EARTH SURFACE PROCESSES AND LANDFORMS
Earth Surf. Process. Landforms 34, 2070–2077 (2009)
Copyright © 2009 John Wiley & Sons, Ltd.
Published online 5 November 2009 in Wiley InterScience
(www.interscience.wiley.com) DOI: 10.1002/esp.1892
Effects of the largest daily events on total soil
erosion by rainwater. An analysis of the
USLE database
J.C. González-Hidalgo,1* M. de Luis1 and R. J. Batalla2,3
Department of Geography, University of Zaragoza, 50009, Spain
2
Department of Environment and Soil Sciences, University of Lleida, 25198 Spain
3
Forestry and Technology Center of Catalonia, 25280 Solsona, Spain
1
Received 29 November 2007; Accepted 7 July 2009
*Correspondence to: J.C. González-Hidalgo, Department of Geography, University of Zaragoza, 50009, Spain. Email: [email protected]
ABSTRACT: Soil erosion by water (rain and wash) is a discrete process, and there are several questions relating to this that have
yet to be answered. For instance, for how long should measurements be taken in order to obtain realistic mean erosion rates, and
what is the effect of the largest daily events on soil erosion, are questions still not fully answered. To make progress on these
issues, detailed information is needed, but usually not available. This work has analysed the USLE database compiled by the
United States Department of Agriculture. A total of 27 857 daily erosion events were examined, monitored over 310 erosion plots
representing 3195 plot-years of soil erosion measurements. Periods of measurement varied between plots, ranging from 2 years
to 32 years. Data have been analysed by calculating the percentage of soil eroded by the largest to the tenth largest daily events
over the entire length of the record. The percentage of soil eroded during the n-largest events follows a power function y = x−b,
where y is the percentage of soil eroded in the selected n-largest event, and x the total of measured daily events. Results showed
that the top 10% of total daily erosive events produce a mean of 50% of eroded soil. Soil erosion measured over short periods,
e.g. typically less than 5 to 10 years, is compressed into a few daily events, whatever their magnitude; therefore, mean erosion
rates estimated under such small time frames cannot be taken as a good descriptor of the real processes. Analysis suggests that
to weight the dependence of soil erosion on the largest daily values, a minimum number of events should be considered. In
general, if it is wished to reduce the effect of the largest events to less than 50% of total soil erosion, the mean number of recorded
daily events should be between 75 and 100. They represent, on average, 10 years of measurements for the database used here.
Copyright © 2009 John Wiley & Sons, Ltd.
KEYWORDS: soil erosion; daily events; largest events; USLE database
Introduction
Research into soil erosion has not solved various problems
both from a methodological (Stroosnijder, 2005) and epistemological point of view (Boardman, 2006). In addition to
other issues, these two authors pointed out the difficulties
arising when data from plots are extrapolated to real landscape scale, the minimum period of records needed to achieve
realistic mean values, and the effects of the largest and extreme
events on total soil erosion. All of these points can be summarized in what Boardman (2006) called ‘the temporal and
spatial context of the erosion’.
Conservationists recognize that the maximum average soil
loss rates permitting maintenance of crop production need to
be specified for the design of soil and water conservation
systems (Burwell and Kramer, 1983). Also, current technology
assumes that annual erosion can be quantified from empirical
and theoretical relationships based on different parameters
(Zuzel et al., 1993). However, soil erosion processes vary
tremendously on both the spatial and temporal scale, and this
unexplained variability is a critical issue when using erosion
data to evaluate the performance of soil erosion models and
for experimental design (Nearing et al., 1999).
Many studies suggest that a large amount of soil loss is often
associated with an extremely limited number of observations,
i.e. days, and this is true for different temporal scales (Table
I). Rare storms introduce weakness in designing conservation
management systems (Larson et al., 1997), supporting the
hypothesis that temporal distribution of extreme events must
be defined to predict long-term erosion rates (Zuzel et al.,
1993), and suggest the possibility that they are partly responsible for unexplained variance. By definition those events are
rare, difficult to study and, consequently, short monitoring
periods may or may not yield useful samples (Burt, 1994;
Coppus and Imeson, 2002; Lane and Kidwell, 2003; Diodato,
2004). However, because planning efficient soil conservation
programs needs knowledge of sediment contribution from
extreme rainstorms (Piest, 1965; Larson et al., 1997; Boardman,
EFFECTS OF THE LARGEST DAILY EVENTS ON TOTAL SOIL EROSION
2071
Table I. A review of daily events on soil erosion
Reference
Comments literally, and context.
