Global Navigation Satellite System (GNSS) vertical displacements measuring the elastic response of Earth's crust to changes in hydrologic mass have been used to produce terrestrial water storage change (∆TWS) estimates for studying both annual ∆TWS as well as multi‐year trends. However, these estimates require a high observation station density and minimal contamination by nonhydrologic deformation sources. The Gravity Recovery and Climate Experiment (GRACE) is another satellite‐based measurement system that can be used to measure regional TWS fluctuations. The satellites provide highly accurate ∆TWS estimates with global coverage but have a low spatial resolution of ∼400 km. Here, we put forward the mathematical framework for a joint inversion of GNSS vertical displacement time series with GRACE ∆TWS to produce more accurate spatiotemporal maps of ∆TWS, accounting for the observation errors, data gaps, and nonhydrologic signals. We aim to utilize the regional sensitivity to ∆TWS provided by GRACE mascon solutions with higher spatial resolution provided by GNSS observations. Our approach utilizes a continuous wavelet transform to decompose signals into their building blocks and separately invert for long‐term and short‐term mass variations. This allows us to preserve trends, annual, interannual, and multi‐year changes in TWS that were previously challenging to capture by satellite‐based measurement systems or hydrological models, alone. We focus our study in California, USA, which has a dense GNSS network and where recurrent, intense droughts put pressure on freshwater supplies. We highlight the advantages of our joint inversion results for a tectonically active study region by comparing them against inversion results that use only GNSS vertical deformation as well as with maps of ∆TWS from hydrological models and other GRACE solutions. We find that our joint inversion framework results in a solution that is regionally consistent with the GRACE ∆TWS solutions at different temporal scales but has an increased spatial resolution that allows us to differentiate between regions of high and low mass change better than using GRACE alone.
The vulnerability of coastal environments to sea-level rise varies spatially, particularly due to local land subsidence. However, high-resolution observations and models of coastal subsidence are scarce, hindering an accurate vulnerability assessment. We use satellite data from 2007 to 2020 to create high-resolution map of subsidence rate at mm-level accuracy for different land covers along the ~3,500 km long US Atlantic coast. Here, we show that subsidence rate exceeding 3 mm per year affects most coastal areas, including wetlands, forests, agricultural areas, and developed regions. Coastal marshes represent the dominant land cover type along the US Atlantic coast and are particularly vulnerable to subsidence. We estimate that 58 to 100% of coastal marshes are losing elevation relative to sea level and show that previous studies substantially underestimate marsh vulnerability by not fully accounting for subsidence.
GRACE‐D accelerometer data show significant bias jumps since one month after the launch of the GRACE Follow‐On (GRACE‐FO) satellites in May 2018, making them inapplicable for correcting GRACE‐FO products for non‐gravitational accelerations. The GRACE‐FO Science Data System (SDS) compensated this issue by transplanting the GRACE‐C accelerometer data toward that of GRACE‐D. Recently, GRACE‐FO SDS implemented an updated transplant method, used in the latest release of GRACE‐FO data. Here, we examine the impact of updated accelerometer transplant data (ACH) on GRACE‐FO measurements at all levels: (a) Level‐1B inter‐satellite laser ranging residuals measured along satellite orbit, (b) Level‐2 time‐variable gravity solutions from all SDS centers (JPL, CSR, and GFZ), and (c) Level‐3 mascon solutions. We show that inter‐satellite laser ranging residuals are modified at low frequencies below 1 mHz, affecting the along‐orbit analysis of large‐scale time‐variable gravity signals. When mapped into monthly Level‐2 spherical harmonic coefficients of geopotential, the low‐frequency change in inter‐satellite ranging residuals leads to substantial improvement of coefficients associated with resonant orders (i.e., 15, 30, 45, etc.) and C30. We also present an improved SLR‐derived C30 which significantly improves the agreement with updated GRACE‐FO C30 at seasonal and interannual timescales. Moreover, we demonstrate the noise reduction in mass change estimates from new GRACE‐FO Level‐2 data over oceans, Greenland, and Antarctica for all SDS solutions. GRACE‐FO mascon solutions show a moderate change in the updated release. Our comprehensive analyses demonstrate high‐quality estimates of non‐gravitational accelerations by the updated transplant method, resulting in more accurate GRACE‐FO time‐variable gravity and mass change observations.
