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Global Climatology and Ecodynamics Anthropogenic Changes to Planet Earth Arthur P Cracknell, Vladimir F Krapivin, Costas A Varotsos Global Climatology and Ecodynamics Anthropogenic Changes to Planet Earth Published in association with Praxis Publishing Chichester, UK Professor Arthur P Cracknell Department of Applied Physics and Electronic Engineering University of Dundee Dundee UK Professor Vladimir F Krapivin Institute of Radioengineering and Electronics Russian Academy of Sciences Moscow Russia Professor Costas A Varotsos University of Athens Faculty of Physics Department of Applied Physics Laboratory of Upper Air Athens Greece The photograph reproduced on the back cover of Kirill Kondratyev, to whom this book is dedicated, is reproduced with the kind permission of his widow, Svetlana Kondratiev SPRINGER±PRAXIS BOOKS IN ENVIRONMENTAL SCIENCES SUBJECT ADVISORY EDITOR: John Mason B.Sc., M.Sc., Ph.D ISBN 978-3-540-78208-7 Springer Berlin Heidelberg New York Springer is part of Springer-Science + Business Media (springer.com) Library of Congress Control Number: 2008926500 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency Enquiries concerning reproduction outside those terms should be sent to the publishers # Praxis Publishing Ltd, Chichester, UK, 2009 Printed in Germany The use of general descriptive names, registered names, trademarks, etc in this publication does not imply, even in the absence of a speci®c statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use Cover design: Jim Wilkie Project management: Originator Publishing Services, Gt Yarmouth, Norfolk, UK Printed on acid-free paper Contents Preface xiii List of ®gures xv List of tables xxi List of abbreviations and acronyms xxiii List of contributors xxvii About the authors xxxi The seminal nature of the work of Kirill Kondratyev 1.1 Introduction 1.2 Early radiation studies 1.3 Balloon and aircraft observations in the context of climate studies 1.4 Satellite remote sensing 1.5 Limnological studies 1.6 Global change studies 1.7 International collaboration 1.8 The Research Center of Ecological Safety and the NIERSC 1.9 Conclusion 1.10 References and list of selected publications by K.Ya Kondratyev 1 10 11 12 13 Kirill Kondratyev and the IPCC: His opposition to the Kyoto Protocol 2.1 Introduction 2.2 Kondratyev's life from circa 1990 to 2006 and his involvement with climate skeptics 2.2.1 The last 15±20 years of Kondratyev's life 2.2.2 The journal Energy and Environment 17 17 18 18 21 vi Contents 2.3 2.4 including Russia's signing of 21 Earth radiation budget, 20 years later (1985±2005) Introduction The ScaRaB project and instrument Earth radiation budget observations for climate research 3.3.1 Trends 3.3.2 Mathematical modeling for spatio-temporal variability of outgoing radiation ®elds 3.3.3 Problem of climate signal detection 3.3.4 Methods of signal detection 3.4 Multichannel Singular Spectrum Analysis (MSSA) 3.5 Mutual evolution of the outgoing longwave and shortwave radiation anomalies for the last two decades 3.6 Principal Oscillation Pattern (POP) analysis 3.7 POP as a predictive tool 3.8 The Earth radiation budget and global warming 3.9 Conclusions 3.10 References 37 37 39 41 43 2.5 2.6 Kondratyev and the IPCC Kondratyev and the Kyoto Protocol, the Protocol Conclusion References The 3.1 3.2 3.3 25 32 34 44 45 46 47 48 50 51 53 57 58 Aerosol and atmospheric electricity 4.1 Introduction 4.2 The relation of aerosol extinction of optical radiation with the electric ®eld under haze conditions 4.3 Results of measurements 4.4 Correlation between aerosol extinction of radiation and the atmospheric electric ®eld under smoke conditions 4.5 Discussion of results 4.6 Conclusions 4.7 References 63 63 Remote sensing of terrestrial chlorophyll content 5.1 Introduction 5.2 Spectral properties of vegetation 5.2.1 Visible region 5.2.2 Near-infrared region 5.2.3 Middle-infrared region 5.2.4 The red edge 5.3 Imaging spectrometry 5.4 Methods used to estimate chlorophyll content using sensed data 77 77 79 79 81 81 81 82 remotely 64 65 68 69 74 75 84 Contents vii 84 87 95 96 96 97 99 99 Regarding greenhouse explosion 6.1 Introduction 6.2 Radiation balance at the surface within the framework of a model of a gray atmosphere; Several stationary thermal states of the hypothetical Earth 6.3 Molecular transmittance functions of the Earth's atmosphere in the region from cmÀ1 to 4,000 cmÀ1 at the stationary states of surface temperatures: 288.2 K, 365 K 6.4 Regarding the radiation balance of the Earth at the top of the atmosphere 6.5 Discussion regarding greenhouse explosion on the Earth 6.6 References 107 107 5.5 5.6 5.7 5.4.1 Colorimetric method 5.4.2 Red-edge position Applications of remotely sensed 5.5.1 Vegetation productivity 5.5.2 Vegetation stress 5.5.3 Land cover mapping Conclusion References chlorophyll content data 110 118 120 124 130 Model-based method for the assessment of global change in the nature± society system 7.1 Introduction 7.2 A new type of global model 7.3 Mathematical model of nature±society system (NSS) dynamics 7.3.1 General description of the global model 7.3.2 Model of the global biogeochemical cycle of carbon dioxide 7.3.3 Global model units for other biogeochemical cycles 7.3.4 The oceans' bioproductivity unit 7.3.5 Units of biogeocenotic, hydrologic, and climatic processes 7.3.6 Demographic unit 7.4 Global simulation experiments 7.5 Concluding remarks 7.6 References 142 159 169 170 173 174 177 178 Self-learning statistical short-term climate predictive model for Europe 8.1 Introduction 8.2 Atmospheric circulation in the Atlantic±European system 8.3 Forecasting methodology 8.4 Fuzzy algorithm 8.5 Low-oscillation dynamic and predictability of precipitation rate 185 185 187 188 190 191 133 133 134 138 138 viii Contents 8.6 Fuzzy classi®cation of regime circulation distribution over Europe 8.7 Model description 8.8 Forecast skill evaluation 8.9 Discussion 8.10 References and