A new instream flow assessment method based on fuzzy habitat suitability and large scale river modelling

This paper proposes an integrated tool for the assessment of fish habitat suitability based on synthetic hydraulic and water quality parameters. There are three innovative features in this study: (a) The proposed approach seeks to improve the capabilities of IFIM (Instream Flow Incremental Methodology) by extending the assessment to a larger spatial scale, which can be helpful for river managers in decision making. (b) The method is based on the suitability response of target species to hydraulic and water quality parameters through a fuzzy model, which is a novel application of a Takagi-Sugeno fuzzy logic. (c) The introduction of simulated river conditions enables the generation of a wide range of scenarios and the detection of potentially critical situations. After introducing the main algorithm, a sensitivity analysis is provided for the assessment of critical river segments and for ranking the influence of each parameter on the habitat. Then, a second algorithm is developed to produce an instream flow assessment method by determining the range of admissible flows that preserve the habitat suitability to a prescribed degree. The combined method is demonstrated with the application to two Italian rivers in the Tuscany region. In the case of the Arno River, the method highlights the habitat diversity for the two target species along its course, and the critical conditions that may develop during the summer low flow. In the case of the Serchio River, the analysis helps to assess habitat alterations likely to be caused by a planned diversion to feed a nearby lake. In both instances, with a minimum requirement of field data, this method shows its flexibility and seems better able to detect critical situations than the conventional IFIM approach.

[1]  Stefano Marsili-Libelli,et al.  A new definition of Minimum Sustainable Flow based on water quality modelling and fuzzy processing , 2008 .

[2]  Alessandro Massolo,et al.  The influence of calcium concentration on the distribution of the river bullhead Cottus gobio L. (Teleostes, Cottidae) , 2009 .

[3]  C. Aricò,et al.  Using unsteady-state water level data to estimate channel roughness and discharge hydrograph , 2009 .

[4]  W. J. Young,et al.  Development of an environmental flows decision support system , 2000, Environ. Model. Softw..

[5]  W. J. Miller,et al.  Habitat Suitability Index Models and Instream Flow Suitability Curves: Chinook Salmon. , 1986 .

[6]  Matthias Schneider,et al.  Nature knows: heuristic search algorithms optimizing a fuzzy rule-based fish habitat suitability model for rivers. , 2006, Communications in agricultural and applied biological sciences.

[7]  B. L. Lamb,et al.  Stream habitat analysis using the instream flow incremental methodology , 1998 .

[8]  Tony Prato,et al.  Assessing ecosystem sustainability and management using fuzzy logic , 2007 .

[9]  W. V. Winkle,et al.  Individual-based model of sympatric populations of brown and rainbow trout for instream flow assessment: model description and calibration , 1998 .

[10]  Hervé Capra,et al.  Simple predictions of instream habitat model outputs for target fish populations , 2002 .

[11]  Piotr Parasiewicz,et al.  The MesoHABSIM model revisited , 2007 .

[12]  Thomas E. McMahon,et al.  Habitat suitability index models: Creek chub , 1982 .

[13]  Andrea Castelletti,et al.  Putting humans in the loop: Social computing for Water Resources Management , 2012, Environ. Model. Softw..

[14]  Matthias Schneider,et al.  Development of a user-friendly software for ecological investigations on river systems, integration of a fuzzy rule-based approach , 2001 .

[15]  Stefano Marsili-Libelli,et al.  MODELLING A COASTAL LAKE FOR FLOOD AND QUALITY MANAGEMENT , 2011 .

[16]  A. Montanari,et al.  Uncertainty in river discharge observations: a quantitative analysis , 2009 .

[17]  Mark E. Borsuk,et al.  Bridging uncertain and ambiguous knowledge with imprecise probabilities , 2012, Environ. Model. Softw..

[18]  Bernard De Baets,et al.  Knowledge-based versus data-driven fuzzy habitat suitability models for river management , 2009, Environ. Model. Softw..

[19]  Bernard De Baets,et al.  Data-driven fuzzy habitat suitability models for brown trout in Spanish Mediterranean rivers , 2011, Environ. Model. Softw..

[20]  P. Nelson,et al.  Habitat Suitability Index Models and Instream Flow Suitability Curves: Warmouth , 1984 .

[21]  Stephanie Hirsh.  A New Definition , 1962, Social Service Review.

[22]  B. Baets,et al.  Fuzzy rule-based macroinvertebrate habitat suitability models for running waters , 2006 .

[23]  Chris Soulsby,et al.  PHABSIM modelling of Atlantic salmon spawning habitat in an upland stream: testing the influence of habitat suitability indices on model output , 2005 .

