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Lake Baikal ice: analysis of AVHRR imagery and simulation of under-ice phytoplankton bloom
Journal of Marine Systems 27 Ž2000. 117–130 www.elsevier.nlrlocaterjmarsys
Lake Baikal ice: analysis of AVHRR imagery and simulation of under-ice phytoplankton bloom q Sergei V. Semovski a,) , Nickolai Yu. Mogilev b, Pavel P. Sherstyankin a a
Hydrology and Hydrophysic Laboratory, Limnological Institute, Siberian Branch Russian Academy of Sciences (SB RAS), PO Box 4199, Irkutsk 664033, Russia b Institute of Solar–Terrestrial Physics, SB RAS, Irkutsk, Russia Received 8 February 1999; accepted 15 May 2000
Abstract Generation and sample applications of an integrated set of multi-spectral remotely sensed products for investigations of Lake Baikal’s ice cover variability are presented. Different ice-snow cover classes and unfrozen water distributions are estimated from calibrated and navigated Advanced Very High Resolution Radiometer ŽAVHRR. 1.1-km imagery of Lake Baikal for January 1994 through May 1998. The calculated downward irradiance field in the photosynthetic available radiance band has served as input for a bio-optical model of phytoplankton dynamics. It is shown that the relationship between the production of main taxonomic groups of Baikal’s phytoplankton is highly sensitive to the variability of ice conditions. q 2000 Elsevier Science B.V. All rights reserved. Keywords: lake ice; remote sensing; phytoplankton; hydro-optics; ecological modelling
1. Introduction Lake Baikal that was entered in the UNESCO World heritage list, contains about 20% of the world’s total surface fresh water ŽFig. 1.. Baikal’s pristine waters, its endemic ecosystem, and the region’s recreational opportunities not yet substantially deteriorated by human activities — all these factors provide unprecedented and important research opportunities regarding the dynamics of Baikal’s ecosystems q
Part of this work was published in Proceedings of the First Asian-Pacific Conference on Remote Sensing of Environment, Beijing, China, September 1998 ŽSemovski et al., 1998a.. ) Corresponding author. Tel.: q7-3952-460768; fax: q7-3952460405. E-mail address: [email protected] ŽS.V. Semovski..
and its possible changes caused by climate change and man-induced effects. The lake lies in the centre of the Asian continent in an area with a sharply continental climate, and its ice cover persists during 5 to 6 months. The lake’s dimensions Žlength 600 km, depth down to 1642 m. give an opportunity to use, studying it, methods developed for marine systems. Aulacoseira baicalensis diatom is a dominant pelagic endemic species of Lake Baikal ŽKozhova and Izmest’eva, 1998. during spring. The diatom begins to multiply in February–March under the ice at 0.1–0.28C; its concentration reaches a maximum in April–May at 1–38C. The under-ice bloom intensity of diatoms shows a strong interannual variability. The causes of these changes are unclear yet; however, the photosynthetic active irradiance trans-
0924-7963r00r$ - see front matter q 2000 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 4 - 7 9 6 3 Ž 0 0 . 0 0 0 6 3 - 4
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Fig. 1. Lake Baikal bathymetry, depth in meters.
mission through the ice during the spring time depends largely on ice optical properties. The heatdriven convection intensity, turbulence and stratification structure is also associated with changes in persistence of the ice cover. Both optical and hydrodynamic factors form conditions for diatom growth. The known spatial and temporal distribution of ice for the whole lake can be used in bio-optical models of water ecosystem dynamics for spring bloom simulation. The role played by picophytoplankton in the pelagic ecosystem has been known since the 1970s to 1980s ŽNagata et al., 1997., Synechocistis limnetica Popowsk. is the most abundant. According to current concept, main picoplankton bloom occurs during summer, and its production comprises about 60% of the total annual primary production for the lake. However, data on interannual variability of picoplankton concentration and its correlation with changes in diatom bloom intensity are lacking at present. Baikal’s ice surveillance has been underway for many decades now ŽObruchev, 1892.. Researchers from the Limnological Institute, Siberian Branch Russian Academy of Sciences ŽSB RAS., as part of
numerous expeditions, investigated the main forms of ice and their dynamics ŽVerbolov et al., 1965., the optical properties of ice ŽSherstyankin, 1975., and climatic patterns of formation and decay of the ice cover ŽKozhova and Izmest’eva, 1998.. Until 1990, the hydrometeorological service was performing regular aerial surveys of Baikal during freeze-up and break-up periods. Examples of sharing results derived from aerophotography and satellite images in the optical range for mapping the types of snow-ice cover are given by Sitnikova et al. Ž1984.. As yet, multi-spectral satellite information has not been used on a regular basis in investigating the dynamics and properties of Baikal’s ice cover. Images acquired by the Advanced Very High Resolution Radiometer ŽAVHRR. aboard the NOAA-series satellites, with a resolution of 1.1 km at the nadir, can be used to analyse many phenomena on the lake’s surface. Because the AVHRR data are acquired on a regular basis and provide global coverage of the terrestrial surface, they are also widely used in the analysis of the ice cover of seas and oceans Žsee, e.g., Burns et al., 1992; de Abreu et al., 1994, and other publications.. This information was
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used much less frequently to investigate ice on lakes ŽYoung, 1991; Burda and Antropov, 1993., however. AVHRR data was used successfully for thermal fronts studies ŽSemovski et al., 1998b.. One of the purposes of this paper is to give an account of how the AVHRR data covering the time interval from 1994 till the present have been used in investigating the growth and decay history of Baikal’s ice cover and the distribution of the main types of snow-ice cover. Main classes of surfaces Žclouds, fog, water, and different states of snow and ice. are identified by outlining peaks on the two-dimensional distribution histogram of the first two Empirical Orthogonal Functions ŽEOFs.. Smoothed characteristics of a change in the state of snow-ice cover during the winter are presented, with some preliminary results concerning interannual variability.
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In Section 2, optical properties of derived ice classes and available downward irradiance on the water surface are estimated using field observational data. This information is used as external fluxes in the bio-optical model of phytoplankton dynamics. The model equations are based on general principles of population dynamics. The impact in optically active ecosystem components in the underwater spectral irradiance field is simulated.
