Hydrology and Noise Affecting Land-Based Gravity Measurements

1. Hydrology and Noise Affecting Land-Based Gravity Measurements M. Van Camp1, L. Métivier2, O. de Viron3, S.D.P. Williams4, B. Meurers5 1Royal Observatory of Belgium; 2IGN; 3IPGP; 4Proudman Oceanographic Laboratory; 5U. Vienna mvc@oma.be The problem 1 yr • Gravity measurements suffer from hydrological effects (Figure 1a). • Global hydrological models are not precise enough, because of their poor spatial resolution (Figure 1b & Figure 2, Table 1). • Performing local hydrogeological investigations makes it possible to correct these effects (Figure 1c). • Heavy process, difficult to carry out everywhere. • The longest land-based gravity time series does not extend longer than 1995, making it difficult to investigate long-term hydrological effects on gravity. • Does hydrological effects induce a “random-walk” process (Figure 4), which would mask slow tectonic deformation ( 1 mm/year)? How does the long periodic noise look like? 5 µGal Figure 1 : Time series from the absolute and superconducting gravimeters, and the prediction from the LaD model at Membach, Belgium. (A) Before correcting hydrological effects; (B) After applying the LaD model; (C) After applying the local model. Model not really efficient in the time domain ! Figure 2 : Time series from the superconducting gravimeters (black) and the LaD model (red). But interesting information in the frequency domain ! The solution • Discrepancies in time domain partly due to phase shifts. But the SG and LaD amplitudes are more or less in agreement. • Have a look in the frequency domain! • Does the PSDs really flatten? • Advantage of Hydrological models: allow us to go back further back in time than SGs: LaD (Milly & Schmakin 1°x1°, monthly) starts in 1980 ( > 1995 for the SG) (Figure 2) Table 1: Standard deviation [nm/s²] of the SG time series before and after applying the LaD model. Is also shown the relative difference in percent. Applying the correction does not usually improve the SNR.

2. Table 2: Time necessary (years) to be able to measure a slope with an uncertainty of 1 nm/s² /yr ( 0.5 mm/yr) (1). Figure 3 : Power spectral densities of the SG and LaD time series. An annual component appears clearly; PSDs usually flatten at low frequency (T > 1 year or n < 10-8 Hz). The good news P(f) • For most of the GGP stations, SG and LaD amplitudes agree fairly for periods longer than 100 days, though they are located in quite different climatic regimes (Figure 3). • Using LaD to look at long period (T>10 yrs) and compare it with SGs makes sense. • PSDs of the LaD model confirm that the hydrological noise flattens at low frequency (Figure 3): 1st order or generalized Gauss-Markov noise (Figure 4), even at “noisy” stations; •  Hydrological effects should not prevent one to measure slow tectonic processes (unless climate changes?)  Measuring slow processes using an absolute gravimeter should not be hopeless (see Table 2). f -2: random walk (Brownian) Generalized Gauss-Markov Gauss-Markov 1storder f 0: white noise f -1: flicker f Figure 4 : PSDs of different noises. The Random Walk process (or processes with spectral index <-2) is the most problematic: having more observations does not necessarily reduce the uncertainty. A generalized Gauss-Markov process is free to have any spectral index at high frequencies. By-product Acknowledgements Usually, land-based absolute and superconducting gravimeters are not the most appropriate way to investigate hydrology. However, the data do exist and are of high quality, incorporating them in data assimilation process for improving hydrological models should be done. We are grateful to Leslie Vandercoilden for processing the GGP time series, as well as to all the GGP participants, for making available the SG data.