Alex Petrakis - River management report

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Alexandros Petrakis

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Managing Rivers and Coasts

River management report for the Bristol Frome near Chipping

Sodbury

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Table of Contents Contents ..................................................................................................................................... 2 Introduction ................................................................................................................................ 3 Assessing flood risk .................................................................................................................... 4 Table of available assessment techniques ................................................................................. 5 Evaluation of chosen method for predicting flood risk .............................................................. 7 Applied river condition assessment method………………………………………………………………………….8 River condition assessment results .......................................................................................... 14 Management plan objectives ……………………………………………..………………………………………………18 Table of available management options …………………………………………………………………………….19 Evaluation of chosen flood management option…………………………………………………………………21 Recommended actions for the selected reach……………………………………………………………………..23 References………………………………………………………………………………………………………………………….29 Appendices…………………………………………………………………………………………………………………………35

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Introduction

The River Frome near chipping Sodbury will be introduced focusing on its location and catchment characteristics. The condition of the reach of the channel studied will be assessed in terms of the flood risk it poses, and a management plan with recommended actions will be produced accordingly. Assessing and managing flood risk is a crucial aspect of fluvial geomorphology, as freshwater flooding is known as the most impacting natural disaster in terms of number of people affected and economic damages (Brath et al, 2015). The foresight project carried out in 2004 found two hundred billion pounds worth of property is at risk (Government office for science, 2004). It’s also important to manage and assess flooding due to the social and ecological implications. While it may not be the greatest cause of loss of life, flooding has effected around 2.2bn people from 1975-2001, through the loss of homes, possessions or destruction to farmland, and is the most frequently occurring natural disaster. Flooding is something that many different stakeholders share as a concern such as politicians, catchment resource managers, ecologists and even the general public, and is therefore a vital matter that needs to be addressed over a local, regional and global scale (Tamayo et al, 2015).

The Bristol Frome catchment is located in South West England as shown in figure 1, and measures at around 78.5km2. The catchment is mainly rural, however passes through several small areas such as the town of Yate, Chipping Sodbury and the village of Frampton Cotterell. Further upstream from the study site is a pumping station as well as several small detention lakes. The Bristol Frome experiences a maritime climate, with an average annual rainfall of 799mm in the years 1961-1990. (NRFA, 2014). Figure 2 shows the geology breakdown of the catchment.

Figure 1: Map showing location of Frome catchment

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Assessing flood risk status:

Flood risk can be defined by the equation “Flood risk=Probability X Consequence”. The probability of flooding encompasses two components; the channel conveyance capacity and the catchment hydrological regime (figure 3). The hydrology can be derived differently depending on if the station is gauged or ungauged. If gauged, annual maximum floods and Peaks Over Threshold methods can be sucessful. If the station/point of interest is ungauged, then statistical approaches such as reviweing historic trends or comparing to other points with similar characteristics can be used. (Black and Burns, 2002). The channel conveyance capacity can be identified by measuring the cross sectional area in the field, and multiplying it by the predicted mean bankfull velocity, resulting in a predicted bankfull flow discharge. Flood consequences are found using the summation of all the quantified damage costs within a given area/elevation that would occur if a particular flow event took place. Table 1 shows some of the available flood risk assessment techniques that have been used to predict the catchment hydrology, channel conveyance capacity and consequences of a flood event.

Figure 2: Catchment geology

Figure 3 - Breakdown of flood risk

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Assessment technique 1, 2 or 3D Reference

SSARR model - Streamflow synthesis and reservoir

regulation

Mascarenhas, F.C.B, Toda, M., Migues M.G. and Inoue K (2006) 2Solution methods - models for one dimensional natural floods", in Mascarenhas, F.C.B., Toda, M., Migues M.G. and Inoue K (ed.)

Flood risk simulation, Southampton: WIT Press, pp. 46.

National weather service river forecast system (NWSRFS)

US Oceanic and Atmospheric Administration, National Weather Service (n.d.) NWSRFS overview, available from:

www.nws.noaa.gov/iao/pdf/Manual.pdf (accessed 08.10.15)

SWMLR ModelDawson et al (2006) Flood Estimation at ungauged sites using

artificial neurol reports, Journal of Hydrology, vol. 319, pp. 391-409

Regional Flood Frequency Method

Cunnane. C (1988) 'Methods and merits of regional flood frequency analysis', Journal of Hydrology, 100(1-3), pp. 269–290.

ARMA (Auto regressive moving average) Models

Brath et al (2000) Comparison of short term rainfall prediction models for real time flood forecasting, Journal of Hydrology, vol.

239. pp. 132-147

KNN Method (K-Nearest Neighbour)

Brath et al (2000) Comparison of short term rainfall prediction models for real time flood forecasting, Journal of Hydrology, vol.

239. pp. 132-147

GEV (Generalised Extreme Value)

Lima et al (2015) 'A climate informed model for nonstationary flood risk prediction: Application to Negro River at Manaus,

Amazonia', Journal of hydrology, 522(), pp. 594-602.

