Numerical study on the mixed model in the GOCE polar gap problem

Gravity gradients acquired by the Gravity field and steady-state Ocean Circulation Explorer(GOCE) do not cover the entire earth because of its sun-synchronous orbit leaving data gaps with a radius of about 6.5° in the polar regions.Previous studies showed that the loss of data in the polar regions deteriorates the accuracy of the low order(or near zonal) coefficients of the earth gravity model,which is the so-called polar gap problem in geodesy.In order to find a stable solution for the earth gravity model from the GOCE gravity gradients,three models,i.e.the Gauss-Markov model,light constraint model and the mixed model,are compared and evaluated numerically with the gravity gradient simulated with the EGM2008.The comparison shows that the Best Linear Uniformly Unbiased Estimation(BLUUE) estimator of the mixed model can solve the polar gap problem as effectively as the light constraint model;furthermore,the mixed model is more rigorous in dealing with the supplementary information and leads to a better accuracy in determining the global geoid.     

On the influence of the ground track on the gravity field recovery from high–low satellite-to-satellite tracking missions: CHAMP monthly gravity field recovery using the energy balance approach revisited

In this paper, the influence of the ground track coverage on the quality of a monthly gravity field solution is investigated for the scenario of a high–low satellite- to-satellite tracking mission. Data from the CHAllenging Minisatellite Payload (champ) mission collected in the period April 2002 to February 2004 has been used to recover the gravity field to degree and order 70 on a monthly basis. The quality is primarily restricted by the accuracy of the instruments. Besides, champ passed through a 31/2 repeat mode three times during the period of interest resulting in an insufficient spatial sampling and a degraded solution. Contrary to the rule of thumb by Colombo (The global mapping of gravity with two satellites, Publications on Geodesy, vol 7(3), Netherlands Geodetic Commission, The Netherlands, 263 pp, 1984), see also Wagner (J Geod 80(2): 94–103, 2006), we found that the monthly solutions themselves could be recovered to about degree 30, not 15. In order to improve the monthly gravity solutions, two strategies have been developed: the restriction to a low degree, and the densification of the sampling by the introduction of additional sensitive measurements from contemporaneous satellite missions. The latter method is tested by combining the champ measurements with data from the Gravity Recovery And Climate Experiment (grace). Note that the two grace satellites are considered independent here, i.e. no use is made of the K-band ranging data. This way, we are able to almost entirely remove the influence of the ground track leaving the accuracy of the instruments as the primary restriction on the quality of a monthly solution. These findings are especially interesting for the upcoming swarm-mission since it will consist of a similar configuration as the combined champ and (grace) missions.     

Comparison of methods for a 3-D density inversion from airborne gravity gradiometry

In this study, we apply Tikhonov’s regularization algorithm for a 3-D density inversion from the gravity-gradiometry data. To reduce the non-uniqueness of the inverse solution (carried out without additional information from geological evidence), we implement the depth-weighting empirical function. However, the application of an empirical function in the inversion equation brings the bias problem of the regularization factor when a traditional Tikhonov’s algorithm is applied. To solve the bias problem of regularization factor selection, we present a standardized solution that comprises two parts for solving a 3-D constrained inversion equation, specifically the linear matrix transformation and Tikhonov’s regularization algorithm. Since traditional regularization techniques become numerically inefficient when dealing with large number of data, we further apply methods which include the Simultaneous Iterative Reconstruction Technique (SIRT) and the wavelet compression combined with Least Squares QR-decomposition (LSQR). In our simulation study, we demonstrate that SIRT as well as the wavelet compression plus LSQR algorithm improve the computation efficiency, while provide results which closely agree with that obtained from applying Tikhonov’s regularization. In particular, the algorithm of wavelet compression plus LSQR shows the best computing efficiency, because it combines the advantages of coefficients compression of big matrix and fast solution of sparse matrix. Similar findings are confirmed from the vertical gravity gradient data inversion for detecting potential deposits at the Kauring (near Perth, Western Australia) testing site.     

Attribute trajectory analysis: a framework to analyse attribute changes using trajectory analysis techniques

Trajectory analysis has attracted growing attention in the research field of geography. Beyond traditional moving object trajectories, another type of trajectory exists in which the coordinates are object attributes rather than geographical coordinates. In this paper, a framework to analyse these so-called attribute trajectories is proposed that uses four techniques typically employed in the analysis of moving object trajectories: the Reeb graph, the similarity matrix, the convoy and the mega-convoy. The Reeb graph provides the ability to visualise the temporal dynamics of attribute similarities. The similarity matrix is a supplement of the Reeb graph whose purpose is to visualise the pairwise similarities among the attributes. Moreover, the similarity matrix forms a basis for clustering. The convoy highlights objects whose attributes remain similar for a sufficiently long period. The mega-convoy reduces the number of convoys and reveals their evolutionary histories by merging overlapping convoys. A small real-world meteorological dataset is used as an example to illustrate the attribute trajectory analysis framework and the techniques. This paper aims to form a starting point for applying trajectory analysis techniques in many research fields.     

