Large earthquakes, especially those occurring in a city or population centers, create devastation and havoc, and often times kindle several deaths and injuries, and significant infrastructure damage that lead to several billions of dollars in losses. Marine earthquakes are the leading cause of large tsunamis which cause deaths, destruction, displacement of population, and a possible nuclear meltdown. Thus, prediction of earthquake or its aftershocks or earthquake early warning system has a great potential to mitigate the loss of life as well as different kinds of damage. Earthquake prediction would mean forecasting the occurrence of an earthquake by providing both its magnitude estimate and accurate location. Earthquake prediction has been an important area of seismology research for quite a while, and it looks like it will continue to be an important area of research. Recently, with the implementation of deep learning in seismology, scientists have been able to detect, predict, and model seismic waves and earthquake aftershocks. Earthquake aftershocks are generally triggered by changes in stress formed by large earthquakes that happen within, or surrounding a given fault network system. The main goal of this study is to investigate the improvement of aftershock pattern predictions with the implementation of tuning and optimizing of deep learning parameters. To achieve these goals, we have developed an algorithm that can help first gather mainshock-aftershock sequence data. Some of the criteria used in identifying earthquakes that initiate an aftershock is to look at earthquakes that happen within a certain radius, the values we attempted are within about 0.5 degrees range, and within a certain period, from few seconds to several weeks of the occurrence of the main shock. For the sequence identification, we have been using seismic data from the United States Geological Survey (USGS)-National Earthquake Information Center (NEIC). We are also looking at different open-source data gathered by researchers for a similar study. The deep neural networks we are implementing make use of Keras python Toolkit, and Theano and Tensorflow libraries, with a plan to use PyTorch python library instead of Theano library in the future because of some maintenance issues. To this point our attempts have shown a good progress.

Seismology is a data-driven science with a huge amount of data gathered for over a century. Though seismic data recording started in 1900, the growth of seismic data has obviously been exponentially in the last three decades. This data growth can be easily noticed if one takes a close look at just one of the largest seismological data centers in the US, the Data Management Center (DMC) of the Integrated Research Institutions in Seismology (IRIS). Data at the DMC grew from less than 10 Tebibytes in 1992 to about 800 Tebibytes in 2022. With the availability of such a large amount of seismic data, it is paramount to develop new seismic data processing and management tools to help analyze and find new and better seismic models. Developing new big seismic data processing and management tools will be helpful to make the best use of such growing big seismological data sets. The main goal of this investigation is the development of efficient data manipulation and processing tools for retrieval, processing, merging, aggregation, and management of big seismic data from disparate data sources. In this study, such big seismic data processing tools are being developed using python programming language and open source python libraries, and the tools we are developing will be helpful to extract, split, and convert, merge and process big seismic data. In addition, python is very suitable for data science and has powerful libraries to process and manage data and applications. Significant contributions have been made in recent python based libraries for seismic data processing, though there are still some rooms for improvement when it comes to seismic applications to merge, convert, manage  and process big seismic data from disparate data sources and converting different file formats. Seismic data from different networks surrounding the Rio Grande Valley have been collected from different data sources. Our attempt is to test the developed tools and evaluate their performance. This study has made important progress in this regard and the results are promising. 

