Abstract

This study analyzes effects of climate change (CCh) and of the increase of impervious surfaces on the groundwater recharge in the Alto Atoyac sub-basin (Oaxaca, southern Mexico). Water recharge was modeled based on HELP 3.95D; temperature and precipitation were derived, for a near (2015–2039) and a far distant future scenario, from GFDL-CM3 global circulation model (GCM), which describes the climate of Mexico under the RCP8.5 scenario. Potential recharge loss zones for the period of 1979–2013 were estimated through a remote sensing analysis. The actual estimated mean annual recharge of 169 million cubic metres could be reduced by 17.97% and 65.09% according to the analyzed CCh scenarios, and the loss of 135 km2 of permeable soil would represent additionally 2.65 × 106 m3 of non-infiltrated water. This study indicates three sites, with high recharge potential, and it can be used to propose local adaptations to guarantee the availability of the water resource in the studied sub-basin.

INTRODUCTION

Water resources in Mexico are under such high stress that several communities do not have access to drinking water, and it is a matter of national security. Although the states in southern Mexico, such as Oaxaca, have larger water resources in comparison with the northern states, there are still zones at risk due to overexploitation and contamination, limiting the population's access to drinking water. According to recent studies, almost 80% of the Earth's population is exposed to significant stress factors that put the security of drinking water at risk (Vörösmarty et al. 2010). Water stress may result from the overexploitation of groundwater resources as well as the reduction in precipitations and decreases in stored water supplies (Parish et al. 2012), the latter one being a consequence of climate change (CCh). In this context, the population is directly affected due to the lack of sufficient drinking water per capita. It is important to understand that CCh represents a challenge, particularly with regard to groundwater which can be affected both directly and indirectly by CCh in ways that today have yet to be explored (Dettinger & Earman 2007; Green et al. 2011).

A great amount of data and climatic predictions provide evidence that water resources are vulnerable to CCh and easily affected by it, which puts society and ecosystems at risk (Bates et al. 2008). The most obvious CCh impacts on water resources are changes in phreatic levels and in the surficial water quality (Leith & Whitfield 1998) but potential effects on the amount and quality of groundwater should also be considered (Bear & Cheng 1999).

CCh could affect water recharge and put water security at risk, giving rise to agriculture production problems and possibly food security (Aguilera & Murillo 2009). Some of these studies have predicted a recharge reduction due to these phenomena (Herrera-Pantoja & Hiscock 2008). Contrastingly, Jyrkama & Sykes (2007) consider that these effects may not always be negative throughout time. The objective of this study is to analyze the effects of the increase of impervious surface and of the CCh on the water recharge inside the Alto Atoyac sub-basin, a region with poor agriculture practices, growing urbanization, and deforestation. Its main economic activity is based on water resource, with 87.6% of the groundwater withdrawals used for agriculture, and 9.5% of withdrawals for public and urban services (Pérez et al. 2010). Remote sensing techniques and geographic information systems (GIS) were used to estimate permeable soil loss. This information was used to evaluate recharge using the Hydrology Evaluation of Landfill Performance (HELP 3.95D) software. Assuming soil properties remain unchanged through time, water recharge in the sub-basin was evaluated under the RCP8.5 scenario, for two time horizons, 2015–2039 and 2075–2099. In particular, precipitation was provided by two global circulation models (GCMs), GFDL-CM3 and HADGEM2-ES, but GFDL-CM3 was taken as a reference for this study.

Study area

The Alto Atoyac sub-basin is located between 16°30′ and 17°25′ north latitude, and 96°15′ and 97°00′ west longitude. It comprises the Central Valleys Region of the State of Oaxaca, which includes the Etla, Tlacolula, and Zaachila valleys as shown in Figure 1(a). It is limited to the west by the Mixteca Region, to the northeast by the Cañada Region, to the north by the Sierra de Juarez, to the east by the Tehuantepec Isthmus Region, and to the south by the Sierra Madre del Sur (Figure 1(b)). In the Central Valleys, 100% of drinking water is derived from underground. A total of 87.6% of the water used in agriculture in the entire region also has its origin in the sub-soil. This dependence and the fact that groundwater is the only water resource in a surface of 3,744.64 km2 with an exploitation surface of 1,130 km2 explains why it is really important to assess the possible impacts of CCh on groundwater.

Figure 1

(a) Location of the study area, climate stations and soil type distribution. (b) Regional location of the study area (TMVB: Trans-Mexican Volcanic Belt). (c) Simplified geological map of the study area according to Secretary of Economy (SE 1997, 2007), and Secretary of Economy (in press).

Figure 1

(a) Location of the study area, climate stations and soil type distribution. (b) Regional location of the study area (TMVB: Trans-Mexican Volcanic Belt). (c) Simplified geological map of the study area according to Secretary of Economy (SE 1997, 2007), and Secretary of Economy (in press).

