fiesta bogota final report report.pdf · la contribución por neblina es relativamente baja en la...
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ANALISIS CIENTIFICO DETALLADO DEL IMPACTO DEL CAMBIO DEL
USO DEL SUELO EN EL SUMINISTRO DE RECURSOS HIDRICOS PARA
LA CIUDAD DE BOGOTA D.C E IMPLICACIONES PARA EL
DESARROLLO DE ESQUEMAS PES
Policy brief
Por
Luis Leonardo Sáenz Cruz y Dr. Mark Mulligan
Department of Geography, King’s College London
2007
El presente estudio usó el modelo FIESTA (http://www.ambiotek.com/fiesta) e
implementó dos componentes adicionales: un modelo de escurrimiento con un
componente paramo y un componente para simular el llenado de embalses, buscando
ayudar a mejorar el entendimiento acerca de los impactos producidos por el cambio
de coberturas vegetales naturales e impactos de conservación a través de esquemas
PES sobre los recursos hídricos en áreas aledañas a la capital de Colombia, Bogotá
D.C. Los principales resultados indican que la contribución hídrica como resultado de
la intercepción de neblina (la cual ocurre de manera mucho más eficiente en bosques
que en otro tipo de coberturas vegetales) es estacionalmente importante para el
mantenimiento de caudales que alimentan las represas localizadas en las zonas mas
secas de la región (Represas del Sisga, Tomine, Chivor y Copa y el lago de Tota).
Sin embargo, la continuada deforestación es probable que incremente los
caudales en toda la zona aumentando la disponibilidad de volúmenes de agua
superficiales para el suministro de agua potable y la generación de energía
hidroeléctrica a pesar del descenso en la intercepción de neblina. Este fenómeno se
explica teniendo en cuenta que los bosques evapotranspiran mas agua que otros usos
del suelo. Por lo tanto, el incremento en los volúmenes de agua superficial a
consecuencia de la deforestación (debido a la menor evapotranspiración) es mayor en
la mayoría de los casos que las perdidas en recursos hídricos superficiales debido a la
disminución en la intercepción de neblina.
Por otro lado, la conversión de paramo a otros usos del suelo lleva a una perdida
significativa de la capacidad de almacenamiento de agua local. Teniendo en cuenta
que el paramo almacena agua durante periodos húmedos y la libera durante periodos
secos, la perdida de paramo podría traer como consecuencia periodos de escasez de
agua en estaciones secas. La implementación de esquemas PES en áreas de paramo
podría así potencialmente ayudar a mantener tales recursos hídricos, especialmente
aquellos que alimentan las principales represas para suministro de agua potable y
generación eléctrica en la región (Golillas y Guavio y la laguna Chingaza).
Palabras clave: FIESTA, Cloud Forest, Paramo, Chingaza Park, Sumapaz,
Bogotá, PES.
Supervisor Tesis: Dr. Mark Mulligan
Reader in Geography
Department of Geography, King’s College London
El presente trabajo fue desarrollado para suministrar una base científica mejorada para
progresar en el entendimiento del papel que podrían jugar los esquemas de pago por
servicios ambiéntales (PES) en el mantenimiento de recursos hídricos para el
abastecimiento de agua potable y la generación de energía hidroeléctrica
suministrados por bosques de niebla (TMCFs) y ecosistemas de paramo para la ciudad
de Bogotá D.C., capital de Colombia.
Contribución por intercepción de neblina e hidrología de
bosques de niebla en el área de influencia de Bogota D.C
“Bogotá región” e implicaciones para sus principales
represas
La contribución por neblina es relativamente baja en la Sabana de Bogotá (áreas entre
2500 y 2800msnm), encontrándose entre los 70 a los 80mm año-1 en promedio anual y
es aun menor en áreas de paramo (áreas por encima de 3200msnm), representando de
40 a 60mm año-1. Sin embargo, la intercepción de neblina es el doble de aquella
observada en la Sabana de Bogotá en áreas de bosque de niebla que rodean los
paramos de Chingaza y Sumapaz (150mm año-1) y en zonas de menor altitud (entre
2300 y 700msnm) hacia la cuenca del Magdalena al occidente y las pendientes mas
húmedas hacia el pie de monte llanero y la cuenca del Orinoco al oriente, donde las
áreas de mayor exposición a vientos predominantes y nubosidad con cobertura de
bosques de niebla muestran contribuciones hídricas por indecepción de neblina de
hasta 300mm año-1.
No obstante, la contribución por intercepción de neblina es importante en áreas
secas al norte de la Bogotá región (áreas al norte de la cuenca del río Bogotá, cuenca
del río Ubate-Suarez, cuenca de Chicamocha y los alrededores de las represas del
Sisga, Tomine, Chivor, Copa y el lago de Tota), donde asciende hasta un 40% del
balance hídrico y representa hasta un 10% de los caudales en los meses de Diciembre,
Enero y Febrero. Aunque estas proporciones están por lo general por debajo del 8%
del balance hídrico en la Sabana de Bogotá, áreas de paramo y las mucho mas
húmedas áreas bajas de la región hacia la cuenca del Orinoco, donde la proporción de
los caudales correspondiente a la intercepción de neblina se reduce marcadamente
(por debajo del 4%).
En general: Las contribuciones por intercepción de neblina son de mayor importancia en
áreas secas hacia el norte de la Bogotá región y en los bordes occidentales de la misma hacia
la cuenca del Magdalena, donde las represas del Sisga, Tomine, Chivor, Prado y Copa y el
Lago de Tota se benefician de importantes contribuciones hídricas especialmente en
estaciones secas.
Impactos de deforestación en la contribución por
intercepción de neblina e implicaciones sobre las principales
represas
La deforestación en la Bogotá región en el periodo 1977 – 2000 llevó a una perdida
hídrica potencial de hasta 150mm año-1 por reducciones en la intercepción de neblina,
la cual tuvo lugar especialmente en los bordes occidentales de la región hacia la
cuenca del río Magdalena (por debajo de los 2500msnm), donde se observó la mas
alta deforestación. Esto trajo consigo un descenso en los caudales de hasta cerca de
0.1m3 s-1, equivalente a perdidas anuales acumulados de alrededor de 3.15 Mm3 año-1
en los principales ríos.
Sin embargo, esta reducción fue mucho menos significativa (de 10 a
20mm año-1) en la Sabana de Bogotá (2500 – 2800msnm) y las áreas de paramo.
Menor deforestación observada en esta zona sobre dicho periodo, donde existía ya
muy poco bosque para convertir a otros usos del suelo podría explicar la baja
reducción en la intercepción de neblina en la Sabana de Bogotá. Adicionalmente, el
menor tamaño y sobre todo la baja capacidad estructural de interceptación de neblina
de la vegetación de paramo comparada con la de los bosques de niebla explica el
menor impacto sobre la intercepción de neblina cuando el paramo es convertido a
otros usos del suelo.
Por otro lado, las perdidas por evapotranspiración cayeron a lo largo de toda el
área entre 50 y 250mm año-1 debido a la deforestación. Por lo tanto, los balances
hídricos aumentaron en áreas deforestadas con la consecuente adición de agua por
escurrimiento de hasta 4m3 s-1 a los caudales superficiales en las zonas bajas (cerca de
700 msnm) en los ríos más caudalosos de la región (Guavio y Chivor) a pesar de las
pérdidas potenciales por disminución en la intercepción de neblina.
En el lugar de las represas Guavio y Chivor se observaron los incrementos mas
altos en caudales (de hasta 2.54m3 s-1) con efectos positivos potenciales sobre la
cantidad de agua almacenada para la generación hidroeléctrica pero al mismo tiempo
efectos negativos sobre la seguridad de operación y el mantenimiento de la represa
(sedimentación y aumento de caudales pico). Sin embargo, la disminución en la
contribución por neblina a los caudales podría haber afectado la regulación de flujos
base en las represas del Sisga, Tomine, Chivor, Copa y el lago de Tota en estaciones
secas.
Perdida de paramo e implicaciones para las represas en la
región
Buscando mejorar el entendimiento sobre el impacto de la remoción de paramo en
áreas especificas de la región el presente estudio acopló al modelo FIESTA un
modelo de escurrimiento y flujo subsuperficial con un componente para simular la
retención y la liberación de agua del paramo. Los modelos fueron parametrizados para
la cuenca del Guavio en el Departamento de Cundinamarca debido a que esta cuenta
con una importante proporción de áreas de paramo pertenecientes al Parque Nacional
Natural de Chingaza y con dos de las mas importantes represas de la región, Guavio y
Golillas.
Un año de simulación hidrológica del fenómeno de precipitación – escorrentía
superficial y subsuperficial en la cuenca del Guavio indica que bajo condiciones de
muy baja evapotranspiración observadas en las áreas de paramo, la vegetación de
paramo, la cual cubre el suelo con una importante capa de biomasa (2.8kg m-2), juega
En general: La contribución por neblina se encuentra ya en sus niveles más bajos en la
sabana de Bogotá debido a que muchos de sus bosques de niebla han sido actualmente
convertidos a otros usos de suelo, pero no así en las pendientes inclinadas que rodean los
parques de Chingaza y Sumapaz y en algunas áreas de los bordes orientales y occidentales de
la Bogotá región (por debajo de los 2500msnm). La deforestación ha incrementado
potencialmente los caudales hacia la mayoría de las represas en la región. Sin embargo, un
aumento en la deforestación amenaza la regulación de flujos base estacionalmente
especialmente en las áreas mas secas de las cuencas de los ríos Bogotá, Ubate-Suarez y
Chicamocha, donde la contribución hídrica por intercepción de neblina corresponde a una
proporción importante de los balances hídricos.
un rol esencial en el almacenamiento inicial de agua con una muy baja tasa de
liberación vertical y horizontal (cerca de 0.2mm hr-1), antes de que el agua se infiltre
en el perfil de suelo (infiltración vertical al suelo y flujo subsuperficial horizontal).
Los resultados de un año de simulación hidrológica en la cuenca del Guavio
indicaron que la perdida de paramo sobre el periodo 1977 – 2000 condujo a una
reducción ligera de los caudales en los lugares de las represas de Guavio y Golillas de
cerca de 0.17 y 0.0303m3 s-1 (0.24% y 0.35% de los caudales de los ríos Guavio y
Chuzque en el año 1977 respectivamente), lo cual representó la perdida de un
volumen de almacenamiento de cerca de 5.6 y 1 Mm3 para ambas represas al año
2000 respectivamente.
Mas investigación es necesaria para entender mejor el impacto de la remoción
de paramo sobre el fenómeno de escurrimiento en cuencas dominadas por vegetación
de paramo sobre escalas de tiempo mas largas (mas de un año de simulación
hidrológica).
Finalmente, a pesar de los beneficios potenciales de mantener la cantidad de la
oferta hídrica, la conservación de paramo es probablemente mas importante para
mitigar los procesos erosivos y de sedimentación que ocurren en todos pero
especialmente en los mas extremos eventos de precipitación, particularmente en las
pendientes mas inclinadas de los Andes orientales, y que junto con servicios
ambientales como el control de inundaciones son esenciales para la operación segura,
económica y eficiente de las represas en la región.
En general: Las condiciones climáticas y topográficas y los altos balances hídricos
comparados con aquellos observados en la Sabana de Bogotá hacen esenciales las áreas de
paramo para el suministro sostenible de recursos hídricos para la ciudad de Bogotá. La
importancia de la conservación del paramo esta intrínsicamente ligada a el mantenimiento
de la calidad del agua y la regulación hídrica. Mas investigación es necesaria para entender
mejor la generación del fenómeno de escurrimiento en cuencas de paramo con y sin
intervención humana.
Implicaciones para la implementación de esquemas PES
La deforestación ha llevado y llevará al aumento de los caudales de los ríos que
suplen las principales represas en la región, debido a la reducción en
evapotranspiración, y esto potencialmente ha generado y generará incrementos en la
cantidad de los recursos hídricos disponibles en las represas para el suministro de
agua potable y la generación hidroeléctrica. Aunque no así en áreas secas del norte y
el suroeste de la región, en los alrededores de las represas del Sisga, Chivor, Copa
Prado y el lago de Tota, donde la reducción en la contribución hídrica por pedida de
intercepción de neblina afecta los caudales de los ríos estacionalmente (periodos
secos). Esto ocurre similarmente en las cuencas con cobertura de paramo
correspondientes a las represas de Golillas, la Regadera y la Laguna de Chingaza,
donde la disminución en evapotranspiración es casi nula o incluso esta puede
incrementarse en algunos casos cuando el paramo es convertido a cultivos o pastos de
mayor evapotranspiración comparada con la de la vegetación de paramo.
Para los equipamientos y obras de infraestructura destinados al
aprovechamiento, manejo y control de recursos hídricos, los cuales han sido
construidos con base en diseños ingenieriles basados en registros históricos y sobre
los cuales existe una gran dependencia de comunidades humanas, todo cambio en los
regimenes de caudales, bien sea aumentos o reducciones, por fuera de los valores de
diseño representa un perjuicio y un aumento en el riesgo de operación. Por lo tanto, la
conservación de las coberturas naturales a través de la implementación de esquemas
PES garantiza el mantenimiento de dichos regimenes de caudal actual, lo cual es
soportado en la presente investigación.
En este contexto, los esquemas PES podrían muy seguramente mantener la
seguridad estructural de las obras de infraestructura hidráulica del acueducto de
Bogotá situadas en el Parque Nacional Natural de Chingaza, las cuales no enfrentarían
el peligro potencial del aumento en flujos pico sobre las especificaciones de diseño
como consecuencia de la continuación de los procesos de deforestación dentro del
parque. Sin embargo, el impacto económico de la falla estructural de las obras de
infraestructura hidráulica debido al incremento de flujos pico no se ha estimado aun.
Cuando se incorporo al modelo FIESTA un componente para simular el llenado
represas y ver el impacto de la remoción de paramo sobre las represas del Guavio y
Golillas, los resultados indicaron que la implementación de esquemas PES para
prevenir la perdida de paramo en el Parque Nacional Natural de Chingaza podría
potencialmente mantener hasta un 0.9% de la generación hidroeléctrica de la represa
del Guavio al año 2050 (representando potencialmente hasta cerca de 3.2 USD
millones año-1). De manera similar, la implementación de esquemas PES para la
conservación de áreas de paramo podría potencialmente mantener hasta un 3.8% la
contribución hídrica para la represa de Golillas al año 2050 (hasta cerca de 6.2 USD
millones año-1). Adicionalmente, la ampliación del área protegida de paramo en la
cuenca que nutre las represas del Guavio y Golillas ayudaría a mantener recursos
hídricos en estaciones secas que de otra manera se perderían si la perdida de paramo
continua a la misma tasa actual.
Los resultados de la protección del paramo son estimulantes toda vez que los
esfuerzos de conservación podrían potencialmente representar impactos económicos
positivos. Sin embargo, dichos resultados deben ahora ser validados y comparados
con información suministrada por organizaciones relevantes involucradas en el
suministro de agua potable, la generación de energía hidroeléctrica y la conservación
del medio ambiente, y entendidos en el marco del contexto económico del suministro
de dichos servicios en la región.
Finalmente, la conservación de bosques de niebla y paramo debe abordarse de
una manera mas integral, considerando no solamente los beneficios potenciales
asociados al aumento o reducción de volúmenes hídricos pero además teniendo en
cuenta otros servicios ambientales tales como el control de la erosión y la
sedimentación, especialmente en las pendientes mas inclinadas de los Andes
orientales, y la mitigación de inundaciones, los cuales son esenciales para la eficiente,
económica y segura operación de las represas en la región.
Leonardo Saenz Cruz y Mark Mulligan
Octubre, 2007
A DETAILED SCIENTIFIC ANALYSIS OF THE IMPACT OF LAND USE
CHANGE ON WATER RESOURCE PROVISION TO BOGOTA D.C. AND
IMPLICATIONS FOR THE DEVELOPMENT OF PES SCHEMES
Policy brief
by
Luis Leonardo Sáenz Cruz and Dr. Mark Mulligan
Department of Geography, King’s College London
2007
This study used the FIESTA (http://www.ambiotek.com/fiesta) model and
implemented two other model components : a runoff model with a paramo component
and a reservoir dam filling component, in order to help understand the impacts of land
cover change and conservation through PES schemes in areas surrounding Bogotá
city, Colombia’s capital. Main results indicate that inputs of water from fog (which
occur more efficiently to forest than to other land uses) are seasonally important for
the maintenance of river flows for the dams located in the driest areas of the region
(Sisga, Tomine, Chivor and Copa dams and Tota lake). However, continued forest
loss is likely to increase river flows in the whole region leading to increases in the
availability of water quantity for drinking water provision and hydroelectric
production in spite of the potential decline in fog interception. This is because forests
evaporate more water than other land uses and the increased availability of surface
water on deforestation (because of reduced evaporation) is greater in most cases than
the losses of surface water because of reduced fog interception.
