The identification of a prospective groundwater recharge zone is crucial for supplementing groundwater resources. It's especially critical in the hard rock region, where groundwater is the principal source of potable water and is fast disappearing due to uncontrolled mining. The present study used a combination of modern methodologies and technologies to analyze the groundwater potential zone occurrence, including geographic information system (GIS), remote sensing (RS), electrical resistivity i.e. VES, and multi-criteria decision analysis (MCDA). Several thematic layers were prepared, including geomorphology, lineament density, drainage density, soil type, geology, rainfall, soil texture, elevation, and land use land cover (LULC), which were weighed according to their impact on the groundwater prospect zone. The analytical hierarchical process (AHP) was used in this study to apply normalization to relative assistance. Vertical electrical sounding was used to find water bearing formations/fracture zones at various points throughout the selected region. The five prospective groundwater prospect zones that were delineated using these methods have been classified as very low, low, moderate, high, and very high. The delineated high groundwater potential zones were found in the northeastern part and a little below the central region, while low to moderate zones were found almost evenly distributed all over the region. The acquired result was validated using well yield data, which showed a 72 percent accuracy with our delineated groundwater potential zone. Hence, the AHP model in the current work has outperformed the competition in terms of prediction accuracy.

In Iran, both local and regional scale land-surface deformation has resulted from the decline in groundwater levels \cite{Motagh_2008}. Moreover, the gap between groundwater extraction and renewal is so large that the resulting short-term impacts are likely to be irreversible \cite{Olen_2021}.Here we use Earth Observation (EO) data to calculate vertical subsidence rates due to groundwater extraction in Tehran, Iran’s capital city. This data includes Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR); very high-resolution (VHR) Pléiades optical stereo imagery; and ICESAT-2 laser altimetry data.The Centre for Observing and Monitoring Earthquakes and Tectonics (COMET) Looking into Continents from Space (LiCSAR) automated processing system is used to process six years (2015-2021) of Sentinel-1 SAR acquisitions \cite{Lazeck__2020} for interferometric (InSAR) analysis. The system generates short baseline networks of interferograms. We also correct for atmospheric noise using the GACOS system \cite{Yu_2018a,Yu_2018} and perform time-series analysis using open-source LiCSBAS software \cite{Morishita_2020}. Vertical and horizontal (east-west) velocities are calculated and mapped to produce nationwide and regional velocity fields \cite{Watson_2022}. Preliminary results indicate maximum subsidence rates in Tehran exceed 100 mm/year.VHR Digital Elevation Models (DEMs) and laser altimetry ground returns are also used to calculate land subsidence rates in Tehran. By comparing rates calculated using all three EO techniques we aim to validate InSAR velocities whilst investigating and constraining the benefits, drawbacks, and biases associated with each technique. Phase bias \cite{De_Zan_2015}, for example, may be introduced to calculated InSAR velocities when using the LiCSBAS short baseline network strategy as subsiding regions in Iran are often vegetated cropland.Publishing the open-source COMET-LiCS Subsidence Portal was the focus of previous work (https://comet-subsidencedb.org/). The portal presents automatically processed LiCSAR Sentinel-1 interferograms and LiCSBAS velocity timeseries for 99 subsiding regions across Iran. Interactive tools allow stakeholders to make quick, critical assessments related to extents and rates of subsidence. Validating portal data using DEMs and laser altimetry is essential before expanding the portal to have a global focus.

A document by Ayomide Hamzat. Click on the document to view its contents.