Hydrological and geological features

Figure 1(c), shows the simplified geologic map of the study area according to the Secretary of the Economy (SE 1997, 2007), and the Secretary of the Economy (in press) and also Campos-Enriquez et al. (2010, 2013). The presence of rocks of Precambrian, Cretaceous, and Tertiary ages that point to a complex geologic history can be noted.

The study area straddles the boundary between the Oaxaca (Zapoteco, Oaxaquia) and Juarez (Cuicateco) tectonostratigraphic terranes (Campa & Coney 1983; Sedlock et al. 1993; Alaníz-Alvarez et al. 1994) (Figure 1(c)). The Oaxaca Fault is a major Tertiary fault located along the western boundary of the 10–15 km wide, polyphase mylonitic, Juarez shear zone that forms the boundary between the above-mentioned tectonostratigraphic terranes. Most of the sub-basin is located on the Oaxaca terrane, whose basement is constituted by the Oaxaca Complex.

The metamorphic Oaxaca Complex comprises paragneisses and arc volcanic rocks intruded by within-plate, rift-related orthogneisses at P1140 Ma and intruded by a 1012 ± 12 Ma anorthosite-charnockite-granite suite. Polyphase deformation under granulite-facies metamorphic conditions occurred during the Zapotecan orogeny at 1,004 ± 3–979 ± 3 Ma (Keppie et al. 2003; Solari et al. 2003). This was followed by intrusion of the 917 ± 6 Ma, arc-related, Etla granitoid pluton (Ortega-Obregón et al. 2003). These Precambrian rocks are unconformably overlain by latest Cambrian–earliest Ordovician (Tremadocian) clastic and carbonate rocks (Tiñu Formation) containing a Gondwanan faunal assemblage (Landing et al. 2007). These are overlain by Carboniferous-Permian clastic and carbonate rocks, which are overstepped by Upper Jurassic and Cretaceous continental shallow marine clastic and carbonate rocks, and Cenozoic red shallow marine clastic and carbonate rocks, and Cenozoic red beds and volcanic arc rocks.

The aquifer under study is unconfined and constituted by alluvial material that includes unconsolidated sediments such as pebbles, gravels, sand, clay and silt, which constitutes a heterogeneous mixture with thicknesses that vary between 10 and 100 m, thinning toward its edges. The basement consists of metamorphic rocks and, in some zones, limestone, and rhyolites (that have been cut in boreholes). Laterally, the aquifer is delimited by impermeable material composed of metamorphic rocks (gneiss and schist) and extrusive volcanic rocks that bound the central valleys. The saturated thickness ranges between 15 and 100 m.

GCMs for Mexico

There are several coupled atmosphere-ocean general circulation models selected by the United Nations Framework Convention on CCh that provide reliable results for the area of Mexico (Conde et al. 2011; Fernandez-Eguiarte et al. 2014). Among them, MPI-ESM-LR (Max-Plank Institute), GFDL-CM3 (Geophysical Fluid Dynamics Laboratory), and HADGEM2-ES (Met Office Hadley) provide good resolutions for Mexico (Conde et al. 2011). However, for southern Mexico, the performance of the models in the precipitation and the Reliability Ensemble Averaging (REA) presents a high variability. HADGEM2 and MPI-ESM-LR are models with a high standard deviation (STD). GFDL-CM3 and HADGEM2 are models with high mean absolute error (MAE) and root mean square error (RMSE) in Maximum temperature which significantly reduces their REA. For the Minimum temperature all the mentioned models have low MAE, RMSE and similar STD (Cavazos et al. 2013).

METHOD AND DATA

Temperature and precipitation in our study area, for a near (2015–2039) and far time horizon (2075–2099), respectively, were obtained from the RCP8.5 scenario (Riahi et al. 2011) based on GFDL-CM3 and HADGEM2-ES atmosphere-ocean coupled GCMs (Conde et al. 2011; Intergovernmental Panel on Climate Change (IPCC) 2013; Fernandez-Eguiarte et al. 2014).

The 3.95 version of the hydrologic model HELP enabled us to estimate recharge rates, both for the historical climate data and for the two mentioned time horizons. HELP is a hydrological model that allows one to analyze the water balance. This model performs a quasi-two-dimensional analysis and simulates the daily water flow in the subsoil, as well as the amounts of water movement in each of its forms (surficial storage, flow, evapotranspiration, vegetation interception, vegetative growth). It considers the effects of temperature on the water balance (Schroeder et al. 1994; Berger & Schroeder 2013). This model has been rigorously tested and constitutes a user-friendly program. It uses accessible parameters (soil types, hydraulic conductivity, land use, runoff, etc.). Several studies (Berger 2000; Gogolev 2002; Risser et al. 2005; Jyrkama & Sykes 2007) report that it provides accurate results. The estimation of recharge zone loss was based on a supervised analysis of the available Landsat images (Berger & Schroeder 2013).

