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Of the approximately 6700 lakes and reservoirs larger than 1 km2 in the Contiguous United States (CONUS), only ~430 (~6%) are actively gaged by the United States Geological Survey (USGS) or their partners and are available for download through the National Water Information System database. Remote sensing analysis provides a means to fill in these data gaps in order to glean a better understanding of the spatiotemporal water level changes across the CONUS. This study takes advantage of two-plus years of NASA’s ICESat-2 (IS-2) ATLAS photon data (ATL03 products) in order to derive water level changes for ~6200 overlapping lakes and reservoirs (>1 km2) in the CONUS. Interactive visualizations of large spatial datasets are becoming more commonplace as data volumes for new Earth observing sensors have markedly increased in recent years. We present such a visualization created from an automated cluster computing workflow that utilizes tens of billions of ATLAS photons which derives water level changes for all of the overlapping lakes and reservoirs in the CONUS. Furthermore, users of this interactive website can download segmented and clustered IS-2 ATL03 photons for each individual waterbody so that they may run their own analysis. We examine ~19,000 IS-2 derived water level changes that are spatially and temporally coincident with water level changes from USGS gages and find high agreement with our results as compared to the in situ gage data. The mean squared error (MSE) and the mean absolute error (MAE) between these two products are 1 cm and 6 cm, respectively.

Interferometric synthetic aperture radar (InSAR) processing techniques have been widely used to derive surface deformation or retrieve terrain elevation. Over the development of the past few decades, most research has mainly focused on its application, new techniques for improved accuracy, or the investigation of a particular error source and its correction method. Therefore, a thorough discussion about each error source and its influence on InSAR-derived products is rarely addressed. Additionally, InSAR is a challenging topic for beginners to learn due to the intricate mathematics and the necessary signal processing knowledge required to grasp the core concepts. This results in the fact that existing papers about InSAR are easy to understand for those with a technical background but difficult for those without. To cope with the two issues, this paper aims to provide an organized, comprehensive, and easily understandable review of the InSAR error budget. In order to assist readers of various backgrounds in comprehending the concepts, we describe the error sources in plain language, use the most fundamental math, offer clear examples, and exhibit numerical and visual comparisons. In this paper, InSAR-related errors are categorized as intrinsic height errors or location-induced errors. Intrinsic height errors are further divided into two subcategories (i.e., systematic and random error). These errors can result in an incorrect number of phase fringes and introduce unwanted phase noise into the output interferograms, respectively. Location-induced errors are the projection errors caused by the slant-ranging attribute of the SAR systems and include foreshortening, layover, and shadow effects. The main focus of this work is on systematic and random error, as well as their effects on InSAR-derived topographic and deformation products. Furthermore, because the effects of systematic and random errors are greatly dependent on radar wavelengths, different bands are utilized for comparison, including L-band, S-band, C-band, and X-band scenarios. As examples, we used the parameters of the upcoming NISAR operation to represent L-band and S-band, ERS-1 and Sentinel-1 to represent C-band, and TerraSAR-X to represent X-band. This paper seeks to bridge this knowledge gap by presenting an approachable exploration of InSAR error sources and their implications. This robust and accessible analysis of the InSAR error budget is especially pertinent as more SAR data products are made available (e.g., NISAR, ICEYE, Capella, Umbra, etc.) and the SAR user-base continues to expand. Finally, a commentary is offered to explore the error sources that were not included in this work, as well as to present our thoughts and conclusions.

Increased demand for power generation coupled with changing seasonal water uncertainty has caused a worldwide increase in the construction of large hydrologic engineering structures. That said, the soon-to-be-completed Grand Ethiopian Renaissance Dam (GERD) will impound the Blue Nile River in Western Ethiopia and its reservoir will encompass ~ 1763 km2 and store ~ 67 Gt (km3) of surface water. The impoundment will undergo maximum seasonal load changes of ~ 28 to ~ 36 Gt during projected seasonal hydroelectric operations. The GERD impoundment will cause significant subsurficial stresses, and could possibly trigger seismicity in the region. This study examines Coulomb stress and hydrologic load centroid movements for several GERD impoundment and operational scenarios. The maximum subsurficial Coulomb stress applied on optimally oriented fault planes from the full impoundment is ~ 186 kPa and over 30% of our model domain incurs Coulomb stresses ≥ 10 kPa, regardless of the impoundment period length. The main driver behind Coulomb stress and load centroid motion during impoundment is the annual, accumulated daily reservoir storage change. The maximum Coulomb stresses from the highest amplitude season of five long-term operational scenarios are around 36, 33, 29, 41, and 24% of the total maximum stresses from the entire GERD impoundment. Variations in annual Coulomb stresses during modeled GERD operations are attributed to the seasonal load per unit area, and partially to the initial seasonal water level. The spatial patterns and amplitudes of these stress tensors are closely linked to both the size and timing of GERD inflow/outflow rates, and an improved understanding of the magnitude and extent of these stresses provides useful information to water managers to better understand potential reservoir triggered seismic events from several different operational and impoundment strategies.