The interpretation of ERT data requires inversion, which is challenging due to its nonlinear and ill­posed nature. We aim to solve the inverse problem using a machine learning technique that approximates the complex non-linearity using the data provided without specifying any geophysical or mathematical equations. Specifically, we make use of CNN with U-Net architecture to estimate the true subsurface resistivity model from observed apparent resistivity data (pseudosection). U-Net is selected because it works well when both input and output data are in the form of images, and in the present work, input (pseudosection) and output (subsurface resistivity model) have spatial correspondence and existing local patterns. For ERT inversion, U-Net learns by minimizing pixel mean squared error between the true and predicted subsurface resistivity images. The ERT dataset was generated by simulating a dipole­dipole survey over a 400m x 100m earth containing two anomalous blocks with varying resistivity, size, and position in a variable homogeneous background resistivity. We used a finite element approach with a triangular mesh grid for forward modelling (using pyGIMLi) and added Gaussian noise to the calculated pseudosection so that simulated data resembles the field data. The U-Net model was trained on 4000 samples (20% of training samples used for validation) and tested on 1000 samples. The architecture consisted of an encoder and decoder subnetwork connected by a bridge section. The encoder depth was 7, and each block in the encoder subnetwork consisted of two sets of convolutional and ReLU layers, followed by a max pooling layer. The decoder subnetwork consisted of a transposed convolution layer for upsampling, followed by two sets of convolutional and ReLU layers. The weights of all the convolutional layers were initialized using the He Normal initializer. We used Adam optimizer with a learning rate of 0.0001. The U-Net model was allowed to train for 100 epochs with a batch size of 128. The trained U-Net model gave good predictions of the anomalous blocks and background resistivity with a root mean square misfit of 2.86% on test data and 2% on training data. The results confirm the efficacy and effectiveness of the proposed U-Net architecture in predicting the subsurface resistivity variation from the DC resistivity data.

Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.

Through machine learning and remote sensing, a high-end model with a finer resolution for groundwater recharge has been developed for the region of South-East Asia. The groundwater recharge coefficient can be found by the application of Random Forest regression followed by the implication of the water budget method to calculate the Groundwater Recharge values. Climatic factors such as precipitation and actual evapotranspiration to map Groundwater Recharge has been framed with a sophisticated machine learning method to be considered as a scale predicting model. A comprehensive visualization of the dataset has been done; the accuracy of the model is noted through random forest regression. Thus, the model can be used for various regions of the dataset specifically for the area where there is a lack of reach for data. It can be successfully used to form a sophisticated end-to-end ML model. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.

The Gravity Recovery and Climate Experiment (GRACE) data help to determine the total water storage anomalies (TWS) across the global scale. The various other important components such as Groundwater storage (GWS) and evapotranspiration for the region of South –East Asia have been determined. With the study of the gravity variation across the globe the long-term changes in the hydrological cycle can be determined which can be related to climate science or the influence of anthropogenic activities. The variation between the Groundwater storage (GWS) and the Total water storage (TWS) of the study area has been calculated for the pre and post-monsoon season of the study area. The variation between groundwater storage and total water storage can be visualized through geospatial analysis. Therefore, the regions with a substantial decrease in water storage can be related to various climate and anthropogenic factors hence implying a sustainable use of groundwater as a resource. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.

Natural hazards such as floods, hurricanes, heatwaves, and wildfires cause significant economic losses (e.g., agricultural and property damage) as well as a high number of fatalities. Natural hazards are often driven by univariate or multivariate hydrometeorological drivers. Therefore, it is crucial to understand how and which hydrometeorological variables (i.e., drivers) combine to contribute to the impacts of these hazards. Additionally, when multiple drivers are associated with a hazard, traditional univariate risk assessment approaches are insufficient to cover the full spectrum of impact-relevant conditions originating from different combinations of multiple drivers. Based on historical socioeconomic loss data, we develop an impact-based approach to assess the influence of different hydrometeorological drivers on the impacts caused by different hazard event types. We use the Spatial Hazard Events and Losses Database for the United States (SHELDUS™) to identify the historical hazard events that caused socioeconomic impacts (property and crop damage, injuries, and fatalities) in our case study area, Miami-Dade County, in south Florida. For 9 different hazard types, we obtained data for 13 hydrometeorological drivers from historical in-situ observations and reanalysis products corresponding to the timing and locations of the hazard events found in the SHELDUS database. The relative importance of each hazard driver in generating impacts and the frequency of multiple drivers was then assessed. We found that many high-impact events were caused by multiple hydrometeorological drivers (i.e., compound events). For example, 61% of the recorded flooding events were compound events rather than univariate hazards and these contributed 99% of total property damage and 98.2% of total crop damage in Miami-Dade County. For several hazards, such as hurricanes/tropical storms and wildfires, all the events that caused damage are classified as compound events in our framework. Our findings emphasize the benefit of including socioeconomic impact information when analyzing hazard events, as well as the importance of analyzing all relevant hydrometeorological drivers to identify compound events.