Soil type, recharge zone information, water table depths, hydraulic conductivity, runoff, and the normal climates corresponding to the climate stations, and those resulting from the GCMs were integrated into a GIS to estimate the water recharge according to the methodologies of Jyrkama et al. (2002).

Climate information

Climate stations with time series of more than 30 years were selected. Five climate stations in the study area were appropriate for our analysis (Figure 1(a)). The respective climatic information was obtained from the National Meteorological System (SMN 2010). The analyses comprise a database of 60 years. Table 1 indicates the climate station names together with their mean climatic parameters, as well as coordinates. Figure 2(a)2(e) shows the behavior of the climate normal for these five stations located in the Alto Atoyac sub-basin.

Table 1

Climate stations located in the Alto Atoyac sub-basin (locations in Figure 1(a))

Climate station Name Average rainfall (mm/year) Average temperature (°C) Coordinates (lat/long) 
20151 San Francisco Telixtlahuaca 774.4 19 17°18′00′′N, 96°54′00′′W 
20034 Etla 753.5 19.7 17°12′26′′N, 96°47′59′′W 
20079 Oaxaca 746 21.3 17°04′59′′N, 96°42′35′′W 
20044 Jalapa del Valle 761.6 18.9 17°03′57′′N, 96°52′42′′W 
20118 San Miguel Ejutla 671.9 20.9 16°34′46′′N, 96°44′14′′W 
Climate station Name Average rainfall (mm/year) Average temperature (°C) Coordinates (lat/long) 
20151 San Francisco Telixtlahuaca 774.4 19 17°18′00′′N, 96°54′00′′W 
20034 Etla 753.5 19.7 17°12′26′′N, 96°47′59′′W 
20079 Oaxaca 746 21.3 17°04′59′′N, 96°42′35′′W 
20044 Jalapa del Valle 761.6 18.9 17°03′57′′N, 96°52′42′′W 
20118 San Miguel Ejutla 671.9 20.9 16°34′46′′N, 96°44′14′′W 
Figures 2

(a)–(e) Climate normal (rainfall and temperature) of the local climate stations located in the study area (Alto Atoyac sub-basin). Locations of the climate stations in Figure 1(a).

Figures 2

(a)–(e) Climate normal (rainfall and temperature) of the local climate stations located in the study area (Alto Atoyac sub-basin). Locations of the climate stations in Figure 1(a).

General circulation models and CCh scenarios

The information used in this study comprises a climatology database spanning from 1950 to 2000 developed by Hijmans et al. (2005). These data series were modeled under given scenarios. This information has an original spatial resolution of 0.5° × 0.5° (55 × 55 km approximately). The calculated anomalies were downscaled by spline interpolation resulting in grids of 30″ × 30″ (926 × 926 m approximately). Later, the downscaled anomalies were added to the climatology database and high spatial resolution scenarios resulted that include the topographic effect (Fernandez-Eguiarte et al. 2014).

The assessment of the effects of CCh on the water recharge was based on two GCMs. After a comparison of the climate anomalies, GFDL-CM3 and HADGEM2-ES models were selected. Considering that significant differences between simulation results and reality can occur, and that the effectiveness of a particular linear system may be drastically overestimated, analysis of the effects on water recharge arising from a very pessimistic CCh scenario based on the actual state of knowledge, will help to realize and assess the degree of the affectations that would occur in such a situation (Berger 2000). This analysis considers the RCP8.5 scenario which represents an extreme situation corresponding to the pathway with a large population and a relatively slow income growth with modest rates of technological change and energy intensity improvement, leading to a long-term high energy demand and the highest Greenhouse Gas (GHG) emission in the absence of CCh policies, and it does not include any specific climate mitigation target (Riahi et al. 2011). This extreme case scenario might help to sensitize politicians on the problems of water preservation.

HELP parameters

The HELP model, used for predicting landfill hydrologic processes, can also be used to estimate groundwater recharge rates, requiring the following input: (1) weather (precipitation, solar radiation, temperature, and evapotranspiration), (2) soil (porosity, field capacity, wilting point, and hydraulic conductivity), and (3) engineering design data (liners, leachate and runoff collection systems, and surface slope) (Schroeder et al. 1994; Allen et al. 2004; Berger & Schroeder 2013).

The numerical solution accounts for the effects of runoff, infiltration, evapotranspiration, vegetation grown, soil moisture storage, surface storage, and some engineering parameters. The components of the simulated water balance are precipitation, interception of rainwater by leaves, evaporation by leaves, surface runoff, evaporation from soil, plant transpiration and percolation of water through the soil profile (Schroeder et al. 1994; Allen et al. 2004; Berger & Schroeder 2013).