Barnett and Hendrickson, 1960 in Larson et al., 1997
✓ Slightly more than 21% of the average annual rainfall was partitioned as runoff.
Eleven excessive-rate storms contributed 25% of the total rainfall during the study
period and resulted in 56% of the total runoff and 86% of the total soil loss.
Watkinsville GA, 20 years 1940–1959, microcatchment.
Wischmeier, 1962
✓ 51% of the loss occurred in 3 of the 27 years. Only 14% of the total rainfall
occurred during those 3 years. Guthrie, OK, 27 years.
✓ 81% of the total soil loss occurred during 3 years of the 17-years period. Those 3
years had only 18% of the total 17-years rainfall. Blacksburg Virginia, 17 years.
✓ 40% of the total soil loss occurred during the 2 years that accounted for 20% of
the 12-years rainfall. Clarinda Iowa, 12 years.
✓ 51% of the total soil loss occurred during 3 years that had 29% of the 10-years
rainfall. Bethany Missouri 10 years.
✓ About three-fourths of the total long-time soil loss was caused by an average of
four storms a year and about 1/3 of the total soil loss was due to extreme storms
having return periods greater than 2 years.
Piest, 1965
✓ The suspended sediment yield caused by large storms return period >2 year varied
from 3 to 46% of total suspended sediment yield. The relative sediment
contribution of any given storm tends to decrease with increasing watershed size
Watershed ranging from 100 to 100 000 acres, record periods between 12 and 6
years.
Burwell and Kramer, 1983
✓ Almost 60% of the 24-year soil loss occurring only during 2 years. Kingdom City,
MO, 24 years.
Hjelmfelt et al, 1986
✓ The large events account for 65% of the total 37-years soil loss 513 events. The
seven largest events account for 25% of the total soil loss. The twenty largest
events account for more than half. Kingdom City MO, 37 years.
✓ The three largest account for more than 25% of the total 18-year soil loss. The
large events account for 75% of the total 18-year soil loss 357 events. the thirteen
largest event account for more than half of the total. Treynor, IA, watershed 83
acre, 18 years.
Edwards and Owens, 1991
✓ Single biggest erosion event accounted for more than 25% of the total erosion
measured in the study period
✓ The three biggest erosion-producing events on the average accounted for more
than 50%.
✓ Five largest erosion-producing storms accounted for 66% of the soil loss during
28-years period, and on one watershed a single storm caused more than half of the
long-term measured erosion 9 watershed, c. 1·5 acres in size, 28 years.
Langdale et al. 1992., in Larson et al., 1997
✓ The largest 11 storms accounted for only about 1·2% of all storms during the study
period but resulted in 41·8% of the erosion. Watkinsville GA, microcatchment
19-year period, 1972–1990
Zuzel et al., 1993
✓ Nearly all of the total erosion 99% was produced by only 50% of the runoff events
for all treatments; only 10% of the runoff events produced 60–70 % of erosion,
depending on the treatments. Pendleton, OR, 1978–1990.
2006), they are necessary to assess total soil erosion for a
correct evaluation of any management practice (Edwards and
Owens, 1991).
A second question emerges if we consider the largest event,
whatever the magnitude of soil eroded involved. In the western
Mediterranean area, a recent review has demonstrated that
more than half of the interannual soil eroded depends largely
on a few largest events, although these events were not necessarily extreme in magnitude (González Hidalgo et al., 2007).
