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Use of Ecosystem Functional Types to represent the interannual variability of vegetation biophysical properties in regional models

  • National Center for AgroMeteorology (NCAM)

Abstract and Figures

CLIVAR is an international research programme dealing with climate variability and predictability on time-scales from months to centuries. CLIVAR is a component of the World Climate Research Programme (WCRP). WCRP is sponsored by the World Meteorological Organization, the International Council for Science and the Intergovernmental Oceanographic Commission of UNESCO. Fig.2: Annual-mean sea surface temperature and surface currents, from the Simple Ocean Data Assimilation (SODA) Reanalysis. Note the strong SST gradient in the Atlantic known as the Angola-Benguela front. To its north, at approximately 10E/10S, lies the Angola dome. In contrast, the southeast Pacific has no comparable SST gradients or thermocline domes. Plot courtesy of Dr. Mingkui Li. //Temperatura media anual de la superficie del mar y corrientes en superficie del Reanálisis de la Asimilación Simple de Datos Oceánicos (SODA). Nótese el fuerte gradiente de SST en el Atlántico, conocido como el frente Angola-Benguela. Al norte de éste, a 10E/10S aproximadamente, se encuentra el Domo de Angola. En contraste, el Pacífico Sudoriental no tiene gradientes de SST comparables ni domos en la termoclina. Figuras cortesía de Dr. Mingkui Li. (see article by Paquita Zuidema et al on page 12) (ver artículo de Paquita Zuidema et al en la página 12) Exchanges
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Joint Edition of the Newsletter of the
Climate Variability and Predictability Project
(CLIVAR) Exchanges and the
CLIVAR Variability of the American Monsoon
Systems Project (VAMOS)
No. 7 2011 Vol. 16 No. 1
No. 55
January 2011
ISSN 1813-6478 ISSN 1026-0471
CLIVAR is an international research programme dealing with climate variability and
predictability on time-scales from months to centuries. CLIVAR is a component
of the World Climate Research Programme (WCRP). WCRP is sponsored by the
World Meteorological Organization, the International Council for Science and the
Intergovernmental Oceanographic Commission of UNESCO.
International CLIVAR Project Ofce,
National Oceanography Centre, Southampton SO14 3ZH, United Kingdom
Tel: +44 23 80 596777 / Fax: +44 23 80 596204 / email:
web address:
VAMOS Newsletter
c/o Departamento de Ciencias de la Atmosfera - UBA
Pabellon II - 2° piso - Ciudad Universitaria - 1428 Buenos Aires
tel: (54-11) 4576-3356 or 4576-3364 ext. 20: Fax (54-11) 4576-3356 or 4576-3364
Fig.2: Annual-mean sea surface temperature and surface currents, from the Simple Ocean
Data Assimilation (SODA) Reanalysis. Note the strong SST gradient in the Atlantic known
as the Angola-Benguela front. To its north, at approximately 10E/10S, lies the Angola
dome. In contrast, the southeast Pacic has no comparable SST gradients or thermocline
domes. Plot courtesy of Dr. Mingkui Li. //Temperatura media anual de la supercie del
mar y corrientes en supercie del Reanálisis de la Asimilación Simple de Datos Oceánicos
(SODA). Nótese el fuerte gradiente de SST en el Atlántico, conocido como el frente Angola-
Benguela. Al norte de éste, a 10E/10S aproximadamente, se encuentra el Domo de Angola.
En contraste, el Pacíco Sudoriental no tiene gradientes de SST comparables ni domos en
la termoclina. Figuras cortesía de Dr. Mingkui Li. (see article by Paquita Zuidema et al on
page 12) (ver artículo de Paquita Zuidema et al en la página 12)
- 23 -
Use of Ecosystem Functional Types
to represent the interannual variability
of vegetation biophysical properties in
regional models
Uso de los Tipos Funcionales de Ecosistemas
para representar la variabilidad interanual de
las propiedades biofísicas de la vegetación en
modelos regionales
Climate is the main regional driver of ecosystem structure and
functioning by determining the timing and amount of energy (both
heat and solar radiation) and water that is available in the system
(Stephenson, 1990). Conversely, ecosystems also inuence climate
through multiple pathways, primarily by determining the energy,
momentum, water, and chemical balance (e.g. albedo, longwave
radiation, surface roughness, evapotranspiration, greenhouse gases,
or aerosols) between the land-surface and the atmosphere (Chapin
Iii et al., 2008). Hence, extensive impacts on ecosystems, both from
natural and human origin, may alter one or several pathways of the
ecosystem–climate feedbacks that may end up affecting the regional
and global climate.
Vast areas of South America are suffering from human-induced
changes in land cover and management practices of crop-systems
that may affect ecosystem-climate feedbacks, with deforestation
and land-clearing for agriculture and cattle ranging being the most
important ones (Foley et al., 2007; Volante et al., in revision).
According to Bonan (2008), land-clearing produces an increase in
albedo, a reduction of transpiration, and a net release of CO2 that
increases the heat-trapping capacity of the atmosphere. On the other
hand, other extensive land-use changes in South America, such as
grassland afforestation (Beltrán-Przekurat et al., 2010), produce a
decrease in albedo, a rise of evapotranspiration, and greater surface
roughness. Yet, other effects on ecosystem-climate feedbacks
would be the extensive practice of no-tillage agriculture and the
also extensive expansion of irrigated agriculture over drylands
(De Oliveira et al., 2009), which increases evapotranspiration and
decreases albedo.
