Predicting post‐fire debris flow grain sizes and depositional volumes in the Intermountain West, United States
Predicting post‐fire debris flow grain sizes and depositional volumes in the Intermountain West, United States
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DOI：
10.1002/esp.5480
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发表时间：
2022-09
影响因子：
3.3
通讯作者：
S. Wall;B. Murphy;P. Belmont;L. Yocom
中科院分区：
文献类型：
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作者：
S. Wall;B. Murphy;P. Belmont;L. Yocom
火灾后的泥石流是与野火严重程度增加相关的最具侵蚀性的后果之一，对其下游影响的调查有限。最近的研究进展将现有的水文地貌模型联系起来，以预测流域尺度上火灾后侵蚀对下游水资源的潜在影响。在这里，我们解决了当前模型的两个关键局限性：(1)在没有触发风暴降雨强度的情况下准确预测火灾后泥石流体积；(2)了解火灾后泥石流产生的粒度控制。我们编制并分析了一个新的数据集，该数据集包含了通过实地调查和文献收集的59个山间西部（IMW）火灾后泥石流的沉积体积和粒度分布（gsd）。我们首先评估了现有模型在火灾后泥石流体积预测中的效用，这些模型主要是为南加州开发的。然后，我们使用随机森林模型和回归分析相结合的方法构建了一个新的IMW火灾后泥石流体积预测模型。我们发现地形和烧伤严重程度是重要的变量，而火灾前土壤有机质的百分比是一个重要的预测变量。我们的模型也能够在没有触发风暴数据的情况下预测泥石流体积，这表明降雨可能更重要的是作为一个存在/不存在的预测因子，而不是一个缩放变量。我们还构建了第一个预测火灾后泥石流产生的中位数、第16百分位和第84百分位颗粒尺寸以及巨石尺寸的模型。这些模型表明，与土地覆盖、物理和化学风化以及山坡沉积物运输过程有关的泥石流gsd的景观控制是一致的。这项工作提高了我们预测火灾后沉积物脉冲如何通过流域运输的能力。我们的模型可以改进IMW不同流域的火灾前后风险评估。
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.