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Modeling the potential impact of climate change in northern Mexico using two environmental indicators
Atmósfera 26(4), 479-498 (2013)
Modeling the potential impact of climate change in northern Mexico using two environmental indicators A. LÓPEZ SANTOS Unidad Regional Universitaria de Zonas Áridas, Universidad Autónoma Chapingo, Apartado Postal 8, 35230 Bermejillo, Durango, México Miembro de la Red Temática del Agua del CONACyT Corresponding author; e-mail: [email protected] J. PINTO ESPINOZA Instituto Tecnológico de Durango, Blvd. Felipe Pescador 1830 Ote., Col. Nueva Vizcaya, 34080 Durango, Durango, México E. M. RAMÍREZ LÓPEZ Universidad Autónoma de Aguascalientes, Av. Universidad 940, Ciudad Universitaria, 20131 Aguascalientes, Aguascalientes, México M. A. MARTÍNEZ PRADO Instituto Tecnológico de Durango, Blvd. Felipe Pescador 1830 Ote., Col. Nueva Vizcaya, 34080 Durango, Durango, México Received June 4, 2012; accepted May 28, 2013 RESUMEN La modelación de impactos locales, caracterizados por deterioro de los recursos naturales –especialmente agua y suelo– ante los efectos globales del cambio climático, se ha convertido en una poderosa herramienta en la búsqueda de medidas de mitigación y adaptación. Los objetivos de la presente investigación fueron: 1) evaluar mediante procesos de modelación el impacto potencial del cambio climático para el periodo 2010 \ DGYHUWLUVREUHULHVJRVIXWXURVDSDUWLUGHODLGHQWL¿FDFLyQGHIRU]DQWHVUDGLDWLYRVORFDOHVRiUHDV críticas, considerando el índice de aridez (IA) y erosión laminar del suelo causada por el viento (ELV) como dos indicadores de calidad ambiental. Se usaron técnicas de evaluación de los recursos naturales empleadas por el Instituto Nacional de Ecología y Cambio Climático (INECC) para los estudios de ordenamiento ecológico territorial. Los insumos empleados comprenden información climática actual y futura, cubiertas GHVXHORV\SURSLHGDGHVHGi¿FDVDVRFLDGDVDOPXQLFLSLRGH*yPH]3DODFLR'XUDQJR0p[LFR 1\ : /RVFiOFXORVUHDOL]DGRVDSDUWLUGHODVDQRPDOtDVSDUDORVSURPHGLRVDQXDOHVGHSUHFLSLWDFLyQ\ WHPSHUDWXUDLQGLFDQTXHHOWHUULWRULRPXQLFLSDOHQHOHVFHQDULR$SRGUtDWHQHUXQLPSDFWRSURPHGLRGH causado por la ELV, en tanto que el IA probablemente cambie su promedio histórico de 9.3 a 8.7; se estima TXHHOLPSDFWRSURPHGLRVREUHHVWHtQGLFHHQHOIXWXURVHUiGH ABSTRACT Modeling the deterioration of natural resources, especially water and soil that results from the global effects of climate change has become a powerful tool in the search for mitigation and adaptation measures. The objectives of this research were: (1) to model the potential impact of climate change for the period 20102039, and (2) to offer advice about future risks based on local radiative forcing or critical areas and taking LQWRDFFRXQWWZRLQGLFDWRUVRIHQYLURQPHQWDOTXDOLW\WKHDULGLW\LQGH[ $, DQGODPLQDUZLQGHURVLRQ /:( .
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Evaluation techniques for natural resources, similar to those applied by the Instituto Nacional de Ecología y Cambio Climático (National Institute of Ecology and Climate Change) were used for studies of ecological land use. The inputs include climate information (current and future), soil cover and edaphic properties UHODWHGWRWKHPXQLFLSDOLW\RI*yPH]3DODFLR'XUDQJR0H[LFR 1: . According to calculations estimated from the anomalies for the mean annual rainfall and mean annual temperature, in a IXWXUHFOLPDWHFKDQJHVFHQDULRDQDYHUDJHLPSDFWRIDSSUR[LPDWHO\ZRXOGEHFDXVHGE\/:(DQGWKH AI would change from its historical value of 9.3 to 8.7. It is estimated that the average impact on the AI in WKHIXWXUHZLOOEH Keywords: Climate scenarios, modeling, ecological zoning.
1. Introduction In less than a decade, modeling climate processes to assess the deterioration of natural resources has become a powerful tool for determining trends, esSHFLDOO\IRUZDWHUDQGVRLO
critical areas and taking into account two indicators of environmental quality, the aridity index (AI) and soil erosion. 2. Methodology 2.1 Study area, characteristics and location *yPH]3DODFLRLVRQHRIWKH PXQLFLSDOLWLHVRI the state of Durango. It has an area of 842.32 km2, DQGLWLVORFDWHG 1: LQDJHRgraphical-ecological region known as the Bolsón de Mapimí. This region presents a climate gradient from south to north, ranging from dry (BSohw) to YHU\GU\ %:KZ 7KHDQQXDODYHUDJHWHPSHUDWXUH YDULHVIURPWR&DQGWKHPHDQDQQXDOUDLQIDOOLVDSSUR[LPDWHO\PP *DUFtD 2QH of its salient characteristics is that approximately RI WKH WHUULWRU\ LV XVHG IRU DJULFXOWXUH ZLWK irrigation, as is favored by its topography (slope < WKHUHPDLQLQJFRUUHVSRQGVWRXUEDQDQG rural (Fig. 1). 2.2 Study baseline and indicators of environmental quality (IQA) The baseline or reference for this study were the conditions of 2010, which serve as a basis for comparison to assess the impact of climate change on a 30-year (2010-2039) time horizon. The environmental characteristics chosen as indicators were the laminar wind HURVLRQ /:( DQG$,WKH¿UVWDVWKHORVVRIVRLOGXH to the wind action on the soil surface, and the second as drought stress. Each is directly correlated with the changes under future scenarios for temperature and rainfall, as described by Magaña et al. (2012) for the northeast of Mexico. 2.3 IQA modeling and input data 7KHPRGHOLQJRIWKH,4$/:(DQG$,ZDVGHYHORSHGWKURXJKWZRWHFKQLTXHV IRUWKH/:(LQGLFD-
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636000
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652000
660000
668000
652000
660000
668000
2856000 2848000
2848000
2832000
2832000
2840000
2840000
UTM
2856000
2864000
2864000
636000
481
UTM
Bolsón de Mapimí
Legend Municipal limit
Tlahualilo
Railroad Road
Mapimí Gómez Palacio
Scale 22800
Lerdo
Meters
)LJ*HRJUDSKLFDOORFDWLRQDQGWRSRJUDSKLFIHDWXUHVRIWKHPXQLFLSDOLW\RI*yPH]3DODFLR'XUDQJR0H[LFR
tor, the loss of soil in t ha–1 yr–1 was estimated based on the methodology proposed by INE (1998) for studies of ecological-use zoning, and (2) AI was determined by the De Martonne’s aridity index, which has been used by Mercado-Mancera et al. (2010) as DQHVWLPDWRURIWKHDULGLW\DQGGHVHUWL¿FDWLRQLQDULG land in northwestern Mexico. The input for the baseline year modeling was the historical data set of weather from the Servicio 0HWHRUROyJLFR 1DFLRQDO 601 1DWLRQDO :HDWKHU
Service), as well as measurements related to the biotic physical environment such as topography, soil and vegetation. Meanwhile, to assess the potential impact on the IQA by future scenarios (2010-2039), projections (metadata) for regional climate change of Mexico were downloaded (INE-SEMARNAT, 2011). These data were generated by downscaling WKHUHVXOWVRIWKHJOREDOFLUFXODWLRQPRGHOV *&0 used in the Fourth Assessment Report of the IPCC in 2007 (IPCC, 2008).
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2.4 Meaning and process for determining LWE 7KH /:( DV DQ HQYLURQPHQWDO TXDOLW\ LQGH[ UHSresents the magnitude (t ha–1 yr–1) of soil loss by wind, which theoretically is incorporated as an additional burden at different heights in the atmosphere. In the lower layer, the air carrying soil has direct effects on human health because it is the layer of air that people breathe. Such impacts can reach many kilometers from where it is produced, affecting both rural and urban areas. &DOFXODWLQJWKHEDVHOLQH/:( IRU EHJDQ with the determination of the dominant environmental factors in erosion, the action of water or wind. The values depend on indices related to edaphic properties (CATEX, Spanish acronym), use of soil (CAUSO, Spanish acronym), susceptibility of soil to erosion (CAERO, Spanish acronym) and topographical conditions (CATOP, Spanish acronym), which apply according to the predominant process in the erosion. The basis for the calculation of the erosion rates is in both cases (water or wind) related to the availability of humidity as a result of the presence of rain > 10 mm. Therefore, the mean annual rainfall (MAR) was determined to estimate the rain aggressiveness LQGH[ 5$, DQGZLQGDJJUHVVLYHQHVVLQGH[ :$, Both indices depend on MAR, which determines WKHJURZWKSHULRG *523( GH¿QHGDVWKHQXPEHU of days per year with availability of water and temperatures conducive to the development of a crop. 7KLV*523(LQGH[KDVEHHQVXFFHVVIXOO\XVHGE\ Monterroso et al. (2011) in a recent study in which they assess the impact of climate change on rain-fed maize in Mexico (Fig. 2). 7KH¿UVWVWHSRIWKLVSURFHVVZDVWRPDNH]RQLQJ PDSVIRUWKH5$,DQG:$,ZKLFKZHUHFDOFXODWHG
using digitalization processes (interpolation, reclasVL¿FDWLRQDQGUDVWHUYHFWRU LQ$UF*LV (65, according to the following equations: *523( 0$5 ± (MAR)2 – 33.1019
(1)
5$, *523( ±
:$, ± *523(
There are three rules for determining the dominant factor in soil erosion: (1) if the value of the RAI is JUHDWHUWKDQ ! LWLVFRQVLGHUHGD]RQHRILQÀXHQFHIRUWKHVWXG\RIZDWHUHURVLRQ LIWKHYDOXH RIWKH:$,LVJUHDWHUWKDQ ! LWLVFRQVLGHUHG D]RQHRILQÀXHQFHIRUWKHVWXG\RIZLQGHURVLRQ DQG (3) if both factors reach the threshold values, then the magnitude of erosion is calculated separately, or without erosion. In accordance with these decision rules, the applied analysis (Fig. 2) determined that laminar water HURVLRQLQQRWGRPLQDQWLQWKHWRZQRI*yPH]3DODcio, so this part of the methodology only describes the procedure to determine the magnitude and distribution of laminar soil erosion by wind. To calculate /:(LQWKD–1 yr–1, the following equation was used: /:( :$, &$7(; &$862
2.5 Aridity index as a drought indicator De Martonne’s AI has been used to characterize climate and indicate drought (Mercado-Mancera et al., 2010). For this study, the following expression was applied:
RAI (Eq 2)
Index (>50)
Water erosion Defining areas by type of erosion
Zoning Maps
GROPE (Eq 1) Wind erosion
Index (>20)
WAI (Eq 3)
Fig. 2. Flowchart of zoning maps for the generation of aggressiveness indices IRUUDLQ 5$, DQGZLQG :$,
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:KHUH$,LVWKHDULGLW\LQGH[ZKLFKWDNHVGLPHQVLRQOHVVYDOXHVEHWZHHQDQG! 0$5LVWKHPHDQ annual rain from historical records and the A2 scenario; MAT is the mean annual temperature from historical records and the A2 scenario; and 10 is a constant value derived from De Martonne’s model (Table I). Table I. De Martonne’s aridity index. AI value 10-20 20-30 !
&ODVVL¿FDWLRQ Desert (hyperarid) Semi-desert (arid) Semiarid Mediterranean type Subhumid :HW Perwet
Source: http://www.miliarium.com/prontuario/MedioAmbiente/Atmosfera/IndicesClima.htm#De_Martonne.
2.6 Origin and management of climate data 'DWDIURPZHDWKHUVWDWLRQV :6 ZHUHXVHG from the SMN and one from the Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias (INIFAP, National Institute for Forestry, Agricultural and Livestock Research), located at the northern region of the municipality. All of them fell within a UDGLXVRIDSSUR[LPDWHO\NP)RXU:6FRUUHVSRQG WR WKH VWDWH RI &RDKXLOD :6 ,' QXPEHUV DQG DQGWKHRWKHUQLQHWRWKHVWDWH of Durango. The metadata for the future scenarios are in this coverage as well (Fig. 3). 2.7 Indices related to edaphic properties and land use: CATEX and CAUSO The estimated values of the indices CATEX and CAU62IRUWKHPXQLFLSDOLW\RI*yPH]3DODFLRZHUHFUHDWHG by manipulating the attribute tables of vector data sets containing the CATEX index. The indices were calculated according to matching criteria between the type RIVRLOVXVHGLQ0H[LFRE\,1(*,LQWKHFDUWRJUDSKLF
ºW
Coordinates y x
Height m 1136
10085
Presa Cuije
103.300
25.700
1116
5028 5029
Presa Guadalupe Presa La Flor
103.230 103.350
25.767 25.083
1112 1276
10004
Presa de Fernández
103.750
25.283
1360
10009
Cd. Lerdo (SMN)
103.520
25.533
1135
10045
Mapimí (km 29)
103.850
25.817
1325
10055
Pedriceñas
103.750
25.083
1330
10108 10140
Cd. Lerdo (DGE) La Cadena
103.370 104.167
25.500 25.533
1139 2230
10049
Nazas
104.117
25.233
1264
10085
Tlahualilo
103.483
26.168
1096
CNID-RASPA
103.476
25.886
1140
Tlahualilo
26
Col. Torreón Jardín
5027
COAHUILA STATE
Mapimí 26
5006
--- degrees --103.400 25.533
NOR
DURANGO STATE
10045 Gómez Palacio
5028
5027
25.5
WS Name
103
103.5 N
Weather stations (WS) selected to characterize the climate of Gomez Palacio Id WS
104
ºN
MAR 10 + MAT
Lerdo 10009
10140
NOR 5006 25.5
AI =
483
10108
Id WS= Identification key for WS; Coord= Coordinate; NOR = Not oficially registered in the WS-SMN.
