The author provides stable and safe designs of coastal and river banks using a new structural concept which stably keeps frame structure inside of the embankments, and therefore protects the backside of lowland areas, where more than ten millions of people live and social investment facilities are distributed. The projects can proceed under the framework of Public Private Partnership (PPP). This new proposal simplifies the design of multi water hazards from the probabilistic collapse risks to deterministic planning for human lives and accumulated social public facilities around coastal mega cities such as Tokyo and Nagoya in Japan. Those Metropolitan citizens experienced historical Kanto earthquake in 1923 and largest Tone-River flooding in 1947. On the other hand, heaviest storm surge in Japan by Typhoon Isewan in 1959 around Nagoya City in the Ise Bay facing Pacific Ocean. These regions distribute in flat low elevation areas for conveniences of foreign trade with port facilities, industries, and traffic infrastructures including hub airports. Mean while, those coastal districts and citizens sometimes remains in legendary collapse risks, such as gigantic fires or pandemic infection diseases nowadays along with the catastrophic destructions by M8 to M9 earthquakes, inundation and river flooding against social frameworks to keep human cultural livelihoods in the hazardous era of this modern earth of Gaia.

Most atmospheric models consider radiative transfer only in the vertical direction (1D), as 3D radiative transfer calculations are too costly. Thereby, horizontal transfer of radiation is omitted, resulting in incorrect surface radiation fields. The horizontal spreading of diffuse radiation results in darker cloud shadows, whereas it increases the surface radiation in clear sky patches (cloud enhancement). In this study, we developed a simple method to account for the horizontal transfer of diffuse radiation. We spatially filter the surface diffuse radiation field with a Gaussian filter, which is conceptually simple and computationally efficient. We applied the filtering to the results of Large-Eddy Simulations for two summer days in Cabauw, the Netherlands, on which shallow cumulus clouds formed during the day. We obtained the optimal filter size by matching the simulation results with detailed high-quality observations (1Hz). Without the filtering, cloud enhancements are not captured, and the probability distribution of global radiation is unimodal, whereas the observed distribution is bimodal. After filtering, the probability distribution of global radiation is bimodal and cloud enhancements are simulated, in line with the observations. We found that small changes in the filter width do not strongly influence the results. Furthermore, we showed that the width of the filter can be parameterized as a linear function of e.g. the cloud cover. Hence, this work presents a proof-of-concept for our method to come to more realistic surface irradiances by filtering diffuse radiation at the surface.

We use the Space Weather Modeling Framework (SWMF) Geospace configuration to simulate a total of 122 storms from the period 2010-2019. With the focus on the storm main phase, each storm period was run for 54 hours starting from 6 hours prior to the start of the Dst depression. The simulation output of ground magnetic variations were compared with ground magnetometer station data provided by SuperMAG to statistically assess the Geospace model regional magnetic perturbation prediction performance. Our results show that the regional predictions at mid-latitudes are quite accurate, but the high-latitude regional disturbances are still difficult to predict.

Hyperpycnal flows are produced when the density of a fluid flowing in a relatively quiescent basin is greater than the density of the fluid in the basin. The density differences can be due to the difference in temperatures, salinity, turbidity, concentration, or a combination of them. Turbulence-resolved numerical simulations of such flows, in particular DNS (Direct Numerical Simulations), generate vasts amounts of resulting data. In the case of poli-disperse particle laden gravity currents simulations, where several sediments diameters are considered, very detailed data of the concentration field is available near bed. It can be post processed and analysed as a deposition map of one geological event. Traditional visualization tools lack the geological visual metaphor, and a new visual and interactive tool is proposed in this work. The aim of this new tool is to provide a better way to visualize numerical simulation results of particle laden gravity currents, plotting the results with the visual resemblance of a stratigraphic image. Since numerical simulation results usually have better spatio-temporal resolution compared to traditional stratigraphy, as the resolution depends exclusively on the amount of computing power available and it gets higher each day, the proposed interactive tool let the user visualize how the deposition map evolves in time and space. This tool can be employed to analyse the link between the deposition map and the turbulent flow that produced it, and the influence of all governing parameters. Numerical data was provided by Incompact3d, a code based on a Boussinesq system for incompressible fluids, designed for supercomputers. However this particular approach is a data driven post processing tool, thus it should be compatible with any numerical solver.

