While in-situ estuarine discharge has been correlated and reconstructed well with localized remotely-sensed data and hydraulic variables since the 1990s, its correlation and reconstruction using averaged GPS-inferred water storage from satellite gravimetry (i.e., GRACE) at the basin upstream based on the water balance standardization (WBS) approach remains unexplored. This study aims to illustrate the WBS approach for reconstructing monthly estuarine discharge (in the form of runoff (R)) at Mekong River Delta, by correlating the averaged GPS-inferred water storage from GRACE of the upstream Mekong Basin with the in-situ R at the Mekong River Delta estuary. The resulting R based on GPS-inferred water storage is comparable to that inferred from GRACE, regardless of in-situ stations within Mekong River Delta being used for the R reconstruction. The resulting R from the WBS approach with GPS water storage converted by GRACE mascon solution attains the lowest normalized root-mean-square error of 0.066, and the highest Pearson correlation coefficient of 0.974 and Nash-Sutcliffe efficiency of 0.950. Regardless of using either GPS-inferred or GRACE-inferred water storage, the WBS approach shows an increase of 1–4% in accuracy when compared to those reconstructed from remotely-sensed water balance variables. An external assessment also exhibits similar accuracies when examining the R estimated at another station location. By comparing the reconstructed and estimated Rs between the entrance and the estuary mouth, a relative error of 1–4% is found, which accounts for the remaining effect of tidal backwater on the estimated R. Additional errors might be caused by the accumulated errors from the proposed approach, the unknown signals in the remotely-sensed water balance variables, and the variable time shift across different years between the Mekong Basin at the upstream and the estuary at the downstream.

We present and discuss JTRF2014, the Terrestrial Reference Frame (TRF) the Jet Propulsion Laboratory constructed by combining space‐geodetic inputs from very long baseline interferometry (VLBI), satellite laser ranging (SLR), Global Navigation Satellite Systems (GNSS), and Doppler orbitography and radiopositioning integrated by satellite submitted for the realization of ITRF2014. Determined through a Kalman filter and Rauch‐Tung‐Striebel smoother assimilating position observations, Earth orientation parameters, and local ties, JTRF2014 is a subsecular, time series‐based TRF whose origin is at the quasi‐instantaneous center of mass (CM) as sensed by SLR and whose scale is determined by the quasi‐instantaneous VLBI and SLR scales. The dynamical evolution of the positions accounts for a secular motion term, annual, and semiannual periodic modes. Site‐dependent variances based on the analysis of loading displacements induced by mass redistributions of terrestrial fluids have been used to control the extent of random walk adopted in the combination. With differences in the amplitude of the annual signal within the range 0.5–0.8 mm, JTRF2014‐derived center of network‐to‐center of mass (CM‐CN) is in remarkable agreement with the geocenter motion obtained via spectral inversion of GNSS, Gravity Recovery and Climate Experiment (GRACE) observations and modeled ocean bottom pressure from Estimating the Circulation and Climate of the Ocean (ECCO). Comparisons of JTRF2014 to ITRF2014 suggest high‐level consistency with time derivatives of the Helmert transformation parameters connecting the two frames below 0.18 mm/yr and weighted root‐mean‐square differences of the polar motion (polar motion rate) in the order of 30 μas (17 μas/d).

We present GPS, hydrological, and GRACE (Gravity Recovery and Climate Experiment) observations in southern Apennines (Italy) pointing to a previously unnoticed response of the solid Earth to hydrological processes. Transient patterns in GPS horizontal time series near to large karst aquifers are controlled by seasonal and interannual phases of groundwater recharge/discharge of karst aquifers, modulating the extensional ∼3 mm/yr strain within the tectonically active Apennines. We suggest that transient signals are produced, below the saturation level of the aquifers and above a poorly constrained depth in the shallow crust, by time‐dependent opening of subvertical, fluid‐filled, conductive fractures. We ascribe this process to the immature karstification and intense tectonic fracturing, favoring slow groundwater circulation, and to multiyear variations of the water table elevation, influenced by variable seasonal recharge. The vertical component displays seasonal and multiyear signals more homogeneously distributed in space and closely correlated with estimates of total water storage from GRACE, reflecting the elastic response of the lithosphere to variations of surface water loads. The different sensitivities of vertical and horizontal components to the hydrologically induced deformation processes allow us to spatially and temporally resolve the different phases of the water cycle, from maximum hydrological loading at the surface to maximum hydrostatic pressure beneath karst aquifers. Finally, we suggest that transient deformation signals in the geodetic series of the Apennines are correlated to large‐scale climatic patterns (Northern Atlantic Oscillation) through their influence on precipitation variability and trends at the regional scale.

