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

农业用水含盐量增加是一个全球性问题，具有重要的经济和生态影响。当农田用中等含盐量的水灌溉时，盐在土壤中积累，导致作物产量下降或土地永久休耕。虽然这个问题既不是新问题（古老的灌溉系统导致美索不达米亚曾经肥沃的土壤退化），也不是在规模上下降（土壤盐碱化现在估计影响了全世界近三分之一的灌溉土地），但很少有决策工具来评估干预的技术经济可行性，这是本提案的重点。开发决策工具对于向农民、管理人员和其他关心减缓盐碱化趋势和确保全球粮食系统可持续性和复原力的人提供信息至关重要。农业航道盐渍化造成了巨大的经济和生态损失。拟议的研究将制定评估这些损害的方法，并比较技术和政策干预的成本效益。提出的研究还将开发新的方法来有效地生成广泛的、多分辨率的农业水道盐渍化数据集，这对实施这些决策模型至关重要。最后，本研究将在多目标决策框架下评估降低盐碱度干预措施的技术经济可行性，包括补贴土地休耕、限制瓦片排放和淡化含盐农业用水。本研究将在Panoche水与排水区和Westlands水区、加州圣华金河流域受盐分影响的地区以及附近的加州大学默塞德分校测试并实施所提出的方法。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个方法上的挑战。pi建议开发一个全面的决策分析框架，利用远程和自主传感快速和经济有效地评估农业水道的盐度管理实践。多模态传感器网络的设计和集成将为农业环境中水质、土壤盐度、生态健康和土地利用之间的基本关系提供信息。将这些信息整合到决策分析框架中，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盐度管理的技术和政策，这些技术和政策涉及多个通常相互竞争的目标。提出的研究的第一个目标是实施一个评估模型，以评估减少盐分对农业和生态系统的好处。第二个目标是开发一种分层、多模式的数据收集方法，使用遥感、自主机器人船舶传感器和稀疏、分布式、静态传感器网络，以高效、经济地开发农业水盐渍化模型。第三个目标是评估农业生产者和监管机构可用的分布式盐度降低技术和政策的成本效益，以最大限度地减少与盐化农业水道相关的经济和生态影响。这将产生新的、分层的、低成本的方法来生成农业水道盐度的高分辨率数据集，这些数据集可以扩展到农业工业中其他非点源排放的检测和监测。拟议的研究还将开发新的评估方法，以量化减少农业水道含盐量对私人行为者（种植者）和公众（生态系统）的好处，并以足够的解决方案为政策和技术实施提供信息，这是对抗盐碱化问题的政策和技术开发的主要障碍。最后，提出的研究将开发静态传感器的定位算法，以最大限度地提高所收集信息的价值。在整个过程中，pi将开发管理大型数据集处理中的不确定性的方法。拟议的工作将通过提供一个定量决策框架来比较减少盐分的公共和私人成本和收益，从而提高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，促进自主和机器人传感环境决策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民族服务机构，参加课程学分或全职暑期工作的学生将接受当地农业新兴高科技工作的培训。最后，pi将继续为高中学生开发一个教育模块，通过部署自主机器人船舶传感器，为学生提供环境科学、机器人技术和数据处理的实践机会。

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.