2. Data and methods 2.1. Study region The study region includes the entire basin of Lake Baikal ŽFig. 1.. The winter sets differently in the
Fig. 2. Lake Baikal integral daily PhAR on the water surface for winter–spring period wmWP cmy2 x. 1 is for South basin, 2 is for Central basin, 3 is for North basin. ŽA. Denotes the clean transparent ice scenario, and ŽB. scenario of snow-covered ice.
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three main lake’s basins. Initially, the ice cover forms in the Northern part Žnormally in December., and the last freeze-up corresponds to the area of the
Angara outfall, the western shore of the Southern part of the lake Žin January.. The Angara outfall itself and its upper reaches remain unfrozen during
Fig. 3. Two first EOFs of 1994–1998 Lake Baikal ice and snow coverage based on AVHRR imagery Ža., and corresponding two-dimensional scattering diagram Žb. Žhistogram, darker colours correspond to higher values..
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the entire wintertime. Break-up of ice occurs in the Southern part in May, while in the Northern part, some ice fields can persist in the middle of the lake till mid-June. 2.2. AVHRR imagery The best quality images for the period January– June, containing no more than 10%-cloudiness over the region from the overall database for 1994–1998, were used in the analysis. It is well known that of the five AVHRR channels, two correspond to the visible and near infrared spectral regions, and the central wavelengths for these channels are, respectively, about 0.6 and 0.9 mm Žhereafter referred to as channels 1 and 2.. Yet another three channels are designed to receive information about the state of the terrestrial surface in the infrared spectral region at wavelengths of 3.7 Žchannel 3., 11.0 Žchannel 4., and 12.0 mm Žchannel 5.. The AVHRR instrument has the field of view of 1.1 km at the nadir.
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Images acquired at the Centre of Satellite Monitoring of the Institute of Solar–Terrestrial Physics ŽISTP., SB RAS were then processed using the XV-Image program package ŽZakharov et al., 1995.. Channels 1 and 2 were calibrated with the use of coefficients calculated by the NOAA–NASA AVHRR Pathfinder Calibration Working Group. A non-linear calibration procedure that was developed at NASA, was used for channels 3, 4 and 5. An atmospheric correction was made by taking into account the optical thickness of the atmosphere determined from the zenith angle of view. In the analysis, we used values of albedo Žon a percentage basis. inferred by applying the calibration procedure for visible channels, and brightness temperatures ŽK. for temperature channels. Calibration information was converted to geographical coordinates using additional referencing to control points on the lake’s shoreline in channel 2. The area on each picture corresponding to the lake’s water area, was singled out for the analysis using a standard mask.
Fig. 4. Surface classes for 9-01-1997, classes 1–4 correspond to the open water, young ice and fog in Southern and Central basins.
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We used the method of EOFs to identify the main possible states of the underlying surface on Lake Baikal’s water area during the winter period, based on a sequence of multi-spectral images. In the analysis, we used a seven-dimensional space of signatures which, in addition to the normalised albedo for channels 1 and 2 and the brightness temperature for channels 3, 4 and 5, included also the brightness temperature difference for channels 3 and 4 Ž p2 y p3. and the normalised vegetation index NDVI calculated by the formula NDVI s Ž p2 y p1rp2 q p1. where p2 and p1 stand for the normalised albedo for AVHRR channels 2 and 1, respectively. The IDL program system, Research Systems Inc., has been used in developing algorithms and in the data analysis. 2.3. Calculating the aÕailable surface irradiance The feature peculiar to Baikal is the presence of areas of extremely transparent crystal ice, whose
optical properties were investigated by Verbolov et al. Ž1965. and Sherstyankin Ž1975.. Ice of such a type transmits up to 85% of the luminous energy, which affects the input of photosynthetic available radiation ŽPhAR. and the warming of the layer under the ice. Note for comparison that the transmission of incident solar radiation through the ice covered with 5-cm layer of snow does not exceed 5%. With an increase in solar-induced warming, the upper snow cover melts away to produce needle ice ŽApipkrakeB . forms. According to observations reported by Sherstyankin Ž1975., the transmission of solar radiation by a wet ice changes drastically in the course of the daytime and can increase from 20% to 80%. By analysing the ice and snow albedo in No. 1 and 2 channels, as well as the brightness temperature, after separating different types of underlying surface spatially and temporally, it is possible to estimate to a first approximation the optical properties of ice and the characteristic of solar radiation transmission using field observations ŽSherstyankin, 1975..
Fig. 5. Surface classes for 19-04-1996, class 4 corresponds to the transparent ice Žwestern part of the Southern basin., classes 5–8 correspond to the snow ŽCentral and Northern basins..
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The available irradiance variability at the water level in the PhAR band was taken for three lake basins from Stepanova and Sherstyankin Ž1984.. The annual input of direct solar radiation Žabout 2500 MJ P my2 . exceeds that over the surrounding regions by 10% due to high spectral and integral transparency of the atmosphere and low frequency of low clouds over the lake. Maximum potential values of total and direct PhAR on a horizontal surface change from 440 W P my2 in June to 100 W P my2 in December. For the calculation of available PhAR under the ice cover, transmission spectra were allowed for every type of underlying ice and snow surface. For the March wet ice, we used transmission values that changed during the daytime according to observations. Daily integrated values of spring available solar irradiance on the water surface are presented in Fig. 2 for three Lake basins, for transparent ice and for 2.5-cm snow cover.
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2.4. Bio-optical model of water ecosystem We used the modification of Semovski Ž1999. in the bio-optical, vertically resolved phytoplankton dynamics model in the water ecosystem to investigate under-ice phytoplankton bloom for different ice conditions. The one-dimensional model includes a spectral bio-optical block, a spectral primary production algorithm of Wozniak et al. Ž1992. based on quantum yield of photosynthesis, and the block of vertical mixing. The presented model is extended to include two groups of phytoplankton: diatoms and picophytoplankton, and the same spectral model of primary production is used for both of them. These are the following differences in properties for different phytoplankton groups: the nutrient assimilation rate for picophytoplankton is growth with temperature Žsee Nagata et al., 1997., diatoms use silicon as additional nutrient and can be limited by it, and the
Fig. 6. Surface classes for 20-04-1998, classes 1–2 correspond to the open water ŽSouthern basin..
