scholarly journals Stochastic Urban Pluvial Flood Hazard Maps Based upon a Spatial-Temporal Rainfall Generator

Water ◽  
2015 ◽  
Vol 7 (12) ◽  
pp. 3396-3406 ◽  
Author(s):  
Nuno Simões ◽  
Susana Ochoa-Rodríguez ◽  
Li-Pen Wang ◽  
Rui Pina ◽  
Alfeu Marques ◽  
...  
Keyword(s):  
Flood Hazard ◽  
Hazard Maps ◽  
Flood Hazard Maps

scholarly journals Design Floods Considering the Epistemic Uncertainty

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1601
Author(s):  
Radu Drobot ◽  
Aurelian Florentin Draghia ◽  
Daniel Ciuiu ◽  
Romică Trandafir
Keyword(s):  
Flood Hazard ◽  
Epistemic Uncertainty ◽  
Statistical Distributions ◽  
Hazard Maps ◽  
Design Flood ◽  
Flood Hydrograph ◽  
Recorded Data ◽  
Different Shapes ◽  
Significant Spread ◽  
Flood Hazard Maps

The Design Flood (DF) concept is an essential tool in designing hydraulic works, defining reservoir operation programs, and identifying reliable flood hazard maps. The purpose of this paper is to present a methodology for deriving a Design Flood hydrograph considering the epistemic uncertainty. Several appropriately identified statistical distributions allow for the acceptable approximation of the frequent values of maximum discharges or flood volumes, and display a significant spread for their medium/low Probabilities of Exceedance (PE). The referred scattering, as a consequence of epistemic uncertainty, defines an area of uncertainty for both recorded data and extrapolated values. In considering the upper and lower values of the uncertainty intervals as limits for maximum discharges and flood volumes, and by further combining them compatibly, a set of DFs as completely defined hydrographs with different shapes result for each PE. The herein proposed procedure defines both uni-modal and multi-modal DFs. Subsequently, such DFs help water managers in examining and establishing tailored approaches for a variety of input hydrographs, which might be typically generated in river basins.

Moving to 3-D flood hazard maps for enhancing risk communication

2019 ◽  
Vol 111 ◽  
pp. 510-522 ◽  
Author(s):  
Francesco Macchione ◽  
Pierfranco Costabile ◽  
Carmelina Costanzo ◽  
Rosa De Santis
Keyword(s):  
Risk Communication ◽  
Flood Hazard ◽  
Hazard Maps ◽  
Flood Hazard Maps

Combination of geomorphic classifiers through Machine Learning-based techniques for flood hazard assessment 

2021 ◽  
Author(s):  
Andrea Magnini ◽  
Michele Lombardi ◽  
Simone Persiano ◽  
Antonio Tirri ◽  
Francesco Lo Conti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hazard Assessment ◽  
Water Depth ◽  
Flood Hazard ◽  
Research Centre ◽  
Hazard Mapping ◽  
Hazard Maps ◽  
Flood Hazard Assessment ◽  
Flood Prone Areas ◽  
Flood Hazard Maps

<p><span xml:lang="EN-US" data-contrast="auto"><span>Every year flood events cause worldwide vast economic losses, as well as heavy social and environmental impacts, which have been steadily increasing for the last five decades due to the complex interaction between climate change and anthropogenic pressure (</span></span><span xml:lang="EN-US" data-contrast="auto"><span>i.e.</span></span><span xml:lang="EN-US" data-contrast="auto"><span> land-use and land-cover modifications). As a result, the body of literature on flood risk assessment is constantly and rapidly expanding, aiming at developing faster, computationally lighter and more efficient methods relative to the traditional and resource</span></span><span xml:lang="EN-US" data-contrast="auto"><span>-</span></span><span xml:lang="EN-US" data-contrast="auto"><span>intensive hydrodynamic numerical models. Recent and reliable fast-processing techniques for flood hazard assessment and mapping consider binary geomorphic classifiers retrieved from the analysis of Digital Elevation Models (DEMs). These procedures (termed herein “DEM-based methods”) produce binary maps distinguishing between floodable and non-floodable areas based on the comparison between the local value of the considered geomorphic classifier and a threshold, which in turn is calibrated against existing flood hazard maps. Previous studies have shown the reliability of DEM-based methods using a single binary classifier, they also highlighted that different classifiers are associated with different performance, depending on the geomorphological, climatic and hydrological characteristics of the study area. The present study maps flood-prone areas and predicts water depth associated with a given non-exceedance probability by combining several geomorphic classifiers and terrain features through regression trees and random forests. We focus on Northern Italy (c.a. 100000 km</span></span><sup><span xml:lang="EN-US" data-contrast="auto"><span>2</span></span></sup><span xml:lang="EN-US" data-contrast="auto"><span>, including Po, Adige, Brenta, Bacchiglione and Reno watersheds), and we consider the recently compiled MERIT (Multi-Error Removed Improved-Terrain) DEM, with 3sec-resolution (~90m at the Equator). We select the flood hazard maps provided by (</span></span><span xml:lang="EN-US" data-contrast="auto"><span>i</span></span><span xml:lang="EN-US" data-contrast="auto"><span>) the Italian Institute for Environmental Protection and Research (ISPRA), and (ii) the Joint Research Centre (JRC) of the European Commission as reference maps. Our findings (a) confirm the usefulness of machine learning techniques for improving univariate DEM-based flood hazard mapping, (b) enable a discussion on potential and limitations of the approach and (c) suggest promising pathways for further exploring DEM-based approaches for predicting a likely water depth distribution with flood-prone areas.</span></span><span> </span></p>

