High producing grazed pastures occupy almost one third of Aotearoa New Zealand (268,000 km2) and produce exported protein to feed more than 40 million people. Trends within farming systems include increasing rates of urea fertiliser use and greater concentrations of N via urine deposition, which enhance N2O emissions and NO3 losses. Focussing on N-sensitive lake catchments, we ask: what tools can reduce uncertainties in the sources and magnitude of N losses, quantify the potential to reduce excess N, and clarify rates of change in N budgets. Dual-isotope NO3 measurements differentiate urine and urea-derived sources (δ15N < 4 ‰) from mineralized soil organic N (δ15N of 4–8 ‰). Nitrate in streams draining the Rotorua region’s pumice soils and aquifers is dominated by urine and urea sources, compared to streams flowing from finer soils that only show these lower δ15N values when large runoff events activate surface flow paths. We have confirmed that shifts in stream water δ2H and δ18O toward the values observed in the major rainfall event coincide with elevated [NO3] and low δ15N representative of urine and urea-derived sources. A combination of δ2H and δ18O and Δ14C in dissolved inorganic carbon (DIC) largely confirmed tritium-based assessments, suggesting lag times of many decades in some aquifers, but rapid responses to recent N inputs elsewhere. In the Southland region, where tile drainage enables effective pasture growth, we explored flow responses in a drainage tile where NO3 consistently showed an imprint of denitrification (δ15N > 12 ‰). In this location following major rainfall, [NO3] remained stable but dissolved organic N concentrations increased, at times associated with stormwater δ2H and δ18O shifts. The Δ14C in DIC yielded apparent ages of several hundred years during low-flow periods, suggesting ongoing breakdown of soil organic matter releases N, which should be considered in farm and catchment N budgets. We conclude that monitoring N concentrations and multiple isotope species can resolve control points of N excess, which reveal targets for potential mitigation. Specifically, N content in clover-ryegrass pastures seasonally exceeds N demand in grazing animals, suggesting alternate species or feeds could reduce animal urinary N excretion, and therefore limit soil-derived N2O emissions and NO3 losses.

Thе usе of unsupеrvisеd machinе lеarning tеchniquеs,  spеcifically K-mеans clustеring algorithms,  in urban studiеs has gainеd significant attеntion in rеcеnt yеars.  Thеsе tеchniquеs havе provеn to bе valuablе in analyzing and undеrstanding various aspеcts of urban dеsign,  such as land usе pattеrns,  transportation systеms,  and population distribution.  This articlе aims to providе a comprеhеnsivе rеviеw of thе application of K-mеans clustеring algorithms in urban studiеs. Thе findings of this rеviеw dеmonstratе thе widе rangе of applications of K-mеans clustеring in urban studiеs,  from idеntifying distinct land usе catеgoriеs to undеrstanding thе spatial distribution of social amеnitiеs.  Furthеrmorе,  it is rеvеalеd that thе usе of K-mеans clustеring in urban studiеs allows for thе idеntification and charactеrization of hiddеn pattеrns and similaritiеs among urban arеas that might not bе immеdiatеly apparеnt through traditional analysis mеthods.  Ovеrall,  thе usе of K-mеans clustеring algorithms providеs a valuablе tool for urban plannеrs and rеsеarchеrs in gaining insights and making informеd dеcisions in urban dеsign.  keywords: k-means clustering,unsupervised Machine Learning ,urban studies  

The higher-order turbulence scheme, Cloud Layers Unified by Binormals (CLUBB), is known for effectively simulating the transition from cumulus to stratocumulus clouds within leading atmospheric climate models. This study investigates an underexplored aspect of CLUBB: its capacity to simulate near-surface winds and the Planetary Boundary Layer (PBL), with a particular focus on its coupling with surface momentum flux. Using the GFDL atmospheric climate model (AM4), we examine two distinct coupling strategies, distinguished by their handling of surface momentum flux during the CLUBB’s stability-driven substepping performed at each atmospheric time step. The static coupling maintains a constant surface momentum flux, while the dynamic coupling adjusts the surface momentum flux at each CLUBB substep based on the CLUBB-computed zonal and meridional wind speed tendencies. Our 30-year present-day climate simulations (1980-2010) show that static coupling overestimates 10-m wind speeds compared to both control AM4 simulations and reanalysis, particularly over the Southern Ocean (SO) and other midlatitude ocean regions. Conversely, dynamic coupling corrects the static coupling 10-m winds biases in the midlatitude regions, resulting in CLUBB simulations achieving there an excellent agreement with AM4 simulations. Furthermore, analysis of PBL vertical profiles over the SO reveals that dynamic coupling reduces downward momentum transport, consistent with the found wind-speed reductions. Instead, near the tropics, dynamic coupling results in minimal changes in near-surface wind speeds and associated turbulent momentum transport structure. Notably, the wind turning angle serves as a valuable qualitative metric for assessing the impact of changes in surface momentum flux representation on global circulation patterns.