We introduce Global Positioning System (GPS) Imaging, a new technique for robust estimation of the vertical velocity field of the Earth's surface, and apply it to the Sierra Nevada Mountain range in the western United States. Starting with vertical position time series from Global Positioning System (GPS) stations, we first estimate vertical velocities using the MIDAS robust trend estimator, which is insensitive to undocumented steps, outliers, seasonality, and heteroscedasticity. Using the Delaunay triangulation of station locations, we then apply a weighted median spatial filter to remove velocity outliers and enhance signals common to multiple stations. Finally, we interpolate the data using weighted median estimation on a grid. The resulting velocity field is temporally and spatially robust and edges in the field remain sharp. Results from data spanning 5–20 years show that the Sierra Nevada is the most rapid and extensive uplift feature in the western United States, rising up to 2 mm/yr along most of the range. The uplift is juxtaposed against domains of subsidence attributable to groundwater withdrawal in California's Central Valley. The uplift boundary is consistently stationary, although uplift is faster over the 2011–2016 period of drought. Uplift patterns are consistent with groundwater extraction and concomitant elastic bedrock uplift, plus slower background tectonic uplift. A discontinuity in the velocity field across the southeastern edge of the Sierra Nevada reveals a contrast in lithospheric strength, suggesting a relationship between late Cenozoic uplift of the southern Sierra Nevada and evolution of the southern Walker Lane.

We developed a new boundary-included inversion model to improve the terrestrial water storage (TWS) inverted from regional GPS vertical deformation data. Through defining a new disc load empirical function (DLEF) and considering the mass change effect from the near but outside region, the result shows the TWS is more reasonable than the one inverted directly. Six simulation tests further confirmed the effectiveness of the boundary-included model. Finally, our new boundary-included model was used to derive the TWS in the Pacific Rim of the western United States based on the GPS-observed vertical deformation information. The inversion results show that our boundary-included inversion model can effectively improve the inversion results by 10–20% in terms of variance reduction in the boundary regions.

This study uses the observed vertical displacements of Global Positioning System (GPS) time series obtained from the Crustal Movement Observation Network of China (CMONOC) with careful pre- and post-processing to estimate the seasonal crustal deformation in response to the hydrological loading in lower three-rivers headwater region of southwest China, followed by inferring the annual EWH changes through geodetic inversion methods. The Helmert Variance Component Estimation (HVCE) and the Minimum Mean Square Error (MMSE) criterion were successfully employed. The GPS inferred EWH changes agree well qualitatively with the Gravity Recovery and Climate Experiment (GRACE)-inferred and the Global Land Data Assimilation System (GLDAS)-inferred EWH changes, with a discrepancy of 3.2–3.9 cm and 4.8–5.2 cm, respectively. In the research areas, the EWH changes in the Lancang basin is larger than in the other regions, with a maximum of 21.8–24.7 cm and a minimum of 3.1–6.9 cm.

