1. Introduction

In many countries with complex socioenvironmental and economic problems ^[1,2,3,4], water-eroded soil (WES) is considered to be the main cause of land degradation ^[5,6,7]. Under natural conditions, each millimeter of eroded soil produces lower levels of biological wealth ^[8,9], whereas in a farming system, this measure is related to a permanent need for increased amendments to crops to obtain the highest price of goods ^[1,10]. For example, Gao et al. ^[10] argue that when black soils in northeastern China were eroded to a depth of 0.4 m, soybean production decreased by nearly 10%.

Currently, 56% of the 3.5 billion of hectares of land in the world present signs of degradation, affecting approximately 1.5 billion people living in these areas ^[2,5,11]. Stavi and Lal ^[2] reported on historical changes in potential erosion levels occurring over the last century (1901 to 1980) and maintained that such changes have been caused by an increase in human activities such as deforestation, land use intensification and land use change, with up to 60% of these land degradation trends detected across all continents except for Europe.

Other reports ^[4,12] have estimated soil losses ranging from 24 to 75 billion tons of fertile soil. For example, Wang et al. ^[4] reported that arable land has been lost due to soil erosion at a rate of more than 10 million ha per year.

In Mexico, an average estimate based on several reports ^[13,14,15] shows that 69.7% (1354 thousand of km²) of the country's land (1949.8 thousand of km²) presents some degree of degradation in the soil component of land, with WES representing the highest proportion with 25.4% (494 thousand of km²), followed by chemical degradation and wind erosion measured at 20.1% (390 thousand of km²) each, and finally physical degradation at 4.1% (79 thousand of km²).

Climatic variability has worsened in recent years, affecting many countries, including Mexico ^[16]. Mexico's susceptibility to water erosion hazards is high because approximately half of the country's territory (42.2%) has a slope greater than three degrees. This topographic feature, along with the inadequate management of forests, agriculture and grazing, promotes the generation of runoff, which erodes topsoil ^[13,17,18].

The State of Aguascalientes is currently suffering from a critical loss in natural resource quality. Since the start of the previous decade, it has been officially reported that ^[19,20] approximately 2853 km² of land has shown a certain degree of degradation, representing 51% of the state's territory (5621 km²), with 1307 km² exhibiting WES, representing 46% (1307/2853 km²) of the affected area and 23.2% (1307/5621 km²) of the territory of the state. In a national ranking of states with the highest degrees of degradation from WES, Aguascalientes is ranked fifth after Guerrero (31.8%), Michoacán (27.1%), Mexico (25.7%) and Jalisco (25.3%).

Generally, the following three forms of soil erosion occur in Mexico: ⅰ) land deformation (26,604.3 km² (1.4%)); ⅱ) topsoil loss (200,039.7 km² (10.5%)); and ⅲ) off-site soil loss (613.1 km² (0.03%)). For Aguascalientes, the third form has not been measured (ⅲ), but evidence shows that the first (ⅰ) ^[21] and second (ⅱ) processes are more significant, appearing over 199.9 km² (3.7%) and 1,106.7 km² (20.5%) of the territory, respectively ^[14,16].

Land deformations due to land subsidence, surface faults and surface cracks are a typical problem related to groundwater extraction, and for unconsolidated alluvial sediments underground, both factors have been described by Aranda-Gómez ^[22] and Pacheco-Martínez et al. ^[21] in reference to the Aguascalientes Valley.

Estimating topsoil loss as laminar soil loss, Sun et al. ^[1] argue that the Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation (RUSLE), Erosion Productivity Impact Calculator (Ehttps://www.aimspress.com/aimspress-data/aimsgeo/2017/3/PIC) and Water Erosion Prediction Project (WEPP) are accepted methods for estimating soil erosion rates. The USLE, or the RUSLE model, is a universally accepted method that can be used as the best fitted model for monitoring rates. The applicability of this model has been proven over recent decades, and it is now widely accepted ^{[1,6,23,24,25]}.