Nazas
10004
10049 Leyend WS, Id number Node scenarios GP, municipality
5029 25
Municipal limit
S. Bolivar
10055 25
Note: WS and nodes distributed every 50 km for Future Scenarios 2010-2039.
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Table II. CATEX and CAUSO indexes. CATEX
TCPP
&,1(*,
CAUSO
Category and use
0.87
1 2 3 SPB
Coarse Medium Fine 33*
0.70 0.20 0.30
C1, rainfed agriculture C2, irrigated agriculture C3, scrubland C4, grassland
TCPP: Textural class and physical phase; SPB: Stony phase or burdensome; &,1(*, 7H[WXUDOFODVVDFFRUGLQJWR,1(*,
series I and II. Series I was elaborated based on the VRLOV FODVVL¿FDWLRQ GHYHORSHG E\ )$281(6&2 LQ DQG PRGL¿HG E\ &(7(1$/ LQ .UDVLOnikov et al 6HULHV,,ZDVEDVHGRQWKH:RUOG Reference Base for Soils (FAO-ISRIC-ISSS, 1998). The soils of the municipality are almost entirely of FDOFDUHRXVRULJLQVRZHVHOHFWHGWH[WXUHTXDOL¿FDWLRQ and physical phase. The CAUSO index, associated with the distribution and abundance of vegetation, was HVWLPDWHGEDVHGRQWKHLGHQWL¿FDWLRQRIIRXUYHJHWDWLRQ groups, as well as the land dedicated to agricultural use and under irrigation (Table II). 2.8 Metadata for regionalized climate scenarios (2010-2039) to determine the LWE To assess the climate changes, metadata were downloaded from the SEMARNAT-INE website (http:// zimbra.ine.gob.mx/escenarios/) corresponding to MAT and MAR for three future scenarios: A2, A1B and B1 for the 2010-2039 period. In the original metadata, each RQHKDVDVSHFL¿FIRUPDW UDLQDQRPDOLHVDUHLQSHUFHQW and the temperature is in degrees Celsius (Table III). The climatic future scenarios produced by Magaña and Caetano (2007), with anomalies for rain and temperature (shown in Table III), are the result of numerous experiments based on the 24 *&0SURSRVHGE\WKH,3&&,QIDFWWKLVZRUNLVWKH RULJLQRI*+*HPLVVLRQOLQHV ,3&& ZKRVH characteristics are as follows: Scenario A1: Assumes a very rapid global economic growth, up to doubling the world population by mid-century, and the rapid introduction of new and PRUHHI¿FLHQWWHFKQRORJLHV,WLVGLYLGHGLQWRWKUHH JURXSVZKLFKUHÀHFWWKUHHDOWHUQDWLYHGLUHFWLRQVIRU technological change: intensive fossil fuels (A1FI), non-fossil energy (A1T), and a balance between the various energy sources (A1B). Scenario B1: Describes a convergent world with the same population as 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 and both slow economic development and technological change. 2.9 Geostatistical analysis and assessing impact The impact analysis of future climate variability in rainfall and temperature began with the creation of a projected layer (shp), according to the locations (x, y) of the weather stations. The data were interpolated for each of the variables included in the study (zi), XVLQJDQLQYHUVHGLVWDQFHZHLJKWHG ,': PHWKRG in ArcMap 10 (ESRI), the same method used by .DUDFD 7KH¿UVWSURGXFWRIWKLVSURFHVVZDV the creation of a raster layer adjusted to maximum and minimum extreme values. The second product was a change of the properties of the statistical and pixel raster image in tree classes for calculating the changes from historic or current data to the future VFHQDULRV1H[WZHFRQYHUWHGWKHFODVVL¿HGUDVWHU image to a vector format, from which it was possible to determine the surface terms most impacted. 3. Results and discussion 7KH :6 GDWD VHW VKRZQ DERYH 7DEOH ,,, ZDV calculated for each variable; for the mean annual rainfall, percent anomalies were subtracted, and for the temperatures, the anomaly value was directly added. The percentage anomalies for both MAR and MAT were very similar among the three future scenarios described by Magaña and Caetano (2007), A2, A1B and B1. Therefore, this single study focused only on local impacts of the A2 scenario. 3.1 Analysis of the MAR impact The values in Table IV, which compares the projected 0$5WRWKHKLVWRULF +0$5 DUHFRQVLVWHQWZLWK WKH PD[LPXP PP DQG PLQLPXP UDLQIDOOV
7DEOH,,,+0$5DQG+0$7DQGDQRPDOLHVUHJLRQDOL]HGIRU0H[LFRIRUWKUHHIXWXUHVFHQDULRVRIWKH SHULRG Coordinates :6,G
:6QDPH
/RQJ:
Lat. N
10004 10009 10108 10140 10049 NOR
Col. Torreón Jardín Presa Cuije 3UHVD*XDGDOXSH Presa La Flor Cañón de Fernández Cd. Lerdo (SMN) Mapimí (km 29) Pedriceñas &G/HUGR '*( La Cadena Nazas Tlahualilo CENID-RASPA
103.4 103.3 103.23 103.37 104.117 103.483
Aver. Stdev
1112 1330 1139 2230 1127 1280 301
194 203.3 280.3 320.4 320.1 270.1 274.2 348.9
Anomaly A2
A1B
B1
MAT +0$7
± –3.32 –3.32 ± –3.98 –2.98 –2.98 –3.98 ± ± –2.81 ± –3.32 ±
–3.08 –3.08 –3.08 –2.87 –3.94 ± ± –3.94 –2.87 –2.84 ± –2.12 –3.07 ±
Anomaly A2
A1B
B1
0.929 0.929 0.973 0.92 0.933
0.838 0.838 0.838 0.823 0.823 0.834 0.827 0.838 0.007
& –2.79 –2.79 –2.79 –2.94 ± –2.24 –2.24 ± –2.94 –2.73 ± –2.74 –2.78 –2.84 0.44
22 21.9 20.9 22.1 21 19.3 21 21.2 20.3 20.9
0.883 0.008
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MAR Elevation +0$5 m mm
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Table IV. The relative impact (RI) on MAR in the future scenario A2. MAR Range
+LVWRULF +0$5
Impact
Scenario A2 (MARScA2)
ACh +0$5±0$5ScA2)
mm Lower Upper Average
202.9
237.4
9
RI 3.2 3.13
0$5 0HDQDQQXDOUDLQIDOO $&K $EVROXWHFKDQJH 5, $&K+0$5
PP LQ WKH :6 LQFOXGHG LQ WKH SUHVHQW study. For the future scenario A2 (MARScA2), which is given by subtracting the average negative anomalies, WKHORZHUOLPLW PP LVPRUHDIIHFWHGZLWKD OLNHO\GHFUHDVHRIPPSHU\HDU &RPSDUHGWR+0$5, in the MARScA2, the impact of such anomalies on the spatial distribution of the gradient in annual rainfall from west to east (Fig. 4b) is to decrease MAR for both the lower and upper UDQJHVDWDQG PPUHVSHFWLYHO\ZKLFKHTXDOV DQDYHUDJHLPSDFWRI 7DEOH,9 To complement the analysis of the changes in MAR IURP+0$5WR WKH$VFHQDULR DQDGGLWLRQDO digital process was performed over the “raster” images WKDWUHSUHVHQWWKHGLVWULEXWLRQRIWKH+0$5ZLWKWKH objective of comparing similar ranges. Data for each WLPHSHULRGZDVUHFODVVL¿HGLQWRWKUHHFDWHJRULHVDQG the involved surfaces were calculated (Fig. 4c). :HWKHQFRPSDUHGWKHKLVWRULFDOQXPEHURISL[HOV and the surface area in each category to the projected scenario. Pixels from each period were divided into WKUHHFODVVHV DDDQGDIRU+0$5DQGEE and b3 for MARScA2). The lowest category included lower values in MARScA2)URP+0$5WR$ of the surface shifted from class 1 to 2, and between FODVVDDQGEWKHVXUIDFHORZHUHG,QVFHnario A2, the class with the lowest values for MAR WKH VXUIDFH DUHD LQFUHDVHG while the surface of the class with the highest values GHFUHDVHG FKDQJLQJ IURP WR KD,WLVDOVRLPSRUWDQWWRPHQWLRQ the following: The average MAR decreased 11.2 mm, to move IURPWRPPDQQXDOO\EHWZHHQKLVWRULFDO values and the future (Fig. 4c). In the above-mentioned class with the highest YDOXHV WKHDIIHFWHGDUHDH[WHQGVIURP
the eastern boundary with the municipality of Matamoros, Coahuila, to the north by Tlahualilo, to the western boundary near Arcinas and Pastor Rovaix, and to the north near Lucero and Banco Nacional (Fig. 4b). There is a concentration of pixels in the lower UDQJHLQVFHQDULR$ E ZKLFKPDJQL¿HVWKHLPpact. Although the mean difference between a3 and b3 is 8.9 mm, the impact is much greater because of the number of values concentrated towards the lower limit of that range (Fig. 4d). Finally, it is important to note the anomalies DIIHFWLQJWZR:6 &DxRQGH)HUQiQGH] ,G DQG3HGULFHxD ,G $WWKHVHVWDWLRQVUXQRIIRI surface water begins to feed the reservoirs (Francisco =DUFRGDP DQGWKHDJULFXOWXUDOLUULJDWLRQRI*yPH] Palacio depends on this runoff. In absolute terms, the MAR could exceed the average for the three scenarios; i.e., the consequence of this declination index is DSSUR[LPDWHO\ 3.2 Calculation and zoning of the GROPE 7KH*523(LVDQLQGH[WKDWZDVFDOFXODWHGEDVHGRQ Eq. (1), where the independent variable is the MAR. Therefore, it compares anomalies in average annual UDLQIDOOLQKLVWRULFDODQGWKHIXWXUHVFHQDULR$:H FRPSDUHG+*523(WR*523(ScA27KH+*523( LQGH[IHOOE\DQDYHUDJHRILQWKHIXWXUH$ VFHQDULR *523(ScA2). In addition, the boundaries of the ranges change between the historic values DQGWKH*523(ScA2 (12.8-31.1), as shown in Table V. Based on the relationship that exists between the 0$5DQG*523(LQGH[ZHZRXOGH[SHFWWKH0$5 to be affected to the same extent, although it is not easy to determine the magnitude of the impacts of this signal in the limits of the range considered. To clarify
Modeling the potential impact of climate change
487
(a)
(b) a2
Arturo Martínez Adame
Arturo Martínez Adame
Seis de Octubre
Seis de Octubre a1
b1
b2 a2
b3
a3 San Martín
San Martín
HMAR
MARScA2
Rank, mm 287.1
Rank, mm 278.5 196.4
Analysis of the relative impact (IR) on mean annual rainfall (MAR) for the future scenario A2 Surface ha
a) HAAR
281.7
(d)
250000
(c)
Mean annual rainfall, MAR
Gómez Palacio
259.7
Inhabited areas Municipal limit Road Railroad Class limit
Mean
Inhabited areas Municipal limit Road Railroad Class limit
237.6
Gómez Palacio
200000
Class
Range mm
IR %
HMAR
a1 a2 a3
202.9 – 237.6 237.6 – 259.7 259.7 – 287.1
19601 24750 39887
23.27 29.38 47.35
MARScA2
b1 b2
196.4 – 237.6 237.6 – 259.7
26505 32777
31.46 38.91
b3
259.7 – 278.2
24955
29.62
Pixels
202.9
150000
Range a1
Range a3
Range a2
100000 50000 0
281.7
259.7
b) A2 AAR
266.0893936 287.120239
225.058548
Mean
250000
224.0277023
237.6
202.9968567
Pixels
200000 150000
Range b2
Range b1
Range b3
100000 50000 0 196.4049835 216.8509729 237.2969624 257.7429518 278.188941
Rain, mm
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Impact
+LVWRULF +*523(
Scenario A2 *523(ScA2
ACh +*523(*523(ScA2)
5,
14.2 32.9
12.7 31.1 21.9
1.8 1.7
7.18
5, $&K+*523(
WKLVLQWHUPVRIWKHLPSDFWDWWKHVXUIDFHOHYHO )LJ WKDW ZRXOG EH DIIHFWHG WKH *523( LQGH[ LPDJHV ZHUH DOVR UHFODVVL¿HG LQWR WKUHH FODVVHV )LJ F and compared. First, between classes a1 and b1, there is a projectHGLQFUHDVHRI KDHTXLYDOHQWWRZKLFK would imply an extension of 2.7 km from east to west. This extension would occur from Esmeralda, where FXUUHQWO\WKH+*523(LVORFDWHGWRWKHZHVWLQWKH vicinity of Pastor Rovaix and Consuelo. Second, between classes a2 and b2, there is a projected increase RIKDHTXLYDOHQWWR ZKLFKZRXOGLPSO\ DQ H[WHQVLRQ RI DSSUR[LPDWHO\ NP IURP HDVW WR west. This extension would occur from La Popular, ZKLFKLVFXUUHQWO\+*523(WRWKHZHVWQHDUWR(O Cairo, Estación Noé, La Plata and Bucareli. Third, there is a double impact in the shift between classes DDQGE7KH*523(LQGH[ZRXOGKDYHDUHGXFWLRQ RIWZRXQLWVIURPWKH+*523( WRWKH *523(ScA2 DQGWKHLQGH[DOVRKDVORZHU PD[LPXPYDOXHVLQFODVVE )LJG 3.3 Calculation and zoning of the WAI $VLQWKHSUHYLRXVFDVHVWKH:$,FRXOGEHDIIHFWHG LQWKHIXWXUH$VFHQDULR :$,ScA2) because of the way the MAR is affected, although the relationship LVPRUHGLUHFWIRU*523(DVGHVFULEHGLQWKHPHWKRGRORJ\ (T :$,FRXOGWKHUHIRUHEHLPSDFWHG LQ FOLPDWH VFHQDULR$ IRU WKH WHUULWRU\ RI *yPH] 3DODFLRE\DQDYHUDJHUHGXFWLRQRIDQGDFKDQJH LQWKHUDQJHRIYDOXHVIURPKLVWRULFDOOHYHOV WRWKH$VFHQDULR DVVKRZQLQ Table VI. 7KHVSDWLDOGLVWULEXWLRQRIWKH:$,LVSUHVHQWHGLQ )LJXUH2QHVDOLHQWFKDQJHLVWKHJURZWKEHWZHHQ classes a2 and b2, which is estimated to affect a WRWDORIKDGHULYHGIURPDQLQFUHDVHRI EHWZHHQ+:$,DQG:$,ScA2 )LJF 7KLVLQFUHDVH
