<p>Rivers are constantly undergoing change due to erosion and sedimentation along their banks. Although these processes generally occur gradually, flood events can significantly accelerate river migration, creating a risk for human life and infrastructure. As a result, it is important to identify river reaches that are prone to channel migration and determine the extent of migration. However, detailed information about river migration across entire river networks is not readily available. This study seeks to develop a Python-based tool that can generate river migration rasters across large watersheds using Landsat imagery. The methodology involves extracting the centerlines of river features in Landsat imagery using the Modified Normalized Difference Water Index (MNDWI) and the Skeletonize function available in the scikit-image library, followed by the application of the Particle Image Velocimetry (PIV) algorithm to compute the river channel migration. The PIV algorithm generates a set of migration rasters that are analyzed to extract the long-term migration of each of the reaches. The tool also creates intermediate outputs, such as the MNDWI raster, binary land-water raster, and skeletonized river centerlines, which can be further analyzed to gain insights into the river's behavior. The methodology is implemented in the Wabash and Lower Mississippi River Basins, and the tool's effectiveness is validated against manual measurements of the river migration available for the Wabash Basin. In addition, this study analyzes the correlation between long-term migration and various factors, such as reach sinuosity, drainage area, geology, and streamflow. The results of the analysis show that drainage area is highly correlated with river migration. The correlation results are compared with the prior literature, thereby serving to validate the developed framework. This framework has the potential to aid decision-makers and policymakers in identifying the long-term patterns of river channel migration, facilitating their efforts to plan for infrastructure resilience. By utilizing this methodology, river managers and other stakeholders can gain insights into river migration across large watersheds and identify areas that require further monitoring and management.</p>

<div><div><div><p>The radionuclide 10Be is produced in the atmosphere and is delivered to Earth’s surface in meteoric rain and aerosols. The stable nuclide 9Be is present in trace concentrations within rocks in Earth’s crust and is released via chemical weathering. Together, these two isotopes have been employed to study a wide range of Earth processes. Here I explore new terrestrial archives of Be isotopes: cave speleothems and terrestrial Mn-oxides. Until this point, these archives have barely been studied. Only one published dataset of Be isotopes in cave speleothems exists (Lundblad, 2006), and to my knowledge, terrestrial manganese oxides are yet to be explored. However, since speleothems and Mn-oxides precipitate from groundwater, they have the potential to encode temporal variations in the 10Be/9Be ratio of water and colloids in the vadose zone.</p><p>I develop a framework for using the 10Be/9Be ratio in the dissolved phase and/or secondary weathering products as a metric of chemical weathering rate. I am motivated by several over-arching questions:</p><ol><li><p>1) Which factor, or factors, is/are dominant in controlling Be isotopes in speleothems and terrestrial Mn-oxides?</p></li><li><p>2) Can Be isotopes in speleothems be used as a metric of weathering rate over time, particularly across glacial/interglacial cycles?</p></li><li><p>3) Can Be isotopes be used to date the formation of terrestrial Mn-oxides?</p></li></ol><p>I measure Be-isotope concentrations in speleothems from Soreq Cave, Israel. By applying an equation that I derive in this thesis, I use the temporal variation in the speleothem10Be/9Be ratio to calculate chemical weathering rates over the last 168 ka. Chemical weathering varies with independent proxies for temperature. The weathering-temperature relationship can be fit to an Arrhenius relationship, and the calculated activation energy (Ea) matches other field-based estimates for feldspar, an abundant mineral in the soil above the cave. In the Appendices I present additional results of Be-isotope measurements in a flowstone from Buffalo Cave in South Africa, as well as Mn-oxides from the Appalachians.</p></div></div></div>