The vertical motion of the Earth’s surface is dominated by the hydrologic cycle on a seasonal scale. Accurate land deformation measurements can provide constructive insight into the regional geophysical process. Although the Global Positioning System (GPS) delivers relatively accurate measurements, GPS networks are not uniformly distributed across the globe, posing a challenge to obtaining accurate deformation information in data-sparse regions, e.g., Central South-East Asia (CSEA). Model simulations and gravity data (from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO)) have been successfully used to improve the spatial coverage. While combining model estimates and GRACE/GRACE-FO data via the GRACE/GRACE-FO data assimilation (DA) framework can potentially improve the accuracy and resolution of deformation estimates, the approach has rarely been considered or investigated thus far. This study assesses the performance of vertical displacement estimates from GRACE/GRACE-FO, the PCRaster Global Water Balance (PCR-GLOBWB) hydrology model, and the GRACE/GRACE-FO DA approach (assimilating GRACE/GRACE-FO into PCR-GLOBWB) in CSEA, where measurements from six GPS sites are available for validation. The results show that GRACE/GRACE-FO, PCR-GLOBWB, and GRACE/GRACE-FO DA accurately capture regional-scale hydrologic- and flood-induced vertical displacements, with the correlation value and RMS reduction relative to GPS measurements up to 0.89 and 53%, respectively. The analyses also confirm the GRACE/GRACE-FO DA’s effectiveness in providing vertical displacement estimates consistent with GRACE/GRACE-FO data while maintaining high-spatial details of the PCR-GLOBWB model, highlighting the benefits of GRACE/GRACE-FO DA in data-sparse regions.