includes the area known as Perímetro Lavín, where the following stations are located: El Cairo, Competencia, Noé, Dolores, Numancia and Britingham )LJE 3.4 Calculation and zoning of the CAUSO index 7KH&$862LVDQLQGH[GH¿QHGE\FXUUHQWODQGXVH (Table II), and the determination of the surfaces that will be occupied by urban nuclei on a horizon of approximately 30 years (2010-2039) was based on the programa de desarrollo urbano (PDU, program RIXUEDQGHYHORSPHQW RIWKHPXQLFLSDOLW\RI*yPH] Palacio (SEMARNAT, 2012). The distribution of the CAUSO index is shown in Figure 7, where future impacts can be assumed because of the drastic changes LQFDWHJRULHV&DQG&)RUWKH¿UVWFDWHJRU\WKHUH is a probable decrease in scrubland located in the western part of the municipality near Poanas, Dinamita and San Martín. The extent would fall from the KD FXUUHQWO\HVWLPDWHGWR KD E\)RUWKHVHFRQGFDWHJRU\WKHUH is an estimated increase in the vegetation associated ZLWKVDQG\GHVHUWV +DORSK\WH LQWKHQRUWKRIWKH municipality, which would grow from 4473.1 ha WR KD )LJF 3.5 Estimation and zoning of the LWE rate The rate of /:(QRWRQO\UHSUHVHQWVWKHTXDQWLW\RI soil loss per year but is also a way to evaluate enviURQPHQWDOORVV7RGHWHUPLQHWKH/:(UDWHDGLJLWDO process was conducted based on algebra maps using matrix operations and between digital layers (raster) using Eq. (4). The inputs described above in the context of the A2 scenario were used, as in CAUSO DQG:$,ZLWKWKHH[FHSWLRQRIWKHHGDSKLFLQGH[ (CATEX). Edaphic characters such as the texture and the physical phase (stoniness factor) are changes not easily predictable in a short period of time.
Modeling the potential impact of climate change
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(b) a2 Arturo Martínez Adame
Arturo Martínez Adame
Seis de Octubre
Seis de Octubre b1
a1
b2
a2
a3
b3
San Martín San Martín HGROPE Rank High: 32.9
GROPE ScA2 Rank High: 31.1
Low: 14.2
Low: 12.7 Gómez Palacio
Class limit Municipal limit Inhabited areas Road Railroad
Class limit Municipal limit Inhabited areas Road Railroad
Gómez Palacio
Class
HMAR
a1 a2 a3
14.2 – 21.9 21.9 – 26.8 26.8 – 32.9
19101 24281 40856
22.67 28.82 48.50
b1 b2
12.7 – 21.9 21.9 – 26.8
26038 31957
30.91 37.94
b3
26.8 – 31.1
26244
31.15
Surface ha
IR %
Pixels
32.9
200000
GROPE index
Range (index)
26.8
a) HGROPE
Mean
250000
Analysis of the relative impact (RI) in the GROPE index for the future scenario A2.
21.9
(d) (c)
150000
Range a1
Range a3
Range a2
100000
50000 0
31.1
b) GROPEScA2
26.8
250000
Mean
202.9968567 287.1202 266.0893936 32.9699 224.0277023 23.60838509 225.058548 28.28917027 14.24681473 18.92759991
21.9
GROPEScA2
Pixels
200000 150000
Range b2
Range b1
Range b3
100000 50000 0 196.4049835 17.33757305 278.1889 257.7429518 31.0779 216.8509729 21.91771126 237.2969624 26.49784946 12.75743484
Rain, mm
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Impact
+LVWRULF +:$,
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ACh +:$,±:$,ScA2)
5,
149.9 142.7
137.0 144.12
1.42
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The results indicate an average impact on the RUGHURIGHULYHGIURPDFKDQJHLQWKHUDQJH LQWKHKLVWRULFDOODPLQDUZLQGHURVLRQ +/:( UDWH IURPWRWKD–1 yr–1 in the future for WKH$VFHQDULR /:(ScA2), as shown in Figure 8c. A more detailed analysis of the impact associated with the increased susceptibility of soils to wind erosion and by a lower availability of moisture because of the lower MARScA2 indicates that this HIIHFW PLJKW EH JUHDW DQG FRXOG LQÀXHQFH QHDUO\ one third of the municipality (Fig. 8d). The spatial distribution of the three classes of erosion is shown in Figure 8b. 3.6 Analysis of the variables related to the aridity index ,Q DGGLWLRQ WR WKH FDOFXODWLRQ IRU /:( DV VKRZQ above, the aridity index provides a good complement to determine the magnitude of probable impacts for decreasing MAR and increasing MAT. AI has been examined in the three climate scenarios analyzed for the north and northeast of Mexico (Magaña and Caetano, 2007; Magaña et al., 2012). 3.7 Changes in mean annual temperature According to the climate scenarios in the report of the IPCC (2007), the mean temperature in the northern hemisphere during the second half of the 20th cenWXU\ZDVOLNHO\KLJKHUWKDQDQ\RWKHU\HDUSHULRG LQWKHODVWDQGPRVWOLNHO\WKHKLJKHVWRYHUWKH past 1300 years. $FFRUGLQJWRWKH,3&&UHSRUW VLJQL¿FDQW changes were observed in the data for physical (snow, ice and frozen ground; hydrology; and coastal processes) and biological systems (land, marine and freshwater biological systems), and there was great variation in the air surface temperature during the period 1970-2004. The temperature changed on
WKHRUGHURIWR&ZLWKLQNPWRWKHQRUWKRI WKH7URSLF RI &DQFHU 1 7KLV LQFUHDVH ZLOO affect almost all of the municipality, but especially the northern area, where Seis de Octubre and Arturo Martínez Adame are located. Temperature and rain are obviously fundamental variables in the analysis of the deterioration of natural resources. Both are processed for the historic data set and later were calculated since the anomalies. The results in Table III suggest that the temperature change HVWLPDWHGE\+0$7ZRXOGEHIURPWR& in the future A2 scenario (MATScA2). 3.8 Changes in the aridity index *LYHQWKHUHODWLRQVKLSWKH'H0DUWRQQHPRGHO (T HVWDEOLVKHV IRU 0$5 DQG 0$7 DV PHQWLRQHG above, a decrease of rain will accompany an increase in temperature. In a future scenario there may be environmental deterioration manifested by an increase in aridity and prolonged drought as reported previously for North America and particularly for Mexico )UHGHULFN DQG *OHLFN 8$&+&21$=$ 6('(62/6$*$53$ ,3&& According to the calculations made from anomalies already described for the MAR and the MAT (Table III), the aridity index in the municipality of *yPH]3DODFLRZRXOGFKDQJHIURPWKHKLVWRULFYDOXHV +$, RI WRLQWKHIXWXUHVFHQDULR$ $,ScA2). It is estimated that the average impact on this index LQWKHIXWXUHFRXOGEH± 7DEOH9,, The estimated impact, in terms of surface area IRUWKH$,LQGLFDWHGWKDWWKHPXQLFLSDOLW\RI*yPH] Palacio would most likely become more sensitive due to the tendency toward a hyper-arid condition, and UHDFKLQGH[YDOXHVLQWKHUDQJHRIWRDVDOUHDG\ VSHFL¿HG DERYH 7DEOH , 7KH LPSDFW LQ WKLV FDVH could cover an area of 13 072 ha, equivalent to the RIWKHPXQLFLSDOWHUULWRU\ )LJ F
Modeling the potential impact of climate change
491
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a2 Arturo Martínez Adame
Arturo Martínez Adame
Seis de Octubre
Seis de Octubre a3
b3
a2 b2 a1 San Martín
b1 San Martín WAIScA2 Rank High: 151.0
HWAI Rank High: 149.9 Low: 135.5
Low: 137.0 Gómez Palacio
Class limit
Class limit
Municipal limit Inhabited areas Road Railroad
Gómez Palacio
Municipal limit Inhabited areas Road Railroad
Class
Range (index)
HWAI
a1 a2 a3
135 – 140.1 140.1 – 143.8 143.8 – 149.85
38972 24792 20474
46.26 29.43 24.31
WAIScA2
b1 b2
137.2 – 140.1 140.1 – 143.8
23622 33218
28.04 39.43
b3
143.8 – 151.05
27399
32.52
Surface ha
IR %
200000
Pixels
WAI (index)
150000
149.86
143.8
a) HWAI
Mean
250000
Analysis of the impact on historical index of aggressiveness of the wind (HWAI) and the rate of aggression for the future scenario A2 (WAI Sc A2)
140.1
(d) (c)
Range a1
Range a2
Range a3
100000
50000 0
Pixels
200000 150000
Range b1
Range b2
151.05
Mean
140.1
b) WAIScA2 250000
143.8
202.9968567 266.0893936 287.120239 224.0277023 142.7130514 225.058548 146.2844742 135.5702057 139.1416285 149.85589
Range b3
100000 50000 0 196.4049835 110.5278397 257.7429518 216.8509729 111.0362211 237.2969624 117.5116091 278.188941 137.019155 151.05299
Rain, mm
)LJ7KHVSDWLDOGLVWULEXWLRQRI+:$, D DQG:$,ScA2 (b); analysis of relative impact (RI) based on affected surface F UHVXOWVRIWKHUHFODVVL¿FDWLRQRILPDJHVDDQGE G
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(a)
(b)
Arturo Martínez Adame
Arturo Martínez Adame
Seis de Octubre
Seis de Octubre
San Martín
n
San Martín Index CAUSO 2010 0.15 0.2 0.3 Inhabited areas Artificial surface Nazas river Road Railroad
CAUSOScA2 Index 0.15 0.2 0.3 Inhabited areas Artificial surface Municipal limit Nazas river Road Railroad
Gómez Palacio
Gómez Palacio
(c) 60 50 Surface, ha (thousand)
40 30 20 10 0 Scrubland C3
Agricultural irrigation C2
Grassland C4
CAUSO 2010
19 793.5
49 336.9
4 473.1
CAUSOScA2
8 675.5
47 019.7
15 390.2
Figure 7. Spatial distribution of CAUSO current (a) and CAUSOScA2 (b); impact analysis based on surface and relative importance (c).
It is also important to mention that in addition to the reduction in the range of index values from hisWRULFDOOHYHOV WRWKHIXWXUHVFHQDULR$,ScA2 WKHDYHUDJHRIWKLVLQGH[FKDQJHGIURP WR $GGLWLRQDOO\ WKHUH LV JUHDWHU DULGLW\ in the highest class, as seen in the density of pixels between the two cases (Fig. 9d). 3.9 Environmental quality analysis and probable impacts Environmental quality is a term that relates to certain conditions of “comfort” for people and natural environments or biological systems. The critical elements
are the availability of fresh water, air quality and WHPSHUDWXUH .DUDFD .XR-HQ et al., 2012). The water in the form of rain becomes important in regulating temperature and its relationship with extreme events, such as drought and dust storms, among others (Sun et al., 2003; Batjargal et al Zhang et al., 2012). Arid and semiarid regions will likely experience an increase in temperature and decrease in rain (Rivera et al *DUFtD3iH]DQG&UX]0HGLQD Magaña et al., 2012), especially for latitudes similar WR WKH %ROVyQ GH 0DSLPt WKH DUHD ZKHUH *yPH] Palacio is located.
