Progress in Physical Geography 28,3 (2004) pp. 340 – 365
Quantifying soil erosion by water
in the UK: a review of monitoring
and modelling approaches
Richard Brazier
Department of Geography, University of Sheffield, Winter Street,
Sheffield S10 2TN, UK
Abstract: The role of erosion by water in the UK is considered. A summary of available data
describing water erosion is presented providing insights into rates of erosion from the hillslope
scale to the large catchment scale. Evidence suggests that soil erosion rates in excess of
acceptable thresholds occur on a wide range of soils and under a wide range of land uses
throughout the country. Given the recent shift towards erosion modelling and away from
erosion monitoring, discussion of the quality of existing available observed data in the context
of model evaluation is made. Much quality data exist in the UK to describe erosion by water,
but it is argued here that few datasets provide the necessary detail with which to evaluate
model performance accurately, especially when the description of the spatial heterogeneity of
soil loss is a goal. Furthermore, the paradox between data collection (to improve models) and
erosion modelling (to replace data collection) is highlighted as an issue that must be addressed
within the discipline if full use of datasets and improvement of models is to be made.
Key words: modelling, monitoring, soil erosion, UK, water erosion.
I
Introduction
Soil erosion by water in the UK, at least in a qualitative sense has been observed and
documented since the eighteenth century (White, 1788; Defoe cited by Hoskins, 1970).
Furthermore, the role of agriculture in promoting this natural phenomenon has also
been recognized for well in excess of 100 years (Fisher, 1868). Despite such lines of
evidence, the treatment of soil erosion as a serious issue with potentially significant
Tel: þ44 (0)114 222 7946; fax: þ 44 (0)114 279 7912; e-mail: [email protected]
C Arnold 2004
W
10.1191/0309133304pp415ra
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R. Brazier
341
environmental and economic impacts did not occur until Evans (1971) underlined the
growing need for soil conservation in the UK. Subsequently, as the study of soil erosion
has evolved into a more quantitative discipline, numerous techniques have been
employed to monitor soil erosion rates at a range of temporal and spatial scales. In
part, this paper seeks to synthesize the results of these monitoring experiments, and
other complementary datasets, for two reasons. First, to provide a comprehensive review
of the extent and magnitude of soil erosion in the UK and secondly, to allow an insight
into the quality of these data for the evaluation of predictive models that are now commonly used in this field. The rationale behind this latter approach stems from the succession of monitoring experiments by a whole suite of modelling tools and, as a
consequence of this, the reliance upon model predictions over field data collection.
In order to distinguish which erosion datasets are best suited to evaluate erosion
predictions some distinction must be made between the different underlying aims
of soil erosion models. Whilst much work throughout the 1980s and 1990s focused
on the construction of models which could (potentially) simulate fine-scale physical
processes (for examples see Nearing et al., 1989; Morgan et al., 1994). More recent work
(Nearing, 2003) has suggested that, at least for soil conservation purposes, the goal of
process-based models to accurately ‘understand’ and represent this understanding in
the form of model predictions, is over-complex; a more realistic goal being to simply
predict the direction of change of erosion, under a different land use or climate scenario, for example. Viewed in this light, it may seem less important to continue data
collection for the evaluation of such models. Here I argue that is certainly not the
case, as it is recognized by Kirkby et al. (1993) that models will never be more than analogies of the real world and consequently it is crucial to model development (whatever the goals of the modeller are) that continual reference is made back to the
quality, observed data that these models attempt to predict. Furthermore, Kirkby
(1987) states that ‘untestable models are as unacceptable as unstructured data collection’ implying that, as modelling has for the most part replaced data collection, a certain paradox is evident. Can improvements in the predictive capabilities of erosion
models be made without the continuing collection of quality field data to match
model predictions? Given that data collection is currently the exception rather than
the rule in the UK, this paper seeks to identify which existing data can be used and
which further data should be collected to complement the development of models
and to aid the necessary validation efforts which must be rigorously pursued to
improve the predictive capability of soil erosion models (Brazier et al., 2000).
The range of soil erosion monitoring experiments and related data collection in the
UK is now discussed with particular reference to water erosion. This paper aims to
highlight the limitations of comparing data collected at different scales and outlines
the advantages of each approach in terms of aiding model development. The wider
picture of soil erosion in the UK is summarized and the need for further observed
data for model evaluation purposes, collected within an integrated monitoring
and modelling framework is then argued.
II Monitoring soil erosion by water
Observed data in this article are presented as a synthesis of representative data from
numerous sources in the UK. In order to draw comparisons between disparate
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Quantifying soil erosion by water: monitoring and modelling approaches
datasets an average bulk density of 1.4 g cm23 is assumed for all soils previously surveyed in volumetric units (Brazier, 2000) and units of t ha 21 yr21 are employed
throughout.
1
Plot studies
Use of plots to monitor erosion allows precise control of management conditions and
accurate measurement of both runoff and soil loss fluxes from the hillslope. Replicates can be used to provide average results, thus decreasing any bias introduced
during extreme events or resulting from extreme conditions at each plot, but they
are also useful to examine variation within supposedly identical sites (see Fullen
and Reed, 1986; Catt et al., 1994; Nearing, 1998; Nearing et al., 1999). The impact of
different management scenarios can be studied and combined with meteorological
data, detailed response of the hillslope to individual storms and seasonal changes
can be monitored.
Certain drawbacks exist with the use of plot data, largely when inferences are
made about the wider landscape. Plots are often sited where erosion is a priori
known to occur; the Woburn erosion plots, for example, were established on the
basis of historical knowledge and observations by Catt (1992). It is therefore fair to
assume that plot-based erosion rates will represent the immediate locality, but
may well overestimate rates in the surrounding landscape (Evans, 1995). It must
be noted that output from plot studies will only reflect an aggregate output across
the whole plot and cannot provide details of redeposition or redistribution within
the plot (Walling and Quine, 1990). This limitation is further discussed by Rejman
and Usowicz (2002) and Wainwright et al. (2001, 2003), the latter arguing that, as erosion does not occur in a uniform fashion across the whole plot, it is misleading to
consider soil loss in terms of mass per unit area, the conclusion being that the consideration of sediment flux as a function of travel distance (or length of plot) is a
more informative way of presenting results.