rain rate spatial Theory of series of exponents and their application for analysis of radiation processes 9.1 Introduction 9.2 Exact expansions of the transmission function in a series of exponents 9.3 The series of exponents and the radiative transfer equation 9.3.1 Integration of the radiative transfer equation over the frequency spectrum (kinetic equation) 9.3.2 Radiation ¯uxes in the aerosol±molecular medium 9.3.3 Molecular atmosphere 9.4 The series of exponents as a means for calculation simpli®cations 9.4.1 Equivalent line and overlapping bands 9.4.2 Small pressures 9.4.3 Inhomogeneous media 9.4.4 One-parametric approximation formulas 9.5 Conclusion 9.6 References 10 Forecast of biosphere dynamics using small-scale models 10.1 Introduction 10.2 The worst case scenario principle and minimal models of the biosphere 10.2.1 Initial minimal model of the biosphere 10.2.2 Results of modeling 10.2.3 Integrated minimal model of long-term carbon dioxide dynamics in the biosphere 10.2.4 Model veri®cation results 10.2.5 Forecasts of the future dynamics of the biosphere 10.3 The carbon cycle; the study of chlorophyll global dynamics and net primary production (NPP) by satellite methods 10.3.1 Introduction 10.3.2 Trends in the global photosynthetic activity of land vegetation 10.3.3 Long-term dynamics of chlorophyll concentration in the ocean surface layer (from space data) 195 198 200 206 206 211 211 212 217 217 220 224 228 228 232 234 237 237 238 241 241 245 245 250 253 256 256 258 258 259 264 Contents ix 10.3.4 Seasonal variations in oceanic phytopigment values in the northern and southern hemispheres averaged by three climatic zones (northern hemisphere starting from 30 N, southern hemisphere starting from 30 S, and the tropical zone) 10.3.5 Minimal model of carbon dioxide seasonal dynamics 10.4 Unicellular organism based experimental closed microecosystems as models of biosystems similar to the biosphere 10.4.1 A microecosystem (MES) mathematical model 10.4.2 Experimental technique 10.4.3 Experimental results 10.5 Discussion and conclusion 10.6 References 11 Air temperature changes at White Sea shores and 20th centuries 11.1 Introduction 11.2 Materials and methods 11.3 The regime of air temperature 11.4 Long-term changes of air temperature 11.5 Conclusions 11.6 References islands in the 19th and 12 Climatic characteristics of temperature, humidity, and wind velocity in the atmospheric boundary layer over western Siberia 12.1 Introduction 12.2 Description of initial data and some methodological aspects of their statistical processing 12.3 Some special features of the vertical structure of average temperature, humidity, and wind velocity ®elds in the atmospheric boundary layer 12.3.1 Basic features of the vertical distribution of average temperature and humidity 12.3.2 Special features of the vertical distributions of average zonal and meridional wind 12.4 Special features of the vertical distributions of temperature, humidity, and wind velocity variability above di€erent parts of western Siberia 12.4.1 Some special features of the vertical distributions of the variability of air temperature and humidity 12.4.2 Special features of the vertical distributions of zonal and meridional wind variability 12.5 Basic laws and special features of the vertical correlation relations for temperature, humidity, and wind velocity 267 268 275 276 287 289 292 296 301 301 303 306 316 330 330 333 333 334 339 339 343 347 348 349 353 x Contents 12.5.1 Interlevel correlation of temperature and humidity 12.5.2 Interlevel correlation relations for wind velocity 12.6 References 13 Ecological safety and the risks of hydrocarbon transportation in the Baltic Sea 13.1 Introduction 13.2 Objects of the study and methods of generalization 13.3 Ecological risk 13.4 North European Gas Pipeline and ecological safety of the Baltic Sea 13.5 Monitoring system for hydrocarbon transportation 13.6 Ecological safety of oil transportation in the Baltic Sea 13.7 Conclusion 13.8 References 353 358 361 363 363 364 365 367 372 373 377 377 14 New directions in biophysical ecology 14.1 Introduction 14.1.1 Experiment in ecology 14.1.2 Complexity of ecosystems 14.1.3 Non-trophic regulation of ecosystems 14.1.4 Hierarchy of ecosystems 14.2 Fundamentals of water ecosystem similarity theory 14.3 Growth acceleration; a new integral index of the cumulative e€ect of all the regulators in a monoculture 14.4 Bioassay system as a new method of description of the state and dynamics of ecosystems, and the alternative of Maximum Permissible Concentration (MPC) 14.5 Arguments supporting the statement about the degree of dependency of population-selective parameters during selection modeling 14.6 Experimental modeling of the phenomenological laws of migration of aquatic organisms 14.7 Conclusion; the future monitoring of aquatic ecosystems 14.8 References 379 379 381 381 381 382 382 15 The Earth as an open ecosystem 15.1 Introduction 15.2 Evolution processes on the Earth 15.3 E€ect of greenhouse gases and aerosols on climate 15.4 The role of water in the variability and evolution of the ment 15.5 Sun±Earth interaction and global catastrophes 15.5.1 Tectonic processes 397 397 398 401 environ 386 388 393 394 394 395 404 409 409 Sec 13.6] 13.6 Ecological safety of oil transportation in the Baltic Sea 373 Table 13.2 Main purposes and tasks for an instrument complex to monitor hydrocarbon transportation routes Purposes and tasks Instrument complex From underwater carriers Identi®cation of location of pipeline shifts and measurement Television system, magnetic gravitation sensors, electrical and magnetic devices, acoustic pro®le graph, sector observation hydrolocator, GPS Identi®cation of pipeline exposure Hydrolocator and sector observation pro®le graph oriented magnetometer Inspection of the bottom terrain along the pipeline Lateral observation hydrolocator, echolot Identi®cation of other objects (stones, metal, chemical weapons) Lateral observation hydrolocator Detection of