Geomorphic and Land Use Controls on Sediment Yield in Eastern USA

Thesis advisor: Noah P. SnyderThesis advisor: Gabrielle C. DavidThe Reservoir Sedimentation Database (ResSed), a catalogue of reservoirs and depositional data that has recently become publicly available, allows for rapid calculation of sedimentation and capacity-loss rates over short (annual to decadal) timescales. This study is a statistical investigation of factors controlling average sediment yield (Y) in eastern United States watersheds. I develop an ArcGIS-based model that delineates watersheds upstream of ResSed dams and calculate drainage areas to determine Y for 191 eastern US watersheds. Geomorphic, geologic, regional, climatic, and land use variables are quantified within study watersheds using GIS. Sediment yield exhibits a large amount of scatter, ranging from 4.7 to 3336 tonnes1km 2year-1. A weak inverse power law relationship between drainage area (A) and Y (R2 = 0.09) is evident, similar to other studies (e.g., Koppes and Montgomery, 2009). Linear regressions reveal no relationship between mean watershed slope (S) and Y, possibly due to the relatively low relief of the region (mean S for all watersheds is 6°). Analysis of variance shows that watersheds in formerly glaciated regions exhibit a statistically significant lower mean Y (159 tonnes1km-2year-1) than watersheds in unglaciated regions (318 tonnes1km-2year-1), while watersheds with different dam purposes show no significant differences in mean Y. Linear regressions reveal no relationships between land use parameters like percent agricultural, and percent impervious surfaces (I) and Y, but classification and regression trees indicate a threshold in highly developed regions (I > 34%) above which the mean Y (965 tonnes1km-2year-1) is four times higher than watersheds in less developed (I < 34%) regions (237 tonnes1km 2year-1). Further, interactions between land use variables emerge in formerly glaciated regions, where increased agricultural land results in higher rates of annual capacity loss in reservoirs (R2 = 0.56). Plots of Y versus timescale of measurement (e.g., Sadler and Jerolmack, 2014) show that nearly the full range of observed Y, including the highest values, are seen over short survey intervals (< 20 years), suggesting that whether or not large sedimentation events (such as floods) occur between two surveys may explain the high degree of variability in measured rates.Thesis (MS) — Boston College, 2014.Submitted to: Boston College. Graduate School of Arts and Sciences.Discipline: Earth and Environmental Sciences

Automated Satellite-Based Landslide Identification Product for Nepal

Landslide event inventories are a vital resource for landslide susceptibility and forecasting applications. However, landslide inventories can vary in accuracy, availability, and timeliness as a result of varying detection methods, reporting, and data availability. This study presents an approach to use publicly available satellite data and open source software to automate a landslide detection process called the Sudden Landslide Identification Product (SLIP). SLIP utilizes optical data from the Landsat 8 OLI sensor, elevation data from the Shuttle Radar Topography Mission (SRTM), and precipitation data from the Global Precipitation Measurement (GPM) mission to create a reproducible and spatially customizable landslide identification product. The SLIP software applies change detection algorithms to identify areas of new bare-earth exposures that may be landslide events. The study also presents a precipitation monitoring tool that runs alongside SLIP called the Detecting Real-time Increased Precipitation (DRIP) model that helps identify the timing of potential landslide events detected by SLIP. Using SLIP and DRIP together, landslide detection is improved by reducing problems related to accuracy, availability, and timeliness that are prevalent in the state-of-the-art of landslide detection. A case study and validation exercise was performed in Nepal for images acquired between 2014 and 2015. Preliminary validation results suggest 56% model accuracy, with errors of commission often resulting from newly cleared agricultural areas. These results suggest that SLIP is an important first attempt in an automated framework that can be used for medium resolution regional landslide detection, although it requires refinement before being fully realized as an operational tool

Assessing the utility of remote sensing data to accurately estimate changes in groundwater storage

