Much information about the North American lithosphere has been gained by imaging seismic wave velocities. Additional constraints on the state of the subsurface can be gained by studying seismic attenuation, which has different sensitivity to physical properties. We produce a model of lateral variations in attenuation across the conterminous U.S. by analyzing P waveforms from deep earthquakes recorded by the EarthScope Transportable Array using a time-domain waveform matching approach. We divide the study area into 12 overlapping tiles and differential attenuation is measured in each tile independently; with analysis being repeated independently for 4 of the tiles. Measurements are combined into a smooth map using a linear inversion. Comparing results for adjacent tiles and for repeated tiles shows that the imaged features are robust. The final map is produced by combining all the measurements and shows generally higher attenuation west of the Rocky Mountain Front than east of it, with significant small length scale variations superimposed on that broad pattern. In general, there is a strong anticorrelation between differential attenuation and shear wave velocities at 90 km depth. However, a given change in velocity may correspond to large or small change in attenuation, depending on the area; suggesting that different physical mechanisms are operating. In some cases, most notably in the Snake River Plain, attenuation and velocity do not show the expected anticorrelation. The southern Intermountain Seismic Belt coincides with a high gradient in the attenuation signal, but even larger gradients further inland do not show any association with seismicity.

In municipal construction, to avoid casualty and property loss the buried pipelines beneath the construction site should be accuracy located. There are many locating method such as magnetic method, electromagnetic method, infrared thermography, ground penetrating radar, acoustic location system etc. All these methods mentioned above can only make an acceptable result at the depth down to 5 m. In this study, improved frequency domain electromagnetic method (FDEM) is used to locate deep buried metal pipeline (>10 m). In this method, the mathematic model is rebuilt and special probe with the diameter of 20mm is designed. It was conduct by two steps. First, Using the regular FDEM method to estimate the approximately depth and horizontal location. Then, the electromagnetic field induced by metal pipeline is measured by the probe in the borehole drilled within 5 m away from the projection of pipeline on the ground and the full curve inversion method is used to give the accuracy depth and planimetric position. Result show that the improved FDEM method can detect the deep buried (>10 m) pipeline with the relative accuracy of and planimetric position with the relative accuracy of . With the advantage of high accuracy and trenchless, it can improve efficiency by replacing excavation verification in the buried pipeline on site investigation. Regular FDEM method usually locates the underground metal pipeline by measuring the eigenvalue of specific frequency electromagnetic characteristic curve upon the vertical direction. Improved FDEM method combine the eigenvalue method and full curve inversion method. Furthermore by detecting electromagnetic field in the borehole the detection line is closer to the pipeline and the interference is less which leads to a high S/N. That why the improved FDEM have a high accuracy in deep buried metal pipeline locating. On the other hand, the probe is designed to well match the current instrument, so it need not to add special receiver which make the method more economical to apply. The location without error of deep buried pipeline especially the deep buried oil and gas metal pipeline is vital. The improved FDEM could provide an effective way in locating the deep buried metal pipeline.

Rainfall-runoff models are commonly evaluated against statistical evaluation metrics. However, these metrics do not provide much insight into what is hydrologically wrong if a model fails to simulate observed streamflow well and they are also not applicable for ungauged catchments. Here, we propose a signature-based hydrologic efficiency (SHE) metric consisting of hydrologic signatures that can be regionalized for model evaluation in ungauged catchments. We test our new efficiency metric across 633 catchments from Great Britain. Strong correlations with Spearman rank and Pearson correlation values around 0.8 are found between our proposed metric and commonly used statistical evaluation metrics (NSE, KGE, NP…) demonstrating that the proposed SHE metric is related to existing metrics as much as these metrics are related to each other. For ungauged catchments, we regionalise the three signatures included in SHE and find that 78% of catchments have an absolute difference of SHE values between gauged and ungauged cases of less than 0.2. This difference increases where the regionalized bias and variance signature values are different to the observed ones. It means that SHE metric is applicable for model evaluation in ungauged catchments if its signatures can be regionalized well.

