Effects of land cover changes on the water
balance of the Palo Verde Wetland, Costa Rica
José Antonio Guzmán Álvarez
March, 2007
Effects of land cover changes on the water balance
of the Palo Verde Wetland, Costa Rica
by
José Antonio Guzmán Alvarez
Thesis submitted to the International Institute for Geo-information Science and Earth
Observation in partial fulfilment of the requirements for the degree of Master of Science in Geoinformation Science and Earth Observation, Specialisation: Advanced use of Remote Sensing in
Water Resource Management, Irrigation and Drainage.
Thesis Assessment Board
Chairman:
External Examiner:
Member
First Supervisor:
Second Supervisor:
Prof. Dr. Ir. Z. Su
Dr. L. Jia
Ir. A.M. van Lieshout
Dr. Z. Vekerdy
Ir. MSc. G. N. Parodi
Head-WRS Department, ITC, Enschede
Wageningen University and Research-ALTERRA
WRS Deparment, ITC, Enschede
WRS Deparment, ITC, Enschede
WRS Deparment, ITC, Enschede
INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH
OBSERVATION
ENSCHEDE, THE NETHERLANDS
Disclaimer
This document describes work undertaken as part of a programme of study at the
International Institute for Geo-information Science and Earth Observation. All views and
opinions expressed therein remain the sole responsibility of the author, and do not
necessarily represent those of the institute.
Dedicated to the Lord and my Dear family
It is God who arms me with strength and make my way perfect
II Samuel 22:
Abstract
Wetlands are ecosystems, which are under increasing threats. Monitoring the wetland vegetation
and the main component of the water budget allows us to know the status of the wetland and
develop optimal strategies of environmental management. Evapotranspiration is one of the most
significant components of the hydrologic budget. Remote sensing techniques offer an excellent
solution for the monitoring of ecosystems by using satellite images; e.g., with medium
resolution ASTER images or low resolution MODIS images. These tools allow us to know the
land cover changes and the evapotranspiration losses in the time.
Palo Verde wetland, in Costa Rica has suffered considerable changes in the vegetation in the
last 20 years; including a restoration process in the last 5 years. Three satellite images of the dry
months of 2003, 2005 2006 were processed to analyze the changes in the land cover and the
estimation of actual evapotranspiration using the Surface Energy Balance System (SEBS)
model. Furthermore, a time series of the actual evapotranspiration calculated from MODIS
images was analysed for the period among January 31, 2004 to February, 22 2004 to determine
the losses in the wetland. The results were compared to the measured water levels to calculate
percolation losses.
Aerodynamic roughness is a significant parameter in the estimation of actual evapotranspiration
using the surface energy balance. In this study, two methods were compared to estimate the
vegetation height: one is obtained from LIDAR (LVIS) data, the other uses an empirical
relationship with the normalized difference vegetation index.
This research sowed that ASTER images give a good representation of the effect of each land
cover type on the actual evapotranspirtacion. MODIS images are very useful in the estimation
of evapotranspiration time series, and using this data, the losses in all the wetland can be
calculated. MODIS-based ETa estimates differ by only 5% from the ASTER-based values.
Aerodynamic roughness values calculated from LIDAR information in the estimation of actual
evapotranspiration affected the results by 15 % in this study. This value is just a first estimate:
the proper use of LIDAR data has to be further studies. This research recommends the extension
of the water balance study to the rainy season.
Key words: Wetlands, actual evapotranspiration, SEBS, MODIS, ASTER, LVIS, Costa Rica.
i
Acknowledgements
I’m very grateful and thank the Royal Netherlands Government through NFP who provided the
necessary financial resources needed to pursue my studies at ITC.
I am profoundly grateful to my first supervisor, Dr. Zoltan Vekerdy, for his guidance during the
research work and for his support and friendship; to my second supervisor Ir. MSc. Gabriel
Norberto Parodi for his help and advices.
I also extend my thanks to the Organization for Tropical Studies, Costa Rica for the data
provided and the support offered in my fieldwork.
My sincere gratitude goes to Dr. Julio Calvo, for his advises and support always and providing
me the logistics the fieldwork period. Also thanks to my friends Oscar Arias R. and Juan Carlos
Solano for their support in the fieldwork.
There are also many ITC staffs that help me to increase the experiences in doing academic
researches. Special thanks go to the Department of Water Resources and Environmental
Management.
I also wish to thank all “Latin community” and my friends for providing an environment of home
feeling. Of course I will always remember my friend of WREM.2 and WREM.3.
Finally, I am eternally grateful to my family for their love, for all the moral support they gave
me during my study and every phase of my life. Also I want to thank to all my friends for their
prayers and to the ITC Christian Fellowship.
ii
Table of contents
1.
Introducion ............................................................................................................................ 1
1.1. Problem definition......................................................................................................... 2
1.2. General objective........................................................................................................... 3
1.3. Specific objective .......................................................................................................... 4
1.4. Research question.......................................................................................................... 4
1.5. Reseach hyphotesis ....................................................................................................... 4
1.6. Reseach methods ........................................................................................................... 4
2. Description of the Study Area............................................................................................... 7
2.1. Location......................................................................................................................... 7
2.2. Types of wetlands.......................................................................................................... 8
2.3. Climate .......................................................................................................................... 9
2.3.1.
Rainfall .................................................................................................................. 9
2.3.2.
Temperature and relative humidity ..................................................................... 10
2.4. Geology, geomorphology and soils............................................................................. 11
2.5. Vegetation and land cover........................................................................................... 12
3. Overview of Water Balance ................................................................................................ 15
3.1. Concept of evapotranspiration .................................................................................... 15
3.2. Wetland evaporation ................................................................................................... 16
3.3. Methods of evapotranspiration estimation .................................................................. 16
3.3.1.
Methods based on field measurements................................................................ 16
3.3.2.
Hydrological models ........................................................................................... 17
3.3.3.
Remote sensing techniques ................................................................................. 17
3.4. Surface energy balance system (SEBS) ...................................................................... 17
3.4.1.
Surface energy balance terms.............................................................................. 18
3.4.2.
Net radiation, Rn .................................................................................................. 18
3.4.3.
The soil heat flux, G0........................................................................................... 19
3.4.4.
The sensible heat flux.......................................................................................... 19
3.4.5.
Evaporative fraction ............................................................................................ 20
3.4.6.
The roughness length for heat transfer ................................................................ 21
3.4.7.
Turbulent heat fluxes and actual evaporation...................................................... 22
4. Data Processing ................................................................................................................... 25
4.1. ASTER Images............................................................................................................ 25
4.1.1.
ASTER pre-processing........................................................................................ 25
4.2. Atmospheric correction ............................................................................................... 26
4.2.1.
Atmospheric correction with SMAC................................................................... 26
4.2.2.
Atmospheric correction for thermal bands .......................................................... 28
4.3. MODIS images............................................................................................................ 29
4.3.1.
Acquirement of the MODIS images.................................................................... 29
4.4. Meteorological data pre-processing ............................................................................ 30
4.5. Bio-physical parameters estimation ............................................................................ 34
4.5.1.
Albedo (ro)........................................................................................................... 34
4.5.2.
Normalized difference vegetation index (NDVI)................................................ 34
4.5.3.
Fractional vegetation cover (fc)........................................................................... 34
iii
4.5.4.
Leaf area index .................................................................................................... 35
4.5.5.
Land surface emissivity....................................................................................... 35
4.5.6.
Aerodynamic roughness height ........................................................................... 35
4.5.7.
Vegetation height ................................................................................................ 36
4.5.8.
Displacement height ............................................................................................ 36
4.6. Land cover................................................................................................................... 36
4.6.1.
In situ observations.............................................................................................. 37
4.6.2.
Definition of mapped land covers types.............................................................. 39
4.6.3.
Land cover maps ................................................................................................. 40
4.7. LVIS Data ................................................................................................................... 42
4.7.1.
LVIS pre-processing ........................................................................................... 42
4.7.2.
Digital terrain model and digital canopy model .................................................. 44
4.7.3.
Verification of the digital canopy model............................................................. 44
4.8. Percolation aproximation ............................................................................................ 46
5. Actual Evapotranspiration and Water Balance Results....................................................... 47
5.1. Spatial-temporal distribution of ETa........................................................................... 47
5.1.1.
11 May, 2003 ...................................................................................................... 47
5.1.2.
24 January, 2005 ................................................................................................. 49
5.1.3.
16 March, 2006 ................................................................................................... 50
5.2. Comparison of aerodynamic roughness values ........................................................... 53
5.3. Bathymetric analysis of the Palo Verde wetland......................................................... 54
5.4. Time series of MODIS images for February 2004...................................................... 55
5.5. Water balance analysis ................................................................................................ 57
6. Conclusions and Recommendations.................................................................................... 59
6.1. Conclusions ................................................................................................................. 59
6.2. Recommendations ....................................................................................................... 60
References ................................................................................................................................... 61
Appendix ..................................................................................................................................... 65
iv
List of figures
Figure 1.1 The “fangueo” activity................................................................................................. 3
Figure 1.2 Management with fires ................................................................................................ 3
Figure 1.3 Restoration of the Huerton Stream .............................................................................. 3
Figure 1.4 Restoration results........................................................................................................ 3
Figure 1.5 Flow chart of the research methods ............................................................................. 5
Figure 2.1 Palo Verde National Park, Costa Rica. ........................................................................ 7
Figure 2.2 Climate diagram for Palo Verde National Park. .......................................................... 9
Figure 2.3 Annual Rainfall of Palo Verde meteorological station from the period 2000-2005. . 10
Figure 2.4 Mean monthly Temperature for a period of 2000-2006 in the Palo Verde Wetland. 10
Figure 2.5 Mean monthly variation of temperature and Relative humidity ................................ 11
Figure 2.6 Geology map.............................................................................................................. 11
Figure 2.7 Land cover changes on Palo Verde Wetland from 1975 to 2000 .............................. 13
Figure 2.8 Land cover wetland 2003........................................................................................... 13
Figure 2.9 Density of Palo Verde................................................................................................ 13
Figure 4.1 Indication of ozone at March 16, 2005. ..................................................................... 27
Figure 4.2 ASTER images from the16 March, 2006 without atmospheric corrections .............. 28
Figure 4.3 Atmospheric corrected image of 16 March 2006....................................................... 28
Figure 4.4 Location of Palo Verde plots and trees ...................................................................... 37
Figure 4.5 Area cover by Typha in 2003 and cattail plants. ....................................................... 38
Figure 4.6 Location of the grass plots and three reference photographs..................................... 38
Figure 4.7 Land cover classification May, 2003......................................................................... 41
Figure 4.8 Land cover classification January, 2005.................................................................... 41
Figure 4.9 Land cover classification March, 2006...................................................................... 41
Figure 4.10 Land cover change from 2003 to 2006 .................................................................... 42
Figure 4.11 Individual waveform of LVIS................................................................................. 43
Figure 4.12 Correlation between the Digital Canopy Model and the elevation of the plots....... 44
Figure 4.13 Digital Elevation Model (DEM) and Digital Canopy Model (DCM)...................... 45
Figure 4.14 Soil samples ............................................................................................................. 46
Figure 5.1 Actual evapotranspiration 11 May, 2003................................................................... 52
Figure 5.2 Actual evapotranspiration 24 January, 2005.............................................................. 52
Figure 5.3 Actual evapotranspiration 16 March, 2006................................................................ 52
Figure 5.4 Actual evapotranspiration changes by land cover ..................................................... 53
Figure 5.5 Relation between the flooded area and the water level.............................................. 54
Figure 5.6 Relation between water storage and the water level .................................................. 55
Figure 5.7 Delimitation of the wetland to 80 cm of depth .......................................................... 56
Figure 5.8 Loses in the Palo Verde wetland from 31 January to 22 February, 2004 .................. 56
Figure 5.9 Time series of actual evapotranspiration ................................................................... 57
v
List of tables
Table 2.1 Wetland vegetation characteristics.............................................................................. 12
Table 4.1 Characteristics of the 3 ASTER sensor systems. ........................................................ 25
Table 4.2 Input parameter of optical depth for the atmospheric corrections............................... 27
Table 4.3 Input parameter of water vapour for the atmospheric correction. ............................... 27
Table 4.4 Input for SMAC of Sun/Satellite angle data ............................................................... 27
Table 4.5 Characteristics of MODIS visible and thermal bands ................................................. 29
Table 4.6 Climate data of the Palo Verde wetland over satellite passing time .......................... 33
Table 4.7 Average of the measures in the Palo Verde Plots by category.................................... 37
Table 4.8 Elevation and Biomass in the samples plots of grass.................................................. 39
Table 4.9 LVIS Ground Elevation data (.lge) ............................................................................. 43
Table 4.10 Approximation of saturated hydraulic conductivity.................................................. 46
Table 5.1 Summary of results of the ETa estimations for 11 May, 2003.................................... 47
Table 5.2 Actual evapotranspiration from ASTER and MODIS images for 11May, 2003 ........ 49
Table 5.3 Summary of results of the ETa estimations for 24 January, 2005 .............................. 49
Table 5.4 Actual evapotranspiration from ASTER and MODIS images for 24 January, 2005 .. 50
Table 5.5 Summary of results of the ETa estimations for 16 March, 2006 ................................ 51
Table 5.6 Actual evapotranspiration from ASTER and MODIS images for 16 March, 2006 .... 51
Table 5.7 Aerodynamic roughness height from NDVI and LVIS............................................... 54
Table 5.8 Water balance for the dry periods ............................................................................... 57
Table 5.9 Water balance for the dry period using the estimation of ETa.................................... 57
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EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
1.
Introduction
Wetlands are among the most valuable and productive ecosystems in the world. They provide
multiple resources, products, and services – depending on their biological, chemical, and
physical characteristics such as: improve water quality by absorbing and filtering out pollutants
and sediments, store floodwater and slow down the force of flood and storm waters as they
travel downstream. They offer habitat for wildlife and support biodiversity. The variety of
living organisms found in wetlands contributes to the health of our planet and our own lives.
Wetlands also provide valuable open space for recreation opportunities (Melesse, Oberg,
Nangia, Beeri, & Baumgartner, 2006).
According to the Ramsar Convention (the 1971 Ramsar Convention on Wetlands of
International importance) wetlands are defined as: “Areas of marsh, fen, peatland, or natural or
artificial water, permanent or temporary, with static water or flowing, fresh, brackish or salt,
including areas of marine water the depth of which at low tide does not exceed 6 meters”.
Wetlands are like an interface between terrestrial and aquatic habitats. There are several types of
them, such as estuaries, aquatic beds, deltas, coastal lagoons, floodplains, shallow lakes,
seasonally flooded areas, freshwater marshes, saline pans and marshes, reservoirs, peatlands,
mangrove swamps freshwater swamps and riparian ecosystems etc. The water depth, duration of
flooding, plants, animals and microbes are different in them, depending on the type, the size and
location of the wetland (Balirwa, 1998).
The resources, services, and production provided by the wetlands arise from four basic
functions: the regulation, carrier, production and information. The regulation functions describe
the capacity of ecosystems to regulate ecological processes that contribute to a healthy
environment; the carrier functions represent the provision by the wetland of suitable space,
substrate, or medium for human activities; the information functions include aesthetic spiritual
and religious, historical and archaeological elements, education, and scientific activities
(Schutter, 2003).
Many wetlands around the world are being lost or are under threat; experts estimate that half of
the world’s wetlands have disappeared since 1900. In many cases losses are caused by the
human impacts such as: irrigation systems, conversion to agriculture land, reclamation for urban
expansion, water flow regulation, pollution, and habitat destruction; however, the ecological
changes can be natural as a result of vegetation succession, erosion, sea-level rise, drought and
hurricanes (Schutter, 2003).
Wetland restoration is designed to restore the functions and values of wetland ecosystems that
have been altered or impacted through removal of vegetation, cropping, construction, filling,
grading, and changes in water levels and drainage patterns. The main goal of a wetland
restoration is to restore the hydrology and vegetation back to their original condition as well as
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EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
to ensure ecological integrity. The first step in wetlands restoration is to restore the hydrology or
water back to the wetland area (Melesse et al., 2006).
Among the various indicators of success in wetlands restoration, hydrology is the most
important and relatively easy to monitor. The hydrology process can be monitoring through the
water balance. As a part in the water balance the evapotranspiration is altered by the
ecohydrologic changes and the restoration activities.
The application of remote sensing methods to estimate evapotranspiration has the advantage of
good spatial resolution and excellent spatial coverage. It is an important tool to study the energy
and material exchanges for large aquatic ecosystems, using data of surface reflection and
emission of radiation in the visible and infrared wavelengths. The availability of a temporal
series of satellite measured data of an ecosystem synchronised with in situ measurements can
also greatly improve the spatial and temporal understanding of biological and abiotic cycles of
the system (Loiselle, Bracchini, Bonechi, & Rossi, 2001).
1.1.
Problem definition
In the last twenty years, changes in the land use on the watershed and in the land cover of the
Palo Verde National Park, Costa Rica, have deteriorated completely the conditions of the
wetlands. Irrigation projects have diverted large amounts of wastewaters to the Park and
changes in hydrologic conditions have lead to the marsh be covered by plant cattail Typha
dominguensis, which colonized about a 60 % of the wetland’s surface until 2002 and a woody
species Palo Verde Parkinsonia aculata (Castillo & Guzman, 2004). Due to their uncontrolled
advances, the marsh’s plant composition, the marsh’s plant composition, structure and
biological function were severely affected. Almost no shallow water bodies could be observed
in the wetland; these changes affect the population of birds negatively. For example, in the past,
there were 35 000 piches Dendrocygna autumnlis and 25 000 Anass discors observed, but in the
late 80’s, the bird population decreased to 5000 individuals. Probably this wetland would have
been disappeared, but it hadn’t, thanks to the restoration activities, i.e. cutting cattail and Palo
Verde trees, and restoration of the flow in the stream Huerton in 2001. Although these efforts
resulted in visible increase of open water surfaces, and as a result, increasing numbers of water
fowls, the effects of those changes on the water balance are unknown, as it is also (partially)
unclear, how the changes in the land cover can affect the hydrological processes in the wetland.
