Assessment of soil erosion vulnerability in rural communities in the vicinity of Motozintla de Mendoza, Chiapas, Mexico : a physical and sociological approach

Soil erosion is a natural geological phenomenon resulting from removal and transportation of soil particles by water, wind, ice and gravity. As soil erosion may be affected from cultural factors as well. The physical and social phenomena of soil erosion are researched in six communities in the upper part of Rio Grijalva Basin in the vicinity of Motozintla de Mendoza, Chiapas, Mexico. For this study, the USDA RUSLE model was applied to estimate soil erosion rates in the six communities based on the available data. The RUSLE model is based on soil properties, topography, and land cover and management factors. These results showed that estimated soil erosion rates ranged from a high of 2,050 metric ton ha-1 yr-1 to a low of 100 metric ton ha-1 yr-1. A survey concerning knowledge, attitudes and practices (KAP) related to soil erosion was also conducted in all 236 households in the six communities. The main findings of the KAP survey were: 69% of respondents did not know what soil erosion was, while over 40% of the population perceived that hurricanes are the biggest factors that cause soil erosion, and about 20 % of the interviewees said that the landslides are the consequences of the soil erosion. People in communities did not perceive cultural factors as important in conservation efforts for reduce vulnerability to erosion; therefore, the results obtained are suggested to be useful for informing efforts to educate stakeholders.

SAMPLING BIAS IN EVALUATING THE PROBABILITY OF SEISMICALLY INDUCED SOIL LIQUEFACTION WITH SPT & CPT CASE HISTORIES

Several deterministic and probabilistic methods are used to evaluate the probability of seismically induced liquefaction of a soil. The probabilistic models usually possess some uncertainty in that model and uncertainties in the parameters used to develop that model. These model uncertainties vary from one statistical model to another. Most of the model uncertainties are epistemic, and can be addressed through appropriate knowledge of the statistical model. One such epistemic model uncertainty in evaluating liquefaction potential using a probabilistic model such as logistic regression is sampling bias. Sampling bias is the difference between the class distribution in the sample used for developing the statistical model and the true population distribution of liquefaction and non-liquefaction instances. Recent studies have shown that sampling bias can significantly affect the predicted probability using a statistical model. To address this epistemic uncertainty, a new approach was developed for evaluating the probability of seismically-induced soil liquefaction, in which a logistic regression model in combination with Hosmer-Lemeshow statistic was used. This approach was used to estimate the population (true) distribution of liquefaction to non-liquefaction instances of standard penetration test (SPT) and cone penetration test (CPT) based most updated case histories. Apart from this, other model uncertainties such as distribution of explanatory variables and significance of explanatory variables were also addressed using KS test and Wald statistic respectively. Moreover, based on estimated population distribution, logistic regression equations were proposed to calculate the probability of liquefaction for both SPT and CPT based case history. Additionally, the proposed probability curves were compared with existing probability curves based on SPT and CPT case histories.