The tectonic development of the Southeast Anatolian Orogenic Belt (SAOB) is closely related to the demise of the NeoTethys Ocean, which was located between the Arabian and Eurasian plates from the late Cretaceous to Late Miocene. The ocean contained several continental slivers and intra-oceanic magmatic arcs. The continental slivers represent narrow tectonic belts rifted off and drifted away from the Arabian Plate while the NeoTethyan Ocean and the back-arc basins were opened. Later they collided with one another during the branches of the oceans were eliminated. In these periods, the continental slivers were involved in the subduction zone and turned into metamorphic massifs. During the Late Cretaceous, the first collision occurred when an accretionary complex was thrust over the Arabian Plate’s leading edge. Despite the collision, the ocean survived in the North and Its northward subduction generated a new intra oceanic arc, which collided later with the northerly located continental slivers. In this period, the metamorphic massifs and the intra-oceanic arc front migrated to the South. The new magmatic arc collided with the southerly transported nappe package during the Late Eocene. The amalgamated nappe pile eventually obducted onto the Arabian Plate during the Late Miocene. The collision produced escape structures during the Neotectonic period.
The East Anatolian High Plateau, part of the Alpine-Himalayan orogen, is a 200 km wide, approximately E-W trending belt surrounded by two peripheral mountains of the Anatolian Peninsula. The plateau is covered by a thick, interbedded Neogene volcanic and sedimentary rocks. Outcrops of the underlying rocks are rare. Therefore, contrasting views were proposed on the nature of the basement rocks. New geological and geophysical data suggest the presence of an ophiolitic mélange-accretionary complex under cover rocks of Eastern Anatolia. The cover units began to be deposited during the closure of the NeoTethyan Ocean that was located between the Pontide arc to the north, and the continental slivers drifted away from the Arabian Plate to the south. The surrounding orogenic belts experienced different orogenic evolution. The Eastern Anatolian orogen was formed during the later stages of the development of the surrounding orogenic belts. In this period, the melange-accretionary prism that occupied a large terrain behaved like a wide and thick cushion, which did not allow a head-on collision of the bordering continents. NeoTethyan oceanic lithosphere was eliminated from entire eastern Turkey by the Late Eocene. The eastern Anatolia began to rise when the northern advance of the Arabian Plate continued after the total demise of the oceanic lithosphere. The present stage of the elevation of the East Anatolian Plateau as a coherent block started during the Late Miocene.
Electrical and electromagnetic (EM) techniques are used to map the electrical resistivity of the subsurface. The injection of carbon dioxide (CO2) is likely to form a lenticular, slab or wedge-like bodies of increased resistivity, and therefore resistive thin-layer and sphere models are used for detectability analyses. Thin resistive layer models are used to determine the maximum depth at which resistive layer be detected, and the maximum depth at which time changes in the layer could be monitored. Sphere models are used for quantitatively studying the basic properties of the secondary fields produced on the surface by a finite body at variable depth and in a variety of source fields, and for order-of magnitude rules for depth and size determination of a confined volume filled with CO2. Both surface based EM and DC resistivity methods have limitations for detecting and delineating flat lying resistive features. Configurations with EM sources at depth, which induce vertical currents that are sensitive to the transverse (vertical) resistivity show great promise for detecting and monitoring the emplacement of tabular zones or bodies of resistive CO2.
The National Oceanic and Atmospheric Administration (NOAA) research to operations (R2O) experiment called the Big Data Project (BDP) was envisioned as a scalable approach for disseminating exponentially increasing NOAA observation, model, and research datasets to the public using commercial cloud services. At the start of the project, during the concept development phase, it was unclear how the specifics might work so a spiral development approach was adopted. It was expected that the number of data sets would increase, and the data extent would grow to cover complete records of some holdings, and that format experimentation would be needed to determine optimal cloud offerings. This dissemination model would require a new way for the BDP and NOAA to engage with end-users, who could range from large enterprises to small businesses and individuals. The BDP was expected to change the game-not just by reaching a broad and diverse set of users but by encouraging new ones. As Dr. Kathy Sullivan, former NOAA Administrator under whom the BDP began, noted, “The agency’s aim is to ‘spur innovation’ and to explore how to create a ’global economic return on investment” (Konkel, 2015). This Chapter describes the journey of BDP as it developed, transitioned and evolved from an experiment to an operational enterprise function for NOAA, now known as NOAA Open Data Dissemination (NODD). Obstacles to the Public’s Use of NOAA Environmental Data NOAA’s mission is to understand and predict changes in climate, weather, oceans, and coasts, to share that knowledge and information with others, and to conserve and manage coastal and marine ecosystems and resources. The agency takes seriously the need for communication of NOAA’s research, data, and information for use by the Nation’s businesses and communities to allow preparation, response and resilience to sudden or prolonged changes in our natural systems. This includes climate predictions and projections; weather and water reports, forecasts and warnings; nautical charts and navigational information; and the continuous delivery of a range of Earth observations and scientific data sets for use by public, private, and academic sectors (NOAA About our agency, 2021).
