Recent advances in reconstructing Earth’s magnetic field and associated currents by utilizing data mining of in situ magnetometer observations in the magnetosphere have proven remarkably accurate at reproducing observed ion diffusion regions. We investigate the effect of placing regions of localized resistivity in global simulations of the magnetosphere at specific locations inspired by the data mining results for the substorm occurring on July 6, 2017. When explicit resistivity is included, the simulation forms an x-line at the same time and location as the MMS observation of an ion diffusion region at 15:35 UT on that day. Without this explicit resistivity, reconnection forms later in the substorm and far too close to Earth ($\gtrsim-15R_E$), a common problem with global simulations of Earth’s magnetosphere. A consequence of reconnection taking place farther down the tail due to localized resistivity is that the reconnection outflows transport magnetic flux Earthward and thus prevent the current sheet from thinning enough for reconnection to take place nearer Earth. As these flows rebound tailward from the inner magnetosphere, they can temporarily and locally (in the dawn-dusk direction) stretch the magnetic field allowing for small scale x-lines to form in the near Earth region. Due to the narrow cross-tail extent of these x-lines ($\lesssim5R_E$) and their short lifespan ($\lesssim5$min), they would be difficult to observe with in situ measurements. Future work will explore time-dependent resistivity using 5 minute cadence data mining reconstructions.

Floods inflict significant damage even outside the 100-year floodplain. Thus, restricting flood risk analysis to the 100-year floodplain (special flood hazard area (SFHA) in the U.S.A.) is misleading. Flood risk outside the SFHA is often underestimated because of minimal flood-related insurance requirements and regulations and sparse flood depth data. This study proposes a systematic approach to predict flood risk for a single-family home using average annual loss (AAL) in the shaded X Zone – the area immediately outside the SFHA (i.e., the 500-year floodplain), which lies between the 1.0- and 0.2-percent annual flood probability. To further inform flood mitigation strategy, annual flood risk reduction with additional elevation above an initial first-floor height () is estimated. The proposed approach generates synthetic flood parameters, quantifies AAL for a hypothetical slab-on–grade, single-family home with varying attributes and scenarios above the slab-on-grade elevation, and compares flood risk for two areas using the synthetic flood parameters vs. an existing spatial interpolation-estimated flood parameters. Results reveal a median AAL in the shaded X Zone of 0.13 and 0.17 percent of replacement cost value () for a one-story, single-family home without and with basement, respectively, at and 500-year flood depth < 1 foot. Elevating homes one and four feet above substantially mitigates this risk, generating savings of 0.07–0.18 and 0.09–0.23 percent of for a one-story, single-family home without and with basement, respectively. These results enhance understanding of flood risk and the benefits of elevating homes above in the shaded X Zone.

To assess zonal temperature and biogeographical patterns in the Paleogene of the Southern Ocean, we present new multi-proxy air and sea surface temperature data for the latest Paleocene (~57–56 Ma) and the Paleocene-Eocene Thermal Maximum (PETM; ~56 Ma) from the northern margin of the Australo-Antarctic Gulf (AAG). The various proxies document the well-known late Paleocene gradual warming and, superimposed, two late Paleocene pre-cursor warming events, hundreds of kyrs prior to the PETM. Remarkably, however, air and sea surface temperature reconstructions for the AAG and SW Pacific during the latest Paleocene, PETM and Early Eocene Climatic Optimum (~53–49 Ma) show similar trends and, within proxies, similar absolute temperatures. The record implies that the exceptional warmth previously recorded in the SW Pacific extended westward into the AAG. This contrasts with the modeled circulation and temperature patterns. We suggest that simulations of ocean circulation underestimate heat transport in the SW Pacific due insufficient resolution, not allowing for mesoscale eddy-related heat transport. We argue for a systematic approach to tackle model and proxy biases in marginal marine settings, including assessment of underexplored factors as high-latitude proxy mechanisms to confidently assess temperature in these non-analogue climates.

 inland, or 37-44%. Our results document that the extremely warm summer of 2012 produced near-surface ice layers at higher elevations, enabling ice slabs to develop with only moderate melting in the following summers. With repeat flights along a transect in southwest Greenland, we show that moderate melting primarily causes slab thickening through uniform accretion on top of the ice slabs, while large melting events can also trigger localised accretion below existing ice slabs.

