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1. Scientific Data Annotation and Dissemination: Using the ‘Ike Wai Gatewayto Manage Research Data

   Cleveland, Sean B ; Geis, Jennifel Jacobs ( July 2020 , Practice and Experience in Advanced Research Computing)

   . Granting agencies invest millions of dollars on the generation and analysis of data, making these products extremely valuable. However, without sufficient annotation of the methods used to collect and analyze the data, the ability to reproduce and reuse those products suffers. This lack of assurance of the quality and credibility of the data at the different stages in the research process essentially wastes much of the investment of time and funding and fails to drive research forward to the level of potential possible if everything was effectively annotated and disseminated to the wider research community. In order to address this issue for the Hawai'i Established Program to Stimulate Competitive Research (EPSCoR) project, a water science gateway was developed at the University of Hawai‘i (UH), called the ‘Ike Wai Gateway. In Hawaiian, ‘Ike means knowledge and Wai means water. The gateway supports research in hydrology and water management by providing tools to address questions of water sustainability in Hawai‘i. The gateway provides a framework for data acquisition, analysis, model integration, and display of data products. The gateway is intended to complement and integrate with the capabilities of the Consortium of Universities for the Advancement of Hydrologic Science's (CUAHSI) Hydroshare by providing sound data and metadata management capabilities for multi-domain field observations, analytical lab actions, and modeling outputs. Functionality provided by the gateway is supported by a subset of the CUAHSI’s Observations Data Model (ODM) delivered as centralized web based user interfaces and APIs supporting multi-domain data management, computation, analysis, and visualization tools to support reproducible science, modeling, data discovery, and decision support for the Hawai'i EPSCoR ‘Ike Wai research team and wider Hawai‘i hydrology community. By leveraging the Tapis platform, UH has constructed a gateway that ties data and advanced computing resources together to support diverse research domains including microbiology, geochemistry, geophysics, economics, and humanities, coupled with computational and modeling workflows delivered in a user friendly web interface with workflows for effectively annotating the project data and products. Disseminating results for the ‘Ike Wai project through the ‘Ike Wai data gateway and Hydroshare makes the research products accessible and reusable. 

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2. Optimizing Automated Kriging to Improve Spatial Interpolation of Monthly Rainfall over Complex Terrain

   https://doi.org/10.1175/JHM-D-21-0171.1  

   Lucas, Matthew P. ; Longman, Ryan J. ; Giambelluca, Thomas W. ; Frazier, Abby G. ; Mclean, Jared ; Cleveland, Sean B. ; Huang, Yu-Fen ; Lee, Jonghyun ( April 2022 , Journal of Hydrometeorology)

   Gridded monthly rainfall estimates can be used for a number of research applications, including hydrologic modeling and weather forecasting. Automated interpolation algorithms, such as the “autoKrige” function in R, can produce gridded rainfall estimates that validate well but produce unrealistic spatial patterns. In this work, an optimized geostatistical kriging approach is used to interpolate relative rainfall anomalies, which are then combined with long-term means to develop the gridded estimates. The optimization consists of the following: 1) determining the most appropriate offset (constant) to use when log-transforming data; 2) eliminating poor quality data prior to interpolation; 3) detecting erroneous maps using a machine learning algorithm; and 4) selecting the most appropriate parameterization scheme for fitting the model used in the interpolation. Results of this effort include a 30-yr (1990–2019), high-resolution (250-m) gridded monthly rainfall time series for the state of Hawai‘i. Leave-one-out cross validation (LOOCV) is performed using an extensive network of 622 observation stations. LOOCV results are in good agreement with observations (R²= 0.78; MAE = 55 mm month⁻¹; 1.4%); however, predictions can underestimate high rainfall observations (bias = 34 mm month⁻¹; −1%) due to a well-known smoothing effect that occurs with kriging. This research highlights the fact that validation statistics should not be the sole source of error assessment and that default parameterizations for automated interpolation may need to be modified to produce realistic gridded rainfall surfaces. Data products can be accessed through the Hawai‘i Data Climate Portal (HCDP;http://www.hawaii.edu/climate-data-portal).

