Disclaimer:
The authors are solely responsible for the content of this report. Material included herein does not represent the opinion of the European Community, and the European Community is not responsible for any use that might be made of it.
Back to overview reports
Table 1 – Overview of the data collected (main parameters) for each estuary, indicating the time periods and number of stations (in between brackets)
For the Scheldt and Elbe topo-bathymetric grids were directly available, respectively for the years 2001 and 2006. The Scheldt grid has a resolution varying between 5 x 5 m and 20 x 20 m, the Elbe grid has a resolution of 10 x 10 m. For the Weser and Humber, point datasets were delivered which still needed to be interpolated to grids. For the Weser the TopoToRaster interpolation method was used (ArcGIS software), while for the Humber (single beam data) the digipol methodology (Qinsy software) was applied, which is especially developed for single beam datasets. The input datasets were respectively from 2009 and 2005 for the Weser and Humber, and interpolation grids were created with a resolution of respectively 20 x 20 m and 10 x 10 m.
To make salinity data comparable between the different estuaries all salinity data were converted to PSU/ppt¹ values. For the Scheldt chlorinity data were provided which were converted to ppt values according to (Forch et al., 1902):
¹ PSU values represent the conductivity ratio of a water sample compared to a standard KCl solution whereas ppt values represent grams of salt per kilogram solution. Although the definition for both units is different, the difference in absolute values between PSU and ppt is small and negligible.
Salinity = 0.03 + (1.805*Chlorinity)
The Elbe dataset contained conductivity values. Based on the UNESCO formula (Fofonoff and Millard, 1983) conductivity values were converted to PSU values. For the Weser, data were directly provided as PSU values and for the Humber the dataset contained ppt conductivity values.
For every sampling station a mean value was calculated. By connecting the mean values of all stations a mean salinity profile was constructed for every estuary. To cover the variation in salinity caused by high and low freshwater discharges respectively the P(5%) (percentile 5%) and P(95%) (percentile 95%) values were calculated for every station. Connecting the P(5%) and P(95%) values results in a longitudinal salinity profile for respectively a flushing event and a dry event, which can be considered as representative for respectively a winter and summer situation.
Differences in salinity are not only explained by variations in riverine discharge, but are also influenced by the tidal intrusion in the estuary. As a consequence, measured salinity values at low water slack will be lower than during high water slack (Figure 1 and Figure 2). To cover this variation in salinity between low and high water slack two envelopes were created (one for a flushing and one for a dry event) where the lower limit of the envelopes represents low water slack and the upper limit high water slack. Comparable to the methodology for a flushing/dry event, percentile values were determined for the envelope limits. To determine the threshold values for the percentiles 3 salinity stations in the Elbe (continuous data available for all stations) were selected with one station close to the open sea, a station where the variation in salinity between P(5%) and P(95%) is the largest, and a station close to the freshwater zone. For these 3 stations two days (15/02/2004 and 20/10/2004) were selected for which the mean daily salinity is comparable with the P(5%) respectively the P(95%) value (Figure 1 and Figure 2). For each day the minimum and maximum values were selected which correspond with the salinity at respectively low and high water slack. Based on the continuous dataset over a period of one year the percentiles values were determined which correspond with the found minimum and maximum values for both days. Finally, for a flushing event the envelope boundaries were found at P(0.2%) and P(30%), for a dry event at P(70%) and P(99%). The same threshold values were used to construct the envelopes for the other estuaries.
Table 2 – Overview of the available SPM datasets and sampling methodology
Within this study, a mean SPM value is calculated for each location based on all available measuring campaigns. Only the sampling locations are included which were measured every campaign (or campaigns in the same time period of 2 weeks in case of the Humber). In this way, mean SPM values are comparable for each selected location. It is important to note that the mean SPM values for the datasets Scheldt1, Scheldt2 and Humber are based on SPM data sampled during various time steps within a tidal cycle, whereas for the datasets Scheldt 3 and Elbe/Weser sampling took place at a fixed time in the tidal cycle (respectively at half tide and low water, see Table 2). The variation on the calculated mean SPM value is presented by the standard deviation. Within the interestuarine comparison (§5), dataset Scheldt1 was used for the Scheldt as this dataset can be compared with the surface SPM datasets of the other estuaries (see Table 2).
