Hindawi
Applied and Environmental Soil Science
Volume 2021, Article ID 5753942, 21 pages
https://doi.org/10.1155/2021/5753942
Research Article
Geospatial Analysis of Soil Erosion including Precipitation
Scenarios in a Conservation Area of the Amazon Region in Peru
Ligia Garcı́a ,1 Jaris Veneros ,1 Franz Pucha-Cofrep ,2 Segundo Chávez ,1
Danilo E. Bustamante ,3 Martha S. Calderón ,3 Eli Morales ,3 and Manuel Oliva 3
1Instituto de Investigación,Innovación y Desarrollo para el Sector Agrario y Agroindustrial de la Región Amazonas IIDAA,
Universidad Nacional Toribio Rodŕıguez de Mendoza de Amazonas,Perú,
Calle Higos Urco No. 342-350-356-Calle Universitaria No. 304, Amazonas, Peru
2Departamento de Geoloǵıa, Minas e Ingenieŕıa Civil (DGMIC), Hidrologı́a y Climatoloǵıa Working Group,
Universidad Técnica Particular de Loja, Ecuador, Calle Paŕıs No. 1101608, de Loja, Ecuador
3Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva INDES-CES,
Universidad Nacional Toribio Rodŕıguez de Mendoza de Amazonas,Perú,
Calle Higos Urco No. 342-350-356-Calle Universitaria No. 304, Amazonas, Peru
Correspondence should be addressed to Jaris Veneros; jaris.veneros@untrm.edu.pe
Received 19 May 2021; Accepted 13 August 2021; Published 8 September 2021
Academic Editor: Fedor Lisetskii
Copyright © 2021 Ligia Garćıa et al. ,is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,e Tilacancha Private Conservation Area provides fresh water to the city of Chachapoyas. ,erefore, the amount of soil lost in
the year and under precipitation scenarios was determined. Individually, the values of the factors were obtained: rain erosivity
(R) in 2019 and simulating increase and decrease of 15% of rainfall, soil erodibility (K), length and degree of slope (LS), land
cover (C), and conservation practices (P); they were integrated into USLE, obtaining A �R ∗ K ∗ LS ∗ PC, (t/ha.yr). Six
ranges of erosion were found, and the ACP had areas where from 0.4 to 665.20 t/ha.yr of soil was lost. A 15% reduction in
rainfall would represent a loss of soil from 0.20 to 301.56 t/ha.yr and an increase in rainfall by 15%, and the erosion ranges
would vary from 0.2 to 1028.84 t/ha.yr.
1. Introduction consumption increased almost eight times during the last
century [8].
Peru has two types of nongovernmental protected areas: Currently, the ACP is threatened by anthropic factors
Private Conservation Areas (ACPs) and conservation con- such as agriculture, livestock, deforestation, slashing, and
cessions [1]. Tilacancha is one of the 141 ACPs [2], created by burning of grasslands and natural forests, which degrade the
Resolution of the Ministry of Environment, on July 6, 2010, soil [3, 5]. Also, despite the known benefits of plant com-
for 20 years, and is located in Chachapoyas, Amazon region munities when they grow, they modify the physical,
in northern Peru [3]. According to the Ecosystem Map of chemical, biological, and other properties of the soil, with
Peru, the Tilacancha ACP has 6800.48 ha and it presents four consequent effects on plant survival and growth [9], and the
ecosystems: Yunga Altimontane Forest (pluvial), Jalca, loss of soil is progressive and is considered an irreversible
Pastures/Herbazales, and secondary vegetation [4]. phenomenon [10].
,e Tilacancha ACP is the only source of water for the Soil erosion affects the storage, filtering, and cleaning of
city of Chachapoyas, and this city has a population of more water, as well as the habitats and genetic reserves of species
than 32 thousand inhabitants [5]. ,e current overexploi- [11, 12]. Water erosion in the world is intensified because
tation of water resources in Peru [6], the growing demand of different climatic conditions and land use to act on the
households for freshwater [7], and the increase in world various natural conditions, mold the soil, and, on certain
2 Applied and Environmental Soil Science
occasions, degrade it [13]. ,is implies that providing a A � R∗K∗ LS∗C∗P, (1)
perspective view of the effects of climate change on soil loss
can guide decision makers in environmental management where A: average annual soil loss expressed in T/ha.yr, R:
and planning [14]. rain erosivity factor expressed in kinetic energy per unit area
Agroecosystems and buffer zones around the ACP de- in MJ ∗ mm ∗ ha−1 ∗ h−1 ∗ year−1, K: soil erodibility
pend on ecosystem conditions and regulating ecosystem factor expressed in T ∗ ha ∗ h ∗ MJ−1 ∗ mm−1 ∗ ha−1, LS:
services, for example, the action of vegetation cover against length factor and grade of slope, C: plant cover factor, and P:
soil erosion or as a function of water quality and quantity. applied conservation practices factor.