   A new method is developed to map rainfall in Hawai‘i using an optimized geostatistical kriging approach. A machine learning technique is used to detect erroneous rainfall maps and several conditions are implemented to select the optimal parameterization scheme for fitting the model used in the kriging interpolation. A key finding is that optimization of the interpolation approach is necessary because maps may validate well but have unrealistic spatial patterns. This approach demonstrates how, with a moderate amount of data, a low-level machine learning algorithm can be trained to evaluate and classify an unrealistic map output.

    

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3. The Isotopic Composition of Rainfall on a Subtropical Mountainous Island

   https://doi.org/10.1175/JHM-D-21-0204.1  

   Torri, Giuseppe ; Nugent, Alison D. ; Popp, Brian N. ( April 2023 , Journal of Hydrometeorology)

   Abstract Tropical islands are simultaneously some of the most biodiverse and vulnerable places on Earth. Water resources help maintain the delicate balance on which the ecosystems and the population of tropical islands rely. Hydrogen and oxygen isotope analyses are a powerful tool in the study of the water cycle on tropical islands, although the scarcity of long-term and high-frequency data makes interpretation challenging. Here, a new dataset is presented based on weekly collection of rainfall H and O isotopic composition on the island of O‘ahu, Hawai‘i, beginning from July 2019 and still ongoing. The data show considerable differences in isotopic ratios produced by different weather systems, with Kona lows and upper-level lows having the lowest δ 2 H and δ 18 O values, and trade-wind showers the highest. The data also show significant spatial variability, with some sites being characterized by higher isotope ratios than others. The amount effect is not observed consistently at all sites. Deuterium excess shows a marked seasonal cycle, which is attributed to the different origin and history of the air masses that are responsible for rainfall in the winter and summer months. The local meteoric water line and a comparison of this dataset with a long-term historical record illustrate strong interannual variability and the need to establish a long-term precipitation isotope monitoring network for Hawai‘i. Significance Statement The isotopic composition of water is often used in the study of island water resources, but the scarcity of high-frequency datasets makes the interpretation of data difficult. The purpose of this study is to investigate the isotopic composition of rainfall on a mountainous island in the subtropics. Based on weekly data collection on O‘ahu, Hawai‘i, the results improve our understanding of the isotopic composition of rainfall due to different weather systems, like trade-wind showers or cold fronts, as well as its spatial and temporal variability. These results could inform the interpretation of data from other mountainous islands in similar climate zones. 

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4. Deriving Gridded Hourly Rainfall on O‘ahu by Combining Gauge and Radar Rainfall

   https://doi.org/10.1175/JHM-D-22-0196.1  

   Huang, Yu-Fen ; Tsang, Yinphan ; Nugent, Alison D. ( December 2023 , Journal of Hydrometeorology)

   High temporal and spatial resolution precipitation datasets are essential for hydrological and flood modeling to assist water resource management and emergency responses, particularly for small watersheds, such as those in Hawai‘i in the United States. Unfortunately, fine temporal (subdaily) and spatial (<1 km) resolutions of rainfall datasets are not always readily available for applications. Radar provides indirect measurements of the rain rate over a large spatial extent with a reasonable temporal resolution, while rain gauges provide “ground truth.” There are potential advantages to combining the two, which have not been fully explored in tropical islands. In this study, we applied kriging with external drift (KED) to integrate hourly gauge and radar rainfall into a 250 m × 250 m gridded dataset for the tropical island of O‘ahu. The results were validated with leave-one-out cross validation for 18 severe storm events, including five different storm types (e.g., tropical cyclone, cold front, upper-level trough, kona low, and a mix of upper-level trough and kona low), and different rainfall structures (e.g., stratiform and convective). KED-merged rainfall estimates outperformed both the radar-only and gauge-only datasets by 1) reducing the error from radar rainfall and 2) improving the underestimation issues from gauge rainfall, especially during convective rainfall. We confirmed the KED method can be used to merge radar with gauge data to generate reliable rainfall estimates, particularly for storm events, on mountainous tropical islands. In addition, KED rainfall estimates were consistently more accurate in depicting spatial distribution and maximum rainfall value within various storm types and rainfall structures.

