scholarly journals TSUNAMI MODELLING AND RISK MAPPING FOR EAST COAST OF SABAH, MALAYSIA

2011 ◽  
Vol 1 (32) ◽  
pp. 55 ◽  
Author(s):  
Claus Pedersen ◽  
Ziauddin Abdul Latif ◽  
Caroline Lai
Keyword(s):  
Overland Flow ◽  
East Coast ◽  
Regional Scale ◽  
Risk Mapping ◽  
Hazard Mapping ◽  
Flow Modelling ◽  
Topographic Data ◽  
Data Limitations ◽  
Tsunami Risk ◽  
Main Population

A tsunami risk mapping study for the east coast of the state of Sabah, Malaysia, has been initiated by the Government of Malaysia. The main objective is to produce tsunami risk maps that can be taken into account in local planning for the coastal zone. The study covers a coastline of more than a thousand kilometers which generally is sparsely populated outside the main population centers. The study is regional in character, but with additional focus on the main population centers. The paper outlines the overall steps of hazard mapping through source identification and modelling followed by tsunami wave propagation and potential modelling of overland flow. Modelling challenges related to sparse bathymetry data, limitations to resolution due to the large coverage required combined with a complex bathymetry with coral reefs, islands and outcrops is discussed. Data and model resolution for overland flow modelling is discussed. For the present study, it was found that the inaccuracies in the topographic data is of similar magnitude to the expected inundation levels, and caution has to be exercised in deriving hazard levels from the overland flow modelling. For a regional scale risk mapping exercise, and in the absence of very detailed topographic data, it may be preferable to use the wave height along the coastline as a hazard indicator rather than potentially inaccurate inundation levels and overland flow velocities.

scholarly journals Identifying Dominant Runoff Processes at a Regional Scale – A GIS - Based Approach

2017 ◽  
Vol 11 (2) ◽  
pp. 19-33
Author(s):  
Fagbohun Babatunde Joseph ◽  
Olabode Oluwaseun Franklin ◽  
Adebola Abiodun Olufemi
Keyword(s):  
Overland Flow ◽  
Expert Knowledge ◽  
Regional Scale ◽  
Field Investigation ◽  
Deep Percolation ◽  
Flood Prediction ◽  
Percolation Process ◽  
Least Area ◽  
Expected Increase

Abstract Identifying landscapes with similar hydrological characteristics is useful for the determination of dominant runoff process (DRP) and flood prediction. Several approaches used for DRP-mapping differ in respect to time and data requirement. Manual approaches based on field investigation and expert knowledge are time consuming and difficult to implement at regional scale. Automatic GIS-based approach on the other hand require simplification of data but are easier to implement and it is applicable on regional scale. In this study, GIS-based automated approach was used to identify the DRPs in Anambra area. The result showed that Hortonian Overland Flow (HOF) has the highest coverage of 1508.3 Km2 (33.5%) followed by Deep Percolation (DP) with coverage of 1455.3 Km2 (32.3%). Subsurface Flow (SSF) is the third dominant runoff process covering 920.6 Km2 (20.4%) while Saturated Overland Flow (SOF) covers the least area of 618.4 Km2 (13.7%) of the study area. The result reveal that considerable amount of precipitated water would be infiltrated into the subsurface through deep percolation process contributing to groundwater recharge in the study area. However, it is envisaged that HOF and SOF will continue to increase due to the continuous expansion of built-up area. With the expected increase in HOF and SOF and the change in rainfall pattern associated with perpetual problem of climate change, it is paramount that groundwater conservation practices be considered to ensure continued sustainable utilization of groundwater in the study area.

scholarly journals The Impact of COVID-19 on NO2 and PM2.5 Levels and Their Associations with Human Mobility Patterns in Singapore

2021 ◽  
Author(s):  
Yangyang Li ◽  
Yihan Zhu ◽  
Jia Yu Karen Tan ◽  
Hoong Chen Teo ◽  
Andrea Law ◽  
...  
Keyword(s):  
Air Pollution ◽  
East Coast ◽  
Human Mobility ◽  
Regional Scale ◽  
Air Pollutant ◽  
Pollution Level ◽  
The South ◽  
High Association ◽  
Pollutant Level ◽  
The Impact

