Hecht and Oguchi Progress in Earth and Planetary Science (2017) 4:40
DOI 10.1186/s40645-017-0156-3

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open Access

Global evaluation of erosion rates inrelation to tectonics
Hagar Hecht1* and Takashi Oguchi1,2

Abstract
Understanding the mechanisms and controlling factors of erosion rates is essential in order to sufficiently comprehendbigger processes such as landscape evolution. For decades, scientists have been researching erosion rates where one of
the main objectives was to find the controlling factors. A variety of parameters have been suggested ranging fromclimate-related, basin morphometry and the tectonic setting of an area. This study focuses on the latter. We use previously
published erosion rate data obtained mainly using 10Be and sediment yield and sediment yield data published by theUnited States Geological Survey. We correlate these data to tectonic-related factors, i.e., distance to tectonic plate boundary,peak ground acceleration (PGA), and fault distribution. We also examine the relationship between erosion rate and meanbasin slope and find significant correlations of erosion rates with distance to tectonic plate boundary, PGA, and slope. The
data are binned into high, medium, and low values of each of these parameters and grouped in all combinations. We findthat groups with a combination of high PGA (> 0.2.86 g) and long distance (> 1118.69 km) or low PGA (< 0.68 g) and shortdistance (< 94.34 km) are almost inexistent suggesting a strong coupling between PGA and distance totectonic plate boundary. Groups with low erosion rates include long distance and/or low PGA, and groups with higherosion rates include neither of these. These observations indicate that tectonics plays a major role in determining erosion
rates, which is partly ascribable to steeper slopes produced by active crustal movements. However, our results show
relatively unclear correlation of slope with erosion rates, pointing to problems with using mean basin-wide slope as a slope
indicator because it does not represent the complex slope distribution within a basin.
Keywords: Erosion rate, Sediment yield, Basin morphometry, Tectonic plates, Peak ground acceleration, Slope, Geographic
information system

Introduction
Understanding the mechanisms and controlling factors oferosion rates is of great importance as they are related tobigger processes such as tectonic plate movements andclimate change. Primarily, erosion is part of the sediment
cycling processes. The material eroded on earth surfaces istransported over time into the oceans, subducted into themantle, and then returns to the surface through volcanism
and tectonic uplift. In this way, estimates of erosion rates
are a vital component of both sediment and geochemical
mass balance studies. The geochemical mass balance includes the carbon cycle as the erosion of silicates and carbonate minerals serves as a transformer of carbon dioxide
between the atmosphere and lithosphere. For this reason,* Correspondence: hhecht@csis.u-tokyo.ac.jp
1
Department of Natural Environmental Studies, The University of Tokyo, 5-1-5
Kashiwanoha, Kashiwa City, Chiba 277-8563, Japan
Full list of author information is available at the end of the article

understanding the controlling factors of erosion rates will
not only help understand landscape evolution but also climate change (Kump et al. 2000).
Much work has been done and a lot of energy has been
invested in understanding erosion rates. Most studies haveconcentrated on investigating controlling factors on a
local scale and have come to a variety of conclusions regarding what most affects erosion rates ranging fromprecipitation (e.g., Galy and France-Lanord 2001; Griffiths
1979; Griffiths 1981) to mass wasting events (Hovius et al.
1997) and lithology (Matsushi et al. 2006). In addition,
global data have been compiled with the intention of identifying the controlling factors of erosion rates. Ahnert(1970) found that erosion rates are dependent on basin relief, and Milliman and Syvitski (1992) found a link
between erosion rates and basin area. Portenga andBierman (2011) found that while basin-wide erosion rates
are correlated best to basin slope, erosion rates for© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.