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sinking rate for diatoms is higher because of their larger size and silicon skeleton. The optical part of the model includes a spectral underwater irradiance field simulation by the algorithm including main optical active components: pure water, phytoplankton pigments, dissolved organic matter, detritus, and mineral suspended matter. For the under-ice irradiance field simulation, the bio-optical block uses the observational data on transmission for different icesnow classes from Sherstyankin Ž1975.. The following biological variables are included in the population dynamics model: zooplankton Žpredator for diatoms., heterotrophic bacterioplankton Žprey for flagellates., and flagellates Žpredator for picoplank-
ton and bacteria.. The vertical physical model uses climatic values taken from Kozhova and Izmest’eva Ž1998. for the mixing layer Žhypolimnion. depth, and its mean temperature and intensity of vertical mixing. For the phytoplankton component, main equations can be found in Semovski Ž1999.. 3. Results 3.1. Delineation and mapping of ice-snow coÕer classes Upon calculating the eigennumbers and eigenvectors of the correlation matrix calculated for all pic-
Fig. 7. Diagram of percentage of the area occupied by ice-snow classes, Ža. 1995, Žb. 1997. Classes from 1 to 4 Ždark colours. correspond to the open water, transparent ice and wet ice; classes from 5 to 8 Žlight colours. correspond to the dry snow cover.
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tures in accordance with the concepts embodied in the EOF technique, it was found that the first two modes describe 85% of the total variance. Weight coefficients of the contribution from different channels to the first two eigenvectors are presented in Fig. 3a. The next step involved calculating the frequency distribution histogram of the first two main components for the entire sequence of pictures Žsee Fig.
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3b.. Peaks of the two-dimensional distribution were interpreted as different classes of state of the underlying surface. The classes were identified manually by outlining the histogram peaks using a special-purpose IDL program. This was followed by a mapping of the resulting classes for each picture and a visual determination of the correspondence of each class and type of underlying surface depending on the month. Classes cor-
Fig. 8. Simulation of the Baikal phytoplankton annual cycle for different ice types during winter. Section time-depth of total phytoplankton concentration wmg N P m I3 x, South Baikal, 1998 year conditions Ža.; 1997 simulated column diatoms–phytoplankton relationship wmg N P m I2 x: Žb. South Baikal, Žc. North Baikal.
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responding to clouds and fog above the water, a frequent occurrence on Baikal in January, were discarded in the analysis. Note that a classification using the entire set of images covering both the freeze-up and break-up periods can lead to the inclusion in a single group of some types of ice typical of these periods only. Conceivably as soon as a more representative data
set has been accumulated, it would be appropriate to carry out a classification for the periods of freeze-up ŽJanuary., stable ice cover ŽFebruary–April. and break up ŽApril–June.. Quite good atmospheric conditions prevail in the area of Lake Baikal in the wintertime. For the period from January to May, the number of high-quality images, with the satellite located at the nadir and the
Fig. 9. Ža. Surface classes for 28-05-1998, North Baikal, classes 5–7 correspond to the open water. Žb. Mosaic of ERS SAR images of North Baikal, 28-05-98.
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Fig. 9 Ž continued ..
cloud amount over the lake not exceeding 10%, can reach 56 Žthe year 1997.. However, during December and early January when South Baikal is not yet frozen up, and a freezing weather occurs in East Siberia, unfrozen water areas are almost constantly obscured with fog, which is clearly identified from high values of the brightness temperature difference of channels 3 and 4. Application of the EOF method, however, makes it possible to clearly recognise the unfrozen water areas, the constant ice edge, and areas probably covered with a young transparent ice
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ŽVerbolov et al., 1965.. Fig. 4 presents a distribution of the main types of underlying surface in the first half of January, 1997. During the period from mid-January to the end of April, the lake is completely covered with ice, and by analysing satellite information, it is possible to investigate in detail the distribution and dynamics of the main types of snow-ice cover. Fig. 5 is an example of the distribution of the types of snow-ice cover for 19 April, 1996. Baikal’s break-up starts usually in early May at the western shore in the Southern part. The position of the ice-edge and the distribution of the main types of snow-ice cover are presented in Fig. 6 for late April 1998. The break-up date, and also the freeze-up date provide a good climatic indicator of temperature variability in the Baikal region, and this indicator has an integral character because of the lake’s large thermal inertia. More detailed studies of the interannual variability of the lake’s freeze-up and break-up process and of the movement of the ice edge must give important information about the character of climate change in the Siberian region. The point here is that different parts of the lake are in different climatic conditions. The climate of the Northern part is distinguished by a greater severity due to perceptible effects of the Yakut anticyclone. Different parts of the lake have their peculiar atmospheric circulation and precipitation systems having their origins in relief elements. Of great interest also are the interannual variability studies into the distribution dynamics of different types of Baikal’s snow-ice cover. Different authors suggested that the freeze-up and break-up dates, the ice cover thickness, and the appearance of transparent ice areas are factors, which have a substantial influence upon the functioning of the lake’s ecosystem. It is likely, in particular, that the known substantial interannual variability in intensity of diatom blooming during the springtime ŽKozhova and Izmest’eva, 1998. is also associated with the time of lake freezing and with character of the snow-ice cover during the wintertime. As has been pointed out above, the transmission of incident solar irradiation through the ice cover of different types can differ nearly by an order of magnitude. Fig. 7 presents the distribution diagrams for different types of snow-ice cover percentage during
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January–May 1995 and 1997. The different structure of ice-snow coverage of the lake can be clearly seen from the plot. For every region of the lake, we can produce time series of ice conditions variability, and this information can be used to calculate fluxes in the ecodynamical model. 3.2. Simulation of the phytoplankton bloom in different ice conditions A simulation of the phytoplankton annual variability using actual ice conditions and available irradiance on the water surface that was calculated in accordance with Section 2.3 demonstrates general known features. Fig. 8a presents the evolution of the total South Baikal phytoplankton vertical distribution for the year with low spring bloom. Under-ice diatoms bloom is clearly visible in the figure; the concentration then decreases during spring homothermy and intensive mixing, picoplankton bloom occurs during summer with a shallow subsurface maximum, and the concentration of early autumn diatoms rises again moderately. The variability in ice-snow conditions substantially controls the relationship between concentrations of phytoplankton taxonomic groups. For the diatom-rich year in the South Baikal ŽFig. 8b., the intensity of summer picoplankton bloom is lower because of low nutrients assimilated by diatoms during summer. During the same year, the simulated North Baikal situation ŽFig. 8c. is quite different. Due the predominance of a snow-covered ice during winter and a longer period of ice-cover, picoplankton is responsible for the greater part of a total annual production, diatoms bloom occurs later, and its intensity is lower. Annual variability of the vertical structure of Baikal phytoplankton was not presented systematically in the literature. One example of chlorophyll vertical changes shown in ŽKozhova and Izmest’eva, 1998. demonstrates qualitative agreement with results of our simulation.