AbstractThe decline in NO2 and PM2.5 pollutant levels were observed during COVID-19 around the world, especially during lockdowns. Previous studies explained such observed decline with the decrease in human mobility, whilst overlooking the meteorological changes (e.g., rainfall, wind speed) that could mediate air pollution level simultaneously. This pitfall could potentially lead to over-or under-estimation of the effect of COVID-19 on air pollution. Consequently, this study aims to re-evaluate the impact of COVID-19 on NO2 and PM2.5 pollutant level in Singapore, by incorporating the effect of meteorological parameters in predicting NO2 and PM2.5 baseline in 2020 using machine learning methods. The results found that NO2 and PM2.5 declined by a maximum of 38% and 36%, respectively, during lockdown period. As two proxies for change in human mobility, taxi availability and carpark availability were found to increase and decrease by a maximum of 12.6% and 9.8%, respectively, in 2020 from 2019 during lockdown. To investigate how human mobility influenced air pollutant level, two correlation analyses were conducted: one between PM2.5 and carpark availability changes at regional scale and the other between NO2 and taxi availability changes at a spatial resolution of 0.01°. The NO2 variation was found to be more associated with the change in human mobility, with the correlation coefficients vary spatially across Singapore. A cluster of stronger correlations were found in the South and East Coast of Singapore. Contrarily, PM2.5 and carpark availability had a weak correlation, which could be due to the limit of regional analyses. Drawing to the wider context, the high association between human mobility and NO2 in the South and East Coast area can provide insights into future NO2 reduction policy in Singapore.Graphical Abstract

Examining the Discrepancy between Forecast and Observed Liquefaction from the 2015 Gorkha, Nepal, Earthquakes

2017 ◽  
Vol 33 (1_suppl) ◽  
pp. 73-83 ◽  
Author(s):  
Robb Eric S. Moss ◽  
Laurie G. Baise ◽  
Jing Zhu ◽  
Diwakar Kadkha
Keyword(s):  
Regional Scale ◽  
Groundwater Table ◽  
Earthquake Sequence ◽  
Hazard Mapping ◽  
Kathmandu Valley ◽  
Surface Shear ◽  
Near Surface ◽  
Global Database ◽  
Crust Layer ◽  
Liquefaction Hazard

Many ground failures resulted from the 2015 Nepal earthquake sequence, including landslides, rockfalls, liquefactions, and cyclic failures. And whereas the amount and extent of landsliding were relatively consistent with predictions for a Mw 7.8 main shock, the amount and extent of liquefaction were not. We present a summary of liquefaction field observations that we made as part of the Geotechnical Extreme Events Reconnaissance (GEER) investigations. The liquefaction that did occur in the Kathmandu Valley was limited in its spatial extent, and the postliquefaction deformations were small. Prior earthquakes in this region have been reported to have caused greater liquefaction-related failures, and liquefaction hazard–mapping studies predicted widespread liquefaction hazard from an event of this size. We explore two possible reasons at the regional scale for the limited liquefaction from this earthquake sequence: drawdown of the groundwater table and high near-surface shear wave velocity. Our study finds that pumping has depressed the groundwater table across the Kathmandu Valley by 13–40 m since 1980, thereby decreasing the amount of near-surface liquefiable material and increasing the nonliquefiable “crust” layer. The regional slope-based V S30 for the valley is on average higher than that for liquefaction sites in a global database of observed liquefaction. A global geospatial model for liquefaction occurrence shows low liquefaction potential in the Kathmandu Valley consistent with the observed patterns.

