We present a Bayesian geospatial modeling framework developed for the synthesis of point prevalence and health facility catchment data of mixed types: Plasmodium parasite prevalence and malaria febrile incidence. Since the clinical case definition for health facility record keeping is less strict than that used in cohort studies used to construct previous parasite prevalence to clinical incidence relationships (the latter usually incorporating a parasite density threshold to remove background fevers accompanied by coincidence asymptomatic parasite infections) our model learns a smooth prevalence-to-incidence conversion during posterior sampling. Also jointly fitted are a catchment model based on the relative travel times between each pixel location and its nearby health facilities, as well as a distribution regression-based covariance structure for explaining the residual errors at health facility level based on the similarity between the ‘bags’ of covariate values in their respective catchments.

Stochastic parameterizations are used in numerical weather prediction and climate modeling to help capture the uncertainty in the simulations and improve their statistical properties. Convergence issues can arise when time integration methods originally developed for deterministic differential equations are applied naively to stochastic problems. (Hodyss et al 2013, 2014) demonstrated that a correction term to various deterministic numerical schemes, known in stochastic analysis as the Itô correction, can help improve solution accuracy and ensure convergence to the physically relevant solution without substantial computational overhead. The usual formulation of the Itô correction is valid only when the stochasticity is represented by white noise. In this study, a generalized formulation of the Itô correction is derived for noises of any color. It is applied to a test problem described by an advection-diffusion equation forced with a spectrum of fast processes. We present numerical results for cases with both constant and spatially varying advection velocities to show that, for the same time step sizes, the introduction of the generalized Itô correction helps to substantially reduce time integration error and significantly improve the convergence rate of the numerical solutions when the forcing term in the governing equation is rough (fast varying); alternatively, for the same target accuracy, the generalized Itô correction allows for the use of significantly longer time steps and hence helps to reduce the computational cost of the numerical simulation.

We are studying the generation and propagation of whistler-mode waves from the upper ionosphere into the magnetosphere. We are using a ray-tracing approach to solve the wave propagation problem, which enables low computational-cost, rapid parameter scans. This approach also gives insights into the physics of wave propagation, through the use of simplified models. We have developed a new python framework called Maestro. It is designed to orchestrate the ray tracing simulations, making them easier to setup, run, and analyze once complete. Maestro ray tracing results will be shown for whistler waves in simplified plasma geometries, illustrating the physics of whistler propagation, ionospheric ducting of VLF waves, and magnetospheric reflection. Results will also be shown for rays propagated in a more realistic ionospheric model, as computed by the SAMI2/3 code. This research is supported by the DARPA Defense Science Office

To characterize how moderately large impactors might alter the differentiation and internal structure of Mars, we examine the fractional crystallization of intermediate depth magma oceans, and document that residual liquids ultimately become denser than normal Martian mantle, establishing unstable density gradients and inducing extensive magma descent within an evolving Mars. Fractional crystallization of intermediate depth magma oceans on Mars is likely to produce liquids that are dense enough to descend to the core-mantle boundary (CMB) and thus form a stably stratified thermochemical boundary layer at the Martian CMB. If this layer cooled sufficiently to crystallize, its mineralogy would be dominated by garnet and ferropericlase, or stishovite and ringwoodite, with changes in the descending liquid’s bulk composition having relatively minor effects on the resulting phase assemblage. While the size of Mars’ core remains uncertain, the addition of such a thermal boundary layer would impede the stabilization of (Mg, Fe)SiO3-perovskite at depth in Mars, although it would contain modest amounts of CaSiO3-perovskite. Such a compositionally distinct thermal boundary layer at the base of the Martian mantle would substantially elevate the inferred temperature of the Martian core, and also produce markedly lowered heat flow at the top of its core, with a potentially causal relation with the current lack of an internally generated Martian magnetic field. We calculate the seismic velocity anomalies that would be expected to be associated with such a layer, and find that the shift in mineralogy at depth should produce a seismic discontinuity that could prospectively be detectable by Martian seismic deployments.

Streamers are millimeter-diameter cold plasma discharges that can extend, branch, and interact with complex collective dynamics to form meter-scale systems. Such streamer systems can be expected to play an important role in lightning. Small-scale streamer systems may grow to provide sufficient ionization and intensification of field to support initiation of hot plasma channel development. Streamer systems developing ahead of the lightning channel may guide channel extension and help explain the observed step-wise extension process. Rapidly-developing streamer systems may also help explain recent observations of fast positive and negative breakdown. We present simulations of streamer system development based on approximate particle-like treatment of individual streamer behavior but including large-scale system dynamics including interaction, collision/connection, and secondary streamer initiation. Here we focus on the observable effects of such streamer system development, including electrostatic field change and electromagnetic wave emissions with frequencies up to 500 MHz. Preliminary results suggest high-frequency (HF) emissions due to streamer acceleration and interaction in isolated streamer systems are comparable in scale to observations of HF emission from lightning and develop before detectable static field change occurs, but these results depend on simulation parameters.