WaterGAPAlcamo et al (2006) 'Estimating the Impact of Global Change on Flood and Drought Risks in Europe: A Continental, Integrated

Analysis', Climate change, 75(3), pp. 273-299.

HEC-HMSUS Army Corps of Engineers, Hydrologic Engineer Centre, HEC-HMS

(n.d) Available from: www.hec.usace.army.mill/software/hec-hms/

Muskingum Model



HEC-RAS 1D and 2D availableBrunner. G. W. (1995) HEC-RAS River Analysis System. Hydraulic

Reference Manual. Version 1.0., : HYDROLOGIC ENGINEERING CENTER DAVIS CA

Mannings roughness 1DDoncker et al (2009) 'Determination of the Manning roughness

coefficient influenced by vegetation in the river Aa and Biebrza river', Environmental fluid mechanics, 9(5), pp. 549-567.

Flood spreading method 2DGouldby et al (2008) 'A methodology for regional-scale flood risk assessment', Proceedings of the Institution of Civil Engineers -

Water Management, 161(3), pp. 169-182.

Methods to predict catchment hydrology - Probability

Methods that predict channel conveyance capacity - Probability

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Table 1 - flood risk assessment techniques

FLO-2D 2DFLO-2D Software (2013) Welcome to FLO-2D Software, Available at:

http://www.flo-2d.com (Accessed: 08.10.15)

RFSM 2DJamieson et al (2012) 'A highly efficient 2D flood model with sub-

element topography', Proceedings of the Institution of Civil Engineers - Water Management, 165(10), pp. 581-595.

Finite Elements Model 1D



Wetted perimeter method 1DGippard and Stewardson (1998) 'Use of wetted perimeter in

defining minimum environmental flows', REGULATED RIVERS RESEARCH & MANAGEMENT, 14(), pp. 53-67.

Conveyance and Afflux Estimation System

1D, 2D and 3DGahey et al (2008) 'Estimating river flow capacity in practice',

Journal of Flood Risk Management, 1(), pp. 23-33.

prandtl-von karman resistance relationship

1DWillets et al (1999) 'CONVEYANCE PREDICTION FOR MEANDERING

TWO-STAGE CHANNEL FLOWS. ', Journal of Flood Risk Management, 136(3), pp. 153-166 .

ISIS/Flood Modeller Pro 1DCheng et al (2013) Journal of flood risk management, broad scale

model for flood simulation in the Taihu Basin, China. Vol. 6, pp. 33-41

Applying building use and water depth

B. Merz, H. Kreibich, A. Thieken, R. Schmidtke. Estimation uncertainty of direct monetary ood damage to buildings. Natural

Hazards and Earth System Science, 2004, 4 (1), pp.153-163.

linear regression models using multiple input variables

Lowe, D., Emsley, M., and Harding, A. (2006). ”Predicting Construction Cost Using Multiple Regression Techniques.” J.

Constr. Eng. Manage. , 132(7), 750–758.

Flood inundation analysis and multi dimensional flood

damage analysis

Sung Yi et al (2010) 'GIS-based distributed technique for assessing economic loss from flood damage: pre-feasibility study for the

Anyang Stream Basin in Korea', Natural hazards, 55(2), pp. 251-272.

Tangible and intangible damages and damage

classification

Merz et al (2010) 'Assessment of economic flood damage', Natural hazards and earth system sciences, 10(8), pp. 1697-1724.

FLEMOpsThieken et al (2008) 'Development and evaluation of FLEMOps - a

new Flood Loss Estimation MOdel for the private sector', Flood recovery, innovation and response, 55(2), pp. 315-324 .

HAZUS-MHScawthorn et al (2006) 'HAZUS-MH flood loss estimation

methodology. II. damage and loss assessment', Multihazards Loss Estimation and HAZUS, 7(2), pp. 1527-6988.

FEH (Flood Estimation Handbook)

Centre for Ecology and Hydrology (2015) Flood Estimation Handbook , Available at: http://www.ceh.ac.uk/services/flood-

estimation-handbook (Accessed: 26th October 2015).

Methods that predict flood consequences

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Evaluation of chosen method:

This particular section comprises of an in depth review of the Regional Flood Frequency Method (RFFM) or flood frequency analysis (FFA). It first briefly attempts to describe some of the scientific methods behind the process, and later on will discuss some of the issues or disadvantages with the method.

FFA attempts to predict the hydrology of ungauged points along a river channel, by combining the flow of hydrologically similar stations in order to extend the flow record period. It’s something that takes into account past flow records, and therefore shares a common denominator with the method, however simplified, being applied to the Frome at Chipping Sodbury, which will be discussed furthermore in this report. An RFFM is often applied when considering the construction of bridges/dams, where its results effect/determine a level of economic invest (Kidson and Richards, 2005).

