Keywords
climatic variability
Saccharum officinarum
Jatropha curcas
Moringa oleifera
How to Cite
##article.highlights##
- La introducción simulada de Saccharum officinarum, Jatropha curcas y Moringa oleifera se analizó.
- El análisis climático se hizo con el Modelo Climático Regional (RegCM4).
- Las regiones que actualmente son de uso agrícola tendrían mayor variabilidad climática.
- S. officinarum generaría las mayores alteraciones térmicas (-0.7 °C) si se introdujera en la región.
- En la precipitación, el sesgo del RegCM4 fue alto debido a las amplias variaciones altitudinales.
Abstract
Introduction: The establishment of new crops may cause climatic alterations at the local or regional level.
Objective: To analyze temperature and rainfall variation by simulated replacement of current vegetation through the introduction of sugarcane (Saccharum officinarum L.), jatropha (Jatropha curcas L.) and moringa (Moringa oleifera Lam.) in the central region of the state of Veracruz, Mexico.
Materials and methods: Simulations of environmental temperature and rainfall for each crop and the control (current conditions: mixed crop, perennial trees, mixed forest and irrigated agriculture) were made with the Regional Climate Model (RegCM4). The model was evaluated by comparative analysis between simulations and observed data, using the mean square error and the root-mean-square error as measures of dispersion.
Results and discussion: Regions with soils devoid of natural vegetation, such as agricultural soils, would have greater climatic variability. In these soils, the displacement of current vegetation by sugarcane would generate the greatest thermal alterations with a decrease of 0.7 °C, while with jatropha and moringa, the decrease would be 0.3 °C. Regarding rainfall, the RegCM4 bias increases when there are high variations in elevation, thus other models should be explored.
Conclusions: The introduction of moringa or jatropha for bioenergy purposes would be a low climatic impact alternative, while sugarcane is not considered suitable for these purposes due to the greater climatic impact that it would have in the region.
References
Agüero-Rodríguez, J. C., Tepetla-Montes, J., & Torres-Beristaín, B. (2015). Producción de biocombustibles a partir de la caña en Veracruz, México: perspectivas y riesgos socio-ambientales. CienciaUAT, 9(2), 74–84. Retrieved from http://www.scielo.org.mx/pdf/cuat/v9n2/2007-7858-cuat-9-02-00074.pdf
Ali, S., Li, D., Congbin, F., & Yang, Y. (2015). Performance of convective parameterization schemes in Asia using RegCM : Simulations in three typical regions for the period 1998-2002. Advances in Atmospheric Sciences, 32, 715–730. doi: https://doi.org/10.1007/s00376-014-4158-4
Andrade, M. F., & Blacutt, L. A. (2010). Evaluación del modelo climático regional PRECIS para el área de Bolivia: Comparación con datos de superficie. Revista Boliviana de Física, 17(17). Retrieved from http://www.scielo.org.bo/scielo.php?script=sci_arttext&pid=S1562-38232010000100001
Bonilla-Ovallos, C. A., & Mesa, O. J. (2017). Validación de la precipitación estimada por modelos climáticos acoplados del proyecto de intercomparación CMIP5 en Colombia. Revista de la Academia Colombiana de Ciencias Exactas Físicas y Naturales, 41(158), 107–118. doi: https://doi.org/10.18257/raccefyn.427
Caiazzo, F., Malina, R., Staples, M. D., Wolfe, P. J., Yim, S. H. L., & Barrett, S. R. H. (2014). Quantifying the climate impacts of albedo changes due to biofuel production : a comparison with biogeochemical effects. Enviromental Research Letters, 9(2), 1–10. doi: https://doi.org/10.1088/1748-9326/9/2/024015
Camargo-Bravo, A., & García-Cueto, R. O. (2012). Evaluación de dos modelos de reducción de escala en la generación de escenarios de cambio climático en el valle de Mexicali en México. Información Tecnológica, 23(3), 11–20. doi: https://doi.org/10.4067/S0718-07642012000300003
Comisión Nacional del Agua (CONAGUA) (2010). Información estadística climatológica. Retrieved February 2, 2019 from https://smn.conagua.gob.mx/es/climatologia/informacion-climatologica/informacion-estadistica-climatologica
Comité Nacional para el Desarrollo Sustentable de la Caña de Azúcar (CONADESUCA). (2015). Ficha técnica del cultivo de la caña de azúcar (Saccharum officinarum L.). Retrieved from https://www.gob.mx/cms/uploads/attachment/file/141823/Ficha_T_cnica_Ca_a_de_Az_car.pdf
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., … Vitart, F. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553–597. doi: https://doi.org/10.1002/qj.828
Dickinson, R. E., Henderson-Sellers, A., & Kennedy, P. J. (1993). Biosphere-Atmosphere Transfer Scheme (BATS) version 1e as coupled to the NCAR community climate model. USA: National Center for Atmospheric Research. doi: https://doi.org/10.5065/D67W6959
Elguindi, N., Bi, X., Giorgi, F., Nagarajan, B., Pal, J., Solmon, F., … Giuliani, T. (2013). Regional Climatic Model RegCM user manual version 4.3. Trieste, Italy: International Center for Theoretical Physics.