The Congo basin is one of the most hydrologically active and pristine locations with limited understanding of how precipitation changes impacts on stream flow dynamics and variations in catchment stores. Given that the basin is among the three prominent convective regions that dominates global rainfall climatology during transition seasons, historical space-time variability of rainfall (1901-2014) over the basin in relation to river discharge is analyzed in order to understand significant hydro-climatic shift. Based on advance multivariate analyses, the total variability of the leading modes (annual variations) of rainfall increased during the 1931-1960 (56.3%) and 1961-1990 (57.3%) periods compared to the 1901-1930 baseline period (51.3%). It varied less between 1991 and 2014 (55.4%) as opposed to the two climatological periods between 1931 and 1990. Furthermore, the total variability in the multi-annual rainfall signals declined from 16.5% at the start of the century (1901-1930) to 13.6% in the 1991-2014 period while the total variability accounted for by other short-term meteorological signals oscillated between 4.0% and 2.7% during the entire period. Between 1995 and 2010 there seems to be a change in the hydrological regimes of the Congo river as the cumulative departures of rainfall and discharge were in opposite directions. The considerable association of discharge with rainfall in catchments characterized by strong annual and seasonal amplitudes in rainfall implies that the wetland hydrology of the basin is largely nourished by rainfall, in addition to possible exchange of fluxes within the Congo floodplain wetlands. Notably, a significant proportion of changes in the dominant rainfall patterns is still not explained by those of river discharge. This information signals the threshold of complex hydrological processes in the region, and perhaps suggest the influence of anthropogenic contributions (e.g., deforestation) and strong multi-scale ocean-atmosphere phenomena as key secondary drivers of hydrologic variability.
Understanding the impacts of climate on surface water hydrology is required to predict consequences and implications on freshwater habitats, ecological assets, and wetland functions. Although the Congo basin is considerably a freshwater-rich region, largely characterised by numerous water resources after the similitude of the Amazon basin, recent accounts of droughts in the basin are indications that even the most humid regions of the world can be affected by droughts and its impacts. Given the scarcity and limited availability of hydrological data in the region, GRACE (Gravity Recovery and Climate Experiment) observations are combined with model and SPEI (standardized precipitation evapotranspiration index) data to investigate the likelihood of such impacts on the Congo basin’s surface water hydrology. By integrating multivariate analysis with support vector machine regression (SVMR), this study provides some highlights on the characteristics (intensity and variability) of drought events and GRACE-derived terrestrial water storage (TWS) and the influence of global climate on the Congo river discharge. The southern section of the basin shows considerable variability in the spatial and temporal patterns of SPEI and extreme droughts over the Congo basin appear to have persisted with more than 40% coverage in 1994. However, there has been a considerable fall in drought intensities since 2007 and coincides with periods of strong positive anomalies in discharge (i.e., 2007-010). GRACE-derived TWS over the Congo basin is driven by annual fluctuations in rainfall (r = 0.81 at three months phase lag) and strong inter-annual variations of river discharge (r = 0.88, α= 0.05). Generally, results show that changes in the surface water variations (from gauge and model output) of the Congo basin is a key component of the GRACE water column. The outputs of the SVMR scheme indicate that global climate through sea surface temperature anomalies of the Atlantic (r = 0.79, α= 0.05), Pacific (r = 0.79, α= 0.05), and Indian (r = 0.74, α= 0.05) oceans are associated with fluctuations in the Congo river discharge, and confirm the importance of climatic influence on surface water hydrology in the Congo basin.
