Impact of the North Atlantic Oscillation on Southern Europe Water Distribution: Insights from Geodetic Data

Valty, P.; Viron, O. de; Panet, I.; Collilieux, Xavier

doi:10.1175/ei-d-14-0028.1

Cited by 5 publications

(3 citation statements)

References 72 publications

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“…The time series of surface mass maps provided by the GRACE mission also illustrate these spatial correlations (Tapley et al, 2010). Correlations of GRACE-inferred loading displacements with GNSS coordinate time series have long been reported (Davis et al, 2004;King et al, 2006) even at the inter-annual time scale (Valty et al, 2015). However, loading displacements derived from either GRACE or atmosphere, ocean and hydrology models do not explain all spatially correlated variations observed in GNSS time series (Collilieux et al, 2012;Chanard et al, 2018).…”

Section: Introductionmentioning

confidence: 92%

Accounting for spatiotemporal correlations of GNSS coordinate time series to estimate station velocities

Benoist

Collilieux

Rebischung

et al. 2020

Journal of Geodynamics

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It is well known that GNSS permanent station coordinate time series exhibit time-correlated noise. Spatial correlations between coordinate time series of nearby stations are also long-established and generally handled by means of spatial filtering techniques. Accounting for both the temporal and spatial correlations of the noise via a spatiotemporal covariance model is however not yet a common practice. We demonstrate in this paper the interest of using such a spatiotemporal covariance model of the stochastic variations in GNSS time series in order to estimate long-term station coordinates and especially velocities. We provide a methodology to rigorously assess the covariances between horizontal coordinate variations and use it to derive a simple exponential spatiotemporal covariance model for the stochastic variations in the IGS repro2 station coordinate time series. We then use this model to estimate station velocities for two selected datasets of 10 time series in Europe and 11 time series in the USA. We show that coordinate prediction as well as velocity determination from short time series are improved when using this spatiotemporal model, as compared with the case where spatiotemporal correlations are ignored.

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Section: Introductionmentioning

confidence: 92%

Accounting for spatiotemporal correlations of GNSS coordinate time series to estimate station velocities

Benoist

Collilieux

Rebischung

et al. 2020

Journal of Geodynamics

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“…Correcting for ATML increases the variance of heights for the sites included in the computation of the vertical stack without resulting in significant variations of the vertical component behavior and maintaining the homogeneity between sites inside and outside the karst area. Therefore, given the results of Valty et al [], pointing to a dominant role of hydrological loads in controlling vertical displacements in Southern Europe, we only consider and show the results without the atmospheric correction.…”

Section: Gps Data and Analysismentioning

confidence: 99%

Transient deformation of karst aquifers due to seasonal and multiyear groundwater variations observed by GPS in southern Apennines (Italy)

et al. 2016

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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.

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“…The relationship between climate drivers and hydrological characteristics in Europe has been documented by many researchers (e.g., Valty et al 2015, Wrzesiński & Paluszkiewicz 2011. Results of the analysis are to a large extent consistent with results obtained by Giuntoli et al (2013) where positive links between the annual discharge volume deficit and the annual NAO and AMO were revealed in many rivers in France, providing insights into the increase of drought severity.…”

Section: Relations With Large-scale Atmospheric Circulationsupporting

confidence: 83%

Trends in flow intermittence for European Rivers

Tramblay

Rutkowska²,

Sauquet

et al.

Authorea

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Impact of the North Atlantic Oscillation on Southern Europe Water Distribution: Insights from Geodetic Data

Abstract: From space gravity and station position data over southern Europe from 2002Europe from to 2010

Cited by 5 publications

References 72 publications

Accounting for spatiotemporal correlations of GNSS coordinate time series to estimate station velocities

Accounting for spatiotemporal correlations of GNSS coordinate time series to estimate station velocities

Transient deformation of karst aquifers due to seasonal and multiyear groundwater variations observed by GPS in southern Apennines (Italy)

Trends in flow intermittence for European Rivers

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