On the other hand, conversion of Paramo to other land uses tends to lead to a
significant decrease in local water storage. Since this water is stored during wet
periods and released slowly during dry periods, a loss of this storage could lead to
seasonal water shortages. PES implementation in paramo areas could thus potentially
help maintain water resources to dams for hydroelectric power generation and
drinking water provision.
Keywords: FIESTA, Cloud Forest, Paramo, Chingaza Park, Bogotá, PES
Dissertation Supervisor: Dr. Mark Mulligan
Reader in Geography
Department of Geography, King’s College London
The work was carried out to provide an improved scientific basis to progress in the
understanding of the role of Payments for Environmental Services (PES) schemes in
the maintenance of water resources for drinking water and hydroelectric power
generation provided by cloud forest (TMCFs) and paramo ecosystems to the Bogotá
city, Colombia’s capital.
Fog inputs and cloud forest hydrology in the Bogotá region
and implications to dams
Fog inputs are relatively small in the high Bogotá sabana (areas between 2500 to
2800masl), from about 70 to 80mm year-1 and are even lower in paramo areas (areas
above 3000masl), between 40 to 60 mm year-1. However, fog inputs are doubled
(about 150mm year-1) in areas below, between 2300 and 700masl towards both, the
Magdalena basin to the west and the much wetter fringes of the Orinoquia basin to the
east, where highly exposed areas of cloud forest cover show fog inputs of up to 300
mm year-1.
Nonetheless, fog inputs are seasonally important in dry areas to the north of the
Bogotá region (areas coinciding with the Chicamocha catchment and surrounding
Tota lake and Copa, Sisga, Tomine and Chivor dams), where they can be as high as
40% of the water balance and represent as much as 10% of river discharges for the
months of December, January and February. Though, these proportions are generally
below 8% of the water balance in the high Bogotá sabana, paramos and the much
wetter lowlands towards the orinoquia catchment, where fog contributions to river
flows are also very low (below 4%).
Deforestation impacts on fog inputs and implications to
dams
Deforestation over the period 1977 – 2000 led to potential losses of fog interception
of up to 150mm year -1, especially in the western fringes of the region towards the
Magdalena catchment (below 2300masl), where deforestation was greatest, leading to
a decline in river flows of up to about 0.1m3 s-1 (accumulated runoff volume of 3.15
Mm3 year-1) for the main rivers.
However, much less decline in fog inputs (from 10 to 20mm year-1) was
observed in the high Bogotá sabana and paramo areas (above 2500). Low
deforestation in these areas, where there was already little forest to convert to pasture
or crops could explain the low changes in fog interception. Further more, the lower
stature and overall reduced interception capacity of paramo vegetation compared to
that of cloud forest also explains the minor impact of paramo removal on fog inputs.
On the other hand, evapo-transpiration losses dropped throughout from 50 to
250mm year-1 due to deforestation. Therefore, water balances increased in deforested
areas with a consequent addition of up to 4m3 s-1 of runoff to river flows for the
largest rivers (Guavio and Chivor) in spite of the potential reductions in fog
contributions to river flows.
Overall: Fog inputs are of major importance in dry areas to the north of the Bogotá region
and in the western fringes towards the Magdalena basin, where Tota lake and Copa, Sisga,
Tomine, Chivor and Prado dams receive important seasonal fog inputs.
At the dam location, Guavio and Chivor faced the greatest increments in river
flows (up to 2.54m3 s-1) with potential positive effects on water quantity for
hydroelectric power generation but also negative effects upon dam operation safety
and maintenance (sedimentation). However, the drop in fog contribution to river
flows seasonally could potentially have affected the regulation of base flows to Copa,
Tomine, Sisga and Chivor dams in the dry seasons.
Paramo loss and implications to dams
In order to gain any understanding of the impact of paramo removal in specific areas
of the region a runoff – subsurface flow model with a component to simulate paramo
water retention and release was coupled to the FIESTA model. The models were
parameterized for the Guavio catchment in the Cundinamarca county, because it
accounts for an important proportion of paramo areas of the Chingaza National Park
and because two of the most important dams in the region, Guavio and Golillas are
located in this catchment.
A year simulation of surface – subsurface runoff as a response to rainfall in the
Guavio catchment indicates that under the very low evapo-transpiration conditions
observed in paramo areas, the paramo vegetation which covers the soil with a fairly
thick biomass layer (2.8kg m-2 of biomass), plays a significant role as initial water
storage with regular and very low vertical and horizontal water release (0.2mm h-1)
before water infiltrates to the soil profile (vertical infiltration to the soil and horizontal
subsurface flow).
Overall: Fog inputs are already minor in most of the flat areas of the high Bogotá sabana
since most of the cloud forest resources have already been converted to other land uses, but
not so in the steep slopes surrounding Chingaza and Sumapaz parks and in the western and
eastern fringes of the region below 2500masl. Deforestation has already potentially increased
flows for most of the dams in the region. Further deforestation threatens the seasonal
regulation of base flows to dams especially in the driest areas of the Bogotá, Ubate – Suarez
and Chicamocha catchments, where fog inputs are an important proportion of seasonal
water balances.
Results from the one year hydrological simulation in the Guavio catchment
indicate that paramo loss over the period 1977 – 2000 led to a slight reduction in river
flows at the Guavio and Golillas dams of about 0.17 and 0.03m3 s-1 (0.24% and 0.35%
of river flows for Guavio and Chuzque rivers respectively in 1977), which represent
an annual volume loss of 5.6 and 1 Mm3 for both dams respectively to the year 2000.
. Much research is needed to better understand the impact of paramo vegetation
removal on the runoff phenomenon in paramo-dominated catchments over longer
timescales.
In spite of the potential benefits of maintaining water quantity, the conservation
of paramo is likely to be important for erosion and sedimentation mitigation for all but
the most extreme rainfall events, especially in the steep slopes of the eastern Andes,
which together with flood mitigation services are essential to the efficient, economic
and safe operation of dams in the region.
Implications to PES
Deforestation has led and will lead to increased river flows for the main dams in the
region due to reduced evapo-transpiration, and this has and will consequently increase
potentially water resources for drinking water and hydroelectric power generation in
the region. Though not so in dry areas to the north and south west surrounding Tota
lake and Sisga, Copa, Chivor and Prado dams, where a reduction in fog interception
affects river flows seasonally. Also in paramo catchments of Chingaza lake, Golillas
and regadera dams, where the reduction in evapo-transpiration is less or even could
Overall: Climatic and topographical conditions and high water balances with regard to the
high Bogotá sabana make paramo areas essential to sustained water supplies to Bogotá City.
The importance of paramo conservation is with respect to maintaining water quality and
regulation. Much research is needed to better understand the generation of the runoff
phenomenon in undisturbed and intervened paramo catchments.
increment when paramo is converted to pasture and crops with greater evapo-
transpiration compared to that of paramo vegetation.
In a highly engineered system on which many people depend change is bad,
whether it be an increase or a decarease in flows. Therefore, conservation of the
existing land cover through the implementation of PES schemes guarantees the
maintenance of current river flows regimes and is generally supported here.
In this context, PES schemes could safely maintain the structural safety of
hydraulic structures of the Bogotá aqueduct in the Chingaza Park, which will not face
the potential danger of increased peak flows above design specifications as a
consequence of further deforestation within the park. The economic impact of
infrastructural failure due to increased peak flows has yet to be estimated.
When a dam filling component was included to study the economic impact of
this reduction on Guavio and Golillas dams, results showed that the implementation
of PES schemes preventing the loss of paramo in the Chingaza park could potentially
maintain up to 0.9% of hydropower generation of the Guavio dam to the year 2050
(up to about 3.2 USD million year-1). Similarly PES implementation could potentially
conserve up to 3.8% of water inputs to Golillas dam from paramo conservation to the
year 2050 (up to about 6.2 USD million year-1). Further enhancements of the paramo
protected area in the catchment would help maintain seasonal water resources that
would otherwise be lost if paramo loss takes place at the same rates of today.
Results from paramo conservation are encouraging since conservation efforts
through PES schemes could potentially represent positive economic impacts.
However, they the study outcomes must now be validated and compared with
information from respective relevant drinking water, hydroelectric and environmental
organizations and understood within the economic context of the provision of these
services in the region.
Finally, conservation of cloud forest and paramo resources should be tackled in
a more integrated way, considering not only benefits from potential water quantity
increases or reductions but taking into account other environmental services such as
erosion and sedimentation mitigation especially in the steep slopes of the eastern
Andes, and flood mitigation services which are essential to the efficient, economic
and safe operation of dams in the region.
Leonardo Sáenz Cruz and Mark Mulligan
October, 2007
Acknowledgements
I would like to thank my supervisor, Dr Mark Mulligan for all his help, generosity,
ideas, guidance and encouragement for the development of this thesis, as well as for
providing with essential information for the analysis of this study.
This work would not have been possible without the financial support of Alban
programme, King’s College London, CIAT and TNC, their support is greatly
acknowledged.
Particularly I would like to thank Dr. Satish Kundaiker for all his generosity.
Finally, I would like to thank and dedicate this thesis to Alfredo and Miryam, my
parents.
Table of contents
Policy brief ................................................................................. 1
Contribución por intercepción de neblina e hidrología de
bosques de niebla en el área de influencia de Bogota D.C
“Bogotá región” e implicaciones para sus principales represas .. 3
Impactos de deforestación en la contribución por intercepción de
neblina e implicaciones sobre las principales represas ............... 4
Perdida de paramo e implicaciones para las represas en la región
.................................................................................................... 5
Implicaciones para la implementación de esquemas PES .......... 7
Policy brief ................................................................................. 9
Fog inputs and cloud forest hydrology in the Bogotá region and
implications to dams ................................................................. 10
Deforestation impacts on fog inputs and implications to dams 11
Paramo loss and implications to dams ...................................... 12
Implications to PES .................................................................. 13
Acknowledgements................................................................... 16
List of figures ............................................................................ 21
List of Tables ............................................................................ 25
Chapter 1: Introduction ............................................................. 26
Chapter 2: Objective ................................................................. 29
Chapter 3: Literature review and Background .......................... 30
3.1 Cloud forest and modelling fog interception processes ............................... 30
3.2 Paramo ......................................................................................................... 30
3.2.1 Structural characteristics of paramo to water storage .............................. 30
3.3 Hydroelectric potential and Dam filling modelling ..................................... 32
Chapter 4: Methodology ........................................................... 34
4.1 Description of the study area ....................................................................... 34
4.2 Multi-temporal Land cover change analysis and creation of land cover
scenarios ................................................................................................................... 35
4.2.1 Data acquisition, image classification and change detection analysis ..... 36
4.3 Application of the FIESTA model .................................................................... 41
4.3.1 Land cover change scenarios ................................................................... 42
4.3.2 Land cover projections from the year 2000 to the year 2050 .................. 43
4.3.3 Validation of flow data results from FIESTA .......................................... 45
4.4 Development of a paramo storage component for a runoff – subsurface flow
model 49
4.4.1 Development of the paramo storage component ..................................... 52
4.4.1.1 No saturated paramo ................................................................................ 53
4.4.1.2 Saturated paramo ..................................................................................... 54
4.4.1.3 Drying process ........................................................................................... 55
4.4.1.4 Component parameterization ................................................................... 55
The model was parameterized with land cover change data for the Guavio
catchment and according to the following values. ................................................... 55
4.4.1.5 Model verification, sensitivity analysis and validation ............................ 55
4.5 Development of a dam component for the understanding of water resources to
dams ......................................................................................................................... 59
4.5.1 Parameters to Dam filling modelling ....................................................... 61
4.5.2 Reservoir topography ............................................................................... 62
4.5.3 Reservoir water balance ........................................................................... 64
4.5.4 Potential hydropower production ............................................................. 66
4.5.5 Potential drinking water production ......................................................... 67
4.5.6 Model verification, sensitivity analysis and validation ............................ 67
Model verification was carried out against a common sense of the processes
modelled (increase in water stored and water heads mainly). ................................. 67
Chapter 5: Results ..................................................................... 70
5.1. FIESTA model results .................................................................................. 70
5.1.1 Fog inputs baseline2000 scenario ............................................................ 70
5.1.1.1 Fog inputs as a proportion of rainfall and water balance ......................... 72
5.1.1.2 Runoff from fog as a component of river discharge ................................ 74
5.1.1.3 Fog contributions and National parks ...................................................... 74
5.1.1.4 Monthly contributions from fog to dams in the region ............................ 75
5.1.2 General impacts of land cover change. Comparison between
baseline1977 and baseline2000 scenario ................................................................. 76
5.1.3 Conservation impacts: PES scenario to 2050 .......................................... 78
5.1.4 Conservation impacts: NOPES scenario .................................................. 80
5.1.5 Conservation impacts: Naturalcoversremoved (NONC) ......................... 80
5.2. Results from the Runoff – subsurface model with a paramo component .... 82
5.2.1 Guavio dam .............................................................................................. 83
5.2.2 Golillas dam ............................................................................................. 84
5.4 Results from the application of a dam filling model .................................... 86
5.4.1 General results for the Bogotá region using FIESTA river flows ........... 86
5.4.2 General results for the Guavio Catchment ............................................... 91
Chapter 6: Conclusions and recommendations ......................... 97
References .............................................................................. 100
Appendix 1: Datasets for FIESTA model parameterization ... 106
• Cloud cover .................................................................................................... 107
• Potential Solar Radiation ............................................................................... 109
• Temperature and daily temperature range ..................................................... 110
• Precipitation ................................................................................................... 111
• Humidity ........................................................................................................ 113
• Mean sea level pressure ................................................................................. 114
• Wind speed..................................................................................................... 114
• Topographical exposure to wind .................................................................... 115
Appendix 2: River flow stations in the Bogotá region ........... 116
Appendix 3: Tables of characteristics of soils for the study area.
From. IGAC (2000) Estudio general de suelos y zonificación de
tierras del Departamento de Cundinamarca. Bogotá: IGAC... 121
List of figures
Figure 1. Epiphyte FI, drip and storage dynamics. Source: Mulligan et al (2006a). ............... 31
Figure 2. Diagram of the components of a conventional hydropower plant with
reservoir. .................................................................................................................................. 32
Figure 3. Geographical location of the Bogotá region, boundary area, main dams in
the region and National National Parks of Chingaza, Sumapaz and Iguaque. Source:
Google Earth (2007) ................................................................................................................ 35
Figure 4. Mosaics of Landsat images covering the Bogotá region. a. baseline2000
scenario. b. baseline1977 scenario. Numbers are a reference to image description on
the table. ................................................................................................................................... 36
Figure 5. Steps to estimate cloud forest and paramo resources in the Bogotá region
and create the baseline1977 and basline2000 scenarios. ......................................................... 38
Figure 6. Multi-temporal land cover classification for the Bogotá region. a.
baseline1977. b. baseline2000 scenario. .................................................................................. 39
Figure 7. a. Area (ha) of cloud forest and paramo loss between the baseline1977 and
baseline2000 scenarios in the Bogotá region. b and c. Extent (ha) and percentage (%)
of area converted from Cloud forest and paramo to crops-grassland, and bare soil. ............... 40
Figure 8. Land cover change scenarios indicating percentages of tree cover (scenario
1977 and scenario 2000) for the Bogotá region. ...................................................................... 43
Figure 9. Roads and rivers datasets used to project scenarios of land use change in the
Bogotá region. Source: Gobernacion de Cundinamarca (2006b) and Google Earth
2007.......................................................................................................................................... 44
Figure 10. PES and NOPES scenarios, differences regarding the baseline2000
scenario and detail of natural cover loss in the Chingaza National Park. ................................ 45
Figure 11. Map with the location of flow stations for validation purposes in the
Bogotá region. .......................................................................................................................... 46
Figure 12. Graphs for FIESTA performance with the use of WorldClim and TRMM
datasets. .................................................................................................................................... 47
Figure 13. Distribution of areas according to the performance of FIESTA model with
the use of WorldClim and TRMM datasets. ............................................................................ 49
Figure 14. Location of the Guavio catchment in the Bogota region and association
with Chingaza National Park. .................................................................................................. 50
Figure 15. Datasets used for application of a runoff model in the Guavio Catchment.
a. soils map (soils study of Cundinamarca) and sample points of soil depth, porosity
and texture. b. Porosity map derived by the model. c. Soil depth derived. d. Wetness
index of similarity. ................................................................................................................... 51
Figure 16. Components of the hydrological cycle in a paramo catchment. Storage and
release water curve for mosses (Mulligan et al 2006a). Drawing by author. ......................... 52
Figure 17. Verification of paramo component. a. Modelled discharges (m3 s-1) with
and without a Paramo component. b. difference between runoff estimates (mm ha-1).
c. Rainfall dataset used in the simulation (mm). Data reported to the Chusneque flow
station, Guavio catchment. ....................................................................................................... 57
Figure 18. Change in runoff (m3 s-1) with the change in paramo extent (Chusneque
flow station, Guavio catchment). ............................................................................................. 58
Figure 19. Correlation between observed and modelled river discharges (m3 s-1) in the
Guavio catchment. ................................................................................................................... 58
Figure 20. Main dams in the Bogotá region. .......................................................................... 61
Figure 21. Procedure and datasets used for reservoir topography characterization.