Goundwater investigations are increasingly becoming important for Indonesia. It is natural resources for sustainable development of a region. In this study, PT Sejahtera Alam Energy conducted an investigation of subsurface conditions, especially related to groundwater potential, carried out by a resistivity investigation method by means of Vertical Electrical Sounding (VES) or Geoelectricity in Embung Area, Baturaden Geothermal Project, Pandansari Village, Paguyangan Subdistrict, Brebes Regency. This method is one of the subsurface estimation methods that is considered suitable for water investigations as well as in terms of accuracy as well as in terms of low cost and faster implementation time compared to other geophysical methods.

The five large moons of Uranus are important targets for future spacecraft missions. To motivate and inform the exploration of these moons, we model their internal evolution, present-day physical structures, and geochemical and geophysical signatures that may be measured by spacecraft. We predict that if the moons preserved liquid until present, it is likely in the form of residual oceans less than 30 km thick in Ariel, Umbriel, Titania, and Oberon. The preservation of liquid strongly depends on material properties and, potentially, on dynamical circumstances that are unknown. Miranda is unlikely to preserve liquid until present unless it experienced tidal heating a few tens of million years ago. The triaxial shapes estimated from Voyager 2 data for Miranda and Ariel further support the prospect that these moons are internally differentiated with a rocky core and icy shell. We find that since the thin residual layers may be hypersaline, their induced magnetic fields could be detectable by future spacecraft-based magnetometers. However, if the ocean is maintained primarily by ammonia, and thus well below the water freezing point, then its electrical conductivity may be too small to be detectable by spacecraft. Lastly, our calculated tidal Love number (k2) and dissipation factor (Q) are consistent with the Q/k2 values previously inferred from dynamical evolution models. In particular, we find that the low Q/k2 estimated for Titania supports the hypothesis that Titania currently holds an ocean.

Tropical anvil clouds are an important player in Earth’s climate and climate sensitivity, but simulations of anvil clouds are uncertain. Here we pinpoint one source of uncertainty by demonstrating a marked increase of anvil cloud fraction with resolution in cloud-resolving simulations of radiative-convective equilibrium. This increase in cloud fraction can be traced back to the resolution dependence of horizontal mixing between clear and cloudy air. A mixing timescale is diagnosed for each simulation using the cloud fraction theory of Seeley et al. (2019), and is found to scale linearly with grid spacing, as expected from a simple scaling law. Thus mixing becomes more efficient with increasing resolution, generating more evaporation, decreased precipitation efficiency, greater mass flux, and hence greater detrainment and cloud fraction.The decrease in precipitation efficiency also yields a marked increase in relative humidity with resolution.

The development of groundwater levels (GWL) simulations, based on deep learning (DL) models, is gaining traction due to their success in a wide range of hydrological applications. GWL Simulations allow generating reconstructions to be used for exploring past temporal variability of groundwater resources or provide means to generate projections under climate change on decadal scales. Owing to the diversity of large-scale and local scale forcing factors involved in explaining GWL variability, machine learning or even deep learning approaches reveal relevant tools to simulate GWL. In addition, such methods do not require too much-extended knowledge of physical variables in the links between climate variables and GWL.In this paper, we investigated the capacities of three deep learning models (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Bidirectional LSTM (BLSTM)) to reproduce GWL variations over time. Among the three deep learning models, GRU performed relatively better in most cases. Another aspect was to evaluate the input data’s impact and usefulness of wavelet pre-processing considering its limitations and best practices. Two different input datasets are compared to each other, one considering Effective Precipitation only, the other considering Precipitation and Temperature.Maximum Overlap Discrete Wavelet Transform (MODWT) preprocessing was used to decompose the input variables to explore the impact of wavelet transform in improving the simulations on several types of GWL time series by unravelling “hidden” though useful information in input data. Results show that the preprocessing (MODWT) helps the models generate better simulations. This improvement is higher with raw climate data (precipitation & temperature) as compared to when effective precipitation was used as input. Finally, the Shapley Additive exPlanations (SHAP) approach was used to interpret the impact of input variables on the model simulations. Analysis of SHAP values indicated that the sources of the information content preferentially learned by the models to achieve best simulations. For instance, it was clear that simulation of inertial and mixed GWL required the models to learn from low-frequency variability presented in the input data.

