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A modeling-GIS approach for assessing irrigation effects on soil salinisation under global warming conditions
Agricultural Water Management 50 (2001) 53±63
A modeling-GIS approach for assessing irrigation effects on soil salinisation under global warming conditions Angel Utseta,*, Matilde Borrotob a
Agricultural University of Havana, Agrophysics Research Unit (GIAF), Apdo. Postal 18, San Jose de las Lajas, Havana 32700, Cuba b Institute of Soils, Finca ``Las Margaritas'', Capdevila, Havana, Cuba Accepted 14 December 2000
Abstract Soil salinisation is very often due to excessive irrigation. However, irrigation is absolutely essential for obtaining reliable crop yields, particularly under predicted global warming conditions. A simple methodology for assessing the salinisation risk for any water management situation and under predicted global warming conditions is presented. The methodology is illustrated by the assessment of irrigation effects on soil salinity at San Antonio del Sur Valley, in the southeast of Cuba. Irrigation from a new dam will support agriculture in the Valley, but at the same time soil salinity is expected to increase. Soil electrical conductivity at several depths and topographical altitudes were used to create raster layers in a Geographic Information System (GIS), thus, determining the border of the saline-affected zones by a GIS analysis. Water-table depth at the border of the saline zones was assumed to be 2 m. The physically based SWAP model was used to predict future water-table depths after irrigation begins and under global warming conditions. Future temperature and precipitation daily values were calculated from a linear increase/decrease of the daily values corresponding to a typical year, according to a global-change forecast for the zone. Soil hydraulic properties were estimated from pedotransfer function and published soil data. Simulated results predict a fast water-table raise of 1 m, due to the increase of irrigation water. Borders of the new saline zones under these conditions (i.e. the places where the water-table is at a 2 m depth) were calculated using a digital terrain model, assuming that the water-table rose 1 m over the whole valley. According to the simulation results, the original saline zones of the valley will be enlarged from 31.4 to 96.8 ha 15 years after the scheduled start of irrigation. The methodology could be used by farmers and decision-makers to select the most suitable water management solution considering both economical and environmental criteria. # 2001 Elsevier Science B.V. All rights reserved. Keywords: Soil salinity; Hydrological modeling; Soil electrical conductivity; Geographic Information System; Global warming * Corresponding author. Tel.: 53-64-63013; fax: 53-72-40942. E-mail addresses: [email protected], [email protected] (A. Utset).
0378-3774/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 3 7 7 4 ( 0 1 ) 0 0 0 9 0 - 7
54
A. Utset, M. Borroto / Agricultural Water Management 50 (2001) 53±63
1. Introduction The greenhouse effect will produce a general warming in the coming years (IPCC, 1996). Even though the total temperature increase is not expected to be high in the tropics, this effect will significantly increase the evaporative demand of crops (Scholes and Van Breemen, 1997). Therefore, irrigation water requirements will increase resulting in higher water-tables and increased soil salinity if the leaching fraction remains unchanged. Excessive irrigation has been shown to be the main cause of soil salinisation in more than one million hectares in the valleys of Cuba due to the raising of saline watertables in lower lands (Ortega et al., 1982). Hence, an increase in irrigation water could increase the soil saline area in these Cuban valleys. Although the spatial variability of soil salinity is generally high, the average levels often reflect topography. Lower lands are generally more saline due to shallow saline water-tables generated in part because of downward lateral flows from the irrigated higher surrounding lands. The relationship between salinity and topography has been used for sampling purposes (Utset et al., 1998), considering the slope as the maximum direction of electrical conductivity (EC) variability. Also, the relationship has been used for salinity risk studies with the aid of a Geographic Information System (GIS) (Bui et al., 1996), since the risk decreases with altitude. The minimum depth at which the water-table should be located, in order to avoid soil salinisation due to capillary action, is usually known from drainage and other related studies. Consequently, the areas of salinity risk could be estimated as those where the water-table depth is shallower than this minimum depth. Water-table depths could be measured directly, monitoring the salinity risk in any zone. This kind of measurement has practical difficulties and, hence, in most agricultural areas in tropical countries there is no information available on water-table depths. Nevertheless, the water-table depth under any water management or climate conditions can be estimated through hydrological models. These models are able to estimate water-table depths, drainage flux, and many other hydrological variables by simulating soil water movement (Van Genuchten, 1994; Leenhardt et al., 1995). Models have been used to predict agricultural yields under global warming conditions (Wilks, 1988; Nonhebel, 1993; Semenov and Porter, 1995; Rosenzweig and Tubiello, 1996) and for calculating future hydrological variables in high risk catchment areas (Gleick, 1987; Caspary, 1990; Valdes et al., 1994; Rao and Al-Wagdany, 1995). However, until now, these models have not been used to predict water-table depths and salinity risks under global warming conditions. This paper shows a simple methodology for assessing future soil salinisation due to the increase of irrigation water under global warming conditions by using hydrological models in a GIS environment. 2. Materials and methods The study was conducted at San Antonio del Sur Valley. It is located at 208010 N and 758160 W, in the southeast semi-arid zone of the island of Cuba. Annual precipitation records lower than 600 mm characterize the climate of the Valley. Precipitation is