Erosion and sediment yield monitoring programs are often
conducted over short time periods and the resulting databases
are used for a variety of purposes including the estimation of
annual soil erosion rates, mean annual sediment yield, and
rates of landscape evolution. Results of such studies are highly
variable (Lane and Kidwell, 2003), thus there is a great deal
of uncertainty in the estimation of mean values, and they may
also be biased by the occurrence or absence of extreme soil
loss events (Burwell and Kramer, 1983; Hjelmfelt et al., 1986;
Copyright © 2009 John Wiley & Sons, Ltd.
Lane and Kidwell, 2003). In any case, short-term studies
produce different results depending upon which study period
is selected (Hjelmfelt et al., 1986); moreover, long-term studies
show that individual years can result in different soil loss
volumes, depending on the occurrence of infrequent severe
storms. Finally, short records show compressed variances and
simple extrapolation is likely to produce large errors (Kirkby,
1984). It follows that the level of conservation management
dictated by average weather conditions can provide adequate
erosion control in most years, but unacceptable soil losses can
still be expected to occur from severe storms.
Previous paragraphs suggest that there are two relevant
temporal scales for research into erosion: the single event for
the design of erosion control technologies and the annual
average for conservation planning (Stroosnijder, 2005). To be
successful over a long period, erosion control practices must
not only consider erosion from severe storms (Edwards and
Owens, 1991) but also the temporal and spatial variability
Earth Surf. Process. Landforms, Vol. 34, 2070–2077 (2009)
DOI: 10.1002/esp
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J.C. GONZÁLEZ-HIDALGO ET AL.
Table I. A review of daily events on soil erosion (II)
Reference
Comments literally, and context.
Larson et al., 1997
✓ Soil loss in 1962 cropping season accounted c. 60% of the total soil loss during 10
years. Morris, MN, 1962–1971.
Douglas et al., 1999
✓ The cumulative plot of daily suspended sediment load against discharge shows the
key role of six storms which carried more than 25% of all sediment carried in eight
years and five months. Malaysia, 1990–1997.
Zhang and Garbrecht, 2002
✓ Soil erosion from the largest 2% of storms accounted for 60 to 85% of total soil
erosion in different farming systems. Oklahoma.
Lenzi et al., 2003
✓ Between 1986–2001, 38% of the total sediment occurred during two flood events
Italy, catchment, 5 years.
Lane and Kidwell, 2003
✓ The years with the largest sediment yield accounted for about 18 to 26% of the
mean annual sediment yield. The four years with the largest sediment yield
accounted for about 54 to 66% of the mean, and the 8 years with the largest
sediment yields accounted for the 80 to 90% of the mean annual sediment yield
on the different watersheds 16-years from 1976 to 1991.
McBroom et al., 2003
✓ A single storm resulted in over 73% of the annual flow and over 95% of the
annual sediment for 2001. Similarly in 1999 a single storm with 10-year 1-day
return period resulted in over 95% of the total annual sediment. Alto experimental
watershed TX 19992001
Lenzi, 2005
✓ Most of the sediment as bed load was supplied during two single floods between
1987–2004. Italy catchment, 7 years.
González Hidalgo et al., 2007
✓ In western Mediterranean basin, more than 50% of the annual erosion is
concentrated in the 5th largest daily erosion events. More than 50% of total
interannual erosion depends on 5th largest soil erosion events.
(Nearing, 1998). This means that long-term records should be
used (Piest, 1965; Edwards and Owens, 1991; Larson et al.,
1997). Stroosnijder (2005) has identified a crisis in erosion
measurement because there are insufficient empirical data of
adequate quality to overcome problems of methodology and
application.
In this paper we analyse the effects of the largest daily
events on total soil erosion using the USLE database. Our
approach differs from classic magnitude–frequency analyses
in that we distinguish between extremes by magnitude from
largest by rank. We discuss the effects of long- and short-term
monitoring programmes on soil erosion measurement and
propose a minimum time threshold for agricultural soil to
achieve a representative erosion value that weights the effects
of the largest events.