A devastating tsunami struck Palu Bay in the wake of the 28 September 2018 M$_{\mathrm{w}}=7.5$ Palu earthquake (Sulawesi, Indonesia). With a predominantly strike-slip mechanism, the question remains whether this unexpected tsunami was generated by the earthquake itself, or rather by earthquake-induced landslides. In this study we examine the tsunami potential of the co-seismic deformation. To this end, we present a novel geodetic dataset of GPS and multiple SAR-derived displacement fields to estimate a 3D co-seismic surface deformation field. The data reveal a number of fault bends, conforming to our interpretation of the tectonic setting as a transtensional basin. Using a Bayesian framework, we provide robust finite fault solutions of the co-seismic slip distribution, incorporating several scenarios of tectonically feasible fault orientations below the bay. These finite fault scenarios involve large co-seismic uplift (~2 m) below the bay due to thrusting on a restraining fault bend that connects the offshore continuation of two parallel onshore fault segments. With the co-seismic displacement estimates as input we simulate a number of tsunami cases. For most locations for which video-derived tsunami waveforms are available our models provide a qualitative fit to leading wave arrival times and polarity. The modeled tsunamis explain most of the observed runup. We conclude that co-seismic deformation was the main driver behind the tsunami that followed the Palu earthquake. Our unique geodetic dataset constrains vertical motions of the sea floor, and sheds new light on the tsunamigenesis of strike-slip faults in transtensional basins.

Plant phenotyping technologies have been developing rapidly over the last 2 decades. Plant sensors are becoming more accurate, faster, and easier to use. However, there are still bottleneck issues in plant sensing, including the changing environmental conditions, the plant’s diurnal activities, and the complicated Genotype-by-Treatment-by-Environment interactions. These issues keep plant phenotyping difficult and limit further application of the sensor technologies in precision agriculture. The Purdue Ag Engineers have been working innovatively to develop the next generation sensor technologies to address these issues. In this presentation, Dr. Jin will firstly introduce the four automatic high-throughput phenotyping facilities recently developed at Purdue and explain why they were built and how they’ve been successfully used in detecting diseases, nutrients deficiencies, and chemical damages. He will then introduce ABE’s most recent plant sensor technologies, including the 2021 Davidson Prize winner, LeafSpec, and the new drone and ground-based robots for automatic field phenotyping in different scenarios. Dr. Jin will also briefly share some discovery stories in plant phenotyping, such as how the crop images diurnal variances, and how to leverage the advantages of remote sensing and proximal sensing. 

