Abstract
This study quantifies and ranks variables of significance to predict mean values of Secchi depth in small glacial lakes. The work is based on a new, extensive set of data from 88 Swedish lakes and their catchments. Several empirical models based on catchment and lake morphometric parameters are presented. These empirical models can only be used to predict Secchi depth for lakes of the same type, and the models based on “geological map” parameters can evidently not be used for time-dependent and site typical predictions of Secchi depth. However, many of the principles behind the results ought to be valid for lakes in general. Various hypotheses concerning the factors regulating the variability in mean Secchi depth among lakes are formulated and tested. The most important variables are: Lake colour (expressing allogenic input of different types of humic materials), total-P and lake temperature (measures of production of autogenic materials). The most important “map” parameters are: The mean depth (linked to resuspension and lake morphometry) and the ratio between the drainage area and lake area (expressing the linkage between catchment and lake). The predictability of some of the models cannot be markedly improved by accounting for the distribution of the characteristics in the drainage area (using the drainage area zonation technique). The variability in mean Secchi depth from other factors, such as precipitation and anthropogenic load, may then be quantitatively differentiated from the impact of these “geological” factors, which can statistically explain 68% of the variability in Secchi depth among these lakes. The model based on map parameters can also be used to estimate natural, preindustrial reference values of Secchi depth.
Key words
Lakes Secchi depth predictive models water chemistry morphometry catchment characteristics natural Secchi depthPreview
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References
- Ahlgren, I., T. Frisk and L. Kamp-Nielsen, 1988. Empirical and theoretical models of phosphorus loading, retention and concentration vs. lake trophic state. Hydrobiologia 170:285–303.Google Scholar
- Carlson, R.E., 1980. More complications in the chlorophyll — Secchi disk relations. Limnol. Oceanogr. 25(2):379–382.Google Scholar
- Chapra, S.C. and K. Reckhow, 1979. Expressing the phosphorus loading concept in probabalistic terms. J. Fish. Res. Bd. Can. 36:225–229.Google Scholar
- Dillon, P.J. and F.H. Rigler, 1974. A test of a simple nutrient budget model predicting the phosphorus concentration in lake water. J. Fish. Res. Board Can. 31:1771–1778.Google Scholar
- Dillon, P.J. and F.H. Rigler, 1975. A simple method for predicting the capacity of a lake for development based on a lake trophic status. J. Fish. Res. Board Can. 32:1519–1531.Google Scholar
- Håkanson, L., 1991. Ecometric and dynamic modelling — exemplified by cesium in lakes after Chernobyl. Springer-Verlag, Berlin, 158 p.Google Scholar
- Håkanson, L., 1992. Considerations on representative water quality data. Int. Rev. ges. Hydrobiol. 77:497–505.Google Scholar
- Håkanson, L., 1993. A model to predict lake colour. Int. Rev. ges. Hydrobiol. 78:107–137.Google Scholar
- Håkanson, L., 1994a. The LEEDS-model. Lake Eutrophication, Effect, Dose, Sensitivity model. Inst. of Earth Sciences, Uppsala Univ., Sweden, Version, 1990-10-01.Google Scholar
- Håkanson, L., 1994b. Models to predict lake mean annual total phosphorus. J. Aquatic. Ecosyst. Health (in press).Google Scholar
- Håkanson, L., 1994c. A model to predict gross sedimentation in small glacial lakes. Hydrobiologia 284:19–42.Google Scholar
- Håkanson, L., 1994d. Variability and representativness of lake variables. Int. Rev. ges. Hydrobiol. 79:175–193.Google Scholar
- Håkanson, L., 1994e. Time compatibility of water chemical lake variables. Int. Rev. ges. Hydrobiol. 79:195–210.Google Scholar