Addressing seasonal water uncertainties and increased power generation demand has sparked a global rise in large-scale hydropower projects. To this end, the Blue Nile impoundment behind the Grand Ethiopian Renaissance Dam (GERD) will encompass an areal extent of ~1763.3 km2 and hold ~67.37 Gt (km3) of water with maximum seasonal load changes of ~27.93 (41% of total)—~36.46 Gt (54% of total) during projected operational scenarios. Five different digital surface models (DSMs) are compared to spatially overlapping spaceborne altimeter products and hydrologic loads for the GERD are derived from the DSM with the least absolute elevation difference. The elastic responses to several filling and operational strategies for the GERD are modeled using a spherically symmetric, non-rotating, elastic, and isotropic (SNREI) Earth model. The maximum vertical and horizontal flexural responses from the full GERD impoundment are estimated to be 11.99 and 1.99 cm, regardless of the full impoundment period length. The vertical and horizontal displacements from the highest amplitude seasonal reservoir operational scenarios are 38–55% and 34–48% of the full deformation, respectively. The timing and rate of reservoir inflow and outflow affects the hydrologic load density on the Earth’s surface, and, as such, affects not only the total elastic response but also the distance that the deformation extends from the reservoir’s body. The magnitudes of the hydrologic-induced deformation are directly related to the size and timing of reservoir fluxes, and an increased knowledge of the extent and magnitude of this deformation provides meaningful information to stakeholders to better understand the effects from many different impoundment and operational strategies.

The Slumgullion landslide, located in southwestern Colorado, has been active since the early 1700s and current data suggests that the most active portion of the slide creeps at a rate of ~1.5–2.0 cm/day. Accurate deformation measurement techniques are vital to the understanding of persistent, yet slow-moving landslides like the Slumgullion. The factors that affect slope movements at the Slumgullion are on-time scales that are well suited towards a remotely sensed approach to constrain the 12 different kinematic units that make up the persistent creeping landslide. We derive a time series of motion vectors (magnitude and direction) using subpixel offset techniques from very high resolution TerraSAR-X Staring Spotlight ascending/descending data as well as from a novel high-resolution amalgamation of airborne lidar and unmanned aerial systems (UAS) Structure from Motion (SfM) digital surface model (DSM) hillshades. Deformation rates calculated from the spaceborne and airborne datasets show high agreement (mean difference of ~0.9 mm/day), further highlighting the potential for the monitoring of ongoing mass wasting events utilizing unmanned aircraft systems (UAS) We compare pixel offset results from an 11-day synthetic aperture radar (SAR) pair acquired in July of 2016 with motion vectors from a coincident low-cost L1 only Global Navigation Satellite System (GNSS) field campaign in order to verify the remotely sensed results and to derive the accuracy of the azimuth and range offsets. We find that the average azimuth and range pixel offset accuracies utilizing the methods herein are on the order of 1/18 and 1/20 of their along-track and slant range focused ground pixel spacing values of 16.8 cm and 45.5 cm, respectively. We utilize the SAR offset time series to add a twelfth kinematic unit to the previously established set of eleven unique regions at the site of an established minislide within the main landslide itself. Lastly, we compare the calculated rates and direction from all spaceborne- and airborne-derived motion vectors for each of the established kinematic zones within the active portion of the landslide. These comparisons show an overall increased magnitude and across-track component (i.e., more westerly angles of motion) for the descending SAR data as compared to their ascending counterparts. The processing techniques and subsequent results herein provide for an improved knowledge of the Slumgullion landslide’s kinematics and this increased knowledge has implications for the advancement of measurement techniques and the understanding of globally distributed creeping landslides.

Understanding the Tibetan Plateau's role in environmental change has gained increasing scientific attention in light of warming and changes in land management. We examine changes in greenness over the Tibetan Plateau using the Normalized Difference Vegetation Index (NDVI) from the Global Inventory Monitoring and Modeling Study (GIMMS 3 g) to identify significant changes over the entire plateau, six ecoregions, and protected areas based on a multiyear time series of July imagery from 1982 to 2015. We also test whether there have been changes in human populations in protected areas. There has been relatively little change in mean NDVI over the Tibetan Plateau or ecoregions, however, there were significant changes at the pixel level. There are sixty-nine protected areas on the Tibetan Plateau; sixty-two protected areas had no significant change in mean NDVI and seven protected areas experienced a significant increase in NDVI. There has been an increase in population within protected areas from 2000 to 2015; however, mean populations significantly increased in two protected areas and significantly decreased in four protected areas. Results suggest a slow greening of the Tibetan Plateau, ecoregions, and protected areas, with a more rapid greening in northern Tibet at the pixel level. Most protected areas are experiencing minor changes in NDVI independent of human population.

