The application of absolutely calibrated piezoelectric (PZT) sensors is increasingly used to help interpret the information carried by radiated elastic waves of laboratory/in situs acoustic emissions (AEs) in nondestructive evaluation. In this paper, we present the methodology based on the finite element method (FEM) to characterize PZT sensors. The FEM-based modelling tool is used to numerically compute the true Green’s function between a ball impact source and an array of PZT sensors to map active source to theoretical ground motion. Physical-based boundary conditions are adopted to better constrain the problem of body wave propagation, reflection and transmission in/on the elastic medium. The modelling methodology is first validated against the reference approach (generalized ray theory) and is then extended down to 1 kHz where body wave reflection and transmission along different types of boundaries are explored. We find the Green’s functions calculated using physical-based boundaries have distinct differences between commonly employed idealized boundary conditions, especially around the anti-resonant and resonant frequencies. Unlike traditional methods that use singular ball drops, we find that each ball drop is only partially reliable over specific frequency bands. We demonstrate, by adding spectral constraints, that the individual instrumental responses are accurately cropped and linked together over 1 kHz to 1 MHz after which they overlap with little amplitude shift. This study finds that ball impacts with a broad range of diameters as well as the corresponding valid frequency bandwidth, are necessary to characterize broadband PZT sensors from 1 kHz to 1 MHz.

Floods are convincingly the most frequent and widespread natural hazard across the world. With an ample amount of literature forecasting increase in its frequency and magnitude further in the future, highly credible and efficient algorithms and tools are crucial for real-time flood monitoring. In this study, a highly efficient tool, Multi-Mission Flood Mapper, has been developed to delineate flood inundation extent without any human intervention from SAR images captured by multiple microwave SAR satellite missions, including ALOS PALSAR CEOS, ALOS 2 CEOS, COSMO-SkyMed, ENVISAT ASAR, ERS 1/2 CEOS, ERS 1/2 SAR(.E1, .E2), ICEYE, JERS CEOS, KOMPSAT-5, PAZ, RADARSAT-1 & -2, RCM, SAOCOM, SeaSat, Sentinel-1, TerraSAR-X, and TanDEM-X. The efficacy of the developed tool is assessed by performing a test on a significant number of flood events in India having diverse flooding patterns and landforms. To manifest the performance of the tool, the step-by-step processing at the backend of the tool is discussed in detail in this study by taking a flood event along the Ganga River in India as a case study. The algorithm of the tool includes various processing steps: pre-processing that incorporate applying orbit file, calibrate to sigma naught, speckle filtering, terrain correction and linear to decibel conversion; thematic analysis that involves multi-segmentation and Otsu’s thresholding techniques; post-processing that consists of the elimination of hill shadows, applying majority filter, and masking out permanent water bodies. Thus derived flood inundation layer is observed to be highly accurate compared to the master image. The total time taken by the tool for processing is about 4 minutes for the given image. The developed tool would be beneficial for rapid flood inundation map generation on a timely basis for flood monitoring and relief management during a disaster. In addition, the flood inundation layers can also be used for calibration/validation of hydrological/hydraulic models, geospatial planning, and generating flood hazard maps. Also, the Multi-Mission Flood Mapper tool is facilitated with a user-friendly Graphical User Interface (GUI), making it look simple and easy to use.

In this work, we develop gradient boosting machines (GBMs) for forecasting the SYM-H index multiple hours ahead using different combinations of solar wind and interplanetary magnetic field (IMF) parameters, derived parameters, and past SYM-H values. Using Shapley Additive Explanation (SHAP) values to quantify the contributions from each input to predictions of the SYM-H index from GBMs, we show that our predictions are consistent with physical understanding while also providing insight into the complex relationship between the solar wind and Earth’s ring current. In particular, we found that feature contributions vary depending on the storm phase. We also perform a direct comparison between GBMs and neural networks presented in prior publications for forecasting the SYM-H index by training, validating, and testing them on the same data. We find that the GBMs have a comparable root mean squared error as the best published black-box neural network schemes.

