15 The increasing pressure on wetland resources continues to threaten the role wetlands play in 16 maintaining the ecological balance of watersheds. The Cuvette Centrale of the Congo is the 17 greatest intertropical peatland in the world. To fully understand its role in water resources and 18 ecological services linked to the quality of water and life in the basin, we first need to quantify 19 its role in the hydrological dynamics. To achieve this aim, we used the Soil and Water 20 Assessment Tool model (SWAT)-modified for tropical environments-in combination with 21 monthly discharge data. We analyzed water fluxes entering and flowing out of the Cuvette 22 Centrale of the Congo River Basin on a monthly time scale for the 2000-2012 period. The 23 model was calibrated, validated, and compared with discharge from gauging stations and 24 surface water elevation from radar altimetry. Results showed that upland runoff from the 25 Congo River was the highest contributor to the Cuvette Centrale (33 percent) followed closely 26 by efficient precipitation inside the Cuvette Centrale (31 percent) with right bank and left bank 27 tributaries contributing 25 percent and 11 percent respectively. We simulated monthly mean 28 interannual inflows of approximately 34,150 m 3 s-1 (88 billion m 3) with the main flood peaking 29 in November (45,310 m 3 s-1) and total outflows averaging around 39,860 m 3 s-1 (100 billion 30 m 3) peaking at 52,430 m 3 s-1 in December for the simulation period. We subsequently estimated 31 a negative monthly mean interannual variation of storage in the Cuvette Centrale wetlands in 32 the order of 5,700 m 3 s-1 suggesting that the Cuvette Centrale supplies the river during low 33 water periods. This highlights the important regulatory function of the Cuvette Centrale and 34 the need for protection of groundwater resources in order to maintain wetland water quantities 35 and quality. 36

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 .

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.

A thermodynamic model to calculate the “Sulfide Content at Sulfide Saturation” or SCSS of basaltic and intermediate composition silicate melts has been built from four independently measurable thermodynamic entities, namely the standard state Gibbs free energy of the saturation reaction, the “sulfide capacity”, and the activities of FeO in the silicate melt and of FeS in the coexisting sulfide: ln [S2-]SCSS = ∆G(FeO-FeS)/RT + ln C(S2- )- ln a(FeO)(sil melt) + ln a(FeS)sulf The model was calibrated for silicate melts of basic and intermediate composition from published experimental results as a function of temperature, silicate melt composition, and sulfide matte composition in the system Fe-Ni-Cu-S-O at 1 bar. The likely effects of pressure and H2O content on SCSS were included in an exploratory way. The model was tuned against the large dataset of S contents in OFB glasses of Jenner and O’Neill (2012), giving it a precision comparable to that of the S analyses themselves, which is ~ 5%. All but 3% the OFB glasses were found to be sulfide saturated within uncertainty; these 3% have lost S by devolatization, revealed by their low S/Se. Applying the model to other OFB datasets suggests sulfide saturation is ubiquitous, including olivine-hosted melt inclusions proposed previously to be sulfide undersaturated. The sulfur fugacity (fS2) of undegassed Ocean Floor Basalts varies proportionally to fO2, with log10fS2 typically within the range -0.6 to +0.4.

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.

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.

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.