The growth of the world’s population living in urban areas has led to change from natural to highly dense and impermeable landscapes. Moreover, the intensity and frequency increase of extreme events resulting from climate change (e.g. inundations, heat waves) produce a degradation of urban systems resilience capacity. The integration of nature-based solutions NbS in urban spaces has been widely accepted, like a sustainable strategy that allows to tackle these urban challenges while building resilience and improving well-being and health. NbS are inspired by the nature, favouring natural processes in the cities like water infiltration or evapotranspiration (ET). Thanks to the latest, the vegetation shadow and wind dynamics favours air temperature reduction during heatwave events or mitigation of Urban Heat Island (UHI) phenomenon. Consequently, the distribution of urban green spaces might impact heat fluxes dynamics, by reducing temperature and its perception. They may also reduce virus spreads and also the sensitivity factors to them. Following a spatial approach, this study aims to analyse the green space distribution across different scales in the agglomeration located in the east of Paris (France) and various scenarios of its development. To achieve this goal, the spatial simulation model “Fractalopolis” is used. This allows to generate fractal urban forms using an iterative downscaling scheme (IFS: Iterative Function System) applied to existing large scale patterns of green areas. This work enables to analyse i) the access of population to the nearest green area and the fractal dimension of public urban green spaces; and ii) the impact of temperature reduction of different land-use scenarios by coupling a multifractal analysis of ET scaling behaviour measured at the local scale.

A novel approach to improve seasonal to interannual sandy shoreline predictions is presented, whereby model free parameters can vary in time, adjusting to potential non-stationarity in the underlying model forcing. This is achieved by adopting a suitable data assimilation technique (Dual State-Parameter Ensemble Kalman Filter) within the established shoreline evolution model, ShoreFor. The method is first tested and evaluated using synthetic scenarios, specifically designed to emulate a broad range of natural sandy shoreline behavior. This approach is then applied to a real-world shoreline dataset, revealing that time-varying model free parameters are linked through physical processes to changing characteristics of the wave forcing. Greater accuracy of shoreline predictions is achieved, compared to existing stationary modelling approaches. It is anticipated that the wider application of this method can improve our understanding and prediction of future beach erosion patterns and trends in a changing wave climate.

The bulk of the sediment found on the abyssal plain is transported from the shelf to the deep ocean by marine turbidity currents. They consecutively erode, transfer and deposit large amounts of sediment determining the characteristics of the marine environment. Further, their destructive nature is evidenced by several failures of subsea infrastructures that have been associated with their passage. Many numerical models have been developed to study field-scale turbidity currents. However, these models do not include sediment transport processes typical of coastal settings. As such they currently require the prescription of boundary conditions usually difficult to assess. Here we present a new modeling approach to hindcast marine turbidity currents that is based on a three-dimensional application of the process-based model Delft3D capable of predicting the initiation of sediment gravity flows driven by wind- and wave-induced processes. Detailed numerical simulations were carried out to investigate the nature of a turbidity current that impacted upon a submarine pipeline offshore Philippines after the nearby landfall of a tropical cyclone. Our simulations predict the triggering of a severe turbidity current only after the passage of the aforementioned tropical cyclone and the absence of any significant undercurrent associated with the other most relevant cyclones that passed near the study area during the lifespan of the pipeline, matching field observations in the form of pipeline shifting. Numerical results describe the development of rip currents that flush out water and sediment in cross-shore direction, ultimately triggering a turbidity current into the submarine canyon that cross the pipeline where its displacement was detected. Collapse of similarity profiles of the predicted undercurrent with experimental measurements and field observations demonstrates the reliability of the model in capturing the vertical structure of turbidity currents. As Delft3D has been demonstrated to accurately reproduce sediment transport processes associated with different environmental pressures in various geomorphological settings, the proposed modelling approach will allow for a deeper understanding of the mechanisms involved in the initiation of marine turbidity currents and their potential forecast.

