... geostationary HOTBIRD satellite and can therefore be obtained routinely. Alternatively data may be obtained through U-MARF (EUMETSAT's archive facility).
DETERMINATION OF LAKE TANA EVAPORATION BY THE COMBINED USE OF SEVIRI, AVHRR AND IASI Ambro Gieske1,Tom Rientjes1, Alemseged Tamiru Haile1, Abeyou Wale Worqlul2, Getachew Hadush Asmerom3 (1) ITC (2) University of Bahrdar (3) Ministry of Water Resources, Mines and Energy
ABSTRACT Lake evaporation is traditionally estimated by land-based hydrometeorological networks, using empirical relations to infer evaporative conditions over large water surfaces. Common theoretical frameworks include energy balance approaches, Penman approximations and complementary relationship models. This study explores the possibility of using a novel array of Earth Observation sensors to make an independent check of the traditional approaches and assumptions. The AVHRR and IASI sensors on board of the polar orbiting METOP-A satellite, coupled with the SEVIRI observations from the geostationary MSG-1 satellite provide high accuracy data on the Earth’s surface and atmosphere. Moreover, all these data are transmitted through the EUMETCAST system through transmission by the geostationary HOTBIRD satellite and can therefore be obtained routinely. Fourier inversion of the IASI interferograms produces high resolution atmospheric spectra in the range from 3 to 15 μm. Inverse radiative transfer modelling of these spectra then yields (among many other parameters) temperature, humidity, ozone and aerosol profiles of the atmosphere from which the longwave energy balance components above lake surfaces can in principle be determined. Because AVHRR visible imagery is determined simultaneously from the same METOP platform, the visible spectra may be corrected for atmospheric effects with unprecedented accuracy, yielding independent values for albedos and short-wave energy balance components. Diurnal patterns can be analyzed by assimilating the IASI/AVHRR data with the VNIR, SWIR and TIR channels of MSG-1. The new approach to the determination of lake evaporation is being tested on Lake Tana (Ethiopia) which is a high altitude lake in the source area of the Blue Nile (altitude 1786 m, average area 3000 km2). A ground survey in the Lake Tana catchment area was carried out in August 2007 in cooperation with the Ethiopian Ministry of Water Resources, Bahrdar University and Lake Tana Research Center. Additional information with regard to lake temperatures and synoptic meteorological data was collected since the start of 2008. This paper presents the first findings of the study which is expected to continue for several years.
INTRODUCTION The hydrology of the Nile Basin has lately received much attention. Two main aspects can be distinguished: first, there is the problem of optimizing the use of the available water resources for all countries along the Nile, and second there is the question how the Nile Basin is responding to climate changes. An excellent introduction into these aspects was recently given by Kebede et al. (2006). Earlier work on the Nile may be found in many textbooks, for example, Sutcliffe and Parks (1999). Whereas the hydrology of the White Nile with its many tributaries, lakes and swamps, has been well documented, the hydrology of the Blue Nile has received less attention in scientific literature, although the flow of the Blue Nile exceeds that of the White Nile by far. Lake Tana is situated on the head waters of the Blue Nile and its outflow contributes about 8% of the total Blue Nile flow. Detailed hydrological description of Lake Tana and its environment (Fig. 1) may be found in Grabham and Clark, 1925, Shahin, 1985, Hurst et al, 1959 and Sutcliffe and Parks, 1999. A first bathymetric survey of the lake was made in 1937 (Morandini, 1940). Later the bathymetric survey was repeated by Pietrangeli (1990). Essayas (2007) conducted a third bathymetric survey with sonar and GPS. As mentioned above, Kebede et al. (2006) carried out a study of the water balance in the period from 1960-1992 and developed a preliminary hydrological model at monthly time steps. This modelling exercise was repeated by SMEC (2007) basically for the same time period and also at monthly time steps. Wale et al. (2008) studied the problem of determining the contribution of the ungauged flow into
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the lake by regionalization of the river catchments in the Lake Tana Basin, while Getachew (2008) included the solute mass balance. In this paper a study is made of the open water evaporation, as one of the most important components of the lake’s water balance. The long-term objective is to try and reduce the error in all components of the lake’s water balance. As is clear from the outflow records, the construction of the regulator weir at Chara Chara has played an increasingly important role from 1996 onwards. It is also clear that the situation will change even more after the completion of the Tana-Beles Hydropower Scheme and after the completion of the newly planned reservoirs in Lake Tana’s Basin. With these developments in mind, it becomes even more important to improve our understanding of the lake’s water balance and increase the present river and lake monitoring activities.
