The dynamic topography of the core-mantle boundary (CMB) provides important constraints on dynamic processes in the mantle and core. However, inferences on CMB topography are complicated by the uneven coverage of data with sensitivity to different length scales and strong heterogeneity in the lower mantle. Particularly, a trade-off exists with density variations, which ultimately drive mantle flow and are vital for determining the origin of mantle structures. Here, I review existing models of CMB topography and lower mantle density, focusing on seismological constraints. I develop average models and vote maps with the aim to find model consistencies and discuss what these may teach us about lower mantle structure and dynamics. While most density models image two areas of dense anomalies beneath Africa and the Pacific, their exact location and relationship to seismic velocity structure differs between studies. CMB topography strongly influences the retrieved density structure, which helps to resolve differences between recent studies based on Stoneley modes and tidal measurements. Current CMB topography models vary both in pattern and amplitude and a discrepancy exists between models based on body-wave and normal-mode data. As existing models feature elevated topography below the Large-Low-Velocity Provinces (LLVPs), very dense compositional anomalies may currently be ruled out as possibility. To achieve a similar consistency as observed in lower mantle models of S-wave and P-wave velocity, future studies should combine multiple data sets to break existing trade-offs between CMB topography and density. Important considerations in these studies should be the choice of theoretical approximation and parameterisation. Efforts to develop models of CMB topography consistent with body-wave, normal-mode and geodetic data should be intensified, which will aid in narrowing down possible explanations for the LLVPs and provide additional insights into mantle dynamics.
Fragments of former oceans are commonly observed in mountain belts: blueschists and eclogites, on the one hand, and ophiolites, on the other hand, are all that remains of ancient oceanic lithosphere. Though volumetrically subordinate, they provide essential insights into past geodynamics and into the processes involved in the formation and destruction of oceanic lithosphere. This contribution apprehends these two types of oceanic fragments jointly and shows the advantage of doing so for understanding the dynamics of oceanic convergence, i.e. subduction and obduction. We examine the intimate relationships between blueschists/eclogites and ophiolites, as well as the similarities and differences in the mechanisms leading to their preservation. While the extensive, unmetamorphosed true ophiolites markedly differ from fragments of oceanic lithosphere offscraped from the slab during subduction, at shallow or great depths, both types record the mechanical behavior and ‘hiccups’ of the subduction plate boundary. Their preservation also highlights the importance of the evolution of the subduction regime through time, from the onset of intra-oceanic subduction to the cessation of continental subduction.
Flood disasters have regularly been reported in the Congo Basin with significant damages to human lives, food production systems and infrastructure. Losses incurred by these damages are huge and represent a major challenge for economic expansion in developing nations. In the Congo River Basin, where availability of in-situ data is a significant challenge, new approaches are needed to investigate flood risks and enable effective management strategies. This study uses recently developed global flood prediction data in order to produce flood risk maps for the Congo River Basin, where flood information currently does not exist. Flood hazard maps that estimate fluvial flooding at a grid cell resolution of 3 arc-seconds (~ 90 m), gridded population density data of 1 arc-second (~ 30 m) spatial resolution, and a spatial layer of infrastructure dataset are used to addresses flood risk at the scale of the Congo Basin. The global flood data provides different return periods of exposure to flooding in the Congo Basin and identifies flood extents. The risk analysis results are presented in terms of the percentage of population and infrastructure at flood risk for six return periods (5, 10, 20, 50, 75 and 100 year). Of the 525 administrative territories, 374 are AGU Books
The Gulf of Mexico is a prolific petroleum basin with more than a century-long exploration history. Tectonic models proposed for the basin vary dramatically in many aspects, ranging from the pre-rift locations of the crustal blocks, the timing of the break-up to even the order of tectonic events. The reason for these disagreements is in a thick and complex overburden that obscures seismic imaging of crustal structures. To overcome that, we integrated seismic data with gravity and magnetic fields to determine the crustal architecture in different parts of the basin, as well as to map the location of the key tectonic features. The subsequent spatial analysis of potential fields allowed us to trace the tectonic structures outside of seismic coverage. As a result, a set of new geological constraints was derived including the Triassic rifts, regions of Seaward Dipping Reflectors (SDR), and Jurassic pre-salt sedimentary basins in the eastern Gulf of Mexico and along the Yucatan margin, and two distinct crustal zones in the oceanic domain. We ensure the pre-breakup alignment of the crustal blocks based on the mapped geological features on the conjugate margins. Our tectonic reconstruction takes into account an apparent temporal variability of the magmatic regime during basin formation that ranged from CAMP (~200 Ma) to an ultra-slow amagmatic spreading during the initial stage of the GoM opening (~ 165 Ma). Our reconstruction also includes a major ridge reorganization (~ 152 Ma) associated with increased magmatic supply. This second phase of oceanic spreading ceased at early Cretaceous (~ 135 Ma) based on published correlation analysis of seismic and well data. Overall, the tectonic reconstruction presented here takes into account previously known and newly derived geological constraints and integrates various geophysical datasets, namely seismic, gravity, and magnetics.