Shifts in Southern Ocean (SO, 40-85S) shortwave (SW) cloud feedback (SWFB) towards more positive values are the dominant contributor to higher effective climate sensitivity (ECS) in Coupled Model Intercomparison Project phase 6 (CMIP6) models. The positive shift in SWFB in CMIP6 global climate model (GCMs) can be traced back to the greater reduction in low cloud cover and the weaker cloud liquid water response to warming in the SO. To evaluate how realistic the CMIP6 cloud response is, we connect the SO SWFB to changes in column-integrated liquid water mass (LWP) and the susceptibility of albedo to LWP in 50 CMIP5 and CMIP6 GCMs. In turn, we predict the responses of SO LWP to warming using a cloud-controlling factor (CCF) model. The combination of the CCF model and radiative susceptibility explains about 50% of the variance in the GCM-simulated SWFB in the SO. Observations of SW radiation fluxes, LWP, and reanalysis of CCFs are used to constrain the SO SWFB. This yields a constrained response of SO LWP to warming of 2.89-4.41 gm-2K-1, relative to the total GCM range of -0.48-9.33 gm-2K-1. The susceptibility of albedo to LWP is constrained to be 0.41-0.86 (kgm-2)-1, relative to the GCM range of 0.23-3.62 (kgm-2)-1, where albedo is unitless. The overall constraint on the contribution of SO SWFB to global cloud feedback is -0.19-0.05 Wm-2K-1, relative to GCM range of -0.28-0.27 Wm-2K-1. In summary, observations suggest a moderate negative to weak positive SO SWFB.

We reprocessed and interpreted Chang’E-4 Lunar Penetrating Radar (LPR) data collected until 14th February 2023, exploiting a new Deep Learning-based algorithm to automatically extract reflectors from a processed radar dataset. The results are in terms of horizon probability and have been interpreted by integrating signal attribute analysis with orbital imagery. The approach provides more objective results by minimizing the subjectivity of data interpretation allowing to link radar reflectors to their geological context and surface structures. For the first time, we imaged dipping layers and at least 20 shallow buried crateriform structures within the regolith using LPR data. We further recognized four deeper structures similar to craters, locating ejecta deposits related to a crater rim crossed by the rover path and visible in satellite image data.

Quantifying warming of the Earth’s climate system since the late 19th century and the ratio of regional to global warming are required when examining implications of Paris Agreement global warming targets. To estimate these terms reliably, the limitations in quality and length of observed records, differences between climate models and observations, and different results dependent on temporal and forcing contexts must be taken into account. Here we use observational datasets currently available back to 1860 and the latest set of global climate model simulations from CMIP5/6 to examine the warming of Australia in the past and projected future. We find that Australia has warmed by 1.5 °C (1.3–1.8 °C) during 1850 - 2019, at a ratio of ~1.4 times the global warming of ~1.1 °C. Models generally produce a lower ratio of Australian to global warming than in observations, which may be related to model biases or internal climate variability.

We explore seasonal mass oscillations by continental water storage in Southeast Asia and Himalayan arc region using continuous Global Positioning System (cGPS) measurements and satellite data from the Gravity Recovery and Climate Experiment (GRACE). While the interaction between seasonally induced non-tectonic and tectonic deformation along the Himalayan plate boundary is still debated, we propose that tectonic deformation along this plate boundary can be significantly influenced by the deformation induced by the non-tectonic hydrological loading cycles. We suggest that the substantially higher transient displacements above the base of the seismogenic zone indicate a role of changes in aseismic slip rate on the deep megathrust that may be controlled by seasonal hydrological loading. We invoke modulation of aseismic slip on the megathrust down-dip of the seismogenic zone due to a fault resonance process induced by the seasonal stress changes. This process modulates mid-crustal ramp associated micro-seismicity and influences the timing of Central Himalayan earthquakes.

Supercritical CO2 (ScCO2) invades oilwell cement under geological CO2 sequestration conditions. With the penetration of ScCO2, cement structure prone to damage when the coupled effects of capillarity and carbonation were found. Microstructural evolution of oilwell cement samples was investigated by the CT scanning and the quantitative image-based analysis and show that ScCO2 with the high humid condition would penetrate much deeper than the dry ScCO2 because of the capillarity effects. Due to the deep saline condition in the sequestration formation, the penetration of ScCO2 was retarded by the salt deposition, comparing with the ultrapure water (UP water) conditions. For further assessment of this coupled mechanism, the permeability property and contact angle changes were proposed to analyse the interface region between ScCO2, saline/UP water and oilwell cement.