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Revista de Matemática Teoría y Aplicaciones

versão impressa ISSN 1409-2433

Rev. Mat vol.22 no.2 San José Jul./Dez. 2015

 

Articles

Classification and multivariate analysis of differences in gross primary production at different elevations using Biome-bgc in the páramos, ecuadorian andean region

Clasificación y análisis multivariado de diferencias en producción primaria bruta en diferentes elevaciones usando Biome-bgc en los páramos, región andina ecuatoriana

Veronica Minaya 1  

Gerald Corzo 2  

Johannes Van Der Kwast 3  

Remigio Galárraga 4  

Arthur Mynett 5  

1UNESCO-IHE, Institute for Water Education, Delft, The Netherlands; &. E-Mail: v.minayamaldonado@unesco-ihe.org

2UNESCO-IHE, Institute for Water Education, Delft, The Netherlands. E-Mail: g.corzo@unesco-ihe.org

3UNESCO-IHE, Institute for Water Education, Delft, The Netherlands.E-Mail: h.vanderkwast@unesco-ihe.org

4Escuela Politécnica Nacional, Quito, Ecuador. E-Mail: remigala@mail.epn.edu.ec, remigala@hotmail.com

5UNESCO-IHE, Institute for Water Education & Technological University of Delft, TU-Delft, Delft, The Netherlands. E-Mail: a.mynett@unesco-ihe.org

Abstract

Gross primary production (GPP) in climate change studies with multispecies and elevation variables are difficult to measure and simulate. Models tend to provide a representation of dynamic process through long-term analysis by using generalized parameterizations. Even, current approaches of modelling do not contemplate easily the variation of GPP at different elevations for different vegetation types in regions like páramos, mainly due to data unavailability. In these models information from cells is commonly averaged, and therefore average elevation, ecophysiology of vegetation, as well as other parameters is generalized. The vegetation model BIOMEBGC was applied to the Ecuadorian Andean region for elevations greater than 4000 masl with the presence of typical vegetation of páramo for 10 years of simulation (period 2000-2009). An estimation of the difference of GPP obtained using a generalized altitude and predominant type of vegetation could lead to a better estimation of the uncertainty in the magnitude of the errors in global climate models. This research explores GPP from 3 different altitudes and 3 vegetation types against 2 main climate drivers (Short Wave Radiation and Vapor Pressure Deficit). Since it is important to measure the possible errors or difference in the use of averaged meteorological and ecophysiological data, here we present a multivariate analysis of the dynamic difference of GPP in time, relative to an altitude and type of vegetation. A copula multivariable model allows us to identify and classify the changes in GPP per type of vegetation and altitude. The Frank copula model of joint distributions was our best fit between GPP and climate drivers and it allowed us to understand better the dependency of the variables. These results can explore extreme situations where averaged simplified approaches could mislead. The change of GPP over time is essential for future climate scenarios of the ecosystem storage and release of carbon to the atmosphere. Our findings suggest that a classification of the difference is highly important to be extended to cells that have similar properties.

Keywords: multivariate classification; copula; BIOME-BGC; GPP; páramos

Resumen

La producción primaria (GPP) es difícil de medir y simular en estudios de cambio climático con múltiples especies de vegetación y con variabilidad en elevación. Los modelos tienden a proveer una representación de los procesos dinámicos a través de análisis a largo plazo usando parametrizaciones generalizadas. Incluso métodos actualizados de modelación no contemplan fácilmente la variación de GPP a diferentes elevaciones y para diferentes tipos de vegetación en regiones como los páramos, debido principalmente a la inexistencia de datos. En estos modelos, la información de las celdas son comúnmente promediadas y por lo tanto factores como la elevación media,eco-fisiología de la vegetación y otros parámetros son generalizados. El modelo de vegetación BIOME-BGC fue aplicado en un área de estudio dentro de la región andina Ecuatoriana a elevaciones superiores a los 4000 msnm donde existe una presencia típica de vegetación de páramo para 10 años de simulación (periodo 2000-2009). La estimación de la diferencia de la GPP obtenida usando una generalización de altura y tipo de vegetación predominante puede conducir a una mejor estimación de la incertidumbre en la magnitud de los errores en modelos climáticos globales. Este estudio explora la relación entre la GPP de tres tipos de vegetación agrupados de acuerdo a sus formas de crecimiento a tres rangos altitudinales y dos factores climáticos (Radiación de onda corta y deficiencia de presión de vapor). Debido a la importancia de la medición de posibles errores o las diferencias en el uso de valores promedio de datos meteorológicos e ecofisiológicos, aquí presentamos un análisis multivariado de la diferencia dinámica de la GPP en el tiempo con respecto al rango altitudinal y al tipo de vegetación. El modelo multivariable Copula nos permite identificar y clasificar los cambios de GPP por tipo de vegetación y por rango altitudinal. El modelo cópula distribuido Frank fue el que mejor se acopló entre la GPP y las variables climáticas y nos permitió entender mejor la dependencia entre estas variables. Los resultados podrían explorar situaciones extremas donde estrategias simplificadas promedio podrían confundir. El cambio de GPP en el tiempo es esencial para futuros escenarios climáticos del almacenamiento y liberación de carbón del ecosistema hacia la atmósfera. Nuestros resultados sugieren que la clasificación de esta diferencia es muy importante que sea extendida a celdas que tienen propiedades similares.

Palabras clave:  clasificación multivariada; cópula; BIOME-BGC; GPP; páramos

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Acknowledgment

Financial support came from SENESCYT (Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación) and from the Dutch Ministry of Foreign Affairs (DUPC program at UNESCO-IHE). Other institutions that also cooperated in the provision of key information and data are EPMAPS, EPN, INAMHI, IRD, INIGEMM, Ministry of Environment, Herbario Nacional del Ecuador. The opinions expressed herein are those of the author(s) and do not necessarily reflect views of any of the Institutions named above. Our thanks also go to Adriana Guatame, Santiago Oña, Franz Betancourt and Washington Lomas.

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1Mathematics Subject Classification: 62H99.

Received: February 24, 2014; Revised: March 06, 2015; Accepted: May 20, 2015

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