The Congo Basin exhibits tremendous heterogeneities, out of which it emerges as an intricate system where complexity will vary consistently over time and space. Increased complexity in the absence of adequate knowledge will always result in increased uncertainties. One way of simplifying this complexity is through an understanding of organisational relationships of the landscape features, which is termed here as catchment classification. The need for a catchment classification framework for the Congo Basin is obvious given the basin’s inherent heterogeneities, the ungauged nature of the basin, and the pressing needs for water resources management that include the quantification of current and future supplies and demands, which also encompass the impacts of future changes associated with climate and land use, as well as water resources operational policies. The need is also prompted by many local-scale management concerns within the basin. This study uses an a priori approach to determine homogenous climatic-physiographic regions that are expected to underline dominant hydrological processes characteristics. A set of 1740 catchment units are partitioned across the whole basin, based on a set of comprehensive criteria, including natural break of the elevation gradient (199 units), inclusion of socio-economic and anthropogenic systems (204 units), and water management units based on traditional nomenclature of the rivers within the basin (1337 units). The identified catchment units are used to assess existing datasets of the basin physical properties, necessary to derive descriptors of the catchments characteristics. An unsupervised classification, based on Hierarchical Agglomerative Cluster algorithm is used, that yields 11 homogenous groups that are consistent with the current perceptual understanding of the Congo Basin physiographic and climatic settings. These regions represent therefore an a priori classification that will be further used to derive functional relationships of the catchments, necessary to enable hydrological prediction and water management in the basin.
The study aims to assess the local response of the regional climate model version 4.6 (RegCM4.6) to the coupling of ocean-atmosphere interaction in Central Africa. The ability of the model is evaluated over six years (first January 2001, to thirty-first December 2006) by conducting two different experiments with the Grell convective scheme. The experiments are carried out monthly with a spatial resolution of 40 km. The model was forced by ERA-Interim reanalyses and validated by GPCP (Global Precipitation Climatology Project) observational data, ERA 5 and ERA-Interim reanalyses. To evaluate the influence of the slab-ocean, we carried out two different experiments: The first experiment is designed to produce the climatology and force the surface limits of RegCM with the sea surface temperature. The second experiment is designed to couple RegCM with the slab-ocean, which provides mutual interaction between the ocean and the atmosphere. Using statistical tools, we evaluated the model’s ability to simulate precipitation, surface temperature and wind. Both experiments reasonably reproduce the main characteristics of the rainfall regime, temperature and wind. A comparative analysis of the different experiments reveals that the performances of the experiments are similar in Central Africa and in the different homogeneous sub-regions as far as rainfall is concerned, but there are subtle differences. Slab-ocean improvement varies from season to season and from the sub-region to sub-region. However, we note a significant improvement in temperature and rainfall over the Indian Ocean.
The Congo River provides potential for socio-economic growth at the regional scale, but with limited information on the river dynamics it is difficult for basin countries to benefit from this potential, and to invest in the development of water resources. In recent years, the number of hazards related to navigation and flooding has sharply increased, resulting in high loss of human lives as well as economic losses. Associated problems of river management in the Congo also include inefficiency in hydropower production, an increase in rate of river sedimentation and land use changes. Accurate information is needed to support adequate management strategies such as prediction of navigation water levels and sediment movement, and assessment of environmental impacts and engineering implications of water resources infrastructure. Modelling approaches and space observations have been used to understand the Congo River dynamics, but their effective application has proved difficult due to a lack of ground-based observational data for validation. Recent developments in data capture with acoustic Doppler technologies have considerably improved measurements of river dynamics. As well measuring river discharge, they also allow the analysis of the multiple hydrodynamic features occurring in fluvial systems. This paper presents the results of field measurement campaigns carried out in the middle reach of the Congo River and the Kasai tributary using state of the art measurement technology (ADCP, Sonar, GNSS) for investigation of large rivers. The measurements relate to river flow at multiple transects, river bathymetry, static and continuous water surface elevation, and targeted sediment sampling along the river. The paper provides a descriptive summary of the measurement results, a discussion on the application and performance of the equipment used in the Congo River, and lessons for future use of this equipment for measurements of large rivers in a data scarce environment such as the Congo Basin.
