Numerical simulation of agricultural sediment and pesticide runoff: RZWQM and PRZM comparison

Agricultural sediment and pesticide runoff is a widespread ecological and human health concern. Numerical simulation models, such as Root Zone Water Quality Model (RZWQM) and Pesticide Root Zone Model (PRZM), have been increasingly used to quantify off‐site agricultural pollutant movement. However, RZWQM has been criticized for its inability to simulate sedimentation processes. The recent incorporation of the sedimentation module of Groundwater Loading Effects of Agricultural Management Systems has enabled RZWQM to simulate sediment and sediment‐associated pesticides. This study compares the sediment and pesticide transport simulation performance of the newly released RZWQM and PRZM using runoff data from 2 alfalfa fields in Davis, California. A composite metric (based on coefficient of determination, Nash–Sutcliffe efficiency, index of agreement, and percent bias) was developed and employed to ensure robust, comprehensive assessment of model performance. Results showed that surface water runoff was predicted reasonably well (absolute percent bias <31%) by RZWQM and PRZM after adjusting important hydrologic parameters. Even after calibration, underestimation bias (?89% ≤ PBIAS  ≤ ?36%) for sediment yield was observed in both models. This might be attributed to PRZM's incorrect distribution of input water and uncertainty in RZWQM's runoff erosivity coefficient. Moreover, the underestimation of sediment might be less if the origin of measured sediment was considered. Chlorpyrifos losses were simulated with reasonable accuracy especially for Field A (absolute PBIAS  ≤ 22%), whereas diuron losses were underestimated to a great extent (?98% ≤ PBIAS  ≤ ?65%) in both models. This could be attributed to the underprediction of herbicide concentration in the top soil due to the limitations of the instantaneous equilibrium sorption model as well as the high runoff potential of herbicide formulated as water‐dispersible granules. RZWQM and PRZM partitioned pesticides into the water and sediment phases similarly. According to model predictions, the majority of pesticide loads were carried via the water phase. On the basis of this study, both RZWQM and PRZM performed well in predicting runoff that carried highly adsorptive pesticides on an event basis, although the more physically based RZWQM is recommended when field‐measured soil hydraulic properties are available.     

Pesticide Health Risk Mapping and Sensitivity Analysis of Parameters in Groundwater Vulnerability Assessment

The extensive use of pesticides for increasing the agricultural production is affecting the quality of groundwater. The objectives of this article are to (i) develop pesticide relative leaching ranks for well sites, (ii) develop maps for human health risks due to pesticide applications, and (iii) identify the most significant parameters in pesticide simulations for groundwater vulnerability assessment. The methods include (i) development of acifluorfen relative leaching ranks for 25 well sites using ArcPRZM‐3, (ii) development of health risk maps using model simulated maximum dissolved bentazon concentrations on the basis of USA drinking water quality guidelines, (iii) sensitivity analysis for 14 ArcPRZM‐3 input parameters using the Plackett–Burman method. ArcPRZM‐3 is a user‐friendly system for spatial modeling of pesticide leaching from surface to groundwater. Thirteen acifluorfen relative leaching potential ranks were developed in which the pesticide leaching decrease from 1 to 13. The model predicted ranks for well 34 and well 9 were 2nd and 3rd, respectively, and acifluorfen was detected in both wells during the physical monitoring. The percentages of high health risks in the agricultural areas were 48.38 and 72.72% for Randolph and Independence Counties, respectively. The most significant parameters were thickness of horizon compartment, runoff curve number of antecedent moisture condition II for cropping, soil bulk density, and total application of pesticide. The irrigation, soil permeability, and numerical dispersion could impact the pesticide leaching in soils toward groundwater. The ArcPRZM‐3 system could be efficiently applied for spatial modeling and mapping of pesticide concentrations for groundwater vulnerability assessment on a large scale.     

