科尔沁沙丘-草甸相间地区表土饱和导水率的土壤传递函数研究

本研究以科尔沁沙地典型坨-甸相间地区为研究区,野外布设240个采样点,对流动沙丘、半固定沙丘、固定沙丘、沙丘区杨树林、沙丘区耕地、低覆盖度草甸、高覆盖度草甸、草甸区耕地、撂荒地九种地貌类型下的表层土壤进行了采样,测定了这些样点的含水率、干容重、有机质、饱和导水率等理化特性参数,分析了不同地貌类型下表层土壤理化参数差异。选取Campbell、Cosby、Wosten1997/1999、Saxton四种土壤传递函数,对该地区表土饱和导水率进行了预测,结果显示这几种土壤传递函数预测值与实测值偏差较大,相关系数均小于0.3,由此可见这几种传统的土壤传递函数在本地区应用具有一定的局限性。在此基础上,选取土壤容重、有机质含量、饱和含水率、平均粒径、粒径标准偏差五种土壤特性参数作为输入变量,采用逐步多元统计回归、主成分分析及非线性回归分析相结合的方法,重新建立了预测本地区表土饱和导水率的土壤传递函数,结果显示预测值与实测值相关系数为0.648,该传递函数可应用于科尔沁沙地表层土壤饱和导水率的预测。     

基于野外实测Vis-NIR光谱的土壤肥力估算研究

为探讨野外实测光谱数据对土壤肥力的估算能力,采集青海省湟水流域表层0 ~ 20 cm土壤样品220份,同步测量其采样位置的野外实测光谱数据,实验室对土壤养分、机械组成含量以及pH值进行分析。基于上述数据,对野外实测光谱反射率进行多元散射校正（Multiplicative scatter correction,MSC）、SG-一阶导数变换（SG - First Derivative,SG-1st）预处理,采用稳定性竞争自适应重加权采样法（stability competitive adaptive reweighted sampling,SCARS）提取不同土壤养分、机械组成含量以及pH值的特征波段,以偏最小二乘回归（partial least squares regression,PLSR）模型对土壤全碳（TC）、有机质（OM）、全氮（TN）、碱解氮（AN）、pH、黏粒（clay）、粉粒（silt）、砂粒（sand）含量进行估算并对比分析,构建土壤养分含量、pH值以及机械组成含量的最优野外实测光谱估算模型。结果表明:通过MSC校正和SG-1st变换能够有效增强野外光谱特征;经SCARS选取的特征波段主要集中于近红外波段。基于野外实测光谱数据建立的PLSR模型能够对研究区土壤TC、OM、TN、AN含量以及pH值进行粗略估算;其中,对于TC、OM、TN含量及pH值而言,最佳估算模型为经SG-1st处理后的SCARS-PLSR模型,RPD值均达到1.70以上（RPD_TC = 1.76; RPD_OM = 1.82;RPD_TN = 2.04;RPD_pH = 1.89）,RPIQ值均达到1.90以上（RPIQ_TC = 1.91;RPIQ_OM = 2.53;RPIQ_TN = 2.98;RPIQ_pH = 2.03）;对于土壤AN含量而言,经MSC处理后的SCARS-PLSR模型最佳,其RPD_AN值高达1.91,RPIQ值高达2.39。对土壤clay、silt以及sand含量野外光谱均无法估算,RPD值均在1.00左右,RPIQ值在1.20左右。     

科尔沁沙丘-草甸相间地区表土饱和导水率的土壤传递函数研究

以科尔沁沙地典型坨-甸相间地区为研究区,野外布设240个采样点,对流动沙丘、半固定沙丘、固定沙丘、沙丘区杨树林、沙丘区耕地、低覆盖度草甸、高覆盖度草甸、草甸区耕地、撂荒地9种地貌类型下的表层土壤进行了采样,测定了其含水率、干容重、有机质、饱和导水率等理化特性,分析了不同地貌类型下表层土壤理化参数差异。选取Campbell、Cosby、Wosten等、Saxton等4种土壤饱和导水率传递函数,对该地区表土饱和导水率进行了预测。结果显示这几种土壤传递函数预测值与实测值偏差较大,相关系数均小于0.3,精度难以满足本地区应用。在此基础上,选取土壤容重、有机质含量、饱和含水率、平均粒径、粒径标准偏差5种土壤特性参数作为输入变量,采用主成分分析与非线性回归分析相结合的方法,重新建立了预测本地区表土饱和导水率的土壤传递函数,结果显示预测值与实测值相关系数为0.661,该传递函数可用于科尔沁沙地表层土壤饱和导水率的预测。     

