The insular Caribbean is a region influenced by Atlantic Ocean climate variability. Effects of low-frequency atmospheric circulation patterns on the precipitation of the Caribbean have been well documented. However, individual modes of variability are usually only considered in isolation. Here we analyse the combined and individual effects of the North Atlantic Oscillation (NAO) and the Atlantic Meridional Mode (AMM) on insular Caribbean precipitation. This work focuses on the Early Rainfall Season (ERS, April-July), which explains much of the interannual variability in precipitation for this region, from 1960-2016. Correlation analysis compare monthly NAO and AMM indices from the National Oceanic and Atmospheric Administration (NOAA) against monthly Caribbean precipitation from the Climate Research Unit (CRU) year-by-year climate variables by country. Sea surface temperature (SST) and sea level pressure (SLP) composites using NOAA data were also created to analyse regional patterns. Analysis of the results show that the NAO and AMM presented a correlation of opposite signs and affected the Eastern Caribbean (from Dominican Republic to Grenada) during ERS, resulting in precipitation anomalies above/below ± 10%. The combined and individual effects of NAO and AMM indicate that Feb-Mar NAO and AMM are significant correlated to May-Jun Eastern Caribbean precipitation anomalies. More frequent and consistent regional effects on precipitation anomalies, and more regionally spread and persistent SLP and SST were registered when both NAO and AMM occurred together in the previous winter. These results could be helpful in seasonal forecasting, by indicating whether a wetter or drier ERS would be expected based on the previous season NAO and AMM activity.

The Southwest U.S. comprising of the four states-Arizona, New Mexico, Colorado, and Utah-is the hottest and driest region of the United States. Most of the precipitation arrives during the winter season, but the summer precipitation makes a significant contribution to the reliability of water resources and the health of ecology. However, summer precipitation and its extremes, over this region exhibit high degree of spatial and temporal variability. In this study we developed a novel spatial Bayesian hierarchical model to capture the space-time variability of –summer season 3-day maximum precipitation over the southwest U.S. In modeling framework, the data layer the extremes at each station are assumed to be distributed as Generalized Extreme Value (GEV) distribution with non-stationary parameters. In addition, the extremes across space is assumed to be related via a Gaussian Copula. In the process layer, the parameters are modeled as a linear function of large scale climate variables and regional mean precipitation covariates. This is akin to a Generalized Linear Model (GLM). The parameters of the covariates at each station are spatially modeled using spatial Gaussian processes to capture the spatial dependency and enable generating the spatial field of the hydroclimate extremes. The likelihood estimates of the GLM at each station form the initial priors. The posterior distribution of the model parameters and consequently the predictive posterior GEV distribution of the hydroclimate extremes at any arbitrary location, or grid and for any year are obtained. The model is demonstrated by application to extreme summer precipitation at 73 stations from this region. The model validation indicates that return levels and their associated uncertainty have a well-defined spatial structure and furthermore, they capture the historical variability very well. The posterior distribution of the GEV parameters were generated on a 1/8th degree grid, providing maps of various return levels for all the years. Maps of return levels provide information about the spatial and temporal variations of the risk of extreme precipitation in the Southwest U.S. that will be of immense help in management and planning of natural resources and infrastructure.