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seasonally distributed. More than 80% of total annual rainfall is recorded between May and October. Mean daily temperature is 26.08C. Average air humidity is 75%. Mean evaporation, as measured in a class A pan evaporimeter, is 2345 mm. Soils of San Antonio del Sur Valley are fluvisols (FAO, 1988). These are shallow loamy soils of 50±80 cm effective depth and medium±low fertility. The impermeable C-horizon is at about 3 m depth. Borroto (1997) provides some physical and chemical properties of soils in San Antonio del Sur Valley. Most of the agricultural lands of the valley have been devoted to non-irrigated grass, although irrigated family agriculture can be found in about 70 ha (Borroto, 1997). However, irrigation water is now available from a new dam and the irrigated area of the valley will be increased to more than 400 ha (about 75% of the valley area) in the near future. Irrigation is foreseen to support cattle raising (Borroto, 1997). According to INRH (1997), a total amount of 900 mm of irrigation water could be available, scheduled in three monthly irrigations during the dry season and two monthly irrigations in the wet season. Borroto (1997) pointed out that soil salinity in the lower lands of San Antonio del Sur has been significantly increased in the last 30 years, which is related to the decrease in milk production in the valley. Likewise, Ortega et al. (1982) studied the water-table effects on soil salinity in the same geographical zone. They show that if the water-table is not deeper than 2 m, a salinity risk should be expected in the soil surface due to soluble salts rising from water-table. Hence, the availability of more water in the San Antonio del Sur agricultural area could lead to shallower water-tables in some parts of the valley. Correspondingly, the soil salinity area is expected to increase. Soil electrical conductivity (EC) was measured in the agricultural areas of the valley in the spring of 1997. Soil samples were taken at 0±20, 20±40, 40±60 and 60±100 cm depth in a 200 m spatied square grid. The EC measurements were made by the saturationextract technique. The 1:50,000 contour lines of the topographical map of the valley were digitized and introduced in the GIS ``TeleMap'' (GeoCuba, 1997). A digital terrain model (DTM) of the valley was created in the GIS from the introduced topographical contour lines. EC values at each depth were also introduced in the GIS and converted to raster layers. Fig. 1 shows the 3D soil surface salinity map (EC at 0±20 cm) of the agricultural areas in San Antonio del Sur Valley superimposed over the DTM. As can be seen in Fig. 1, a relatively high mountain is located to the northeast of the valley, determining the general slope of the valley from north (mountains) to south (coast). However, some hills are located at the southeast end of the valley (see Fig. 1). There is a clear correlation between soil salinity and topography. Soil salinity is highest in the lower areas of the valley. The SWAP model (Van Dam et al., 1997) was used in this study. SWAP is a physically based hydrological model developed from the SWATRE model (Belmans et al., 1983). The model is a functional combination of advanced soil water simulation models and a crop-growth simulation model, containing grass and several other crops. The Richards equation for soil water movement is solved in SWAP by a numerical scheme, including several practical options for the initial and boundary conditions. According to Eatherall (1997), mechanistic models are the most reliable for assessing ecosystem processes under global warming conditions. Physically based mechanistic models are able to account for any meteorological condition, whereas empirical models could give unrealistic results for conditions different than those where they were obtained (Eatherall, 1997).
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Fig. 1. Salinity map of the agricultural areas of San Antonio del Sur Valley superimposed over the digital terrain model (DTM).
Soil hydraulic properties, as required in SWAP inputs, were estimated from Rawls et al. (1982, 1998) pedotransfer functions. Physical soil data needed for these estimations, i.e. mechanical composition, soil density, and soil organic matter content, were obtained from published data (Borroto, 1997). An impermeable layer at 3 m depth was considered as the bottom boundary condition. The soil water content at the whole-simulated layer (0±300 cm) was assumed as field capacity for the first simulation day (January 1, 2000), according to Borroto (1997) published values. The INRH (1997) proposed irrigation schedule was considered in the SWAP simulations for the whole agricultural area of the valley. The grass crop function included in SWAP (Van Dam et al., 1997) was used for the crop water-uptake calculations. Centella et al. (1997) provided the global warming forecast for the next 50 years in Cuba and the surrounding Caribbean Sea. They assumed the KyotoA1 scenario for greenhouse gases emission and calculated the temperature increase and the precipitation
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Fig. 2. Meteorological variables generated from the global change climate scenario. (A) Yearly averaged mean daily temperature for the simulated period. (B) Daily precipitation values for the first and the last simulated years.
changes (in percent) for each year through the simple MAGIC model (Hulme et al., 1995). This information was used to generate all the daily meteorological data needed in SWAP for calculating potential evapotranspiration (ETp) by the modified Penman method (Doorenbos and Pruitt, 1977), i.e. mean temperature, sun radiation, wind speed, and air humidity. Since meteorological data for hydrological models must be introduced in the crop models on a daily basis (Nonhebel, 1994), the 1996 daily data were taken as the starting point. This was a non-dry, non-wet year for the east region of Cuba (Borroto, 1997). Daily temperature and precipitation values were changed each year according to Centella et al. (1997) predictions. The other meteorological variables needed for the ETp calculations remained at the same 1996 values. Fig. 2A shows the yearly averaged mean daily temperatures from 2000 to 2030 and Fig. 2B shows the daily precipitation values for 2000 and 2030. As can be seen in Fig. 2, values changed linearly according to the forecasted increase/decrease. Nonhebel (1993) and Rao and Al-Wagdany (1995) followed
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a similar approach to obtain the input meteorological data in hydrological model applications. 3. Results A GIS analysis was performed in order to determine the water-table affected saline zones. These zones are those where EC is higher in the soil surface and decreases with depth. In those areas where EC increases with depth, the water-table is deep enough and there is, as yet, no salinisation risk. Therefore, a comparison among EC raster layers in the GIS was made, obtaining those pixels where the condition EC0 20 > EC20 40 > EC40 60 > EC60 100 was met. Those zones are depicted in Fig. 3. As expected, the saline zones affected by shallow water-tables are those located in lower topographical altitudes. There are two main saline zones in the valley. One of these zones is near the coast and the other is between the mountains (see Figs. 1 and 3). According to our assumptions, the border of the saline zones must agree with the minimum water-table depth for which soluble salts can reach the soil surface. Therefore, water-table depth in the border of the saline zones shown in Fig. 3 could be estimated as 2 m. Water-table
Fig. 3. Electrical conductivity values at 0±20 cm depth meeting the condition that surface EC is higher than deeper EC values.
