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Unraveling the Lagged Effect of Hydro-meteorological Conditions On the Trophic State of a Reservoir By Applying Dynamic Regression

Author

Listed:
  • Pablo F. Andreoni

    (Universidad de Buenos Aires
    Subgerencia Centro de la Región Semiárida, Instituto Nacional del Agua
    Universidad Católica de Córdoba)

  • Marcia A. Ruiz

    (Subgerencia Centro de la Región Semiárida, Instituto Nacional del Agua
    Universidad Católica de Córdoba)

  • María Inés Rodríguez

    (Subgerencia Centro de la Región Semiárida, Instituto Nacional del Agua)

  • Ana Laura Ruibal-Conti

    (Subgerencia Centro de la Región Semiárida, Instituto Nacional del Agua
    Universidad Católica de Córdoba)

Abstract

In this study we develop a novel approach to quantify the relative importance of hydro-meteorological (HM) conditions on the trophic state index (TSI) of a water reservoir (San Roque, Córdoba, Argentina). Seven HM variables measured at four reservoir sites and different depths over a time period of near 2 decades are used. We propose a dynamic regression model to predict the TSI from these variables aggregated over a range of time lags, which has not been applied in such a complex setting so far. By performing coefficient analysis, we quantify the relative importance of these variables on the TSI, as well as the time duration over which they have significant impact (lagged effect). Additionally, the analysis of the autoregressive and moving average (ARIMA) terms reveals the impact of the residual effects of previous trophic states on the current trophic state. We find that surface temperature and precipitation have the largest direct relationship to the TSI in the short-term, while the reservoir water level is inversely related to the TSI in the short- to mid-term. Also, the residual effects of the trophic state impact from 1 month (generally) up to 2 years (exceptionally). This approach can be applied to other water bodies affected by similar eutrophication phenomena. Graphical abstract

Suggested Citation

  • Pablo F. Andreoni & Marcia A. Ruiz & María Inés Rodríguez & Ana Laura Ruibal-Conti, 2022. "Unraveling the Lagged Effect of Hydro-meteorological Conditions On the Trophic State of a Reservoir By Applying Dynamic Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4275-4291, September.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:11:d:10.1007_s11269-022-03254-6
    DOI: 10.1007/s11269-022-03254-6
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Icen Yoosefdoost & Abbas Khashei-Siuki & Hossein Tabari & Omolbani Mohammadrezapour, 2022. "Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1191-1215, March.
    3. Manish Kumar & Ahmed Elbeltagi & Chaitanya B. Pande & Ali Najah Ahmed & Ming Fai Chow & Quoc Bao Pham & Anuradha Kumari & Deepak Kumar, 2022. "Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2201-2221, May.
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