IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v30y2016i15d10.1007_s11269-016-1394-7.html
   My bibliography  Save this article

Environmental Flow Assessment Based on Different Metrics of Hydrological Alteration

Author

Listed:
  • David J. Peres

    (University of Catania)

  • Antonino Cancelliere

    (University of Catania)

Abstract

The concepts of hydrological alteration and the related natural flow paradigm conceive variable environmental flows that preserve as much as possible the natural variability of flows, with a particular focus on a suite of specific characteristics, the so-called indicators of hydrological alteration (IHA). In the paper we propose a simple simulation approach for a preliminary desk-top assessment environmental flows, whose principle is to maximize the possible utilization of water while complying with the alteration targets according to a global alteration metric. We investigate the use of three different alteration metrics, with the aim of measuring the sensitivity of environmental flow assessments respect to the index and the corresponding low and moderate alteration target thresholds. An application of the methodology to a case study area in Sicily, comprising several rivers sections, is carried out. Results show that a significant sensitivity of the optimal environmental flows to the alteration metric, both in the pattern and in the amount. While some metrics privilege environmental flow patterns that follow the natural variability of IHA parameters, other yield to optimal environmental flows that follow the long-term means of the IHA parameters. Results also show that in general the attainment of the low alteration target is quite demanding, since at least the 30 % of natural flows should be addressed to environmental flows, while for a moderate alteration hydrological status this percentage reduces to 15 %.