Modeling the potential impact of climate change
493
(a)
(b)
Arturo Martínez Adame Arturo Martínez Adame
Seis de Octubre Seis de Octubre
San Martín San Martín
HLWE Class Low Moderate High Inhabited areas (2010) Artificial surface Municipal limit Nazas river Road Railrad
LWE ScA2 Class Low Moderate High Inhabited areas (2035) Artificial surface Municipal limit Nazas river Road Railrad
Gómez Palacio
(c)
(d) Changes between class erosion to HLWE and LWE ScA2
Analysis impact of the LWE in the future A2 scenario Rank limit
From HLWE
to LWEScA2
--- Impacts --ACh RI
---------- t ha-1 yr -1 ---------Lower Upper Average
36 106 71
Gómez Palacio
36 151.4 93.7
0 45.4 22.7
Rank
Surface
RI
HLWE
Class Low Moderate High
(t ha -1 yr -1) 36 - 41 41 - 55 55 - 106
ha 23 435 55 130 3 023
% 28.7 67.6 3.7
Low Moderate High
36 - 41
LWEScA2
7 303 45 950 17 296
10.4 65.1 24.5
Period
% 0 126.11 63.06
41 - 55 55 - 151.4
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Although this study used an average temperature LQFUHDVHRIOHVVWKDQ &DQGDUDLQIDOOGHFOLQHRI DSSUR[LPDWHO\WKHFKDQJHVPD\EHODUJHU)RU example, Magaña et al. (2012) remarked that for the north of Mexico in the future A2 scenario, the average temperature towards the end of the 21st century FRXOGLQFUHDVHIURP& ZLWKH[WUHPHVIRU WKHGULHUPRQWKV 0DUFK$SULODQG0D\ XSWR& ZKLOHUDLQIDOOFRXOGGHFOLQHE\XSWR This decrease in humidity, as shown in Figure 8, could not only negatively impact the rates of soil erosion, with probable losses in the future scenario $RIXSWRWKD–1 yr–1, but could also result in changes in the systems of biological feedback (Breshears et al. +DUSHUet al., 2010), a crisis
of freshwater availability and negative effects on the air quality due to a drastic increase in suspended particles. This change is potentially harmful to people´s health (Razo et al., 2004; Rashki et al., 2011) and could affect the albedo at the level of the surface of the Earth and the atmosphere (Batjargal et al. +DN6XQJet al., 2011). Figure 10 shows DQDHULDOYLHZRI*yPH]3DODFLRPXQLFLSDOLW\DQG an example of qualitative laminar wind erosion. 7KHUDWHRIODPLQDUZLQGHURVLRQZDVWKD–1 –1 yr , equivalent to the removal and transport of a soil OD\HUEHWZHHQDQGPPWKLFN,IWKHVRLOLV medium textured over the entire area of study, this amount is between 1100 and 1200 kg m–3, which implies a potential loss of approximately 0.4 m in
A. López Santos et al.
494
7DEOH9,,(VWLPDWLRQRIFKDQJHVLQWKH$,IURPWKHKLVWRULF +$, WRIXWXUH$VFHQDULR $,ScA2 LQ*yPH] Palacio. Coordinates :6,G
Name :6601
Long.
Aridity Index
Lat.
+$,
Degrees 10004 10009 10108 10140 10049 NOR
Col. Torreón Jardín Presa Cuije 3UHVD*XDGDOXSH Presa La Flor Cañón de Fernández Cd. Lerdo (SMN) Mapimí (km 29) Pedriceña &G/HUGR '*( La Cadena Nazas Tlahualilo CENID-RASPA
103.400 103.300 103.230 103.370 104.117 103.483
AIScA2
ACh
Index
Aver. Stddev
8.0 9.1 10.0 9.2 10.9 8.7 8.8 8.7 9.3 2.1
9.3 8.7 10.3 12.7 8.3 8.2 10.9 8.3 8.7 2.0
0.41 0.38 0.47 0.91 0.38 0.4 0.20
$&K $EVROXWHFKDQJHRQDULGLW\LQGH[ +$,±$,ScA2); the value of the historic temperature (bold) for the :6601ZDVDYHUDJHGXVLQJWKHYDOXHVIURPWKHQHDUHVWQHLJKERUKRRG $YHU $YHUDJH 6WGGHY 6WDQGDUGGHYLDWLRQ 125 1RWRI¿FLDOO\UHJLVWHUHGLQWKH:6601
30 years. For this type of soil, these values exceed WKHUDWHVRIVRLOORVVWROHUDQFH WKD–1 yr–1) proposed by the United States Department of Agriculture (USDA) (Montgomery, 2007). An example of changes in the biological feedback is that the removal of the soil directly affects the role of nitrogen (N). N determines plant productivity, and in arid environments, the soil frequently presents large accumulations of calcium carbonate that push the soil S+WRDUDQJHRIWR$WWKLVS+SKRVSKRUXV 3 LV in complex forms and not available (Munson et al., 2011; Schlesinger et al., 2011), a set of conditions that matches the selection criteria of the parameters for the CATEX index that was described in the methodoloJ\,QUHVSRQVHWRDGH¿FLHQF\RI1DQG3Larrea tridentataVKRZVWKHKLJKHVWOHYHOVRIHI¿FLHQWXVHRI nutrients that have been found in woody plants. 4. Conclusions 7KH PRGHOV IRU WKH /:( DQG WKH$, VXJJHVW WKDW in the immediate future (2010-2039) the climatic conditions of the area of study and its surroundings will deteriorate and could lead to a steady decline of environmental quality and health. There are two
important implications of this study. First, the models can serve to warn people of the risks to human and natural environments from extreme conditions such as droughts and dust storms. Secondly, it is possible to identify local changes in use of the soil and deforestation. Finally, it is important to mention that the magnitude and distribution of the impact on the territory are relevant to planning the management of natural resources. This research should be taken into account in decision-making about preventing impacts on natural resources now and in the future. Acknowledgements This article is part of the results of the research carried out within the framework of the Programa Estatal de Acciones ante el Cambio Climático (State Program of Action on Climate Change) Durango 3($&&'*2 DQG$JXDVFDOLHQWHV 3($&&$*6 during a post-doctoral stay conducted between 2011 and 2012. Thanks to Joaquín Pinto Espinoza and Adriana Martínez Prado, both responsible for the 3($&&'*2 DQG (OVD 0DUFHOD 5DPtUH] /ySH] KHDGRI3($&&$*6
Modeling the potential impact of climate change
495
(a)
(b)
Arturo Martínez Adame
Arturo Martínez Adame
Seis de Octubre
Seis de Octubre
San Martín San Martín HAI Clase Low Moderate High Inhabited areas (2010) Artificial surface Municipal limit Road railroad
AI ScA2 Clase Low Moderate High Inhabited areas (2035) Artificial surface Municipal limit Road railroad
Gómez Palacio
Gómez Palacio
High Moderate
6.11 – 7.7 7.7 – 8.5
Low
8.5
– 9.62
9.74 29.75 60.51
21288.9 23998.5
25.26 28.48
0
38981.6
46.26
202.9968567 6.555311203
8.5
Mean Range
Range
High
Moderate
Low
50
150
224.0277023 7.323812962
b) AIScA2
225.058548 8.09231472
287.12023 266.0893936 9.62931 8.860816479
9.17
AIScA2
8209.7 25066.9 50984.5
Range 100
8.5
HAI
6.5 7.7 8.5
RI %
Mean
– 7.7 – 8.5 – 9.62
High Moderate Low
Surface ha
7.7
Rank index
Class
a) HAI
Pixels
Period
7.7
150
Analysis of the relative impact (RI) in the aridity index (AI) to the future scenario A2 (IAScA2).
9.62
(d) (c)
Pixels
100 Range
Range
Range
High
Moderate
Low
50
0
196.4049835 6.880219316 278.18894 257.7429518 9.16679 216.8509729 7.642412376 237.2969624 8.4046054636 6.118026257
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Scrubland & agricultural irrigation. Scale 500 m
Scrubland Scale 500 m
Municipal limit Scale 22.9 km
)LJ&XUUHQWDHULDOYLHZDWGLIIHUHQWVFDOHVRIWKHVXSHU¿FLDODUHDRI*yPH]3DODFLRDQGDFORVHXS to the high critical zone of laminar wind erosion.
References %DWMDUJDO = - 'XODP DQG < 6 &KXQJ 'XVW storms are an indication of an unhealthy environment in East Asia. Environ. Monit. Assess. 114 GRL V %UHVKHDUV''--:KLFNHU03-RKDQVHQDQG-( 3LQGHU:LQGDQGZDWHUHURVLRQDQGWUDQVSRUWLQ semi-arid shrub land, grassland and forest ecosystems: Quantifying dominance of horizontal wind-driven transport. Earth Surf. Proc. Land. 28, 1189-1209, doi:10.1002/esp.1034. )$2,65,&,666 :RUOG UHIHUHQFH EDVH IRU VRLO UHVRXUFHV:RUOG6RLO5HVRXUFHV5HSRUWV)RRGDQG Agriculture Organization of the United Nations, Rome. $YDLODEOH DW KWWS ZZZIDRRUJGRFUHS: ( : (KWP )UHGHULFN.'DQG3+*OHLFN3RWHQWLDOLPSDFWV on US water resources. In: Climate change, science strategy and solutions (E. Claussen, V. Arroyo Cochran DQG ' 3 'DYLV (GV 7KH 3RZ &HQWHU RQ *OREDO &OLPDWH&KDQJH%ULOO%RVWRQSS
*DUFtD ( 'LVWULEXFLyQ GH OD SUHFLSLWDFLyQ HQ OD República Mexicana. ,QYHVWLJDFLRQHV *HRJUi¿FDV 50 *DUFtD3iH])DQG,5&UX]0HGLQD 9DULDELOLGDG GHODSUHFLSLWDFLyQSOXYLDOHQODUHJLyQ3DFt¿FRQRUWH de México. Agrociencia 43, 1-9. +DN6XQJ.&
Modeling the potential impact of climate change
to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (M. L. Parry, O. F. Canziani, J. P. Palutikof, P. J. van der Linden and C. (+DQVRQ(GV &DPEULGJH8QLYHUVLW\3UHVV&DPEULGJH SS IPCC, 2007. Cambio climático 2007: Informe de síntesis. Contribución de los grupos de trabajo I, II y III al Cuarto Informe de Evaluación del Grupo Intergubernamental de Expertos sobre el Cambio Climático (R. .3DFKDXULDQG$5HLVLQJHU(GV ,3&&*HQHYH 104 pp. .DUDFD ) 'HWHUPLQDWLRQ RI DLU TXDOLW\ ]RQHV in Turkey. JAPCA J. Air Waste Ma. 62, 408-419, GRL .UDVLOQLNRY30&*XWLpUUH]&DVWRUHQD5-$KUHQV& 2&UX]*DLVWDUGR66HGRYDQG(6ROOHLUR5HEROOHdo, 2013. The soils of Mexico:RUOG6RLOV%RRN6HULHV 6SULQJHUSSGRL B .XR-HQ/3$PDU(7DJDULVDQG$*5XVVHOO Development of risk-based air quality management strategies under impacts of climate change. JAPCA J. Air Waste Ma. 62GRL Magaña V. O., J. L. Vázquez, J. L. Pérez and J. B. Pérez, 2003. Impact of El Niño on precipitation in Mexico. Geofí. Int. 42, 313-330. Magaña V. O. and E. Caetano, 2007. Pronóstico climático estacional regionalizado para la República Mexicana como elemento para la reducción de riesgo, para la LGHQWL¿FDFLyQ GH RSFLRQHV GH DGDSWDFLyQ DO FDPELR climático y para la alimentación del sistema: cambio FOLPiWLFRSRUHVWDGR\SRUVHFWRU LQIRUPH¿QDO 'LUHFFLyQ*HQHUDOGH,QYHVWLJDFLyQVREUH&DPELR&OLPiWLFR SEMARNAT-INE. 41 pp. Magaña V. O., 2010. Guía para generar y aplicar escenarios probabilísticos regionales de cambio climático en la toma de decisiones. Centro de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México, 80 pp. Magaña V. O., D. Zermeño and C. Neri, 2012. Climate change scenarios and potential impacts on water availability in northern Mexico. Clim. Res. 51, 171-184, GRL &5 0HUFDGR0DQFHUD*(7UR\R'LpJXH]$$JXLUUH*ymez, B. Murillo-Amador, L. F. Beltrán-Morales and J. /*DUFtD+HUQiQGH]&DOLEUDFLyQ\DSOLFDFLyQ del índice de aridez de De Martonne para el análisis del Gp¿FLWKtGULFRFRPRHVWLPDGRUGHODDULGH]\GHVHUWL¿cación en zonas áridas. Universidad y Ciencia 26
497
0RQWHUURVR$,$&&RQGH'5RVDOHV-'*yPH] DQG&*D\$VVHVVLQJFXUUHQWDQGSRWHQWLDOUDLQfed maize suitability under climate change scenarios in Mexico. Atmósfera 24 Montgomery D. R., 2007. Soil erosion and agricultural sustainability. P. Natl. Acad. Sci. USA 104 0XQVRQ 6 0 - %HOQDSD DQG * 6 2NLQE 5Hsponses of wind erosion to climate-induced vegetation changes on the Colorado Plateau. P. Natl. Acad. Sci. USA 108 318'Marco de políticas de adaptación al cambio climático: desarrollo de estrategias, políticas y medidas (B. Lim and E. Spanger-Siegfried, Eds.). Programa de las Naciones Unidas para el Desarrollo, New York, 274 pp. 5DVKNL$&-5DXWHQEDFK3*(ULNVVRQ'*.DVNDRXWLVDQG3*XSWD7HPSRUDOFKDQJHVRISDUticulate concentration in the ambient air over the city of Zahedan, Iran. Air Quality, Atmosphere & Health, GRL V Razo I., L. Carrizales, J. Castro, F. Díaz-Barriga and M. Monroy, 2004. Arsenic and heavy metal pollution of soil, water and sediments in a semi-arid climate mining area in Mexico. Water Air Soil Poll. 152 GRL % :$7(F 5LYHUDGHO5tR5*&UHVSR3LFKDUGR5$UWHDJD5DPtUH] and A. Quevedo Nolasco, 2007. Comportamiento espacio temporal de la sequía en el estado de Durango, México. Terra Latinoamericana 25, 383-392. 5RVH&:(URVLRQE\ZDWHUPRGHOLQJ,Q Encyclopaedia of Soil Science (R. Lal, Ed.). Marcel Decker, 1HZODVW DFFHVVHG RQ0DUFK@ SEMARNAT-INE, 2009. México, cuarta comunicación nacional ante la Convención Marco de las Naciones Unidas sobre Cambio Climático. Secretaría de Medio Ambiente y Recursos Naturales, Instituto Nacional de Ecología, México, 274 pp. Available at: http://www.