These kinds of ecosystem-climate feedbacks are a central problem
for modeling the land-atmosphere interactions of the climate system
(Mahmood et al., 2010), but their incorporation in current regional
and global circulation models is not straightforward. Many models
use land-cover maps of different plant functional types (i.e. groups
of plants that share functional traits) to estimate maps of biophysical
properties (West et al., in press). Such estimates rely on the
relationship between particular plant functional traits and different
ecosystem functioning properties (Smith et al., 1997). However,
several works have shown that plant functional types classications
are not reliable to predict ecosystem functioning (Wright et al.,
2006; Bret-Harte et al., 2008). In addition, these land-cover maps
are difcult to update in a yearly basis and are mainly dictated by
structural features of vegetation (such as leave life-span) that have
little sensitivity to environmental changes. Overall, this representation
of vegetation may result in a delayed response and reduces the
ability of models to represent rapid changes including land-use shifts,
res, oods, droughts, and insect outbreaks. Hence, to account for
land-use/cover change effects on climate models it is necessary to
improve the way the spatial and interannual variability of vegetation
dynamics are considered in the coupling of the atmosphere and the
Functional attributes of vegetation, which are descriptors of the
energy and matter exchange between the biota and the atmosphere
at the ecosystem scale (Valentini et al., 1999; Virginia et al., 2001)
may help to fulll these needs since they show a quicker response
to environmental changes than structural ones (Mcnaughton et al.,
1989). Additionally, they are relatively easy to monitor using the
satellite-derived Normalized Difference Spectral Index (NDVI) to get
El clima es el principal motor regional de la estructura y funciona-
miento de los ecosistemas, al determinar el momento y la cantidad
de energía (calor y radiación solar) y agua disponibles (Stephenson,
1990). Por el contrario, los ecosistemas también afectan el clima
mediante varios caminos, principalmente determinando la energía,
cantidad de movimiento, agua y balance químico (por ejemplo, el
albedo, la radiación de onda larga, la rugosidad de la supercie, la
evapotranspiración, los gases de invernadero y los aerosoles) entre
la supercie de la tierra y la atmósfera (Chapin Iii et al., 2008). Por
consiguiente, los amplios impactos sobre los ecosistemas, tanto de
origen natural como humano, pueden alterar uno o varios de los
caminos de las retroacciones ecosistema-clima que pueden terminar
afectando el clima regional y global.
Vastas áreas de América del Sur están sufriendo cambios inducidos
por el hombre en la cobertura de la tierra y las prácticas de manejo
de los sistemas de cultivos que pueden afectar las retroacciones
ecosistema-clima. Los más importantes de ellos son la deforesta-
ción y el desmonte para agricultura y ganadería (Foley et al., 2007;
Volante et al., en revisión). Según Bonan (2008), el desmonte pro-
duce un aumento en el albedo, una reducción en la transpiración
y una liberación neta de CO2 que incrementa la capacidad de la
atmósfera de retener calor. Por otro lado, otros cambios extensos
en el uso de la tierra en América del Sur, como la aforestación de
pastizales (Beltrán-Przekurat et al., 2010), dan lugar a una disminu-
ción del albedo, un aumento en la evapotranspiración y una mayor
rugosidad de la supercie. Otros efectos sobre las retroacciones
ecosistema-clima son el amplio uso de la siembra directa y la ex-
pansión también generalizada de la agricultura de riego en zonas
áridas (De Oliveira et al., 2009), que aumenta la evapotranspiración
y disminuye el albedo.
Este tipo de retroacciones ecosistema-clima constituyen un proble-
ma central para el modelado de las interacciones tierra-atmósfera
del sistema climático (Mahmood et al., 2010), pero su incorporación
en los modelos global y regionales actuales de circulación no es
sencilla. Muchos modelos usan mapas de la cobertura del suelo de
diferentes tipos funcionales de plantas (es decir, grupos de plantas
que comparten rasgos funcionales) para estimar los mapas de las
propiedades biofísicas (West et al.,en prensa). Dichas estimaciones
se apoyan en la relación entre los rasgos funcionales de plantas
particulares y diferentes propiedades del funcionamiento de los
ecosistemas (Smith et al., 1997). Sin embargo, varios trabajos han
mostrado que las clasicaciones de tipos funcionales de plantas
no son conables para predecir el funcionamiento de los ecosis-
temas (Wright et al., 2006; Bret-Harte et al., 2008). Además, estos
mapas de la cobertura del suelo son difíciles de actualizar anual-
mente y están principalmente condicionados por las características
estructurales de la vegetación (como el tiempo de vida de las hojas)
cuya sensibilidad a los cambios ambientales es baja. En términos
generales, esta representación de la vegetación puede resultar en
un retraso en la respuesta y reduce la habilidad de los modelos de
representar cambios rápidos incluyendo los cambios en el uso del
suelo, los incendios, las inundaciones, las sequías y los brotes de
insectos. Por consiguiente, para explicar los efectos del cambio en
el uso/cobertura del suelo en los modelos climáticos es necesario
mejorar el modo en que se considera la variabilidad espacial e in-
teranual de la dinámica de la vegetación en el acoplamiento de la
atmósfera y la supercie del suelo.
- 24 -
Figure 1: Ecosystem Functional Types distribution in South America based on the NDVI dynamics for the 1988 and b) 1998 years and for the c) median
distribution of the 1982-1999 period. // Figura 1. Distribución de los Tipos Funcionales de Ecosistemas en América del Sur basada en la dinámica del
NDVI para a) los años 1988 y b) 1998 y para c) la distribución de medianas del período 1982-1999.
surrogates for productivity, seasonality, and phenology of carbon
gains. These functional attributes of vegetation can be used to map
Ecosystem functional types (EFTs), dened as patches of the land
surface that exchange mass and energy with the atmosphere in a
common way, and that show a coordinated and specic response
to environmental factors (Valentini et al., 1999; Soriano & Paruelo,
1992; Paruelo et al., 2001; Alcaraz-Segura et al., 2006). EFTs can
be considered a top-down approach to capture the spatial and
temporal heterogeneity of ecosystem functioning at a higher level of
the biological hierarchy than the more traditional bottom-up approach
that classies land-cover types based on plant functional types to
derive ecosystem properties (Alcaraz-Segura et al., in preparation).
