Collaborative Research: INFEWS: N/P/H2O: Remote and autonomous sensing for managing the economic and environmental consequences of salinity-impacted agricultural waterways

1604853/1604906 Mauter/Viers农业水道盐度增加是一个具有严重经济和生态影响的全球性问题。当农田用中等咸水灌溉时，盐分会在土壤中积累，导致作物减产或永久休耕。虽然这个问题既不是新问题(古代灌溉系统导致美索不达米亚曾经肥沃的土壤退化)，也不是规模下降(据估计，土壤盐碱化现在影响了全世界近三分之一的灌溉土地)，但几乎没有决策工具来评估干预措施的技术经济可行性，这是本提案的重点。开发决策工具对于向农民、管理者和其他对减缓盐碱化趋势和确保全球粮食系统的可持续性和复原力感兴趣的人提供信息至关重要。农业水路盐碱化造成相当大的经济和生态破坏。拟议的研究将开发评估这些损害的方法，并比较技术和政策干预的成本效益。这项拟议的研究还将开发新的方法，有效地生成广泛的、多分辨率的农业水道盐碱化数据集，这对实施这些决策模型至关重要。最后，拟议的研究将在多目标决策框架内评估减少盐分干预措施的技术经济可行性，包括补贴土地休耕、限制瓦片排水沟排放和咸化农业水域海水淡化。这项研究将在帕诺什水利区和韦斯特兰兹水区、加利福尼亚州圣华金河流域受盐分影响的地区以及非常邻近的加州大学默塞德分校测试和实施拟议的方法。在农业系统中，收集、处理和利用高分辨率数据为环境决策提供信息仍然是一项方法论挑战。投资促进机构建议开发一个综合决策分析框架，利用遥感和自主遥感，以具有成本效益的方式快速评估农业水道的盐度管理做法。多模式传感器网络的设计和集成将为农业环境中水质、土壤盐分、生态健康和土地利用之间的基本关系提供信息。将这些信息纳入决策分析框架将有助于农业生产者、监管机构和国家基础设施管理者评估跨多个往往相互竞争的目标的盐分管理技术和政策。拟议研究的第一个目标是实施一个评估模型，以评估降低盐度对农业和生态系统的益处。第二个目标是利用遥感、自主机器人船艇传感器和稀疏、分布式、静态传感器网络，开发一种层次化、多模式数据收集方法，以高效和具有成本效益的方式开发农业水盐化模型。第三个目标是评估农业生产者和监管者可利用的分布式降盐技术和政策的成本效益，以尽量减少与盐碱化农业水道有关的经济和生态影响。这将产生新的、分级的、低成本的方法，用于生成关于农业水道盐度的高分辨率数据集，可扩展到对农业行业其他非点源排放的检测和监测。拟议的研究还将制定新的评估方法，以量化降低农业水道盐度对私营行为者(种植者)和公众(生态系统)的惠益，并以足够的分辨率为政策和技术执行提供信息，这是为解决盐碱化问题而制定政策和技术的一个主要障碍。最后，建议的研究将开发定位静态传感器的算法，以最大化收集的信息的价值。在整个过程中，绩效指标将开发方法来管理大型数据集处理过程中的不确定性。拟议的工作将提供一个量化的决策框架，在其中比较公共和私人降低盐分的成本和收益，从而加强灌溉农业的可持续性。它还将通过让加州大学默塞德分校的本科生参与数据收集、数据管理和数据可视化研究，促进环境决策自主和机器人传感新兴领域的劳动力发展。加州大学默塞德分校是加州中央山谷的一所少数族裔服务机构，参加课程学分或全职暑期工作的学生将接受培训，以便在当地农业行业从事新兴的高科技工作。最后，PI将继续为高中生开发一个教育模块，通过部署自主机器人船舶传感器，提供对环境科学、机器人和数据处理的动手接触。

1604853 / 1604906Mauter / ViersIncreasing salinity of agricultural waterways is a global problem with critical economic and ecological impacts. When cropland is irrigated with moderately saline waters, salts accumulate in the soil leading to reduced crop yields or permanent land fallowing. While this problem is neither new (ancient irrigation systems led to the degradation of the once fertile soils across Mesopotamia), nor declining in scale (soil salinization is now estimated to impact nearly one-third of all irrigated land worldwide), there are few decision tools for evaluating the techno-economic feasibility of intervention which is the focus of this proposal. Developing decision tools is critical to informing farmers, managers and others who are interested in slowing salinization trends and ensuring the sustainability and resilience of global food systems.Agricultural waterway salinization imposes considerable economic and ecological damages. The proposed research will develop methods for valuing those damages and comparing the cost-effectiveness of technology and policy interventions. The proposed research will also develop novel methods for efficiently generating a broad, multi-resolution dataset of agricultural waterway salinization critical to implementing these decision models. Finally, the proposed research will evaluate the techno-economic feasibility of salinity reduction interventions, including subsidizing land fallowing, limiting tile drain discharge, and desalinating saline agricultural waters, in a multi-objective decision framework. This research will test and implement the proposed methodology in the Panoche Water and Drainage District and the Westlands Water District, salinity-impacted districts within the San Joaquin River Basin of California and very nearby UC Merced. Collecting, processing, and leveraging data high-resolution data to inform environmental decisions remains a methodological challenge in agricultural systems. The PIs propose to develop a comprehensive decision analysis framework that leverages remote and autonomous sensing to rapidly and cost-effectively evaluate salinity management practices for agricultural waterways. Design and integration of multi-modal sensor networks will inform fundamental relationships between water quality, soil salinity, ecological health, and land use in agricultural environments. Incorporating this information into a decision analysis framework will aid agricultural producers, regulators, and state infrastructure managers in evaluating technologies and policies for salinity management across multiple, often competing, objectives. The first objective of the proposed research is to implement a valuation model to assess the benefits of salinity reduction to agricultural and ecological systems. The second objective is to develop a hierarchical, multi-modal data collection methodology using remote sensing, autonomous robotic watercraft sensors, and sparse, distributed, static sensor networks for efficiently and cost-effectively developing models of agricultural water salinization. The third objective is to evaluate the cost-effectiveness of distributed salinity reduction technologies and policies available to agricultural producers and regulators to minimize the economic and ecological impacts associated with salinized agricultural waterways. This will result in novel, hierarchical, low cost methods for generating high-resolution data sets on agricultural waterway salinity that can be extended to the detection and monitoring of other non-point source emissions in the agricultural industry. The proposed research will also develop novel valuation methods for quantifying the benefits of reducing agricultural waterway salinity for private actors (growers) and the public (ecosystems) at a sufficient resolution to inform policy and technology implementation, a major barrier to policy and technology development for combating salinization issues. Finally, the proposed research will develop algorithms for positioning static sensors to maximize the value of information collected. Throughout, the PIs will develop methods to manage uncertainty in the processing of large datasets. The proposed work will enhance the sustainability of irrigated agriculture by providing a quantitative decision framework in which to compare public and private costs and benefits of salinity reduction. It will also promote workforce development in the emerging field of autonomous and robotic sensing for environmental decision-making by engaging UC Merced undergraduate students in data collection, data management, and data visualization research. UC Merced is a minority serving institution in the Central Valley of CA, and students participating for course credit or full time summer employment will be trained for emerging, high tech jobs in the local agricultural industry. Finally, the PIs will continue the development of an education module for high school students that provides hands-on exposure to environmental science, robotics, and data processing via deployment of autonomous robotic watercraft sensors.