Mumbai 2005, Bihar 2008 Flood Reflected in Mass Changes Seen by GRACE Satellites

Gravity Recovery And Climate Experiment (GRACE) satellites were launched on March 17 2002 to derive with unprecedented accuracy, estimates of the global high resolution model of the earth’s gravity field. Local gravity changes with change in mass or mass redistribution. The mass changes can be due to hydrological events, seismic events or postglacial rebound, majorly. GRACE is sensitive to changes at large spatial scale since the resolution of GRACE is 400 km. Hydrological activities over basins provide sufficient mass changes to be detected by GRACE. In this research paper the discussion would be about two major flooding events in India, one being the 2005 monsoon flooding in Mumbai and nearby states and other being flood experienced by Bihar in 2008. The GRACE data is in the form of matrix consisting spherical harmonic coefficients. These coefficients are processed to obtain mass changes in terms of equivalent water height at a spatial scale of 400 km. The strategy of analysis is also discussed which need to be followed depending upon limitations of GRACE observation and requirement of application, here in this case application is flood induced mass change detection. Time-series and residual plots are generated and they show the flooding events for the concerned area as outliers. Better visualisation is obtained by residual plot, if there is a trend or systematic behaviour in time-series. This work points towards the qualitative capability of GRACE to detect flooding events at large spatial scale. Quantitative analysis requires in-situ data over the period of GRACE which is not possible for the cases discussed here.     

Review: From multi-scale conceptualization to a classification system for inland groundwater-dependent ecosystems

Aquifers provide water, nutrients and energy with various patterns for many aquatic and terrestrial ecosystems. Groundwater-dependent ecosystems (GDEs) are increasingly recognized for their ecological and socio-economic values. The current knowledge of the processes governing the ecohydrological functioning of inland GDEs is reviewed, in order to assess the key drivers constraining their viability. These processes occur both at the watershed and emergence scale. Recharge patterns, geomorphology, internal geometry and geochemistry of aquifers control water availability and nutritive status of groundwater. The interface structure between the groundwater system and the biocenoses may modify the groundwater features by physicochemical or biological processes, for which biocenoses need to adapt. Four major types of aquifer-GDE interface have been described: springs, surface waters, peatlands and terrestrial ecosystems. The ecological roles of groundwater are conditioned by morphological characteristics for spring GDEs, by the hyporheic zone structure for surface waters, by the organic soil structure and volume for peatland GDEs, and by water-table fluctuation and surface floods in terrestrial GDEs. Based on these considerations, an ecohydrological classification system for GDEs is proposed and applied to Central and Western-Central Europe, as a basis for modeling approaches for GDEs and as a tool for groundwater and landscape management.     

Assessment of two techniques to merge ground-based and TRMM rainfall measurements: a case study about Brazilian Amazon Rainforest

The availability of accurate rainfall data with high spatial resolution, especially in vast watersheds with low density of ground-measurements, is critical for planning and management of water resources and can increase the quality of the hydrological modeling predictions. In this study, we used two classical methods: the optimal interpolation and the successive correction method (SCM), for merging ground-measurements and satellite rainfall estimates. Cressman and Barnes schemes have been used in the SCM in order to define the error covariance matrices. The correction of bias in satellite rainfall data has been assessed by using four different algorithms: (1) the mean bias correction, (2) the regression equation, (3) the distribution transformation, and (4) the spatial transformation. The satellite rainfall data were provided by the Tropical Rainfall Measuring Mission, over the Brazilian Amazon Rainforest. Performances of the two merging data techniques are compared, qualitatively, by visual inspection and quantitatively, by a statistical analysis, collected from January 1999 to December 2010. The computation of the statistical indices shows that the SCM, with the Cressman scheme, provides slightly better results.     

Education in cartography: what is the status of young people’s map-reading skills?

Due to recent technological progress, maps have become more popular than ever before. This is especially true for young people, who interact with these technologies on a daily basis. Therefore, it is essential that these potential map users possess sufficient knowledge and skills to process the content of cartographic products. A user study was conducted during which pupils (aged 11–18 years) and geography students (>18 years) had to solve a number of cartography questions using topographic maps. The data were analyzed statistically, taking into account a number of potentially influencing factors (user characteristics) on the participants’ results: age, gender, youth club membership, knowledge about the area, among others. The results show a rising trend in the pupils’ scores with increasing age, which can be explained by education in cartography at school. Geography students perform much better, but no influence of any other user characteristics was detected. For pupils, knowledge about the area and gender might be considered as influencing factors. However, the detected influence of gender depends on the scoring system.     

Global energy security under different climate policies,GDP growth rates and fossil resource availabilities

Energy security is one of the main drivers of energy policies. Understanding energy security implications of long-term scenarios is crucial for informed policy making, especially with respect to transformations of energy systems required to stabilize climate change. This paper evaluates energy security under several global energy scenarios, modeled in the REMIND and WITCH integrated assessment models. The paper examines the effects of long-term climate policies on energy security under different assumptions about GDP growth and fossil fuel availability. It uses a systematic energy security assessment framework and a set of global and regional indicators for risks associated with energy trade and resilience associated with diversity of energy options. The analysis shows that climate policies significantly reduce the risks and increase the resilience of energy systems in the first half of the century. Climate policies also make energy supply, energy mix, and energy trade less dependent upon assumptions of fossil resource availability and GDP growth, and thus more predictable than in the baseline scenarios.