Hecht and Oguchi Progress in Earth and Planetary Science (2017) 4:40

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outcrops are most strongly correlated to mean annual
precipitation.
Another dominant controlling factor that shows up in
many studies, both global and local, is the rate of tectonic
uplift. A study in South Africa concluded that the reason for
the very slow erosion rates despite alpine topography is
tectonic stability (Scharf et al. 2013). A similar example is Sri
Lanka where even though there are high temperatures, precipitation, and relief, erosion rates are relatively low because
the country is situated in a cratonic area that lacks tectonic
activity (Von Blanckenburg 2005; Von Blanckenburg et al.
2004). Cyr et al. (2010) found that 10Be-derived erosion rates
increase linearly with increasing rock uplift rates. Some studies even derived uplift rates from observed denudation rates
assuming that they are primarily controlled by rock uplift(Morell et al. 2015; Roux-Mallouf et al. 2015).
Despite the strong relationship between erosion rate andtectonic activity, a simpler way to make this connection isstill missing since tectonic activity and uplift rates are difficult
to measure. This study comprehensively compiles previously
published erosion rate data derived by various methods,
namely, 10Be and sediment yield, and erosion rates calculated
from suspended sediment load published by the UnitedStates Geological Survey (USGS) and correlates them to data
related to tectonics, i.e., distance to tectonic plate boundary,peak ground acceleration, fault distribution, and slope.

from sediment yield (n = 447), and erosion rates derived
from other methods such as channel incision rates basedon 36Cl and 14C (n = 88). In addition, we downloaded suspended sediment loads from the USGS sediment data
portal (U.S. Geological Survey) (n = 47). Only sites with atleast 30 years of records were chosen. Of these, sites withno information about upstream drainage area or records
for only several months of the year were discarded. Following previous studies (e.g., Cyr and Granger 2008; Olenet al. 2016), a rock density of 2.6 g cm−3 was used in orderto estimate basin-wide erosion rates from the sedimentyield data. Figure 1 shows the distribution of all data
points used in this compilation.
It is important to note that the two main types of data usedin this compilation, i.e., 10Be- and sediment yield-derived erosion rates, are inherently different in that they represent different time scales. While sediment yields are measured overthe past few decades, 10Be-derived data represent erosion
rates from 103 to 106 years ago, both of which refer toshorter time scales than large-scale tectonic processes; therefore, they deal with generally unchanged tectonic settings.
Another important point is the skewed distribution of
the data. Most of the data originate from the USA, thewest of South America, central Europe, the Himalayas,
and the Yangtze basin. These areas are studied more
frequently than others due to many reasons, some being
convenient and easily accessible and some are points ofspecial interest, partly because they are located in tectonically active regions. Therefore, an effort was made to finddata from less represented areas such as central Asia andAfrica. Altogether, the area of the basins analyzed in thisstudy represents about 10% of the Earth’s land surface.

Methods/experimental
This work uses several datasets. The largest dataset is the10
Be-derived erosion rate data compiled by Portenga andBierman (2011) (n = 1555). We also added newer published 10Be-derived data (n = 546), erosion rates derived

Fig. 1 Geographical distribution of data used for this study

Hecht and Oguchi Progress in Earth and Planetary Science (2017) 4:40

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Basin boundaries were extracted using the basin tool of
the hydrology toolset in ArcGIS. In most cases, the highresolution filled 1 arcsecond Advanced Spaceborne Thermal
Emission and Reflection Radiometer (ASTER) Global Digital
Elevation Model (GDEM) (U.S./Japan ASTER Science Team
2011) was used for the extraction, but the filled 3 arcsecond
STRM DEM (Jarvis et al. 2008) was used for the larger basins. Since there are cases of multiple studies in the same
area or data representing sub-basins, 211 selected basins
were delineated with an area ranging from about 2.5 × 100
to 6.5 × 106 km2. For each basin, a basin-wide mean slope
was calculated using the ArcGIS slope tool. Fault data derived from a map published by ESRI and attributed to the
USGS (ESRI 2014) were used to calculate fault density foreach basin. Additional data used in this study include tectonic plate boundary data published by Nordpil (2014) basedon Bird (2003) and peak ground acceleration (PGA) data
published by the Global Seismic Hazard Assessment Program (Giardini et al. 1999). This is a map produced bycompiling regional maps representing the peak ground acceleration with a 10% chance of being exceeded within
50 years at a certain location due to earthquakes.
Using JMP pro 13.0, we examined the bilinear relationship of erosion rates with five tectonic-related factors.
These include distance to fault, PGA, distance to tectonic
plate boundaries, fault density, and mean basin-wideslope. From these, we chose those that correlated well toerosion rates and defined high and low value ranges foreach as the upper and lower quartiles, respectively. Then,we grouped them in all possible combinations and calculated the mean erosion rate for each group.

