A Stochastic Dynamic Methodology (StDM) for reservoir's water quality management: Validation of a multi-scale approach in a south European basin (Douro, Portugal)

Abstract Worldwide aquatic ecosystems have been impacted by broad-scale environmental pressures such as agriculture, point and non-point-source pollution and land-use changes overlapping in space and time, leading to the disruption of the structure and functioning of these systems. The present paper examined the applicability of a holistic Stochastic Dynamic Methodology (StDM) in predicting the tendencies of phytoplankton communities and physicochemical conditions in reservoirs as a response to the changes in the respective watershed soil use. The case of the Douro's basin (Portugal) was used to test the StDM performance in this multi-scale approach. The StDM is a sequential modelling process developed in order to predict the ecological status of changed ecosystems, from which management strategies can be designed. The data used in the dynamic model construction included true gradients of environmental changes and was sampled from 1995 to 2004. The dynamic model developed was preceded by a conventional multivariate statistical procedure performed to discriminate the significant relationships between the selected ecological components. The model validation was based on independent data, for all the state variables considered. Overall, the simulation results are encouraging since they seem to demonstrate the StDM reliability in capturing the dynamics of the studied reservoirs. The StDM model simulations were validated for the most part of the twenty-two components selected as ecological indicators, with a performance of 50% for the physicochemical variables, 75% for the phytoplankton variables, and 100% for the Carlson trophic state indices (TSI). This approach provides a useful starting point, as a contribution for the practical implementation of the European Water Framework Directive, allowing the development of a true integrated assessment tool for water quality management, both at the scale of the reservoir body and at the scale of the respective river watershed dynamics.

[1]  Robert C. Bailey,et al.  Integrating stream bioassessment and landscape ecology as a tool for land use planning , 2007 .

[2]  Ute Mischke,et al.  Cyanobacteria associations in shallow polytrophic lakes: influence of environmental factors , 2003 .

[3]  Edna Cabecinha,et al.  Applying a stochastic-dynamic methodology (StDM) to facilitate ecological monitoring of running waters, using selected trophic and taxonomic metrics as state variables. , 2007 .

[4]  R. Carlson.  DISCUSSION.: "Using Differences Among Carlson's Trophic State Index Values in Regional Water Quality Assessment," by Richard A. Osgood , 1983 .

[5]  Patrick L. Brezonik,et al.  A CARLSON-TYPE TROPHIC STATE INDEX FOR NITROGEN IN FLORIDA LAKES , 1981 .

[6]  Sven Erik Jørgensen,et al.  Ecological Modelling: editorial overview 2000–2005 , 2005 .

[7]  Alexey Voinov,et al.  Introduction: Spatially Explicit Landscape Simulation Models , 2004 .

[8]  C. T. Hunsaker,et al.  Interpreting results of ecological assessments , 2004 .

[9]  R. Wetzel.  Limnology: Lake and River Ecosystems , 1975 .

[10]  Klaus Henle,et al.  Biological Indicator Systems in Floodplains – a Review , 2006 .

[11]  C. Reynolds.  Eutrophication and the management of planktonic algae: What Vollenweider couldn't tell us , 1992 .

[12]  B. Statzner,et al.  Perspectives for biomonitoring at large spatial scales: a unified measure for the functional composition of invertebrate communities in European running waters , 2001 .

[13]  Peter Blomqvist,et al.  AMMONIUM-NITROGEN - A KEY REGULATORY FACTOR CAUSING DOMINANCE OF NON-NITROGEN-FIXING CYANOBACTERIA IN AQUATIC SYSTEMS , 1994 .

[14]  Sven Erik Jørgensen,et al.  State-of-the-art of ecological modelling with emphasis on development of structural dynamic models , 1999 .

[15]  F. Pick,et al.  Factors regulating phytoplankton and zooplankton biomass in temperate rivers , 1996 .

[16]  João Alexandre Cabral,et al.  Development of a stochastic dynamic model for ecological indicators’ prediction in changed Mediterranean agroecosystems of north-eastern Portugal. , 2004 .

[17]  Rita B. Domingues,et al.  Phytoplankton and environmental variability in a dam regulated temperate estuary , 2007, Hydrobiologia.

[18]  Robert V. O'Neill,et al.  Considerations for the development of a terrestrial index of ecological integrity , 2001 .

[19]  Ana Maria A. C. Rocha,et al.  A stochastic dynamic methodology (SDM) to facilitate handling simple passerine indicators in the scope of the agri-environmental measures problematics , 2007 .

[20]  R. Carlson.  A trophic state index for lakes1 , 1977 .