This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them. Each commentary focuses on a different perspective as follows: (i) field, experimental, remote sensing, and real-time data research and application (Section 1); (ii) Inclusive, equitable, and accessible science: Involvement, challenges, and support of early career, marginalized racial groups, women, LGBTQ+, and/or disabled researchers (Section 2); and (iii) an ICON perspective on machine learning for multiscale hydrological modeling (Section 3). Hydrologists depend on data monitoring, analyses, and simulations from these diverse scientific disciplines to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (in-situ: lab, plots, and field experiments) and secondary sources (ex-situ: remote sensing, UAVs, hydrologic models) that are typically openly available and discoverable. Hydrology-oriented organizations have pushed our community to increase coordination of the protocols for generating data and sharing model platforms. In addition, networking at all levels has emerged with an invigorated effort to activate community science efforts that complement conventional data collection methods. With increasing amounts of data, it has become difficult to decipher various complex hydrologic processes. However, machine learning, a branch of artificial intelligence, provides accurate and faster alternatives to understand different biogeochemical and hydrological processes better. Diversity, equity, and inclusivity are essential in terms of outreach and integration of peoples with historically marginalized identities into this professional discipline and respecting and supporting the local environmental knowledge of water users.

This is a test-case study assessing the ability of deep learning methods to generalize to a future climate (end of 21st century) when trained to classify thunderstorms in model output representative of the present-day climate. A convolutional neural network (CNN) was trained to classify strongly-rotating thunderstorms from a current climate created using the Weather Research and Forecasting (WRF) model at high-resolution, then evaluated against thunderstorms from a future climate, and found to perform with skill and comparatively in both climates. Despite training with labels derived from a threshold value of a severe thunderstorm diagnostic (updraft helicity), which was not used as an input attribute, the CNN learned physical characteristics of organized convection and environments that are not captured by the diagnostic heuristic. Physical features were not prescribed but rather learned from the data, such as the importance of dry air at mid-levels for intense thunderstorm development when low-level moisture is present (i.e., convective available potential energy). Explanation techniques also revealed that thunderstorms classified as strongly rotating are associated with learned rotation signatures. Results show that the creation of synthetic data with ground truth is a viable alternative to human-labeled data and that a CNN is able to generalize a target using learned features that would be difficult to encode due to spatial complexity. Most importantly, results from this study show that deep learning is capable of generalizing to future climate extremes and can exhibit out-of-sample robustness with hyperparameter tuning in certain applications.

The High Accuracy Satellite Drag Model (HASDM) is the operational thermospheric density model used by the US Space Force (USSF) Combined Space Operations Center (CSpOC). By using real-time data assimilation, HASDM can provide density estimates with increased accuracy over empirical models. With historical HASDM density data being released publicly for the first time, we can analyze the data to identify dominant modes of variations in the upper atmosphere. As HASDM is a close relative to the Jacchia-Bowman 2008 Empirical Thermospheric Density Model (JB2008), we look at time-matched density data to better understand the models’ characteristics. This model comparison is conducted through the use of Principal Component Analysis (PCA). We then compare both datasets to the CHAllenging Minisatellie Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) accelerometer-derived density estimates. By looking at the principal components and PCA scores from the two models, we confirm the increased complexity of the HASDM dataset while the CHAMP and GRACE comparisons show that HASDM more closely matches the accelerometer-derived densities with mean absolute differences of 23.81% and 30.84% compared to CHAMP and GRACE-A, respectively.

Poleward ocean heat transport is a key process in the earth system. We detail and review the northward Atlantic Water (AW) flow, Arctic Ocean heat transport, and heat loss to the atmosphere since 1900 in relation to sea ice cover. Our synthesis is largely based on a sea ice-ocean model forced by a reanalysis atmosphere (1900-2018) corroborated by a comprehensive hydrographic database (1950-), AW inflow observations (1996-), and other long-term time series of sea ice extent (1900-), glacier retreat (1984-) and Barents Sea hydrography (1900-). The Arctic Ocean, including the Nordic and Barents Seas, has warmed since the 1970s. This warming is congruent with increased ocean heat transport and sea ice loss and has contributed to the retreat of marine-terminating glaciers on Greenland. Heat loss to the atmosphere is largest in the Nordic Seas (60% of total) with large variability linked to the frequency of Cold Air Outbreaks and cyclones in the region, but there is no long-term statistically significant trend. Heat loss from the Barents Sea (~30%) and Arctic seas farther north (~10%) is overall smaller, but exhibit large positive trends. The AW inflow, total heat loss to the atmosphere, and dense outflow have all increased since 1900. These are consistently related through theoretical scaling, but the AW inflow increase is also wind-driven. The Arctic Ocean CO2 uptake has increased by ~30% over the last century - consistent with Arctic sea ice loss allowing stronger air-sea interaction and is ~8% of the global uptake.