The discharges at the different stations are daily discharges. An overview of the time periods for which discharge data were available is given in Table 1. Based on the daily discharges a mean value was calculated for the total river discharge into each estuary. Moreover, the P(5%) and P(95%) values were calculated returning a low and high freshwater discharge value, representative for respectively a dry and a flushing event in the estuary. The calculated mean, P(5%) and P(95%) discharge values are input parameters for the cubage and residence time calculations (see respectively §3.2.1 and §3.2.5).
Scheldt
For the selected marsh site in the Scheldt estuary, platform elevation data were available for the years 1931, 1951, 1963, 1992, 2004 and 2010. For the years 1931, 1951, 1963 and 1992 topographic surveys (in a grid) were carried out and data were provided as Digital Elevation Models (DEM) with a 20 x 20 m grid resolution. For the more recent time steps (2004 and 2010), DEMs with a 2 x 2 m resolution were available based on LIDAR data. The DEMs based on LIDAR data were corrected for vegetation.
All provided DEMs include the tidal channel network. To exclude grid cells located within the tidal channel network we delineated the tidal channel network for 1931 and 2010 based on aerial photographs. The channel network for 1931 and 2010 were merged and used as a mask to exclude grid cells located within the tidal channel network. As no significant changes occurred between the tidal channel networks in 1931 and 2010 we also used this mask for the intervening times steps. Besides the mean platform elevation of the selected site, additionally the standard deviation was calculated representing the spatial variation in marsh platform elevation.
Historical data on MHWL were derived from the nearby water level station at Bath. Besides the yearly MHWL also the yearly mean high water levels at spring tide (MHWLS) were available for this station.
From 1931 to 2010 the marsh evolved from a low elevated tidal marsh (mean platform elevation below MHWL) towards a high elevated tidal marsh (mean platform elevation above MHWL).
Elbe
For the Elbe marsh site, platform elevation data were available for 1958, 1966, 1977, 1997 and 2008. Elevation data were provided as point data (xyz), located along transects perpendicular to the Elbe main channel (same transects for every time step). To calculate mean platform elevations, DEM were built (1 x 1 m) for every time step using the TopoToRaster interpolation in ArcGIS 9.2. Based on the constructed DEM, mean values and standard deviations were calculated of the marsh platform, and this for every time step.
Historical data on MHWL were derived from the nearby water level station at Osteriff. No data on spring tide were available.
During the considered time period (1958 – 2008), the selected marsh site evolved from a low elevated tidal marsh (mean platform elevation below MHWL), towards a high elevated tidal marsh (mean platform elevation above MHWL)
Back to top
How can residence times (water/particle) be calculated?
How can sediment transport be managed?
How can we prevent excessive degradation or loss of tidal marshes?
What are the limiting factors of sediment management in the TIDE estuaries regarding hydrogeomorphology?
What factors determine the distribution of suspended sediments in an estuary?
What is important in establishing a zonation for estuaries?
What is the impact of deep subtidal habitat dimension on tidal amplification?
What is the importance of sediment management for integrated estuarine management?
What measures are successful for the dissipation of tidal energy?
Which measures are suitable to achieve specific morphological and hydrological targets?
“Working with nature”: What are the opportunities for sediment management in estuaries?