Case: Guavio dam. a. DEM, reservoir boundary and buffer area. b. Slope of the
terrain surrounding the dam. c. aspect. d. Reservoir topography below the water
surface. ..................................................................................................................................... 63
Figure 22. Characteristic of a model reservoir assumed for the dams model. ......................... 64
Figure 23. a. Modelled reservoir capacity (Mm3) and water head (m) for the Guavio
dam. b. Monthly progression of potential hydroelectric power simulated by the model
(MW)........................................................................................................................................ 68
Figure 24. Fog inputs (mm) in the Bogotá region, baseline scenario2000. ............................. 71
Figure 25. a. Fog inputs by altitudinal bands (mm) and b. altitudinal bands in the
Bogotá region (masl)................................................................................................................ 72
Figure 26. a. Fog inputs as proportion annual rainfall (mm) and b. Fog inputs as
proportion annual water budget (mm). c. Annual water budget (mm). ................................... 73
Figure 27. Runoff from fog as part of river discharge. a. Runoff from fog (m3 s-1). b.
River discharge (m3 s-1). Detail for the Guavio river that feeds the Guavio Dam. ................. 74
Figure 28. Fog contributions and national parks in the Bogotá region. a. % of water
balance. b. Fog contributions (mm). ....................................................................................... 75
Figure 29. Monthly progression of fog as a proportion of runoff in the Bogotá region. ......... 76
Figure 30. Differences in a. Fog interception (mm), b. Evapo-transpiration (mm) and
c. Water balance (mm) between the baseline1977 and baseline2000 scenarios. ..................... 78
Figure 31. a. Detail of increase in runoff (m3 s-1) for the Guavio dam, baseline2000
scenario. b. Reduction in fog contribution to runoff (m3 s-1). .................................................. 78
Figure 32. Differences in a. fog interception, b. evapo-transpiration and c. water
balance between the PES and baseline2000 scenarios. ........................................................... 79
Figure 33. a. Detail of runoff (m3 s-1) for the Guavio dam baseline2000 scenario. b.
Reduction in fog contribution to runoff (m3 s-1). ..................................................................... 80
Figure 34. Differences in a. fog interception (mm), b. evapo-transpiration (mm) and
c. water balance (mm) between the NOPES and baseline2000 scenarios. .............................. 81
Figure 35. a. NONC scenario . Detail of runoff (m3 s-1) for the Guavio dam. b.
Reduction in fog contribution to runoff (m3 s-1). ..................................................................... 82
Figure 36. Guavio catchment. Land covers, main dams and the location of flow
stations are represented. ........................................................................................................... 82
Figure 37. a. Annual change in river discharge (m3 s-1) to the Guavio dam for Baseline
and PES Scenarios. b. Seasonal changes in river discharge (m3 s-1). c. Seasonal
differences (m3 s-1). .................................................................................................................. 84
Figure 38. a. Annual change in river discharge (m3 s-1) to the Golillas dam for
Baseline and PES Scenarios. b. Seasonal changes in river discharge (m3 s-1). c.
Seasonal differences (m3 s-1). ................................................................................................... 85
Figure 39. a. Change in the regional water stored in dams in the Bogotá region. b.
Seasonal contribution from every dam in the region. .............................................................. 87
Figure 40. Change in the economic value of drinking water resources (Million USD).
a. Regional. B. Contribution per dam. .................................................................................... 89
Figure 41. Change in the potential hydroelectric energy produced in the Bogotá
region (GWh year-1). a. Economic change (MUSD). B. Contribution per dam (GWh
year-1). ...................................................................................................................................... 90
Figure 42. Change in the economic value of hydroelectric energy production
(MUSD). a. Loses from paramo loss. b. Gains from deforestation. ........................................ 92
Figure 43. Change in the economic value of drinking water provision (MUSD). a.
Loses from paramo loss. b. Gains from deforestation. ............................................................ 94
Figure 44. Digital Elevation Model for the Bogotá region. ................................................... 106
Figure 45. Cloud cover frequency for the seasons (DJF, MAM, JJA, SON) in the
Bogotá region. ........................................................................................................................ 107
Figure 46. Cloud cover frequency for the daily cycle (Early morning, morning,
afternoon, evening) in the Bogotá region. ............................................................................. 108
Figure 47. Progression of monthly solar radiation (Wm2) for the Bogotá region. ............... 109
Figure 48. Progression of Monthly temperature (°C) for the Bogotá region. ........................ 110
Figure 49. Monthly progression of precipitation (mm) for the Bogota region according
to WorldClim. ........................................................................................................................ 111
Figure 50. TRMM precipitation dataset (mm) for the Bogotá region. .................................. 112
Figure 51. Monthly progression of relative humidity (%) in the Bogotá region. ................. 113
Figure 52. Progression of monthly wind speed (m s-1) for the Bogotá region....................... 114
Figure 53. Topographical exposure to winds in the Bogotá region for E, N, W, S, NE,
SE, SW, NW cardinal directions............................................................................................ 115
Figure 54. Observed and modelled river discharges below 5m3 s-1 with the use of
WorldClim and TRMM datasets. ........................................................................................... 120
List of Tables
Table 1. Landsat imagery used for a land cover change analysis of the Bogotá region.
Source ESDI (2007). ................................................................................................................ 37
Table 2. Validation results for the FIESTA model with the use of WorldClim and
TRMM rainfall datasets. The comparison of model performance for discharges
greater or lesser than 5m3 s-1 is also reported. ......................................................................... 48
Table 3. Observed and modelled discharges in the Guavio Catchment. ................................. 58
Table 4. Characteristics of the main dams in the Bogotá region. Sources. Database of
tropical dams (Mulligan et al 2006b) ....................................................................................... 60
Table 5. Change in water storage (Mm3) for the Bogotá region according to different
scenarios of land use change (Negative values indicate loses in water volume). .................... 86
Table 6. Change in drinking water provision (Mm3) and Economic value Million
USD.......................................................................................................................................... 88
Table 7. Change in annual hydroelectric energy production, GWh year-1 and
economic value. ....................................................................................................................... 90
Table 8. Hydroeletric energy production and economic impact of land use change and
conservation in the Guavio catchment (Guavio dam). ............................................................. 93
Table 9. Drinking water production and economic impact of land use change and
conservation in the Guavio catchment (Golillas dam). ............................................................ 95
Chapter 1: Introduction
Cloud forest and paramo ecosystems of the Colombian eastern Andes are the main
source of high quality water for drinking water and hydroelectric power purposes
supplying Bogotá D.C, Colombia’s capital (Mulligan and Burke 2005a; Sáenz and
Mulligan 2007). This water provision fulfils demands of about 30% of Colombia’s
population concentrated in Bogotá’s influence area (about 15 million people) and
generates a similar proportion of the country’s GDP, which makes Bogotá D.C the
eighth most important economy in Latin America (DANE 1999; Ramirez 2002;
DANE 2007).
However, marked population increase and economic growth concentrated in this
region over the last three decades have led to significant deforestation and
transformation of cloud forest and paramo with associated detriment to water quantity
and quality (Etter and Wyngaarden 2000; Etter and Villa 2000; Cavelier and Etter
1995; Mulligan and Burke 2005a; Sáenz and Mulligan 2007) (Lombana 2000; Lora
2006). Furthermore, the establishment of Chingaza national park, for instance, proved
to be difficult to police and has not prevented the establishment of illegal farming
(Lora 2007).
Since about 30% of Colombia’s hydroelectric power generation is produced in
Bogotá D.C surrounding areas together with about 512 million m3 s-1 year-1 (16.3 m3
s-1) of high quality water for human, industrial and commercial consumptions (CAF
1998; EMGESA 1999; EEB 2000; MA 2001), a further degradation of hydrological
services becomes a major constraint to sustained economic development of the region.
Hydrological studies of the dependence of downstream communities to water
resources from mountainous areas, especially of montane cloud forest and paramo
cover, are limited in Colombia. Nor the resulting economic impact to dams due to
cloud forest and paramo loss has been studied in Colombia.
At global scales Mulligan and Burke (2005a) presented the most comprehensive
study of this nature for tropical mountainous areas with cloud forest cover. Moreover,
Mulligan and Burke (2005b) reported the development and application of the FIESTA
model in Costa Rica (the most sophisticated hydrological model for montane
ecosystems hydrology) in order to understand the role of fog interception by cloud
forest vegetation to water balances at national scale and help support PES schemes
implementation processes.
In countries such as Costa Rica, payments for environmental services PES
schemes have been established successfully to provide funds for better management
of water key areas by taxation of water users downstream (Castro et al 2000; Johnson
et al 2001; Perrot-Maître and Davis 2001). The establishment of these schemes in
Colombia is hindered by a poor understanding of both the hydrology of the water
catchment areas (cloud forests, paramo and agricultural land) and the resulting
economic impact of degraded water quantity and quality.
This thesis aims to improve our understanding of the hydrological contributions
of cloud forest and paramo ecosystems to water resources of the Bogotá region by
using the FIESTA model (Mulligan and Burke 2005b) and other model components
implemented in the study. In particular, the thesis seeks to examine impacts of cloud
forest and paramo loss to water resources for drinking water and hydroelectric power
generation purposes in Bogotá D.C and assess the suitability of implementing
Payments for Environmental Services (PES) schemes and an enhanced watershed
protection to protect water key areas. These questions are of paramount importance
for the sustained economic development of the region with environmental
conservation values
The thesis is organized in six chapters. Chapter 2 states the general objective of
the study. Chapter 3 gathers meaningful insight for the process modelled in cloud
forest and paramo ecosystems of the Bogotá region. Chapter 4 describes the methods
for the research. Chapter 5 presents the main thesis results and Chapter 6 presents the
main conclusions and recommendations.
Chapter 2: Objective
Estimation of the magnitude of degradation of water resources, mainly water quantity,
to Bogotá region as a results of land cover change in cloud forest and páramo areas
and evaluation of the potential of implementing Payments for Environmental Services
(PES) schemes to provide solutions.
Chapter 3: Literature review and Background
3.1 Cloud forest and modelling fog interception processes
Cloud forests are a hydrologically important type of forest because they do intercept
fog water and tend to occur in some of the wettest mountainous areas in the tropics
where high rainfall inputs and reduced evapo-transpiration allow high water balances
(Grubb 1977; Mulligan and Burke 2005a). Cloud forest can be very important
seasonally upstream of dry lowland areas with fog contributions of up to 30% in some
areas of southern México (Mulligan and Burke 2005b), though these contributions are
only a low percentage of river discharges in the lowlands of wet tropical areas (1 to
2%) (Bruijnzeel et al 2006).
3.2 Paramo
Paramo is an equatorial tree-less high wetland ecosystem endemic of the northern
Andes. Paramo vegetation is taller and more complex than low land grasslands and is
comprised by bryophytes (mainly mosses), endemic stem rosettes (frailejones) genus
Espeletia of up to 10m height, riparian shrub patches and short growing grasses. This
vegetation structure together with accumulation of high aboveground biomass and
low decomposition favours high water retention (Hofstede et al 1995; Buytaert et al
2005; Ospina 2003).
Volcanic soils dominate in paramo regions. These soils show low depth, low
apparent density, high porosity, high hydraulic conductivity and high water retention
at field capacity. Their water retention is also high below wilting point (-1500 kPa)
(about 0.4 ml cm-3) indicating that the paramo soils potentially hold a significant
proportion of inactive water that is not released to the surface part of the hydrological
cycle (Buytaert 2004; Buytaert et al 2005).
3.2.1 Structural characteristics of paramo to water storage
Very few studies have looked at the structure of paramo vegetation but none have
looked at the hydrology of paramo vegetation as a system. However, some studies
have brought meaningful insight to understand paramo vegetation water storage
dynamics.
Hofstede (1995), studying the impacts of grassing and burning to biomass loss
in four paramo areas of Colombia, reports an average of above ground biomass in
undisturbed paramo of about 2800 g m-2.
Mulligan et al (2006a) informs the water interception and storage dynamics of
moss and their implications for fog interaction processes in cloud forest catchments.
Under laboratory conditions the study informs a saturated storage capacity of moss of
about 5.91 times the dry weight and a rate of water release of 0.013 ml g dry biomass-
1 hr-1 when mosses reach 75% of saturation (Figure 1).
0
0.5
1
1.5
2
2.5
3
3.5
9 19 29 9 19 29 39 49 59 69
Hours
Epip
hyte
wat
er s
tora
ge m
l/g d
ry b
iom
ass
Epiphyte water storageExperiment 3Experiment 4
rapid uptakeno fog-drip
no evaporation
slowerwater uptake
fog drip occurringno evaporation
rapid water lossrapid fog drip and evaporation
slower water lossevaporation only
wetting experimentdrying experiment
schematic
Figure 1. Epiphyte FI, drip and storage dynamics. Source: Mulligan et al (2006a).
No attempts have been reported to water storage modelling in paramo areas.
Moreover the water regulation capacity of paramo has been attributed to soil, climate
and topographical conditions of paramo areas themselves given negligible impact to
paramo vegetation (Lombana 2000; Buytaert et al 2005; Buytaert 2004).
3.3 Hydroelectric potential and Dam filling modelling
Hydroelectric power generation depends upon water flows inputs and the potential
energy of water (Kerola 2006). The conventional hydroelectric system is comprised
by a main river catchment area, a reservoir and a power plant (Novak et al 2001).
Figure 2 shows a diagram of the components of a conventional hydroelectric
power system with reservoir.
Figure 2. Diagram of the components of a conventional hydropower plant with reservoir.
The potential hydroelectric power in the system is proportional to water
discharge, water head (difference in water height between the water surface in the
reservoir and the turbines) and the efficiencies of the turbines and generators (Novak
et al 2001; Kerola 2006).
Depending upon topographical characteristics and dam purposes there is usually
an inactive water capacity in the reservoir called the death volume as well as a
reservoir capacity destined for flood control only. The reservoir capacity actually used
Source: ESA (2007)
for hydropower generation is called operational capacity (EEB 2000; Novak et al
2001; INGETEC 2004).
Chapter 4: Methodology
4.1 Description of the study area
The definition of the extension of the study area was based upon the concept of
Bogotá region (Alcaldía de Bogotá 2004). This is a project, which considers Bogotá
D.C interconnected geographically and economically to the whole high Bogotá
Sabana (between 2300 and 2900m) and associated municipalities towards the
Magdalena valley and towards the eastern planes.
In addition, this study assumes a hydrological approach for the delineation of
the area, which was based firstly, upon definitions of catchment areas that feed the
most important dams providing hydropower energy or drinking water to the Bogotá
city and secondly by establishing a minimum hydrological altitude limit (about 700m)
in order to consider all the extent of cloud forest resources in the region.
The so defined Bogotá region covers an area of 4000160ha (40001.6km2) and is
located between geographical coordinates 3°14’ and 5°52’ N Latitude and 72°38’ 75°
W Longitude and extents in the Cundinamarca, Boyaca, Tolima, Meta and Huila
counties. Three National Natural Parks, Chingaza, Sumapaz and Iguaque, are located
in the region, which are threatened by population and economic growth.
Figure 3 presents the localization of the study area in central Colombia
indicating the main dams and National protected areas (PNNC 2007).
Figure 3. Geographical location of the Bogotá region, boundary area, main dams in the region
and National National Parks of Chingaza, Sumapaz and Iguaque. Source: Google Earth (2007)
4.2 Multi-temporal Land cover change analysis and creation of land cover
scenarios
This study attempts to improve the estimate of Cloud forest and paramo resources in
the Bogotá region to the year 2000 and estimate the potential rate of cloud forest and
paramo loss with regard to an initial scenario for the year 1977. Finally initial and
final land cover scenarios were produced for hydrological modelling purposes.
