PCA study of the interannual variability of GPS heights and environmental parameters

2020 ◽  
Author(s):  
Letizia Elia ◽  
Susanna Zerbini ◽  
Fabio Raicich
Keyword(s):  
Time Series ◽  
Interannual Variability ◽  
Water Storage ◽  
Environmental Parameters ◽  
Space Time ◽  
Terrestrial Water Storage ◽  
Coupled Modes ◽  
Entire Study ◽  
Terrestrial Water ◽  
The Mediterranean

<p>Time series of GPS coordinates longer than two decades are now available at many stations around the world. The objective of our study is to investigate large networks of GPS stations to identify and analyze spatially coherent signals present in the coordinate time series and, at the same locations, to identify and analyze common patterns in the series of environmental parameters and climate indexes. The study is confined to Europe and the Mediterranean area, where 107 GPS stations were selected from the Nevada Geodetic Laboratory (NGL) archive on the basis of the completeness and length of the data series. The parameters of interest for this study are the stations height (H), the atmospheric surface pressure (AP), the terrestrial water storage (TWS) and the various climate indexes, such as NAO (North Atlantic Oscillation), AO (Artic Oscillation), SCAND (Scandinavian Index) and MEI (Multivariate ENSO Index). The Empirical Orthogonal Function (EOF) is the methodology adopted to extract the main patterns of space/time variability of these parameters. We also focus on the coupled modes of space/time interannual variability between pairs of variables using the singular value decomposition (SVD) methodology. The coupled variability between all the afore mentioned parameters is investigated. It shall be pointed out that EOF and SVD are mathematical tools providing common modes on the one hand, and statistical correlations between pairs of parameters on the other. Therefore, these methodologies do not allow to directly infer the physical mechanisms responsible for the observed behaviors which should be explained through appropriate modelling. Our study has identified, over Europe and the Mediterranean, main modes of variability in the time series of GPS heights, atmospheric pressure and terrestrial water storage. For example, regarding the station heights, the EOF1 explains about 30% of the variance and the spatial pattern is coherent over the entire study area. The SVD analysis of coupled parameters, namely H-AP, TWS-AP and H-TWS, showed that most of the common variability is explained by the first 3 modes. In particular, 70% for the H-AP, 67% for the TWS-AP and 49% for the H-TWS pair. Moreover, we correlated the stations heights with the NAO, AO, SCAND and MEI indexes to investigate the possible influence of climate variability on the height behavior. To do so, the stations heights were represented using the first three EOFs to reduce the potential effect of local anomalies. More than 30 stations, over the total of 107, show significant correlations up to about 0.3 with the AO and SCAND indexes. The correlation coefficients with MEI turn out to be significant and up to 0.5 for about half of the stations.</p>

scholarly journals Interannual Variability of GPS Heights and Environmental Parameters over Europe and the Mediterranean Area

2021 ◽  
Vol 13 (8) ◽  
pp. 1554
Author(s):  
Letizia Elia ◽  
Susanna Zerbini ◽  
Fabio Raicich
Keyword(s):  
Time Series ◽  
Interannual Variability ◽  
Environmental Parameters ◽  
Mediterranean Area ◽  
Space Time ◽  
The Arctic ◽  
Climate Indices ◽  
Geological Processes ◽  
Second Mode ◽  
The Mediterranean

Vertical deformations of the Earth’s surface result from a host of geophysical and geological processes. Identification and assessment of the induced signals is key to addressing outstanding scientific questions, such as those related to the role played by the changing climate on height variations. This study, focused on the European and Mediterranean area, analyzed the GPS height time series of 114 well-distributed stations with the aim of identifying spatially coherent signals likely related to variations of environmental parameters, such as atmospheric surface pressure (SP) and terrestrial water storage (TWS). Linear trends and seasonality were removed from all the time series before applying the principal component analysis (PCA) to identify the main patterns of the space/time interannual variability. Coherent height variations on timescales of about 5 and 10 years were identified by the first and second mode, respectively. They were explained by invoking loading of the crust. Single-value decomposition (SVD) was used to study the coupled interannual space/time variability between the variable pairs GPS height–SP and GPS height–TWS. A decadal timescale was identified that related height and TWS variations. Features common to the height series and to those of a few climate indices—namely, the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), the East Atlantic (EA), and the multivariate El Niño Southern Oscillation (ENSO) index (MEI)—were also investigated. We found significant correlations only with the MEI. The first height PCA mode of variability, showing a nearly 5-year fluctuation, was anticorrelated (−0.23) with MEI. The second mode, characterized by a decadal fluctuation, was well correlated (+0.58) with MEI; the spatial distribution of the correlation revealed, for Europe and the Mediterranean area, height decrease till 2015, followed by increase, while Scandinavian and Baltic countries showed the opposite behavior.