Flood forecasters are often expected to find the probability of exceedance related to a particular flow along a river channel. In order to do this, an estimation is made on the relationship of an extreme flow value and it’s returned period, which is often referred to as a “Q-T” relationship. It is often the case that the selected time period is greater than the duration of a specific river/point’s flow record. To compensate, FFA is applied whereby the historic flows of catchments with similar characteristics are combined to allow that flood forecast to represent the appropriate time period. (Burn, 1990).

There are various ways of grouping catchments with similar characteristics; in terms of geology, precipitation, topography, land use and even statistical methods. Wiltshire (1986) has developed a homogeneity test for different catchments using a region of influence approach, by assuming that the annual maximum flood populations’ flood frequency relationships have similar slopes on a probability plot. Since the slope is related to the coefficient of variation, this allows a homogeneity test take place on regional variability. The advantages of this method are that it is distribution free, and it also provides a numerical value which is easily comparable. An alternative statistical method is to analyse the scatter about the fitted region average distribution. (Wiltshire, 1986). An additional statistical method for Flood Frequency analysis involves the use of Canonical Correlation Analysis (CCA). This is used to establish a link between two groups of variables, whereby it identifies combinations from the first group that linearly correlate to combinations from the second group, thus providing evidence of homogeneity (Bobee et al, 2001). Cavadias (1995) has split homogenous regions into 3 categories, geographically continuous, non-continuous and hydrologic neighbourhoods. He found that geographically continuous regions may not be hydrologically similar, and has applied the use of CCA into hydrologic neighbourhoods and has found that this has been very successful when regionalising flood flows. There are a few general issues with using the RFFM. One is that when obtaining historical flow records for a given time period, one assumes that the rivers flow is uniform and will continue that uniform trend into the period of flood forecasting. For example, the flow record may be 30 years, and 31 years ago there could’ve been a very large/rare flood event that is not accounted for when making the calculations. Also with recent climate change/climate change projections, it would cast reasonable doubt on historic trends e.g. changes in precipitation patterns, therefore having a direct impact on river flow. According to Cunnane (1987), the main disadvantages are either model or sampling errors, and the process should be more sensitive.

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It has been recognised that many papers have accounted for the issues related to the statistical aspect of the RFFM, but have failed to acknowledge the hydrological processes that can cause floods, field related issues (measurements and data collection) and water resource problems for which the analysis is used (Potter, 1987). Using a RFFM amongst arid/semi-arid areas can also be subject to constraints. This refers to problems with limited gauging station information/annual maxima of nearly zero. Scaling factors in arid/semi-arid areas can be difficult to identify, with quite often the 5 year flood being more appropriate (Sutcliffe et al, 1992). In agreement to Potters aforementioned idea on the focus of literature being too much on the statistical aspect of Flood Frequency analysis is Kidson and Richards (2005) work. They believe that it has lacked hydrological justification. The RFFM is a type of extrapolation technique which means it requires the fitting of a model, and evident in any fitting of a model is one common error; a priori assumption about the underlying distribution generating flood events, and cannot be tested within human time scales. Table 2 shows the uncertainty/error to consider with RFFM results:

It is believed that if an error is made in one of the steps, this could result in a knock on effect and have dire consequences on the result of the RFFM. When choosing the parameters it is standard practice to apply a “goodness of fit test” which is another limitation, as different tests will favour different models, and not all of these are applicable to the same test.(Kidson and Richards, 2005). A major area of concern for a RFFM is that “stationarity” is assumed, and the main reason that this is an issue is due to increased urbanisation/human interference. With a growing population and increased awareness/requirements for flood risk assessment, channel straightening, dredging and other hard engineering techniques have and will continue to affect a rivers flow, not to mention the effect of changing the land use in a catchment. As well as this, there are limitations related to the statistics. Assumptions take place on the probability distribution function (PDF) curve of annual maximum flood peaks that result in a return period. Samuel and Sivapalan (2009) have stated that too many assumptions are made when using an FFM, and that these underpin the results obtained, and that an alternative flood assessment framework is required in such a fast changing environment. They believe that this poses a serious risk to flood management. (Samuel and Sivapalan, 2009). Applied river condition assessment method:

This section contains a description of the assessment technique used at the selected reach of the Frome at Chipping Sodbury. To predict the flood risk requires predicting the conveyance capacity, hydrology and consequences at each of our 10 points, so the methodology will be organised into these categories respectively. Observations were also

Uncertainty type Sensitive toNatural Non stationary conditionsModel Choice of model

Parameter Fitting techniqueData Data choice; Accuracy of observed/gauged data

Operational Human errors/decisionsTable 2: Yen (2002)-Uncertainty/error

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made up and down stream to identify any opportunities available or threats to the flood risk status of the river at a larger scale.

Ten points were sufficient given the time period, placed 50m apart providing a fair represenation of the reach. The reach was chosen for several reasons; acessibility, management already in place, and the riparian zones are fairly urbanised meaning there’s a higher demand for flood risk management (figure 4).

