Geology Reports⌕ Search

USGS · 70256109

Post-fire sediment yield from a central California watershed: Field measurements and validation of the WEPP model

Abstract

In a warming climate, an intensifying fire regime and higher likelihood of extreme rain are expected to increase watershed sediment yield in many regions. Understanding regional variability in landscape response to fire and post-fire rainfall is essential for managing water resources and infrastructure. We measured sediment yield resulting from sequential wildfire and extreme rain and flooding in the upper Carmel River watershed (116 km 2 ), on the central California coast, USA, using changes in sediment volume mapped in a reservoir. We determined that the sediment yield after fire and post-fire flooding was 854–1,100 t/km 2 /yr, a factor of 3.5–4.6 greater than the long-term yield from this watershed and more than an order of magnitude greater than during severe drought conditions. In this first large-scale field validation test of the WEPPcloud/ wepppy framework for the Water Erosion Prediction Project (WEPP) model on a burned landscape, WEPP predicted 81%–106% of the measured sediment yield. These findings will facilitate assessing and predicting future fire effects in steep watersheds with a Mediterranean climate and indicate that the increasingly widespread use of WEPP is appropriate for evaluating post-fire hillslope erosion even across 100-km 2 scales under conditions without debris flows.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 36.29738349401494° to 36.57207874053866° latitude; -121.95235756143774° to -121.59272197628316° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Amy E. East, Joshua B. Logan, Helen Willemien Dow, Douglas P. Smith, Pat Iampietro, Jonathan A. Warrick, Thomas Lorenson, Leticia Hallas, Benjamin Kozlowicz. 2024-07-20. Post-fire sediment yield from a central California watershed: Field measurements and validation of the WEPP model. https://doi.org/10.1029/2024ea003575

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

KEEP EXPLORING

Related USGS reports

A new amphibious earthquake catalog for the Alaska Peninsula: Training, testing, and applying machine learning earthquake picking in offshore and amphibious environments

The dual seismological developments of large-scale offshore observational networks and high-quality machine-learning earthquake detection and picking allow unprecedented seismicity detection in high hazard subduction zones. We develop a high resolution machine learning enhanced seismicity catalog for the Alaska Peninsula stretch of the Alaska-Aleutian Subduction Zone, which was instrumented with both land and ocean bottom seismometers during the Alaska Amphibious Community Seismic Experiment (AACSE) in 2018–2019. We first use an analyst-checked AACSE catalog to develop new local land and ocean-bottom earthquake waveform training data sets, which we then use to fine-tune the EQTransformer machine learning picking model. In the process, we test multiple training strategies for best picking performance on ocean-bottom waveform data. We evaluate the effect of choice of pre-trained starting model, addition of waveform bandpass filtering, and choice of random metadata split. We find no training strategy to be substantially better than the others. Instead, simply having a local waveform data set to fine-tune the picking model substantially improves performance on both the ocean-bottom and land seismic data from AACSE. We finally apply the separately trained land and ocean-bottom models to continuous seismic data for AACSE and use an iterative catalog refinement method to detect new small-magnitude seismicity. The machine learning enhanced catalog has roughly 3x more earthquakes than the analyst-checked catalog, and the magnitude of completeness is regionally variable but decreases 0.1–0.4 magnitude units. With more small seismicity detected, seismicity patterns mirror prior observations in the region. Detection is especially improved far offshore and in the outer rise.

Alaska↗

Widespread landslide activity in an extreme wet season and implications for regional sediment management, eastern San Francisco Bay area, California

Watershed sediment production is expected to increase in a warmer future with more extreme rain, with cascading effects throughout drainage and sediment-transport networks. This study investigated landscape-scale sediment movement in the eastern San Francisco Bay area, California, USA, during the extreme 2016–2017 wet season that brought major rainfall, landslides, and flooding. Mapping 8,928 landslides across a 1,050-km 2 study area revealed new sediment yield of 510–956 t/km 2 , equivalent to denudation of 193–361 mm/ky. These results correspond closely to long-term denudation rates in the northern and central California Coast Ranges, indicating that mass wasting in very wet years dominates long-term sediment mobilization. However, due to long residence times in drainage networks, the 2017 landslides contributed at most ∼1%–2% of the estimated locally derived fluvial sediment transport to San Francisco Bay. Although the amount of sediment mobilized did not threaten municipal water supplies, small rangeland impoundments in this mixed-use landscape lost storage capacity to new sedimentation. Considering regional sediment supply and demand, even the exceptionally large sediment delivery in an extreme wet year cannot meet the need for sediment to accrete tidal wetlands in the bay. To keep pace with rising sea levels, this abnormally high terrestrial sediment input would need to occur in 50 of the next 75 years, an unlikely occurrence due to the prevalence of recent drought years. Shoreline protection and restoration in the bay would need additional sources of sediment, such as through management of dredged sediment through beneficial-reuse programs.

California↗

Patterns and drivers of cliff erosion in Big Sur, California, USA using repeat photogrammetry, 2017–2023

Seacliff erosion in steep terrain poses major risks to transportation and critical infrastructure. In Big Sur, California, USA, seacliff erosion threatens the sustainability of the central coast stretch of California State Route 1, a transportation corridor that is critical to the region's economy. Published cliff retreat rates for the region range from 1 to 40 cm yr −1 , highlighting that high-resolution, process-based studies could enhance understanding of the causes of spatial and temporal variability. We quantified cliff erosion and investigated its drivers along ∼13 km of the Big Sur coastline at week–month timescales during the late fall to early spring wet seasons between January 2017 and June 2023 by analyzing 3D point clouds developed from aerial imagery using four-dimensional structure-from-motion (4D SfM) photogrammetry techniques. We calculated cliff face retreat rates of 2.23 ± 3.06 cm yr −1 (mean ±1 σ ), an order of magnitude lower than long-term estimated rates for the region (which included large deep-seated landslides), but in line with short-term rates reported across California. Change detection imagery comparison, cliff profiles through time, and statistical analysis reveal a cyclical cliff evolution process in which erosion by wave action at the cliff base destabilizes the cliff and primes it for subsequent failure during precipitation events. Although more erosion by volume could be attributed to precipitation-induced increases in soil moisture (784 m 3 km −1 yr −1 ) compared with erosion attributed to wave power (282 m 3 km −1 yr −1 ), our observations underscore the coupled nature of these processes in driving cliff evolution, consistent with established theory and observations.

California↗