scholarly journals Comparison and Assessment of Three Advanced Land Surface Models in Simulating Terrestrial Water Storage Components over the United States

2017 ◽  
Vol 18 (3) ◽  
pp. 625-649 ◽  
Author(s):  
Youlong Xia ◽  
David Mocko ◽  
Maoyi Huang ◽  
Bailing Li ◽  
Matthew Rodell ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Land Surface ◽  
Water Storage ◽  
The United States ◽  
Major Contributor ◽  
Terrestrial Water Storage ◽  
Land Surface Models ◽  
Community Land Model ◽  
Surface Models ◽  
Terrestrial Water

Abstract To prepare for the next-generation North American Land Data Assimilation System (NLDAS), three advanced land surface models [LSMs; i.e., Community Land Model, version 4.0 (CLM4.0); Noah LSM with multiphysics options (Noah-MP); and Catchment LSM-Fortuna 2.5 (CLSM-F2.5)] were run for the 1979–2014 period within the NLDAS-based framework. Unlike the LSMs currently executing in the operational NLDAS, these three advanced LSMs each include a groundwater component. In this study, the model simulations of monthly terrestrial water storage anomaly (TWSA) and its individual water storage components are evaluated against satellite-based and in situ observations, as well as against reference reanalysis products, at basinwide and statewide scales. The quality of these TWSA simulations will contribute to determining the suitability of these models for the next phase of the NLDAS. Overall, it is found that all three models are able to reasonably capture the monthly and interannual variability and magnitudes of TWSA. However, the relative contributions of the individual water storage components to TWSA are very dependent on the model and basin. A major contributor to the TWSA is the anomaly of total column soil moisture content for CLM4.0 and Noah-MP, while the groundwater storage anomaly is the major contributor for CLSM-F2.5. Other water storage components such as the anomaly of snow water equivalent also play a role in all three models. For each individual water storage component, the models are able to capture broad features such as monthly and interannual variability. However, there are large intermodel differences and quantitative uncertainties, which are motivating follow-on investigations in the NLDAS Science Testbed developed by the NASA and NCEP NLDAS teams.

scholarly journals Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical Basins

2019 ◽  
Vol 11 (21) ◽  
pp. 2487 ◽  
Author(s):  
Melo ◽  
Getirana
Keyword(s):  
Time Series ◽  
Amazon Basin ◽  
Anthropogenic Impacts ◽  
Water Storage ◽  
Principal Component ◽  
Water Levels ◽  
Terrestrial Water Storage ◽  
Validation Experiment ◽  
Physically Based ◽  
Terrestrial Water

The Gravity Recovery and Climate Experiment (GRACE) mission has provided us with unforeseen information on terrestrial water-storage (TWS) variability, contributing to our understanding of global hydrological processes, including hydrological extreme events and anthropogenic impacts on water storage. Attempts to decompose GRACE-based TWS signals into its different water storage layers, i.e., surface water storage (SWS), soil moisture, groundwater and snow, have shown that SWS is a principal component, particularly in the tropics, where major rivers flow over arid regions at high latitudes. Here, we demonstrate that water levels, measured with radar altimeters at a limited number of locations, can be used to reconstruct gridded GRACE-based TWS signals in the Amazon basin, at spatial resolutions ranging from 0.5 to 3, with mean absolute errors (MAE) as low as 2.5 cm and correlations as high as 0.98. We show that, at 3 spatial resolution, spatially-distributed TWS time series can be precisely reconstructed with as few as 41 water-level time series located within the basin. The proposed approach is competitive when compared to existing TWS estimates derived from physically based and computationally expensive methods. Also, a validation experiment indicates that TWS estimates can be extrapolated to periods beyond that of the model regression with low errors. The approach is robust, based on regression models and interpolation techniques, and offers a new possibility to reproduce spatially and temporally distributed TWS that could be used to fill inter-mission gaps and to extend GRACE-based TWS time series beyond its timespan.

