Remote estimation of geologic composition using interferometric synthetic-aperture radar in California's Central Valley

California's Central Valley is the national agricultural center, producing 1/4 of the nation's food. However, land in the Central Valley is sinking at a rapid rate (as much as 20 cm per year) due to continued groundwater pumping. Land subsidence has a significant impact on infrastructure resilience and groundwater sustainability. In this study, we aim to identify specific regions with different temporal dynamics of land displacement and find relationships with underlying geological composition. Then, we aim to remotely estimate geologic composition using interferometric synthetic aperture radar (InSAR)-based land deformation temporal changes using machine learning techniques. We identified regions with different temporal characteristics of land displacement in that some areas (e.g., Helm) with coarser grain geologic compositions exhibited potentially reversible land deformation (elastic land compaction). We found a significant correlation between InSAR-based land deformation and geologic composition using random forest and deep neural network regression models. We also achieved significant accuracy with 1/4 sparse sampling to reduce any spatial correlations among data, suggesting that the model has the potential to be generalized to other regions for indirect estimation of geologic composition. Our results indicate that geologic composition can be estimated using InSAR-based land deformation data. In-situ measurements of geologic composition can be expensive and time consuming and may be impractical in some areas. The generalizability of the model sheds light on high spatial resolution geologic composition estimation utilizing existing measurements.Comment: 10 pages, 7 figures, NeurIPS 202

Origin of interannual variability in global mean sea level

Author Posting. © National Academy of Sciences, 2020. This article is posted here by permission of National Academy of Sciences for personal use, not for redistribution. The definitive version was published in Proceedings of the National Academy of Sciences of the United States of America 117(25), (2020): 13983-13990, doi: 10.1073/pnas.1922190117.The two dominant drivers of the global mean sea level (GMSL) variability at interannual timescales are steric changes due to changes in ocean heat content and barystatic changes due to the exchange of water mass between land and ocean. With Gravity Recovery and Climate Experiment (GRACE) satellites and Argo profiling floats, it has been possible to measure the relative steric and barystatic contributions to GMSL since 2004. While efforts to “close the GMSL budget” with satellite altimetry and other observing systems have been largely successful with regards to trends, the short time period covered by these records prohibits a full understanding of the drivers of interannual to decadal variability in GMSL. One particular area of focus is the link between variations in the El Niño−Southern Oscillation (ENSO) and GMSL. Recent literature disagrees on the relative importance of steric and barystatic contributions to interannual to decadal variability in GMSL. Here, we use a multivariate data analysis technique to estimate variability in barystatic and steric contributions to GMSL back to 1982. These independent estimates explain most of the observed interannual variability in satellite altimeter-measured GMSL. Both processes, which are highly correlated with ENSO variations, contribute about equally to observed interannual GMSL variability. A theoretical scaling analysis corroborates the observational results. The improved understanding of the origins of interannual variability in GMSL has important implications for our understanding of long-term trends in sea level, the hydrological cycle, and the planet’s radiation imbalance.The research was carried out at JPL, California Institute of Technology, under a contract with NASA. This study was funded by NASA Grants NNX17AH35G (Ocean Surface Topography Science Team), 80NSSC17K0564, and 80NSSC17K0565 (NASA Sea Level Change Team). The efforts of J.T.F. in this work were also supported by NSF Award AGS-1419571, and by the Regional and Global Model Analysis component of the Earth and Environmental System Modeling Program of the US Department of Energy's Office of Biological & Environmental Research via National Science Foundation Grant IA 1844590. C.G.P. was supported by the J. Lamar Worzel Assistant Scientist Fund and the Penzance Endowed Fund in Support of Assistant Scientists at the Woods Hole Oceanographic Institution.2020-12-0

The Dominant Global Modes of Recent Internal Sea Level Variability

The advances in the modern sea level observing system have allowed for a new level of knowledge of regional and global sea level in recent years. The combination of data from satellite altimeters, Gravity Recovery and Climate Experiment (GRACE) satellites, and Argo profiling floats has provided a clearer picture of the different contributors to sea level change, leading to an improved understanding of how sea level has changed in the present and, by extension, may change in the future. As the overlap between these records has recently extended past a decade in length, it is worth examining the extent to which internal variability on timescales from intraseasonal to decadal can be separated from long‐term trends that may be expected to continue into the future. To do so, a combined modal decomposition based on cyclostationary empirical orthogonal functions is performed simultaneously on the three data sets, and the dominant shared modes of variability are analyzed. Modes associated with the trend, seasonal signal, El Niño–Southern Oscillation, and Pacific decadal oscillation are extracted and discussed, and the relationship between regional patterns of sea level change and their associated global signature is highlighted

