Geology Reports⌕ Search
Home/Archive/USGS

USGS · 70205360

Characterization and evaluation of controls on post-fire streamflow response across western U.S. watersheds

Samuel Saxe·Terri S. Hogue·Lauren E. Hay
View original source ↗Export metadata ↓

Abstract

This research investigates the impact of wildfires on watershed flow regimes, specifically focusing on evaluation of fire events within specified hydroclimatic regions in the western United States, and evaluating the impact of climate and geophysical variables on response. Eighty-two watersheds were identified with at least 10 years of continuous pre-fire daily streamflow records and 5 years of continuous post-fire daily flow records. Percent change in annual runoff ratio, low flows, high flows, peak flows, number of zero flow days, baseflow index, and Richards–Baker flashiness index were calculated for each watershed using pre- and post-fire periods. Independent variables were identified for each watershed and fire event, including topographic, vegetation, climate, burn severity, percent area burned, and soils data. Results show that low flows, high flows, and peak flows increase in the first 2 years following a wildfire and decrease over time. Relative response was used to scale response variables with the respective percent area of watershed burned in order to compare regional differences in watershed response. To account for variability in precipitation events, runoff ratio was used to compare runoff directly to PRISM precipitation estimates. To account for regional differences in climate patterns, watersheds were divided into nine regions, or clusters, through k-means clustering using climate data, and regression models were produced for watersheds grouped by total area burned. Watersheds in Cluster 9 (eastern California, western Nevada, Oregon) demonstrate a small negative response to observed flow regimes after fire. Cluster 8 watersheds (coastal California) display the greatest flow responses, typically within the first year following wildfire. Most other watersheds show a positive mean relative response. In addition, simple regression models show low correlation between percent watershed burned and streamflow response, implying that other watershed factors strongly influence response. Spearman correlation identified NDVI, aridity index, percent of a watershed's precipitation that falls as rain, and slope as being positively correlated with post-fire streamflow response. This metric also suggested a negative correlation between response and the soil erodibility factor, watershed area, and percent low burn severity. Regression models identified only moderate burn severity and watershed area as being consistently positively/negatively correlated, respectively, with response. The random forest model identified only slope and percent area burned as significant watershed parameters controlling response. Results will help inform post-fire runoff management decisions by helping to identify expected changes to flow regimes, as well as facilitate parameterization for model application in burned watersheds.

Explore related subjects

Hydrology and Earth System SciencesArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Samuel Saxe, Terri S. Hogue, Lauren E. Hay. 2018-02-13. Characterization and evaluation of controls on post-fire streamflow response across western U.S. watersheds. https://doi.org/10.5194/hess-22-1221-2018

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

KEEP EXPLORING

Related USGS reports

USGS2026

Predicting streamflow drought in the conterminous United States using machine learning and a donor-gage approach, 1982–2020

Drought is a highly consequential natural disaster that may likely increase in both severity and extent across the conterminous United States (CONUS). The mechanisms affecting the propagation of drought from the atmosphere to streamflow are complex and interactive, making the prediction of streamflow drought difficult with current modeling approaches. Machine learning is an emerging tool in the field of hydrology that may be well-suited to prediction of streamflow drought across large and topographically diverse areas. Here, we train and analyze 3198 random forest models at U.S. Geological Survey streamgages to understand common meteorological drivers of streamflow drought and to define physiographic characteristics of basins sensitive to these drivers. We also develop a novel dynamic regionalization approach using donor gages to predict daily streamflow drought at pseudo-ungaged locations. Our results show that teleconnections, temperature, evaporative demand, and snow–water equivalent are important drivers of streamflow drought in the West, Southwest, and Northern Rocky Mountains (Northern Rockies) regions of the United States, and precipitation and soil moisture are primary drivers of streamflow drought in the Northeast, Southeast, and the Northwest regions. Prediction using dynamic regionalization shows comparable performance to at-site models. This method may be applicable to other hydrologic prediction problems requiring transferability to ungaged locations.

conterminous United States↗
USGS2026

Proposing sources for discrete groundwater discharges to patterned pools in three regional raised northern peat bogs

Raised northern peat bogs are generally assumed to be entirely precipitation-fed (ombrogenous), suggesting they are void of groundwater (minerogenous) inflow from underlying sediments. Patterned pools in raised bogs have often been attributed to surficial flow filling depressions along the peat surface that produces subtle differences in peat pore-water chemistry. However, we present evidence that certain patterned pools may be partially fed by localized upwelling of minerogenous groundwater from underlying glacial sediments in three northern peat bogs of Maine, USA. Underlying permeable glacial deposits, embedded in hydraulically confining glacio-marine clay deposits, were delineated using ground-penetrating radar and transient electromagnetic surveys. Paired point measurements of temperature and specific conductance (SpC) were surveyed around pools, and statistical relationships indicative of groundwater upwelling were established. Uncrewed aerial systems (UAS) thermal infrared (TIR) mapping was conducted, augmented by handheld TIR imaging, to examine characteristic groundwater temperatures in cold and warm seasons across pool surfaces. Surface water samples were acquired to assess the relationship between SpC / temperature signals and elevated iron and manganese concentrations that could be indicative of glacial aquifer sources. The combined datasets present evidence for localized upwelling in pools underlain by glacial structures, and the possibility of minerogenous groundwater contributions. Upwelling through the peat matrix is possibly partially facilitated by macropore, “peat pipe” features that serve as preferential flowpaths of varying lengths to the surface. Such upwelling could drive a positive feedback loop where elevated concentrations of ionic constituents repeatedly dilate the peat matrix and/or terminal electron acceptors enhance anaerobic respiration and microbial activity, accelerating the humification of peat around patterned pools and potentially magnifying carbon loss.

Maine↗
USGS2025

Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model

Despite their ubiquity and importance as freshwater habitat, small headwater streams are under-monitored by existing stream gage networks. To address this gap, we describe a low-cost, non-contact, and low-effort method that enables organizations to monitor relative streamflow dynamics in small headwater streams. The method uses a camera to capture repeat images of the stream from a fixed position. A person then annotates pairs of images, in each case indicating which image has more apparent streamflow or indicating equal flow if no difference is discernible. A deep learning modeling framework called streamflow rank estimation (SRE) is then trained on the annotated image pairs and applied to rank all images from highest to lowest apparent streamflow. From this result a relative hydrograph can be derived. We found that our modeled relative hydrograph dynamics matched the observed hydrograph dynamics well for 11 cameras at 8 streamflow sites in western Massachusetts. Higher performance was observed during the annotation period (median Kendall's Tau rank correlation of 0.75, with a range of 0.6–0.83) than after it (median Kendall's Tau of 0.59, with range 0.34–0.74). We found that annotation performance was generally consistent across the 11 camera sites and 2 individual annotators and was positively correlated with streamflow variability at a site. A scaling simulation determined that model performance improvements were limited after 1000 annotation pairs. Our model's estimates of relative flow, while not equivalent to absolute flow, may still be useful for many applications, such as ecological modeling and calculating event-based hydrological statistics (e.g., the number of out-of-bank floods). We anticipate that this method will be a valuable tool to extend existing stream monitoring networks and provide new insights on dynamic headwater systems.

Massachusetts↗