The Mars Atmosphere and Volatile Evolution (MAVEN) mission instrument suite includes an ultraviolet echelle spectrograph with high-spectral resolution designed to resolve D and H Lyman-α emissions. The high-spectral resolution mode was previously characterized in the lab and in the cruise phase to Mars and had been calibrated using observations and models of interplanetary hydrogen Lyman-α emissions. This work presents improved characterizations of the high-spectral resolution mode using in-orbit observations that allow for more robust detections of the faint D Lyman-α emission line. Additionally, the instrument was re-calibrated using simultaneous and comparable observations made with the Hubble Space Telescope high-spectral resolution instrument. Comparisons to Lyman-α observations made with the low-resolution UV channel on the spectrometer, that had been calibrated with stars, showed consistency in the brightness values for measurements obtained at similar observational conditions. The combined upgrades to the faint-emission fitting and new calibration techniques of the MAVEN echelle channel have resulted in an improved data-reduction pipeline with favorable implications for the science utility of D and H Lyman-α emissions.

Tropical marginal seas host important sedimentary archives that may be exploited to reveal past changes in continental erosion, chemical weathering, and ocean dynamics. However, these records can be challenging to interpret due to the complex interactions between climate and particulate transport across ocean margins. For the southern South China Sea over the last 90 kyr, we observe a contrasting temporal relationship between the deposition of clay minerals and magnetic minerals, which were associated with two different hydrodynamic modes. Fine-grained clay minerals can be carried in suspension by ocean currents, leading to a rapid response to regional climate-driven inputs. In contrast, changes in magnetic mineralogy were associated with bedload transport and resuspension, leading to a control by glacial-interglacial sea-level variability. Overall, this study indicates that the transfer pathways and mechanisms imparted by varying hydrodynamic conditions exert a substantial influence on the distribution of terrigenous material in continental shelf and margin sediments.

The relationship between Gulf Stream (GS) transport and coastal sea level is investigated using monthly GS transport between 1993–2019 at Florida Straits and ten altimeter tracks. The results show that GS transport decorrelates quickly along its path, indicating it is misleading to assume that transport at a particular location represents strength of the GS as a whole. GS transport south of Cape Hatteras is significantly correlated with coastal sea level in both the South Atlantic Bight (SAB) and Middle Atlantic Bight (MAB). However, the significant correlations in MAB are due to their concurrent response wind on the shelf—the correlation becomes insignificant once the influence of local winds is removed. North of Cape Hatteras, the influence of GS transport on sea level is mostly over the deep ocean and rarely on the shelf, indicating that there is no dynamic link between the GS transport and coastal sea level in this region.

The ion composition of the tail current sheet is essential to understanding the Martian ion escape, however, our current knowledge is poor. Using measurements by Mars Atmosphere and Volatile EvolutioN (MAVEN), we investigate the density of different ion species in the Martian magnetotail current sheet. We find that events we survey of the current sheet, the average occurrence rate of current sheets with dominant heavy ions (O+ and O2+) is about 50%, while the rate of those with dominant H+ is about 20%. A current sheet closer to the terminator is mostly dominated by heavy ions, regardless of upstream solar wind, but it could be dominated sometimes by H+ when it is beyond the downstream distance of 0.75 RM (RM is Mars’ radius), where the occurrence rate of current sheets with dominant H+ (heavy ions) weakly increases (decreases) with solar wind density and dynamic pressure.

Groundwater discharge along channels can affect stream runoff, chemistry, and ecological communities. Although the spatial distribution of groundwater springs can be investigated by areal thermal remote sensing of wide rivers, this technique is difficult to apply to forested headwater streams because their channels are often covered by riparian trees. We present a method of mapping groundwater springs for forested headwater streams based on recording thermal video while walking along channels. We applied this method at two sites in Hokkaido, Japan. At one site, groundwater springs were spaced every ~100 m, and their distribution did not follow the topography at a 1.3-km-long reach underlain by Pleistocene andesite lava. Here, almost all of the springs were colder than stream water and had similar chemistries to each other. At the second site, cold and warm springs were underlain by Holocene volcanic ash. The cold springs mainly seeped from the streambed at the downstream part of the site while warm springs were located at the footslope. Some cold springs had much higher solute concentrations than the stream and warm springs, which suggests that the groundwater discharge to the stream had multiple sources. We also used our method to map the spatial heterogeneity of the stream temperature associated with groundwater inputs. This method is suitable for wide-area surveys because it can easily map the spatial distribution of the surface water temperature and the groundwater discharge along headwater streams.

This study presents the longitudinal distribution of thermospheric density around the terminator (in the dawn and dusk sectors), using observations collected by the atmospheric density detector onboard the Chinese satellite APOD (Atmospheric density detection and Precise Orbit Determination) from 2017 to 2018. The APOD observations show a significant relative longitudinal variation of thermospheric density with global maxima (Δρrmax) near the geomagnetic pole, especially in the winter hemisphere. The annual maximum of Δρrmax appears in the Southern Hemisphere around the June solstices and reaches 26.3% and 39.6% at dawn and dusk, respectively. Compared with at dawn, Δρrmax occurs at a higher latitude with a larger value at dusk. The auroral heating and meridional wind might play an important role in the longitudinal variation of thermospheric density. We further compare the APOD observations with the NRLMSIS 2.0 model predictions under low solar activity condition. The NRLMSIS 2.0 model reproduces similar longitudinal variations to the observations, with hemispheric asymmetry and local time difference.