   The results of this study show the effectiveness of utilizing kriging with external drift (KED) in merging gauge and radar rainfall data to produce highly accurate, reliable rainfall estimates in mountainous tropical regions, such as O‘ahu. The validated KED dataset, with its high temporal and spatial resolutions, offers a valuable resource for various types of rainfall-related research, particularly for extreme weather response and rainfall intensity analyses in Hawai’i. Our findings improve the accuracy of rainfall estimates and contribute to a deeper understanding of the performance of various rainfall estimation methods under different storm types and rainfall structures in a mountainous tropical setting.

    

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5. An Exploration of the Effects of Enterprise Social Media Use for Classroom Teams.

   Cherchiglia, L. ; Van Osch, W. ; Liang, Y. ; Averkiadi, E. ( December 2020 , SIGHCI 2020 Proceedings)

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   Cherchiglia et al. Effects of ESM Use for Classroom Teams Proceedings of the Nineteenth Annual Pre-ICIS Workshop on HCI Research in MIS, Virtual Conference, December 12, 2020 1 An Exploration of the Effects of Enterprise Social Media Use for Classroom Teams Leticia Cherchiglia Michigan State University leticia@msu.edu Wietske Van Osch HEC Montreal & Michigan State University wietske.van-osch@hec.ca Yuyang Liang Michigan State University liangyuy@msu.edu Elisavet Averkiadi Michigan State University averkiad@msu.edu ABSTRACT This paper explores the adoption of Microsoft Teams, a group-based Enterprise Social Media (ESM) tool, in the context of a hybrid Information Technology Management undergraduate course from a large midwestern university. With the primary goal of providing insights into the use and design of tools for group-based educational settings, we constructed a model to reflect our expectations that core ESM affordances would enhance students’ perceptions of Microsoft Teams’ functionality and efficiency, which in turn would increase both students’ perceptions of group productivity and students’ actual usage of Microsoft Teams for communication purposes. In our model we used three core ESM affordances from Treem and Leonardi (2013), namely editability (i.e., information can be created and/or edited after creation, usually in a collaborative fashion), persistence (i.e., information is stored permanently), and visibility (i.e., information is visible to other users). Analysis of quantitative (surveys, server-side; N=62) and qualitative (interviews; N=7) data led to intriguing results. It seems that although students considered that editability, persistency, and visibility affordances within Microsoft Teams were convenient functions of this ESM, problems when working collaboratively (such as connectivity, formatting, and searching glitches) might have prevented considerations of this ESM as fast and user-friendly (i.e., efficient). Moreover, although perceived functionality and efficiency were positively connected to group productivity, hidden/non-intuitive communication features within this ESM might help explain the surprising negative connection between efficiency and usage of this ESM for the purpose of group communication. Another explanation is that, given the plethora of competing tools specifically designed to afford seamless/optimal team communication, students preferred to use more familiar tools or tools perceived as more efficient for group communication than Microsoft Teams, a finding consistent with findings in organizational settings (Van Osch, Steinfield, and Balogh, 2015). Beyond theoretical contributions related to the impact that ESM affordances have on users’ interaction perceptions, and the impact of users’ interaction perceptions on team and system outcomes, from a strategic and practical point of view, our findings revealed several challenges for the use of Microsoft Teams (and perhaps ESM at large) in educational settings: 1) As the demand for online education grows, collaborative tools such as Microsoft Teams should strive to provide seamless experiences for multiple-user access to files and messages; 2) Microsoft Teams should improve its visual design in order to increase ease of use, user familiarity, and intuitiveness; 3) Microsoft Teams appears to have a high-learning curve, partially related to the fact that some features are hidden or take extra steps/clicks to be accessed, thus undermining their use; 4) Team communication is a complex topic which should be further studied because, given the choice, students will fall upon familiar tools therefore undermining the full potential for team collaboration through the ESM. We expect that this paper can provide insights for educators faced with the choice for an ESM tool best-suited for group-based classroom settings, as well as designers interested in adapting ESMs to educational contexts, which is a promising avenue for market expansion. 

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