Key Points: • Machine learning (ML) helps model the interaction between clouds and climate using large datasets. • We review physics-guided/explainable ML applied to cloud-related processes in the climate system. • We also provide a guide to scientists who would like to get started with ML. Abstract: Machine learning (ML) algorithms are powerful tools to build models of clouds and climate that are more faithful to the rapidly-increasing volumes of Earth system data than commonly-used semiempirical models. Here, we review ML tools, including interpretable and physics-guided ML, and outline how they can be applied to cloud-related processes in the climate system, including radiation, microphysics, convection, and cloud detection , classification, emulation, and uncertainty quantification. We additionally provide a short guide to get started with ML and survey the frontiers of ML for clouds and climate .
Finding optimum balances between conflicting interests in multipurpose reservoirs often represents an important challenge for decision makers. This study assesses the use of different computational tools to obtain optimal reservoir operations applied to the Hatillo dam in the Dominican Republic. A multiobjective optimization approach is used, in which non-dominated sorting genetic algorithm II (NSGAII) and multi-objective evolutionary algorithm based on decomposition (MOEA/D) optimizers are applied to models that simulate reservoir operations. Three different Machine Learning (ML) models, namely, the multilayer perceptron (MLP), the radial basis network (RBN) and the linear function (LF), are employed to learn the current operation of the system. Subsequently, a general model is proposed to simulate daily reservoir operations (2009-2019), integrating water balances, physical constraints of the dam components and the ML models, the latter defining daily controlled discharges. In the optimization process, the ML parameters are the decision variables, while the objectives evaluated are irrigation, hydropower generation and flood control. The results are compared with the actual operation of the reservoir. Three dimensional Pareto fronts are obtained, from which, the wide variety of operations can be evidenced. The flood control objective was found to have a wide room for improvement over the current operation of the reservoir, and several of the solutions found improve the current operation for the three proposed objectives. The MLP models tend to generate the best results for this case study and the NSGAII optimizer generates the best optimization results.
Skillful forecasts of weather phenomena in numerical models begin with the most accurate set of initial conditions achievable from observational datasets. The process of combining observations with numerical model predictions is called data assimilation. This chapter describes the types of observations available for data assimilation in models that predict the transport, fate, and impacts of smoke pollution. Observation properties needed for effective data assimilation are identified based on experiences with a variety of observation types in data assimilation experiments, compiled from the published literature. The second half of the chapter surveys the data assimilation methodologies that have been applied to smoke aerosols, and describes specific problems associated with the smoke observations that require innovative techniques in data assimilation. The chapter concludes by providing an outlook for future research and development in data assimilation for smoke prediction models. Data assimilation for prediction of smoke is an emerging area of development that promises to greatly improve forecast skill as new datasets and techniques are applied.
The viscosity of the mantle affects every aspect of the thermal and compositional evolution of Earth's interior. Radial variations in viscosity can affect the sinking of slabs, the morphology of plumes, and the rate of convective heat transport and thermal evolution. Below the mantle transition zone, we detect changes in the long-wavelength pattern of lateral heterogeneity in global tomographic models, a peak in the the depth-distribution of seismic scatterers, and changes in the dynamics plumes and slabs, which may be associated with a change in viscosity. We analyze the long-wavelength structures, radial correlation functions, and spectra of four recent global tomographic models and a suite of geodynamic models. We find that the depth-variations of the spectral slope in tomographic models are most consistent with a geodynamic model that contains both a dynamically significant phase transition and a reduced-viscosity region at the top of the lower mantle. We present new inferences of the mantle radial viscosity profile that are consistent with the presence of such a feature.