Profile structure

The profile structure can be multi-layered, consisting of a combination of natural (soil) and artificial materials (Schroeder et al. 1994; Allen et al. 2004; Berger & Schroeder 2013). In the current HELP application, only natural geological materials found in the central valleys of Oaxaca were used. A homogeneous stratigraphy was considered in every analyzed profile. Soil type information was provided by the National Institute of Statistics and Geography (INEGI 2010a, 2010b).

Hydraulic conductivity, K, is a measure of how easily water can pass through subsurface: high values indicate permeable material; low values indicate impermeable material and it is defined by Darcy's law. K-values were assigned according to the soil textures following the methodology of the US Department of Agriculture (USDA 1985). Table 2 indicates the soil classification and respectively adopted hydraulic conductivities.

Table 2

Soil classification and hydraulic conductivity (USDA 1985) (for distribution see Figure 1(a))

Soil textures Saturated hydraulic conductivity (cm/s) 
Silt clay loam 1.9 × 10−6 
Sand clay loam 2.7 × 10−6 
Silt loam 9.0 × 10−6 
Soil textures Saturated hydraulic conductivity (cm/s) 
Silt clay loam 1.9 × 10−6 
Sand clay loam 2.7 × 10−6 
Silt loam 9.0 × 10−6 

Figure 3 presents the average depth distribution of the water level from 2001 to 2013 based on field monitoring during the past years. Depths between 1 and 10 metres to the water level are predominant.

Figure 3

Water table depths (m) obtained from a 12-year field monitoring (2001–2013).

Figure 3

Water table depths (m) obtained from a 12-year field monitoring (2001–2013).

Runoff

The rainfall-runoff process is modeled in HELP by using the USDA Soil Conservation Service curve-number method (USDA 1985). The curve-number method is a procedure amply used for four reasons: (1) it is widely accepted, (2) it is computationally efficient, (3) the required input is generally available, and (4) it can conveniently handle a variety of soil types, land uses and management practices (Schroeder et al. 1994).

The curve number (CN) is defined with respect to the runoff-retention parameter (S), which is a measure of the maximum retention of rainwater after runoff starts (in length units). HELP uses different procedures to adjust the CN value to the surface slope, soil texture, and vegetation class. The maximum value of CN (100) occurs when there is no infiltration. A smaller CN value indicates more rainwater infiltrating into the soil. A previous work conducted in the study area permitted the CN values to be established (Reyes-López et al. 2009).

Evapotranspiration

The input parameters to HELP to calculate the evapotranspiration include evaporative depth zone, maximum leaf-area index, start and end day of growing season, average wind speed and quarterly relative humidity. For this study a depth of 100 cm was used (maximum depth where water can be removed by evapotranspiration), which corresponds to the average plant root length in the area.

Land change analysis

Three Landsat images from the Global Land Cover Facility (GLCF) and of the United States Geological Survey (USGS) from 1979 to 2013 were used to analyze land use changes (LUC) along 34 years to quantify impervious surface increase. Table 3 indicates the different satellites used, with respective bands, sensors, and spatial resolutions. A total of five land covers (urban areas, coniferous, agriculture areas, rangeland and deciduous forest) were selected according to the IV map series published by the National Institute of Statistics and Geographic Information (INEGI for its abbreviation in Spanish). Table 4 reports the land uses considered for this analysis. Figures 4 and 5 show the calculated land uses for 1979 and 2013.

Table 3

Landsat image characteristics.

Satellite Date Bands Sensor Spatial Resolution Source 
LandSat4 08/11/1979 1,2,34 MSS 60 m GLCF 
LandSat8 02/02/2013 1,2,3,4,5,6,7,8,9,10,11 OLIS/TIRS 30 m USGS 
Satellite Date Bands Sensor Spatial Resolution Source 
LandSat4 08/11/1979 1,2,34 MSS 60 m GLCF 
LandSat8 02/02/2013 1,2,3,4,5,6,7,8,9,10,11 OLIS/TIRS 30 m USGS 

Sources:GLCF and USGS.

Table 4

Land use and covers selected for the supervised classification analysis of the satellite images of the Alto Atoyac sub-basin

Land cover Observations 
Urban areas Urban areas, roads 
Coniferous Pine oak forest 
Agriculture areas Rainfed and irrigated agriculture 
Rangeland Induced grassland, pasture-farming 
Deciduous forest Secondary forest 
Land cover Observations 
Urban areas Urban areas, roads 
Coniferous Pine oak forest 
Agriculture areas Rainfed and irrigated agriculture 
Rangeland Induced grassland, pasture-farming 
Deciduous forest Secondary forest 
Figure 4

Land use, for 1979, in the Alto Atoyac sub-basin.