Accurate and timely estimates of groundwater storage changes are critical to the sustainable management of aquifers worldwide, but are hindered by the lack of in-situ groundwater measurements in most regions. Hydrologic remote sensing measurements provide a potential pathway to quantify groundwater storage changes by closing the water balance, but the degree to which remote sensing data can accurately estimate groundwater storage changes is unclear. In this study, we quantified groundwater storage changes in California\u27s Central Valley at two spatial scales for the period 2002 through 2020 using remote sensing data and an ensemble water balance method. To evaluate performance, we compared estimates of groundwater storage changes to three independent estimates: GRACE satellite data, groundwater wells and a groundwater flow model. Results suggest evapotranspiration has the highest uncertainty among water balance components, while precipitation has the lowest. We found that remote sensing-based groundwater storage estimates correlated well with independent estimates; annual trends during droughts fall within 15% of trends calculated using wells and groundwater models within the Central Valley. Remote sensing-based estimates also reliably estimated the long-term trend, seasonality, and rate of groundwater depletion during major drought events. Additionally, our study suggests that the proposed method estimate changes in groundwater at sub-annual latencies, which is not currently possible using other methods. The findings have implications for improving the understanding of aquifer dynamics and can inform regional water managers about the status of groundwater systems during droughts

Socioeconomic Impact Evaluation for Near Real-time Flood Detection in the Lower Mekong River Basin

Flood events pose a severe threat to communities in the Lower Mekong River Basin. The combination of population growth, urbanization, and economic development exacerbate the impacts of these events. Flood damage assessments, critical for understanding the effects of flooding on the local population and informing decision-makers about future risks, are frequently used to quantify the economic losses due to storms. Remote sensing systems provide a valuable tool for monitoring flood conditions and assessing their severity more rapidly than traditional post-event evaluations. The frequency and severity of extreme flood events are projected to increase, further highlighting the need for improved flood monitoring and impact analysis. In this study we integrate a socioeconomic damage assessment model with a near real-time flood remote sensing and decision support tool (NASA's Project Mekong). Direct damages to populations, infrastructure, and land cover are assessed using the 2011 Southeast Asian flood as a case study. Improved land use/land cover and flood depth assessments result in rapid loss estimates throughout the Mekong River Basin. Results suggest that rapid initial estimates of flood impacts can provide valuable information to governments, international agencies, and disaster responders in the wake of extreme flood events

Remote Sensing-Based Estimates of Changes in Stored Groundwater at Local Scales: Case Study for Two Groundwater Subbasins in California’s Central Valley

Sustainable groundwater management requires high-quality and low-latency estimates of changes in groundwater storage (∆Sgw). However, estimates of ∆Sgw produced using traditional methods, including groundwater models and well-based measurements, typically lag years behind the present because collecting the required on-the-ground data is a time consuming, expensive, and labor-intensive task. Satellite remote sensing measurements provide potential pathways to overcome these limitations by quantifying ∆Sgw through closing the water balance. However, the range of spatial scales over which ∆Sgw can be accurately estimated using remote sensing products remains unclear. To bridge this knowledge gap, this study quantified ∆Sgw for the period of 2002 through to 2021 using the water balance method and multiple remote sensing products in two subbasins (~2700 km2–3500 km2) within California’s Central Valley: (1) the Kaweah–Tule Subbasin, a region where the pumping of groundwater to support agriculture has resulted in decades of decline in head levels, resulting in land subsidence, damage to infrastructure, and contamination of drinking water and (2) the Butte Subbasin, which receives considerably more rainfall and surface water and has not experienced precipitous drops in groundwater. The remote sensing datasets which we utilized included multiple sources for key hydrologic components in the study area: precipitation, evapotranspiration, and soil moisture. To assess the fidelity of the remote sensing-based model, we compared estimates of ∆Sgw to alternative estimates of ∆Sgw derived from independent sources of data: groundwater wells as well as a widely used groundwater flow model. The results showed strong agreement in the Kaweah–Tule Subbasin in long-term ∆Sgw trends and shorter-term trends during droughts, and modest agreement in the Butte Subbasin with remote sensing datasets suggesting more seasonal variability than validation datasets. Importantly, our analysis shows that the timely availability of remote sensing data can potentially enable ∆Sgw estimates at sub-annual latencies, which is timelier than estimates derived through alternate methods, and thus can support adaptive management and decision making. The models developed herein can aid in assessing aquifer dynamics, and can guide the development of sustainable groundwater management practices at spatial scales relevant for management and decision making