Likewise, the replacement of forest by other land uses can ,e R factor represents the erosivity factor of the rain,
cause serious impacts on the quality of river water, altering and it refers to the sum of all the annual rain events and their
its physical, chemical, and biological characteristics, so respective maximum intensities, which gives us an idea of
Permanent Preservation Areas (PPAs) should be established, the degree of aggressiveness of precipitation to soil degra-
in order to assess the variability of its quality because it has dation. Wischmeier and Smith [19] represented an erosivity
been shown that degraded watersheds presented higher index based on the direct relationship between kinetic en-
values of solids, turbidity, nutrients, and coliforms, in ad- ergy (E) and the intensity of rain (I) [24] (Figure 3(a)).
dition to presenting greater variability of temporal data Equation (2) [10] measured in MJ ∗ mm ∗ ha−1
compared to forested watersheds [15]. ,us, assessments of ∗ h−1 ∗ year−1 was used:
ecosystem services, conditions, and interactions are very 9.28P − 8.393
important to understand the relationships in highly man- R � I30 ∗ , (2)
aged systems [16]. 1000
,e results obtained in this research, on estimating the where I30: 75mm/h, the value recommended by Wischmeier
spatial distribution of current water erosion and under [25], and P: annual mean precipitation (mm). ,e precipi-
scenarios of ±15% of rainfall for the Tilacancha ACP, tation values for the Tilacancha ACP were obtained from the
provide an urgent response to the need to map, not only the Global Climate Data, Worldclim version 2, which contain
current state of the water erosion index but also to determine meteorological data worldwide from the 1970s to the 2000s
the influences of climate change scenarios [17], and the with a resolution 1 km2 [26, 27].,e data were extracted using
results will be used to manage and avoid the water erosion of the “extract by mask” tool of the ArcGis 10.7 program; this
the soil in the ACP for its conservation. tool allows you to extract a part of a raster dataset based on a
template extension. ,e clip output includes the pixels that
2. Materials and Methods intersect with feature datasets for the ACP. Besides, this
methodology solved the lack of information on precipitation
2.1. Study Area. ,e Tilacancha ACP is located between data in the place, as happens in several regions of the world
2700 and 3490m.a.s.l. in the stream of the same name, [28, 29]. ,e evaluation ranges were considered in five scales
within the Utcubamba River basin, a tributary of the and are the following [30]: 144–213MJ ∗ mm ∗ ha−1 ∗ h−1
Marañón River, in the Amazon region of Peru (Figure 1). ∗ year−1; 214–248MJ ∗ mm ∗ ha−1 ∗ h−1 ∗ year−1;
,is ACP is located on lands of the communities of San 249–285MJ ∗ mm ∗ ha−1 ∗ h−1 ∗ year−1; 286–319MJ ∗
Isidro de Maino and Levanto and has an area of 6,800.48 ha mm ∗ ha−1 ∗ h−1 ∗ year−1; and 320–355MJ ∗ mm ∗
[18]. ha−1 ∗ h−1 ∗ year−1 (Figure 3(a)).