Absorption muon imaging is a technique that can measure density variations underground down to a few hundred meters. Then, it can prove to be useful in a mining environment: to help assess the ore bodies volumes and/or to monitor the underground for natural hazards that can happen at the surface because of the mining exploitation. Here we report the result of an experiment designed to test the capabilities and resolution power of a cosmic muon measurement in a mining environment compared to other standard geophysics tools: gravimetry and seismic studies. It consists of three independent measurement of a subset of the decommissioned iron mine of May-sur-Orne (France). The first one was made using a 50x50cm² micromegas based muon telescope installed at the deepest non-submerged level (50m underground) during 3 months. The second one is a gravimetry survey of the surface area inside the muon telescope acceptance cone. And the third one is a study of refracted and reflected seismic waves along a single line above the muon telescope location. The investigation volume was chosen because of the presence of surface risks (neighborhood), the unknown of some uncharted volume and the presence of an ore storage volume of several meter cubed that was used during the mine exploitation and which filling state is unknown. The data analysis showed that while muon tomography is able to detect the negative density anomaly of the storage volume, the gravimetry measurement is not sensible to it. However, the seismic study was able to detect the volume as well and its location and extension is compatible with the muon measurement.

Precision monitoring of the subsurface carbon-dioxide plume ensures long-term, sustainable geological carbon storage. Carrigan et al. (2013) and Yang et al. (2014) showed that electrical resistivity tomography (ERT) can accurately map the evolution of the CO2 saturation during geological carbon storage. To better monitor the CO2 plume migration in a storage reservoir, we develop an unsupervised spatiotemporal clustering to process the CO2 saturation maps derived from the ERT measurements acquired over 80 days by Carrigan et al. (2013). Using dynamic time wrapping (DTW) K-means clustering, four distinct clusters were identified in the CO2-storage reservoir. The four clusters exhibit a Davies-Bouldin (DB) index of 0.71, a Calinski-Harabasz (CH) index of 262791, and a DTW-silhouette score of 0.58. Unlike traditional clustering methods, the DTW K-means incorporates a temporal distance metric. Traditional clustering methods, such as Euclidean K-means, agglomerative and meanshift clustering, exhibit a lower performance with DB index of 0.83, 0.95, and 1.01, respectively, and CH index of 157866, 131593, and 69438, respectively. Subsequent statistical analysis indicates that contrast stretching and fast-Fourier transform are strong geophysical signatures of the spatiotemporal evolution of CO2 plume. We also identified a strong correlation between injection flow rate and the spatial evolution of regions with high CO2 content. Finally, the previously computed spatiotemporal clusters were further clustered to discover distinct temporal sequences emerging with respect to the overall CO2 plume distribution in the subsurface. Six distinct temporal clusters of CO2 plume evolution were detected over a period of 2 months. A tensor-based feature extraction was critical for processing the ERT data acquired over 80 days to capture both the temporal and spatial components relevant to the evolution of CO2 plume in the storage reservoir.

A document by Luke Conibear. Click on the document to view its contents.