We present an intercomparison and consistency study of aerosol optical depth (AOD) retrievals from the 1625 nm channel that has recently been added to the SURFRAD’s Multi-Filter Rotating Shadowband Radiometer (MFRSR) instruments. In addition, a Multi-Filter Radiometer (MFR) has been installed at the SURFRAD sites to measure upwelling radiation in the same wavelength bands. SURFRAD (Surface Radiation Budget Monitoring) is a NOAA measurement network with the prime directive to determine the surface radiative budget. It is composed of seven sites across the United States and two mobile facilities. Each site is equipped with an MFRSR which measures global and diffuse solar radiation in one broadband and six narrow band spectral channels. MFRSR retrivals include several aerosol optical properties including the spectrally resolved AOD at 415, 500, 673, 870, 940, and 1625 nm. With particular focus on the AOD from the 1625 nm channel we discuss measurement uncertainties based on instrument limitations, uncertainties from the retrieval algorithm, and statistical considerations. Some of the SURFRAD sites have a colocated CIMEL sunphotometer that in some instances is equipped with a 1625 nm channel. While the two datasets are highly correlated we observe various deviations. We furthermore present an aerosol particle size distribution retrieval based on the AOD from 5 narrow band MFRSR channels. Here we put particular emphasis on the 1625 nm channel and its potential to improve the retrieval of the coarse mode fraction of the size distribution.

Understanding the consequences of different management practices on vegetation phenology, forage production and quality, plant and microbial species composition, greenhouse gas emissions, and water budgets in tallgrass prairie systems is vital to identify best management practices. As part of the Southern Plains Long-Term Agroecosystem Research (SP-LTAR) grassland study, a long-term integrated Grassland-LivestOck Burning Experiment (iGLOBE) has been established with a cluster of six eddy covariance (EC) systems on differently managed (i.e., different burning and grazing regimes) native tallgrass prairie systems located in different landscape positions. The purpose of this paper is to describe this long-term experiment, report preliminary results on the responses of differently managed tallgrass prairies under variable climates using satellite remote sensing and EC data, and present future research directions. In general, vegetation greened-up and peaked early, and produced greater forage yields in burned years. However, drought impacts were greater in burned sites due to reductions in soil water availability by burning. The impact of grazing on vegetation phenology was confounded by several factors (e.g., cattle size, stocking rate, precipitation). Moreover, prairie systems located in different landscapes responded differently, especially in dry years due to differences in water availability. The strong correspondence between vegetation phenology and eddy fluxes was evidenced by strong linear relationships of greenness index (i.e., enhanced vegetation index) with evapotranspiration and gross primary production. Results indicate that impacts of climate and management practices on vegetation phenology may profoundly impact carbon and water budgets of tallgrass prairie. Interacting effects of multiple management practices and inter-annual climatic variability on the responses of tallgrass prairie highlight the necessity of establishing an innovative and comprehensive long-term experiment to address inconsistent responses of tallgrass prairie to different intensities, frequencies, timing, and duration of management practices, and to identify best management practices.

Large Igneous Provinces (LIPs) are among the greatest magmatic events in Earth history withvolumes in excess of ∼500,000 km3 of predominantly basaltic lavas covering hugecontinental and ocean regions (>100,000 km2). Field observations suggest that lava flowfields in LIPs are made largely of sheet pāhoehoe lava lobes and the 10-100 m thick flows areformed by inflation. Understanding the emplacement history of these lava lobes can help usinfer the magnitude and temporal dynamics of past events.We use a phase-field model to describe solidification and re-melting of sequentially emplacedlava flows. We calibrate model parameters using field measurements at Makaopuhi lava lakeand perform extensive numerical simulations by varying the thickness of individual flow and thetime intervals between eruptions. These results help quantify the complex interplay betweenthermal evolution, flow thickness and emplacement frequency. If flows are thick enough andthe interval between emplacement short enough, reheating and re-melting may remove thetextural record of flow contacts – making flows appear thicker than they actually were. Guidedby field observations in Columbia River Basalt and Deccan Traps, we illustrate how the finalmorphology of sequentially emplaced lava is controlled by both the time scale of emplacementintervals and the time scale of cooling. We summarize our results to provide theoreticalconstraints on the thickness and emplacement intervals of individual LIP lava flows.