Mosquitoes are known vectors for disease transmission that cause over one million deaths globally each year. The majority of natural mosquito habitats are areas containing standing water such as ponds, lakes, and marshes. These habitats are challenging to detect using conventional ground-based technology on a macro scale. Contemporary approaches, such as drones, UAVs, and other aerial imaging technology are costly when implemented. Multispectral imaging technology such as Lidar is most accurate on a finer spatial scale whereas the proposed convolutional neural network(CNN) approach can be applied for disease risk mapping and further guide preventative efforts on a more global scale. By assessing the performance of autonomous mosquito habitat detection technology, the transmission of mosquito borne diseases can be prevented in a cost-effective manner. This approach aims to identify the spatiotemporal distribution of mosquito habitats in extensive areas that are difficult to survey using ground-based technology by employing computer vision on satellite imagery. The research presents an evaluation and the results of 3 different CNN models to determine their accuracy of predicting large-scale mosquito habitats. For this approach, a dataset was constructed utilizing Google Earth satellite imagery containing a variety of geographical features in residential neighborhoods as well as cities across the world. Larger land cover variables such as ponds/lakes, inlets, and rivers were utilized to classify mosquito habitats while minute sites such as puddles, footprints, and additional human-produced mosquito habitats were omitted for higher accuracy on a larger scale. Using the dataset, multiple CNN networks were trained and evaluated for accuracy of habitat prediction. Utilizing a CNN-based approach on readily available satellite imagery is cost-effective and scalable, unlike most aerial imaging technology. Testing revealed that YOLOv4 obtained greater accuracy in mosquito habitat detection than YOLOR or YOLOv5 for identifying large-scale mosquito habitats. YOLOv4 is found to be a viable method for global mosquito habitat detection and surveillance.

While hydrogen is one of plausible major light elements in the core, the high-temperature equation of state (EoS) of Fe-H alloy has not been experimentally examined to the core pressure range. Here we measured the volume (V) of non-magnetic (NM) fcc FeH at high pressure and temperature (P-T) to 142 GPa and 3660 K in a laser-heated diamond-anvil cell (DAC) and obtained its P-V-T EoS. An increase in the lattice volume of Fe per H atom, ΔVH, determined as functions of P and T is found to be substantially smaller than the volume of metallic H that has been used to estimate H concentration in Fe-H alloy. The ΔVH is almost identical between fcc and dhcp phases in the NM state, suggesting that it is applicable to hcp. The extrapolation of ΔVH to inner core conditions indicates its maximum H content to be 0.8–0.9 wt%.

Plate motions in Southern California have undergone a transition from compressional and extensional regimes to a dominantly strike-slip regime in the Miocene. Strike-slip motion is most easily accommodated on vertical faults, and major transform fault strands in the region are typically mapped as near-vertical on the surface. However, some previous work suggests these faults have a dipping or listric geometry at depth. We analyze receiver function arrivals that vary harmonically with backazimuth at all available broadband stations in the region. The results show a dominant signal from contrasts in dipping foliation as well as dipping isotropic contrasts from all crustal depths, including from the ductile middle to lower crust. We interpret these receiver function observations as a dipping fault-parallel structural fabric that is pervasive throughout the region. The strike of these structures and fabrics is parallel to that of nearby fault surface traces. We also plot microseismicity on depth profiles perpendicular to major strike-slip faults and find consistently NE-dipping lineations in seismicity shallowing in dip from near vertical (80-85) on the Elsinore Fault near the coastal ranges to 60-65 slightly further inland on the San Jacinto Fault to 50-55 on the San Andreas Fault. Taken together, the dipping features in seismicity and in rock fabric suggest that preexisting fabrics and faults likely act as strain guides in the modern slip regime, with reactivation-like mechanisms operating both above and below the brittle-ductile transition.

Key Points: • The frequency-dependent ratio of pore pressure/confining pressure is obtained using the dynamic poroelasticity. • Hydraulic-parameter estimation depends on the varied solid/fluid coupling. • The dynamic responses of water level to seismic waves are estimated across frequencies. Abstract Estimating the hydraulic properties of subsurface aquifer is crucial to properly evaluating groundwater resources. There are many methods for estimating aquifer parameter using well water-level response using quasi static poroelastic-ity. However, these approaches have ignored the possible frequency-dependent solid/fluid coupling in the porous aquifer. In this study, three great earthquakes (2011 Mw9.1 Tohoku, 2012 Mw 8.6 Sumatra, and 2018 Mw7.6 Minahassa Peninsula) induced coseismic water-level variations in Tengchong (TC) well are observed and estimated using dynamic poroelasticity. Pore pressure/stress ratio across frequency ranges may be switched, and the permeability is estimated and validated in different frequency domains by the seismic-wave time series. The predicted coseismic water levels due to the three great earthquakes are better consistent than those using quasi static poroelasticity. The method proposed in this study is useful to in evaluating the continuous permeability variations by earthquakes and their aftershocks.