The redistribution of atmospheric, oceanic and hydrological masses on the Earth's surface varies in time and this in turn loads and deforms the surface of the solid Earth. Analyzing such environmental loading signal and modeling its induced elastic displacements are of great importance for explaining geophysical phenomena. Based on the well-established loading theory, this thesis makes use of two different space-borne measurements, i.e. GPS and GRACE, along with other environmental loading data to investigate three different aspects of environmental loading and its induced elastic deformations: Firstly, an increasing concern is observed recently over time variable seasonal signals in geodesy. Several model based approaches were applied to extract amplitude and phase modulated annual and semiannual signals. In view of this phenomenon, this thesis introduces an alternative approach, namely, singular spectrum analysis (SSA). With respect to these model-dependent approaches, the advantage of SSA lies in data-driven and model-independence. Several aspects regarding the application of SSA, e.g. optimal choice of window size, are investigated before showing its abilities. Through applying SSA to the lake level time series of Lake Urmia (Iran) and the basin averaged equivalent water height time series of the Congo basin, the capabilities of SSA in separating time varying seasonal signals are demonstrated. In addition, we find that SSA is also able to extract the non-linear trend as well as long-term oscillations from geodetic time series. Secondly, we look into the comparison between GPS and GRACE with an emphasis on GRACE data filtering. Three types of deterministic filters and two types of stochastic filters are studied and compared over GPS sites from two regions, i.e. the Europe area and the Amazon area. The comparisons indicate that no single filtering scheme could provide consistently better performance over other considered filters. However, we find that the stochastic filters generally show better performance than the deterministic filters. The DDK 1 filter outperforms other filters in the Europe area and the regularization filter of parameter lambda=4, which follows the concept of the DDK filters, shows optimal performance in the Amazon area. The combination of the isotropic Gaussian filter of a low smoothing radius, e.g. around 300 km with the destriping filter is proved to be optimal filter choice if only the deterministic filters are considered. Thirdly, based on an overview of displacements modeling at various spatial scales, we evaluate three methods, i.e. two types of half-space approaches and the classic Green function approach, by using a high spatial resolution local load data along the lower Mississippi river when a severe flood happened in 2011. The equivalence between the two half-space approaches, i.e. point load approach and surface load approach, are demonstrated with the local load data. However, the point load approach is recommended for practical use in terms of computational efficiency. In addition, within such a limited spatial extent, we investigate the differences between the half-space approach and the Green function approach. It is shown that the half-space approach predicts larger displacements than the Green function approach and agrees better with the observed deformations at 11 considered GPS sites. Meanwhile, strong global environmental loading effects are found via two global hydrological models, i.e. GLDAS and MERRA. Thus, a reduction of these far-field loading effects beforehand is suggested before probing the local crustal structure using the half-space approach. Last but not least, based on the local load data, the effects of site-dependent Green functions are studied with two types of site-dependent Green functions, which were generated by modifying the local crustal structure of the REF Earth model using the CRUST 1.0 and CRUST 2.0 models. A relative RMS of differences of more than 5% in vertical component and 25% in horizontal components are found with respect to the PREM Earth model based Green functions. It indicates that the Green functions could contribute more uncertainties in loading induced displacements modeling than reported in the literature. Die Massenumverteilungen zwischen Atmosphare, Ozeanen und Hydrologie der Erdoberflache variieren stetig und fuhren im Gegenzug zu Auflasten und Deformationen der festen Erde. Die Untersuchung dieser Auflastsignale und die Modellierung der induzierten elastischen Verformungen sind von enormer Bedeutung fur die Erklarungen geophysikalischer Phanomene. Basierend auf gangigen Theorien werden in dieser Dissertation die beiden Satellitenverfahren GPS und GRACE zusammen mit anderen Auflastdaten genutzt, um drei Aspekte von Auflasten und die von diesen verursachte elastischen Deformationen naher zu untersuchen: Zunachst lasst sich in der Geodasie eine wachsende Tendenz erkennen, auch zeitlich variable, saisonale Signale besser zu untersuchen. Zahlreiche, auf Modellen basierende Verfahren werden angewendet, um Amplituden und Phasen aus jahrlichen und halbjahrlichen Signalen zu erfassen. Fur diese Aufgabe wird in dieser Arbeit die "singular spectrum analysis" (SSA) als alternative Methode dargestellt. Im Gegensatz zu den ublichen modellbasierten Verfahren arbeitet die SSA modellunabhangig nur auf Grundlage der Daten. Verschiedene Freiheitsgrade in der SSA, wie zum Beispiel die Wahl der optimalen Fenstergrose, werden vor der Verwendung untersucht. Die Fahigkeit der SSA, saisonale Signale zu trennen, wird sowohl an der Bestimmung des Wasserstandes fur den Urmia-See (Iran) als auch anhand der Zeitreihe der uber dem Kongo-Becken gemittelten Massenanderungen demonstriert. Zusatzlich konnen mit SSA auch nichtlineare Trends und langfristige Oszillationen aus geodatischen Zeitreihen extrahiert werden. Zweitens werden die Daten von GPS und GRACE unter besonderer Berucksichtigung der Filterung von GRACE-Daten verglichen. Drei Arten deterministischer Filter sowie zwei Arten stochastischer Filter werden untersucht und fur die GPS-Stationen von zwei ausgewahlten Regionen in Mitteleuropa und dem Amazonasgebiet gegenubergestellt. Der Vergleich bestatigt, dass keiner der Filter grundsatzlich den Anderen uberlegen ist. Jedoch kann gezeigt werden, dass die stochastischen Filter im Allgemeinen besser abschneiden als die deterministischen Ansatze. Der DDK 1 Filter fuhrt in Europa zu den besten Ergebnissen, wahrend ein "Regularisierungsfilter", der dem Konzept der DDK Filter nachempfunden ist, mit einem Regularisierungsparameter lambda = 4 die besten Ergebnisse in fur das Amazonasbecken liefert. Die Kombination aus einem isotropen Gausfilter mit kleinem Glattungsradius, z.B. 300 km, und einem "De-striping"-Filter konnte als optimaler Filter bestatigt werden, solange nur deterministische Filter verwendet werden Drittens werden nach einer Zusammenfassung der Deformationsmodelle auf verschiedenen raumlichen Skalen drei Methoden -- zwei Arten von "half-space"-Ansatzen und die klassische Methode der Greenfunktionen -- auf die raumlich hochauflosenden Daten der Uberflutungen im Mississippi-Becken (2011) angewendet. Die Gleichwertigkeit der beiden "half-space"-Ansatze, einerseits der "point load approach" und andererseits der "surface load approach" werden fur lokale Auflastdaten bestatigt. Aus Grunden der numerischen Effizienz ist jedoch der "point load approach" zu bevorzugen. Auserdem werden innerhalb des raumlich begrenzten Gebietes auch die Unterschiede zwischen den "half-space"-Ansatzen und die Methode der Greenfunktionen gegenubergestellt. Es wird gezeigt, dass die "half-space"-Ansatze grosere Deformationen vorhersagen als die Greenfunktionen, wobei die Ergebnisse besser zu den Beobachtungen der 11 GPS-Stationen passen. Inzwischen konnen auch grosere globale Auflasteffekte in den globalen hydrologischen Modellen, wie z.B. GLDAS und MERRA, gefunden werden. Daher wird eine vorherige Reduktion der Fernwirkung von Auflasteffekten empfohlen, ehe man eine lokale Krustenstruktur mit den "half-space"-Ansatzen erforscht. Nicht zuletzt werden auch die Effekte der ortsabhangigen Greenfunktionen fur die lokalen Auflastdaten studiert, wobei die beiden ortsabhangen Varianten der Greenfunktionen aus der lokalen Krustenstruktrur des REF Erdmodells mit den Modellen CRUST 1.0 bzw. CRUST 2.0 erzeugt werden. Im Vergleich zu den Greenfunktionen aus dem PERM Erdmodell wird ein relativer RMS der Differenzen von mehr als 5% in der Vertikalkomponente und 25% in der Horizonalkomponente beobachtet. Daraus kann man ablesen, dass die Greenfunktionen deutlich mehr Unsicherheiten zu den auflastinduzierten Deformationen beitragen, als dies bisher in der Literatur wahrgenommen wird.

