Meso-scale hazard zoning of potentially flood prone areas

The delimitation of flood-prone areas is an important non-structural measure that proves to be effective in the long term in reducing food risk.In Italy, more than 20 basin&#8217;s Units of Management (UoMs) were in charge to delineate the flood hazard zoning (FHZ) for three different flood return periods. Mostly, FHZ was prepared using physically based models i.e., considering the rainfall-runoff transformation and simulating the flood discharge through the river network. Physically-based models require many inputs and boundary conditions including: hydro-meteorological data, detailed characterization of the geometry of the riverbeds, roughness, infiltration parameters and also real hydrometric measurements in order to be calibrated. Physically based modelling is therefore a long, time consuming and resource intensive process that should be frequently updated to take into account the river channel changes. As a consequence, the Italian FHZ suffers from an underlying lack of homogeneity across the different UoMs, resulting in significant differences on the percentage of the river network for which the flood-prone areas were delineated.As alternatives to physically based models, in recent years many authors have produced maps of flood susceptibility or hazard using expert (e.g. Analytic Hierarchy Process) or data-driven (e.g.&#160; multivariate statistics or machine learning) approaches. Such methods were mostly used in ungauged territories where hydro-meteorological data is not available.Here we present a procedure, named Flood-SHE (Flood - Statistical Hazard Evaluation), which is aimed at the delineation of flood-prone areas and the corresponding expected water depth, using a multivariate statistical classification model. Flood-SHE was applied to the entire Italian territory with the aim to integrate the UOMs FHZ where it is not available or incomplete. The classification model was trained exploiting the existing UoMs FHZ and using, as independent variables, a set of geomorphometric layers (derived at 10x10 meters ground resolution) which includes the distance and height to the closest rivers and to the basins outlets, the local DEM slope, a stream order classification criterion and the DEM local roughness. Random training and validation areas were used for the classification model in order to obtain an estimation of the uncertainty of the values of the predictive performance indexes. Results highlight (i) the significance of the the variables distance and height to the closest rivers, roughness and stream order in predicting the flood-prone areas, (ii) the impact of the UoMs morphology and the quality of UoMs FHZ on the reliability of the statistically modeled flood-prone areas.

Download Full-text

Source Credibility and Psychological Well-Being among Residents Lived at Flood Prone Areas

10.15405/epsbs.2016.08.25 ◽

2016 ◽

Author(s):

Che Su Mustaffa

Keyword(s):

Source Credibility ◽

Well Being ◽

Psychological Well Being ◽

Flood Prone Areas

Download Full-text

Meso-scale geometrid guild dynamics in a geographically complex sub-arctic birch forest ecosystem

10.1603/ice.2016.107736 ◽

2016 ◽

Author(s):

Ole-Petter L. Vindstad

Keyword(s):

Forest Ecosystem ◽

Birch Forest ◽

Meso Scale

Download Full-text

Impingement Heat Transfer with Meso-scale Fluid Jets

10.1615/ihtc12.3310 ◽

2002 ◽

Cited By ~ 1

Author(s):

Chong Fang Ma

Keyword(s):

Heat Transfer ◽

Impingement Heat Transfer ◽

Fluid Jets ◽

Meso Scale

Download Full-text

ANALYSIS OF FLOOD PRONE AREAS IN NAGAN RAYA REGENCY ACEH PROVINCE

MALAYSIAN JOURNAL OF COMPUTING ◽

10.24191/mjoc.v5i1.7805 ◽

2020 ◽

Vol 5 (1) ◽

pp. 414

Author(s):

Amsar Yunan

Keyword(s):

Land Use ◽

Spatial Data ◽

Flood Hazard ◽

Parameter Analysis ◽

Thematic Maps ◽

Aceh Province ◽

Object Area ◽

Use Factors ◽

Hazard Levels ◽

Flood Prone Areas

Maps or remote sensing can be interpreted as the process of reading using various sensors where data collected remotely can be analyzed to obtain information about the object, area or phenomenon. In this study, the author develops a flood disaster mapping information system applying overlays with scoring between the parameters. The determinant factors to provide flood hazard levels includes rainfall factors in the dasarian unit, land-use factors and land-use arbitrary factors. Of all these parameters, a scoring process will be carried out by assigning weights and values according to their respective classifications, then an overlay process will be performed using ArcGIS software. The author conducted this study in Nagan Raya Regency since this area experiences flooding annually. Framing a thematic map of flood-prone areas in Nagan Raya Regency was designed using the flood hazard method. Spatial data that has been presented in the form of thematic maps as parameters are land use maps, landform maps, and dasarian rainfall maps (per 10 daily). The design of thematic maps that are prone to flooding is done by overlapping (overlay process). In contrast, the determination of the classification is done by adding scores to each parameter, with low, medium and high hazard levels. Parameter analysis shows the level of flood vulnerability in Nagan Raya Regency of each district, namely Beutong: high 0.21%, medium 13.68%, low 86.12%. Seunagan District: high 51.17%, medium 48.83%, low 0%. Seunagan Timur District: high 10.07%, medium 46.18%, low 43.75%. Kuala Subdistrict: high 29.66%, medium 68.99%, low 1.35%. Darul Makmur District: high 8.57%, medium 63.37%, low 28.06%. From the overall results of the study, it can be concluded that the danger of flooding in Nagan Raya Regency with a level of vulnerability: high 9.92%, moderate 42.65% and low 47.43%.

Download Full-text