The multi-temporal land cover change analysis was performed with the use of
Landsat MSS, TM and ETM+ datasets acquired from the Global Land Cover Facility
(ESDI 2007) for a combination of years from between 1977 and 2000.
4.2.1 Data acquisition, image classification and change detection analysis
Five different Landsat scenes were needed for the production of each scenario due to
the large extent of the study area. Furthermore, high cloud cover contamination
limited the selection of images to produce scenarios that represented single years for
initial and final periods (though more than 50% of the area was covered with imagery
for the years 1977 and 2000). For convention purposes from here and after the land
cover scenarios will be referred as baseline1977 and baseline2000 in accordance with
the greater proportion of area covered with images that corresponded to these years.
Figure 4 and Table 1 present the imagery used for the production of the
baseline1977 and baseline2000 scenarios, which include a MSS image for the year
1977.
Figure 4. Mosaics of Landsat images covering the Bogotá region. a. baseline2000 scenario. b.
baseline1977 scenario. Numbers are a reference to image description on the table.
Km0 75 0 75
Km
NN
1 2
3 4
5
1 2
3 4
5a b
Table 1. Landsat imagery used for a land cover change analysis of the Bogotá region. Source ESDI (2007). Scenario Scene Date Satellite Type of
imagery Spatial resolution (m)
Processing Level
baseline 1977
1 1987-12-17
Landsat 5
TM 28.5 Ortho-GeoCover
baseline 1977
2 1992-09-02
Landsat 5
TM 28.5 Ortho-GeoCover
baseline 1977
3 1977-01-07
Landsat 3
MSS 79 L1G
baseline 1977
4 1986-01-13
Landsat 5
TM 28.5 L1G
baseline 1977
5 1988-01-12
Landsat 5
TM 28.5 Ortho-GeoCover
baseline 2000
1 2000-02-04
Landsat 7
ETM+ 28.5 Ortho-GeoCover
baseline 2000
2 2000-12-13
Landsat 7
ETM+ 28.5 Ortho-GeoCover
baseline 2000
3 1995-02-14
Landsat 5
TM 28.5 Ortho-GeoCover
baseline 2000
4 2000-12-13
Landsat
7
ETM+ 28.5 Ortho-
GeoCover
baseline 2000
5 2001-01-05
Landsat 7
ETM+ 28.5 Ortho-
GeoCover
Image classification considered seven classes: forest, paramo, crop-grasslands,
bare soil, clouds, shadows and water and was performed with an unsupervised
classification approach due to absence of ground truth data for training signatures
(Anderson et al 1976; Tole 2002; Yang and Lo 2002; Hung and Wu 2005; Envi 2003;
SIC 2007).
Figure 5 shows a diagram, which summarises the steps undertaken to produce
the land cover scenarios.
Figure 5. Steps to estimate cloud forest and paramo resources in the Bogotá region and create the
baseline1977 and basline2000 scenarios.
Landsat MSS, TM, ETM+ datasets. Ten images MSS (1977), TM
(1986, 1987, 1988, 1992, 1995) and ETM+ (2000, 2001)
Image Registration and Projection
Image subset to Bogotá region boundary
Enhancement and creation of false colour composite images to
highlight vigorous vegetation Principal
Components Analysis for the
MSS bands Creation of NDVI maps
Unsupervised classification. Use of ISODATA with 40
classes to reduce misclassification of paramo
Cluster Labelling
Re-classification, reduction of anomalous pixels, masking of clouds and water and reclassification of shadow areas
according to visual inspection of signatures
Use of other various datasets to assign signature clusters to
classes, such as Land cover map Cundinarmarca
(Gobernacion de Cundinamarca 2006a),
Pancromatic ETM+ images.
Change detection
Integration of Landsat scenarios
with MODIS VCF 2001.
Production of final scenarios improved from
cloud contamination
Results Forest and paramo
extent and loss
Attempt of accuracy assessment
STRM 90m Mapping of cloud
forest and paramos
The multi-temporal land cover classification for basliene1977 and baseline2000
scenarios of the Bogotá region is presented in Figure 6.
Results from the change detection analysis are also reported indicating the
extent and rate of cloud forest and paramo loss.
Figure 6. Multi-temporal land cover classification for the Bogotá region. a. baseline1977. b.
baseline2000 scenario.
Figure 6 and Figure 7 show a significant decrease in cloud forest resources from
about 1761282ha (44% of the study area) to about 1216177ha (Figure 7a) in the
region, which indicates a 30% cloud forest decrease at a potential deforestation rate of
about 23000ha year-1 (1.3% of forest resources) between baseline1977 and
baseline2000 scenarios.
Similarly the paramo cover dropped from about 371088ha (9% of Bogotá
region) to 328364ha (Figure 7a), indicating a 12% decrease at a paramo loss rate of
about 1860ha year-1 (0.5% of paramo resources). Though, less significant than the
reduction in cloud forest, these figures show the pressures over this important
ecosystem.
N
ParamoForest Crops Bare soil
Land Cover Classes ParamoForest Crops Bare soil
Land Cover Classes Baseline1977
Baseline2000
Cloud contaminated areas amounted to about 124000ha (3.1% of the study area)
and water bodies represented about 0.5% of the Bogotá region (about 18000ha).
Figure 7. a. Area (ha) of cloud forest and paramo loss between the baseline1977 and
baseline2000 scenarios in the Bogotá region. b and c. Extent (ha) and percentage (%) of area
converted from Cloud forest and paramo to crops-grassland, and bare soil.
1761282
1216177
371088 328364
0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1800000
2000000
baseline1977 baseline2000
Land cover change scenarios
Are
a (h
a)
Cloud Forest extent Paramo Extent
-30%
-11%
a
0 6049
529578
422791204 0
372544265
0
100000
200000
300000
400000
500000
600000
Cloud forest Paramo Crops-grassland Bare soil
Land covers converted from forest and paramo
Are
a (h
a)
Cloud forest to other land covers Paramo to other land covers
b
0 1
92
73 0
87
10
0
10
20
30
40
50
60
70
80
90
100
Cloud forest Paramo Crops-grassland Bare soil
Land covers converted from forest and paramo
Are
a (h
a)
Cloud forest to other land covers Paramo to other land covers
c
For the hydrological analysis the scenarios presented above were improved with
the MODIS VCF dataset year 2001 in high cloud contaminated areas. To use MODIS
the assumption considered was that since the cloud cover area was below 5% of the
regional area, the correction applied did not affect the integrity of the land covers for
the hydrology analysis but instead ensured to restore forested areas that were likely
forested in the baseline1977 scenario.
A threshold of 10% for the existence fractional forest cover was assumed
(Mulligan and Burke 2005a; Sáenz and Mulligan 2007). Areas of fractional values
below 10% above 3200m were reclassified as paramo. Below 3200m these areas were
assigned a crops-grass classification. Initial and final scenarios produced are
presented in the section 4.3.1.
4.3 Application of the FIESTA model
The FIESTA delivery model is the most sophisticated process-based spatially
distributed hydrological model to provide national scale estimates of fog interception
and understand its likely impact on terrestrial water resources. FIESTA model was
parameterized for the Bogotá region considering the following input datasets, which
were spatially adjusted to 92m spatial resolution in this study by using a nearest
neighbour method.
• 90m Digital Elevation Model (DEM) of the Bogotá region (Appendix 1).
• Cloud cover (Appendix 1).
• Potential Solar Radiation (Wm2) (Appendix 1).
• Temperature (°C) and daily temperature (°C) (Appendix 1).
• Precipitation (Appendix 1).
• Humidity (%)(Appendix 1).
• Mean sea level pressure (mb) (Appendix 1).
• wind speed (m s-1) (Appendix 1).
• Topographical exposure to wind. cardinal directions (E, N, W, S, NE, SE, SW,
NW).
Other datasets for FIESTA implementation developed in the project are described
subsequently.
4.3.1 Land cover change scenarios
Land cover data is essential to simulate fog interception by the vegetation. The
land cover scenarios described in the section 4.2 were used as inputs for the FIESTA
model.
The scenarios were adjusted to FIESTA requirements as follows. Firstly, three
land cover classes were considered: Cloud forest, paramo and crops-grasslands.
Secondly, cloud forested pixels were assigned a value of 100% of tree cover in
basline1977 and baseline2000 scenarios, since FIESTA takes into account the forest
proportion of pixels with a percentage of forest above 10%. Thirdly, below this
threshold pixels were considered tree-less (crops-grasslands) and were assigned a
value of 0 (0 cloud forest).
Since paramo ecosystems are more complex vegetation cover than grasslands or
crops (Hofstede et al 1995; Ospina 2003; Buytaert et al 2005) a different
classification was assigned. Paramo is taller vegetation than grasslands (Hofstede et al
1995) and accounts for relatively high types of vegetation such as endemic stem
rosettes (frailejon) genus Espeletia of up to 10m height and riparian shrub patches.
Neither field studies report the proportion of these covers in the paramo nor the
remote sensing analysis allowed the classification of specific types of vegetation due
to spatial resolution constraints (28.5m). Therefore for the purpose of the hydrological
analysis this study assumes that paramo presents vegetation types, which behave as
fog interceptors and that the proportion of these species is about 20% of the paramo
pixels (personal knowledge of the area).
Figure 8 shows the baseline1977 and baseline2000 scenarios produced.
Figure 8. Land cover change scenarios indicating percentages of tree cover (scenario 1977 and
scenario 2000) for the Bogotá region.
4.3.2 Land cover projections from the year 2000 to the year 2050
Land use change projections were used to study the impacts of conservation through
PES schemes implementation to the year 2050. Three projection scenarios were
considered: PES, NOPES and Naturalcoversremoved (NONC).
The scenarios were created with a PCRASTER script, which implemented the
windoaverage instruction in order to project a pattern of deforestation by considering
roads and rivers pixels as seeds of a deforestation pattern as well as tree-less and
paramo-less pixels from the baseline2000. The script was parameterized and validated
against the rates of deforestation and paramo loss highlighted in section 4.2.1.
Figure 9 presents the roads and river datasets used in the projections of land
cover scenarios to the year 2050.
baseline 1977 baseline 2000
N
Figure 9. Roads and rivers datasets used to project scenarios of land use change in the Bogotá
region. Source: Gobernacion de Cundinamarca (2006b) and Google Earth 2007.
• PES scenario
The PES scenario allows deforestation and paramo loss in areas outside protected
areas but not inside protected areas where PES conservation activities take place
(Figure 10a).
• NOPES scenario
Deforestation and paramo loss take place inside protected areas since no PES
conservation measures are enforced to the year 2050 (Figure 10b).
• Naturalforestremoved (NONC).
Extreme scenario in which all forest and paramo areas are lost in the Bogota region.
N
Figure 10 shows the projection scenarios produced and the differences
regarding the baseline2000 scenario in percentage. For comparison purposes of
deforestation and paramo loss effects cloud forest and paramo were assigned the same
value of 100% here.
Figure 10. PES and NOPES scenarios, differences regarding the baseline2000 scenario and detail
of natural cover loss in the Chingaza National Park.
4.3.3 Validation of flow data results from FIESTA
FIESTA annual runoff results produced with the use of WorldClim and TRMM
datasets were validated against data of river discharge for 129 flow stations in the
Bogotá region from Corporación Autónoma Regional de Cundinamarca - CAR and
the Instituto de Hydrologia y Meteorologia IDEAM (CAR 2005; IDEAM 2007). Lack
of accurate coordinates indicating the location of some of the stations in the rivers
introduced uncertainty to the validation since the stations had to be relocated to
represent the actual river flows. Figure 11 and Appendix 2 present the location and
description of the flow stations used for validation.
79,000 0 79,00039,500 Kilometers
N
11,000 0 11,0005,500 Kilometers
Figure 11. Map with the location of flow stations for validation purposes in the Bogotá region.
The performance of the model was studied with measures of relative and
absolute error such as the Coefficient of efficiency E1, the modified Index of
Agreement d1 and the Mean Absolute Error MAE since they provide a more efficient
evaluation of the goodness of fit of hydrological models (Legates and McCabe 1999;
Mulligan and Burke 2005b; Sáenz 2007a).
The validation showed a reasonable good fit of FIESTA results, with
particularly better agreement when using the WorldClim dataset (d1 = 0.8 and E1 =
0.64 ) (Figure 10 and Table 2). Though, the model overestimates the average regional
flow with both datasets (worlClim = -2.32, TRMM = -1.33) (Figure 12 and Table 2).
Figure 12. Graphs for FIESTA performance with the use of WorldClim and TRMM datasets.
When looking at the discharges below 5m3 s-1 the model performance dropped
slightly though still indicating a high agreement regarding ground truth data (d1 = 0.75
and E1 = 0.51). Still higher agreement for WorldClim is noted (Table 2 and Appendix
2).
0
20
40
60
80
100
120
140
160
0 20 40 60 80 100 120
River discharge, CAR - IDEAM stations (m3 s-1)
FIES
TA -
Wor
ldC
lim ru
noff
(m3 s
-1)
0
20
40
60
80
100
120
0 20 40 60 80 100 120
River discharge, CAR - IDEAM stations (m3 s-1)
FIES
TA -
TRM
M ru
noff
(m3 s
-1)
d1 = 0.8 E1 = 0.64 R2 = 0.87 MAE = 0.36 Bias = -2.32
d1 = 0.71 E1 = 0.43 R2 = 0.76 MAE = 0.57 Bias = -1.33
Table 2. Validation results for the FIESTA model with the use of WorldClim and TRMM rainfall
datasets. The comparison of model performance for discharges greater or lesser than 5m3 s-1 is
also reported.
Model validation results
Accumulated Water Balance from TRMM
Accumulated Water Balance from WorldClim
Statistics
Q Total (m3 s-1)
Q>5 (m3 s-1)
Q<=5 (m3 s-1)
Q Total (m3 s-1)
Q>5 (m3 s-1)
Q<=5 (m3 s-1)
Observed mean (m3 s-1) 7.21 23.24 1.23 7.21 23.24 1.23 Observed sd (m3 s-1) 17.50 28.16 1.18 17.50 28.16 1.18 Modelled mean (m3 s-1) 8.54 29.60 1.82 9.53 30.48 2.22 Modelled sd (m3 s-1) 17.67 27.38 1.95 19.20 30.02 2.20
Bias (m3 s-1) -1.33 -6.36 -0.60 -2.32 -7.25 -0.99 Mean Absolute Error (MAE) (m3 s-1) 0.57 0.47 0.01 0.36 8.32 0.52 Root Mean Squared Error (m3 s-1) 2.71 3.24 0.10 1.54 11.71 0.75
R2 0.76 0.65 0.57 0.87 0.82 0.59 Modified Index of Agreement (d1) 0.71 0.76 0.69 0.80 0.82 0.75 Modified coefficient of efficiency (E1) 0.43 0.52 0.42 0.64 0.64 0.51
In general the model overestimates large flows to the low western fringes of the
region towards the Magdalena basin (altitudinal gradients between 700 to 2300m) for
WorldClim and TRMM respectively (Figure 13). In contrast, similar discharges in
magnitude at similar elevations are underestimated to the south east of the region
draining from Sumapaz and Chingaza paramos towards the Amazon and Orinoquia
basins.
Low density of rain stations in these areas providing data to the WorldClim as
well as low rainfall inputs reported by TRMM at elevations above about 3000m could
explain the underestimations (Figure 13). Finally, there is a slight better agreement
throughout the high Bogotá sabana for both datasets.
Figure 13. Distribution of areas according to the performance of FIESTA model with the use of
WorldClim and TRMM datasets.
Providing the better agreement achieved with the use of WorldClim subsequent
results are described from the use of this dataset only.
4.4 Development of a paramo storage component for a runoff – subsurface flow
model
In order to produce a closer estimate of water resources in sensitive areas of the
Bogotá region, the results from FIESTA, particularly monthly fog runoff (mm) and
actual evaporation (mm) were coupled with the runoff – subsurface flow model
reported by Sáenz (2007b).
The model was parameterized with soils data of the Guavio catchment located
to the north east of Bogotá city, which covers areas of Chingaza National Park and
accounts for Golillas and Guavio dams the most important for Drinking water
provision and hydroelectric power generation in the Bogotá region (Figure 14).
Orinoquia
Magdalena
Amazon
Bogotá Sabana
Figure 14. Location of the Guavio catchment in the Bogota region and association with Chingaza
National Park.