Results
Erosion rates in relation to tectonics

Figure 2 and Table 1 show bivariate correlations of erosion rates with tectonic-related factors. The correlation
between erosion rates and distance to tectonic plateboundary shows a statistically significant relationship.

Fig. 2 Bivariate correlations of erosion rates with the different tectonic-related factors: a distance to tectonic plate boundary, b peak groundacceleration (PGA), c distance to fault, d fault density, and e mean basin-wide slope

Hecht and Oguchi Progress in Earth and Planetary Science (2017) 4:40

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Table 1 Bivariate correlations of tectonic-related factors to erosion rate
Distance to the tectonic plate boundary (km)PGA (g)Distance to the fault (km)

Fault density (km/km2)

Mean basin-wide slope (°)

P*< 0.0001< 0.0001

0.33410.0127< 0.0001

R**− 0.4861

0.4816− 0.0173

0.04800.4093*P value**Square root of R squared, coefficient of determination

The correlation is negative, which means that the further
away from tectonic plate boundaries, the slower the erosion rates. There is a statistically significant positive correlation between erosion rate and PGA. Higher PGA
values correspond to higher erosion rates, and a similar
R value is obtained for the correlation between erosion
rate and distance to boundary. The correlation between
erosion rate and distance to fault is statistically insignificant. Although a slightly positive correlation with fault
density is more significant, the R value is very low. Thereis a statistically meaningful positive correlation between
erosion rate and mean basin-wide slope.
Grouping analysis

Taking into account the poor correlations of fault distribution data with erosion rates, we did not use the distance to fault and fault density data in our discussion.
The high, middle, and low values of the three remaining
factors, i.e., distance to tectonic plate boundary, PGA,
and mean basin-wide slope, were defined using theupper quartile, interquartile range, and lower quartile ofdata distribution: long, middle, and short distances fromtectonic plate boundary (LD, MD, and SD respectively);
high, middle, and low PGA (HP, MP, and LP, respectively); and steep, middle, and gentle slopes (SS, MS, andGS, respectively) (Table 2). The complete dataset consists of 2683 data points with a mean erosion rate of559.16 m/Myr. Out of the 27 possible combinations,four had no data points that met all the criteria and are
empty groups. They are steep slope, high PGA, and long
distance (SS HP LD); middle slope, high PGA, and long
distance (MS HP LD); middle slope, low PGA, and shortdistance (MS LP SD); and gentle slope, high PGA, andlong distance (GS HP LD). In addition, six other groups
had 20 data points or less, which are not included forfurther analysis.
Table 3 shows the mean erosion rates of the remaining
17 groups, arranged from low to high mean erosion
rates. The groups with the highest mean erosion rates
ranging from 1085.27 to 11,642.11 m/Myr are middle

slope, high PGA, and short distance (MS HP SD); steep
slope, high PGA, and short distance (SS HP SD); andgentle slope, high PGA, and short distance (GS HP SD).
The groups with the lowest erosion rates ranging from
10.13 to 18 m/Myr are gentle slope, middle PGA, andlong distance (GS MP LD); middle slope, low PGA, andlong distance (MS LP LD); and gentle slope, low PGA,
and long distance (GS LP LD. The group with the largest
number of data points has a mean erosion rate of
267.22 m/Myr and is middle slope, middle PGA, andmiddle distance (MS MP MD).

Discussion
The mean erosion rate for this study (about 559 m/Myr,
Table 3) is significantly higher than the previously reported rate by Portenga and Bierman (2011): a mean of
218 m/Myr for basins and 12 m/Myr for outcrops. Thereis one major difference between this study and that ofPortenga and Bierman (2011). The latter used a compilation of 10Be-derived data only, but this study compiled
different types of data including sediment yield. Erosion
rates from sediment yield are those for recent years
when human activity is considered to accelerate erosion
rates (e.g., Saunders and Young 1983; Von Blanckenburg
2005; Walling and Webb 1996) and could account forthe overall higher mean erosion rate.
Interpretation of bivariate correlation

The correlation of erosion rates to distance to tectonic
plate boundary is negative (Fig. 2a), suggesting that enhanced tectonic activity along the contact lines of theplates is favorable for erosion. PGA is an expression ofprobable ground acceleration due to earthquakes; thus,
it is a direct indicator of tectonic activity. Figure 2b
shows that higher erosion rates tend to occur at locations with higher PGA values. The strongest correlationof erosion rates is that with distance to tectonic plateboundary, although its R value is similar to that of PGA(Table 1). This emphasizes the strength of the former asa simple geographical predictor of erosion rates.