基于遥感和GIS的建筑物震害损失评估方法研究与实现

介绍了一种使用遥感技术进行震后损失快速评估的方法,评估流程包括震害提取和损失评估两个部分。评估方式根据需求可以分为基于震害指数的评估和基于图像分类结果的评估两种模式,前者通过震害指数与烈度的关系得到地面居民住地的宏观震害情况,结合地震应急数据进行建筑物损失评估;后者采用图像处理技术,选取典型震害样本以图像分类技术得到地面居民住地的分级分类的震害结果,然后计算参数进行损失评估。损失评估的计算根据震害提取模式而不同。这种遥感震害评估方法和计算流程结合了遥感和GIS技术,以满足实际工作需求目标,提高了震害提取的自动化程度,实现多人工作模式,通过有效率的人机交互来提高遥感震害评估的速度。     

Planning of Soil-Pore Water Sampling Campaigns Using Pesticide Transport Modeling

Soil-pore water sampling by suction lysimeters monitors the fate of soil contaminants as a function of depth and time. However, sampling campaigns must be planned to most effectively monitor the migration of contaminants with a minimum expenditure of resources. The vertical migration of pesticides was studied at two sites treated with systemic s-triazine herbicides and equipped with suction lysimeters. The measured concentrations were compared with those calculated by a simulation model. This modeling was based on the processes that control the transport and fate of pesticide within the soil. The usefulness of such a tool was demonstrated by the good approximation obtained for pesticide concentrations and arrival times. Moreover, the significant spatial variability of concentrations observed justifies the use of a stochastic approach in modeling that takes into account the spatial variability of soil parameters. Also, the rapid transformation of herbicides observed in unsaturated soil zones demonstrates the importance of taking into account the sum of the toxic residues when evaluating the fate of s-triazines in soil.     

LAND-USE IMPACT ON WATERSHED RESPONSE: THE INTEGRATION OF TWO-DIMENSIONAL HYDROLOGICAL MODELLING AND GEOGRAPHICAL INFORMATION SYSTEMS

The integration of a two-dimensional, raster-based rainfall–runoff model, CASC2D, with a raster geographical information system (GIS), GRASS, offers enhanced capabilities for analysing the hydrological impact under a variety of land management scenarios. The spatially varied components of the watershed, such as slope, soil texture, surface roughness and land-use disturbance, were characterized in GRASS at a user-specified grid cell resolution for input into the CASC2D model. CASC2D is a raster-based, single-event rainfall–runoff model that divides the watershed into grid cell elements and simulates the hydrological processes of infiltration, overland flow and channel flow in response to distributed rainfall precipitation. The five-step integration of CASC2D and GRASS demonstrates the potential for analysing spatially and temporally varied hydrological processes within a 50 square mile semi-arid watershed. By defining possible land-use disturbance scenarios for the watershed, a variety of rainfall–runoff events were simulated to determine the changes in watershed response under varying disturbance and rainfall conditions. Additionally, spatially distributed infiltration outputs derived from the simulations were analysed in GRASS to determine the variability of hydrological change within the watershed. Grid cell computational capabilities in GRASS allow the user to combine the scenario simulation outputs with other distributed watershed parameters to develop complex maps depicting potential areas of hydrological sensitivity. This GIS–hydrological model integration provides valuable spatial information to researchers and managers concerned with the study and effects of land-use on hydrological response.     

Uncertainty based modeling of rainfall-runoff: Combined differential evolution adaptive Metropolis (DREAM) and K-means clustering

Simulation of rainfall-runoff process in urban areas is of great importance considering the consequences and damages of extreme runoff events and floods. The first issue in flood hazard analysis is rainfall simulation. Large scale climate signals have been proved to be effective in rainfall simulation and prediction. In this study, an integrated scheme is developed for rainfall-runoff modeling considering different sources of uncertainty. This scheme includes three main steps of rainfall forecasting, rainfall-runoff simulation and future runoff prediction. In the first step, data driven models are developed and used to forecast rainfall using large scale climate signals as rainfall predictors. Due to high effect of different sources of uncertainty on the output of hydrologic models, in the second step uncertainty associated with input data, model parameters and model structure is incorporated in rainfall-runoff modeling and simulation. Three rainfall-runoff simulation models are developed for consideration of model conceptual (structural) uncertainty in real time runoff forecasting. To analyze the uncertainty of the model structure, streamflows generated by alternative rainfall-runoff models are combined, through developing a weighting method based on K-means clustering. Model parameters and input uncertainty are investigated using an adaptive Markov Chain Monte Carlo method. Finally, calibrated rainfall-runoff models are driven using the forecasted rainfall to predict future runoff for the watershed. The proposed scheme is employed in the case study of the Bronx River watershed, New York City. Results of uncertainty analysis of rainfall-runoff modeling reveal that simultaneous estimation of model parameters and input uncertainty significantly changes the probability distribution of the model parameters. It is also observed that by combining the outputs of the hydrological models using the proposed clustering scheme, the accuracy of runoff simulation in the watershed is remarkably improved up to 50% in comparison to the simulations by the individual models. Results indicate that the developed methodology not only provides reliable tools for rainfall and runoff modeling, but also adequate time for incorporating required mitigation measures in dealing with potentially extreme runoff events and flood hazard. Results of this study can be used in identification of the main factors affecting flood hazard analysis.     