To work out the channel conveyance capacity requires collecting the following data:

• Bank full width • Bankfull depth • Channel roughness or Mannings “n” – Using Cowan’s look up chart • Slope – Using a survey level • Two bank full width’s and depth’s were measured to ensure that any exceedance of

the second bank full would have economic consequences (figure 5).

Figure 4-Location of reach and sample points

Width 2

Width 1

Figure 5 - example of width measurements

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The predicted bank full flow discharge was then calculated for each bank full level on excel (figure 6).

Figure 6 - Bank full discharge capacity equation where QBF is the bank full discharge capacity, A is the cross sectional area and u is the predicted mean flow velocity.

To predict the hydrology for each point involved creating a drainage area raster for

the catchment using ArcGIS. By knowing the drainage area and flows at the flow gauge at Frampton Cotterell, an equation was created to establish the flows of each point from 1978-2013 and a hydrograph made to represent each point. The calculated QBF was added to the hydrographs (figure 7), and we could divide the number of times of exceedance by the number of years from 1978-2013 to produce an annual probability of flooding value. The hydrology was also predicted for the year 2050 by adding 11.1% to the flow values. Which was the median value for Southern England according to Jakob et al (2003).

Figure 7 - example of hydrograph

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To predict the consequences of a flood event a number of tasks were carried out: • Colour coded map produced outlining the riparian land use at the reach • Right move and EC Harris used for prices for housing and industrial property • The symbology of the DTM changed to show what would flood if the water level rose

to a certain elevation (using the bank full depths – figure 8). To then obtain a value for the flood risk for each point, the probability of flooding was multiplied by the consequences of flooding, and presented in a table.

Step 1 – Mastermap data for chipping Sodbury added from digimap to ArcMap.

Step 2 – Contour data and Frome river shapefile added to ArcMap.

Step 3 – Topo to raster carried out to create a DTM using the Frome as a stream input, the contours as a contour input and the catchment as the boundary.

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Step 4 – 10 sample points created as separate feature classes 50m apart along the reach. A 25m buffer made around each point.

Step 5 – DTM copied and pasted and set to the extent of each sample point buffer.

Step 6 – Table produced showing each point’s elevation in addition to; the highest 1st bank full measured depth, the highest 2nd bank full measured depth and the highest 2nd bank full measured depth +0.5m, to show the classes of the DTM that needed to be coloured.

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Step 7 - Manual classes were set using the table and coloured accordingly.

Step 8 – Using qualitative information from the field day, surrounding building types were colour coded.

Figure 8 - Flow diagram outlining method for producing flood extent

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River Condition assessment results: Table 3 shows that flood risk was £0/year at all cross-sections other than Point 4 where there is a risk of £90,100/year. There was probability of flooding of the first bank full depth at all points except sample point 1, which could be explained by the culvert which prevents the main road from flooding (figure 9). These values ranged from less than one day per year to over 14 days per year (table 3). However there were no consequences here hence flood risk being 0. At most points, flooding of the first bank full depth did result in the flooding of the public footpath (figure 10), and although this cannot be quantified, it certainly effects the locals who use the path.

Table 3-results

Point numberAnnual probability

(1st bankfull) days/year

Annual probability (2nd

bankfull) days/year

Consequences of 1st bank full exceedance - £/flood event

Consequences of second bank full

exceedance (+0.5m) - £/flood event

Flood risk - £/year

1 0 0 £32,433.70 64867.4 02 14.08108108 0 0 32433.7 03 11.40540541 0 0 32433.7 04 3.891891892 0 23,150.90 92603.6 90100.85 2.72972973 0 0 23,150.90 06 1 0 0 0 07 0.648648649 0 0 0 08 0.702702703 0 0 11436.98 09 2.486486486 0 0 0 0

10 13.89189189 0 0 0 0

Figure 9 - Culvert at sample point 1

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Many of the points show significant consequences if a rare flow event occurred, and the second bank full was exceeded (table 3), which maybe the data set did not account for as it only represented 1978-2013. The climate change analysis showed the enhanced flow values only had an effect on the flood risk at point 4 (by increasing the probability of exceedance at the first bank full level) as shown in figure 11. The projected flood risk for 2050 was increased from £90,100.80/year (table 3) per year to £125,765.70/year. Figure 12 shows the flood extent of the reach according to ArcMaps.

Figure 11 - Hydrograph for sample point 4 with added climate change predictions (year 2050)

Figure 10 – Public footpath alongside Frome

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Figure 12 - Flood extent

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River Condition Summary

Strengths

· Flood risk is 0 at nine out of the ten points assessed.

Weaknesses

· Flood risk at point 4 is £90,100.80 per year.

Opportunities

· Excess channel conveyance capacity at most points.

Threats

· Climate change may effect precipitation patterns in Chipping Sodbury by a greater magnitude than anticipated.