The increase in world population, added to socioeconomic development and climate change, have highlighted one of the biggest problems worldwide: the depletion of water resources. The La Ligua and Petorca river basins, in central Chile, are an example of this problem, as rainfall has decreased in recent years, while socio-economic activities, mainly agriculture have increase. This situation has led to a severe water stress, and the need for integrated and sustainable river basin management, aimed at understanding the behavior of basins, aquifers, and the exchange of flows between them. Therefore, the main objective of this research is to quantify the impacts of climate change, in terms of groundwater scarcity, in semi-arid basins using integrated modeling of water resources. For this purpose, groundwater/surface waters integrated models of La Ligua and Petorca basins were developed using WEAP and MODFLOW. Both basins present different hydrological, social, and geographical characteristics. Different scenarios were evaluated to quantify groundwater depletion. These scenarios depend on climatic forcings, such as precipitation and temperature, which were obtained from the Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Results forecast that annual precipitation will decrease, whereas average annual temperature will increase in these semi-arid regions. As a consequence, the aquifer’s recovery rate will reduce, decreasing the number of wells that provide drinking water in rural and agricultural areas. In conclusion, the coupling of hydrological and hydrogeological models is a tool that allows researchers and stakeholders to make opportune and appropriate decisions on the management of basins and aquifers, which is even more important in basins that are expected to be or are already under severe water stress.

The use of modeling tools for integrated water resources management is a complex task due to the large number of processes involved in a basin. Moreover, these modeling tools commonly require information that is not readily available, such as illegal water withdrawals, or other data difficult to obtain, which results in groundwater models that fail to capture the aquifer dynamics. In recent years, machine learning algorithms have shown outstanding performance as prediction tools. Despite being questioned for not having a physical basis, they have been used in areas such as hydrology and hydrogeology (e.g., for flow prediction, rain forecast). Thus, the objective of this research is to estimate groundwater withdrawals using machine learning algorithms and integrated water management models. To achieve this objective, ensembles of groundwater levels were generated with a previously calibrated groundwater/surface water integrated model. Then, these ensembles were used as input parameters for Gaussian process regression (GPR) and artificial neural network (ANN) models to construct time series of water withdrawals throughout a basin. This method was applied in the Petorca and La Ligua basins, in central Chile, as they exhibit a contrasting reality in terms of water availability even when they have geographical proximity. Also, these basins are within an effective extraction monitoring program lead by the Chilean water authority that can be used to validate the users’ water withdrawal. Our results show that the GPR model, compared to ANNs, adequately estimates the spatiotemporal distribution of groundwater withdrawals in the pilot basins. Thus, the use of machine learning algorithms improves the performance of integrated water resources management models.

The rapid acceleration of Greenland Ice Sheet mass loss over, particularly the last two decades, is well documented.However, limits in early remote sensing restricted the details with which we could examine local changes on an ice-sheet-wide scale, particularly in areas of slow motion, along shear margins and complex coastal terrain. We explore the localcharacter of rapid contemporary change marine-terminating glaciers using satellite-derived ice sheet surface velocities,glacier terminus advance/retreat history, and surface elevation-change data from the 1980s to the present. Widespread glacierterminus retreat is a strong and more consistent climate response indicator than velocity change, but local changes in velocityare critical indicators of rapid ice sheet reconfiguration. Ice thickness changes related to changing ice dynamics often providethe first evidence of processes that initiate outlet glacier retreats and mass loss, such as the development of sub-ice shelfcavities and subglacial hydrology changes. Reconfiguration is observed locally as narrowing zones of fast-flow, ice flowrerouting, shear margin migration, and likely glacier outlet abandonment. These patterns are apparent in all ice sheet sectorsand observable from space-borne instruments. The rapid reconfiguration now well underway in Greenland has wide-rangingimplications, including expected changes in subglacial hydrology, ice discharge, freshwater flux to the ocean, and transport ofnutrients and sediment. Lacking detailed observations of earlier deglaciations and current limits on ice-sheet modelcapabilities, the expanding details of these combined observational records may provide a valuable analog for studying pastice sheet dynamics and projecting future ice loss.