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semarnat.gob.mx/informacionambiental/documents/ sniarn/pdf/Cuarta_Comunicacion_Nacional.pdf. SEMARNAT-INE, 2011. Sistema de información de escenarios de cambio climático regionalizados (SIECCRe). 0HWDGDWRV \ PDSDV SDUD 6,*$YDLODEOH DW KWWS ]LPEUDLQHJREP[HVFHQDULRV>ODVWDFFHVVHGRQ6HSWHPEHU@ 6XQ / ; =KRX - /X<3 .LP DQG<6HXQJ &KXQJ 2003, Climatology, trend analysis and prediction of sandstorms and their associated dust fall in China. Water Air Soil Poll. 3 8$&+&21$=$ Escenarios climatológicos de la República Mexicana ante el cambio climático.
Universidad Autónoma Chapingo, Comisión Nacional GH=RQDVÈULGDV0p[LFR SS 9DQ5RRVPDOHQ/726RQQHQERUJ.+-HQVHQDQG -+&KULVWHQVHQ&RPSDULVRQRIK\GURORJLFDO simulations of climate change using perturbation of observations and distribution-based scaling. Vadose Zone J. 10GRL Y]M
Modeling the potential impact of climate change in northern Mexico using two environmental indicators A. LÓPEZ SANTOS Unidad Regional Universitaria de Zonas Áridas, Universidad Autónoma Chapingo, Apartado Postal 8, 35230 Bermejillo, Durango, México Miembro de la Red Temática del Agua del CONACyT Corresponding author; e-mail: [email protected] J. PINTO ESPINOZA Instituto Tecnológico de Durango, Blvd. Felipe Pescador 1830 Ote., Col. Nueva Vizcaya, 34080 Durango, Durango, México E. M. RAMÍREZ LÓPEZ Universidad Autónoma de Aguascalientes, Av. Universidad 940, Ciudad Universitaria, 20131 Aguascalientes, Aguascalientes, México M. A. MARTÍNEZ PRADO Instituto Tecnológico de Durango, Blvd. Felipe Pescador 1830 Ote., Col. Nueva Vizcaya, 34080 Durango, Durango, México Received June 4, 2012; accepted May 28, 2013 RESUMEN La modelación de impactos locales, caracterizados por deterioro de los recursos naturales –especialmente agua y suelo– ante los efectos globales del cambio climático, se ha convertido en una poderosa herramienta en la búsqueda de medidas de mitigación y adaptación. Los objetivos de la presente investigación fueron: 1) evaluar mediante procesos de modelación el impacto potencial del cambio climático para el periodo 2010 \ DGYHUWLUVREUHULHVJRVIXWXURVDSDUWLUGHODLGHQWL¿FDFLyQGHIRU]DQWHVUDGLDWLYRVORFDOHVRiUHDV críticas, considerando el índice de aridez (IA) y erosión laminar del suelo causada por el viento (ELV) como dos indicadores de calidad ambiental. Se usaron técnicas de evaluación de los recursos naturales empleadas por el Instituto Nacional de Ecología y Cambio Climático (INECC) para los estudios de ordenamiento ecológico territorial. Los insumos empleados comprenden información climática actual y futura, cubiertas GHVXHORV\SURSLHGDGHVHGi¿FDVDVRFLDGDVDOPXQLFLSLRGH*yPH]3DODFLR'XUDQJR0p[LFR 1\ : /RVFiOFXORVUHDOL]DGRVDSDUWLUGHODVDQRPDOtDVSDUDORVSURPHGLRVDQXDOHVGHSUHFLSLWDFLyQ\ WHPSHUDWXUDLQGLFDQTXHHOWHUULWRULRPXQLFLSDOHQHOHVFHQDULR$SRGUtDWHQHUXQLPSDFWRSURPHGLRGH causado por la ELV, en tanto que el IA probablemente cambie su promedio histórico de 9.3 a 8.7; se estima TXHHOLPSDFWRSURPHGLRVREUHHVWHtQGLFHHQHOIXWXURVHUiGH ABSTRACT Modeling the deterioration of natural resources, especially water and soil that results from the global effects of climate change has become a powerful tool in the search for mitigation and adaptation measures. The objectives of this research were: (1) to model the potential impact of climate change for the period 20102039, and (2) to offer advice about future risks based on local radiative forcing or critical areas and taking LQWRDFFRXQWWZRLQGLFDWRUVRIHQYLURQPHQWDOTXDOLW\WKHDULGLW\LQGH[ $, DQGODPLQDUZLQGHURVLRQ /:( .
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Evaluation techniques for natural resources, similar to those applied by the Instituto Nacional de Ecología y Cambio Climático (National Institute of Ecology and Climate Change) were used for studies of ecological land use. The inputs include climate information (current and future), soil cover and edaphic properties UHODWHGWRWKHPXQLFLSDOLW\RI*yPH]3DODFLR'XUDQJR0H[LFR 1: . According to calculations estimated from the anomalies for the mean annual rainfall and mean annual temperature, in a IXWXUHFOLPDWHFKDQJHVFHQDULRDQDYHUDJHLPSDFWRIDSSUR[LPDWHO\ZRXOGEHFDXVHGE\/:(DQGWKH AI would change from its historical value of 9.3 to 8.7. It is estimated that the average impact on the AI in WKHIXWXUHZLOOEH Keywords: Climate scenarios, modeling, ecological zoning.
1. Introduction In less than a decade, modeling climate processes to assess the deterioration of natural resources has become a powerful tool for determining trends, esSHFLDOO\IRUZDWHUDQGVRLO
critical areas and taking into account two indicators of environmental quality, the aridity index (AI) and soil erosion. 2. Methodology 2.1 Study area, characteristics and location *yPH]3DODFLRLVRQHRIWKH PXQLFLSDOLWLHVRI the state of Durango. It has an area of 842.32 km2, DQGLWLVORFDWHG 1: LQDJHRgraphical-ecological region known as the Bolsón de Mapimí. This region presents a climate gradient from south to north, ranging from dry (BSohw) to YHU\GU\ %:KZ 7KHDQQXDODYHUDJHWHPSHUDWXUH YDULHVIURPWR&DQGWKHPHDQDQQXDOUDLQIDOOLVDSSUR[LPDWHO\PP *DUFtD 2QH of its salient characteristics is that approximately RI WKH WHUULWRU\ LV XVHG IRU DJULFXOWXUH ZLWK irrigation, as is favored by its topography (slope < WKHUHPDLQLQJFRUUHVSRQGVWRXUEDQDQG rural (Fig. 1). 2.2 Study baseline and indicators of environmental quality (IQA) The baseline or reference for this study were the conditions of 2010, which serve as a basis for comparison to assess the impact of climate change on a 30-year (2010-2039) time horizon. The environmental characteristics chosen as indicators were the laminar wind HURVLRQ /:( DQG$,WKH¿UVWDVWKHORVVRIVRLOGXH to the wind action on the soil surface, and the second as drought stress. Each is directly correlated with the changes under future scenarios for temperature and rainfall, as described by Magaña et al. (2012) for the northeast of Mexico. 2.3 IQA modeling and input data 7KHPRGHOLQJRIWKH,4$/:(DQG$,ZDVGHYHORSHGWKURXJKWZRWHFKQLTXHV IRUWKH/:(LQGLFD-
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UTM
2856000
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2864000
636000
481
UTM
Bolsón de Mapimí
Legend Municipal limit
Tlahualilo
Railroad Road
Mapimí Gómez Palacio
Scale 22800
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Meters
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tor, the loss of soil in t ha–1 yr–1 was estimated based on the methodology proposed by INE (1998) for studies of ecological-use zoning, and (2) AI was determined by the De Martonne’s aridity index, which has been used by Mercado-Mancera et al. (2010) as DQHVWLPDWRURIWKHDULGLW\DQGGHVHUWL¿FDWLRQLQDULG land in northwestern Mexico. The input for the baseline year modeling was the historical data set of weather from the Servicio 0HWHRUROyJLFR 1DFLRQDO 601 1DWLRQDO :HDWKHU
Service), as well as measurements related to the biotic physical environment such as topography, soil and vegetation. Meanwhile, to assess the potential impact on the IQA by future scenarios (2010-2039), projections (metadata) for regional climate change of Mexico were downloaded (INE-SEMARNAT, 2011). These data were generated by downscaling WKHUHVXOWVRIWKHJOREDOFLUFXODWLRQPRGHOV *&0 used in the Fourth Assessment Report of the IPCC in 2007 (IPCC, 2008).
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2.4 Meaning and process for determining LWE 7KH /:( DV DQ HQYLURQPHQWDO TXDOLW\ LQGH[ UHSresents the magnitude (t ha–1 yr–1) of soil loss by wind, which theoretically is incorporated as an additional burden at different heights in the atmosphere. In the lower layer, the air carrying soil has direct effects on human health because it is the layer of air that people breathe. Such impacts can reach many kilometers from where it is produced, affecting both rural and urban areas. &DOFXODWLQJWKHEDVHOLQH/:( IRU EHJDQ with the determination of the dominant environmental factors in erosion, the action of water or wind. The values depend on indices related to edaphic properties (CATEX, Spanish acronym), use of soil (CAUSO, Spanish acronym), susceptibility of soil to erosion (CAERO, Spanish acronym) and topographical conditions (CATOP, Spanish acronym), which apply according to the predominant process in the erosion. The basis for the calculation of the erosion rates is in both cases (water or wind) related to the availability of humidity as a result of the presence of rain > 10 mm. Therefore, the mean annual rainfall (MAR) was determined to estimate the rain aggressiveness LQGH[ 5$, DQGZLQGDJJUHVVLYHQHVVLQGH[ :$, Both indices depend on MAR, which determines WKHJURZWKSHULRG *523( GH¿QHGDVWKHQXPEHU of days per year with availability of water and temperatures conducive to the development of a crop. 7KLV*523(LQGH[KDVEHHQVXFFHVVIXOO\XVHGE\ Monterroso et al. (2011) in a recent study in which they assess the impact of climate change on rain-fed maize in Mexico (Fig. 2). 7KH¿UVWVWHSRIWKLVSURFHVVZDVWRPDNH]RQLQJ PDSVIRUWKH5$,DQG:$,ZKLFKZHUHFDOFXODWHG
using digitalization processes (interpolation, reclasVL¿FDWLRQDQGUDVWHUYHFWRU LQ$UF*LV (65, according to the following equations: *523( 0$5 ± (MAR)2 – 33.1019
(1)
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There are three rules for determining the dominant factor in soil erosion: (1) if the value of the RAI is JUHDWHUWKDQ ! LWLVFRQVLGHUHGD]RQHRILQÀXHQFHIRUWKHVWXG\RIZDWHUHURVLRQ LIWKHYDOXH RIWKH:$,LVJUHDWHUWKDQ ! LWLVFRQVLGHUHG D]RQHRILQÀXHQFHIRUWKHVWXG\RIZLQGHURVLRQ DQG (3) if both factors reach the threshold values, then the magnitude of erosion is calculated separately, or without erosion. In accordance with these decision rules, the applied analysis (Fig. 2) determined that laminar water HURVLRQLQQRWGRPLQDQWLQWKHWRZQRI*yPH]3DODcio, so this part of the methodology only describes the procedure to determine the magnitude and distribution of laminar soil erosion by wind. To calculate /:(LQWKD–1 yr–1, the following equation was used: /:( :$, &$7(; &$862
2.5 Aridity index as a drought indicator De Martonne’s AI has been used to characterize climate and indicate drought (Mercado-Mancera et al., 2010). For this study, the following expression was applied:
RAI (Eq 2)
Index (>50)
Water erosion Defining areas by type of erosion
Zoning Maps
GROPE (Eq 1) Wind erosion
Index (>20)
WAI (Eq 3)
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:KHUH$,LVWKHDULGLW\LQGH[ZKLFKWDNHVGLPHQVLRQOHVVYDOXHVEHWZHHQDQG! 0$5LVWKHPHDQ annual rain from historical records and the A2 scenario; MAT is the mean annual temperature from historical records and the A2 scenario; and 10 is a constant value derived from De Martonne’s model (Table I). Table I. De Martonne’s aridity index. AI value 10-20 20-30 !
