scholarly journals Warning! The use of meteorological information during a flash-flood warning process

2009 ◽  
Vol 3 (1) ◽  
pp. 99-103 ◽  
Author(s):  
L. Créton-Cazanave
Keyword(s):  
Emergency Services ◽  
Flash Flood ◽  
Local Population ◽  
Flash Floods ◽  
Decision Makers ◽  
Classical Approach ◽  
The Real ◽  
Flood Warning ◽  
Integrated Studies

Abstract. Warning is a key issue to reduce flash floods impacts. But, despite many studies, local and national authorities still struggle to issue good flash floods warnings. We will argue that this failure results from a classical approach of warnings, based on a strict separation between the assessment world and the action world. We will go further than the previous criticisms (Pielke and Carbone, 2002) and show that forecasters, decision makers, emergency services and local population have quite similar practices during a flash-flood warning. Focusing on the use of meteorological information in the warning process, our case study shows that more research about the real practices of stakeholders would be another step towards integrated studies.

scholarly journals Rainfall Threshold for Flash Flood Warning Based on Model Output of Soil Moisture: Case Study Wernersbach, Germany

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1061
Author(s):  
Thanh Thi Luong ◽  
Judith Pöschmann ◽  
Rico Kronenberg ◽  
Christian Bernhofer
Keyword(s):  
Soil Moisture ◽  
Flash Flood ◽  
Rainfall Threshold ◽  
Probability Of Detection ◽  
Water Levels ◽  
Critical Success ◽  
Flood Warning ◽  
Critical Discharge ◽  
Critical Success Index

Convective rainfall can cause dangerous flash floods within less than six hours. Thus, simple approaches are required for issuing quick warnings. The flash flood guidance (FFG) approach pre-calculates rainfall levels (thresholds) potentially causing critical water levels for a specific catchment. Afterwards, only rainfall and soil moisture information are required to issue warnings. This study applied the principle of FFG to the Wernersbach Catchment (Germany) with excellent data coverage using the BROOK90 water budget model. The rainfall thresholds were determined for durations of 1 to 24 h, by running BROOK90 in “inverse” mode, identifying rainfall values for each duration that led to exceedance of critical discharge (fixed value). After calibrating the model based on its runoff, we ran it in hourly mode with four precipitation types and various levels of initial soil moisture for the period 1996–2010. The rainfall threshold curves showed a very high probability of detection (POD) of 91% for the 40 extracted flash flood events in the study period, however, the false alarm rate (FAR) of 56% and the critical success index (CSI) of 42% should be improved in further studies. The proposed adjusted FFG approach has the potential to provide reliable support in flash flood forecasting.

scholarly journals A staggered approach to flash flood forecasting – case study in the Cévennes region

2011 ◽  
Vol 29 ◽  
pp. 13-20 ◽  
Author(s):  
L. Alfieri ◽  
P. J. Smith ◽  
J. Thielen-del Pozo ◽  
K. J. Beven
Keyword(s):  
Flash Flood ◽  
Early Stage ◽  
Mechanistic Model ◽  
Flood Forecasting ◽  
Flash Floods ◽  
Decision Makers ◽  
System A ◽  
Flood Alert ◽  
A Chain ◽  
Flash Flood Forecasting

Abstract. A staggered approach to flash flood forecasting is developed within the IMPRINTS project (FP7-ENV-2008-1-226555). Instead of a single solution system, a chain of different models and input data is being proposed that act in sequence and provide decision makers with information of increasing accuracy in localization and magnitude as the events approach. The first system in the chain is developed by adapting methodologies of the European Flood Alert System (EFAS) to forecast flash floods and has the potential to provide early indication for probability of flash floods at the European scale. The last system in the chain is an adaptation of the data based mechanistic model (DBM) to probabilistic numerical weather predictions (NWP) and observed rainfall, with the capability to forecast river levels up to 12 h ahead. The potential of both systems to provide complementary information is illustrated for a flash flood event occurred on 2 November 2008 in the Cévennes region in France. Results show that the uncertainty in meteorological forecasts largely affects the outcomes. However, at an early stage, uncertain results are still valuable to decision makers, as they raise preparedness towards prompt actions to be taken.

