Use of Doppler velocity radars to monitor and predict debris and flood wave velocities and travel times in post-wildfire basins

IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY Journal of Hydrology X Pub Date : 2024-06-25 DOI:10.1016/j.hydroa.2024.100180
John W. Fulton , Nick G. Hall , Laura A. Hempel , J.J. Gourley , Mark F. Henneberg , Michael S. Kohn , William Famer , William H. Asquith , Daniel Wasielewski , Andrew S. Stecklein , Amanullah Mommandi , Aziz Khan
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To address this challenge, a sensor ensemble consisting of noncontact, ground-based (near-field), Doppler velocity (velocity) and pulsed (stage or gage height) radars, rain gages, and a redundant radio communication network was leveraged to monitor flood wave velocities, to validate travel times, and to compliment observations from NEXRAD weather radar. The sensor ensemble (DEbris and Floodflow Early warNing System, DEFENS) was deployed in Waldo Canyon, Pike National Forest, Colorado, USA, which was burned entirely (100 percent burned) by the Waldo Canyon fire during the summer of 2012 (<span>MTBS, 2020</span>).</p><p>Surface velocity, stage, and precipitation time series collected during the DEFENS deployment on 10 August 2015 were used to monitor and predict flood wave velocities and travel times as a function of stream discharge (discharge; streamflow). 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Abstract

The magnitude and timing of extreme events such as debris and floodflows (collectively referred to as floodflows) in post-wildfire basins are difficult to measure and are even more difficult to predict. To address this challenge, a sensor ensemble consisting of noncontact, ground-based (near-field), Doppler velocity (velocity) and pulsed (stage or gage height) radars, rain gages, and a redundant radio communication network was leveraged to monitor flood wave velocities, to validate travel times, and to compliment observations from NEXRAD weather radar. The sensor ensemble (DEbris and Floodflow Early warNing System, DEFENS) was deployed in Waldo Canyon, Pike National Forest, Colorado, USA, which was burned entirely (100 percent burned) by the Waldo Canyon fire during the summer of 2012 (MTBS, 2020).

Surface velocity, stage, and precipitation time series collected during the DEFENS deployment on 10 August 2015 were used to monitor and predict flood wave velocities and travel times as a function of stream discharge (discharge; streamflow). The 10 August 2015 event exhibited spatial and temporal variations in rainfall intensity and duration that resulted in a discharge equal to 5.01 cubic meters per second (m3/s). Discharge was estimated post-event using a slope-conveyance indirect discharge method and was verified using velocity radars and the probability concept algorithm. Mean flood wave velocities – represented by the kinematic celerity ck=2.619meterspersecond,m/s±0.556percent and dynamic celerity cd=3.533m/s±0.181percentandtheiruncertainties were computed. L-moments were computed to establish probability density functions (PDFs) and associated statistics for each of the at-a-section hydraulic parameters to serve as a workflow for implementing alert networks in hydrologically similar basins that lack data.

Measured flood wave velocities and travel times agreed well with predicted values. Absolute percent differences between predicted and measured flood wave velocities ranged from 1.6 percent to 49 percent and varied with water slope, hydraulic radius, and depth. The kinematic celerity was a better predictor for steep slopes and wide flood plains associated with the Upper Waldo and Middle Waldo radar streamgages; whereas, the dynamic celerity was a better surrogate for shallow slopes and incised channels such as the Lower Waldo radar streamgage.

The method demonstrates the potential extensibility of a post-wildfire warning system by (1) leveraging multiple systems (i.e., weather radar, near-field velocity and stage radars, and rain gages) for accurate and timely warnings of debris and floodflows, (2) establishing an order of operations to site, install, and operate near-field radars and conventional rain gages to record floodflows, forecast travel times, and document geomorphic change in this basin and hydrologically similar basins that lack data, and (3) communicating data operationally with the Colorado Department of Transportation engineering staff, National Weather Service forecasters, and emergency managers.

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利用多普勒速度雷达监测和预测野火后流域的泥石流和洪水波速度及行进时间
野火后流域的泥石流和洪峰流量(统称为洪峰流量)等极端事件的规模和时间很难测量,更难预测。为了应对这一挑战,我们利用了由非接触式、地基(近场)、多普勒速度(流速)和脉冲(阶段或水位计高度)雷达、雨量计和冗余无线电通信网络组成的传感器组合来监测洪波速度、验证传播时间并补充 NEXRAD 气象雷达的观测结果。传感器组合(DEBRIS 和洪流早期预警系统,DEFENS)部署在美国科罗拉多州派克国家森林公园的瓦尔多峡谷,该峡谷在 2012 年夏季被瓦尔多峡谷大火完全烧毁(100% 烧毁)(MTBS,2020 年)。在 2015 年 8 月 10 日部署 DEFENS 期间收集的地表速度、阶段和降水时间序列被用于监测和预测洪波速度和行进时间与溪流排水量(排水量;溪流流量)的函数关系。2015 年 8 月 10 日的事件在降雨强度和持续时间方面表现出空间和时间变化,导致每秒 5.01 立方米(m3/s)的排水量。事件发生后,使用斜坡输送间接排水法估算了排水量,并使用速度雷达和概率概念算法进行了验证。计算了平均洪波速度--以运动流速 ck=2.619 米/秒(米/秒)±0.556% 和动力流速 cd=3.533 米/秒(米/秒)±0.181% 表示--及其不确定性。通过计算 L 矩,建立了每个断面水力参数的概率密度函数 (PDF) 和相关统计量,作为在缺乏数据的类似水文流域实施预警网络的工作流程。预测洪波速度和测量洪波速度之间的绝对百分比差异从 1.6% 到 49% 不等,并随水流坡度、水力半径和深度的变化而变化。对于与上沃尔多和中沃尔多雷达测流仪相关的陡坡和宽泛的洪泛平原,运动时速是更好的预测指标;而对于浅坡和切入河道(如下沃尔多雷达测流仪),动态时速是更好的替代指标、(1) 利用多个系统(即气象雷达、近场速度和水位雷达以及雨量计)准确及时地发出泥石流和洪水警报;(2) 建立操作顺序,以选址、安装和操作近场雷达和传统雨量计,从而记录洪水流量、预报行程时间,并记录该流域以及缺乏数据的类似水文流域的地貌变化;(3) 与科罗拉多州交通部工程人员、国家气象局预报员和应急管理人员在操作上沟通数据。
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来源期刊
Journal of Hydrology X
Journal of Hydrology X Environmental Science-Water Science and Technology
CiteScore
7.00
自引率
2.50%
发文量
20
审稿时长
25 weeks
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