Comparative lahar hazard mapping at Volcan Citlaltépetl, Mexico using SRTM, ASTER and DTED-1 digital topographic data

2007 ◽  
Vol 160 (1-2) ◽  
pp. 99-124 ◽  
Author(s):  
Bernard E. Hubbard ◽  
Michael F. Sheridan ◽  
Gerardo Carrasco-Núñez ◽  
Rodolfo Díaz-Castellón ◽  
Sergio Raúl Rodríguez
Keyword(s):  
Hazard Mapping ◽  
Topographic Data

scholarly journals Tsunami risk mapping simulation for Malaysia

2011 ◽  
Author(s):  
S. Y. Teh ◽  
H. L. Koh ◽  
Y. T. Moh ◽  
D. L. DeAngelis ◽  
J. Jiang
Keyword(s):  
Risk Mapping ◽  
Tsunami Risk

scholarly journals Identifying Dominant Runoff Processes at a Regional Scale – A GIS - Based Approach

2018 ◽  
Vol 12 (1) ◽  
pp. 19-33
Author(s):  
Babatunde Joseph Fagbohun ◽  
Oluwaseun Franklin Olabode ◽  
Abiodun Olufemi Adebola
Keyword(s):  
Overland Flow ◽  
Expert Knowledge ◽  
Regional Scale ◽  
Field Investigation ◽  
Deep Percolation ◽  
Flood Prediction ◽  
Percolation Process ◽  
Least Area ◽  
Expected Increase

Abstract Identifying landscapes with similar hydrological characteristics is useful for the determination of dominant runoff process (DRP) and flood prediction. Several approaches used for DRP-mapping differ in respect to time and data requirement. Manual approaches based on field investigation and expert knowledge are time consuming and difficult to implement at regional scale. Automatic GIS-based approach on the other hand require simplification of data but are easier to implement and it is applicable on regional scale. In this study, GIS-based automated approach was used to identify the DRPs in Anambra area. The result showed that Hortonian Overland Flow (HOF) has the highest coverage of 1508.3 Km2 (33.5%) followed by Deep Percolation (DP) with coverage of 1455.3 Km2 (32.3%). Subsurface Flow (SSF) is the third dominant runoff process covering 920.6 Km2 (20.4%) while Saturated Overland Flow (SOF) covers the least area of 618.4 Km2 (13.7%) of the study area. The result reveal that considerable amount of precipitated water would be infiltrated into the subsurface through deep percolation process contributing to groundwater recharge in the study area. However, it is envisaged that HOF and SOF will continue to increase due to the continuous expansion of built-up area. With the expected increase in HOF and SOF and the change in rainfall pattern associated with perpetual problem of climate change, it is paramount that groundwater conservation practices be considered to ensure continued sustainable utilization of groundwater in the study area.

scholarly journals Assessment of Landslide Hazard in Jiangxi Using Geo-information Technology

2021 ◽  
Vol 9 ◽  
Author(s):  
Penghui Ou ◽  
Weicheng Wu ◽  
Yaozu Qin ◽  
Xiaoting Zhou ◽  
Wenchao Huangfu ◽  
...  
Keyword(s):  
Regional Scale ◽  
Binary Logistic Regression ◽  
Technical Support ◽  
Landslide Hazard ◽  
Information Value ◽  
Hazard Mapping ◽  
Landslide Risk ◽  
Risk Modeling ◽  
Binary Logistic Regression Model ◽  
Linear Features

Landslides constitute a severe environmental problem in Jiangxi, China. This research was aimed at conducting landslide hazard assessment to provide technical support for disaster reduction and prevention action in the province. Fourteen geo-environmental factors, e.g., slope, elevation, road, river, fault, lithology, rainfall, and land cover types, were selected for this study. A test was made in two cases: (1) only based on the main linear features, e.g., main rivers and roads, and (2) with detailed complete linear features including all levels of roads and rivers. After buffering of the linear features, an information value (IV) analysis was applied to quantify the distribution of the observed landslides for each subset of the 14 factors. The results were inputted into the binary logistic regression model (LRM) for landslide risk modeling, taking the known landslide points as a training set (70% of the total 9,525 points). The calculated probability of a landslide was further classified into five grades with an interval of 0.2 for hazard mapping: very high (3.70%), high (4.05%), moderate (18.72%), low (27.17%), and stable zones (46.36%). The accuracy was evaluated by AUC [the area under the receiver operating characteristic (ROC) curve] vs. the validation set (30%, the remaining landslides). The final results show that with increasing the completeness of the linear features, the modeling reliability also significantly increased. We hence concluded that the tested methodology is capable of achieving the landslide hazard prediction at regional scale, and the results may provide technical support for geohazard reduction and prevention in the studied province.

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