,e erodibility index (K factor) measures the suscepti-
2.2. Implementation of Available Datasets into USLE. To bility of the soil to water erosion [11], and the K factor in the
determine the loss of soil due to water erosion in the ACP, International System of Units is expressed in T ∗ ha ∗ h/
the USLE equation was used [19], where five factors are used MJ.mm ∗ ha, which expresses the resistance of the soil in
that are finally integrated into equation (1). ,e methodo- surface and time, concerning the energy of rain [31]. It was
logical process is shown in Figure 2. obtained directly through the following equation
,e function that describes the estimation of water (Figure 3(b)):
erosion is expressed as follows [20–23]:
􏽨2.1∗ 10− 4 ∗ (12 − MO)∗M1.14
+ 3.25∗ (s − 2) + 2.5∗ (p − 3)􏽩
K � 1.313∗ , (3)
100
where OM: percentage of organic matter (OM) of the sam- ,e textural class was obtained by the Bouyoucos
ples, s: soil structure code, p: permeability code, andM: factor Method [32], from a total of 108 soil samples representative
given by the product of the sum of the percentages of silt and of the ACP [33, 34] resulting in five classes: sandy (S), sandy
very fine sand with the sum of the percentages of sand and silt. loam (SL), loam (S), loamy sand (LS), and sandy clay loam
,at is, (%silt + very fine sand) ∗ (100−%clay). (SCL) (Figure 4(a)). Organic Matter was obtained with the
Applied and Environmental Soil Science 3
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Pacific Ocean
9300000 9300000
AMAZON CHACHAPOYAS
REGION PROVINCE
9298000 9298000
9296000 9296000
Kilometers
0 3
9294000 9294000
182000 184000 186000 188000 190000 192000 194000 196000 Tilacancha ACP
Chachapoyas
Contor lines
Primary
Secondary
Rivers
Tilacancha ACP
Figure 1: Location of the Tilacancha ACP.
Universal Soil Loss Equation for Tilacancha ACP
A = R ∗ K ∗ LS ∗ C ∗ P
Rain Data Soil Data DEM Alos Palsar Satellite data
Flow Accumulation Slope Land Cover and Land Use
R Factor K Factor LS Factor C Factor P Factor
Annual Soil Loss for Tilacancha ACP 2019
Figure 2: Methodological €ow to integrate the Universal Soil Loss Equation (USLE), adapted from the work of Rizeei et al. [13], where A is
annual soil loss in the Tilacancha ACP for 2019, R factor: rain data, K factor: soil data, LS factor: €ow and slope accumulation, C factor: land
cover and land use, and P factor: conservation practices.
Walkley and Black method [35], with ranges from 1.9 to with a spatial resolution of 12.5m× 12.5m [40–42], which
9.9% (Figure 4(b)). allowed calculating the topographic factor (LS). e
e LS factor (Figure 3(c)) was calculated using the cloudless image was downloaded from the Earth Explorer
interaction between the topography, percentage, and length portal (https://earthexplorer.usgs.gov/) of the United States
of the slope and the accumulation of €ow [36, 37]. e €ow Geological Survey (USGS). Landsat 8 satellite images with a
direction and €ow accumulation model were implemented spatial resolution of 30m× 30m for August 2019 were used.
with the ArcHydro extension for ArcGIS [38, 39]. e e slope map was implemented, and it was reclassi˜ed,
Digital Elevation Model (DEM) was obtained from the obtaining the spatial distribution of the LS factor using
Alaska Satellite Facility geoserver of the Alos Palsar Satellite equations (4) and (5) [39].
4 Applied and Environmental Soil Science
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0 3
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K Factor
S SCL
SL Rivers
L Tilacancha ACP
LS
(a)
Figure 3: Continued.
Applied and Environmental Soil Science 5
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Organic Matter (%)
1.9 - 3.6 7.1 - 7.5
3.7 - 4.5 7.6 - 8.1
4.6 - 5.1 8.2 - 9
5.2 - 5.8 Rivers
5.9 - 6.4 Tilacancha ACP
6.5 - 7.0
(b)
Figure 3: Maps resulting from the individual factors for the USLE calculations in the Tilacancha ACP. (a) Erosivity, (b) erodibility, and
(c) LS factor.
(sin(θ/0.0896)) where M factor “Factor β ”/(“Factor β” + 1).
β  , (4)
3.0(sin θ)0.8 + 0.56 e calculation of the L factor with the contributing
drainage area was carried out with the Flow Direction and
where θ the angle of the slope according to Flores-López Flow Accumulation tools, respectively. Once these two
et al. [43]. In ArcMap with Raster Calculator, the following images were obtained, the L factor (equation (6) using
formula is used to obtain the factor β, where factor β ((sin equation (5)) was obtained [44, 45].