&ODVVL¿FDWLRQ Desert (hyperarid) Semi-desert (arid) Semiarid Mediterranean type Subhumid :HW Perwet
Source: http://www.miliarium.com/prontuario/MedioAmbiente/Atmosfera/IndicesClima.htm#De_Martonne.
2.6 Origin and management of climate data 'DWDIURPZHDWKHUVWDWLRQV :6 ZHUHXVHG from the SMN and one from the Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias (INIFAP, National Institute for Forestry, Agricultural and Livestock Research), located at the northern region of the municipality. All of them fell within a UDGLXVRIDSSUR[LPDWHO\NP)RXU:6FRUUHVSRQG WR WKH VWDWH RI &RDKXLOD :6 ,' QXPEHUV DQG DQGWKHRWKHUQLQHWRWKHVWDWH of Durango. The metadata for the future scenarios are in this coverage as well (Fig. 3). 2.7 Indices related to edaphic properties and land use: CATEX and CAUSO The estimated values of the indices CATEX and CAU62IRUWKHPXQLFLSDOLW\RI*yPH]3DODFLRZHUHFUHDWHG by manipulating the attribute tables of vector data sets containing the CATEX index. The indices were calculated according to matching criteria between the type RIVRLOVXVHGLQ0H[LFRE\,1(*,LQWKHFDUWRJUDSKLF
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Coordinates y x
Height m 1136
10085
Presa Cuije
103.300
25.700
1116
5028 5029
Presa Guadalupe Presa La Flor
103.230 103.350
25.767 25.083
1112 1276
10004
Presa de Fernández
103.750
25.283
1360
10009
Cd. Lerdo (SMN)
103.520
25.533
1135
10045
Mapimí (km 29)
103.850
25.817
1325
10055
Pedriceñas
103.750
25.083
1330
10108 10140
Cd. Lerdo (DGE) La Cadena
103.370 104.167
25.500 25.533
1139 2230
10049
Nazas
104.117
25.233
1264
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Tlahualilo
103.483
26.168
1096
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103.476
25.886
1140
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26
Col. Torreón Jardín
5027
COAHUILA STATE
Mapimí 26
5006
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NOR
DURANGO STATE
10045 Gómez Palacio
5028
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25.5
WS Name
103
103.5 N
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10055 25
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Table II. CATEX and CAUSO indexes. CATEX
TCPP
&,1(*,
CAUSO
Category and use
0.87
1 2 3 SPB
Coarse Medium Fine 33*
0.70 0.20 0.30
C1, rainfed agriculture C2, irrigated agriculture C3, scrubland C4, grassland
TCPP: Textural class and physical phase; SPB: Stony phase or burdensome; &,1(*, 7H[WXUDOFODVVDFFRUGLQJWR,1(*,
series I and II. Series I was elaborated based on the VRLOV FODVVL¿FDWLRQ GHYHORSHG E\ )$281(6&2 LQ DQG PRGL¿HG E\ &(7(1$/ LQ .UDVLOnikov et al 6HULHV,,ZDVEDVHGRQWKH:RUOG Reference Base for Soils (FAO-ISRIC-ISSS, 1998). The soils of the municipality are almost entirely of FDOFDUHRXVRULJLQVRZHVHOHFWHGWH[WXUHTXDOL¿FDWLRQ and physical phase. The CAUSO index, associated with the distribution and abundance of vegetation, was HVWLPDWHGEDVHGRQWKHLGHQWL¿FDWLRQRIIRXUYHJHWDWLRQ groups, as well as the land dedicated to agricultural use and under irrigation (Table II). 2.8 Metadata for regionalized climate scenarios (2010-2039) to determine the LWE To assess the climate changes, metadata were downloaded from the SEMARNAT-INE website (http:// zimbra.ine.gob.mx/escenarios/) corresponding to MAT and MAR for three future scenarios: A2, A1B and B1 for the 2010-2039 period. In the original metadata, each RQHKDVDVSHFL¿FIRUPDW UDLQDQRPDOLHVDUHLQSHUFHQW and the temperature is in degrees Celsius (Table III). The climatic future scenarios produced by Magaña and Caetano (2007), with anomalies for rain and temperature (shown in Table III), are the result of numerous experiments based on the 24 *&0SURSRVHGE\WKH,3&&,QIDFWWKLVZRUNLVWKH RULJLQRI*+*HPLVVLRQOLQHV ,3&& ZKRVH characteristics are as follows: Scenario A1: Assumes a very rapid global economic growth, up to doubling the world population by mid-century, and the rapid introduction of new and PRUHHI¿FLHQWWHFKQRORJLHV,WLVGLYLGHGLQWRWKUHH JURXSVZKLFKUHÀHFWWKUHHDOWHUQDWLYHGLUHFWLRQVIRU technological change: intensive fossil fuels (A1FI), non-fossil energy (A1T), and a balance between the various energy sources (A1B). Scenario B1: Describes a convergent world with the same population as 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 and both slow economic development and technological change. 2.9 Geostatistical analysis and assessing impact The impact analysis of future climate variability in rainfall and temperature began with the creation of a projected layer (shp), according to the locations (x, y) of the weather stations. The data were interpolated for each of the variables included in the study (zi), XVLQJDQLQYHUVHGLVWDQFHZHLJKWHG ,': PHWKRG in ArcMap 10 (ESRI), the same method used by .DUDFD 7KH¿UVWSURGXFWRIWKLVSURFHVVZDV the creation of a raster layer adjusted to maximum and minimum extreme values. The second product was a change of the properties of the statistical and pixel raster image in tree classes for calculating the changes from historic or current data to the future VFHQDULRV1H[WZHFRQYHUWHGWKHFODVVL¿HGUDVWHU image to a vector format, from which it was possible to determine the surface terms most impacted. 3. Results and discussion 7KH :6 GDWD VHW VKRZQ DERYH 7DEOH ,,, ZDV calculated for each variable; for the mean annual rainfall, percent anomalies were subtracted, and for the temperatures, the anomaly value was directly added. The percentage anomalies for both MAR and MAT were very similar among the three future scenarios described by Magaña and Caetano (2007), A2, A1B and B1. Therefore, this single study focused only on local impacts of the A2 scenario. 3.1 Analysis of the MAR impact The values in Table IV, which compares the projected 0$5WRWKHKLVWRULF +0$5 DUHFRQVLVWHQWZLWK WKH PD[LPXP PP DQG PLQLPXP UDLQIDOOV
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Lat. N
10004 10009 10108 10140 10049 NOR
Col. Torreón Jardín Presa Cuije 3UHVD*XDGDOXSH Presa La Flor Cañón de Fernández Cd. Lerdo (SMN) Mapimí (km 29) Pedriceñas &G/HUGR '*( La Cadena Nazas Tlahualilo CENID-RASPA
103.4 103.3 103.23 103.37 104.117 103.483
Aver. Stdev
1112 1330 1139 2230 1127 1280 301
194 203.3 280.3 320.4 320.1 270.1 274.2 348.9
Anomaly A2
A1B
B1
MAT +0$7
± –3.32 –3.32 ± –3.98 –2.98 –2.98 –3.98 ± ± –2.81 ± –3.32 ±
–3.08 –3.08 –3.08 –2.87 –3.94 ± ± –3.94 –2.87 –2.84 ± –2.12 –3.07 ±
Anomaly A2
A1B
B1
0.929 0.929 0.973 0.92 0.933
0.838 0.838 0.838 0.823 0.823 0.834 0.827 0.838 0.007
& –2.79 –2.79 –2.79 –2.94 ± –2.24 –2.24 ± –2.94 –2.73 ± –2.74 –2.78 –2.84 0.44
22 21.9 20.9 22.1 21 19.3 21 21.2 20.3 20.9
0.883 0.008
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MAR Elevation +0$5 m mm
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Table IV. The relative impact (RI) on MAR in the future scenario A2. MAR Range
+LVWRULF +0$5
Impact
Scenario A2 (MARScA2)
ACh +0$5±0$5ScA2)
mm Lower Upper Average
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RI 3.2 3.13
0$5 0HDQDQQXDOUDLQIDOO $&K $EVROXWHFKDQJH 5, $&K+0$5
PP LQ WKH :6 LQFOXGHG LQ WKH SUHVHQW study. For the future scenario A2 (MARScA2), which is given by subtracting the average negative anomalies, WKHORZHUOLPLW PP LVPRUHDIIHFWHGZLWKD OLNHO\GHFUHDVHRIPPSHU\HDU &RPSDUHGWR+0$5, in the MARScA2, the impact of such anomalies on the spatial distribution of the gradient in annual rainfall from west to east (Fig. 4b) is to decrease MAR for both the lower and upper UDQJHVDWDQG PPUHVSHFWLYHO\ZKLFKHTXDOV DQDYHUDJHLPSDFWRI 7DEOH,9 To complement the analysis of the changes in MAR IURP+0$5WR WKH$VFHQDULR DQDGGLWLRQDO digital process was performed over the “raster” images WKDWUHSUHVHQWWKHGLVWULEXWLRQRIWKH+0$5ZLWKWKH objective of comparing similar ranges. Data for each WLPHSHULRGZDVUHFODVVL¿HGLQWRWKUHHFDWHJRULHVDQG the involved surfaces were calculated (Fig. 4c). :HWKHQFRPSDUHGWKHKLVWRULFDOQXPEHURISL[HOV and the surface area in each category to the projected scenario. Pixels from each period were divided into WKUHHFODVVHV DDDQGDIRU+0$5DQGEE and b3 for MARScA2). The lowest category included lower values in MARScA2)URP+0$5WR$ of the surface shifted from class 1 to 2, and between FODVVDDQGEWKHVXUIDFHORZHUHG,QVFHnario A2, the class with the lowest values for MAR WKH VXUIDFH DUHD LQFUHDVHG while the surface of the class with the highest values GHFUHDVHG FKDQJLQJ IURP WR KD,WLVDOVRLPSRUWDQWWRPHQWLRQ the following: The average MAR decreased 11.2 mm, to move IURPWRPPDQQXDOO\EHWZHHQKLVWRULFDO values and the future (Fig. 4c). In the above-mentioned class with the highest YDOXHV WKHDIIHFWHGDUHDH[WHQGVIURP
the eastern boundary with the municipality of Matamoros, Coahuila, to the north by Tlahualilo, to the western boundary near Arcinas and Pastor Rovaix, and to the north near Lucero and Banco Nacional (Fig. 4b). There is a concentration of pixels in the lower UDQJHLQVFHQDULR$ E ZKLFKPDJQL¿HVWKHLPpact. Although the mean difference between a3 and b3 is 8.9 mm, the impact is much greater because of the number of values concentrated towards the lower limit of that range (Fig. 4d). Finally, it is important to note the anomalies DIIHFWLQJWZR:6 &DxRQGH)HUQiQGH] ,G DQG3HGULFHxD ,G $WWKHVHVWDWLRQVUXQRIIRI surface water begins to feed the reservoirs (Francisco =DUFRGDP DQGWKHDJULFXOWXUDOLUULJDWLRQRI*yPH] Palacio depends on this runoff. In absolute terms, the MAR could exceed the average for the three scenarios; i.e., the consequence of this declination index is DSSUR[LPDWHO\ 3.2 Calculation and zoning of the GROPE 7KH*523(LVDQLQGH[WKDWZDVFDOFXODWHGEDVHGRQ Eq. (1), where the independent variable is the MAR. Therefore, it compares anomalies in average annual UDLQIDOOLQKLVWRULFDODQGWKHIXWXUHVFHQDULR$:H FRPSDUHG+*523(WR*523(ScA27KH+*523( LQGH[IHOOE\DQDYHUDJHRILQWKHIXWXUH$ VFHQDULR *523(ScA2). In addition, the boundaries of the ranges change between the historic values DQGWKH*523(ScA2 (12.8-31.1), as shown in Table V. Based on the relationship that exists between the 0$5DQG*523(LQGH[ZHZRXOGH[SHFWWKH0$5 to be affected to the same extent, although it is not easy to determine the magnitude of the impacts of this signal in the limits of the range considered. To clarify
Modeling the potential impact of climate change
487
(a)
(b) a2
Arturo Martínez Adame
Arturo Martínez Adame
Seis de Octubre
Seis de Octubre a1
b1
b2 a2
b3
a3 San Martín
San Martín
HMAR
MARScA2
Rank, mm 287.1
Rank, mm 278.5 196.4
Analysis of the relative impact (IR) on mean annual rainfall (MAR) for the future scenario A2 Surface ha
a) HAAR
281.7
(d)
250000
(c)
Mean annual rainfall, MAR
Gómez Palacio
259.7
Inhabited areas Municipal limit Road Railroad Class limit
Mean
Inhabited areas Municipal limit Road Railroad Class limit
237.6
Gómez Palacio
200000
Class
Range mm
IR %
HMAR
a1 a2 a3
