Identifying the Impact of Human Made Transformations of

Historical Watercourses and Flood Risk

Thomas Moran

, Sivaraj Valappil

and David Harding

Waste Innovation – Thames Water Utilities Limited, Island Road, Reading, U.K.

Waste Planning & Optimisation – Thames Water Utilities Limited, Island Road, Reading, U.K.

Keywords: Geographical Information System, Lidar, Culvert, Watercourse, Digital Terrain Model, Sewer, Flood.

Abstract: In the past, many urban rivers were piped and buried either to simplify development, hide pollution or in an

attempt to reduce flood risk and these factors define a culverted watercourse. A large amount of these

watercourses are not mapped, and if they are, then their original nature is not clearly identifiable due to being

recorded as part of the sewer network. Where these culverted watercourses are not mapped due to being lost

to time and development, we expressed these to be so-called ‘lost rivers’. There is a lack of awareness of the

flood risk in catchments housing these rivers, and because many of them are incorrectly mapped as sewers,

there is often confusion over their legal status and responsibility for their maintenance. To identify the

culverted watercourses many datasets were used including LiDAR data (Ground Elevation Data), historical

maps (earliest 1840's), asset data (Sewer network), and the river network. Automatic and manual identification

of potential culverted watercourses were carried out and then the mapped assets are analysed with flooding

data to understand the impacts. A GIS map has been created showing all potential lost rivers and sites of

culverted watercourses in the North London area.

1 INTRODUCTION

London has a large legacy population of culverted

and concealed watercourses, dating from the 19th and

20th centuries. Since these structures were built,

changes to the governance of drainage have resulted

in many assets being transferred between authorities

and in the process, comprehensive records have not

always survived. There is often uncertainty

surrounding the legal status and responsibility for the

maintenance of culverts. For example, many

culverted watercourses in London were included on

the map of public sewers, where their original status

has become obscured over time. This can be a

significant obstacle to the proper stewardship of the

structures. In addition, the culverting of watercourses

causes problems such as increasing upstream flood

risk due to blockages, reduced ecological value

within concrete channels and with reduced daylight

and adverse effects on environmental features and

wildlife. The issues are summarised as:

Inadequate maintenance and investment – the

responsibility for drainage assets varies according to

their legal status. For example responsibility for a

watercourse normally rests with the owners of land

through which it flows (riparian owners). Where a

watercourse is incorrectly mapped or not mapped at

all, owners may be unaware of their responsibilities.

Different agencies often assume that others are

responsible for such assets, and as a result appropriate

maintenance regimes are not in place. Many of these

assets are critical structures with a high impact of

failure. They should be subject to regular inspection

and have adequate investment plans for their

maintenance and eventual replacement. More

immediately, culverts often have grilles at their inlets

and outlets which can become easily blocked, or

debris causes a blockage within the culvert,

potentially leading to flooding.

Poor understanding of flood risk – culverted

sections of watercourse may drain large, upstream

catchments that extend far beyond the urban area.

Such a situation may not be clear from drainage

records and if it is not appreciated, can lead to

understatement of the flood risk as well as concealing

potential upstream solutions to flooding. A recent

study on drainage capacity relating to the surface

water drainage system around the Mill Hill Circus

junction in London by Transport for London (TfL) is

a good example of this. During periods of medium to

heavy rainfall, the Mill Hill Circus roundabout floods

Moran, T., Valappil, S. and Harding, D.

Identifying the Impact of Human Made Transformations of Historical Watercourses and Flood Risk.

DOI: 10.5220/0006672701730179

In Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2018), pages 173-179

ISBN: 978-989-758-294-3

173

in three different areas. These flooded areas spread

across lane 1, 2 and a footway (Transport for London,

2014).

Differing legislature and flood risk management –

different asset types are governed by various pieces

of legislation, which give design criteria for flood risk

management and define stewardship responsibilities

for agencies. Floods in urbanised areas have a greater

impact due to the exclusion of rivers in those areas.

The watercourses have been substituted by sewers

which are not designed to convey intense rainfall as

effectively as flood defences would be. Also, whilst

rivers have a degree of protection against

development with regard to flood risk, developers and

property owners have right to connect to sewers,

regardless of flood risk.