(“Slope” ∗ 0.01745)/0.0896)/ 2 m+1
(3 ∗ Power(Sin(“Slope” ∗ 0.01745), 0.8)) + 0.56)). Once the (A(i,j) +D ) − A(i,j) m + 1
L( )  (6)
i,j
factor β was obtained, the M factor was obtained. Equation xm +Dm+2 ,
∗ (22.13)m
(7) is used in the “Raster Calculator.” where A(i, j) [m] the unit contributing area at the input of
β a pixel, D  the size of the pixel, and X  the shape
m  , (5) correction factor. L factor  (Power((“€ow_acc” + 625),
(β + 1)
6 Applied and Environmental Soil Science
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0 3
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Land cover
Water bodies Bare ground
Montane grasslands Agricultural area
Pine tree Rivers
Relict forest and shrubs Tilacancha ACP
Figure 4: Resulting maps to determine the K factor: (a) soil texture and (b) organic matter in the Tilacancha ACP.
(“Factor_M” + 1))Power (“€ow_acc,” (“Factor_M” + 1)))/ considered that the angle has to be converted to radians [39].
Power(25, (“Factor_M”+2)) ∗ Power(22, 13“Fac- Once all the previous factors had been obtained, the LS factor
tor_M”)), which refers to equation (6). that is the object of this methodology was calculated. For this,
Otherwise, to calculate factor S, the following equation equation (8) was used, which refers to equations (6) and (7)
was used: (Figure 3(c)). is ˜gure has the following ranges for the slope
in percent (%): o: 0–3; p: 4–12; q: 13–18; r: 19–24; s: 25–30; t:
 10.8 sin β(i,j) + 0.03, tan β
 (i,j) < 0.09, ( ) 31–60, u: 61–70, v: 71–100, and w: >101.
S(i,j)  7
16.8 sin β(i,j) − 0.5, tan β(i,j) ≥ 0.09, LS Factor  “Factor_L” ∗ “Factor_S.” (8)
where S(i, j) slope of the coordinate factor (i, j)·and β(i, j) e C factor ranges from 0 to 1 (Table 1). A value equal
slope in degrees with coordinates (i, j). S factorwith ((tan to 1 indicates that there is no cover and the surface is treated
(“Slope” ∗0.01745) <0.09), (10.08 ∗ sin (“Slope” ∗ as barren soil, while a C value close to 0 indicates very strong
0.01745)+0.03), (16.8 ∗Sin (“Slope” ∗0.01745))− 0.5)), which cover e©ects and well-protected soil [19, 23]. For the cal-
refers to equation (8). e subfactor raster of (S) is the slope of culation of C factor, the supervised classi˜cation technique
the terrain, where the angle is taken as the mean angle to all [46] was used, through Landsat 8. e types of land cover
subgrids in the direction of the steepest slope [38]. When this within the Tilacancha ACP were determined with the su-
formula is applied in the ArcGIS Raster Calculator, it must be pervised classi˜cation technique [46], using Landsat 8 Image
Applied and Environmental Soil Science 7
Table 1: Soil coverage (C factor) for ACP Tilacancha.
Cover type Color Area (ha) Percentage C factor
Water bodies 104.34 1.53 —
Montane grasslands 2246.82 33.04 0.10
Pine tree 766.79 11.28 0.01
Relict forests and shrubs 1760.42 25.89 0.01
Bare ground 1367.3 20.11 1.00
Agricultural areas 554.81 8.16 0.70
Total 6800.48 100 —
from August 18, 2019. ,e atmospheric correction of their 9.28(P ± 15%) − 8.393
bands was performed through QGIS 3.14 software [47], and R � I30 ∗ , (9)
1000
to obtain an image of natural color, a combination of bands
(4-3-2) was performed using the ArcGis software [48]. where I30 is equal to 75mm/h, the value which was rec-
,e classification of land cover has six types of pre- ommended by Wischmeier [57], and P corresponds to the
dominant classes [49, 50] (Table 1 and Figure 5): water mean annual precipitation (mm) in ±15%. ,e resulting
bodies, montane grasslands, pine tree, relict forests and erosivity factors Rwere added to themultiplication of factors
shrubs, bare ground, and agricultural areas. Likewise, the in the USLE equation. To perform the sensitivity analysis of
numerical value of the C factor was determined from the soil loss due to water erosion in the ACP, two precipitation
literature review due to the lack of this information for local change scenarios were used, referring to the decrease
conditions [51–53] and was entered into the USLE (Figure 8(b)) and increase (Figure 8(c)) of the coefficient of
equation. variation of the historical data group for precipitation in this
,e bodies of water correspond to lakes, lagoons, rivers, area (Figure 8(a)). Annual erosivity provides information on
and springs. Otherwise, the paramo is the neotropical alpine the total rainfall energy but does not provide information on
wetland ecosystem that is covering the highest region of the the time distribution of the events [58]. ,e values obtained
Northern Andes [54]. ,e grasslands predominate the area for each factor of the USLE equation were obtained in the
and are located in the upper middle part of the mountains raster format. ,en, the Algebra Maps tool of the ArcGis
that delimit the ACP. Pine forests are distributed in high program was applied to obtain the current erosion map and
areas and with not very steep slopes in the ACP. Similarly, the two water erosion susceptibility maps under two
the relict and shrub forests correspond to areas of trees and scenarios.