scholarly journals Neural network modeling and geochemical water analyses to understand and forecast karst and non-karst part of flash floods (case study on the <i>Lez</i> river, Southern France)

2015 ◽  
Vol 369 ◽  
pp. 43-48 ◽  
Author(s):  
T. Darras ◽  
F. Raynaud ◽  
V. Borrell Estupina ◽  
L. Kong-A-Siou ◽  
S. Van-Exter ◽  
...  
Keyword(s):  
Flash Flood ◽  
Mediterranean Area ◽  
Flash Floods ◽  
Neural Network Modeling ◽  
Heterogeneous Structure ◽  
Nonlinear Behaviour ◽  
Complex Behaviour ◽  
Future Events ◽  
Water Analyses

Abstract. Flash floods forecasting in the Mediterranean area is a major economic and societal issue. Specifically, considering karst basins, heterogeneous structure and nonlinear behaviour make the flash flood forecasting very difficult. In this context, this work proposes a methodology to estimate the contribution from karst and non-karst components using toolbox including neural networks and various hydrological methods. The chosen case study is the flash flooding of the Lez river, known for his complex behaviour and huge stakes, at the gauge station of Lavallette, upstream of Montpellier (400 000 inhabitants). After application of the proposed methodology, discharge at the station of Lavallette is spited between hydrographs of karst flood and surface runoff, for the two events of 2014. Generalizing the method to future events will allow designing forecasting models specifically for karst and surface flood increasing by this way the reliability of the forecasts.

Organisational readiness for the adoption of AI diagnostic tools in clinical workflows – a case study from the National Breast Screening Programme in the East Midlands of England. (Preprint)

2020 ◽  
Author(s):  
Niamh Lennox-Chhugani ◽  
Simon Harris ◽  
Jacqueline Moxon ◽  
Vipul Patel
Keyword(s):  
Real World ◽  
Breast Screening ◽  
Screening Programme ◽  
Decision Makers ◽  
Diagnostic Tools ◽  
Healthcare Organisation ◽  
Breast Screening Programme ◽  
The Real ◽  
National Breast

BACKGROUND Application of artificial intelligence (AI) in healthcare is accelerating but relatively little is yet known about the real-world implementation of AI in clinical workflows. OBJECTIVE In this paper, we have focused on one application of AI as a second reader of breast mammograms in the context of a national breast screening programme. We look at the development and testing of an AI image reading tool for mammograms and the effect of organisational readiness for AI tool adoption. We focus on two aspects of organisational readiness as conceptualised by Weiner (2009) for AI technology specifically and answer the questions (1) what are the views of the technology adopters in a healthcare organisation to the use of AI technology in the case of breast screening? (2) What are some of the emerging organisation factors that are likely to effect adoption and spread and are any unique to AI technology? METHODS A prospective mixed methods study of the real-world development of AI tools for use in the National Breast Screening Programme in England. We recruited 67 radiologists and reporting radiographers in four breast screening services and 18 organisational leaders who were the AI project decision-makers. Data was collected using an online survey of breast screening staff (adopters), semi-structured interviews with organisational leaders, participant observation of project meetings and document review. Data regarding organisational and adopter readiness for technology adoption was analysed over the duration of the project. RESULTS Sixty-seven clinicians and eighteen organisational leaders participated the study. Commitment to adoption is positive but adopters want to see clinical evidence of AI safety and accuracy. Decision-makers and other organisational adopters do not yet have shared views on their resources, capacity and capability to adopt and spread the technology and significant challenges related to task demands and situational factors emerged during the project causing substantial delays to adoption. The nature of AI and ML technology surfaced novel complexities not encountered by traditional health technology related to explainability and meaningful decision-support. CONCLUSIONS The case study shows that adopter commitment in this case and AI technology in breast screening is growing but gaps remain in the collective capability of organisations to adopt these novel technologies. CLINICALTRIAL Not applicable

scholarly journals A New Flash Flood Warning Scheme Based on Hydrodynamic Modelling