In this study, we investigate the oceanic and atmospheric processes that have contributed to the Rapid Intensification (RI) and Rapid Weakening (RW) of Cyclone Ockhi using the HYbrid Coordinate Ocean Model (HYCOM) simulations and Global Forecast System (GFS) outputs. The environmental conditions prevailed before RI showed the presence of thick warm and fresh waters, ample supply of mid-tropospheric relative humidity, and moderate wind shear. The intrusion of dry air, strong vertical wind shear, and unfavourable oceanic conditions annihilated the storm intensity during the RW stage. Compared to the ocean temperature, the vertical structure of salinity showed remarkable differences between the RI and RW locations resulting in contrasting upper-ocean stratification. The dynamic temperature (T$_{dy}$) under the TC core evolved under the influence of upper-ocean stratification showed a large drop at RW compared to RI. T$_{dy}$ provided a better representation of the ocean’s negative feedback on the rapid intensity changes of TC Ockhi compared to TCHP, especially for the region like RI, which was primarily influenced by the salinity stratification. Hence, this study demonstrates the importance of multi-parameter metric like T$_{dy}$ in the assessment of oceanic feedback to TC and its intensity changes.

Seismic wave extractions have been performed using ambient noise records observed by distributed acoustic sensing (DAS) technology. Extractions of microseisms can be investigated at a local scale using such DAS records observed in the ocean. Here, we show P and Scholte wave extractions from ambient seafloor noise observed by DAS along a submarine cable deployed off Cape Muroto in the Nankai subduction zone, Japan. The P waves can be observed at a frequency band of 0.1–0.3 Hz and up to a distance of 6–7 km. The distance in which the P waves can be observed is controlled by the P incident angle and the DAS sensitivity to the observable apparent velocity. The effective extractions of P and Scholte waves are performed in correspondence to large significant wave heights of the sea surface, which indicate that these waves are originated from fluid distturbances at the sea surface.

Unmanned Aerial Vehicle (UAV) based Structure-from-Motion (SfM) techniques have renovated 3D topographic monitoring of earth surface, offering low-cost, rapid and reliable data acquisition and processing. Multi-temporal models of the river environment can be produced by autonomous operation in order to determine erosion, subsidence, landslide, soil transport and surface deformation in the riverbeds. Herein, the acquisition of repeated topographic surveys helps us to characterize the flow regime and to monitor the sediment dynamics. This study presents the hydromorphological changes of the meandering structures by using UAV-generated point clouds and Digital Surface Models (DSMs) produced by SfM at different times in the Büyük Menderes Basin located in the western part of Turkey. The processing of the data obtained with the flights were made in January and June 2018 at selected three meander locations with the highest visible changes according to the long-term satellite imageries. Especially, riverbank erosion along the river was determined by digitizing the edges and volumetric calculations of the eroded/deposited sediments derived from UAV-based measurements. In addition to the periodic volumetric differences of the meander structures, the differences in volumetric comparison methods for the same meander structures have been evaluated. Ultimately, the sediment profiles were extracted along the river banks at the selected part of the meanders and the amount of deposited sediments were determined to increase in a range between 1.5% and 3.3% of the total sediment. In conclusion, it is estimated that UAVs will be used instead of conventional photogrammetry aircraft in many future projects, considering the data production times and costs in large areas. Further, various digital cameras and sensors can be mounted on UAVs in order for examining the sediment effect on the health and productivity of plants in agricultural areas around the meanders.

Health and environmental hazards related to high pollutant concentrations have become a serious issue from the perspectives of public policy and human health. The objective of this research is to improve the estimation of grid-wise PM2.5, a criteria pollutant, by reducing systematic bias in estimating PM2.5 empirically from speciation provided by MERRA-2 using a ML approach. We present a unique application of machine learning (ML) for estimating hourly PM2.5 concentrations at grid points of Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). The model was trained using various meteorological parameters and aerosol species simulated by MERRA-2 and ground measurements from Environmental Protection Agency (EPA) air quality system (AQS) stations. monitors. The ML approach significantly improved performance and reduced mean bias in the 0-10 µg m-3 range. We also used the Random Forest ML model for each EPA region using one year of collocated datasets. The resulting ML models for each EPA region were validated and the aggregate data set has a Pearson correlation of 0.88 (RMSE = 4.8 µg m-3) and 0.82 (RMSE = 5.8 µg m-3) for training and testing, respectively. The correlation (and RMSE) increased to 0.89 (4.0), 0.95 (1.6), 0.94 (1.1) for daily, monthly, and yearly average comparisons. The results from initial implementation of the ML model for global region are encouraging but require more research and development to overcome challenges associated with data gaps in many parts of the world.