transported substance leak Acoustic pro®le graph, metal detector From ships Investigation of shelf and sea currents, special location of main biological objects Laboratory complex installed on the research vessel Chemical ecological investigation Ground and water sampling with subsequent physico-chemical analysis Detection of transported substance leakage (gas, fuel, etc.) Gas analyzer From air 10 Monitoring the blossoming dynamics of Multiscanner MODIS harmful micro-algae 11 Detection of transported substance leakage (gas, fuel, etc.) Remotely controlled laser gas analyzer with a wavelength of 1.65 mm, GPS 12 Detection of oil product spills Radiolocator with synthesized equipment 13.6 ECOLOGICAL SAFETY OF OIL TRANSPORTATION IN THE BALTIC SEA Cargo turnover through the ports of the Gulf of Finland and the Baltic Sea has been increasing exponentially After commissioning the port of Primorsk, tankers with a 374 Ecological safety and the risks of hydrocarbon transportation in the Baltic Sea [Ch 13 deadweight up to 150,000 t and a loading draught over 15 m started to enter the Baltic Sea For ships heading for Gotland, the boundary of the deepwater navigation channel runs along the 16 m±17 m depth contour, which increases the probability of their running into a shoal Since the end of 2006 the Baltic pipeline has provided annual oil transportation of 72 Mt to Primorsk port After the construction of new Russian oil terminals on the coast of the Gulf of Finland, including the construction of the pipeline branch from Primorsk to Vysotsk port, oil transportation will reach 78 Mt per year by 2015 Taking into consideration the fact that world oil transportation amounts to 2.2 Gt annually, the share of the Baltic Sea will be about 10% of the entire world transportation, which will result not only in the increasing intensity of navigation but also in a considerable deterioration of the ecological situation in the Baltic Sea area Up to 10,000 t of oil products leak into the Baltic Sea annually Such intensive development of tanker trac in the Baltic will result in a situation by 2015 where the risk of oilspills up to 1,000 t in size will increase by 50%, while that of oilspills over 1,000 t will increase by 25% The risk of emergency situations is especially high for oil transportation by tankers The probability of large oilspills (over 150 t) during pipeline transportation and in the process of drilling works is reduced two to three times (Semanov, 2005) Estimation of oilspill risks at sea implies identi®cation of the potential source of oilspills in the sea; calculation of oilspill volumes and frequency of their occurrence; identi®cation of natural resources and industrial facilities that may be contaminated as a result of oilspills; development of scenarios of oil behavior on the sea surface that should take into account oil spreading and weathering, depending on conditions in the spill area and the length of the a€ected coastal area Risk estimation can be the basis for designing measures to reduce emergency occurrences and their consequences, their elimination costs, and taking decisions to justify planned activities The basic component of risk estimation is calculation of oilspill volumes and their frequency This parameter is essential for the systemization of emergencies at sea and calculation of the resources required for oilspill elimination The main sources of oilspills are loading activities at oil terminals, accidents involving oil and oil product carrying tankers, illegal dumping of oil-containing wastes and accidents at oilrigs Figure 13.1 shows cases of oilspills in the Baltic Sea occurring as a result of shipwrecks and during loading activities at oil terminals in 2005 According to Russian legislation concerning measures pertaining to oilspills, the following classi®cation of oilspill emergencies at sea is adopted: A local oilspill is an oilspill for whose elimination the resources available at the facility or its vicinity are sucient This spill does not exceed 500 t It is handled by local resources or by the resources of cooperating organizations hired on a contract basis Sec 13.6] 13.6 Ecological safety of oil transportation in the Baltic Sea 375 A regional oilspill is one for whose elimination the resources available in the region are sucient Normally these are spills not exceeding 5,000 t The Basin Administration of the Marine Rescue Service (BAMRS) is responsible for their handling and elimination BAMRS is also involved in the elimination of local spills if they occur beyond the zone of responsibility of the organization involved in oil transportation activities or if this organization is not able to eliminate oilspills with its own resources A federal oilspill is one exceeding 5,000 t and its elimination requires the involvement of resources from other basins and neighboring states The Federal Service of Maritime and River Transportation of Russia's Ministry of Transportation is responsible for oil collection activities in the sea The main sources of oilspills are loading activities at terminals where accidents, including ¯exible pipe rupture, loading device failure, tank over®lling, and loading tank damage, may occur during landing activities The frequency of oilspills over t per terminal can be considered equal to  10 À4 , with the spill share within the t À 10 t range being 0.79%, that of 10 t±100 t being 0.036%, and over 1,000 t being 0.008% (i.e., 96% of all spills at terminals not exceed 100 t; Tables 13.3 and 13.4) Table 13.3 The probability of spilling more than 100 tons of oil during accidents involving single-hull and double-hull tankers Parameter Single-hull tankers Probability (P) of spill/accident Double-hull tankers P spill under 100 t P spill over 100 t P spill under P spill over 100 t 100 t Shoal running 0.25 0.04 0.03 0.09 Collisions 0.25 0.04 0.03 0.09 Damage to structural elements 0.05 0.16 0.05 0.09 Fire, explosion 0.1 0.14 0.1 0.09 Table 13.4 Estimated mean volumes of oilspills