External drift kriging of NOx concentrations with dispersion model output in a reduced air quality monitoring network

In the mid nineteen eighties the Dutch NO_x air quality monitoring network was reduced from 73 to 32 rural and city background stations, leading to higher spatial uncertainties. In this study, several other sources of information are being used to help reduce uncertainties in parameter estimation and spatial mapping. For parameter estimation, we used Bayesian inference. For mapping, we used kriging with external drift (KED) including secondary information from a dispersion model. The methods were applied to atmospheric NO_x concentrations on rural and urban scales. We compared Bayesian estimation with restricted maximum likelihood estimation and KED with universal kriging. As a reference we also included ordinary least squares (OLS). Comparison of several parameter estimation and spatial interpolation methods was done by cross-validation. Bayesian analysis resulted in an error reduction of 10 to 20% as compared to restricted maximum likelihood, whereas KED resulted in an error reduction of 50% as compared to universal kriging. Where observations were sparse, the predictions were substantially improved by inclusion of the dispersion model output and by using available prior information. No major improvement was observed as compared to OLS, the cause presumably being that much good information is contained in the dispersion model output, so that no additional spatial residual random field is required to explain the data. In all, we conclude that reduction in the monitoring network could be compensated by modern geostatistical methods, and that a traditional simple statistical model is of an almost equal quality.

Monitoring and estimating the flow conditions and fish presence probability under various flow conditions at reach scale using genetic algorithms and kriging methods

The combination of current velocity and water depth influences stream flow conditions, and fish activities prefer particular flow conditions. This study develops a novel optimal flow classification method for identifying types of stream flow based on the current velocity and the water depth using a genetic algorithm. It is applied to the Datuan stream in northern Taiwan. Fish were sampled and their habitat investigated at the study site during the spring, summer, fall and winter of 2008-2009. The current velocity, water depth and maps of the presence probability of fish were estimated by ordinary and indicator kriging. The optimal classification results were compared with the classification results obtained using the Froude number and empirical methods. The flow classification results demonstrate that the proposed optimal flow classification method that considers depth-velocity and optimally identified criteria for classifying flow types, yields a current velocity and water depth of 0.32 (m/s) and 0.29 (m), respectively, and classifies the flow conditions in the study area as pool, run, riffle and slack. The variography results of the current velocity and the water depth data reveal that seasonal flows are not spatially stationary among seasons in the study area. Kriging methods and a two-dimensional hydrodynamic model (River 2D) with empirical and optimal flow classification methods are more effective than the Froude number method in classifying flow conditions in the study area. The flow condition classifications and probability maps were generated by River 2D, ordinary kriging and indicator kriging, to quantify the flow conditions preferred by Sicyopterus japonicus in the study area. However, the proposed optimal classification method with kriging and River 2D is an effective alternative method for mapping flow conditions and determining the relationship between flow and the presence probability of target fish in support of stream restoration.     

Ordinary kriging for function-valued spatial data

In various scientific fields properties are represented by functions varying over space. In this paper, we present a methodology to make spatial predictions at non-data locations when the data values are functions. In particular, we propose both an estimator of the spatial correlation and a functional kriging predictor. We adapt an optimization criterion used in multivariable spatial prediction in order to estimate the kriging parameters. The curves are pre-processed by a non-parametric fitting, where the smoothing parameters are chosen by cross-validation. The approach is illustrated by analyzing real data based on soil penetration resistances.     

禹城地区土壤铅含量空间分布的指示克里格估值

以鲁西北禹城地区土壤重金属Pb含量为例,针对其存在特异值与偏态分布的特点,利用指示克里格法研究了土壤Pb含量小于特定阈值的条件概率分布,分析了该地区土壤Pb含量的空间分布特征,并对普通克里格和指示克里格的估值效果进行交叉验证。结果表明：研究区土壤Pb含量总体较低,未超过国家标准的限值,但不同部位土壤Pb含量差异较大;普通克里格法和指示克里格法形成的土壤Pb含量空间分布格局相似,但普通克里格法的平滑效果强于指示克里格法;研究区域土壤环境质量总体良好,Pb含量高值区主要集中在各乡镇驻地附近的农田;指示克里格对土壤Pb含量空间估值的精度优于普通克里格,指示克里格法更适用于原始数据呈非正态分布且特异值对估值结果有较大影响的土壤属性。本研究结果为该地区土壤环境质量评价和重金属污染防控提供科学依据。     