A document by Chang Liao. Click on the document to view its contents.

In the ocean, temperature extremes have adverse effects on precipitation patterns, sea level change, and migration/damage of ecosystems. It has been found that most species are more sensitive to extreme events like marine heatwaves (MHWs), implying the severe impacts of MHWs on ecology. These events are driven by various atmospheric and oceanic processes. In recent years, these extreme events are more frequent and intense globally and their increasing trend is expected to continue in the upcoming decades. They have the potential to devastate marine habitats, and ecosystems together with ensuing socioeconomic consequences. It recently attracted public interest and scientific researchers, which motivates us to analyze the recent MHW events in the Bay of Bengal region. we have isolated 107 MHW events (above the 90th percentile threshold) in this region of the Indian Ocean and investigated the variation in duration, intensity, and frequency of MHW events during our test period (1982-2021). Our study reveals that the average of three MHW events per year in the study region with an increasing linear trend of 1.11 MHW events per decade. In the analysis, we found the most intense event has a maximum intensity was 5.29°C (above the climatology mean), while the mean intensity was 2.03°C. In addition, we observed net heat flux accompanied by anticyclonic eddies to be the primary cause of these events. Also, an effort has been made to understand the relationship between climate modes, sea surface height, and the difference between evaporation and precipitation with the occurrence of MHW events.

Machine Learning and Remote sensing method to determine the relationship between Climate and Groundwater Recharge. Adya Aiswarya Dash1, Abhijit Mukherjee1,2,3. 1Department of Geology and Geophysics, Indian Institute of Technology Kharagpur, West Bengal 721302, India 2School of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India 3Applied Policy Advisory for Hydrogeoscience (APAH) Group, Indian Institute of Technology Kharagpur, West Bengal 721302, India Abstract Through machine learning and remote sensing, a high-end model with a finer resolution for groundwater recharge has been developed for the region of South-East Asia. The groundwater recharge coefficient can be found by the application of Random Forest regression followed by the implication of the water budget method to calculate the Groundwater Recharge values. Climatic factors such as precipitation and actual evapotranspiration to map Groundwater Recharge has been framed with a sophisticated machine learning method to be considered as a scale predicting model. A comprehensive visualization of the dataset has been done; the accuracy of the model is noted through random forest regression. Thus, the model can be used for various regions of the dataset specifically for the area where there is a lack of reach for data. It can be successfully used to form a sophisticated end-to-end ML model. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.

Detection and monitoring of tropical forest degradation is crucial to climate change mitigation and biodiversity conservation efforts. Several algorithms have been recently developed to monitor forest degradation and disturbance using remote sensing. However, these algorithms differ in local predictions due to the variation in the biogeophysical parameters used as degradation proxies. It is crucial to assess their relative performance and shortcomings in order to develop a clear understanding of the conditions under which each algorithm will detect a disturbance. In this study, we used GEDI lidar data on forest structure to examine the sensitivity of widely used forest disturbance and degradation products in a frontier tropical forest landscape in the Peruvian Amazon. We compared a leading spectral-based degradation algorithm (Continuous Degradation Detection (CODED)) with a radar-based algorithm (ALOS-2 PalSAR-2 based Radar Forest degradation Index (RFDI)). Given the sensitivity of radar to canopy cover and volume, we hypothesized that a single radar observation may detect degradation better than a long spectral time series. We first identified stable forests for reference structure in two ways: using disturbance stratification data from CODED, and using Peruvian protected areas. Our analysis showed that CODED performed below expectations in detecting forest degradation, often including patches that were regrowing after clear-felling in its “degraded” class. As CODED classified spectral changes over time rather than capturing structural variability, it classified 82% of palm plantations area as “degraded.” CODED also failed to detect degradation in forest areas that were likely partially disturbed (i.e., with low height and high cover). By contrast, the PalSAR-2 RFDI showed a significant relationship with forest height (detecting low height in degraded forests), although its predictive ability was limited due to high variability and signal saturation. Our study supports the conclusion that radar-based observation can detect forest degradation that the time series observation failed to detect. Given the limited correspondence between radar and spectral algorithms, we suggest that integrations of spectral and radar data may be beneficial for mapping forest degradation.

