This Show and Tell session provides an introductory tour of the modernized USGS Water Data for the Nation interface and tools available at waterdata.usgs.gov.
The Toolbox for River Velocimetry using Images from Aircraft (TRiVIA) provides an end-to-end workflow for mapping velocities in rivers from videos or image sequences. The software includes modules for extracting frames, stabilizing and geo-referencing images, defining a region of interest, enhancing images, performing particle image velocimetry (PIV), visualizing results, assessing accuracy, and calculating discharge. In this introductory session, we will provide a brief overview TRiVIA's capabilities using an example data set from a river in Oregon collected using an uncrewed aircraft systems (UAS). Our target audience is any hydrologist who wants to learn about image-based techniques for estimating flow velocities in rivers.
Precipitation is a challenging parameter to model and measure accurately, making in situ observations from networks, like the approximately 3,400 USGS precipitation stations, especially valuable. Ensuring the accuracy of these observations is critical, particularly because provisional data is made publicly available in near real time. Station measurements are affected by various types of errors, several of which occur randomly in an unpredictable manner. The DRIP application is a threshold-based tool that leverages existing NOAA and USGS data to automatically identify potentially anomalous measurements to help maximize station uptime, prevent skewing of precipitation totals, and streamline field response.
The NWDi development team wants to hear why you don’t use NWDi/WADERS and roadblocks/usability issues you come up against. We want to hear your ideas for enhancements. NWDi team plans to take these ideas/issues to drive development and communication on the application.
NWDi/WADERS is an interactive web application that brings together an array of data and approaches to view and manage the USGS water network operations and internal business operational systems in a unified, standardized way for real-time management, resource planning, and situational awareness.
This training will demonstrate how to use the dataretrieval package in R/Python to access USGS water data. We will cover recent changes and updates that have occurred in dataretrieval to leverage new Water Data APIs. We’ll show examples for how to replace legacy workflows with modern alternatives, including how to obtain data for a site list or area of interest. We’ll discuss future updates and how to get the current status of dataretrieval workflows. Attendees are encouraged to bring existing scripts and workflows to the session to get hands on help in adapting to the new modern dataretrieval functions.
Introduction to Campbell Scientific SCWID (Shortcut for Web Interface Design), a web based GUI that lives on the datalogger and can be customized for your application. How they work, what they can do, and how you can design your own.
This session introduces a set of state-level Python notebooks that use hyswap to generate maps and charts of current water conditions. These notebooks are designed to be easy to run and modify, providing a practical entry point for creating visuals that were previously available through legacy systems like WaterWatch.
Participants will learn how to produce 7-, 14-, and 28-day percentile maps, along with HUC-based runoff maps, using reproducible workflows that can be adapted to different states or regions. The session will walk through how the notebooks are structured, how they access and process data, and how outputs can be customized for specific communication or analysis needs.
This is a hands-on, work-along session. Attendees are encouraged to bring a laptop and follow along as we get the notebooks running locally, step through the code, and make simple modifications. Support will be provided to help participants set up Python environments and troubleshoot issues so they can continue using the notebooks independently after the session. The session is open to anyone interested in learning how to generate state-level water condition visuals using Python, regardless of prior experience.