GuavioGolillas
Guavio catchment
Chingaza Park
Bogotá City
Data of soil depth, texture (sand, clay and silt) and porosity of 315 locations in
the catchment were digitized from the Cundinarmarca soils study scale: 1:100000
(IGAC 2000) and were used together with a SRTM 90m DEM for model
parameterization. A 3h rainfall timeseries dataset covering the whole catchment was
incorporated from the TRMM (Mulligan 2007b).
Figure 15 and Appendix 3 present the main datasets used for parameterization
of the runoff model for the Guavio catchment.
Figure 15. Datasets used for application of a runoff model in the Guavio Catchment. a. soils map
(soils study of Cundinamarca) and sample points of soil depth, porosity and texture. b. Porosity
map derived by the model. c. Soil depth derived. d. Wetness index of similarity.
0 4,250 8,500 12,750 17,0002,125Kilometers
N
0 4,250 8,500 12,750 17,0002,125Kilometers
N
a b
c d
4.4.1 Development of the paramo storage component
Paramo vegetation types such as mosses and the accumulation of high aboveground
biomass favours high water retention with very low release rates (Hofstede et al 1995;
Ospina 2003; Buytaert et al 2005; Mulligan et al 2006a).
This study attempts to develop a component for the runoff – subsurface flow
model to help understand the effects of paramo vegetation, mainly mosses, on the
water resources to the Guavio catchment.
The model assumes that paramo areas behave as initial water storage of
incoming rain, and runoff water from upstream cells, before the water infiltrates to the
soil (Figure 16).
Figure 16. Components of the hydrological cycle in a paramo catchment. Storage and release
water curve for mosses (Mulligan et al 2006a). Drawing by author.
0
0.5
1
1.5
2
2.5
3
3.5
9 19 29 9 19 29 39 49 59 69
Hours
Epip
hyte
wat
er s
tora
ge m
l/g d
ry b
iom
ass
Epiphyte water storageExperiment 3Experiment 4
rapid uptakeno fog-drip
no evaporation
slowerwater uptake
fog drip occurringno evaporation
rapid water lossrapid fog drip and evaporation
slower water lossevaporation only
wetting experimentdrying experiment
schematic
4.4.1.1 No saturated paramo
Water from rainfall, R [mm] and upstream runoff, Q [mm] is stored at a constant rate
until paramo reaches its maximum water storage capacity (saturation). The maximum
water storage capacity of paramo for a given cell can be calculated as follows:
CAPCAdW sp
mp
**=θ
(1)
where θmp is the maximum water storage capacity of paramo [mm], dWp is the
dry weight of paramo biomass [kg m-2], CA is the cell area (m2) and Ps is the
maximum amount of water storage [kg] per amount of dry mass [kg].
No water release from paramo to the soil takes place until paramo reaches 75%
of θmp. Once this threshold is reached water starts to be realised in a vertical
dimension to the soil at a constant rate of paramo release Pr (Figure 1 and Figure 16).
At each timestep under no saturated conditions and below 75% of saturation the
paramo storage can be calculated as:
QRsis ++= θθ (2)
where θs [mm] is paramo storage and θsi [mm] is the initial paramo storage per
timestep.
Above 75% of maximum storage capacity the paramo storage can be calculated
as:
rsis PQR −++= θθ (3)
4.4.1.2 Saturated paramo
Modelling water interchange dynamics in the boundary layer between soil and
paramo under paramo saturated condition is more complex due to evaporation and
significantly different paramo release rates and high hydraulic conductivities (100 to
200 mm h-1), thus the following assumptions were made.
- Evaporation in mosses takes place in the drying process after the vegetation
reaches saturation and the water inputs cease (Mulligan et al 2006a) (Figure
1). Since FIESTA model already calculates evapo-transpiration by the
vegetation and these results are incorporated to the runoff – subsurface model,
evaporation losses are not calculated in the paramo component.
- When paramo reaches saturation the vertical contribution of water to the soil
continues at the same Pr rate. However, since water movement is governed by
energy gradients the paramo component assumes that under saturation
conditions water starts to move horizontally in the paramo layer from
upstream cells to downstream cells at the same Pr rate describing a two
dimensional flow (analogy to horizontal flow in saturated soils reported by
Howe (2000) and Sáenz (2007b).
At each timestep the paramo horizontal water flow Phw that enters a cell from
upstream cells can be calculated as:
( )rhw PlddupstremP ,= (4)
Therefore paramo storage θs at each timestep can be rewritten as:
( )mshwrsis PPQR θθθ ,2min +−++= (5)
- When water inputs are greater than the maximum paramo storage capacity
(θmp) (paramo saturated conditions) the model assumes that the water excess
becomes overland flow.
At each timestep the Water excess We [mm] to soil infiltration can be calculated as:
( ) mshwrsie PPQRW θθ −−−++=
(6)
4.4.1.3 Drying process
Water release from paramo, after water inputs from rainfall, runoff and horizontal
flow have ceased, continues at the constant rate Pr until paramo storage falls below
45% of θmp. After this threshold value paramo losses water according to evapo-
transpiration mechanisms.
4.4.1.4 Component parameterization
The model was parameterized with land cover change data for the Guavio catchment
and according to the following values.
dWp: dry weight of paramo biomass is 2.83 kg m-2 (Hofstede et al 1995) (section
3.2.1). This study assumes that 100% of paramo biomass corresponds to mosses.
CA : is 8574 m2
Ps : 5.9 kg of water per kg of dry mass (section 3.2.1).
Pr : 0.013 ml g dry biomass hr-1 (about 0.2mm 3hr-1)
4.4.1.5 Model verification, sensitivity analysis and validation
Model verification was carried out following a common sense of the phenomena
modelled. Since verification of model processes were verified for the runoff model in
the area (Sáenz 2007b), this study concentrates on verification of the paramo
component only.
The verification was achieved comparing model discharges by coupling the
paramo component with paramo areas from the baseline2000 scenario, with regard to
model results without considering land cover effects. Comparisons were made at the
Chusneque flow station of the Guavio catchment (Figure 36). The differences were
assumed to be the impact of paramo on the estimates.
Figure 17 shows slightly higher river discharges with the use of the paramo
component (about 0.8% on annual average). This output is consequent with the
assumption made indicating that subsurface horizontal flow within the paramo layer
and overland flow from paramo occur once paramo reaches saturation. Since the rate
of paramo water release is very low (0.2mm 3h-1) the availability of water to infiltrate
to the soil is reduced and therefore water excess of the paramo is likely to become
overland flow under paramo saturation.
In addition, paramo runoff is about 0.04mm on average throughout the year
(Figure 17b), which is about the sum of vertical and horizontal paramo fluxes at
saturation. Moreover, paramo runoff to rainfall ratio is still very small (0.03) when
compared to the total runoff to rainfall ratio for the catchment (0.86). Finally, paramo
contribution to runoff can be as high as 2mm at the highest rainfall intensities, which
could take place at saturated conditions when overland flow becomes the dominant
process (Figure 17b).
However, energy gradients and diffusion processes in the boundary layer
between soil and paramo might increase the rate at which paramo releases water to the
soil. In addition, much higher hydraulic conductivities (from 100 to 200 mm h-1) for
the soils of the Guavio catchment than the rate of paramo release could increase the
fluxes between the two media. These aspects represent high uncertainty in the
application of the model.
Figure 17. Verification of paramo component. a. Modelled discharges (m3 s-1) with and without a
Paramo component. b. difference between runoff estimates (mm ha-1). c. Rainfall dataset used in
the simulation (mm). Data reported to the Chusneque flow station, Guavio catchment.
The sensitivity of the model was tested against paramo extent, which is likely to
determine the overall impact of paramo on river discharges in the catchment. The
results confirm this assumption where runoff increases directly with the extent of
paramo (Figure 18).
c
a
b
Figure 18. Change in runoff (m3 s-1) with the change in paramo extent (Chusneque flow station,
Guavio catchment).
Model validation showed good agreement with data of river discharge for the stations
La Vega, Chusneque, Ubala and el Campamento of the Guavio and Golillas dams
(Figure 18 and Table 3).
Figure 19. Correlation between observed and modelled river discharges (m3 s-1) in the Guavio
catchment.
Table 3. Observed and modelled discharges in the Guavio Catchment.
The overall conclusion outlining verification and validation of the paramo
component is that much research is needed to understand water interchange
Station Observed discharge (m3 s-1) Modelled discharge (m3 s-1) Chusneque 27.90 31.80Ubala 54.81 63.40La Vega 71.17 71.00Dedal 9.60 9.30
31.6
31.6
31.7
31.7
31.8
31.8
31.9
31.9
32.0
0 5000 10000 15000 20000
Area of paramo (ha)
Run
off (
m3 s
-1)
0
10
20
30
40
50
60
70
80
0 10 20 30 40 50 60 70 80
Observed discharge (m3 s-1)
Mod
elle
d di
scha
rge
(m3
s-1)
d1 =0 9
mechanisms in the boundary layer between soil and paramo at paramo saturated
conditions, especially considering the low paramo water release intensities. Therefore,
results from the use of this model must be verified with field and laboratory data,
though they are an important contribution to gaining any understanding of the role of
paramo and the impacts of its removal on paramo catchments.
4.5 Development of a dam component for the understanding of water resources
to dams
This study reports the implementation of a physical model in PCRASTER that
simulates the potential annual and seasonal water storage in the main dams of the
Bogotá region and the potential seasonal and annual hydroelectric power generation
and drinking water production.
The model was used to help understand the impacts of land cover change and
PES Schemes conservation to water resources management in the Bogotá region. The
model does not consider operational costs; it takes the market values per kWh of
energy generated and m3 of water produced to estimate a potential change in expected
revenues.
Table 4 and Figure 20 present the characteristics and location of the main dams
in the Bogotá region.
Table 4. Characteristics of the main dams in the Bogotá region. Sources. Database of tropical
dams (Mulligan et al 2006b)
Dam Hhight (masl)
Hlow (masl)
hr (m)
hext (m)
volume (1000m3) area m2 Q m3 s-1
Guavio 1727 1582 145 955 933693 12289891
62 Reporte
d
Golillas 3050 2930 120 100 223000 3441438
7 Reporte
d
Chingaza Lagoon 3219 3092 127 100 26231 780423
5 Reporte
d
Chivor 1345 1146 199 800 700985 7639889
62 Reporte
dPrado 366 170 196 0 530395 25865442 115
Tomine 2581 2549 32 0 705500 22007528
4 Reporte
dSisga 2660 2641 19 0 96500 5174740 3Neusa 2967 2956 11 0 99900 8934441 2
regadera 2969 2957 12 0 4100 333246
1 Reporte
dCopa 2657 2644 13 0 72000 5524391 3Tota 3014 2980 34 0 1920000 56347235 4
Figure 20. Main dams in the Bogotá region.
4.5.1 Parameters to Dam filling modelling
Availability of water inputs (river discharge), maximum storage capacity in the
reservoir and maximum operational discharge control the operational capacities and
operation purposes for dam management (Kerola 2006).
To model the dam reservoir system for operational capacities it is necessary to know.
- Reservoir topography: Reservoir DEM, which gives sensitivity the changes in
water head.
- Water inputs: river discharge, Qr [m3 s-1].
- Maximum storage capacity in the reservoir: Vmax [m3].
- Minimum operational storage capacity in the reservoir: Vmin [m3]. Reservoir
storage which is neither death volume Vdeadth [m3] nor that intended for flood
control processes Vfcontrol [m3].
- Maximum and average operational discharge: Dam discharge Qdam [m3 s-1].
4.5.2 Reservoir topography
Reservoir topographical characteristics determine maximum storage capacity Vmax
[m3], minimum operational storage capacities Vmin_o [m3] as well as potential water
head energies for hydroelectric power generation.
This information is not easily available from open sources and time constraints
did not allow the provision of this information on time for this study from dam
managing bodies. Use of Lidar datasets were not available and time restrictions did
not allow interpretation of aerial photography. These were limitations to the
application of the model.
Nonetheless, this study considered the following assumptions to give an idea of
topographical characteristics of the reservoir.
- Dam topography below water surface responds to the topographical
characteristic of the boundary areas of the reservoir above water surface.
- The maximum reservoir depth below the water surface at the dam place was
assumed to be equal to the dam wall height. The maximum reservoir altitude
was the water surface altitude according to the SRTM 90m DEM.
In order to implement these assumptions a 500m buffer around the reservoir
edges and a SRTM 90m DEM were used to consider the topography of the boundary
areas of the reservoir water surface. The 500m buffer was chosen as an average
distance where the surrounding topographical variables (slope, aspect) did not change
drastically and thus conserving theoretically similar water accumulation properties.
Reservoir altitudes in the inner surfaces (topography below water) were thus
interpolated using a minimum curvature spline (tension) technique.
Figure 21 presents the datasets used for the reproduction of reservoirs
topography in the main dams of the Bogotá region.
Figure 21. Procedure and datasets used for reservoir topography characterization. Case: Guavio
dam. a. DEM, reservoir boundary and buffer area. b. Slope of the terrain surrounding the dam.
c. aspect. d. Reservoir topography below the water surface.
Guavio
dam
Guavio
dam
Guavio
dam
a b
c d
The reservoir topography and thus the calculated volume of the dam were
validated against data of maximum storage capacity in the reservoir Vmax [m3] for
every dam reported by the tropical dams database (Mulligan et al 2006b). In cases
where the reservoir volume was underestimated or overestimated the maximum
altitude for the water surface in the DEM (altitude Hmax (masl)) for the dam was
adjusted from the interpolated surface. Figure 22 presents the characteristics taken
into consideration for topography and dam modelling.
Figure 22. Characteristic of a model reservoir assumed for the dams model.
4.5.3 Reservoir water balance
The input volume to the reservoir each timestep was calculated as:
timestepTimeQV rinput _*= (7)
where Vinput [m3] is volume that enters to the reservoir and Qr [m3 s-1] is river
discharge and Time_timestep [s] is the period of time the timestep represents.
The output volume from the reservoir each time step was calculated as:
timestepTimeQV damoutput _*= (8)
where Voutput [m3] is the volume output from the reservoir and Qdam [m3 s-1] is the dam
discharge.
At each timestep the reservoir storage is calculated as:
( )max1 ,min VVVVV outpuinputii −+= − (9)
where Vi is reservoir storage at the timestep i [m3], and Vmax is maximum reservoir
storage [m3].
When Vi exceeds Vmax this water is supposed to be returned to the river downstream
(spillway) and is not taken into account for further calculations.
4.5.3.1 water balance parameterization
• Maximum storage
The maximum storage capacity Vmax [m3] for every dam was taken from the database
of tropical dams (Table 4).
• Dam discharge and Minimum operational storage capacity
The literature informs a minimum operational storage capacity Vmin [m3] of 20% of
maximum storage for the Guavio dam only (EEB 2000) (Table 4). Also a minimum
aggregated national operational storage capacity of 20% for hydroelectric power
generation under extreme events such as the niño is also pointed out (UPME 2003).
Dam discharges are taken from the literature for six dams in the study area
(Table 4). For the remaining dams (smaller dams) the dam discharge is assumed to be
the annual average runoff in order to model the average water storage and drinking
water production.
The model assumes that dam discharges Qdam [m3 s-1] occur only above the
minimum operational storage capacity Vmin [m3] (20% of the maximum reservoir
capacity Vmax [m3]).
Water losses of the reservoir below the minimum operational storage capacity
are supposed to occur due to evaporation of water surfaces.
Since the model runs with data for one year provided by FIESTA and the
runoff-subsurface model the dams have not water storage at the beginning of the
simulation. In order to represent higher volumes that are likely to occur in an average
season and allow initial discharges from the dams (above minimum capacity) the
model assumes an initial storage Vi [m3] in the reservoirs of 30%.
4.5.4 Potential hydropower production
The potential power produced by the water stored in the reservoir at each timestep can
be calculated as:
gtextrdami hhQgp ηη **)(** += (10)
where Pi [kW] is the potential nominal hydroelectric power of the dam, Qdam [m3 s-1]
is the dam discharge (turbines flow), hr and hext [m] are the water head in the reservoir
and the external water head from the base of the dam to the power plant. ηt and ηg are
the efficiency of the turbine and generator respectively (Fay 1994; Novak et al 2001;
Kerola 2006). g [m s-1] represents the gravitational constant (9.81 m s-1).
At each time step the water head in the reservoir hr is calculated as:
)1()1(
)(
−−
−+=
ir
outputinputirri A
VVhh (10)
where hri [m] is the water head at the timestep i, hri-1 [m] is the water head at the
timestep i-1 Vinput and Voutput [m3] are the input and output volumes of the dam, and
Ar(i+1) is the area of the reservoir at the water surface level.