Figure 4

Land use, for 1979, in the Alto Atoyac sub-basin.

Figure 5

Land use, for 2013, in the Alto Atoyac sub-basin.

Figure 5

Land use, for 2013, in the Alto Atoyac sub-basin.

Imagery correction: atmospheric correction and resample

The atmospheric correction transformed the sensor digital data into radiance values to avoid errors of atmospheric spreading and energy absorption. The input parameters for the atmospheric correction are available imagery data, wavelength, digital values, gain, offset, and sun elevation. Since imagery was produced by sensors, and at different dates, they have a different spatial resolution, so they needed to be matched by means of a GIS resample tool (IDIRISI in this case).

Band composite, supervised classification and class validation

The selected RGB (Red, Green and Blue) composite for the imagery classification comprises a false color composition of the MSS (Multispectral Scanner System) sensor RGB321 and of the OLI (Operational Land Imager) sensor RGB543. Field work, INEGI land use GIS layer, and Google Earth permitted one to delineate training polygons, in the study area, to conduct the supervised classification. The agreement kappa index enables validation of the reliability among calculated classes (López de Ullibarri & Pita 2001).

Identification of potential recharge zones

Use of GIS and HELP allowed one to delimit the recharge zone and estimate groundwater recharge rates by combining GIS layers of land use, soil type, daily weather data (precipitation, temperature, solar radiation), and evapotranspiration data (evaporative zone depth, leaf area index, curve numbers, average wind speed, relative humidity, and growing season) (Jyrkama et al. 2002; Jyrkama & Sykes 2007).

RESULTS AND DISCUSSION

Climate scenarios and model uncertainty

Model performance comparison indicates that differences between the normal and the RCP8.5 scenario projections are small, as presented in Figure 6. In the first period (2015–2039), the GFDL-CM3 indicates small but noticeable precipitation reductions from April to August (planting season in the Alto Atoyac sub-basin) (see Table 5), with annual accumulative reductions of 69.15 mm and of 105.24 mm. Contrastingly, the HADGEM2-ES based scenario indicates an annual increase of 21 mm for the near time horizon (2015–2039) and a decrease of 86.81 mm for the far time horizon (2075–2099). According to the GFDL-CM3 the average annual temperature would increase by 1.5°C and 5.13°C for the 2015–2039 horizon and for the 2075–2099 horizon, respectively, while HADGEM2-ES indicates increases of 1.21°C and 5.38°C, respectively, for the near and far time horizons as shown in Table 5. Precipitation data from the GFDL-CM3 have a lower STD in comparison to the HADGEM2-ES based values. Concerning the average temperature data, the HADGEM2-ES data seem to have a better correlation and a lower STD.

Table 5

GCMS climate anomalies respect to the historical climate normal

Months GFDL CM3 GFDL CM3 HADGEM2-ES HADGEM2-ES 
(2015–2039)
 
(2075–2099)
 
(2015–2039)
 
(2075–2099)
 
Anomalies R (mm/month) Anomalies T (°C) Anomalies R (mm/month) Anomalies T (°C) Anomalies R (mm/month) Anomalies T (°C) Anomalies P (mm/month) Anomalies T (°C) 
January −4.16 −0.51 −4.16 −4.91 −1.16 −0.37 0.84 −5.01 
February 3.8 −1.91 3.8 −5.21 3.8 −0.94 4.8 −5.31 
March 2.12 −1.57 −1.88 −4.77 4.79 −0.72 9.62 −5.17 
April 10.37 −1 10.62 −4.4 7.92 −1.42 31.12 −5.47 
May 21.21 −1.43 8.88 −4.71 −5.46 −1.27 12.87 −5.52 
June 1.6 −1.4 9.85 −5.05 11.1 −1.44 10.35 −5.28 
July 20.41 −1.8 20.41 −5.7 2.91 −1.64 9.41 −5.44 
August 12.07 −1.92 28.49 −5.6 6.07 −1.3 0.32 −5.43 
September 6.76 −1.54 8.76 −5.61 −13.49 −1.12 −10.49 −5.23 
October 10.05 −1.71 17.55 −5.21 −9.45 −1.19 11.55 −5.23 
November −4.46 −1.75 1.04 −4.94 −21.96 −1.4 4.04 −5.49 
December −10.62 −1.48 1.88 −5.48 −6.12 −1.68 2.38 −6.01 
STD 9.72 0.40 9.62 0.41 9.79 0.38 9.93 0.25 
R accumulative/T Average 69.15 −1.50 105.24 −5.13 −21.05 −1.21 86.81 −5.38 
Months GFDL CM3 GFDL CM3 HADGEM2-ES HADGEM2-ES 
(2015–2039)
 
(2075–2099)
 
(2015–2039)
 
(2075–2099)
 