The combined application of continuous Global Positioning System data (high temporal resolution) with spaceborne interferometric synthetic aperture radar data (high spatial resolution) can reveal much more about the complexity of large landslide movement than is possible with geodetic measurements tied to only a few specific measurement sites. This approach is applied to an ~4 km reactivated translational landslide in the Columbia River Gorge (Washington State), which moves mainly during the winter rainy season. Results reveal the complex three-dimensional shape of the landslide mass, how onset of sliding relates to cumulative rainfall, how surface velocity during sliding varies with location on the topographically complex landslide surface, and how the ground surface subsides slightly in weeks prior to downslope sliding.

The Global Positioning System (GPS) records monsoonal precipitable water vapor (PWV) and vertical crustal displacement (VCD) due to hydrological loading, and can thus be applied jointly to diagnose meteorological and hydrological droughts. We have analyzed the PWV and VCD observations during 2007.0–2015.0 at 26 continuous GPS stations located in Yunnan province, China. We also obtained equivalent water height (EWH) derived from the Gravity Recovery And Climate Experiment (GRACE) and precipitation at these stations with the same period. Then, we quantified the annual variations of PWV, precipitation, EWH and VCD and provided empirical relationships between them. We found that GPS-derived PWV and VCD (positive means downward movement) are in phase with precipitation and GRACE-derived EWH, respectively. The annual signals of VCD and PWV show linearly correlated amplitudes and a two-month phase lag. Furthermore, the results indicate that PWV and VCD anomalies can also be used to explore drought, such as the heavy drought during winter/spring 2010. Our analysis results verify the capability of GPS to monitor monsoon variations and drought in Yunnan and show that a more comprehensive understanding of the characteristics of regional monsoon and drought can be achieved by integrating GPS-derived PWV and VCD with precipitation and GRACE-derived EWH.

The Geodesy Advancing Geosciences and EarthScope (GAGE) Facility Global Positioning System (GPS) Data Analysis Centers produce position time series, velocities, and other parameters for approximately 2000 continuously operating GPS receivers spanning a quadrant of Earth's surface encompassing the high Arctic, North America, and Caribbean. The purpose of this review is to document the methodology for generating station positions and their evolution over time and to describe the requisite trade‐offs involved with combination of results. GAGE GPS analysis involves formal merging within a Kalman filter of two independent, loosely constrained solutions: one is based on precise point positioning produced with the GIPSY/OASIS software at Central Washington University and the other is a network solution based on phase and range double‐differencing produced with the GAMIT software at New Mexico Institute of Mining and Technology. The primary products generated are the position time series that show motions relative to a North America reference frame and secular motions of the stations represented in the velocity field. The position time series themselves contain a multitude of signals in addition to the secular motions. Coseismic and postseismic signals, seasonal signals from hydrology, and transient events, some understood and others not yet fully explained, are all evident in the time series and ready for further analysis and interpretation. We explore the impact of analysis assumptions on the reference frame realization and on the final solutions, and we compare within the GAGE solutions and with others.