Risk management of BTEX contamination in ground water--an integrated fuzzy approach

An integrated fuzzy simulation-assessment method (FSAM) was developed for assessing environmental risks from petroleum hydrocarbon contamination in ground water. In the FSAM, techniques of fuzzy simulation and fuzzy risk assessment were coupled into a general framework to reflect a variety of system uncertainties. A petroleum-contaminated site located in western Canada was selected as a study case for demonstrating applicability of the proposed method. The risk assessment results demonstrated that system uncertainties would significantly impact expressions of risk-level outputs. A relatively deterministic expression of the risks would have clearer representations of the study problem but may miss valuable uncertain information; conversely, an assessment under vaguer system conditions would help reveal potential consequences of adverse effects but would suffer from a higher degree of fuzziness in presenting the modeling outputs. Based on the risk assessment results, a decision analysis procedure was used to calculate a general risk index (GRI) to help identify proper responsive actions. The proposed method was useful for evaluating risks within a system containing multiple factors with complicated uncertainties and interactions and providing support for identifying proper site management strategies.     

Agricultural Chemicals and Ground Water Quality - Issues and Challenges

Anthropogenic agricultural chemicals of concern in ground water include nitrate and pesticides. Increased legislation and regulation of contaminant levels in ground water can be expected. Ground water contamination should be prevented from getting worse, but more research is necessary so as to base regulations on sound criteria. Health effects and acceptable risks must be better formulated. More research on chemical movement in the vadose zone is necessary for accurate predictive modeling of pesticide transport to ground water. Best management practices need to be developed so that farmers will be able to farm profitably while complying with regulations for maximum contaminant levels in underlying ground water. People from all concerned disciplines, citizens'groups, and policy-makers need to work together to develop realistic regulatory policies and management practices that will effectively protect public health while ensuring a viable and sustainable agriculture.     

基于三维GIS技术的地震灾情场景模拟系统

设计并实现了基于三维GIS技术的地震灾情场景模拟系统。根据现场救援工作的特点和需求,对基于基础地理信息数据的自动三维建模方法和场景生成技术进行了研究,建立了基于地震动影响场和结构易损性的地震灾情场景模型;基于ArcGISEngine开发组件,实现了灾前、灾后场景的自动生成和直观显示,实现了整体破坏情况分析、重点搜索区域判定、关键目标震害与属性查询等灾情分析和辅助决策功能。该系统以震区基础地理信息数据和地震基本参数作为数据源,可以在获得地震基本参数之后快速地估计和分析震区建筑结构的破坏及其空间分布情况,预判重点搜索救援区域,为应急指挥决策和救援行动提供有效的支持,适合在现场救援工作中实际应用。     

基于GIS的地质数据库系统:研究现状和发展趋势

有效地存储、管理、交流、进而充分利用正日益增多的地质资料和数据，离不开功能强大的数据库管理系统，然而，地学数据显著的空间特征和复杂的结构属性又不能简单运用常规的数据库管理系统进行表述、处理，地理信息系统（GIS）技术，以其对空间数据强大的储存查询和分析处理功能而鲜明地区 地普通管理信息系统，正适合于对复杂的地球空间数据进行采集、储存、分类、检索查询、刻划表达、以及分析建模，因此，先进的GIS技术与强大的地质数据库系统相结合，亦即是，基于GIS的地质数据库系统的开发和应用，是计算机技术应用于地学研究的发展方向和应用趋势，是当今地学发展所必需的基础技术之一，本文结合我们的近期工作概述了这一新兴领域的研究现状和发展趋势。     

