Channel cross-section analysis for automated stream head identification

Ethan J. Shavers; Lawrence V. Stanislawski

doi:10.1016/j.envsoft.2020.104809

Home/Archive/USGS

USGS · 70225726

Channel cross-section analysis for automated stream head identification

Ethan J. Shavers·Lawrence V. Stanislawski

View original source ↗Export metadata ↓

Abstract

Headwater streams account for more than half of the streams in the United States by length. The substantial occurrence and susceptibility to change of headwater streams makes regular updating of related maps vital to the accuracy of associated analysis and display. Here we present work testing new methods of completely automated remote headwater stream identification using metrics derived from channel Digital Elevation Model (DEM) cross-sections. A jump in standard deviation of curvature (sK) is found to correlate with the presence of stream heads. Field and remotely validated stream and channel initiation points from 4 diverse study areas in North Carolina as well as a simulated surface are used to test the sK findings. The sK value within individual catchments equal to 0.5*Tukey's upper inner fence is found to be a reliable threshold for identifying the upslope extent of channels in varied landscapes.

Explore related subjects

Environmental Modelling & Software Article

Source-reported bounding extent: 25.08° to 49.38905° latitude; -124.68721° to -66.96466° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Ethan J. Shavers, Lawrence V. Stanislawski. 2020. Channel cross-section analysis for automated stream head identification. https://doi.org/10.1016/j.envsoft.2020.104809

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Multivariate SWAT streamflow and surface water storage calibration enables upper Mississippi River Basin wetland change scenarios

Surface water storage (e.g., wetlands, lakes) is not typically considered in hydrological model calibrations. We tested a multivariate calibration process, incorporating Sentinel-1 and -2 surface water storage, for a Soil and Water Assessment Tool model across the 0.5 million km 2 Upper Mississippi River Basin. While 19% of the 2000 parameter sets adequately simulated discharge (Kling-Gupta efficiency >0.5), only 5% also adequately simulated surface water storage (mean absolute error <2 m), reducing model output uncertainty. Using the best calibrated model, we found that changes in surface water storage capacity most strongly affected discharge during the first annual peak flow (i.e., floods), when storage was filling. Increases in upstream surface water storage capacity resulted in projected decreases in peak flow and flashiness, with changes persisting downstream to the watershed outlet. Our findings demonstrate the importance of surface water storage in multivariate model calibration processes to inform river discharge and flood impact predictions.

Illinois, Indiana, Iowa, Minnesota, Missouri, Wisc↗

USGS2026

Artificial intelligence with earth observations provides continuous streamflow data across varying wildfire recurrence and recovery scenarios

Wildfires continue to alter watershed hydrology globally. Numerous studies have analyzed post-fire hydrology through before- and after-fire comparisons—often focusing on a single fire event. This has led to a significant gap in research, where continuous, daily streamflow data are needed to capture both the short- and long-term effects of recurring fires. Our study, for the first time, demonstrates how Artificial Intelligence (AI) through Machine Learning/Deep Learning (ML/DL) models can fill this gap by harnessing Earth Observations of burn severity, fire radiative power, and burn dates alongside traditional watershed attributes and drought indicators. We present a highly efficient DL model based on the Long Short-term Memory (LSTM) algorithm that is both reproducible and generalizable across multiple fire-affected watersheds in the western United States. Despite watersheds’ diverse climate conditions and markedly different hydrologic characteristics, the model consistently achieves strong performance, with Kling Gupta Efficiency ranging between 0.7 and 0.9. Our primary contribution is testing the model at a daily time-scale to model three distinct fire scenarios: no significant fire history yet the occurrence of a large fire, a longer return interval and no-fire period between phases of recurring fires, and frequent and repeated fire activity. We further investigate whether a relatively simple ML model such as Support Vector Machine (SVM) can achieve comparable accuracy in capturing these complex post-fire scenarios. By offering this fully open-source, generalizable AI solution powered by Earth Observations, newer and better modeling tools can provide post-fire daily streamflow data—a step in the right direction to safeguard water supply reliability and support watershed management across fire-prone regions worldwide.

California, Colorado, Idaho, Montana↗

USGS2025

METRIC: An interactive framework for integrated visualization and analysis of monitored and expected load reductions for nitrogen, phosphorus, and sediment in the Chesapeake Bay watershed

Reductions of nitrogen, phosphorus, and sediment loads have been the focus of watershed restoration in many regions for improving water quality, including the Chesapeake Bay. Watershed models and riverine monitoring data can provide important information on the progress of load reductions but do not always generate consistent interpretations. A new framework for integrated visualization and analysis of monitoring and modeling data, named “Monitored and Expected Total Reduction Indicator for the Chesapeake (METRIC),” was developed to provide spatially explicit trends for the subwatersheds of the Chesapeake Bay. METRIC contains up-to-date information on nitrogen, phosphorus, and sediment at 83, 66, and 66 stations, respectively, which can help watershed managers gauge expectations on the trajectory and pace of progress at localized scales. These results were further synthesized to better understand the spatial patterns of the response classes ( i.e. , agreement between the expected and monitored trends) across the Chesapeake Bay watershed.

Chesapeake Bay watershed↗