The Earth’s surface fluid mass redistribution, e.g., groundwater depletion and severe drought, causes the elastic surface deformation, which can be measured by global positioning system (GPS). In this paper, the continuous GPS observations are used to estimate the terrestrial water storage (TWS) changes in southwestern USA, which have a good agreement with TWS changes derived from Gravity Recovery And Climate Experiment (GRACE) and hydrological models. The seasonal variation is mostly located in the Rocky mountain range and Mississippi river watershed. The largest amplitude of the seasonal variation is between 12 and 15 cm in equivalent water thickness. The timing and duration of TWS anomalies caused by the severe drought in 2012 are observed by the GPS-derived TWS, which are confirmed by the GRACE results. Different hydrological models are further used for comparison with GPS and GRACE results. The magnitude of TWS depletion from GRACE and GPS observations during the drought is larger than that from hydrological models, which indicates that the drought was caused by comparable groundwater and surface water depletion. The interannual TWS changes from GPS are also consistent with the precipitation pattern over the past 6 years, which further confirms the severe drought in 2012. This study demonstrates that continuous GPS observations have the potential as real-time drought indicator.

Surface mass variations inferred from the Global Positioning System (GPS), and observed by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) complement each other in terms of spatial and temporal coverage. This paper presents an analysis of regional surface mass variations inverted from GPS vertical displacements under different density distributions of GPS stations, and compares the GPS-derived mass variations with GRACE/GFO inversion results in spatial and temporal domains. To this end, GPS vertical displacement data from a total of 85 permanent GPS stations of the Crustal Movement Observation Network of China (CMONOC), the latest GRACE/GFO RL06 spherical harmonic (SH) solutions and GRACE RL06 mascon solutions are used to investigate surface mass variations in four regions or basins, including the Yunnan Province (YNP), Min River Basin (MRB), Jialing River Basin (JLRB), and Wu River Basin (WRB) in Southwest China. Our results showed that the spatial distributions and seasonal characteristics of GPS-derived mass change time series agree well with those from GRACE/GFO observations, especially in regions with relatively dense distributions of GPS stations (e.g., in the YNP and MRB), but there are still obvious discrepancies between the GPS and GRACE/GFO results. Scale factor methods (both basin-scaled and pixel-scaled) were employed to reduce the amplitude discrepancies between GPS and GRACE/GFO results. The results also showed that the one-year gap between the GRACE and GFO missions can be bridged by scaled GPS-derived mass change time series in the four studied regions, especially in the YNP and MRB regions (with relatively dense distributions of GPS stations).

On 26 August 2017, Hurricane Harvey struck the Gulf Coast as a category four cyclone depositing ~95 km 3 of water, makingitthewettestcycloneinU.S.history.WaterleftinHarvey ’ swakeshouldcauseelasticloadingandsubsidenceof Earth ’ s crust, anduplift as it drains intotheocean andevaporates. To track dailychanges of transient water storage, we use Global Positioning System (GPS) measurements, finding a clear migration of subsidence (up to 21 mm) and horizontal motion (up to 4 mm) across the Gulf Coast, followed by gradual uplift over a 5-week period. Inversion of these data shows that a third of Harvey ’ s total stormwater was captured on land (25.7 ± 3.0 km 3 ), indicating that the rest drained rapidly into the ocean at a rate of 8.2 km 3 /day, with the remaining stored water gradually lost over the following 5 weeks at ~1 km 3 /day, primarily by evapotranspiration. These results indicate that GPS networks can re-motely track the spatial extent and daily evolution of terrestrial water storage following transient, extreme precipitation events, with implications for improving operational flood forecasts and understanding the response of drainage systems to large influxes of water.