Funding – funding for different types of drainage

comes from multiple sources e.g. Sewerage

investment is funded from customer bills, while land

drainage comes from a combination of local taxes and

levies and central government grants. If an asset is

assigned to the wrong owner, they may not be able to

access funds to maintain it. An example of a

watercourse that encapsulates all of the issues is the

Caterham Bourne, a chalk-fed river that flows from

the North Downs, into South London. Much of its

length is culverted and different culverts are variously

mapped as ordinary watercourses, main rivers or as

sewers (Surrey County Council, 2015). During a

recent severe flood event, there was considerable

dispute over responsibility for the different culverts,

leading to delays in clearing blockages and the

prolonging of the significant traffic disruption, caused

by the flooding. In the subsequent investigation into

the flooding, the different legal statuses of culverts

meant the different agencies applied different

thresholds of risk, since their origins are not clear.

This is hampering the development of a coherent

flood alleviation strategy and is an obstacle to

identifying funding for investment.

Deculverting (or daylighting) these watercourses

can instigate advantages including ecological

benefits, reduced flood risks, recreation for local

communities and a stimulus for regeneration. The

evidence for these impacts are sparse, however these

are the opportunities that present themselves when

considering daylighting the watercourses.

2 METHODOLOGY

To identify the culverted watercourses many datasets

will need to be used including LiDAR data, historical

maps, asset data, and the river network. The potential

culverts can then be drawn on GIS and plans can be

put in place to ensure they receive the correct

maintenance. This section outlines the datasets used

and the mapping of the lost rivers through GIS.

2.1 GIS Asset Data

This dataset included:

 Gravity sewers

 Invert levels

 Manholes

 Operational sites

 Sewer end items

During analysis, gravity sewer coverage was the

most useful with surface water sewers being

identified as the most likely candidates for being

culverts. Of these sewers, pipes with a large

(>500mm) diameter were seen to have a higher

probability.

The following sewer types were included in the

analysis:

 Surface (S)

 Storm Overflow (SO)

 Other (O)

Sewer end types were also thought to be useful. The

dataset was filtered to include only those that had

notes in the watercourse attribute or which had

“inlet”, “outfall” or “culvert” in the comments.

2.2 Historical Mapping

Datasets from around 1840 and 1935 were provided

by the National Library of Scotland and were

available at various scales; enabling identification of

field boundaries. Rivers and watercourses were

digitised from this mapping where they were present

on the mapping but not on the EA main river network

layer. Some smaller watercourses were also identified

as the Ordinance Survey (OS) labelled them with

flow direction.

2.3 EA Main River Network

This data was in the form of a shapefile showing the

centroids of the main river channels as defined by the

EA. The dataset shows both currently exposed

watercourses and a number of culverted rivers.

However, there did not seem like there was particular

logic as to which of these covered watercourses were

mapped, and the provenance of the data is

unavailable.

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Figure 1: 1:10,500 mapping showing flow direction arrow.

2.4 EA Lidar Data

This dataset recently became open data, but the

quality and resolution of the available data was

variable. However, the 2m digital terrain model

(DTM) data was selected to be utilised as it was

adequate for picking out drainage channels and it also

provided the most complete coverage.

The data is supplied in ESRI ASCII format (.asc

files) in 10km by 10km tiles. These were converted in

Quantum GIS (QGIS) to ERDAS IMAGINE format

(.img files) and mosaicked to form a seamless dataset

over the study area. This data was run through an

automated drainage extraction routine in QGIS since

the data is inherently noisy and a number of man-

made features interfere with the natural drainage

patterns (roads, railways etc.)

Figure 2: Cuttings and embankments in the EA DTM.

However, this is true of all DTM products. The

full resolution 2m DTM was found to give a dense

network of drainage, far too detailed for the purpose.

Figure 3: Detailed drainage from 2m DTM overlain on

1900 1:10,500 mapping.

3 GIS ANALYSIS

It was decided to reduce the scale of the DTM to 10m

resolution and use thinning and cleaning techniques

to produce the final drainage output from the EA

LiDAR data.

The 2m DTM was resampled to 10m, then the dataset

was run through the r.watershed routine in QGIS. The

parameters applied to the dataset include:

 Minimum size of exterior watershed basin:

100

 Maximum length of surface flow: 0

 Convergence factor for MFD: 5

 Beautify flat areas – selected

The process produced a raster output with pixels

of varying value tracing the drainage pathways. This

was then run through a thinning routing (r.thin in

QGIS) that removed excess pixels from the drainage,

outputting a single pixel path for the drainage. The

raster dataset was then converted to shapefile by the

r.to.vect routine in QGIS.

This still resulted in a fairly complex drainage

pattern, so in order to simplify it a little more a

cleaning routine was run to remove dangling vectors

under 100m in length. It was then run through the

v.clean routine in QGIS using rmdangle as the

cleaning tool and 100 as the threshold. A considerably

simplified drainage output was the result of this

process.