Since EFTs can be dened in a year-to-year basis, they can give a
much better representation of time-varying land surface properties
Los atributos funcionales de la vegetación, que son descriptores
del intercambio de energía y masa entre la biota y la atmósfera en
escala de ecosistemas (Valentini et al., 1999; Virginia et al., 2001)
pueden contribuir a satisfacer esas necesidades dado que mues-
tran una respuesta más rápida a los cambios ambientales que a los
estructurales (Mcnaughton et al., 1989). Además, son relativamente
fáciles de monitorizar utilizando el Índice Espectral de Vegetación
de Diferencias Normalizadas obtenido de satélites (NDVI, por sus
siglas en inglés) para obtener sustitutos de la productividad, la esta-
cionalidad y la fenología de la ganancia de carbono. Estos atributos
funcionales de la vegetación pueden utilizarse para realizar mapas
de los Tipos funcionales de ecosistemas (TFEs), denidos como
parches de la supercie del suelo que intercambian masa y energía
con la atmósfera de un modo común, y que muestran una respuesta
coordinada y especíca a los factores ambientales (Valentini et al.,
that reect the actual characteristics of vegetation functioning and not
just time-xed vegetation types. In this sense, the use of time-varying
EFTs captures the effect of human-driven changes in land use and
management. In addition, the NDVI dynamics of a particular year
does not only reect the vegetation response to the environmental
conditions of that particular year, but it also exhibits the memory of
the system to the climatic conditions and disturbance effects from
previous years (Wiegand et al., 2004).
In this note, we use Ecosystem Functional Types to describe the
interannual variability of selected biophysical properties in southern
South America and propose a method to replace the traditional land-
cover types in regional climate models by time-varying EFTs. We
rst produced annual EFTs maps from 1982 to 1999 using three
metrics of the NDVI dynamics from the AVHRR-LTDR datarecord
(this methodology is discussed in Alcaraz-Segura et al. 2006, 2010,
in preparation). Then, we estimated the biophysical properties of
each EFT based on the Noah land-surface model parameterization
for the USGS land-cover classes. Finally, we formally evaluated the
1999; Soriano & Paruelo, 1992; Paruelo et al., 2001; Alcaraz-Segura
et al., 2006). Puede considerarse que los TFEs constituyen un enfo-
que top-down para capturar la heterogeneidad espacial y temporal
del funcionamiento de los ecosistemas en un nivel más alto de la
jerarquía biológica que el más tradicional enfoque bottom-up que
clasica los tipos de cobertura del suelo sobre la base de los tipos
funcionales de plantas para obtener las propiedades de los ecosis-
temas (Alcaraz-Segura et al., en preparación).
Al poder denir los TFEs sobre una base anual, se tiene una re-
presentación mucho mejor de las propiedades de la supercie del
suelo variables en el tiempo que reejan las características reales
del funcionamiento de la vegetación en lugar de simples tipos de ve-
getación invariantes con el tiempo. En este sentido, el uso de TFEs
variables en el tiempo captura el efecto de los cambios inducidos
por el hombre en el uso y manejo del suelo. Además, la dinámica
del NDVI de un año en particular no sólo reeja la respuesta de la
vegetación a las condiciones ambientales de ese año, sino que tam-
bién muestra la memoria del sistema a las condiciones climáticas y
- 25 -
los efectos de las perturbaciones de los años anteriores (Wiegand
et al., 2004).
En esta nota, utilizamos los Tipos Funcionales de Ecosistemas para
describir la variabilidad interanual de propiedades biofísicas selec-
cionadas en el sur de América del Sur y proponemos un método
para reemplazar los tipos tradicionales de cobertura de la tierra de
los modelos climáticos regionales por TFEs variables en el tiempo.
En primer lugar, generamos mapas anuales de TFEs desde 1982
hasta 1999 utilizando tres métricas de la dinámica del NDVI del
registro de AVHRR-LTDR (se analiza esta metodología en Alcaraz-
Segura et al. 2006, 2010, en preparación). Luego, estimamos las
propiedades biofísicas de cada TFE sobre la base de la parametri-
zación de Noah para modelos de la supercie de la tierra para las
clases de cobertura de la tierra de USGS. Finalmente, evaluamos
formalmente el efecto de nuestro enfoque en la variabilidad espacial
e interanual de las propiedades de la supercie de la tierra en el sur
de América del Sur y probamos la sensibilidad de las simulaciones
a las propiedades de la supercie.
Los Tipos Funcionales de Ecosistemas (mediana para 1982-1999)
de la Fig. 1c muestran una caracterización promedio del funciona-
miento de los ecosistemas. En promedio, los ecosistemas de áreas
templadas de América del Sur presentan máximos en otoño y vera-
no. Con los máximos de verano, los TFEs tienden a presentar una
productividad media a baja y una estacionalidad alta, mientras que
los máximos de otoño y primavera de los representan la mayoría de
las combinaciones posibles entre productividad y estacionalidad.
Los TFEs con máximos invernales de NDVI tienden a mostrar una
productividad muy baja o muy alta con valores muy bajos de es-
tacionalidad. Entre 1988 y 1999 se observan grandes diferencias
effect of our approach on the spatial and interannual variability of
land-surface properties over southern South America and tested the
sensitivity of the simulations to the surface properties.