In southern Tibet, ongoing vertical and horizontal motions due to the collision between India and Eurasia are monitored by large numbers of global positioning system (GPS) continuous and campaign sites installed in the past decade. Displacements measured by GPS usually include tectonic deformation as well as non-tectonic, time-dependent signals. To estimate the regional long-term tectonic deformation using GPS more precisely, seasonal elastic deformation signals associated with surface loading must be removed from the observations. In this study, we focus on seasonal variation in vertical and horizontal motions of southern Tibet by performing a joint analysis of GRACE (Gravity Recovery and Climate Experiment) and GPS data, not only using continuous sites but also GPS campaign-mode sites. We found that the GPS-observed and GRACE-modeled seasonal oscillations are in good agreements, and a seasonal displacement model demonstrates that the main reason for seasonal variations in southern Tibet is from the summer monsoon and its precipitation. The biggest loading appears from July to August in the summer season. Vertical deformations observed by GPS and modeled by GRACE are two to three times larger than horizontal oscillations, and the north components demonstrate larger amplitudes than the east components. We corrected the GPS position time series using the GRACE-modeled seasonal variations, which gives significant reductions in the misfit and weighted root-mean-squares (WRMS). Misfit (χ2 divided by degree of freedom) reductions for campaign sites range between 20% and 56% for the vertical component, and are much smaller for the horizontal components. Moreover, time series of continuous GPS (cGPS) sites near the 2015 Nepal earthquakes must be corrected using appropriate models of seasonal loading for analyzing postseismic deformation to avoid biasing estimates of the postseismic relaxation.

Hydrologically induced deformation of Earth's surface can be measured with high precision geodetic techniques, which in turn can be used to study the underlying hydrologic process. For geodetic study of other Earth processes such as tectonic and volcanic deformation, or coastal subsidence and its relation to relative sea level rise and flood risk, hydrological loading may be a source of systematic error, requiring accurate correction. Accurate estimation of the hydrologic loading deformation may require consideration of local as well as regional loading effects. We present a new hybrid approach to this problem, providing a mathematical basis for combining local (near field) and regional to global (far field) loading data with different accuracies and spatial resolutions. We use a high‐resolution hydrological model (WGHM) for the near field and GRACE data for the far field. The near field is defined as a spherical cap and its contribution is calculated using numerical evaluation of Green's functions. The far field covers the entire Earth, excluding only the near‐field cap. The far‐field contribution is calculated using a modified spherical harmonic approach. We test our method with a large GPS data set from central North America. Our new hybrid approach improves fits to GPS‐measured vertical displacements, with 25% and 35% average improvement relative to GRACE‐only or WGHM‐only spherical harmonic solutions. Our hybrid approach can be applied to a wide variety of environmental surface loading problems.

Hydrogeodesy, a relatively new field within the earth sciences, is the analysis of the distribution and movement of terrestrial water at Earth's surface using measurements of Earth's shape, orientation, and gravitational field. In this paper, we review the current state of hydrogeodesy with a specific focus on Global Navigation Satellite System (GNSS)/Global Positioning System measurements of hydrologic loading. As water cycles through the hydrosphere, GNSS stations anchored to Earth's crust measure the associated movement of the land surface under the weight of changing hydrologic loads. Recent advances in GNSS‐based hydrogeodesy have led to exciting applications of hydrologic loading and subsequent terrestrial water storage (TWS) estimates. We describe how GNSS position time series respond to climatic drivers, can be used to estimate TWS across temporal scales, and can improve drought characterization. We aim to facilitate hydrologists' use of GNSS‐observed surface deformation as an emerging tool for investigating and quantifying water resources, propose methods to further strengthen collaborative research and exchange between geodesists and hydrologists, and offer ideas about pressing questions in hydrology that GNSS may help to answer.