Climate change and a growing global population pose ongoing threats to critical resources. As resources required by the agriculture sector continue to diminish, it is critical to leverage the emerging technologies and new solutions within the sector. New cultivation practices have emerged over the years, allowing food to be grown within urban areas. Greenhouses are versatile in the resources needed for their operation, as well as the foods that can be grown. While greenhouses provide a potential for a more constant food supply, there is a lack of optimization between the components. There are benefits to having modular components of a greenhouse, allowing for adjustments or repairs to singular pieces. However, there is inefficiency in the entire system, since each component functions without considering the others. To improve greenhouse efficiency, a closed-loop system can be introduced. A greenhouse is a closed system, and by repurposing, reusing, and recirculating resources, a greenhouse can evolve to have a closed-loop system. This enables the components of a system to share resources more effectively, communicate any systems changes that are required, and minimize waste outputs.This research explores the current technology in the space of agriculture and computer science to create a fully closed-loop system. The most noticeable system components are food waste, nutrient systems, water systems, growing media, and heating and energy. Not all components within a greenhouse can leverage the same artificial intelligence methods and techniques based on existing findings. There are methods in place that allow the components to interpret data gathered from the greenhouse and alter its operational patterns. There remains a lack in communicating this information to other aspects of the system to have it make informed data-driven decisions as well. One can optimize singular components thereby reducing resource reliance, to a certain threshold until it impacts the plant’s development and yield. When all the systems components’ resource needs and outputs converge the functionality of the system can be optimized to utilize resources at a higher efficiency. Results are indicative of very siloed and isolated research, exploring closed-loop systems within greenhouses, but not leveraging its full capabilities.

Understanding the nature and behavior of the rocks in boundary zones between tectonic plates is important to improve our understanding of earthquake-associated hazard. Laboratory experiments can derive models that explain material behavior on small scales and under controlled conditions. These models can also be tested on observations of surface motion near plate boundaries: Fitting surface displacements from earthquakes (either shortterm offsets or longterm motion) yields estimates of rock properties for each model. However, using only observations from a single earthquake (from immediately after the quake and/or the subsequent years), may not allows us to confidently distinguish between models. In this study, we investigate the potential of using the displacement timeseries from multiple earthquakes, as well as the period between the quakes, to distinguish between proposed models. We use methods that enable comparison between models and parameters taking into account uncertainties, and perform our assessment on an artificial dataset.

Low marine clouds are a major source of uncertainty in cloud feedbacks across climate models and in forcing by aerosol-cloud interactions. The evolution of these clouds and their response to aerosol are sensitive to the ambient environmental conditions, so it is important to be able to determine different responses over a representative set of conditions. Here, we propose a novel approach to encompassing the broad range of conditions present in low marine cloud regions, by building a library of observed environmental conditions. This approach can be used, for example, to more systematically test the fidelity of Large Eddy Simulations (LES) in representing these clouds. ERA5 reanalysis and various satellite observations are used to extract and derive macrophysical and microphysical cloud-controlling variables (CCVs) such as SST, estimated inversion strength (EIS), subsidence, and cloud droplet number concentrations. A few locations in the stratocumulus (Sc) deck region of the Northeast Pacific during summer are selected to fill out a phase space of CCVs. Thereafter, Principal Component Analysis (PCA) is applied to reduce the dimensionality and to select a reduced set of components that explain most of the variability among CCVs in order to efficiently select cases for LES simulations that encompass the observed CCV phase space. From this phase space, 75-100 cases with distinct environmental conditions will be selected and used to initialize 2-day LES modeling to provide a spectrum of aerosol-cloud interactions and Sc-to-Cumulus transition under observed ambient conditions. Such a large number of simulations will help create statistics to assess how well the LES can simulate the cloud lifecycle when constrained by the ‘best estimate’ of the environmental conditions, and how sensitive the modeled clouds are to changes in these driving fields.