Every year incidents of building collapse claim many lives and cause enormous financial losses around the world, which are often blamed on low‐quality materials, non‐compliance with standards, lack of oversight, and failure to enforce building codes. Here, we highlight the role of land subsidence in triggering unprecedented collapses in the city of Lagos, Nigeria, which has reported over 200 casualties during 152 building failures since 2005. We used acquisitions from radar satellites for 2018–2021 and provided data that link subsidence to foundation damage and high building failure risk in the region. We estimate that an area of 5–81 km2 and 255–4,000 buildings are exposed to a high to very high risk of collapse for short‐term (10 years) to long‐term (75 years) periods. Differential land subsidence can trigger building collapse, and the data presented here will enable authorities to create adequate building codes and standards and devise mitigation strategies.
Future projections of sea‐level rise (SLR) used to assess coastal flooding hazards and exposure throughout the 21st century and devise risk mitigation efforts often lack an accurate estimate of coastal vertical land motion (VLM) rate, driven by anthropogenic or non‐climate factors in addition to climatic factors. The Chesapeake Bay (CB) region of the United States is experiencing one of the fastest rates of relative sea‐level rise on the Atlantic coast of the United States. This study uses a combination of space‐borne Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite System (GNSS), Light Detecting and Ranging (LiDAR) data sets, available National Oceanic and Atmospheric Administration (NOAA) long‐term tide gauge data, and SLR projections from the Intergovernmental Panel on Climate Change (IPCC), AR6 WG1 to quantify the regional rate of relative SLR and future flooding hazards for the years 2030, 2050, and 2100. By the year 2100, the total inundated areas from SLR and subsidence are projected to be 454(316–549)–600(535–690) normalknormalm2 ${\mathrm{k}\mathrm{m}}^{2}$ for Shared Socioeconomic Pathways (SSPs) 1–1.9 to 5–8.5, respectively, and 342(132–552)–627(526–735) normalknormalm2 ${\mathrm{k}\mathrm{m}}^{2}$ only from SLR. The effect of storm surges based on Hurricane Isabel can increase the inundated area to 849(832–867)–1,117(1,054–1,205) km2 under different VLM and SLR scenarios. We suggest that accurate estimates of VLM rate, such as those obtained here, are essential to revise IPCC projections and obtain accurate maps of coastal flooding and inundation hazards. The results provided here inform policymakers when assessing hazards associated with global climate changes and local factors in CB, required for developing risk management and disaster resilience plans.
California’s arid Central Valley relies on groundwater pumped from deep aquifers and surface water transported from the Sierra Nevada to produce a quarter of the United States’ food demand. The natural recharge to deep aquifers is thought to be regulated by the adjacent high Sierra Nevada mountains, but the underlying mechanisms remain elusive. We investigate large sets of geodetic remote sensing, hydrologic, and climate data and employ process-based models at annual time scales to investigate possible recharge mechanism. Peak annual groundwater storage in the Central Valley lags several months behind that of groundwater levels, which suggests a longer transmission time for water flow than pressure propagation. We further find that peak groundwater levels lag the Sierra Nevada snowmelt by about one month, consistent with an ideal fluid pressure diffusion time in the Sierra’s fractured crystalline body. This suggests that Sierra Nevada snowpack changes likely impact freshwater availability in the Central Valley aquifers. Our datasets, analysis and process-based models link the current precipitation and meltwater in the high mountain Sierra to deep Central Valley aquifers through the mountain block recharge process. We call for new hydroclimate models to account for the role of the Sierra in California’s water cycle and for revision of the current management and drought resiliency plans.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.