Various teaching methods including classroom lectures, physical experiments, and field excursions are useful for students to learn and understand the basic concepts of geography and earth sciences. However, due to constraints in the current curriculum of geographic education in Japanese schools, physical experiments and field excursions are rarely conducted, and classroom lectures tend to focus on memorizing some technical terms. This environment is not ideal for teaching processes and mechanisms of geographic phenomena. High-frequency, high-definition topographic data obtained using a TLS (Terrestrial Laser Scanner) and SfM-MVS photogrammetry with a UAS (Unmanned Aerial System) have become popular in geoscience. Those surveying approaches allow us to directly monitor rapidly changing landforms, while we can also use the obtained data to visualize geographic phenomena by various methods and materials including 3D print models, 3D virtual models, pictures, videos, and virtual/mixed reality. Here we explore the use of high-frequency, high-definition topographic data for educating geographic thinking. We arranged and conducted experimental teaching classes for elementary school students. First, we showed two 3D print models of the same sea cliff for years 2015 and 2017 constructed form high-definition topographic data. When students touched the two models, they were able to feel topographic changes due to erosion and sedimentation effectively. Furthermore, after exploring the 3D print models, many students were able to imagine how the sea cliff would change in the future. Next, we showed two images of fluvial deposits along a river segment in the area where the students live for July 2017 and September 2017. Then, they were able to imagine the transportation force of river flow. They also understood that the river flows typically in quiet but becomes powerful at high flow to move more sediment, and it might cause a disaster. Such visualized and touchable learning materials derived from high-frequency, high-definition topographic data enable students to enhance their geographic imagination of landforms, which are familiar to them but unexpectedly changing, at appropriate spatial and time scales.

Here we assess to what extent the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM) can reproduce the climatological variations of vertical Total Electron Content (vTEC) in the Canadian sector. Within the auroral oval and polar cap, E-CHAIM is found to exhibit Root Mean Square (RMS) errors in vTEC as low 0.4 TECU during solar minimum summer but as high as 5.0 TECU during solar maximum equinox conditions. These errors represent an improvement of up to 8.5 TECU over the errors of the International Reference Ionosphere (IRI) in the same region. At sub-auroral latitudes, E-CHAIM RMS errors range between 1.0 TECU and 7.4 TECU, with greatest errors during the equinoxes at high solar activity. This represents an up to 0.5 TECU improvement over the IRI during summer but worse performance by up to 2.4 TECU during the winter. Comparisons of E-CHAIM performance against in situ measurements from the European Space Agency’s Swarm mission are also conducted, ultimately finding behaviour consistent with that of vTEC. In contrast to the vTEC results, however, E-CHAIM and the IRI exhibit comparable performance at Swarm altitudes, except within the polar cap, where the IRI exhibits systematic underestimation of electron density by up to 1.0e11 e/m^3. Conjunctions with mid-latitude ionosondes demonstrate that E-CHAIM’s errors appear to result from compounding same-signed errors in its NmF2, hmF2, and topside thickness at these latitudes. Overall, E-CHAIM exhibits strong performance within the polar cap and auroral oval but performs comparably to the IRI at sub-auroral latitudes.

We decompose the monthly global Ocean Bottom Pressure (OBP) from GRACE(-FO) mass concentration solutions, with trends and seasonal harmonics removed from the signal, to extract 23 significant regional modes of variability. The 23 modes are analyzed and discussed considering Sea-Level Anomalies (SLA), Wind Stress Curl (WSC), and major climate indices. Two-thirds of the patterns correspond to extratropical regions and are substantially documented in other global or regional studies. Over the equatorial band, the identified modes are unprecedented, with an amplitude ranging between 0.5 and 1 centimeter. With smaller amplitude than extratropical patterns, they appear to be less correlated with the local SLA or WSC; yet, they present significantly coherent dynamics. The Pacific Ocean modes show significant correlations with the Pacific Decadal Oscillation (PDO) and El Niño Southern Oscillation (ENSO).

Coastal cities face recurrent flooding from storm events and rising seas. A contributing factor to flooding in these low relief areas is the groundwater table, which, already relatively shallow, can quickly rise towards the land surface during storm events. This leads to increased surface runoff entering stormwater drainage systems and a greater probability of flooding. As such, groundwater table forecasts could be an important component of real-time flood forecasting systems, but are generally unavailable. Because traditional physics-based models require extensive amounts of subsurface data that is difficult to obtain, especially in urban environments, this research evaluates two types of machine learning models, Recurrent Neural Networks (RNN) and Long Short-term Memory neural networks (LSTM), for creating groundwater table forecasts. The two types of networks were built with Tensorflow/Keras to forecast the groundwater table response to forecasted storm events and appropriate hyperparameters were tuned using the Hyperas library. Using observed hourly groundwater levels, rainfall, and tide from the City of Norfolk, Virginia, the networks were trained with data from 2010-2016 and tested with data from 2016-2018. Archived forecast rainfall and tide from two large storms in the test period (Hurricane Hermine and Tropical Storm Julia) were then used to evaluate the effect of forecast inputs on model performance. Results indicate that LSTM is slightly more accurate when forecasting the groundwater table than RNN, likely because of its increased ability to preserve and learn from past information. Average root mean squared error and Nash-Sutcliffe efficiency values for an 18hr forecast for the LSTM were 0.06m and 0.89, respectively, and 0.07m and 0.85, respectively, for the RNN. These forecasts could provide valuable information to aid in planning and response to storm events and will become an increasingly important part of effectively modeling and predicting coastal urban flooding as sea level rises.