The physical constraints of plot boundaries are also a potential problem. Most plot
sites fail to recreate the conditions found within the field, as boundaries eliminate
any convergence or divergence of flow paths in and out of the plot and interrupt
the movement of sediment by splash along the plot margins (Wainwright et al.,
2000). Plots used by the United States Department of Agriculture (USDA) for calibration of the Universal Soil Loss Equation (USLE) and Water Erosion Prediction
Project (WEPP) models, for example, measure only 22.13 m in length and 1.83 m in
width – being equivalent to 1/100th acre (Wischmeier and Smith, 1978). The precise
influence of slope length upon soil erosion is, however, undecided (Speirs and Frost
1987; Evans, 1990a), though the use of bounded plots will clearly have some effect
upon runoff generation and consequent soil erosion, as is summarized by Wainwright et al. (2000). Examination of plot data is nonetheless informative if considered
within the context of these limitations.
Plots established on the loamy sands of the Bridgnorth series, in Shropshire
(Fullen, 1992) show average soil erosion rates of 11.3 t ha21yr21 and maximum erosion rates of 34.2 t ha21 yr21 over a seven year period (see Table 1). These data were
obtained from bare soils but also, because of the aforementioned issues, should probably be viewed as describing worst-case erosion scenarios. Consequently, these data
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Hilton experimental
site, Shropshire,
bare plots
Hilton experimental
site, Shropshire,
grassland plots
Woburn experimental
farm, Bedfordshire
Houndean Farm,
Lewes, Sussex
Shropshire
Fullen (1992)
Quinton (personal
communication, 2002)
Robinson and Boardman
(1988)
Reed (1983, 1986)
Study site
Loamy sands:
Bridgnorth
1986– 91
1964– 86
1985– 86
Sandy loam:
Cottenham
Silty loam:
Andover
Sands
Loamy sands:
Bridgnorth
1985– 91
1990– 95
Soil type and
series
Study
period
16
7
11
14
Min
30
7
25
30
Max
Slope gradient
(%)
Observed soil erosion rates from plot experiments in the UK
Author
Table 1
n/a
180
36
10
Plot length
(m)
16
15.03
2.0
0.008
11.3
Average soil
erosion
(t ha21 yr21)
. 40
45.34
2.93
n/a
34.2
Maximium
soil erosion
(t ha21 yr21)
R. Brazier
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343
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Quantifying soil erosion by water: monitoring and modelling approaches
indicate the potential soil loss from sites of loamy sand soil type and are thus useful
in their own right. At certain times of year, notably in the autumn, such soils found
under winter wheats are bare (Arden-Clarke and Evans, 1990) and are at high risk
from water erosion (Boardman and Robinson, 1985). In contrast, the corresponding
grassland plots produce consistently low runoff and insignificant soil erosion, highlighting the importance of a year-round cover of vegetation and illustrating the
high variability of soil erosion rates within soils with the same basic erodibility
characteristics.
Plot data from the Woburn experimental farm, Bedfordshire, are perhaps more
representative of the surrounding locality in as far as the plots were managed on a
rotation of potatoes, winter wheat, winter barley, spring barley, winter wheat, winter
barley, as is common for that part of Bedfordshire (Catt et al., 1994). Eight plots split
into pairs of replicates, each pair employing different combinations of management
techniques (cultivation downslope or across slope and residue removal or residue
retention), yield average soil erosion rates of 2.0 t ha21 yr21 over a period of five
years (J.N. Quinton, personnal communication, 2002). Annual treatment of plots,
in this case identical to the management practices of the surrounding locality, adds
some credence to the extrapolation of these results to the wider landscape. As a
good sample of crops and management techniques are included in the study period,
such results are thought to be genuinely representative of soil erosion rates at the plot
scale for this part of the UK.
Further plot-based soil erosion rates are available from Houndean Farm, near
Lewes in the South Downs (Robinson and Boardman, 1988). A set of seven plots
was monitored for the year 1985 – 86 on the silty loams of the Andover series. Each
of the plots represented different cropping and management techniques, so results
varied considerably from 0.014 t ha21 yr21 to 45.346 t ha21 yr21, based on an assessment of rill erosion volume, with a mean of 15.03 t ha21 yr21. It is suggested that
such studies are allowed to run for a number of years before any longer term or
wider assumptions are drawn from the results, though again, it is informative to
see the range of soil erosion rates from plot studies, underlining the complex nature
of the hillslope system and the potential for serious erosion to occur on such soils.
Clearly, there are many factors causing variability at the plot scale and a completely inappropriate dataset to describe these factors for all soils in the UK (in comparison to the USDA USLE database, for instance). Therefore, though it is recognized that
the variability between plots (and soils) may be well described in a relative sense
with such experiments, this limitation must be borne in mind if extrapolation of
plot results is made to the wider landscape.
2
Overflight with field survey data
In 1982 The Ministry of Agriculture, Fisheries and Food (MAFF) commissioned the
then Agricultural Development and Advisory Service (ADAS) in collaboration with
the Soil Survey of England and Wales (SSEW) to begin an extensive survey of water
erosion in lowland England and Wales. Seventeen locations were chosen, largely on
the basis of previously observed erosion in the localities (Evans, 1988, 1993). The
areas ranged in size from 31 km2 -to 105 km2, covering a variety of different soil
series, management practices and topography throughout England and Wales.
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R. Brazier
345
Survey methods involved aerial photography of each transect (weather conditions
permitting) on an annual basis, followed by field validation of areas that appeared to
have eroded, or contained erosional/depositional features too small to identify from
the overflight data (Evans, 1988). Adopting this method, it was intended that field
observations could capture the features that the 1:10 000 scale photographs missed
whilst also ensuring that not every field had to be visited, which was deemed to
be too time-consuming.
Some criticism can be made of these data as overall they are biased, being observations from sites that are known to erode. Also, the rill-survey technique does not
take into account the contribution of sediment from the interrill areas from processes
such as rain splash and sheet wash, which generally result in surface lowering. However, it is argued that rilling and ephemeral gullying of arable land are the dominant
processes and that other processes may only lead to the removal of less than
0.7 t ha21 yr21 (Evans and Cook, 1986).
General results from the overflight data are summarized in Table 2. Maximum erosion rates are included as they show more clearly the relative differences between
areas throughout England and Wales than the median values. Evans (1993) also
points out that these rates reflect the ‘full erosive potential’ of the soil, providing
an upper limit to the range of observed values across England and Wales. Notably,
for example, sites with low median rates can still produce high maximum rates;
Staffordshire being the best example where fields down to row crops such as sugar
beet and potatoes yield very high localized erosion rates in excess of 100 t ha21 yr21.