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depths inside the saline zones are lower than 2 m and outside the zones are deeper than 2 m. The SWAP mechanistic simulations, since obtained from the solution of the Richards equation for the vertical direction, are point-based and spatially variable. However, we assumed the same model inputs and outputs for the whole border of each saline zone. Hence, a single SWAP simulation must predict the water-table time behavior in these borders, considering that the initial water-table depth at all the points of these lines was 2 m. Besides, lateral flows from the higher lands outside the border of the saline zones were assumed as equal to the lateral flows to the surrounding lower lands, inside the saline zones, and were ignored. Therefore, the changes of water-table depths in the borders of the saline zones were considered as a function of irrigation, precipitation, and evapotranspiration. The simulation results for 2000±2030 are shown in Fig. 4. Fig. 4A depicts the total yearly values of potential evapotranspiration, as calculated from the modified Penman method. As can be seen in the figure, potential evapotranspiration increases linearly each year, following the same behavior as mean daily temperature (see Fig. 2A). This result is somehow unrealistic, but agrees with the meteorological input data. Due to this uncertainty, the results have mainly a methodological value. Fig. 4B shows the ratio between the yearly averaged real and potential evapotranspiration. This ratio is an effective index of the crop water requirements. After the beginning of irrigation (assumed to start in January 2000), the ratio between real and potential evapotranspiration increases rapidly to 1 and remains close to unity until the year 2015. The ratio is reduced again after 2015, due to the increase in potential evapotranspiration caused by global warming. According to this result, the scheduled irrigation proposed by INRH (1997) is enough to satisfy the grass water requirements until 2015. Irrigation should be increased slightly after this year otherwise the grass will suffer a considerable water stress around 2025±2030, which could affect cattle productivity. The simulated water-table depths are shown in Fig. 4C. Similar to Fig. 4B, the water-table rises rapidly after irrigation starts and reaches a 1 m depth at the beginning of the 21st century. Watertable depths are gently deeper each year after the initial rise; however, it remains at depths shallower than 150 cm until 2025. According to the modeling results, the water-table raises from 2 to 1 m by the year 2000. Therefore, the water-table depth difference due to the INRH (1997) proposed irrigation schedule is about 1 m. The water-table influence on soil surface salinity will remain in the saline zones shown in Fig. 3 for more than 20 years. Assuming that the water-table raises from 2 to 1 m in the borders of the saline zones (see Fig. 3), the new borders were calculated using the DTM. This was done by creating masks of each saline zone and subtracting the altitude of the border (1 m incremented from the original altitude) from all the altitudes of the mask. The negative values of the calculated GIS variable correspond to those zones where water-table depths are shallower than 2 m. Therefore, soluble-salt rising by capillary action from the water-table could affect them. The zones were depicted on the screen and digitized directly. The results are shown in Fig. 5. Both the actual (Fig. 5A) and the future (Fig. 5B) saline zones are shown in the figure. The saline zone near the coast will expand its area from 28.9 to 56.4 ha around 2015, whereas the saline zone between the mountains will increase from 25.1 to 40.4 ha in this period. Practically, all the agriculture land near the coast will be lost due to
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Fig. 4. Simulation results. (A) Simulated yearly potential evapotranspiration. (B) Yearly averaged ratio between simulated actual and potential evapotranspiration. (C) Simulated water-table depths.
soil salinisation. However, most of the agricultural land in the higher areas will remain unaffected. As shown above, simulation results are not strictly reliable but they can give an idea to farmers and decision-makers about the salinity risk to be expected if the proposed
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Fig. 5. Saline areas in the agricultural zones of San Antonio del Sur valley. (A) Actual saline zones (year 2000). (B) Predicted saline zones (year 2015).