This chapter discusses the sounds emitted by gas bubbles when they are generated underwater. Here we define bubbles to be volumes of gas, surrounded by liquid (here, taken to be water), having surface tension forces (the so-called Laplace pressure) generated by a single wall, and so are distinguished from the soap bubbles familiar in children’s games, where the volume of gas is surrounded by two gas/liquid boundaries1. In comparison with other acoustic sources, such as marine mammals, ships and tectonic events, a single bubble may seem insignificant. Indeed, without ideal conditions it can be difficult to observe the sound of a single bubble from a distance of more than a few tens of centimetres. However, natural processes rarely produce single bubbles, and in fact can generate them in their millions at which point the sound generation is significant. With the formation of bubbles as a result of gas seeps, rainfall and breaking waves being a major component of ambient noise in the marine environment and can even alter the propagation of sound waves from other sources. This chapter focuses on the passive emissions of bubbles as they are formed, released, or injected into water, and here the volume pulsations are linear. In this chapter we will discuss the mechanics behind an individual bubble’s acoustic signature, in particular the Minnaert equation and other relevant properties, before discussing the formation of bubbles from subsurface gas migration, rainfall and wave action, characterizing the acoustic nature of each process. The primary focus will be on the sound resulting from bubble generation from each of these sources. A number of different units are used to define each acoustic source, while this may appear confusing and make direct comparison difficult, this is done to be consistent with the literature. The topics covered here are broad, so the approach taken is to summarise the key principles and state of the field, while providing substantial linkage to the literature.
A multi-model hydrological assessment in the Congo Basin is performed to assess water availability conditions for historical and future periods (1913–2099). With models limited by scarce in situ observations, a combination of GRACE satellite data and soil-moisture-based drought indices is shown to be capable of estimating water budget, streamflow, and drought and storage variability. Changes in land use and land cover played a role in modifying the hydrologic responses but were found to be within the uncertainties of other inputs, including weather, soil, and model parameters. Seasonal and annual variability in total water storage anomalies (TWSAs) and the modified Palmer drought severity index (MPDSI) display a good correlation with each other. A selected set of global climate models is used to characterize the future temperature and precipitation patterns. It is expected that subbasin-scale variability in future temperature and precipitation increases will result in increased evapotranspiration, decreased runoff, and more drought events in the Congo Basin.
This chapter discusses efforts to measure surface observations of air pollution at the country-scale. The countries with the most comprehensive regulatory systems to monitor air pollution are the older industrial nations such as countries in the United Kingdom and the United States. Recent proliferation of low-cost air quality monitors (LCAQM) are making near-real-time air pollution monitoring more prevalent across the globe. While unique challenges exist between regulatory and LCAQM data access and usability, there are common challenges in using these data for decision support and research applications. This chapter discusses common statistical methods for estimating air pollution including spatial interpolation methods, statistical regression methods, machine learning, and chemical transport modeling.
A sequential inversion methodology for combining geophysical data types of different resolutions is developed and applied to monitoring of large-scale CO2 injection. The methodology is a two-step approach within the Bayesian framework where lower resolution data are inverted first, and subsequently used in the generation of the prior model for inversion of the higher resolution data. For the application of CO2 monitoring, the first step is done with either controlled-source electromagnetic (CSEM) or gravimetric data, while the second step is done with seismic amplitude-versus-offset (AVO) data. The Bayesian inverse problems are solved by sampling the posterior probability distributions using either the ensemble Kalman filter or ensemble smoother with multiple data assimilation. A carefully designed parameterization is used to represent the unknown geophysical parameters: electric conductivity, density, and seismic velocity. The parameterization is well suited for identification of CO2 plume location and variation of geophysical parameters within the regions corresponding to inside and outside of the plume. The inversion methodology is applied to a synthetic monitoring test case where geophysical data are made from fluid-flow simulation of large-scale CO2 sequestration in the Skade formation in the North Sea. The numerical experiments show that seismic AVO inversion results are improved with the sequential inversion methodology using prior information from either CSEM or gravimetric inversion.