Natural and anthropogenic controls on sediment rating curves in northern California coastal watersheds

Fisher, Adam; Belmont, Patrick; Murphy, Brendan P.; MacDonald, Lee H.; Ferrier, Ken L.; Hu, Kai

doi:10.1002/esp.5137

Cited by 8 publications

(2 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is because the No Management stratum tends to occupy higher elevation portions of the inventory basins with steeper slopes, both of which are key determinants of rainfall thresholds for landslide initiation (Marin et al, 2020; Minder et al, 2009). Moreover, variable bed‐rock geology and tectonic uplift rates between strata may impart differences in sediment generation and downstream geomorphic response (Bywater‐Reyes et al, 2017; Fisher et al, 2021), effects we have not evaluated in this study. However, because the distributions of elevation, hillslope gradient and precipitation were strongly overlapping between all three management strata (not shown) and because the final two decades in the inventory were characterized by lower rates of landslide volume generation in Ongoing and Legacy Management lands versus No Management lands (Figure 5a), we believe the documented landslide timeseries suggests that logging reduction and regulation have reduced sediment supply to near‐background levels.…”

Section: Discussionmentioning

confidence: 99%

Forest management history influences eight decades of shallow landsliding in the northwest Cascade Mountains, USA

Seixas,

Veldhuisen

2023

Earth Surf Processes Landf

View full text Add to dashboard Cite

Forest management‐driven increases in landsliding may impact aquatic habitat and water quality, yet it is difficult to disentangle the effects of hydroclimate from evolving land‐use practices when interpreting landslide rate fluctuations over decadal timescales. We compiled shallow landslide inventories starting in the 1940s from nine small watersheds in the northwest Cascade Mountains, USA, and updated the mapping through 2019. We stratified the landscape by management regime (industrial/state and federally managed forests and forests with no history of management) and compared landslide volume generation rates against timeseries representing regional climate, precipitation and river discharge, timber harvest rate, and instability ratings from the topographic model SHALSTAB. Results demonstrate a bell‐shaped temporal trend in composite landsliding that peaked in the 1970s and 1980s and is unlikely to have arisen from random variation alone. The signal from unmanaged forests correlates with regionally warm/wet climatic conditions at decadal scales, but we found mixed quantitative support for storm hydrology in explaining the landslide trends. Landsliding in federal managed forests was greatly reduced following cessation of clearcut logging in the mid‐1990s in most basins. In contrast, topographic and stand age modelling suggest the private industrial and state forest trend is consistent with reduced landsliding resulting from regulations requiring hazard avoidance in logging unit design—not changes to logging rate—demonstrated by a decline in probability of recent clearcuts on highly unstable terrain relative to more stable terrain following the 1980s in most basins. The analysed covariates could not explain all trends in all watersheds, suggesting the presence of methodological or data‐based limitations in the analyses. Late 20th century landslide sediment delivery acutely impacted tributaries but likely became muted downstream due to lag effects and mixing with other sources; our results suggest that proximal sediment supply impacts may be greatly reduced.

show abstract

Section: Discussionmentioning

confidence: 99%

Forest management history influences eight decades of shallow landsliding in the northwest Cascade Mountains, USA

Seixas,

Veldhuisen

2023

Earth Surf Processes Landf

View full text Add to dashboard Cite

show abstract

“…We first used Random Forest (RF) machine learning models to evaluate the relative importance of all included predictor variables (Breiman, 2001). RF models are a type of classification or regression tree analysis used to evaluate complex relationships between predictor and response variables by combining observations from an ensemble of trees (Cutler et al, 2007; Fisher et al, 2021; Vaughan et al, 2017). We initially removed any variables that demonstrated high covariance by using the variance inflation factor (VIF) and then examined the importance of the predictor variables using variable importance plots (Gemuer et al, 2012), which rank variables based on the mean decrease in model accuracy that would occur if they were removed from the RF model.…”

Section: Methodsmentioning

confidence: 99%

Predicting post‐fire debris flow grain sizes and depositional volumes in the Intermountain West, United States

Wall

Murphy

Belmont

et al. 2022

Earth Surf Processes Landf

Self Cite

View full text Add to dashboard Cite

Post-fire debris flows represent one of the most erosive consequences associated with increasing wildfire severity and investigations into their downstream impacts have been limited. Recent advances have linked existing hydrogeomorphic models to predict potential impacts of post-fire erosion at watershed scales on downstream water resources. Here we address two key limitations in current models: (1) accurate predictions of post-fire debris flow volumes in the absence of triggering storm rainfall intensities and (2) understanding controls on grain sizes produced by post-fire debris flows. We compiled and analysed a novel dataset of depositional volumes and grain size distributions (GSDs) for 59 post-fire debris flows across the Intermountain West (IMW) collected via fieldwork and from the literature. We first evaluated the utility of existing models for post-fire debris flow volume prediction, which were largely developed for Southern California. We then constructed a new post-fire debris flow volume prediction model for the IMW using a combination of Random Forest modelling and regression analysis. We found topography and burn severity to be important variables, and that the percentage of pre-fire soil organic matter was an essential predictor variable. Our model was also capable of predicting debris flow volumes without data for the triggering storm, suggesting that rainfall may be more important as a presence/absence predictor, rather than a scaling variable. We also constructed the first models that predict the median, 16th percentile, and 84th percentile grain sizes, as well as boulder size, produced by post-fire debris flows. These models demonstrate consistent landscape controls on debris flow GSDs that are related to land cover, physical and chemical weathering, and hillslope sediment transport processes. This work advances our ability to predict how post-fire sediment pulses are transported through watersheds. Our models allow for improved pre-and post-fire risk assessments across diverse ranges of watersheds in the IMW.

show abstract

Understanding Marsh Elevation and Accretion Processes and Vulnerability to Rising Sea Levels Across Climatic and Geomorphic Gradients in California, USA

Thorne,

Bristow,

Rankin

et al. 2023

Estuaries and Coasts

View full text Add to dashboard Cite

Natural and anthropogenic controls on sediment rating curves in northern California coastal watersheds

Cited by 8 publications

References 83 publications

Forest management history influences eight decades of shallow landsliding in the northwest Cascade Mountains, USA

Forest management history influences eight decades of shallow landsliding in the northwest Cascade Mountains, USA

Predicting post‐fire debris flow grain sizes and depositional volumes in the Intermountain West, United States

Understanding Marsh Elevation and Accretion Processes and Vulnerability to Rising Sea Levels Across Climatic and Geomorphic Gradients in California, USA

Contact Info

Product

Resources

About