Port Cargo (10 t) Cargo (10 t) 2004 2010 1,356 10,000 937 Primorsk 44,565 52,000 2,500 Vysotsk 1,515 14,000 1,250 St Petersburg Average oilspill (t) 376 Ecological safety and the risks of hydrocarbon transportation in the Baltic Sea [Ch 13 Figure 13.3 Number of reported accidents in the Baltic Sea during the period 2000±2006 Figure 13.3 shows that the accident occurrence on oil vessels in the Baltic Sea in 2005, according to HELCOM data is most common in the Danish straits of the Baltic Sea In 2007 while leaving Primorsk port a Greek oiltanker with a capacity of 100, 000 t was shipwrecked and only the fact that it was a double-hull tanker prevented it from causing an oilspill As can be seen from Figure 13.3 most accidents in 2003±2005 were not accompanied by signi®cant contamination of the environment Thus, according to the statistics, for every 100,000 loadings at a terminal there may be two oilspills with a mass of 100 t or more Based on this, there is a probability that when the Primorsk terminal has achieved its planned capacity of 60 Mt per year there is expected to be one oilspill in 400 years during oil loading in tankers with a deadweight of 120,000 t Calculation of the frequency and size of oilspills as a result of tanker accidents is based on statistics from the International Maritime Organization (IMO), according to which accident frequency (for seas with intensive navigation) includes shoal accidents 5.4 per 170 km, collisions 1.9 per 170 km, and ®re or explosion 0.063 per 170 km To calculate the amount of damage it is necessary to estimate the volume of possible leaks (spills) resulting from potential accidents The consequences of possible oilspills to a considerable extent will be determined by the size of oil product slick and the extent of sensitivity of the contacting components of the environment: land, water, and air Statistical data testify that most contaminants ending up in the water basin of the Gulf of Finland are contributions from river ¯ows containing waste water from industrial enterprises (28%) and from ballast water (23%) This is con®rmed by data from the routine practice of the emergency services On the other hand, it is clear that oil product contribution from ship accidents does not exceed 5%±10% However, it is these accidents that get most publicity, as in these cases thousands of tonnes of oil are spilled causing vast amounts of damage Hydrocarbon contamination of the Baltic Sea results in its eutrophication, and according to data from the MODIS spectro- Sec 13.8] 13.8 References 377 radiometer encourages the concentration of blue-green algae, suspended particles in water basins experiencing the most intensive navigation, and in ®sh spawning areas (in particular, in the eastern part of the Gulf of Finland) 13.7 CONCLUSION The ®ndings of our investigation show that ecological risks involved in the construction of the NEGP on the bottom of the Baltic Sea are considerably lower than in the case of oil transportation by ships The risk of a contamination emergency is especially high during oil transportation by tankers, and though natural gas is less hazardous than oil and its products both fuels when they get into seawater cause contamination, eutrophication, and changes in the food chains of the Baltic Sea ecosystem Thus, ecological monitoring of hydrocarbon transportation routes should be comprehensive and regular, with permanent stations for automatic monitoring provided for the most hazardous locations of oil and gas transportation routes The capacities of GIS technologies (as exempli®ed by MapInfo) were used to provide an initial database of the Baltic Sea ecosystem with an estimation module to estimate ecological risks and potential economic damage from transported hydrocarbons, as well as for optimization of measures to eliminate the consequences of possible emergencies during the extraction, transportation, storage, and reloading of hydrocarbons Ensuring the ecological safety of plant and animal wildlife in the Baltic Sea area and the entire sea ecosystem should be implemented within the framework of international legislation and close cooperation between the Baltic Sea countries 13.8 REFERENCES Anon (2006) Baltic Sea Day Seventh Int Environmental Forum: Materials, March 22±23, 2006, St Petersburg OOO Dialog, 592 pp Binenko V.I (2006) Terrorism statistics in the Russian Federation: Ecological extremism and safety problems Problems of Safety and Emergency, 4, 45±56 [in Russian] Binenko V.I and Berkovits A.V (2006) Ecological risks connected with transportation of hydrocarbons with estimation of the proposed construction of the North European Gas Pipeline (NEGP) and the safety of the Baltic Sea Problems of Safety and Emergency, 3, 83±96 [in Russian] Binenko V.I., Khramov G.N., and Yakovlev V.V (2004) Emergency Situations in the Modern World and the Safety of Human Activity St Petersburg University, St Petersburg, 400 pp [in Russian] Furman E., Munsterhulm R., Salemna H., and Vjalipakka P (eds.) (2002) The Baltic Sea: The Environment and Ecology HELCOM, Digitone Oy, Helsinki, 39 pp Goncharov V.K and Pimkin V.G (2000) Forecasting the ecological consequences of PS penetration into seawater from the aged chemical weapons dumped in the Baltic Sea Ecological Chemistry, 9(3), 196±204 [in Russian] 378 Ecological safety and the risks of hydrocarbon transportation in the Baltic Sea [Ch 13 Khristenko V.B (2006) Russia's energy strategy: On the prospects for development and application of transportation of hydrocarbon raw materials and products Transportation Safety and Technology 4(9), 22±29 [in Russian] Kojima J., Kato Y., and Asakawa K (1997) Development of autonomous underwater vehicle ``Aqua Explorer-2'' for inspection of underwater cables Proceedings of the Oceans '97 MTS/IEEE Conference, October 6±9, 1997 World Trade and Convention Centre, Halifax, Nova Scotia, Canada, pp 1007±1012 Krapivin V.F and Kondratyev K.Ya (2002) Global Environmental Change: Ecoinformatics St