Flow recession analysis, relating discharge Q and its time rate of change -dQ/dt, has been widely used to understand catchment scale flow dynamics. However, data points in the plot of -dQ/dt versus Q typically form a wide point cloud due to noise and hysteresis, and it is still unclear what information we can extract from the data points and how to understand the information. In this study, we utilize a machine learning tool to capture the point cloud using the past trajectory of discharge. Our results show that most of the data points can be captured using 5 days of past discharge. While analyzing the machine learning model structure and the trained parameters is a daunting task, we show that we can learn the catchment scale flow recession dynamics from what the machine learned. We analyze patterns learned by the machine and explain and hypothesize why the machine learned those characteristics. The hysteresis in the plot mainly occurs during the early time dynamics, and the flow recession dynamics eventually converge to an attractor in the plot, which represents the master recession curve. We also illustrate that a hysteretic storage-discharge relationship can be estimated based on the attractor.

Continental-scale river hydrodynamic modeling is useful for understanding the global hydrological cycle, and model evaluation is essential for robust calibration and assessing model performance. Although many models have been robustly evaluated using several variables separately, methods for the integrated multivariable evaluation of models have yet to be established. Here, we propose an evaluation method using the overall basin skill score (OSK), based on considering the spatial distribution of different variables via a sub-basin approach. The OSK approach integrates multiple variables to overcome observation-related limitations, such as the distinct temporal and spatial dimensions and unit of measurement unique to each variable, thus judging model performance objectively at the sub-basin and basin scales. As a case study, the global river model, CaMa-Flood, was evaluated using three variables¾discharge, water surface elevation, and flooded area¾for the Amazon Basin, focusing on the impact of using different types of baseline topography data (SRTM and MERIT digital elevation models [DEMs]). CaMa-Flood with the MERIT DEM performed robustly well over a wide range of river depth parameters with a maximum OSK of 0.51 against 0.46 for the SRTM DEM. Single-variable evaluation for all three variables proved inadequate due to low sensitivity for river bathymetry, with good performance outcomes potentially arising for the wrong reasons. This study confirmed that model evaluation using this method enables a balanced evaluation of different variables and a robust estimation of the best parameter set. The proposed method proved useful for flexible, integrated multivariable model evaluation, with modifications allowed per the user’s requirements.

Ambient nitrogen dioxide (NO2) and fine particulate matter (PM2.5) pollution threaten public health in the United States (U.S.), and systemic racism has led to modern-day disparities in the distribution and associated health impacts of these pollutants. Many studies on environmental injustices related to ambient air pollution focus only on disparities in pollutant concentrations or provide only an assessment of pollution or health disparities at a snapshot in time. In this study we aim to document changing disparities in pollution-attributable health burdens over time and, for the first time, disparities in NO2-attributable health impacts across the entire U.S. We show that, despite overall decreases in the public health damages associated with NO2 and PM2.5, ethnoracial relative disparities in NO2-attributable pediatric asthma and PM2.5-attributable premature mortality in the U.S. have widened during the last decade. Racial disparities in PM2.5 attributable premature mortality and NO2-attributable pediatric asthma have increased by 19% and 16%, respectively, between 2010 and 2019. Similarly, ethnic disparities in PM2.5-attributable premature mortality have increased by 40% and NO2-attributable pediatric asthma by 10%. These widening trends in air pollution disparities are reversed when more stringent air quality standard levels are met for both pollutants. Our methods provide a semi-observational approach to tracking changes in disparities in air pollution and associated health burdens across the U.S.

Low-frequency earthquakes are peculiar energy-release events mostly occurring at the transition between the seismogenic and the freely creeping zones of a subducting slab. The source characterization of these events is of fundamental importance to understand physical processes that govern the slow out of equilibrium evolution of the subduction interface that may lead to the generation of large, destructive earthquakes. Nevertheless, their source mechanisms still remain unclear. Here, we estimate the source parameters of ~23,000 low-frequency earthquakes continuously detected from 2013 to 2015 in Shikoku, Japan. We show that a cubic moment-duration scaling characterizes these events, suggesting a self-similar process as for regular earthquakes. However, their high-frequency fall-off suggests an omega-cube decay in contrast to the omega-squared model of earthquakes. Source characteristics do not change when low-frequency earthquake bursts occur during the analyzed three years. On the other hand, we observe a coherent along-strike variation of the product of stress drop and the cube of rupture velocity, possibly related to a weaker behavior of tremor patches in central Shikoku. Secondary microseismic noise and network-dependent completeness magnitude lead to missing event detections that do not allow discriminating between Gutenberg-Richter event size distribution and any deviation from it. Our findings suggest that the same observational limits might affect worldwide detection of low-frequency earthquakes.