Erosion rates averaged over the whole field can therefore be low, often less
than 1.4 t ha21 yr21, a threshold recognized as being acceptable by authors such
Table 2 Observed soil erosion rates from overflight and field survey in the UK
1982 – 86
Study site
Soil type
Average soil
erosion
(t ha21 yr21)
Maximium
soil erosion
(t ha21 yr21)
Cambridgeshire/Bedfordshire
Cumbria
Devon
Dorset
Gwent
Hampshire
Herefordshire
Isle of Wight
Kent
Norfolk East
Norfolk West
Nottinghamshire
Shropshire
Somerset
Staffordshire
Sussex East
Sussex West
Clays and medium loams
Medium and light loams with sands
Medium loams clays and medium silts
Mostly clays, medium loams
Medium loams, medium silts, clays
Loams and medium silts
Medium silts
Light loams and sands
Medium silts and clays
Light loams and sands
Light loams and sands
Sands and light loams
Sands and light loams, medium loams
Medium silts and loams
Sands and light loams
Silts and medium loams
Silts and medium silts
0.36
0.22
2.07
1.29
1.08
0.97
0.99
2.05
4.51
1.48
0.36
1.11
1.28
4.89
1.38
0.48
0.45
3.30
5.07
8.32
31.08
21.87
29.71
13.22
28.63
17.86
9.45
11.96
66.15
49.34
55.64
108.28
9.41
10.01
Source: after Evans (1988, 1993); Skinner and Chambers, (1996).
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346
Quantifying soil erosion by water: monitoring and modelling approaches
as Evans (1981) and (Morgan, 1980), but these figures can hide the potential rates
from contributing areas within the fields, specifically from the steeper hillslopes. Significantly, all locations surveyed show maximum rates in excess of this ‘acceptable’
level of erosion and, with the exception of Devon and Cumbria, numerous fields
within each locality were observed to erode on multiple occasions.
Further work by Evans (1990b) suggests a relationship between erosion rates and
soil texture, sandy and light textured soils in general being more susceptible to structural breakdown and subsequent erosion than heavier, clay rich soils. The sites in
Sussex, Cambridgeshire/Bedfordshire and Hampshire all exemplify this suggestion
with median erosion rates less than 1 t ha21 yr21. Furthermore, this hypothesis is
supported by conclusions drawn by Speirs and Frost (1985), Colborne and Staines
(1985) and Boardman (1990b) who also observe greater erosion in silty soils when
compared with those with high clay contents (see discussion below). Data from Somerset, Kent and the Isle of Wight also support this relationship, with high average soil
erosion rates and, in addition, amongst the highest maximum rates. However,
despite this trend and the diverse range of conditions that exist between sites, the
general conclusion that can be drawn is that erosion can occur to significant levels
throughout the SSEW/MAFF sites, pointing to the fact that soil erosion by water is
a relatively widespread phenomenon throughout the UK.
3
137
Cs survey data
Whilst plot and field survey data provide an insight into soil erosion over the period
of monitoring, the analysis of 137Cs data (Quine and Walling, 1991; Walling and
Quine, 1990, 1991) establishes a rate of soil loss for a longer time period, which
also represents erosion rate as a sum of all erosive processes (Walling and Quine,
1990) including those that are not measured by the other techniques reviewed
here. A brief description of the methodology involved is warranted, prior to examination of estimated soil erosion rates.
Global stratospheric distribution of 137Cs as a result of nuclear weapons testing
occurred between the 1950s and 1970s, leading to deposition by rainfall and washout. The pattern of 137Cs in the UK therefore reflects that of rainfall, with wetter
areas typically exhibiting higher total loading. At the field and small catchment
scale, the distribution of 137Cs is relatively homogeneous as it has a strong affinity
with clays in the soil, this results in the redistribution of soil corresponding well to
the redistribution of 137Cs. Depth profiles and total loading within the soil of 137Cs
from cultivated sites can therefore be compared with a local reference profile, typically from undisturbed pasture, to distinguish between sites of erosion or deposition.
Using this method, Walling and Quine (1990, 1991) established erosion rates for a
number of field-sized areas throughout the UK. In many cases, these sites tie in with
the previous monitoring schemes of Fullen (1992), Boardman (1990a), Boardman and
Favis-Mortlock (1993) and Evans (1993), allowing comparisons to be drawn between
measurements taken by the different techniques. Table 3. summarizes the range of
137
Cs results. Dalicott Farm in Shropshire, for instance, shows average soil erosion
rates of 6.5 t ha21 yr21 on the loamy sands of the Bridgnorth series (Walling and
Quine, 1991); the same soil series under plots (Fullen, 1992) erodes at an average
annual rate of 11.3 t ha21 yr21. Perhaps this is due to plots recording the erosion
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R. Brazier
Table 3
137
347
Cs survey soil erosion rates (t ha21 yr21)
Site
137
137
Yendacott, Devon
Wootton, Herefordshire
Keysoe Park, Bedfordshire
Higher, Dorset
Fishpool, Gwent
West Street, Kent
Manor House, Norfolk
Hole Farm, Norfolk
Rufford, Nottinghamshire
Dalicott, Shropshire
Mountfield, Somerset
Lewes, Sussex
1.9
2.8
0.6
3.1
1.9
4.3
2.4
3.0
10.5
6.5
2.2
1.4
5.3
6.4
2.2
5.2
5.1
7.7
6.3
6.3
12.2
10.2
4.6
4.3
Cs Net erosion
Cs Gross erosion
Source: after Quine and Walling (1991).
rather than both the erosion and the deposition, which are quantified by the 137Cs
technique, resulting in the ‘net’ soil loss of 6.5 t ha21 yr21, or the different timescale
involved, with its consequent averaging of different climate and land use conditions.
The higher erosion rates from the Fullen (1992) data may also be due to the use of
exposed, bare ground all year round for the plots, compared with the 137Cs observations which are . 30 year average data from cultivated land. Comparisons can
also be drawn with the Evans (1993) data for soils of the Bridgnorth series. The
five years of the overflight survey (1982– 86) show lower erosion rates than those predicted by Walling and Quine (1991) with an average of 1.28 t ha21 yr21. However, as
discussed above, Evan’s survey technique only measures soil loss in rills and ephemeral gullies, not that which is due to rainsplash or sheetwash, which may account
for some of the disparity between the datasets (Evans and Cook, 1986). Finally, comparisons can also be drawn with the work of Reed (1983) (see Table 4) who observed
soil erosion in Shropshire from over 1000 sites between 1965 and 1983. Though much
of this work was subjective, with little in the way of quantitative measurements, rates
of soil erosion are estimated to be in excess of 40 t ha21 yr21 in parts of eastern Shropshire, providing an example of potential erosion rates given steepest slopes and, in
this case, high levels of man-induced compaction.