irrigation schedule goes ahead. Land reclamation of these saline areas is a very complicated and expensive task. Hence, if decision-makers choose to begin the proposed irrigation management system they would lose the agricultural areas to increased salinity, as estimated in this paper. 4. Conclusions Farmers and decision-makers can rely on a simple methodology for assessing the salinity risk in agricultural lands where irrigation will be introduced/increased under global warming conditions. The methodology comprises the following general steps: 1. Calculating the saline areas and their borders by using DTM and soil EC measurements at several depths. 2. Estimating the time changes in the water-table depth through a hydrological model and considering a climate scenario. 3. Predicting the new saline areas by combining the DTM with the new water-table depths at the borders of the saline zones. Climate scenarios could be unreliable and, hence, daily or yearly results are of less practical interest. However, general trends in soil salinisation for a specific water
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management system could effectively help decision-makers and farmers to select the most suitable solution. Decision-makers must evaluate many economical and environment issues before taking any decision, but they can rely on this simple methodology for assessing the possible effects of any water management solution. References Belmans, C., Wesseling, J., Feddes, R., 1983. Simulation model of the water balance of a cropped soil: SWATRE. J. Hydrol. 63, 271±286. Borroto, M., 1997. RehabilitacioÂn ecoloÂgica del Valle de San Antonio del Sur. Ministry of Sciences and Technology, Cuban Global-Change Research Program, Project 013004, Havana, 23 pp. Bui, E., Keith, R.J., Moran, C.J., William, J., 1996. Use of soil survey information to assess regional salinisation risk using Geographical Information Systems. J. Environ. Qual. 25, 433±439. Caspary, H.J., 1990. An ecohydrological framework for water yield changes of forested catchments due to forest decline and soil acidification. Water Resourc. Res. 26, 1121±1131. Centella, A., GutieÂrrez, T., Limia, M., Rivero, R., 1997. Climatic change scenarios for impact assessment in Cuba. Institute of Meteorology Report, Havana, 16 pp. Doorenbos, J., Pruitt, W., 1977. Guidelines for Predicting Crop Water Requirements. Irrigation and Drainage Paper 24, 2nd Edition. FAO, Rome, 156 pp. Eatherall, A., 1997. Modelling climate impacts on ecosystems using linked models and a GIS. Climatic change 35, 17±34. FAO (Food and Agriculture Organization), 1988. FAO±UNESCO Soil Map of the world. FAO Report 60. FAO, Rome. GeoCuba, 1997. TeleMap: Un Sistema de InformacioÂn GeograÂfica dedicado para las Geociencias, User Manual. GeoCuba, Havana, 95 pp. Gleick, P.H., 1987. The development and testing of a water balance model for climate impact assessment: modelling the Sacramento river basin. Water Resourc. Res. 20, 793±802. Hulme, M., Jiang, T., Wigley, T., 1995. A Climate Change Scenario Generator: A User Manual. Climate Research Unit, UEA, Norwich, UK, 38 pp. INRH (Instituto Nacional de Recursos HidraÂulicos), 1997. Estudio de factibilidad del riego en las aÂreas agrõÂcolas del Valle de San Antonio del Sur. Research Report. INRH, GuantaÂnamo, 17 pp. IPCC (International Panel on Climate Change), 1996. Impacts, Adaptations and Mitigation of Climate Change: Scientific±Technical Analysis. IPCC, Cambridge University Press, Cambridge, 879 pp. Leenhardt, D., Voltz, M., Rambel, S., 1995. A survey of several agroclimatic soil water balance models with reference to their spatial application. Eur. J. Agron. 41, 1±14. Nonhebel, S., 1993. Effects of changes in temperature and CO2 concentration on simulated spring wheat yields in the Netherlands. Climatic Change 24, 311±329. Nonhebel, S., 1994. The effects of use of average instead of daily weather data in crop growth simulation models. Agric. Syst. 44, 377±396. Ortega, F., MartõÂnez, M., Herrero, L., 1982. Causas de la variacioÂn del manto freaÂtico y su relacioÂn con la salinidad de los suelos en el Valle de GuantaÂnamo. Cienc. Agric. 12, 63±73. Rao, R., Al-Wagdany, A., 1995. Effects of climatic change in Wabash river basin. J. Irrig. Drain. Eng. 121, 207±215. Rawls, W., Brakensiek, D., Saxton, K., 1982. Estimation of soil water properties. Trans. ASAE 25, 305±316. Rawls, D., Gimenez, D., Grossman, R., 1998. Use of soil texture, bulk density and slope of the water retention curve to predict saturated hydraulic conductivity. Trans. ASAE 41, 983±988. Rosenzweig, C., Tubiello, F.N., 1996. Effects of changes in minimum and maximum temperature on wheat yields in the central US Ð a simulation study. Agric. For. Meteorol. 80, 215±230. Scholes, R.J., Van Breemen, N., 1997. The effects of global change on tropical ecosystems. Geoderma 79, 9±24.
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Semenov, M.A., Porter, J.A., 1995. Climatic variability and the modeling of crop yields. Agric. For. Meteorol. 73, 265±283. Utset, A., Ruiz, M.E., Herrera, J., Ponce de LeoÂn, D., 1998. A geostatistical method for soil salinity sample site spacing. Geoderma 86, 143±151. Valdes, J., Seone, R., North, G., 1994. A methodology for the evaluation of global warming impact on soil moisture and runoff. J. Hydrol. 161, 389±413. Van Dam, J.C., Huygen, J., Wesseling, J.G., Feddes, R.A., Kabat, P., Van Walsum, P.E.V., Groenedijk, P., Van Diepen, C.A., 1997. Report 71. Department of Water Resources, Wageningen Agricultural University, 167 pp. Van Genuchten, M., 1994. New issues and challenges in soil physics research. In: Proceedings of the 15th World Congress on Soil Science, Inaugural and State of the Art Conferences, Vol. 1, Acapulco, Mexico. Wilks, D.S., 1988. Estimating the consequences of CO2 induced climatic change on north American grain agriculture using general circulation model information. Climatic Change 13, 19±42.