In reference to Mexico, some studies ^[23,24] have reported soil erosion rates above those that have been previously indicated. For example, Montes-León et al. ^[24] used the USLE model for hydrologic region 12 in Lerma-Santiago, Aguascalientes and reported potential extreme soil losses at rates of > 250 t ha^-1 yr^-1 without considering the effects of climate change. Other studies ^[26,27,28] based on the same model (USLE) have reported higher rates than this.

The aims of this research are 1) to determine the effects of climate change on WES and the distribution thereof within the State of Aguascalientes, Mexico, using the near-future divided world scenario (A2) drawn from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4), and 2) to predict the future risks resulting from the deterioration of soil resources.

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2. Brief Background on Climatic Change Scenarios

To improve our understanding of the complex relationships between the climate system, ecosystems, and human activities, the scientific community has developed scenarios that provide plausible accounts of how key socioeconomic and technological areas and environmental conditions could be affected by greenhouse gas (GHG) emissions and climate change ^[29]. However, the implementation of these scenarios at the local level, which is of significance for vulnerability studies on climate change adaptation, represents a growing challenge ^[2,30].

The future climatic scenarios produced by Conde and Gay ^[31] present precipitation and temperature anomalies; these scenarios are the result of numerous experiments conducted based on the 23 Global Circulation Models (GCMs) proposed by the IPCC. In fact, this work constitutes the basis of the GHG emission trajectories ^[31,32]. These future climatic scenarios have the following characteristics: Scenario A1 assumes a very rapid rate of global economic growth, a doubling of the world population by mid-century, and the rapid introduction of new and more efficient technologies. This scenario is divided into three sub-scenarios that reflect three alternative directions for technological change: intensive fossil fuel use (A1FI), non-fossil energy use (A1T), and a balance between various energy sources (A1B). Scenario B1 describes a convergent world with the same population as that of A1 but with a more rapid evolution of economic structures toward a service and information economy. Scenario A2 describes a very heterogeneous world with strong population growth, slow economic development, and slow technological change.

The GHG emission assessment scenarios (A1B, A2, B2, and B1) show an approximately 3% reduction in annual rainfall levels, a nearly 1℃ increase in the annual average temperature, and a 6℃ temperature increase for the fall. The longest drought occurs from 2010–2039 and is centered on the fragile dry lands of northern Mexico ^[31,32,33].

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3. Materials and Methods

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3.1. Study unit

The State of Aguascalientes in Mexico is located in the central northern region (Figure 1a), covering a total area of 5621.5 km² and representing 0.3% of the national territory ^[34]. According to the INEGI ^[34], there are three main climates within this territory: arid (BS1kw), semiarid (BS1kw(w)) and subhumid (C(w0), C(w1)) (Figure 1b). In addition, this area presents a physiographic conformation that includes vast hills and plains, including the Sierra Madre Occidental, Mesa del Centro and Eje Neovolcanico, ranging from 1800 to 3050 masl (meters above sea level) (Figure 1c).

Based on the climate and physiography briefly described above (Figure 1), the soil, land use and vegetation features are described as follows. According to the WRBS (World Resources Base of Soils), Aguascalientes includes 10 of the 32 soil groups in the world. There are three main soil groups (Phaeozems, PH (31.58%), Leptosols, LP (21.36%) and Durisols, DU (18.89%)) that cover almost three-quarters of the territory of the state.

According to the INEGI ^[35], there are three forms of land use in Aguascalientes. The main land use covers approximately 2386.5 km² (42.5% of the state's land area) and is managed under rainfed (18.6%) and irrigated conditions (23.8%). The second land use involves secondary vegetation, covering 1193 km² (equivalent to 21.2%) and includes vegetation such as oak bushes (7.4%), natural grassland (4.1%), deciduous forest vegetation (5.1%) and scrubland (4.3%). Finally, primary vegetation occupies approximately 988.4 km² (representing 17.6% of the total surface) and includes oak and pine forest (7.2%), natural grassland (5.47%), scrubland (4.1%), and high-altitude moist evergreen and semi-deciduous forest (0.8%).