Restoration process
Palo Verde wetland has an importance for aquatic resident and migratory birds and its
hydrological characteristics were registered as RAMSAR site in 1991 by the Costa Rica
Government. Then in 1993, the wetland was included in the Montreux Record, calling for
specific actions to recuperate the previous conditions of the wetland (Ramsar, 1998).
Starting in 1987, several researches were carrying out to define a method of controlling the
cattail; some methods for controlling cattail was using fire, grazing, cutting under water with
“machete”, and by combination of these techniques. Also a technique adapted from rice culture
called “fangueo” (Figure 2.9) to crush it under water using tractors with padding wheels. This
2
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
management technique gave the most efficient results in the removing the cattail from the
wetland.
The management activities began in July 2002, approximately 180 ha of Typha was controlled
by “fangueo” (crushing cattail stems with the paddling wheels). The following year the
restoration process continued with a controlled fire over 200 ha in the wetland, with the
objective to reduce the biomass of the Typha and to make easier access of tractors to the Typha.
Also were carrying out the cutting 15 ha of Palo Verde trees and controlled fire over 200 ha of
the wetland (Figure 1.2). Another activity was the restoration of the freshwater stream with a
construction of an artificial channel (600 m long and 6 m wide) that collects water from the hills
and brings it to the wetland (Figure 1.3). For the year 2006 the control of the Typha had reach
370 hectares of “fangueo” and the objective this restoration was to create water spaces and
places without cattail as is shown in the Figure 1.4 (Trama, 2005).
Figure 1.1 The “fangueo” activity
Figure 1.2 Management with fires
Figure 1.3 Restoration of the Huerton Stream
Figure 1.4 Restoration results
Pictures: F. Trama, O. Arias and J. Guzmán
1.2.
General objective
ƒ
To define the effects of vegetation removal on the evapotranspiration in the Palo Verde
Wetland.
3
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
1.3.
ƒ
ƒ
ƒ
ƒ
1.4.
Specific objective
To create a land cover map and ETa from ASTER images (2003, 2005 and 2006).
To evaluate the accuracy of the ETa mapping using RS against the field measurements
(meteorological station data and water levels).
To determine a time series of evapotranspiration for the wetland using MODIS.
To establish the water balance of the Palo Verde Wetland.
Research question
ƒ
Do the land cover changes in the wetland have a significant effect on the
evapotranspiration?
ƒ
What is the influence of cattail Typha dominguensis on the actual evapotranspiration?
ƒ
Can the accuracy of the ETa estimation be improved using Remote Sensing?
1.5.
Reseach hyphotesis
The land cover changes affect the evapotranspiration and these effects can be measured using
remote sensing techniques
1.6.
Reseach methods
The following steps describe the procedure of this research:
Literature review
The literature review was based on documents focusing on: wetland ecology, management and
restoration of wetlands, evapotranspiration, SEBS, aerodynamic roughness, wetland
evaporation, LVIS data, ASTER and MODIS images, actual evapotranspiration of cattail.
Collection of the existing data
Some data was colleted from the Organization for Tropical Studies, e.g. the meteorological data,
elevation points, water levels, land cover map, soils map, and the geological map. The ASTER
images were obtained through ITC. The data from MODIS images and LVIS data can be
downloaded from the Internet free of charge.
Fieldwork campaign
In the fieldwork soils samples were taken and biometric measurements were made on plots of
grass. The biomass and the infiltration of the soil samples were analyzed in the Escuela de
Ingeniería Forestal, Instituto Tecnológico de Costa Rica (ITCR), in Cartago.
Data analysis and interpretation
The land cover changes were analyzed using ASTER images. For the analysis of the actual
evapotranspiration ASTER images and a time series of MODIS images were used. Up-scaled
Aster image based ETa maps were used to define the reliability of MODIS-based ETa mapping.
Losses in the wetland were compared to the water levels of the lagoon and with the losses given
4
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
by the time series of actual evapotranspiration. Finally two aerodynamic roughness estimation
methods were compared for the mapping of actual evapotranspiration. One approach uses data
from a LIDAR sensor, that gives the elevation of the top of the canopy and the other approach
uses a relationship of the aerodynamic roughness and the vegetation index (NDVI).
Final assessment
The results obtained in the analysis part were used to identify the effect of the vegetation on the
actual evapotranspiration and to define the losses by percolation. Also is essential to determine
the utility of LIDAR data in the determination of aerodynamic roughness and their effect in the
estimation of actual evapotranspiration.
Literature review
Data Collection
Meteorological data, LVIS Data,
ASTER and MODIS images
Pre-Fieldwork
Fieldwork
Fieldwork
Grass plot and soil
samples
Post-Fieldwork
Data analysis interpretation
Land cover changes
Actual
Evapotranpiration and
time series
LVIS
Digital Canopy
Model and Zom
Final assessment
ETa by land cover,
Losses by ETa and percolation.
ETa integrating LVIS
Figure 1.5 Flow chart of the research methods
5
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
6
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
2.
Description of the Study Area
Costa Rica is located in the tropical zones of the American continent (Neotropics), with a land
area of only 51.100 km2. The geographic position consists in two coasts and mountainous
system, which provides numerous and varied microclimates, these are some of the reasons that
explain this natural wealth, both in terms of species and ecosystems.
2.1.
Location
The Palo Verde Wetland is located in the floodplain areas of the Tempisque Riverin the
Guanacaste Province 20 km to the south of Bagaces City (10˚20’45” North and 85˚20’62”
West), in the northwest of Costa Rica, Central America. This wetland is in the Palo Verde
National Park. The location of the study area is shown in the Figure 2.1
Figure 2.1 Palo Verde National Park, Costa Rica.
The Palo Verde National Park has an extension of 19 800 hectares, almost 60 % of the Park is
covered by seasonal and permanent wetlands. This group of wetlands in the lower Tempisque is
one of the most important wetlands in Central America for the population of migratory and
nesting ducks from North America. It has also a great importance for hosting species that are
considered endangered by the current legislation. Palo Verde wetlands are the site of extreme
importance for nesting, resting and wintering for more than 60 species of aquatic birds. It is the
main habitat for the Jabiru mycteria in Costa Rica, where the characteristics of this species
ensure nesting because of the progressive deterioration of the habitat in the surrounding areas.
(Ramsar, 1998).
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EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
By those reasons, Palo Verde wetland was declared a Ramsar site in 1991, together with the
other wetlands inside the National Park as: Bocana, Poza Verde, Piedra Blanca and Nicaragua
wetlands.
2.2.
Types of wetlands
The water sources determine the type of wetland. Also the magnitude and the period of water
level fluctuation have a profound impact upon the character of wetland. From the point of view
of their plant ecology, three broad, intergrading types can be recognised in the wetland:
permanent, seasonal and fluctuating; wetland vegetation, if it develops at all, is represented by
opportunistic, ephemeral species that temporarily colonise exposed, moist substrate (Baird &
Wilby, 1999).
According to the Ramsar Convention and scientific literature, wetlands can be classified
in tree main types: marine/coastal, inland and artificial (Schutter, 2003):
ƒ
Marine/Coastal Wetlands: Including open coast, coral reefs, estuaries, tidal flats,
mangrove forest, and costal lagoons. From this class Palo Verde wetlands
contain the followings types:
A-Permanent shallow marine waters in most cases less than six metres deep at low tide;
includes sea bays and straits.
G-Intertidal mud, sand or salt flats.
H-Intertidal marshes; includes salt marshes, salt meadows, saltings, raised salt marshes;
includes tidal brackish and freshwater marshes.
I-Intertidal forested wetlands; includes mangrove swamps, nipah swamps and tidal freshwater
swamp forests.
J-Coastal brackish/saline lagoons: brackish to saline lagoons with at least one relatively
narrow connection to the sea.
K-Coastal freshwater lagoons; includes freshwater delta lagoons.
ƒ
Inland Wetlands: including permanent and seasonal rivers, inland deltas and
floodplains, permanents and seasonal lakes and ponds, marsh, freshwater,
swamp forests, and peatlands. In the Palo Verde wetland are possible to find:
M-Permanent rivers/streams/creeks; includes waterfalls.
P-Seasonal/intermittent freshwater lakes (over 8 ha); includes floodplain lakes.
R-Seasonal/intermittent saline/brackish/alkaline lakes and flats.
Ss-Seasonal/intermittent saline/brackish/alkaline marshes/pools.
Ts-Seasonal/intermittent freshwater marshes/pools on inorganic soils; includes sloughs,
potholes, seasonally flooded meadows, sedge marshes.
W-Shrub-dominated wetlands; shrub swamps, shrub-dominated freshwater marshes, shrub
carr, alder thicket on inorganic soils.
Xf-Freshwater, tree-dominated wetlands; includes freshwater swamp forests, seasonally
flooded forests, wooded swamps on inorganic soils.
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EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
ƒ
Artificial: human-make wetlands, including reservoirs, aquaculture ponds, excavations
and borrow pits, wastewater treatment ponds and irrigation canals, ditches, and rice
fields. Palo Verde wetland have four different types of wetlands artificial:
2-Ponds; includes farm ponds, stock ponds, small tanks; (generally below 8 ha).
3-Irrigated land; includes irrigation channels and rice fields.
9-Canals and drainage channels ditches.
2.3.
Climate
The Palo Verde region is a tropical dry area, the climate is characterized by two seasons; one is
the dry period from December to April and the wet period from May to November. During July
and August, there is a period of reduction in rainfall know as “Veranillo de San Juan”, that
occurs due to an increase in the speed of the trade winds and slight southerly shift of the Intertropical Convergence Zone. Figure 2.2 shows a climate diagram where the mean monthly
temperature (˚C) and the monthly precipitation (mm) are scaled to the dry, humid and per humid
months. Dry months are represented by the dotted areas, when there deficit of water; humid
months by the vertical lines and months with rain in excess of 100 mm (per humid months) are
in solid black (Jiménez, Gonzalez, & Mateo-Vega, 2001; Kalácska, 2003).
Figure 2.2 Climate diagram for Palo Verde National Park.
2.3.1.
Rainfall
The collected rainfall data in the Palo Verde biological station started in 1996 with a manual
pluviometer, later in 1999 was installed automatic meteorological station a GroWeather. This
station continued working till 2003 when it was replaced by a Vantage Pro weather station
(www.davisnet.com). The average annual precipitation from 1996 to 2006 is 1300. mm yr-1,
September is the month with the highest rainfall with, around 300 mm, while the lowest
precipitation is observed in the months of January to March. The average rainfall is less than 10
mm in every month (Figure 2.3).
9
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
350
300
Rainfall (mm)
250
200
150
100
50
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Month
Figure 2.3 Annual Rainfall of Palo Verde meteorological station from the period 2000-2005.
2.3.2.
Temperature and relative humidity
The average annual temperature is 27.6 ˚C; the mean maximum temperature is around 34 ˚C and
the minimum in 24 ˚C. Maximum temperatures occur in March and April with 40 ˚C and the
lowest are from September to December, around of the 21 ˚C. in the Figure 2.4 the annual
average of min, mean and max temperatures are shown.
40
38
36
Temperature (C)
34
32
30
28
26
24
22
20
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Months
Max
Mean
Min
Figure 2.4 Mean monthly Temperature for a period of 2000-2006 in the Palo Verde Wetland.
The mean monthly average of relative humidity is 75 %, in the dry months from January to
April the relative humidity reach the ~60%, for the wet period the average is around the 85 %.
10
100
30
90
29
80
28
70
27
60
26
RH %
50
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Mean T
Oct
Nov
Temperature (C)
Relative humidity (%)
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
25
Dec
Months
Figure 2.5 Mean monthly variation of temperature and Relative humidity
2.4.
Geology, geomorphology and soils
A large part of this area is formed by alluvial fill composed mainly by volcanic sediments
transported by the Tempisque River and to a lesser degree, by sediments of Cretaceous origin.
There are also fluvial, colluvial and coastal deposits from the floodplains of the Tempisque
River basin, marsh areas and small-scale intrusive process, in the Figure 2.6 is shown the
geology map. The geomorphology: over all, the Palo Verde National Park is a system with
poorly developed drainage with a few intermittent streams that drain marsh areas during the
rainy season. They have a flat morphology with elevation ranging between 0 and 230 m.a.s.l.
forming a sedimentary unit with influence by annual flooding. Several sectors are affected by
influence of tides, primarily in the lower of the basin (Jiménez et al., 2001).
Figure 2.6 Geology map
11
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
The soil type in the wetland is vertisol. This group is characterized by dark brown or black,
clay-rich soil, wide cracks for some time during the year and have slickensides within 100 cm of
the mineral soil surface. They shrink when dry and swell when moistened. Shrink-well
processes in soils are related to the total content of clay, the content of fine clay and mineralogy.
Vertisols generally have high clay content (50 to 70 %) and a relatively large proportion of fine
clay in the clay fraction (more 50 %). The clays consist of 2:1-layer clay minerals (USDA,
1999). The permeability of these soils is very low and the soil moisture regimen is udic. The
type soil in the wetland is Typic Pellustert/Typic Pelludert.
2.5.
Vegetation and land cover
The behaviour of the seasonal wetland, the topography and the depth of the water are factors
that affect the vegetation. It is possible to find species like Palo Verde Parkinsonia aculata and
Eleocharis sp in shallow places. In depths between 40 and 80 cm there are species of grass and
Typha. Finally, floating species appear when the water gets deeper than 80 cm. When the rainy
season starts the floating species such as, Neptunia plana and Eichornia crassipes dominate the
areas. However, these species disappear in the dry season. There are other areas of the wetland
dominated by emergent vegetation and invading plants as: Palo Verde and Thalia geniculata.
The trees and shrub are distributed around the marsh where the water is shallower, Palo Verde is
the most abundant specie in those areas, the characteristics of these species are shown in the
Table 2.1 (Trama, 2005).
Table 2.1 Wetland vegetation characteristics
Vegetation
Eichornia crassipes
Neptunia plana
Thalia geniculata
Eleocharis mutata
Hymenachne amplexicaulis
Fimbristylis spadicea
Typha dominguensis
Parkinsonia aculata
Life Form
Floating
Floating
Emergent
Emergent
Emergent
Emergent
Emergent
Shrubs
Height
up 40 cm
up 30 cm
2-4 m
up to 1m
up to 3.5 m
up to 2 m
2-4 m
up to 5 m
Source: (Crow, 2002)
The Palo Verde wetland had suffered changes in the land cover, since 1975, when the
predominant (approximate 85 %) the land cover was floating vegetation, open water and
pasture. However in 1986 the Typha had already colonized the 57% of the wetland and the
shrub covered only 6%. By 1992, there was an increase in the area of shrub (to 18%) and a fall
in the Typha (to 53%). Typha and Palo Verde are the categories dominant in 2000, taking an
extension of 815 ha of the wetland a 70% of the area (Figure 2.7) (Castillo & Guzman, 2004).
12
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Figure 2.7 Land cover changes on Palo Verde Wetland from 1975 to 2000
As a part of the restoration program, a research was carried out on the composition of the
vegetation cover in the wetland. (Solano, 2004) studied the distribution and abundance of
Parkinsonia aculeate and Typha dominguensis(Figure 2.8 and Figure 2.9) using MASTER
images (Hook, Myers, Thome, Fitzgerald, & Kahle, 2001).
Figure 2.8 Land cover wetland 2003
Figure 2.9 Density of Palo Verde
13
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
14
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
3.
Overview of Water Balance
(Dingman, 2002) cited the concepts of the water balance approach, which involve the
application of the water balance equation to the water body over a time period ∆t:
W + SWin + GWin – Sw out – Gw out – ET = ∆V
(3.1)
Here, W is precipitation on the wetland; SWin and SWout are the inflows and outflows of the
surface water, respectively; GWin and Gw out are the inflows and outflows of ground water,
respectively; ET is the evapotranspiration and ∆V is the change in the amount of water stored in
the wetland during ∆t; and all quantities have dimensions of either volume or volume per unit
wetland area.
The concept of water balance provides a framework for studying the hydrological behaviour of
a catchment. It is useful for assessing how changes in catchment conditions can alter the
partitioning of rainfall into different components. Precipitation is the largest term in the water
balance equation, and it varies both temporally and spatially. For most of the hydrological
applications it is appropriate to assume that precipitation is independent of vegetation type.
Evapotranspiration is the second or third largest term in the water balance equation, and is
closely linked with the vegetation characteristics. In humid regions it is limited by available
energy (Zhang, Dawes, & Walker, 2001).
3.1.
Concept of evapotranspiration
Evapotranspiration is a collective term of all processes whereby water is lost from the (soil)
surface by evaporation and from the plants by transpiration. Both evaporation and transpiration
occur simultaneously and there is no easy way of separating the two processes (Dingman,
2002). In other words, evapotranspiration is the term applied to the removal of water from the
land surface through a combination of direct evaporation and vegetation transpiration
(Wegehenkel & Kersebaum, 2005).
Potential evapotranspiration (ETpot or ETc) is the maximum evapotranspiration according to
prevailing atmospheric conditions and vegetation properties. The land surface in question
should well supply by water such that soil moisture forms no limitation to the stomatal aperture.
Reference crop evapotranspiration (ETref or ET0) the maximum possible evapotranspiration
of a reference crop (usually clipped grass) according to prevailing atmospheric conditions and
constants biophysical properties. Experimentally determined ratios of ETc /ET0, called crop
coefficients (Kc), are used to relate ETc to ET0 (Allen & Fao, 1998). ETc is given by:
ETc = Kc *ET0
(3.2)
15
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
The actual evapotranspiration (ETa) is an indicator of how much water the crop and
vegetation need for healthy growth and production. This is the real amount of water consumed
by the crops soil and vegetation.
3.2.
