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Neural network modeling of climate change impacts on irrigation water supplies in Arkansas River Basin

dc.contributor.authorElgaali, Elgaali, author
dc.contributor.authorGarcia, Luis, author
dc.contributor.authorColorado State University, publisher
dc.date.accessioned2020-01-29T15:33:18Z
dc.date.available2020-01-29T15:33:18Z
dc.date.issued2004
dc.description24th annual AGU hydrology days was held at Colorado State University on March 10-12, 2004.
dc.descriptionIncludes bibliographical references.
dc.description.abstractThe evidence of climate change is mounting. Climate change in the region that includes the Arkansas River basin may have profound effects on water users. The potential impacts of climate change include changes in snowfall, snowmelt and rainfall amount and intensities. Snowmelt is the main source of water supply in the region. Water supply is a key factor in determining agricultural potential. In scientific studies dealing with modeling irrigation water budgets, water supply is usually assumed sufficient. Such an assumption leads to critical uncertainties in these water budgets. The water supply may be affected by changes in quantity, type (snow or rain) and timing of precipitation. The possible effects of climatic changes on surface water supplies for irrigation in the Arkansas River basin are investigated using Artificial Neural Network (ANN). ANN models have been found useful and efficient, particularly in problems for which the characteristics of the process are difficult to describe using physically based models. ANN is capable of identifying complex nonlinear relationships between input and output data sets without prior knowledge of the internal structure of a system. This study presents a procedure for modeling the impacts of climate change on irrigation water supplies and demonstrates the potential ANN models for simulating such nonlinear hydrologic behavior. Precipitation over the mountains and the basin area coupled with steam flow is used to quantify the impacts of climate changes on surface water supply for irrigation. A feedforward neural network is trained to map the relation between the water diverted for irrigation (output) and the streamflow/precipitation (inputs).
dc.format.mediumborn digital
dc.format.mediumproceedings (reports)
dc.identifier.urihttps://hdl.handle.net/10217/200010
dc.identifier.urihttp://dx.doi.org/10.25675/10217/200010
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofHydrology Days
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.titleNeural network modeling of climate change impacts on irrigation water supplies in Arkansas River Basin
dc.title.alternativeHydrology days 2004
dc.title.alternativeAGU hydrology days 2004
dc.typeText

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