plentiful in water bodies 1–3 and this may be the result why a positive correlation was observed.

	[[Image:OR13031fig15.jpg\|thumb\|center\|~~500px~~\| '''Figure 15'''    Changes in concentrations of TP in cores taken from the six water bodies of the River Nene. Water body 1 (cores 1–6), water body 2 (Cores 7–12), water body 3 (cores 13–18), water body 4 (cores 19–24), water body 5 (cores 25–30) and water body 6 (cores 31–36). Missing values are where Core<sub>D</sub> samples were taken. ]]		[[Image:OR13031fig15.jpg\|thumb\|center\|600px\| '''Figure 15'''    Changes in concentrations of TP in cores taken from the six water bodies of the River Nene. Water body 1 (cores 1–6), water body 2 (Cores 7–12), water body 3 (cores 13–18), water body 4 (cores 19–24), water body 5 (cores 25–30) and water body 6 (cores 31–36). Missing values are where Core<sub>D</sub> samples were taken. ]]

	[[Image:OR13031fig16.jpg\|thumb\|center\|400px\| '''Figure 16'''    Relationships between TP and (a) Total Fe and (b) Total Mn (c) Total Ca in homogenised sediment cores taken from the six water bodies of the River Nene. ]]		[[Image:OR13031fig16.jpg\|thumb\|center\|400px\| '''Figure 16'''    Relationships between TP and (a) Total Fe and (b) Total Mn (c) Total Ca in homogenised sediment cores taken from the six water bodies of the River Nene. ]]

Ajhil: /* Organic matter */

2021-10-19T08:17:36Z

Organic matter

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	Loss on ignition varied between 3.18 and 12.06% in the homogenised cores taken from the six water bodies. Figure 13 shows that there was a general tendency for LOI to increase as distance from the head waters increased. This could be expected based on the assumption that because of slowing water currents the sediment may become slightly finer as more silt and clay settle, these being the particles that organic matter will preferentially bind to. However, the particle size distributions down the river channel previously described would suggest that no consistent fining of sediment particles was found downstream, and that particle size alone may not control organic matter concentration. This was confirmed by the lack of correlation between % clay and LOI (Figure 14a). Included in the clay size fraction will be a range of minerals such as (i) sub-micron oxides, (ii) sub-micron quartz and (iii) sub micron carbonate that do not contribute significant binding surfaces for organic matter. Rawlins (2011)<ref name="Rawlins 2011">Rawlins, B G. 2011. Controls on the phosphorus content of fine stream bed sediments in agricultural headwater catchments at the loandscape-scale. Agriculture, Ecosystems and Environment, 144, 352–363.</ref> found that surface area was a better predictor than particle size for organic matter in sediments and this is largely controlled by the type and amount of clay minerals present. Further investigation by plotting organic matter against aluminium, a major component of clay, shows a positive correlation with organic matter (Figure 14b) suggesting that organic matter in the sediments is likely linked to clay content. A reasonable correlation is also found with titanium (Figure 14c) that is also a significant component of clay sized material.		Loss on ignition varied between 3.18 and 12.06% in the homogenised cores taken from the six water bodies. Figure 13 shows that there was a general tendency for LOI to increase as distance from the head waters increased. This could be expected based on the assumption that because of slowing water currents the sediment may become slightly finer as more silt and clay settle, these being the particles that organic matter will preferentially bind to. However, the particle size distributions down the river channel previously described would suggest that no consistent fining of sediment particles was found downstream, and that particle size alone may not control organic matter concentration. This was confirmed by the lack of correlation between % clay and LOI (Figure 14a). Included in the clay size fraction will be a range of minerals such as (i) sub-micron oxides, (ii) sub-micron quartz and (iii) sub micron carbonate that do not contribute significant binding surfaces for organic matter. Rawlins (2011)<ref name="Rawlins 2011">Rawlins, B G. 2011. Controls on the phosphorus content of fine stream bed sediments in agricultural headwater catchments at the loandscape-scale. Agriculture, Ecosystems and Environment, 144, 352–363.</ref> found that surface area was a better predictor than particle size for organic matter in sediments and this is largely controlled by the type and amount of clay minerals present. Further investigation by plotting organic matter against aluminium, a major component of clay, shows a positive correlation with organic matter (Figure 14b) suggesting that organic matter in the sediments is likely linked to clay content. A reasonable correlation is also found with titanium (Figure 14c) that is also a significant component of clay sized material.