The NASA Mars InSight lander carries two color cameras: The Instrument Context Camera (ICC), a wide-angle camera mounted to the lander, and the Instrument Deployment Camera (IDC), mounted to the lander robotic arm. The cameras provide both valuable information for lander and instrument operation and essential data for a range of scientific studies. In this study, we examine ICC images to investigate whether there is any shift that might be associated with lander movement. Detection of lander movement may lead to potential applications, including a possibility of inferring the background magnetic field. We focused on the data intervals where there were strong winds, many ICC images taken during the Martian day, or during seismometer deployment. Because any potential shift in ICC images is expected to be minuscule, we developed a computer program to search for the shift between images which minimizes the sum of squared differences in brightness. The preliminary results indicate that the lander, ICC, and the mount are very robust. Our image analysis method is suitable for identifying object movement in color images from both InSight cameras.

From 2014 to at least 2018, ecosystem health in the eastern boundary upwelling system along the west coast of North America was significantly impacted by a combination of a marine heatwave known as The Blob and an El Niño event, as well as by ongoing climate change. At the northern limit of this upwelling system, in Queen Charlotte Sound on the highly productive central coast of British Columbia, we have demonstrated that changing conditions on the continental shelf and in coastal waters may be skillfully predicted based on observed open-ocean and large-scale atmospheric conditions on seasonal to interannual timescales. In this work, we build on our understanding of this predictability by presenting a statistical model that relates physical and biogeochemical ocean properties in this region to conditions at and beyond the shelf break and large-scale forcing metrics. The model is based on statistical relationships developed using a multi-decadal archive of hydrographic and biogeochemical data in combination with high-temporal-resolution mooring records collected in Queen Charlotte Sound, and is supported by a conceptual understanding of the upwelling and downwelling regimes in this region. We next use the model to examine specifically how the arrival of The Blob and the subsequent El Niño modified ocean conditions on the continental shelf during both upwelling and downwelling, including impacts on nutrient concentrations, dissolved oxygen levels, stratification, and warming. Our results suggest it may be possible to predict changes in this upwelling system caused by future anomalous events and climate change using readily available large-scale data products such as the Argo dataset and NOAA Upwelling Index.

Light detection and ranging (LiDAR) technologies are changing the ways in which scientists research the Arctic. Unmanned aerial vehicle (UAV)-based LiDAR collects detailed structural landscape data by returning high density point clouds. LiDAR systems are improving the quality and accuracy of data collection compared to field surveys and help to remove some of the logistical barriers of research in remote and complicated terrain. Our study mapped thermokarst depressions in a 3 km2 watershed on the Seward Peninsula near Nome, Alaska in 2017 and 2018. The watershed is characterized as tussock permafrost landscape consisting of grasses and mosses interspersed with patches of dense shrubs. By configuring the UAV with a 32 laser swath and flying slowly at low altitude, we collected high density point clouds of about 4,000 points m2, including high density terrain surface points underneath dense shrubby vegetation. We then modeled the sub-vegetation terrain surface at very fine detail to detect thermokarst depressions. Combining these high resolution data with vegetation surveys and topographic properties, we tested the relationship between permafrost subsidence, thermokarst depressions and vegetation type, specifically the relationships in shrub-associated thermokarst features. By coupling our LiDAR data and analysis with hydrologic models, climate variables (e.g., snow depth, soil moisture), and vegetation surveys, we can infer geospatial relationships between thermokarst development, vegetation, and landscape position throughout the watershed. The technologies used in our study have implications for predicting the development of future thermokarst features and permafrost thaw sites across the Arctic.

This paper presents some pre-earthquake and coseismic water-level and stream-flow changes observed in Japan and Taiwan. The results suggest that: (1) Hydrological precursors do occur; (2) they can be observed at a relatively few sensitive sites; (3) these “sensitive” sites consistently show coseismic changes; (4) the mechanisms of these precursors can be understood, if crustal heterogeneity and pre-earthquake slow-slip events are included in their mechanism consideration. In Japan, the monitored well was sensitive, because it tapped a permeable aquifer connected to a nearby fault, which was under a hydraulic –pressure gradient caused by pumping activity in an underground gallery on the other side of the fault. Also, the fault was in a near-critical condition, such that leakage could be caused by a small crustal disturbance, such as seismic shaking, in the case of coseismic changes, or a stress increment, in the case of precursory changes. In Taiwan, both the sensitive well and the stream gauge were located on the hanging wall of the seismogenic fault of the magnitude-7.6 Chi-Chi earthquake in 1999. The hanging wall probably bulged before the earthquake, causing opening up of fractures along some secondary faults, and allowing stored groundwater to flow down to the monitored stream and caused the observed pre-earthquake stream-flow increase. The continued fracture-opening process toward greater depth then caused down-flow of water into greater depth of the crust and triggered the occurrence of slow-slip events, which propagated down-dip to where these faults met the seismogenic fault and caused slow-slip events to propagate up-dip the seismogenic fault toward the hypocenter, triggering the earthquake and the observed water-level precursor at a well located near the tip of the wall.

