An analytical solution for rapidly predicting post‐fire peak streamflow for small watersheds in southern California
An analytical solution for rapidly predicting post‐fire peak streamflow for small watersheds in southern California
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快速预测南加州小流域火灾后峰值水流的分析解决方案
DOI：
10.1002/hyp.13976
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发表时间：
2020
影响因子：
3.2
通讯作者：
A. Kinoshita
中科院分区：
文献类型：
--
作者：
Brenton A. Wilder;J. Lancaster;P. Cafferata;D. Coe;B. Swanson;D. Lindsay;W. Short;A. Kinoshita
野火之后，洪水和泥石流的可能性增加，对人类生命、下游社区、基础设施和生态系统构成风险。在南加州（美国），Rowe，Countryman，and Storey（RCS）1949方法是一种经验方法，用于快速估算火灾后的峰值流量。我们重新评估了当前条件下33个流域的RCS准确性。火灾前峰值流量预测性能较低，其中2年和10年复发间隔事件的平均R2为0.29，平均RMSE为1.10 cms/km 2。火灾后，RCS性能也较低，2年和10年事件的平均R2为0.26，RMSE为15.77 cms/km 2。我们表明，RCS过度概括流域过程，并没有充分代表野火和极端天气事件的影响，往往低估了峰值流量没有泥沙膨胀因素的系统的空间和时间的变化。机器学习的一种新应用被用来识别关键的流域特征，包括当地的自然地理、土地覆盖、地质、坡度、坡向、降雨强度和土壤烧伤的严重程度，产生了两个随机森林模型，分别有45个和5个参数（RF-45和RF-5）来预测火灾后的峰值流量。RF-45和RF-5的性能优于RCS方法;然而，它们证明了数据可用性的重要性和依赖性。通过机器学习技术确定的重要参数用于创建三维多项式函数，以计算火灾后第一年内加州南部小流域的火灾后峰值流量（R2 = 0.82; RMSE = 6.59 cms/km 2），该函数可用作火灾后风险评估团队的临时工具。我们的结论是，高时空分辨率的降雨强度，径流量和渠道中的泥沙负荷的数据收集的显着增加将有助于指导未来的模型开发，以量化火灾后的洪水风险。
Following wildfires, the probability of flooding and debris flows increase, posing risks to human lives, downstream communities, infrastructure, and ecosystems. In southern California (USA), the Rowe, Countryman, and Storey (RCS) 1949 methodology is an empirical method that is used to rapidly estimate post‐fire peak streamflow. We re‐evaluated the accuracy of RCS for 33 watersheds under current conditions. Pre‐fire peak streamflow prediction performance was low, where the average R2 was 0.29 and average RMSE was 1.10 cms/km2 for the 2‐ and 10‐year recurrence interval events, respectively. Post‐fire, RCS performance was also low, with an average R2 of 0.26 and RMSE of 15.77 cms/km2 for the 2‐ and 10‐year events. We demonstrated that RCS overgeneralizes watershed processes and does not adequately represent the spatial and temporal variability in systems affected by wildfire and extreme weather events and often underpredicted peak streamflow without sediment bulking factors. A novel application of machine learning was used to identify critical watershed characteristics including local physiography, land cover, geology, slope, aspect, rainfall intensity, and soil burn severity, resulting in two random forest models with 45 and five parameters (RF‐45 and RF‐5, respectively) to predict post‐fire peak streamflow. RF‐45 and RF‐5 performed better than the RCS method; however, they demonstrated the importance and reliance on data availability. The important parameters identified by the machine learning techniques were used to create a three‐dimensional polynomial function to calculate post‐fire peak streamflow in small catchments in southern California during the first year after fire (R2 = 0.82; RMSE = 6.59 cms/km2) which can be used as an interim tool by post‐fire risk assessment teams. We conclude that a significant increase in data collection of high temporal and spatial resolution rainfall intensity, streamflow, and sediment loading in channels will help to guide future model development to quantify post‐fire flood risk.
源头水道的干燥沉积物加剧了基岩景观中野火后的泥石流
影响因子：
5.8
作者：
DiBiase, Roman A.;Lamb, Michael P.
通讯作者：
Lamb, Michael P.