反演含碎石碳酸盐岩红土水力特性

为探究喀斯特地区碎石夹层对碳酸盐岩红土水力特性的影响因素,通过室内模拟土柱入渗试验,采用垂直入渗水头法研究3种碎石体积含量(0,40%,80%)和3种碎石埋藏深度(0,5,15 cm)分别与累积入渗量、湿润锋、入渗特性和土壤水分特征曲线之间的关系,并采用3种模型分析了含碎石土壤对传统土壤入渗模型的适用性,将实测入渗数据结合改进的Green-Ampt模型反演Brooks-Corey模型参数。结果表明:在相同碎石埋藏深度下,累积入渗量、湿润锋、初始入渗率、平均入渗率和稳定入渗率均随碎石含量增加而减小;当碎石含量为40%时,埋藏深度是15 cm的土壤稳定入渗率最大(12.71 mm/h),是埋藏深度为0 cm的1.33倍。Horton模型对含碎石夹层土壤入渗规律的适用性要优于Kostiakov模型和Philip模型。反演参数a、n和h_d在同一碎石埋藏深度(0,5 cm)下,随碎石含量增加而增大,而K_s随碎石含量增加而减小。通过土壤水分特征曲线可知,对照组土壤持水性最低;含碎石夹层土壤持水性随碎石含量增加而减弱,但碎石埋藏深度为15 cm时,含碎石夹...     

基于多光谱数据的荒漠矿区土壤有机质估算模型

目前运用高光谱数据估算土壤有机质的模型精度已经可以达到精准农业的要求,但其数据的整理和运算过程较为复杂且观测尺度较小.为节省资源,提高效率并为多光谱遥感估算土壤有机质积累经验,该文将Landsat8_OLI多光谱遥感影像各波段的反射率数据与地面土壤有机质SOM(soil organic matter)实测数据相结合,利用SPSS软件及多元线性回归分析方法建立基于反射率R、反射率倒数1/R、反射率倒数对数LN(1/R)、反射率一阶导数FDR(first derivative reflectance)的土壤有机质定量估算模型,精度检验后择取最优模型通过多光谱遥感波段运算的方式推广至整个研究区.结果表明:FDR模型的精度更高,RMSE为0.215,F检验结果为4.072,预测值与实际值之间的决定系数R2为0.963.基于该模型估算研究区空间范围的土壤有机质含量,得出土壤有机质含量在0～5 g/kg之间的面积占总研究区的84.065％,＞10 g/kg的面积仅仅为0.001 5％.在4种土地类型中工矿用地SOM平均含量为最高的7.35 g/kg,受开采的煤炭中有机质影响较大.裸地面积2 674.44 km2,占研究区面积的63％,SOM平均含量6.12 g/kg;盐渍地和荒漠林地SOM含量偏低.总之,运用多光谱遥感数据估算干旱区土壤有机质的方法可行,也为遥感估算其他地表参数提供参考.     

基于多源环境变量的渭–库绿洲土壤颗粒含量预测研究

本文以渭干河–库车河绿洲(简称渭–库绿洲)土壤颗粒为研究对象,采集了绿洲内50个典型表层(0～10 cm)土壤样本,通过相关软件,提取到遥感指数变量、地形和气候等环境变量,经过相关性分析确定环境变量和预测目标间的关系,使用R语言构建了预测土壤颗粒含量的随机森林(random forest,RF)模型和极端梯度提升(extreme gradient boosting,XGBoost)模型。研究结果表明：XGBoost模型的预测结果整体好于RF模型,其中相关系数介于0.39～0.78;土壤pH、高程及衍生变量、光谱变换变量均是两个模型预测土壤颗粒含量的重要因子;将模型预测结果、实测数据和世界土壤数据库(HWSD)中的3种土壤颗粒数据作对比分析,结果表现出模型预测数据的误差小于HWSD与实测数据的误差。综上所述,通过筛选环境变量建立的XGBoost模型,是预测渭–库绿洲土壤颗粒含量的有效方法。     

