Projecting Impacts of Climate Change on Water Availability Using Artificial Neural Network Techniques
Publication: Journal of Water Resources Planning and Management
Volume 143, Issue 12
Abstract
Lago Loíza reservoir in east-central Puerto Rico is one of the primary sources of public water supply for the San Juan metropolitan area. To evaluate and predict the Lago Loíza water budget, an artificial neural network (ANN) technique is trained to predict river inflows. A method is developed to combine ANN-predicted daily flows with ANN-predicted 30-day cumulative flows to improve flow estimates. The ANN application trains well for representing 2007–2012 and the drier 1994–1997 periods. Rainfall data downscaled from global circulation model (GCM) simulations are used to predict 2050–2055 conditions. Evapotranspiration is estimated with the Hargreaves equation using minimum and maximum air temperatures from the downscaled GCM data. These simulated 2050–2055 river flows are input to a water budget formulation for the Lago Loíza reservoir for comparison with 2007–2012. The ANN scenarios require far less computational effort than a numerical model application, yet produce results with sufficient accuracy to evaluate and compare hydrologic scenarios. This hydrologic tool will be useful for future evaluations of the Lago Loíza reservoir and water supply to the San Juan metropolitan area.
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Published In
Journal of Water Resources Planning and Management
Volume 143 • Issue 12 • December 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Dec 7, 2016
Accepted: Jun 1, 2017
Published online: Sep 22, 2017
Published in print: Dec 1, 2017
Discussion open until: Feb 22, 2018
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