4. Discussion This paper is the first attempt at analysing multispectral satellite imagery data banks with the purpose of investigating the ice cover dynamics on Lake
Baikal and its influence on phytoplankton variability. One should be aware of the limitations arising when using information of moderate spatial resolution in the optical and infrared ranges, such as AVHRR imagery. It is well known that the data of active radar soundings acquired by instruments aboard, for example, ERS satellites, provide higher quality and higher spatial resolution information. In addition, measurements of this kind are virtually independent of atmospheric conditions. However, no regular data sets of radar surveys are currently Žand in the near future. available to us. Moreover, as regards to regularity and availability, the AVHRR data have no equal yet. Consequently, the Baikal Geographic Information System ŽGIS. under development must incorporate, at a later time, databases containing AVHRR multi-spectral imagery, as well as algorithms for investigating the ice cover conditions, which will constitute a further development of the methods presented in this paper. It is appropriate, whenever possible, to make comprehensive undersatellite measurements of physical characteristics of Baikal’s ice. Data of physical and biological observations have much potential for yielding information about the association of optical characteristics of ice Žincluding those acquired from space-borne observations. with physical parameters of the water column and phytoplankton growth. Application of models for development of turbulence and convection under the ice is necessary for the correct description of the under-ice upper layer dynamics. Note, however, that routine observations of the conditions of the snow-ice cover at a stationary hydrometeorological network are usually poor representative, and the main part of the water surface Žabout 80%. remains unexplored by observers. Expedition investigations are, as before, the principal methods of obtaining information about the state of the lake’s ecosystem, but they have an extremely irregular character. And on Baikal during freeze up and break up of the ice cover, they are extremely complicated and even dangerous. The currently available capabilities of accumulating archives of regularly received data are limited to satellite platforms affording low spatial resolution ŽAVHRR, SeaWiFS.. High resolution data of SPOT and Landsat satellites cannot be involved into analysis due to its high price, other data sources are
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unavailable because of isolation of Lake Baikal area from receiving stations. During 1997–1998, the mobile station of the European Space Agency ŽESA., located in Ulan Bator, Mongolia, was receiving the first Baikal images taken by the synthetic aperture radar ŽSAR. from the ERS-1 and ERS-2 satellites. Comparison of AVHRR-based classification of snow and ice cover and simultaneous SAR image is presented in Fig. 9 for the late Spring. We plan to incorporate into analysis of Baikal ice high-resolution multi-spectral data of Resurs satellites, which cover the entire territory of Russia. Both light transmission through the ice and mixing in the upper layer are processes that control productivity of phytoplankton. According to some new knowledge, under-ice turbulence generated by salt convection during ice freezing or by heating during spring can play an important role in diatoms ecology ŽGranin et al., 1999.. Peculiar Baikal diatoms life cycle ŽJewson, 1992. should probably be included in mathematical models because this feature can cause interannual variability. These processes, however, are beyond the scope of the studies presented and can be the subject for future work. It is possible that a Lagrangian attempt to a description of the phytoplankton dynamics ŽWolf and Woods, 1988. will be more appropriate for detailed studies of diatom ecology in a turbulent environment.
Acknowledgements We are thankful to Dr. N.P. Minko and our colleagues from the Centre of Satellite Monitoring at the ISTP SB RAS for the encouragement, and to Prof. M.N. Shimaraev and Prof. G.I. Popowskaya for the useful discussion. Remarks of anonymous referees were essential in improving the paper quality. This work is supported by the Russian Foundation for Basic Research grants No. 99-05-64814 and by project No.6r1 of the Ministry of Science, Russian Federation. Images of ERS SAR instrument were given for our disposal in the framework of European Space Agency announcement of opportunity. IDL and ENVI software ŽResearch Systems. were used intensively in the data analysis.