The Ecosystem Functional Types (median for 1982-1999) presented
in Fig. 1c show an average characterization of ecosystem
functioning. On average, ecosystems of temperate South America
show maxima in autumn and summer. EFTs with summer maxima
tend to show medium-to-low productivity and high seasonality, while
EFTs with autumn and spring maxima represent most of the possible
combinations of productivity and seasonality. EFTs with NDVI
maxima during winter tend to exhibit either very low or very high
productivity under very low seasonality values. Strong differences
in the EFTs distribution are observed between 1988 and 1999 due to
climate factors (e.g., Figs. 1a,b). In 1998 EFTs with high productivity
and low seasonality dominated temperate South America, and
particularly La Plata basin. On the other hand, in 1988 the dominant
EFTs showed high seasonality and medium to low productivity.
The interannual variability of vegetation properties is presented
in Fig. 2. Great interannual variability was found for Surface
Roughness, Stomatal Resistance, and Minimum Leaf Area Index
(Figs. 2a-d). Low interannual variability was observed for Emissivity
and Radiation Stress (Figs. 2e-g). Rooting Depth, Background
Albedo, Green Vegetation Fraction, and Maximum Leaf Area
Index showed intermediate variability. On average, the interannual
coefcient of variation of the entire study area across all biophysical
properties was relatively low (13%). However, some regions (e.g.,
semi-arid areas of the Patagonian steppe, the NW-SE transect
from southeastern Bolivia to Uruguay, and the Brazilian Atlantic
Plateau) repeatedly presented high interannual variability across all
Figure 2: Interannual variability of different biophysical properties measured as the interquartile range over the median (in %). Top row, selected parameters
with large variation. Bottom row, selected parameters with low variation // Figura 2: Variabilidad interanual de distintas propiedades biofísicas medidas
como el rango intercuartil sobre la mediana (en %). Fila superior, parámetros seleccionados con variación alta. Fila inferior, parámetros seleccionados
con variación baja
- 26 -
en la distribución de los TFEs debidas a factores climáticos (por
ejemplo, Figs. 1a,b). En 1998, los TFEs con productividad alta y
baja estacionalidad dominaron la región templada de América del
Sur, y particularmente la cuenca del Plata. Por otra parte, los TFEs
dominantes en 1988 exhibían una alta estacionalidad y una produc-
tividad media a baja.
En la Figura 2 se muestra la variabilidad interanual de las propieda-
des de la vegetación. Se observó una gran variabilidad interanual
en la Rugosidad de la Supercie, la Resistencia Estomática y el
Índice de Área Foliar Mínima (Figs. 2a-d). Se observó una baja va-
riabilidad interanual para el Estrés por Emisividad y Radiación (Figs.
2e-g). La Profundidad Radicular, Background Albedo, la Fracción de
Vegetación Verde y el Índice de Área Foliar Máxima mostraron una
variabilidad intermedia. En promedio, el coeciente interanual de
variación en toda el área de estudio y para todas las propiedades
biofísicas fue relativamente bajo (13%). Sin embargo, algunas re-
giones (por ejemplo, las áreas semiáridas de la estepa patagónica,
la transecta NO-SE desde el sudeste de Bolivia hasta Uruguay y la
Meseta Brasileña del Atlántico) presentaron repetidamente una alta
variabilidad interanual en todas las propiedades.
The sensitivity of near surface temperature and precipitation to the
interannual variability of EFTs was tested with the WRF regional
model by performing seasonal simulations for a low productivity
year (1988) and a high productivity year (1998). Simulations were
done with the corresponding EFT types, and a second set of
simulations was performed reversing their order (a low productivity
year was simulated using the EFTs of the high productivity year
and vice versa). Figures 3a,b show that when using EFTs with high
productivity and a weak seasonal cycle the near surface temperature
for the 1988 and 1998 springs tends to increase by as much as 1°
C in the central and western portions of La Plata Basin. Figures
3c,d show that precipitation differences were in general positive,
regardless of whether it was a dry or a wet year. However, the
patterns are not uniform and exhibit certain patchiness with drier
conditions. This note shows that using Ecosystem Functional
Types instead of the Land Cover Types opens up the possibility of
incorporating interannual changes of biophysical properties into land-
surface and climate models.
Figure 3: Sensitivity studies showing the impact in temperature (top row) and precipitation (bottom row) of using high or low productivity EFTs. 1988 was
a dry year, while 1998 was a wet one// Figura 3: Estudios de sensibilidad que muestran el impacto en la temperatura (la superior) y la precipitación
(la inferior) de utilizar TFEs de baja o alta productividad. 1988 fue un año seco, mientras que 1998 fue húmedo
- 27 -
Domingo Alcaraz-Segura
University of Almería, Spain
Ernesto H. Berbery and S.-J. Lee
University of Maryland, USA
José Paruelo
University of Buenos Aires, Agentina
Para probar la sensibilidad de la temperatura cerca de la supercie y
la precipitación a la variabilidad interanual de los TFEs con el modelo
regional WRF se realizaron simulaciones estacionales para un año
de productividad baja (1988) y otro de alta (1998). Las simulaciones
se hicieron con los TFEs correspondientes, y se realizó un segundo
conjunto de simulaciones invirtiendo su orden (se simuló un año de
baja productividad usando los TFEs del año de productividad alta
y viceversa). Las Figuras 3a,b muestran que cuando se usa TFEs
con alta productividad y un ciclo estacional débil, la temperatura
cerca de la supercie para las primaveras de 1988 y 1998 tiende a
aumentar hasta 1° C en las partes central y oeste de la cuenca del
Plata. En las Figuras 3c,d se ve que las diferencias en precipitación
fueron positivas en general, independientemente de si se trató de un
año seco o húmedo. Sin embargo, los patrones no son uniformes y
muestran cierta presencia de parches bajo condiciones más secas.