灰色关联及非线性规划法构建传递函数估算黑土水力参数

土壤水分特征曲线和饱和导水率是重要的水力参数,为了简便准确获取这些参数,以松嫩平原黑土区南部为研究区域,采集136个采样点土样用于测定不同土层土壤水分特征曲线、饱和导水率以及土壤理化性质,并运用灰色关联分析确定影响土壤水力参数的主要土壤理化性质,采用非线性规划构建土壤分形维数、有机质、干容重、土壤颗粒组成与土壤水分特征曲线、饱和导水率之间的土壤传递函数,并通过与现有土壤传递函数对比分析进行精度验证。结果表明:1)土壤分形维数是估算土壤水分特征曲线模型参数和饱和导水率的主要参数之一,同时,干容重和有机质含量也在不同土层土壤传递函数中起到重要的作用;2)通过验证分析,不同土层各参数平均绝对误差接近于0,均方根误差值也都较小,其中在不同土层土壤传递函数估算的土壤含水率均方根误差分别为0.022、0.017cm~3/cm~3;3)对比分析其他已存的土壤水分特征曲线和饱和导水率的土壤传递函数,该文构建的土壤传递函数均方根误差值均较小,决定系数值都在0.66以上,表明估算精度较高,均好于其他方法估算精度,具有良好的区域适应性。综上,所构建的土壤水分特征曲线和饱和导水率土壤传递函数可以用于松嫩平原黑土区土壤水力参数估算。     

利用高光谱估算博斯腾湖西岸湖滨绿洲土壤有机质含量

以新疆博斯腾湖西岸湖滨绿洲为研究区,利用实测的土壤有机质含量与高光谱数据,通过多元逐步回归与偏最小二乘回归法分别构建反演土壤有机质含量估算模型.结果表明:(1)研究区土壤有机质含量变化范围为5.09～44.00 g·kg-1,均值为16.87 g·kg-1,变异系数为44.69％,呈中等变异;土壤有机质含量与土壤光谱反...     

基于高光谱数据的杨树叶片干物质含量的估算

将杨树叶片实测的理化数据和土壤背景光谱数据作为PROSPECT和PROSAIL模型的输入参数,输出杨树叶片高光谱数据模拟值,通过实测获得叶片尺度和冠层尺度干物质含量、等效水厚度以及高光谱数据,利用统计方法,分别对两种尺度杨树叶片干物质含量进行分析。结果表明:基于归一化指数计算方法,杨树叶片尺度和冠层尺度的最佳估算干物质含量的干物质含量归一化指数(NDMI)波段组合分别为1685,1704nm和1551,2143nm,使用偏最小二乘法分别对筛选波段组成的NDMI(1685,1704)指数和NDMI(1551,2143)指数构建叶片尺度干物质含量和冠层尺度干物质含量的估算模型,叶片尺度干物质含量估算模型精度为R2=0.663,RMSE=0.001g·cm-2,冠层尺度精度为R2=0.91,RMSE=16.7g·m-2。可见,高光谱技术对杨树叶片干物质含量的估算具有较高的精度,可为杨树叶片干物质含量的快速、无损估算提供参考依据。     

基于微波冠层散射模型的水稻生物量遥感估算

为寻求有效的水稻生物量估算方法,尝试开发了微波冠层散射模型。将实测的水稻结构参数作为输入变量,运行微波冠层散射的改进模型来模拟水稻冠层后向散射系数,结合遗传算法优化工具,从星载微波雷达遥感ALOS/PALSAR数据反演水稻的结构参数,进而对水稻生物量进行了空间制图。结果显示,模拟的水稻冠层后向散射系数误差在1 dB以内,估算的水稻生物量的误差小于200 g/m2。表明利用微波遥感机理模型反演水稻结构参数和估算水稻生物量具有应用潜力。     

Estimation of soil saturated hydraulic conductivity by artificial neural networks ensemble in smectitic soils