Application of geostatistical methods to arsenic data from soil samples of the Cova dos Mouros mine (Vila Verde-Portugal)

A total of 286 soil samples were collected in the Cova dos Mouros area. All samples were dry sieved into the <200 mesh size fraction and analysed for Fe, Cu, Zn, Pb, Co, Ni, Bi and Mn by atomic absorption spectrometry (AAS) and for As, Se, Sb and Te by atomic absorption spectrometry-hydrid generation (AAS-HG). Only the results of arsenic are discussed in this paper although the survey was extended to all analysed chemical elements. The purpose of this study was to make a risk probability mapping for arsenic that would allow better knowledge about the vulnerability of the soil to arsenic contamination. To achieve this purpose, the initial variable was transformed into an indicator variable using as thresholds the risk-based standards (intervention values) for soils, as proposed by [Swartjes 1999. Risk based assessment of soil and groundwater quality in the Netherlands: Standards and remediation. J. Geochem. Explor.73 1–10]. To account for spatial structure, sample variograms were computed for the main directions of the sampling grid and a spherical model was fitted to each sample variogram (arsenic variable and indicator variables). The parameters of the spherical model fitted to the arsenic variable were used to predict arsenic concentrations at unsampled locations. A risk probability mapping was also done to assess the vulnerability of the soil towards the mining works. The parameters of the spherical model fitted to each indicator variable were used to estimate probabilities of exceeding the corresponding threshold. The use of indicator kriging as an alternative to ordinary kriging for the soil data of Cova dos Mouros produced unbiased probability maps that allowed assessment of the quality of the soil.     

Spatial analyses of groundwater level differences using geostatistical modeling

The purpose of this study was to determine and evaluate the spatial changes in the depletion of groundwater level differences by using geostatistical methods based on data from 58 groundwater wells during the period from April 1999 to April 2008 in the study area. Geostatistical methods have been used widely as a convenient tool to make decision on the management of groundwater levels. To evaluate the spatial changes in the level of the groundwater, geographic information system is used for the application of universal kriging method with cross-validation leading to the estimation of groundwater levels. The resulting prediction mappings identify the locations of groundwater level fluctuations of the study area. The average range of variogram (spherical model) for the spatial analysis is about 9,200 m. Results of universal kriging for groundwater level differences drops were underestimated by 15 %. Cross-validation errors are within an acceptable level. The maps show that this area of high decrease of groundwater level is located at the southwest. Kriging model helps also to detect sensitively risk prone areas for groundwater withdrawing. Such areas must be protected with an effective management procedure for future groundwater exploitations.     

典型喀斯特区侵蚀性降雨特征及坡面生物措施水土流失防控效应

降雨类型和生物措施是坡面水土流失的重要影响因素.为研究喀斯特区降雨类型和不同坡面生物措施共同作用下的水土保持效益,以贵州蚂蟥田小流域水土保持监测站2014—2018年5年的径流小区实测数据为基础,分析了112场次的侵蚀性降雨特征,并以降雨量、降雨历时、平均降雨强度为指标对其进行分类.同时,分析了不同侵蚀性降雨类型下6个...     

Spatial methods for plot-based sampling of wildlife populations

Classical sampling methods can be used to estimate the mean of a finite or infinite population. Block kriging also estimates the mean, but of an infinite population in a continuous spatial domain. In this paper, I consider a finite population version of block kriging (FPBK) for plot-based sampling. The data are assumed to come from a spatial stochastic process. Minimizing mean-squared-prediction errors yields best linear unbiased predictions that are a finite population version of block kriging. FPBK has versions comparable to simple random sampling and stratified sampling, and includes the general linear model. This method has been tested for several years for moose surveys in Alaska, and an example is given where results are compared to stratified random sampling. In general, assuming a spatial model gives three main advantages over classical sampling: (1) FPBK is usually more precise than simple or stratified random sampling, (2) FPBK allows small area estimation, and (3) FPBK allows nonrandom sampling designs.     

城市降雨径流长期污染负荷模型的探讨

本文以成都市为例,使用修正的STORM模型计算一次标准降雨的径流污染负荷;使用“降雨负荷法”进行城市径流污染年负荷量的估算.文章指出年负荷中非标准降雨冲刷将起着主导作用,仅用标准降雨来估算一个城市的长期污染负荷是不可行的.     