Magmas readily react with their surroundings, which may be other magmas or solid rocks. Such reactions are important in the chemical and physical evolution of magmatic systems and the crust, for example, in inducing volcanic eruptions and in the formation of ore deposits. In this contribution, we conceptually distinguish assimilation from other modes of magmatic interaction and discuss and review a range of geochemical (+/- thermodynamical) models used to model assimilation. We define assimilation in its simplest form as an end-member mode of magmatic interaction in which an initial state (t0) that includes a system of melt and solid wallrock evolves to a later state (tn) where the two entities have been homogenized. In complex natural systems, assimilation can refer more broadly to a process where a mass of magma wholly or partially homogenizes with materials derived from wallrock that initially behaves as a solid. The first geochemical models of assimilation used binary mixing equations and then evolved to incorporate mass balance between a constant-composition assimilant and magma undergoing simultaneous fractional crystallization. More recent tools incorporate energy and mass conservation in order to simulate changing magma composition as wallrock undergoes partial melting. For example, the Magma Chamber Simulator utilizes thermodynamic constraints to document the phase equilibria and major element, trace element, and isotopic evolution of an assimilating and crystallizing magma body. Such thermodynamic considerations are prerequisite for understanding the importance and thermochemical consequences of assimilation in nature, and confirm that bulk assimilation of large amounts of solid wallrock is limited by the enthalpy available from the crystallizing resident magma. Nevertheless, the geochemical signatures of magmatic systems-although dominated for some elements (particularly major elements) by crystallization processes-may be influenced by simultaneous assimilation of partial melts of compositionally distinct wallrock.

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.

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.

Many rivers systems of the world are super-saturated in dissolved CO2 (pCO2) relative to equilibrium with the atmosphere — why? Here we compare the coupled organic matter and pCO2 dynamics of the world’s two largest river systems, the Amazon and Congo, where data sets enable insights into the overall functioning of the respective basins. Discharge is the primary control on particulate (POC) and dissolved organic carbon (DOC) export in both the Amazon and Congo Rivers. Total suspended sediments (TSS) yield from the Amazon is twenty times greater per unit area than the Congo. However, despite low TSS concentrations, the Congo has a POC content approximately five times higher than the Amazon. The organic rich character of both watersheds is reflected in the DOC export, with the Amazon exporting ~ 11% and the Congo ~ 5% of the global land to ocean flux, based on measurements from the last discharge gauging stations. But care should be taken when describing estimates of TSS and carbon to the ocean. Processing and sequestration in tidal and coastal areas can significantly alter TSS and carbon delivery, and last discharge gauging stations are typically hundreds of kilometers from the sea. pCO2 in the Amazon mainstem ranges from 1,000 to 10,000 μatm, with floodplain lakes ranging from 20 to 20,000 μatm. Concentrations in the Congo mainstem are lower, with maximum values of ~5,000 μatm observed. The elevated level of pCO2 even as far as the mouth of such major rivers as the Amazon and Congo, up to thousands of kilometers from CO2-rich small streams, poses a most interesting question — what set of processes maintains such high levels? The answer is presumably some combination of instream metabolism of organic matter of terrestrial and floodplain origin, and/or injection of very high pCO2 water from local floodplains or tributaries.

Iron is present in magmas at concentrations ranging from less than 1 wt% to more 8 than 10 wt% in two valence state. In general, Fe2+ is a network modifier in the melt structure while Fe3+ is a weak network former. The ratio Fe3+/(Fe3+ + Fe2+) depends on temperature, pressure, oxygen fugacity and melt composition. Parametric models allow its calculation, but the complex links between melt composition, iron oxidation state and coordination can be further rationalized using a ionic-polymeric model. Constraining concentration and oxidation state of iron is critical for determining magma density and viscosity, which drive exchanges of matter and heat in the Earth. At high pressures, changes in the coordination of elements, including iron, yield a stiffening and densification of magmas, potentially influencing dynamic and geochemical processes. Near surface, crystallization of Fe-bearing phases changes the residual melt composition, including iron content and oxidation state as well as volatile concentration, ultimately driving large changes in density and viscosity of magmas, and, hence, in the dynamic of fluid flow in volcanic systems. The complex interplay between magma iron content and oxidation state, major element chemistry, crystal and volatile content thus can play a large role on the dynamic of volcanic systems.