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References Burda, N.Yu., Antropov, A.V., 1993. Automatic prediction of the dynamics of ice in Lake Ladoga in spring from aerospace data using a simulation method. Sov. J. Remote Sens. 10 Ž3., 432–443. Burns, B.A., Schmidt-Grottrup, M., Viehoff, T., 1992. Methods for digital analysis of AVHRR sea ice images. IEEE Trans. Geosci. Remote Sens. 30, 589–602. de Abreu, R.A., Key, J., Maslanik, J.A., Serreze, M.C., LeDrew, E.F., 1994. Comparison of in situ and AVHRR-derived surface broadband albedo over Arctic sea ice. Arctic 47, 288–297. Granin, N.G., Jewson, D.H., Grachev, M.A. et al., 1999. Physical processes as a factor for developing diatoms under the ice. Russ. Acad. Sci. Dokl. 366 Ž6., 836–839 Žin Russian.. Jewson, D.H., 1992. Size reduction, reproductive strategy and the life cycle of a centric diatom. Philos. Trans. R. Soc. London 336, 191–213. Kozhova, O.M., Izmest’eva, L.R. ŽEds.., 1998. Lake Baikal, Evolution and Biodiversity. Backhuys Publ., Leiden. Nagata, T., Takai, K., Kawanobe, K. et al., 1997. Autotrophic picoplankton in southern Lake Baikal: abundance, growth and grazing mortality during summer. J. Plankton Res. 16, 945– 959. Obruchev, V.A., 1892. Our knowledge on formation and properties of Angara ice and necessity of new research with volunteer observations of Baikal, East Siberian Branch of Russian Geographical Society. Notes 23 Ž4. Žin Russian.. Semovski, S.V., Mogilev, N.Yu., Minko, N.P., 1998a. Lake Baikal’s ice cover dynamics studies using multispectral AVHRR imagery. In: Green, R.O., Tong, Q. ŽEds.., Hyperspectral Remote Sensing and Application. Proc. SPIE, vol. 350, 2 pp. 270–277 Žin Russian.. Semovski, S.V., Shimaraev, M.N., Minko, N.P., Gnatovsky, R.Yu., 1998b. Lake Baikal’s thermal fronts study using AVHRR IR imagery. Issled. Zemli Kosmosa ŽEarth Studies from Space. 5, 65–75 Žin Russian.. Semovski, S.V., 1999. The Baltic Sea and Lake Baikal underwater bio-optical Fields simulation using ecodynamical model. Ecol. Modell. 116, 149–163. Sherstyankin, P.P., 1975. Experimental investigations of the lake Baikal under-ice light field. Nauka, Moscow Žin Russian.. Sitnikova, G.V., Furman, M.S., Yanter, N.N., 1984. Remote and groundbased methods for researching the dynamics of ice processes on Lake Baikal. Remote and Groundbased Studies of the Dynamics of Natural Processes in Siberia. Institute of Geography, USSR Academy of Science ŽSiberian Branch., Irkutsk, pp. 72–81 Žin Russian.. Stepanova, V.V., Sherstyankin, P.P., 1984. Spectral distribution of available radiation in the visible spectrum at Lake Baikal. Automatisation of Limnological Research and Light Regime of Water Basins. Nauka, Novosibirsk, pp. 112–125 Žin Russian.. Verbolov, V.I., Sokolnikov, V.M., Shimaraev, M.N., 1965. The hydrometeorological regime and heat balance of lake Baikal. Nauka, Moscow Žin Russian..
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Lake Baikal ice: analysis of AVHRR imagery and simulation of under-ice phytoplankton bloom q Sergei V. Semovski a,) , Nickolai Yu. Mogilev b, Pavel P. Sherstyankin a a
Hydrology and Hydrophysic Laboratory, Limnological Institute, Siberian Branch Russian Academy of Sciences (SB RAS), PO Box 4199, Irkutsk 664033, Russia b Institute of Solar–Terrestrial Physics, SB RAS, Irkutsk, Russia Received 8 February 1999; accepted 15 May 2000
Abstract Generation and sample applications of an integrated set of multi-spectral remotely sensed products for investigations of Lake Baikal’s ice cover variability are presented. Different ice-snow cover classes and unfrozen water distributions are estimated from calibrated and navigated Advanced Very High Resolution Radiometer ŽAVHRR. 1.1-km imagery of Lake Baikal for January 1994 through May 1998. The calculated downward irradiance field in the photosynthetic available radiance band has served as input for a bio-optical model of phytoplankton dynamics. It is shown that the relationship between the production of main taxonomic groups of Baikal’s phytoplankton is highly sensitive to the variability of ice conditions. q 2000 Elsevier Science B.V. All rights reserved. Keywords: lake ice; remote sensing; phytoplankton; hydro-optics; ecological modelling
1. Introduction Lake Baikal that was entered in the UNESCO World heritage list, contains about 20% of the world’s total surface fresh water ŽFig. 1.. Baikal’s pristine waters, its endemic ecosystem, and the region’s recreational opportunities not yet substantially deteriorated by human activities — all these factors provide unprecedented and important research opportunities regarding the dynamics of Baikal’s ecosystems q
Part of this work was published in Proceedings of the First Asian-Pacific Conference on Remote Sensing of Environment, Beijing, China, September 1998 ŽSemovski et al., 1998a.. ) Corresponding author. Tel.: q7-3952-460768; fax: q7-3952460405. E-mail address: [email protected] ŽS.V. Semovski..
and its possible changes caused by climate change and man-induced effects. The lake lies in the centre of the Asian continent in an area with a sharply continental climate, and its ice cover persists during 5 to 6 months. The lake’s dimensions Žlength 600 km, depth down to 1642 m. give an opportunity to use, studying it, methods developed for marine systems. Aulacoseira baicalensis diatom is a dominant pelagic endemic species of Lake Baikal ŽKozhova and Izmest’eva, 1998. during spring. The diatom begins to multiply in February–March under the ice at 0.1–0.28C; its concentration reaches a maximum in April–May at 1–38C. The under-ice bloom intensity of diatoms shows a strong interannual variability. The causes of these changes are unclear yet; however, the photosynthetic active irradiance trans-
0924-7963r00r$ - see front matter q 2000 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 4 - 7 9 6 3 Ž 0 0 . 0 0 0 6 3 - 4
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Fig. 1. Lake Baikal bathymetry, depth in meters.
mission through the ice during the spring time depends largely on ice optical properties. The heatdriven convection intensity, turbulence and stratification structure is also associated with changes in persistence of the ice cover. Both optical and hydrodynamic factors form conditions for diatom growth. The known spatial and temporal distribution of ice for the whole lake can be used in bio-optical models of water ecosystem dynamics for spring bloom simulation. The role played by picophytoplankton in the pelagic ecosystem has been known since the 1970s to 1980s ŽNagata et al., 1997., Synechocistis limnetica Popowsk. is the most abundant. According to current concept, main picoplankton bloom occurs during summer, and its production comprises about 60% of the total annual primary production for the lake. However, data on interannual variability of picoplankton concentration and its correlation with changes in diatom bloom intensity are lacking at present. Baikal’s ice surveillance has been underway for many decades now ŽObruchev, 1892.. Researchers from the Limnological Institute, Siberian Branch Russian Academy of Sciences ŽSB RAS., as part of
numerous expeditions, investigated the main forms of ice and their dynamics ŽVerbolov et al., 1965., the optical properties of ice ŽSherstyankin, 1975., and climatic patterns of formation and decay of the ice cover ŽKozhova and Izmest’eva, 1998.. Until 1990, the hydrometeorological service was performing regular aerial surveys of Baikal during freeze-up and break-up periods. Examples of sharing results derived from aerophotography and satellite images in the optical range for mapping the types of snow-ice cover are given by Sitnikova et al. Ž1984.. As yet, multi-spectral satellite information has not been used on a regular basis in investigating the dynamics and properties of Baikal’s ice cover. Images acquired by the Advanced Very High Resolution Radiometer ŽAVHRR. aboard the NOAA-series satellites, with a resolution of 1.1 km at the nadir, can be used to analyse many phenomena on the lake’s surface. Because the AVHRR data are acquired on a regular basis and provide global coverage of the terrestrial surface, they are also widely used in the analysis of the ice cover of seas and oceans Žsee, e.g., Burns et al., 1992; de Abreu et al., 1994, and other publications.. This information was
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used much less frequently to investigate ice on lakes ŽYoung, 1991; Burda and Antropov, 1993., however. AVHRR data was used successfully for thermal fronts studies ŽSemovski et al., 1998b.. One of the purposes of this paper is to give an account of how the AVHRR data covering the time interval from 1994 till the present have been used in investigating the growth and decay history of Baikal’s ice cover and the distribution of the main types of snow-ice cover. Main classes of surfaces Žclouds, fog, water, and different states of snow and ice. are identified by outlining peaks on the two-dimensional distribution histogram of the first two Empirical Orthogonal Functions ŽEOFs.. Smoothed characteristics of a change in the state of snow-ice cover during the winter are presented, with some preliminary results concerning interannual variability.