Aquí se muestra que el uso de Tipos Funcionales de Ecosistemas
en lugar de los Tipos de Cobertura del Suelo abre la posibilidad de
incorporar cambios interanuales en las propiedades biofísicas en los
modelos climáticos y de la supercie del suelo.
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CLIVAR Editorial 2
Introducing Catherine Beswick 2
Membership of CLIVAR Panels and Working Groups 3
South African mid-summer seasonal reainfall prediction performance by a coupled
ocean-atomosphere model
Report of the 6th Meeting of the CLIVAR/CliC/SCAR Southern Ocean Region Panel
and the Workshop on the Upper and Lower Cells of the Meridional Circulation in the
Southern Ocean 6
New CLIVAR relevant Research Centre in Australia 8
Corrigendum 8
RAPID - US AMOC International Science Meeting 9
VAMOS! Editorial 10
Coupled ocean-atmosphere-land processes in the tropical Atlantic 12
VAMOS and extremes in the Americas: an update of the Extremes Working Group
A view forward for North American research 18
Use of Ecosystem Functional Types to represent the interannual variability of vegetation
biophysical perperties in regional models
... Functional attributes of ecosystems, those characterizing the energy and matter exchange between the biota and the atmosphere (Valentini et al. 1999), show a quicker response to environmental changes than structural ones (McNaughton et al. 1989) and are relatively easy to monitor using satellite-derived spectral indices (Paruelo et al. 2001). Based on these concepts, Alcaraz-Segura et al. (2006 developed a method to define ecosystem functional types on a yearly basis. Formally, ecosystem functional types are defined as groups of ecosystems that share functional characteristics in relation to the amount and timing of the exchanges of matter and energy between the biota and the physical environment (Paruelo et al. 2001;Alcaraz-Segura et al. 2006). ...
... Based on these concepts, Alcaraz-Segura et al. (2006 developed a method to define ecosystem functional types on a yearly basis. Formally, ecosystem functional types are defined as groups of ecosystems that share functional characteristics in relation to the amount and timing of the exchanges of matter and energy between the biota and the physical environment (Paruelo et al. 2001;Alcaraz-Segura et al. 2006). Since EFTs are defined from descriptors of the NDVI dynamics on an annual basis, the year-to-year variability of the surface conditions can thus be identified (Alcaraz-Segura et al. 2013a). ...
... EFTs are computed using three metrics of the NDVI seasonal dynamics: (i) the annual mean of NDVI as an estimator of net primary production; (ii) the seasonal coefficient of variation of NDVI as a descriptor of seasonality (the difference between the growing and nongrowing season or amplitude of the annual cycle); and (iii) the date of the absolute maximum normalized difference vegetation index in the given year as a phenological indicator of the growing season. For practical reasons (see Alcaraz-Segura et al. 2006 the range of values of each NDVI descriptor was divided into four fixed intervals, giving a potential number of 4 3 5 64 categories. To divide the range of values of the NDVI annual mean into four categories, its three quartiles were obtained for each year and then, for each quartile, the median across years was calculated. ...
Full-text available
This work discusses the land surface–atmosphere interactions during the severe drought of 2008 in southern South America, which was among the most severe in the last 50 years in terms of both intensity and extent. Once precipitation returned to normal values, it took about two months for the soil moisture content and vegetation to recover. The land surface effects were examined by contrasting long-term simulations using a consistent set of satellite-derived annually varying land surface biophysical properties against simulations using the conventional land-cover types in the Weather Research and Forecasting Model–Noah land surface model (WRF–Noah). The new land-cover dataset is based on ecosystem functional properties that capture changes in vegetation status due to climate anomalies and land-use changes. The results show that the use of realistic information of vegetation states enhances the model performance, reducing the precipitation biases over the drought region and over areas of excessive precipitation. The precipitation bias reductions are attributed to the corresponding changes in greenness fraction, leaf area index, stomatal resistance, and surface roughness. The temperature simulation shows a generalized increase, which is attributable to a lower vegetation greenness and a doubling of the stomatal resistance that reduces the evapotranspiration rate. The increase of temperature has a beneficial effect toward the eastern part of the domain with a notable reduction of the bias, but not over the central region where the bias is increased. The overall results suggest that an improved representation of the surface processes may contribute to improving the predictive skill of the model system.
... Such functional units were later defined as groups of ecosystems showing similar dynamics of primary production [8,20] and can be interpreted as patches of the land surface that exchange mass and energy in a common way, showing coordinated and specified responses to environmental factors [7]. This basic approach has been further developed in several studies to characterize ecosystem functioning (e.g., [8,2122232425), offering the potential of linking atmospheric and ecosystem processes in land-surface and climate modeling [26,27], which has been shown to significantly improve weather forecasts [28,29]. Similar methodological approaches that explicitly include the temporal dynamics of vegetation indices have also been used to characterize environmental heterogeneity at the regional scale. ...
... Ecosystem Functional Types (EFTs) were identified following the approach by Alcaraz-Segura et al. ([21,27]). EFTs were defined using fixed limits between classes, which captures the year-to-year dynamics and allows for inter-annual comparisons. The EFT identification was based on the Enhanced Vegetation Index derived from the MODIS MOD13C1 product for the 2001–2008 period and for the non-tropical portion of South America. ...