Figure 1: The figure shows the location of Lake Tana and the Blue Nile in the Ethiopian Highlands. The yellow dots mark the river gauging stations. New developments are dams (D) and hydropower schemes (red circles).
LAKE EVAPORATION Lake energy budgets can usually be simplified to (e.g. Gianniou and Antonopoulos, 2007; ValletCoulomb et al., 2001)
ΔS = K in − K out + Lin − Lout − H − LE Δt
(1)
Where ΔS/Δt is the rate of change in energy content of the water body, Kin and Kout are the incoming and reflected short-wave radiation components, respectively, Lin is the incoming long-wave atmospheric radiation and Lout the thermal radiation emitted by the water surface. H is the sensible heat flux as a result of temperature difference between water surface and overlying air and finally, LE is the latent heat representing the energy loss due to evaporation. The deep energy loss G (from the water into the bottom sediments) and the advected energy A have been neglected here. The ratio between the reflected and the incoming short-wave radiation is the albedo α, a parameter that is almost routinely determined with RS techniques. The incoming long-wave radiation depends both on the air temperature and the air emissivity, while the surface leaving long-wave radiation depends on surface temperature and on surface emissivity. A number of equations exist for air emissivity usually depending on vapour pressure, air temperature and cloud cover. It should be noted
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that the state of the atmosphere does not only play a direct role in the physical processes governing evaporation, it also determines the corrections required for the visible and thermal imagery. These corrections are important in the determination of the “true” surface temperature and albedo, and in assessing the spatial variability of these parameters. A simple energy balance model was developed to illustrate some of the main aspects of the lake’s energy balance and to show how multi-sensor observations may be used to improve the water and energy balance assessment in data scarce environments. The change in energy content ΔS is simply written as
ΔS = ρ mCw ΔT
(2)
where Cw is the specific heat of water (4200 Jkg-1K-1), m the total mass of water, ρ the density of water (1000 kgm-3) and T temperature. Lake temperature stratification with depth is usually taken into account. However, here this stratification is ignored and m simply has the meaning of an effective mass participating in the annual cycle of energy exchange between water and overlying air. The net radiation Rn (the sum of all short-wave and long-wave components) is written as:
Rn = (1 − α )τ day K exo − 110
τ day λ
(3)
where the first term on the right-hand side of (3) represents the net short-wave radiation, with albedo α (about 0.05) and daily transmissivity τday (about 0.7). Kexo is the daily extraterrestrial radiation which is evaluated in a standard way. The second term on the right-hand side is a simple expression for the net long-wave radiation, where λ is the latent heat of vaporization (about 2.45 MJkg-1). As a last step we assume that H+LE (the total energy loss to the atmosphere) is proportional to the water temperature (in Celsius):
H + LE ≈ β T
(4)
Combining (1)-(4), and taking Δt as 1 day, we obtain
γΔT = Rn − βT
(5)
where γ is ρmCw/A. The lake area A is taken as constant and equal to 3000 km2. Division by A is required to be able to express all terms in (5) in MJm-2d-1. The final step is to write (5) in finite difference form as
Tt +1 = Tt +
Rn
γ
−
βT γ
(6)
It is shown in the next sections how (6) may be solved by making use of a long series of SEVIRI images. As values for β and γ may be obtained by model inversion, a deeper insight may be gained through γ into the depth of water participating in the multi-annual energy exchange cycle and into the energy loss due to H and LE through parameter β. REMOTE SENSING ASPECTS The AVHRR and IASI sensors on board of the polar orbiting METOP-A satellite, coupled with the SEVIRI observations from the geostationary MSG-1 satellite provide high accuracy data on the Earth’s surface and atmosphere. Moreover, all these data are transmitted through the EUMETCAST system through transmission by the geostationary HOTBIRD satellite and can therefore be obtained routinely. Alternatively data may be obtained through U-MARF (EUMETSAT’s archive facility).
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Figure 2 : The diagram shows the setup of the Eumetcast data dissemination to Europe, Africa and America, while the picture on the right shows the ordinary satellite dish required to receive the data in Europe.
Fourier inversion of the IASI interferograms produces high resolution atmospheric spectra in the range from 3 to 15 μm (Camy-Peyret and Eyre, 1998). Inverse radiative transfer modeling (RTIASI) of these spectra then yields (among many other parameters) temperature, humidity, ozone and aerosol profiles of the atmosphere from which the long-wave energy balance components above lake surfaces can in principle be determined (see e.g. Knuth and Stamnes, 1998). Because AVHRR visible imagery is determined simultaneously from the same METOP platform, the visible spectra may be corrected for atmospheric effects with unprecedented accuracy, yielding independent values for albedos and shortwave energy balance components. Diurnal and annual patterns can be analyzed by assimilating the IASI/AVHRR data with the VNIR, SWIR and TIR channels of MSG-1. The instrument is also destined to provide a wealth of data on various components of the atmosphere to further our understanding of atmospheric processes and the interactions between atmospheric chemistry, climate and pollution. In addition, the IASI will deliver data on land-surface emissivity and sea-surface temperature (in cloudfree conditions).