The saturated hydraulic conductivity (K_s) of the soil is one of the main soil physical properties. Indirect estimation of this parameter using pedo-transfer functions (PTFs) has received considerable attention. The Purpose of this study was to improve the estimation of K_s using fractal parameters of particle and micro-aggregate size distributions in smectitic soils. In this study 260 disturbed and undisturbed soil samples were collected from Guilan province, the north of Iran. The fractal model of Bird and Perrier was used to compute the fractal parameters of particle and micro-aggregate size distributions. The PTFs were developed by artificial neural networks (ANNs) ensemble to estimate K_s by using available soil data and fractal parameters. There were found significant correlations between K_s and fractal parameters of particles and microaggregates. Estimation of K_s was improved significantly by using fractal parameters of soil micro-aggregates as predictors. But using geometric mean and geometric standard deviation of particles diameter did not improve K_s estimations significantly. Using fractal parameters of particles and micro-aggregates simultaneously, had the most effect in the estimation of K_s. Generally, fractal parameters can be successfully used as input parameters to improve the estimation of K_s in the PTFs in smectitic soils. As a result, ANNs ensemble successfully correlated the fractal parameters of particles and micro-aggregates to K_s.     

Support Vector Machine and Nonlinear Regression Methods for Estimating Saturated Hydraulic Conductivity

Pedotransfer functions (PTFs) are widely used for hydrological calculations based on the known basic properties of soils and sediments. The choice of predictors and the mathematical calculus are of particular importance for the accuracy of calculations. The aim of this study is to compare PTFs with the use of the nonlinear regression (NLR) and support vector machine (SVM) methods, as well as to choose predictor properties for estimating saturated hydraulic conductivity (K_s). K_s was determined in direct laboratory experiments on monoliths of agrosoddy-podzolic soil (Umbric Albeluvisol Abruptic, WRB, 2006) and calculated using PTFs based on the NLR and SVM methods. Six classes of predictor properties were tested for the calculated prognosis: K_s-1 (predictors: the sand, silt, and clay contents); K_s-2 (sand, silt, clay, and soil density); K_s-3 (sand, silt, clay, soil organic matter); K_s-4 (sand, silt, clay, soil density, organic matter); K_s-5 (clay, soil density, organic matter); and K_s-6 (sand, clay, soil density, organic matter). The efficiency of PTFs was determined by comparison with experimental values using the root mean square error (RMSE) and determination coefficient (R²). The results showed that the RMSE for SVM is smaller than the RMSE for NLR in predicting K_s for all classes of PTFs. The SVM method has advantages over the NLR method in terms of simplicity and range of application for predicting K_s using PTFs.     

Pedo-transfer functions for estimating the hydraulic properties of paddy soils in subtropical central China

Pedo-transfer functions (PTFs) have been widely used to estimate soil hydraulic properties in the simulation of catchment eco-hydrological processes. However, the accuracy of existing PTFs is usually inadequate for use. To develop PTFs for local use, soil columns were collected from a double rice-cropped agricultural catchment in subtropical central China. The PTFs for saturated soil hydraulic conductivity (K_s) and parameters (θ_s, α, and n) of the van Genuchten model for the soil water retention curve (SWRC) were obtained based on soil’s basic properties, and compared with models developed by Li et al. in 2007 and Wösten et al. in 1999, respectively. Our results indicated that K_s in the range of 0.04–1087 cm d^?1 and θ_s in the range of 0.34–0.51 cm³ cm^?3 were both well estimated with the R²_adj of 0.72 and 0.87, respectively, but α (0.04–0.65 cm^?1) and n (1.05–1.21) were relatively poorly predicted with the respective R²_adj of 0.38 and 0.55, despite the use of more input parameters. Our local derived PTFs outperformed the other two existing models. However, if the local PTFs for paddy soils are not available, the Wösten et al. 1999 model can be proposed as a useful alternative. Therefore, this study can improve our understanding of the development and application of PTFs for predicting paddy soil hydraulic properties in China.     

