Yuning Fu

Recent studies have identified a near six-year oscillation (SYO) in Global Navigation Satellite Systems (GNSS) surface displacements, with a degree 2, order 2 spherical harmonic (SH) pattern and retrograde motion. The cause is uncertain, with proposals ranging from deep Earth to near-surface sources. This study investigates the SYO and possible causes from surface loading. Considering the irregular spatiotemporal distribution of GNSS data and the variety of contributors to surface displacements, we used synthetic experiments to identify optimal techniques for estimating low degree SH patterns. We confirm a reported retrograde SH degree 2, order 2 displacement using GNSS data from the same 35 stations used in a previous study for the 1995–2015 period. We also note that its amplitude diminished when the time span of observations was extended to 2023, and the retrograde dominance became less significant using a larger 271-station set. Surface loading estimates showed that terrestrial water storage (TWS) loads contributed much more to the GNSS degree 2, order 2 SYO, than atmospheric and oceanic loads, but TWS load estimates were highly variable. Four TWS sources—European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5), Modern-Era Retrospective analysis for Research and Applications (MERRA), Global Land Data Assimilation System (GLDAS), and Gravity Recovery and Climate Experiment (GRACE/GRACE Follow-On)—yielded a wide range (24% to 93%) of predicted TWS contributions with GRACE/GRACE Follow-On being the largest. This suggests that TWS may be largely responsible for SYO variations in GNSS observations. Variations in SYO GNSS amplitudes in the extended period (1995–2023) were also consistent with near surface sources.

Geodetic methods can monitor changes in terrestrial water storage (TWS) across large regions in near real‐time. Here, we investigate the effect of assumed Earth structure on TWS estimates derived from Global Navigation Satellite System (GNSS) displacement time series. Through a series of synthetic tests, we systematically explore how the spatial wavelength of water load affects the error of TWS estimates. Large loads (e.g., >1,000 km) are well recovered regardless of the assumed Earth model. For small loads (e.g., <10 km), however, errors can exceed 75% when an incorrect model for the Earth is chosen. As a case study, we consider the sensitivity of seasonal TWS estimates within mountainous watersheds of the western U.S., finding estimates that differ by over 13% for a collection of common global and regional structural models. Errors in the recovered water load generally scale with the total weight of the load; thus, long‐term changes in storage can produce significant uplift (subsidence), enhancing errors. We demonstrate that regions experiencing systematic and large‐scale variations in water storage, such as the Greenland ice sheet, exhibit significant differences in predicted displacement (over 20 mm) depending on the choice of Earth model. Since the discrepancies exceed GNSS observational precision, an appropriate Earth model must be adopted when inverting GNSS observations for mass changes in these regions. Furthermore, regions with large‐scale mass changes that can be quantified using independent data (e.g., altimetry, gravity) present opportunities to use geodetic observations to refine structural properties of seismologically derived models for the Earth's interior structure.

Geodetic methods can monitor changes in terrestrial water storage (TWS) across large regions in near real-time. Here, we investigate the effect of assumed Earth structure on TWS estimates derived from Global Navigation Satellite System (GNSS) displacement time series. Through a series of synthetic tests, we systematically explore how the spatial wavelength of water load affects the error of TWS estimates. Large loads (e.g., >1000 km) are well recovered regardless of the assumed Earth model. For small loads (e.g., <10 km), however, errors can exceed 75% when an incorrect model for the Earth is chosen. As a case study, we consider the sensitivity of seasonal TWS estimates within mountainous watersheds of the western U.S., finding estimates that differ by over 13% for a collection of common global and regional structural models. Errors in the recovered water load generally scale with the total weight of the load; thus, long-term changes in storage can produce significant uplift (subsidence) enhancing errors. We demonstrate that regions experiencing systematic and large-scale variations in water storage, such as the Greenland ice sheet, exhibit significant differences in predicted displacement (over 20 mm) depending on the choice of Earth model. Since the discrepancies exceed GNSS observational precision, an appropriate Earth model must be adopted when inverting GNSS observations for mass changes in these regions. Furthermore, regions with large-scale mass changes that can be quantified using independent data (e.g., altimetry, gravity) present opportunities to use geodetic observations to refine structural deficiencies of seismologically derived models for the Earth’s interior structure.

Seasonal seismicity in Japan has been observed and attributed to variations induced by hydrological loading, as well as atmospheric and pore‐fluid pressure. We examine the seasonal variations of crustal (M < 4.5) earthquakes in central Japan near the Biwako‐Seigan Fault Zone (BSFZ), and analyze their correlation with the annual hydrological cycle of nearby Lake Biwa, the largest freshwater lake in Japan. The 2002–2018 lake water storage is estimated using water‐level gauge and satellite altimetry data. Regional surface mass loading from soil moisture and snow predicted by the Global Land Data Assimilation System is used to augment the lake mass variation. We compute the stress and stressing‐rate changes induced by the surface mass loading from lake storage, snow, and soil moisture at seismogenic depth (10 km) along the BSFZ using a finite element model incorporating a 3D Earth structure. The results show that seasonal surface mass loading generates 1–5 kPa peak‐to‐peak Coulomb stress change and 2.5–15 kPa/year stressing‐rate variations on the fault segments along the BSFZ. Two of the four fault segments present significant correlation between local seismicity rate and loading‐derived stress and/or stressing rate changes. Among the hydrological load components, lake water, snow, and soil moisture exhibit different annual phases, and the lake water storage dominates the total annual stress changes. Our analysis indicates that seasonal surface hydrological loads, especially lake water variations, modulate the seismicity rate along the BSFZ in central Japan.