Interestuarine comparison: Hydro-geomorphology
Table of content
- 1. Introduction
- 1a. Outline
- 2. Objectives
- 3. Methodology
- 3a. Main parameters
- 3b. Derived parameters
- 3c. Habitats
- 3d. Mouth definition
- 4. TIDE estuaries
- 4a. Scheldt
- 4b. Elbe
- 4c. Weser
- 4d. Humber
- 4e. Parameters of the 4 estuaries (TIDE km)
- 5. Interestuarine comparison
- 5a. TOPIC 1 – Tidal amplification
- 5b. TOPIC 2 – Habitats
- 5c. TOPIC 3 – Relation between sediment load and tidal/riverine characteristics
- 5d. TOPIC 4 – Residence time
- 5e. TOPIC 5 – Impact of increasing MHWL on tidal marsh ecosystems
- 6. General conclusions
- 7. References
- 8. Appendix A – Estuary convergence
- 9. Appendix B – Habitat maps
- 10. Appendix C – Habitat widths
- 11. Appendix D – Tidal marsh sites
3a. Main parameters
Main parameters are parameters which are directly measured in the estuaries. In this study we focus on the present situation of each estuary and therefore we collected the most recent data, and if appropriate over a time period of several years. An overview of the collected main parameters for each estuary is given in Table 1. A description of each parameter is given in the following paragraphs (§3.1.1 - §3.1.6).Table 1 – Overview of the data collected (main parameters) for each estuary, indicating the time periods and number of stations (in between brackets)
| Scheldt | Elbe | Weser | Humber | |
| Topo-bathymetry | 2001 | 2006 | 2009 | 2005 |
| Tide | 2001-2010 (17) | 2001-2010 (17) | 2001-2010 (12) | 2005 (18) |
| Salinity | 2000-2007 (19) | 2004 (9) | 2010 (9) | 2004-2009 (10) |
| Suspended sediment | 2001-2010 (34) | 2004-2009 (27) | 2005, 2009-2010 (10) | 2004-2009 (12) |
| Freshwater discharge | 2008-2009 | 2001-2010 | 2001-2010 | 2010 |
| Tidal marshes | 1931, 1951, 1963, 1992, 2004, 2010 | 1958, 1966, 1977,1997, 2008 | - | - |
Topo-bathymetry
A topo-bathymetric grid of an estuary represents the elevation of the subtidal, intertidal and supratidal areas of the estuary, located within the dyke lines. In general, the bathymetry grids (based on multibeam or singlebeam measurements) cover the subtidal and lower intertidal parts of the estuary, while the topographic datasets (based on LIDAR data) cover the higher parts of the intertidal areas and tidal marsh areas (supratidal).For the Scheldt and Elbe topo-bathymetric grids were directly available, respectively for the years 2001 and 2006. The Scheldt grid has a resolution varying between 5 x 5 m and 20 x 20 m, the Elbe grid has a resolution of 10 x 10 m. For the Weser and Humber, point datasets were delivered which still needed to be interpolated to grids. For the Weser the TopoToRaster interpolation method was used (ArcGIS software), while for the Humber (single beam data) the digipol methodology (Qinsy software) was applied, which is especially developed for single beam datasets. The input datasets were respectively from 2009 and 2005 for the Weser and Humber, and interpolation grids were created with a resolution of respectively 20 x 20 m and 10 x 10 m.
Tide
For each estuary the main water level parameters (mean high water level = MHWL, mean low water level = MLWL) were delivered for a number of stations. Table 1 gives an overview of the timespan and the number of stations used to present the MHWL and MLWL along the different estuaries. A 10-yearly average (2001-2010) was therefore used, except for the Humber where data from 2005 were used (Table 1). The difference between MHWL and MLWL gives the tidal range. The change in tidal range along the length axis of the estuary returns the tidal range gradient (𝛁 TR). And the ratio between the mean tidal fall and the mean tidal rise returns the tidal asymmetry. An overview of the location of the water level stations is given in Figure 9 (Scheldt), Figure 10 (Elbe), Figure 11 (Weser) and Figure 12 (Humber).Salinity
Salinity data were collected which cover the variation in salinity between high freshwater discharge (representative for a flushing event) and low freshwater discharge (representative for a dry event), and moreover the difference between different tides, and this for as many sampling stations as possible. For the Elbe and Weser continuous salinity data (respectively every 5 and 15 min) were collected over a period of one year, and this for in total 9 stations (Table 1). For the Scheldt and Humber continuous salinity datasets were only available for a limited number of stations and hence could not be used to cover the salinity gradient along the estuary. Therefore salinity data were collected that were measured along longitudinal transects at high water or low water slack. For the Scheldt these measurement campaigns are performed about every month since the 1970’s (both at high and low water slack). For this study we only collected data for the present situation and this over a period of 8 years (Table 1). For the Humber less measurement campaigns were performed but still sufficiently enough to cover the variation in salinity between high and low freshwater discharge, and between high and low water slack.To make salinity data comparable between the different estuaries all salinity data were converted to PSU/ppt¹ values. For the Scheldt chlorinity data were provided which were converted to ppt values according to (Forch et al., 1902):
¹ PSU values represent the conductivity ratio of a water sample compared to a standard KCl solution whereas ppt values represent grams of salt per kilogram solution. Although the definition for both units is different, the difference in absolute values between PSU and ppt is small and negligible.