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In Section 2, optical properties of derived ice classes and available downward irradiance on the water surface are estimated using field observational data. This information is used as external fluxes in the bio-optical model of phytoplankton dynamics. The model equations are based on general principles of population dynamics. The impact in optically active ecosystem components in the underwater spectral irradiance field is simulated.
2. Data and methods 2.1. Study region The study region includes the entire basin of Lake Baikal ŽFig. 1.. The winter sets differently in the
Fig. 2. Lake Baikal integral daily PhAR on the water surface for winter–spring period wmWP cmy2 x. 1 is for South basin, 2 is for Central basin, 3 is for North basin. ŽA. Denotes the clean transparent ice scenario, and ŽB. scenario of snow-covered ice.
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three main lake’s basins. Initially, the ice cover forms in the Northern part Žnormally in December., and the last freeze-up corresponds to the area of the
Angara outfall, the western shore of the Southern part of the lake Žin January.. The Angara outfall itself and its upper reaches remain unfrozen during
Fig. 3. Two first EOFs of 1994–1998 Lake Baikal ice and snow coverage based on AVHRR imagery Ža., and corresponding two-dimensional scattering diagram Žb. Žhistogram, darker colours correspond to higher values..
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the entire wintertime. Break-up of ice occurs in the Southern part in May, while in the Northern part, some ice fields can persist in the middle of the lake till mid-June. 2.2. AVHRR imagery The best quality images for the period January– June, containing no more than 10%-cloudiness over the region from the overall database for 1994–1998, were used in the analysis. It is well known that of the five AVHRR channels, two correspond to the visible and near infrared spectral regions, and the central wavelengths for these channels are, respectively, about 0.6 and 0.9 mm Žhereafter referred to as channels 1 and 2.. Yet another three channels are designed to receive information about the state of the terrestrial surface in the infrared spectral region at wavelengths of 3.7 Žchannel 3., 11.0 Žchannel 4., and 12.0 mm Žchannel 5.. The AVHRR instrument has the field of view of 1.1 km at the nadir.
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Images acquired at the Centre of Satellite Monitoring of the Institute of Solar–Terrestrial Physics ŽISTP., SB RAS were then processed using the XV-Image program package ŽZakharov et al., 1995.. Channels 1 and 2 were calibrated with the use of coefficients calculated by the NOAA–NASA AVHRR Pathfinder Calibration Working Group. A non-linear calibration procedure that was developed at NASA, was used for channels 3, 4 and 5. An atmospheric correction was made by taking into account the optical thickness of the atmosphere determined from the zenith angle of view. In the analysis, we used values of albedo Žon a percentage basis. inferred by applying the calibration procedure for visible channels, and brightness temperatures ŽK. for temperature channels. Calibration information was converted to geographical coordinates using additional referencing to control points on the lake’s shoreline in channel 2. The area on each picture corresponding to the lake’s water area, was singled out for the analysis using a standard mask.
Fig. 4. Surface classes for 9-01-1997, classes 1–4 correspond to the open water, young ice and fog in Southern and Central basins.
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We used the method of EOFs to identify the main possible states of the underlying surface on Lake Baikal’s water area during the winter period, based on a sequence of multi-spectral images. In the analysis, we used a seven-dimensional space of signatures which, in addition to the normalised albedo for channels 1 and 2 and the brightness temperature for channels 3, 4 and 5, included also the brightness temperature difference for channels 3 and 4 Ž p2 y p3. and the normalised vegetation index NDVI calculated by the formula NDVI s Ž p2 y p1rp2 q p1. where p2 and p1 stand for the normalised albedo for AVHRR channels 2 and 1, respectively. The IDL program system, Research Systems Inc., has been used in developing algorithms and in the data analysis. 2.3. Calculating the aÕailable surface irradiance The feature peculiar to Baikal is the presence of areas of extremely transparent crystal ice, whose
optical properties were investigated by Verbolov et al. Ž1965. and Sherstyankin Ž1975.. Ice of such a type transmits up to 85% of the luminous energy, which affects the input of photosynthetic available radiation ŽPhAR. and the warming of the layer under the ice. Note for comparison that the transmission of incident solar radiation through the ice covered with 5-cm layer of snow does not exceed 5%. With an increase in solar-induced warming, the upper snow cover melts away to produce needle ice ŽApipkrakeB . forms. According to observations reported by Sherstyankin Ž1975., the transmission of solar radiation by a wet ice changes drastically in the course of the daytime and can increase from 20% to 80%. By analysing the ice and snow albedo in No. 1 and 2 channels, as well as the brightness temperature, after separating different types of underlying surface spatially and temporally, it is possible to estimate to a first approximation the optical properties of ice and the characteristic of solar radiation transmission using field observations ŽSherstyankin, 1975..
Fig. 5. Surface classes for 19-04-1996, class 4 corresponds to the transparent ice Žwestern part of the Southern basin., classes 5–8 correspond to the snow ŽCentral and Northern basins..
