Redalyc.Trend Analysis to Determine Hazards Related to Climate Change
Total Page：16
File Type：pdf, Size：1020Kb
Agronomía Colombiana ISSN: 0120-9965 [email protected] Universidad Nacional de Colombia Colombia Peña Q., Andrés J.; Arce B., Blanca A.; Boshell V., J. Francisco; Paternina Q., María J.; Ayarza M., Miguel A.; Rojas B., Edwin O. Trend analysis to determine hazards related to climate change in the Andean agricultural areas of Cundinamarca and Boyacá Agronomía Colombiana, vol. 29, núm. 2, mayo-agosto, 2011, pp. 275-285 Universidad Nacional de Colombia Bogotá, Colombia Available in: http://www.redalyc.org/articulo.oa?id=180322766013 How to cite Complete issue Scientific Information System More information about this article Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Journal's homepage in redalyc.org Non-profit academic project, developed under the open access initiative Trend analysis to determine hazards related to climate change in the Andean agricultural areas of Cundinamarca and Boyacá Análisis de tendencias para determinar amenazas relacionadas con el cambio del clima en zonas agrícolas altoandinas de Cundinamarca y Boyacá Andrés J. Peña Q.1, Blanca A. Arce B.2, J. Francisco Boshell V.3, 6, María J. Paternina Q.4, Miguel A. Ayarza M.5, and Edwin O. Rojas B.5 ABSTRACT RESUMEN Recognizing the threat from climate change that is facing and Reconocer la amenaza climática a la que se enfrentan y se will face agroecosystems is the first step in determining adap- enfrentaran los agroecosistemas es el primer paso para deter- tation to climate change. One way is through Global Climate minar las medidas de adaptación frente al cambio climático. Models (GCMs), but their spatial resolution is not best suited for Una forma de hacerlo es a través de los Modelos Climáticos making decisions locally, further reducing scale, seen as a way Globales (MCG), sin embargo la resolución espacial de éstos no to resolve the resolution problem, has not yielded the expected es la más indicada para tomar decisiones a escala local; además, results. This study puts forth an exercise in which we study the la reducción de escala, vista como una forma de mejorar el climatic time series of precipitation and temperature to deter- problema de resolución, no ha dado los resultados esperados. mine if there are effects of climate change on one of the most Se plantea un ejercicio en el que se estudian las series de tiempo important national agricultural areas, using the Mann-Kendall climáticas de precipitación y temperatura para determinar si analysis to determine the existence of statistically significant hay efectos del cambio climático en una de las zonas agro- trends, i.e. signs of change in the variables analyzed. It was pecuarias de mayor importancia a nivel nacional. Se plantea found that the variable that presents the most significant trends el análisis de Mann-Kendall para determinar la existencia de is the average maximum temperature, while precipitation and tendencias estadísticamente significativas, es decir señales de average minimum temperature do not. cambio en las variables analizadas. Se encontró que la variable que presenta tendencias más significativas es la temperatura máxima media, mientras que la precipitación y la temperatura mínima media no. Key words: mathematical models, climate observations, Palabras clave: modelos matemáticos, observaciones del clima, temperature, mountain farming. temperatura, agricultura de montaña. Introduction 1986), more frequent cyclical phenomena (Pavia et al., 2009) and changes in vegetation cover (McGregor and Nieuwolt, According to the IPCC (2007), climate change is the varia- 1998), the emission of greenhouse gases (GHGs), the prod- tion (statistically significant) in average climatic conditions uct of human economic activity and its accumulation in or in its variability over an extended period, typically the atmosphere have increased the radiative force (IPCC, decades or longer. Dry seasons becoming more frequent, 2007) impacting the current climate. higher temperatures than usual, very short rainy season in previous dry periods, droughts, floods, among other Furthermore, as the atmosphere has no limits or southern consequences, attributed to climate change, are considered zone, GHGs are significant, determinative factors for the the main threat to human development in our generation global climate and its effects can be modeled at the global (UNDP, 2007). In addition to natural climate change, re- level through Global Climate Models (GCMs), which can lated to changes in obliquity, eccentricity and precession generate future climate scenarios, based on previously (Hays et al., 1976; Imbrie et al., 1984; Herbert and Fischer, determined emission scenarios (IPCC, 1997). However, Recceived for publication: 31 August, 2010. Accepted for publication: 2 June, 2011. 1 Agroclimatalogy, Centro Nacional de Investigaciones de Café – Cenicafé. Chinchiná (Colombia). 2 Grazing and Forage Network, Tibaitatá Research Center, Corporación Colombiana de Investigación Agropecuaria (Corpoica). Mosquera (Colombia). 3 Department of Agronomy, Faculty of Agronomy, Universidad Nacional de Colombia, Bogota (Colombia). 