[24]  Laurence Smith,et al.  The role of expert opinion in environmental modelling , 2012, Environ. Model. Softw..

[25]  K. Beven,et al.  Uncertainty in the calibration of effective roughness parameters in HEC-RAS using inundation and downstream level observations , 2005 .

[26]  Yves Souchon,et al.  Simple predictions of instream habitat model outputs for fish habitat guilds in large streams , 2002 .

[27]  Ralf Wieland,et al.  A new method for semi-automatic fuzzy training and its application in environmental modeling , 2011, Environ. Model. Softw..

[28]  Keith Beven,et al.  Influence of uncertain boundary conditions and model structure on flood inundation predictions. , 2006 .

[29]  G. Petts.  Instream Flow Science For Sustainable River Management 1 , 2009 .

[30]  Piotr Parasiewicz.  Overcoming the limits of scales , 2007 .

[31]  Stefano Marsili-Libelli,et al.  Una nuova definizione di Deflusso Minimo Vitale basata su parametri di qualità dell'acqua e sulla logica fuzzy , 2010 .

[32]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[33]  M. Abramowitz,et al.  Handbook of Mathematical Functions With Formulas, Graphs and Mathematical Tables (National Bureau of Standards Applied Mathematics Series No. 55) , 1965 .

[34]  Matthias Schneider,et al.  Fish habitat modelling as a tool for river management , 2007 .

[35]  S. Marsili-Libelli,et al.  Water quality modelling for small river basins , 2008, Environ. Model. Softw..

[36]  Bernard De Baets,et al.  Interpretability-preserving genetic optimization of linguistic terms in fuzzy models for fuzzy ordered classification: An ecological case study , 2007, Int. J. Approx. Reason..

[37]  B. Baets,et al.  Effect of model formulation on the optimization of a genetic Takagi–Sugeno fuzzy system for fish habitat suitability evaluation , 2011 .

[38]  Matthias Schneider,et al.  Optimisation of a fuzzy physical habitat model for spawning European grayling ( Thymallus thymallus L.) in the Aare river (Thun, Switzerland) , 2008 .

[39]  Zdneka Svobodova,et al.  Water quality and fish health , 1993 .

[40]  Steven C. Chapra,et al.  QUAL2K: A Modeling Framework for Simulating River and Stream Water Quality , 2004 .

[41]  Shinji Fukuda,et al.  Consideration of fuzziness: is it necessary in modelling fish habitat preference of Japanese medaka (Oryzias latipes)? , 2009 .

[42]  Viliam Macura,et al.  INFLUENCE OF THE MORPHOLOGICAL AND HYDRAULIC CHARACTERISTICS OF MOUNTAIN STREAMS ON FISH HABITAT SUITABILITY CURVES , 2012 .

[43]  Gary W. Brunner,et al.  HEC-RAS (River Analysis System) , 1996 .

[44]  Pierluigi Claps,et al.  A hydraulic study on the applicability of flood rating curves , 2010 .

[45]  S Marsili-Libelli,et al.  A fuzzy quality index for the environmental assessment of a restored wetland. , 2011, Water science and technology : a journal of the International Association on Water Pollution Research.

[46]  D. Morgan.  A new definition. , 1983, Journal of the American Dental Association.

[47]  Steven C. Chapra,et al.  QUAL2Kw - A framework for modeling water quality in streams and rivers using a genetic algorithm for calibration , 2006, Environ. Model. Softw..

[48]  Tullio Facchinetti,et al.  F-IND: A framework to design fuzzy indices of environmental conditions , 2009 .

[49]  Matthias Schneider,et al.  Application of MesoCASiMiR: assessment of Baetis rhodani habitat suitability , 2006 .

[50]  B. L. Lamb,et al.  The Instream Flow Incremental Methodology: A Primer for IFIM. , 1995 .

[51]  Robert T. Milhous,et al.  Physical habitat simulation system reference manual: version II , 1989 .

[52]  Piotr Parasiewicz,et al.  MesoHABSIM: A concept for application of instream flow models in river restoration planning , 2001 .

[53]  Daniel Ayllón,et al.  THE INFLUENCE OF VARIABLE HABITAT SUITABILITY CRITERIA ON PHABSIM HABITAT INDEX RESULTS , 2012 .

[54]  R. Raleigh,et al.  Habitat Suitability Index models and Instream Flow Suitability curves: brown trout , 1984 .

[55]  Kazuaki Hiramatsu,et al.  Prediction ability and sensitivity of artificial intelligence-based habitat preference models for predicting spatial distribution of Japanese medaka (Oryzias latipes) , 2008 .