Strong solar flares are indeed rare events, which make the flare classification task a rare-event problem. Solar energetic particle events are even rarer space weather events as only a few instances of them are recorded each year. With the unprecedented growth in employment of Machine Learning algorithms for rare-event classification/forecast problems, a proper evaluation of rare-event models becomes a necessary skill for domain experts to have. This task remains to be an outstanding challenge as both the learning process and the metrics used for quantitative verification of models can easily obscure or skew the true performance of models and yield misleading and biased results. To help mitigate this effect we introduce a bounded semimetric space that provides a generic representation for any deterministic performance verification metric. This space, named Contingency Space, can be easily visualized and shed light on models’ performance as well as on the metrics’ distinct behavior. An arbitrary model’s performance can be mapped to a unique point in this space, which allows comparison of multiple models at the same time, for a given metric. Using this geometrical setting we show the difference between a metric’s interpretation of performance and the true performance of the model. Using this perspective, models which are seemingly different but practically identical, or only marginally different, can be easily spotted. By tracking down a learner’s performance at each epoch, we can also compare different learners’ learning paths, which provides a deeper understanding of the utilized algorithms and their challenge in the learning process. Moreover, in the Contingency Space, a given verification metric can be represented by a geometrical surface, which allows a visual comparison between different metrics—a task that without this concept could be done only by the tedious algebraic comparison of metrics’ formulae. Moreover, using such a surface, for the first time we can see and quantify the impact of scarcity of data (intrinsic to rare-even problems) on different metrics. This extra knowledge provides us with the information we need to choose an appropriate metric for evaluation of our rare-event models.

The main objectives of the study were to examine the magnitude and rate of the current status of Accacian decurrens expansions, to identify the driving force of the local communities to shift, crops to tree plantation land use, to evaluate its economic benefits and its problems and opportunities for the sustainable future tree plant production in Fagita Lekoma district. ArcGIS 10.4 and ERDAS Imagine 2014 software was used for spatial analysis and land use classification. Landsat images were used to generate the land use maps by using the maximum likelihood algorithm of supervised classification. The results show that cultivated lands, grasslands, and wetlands are declining while forests and settlements are increasing. Forest cover was the most widespread land use, increasing by 210% during the study year. The conversion rate of forest land is 15.1%, which is the highest value in the general study period of the district. The rationale behind the expansion of forest land use is the introduction and spread of new species of trees known as Accacian decurrens plantations. Accacian decurrens forestation is the main cause of the change of use, system from arable land to forest system. These plantation-based land uses have been shown to be a way to increase economic benefits, employment opportunities and reduce soil erosion and increase soil fertility in the area.

Ionospheric plasma irregularities can be successfully studied with the Swarm satellites. Parameters derived from the in situ plasma measurements and from the topside ionosphere total electron content provide a comprehensive dataset for characterising plasma structuring along the orbits of the Swarm satellites. The Ionospheric Plasma IRregularities (IPIR) data product summarizes these parameters and has already been used in studies related to structuring and variability of ionospheric plasma. We provide a detailed description of the algorithms behind the IPIR data product and demonstrate its use for ionospheric studies.

Microbes play a critical role in regulating the size, composition, and turnover of dissolved organic matter (DOM), which is one of the largest pools of carbon in aquatic ecosystems. Global change may alter DOM-microbe associations with implications for biogeochemical cycles, although disentangling these complex interactions remains a major challenge. Here we develop a framework called Energy-Diversity-Trait integrative Analysis (EDTiA) to examine the associations between DOM and bacteria along temperature and nutrient gradients in a manipulative field experiment on mountainsides in contrasting subarctic and subtropical climates. In both study regions, the chemical composition of DOM correlated with bacterial communities, and was primarily controlled by nutrients and to a lesser degree by temperature. At a molecular-level, DOM-bacteria associations depended strongly on the molecular traits of DOM, with negative associations indicative of decomposition as molecules are more biolabile. Using bipartite networks, we further demonstrated that negative associations were more specialized than positive associations indicative of DOM production. Nutrient enrichment promoted specialization of positive associations, but decreased specialization of negative associations particularly at warmer temperatures in subtropical climate. These global change drivers influenced specialization of negative associations most strongly via molecular traits, while both molecular traits and bacterial diversity similarly affected positive associations. Together, our framework provides a quantitative approach to understand DOM-microbe associations and wider carbon cycling across scales under global change.