A debris-flow alarm system for the Alpine Illgraben catchment: design and performance

We describe the development, implementation, and first analyses of the performance of a debris-flow warning system for the Illgraben catchment and debris fan area. The Illgraben catchment (9.5km2), located in the Canton of Valais, Switzerland, in the Rhone River valley, is characterized by frequent and voluminous sediment transport and debris-flow activity, and is one of the most active debris-flow catchments in the Alps. It is the site of an instrumented debris-flow observation station in operation since the year 2000. The residents in Susten (municipality Leuk), tourists, and other land users, are exposed to a significant hazard. The warning system consists of four modules: community organizational planning (hazard awareness and preparedness), event detection and alerting, geomorphic catchment observation, and applied research to facilitate the development of an early warning system based on weather forecasting. The system presently provides automated alert signals near the active channel prior to (5-15min) the arrival of a debris flow or flash flood at the uppermost frequently used channel crossing. It is intended to provide data to support decision-making for warning and evacuation, especially when unusually large debris flows are expected to leave the channel near populated areas. First-year results of the detection and alert module in comparison with the data from the independent debris-flow observation station are generally favorable. Twenty automated alerts (alarms) were issued, which triggered flashing lights and sirens at all major footpaths crossing the channel bed, for three debris flows and 16 flood flows. Only one false alarm was issued. The major difficulty we encountered is related to the variability and complexity of the events (e.g., events consisting of multiple surges) and can be largely solved by increasing the duration of the alarm. All of the alarms for hazardous events were produced by storms with a rainfall duration and intensity larger than the threshold for debris-flow activity that was defined in an earlier study, supporting our intention to investigate the use of rainfall forecasts to increase the time available for warning and implementation of active countermeasure

Recommender Thermometer for Measuring the Preparedness for Flood Resilience Management

A range of various thermometers and similar scales are employed in different human and resilience management activities: Distress Thermometer, Panic Thermometer, Fear Thermometer, fire danger rating, hurricane scales, earthquake scales (Richter Magnitude Scale, Mercalli Scale), Anxiety Thermometer, Help Thermometer, Problem Thermometer, Emotion Thermometer, Depression Thermometer, the Torino scale (assessing asteroid/comet impact prediction), Excessive Heat Watch, etc. Extensive financing of the preparedness for flood resilience management with overheated full-scale resilience management might be compared to someone ill running a fever of 41°C. As the financial crisis hits and resilience management financing cools down it reminds a sick person whose body temperature is too low. The degree indicated by the Recommender Thermometer for Measuring the Preparedness for Flood Resilience Management with a scale between Tmin=34,0° and Tmax=42,0° shows either cool or overheated preparedness for flood resilience management. The formalized presentation of this research shows how changes in the micro, meso and macro environment of resilience management and the extent to which the goals pursued by various interested parties are met cause corresponding changes in the “temperature” of the preparedness for resilience management. Global innovative aspects of the Recommender Thermometer developed by the authors of this paper are, primarily, its capacity to measure the “temperature” of the preparedness for flood resilience management automatically, to compile multiple alternative recommendations (preparedness for floods, including preparing your home for floods, taking precautions against a threat of floods, retrofitting for flood-prone areas, checking your house insurance; preparedness for bushfires, preparedness for cyclones, preparedness for severe storms, preparedness for heat waves, etc.) customised for a specific user, to perform multiple criteria analysis of the recommendations, and to select the ten most rational ones for that user. Across the world, no other system offers these functions yet. The Recommender Thermometer was developed and fine-tuned in the course of the Android (Academic Network for Disaster Resilience to Optimise educational Development) project

Characteristic analysis of a flash flood-affected creek catchment using LiDAR-derived DEM

Flooding occurred across a large area of southern and central Queensland in December 2010 and January 2011. Intense rainfall over the Gowrie Creek catchment caused severe flash flooding through the Toowoomba CBD (Central Business District) on the afternoon of Monday, 10 January 2011, taking lives and damaging the community. Flash floods are sudden and unexpected floods that arise from intense rainfall, generally over a small, steep catchment area. Smaller and steeper catchments have shorter critical storm duration, and they respond more quickly to rainfall events. The resulting flood wave is characterized by very high water flows and velocities and abrupt water level rises, leading to extremely hazardous conditions. Effective flash flood forecasting for specific locations is a big challenge because of the behaviour of intense thunderstorms. A flash flood forecasting and warning system calls for accurate spatial information on catchment characteristics. A high-resolution DEM is a key spatial dataset for the characterization of a catchment to design possible flood mitigation measures. The characteristics of a catchment have a strong influence on its hydrological response. The nature of floods is dependent on both the intensity and duration of the rainfall and the catchment characteristics such as catchment area, drainage patterns and waterway steepness. Therefore, analysis of catchment characteristics is critical for hydrologic modelling and planning for flood risk mitigation. The analysis of catchment characteristics can support hydrological modelling and planning for flood risk mitigation. For example, the shape indices of sub-catchments can be used to compare the hydrological behaviour of different subcatchments. The longitudinal profiles of the creeks illustrate the slope gradients of the waterways. A hypsometric curve for each sub-catchment provides an overall view of the slope of a catchment and is closely related to ground slope characteristics of a catchment. Airborne light detection and ranging (LiDAR), also referred to as airborne laser scanning (ALS), is one of the most effective means of terrain data collection. Using LiDAR data for generation of DEMs is becoming a standard practice in the spatial science community. This study used airborne LiDAR data to generate a high-resolution DEM for characteristic analysis of Gowrie Creek catchment in Toowoomba, Queensland, Australia, which was affected by a flash flood in January 2011. Drainage networks and sub-catchment boundaries were extracted from LiDAR-derived DEM. Catchment characteristics including sub-catchment areas and shape indices, longitudinal profiles of creeks and hypsometric curves of sub-catchments were calculated and analysed