In general, the results of the 137Cs surveys diverge from those presented by other
authors. Results from the ten (Walling and Quine, 1990) survey sites that tie in with
the Evans (1993) overflight survey data are presented in Figure 1. It is apparent from
the comparison of the results that the two measurement techniques rarely provide
compatible assessments of erosion rates. Since this is arguably the case for all different methods of quantifying erosion, great care must be taken in drawing conclusions
as to which results are ‘correct’. Here, it is argued that attention must be given to the
nature of the observed data when considering rates of soil loss at a specific site or on
a specific soil series. Owing to the temporal and spatial scales involved and the
wholly different nature of the techniques employed it is hardly surprising that varying assessments of extent of erosion or magnitude are presented. Therefore, each set
of observed data must be treated with reference to its limitations, especially when
these data are considered for model validation as is demonstrated by Quine (1999).
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Kenton, Devon
Ashcombe, Devon
Starcross, Devon
Penallt, Gwent
Bicton, Devon
Morfe Valley, Salop
Bollitree, Herefordshire
Llanishen, Gwent
Ridgmont, Bedfordshire
Mileoak, W. Sussex
Houndean, E. Sussex
Dalicott, Salop
1990– 94
0.03– 0.05
0.03– 0.07
Clay
Clay
0.02
0.2225
6.3
0.085
0.185
, 0.01
0.105
0.1175
no erosion
0.02
0.155
0.135
no erosion
0.6– 2.1
Chalky
Loamy sand/sandy loam – Newport/
Wick
Loamy sand/sandy loam – Frilford
Silty clay loam – Andover
Silty clay loam – Andover
Loamy sand/sandy loam – Newport/
Wick
Loamy sand/sandy loam – Cuckney
Sandy loam – Earniston/Hodnet
Sandy loam/sandy silty loam –
Eardiston
Sandy silty loam – Milford
Loamy sand/sandy loam –
Bridgnorth
Clay loam – Crediton
Loamy sand/sandy loam –
Bridgnorth
Loamy sand/sandy loam –
Bridgnorth
1.0– 4.5
0.06– 2.4
0.00– 0.001
0.6– 2.9
2.1
Average soil
erosion
(t ha21 yr21)
Sand
Sandy loam – Cottenham series
Sandy loam – Cottenham series
Yeovil and Pennard sands
1982– 83
1973– 79
Silty clay loams/silt loams – Andover
Soil type and series
1982– 92
Study
period
Note: aResults represent net median and maximum rate over 4-year period, 1 year for the Starcross site.
Chambers and Garwood
(2000)a
Eastern south Downs, southeast
England
Somerset
Boardman and
Favis-Mortlock (1993)
Colborne and Staines
(1985)
Morgan (1985a)
Silsoe, Bedfordshire, bare ground
Woburn, Bedfordshire, cereals
Maulden, Bedfordshire,
woodland
Ashwell, Bedfordshire, winter
wheat
Meppershall, Bedfordshire,
winter wheat/spring barley
Pulloxhill, Bedfordshire, spring
barley
Hempstead, Norfolk
Study site
Observed soil erosion rates from field survey in the UK
Author
Table 4
1.15
0.825
20.0
11.75
1.425
1.0
0.575
10.75
no erosion
35.75
4.25
3.0
no erosion
n/a
n/a
n/a
n/a
n/a
n/a
n/a
7
Maximium
soil erosion
(t ha21 yr21)
348
Quantifying soil erosion by water: monitoring and modelling approaches
R. Brazier
349
Finally, it is suggested that positive use can be made of the difference in measurement techniques from the methods surveyed. Consistent differences, between 137Cs
and field survey data (for example) as illustrated in Figure 1, where 137Cs rates generally exceed survey results, may be consistently describing the same role of alternative processes. Thus, instead of focusing on the inadequacies of various
measurement techniques, it may be possible, with future work, to attribute the
diverse soil erosion rates to the different processes measured, in this case translocation by tillage perhaps explaining the discrepancies between the datasets.
4
Field survey data
Numerous sites in England and Wales have been monitored for water erosion of soils
by means of field survey. Typically these surveys have involved the volumetric
measurement of rills or erosional features such as ephemeral gullies (Morgan,
1985b, Colborne and Staines, 1985; and Boardman, 1990a), which can then be
converted to a soil loss in mass per unit area by applying a bulk density figure for
the soil in question. Such literature also includes the qualitative estimation of erosion rates over, in some cases, quite considerable time periods, giving an added
Figure 1 Average observed erosion rates from overflight surveys
(mean, min, max) against net erosion from 137Cs surveys (t ha21
yr21)
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350
Quantifying soil erosion by water: monitoring and modelling approaches
perspective to the assessment of erosion in the UK (Reed, 1983, 1986). In general,
these data relate to intensive periods of a few years over which surveys are carried
out, the notable exception being the longer term . 10-year monitoring carried out by
Boardman on the South Downs. Recourse to these datasets is now made to summarize the rates of erosion recorded, drawing comparisons between both this type of
data and other data that exist for the study sites.
Direct comparison of field survey data collected between 1982 and 1992 (Boardman and Favis-Mortlock, 1993) and plot data collected on soils of the Andover series
(Boardman and Robinson, 1985) can be made, as both datasets were based on the
volumetric measurement of rills and ephemeral gullies. Erosion rates monitored in
a 36 km2 area of the South Downs during the 10-year period of field survey ranged
from 0.84 to 7 t ha21 yr21, whereas rates recorded on the plots ranged from 0.014 to
45.34 t ha21 yr21 with an average annual rate of erosion across all plots of
15.01 t ha21 yr21. Those plots down to winter cereals (the dominant land use in
the field survey area, Boardman and Favis-Mortlock, 1993), exhibit a range of erosion
rates that may offer some explanation of the rates observed in the field. Low rates of
0.014 t ha21 yr21 are recorded on the plot where winter wheats are directly drilled,
higher rates where organic matter is removed by burning prior to drilling and the
highest rates occur on plots where seedbed preparation in the form of ploughing
and harrowing is practised. Thus, though the observed rates on the plots do not
exactly match those from the field survey, they give some indication of which management practices may lead to increased erosion and the unsustainable rates that are
observed in some of the fields.
A further conclusion to be drawn from the Boardman field survey dataset is that
for six of the ten years monitored, average erosion rates exceeded the level of
1.4 t ha21 yr21 deemed to be acceptable by authors such as Evans (1981) and Morgan
(1980). This is also true of rates observed by Colborne and Staines (1985) in Somerset,
Morgan (1985a) in Bedfordshire and at one of the sites monitored by Chambers and
Garwood (2000) in Devon. Furthermore, with the exception of two sites, all field surveys that return maximum erosion rates demonstrate that levels of erosion are unacceptably high if extreme events are considered.
