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1. Effects of meteorological forcing uncertainty on high-resolution snow modeling and streamflow prediction in a mountainous karst watershed

   https://doi.org/10.1016/j.jhydrol.2023.129304  

   Tyson, Conor; Longyang, Qianqiu; Neilson, Bethany T.; Zeng, Ruijie; Xu, Tianfang (April 2023, Journal of Hydrology)

   Marco Borga; Francesco Avanzi (Ed.)
2. High resolution water body mapping for SWAT evaporative modelling in the Upper Oconee watershed of Georgia, USA

   https://doi.org/10.1002/hyp.11398  

   Ignatius, Amber R.; Jones, John W. (January 2018, Hydrological Processes)
3. High-resolution precipitation monitoring with a dense seismic nodal array

   https://doi.org/10.1038/s41598-023-38008-w  

   Hua, Junlin; Wu, Mengxi; Mulholland, Jake_P; Neelin, J_David; Tsai, Victor_C; Trugman, Daniel_T (July 2023, Scientific Reports)

   Abstract Accurate precipitation monitoring is crucial for understanding climate change and rainfall-driven hazards at a local scale. However, the current suite of monitoring approaches, including weather radar and rain gauges, have different insufficiencies such as low spatial and temporal resolution and difficulty in accurately detecting potentially destructive precipitation events such as hailstorms. In this study, we develop an array-based method to monitor rainfall with seismic nodal stations, offering both high spatial and temporal resolution. We analyze seismic records from 1825 densely spaced, high-frequency seismometers in Oklahoma, and identify signals from nine precipitation events that occurred during the one-month station deployment in 2016. After removing anthropogenic noise and Earth structure response, the obtained precipitation spatial pattern mimics the one from a nearby operational weather radar, while offering higher spatial (~ 300 m) and temporal (< 10 s) resolution. We further show the potential of this approach to monitor hail with joint analysis of seismic intensity and independent precipitation rate measurements, and advocate for coordinated seismological-meteorological field campaign design. 

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4. Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed

   https://doi.org/10.1029/2021WR030993  

   Xu, Tianfang; Longyang, Qianqiu; Tyson, Conor; Zeng, Ruijie; Neilson, Bethany T. (March 2022, Water Resources Research)

   Abstract Snow dominated mountainous karst watersheds are the primary source of water supply in many areas in the western U.S. and worldwide. These watersheds are typically characterized by complex terrain, spatiotemporally varying snow accumulation and melt processes, and duality of flow and storage dynamics because of the juxtaposition of matrix (micropores and small fissures) and karst conduits. As a result, predicting streamflow from meteorological inputs has been challenging due to the inability of physically based or conceptual hydrologic models to represent these unique characteristics. We present a hybrid modeling approach that integrates a physically based, spatially distributed, snow model with a deep learning karst model. More specifically, the high‐resolution snow model captures spatiotemporal variability in snowmelt, and the deep learning model simulates the corresponding response of streamflow as influenced by complex surface and subsurface properties. The deep learning model is based on the Convolutional Long Short‐Term Memory (ConvLSTM) architecture capable of handling spatiotemporal recharge patterns and watershed storage dynamics. The hybrid modeling approach is tested on a watershed in northern Utah with seasonal snow cover and variably karstified carbonate bedrock. The hybrid models were able to simulate streamflow at the watershed outlet with high accuracy. The spatial and temporal recharge and discharge patterns learned by the ConvLSTM model were then examined and compared with known hydrogeologic information. Results suggest that ConvLSTM simulates streamflow with higher accuracy than reference models for the study area and provides insight into spatially influenced hydrologic responses that are unavailable within lumped modeling approaches. 

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5. High-Resolution Conductivity Mapping with STEM EBIC

   https://doi.org/10.31399/asm.cp.istfa2022p0251  

   Hubbard, William A; Chan, Ho Leung; Regan, B. C. (October 2022, International Symposium for Testing and Failure Analysis)

   Abstract Modern electronic systems rely on components with nanometer-scale feature sizes in which failure can be initiated by atomic-scale electronic defects. These defects can precipitate dramatic structural changes at much larger length scales, entirely obscuring the origin of such an event. The transmission electron microscope (TEM) is among the few imaging systems for which atomic-resolution imaging is easily accessible, making it a workhorse tool for performing failure analysis on nanoscale systems. When equipped with spectroscopic attachments TEM excels at determining a sample’s structure and composition, but the physical manifestation of defects can often be extremely subtle compared to their effect on electronic structure. Scanning TEM electron beam-induced current (STEM EBIC) imaging generates contrast directly related to electronic structure as a complement the physical information provided by standard TEM techniques. Recent STEM EBIC advances have enabled access to a variety of new types of electronic and thermal contrast at high resolution, including conductivity mapping. Here we discuss the STEM EBIC conductivity contrast mechanism and demonstrate its ability to map electronic transport in both failed and pristine devices. 

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