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1221 ◽  
Author(s):  
Wei Huang ◽  
Zhixian Cao ◽  
Minghai Huang ◽  
Wengang Duan ◽  
Yufang Ni ◽  
...  
Keyword(s):  
Flash Flood ◽  
Rapid Onset ◽  
Disaster Mitigation ◽  
Hydrodynamic Modelling ◽  
Flood Warning ◽  
Mitigation And Adaptation ◽  
Critical Rainfall ◽  
Moisture Conditions ◽  
Flood Flow

Flash flooding is one of the most severe natural hazards and commonly occurs in mountainous and hilly areas. Due to the rapid onset of flash floods, early warnings are critical for disaster mitigation and adaptation. In this paper, a flash flood warning scheme is proposed based on hydrodynamic modelling and critical rainfall. Hydrodynamic modelling considers different rainfall and initial soil moisture conditions. The critical rainfall is calculated from the critical hazard, which is based on the flood flow depth and velocity. After the critical rainfall is calculated for each cell in the catchment, a critical rainfall database is built for flash flood warning. Finally, a case study is presented to show the operating procedure of the new flash flood warning scheme.

scholarly journals Identification of spatial and temporal contributions of rainfalls to flash floods using neural network modelling: case study on the Lez basin (southern France)

2015 ◽  
Vol 19 (10) ◽  
pp. 4397-4410 ◽  
Author(s):  
T. Darras ◽  
V. Borrell Estupina ◽  
L. Kong-A-Siou ◽  
B. Vayssade ◽  
A. Johannet ◽  
...  
Keyword(s):  
Neural Network ◽  
Global Climate ◽  
Flash Floods ◽  
Crucial Importance ◽  
Network Modelling ◽  
Southern France ◽  
Flood Warning ◽  
Run Off ◽  
Warning Services

Abstract. Flash floods pose significant hazards in urbanised zones and have important implications financially and for humans alike in both the present and future due to the likelihood that global climate change will exacerbate their consequences. It is thus of crucial importance to improve the models of these phenomena especially when they occur in heterogeneous and karst basins where they are difficult to describe physically. Toward this goal, this paper applies a recent methodology (Knowledge eXtraction (KnoX) methodology) dedicated to extracting knowledge from a neural network model to better determine the contributions and time responses of several well-identified geographic zones of an aquifer. To assess the interest of this methodology, a case study was conducted in southern France: the Lez hydrosystem whose river crosses the conurbation of Montpellier (400 000 inhabitants). Rainfall contributions and time transfers were estimated and analysed in four geologically delimited zones to estimate the sensitivity of flash floods to water coming from the surface or karst. The Causse de Viols-le-Fort is shown to be the main contributor to flash floods and the delay between surface and underground flooding is estimated to be 3 h. This study will thus help operational flood warning services to better characterise critical rainfall and develop measurements to design efficient flood forecasting models. This generic method can be applied to any basin with sufficient rainfall–run-off measurements.

scholarly journals Environmental and Economic Impact of Cloudburst Triggered Debris Flows and Flash Floods in the Himalaya: A Case Study

2021 ◽  
Author(s):  
Vishwambhar Prasad Sati ◽  
Saurabh Kumar Dubey
Keyword(s):  
Economic Impact ◽  
Debris Flows ◽  
Flash Flood ◽  
Economic Valuation ◽  
Arable Land ◽  
Flash Floods ◽  
Large Area ◽  
Household Level ◽  
The Himalaya

Abstract This paper examines the environmental and economic impact of cloudburst triggered debris flows and flash floods in the Himalaya. A case study of four villages affected by cloudburst calamity was conducted. Data were gathered by a household-level survey of affected villages and households. The authors visited the affected villages immediately after the cloudburst calamity and interviewed the head of all affected households. A total of 143 households were surveyed. First, the damage of houses, cowsheds, bridges, trees (forests and fruits) dislocation, degradation of total land along the streams and arable land in and around the villages were measured with the help of the head of households (environmental impact). The volume of debris, boulders, pebbles, gravels and mud was measured. Economic valuation of all losses was noted (economic impact). This study finds out that a large area of all villages was severely affected by cloudburst triggered debris flow and flash flood, because, they are located in a severely vulnerable landscape. This study reveals that Nirakot village needs to be rehabilitated entirely and in other villages all households, which are severely affected need to be rehabilitated as soon as possible to escape from the future cloudburst catastrophe.