Sites in Somerset have been monitored by aerial/ground surveys, 137Cs surveys
(as detailed above) and also field-based surveys (Colborne and Staines, 1985).
Good agreement exists between the results with average rates ranging from 0.6 to
2.9 t ha21 yr21 from the field surveys, 2.2 t ha21 yr21 from the net 137Cs erosion
and 4.89 t ha21 yr21 from the Evans surveys. These separate surveys were all carried
out in the same locality though, as discussed above, some differences exist in the
timescale over which measurements were made. The similarity of results between
these surveys seems to confirm that soil erosion is a problem on the medium silts
and loams of this area.
Evidence from field survey data throughout England and Wales, despite its limitations in terms of survey technique, does provide an effective means of assessing rill
erosion especially on agricultural land that is easily accessed and can be regularly
monitored. All sites monitored contain fields that erode heavily and those schemes
that provide longer term surveys (notably those of Boardman on the South
Downs) show that such rates have occurred throughout the study periods. It is
stressed here that the value of such surveys increases with the length for which
they are conducted by affording a valuable long-term perspective on rates of change
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R. Brazier
351
of soil erosion and also as a means of evaluating field-scale spatial predictions that
are made by soil erosion models.
III Related data describing erosion by water
The following section describes techniques from which soil erosion rates can be
inferred, not directly measured. Nonetheless, if interpreted with care, these data provide a further perspective on erosion rates in the UK and may be used to aid in the
evaluation of model performance at the catchment scale.
1
Reservoir sedimentation data
The use of bathymetric survey techniques to establish sedimentation rates in reservoirs and consequent sediment yield rates from upland catchments will now be considered. Though in the short term this approach is difficult to reconcile with erosion
rates because of the lack of consideration of sediment storage in the system (Boardman and Evans, 1994), over longer timescales average sediment yield figures provide
useful datasets describing the movement of sediment over (for instance) the past 100
years – see Table 5. A further limitation of this approach is the assumption that all
sediment yield into reservoirs in upland areas is due to erosion by water. Whilst
this is unlikely to be the case, as (for example) translocation by tillage or wind erosion
may move sediment, water erosion (which may include rill, interrill, gullying or
bank erosion) is the dominant process that supplies sediment from upland catchments.
Unlike the previous techniques, this method is not born out of a direct interest in
soil erosion; moreover, the problem of sedimentation of reservoirs is often perceived
as a water resource problem, owing to the reduced capacity and life of such structures (Verstraeten and Poesen, 2000). Nonetheless volumetric surveys of sediment
accumulation, combined with appropriate characterization of bulk density and use
of a trap efficiency term, enable the calculation of sediment yields from catchments
(e.g., Rowan et al., 1995), providing a useful insight into spatial variations in sediment yield estimates (Butcher et al., 1993). The following section reviews a range
of reservoir sedimentation studies as a means of quantifying long-term erosion
rates typically in upland areas of the UK.
Perhaps the most detailed study of reservoir sedimentation rates in the UK was
carried out by Butcher et al. (1993) who focused on an area of the Southern Pennines,
completing surveys of some 113 lakes and reservoirs between 1984 and 1991. In the
interest of compiling a high quality dataset, only 28 of these sites were further analysed. The results from this study (and those of other reservoirs throughout the UK)
are presented in Table 5. A considerable range of sediment yield rates is observed,
not only at the Southern Pennines sites but also throughout the other sites studied.
However, these rates, which represent average erosion rates from the catchments
over the long term (often in excess of 100 years), only exceed acceptable levels of erosion as established by Evans (1981) and Morgan (1980) at 11 of the 35 sites. On this
evidence it appears that erosion in the upland areas where these catchments are
situated is less significant than corresponding rates on lowland agricultural land,
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Barden Upper
Blackmoor-foot
Broomhead
Chew
Cod Beck
Deanhead
Embsay
Gorple Upper
Gorpley
Graincliffe
Green Withens
Holme Styles
Ingbirchworth
Kinder
Langsett
Mixenden
Reva
Silsden
Snailsden
Strines
Thornton Moor
Widdop
Strines, South Pennines
Catcelugh, Northumberland
Butcher et al. (1993)
Young (1958)a
Hall (1967)b
Reservoir
1882
1876
1929
1914
1953
1840
1909
1934
1905
1885
1898
1840
1868
1912
1905
1873
1894
1860
1899
1871
1885
1978
Year of
construction
Sediment yield from reservoir sedimentation studies
Author
Table 5
1989
1988
1988
1987
1989
1986
1989
1989
1990
1989
1990
1988
1990
1987
1989
1989
1990
1990
1990
1988
1989
1988
Year of
survey
6.34
8.20
21.96
2.92
7.12
2.00
2.95
3.80
2.80
5.00
3.40
2.20
7.72
8.95
21.06
0.77
2.91
7.85
0.84
11.70
5.12
8.90
Catchment
area (km2)
125.05
89.81
51.00
212.69
74.36
37.90
165.39
64.24
143.34
69.40
21.73
2.90
88.25
135.14
169.30
11.00
286.14
221.61
289.46
113.40
35.11
101.30
39.41
43.1
Estimated
sediment yield
(t km22 yr21)
1.25
0.90
0.51
2.13
0.74
0.38
1.65
0.64
1.43
0.69
0.22
0.03
0.88
1.35
1.69
0.11
2.86
2.22
2.89
1.13
0.35
1.01
0.39
0.43
Estimated
sediment yield
(t ha21 yr21)
352
Quantifying soil erosion by water: monitoring and modelling approaches
North Esk, SE Scotland
Hopes, SE Scotland
Kelly, Strathclcyle
Trentabank, Macclesfield Forest
Glenfarg and Glenquey, S. Scotland
Pinmacher
Holl
Earlsburn
North Third
Lambieletham
Harpleas
Drumain
Cullaloe
Glanfrag
Glenquey
Carron Valley
Abbeystead, Lancashire
Notes
a
Cited in McManus and Duck (1985).
b
after Walling and Webb (1987).