Anomalies R (mm/month) Anomalies T (°C) Anomalies R (mm/month) Anomalies T (°C) Anomalies R (mm/month) Anomalies T (°C) Anomalies P (mm/month) Anomalies T (°C) 
January −4.16 −0.51 −4.16 −4.91 −1.16 −0.37 0.84 −5.01 
February 3.8 −1.91 3.8 −5.21 3.8 −0.94 4.8 −5.31 
March 2.12 −1.57 −1.88 −4.77 4.79 −0.72 9.62 −5.17 
April 10.37 −1 10.62 −4.4 7.92 −1.42 31.12 −5.47 
May 21.21 −1.43 8.88 −4.71 −5.46 −1.27 12.87 −5.52 
June 1.6 −1.4 9.85 −5.05 11.1 −1.44 10.35 −5.28 
July 20.41 −1.8 20.41 −5.7 2.91 −1.64 9.41 −5.44 
August 12.07 −1.92 28.49 −5.6 6.07 −1.3 0.32 −5.43 
September 6.76 −1.54 8.76 −5.61 −13.49 −1.12 −10.49 −5.23 
October 10.05 −1.71 17.55 −5.21 −9.45 −1.19 11.55 −5.23 
November −4.46 −1.75 1.04 −4.94 −21.96 −1.4 4.04 −5.49 
December −10.62 −1.48 1.88 −5.48 −6.12 −1.68 2.38 −6.01 
STD 9.72 0.40 9.62 0.41 9.79 0.38 9.93 0.25 
R accumulative/T Average 69.15 −1.50 105.24 −5.13 −21.05 −1.21 86.81 −5.38 
Figure 6

Historical climate normal obtained from the local climate stations and climate normal from RCP8.5, 2015–2039 and 2075–2099 horizons, modeled by GFDL-CM3 and HADGEM2-ES GCMs (Fernandez-Eguiarte et al. 2014).

Figure 6

Historical climate normal obtained from the local climate stations and climate normal from RCP8.5, 2015–2039 and 2075–2099 horizons, modeled by GFDL-CM3 and HADGEM2-ES GCMs (Fernandez-Eguiarte et al. 2014).

The model and scenarios do not intend to reproduce or predict the future. Their purpose is to provide possible scenarios under some assumptions and conditions in order to analyze CCh challenges to natural resources, groundwater in this case, in such conditions.

Decrease of water recharge areas

Impervious surfaces are features of anthropogenic origin. They include roads, buildings, and parking lots for which water cannot infiltrate through the soil (Flanagan & Civco 2001). The decrease of pervious surfaces leads to an increase of surface runoff during a given storm and reduces lag-time and time of concentration, contributing to much greater peak flows. The natural storage along the stream channels is reduced, while the downstream discharge is increased (Micklin & Hodler 1983). Overflow and high runoff can clog soil interstices, reducing the aquifer water recharge. Assuming urban areas as impervious zones, a reduction of 135 km2 of permeable soil was obtained for the 34-year analysis, which amounts to 2.65 × 106m3 of non-infiltrated water. This estimation considers that this amount of water will not infiltrate somewhere else. As already mentioned, in the Alto Atoyac sub-basin, the urban growth is disordered (no urban planning exists). Consequently, it is inferred that this loss presents a big challenge that water resources will be facing in the future because high potential recharge zones will be lost, which in turn will lead to an increase in surface runoff and overflows. Water availability and resource quality will be put at risk. Figure 7 summarizes the results of the analysis of land change in the sub-basin from 1979 up to 2013.

Figure 7

Net gains and losses in the Alto Atoyac sub-basin between 1979 and 2013 according to the remote sensing analysis.

Figure 7

Net gains and losses in the Alto Atoyac sub-basin between 1979 and 2013 according to the remote sensing analysis.

Another land use change is represented by deciduous and coniferous forest losses, which have been largely reduced. A mean annual forest loss of 0.29% is estimated, which together with the urban area growth will increase pressure on the natural resources.

Forests influence climate through physical, chemical, and biological processes that affect the hydrologic cycle and atmospheric composition. These complex and nonlinear forest–atmosphere interactions can dampen or amplify anthropogenic CCh since they can attenuate global warming through carbon sequestration. However, the evaporative effect of temperate forests is yet unclear (Bonan 2008). Studies indicate that the loss of forest decreases the evapotranspiration as well as the rainfall. Nevertheless, the calculated reductions in precipitation are larger than the calculated decrease in evapotranspiration, indicating a reduction in the regional moisture convergence and also an increase in the length of the dry season (Nobre et al. 1991). The decrease in rainfall and increase in temperature will result in an increase in evapotranspiration (Abtew & Melesse 2013).

