A comparison on GIS-based hazard assessment of regional debris flow mapping

Cheng Weiming and Wang Nan

In this paper, three analysis methods such as factor superposition model, information amount model and logistic regression model are presented and analyzed. On the basis of spatial distributions of 339 gullies of debris flows from field surveying in Jundu Mountains of Beijing, hazard assessment maps are obtained based on the three methods and then the assessment results are compared. The conclusions can be drawn as follows: 1) The roughly equal percent of hazard levels from the three methods can be obtained which can classified into 5 levels such as very low, low, medium, high and very high, of hazard results of debris flows. 2) Based on the results of the three assessment methods, on the whole, gully numbers of debris flows increase in the hazard zones when hazard assessment levels change from low to high. 3) Taking the actual gully numbers of debris flows located in different hazard zones as judging standard, information amount model can obtain better results which possesses more than 77.28% of gullies in high and very high hazard zones; logistic regression model can reach 70.2% in the same hazard zones. The numbers of five hazard level from very low to very high are 5, 6, 9, 5 and 7 respectively in Jundu Mountain region by the three methods. Taking ratio of gully numbers within unit area and gully distribution of debris flows as judging standard, information amount model is relatively good to the general gully distribution of debris flows which possesses high precision evaluation. Logistic regression model also covers most of gully distribution of debris flows in high and very high hazard zones. The results obtained by factor superposition model have no obvious difference in high and very high hazard zones and a good gradient is not formed.

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Effect of Shear Modulus Correlation on Site Response Study

Anbazhagan P., Manohar D.R., Sayed S.R. Moustafa and Nassir S. Al-Arifi

Site response analysis requires dynamic/shear moduli of subsurface layers. A low strain shear modulus plays a fundamental role in the geotechnical earthquake engineering to estimate the hazard parameters for site response studies and seismic microzonation. Shear modulus is usually obtained from measured shear wave velocity and density or from standard penetration test (SPT) N values using correlation between SPT N and shear modulus. Many shear modulus correlations between N and shear modulus (Gmax) are available in the literature but selected few correlations are repeatedly used to obtain site response parameters. Anbazhagan et al2,3 presented a detailed review of the available fifteen Gmax correlations with SPT N and a proposal of new correlation applicable to any region. The objective of this study is to identify the suitable Gmax correlation for different soil types such as sand, clay and gravel or the mixture of all (sand, clay, gravel, sandy soil) considering recorded ground motion data with soil profile. In this study, sites with earthquake data recorded at the surface, drilled soil profiles along with SPT N values and shear wave velocity are selected from K-NET (Japanese website) data base. Shear wave velocity is used to classify the sites. As bedrock recorded ground motion data is not available for the most of site with SPT N values, ground motion recorded in site class A and B is used as input to understand the response of site class C, D and E. Collected earthquake data consists of moment magnitude (MW) of 5.0 to 9.0 which are recorded at different epicentral distances. Surface ground motion and response spectrum are obtained by considering dynamic properties from 16 Gmax correlations. The estimated values are compared with surface recorded data of the same event. The study shows that peak ground acceleration (PGA), amplification factor (AF) and average horizontal spectral amplification (AHSA) are obtained from very few Gmax correlations comparable with recorded values. Gmax relation giving values close to record data is considered as a suitable correlation for specific soil type.

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Ionospheric Precursor related to 03 August, 2014, Mw =6.1 China’ Yunnan Earthquake: Two-Dimensional Principal Component Analysis

Lin Jyh-Woei

Two-dimensional principal component analysis (2DPCA) has been used to determine ionospheric two-dimensional total electron content (TEC) anomaly, which was 5 days before China’s Yunnan earthquake at 08:30:13UT on 03 August, 2014 (Mw=6.1)25. The TEC anomaly was more intense localized at 06:15 to 06:20 (UT) on 01 August 2014, 3 days before the earthquake. Potential reason of the TEC anomaly, which might be a density variance, is gas release. The duration time of the TEC anomaly was for 5 minutes.

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Assessment of Sustainable Land Management and Food Security among Climatic Shocks’ exposed African Farmers

Abayomi Samuel Oyekale

Climate change is one of the major challenges of agricultural production in many developing countries. This paper analyzed the impact of sustainable land use on monthly food shortages among farmers in selected African countries. Data were analyzed with descriptive statistics and Negative Binomial (NB) regression model. The results showed that majority of the farmers from Senegal had no formal education while average numbers of months when farm households were unable to meet households’ food needs were highest in Ethiopia (6.55), Tanzania (5.36) and Ghana (4.44). Negative binomial regression results showed that monthly food shortages significantly increased (p<0.05) with exposure to climatic shocks, introduction of new crops, late planting, use of mulching and stopping irrigation. It however reduced significantly (p<0.05) with food cropland owned, vegetable cultivation, fish production, remittance income, access to formal and informal loans, income from renting land, stop planting a variety, improved irrigation and use of integrated crop management. It was concluded that the farmers were adjusting their farming systems in response to climate change and efforts at promoting sustainable farming system will enhance their adaptive capacity and food security given the current climatic changes.

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