Fracture geometry, density, and distribution control fluid and thermal transport in low porosity rocks, as well as their seismic attributes. The creation of cracks throughout the matrix can result in large and disparate reductions in P- and S-wave velocities with negligible porosity development, which breaks the classic velocity-porosity relationship. Properly interpreting these signatures in seismic surveys from stimulated geothermal, oil, and gas reservoirs or seismogenic areas requires a rock physics model that uses realistic elastic properties of fractures. Conventional models for cracked rocks employ specific inclusion shapes such as spheroids with small aspect ratios. However, all these exhibit the same normal-to-shear compliance ratio -- regardless of aspect ratio -- and therefore impact the bulk and shear moduli in the same qualitative way. As such they are inadequate for describing many of the complementary evolutions of P- and S-wave velocities that transpire with the generation of cracks. Using a differential model of decoupled compliances, we analyze published velocities for granite, basalt, and limestone to quantify typical compliance ratios and find that it is often different from that assumed in conventional crack models, which then affects crack density estimates. We also investigate how the normal and shear compliances change due to certain thermo-hydro-chemo-mechanical processes that generate large crack densities, and conclude that the final compliance ratios often still deviate from that of conventional models. Finally, we demonstrate how the model can be used to describe scatter in both P- and S-wave velocity-porosity trends. Implications for energy extraction and seismic monitoring are discussed.

The South Atlantic Ocean plays an important role in the Atlantic meridional overturning circulation (AMOC), connecting it to the Indian and Pacific Oceans as part of the global overturning circulation system; yet the detailed time mean circulation structure in this region and the large-scale spatial pattern of the AMOC variability remain unclear. Using model outputs from a 60-year, eddying global ocean-sea ice simulation validated against observations at a zonal section at 34°S, a meridional section at 65°W in the Drake Passage, and a meridional section southwest of Africa, we show that the upper limb of the AMOC originates primarily from the Agulhas leakage and that, while the cold Pacific water from the Drake Passage does not contribute significantly to the AMOC, it does play a role in setting the temperature and salinity properties of the water masses in the subtropical South Atlantic. We also find that the North Atlantic deep water (NADW) in the lower limb of the AMOC flows southward as a deep western boundary current all the way to 45°S and then turns eastward to flow across the Mid-Atlantic Ridge near 42°S, and that the recirculation around the Vitoria-Trindade seamount chain brings some NADW into the Brazil Basin interior. Finally, we find that the modeled AMOC variability is coherent on interannual to decadal timescales from 35°S to about 35°N, where diapycnal water mass transformations between the upper and lower limbs of the AMOC are expected to be small.

Plasmas in Earth’s outer magnetosphere, magnetosheath, and solar wind are essentially collisionless. This means particle distributions are not typically in thermodynamic equilibrium and deviate significantly from Maxwellian distributions. The deviations of these distributions can be further enhanced by plasma processes, such as shocks, turbulence, and magnetic reconnection. Such distributions can be unstable to a wide variety of kinetic plasma instabilities, which in turn modify the electron distributions. In this paper the deviations of the observed electron distributions from a bi-Maxwellian distribution function is calculated and quantified using data from the Magnetospheric Multiscale (MMS) spacecraft. A statistical study from tens of millions of electron distributions shows that the primary source of the observed non-Maxwellianity are electron distributions consisting of distinct hot and cold components in Earth’s low-density magnetosphere. This results in large non-Maxwellianities in at low densities. However, after performing a stastical study we find regions where large non-Maxwellianities are observed for a given density. Highly non-Maxwellian distributions are routinely found are Earth’s bowshock, in Earth’s outer magnetosphere, and in the electron diffusion regions of magnetic reconnection. Enhanced non-Maxwellianities are observed in the turbulent magnetosheath, but are intermittent and are not correlated with local processes. The causes of enhanced non-Maxwellianities are investigated.