The interconnection between anthropogenic and natural surface loads and seismicity continues to be poorly understood. The metropolitan Salt Lake City in Utah hosts various industrial, hydrological, and tectonic processes, including the Bingham Canyon mine and its associated tailings facility, precipitation and water storage at the surface and in aquifers, as well as the seismically active Wasatch Fault Zone. The March 18, 2020 M5.7 Magna earthquake occurred east of a mine tailings impoundment that receives ∼60 million tons/yr of ore waste products since the early 1900s. Here we investigate the spatiotemporal elastic stress changes due to anthropogenic mass transfer and natural hydrological loading and unloading. Two local earthquake clusters host persistent microseismicity and the 2020 M5.7 Magna earthquake sequence. The amplitude and sign of the computed Coulomb stress changes at seismogenic depths strongly vary with the receiver fault geometry, the frictional coefficient, and the location, and can reach tens of kPa and ∼1 kPa/yr due to the tailings loads, a substantial fraction of background tectonic loading. The long‐term and seasonal stress changes from regional hydrological processes are only up to a few kPa. A lack of statistically significant seasonality in seismicity across Utah suggests a weak control by cyclic hydrological loads. Explicit knowledge of the fault architecture is essential to allow for seismic hazard assessment considering external stress loading.

The Great Lakes region is usually considered to be seismically inactive. However, earthquakes do occur around this region and may be related to stress changes caused by water level fluctuations. We perform a systematic template matching analysis of regional seismicity in 2013-2020 and calculate the Coulomb stress change caused by water loading. The new catalog reveals 20-40 M>0 earthquakes/year before 2019. The high seismicity rate in 2019 is dominated by active aftershocks following the M _L 4.0 Ohio earthquake. Given the limited number of earthquakes, neither seasonal pattern nor obvious increasing trend of seismicity with fluctuating water levels can be established. However, we cannot rule out the role of increasing water level in reactivating the faults that host the 2019 Ohio earthquake sequence. The lake loading induced stress change is found to increase with water level at low effective friction coefficient, with maximum positive stress change of ~0.2 KPa (µ = 0.2).

SUMMARY: Crustal deformation caused by hydrological processes has long been detected using space geodetic techniques, yet questions remain about the relative contributions of surface water and groundwater to the geodetic signals in different regions. Here, we investigate forward models of elastic loading deformation caused by a variety of water-storage changes within the Great Lakes region, including fluctuations in lake-water volume, soil moisture and snow load. We use lake-level data from the Great Lakes Environmental Research Laboratory, soil-moisture content from the North American Land Data Assimilation System (NLDAS), snow load from the Snow Data Assimilation System (SNODAS) and background hydrological load at the global scale from Gravity Recovery and Climate Experiment (GRACE). We compare the modelled surface deformation with estimates of hydrological loading deformation inferred from Global Navigation Satellite System (GNSS) measurements. We find that seasonal deformation measured by GNSS is dominated by regional-scale hydrological loading based on strong correlations with the modelled loading displacements. The mean correlation coefficient for the study network is 0.56. The correlation coefficients vary spatially within the study region and exceed 0.9 at some stations near to the Great Lakes. We assess the relative contribution of each individual hydrological component to the total integrated hydrological load. We find that soil moisture consistently explains the largest percentage (27–69 per cent) of the total vertical loading deformation for 87 per cent of GNSS stations in the Great Lakes region. Snow loading and soil moisture contribute relatively equally in the northern reaches of the study area (e.g. Canadian shield, northern Superior basin). Lake loading accounts for about 10–25 per cent of the total loading signal in the immediate vicinity of the lakes. We also investigate the sensitivities of the surface loading displacements to three different Earth models, including two with lateral variations in structure. The structural variations considered here have limited impact (<0.2 mm) on the predicted hydrological loading displacements and could be neglected at the current level of observational precision.

Surface load variations from large water bodies alter the state of stress on faults and potentially affect earthquake nucleation processes. We report observational evidence for seasonal variations of seismicity on the border faults of the western branch in the East African Rift System. Relying on water level changes of Lake Victoria and the surrounding rift lakes and soil moisture from the Global Land Data Assimilation System model, we calculate the crustal stress changes using a finite element model. Our results suggest the seasonal hydrological variations produce a 0.2–0.8 kPa Coulomb stress change on the border faults. The primary annual peak in seismicity is coincident with the peak Coulomb stress with an insignificant time lag. The correlation suggests seasonal variations of seismicity rates in the western branch of East African Rift System are modulated by the hydrological loading from Lake Victoria and the large rift lakes in the surrounding region.