Suggested Citation

  • David J. Peres & Antonino Cancelliere, 2016. "Environmental Flow Assessment Based on Different Metrics of Hydrological Alteration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(15), pages 5799-5817, December.
  • Handle: RePEc:spr:waterr:v:30:y:2016:i:15:d:10.1007_s11269-016-1394-7
    DOI: 10.1007/s11269-016-1394-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-016-1394-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11269-016-1394-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. A. Cancelliere & G. Giuliano & A. Ancarani & G. Rossi, 2002. "A Neural Networks Approach for Deriving Irrigation Reservoir Operating Rules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 16(1), pages 71-88, February.
    2. Pasquale Cutore & Gabriella Cristaudo & Alberto Campisano & Carlo Modica & Antonino Cancelliere & Giuseppe Rossi, 2007. "Regional Models for the Estimation of Streamflow Series in Ungauged Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(5), pages 789-800, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mummidivarapu Satish Kumar & P. N. Chandi Priya & Rehana Shaik & Shailesh Kumar Singh, 2023. "Environmental Flows Allocation for a Tropical Reservoir System by Integration of Water Quantity (SWAT) and Quality (GEFC, QUAL2K) Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 113-133, January.
    2. Jorge Andres Garcia & Angelos Alamanos, 2022. "Integrated Modelling Approaches for Sustainable Agri-Economic Growth and Environmental Improvement: Examples from Greece, Canada and Ireland," Land, MDPI, vol. 11(9), pages 1-19, September.
    3. Han-Chung Yang & Jian-Ping Suen & Shih-Kai Chou, 2016. "Estimating the Ungauged Natural Flow Regimes for Environmental Flow Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4571-4584, October.
    4. Gokmen Tayfur & Bihrat Onoz & Antonino Cancelliere & Luis Garrote, 2016. "Editorial: Water Resources Management in a Changing World: Challenges and Opportunities," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(15), pages 5553-5557, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhenghua Gu & Xiaomeng Cao & Guoliang Liu & Weizhen Lu, 2014. "Optimizing Operation Rules of Sluices in River Networks Based on Knowledge-driven and Data-driven Mechanism," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3455-3469, September.
    2. Chang-ming Ji & Ting Zhou & Hai-tao Huang, 2014. "Operating Rules Derivation of Jinsha Reservoirs System with Parameter Calibrated Support Vector Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2435-2451, July.
    3. Goksel Ezgi Guzey & Bihrat Önöz, 2023. "Performance Assessment Comparison between Physically Based and Regression Hydrological Modelling: Case Study of the Euphrates–Tigris Basin," Sustainability, MDPI, vol. 15(13), pages 1-15, July.
    4. K. Ramakrishnan & C. Suribabu & T. Neelakantan, 2010. "Crop Calendar Adjustment Study for Sathanur Irrigation System in India Using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(14), pages 3835-3851, November.
    5. Evangelos Rozos, 2019. "Machine Learning, Urban Water Resources Management and Operating Policy," Resources, MDPI, vol. 8(4), pages 1-13, November.
    6. Alireza Dariane & Farzane Karami, 2014. "Deriving Hedging Rules of Multi-Reservoir System by Online Evolving Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3651-3665, September.
    7. Shinuk Kang & Sangho Lee & Taeuk Kang, 2017. "Development and Application of Storage-Zone Decision Method for Long-Term Reservoir Operation Using the Dynamically Dimensioned Search Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 219-232, January.
    8. Abdüsselam Altunkaynak, 2007. "Forecasting Surface Water Level Fluctuations of Lake Van by Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(2), pages 399-408, February.
    9. Stavros Yannopoulos & Mike Spiliotis, 2013. "Water Distribution System Reliability Based on Minimum Cut – Set Approach and the Hydraulic Availability," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(6), pages 1821-1836, April.
    10. A. kumar & Manish Goyal & C. Ojha & R. Singh & P. Swamee & R. Nema, 2013. "Application of ANN, Fuzzy Logic and Decision Tree Algorithms for the Development of Reservoir Operating Rules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 911-925, February.
    11. Chang-Shian Chen & Frederick Chou & Boris Chen, 2010. "Spatial Information-Based Back-Propagation Neural Network Modeling for Outflow Estimation of Ungauged Catchment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(14), pages 4175-4197, November.
    12. Dave Deckers & Martijn Booij & Tom Rientjes & Maarten Krol, 2010. "Catchment Variability and Parameter Estimation in Multi-Objective Regionalisation of a Rainfall–Runoff Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(14), pages 3961-3985, November.
    13. Muhammad Sulaiman & Ahmed El-Shafie & Othman Karim & Hassan Basri, 2011. "Improved Water Level Forecasting Performance by Using Optimal Steepness Coefficients in an Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(10), pages 2525-2541, August.
    14. Murat Cobaner & Tefaruk Haktanir & Ozgur Kisi, 2008. "Prediction of Hydropower Energy Using ANN for the Feasibility of Hydropower Plant Installation to an Existing Irrigation Dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(6), pages 757-774, June.
    15. Misgana Muleta & John Nicklow, 2004. "Joint Application of Artificial Neural Networks and Evolutionary Algorithms to Watershed Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(5), pages 459-482, October.
    16. Pan Liu & Shenglian Guo & Lihua Xiong & Wei Li & Honggang Zhang, 2006. "Deriving Reservoir Refill Operating Rules by Using the Proposed DPNS Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(3), pages 337-357, June.
    17. L. Karthikeyan & D. Kumar & Didier Graillot & Shishir Gaur, 2013. "Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 871-883, February.
    18. A. Agarwal & R. Maheswaran & J Kurths & R. Khosa, 2016. "Wavelet Spectrum and Self-Organizing Maps-Based Approach for Hydrologic Regionalization -a Case Study in the Western United States," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4399-4413, September.
    19. Mingyong Cai & Shengtian Yang & Hongjuan Zeng & Changsen Zhao & Shudong Wang, 2014. "A Distributed Hydrological Model Driven by Multi-Source Spatial Data and Its Application in the Ili River Basin of Central Asia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 2851-2866, August.
    20. Ahmed El-Shafie & Alaa Abdin & Aboelmagd Noureldin & Mohd Taha, 2009. "Enhancing Inflow Forecasting Model at Aswan High Dam Utilizing Radial Basis Neural Network and Upstream Monitoring Stations Measurements," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(11), pages 2289-2315, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:30:y:2016:i:15:d:10.1007_s11269-016-1394-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.