Emil Bayramov

Differences of MMF and USLE Models for Soil Loss Prediction along BTC and SCP Pipelines

The main goal of this study is to assess the Morgan-Morgan-Finney (MMF) and the universal soil loss equation (USLE) erosion models in the prediction of soil degradation along the corridor of oil and gas pipelines. In the comparative analysis, the MMF model revealed a larger coefficient of variation (COV) in predicted soil loss rates. Based on the pair-sample t-test, the predictions of the two models were significantly different in the spatial distribution of soil loss along the rights-of-way (RoW) of the pipelines. Sensitivity of the MMF and USLE models to terrain morphometric elements was also assessed. Slope gradient was one of the controlling factors of erosion processes, but not of the soil loss rates. The MMF and USLE models did not reveal any sensitivity to slope aspects. In terms of elevation, the MMF model revealed higher soil loss rates in the lower elevations than with the USLE model, leading to the conclusion that the USLE model is more sensitive to elevation change than the MMF model. The USLE model revealed higher sensitivity to the terrain curvature than the MMF model because it had larger variations within concave and flat terrain curvature types. Both models were sensitive to increasing vegetation cover (VC) percentage. Both models revealed different sensitivities; therefore, better understanding of these sensitivities may contribute to the selection of the most suitable model, depending on the terrain, to yield the highest soil loss prediction accuracy. Qualitative validation of the spatial distribution of USLE- and MMF-predicted erosion-prone areas was performed using 6 years of ongoing surveillance and measurement of erosion occurrences. Quantitative validation of the predicted soil loss was performed using 3 years of monitoring of field erosion plots. The USLE model performed better than the MMF model in terms of the frequency ratio of erosion occurrences within the critical erosion classes (soil loss > 10 t/ha). The USLE-predicted soil loss rates were more reliable than the MMF rates not only in terms of spatial distributions of critical erosion classes, but also in quantitative terms of soil loss rates because of the high correlation with the soil loss measurements of field erosion plots. The number of erosion-prone pipeline segments realistically predicted by the USLE model, e.g., soil loss more than 10 t/ha, was 88, whereas the MMF model predicted only 76 erosion-prone pipeline segments. The regression analysis between the total of 354 USLE and MMF erosion-prone segments revealed an R2 equal to 0.33, which means that the predictions by the USLE and MMF erosion models are significantly different on the level of pipeline segments.

Prediction reliability, quantitative differences and spatial variations of erosion models for long-range petroleum and gas infrastructure

The main goal of this study was to assess the prediction reliability, the quantitative differences and the spatial variations of the Morgan––Morgan–Finney (MMF) and the Universal Soil Loss Equation (USLE) erosion prediction models along the 442-km-long and 44-m-wide Right-of-Way of Baku–Tbilisi–Ceyhan oil and South Caucasus gas pipelines. USLE performed better than MMF erosion model by the accurate prediction of 61% of erosion occurrences. Paired-samples T-test with p-value less than 0.05 and bivariate correlation with the Pearsons correlation coefficient equal to 0.23 showed that the predictions of these two models were significantly different. MMF model revealed more clustered patterns of predicted critical erosion classes with a soil loss of more than 10 ton/ha/year in particular ranges of pipelines rather than USLE model with the widespread spatial distribution. The average coefficients of variation of predicted soil loss rates by these models and the number of accurately predicted erosion occurrences within the geomorphometric elements of terrain, vegetation cover and landuse categories were larger in the USLE model. This supported the hypothesis that larger spatial variations of erosion prediction models can contribute to the better soil loss prediction performance and reliability of erosion prediction models.

Quantitative assessment of vegetation cover and soil degradation factors within terrain units for planning, monitoring and assessment of renaturation along oil and gas pipelines

This paper evaluates the renaturation activities applying the quantification of vegetation cover (VC), the site suitability analysis (SSA) based on the predefined criteria (slope steepness category (SSC), soil erodibility factor (K) and VC) and soil erosion model (SEM) results within the terrain units (TUs) along pipeline rights-of-way (RoW). Quantification of VC percentage is performed to assess the overall restored VC from 2005 to 2007. The results of the quantitative analysis in 2007 show that the total area of restored VC is 10.7 km2, and 8.9 km2 still needs to be restored to comply with the environmental acceptance criteria. As a result of SSA, TUs were prioritized by erosion vulnerability and this allowed to better understand the landscape behaviour in regards to erosion processes. SEM provided more detailed predictions of erosion classes falling into TUs. SEM identified 40% of erosion sites occurred from 2005 to 2010.

Modelling of oil spill frequency, leak sources and contamination probability in the Caspian Sea using multi-temporal SAR images 2006–2010 and stochastic modelling

The main goal of this research was to detect oil spills, to determine the oil spill frequencies and to approximate oil leak sources around the Oil Rocks Settlement, the Chilov and Pirallahi Islands in the Caspian Sea using 136 multi-temporal ENVISAT Advanced Synthetic Aperture Radar Wide Swath Medium Resolution images acquired during 2006–2010. The following oil spill frequencies were observed around the Oil Rocks Settlement, the Chilov and Pirallahi Islands: 2–10 (3471.04 sq km), 11–20 (971.66 sq km), 21–50 (692.44 sq km), 51–128 (191.38 sq km). The most critical oil leak sources with the frequency range of 41–128 were observed at the Oil Rocks Settlement. The exponential regression analysis between wind speeds and oil slick areas detected from 136 multi-temporal ENVISAT images revealed the regression coefficient equal to 63%. The regression model showed that larger oil spill areas were observed with decreasing wind speeds. The spatiotemporal patterns of currents in the Caspian Sea explained the multi-directional spatial distribution of oil spills around Oil Rocks Settlement, the Chilov and Pirallahi Islands. The linear regression analysis between detected oil spill frequencies and predicted oil contamination probability by the stochastic model showed the positive trend with the regression coefficient of 30%.

Optimization of environmental monitoring principles along oil and gas pipelines using GIS and remote sensing

The main objectives of this research were to evaluate the vegetation restoration progress and to predict erosion-prone areas along the BTC Oil and the SCP Gas pipelines. Based on the GIS and Remote Sensing analysis of high resolution multispectral satellite images, the total area of restored vegetation cover between 2005 and 2007 was detected to be 10.7 million sq. m. An area of 8.9 million sq. m. of ground vegetation needed restoration. USLE performed better than MMF model by identifying of 192 erosion occurrences out of 316 along the pipelines.