Table 2 High, middle, and low range values for each parameter defined using quartiles
Distance to tectonic the plate boundary (km)PGA (g)Upper quartileLD> 1118.69

HP> 2.86SS> 15.03

Interquartile range

MD

94.34–1118.69

MP

0.68–2.86MS

8.1–15.03

Lower quartileSD< 94.34

LP< 0.68GS< 8.1

Mean basin-wide slope (°)

Hecht and Oguchi Progress in Earth and Planetary Science (2017) 4:40

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Table 3 Mean erosion rate (m/Myr) and its standard deviation(SD), median, minimum, and maximum for each of the groups
that contain more than 20 data points as well as the complete
data setboundary and is also apparent in the distribution of PGA
as a function of distance to plate (Fig. 3).
Table 3 has implications on the effect of each parameter on erosion rates. The seven groups with the lowest
erosion rates all include long distance (LD) and/or low
PGA (LP), whereas the other 10 groups with highest erosion rates did not include any of these. In addition, thetop part of Table 3 with low erosion rates is dominatedby groups including long distance (LD), while the bottom part of the table with high erosion rates is dominated by groups including short distance (SD). A similar
trend is observed for groups with the correlation of low
PGA with low erosion rates and high PGA with higherosion rates but to a smaller effect. No such trend exists for groups with gentle and steep slopes. This points
out that distance to tectonic plate boundary is the mostimportant factor and emphasizes its strong effect on erosion rates. Although there is no apparent correlation between slope and erosion rates, numerous studies haveshown that a steeper terrain is more erodible than agentler terrain, which is naturally acceptable in relation
to change in slope stability. Indeed, the data we usedshow a significant relationship between erosion rate andslope when examining a bivariate correlation of all thedata, even though they are scattered. However, when thedata are separated into groups with different characteristics and sections of the data are separately examined,
this relationship becomes less apparent. It is important
to note that the slope parameter used in this study is themean basin-wide slope, and its lumped characteristics
do not reflect the actual complex distribution of slope
within a basin. To address this problem, we take a closer
look at the group with the highest mean erosion rate,i.e., GS HP SD, which has a mean erosion rate of
11,642.11 m/Myr that is an order of magnitude higher
than the next group (SS HP SD, 2553.63 m/Myr). In thisn
All data

Mean

2683 559.16

SD

Median Minimum Maximum

2656.3765.7

080,700

GS MP LD

130

10.13

15.27

3.04

0.29

113.25MS LP LD

292

16.27

15.14

11.6

1

115.68

GS LP LD

154

18

25.06

9.93

0.88

186.48MS MP LD

72

26.34

11.3

25.01

4.85

57.2

GS LP MD

97

65.2

173.36

11.73

0.28

1513.6MS LP MD

61

70.53

58.76

47.81

0.32

223

SS LP MD

36

71.39

50

55.1

14.6

233.6

GS MP MD 165

183.86295.73

74.61

0.06

2190

SS HP MD100

193.96286.4993.38

0.2

1773.83

MS MP SD

133

215.18433.57

43

0.24

4131

MS MP MD 468

267.22516.68

98.33

0.08

4151.28

GS MP SD66

333.88403.15180.970.22

1987.16

SS MP MD

306

357.01456.15174.345 0.02

3236.7MS HP MD 95

829.492740.38

118.490.56

18,400MS HP SD

248

1085.27

2360.73

357.7

15.43

24,300SS HP SD178

2553.63

4131.26800

029,500

GS HP SD

28

11,642.11 18,242.28 7000

80

80,700As noted, the erosion rate data we used show a slightly
positive correlation to distance to fault and a slightly negative correlation to fault density (Fig. 2c, d), but they arestatistically insignificant. This may be because faults havequite different levels of activity even locally, and hence,each fault does not represent regional tectonics well, in
contrast to each plate boundary. On a local scale, a connection between erosion rates and faults has been shownin the past, for example, erosion has been shown to influence seismicity along faults (Steer et al. 2014). If enoughdata are collected from a single basin, a correlation could
be found between erosion rate and fault distribution. In
addition, more information regarding the activity level ofthe faults might also improve the correlation. However,
the global fault data used here are not comprehensive andtectonic activity on a larger scale related to plate boundaries correlates better to erosion rates.
Implication of group analysis