Wetland evaporation
Different functional groups of vegetation may have very different rates of water conductance to
the atmosphere. Rooted, emergent vascular plants may be of particular importance with respect
to ET, because transpiration may progress unimpeded even in wetlands with no standing water
as long as roots have access to the groundwater table. Other aspects of vegetation may also
influence ET including plant density, species diversity, height and roughness of the dominant
canopy, number of canopies, leaf characteristics, depth of litter layer and phenology. The albedo
of vegetation may have a strong influence over ET depending on biochemical properties,
orientation of leaves and leaf area index. Albedo of wetlands may change during the year due to
emergence and senescence of the dominant vegetation, which in turn changes the relative cover
of vegetation, open water and bare ground (Drexler, Snyder, Spano, & Paw, 2004).
The characteristics of open water areas that affect ET include depth of water, whether water is
standing or flowing and water temperature. These factors influence how much energy the water
will ultimately absorb, which in turn affects how much energy is subsequently available for ET.
In most of the situations, ET is greatly increased by a dense stand o actively growing littoral
vegetation, as compared with evaporation rates from open water (Baird & Wilby, 1999).
Although there has been considerable controversy over whether open water or vegetated areas
contribute more to ET in wetlands the fact that wetlands are often a complex mixture of both
leads to considerable difficulties in measurement. ET Wetlands surrounded by surfaces with low
evapotranspiration (e.g. bare dry soil) tend to have higher ET than those surrounded by forests
(i.e. the oasis effect). Furthermore, long narrow wetlands such as riparian zones and marsh
fringes around lakes tend to have higher ET rates than large expanses of wetlands with greater
area-to perimeter ratios (Drexler et al., 2004).
3.3.
Methods of evapotranspiration estimation
Three methods can be used to estimate the evaporation at a regional scale: by up-scaling point
or field measurements, by remote sensing techniques and by hydrological modelling. Each of
the three methods has its limitations, and an optimal procedure probably would be a
combination of the three approaches (Mohamed, Bastiaanssen, & Savenije, 2004).
3.3.1.
Methods based on field measurements
Traditional means, such as the pan evaporation method, the FAO56 method, the Bowen ratio,
eddy correlation, and aerodynamic techniques. A root-zone soil water balance approach, based
on water budget is also a technique used to estimate ET as a residual variable, flux profile
measurements. Relatively simpler point methods use lysimeter instrumentation, scintillometer
measurements (Abtew, 1996, 2001; Drexler et al., 2004; Gieske, 2003; Immerzeel, Droogers, &
Gieske, 2006; Lott & Hunt, 2001; Melesse et al., 2006; Rosenberry, Stannard, Winter, &
16
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Martinez, 2004). There are other methods such as Penman-Monteith, Makkink, Turc Hargreaves
(Jacobs, Anderson, Friess, & Diak, 2004)
3.3.2.
Hydrological models
Many models exist that take into account the subsurface processes to account for losses due to
surface runoff, bare soil evaporation, plant respiration and deep ground water recharge, e.g.
modelling of moisture transport though the unsaturated zone may be used to assess soil
evaporation and plant respiration rates, surface runoff modelling on a basin wide scale may be
used to arrive at spatially and temporally varying soil moisture conditions, and through this at
the actual evapotranspiration all locations of the basin (Gieske, 2003). Among of the
hydrological models well know are: Soil-Vegetation-Atmosphere (SVAT), MIKE SHE
(Zacharias, Dimitriou, & Koussouris, 2004); SWAP, SLURP (Kite & Droogers, 2000).
3.3.3.
Remote sensing techniques
The success using remote sensing imagery for estimated at various spatial scales has expanded
greatly during the last 25 years. Remote sensing has the capability to acquire spectral signatures
instantaneously for large areas information; the data allow the extraction of land cover,
vegetation cover, emissivity, albedo, surface temperature and energy flux information (Melesse
et al., 2006). The common denominator in most methods is that they allow spatially distributed
estimates of ET and incorporate (parts of) the energy balance theory, the Monin-Obukhov
similarity hypothesis and/or the flux profile relationships. Different examples of methods found
in literature in increasing order of complexity and data needs (Immerzeel et al., 2006).
The Remote sensing methods can be classified in: Thermal infra-red empirical methods,
feedback approach, land parameterization and Remote Sensing (Chen, Kan, Tan, & Shih, 2002;
Kite & Droogers, 2000) and the models base in the energy balance and similarity theory
methods as, The Surface Energy Balance Algorithm for Land SEBAL, (Bastiaanssen, Menenti,
Feddes, & Holtslag, 1998; Chen et al., 2002; Hailegiorgis, 2006; Jacobs et al., 2004; Jacobs,
Myers, Anderson, & Diak, 2002; Melesse et al., 2006; Mohamed et al., 2004; Su, 2002), TwoSources Energy balance TSEB (French et al., 2005), and Surface Energy Balance System SEBS
(Hailegiorgis, 2006; Jia et al., 2003; Su, 2002; Wenjing, 2006).
3.4.
Surface energy balance system (SEBS)
Surface Energy Balance System was developed for the estimation of atmospheric turbulent
fluxes using satellite earth observation data more coherently. SEBS as proposed by (Su, 2002)
consists of:
- A set of tools for the determination of the land surface physical parameters, such as albedo,
emissivity, temperature, vegetation coverage etc. from spectral reflectance and radiance.
- An extended model for the determination of the roughness length for heat transfer.
- A new formulation for the determination of the evaporative fraction on the basis of energy
balance at limiting cases.
SEBS requires as inputs three sets of information. The first set consists of land surface albedo,
emissivity, temperature, fractional vegetation coverage and leaf area index, and the height of the
17
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
vegetation (or roughness height). When vegetation information is not explicitly available, the
Normalized Difference Vegetation Index (NDVI) is used as a surrogate. These inputs can be
derived from remote sensing data in conjunction with other information about the concerned
surface. The second set includes air pressure, temperature, humidity, and wind speed at a
reference height. The reference height is the measurement height for point application and the
height of the planetary boundary layer (PBL) for regional application. This data set can also be
variables estimated by a large scale meteorological model. The third data set includes downward
solar radiation, and downward longwave radiation which can either be directly measurements,
model output or parameterization (Su, 2005) .
3.4.1.
Surface energy balance terms
The energy coming from the sun and atmosphere in form of short and long wave radiation is
dissipated on the ground. The total available energy is transformed and used for several
purposes: Heat up the soil (Soil heat flux), Heat up the surface environment (sensible heat flux)
and transform water into vapour (latent heat flux). The surface energy balance is written in
SEBS as:
Rn = G0 + H + λE
(3.3)
Where Rn is the net radiation, G0 is the soil heat flux, H is the turbulent sensible heat flux,
and λE is the turbulent latent heat flux ( λ is the latent heat of vaporization and E is the actual
evapotranspiration).
3.4.2.
Net radiation, Rn
The equation to calculate the net radiation is given by
Rn = (1 − α ) ⋅ Rswd + ε ⋅ Rlwd − ε ⋅ σ ⋅ T 04
(3.4)
where α is the albedo, Rswd is the downward solar radiation, Rlwd is the downward longwave
radiation, ε is the emissivity of the surface, σ is the Stefan-Bolzmann constant, and T0 is the
surface radiative temperature measured by a remote sensor. α , ε and T0 can be derived from
remote sensing data from the visible to the thermal infrared spectral range.
Solar radiation ( Rswd )
Rswd = I sc ⋅ e0 ⋅ cosθ z ⋅ exp(− m ⋅τ )
(3.5)
Where, I sc = 1367 W ⋅ m is the solar constant, 0 the eccentricity factor, θ z the solar zenith
angle, m the air mass, and τ the optical thickness. Details on the determination of all the
parameters can be found in (Iqbal, 1983).
−2
18
e
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
The downward longwave radiation Rlwd can be calculated as:
Rlwd = ε aσTa4
(3.6)
where ε a is the emissivity of the atmosphere which can be estimated using the Swinbank
formula as given by (Campbell & Norman, 1998) in the for:
ε a = 9.2 ⋅10 −6 ⋅ (Ta + 273.15)2
(3.7)
With Ta is the air temperature at the reference height.
The out going long wave radiation ( Rlwu ) is determined as a function of surface temperature
and emissivity as:
Rlwu = ε ⋅ σ ⋅ T 04
3.4.3.
(3.8)
The soil heat flux, G0
The equation to calculate the soil heat flux is parameterised as,
G0 = Rn ⋅ [Γc + (1 − f c ) ⋅ (Γs − Γc )]
(3.9)
in which it is assumed that the ratio of soil heat flux to net radiation Γc = 0.05 for full
vegetation canopy by Monteith and Γs = 0.315 for bare soil by Kustas and Daughtry cited in
(Su, 2002). An interpolation is then performed between these limiting cases using the fractional
canopy coverage, f c , which can be determined from remote sensing data.
3.4.4.
The sensible heat flux
To determine the sensible heat flux, similarity theory is applied. The relationships for the mean
wind and temperature profiles are written in integral form as,
⎡ ⎛ z − d0 ⎞
z − d0 ⎞
⎛ z ⎞⎤
⎟⎟ − Ψm ⎛⎜
⎟ + Ψm ⎜ 0 m ⎟⎥
⎢ln⎜⎜
⎝ L ⎠
⎝ L ⎠⎥⎦
⎢⎣ ⎝ z0 m ⎠
(3.10)
⎡ ⎛ z − d0 ⎞
z − d0 ⎞
⎛ z ⎞⎤
⎟⎟ − Ψh ⎛⎜
⎟ + Ψh ⎜ 0 h ⎟⎥
⎢ln⎜⎜
ku* ρC p ⎢⎣ ⎝ z0 h ⎠
⎝ L ⎠⎥⎦
⎝ L ⎠
(3.11)
u=
θ0 − θa =
u*
k
H
where z is the height above the surface, u* = (τ 0 ρ )
12
is the friction velocity, τ 0 is the
surface shear stress, ρ is the density of air, Cρ is the heat capacity of the dry air, k = 0.4 is the
19
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Von Karman constant, d 0 is the zero plane displacement height, z 0 m is the roughness height
for momentum transfer, θ 0 is the potential temperature at the surface, θ a is the potential air
temperature at height z , z 0 h is the scalar roughness height for heat transfer, ψ m and ψ h are
the stability correction functions for momentum and sensible heat transfer respectively, L is the
Obukhov length (m) given by,
L=−
ρC p u*3θ v
kgH
(3.12)
Where g is the acceleration due to gravity, θ v is the potential virtual temperature near the
surface (K). The actual sensible heat flux (H) is determined by using the friction velocity (u*)
and stability length (L) obtained by solving the system of no linear equations (3.10), (3.11) and
(3.12). The generation of the sensible heat flux in SEB models is driving by the difference
between the aerodynamic temperature (Taero) or _0 and the air temperature (Ta) or _a. The
difference between these two temperatures is corrected for stability in the Atmospheric
Boundary Layer (ABL) and Atmospheric Surface Layer (ASL) to obtain the sensible heat flux
(Su, 2002).
3.4.5.
Evaporative fraction
In order to determine the evaporative fraction, energy balance considerations at limiting cases
are used. Under the dry-limit, the latent heat (or the evaporation) becomes zero due to the
limitation of soil moisture, and the sensible heat flux is at its maximum value. From Equation
(3.3), it follows,
λEdry = Rn − G0 − H dry ≡ 0, or
H dry = Rn − G0
(3.13)
Under the wet-limit, where the evaporation takes place at potential rate, λE wet , (i.e. the
evaporation is only limited by the available energy under the given surface and atmospheric
conditions), the sensible heat flux takes its minimum value, H wet , that is
λE wet = Rn − G0 − H wet , or
H wet = Rn − G0 − λE wet
(3.14)
The relative evaporation then can be evaluated as
Λr =
λE − λE
λE
= 1 − wet
λE wet
λE wet
(3.15)
Substitution of Equations (3.3), (3.13) and (3.13) in Equation (3.15) and after some algebra, one
obtains,
20
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Λr = 1−
H − H wet
H dry − H wet
(3.16)
Equations (3.3) to (3.16) constitute the basis formulation of SEBS. Further, in SEBS the actual
sensible heat flux H is obtained by solving a set of non-linear equations and is constrained in
the range set by the sensible heat flux at the wet limit H wet , and the sensible heat flux at the dry
limit H dry given in the by the equation (3.13).
The sensible heat flux at the wet limit is given by,
ρC p es − e ⎞
⎛
⎟
⋅
H wet = ⎜⎜ (Rn − G0 ) −
γ ⎟⎠
rew
⎝
⎛ Δ⎞
⎜⎜1 + ⎟⎟
⎝ γ⎠
(3.17)
The aerodynamic resistance for wet surface conditions can be derived with,
rew =
1 ⎡ ⎛ z − d0
⎢ln⎜
ku* ⎣ ⎜⎝ z 0 h
⎞
⎛ z − d0
⎟⎟ − Ψh ⎜⎜
⎠
⎝ Lw
⎞
⎛z
⎟⎟ + Ψh ⎜⎜ 0 h
⎠
⎝ Lw
⎞⎤
⎟⎟⎥
⎠⎦
(3.18)
The stability length suitable from wet conditions is estimated as,
Lw = −
3.4.6.
ρu*3
kg ⋅ 0.61⋅ (Rn − G0 ) λ
(3.19)
The roughness length for heat transfer
The scalar roughness height for heat transfer, z 0 h , changes with surface characteristics,
atmospheric flow and thermal dynamic state of the surface, can be derived from the roughness
model for heat transfer was proposed by Su. However, in their model a functional form to
describe the vertical structure of the vegetation canopy is needed in order to calculate the
within-canopy wind speed profile extinction coefficient, nec . For local studies, this information
is easily obtained, but for large scale applications, it is generally impossible to obtain detailed
information on the vertical structure of the canopy. In SEBS, nec , is formulated as a function of
the cumulative leaf drag area at the canopy top,
nec =
C d ⋅ LAI
2u*2 u (h )
2
(3.20)
Where C d is the drag coefficient of the foliage elements assumed to take the value of 0.2, LAI
is the one-sided leaf area index defined for the total ground area, u (h) is the horizontal wind
speed at the canopy top. The scalar roughness height for heat transfer, z 0 h , can be derived from
21
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
(
z 0 h = z 0 m / exp kB −1
)
(3.21)
Where B −1 is the inverse Stanton number, a dimensionless heat transfer coefficient. To estimate
the kB −1 value, an extended model of Su et al. (2001) is proposed as follows,
kCd
−1
kB =
4Ct
(
u*
1 − e − nec
u (h )
2
)
k ⋅ u* u (h ) ⋅ z0 m h
f + 2 fc f s
+ kBs−1 f s2
*
Ct
2
c
(3.22)
Where f c is the fractional canopy coverage and f s is its compliment. Ct is the heat transfer
coefficient of the leaf. For most canopies and environmental conditions, Ct is bounded as
0.005 N ≤ Ct ≤ 0.075 N ( N is number of sides of a leaf to participate in heat exchange),
The heat transfer coefficient of the soil is given by
Ct* = Pr −2 / 3 Re *−1 / 2
(3.23)
Where Pr is the Prandtl number, Re is the the roughness Reynolds number
The roughness Reynolds number
Re * = hs u* ν ,
(3.24)
With hs the roughness height of the soil and v is the kinematic viscosity
The kinematic viscosity is given by,
ν = 1.327 ⋅ 10 −5 ( p0 p )(Ta Ta 0 )1.81
(3.25)
With p and Ta the ambient pressure and temperature and p0 = 101.3 kPa and Ta 0 = 273.15
3.4.7.
Turbulent heat fluxes and actual evaporation
The actual sensible and latent heat fluxes is expressed as,
H = (1 − Λ ) ⋅ (Rn − G )
λE = Λ ⋅ (Rn − G )
When the evaporative fraction is known, the daily evaporation can be determined as
22
(3.26)
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
24
E daily = 8.64 × 10 7 × Λ ×
Rn − G0
0
(3.27)
λρW
24
where E daily is the actual evaporation on daily basis ( mm ⋅ d −1 ). Λ is the daily average
0
evaporative fraction, which can be approximated by the SEBS estimate since the evaporative
fraction is conservative. Rn and G0 are the daily net radiation flux and soil heat flux, λ is the
latent heat of vaporization ( JKg −1 ), ρ w is the density of water ( Kg ⋅ m −3 ).
Since the daily soil heat flux is close to zero because of the downward flux in daytime and the
upward flux at night balance each other approximately, the daily evaporation only depends on
the net radiation flux given by
↓
R n = (1 − α ) K 24
+ εL 24
(3.28)
↓
where K 24
is the daily incoming global radiation and L24 is daily net longwave radiation. The
daily average albedo, α , and emissivity, ε , can be approximated easily with the same values as
used previously in the energy balance equation.
↓
The daily incoming global radiation K 24
a function of geometric and atmospheric factors and it
can be estimated as,
⎡⎛
n ⎞ ↓ exo ⎤
↓
K 24
= 11.5741 ⋅ ⎢⎜ a s + bs ⋅ ⎟ ⋅ K 24
⎥
N⎠
⎣⎝
⎦
↓
Where, K 24
exo
is the daily terrestrial solar radiation,
(3.29)
n
is the sunshine fraction, as is 0.25 and
N
bs is 0.5.
↓
Daily terrestrial solar radiation K 24
↓
K 24
exo
=
24
π
exo
at the point of consideration is defined as,
⋅ SC ⋅ E 0 ⋅ sin φ ⋅ sin δ ⋅ (ω s − tan ω s )
(3.30)
Where, Sc is the solar constant, and the sunrise hour angle ωs, is given below,
cos ω s = − tan (φ ) ⋅ tan (δ )
For the sunshine fraction
(3.31)
n
, n is the amount of hours that the sun was actually shining for a
N
certain day and location and it can be obtained from meteorological observation. N is the total
hours of sunshine for a perfect clear day, which is given by,
23
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
N=
360
⋅ ωs
15 ⋅ π
(3.32)
The daily net longwave radiation ( L24 ) is usually estimated as
L24 = −100τ
(3.33)
Where, τ is determined from the sunshine fraction as,
τ = a s + bs ⋅
n
N
(3.34)
For this research the was used a ILWIS script for SEBS algorithm, which was created by (Sine
Hailegiorgis, 2006) in his thesis.