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3.2. LWE calculation and modeling

The LWE calculation and modeling in this work is based on the methodology proposed by the SEDESOL-INE ^[36] for Land Use/Land Cover (LULC), which has been used in similar studies ^[37,38] to model the local effects of climate change in northern Mexico. From this same methodology, we used ^[36] as a reference for four probable classes of soil loss: 1) light, < 10 t ha^-1 yr^-1; 2) moderate, 10-50 t ha^-1 yr^-1; 3) high, 50-200 t ha^-1 yr^-1; and 4) very high, > 200 t ha^-1 yr^-1. The method employed is a function of the available humidity as a consequence of rain, and other factors were considered, as described below.

According to the climatology patterns briefly described above and the climatology dataset used, it is assumed that the dominant environmental process is related to the rain aggressiveness index (RAI) due to the dependence of this index on the mean annual rainfall (MAR), which determines the growth period (GROPE) for annual plants while also defining the humidity levels available to crops for planting, strong root formation and growth ^[39,40]. The number of days (d) for each climatological case are as follows ^[41]: 1–59 d (arid), 60-119 d (semiarid) and subhumid (120–179 d). Both indices are expressed as follows:

$RAI = \left( {1.244 \times GROPE} \right) - 14.7875$

(Eq. 1)

$GROPE = \left( {0.2408 \times MAR} \right) - \left( {0.0000372 \times MA{R^2}} \right) - 33.1019$

(Eq. 2)

Once the bioclimatic variables described above were calculated (Eq. 1 and Eq. 2), the LWE was determined from biophysical properties of anthropogenic influence (land use) for the study area, as shown below:

$LWE = RAI \times CAERO \times CATEX \times CATOP \times CAUSO$

(Eq. 3)

where CAERO (Spanish acronym) is an index that defines a soil layer's susceptibility to environmental effects, such as rain or wind; CATEX (Spanish acronym) is an index associated with the outstanding properties of the same soil layer's texture and physical phase; CATOP (Spanish acronym) is an index related to landforms and dominant slope classes; and CAUSO (Spanish acronym) is an index related to soil uses and the dominant vegetation types.

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3.3. Preparation of input variables for LWE calculation

The input variables for the LWE calculation shown in Eq. 3 were prepared from their original formats (shp and Geotiff) for processing using the raster calculator available through ArcGIS 10.1® (ESRI, Redlands, CA, USA) in the Spatial Analyst module. The primary steps of this procedure are as follows.

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3.3.1. Vector dataset processing

The cartographic coverage used for Aguascalientes State involved modifying the attribute tables of vector datasets edited and distributed by the INEGI (F13–06, F13–09, F14–04 y F14–07) at a scale of 1:250,000. To create the CAERO and CATEX indices, edaphology set Ⅱ ^[41] was used; for CAUSO, Land Use and Vegetation (LUV) set Ⅳ (INEGI, 2007b) was used.

From the original methodology (SEDESOL-INE, 1998), CAERO was defined from the soil properties described by Wischmeier and Smith ^[42], which have been used in several studies ^[43,37,38]. These properties correspond to the soil units and subunits of set Ⅰ. Therefore, it was necessary to standardize these units with the groups and edaphological classifiers of set Ⅱ (Table 1), which were based on the same WRBS classification published in 1998 ^[44] and were officially adopted for soils in Mexico in 2007.

In this same vector set (edaphology set Ⅱ), the field corresponding to the texture and physical phase was extracted to create the CATEX index (Table 2) in a new attribute table representing the soil textural properties and physical phase to a depth of 0.3 m. This attribute table was converted to a raster layer as indicated in Eq. 3.

