CAREER: Hillslope Morphology Never Stops: Validating Hillslope Evolution Models on Transport Limited, Low Relief Landscapes

职业：山坡形态永不停歇：在交通有限、低地势景观上验证山坡演化模型

• 批准号: 2049042
• 负责人: Bradley Miller
• 金额: $ 57.42万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2049042&HistoricalAwards=false
• 关键词: CAREER; Hillslope; Morphology; Never; Stops

Conventional agricultural practices are causing erosion rates that exceed the rates of soil production and threaten agricultural sustainability (Montgomery, 2007). Although climate models predict continued increases in the severity of floods and losses in crop productivity, they currently do not include the feedback loops for how the landscape changes over time. Virtually all models predicting impacts of climate change rely on topographic and soil maps that are assumed to stay the same. We know these assumptions are incorrect, but those static inputs are used because they are the best data available. The ultimate goal of this project is to assemble an erosion-deposition model for landscapes where the rate of sediment transport, not rate of weathering, limits the amount that those processes change the topography and soil properties of hillslopes. The recent implementation of highly accurate, remotely sensed elevation measurements more than a decade apart offers a unique opportunity to validate such a model at the regional scale. This knowledge is crucial for incorporating landscape change into models that predict impacts of climate change, such as effects on flooding and crop productivity. The model will also be used to generate scenarios of landscape change from real landscapes, which can be explored and employed as an educational tool through a publicly accessible, web map interface. Training and curriculum materials will be developed in collaboration with educators experienced with delivering Earth Science content to diverse student audiences.The major barriers limiting the use of hillslope erosion-deposition models to predict topographic change for low relief landscapes are the disciplinary divides between geomorphology and soil science as well as the absence of data for full validation. Geomorphology studies tend to focus on high-relief landscapes. In contrast, soil scientists typically investigate lower relief, agricultural areas in the context of mass removed and effects on soil properties, such as carbon content. Therefore, an integrated landscape model is needed that combines the theories of geomorphology and soil science to predict landscape change in the areas that currently experience the most active erosion and that are crucial to securing food and feed needs in the future. The PI will study hillslope erosion-deposition models and calibrate them to multiple landform regions within the USA Corn Belt. These models will be validated and evaluated through the elevation differences observed between the 2009 and 2020 LiDAR-based elevation data collected for the state of Iowa. This project is jointly funded by the Geomorphology and Land-use Dynamics (GLD) Program and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

传统农业做法造成的侵蚀速度超过了土壤生产速度，威胁到农业的可持续性（蒙哥马利，2007年）。虽然气候模型预测洪水的严重程度和作物生产力的损失将继续增加，但它们目前没有包括景观如何随时间变化的反馈回路。几乎所有预测气候变化影响的模型都依赖于假定不变的地形图和土壤图。我们知道这些假设是不正确的，但使用这些静态输入是因为它们是可用的最佳数据。该项目的最终目标是为地貌组合一个侵蚀-沉积模型，在地貌中，沉积物迁移的速度，而不是风化的速度，限制了这些过程改变山坡地形和土壤特性的程度。最近实施的高精度遥感高程测量相隔十多年，提供了一个独特的机会，在区域范围内验证这样一个模型。这些知识对于将景观变化纳入预测气候变化影响的模型至关重要，例如对洪水和作物生产力的影响。该模型还将用于从真实的景观中生成景观变化情景，可通过一个可公开访问的网络地图界面作为教育工具加以探索和使用。培训和课程材料将开发与教育工作者提供地球科学的内容，以不同的学生audios.The主要障碍限制使用山坡侵蚀沉积模型来预测地形变化的低浮雕景观地貌学和土壤科学之间的学科划分，以及缺乏充分验证的数据。地貌学的研究往往集中在高起伏的景观。相比之下，土壤科学家通常调查地势较低的农业地区，在此背景下，质量被移除，对土壤性质的影响，如碳含量。因此，需要一个综合的景观模型，结合地貌学和土壤科学的理论来预测景观变化的地区，目前经历最活跃的侵蚀，这是至关重要的，以确保未来的粮食和饲料需求。PI将研究山坡侵蚀沉积模型，并将其校准到美国玉米带内的多个地形区域。这些模型将通过在2009年和2020年收集的爱荷华州基于LiDAR的高程数据之间观察到的高程差异进行验证和评估。该项目由地貌学和土地利用动力学（GLD）计划和刺激竞争性研究的既定计划（EPSCoR）共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。