24
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
4.
Data Processing
4.1.
ASTER Images
The Advanced Spaceborne Thermal Emission and Reflection Radiometric (ASTER) is the
Japanese high performance earth observation sensor that is carried on-board of the TERRA
(EOS-AM1), launched on December 19, 1999. The orbit is sun-synchronous, near-polar orbit
with a 10:30 a.m. (±15 min) equatorial crossing time to minimize the cloud effects on
observations of the land and oceans. TERRA is equipped with 5 instruments: ASTER CERES,
MISR, MODIS and MOPITT (Kaufman, Herring, Ranson, & Collatz, 1998). ASTER sensor is
characterized by has 14 channels between the spectral range of 0.5 to 12 µm, repeatability of 16
days, swath of 60 km and with stereo capability. Table 4.1 shows the characteristics of the
ASTER sensor.
Table 4.1 Characteristics of the 3 ASTER sensor systems.
Subsystem
VNIR
SWIR
TIR
Band No.
1
2
3N
3B
4
5
6
7
8
9
10
11
12
13
14
Spectral Range (µm)
0.52-0.60
0.63-0.69
0.78-0.86
0.78-0.86
1.60-1.70
2.145-2.185
2.185-2.225
2.235-2.285
2.295-2.365
2.360-2.430
8.125-8.475
8.475-8.825
8.925-9.275
10.25-10.95
10.95-11.65
Spatial Resolution
15 m
30 m
90 m
Source: (Abrams & Hook, 2002)
4.1.1.
ASTER pre-processing
The ASTER images for the dry period between 2003 and 2006 were found using the USGS
Global Visualization Viewer (http://glovis.usgs.gov/); the acquisition of the images was through
RSG laboratory of the ITC. The following images selected: May 11, 2003, January 24, 2005,
and March 16, 2006. The data are in the level 1B: radio-metrically calibrated, geometrically
corrected and with scaled radiance at the sensor, the format of the files is HDF-EOS
(Hierarchical Data Format). In ILWIS is possible to read ASTER images level 1B. A
25
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
radiometric pre-processing was carried out for converting the radians values to reflectance.
Related equations, parameters and the script are presented in the Appendix A.
4.2.
Atmospheric correction
The presence of the heterogeneous, dense and layered atmosphere composed by different gases,
water vapour and aerosols causes scattering and absorption of the electromagnetic energy. All of
these effects produce disturbances in the signal reaching the sensor, so techniques on
atmospheric corrections (AC) are needed to retrieve the actual at-surface radiance from target.
Generally, these methods are grouped into two classes: relative AC methods, based on known
ground reflectance properties of certain objects; and absolute AC methods based on modelling
atmospheric processes (Parodi, 2006).
Absolute AC methods based on atmospheric processes require a description of the air
components in the atmospheric profile. The output of these methods is an image that matches
the reflectance of the ground pixel with a maximum estimated error of 10 %, if the atmospheric
parameters are known. LOWTRAN, MODTRAN, Code 5S and 6S are all reference Radiative
Transfer Models (RTM). These methods need a complex set of atmospheric parameters like
aerosols properties, ozone and water vapour content.
RTM’s usually used in a research environment, where several scenarios are built to solve the
large number of possible combinations of reflectance, view geometry and atmospheric
conditions. There are also “simplified” radiative transfer models for image processing like
ATCOR, FLAASH and SMAC (Parodi, 2006).
4.2.1.
Atmospheric correction with SMAC
SMAC is a simplified radiative transfer models in which depend on the description of
atmospheric conditions but the load of input information is reduced to some more widely
measured “standard parameters”. Additionally, they are several hundred times faster than the
more detailed radiative transfer models, like 5S. The technique is based on a set of equations
with coefficients, which depend on some spectral bands of the sensor. “Semi-empirical
formulations are used to describe the different interactions (absorption, scattering, etc.) of solar
radiation with atmospheric constituents during its pass through the atmosphere. Sensor specific
coefficients for each equation are determined using a best fit technique against the computations
of the 5S code” (Raupach, 1994).
The input parameters of SMAC are:
ƒ Optical depth at 550 μm
ƒ Water vapour column
ƒ Ozone concentration
ƒ Surface pressure
ƒ Coefficient files
ƒ Satellite and solar angles
26
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Using the parameter of visibility from meteorological data was determined the optical depth for
the 3 mages as shown in Table 4.2. The equation for the estimation of the optical depth can be
founded in the Appendix B
Table 4.2 Input parameter of optical depth for the atmospheric corrections
Acquisition time images
11-May-2003
24-Jan-2005
16-Mar-2006
Visibility (Km)
9
8.2
8.6
α
1.3
1.3
1.3
Angstrom's β
0.24
0.26
0.24
Optical Depth Kaα
0.51
0.55
0.53
The water vapour column was obtained from the meteorological data of relative humidity and
the air temperature by calculated the actual vapour pressure (ea) and the results of water vapour
are shown in Table 4.3
Table 4.3 Input parameter of water vapour for the atmospheric correction.
Acquisition date
11-May-2003
24-Jan-2005
16-Mar-2006
es (KPa)
ea (KPa)
w (g/cm2)
57.16
29.15
5.2
38.69
17.80
3.2
49.74
19.40
3.5
The ozone concentration was got from site: http://toms.gsfc.nasa.gov/ozone/ozone_v8.html
For the May 11, 2003 was
250 DU = 0.25 g*atm * cm
For the 24 January, 2005 was
200 DU = 0.20 g*atm * cm
For the March 16, 2006 was
250 DU = 0.25 g*atm * cm
(Figure 2.1)
Figure 4.1 Indication of ozone at March 16,
2005.
The elevation above sea level in Palo Verde wetland is around 1 to 3 meters, for this reason the
surface pressure was taken 1013 HPa as the input value for the 3 images. The Sun/Satellite
angle was acquired by the satellite overpass predictor and using the header of the images. In the
Table 4.4 is given the sun and solar angles.
Table 4.4 Input for SMAC of Sun/Satellite angle data
Acquisition Images
Date
Time (GTM)
11-May-2003
16:18:07
24-Jan-2005
16:17:14
16-Mar-2006
16:17:17
Solar Zenith
20.94
37.69
26.18
Angles
Solar Azimut
View zenith
66.61
1.97
141.16
2.26
115.49
1.78
View Azimut
281.16
283.12
282.65
Source: http://www-air.larc.nasa.gov/tools/predict.htm
27
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Once founded the input parameters the process was ready to run the SMAC program for the
images of May 11, 2003 (Figure 4.2), January 24, 2005, and March 16, 2006. In the Figure 4.3
is showing the results of the atmospheric corrected images of 11 May, 2003.
Figure 4.2 ASTER images from the16 March, 2006 without atmospheric corrections
Figure 4.3 Atmospheric corrected image of 16 March 2006
4.2.2.
Atmospheric correction for thermal bands
The surface temperature is a parameter using by SEBS in the determination of radiation budget
and sensible heat fluxes. The atmospheric corrections for the thermal bands were used to
derivate the emissivity and surfaces temperature. The algorithm used in ENVI is similar to the
In-Scene Atmospheric Compensation algorithm, ISAC (Johnson & Young, 1998). This
28
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
algorithm assumes that the atmosphere is uniform over the data scene and that a near-blackbody
surface exists within the scene. The emissivity normalization technique calculates the
temperature for every pixel and band in the data using a fixed emissivity value. The highest
temperature for each pixel is used to calculate the emissivity values using the Planck function
(ENVI, 2001; Hernandez-Baquero, 2000; Hook, Gabell, Green, & Kealy, 1992).
4.3.
MODIS images
Moderate Resolution Imaging Spectroradiometer (MODIS) is a key instrument aboard the Terra
(EOS AM) and Aqua (EOS PM) satellites. The MODIS instrument provides high radiometric
sensitivity (12 bit) in 36 spectral bands ranging in wavelength from 0.4 µm to 14.4 µm. Two
bands are imaged at a nominal resolution of 250 m at nadir, with five bands at 500 m, and the
remaining 29 bands at 1 km. A 2,330-km (cross track) by 10 km (along track at nadir) swath and
provides global coverage every one to two days (Kaufman et al., 1998).
Table 4.5 Characteristics of MODIS visible and thermal bands
Band No.
1
2
3
4
5
6
7
31
32
4.3.1.
Spectral Range (µm)
0.62-0.67
0.84-0.87
0.46-0.48
0.54-0.56
1.23-1.25
1.63-1.65
2.11-2.15
10.78 - 11.28
11.77 - 12.27
Pixel Resolution (m)
250
250
500
500
500
500
500
1000
1000
Acquirement of the MODIS images
The parameters derived from remote sensing needed for the estimation of the evapotranspiration
can be obtained from the visible bands and the thermal bands of MODIS standard products.
These products can be found and download free of charge from the Earth Observation System
Data Gateway (http://edcimswww.cr.usgs.gov/pub/imswelcome). Selected:
1) SURFACE REFLECTANCE DAILY L2G GLOBAL 500M SIN GRID V004:These
product (MOD09GHK) is made from the bands 1-7. The data are provided as a gridded
level-2G, 500-meter product in the Sinusoidal projection.
2) LAND SURFACE TEMPERATURE/EMISSIVITY DAILY L3 GLOBAL 1KM SIN
GRID V004: Retrieved at 1-kilometer and 5-kilometer spatial resolutions, these
MOD11A1 data are provided daily as a gridded level-3 product in the Sinusoidal
projection.
For this research the days selected for download the data were: May 11, 2003, January 24, 2005,
and March 16, 2006. Also a time series images from 31/01/2004 to 22/02/2004 except for the
follows days 05, 06, 08, 10, 15 and 18 of February that they had problems with clouds
29
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Images re-projection
MODIS imagery, however, is in a new map projection called the Integerized Sinusoidal (ISIN)
projection, which is not supported by most existing software packages. So before those images
can be used by other software packages, re-projection should be pre-processed. Using MRT
package, MODIS images were converted to GeoTIFF file format, which can be read by ENVI
software. In ENVI the singles GoeTIFF files were joined and saved img format (ERDAS images
format). Finally these files can be imported into LWIS software. The re-projection parameters
are showing below: Resampling type: Nearest Neighbor, Datum: WGS84, Output Projection
Type: UTM, Zone 16 North, Output Pixel Size: 500 m
4.4.
Meteorological data pre-processing
SEBS needs three types of data sets as input: remote sensing, meteorological and radiation. The
data set from meteorological station consist of air pressure, temperature, humidity, and wind
speed at the reference height. The radiation data set includes downward radiation, and
downward longwave radiation which can either be directly measurements or modelled.
The meteorological station is located close to the wetland (Lat 10˚20’46” N and Long
85˚20’20” W) at 3 m.a.s.l. It measures relative humidity, wind speed, air temperature at 2 m
height; rainfall, sunshine hour and evaporation are taken each 30 minute. The meteorological
data and the calculated climate data when the satellite passes over the study area are shown in
the Table 4.6. The other parameters which were not measured were calculated from the
metrological data as follows:
Saturation vapour pressure (es)
As saturation vapour pressure is related to air temperature, it can be calculated from air
temperature. The relationship is expressed by,
⎛ 17.27T ⎞
e s = 0.6108 exp⎜
⎟
⎝ T + 237.3 ⎠
(4.1)
Where, es the saturation vapour pressure (kPa) and T is the air temperature (˚C).
Actual vapour pressure (ea)
The actual vapour pressure can be calculated from the relative humidity using the following
expression,
ea ,mean =
Where, RH is the relative humidity (%)
30
RH mean
⋅ e s ,mean
100
(4.2)
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Slope of saturation vapour pressure curve (∆)
∆ is the slope of relationship between saturation vapour pressure and temperature. The slope of
the curve at a given temperature is given by,
⎡
⎛ 17.27T ⎞⎤
4098⎢0.6108 exp⎜
⎟⎥
⎝ T + 237.3 ⎠⎦
⎣
Δ=
(T + 237.3)2
(4.3)
∆ Slope of saturation vapour is in (kPa ˚C-1), T is the air temperature (˚C).
Specific humidity
Specific humidity is the mass of water vapour present in a unit mass of air; it can be estimated
based on the actual vapour pressure and the surface pressure data:
⎛R
q = ⎜⎜ d
⎝ Rv
⎞ ea
⎟⎟ ⋅
⎠ P
(4.4)
Where, Rd (278.04) and Rv (461.05) are the gas constant for dry air and water vapour air.
Air pressure
The air pressure is the pressure exerted by the weight of the earth’s atmosphere , its depends no
only on the local elevation but also for the local atmospheric conditions and is calculated by,
⎛
⎜
1
⎞
⎟
z ⎞ ⎝ 0.1903 ⎠
⎛
Ps = P0 ⎜1 −
⎟
⎝ 44331 ⎠
(4.5)
Where, P0 is the sea level air pressure [Pa], taken 101325 Pa as default value locally, z the
elevation of calculation height [m].
Potential temperature
⎛ Po ⎞
⎟
⎝P⎠
θ = T⎜
0.286
(4.6)
Where, θ is the potential temperature [K], T is the near surface layer air temperature and surface
temperature [K] and P is the pressure in mbar.
31
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Mean air density at constant pressure
ρ air =
P0
287.15(T + 273.15)
(4.7)
Where, ρair is in Kgm-3
Virtual potential temperature
θ v = (1 + 0.61q )θ
(4.8)
Where, θv is the virtual potential temperature [K].
Emissivity of the atmosphere
The emissivity of the air described in Cambell and Norman (1998) as:
ε a = 9.2 ⋅ 10 −6 ⋅ (Ta + 273.15)2
Where, ε a is the emissivity of the air, Ta is the air temperature.
32
(4.9)
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Table 4.6 Climate data of the Palo Verde wetland over satellite passing time
Julia
Surfac
n
Time
Air
e
RH
Wind
Date
Day
Overpassing
Temp
Temp
Temp
(˚C)
35.3
28.4
32.8
(K)
308.45
301.55
305.95
(K)
306.24
303.22
309.10
(%)
0.51
0.46
0.39
(m/s)
1.3
4.0
6.3
30.9
32.3
31.1
31.2
30.9
30.8
32.6
31.1
30.9
31.3
32.2
31.6
31.8
29.9
30.9
32.5
30.8
304.05
305.45
304.25
304.35
304.05
303.95
305.75
304.25
304.05
304.45
305.35
304.75
304.95
303.05
304.05
305.65
303.95
304.22
303.28
305.55
304.52
302.20
304.10
306.60
303.38
304.42
302.88
306.60
305.76
304.16
302.36
304.60
307.10
304.38
0.48
0.47
0.50
0.43
0.50
0.50
0.39
0.51
0.52
0.49
0.45
0.50
0.43
0.50
0.48
0.41
0.47
1.8
2.2
2.7
4.9
4.5
5.8
4.0
4.0
5.4
4.0
3.6
4.9
4.9
5.4
3.1
3.6
4.0
05/11/03
01/24/05
03/16/06
131
24
75
(hr)
16.302
16.287
16.288
1/31/04
2/1/04
2/2/04
2/3/04
2/4/04
2/7/04
2/9/04
2/11/04
2/12/04
2/13/04
2/14/04
2/16/04
2/17/04
2/19/04
2/20/04
2/21/04
2/22/04
31
32
33
34
35
38
40
42
43
44
45
47
48
50
51
52
53
16.208
16.919
16.005
16.716
15.801
16.309
16.106
15.903
16.613
15.699
16.410
16.207
16.917
16.714
15.800
16.511
15.597
Speed
Pressure
Solar
Radiatio
n
(hpa)
(W/m2)
1010.7
1027
1013.2
711
1008.4
991
MODIS time series
1009.4
719
1009.1
844
1010.1
744
1010.0
820
1009.5
728
1010.0
818
1009.1
786
1009.3
746
1009.9
819
1012.0
750
1011.3
819
1010.9
750
1011.2
906
1011.8
830
1011.0
765
1010.5
843
1012.1
688
es
ea
(Hr)
10.0
10.5
10.5
(kPa)
57.16
38.69
49.74
10.0
10.0
10.5
10.5
10.5
10.5
10.5
10.5
10.5
10.5
10.5
10.5
10.5
10.5
10.5
10.5
10.5
44.67
48.36
45.18
45.44
44.67
44.42
49.18
45.18
44.67
45.70
48.09
46.48
47.01
42.19
44.67
48.91
44.42
Bright
q
es - ea
Ρair
∆
1+∆/γ
(kPa)
29.15
17.80
19.40
0.0174
0.0106
0.0116
(kPa)
2.801
2.089
3.034
1.14
1.17
1.15
(kPa
)
0.32
0.22
0.28
5.7
4.4
5.2
21.44
22.73
22.59
19.54
22.33
22.21
19.18
23.04
23.23
22.39
21.64
23.24
20.21
21.09
21.44
20.05
20.88
0.0128
0.0136
0.0135
0.0117
0.0133
0.0133
0.0115
0.0138
0.0139
0.0133
0.0129
0.0139
0.0121
0.0126
0.0128
0.0120
0.0124
2.323
2.563
2.259
2.590
2.233
2.221
3.000
2.214
2.144
2.331
2.645
2.324
2.680
2.109
2.323
2.885
2.354
1.16
1.16
1.16
1.16
1.16
1.16
1.15
1.16
1.16
1.16
1.16
1.16
1.16
1.16
1.16
1.15
1.16
0.25
0.27
0.26
0.26
0.25
0.25
0.28
0.26
0.25
0.26
0.27
0.26
0.27
0.24
0.25
0.28
0.25
4.8
5.1
4.8
4.9
4.8
4.8
5.1
4.8
4.8
4.9
5.0
4.9
5.0
4.6
4.8
5.1
4.8
Sunshine
33
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
4.5.