- Håkanson, L., H. Borg and R. Uhrberg, 1990a. Reliability of analysis of Hg, Fe, Ca, K, P, pH, alkalinity, conductivity, hardness and colour from lakes. Int. Rev. ges. Hydrobiol. 75:79–94.Google Scholar
- Håkanson, L., T. Andersson and Å. Nilsson, 1990b. A new method of quantitatively describing drainage areas. Env. Geol. and Water Sci. 15:61–69.Google Scholar
- Håkanson, L. and M. Jansson, 1983. Principles of Lake Sedimentology. Springer-Verlag, Berlin, 316p.Google Scholar
- Håkanson, L. and T. Andersson, 1992. Remedial measures against radioactive caesium in Swedish lake fish after Chernobyl. Aquatic Sci. 54:141–164.Google Scholar
- Håkanson, L. and R.H. Peters, 1994. Predictive limnology — methods for predictive modelling. SPB Academic Publishers, Amsterdam (in press).Google Scholar
- Hutchinson, G.E., 1957. A treatise on limnology. I. Geography, physics, and chemistry. Wiley, New York, 1015 p.Google Scholar
- Kiefer, D.A. and R.W. Austin, 1974. The effect of varying phytoplankton concentration on submarine transmission in the Gulf of California. Limnol. Oceanogr. 19:55–64.Google Scholar
- Kranck, K., 1973. Flocculation of suspended sediment in the sea. Nature 246:348–350.Google Scholar
- Kranck, K., 1979. Particle matter grain-size characteristics and flocculation in a partially mixed estuary. Sedimentology 28:107–114.Google Scholar
- Lick, W., J. Lick and C.K. Ziegler, 1992. Flocculation and its effect on the vertical transport of fine-grained sediments. Hydrobiologia 235/236:1–16.Google Scholar
- Nicholls, K.H. and P.J. Dillon, 1978. An evaluation of phosphorus-chlorophyll-phytoplankton relationships for lakes. Int. Revue ges. Hydrobiol. 63:141–154.Google Scholar
- Nilsson, Å. and L. Håkanson, 1992. Relationship between drainage area characteristics and lake water characteristics. Env. Geol. and Water Sci. 19:75–81.Google Scholar
- OECD, 1982. Eutrophication of waters. Monitoring, assessment and control. OECD, Paris, 154 p.Google Scholar
- Peters, R.H., 1986. The role of prediction in limnology. Limnol. Oceanogr. 31:1143–1159.Google Scholar
- Peters, R.H., 1991. A Critique for Ecology Cambridge Univ. Press, Cambridge, 366 p.Google Scholar
- Preisendorfer, R.W., 1986. Secchi disk science: Visual optics of natural waters. Limnol. Oceanogr. 31:909–926.Google Scholar
- Sly, P.G., 1978. Sedimentary Processes in Lakes. In: Lerman, A. (ed.), Lakes: Chemistry, Geology, Physics. Springer-Verlag, Berlin, pp. 65–89.Google Scholar
- Tilzer, M.M., 1988. Secchi disk — chlorophyll relationships in a lake with highly variable phytoplankton biomass. Hydrobiologia 162:163–171.Google Scholar
- Vollenweider, R.A., 1968. The scientific basis of lake eutrophication, with particular reference to phosphorus and nitrogen as eutrophication factors. Tech. Rep. DAS/DSI/68.27, OECD, Paris, 159 pp.Google Scholar
- Vollenweider, R.A., 1976. Advances in defining critical loading levels for phosphorus in lake eutrophication. Mem. Ist. ital. Idrobiol. 33:53–83.Google Scholar
- Vollenweider, R.A., 1990. Eutrophication: conventional and non-conventional considerations on selected topics. In: de Bernardi, R., G. Giussani and L. Barbanti (eds), 1990. Scientific perspectives in theoretical and applied Limnology. Memorie dell'Istituto Italiano di Idrobiologia Dott. Marco de Marchi, Vol. 47, Pallanza, 378 p.Google Scholar
- Wallin, M. and L. Håkanson, 1991. The importance of inherent and properties of coastal areas. Mar. Poll. Bull. 22:381–388.Google Scholar
- Wallin, M., L. Håkanson and J. Petersson, 1992. Load models for nutrients in coastal areas, especially from fish farms (In Swedish with English summary). Nordiska ministerrådet, 1992: 502, 207 p.Google Scholar
- Wallin, M. and L. Håkanson, 1992. Morphometry and sedimentation as regulating factors for nutrient recycling and trophic level in coastal waters. Hydrobiologia 235/236:33–45.Google Scholar
- Wetzel, R.G., 1983. Limnology. Sauders College Publ., 767 p.Google Scholar