1960-2017年重庆市不同量级降雨侵蚀力R值的时空变化特征

降雨是引起土壤侵蚀的主要动力之一,探究不同量级降雨侵蚀力的时空变化特征,以期为重庆市水土流失防治提供参考。选取1960—2017年重庆市及周边42个站点24 h降雨数据,按雨量等级表将其划分为不同量级降雨,利用简易模型计算R值,应用气候倾向率、变异系数、普通Kriging插值分析重庆市各量级降雨侵蚀力的时空演变特征。结果表明:(1)研究期内重庆市年均R值为5 751.55(MJ·mm)/(hm²·h·a),变异系数为0.19,属中等变异;(2)不同量级降雨侵蚀力对年均R值的贡献率为大雨(34.82%)＞暴雨(30.53%)＞中雨(23.93%)＞大暴雨(10.53%),其中大暴雨侵蚀力的气候倾向率最大,对年降雨侵蚀力的上升贡献率最高;(3)降雨侵蚀力年内分配极度不均,54.19%集中分布在夏季,仅有0.91%分布在冬季;(4)年均降雨侵蚀力和各量级降雨侵蚀力空间分布呈东高西低的格局。通过对重庆市不同量级降雨侵蚀力的分析,明确了发生土壤侵蚀可能性较高的时期和地区,为因地制宜制定水土保持措施提供参考。     

Assessing soil spatial variability and delineating site-specific management zones for a coastal saline land in eastern India

ABSTRACT

The present study was to delineate management zones (MZs) in salt affected Mahakalpada block in eastern India by capturing both spatial variability of soil parameters along with satellite derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Grid wise 237 soil samples collected from the study area were analyzed and spatial maps were generated for physicochemical properties, DTPA extractable micronutrients, i.e. iron, zinc, copper, and manganese and major nutrients, i.e. available nitrogen (AN), phosphorous (AP), and potassium (AK). Soil electrical conductivity and AK showed a high CV of 100% and 56.7%, respectively. Principal component analysis was performed using the soil spatial maps, NDVI and EVI maps and only four principal components which produced eigenvalues > 1 and accounting for 75.4% of the total variability were retained for further analysis. Further, fuzzy c-mean clustering was used to delineate the MZs based on fuzzy performance index (FPI) and normalized classification entropy (NCE) was used for identifying the three MZs. There was a significant difference between MZ1 and MZ2 for all the variables except AN and EVI whereas all the variables were significantly different between MZ1 and MZ3 highlighting the usefulness of MZs delineation technique for site-specific nutrient management.     

江西多年平均降水量空间插值模型的选取与比较

为了探究江西省多年平均降水量的空间分布格局,本文选取81个时间序列完整的气象站30 a(1976-2005年)降水数据,运用泛克里格中不同半变异模型对降水数据进行拟合.分别采用65个建模站点交叉验证和16个验证站点的检验,证实了该研究区域多年平均降水量存在较强的空间相关性.通过不同半变异函数模型的对比发现,球面和指数模型在建模站点交叉验证结果中的标准均方根预测误差分别为1.024和1.023,较为接近1,表明其误差较小;其在验证站点检验结果中的标准均方根预测误差分别为1.105和1.104,表明这两种模型的拟合效果较优,能较为真实地反映江西省多年平均降水量的空间分布情况,其中以指数模型拟合效果最优.     

不同方法预测河北省土壤有机碳密度空间分布特征的研究

运用多元线性回归、泛克里格和回归克里格三种方法,结合由DEM获取的地形属性因子预测了河北省土壤有机碳密度的空间分布.多元线性回归预测的残差较大,模型对总方差的解释仅18.6%,采用泛克里格方法后,预测残差降低,预测结果的极差范围变宽,低碳密度区的局部变异得以体现,模型对总方差的解释程度提高到53%.而回归克里格方法应用后预测残差和均方根预测误差进一步降低,模型对总方差的解释程度提高到65%,回归克里格方法也能更好地反映碳密度与地形的关系以及局部变异.三种方法中回归克里格预测效果最好,泛克里格次之,而多元线性回归方法最差.     

缓坡水平梯田土壤水分空间变异性

以江苏省扬州市区北部某梯田为例,分析了小于2°的缓坡水平梯田土壤含水率的空间变异特征,并针对缓坡水平梯田土壤含水率由较高田块到较低田块逐渐增加、同一级梯田内由内侧到外侧含水率逐渐减小的分布特征,提出了适合研究该种地形土壤含水率空间分布的有效方法,即趋势辅助克立格法(简记为KTAI)。该方法同时考虑了不同级梯田高程和同级田块内不同部位对土壤含水率的影响,用它对梯田土壤含水率进行插值,估计方差比传统的普通克立格法(简记为OK)大幅度降低,大大提高了估值精度,减轻了野外采样工作量,对于研究梯田土壤水分空间变异性具有重要意义,同时拓展了地质统计学理论在土壤水分空间变异性研究中的应用范围。     

