The Gravity Recovery and Climate Experiment (GRACE) twin‐satellite gravimetry mission has been monitoring time‐varying changes of the Earth's gravitational field on a near‐global scale since 2002. One of the environmentally important signals to be detected is temporal variations induced by changes in the distribution of terrestrial water storage (i.e., hydrology). Since water is one of Australia's precious resources, it is logical to monitor its distribution, and GRACE offers one such opportunity. The second and fourth releases (referred to as RL02 and RL04) of the ‘standard’ monthly GRACE solutions with respect to their annual mean are analysed. When compared to rainfall data over the same time period, GRACE is shown to detect hydrological signals over Australia, with the RL04 data showing better results. However, the relatively small hydrological signal typical for much of Australia is obscured by deficiencies in the standard GRACE data processing and filtering methods. Spectral leakage of oceanic mass changes also still contaminates the small hydrological signals typical over land. It is therefore recommended that Australia‐focussed reprocessing of GRACE data is needed for useful hydrological signals to be extracted. Naturally, this will have to be verified by independent ‘in situ’ external sources such as rainfall, soil moisture and groundwater borehole piezometer data over Australia.

In order to study in the field of the dynamics and continuous variations in the ionosphere, the ionospheric measurement tools such as ionosondes, incoherent scatter radars, satellites, and Global Positioning System (GPS) networks should be used. Total Electron Content (TEC) is a key parameter in the investigation and identification of ionosphere layer. From observations of dual frequency GPS receivers, the ionospheric TEC can be extracted. Global Ionospheric Maps (GIM) are auxiliary Maps for a study on ionosphere layer in around the world. It is necessary to produce the regional TEC map for precise studying of the ionospheric TEC. Bernese software is used to extract TEC by dual frequency GPS receivers. Regional modeling of ionospheric TEC by Artificial Neural Network (ANN) is a significant domain for prediction TEC at both single and double frequency GPS receivers. Five locations in Iran during the period of 2006-2010 were identified and used in the development of an input space and ANN architecture for the TEC modeling. The input space included the day number (seasonal variation), hour (diurnal variation), sunspot number (a measure of the solar activity) and magnetic index (a measure of magnetic activity). Based on the results, the ANN have capability and flexibility to model and to predict TEC. TEC predicted by ANN A (NN TEC) and TEC obtained from the IRI2007 version of the International Reference Ionosphere (IRI TEC) are compared during equinoxes and solstices. Results show that ANN predicts GPS TEC more accurately than the IRI over Iran. The IRI-2007 model is not a suitable method to produce TEC over IRAN.