Investigation of correlation of the variations in land subsidence (detected by continuous GPS measurements) and methodological data in the surrounding areas of Lake Urmia

Lake Urmia, a salt lake in the north-west of Iran, plays a valuable role in the environment, wildlife and economy of Iran and the region, but now faces great challenges for survival. The Lake is in immediate and great danger and is rapidly going to become barren desert. As a result, the increasing demands upon groundwater resources due to expanding metropolitan and agricultural areas are a serious challenge in the surrounding regions of Lake Urmia. The continuous GPS measurements around the lake illustrate significant subsidence rate between 2005 and 2009. The objective of this study was to detect and specify the non-linear correlation of land subsidence and temperature activities in the region from 2005 to 2009. For this purpose, the cross wavelet transform (XWT) was carried out between the two types of time series, namely vertical components of GPS measurements and daily temperature time series. The significant common patterns are illustrated in the high period bands from 180–218 days band (~6–7 months) from September 2007 to February 2009. Consequently, the satellite altimetry data confirmed that the maximum rate of linear trend of water variation in the lake from 2005 to 2009, is associated with time interval from September 2007 to February 2009. This event was detected by XWT as a critical interval to be holding the strong correlation between the land subsidence phenomena and surface temperature. Eventually the analysis can be used for modeling and prediction purposes and probably stave off the damage from subsidence phenomena

On BIQUE Procedures Applied to GPS Pseudorange Measurements

Best-Invariant-Quadratic-Unbiased-Estimation (BIQUE) of the variance factors is used in this work to evaluate the adequacy, or goodness, of different stochastic models affecting GPS pseudorange measurements (assumed uncorrelated), using the linear Gauss-Markov functional model. Four different stochastic models of the observations are tested, verifying that incorrect results of the BIQUE estimates (i.e. negative variance components) imply large inaccuracies of GPS-derived user positions. Results on real measurement campaigns show that the SNR (Signal to Noise Ratio) is effective in reducing the GPS position errors, by using models with SNR and SNR squared. BIQUE estimations with negative variance components allowed us to reject one of the four chosen stochastic models. No significant differences have been noted using slightly different (high) values of the redundancy r of the observations (r = 20 and r = 28). We use formulas in which the BIQUE methodology does not require the evaluation of least-squares (LS) residuals. Therefore, the BIQUE of the variance and covariance components could be performed in pre-adjustment, without the necessity of cumbersome LS adjustments during each iteration