As noted above, the CAUSO index uses the vector dataset of LUV sets Ⅲ and Ⅳ, with the first extending the reference for groups within each form of land use and the second representing the most recent data (Table 3).

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3.3.2. Landform and slope class processing and preparation

The CATEX index was derived from a 30-meter resolution digital elevation map of Mexico ^[49]. The CATOP values were defined as a function of slope using three classes and three landforms, as shown in Table 4.

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3.4. Definitions of baseline climate data, homogenization and downscaling

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3.4.1. Baseline climate data and homogenization

The baseline or reference year was set as 2010, and daily climatic data for temperature and rainfall were obtained from 18 National Weather Service (WS-NWS) weather stations, with 17 located in Aguascalientes and one located in Jalisco (Id-code: 14122). The WS selection criteria were as follows: 1) at least 30 years of climatic data; 2) database quality; and 3) proximity to the mesh resolution node (50 x 50 km) for climate change scenarios for Mexico (AR4). The first criterion used an average of 54 years of data. For the third criterion, the five nodes identified for this territory have the following locations (longitude, latitude): 1 (102.75, 22.25); 2 (102.25, 22.25); 3 (102.75, 21.75); 4 (102.25, 21.75); and 5 (101.75, 21.75). The quality levels were determined based on the RClimDex©, ver 1.0 daily climate database for homogenization (www.r-project.org), from which 27 indices were obtained for climatic change detection. We selected six key variables, as shown in Table 5.

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3.4.2. Downscaling method

We used the LARS-WG (Long Ashton Research Station Weather Generator) downscaling method as a time stochastic weather generator (TSWG). The LARS-WG can simulate daily weather data variables based on the statistical characteristics of the observed data in one place. The LARS-WG was applied to the 18 WS-NWSs selected (Table 5). Using the LARS-WG, we obtained the synthetic series for climate change scenarios A2 and A1B for 2010–2039 for the same input variables based on the 15 general circulation models (GCMs) that were used for the IPCC AR4 ^[32].

The analysis of current and future climatic patterns to estimate the likely regional impacts of climate change was carried out using the Regionalized Climate Models for Mexico (RCMM) created by the UNAM Center for Atmospheric Sciences, which has a resolution of 2500 km² with nodes equidistant at 50 × 50 km (Figure 2).

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3.4.3. Climate change impact analysis

The technique used to analyze the impacts of climate change on soil erosion involved using MAR for historical values. Scenario A2 was used for the input values to determine the GROPE (Eq. 2) at a spatial distribution that was obtained via the IDW (Inverse Distance Weighting) method (this method has been used in similar studies ^[50,37]. This process allowed for the detection of local impacts once similar ranges had been adjusted for both the historical values and scenario A2 according to the method described by López-Santos et al. ^[37]. The anomalies of climatic change scenarios to A2 and A1B models, were used because they are closer to social and economic trend considerer to Mexico, both have been recommended and used in similar studies ^[31,33,40].

The relative importance of the changes for the three defined classes (high, medium, light) was obtained as follows:

$R{I_{ci}} = \frac{{{S_{ci}}*100}}{{\mathop \sum \nolimits_{i = 1}^n ci}}$

(Eq. 4)

where RIci is the relative value (percentage) of the i-th defined class; Sci is the geospatial surface calculated from the i-th class; n is the number of defined classes; and $\mathop \sum \nolimits_{i = 1}^n$ is the sum of the surface of each class (ci) ranging from 1 to 3.

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4. Results

As shown in Table 6, the historical standardized climatic dataset and the statistically downscaled dataset were analyzed using the mean annual temperature and rainfall values for each WS selected, and the anomalies for the A2 and AIB scenarios were determined. Both scenarios showed differences, but the most important difference concerned rainfall levels. The arithmetic average for the historical rainfall levels was 509 (± 83.1) mm. Thus, when A2 reported 2.2% (± 7.9) less rainfall than that in A1B, these rainfall levels were 8% (± 5.9%) higher. It is also important to note that for the negative standard deviation between the A2 and A1B scenarios, one is negative (-5.7%) while the other is positive (+2.5).