For catchment-scale streamflow estimation, recently, the preference is shifting towards the use of innovative remote sensing (RS)-based approaches at the remote gauging stations. In this context, this study evaluates the performances of three RS-based models designed with the near-infrared (NIR) bands of Landsat images at the Jenapur outlet of the Brahmani River basin in eastern India. These RS-based models are designed using the spectral behavior of land (C) and water (M) pixels in the NIR region of the electromagnetic spectrum in the presence and absence of water surrounding the streamflow gauging station. Further, the computed pixel ratio (C/M) is used as a parameter for the discharge estimation, in which four years (2009-2013) of Landsat images are used during calibration and three years (2014-2016) of these images are used during validation. Model-I uses the C/M method in which a box-matrix is conceptualized to analyze the optimal location of the land pixel (C0); and subsequently, the time series of C/M is calibrated with the in-situ discharge (Q) time series. The best pixel ratio (C0/M) time series is preprocessed with an exponential smoothing filter to derive the best filtered-pixel ratio (C0/M*) time series, which is used in the regression model to estimate the river discharge. Model-II corresponds to the multi-pixel ratio (MPR) method, where a 3×3 window is used to calculate the average reflectance of both the C and M pixels within the box-matrix, and subsequently, to obtain the best pixel (C0ʹ/Mʹ) ratio as in the case of Model-I to develop the spectral relationship between C0ʹ/Mʹ and Q time series. Model-III uses both the C/M and water width-based function to estimate the streamflow. The performance evaluation of the models is carried out using the Nash-Sutcliffe efficiency, Percentage bias, and Mean absolute error, which reveals that the model performance varies in the order: Model-III > Model-II > Model-I. This proposed RS-based discharge estimation model framework has the potential to be used in many world- rivers with varying cross-sections.

At altitudes below about 600 km, satellite drag is one of the most important and variable forces acting on a satellite. Neutral mass density predictions in the upper atmosphere are therefore critical for (1) designing satellites; (2) performing adjustments to stay in an intended orbit; and (3) collision avoidance maneuver planning. Density predictions have a great deal of uncertainty, including model biases and model misrepresentation of the atmospheric response to energy input. These may stem from inaccurate approximations of terms in the Navier-Stokes equations, unmodeled physics, incorrect boundary conditions, or incorrect parameterizations. Two commonly parameterized source terms are the thermal conduction and eddy diffusion. Both are critical components in the transfer of the heat in the thermosphere. Determining how well the major constituents ($N_2$, $O_2$, $O$) are as heat conductors will have effects on the temperature and mass density changes from a heat source. This work shows the effectiveness of using the retrospective cost model refinement (RCMR) technique at removing model bias caused by different sources within the Global Ionosphere Thermosphere Model (GITM). Numerical experiments, Challenging Minisatellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) data during real events are used to show that RCMR can compensate for model bias caused by both inaccurate parameterizations and drivers. RCMR is used to show that eliminating model bias before a storm allows for more accurate predictions throughout the storm.

The overturn of mantle cumulates following the crystallization of the lunar magma ocean – particularly the sinking of ilmenite-bearing cumulates (IBC) – provides an explanation for several aspects of the Moon’s evolution. However, the growth of a stagnant lid due to the temperature dependence of the viscosity tends to prevent IBC from sinking. Here, we investigate the dynamics of the overturn based on a composition-dependent rheology coupling diffusion and dislocation creep of major lunar mantle minerals via three alternative mixing models: isostrain, isostress and Minimized Power Geometric (MPG). The pre-overturn structure is obtained from a fractional crystallization model of the lunar magma ocean, which predicts the formation of a 36-km-thick IBC layer. The possibility of overturning this layer strongly depends on the choice of the rheological mixing model. The isostress model allows for a rapid and complete overturn, while the isostrain and the experiment-based MPG models do not allow IBC to sink. If IBC started sinking and mixing with the underlying mantle during magma ocean solidification, IBC could be initially distributed across a layer with a thickness of up to 150 km, whose partial overturn is always possible, independent of the rheological model. These results highlight the importance of improving rheological models for relevant lunar materials. In all cases, the overturn occurs via small-scale instabilities.