考虑区域水资源地震破坏后的信息管理模型设计

传统水资源信息管理模型采用GIS网络技术,对水资源信息进行优化配置,未对地震多发区域水资源管网进行综合规划,存在地震破坏后受污染水资源信息管理性能差的问题。设计考虑区域水资源地震破坏后污染的信息管理模型,模型包括受污染水资源信息规划设计模块、信息监测和采集模块、信息管理模块。3个模块分别实现对地震破坏后污染的水资源管网的合理规划、监测和采集以及污染信息的综合管理。实验结果表明,所设计模型对区域水资源地震破坏后污染的信息管理的平均时间达到1.38 s,且在管理过程中各项功能的评分都高于93.5分,具有较高的管理性能。     

ESTIMATION OF SOIL PARAMETERS FOR ASSESSING POTENTIAL WETNESS: COMPARISON OF MODEL RESPONSES THROUGH GIS

A geographic information system (GIS) is utilized to model wetness potential for a portion of Uwharrie National Forest, North Carolina. The wetness index is derived from TOPMODEL, a hillslope-scale runoff simulation model. The wetness index is a distributed-parameter model, with the input parameters obtained from a digital elevation model (DEM) and Soil Conservation Service (SCS) soils data. The primary objectives of the research are to: (1) compare methods of estimating soil parameters for input into the wetness potential model; and (2) determine how the model outputs vary spatially as a consequence of different methods of estimating soil parameters. Three methods of estimating soil parameters are used: (a) assuming uniform soil properties; (b) using SCS data presented as ranges; and (c) using alternative literature-based estimates of soil parameters. Results indicate that the wetness model responds similarly regardless of how the soil parameters are estimated, but differences in the spatial variability of the wetness potentials occur as a result of estimating soil parameters through alternative approaches. Correlation, pair-wise regression and analysis of regression residuals are used to compare model responses within a GIS environment.     

Ground Water Loading of Pesticides in the Atlantic Coastal Plain

Pollution of ground water by agricultural practices has gained considerable attention in recent years. Mathematical models have been developed to evaluate the effects of agricultural best management practices on pesticide transport to ground water. The GLEAMS model was evaluated to determine its suitability for use in predicting managerial effects on pesticide leaching from agricultural systems in the Atlantic Coastal Plain. Model predictions of pesticide concentrations in percolation from the root zone were compared to observed concentrations in ground water beneath field-sized areas. The model underpredicted runoff from these areas. Model predictions were used to produce rankings of the magnitudes of pesticide losses that paralleled rankings of observed ground water data in some cases but not others. Differences in observed and predicted rankings were assessed to be the result of sampling schedule inefficiences. The magnitudes of predicted pesticide concentrations in leachate from the root zone were three to seven times higher than observed concentrations in shallow ground water. Results support the use of GLEAMS for comparison of managerial effects on pesticide movement to ground water if appropriate limitations are recognized.     

基于GIS的城市震害单元化应急医疗救助辅助系统

在城市震害应急管理单元的基础上,提出了城市震害单元化应急医疗资源需求模型,并给出了该模型的GIS集成方案,进而整合现有震害应急救助相关理论和方法,结合面向对象与数据库技术,采用组件式集成二次开发方式,详细阐述了建立城市震害单元化应急医疗救助辅助系统的系统设计目标、技术路线、体系结构及功能构建等,形成了基于GIS的城市震害单元化应急医疗救助辅助系统的技术思路和解决方案,并以重庆邮电大学为应急管理单元,建立了初步试验系统。     

地震灾情场景计算机在线模拟系统设计

传统大型多道地震物理模拟系统缺乏对地震灾情场景的图像直观整合,可观赏效果差。设计基于GIS的地震灾情场景计算机在线模拟系统,从系统层次划分以及软件实现两方面完成地震灾情场景的在线模拟,系统由数据层、逻辑层和应用层构成。利用逻辑层中灾情场景生成模块自动整合功能,依据研究地区的GIS数据及其开发部件的底层支持,构建建筑物的三维像素点集合模型,实现灾后场景构建;通过地震动场判定和三维坐标转换,构建灾后场景。实验结果表明,所设计的系统模拟地震灾情场景图像最低误差均值仅为3.4,观赏性好,系统功能及性能满足实际需求。     