Conventional earthquake detection methods suffer significant degradations in completeness during high-rate sequences such as aftershock sequences or volcanic swarms. Missed earthquakes during the early periods of aftershock sequences can affect aftershock forecasts and hazard estimates. Missed events during volcanic unrest sequences can impact rate estimations, leading to the sequence being mis-characterized. Much recent work has addressed how matched-filters can be used to overcome some aspects of catalog incompleteness during high-rate sequences, by detecting similar events using cross-correlation. Here we describe the application of open-source (GPL v3.0) software to the near-real-time implementation of matched-filter earthquake detection. Our software (RT-EQcorrscan) is written in Python, and leverages the extensive Obspy package, as well as EQcorrscan and Obsplus to provide matched-filter methods and database handling respectively. RT-EQcorrscan is designed to be modular, so that users can readily utilize only the components they require, or make use of pre-built command-line utilities controlled by simple that can handle thousands of templates over tens of channels of seismic data within the processing capacity (memory and CPU usage) of a standard desktop personal computer. Detections are made within a few seconds of data arriving, with latency due to data delivery and a requirement for full network move-out. At the same time, RT-EQcorrscan has an overarching “Reactor” module to listen to a web-service and respond to new events. If an event occurs that meets user-defined criteria, the Reactor will initiate a near-real-time matched-filter process encompassing the region surrounding the trigger event. Subsequent trigger events in different regions can also be handled with threaded operations. This system is backed-up by a constantly updating template database built on Obsplus, allowing groups of templates to be rapidly deployed. In this presentation we will discuss the key implementation details, as well as showcasing some examples of the system in operation.

Plate tectonics and mantle plumes are two of the most fundamental solid-Earth processes that operated through much of the Earth’s history. For the past 300 million years, mantle plumes are known to derive mostly from two large low shear velocity provinces (LLSVPs) above the core-mantle boundary, referred to as the African and Pacific superplumes. Whether such LLSVPs and their plume products are stationary through Earth history, or dynamically linked to global plates motions and subduction geometry, remains a first-order question for the geoscience community. Here, we demonstrate that transition elements (Ni, Cr and Fe/Mn) in basaltic rocks can be used as a tool to trace plume-related magmatism through Earth history. An analysis of the global petro-chemical database indicates the presence of a direct relationship between the intensity of plume magmatism and the supercontinent cycle. Such results favour the presence of a dynamic supercontinent-superplume coupling over the fixed-LLSVP geodynamic model. In addition, our analysis shows a consistent sudden drop in MgO, Ni and Cr at ~3.2–3.0 billion years ago, possibly indicating an abrupt change in mantle temperature at the start of global plate tectonics.

Planning for hydropower, water resources management, and climate change adaptation requires statistically unbiased hydrologic predictions. However, all hydrologic models contain systematic errors, e.g., incorrect mathematical representations of physical processes and effects of uncertainties in data sources. Statistical post-processing, or bias correction, is often used to reduce the effects of these systematic errors in model outputs. A large number of techniques for performing bias correction has been developed, primarily in the context of correcting statistical properties of independent locations. However, when bias correcting streamflow predictions within the same stream network, this assumption of spatial independence breaks down. Independently bias correcting locations from the headwaters to the mouth of a river system destroys the spatial consistency of the streamflow across a river network. We describe work toward maintaining spatial consistency in streamflow bias correction using a number of locations in the western United States. We simulate the hydrology of the Columbia River in the Pacific Northwestern United States, a river system that spans a number of hydroclimatic and flow regimes that contains a large number of flow gages. We develop a mapping from the modeled output at the gages with flow observations, which we use as the basis for training a machine learning (ML) model to perform the site-specific bias correction. We then apply the ML model to local streamflow contributions for each river segment, including river segments without flow observations. Finally, we combine the local bias corrections across the stream network, to create accumulated bias-corrected streamflow time series that are spatially-consistent across the stream network. We compare our method against several commonly used bias correction techniques to evaluate both model performance and spatial consistency.