Rowan et al. (1995)
Ledger et al. (1974)
Stott and Duck (1988)
McManus and Duck (1985)
Duck and McManus (1990)
Ledger et al. (1974)
1851
1876
1930
1948
1963
1970
1980
1991
48.70
34.5– 49.3
26.1– 31.3
25.00
26.00
41.00
0.345 – 0.493
0.261 – 0.313
50.90
72.30
206.20
205.40
2.10
13.80
3.90
30.80
52.00
15.10
141.90
242.00
78.00
373.00
369.00
170.00
208.00
96.00
0.51
0.72
2.06
2.05
0.02
0.14
0.04
0.31
0.52
0.15
1.42
2.42
0.78
3.73
3.69
1.70
2.08
0.96
0.25
0.26
0.41
R. Brazier
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353
354
Quantifying soil erosion by water: monitoring and modelling approaches
as discussed above. However, the catchment values include storage before the reservoir, so it would be possible to have unacceptable erosion within catchment that is
deposited elsewhere in the system. Despite this catchment storage, over the long
term, records describing sedimentation of all the reservoirs studied still indicate
that erosion is widespread and a significant concern for land degradation and
water resource issues alike. Maximum rates in these areas are not easily derived
from survey techniques, therefore comparison of event-based rates or even annual
sediment yields are not made here. Doubtless low-frequency, high-magnitude events
are responsible for significant portions of long-term sediment yields, as mass movements (for example) will tend to contribute large amounts of sediment directly to
channels in upland areas. These fluctuations in sediment delivery ratio underline
the fact that such data cannot be used to determine sediment yields on a short temporal basis and are best used to assess changes in sediment flux over long time
periods.
Previous studies by Walling and Webb (1987) and Newson and Leeks (1985)
suggest that long-term sediment yields from upland areas are in the region of 0.3
and 0.5 t ha21 yr21, respectively. Results from sites reviewed here are generally
greater than these estimates, perhaps because of the anthropogenic influence upon
erosion, as in lowland areas. For instance, results derived from the Abbeystead reservoir surveys peak between 1930 and 1948. Rowan et al. (1995) propose that this is due
to the improvement of land in order to increase food productivity, and that subsequently rates have decreased as land has largely been returned to grazing; an
observation that is also supported by Foster et al. (1990).
Reservoir sedimentation data provide a unique means of assessing sediment
yields in upland areas. Combined with accurate trap efficiency terms, such data
afford useful insights into variation in sediment yield between catchments (which
is considerable) and quality data that can be compared with sediment yield and erosion rates throughout the UK. It is apparent from the studies summarized here that
though erosion rates may be less significant than those on lowland sites in the UK, in
the long term (c. 100 years) upland erosion occurs to a sufficient magnitude and
extent to warrant further monitoring and modelling efforts. Consequently these
data may be useful to aid model evaluation at the catchment scale at which they
are collected (Molino et al., 2001). Furthermore, improvements in measurement techniques that separate suspended sediment and bedload (Duck and McManus, 1994)
may provide data for validation of the process description of the movement of sediment within catchment-scale erosion models that explicitly model bedload and suspended sediment separately.
2
Suspended sediment data
Monitoring concentrations of suspended sediment in waterways provides an
important source of information to aid understanding of the off-site impacts of soil
erosion. Numerous authors (Table 6 describes a representative selection) have monitored a wide range of catchments for sediment fluxes. Robson and Neal (1997)
describe results over the last 20 years of the Harmonised Monitoring Scheme,
which has sought to quantify not only the input of suspended solids from rivers
to estuaries but also a suite of water quality and chemical loads at the large
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R. Brazier
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Table 6 Suspended sediment concentrations from selected monitoring schemes
throughout the UK
Author
River
Al-Ansari et al. (1977)
Bridges and Harding
(1971)
Geikie (1868)
Almond, Tayside
Lower Swansea valley,
West Glamoragan
Nith, Dumfries and
Galloway
Clyth, Highland
Tyne, Northumberland
Derwent, Derbyshire
Grains Gill, Cumbria
Hodge Beck, Yorkshire
Tweed
Tyne, Northumberland
Wear
Tees
Ouse, Yorkshire
Wharfe
Derwent, Derbyshire
Aire
Don
Trent
Humber
Nene and Welland
Ouse, Bedfordshire
Blackball Stream
Barle
Upper Exe
Batherm
Exe
Lowman
Dart
Exe
Jackmoor Brook
Creedy
Clum
Clum
Clyst
Hall (1967)
Harvey (1974)
Imeson (1971)
Robson and Neal (1997)
Walling and Webb (1987)
Site
Catchment
area (km2)
Suspended
sediment yield
(t ha21 yr21)
2.7 –9.4
0.60
0.68
Norham
Wylam
Lamb Bridge
Low Worsall
Naburn
Tadcaster
Loftsome Bridge
Beal
Doncaster
Dunham
–
–
Earith
Lyshwell
Brushford
Pixton
Bampton
Stoodleigh
Tiverton
Bickleigh
Thorverton
Pynes Cottage
Cowley
Woodmill
Rewe
Clyst Honiton
2.1
128.0
160.0
64.5
422.0
53.7
46.0
601.0
9.8
262.0
226.0
273.0
98.2
0.25
0.68
1.17
400.00
4.80
0.13
0.19
0.33
0.16
0.19
0.21
0.12
0.30
0.35
0.13
0.18
0.09
0.06
0.04
0.16
0.19
0.35
0.20
0.52
0.58
0.28
0.30
0.39
0.32
0.20
0.26
catchment scale. Prior to this, Walling and Webb (1987) instrumented a number of
catchments in the southwest of England to quantify suspended sediment transport
in UK rivers. However, care must be taken when interpreting results from such
monitoring schemes as suspended sediment only represents a portion of the total
sediment yielded from catchments. For example, Duck and McManus (1994)
found that suspended sediment accounted for only 54.4% of the total sediment
yield in the Pinmacher catchment and it is argued by Lewin et al. (1974) that only
20% of the total sediment load may be in suspension. Furthermore, Newson (1981)
postulates that in lower gradient channels, suspended sediment may only account
for , 11% of sediment yield. Nonetheless, examination of these datasets is
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Quantifying soil erosion by water: monitoring and modelling approaches
worthwhile as they provide a means of assessing long-term trends in the off-site
impacts of soil erosion (in terms of river water quality) and also large spatial scale
data on the flux of sediment in UK rivers.
Highest sediment yield rates included in Table 5 obtain from a gullied system
(Harvey, 1974) and a disturbed moorland catchment that was affected by burning
(Imeson, 1971). Though these rates are therefore not representative of sediment
yields in UK rivers in general, they provide an insight into the potential yields of suspended sediment in catchments that are highly erosive. Perhaps more representative
of typical UK conditions are the Exe basin (Walling and Webb, 1987) and Harmonised Monitoring Scheme (HMS) datasets (Simpson, 1980), which describe relatively
low average rates from catchments at a wide range of scales. In part, these average
figures may be low because of the way in which such data are collected, for instance,
long timesteps between sampling (c. 2 weeks with the HMS data), which may prevent sampling of hydrograph peaks (when turbidity may be highest). However,
data collected at most sites are consistently of the same order of magnitude, and
are well below the aforementioned levels of ‘acceptable’ erosion rates. Though suspended sediment only represents a portion of the total sediment transported in channel, such results reflect the fact that UK rivers carry low levels of suspended
sediment unless in spate (Robson and Neal, 1997).