scholarly journals Random Forest-Based Reconstruction and Application of the GRACE Terrestrial Water Storage Estimates for the Lancang-Mekong River Basin

2021 ◽  
Vol 13 (23) ◽  
pp. 4831
Author(s):  
Senlin Tang ◽  
Hong Wang ◽  
Yao Feng ◽  
Qinghua Liu ◽  
Tingting Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Correlation Coefficient ◽  
River Basin ◽  
Water Storage ◽  
Mekong River ◽  
Climatic Research Unit ◽  
Terrestrial Water Storage ◽  
Mekong River Basin ◽  
Terrestrial Water

Terrestrial water storage (TWS) is a critical variable in the global hydrological cycle. The TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) allow us to better understand water exchanges between the atmosphere, land surface, sea, and glaciers. However, missing historical (pre-2002) GRACE data limit their further application. In this study, we developed a random forest (RF) model to reconstruct the monthly terrestrial water storage anomaly (TWSA) time series using Global Land Data Assimilation System (GLDAS) and Climatic Research Unit (CRU) data for the Lancang-Mekong River basin. The results show that the RF-built TWSA time series agrees well with the GRACE TWSA time series for 2003–2014, showing that correlation coefficients (R) of 0.97 and 0.90 at the basin and grid scales, respectively, which demonstrates the reliability of the RF model. Furthermore, this method is used to reconstruct the historical TWSA time series for 1980–2002. Moreover, the discharge can be obtained by subtracting the evapotranspiration (ET) and RF-built terrestrial water storage change (TWSC) from the precipitation. The comparison between the discharge calculated from the water balance method and the observed discharge showed significant consistency, with a correlation coefficient of 0.89 for 2003–2014 but a slightly lower correlation coefficient (0.86) for 1980–2002. The methods and findings in this study can provide an effective means of reconstructing the TWSA and discharge time series in basins with sparse hydrological data.

scholarly journals Estimation of Terrestrial Water Storage Variations in Sichuan-Yunnan Region from GPS Observations Using Independent Component Analysis

2022 ◽  
Vol 14 (2) ◽  
pp. 282
Author(s):  
Bin Liu ◽  
Wenkun Yu ◽  
Wujiao Dai ◽  
Xuemin Xing ◽  
Cuilin Kuang
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Hydrological Model ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Coordinate Time ◽  
Yunnan Region ◽  
Grace Data ◽  
Terrestrial Water ◽  
Gps Observations

GPS can be used to measure land motions induced by mass loading variations on the Earth’s surface. This paper presents an independent component analysis (ICA)-based inversion method that uses vertical GPS coordinate time series to estimate the change of terrestrial water storage (TWS) in the Sichuan-Yunnan region in China. The ICA method was applied to extract the hydrological deformation signals from the vertical coordinate time series of GPS stations in the Sichuan-Yunnan region from the Crustal Movement Observation Network of China (CMONC). These vertical deformation signals were then inverted to TWS variations. Comparative experiments were conducted based on Gravity Recovery and Climate Experiment (GRACE) data and a hydrological model for validation. The results demonstrate that the TWS changes estimated from GPS(ICA) deformations are highly correlated with the water variations derived from the GRACE data and hydrological model in Sichuan-Yunnan region. The TWS variations are overestimated by the vertical GPS observations the northwestern Sichuan-Yunnan region. The anomalies are likely caused by inaccurate atmospheric loading correction models or residual tropospheric errors in the region with high topographic variability and can be reduced by ICA preprocessing.

