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Climate Change Impacts on Water Resources Management with Particular Emphasis on Southern Italy

  • Michele Vurro
  • Ivan Portoghese
  • Emanuela Bruno
Conference paper
Part of the NATO Science for Peace and Security Series C: Environmental Security book series (NAPSC)

Abstract

A methodology to use climate change information in water resources evaluation is developed through a meaningful case study in southern Italy (the Apulia region). The problem of the effective information of climate model simulations with respect to small scale impact studies is developed taking into account the limited predictive capability of climate models. Therefore downscaling and bias-correction requirements are treated through a specific methodology based on a quantile variable correction adopting ground based observation of climate variables. The meteorological forcing for the impact study are obtained through the downscaling of atmospheric variables produced by a Regional Climate Model (RCM) called Protheus. The impact assessment on the water balance of the Apulia region (southern Italy) revealed a marked increase in the variability of hydrologic regimes (both runoff and groundwater recharge) as consequence of the increased rainfall variability predicted for the twenty-first century, while preserving a decreasing in the annual trend. Moreover, the analysis of climate change effects was performed focusing on the rainfall-discharge process of a strategic karst spring supplying the Apulia aqueduct. In this case study, no substantial variations in the annual mean discharge are recognized, although a marked decrease in the mean monthly discharge was found between October and December, which represent the start of the recharge period of Apennine aquifers. Such results represent a crucial water management issue that has to be addressed in terms of adaptation to meet future water resources requirements.

Keywords

Groundwater Recharge Regional Climate Model Hydrological Model Spring Discharge Water Balance Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research was undertaken under the European Union funded project CIRCE (FP6 Project No. 036961).

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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Michele Vurro
    • 1
  • Ivan Portoghese
    • 1
  • Emanuela Bruno
    • 1
  1. 1.Water Research InstituteNational Research Council of Italy (CNR-IRSA)BariItaly

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