Anthropogenic subsurface urban heat islands (SUHIs) in groundwater under cities are known worldwide. SUHIs are potentially threats to springs because much spring fauna, like trout, amphipods, and rare plants, is cold stenothermal. The city of Minneapolis, Minnesota, USA, has a SUHI documented by the temperature of an underground spring, dubbed “Little Minnehaha Falls,” inside Schieks Cave, which is located 23 m below the central core of the city. In 2000 the temperature of that spring was elevated 11°C above regional background groundwater temperatures (8°C) at this latitude (45°N). A thermometric survey of the cave and nearby tunnel seepages in 2007 found that an abandoned drill-hole through the bedrock ceiling of the cave was discharging groundwater with a temperature of 17.9°C. By comparison, groundwater in the deep water-table below the cave was closer to natural background temperatures for the region. The unusually warm groundwater was thereby localized to the strata above the cave. This is the strongest signal of anthropogenic groundwater warming in the state of Minnesota and is attributed to vertical heat conduction from basements and pavements. Minneapolis is unique among SUHIs in that a cave forms a natural collection gallery deep below the city surface, whereas the literature is almost exclusively based on data from observation wells.
Western Anatolia is located at the boundary between the Aegean and Anatolian microplates. It is considered a type-location for marking a significant transition between compressional and extensional tectonics across the Alpine-Himalayan chain. The onset of lateral extrusion in Western Anatolia and the Aegean during the Eocene is only one of its transitional episodes. The region has a geological history marked by diverse tectonic events starting from the Paleoproterozoic through the Cambrian, Devonian, and Late Cretaceous, as recorded by its suture zones, metamorphic history, and intrusions of igneous assemblages. Extension in Western Anatolia initiated in a complex lithospheric tectonic collage of multiple sutured crustal fragments from ancient orogens. This history can be traced to the Aegean microplate, and today both regions are transitioning or have transitioned to a stress regime dominated by strike-slip tectonics. The control for extension in Western Anatolia is widely accepted as the rollback of the African (Nubian) slab along the Hellenic arc, and several outstanding questions remain regarding subduction dynamics. These include the timing and geometry of the Hellenic arc and its connections to other subduction systems along strike. Slab tear is proposed for many regions across the Anatolian and Aegean microplates, either trench-parallel or perpendicular, and varies in scale from regional to local. The role of magma in driving and facilitating extension in Western Anatolia and where and why switches in stress regimes occurred along the Anatolia and Aegean microplates are still under consideration. The correlation between Aegean and Anatolian tectonic events requires a better understanding of the detailed metamorphic history recorded in Western Anatolia rocks, possible now with advances in garnet-based themobarometric approaches. Slab tear and ultimate delamination impact lithospheric dynamics, including generating economic and energy deposits, facilitating lithospheric thinning, and influencing the onset of transfer zones that accommodate deformation and provide conduits for magmatism.
The Hellenic arc, where the African (Nubian) slab subducts beneath the Aegean and Anatolian microplates, has emerged as a type-locality for understanding subduction dynamics, including slab tear, slab fragments, drips, and transfer zones. Based on field evidence and geophysical, tectonics, and geochemical studies, it has been recognized that the subducting African slab is a primary driver for extension in the Aegean and Anatolian microplates and plays a significant role in accommodating present-day westward extrusion of the Anatolian microplate. Thus, understanding the Hellenic arc subduction zone initiation (SZI) age is critical in deciphering ancient mantle flow, how plate tectonics is maintained, and the mechanisms involved in triggering the onset of subduction. The SZI for the Hellenic arc has two disparate ages based on different lines of evidence. A Late Cenozoic (Eocene-Pliocene) SZI is proposed using the analysis of topography combined with estimates of slab age and depth, paleomagnetism, the timing of metamorphism, and volcanic activity, and timing of sedimentation within its accretionary wedge, the Mediterranean Ridge. This age follows an induced-transference SZI model, where a new subduction zone initiates following the jamming of an older subduction zone by buoyant crust due to regional compression, uplift, and underthrusting. A Late Cretaceous-Jurassic SZI age has also been proposed using reconstructions of images of subducted slabs seen using tomography and timing of obducted ophiolite fragments thought to be related to the system. In this case, the induced-transference SZI model fails, and a single subduction zone persists. As a result, continental lithospheric fragments and the ancient oceans between them become incorporated into the overall system without creating a new subduction zone. The presence of a long-lived subduction zone has implications for understanding Earth’s mantle dynamics and how plate tectonics operates. This paper describes and summarizes the evidence for both models in the Aegean-Western Anatolia region.