The dominant global modes of recent internal sea level variability

Author Posting. © American Geophysical Union, 2019. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research-Oceans 124(4), (2019):2750-2768, doi: 10.1029/2018JC014635.The advances in the modern sea level observing system have allowed for a new level of knowledge of regional and global sea level in recent years. The combination of data from satellite altimeters, Gravity Recovery and Climate Experiment (GRACE) satellites, and Argo profiling floats has provided a clearer picture of the different contributors to sea level change, leading to an improved understanding of how sea level has changed in the present and, by extension, may change in the future. As the overlap between these records has recently extended past a decade in length, it is worth examining the extent to which internal variability on timescales from intraseasonal to decadal can be separated from long‐term trends that may be expected to continue into the future. To do so, a combined modal decomposition based on cyclostationary empirical orthogonal functions is performed simultaneously on the three data sets, and the dominant shared modes of variability are analyzed. Modes associated with the trend, seasonal signal, El Niño–Southern Oscillation, and Pacific decadal oscillation are extracted and discussed, and the relationship between regional patterns of sea level change and their associated global signature is highlighted.The satellite altimetry grids are available from NASA JPL/PO.DAAC at the following location: https://podaac.jpl.nasa.gov/dataset. GRACE land water storage data are available at http://grace.jpl.nasa.gov, supported by the NASA MEaSUREs Program. The gridded fields based on Argo data used to compute the steric sea level data are available at http://www.argo.ucsd.edu/Gridded_fields.html. The gridded fields based on Argo data used to compute the steric sea level data are available at http://www.argo.ucsd.edu/Gridded_fields.html. The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. B. D. H., F. W. L., J. T. R., and P. R. T. acknowledge support from NASA grant 80NSSC17K0564 (NASA Sea Level Change Team). C. G. P. acknowledges support from NSF awards OCE‐1558966 and OCE‐1834739. K. Y. K. was partially supported for this research by the National Science Foundation of Korea under the grant NRF‐ 2017R1A2B4003930.2019-09-2

Ocean mass, sterodynamic effects, and vertical land motion largely explain US coast relative sea level rise

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Harvey, T., Hamlington, B. D., Frederikse, T., Nerem, R. S., Piecuch, C. G., Hammond, W. C., Blewitt, G., Thompson, P. R., Bekaert, D. P. S., Landerer, F. W., Reager, J. T., Kopp, R. E., Chandanpurkar, H., Fenty, I., Trossman, D. S., Walker, J. S., & Boening, C. W. Ocean mass, sterodynamic effects, and vertical land motion largely explain US coast relative sea level rise. Communications Earth & Environment, 2(1), (2021): 233, https://doi.org/10.1038/s43247-021-00300-w.Regional sea-level changes are caused by several physical processes that vary both in space and time. As a result of these processes, large regional departures from the long-term rate of global mean sea-level rise can occur. Identifying and understanding these processes at particular locations is the first step toward generating reliable projections and assisting in improved decision making. Here we quantify to what degree contemporary ocean mass change, sterodynamic effects, and vertical land motion influence sea-level rise observed by tide-gauge locations around the contiguous U.S. from 1993 to 2018. We are able to explain tide gauge-observed relative sea-level trends at 47 of 55 sampled locations. Locations where we cannot explain observed trends are potentially indicative of shortcomings in our coastal sea-level observational network or estimates of uncertainty.The research was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. C.G.P. was supported by NASA grant 80NSSC20K1241. B.D.H., T.C.H., and T.F. were supported by NASA JPL Task 105393.281945.02.25.04.59. R.E.K. and J.S.W. were supported by U.S. National Aeronautics and Space Administration (grants 80NSSC17K0698, 80NSSC20K1724 and JPL task 105393.509496.02.08.13.31) and U.S. National Science Foundation (grant ICER-1663807). P.R.T. acknowledges financial support from the NOAA Global Ocean Monitoring and Observing program in support of the University of Hawaii Sea Level Center (NA11NMF4320128). The ECCO project is funded by the NASA Physical Oceanography; Modeling, Analysis, and Prediction; and Cryosphere Programs

Assimilation of GRACE Terrestrial Water Storage Observations into a Land Surface Model for the Assessment of Regional Flood Potential