Data and Methods
We have analysed soil erosion data from the USLE database
on a daily basis. This database is available at the USDA-ARS
web site http://topsoil.nserl.purdue.edu/usle/index.html (April,
2008), West Lafayette, Indiana. USLE stands for Universal Soil
Loss Equation, which was developed by Walter H. Wischmeier
and Dwight Smith. In 1929 Hugh Hammond Bennett successfully campaigned for funding from the US Congress to begin
researching into soil erosion in the USA. He initiated the collection of data, which Wischmeier later compiled into one
large database, referred to here as the USLE Database. The
USLE database is a collection of files containing over 11 000
plot-years of data from 47 locations in 24 states. This data was
collected in the 1930s, 1940s, and 1950s, and entered into
the computer using punch cards during the 1950s. There are
three types of these files available for download on this site:
Site Specific Data, Storm Data, and Soil Loss and Runoff Data.
Copyright © 2009 John Wiley & Sons, Ltd.
Data from 310 plots at 16 sites representing more than
3195 plot-years of erosion and more than 27 857-daily
events are available. The spatial distribution of sites is
shown in Figure 1. The database represents a variety of soil
conditions, farming and conservation practices, plant cover,
crop rotation and climate, although not all of this information is included. The database has previously been analysed
for different purposes (Wischmeier, 1962, 1976; Nearing,
1998; Nearing et al., 1999; Risse et al., 1993), including
calibration of the MUSLE, RUSLE and WEPP models.
Measurement periods vary from 2 years to 32 years, although
many plots display several different periods of no data.
Therefore, we have taken the total number of daily events
recorded at each plot as the time frame for analysis, instead
of the total years.
To analyse the effects of daily events on soil erosion we
have adopted a different approach from the traditional
magnitude–frequency analysis. At this point, it is critical to
clarify the concepts of largest and extreme events. Extreme
event signifies some deviation from mean value, median
value, quartile distance, etc., and represents a high magnitude
by definition. Large event means the first, second, . . . , i.e.
the order in a set of data whatever its particular magnitude. In
this paper, we considered the largest daily events instead of
extreme events along the period of record.
To describe the effects of the largest daily events on total
soil erosion, events were ordered in each plot from the largest
to the smallest, while ranking was based on measured soil
losses rather than on individual storm rainfall, intensity or
volume. Subsequently, the cumulative erosion of n-largest
events as a percentage over total erosion was calculated, and
compared with the total number of daily erosion events,
instead of the number of years. We also calculated the total
amount of precipitation and runoff produced during the largest
daily soil erosion events.
Earth Surf. Process. Landforms, Vol. 34, 2070–2077 (2009)
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EFFECTS OF THE LARGEST DAILY EVENTS ON TOTAL SOIL EROSION
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Figure 1. USLE-database field sites (http://topsoil.nserl.purdue.edu/usle/index.html).
Results
The total amount of soil eroded between sites and plots varies
tremendously and mainly represents the effects of the length
of the monitoring period, but also the differences between rain
erosivity, soil characteristics, plant cover, farming practices
etc. from site to site. However, an analysis of the relative
contribution of the n-largest events to the total soil eroded
reveals a general pattern. The contribution of the largest
n-daily event accumulated over total soil erosion varies
according to the total amount of daily events recorded and it
can be fitted empirically into a power law (P < 0·05). Figure
2 shows the relationship between the number of daily events
and the percentage of soil eroded, computed for the largest 3,
5 and 10 largest events. It should be remembered that the
number of daily events as an independent variable acts as a
surrogate for time, instead of the number of years.
The mean value of soil eroded predicted by the different
n-largest events is presented in Table II. Results clearly indicate that, when the amount of daily erosion events recorded
is low, the soil erosion produced by n-largest daily events is
very high, i.e. if only one isolated event was recorded, the soil
erosion accumulated would be almost 100%.
This behavior drew our attention to the minimum number
of events needed for weighting the effects of the largest events
on total soil erosion estimations, whatever the magnitude of
soil erosion is involved. In general, to reduce the largest events
to less than 50% of total soil erosion, the mean number of
recorded daily events should be between 75 and 100 for the
database used here.
Copyright © 2009 John Wiley & Sons, Ltd.