Critical freshwater resources lying within mid-latitude mountain glaciers are vulnerable to a rapidly changing climate. The Lehman Rock Glacier is the only extant glacier mapped within the Great Basin National Park in Nevada. As part of an effort to understand this specialized alpine environment, we have been studying this area and conducting observations with annual student research visitations since 2005. Deploying mixed methods including an embedded sensor network, paleoclimate reconstructions, hydrological observations, and unmanned aerial system (UAS) operations, our team has documented diverse evidence of climate change over interannual to millennial scales. Starting in 2015, we conducted annual surveys of the rock glacier to measure topographic changes. Initially, we used balloon-borne photogrammetry, capturing 600+ images over about 0.1 km2 at an altitude of 500m above ground level (AGL). Despite impartial terrain coverage (50-80%) caused by limited control of the balloon rig in the air, digital elevation modeling (DEM) differencing resolved a net volume loss of 5,300m3 between 2015 and 2016. Submission of a Certification Of Approval (COA) granted our team permission to fly a UAV for the first time within the National Park in 2018 and 2019 to map the rock glacier. UAS surveying over successive days in August with > 80% horizontal and vertical overlap helped achieved 100% coverage with 900+ photos. Using previous year’s DEMs, we have optimized autonomous flight planning at 80m AGL and 168m AGL at 8.5mh and 20kmh, respectively. We will present our most recent computations of the glacier changes from years 2018 and 2019 and discuss how UAS instrumentation techniques are helping us observe changing glacier conditions at centimeter-scale resolution, better understand ecosystem relationships, and improve capabilities to model future landscapes all while mitigating mountain safety issues.

Not only are reservoir managers and aquatic scientists concerned with the environmental effects of water quality, civil engineers must also consider water quality to comply with regulations in the construction of new reservoirs, or in making structural and operational modifications to existing reservoirs. This study establishes a machine learning approach for predicting Carlson’s Trophic State Index (CTSI), which is a frequently used metric of water quality in reservoirs. Data collected over ten years (1995-2016) from the stations at 20 reservoirs in Taiwan were preprocessed as the input for the modeling system. Four well-known artificial intelligence (AI) techniques, ANN (Artificial Neural Network), SVM (Support Vector Machine), CART (Classification And Regression Technique), and LR (Linear Regression), were used to analyze in baseline and ensemble scenarios. Moreover, one variation of support vector machine was integrated with a metaheuristic optimization algorithm to develop a hybrid AI model. The comprehensive comparison demonstrated that the ensemble ANN model, based on tiering method, is more accurate than the other single, ensemble, and hybrid models. The novelty of this study is providing a new approach of AI models, reducing the complexity of measuring three traditional parameters of CTSI formula, as an alternative to the conventional approach to predicting CTSI. This work contributes to the improvement of water quality management by providing a versatile technique that offers diverse predictive methods to meet the specific requirements of practitioners.

Throughout the last years, there is an increasing interest of the geoscientific community in using terrestrial gravimetry as an integrative and non-invasive method for observing mass change and mass redistribution in the environment due geophysical processes. The nature of the observed processes and the need for nearby data collection often require the gravimeters to be installed at remote field sites. In contrast to classical deployment at permanent observatory sites, this often is a challenge because there are three main requirements to be fulfilled for continuous high-quality operation of the gravimeter: electrical power, stable site conditions, and data connection. Whereas the latter can usually be accomplished by wireless solutions, the second requirement is more demanding as it requires an adequate design of a gravimeter enclosure and a stable pillar, while the first requirement so far has been practically insolvable in the absence of a power line. Here, we present the prototype of a mobile field container for gravity monitoring that fulfils all above requirements: the gPhone-solar-cube. The container consists of a cubic steel container as used by ships and trucks with edge length of about 2 meters. We optimized all components to host a continuously operating gPhoneX. Components include temperature shielding, ventilation, solar panels, power management and monitoring, storage batteries and an integrated backup generator to guarantee self-sufficient power supply, data loggers and wireless data transfer components. Furthermore we developed a new type of gravimeter pillar which is simple to install and to remove, without connection to the container floor to avoid vibration transfer. The pillar is large enough to accommodate two CG-6 field gravimeters, next to the gPhoneX. Other instruments integrated are a complete weather station and a cosmic ray neutron probe. The gPhone-solar-cube has been installed in the Ore mountains, Germany, as a continuously operating gravity reference station for time-lapse field surveys with CG-6 gravimeters to assess water storage changes in the course of heavy precipitation events. After 6 months of field operation, all requirements concerning data transmission, remote access, energy consumption, pillar stability and reliable gravity data were continuously met.