Suspended sediment time series will not provide an accurate description of total
sediment yield from a catchment. Such data can be useful, however, if a description
of the movement of fines is needed. One example might be to determine whether
the movement of sediment into water courses may impact upon the construction
of salmon ‘redds’ (Crisp and Carling, 1989; Carling, 1995). Also much recent work
(Sharpley et al., 2000; Withers et al., 2000) has recognized the need for control of nutrients from farm lands, often associated with fine sediments, into channel networks.
Thus, monitoring suspended sediment within schemes such as the HMS plays an
important role in the quantification of sediment fluxes.
A further role that these datasets can play is in providing validation data for largescale modelling efforts, which are notoriously hard to evaluate given the paucity
of observed data (Brazier et al., 2001a). Modelling approaches such as ANSWERS
(Beasley et al., 1980) and MEDRUSH (Abrahart et al., 1994), which simulate soil
loss at the catchment or landscape scale, require quality time series of data (typically
both flow and sediment) against which to validate model performance. If such
models are to be implemented and indeed improved it is argued here that use of
long-term large-scale datasets (for example the HMS data) is made to both assess
model predictions and provide confidence limits with which to bracket predictions,
as is illustrated in Brazier et al. (2000).
IV Discussion
1
The need for continued monitoring
It is apparent from the monitoring schemes reviewed here that a range of high quality data exist that describe erosion by water in the UK. Furthermore, it is recognized
that monitoring has an integral role to play in the sustainable management of lowland agricultural land in particular. Despite the movement towards modelling of
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R. Brazier
357
soil loss, field observations or implementation of remediation strategies may yield
more economical (and efficient) solutions in local areas than the application of the
existing generation of erosion models (Boardman, 1990a). Thus, though continued
monitoring of soil loss is vital for model improvement, it must also be treated as a
means in itself to aid soil conservation and to inform catchment managers and
decision/policy makers alike as to the erodibility of soils.
It is difficult to conclude which datasets provide the most accurate description of
soil erosion in the UK and thus to conclude definitively what the true extent and
magnitude of water erosion is. Indeed, it seems most prudent to treat each data
source as separate, representing slightly different perspectives on erosion rates
over varying spatial and temporal scales. Despite these differences, from the variety
of sources of evidence reviewed here it is clear that erosion by water occurs throughout the UK. Whilst the numerous monitoring experiments and indirect measures of
soil erosion produce a considerable range of soil erosion rates within the UK, it is
further clear that water erosion can exceed acceptable levels on a variety of different
soils and under a variety of different management practices. The continuous monitoring of soil erosion in the UK, however, has declined after a ‘peak’ of interest in
the discipline in the mid-1980s (Favis-Mortlock, 1994). Whether this is due to lack
of funding for data collection or a concentration of efforts in other areas (such as
modelling), is not relevant, as the paradox that this lack of recent data collection creates still remains. Whilst modelling may provide an efficient means of assessing (predicting) soil erosion at a given scale, neither improvements in the level of model
accuracy (or uncertainty surrounding model results) nor in the process representation of those physically based models can be made without recourse to high quality
observed data. Therefore, if models replace data collection, will model results ever
truly be a substitute for field data? Here it is argued that will not be the case. Furthermore it is argued that a return to collection and use of datasets that closely match the
output of soil erosion models is crucial for the development of the discipline as a
whole.
To underline this, an example of work evaluating the uncertainty surrounding
results produced by the WEPP soil erosion model (Nearing et al., 1989) is now discussed. The WEPP was initiated by the USDA in 1985 to succeed the USLE and provide a ‘new generation of water erosion prediction technology . . .’ (Nearing et al.,
1989). Though claiming to be ‘process-based’, certain sub-models (notably those
relating to erodibility) are, in fact, parameterized by regression relationships
drawn from empirical plot data collected throughout the USA (Lane and Nearing,
1989).
The WEPP model was evaluated against similar plots that were not included in the
original parameterization (Zhang et al., 1996) producing results which were reasonable (estimated soil loss versus observed soil loss r 2 50%). Thus, data at the correct
spatial and temporal scales were employed to test the accuracy of WEPP in the kind
of environment for which the model was developed. However, conclusions were
drawn that emphasized the need for further validation on a wider range of scenarios
before the model could be used with confidence at other sites (Zhang et al., 1996),
notably those outside the USA. Further to this, it was argued by Brazier et al.
(2000) that given the validation data that exist for WEPP, more in-depth analysis
was needed prior to use of the model on sites other than those for which it was developed (i.e., anything other than standard USLE plots). Results of the uncertainty
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Quantifying soil erosion by water: monitoring and modelling approaches
analyses implemented by Brazier et al. (2000) show that even on these sites, uncertainty surrounding model predictions is considerable and for sites in the UK and
USA must be considered alongside standard model output to qualify accuracy of
the results produced; a similar conclusion to that drawn by Quinton (1997) with
respect to the EUROSEM model.
In spite of these recommendations and work by authors such as Oreskes et al.
(1994) who warn that ‘models can only be evaluated in relative terms, and their predictive value is always open to question’, use of WEPP as a management tool has
been promoted since its early development (Lane and Nearing, 1989; Laflen et al.,
1991), a clear case of model capability being outpaced by end-user demands or
unrealistic expectations of the model. It is suggested by Brazier et al. (2001a) and
in this paper that use of the high quality observed data that exist should be made
to improve reliability of results and constrain uncertainty prior to reliance upon
the accuracy of model predictions within a decision-making framework.
2
Soil erosion policy in the UK
The increasing need for policy relating to soil erosion in the UK is another area within
which the aforementioned paradox becomes relevant. Despite recognition by
authors of the need for a nationwide soil erosion policy (Boardman, 1988a; Morgan
and Rickson, 1990), there has been little change in local or central government policy
to deal with soil erosion and related problems. In part, this must be because soil erosion is not perceived as a problem in the UK and will therefore not be under current
agricultural policy at the EU level. Some guidance exists in the form of the Soil Code
‘code of good agricultural practice’, however this only represents advice and as such
is not enforceable by law (Brazier, 2000). Most recently the draft soil strategy for
England was published (see http://www.defra.gov.uk/environment/consult/
dss/response/index.htm March 2001) inviting comments and outlining a strategy
that included the monitoring of soil for quality, diversity and rate of change but making only minimal reference to that of soil erosion. An overview of responses has now
been published and though it contains references to problems such as the loss of
organic matter in soils, which may lead to increased erosion, it details very little in
the way of policy relating directly to soil erosion monitoring or prediction. Whilst
a soil strategy is welcomed it is argued here that further monitoring for erosion
and closely related modelling of erosion should form an integrated part of that strategy in order to provide the basis for policy formulation.