区域房屋震灾脆弱性模拟评估系统

以中国县级尺度房屋结构数据库为基础,基于B/S（Browser/Server）架构,利用地震烈度图、地震烈度与房屋受损关系研究成果,构建了区域房屋震灾脆弱性模拟评估系统,实现了对各区域在不同等级地震发生时的房屋受损、受灾人口等情况进行模拟评估,分行政区和地震烈度区对灾害损失进行各种统计与GIS可视化表达,以及根据损失情况对灾害影响区进行脆弱性等级划分等功能。基于B/S架构的系统便于系统的应用普及,将进一步促进研究成果向应用的转化。本系统的构建将为从全国尺度和区域尺度把握房屋震灾脆弱性,以及制定各地区的防灾、减灾规划和灾后应急对策提供有效支撑,并为实现灾害防治的信息化管理提供参考。     

Spatial modeling of environmental vulnerability of marine finfish aquaculture using GIS-based neuro-fuzzy techniques

Combining GIS with neuro-fuzzy modeling has the advantage that expert scientific knowledge in coastal aquaculture activities can be incorporated into a geospatial model to classify areas particularly vulnerable to pollutants. Data on the physical environment and its suitability for aquaculture in an Irish fjard, which is host to a number of different aquaculture activities, were derived from a three-dimensional hydrodynamic and GIS models. Subsequent incorporation into environmental vulnerability models, based on neuro-fuzzy techniques, highlighted localities particularly vulnerable to aquaculture development.The models produced an overall classification accuracy of 85.71%, with a Kappa coefficient of agreement of 81%, and were sensitive to different input parameters. A statistical comparison between vulnerability scores and nitrogen concentrations in sediment associated with salmon cages showed good correlation.Neuro-fuzzy techniques within GIS modeling classify vulnerability of coastal regions appropriately and have a role in policy decisions for aquaculture site selection.     

Pesticide Pollution in Surface‐ and Groundwater by Paddy Rice Cultivation: A Case Study from Northern Vietnam

This study was designed to examine the environmental exposure of surface‐ and groundwater pollution in remote mountainous regions of northern Vietnam. In 2008, we monitored the loss of four commonly applied pesticides (imidacloprid, fenitrothion, fenobucarb, dichlorvos) from paddy rice farming systems to a receiving stream on the watershed scale and quantified groundwater pollution. For the entire monitoring period, runoff loss of pesticides from the watershed was estimated to range between 0.4% (dichlorvos) and 16% (fenitrothion) of the total applied mass. These losses were correlated well with the octanol–water partition coefficient and water solubility of pesticides (r² = 0.78–0.99). In the groundwater collected from eight wells, all target pesticides were frequently detected. Maximum measured concentrations were 0.47, 0.22, 0.17, and 0.07 µg L^?1 for fenitrothion, imidacloprid, fenobucarb, and dichlorvos, respectively. Our results strongly indicate that under the current management practice pesticide use in paddy fields poses a serious environmental problem in mountainous regions of northern Vietnam.     

Mobile GIS技术在灾害数据采集中的应用

以Mobile GIS相关关键技术为基础,在Windows Mobile 5.0系统下设计开发了"应急通"灾害数据采集软件.采集数据包括文字、图片、音频、视频、xml表单.系统通过通讯网络及时向后台服务器发回所采集的数据,满足基本灾害数据采集需求,能够为灾害应急管理提供决策支持.     

基于土地利用动态变化的太湖地区景观生态风险评价

在GIS和RS技术支持下,以遥感数据和土地利用数据为基础,以行政区划为评价单元,定量分析太湖地区1990-2008年土地利用变化及其转化关系;从土地利用变化和景观结构角度构建景观生态风险评价模型,定量评价了研究区内景观生态风险的时空动态变化特征.结果表明:太湖地区景观生态风险分布与区内土地利用方式和强度具有较高关联.景...