Groups with the combinations of high PGA and long distance and low PGA and short distance are either empty or
include very few data points. Groups with a relatively large
number of data points are generally those with the combination of low PGA and long distance, high PGA andshort distance, and middle PGA and middle distance(Table 3). This confirms that PGA, and therefore tectonic
activity, is directly affected by distance to tectonic plate

Fig. 3 PGA as a function of distance to tectonic plate boundary

Hecht and Oguchi Progress in Earth and Planetary Science (2017) 4:40

Page 6 of 9group, all but two data points are located on the southwestern side of Taiwan. Taiwan is known for its exceptionally high erosion rates due to a combination of
extremely wet climate and a tectonically active environment (Chen-Feng et al. 1988; Dadson et al. 2003).
Taiwan’s topography consists of roughly north–southorienting mountain belts with peaks of almost 4000 m inelevation. This means that even though the mean basinwide slope of the southwest basins is lower due to thegentle slopes of the lower extensive depositional plains,
the rivers originate higher up in the mountain range
where slopes are steeper and are able to produce
abundant sediment. A distribution map of all data points
from Taiwan shows that the majority of the points belong to the SS HP SD group, which is theoretically thegroup with the highest expected erosion rates as it represents steep slopes and active tectonics. The otherpoints mainly belong to the gentle slope and active tectonic group (GS HP SD) (Fig. 4). The two data points of
the GS HP SD group from areas other than Taiwan arelocated on a tributary of the Orinoco basin in Colombia
basin and in the upstream part of a tributary of theIndus River. These cases also have steeper slopes in the
upstream areas, but the overall basin slope is low due towide depositional areas. The same applies to the case of
the next group in line, i.e., middle slope and active tectonics (MS HP SD). Here, most data points are from theHimalayan origins of the Ganges River, the Andean origins of the Magdalena River in Colombia, the San
Gabriel mountain and the San Bernardino mountains

Fig. 4 Distribution of data points in Taiwan: SS HP SD (squares), GS HP SD (crosses), and MS HP SD (diamonds)

Hecht and Oguchi Progress in Earth and Planetary Science (2017) 4:40

Page 7 of 9along the San Andreas Fault, and the southwest part of
Taiwan. As indicated, steep slopes in these areas are notproperly represented when referring to mean basin-wideslope because broad depositional areas are included. It isquite common that steep mountainous river basins withhigh erosion rates produce broad depositional landforms
such as alluvial fans in piedmont areas. Therefore, whilea dissonance between existing steep slopes in a basin
and its significantly lower mean basin-wide slope can besmoothed when examining large data, this is more problematic when looking at smaller data sets and superficial
correlations of lower mean basin slope with high erosion
rates can occur. This should be taken into account in future erosion rate studies.
It is expected that the group with the lowest erosion
rates would be that of gentle slope, low PGA, and long
distance (GS LP LD). Results show that this group indeed has a low mean erosion rate, but it is not thelowest. The MS LP LD group has a very similar meanerosion rate and is also dominated by a tectonically inactive setting (low PGA and long distance), and the GS
MP LD group has the lowest mean erosion rate eventhough it is from middle PGA areas. This group is dominated by data originating from the middle and southernparts of Australia, where basins have a low mean annual
precipitation ranging from about 200 to 425 mm/year.
Here, the dry climate might contribute to the slow erosion rates.

