ABSTRACT:

This resource provides users with valuable access to the NOAA National Water Model (NWM) CONUS Retrospective Dataset version 2.1. The data, offered in Zarr format, can be downloaded and converted into user-specific CSV files, corresponding to individual forecast points identified by the user. Accompanying the code, users will discover a comprehensive list of USGS stations, each corresponding to a forecast point from the NWM, aiding in the forecast precision and data extraction process. The resource is further enhanced by a station description file, providing in-depth information about various streams and drainage areas retrieved from the NHDPlus Version 2dataset. The combination of these tools and datasets offers an effective means to analyze and visualize hydrological conditions across the CONUS region, benefitting researchers, planners, and policy-makers in their water management decisions.

ABSTRACT:

This HydroShare resource provides a detailed tutorial on the Stevens River Ice Mapping System, a novel platform that combines multi-satellite imagery and citizen science data through Google Earth Engine to monitor river ice dynamics in the United States and Canada. Addressing the challenge of river ice monitoring, particularly in remote areas like Alaska, this document provides users with instructions on navigating the system's interface, visualizing ice conditions, and downloading relevant data.
The system is accessible at: https://web.stevens.edu/ismart/land_products/rivericemapping.html

ABSTRACT:

This resource contain the training materials from a workshop held at the 2nd Annual Developers Conference at the University of Utah. It delves into the integration of ground-based observations with remote sensing datasets. The workshop facilitated hands-on experience in employing cloud-based technologies such as Google Earth Engine, Compute Engine, and Cloud Storage for data dissemination. Participants learned to create automated systems for data upload, processing, and dissemination, featuring the Stevens River Ice Monitoring System. This approach enhances collaboration and efficiency in environmental studies by streamlining data handling workflows.

ABSTRACT:

This resource allows users to obtain the location and metadata of USGS cameras from the Hydrologic Imagery Visualization and Information System (HIVIS). It provides a Python notebook for accessing and processing data, including the retrieval of camera locations and related information directly from the USGS API. Users can filter data based on specific attributes, generate URLs for individual camera stations, and save the filtered data locally. Additionally, the resource includes functionality to clip camera data using a shapefile of a selected area, allowing for targeted analysis. The Python notebook uses common libraries such as pandas and geopandas, making it accessible to those familiar with basic data manipulation and geographical data handling.

ABSTRACT:

This resource enables users to retrieve Multi-Radar Multi-Sensor (MRMS) rainfall data for selected locations based on a given shapefile. The included Jupyter Notebook guides users to perform geospatial analysis of the region of interest and retrieve data accordingly. It facilitates the visualization of rainfall data over specified areas and time periods. This tool supports environmental studies, urban planning, and any field where precise weather data analysis is crucial. The resource is designed to be user-friendly, accommodating users with varying levels of technical expertise in handling and analyzing geospatial data. This resource was developed as part of the activities for developing low-cost rainfall sensors under the CUAHSI INSTRUMENTATION DISCOVERY TRAVEL GRANT. It serves as a supportive tool for validating and calibrating rainfall measurements obtained from these sensors.

ABSTRACT:

This resource provides water depth data collected during a significant rainfall event on September 29, 2023, in Hoboken, NJ, capturing high water marks and visual documentation of the flood extent. The included HTML file offers a spatial visualization of the measurements, while the CSV file provides a summary of the locations of high water marks above ground level, critical for flood modeling efforts. Additionally, the accompanying images within the ZIP file serve as a qualitative tool for assessing the accuracy and realism of flood inundation models. This resource was developed as part of the activities for developing low-cost flood sensors under the CUAHSI INSTRUMENTATION DISCOVERY TRAVEL GRANT. It serves as a supportive tool for validating and calibrating water depth measurements obtained from these sensors.