While the notion that injecting fluids into the subsurface can reactivate faults by reducing frictional resistance is well established, the ensuing evolution of slip is still poorly understood. What controls whether the induced slip remains stable and confined to the fluid-affected zone or accelerates into a runaway earthquake? Are there observable indicators of the propensity to earthquakes before they happen? Here, we investigate these questions by modeling a unique fluid-injection experiment on a natural fault with laboratory-derived friction laws. We show that a range of fault models with diverging stability with sustained injection reproduce the slip measured during pressurization. Upon depressurization, however, the most unstable scenario departs from the observations, suggesting that the fault is relatively stable. The models could be further distinguished with optimized depressurization tests or spatially distributed monitoring. Our findings indicate that avoiding injection near low-residual-friction faults and depressurizing upon slip acceleration could help prevent large-scale earthquakes.

Future precipitation changes are controlled by the atmospheric energy budget, with temperature, water vapor, and absorbing aerosols playing dominant roles in driving radiative changes. Atmospheric energy budgets are calculated for different Shared Socioeconomic Pathways (SSPs) using ScenarioMIP projections from phase 6 of the Climate Model Intercomparison Project and are used to quantify the influence of 21st century aerosol cleanup on precipitation. Absorbing aerosol influences on shortwave absorption are isolated from the effects of water vapor. Apparent hydrologic sensitivity is ~40% higher for the “Middle of the Road” (SSP2-4.5) scenario with aerosol cleanup than for the “Regional Rivalry” (SSP3-7.0) scenario that maintains aerosol. Regionally, cleanup-induced changes in the atmospheric energy budget are of a similar magnitude to the precipitation increases themselves and are larger than the influence of changes in atmospheric circulation. Policy choices about future absorbing aerosol emissions will therefore have major impacts on global and regional precipitation changes.

Torrential rainfall and rapid snowmelt in April 2017 caused deadly floods in northwestern Iran. An atmospheric river (AR), propagating across the Middle East and North Africa, was found responsible for this extreme event. The snowmelt was triggered by precipitation and warm advection associated with the AR. Total satellite-based rainfall for April 2017 was moderately below normal, suggesting that a heavy flood can happen during dry years. The AR was fed by moisture from the Mediterranean and Red Seas. Despite its adverse societal consequences, this event was beneficial to the recovery of the desiccating Lake Urmia. The impacts of this AR were not limited to flooding; it also facilitated dust transport to the region. This distinct characteristic of the ARs in the Middle East is attributed to major mineral dust sources located along their pathways. This event was reasonably predicted at 7-day lead time, crucially important for successful early warning systems.

The mainstream media and popular science platforms are rife with misunderstandings about what a “polar vortex” is. The term most aptly describes the stratospheric polar vortex, a single feature dominating the cool-season circulation at ∼15–50 km altitude. Regional upper tropospheric jet stream variations dominate the tropospheric circulation, which is not well-described by the idea of a polar vortex; indeed, there is no single consistent definition of a tropospheric polar vortex in the literature. Stratospheric polar vortex disturbances profoundly influence extreme weather events such as cold air outbreaks (CAO). How the stratospheric polar vortex affects the tropospheric jets, local excursions of which drive CAOs, is not yet fully understood. The most public-facing parts of publications describing research on this topic are sometimes unclear about how the “polar vortex” is defined; greater clarity could help improve communications both within the community and with non-specialist audiences.

Large Antarctic ice volume changes characterized the middle to Late Oligocene and the first million years of climate evolution during the Miocene. However, the sea surface temperature (SST) evolution over this period remains poorly constrained, as only a few records from contrasting proxies are available. In this study, we present a long-term alkenone-derived SST record from sediments drilled by the Ocean Drilling Program (ODP) at Site 1168 in the west Tasmanian Sea spanning 29.8 Ma to 16.7 Ma. The SST record highlight that the long-term warming in the Late Oligocene linked to the end of the Middle Oligocene Glacial Interval can be recognized also at mid-to-high latitudes of the Southern Hemisphere. Warmer average temperatures (25.5°C) characterize the period from 24.6 to 22 Ma; average temperatures then decrease by 1 to 2°C into the Miocene and stabilize by 20.1 Ma. The reconstructed temperatures are highly variable in the warm Late Oligocene waters, and more stable and slightly colder in the Early to Middle Miocene. We confirm that this temperature trend is not an artefact of the latitudinal drift of the site, as the temperature anomaly relative to the modern water temperature at the paleolocation confirms the SST trends of the Oligocene. This is the first alkenone-derived record to reproduce the long-term Oligocene climate trend previously interpreted from the benthic δ18O, which recorded a warming and/or reduction in ice volume from the Middle Oligocene Glacial Interval through the latest Oligocene.