The effects of the largest daily events in relation to different
monitoring periods are shown, as an example, for the Temple
Texas sites (Figure 3). Figure 4 shows the increment of the
percentage of soil eroded in the 3, 5 and 10 largest events.
The results show decreasing effects of the largest daily events
if monitoring periods increase, while the total accumulated
soil eroded increases. The example is particularly noteworthy,
because in this plot the largest soil erosion event by magnitude
was recorded at the end of the monitoring period, while
maximum rainfall and runoff daily events occurred many
years before.
Discussion
Models are the most powerful tool to predict soil erosion, and
hence to serve as the basis for designing correction measurements, but models need field data of high quality to be validated (Brazier, 2004), since they will never be more than
analogies (Kirkby et al., 1993). Within this context, long-term
studies are scarce and data poorly understood (Burt, 1994),
and this issue is particularly relevant under the present global
climate change conditions (Williams et al., 2001; Michael et
al., 2005).
The analysis of the most complete, available daily erosion
dataset, i.e. from the USLE repository manifests that the n-largest daily erosion events accumulate a high proportion of soil
loss, following a power law. Power law relationships have
been found in many natural processes (Leopold and Maddock,
1953; Wolman and Miller, 1960; Whittaker and Marks, 1975,
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J.C. GONZÁLEZ-HIDALGO ET AL.
100
Soil erosion (%) = 297.5 * Daily events (-0.51) (r2 = 0.67)
80
a)
60
40
20
0
100
Soil erosion (in %)
Soil erosion (%) = 316.5 * Daily events (-0.46) (r2 = 0.70)
80
b)
60
40
20
0
100
Soil erosion (%) = 302.1 * Daily events (-0.38) (r2 = 0.72)
80
c)
60
40
20
0
0
50
100
150
200
250
300
350
Daily events
Figure 2. The effects of (a) 3, (b) 5 and (c) 10 largest daily events on total soil erosion. Solid and dashed lines represent fitting line and confidence
intervals (P < 0·05).
Table II. Predicted soil erosion (in %) in n-largest events
Total daily events
30
50
100
3 largest
5 largest
10 largest
51%
39%
27%
65%
51%
37%
83%
68%
52%
for classic examples, and Cox, 2007, for recent comments),
but still need an explanation at present not fully attained.
Long-term records should, in principle, include more extreme
events than short-term ones. Notwithstanding, magnitude–
frequency analyses show that for rain-wash erosion there are
finite-size effects, which constrain the maximum erosion value
events that can occur in a given area (Boardman and FavisMortlock, 1999). Thus, as the time record enlarges, the number
of events lower than the extreme values will increase. Whatever
the magnitude, longer records under such an assumption
should result in a decrease of the percentage of largest events.
Further research should be done on this issue while, in the
Copyright © 2009 John Wiley & Sons, Ltd.
meantime, the application of fitting lines should be done only
within the limits of the original data.
There is a long-running debate about the length of time
needed for records to achieve representative values of soil
erosion or annual rate values. Wischmeier and Smith (1978)
stated that care must be taken to ensure that the duration is
sufficient to account for cyclical effects and random fluctuations in uncontrolled variables whose effects are averaged in
the USLE factor values. Risse et al. (1993) suggested that,
ideally, a study should only contain plots with monitoring
periods of 22 years or more. A similar duration of at least 16
years has been suggested by Lane and Kidwell (2003); to a
lesser extent, Ollesch and Vacca (2002) state that at least 3
years of records are needed to reduce the temporal uncertainty
of erosion plot experiments. However there is very little longterm soil loss data available. Our research would indicate a
minimum of 100 daily events, whatever the number of years
needed to complete them, to weight the effects of the largest
daily events; if not, the mean erosion rate or any other descriptor will be biased, independently of the real erosion values,
Earth Surf. Process. Landforms, Vol. 34, 2070–2077 (2009)
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EFFECTS OF THE LARGEST DAILY EVENTS ON TOTAL SOIL EROSION
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Date (dd/mm/yy)
4
2
3
4
2
6
1
8
0
10
Rainfall (in inches)
Runoff (in inches)
23/06/1932
19/05/1935
23/05/1938
11/05/1941
13/02/1944
25/03/1946
28/04/1949
05/01/1931
04/04/1934
03/11/1936
02/07/1940
10/06/1942
11/02/1945
25/11/1946
22/05/1951
0
5
22
20
18
Soil Erosion (in Tons/acre)
16
14
12
10
8
6
4
2
0
05/01/1931
04/04/1934
03/11/1936
02/07/1940
10/06/1942
11/02/1945
25/11/1946
22/05/1951
23/06/1932
19/05/1935
23/05/1938
11/05/1941
13/02/1944
25/03/1946
28/04/1949
Date (dd/mm/yy)
Figure 3. Daily rainfall, runoff and erosion (top-bottom) at Temple USLE field site (Texas, Plot 1_7)
because the n-largest events that represent 10% of the total
daily records produce 50% of the total soil erosion (see Table
II). A mean value of 100 daily erosion events represents a time
threshold between 7 and 10 years for the dataset used here.
