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  1. Abstract

    Surface meteorological analyses are an essential input (termed “forcing”) for hydrologic modeling. This study investigated the sensitivity of different hydrologic model configurations to temporal variations of seven forcing variables (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure). Specifically, the effects of temporally aggregating hourly forcings to hourly daily average forcings were examined. The analysis was based on 14 hydrological outputs from the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model for the 671 Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) basins across the contiguous United States (CONUS). Results demonstrated that the hydrologic model sensitivity to temporally aggregating the forcing inputs varies across model output variables and model locations. We used Latin hypercube sampling to sample model parameters from eight combinations of three influential model physics choices (three model decisions with two options for each decision, i.e., eight model configurations). Results showed that the choice of model physics can change the relative influence of forcing on model outputs and the forcing importance may not be dependent on the parameter space. This allows for model output sensitivity to forcing aggregation to be tested prior to parameter calibration. More generally, this work provides a comprehensive analysis of the dependence of modeled outcomes on input forcing behavior, providing insight into the regional variability of forcing variable dominance on modeled outputs across CONUS.

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  2. Abstract

    We describe a recommendation system for HydroShare, a platform for scientific water data sharing. We discuss similarities, differences and challenges for implementing recommendation systems for scientific water data sharing. We discuss and analyze the behaviors that scientists exhibit in using HydroShare as documented by users’ activity logs. Unlike entertainment system users, users on HydroShare tend to be task-oriented, where the set of tasks of interest can change over time, and older interests are sometimes no longer relevant. By validating recommendation approaches against user behavior as expressed in activity logs, we conclude that a combination of content-based filtering and a latent Dirichlet allocation (LDA) topic modeling of user behavior—rather than and instead of LDA classification of dataset topics—provides a workable solution for HydroShare and compares this approach to existing recommendation methods.

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  3. Abstract

    Many have argued that datasets resulting from scientific research should be part of the scholarly record as first class research products. Data sharing mandates from funding agencies and scientific journal publishers along with calls from the scientific community to better support transparency and reproducibility of scientific research have increased demand for tools and support for publishing datasets. Hydrology domain‐specific data publication services have been developed alongside more general purpose and even commercial data repositories. Prominent among these are the Hydrologic Information System (HIS) and HydroShare repositories developed by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI). More broadly, however, multiple organizations have been involved in the practice of data publication in the hydrology domain, each having different roles that have shaped data publication and reuse. Bibliographic and archival approaches to data publication have been advanced, but both have limitations with respect to hydrologic data. Specific recommendations for improving data publication infrastructure, support, and practices to move beyond existing limitations and enable more effective data publication in support of scientific research in the hydrology domain include: improving support for journal article‐based data access and data citation, considering the workflow for data publication, enhancing support for reproducible science, encouraging publication of curated reference data collections, advancing interoperability standards for sharing data and metadata among repositories, developing partnerships with university libraries offering data services, and developing more specific data management plans. While presented in the context of CUAHSI's data repositories and experience, these recommendations are broadly applicable to other domains.

    This article is categorized under:

    Science of Water > Methods

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  4. This resource includes Jupyter Notebooks that combine (merge) model results with observations. There are four folders: - NWM_SnowAssessment: This folder includes codes required for combining model results with observations. It also has an output folder that contains outputs of running five Jupyter Notebooks within the code folder. The order to run the Jupyter Notebooks is as follows. First run Combine_obs_mod_[*].ipynb where [*] is P (precipitation), SWE (snow water equivalent), TAir (air temperature), and FSNO (snow covered area fraction). This combines the model outputs and observations for each variable. Then, run Combine_obs_mod_P_SWE_TAir_FSNO.ipynb. - NWM_Reanalysis: This folder contains the National Water Model version 2 retrospective simulations that were retrieved and pre-processed at SNOTEL sites using and - SNOTEL: This folder contains preprocessed SNOTEL observations that were created using - GEE: This folder contains MODIS observations that we downloaded using Note that the existing CSV file is the merged file of the downloaded CSV files. 
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  5. Geospatial research and education have become increasingly dependent on cyberGIS to tackle computation and data challenges. However, the use of advanced cyberinfrastructure resources for geospatial research and education is extremely challenging due to both high learning curve for users and high software development and integration costs for developers, due to limited availability of middleware tools available to make such resources easily accessible. This tutorial describes CyberGIS-Compute as a middleware framework that addresses these challenges and provides access to high-performance resources through simple easy to use interfaces. The CyberGIS-Compute framework provides an easy to use application interface and a Python SDK to provide access to CyberGIS capabilities, allowing geospatial applications to easily scale and employ advanced cyberinfrastructure resources. In this tutorial, we will first start with the basics of CyberGISJupyter and CyberGIS-Compute, then introduce the Python SDK for CyberGIS-Compute with a simple Hello World example. Then, we will take multiple real-world geospatial applications use-cases like spatial accessibility and wildfire evacuation simulation using agent based modeling. We will also provide pointers on how to contribute applications to the CyberGIS-Compute framework. 
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  6. null (Ed.)
    We present the design and development of an open-source web application called Water Data Explorer (WDE), designed to retrieve water resources observation and model data from data catalogs that follow the WaterOneFlow and WaterML Service-Oriented Architecture standards. WDE is a fully customizable web application built using the Tethys Platform development environment. As it is open source, it can be deployed on the web servers of international government agencies, non-governmental organizations, research teams, and others. Water Data Explorer provides uniform access to international data catalogs, such as the Consortium of Universities for the Advancement of Hydrologic Science (CUAHSI) Hydrologic Information System (HIS) and the World Meteorological Organization (WMO) Hydrological Observing System (WHOS), as well as to local data catalogs that support the WaterOneFlow and WaterML standards. WDE supports data discovery, visualization, downloading, and basic data interpolation. It can be customized for different regions by modifying the user interface (i.e., localization), as well as by including pre-defined data catalogs and data sources. Access to WDE functionality is provided by a new open-source Python package called “Pywaterml” which provides programmable access to WDE methods to discover, visualize, download, and interpolate data. We present two case studies that access the CUAHSI HIS and WHOS catalogs and demonstrate regional customization, data discovery from WaterOneFlow web services, data visualization of time series observations, and data downloading. 
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  7. null (Ed.)
    In recent years, data and file sharing have advanced significantly, opening doors for engineers from all over the world to stay connected with each other and share data, models, scripts and other information required for scientific and engineering purposes. HydroShare ( was developed by a consortium of universities sponsored by the National Science Foundation (NSF) as a means for improving data and model sharing. Originally released in 2014, and continually updated since that time, HydroShare has proven to be a valuable resource for a growing number of active users in the field of water resources and environmental research. The graphical user interface is relatively simple and easy to understand and the system provides users with a large amount of free data storage, which makes it particularly useful for academics, researchers, and scientists as well as practicing engineers. This project report presents the design and development of a web-based application (web app) that demonstrates all core functions of HydroShare via a published application programmer interface (API). The resulting web app was developed using the Tethys Platform which is intended for creating web-based applications with database and mapping capabilities. This app demonstrates the use of all of the core functions of the HydroShare Python REST client and includes sample code and instructions for using these functions. The overarching goal of this work is to increase the use and usability of HydroShare via its API and to simplify using the API for student and other programmers developing their own web applications. 
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  8. null (Ed.)