Near Surface Air Temperature is an important climatic variable that affects the hydrological response of a river basin, and forms an input to most of the hydrological models. General Circulation Models (GCMs) simulate the response of temperature and other climate variables to the variations in emission concentrations, but their outputs are too coarse to be used in most hydrological models. A multi stage statistical downscaling approach is proposed for downscaling GCM predicted temperatures. In the first stage the Relevance Vector Machine (RVM) is used to develop a statistical model between the GCM simulated historical climate variables and the observed historical temperature for spatially downscaling monthly GCM simulations. A weather generator is then used to generate daily data from the spatially downscaled temperature data. On fine scales, lack of correlation between precipitation and temperature data used for hydrological modelling can lead to large uncertainties in the generated hydrological series. Thus, a distribution free post processing is performed for reproducing the observed regional correlation between temperature and precipitation, in the generated temperature data. The methodology is then applied to the Bharathapuzha catchment in Kerala, India, to downscale temperature from the climate models BNU-ESM, CESM1-BGC, CMCC-ESM2, FGOALS-G2, FIO-ESM-2.0 and MIROC4h. The statistical models set up using RVM show consistent performance during the calibration (1969-1980) and validation (1981-2005) phases, with Nash-Sutcliffe efficiency (NSE) between 0.64 to 0.83. The weather generator is then run to generate daily temperature data from the monthly downscaled series. Across the different climate models, daily maximum temperature is generated with RMSE between  2.5°C to 3.3°C, while the minimum temperature has RMSE ranging from 1.7°C to 2.0°C. The probabilistic nature of the procedure enables the generation of multiple series from the same set of predictors. The simulation band from the multiple GCMs is studied for the period 2016 to 2021 to understand the deviation in predicted temperature for the future scenario. The prediction band for maximum temperature has an average band width of 6.7°C and for minimum temperature, the average band width is 4.9°C.

The current contribution presents wintertime climatology from 2012 to 2020 of mixed-phase clouds and their radiative effects when coupled to the sea ice states. Measurements from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) at the North Slope Alaska (NSA) site in Utqiagvik, Alaska are being analyzed.Classification of cloud hydrometeors in the liquid, ice or mixed-phase states was primary determined by the Cloudnet algorithm, developed by the Finish Meteorological Institute, and applied to a set of ground-based remote sensing instruments from NSA . To evaluate the influence by sea ice, which plays an important role on the Arctic surface-atmosphere interaction, the statistics are separated into cases when clouds are coupled or decoupled to specific sea ice conditions, like presence of leads or polynyas in the vicinity of NSA .We found that clouds coupled to sea ice with presence of leads have shown distinguished features like the increase of total liquid content, lower cloud base heights and less ice content when compared to decoupled cases. Nevertheless, these results rely on Cloudnet accurately detecting cloud droplets within mixed-phase clouds. Arctic cloud radiative effects (CRE) have already been studied from short expeditions like the SHEBA campaign (Shupe et al., 2004) and middle-term ground observations in Barrow (Shupe et al., 2015) and Ny-Ålesund, Svalbard (Ebell et al., 2020). We extend similar CRE studies for 8 years during wintertime, where longwave up- and down-welling flux measurements from NSA are used to estimate surface net fuxes and other cloud radiative features for cases when clouds are coupled or decoupled to sea ice conditions and their sensitivity to different gradients of air-surface temperature when leads or polynyas are present. 

Bentonite is a fine-grained geologic material consisting mainly of montmorillonite clay. It presents a low permeability, a high swelling pressure, and a strong capacity to retain radionuclides that make it an important component in current efforts to design engineered barrier systems for the isolation of radioactive waste. In these barriers, the thermal gradient generated by radioactive decay is expected to lead to coupled thermal-hydrologic-mechanical-chemical (THMC) processes that may impact barrier performance. However, constitutive relations characterizing the THMC coupled properties of bentonite in variable temperature, aqueous chemistry, and dry density conditions remain incompletely understood. Here, we use high-performance molecular dynamics (MD) simulations to gain insight into the THMC constitutive relations of compacted montmorillonite clay. Specifically, we report large-scale MD simulations of water-saturated clay assemblages containing 27 montmorillonite particles performed using the codes GROMACS and LAMMPS (Fig. 1). Simulations were carried out using the replica-exchange MD (REMD) technique, with 96 replicas of the system with a wide range of temperatures up to 100 °C. In addition, simulated systems were progressively dehydrated to examine a range of dry densities. Results were analyzed to determine a series of properties including hydraulic conductivity, water and ion self-diffusivity, heat capacity, thermal expansion, and swelling pressure as a function of temperature, dry density, and the type of exchangeable cations (Na, K, Ca). Finally, simulation predictions were validated and refined by benchmarking against experimental results and previous MD simulation predictions. This research provides new insight into the coupled THMC properties of clay barrier systems and advances efforts to predict the performance of engineered clay barriers over a long timescale.

