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|Resource type:||Composite Resource|
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|Created:||Nov 04, 2017 at 1:58 a.m.|
|Last updated:|| Dec 20, 2017 at 12:11 a.m.
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The purpose of this research effort is to improve understanding of two key drivers of water use in irrigated landscapes: 1) irrigation technology adoption and 2) irrigation district hierarchies. The methodology used to address the first driver focuses on integrating economic models of optimal investment with hydrologic models of annual streamflow. Specifically, this approach quantifies how streamflow trends and streamflow variability influence the adoption of more efficient irrigation technologies in three watershed in Cache Valley. This improved understanding of the influence of streamflow trends and variability can be used to predict how one specific form of climate adaptation (uptake of water-saving irrigation technologies) will evolve in response to climate trends and variability. The methodology used to address the second driver is an agent-based model of the behaviors of canal district managers and owners. This methodology allows us to specify both economic and non-economic behaviors employed by three agent types (canal manager, residential water users, agricultural water users) in a specific canal in Cache Valley. The model accounts for realistic hydrologic aspects of the canal, the hierarchical relationship between canal managers and water users, as well as the variety of water use objectives held by owners of the canal company. This research provides an initial glimpse of the relative importance of institutions, social norms, and economics in water use decisions. These research efforts help solidify the links between RFA2 and RFA3.
Resource Level Coverage
This project contains two sets of files. The first set solves for the optimal time for water irrigation investments and uses the following files: chapteronecode0207.m is the main Matlab file augustinamff0207.m is the model function file that supplies model structure to chapteronecode0207.m Water GBM simulations.xls calculates the parameter values for the stochastic differential equation that describes streamflow for the three rivers. These are used as input for chapteronecode0207.m is the main Matlab file The second set of files solves a bilevel programming problem for optimal canal management: ABM Decision Rules.xlsx catalogs parameter values and data sources as well as describes the modeled scenarios ABM_QuantityOnly.m is a Matlab file that solves the optimal bi-level programming problem for canal management
This resource was created using funding from the following sources:
|Agency Name||Award Title||Award Number|
|National Science Foundation||iUTAH-innovative Urban Transitions and Aridregion Hydro-sustainability||1208732|
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.http://creativecommons.org/licenses/by/4.0/