· The current channel straightening has increased the flood risk downstream in areas more vulnerable e.g. Bristol and Yate.· Aggradation underneath the bridge located just after sample point 6 (figure 13). This reduces the conveyance capacity so there’s a greater risk of exceedance.· Threat of vegetation being trapped underneath the bridge.

Figure 13 - Bridge between sample points 6&7

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River Management Plan

Table 4 shows the available management options for the objectives above.

Objective 1: To reduce the flood risk of point 4 to 0 by 2050.

Objective 2:To maintain the level of flood risk at 0 at all other samplepoints including mitigating the effects of future climate changeand any change in channel form by 2050.

Objective 3:To take advantage of excess conveyance capacity along theChipping Sodbury reach to reduce the flood risk furtherdownstream e.g. (Bristol and Yate).

Objective 4: To reduce the consequences of potential future flooding.

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Management approach Management optionReach/Catchment

scaleSoft/hard

Management objective covered

Reference

Building design - elevated sockets

Reach scale Soft 1, 2 and 4

Building design - Water resistant materials


Flood warning systems e.g. floodline direct

Reah scale Soft 1,2 and 4Balmforth. D (2012) '', Flood

risk management, 5(2), pp. 91

Building design - elevated construction


"Floating homes" in the Netherlands

Reach scale Soft 2 and 4

Making space for water Land use planning Reach scale Soft 2 and 4

Juergensmeyer & Roberts (2013) Land Use Planning &

Development Regulation Law , 3 edn., Georgia: Faculty

publications.

Sustainable urban drainage systems (SUDS) - swales

Catchment scale Soft 2

SUDS - Wetlands Catchment scale Soft 1,2 and 3

SUDS - Retention reservoirs Catchment scale Soft 1, 2 and 3

SUDS - Permeable paving Catchment scale Soft 2

SUDS - Green roofing Catchment scale Soft 2

SUDS - Infiltration trenches Catchment scale Soft 2

SUDS - Rainwater harvesting systems


SUDS - Soakaways Catchment scale Soft 2

SUDS - Bioretention Catchment scale Soft 2

SUDS - Sand filters Catchment scale Soft 2

Tree shelterbelts Catchment scale Soft 2

Caroll et al (2004) "Can tree shelterbelts on agricultural land reduce flood risk?" Soil

use and Management , 20(3), pp. 357-359.

Hedgerow restoration Catchment scale Soft 2

Wheater et al (2008) 'The impact of upland land

management on flooding: insights from a multiscale

experimental and modelling programme', Flood risk

management, 1(2), pp. 71-80.

Borrows. P (2006) 'Living with flooding', Irrigation and

drainage, 55(S1), pp. 33-40.

Frans Klijn, Michaël van Buuren, Sabine A. M. van Rooij (2004) 'Flood-risk

Management Strategies for an Uncertain Future: Living with Rhine River Floods in

The Netherlands?', A Journal of the Human Environment,

33(3), pp. 141-147.

Living with flooding

Tassi and Poleto (2012) 'Sustainable Urban Drainage

Systems', Drainage Systems, (), pp. 56-70.

B Woods-Ballard, R Kellagher, P Martin, C Jefferies, R Bray, P

Shaffer (2012) The SUDS manual , Norfolk: Norfolk

county council.

Impacting the hillslope response function

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Dredging Reach scale Hard 2

Silver et al (2004) 'Flood management options for The Netherlands', International

Journal of River Basin Management, 2(2), pp. 101-

Re sectioning Reach scale Hard 1 and 2Downs and Greogory (2014) RIver channel management ,

New York: Routledge.

Channel re alignment Reach scale Hard 1 and 2

Winkely BR (1982) Response of the Lower Mississippi to

river training and realignment , Chichester: John

Wiley and sons.

Fluvial dike/levee system Reach scale Hard 1 and 2

Dawson. R, Hall. J, Syers. P, Bates. P, Rosu. C (2005)

'Sampling-based flood risk analysis for fluvial dike

systems', Stochastic Environmental Research and Risk Assessment, 19(6), pp.

388-402.

Flood walls and embankments Reach scale Hard 1 and 2

Kenyon. W (2007) 'Evaluating flood risk management options in Scotland: A

participant-led multi-criteria approach', Ecological

Economics, 64(1), pp. 70-81.

Restoring the sinuosity Reach scale Soft 3

Brooks. A (1987) 'Restoring the sinuosity of artificially

straightened stream channels',Environmental

Geology and Water Sciences, 10(1), pp. 33-41.

Vegetation removal Reach scale Hard 1 and 2

Gregory K J (1992) Vegetation and river channel process

interactions , New York: John wiley and sons.

revegetation Reach scale Soft 3

Sparks et al (1998) 'Naturalization of the Flood

Regime in Regulated Rivers',Bioscience, 48(9), pp.

706-720.

Impoundment - Damming and reservoirs

Catchment scale Hard 1 and 2

Merz et al (2010) 'Fluvial flood risk management in a

changing world', Natural Hazards and Earth System Sciences, 10(), pp. 509-527.