The various extreme weather events that occurred globally in 2021, from Europe to China to North America, served as yet another reminder that robust strategies for climate adaptation are crucial at a time of rapid global warming. Building resilient communities and lessening the impact that natural disasters have on vulnerable infrastructure can be aided by automated systems driven by machine learning algorithms trained on Earth observation data. When deployed, computer vision models can analyze satellite imagery in real time and inform decision makers and nongovernmental organizations about the timely and targeted allocation of resources and humanitarian aid personnel to affected areas. Here, we overview several specific 2021 extreme events and the factors that caused the loss of life, damage to infrastructure, and economic loss. The events surveyed include flooding in Germany, wildfires in Greece, and Hurricane Ida in the Eastern United States. Taking this information into account, we further discuss barriers to the large-scale deployment of current machine learning technologies, especially models trained on Earth observation data. We examine the limitations of satellite imagery and big data applications in detecting damage and building collapse and how Interferometric Synthetic Aperture Radar (InSAR) can be a tool to resolve existing issues. The aim of this work is to understand why many state-of-the-art models being developed have not yet been successfully and extensively deployed in the real world and to foster discussion about optimizing the use of deep learning technology to save lives and lead effective disaster management efforts.

A document by Kavyashree N Kalkura. Click on the document to view its contents.

Identifying areas of the land surface and surface water features (e.g., rivers, lakes, etc.) likely to contribute groundwater recharge to a public groundwater supply well is typically a first step toward source water protection. Identifying the contributions from these areas is important for assessing contamination sources, developing land use management strategies, and mitigating groundwater risk for drinking water supply. Simple analytical solutions that employ Darcy’s Law are unable to account for surface water boundary conditions within the flow system. Therefore, capture zone delineation is typically performed using three-dimensional, fully distributed numerical models that require considerable numbers of parameters, stratigraphic data, and user expertise. However, advanced analytical solutions exist that can provide approximations to such solutions using few parameters.In this work, the R Shiny web platform is developed to create an open-source application to allow Internet users to visualize potential flow systems near wells in the vicinity of surface water features. Assumptions include homogeneous stratigraphy and aquifer thickness, a steady state flow field, and relatively simple aquifer geometry. The web tool is currently being developed with a focus on Yukon Territory in northern Canada, where most of the population relies on groundwater, but less work has been done on the analysis of well vulnerability and source water protection than in southern Canada. The results are intended for estimation and education purposes and will be compared with numerical model results for some sites with pre-existing investigations. Abstract ID: 1168805