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The available irradiance variability at the water level in the PhAR band was taken for three lake basins from Stepanova and Sherstyankin Ž1984.. The annual input of direct solar radiation Žabout 2500 MJ P my2 . exceeds that over the surrounding regions by 10% due to high spectral and integral transparency of the atmosphere and low frequency of low clouds over the lake. Maximum potential values of total and direct PhAR on a horizontal surface change from 440 W P my2 in June to 100 W P my2 in December. For the calculation of available PhAR under the ice cover, transmission spectra were allowed for every type of underlying ice and snow surface. For the March wet ice, we used transmission values that changed during the daytime according to observations. Daily integrated values of spring available solar irradiance on the water surface are presented in Fig. 2 for three Lake basins, for transparent ice and for 2.5-cm snow cover.
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2.4. Bio-optical model of water ecosystem We used the modification of Semovski Ž1999. in the bio-optical, vertically resolved phytoplankton dynamics model in the water ecosystem to investigate under-ice phytoplankton bloom for different ice conditions. The one-dimensional model includes a spectral bio-optical block, a spectral primary production algorithm of Wozniak et al. Ž1992. based on quantum yield of photosynthesis, and the block of vertical mixing. The presented model is extended to include two groups of phytoplankton: diatoms and picophytoplankton, and the same spectral model of primary production is used for both of them. These are the following differences in properties for different phytoplankton groups: the nutrient assimilation rate for picophytoplankton is growth with temperature Žsee Nagata et al., 1997., diatoms use silicon as additional nutrient and can be limited by it, and the
Fig. 6. Surface classes for 20-04-1998, classes 1–2 correspond to the open water ŽSouthern basin..
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sinking rate for diatoms is higher because of their larger size and silicon skeleton. The optical part of the model includes a spectral underwater irradiance field simulation by the algorithm including main optical active components: pure water, phytoplankton pigments, dissolved organic matter, detritus, and mineral suspended matter. For the under-ice irradiance field simulation, the bio-optical block uses the observational data on transmission for different icesnow classes from Sherstyankin Ž1975.. The following biological variables are included in the population dynamics model: zooplankton Žpredator for diatoms., heterotrophic bacterioplankton Žprey for flagellates., and flagellates Žpredator for picoplank-
ton and bacteria.. The vertical physical model uses climatic values taken from Kozhova and Izmest’eva Ž1998. for the mixing layer Žhypolimnion. depth, and its mean temperature and intensity of vertical mixing. For the phytoplankton component, main equations can be found in Semovski Ž1999.. 3. Results 3.1. Delineation and mapping of ice-snow coÕer classes Upon calculating the eigennumbers and eigenvectors of the correlation matrix calculated for all pic-
Fig. 7. Diagram of percentage of the area occupied by ice-snow classes, Ža. 1995, Žb. 1997. Classes from 1 to 4 Ždark colours. correspond to the open water, transparent ice and wet ice; classes from 5 to 8 Žlight colours. correspond to the dry snow cover.
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tures in accordance with the concepts embodied in the EOF technique, it was found that the first two modes describe 85% of the total variance. Weight coefficients of the contribution from different channels to the first two eigenvectors are presented in Fig. 3a. The next step involved calculating the frequency distribution histogram of the first two main components for the entire sequence of pictures Žsee Fig.
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3b.. Peaks of the two-dimensional distribution were interpreted as different classes of state of the underlying surface. The classes were identified manually by outlining the histogram peaks using a special-purpose IDL program. This was followed by a mapping of the resulting classes for each picture and a visual determination of the correspondence of each class and type of underlying surface depending on the month. Classes cor-
Fig. 8. Simulation of the Baikal phytoplankton annual cycle for different ice types during winter. Section time-depth of total phytoplankton concentration wmg N P m I3 x, South Baikal, 1998 year conditions Ža.; 1997 simulated column diatoms–phytoplankton relationship wmg N P m I2 x: Žb. South Baikal, Žc. North Baikal.
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responding to clouds and fog above the water, a frequent occurrence on Baikal in January, were discarded in the analysis. Note that a classification using the entire set of images covering both the freeze-up and break-up periods can lead to the inclusion in a single group of some types of ice typical of these periods only. Conceivably as soon as a more representative data
set has been accumulated, it would be appropriate to carry out a classification for the periods of freeze-up ŽJanuary., stable ice cover ŽFebruary–April. and break up ŽApril–June.. Quite good atmospheric conditions prevail in the area of Lake Baikal in the wintertime. For the period from January to May, the number of high-quality images, with the satellite located at the nadir and the
Fig. 9. Ža. Surface classes for 28-05-1998, North Baikal, classes 5–7 correspond to the open water. Žb. Mosaic of ERS SAR images of North Baikal, 28-05-98.
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Fig. 9 Ž continued ..