The present atmosphere of Venus contains almost no water, but recent measurements indicate that in its early history Venus had an Earth-like ocean. Understanding how the Venusian atmosphere evolved is important not only for Venus itself, but also for understanding the evolution of other planetary atmospheres. In this study, we quantify the escape rates of oxygen ions from the present Venus to infer the past of the Venusian atmosphere. We show that an extrapolation of the current escape rates back in time leads to the total escape of 0.02-0.6 m of a global equivalent layer of water. This implies that the loss of ions to space, inferred from the present state, cannot account for the loss of an historical Earth-like ocean. We find that the O+ escape rate increases with solar wind energy flux, where more energy available leads to a higher escape rate. Oppositely, the escape rate decrease slightly with increased EUV flux, though the small variation of EUV flux over the measured solar cycle may explain the weak dependency. These results indicate that there isn’t enough energy transferred from the solar wind to Venus’ upper atmosphere that can lead to the escape of the atmosphere over the past 3.9 billion years. This means that the Venusian atmosphere didn’t have as much water in its atmosphere as previously assumed or the present-day escape rates don’t represent the historical escape rates at Venus. Otherwise, some other mechanisms have acted to more effectively remove the water from the Venusian atmosphere.

Diverse, complex data are a significant component of Earth Science’s “big data” challenge. Some earth science data, like remote sensing observations, are well understood, are uniformly structured, and have well-developed standards that are adopted broadly within the scientific community. Unfortunately, for other types of Earth Science data, like ecological, geochemical and hydrological observations, few standards exist and their adoption is limited. The synthesis challenge is compounded in interdisciplinary projects in which many disciplines, each with their own cultures, must synthesize data to solve cutting edge research questions. Data synthesis for research analysis is a common, resource intensive bottleneck in data management workflows. We have faced this challenge in several U.S. Department of Energy research projects in which data synthesis is essential to addressing the science. These projects include AmeriFlux, Next Generation Ecosystem Experiment (NGEE) - Tropics, Watershed Function Science Focus Area, Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE), and a DOE Early Career project using data-driven approaches to predict water quality. In these projects, we have taken a range of approaches to support (meta)data synthesis. At one end of the spectrum, data providers apply well-defined standards or reporting formats before sharing their data, and at the other, data users apply standards after data acquisition. As these projects continue to evolve, we have gained insights from these experiences, including advantages and disadvantages, how project history and resources led to choice of approach, and enabled data harmonization. In this talk, we discuss the pros and cons of the various approaches, and also present flexible applications of standards to support diverse needs when dealing with complex data.

Marine sedimentary records are a key archive when reconstructing past climate; however, mixing at the seabed (bioturbation) can strongly influence climate records, especially when sedimentation rates are low. By commingling the climate signal from different time periods, bioturbation both smooths climate records, by damping fast climate variations, and creates noise when measurements are made on samples containing small numbers of individual proxy carriers, such as foraminifera. Bioturbation also influences radiocarbon-based age-depth models, as sample ages may not represent the true ages of the sediment layers from which they were picked. While these effects were first described several decades ago, the advent of ultra-small-sample 14C dating now allows samples containing very small numbers of foraminifera to be measured, thus enabling us to directly measure the age-heterogeneity of sediment for the first time. Here, we use radiocarbon dates measured on replicated samples of 3-30 foraminifera to estimate age-heterogeneity for five marine sediment cores with sedimentation rates ranging from 2-30 cm / kyr. From their age-heterogeneities and sedimentation rates we infer mixing depths of 10-20 cm for our core sites. Our results show that when accounting for age-heterogeneity, the true error of radiocarbon dating can be several times larger than the reported measurement. We present estimates of this uncertainty as a function of sedimentation rate and the number of individuals per radiocarbon date. A better understanding of this uncertainty will help us to optimise radiocarbon measurements, construct age models with appropriate uncertainties and better interpret marine paleo records.

