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Heuristic Modelling of the Water Resources Management in the Guadalquivir River Basin, Southern Spain

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  • Inmaculada Pulido-Calvo
  • Juan Gutiérrez-Estrada
  • Dragan Savic

Abstract

A model comprising blocks of artificial neural networks (ANNs) combined in sequence was used to simulate the inflow and outflow in a water resources system under a shortage of water. We assessed the selection of appropriate input data using linear and non-linear cross-correlation functions and sensitivity analysis. The potential model inputs were flow, precipitation and temperature data from various gauging stations throughout the upper watershed of the ‘Guadiana Menor’ River (southern Spain), and the model considered various input time lags. The ANNs based on the selected inputs were effective relative to those with no relevant inputs, and produced more parsimonious models. We also investigated conceptual analogies inherent in the ANN models by analyzing the response profiles of the modelled variables (inflow and outflow) in relation to each of the selected input data. The results demonstrate that the neural approach approximated the behaviour of various components of the water resources system in terms of various hydrologic cycle processes and management rules. Our findings suggest that in dry periods a mean temperature increase of 1°C in low altitude locations of the region will result in a mean decrease of approximately 2% in the inflow to the water resources system, and a mean increase of approximately 12% in the outflow requirements for irrigation purposes. Copyright Springer Science+Business Media B.V. 2012

Suggested Citation

  • Inmaculada Pulido-Calvo & Juan Gutiérrez-Estrada & Dragan Savic, 2012. "Heuristic Modelling of the Water Resources Management in the Guadalquivir River Basin, Southern Spain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(1), pages 185-209, January.
    • Handle: RePEc:spr:waterr:v:26:y:2012:i:1:p:185-209
    DOI: 10.1007/s11269-011-9912-0
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    References listed on IDEAS

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    1. Ashu Jain & Ashish Kumar Varshney & Umesh Chandra Joshi, 2001. "Short-Term Water Demand Forecast Modelling at IIT Kanpur 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. 15(5), pages 299-321, October.
    2. Gutiérrez-Estrada, Juan C. & Bilton, David T., 2010. "A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters," Ecological Modelling, Elsevier, vol. 221(11), pages 1451-1462.
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    Cited by:

    1. Isselhorst, Sarah & Berking, Jonas & Schütt, Brigitta, 2018. "Water pricing following rainfall distribution and its implications for irrigation agriculture," Agricultural Water Management, Elsevier, vol. 199(C), pages 34-47.
    2. Shan Huang & Qi Feng & Zhixiang Lu & Xiaohu Wen & Ravinesh C. Deo, 2017. "Trend Analysis of Water Poverty Index for Assessment of Water Stress and Water Management Polices: A Case Study in the Hexi Corridor, China," Sustainability, MDPI, vol. 9(5), pages 1-17, May.
    3. Ana Iglesias & Berta Sánchez & Luis Garrote & Iván López, 2017. "Towards Adaptation to Climate Change: Water for Rice in the Coastal Wetlands of Doñana, Southern Spain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(2), pages 629-653, January.

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