SEVIRI TIME SERIES
Figure 3: The image shows a resampled MSG-2 image (10.8 μm TIR) of 27 Jan 2007 (see Fig. 1 for comparison). The white line represents the mask outline of the water pixels. A time series was collected by processing images of 0200 and 0900 UTC (0500 and 1200 local time) for each day from Sep 2005 until Aug 2008. The figure on the right shows the two time series for the TOA water temperatures.
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Figure 3 shows a resampled MSG-2 image (10.8 μm TIR) of 27 Jan 2007 (see also Fig. 1 for comparison. The white line represents a mask outline of the water pixels. There are about 300 pixels of 2x2 km inside the mask which was taken to be well inside the coast line to avoid mixed land-water pixels. A time series was collected by processing images of 0200 and 0900 UTC (0500 and 1200 local time) for each day from Sep 2005 until August 2008. Outliers were removed from each group of pixels belonging to a certain time and day. Then the maximum value of each group of 300 pixels was taken as representative of the TOA surface temperature. It was assumed that the other pixel values were influenced by various types of clouds. Figure 3 on the right show the two time series for the water temperatures obtained in this way. The graphs of figure 3 clearly show the annual hydrological cycle of the lake. The rainy season is from June until October, with highest rainfall in the months July and August. Temperatures are low and erratic during the rainy season. This is mainly caused by the dense cloud cover during the rainy season. Cloud top temperatures are observed rather than lake temperatures during this period. Other reasons for a decrease in temperature during these months might be the direct rainfall on the lake or inflow from cold river water. However, sunny conditions prevail during the dry months and the satellite derived temperatures are then more representative of the lake water temperature. Inspection of the graphs for 0500 and 1200 hrs (local time) shows that temperatures during midday are generally 4 degrees higher than those in the early morning. This gradient is responsible for the downward transport and mixing of warmer surface water with colder water below the surface. Generally, a strong sea/lake breeze occurs during the afternoon, leading to strong wave action, enhancing the mixing process. However, wind decreases during the calm nights, and in the morning the temperature gradient below the water surface has usually disappeared. However, on an annual basis periodic changes in water temperatures appear to occur, leading to a minimum around December-January. This is indicated in figure 3 by the red lines. The question was therefore whether this periodic temperature behaviour might be explained by annual variations in the extraterrestrial radiation Kexo (see equation 3). The simple model described before can be easily implemented in Excel or a computer code and adjustment of the model parameters to the period from October to March yielded the following solution: β=0.8 and γ=30 (with albedo α=0.05 and transmissivity τday=0.70). The solutions are shown in Figure 4.
Figure 4: The top figure shows the modelled water temperatures where parameters are adjusted to the dry season temperature behaviour (November until March). The bottom figure shows the phase shift between solar forcing (Kexo) and the lake temperatures (in phase with H+LE).
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Whereas the curve fits the periodic behaviour in the dry season quite well, it is clear that the situation in the wet season is complex and requires more field data before conclusions can be drawn The bottom graph of Figure 4 shows the curves for Rnet and H+LE. Note that the graph for H+LE is in phase with the modelled lake temperatures because of the approximation H+LE=βT. The phase of te lake temperatures lags the solar forcing by about 40 days because the lake acts as a large heat reservoir. It takes time to warm up and cool down again. The lake’s system response time to the solar forcing is equal to β/γ=38 days. The value of γ=30 leads to an effective annual mixing depth of 7 m (see Equation 5). The partitioning of H+LE into H and LE is another topic for future research. Basically H is evaluated as
H = ρ aC p
Δt rah
(7)
where ρa is the density of air, Cp the specific heat of air, Δt the temperature difference between surface and elevation z and rah the aerodynamic resistance to heat transport which in the case of water can be simplified to
1 ⎡ ⎛ z ⎞⎤ rah = 2 ⎢ln⎜⎜ ⎟⎟⎥ k u ⎣ ⎝ z0 ⎠ ⎦
2
(8)
where k is von Karman’s constant, u the wind speed, z height of measurement and z0 surface roughness for momentum and heat transport. Observations on the lake with temperature sensors (Figure 5) on the 24th June 2008 show that the average temperature difference was 1.2±1.4 C during that particular day
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temperature (C)
24 June 2008 25
20
15 5
6
7
8
9
10
11
12
13
local time (hrs)
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15
16
temp water
17
18
19
temp air
Figure 5: Observations of water-air temperature differences (z=3 m) on Lake Tana (24 June 2008).
