伊犁河南岸土地盐渍化现状分析与风险评估
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摘要
土壤盐渍化是世界干旱、半干旱地区土地农业开发所面临的最突出的生态环境问题之一,而盐渍化治理又是一个费时、耗力、难度很大的系统工程。随着伊犁河南岸大渠引水工程的实施,伊犁河谷土地如何安全可持续地农业开发成为一个亟待解决的科学问题。本研究利用大地电导率仪(EM38)于2008年10月、2009年5月和2010年10月分别对伊犁河南岸199个观测点进行表观电导率的连续监测,结合11个层次(0-200cm)取样分析和实验室分析。利用传统统计和地统计学方法对研究区土壤盐分、水分、地下水以及土层厚度等指标进行分析,应用遥感数据进行盐渍化变化分析,利用综合指标法进行盐渍化风险评价。具体研究结果如下：
1、在伊犁河南岸中心位置150km2的范围内均匀布置22个地下水监测点,通过监测发现该区域地下水埋深在空间上呈现南部深于北部,在时间上(年度内)呈现从深逐渐变浅,而后逐渐变深趋势；地下水的矿化度呈现东北高、西南低,年度内呈现高-低-高的变化趋势。
2、建立了大地电导仪(EM38)的表观电导率预测有效土层厚度的模型1nD=0.97381nECav+1.6448。据此模型制作了有效土层厚度分布图,研究区有效土层厚度呈现中部偏北最厚,向南和最北逐渐变薄的分布状况。
3、通过对研究区2008和2009采样点土壤含水量的聚类分析,发现研究区土壤剖面水分存在表聚型、中聚型和底聚型3种分布特征,并且表聚型、中聚型和底聚型三种类型在2008年所占比例为51.35%、35.14%和13.51%,2009年为22%、26%和52%。建立以表观电导率(EM38)预测土壤含水量0-10cm的最优回归方程1nW=0.27601nECav+0.50671nECaH+1.7105。
4、通过对研究区2008和2009采样点土壤盐分含量的聚类分析,确定了研究区土壤剖面盐分存在表聚型、中聚型和底聚型3种分布特征。同时土壤盐分含量与表观电导率(EM38测定结果)存在显著相关性,其相关性表现出水田大于灌溉旱田,旱田大于裸地,秋季大于春季。据此以总体测量值所建立的0-10cm土层的回归方程lnS=0.3515InECaV+0.4321InECaH-1.6867乍为EM38预测土壤盐分的模型。
5、利用伊犁河谷2008年和2009年的两期的TM遥感影像结合实地调查分析数据,分析确定了TM4-5-3波段组合是伊犁河南岸土地遥感监测盐渍化最佳选择,并建立了影像灰度值与土壤含盐量关系模型。以实地调查的土壤含盐量数据为依据,使用监督分类的最大似然法对研究区进行了分类并检验混淆矩阵Kappa系数,分类精度达到86.72%,Kappa系数为0.82和0.80。
6、通过分析伊犁河南岸土地盐渍化各种影响因素,构建了伊犁河南岸以有效土层厚度、土地利用方式、土壤表层含盐量、2m土体平均含盐量、积盐层部位、盐分运移方向、土壤质地与层次、地下水埋深、地下水矿化度、地形地貌等10个指标为核心评价指标体系,利用综合指数法进行土壤盐渍化风险评估,并根据评价结果提出了适宜的开发利用管理措施。
总之,本研究建立了利用大地电导仪预测有效土层厚度、土壤水分、土壤盐分的模型,该模型为数字土壤信息获取提供了无损快捷的手段和方法。研究区盐分演变与风险评价结果可为伊犁河南岸土地农业可持续开发与利用提供科学依据。
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.
1、在伊犁河南岸中心位置150km2的范围内均匀布置22个地下水监测点,通过监测发现该区域地下水埋深在空间上呈现南部深于北部,在时间上(年度内)呈现从深逐渐变浅,而后逐渐变深趋势；地下水的矿化度呈现东北高、西南低,年度内呈现高-低-高的变化趋势。
2、建立了大地电导仪(EM38)的表观电导率预测有效土层厚度的模型1nD=0.97381nECav+1.6448。据此模型制作了有效土层厚度分布图,研究区有效土层厚度呈现中部偏北最厚,向南和最北逐渐变薄的分布状况。
3、通过对研究区2008和2009采样点土壤含水量的聚类分析,发现研究区土壤剖面水分存在表聚型、中聚型和底聚型3种分布特征,并且表聚型、中聚型和底聚型三种类型在2008年所占比例为51.35%、35.14%和13.51%,2009年为22%、26%和52%。建立以表观电导率(EM38)预测土壤含水量0-10cm的最优回归方程1nW=0.27601nECav+0.50671nECaH+1.7105。
4、通过对研究区2008和2009采样点土壤盐分含量的聚类分析,确定了研究区土壤剖面盐分存在表聚型、中聚型和底聚型3种分布特征。同时土壤盐分含量与表观电导率(EM38测定结果)存在显著相关性,其相关性表现出水田大于灌溉旱田,旱田大于裸地,秋季大于春季。据此以总体测量值所建立的0-10cm土层的回归方程lnS=0.3515InECaV+0.4321InECaH-1.6867乍为EM38预测土壤盐分的模型。
5、利用伊犁河谷2008年和2009年的两期的TM遥感影像结合实地调查分析数据,分析确定了TM4-5-3波段组合是伊犁河南岸土地遥感监测盐渍化最佳选择,并建立了影像灰度值与土壤含盐量关系模型。以实地调查的土壤含盐量数据为依据,使用监督分类的最大似然法对研究区进行了分类并检验混淆矩阵Kappa系数,分类精度达到86.72%,Kappa系数为0.82和0.80。
6、通过分析伊犁河南岸土地盐渍化各种影响因素,构建了伊犁河南岸以有效土层厚度、土地利用方式、土壤表层含盐量、2m土体平均含盐量、积盐层部位、盐分运移方向、土壤质地与层次、地下水埋深、地下水矿化度、地形地貌等10个指标为核心评价指标体系,利用综合指数法进行土壤盐渍化风险评估,并根据评价结果提出了适宜的开发利用管理措施。
总之,本研究建立了利用大地电导仪预测有效土层厚度、土壤水分、土壤盐分的模型,该模型为数字土壤信息获取提供了无损快捷的手段和方法。研究区盐分演变与风险评价结果可为伊犁河南岸土地农业可持续开发与利用提供科学依据。
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.
引文
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