From this this brief comparison and given that the trends in Mexico reflect very heterogeneous patterns characterized by strong population growth and slow economic development and technological change, scenario A2 was used in this work.

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4.1. The effects of climate change on key variables

The results of the analysis of the impacts of climate change on the main study variables (MAR, GROPE, RAI) are presented below.

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4.1.1. Changes on mean annual rain

The changes in and effects of climate change on MAR were determined for both raster datasets. The raster images were segmented into equal ranges. Three comparable ranges were defined to analyze the changes in HMAR and MAR_ScA2 (a1 vs. b1; a2 vs. b2; a3 vs. b3), thus clarifying the hazard magnitudes in terms of area. Positive and negative changes can thus be a "benefit or detriment" based on the variability of the data listed above (Table 6), which can manifest in future scenarios as class b1, the lower limit, or as class b3, the upper limit. For the first case, by averaging the MAR and given on the historical average of 509 mm for each class, we found that the increase in the future class b1 (MAR_ScA2 b1) only reached 8.3 mm, while for class b3 (MAR_ScA2 b3), the average rainfall reached 45.1 mm, denoting positive effects of 1.6 and 8.9%, respectively (Table 7).

The same comparative analysis between the classes (a1 vs. b1) shows that the MAR may increase by 19.5% in Aguascalientes when comparing the historical and future scenarios (2,039), changing from an area of 337.5 to 1,096.7 km². For classes b2 and b3, the following results were observed: (1) The b2 class will maintain the same amount of rain (479 mm) but within an 11.4% smaller area than that observed for the historical scenario, changing from 3153.5 to 2510.2 km²; and (2) the b3 class, with a 4.15 mm increase (+ 8.9%), will increase in area by equivalent of 35.8% of the total area of Aguascalientes (Table 7).

The spatial distributions of HMAR and PMA_ScA2 (Figure 3) exhibit the following features:

(1) The MAR increase (1.7%) for the b1 class in scenario A2 was distributed across the north and southeast over 13.5% of the total area (5,621.5 km²) of Aguascalientes. The municipalities of Cosío, Rincón de Romos and Tepezalá are located in the first (north) area, and the municipalities of Asientos, El Llano and Aguascalientes are located in the second (southeast) area.

(2) Although no increases are expected for MAR_ScA2 in b2, we observed a decrease of 11.4% for the total study unit (SU) area, which in real terms indicates a reduced volume of water for municipalities located in the central area of the state (San José de Gracia, Jesús María, Pabellón de Arteaga and Aguascalientes) as well as in the east (portions of Tepezalá, Asientos and El Llano).

(3) Although there could be an MAR increase of 9.4% for the b3 class, this would be the result of a 2.1% smaller area than that observed in HMAR a3. The area to the west of the state corresponds to the municipalities of Calvillo, San José de Gracia, Asientos, San Francisco de los Romo and Aguascalientes.

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4.1.2. Changes in and effects on GROPE and RAI

The stratified analysis of the three comparable classes, as in the previous case for GROPE and RAI, shows consistencies in the spatial distribution of MAR. Both indices, from the relative to the historical, show that for the future scenario, the impact could be of the same magnitude because in the b1 class (GROPE_ScA2 b1 and RAI_ScA2 b1), the impacts would reach 2.4 and 2.8%, whereas for the b3 class (GROPE_ScA2 b3 and RAI_ScA2 b3), the increases could reach 13.2 and 15.3%, respectively (Table 8).

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4.2. Description of the input variables in the erosion model

Our analysis of the relative importance and spatial distribution of the erosion model input variables (Eq. 3) reveals the gradient of soil susceptibility for the territory of Aguascalientes, as shown below.

