ISSN:
1573-5117
Keywords:
reservoir
;
quality management
;
modelling
;
algae
;
zooplankton
;
fish
Source:
Springer Online Journal Archives 1860-2000
Topics:
Biology
Notes:
Abstract Bankside storage reservoirs are used as a major water supply resource in the lower Thames Valley, England. They form the link between the River Thames and the water treatment works of the Greater London area. The reservoirs act as both a water reserve in times of low river flows, and a quality ‘buffer’ between the river and the treatment works. The load on the water treatment works (particulate material, physico-chemical characteristics) primarily reflects the water qualities of the reservoirs. Management of such reservoirs thus seeks to reduce the adverse impacts which would otherwise arise from direct river use, and to ensure as far as possible that the ecological processes within the reservoirs do not introduce new challenges to the water treatment. Reservoir management clearly needs a good understanding of those ecological processes and their interactions, and, hopefully, a means to exploit that understanding in hindcasting to explain past events, in forecasting near- or far-future events, and to help in exploring operational options to ameliorate any foreseable difficulties. The reservoirs consist of a variety of configurations, physical dimensions and operational circumstances. They have, importantly, basically simple morphologies, known hydraulic regimes and physico-chemical qualities. Nonetheless, they appear to behave essentially as small (1–50 Mm3), eutrophic lakes; and various aspects of their ecology has been studied for the past 65 years. Their attributes and operational involvement make them ideal candidates for ecological modelling, which has been applied to them in varying extents for the past 30 years. The major conclusion which may be drawn from these studies is that even in such relatively simple water bodies, current (and probably future) models can only encompass their broad ecological characteristics. Detailed operational needs have to be met by a variety of modelling approaches, mainly predicated on the basis of only being able to know a lot about a little or a little about a lot. The operational needs for modelling fall into the following broad types: (a) understanding: why did those events occur, or where is our ignorance greatest? (b) short-term forecasts: how will the current situation develop in the short-term (weeks)? (c) what-if considerations: what would happen if some management facility were employed or used differently? (d) optimisation: what are the optimal volume– quality supply arrangements? (e) long-term prediction: what is the longer-term (years) outlook under foreseeable scenarios? (f) projective evaluation: how would potential, as yet non-existant reservoirs behave under prescribed circumstances? Examples of how these needs have been met are outlined, with examples ranging from simple models of the diatom ecology of the reservoirs to much broader trophic–dynamic descriptions which can allow expression of fish–zooplankton–phytoplankton interactions. This is crucial for present and future management of cyanobacterial phases. It is clear that considerable management insight and control can result from modelling assistance, but only if the appropriate questions are asked. Whilst simple short-term modelling is less demanding, any attempt to model the full complexity of the ecology of even these relatively simple water-bodies is probably doomed to founder on complexity–understanding difficulties, unless these are resolved to much more constrained system aspects. This is particularly so for the qualitative biology. The best that may presently be foreseen is for development of the newer multi-biological type models, with reasonably realistic and dynamic physical and chemical environment sub-models, being able to manifest the general characteristics of the ecosystem in question. Despite such difficulties, new reservoir management insights and approaches will inevitably be founded on critical modelling of those ecosystems.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1017041417145
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