The observed zonal winds at Jupiter’s cloud tops have been shown to be closely linked to the asymmetric part of the planet’s measured gravity field. However, other measurements suggest that in some latitudinal regions the flow below the clouds might be somewhat different from the observed cloud-level winds. Here we show, using both the symmetric and asymmetric parts of the measured gravity field, that the observed cloud-level wind profile between 25oS and 25oN must extend unaltered to depths of thousands of kilometers. Poleward, the midlatitude deep jets also contribute to the gravity signal, but might differ somewhat from the cloud-level winds. We analyze the likelihood of this difference and give bounds to its strength. We also find that to match the gravity measurements, the winds must project inward in the direction parallel to Jupiter’s spin axis, and that their decay inward should be in the radial direction.

Most of the classic wind-driven circulation theories based on the Sverdrup balance have neglected the profound influence of eddy mixing on the large-scale distribution of the potential vorticity (PV), thus failing to explain some prominent features of the observed circulation. In this study, using a series of numerical experiments based on the MITgcm, we diagnose the PV balance to quantify the effect of eddy mixing on the subtropical gyre. Four grid-spacings of 1, 1/3.2, 1/10, and 1/32 degrees are selected to compare the structure of the upper-ocean circulation. In the 1° grid case, the structure of the thermocline is as predicted by the Sverdrup dynamics, with its maximum depth located in the subtropical interior where the wind stress curl is strongest. With increasing resolution, however, this maximum depth is displaced toward the subtropical front, which more closely resembles the observed thermocline. From 1° to 1/32°, the enhanced eddy mixing tends to homogenize the macroscopic PV in the subtropical gyre and reduces the latitudinal PV range to about 25% of the non-eddy solution; and the region where the Sverdrup balance holds is relegated to isolated patches, with its area reduced by about 60%. Furthermore, sensitivity experiments show that the observed thermocline structure is well reproduced in eddy-resolving runs, indicating that the PV mixing provides a better explanation of the subtropical circulation than the Sverdrup dynamics. Our results suggest that the Sverdrup relationship should be treated carefully in the eddy-rich region, even in the subtropical interior.

Given the recent availability of high-resolution data on both current forest carbon stocks and restoration potential, the next geospatial and computational challenge is to utilize this data to identify priority areas for reforestation. Strategic reforestation activities, which account for both carbon sequestration potential as well as co-benefits, such as biodiversity protection, riparian management, and economic opportunity, can provide particularly attractive options for policy-makers who must manage competing social and environmental goals. The objective of this work is to identify potential future carbon corridors that can advance habitat connectivity while maximizing co-benefits for climate mitigation. While there have been efforts to map existing habitat corridors, we identify future corridors to incorporate strategic reforestation into land-use planning. First, we mapped current protected areas and the distribution of priority habitat across Maryland (USA) using the MD BioNet and PAD-US databases. Then, using high-resolution NASA Carbon Monitoring System forest carbon products, we identified optimal future corridors between existing protected areas in the state based on established viability factors, including the: amount of carbon stored, time to achieve habitat requirement, path length (land required), land ownership, and current land-use. Using a least-cost corridor model (prominently used by Jantz et al. 2014 to identify current carbon-habitat corridors in the Amazon), we found that reforesting a 1km habitat corridor connecting all protected areas larger than 20 ha in size results in 48% of the state’s land area being protected. Such a corridor would sequester an additional 80 Tg C and protect more than 132 Tg C in total, including the ongoing growth of existing trees along corridor pathways. This estimate is close to 50% of the state’s remaining carbon sequestration potential and would advance the state’s climate goals outlined in the Maryland Greenhouse Gas Reduction Act. More broadly, this approach to reforestation is useful for states interested in facilitating species migration in the face of ongoing environmental change while maximizing co-benefits.