A modeling-GIS approach for assessing irrigation effects on soil salinisation under global warming conditions Angel Utseta,*, Matilde Borrotob a
Agricultural University of Havana, Agrophysics Research Unit (GIAF), Apdo. Postal 18, San Jose de las Lajas, Havana 32700, Cuba b Institute of Soils, Finca ``Las Margaritas'', Capdevila, Havana, Cuba Accepted 14 December 2000
Abstract Soil salinisation is very often due to excessive irrigation. However, irrigation is absolutely essential for obtaining reliable crop yields, particularly under predicted global warming conditions. A simple methodology for assessing the salinisation risk for any water management situation and under predicted global warming conditions is presented. The methodology is illustrated by the assessment of irrigation effects on soil salinity at San Antonio del Sur Valley, in the southeast of Cuba. Irrigation from a new dam will support agriculture in the Valley, but at the same time soil salinity is expected to increase. Soil electrical conductivity at several depths and topographical altitudes were used to create raster layers in a Geographic Information System (GIS), thus, determining the border of the saline-affected zones by a GIS analysis. Water-table depth at the border of the saline zones was assumed to be 2 m. The physically based SWAP model was used to predict future water-table depths after irrigation begins and under global warming conditions. Future temperature and precipitation daily values were calculated from a linear increase/decrease of the daily values corresponding to a typical year, according to a global-change forecast for the zone. Soil hydraulic properties were estimated from pedotransfer function and published soil data. Simulated results predict a fast water-table raise of 1 m, due to the increase of irrigation water. Borders of the new saline zones under these conditions (i.e. the places where the water-table is at a 2 m depth) were calculated using a digital terrain model, assuming that the water-table rose 1 m over the whole valley. According to the simulation results, the original saline zones of the valley will be enlarged from 31.4 to 96.8 ha 15 years after the scheduled start of irrigation. The methodology could be used by farmers and decision-makers to select the most suitable water management solution considering both economical and environmental criteria. # 2001 Elsevier Science B.V. All rights reserved. Keywords: Soil salinity; Hydrological modeling; Soil electrical conductivity; Geographic Information System; Global warming * Corresponding author. Tel.: 53-64-63013; fax: 53-72-40942. E-mail addresses: [email protected], [email protected] (A. Utset).
0378-3774/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 3 7 7 4 ( 0 1 ) 0 0 0 9 0 - 7
54
A. Utset, M. Borroto / Agricultural Water Management 50 (2001) 53±63
1. Introduction The greenhouse effect will produce a general warming in the coming years (IPCC, 1996). Even though the total temperature increase is not expected to be high in the tropics, this effect will significantly increase the evaporative demand of crops (Scholes and Van Breemen, 1997). Therefore, irrigation water requirements will increase resulting in higher water-tables and increased soil salinity if the leaching fraction remains unchanged. Excessive irrigation has been shown to be the main cause of soil salinisation in more than one million hectares in the valleys of Cuba due to the raising of saline watertables in lower lands (Ortega et al., 1982). Hence, an increase in irrigation water could increase the soil saline area in these Cuban valleys. Although the spatial variability of soil salinity is generally high, the average levels often reflect topography. Lower lands are generally more saline due to shallow saline water-tables generated in part because of downward lateral flows from the irrigated higher surrounding lands. The relationship between salinity and topography has been used for sampling purposes (Utset et al., 1998), considering the slope as the maximum direction of electrical conductivity (EC) variability. Also, the relationship has been used for salinity risk studies with the aid of a Geographic Information System (GIS) (Bui et al., 1996), since the risk decreases with altitude. The minimum depth at which the water-table should be located, in order to avoid soil salinisation due to capillary action, is usually known from drainage and other related studies. Consequently, the areas of salinity risk could be estimated as those where the water-table depth is shallower than this minimum depth. Water-table depths could be measured directly, monitoring the salinity risk in any zone. This kind of measurement has practical difficulties and, hence, in most agricultural areas in tropical countries there is no information available on water-table depths. Nevertheless, the water-table depth under any water management or climate conditions can be estimated through hydrological models. These models are able to estimate water-table depths, drainage flux, and many other hydrological variables by simulating soil water movement (Van Genuchten, 1994; Leenhardt et al., 1995). Models have been used to predict agricultural yields under global warming conditions (Wilks, 1988; Nonhebel, 1993; Semenov and Porter, 1995; Rosenzweig and Tubiello, 1996) and for calculating future hydrological variables in high risk catchment areas (Gleick, 1987; Caspary, 1990; Valdes et al., 1994; Rao and Al-Wagdany, 1995). However, until now, these models have not been used to predict water-table depths and salinity risks under global warming conditions. This paper shows a simple methodology for assessing future soil salinisation due to the increase of irrigation water under global warming conditions by using hydrological models in a GIS environment. 2. Materials and methods The study was conducted at San Antonio del Sur Valley. It is located at 208010 N and 758160 W, in the southeast semi-arid zone of the island of Cuba. Annual precipitation records lower than 600 mm characterize the climate of the Valley. Precipitation is
A. Utset, M. Borroto / Agricultural Water Management 50 (2001) 53±63
55