natural shrubs that remained as a vestige of what once
existed. ,e areas of bare soil are distributed mainly in the
middle and upper parts of the ACP and with not very steep 3. Results
slopes; these zones are made up of areas without coverage
and highly exposed to erosion. ,e agricultural area is made ,e estimate of the amount of soil lost in the Tilacancha
up of coverage with crops and pastures and is located mostly ACP, for the year 2019, has values from 0.4 to 665.2 t/ha.yr,
in the lower and middle parts of the ACP. represented in the distribution spatial analysis of the ACP,
,e conservation practices factor (P) has values between cataloging the soil loss in six ranges that go from low to
0 and 1 [55]. To calculate the PC values, we use the extreme (Table 2 and Figure 6).
methodology proposed by Gelagay andMinale [56]. Factor P ,e low range of erosion (light green color in Figure 6
does not present units of measurement, and the value of 1.0 and Table 2) had soil losses from 0.4 to 50 t/ha.yr, that is,
[50] was used since, in the Tilacancha ACP, conservation 31.1% of the total area of the ACP. When estimating the
practices are not applied. Finally, Figure 6(b) corresponds to erosion decrease and increase in 15%, the level of erosion
the potential erosion t/ha.yr considering the R factor, K was determined by 56.48% and 5.8%, respectively. Similarly,
factor, LS factor, and P factor. the average range of erosion is represented by the dark green
color in Figure 6 and Table 2, with soil losses from 51 to
100 t/ha.yr, and this corresponds to 2251 ha, that is, 33.1% of
2.3. Simulation under Two Scenarios of Soil Loss Due toWater the total area of the ACP.
Erosion for the R Factor (Erosivity) Values. In Figure 7, the When estimating erosion with a 15% decrease in annual
methodology of the two precipitation change scenarios is precipitation, a decrease of the level of erosion was deter-
presented, referring to the 15% decrease and 15% increase in mined by 20.29% and an increase of 2.1%. Soil erosion ranging
the coefficient of variation of precipitation (factor R) (Fig- from 101 to 150 t/ha.yr corresponds to the considerable range,
ure 8) in the PCA for estimating soil loss. ,en, the values represented by the yellow color in Figure 6 and Table 2, where
were integrated into the value of the erosivity factor R with there is 1414 ha with this erosion range and corresponds to
the methodology proposed by Morgan [10] adapting the 20.8% of the total area of the ACP in 2019. An increase and
estimated values according to the following equation: decrease of 15% in precipitation led to an increase in area with
8 Applied and Environmental Soil Science
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Erosivity
(MJ∗mm/ha∗h∗year)
144 - 213 320 - 355
214 - 248 Rivers
249 - 285 Tilacancha ACP
286 - 319
(a)
Figure 5: Continued.
Applied and Environmental Soil Science 9
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Erodability
(t∗ha∗h/MJ∗mm∗ha)
0.0004 - 0.0110 0.0250 - 0.0410
0.0120 - 0.0150 Rivers
0.0160 - 0.0190 Tilacancha ACP
0.0200 - 0.0240
(b)
Figure 5: Continued.
10 Applied and Environmental Soil Science
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0 3
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LS Factor
o u
p v
q w
r Rivers
s Tilacancha ACP
t
(c)
Figure 5: Land cover map for the Tilacancha ACP (C factor).
considerable erosion ranges of +2.1% and a reduction of maps in Figure 6 represents extreme soil erosion, that is,
−20.29% in these areas. e loss of soil erosion of high range, values of soil loss that are equal to or greater than 251 t/ha.yr.