Pedotransfer Functions for the Estimation of Saturated Hydraulic Conductivity for Some Indian Sandy Soils

Determination of the saturated hydraulic conductivity (k_s) is needed in many studies and applications related to irrigation, drainage, water movement and solute transport in the soil. Although many advances are made for direct measurements of k_s, they are usually time consuming and costly. Some attempts have been made to indirectly predict the saturated hydraulic conductivity from the more easily or readily available basic soil properties. The objective of this study was to develop and validate Pedotransfer Functions (PTFs) for estimation of saturated hydraulic conductivity using multiple non-linear regression technique. One hundred and one soil samples were collected from agricultural and forest soils at different depths, at different locations in the Pavanje River basin that lies in the southern coastal region of Karnataka, India. Saturated hydraulic conductivity was measured, by variable falling head method through Permeameter in the laboratory. Prediction accuracies were evaluated using coefficient of determination, root mean square error, mean error, geometric mean error ratio and geometric standard deviation of the error ratio between measured and predicted values. The results show that, the PTFs for the estimation of saturated hydraulic conductivity could be used appropriately for the soils with loamy sand and sandy loam textures falling in this area of the coastal region of southern India.     

青海省东部不同土地利用方式下土壤饱和导水率分布及其影响因素

研究高寒地区土壤饱和导水率分布特征及其影响因素可为评估脆弱生态系统水源涵养能力和构建区域水文模型提供参数.通过测定青海省东部南北样线24个样点(0—30 cm)土壤基本理化性质和饱和导水率(Ks),分析了不同土地利用方式下Ks分布特征及其影响因素.结果表明:Ks均值表现为林地(1.89 cm/h)>草地(1.62 cm...     

Field-scale spatial variability of saturated hydraulic conductivity on a recently constructed artificial ecosystem

Saturated hydraulic conductivity (K_s) influences water storage and movement, and is a key parameter of water and solute transport models. Systematic field evaluation of K_s and its spatial variability for recently constructed artificial ecosystems is still lacking. The objectives of the present study were; (1) to determine saturated hydraulic conductivity of an artificial ecosystem using field methods (Philip-Dunne, and Guelph permeameters), and compare their results to the constant-head laboratory method; (2) to evaluate the spatial variability of K_s using univariate and geostatistical analyses, and (3) to evaluate the ability of five pedotransfer functions to predict K_s. The results showed that K_s varied significantly (p < 0.05) among methods, probably reflecting differences in scales of measurement, flow geometry, assumptions in computation routines and inherent disturbances during sampling. Mean K_s values were very high for all methods (38.6-77.9 m day^− 1), exceeding values for natural sandy soils by several orders of magnitude. The high K_s values and low coefficients of variation (26-44%) were comparable to that of well-sorted unconsolidated marine sands. Geostatistical analysis revealed a spatial structure in surface K_s data described by a spherical model with a correlation range of 8 m. The resulting kriged map of surface K_s showed alternating bands of high and low values, consistent with surface structures created by wheel tracks of construction equipment. Vertical K_s was also spatially structured, with a short correlation range of 40 cm, presumably indicative of layering caused by post-construction mobilization and deposition of fine particles. K_s was linearly and negatively correlated with dry soil bulk density (ρ_b) (r² = 0.73), and to a lesser extent silt plus clay percentage (Si + C) (r² = 0.21). Combining both ρ_b and Si + C significantly (p < 0.05) improved the relationship and gave the best predictor of K_s (r² = 0.76). However, evaluation of five PTFs developed for natural soils showed that they all underestimated K_s by an order of magnitude, suggesting that application of water balance simulation models based on such PTFs to the present study site may constitute a bias in model outputs. Overall, the study demonstrated the influence of material handling, construction procedures and post-construction processes on the magnitude and spatial variability of K_s on a recently constructed artificial ecosystem. These unique hydraulic properties may have profound impacts on soil moisture storage, plant water relations and water balance fluxes on artificial ecosystems, particularly where such landforms are intended to restore pre-disturbance ecological and hydrological functions.     