Presenting Author: [email protected] roll is the most common coherent noise observed in the land seismic data, mainly characterized by low frequency and low velocities. In this study, we are demonstrating the application of a model-based surface wave attenuation technique for the efficient removal of ground roll from 2D seismic data. The method employs inversion and adaptive subtraction filtering of surface waves. A 1D viscoelastic model is characterized by considering layer thickness, P & Sv wave velocities, density, and P & Sv wave quality factors (Qp & Qs) for each layer, to compute a synthetic seismogram. A linear Radon transform is used to generate dispersion spectra of input shot gather and synthetic shot gather generated from the model. The misfit between the dispersion spectra of input shot gather and synthetic shot gather is given by an objective function ‘J’,\(J\left(m\right)=\ {|\left|s-o\right||}_{2}\), where ‘s’ is the dispersion spectrum of the synthetic data generated by the model, ‘o’ is the dispersion spectrum of the shot gather and \({||.||}_{2}\)is the L2-norm of the difference between ‘s’ and ‘o’. The main focus is to minimize the objective function J. The local minima always seems to be the bottleneck for most of the non-linear inversion problems. To overcome this constraint, we implemented the Genetic Algorithm (GA) method in the surface-wave inversion process using the tool available in a commercial software. GA is a derivative-free search approach towards global minima, for the solution of the inversion problem. Once an optimized model is achieved from GA, synthetic data is generated and is adaptively subtracted from the shot gather. As the 1D model obtained cannot represent any lateral variations of physical parameters in the earth, adaptive subtraction and GA are implemented several times to mitigate this limitation.The proposed technique is applied to process a 2D shot gather data (Fig. 1-a) accessed from the SEG wiki Open data set collection of oz Yilmaz-40 shots. In this data, ground roll is masking the reflection events in the shot gather. After implementation of the proposed technique, it is observed that the ground roll is efficiently attenuated and reflections became more prominent by restoring the actual amplitudes, as shown in Fig. (1-b). The dispersion spectrum of both shot gathers is shown in Fig. 1 (c-d), it is observed that the ground roll present in the low-frequency zone (0-15 Hz) is attenuated. The results of the study suggest that the proposed method is quite efficient for surface wave attenuation.

Models of fault slip development generally consider interfacial strength to be frictional and deformation of the bounding medium to be elastic. The frictional strength is usually considered as sliding rate- and state-dependent. Their combination, elastic deformation due to differential slip and rate-state frictional strength, leads to nonlinear partial differential equations (PDEs) that govern the spatio-temporal evolution of slip. Here, we investigate how data on fault slip rate and stress can directly discover the complex system (of PDEs) that governs aseismic slip development. We first prepare (synthetic) data sets by numerically solving the forward problem of slip rate and fault stress evolution with models, such as a thin laterally deformable layer over a thick substrate. We now identify the variables, for example, slip rate or friction state variable, and use nonlinearity identification algorithms to discover the governing PDE of the chosen variable.In particular, we use sparse identification of nonlinear dynamics algorithm (SINDy, Brunton et al., 2016) where we solve a regression problem, Ax=y. Here, y is the time derivative of the variable of interest, for example, slip rate. A is a large matrix (library) with all possible candidate functions that may appear in the slip rate evolution PDE. The entries in x, to be solved for, are coefficients corresponding to each library function in matrix A. We update A according to the solutions x so that A's column space can span the dynamics we seek to find. To find the suitable column space for A, we encourage sparse solutions for x, suggesting that only a few columns in matrix A are dominant, leading to a parsimonious representation of the governing PDE.We show that the algorithm successfully recovers the terms of the PDE governing fault slip and could also find the frictional parameter, for example, a/b, where a and b, respectively, are the magnitudes that control direct and evolution effects. Moreover, the algorithm can also determine whether the associated state variable evolves as aging- or slip-law types or their combination. Further, with the data set prepared from distinct initial conditions, we show that the SINDy can also determine the problem parameter’s spatial distribution (heterogeneities) from fault slip rate and stress data.