With u=2 ms-1, k=0.41, ρCp=1000 Jm-3K-1, rah=52 sm-1, Δt=1.2 C the sensible heat H can be evaluated as 2 MJm-2d-1 or 300 mmyr-1. However, in view of the large standard deviation the value could be between 0 and 600 mmyr-1. Further work is required to determine the sensible heat fluxes during the year.
DISCUSSION AND FURTHER WORK Notwithstanding the simplicity of the model, it illustrates very well how the solar radiation influences the lake’s temperature behaviour during the dry season. To improve the modelling a combination of fieldwork and remote sensing is required. The fieldwork in the coming months will include temperature measurements of the lake water at various depths, air temperature and humidity measurements at several heights above the surface. Moreover, a sonic anemometer (CSAT3) will be used to determine the sensible heat flux at 20 Hz, while all four short- and long-wave components will be measured simultaneously with a CNR1. A small automatic weather station has been installed on the lake shore
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(near Woreta). This will yield supplementary data on pressure, relative humidity, air temperature, wind speed and direction, incoming global radiation, soil moisture and rain. It is intended to focus further on improvement of the simple model through the planned field validation and development of algorithms for the METOP-AVHRR and IASI sensors, especially with regard to lake surface temperatures, albedos and emissivities. The state of the atmosphere will be assessed through level 1c IASI images and associated products: atmospheric temperature and water vapour (twt), ozone (ozo), emissivities (ems) and cloud parameters (clp). Work is under way to develop the necessary processing chain. The decoding and validation of the EUMETCAST/BUFR formats took longer than anticipated, but will hopefully be completed soon.
REFERENCES Essayas Kaba (2007): Validation of altimetry lake level data and its application in water resources management. MSc Thesis, ITC, Enschede, The Netherlands. Camy-Peyret, C. and Eyre, J. (1998): IASI Science Plan, A Report from the IASI Sounding Science Working Group (ISSWG), EUMETSAT, Darmstadt, Germany. Getachew Hadush Asmerom (2008): Groundwater contribution to the flow of the upper Blue Nile. ITC MSc Thesis, The Netherlands. Grabham, G.W. and Black, R.P. (1925): Report of the Mission to Lake Tana, Government Press, Cairo, Egypt. Gianniou, S.K. and Antonopoulos, V. Z. (2007): Evaporation and energy budget in Lake Vegoritis, Greece. J. Hydrol., 345: 212-223. Hurst, H.E., R.P. Black and Simaika, Y.M. (1959): The Nile Basin, Vol IX, Government Printing, Cairo Egypt, 206 pp. Kebede, S., Y, Travi, T. Alemayehu and Marc V. (2006): Water balance of Lake Tana and its sensitivity to fluctuations in rainfall, Blue Nile basin, Ethiopia. J. Hydrol., 316: 233-247. Knuth, G.E. and Stamnes, K. (1998): Radiative Transfer in the Atmosphere and Ocean. Cambridge University Press, London, UK, 514pp. Morandini, G. (1940): Missione di studio al Lago Tana, Vol. 3. Richerche Limnologiche, Parte Prima, Geografia Fisica, Reale Academia d’Italia, Roma, Italia, 319pp. Pietrangeli. (1990): Studio Pietrangeli, Hydrological Report. Government of Ethiopia. Addis Abeba. Shahin M. (1985): Hydrology of the Nile Basin. Elsevier, Amsterdam, The Netherlands, 575pp. SMEC. (2007): Hydrological Study of the Tana-Beles Sub-Basins. Draft Inception Report. Snowy Mountains Engineering Corporation, Australia. Sutcliffe, J.V. and Parks Y.P. (1999): The Hydrology of the Nile. IAHS Special Publication no. 5, IH, Wallingford, UK, 179pp. Vallet-Coulomb, C., Dagnachew Legesse, F. Gasse, Y. Travi, Tesfaye Chernet. (2001): Lake evaporation estimates in tropical Africa (Lake Ziway, Ethiopia). J. Hydrol., 245:1-18. Wale A.W., Rientjes, T.H.M., Gieske, A.S.M. and Getachew, H.A.. (2008): Ungauged catchment contributions to Lake Tana’s water balance. Hydrological Processes (in print).
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