4.5.4.1 Hydropower parameterization
Dam discharges below the minimum operational reservoir capacity and thus the
hydroelectric power generation is 0. Water heads are never higher than the water head
at the maximum operational storage capacity.
No information was available referring to the characteristics of the turbines and
generators of the power plants in the Bogotá region. Values were assigned from
typical values reported in the literature (Fay 1994; Kerola 2006).
ηt : values between 0.75 and 0.9. 0.9 was assigned.
ηg : values between 0.92 and 0.97. 0.95 was assigned.
The price per kWh to the public was $292 (Colombian pesos) according to SSPD
(2007).
4.5.5 Potential drinking water production
Drinking water production was calculated taken into consideration the dam discharge
Qdam [m3 s-1] for the dams providing drinking water in the Bogotá region.
The price of m3 of drinking water to the public was $1862 (Colombian pesos)
according to EAAB (2007).
4.5.6 Model verification, sensitivity analysis and validation
Model verification was carried out against a common sense of the processes modelled
(increase in water stored and water heads mainly).
Unfortunately no validation of results produced by the model was feasible due
to availability of data constraints for the dams under study.
Verification of modelled seasonal reservoir storage capacities for the Guavio
dam is presented in Figure 23a.
The model reproduces the seasonality of the filling process of the reservoir
according to the river flows data from FIESTA with average water storage of
480Mm3 throughout the year (Figure 23a). Figure 23 also shows the change in
nominal water head (reservoir head + external head) in the reservoir, which is
proportional to the increase in storage volume and exhibits sensibility at the seasonal
changes.
Figure 23. a. Modelled reservoir capacity (Mm3) and water head (m) for the Guavio dam. b.
Monthly progression of potential hydroelectric power simulated by the model (MW).
The potential hydroelectric power (MW) changes in accordance with the change
in water head in the reservoir (Figure 23b). The model reports an average potential
hydroelectric power of about 480MW and a potential energy generation 3143GWh
year-1, considering 75% potential operation throughout the year. These figures are
within the margin of designed installed capacities for the dam according to the
tropical database of dams (1150MW and 5600 GWh year-1 respectively) (Mulligan et
al 2006b). However no data of actual operation capacities was available for
verification.
300
350
400
450
500
550
600
650
700
Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec
Months
MW
b
0
100
200
300
400
500
600
700
800
Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec
Time, Months
Res
ervo
ir ca
pcity
, Mm
3
940
960
980
1000
1020
1040
1060
1080
1100
Wat
er h
ead,
hr (
m)
Modelled reservoir capacity Water head reservoir, hr
a
This is a clear limitation to the validation of power, energy and economic results
produced by the model. Also the assumptions made for consideration of reservoir
topography introduce significant uncertainty to the estimation of these variables.
Therefore, the results presented subsequently do not intend to represent the
reality of hydroelectric power generation or drinking water provision in the region,
but instead they aim for improving our understanding of the overall impacts of land
cover change in the areas surrounding its main dams.
Chapter 5: Results
5.1. FIESTA model results
This section outlines the main results of the FIESTA model and their relevance for the
largest dams in the region highlighting fog interception contributions to water
balances and impacts of land cover change and conservation measurements from PES
implementation.
5.1.1 Fog inputs baseline2000 scenario
Overall fog inputs magnitudes are clearly dependent upon exposure to incoming
winds and are inversely correlated to altitudinal bands in the Bogotá region (Figure 24
and Figure 25). The greatest fog inputs occur in the lowlands (below about 2300m)
from 100 to 150mm year-1, with highly exposed areas showing between 200
and 300 mm year-1 (Figure 24, Figure 25a and Figure 25b). On the other hand, fog
inputs just average about 70mm year-1 in the high Bogotá Sabana (2300 to 2800m)
and at higher altitudes coinciding with paramo areas (above 3200m) these are as low
as 40mm year-1 (Figure 24, Figure 25a and Figure 25b).
Highest fog inputs are observed in dams located below 2000m, particularly
areas surrounding Guavio, Prado and Chivor (100 to 140mm year-1). The rest of the
dams including Golillas and Chingaza Lagoon, receive less than 70mm year-1.
Figure 24. Fog inputs (mm) in the Bogotá region, baseline scenario2000.
N
0 40 80 120 16020Kilometers
Sisga
Tomine
Neusa
Regadera
PradoChingaza lagoon
Golillas
Guavio
Chivor
Copa
Tota
Figure 25. a. Fog inputs by altitudinal bands (mm) and b. altitudinal bands in the Bogotá region
(masl).
5.1.1.1 Fog inputs as a proportion of rainfall and water balance
Fog inputs represent from about 6% to 9% of rainfall and water balance in the high
Bogotá sabana (2300 to 2800masl), though they only amount from 1% to 2% of the
much wetter low lands, in the eastern ridges of the Bogotá region, and of high areas
above 3000m (Figure 26). However, they represent from 10% to 15% of the drier low
lands towards the Magdalena basin (Figure 26).
Fog inputs are an important proportion of water budget in areas surrounding
Chivor, Tomine, Sisga, Copa, Tota Lake and Prado (from 8% to 15%) (Figure 26),
though not so for Golillas and Chingaza Lagoon located in paramo areas of the
Chingaza Park (Figure 26).
N
Sisga
Tomine
Neusa
Regadera
PradoChingaza lagoon
Golillas
Guavio
Chivor
Copa
Tota
a b
Sisga
Tomine
Neusa
Regadera
PradoChingaza lagoon
Golillas
Guavio
Chivor
Copa
Tota
Figure 26. a. Fog inputs as proportion annual rainfall (mm) and b. Fog inputs as proportion
annual water budget (mm). c. Annual water budget (mm).
N
Sisga
Tomine
Neusa
Regadera
PradoChingaza lagoon
Golillas
Guavio
Chivor
Copa
Sisga
Tomine
Neusa
Regadera
PradoChingaza lagoon
Golillas
Guavio
Chivor
Copa
Tota Tota
a b
Sisga
Tomine
Neusa
Regadera
PradoChingaza lagoon
Golillas
Guavio
Chivor
Copa
Tota
However, Figure 26c shows much higher water budgets in paramo areas (areas above
3200masl) of up to 1000mm year-1 compared to those of the high Bogotá sabana
(about 500mm year-1) confirming the importance of these ecosystems for the
provision of water resources to Bogotá city.
5.1.1.2 Runoff from fog as a component of river discharge
Runoff from fog in most of the cases is a proportion below 4% of river discharge to
the main dams in the region. Fog represents about 4% of the average runoff for
Guavio, Chivor and Prado dams (2 to 3 m3 s-1) (Figure 27). However, fog runoff is
more significant in the drier areas of the high valleys feeding Tota, Copa, Tomine,
Neusa, Sisga and Regadera dams where it amounts from 5% to 10% of river
discharges.
Figure 27. Runoff from fog as part of river discharge. a. Runoff from fog (m3 s-1). b. River
discharge (m3 s-1). Detail for the Guavio river that feeds the Guavio Dam.
5.1.1.3 Fog contributions and National parks
Fog inputs represent a relatively low proportion of water budgets in national parks
corresponding to 3.6%, 3.2% and 4.5% (68mm year-1, 72 mm year-1 and 54mm year-1)
for Sumapaz, Chingaza and Iguaque respectively. However, Golillas and Guavio dam
and Chingaza Lagoon benefit from fog inputs provided by Chingaza (Figure 28).
illas
Guavio
illas
Guavioa b
Figure 28. Fog contributions and national parks in the Bogotá region. a. % of water balance. b.
Fog contributions (mm).
5.1.1.4 Monthly contributions from fog to dams in the region
Seasonally fog is a high proportion of runoff in the months of December, January and
February in the high valleys surrounding Tota and Copa (Towards the Chicamocha
Region). January is the month of greatest fog contributions amounting from 15% to
40% of water balance (Figure 29). To the middle of the year, months of July and
August, the contribution becomes higher to the west of Bogotá region (low areas of
Sumapaz municipality) (from 10 to about 30%) (Figure 29).
Therefore, fog inputs are seasonally important for the maintenance of regular
flows in the dry areas surrounding Tota Lake and Copa dam as well as for Chivor,
Sisga, Tomine and Guavio, but especially in January (Figure 29).
Sisga
Tomine
Neusa
Regadera
PradoChingaza lagoon
Golillas
Guavio
Chivor
Copa
Tota
Sumapaz Park
ChingazaPark
Iguaque Park
Sumapaz Park
ChingazaPark
Iguaque Park
Figure 29. Monthly progression of fog as a proportion of runoff in the Bogotá region.
5.1.2 General impacts of land cover change. Comparison between baseline1977
and baseline2000 scenario
Comparing the baseline1977 and baseline2000 scenarios, there has been already a
reduction in fog interception from 10 to about 20mm year-1 for the main dams in the
region. The greatest drop (down to 150mm year-1) has taken place towards the
Magdalena catchment where most of the deforestation took place over the period
(Figure 30a).
In addition, evapo-trasnpiration has decreased throughout the region in
deforested areas from 50 to about 250mm year-1, and as much as 1200mm year-1 (bare
and urban soil) (Figure 30b). Consequently, water budgets have increased throughout
from 200 to 400mm year-1 in spite of the losses from reduced fog interception (Figure
30c).
Flows have increased up to 4m3 s-1 for some of the largest rivers in the region.
Particularly, for Guavio flows have increased about 2.5m3 s-1 (Figure 31a), whereas
fog contribution to runoff declined about 0.1 m3 s-1 (3.15 Mm3) (Figure 31b).
Sisga
Tomine
Neusa
Regadera
PradoChingaza lagoon
Golillas
Guavio
Chivor
Copa
Tota
N
J F M
M J J A
S O N D
A
Sisga
Tomine
Neusa
Regadera
PradoChingaza lagoon
Golillas
Guavio
Chivor
Copa
Tota
Sisga
Tomine
Neusa
Regadera
PradoChingaza lagoon
Golillas
Guavio
Chivor
Copa
Totaa
b
Figure 30. Differences in a. Fog interception (mm), b. Evapo-transpiration (mm) and c. Water
balance (mm) between the baseline1977 and baseline2000 scenarios.
Figure 31. a. Detail of increase in runoff (m3 s-1) for the Guavio dam, baseline2000 scenario. b.
Reduction in fog contribution to runoff (m3 s-1).
5.1.3 Conservation impacts: PES scenario to 2050
Considering the PES scenario with regard to the baseline2000 scenario, further
deforestation outside protected areas leads to increases in water budgets due to
reduced evapo-transpiration (Figure 32). No change takes place within the protected
Guavio Guavioa b
Sisga
Tomine
Neusa
Regadera
PradoChingaza lagoon
Golillas
Guavio
Chivor
Copa
Tota
c
areas (Figure 32). However, total flows are increased up to 2.8 m3 s-1 for the whole
region in spite of a decline in fog contribution to flows of about 0.05 m3 s-1 (Figure
33a and Figure 33b).
Figure 32. Differences in a. fog interception, b. evapo-transpiration and c. water balance
between the PES and baseline2000 scenarios.
Sisga
Tomine
Neusa
Regadera
PradoChingaza lagoon
Golillas
Guavio
Chivor
Copa
Sisga
Tomine
Neusa
Regadera
PradoChingaza lagoon
Golillas
Guavio
Chivor
Copa
Sisga
Tomine
Neusa
Regadera
PradoChingaza lagoon
Golillas
Guavio
Chivor
Copa
Tota
a b
c
Tota Tota
Figure 33. a. Detail of runoff (m3 s-1) for the Guavio dam baseline2000 scenario. b. Reduction in
fog contribution to runoff (m3 s-1).
5.1.4 Conservation impacts: NOPES scenario
Similar trends as those observed in the PES scenario occur here but this time within
the protected areas as well. Fog inputs decrease down to 40mm year-1 in low land
areas of Chingaza park, though much less in paramo areas (between 0 to 10mm year-
1) (Figure 34a). Further gains in water budget from removing paramo are also very
low (0 to 20 mm year-1) (Figure 34c)
5.1.5 Conservation impacts: Naturalcoversremoved (NONC)
When cloud forest and paramo are removed an additional increase in river flows of
about 3m3 s-1 is observed throughout the whole region (Figure 35). In contrast, a
great decline in fog contribution to river flows is observed as well (0.1 to 0.15m3 s-1,
4.7Mm3 for the Guavio dam).
Guavio Guavioa b
Figure 34. Differences in a. fog interception (mm), b. evapo-transpiration (mm) and c. water
balance (mm) between the NOPES and baseline2000 scenarios.
Sisga
Tomine
Neusa
Regadera
PradoChingaza lagoon
Golillas
Guavio
Chivor
Copa
Sisga
Tomine
Neusa
Regadera
PradoChingaza lagoon
Golillas
Guavio
Chivor
Copa
a b
Sisga
Tomine
Neusa
Regadera
PradoChingaza lagoon
Golillas
Guavio
Chivor
Copa
Tota
c
Figure 35. a. NONC scenario. Detail of runoff (m3 s-1) for the Guavio dam. b. Reduction in fog
contribution to runoff (m3 s-1).
5.2. Results from the Runoff – subsurface model with a paramo component
The paramo component integrated to the runoff – subsurface flow model was
implemented with land cover data of the Guavio catchment to gaining any
understanding of the impacts of paramo loss and conservation from PES schemes to
Golillas and Guavio dams. Figure 36 indicates the land covers, main dams and flow
stations in the area.
Figure 36. Guavio catchment. Land covers, main dams and the location of flow stations are
represented.
Guavio
Guavio
a b
5.2.1 Guavio dam
Overall replacement of paramo to crops-grasslands reduces slightly water inputs to
the Guavio dam. Comparison of annual flows for baseline1977 and baseline2000
scenarios indicates that the water flows to the dam have potentially dropped about
0.14m3 s-1 on average (0.24%) (Figure 37a).
Water flows reductions are greatest for NOPES and NONC scenarios with
regard to baseline2000 amounting to 1.2% of river flows (0.32 and 0.85m3 s-1
respectively) (Figure 37a and Figure 37b). Less reduction is observed in the PES
scenario (Figure 37a).
55
60
65
70
75
80
85
Ene Feb Mar Apr May June July Aug Sep Oct Nov Dec
Time, Months
Dis
char
ge, m
3 s-1
baseline2000 baseline1977 Pes NoPes NONC
b
71.0570.89
70.7970.66
70.04
69.4069.6069.8070.0070.2070.4070.6070.8071.0071.20
base
line19
77
base
line20
00PES
NOPES
NoNaturalco
vers
Landcover scenarios
Disc
harg
e, m
3 s-1
a
+0.24% 0.0 -0.13% -0.22%
-1.20% +0.17m3s-1
0.0 +0.10m3s-1 -0.32m3s-1
-0.85m3s-1
Figure 37. a. Annual change in river discharge (m3 s-1) to the Guavio dam for Baseline and PES
Scenarios. b. Seasonal changes in river discharge (m3 s-1). c. Seasonal differences (m3 s-1).
In addition, the impact of paramo removal and conservation could be better
understood in Figure 37c and Figure 37b (positive part represents higher budgets in
the baseline2000 scenario), in which the greatest reductions in the NONC scenario for
the wet season (months, June and July) coincide with higher flows considering the
baseline1977, in which paramo resources are greatest (Figure 37c). Furthermore,
higher and more regular flows noted in the baseline1977 scenario in the driest months
(first five months) (Figure 37b and Figure 37c, orange line) could indicate a potential
regulation effect of paramo on river flows.
5.2.2 Golillas dam
Similar trends are observed for the Golillas dam, though the relative impacts of
paramo removal are almost double compared to those for Guavio (0.9, 2 and 2.3% for
PES, NOPES and NONC scenarios respectively). This could be attributed to the fact
that the dam is located right within paramo areas of Chingaza park and thus it is more
sensitive to impacts from paramo loss (Figure 38a and Figure 36).
-2-1.5
-1-0.5
00.5
1
1.52
2.53
Ene Feb Mar Apr May June July Aug Sep Oct Nov Dec
Time, Months
Diff
eren
ce d
isch
arge
, m3 s
-1
baseline1977 Pes NoPes NONCc
Figure 38. a. Annual change in river discharge (m3 s-1) to the Golillas dam for Baseline and PES
Scenarios. b. Seasonal changes in river discharge (m3 s-1). c. Seasonal differences (m3 s-1).