In light of climate change, wildfires are concurrently becoming more frequent and devastating worldwide. Though we have a good understanding of fire frequency changes in the past using charcoal analysis, records of the characteristics of these fires, such as fire severity, are lacking. This limits our ability to model how fire severity responds to climate change. Boron isotopes in clay minerals show promise as a novel fire severity proxy, where increased 𝛿11B is correlated with higher fire severity. Through reacting boron leached from experimentally combusted plants with clays, we determine that the observed correlation with fire severity is likely caused by input of isotopically heavier boron from combusted leaves. In contrast, combusted barks lower the 𝛿11B of clays upon reaction. Despite the different results, in both experiments with barks and leaves, similar boron isotope fractionation is observed during boron adsorption onto clays, where the lighter 10B is preferentially adsorbed. Therefore, the different results are likely caused by the different boron isotope composition of leaves and barks, where leaves have a much higher 𝛿11B (~30 ‰) than bark (~9 ‰). Combustion temperature can also affect the 𝛿11B of clays: changes to the 𝛿11B of clays were observed only when reacting with bark combusted at >300 °C, or with leaves combusted at >550 °C. This could be because more boron can be leached into solution from materials combusted at higher temperatures, which in turn results in greater adsorption onto clays during reactions. Clays have higher 𝛿11B in soils affected by high severity fires that consume tree crowns, because these fires combust more leaves that then deposit their isotopically heavier boron content into the soil. This relationship could help complete our fire record and improve our ability to predict future fire characteristics.

Recently, a tailored gravity field model was developed to fit local terrestrial gravity data by integrating Global Gravitational Models (GGMs), terrestrial gravity data, and Digital Elevation Models (DEMs). The numerical analysis of the newly developed tailored gravity model showed a substantial improvement by means of its possible application for geophysical exploration by exhibiting known geological features over the Southern Benue Trough of Nigeria. In this study, we apply a similar technique to develop a tailored gravity field model at the Limpopo Province in South Africa using a total of 8,603 terrestrial gravity measurements. Validation of results indicates that our tailored gravity model could reproduce the observed gravity data with the accuracy specified by a standard deviation of 8.9 mGal and with a systematic bias less than 0.1 mGal within the study area. We then inspected a possibility of using our tailored gravity field model to improve the accuracy of existing geoid/quasi geoid models at the study area. For this purpose, we compute a new (quasi)geoid model by applying the Remove-compute-restore numerical technique that treats separately the detailed gravity pattern that is closely correlated spatially with the topographic relief, the higher-to-medium gravity signal that is mostly captured by local/regional gravity data, and the long-wavelength gravity signal that is modelled by using GGMs. The accuracy of the new (quasi)geoid model was assessed by using the most recent South African gravimetric quasi-geoid model CDSM09A and the latest hybrid quasi-geoid model of South AfricaSAGEOID10. The comparison of our quasi-geoid model with the CDSM09A and SAGEOID10 quasi-geoid models was done at 7,225 quasi-geoid grid points. The comparison revealed that our new quasi-geoid model closely agrees with the CDSM09A and SAGEOID10 models. The differences between our and CDSM09A quasi-geoid models vary within-0.31 and 0.70 m, with a mean of 0.05 m and a standard deviation of 0.12 m. The corresponding differences between our and SAGEOID10 quasi-geoid models are between-0.35 and 0.70 m with a mean of 0.06 m and a standard deviation 0.12 m. The numerical analysis revealed that the new tailored gravity model could efficiently be used in various geophysical and geodetic applications.

A document by Balaji Senapati. Click on the document to view its contents.

Rapid urbanization has caused extensive land use changes, which may impact the local climatology and trigger extreme rainfall events. Wind, another important factor in rainfall, has been examined to determine its role in generating extreme rainfall in conjunction with land use changes. This study evaluates the variability of extreme rainfall in the Dallas Fort-Worth (DFW) area. The study area has been divided into 256 equal sized grids and grouped into 4 clusters. Spatial Synoptic Classification (SSC) technique has been adopted to select days with benign synoptic activity so that the role of large-scale weather patterns is reduced. Extreme rainfall days are defined as those exceeding the 95th percentile threshold of the daily rainfall observed at the DFW airport. After filtering data based on SSC and extreme rainfall criteria, 114 days are selected for the study period from 2000-2016. The Multi-Radar Multi-Sensor rainfall product has been used to examine the variability of extreme rainfall in grids and clusters. The variation in extreme rainfall for different land covers (e.g., high, medium, and low intensity developed land, and open space) has also been examined. The results reveal that the “Urban Core” cluster receives maximum rainfall across the study period. Also, the difference in cumulative maximum rainfall between highly urbanized grids and the lowest urbanized grid is highest in the “Urban Core” cluster. The grid with the highest percentage of “High intensity developed land” received the most rainfall. The wind regime associated with the most rainfall across the study period is from the South-West. Less urbanized grids located downwind of the highest urbanized grids received relatively more rainfall. Urbanization and the magnitude of extreme rainfall is proportional in most of the grids. The role of the Urban Heat Island will also be examined to determine its impact on extreme rainfall variability.  