Quantification of heterogeneous multiscale permeability in geologic porous media is key for understanding and predicting flow and transport processes in the subsurface. Recent utilization of in situ imaging, specifically positron emission tomography (PET), enables the measurement of three-dimensional (3-D) time-lapse radiotracer solute transport in geologic media. However, accurate and computationally efficient characterization of the permeability distribution that controls the solute transport process remains challenging. Leveraging the relationship between local permeability variation and solute advection rates, an encoder-decoder based convolutional neural network (CNN) is implemented as a permeability inversion scheme using a single PET scan of a radiotracer pulse injection experiment as input. The CNN consists of Densely Connected Neural Networks that can accurately capture the 3-D spatial correlation between the permeability and the radiotracer solute arrival time difference maps in geologic cores. We first test the inversion accuracy using 500 synthetic test datasets. We then use a suite of experimental PET imaging datasets acquired on four different geologic cores. The network-inverted permeability maps from the geologic cores are used to parameterize forward numerical models that are directly compared with the experimental PET imaging datasets. The results indicate that a single trained network can generate robust, denoised 3-D permeability inversion maps in seconds. Numerical models parameterized with these permeability maps closely capture the experimental solute arrival time behavior. This approach presents an unprecedented improvement for efficiently characterizing multiscale permeability heterogeneity in complex geologic materials.

A document by Julia G Mason. Click on the document to view its contents.

Previous studies have shown that Strong Thermal Emission Velocity Enhancement (STEVE) events occur at the end of a prolonged substorm expansion phase. However, the connection between STEVE occurrence and substorms and the global high-latitude ionospheric electrodynamics associated with the development of STEVE and non-STEVE substorms are not yet well understood. The focus of this paper is to identify electrodynamics features that are unique to STEVE events through a comprehensive analysis of ionospheric convection patterns estimated from SuperDARN plasma drift and ground-based magnetometer data using the Assimilative Mapping of Geospace Observations (AMGeO) procedure. Results from AMGeO are further analyzed using principal component analysis and superposed epoch analysis for 32 STEVE and 32 non-STEVE substorm events. The analysis shows that the magnitude of cross-polar cap potential drop is generally greater for STEVE events. In contrast to non-STEVE substorms, the majority of STEVE events investigated accompany with a pronounced extension of the dawn cell into the pre-midnight subauroral latitudes, reminiscent of the Harang reversal convection feature where the eastward electrojet overlaps with the westward electrojet, which tends to prolong over substorm expansion and recovery phases. This is consistent with the presence of an enhanced subauroral electric field confirmed by previous STEVE studies. The global and localized features of high-latitude ionospheric convection associated with optical STEVE events characterized in this paper provide important insights into cross-scale magnetosphere-ionosphere coupling mechanisms that differentiate STEVE events from non-STEVE substorm events.

The alignment of intrinsically anisotropic olivine crystals under convection is typically invoked as the cause of the bulk of seismic anisotropy inferred from shear-wave splitting (SWS). This provides a means of constraining the interplay between continental dynamics and the deep mantle, in particular for densely instrumented regions such as North America after USArray. There, a comparison of “fast orientations” from SWS with absolute plate motions (APM) suggests that anisotropy is mainly controlled by plate motions. However, large regional misfits and the limited realism of the APM model motivate us to further explore SWS based anisotropy. If SWS is estimated from olivine alignment in mantle circulation instead, plate-driven flow alone produces anisotropy that has large misfits with SWS. The addition of large-scale mantle density anomalies and lateral viscosity variations significantly improves models. Although a strong continental craton is essential, varying its geometry does, however, not improve the plate-scale misfit. Moreover, models based on higher resolution tomography degrade the fit, indicating issues with the flow model assumptions and/or a missing contributions to anisotropy. We thus compute a “lithospheric complement” to achieve a best-fit, joint representation of asthenospheric and frozen-in lithospheric anisotropy. The complement shows coherent structure and regional correlation with independently imaged crustal and upper mantle anisotropy. Dense SWS measurements therefore provide information on depth-dependent anisotropy with implications for tectonics, but much remains to be understood about continental anisotropy and its origin.