Nile water pollution and technical efficiency of crop production in Egypt: an assessment using spatial and non-parametric modelling

Agriculture is considered one of the vital activities in Egypt; it consumes about 83 % of the Egyptian Nile water quota. This activity is becoming negatively affected by water pollution causing negative repercussion on land productivity and subsequently food security. This paper assesses the water quality for agriculture along the mainstream of the Nile in Egypt through spatial distributions modelling of total dissolved solids (TDS), using spatial statistical analysis. The study’s sample frame consists of 78 sampling points collected in February 2008 and located on the Nile mainstream and its two branches, Rosetta and Damietta. Exploratory spatial data analysis is carried out on the TDS, followed by plotting and modelling the experimental semi-variogram. Then, cross validation is executed in order to determine the best fitting model. Finally, surface mapping is generated by performing spatial interpolation, using kriging technique. The generated surface map shows that the TDS levels increase from Upper to Lower Egypt, exceeds the standard limit in Beni-Suef and Rosetta branch. In fact, high levels of TDS are known to have a negative effect on Egyptian agriculture through harmfully affecting the soil and consequently the crop yields. Therefore, an analysis of the effect of water pollution on technical efficiency of crop production is conducted using a non-parametric mathematical programming approach to frontier estimation. The results of this estimation indicated that the TDS is overutilized in all governorates except Aswan.     

太湖地区稻田氮素损失特征及环境效应分析

通过氮肥减量小区试验,研究了太湖地区稻田氮素径流损失、渗漏损失、氨挥发损失以及氨挥发通量的动态变化特征,阐述了氮素损失量、水稻产量与施氮量之间的关系。结果表明：稻季氮素径流损失和氨挥发损失均随施氮量的增加不断增加,而渗漏损失与施氮量没有显著相关性。综合整个稻季,氨挥发损失以分蘖肥期最高,基肥期次之,穗肥期最低。稻季氮素总损失为13.7～59.8 kg·hm-2,占总施氮量的16.5%～22.2%,且随施氮量的增加而不断增加,其中氨挥发损失占42.2%～72.0%,径流损失占22.2%～38.4%,渗漏损失占5.8%～22.7%。稻季181 kg·hm-2的氮肥用量,较常规施氮量减少了33%的氮肥,增加了10.3%的产量,降低了48.5%的氮素损失,较好地兼顾了粮食产量和环境效应;而对于重要环境区域或高污染区域,还可以尝试更低的氮肥投入,以达到更好的环境效益。     

Hierarchical Bayesian scaling of soil properties across urban, agricultural, and desert ecosystems.

Ecologists increasingly use plot-scale data to inform research and policy related to regional and global environmental change. For soil chemistry research, scaling from the plot to the region is especially difficult due to high spatial variability at all scales. We used a hierarchical Bayesian model of plot-scale soil nutrient pools to predict storage of soil organic carbon (oC), inorganic carbon (iC), total nitrogen (N), and available phosphorus (avP) in a 7962-km2 area including the Phoenix, Arizona, USA, metropolitan area and its desert and agricultural surroundings. The Bayesian approach was compared to a traditional approach that multiplied mean values for urban mesic residential, urban xeric residential, nonresidential urban, agricultural, and desert areas by the aerial coverage of each land-use type. Both approaches suggest that oC, N, and avP are correlated with each other and are higher (in g/m2) in mesic residential and agricultural areas than in deserts or xeric residential areas. In addition to traditional biophysical variables, cultural variables related to impervious surface cover, tree cover, and turfgrass cover were significant in regression models predicting the regional distribution of soil properties. We estimate that 1140 Gg of oC have accumulated in human-dominated soils of this region, but a significant portion of this new C has a very short mean residence time in mesic yards and agricultural soils. For N, we estimate that 130 Gg have accumulated in soils, which explains a significant portion of "missing N" observed in the regional N budget. Predictions for iC differed between the approaches because the Bayesian approach predicted iC as a function of elevation while the traditional approach employed only land use. We suggest that Bayesian scaling enables models that are flexible enough to accommodate the diverse factors controlling soil chemistry in desert, urban, and agricultural ecosystems and, thus, may represent an important tool for ecological scaling that spans land-use types. Urban planners and city managers attempting to reduce C emissions and N pollution should consider ways that landscape choices and impervious surface cover affect city-wide soil C, N, and P storage.     

Sensitivity of ecological models to their climate drivers: statistical ensembles for forcing.