	[[Image:OR13031fig13.jpg\|thumb\|center\|~~500px~~\| '''Figure 13'''    Changes in organic matter as determined by loss on ignition (%) for the homogenised cores collected from the six water bodies of the River Nene. Water body 1 (cores 1–6), water body 2 (Cores 7–12), water body 3 (cores 13–18), water body 4 (cores 19–24), water body 5 (cores 25–30) and water body 6 (cores 31–36). ]]		[[Image:OR13031fig13.jpg\|thumb\|center\|600px\| '''Figure 13'''    Changes in organic matter as determined by loss on ignition (%) for the homogenised cores collected from the six water bodies of the River Nene. Water body 1 (cores 1–6), water body 2 (Cores 7–12), water body 3 (cores 13–18), water body 4 (cores 19–24), water body 5 (cores 25–30) and water body 6 (cores 31–36). ]]

	[[Image:OR13031fig14.jpg\|thumb\|center\|400px\| '''Figure 14'''    Relationship between organic matter content with (a) clay, (b) total Al and (c) total Ti. ]]		[[Image:OR13031fig14.jpg\|thumb\|center\|400px\| '''Figure 14'''    Relationship between organic matter content with (a) clay, (b) total Al and (c) total Ti. ]]

Ajhil: /* Particle size distribution (PSD) of sediments */

2021-10-19T08:17:19Z

Particle size distribution (PSD) of sediments

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	Figure 12 shows how the percentage sand, silt and clay fractions in the 36 sediment cores change with distance downstream. Overall, there appeared to be a slight fining of particles with distance downstream but no consistent pattern is evident. The major influence on the particle sizes is likely the depositional environment (e.g. position in relation to current, density of plants that can trap sediment, etc). In addition, Fisher et al. (2004)<ref name="Fisher 2004">Fisher, S G, Sponsellar, R A, and Heffernan, J B. 2004. Horizons in stream biogeochemistry: Flowpaths to progress. Ecology, 85(9), 2369–2379.</ref> suggest that tributaries flowing into a river will provide pulses of fresh material and energy that can affect sediment deposition. It is also apparent that the cores represent a history of deposition and would represent changes in the depositional environment with time. In addition, the local geology may play a role in determining PSD as eroding river banks will add to the load. For example, where the river is cutting through sand and gravel deposits ([[Media:OR13031fig1.jpg\|Figure 1]]) it is likely that cores taken from these areas may have a coarser particle size distribution, particularly as the sediment was found largely along the margins of the channel.		Figure 12 shows how the percentage sand, silt and clay fractions in the 36 sediment cores change with distance downstream. Overall, there appeared to be a slight fining of particles with distance downstream but no consistent pattern is evident. The major influence on the particle sizes is likely the depositional environment (e.g. position in relation to current, density of plants that can trap sediment, etc). In addition, Fisher et al. (2004)<ref name="Fisher 2004">Fisher, S G, Sponsellar, R A, and Heffernan, J B. 2004. Horizons in stream biogeochemistry: Flowpaths to progress. Ecology, 85(9), 2369–2379.</ref> suggest that tributaries flowing into a river will provide pulses of fresh material and energy that can affect sediment deposition. It is also apparent that the cores represent a history of deposition and would represent changes in the depositional environment with time. In addition, the local geology may play a role in determining PSD as eroding river banks will add to the load. For example, where the river is cutting through sand and gravel deposits ([[Media:OR13031fig1.jpg\|Figure 1]]) it is likely that cores taken from these areas may have a coarser particle size distribution, particularly as the sediment was found largely along the margins of the channel.

	[[Image:OR13031fig12.jpg\|thumb\|center\|~~400px~~\| '''Figure 12'''    Changes in the sand, silt and clay particle size distributions in the 36 cores taken from the River Nene’s six water bodies. Water body 1 (cores 1–6), water body 2 (Cores 7–12), water body 3 (cores 13–18), water body 4 (cores 19–24), water body 5 (cores 25–30) and water. ]]		[[Image:OR13031fig12.jpg\|thumb\|center\|600px\| '''Figure 12'''    Changes in the sand, silt and clay particle size distributions in the 36 cores taken from the River Nene’s six water bodies. Water body 1 (cores 1–6), water body 2 (Cores 7–12), water body 3 (cores 13–18), water body 4 (cores 19–24), water body 5 (cores 25–30) and water. ]]

	==Organic matter==		==Organic matter==

Dbk: 1 revision imported

2016-12-07T11:03:18Z

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Ajhil: /* Uptake of SRP from river water in each water body */