青藏高原土壤可蚀性K值的空间分布特征

土壤可蚀性反映了土壤对水力侵蚀作用的敏感性,是进行土壤侵蚀评价和预报的重要参数。收集了青藏高原1 255个典型土壤剖面资料,采用模型计算和面积加权分析方法确定了每一个土壤亚类的土壤可蚀性K值,结合青藏高原1∶100万土壤类型图,分析了青藏高原土壤可蚀性K值的空间格局特征。结果表明,青藏高原土壤可蚀性K值平均为0.230 8,低可蚀性、较低可蚀性、中等可蚀性、较高可蚀性和高可蚀性土壤面积分别占该区面积的5.60%,18.23%,24.35%,44.02%和7.80%。土壤可蚀性以中等可蚀性和较高可蚀性为主,二者分布面积之和达1.77×106 km2,占青藏高原总面积的68.37%;较高可蚀性、高可蚀性土壤主要分布在青藏高原中西部的羌塘高原、柴达木盆地和横断山区的低海拔河谷中。青藏高原土壤可蚀性K值具有明显的垂直分异特征,在横断山区最为显著,土壤可蚀性随海拔高度升高而降低。不同海拔高度的水热分异影响了土壤的理化特性,进而决定了青藏高原土壤可蚀性的垂直分带特征。     

晋西北黄土丘陵区土壤饱和导水率的空间分布特征及影响因素

土壤饱和导水率（K_s）是反映土壤入渗性能与土壤持水能力的重要参数，为探究流域尺度下土壤K_s的空间分布特征及影响因素，更好地掌握土壤水文过程与调节机理，选取晋西北黄土丘陵区朱家川流域横向梯度（上游、中游、下游）不同土地利用方式下的土壤（70个样点）为研究对象，采用定水头法测定土壤K_s，并获取样点地形因子和其他土壤理化性质，通过建立土壤K_s偏最小二乘回归模型（PLSR），分析影响土壤K_s空间分布格局的主要因素。结果表明：（1）除土壤容重和砂粒含量为弱变异外，区域土壤理化性质其余因子均为中等变异；土壤K_s在横向梯度下表现为上游 > 中游 > 下游；（2）不同土地利用方式下土壤K_s差异显著（P<0.05），由高到低顺序为林地 > 农地 > 草地；（3）林地（VIP=1.997）与草地（VIP=1.710）利用方式、土壤容重（VIP=1.548）、土壤有机质（VIP=1.323）、大团聚体（VIP=1.266）、粉粒含量（VIP=1.062）和黏粒含量（VIP=1.049）是土壤K_s变化的主要因素，林地利用方式影响程度最大。土地利用、土壤性质、地形因子均是影响黄土丘陵区土壤K_s空间分布的主要因素，是用来模拟预测土壤K_s空间分布的重要因子。     

Controlling effects of surface crusts on water infiltration in an arid desert area of Northwest China

Purpose

Surface crusts are important features in arid desert areas and are critical to hydrological processes and ecosystem development. This paper aims to understand the effects of crusts on water movement in the soil and the factors that affect this and to provide the soil parameters for estimation of saturated hydraulic conductivity (K _s) in ecohydrological models.

Materials and methods

The study area was located in the middle and lower reaches of the Heihe River Basin, an arid desert area in Northwest China. There were three crust types in this region: physical soil crusts (PSCs, formed by water drop and erosion), biological soil crusts (BSCs, formed by microorganisms, moss, algae, lichen, and soil materials), and salt soil crusts (SSCs, formed by soluble salts). The infiltration rates of different soil and crust types and scalped soils were determined in situ in the field conditions using a disc infiltrometer with three repetitions. Crusts and soils were collected, and their properties were determined in the laboratory.

Results and discussion

The K _s of crust were significantly lower than that of scalped soils with a decrease of 13–70 %. The K _s of crusts were related to the type of crust and the properties of soil beneath the crusts. In this region, the soil textures are similar throughout, due to ubiquitous loess sedimentation, so textural differences had no significant effect on K _s. Soil organic matter (SOM) played a weak negative role on K _s because most crusts had higher SOM than the underlying soil. However, both crust thickness and electrical conductivity (EC, an index of salt concentration) showed significantly negative exponential relationship with K _s. Therefore, the SSC with high EC and thick crust have the lowest K _s among all crust types. Because soil development is related to salt accumulation, structure, and crust formation, the K _s follows the order of Solonchaks < Cambisols < Regosols, from lowest to highest.

Conclusions

Crusts have different characteristics compared with original soils and are the limiting layer of water infiltration in these arid soils. Therefore, the characteristics of crust must be considered in ecohydrological models. The main apparent controlling parameters of water infiltration rate in this area are crust thickness and EC.