To close the water use budget in irrigated agricultural fields in flood plains with substantial riparian corridors, it is necessary to understand groundwater usage by dominant phreatophyte vegetation, particularly when the primary source of the water for irrigation comes from groundwater abstraction. We report here results of measurements of sap flow in phreatophyte vegetation in a riparian corridor, which is part of a watershed located along the coast in Santa Cruz County, California. The riparian corridor is within a study area of 75 to 140 meters wide in the lower portion of Scotts Creek watershed, which is bounded to the west by the Pacific Ocean. Canopy cover in the study area often approaches 100 percent, with dominant trees being red alder (Alnus rubra), arroyo willow (Salix lasiolepis), and pacific willow (Salix lasiandra var. lasiandra). Other trees include boxelder (Acer negundo), big leaf maple (Acer macrophyllum), and California bay laurel (Umbellularia californica). Common understory vegetation includes California blackberry (Rubus ursinus), stinging nettle (Urtica dioica subsp. gracilis), poison hemlock (Conium maculatum), Cape ivy (Delairea odorata), and Italian thistle (Carduus pycnocephalus). For the study reported here, only the two most dominant phreatophyte species, namely red alders and arroyo willows, were instrumented with sap flow sensors. In addition to diurnal fluctuations, sap flow data collected hitherto also shows expected seasonal variation with summer maxima and winter minima, with transition fall and spring periods. Sap flow measurements from the study area are projected across the entire riparian forest using sampled tree sapwood area and used to estimate forest evapotranspiration (ET). The ET is then used in a groundwater flow model to more accurately predict observed groundwater fluctuations and usage by riparian vegetation.

Wave statistics of the North Atlantic Ocean, instrumental in designing merchant ships, were revisited by an in-house high-resolution 25-year wave hindcast (TodaiWW3-NK). The tail of the exceedance probability of $H_s$ was extended to 20 m and compared surprisingly well against the satellite altimeter. Moreover, we have found that the largest storm event in 25 years with the highest wave over 21 m in January 2014 significantly enhanced the tail of the $H_s$ distribution, which is a feature that was common among the three wave-hindcasts (ERA5/ECMWF, IOWAGA/IFREMER, and TodaiWW3-NK). Paradoxically, the satellite altimeter did not detect the $H_s$ at the peak of this storm. We found that extreme wave heights from models and satellites of three storms in 2007, 2011, and 2014 may deviate about a few meters among the estimates, particularly the altimeter having a large uncertainty. In-situ observations of the extreme wave events are urgently in need.

The frequent activity taking place at Mount Etna may pose risks to tourists and specialists visiting the summit craters as well as to the nearby population and thus requires constant monitoring. Infrasound recordings play an important role in volcanic observation because shallow explosive activity, shallow tremor processes or other volcanic phenomena coupled with the atmosphere are easier to identify with sound waves travelling through the air than with seismic waves, which are more effective for the characterization of buried sources but are strongly affected by scattering within the volcano edifice. Similar to seismic waves, infrasound signals often interfere with noise sources, in case of Mount Etna mostly wind induced noise. The manual distinction of noisy data from real volcanic signals is often not easy, and becomes unrealistic when a large amount of data has to be processed. Currently five summit craters at Mt Etna are active, showing intermittent and fluctuating levels of activity. This leads to a wide variety of infrasound signal patterns coming along with changing noise levels. In order to distinguish between noise induced signals and different kinds of volcano induced signals – such as infrasound events and tremor – in the waveform data we apply unsupervised pattern recognition techniques. We show that by extracting features from the amplitude spectrum, a simple clustering analysis with K-Means yields a set of different activity regimes. A more indepth analysis of the patterns is possible by means of Self-Organizing maps (SOMs) which also allow for the identification of transitional activity regimes and provide the option to color-code the results for an intuitive interpretation. We create a reference data set from multiple months of infrasound waveforms to include as many activity regimes as possible. We apply the results to a test set of data showing how new data can be treated fast and efficiently.

Strong winds in Southern Ocean storms drive air-sea carbon and heat fluxes. These fluxes are integral to the global climate system and the wind speeds that drive them are increasing. The current scatterometer constellation measuring vector winds remotely undersamples these storms and the higher winds within them, leading to potentially large biases in Southern Ocean wind reanalyses and the fluxes that derive from them. This observing system design study addresses these issues in two ways. First, we describe an addition to the scatterometer constellation, called Southern Ocean Storms -- Zephyr, to increase the frequency of independent observations, better constraining high winds. Second, we show that potential reanalysis wind biases over the Southern Ocean lead to uncertainty over the sign of the net winter carbon flux. More frequent independent observations per day will capture these higher winds and reduce the uncertainty in estimates of the global carbon and heat budgets.