The geospace plume, referring to the combined processes of the plasmaspheric and the ionospheric storm-enhanced density (SED)/total electron content (TEC) plumes, is one of the unique features of geomagnetic storms. The apparent spatial overlap and joint temporal evolution between the plasmaspheric plume and the equatorial mapping of the SED/TEC plume indicate strong magnetospheric-ionospheric coupling. However, a systematic modeling study of the factors contributing to geospace plume development has not yet been performed due to the lack of a sufficiently comprehensive model including all the relevant physical processes. In this paper, we present a numerical simulation of the geospace plume in the March 31, 2001 storm using the Multiscale Atmosphere Geospace Environment model. The simulation reproduces the observed linkage of the two plumes, which, we interpret as a result of both being driven by the electric field that maps between the magnetosphere and the ionosphere. The model predicts two velocity channels of sunward plasma drift at different latitudes in the dusk sector during the storm main phase, which are identified as the sub-auroral polarization stream (SAPS) and the convection return flow, respectively. The SAPS is responsible for the erosion of the plasmasphere plume and contributes to the ionospheric TEC depletion in the midlatitude trough region. We further find the spatial distributions of the magnetospheric ring current ions and electrons, determined by a delicate balance of the energy-dependent gradient/curvature drifts and the E´B drifts, are crucial to sustain the SAPS electric field that shapes the geospace plume throughout the storm main phase.

Climate change has been a common interest and the forefront of crucial political discussion and decision-making for many years. Shallow clouds play a significant role in understanding the Earth’s climate but they are challenging to interpret and represent in a climate model. By classifying these cloud structures there is a better possibility of understanding the physical structures of the clouds which would improve the climate model generation resulting in a better prediction of climate change or forecasting weather update. Clouds organise in many forms which makes it challenging to build traditional rule-based algorithms to separate cloud features. In this paper classification of cloud organization patterns was performed using a new scaled-up version of Convolutional Neural Network (CNN) named as EfficientNet as the encoder and UNet as decoder where they worked as feature extractor and reconstructor of fine grained feature map and was used as a classifier which will help experts to understand how clouds will shape the future climate. By using a segmentation model in a classification task it was shown that with a good encoder alongside UNet it is possible to obtain good performance from this dataset. Dice coefficient has been used for the final evaluation metric which gave the score of 66.26% and 66.02% for public and private (test set) leaderboard on Kaggle competition respectively.

The calculations of atmospheric radiative transfer are among the most time-consuming components of the numerical weather prediction (NWP) models. Therefore, using deep learning to achieve fast radiative transfer has become a popular research direction. We propose a physics-incorporated framework for the radiative transfer model training, in which the thermal relationship between fluxes and heating rates is encoded as a layer of the network so that the energy conservation can be satisfied. Based on this framework, we compared various types of neural networks and found that the model structures with global receptive fields are more suitable for the radiative transfer problem, among which the Bi-LSTM model has the best performance.

A three-dimensional physical-biogeochemical ocean numerical model with eddy-permitting horizontal resolution was applied to simulate upper-ocean carbon cycle variabilities in the Tropical Maritime Continent (TMC) over the last decade (2010–2019). Forced by atmospheric and oceanic reanalysis products with high temporal resolution, the model showed robust consistency with the observed seasonality of pCO2 and atmospheric CO2 sink/source characteristics across the modeling domain. Within the TMC, the model results indicated strong CO2 degassing along the west of Sumatra-south of Java associated with the seasonal cycle of the upwelling system in the area. While acting as a full-year atmospheric CO2 source, the TMC exhibited pronounced interannual modulation in both pCO2 and sea-air CO2 flux over the last decade. Large-scale anomalous strong CO2 degassing from 2015 to 2016 in response to the evolution of the 2015/2016 El Niño was observed from the simulation results. Modulations related to the Indian Ocean Dipole (IOD), on the other hand, were confined along the west of Sumatra-south of Java with a higher magnitude compared with anomalies related to El Niño/La Niña. Simulation results also captured the asymmetric response of the upper-ocean carbon cycle to the IOD over the last decade, where anomalies during negative IOD (nIOD) were notably strong despite being indicated as weak nIOD events by the Dipole Mode Index.