Full-text available
The regional controls of biodiversity patterns have been traditionally evaluated using structural and compositional components at the species level, but evaluation of the functional component at the ecosystem level is still scarce. During the last decades, the role of ecosystem functioning in management and conservation has increased. Our aim was to use satellite-derived Ecosystem Functional Types (EFTs, patches of the land-surface with similar carbon gain dynamics) to characterize the regional patterns of ecosystem functional diversity and to evaluate the environmental and human controls that determine EFT richness across natural and human-modified systems in temperate South America. The EFT identification was based on three descriptors of carbon gain dynamics derived from seasonal curves of the MODIS Enhanced Vegetation Index (EVI): annual mean (surrogate of primary production), seasonal coefficient of variation (indicator of seasonality) and date of maximum EVI (descriptor of phenology). As observed for species richness in the southern hemisphere, water availability, not energy, emerged as the main climatic driver of OPEN ACCESS Remote Sens. 2013, 5 128 EFT richness in natural areas of temperate South America. In anthropogenic areas, the role of both water and energy decreased and increasing human intervention increased richness at low levels of human influence, but decreased richness at high levels of human influence.
... Third, the date of maximum EVI (DMAX) was used as a phenological indicator of the growing season . These three EVI metrics are widely known to capture most of the variability in the EVI time series (Alcaraz-Segura et al., 2011. ...
... Third, the date of maximum EVI (DMAX) was used as a phenological indicator of the growing season (Paruelo et al. 2001;Pettorelli et al. 2005;Alcaraz-Segura et al. 2006). These three EVI metrics are widely known to capture most of the variability in the EVI time series (Alcaraz-Segura et al. 2006, 2011. The ranges of the three EVI metrics were divided into four intervals using the approach proposed by Alcaraz-Segura et al. (2013), potentially leading to 4 × 4 × 4 = 64 EFTs. ...
Full-text available
Policymakers and ecologists are appealing for a comprehensive understanding of how protected areas reconcile biodiversity and carbon conservation strategies. Satellite-based functional characterization of ecosystems offers the possibility of exploring the gaps and spatial congruence between biodiversity and carbon conservation priorities using common protocols that can be implemented worldwide. In this study, we evaluated the effectiveness of three national park networks in Portugal, Spain and Morocco for capturing the ecosystem functional diversity and providing carbon gains in these countries. The approach was based on identification of ecosystem functional types (EFTs), groups of ecosystems that share functional diversity characteristics related to the dynamics of matter and energy exchanges between biota and the atmosphere, and quantification of carbon gains using the Enhanced Vegetation Index (EVI) derived from MODIS images. The effectiveness of each network in capturing EFTs diversity was evaluated by quantifying the number of EFTs included in the parks (representativeness) and their singularity or distinctiveness (rarity). The Portuguese network, with only one national park, showed the least representativeness (17.2%) and accumulated rarity (18.4%), and much higher carbon gains than 50% of the natural areas of the country. The Spanish Network (7 parks) showed medium representativeness (38.1%) and rarity (44.8%), and most parks (86%) had higher carbon gains than the country median. The Moroccan national park network had the highest representativeness (54.4%) and rarity (66.3%) and half of the parks had higher carbon gains than the country median. Although some parks may be considered close to win-win options, the lack of clear synergies between biodiversity and carbon conservation strategies pointed out the need to aim the win-win condition for the whole networks rather than the individual parks. This strategy could be particularly important outside tropical regions where high biodiversity and other ES may be associated with non-forested areas. Our assessment could be useful as support for conservation planning and decision-making, and to the whole society by adding value to the parks as a contribution to climate change policies.
... Use of EFT data for lower boundary conditions may offer a more realistic representation of the land surface for regional climate investigations. Segura et al. (2011) derived a consistent set of biophysical properties of the land surface based on EFTs, and examined their changes over LPB. They found that ecosystems in temperate South America have NDVI maxima in summer and autumn. ...
Full-text available
In this paper, the effects of land cover changes on the climate of the La Plata Basin in southern South America are investigated using the Weather and Research Forecasting (WRF) Model configured on a 30/10-km two-way interactive nested grid. To assess the regional climate changes resulting from land surface changes, the standard land cover types are replaced by time-varying Ecosystem Functional Types (EFTs), which is a newly devised land-cover classification that characterizes the spatial and interannual variability of surface vegetation dynamics. These variations indicate that natural and anthropogenic activities have caused changes in the surface physical parameters of the basin, such as albedo and roughness length, that contributed to regional climate changes. EFTs are obtained from functional attributes of vegetation computed from properties of the Normalized Difference Vegetation Index (NDVI) to represent patches of the land surface with homogeneous energy and gas exchanges with the atmosphere. Four simulations are conducted, each experimental period ranging from September to November in two contrasting years, 1988 and 1998. The influence of an identical EFT change on the surface heat fluxes, 2-m temperature and humidity, 10-m winds, convective instabilities and large-scale moisture fluxes and precipitation are explored for 1988 (a dry year) and 1998 (a wet year). Results show that the surface and atmospheric climate has a larger response to the same EFT changes in a dry year for 2-m temperature and 10-m wind; the response is larger in a wet year for 2-m water vapor mixing ratio, convective available potential energy, vertically integrated moisture fluxes and surface precipitation. For EFTs with high productivity and a weak seasonal cycle, the near-surface temperature during the spring of 1988 and 1998 increased by as much as 1◦C in the central and western portions of La Plata Basin. Additionally, for higher productivity EFTs, precipitation differences were generally positive in both dry and wet years, although the patterns are not uniform and exhibit certain patchiness with drier conditions.