Petersburg State University, St Petersburg, 724 pp [in Russian] Medvedeva N.G., Sukharevich V.I., Poliak Yu.M., Zaitseva T.B., and Gridneva Yu (1996) Russian Federation Patent No 2103357 ``Biodegradation technology for yperitecontaining mixture, Pseudomonas bacteria yperite biodegrader, bacteria Pseudomonas duodoro 70-11-yperite biodegrader, bacteria Corynebacterium sp., KSBÐyperite biodegrader'' (AC12N1/20, C02 F 3/34) Ecological Safety Research Center of the Russian Academy of Sciences (®led 23.05.1996) Rastoskuev V.V and Shalina E.V (2006) Geoinformation Technologies for Solution of Ecological Safety Problems St Petersburg University, St Petersburg, 256 pp Semanov G.N (2005) Oil spills in sea and provision of immediate response measures Available at http://www.secupress.ru/issue/Tb/2005-2/neft-rasliv.htm/ Smirnova N.F and Smirnov N.P (2005) Atlantic Cod and Climate St Petersburg University, St Petersburg, 222 pp [in Russian] Turkin V (2004) Estimation of the ecological risk of o€shore oil extraction Proceedings of International Conference Modeling and Analysis of Safety and Risks: Complex Systems, MASR-2004, June 22±25, 2004, St Petersburg, pp 430±433 14 New directions in biophysical ecology Andrey G Degermendzhi 14.1 INTRODUCTION It can be argued that biophysical ecology (i.e., the science concerned with studying the subject matter of ecology from the physical±mathematical point of view) is developing rather slowly The rate of development of this science, which is highly important for developing scienti®cally based management of ecosystems and the biosphere, is limited by the following factors: (1) the absence of systematic experimental approaches (of the type used in physics) connected with the impossibility to make experiments with the ecological object which is unique (e.g., unique is the biosphere itself, a certain lake, river ecosystem, etc.); (2) the rare procedures for the veri®cation of ecosystem mathematical models using ®eld and/or experimental data; (3) the variety of interactions within ecosystems in terms of energy, matter, and control even for small-species communities; and (4) the absence of strict methods for the transfer of laboratory-scale experimental data to full scale In this chapter we shall discuss some solutions to the situation We shall consider water resources as an example The rapidly increasing consumption of water will soon make the lack of freshwater a factor that will limit the development of civilization as severely as diminishing energy resources will As a rule, the interests of water users are con¯icting However, almost all of them pollute water environments, seriously interfering with ecosystems and making harmful alterations to them Aquatic ecology must be able both to predict the environmental consequences of the activities of water users and also to satisfy their needs in the best possible way As a fundamental science, the biophysics of aquatic ecosystems studies the physical and biochemical principles of ecological mechanisms responsible for the stability, controllability, and variability of aquatic ecosystems for short times (successions) and for long times (microevolution) The biophysics of ecosystems has three major branches with their own physical±mathematical methods: namely, 380 New directions in biophysical ecology [Ch 14 (a) monitoring the integrated parameters of ecosystems, (b) the kinetic experimental approach, and (c) mathematical modeling, which is based on the ®rst two branches In its methodology, the biophysics of ecosystems currently tends towards reductionism, maybe because it has been used successfully in physical sciences Investigations address the spatio-temporal distribution and dynamics of various ecological structures of aquatic ecosystems (species, age, sex, functional structure, and trophic structure) and the hydrochemical conditions of a water body More speci®cally, the biophysics of ecosystems deals with Ð biochemical and population mechanisms: self-regulation of growth in aquatic communities, substrate consumption, material cycling, inter-speci®c relationships in the community; Ð contribution of density and limiting factors to the stability of aquatic communities; Ð physical principles underlying the theory of the search for limiting factors; Ð laws of the stable coexistence of interacting populations; Ð principles and theory of material cycling in aquatic communities; Ð experiments, mechanisms, and the theory of migration behavior of aquatic organisms (plankton); Ð scale-up of ecosystems; Ð construction of ecosystems with tailored properties; Ð ecosystems with closed material loops as models of biosphere-like systems The purpose of ecosystem biophysics is to reach such a level of knowledge about the elementary physical±biochemical mechanisms responsible for the functioning of aquatic ecosystems that would be sucient to make valid prognoses of their natural and human-induced dynamics and to control their state A very important part of ecosystem biophysics is theoretical prediction of the development of aquatic ecosystems, including water quality An instrument of prognosis (i.e., the theory and models of aquatic ecosystems) must be regarded as equal to the methods of biological monitoring (Kratasyuk et al., 1996), including remote control, and physicochemical analysis of the state of a water body Until recently, modeling of aquatic ecosystems has been only (and rather weakly) related to data of the classical monitoring of water bodies The existing procedure of model identi®cation and veri®cation (actually ®tting to ®eld data) does not allow an extrapolation of constructed models to other water bodies, because it disguises and mixes up the errors of measurements of ecosystem inputs and the lack of knowledge of mechanisms responsible for the functioning of ecosystems The most serious drawback of the existing method of modeling aquatic ecosystems (compared with physics) is that modeling is unrelated to experimental investigations Thus, we