Regional Directing Water Management Plans (SDAGE) -

FranceCatchment scale Soft 1 and 2

Owens et al (2005) 'Fine Grained sediment in River systems: Environmental

significance and management issues', River research and applications, 21(), pp. 693-

717.

Rough flood plains Catchment scale Soft 1 and 2

Pasche and Rouve (1985) 'Overbank Flow with

Vegetatively Roughened Flood Plains',Journal of

hydraulic engineering, 111(9), pp. 1262-1278.

Flood plain reconnection Catchment scale Soft 1 and 2

Opperman. J, Galloway. G, Fargione. J, Mount. J, Richter.

B, Secchi. S (2009) 'Sustainable floodplains

through large scale reconnection to rivers',

Science, 326(), pp. 1487-1488.

Impacting the conveyance capacity

Impacting the channel routing function

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Evaluation of chosen flood management option: This section provides an in depth review of some of the issues related to fluvial impoundment. A brief introduction to damns and their application is given followed by a review of the literature that discusses and highlights damning and its associated effects on flood risk and the environment. River impoundment is a hard constructive approach taken at a catchment scale to manage flood risk through the use of damming, which deliberately holds back water to reduce flood risk downstream, creating reservoirs and retention lakes. In the northern hemisphere, 77% of rivers are controlled by impoundment (Rypel, 2010), and damming is responsible for a 0.55mm reduction in sea level per year (Chao et al, 2008). Damns are subject to controversy from many stakeholders including ecologists, hydrologists and even the general public whereby they may reduce flood risk, but have detrimental effects on the environment, such as diminishing the aquatic flora and fauna (Donnelly, 1993). A major change has taken place worldwide amongst riparian ecosystems due to the effects of 40,000 large dams being implemented globally. These ecosystems provide habitats for many species, function as filters between land and water, and serve as pathways for dispersing/migrating organisms, as well as having economic and recreational value (Nilsson and Berggren, 2000). Riparian and fresh water aquatic zones offer the most complex and varying ecosystems, and are a very useful indicator for environmental change.

Hydraulic Flushing Catchment scale Hard 2

Sumi. T (2004) 'Reservoir sedimentation management

with bypass tunnels in Japan', Proceedings of the Ninth

International Symposium on River Sedimentation October 18 – 21, 2004, Yichang, China,

(), pp. 1036-1043.

vegetating river banks Catchment scale Soft 2

Sediment routing methods. Catchment scale Soft 2

Removing forest roads (USA) Catchment scale Soft 2

MARY ANN MADEJ (2001) 'Erosion and sediment

delivery following removal of forest roads', Earth Surface Processes and Landforms,

26(), pp. 175-190.

root reinforcement on river banks


Docker and Hubble (2008) 'Quantifying root-

reinforcement of river bank soils by four Australian tree species', Geomorphology,

100(3-4), pp. 401-418.

Soil bioengineering Catchment scale Hard 2

Gray Donald and Sotir Robin (1996) Biotechnical and soil

bioengineering slope stabilization; a guide to

erosion control , Canada: John wiley and sons.

Owens et al (2005) 'Fine Grained sediment in River systems: Environmental

significance and management issues', River research and applications, 21(), pp. 693-

717.

reducing sediment delivery from

upstream

Table 4 - Management options

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One key issue that arises from the implementation of a river dam is sediment restriction. Due to the construction itself, sediment is unable to be carried downstream, and the fluvial system is responsible for transporting around 15-20Gt of sediment per year to the world’s oceans, therefore being a valuable feature of the biochemistry and geology of our planet. Its impacts are thought to have sometimes irreversible consequences for the morphology of river channels (Meybeck et al, 2003). Owens et al (2009) found that there was a link between sediment dynamics and fish habitats in British Columbia. It was identified that sedimentation was occurring amongst the salmonid spawning areas, resulting in a reduction of biodiversity from an ecological point of view.

In Southern Alberta, damming has also been linked with the decline in downstream forestry. Rood and Heinze-Milne (1989), found a 23% decline in poplar forest when compared with an undammed neighbouring river, and have identified that the causes are likely to be the drought mortality of seedlings, decreasing the riparian biodiversity. Rood and Mahoney (1995) have also discovered a large deficiency in riparian cottonwood seedlings downstream of the Tiber Dam in Montana, likely to be caused by the dam itself stabilising flows, and starving the downstream areas of sediment/nutrients. The impoundment of rivers is also held responsible for the decline in freshwater mussels in Oklahoma. A study carried out found that a linear decrease in freshwater mussels occurred downstream from the dam. The dam had caused alterations in flow seasonality, temperature regimes, particle size, sediment scour and deposition, and the transport of organic matter, which are feeders for freshwater mussels (Vaughn and Taylor, 1999).