Salinity = 0.03 + (1.805*Chlorinity)
The Elbe dataset contained conductivity values. Based on the UNESCO formula (Fofonoff and Millard, 1983) conductivity values were converted to PSU values. For the Weser, data were directly provided as PSU values and for the Humber the dataset contained ppt conductivity values.
For every sampling station a mean value was calculated. By connecting the mean values of all stations a mean salinity profile was constructed for every estuary. To cover the variation in salinity caused by high and low freshwater discharges respectively the P(5%) (percentile 5%) and P(95%) (percentile 95%) values were calculated for every station. Connecting the P(5%) and P(95%) values results in a longitudinal salinity profile for respectively a flushing event and a dry event, which can be considered as representative for respectively a winter and summer situation.
Differences in salinity are not only explained by variations in riverine discharge, but are also influenced by the tidal intrusion in the estuary. As a consequence, measured salinity values at low water slack will be lower than during high water slack (Figure 1 and Figure 2). To cover this variation in salinity between low and high water slack two envelopes were created (one for a flushing and one for a dry event) where the lower limit of the envelopes represents low water slack and the upper limit high water slack. Comparable to the methodology for a flushing/dry event, percentile values were determined for the envelope limits. To determine the threshold values for the percentiles 3 salinity stations in the Elbe (continuous data available for all stations) were selected with one station close to the open sea, a station where the variation in salinity between P(5%) and P(95%) is the largest, and a station close to the freshwater zone. For these 3 stations two days (15/02/2004 and 20/10/2004) were selected for which the mean daily salinity is comparable with the P(5%) respectively the P(95%) value (Figure 1 and Figure 2). For each day the minimum and maximum values were selected which correspond with the salinity at respectively low and high water slack. Based on the continuous dataset over a period of one year the percentiles values were determined which correspond with the found minimum and maximum values for both days. Finally, for a flushing event the envelope boundaries were found at P(0.2%) and P(30%), for a dry event at P(70%) and P(99%). The same threshold values were used to construct the envelopes for the other estuaries.
3a. Suspended particle matter (SPM)
A large variability exists in the collected SPM data for the different estuaries. Variability is caused by differences in number of measuring campaigns, number of sampling locations, position of sampling (surface, watercolumn), and time of sampling (low water, high water, half tide). A detailed overview of the collected SPM data is given in Table 2.Table 2 – Overview of the available SPM datasets and sampling methodology
| Scheldt1 | Scheldt2 | Scheldt3 | Elbe | Weser | Humber | |
| Time period | 2001-2010 | 2001-2010 | 2009 | 2004-2009 | 2005, 2009-2010 | 2004-2009 |
| Sampling position (S: surface; W: watercolumn) | S | W | W | S | S | S |
| Frequency campaigns | monthly | monthly | monthly | 2-monthly | 6-monthly | monthly |
| Number of sampling locations | 34 | 27 | 17 | 29 | 10 | 12 |
| Sampling time (Low Water, High Water, Half Tide) | LW, HW, HT | LW, HW, HT | HT | LW | LW | LW, HW, HT |
Within this study, a mean SPM value is calculated for each location based on all available measuring campaigns. Only the sampling locations are included which were measured every campaign (or campaigns in the same time period of 2 weeks in case of the Humber). In this way, mean SPM values are comparable for each selected location. It is important to note that the mean SPM values for the datasets Scheldt1, Scheldt2 and Humber are based on SPM data sampled during various time steps within a tidal cycle, whereas for the datasets Scheldt 3 and Elbe/Weser sampling took place at a fixed time in the tidal cycle (respectively at half tide and low water, see Table 2). The variation on the calculated mean SPM value is presented by the standard deviation. Within the interestuarine comparison (§5), dataset Scheldt1 was used for the Scheldt as this dataset can be compared with the surface SPM datasets of the other estuaries (see Table 2).