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4.2.1. CAERO Index

The CAERO index, which denotes the degree of soil susceptibility to the erosive effects of rain, exhibits the following degree of relative importance and spatial distribution (Figure 4a):

(1) Forty-six percent of the soils in Aguascalientes has a CAERO index of 2, demonstrating a high susceptibility to water erosion, as these soils are associated with the following soil groups: Calcisol (CL), Durisol (DU), Leptosol (LP) and Planosol (PL). These soil groups correspond to the municipalities of Jesús María, San José de Gracia and Pabellón de Arteaga. For the northeastern part of the state, these soil groups correspond to the municipalities of Cosío, Tepezalá, Asientos and part of the municipality of El Llano.

(2) In contrast, 32% of the territory had a CAERO index of 0.5, representing a low susceptibility to erosion in places where only the Phaeozem (PH) soil group was identified. This index (CAERO) value is distributed along the central-southern area of the state, including the municipalities of Aguascalientes, San Francisco de los Romo, Jesús María, El Llano and Calvillo.

(3) Finally, 18% of the territory had a CAERO index of 1, indicating a moderate susceptibility to erosion, and the following soil groups were identified: Cambisol (CM), Fluvisol (FL), Kastanozem (KS), Luvisol (LV), and Regosol (RG). This CAERO value was distributed in the western part of the state in the municipalities of Calvillo, San José de Gracia and Rincón de Romos; for the eastern area, this CAERO value was distributed in the municipalities of San Francisco de los Romo, Aguascalientes, El Llano and Asientos.

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4.2.2. CATEX index

The CATEX index denotes the gradient of soil resistance to erosion processes associated with dispersion, flow formation and sedimentation. The relative importance and spatial distributions (Figure 4b) of this index are described below:

(1) Most of the territory of the state (82.8%) has a CATEX value of 0.3 because the soils have a fine texture (class 2), denoting a moderate susceptibility to erosion. This CATEX value extends over almost the entire territory, excluding certain central and southwestern areas.

(2) A CATEX value of 0.5 was assigned to 10.9% of the territory due to the presence of a stony or rocky phase denoting high susceptibility to erosion. This CATEX value is distributed across the southwestern area of the state (including the municipalities of Calvillo and Jesús María) and across the eastern area (in the municipalities of El Llano, Asientos and San José de Gracia).

(3) Finally, 3.27% of the territory presented a CATEX value of 0.2 due to the presence of coarse soils (class 1) (which are highly susceptible to erosion). Territories with this value were found to be distributed in the central area of the state across the municipalities of Jesús María, Pabellón de Arteaga, San José de Gracia and Rincón de Romos.

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4.2.3. CATOP index

The topographic properties of the study area proved to be very useful as they allowed us to identify areas where slope conditions and ground discontinuities determine erosion susceptibility, as described below:

(1) A CATOP index of 0.35 was assigned to 60% of the state territory because this area includes valleys, which are less susceptible to erosion. This CATOP value is distributed from the central to the eastern part of the state in the municipalities of Cosío, Rincón de Romos, Tepezalá, Pabellón de Arteaga, Asientos, Jesús María, San Francisco de los Romo, Aguascalientes y El Llano (Figure 4c).

(2) A CATOP index of 3.5 was assigned to the 27.4% of the area, denoting a moderate susceptibility to erosion. This area is mainly distributed in the western region of the state in the municipalities of Cosío, Rincón de Romos, San José de Gracia, Calvillo, Jesús María and Aguascalientes, and in the eastern area in some parts of the municipalities of Tepezalá, Asientos and El Llano (Figure 4c).

(3) Finally, 12.5% of the state territory is classified with a CATOP value of 11 due to the presence of sierras and falls, which are highly susceptible to erosion. This CATOP value is found in the same municipalities as the CATOP value of 3.5 (Figure 4c).

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4.2.4. CAUSO index

The CAUSO index evaluates erosion susceptibility levels based on land use in Aguascalientes for 2010, and the results (Table 10) are as follows:

(1) With a CAUSO index of 0.3, 23.5% of the territory includes grasslands, denoting a moderate susceptibility to erosion. This CAUSO value is distributed across the central area of the state in the municipalities of Cosío, San José de Gracia, Jesús María and Aguascalientes (Figure 4d).