It was clear that the watercourses digitised from

the 1900 historical mapping were the primary

indicator of potential lost rivers. A number of large

diameter surface water sewers were observed closely

following the course of the original watercourses and

these became high confidence targets.

Identifying the Impact of Human Made Transformations of Historical Watercourses and Flood Risk

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3.1 Buffer Zones

As the sewers did not exactly follow the original

watercourses, it was necessary to add a buffer zone

around the line of the watercourses. A buffer of 50

meters was used for the historical rivers and EA river

network in order to include those sewers that run

parallel to the original watercourse. This figure was

derived from trial and error so as to include known

targets.

As the EA LiDAR drainage was less well defined,

two buffer zones of 30m and 100m were used; the

first to capture high probability targets and the second

to capture lower probability targets.

3.2 Sewer end Items

Sewer end items that include a watercourse name or

“inlet, “outfall” or “culvert in the comments were

felt to be indicative of natural drainage. These were

filtered from the original dataset, buffered to 10m to

ensure intersection with the sewer network and then

used to select output vectors from the 100m buffered

EA LiDAR drainage dataset.

3.3 Lost Rivers Model

The model was constructed in ERDAS IMAGINE

Spatial Modeller (Sterling Geo, 2016).

3.3.1 Examples

The following demonstrates some of the features

found in this investigation.

Firstly, here is the OpenStreetMap (OSM) data

over an area in West London, where there is no trace

of surface watercourses:

Figure 4: OSM of an area with no surface watercourses.

And this is what the same area looked like around

1900:

Figure 5: OS 1:10,500 Historical Mapping.

Now, the digitised drainage (blue line), the EA

river buffer (green shading) and the drainage

extracted from the EA LiDAR (green line) can be

overlaid.

Figure 6: Overlay showing extracted drainage.

It is clear that the EA LiDAR drainage follows the

river quite well, but the railway interferes with the

drainage path. The EA main river network is mostly

good, but it cuts a corner on the 1900 river path.

This matches up to the filtered sewer network in

the following way:

Figure 7: Overlay of drainage buffers and sewer network.

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The large (>300 diameter) sewers (thick red line)

in this instance provide a close match to the digitised

drainage network, with the smaller diameter sewers

not relevant.

Figure 8: Target high probability sewers over OSM.

Figure 9: Sewer end targets in yellow.

In other areas, a high concentration of sewer end

targets (figure 9 in yellow) may also be an indicator

of former watercourses.

3.4 Output

The following seven shapefiles were produced:

Table 1: Shapefiles in order of probability of being a

culverted sewer.

Group

Diameter

of sewer

Data used

>300mm

Within 50m of the digitised rivers

from historical mapping.

>300mm

Within 50m of the EA Main River

Network.

<300mm

Within 50m of the digitised rivers

from historical mapping.

>300mm

Within 30m of the drainage network

extracted from EA LiDAR DTM.

<300mm

Within 50m of the EA Main River

Network.

>300mm

Within 100m of the drainage

network extracted from EA LiDAR

DTM that also intersect with the

filtered sewer end

outfall/inlet/culvert/watercourse

points.

>300mm

Within 100m of the drainage

network extracted from EA LiDAR

DTM.

These criteria proved to be too broad and

identified over 1023km of pipes as culverted

watercourses, out of the 5245km of pipe in the trial

area of North London. Therefore, we decided to use

the digitised rivers from historical mapping along

with the EA main river network to perform further

analysis. Also included was the whole gravity sewer

network in the trial area, filtered so only surface (S)

and surface overflow (SO) with diameter over

300mm were considered. These pipes were then

classified as “highly likely”, “possible” and “not

likely” to be a culverted sewer in the following way:

Figure 10: Example of highly likely culverted watercourses.

A sewer (green) connecting two watercourses

(blue) or following closely to the digitised lost river

(pink) were classed as “highly likely” if the diameter

is greater than 600mm, or “possible” if between

300mm and 600mm.

In addition, when an Ordinance Survey (OS)

watercourse ends but the EA river network continues,

the sewers connected to this have been classed as

possible. See figure 11 for a “possible” watercourse

shown in orange:

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Figure 11: Example of a possible culverted watercourse.

All other surface and storm overflow sewers

greater than 300mm are classed as “not likely”. Since

some of these watercourses were not all connected

but had the same GISID, it was necessary to

categorise the lost rivers by a letter (describing its

likelihood due to positioning) and a number (detailing

the rivers connected ID). The details of the categories

are as follows:

Table 2: Category description.