Global and regional numerical models for terrestrial ecosystem dynamics require fine spatial resolution and temporally complete historical climate fields as input variables. However, because climate observations are unevenly spaced and have incomplete records, such fields need to be estimated. In addition, uncertainty in these fields associated with their estimation are rarely assessed. Ecological models are usually driven with a geostatistical model's mean estimate (kriging) of these fields without accounting for this uncertainty, much less evaluating such errors in terms of their propagation in ecological simulations. We introduce a Bayesian statistical framework to model climate observations to create spatially uniform and temporally complete fields, taking into account correlation in time and space, spatial heterogeneity, lack of normality, and uncertainty about all these factors. A key benefit of the Bayesian model is that it generates uncertainty measures for the generated fields. To demonstrate this method, we reconstruct historical monthly precipitation fields (a driver for ecological models) on a fine resolution grid for a climatically heterogeneous region in the western United States. The main goal of this work is to evaluate the sensitivity of ecological models to the uncertainty associated with prediction of their climate drivers. To assess their numerical sensitivity to predicted input variables, we generate a set of ecological model simulations run using an ensemble of different versions of the reconstructed fields. We construct such an ensemble by sampling from the posterior predictive distribution of the climate field. We demonstrate that the estimated prediction error of the climate field can be very high. We evaluate the importance of such errors in ecological model experiments using an ensemble of historical precipitation time series in simulations of grassland biogeochemical dynamics with an ecological numerical model, Century. We show how uncertainty in predicted precipitation fields is propagated into ecological model results and that this propagation had different modes. Depending on output variable, the response of model dynamics to uncertainty in inputs ranged from uncertainty in outputs that matched that of inputs to those that were muted or that were biased, as well as uncertainty that was persistent in time after input errors dropped.     

Accounting for geophysical information in geostatistical characterization of unexploded ordnance (UXO) sites

Efficient and reliable unexploded ordnance (UXO) site characterization is needed for decisions regarding future land use. There are several types of data available at UXO sites and geophysical signal maps are one of the most valuable sources of information. Incorporation of such information into site characterization requires a flexible and reliable methodology. Geostatistics allows one to account for exhaustive secondary information (i.e.,, known at every location within the field) in many different ways. Kriging and logistic regression were combined to map the probability of occurrence of at least one geophysical anomaly of interest, such as UXO, from a limited number of indicator data. Logistic regression is used to derive the trend from a geophysical signal map, and kriged residuals are added to the trend to estimate the probabilities of the presence of UXO at unsampled locations (simple kriging with varying local means or SKlm). Each location is identified for further remedial action if the estimated probability is greater than a given threshold. The technique is illustrated using a hypothetical UXO site generated by a UXO simulator, and a corresponding geophysical signal map. Indicator data are collected along two transects located within the site. Classification performances are then assessed by computing proportions of correct classification, false positive, false negative, and Kappa statistics. Two common approaches, one of which does not take any secondary information into account (ordinary indicator kriging) and a variant of common cokriging (collocated cokriging), were used for comparison purposes. Results indicate that accounting for exhaustive secondary information improves the overall characterization of UXO sites if an appropriate methodology, SKlm in this case, is used.     

Comparison of Spatial Methods for the Assessment of Planktonic Patches

Planktonic patches are defined as areas where the abundance of plankters is above a threshold value τ. The estimation of patch size and shape can be approached using spatial statistical tools, using truncated random fields or indicator random fields as classifiers. In all cases there is the risk of false positive and false negative errors. In this paper we present the results of a comparative study on the performance of four commonly used methods: conditional simulation and kriging, both in the original measurement units of the data and under an indicator transform. We used a misclassification cost function to compare the four methods. Our results show that conditional simulation in the original measurement units attains the lowest misclassification cost. We also illustrate how the point at which this minimum is attained can be used to chose an optimal cut-off value for binary classification. Received: December 2003 / Revised: June 2005     

北京市地面街尘与径流中重金属的污染特征（Characteristics of Heavy Metals in Street Dusts and Urban Runoff in Beijing）

街尘作为城市各种污染物的载体和地表径流污染物的主要来源对水环境的影响日益受到关注.论文分析了北京市城区街尘与地表径流中重金属浓度和颗粒的粒径分布.结果表明:不同城市土地利用类型对街尘和径流中的重金属含量、颗粒粒径分布具有重要影响.在商业区、主要道路、住宅区、城中村4种土地利用类型中,街尘重金属浓度和地面单位面积重金属质量均以主要道路最高;径流重金属浓度由高到低顺序依次为:主要道路>城中村>居民区;主要道路和商业区街尘颗粒中细粒径占的比例较高,在全部土地利用类型的径流水样中颗粒物粒径分布差别不大;随着街尘中颗粒物粒径的减小,重金属浓度增加;街尘中小于149μm的颗粒物质量百分比和重金属浓度均较高,且在径流中这个粒径段的颗粒物含量也高,体积比达80%以上.建议在今后的城市街尘面源污染控制中应特别关注土地利用类型和街尘粒径的影响.     

