Neural network modeling of climate change impacts on irrigation water supplies in Arkansas River Basin
| dc.contributor.author | Elgaali, Elgaali, author | |
| dc.contributor.author | Garcia, Luis, author | |
| dc.contributor.author | Colorado State University, publisher | |
| dc.date.accessioned | 2020-01-29T15:33:18Z | |
| dc.date.available | 2020-01-29T15:33:18Z | |
| dc.date.issued | 2004 | |
| dc.description | 24th annual AGU hydrology days was held at Colorado State University on March 10-12, 2004. | |
| dc.description | Includes bibliographical references. | |
| dc.description.abstract | The 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.medium | born digital | |
| dc.format.medium | proceedings (reports) | |
| dc.identifier.uri | https://hdl.handle.net/10217/200010 | |
| dc.identifier.uri | http://dx.doi.org/10.25675/10217/200010 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | Hydrology Days | |
| dc.rights | Copyright 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.title | Neural network modeling of climate change impacts on irrigation water supplies in Arkansas River Basin | |
| dc.title.alternative | Hydrology days 2004 | |
| dc.title.alternative | AGU hydrology days 2004 | |
| dc.type | Text |
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