4 Applied Stastistics. Fundación Universitaria Los Libertadores. Bogota (Colombia). 5 Climate Change and Agriculture Network, Tibaitatá Research Center, Corporación Colombiana de Investigación Agropecuaria (Corpoica). Mosquera (Colombia). 6 Corresponding author. [email protected] Agronomía Colombiana 29(2), 275-285, 2011 their low resolution (Molina et al., 2000) combined with prioritized by areas and production systems at regional and methodological and operational difficulties arising from local levels. 5) It is important and necessary to review the the reduction of scale (and statistics) has led to a need for time series of regional-scale climate variables to determine assessing the presence of trends within the time series of any trends in them. Those areas that recorded the most climatic variables to understand what is happening locally significant trends represent a major threat to agricultural and determine the level of threat and future climate risk. production activities; therefore, there should be prioritized actions and strategies of adaptation. It is worth noting that, This work takes into account the following five aspects: although in this paper we propose a qualitative scenario, 1) current climate (2001-2008) measured at some stations as the product of an empirical (statistical) analysis, this located in the Colombian Andes is different to past climae does not ensure a reduction in uncertainty regarding the (1970-1980) and the changes could modify the irrigation MCG, but because local settings are an important factor for (Pena et al., 2008; Pena et al., 2010), or accelerate life cycles decision makers (Alcamo et al., 2006) and can improve the of poikilothermic organisms, such as insects, weeds and identification of adaptation measures against these threats crops, or determine cultivated species altitudinal migration primarily by farmers. in response to the search for optimal soil and climate (Jarvis and Ramirez, 2009). 2) Because the study area is a region Materials and methods of horticultural and livestock (dairy) importance (Madrid, 2006; ITC, 2009), the effect of climate change on the high- Study area lands of Cundinamarca and Boyacá can affect the country’s food security. 3) The MCG have very low resolution and We analyzed the time series of the weather elements mea- therefore do not detect local variations and/or the regional sured at weather stations located in the Cordillera Oriental, level. 4) Adaptation strategies to climate change should be in the departments of Cundinamarca and Boyaca (Fig. 1). FIGURE 1. Location of weather stations in the Andean agricultural areas of Cundinamarca and Boyacá (Colombia). 276 Agron. Colomb. 29(2) 2011 Most of these are in the highlands of Cundinamarca and the first rainy season of the year (FRSY). July, August and Boyaca, which comprises a set of high-altitude basins, along September are dry, but in September, depending on the area with the upper parts of the rivers Chicamocha, Bogota and can be characterized as a transitional month and comprise Suarez (Valencia, 2002). The plateau of this area is compri- the second dry season of the year (SDSY), whereas the last sed of a set of mesas that are located between 2,500 and three months of the year are rainy and form the second 2,600 m a.s.l., surrounded by mountains up to 4,000 m. rainy season (SRS) (Boshell, 2009). According to Boshell, Climatically, it is considered a dry island, compared with during the SDSY and FDSY average temperature tends its surroundings; the precipitation has a strong spatial va- to decrease as the result of radiative loss, associated with riation, since the annual rainfall ranges from 600 to 1,500 clear skies at night, while in the FRSY and SRS it tends to mm. The temperatures are determined by the height above increase for the opposite reason. sea level (Valencia, 2002). Climatic time series In this region, intra-annual temporal variation in pre- Information for average annual maximum temperature cipitation and temperature is marked by the double pass (Tmax) and minimum temperature (Tmin) and the cu- of intertropical convergence zone (ITCZ). The first three mulative annual rainfall (Prec) was used from 31 meteo- months of the year are dry (January-March), forming the rological stations located in the Andean highlands in the first dry season of the year (FDSY), the three subsequent departments of Cundinamarca and Boyacá, for a total of months are rainy (April-June), especially in April and May, 87 time series (Tab. 1), with a minimum length of 23 years. TABLE 1. Time series used in the Andean agricultural areas of Cundinamarca and Boyacá (Colombia). Tmax Tmin Precipitation No. Code Municipality Department M a.s.l. Longitude Latitude Initial Final Initial Final Initial Final 1 509503 Cuítiva Boyacá 3000 -72.943 5.572 1983 2008 1978 2008 1971 2008 2 403513 Tunja Boyacá 2690 -73.355 5.553 1979 2008 1979 2008 1969 2008 3 401522 Samacá Boyacá 2600 -73.495 5.511 1978 2008 1978 2008 1969 2008 4 508502 Rondón Boyacá 2120 -73.203 5.358 1978 2008 1978 2008 1971 2008 5 508504 Miraflores Boyacá 1640 -73.144 5.192 1984 2008 1984 2008 2008 6 401530 V.