Copyright © 2009 John Wiley & Sons, Ltd.
The long-term record approach to soil erosion monitoring
programs introduces the problem of soil exhaustion. Bounded
plots create closed systems that could suffer from soil exhaustion. Stoniness and sustained temporal decreases of erosion
Earth Surf. Process. Landforms, Vol. 34, 2070–2077 (2009)
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J.C. GONZÁLEZ-HIDALGO ET AL.
240
10 largest
5 largest
3 largest
Erosion
90
80
220
200
180
70
160
60
140
50
120
40
100
80
30
60
20
40
10
Accumulated annual soil erosion (Tons/acre)
Soil erosionincluded in n-largest daily events (in %)
100
20
0
0
20
40
60
80
100
120
140
160
180
0
200
Years from beginning (expressed as total daily events)
Figure 4. Daily events accumulated over the years of study periods (Temple, TX, Plot 1_7)
rates have been reported as indicators of soil exhaustion
(Ollesh and Vacca, 2002). Under such circumstances soil
erosion processes could be changed from transport limited to
detachment limited conditions. This is a normal process for
small plots and non-agricultural soils, but does not seem to
apply generally to the USLE database, which gives data from
large plots, located on agricultural soils, repeatedly subjected
to conservation practices, conservation tillage systems, and
with crop rotation or lying fallow. Furthermore, maximum
absolute values of daily erosion have occasionally been
recorded after many years of monitoring, while absolute and
extreme values of daily precipitation and runoff were recorded
years before.
Temporal concentration of soil erosion does not mean,
however, that extreme amounts of rainfall occurred during
those extreme erosion events (Wischmeier, 1962; Boardman,
2006), and this is because the interplay between rainfall,
runoff and soil loss is not simple (Boardman and FavisMortlock, 1999). In the dataset analysed, the mean percentage
of rainfall and runoff that caused soil to erode in the n-largest
events represented a low value, and suggests that for the most
part precipitation and, to a lesser extent, runoff, are not useful
to assess high erosion rates and soil erosion volumes (Table
III). Notwithstanding this, a single daily erosion event can
produce a large amount of eroded soil (Wischmeier, 1962;
Edwards and Owens, 1991; Zuzel et al., 1993; McBroom et
al., 2003, among many others)
It follows that daily precipitation or mean precipitation
values do not seem to be good predictors of soil erosion, and
caution should be taken when rainfall is considered in soil
erosion prediction by means of different rain erosivity indices.
Most precipitation does not, in fact, generate soil erosion.
Table III. Precipitation, runoff and soil erosion mean values (in %)
during n-largest soil erosion events
n-largest daily events
3 largest
5 largest
10 largest
Precipitation
Runoff
Soil erosion
5·0 %
7·7 %
13·8 %
16·3 %
23·0 %
35·6 %
39·8 %
50·9 %
65·7 %
However, a common trend is that the largest events represent
a high percentage of total soil erosion – the largest 10% of
events account for at least 50% of total erosion, independently
of the length of record, soil characteristics, plant cover or
farming practices. This indicates that data from at least 100 daily
events, whatever the number of years needed to produce them,
are necessary to weight and integrate the effects of large, significant events (the 10%). Data from a minimum of 100 events
help to account for cyclical or random effects, and hence
produce realistic estimates of mean erosion rates. The data
relate to agricultural soils and a specific set of environmental
conditions, so it is important to stress that inferences about
erosion for other soil types and conditions should be avoided.