seasonally distributed. More than 80% of total annual rainfall is recorded between May and October. Mean daily temperature is 26.08C. Average air humidity is 75%. Mean evaporation, as measured in a class A pan evaporimeter, is 2345 mm. Soils of San Antonio del Sur Valley are fluvisols (FAO, 1988). These are shallow loamy soils of 50±80 cm effective depth and medium±low fertility. The impermeable C-horizon is at about 3 m depth. Borroto (1997) provides some physical and chemical properties of soils in San Antonio del Sur Valley. Most of the agricultural lands of the valley have been devoted to non-irrigated grass, although irrigated family agriculture can be found in about 70 ha (Borroto, 1997). However, irrigation water is now available from a new dam and the irrigated area of the valley will be increased to more than 400 ha (about 75% of the valley area) in the near future. Irrigation is foreseen to support cattle raising (Borroto, 1997). According to INRH (1997), a total amount of 900 mm of irrigation water could be available, scheduled in three monthly irrigations during the dry season and two monthly irrigations in the wet season. Borroto (1997) pointed out that soil salinity in the lower lands of San Antonio del Sur has been significantly increased in the last 30 years, which is related to the decrease in milk production in the valley. Likewise, Ortega et al. (1982) studied the water-table effects on soil salinity in the same geographical zone. They show that if the water-table is not deeper than 2 m, a salinity risk should be expected in the soil surface due to soluble salts rising from water-table. Hence, the availability of more water in the San Antonio del Sur agricultural area could lead to shallower water-tables in some parts of the valley. Correspondingly, the soil salinity area is expected to increase. Soil electrical conductivity (EC) was measured in the agricultural areas of the valley in the spring of 1997. Soil samples were taken at 0±20, 20±40, 40±60 and 60±100 cm depth in a 200 m spatied square grid. The EC measurements were made by the saturationextract technique. The 1:50,000 contour lines of the topographical map of the valley were digitized and introduced in the GIS ``TeleMap'' (GeoCuba, 1997). A digital terrain model (DTM) of the valley was created in the GIS from the introduced topographical contour lines. EC values at each depth were also introduced in the GIS and converted to raster layers. Fig. 1 shows the 3D soil surface salinity map (EC at 0±20 cm) of the agricultural areas in San Antonio del Sur Valley superimposed over the DTM. As can be seen in Fig. 1, a relatively high mountain is located to the northeast of the valley, determining the general slope of the valley from north (mountains) to south (coast). However, some hills are located at the southeast end of the valley (see Fig. 1). There is a clear correlation between soil salinity and topography. Soil salinity is highest in the lower areas of the valley. The SWAP model (Van Dam et al., 1997) was used in this study. SWAP is a physically based hydrological model developed from the SWATRE model (Belmans et al., 1983). The model is a functional combination of advanced soil water simulation models and a crop-growth simulation model, containing grass and several other crops. The Richards equation for soil water movement is solved in SWAP by a numerical scheme, including several practical options for the initial and boundary conditions. According to Eatherall (1997), mechanistic models are the most reliable for assessing ecosystem processes under global warming conditions. Physically based mechanistic models are able to account for any meteorological condition, whereas empirical models could give unrealistic results for conditions different than those where they were obtained (Eatherall, 1997).
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Fig. 1. Salinity map of the agricultural areas of San Antonio del Sur Valley superimposed over the digital terrain model (DTM).
Soil hydraulic properties, as required in SWAP inputs, were estimated from Rawls et al. (1982, 1998) pedotransfer functions. Physical soil data needed for these estimations, i.e. mechanical composition, soil density, and soil organic matter content, were obtained from published data (Borroto, 1997). An impermeable layer at 3 m depth was considered as the bottom boundary condition. The soil water content at the whole-simulated layer (0±300 cm) was assumed as field capacity for the first simulation day (January 1, 2000), according to Borroto (1997) published values. The INRH (1997) proposed irrigation schedule was considered in the SWAP simulations for the whole agricultural area of the valley. The grass crop function included in SWAP (Van Dam et al., 1997) was used for the crop water-uptake calculations. Centella et al. (1997) provided the global warming forecast for the next 50 years in Cuba and the surrounding Caribbean Sea. They assumed the KyotoA1 scenario for greenhouse gases emission and calculated the temperature increase and the precipitation
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Fig. 2. Meteorological variables generated from the global change climate scenario. (A) Yearly averaged mean daily temperature for the simulated period. (B) Daily precipitation values for the first and the last simulated years.
changes (in percent) for each year through the simple MAGIC model (Hulme et al., 1995). This information was used to generate all the daily meteorological data needed in SWAP for calculating potential evapotranspiration (ETp) by the modified Penman method (Doorenbos and Pruitt, 1977), i.e. mean temperature, sun radiation, wind speed, and air humidity. Since meteorological data for hydrological models must be introduced in the crop models on a daily basis (Nonhebel, 1994), the 1996 daily data were taken as the starting point. This was a non-dry, non-wet year for the east region of Cuba (Borroto, 1997). Daily temperature and precipitation values were changed each year according to Centella et al. (1997) predictions. The other meteorological variables needed for the ETp calculations remained at the same 1996 values. Fig. 2A shows the yearly averaged mean daily temperatures from 2000 to 2030 and Fig. 2B shows the daily precipitation values for 2000 and 2030. As can be seen in Fig. 2, values changed linearly according to the forecasted increase/decrease. Nonhebel (1993) and Rao and Al-Wagdany (1995) followed
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a similar approach to obtain the input meteorological data in hydrological model applications. 3. Results A GIS analysis was performed in order to determine the water-table affected saline zones. These zones are those where EC is higher in the soil surface and decreases with depth. In those areas where EC increases with depth, the water-table is deep enough and there is, as yet, no salinisation risk. Therefore, a comparison among EC raster layers in the GIS was made, obtaining those pixels where the condition EC0 20 > EC20 40 > EC40 60 > EC60 100 was met. Those zones are depicted in Fig. 3. As expected, the saline zones affected by shallow water-tables are those located in lower topographical altitudes. There are two main saline zones in the valley. One of these zones is near the coast and the other is between the mountains (see Figs. 1 and 3). According to our assumptions, the border of the saline zones must agree with the minimum water-table depth for which soluble salts can reach the soil surface. Therefore, water-table depth in the border of the saline zones shown in Fig. 3 could be estimated as 2 m. Water-table
Fig. 3. Electrical conductivity values at 0±20 cm depth meeting the condition that surface EC is higher than deeper EC values.