Bio-physical parameters estimation
The determination of biophysical parameters is one of the most important processes in the
estimation of evapotranspiration using the remote sensing data and techniques. In SEBS the
information as input from remote sensing are: the land surface albedo, emissivity, temperature,
fractional vegetation coverage (fc) and leaf area index (LAI), and the height of the vegetation (or
roughness height) and NDVI.
4.5.1.
Albedo (ro)
Albedo is the fraction of the incident sunlight that is reflected back to the atmosphere. The
surface albedo is an important physical parameter in the determination of the net radiation and
also is used in climate models. The total shortwave broadband albedo is given by the equations
determined by (Liang, 2001; Liang et al., 2002),
α ASTER = 0.484α 1 + 0.335α 3 - 0.324α 5 + 0.551α 6 + 0.305α 8 - 0.367α 9 - 0.0015
α MODIS = 0.160α 1 + 0.291α 2 + 0.243α 3 + 0.116α 4 + 0.112α 5 + 0.081α 7 - 0.0015
(4.10)
(4.11)
Where, αMODIS or ASTER is a simulated shortware albedo, αi (i = 1~9) is the narrow band albedo of
MODIS and ASTER shortwave bands.
4.5.2.
Normalized difference vegetation index (NDVI)
The NDVI is a good indicator of photosynthetic activity on vegetation at the surface, due to the
strong spectral absorption of chlorophyll in the visible region (0.475 to 0.65 μm) and the high
reflectance of vegetation on in the near infrared part of the spectrum. The NDVI index range is
between -1 and +1. The index lies roughly between 0 and 1 for green surface.
NDVI =
ρ nir − ρ red
ρ nir + ρ red
(4.12)
Where, ρnir and ρred are atmospherically corrected ground reflectance in the near infrared and red
band respectively. In ASTER the ρnir is the band 3 and ρred is the band 2. For MODIS the ρnir is
the channel 2 and ρred is the channel 1.
4.5.3.
Fractional vegetation cover (fc)
Fractional vegetation cover is used to separate non-vegetated, partially vegetated and densely
vegetated land surface. This parameter is using in SEBS algorithm to derive surface
temperature, LAI and ground heat flux. The formula developed by (Choudhury, Ahmed, Idso,
Reginato, & Daughtry, 1994) was applied to determine the parameter as:
⎛ NDVI max − NDVI
f c = 1 − ⎜⎜
⎝ NDVI max + NDVI min
34
⎞
⎟⎟
⎠
p
(4.13)
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Where:
The exponent, p, represents the ratio of the leaf angle distribution taken as a constant of 0.625
NDVImax is the NDVI value of the full vegetation cover, NDVImin is the NDVI value of the bare
soil and NDVI is the NDVI value of the current pixel.
The NDVImax and NDVImin are defined from the frequency of histogram, as the lower and upper
2-5% of each NDVI maps (Gutman & Ignatov, 1998)
4.5.4.
Leaf area index
Leaf area index represents the total biomass and is indicate of crop yield, canopy resistance and
heat flux. LAI is defined as the ratio of the total area of all leaves on a plant to the ground area
covered by the plant. For most plants leaf area index increases with the age and reaches
maximum of 2 to 5. There are several methods to relate the vegetation index with the LAI. For
this study the relation of LAI is given by Choudhury (1987) cited in (French, Schmugge,
Kustas, Brubaker, & Prueger, 2003).
LAI =
log(1 − f x )
−∧
(4.14)
Where: - fc is the fractional canopy cover derived in the equation
-Λ is the leaf angle distribution function taken to be 0.5.
4.5.5.
Land surface emissivity
The broad-band emissivity is a key factor in the on the temperature measurement. Empirical
relationships using the vegetation cover method of Valor and Caselles (Valor & Caselles, 1996)
together with the landuse map used to derive surface emssivity.
ε 0 = ε c f c + ε s (1 − f c ) + 4〈 dε 〉 f c (1 − f c )
(4.15)
Where:
εc is emissivity of full vegetation cover that is taken as 0.985
εs is emissivity of bare soil that is taken as 0.96
fc is the fractional vegetation cover
‹d ε›: is the vegetation structure parameter that is taken 0.015
4.5.6.
Aerodynamic roughness height
Aerodynamic roughness height (Zom) is a very important parameter in the surface energy
balance model. The roughness height for momentum transfer is taken as reference height for
momentum flux calculations. It approximates the height at which the fluid flow changes from
being turbulent to be laminar. Even though remote sensing observation provide most of the
vegetation information, the estimation of roughness height is still remains a challenge for
35
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
regional modelling of turbulent transport. There are several methods to retrieve this parameter
but for this study were used the following two:
1) Using the relationship that was used to derive the roughness length for momentum
transfer as follows (Su & Jacobs, 2001),
Z om
⎛ NDVI
= 0.005 + 0.5 ⋅ ⎜⎜
⎝ NDVI max
⎞
⎟⎟
⎠
2.5
(4.16)
2) Aerodynamic roughness height can be estimated by vegetation height (h), when this
information is provaited. For this research the elevation height was derivated from the
LIDAR data, therefore, the aerodynamic roughness were estimate using the expresion
cited by (Allen & Fao, 1998; Verhoef, McNaughton, & Jacobs, 1997) which is given
by,
Z om = 0.136 ⋅ h
4.5.7.
(4.17)
Vegetation height
Vegetation height (h) can be estimated by inverting the equation that calculates the zom based on
the vegetation height, the objective of this estimation is to use in the derivation of the
displacement height.
h=
4.5.8.
Z om
0.136
(4.18)
Displacement height
Displacement height (d0), same as zero-plane displacement, a height scale in turbulent flow over
tall rough elements associated with the average level of action of momentum transfer between
the flow and the roughness elements. It is the height that the surface level is normally displaced
to a level just below the vegetation canopy due to tall vegetation canopy, where the wind speed
is zero. It can be estimated using approximation empirical relationship vegetation height and
displacement height given in (Allen & Fao, 1998; Verhoef et al., 1997) but can be estimate from
LAI and Zom.
d0 =
4.6.
2
⋅h
3
(4.19)
Land cover
Land cover change is an important factor to understand the environmental processes in
wetlands. Thus, land cover maps were produced from ASTER images from 2003, 2005 and
2006. The supervised classification was made in ERDAS software using a maximum likelihood
36
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
algorithm. Field measures of samples plots in Palo Verde, Typha and grass, additionally,
panoramic photos and aerial photographs of March 2003 and February 2005 were assisting the
selection of training areas.
4.6.1.
In situ observations
Palo Verde
In a previous research on 2004 were made 40 sample plots (100 m2 each) of Palo Verde and The
results proved that Palo Verde tree have three density classes, the other parameter measure in
the plot are shown in the Table 4.7. Palo Verde trees are located over the natural dike of the
Tempisque River, in the Figure 4.4 are shown the distribution of the plots and trees.
Figure 4.4 Location of Palo Verde plots and trees
Table 4.7 Average of the measures in the Palo Verde Plots by category
Categories
Category 1
Category 2
Category 3
Diameter (cm)
10.0
7.8
7.2
Height (m)
4.1
3.9
3.7
Density (trees/ha
811
1550
2436
Source: (Solano, 2004)
Typha
The other land cover type measured in 2004 was Typha, in the field were made 32 plots of 1 m2
each one. For Typha it is possible to identify two classes according to the density. The height is
2.34m for the class 1 and 2.15 m for the class 2; the biomass in the class 1 is 695 gm-2 and for
class 2 is 489 g m-2 the total area covered by Typha is ~120 ha. The biomass average of Cattail
is 5344 kg/ha. That gives a result of 64.98 tons for all the wetland (Solano, 2004). In Figure 4.5
was shown an aerial photograph of the area cover by cattail in March of 2003,
37
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Figure 4.5 Area cover by Typha in 2003 and cattail plants.
Grass
During the fieldwork in October, 2006, thirty plots of 1 x 1 m were established, measuring the
height in 10 of them and then all the biomass was collected. Figure 4.6 is shows the location of
the grass plot in the wetland. Pasture and mixed vegetation were classified in 5 categories
according to the species. This classification was made with the objective to determine the
difference between the grasses, and if these differences are reflected in the separation of land
covers using the spectral information in the supervised classification of the images.
Figure 4.6 Location of the grass plots and three reference photographs
The five categories of grass and the average collected values as the biomass and height are in
Table 4.8.
38
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Table 4.8 Elevation and Biomass in the samples plots of grass
Species
Thalia geniculata
Eleocharis mutata
Hymenachne amplexicaulis
Fimbristylis spadicea
Mixed plots
4.6.2.
Biomass (g)
1278.04
464.44
797.28
773.19
775.45
Height (m)
1.42
0.56
0.93
0.64
1.14
Definition of mapped land covers types
Based on the preliminary land cover classification, created by previous research (March 2003)
using the MASTER images as was observed in Figure 2.8; were identified 23 vegetation
categories, using the spatial and spectral resolution with 20 bands in the visible part and 10 m of
spatial resolution as characteristics of the sensor. It was not necessary to achieve this level of
detail for the present work, so a simplified approach was used. Taking into consideration the
fieldwork experience and the sample plots, and the characteristic of the sensor (ASTER), 12
categories of land cover were defined for the wetland:
1) Forests: It is located in the northern part of the images, outside of the wetland area. The
type of forest is a deciduous or semi-evergreen, which is characterized by looses of
leafs in the dry period (Figure 2.2). This phenological condition can be cause problems
in the classification of forested areas using satellite images due to the open canopy and
is possible to confuse with the ground
2) Pasture 1: This type of vegetation is grass, dominated by Hymenachne amplexicaulis
and Thalia geniculata. The grasses with high biomass and elevation.
3) Pasture 2: Pasture 2 is a sub-division of the categories of pastures and it is dominated
by the species of Eleocharis mutate and Fimbristylis spadicea. This type of coverage is
located close to the Chirca tidal flats in the southeast of the Palo Verde Wetland. In this
categories are the species with less biomass and the average is ~ 60 cm.
4) Pasture-Shrubs: are a combination of grass, trees and Palo Verde. This class represents
the category 2 of Palo Verde in the Solano’s map with an average density of 1550
trees/ha.
5) Shrubs: This category is a mixture of Palo Verde trees and other species of shrubs such
as: Acacia farnesiana and Pithecellobium laceolatum. The density of Palo Verde trees
in this landcover is around of 2436 trees/ha and it is called category 3 in the map
generated with MASTER images.
6) Palo Verde: It is the land cover dominantly by Palo Verde trees. The density of these
trees are 811/ha and it’s represents by the category 1 in the map created in 2003.
7) Typha: This land cover type is the cattail vegetation.
39
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
8) Dead Typha: This land cover corresponds to areas with mechanically smashed cattail.
The cattail was crushed down below the water level, as Figure 2.9 shows it. Some parts
of it can be areas with bare soil.
9) Floating water vegetation: The vegetation land cover type is a class with floating
vegetation, when the wetland has water. The characteristics species are Eichornia
crassipes, Neptunia plana and other. When the wetland is dry other plants emergent or
is converted in bare soil.
10) Mangrove: includes all the areas with mangrove forests.
11) Open water: are sectors in the wetland with water and without any vegetation.
12) Water: These are the areas of the Tempisque River.
4.6.3.
Land cover maps
The land cover maps were classified using the above-mentioned land cover types. In May, 2003
when the wetland was dry and some process of restoration had been started in the years before,
was founded Typha crushing down; the land cover type vegetation in this cases is characterized
by the absence of floating vegetation, and most of the cover correspond to bare soil or others
emergent plants; still there are a very compact and with high density cattail that never been
restored (around 80 ha), that’s represents a 6 % of the total area of the wetland; Palo Verde is
well defined in the highest part of the wetland. Comparing with the results of the land use map
generated with MASTER images is possible to say the land cover determined with ASTER
images are a good product and is probable to find values highest to 80 % of the accuracy. The
distribution of the land cover for May 2003 is shown in the Figure 4.7
In 2005 is when the activities vegetation removal reach his maximum area ~210 ha a 15% of the
wetland, but still some areas are small areas are cover by Typha (30 ha). Now the floating
vegetation appears is specially in those area that before was cattail; other big change is given by
pasture-shrub, they decrease in a 50 % from 2003 to 20005, in these areas the most part is
covered by the pasture 2; one explanation can be that the pasture now is green and the density of
the shrub is very low, the other possible explanation is the product of losses in the tree and
shrub coverage, due to the more flooded periods that caused the dead on the trees. Those
changes are presents in the Figure 4.8
For the year of 2006 the changes in the vegetation are given by the reappearance of the cattail in
those areas that was cover by floating vegetation. There are small patches of cattail distributed
for all the wetland but now this cattail has less density and size. Also there converges with an
increase in them area such as: Pasture that has a 30 % of the total areas and shrub that reach
~15%.The results of the land cover for 2006 are shown in the Figure 4.9 and the variations of
the land cover types according to the area in the period of 2003 to 2006 are given in the Figure
4.10.
40
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Figure 4.7 Land cover classification May, 2003.
Figure 4.8 Land cover classification January, 2005
Figure 4.9 Land cover classification March, 2006
41
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
35
2003
2005
2006
Percentage of Area
30
25
20
15
10
5
0
Forest
Pasture 1
Pasture 2
PastureShrub
Shrub
Palo Verde
Typha
Dead Typha
Vegetation
Mangrove
Open Water
Land Cover
Figure 4.10 Land cover change from 2003 to 2006
4.7.
LVIS Data
The Laser Imaging Vegetation System (LVIS) is a sensor developed by the Laser Remote
Sensing Laboratory of the Goddard Space Flight Center from the National Aerospace
Administration of USA (NASA). LVIS sensor is part of the LIDAR (Light Detection and
Ranging) remote sensing technologies. The operative principle of these sensors is that they
direct a laser pulse toward a target and, by studying the returned light it may be used to retrieve
information about the object that have interacted with the laser pulse (Blair, Rabine, & Hofton,
1999; Lillesand, Kiefer, & Chipman, 2004). LVIS is an airborne scanning/imaging system,
looking down adjacent -, along-, and across-track footprint within a 1-2 km wide swath, it is a
large footprint, can be operated at a variable footprint size, from 5-50 mm (typically ~20 m).
LVIS are flown heights of 5 to 10 km, and digitize the entire record of reflectance energy,
resulting in what is commonly referred to as waveform.
LVIS have been successfully implemented in the Costa Rican tropical rain forest to determine
canopy and sub canopy height (Clark, Clark, & Roberts, 2004; Drake et al., 2002; Dubayah,
Peterson, Rhoads, & Dietrich, 2005; Hurtt et al., 2004). Also the LIDAR provided information
about forest height and roughness (Straatsma & Middelkop, 2006,) and LAI determination
(Houldcroft, Campbell, Davenport, Gurney, & Holden, 2005; Lefsky et al., 1999; Morsdorf,
Kotz, Meier, Itten, & Allgower, 2006). Due to the quality and the information generated from
this LIDAR such as the elevation of the terrain and the elevation over the vegetated coverage
made this data very important and useful as source in this research, also taking in consideration
that a fly for obtain information from LIDAR is very expensive but in this case is free of charge.
4.7.1.
LVIS pre-processing
In the months of March and April, 2006, NASA carried out a project in Costa Rica (CARTA II)
with the objective of National Mapping and Volcano Gas Emissions Study. An aircraft WB57
carried sensors such as: MASTER, DCS, RC-30, Hymap and LVIS. The data of the LVIS
sensor can be downloaded from https://lvis.gsfc.nasa.gov/index.php.
42
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
There are 3 types of files offered in the web pages: the LVIS Canopy Elevation (.lce), LVIS
Ground Elevation (.lge) and LVIS Geolocated Waveform (.lgw). The format of the files is
binary. Lcc-win32 is software for converting binary released LVIS data into ASCII output. The
ASCII file can be open in any GIS software. The data has a reference Frame: International
Terrestrial Reference Frame (ITRF 2000) / WGS-84 Ellipsoid. In the Table 4.9 is shown the
description of the LVIS Ground Elevation data (.lge). The “zg” values correspond to the
elevation of the ground and the “rh 100” to canopy top.
Table 4.9 LVIS Ground Elevation data (.lge)
Item
Item Description
Lfid
LVIS file identification
Shotnumber
laser shot assigned during collection
Time
UTC decimal seconds of the day
Glon
longitude of the lowest detected mode within the waveform (degrees east)
glat
latitude of the lowest detected mode within the waveform (degrees north)
Zg
mean elevation of the lowest detected mode within the waveform (m)
rh25
height (relative to zg) at which 25% of the waveform energy occurs (m)
rh50
height (relative to zg) at which 50% of the waveform energy occurs (m)
rh75
height (relative to zg) at which 75% of the waveform energy occurs (m)
rh100
height (relative to zg) at which 100% of the waveform energy occurs (m)
Source: https://lvis.gsfc.nasa.gov/index.php
Figure 4.11 Individual waveform of LVIS
43
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
4.7.2.
Digital terrain model and digital canopy model
The digital terrain model (DTM) and canopy-height estimation are two LIDAR products that
have an immense potential for research in ecology and management. DTM describes the
variations of elevation across a landscape and has been used in applications including mapping
drainage basin geomorphology, flood modelling, calculation of biophysical controls on
vegetation distribution, and spatial analysis of soil properties. Canopy-Height estimates from
remote sensing technology have a variety of potential applications such as calculating surface
roughness for atmosphere-land interaction models, spatial analyses of forest dynamics,
modelling canopy intersection, and light penetration (Clark et al., 2004).
The DTM was interpolated from the “zg” values of LVIS, using a 35 985 points over the
wetland. The average distance between neighbouring points is 20 m. Furthermore, 120 points of
elevation taken in the field were added to the interpolation. Geostatistical techniques were used
to interpolate the DTM. Ordinary kriging with the spherical model give a root mean square error
of 0.88 m in the interpolation. In the case of the Digital Canopy model (DCM) the data from the
“rh100”or zt (depends of the file) was used, this correspond to the height of the vegetation in
meter. The root mean square error was 1.16 m. in the estimation of the interpolation using the
same algorithm. The interpolated maps are shown in the Figure 4.13.