中国土壤温度的空间预测研究

土壤温度栅格数据是很多区域性宏观研究的重要基础,对离散的土壤温度数据进行空间预测分析获取空间上连续的土壤温度数据具有重要意义。本文用我国698个气象站点的年均土壤温度和年均气温数据以及数字高程模型数据,分析不同气象和地形因素对年均土壤温度的影响;根据全国各地可获取数据源的不同,分别用3组不同的影响因素为辅助变量:(1)年均气温;(2)经度、纬度和海拔;(3)年均气温、经度、纬度和海拔,采用回归克里格法预测我国年均土壤温度空间分布。结果表明年均气温、经度、纬度和海拔对年均土壤温度空间变异均有显著影响。土壤温度空间预测结果的准确性检验显示用经度、纬度和海拔预测土壤温度的精度最高,基于年均气温、经度、纬度和海拔预测的稍差,只用年均气温预测的最差。辅助变量数据的精度及其与年均土壤温度的相关性对预测效果的影响较大。     

普通克里格评估土壤污染物空间分布精度控制研究

从数据的统计特征分析、异常值筛选及处理、实验半变异函数计算及拟合等方面对普通克里格计算过程中的精度控制问题进行了初步研究,并以天津市东南郊区土壤砷（As）空间分布评估为例,分析了在多步骤精度控制下普通克里格法估计土壤污染物空间分布的可靠性。结果表明,研究区土壤As表现出了明显的累积特征,且监测值为正态分布,异常值较少且为局部异常值,实验半变异函数表现出明显的各向异性。在较严格的精度控制下,普通克里格估值的最优无偏特性可以得到较好的体现,其对土壤As的空间分布的估计精度可以达到较高的水平。     

杞麓湖盆地土壤有机质多时相空间分布与演变

基于四期土壤采样点数据,结合样点调查和统计年鉴数据,采用普通克里格、多元回归和探索性回归方法预测杞麓湖盆地土壤有机质(SOM)空间分布并分析其影响因素。结果表明指数和椭球模型分别较好地拟合了2008和2011年、2013和2015年的SOM的空间变异性。四个时相的SOM均由西南到中部再到东北部呈现出低-高-低的分布特征,并且高值核心区的位置不变,但高值区范围逐年扩大。前三个时间段内SOM连续增加的区域面积是连续减小的区域面积的两倍多。土壤亚类类型、农业设施和土壤质地各解释SOM变异的14.3%、2.6%和1.3%。SOM的降低主要由粮食和蔬菜的高产出所导致,适当地减少氮肥施用量并增加复合肥施用量能在一定程度上维持甚至提升SOM含量。     

中国禹城土壤盐渍化的时空变异及其预测

This research used both geostatistics and GIS approach to compare temporal change of soil salt between 1980 and 2003, to analyze the spatial distribution of surface soil salt, to developed methods for predicting soil salinization potential based on recent improvements to the Dempster-Shafer theory, and to develop probability maps of potential salinization in Yucheng City, China. A semivariogram model of soil salt content was developed from the spherical model, and then employing kriging interpolation the spatial distribution of salt content in 2003 was obtained utilizing data from 100 soil sampling points. Potential salinization distribution was mapped using an approach that integrated soil data of the second general survey in 1980 in Yucheng City, which included groundwater salinity, groundwater depth, soil texture, soil organic matter content, and geomorphic maps. With the support of Dempster-Shafer theory and fuzzy set technique the factors that affected potential soil salinization were characterized and integrated;and then soil salinization was predicted. Finally a prognosis map of potential salinization distribution in the research area was obtained, with higher probability values indicating higher hazards to salinity processes. The distribution of the potential soil salinization probability was a successive surface.     

Effect of sampling density on regional soil organic carbon estimation for cultivated soils

An extensive knowledge of how sampling density affects soil organic C (SOC) estimation at regional scale is imperative to reduce uncertainty to a meaningful confidence level and aid in the development of sampling schemes that are both rational and economical. Using kriging prediction, this paper examined the effect of sampling density on regional SOC‐concentration estimations in cultivated topsoils at six scales in a 990 km² area of Yucheng County, a typical region in the N China Plain. Except the original data set (n = 394), five other sampling densities were recalculated using grids of 8 km × 8 km (n = 28), 8 km × 4 km (n = 44), 4 km × 4 km (n = 82), 4 km × 2 km (n = 142), and 2 km × 2 km (n = 257), respectively. Experimental SOC semivariances and kriging interpolations at six sampling density scales were calculated and modeled to estimate regional SOC variability. Accuracy of the effects of the five sampling densities on regional SOC estimations was assessed using the indices of mean error (ME) and root mean square error (RMSE) with 100 independent validation samples. By comparison with the kriged grid map derived from the 394 samples data set, the relative error (RE,%) was spatially calculated to highlight the spatial variability of prediction errors at five sampling‐density scales due to the intrinsic limitations of ME and RMSE in accuracy assessment. The results indicated that sampling density significantly affected the estimation of regional SOC concentration. Particularly when the sampling density was < 4 km × 4 km, the large spatial variation of SOC was concealed. Semivariance analysis indicated that different sampling density had significant effect on reasonable detection of the dominant factors which influenced SOC spatial variation. Greater sampling density could more exactly reveal regional SOC variation caused by human management. The prediction accuracy for regional SOC estimation increased with the increasing of sampling density. The critical areas with larger RE values should be intensified in the future sampling scheme, and the areas of lower RE values should be decreased relatively. A specific sampling scheme should be considered in accordance with the demand to the estimation accuracy of regional SOC stock at a certain confidence level. Our results will facilitate a better understanding of the effect of sampling density on regional SOC estimation for future sampling schemes by providing meaningful confidence levels.