Concept of a Polish Database of a Multi-Dimensional Cadastral System with Particular Focus on Geo-Hazards

2017 ◽  
Author(s):  
Agnieszka Trystuła
Keyword(s):  
Crisis Management ◽  
New Technologies ◽  
Flood Hazard ◽  
Data Visualisation ◽  
Hazard Maps ◽  
Multiple Factors ◽  
Dynamic Growth ◽  
Cadastral System ◽  
Flood Hazard Maps ◽  
Cadastral Systems

The dynamic growth of contemporary cadastral systems depends on multiple factors, which include, e.g. economic policy of a given country and possibilities of implementing activities supporting innovation and transfer of new technologies. A modern cadastre should satisfy not only its leading functions, which include, e.g. fiscal, information, legal or record functions. It should also be oriented towards new challenges, including 3D geovisualisation, which will enable multidimensional visualisation of cadastral objects. New data visualisation methods will contribute to extending the existing functions of cadastral systems and to emergence of new functions, e.g. related to ensuring public safety as a basic aim of crisis management, being an important element of sustainable development. This paper presents a concept of a database of multidimensional cadastral system enabling, for instance, 3D visualisation of system objects, incorporating its known functions (e.g. fiscal, information or legal functions), and also a new purpose –support for crisis management. Additionally, the study indicates sources of data that should be used for this type of undertaking (e.g. flood hazard maps, maps of areas at risk of mass land movements, orthophotomaps).

scholarly journals Flood hazard assessment of a flood-prone intensively urbanized area – A case study from Samsun Province, Turkey

Geofizika ◽  
2020 ◽  
Vol 37 (1) ◽  
pp. 1-25
Author(s):  
Neslihan Beden ◽  
Aslı Ülke Keskin
Keyword(s):  
Flood Hazard ◽  
Wide Spectrum ◽  
Flood Plain ◽  
Flood Management ◽  
Flood Damage ◽  
The Black Sea ◽  
Hazard Maps ◽  
Flood Depth ◽  
The Social ◽  
Flood Hazard Maps

Flooding is one of the most catastrophic events among the wide spectrum of natural disasters that impact human communities. The identification of floodprone areas and the probability of occurrence, or estimated return period, of flood events are fundamental to proper planning for flood management and minimization of the social and economic costs of flood damage. In this study, 1D/2D coupled flood models of the Mert River, which flows into the Black Sea at Samsun in north-central Turkey, were developed. Based on the flood modeling results, flood extent, flood depth and flood hazard maps for the river were produced and they showed that the study area is particularly flood prone, as evidenced by catastrophic flooding in 2012. Specifically, the estimated 100, 500 and 1000-year peak discharges would affect 184 ha, 262 ha and 304 ha, respectively, of the 1,200 ha study area. Hazard ratings for the areas expected to be affected are shown in the flood hazard maps generated. The results of this research can be used by local government agencies in Samsun for the development of policies, strategies and actions that would help minimize the social and economic impacts of flooding, especially adjacent to the downstream sections where there is intensive development on the flood plain.

scholarly journals Using 1D HEC-RAS Modeling and LiDAR Data to Improve Flood Hazard Maps Accuracy: A Case Study from Jijia Floodplain (NE Romania)

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1624 ◽  
Author(s):  
Elena Huţanu ◽  
Alin Mihu-Pintilie ◽  
Andrei Urzica ◽  
Larisa Elena Paveluc ◽  
Cristian Constantin Stoleriu ◽  
...  
Keyword(s):  
Geographical Information Systems ◽  
Large Scale ◽  
Flood Hazard ◽  
Hydraulic Modeling ◽  
Geographical Information ◽  
Computer Algorithms ◽  
Hazard Maps ◽  
Modeling Software ◽  
Recurrence Intervals ◽  
Flood Hazard Maps