Water recharge

Based on the 60-year period database, the obtained mean annual recharge associated with the vertical flow is 45.2 mm per year. In an area of 3,744.64 km2, this corresponds to 169 million cubic metres annually. It was not possible to compare this estimation with results from other studies, as suggested by Jyrkama & Sykes (2007) and Risser et al. (2005), because of the costs and difficulty in accessing data from other numerical models. Nevertheless, it was possible to compare our results with other studies on water availability and recharge. Table 6 summarizes the recharge estimated from the HELP analysis and results from other studies on water availability conducted in the study area by different institutions (Pérez et al. 2010). It is important to note that previous recharge studies are based on other methodologies and did not take into account the increase of impervious surfaces or the future CCh affectation. The good correlation observed add reliability to the recharge water estimated in this study.

Table 6

Comparison of water recharge studies carried out in the Alto Atoyac sub-basin (Pérez et al. 2010)

Institution Water recharge in millions of cubic metres 
COPEI 2001 162.8 
CONAGUA 2003 153.6 
CONAGUA 2009 153.6 
UACH 2010 153.6 
Mean 155.9 
Standard deviation 4.6 
Institution Water recharge in millions of cubic metres 
COPEI 2001 162.8 
CONAGUA 2003 153.6 
CONAGUA 2009 153.6 
UACH 2010 153.6 
Mean 155.9 
Standard deviation 4.6 

Figure 8 represents water recharge, runoff and real evapotranspiration for the historical period. Water recharge for two time horizons (2015–2019 and 2075–2099) based on: (1) the RCP8.5 scenario as modeled with the GFDL-CM3 GCMs (i.e., Conde et al. 2011; Fernandez-Eguiarte et al. 2014) and (2) the HELP based water balance (water recharge and real evapotranspiration) are shown in Figures 9 and 10. These figures enable a comparison of the water recharge, runoff, and real evapotranspiration between the historical period and the 2015–2019 and 2075–2099 time horizons.

Figure 8

Water balance variables for the Alto Atoyac sub-basin for the historical period 1951–2010.

Figure 8

Water balance variables for the Alto Atoyac sub-basin for the historical period 1951–2010.

Figure 9

Water balance variables for the Alto Atoyac sub-basin for the period 2015–2039.

Figure 9

Water balance variables for the Alto Atoyac sub-basin for the period 2015–2039.

Figure 10

Water balance variables for the Alto Atoyac sub-basin for the period 2075–2099.

Figure 10

Water balance variables for the Alto Atoyac sub-basin for the period 2075–2099.

According to Figures 8 and 9, water recharge already shows a decrease in the first time horizon (2015–2039). The recharge decreases notoriously for the 2075–2099 time horizon (Figure 10). As temperature increases and precipitation decreases, water recharge diminishes in a proportional way. Contrastingly, the real evapotranspiration increases together with runoff, which is also increased by the reduction of recharge zones. Figures 11 and 12 show, respectively, the monthly and total water recharge for the analyzed periods. Figure 11 presents the mean monthly recharge. A sequential recharge decrease from the present up to the 2075–2099 time horizon can be observed. During June and July, recharge rates are relatively high, but in August a notable decrease is observed. In the 2075–2099 time horizon, from June to August the recharge is already very low, which can endanger the agricultural activities in the study area. In Figure 12, it can be noted how the water recharge in the near and far climate horizons tends to decrease in comparison to the historical recharge relatively by 17.97% and 65.09%, respectively. Figures 1315 show the water recharge in the Alto Atoyac sub-basin for the historical period as well as for the climate horizons of 2015–2019 and 2075–2099, as obtained from the RCP8.5 scenario.

Figure 11

Monthly historical water recharge compared to water recharge for the two climate horizons 2015–2039 and 2075–2099.

Figure 11

Monthly historical water recharge compared to water recharge for the two climate horizons 2015–2039 and 2075–2099.

Figure 12

Historical water recharge compared to water recharge for the two climate horizons 2015–2039 and 2075–2099.

Figure 12

Historical water recharge compared to water recharge for the two climate horizons 2015–2039 and 2075–2099.

Figure 13

Water recharge for the historical period 1951–2010.

Figure 13

Water recharge for the historical period 1951–2010.

Figure 14

Water recharge in the Alto Atoyac sub-basin for the horizon 2015–2039.

Figure 14

Water recharge in the Alto Atoyac sub-basin for the horizon 2015–2039.

Figure 15

Water recharge in the Alto Atoyac sub-basin for the horizon 2075–2099.

Figure 15

Water recharge in the Alto Atoyac sub-basin for the horizon 2075–2099.

There are three main recharge sites. The first one is hosted in the northeastern part of the Etla Valley. The second one is located in the southern part of the study area, in the Tlacolula Valley. The third one is comprised in the southwestern part of the Zaachila Valley. This last recharge site presents a lesser infiltration potential.