4.7.3.
Verification of the digital canopy model
The validation of the elevation from the canopy model was made using the average elevation of
the samples plots of Palo Verde and grass. The parcels of Typha were not considered in this
analysis due to the cover of Typha was removed after 2004 and the LIDAR data was taken in
March, 2005. Moreover not all the LVIS data cover the wetland, so only 37 plots could be used
in the validation: 20 plots of grass and 17 plots of Palo Verde. The general correlation
coefficient between the elevation from the DCM and the average elevation in the plots was 0.86
and the standard error of 0.81. A major research is need for to try to understand the why there an
overestimation in the height given by the LIDAR data; in the Figure 4.12 is possible to see a big
offset from the relation 1:1 line in all the coverage, vegetation very shorter as grass that’s in the
field measured were ~ 1m and with the canopy elevation give values over 2 m but the same
error are presented in the Palo Verde land cover. For this research due to the time and the
objectives more studies in the validation LVIS data and the filtering were skipped.
8
Canopy Elevation (m)
7
6
5
4
3
2
1
0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
Plots elevation (m)
Figure 4.12 Correlation between the Digital Canopy Model and the elevation of the plots
44
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Figure 4.13 Digital Elevation Model (DEM) and Digital Canopy Model (DCM)
45
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
4.8.
Percolation aproximation
Percolation is one of the most difficult factors to measure in the water balance. In most of the
cases the percolation is the residual of the water balance equation. To assess the order of
magnitude of possible percolation, during the fieldwork, 5 samples of 10 cm of undisturbed soil
each 10 cm of depth were taken from the least permeable layer of the (sub) soil in two different
places of the wetland. In the laboratory were aggregated a column of 10 cm of water, as is
shown in the Figure 4.14.
Figure 4.14 Soil samples
The result of this approximation is that for Palo Verde wetland the percolation is around of 2.4
mm day-1 with a 10 cm of pressure head and it is shown in the Table 4.10. Based on the
literature, in vertisols the hydraulic conductivity ranges between 0.2 mm day-1 and 15.8 mm
day-1 with an average of 4.3 mm day-1(Diaz, Duarte, Cerana, & Fontanini, 2003). This
percolation value give an idea of how much could be the losses by percolation, also in this
research were tried to estimate the percolation with the use of the remote sensing data (time
series of MODIS) and the field measurement of the water lever.
Table 4.10 Approximation of saturated hydraulic conductivity
Place
1
2
46
Depth
0-10
10-20
20-30
30-40
40-50
0-10
10-20
20-30
30-40
40-50
30-Oct
6.35
2.31
0.00
1.62
0.00
2.08
0.45
3.12
1.96
Day
31-Oct
1-Nov
5.94
5.43
5.43
5.83
2.26
2.54
1.50
1.62
0.00
0.00
1.99
2.09
0.38
0.47
4.27
4.62
1.96
2.09
2-Nov
4.16
5.89
2.42
1.43
0.00
0.21
0.62
4.27
2.14
Percolation
(mm)
5.47
4.86
1.81
1.54
0.00
1.59
0.48
4.07
2.04
Profile
average
2.74
2.05
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
5.
Actual Evapotranspiration and Water
Balance Results
This chapter focuses on the analysis and discussion of the actual evapotranspiration in the Palo
Verde wetland. A comparison between the actual ET and the land cover changes from 2003 to
2006 using the SEBS algorithm applied to ASTER and MODIS images is presented.
Furthermore, two methods for calculating the surface roughness are compared: one uses the
NDVI the other is based on the vegetation canopy height data provided by the LIDAR. Finally,
a comparison of the losses was attempted, using the evapotranspiration results to define the
percolation while closing the water balance.
5.1.
Spatial-temporal distribution of ETa
5.1.1.
11 May, 2003
In the year of 2003, the rainfall was about 10 mm in five months (Dec, 2002 to May 2003), thus
the wetland was completely dry. The potential evaporation given by the meteorological station
calculated using the FAO modified Penman equation (Allen & Fao, 1998) was 5.35 mm day-1
and the average ETa estimated for all the wetland was 5.13 mm day-1; this value is in the range
of the values reported in the literature for wetlands (4-8 mm day-1) (Abtew, 1996, 2001; Allen,
Prueger, & Hill, 1992; Chen et al., 2002; Jacobs, Mergelsberg, Lopera, & Myers, 2002; Jennifer
M. Jacobs et al., 2002; Lott & Hunt, 2001; Price, 1994; Sanchez-Carrillo, Angeler, SanchezAndres, Alvarez-Cobelas, & Garatuza-Payan, 2004; Souch, Wolfe, & Grimmond, 1996). The
ratio of ETa/Eto=0.96. The average albedo was 0.21 and the literature mention 0.2 for wetlands.
The Table 5.1 is shown the results of the estimation of ETa and other parameters by land cover
types.
Table 5.1 Summary of results of the ETa estimations for 11 May, 2003
Land Cover
Forest
Pasture I
Pasture II
Pasture-Shrub
Shrub
Palo Verde
Typha
Dead Typha
F. Vegetation
Mangrove
Water
Albedo
0.18
0.25
0.18
0.21
0.18
0.17
0.32
0.28
0.22
0.17
0.16
NDVI
0.75
0.35
0.44
0.41
0.57
0.57
0.31
0.28
0.43
0.78
0.30
ZomNDVI
(m)
0.43
0.07
0.12
0.11
0.24
0.23
0.05
0.04
0.12
0.47
0.06
Surface Temp
(K)
303.9
308.7
306.2
307.9
304.9
304.6
307.5
308.8
307.8
301.6
299.3
ETa
(mmday-1)
6.0
4.9
5.9
5.2
5.8
6.1
4.5
4.7
5.3
6.2
5.8
Kc
1.13
0.92
1.10
0.97
1.08
1.15
0.84
0.88
1.00
1.15
1.09
According to the results of the Table 5.1 the highest values of ETa were found in the forest and
shrubs (Forest, Mangrove and Palo Verde); due to these coverages have high photosynthesis
47
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
activity and in this period they still had kept the leaves, the aerodynamic roughness height was
also the highest and the surface temperature is lower. The second group is the coverage
containing the grasses (Pasture 1, Pasture 2 and vegetation) they have similar values of albedo
but the NDVI and roughness height is lower than forested areas given less ETa. In the case of
Typha and dead Typha are the lowest values because they have the highest albedo, lower NDVI
and roughness. The lower NDVI can be explained because was a dry period and the other part
of the cattail was crushed down. The lower values of roughness height are due to the high
density and compact distribution in the cattail. In the Figure 5.1 is shown the evapotranspiration
map for 11 May, 2003.
Comparison of ASTER and MODIS
The daily estimation of evapotranspiration calculated from the data of two sensors for 11 May
2005. The ASTER images were up-scaled by averaging the values of the pixels contained in a
500 x 500 m MODIS pixel. The differences between both images were compared using a simple
equation of the relative error that expresses the changes in percentages for the ASTER image
from 15m to 500 m. The same procedure was used the comparison between ASTER and
MODIS. The relation is given by,
X =
ETa new − ETa ref
ETa ref
⋅ 100%
(5.1)
Where X is the percentage of change in ET, ETnew is the newly computed value from ASTER of
15 m and ETref is the ETa with a resolution of 15 m. This equation is also used for the
comparison between ETa of ASTER resolution with 500 m and ETa from MODIS images.
Where ETnew are the values from MODIS and ETref is the values from the ASTER images with
500 m resolution.
The transformation of pixel size from 15 m to 500 m gives different average values of ETa for
the wetland and in each land cover; the difference in all the wetland occurs due to the
delimitation of the boundary, using 15 m resolution we have more detail in the boundary than
using 500 by 500 and take some values from the areas outside of the wetland. The differences in
the ETa by land covers are caused by data due to the high mixture of pixel information.
For the year of 2003 the differences are presented in the Table 5.2. In general the up-scaling has
trend to over estimate the MODIS images, Some problems in the thermal band in this MODIS
image is the reason for the missing data in the estimation of ETa and the big (~ 11.6 %)
different the estimation for all the wetland comparing with the ASTER image. Individually the
mayor difference was found in the estimation of Cattail in more than 20 % of the changes; also
forest cover has a high value: 13.5 % of the change. In general the MODIS image is not very
good parameter when is compared with ASTER, due to few pixels were used in the comparison
in the missing surface temperature data, but it give some ideas about the expected behaviour in
the others images where the information is completed.
48
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Table 5.2 Actual evapotranspiration from ASTER and MODIS images for 11May, 2003
ETa
ETa
X
X
ETa
Land Cover
ASTER500m
MODIS
ASTER500m
MODIS
ASTER15m
(mmday-1)
(mmday-1)
(%)
(mmday-1)
(%)
Forest
Pasture I
Pasture II
Pasture-Shrub
Shrub
Palo Verde
Typha
Dead Typha
F. Vegetation
Mangrove
Water
6.0
4.9
5.9
5.2
5.8
6.1
4.5
4.7
5.3
6.2
5.8
5.4
5.3
5.8
5.6
5.9
5.8
4.9
5.1
5.2
6.0
5.8
6.2
6.0
5.9
6.1
6.3
-
-10.1
7.1
-1.2
6.9
1.9
-4.7
10.3
7.0
-2.8
-2.3
0.0
13.5
Average
5.1
5.4
6.0
6.1
11.6
5.1.2.
7.9
1.2
22.7
3.8
24 January, 2005
On the 24 of January the wetland had a depth of 55.7 cm, the last rainfall occur in December
2004 (two months without rainfall), and the potential evaporation calculated at the
meteorological station was 4.1 mm day-1. The actual evapotranspiration estimated by Remote
Sensing over the wetland coincide with the PET. The average albedo was 0.16 and the surface
temperature average was 301.25 K. Table 5.3 presents the average ETa and other parameters by
land cover where was founded that the water from the river has the highest ETa value, followed
by open water. Typha and dead Typha have 4.8 mm due to two factors: for the high
photosynthetic activity with NDVI highest values and caused by the mixture between the water
and the cattail: the literature (Price, 1994) found the same ETa for Typha. In the case of open
water the low albedo determine the high ETa. The ETa lower rates are located in the
combination of grasses and shrubs with lower values of NDVI and roughness.
Table 5.3 Summary of results of the ETa estimations for 24 January, 2005
ZomNDVI
Surface
ETa
Kc
Land Cover
Albedo
NDVI
(m)
Temp (K)
(mmday-1)
0.14
0.62
0.29
Forest
301.9
4.2
1.01
0.18
0.59
0.26
Pasture I
301.5
3.9
0.94
Pasture II
0.22
0.39
0.10
303.3
3.5
0.84
Pasture-Shrub
0.19
0.45
0.14
302.7
3.8
0.92
Shrub
0.16
0.69
0.36
301.3
3.9
0.95
Palo Verde
0.18
0.46
0.14
302.7
3.9
0.93
Typha
0.18
0.77
0.47
299.1
4.8
1.16
Dead Typha
0.16
0.66
0.17
300.0
4.8
1.16
F. Vegetation
0.16
0.48
0.33
301.4
4.3
1.03
Mangrove
0.14
0.74
0.42
300.8
4.3
1.03
Open Water
0.09
0.25
0.28
300.5
4.8
1.16
Water
0.11
0.61
0.05
300.4
5.7
1.38
49
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Comparison of ASTER and MODIS
The Table 5.4 shows the comparison between the ETa from ASTER up-scaled and ETa from
MODIS. The average ETa over the wetland with the ASTER15m is 4.1 mm, when the image is
up-scaled the percentage change in ~ 5 % for all the wetland but there some coverage with
changes very high such as: vegetation (19.2%), pasture 1 (14.2 %) and shrubs (8.7 %), in the
majority of the cases the results is an over estimation of the original image. These changes can
be explain if is taking in consideration that the resolution of 500 x 500 caused a mixture of
pixels, there not pixel without contamination from others land cover types. The overestimation
or underestimation depends on the values ETa of the neighbors land cover types. The results
obtained from MODIS images are very close to the ASTER image for all the wetland with a
difference of 3.5 % less than ASTER, the errors divert when the data is analyzed by coverage.
This is due, the the highest errors were founded in shrubs, Pasture 1 and dead Typha .
Table 5.4 Actual evapotranspiration from ASTER and MODIS images for 24 January, 2005
ETa
ETa
X
X
ETa
Land Cover
ASTER500m
MODIS
ASTER500m
MODIS
ASTER15m
(mmday-1)
(mmday-1)
(%)
(mmday-1)
(%)
Forest
4.2
4.4
4.5
6.5
2.3
Pasture I
3.9
4.4
3.9
14.2
-10.9
Pasture II
3.5
3.6
3.8
4.0
3.9
Pasture-Shrub
3.8
3.7
3.8
-0.8
1.9
Shrub
3.9
3.6
5.0
-8.7
40.8
Palo Verde
3.9
3.9
4.0
0.3
3.1
Typha
4.8
4.9
4.4
2.1
-9.2
Dead Typha
4.8
4.6
4.1
-4.2
-10.7
F. Vegetation
4.3
5.1
4.7
19.2
-6.7
Mangrove
4.3
4.4
4.2
4.2
-5.2
Open Water
4.8
4.7
4.6
-2.1
-2.8
Average
4.1
4.3
4.2
4.9
-3.5
5.1.3.
16 March, 2006
The estimation of actual evapotranspiration over the study area on 16 March, 2006 was 3.8 mm
day-1. The wetland had a depth of 67 cm, and the evapotranspiration from the meteorological
station of 6.1 mm. The total rainfall since December 2005 was ~10 mm; the average of the
surface temperature was 308.6 K and average albedo 0.19. According with the Table 5.5 the
highest value of ETa was founded in the water with 6.3 mm day-1. Other land covers such as
open water mangrove and vegetation have ETa values from 4 to 4.8. The lower values are found
in the grass and Typha with values from 2.9 to 3.5 mm day-1. The spatial distribution of the
actual evapotranspiration over the wetland is shown in the Figure 5.3.
Comparison of ASTER and MODIS
Table 5.6 shows that the average of the actual evapotranspiration over de wetland from ASTER
and MODIS images are very close, the difference between both are a 2.5 %. MODIS over
estimate the values ETa of Palo Verde and mangrove but under estimate the ETa from the land
cover of Pasture-Shrubs, dead Typha, vegetation and open water.
50
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Table 5.5 Summary of results of the ETa estimations for 16 March, 2006
ZomNDVI
Surface
ETa
Land Cover
Albedo
NDVI
(m)
Temp (K)
(mmday-1)
0.16
0.62
0.25
Forest
307.0
3.6
Kc
0.59
Pasture I
0.23
0.44
0.11
307.6
3.4
0.55
Pasture II
0.24
0.32
0.05
308.7
3.5
0.58
Pasture-Shrub
0.25
0.36
0.06
309.5
2.9
0.48
Shrub
0.18
0.56
0.20
307.0
3.5
0.58
Palo Verde
0.20
0.40
0.09
308.5
3.7
0.60
Typha
0.21
0.61
0.24
306.9
3.2
0.53
Dead Typha
0.23
0.29
0.04
308.4
3.7
0.61
F. Vegetation
0.18
0.55
0.19
306.6
4.0
0.65
Mangrove
0.16
0.83
0.48
304.3
4.5
0.74
Open Water
0.16
0.49
0.16
305.3
4.8
1.04
Water
0.15
0.31
0.07
302.1
6.3
0.79
Table 5.6 Actual evapotranspiration from ASTER and MODIS images for 16 March, 2006
ETa
ETa
X
X
ETa
Land Cover
ASTER500m
MODIS
ASTER500m
MODIS
ASTER15m
(mmday-1)
(mmday-1)
(%)
(mmday-1)
(%)
Forest
Pasture 1
Pasture 2
Pasture-Shrub
Shrub
Palo Verde
Typha
Dead Typha
F. Vegetation
Mangrove
Open Water
3.6
3.4
3.5
2.9
3.5
3.7
3.2
3.7
4.0
4.5
4.8
3.6
3.6
3.7
3.0
3.4
3.4
4.1
3.7
4.1
4.4
4.6
3.7
3.5
3.7
2.4
3.5
4.8
4.0
1.8
3.4
5.4
4.0
-0.8
8.1
4.0
1.4
-3.4
-7.6
28.2
-1.4
4.3
-2.4
-4.2
2.5
-4.7
-0.3
-18.3
2.6
41.9
-4.3
-52.1
-16.7
23.2
-13.0
Average
3.8
3.7
3.6
-2.9
-2.5
According to Figure 5.4 the year of 2003 has the highest values of ETa of all the years.
Coverage as water, open water, Typha and dead Typha they keep a similar values in the three
years, e.g. the water the ETa is between 5.7 and .6.3 mm day-1.In the case of open water has the
same value for 2005 and 2006. The range over Cattail were from 3.2 to 4.8 similar to the
founding in the literature by (Price, 1994) with 4.8 mmday-1, (Sanchez-Carrillo et al., 2004)
with an average of 4.9 mm day-1, both using the energy balance method, other such (Abtew,
2001; Abtew & Obeysekera, 1995; Allen et al., 1992; Lott & Hunt, 2001) found values between
the 3.5 to 3.9 mm day-1, using information from lysimeter data and other techniques in the
estimation of the actual evapotranspiration. The use of remote sensing was implemented by
(Chen et al., 2002; Jennifer M. Jacobs et al., 2002) in the determination of ETa for Typha their
results were between 3 mm to 5.35 mm day-1.