9.299.25
9.18
9.079.04
8.908.959.009.059.109.159.209.259.309.35
base
line19
77
base
line20
00PES
NOPES
NoNaturalco
vers
Landcover scenarios
Disc
harg
e, m
3 s-1
+0.35% 0.0 -0.86% -1.96%
-2.29% +0.03m3s-1
0.0 +0.08m3s-1 -0.18m3s-1
-0.21m3s-1
c
8.5
8.7
8.9
9.1
9.3
9.5
9.7
Ene Feb M ar Apr M ay June July Aug Sep Oct Nov Dec
Time, Months
Dis
char
ge, m
3 s-1
baseline2000 baseline1977 Pes NoPes NONC
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
Ene Feb M ar Apr M ay June July Aug Sep Oct Nov Dec
Time, months
Diff
eren
ce d
isch
arge
, m3 s
-1
baseline1977 Pes NoPes NONC
b
a
5.4 Results from the application of a dam filling model
5.4.1 General results for the Bogotá region using FIESTA river flows
5.4.1.1 Aggregated volume, Bogotá region
The model informs an increase in the water stored for all the dams in the region for
PES, NOPES and NONC scenarios, regarding the baseline2000 scenario (2465Mm3).
Increases are about 21.2, 22.5 and 25.4 Mm3 for the three scenarios respectively (the
highest being just above 1% of that of baseline2000). The model reports a potential
change already occurred from baseline1977 to baseline2000 of about 16Mm3 (Table 5
and Figure 40).
Table 5. Change in water storage (Mm3) for the Bogotá region according to different scenarios of
land use change (Negative values indicate loses in water volume).
Storage Volume (m3)
Dam Baseline 2000
Baseline 1977 PES NOPES NONC
Diff (%)baseline2000-NONC
Tota 598.98 599.05 598.95 598.95 598.92 0.00
Chingaza_lagoon 10.74 10.75 10.74 10.74 10.74 0.04
Chivor 511.62 500.58 517.45 517.45 517.48 -1.15
Copa 60.52 63.48 60.52 60.52 60.58 0.11
Golillas 79.75 79.07 79.75 79.82 79.85 -0.13
Guavio 462.38 454.24 468.04 468.92 470.96 -1.85
Neusa 51.36 49.99 51.40 51.40 51.42 -0.11
Prado 249.91 250.04 250.98 250.96 250.95 -0.42
Regadera 4.54 4.50 4.54 4.54 4.54 -0.02
Sisga 48.35 47.05 48.71 48.75 48.75 -0.83
Tomine 238.97 239.45 239.74 239.74 239.82 -0.36
Total Bogotá capacity 2465.28 2449.29 2486.49 2487.77 2490.65
Difference baseline -15.99 +21.22 +22.49 +25.37
There is less to be gained from deforestation for dams in drier areas where fog
inputs are greater, especially for Tota, Copa and Prado, where fog inputs are a
significant proportion of water budgets seasonally (Figure 29). In addition, areas with
important proportions of paramo cover (low evapo-transpiration) such as Chingaza
Lagoon, Golillas and Regadera also show smaller increases (Figure 34c).
Figure 39. a. Change in the regional water stored in dams in the Bogotá region. b. Seasonal
contribution from every dam in the region.
5.4.1.2 Drinking water provision
In terms of the aggregated drinking water provision to the region (Tomine, Sisga,
Neusa, Golillas, Chingaza Lake and Copa dams together), the model reports increases
of 1.9, 3.3 and 4.7Mm3 for the PES, NOPES and NONC scenarios with regard to
baseline2000 (Table 6). These increases represent from 1.8 to 4.2 million USD
(Figure 40).
0
250
500
750
1000
1250
1500
1750
2000
2250
2500
2750
3000
Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec
Months
Mm
3
tota
chingaza_lake
chivor
copa
golillas
guavio
neusa
prado
regadera
sisga
tomine
Bogota_Capacity
b
a -20.0-15.0-10.0-5.00.05.0
10.015.020.025.030.0
baseline1977 PES NOPES NONC
Scenario
Diff
eren
ce in
wat
er s
tore
d, M
m3
Table 6. Change in drinking water provision (Mm3) and Economic value Million USD.
Dam drinking water provision Mm3
Dam Baseline 2000
Baseline 1977 PES NOPES NONC
Tota 74.6 77.5 74.8 74.8 75.0
Chingaza Lagoon 75.8 75.2 75.8 75.9 76.6
Copa 62.0 63.2 62.0 62.0 62.0
Golillas 81.1 78.9 81.1 82.0 82.6
Neusa 42.6 39.5 42.7 42.7 42.8
Regadera 28.1 27.5 28.1 28.1 28.1
Sisga 53.0 51.3 53.5 53.6 53.5
Tomine 55.8 56.5 57.0 57.0 57.1
Total 473.0 469.7 475.0 476.0 477.7
Difference Mm3 -3.3 +1.9 +3.0 +4.7
Economic value of change in water provision Million USD
Dam Baseline 2000
Baseline 1977 PES NOPES NONC
Tota 67.2 69.7 67.3 67.3 67.5
Chingaza Lagoon 68.2 67.7 68.2 68.3 69.0
Copa 55.8 56.9 55.8 55.8 55.8
Golillas 73.0 71.0 73.0 73.8 74.4
Neusa 38.4 35.6 38.4 38.4 38.5
Regadera 25.2 24.7 25.2 25.2 25.2
Sisga 47.7 46.2 48.1 48.2 48.2
Tomine 50.2 50.9 51.3 51.3 51.4
Total economic value MUSD 425.7 422.7 427.5 428.4 429.9
Difference -3.0 +1.8 +2.7 +4.2
Figure 40. Change in the economic value of drinking water resources (Million USD). a. Regional.
B. Contribution per dam.
5.4.1.3 Hydroelectric power generation
The aggregated hydroelectric energy production (GWh year-1) from the Guavio,
Chivor and Prado dams increases in the PES, NOPES and NONC scenarios in about
27, 29 and 35 GWh year-1, which represent an augment from 2.9 to 3.8 Million
USD (Table 7 and Figure 1). This increase was potentially greatest over the period
between baseline1977 and baseline2000 (84GWh year-1) (Table 7).
0
2
4
6
8
10
12
14
Jan
Feb
Mar
Apr
May
June
July
Aug
Sep Oct
Nov
Dec
Months
Econ
omic
val
ue, M
USD
totachingaza_lakecopagolillasneusaregaderasisgatominetotal_capacity
a
b
-20.0
-15.0
-10.0
-5.0
0.0
5.0
10.0
15.0
20.0
25.0
30.0
baseline1977 PES NOPES NONC
Scenario
Econ
omic
impa
ct, M
USD
Table 7. Change in annual hydroelectric energy production, GWh year-1 and economic value.
Hydroelectric energy production GWh year-1
Dam Baseline 2000
Baseline 1977 PES NOPES NONC
Chivor 2475 2420 2477 2477 2477 Guavio 3294 3265 3317 3319 3325 Prado 101 102 103 103 103 Total_region 5871 5787 5897 5900 5906 Difference -84 +27 +29 +35 Economic value of the energy production (GWh year-1) Million USD pesos)
Dam Baseline 2000
Baseline 1977 PES NOPES NONC
Chivor 269.8 263.8 270.0 270.0 270.0
Guavio 359.1 355.9 361.5 361.8 362.4
Prado 11.0 11.1 11.3 11.3 11.3
Total_region 639.9 630.8 642.8 643.1 643.7
Difference 9.2 -2.9 -3.1 -3.8
Figure 41. Change in the potential hydroelectric energy produced in the Bogotá region (GWh
year-1). a. Economic change (MUSD). B. Contribution per dam (GWh year-1).
b
0
100
200
300
400
500
600
Jan
Feb
Mar Apr MayJu
ne July
Aug Sep OctNov Dec
Months
Ener
gy p
rodu
ctio
n, G
Wh
year
-1
chivor guavio prado total_capacity
-10.0
-8.0
-6.0
-4.0
-2.0
0.0
2.0
4.0
6.0
baseline1977 PES NOPES NONC
Scenario
Econ
omic
impa
ct, M
USD
a
5.4.2 General results for the Guavio Catchment
Implications of paramo removal on hydroelectric power generation and drinking
water production for Guavio and Golillas dams were studied by using river flows
simulated with a runoff model with paramo component.
To difference of the effect of deforestation, which increases potential water
resources in the Bogotá region (though not likely water quality), losing paramo could
bring negative consequences for the provision of these services from Guavio
catchment.
5.4.2.1 Hydroelectric energy production
A decrease in hydroelectric energy production as a result of paramo removal is
observed. Energy production drops about 25, 54 and 64GWh year-1 for PES, NOPES
and NONC scenarios respectively (Table 8). A potential decrease in energy
production of about 10GWh year-1 is also reported from baseline1977 to baseline2000
(Table 8).
This reductions appear to be higher than potential gains from deforestation,
which are about 23, 25 and 31 GWh year-1 for PES NOPES and NONC scenarios
respectively (according to FIESTA river flows without a paramo component, though
these figures cannot be compared directly because of the different rainfall datasets
used).
Therefore, the potential economic loss of removing paramo would amount to
about 2.7, 5.9 and 7 million USD year-1, for PES, NOPES and NONC respectively
(Table 8 and Figure 42). This loss is greater than the gains in water river flows from
deforestation (2.5, 2.7 and 3.4 million USD respectively) (Table 8 and Figure 42).
Thus, results point out a potential positive economic impact of up to 3.2 million
USD from conserving paramo in Chingaza by implementing PES schemes to the year
2050. In addition, a further enhance in paramo protection in the catchment could
represent as much as 2.7 million USD to the year 2050.
Figure 42. Change in the economic value of hydroelectric energy production (MUSD). a. Loses
from paramo loss. b. Gains from deforestation.
River flows Runoff - paramo component
-8.0
-6.0
-4.0
-2.0
0.0
2.0
4.0
baseline1977 PES NOPES NONC
Scenario
Econ
omic
impa
ct, M
USD
FIESTA river flows with no paramo component
-4
-3
-2
-1
0
1
2
3
4
baseline1977 PES NOPES NONC
Scenario
Econ
omic
impa
ct, M
USD
a
b
Table 8. Hydroeletric energy production and economic impact of land use change and
conservation in the Guavio catchment (Guavio dam).
5.4.2.2 Drinking water provision.
The change in drinking water provision was studied for the Golillas dam, where
reductions in potential annual water provision of about 4.7, 11 and 10Mm3 for PES,
NOPES and NONC scenarios respectively are highlighted (Table 9). Current paramo
loss had already led to a potential 1.5 Mm3 annual reduction in water provision from
the dam (Table 9). These figures have to be validated against data from the water
company.
Hydroelectric energy production GWh year-1, Guavio dam
dam Baseline 2000
Baseline 1977 PES NOPES NONC
Runoff model 3289 3299 3264 3235 3225
Total Guavio catchment 3289 3299 3264 3235 3225
Difference +10 -25 -54 -64
FIESTA 3294 3265 3317 3319 3325
Total Guavio catchment 3294 3265 3317 3319 3325
Difference -29 +23 +25 +31
Economic value of the energy production Million USD. Guavio dam
dam Baseline 2000
Baseline 1977 PES NOPES NONC
Runoff model Economic value Million USD 358.5 356.0 355.8 352.6 351.5
Total Guavio Catchment 358.5 356.0 355.8 352.6 351.5
Difference +2.5 -2.7 -5.9 -7.0
FIESTA Economic value Million USD 359.0 355.9 361.6 361.8 362.4
Total Guavio catchment 359.0 355.9 361.6 361.8 362.4
Difference -3.2 +2.5 +2.7 +3.4
Coupling river flows from FIESTA the model predicts slight increases in the
availability of potential drinking water resources of just above 1.2% for the NONC
scenario (0, 0.8 and 1.5Mm3 between PES, NOPES and NONC scenarios), indicating
that there is much less to be gained in water flows from paramo vegetation removal.
The economic value of losing paramo for this dam could represent between 4.2 and
10.4 million USD year -1 to the year 2050 for PES and NOPES scenarios respectively
(Table 9 and Figure 43), indicating a potential economic benefit of conserving paramo
of up to 6.2 million USD year-1 by implementing PES schemes and a further 4.2
million year-1 as a result of extending the paramo protected area (Table 9 and Figure
43).
Figure 43. Change in the economic value of drinking water provision (MUSD). a. Loses from
paramo loss. b. Gains from deforestation.
River flows Runoff - paramo component
-12.0
-10.0
-8.0
-6.0
-4.0
-2.0
0.0
2.0
baseline1977 PES NOPES NONC
Scenario
Econ
omic
impa
ct, M
USD
FIESTA river flows with no paramo component
-2.5-2
-1.5-1
-0.50
0.51
1.52
baseline1977 PES NOPES NONC
Scenario
Econ
omic
impa
ct, M
USD
a
b
Table 9. Drinking water production and economic impact of land use change and conservation
in the Guavio catchment (Golillas dam).
Drinking water provision Mm3, Golillas
dam Baseline 2000
Baseline 1977 PES NOPES NONC
Runoff model 177.0 178.5 172.3 165.5 167.0
Total Guavio catchment 177.0 178.5 172.3 165.5 167.0
Difference +1.5 -4.7 -11.6 -10.0
FIESTA 81.1 78.9 81.1 82.0 82.6
Total Guavio catchment 81.1 78.9 81.1 82.0 82.6
Difference -2.2 +0.0 +0.8 +1.5
Economic value of drinking water provision Million USD. Golillas dam
Dam Baseline 2000
Baseline 1977 PES NOPES NONC
Runoff model Economic value Million USD 159.3 160.7 155.1 148.9 150.3
Total Guavio catchment 159.3 160.7 155.1 148.9 150.3
Difference +1.4 -4.2 -10.4 -9.0
Runoff model Economic value Million USD FIESTA 73.0 71.0 73.0 73.8 74.4
Total Guavio catchment 73.0 71.0 73.0 73.8 74.4
Difference -2.0 0.0 +0.8 +1.4
Chapter 6: Conclusions and recommendations
FIESTA model was used to understand water resources to the Bogotá region and the
impacts of land cover change and conservation through PES schemes implementation.
The model was coupled with other model components such as a runoff model with a
paramo component and a dam filling model to help understand impacts of
deforestation on the provision of drinking water and hydroelectric energy generation
for the whole region in general and the implications of paramo removal, in particular,
for a smaller catchment of the region (Guavio catchment).
Study outcomes indicate that fog inputs are seasonally important in magnitude
especially for dry areas to the north of the Bogotá region where they can be as high as
40% of water balances and represent up to 10% of river discharges seasonally, though
these contributions are generally a low proportion in the lowlands (below 4%).
Deforestation has led to potential losses in river flows of up to 0.1m3 s-1
(3.15Mm3) in the whole region due to declines in fog interception with regard to an
early scenario to the year 1977. Projected deforestation scenarios to the year 2050
reduce fog contribution to rivers in similar amounts. However, river flows are
generally increased up to 3 m3 s-1 throughout the whole region due to reduced evapo-
transpiration even when the losses from reduced net fog interception are considered.
The general increase in river flows due to deforestation for all land cover
change and conservation scenarios considered (baseline2000, PES NOPES and
NONC) leads to an enhance in water resources for drinking water provision and an
increase in the hydroelectric energy produced by the whole region (up to 4.7Mm3
amounting to 4.2 Million USD and up to 35GWh year-1 amounting to 3.8 Million
USD respectively). However, there is much less to be gained from deforestation in
dry areas surrounding Tota Lake and Copa and Prado dam and paramo areas
surrounding Chingaza Lagoon, Golillas and Regadera.
In contrast, the effects of paramo removal could represent a reduction in water
resources to dams in paramo areas such as Chingaza and therefore on the economic
productivity of drinking water and hydroelectric generation. The model indicates a
potential drop in river flows down to 1.2% and 2.2% for Guavio and Guaitiquia rivers
feeding Guavio and Golillas dams respectively.
Results for Guavio catchment point out that PES schemes implementation to
the 2050 could help save about 3.2 million USD year-1 of economic hydroelectric
productivity from paramo conservation in the Chingaza National Park and that
another 2.7 million USD could be added to this figure if conservation measures are
extended to unprotected paramo resources surrounding the Park.
Similar results are observed for the case of drinking water production, for
which a PES schemes establishment could help maintain water inputs representing 6.2
million USD year-1 and in which a further enhancement of the protected area could
represent another 4.2 million USD.
Results from paramo conservation are encouraging since conservation efforts
could potentially represent positive economic impacts and are a useful exercise to
help understand the potential benefits of conserving paramo. However, they must be
validated now as well as model assumptions and compared with information from
respective relevant drinking water, hydroelectric and environmental organizations and
understood within the economic context of the provision of these services in the
region.