scholarly journals Identification of spatial and temporal contributions of rainfalls to flash floods using neural network modelling: case study on the Lez Basin (Southern France)

2015 ◽  
Vol 12 (4) ◽  
pp. 3681-3718
Author(s):  
T. Darras ◽  
V. Borrell Estupina ◽  
L. Kong-A-Siou ◽  
B. Vayssade ◽  
A. Johannet ◽  
...  
Keyword(s):  
Neural Network ◽  
Global Climate ◽  
Flash Floods ◽  
Rainfall Runoff ◽  
Crucial Importance ◽  
Network Modelling ◽  
Southern France ◽  
Flood Warning ◽  
Warning Services

Abstract. Flash floods pose significant hazards in urbanised zones and have important human and financial implications in both the present and future due to the likelihood that global climate change will exacerbate their consequences. It is thus of crucial importance to better model these phenomena especially when they occur in heterogeneous and karst basins where they are difficult to describe physically. Toward this goal, this paper applies a recent methodology (KnoX methodology) dedicated to extracting knowledge from a neural network model to better determine the contributions and time responses of several well-identified geographic zones of an aquifer. To assess the interest of this methodology, a case study was conducted in Southern France: the Lez hydrosystem whose river crosses the conurbation of Montpellier (400 000 inhabitants). Rainfall contributions and time transfers were estimated and analysed in four geologically-delimited zones to estimate the sensitivity of flash floods to water coming from the surface or karst. The Causse de Viol-le-Fort is shown to be the main contributor to flash floods and the delay between surface and underground flooding is estimated to be three hours. This study will thus help operational flood warning services to better characterise critical rainfall and develop measurements to design efficient flood forecasting models. This generic method can be applied to any basin with sufficient rainfall–runoff measurements.

scholarly journals Development of a new rainfall‐triggering index of flash flood warning‐case study in Yunnan province, China

2020 ◽  
Author(s):  
Meihong Ma ◽  
Huixiao Wang ◽  
Yan Yang ◽  
Gang Zhao ◽  
Guoqiang Tang ◽  
...  
Keyword(s):  
Yunnan Province ◽  
Flash Flood ◽  
Flood Warning

scholarly journals A GIS-based model to estimate flood consequences and the degree of accessibility and operability of strategic emergency response structures in urban areas

2014 ◽  
Vol 14 (11) ◽  
pp. 2847-2865 ◽  
Author(s):  
R. Albano ◽  
A. Sole ◽  
J. Adamowski ◽  
L. Mancusi
Keyword(s):  
Risk Reduction ◽  
Urban Area ◽  
Emergency Response ◽  
Urban Areas ◽  
Emergency Services ◽  
Urban System ◽  
Decision Makers ◽  
Practical Applications ◽  
Proposed Model

Abstract. Efficient decision-making regarding flood risk reduction has become a priority for authorities and stakeholders in many European countries. Risk analysis methods and techniques are a useful tool for evaluating costs and benefits of possible interventions. Within this context, a methodology to estimate flood consequences was developed in this paper that is based on GIS, and integrated with a model that estimates the degree of accessibility and operability of strategic emergency response structures in an urban area. The majority of the currently available approaches do not properly analyse road network connections and dependencies within systems, and as such a loss of roads could cause significant damages and problems to emergency services in cases of flooding. The proposed model is unique in that it provides a maximum-impact estimation of flood consequences on the basis of the operability of the strategic emergency structures in an urban area, their accessibility, and connection within the urban system of a city (i.e. connection between aid centres and buildings at risk), in the emergency phase. The results of a case study in the Puglia region in southern Italy are described to illustrate the practical applications of this newly proposed approach. The main advantage of the proposed approach is that it allows for defining a hierarchy between different infrastructure in the urban area through the identification of particular components whose operation and efficiency are critical for emergency management. This information can be used by decision-makers to prioritize risk reduction interventions in flood emergencies in urban areas, given limited financial resources.

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