In its three-dimensional (3-D) characterization, drought is approached as an event whose spatial extent changes over time. Each drought event has an onset and end time, a location, a magnitude, and a spatial trajectory. These characteristics help to analyze and describe how drought develops in space and time, i.e., drought dynamics. Methodologies for 3-D characterization of drought include a 3-D clustering technique to extract the drought events from the hydrometeorological data. The application of the clustering method yields small ‘artifact’ droughts. These small clusters are removed from the analysis with the use of a cluster size filter. However, according to the literature, the filter parameters are usually set arbitrarily, so this study concentrated on a method to calculate the optimal cluster size filter for the 3-D characterization of drought. The effect of different drought indicator thresholds to calculate drought is also analyzed. The approach was tested in South America with data from the Latin American Flood and Drought Monitor (LAFDM) for 1950–2017. Analysis of the spatial trajectories and characteristics of the most extreme droughts is also included. Calculated droughts are compared with information reported at a country scale and a reasonably good match is found.

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.

Health outcomes attributable to wildfire smoke pollution exposure are an increasingly important global health issue especially as wildfires are increasing in frequency and intensity with climate change. In this chapter, we present an up-to-date overview of the literature regarding the health consequences of wildfire smoke pollution exposure experienced by adults, identify research gaps, and propose possible areas for future epidemiological studies. We also discuss existing interventions to reduce the negative health outcomes associated with wildfire smoke pollution exposure.

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.

The Tropical Rainfall Measuring Mission (TRMM) daily (3B42) and monthly (3B43) rainfall products are evaluated relative to synoptic weather station observations in Cameroon and according to the main agro-climatic regions. In order to achieve this goal, deterministic and categorical metrics were used, as well as inter annual variability and seasonnal distributions. Outcomes of the comparison showed that synoptic weather station data are strongly correlated with the TRMM 3B43 data and that rainfall distribution is characteristic for each agro-climatic region. The highest skill scores were observed in the Sudano-sahelian, High Savannah, and Western Highlands zones, while the Uni-modal Equatorial zone displayed the lowest correspondence scores between TRMM rainfall estimates and station-based observations. Daily TRMM 3B42 showed good performance in detecting rainy events, especially for light and moderate intensity rainfall events. TRMM 3B42 overestimates rainfall intensities except in the uni-modal region where rainfall intensities are underestimated. Rainfall seasonnality, as well convective zone are well reproduced by the TRMM datasets. Overall, the skill of TRMM 3B42 decreases for increasing precipitation intensities.

The Himalayan orogen exposes a range of metamorphosed assemblages, from low-grade Indian shelf sediments of the Tethyan Formation to eclogite and ultra-high pressure rocks documented near the suture zone between the Indian craton and Asian subcontinent. Barrovian-grade pelites and mafic protoliths are exposed in the Himalayan core and include the Greater Himalayan Crystallines and Lesser Himalayan Formations. These units are separated by the Main Central Thrust (MCT). This fault system accommodated a significant amount of India-Asia convergence and is the focus of several models that explore ideas about the development of the range and collisional belts in general. These units provide critical information regarding the mechanisms of heat transfer within collisional belts. Garnets collected across the MCT record their growth history through changes in chemistry. These chemical changes can be extracted and modeled using a variety of thermodynamic approaches. This paper reviews the geological framework of the Himalayas with a focus on the protolith of its metamorphosed assemblages. It describes and applies particular thermobarometric techniques to decipher the metamorphic history of several garnet-bearing rocks collected across the MCT in central Nepal. Comparisons are made between the results of previously-reported conventional rim P-T conditions and P-T paths extracted using the Gibb’s method to isopleth thermobarometry and high-resolution P-T path modeling using the same data and assemblages. Predictions of the paths on garnet zoning are also presented for the high-resolution P-T path modeling and Gibb’s method using the program TheriaG. Although the approaches yield different absolute conditions and P-T path shapes, all are consistent with the development of the MCT shear zone due to imbrication of distinct rock packages. Greater Himalayan Crystalline garnets experienced higher-grade conditions that make extracting its P-T conditions and paths a challenge. Lesser Himalayan garnets appear to behave as closed systems and are ideally suited for thermodynamic approaches.