Vertical crustal deformations and climate variability through PCA and SVD

2021 ◽  
Author(s):  
Letizia Elia ◽  
Susanna Zerbini ◽  
Fabio Raicich
Keyword(s):  
Time Series ◽  
Principal Component ◽  
Space Time ◽  
Time Variability ◽  
Large Network ◽  
Coupled Modes ◽  
Entire Study ◽  
Modes Of Variability ◽  
Multivariate Enso Index ◽  
Enso Index

<p>We investigated a large network of permanent GPS stations to identify and analyse common patterns in the series of the GPS height, environmental parameters, and climate indexes.</p><p>The study is confined to Europe, the Mediterranean, and the North-eastern Atlantic area, where 114 GPS stations were selected from the Nevada Geodetic Laboratory (NGL) archive. The GPS time series were selected on the basis of the completeness and the length of the series.</p><p>In addition to the GPS height, the parameters analysed in this study are the atmospheric surface pressure (SP), the terrestrial water storage (TWS), and a few climate indexes, such as MEI (Multivariate ENSO Index). The Principal Component Analysis (PCA) is the methodology adopted to extract the main patterns of space/time variability of the parameters.</p><p>Moreover, the coupled modes of space/time interannual variability between pairs of variables was investigated. The methodology adopted is the Singular Value Decomposition (SVD).</p><p>Over the study area, main modes of variability in the time series of the GPS height, SP and TWS were identified. For each parameter, the main modes of variability are the first four. In particular, the first mode explains about 30% of the variance for GPS height and TWS and about 46% for SP. The relevant spatial patterns are coherent over the entire study area in all three cases.</p><p>The SVD analysis of coupled parameters, namely H-AP and H-TWS, shows that most of the common variability is explained by the first 3 modes, which account for almost 80% and 45% of the covariance, respectively.</p><p>Finally, we investigated the relation between the GPS height and a few climate indexes. Significant correlations, up to 50%, were found between the MEI (Multivariate Enso Index) and about half of the stations in the network.</p>

scholarly journals Forecasting terrestrial water storage changes in the Amazon Basin using Atlantic and Pacific sea surface temperatures

2013 ◽  
Vol 10 (10) ◽  
pp. 12453-12483 ◽  
Author(s):  
C. de Linage ◽  
J. S. Famiglietti ◽  
J. T. Randerson
Keyword(s):  
Interannual Variability ◽  
Lead Time ◽  
Amazon Basin ◽  
Water Storage ◽  
Sea Surface ◽  
Sea Surface Temperatures ◽  
Terrestrial Water Storage ◽  
Combined Model ◽  
Northeastern Region ◽  
Terrestrial Water

Abstract. Floods and droughts frequently affect the Amazon River basin, impacting transportation, river navigation, agriculture, and ecosystem processes within several South American countries. Here we examined how sea surface temperatures (SSTs) influence interannual variability of terrestrial water storage anomalies (TWSAs) in different regions within the Amazon basin and propose a modeling framework for inter-seasonal flood and drought forecasting. Three simple statistical models forced by a linear combination of lagged spatial averages of central Pacific (Niño 4 index) and tropical North Atlantic (TNAI index) SSTs were calibrated against a decade-long record of 3°, monthly TWSAs observed by the Gravity Recovery And Climate Experiment (GRACE) satellite mission. Niño 4 was the primary external forcing in the northeastern region of the Amazon basin whereas TNAI was dominant in central and western regions. A combined model using the two indices improved the fit significantly (p < 0.05) for at least 64% of the grid cells within the basin, compared to models forced solely with Niño 4 or TNAI. The combined model explained 66% of the observed variance in the northeastern region, 39% in the central and western regions, and 43% for the Amazon basin as a whole with a 3 month lead time between the SST indices and TWSAs. Model performance varied seasonally: it was higher than average during the rainfall wet season in the northeastern Amazon and during the dry season in the central and western regions. The predictive capability of the combined model was degraded with increasing lead times. Degradation was smaller in the northeastern Amazon (where 49% of the variance was explained using an 8 month lead time vs. 69% for a 1 month lead time) compared to the central and western Amazon (where 22% of the variance was explained at 8 months vs. 43% at 1 month). These relationships may enable the development of an early warning system for flood and drought risk. This work also strengthens our understanding of the mechanisms regulating interannual variability in Amazon fires, as water storage deficits may subsequently lead to decreases in transpiration and atmospheric water vapor that cause more severe fire weather.

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