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depths inside the saline zones are lower than 2 m and outside the zones are deeper than 2 m. The SWAP mechanistic simulations, since obtained from the solution of the Richards equation for the vertical direction, are point-based and spatially variable. However, we assumed the same model inputs and outputs for the whole border of each saline zone. Hence, a single SWAP simulation must predict the water-table time behavior in these borders, considering that the initial water-table depth at all the points of these lines was 2 m. Besides, lateral flows from the higher lands outside the border of the saline zones were assumed as equal to the lateral flows to the surrounding lower lands, inside the saline zones, and were ignored. Therefore, the changes of water-table depths in the borders of the saline zones were considered as a function of irrigation, precipitation, and evapotranspiration. The simulation results for 2000±2030 are shown in Fig. 4. Fig. 4A depicts the total yearly values of potential evapotranspiration, as calculated from the modified Penman method. As can be seen in the figure, potential evapotranspiration increases linearly each year, following the same behavior as mean daily temperature (see Fig. 2A). This result is somehow unrealistic, but agrees with the meteorological input data. Due to this uncertainty, the results have mainly a methodological value. Fig. 4B shows the ratio between the yearly averaged real and potential evapotranspiration. This ratio is an effective index of the crop water requirements. After the beginning of irrigation (assumed to start in January 2000), the ratio between real and potential evapotranspiration increases rapidly to 1 and remains close to unity until the year 2015. The ratio is reduced again after 2015, due to the increase in potential evapotranspiration caused by global warming. According to this result, the scheduled irrigation proposed by INRH (1997) is enough to satisfy the grass water requirements until 2015. Irrigation should be increased slightly after this year otherwise the grass will suffer a considerable water stress around 2025±2030, which could affect cattle productivity. The simulated water-table depths are shown in Fig. 4C. Similar to Fig. 4B, the water-table rises rapidly after irrigation starts and reaches a 1 m depth at the beginning of the 21st century. Watertable depths are gently deeper each year after the initial rise; however, it remains at depths shallower than 150 cm until 2025. According to the modeling results, the water-table raises from 2 to 1 m by the year 2000. Therefore, the water-table depth difference due to the INRH (1997) proposed irrigation schedule is about 1 m. The water-table influence on soil surface salinity will remain in the saline zones shown in Fig. 3 for more than 20 years. Assuming that the water-table raises from 2 to 1 m in the borders of the saline zones (see Fig. 3), the new borders were calculated using the DTM. This was done by creating masks of each saline zone and subtracting the altitude of the border (1 m incremented from the original altitude) from all the altitudes of the mask. The negative values of the calculated GIS variable correspond to those zones where water-table depths are shallower than 2 m. Therefore, soluble-salt rising by capillary action from the water-table could affect them. The zones were depicted on the screen and digitized directly. The results are shown in Fig. 5. Both the actual (Fig. 5A) and the future (Fig. 5B) saline zones are shown in the figure. The saline zone near the coast will expand its area from 28.9 to 56.4 ha around 2015, whereas the saline zone between the mountains will increase from 25.1 to 40.4 ha in this period. Practically, all the agriculture land near the coast will be lost due to
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Fig. 4. Simulation results. (A) Simulated yearly potential evapotranspiration. (B) Yearly averaged ratio between simulated actual and potential evapotranspiration. (C) Simulated water-table depths.
soil salinisation. However, most of the agricultural land in the higher areas will remain unaffected. As shown above, simulation results are not strictly reliable but they can give an idea to farmers and decision-makers about the salinity risk to be expected if the proposed
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Fig. 5. Saline areas in the agricultural zones of San Antonio del Sur valley. (A) Actual saline zones (year 2000). (B) Predicted saline zones (year 2015).