As well as having downstream effects on the environment, impoundment also reduces the ecological status of the reservoir itself. Increased siltation results in a higher level of suspended solids in the water, which in turn decrease the light penetration and transparency of the water. There is also a lack of turbulence in reservoirs which decreases the amount of dissolved oxygen in the water, which reduces the water quality. A reduction in water quality is associated with the death and decay of macrophytes, and the destruction of the benthic zone. This is supported by the study carried out by Ogbeibu and Oribhabor (2002) in Nigeria. The water quality issues caused by impoundment have also influenced the migratory behaviour of Striped bass in Tennessee. It was found that the bass were less mobile, and only restricted to areas where the dissolved oxygen was 4mg/L or more (Cheek et al, 1985). A major issue when considering dam construction is the flood risk as a result of failure principle. Such an issue would result in serious social, economic and environmental

Change in fine-grained sediment delivery EffectsChannel incision

Undermining of engineering structures, causing a hazard and associated costs for reconstruction

Changes in channel morphology (such as channel width, sinuosity, braiding index etc.)

Reduced supply of sediment to coastal zones and possible increase in coastal erosion

Possible loss of valuable sediment-based habitats such as floodplains, mud flats and deltas

Threat to salmonids by changing particle size of spawning gravel

Threat to estuarine and coastal fish stocks

Too little sediment

Table 5 - Owens et al (2009); Impacts of impoundment

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consequences, some which cannot be given a value or be reversed e.g. loss of life. The potential flooding that could result from dam failure has recently been acknowledged as a lesson from failures such as the Teton Dam and Johnstown Dam in the U.S, which had significant effects due to the sudden release of water. A probability of dam failure has been estimated at 0.0001 (Costa, 1985), but the consequences are much greater. For example, in the last 100 years, 200 dam failures have accounted for the loss of around 11,000 lives around the world. If no warning is in place, the loss of life on average is 19 times greater as a result of dam failure. The main causes of failure include; inadequate spillway, foundation defects and piping and seepage. As well as the immediate consequences e.g. loss of life, damage to property, dam failure can have a number of implications on the river channel morphology. Trapped sediment upstream can result in high levels of aggradation, the rapid draw down of water can trigger landslides in more unstable areas, and high levels of deposition further downstream occurs. The stream powers produced by dam failures are comparable to the highest rates of precipitation ever recorded, and therefore have extreme erosion potential of the valley sides (Costa, 1985). River impoundment also requires a high level of maintenance in order to keep it working efficiently. Due to the decreased in flows amongst reservoirs, a rivers transport capacity is significantly reduced. This results in the deposition of sediment occurring behind the dam, a decrease in bank full depth which reduces the reservoirs capacity. As a result, sediment needs to be removed through processes such as dredging, which incurs additional costs and time.

Recommended actions for the selected reach of the River Frome, Chipping Sodbury

To achieve Objective 1:

• Improving communications with the public when issuing flood warnings • Maintenance of the channel capacity

The latter will decrease the channel roughness/manning’s “n” value which will increase the bank full discharge capacity. If the vegetation value was decreased to 0.0107, the flood risk of point 4 would reduce to 0. An area to carry out the maintenance is highlighted below (figure 14). The improvement in flood warning will also help to achieve objection 1. Johnson (1988) believes that message dissemination is key to increasing lead times so that damage reduction actions can be taken, such as moving valuable possessions away from possible flood areas of the home.

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To achieve objective 2

• Bank stabilisation upstream • Land use planning • Expansion of the wetland area.

Bank stabilisation upstream (figure 15) involves planting vegetation in the river banks in order to make the soil more cohesive to prevent erosion and reduce aggradation downstream. The particular concern at chipping Sodbury is potential aggradation under the bridge located between points 6 and 7 and as soil erosion from banks is one of the largest causes of aggradation (Owens et al, 2005), it is believed that this would prevent/reduce the rate of sediment deposition, prevent/slow down the reduction in channel capacity so maintain the flood risk at 0.

Figure 14 - Vegetation removal location (sample point 4)

Figure 15 - Bank stabilization suggested location

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Land use planning would be useful to reduce the effects of future climate change. As there is a large variance in the magnitude of flow increase amongst scientists as a result of climate change, if land use planning is efficient enough, this would eliminate any consequences of future flooding. Also reducing the effects of future climate change would be to expand the wetland area (figure 16), which would allow the natural processes to take place without having any consequences.


• Restore the river’s sinuosity • Revegetation Restoring the sinuosity would reduce river slope by increasing the valley distance the river

travels. This in turn reduces the bank full discharge capacity in these areas that can afford it e.g. point 8 can accommodate an extra 240% of flow, which means less flow reaches downstream in more vulnerable area’s such as Bristol and Yate. It is critical to ensure the sinuosity ratio does not fall below 0.62 without jeopardising the flood risk in Chipping Sodbury. Figure 17 shows a suitable area for this, from sample points 7-10. The new channel is 246m long, the maximum length it can be without consequences.