202.9 – 237.6 237.6 – 259.7 259.7 – 287.1
19601 24750 39887
23.27 29.38 47.35
MARScA2
b1 b2
196.4 – 237.6 237.6 – 259.7
26505 32777
31.46 38.91
b3
259.7 – 278.2
24955
29.62
Pixels
202.9
150000
Range a1
Range a3
Range a2
100000 50000 0
281.7
259.7
b) A2 AAR
266.0893936 287.120239
225.058548
Mean
250000
224.0277023
237.6
202.9968567
Pixels
200000 150000
Range b2
Range b1
Range b3
100000 50000 0 196.4049835 216.8509729 237.2969624 257.7429518 278.188941
Rain, mm
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7DEOH9,PSDFWDQDO\VLVLQWKH*523(LQGH[DVDUHVXOWLQWKH0$5FKDQJHV *523( Range limit Lower +LJKHU Average
Impact
+LVWRULF +*523(
Scenario A2 *523(ScA2
ACh +*523(*523(ScA2)
5,
14.2 32.9
12.7 31.1 21.9
1.8 1.7
7.18
5, $&K+*523(
WKLVLQWHUPVRIWKHLPSDFWDWWKHVXUIDFHOHYHO )LJ WKDW ZRXOG EH DIIHFWHG WKH *523( LQGH[ LPDJHV ZHUH DOVR UHFODVVL¿HG LQWR WKUHH FODVVHV )LJ F and compared. First, between classes a1 and b1, there is a projectHGLQFUHDVHRI KDHTXLYDOHQWWRZKLFK would imply an extension of 2.7 km from east to west. This extension would occur from Esmeralda, where FXUUHQWO\WKH+*523(LVORFDWHGWRWKHZHVWLQWKH vicinity of Pastor Rovaix and Consuelo. Second, between classes a2 and b2, there is a projected increase RIKDHTXLYDOHQWWR ZKLFKZRXOGLPSO\ DQ H[WHQVLRQ RI DSSUR[LPDWHO\ NP IURP HDVW WR west. This extension would occur from La Popular, ZKLFKLVFXUUHQWO\+*523(WRWKHZHVWQHDUWR(O Cairo, Estación Noé, La Plata and Bucareli. Third, there is a double impact in the shift between classes DDQGE7KH*523(LQGH[ZRXOGKDYHDUHGXFWLRQ RIWZRXQLWVIURPWKH+*523( WRWKH *523(ScA2 DQGWKHLQGH[DOVRKDVORZHU PD[LPXPYDOXHVLQFODVVE )LJG 3.3 Calculation and zoning of the WAI $VLQWKHSUHYLRXVFDVHVWKH:$,FRXOGEHDIIHFWHG LQWKHIXWXUH$VFHQDULR :$,ScA2) because of the way the MAR is affected, although the relationship LVPRUHGLUHFWIRU*523(DVGHVFULEHGLQWKHPHWKRGRORJ\ (T :$,FRXOGWKHUHIRUHEHLPSDFWHG LQ FOLPDWH VFHQDULR$ IRU WKH WHUULWRU\ RI *yPH] 3DODFLRE\DQDYHUDJHUHGXFWLRQRIDQGDFKDQJH LQWKHUDQJHRIYDOXHVIURPKLVWRULFDOOHYHOV WRWKH$VFHQDULR DVVKRZQLQ Table VI. 7KHVSDWLDOGLVWULEXWLRQRIWKH:$,LVSUHVHQWHGLQ )LJXUH2QHVDOLHQWFKDQJHLVWKHJURZWKEHWZHHQ classes a2 and b2, which is estimated to affect a WRWDORIKDGHULYHGIURPDQLQFUHDVHRI EHWZHHQ+:$,DQG:$,ScA2 )LJF 7KLVLQFUHDVH
includes the area known as Perímetro Lavín, where the following stations are located: El Cairo, Competencia, Noé, Dolores, Numancia and Britingham )LJE 3.4 Calculation and zoning of the CAUSO index 7KH&$862LVDQLQGH[GH¿QHGE\FXUUHQWODQGXVH (Table II), and the determination of the surfaces that will be occupied by urban nuclei on a horizon of approximately 30 years (2010-2039) was based on the programa de desarrollo urbano (PDU, program RIXUEDQGHYHORSPHQW RIWKHPXQLFLSDOLW\RI*yPH] Palacio (SEMARNAT, 2012). The distribution of the CAUSO index is shown in Figure 7, where future impacts can be assumed because of the drastic changes LQFDWHJRULHV&DQG&)RUWKH¿UVWFDWHJRU\WKHUH is a probable decrease in scrubland located in the western part of the municipality near Poanas, Dinamita and San Martín. The extent would fall from the KD FXUUHQWO\HVWLPDWHGWR KD E\)RUWKHVHFRQGFDWHJRU\WKHUH is an estimated increase in the vegetation associated ZLWKVDQG\GHVHUWV +DORSK\WH LQWKHQRUWKRIWKH municipality, which would grow from 4473.1 ha WR KD )LJF 3.5 Estimation and zoning of the LWE rate The rate of /:(QRWRQO\UHSUHVHQWVWKHTXDQWLW\RI soil loss per year but is also a way to evaluate enviURQPHQWDOORVV7RGHWHUPLQHWKH/:(UDWHDGLJLWDO process was conducted based on algebra maps using matrix operations and between digital layers (raster) using Eq. (4). The inputs described above in the context of the A2 scenario were used, as in CAUSO DQG:$,ZLWKWKHH[FHSWLRQRIWKHHGDSKLFLQGH[ (CATEX). Edaphic characters such as the texture and the physical phase (stoniness factor) are changes not easily predictable in a short period of time.
Modeling the potential impact of climate change
489
(a)
(b) a2 Arturo Martínez Adame
Arturo Martínez Adame
Seis de Octubre
Seis de Octubre b1
a1
b2
a2
a3
b3
San Martín San Martín HGROPE Rank High: 32.9
GROPE ScA2 Rank High: 31.1
Low: 14.2
Low: 12.7 Gómez Palacio
Class limit Municipal limit Inhabited areas Road Railroad
Class limit Municipal limit Inhabited areas Road Railroad
Gómez Palacio
Class
HMAR
a1 a2 a3
14.2 – 21.9 21.9 – 26.8 26.8 – 32.9
19101 24281 40856
22.67 28.82 48.50
b1 b2
12.7 – 21.9 21.9 – 26.8
26038 31957
30.91 37.94
b3
26.8 – 31.1
26244
31.15
Surface ha
IR %
Pixels
32.9
200000
GROPE index
Range (index)
26.8
a) HGROPE
Mean
250000
Analysis of the relative impact (RI) in the GROPE index for the future scenario A2.
21.9
(d) (c)
150000
Range a1
Range a3
Range a2
100000
50000 0
31.1
b) GROPEScA2
26.8
250000
Mean
202.9968567 287.1202 266.0893936 32.9699 224.0277023 23.60838509 225.058548 28.28917027 14.24681473 18.92759991
21.9
GROPEScA2
Pixels
200000 150000
Range b2
Range b1
Range b3
100000 50000 0 196.4049835 17.33757305 278.1889 257.7429518 31.0779 216.8509729 21.91771126 237.2969624 26.49784946 12.75743484
Rain, mm
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7DEOH9,,PSDFWDQDO\VLVRIWKH:$,DVDFRQVHTXHQFHRIWKH*523(FKDQJHV RQWKHPXQLFLSDOLW\RI*yPH]3DODFLR :$, Range limit Lower +LJKHU Average
Impact
+LVWRULF +:$,
Scenario A2 :$,ScA2)
ACh +:$,±:$,ScA2)
5,
149.9 142.7
137.0 144.12
1.42
1.12 0.77 1.00
5, $&K+*523(
The results indicate an average impact on the RUGHURIGHULYHGIURPDFKDQJHLQWKHUDQJH LQWKHKLVWRULFDOODPLQDUZLQGHURVLRQ +/:( UDWH IURPWRWKD–1 yr–1 in the future for WKH$VFHQDULR /:(ScA2), as shown in Figure 8c. A more detailed analysis of the impact associated with the increased susceptibility of soils to wind erosion and by a lower availability of moisture because of the lower MARScA2 indicates that this HIIHFW PLJKW EH JUHDW DQG FRXOG LQÀXHQFH QHDUO\ one third of the municipality (Fig. 8d). The spatial distribution of the three classes of erosion is shown in Figure 8b. 3.6 Analysis of the variables related to the aridity index ,Q DGGLWLRQ WR WKH FDOFXODWLRQ IRU /:( DV VKRZQ above, the aridity index provides a good complement to determine the magnitude of probable impacts for decreasing MAR and increasing MAT. AI has been examined in the three climate scenarios analyzed for the north and northeast of Mexico (Magaña and Caetano, 2007; Magaña et al., 2012). 3.7 Changes in mean annual temperature According to the climate scenarios in the report of the IPCC (2007), the mean temperature in the northern hemisphere during the second half of the 20th cenWXU\ZDVOLNHO\KLJKHUWKDQDQ\RWKHU\HDUSHULRG LQWKHODVWDQGPRVWOLNHO\WKHKLJKHVWRYHUWKH past 1300 years. $FFRUGLQJWRWKH,3&&UHSRUW VLJQL¿FDQW changes were observed in the data for physical (snow, ice and frozen ground; hydrology; and coastal processes) and biological systems (land, marine and freshwater biological systems), and there was great variation in the air surface temperature during the period 1970-2004. The temperature changed on
WKHRUGHURIWR&ZLWKLQNPWRWKHQRUWKRI WKH7URSLF RI &DQFHU 1 7KLV LQFUHDVH ZLOO affect almost all of the municipality, but especially the northern area, where Seis de Octubre and Arturo Martínez Adame are located. Temperature and rain are obviously fundamental variables in the analysis of the deterioration of natural resources. Both are processed for the historic data set and later were calculated since the anomalies. The results in Table III suggest that the temperature change HVWLPDWHGE\+0$7ZRXOGEHIURPWR& in the future A2 scenario (MATScA2). 3.8 Changes in the aridity index *LYHQWKHUHODWLRQVKLSWKH'H0DUWRQQHPRGHO (T HVWDEOLVKHV IRU 0$5 DQG 0$7 DV PHQWLRQHG above, a decrease of rain will accompany an increase in temperature. In a future scenario there may be environmental deterioration manifested by an increase in aridity and prolonged drought as reported previously for North America and particularly for Mexico )UHGHULFN DQG *OHLFN 8$&+&21$=$ 6('(62/6$*$53$ ,3&& According to the calculations made from anomalies already described for the MAR and the MAT (Table III), the aridity index in the municipality of *yPH]3DODFLRZRXOGFKDQJHIURPWKHKLVWRULFYDOXHV +$, RI WRLQWKHIXWXUHVFHQDULR$ $,ScA2). It is estimated that the average impact on this index LQWKHIXWXUHFRXOGEH± 7DEOH9,, The estimated impact, in terms of surface area IRUWKH$,LQGLFDWHGWKDWWKHPXQLFLSDOLW\RI*yPH] Palacio would most likely become more sensitive due to the tendency toward a hyper-arid condition, and UHDFKLQGH[YDOXHVLQWKHUDQJHRIWRDVDOUHDG\ VSHFL¿HG DERYH 7DEOH , 7KH LPSDFW LQ WKLV FDVH could cover an area of 13 072 ha, equivalent to the RIWKHPXQLFLSDOWHUULWRU\ )LJ F
Modeling the potential impact of climate change
491
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(b) b2
a2 Arturo Martínez Adame
Arturo Martínez Adame
Seis de Octubre
Seis de Octubre a3
b3
a2 b2 a1 San Martín
b1 San Martín WAIScA2 Rank High: 151.0
HWAI Rank High: 149.9 Low: 135.5
Low: 137.0 Gómez Palacio
Class limit
Class limit
Municipal limit Inhabited areas Road Railroad
Gómez Palacio
Municipal limit Inhabited areas Road Railroad
Class
Range (index)
HWAI
a1 a2 a3
135 – 140.1 140.1 – 143.8 143.8 – 149.85
38972 24792 20474
46.26 29.43 24.31
WAIScA2
b1 b2
137.2 – 140.1 140.1 – 143.8
23622 33218
28.04 39.43
b3
143.8 – 151.05
27399
32.52
Surface ha
IR %
200000
Pixels
WAI (index)
150000
149.86
143.8
a) HWAI
Mean
250000
Analysis of the impact on historical index of aggressiveness of the wind (HWAI) and the rate of aggression for the future scenario A2 (WAI Sc A2)
140.1
(d) (c)
Range a1
Range a2
Range a3
100000
50000 0
Pixels
200000 150000
Range b1
Range b2
151.05
Mean
140.1
b) WAIScA2 250000
143.8
202.9968567 266.0893936 287.120239 224.0277023 142.7130514 225.058548 146.2844742 135.5702057 139.1416285 149.85589
Range b3
100000 50000 0 196.4049835 110.5278397 257.7429518 216.8509729 111.0362211 237.2969624 117.5116091 278.188941 137.019155 151.05299
Rain, mm
)LJ7KHVSDWLDOGLVWULEXWLRQRI+:$, D DQG:$,ScA2 (b); analysis of relative impact (RI) based on affected surface F UHVXOWVRIWKHUHFODVVL¿FDWLRQRILPDJHVDDQGE G
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(a)
(b)
Arturo Martínez Adame
Arturo Martínez Adame
Seis de Octubre
Seis de Octubre
San Martín
n
San Martín Index CAUSO 2010 0.15 0.2 0.3 Inhabited areas Artificial surface Nazas river Road Railroad
CAUSOScA2 Index 0.15 0.2 0.3 Inhabited areas Artificial surface Municipal limit Nazas river Road Railroad
Gómez Palacio
Gómez Palacio
(c) 60 50 Surface, ha (thousand)
40 30 20 10 0 Scrubland C3
Agricultural irrigation C2
Grassland C4
CAUSO 2010
19 793.5
49 336.9
4 473.1
CAUSOScA2
8 675.5
47 019.7
15 390.2
Figure 7. Spatial distribution of CAUSO current (a) and CAUSOScA2 (b); impact analysis based on surface and relative importance (c).