irrigation schedule goes ahead. Land reclamation of these saline areas is a very complicated and expensive task. Hence, if decision-makers choose to begin the proposed irrigation management system they would lose the agricultural areas to increased salinity, as estimated in this paper. 4. Conclusions Farmers and decision-makers can rely on a simple methodology for assessing the salinity risk in agricultural lands where irrigation will be introduced/increased under global warming conditions. The methodology comprises the following general steps: 1. Calculating the saline areas and their borders by using DTM and soil EC measurements at several depths. 2. Estimating the time changes in the water-table depth through a hydrological model and considering a climate scenario. 3. Predicting the new saline areas by combining the DTM with the new water-table depths at the borders of the saline zones. Climate scenarios could be unreliable and, hence, daily or yearly results are of less practical interest. However, general trends in soil salinisation for a specific water
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management system could effectively help decision-makers and farmers to select the most suitable solution. Decision-makers must evaluate many economical and environment issues before taking any decision, but they can rely on this simple methodology for assessing the possible effects of any water management solution. References Belmans, C., Wesseling, J., Feddes, R., 1983. Simulation model of the water balance of a cropped soil: SWATRE. J. Hydrol. 63, 271±286. Borroto, M., 1997. RehabilitacioÂn ecoloÂgica del Valle de San Antonio del Sur. Ministry of Sciences and Technology, Cuban Global-Change Research Program, Project 013004, Havana, 23 pp. Bui, E., Keith, R.J., Moran, C.J., William, J., 1996. Use of soil survey information to assess regional salinisation risk using Geographical Information Systems. J. Environ. Qual. 25, 433±439. Caspary, H.J., 1990. An ecohydrological framework for water yield changes of forested catchments due to forest decline and soil acidification. Water Resourc. Res. 26, 1121±1131. Centella, A., GutieÂrrez, T., Limia, M., Rivero, R., 1997. Climatic change scenarios for impact assessment in Cuba. Institute of Meteorology Report, Havana, 16 pp. Doorenbos, J., Pruitt, W., 1977. Guidelines for Predicting Crop Water Requirements. Irrigation and Drainage Paper 24, 2nd Edition. FAO, Rome, 156 pp. Eatherall, A., 1997. Modelling climate impacts on ecosystems using linked models and a GIS. Climatic change 35, 17±34. FAO (Food and Agriculture Organization), 1988. FAO±UNESCO Soil Map of the world. FAO Report 60. FAO, Rome. GeoCuba, 1997. TeleMap: Un Sistema de InformacioÂn GeograÂfica dedicado para las Geociencias, User Manual. GeoCuba, Havana, 95 pp. Gleick, P.H., 1987. The development and testing of a water balance model for climate impact assessment: modelling the Sacramento river basin. Water Resourc. Res. 20, 793±802. Hulme, M., Jiang, T., Wigley, T., 1995. A Climate Change Scenario Generator: A User Manual. Climate Research Unit, UEA, Norwich, UK, 38 pp. INRH (Instituto Nacional de Recursos HidraÂulicos), 1997. Estudio de factibilidad del riego en las aÂreas agrõÂcolas del Valle de San Antonio del Sur. Research Report. INRH, GuantaÂnamo, 17 pp. IPCC (International Panel on Climate Change), 1996. Impacts, Adaptations and Mitigation of Climate Change: Scientific±Technical Analysis. IPCC, Cambridge University Press, Cambridge, 879 pp. Leenhardt, D., Voltz, M., Rambel, S., 1995. A survey of several agroclimatic soil water balance models with reference to their spatial application. Eur. J. Agron. 41, 1±14. Nonhebel, S., 1993. Effects of changes in temperature and CO2 concentration on simulated spring wheat yields in the Netherlands. Climatic Change 24, 311±329. Nonhebel, S., 1994. The effects of use of average instead of daily weather data in crop growth simulation models. Agric. Syst. 44, 377±396. Ortega, F., MartõÂnez, M., Herrero, L., 1982. Causas de la variacioÂn del manto freaÂtico y su relacioÂn con la salinidad de los suelos en el Valle de GuantaÂnamo. Cienc. Agric. 12, 63±73. Rao, R., Al-Wagdany, A., 1995. Effects of climatic change in Wabash river basin. J. Irrig. Drain. Eng. 121, 207±215. Rawls, W., Brakensiek, D., Saxton, K., 1982. Estimation of soil water properties. Trans. ASAE 25, 305±316. Rawls, D., Gimenez, D., Grossman, R., 1998. Use of soil texture, bulk density and slope of the water retention curve to predict saturated hydraulic conductivity. Trans. ASAE 41, 983±988. Rosenzweig, C., Tubiello, F.N., 1996. Effects of changes in minimum and maximum temperature on wheat yields in the central US Ð a simulation study. Agric. For. Meteorol. 80, 215±230. Scholes, R.J., Van Breemen, N., 1997. The effects of global change on tropical ecosystems. Geoderma 79, 9±24.
A. Utset, M. Borroto / Agricultural Water Management 50 (2001) 53±63
63
Semenov, M.A., Porter, J.A., 1995. Climatic variability and the modeling of crop yields. Agric. For. Meteorol. 73, 265±283. Utset, A., Ruiz, M.E., Herrera, J., Ponce de LeoÂn, D., 1998. A geostatistical method for soil salinity sample site spacing. Geoderma 86, 143±151. Valdes, J., Seone, R., North, G., 1994. A methodology for the evaluation of global warming impact on soil moisture and runoff. J. Hydrol. 161, 389±413. Van Dam, J.C., Huygen, J., Wesseling, J.G., Feddes, R.A., Kabat, P., Van Walsum, P.E.V., Groenedijk, P., Van Diepen, C.A., 1997. Report 71. Department of Water Resources, Wageningen Agricultural University, 167 pp. Van Genuchten, M., 1994. New issues and challenges in soil physics research. In: Proceedings of the 15th World Congress on Soil Science, Inaugural and State of the Art Conferences, Vol. 1, Acapulco, Mexico. Wilks, D.S., 1988. Estimating the consequences of CO2 induced climatic change on north American grain agriculture using general circulation model information. Climatic Change 13, 19±42.