TXT-tool 2.081-5.1: High-Resolution Rainfall Prediction for Early Warning of Landslides

2017 ◽  
pp. 415-423
Author(s):  
Ryo Onishi ◽  
Keigo Matsuda ◽  
Keiko Takahashi
Keyword(s):  
High Resolution ◽  
Early Warning ◽  
Rainfall Prediction

Urban Drainage Systems modelling for Early Warning Service Using Data-Driven Modelling

2020 ◽  
Author(s):  
Solomon Seyoum ◽  
Boud Verbeiren ◽  
Patrick Willems
Keyword(s):  
High Resolution ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Rainfall Data ◽  
Data Driven ◽  
Peak Flows ◽  
Previous Time ◽  
Pluvial Flooding ◽  
Drainage Channels

<p>Urban catchments are characterized by a high degree of imperviousness, as well as a highly modified landscape and interconnectedness. The hydrological response of such catchments is usually complex and fast and sensitive to precipitation variability at small scales. To properly model and understand urban hydrological responses, high-resolution precipitation measurements to capture spatiotemporal variability is crucial input.</p><p>In urban areas floods are among the most recurrent and costly disasters, as these areas are often densely populated and contain vital infrastructure. Runoff from impervious surfaces as a result of extreme rainfall leads to pluvial flooding if the system’s drainage capacity is exceeded. Due to the fast onset and localised nature of pluvial flooding, high-resolution models are needed to produce fast simulations of flood forecasts for early warning system development. Data-driven models for predictive modelling have been gaining popularity, due to the fact they require minimal inputs and have shorter processing time compared to other types of models.</p><p>Data-driven models to forecast peak flows in drainage channels of Brussels, Belgium are being developed at sub-catchment scale, as a proxy for pluvial flooding within the FloodCitiSense project. FloodCitiSense aims to develop an urban pluvial flood early warning service. The effectiveness of these models relies on the input data resolution among others. High-temporal resolution rainfall and runoff data from 13 rainfall and 13 flow gauging stations in Brussels for several years is collected (Open data from Flowbru.be) and the data-driven models for forecasting peak flows in drainage channels are build using the Random Forest classification model.</p><p>Optimal model inputs are determined to increase model performance, including rainfall and runoff information from the current time step, as well as additional information derived from previous time steps.</p><p>The additional inputs are determined by progressively including rainfall data from neighboring stations and runoff from previous time steps equivalent to the lag time equal to the forecasting horizon, in our case two hours. The data-driven model we develop has the form as shown in the following equation.</p><p><strong><em>Q<sub>t</sub> = f(Q<sub>t-lag</sub>, ∑RF<sub>i,j</sub>)  </em></strong><em>for <strong>i</strong> is the number of rainfall stations considered and <strong>j</strong> is the time  from <strong>t-lag</strong> to <strong>t</strong></em></p><p>Where <strong><em>Q<sub>t</sub>  </em></strong>is the flow at a flow station at time <strong><em>t</em></strong>, <strong><em>Q<sub>t-lag </sub></em></strong>is the lagged flow at the station and <strong><em>RF<sub>i,j </sub></em></strong>is the rainfall values for station <strong><em>i</em></strong> and time <strong><em>j</em></strong>.</p><p>For Brussels nine relevant sub-catchments were identified based on historical flood frequency for which we are building data-driven flood forecasting models. For each sub-catchment, RF models are being trained and tested. More than 200,000 data point were available for training and testing the models. For most of the flow stations the data-driven models perform well with R-squared values up to 0.84 for training and 0.6 for testing for a 2-hour forecast horizon. </p><p>To improve the reliability of the data-driven models, as next step, we are including radar rainfall data input, which has the ability to capture temporal and spatial variability of rainfall from localized convective storms to large scale moving storms.</p><p><strong>KEYWORDS</strong></p><p>Data driven models, FloodCitiSense, Flood Early Warning System, Urban pluvial flooding</p>

scholarly journals An EARLINET Early Warning System for atmospheric aerosol aviation hazards

2020 ◽  
Author(s):  
Nikolaos Papagiannopoulos ◽  
Giuseppe D'Amico ◽  
Anna Gialitaki ◽  
Nicolae Ajtai ◽  
Lucas Alados-Arboledas ◽  
...  
Keyword(s):  
High Resolution ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Mount Etna ◽  
Depolarization Ratio ◽  
Backscatter Coefficient ◽  
Desert Dust ◽  
Aviation Hazards ◽  
Linear Depolarization Ratio