Fuentes-Franco, R., Coppola, E., Giorgi, F., Graef, F., & Pavia, E. G. (2014). Assessment of RegCM4 simulated inter-annual variability and daily-scale statistics of temperature and precipitation over Mexico. Climate Dynamics, 42, 629–647. doi: https://doi.org/10.1007/s00382-013-1686-z
Georgescu, M., Lobell, D. B., & Field, C. B. (2009). Potential impact of U . S . biofuels on regional climate. Geophysical Research Letters, 36(21), 1–6. doi: https://doi.org/10.1029/2009GL040477
Georgescu, M., Lobell, D. B., Field, C. B., & Mahalov, A. (2013). Simulated hydroclimatic impacts of projected Brazilian sugarcane expansion. Geophysical Research Letters, 40(5), 972–977. doi: https://doi.org/10.1002/GRL.50206
Hallgren, W., Schlosser, C. A., Monier, E., Kicklighter, D., Sokolov, A., & Melillo, J. (2013). Climate impacts of a large-scale biofuels expansion. Geophysical Research Letters, 40(8), 1624–1630. doi: https://doi.org/10.1002/grl.50352
Hassan, M., Penfei, D., Iqbal, W., Can, W., Wei, F., & Ba, W. (2014). Temperature and precipitation climatology assessment over South Asia using the Regional Climate Model (RegCM4.3): An evaluation of the model performance. Earth Science & Climatic Change, 5(7), 1–8. doi: https://doi.org/10.4172/2157-7617
Hernández-Rodríguez, M. A., & Hernández-Zárate, J. A. (2008). Verdades y mitos de los biocombustibles. Elementos: Ciencia y Cultura, 71(15), 15–18. Retrieved from https://elementos.buap.mx/directus/storage/uploads/00000002099.pdf
Hua, W. J., Chen, H. S., & Li, X. (2015). Effects of future land use change on the regional climate in China. Science China Earth Sciences, 58(10), 1840–1848. doi: https://doi.org/10.1007/s11430-015-5082-x
Instituto Nacional de Estadística Geografía e Informática (INEGI). (2015). Guía para la interpretación de cartografía: Uso de suelo y vegetación, escala 1:250,000, serie V. México: Author.
Ines, A. V. M., & Hansen, J. W. (2006). Bias correction of daily GCM rainfall for crop simulation studies. Agricultural and Forest Meteorology, 138, 44–53. doi: https://doi.org/10.1016/j.agrformet.2006.03.009
Intergovernmental Panel on Climate Change (IPCC). (2014). Informe de síntesis. In R. K. Pachauri & L. A. Meyer (Eds.), Cambio climático 2014 (pp. 157). Ginebra: Author.
Ji, Z., & Kang, S. (2015). Evaluation of extreme climate events using a regional climate model for China. Royal Meteorological Society, 35(6), 888–902. doi: https://doi.org/10.1002/joc.4024
Khanal, S., Anex, R. P., Anderson, C. J., & Daryl, E. (2013). Implications of biofuel policy-driven land cover change for rainfall erosivity and soil erosion in the United States. Bioenergy, 5(6),713–722. doi: https://doi.org/10.1111/gcbb.12050
Kueppers, L. M., Snyder, M. A., & Sloan, L. C. (2007). Irrigation cooling effect : Regional climate forcing by land-use change. Geophysical Research Letters, 34(3), 1–5. doi: https://doi.org/10.1029/2006GL028679
Lee, S., & Berbery, H. (2015). Land cover change effects on the climate of the La Plata basin. Journal of Hydrometeorology, 13, 84–102. doi: https://doi.org/10.1175/JHM-D-11-021.1
Lobell, D. B., Bala, G., & Duffy, P. B. (2006). Biogeophysical impacts of cropland management changes on climate. Geophysical Research Letters, 33(6), 1–4. doi: https://doi.org/10.1029/2005GL025492
Mahmood, R., Pielke, R. A., Hubbard, K. G., Niyogi, D., Dirmeyer, P. A., McAlpine, C., …Fall, S. (2014). Land cover changes and their biogeophysical effects on climate. Royal Meteorological Society, 34(4), 929–953. doi: https://doi.org/10.1002/joc.3736
Malhi, Y., Roberts, J. T., Betts, R. A., Killeen, T. J., Li, W., & Nobre, C. A. (2008). Climate change, deforestation, and the fate of the Amazon. Science, 319(5860), 169–172. doi: https://doi.org/10.1126/science.1146961
Marcella, M. P., & Eltahir, E. A. B. (2014). Introducing an irrigation scheme to a regional climate model : a case study over West Africa. Journal of Climate, 27, 5708–5723. doi: https://doi.org/10.1175/JCLI-D-13-00116.1
