On the relationship between the Indian summer monsoon and river flow in the Aral Sea basin

We study the contemporaneous relationship between the intensity of the Indian Summer Monsoon (ISM) and runoff in the major rivers of the Aral Sea basin (Amudarya, Syrdarya) and some of their subcatchments. To this end, we use All-India rainfall (AIR) data, CRU surface observations of precipitation and temperature, ERA40 atmospheric data, and natural discharge data corrected for human interference. We show that there is a highly significant positive correlation between ISM intensity and Amudarya runoff. This finding cannot be explained by the spill-over of ISM precipitation over the Hindu Kush into the Amudarya basin. Instead, we suggest that the observed co-variability is mediated by tropospheric temperature variations due to fluctuations in the ISM intensity. These variations are known to be due to Rossby-wave propagation in response to condensational heating during monsoon precipitation. We hypothesise that the corresponding anomalies in surface temperatures imply anomalies in meltwater formation.

Phytoplankton parameters in the Large Aral Sea in June 2008

Species composition, abundance, and biomass of phytoplankton in the surface water layer were determined at 10 stations in the central part of the Western Basin (WB) and at one station in the Eastern Basin (EB) of the Large Aral Sea. 42 algal species were found. Diatoms had the highest number of species. Similarity of phytoplankton composition in the WB was high, whereas phytoplankton composition in the WB and EB differed significantly. In WB abundance and biomass of phytoplankton varied from 826x10**3 to 6312x10**3 cells/l (aver. 1877x10**3 cells/l) and from 53 to 241 ?g C/l (aver. 95 ?g C/l). In EB the phytoplankton abundance was 915x10**3 cells/l and 93 ?g C/l. Vertical distribution of phytoplankton in upper 35 m was investigated at one station in WB. Maximum values of phytoplankton abundance and biomass were recorded under the thermocline at 20 m depth. Integrated biomass of phytoplankton was 14 g C/m**2.

Updated bathymetric chart of the East Aral Sea

The Aral Sea is located in an arid region with much sand and salt deposits in the surrounding barren open land. Hence, significant displacements of sediments into the lakebed by the action of wind, water, gravity, or snow are likely. The bathymetry of the lake was last observed in the 1960s, and within the last half century, the structure of the lakebed has changed. Based on satellite observations of the temporal changes of shoreline (Landsat optical remote sensing) and water level (multi-mission satellite altimetry) over more than one decade an updated bathymetric chart for the East Basin of the Aral Sea has been generated. During this time, the geometry of the shallow East Basin experienced strong fluctuations due to the occurrence of periods of drying and strong inflow. By the year 2014 the East Basin fell dry. The dynamic behavior of the basin allowed for estimating the lake's bathymetry from a series of satellite-based information. The river mouth made its impression in the present East Aral Sea, because its shrinking led to the inflow of much sediment into the lake's interior. In addition, salt deposits along the shorelines increased the corresponding elevation, a phenomenon that is more pronounced in the reduced lakebed because of increased salinity. It must be noted that height estimates from satellite altimetry were only possible down to a minimum elevation of 27 m above sea level due to a lack of reliable altimetry data over the largely reduced water surface. In order to construct a complete bathymetric chart of the lakebed of the East Aral Sea heights below 27 m were obtained solely from Landsat optical images following the SRB (Selected Region Boundary) approach as described by Singh et al. (2015).

On the origin of the ultradeep East Barents Sea basin

Very large subsidence, with up to 20 km thick sediment layers, is observed in the East Barents Sea basin. Subsidence started in early Paleozoic, accelerated in Permo-Triassic times, finished during the middle Cretaceous, and was followed by moderate uplift in Cenozoic times. The observed gravity signal suggests that the East Barents Sea is at present in isostatic balance and indicates that a mass excess is required in the lithosphere to produce the observed large subsidence. Several origins have been proposed for the mass excess. We use 1-D thermokinematic modeling and 2-D isostatic density models of continental lithosphere to evaluate these competing hypotheses. The crustal density in 2-D thermokinematic models resulting from pressure-, temperature-, and composition-dependent phase change models is computed along transects crossing the East Barents Sea. The results indicate the following. (1) Extension can only explain the observed subsidence provided that a 10 km thick serpentinized mantle lens beneath the basin center is present. We conclude that this is unlikely given that this highly serpentinized layer should be formed below a sedimentary basin with more than 10 km of sediments and crust at least 10 km thick. (2) Phase changes in a compositionally homogeneous crust do not provide enough mass excess to explain the present-day basin geometry. (3) Phase change induced densification of a preexisting lower crustal gabbroic body, interpreted as a mafic magmatic underplate, can explain the basin geometry and observed gravity anomalies. The following model is proposed for the formation of the East Barents Sea basin: (1) Devonian rifting and extension related magmatism resulted in moderate thinning of the crust and a mafic underplate below the central basin area explaining initial late Paleozoic subsidence. (2) East-west shortening during the Permian and Triassic resulted in densification of the previously emplaced mafic underplated body and enhanced subsidence dramatically, explaining the present-day deep basin geometry.

Statistical learning theory for geospatial data. Case study: Aral sea

In recent years there has been an explosive growth in the development of adaptive and data driven methods. One of the efficient and data-driven approaches is based on statistical learning theory (Vapnik 1998). The theory is based on Structural Risk Minimisation (SRM) principle and has a solid statistical background. When applying SRM we are trying not only to reduce training error ? to fit the available data with a model, but also to reduce the complexity of the model and to reduce generalisation error. Many nonlinear learning procedures recently developed in neural networks and statistics can be understood and interpreted in terms of the structural risk minimisation inductive principle. A recent methodology based on SRM is called Support Vector Machines (SVM). At present SLT is still under intensive development and SVM find new areas of application (www.kernel-machines.org). SVM develop robust and non linear data models with excellent generalisation abilities that is very important both for monitoring and forecasting. SVM are extremely good when input space is high dimensional and training data set i not big enough to develop corresponding nonlinear model. Moreover, SVM use only support vectors to derive decision boundaries. It opens a way to sampling optimization, estimation of noise in data, quantification of data redundancy etc. Presentation of SVM for spatially distributed data is given in (Kanevski and Maignan 2004).