The ability to extract flood hazard settings in highly vulnerable areas like populated floodplains by using new computer algorithms and hydraulic modeling software is an important aspect of any flood mitigation efforts. In this framework, the 1D/2D hydraulic models, which were generated based on a Light Detection and Ranging (LiDAR) derivate Digital Elevation Model (DEM) and processed within Geographical Information Systems (GIS), can improve large-scale flood hazard maps accuracy. In this study, we developed the first flood vulnerability assessment for 1% (100-year) and 0.1% (1000-year) recurrence intervals within the Jijia floodplain (north-eastern Romania), based on 1D HEC-RAS hydraulic modeling and LiDAR derivate DEM with 0.5 m spatial resolution. The results were compared with official flood hazards maps developed for the same recurrence intervals by the hydrologists of National Administration “Romanian Waters” (NARW) based on MIKE SHE modeling software and a DEM with 2 m spatial resolutions. It was revealed that the 1D HEC-RAS provides a more realistic perspective about the possible flood threats within Jijia floodplain and improves the accuracy of the official flood hazard maps obtained according to Flood Directive 2007/60/EC.

scholarly journals DESIRABLE UTILIZATION OF FLOOD HAZARD MAPS FOR RISK COMMUNICATION

2007 ◽  
Vol 63 (4) ◽  
pp. 498-508 ◽  
Author(s):  
Toshitaka KATADA ◽  
Shuji KIMURA ◽  
Makoto KODAMA
Keyword(s):  
Risk Communication ◽  
Flood Hazard ◽  
Hazard Maps ◽  
Flood Hazard Maps

Probabilistic Flood Hazard Maps at Ungauged Locations Using Multivariate Extreme Values Approach

2020 ◽  
Author(s):  
Kiran Kezhkepurath Gangadhara ◽  
Srinivas Venkata Vemavarapu
Keyword(s):  
Flood Risk ◽  
Flood Hazard ◽  
Flood Plain ◽  
Hydrologic Model ◽  
Regional Frequency Analysis ◽  
Hazard Map ◽  
Hazard Maps ◽  
Flood Hydrograph ◽  
Flood Hazard Map ◽  
Flood Hazard Maps

<p>Flood hazard maps are essential for development and assessment of flood risk management strategies. Conventionally, flood hazard assessment is based on deterministic approach which involves deriving inundation maps considering hydrologic and hydraulic models. A flood hydrograph corresponding to a specified return period is derived using a hydrologic model, which is then routed through flood plain of the study area to estimate water surface elevations and inundation extent with the aid of a hydraulic model. A more informative way of representing flood risk is through probabilistic hazard maps, which additionally provide information on the uncertainty associated with the extent of inundation. To arrive at a probabilistic flood hazard map, several flood hydrographs are generated, representing possible scenarios for flood events over a long period of time (e.g., 500 to 1000 years). Each of those hydrographs is routed through the flood plain and probability of inundation for all locations in the plain is estimated to derive the probabilistic flood hazard map. For gauged catchments, historical streamflow and/or rainfall data may be used to determine design flood hydrographs and the corresponding hazard maps using various strategies. In the case of ungauged catchments, however, there is a dearth of procedures for prediction of flood hazard maps. To address this, a novel multivariate regional frequency analysis (MRFA) approach is proposed. It involves (i) use of a newly proposed clustering methodology for regionalization of catchments, which accounts for uncertainty arising from ambiguity in choice of various potential clustering algorithms (which differ in underlying clustering strategies) and their initialization, (ii) fitting of a multivariate extremes model to information pooled from catchments in homogeneous region to generate synthetic flood hydrographs at ungauged target location(s), and (iii) routing of the hydrographs through the flood plain using LISFLOOD-FP model to derive probabilistic flood hazard map. The MRFA approach is designed to predict flood hydrograph related characteristics (peak flow, volume and duration of flood) at target locations in ungauged basins by considering watershed related characteristics as predictor/explanatory variables. An advantage of the proposed approach is its ability to account for uncertainty in catchment regionalization and dependency between all the flood hydrograph related characteristics reliably. Thus, the synthetic flood hydrographs generated in river basins appear more realistic depicting the observed dependence structure among flood hydrograph characteristics. The approach alleviates several uncertainties found in conventional methods (based on conceptual, probabilistic or geomorphological approaches) which affect estimation of flood hazard. Potential of the proposed approach is demonstrated through a case study on catchments in Mahanadi river basin of India, which extends over 141,600 km<sup>2</sup> and is frequently prone to floods. Comparison is shown between flood hazard map obtained based on true at-site data and that derived based on the proposed MRFA approach by considering the respective sites to be pseudo-ungauged. Coefficient of correlation and root mean squared error considered for performance evaluation indicated that the proposed approach is promising.</p>

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