brown color in Figure 6 and Table 2, ranges from 151 to 200 t/ In the ACP, it represents a value of 0.26% of the area for the
ha.yr and represents 11.3% of the total area of the ACP, and year of study; the estimates of soil loss with an increase and
the estimates with an increase and a decrease of the 15% of decrease of 15% in precipitation result in variations of
precipitation show a decrease in soil loss in the number of −0.259% for the current range when it occurs a decrease in
areas of 11.26% and 0.6%, respectively. precipitation; on the contrary, a 15% increase in precipi-
Following, there is 238.33 ha that corresponds to 3.5% of tation shows an increase in areas for the ACP by 1.78%.
the area of the ACP and corresponds to the ranges of soil loss For the entire ACP, the following values of soil loss due to
from 201 to 250 t/ha.yr, pink in Figure 6 and Table 2, and water erosion were presented: in 2019, a minimum value of
these represent very high ranges of erosion. e estimates of 0.4 t/ha.yr and a maximum value of 665.2 t/ha.yr and total
−15% and +15% of precipitation estimated a change of areas average 60 t/ha.yr with a standard deviation of 38.8%. With a
for this level of erosion with a decrease of 3.49% and an scenario of a 15% reduction in average annual precipitation, a
increase of 0.32%, respectively. Finally, the red color in the minimum erosion value of 0.20 t/ha.yr is estimated, a
Applied and Environmental Soil Science 11
A Factor: Average Annual Soil Loss in the Tilacancha ACP, 2019
A = R∗K∗LS∗C∗P
Estimation 01: Assuming a 15% decrease Estimation 2: Assuming a 15% increase
in the variation of the R Factor in variation of the R Factor
A = ( –15%R)∗K∗LS∗C∗P A = (+15%R)∗K∗LS∗C∗P
Soil Loss Figure 8a Soil Loss Figure 8c
Figure 6: Estimation of water erosion using USLE in the Tilacancha ACP: (a) estimated water erosion with −15% of annual pp, (b) current
water erosion with annual pp, and (c) estimated water erosion with an increase of +15% of annual pp.
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0 Kilometers 3
9294000 9294000
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Precipitation (mm) Rivers
1400 Tilacancha ACP
1100
(a)
Figure 7: Continued.
12 Applied and Environmental Soil Science
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0 Kilometers 3
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Erosivity –15% Rivers
(MJ∗mm/ha∗yr) Tilacancha ACP
207
29
(b)
Figure 7: Continued.
Applied and Environmental Soil Science 13
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0 Kilometers 3
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Erosivity +15% Rivers
(MJ∗mm/ha∗yr) Tilacancha ACP
503
260
(c)
Figure 7:Methodological €ow for the simulation under two scenarios of soil loss due to water erosion for values of the R factor (erosivity), in
the Tilacancha ACP, where factor A is the average annual soil loss in the Tilacancha ACP in 2019.
maximum value of 301.56 t/ha.yr and an average value of eªciently predict soil erosion under di©erent conditions
28.50 t/ha.yr with a standard deviation of 18.49%. Otherwise, [19, 25]. USLE generated information that allows deter-
the erosion estimate when increasing 15% of annual pre- mining speci˜c strategies according to the volume and the
cipitation resulted in a minimum value of 0.2 t/ha.yr, which spatial distribution of soil erosion, as Gaspari et al. [13] did,
can reach maximum values of 1028.84 t/ha.yr, and an average de˜ning and selecting cultivation and management com-
total of 61.59 t/ha.yr with a standard deviation of 60.0%. binations for adequate control of erosion in a Serrana
Bonaerense Basin, Argentina. e precision and accuracy of
4. Discussion DEMs become increasingly important as we expand their
use for the spatial prediction of soil attributes [60].
e soil erosion estimates from our study were possible e work of Basuki et al. [61] agrees with this research,
using USLE [59], thanks to the wide use of the model to since using the DEM called ALOS PALSARwith a resolution
14 Applied and Environmental Soil Science
182000 184000 186000 188000 190000 192000 194000 196000
9306000
N 9306000
9304000 9304000
9302000 9302000
9300000 9300000
9298000 9298000
9296000 9296000
0 Kilometers 3
9294000 9294000
184000 186000 188000 190000 192000 194000 196000
Soil Loss -15%
(Tn/ha∗yr)
0 - 50 151 - 200 Rivers
51 - 100 201 - 250 Tilacancha ACP
101 - 150 > 250
(a)
Figure 8: Continued.