Great Lakes water levels rose 0.7–1.5 m from 2013 to 2019, increasing surface water volume by 285 km3. Solid Earth's elastic response to the increased mass load is nearly known: The Great Lakes floor fell 8–23 mm, and the adjacent land fell 3–14 mm. Correcting GPS measurements for this predicted elastic loading (1) straightens position‐time series, making the evolution of position more nearly a constant velocity and (2) reduces estimates of subsidence rate in Wisconsin, Michigan, and southern Ontario by 0.5–2 mm/yr, improving constraints on postglacial rebound. GPS records Wisconsin and Michigan to have subsided at 1–4 mm/yr. We find this sinking to be produced primarily by viscous collapse of the former Laurentide ice sheet forebulge and secondarily by elastic Great Lakes loading. We infer water on land in the Great Lakes watershed to be total water change observed by GRACE minus Great Lakes surface water smeared by a Gaussian distribution. Water stored on land each year reaches a maximum in March, 6 months before Great Lakes water levels peak in September. The seasonal oscillation of water on land in the Great Lakes basin, 100 km3 (0.20 m water thickness), is twice that in a hydrology model. In the seasons, groundwater in the Great Lakes watershed increases by 60 km3 (0.12 m) each autumn and winter and decreases by roughly an equivalent amount each spring and summer. In the long term, groundwater volume remained constant from 2004 to 2012 but increased by 50 km3 (0.10 m) from 2013 to 2019.

Global navigation satellite systems (GNSS) techniques, such as GPS, can be used to accurately record vertical crustal movements induced by seasonal terrestrial water storage (TWS) variations. Conversely, the TWS data could be inverted from GPS-observed vertical displacement based on the well-known elastic loading theory through the Tikhonov regularization (TR) or the Helmert variance component estimation (HVCE). To complement a potential non-uniform spatial distribution of GPS sites and to improve the quality of inversion procedure, herein we proposed in this study a novel approach for the TWS inversion by jointly supplementing GPS vertical crustal displacements with minimum usage of external TWS-derived displacements serving as pseudo GPS sites, such as from satellite gravimetry (e.g., Gravity Recovery and Climate Experiment, GRACE) or from hydrological models (e.g., Global Land Data Assimilation System, GLDAS), to constrain the inversion. In addition, Akaike’s Bayesian Information Criterion (ABIC) was employed during the inversion, while comparing with TR and HVCE to demonstrate the feasibility of our approach. Despite the deterioration of the model fitness, our results revealed that the introduction of GRACE or GLDAS data as constraints during the joint inversion effectively reduced the uncertainty and bias by 42% and 41% on average, respectively, with significant improvements in the spatial boundary of our study area. In general, the ABIC with GRACE or GLDAS data constraints displayed an optimal performance in terms of model fitness and inversion performance, compared to those of other GPS-inferred TWS methodologies reported in published studies.

GPS monitoring of solid Earth deformation due to surface loading is an independent approach for estimating seasonal changes in terrestrial water storage (TWS). In western United States (WUSA) mountain ranges, snow water equivalent (SWE) is the dominant component of TWS and an essential water resource. While several studies have estimated SWE from GPS-measured vertical displacements, the error associated with this method remains poorly constrained. We examine the accuracy of SWE estimated from synthetic displacements at 1,395 continuous GPS station locations in the WUSA. Displacement at each station is calculated from the predicted elastic response to variations in SWE from SNODAS and soil moisture from the NLDAS-2 Noah model. We invert synthetic displacements for TWS, showing that both seasonal accumulation and melt as well as year-to-year fluctuations in peak SWE can be estimated from data recorded by the existing GPS network. Because we impose a smoothness constraint in the inversion, recovered TWS exhibits mass leakage from mountain ranges to surrounding areas. This leakage bias is removed via linear rescaling in which the magnitude of the gain factor depends on station distribution and TWS anomaly patterns. The synthetic GPS-derived estimates reproduce approximately half of the spatial variability (unbiased root mean square error 50%) of TWS loading within mountain ranges, a considerable improvement over GRACE. The inclusion of additional simulated GPS stations improves representation of spatial variations. GPS data can be used to estimate mountain-range-scale SWE, but effects of soil moisture and other TWS components must first be subtracted from the GPS-derived load estimates.