(2) In Aguascalientes, 22.5% of the land has a CAUSO index of 0.2, and because this land is utilized for irrigated agriculture, it exhibits a moderate susceptibility to erosion. This CAUSO value is distributed over the central area of the territory in the municipalities of Cosío, Rincón de Romos, Tepezalá, Pabellón de Arteaga, San Francisco de los Romo, Jesús María and Aguascalientes as well as in the southwest in Calvillo (Figure 4d).

(3) In total, 21.7% of the State of Aguascalientes has a CAUSO index of 0.7, and this land is used for rainfed agriculture, denoting a high susceptibility to erosion. This CAUSO value is focused in the eastern part of the state in the municipalities of Tepezalá, Asientos and El Llano (Figure 4d).

(4) Forested areas cover 16.6% of the state, representing a CAUSO value of 0.1 and indicating a low susceptibility to erosion. These areas are distributed across the western part of the state in the municipalities of San José de Gracia, Calvillo and Jesús María.

(5) Scrubland, which covers approximately 12% of the land in Aguascalientes, has a CAUSO value of 0.15, denoting a low susceptibility to erosion. These areas are distributed across the northern and central areas of the state in the municipalities of Tepezalá, Asientos, Pabellón de Arteaga, San Francisco de los Romo and San José de Gracia and in the southwestern area in the municipality of Calvillo.

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4.3. Estimating the effects of climate change on LWE

The effects of climate change on LWE were estimated based on the input variables described above (3.1 and 3.2). Once transformed from vector files (shp) into raster images (vector ↔ raster), these input variables were used to calculate the soil erosion rates in the study area based on the raster calculator available in ArcGIS 10.1 (Spatial Analyst). The results are described below (Table 9).

(1) The average of the historical LWE variable was 279.3 t ha^-1 yr^-2, and the variable ranged between the minimum (0 t ha^-1 yr^-2) and maximum (558.6 t ha^-1 yr^-2) values shown above (Table 9).

(2) Changes in the soil water erosion rates between the historical values and scenario A2 (HLWE and LWE_ScA2) are not evident, with the exception of class 4, which presents an estimated increase of 192.1 t ha^-1 yr^-1. This value is equivalent to 34.4% of the average historical laminar water erosion (HLWE) value, which changes from 558.6 to 750.7 t ha^-1 yr^-1.

(3) A decrease of 110.5 km² was estimated in the spatial distribution of classes 2 and 3 (moderate and high, respectively), representing 2% of the total area of Aguascalientes.

(4) Inversely, for classes 1 and 4, an increase of 110.5 km² was estimated. Class 1 will increase by 101 km², and class 4 will increase by 9.5 km².

In the SU, the spatial distribution of soil erosion susceptibility for the four analyzed classes is described in the following paragraph and in Figure (Figure 5).

Classes 1 and 2 (light and moderate, respectively) are located in all the municipalities of the state, mainly in the central and eastern regions. The municipalities covered by class 3 are mainly located in the west (Cosío, San José de Gracia, Calvillo, Jesús María) and in parts of the east (Tepezalá, Asientos and El Llano). Class 4 (very high) is found primarily in San José de Gracia, Calvillo, El Llano and Asientos.

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5. Discussion

Analysis of the local impacts of climate change on MAR and LWE

Rain variability in Aguascalientes is determined by El Niño (EN), La Niña and the EN/southeast oscillation (ENSO), with the main effects derived from the Pacific ^[51,52]. When these phenomena are combined with orographic variations, they produce arid and semiarid climatic gradients with rainfall levels of over 300 mm and up to 700 mm, respectively ^[53].