Applied and Environmental Soil Science 15
182000 184000 186000 188000 190000 192000 194000 196000
9306000
N 9306000
9304000 9304000
9302000 9302000
9300000 9300000
9298000 9298000
9296000 9296000
0 Kilometers 3
9294000 9294000
184000 186000 188000 190000 192000 194000 196000
Soil Loss
(Tn/ha∗yr)
Low (0 - 50) High (151 - 200) Rivers
Medium (51 - 100) Very high (201 - 250) Tilacancha ACP
 Considerable (101 - 150) Extreme (> 250)
(b)
Figure 8: Continued.
16 Applied and Environmental Soil Science
182000 184000 186000 188000 190000 192000 194000 196000
9306000
N 9306000
9304000 9304000
9302000 9302000
9300000 9300000
9298000 9298000
9296000 9296000
0 Kilometers 3
9294000 9294000
184000 186000 188000 190000 192000 194000 196000
Soil Loss +15%
(Tn/ha∗yr)
0 - 50 151 - 200 Rivers
51 - 100 201 - 250 Tilacancha ACP
101 - 150 > 250
(c)
a. Erosion -15%
b. Current erosion
c. Erosion +15%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Low High
Medium Very high
Considerable Extreme
(d)
Figure 8: Precipitationmaps for erosivity factor: (a) observed annual precipitation, (b) precipitation with a 15% decrease in the coeªcient of
variation of the historical data group, and (c) precipitation with a 15% increase in the coeªcient of variation of the historical data group for
the ACP.
Applied and Environmental Soil Science 17
Table 2: Comparison of soil loss for the year 2019 and two scenarios with a reduction of and an increase of 15% in annual rainfall for the
ACP.
Soil loss (t/ha·yr)
Level Color Value range Observed rainfall (ha) % area (−) 15% rainfall (ha) % area (+) 15% rainfall (ha) % area
Low 0.20–50 2113 31.1 5956 87.58 1722 25.3
Medium 51–100 2251 33.1 806 11.85 2397 35.2
Considerable 101–150 1414 20.8 34.9 0.51 1554 22.9
High 151–200 767 11.3 2.99 0.04 729.1 10.7
Very high 201–250 238.3 3.5 0.69 0.01 259.9 3.82
Extreme >251 17.57 0.26 0.08 0.001 138.8 2.04
Total 6800 100 6800 100 6800 100
of 10m determined greater precisionmore than the Landsat- 20th century are well studied, knowledge about the
7 ETM and the annual average of soil loss has been widely changes at century-scale is limited [72], and this uncer-
used to determine the causes of water erosion [62]. ,e tainty is understandable in the ACP after observing that
average volume of soil eroded in Tilacancha was 60 t/ha.yr the estimates of increase and reduction of 15% in rainfall
for the year 2019 in the ACP. ,e value exceeds averages for lead to soil losses due to water erosion that would reach
places that are also important for conserving water, such as maximum volumes of 1028.84 t/ha.yr and 301.56 t/ha.yr,
the subhumid Gumara Basin where 42.67 t/ha.yr of lost soil respectively.
volume was estimated [63], Anjeni Watershed, with 24.6 t/ ,e values confirm that water erosion is considered the
ha.yr [64], 47 93 t/ha.yr in KogaWatershed [56], Upper Blue most dangerous form of soil degradation [73]. ,en, the
Nile Basin 27.5 t/ha.yr [65], and Andassa Watershed 23.7 t/ monitoring of water erosion and the factors that control
ha.yr [66]. ,e 69.59% of the Tilacancha ACP corresponded the loss of soil and water are essential for conservation
to erosion of 10 to 25 t/ha.yr, a value that doubles in range planning [74].
when compared with 74% of the total area of a Microbasin of
the Madı́n Water Dam in Mexico where it erodes from 5. Conclusions
0–10 t/ha.yr [62].
Climate and land use trends contribute to reducing the USLE allowed estimating the loss of soil in the Tilacancha
volume of water infiltration and increasing the runoff ACP. ,e factors were obtained individually based on the
generation [67]. Although the basic elements of water col- equations of the factors: rain erosivity (R) in 2019 and
lection, storage, and discharge are not well understood in the simulating increase and decrease of 15% of rainfall, soil
ACP, subsurface runoff dominates the hydrology of the site erodibility (K), length and degree of slope (LS), coverage
and many humid and steep regions, predisposing surfaces to (C), and conservation practices (P). In 2019, there was a
a greater volume of soil erosion [68]. For this reason, the range of 0.4 to 665.2 t/ha.yr of soil lost by water erosion
precipitation scenarios were estimated in the ACP, a place according to six ranges, ranging from low to extreme soil
that is subject to future increases in the intensity of pre- loss according to the spatial distribution of the volume of
cipitation, and are an important aspect of climate change soil lost.