Juno’s arrival at Jupiter in 2016 revealed unprecedented details about Jupiter’s ultraviolet aurorae thanks to its unique suite of remote sensing and in situ instruments. Here we present results from in situ observations during Juno flybys above specific bright auroral spots in Jupiter’s polar aurora. We compare data observed by Juno-UVS, JEDI, JADE, Waves, and MAG instruments when Juno was magnetically connected to bright polar auroral spots during perijove 3 (PJ3), PJ15, and PJ33. The highly energetic particles observed by JEDI show enhancements dominated by upward electrons, which suggests that the particle acceleration region takes place below the spacecraft. Moreover, both brightness and upward particle flux were higher for the northern bright spot in PJ3 compared to the southern spots found in PJ15 and PJ33. In addition, we notice the intensification of whistler-mode waves at the time of the particle enhancements, suggesting that wave-particle interactions contribute to the acceleration of particles which cause the UV aurorae. The MAG data reveal magnetic perturbations during the PJ3 spot detection by Juno, which suggests the presence of significant field-aligned electric currents. While the stable position of the bright spots in System III suggests that the phenomenon is fixed with respect to the rotation of the planet, the presence of field-aligned currents leaves open the possibility of an origin rooted much farther in the magnetosphere.

Why is plane-bed topography unstable under certain flow conditions? We investigate the grain-scale mechanisms responsible for topographic instability at the onset of bedform development. Measurements of fluorescent tracer particle motion were used to estimate the ensemble mean particle activity, entrainment rate, hop distance, travel time, and particle velocity characteristic of flow conditions straddling the threshold stress for bedform development. Based on these data, we propose two hypotheses to explain the destabilization of planar topography with rising transport conditions. Hypothesis 1: plane-bed topography is unstable above a theory-predicted entrainment rate threshold that varies primarily as a function of particle diameter. Hypothesis 2: plane-bed topography is unstable above a threshold particle collision frequency that is proportional to bedload flux.The threshold particle collision frequency is predicted analogously to the propensity for congestion shockwaves in vehicular traffic flow theory.

Hydrological distributed modeling is a key point for a comprehensive assessment of the feedback between the dynamics of the hydrological cycle, climate conditions, and land use. Such modeling results are markedly relevant in the fields of water resources management. Here TopModel (TOPography based hydrological MODEL) is employed for the hydrological modeling of an area in the Middle Magdalena Valley (MMV), a tropical basin located in Colombia. This study is located in the intertropical convergence zone and is characterized by special meteorological conditions, with fast water fluxes over the year. It has been subject to significant land use changes, as a result of intense economic activities, i.e., agriculture, energy and oil & gas production. The model employees a record of 12 years of: • Daily precipitation database from observed gauges • Daily evapotranspiration database from temperature data • Streamflow database as observed data from calibration Calibration is performed using data from 2000 to 2008, and validation is performed with data from 2009 to 2012. The Nash-Sutcliffe coefficient is used to assess the robustness of our calibration process.(values of this metric being 0.62 and 0.53, respectively for model calibration and validation). The results reveal high water storage capacity in the soil, and a marked subsurface runoff, consistent with the characteristics of the soil types in the regions. The calibrated model provides relevant indications about recharge in the region, which is important to quantify the interaction between surface water and groundwater, especially during the dry season, which is more relevant in climate-change and climate-variability scenarios.

Imaging the Earth’s thermochemical structure is crucial for understanding its dynamics and evolution. Moreover, the increased demand for critical minerals and geothermal energy driven by the energy transition has intensified the need for reliable subsurface models. Multi-Observable Thermochemical Tomography (MTT) is a simulation-based, probabilistic inversion platform designed to harness the combined sensitivities of multiple geophysical datasets and thermodynamic modelling. It produces internally-consistent estimates of the Earth’s interior as probability distributions, offering a powerful means for uncertainty quantification. Here, we present an updated MTT formalism and assess its benefits and limitations to image the thermochemical structure of the lithosphere-asthenosphere system. Individual and combined sensitivities of different observables to parameters of interest (e.g. temperature, composition, crustal architecture) are explored using challenging synthetic models. Our findings demonstrate that a judicious combination of observables can retrieve complex thermochemical structures relevant to greenfields exploration. We then apply MTT to study two cratonic regions of geological and economic significance. In the Superior Craton, we jointly invert receiver functions, gravity anomalies, gravity gradients, geoid anomalies, Rayleigh-wave dispersion curves, absolute elevation and surface heat flow. In the North Australian Craton, we incorporate new data from the Ausarray and add teleseismic P- and S-phase travel times to the datasets. The imaged lithospheric architectures provide new insights into the tectonic evolution of these two regions and the physical meaning of geophysical signatures. Additionally, these models offer unique proxies to guide exploration efforts for clean energy and critical minerals and serve as reference models for future high-resolution studies.