cannot gain any essentially new knowledge about the mechanisms of interactions of biological components, so the heuristic signi®cance of investigations is limited Experimental investigations are laboratory and/or semi-®eld investigations of both the kinetic characteristics of aquatic organisms and the behavior of a community in special experiments Experimental methods in biophysical ecology must, like physical Sec 14.1] 14.1 Introduction 381 ones, provide insight into the internal structure of communities and interactions between populations The deepest insight into the structure of an ecosystem, its parts and their functioning is gained when experimental and ®eld data are coordinated and the logical consistency of this coordination can be veri®ed by mathematical models of various hierarchical levels Although biologists are sometimes skeptical about the achievements of mathematical modeling, this may be the only means to strictly verify ecological hypotheses, particularly in the case of events with multi-directional processes running simultaneously, and the universal method to check the ecological eciency of di€erent scenarios of controlling the state of a water body (Gubanov et al., 1996) Contemporary knowledge of the structure of river, lake, and reservoir ecosystems and the practical positive control of the state of water bodies suggest more questions than answers The answers are less profound than ecological problems The reason is that aquatic ecology as a science encounters some objective diculties related to the following sections (Sections 14.1.1±14.1.4) 14.1.1 Experiment in ecology In contrast to physics, ecology is poor in experimental approaches; we not refer to methods of ®eld observations but rather to experimental approaches similar to physical ones (i.e., a discriminating experiment with a whole ecosystem responding to a sole experimentally calibrated impact) 14.1.2 Complexity of ecosystems The rapid accumulation of ecological knowledge is naturally impeded by speci®c features of aquatic ecosystems An ecosystem consists of numerous variously interrelated components, which are responsible for its counter-intuitive behavior (i.e., the behavior is opposite to what we can predict based on our limited knowledge, which seems to us quite complete) In ecology, this behavior has particularly grave consequences, as the human impact on aquatic ecosystems increases and there is rather limited time for thorough studies to counterbalance counter-intuitiveness In this respect, physics has been in a better position for quite a long time Counter-intuitive behavior can also be caused by changes in interactions between populations (due to adaptation, microevolution) that the ecology researcher is not aware of 14.1.3 Non-trophic regulation of ecosystems In the general case (maybe as a consequence of Section 14.1.2), we adhere to classical concepts and assume that, to make a valid prognosis, it is sucient to know only the trophic±energy structure of an aquatic ecosystem and to have basic knowledge of the species However, an ecosystem comprises organized ¯uxes of energy, matter, and control Processes of control may be even more important for a valid prognosis than material ¯ows Moreover, the e€ective speci®c mechanisms of regulation that have been selected in the course of long-term evolution and that include various (e.g., 382 New directions in biophysical ecology [Ch 14 chemical) special signal systems can in¯uence all species, from bacteria to humans Thus, when we consider the impact of pollutants, we should study not only the processes of their decomposition and biochemical transformation but also their damaging e€ects on regulatory interactions and their interference with regulation, including communications 14.1.4 Hierarchy of ecosystems Presumably, the declared hierarchical principles of the ecosystem structure must help us quickly accumulate ecological knowledge At present, however, we cannot ®nd an example of an actually ecient hierarchy with clearly de®ned rules for the formation of laws at each level The holistic approach, as the antithesis of reductionism, must develop more rapidly and build up its own axiomatic basis Cooperation of the holistic approach and reductionism in the research on one water body (on one problem) may essentially facilitate the establishment of workable hierarchical principles in aquatic ecology Investigations in the biophysics of aquatic ecosystems can be intensi®ed along new lines as described in Section 14.2 14.2 FUNDAMENTALS OF WATER ECOSYSTEM SIMILARITY THEORY If we address the problem of experimenting with real aquatic ecosystems (see Section 14.1.1), leaving aside quite successful experiments with water treatment facilities, we can see that a well-developed methodology is still lacking There is an approach based on the construction of various sizes of experimental micro-ecosystems; there are systems of continuous cultivation of microorganisms, and ®nally there are test-tank or aquarium-type laboratory systems However, all these systems are de®cient in principles, methodology, and methods of extrapolating the results of laboratory and semi-laboratory experiments to natural ecosystems A mathematical theory of scaling of aquatic ecosystems could provide a scienti®c basis for developing the principles of such extrapolation Scaling theory has proved to be useful in hydrodynamics and aerodynamics Let us recall the theory of dimensionality and scaling (Barenblatt, 1982; Sedov, 1972) The main result is contained in the ``S-theorem'' (short for ``similarity theorem'') We suppose that physical value a depends on determining parameters and variables a1 ; ; ak ; ak‡1 ; ; an : a ˆ f …a1 ; ; ak ; ak‡1 ; ; an †: …14:1† If a1 ; ; ak are independent variables then Equation (14.1) can be reduced to the relationship of dimensionless quantities: S ˆ F…1; ; 1; Sk‡1 ; ; Sn †; Sec 14.2] 14.2 Fundamentals of water ecosystem similarity theory 383 pj pj where S ˆ a=a h1 Á Á Á a qk ; Sj ˆ aj =…a 1 Á Á Á a kk †; j ˆ k ‡ 1; ; n, or compactly: S ˆ F…S1 ; ; SnÀk †: …14:2† It follows from (14.2) that S really dependsÐnot on n parametersÐbut rather on n À k parameters Let us apply the S-theorem to the simplest model of an aquatic microbial ecosystem based on the principle of a chemostat Let a population of microorganisms of biomass x…t† develop in the system at speci®c ¯ow rate D (the ratio of volume ¯ux to system volume) and consume some substrate of the background concentration S…t† and the input concentration S0 An increase in biomass of gram requires the consumption of y grams of substrate The dependence of the speci®c growth rate (SGR) of biomass (g) is given as g ˆ S=…Ks ‡ S†, where  is the maximum SGR, and Ks is the half-saturation constant for the substrate Then ) S ˆ '…x…0†; S…0†; S0 ; t; ; Ks ; D; y† …14:3† x ˆ f …x…0†; S…0†; S0 ; t; ; Ks ; D; y†; where dimensionalities are as follows: ‰xŠ ˆ ‰SŠ ˆ ‰x…o†Š ˆ ‰S…o†Š ˆ ‰S0 Š ˆ ‰Ks Š ˆ M=L ; ‰tŠ ˆ T; ‰DŠ ˆ ‰Š ˆ T À1 ; ‰yŠ ˆ 1: As independent variables we take Ks and  Then, according to Equation (14.2), the dimensionless parameters are F ˆ x=Ks , W ˆ S=Ks ,  ˆ t= À1 , V ˆ D=, etc Equations (14.3) will be given as W ˆ '…x…0†=Ks ; S…0†=Ks ; S0 =Ks ; t À1 ; D=; y† or W ˆ '…; V; y† Similarly, F ˆ f …; V; y† In contrast to an empirical search for Equation (14.2) type relationships, for this system there is a known mechanism, and thus dimensionless equations W à ˆ …S0 =Ks À W†V À yWF=…1 ‡ W† and F à ˆ …W=…1 ‡ W† À V†F can be written down In the steady state W ˆ V=…1 À V† A graph of a theoretical dimensionless relationship between the residual concentration of limiting substrate W…ˆ S=Ks † and the dimensional quantity of ¯ow rate V…ˆ D=; D < † together with respective experimental values is presented in Figure 14.1 All undimensioned points are adequate (i.e., belong to) one and the same curve W ˆ V=…1 À V† Even this very simple example shows that the condition of similarity between ®eld ( f ) and laboratory (l) ecosystems (i.e., equality of all dimensionless similarity parameters, f ˆ l ; Vf ˆ Vl , etc.) leads to the requirement of a certain relationship between population microbiological parameter () and ¯ow rate (D) as a hydrodynamic quantity: Df =f ˆ Dl =l Hence, in laboratory experiments, populations growing at higher rates l can be used, and thus higher ¯ow rates Dl can be set The dimensionless laboratory relationship between the background concentration of the limiting substrate and D= will be the same as the ®eld concentration Since tf f ˆ tl l , laboratory time (tl ) of the identical laboratory and ®eld dynamics of the components will be l =f times shorter than the ®eld time Using the S-theorem, one can write down simultaneous ecological±hydrophysical equations for the dynamics of the state of an aquatic ecosystem in dimensionless 384 New directions in biophysical ecology [Ch 14 Figure 14.1 Dimensionless relationship between residual substrate concentration (W) and dimensionless ¯ow rate (V) Experiments: f Saccharomyces carlsbergensis, substrate, glucose (Toda, 1976); i mixed culture of activated sludge, substrate, glucose (Chiu et al., 1972) Theory: ÐÐ W ˆ V=…1 À V† form Thus, new dimensionless parameters can be added to well-known ones (i.e., Reynolds', Froude's, etc.), with ecological micro-parameters used along with hydrophysical ones The future scaling theory for aquatic ecosystems will contain a simultaneous mathematical description of the three main groups of processes: hydrodynamic, hydrochemical, and hydrobiological The ultimate goal must be scaling of the maximally complete system of equations generally consisting of (1) a hydrodynamic unit, (2) a hydrophysical unit, and (3) an ecosystem unit The objective of the hydrodynamic unit is to calculate the spatio-temporal dynamics of current velocity (depending on the morphometry of the water body ¯oor, friction, slopes, water ¯ow, and in¯ow) The objective of the hydrophysical unit is to calculate the dynamics of the following parameters: water temperature (depending on turbulence, Sec 14.2] 14.2 Fundamentals of water ecosystem similarity theory 385 heat balance with the atmosphere, and input of thermal e‚uents); the level of underwater irradiation (depending on the outer light ¯ux, light absorption and re¯ection by microalgae and particles); sedimentation; turbidity; etc The objective of the ecosystem unit is to calculate the dynamics of the concentrations of phytoplankton, zooplankton, bacteria, the main hydrochemical components, and pollutants in the water column, and the dynamics of bottom-water organisms (depending on biological interactions between populations, material cycling, industrial e‚uents, limiting factors, hydrophysical and hydrodynamic conditions, and sludge transport) The author is planning to create a computer system that will simulate these units, in dimensional and dimensionless forms, and inverse algorithms, which will reconstruct ®eld dynamics from laboratory dynamics To understand the interactions between sub-systems it may be interesting to consider various correlations between characteristic relaxation times and the times of impact increase According to the data of other natural sciences, di€erent correlations between these times can cause various instabilities, and consequently isolated or ubiquitous occurrences of a sharp increase in the biomass of aquatic organisms or some other pronounced imbalances These de¯ections from the theoretically monotonic smooth trend of the curve are crucial growth points in scaling theory Having undimensioned macro-parameters of the system of the abovementioned groups of equations, we can make a universal undimensioned description of the dynamics of some ecosystems Then, varying experimental dimensional microparameters, we may be able to ®nd the values of undimensioned macro-parameters equal to real ones and conduct experiments with this small ecosystem Conversely, experimental dynamics must be converted into real dynamics for