[69]; warming will tend to accelerate the cycle general hy- ,e estimates of the volume of soil lost due to water
drological, intensifying wet extremes [70], in addition to dry erosion under scenarios of decrease and increase in rainfall
extremes in some areas [69]. (±15%) demonstrated the need to act on urgent measures
Likewise, variations in dry days and the intensity of to mitigate erosion levels in the Tilacancha ACP, an im-
precipitation in wet days account for more than half of the portant area that harbors water for the city of Chacha-
change in total annual precipitation in regions such as Peru poyas, since, if there is a 15% reduction in precipitation,
(areas between 40°N and 40°S) [69] and consequently in the ranges are estimated from 0.20 to 301.56 t/ha.yr of soil
Tilacancha. While it is an area that stores and provides lost due to water erosion. However, if there is an increase of
water to the city of Chachapoyas, it is expected that its final 15% of precipitation in the ACP, the ranges can go from
destination will be focused on conservation, increasing the 0.20 to 1028.84 t/ha.yr of soil lost by water erosion.
number of intense rains that allow greater capture and Evaluation of proposals according to the level of loss and
retention of the same. However, the values presented in the influence of each factor on the spatial distribution of
research suggest a relationship with the flows impacted by erosion can be the beginning of a series of conservation
rain. ,ey dominate the erosion of the sheet and between strategies under climate change scenarios for the Tila-
furrow and are important to erode the soil rich in nutrients cancha ACP.
and other chemical substances that can have harmful ef-
fects on water quality [71]. In the current models used to Data Availability
analyze climate, there is no consensus on how different
parts of the earth will warm up; although global changes in Data are openly available: Garćıa, Ligia; Veneros, Jaris;
extremes of temperature and precipitation since the mid- Pereyra, César; Chávez, Segundo; Bustamante, Danilo;
18 Applied and Environmental Soil Science
Calderon, Martha S; Idrogo, Guillermo; and Morales, Eli nacional con categorı́a definitivaLima, Peru, 2020, https://
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,e authors declare no conflicts of interest. 1592514146.
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Acknowledgments vol. 28, pp. 94–106, 1974, https://pascal-francis.inist.fr/vibad/
index.php?action=getRecordDetail&idt=PASCALGEODEBR
,e authors would like to thank the members of the DRON- GM7620029622.
UNTRM Project, who accompanied them on the field trips [11] B. Zhu, Z. Li, P. Li, G. Liu, and S. Xue, “Soil erodibility,
to take samples for laboratory analysis and validate maps microbial biomass, and physical-chemical property changes
through 1500 georeferenced points. ,e authors also thank during long-term natural vegetation restoration: a case study
all the residents of the rural communities of Maino and in the Loess Plateau, China,” Ecological Research, vol. 25,
Levanto for their support as guides in the ACP and for their no. 3, pp. 531–541, 2010.
important information provided. Finally, the authors thank [12] H. K. Addis and A. Klik, “Predicting the spatial distribution of
all the people who supported us at the Toribio Rodŕıguez de soil erodibility factor using USLE nomograph in an agri-
Mendoza National University in Amazonas and the Mu- cultural watershed, Ethiopia,” International Soil and Water
nicipal Drinking Water Company for all the logistics pro- Conservation Research, vol. 3, no. 4, pp. 282–290, 2015.
[13] F. Gaspari, M. Delgado, and G. Denegri, “Estimación espacial,
vided. ,is research was supported by the World Bank temporal y económica de la pérdida de suelo por erosión
through the National Fund for Scientific, Technological hı́drica superficial,” Terra Latinoamericana, vol. 27, no. 1,
Development, and Technological Innovation, FONDECYT pp. 43–51, 2009, http://www.scielo.org.mx/pdf/tl/v27n1/
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FONDECYT- BM- IADT- SE (Drone Project), and the [14] H. M. Rizeei, M. A. Saharkhiz, B. Pradhan, and N. Ahmad,
CEINCAFE Public Investment Project (SNIP No. 352439 “Soil erosion prediction based on land cover dynamics at the
and CUI No. 2314883). Semenyih watershed in Malaysia using LTM and USLE
models,” Geocarto International, vol. 31, no. 10,
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