[21]  Sašo Džeroski,et al.  Using machine learning techniques in the construction of models. II. Data analysis with rule induction , 1997 .

[22]  Alexey Voinov,et al.  Landscape simulation modeling : a spatially explicit, dynamic approach , 2003 .

[23]  João Alexandre Cabral,et al.  Testing the Stochastic Dynamic Methodology (StDM) as a management tool in a shallow temperate estuary of south Europe (Mondego, Portugal) , 2008 .

[24]  J. Lund,et al.  The inverted microscope method of estimating algal numbers and the statistical basis of estimations by counting , 1958, Hydrobiologia.

[25]  Robert E. Truitt,et al.  Seasonal and interannual variability in the taxonomic composition and production dynamics of phytoplankton assemblages in Crater Lake, Oregon , 2006, Hydrobiologia.

[26]  D. L. Scarnecchia,et al.  Fundamentals of Ecological Modelling , 1995 .

[27]  M. Dokulil,et al.  Cyanobacterial dominance in lakes , 2000, Hydrobiologia.

[28]  C. Hoyos,et al.  Relationships between diatoms and the environment in Spanish reservoirs , 2005, Limnetica.

[29]  João Alexandre Cabral,et al.  Simulating the impact of socio-economic trends on threatened Iberian wolf populations Canis lupus signatus in north-eastern Portugal , 2007 .

[30]  Patrick L. Brezonik,et al.  “A Carlson‐Type Trophic State Index for Nitrogen in Florida Lakes” by Charles R. Kratzer and Patrick L. Brezonik , 1982 .

[31]  R. A. Osgood.  Using Differences Among Carlson's Trophic State Index Values in Regional Water Quality Assessment , 1982 .

[32]  João Alexandre Cabral,et al.  A Stochastic Dynamic Methodology (SDM) to the modelling of trophic interactions, with a focus on estuarine eutrophication scenarios. , 2006 .

[33]  Colin S. Reynolds,et al.  The ecology of freshwater phytoplankton , 1984 .

[34]  D. Pont,et al.  A probabilistic model characterizing fish assemblages of French rivers: a framework for environmental assessment , 2001 .

[35]  Heiskanen Anna-Stiina,et al.  Relationships between Pressures, Chemical Status, and Biological Quality Elements. Analysis of the Current Knowledge Gaps for the Implementation of the Water Framework Directive , 2005 .

[36]  Sven Erik Jørgensen,et al.  State of the art of ecological modelling in limnology , 1995 .

[37]  János Podani,et al.  Introduction to the exploration of multivariate biological data , 2000 .

[38]  JoAnn M. Hanowski,et al.  Evaluation of geographic, geomorphic and human influences on Great Lakes wetland indicators: A multi-assemblage approach , 2007 .

[39]  J. L. Teranes,et al.  Diatom assemblage response to Iroquoian and Euro-Canadian eutrophication of Crawford Lake, Ontario, Canada , 2007 .

[40]  Gerald J Niemi,et al.  Integrated Measures of Anthropogenic Stress in the U.S. Great Lakes Basin , 2007, Environmental management.

[41]  Jan-Tai Kuo,et al.  A hybrid neural-genetic algorithm for reservoir water quality management. , 2006, Water research.

[42]  Fernando Gonçalves,et al.  The effect of environmental parameters and cyanobacterial blooms on phytoplankton dynamics of a Portuguese temperate Lake , 2006, Hydrobiologia.

[43]  D. Sutcliffe,et al.  Eutrophication : research and application to water supply , 1992 .

[44]  J. Garnier,et al.  An integrated modelling approach to forecast the impact of human pressure in the Seine estuary , 2007, Hydrobiologia.

[45]  N. Simboura,et al.  A synthesis of the biological quality elements for the implementation of the European Water Framework Directive in the Mediterranean ecoregion: The case of Saronikos Gulf , 2005 .

[46]  S. E. Jørgensen,et al.  Models as instruments for combination of ecological theory and environmental practice , 1994 .

[47]  Saso Dzeroski,et al.  Predicting Chemical Parameters of River Water Quality from Bioindicator Data , 2000, Applied Intelligence.

[48]  Lars Håkanson,et al.  Predictive Limnology: Methods for Predictive Modelling , 1995 .

[49]  Milani Chaloupka,et al.  Stochastic simulation modelling of southern Great Barrier Reef green turtle population dynamics , 2002 .

[50]  Edna Cabecinha,et al.  Performance of a stochastic-dynamic modelling methodology for running waters ecological assessment , 2004 .