51
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Figure 5.1 Actual evapotranspiration 11 May, 2003
Figure 5.2 Actual evapotranspiration 24 January, 2005
Figure 5.3 Actual evapotranspiration 16 March, 2006
52
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
7
6
5
4
3
2
1
0
For est
Pastur e I
Pastur e II
Pastur e-
Shr ub
Pal o Ver de
Typha
Dead Typha
Vegetati on
Mangr ove
Water
Open Water
Shr ub
La nd C ov e r
2003
2005
2006
Figure 5.4 Actual evapotranspiration changes by land cover
5.2.
Comparison of aerodynamic roughness values
Aerodynamic roughness influences greatly the turbulent characteristics near the surface where
the heat fluxes originate. For this study two methods were used in the determination of Zom. The
first one uses the vegetation height and the other is based on the NDVI using the equation
(4.16). The vegetation heights were obtained from the LIDAR data and using the equation
(4.17), which is a direct estimation of the vegetation canopy elevation.
The Table 5.7 presents the output of the ETa using two approaches of Zom . The image selected
for this analysis was the ASTER images on 24 January, 2005 because this day is closer to the
day when the LVIS data was fly (March 2005). Considering the results for all the wetland, there
is a difference of about 16 % between the two approaches. The ETa calculated using the LVIS
data gives lower values than the derivate from NDVI.
The possible causes are that the NDVI approach the maximum Zom that can be reached is around
0.5 m. when the vegetation has values of NDVI greatest than 0.7; for the case of the LIDAR
information is possible to get higher values, even around the 1.3 m for the forest that’s very
close to the value given by Müncher et al., 2001 cited in (Su, 2005) of 1.22 for forest coverage.
Using the elevation of the vegetation is better than use the NDVI as approach Zom, because is
more realistic the influence in the roughness given by high than the relationship of
photosynthetic process. The problem is to get information from LIDAR data is very expensive
and not always is possible to find data of the elevation vegetation, for this reason approaches
derivate from parameters as NDVI are very useful taking into account that error can be less than
16 %. The LIDAR data can be use for estimate values Zom characterized the vegetation of region
and to create tables associated land cover with the Zom values as E.g. the PELCOM land use
map in Müncher et al., 2001 cited in (Su, 2005) or start to investigated the possible use of the all
wavelength for the determination of the roughness, helping in future researches in
evapotranspiration estimation. . Other important consideration is to know exactly the error
existent in the LVIS data and how this affects the estimation of ETa. Land covers as water,
floating vegetation, Typha and open water have the less error; less than 3% and also are the
coverage with less values of Zom (less than 0.4m) derivated from the LIDAR data.
53
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Table 5.7 Aerodynamic roughness height from NDVI and LVIS
Land cover
Forest
Pasture I
Pasture II
Pasture-Shrub
Shrub
Palo Verde
Typha
Dead Typha
Floating vegetation
Mangrove
Water
Open Water
Wetland Average
5.3.
ZomNDVI
(m)
0.29
0.25
0.10
0.15
0.36
0.15
0.46
0.17
0.18
0.40
0.03
0.26
ZomLVIS
(m)
1.29
0.61
0.54
0.55
0.89
0.66
0.33
0.61
0.41
1.11
0.00
0.29
ETaNDVI
(mmday-1)
3.9
3.9
3.5
3.8
3.9
3.9
4.8
4.3
4.8
4.2
5.7
5.4
ETaLVIS
(mmday-1)
3.1
3.3
2.8
3.2
3.2
3.3
4.8
3.8
4.6
3.8
5.5
5.4
4.1
3.5
XETa
(%)
-19
-14
-20
-16
-18
-15
0
-11
-3
-9
-3
0
-16
Bathymetric analysis of the Palo Verde wetland
The storage is the changes in the volume in a certain time. Changes in the lagoon volume
determine by continuous stage height measured of the lake surface and detailed bathymetry
integrate all fluxes to and from the water body. These changes can be used to verify other
measured in the water balance. Measurements of the changes in the level or in the area have
been substituted from determinations of the changes in volume. The area can be increases as a
function of the level (Baird & Wilby, 1999). Using the digital elevation model and the water
levels in the wetland this relationship between the depth and the area cover by water and the
volume was determine and shown it the Figure 5.5 and Figure 5.6
1400
1200
Area (ha)
1000
800
600
400
200
0
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
Water level (m)
Figure 5.5 Relation between the flooded area and the water level
54
1.8
2.0
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
10000000
9000000
8000000
Volume (m3)
7000000
6000000
5000000
4000000
3000000
2000000
1000000
0
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
Water Level (m )
Figure 5.6 Relation between water storage and the water level
This relation is given for the water storage by the equation:
3rd degree Polynomial Fit: y=a+bx+cx^2+dx^3...
Coefficient Data:
a=
-130859.56
b=
16815549
c=
-9737822.2
d=
1907505.5
Standard Error: 102404.41
Correlation Coefficient: 0.99
5.4.
Time series of MODIS images for February 2004
MODIS was used for the estimation of time series of actual ET from 31 January to 22 February,
2004. This information was compared with the losses in the wetland expressed by water depth
changes in the same period. The ET average taken from the meteorological station for this
period was 6.6 mm day-1 and the last rainfall was in last days of December of 2003. The initial
depth of the wetland was 82 cm on the 31 of January, measured from the levelogger install in
the deeper part in the wetland; in the Figure 5.7 is shows the location of the meteorological
station, levelogger and the area cover by water with 80 cm from the ground.
The wetland suffers a reduction in the depth of 20.74 cm in 23 days; the average daily drop was
9 mm per day but for this period there are to days when water level increase, the only
explanation could be by the effect of the tide, the water levels are shown in Figure 5.8. In this
dry period, the losses in water level are caused by evapotranspiration and percolation. The
losses by evapotranspiration can be estimated from remote sensing data and the percolation as
residual of the total losses minus the ETa. This analysis was carried out for the water-cover area
on the starting date, when the depth was 82 cm at the levelogger.
55
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Figure 5.7 Delimitation of the wetland to 80 cm of depth
0.85
Water level (m)
0.80
0.75
0.70
0.65
0.60
30
35
40
45
50
55
Julian Day
Figure 5.8 Loses in the Palo Verde wetland from 31 January to 22 February, 2004
Based on the time series of ETa generated from the MODIS images, the average actual
evapotranspiration for this period was of 5.2 mm day-1 in the Figure 5.9 are shown the results of
the time series, The PET average for the same period was 6.6, therefore the relation ETa/PET is
equal to 0.8 (kc factor). According to the findings and considering that the losses occur only by
evapotranspiration and percolation, the percolation was estimated in 3.8 mm day -1.
56
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
8
7
6
mm/day
5
4
3
2
1
ETa
PET
0
30
35
40
45
50
55
Day
Day
31
32
33
34
35
38
40
42
43
44
45
47
48
50
51
52
53
ETa
5.1
5.7
5.0
5.4
4.8
5.4
4.8
4.9
5.4
4.8
5.1
4.9
6.0
6.0
4.9
5.0
4.6
PET
5.5
5.6
6.4
7.5
6.9
7.0
7.5
5.0
4.6
6.9
6.8
4.2
6.5
7.5
6.7
6.9
7.1
Figure 5.9 Time series of actual evapotranspiration
5.5.
Water balance analysis
Due to difficulty to measure all the inflows and outflow in the wetland, the study was limited to
the dry periods when the rainfall is scarce and there is no superficial outflow. This period starts
when the depth of the wetland decreases to 1 m (depth in which there is no superficial outflow).
The results of the balance are presented in Table 5.8 using the potential evapotranspiration
given by the meteorological station. The Table 5.9 shows the water balance using the
evapotranspiration of the meteorological station multiplied by the Kc factor of 0.8 found for the
wetland.
Table 5.8 Water balance for the dry periods
Period
03-04
04-05
05-06
Days
123
137
100
Rain
(mm)
25
31
43
PET
(mmday-1)
804
814
711
Percolation
(mm)
467
521
380
Balance
(mm)
-1247
-1304
-1048
Storage
(mm)
1099
1092
965
Difference
(mm)
-148
-212
-83
Storage
(mm)
1099
1092
965
Difference
(mm)
13
-49
59
Table 5.9 Water balance for the dry period using the estimation of ETa
Period
03-04
04-05
05-06
Days
123
137
100
Rain
(mm)
25
31
43
ETa
(mmday-1)
644
651
569
Percolation
(mm)
467
521
380
Balance
(mm)
-1086
-1141
-906
57
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
58
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
6.
Conclusions and Recommendations
Based on the analysis of the information and the results obtained the major conclusions and
recommendations are described in this chapter.
6.1.
Conclusions
The use of remote sensing allows accurate classification maps of land cover and improves the
analysis of the vegetation changes. The advantage of use ASTER images is that gives a great
detail of the land cover type and spatial distribution. The low cost of the images allows develop
studies in the changes in vegetation or other type of the studies multi-temporal.
Other advantage of the ASTER image is the possibility of the estimation of actual
evapotranspiration using algorithms as SEBS. With this information is possible to identify the
losses caused by evapotranstiration in the wetland by each one of the vegetation and to know
the evaporation rates according to the type of existent coverage.
The overall average actual evapotranspiration for the Typha dominguensis was between 3.2 and
4.8 mm day-1 with standard deviation of 0.8 mm, showing that cattail is among the vegetated
coverage with higher evapotranspiration values, overcome only by the forested areas.
MODIS images are daily available assisting a better description of the ETa. The results for all
the wetland are very close to the obtained by ASTER images (~5% of difference). MODIS is
adequate for large areas or when the fragmentation of the vegetation is little or when here is not
mixed pixels.
The elevation of canopy given in the LVIS data has an overestimation but with the information
available of the ground data is difficult to say exactly how much is the error in the estimation of
the digital canopy height due to the few point in the field and only in two categories of land
cover. The general correlation between the data from the samples plots and the digital canopy
height is 0.8 with a standard error of 0.81 m
Using the elevation of the vegetation from LIDAR in the estimation of the aerodynamic
roughness gives a promising values, especially that have values Zom overcome the 0,6 m, and is
possible to say the estimation with LIDAR can be improve the determination of the actual
evapotranspiration. The range of error between of the actual ET estimations using the LIDAR
data to calculate Zom gives a values 16 % less than obtained using the NDVI, due to the most of
the coverage have a the same behavior.
In wetlands without superficial outflows, the use of remote sensing technique can be improving
the estimation of the percolation when the storage is known.
59
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
6.2.
Recommendations
This study was made for the dry period, when the rainfall is very little with no inflows to the
wetland. The extension to the rainy season can only be done....
This research was based on the analysis of the actual evapotranspiration by land cover after the
restoration process. The condition before of the restoration process is only know the land cover
changes, due to the time and the acquisition of information the analysis before was not carry out
and could be very interesting to estimate the effect evapotranspiration before the restoration
process when the cattail was dominated the wetland area. With the information of the actual
evapotranspiration can be possible to modeling different scenarios.
The validation of the elevation of the canopy height is necessary and determines the errors this
calculation, but later use this information in a second products as the LAI and d0 and Zom. Based
in the results of the variations on ETa using the parameter of Zom derived from the LVIS, the
determination of actual evapotranspiration can be improving developing a new Zom model
associated to the land cover. This will open a research possibility of use the LVIS data using all
the information given in this LIDAR, and the estimation of other parameter as Leaf area index
and canopy structure.
Other research possibility will be the use of the MASTER images taken in 2003 and 2005 by the
NASA projects. This information is available and free of charge, with a good detail having a
resolution between 10 to 20 m for all the bands.
60
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
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EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Appendix
Appendix A: ASTER radiometric calibration
According to (Iqbal, 1983)
The eccentricity correction factor (E0) explains the variances of Earth-Sun distance throughout
the year due to the circle orbit that Earth around the sun. It is give by,
2
⎛r ⎞
E0 = ⎜ 0 ⎟ = 1.000110 + 0.034221cos(Γ) + 0.001280 sin(Γ) + 0.000719 cos(2Γ) + 0.000077 sin(2Γ)
⎝r⎠
Where the day angle (Г) is defined as,
Г = 2п (dn-1)/365
Where dn is the day number of the year in Julian days
Solar zenith angle (θ) is determine from,
cosθz = sinδ sinφ + cosδ cosφ cosω
Where Φ is the latitude of the target area, δ the solar declination angle, and ω hour angle.
Solar declination angle (δ) is the angle between the ecliptic plane and the earth’s equatorial
plane which is describe the position of the sun during different seasons. It can be calculates as,
δ = 0.006918 – 0.399912cos(Г) + 0.070257sin(Г) – 0.006758cos(2 Г) + 0.000907sin(2 Г)
–0.002697cos(3 Г) + 0.00148sin(3 Г)
The Hour angle (ω) is calculated based on the local solar time (LAT),
ω = 15 (LAT-12) (п/180)
When the Universal Time or (GMT) is given, LAT can be calculated as follows,
LAT = UTC + 4* Lc/60 + Et/60
Where, Lc is the longitude at the point of interest, and Et is the so-called equation of time, which
descript the position of the sun during different seasons. It can be calculated as,
Equation of time (Et)
Et = 0.000075 + 0.001868cos(Г) - 0.032077sin(Г) - 0.014615cos(2Г)
- 0.04089sin(2Г) * 229.18
Earth-Sun Distance (d)
⎛ 2 * π * (J − 93.5) ⎞
d = 1 + 0.01672 * sin ⎜
⎟
365
⎠
⎝
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EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Reflectance (ρ)
Reflectance =
Lλ π d 2
ESUN λ cos θ z
Lλ = Radiance at sensor (Wm-2sr-1μm-1)
d2 = Earth-Sun Distance (AU)
θz = Solar Zenith Angle (deg)
ESUN = Band dependent Exoatmospheric Irradiance (Wm-2μm-1)
Table A-1. Exoterrestrial Solar Irradiance (ESUN) for ASTER Images.
ASTER Band
ESUNi
B1
1845.99
B2
1555.74
B3N
1119.47
B4
231.25
B5
79.81
B6
74.99
B7
68.66
B8
59.74
B9
56.92
Source: http://www.cnrhome.uidaho.edu/default.aspx?pid=85984
Scrip
//Latitude,Longitud
Lon.mpr{dom=value.dom;vr=-180.0000:180.0000:0.00001}:=iff(r1>0,crdx(transform(mapcrd(r1),latlon)),0)
Latitude.mpr{dom=value.dom;vr=-90.0000:90:0000:0.00001}:=iff(r1>0,crdy(transform(mapcrd(r1),latlon)),0)
//Calculation of Julian day, Day angle Equation ofTime (min) and Local Apparent Time, eccentricity
ones.mpr{dom=value.dom;vr=1:1:1}:=lon/lon
day.mpr{dom=value.dom;vr=1:365:1}:=ones*131
da.mpr{dom=value.dom;vr=0:10:0.00001}:=0.01721420632*(day-1)
time.mpr{dom=VALUE.dom;vr=-100.0000:100.0000:0.0001} = (0.000075+0.001868*COS(da)-0.032077*SIN(da)0.014615*COS(2*da)-0.04089*SIN(2*da))*229.18
LAPT.mpr{dom=value.dom;vr=0:24:0.00001}:=16.302+4*(lon/60)+time/60
Eo:=1.00011+0.034221*cos(da)+0.00128*sin(da)+0.000719*cos(2*da)+0.000077*sin(2*da)
//Angles: omega, delta, phi, delta, cos (theta).
omega.mpr{dom=value.dom;vr=-20:20:0.0000001}:=15*(LAPT-12)*pi/180
delta.mpr{dom=value.dom;vr=-1:1:0.00001} := pi/180*0.006918-0.399912*cos(DA)+0.070257*sin(DA)0.006758*cos(2*DA)+0.000907*sin(2*DA)-0.002697*cos(3*DA)+0.00148*sin(3*DA)
phi.mpr{dom=value.dom;vr=-1:1:0.000001}:=latitude*pi/180
costh.mpr{dom=VALUE.dom;vr=-1.00000000000:1.00000000000:0.00000100000} =
sin(phi)*sin(delta)+cos(phi)*cos(delta)*cos(omega)
//EARTH SUN DISTANCE
Dist.mpr{dom=VALUE.dom;vr=-2.00000:2.00000:0.00001}:= 1+0.0167*sin(2*PI*(day-93.5)/365)
//Radians to Reflectance
Refl1.mpr{dom=value.dom;vr=-100:100:0.001}:=r1*PI*Dist^2/(1845.99*costh)
Refl2.mpr{dom=value.dom;vr=-100:100:0.001}:=r2*PI*Dist^2/(1555.74*costh)
Refl3.mpr{dom=value.dom;vr=-100:100:0.001}:=r3*PI*Dist^2/(1119.47*costh)
Refl4.mpr{dom=value.dom;vr=-100:100:0.001}:=r4*PI*Dist^2/(231.25*costh)
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EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Refl5.mpr{dom=value.dom;vr=-100:100:0.001}:=r5*PI*Dist^2/(79.81*costh)
Refl6.mpr{dom=value.dom;vr=-100:100:0.001}:=r6*PI*Dist^2/(74.99*costh)
Refl7.mpr{dom=value.dom;vr=-100:100:0.001}:=r7*PI*Dist^2/(68.66*costh)
Refl8.mpr{dom=value.dom;vr=-100:100:0.001}:=r8*PI*Dist^2/(59.74*costh)
Refl9.mpr{dom=value.dom;vr=-100:100:0.001}:=r9*PI*Dist^2/(56.92*costh)
Appendix B Parameter in the AC
Optical depth at 550 μm: The optical depth, also known as optical thickness (OT) or
attenuation coefficient for aerosols as define as,
k aλ = β ⋅ λ−α
In this formula, β is called Angstrom’s turbidity coefficient, α is the wavelength exponent
(normally is adopted as 1.3), and the wavelength λ is in micrometers (in this case is 0.55 μm). It
is called “turbidity” because scattering of solar radiation by matter other than dry air molecules
is called turbidity of the atmosphere (in the optical sense). Consequently, Kaα includes
attenuation due to “dry” as well as “wet dust particles-that is aerosols. The Anstroms’s (β) may
determined from a measurement of visibility in the horizontal direction, using the following
equation (Iqbal, 1983),
β = (0.55)α (3.912 / VIS − 0.01162)[0.02472(VIS − 5) + 1.132]
Where, VIS is in kilometres.
