Land Salinization Situation Analysis And Risk Assessment At The Southern Bank Of Ili River

Soil salinization may be the most severe eco-environment problem presented to agricultural areas development in both semi-arid and arid climatic zones around the globe. Reclaiming salt-affected and salinized soils is both a labor-and time-consuming task. With the implementation of the major irrigation channel diversion project that located at the southern bank of Ili River, securing sustainable agricultural land development this becomes an urgent problem in Ili River Valley. In the research, salinization risk was evaluated by comprehensive index method using electromagnetic conductivity meter (EM38) to continuously monitor apparent electrical conductivity at199observation points at the southern bank of Ili River in2008October,2009May and2010October. Soil samples from eleven soil layers at0-200cm soil profiles were taken repeatedly, which were then subjected to laboratory analysis. Statistical and geo-statistical analysis were performed on key indicators (i.e. soil salinity, soil moisture, groundwater, the effective thickness of soil layer). Remote sensing data and analysis were also used in interpreting soil salinity distribution. The main results are as follows:1. Twenty-two groundwater monitoring points were evenly distributed in150km2at the southern bank of Ili River center position. The monitoring results revealed that the regional groundwater depth was deeper in the south and shallower in the north of the study area. Groundwater tables exhibited deep-shallow-deep pattern in intra-year variability, while the mineralization of groundwater presented northeast high-southwest low pattern, and high-low-high changing trend in observed year.2. A logistic model was fitted to forecast the effective thickness of soil layer with sampling points of apparent conductivity (ECa) by EM38, i.e. lnD=0.97381nECav+1.6448. The spatial distribution of effective soil layer thickness displayed that the soil layer was thick in the north-center, shallower in the south and the north.3. It was found that there were three types of soil profile moisture, i.e. surface-concentrated, middle-concentrated, and bottom-concentrated, through analyzing research area of2008and2009sampling points’soil moisture clustering. The three types shared51.35%,35.14%and13.51%in2008, and22%,26%, and52%in2009. The regression equation for0-10cm layer i.e.lnW=0.2160lnECav+0.5067lnECaH+1.7105, was established to predict soil moisture by the apparent electrical conductivity.4. The soil profile salinity can also be classified into three types, i.e. surface-concentrated, middle-concentrated, and bottom-concentrated, through analyzing sampling points’ clustering of2008and2009in research area, The results also revealed that the regression equation with ECa and soil moisture were extremely significant. As for land use types, the significance of regression followed the order of rice paddies, irrigated cropland, and bare land. For seasons, that of autumn was larger than spring. A0-10cm’s model lnS=0.3515lnECav+0.4321lnECaH-1.6867was established on the overall measurement value of EM38to predict soil salinity.5. Using TM remote sensing image of Ili River Valley in2008and2009combined with field survey data, it was found that TM4-5-3band combination was the best choice for remote sensing monitoring of salinization at the southern bank of Ili River. Also, a regression equation of grey value and salt content was established at study area. Based on measured soil salinity, the maximum proximity method in supervised classification was used to classify the study area. After deleting confounding spots, Kappa coefficient was calculated by classification mix matrix to test the exactness, they were86.72%and84.64%, respectively, with Kappa being0.82and0.80respectively.6. By analyzing the factors affecting land salinization at the south bank of Ili River, evaluation index system was built at the southern bank of Ili River with the effective thickness of soil, land use, soil salt content, average soil salinity at2meters deep, salt migration direction, soil texture and levels, groundwater buried depth, the mineralization degree of groundwater, topography10indicators as the core, evaluate Soil Salinization Risk Assessment using a comprehensive index, and put forward the development and utilization of management suitable measures according to the evaluation results.In conclusion, this study established a method using electromagnetic conductivity meter to predict the effective soil layer thickness, soil moisture, soil salinity model, the model provided a nondestructive and fast tool to acquire digital soil information. The salinity evolution and risk assessment at study area can provide scientific basis for sustainable development and utilization for land agriculture at the southern bank of Ili River.