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Aim To examine the geographical patterns of the interception of photosynthetically active radiation by vegetation and to describe its spatial heterogeneity through the definition of ecosystem functional types (EFTs) based on the annual dynamics of the Normalized Difference Vegetation Index (NDVI), a spectral index related to carbon gains. Location The Iberian Peninsula. Methods EFTs were derived from three attributes of the NDVI obtained from NOAA/AVHRR sensors: the annual integral (NDVI-I), as a surrogate of primary production, an integrative indicator of ecosystem functioning; and the intra-annual relative range (RREL) and month of maximum NDVI (MMAX), which represent key features of seasonality. Results NDVI-I decreased south-eastwards. The highest values were observed in the Eurosiberian Region and in the highest Mediterranean ranges. Low values occurred in inner plains, river basins and in the southeast. The Eurosiberian Region and Mediterranean mountains presented the lowest RREL, while Eurosiberian peaks, river basins, inner-agricultural plains, wetlands and the southeastern part of Iberia presented the highest. Eurosiberian ecosystems showed a summer maximum of NDVI, as did high mountains, wetlands and irrigated areas in the Mediterranean Region. Mediterranean mountains had autumn–early-winter maxima, while semi-arid zones, river basins and continental plains had spring maxima. Based on the behaviour in the functional traits, 49 EFTs were defined. Main conclusions The classification, based on only the NDVI dynamics, represents the spatial heterogeneity in ecosystem functioning by means of the interception of radiation by vegetation in the Iberian Peninsula. The patterns of the NDVI attributes may be used as a reference in evaluating the impacts of environmental changes. Iberia had a high spatial variability: except for biophysically impossible combinations (high NDVI-I and high seasonality), almost any pattern of seasonal dynamics of radiation interception was represented in the Peninsula. The approach used to define EFTs opens the possibility of monitoring and comparing ecosystem functioning through time.
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Latin American countries show a great potential for expanding their irrigated areas. Irrigation is important for strengthening local and regional economy and for enhancing food security. The present paper aimed at providing a brief review on key aspects of irrigation management in Latin America. Poor irrigation management can have great impact on crop production and on environment while good management reduces the waste of soil and water and help farmers maximizing their profits. It was found that additional research is needed to allow a better understanding of crop water requirements under Latin American conditions as well as to provide farmers with local derived information for irrigation scheduling. The advantages of deficit irrigation practices and the present and future opportunities with the application of remote sensing tools for water management were also considered. It is clear that due to the importance of irrigated agriculture, collaborative work among Latin American researchers and institutions is of paramount importance to face the challenges imposed by a growing population, environment degradation, and competition in the global market.
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We analyzed data sets on phytomass production, basal cover, and monthly precipitation of a semiarid grassland in South Africa for good, medium, and poor rangeland condition (a) to investigate whether phytomass production per unit of basal cover differed among rangeland conditions, (b) to quantify the time scales of a carryover effect from production in previous months, and (c) to construct predictive models for monthly phytomass. Finally, we applied the best models to a 73-year data set of monthly precipitation data to study the long-term variability of grassland production. Our results showed that mean phytomass production per unit of basal cover did not vary significantly among the rangeland conditions—that is, vegetated patches in degraded grassland have approximately the same production as vegetated patches in grassland in good condition. Consequently, the stark decline in production with increasing degradation is a first-order effect of reduced basal area. Current-year precipitation accounted for 64%, 62%, and 36% of the interannual variation in phytomass production for good, medium, and poor condition, respectively. We found that 61%, 68%, and 33%, respectively, of the unexplained variation is related to a memory index that combines mean monthly temperature and a memory of past precipitations. We found a carryover effect in production from the previous 4 years for grassland in good condition and from the previous 1 or 3S month for grassland in medium and poor condition. The memory effect amplified the response of production to changes in precipitation due to alternation of prolonged periods of dry or wet years/months at the time scale of the memory. The interannual variability in phytomass production per unit basal cover (coefficient of variation [CV] = 0.42–0.50 for our 73-year prediction, CV = 0.57–0.71 for the 19-year data) was greater than the corresponding temporal variability in seasonal rainfall (CV = 0.29).
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We described, classified, and mapped the functional heterogeneity of temperate South America using the seasonal dynamics of the Normalized Difference Vegetation Index (NDVI) from NOAA/AVHRR satellites for a 10-year period. From the seasonal curves of NDVI, we calculated (a) the annual integral (NDVI-1), used as an estimate of the fraction of photosynthetic active radiation absorbed by the canopy and hence of primary production, (b) the relative annual range of NDVI (RREL), and (c) the date of maximum NDVI (MMAX), both of which were used to capture the seasonality of primary production. NDVI-1 decreased gradually from the northeastern part of the study region (southern Brazil and Uruguay) toward the southwest (Patagonia). High precipitation areas dominated by rangelands had higher NDVI-1 and lower RREL values than neighboring areas dominated by crops. The relative annual range of NDVI was maximum for the northern portion of the Argentine pampas (high cover of summer crops) and the subantarctic forests in southern Chile (high cover of deciduous tree species). More than 25% of the area showed an NDVI peak in November. Around 40% of the area presented the maximum NDVI during summer. The pampas showed areas with sharp differences in the timing of the NDVI peak associated with different agricultural systems. In the southern pampas, NDVI peaked early (October–November); whereas in the northeastern pampas, NDVI peaked in late summer (February). We classified temperate South America into 19 ecosystem functional types (EFT). The methodology used to define EFTs has advantages over traditional approaches for land classification that are based on structural features. First, the NDVI traits used have a clear biological meaning. Second, remote-sensing data are available worldwide. Third, the continuous record of satellite data allows for a dynamic characterization of ecosystems and land-cover changes.