The lithosphere-asthenosphere system is fundamental to our understanding of mantle convection and plate tectonics. Seismic and electromagnetic methods are our primary means of determining its structure and physical properties. These independent constraints with different sensitivities to Earth’s properties hold promise for understanding the system. Here we use the shear velocity model from Rayleigh waves and the MT based resistivity model from near the equatorial Mid-Atlantic Ridge. Cross-plots of the models suggest a linear or near-linear trend that is also in agreement with petrophysical predictions. We therefore map the MT model to a new shear-wave starting model using the petrophysical relationship, which is then used to re-invert for shear-wave velocity. The resulting shear-wave velocity model fits the phase velocity data, and the correlation coefficient between the shear velocity and resistivity models is increased. Much of the model can be predicted by expectations for a thermal half-space cooling model, although some regions require a combination of higher temperatures, volatiles, or partial melt. We use the petrophysical predictions to estimate the melt fraction, melt volatile content, and temperature structure of the asthenospheric anomalies. We find up to 4% melt, with the lowest resistivities and shear velocities explained by up to 20% water or 20% CO2 in the melt or ~1% nearly pure sulfide melt, depending on the set of assumptions used. Melt is required in punctuated anomalies over broad depth ranges, and also in channels at the base of the lithosphere. Melt in the asthenosphere is dynamic, yet persistent on geologic time scales.

Water resources management is a non-trivial process requiring a holistic understanding of the factors driving the dynamics of human-water systems. Policy-induced or autonomous behavioral changes in human systems may affect water and land management, which may affect water systems and feedback to human systems, further impacting water and land management. Currently, hydro-economic models lack the ability to describe such dynamics either because they do not account for the multi-factor/multi-output nature of these systems, and/or are not designed to operate at a river basin scale. This paper presents a flexible and replicable methodological framework for integrating a microeconomic multi-factor/multi-output Positive Multi-Attribute Utility Programming (PMAUP) model with an eco-hydrologic model, the Soil and Water Assessment Tool (SWAT). The connection between the models occurs in a sequential modular approach through a common spatial unit, the “Hydrologic-Economic Representative Units” (HERUs), derived from the boundaries of decision-making entities and hydrologic responsive units. The resulting SWAT-PMAUP model aims to provide the means for exploring the dynamics between the behavior of socio-economic agents and their connection with the water system through water and land management. The integrated model is illustrated by simulating the impacts of irrigation restriction policies on the Río Mundo sub-basin in south-eastern Spain. The results suggest that agents’ adaptation strategies in response to the irrigation restrictions have broad economic impacts and subsequent consequences on surface and groundwater resources. We suggest that the integrated modeling framework can be a valuable tool to support decision-making in water resources management across a wide range of scales.

The current increase in temperature over Greenland and other glaciated regions allows for more surface melt, which poses the question of the impact of this extra amount of meltwater on ice dynamics. As subglacial hydrology models evolve they are now easier to apply to realistic scenarios to quantify the effect of an increase in melt on the dynamics of glaciers. However, a number of processes linking the surface melt to the water pressure at the base of glaciers are still overlooked in models due to a lack of knowledge or an excess of complexity. Here, we apply a subglacial hydrology model coupled to an ice dynamics model to a synthetic geometry to investigate the impact of moulins distribution on the dynamics of the glacier. Our results show that a sparser distribution of moulins leads to the faster development of the efficient drainage system and greatly slows down the glacier.

The Pacific Meridional Mode (PMM) exhibits a marked seasonal variability, with the strongest (weakest) variance in northern spring (fall). Such a phase locking feature is investigated through a combined observational and modeling study. Given the PMM perturbation, the wind induced latent heat flux anomaly leads to a strongest (weakest) heating on local sea surface temperature anomaly (SSTA) in MAM (SON) through positive wind-evaporation-SST feedback. The difference between MAM and SON lies on the strength and area of mean northeasterly trades. Experiments with a simple air-sea coupled model further demonstrate that a PMM-like SSTA perturbation grows much faster in MAM than in SON. The difference is primarily attributed to the seasonal mean wind, not mean SST condition. It is greatest strength and area of the mean northeasterly trade in MAM that leads to most efficient wind-evaporation-SST feedback and thus fastest PMM growth rate.