Water vapour column: The precipitable (cm or g/cm2) is the total amount of water vapour
between surface and TOA, in a vertical column. Consider a column of 1 cm2 extending from the
surface to the TOA. It is the weight of a column of 1 cm2 of atmospheric water, assuming that
all the atmospheric moisture can be condensed.
w = ea ⋅ 0.18
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EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Apendix C Elevation and biomass in the grass plots
Table A-2 Elevation and biomass in the grass plots
Plot Species
Place
Photo
1 Hymenachne amplexicaulis
Palo Verde 8382
2 Hymenachne amplexicaulis
Palo Verde 8400
3 Mixed
Palo Verde 8414
Palo Verde 8429
4 Hymenachne amplexicaulis
Palo Verde 8431
5 Hymenachne amplexicaulis
6 Hymenachne amplexicaulis
Palo Verde 8433
7 Eleocharis mutata
Chirca
8455
8 Eleocharis mutata
Chirca
8457
9 Eleocharis mutata
Chirca
8461
10 Eleocharis mutata
Chirca
8465
11 Eleocharis mutata
Chirca
8467
12 Fimbristylis spadicea
Chirca
8469
13 Fimbristylis spadicea
Chirca
8472
14 Fimbristylis spadicea
Chirca
8474
15 Eleocharis mutata
Chirca
8477
16 Fimbristylis spadicea
Chirca
8480
17 Thalia geniculata
Palo Verde 8484
18 Mixed
Palo Verde 8485
Palo Verde 8486
19 Hymenachne amplexicaulis
Palo Verde
20 Hymenachne amplexicaulis
Palo Verde
21 Hymenachne amplexicaulis
22 Hymenachne amplexicaulis
Palo Verde
23 Mixed
Palo Verde
Palo Verde
24 Hymenachne amplexicaulis
25 Hymenachne amplexicaulis
Palo Verde
26 Hymenachne amplexicaulis
Palo Verde
Palo Verde
62
27 Hymenachne amplexicaulis
28 Mixed
Palo Verde
70
29 Thalia geniculata
Palo Verde
74
30 Thalia geniculata
Palo Verde
75
Palo Verde wetland in May 2003
Palo Verde Wetland in February 2004
68
1
1.10
0.88
1.00
1.28
0.91
0.70
0.44
0.44
0.76
0.67
0.88
0.70
0.60
0.45
0.72
0.95
1.17
1.46
0.60
1.10
0.97
0.81
0.76
1.15
0.78
0.88
1.12
1.09
1.25
2.34
2
1.16
0.85
1.18
1.47
0.89
0.82
0.45
0.39
0.66
0.93
0.50
0.41
0.53
0.66
0.60
0.78
1.10
1.00
0.64
1.22
1.04
0.83
1.11
0.80
0.67
0.89
1.00
0.85
1.19
2.04
3
1.00
0.87
1.10
1.05
1.00
0.69
0.65
0.38
0.79
0.75
0.52
0.58
0.48
0.33
0.54
0.68
1.34
0.70
0.65
0.96
0.98
0.81
1.02
0.95
0.77
0.80
0.84
1.15
0.66
0.90
4
1.09
0.83
1.44
0.78
0.80
0.67
0.60
0.28
0.42
0.43
0.58
0.74
0.63
0.90
0.45
0.75
1.22
0.86
0.93
0.80
0.83
0.80
0.98
0.87
0.92
0.93
0.76
2.12
1.34
2.10
5
1.20
0.73
1.04
1.16
0.88
0.68
0.57
0.37
0.44
0.54
0.53
0.63
0.76
0.68
0.68
0.77
1.28
1.80
0.74
1.20
1.21
0.87
1.59
0.93
0.88
1.12
0.75
1.13
0.70
1.70
6
1.15
1.10
1.54
1.03
0.86
0.70
0.70
0.36
0.78
0.64
0.41
0.68
0.67
0.97
0.71
0.80
1.20
1.10
0.60
1.70
0.83
0.90
0.76
0.73
0.78
1.21
1.12
1.45
1.16
1.60
7
1.12
0.94
1.09
1.13
0.79
0.77
0.86
0.29
0.85
0.77
0.32
0.50
0.58
0.85
0.62
0.55
1.15
1.50
0.59
1.46
1.10
0.81
1.58
0.88
0.79
0.83
1.02
0.82
1.40
2.40
8
1.13
0.87
1.35
1.20
0.96
0.85
0.54
0.28
0.45
0.84
0.49
0.44
0.56
0.89
0.68
0.58
1.00
0.89
0.70
1.70
1.15
1.06
1.00
1.04
0.77
1.13
0.83
0.91
1.19
2.38
9
1.20
0.89
1.36
1.05
0.88
0.83
0.69
0.28
0.79
0.49
0.42
0.61
0.80
0.46
0.64
0.45
1.48
1.15
0.60
1.20
0.83
0.93
0.84
0.96
0.74
0.90
0.90
0.73
0.80
2.00
10 Average Biomass
0.98
1.11
747.9
0.75
0.87
764.2
1.05
1.22
477.1
0.97
1.11
850.9
0.92
0.89
1063.6
0.81
0.75
904.9
0.44
0.59
406.4
0.33
0.34
456.2
0.68
0.66
520.2
0.58
0.66
641.7
0.44
0.51
374.6
0.48
0.58
399.2
0.73
0.63
1032.6
0.53
0.67
697.3
0.37
0.60
387.5
0.38
0.67
963.6
1.25
1.22
694.8
1.11
1.16
464.1
0.65
0.67
259.2
1.12
1.25
476.7
1.02
1.00
738.2
0.81
0.86
1095.8
0.95
1.06
1230.5
0.93
0.92
957.7
0.69
0.78
588.6
1.10
0.98
1066.0
0.79
0.91
851.1
0.94
1.12
930.2
0.79
1.05
1205.4
2.32
1.98
1933.9
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
List of Acronyms
ABL
AC
ASL
ASTER
DCM
DTM
ETa
ILWIS
ISAC
ISIN
LAI
LIDAR
LVIS
MASTER
MODIS
NASA
NDVI
PBL
PET
RTM
SEBAL
SEBS
SMAC
UTM
DEM
Atmospheric Boundary Layer
Atmospheric Corrections
Atmospheric Surface Layer
Advanced Spaceborne Thermal Emission and Reflection Radiometric
Digital Canopy Model
Digital Terrain Model
Actual evapotraspiration
Integrated Land and Water Information System
In-Scene Atmospheric Compensation algorithm
Integerized Sinusoidal
Leaf Area Index
Light Detection and Ranging
Laser Imaging Vegetation System
Moderate Resolution Imaging Spectroradiometer
National Aerospace Administration of USA
Normalized Difference Vegetation Index
PlanetaryBoundary Layer
Potential Evapotranspiration
Radiative Transfer Models
Surface Energy Balance Algorithm for Land SEBAL
Surface energy balance system
Simplified Method for Atmospheric Correction
Universal Transverse Mercator
Digital Elevation Model
69
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
List of Symbols
Symbol
es
T
ea
RH
∆
q
Rd
Rv
P
P0
Interpretation
Saturation vapour pressure
Air temperature
Actual vapour pressure
Relative humidity
Slope of saturation vapour
Specific humidity
The gas constant for dry air
The gas constant for dry air
Air pressure
Air pressure at sea level
Potential temperature
Unit
kPa
˚C
kPa
%
kPa ˚C-1
JKg-1K-1
JKg-1K-1
Pa
Pa
K
Mean air density at constant pressure
Virtual potential temperature
Emissivity of the atmosphere
Kgm-3
K
-
G0
Broadband albedo
Fractional vegetation cover
Land surface emissivity
Aerodynamic roughness height
Displacement height
Net radiation
Soil heat flux
m
m
Wm-2
Wm-2
λE
Latent heat flux
Wm-2
H
Rswd
Sensible heat flux
Downward solar radiation
Wm-2
Wm-2
Rlwd
Downward longwave radiation
Wm-2
σ
ε
z
d0
Stefan-Bolzmann constant
Emissivity
Height above the surface
Friction velocity
Horizontal wind speed
Heat capacity of the dry air
Zero plane displacement height
Wm-2K-4
m
ms-1
ms-1
JKg-1K-1
m
z0m
Roughness height for momentum transfer
m
θa
Potential air temperature at height
K
z 0h
Scalar roughness height for heat transfer
m
ψm
Stability correction for momentum transport
-
ψh
Stability correction for heat transport
-
H wet
Sensible heat flux at the wet limit
Wm-2
Hdry
Sensible heat flux at the dry limit
Wm-2
θ0
ρair
θv
εa
α (Ro)
fc
ε0
Zom
d0
Rn
u*
u
Cρ
70
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
kB −1
Excess resistance to heat transfer
-
L
Monin-Obukhov lingth
Daily incoming global radiation
m
Wm-2
Daily net longwave radiation
Wm-2
Daily terrestrial solar radiation
Wm-2
Trasmissivity
-
K
↓
24
L24
↓
K 24
τ
exo
ILWIS script for SEBS algorithm created by (Sine Hailegiorgis, 2006)
//Conversion of narrow band to broad band (Liang, 2001) reflectance and biophysical parameters
//
Ro.mpr = 0.356*R1+0.13*R3+0.373*R4+0.085*R5+0.072*R7-0.0018
NDVI.mpr{dom=VALUE.dom;vr=-1.0000:1.0000:0.0001} = iff((R4 LE 0.1) and (R3=0),0,(R4R3)/(R4+R3))
fc.mpr = 1-((NDVImax-NDVI)/(NDVImax-NDVImin))^0.625
LAI.mpr = ln(1-fc)/-0.5
//Surface energy balance components//
emm_air.mpr = 9.2e-6*(Tair+273.15)^2*map
L_inc.mpr = 5.67e-8*emm_air*(Tair+273.15)^4
emissivity.mpr = 0.98*fc+0.96*(1-fc)+4*0.002*fc*(1-fc)
L_out.mpr = emissivity*5.67e-8*T_surface^4
Kexo.mpr = 1367*E0(pi,DOY)*CSZ
tau=828/kexo
Knet.mpr = (1-Ro)*Kexo*tau
Lnet.mpr = L_inc*emissivity-L_out
Rn.mpr = Knet+Lnet
Ws.mpr = Acos(-tan(Latitude*pi/180)*tan(decmap))
Kexoday.mpr:=24/pi*1367*0.0036*E0(pi,DOY)*sin(Latitude*pi/180)*sin(decmap)*(Ws-tan(Ws))
Kinc_day.mpr := 11.5741*((0.25+0.5*n/N)*Kexoday)
Tau_day.mpr = Kinc_day/(11.5741*Kexoday)
Rnet_day.mpr = (1-1.1*Ro)*Kinc_day-110*Tau_day
Go.mpr := Rn*(0.05+(1-fc)*(0.315-0.05))
Hdry.mpr := Rn-Go
//Deriving the canopy height and displacement height from (Brutsaert, 1982)//
hc.mpr = Zom/0.136
d.mpr :=2/3*hc
71
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
//Similarity theory//
Thetha_surf.mpr = T_surface*(101325/ps)^0.286
Thetha_v.mpr := (1+0.61*q)*Thetha_surf
Uref.mpr = U10*((ln(100-d)-ln(zom))/(ln(10-d)-ln(zom)))*map
t_c.mpr{dom=VALUE.dom;vr=0.000:10.000:0.001} = ln((100-d)/zoh)/ln((z-d)/zoh)*map
t_pbla.mpr{dom=VALUE.dom;vr=250.000:350.000:0.001} = T_surface*(1-t_c)+T_air*t_c
tetha_air=T_pbla*(101325/P)^0.286
delta_t.mpr{dom=VALUE.dom;vr=-20.000:40.000.0000:0.0001} = thetha_surf-Tetha_air
//Computation of actual and wet limit sensible heat flux//
//Iteration-1 setting initial condition to be neutral stability//
Ustar_1.mpr := 0.41*Uref/ln((100-d)/zom)
v.mpr:= 1.327e-05*(101325/P)*(Tair/273.15)^1.81*map
Restar.mpr := 0.009*Ustar_1/v
Ctstar.mpr := 0.71^-0.66*Restar^-0.5
Ustar_Uh.mpr := 0.32-(0.264*exp(-15.1*0.2*LAI))
nec.mpr:= 0.1*LAI/Ustar_Uh^2
deno.mpr = exp(-nec/2)
term1.mpr = iff(deno=1,0,0.41*0.2*fc^2/(4*0.03*ustar_uh*(1-deno)))
term2.mpr = iff(Zom=0,0,fc^2*fs^2*0.41*ustar_uh*(zom/hc)/ctstar)
fs.mpr:= 1-fc
term3.mpr:= (2.46*Restar^0.25-ln(7.4))*fs^2
kB-1.mpr :=term1+term2+term3
zoh.mpr := iff(Zom/exp(Kb) LE 0.00001, 0.00001,Zom/exp(KB))
rah_1.mpr := ln((100-d)/Zoh)
H_1.mpr:=Iff((delta_t)<0,0.01,(delta_t)*1013*ρair*ustar_1*0.41/rah_1)
L.mpr:= -ρair *1013*Ustar_1^3*Thetha_V/(H_1*9.81*0.41)
Lw.mpr:= -Ustar_1^3*ρair /((Rn-Go)/2450000*0.61*0.41*9.81)
rew_1.mpr = ln((100-d)/zoh)/(0.41*Ustar_1)
Hwet_1.mpr:= ((Rn-Go)-(1013*ρair /rew_1)*es-ea/0.067)/(1+Δ/γ)
y_1.mpr := -(100-d)/L
y_2.mpr = -zom/L
x_1.mpr:= (y_1/0.33)^(1/3)
x_2.mpr:= (y_2/0.33)^(1/3)
phi_o.mpr = (-ln(0.33)+3^(1/2)*0.41*0.33^(1/3)*pi/6)*map
phi_m1.mpr:=ln(0.33+y_1)-3*0.41*y_1^(1/3)+0.142*ln((1+x_1)^2/(1-x_1+x_1^2))+0.492*atan((2*x_11)/3^0.5)+phi_o
phi_m2.mpr :=ln(0.33+y_2)-3*0.41*y_2^(1/3)+0.142*ln((1+x_2)^2/(1x_2+x_2^2))+0.492*atan((2*x_2-1)/3^0.5)+phi_o
phi_h1.mpr := ((1-0.057)/0.78)*ln((0.33+y_1^0.78)/0.33)
phi_h2.mpr:= ((1-0.057)/0.78)*ln((0.33+y_2^0.78)/0.33)
72
EFFECTS OF LAND COVER CHANGES ON THE WATER BALANCE OF THE PALO VERDE WETLAND, COSTA RICA
Iteration-2
Ustar_2.mpr := Uref*0.41/(ln((100-d)/zom)-phi_m1+phi_m2)
Restar.mpr := 0.009*Ustar_2/v
Ctstar.mpr := 0.71^-0.66*Restar^-0.5
term2.mpr = iff(zom=0,0, fc^2*fs^2*0.41*ustar_uh*(zom/hc)/ctstar)
term3.mpr:= (2.46*Restar^0.25-ln(7.4))*fs^2
kB-1.mpr :=term1+term2+term3
zoh.mpr := iff(Zom/exp(Kb) LE 0.000001, 0.000001,Zom/exp(kB-1))
rah_2.mpr :=ln((100-d)/Zoh)-phi_h1+phi_h2
H_2.mpr :=iff((delta_t)<0,0.01,(delta_t)*1013*ρair *ustar_2*0.41/rah_2)
L.mpr := -1013*ρair*Ustar_2^3*Thetha_V/(H_2*9.81*0.41)
Lw.mpr := -Ustar_2^3*ρair/((Rn-Go)*0.41*9.81*0.61/2450000)
yw1.mpr:= -(100-d)/Lw
yw2.mpr = -zom/Lw
xw1.mpr := (yw1/0.33)^(1/3)
xw2.mpr := (yw2/0.33)^(1/3)
phi_hw1.mpr := ((1-0.057)/0.78)*ln((0.33+yw1^0.78)/0.33)
phi_hw2.mpr := ((1-0.057)/0.78)*ln((0.33+yw2^0.78)/0.33)
rew_2.mpr =( ln((100-d)/zoh)-phi_hw1+phi_hw2)/(0.41*Ustar_2)
Hwet_2.mpr := ((Rn-Go)- ρair*1013/rew_2*es-ea/0.067)/ (1+Δ/γ)
y_1.mpr := -(100-d)/L
y_2.mpr= -zom/L
x_1.mpr:= (y_1/0.33)^(1/3)
x_2.mpr := (y_2/0.33)^(1/3)
phi_m1.mpr:=ln(0.33+y_1)-3*0.41*y_1^(1/3)+0.142*ln((1+x_1)^2/(1-x_1+x_1^2))+0.492*atan((2*x_11)/3^0.5)+phi_o
phi_m2.mpr:=ln(0.33+y_2)-3*0.41*y_2^(1/3)+0.142*ln((1+x_2)^2/(1-x_2+x_2^2))+0.492*atan((2*x_21)/3^0.5)+phi_o
phi_h1.mpr := ((1-0.057)/0.78)*ln((0.33+y_1^0.78)/0.33)
phi_h2.mpr:= ((1-0.057)/0.78)*ln((0.33+y_2^0.78)/0.33)
Determination of turbulent heat flux and actual evaporation
Hwet.mpr = iff(Hweti<0,0,Hweti)
Hi.mpr := iff(Hi-1>Hwet,Hi-1,Hwet)
H.mpr := iff(Hi<Hdry,Hi,Hdry)
LEwet.mpr = Rn-Go-Hwet
EFr.mpr:= 1-((H-Hwet)/(Hdry-Hwet))
EF.mpr = EFr*LEwet/(Rn-Go)
ETdaily.mpr := EF*Rnet_day/28.672
73