The accuracy of hydrological model predictions is limited by uncertainties in model structure and parameterization, and observations used for calibration, validation and model forcing. While calibration is usually performed with discharge estimates, the internal model processes might be misrepresented, and the model might be getting the “right results for the wrong reasons”, thus compromising model reliability. An alternative is to calibrate model parameters with remote sensing (RS) observations of the water cycle. Previous studies highlighted the potential of RS-based calibration to improve discharge estimates, focusing less on other variables of the water cycle. In this study, we analyzed in detail the contribution of five RS-based variables (water level (h), flood extent (A), terrestrial water storage (TWS), evapotranspiration (ET) and soil moisture (W)) to calibrate a coupled hydrologic-hydrodynamic model for a large Amazon sub-basin with extensive floodplains. Single-variable calibration experiments with all variables were able to improve discharge KGE from around 6.1% to 52.9% when compared to a priori parameter sets. Water cycle representation was improved with multi-variable calibration: KGE for all variables were improved in the evaluation period. By analyzing different calibration setups, a consistent selection of complementary variables for model calibration resulted in a better performance than incorporating all RS variables into the calibration. By looking at multiple RS observations of the water cycle, inconsistencies in model structure and parameterization were found, which would remain unknown if only discharge observations were considered.

Using E-W and vertical deformation-rate maps derived from radar interferometric time-series, we analyze the deformation field of an entire orogenic segment, i.e., the Tajik depression and its adjoining mountain belts, Tian Shan, Pamir, and Hindu Kush. The data-base consists of 900+ radar scenes acquired over 2.0–4.5 years and global navigation satellite system measurements. The recent, supra-regional kinematics is visualized in an unprecedented spatio-temporal resolution. We confirm the westward collapse of the Pamir-Plateau crust, inverting the Tajik basin into a fold-thrust belt with shortening rates decaying westward from ~15 to 2 mm/yr. Vertical rates in the Hindu Kush likely record slab-dynamic effects, i.e., the progressive break-off of the Hindu Kush slab. At least 10 mm/yr of each, uplift and westward motion occur along the western edge of the Pamir Plateau, outlining the crustal-scale ramp along which the Pamir Plateau overrides the Tajik depression. The latter shows a combination of basin-scale tectonics, halokinesis, and seasonal/weather-driven near-surface effects. Abrupt ~6 mm/yr horizontal-rate changes occur across the kinematically-linked dextral Ilyak strike-slip fault, bounding the Tajik fold-thrust belt to the north, and the Babatag backthrust, the major thrust of the fold-thrust belt, located far west in the belt. The sharp rate decay across the Ilyak fault indicates a locking depth of ≤1 km. The Hoja Mumin salt fountain is spreading laterally at ≤350 mm/yr. On the first-order, the modern 20–5 and fossil (since ~12 Ma) 12–8 mm/yr shortening rates across the fold-thrust belt correspond.

Crystal zonations provide valuable snapshot of the dynamic changes within a magma reservoir. However, crystal zonations are often heterogeneous down to the hand-sample scale, such that deciphering their signatures becomes convoluted. Crystals are reactively precipitated and dissolving as a function of temperature, pressure, and composition. In this manuscript, we investigate what temperature histories crystals experience in a magma lens after its injection into a cooler magma reservoir. We simulate the cooling interface in either hot basaltic or dacitic magmas after their injection into a cooler magma reservoir. We couple fluid dynamics to thermodynamics by resolving flow at the crystalline-scale and allowing crystals with constant density and size to precipitate and dissolve based on ambient melt properties. We infer zonations in our simulated crystals by tracking the magma temperatures they sample over time. Our results show that when thermodynamics and fluid dynamics are coupled, a reactive, crystal-driven instability arises, because the negative buoyancy of crystals pulls along the cooler-than-ambient melt in which they precipitated. As crystals continue to precipitate along the cooling boundary, the instability develops into a sustained convective flow. Our results show that crystals record complex and unique zonations in this crystalline-scale domain, suggesting that zonations and their heterogeneity can be indicative of local instead of system scale processes. Also, our results show that many of the crystals in the instability dissolve and lose their thermal record of the instability. These results highlight the challenges of deciphering system-scale process from crystalline data.