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Project ID:2006UT69B

Title: Irrigation Demand Forecasting for Management of Large Water Systems

Project Type: Research

Start Date: 03/01/2006

End Date: 02/28/2007

Congressional District: UT1

Focus Categories: Models, Irrigation, Management and Planning

Keywords: Water Systems Operation, Short-Term Management, Uncertainty

Principal Investigator: McKee, Mac (Utah State University)

Federal Funds: $31,478

Non-Federal Matching Funds: $57,289

Abstract: The relative scarcity of water in the western United States is increasing due to population and economic growth, pollution, and diversification of the types of demands that are being placed on water use (e.g., traditional consumptive uses such as irrigation and municipal supply, as well as emerging uses for such concerns as water quality maintenance and endangered species protection). This increasing relative scarcity brings: (1) a greater need to more intensively manage the resource, and (2) a requirement for better characterizations of the current and potential future states of our water resources systems--including estimates of the uncertainty contained in these characterizations--so that management decisions can be better informed. However, in spite of these increasing needs for better water resources management information, investments in traditional water resources data collection programs (e.g., point stream flows, snow pack, soil moisture, etc.) are declining at the federal and state levels. In contrast, investments in new data collection methods are increasing (e.g., satellite imagery of land cover, snow cover, ocean surface temperatures, etc.; radar estimation of precipitation; aircraft and satellite imagery for estimation of evapotranspiration) and will have to be used to back-fill the decline in availability of traditional data and to improve the quality of the information base available to managers of large water systems.

Research is needed to develop the data now becoming available from emerging remote sensing sources into useful information for all temporal and geographical scales of water resources management. This must be done in such a way as to maximize the total value of the information coming from both these new, emerging data sources and from the traditional water resources monitoring approaches. Further, the products of such research must be of practical use to the water resources managers who (1) are now losing access to traditional data sources and (2) have not been trained in how to access and use the information flowing from new remote sensing capabilities. In addition, the research products must also be of use to a growing range of stakeholders who have heterogeneous technical backgrounds and skill levels. The purpose of this project is to develop and help implement a significantly enhanced capability within the state of Utah--that will also be appropriate for application in the arid West--to more efficiently manage the states scarce water resources by exploiting emerging technologies in data collection and analysis.

Specific research tasks are to:

  1. Develop and test methodologies from statistical learning theory for combining meteorological and hydrological data from traditional and new remote sensing sources to produce information valuable to managers of large water resources systems. The methodologies will also incorporate inputs that captures information about the economic basis of decision-making by irrigators, including forecast future crop prices and remaining season water rights quantities. These methodologies will be directed at supplying reliable predictions of required canal diversions for periods of one to five days in advance of the time of delivery to irrigators.
  2. Develop and implement inexpensive and effective web-based methodologies to disseminate the resulting decision-relevant information to all potential stakeholders.
  3. Evaluate and report on the results of the application of the methodologies.

Progress/Completion Report, PDF

U.S. Department of the Interior, U.S. Geological Survey
Maintained by: John Schefter
Last Updated: Thursday, January 03, 2008
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