Magaña et al. ^[51] have argued that the occurrence and characteristics of ENSO can modify global climate patterns. During the winter ENSO in Mexico, these features increase precipitation in the northeast, while in the summer, they produce negative precipitation anomalies over much of Mexico. These results complement those of Pavia et al. ^[55], who analyzed data collected from approximately one thousand climatic stations across Mexico.

Among the erosion rates resulting from the calculations (Table 9), class 4 is classified in this case as very high, with an average value around of 475 t ha^-1 yr^-1 for the A2 scenario. This value is equivalent to an annual loss of approximately 0.30 mm of lamina considering a bulk density of 1.2 t m^-3. This means that while the future area of this class could be 9.5 km² (36.3–45.8 km²), due to MAR changes in mountainous areas, as was shown before (Figure 4c), the risks could be more significant due the natural processes described above. Areas without official protection plans ^[19,20] could experience irreversible land degradation beyond that described here.

In reference to similar areas (arid, semiarid and subhumid) as those examined this study show erosion rates almost same ^[26,27,28]. For example, Zhang et al. ^[26] in northwestern Shanxi Province in the Loess Plateau area of China, used the RUSLE model to find high soil erosion rates (>500 t ha^-1 yr^-1) in areas presenting significant terrain alterations, high slopes, and land with sparse vegetation. The authors show that hilly areas, grasslands, and newly constructed mine dumping areas are more degraded and have higher levels of erosion. However, in some cases, reversing these impacts is possible, as described by Jiao et al. ^[56] for the Yellow River Basin, which is located in the Shanxi and Shaanxi provinces in China.

Chaplot ^[27] reported the same erosion rates by modeling variations in rainfall in SWAT (Soil and Water Assessment Tool) for a watershed located in Central Texas (Waco) with similar conditions as part of the SU described above (Figure 4c, 4d). In other words, for class 4, which corresponds to areas with forest vegetation in the upper parts of the west of Aguascalientes, there are high erosion rates (>475 t ha^-1 yr^-1) resulting in a soil loss of 0.3 mm (0.0003 m yr^-1).

The effects of the soil losses that could occur in the near future (2010–2039) are more significant than those reported by Montes-León et al. ^[24]. Extreme potential erosion (>250 t ha^-1 yr^-1) has been estimated for Hydrologic Region No. 12 (RH12), which comprises the hydrologic system of Lerma-Santiago, where Aguascalientes is located. This result is considered a possibility for the following three reasons: 1) the rate of extreme erosion is not the exact upper limit of the range; 2) areas with undulating terrain that are poorly managed and that are subject to torrential seasonal rains are generally more susceptible to erosion; and 3) studies on similar environments that do not consider the effects of climate change have reported erosion rates of up to average cited here (475 t ha^-1 yr^-1) according to the USLE and others models ^{[23,26,27,28]}.

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6. Conclusions

Soil loss represents the initial state of environmental degradation in the SU. According to previous studies, soil loss is likely an underestimated problem, as these studies have not examined climate scenarios with different rain and LWE spatial distributions as principal variables for both the present and the future.

The calculated rate of water erosion shows that for the future scenario LWE_ScA2 (2010–2039), if soil erosion is not addressed through policies, prevention practices, conservation and the protection of natural resources, the soil layer that currently performs fundamental stabilizing functions in ecosystem services and that represents an income source related to primary activities could be lost. While the impacts described here, which are based on annual soil loss rates and climatic seasonal (dry and wet) effects, are not yet clear, we present a good approximation according to the scientific references discussed.

A more comprehensive understanding of this issue is desirable for applying the new climate change scenarios (RCP 5) proposed in the IPCC's Fifth Assessment Report.

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Acknowledgments

This research was conducted as part of the Chapter Vulnerability Studies of Natural Resources to Climate Change project with support from the State Program of Climate Change of Aguascalientes (PEACC-Ags, Spanish acronym) and SEMARNAT-CONACYT sectorial funds (clave: S0010–2008).

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Conflict of interest

Both authors declare no conflicts of interest in this paper.

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