We evaluate performance of the Catchment Land Surface Model (CLSM) under flood conditions after the assimilation of observations of the terrestrial water storage anomaly (TWSA) from NASA’s Gravity Recovery and Climate Experiment (GRACE). Assimilation offers three key benefits for the viability of GRACE observations to operational applications: (1) near-real time analysis; (2) a downscaling of GRACE’s coarse spatial resolution; and (3) state disaggregation of the vertically-integrated TWSA. We select the 2011 flood event in the Missouri river basin as a case study, and find that assimilation generally made the model wetter in the months preceding flood. We compare model outputs with observations from 14 USGS groundwater wells to assess improvements after assimilation. Finally, we examine disaggregated water storage information to improve the mechanistic understanding of event generation. Validation establishes that assimilation improved the model skill substantially, increasing regional groundwater anomaly correlation from 0.58 to 0.86. For the 2011 flood event in the Missouri river basin, results show that groundwater and snow water equivalent were contributors to pre-event flood potential, providing spatially-distributed early warning information

Simulating Human Water Regulation: The Development of an Optimal Complexity, Climate-Adaptive Reservoir Management Model for an LSM

Abstract The widespread influence of reservoirs on global rivers makes representations of reservoir outflow and storage essential components of large-scale hydrology and climate simulations across the land surface and atmosphere. Yet, reservoirs have yet to be commonly integrated into earth system models. This deficiency influences model processes such as evaporation and runoff, which are critical for accurate simulations of the coupled climate system. This study describes the development of a generalized reservoir model capable of reproducing realistic reservoir behavior for future integration in a global land surface model (LSM). Equations of increasing complexity relating reservoir inflow, outflow, and storage were tested for 14 California reservoirs that span a range of spatial and climate regimes. Temperature was employed in model equations to modulate seasonal changes in reservoir management behavior and to allow for the evolution of management seasonality as future climate varies. Optimized parameter values for the best-performing model were generalized based on the ratio of winter inflow to storage capacity so a future LSM user can generate reservoirs in any grid location by specifying the given storage capacity. Model performance statistics show good agreement between observed and simulated reservoir storage and outflow for both calibration (mean normalized RMSE = 0.48; mean coefficient of determination = 0.53) and validation reservoirs (mean normalized RMSE = 0.15; mean coefficient of determination = 0.67). The low complexity of model equations that include climate-adaptive operation features combined with robust model performance show promise for simulations of reservoir impacts on hydrology and climate within an LSM

Simulating Human Water Regulation: The Development of an Optimal Complexity, Climate-Adaptive Reservoir Management Model for an LSM

Abstract The widespread influence of reservoirs on global rivers makes representations of reservoir outflow and storage essential components of large-scale hydrology and climate simulations across the land surface and atmosphere. Yet, reservoirs have yet to be commonly integrated into earth system models. This deficiency influences model processes such as evaporation and runoff, which are critical for accurate simulations of the coupled climate system. This study describes the development of a generalized reservoir model capable of reproducing realistic reservoir behavior for future integration in a global land surface model (LSM). Equations of increasing complexity relating reservoir inflow, outflow, and storage were tested for 14 California reservoirs that span a range of spatial and climate regimes. Temperature was employed in model equations to modulate seasonal changes in reservoir management behavior and to allow for the evolution of management seasonality as future climate varies. Optimized parameter values for the best-performing model were generalized based on the ratio of winter inflow to storage capacity so a future LSM user can generate reservoirs in any grid location by specifying the given storage capacity. Model performance statistics show good agreement between observed and simulated reservoir storage and outflow for both calibration (mean normalized RMSE = 0.48; mean coefficient of determination = 0.53) and validation reservoirs (mean normalized RMSE = 0.15; mean coefficient of determination = 0.67). The low complexity of model equations that include climate-adaptive operation features combined with robust model performance show promise for simulations of reservoir impacts on hydrology and climate within an LSM

Groundwater depletion during drought threatens future water security of the Colorado River

Abstract Streamflow of the Colorado River Basin is the most over-allocated in the world. Recent assessment indicates that demand for this renewable resource will soon outstrip supply, suggesting that limited groundwater reserves will play an increasingly important role in meetin

Groundwater depletion during drought threatens future water security of the Colorado River Basin.

Streamflow of the Colorado River Basin is the most overallocated in the world. Recent assessment indicates that demand for this renewable resource will soon outstrip supply, suggesting that limited groundwater reserves will play an increasingly important role in meeting future water needs. Here we analyze 9 years (December 2004 to November 2013) of observations from the NASA Gravity Recovery and Climate Experiment mission and find that during this period of sustained drought, groundwater accounted for 50.1 km3 of the total 64.8 km3 of freshwater loss. The rapid rate of depletion of groundwater storage (-5.6 ± 0.4 km3 yr-1) far exceeded the rate of depletion of Lake Powell and Lake Mead. Results indicate that groundwater may comprise a far greater fraction of Basin water use than previously recognized, in particular during drought, and that its disappearance may threaten the long-term ability to meet future allocations to the seven Basin states