Economics of water conservation in irrigated landscapes

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.

本研究旨在深化对灌溉景观中水资源利用两大核心驱动因素的认知：其一为灌溉技术采纳行为，其二为灌区层级结构。针对第一类驱动因素的研究方法聚焦于将最优投资经济模型与年度径流水文模型相耦合，具体而言，该方法量化了径流趋势与径流变异性对卡什谷（Cache Valley）三个流域内高效灌溉技术采纳情况的影响机制。上述对径流趋势与变异性影响的深化认知，可用于预测一种特定的气候适应手段——节水灌溉技术的普及——将如何随气候趋势与变异性发生演化。针对第二类驱动因素的研究则采用基于智能体（Agent）的模型模拟渠区管理者与所有者的行为，该方法可针对卡什谷某一特定渠道，设定三类智能体（渠道管理者、居民用水户、农业用水户）所采用的经济与非经济行为模式，模型涵盖了该渠道的实际水文特征、渠道管理者与用水户之间的层级关系，以及渠企所有者所持有的多样化用水目标。本研究首次揭示了制度、社会规范与经济因素在用水决策中的相对重要性，此项研究成果有助于明确RFA2与RFA3之间的内在关联。