India predominately depends on rainfall for its agricultural water requirements. Effective rainfall (ER) is the amount of water needed for crop cultivation without any external water supply support. Hence, ER calculation is necessary for irrigation management. Besides crop water management, ER can also aid in understanding extreme hydrological analysis. This study aims to find the suitability of employing ER as an alternative for meteorological drought severity identification. In this study, one of the most widely used ER calculation methods, the USDA-SC (Smith) method, was employed to calculate ER from 2011 to 2020 throughout India. The Standard Precipitation Index (SPI-3) at 3 months intervals was used to validate the suitability of ER for assessing the drought. Four test sites were selected that cover different climate conditions of India to validate the using ER in alternative to drought. This study reveals that ER values accord with SPI-3 values in all most all 118 months. The ER was classified into 5 classes: extreme drought, moderate to severe drought, normal rainfall, severe wet, and extreme wet to assess the severity of drought. Out of 118 months of the study period, a total of 4, 2, 6 and 5 months fall into extreme drought class in Kundala (Odisha), Mallapur (Telangana), Morbi (Gujarath), and Kishangarh Urf Pharwahi (Punjab), respectively. There are no extreme wet months in Mallapur and moderate to severe drought months in Morbi. This signifies that Mallapur might be experiencing normal rainfall, and Morbi not having drought-prone months during the study period. In the overall analysis, 2011, 2012, 2013, and 2018 were dry years with low ER values. Further, ER can even identify the wet places apart from only severe to extreme dry places. The advantage of using ER as a drought indicator is that it not only classifies the drought character but also gives the magnitude of the available portion of rainfall.

A major challenge to community code development and management is the testing and validation of public contributions. The community-developed GFDL Finite-Volume Cubed Sphere Dynamical Core (FV3) is no exception:  automated testing of contributions made to the FV3 public repository is paramount for ensuring the integrity of the many earth-system models and forecasting applications using FV3 as a dynamical core.  A build and test system for the FV3 dynamical core was developed for internal testing on NOAA Research and Development High Performance Computing Systems (RDHPCS).  We have designed a continuous integration (CI) approach for the FV3 dynamical core Github repository that uses a cloud-based platform to perform automated compilation and reproducibility testing to validate community code contributions. A combination of NOAA RDHPCS Parallel Works virtual machines and containers developed at GFDL are used to compile and test code on the cloud efficiently.   We will also discuss how we adapted the FV3 tests for automated CI.

The West African Monsoon (WAM) strongly drives precipitation variability and seasonality across continental West Africa and the tropical Eastern Atlantic. However, the evolution of the WAM in the late Cenozoic, in response to changes in vegetation, atmospheric CO 2 , orbital forcings, paleogeography, and orography as well as its teleconnections such as the mean location of the African Easterly Jet (AEJ), Tropical Easterly Jet (TEJ), SubTropical Jet (STJ), Inter-Tropical Discontinuity (ITD) and low-level westerly flow is not well constrained. We contribute to understanding past WAM dynamics by performing high-resolution, time-specific paleoclimate simulation using General Circulation Model ECHAM5. We focus our analysis on the migration and intensification of the WAM and its associated atmospheric thermodynamic structure which influence the rainfall seasonality and patterns across the Sahel, Guinea Coast, and Sahara regions.