Current machine learning based shear wave velocity (Vs) inversion using surface wave dispersion measurements utilizes synthetic dispersion curves calculated from existing 3-D velocity models as training datasets. It is shown in the previous studies that the performances of the resulting networks are dependent on the diversity of the training data. We present an improved semi-supervised deep learning algorithm-based method that incorporates both observed and synthetic surface wave dispersion curves in the network training process. The algorithm is termed Wasserstein cycle-consistent generative adversarial networks (Wcycle-GAN), which combines the architecture of cycle-consistent GAN with Wasserstein loss metrics in optimization. Different from conventional supervised deep learning approaches, the GAN architecture also extracts structural information from the observed surface wave dispersion data in the training process that may improve generalization of the resulting network. The cycle-consistent loss addresses soft constraints on the trained neural networks to be reversible and thus reduces the variance of the trained networks. The Wasserstein metric provides weaker topology for convergence and improves spatial continuity of the predicted shear velocity (Vs) models. We demonstrate these improvements by applying the Wcycle-GAN to 4066 fundamental mode Rayleigh wave phase and group dispersion curves obtained in Southern California (SC). In general, the 3-D Vs model predicted by the best training Wcycle-GAN is consistent with previous surface wave tomography studies of SC in the overlapping area, but with smaller data misfit, yields better spatial smoothing, and provides improved images of structures near faults and in the top 5 km. Our results indicate that the proposed Wcycle-GAN algorithm has strong training stability and generalization abilities.

Researchers and end users using climate data face a challenge when they analyze the data they need. Data volumes are increasing very rapidly, and the ability to download all needed data is often no longer possible. Also, it can be complex to install, configure and use some advanced analysis tools on such large datasets. This is especially true when they are stored in a federated architecture like the ESGF. An example of a complex analysis tool used in climate research and adaptation studies is a tool to follow storm tracks. In the context of climate change, it is important to know if storm tracks will change in the future, in both their frequency and intensity. Storms can cause significant societal impacts, hence it is important to assess future patterns. Having access to this type of complex analysis tool is very useful, and integrating them with front-ends like the IS-ENES climate4impact (C4I) would enable the use of those tools by a larger number of researchers and end users. Integrating this type of complex tool is not an easy task. It requires significant development effort, especially if one of the objectives is also to adhere to FAIR principles. The DARE Platform enables research developers to faster develop the implementations of scientific workflows more rapidly. This work presents how such a complex analysis tool has been implemented to be easily integrated with the C4I platform. The DARE Platform also provides easy access to e-infrastructure services like EUDAT B2DROP, to store intermediate or final results and powerful provenance-powered tools to help researchers manage their work and data. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements N°824084 and N°777413.

Zircon Raman dating is an emergent thermochronological method. It exploits the disruption of the zircon lattice due to α-disintegration of trace amounts of 238U, 235U, 232Th, and their daughters. The radiation damage broadens the Raman bands and shifts them to lower wavenumbers. The Raman bandwidths provide a sensitive measure for the accumulated lattice damage (Nasdala et al., 1995; Nasdala et al., 2001). The measured bandwidth and the effective uranium concentration define the Raman age (Härtel et al., 2021). Radiation damage anneals upon heating and the meaning of the Raman age depends on the zircon’s thermal history. The Raman age is a formation age if no annealing has taken place, or a reset age if all pre-existing damage has been annealed by a geological heating event. In the case of partial annealing, however, the Raman age is a mixed age with no obvious geological significance. Mixed ages are difficult to interpret and cannot be distinguished from reset ages using the standard procedure for annealing detection (Nasdala et al., 2001). On the other hand, inhomogeneous damage distributions due to actinide zoning within zircon grains present a problem for zircon Raman dating. Overlapping signals from more and less damaged zones lead to asymmetric Raman bands and overestimated bandwidths (Nasdala et al., 2005). We discuss Raman spectra of zircon from partially annealed samples and spectra with asymmetric bands. We introduce discrimination plots based on the 356, 439, and 1008 cm-1 bandwidths that provide a means for detecting and distinguishing asymmetry and partial annealing. We discuss examples of zircon Raman dating and present a measurement protocol.