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Human activities have modified the environment for thousands of years. Significant population increase, migration, and accelerated socioeconomic activities have intensified these environmental changes over the last several centuries. The climate impacts of these changes have been found in local, regional, and global trends in modern atmospheric temperature records and other relevant climatic indicators. An important human influence on atmospheric temperature trends is extensive land use/land cover change (LULCC) and its climate forcing. Studies using both modeled and observed data have documented these impacts (e.g., Chase et al. 2000; Kalnay and Cai 2003; Cai and Kalnay 2004; Trenberth 2004; Vose et al. 2004; Feddema et al. 2005; Christy et al. 2006; Mahmood et al. 2006b; Ezber et al. 2007; Nuñez et al. 2008). Thus, it is essential that we detect LULCCs accurately, at appropriate scales, and in a timely manner so as to better understand their impacts on climate and provide improved prediction of future climate.
Functional characters are rarely taken into account in most vegetation classification systems. Ignoring these characters represents a major shortcoming when the dynamic aspects of ecosystems need to be recorded. In this paper we define vegetational and environmental units mainly from functional characters of the vegetation (namely the seasonal dynamics of the ANPP or of the green biomass), and we propose for these units the name of Biozones. The above-mentioned functional characters are derived from satellite images. To illustrate our biozones, we discuss the biozones of central Patagonia, using spectral data of the AVHRR/NOAA satellite.
A coupled atmospheric-biospheric model is a particularly valuable tool to study the potential effects of land-use/land-cover changes on near-surface atmosphere since the atmosphere and biosphere are allowed to dynamically interact through the surface and canopy energy balance. GEMRAMS is an ecophysiological process-based model, comprised of the Regional Atmospheric Modeling System (RAMS) and the General Energy and Mass Transport Model (GEMTM), and was used in this study. At a regional and seasonal scale, several spring-early summer simulations were conducted on a southern South America domain. GEMRAMS were able to simulate the observed monthly temperature and precipitation. Sensitivity to lateral boundary conditions was explored for RAMS using NCEP and ECMWF reanalysis as atmospheric forcing. Land-cover scenarios representing current, natural, and afforestation conditions were implemented for this region and used to simulate the impacts of land-cover changes on near-surface atmosphere. Changes in near-surface fluxes and temperature depended on the type of vegetation conversion and the season. Warmer temperatures were found in the conversion from wooded grasslands or forest to agriculture. Afforestation and conversion from grass to agriculture led to a cooler and wetter near-surface atmosphere. Additional simulations with a double CO2 concentration were also performed to assess the relative contributions of the land-cover and doubled CO2 forcing to meteorological and biological variables. At a local and diurnal scale, GEMRAMS was used to evaluate the effects of observed vegetation changes that occurred in the northern Chihuahuan Desert, from grasslands in the mid-1800s to shrublands in the late 1900s. Simulations were performed using detailed vegetation maps for 1858 and 1998. Surface flux changes and the associated effects on near-surface temperature were spatially heterogeneous: different vegetation changes led to different effects, but albedo was the dominant parameter controlling the energy budget. Sensitivity experiments to soil moisture and mesquite cover were also conducted. Results of this study show that simulated shifts in vegetation led to complex interactions between biophysical and physiological characteristics of land and surface fluxes. These results also demonstrate that vegetation itself is a weather and climate variable as it significantly influences temperature, humidity, and surface fluxes.
Plant communities in natural ecosystems are changing and species are being lost due to anthropogenic impacts including global warming and increasing nitrogen (N) deposition. We removed dominant species, combinations of species and entire functional types from Alaskan tussock tundra, in the presence and absence of fertilization, to examine the effects of non-random species loss on plant interactions and ecosystem functioning. After 6 years, growth of remaining species had compensated for biomass loss due to removal in all treatments except the combined removal of moss, Betula nana and Ledum palustre (MBL), which removed the most biomass. Total vascular plant production returned to control levels in all removal treatments, including MBL. Inorganic soil nutrient availability, as indexed by resins, returned to control levels in all unfertilized removal treatments, except MBL. Although biomass compensation occurred, the species that provided most of the compensating biomass in any given treatment were not from the same functional type (growth form) as the removed species. This provides empirical evidence that functional types based on effect traits are not the same as functional types based on response to perturbation. Calculations based on redistributing N from the removed species to the remaining species suggested that dominant species from other functional types contributed most of the compensatory biomass. Fertilization did not increase total plant community biomass, because increases in graminoid and deciduous shrub biomass were offset by decreases in evergreen shrub, moss and lichen biomass. Fertilization greatly increased inorganic soil nutrient availability. In fertilized removal treatments, deciduous shrubs and graminoids grew more than expected based on their performance in the fertilized intact community, while evergreen shrubs, mosses and lichens all grew less than expected. Deciduous shrubs performed better than graminoids when B. nana was present, but not when it had been removed. Synthesis. Terrestrial ecosystem response to warmer temperatures and greater nutrient availability in the Arctic may result in vegetative stable-states dominated by either deciduous shrubs or graminoids. The current relative abundance of these dominant growth forms may serve as a predictor for future vegetation composition.
Ecosystems influence climate through multiple pathways, primarily by changing the energy, water, and green- house-gas balance of the atmosphere. Consequently, efforts to mitigate climate change through modification of one pathway, as with carbon in the Kyoto Protocol, only partially address the issue of ecosystem-climate interactions. For example, the cooling of climate that results from carbon sequestration by plants may be par- tially offset by reduced land albedo, which increases solar energy absorption and warms the climate. The rela- tive importance of these effects varies with spatial scale and latitude. We suggest that consideration of multi- ple interactions and feedbacks could lead to novel, potentially useful climate-mitigation strategies, including greenhouse-gas reductions primarily in industrialized nations, reduced desertification in arid zones, and reduced deforestation in the tropics. Each of these strategies has additional ecological and societal benefits. Assessing the effectiveness of these strategies requires a more quantitative understanding of the interactions among feedback processes, their consequences at local and global scales, and the teleconnections that link changes occurring in different regions.