The Hawaiian Islands have been identified as a global biodiversity hotspot. We examine the Normalized Difference Vegetation Index (NDVI) using Climate Data Records products (0.05 × 0.05°) to identify significant differences in NDVI between neutral El Niño-Southern Oscillation years (1984, 2019) and significant long-term changes over the entire time series (1982–2019) for the Hawaiian Islands and six land cover classes. Overall, there has been a significant decline in NDVI (i.e., browning) across the Hawaiian Islands from 1982 to 2019 with the islands of Lāna’i and Hawai’i experiencing the greatest decreases in NDVI (≥44%). All land cover classes significantly decreased in NDVI for most months, especially during the wet season month of March. Native vegetation cover across all islands also experienced significant declines in NDVI, with the leeward, southwestern side of the island of Hawai’i experiencing the greatest declines. The long-term trends in the annual total precipitation and annual mean Palmer Drought Severity Index (PDSI) for 1982–2019 on the Hawaiian Islands show significant concurrent declines. Primarily positive correlations between the native ecosystem NDVI and precipitation imply that significant decreases in precipitation may exacerbate the decrease in NDVI of native ecosystems. NDVI-PDSI correlations were primarily negative on the windward side of the islands and positive on the leeward sides, suggesting a higher sensitivity to drought for leeward native ecosystems. Multi-decadal time series and spatially explicit data for native landscapes provide natural resource managers with long-term trends and monthly changes associated with vegetation health and stability.

The mechanisms, magnitudes, and the effects of large-scale lacustrine changes can vary drastically over both time and space. These changes can alter the surrounding lithosphere, the interconnected natural systems, as well as the ever-growing connections to human water use. The timing of these changes can occur over several decades, or in some extreme cases these lacustrine changes can occur intra-annually (e.g. seasonal reservoir fluxes) or inter-annually (e.g. initial reservoir impoundments). The first portion of this dissertation work examines the magnitudes and effects of these changes at the Grand Ethiopian Renaissance Dam (GERD) in Ethiopia. Here, the mechanisms behind these changes are related to direct anthropogenic impacts (e.g. dam building and riverine impoundment). To this end, I examine the elastic deformation (Chapter 1) and subsurficial stress responses (Chapter 2) to several different impoundment and seasonal operational cycles at the GERD. Large hydrologic loads can impart notable stress on the surrounding crust and upper mantle, and, as such, I examine these stresses at the GERD and discuss the main drivers of potential reservoir triggered seismic events. The spatial patterns and amplitudes of the stress tensors and hydrologic-induced deformation are closely linked to both the size and timing of reservoir fluxes, and an improved understanding of the magnitude and extent of the stresses and deformation provides useful information to water managers in order to better understand the effects from many different impoundment and operational strategies. Lastly, this work examines the magnitude of water level changes for all waterbodies > 1 km2 within the contiguous United States (CONUS) as derived from spaceborne lidar altimeter data products (Chapter 3). Here, the mechanisms behind these changes are related to both direct and indirect anthropogenic impacts and are quite diverse across the landscape. A more thorough understanding of the spatiotemporal differences in the magnitude of these changes provides the foundation needed to appropriately assess the varying mechanisms behind, and the effects of these changes. There is a notable spatial data gap of in situ water level measurements within the CONUS. To that end, remote sensing analysis provides a means to fill in those data gaps in order to glean a better understanding of the spatiotemporal water level changes across the entirety of the CONUS.

A substantial and rapid expansion beginning in the late 1990s of Siling Co, the largest endorheic lake on the central Tibetan Plateau (TP), has caused a measurable deflection in the region adjacent to the lake. Current broad-scale measuring of this flexural response is mainly derived from DInSAR processing techniques. The underlying lithospheric response to large lake loads in this region is not well understood, with the focus on either a viscoelastic or an elastic response. This thesis intends to use a more efficient LiDAR remote sensing technique to measure the deflection in the vicinity of Siling Co, and to investigate the mechanisms of the observed lithospheric response. A lake-adjacent deflection rate and Siling Co water load variations are calculated utilizing the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice, Cloud and land Elevation Satellite (ICESat) and the joint NASA/USGS Landsat series of earth observing satellites. A downward deflection rate of ~5.6 mm/yr for the first 4 km of lake-adjacent land is determined from the GLAS instrument and this response is compared to the flexural outputs from a spherically symmetric, non-rotating, elastic, and isotropic (SNREI) Earth model in order to better understand the underlying mechanisms of the lithospheric response to the extreme change in Siling Co loads. The modeled elastic response is ~6.7 times lower than the GLAS derived flexure, thereby providing further evidence that a completely elastic lithospheric response cannot explain the deflection in this region, and that a viscoelastic response is more likely. The relationship between the modeled elastic response and the GLAS derived flexure is applied to a long-term lake load change dataset to create the longest-running flexural response curve as caused by the last ~40 years of Siling Co load variations.