太湖典型河网区地表水与沉积物氮、磷和重金属含量空间分异

将无锡城乡交错区河网分为城镇生活区水巷、工业区主河道、农业区主河道、农业区支流和鱼塘5种类型区.通过秋季(旱季)和夏季(雨季)2次采样,研究氮、磷和Cu、Zn、Pb、Cr、Cd在地表水和沉积物中的含量及其空间变化.结果表明:(1)城乡交错区地表水氮、磷来源于生活污染和工业污染的比例大于农田,同时具有来源分散、污染面广的特征.(2)由于城镇地表径流和工业活动影响,各类型区地表水重金属含量均有不同程度提高.但地表水重金属主要沿主河道迁移,旱季和雨季主河道重金属含量均大于农业区支流,而且随着与城镇距离增加,主河道沉积物重金属含量迅速降低,重金属污染影响范围较小.(3)与重金属相比,地表水氮、磷污染仍是太湖水网区主要的环境问题.但城镇及其周围河流沉积物中富集的重金属含量很高,其潜在环境风险不容忽视.     

三峡水库1956—2017年坝下径流特征分析

采用三峡水库坝下宜昌水文站1956—2017年日径流监测数据和长江上游80个国家气象站年均降水量长系列资料,利用一元线性分析法、滑动平均法、线性趋势回归法、非参数统计检验法、Mann-Kendall秩次相关、累积距平法和滑动T检验法分析三峡工程运行前后坝下径流的变化特征,其成果可为三峡工程调度提供参考。结果表明:三峡水库坝下宜昌水文站径流量变化趋势具有显著阶段性。三峡水库蓄水运行前宜昌水文站径流量和上游降水量相关性超过90%,该时期径流量主要受上游天然来水影响。三峡水库运行后,径流量主要受人类活动影响。宜昌水文站径流年际变化减少趋势不显著,长江上游降水量呈显著减少趋势。坝下径流量和上游降水量在2002年发生了突变且变化趋势相同,但径流量减少趋势不显著。径流年内分布发生了一定变化,其中汛期径流量占年径流总量百分比从72%下降到66%,枯期径流量占年径流量总量百分比从11%增加到15%。三峡工程调蓄是人类活动的直接表现,枯期宜昌水文站径流量增大是三峡工程生态效益的具体体现。     

基于DFA的无定河径流长期变化趋势及持续性研究

河川径流量的变化对水资源的开发利用和国民经济的发展具有很大的影响。采用Ko1mogorov-Smirnov检验方法对无定河流域两个主要水文站赵石窑站和白家川站逐月径流量序列进行正态性检验,在此基础上运用DFA分析方法,估算两站时间序列的α标度指数,预测该流域径流量的未来变化趋势及其持续性,并采用随机重排法对估算结果进行了稳定性检验。分析结果显示:赵石窑和白家川两站径流时间序列的α标度指数分别为0.52和0.56,均大于0.5,径流时间序列表现为较强的长程依赖性,表明无定河流域在过去近50a内径流量的总体上呈下降趋势,在未来一段时间内,还将持续一段时间。对原时间序列随机重排后,两站月径流序列的α标度指数都变小,分别为0.48和0.49,接近于0.5,进一步证明原序列确实存在着标度不变性。研究结果对流域的水资源规划、管理与合理利用有帮助。     

对城市雨水地下回灌的分析

城市扩张带来雨水径量增大,排出时间缩短并由此产生一系列后果,对城区雨水回灌地区的效益进行了讨论,分析城区雨水径流水质污污状况及其影响因素,初期雨水径流污染严重,主要受到地面条件,屋面材料,气温,降雨量及降雨强度等影响,根据对雨水水质的研究结果,分析了雨水回灌地下的条件及必要的雨水处理措施,对雨水回灌技术作了简略的论述。