It is also important to mention that in addition to the reduction in the range of index values from hisWRULFDOOHYHOV WRWKHIXWXUHVFHQDULR$,ScA2 WKHDYHUDJHRIWKLVLQGH[FKDQJHGIURP WR $GGLWLRQDOO\ WKHUH LV JUHDWHU DULGLW\ in the highest class, as seen in the density of pixels between the two cases (Fig. 9d). 3.9 Environmental quality analysis and probable impacts Environmental quality is a term that relates to certain conditions of “comfort” for people and natural environments or biological systems. The critical elements
are the availability of fresh water, air quality and WHPSHUDWXUH .DUDFD .XR-HQ et al., 2012). The water in the form of rain becomes important in regulating temperature and its relationship with extreme events, such as drought and dust storms, among others (Sun et al., 2003; Batjargal et al Zhang et al., 2012). Arid and semiarid regions will likely experience an increase in temperature and decrease in rain (Rivera et al *DUFtD3iH]DQG&UX]0HGLQD Magaña et al., 2012), especially for latitudes similar WR WKH %ROVyQ GH 0DSLPt WKH DUHD ZKHUH *yPH] Palacio is located.
Modeling the potential impact of climate change
493
(a)
(b)
Arturo Martínez Adame Arturo Martínez Adame
Seis de Octubre Seis de Octubre
San Martín San Martín
HLWE Class Low Moderate High Inhabited areas (2010) Artificial surface Municipal limit Nazas river Road Railrad
LWE ScA2 Class Low Moderate High Inhabited areas (2035) Artificial surface Municipal limit Nazas river Road Railrad
Gómez Palacio
(c)
(d) Changes between class erosion to HLWE and LWE ScA2
Analysis impact of the LWE in the future A2 scenario Rank limit
From HLWE
to LWEScA2
--- Impacts --ACh RI
---------- t ha-1 yr -1 ---------Lower Upper Average
36 106 71
Gómez Palacio
36 151.4 93.7
0 45.4 22.7
Rank
Surface
RI
HLWE
Class Low Moderate High
(t ha -1 yr -1) 36 - 41 41 - 55 55 - 106
ha 23 435 55 130 3 023
% 28.7 67.6 3.7
Low Moderate High
36 - 41
LWEScA2
7 303 45 950 17 296
10.4 65.1 24.5
Period
% 0 126.11 63.06
41 - 55 55 - 151.4
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Although this study used an average temperature LQFUHDVHRIOHVVWKDQ &DQGDUDLQIDOOGHFOLQHRI DSSUR[LPDWHO\WKHFKDQJHVPD\EHODUJHU)RU example, Magaña et al. (2012) remarked that for the north of Mexico in the future A2 scenario, the average temperature towards the end of the 21st century FRXOGLQFUHDVHIURP& ZLWKH[WUHPHVIRU WKHGULHUPRQWKV 0DUFK$SULODQG0D\ XSWR& ZKLOHUDLQIDOOFRXOGGHFOLQHE\XSWR This decrease in humidity, as shown in Figure 8, could not only negatively impact the rates of soil erosion, with probable losses in the future scenario $RIXSWRWKD–1 yr–1, but could also result in changes in the systems of biological feedback (Breshears et al. +DUSHUet al., 2010), a crisis
of freshwater availability and negative effects on the air quality due to a drastic increase in suspended particles. This change is potentially harmful to people´s health (Razo et al., 2004; Rashki et al., 2011) and could affect the albedo at the level of the surface of the Earth and the atmosphere (Batjargal et al. +DN6XQJet al., 2011). Figure 10 shows DQDHULDOYLHZRI*yPH]3DODFLRPXQLFLSDOLW\DQG an example of qualitative laminar wind erosion. 7KHUDWHRIODPLQDUZLQGHURVLRQZDVWKD–1 –1 yr , equivalent to the removal and transport of a soil OD\HUEHWZHHQDQGPPWKLFN,IWKHVRLOLV medium textured over the entire area of study, this amount is between 1100 and 1200 kg m–3, which implies a potential loss of approximately 0.4 m in
A. López Santos et al.
494
7DEOH9,,(VWLPDWLRQRIFKDQJHVLQWKH$,IURPWKHKLVWRULF +$, WRIXWXUH$VFHQDULR $,ScA2 LQ*yPH] Palacio. Coordinates :6,G
Name :6601
Long.
Aridity Index
Lat.
+$,
Degrees 10004 10009 10108 10140 10049 NOR
Col. Torreón Jardín Presa Cuije 3UHVD*XDGDOXSH Presa La Flor Cañón de Fernández Cd. Lerdo (SMN) Mapimí (km 29) Pedriceña &G/HUGR '*( La Cadena Nazas Tlahualilo CENID-RASPA
103.400 103.300 103.230 103.370 104.117 103.483
AIScA2
ACh
Index
Aver. Stddev
8.0 9.1 10.0 9.2 10.9 8.7 8.8 8.7 9.3 2.1
9.3 8.7 10.3 12.7 8.3 8.2 10.9 8.3 8.7 2.0
0.41 0.38 0.47 0.91 0.38 0.4 0.20
$&K $EVROXWHFKDQJHRQDULGLW\LQGH[ +$,±$,ScA2); the value of the historic temperature (bold) for the :6601ZDVDYHUDJHGXVLQJWKHYDOXHVIURPWKHQHDUHVWQHLJKERUKRRG $YHU $YHUDJH 6WGGHY 6WDQGDUGGHYLDWLRQ 125 1RWRI¿FLDOO\UHJLVWHUHGLQWKH:6601
30 years. For this type of soil, these values exceed WKHUDWHVRIVRLOORVVWROHUDQFH WKD–1 yr–1) proposed by the United States Department of Agriculture (USDA) (Montgomery, 2007). An example of changes in the biological feedback is that the removal of the soil directly affects the role of nitrogen (N). N determines plant productivity, and in arid environments, the soil frequently presents large accumulations of calcium carbonate that push the soil S+WRDUDQJHRIWR$WWKLVS+SKRVSKRUXV 3 LV in complex forms and not available (Munson et al., 2011; Schlesinger et al., 2011), a set of conditions that matches the selection criteria of the parameters for the CATEX index that was described in the methodoloJ\,QUHVSRQVHWRDGH¿FLHQF\RI1DQG3Larrea tridentataVKRZVWKHKLJKHVWOHYHOVRIHI¿FLHQWXVHRI nutrients that have been found in woody plants. 4. Conclusions 7KH PRGHOV IRU WKH /:( DQG WKH$, VXJJHVW WKDW in the immediate future (2010-2039) the climatic conditions of the area of study and its surroundings will deteriorate and could lead to a steady decline of environmental quality and health. There are two
important implications of this study. First, the models can serve to warn people of the risks to human and natural environments from extreme conditions such as droughts and dust storms. Secondly, it is possible to identify local changes in use of the soil and deforestation. Finally, it is important to mention that the magnitude and distribution of the impact on the territory are relevant to planning the management of natural resources. This research should be taken into account in decision-making about preventing impacts on natural resources now and in the future. Acknowledgements This article is part of the results of the research carried out within the framework of the Programa Estatal de Acciones ante el Cambio Climático (State Program of Action on Climate Change) Durango 3($&&'*2 DQG$JXDVFDOLHQWHV 3($&&$*6 during a post-doctoral stay conducted between 2011 and 2012. Thanks to Joaquín Pinto Espinoza and Adriana Martínez Prado, both responsible for the 3($&&'*2 DQG (OVD 0DUFHOD 5DPtUH] /ySH] KHDGRI3($&&$*6
Modeling the potential impact of climate change
495
(a)
(b)
Arturo Martínez Adame
Arturo Martínez Adame
Seis de Octubre
Seis de Octubre
San Martín San Martín HAI Clase Low Moderate High Inhabited areas (2010) Artificial surface Municipal limit Road railroad
AI ScA2 Clase Low Moderate High Inhabited areas (2035) Artificial surface Municipal limit Road railroad
Gómez Palacio
Gómez Palacio
High Moderate
6.11 – 7.7 7.7 – 8.5
Low
8.5
– 9.62
9.74 29.75 60.51
21288.9 23998.5
25.26 28.48
0
38981.6
46.26
202.9968567 6.555311203
8.5
Mean Range
Range
High
Moderate
Low
50
150
224.0277023 7.323812962
b) AIScA2
225.058548 8.09231472
287.12023 266.0893936 9.62931 8.860816479
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7.7
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0
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References %DWMDUJDO = - 'XODP DQG < 6 &KXQJ 'XVW storms are an indication of an unhealthy environment in East Asia. Environ. Monit. Assess. 114 GRL V %UHVKHDUV''--:KLFNHU03-RKDQVHQDQG-( 3LQGHU:LQGDQGZDWHUHURVLRQDQGWUDQVSRUWLQ semi-arid shrub land, grassland and forest ecosystems: Quantifying dominance of horizontal wind-driven transport. Earth Surf. Proc. Land. 28, 1189-1209, doi:10.1002/esp.1034. )$2,65,&,666 :RUOG UHIHUHQFH EDVH IRU VRLO UHVRXUFHV:RUOG6RLO5HVRXUFHV5HSRUWV)RRGDQG Agriculture Organization of the United Nations, Rome. $YDLODEOH DW KWWS ZZZIDRRUJGRFUHS: ( : (KWP )UHGHULFN.'DQG3+*OHLFN3RWHQWLDOLPSDFWV on US water resources. In: Climate change, science strategy and solutions (E. Claussen, V. Arroyo Cochran DQG ' 3 'DYLV (GV 7KH 3RZ &HQWHU RQ *OREDO &OLPDWH&KDQJH%ULOO%RVWRQSS
*DUFtD ( 'LVWULEXFLyQ GH OD SUHFLSLWDFLyQ HQ OD República Mexicana. ,QYHVWLJDFLRQHV *HRJUi¿FDV 50 *DUFtD3iH])DQG,5&UX]0HGLQD 9DULDELOLGDG GHODSUHFLSLWDFLyQSOXYLDOHQODUHJLyQ3DFt¿FRQRUWH de México. Agrociencia 43, 1-9. +DN6XQJ.&
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0RQWHUURVR$,$&&RQGH'5RVDOHV-'*yPH] DQG&*D\$VVHVVLQJFXUUHQWDQGSRWHQWLDOUDLQfed maize suitability under climate change scenarios in Mexico. Atmósfera 24 Montgomery D. R., 2007. Soil erosion and agricultural sustainability. P. Natl. Acad. Sci. USA 104 0XQVRQ 6 0 - %HOQDSD DQG * 6 2NLQE 5Hsponses of wind erosion to climate-induced vegetation changes on the Colorado Plateau. P. Natl. Acad. Sci. USA 108 318'Marco de políticas de adaptación al cambio climático: desarrollo de estrategias, políticas y medidas (B. Lim and E. Spanger-Siegfried, Eds.). Programa de las Naciones Unidas para el Desarrollo, New York, 274 pp. 5DVKNL$&-5DXWHQEDFK3*(ULNVVRQ'*.DVNDRXWLVDQG3*XSWD7HPSRUDOFKDQJHVRISDUticulate concentration in the ambient air over the city of Zahedan, Iran. Air Quality, Atmosphere & Health, GRL V Razo I., L. Carrizales, J. Castro, F. Díaz-Barriga and M. Monroy, 2004. Arsenic and heavy metal pollution of soil, water and sediments in a semi-arid climate mining area in Mexico. Water Air Soil Poll. 152 GRL % :$7(F 5LYHUDGHO5tR5*&UHVSR3LFKDUGR5$UWHDJD5DPtUH] and A. Quevedo Nolasco, 2007. Comportamiento espacio temporal de la sequía en el estado de Durango, México. Terra Latinoamericana 25, 383-392. 5RVH&:(URVLRQE\ZDWHUPRGHOLQJ,Q Encyclopaedia of Soil Science (R. Lal, Ed.). Marcel Decker, 1HZ
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Universidad Autónoma Chapingo, Comisión Nacional GH=RQDVÈULGDV0p[LFR SS 9DQ5RRVPDOHQ/726RQQHQERUJ.+-HQVHQDQG -+&KULVWHQVHQ&RPSDULVRQRIK\GURORJLFDO simulations of climate change using perturbation of observations and distribution-based scaling. Vadose Zone J. 10GRL Y]M