a large ecosystem, which cannot be experimented on Accurate similarity scaling can start a new direction in the experimental modeling of very many ecologically signi®cant phenomena (material cycling in aquatic ecosystems, self-puri®cation, strati®cation of biological components, migration of plankton, microalgal blooms) together with the modeling of hydrophysical parameters (currents, light and temperature ®elds, etc.) It would be good to use experimental facilities that hydraulic engineers have used for similarity scaling of hydrophysical characteristics only For the sake of similarity, it will be necessary to equip these facilities with technical systems of light radiation for microalgal photosynthesis, to prepare model e‚uents, etc The great advantage of this approach is that decision-makers would clearly see the environmental consequences of a given project even before it is practically implemented First, it would be reasonable to construct simple homogeneous ecological ¯ow-through systems and then gradually to move up to spatially heterogeneous ones At the same time, it would be necessary to develop an ecological±hydrophysical scaling theory, later involving the scaling of hydrochemical processes In the course of development, theoretically grounded bans may be placed on simultaneous scaling of ecological±hydrophysical processes that produce an opposite e€ect on scaling parameters, as phappens in  hydrodynamics in the case of wave resistance to movement (Fr ˆ v= lg) and in the case of viscose resistance (Re ˆ vl=) The main concerns of the scaling theory for aquatic ecosystems are (a) the validity of systems of equations and (b) the theoretical limits of similarity scaling 386 14.3 New directions in biophysical ecology [Ch 14 GROWTH ACCELERATION; A NEW INTEGRAL INDEX OF THE CUMULATIVE EFFECT OF ALL THE REGULATORS IN A MONOCULTURE As the question of the complexity of ecosystems (Section 14.1.2) is rather dicult, the question of the non-trophic regulation of ecosystems should be pursued simultaneously To create a stock of valid models, taking into account the mechanisms of population regulation (see Section 14.1.3), it is necessary to amass experimental data on the kinetic parameters of aquatic organisms, with kinetics being de®ned broadly (growth rates, food spectra, types of limiting factors, death rates, nature and intensity of inter-population relationships, etc.) These kinetics must be used in models along with quantitative ®eld observations of the dynamics of ecosystem components so as to verify and identify the structures of model ecosystems That is why the modeler's work cannot be independent of the experimenter's and the naturalist's work They have to design experiments together Experimental methods must play a special part in the development of mathematical models of natural aquatic ecosystems, and speci®cally of microbial aquatic communities The most important biochemical substances are those that are responsible for the sustainability of a microbial community First of all, these are density-dependent growth control factors (DDGCFs; i.e., substances that are released or consumed by a population and that in¯uence the growth of this or another population; Odum, 1971) It is traditional to determine the relationship of the SGR to a speci®c DDGCF (e.g., a Monod-type relationship) However, the question of whether one such relationship is enough is not usually discussed (i.e., whether Liebig's bottleneck principle is valid here or the SGR depends on other DDGCFs, unknown to the researcher) In more general terms, this question can be formulated as follows If we know the relationship of the SGR to some speci®c DDGCF, can we accurately quantify our knowledge of the density-dependent control of this species in a speci®c system? In other words, is there a way to determine the aggregate e€ect of all the DDGCFs on a speci®c population? In contrast to physics, where the types and number of forces and principles of their action are well-known, the situation in aquatic ecology is quite di€erent Any product of the ecosystem's metabolism (innumerable biochemical substances) can potentially be a factor controlling the stability of the community by positive or negative feedback Even if we manage to make a complete list of all the biochemical products of metabolism, the main question remains open as to which of these substances can in¯uence, say, the growth rate of a microbial population and how? Only these substances can be regarded as DDGCFs, which are essential for modeling The fundamental solution to this problem is based on an essentially physical idea The idea is as follows Take a separate microbial population, a monoculture, and assume that it is related to several biochemical DDGCFs by feedbacks Microbiologists know that not only limiting substratesÐbut also metabolitesÐinhibiting or stimulating growth, can be considered to be DDGCFs Then, what is the overall measure of the feedback level in growth control; that is, what is the estimate of the total e€ect produced by all the DDGCFs on population growth? As the theory Sec 14.3] 14.3 Growth acceleration 387 developed previously states (Degermendzhy et al., 1993), this is a change in the growth rate increase B (i.e., acceleration of growth) Or, in other words, it is the rate of change of SGR, g, in response to a pulse disturbance of population concentration DX, under an unchanged (at the moment of disturbance) chemical composition of the environment: ... the transmission function in a series of exponents 9.3 The series of exponents and the radiative transfer equation 9.3.1 Integration of the radiative transfer... substrate concentration and dimensionless ¯ow rate Approach to estimating the experimental level of feedback BE Combined dynamics of concentrations... Return On Energy Invested Explosive Substance EUREKA Project on the TRansport And Chemical Transformation of Environmentally Relevant Trace Constituents in the Troposphere over Europe Federal Center