3
Data requirements and modelling
The type of soil erosion data or monitoring experiments that most closely complement modelling efforts is highly dependent upon the models in question. As discussed, model development will benefit from the collection of datasets that closely
match the output produced by the models. So, hillslope models such as WEPP
(Nearing et al., 1989), EUROSEM (Morgan et al., 1994) and the Erosion Productivity
Impact Calculator (EPIC) (Sharpley and Williams, 1990) that have all been applied
in the UK and Europe (Cabelguenne et al., 1990; Favis-Mortlock, 1994; Quinton,
1994) require hillslope-scale data to quantify levels of error and to improve model
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R. Brazier
359
predictions. It is not satisfactory to simply apply these models in the USA (for
instance) and assume that results will be equally reliable elsewhere in the world.
As an example, it is clear that there is no ‘universal’ soil loss equation, as any empirically based model will always be limited by the data it is built upon. Any predicted
results, therefore, will only describe the range of results from, in the case of the USLE,
plot studies within the USA.
Plot data must be valued for the detailed description of fluxes of water and sediment that are afforded and the high resolution with which soil erosion processes can
be monitored. Thus, here it is stressed that plot data are collected both to provide
data for the ongoing validation and evaluation of existing (and future) erosion
models and also to further the understanding of processes operating at the hillslope
scale that to date are still poorly represented in soil erosion models (see Wainwright
et al., 2001 for an example).
At the catchment scale both hydrographs and sedigraphs (preferably describing
suspended sediment and bedload) are required to validate the output of models
such as the Agricultural Non-Point Source Pollution System (AGNPS) (Young et al.,
1989), ANSWERS (Beasley et al., 1980), GAMES (Dickinson and Rudra, 1990) and
MEDRUSH (Kirkby et al., 1993). Furthermore, given the spatial nature of predictions
provided by some of these models, data monitored within the catchment at different
locations would be advantageous to evaluate model performance at a range of scales
(G. Govers, personal communication, 2002) Quine (1999) argues that 137Cs data may
provide a means of evaluating spatially distributed erosion models, however, explicit consideration of other soil redistribution processes such as translocation by tillage
must be made. Following on from this, if reservoir sedimentation data are to be used
to evaluate soil erosion model output (as demonstrated by Molino et al., 2001), then
holistic modelling approaches, which bring together all the processes of erosion
within such catchments, must be made. Because of the range of processes that
may contribute sediment to upland reservoirs, models that do not take into account
gullying, for example, may grossly underpredict total sediment yields from catchments (Poesen et al., 1996).
The problem of scaling within soil erosion models is well documented by Kirkby
(1998) who call for data nested at different scales, to evaluate whether modelling systems such as the MEDALUS approach accurately address the issue of changing
dominant processes at different scales. Though data describing these changes exist
in the form of suspended sediment flux, no datasets reviewed here would allow a
rigorous spatial validation of catchment-scale soil erosion models. It is suggested
that either existing monitoring schemes are modified to include some assessment
of all sediment sources or future schemes set up to characterise these fluxes and
thus provide time series of total sediment fluxes at a variety of scales within catchments (see Wainwright et al., 2001).
Larger or global scale models (Kirkby and Cox, 1995; Drake et al., 1999; Brazier
et al., 2001b) are not easily validated given the coarse scales at which they operate.
Grid cell sizes may be large; 4 km 4 km in the case of the Drake et al. (1999)
model which cannot, therefore, be validated against any of the directly monitored
datasets reviewed here. A novel approach that has been employed by Zhang et al.
(2002) is to downscale model output to the scale of the observed data that exist.
Limitations with this technique focus on the timescale over which the field measurements were made and the different techniques that were employed to down-scale.
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Quantifying soil erosion by water: monitoring and modelling approaches
However, preliminary results are encouraging and demonstrate the way in which
existing observations can be used to validate coarse scale output (Drake et al.,
1999). Use of related datasets, such as time series of flow and sediment flux measured
at the large catchment scale, could also be made to evaluate the predictions of
these models at least at catchment outlets and potentially at different spatial scales
within catchments; this work is ongoing (N.A. Drake, personal communication,
2002).
A further point that should be stressed was first highlighted by Dunne (1984),
reiterated by Boardman (1990b) and is relevant here also. These authors suggested
that knowledge and observations of a catchment are crucial to the understanding
of erosion, an argument that holds true with respect to the development of erosion
models that attempt to represent these catchments. Though validation may usefully
be carried out ‘blind’ as with the International Geosphere – Biosphere Programme –
Global and Change and Terrestrial Ecosystems (IGBP-GCTE) validation exercise
(Favis-Mortlock et al., 1996; Jetten et al., 1999) improving models post-validation
may well be aided by a ‘real’ or field-based understanding of the observed dataset.
For example, Jetten et al. (1999) underline the importance of predicting the spatial
pattern of runoff rather than just the net output, an outcome which is more easily realized by observing the spatial distribution of overland flow (for example) within a
catchment during events (Brazier et al., 2003). Furthermore, Jetten et al. (1999) report
that so-called ‘soft’ information relating to the physical state of the soil or topography
are vital to improvements in model predictions and must therefore be incorporated
into future model development and future data collection strategies.
V
Conclusions
Though soil erosion rates by water are in general relatively low in the UK, this review
demonstrates that erosion occurs to significant levels throughout the UK on both
upland and lowland areas. It is further concluded that monitoring of erosion must
continue to maintain a long-term perspective on soil erosion as a problem that, to
date, has yet to be fully quantified let alone fully understood.
In general despite the numerous quality datasets that exist in the UK, it seems
clear that a paucity of datasets exist with which to rigorously validate erosion
models. Those datasets that are available provide useful information as to erosion
rates throughout the UK but are not ideally suited to model validation and development, being in some way qualitative and not compatible with model output.
Whilst these data should be used to a greater extent to evaluate the accuracy of
erosion models, other data must also be collected that provide a more suitable
means of assessing the performance of erosion models and, hence, improve the
representation and understanding of physical processes within model frameworks. Future research, through data collection, must ensure that the paradox
that exists between data collection and modelling does not continue. If progress
in both model evaluation techniques and consequent improvement of the predictive capabilities of soil erosion models is to be made, more quality observed data
are required at the relevant spatial and temporal scales. Furthermore, these data
must be collected within integrated projects that allow data collection to closely
match model output.
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361
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