accelerated with the help of increased precipitation onmountain slopes due to the orographic effect (Barros
and Lettenmaier 1994). Furthermore, tectonic activity
produces high relief topography prone to erosion, resulting in higher erosion rates. The effects of slope and relief
on erosion rates have been discussed extensively (e.g.,
Ahnert 1970; Portenga and Bierman 2011; Summerfieldand Hulton 1994). This study also shows that higher
erosion rates generally occur in basins with steeper
slopes (Fig. 2e), and basins closer to tectonic plateboundaries and/or with higher PGA values have higher
mean slopes (Fig. 5). This means that stronger tectonic
activities increase slope steepness and thus increase
slope instability and erosion rates.
This study utilized data from more than 2600 points
and concluded that erosion rates are strongly dependent
on tectonic activity. However, many of the data are fromtectonically active areas, so lower erosion rates in areasof weak tectonics need to be confirmed using more datafrom such areas.

Effect of tectonic activity on erosion rates

The results of this study indicate that erosion rates arestrongly related to tectonic activity. A simple direct explanation would be that tectonic activity with groundmotion weakens and fractures bedrock, creates debris,and hence incites physical erosion (Molnar et al. 2007).
In addition, influences of erosion on tectonics may cause
their correlation. Whipple (2009) has shown that erosionin active mountain belts tends to influence tectonic processes by thinning the crust, which accelerates upliftthrough isostasy. This process would be further

Conclusions
Understanding erosion rate distribution and its controlling factors is necessary for discussing and predicting
landscape formations. Global-scale studies provide general ideas about these processes. As part of these efforts,
this study has utilized global DEM data and an extensive
compilation of erosion rates derived from various
methods. The results of this research show that erosion
rates are strongly related to tectonic activity. A strong
coupling between PGA, which is a direct indicator of
tectonic activity, and distance to tectonic plate boundary
suggests that the latter can also be an indicator of
tectonic activity. This means that the easy-to-measureparameter of distance to plate boundary can be used tomake general correlations between erosion rates and tectonic activity on a global scale, despite the compilationsin accurately measuring tectonic activity. The power ofthis parameter is further emphasized through the group
analysis where the strongest correlation is between highFig. 5 Mean basin slope as a function of PGA and distance to tectonic plate boundary

Hecht and Oguchi Progress in Earth and Planetary Science (2017) 4:40

and low erosion rates and short and long distances totectonic boundaries, respectively, and not between erosion rates and PGA or mean basin-wide slope, eventhough the latter is well known as a parameter that affects erosion rates. The precise way in which tectonic activity is connected to erosion rates is yet unknown;
however, some possibilities include influence throughseismic shattering and fracturing of rocks, a relation
between erosion and isostatic uplift, which is increased
by orographic rainfall on uplifted mountains, and increased slope steepness and instability in such
mountains. These are supported by the strong connections among high PGA, proximity to tectonic boundaries, and steep slopes. Similar to some previous studies,
our study has found that on a global scale, a significant
bivariate correlation between erosion rates and meanbasin-wide slope exists. However, this relationship is notreflected when smaller data sets in the group analysis
are taken into account; therefore, this parameter should
be used with caution and careful consideration since itcannot account for the complex distribution of slopes
within a basin. Although this study also discusses someregional issues, such as some unique drainage basins in
Taiwan, analyses are essentially global and deal with only
general trends. Further research is needed to understandmore detailed mechanisms that determine regional andlocal erosion rates.
Abbreviations
ASTER: Advanced Spaceborne Thermal Emission and Reflection Radiometer;
GS: Gentle slope; HP: High PGA; LD: Long distance; LP: Low PGA; MD: Middle
distance; MP: Middle PGA; MS: Middle slope; PGA: Peak ground acceleration;
SD: Short distance; SS: Steep slope; USGS: United States Geological Survey

Acknowledgements
We thank the editor and two anonymous reviewers for their helpful commentsthat improved the manuscript.

Funding
This work was supported by JSPS KAKENHI Grant Numbers JP15K12452 andJP15H01782.

Authors’ contributions
Both authors read and approved the final manuscript.

Competing interests
The authors declare that they have no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
Author details
1
Department of Natural Environmental Studies, The University of Tokyo, 5-1-5
Kashiwanoha, Kashiwa City, Chiba 277-8563, Japan. 2Center for Spatial
Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa
City, Chiba 277-8568, Japan.

Page 8 of 9

Received: 17 August 2017 Accepted: 2 December 2017

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