Freshwater discharge
The total freshwater discharge of a river is the sum of the freshwater discharges of the main channel and all its tributaries. For the Elbe and Weser discharges were measured along the main channel in the more upstream parts of the river (respectively at Neu Darchau and Intschede, see Figure 10 and Figure 11). These measured discharges can be considered as the total freshwater discharge of the rivers since the tributary discharges are negligible compared to the discharge of the main channel (factor 100). For the Scheldt and Humber all tributaries have a significant contribution to the total river discharge. For the Scheldt discharge data are calculated at Schelle (Figure 9). The calculated discharge at Schelle is the sum of the freshwater discharges measured at each tributary, and thus representative for the total river discharge of the Scheldt. For the Humber discharge data are also available for each tributary. Summing up the tributary discharges returns the total river discharge for the Humber.The discharges at the different stations are daily discharges. An overview of the time periods for which discharge data were available is given in Table 1. Based on the daily discharges a mean value was calculated for the total river discharge into each estuary. Moreover, the P(5%) and P(95%) values were calculated returning a low and high freshwater discharge value, representative for respectively a dry and a flushing event in the estuary. The calculated mean, P(5%) and P(95%) discharge values are input parameters for the cubage and residence time calculations (see respectively §3.2.1 and §3.2.5).
Tidal marshes
Historical data on marsh platform elevation are scarce. We received data on marsh platform elevation for the Scheldt and Elbe estuaries. To be able to analyse the historical changes in marsh platform elevation, the following conditions need to be fulfilled:
- The considered time period should be sufficiently long (± 50 year)
- There should be sufficient time steps with marsh elevation data (at least 3)
- There should be historical data on MHWL
Scheldt
For the selected marsh site in the Scheldt estuary, platform elevation data were available for the years 1931, 1951, 1963, 1992, 2004 and 2010. For the years 1931, 1951, 1963 and 1992 topographic surveys (in a grid) were carried out and data were provided as Digital Elevation Models (DEM) with a 20 x 20 m grid resolution. For the more recent time steps (2004 and 2010), DEMs with a 2 x 2 m resolution were available based on LIDAR data. The DEMs based on LIDAR data were corrected for vegetation.
All provided DEMs include the tidal channel network. To exclude grid cells located within the tidal channel network we delineated the tidal channel network for 1931 and 2010 based on aerial photographs. The channel network for 1931 and 2010 were merged and used as a mask to exclude grid cells located within the tidal channel network. As no significant changes occurred between the tidal channel networks in 1931 and 2010 we also used this mask for the intervening times steps. Besides the mean platform elevation of the selected site, additionally the standard deviation was calculated representing the spatial variation in marsh platform elevation.
Historical data on MHWL were derived from the nearby water level station at Bath. Besides the yearly MHWL also the yearly mean high water levels at spring tide (MHWLS) were available for this station.
From 1931 to 2010 the marsh evolved from a low elevated tidal marsh (mean platform elevation below MHWL) towards a high elevated tidal marsh (mean platform elevation above MHWL).
Elbe
For the Elbe marsh site, platform elevation data were available for 1958, 1966, 1977, 1997 and 2008. Elevation data were provided as point data (xyz), located along transects perpendicular to the Elbe main channel (same transects for every time step). To calculate mean platform elevations, DEM were built (1 x 1 m) for every time step using the TopoToRaster interpolation in ArcGIS 9.2. Based on the constructed DEM, mean values and standard deviations were calculated of the marsh platform, and this for every time step.
Historical data on MHWL were derived from the nearby water level station at Osteriff. No data on spring tide were available.
During the considered time period (1958 – 2008), the selected marsh site evolved from a low elevated tidal marsh (mean platform elevation below MHWL), towards a high elevated tidal marsh (mean platform elevation above MHWL)
Important to know
Reports / Measures / Tools
| Report: | Management measures analysis and comparison |
|---|
Management issues
How and by which management measures can tidal amplification be reduced?How can residence times (water/particle) be calculated?
How can sediment transport be managed?
How can we prevent excessive degradation or loss of tidal marshes?
What are the limiting factors of sediment management in the TIDE estuaries regarding hydrogeomorphology?
What factors determine the distribution of suspended sediments in an estuary?
What is important in establishing a zonation for estuaries?
What is the impact of deep subtidal habitat dimension on tidal amplification?
What is the importance of sediment management for integrated estuarine management?
What measures are successful for the dissipation of tidal energy?
Which measures are suitable to achieve specific morphological and hydrological targets?
“Working with nature”: What are the opportunities for sediment management in estuaries?