2016-12-01T10:29:48Z

Uptake of SRP from river water in each water body

Show changes

← Older revision		Revision as of 09:18, 19 October 2021
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	Concentrations of Olsen extractable P (OEP) for the 5 homogenised cores taken from each water body are shown in Figure 17 and mean values for each water body are shown in Figure 18. Results show a trend of increasing OEP concentrations from water body 1 through to water body 6. This is likely for three reasons. Firstly, the sediment distribution was slightly coarser in the headwaters, suggesting that clay and silt-sized particles that enter the channel are washed downstream by the faster flowing waters. The greater surface area provided by fine sediments will provide a greater sorption capacity for SRP from the river water. Secondly, greater deposition of sediment is found lower down the river as the cumulative catchment size increases, water currents are slower and catchment erosion rates generally increase ([[Media:OR13031tab15.jpg\|Table 15]]). Thirdly, the number of sources of SRP entering the river increase as the distance from the headwaters increase. This is demonstrated by the increase in SRP which appears to be associated with STW’s within the catchment ([[OR/13/031 Results and discussion#River Nene water chemistry \|''see'' River Nene water chemistry]]). Concentrations of OEP vary between ~17 -100 mg kg<sup>-1</sup>. There were no relationships between OEP and the typical sorption surfaces including LOI, Fe and Mn when analysing the whole dataset. However, splitting the data into water bodies 1–3 and 4–6 produced stronger linear regressions (Figure 19). No relationships were found in water bodies 1–3 between OEP and Fe, Mn and Ca. However, for water bodies 4–6, a positive linear regression with Mn and a weak negative linear regression with Ca were found. These results suggest that no specific sorption surface was dominant for OEP in water bodies 1–3, whereas in water bodies 4–6, Mn oxides appeared to assume a greater importance. It was found that OEP was <5% of TP in all instances with most samples being <2%. A positive correlation of rs = 0.72 was found between TP and OEP. However, there is no mechanistic basis for this relationship and it is likely that it is a consequence of more TP containing minerals being found as there is a gradual fining of sediment which also carries a greater sorption capacity.		Concentrations of Olsen extractable P (OEP) for the 5 homogenised cores taken from each water body are shown in Figure 17 and mean values for each water body are shown in Figure 18. Results show a trend of increasing OEP concentrations from water body 1 through to water body 6. This is likely for three reasons. Firstly, the sediment distribution was slightly coarser in the headwaters, suggesting that clay and silt-sized particles that enter the channel are washed downstream by the faster flowing waters. The greater surface area provided by fine sediments will provide a greater sorption capacity for SRP from the river water. Secondly, greater deposition of sediment is found lower down the river as the cumulative catchment size increases, water currents are slower and catchment erosion rates generally increase ([[Media:OR13031tab15.jpg\|Table 15]]). Thirdly, the number of sources of SRP entering the river increase as the distance from the headwaters increase. This is demonstrated by the increase in SRP which appears to be associated with STW’s within the catchment ([[OR/13/031 Results and discussion#River Nene water chemistry \|''see'' River Nene water chemistry]]). Concentrations of OEP vary between ~17 -100 mg kg<sup>-1</sup>. There were no relationships between OEP and the typical sorption surfaces including LOI, Fe and Mn when analysing the whole dataset. However, splitting the data into water bodies 1–3 and 4–6 produced stronger linear regressions (Figure 19). No relationships were found in water bodies 1–3 between OEP and Fe, Mn and Ca. However, for water bodies 4–6, a positive linear regression with Mn and a weak negative linear regression with Ca were found. These results suggest that no specific sorption surface was dominant for OEP in water bodies 1–3, whereas in water bodies 4–6, Mn oxides appeared to assume a greater importance. It was found that OEP was <5% of TP in all instances with most samples being <2%. A positive correlation of rs = 0.72 was found between TP and OEP. However, there is no mechanistic basis for this relationship and it is likely that it is a consequence of more TP containing minerals being found as there is a gradual fining of sediment which also carries a greater sorption capacity.

	[[Image:OR13031fig17.jpg\|thumb\|center\|~~500px~~\| '''Figure 17'''    Changes in OEP concentration (mg kg<sup>-1</sup>) in homogenised cores with distance going down the 6 water bodies sampled of the River Nene. Error bars show the standard deviation of the 3 replicates analysed for each core. Water body 1 (cores 1–6), water body 2 (cores 7–12), water body 3 (cores 13–18), water body 4 (cores 19–24), water body 5 (cores 25–30) and water body 6 (cores 31–36). Missing values are where CoreD samples were taken. ]]		[[Image:OR13031fig17.jpg\|thumb\|center\|600px\| '''Figure 17'''    Changes in OEP concentration (mg kg<sup>-1</sup>) in homogenised cores with distance going down the 6 water bodies sampled of the River Nene. Error bars show the standard deviation of the 3 replicates analysed for each core. Water body 1 (cores 1–6), water body 2 (cores 7–12), water body 3 (cores 13–18), water body 4 (cores 19–24), water body 5 (cores 25–30) and water body 6 (cores 31–36). Missing values are where CoreD samples were taken. ]]

	[[Image:OR13031fig18.jpg\|thumb\|center\|400px\| '''Figure 18'''    Mean Olsen Extractable P concentrations for each water body sampled from the River Nene. Error bars are ±1 standard deviation. ]]		[[Image:OR13031fig18.jpg\|thumb\|center\|400px\| '''Figure 18'''    Mean Olsen Extractable P concentrations for each water body sampled from the River Nene. Error bars are ±1 standard deviation. ]]