Grappling with systemic discrimination and bias in geosciences can be overwhelming to the point that one may feel powerless to fix them. Despite the sweeping nature of this challenge, faculty, principal investigators, and other scientists with leadership roles have unparalleled power to mitigate harm in environments they oversee. Here, we identify ways that scientists in these roles can immediately address bias in three common spaces --- the lab, field, and classroom. We highlight key actions that can be taken to improve the quality of life of marginalized students and other trainees quickly, while important but comparatively slow institutional changes proceed.

Reconstructing water availability in terrestrial ecosystems is key to understanding past climate and landscapes, but there are few proxies for aridity that are available for use at terrestrial sites across the Cenozoic. The isotopic composition of tooth enamel is widely used as paleoenvironmental indicator and recent work suggests the potential for using the triple oxygen isotopic composition of mammalian tooth enamel (∆’17Oenamel) as an indicator of aridity. However, the extent to which ∆’17Oenamel values vary across environments is unknown and there is no framework for evaluating past aridity using ∆’17Oenamel data. Here we present ∆’17Oenamel and δ18Oenamel values from 50 extant mammalian herbivores that vary in physiology, behavior, diet, and water-use strategy. Teeth are from sites in Africa, Europe, and North America and represent a range of environments (humid to arid) and latitudes (34S to 69N), where mean annual δ18O values of meteoric water range from -26.0‰ to 2.2‰ (VSMOW). ∆’17Oenamel values from these sites span 146 per meg (-283 to -137 per meg, where 1 per meg = 0.001‰). The observed variation in ∆’17Oenamel values increases with aridity, forming a wedged-shape pattern in a plot of aridity index vs. ∆’17Oenamel that persists regardless of region. In contrast, the plot of aridity index vs. δ18Oenamel for these same samples does not yield a distinct pattern. We use these new ∆’17Oenamel data from extant teeth to provide guidelines for using ∆’17Oenamel data from fossil teeth to assess and classify the aridity of past environments. ∆’17Oenamel values from the fossil record have the potential to be a widely used proxy for aridity without the limitations inherent to approaches that use δ18Oenamel values alone. In addition, the data presented here have implications for how ∆’17Oenamel values of large mammalian herbivores can be used in evaluations of diagenesis and past pCO2.

Distribution: A. Approved for public release – distribution is unlimited QuakeCast is a novel system for short-term earthquake prediction using global ionosphere Total Electron Content (TEC) data. In the last 20 years, earthquakes have caused over 800,000 deaths and $650 billion in economic damage. While current seismic early warning systems provide up to a minute’s notice by detecting an earthquake’s non-damaging P-waves prior to the damaging S-waves, earlier warnings could allow more significant emergency preparations. Electromagnetic ionospheric phenomena have recently been observed many days before major earthquakes, notably in 2015 in Nepal. QuakeCast explores whether such signals could be used to predict earthquakes in a global dataset of ionosphere and earthquake data. The ionosphere data consists of global TEC data from NASA’s Crustal Dynamics Data Information System (CDDIS), for the years 2005-2015 at 15 minute intervals. The earthquake data consists of over 10,000 events of at least M5 from the International Seismological Centre (ISC-GEM) Global Instrumental Earthquake Catalogue over the same time period. We used the dataset to explore the following classification problem: given a 24-hour sequence of ionosphere TEC data in a 30° latitude by 30° longitude window, is the sequence preseismic (an earthquake occurs in the next time step) or nominal (no earthquake)? We built two models to address this. The first is a classical logistic regression model trained on radially binned data. Analysis of the classifier weights indicate a localized effect in the ionosphere: TEC data spatially and temporally closer to the event contributes more significantly to the prediction. The second model is a deep learning ConvLSTM autoencoder trained to reproduce nominal TEC data sequences. Since autoencoders reproduce data which resemble their training data better than data which does not, we used the reconstruction error to classify sequences as anomalous (preseismic) or nominal. Both methods were found to perform significantly better than a random null classifier, indicating that the ionosphere contains information useful for predicting earthquakes at least 15 minutes in advance. While further research is needed to incorporate additional data features and address noise, we believe that these results are a promising development toward forecasting earthquakes using geoelectric signals.

A document by Richard W Eastes. Click on the document to view its contents.