This work presents an integrated methodology for the assessment of threats on spring quality and quantity in poorly investigated Mediterranean semi-arid karst catchments in Lebanon. Pilot investigations, including 1) high-resolution monitoring of spring water and climate, 2) artificial tracer experiments, and 3) analysis of micropollutants in surface water, groundwater, and wastewater samples were conducted to assess flow and transport in three karst catchments of El Qachqouch, El Assal, and Laban springs. First, the high-resolution in-situ spring data allows the quantification of available water volumes, as well as their seasonal and yearly variability in addition to shortages and floodwaters. Moreover, the statistical analysis of hydrographs and chemographs helps assess the karst typology, spring type and hydrodynamic behavior (storage versus fast flow). Furthermore, a series of artificial tracer experiments provides information about key-transport parameters related to the intrinsic vulnerability of the pilot springs, while the analysis of micropollutants gives insight into the specific types of point source pollution as well as contaminant types and loads. On the one hand, the tracer experiments reveal that any potential contamination occurring in snow-governed areas can be observed at the spring for an extensive time due to its intermittent release by gradual snowmelt, even with enough dilution effect. On the other hand, the assessment of persistent wastewater indicators shows that springs in the lower catchment (including El Qachqouch) are highly vulnerable to a wide range of pollutants from point source (dolines and river) and diffuse percolation. Such contaminants breakthrough is challenging to predict because of the heterogenous duality of infiltration and flow, typical of karst systems. Finally, this set of investigations is essential for the proper characterization of poorly studied systems in developing areas, whereby results can be integrated into conceptual and numerical models to be used by decision-makers as support tools in science-evidenced management plans.
With wildfires increasing in activity in the Western United States and around the world, there is an immediate need to understand the toxic effects of the smoke. This chapter will provide a background of toxicology and apply principle concepts such as dose, duration and frequency to help define the potential effects of smoke exposure. Characteristics that influence toxicity will be discussed, which include particle size, source and temperature and the mixture of chemical constituents. An overview of the routes of exposure, mechanisms of action, toxicokinetics and the role of the immune system will all be covered. The importance and mutual benefits of in vitro, ex vivo and in vivo studies will be discussed. Finally, the chapter concludes by outlining knowledge gaps and research needs.
This study intends to integrate heterogeneous remote sensing observations and hydrological modelling into a simple framework to monitor hydrological variables in the poorly gauged Congo River basin (CRB). It focuses on the possibility to retrieve effective channel depths and discharges all over the basin in near real time (NRT). First, this paper discusses the complexity of calibrating and validating a hydrologic–hydrodynamic model (namely the MGB model) in the CRB. Next, it provides a twofold methodology for inferring discharge at newly monitored virtual stations (VSs, crossings of a satellite ground track with a water body). It makes use of remotely sensed datasets together with in-situ data to constrain, calibrate and validate the model, and also to build a dataset of stage/discharge rating curves (RCs) at 709 VSs distributed all over the basin. The model was well calibrated at the four gages with recent data (Nash-Sutcliffe Efficiency, NSE> 0.77). The satisfactory quality of RCs basin-wide (mean NSE between simulated discharge and rated discharge at VSs, NSEmean = 0.67) is an indicator of the overall consistency of discharge simulations even in ungauged upstream sub-basins. This RC dataset provides an unprecedented possibility of NRT monitoring of CRB hydrological state from the current operational satellite altimetry constellation. The discharges estimated at newly monitored locations proved to be consistent with observations. They can be used to increase the temporal sampling of water surface elevation (WSE) monitoring from space with no need for new model runs. The RC located under the fast sampling orbit of the SWOT satellite, to be flown in 2022, will be used to infer daily discharge in major contributors and in the Cuvette Centrale, as soon as data is released.
Extreme runoff modeling is hindered by the lack of sufficient and relevant ground information and the low reliability of physically-based models. The authors propose to combine precipitation Remote Sensing (RS) products, Machine Learning (ML) modeling, and hydrometeorological knowledge to improve extreme runoff modeling. The approach applied to improve the representation of precipitation is the object-based Connected Component Analysis (CCA), a method that enables classifying and associating precipitation with extreme runoff events. Random Forest (RF) is employed as a ML model. We used 2.5 years of nearly-real-time hourly RS precipitation from the PERSIANN-CCS and IMERG-early run databases (spatial resolutions of 0.04 o and 0.1 o , respectively), and runoff at the outlet of a 3391 km 2-basin located in the tropical Andes of Ecuador. The developed models show the ability to simulate extreme runoff for the cases of long-duration precipitation events regardless of the spatial extent, obtaining Nash-Sutcliffe efficiencies (NSE) above 0.72. On the contrary, we found an unacceptable model performance for a combination of short-duration and spatially-extensive precipitation events. The strengths/weaknesses of the developed ML models are attributed to the ability/difficulty to represents complex precipitation-runoff responses.