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Introduction to Environmental and Economic Consequences of Hypoxia

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  • Robert J. Díaz
  • Rutger Rosenberg

Abstract

Low dissolved oxygen environments (known as hypoxic or dead zones) occur in a wide range of aquatic systems and vary in frequency, seasonality and persistence. While there have always been naturally occurring hypoxic habitats, anthropogenic activities related primarily to organic and nutrient enrichment related to sewage/industrial discharges and land runoff have led to increases in hypoxia and anoxia in both freshwater and marine systems. As a result, over the last 50 years there has been a rapid rise in the areas affected by hypoxia. The future status of hypoxia and its consequences for the environment, society and economies will depend on a combination of climate change (primarily from warming, and altered patterns for wind, currents and precipitation) and land-use change (primarily from expanded human population, agriculture and nutrient loadings). The overall forecast is for hypoxia to worsen, with increased occurrence, frequency, intensity and duration. The consequences of eutrophication-induced hypoxia can be reversed if long-term, broad-scale and persistent efforts to reduce nutrient loads are developed and implemented.

Suggested Citation

  • Robert J. Díaz & Rutger Rosenberg, 2011. "Introduction to Environmental and Economic Consequences of Hypoxia," International Journal of Water Resources Development, Taylor & Francis Journals, vol. 27(1), pages 71-82, March.
    • Handle: RePEc:taf:cijwxx:v:27:y:2011:i:1:p:71-82
    DOI: 10.1080/07900627.2010.531379
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    Cited by:

    1. Beck, Marcus W. & Lehrter, John C. & Lowe, Lisa L. & Jarvis, Brandon M., 2017. "Parameter sensitivity and identifiability for a biogeochemical model of hypoxia in the northern Gulf of Mexico," Ecological Modelling, Elsevier, vol. 363(C), pages 17-30.
    2. García-Nieto, P.J. & García-Gonzalo, E. & Fernández, J.R. Alonso & Muñiz, C. Díaz, 2019. "Modeling of the algal atypical increase in La Barca reservoir using the DE optimized least square support vector machine approach with feature selection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 166(C), pages 461-480.
    3. García Nieto, P.J. & García-Gonzalo, E. & Alonso Fernández, J.R. & Díaz Muñiz, C., 2019. "Water eutrophication assessment relied on various machine learning techniques: A case study in the Englishmen Lake (Northern Spain)," Ecological Modelling, Elsevier, vol. 404(C), pages 91-102.

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