cloud amount over the lake not exceeding 10%, can reach 56 Žthe year 1997.. However, during December and early January when South Baikal is not yet frozen up, and a freezing weather occurs in East Siberia, unfrozen water areas are almost constantly obscured with fog, which is clearly identified from high values of the brightness temperature difference of channels 3 and 4. Application of the EOF method, however, makes it possible to clearly recognise the unfrozen water areas, the constant ice edge, and areas probably covered with a young transparent ice
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ŽVerbolov et al., 1965.. Fig. 4 presents a distribution of the main types of underlying surface in the first half of January, 1997. During the period from mid-January to the end of April, the lake is completely covered with ice, and by analysing satellite information, it is possible to investigate in detail the distribution and dynamics of the main types of snow-ice cover. Fig. 5 is an example of the distribution of the types of snow-ice cover for 19 April, 1996. Baikal’s break-up starts usually in early May at the western shore in the Southern part. The position of the ice-edge and the distribution of the main types of snow-ice cover are presented in Fig. 6 for late April 1998. The break-up date, and also the freeze-up date provide a good climatic indicator of temperature variability in the Baikal region, and this indicator has an integral character because of the lake’s large thermal inertia. More detailed studies of the interannual variability of the lake’s freeze-up and break-up process and of the movement of the ice edge must give important information about the character of climate change in the Siberian region. The point here is that different parts of the lake are in different climatic conditions. The climate of the Northern part is distinguished by a greater severity due to perceptible effects of the Yakut anticyclone. Different parts of the lake have their peculiar atmospheric circulation and precipitation systems having their origins in relief elements. Of great interest also are the interannual variability studies into the distribution dynamics of different types of Baikal’s snow-ice cover. Different authors suggested that the freeze-up and break-up dates, the ice cover thickness, and the appearance of transparent ice areas are factors, which have a substantial influence upon the functioning of the lake’s ecosystem. It is likely, in particular, that the known substantial interannual variability in intensity of diatom blooming during the springtime ŽKozhova and Izmest’eva, 1998. is also associated with the time of lake freezing and with character of the snow-ice cover during the wintertime. As has been pointed out above, the transmission of incident solar irradiation through the ice cover of different types can differ nearly by an order of magnitude. Fig. 7 presents the distribution diagrams for different types of snow-ice cover percentage during
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January–May 1995 and 1997. The different structure of ice-snow coverage of the lake can be clearly seen from the plot. For every region of the lake, we can produce time series of ice conditions variability, and this information can be used to calculate fluxes in the ecodynamical model. 3.2. Simulation of the phytoplankton bloom in different ice conditions A simulation of the phytoplankton annual variability using actual ice conditions and available irradiance on the water surface that was calculated in accordance with Section 2.3 demonstrates general known features. Fig. 8a presents the evolution of the total South Baikal phytoplankton vertical distribution for the year with low spring bloom. Under-ice diatoms bloom is clearly visible in the figure; the concentration then decreases during spring homothermy and intensive mixing, picoplankton bloom occurs during summer with a shallow subsurface maximum, and the concentration of early autumn diatoms rises again moderately. The variability in ice-snow conditions substantially controls the relationship between concentrations of phytoplankton taxonomic groups. For the diatom-rich year in the South Baikal ŽFig. 8b., the intensity of summer picoplankton bloom is lower because of low nutrients assimilated by diatoms during summer. During the same year, the simulated North Baikal situation ŽFig. 8c. is quite different. Due the predominance of a snow-covered ice during winter and a longer period of ice-cover, picoplankton is responsible for the greater part of a total annual production, diatoms bloom occurs later, and its intensity is lower. Annual variability of the vertical structure of Baikal phytoplankton was not presented systematically in the literature. One example of chlorophyll vertical changes shown in ŽKozhova and Izmest’eva, 1998. demonstrates qualitative agreement with results of our simulation.
4. Discussion This paper is the first attempt at analysing multispectral satellite imagery data banks with the purpose of investigating the ice cover dynamics on Lake
Baikal and its influence on phytoplankton variability. One should be aware of the limitations arising when using information of moderate spatial resolution in the optical and infrared ranges, such as AVHRR imagery. It is well known that the data of active radar soundings acquired by instruments aboard, for example, ERS satellites, provide higher quality and higher spatial resolution information. In addition, measurements of this kind are virtually independent of atmospheric conditions. However, no regular data sets of radar surveys are currently Žand in the near future. available to us. Moreover, as regards to regularity and availability, the AVHRR data have no equal yet. Consequently, the Baikal Geographic Information System ŽGIS. under development must incorporate, at a later time, databases containing AVHRR multi-spectral imagery, as well as algorithms for investigating the ice cover conditions, which will constitute a further development of the methods presented in this paper. It is appropriate, whenever possible, to make comprehensive undersatellite measurements of physical characteristics of Baikal’s ice. Data of physical and biological observations have much potential for yielding information about the association of optical characteristics of ice Žincluding those acquired from space-borne observations. with physical parameters of the water column and phytoplankton growth. Application of models for development of turbulence and convection under the ice is necessary for the correct description of the under-ice upper layer dynamics. Note, however, that routine observations of the conditions of the snow-ice cover at a stationary hydrometeorological network are usually poor representative, and the main part of the water surface Žabout 80%. remains unexplored by observers. Expedition investigations are, as before, the principal methods of obtaining information about the state of the lake’s ecosystem, but they have an extremely irregular character. And on Baikal during freeze up and break up of the ice cover, they are extremely complicated and even dangerous. The currently available capabilities of accumulating archives of regularly received data are limited to satellite platforms affording low spatial resolution ŽAVHRR, SeaWiFS.. High resolution data of SPOT and Landsat satellites cannot be involved into analysis due to its high price, other data sources are
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unavailable because of isolation of Lake Baikal area from receiving stations. During 1997–1998, the mobile station of the European Space Agency ŽESA., located in Ulan Bator, Mongolia, was receiving the first Baikal images taken by the synthetic aperture radar ŽSAR. from the ERS-1 and ERS-2 satellites. Comparison of AVHRR-based classification of snow and ice cover and simultaneous SAR image is presented in Fig. 9 for the late Spring. We plan to incorporate into analysis of Baikal ice high-resolution multi-spectral data of Resurs satellites, which cover the entire territory of Russia. Both light transmission through the ice and mixing in the upper layer are processes that control productivity of phytoplankton. According to some new knowledge, under-ice turbulence generated by salt convection during ice freezing or by heating during spring can play an important role in diatoms ecology ŽGranin et al., 1999.. Peculiar Baikal diatoms life cycle ŽJewson, 1992. should probably be included in mathematical models because this feature can cause interannual variability. These processes, however, are beyond the scope of the studies presented and can be the subject for future work. It is possible that a Lagrangian attempt to a description of the phytoplankton dynamics ŽWolf and Woods, 1988. will be more appropriate for detailed studies of diatom ecology in a turbulent environment.
Acknowledgements We are thankful to Dr. N.P. Minko and our colleagues from the Centre of Satellite Monitoring at the ISTP SB RAS for the encouragement, and to Prof. M.N. Shimaraev and Prof. G.I. Popowskaya for the useful discussion. Remarks of anonymous referees were essential in improving the paper quality. This work is supported by the Russian Foundation for Basic Research grants No. 99-05-64814 and by project No.6r1 of the Ministry of Science, Russian Federation. Images of ERS SAR instrument were given for our disposal in the framework of European Space Agency announcement of opportunity. IDL and ENVI software ŽResearch Systems. were used intensively in the data analysis.
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