Acknowledgements—This project was sponsored by the Spanish Government through the research grants CGL2005-04270, CGL200506989-C02-02/HID, CGL2006-11679-C02-01/HID. Special thanks
are due to Damià Vericat for drawing Figures 2 to 5. We thank Professor J. Thornes and an anonymous reviewer for comments that
improved the original manuscript. We also thank USDA for providing
the database via their web site.
References
Summary and Conclusions
This paper assessed the contribution of the largest daily rainfallinduced erosion events to total soil erosion. The assessment was
based on the USLE database that includes 27 857 daily erosion
events monitored at 310 erosion plots. Results highlight the
great variability of total erosion between sites and plots.
Copyright © 2009 John Wiley & Sons, Ltd.
Boardman J. 2006. Soil erosion science: reflections on the limitations
of current approaches. Catena 68: 73–86.
Boardman J, Favis-Mortlock D. 1999. Frequency–magnitude distribution for soil erosion, runoff and rainfall – a comparative analysis.
Zeitschrift für Geomorphologie Supplement Band 115: 51–70.
Brazier R. 2004. Quantifying soil erosion by water in the UK: a review
of monitoring and modeling approaches. Progress in Physical Geography 28: 340–365.
Earth Surf. Process. Landforms, Vol. 34, 2070–2077 (2009)
DOI: 10.1002/esp
EFFECTS OF THE LARGEST DAILY EVENTS ON TOTAL SOIL EROSION
Burt TP. 1994. Long term study of the natural environment- perceptive
science or mindless monitoring? Progress in Physical Geography 17:
475–496.
Burwell RE, Kramer LA. 1983. Long-term annual runoff and soil loss
from conventional conservation tillage corn. Journal of Soil and
Water Conservation 38: 315–319.
Coppus R, Imeson A. 2002. Extreme events controlling erosion and
sediment transport in a semiarid sub-Andean valley. Earth Surface
Processes and Landforms 27: 1365–1375.
Cox NJ. 2007. Kinds and problems of geomorphological explanation.
Geomorphology 88: 46–56.
Diodato N. 2004. Local models for rainstorm-induced hazard analysis
on Mediterranean river-torrential geomorphological systems.
Natural Hazards and Earth Systems Sciences 4: 389–397.
Douglas I, Bidin K, Balamurugan G, Chappel NA, Walsh RPD, Greer
T, Sinun W. 1999. The role of extreme events in the impacts of
selective tropical forestry on erosion during harvesting and recovery
phases at Danum Valley, Sabah. Philosophical Transactions of the
Royal Society London B 354: 1749–1761.
Edwards WM, Owens LB. 1991. Large storm effects on total soil
erosion. Journal of Soil and Water Conservation 46: 75–78.
González-Hidalgo JC, de Luis M, Peña JL. 2007. A review of daily
soil erosion in western Mediterranean areas. Catena 71: 193–199.
Hjelmfelt AT, Kramer LA, Spomer RG. 1986. Role of large events in
average soil loss. In Proceedings of 4th Federal Interagency Sedimentation Conference, 1, USGS, Denver, Colorado; 3·1–3·9.
Kirkby MJ. 1984. The Hurst effect, its implications for extrapolating
processes data. Earth Surface Processes and Landforms 12: 57–67.
Kirkby MJ, Baird AJ, Lockwood JG, McMahon MD, Mitchell PJ, Shao
J, Sheeny JE, Thornes JB, Woodward FI. MEDALUS project A1:
physically based models: final report. In MEDALUS 1 Final Report,
Thornes JB (ed). Unpublished.
Lane LJ, Kidwell MR. 2003. Hydrology and soil erosion. USDA Forest
Services Proceeding RMRS-P-30; 92–100.