Critical rainfall conditions for the initiation of torrential flows: results from the Rebaixader catchment (Central Pyrenees)

Torrential flows like debris flows or debris floods are fast movements formed by a mix of water and different amounts of unsorted solid material. They generally occur in steep torrents and pose high risk in mountainous areas. Rainfall is their most common triggering factor and the analysis of the critical rainfall conditions is a fundamental research task. Due to their wide use in warning systems, rainfall thresholds for the triggering of torrential flows are an important outcome of such analysis and are empirically derived using data from past events. In 2009, a monitoring system was installed in the Rebaixader catchment, Central Pyrenees (Spain). Since then, rainfall data of 25 torrential flows (“TRIG rainfalls”) were recorded, with a 5-min sampling frequency. Other 142 rainfalls that did not trigger torrential flows (“NonTRIG rainfalls”) were also collected and analyzed. The goal of this work was threefold: (i) characterize rainfall episodes in the Rebaixader catchment and compare rainfall data that triggered torrential flows and others that did not; (ii) define and test Intensity–Duration (ID) thresholds using rainfall data measured inside the catchment by with different techniques; (iii) analyze how the criterion used for defining the rainfall duration and the spatial variability of rainfall influences the value obtained for the thresholds. The statistical analysis of the rainfall characteristics showed that the parameters that discriminate better the TRIG and NonTRIG rainfalls are the rainfall intensities, the mean rainfall and the total rainfall amount. The antecedent rainfall was not significantly different between TRIG and NonTRIG rainfalls, as it can be expected when the source material is very pervious (a sandy glacial soil in the study site). Thresholds were derived from data collected at one rain gauge located inside the catchment. Two different methods were applied to calculate the duration and intensity of rainfall: (i) using total duration, Dtot, and mean intensity, Imean, of the rainfall event, and (ii) using floating durations, D, and intensities, Ifl, based on the maximum values over floating periods of different duration. The resulting thresholds are considerably different (Imean = 6.20 Dtot-0.36 and Ifl_90% = 5.49 D-0.75, respectively) showing a strong dependence on the applied methodology. On the other hand, the definition of the thresholds is affected by several types of uncertainties. Data from both rain gauges and weather radar were used to analyze the uncertainty associated with the spatial variability of the triggering rainfalls. The analysis indicates that the precipitation recorded by the nearby rain gauges can introduce major uncertainties, especially for convective summer storms. Thus, incorporating radar rainfall can significantly improve the accuracy of the measured triggering rainfall. Finally, thresholds were also derived according to three different criteria for the definition of the duration of the triggering rainfall: (i) the duration until the peak intensity, (ii) the duration until the end of the rainfall; and, (iii) the duration until the trigger of the torrential flow. An important contribution of this work is the assessment of the threshold relationships obtained using the third definition of duration. Moreover, important differences are observed in the obtained thresholds, showing that ID relationships are significantly dependent on the applied methodology.Peer ReviewedPostprint (author's final draft

A high resolution coupled hydrologic–hydraulic model (HiResFlood-UCI) for flash flood modeling

HiResFlood-UCI was developed by coupling the NWS's hydrologic model (HL-RDHM) with the hydraulic model (BreZo) for flash flood modeling at decameter resolutions. The coupled model uses HL-RDHM as a rainfall-runoff generator and replaces the routing scheme of HL-RDHM with the 2D hydraulic model (BreZo) in order to predict localized flood depths and velocities. A semi-automated technique of unstructured mesh generation was developed to cluster an adequate density of computational cells along river channels such that numerical errors are negligible compared with other sources of error, while ensuring that computational costs of the hydraulic model are kept to a bare minimum. HiResFlood-UCI was implemented for a watershed (ELDO2) in the DMIP2 experiment domain in Oklahoma. Using synthetic precipitation input, the model was tested for various components including HL-RDHM parameters (a priori versus calibrated), channel and floodplain Manning n values, DEM resolution (10 m versus 30 m) and computation mesh resolution (10 m+ versus 30 m+). Simulations with calibrated versus a priori parameters of HL-RDHM show that HiResFlood-UCI produces reasonable results with the a priori parameters from NWS. Sensitivities to hydraulic model resistance parameters, mesh resolution and DEM resolution are also identified, pointing to the importance of model calibration and validation for accurate prediction of localized flood intensities. HiResFlood-UCI performance was examined using 6 measured precipitation events as model input for model calibration and validation of the streamflow at the outlet. The Nash–Sutcliffe Efficiency (NSE) obtained ranges from 0.588 to 0.905. The model was also validated for the flooded map using USGS observed water level at an interior point. The predicted flood stage error is 0.82 m or less, based on a comparison to measured stage. Validation of stage and discharge predictions builds confidence in model predictions of flood extent and localized velocities, which are fundamental to reliable flash flood warning