Figure 16 - Suggested area to expand the wetlands

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Revegetation of the channel could also help reduce flood risk downstream. Channel roughness would be increased decreasing the bank full discharge capacity at this point, again allowing less flow to reach downstream. Whilst it is not the main aim of this objective, revegetation would also improve the ecological status of the river. According to Gore (1985), revegetation has improved water quality and provided habitats for benthic communities and macroinvertebrates. The level of vegetation according to Cowan’s look up chart should not exceed 0.03. This will maintain the flood risk of this particular reach at 0. A suggested area for the revegetation is shown below (figure 18).

Figure 17 - New channel with restored sinuosity

Figure 18 - Suggested location for revegetation

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• Land use planning • Integrated unofficial and official flood warning systems These comply with the European water framework directive’s theory of “living with

flooding” and “making space for water” (Warner et al, 2010). Land use planning involves not building in flood risk zones which will reduce the consequences of flooding and therefore the flood risk. Official flood warning systems often fail, as is shown by Parker and Handmer (2002) whereas integrated with unofficial warnings such as public networks/observations are more beneficial in increasing lead times. The areas of flood awareness are highlighted below (figure 19).

Figure 20 shows an overview of the management plan for the reach of Bristol Frome at Chipping Sodbury.

Figure 19 - Flood awareness zone

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Figure 20 - Overview of management plan

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List of appendices:

Appendix 1 - Flow gauge hydrograph for Bristol Frome at Frampton Cotterell

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Appendix 2 - Hydrograph for sample point 1

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Appendix 12 - Hydrograph for the climate change projection of sample point 1

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Appendix 13 - Hydrograph of the climate change projection for sample point 2

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Sample point 1st area (m2)2nd area

(m2) 1st wetted perimeter

(m)2nd wetted perimeter

(m)1st Hydraulic radius

(m)2nd hydraulic radius

(m)

1 5.9584 11.92714286 6.96 13.57428571 0.856091954 0.8786571252 2.074285714 9.023142857 4.382857143 14.67 0.47327249 0.6150744963 2.595714286 13.62 4.802857143 15.94571429 0.540452112 0.8541480024 3.1784 12.29428571 5.625714286 15.20714286 0.564977146 0.8084546745 3.084 12.29428571 5.668571429 14.89142857 0.544052419 0.8255947816 1.923428571 11.83765714 4.668571429 17.93142857 0.411995104 0.6601625247 2.978571429 17.51671429 5.338571429 17.93142857 0.557934172 0.9768722128 2.627 20.59592857 5.03 25.55857143 0.522266402 0.8058325429 3.351428571 17.44285714 5.827142857 31.16285714 0.575140966 0.559732282

10 2.960571429 16.35442857 5.745714286 25.26857143 0.515266037 0.647224107

Slope mannings n1st predicted bankfull

mean flow velocity (ms-1)2nd predicted bankfull

mean flow velocity (ms-1)1st predicted bankfull flow discharge capacity (ms-3)

2nd predicted bankfull flow discharge capacity

(ms-3)0.016 0.129 0.884064377 0.899531904 5.267609185 10.728845520.007 0.13 0.390852753 0.465469055 0.810740282 4.199993781

0.0045 0.129 0.345028279 0.46813592 0.895594833 6.3760112370.005 0.114 0.423903892 0.538291185 1.347336131 6.6179056290.005 0.099 0.476003674 0.628580784 1.467995331 7.7279517520.013 0.064 0.98640002 1.350700145 1.897269981 15.989125220.017 0.124 0.712620125 1.035208778 2.122589944 18.133456390.016 0.106 0.773895256 1.033357653 2.033022838 21.282960410.009 0.141 0.465319955 0.456971427 1.559486591 7.970887313

0.004 0.14 0.290350763 0.338016902 0.859604173 5.528073273

Appendix 22 - Raw data collected in order to derive bank full discharge capacity

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Sample point elevation (maod)Highest 1st bank full

depth (m)Highest 2nd bank full

depth (m)1st bank full DTM

extent (maod)2nd bank full DTM

extent (maod)

2nd bank full DTM extent +0.5m

(maod)1 88.594994 2.27 3.41 90.864994 92.004994 92.5049942 88.330803 1.09 2.28 89.420803 90.610803 91.1108033 88.076111 1.33 2.42 89.406111 90.496111 90.9961114 87.837944 1.29 2.65 89.127944 90.487944 90.9879445 87.607544 1.19 2.46 88.797544 90.067544 90.5675446 87.362274 0.99 2.24 88.352274 89.602274 90.1022747 87.111137 1.21 2.64 88.321137 89.751137 90.2511378 86.873413 1.18 1.26 88.053413 88.133413 88.6334139 86.636459 1.21 1.77 87.846549 88.406459 88.90645910 86.378595 1.08 1.37 87.458595 87.748595 88.248595

Appendix 23 - Table showing how the DTM extents were derived

Documents

Alex Petrakis - River management report