Noda-Leyva, Y., Pérez-Vázquez, A., & Valdés-Rodríguez, O. A. (2015). Establecimiento de tres especies de oleaginosas bajo asociación. Agronomía Mesoamericana, 26(2), 323–332. doi: https://doi.org/10.15517/am.v26i2.19326
Önol, B. (2012). Effects of coastal topography on climate: high-resolution simulation with a regional climate model. Climate Research, 52, 159–174. doi: https://doi.org/10.3354/cr01077
Otieno, V. O., & Anyah, R. O. (2012). Effects of land use changes on climate in the greater horn of Africa. Climate Research, 52(1), 77–95. doi: https://doi.org/10.3354/cr01050
Pal, J. S., Giorgi, F., Bi, X., Elguindi, N., Solmon, F., Gao, X., … Steiner, A. L. (2007). Regional climate modelling for the developing world: The ICTP RegCM3 and RegCNET. Bulletin of the American Meteorological Society, 88(9), 1395–1409. doi: https://doi.org/10.1175/BAMS-88-9-1395
Pérez, A., Sánchez, T., Armengol, N., & Reyes, F. (2010). Características y potencialidades de Moringa oleifera Lamark. Una alternativa para la alimentación animal. Pastos y Forrajes, 33(4), 1–16. Retrieved from http://scielo.sld.cu/pdf/pyf/v33n4/pyf01410.pdf
Piani, C., Weedon, G. P., Best, M., Gomes, S. M., Viterbo, P., Hagemann, S., & Haerter, J. O. (2010). Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. Journal of Hydrology, 395(3–4), 199–215. doi: https://doi.org/10.1016/j.jhydrol.2010.10.024
Pielke Sr., R. A., Adegoke, J., Beltrán-Przekurat, A., Hiemstra, C. A., Lin, J., Nair, U. S., & Nobis, T. E. (2007). An overview of regional land-use and land-cover impacts on rainfall. Tallus, 59(3), 587–601. doi: https://doi.org/10.1111/j.1600-0889.2007.00251.x
Pitman, A. J., Narisma, G. T., Pielke Sr., R. A., & Holbrook, N. J. (2004). Impact of land cover change on the climate of southwest Western Australia. Journal of Geophysical Researcheophysical Research, 109(D18), 1–12. doi: https://doi.org/10.1029/2003JD004347
Sacks, W. J., Cook, B. I., Buenning, N., Levis, S., & Helkowski, J. H. (2009). Effects of global irrigation on the near-surface climate. Climate Dynamics, 33(2), 159–175. doi: https://doi.org/10.1007/s00382-008-0445-z
Salazar, A., Katzfey, J., Thatcher, M., Syktus, J., Wong, K., & McAlpine, C. (2016). Deforestation changes land – atmosphere interactions across South American biomes. Global and Planetary Change, 139, 97–108. doi: https://doi.org/10.1016/j.gloplacha.2016.01.004
Sanabria, J., Marengo, J., & Valverde, M. (2009). Escenarios de cambio climático con modelos regionales sobre el Altiplano Peruano (Departamento de Puno). Revista Peruana Geo-Atmósfera, 1, 134–149. Retrieved from https://web2.senamhi.gob.pe/rpga/pdf/2009_vol01/art11.pdf
Secretaría de Agricultura y Desarrollo Rural (SAGARPA). (2009). Programa de producción sustentable de insumos para bioenergéticos y de desarrollo científico y tecnológico. Retrieved from https://www.inforural.com.mx//wp-content/uploads/2009/10/PROINBIOS-061009.pdf
Teniente, O. R., Tapia, V. L. M., González, A. A., Zamarripa, C. A., Solís, B. J. L., Martínez, V. B., & Hernández, M. M. (2011). Guía técnica para la producción de piñón mexicano (Jatropha curcas L.) en Michoacán. México: INIFAP.
Tiwari, P. R., Kha, S. C., Mohanty, U. C., Dey, S., Sinha, P., Raju, P. V. S., & Shekhar, M. S. (2015). The role of land surface schemes in the regional climate model (RegCM) for seasonal scale simulations over Western Himalaya. Atmósfera, 28(2), 129–142. doi: https://doi.org/10.1016/S0187-6236(15)30005-9
Zaman, Q., Waqas, K., & Rasul, G. (2011). Land use change study in RegCM3 to see the effect of HKH glaciers melt over agriculture areas of Pakistan. Pakistan Journal of Meteorology, 7(14), 17–24.
Zhao, Y., Fang, Y., Cui, C., & Huang, A. (2012). Effects of irrigation on precipitation in the arid regions of Xinjiang, China. Journal of Arid Land, 4(2), 132–139. doi: https://doi.org/10.3724/SP.J.1227.2012.00132
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