Nonetheless conservation of cloud forest and paramo resources should be
tackle in a more integral way, considering not only benefits from potential water
quantity increases or reductions but taking into account other environmental services
such as erosion and sedimentation mitigation as well as water regulation.
Much research is needed to better understand the hydrological properties of
vegetation covers such as paramo as well as to better represent hydrological
mechanism especially of the paramo – soil interaction at natural conditions.
Availability of more representative rainfall information, especially for the high
mountainous areas exhibiting low density of rainfall stations, remains also as an
important challenge to more representative water resources assessments in the region.
The main limitations to this work were the scarce availability of information
of operational capacities (annual and seasonal) for the main dams in the region, which
restricted the validation of results from the use of a dam filling model.
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Appendix 1: Datasets for FIESTA model parameterization
• 90m Digital Elevation Model (DEM) of the Bogotá region (Appendix 1).
Figure 44. Digital Elevation Model for the Bogotá region.
The DEM 90m model for Bogotá region was derived from the SRTM 90m DEM
topoview interface (Mulligan 2006). About 75% of the Bogotá region is located in the
high Bogotá sabana (between 2300 and 2900m)
• Cloud cover
The cloud cover datasets used represent annual and seasonal frequencies (DJF, MAM,
JJA, SON) as well as different daily periods (Early morning [0300 - 0600], morning
[0700 - 0800], afternoon [1500 - 1700] and evening [1800 - 2000] for the year 2001.
The datasets were processed from MODIS 500m datasets by Mulligan and Burke
(2005b).
Figure 45. Cloud cover frequency for the seasons (DJF, MAM, JJA, SON) in the Bogotá region.
N
Figure 46. Cloud cover frequency for the daily cycle (Early morning, morning, afternoon,
evening) in the Bogotá region.
Early morning Morning
Afternoon Evening
N
• Potential Solar Radiation
1 km Solar radiation (Wm2) is used by FIESTA to simulate evapo-transpiration.
Dataset provided by Mulligan and Burke (2005b).
Figure 47. Progression of monthly solar radiation (Wm2) for the Bogotá region.
January February March April
May June July August
September October November December 0 75 Km
N
• Temperature and daily temperature range
1 km Temperature (°C) and daily temperature (°C) from New et al (2000) and
Mulligan and Burke (2005b) datasets are used to calculate the lifting condensation
level for fog interception and evapo-transpiration (mm).
Figure 48. Progression of Monthly temperature (°C) for the Bogotá region.
January February March April
May June July August
September October November December
0 75 Km
N
• Precipitation
Precipitation is the main input for FIESTA to simulate the water cycle. Two monthly
1km rainfall datasets (WordClim and the TRMM) (Hijmans et al 2005; TRMM 2006)
were compared used and compared. Precipitation in highly exposed areas is supposed
to be under estimated since WorldClim rainfall stations are not corrected by gauge
types, gauge wetting and wind driven (Mulligan and Burke 2005b)
Figure 49. Monthly progression of precipitation (mm) for the Bogota region according to
WorldClim.
January February March April
May June July August
September October November December
0 75 Km
N
Figure 50. TRMM precipitation dataset (mm) for the Bogotá region.
January February March April
May June July August
September October November December
0 75 Km
N
• Humidity
A 1km Humidity (%) dataset is used to calculate the lifting condensation level of the
humid air for fog production (dataset provided by Mulligan and Burke 2005b).
Figure 51. Monthly progression of relative humidity (%) in the Bogotá region.
January February March April
May June July August
September October November December 0 75Km
N
• Mean sea level pressure
Mean sea level pressure (mb)
dataset provided by Mulligan and Burke (2005b).
• Wind speed
FIESTA considers wind speed (m s-1) for fog interception modelling.
0.5 degree New et al (2000) dataset processed to 1km by Mulligan and Burke (2005b)
was used here.
Figure 52. Progression of monthly wind speed (m s-1) for the Bogotá region.
January February March April
May June July August
September October November December
0 75 Km
N
• Topographical exposure to wind
Topographical exposure datasets from eight cardinal directions (E, N, W, S, NE, SE,
SW, NW) were used here (source Mulligan and Burke 2005b).
Figure 53. Topographical exposure to winds in the Bogotá region for E, N, W, S, NE, SE, SW,
NW cardinal directions.
E N W
S NE SE
SW NW 0 75 Km
N
Appendix 2: River flow stations in the Bogotá region
Final list of flow stations in the whole Bogotá catchment
Number Código Nombre
estación Tipo Corriente Instalada Suspendida River
1 2119701
El Profundo LIMNIGRAFICA Sumapaz 1959 Sumapaz
2 2119703 La Playa
LIMNIGRAFICA
Sumapaz
1958
Sumapaz 3 2119709 Dos Mil LM Sumapaz 1969 Sumapaz 4 2306705 Guaduero LG River Negro 1974 Negro 5 2306707 Villeta LM Villeta 1951 Villeta 6 2306709 El Paraiso LM River Negro 1939 Negro 7 2401715 La Boyera LM River Ubate 1960 Ubate 8 3502702 Oro Podrido LM River Negro 1994 Negro 9 3502713 Pte Quevedo LM River Clarin 1985 Clarin
10 3502715 Las Animas LM River Chochal 1985 Chochal 11 3502719 Guacapate LG River Negro 1980 Negro 12 3502720 El Palmar LG River Blanco 1980 Blanco
13 3502721 CASETEJA-DELICIA LM River Negro 1980 Negro
14 3506701 La Gloria LG River Negro 1963 Negro 15 3506702 Ubala LM Guavio 1962 1987 Guavio 16 3506703 Ubala LG River Chivor 1962 Chivor
17 3506704 CHUSNEQUE LM Guavio 1963 1992 Guavio
18 3506709 La Boca LG River Batatas 1971 River Batatas
19 3506710 La Vega LM Guavio 1972 1983 Guavio 20 3506712 Sta Barbara LG Murca 1990 1992 Murca 21 3506713 Mundo Nuevo LG River Rucio 1979 Rucio 22 3506714 Mambita LG River Guavio 1972 1983 Guavio
23 2119705 RINCON SANTO LM
River Sumapaz 1966 1972 Sumapaz
24 2119706 Pte Paquilo LM River Sumapaz 1966 1974 Sumapaz
25 2119718 BOCATOMA PIRINEOS LM
Quebrada Aguas Claras 1998
Quebrada Aguas Claras
26 2119719 COSTA RICA LM Quebrada Salvios 1998 Salvios
27 2119722 JALISCO BAJO LM La Laja 1992 La Laja
28 2119723 Pasca LM El Bosque 1998 El Bosque
29 2119724 Pasca 1 LM River Corrales 1998 Corrales
30 2119726 PTE AGUADITA LM
River Barroblanco 1998
River Barroblanco
31 2119727 PTE ARBELAEZ LM River Cuja 1998 Cuja
32 2119729 PTE CARO LM River Juan Viejo 1998
Juan Viejo
33 2119728 Pte Caracol LM Q. Grande 1998 Q. Grande
34 2119730 Pte Cabrera LM Quebrada Panela 1998
Q. Panela
35 2119732 Pte Los Rios LM River Guavio 1998 Guavio
36 2119733 Pte Negro LM River Negro 1998 Negro
37 2119734 Pte Rojo LM River Cuja 1998 Cuja
38 2119735 Pte San Vicente LM River Batan 1998 Batan
39 2119736 JUANXXIII LM Q. Filadelfia 1999 Filadelfia
40 2120714 Pte. Cndinamarca LM River Bogota 1956 Bogota
41 2120719 SAUCIO LM River Bogota 1940 Bogota
42 2120728 Acequia_Molino LM R. Neusa 1946 Neusa
43 2120732 PTE Carretera LM Neusa 16834 Neusa
44 2120733 Acequiaquinta LM Acequiquinta 1946
Acequiaquinta - neusa
45 2120734 Pte Vargas LG Bogota 1946 Bogota
46 2120735 Pte Virginia LG River Frio 1946 Frio
47 2120739 Embalse Neusa LM Neusa 16834 Neusa
48 2120742 La Balsa LM River Bogota 1939 Bogota
49 2120744 Embalse Sisga LM Sisga 1952 Sisga
50 La Vega-Aves_Guasc LG River Aves 1956 Aves
51 2120752 Pte Galindo-Bojaca-Boja LM River Bojaca 1956 Bojaca
52 2120755 San Jorge LM River Soacha 1960 Soacha
53 2120756 El Recreo LM River Bojaca 1960 Bojaca
54 2120757 AceuiaSanPatricio LM Sanpatricio 1960
SanPatrico
55 2120758 La Muralla LM Subachoque 1960 Subachoque
56 2120766 La Pradera LM Subachoque 1962 Subachoque
57 2120767 Pte Florencia LM River Bogota 1961 Bogota
58 2120783 Canaleta_Parshall LM Neusa 1964 Neusa
59 2120785 Molino LM Neusa 1964 Neusa
60 2120786 El recuerdo LM Patricio 1964 Patricio
61 2120787 STA ISABEL LM River Frio 1964 Frio
62 2120787 Pte_Adobes LM River Teusaca 1964 Teusaca
63 2120790 El Rincon AF Bogota 1984 Bogota
64 2120791 Teusaca LG River Bogota 1976 Bogota
65 2120792 Tocancipa LG River Bogota 1950 Bogota
66 2120793 El Espino LG River Bogota 1967 Bogota
67 2120795 Altamira LG Q. Mancilla 1968 Mancilla
68 2120797 Aguas Clras AF Q. Honda 1968 Q. Honda
69 2120798 San Isidro LM River Siecha 1958 Siecha
70 2120800 Pte Manrique LM River Subachoque 1949
Subachoque
71 2120815 Villapinzon LM River Bogotá 1972 Bogota
72 2120816 Sta Rosa LM River Bogotá 1972 Bogota
73 2120827 Pte Baraya LM River Bogotá 1972 Bogota
74 2120843 San Patricio LM S.Patricio 1960 1978 S.Patricio
75 2120845 El Bosque LM Subachoque 1975 R.Bogota
76 2120864 El Valor LM Neusa 1954 1972 Neusa
77 2120868 Sta. Martha LM San Francisco 1980
San Francisco
78 2120870 La Iberia LM San Francisco
San Francisco
79 2120875 Pte. Checua LM Checua 1986 Checua
80 2120878 El Vergel LM River Teusaca 1985 Teusaca
81 2120880 San Javier LM River Apulo 1988 Apulo
82 2120885 Pena Negra LM River Bajamon 1988
River Bajamon
83 2120886 Java LM River Calandaima 1988
River calandaima
84 2120888 Pte Bocas LM River Calandaima 1987
River calandaima
85 2120892 Manzanares LM River Curi 1988 River Curi
86 2120893 Pto. Brasil LM Q. Mona 1988 Q. Mona
87 2120895 La Pola LM River Lindo 1987 Lindo
88 2120897 La Cascada-Modelia-Viota LM Q. Modelia 1987 Modelia
89 2120900 Pte. Saenz-Q.SanJuana-Viota Q.San Juana 1987 Q.San Juana
90 2120913 El Hato LM River Hato 1992 Hato
91 2120913 La Esperanza-Apulo-LaMesa LM River Apulo 1985 Apulo
92 2120917 Pte.Choconta LM Q.Tejar 1982 Tejar
93 2120925 Pte Calamar LM River Frio 1984 Frio
94 2120930 Cartagena LM Apulo 1992 Apulo
95 2120934 El Chirca-Apulo-Zipac LM Apulo 1995 Apulo
96 2120935 El Manzano-Sausa-Cog LM River Sausa 1996 Sausa
97 2120938 Antes_Acu_Mesitas LM Q.Sta Martha 1996
Q.Sta Martha
98 2120939 Ave-Colombia LM River Sausa 1996 Sausa
99 2120963 VillaBlanca LM River Muna 1997 Muna
100 2120966 Pozo Hondo-Frio_Zipa LM River Frio 1997 Frio
101 2306711 Pte. Naranjal-Villeta LM River Villeta 1998 Villeta
102 2306713 Salitre Blanco LM Cune 1999 Cune
103 2306717 Cabrera-Suchin LM Q. Suchin 1999
Q. Suchin
104 2401704 La Balsa-Suarez LM River Suarez 1934
River Suarez
105 2401710 Corralejas LM Q.Molino 1992 Q.Molino
106 2401714 Tapias LM Lenguazaque 1992 Lenguazaque
107 2401716 El Pino LM River Suta 1992 River Suta
108 2401723 BOQUERON EL LM R. LENGUAZAQUE 1992
R. LENGUAZAQUE
109 2401729 PTE COLORADO LM R. Ubate 1964 Ubate
110 2401730 PTE GUZMAN LM R. SIMIJACA 1964 R. SIMIJACA
111 2401733 PTE la balsa LM River Lenguazaque 1964
River Lenguazaque
112 2401738 Pte.Peralonso LM R. Susa 1964 R. Susa
113 2401745 PTE PINILLA LM R. CHIQUINQUIRA 1964
R. CHIQUINQUIRA
114 2401755 La Mallla LM River Carupa 1967 Carupa
115 2401792 La Florida LM River Sutamarchan 1997
River Sutamarchan
116 2401793 FUQUENE LM Q.Honda 1997 Q.Honda
117 2401794 MONASTERIO LM R. CANDELARIA 1997
R. CANDELARIA
118 3509715 PEREZ LM Q. Aguablanca 1989 1997 Q. Aguablanc
a
119 3509724 LA PLAYA LM River Tobal 1997 1997 River Tobal
120 3509728 Hato-Laguna-Aquit Laguna Hato 01-Sep-89 01-May-97 Laguna Hato
121 3509727 CRIADERO CAR Q. lagoTota 01-Sep-89 01-May-97
Q. lagoTota
122 2120769 LAG LOS TUNJOS LM CHISACA 1963
Laguna Los Tunjos
123 2120747 HERRADERO LG MUGROSO 1951 Chisaca
124 2120746 La Toma LM Chisaca 1951 Chisaca
125 2120706 REGADERA LM Tunjuelo 1928
La Regadera
126 2120717
REGADERA REBOSADER LM Tunjuelo 1939
La Regadera
127 2120722 Represa Muña LM Muña 1944 1964 Muña
128 2120726 Represa San Rafael LM Muña 1929 Teusaca
129 3503701 Boqueron LM La Playa ######## Laguna Chingaza
130 3503704 San Jose LM Guatiquia ######## Laguna Chingaza
131 3503708 DEDAL EL CAMPAMENT LM Chuza 1968 Golillas
132 3503708 Golillas2 LM Chuza 1968 Golillas 133 3507707 El Salitre LM Somondoco 1974 Chivor 134 3507716 Sitio presa LM Bata 1961 Chivor 135 3507716 Chivor3 LM 1961 Chivor
136 3509718 Lago de Tota LM Canal de aduccion Lago de Tota
137 2403744 Paipa LM Lago Sochagota
Lago Sochagota
138 2403742 La Playa LM Embalse La Playa 1972
Copa - La Playa
139 2403702 La Copa LM Tuta 1990 Copa - La Playa
140 Gachaneca LM River Gachaneca 1990
Gachaneca
141 2401717 Isla del Santuario LM Lag. De Fuquene
Laguna de Fuquene
142 2401781 Chinzaque LM Fuquene
Laguna de Fuquene
143 2401781 Chalet Sur LM Fuquene
Laguna de Fuquene
144 2401726 Peñas de Palacio LM Laguna Cucunuba Ramada
145 Hato Laguna Hato Laguna Hato
146 2401030 EL HATILLO LM Lag. Suesca 1960
Laguna de Suesca
147 2120782 Tomine LM Tomine 1962 Tomine
148 2116702 Boqueron LIMNIGRAFICA
Prado ########
Prado
2116708 MORA LA LIMNIGRAFICA NEGRO ######## Prado
Validation of river discharge below 5m3 s-1
WC<5
0
1
2
3
4
5
6
0 2 4 6 8 10 12 14 16
Modelled discharge, m3 s-1
Obs
erve
d di
scha
rge,
m3 s-1
TRMM<5
0
1
2
3
4
5
6
0 2 4 6 8
Modelled discharge, m3 s-1
Obs
erve
d di
scha
rge,
m3 s
-1
Figure 54. Observed and modelled river discharges below 5m3 s-1 with the use of WorldClim and
TRMM datasets.
Appendix 3: Tables of characteristics of soils for the study
area. From. IGAC (2000) Estudio general de suelos y
zonificación de tierras del Departamento de Cundinamarca.
Bogotá: IGAC.