Abstract. A stand-alone lidar-based method for detecting airborne hazards for aviation in near-real-time (NRT) is presented. A polarization lidar allows for the identification of irregular-shaped particles such as volcanic dust and desert dust. The Single Calculus Chain (SCC) of the European Aerosol Lidar Network (EARLINET) delivers high resolution pre-processed data: the calibrated total attenuated backscatter and the calibrated volume linear depolarization ratio time series. From these calibrated lidar signals, the particle backscatter coefficient and the particle depolarization ratio can be derived in temporally-high resolution, and thus provide the basis of the NRT Early Warning System (EWS). In particular, an iterative method for the retrieval of the particle backscatter is implemented. This improved capability was designed as a pilot that will produce alerts for imminent threats for aviation. The method is applied to data during two diverse aerosol scenarios: first, a record breaking desert dust intrusion in March 2018 over Finokalia, Greece, and, second, an intrusion of volcanic particles originating from Mount Etna in June 2019 over Antikythera, Greece. Additionally, a devoted observational period including several EARLINET lidar systems demonstrates the network's preparedness to offer insight into natural hazards that affect the aviation sector.

High-resolution summer rainfall prediction in the JHWC real-time WRF system

2010 ◽  
Vol 46 (3) ◽  
pp. 341-353 ◽  
Author(s):  
Dong-Kyou Lee ◽  
Dae-Yong Eom ◽  
Joo-Wan Kim ◽  
Jae-Bok Lee
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Summer Rainfall ◽  
Rainfall Prediction

Process dynamics, real time monitoring and early warning at an imminent cliff fall (Hochvogel, Allgäu Alps)

2020 ◽  
Author(s):  
Johannes Leinauer ◽  
Benjamin Jacobs ◽  
Michael Krautblatter
Keyword(s):  
High Resolution ◽  
Early Warning ◽  
Slope Failure ◽  
Rock Slope ◽  
Point Clouds ◽  
Aerial Images ◽  
Slope Failures ◽  
High Resolution Data ◽  
Process Dynamics ◽  
Resolution Data

<p>Costs for (re)installation and maintenance of protective structures are increasing while alpine hazards progressively threaten alpine communities, infrastructure and economics. With climatic changes, anticipation and clever early warning of rock slope failures based on the process dynamics become more and more important. The imminent rock slope failure at the Hochvogel summit (2592 m a.s.l., Allgäu Alps) offers a rare possibility to study a cliff fall at a high alpine carbonate peak during its preparation and until failure. In this real case scenario, we can develop and test an operative and effective early warning system.</p><p>The main cleft is two to six metres wide at the summit and at least 60 metres deep at the sides. Several lateral cracks are opening at faster pace and separate different instable blocks. 3D-UAV point clouds reveal a potentially failing mass of 260,000 m³ in six subunits. However, the pre-deformation is yet not pronounced enough to decide on the expected volume. Analysis of historical ortho- and aerial images yields an elongation of the main crack length from 10 to 35 m from 1960 until now. Discontinuous tape extensometer measurements show 35 cm opening of the main cleft between 2014 and 2020 with movement rates up to 1 cm/month. Since July 2018, automatic vibrating wire gauges deliver high-resolution data to an online server. In October 2019, we transferred the system into LoRa with data transmission every 10 min. Automatic warnings via SMS and email are triggered when crossing specific thresholds.</p><p>Here we demonstrate long-term process dynamics and 2-years of high-resolution data of a preparing alpine rock slope failure. Corresponding geodetic, photogrammetric, seismic and gravimetric measurements complete the comprehensive measurement design at the Hochvogel. This will help to decipher anticipative signals of initiating alpine rock slope failures and improve future event predictions.</p>

A systems approach to routing global gridded runoff through local high-resolution stream networks for flood early warning systems

2019 ◽  
Vol 120 ◽  
pp. 104501 ◽  
Author(s):  
Xiaohui Qiao ◽  
E. James Nelson ◽  
Daniel P. Ames ◽  
Zhiyu Li ◽  
Cédric H. David ◽  
...  
Keyword(s):  
High Resolution ◽  
Early Warning ◽  
Systems Approach ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Stream Networks
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