Though precise, most LiDARs are vulnerable to position and pointing errors as deviations from the expected principal axis lead to projection errors on target. While fidelity of location/pointing solutions can be high, determination of uncertainty remains relatively limited. As a result, NASA’s 2021 Surface Topography and Vegetation Incubation Study Report lists vertical (horizontal, geolocation) accuracy as an associated parameter for all (most) identified Science and Application Knowledge Gaps, and identifies maturation of Uncertainty Quantification (UQ) methodologies on the STV Roadmap for this decade. The presented generalized Polynomial Chaos Expansion (gPCE) based method has wide ranging applicability to improve positioning, geolocation uncertainty estimates for all STV disciplines, and is extended from the bare earth to the bathymetric lidar use case, adding complexity introduced by entry angle, wave structure, and sub-surface roughness. This research addresses knowledge gaps in bathy-LiDAR measurement uncertainty through a more complete description of total aggregated uncertainties, from system level to geolocation, by applying a gPCE-UQ approach. Currently, the standard approach is the calculation of the Total Propagated Uncertainty, which is often plagued by simplifying approximations (e.g. strictly Gaussian uncertainty sources) and ignored covariances. gPCE intrinsically accounts for covariance between variables to determine uncertainty in a measurement, without manually constructing a covariance matrix, through a surrogate model of system response. Additionally, gPCE allows arbitrarily high order uncertainty estimates (limited only by the one-time computational cost of computing gPCE coefficients), accurate representation of non-Gaussian sources of error (e.g. wave height energy distributions), and direct integration of measurement requirements into the design of LiDAR systems, by trivializing the computation of global sensitivity analysis.

As all kinds of physics-based and data-driven models are emerging in the fields of hydrologic and hydraulic engineering, Bayesian model averaging (BMA) is one of the popular multi-model methods used to account for the various uncertainty sources in the flood modeling process and generate robust ensemble predictions based on multiple competitive candidate models. The reliability of BMA parameters (weights and variances) determines the accuracy of BMA predictions. However, the uncertainty in the BMA parameters with fixed values, which are usually obtained from the Expectation-Maximization (EM) algorithm, has not been adequately investigated in BMA-related applications over the past few decades. Given the limitations of the commonly used EM algorithm, the Metropolis-Hastings (M-H) algorithm, which is one of the most widely used algorithms in the Markov Chain Monte Carlo (MCMC) method, is proposed to estimate the BMA parameters and quantify their associated uncertainty. Both numerical experiments and the one-dimensional HEC-RAS models are employed to examine the applicability of the M-H algorithm with multiple independent Markov chains. The performances of the EM and M-H algorithms in the BMA analysis are compared based on the daily water stage predictions from 10 model configurations. The results show that the BMA weights estimated from both algorithms are comparable, while the BMA variances obtained from the M-H MCMC algorithm are closer to the given variances in the numerical experiment. Moreover, the normal proposal distribution used in the M-H algorithm can yield narrower distributions for the BMA weights than those from the uniform prior. Overall, the MCMC approach with multiple chains can provide more information associated with the uncertainty of BMA parameters and its prediction performance is better than the default EM algorithm in terms of multiple evaluation metrics as well as algorithm flexibility.

Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.

Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.