Areas with high water recharge ratios represent zones where the aquifer is potentially vulnerable to contamination, and in fact the studies indicate that this aquifer presents a high vulnerability to vertical contamination, due to the permeability of soils and the shallow water levels (Belmonte-Jímenez et al. 2003, 2005; Ramos-Leal et al. 2012).

Adaptation strategies in the Alto Atoyac sub-basin

To better appreciate the reality of the study area it is necessary to emphasize that Oaxaca is Mexico's second poorest state, with highest marginalization indices, and affected by an increasing environmental deterioration, mainly of its water resources. Pollution of the mains rivers that cross the city is large, while many municipalities suffer water scarcity.

In the Alto Atoyac sub-basin, the challenges for water security are associated with: (1) the agriculture production systems, (2) land use changes (LUC), (3) the growing population, (4) the lack of interest of the people in the conservation of natural resources, and (5) the lack of consideration by local and federal governments of the corresponding risks. The identification of measures to be implemented in the near future represents challenges to the stakeholders (local institutions, non-governmental organizations (NGOs), and farmers). The spectra of adaption strategies range from no regret (generating other benefits to the economy or to the environment), reversibility, minimizing environmental impacts, reducing vulnerability or at least not increasing it (De Loë et al. 2001). Cost effectiveness, equity, ease of implementation (feasibility) and effectiveness must be considered in choosing the best adaption strategies.

Conditions in the Alto Atoyac sub-basin region result in negative impacts on the crops. As already mentioned, 87.6% of exploited groundwater resource is allocated to irrigation. Agriculture technology is not well developed despite this economic sector representing the main economic activity. Irrigation itself is considered an adaptation strategy to CCh and variability (De Loë et al. 2001); however, inefficient irrigation methods can put the groundwater system at risk. Farmers need to switch from low- to high-efficiency irrigation methods, to reduce wastage. This can be achieved by irrigation management techniques, such as irrigation scheduling and by considering the water demand of different crops. Appropriate advice, training programs and monitoring of the corresponding achievements would be key in enabling assimilation of the knowledge and respective technologies. Rainwater harvesting and water recharge works represent good strategies that are being implemented by farmers in the Zaachila Valley and providing positive results for them.

LUC and growing population are closely related and both stress the groundwater resources. Reduction of forest and increase of urban areas may result in less water recharge areas and larger water demand for different uses. Effects associated with these items are already being experienced. Government must implement an urban planning policy that limits effects of a growing population and associated LUC. Government must fund studies to identify and assure the preservation of the potential recharge zones, and to plan a re-forestation campaign in the watershed upstream.

The challenge is to make the government aware of the risk to water supply. This can be done by presenting the government with the possible scenarios if no action is taken. Studies like this one can be a large contribution. A collaboration with research centers is necessary and also the establishment of sustainable water conservation policies. This requires more research in the study area since at the moment there are few studies concerning the water resource. Studies about the water degradation and its effect on the poorest communities are necessary.

The lack of interest of the people in the conservation of natural resources, can only be overcome through the education of future generations, and awareness campaigns to explain the importance of the conservation of water resource. The people need to understand that water is a highly vulnerable resource, mainly in the Alto Atoyac sub-basin, and that probably in future years they will not have access to water.

CONCLUSIONS

Land use changes along a 34-year period in the Alto Atoyac sub-basin were established by means of a classification analysis of Landsat imagery. Hydraulic conductivities were assigned to soil types as classified by INEGI by following the USDA (1985) methodology.

Water balance analysis together with land use made it possible to estimate water runoff and the loss of infiltration surface due to urban growth. This information made it possible to further conduct a water balance to estimate the water recharge based on a temperature and rainfall 60-year database from five climate stations located in the study area. The HELP3.95D program was used.

By assuming that the soil properties will not change over time and using the soil temperature and precipitation from the CCh RCP8.5 scenario, the recharge at the time horizons of 2015–2019 and 2075–2099 was estimated. According to the CCh RCP8.5 scenario in the Alto Atoyac sub-basin, there will be a direct reduction of precipitation and an increase in soil temperatures in these two horizons.

From 1979 to 2013, the urban areas in the Alto Atoyac sub-basin increased by 135 square kilometres. That amounts to a loss of a 135 km2 caption surface and of 2.65 × 106 m3 of water infiltration into the subsoil. Assuming that this water amount does not infiltrate somewhere else, this infiltration surface loss represents a significant water recharge loss. According to the HELP based water balance, this will result in a reduction of water recharge into the aquifer of the Alto Atoyac sub-basin. In the case of future urban growth continuing, additional water recharge reductions might result.

Near and far horizons analyzed under the CCh RCP8.5 scenario indicate that reductions of 17.97% and 65.09% of water recharge would take place in the short and long term within the watershed respectively, this represents a challenge to the management of water resources for the future, as farming and human consumption are the most important uses of water.

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