Larson WE, Lindstrom MJ, Schumacher TE. 1997. The role of severe
storm in soil erosion: a problem needing consideration. Journal of
Soil and Water Conservation 52: 90–95.
Lenzi MA. 2005. Experience from sediment transport monitoring and
investigation in the Rio Cordon. Geophysical Research Abstract 7:
06419.
Lenzi MA, Mao L, Comiti F. 2003. Interannual variation of suspended
sediment load and sediment yield in an alpine catchment. Hydrological Science Journal 48: 899–915.
Leopold, LB, Maddock T. 1953. The hydraulic geometry of stream
channels and some physiographic implications. US Geological
Survey Professional Paper 252; 1–57.
McBroom M, Beasley RS, Chang M, Gowin B, Ice G. 2003. Runoff
and sediment losses from annual and unusual storm events from
Copyright © 2009 John Wiley & Sons, Ltd.
2077
the Alto Experimental watersheds, Texas: 23 years after silvicultural
treatments. In Proceedings of First Interagency Conference on
Research in the Watersheds, Benson, AZ.
Michael A, Schmidt J, Enke W, Deutschländer Th, Maliz G. 2005.
Impact of expected increase in precipitation intensities on soil loss
– results of comparative model simulations. Catena 61: 155–164.
Nearing MA. 1998. Why soil erosion models over-predict small soil
losses and under-predict large soil losses? Catena 32: 15–22.
Nearing MA, Govers G, Norton LD. 1999. Variability in soil erosion
data from replicated plots. Soil Science Society of America Journal
63: 1829–1835.
Ollesch G,Vacca A. 2002. Influence of time on measurements results
of erosion plot studies. Soil and Tillage Research 67: 23–39.
Piest RF. 1965. The role of the large storm as a sediment contributor.
In Proceedings of Federal Interagency Sedimentation Conference,
USDA, Miscellaneous Publication Nº 970; 98–108.
Risse LM, Nearing MA, Nicks AD, Laflen JM. 1993. Assessment of
error in the universal soil loss equation. Soil Science Society of
America Journal 57: 825–833.
Stroosnijder L. 2005. Measurement of erosion: is it possible? Catena
64: 162–173.
Williams AN, Nearing M, Habeck M, Southworth J, Pfeifer R, Doering
OC, Lowenberg-Deboer J, Randolph JC, Mazzocc MA. 2001. In
Global climate change: implication of extreme events for soil conservation strategies and crop production in the Midwestern United
States. Sustaining the global farm, Stott DE, Mohtar RH, Steinhardt
GC (eds). Selected paper from 10th International Soil Conservation
Organization Meeting, Purdue University-USDA-ARS Nat. Soil Res.
Laboratory; 509–515.
Whittaker R, Marks PL. 1975. Methods of assessing terrestrial productivity. In Primary Productivity of the Biosphere, Lieth H, Whittaker
R (eds). Springer: New York; 55–118.
Wischmeier WH. 1962. Storms and soil conservation. Journal of Soil
and Water Conservation 17: 55–59.
Wischmeier WH. 1976. Use and misuse of the universal soil loss
equation. Journal of Soil and Water Conservation 31: 5–9.
Wischmeier WH, Smith DD. 1978. Predicting rainfall erosion losses
– A guide to conservation planning. USDA Agriculture Handbook
537, US Government Print Office: Washington DC.
Wolman MG, Miller JP. 1960. Magnitude and frequency of forces in
geomorphic processes. Journal of Geology 68: 54–74.
Zhang J, Garbrecht JD. 2002. Precipitation retention and soil erosion
under varying climate, land use, and tillage and cropping systems.
Journal of the American Water Resources Association 38:
1241–1253
Zuzel JF, Allmaras RR, Greenwalt RN. 1993. Temporal distribution of
runoff and soil erosion at a site in Northeast Oregon. Journal of Soil
and Water Conservation 48: 373–378.
Earth Surf. Process. Landforms, Vol. 34, 2070–2077 (2009)
DOI: 10.1002/esp