Prediction of river discharge and surface water quality using an integrated geographical information system approach

A methodology is developed for the prediction of river discharge and surface water quality (indexed by nitrogen loading) of a predominantly rural catchment using simple models in an integrated Geographical Information System (GIS). River discharge is predicted using the Soil Conservation Service (SCS) runoff Curve Number model, and surface water quality by the export coefficient model. Main input variable to these models is information on land-use along with ancillary information such as soils. Land-use is an important parameter that affects both discharge and water quality, and it can be derived from classification of remotely sensed images. Unlike conventional models, the models employed here do not require large amounts of data on several hydro-meteorological variables. The models are applied to a rural catchment in eastern England where major land-use changes have occurred in the recent past. Historical land-use data are derived from a variety of sources including maps, aerial photographs and remotely sensed satellite images for various dates ranging from 1931 to 1989. A GIS is a valuable means to enable large amounts of spatial data to be integrated, and to facilitate data manipulation for the specific application of the models. Results are validated using observed runoff and water quality records, and it is shown that the model predictions are of acceptable accuracy. This study demonstrated an application of a GIS to employ simple models to predict river discharge and water quality.     

Borehole Optimisation System (BOS)—A GIS based risk analysis tool for optimising the use of urban groundwater

Urban groundwater is generally an underused resource, partially due to the perceived risk of pollution and the strategic difficulties in placing boreholes in built-up areas. The development of a probabilistic risk based management tool that predicts groundwater quality at potential new urban boreholes is beneficial in determining the best sites for future resource development. The Borehole Optimisation System (BOS) is a custom Geographic Information System (GIS) application that has been developed in the ArcView 3.1 environment with the objective of locating the optimum locations for new boreholes in urban areas. It couples three component models, the Catchment Zone Probability Model (CZPM), the Land-use Model (LM) and the Pollution Risk Model (PRM). The CZPM produces probabilistic catchment zones for a user-defined abstraction borehole location under uncertain and variable hydrogeological parameters. The LM identifies current and historical industries located within the selected probabilistic catchment zone. The PRM uses these industrial and the associated hydrogeological and contaminant data to predict probabilistic contaminant concentrations in a particular analysis year. This paper outlines the methodologies employed in the development of BOS and attempts to validate the approach by presenting a simulation that forecasts PCE concentrations at an actual borehole location in the Nottingham urban aquifer. The results predict contaminant levels in the abstracted water that are in agreement with observed values, both being above the UK Drinking Water Standard of 10 μg/l. These demonstrate the applicability of BOS as a tool for informing decision-makers on the development of urban groundwater resources.     

Development and testing of a risk indexing framework to determine field-scale critical source areas of faecal bacteria on grassland

This paper draws on lessons from a UK case study in the management of diffuse microbial pollution from grassland farm systems in the Taw catchment, southwest England. We report on the development and preliminary testing of a field-scale faecal indicator organism risk indexing tool (FIORIT). This tool aims to prioritise those fields most vulnerable in terms of their risk of contributing FIOs to water. FIORIT risk indices were related to recorded microbial water quality parameters (faecal coliforms [FC] and intestinal enterococci [IE]) to provide a concurrent on-farm evaluation of the tool. There was a significant upward trend in Log[FC] and Log[IE] values with FIORIT risk score classification (r² = 0.87 and 0.70, respectively and P < 0.01 for both FIOs). The FIORIT was then applied to 162 representative grassland fields through different seasons for ten farms in the case study catchment to determine the distribution of on-farm spatial and temporal risk. The high risk fields made up only a small proportion (1%, 2%, 2% and 3% for winter, spring, summer and autumn, respectively) of the total number of fields assessed (and less than 10% of the total area), but the likelihood of the hydrological connection of high FIO source areas to receiving watercourses makes them a priority for mitigation efforts. The FIORIT provides a preliminary and evolving mechanism through which we can combine risk assessment with risk communication to end-users and provides a framework for prioritising future empirical research. Continued testing of FIORIT across different geographical areas under both low and high flow conditions is now needed to initiate its long-term development into a robust indexing tool.     

基于GIS和相关数学模型的城市环境空气质量评价系统

环境影响评价对于城市规划管理具有重要意义,它所处理的对象带有明显的空间属性。介绍了城市环境空气质量评价系统利用ArcView GIS对空间数据的编辑、管理和分析功能,通过在VB中开发的应用程序调用环境空气质量数学模型CALINE4和ISCST3,对城市区域环境空气质量进行计算预测,并在香港的市区发展规划项目中得到了成功的应用。     

基于物联网的水质污染监控系统的设计与实现

水质污染监控系统采用物联网架构,利用GIS、网络通信、多媒体、传感器及计算机仿真等技术,构建一套完善的水质污染监测与管理体系。介绍系统总体架构,阐述系统网络通信和关键模块的设计实现,系统通过水质远程查询分析、应急预案数字化和报警综合管理,实现水质污染的实时监控和水源地监测数据的综合应用,有效地为水源地水质安全保障提供支持。     

大气污染暴露风险防控可视化决策支持平台

针对当前我国大气污染防治正逐步由污染治理转向风险防控,而现有空气质量监测设备和平台服务仅限于环境监测而非暴露监测的问题,设计研发了一套基于B/S架构的可视化综合分析与决策支持平台——大气污染暴露风险测量系统（APERMS）。首先,基于大气污染浓度监测数据和暴露时空行为活动模式,耦合集成污染浓度制图、个体暴露测量、人群暴露测量、暴露风险评价这一完整的大气污染暴露风险测量技术路线;其次,基于高可用和可靠原则,进行系统的总体架构设计、数据库设计和功能模块设计;最终,采用GIS与J2EE Web等技术,完成APERMS开发,实现了大气污染浓度分布高时空分辨率模拟、个体和人群大气污染暴露状况精准评估、大气污染暴露风险水平全方位评价等功能。APERMS主要应用于大气污染监控和环境健康管理行业,为风险规避和污染防控提供有效的技术支持。     

Safe river water: A ubiquitous and collaborative water quality monitoring solution

Water quality is vital to human life and economy. However, one sixth of the world’s population suffers from lack of safe drinking and domestic water. Aiming to improve the capability of predicting and responding to river pollution disasters, this project collaborated with local offices of Chinese National Bureau of Water Resource to explore new solutions to coping with the ever-growing threat of river water pollution. We presented a distributed data analysis algorithm, Infinitesimal Dividing and Analysis, to efficiently locate pollution sources with data gathered from a ubiquitous wired/wireless sensor network. We elaborate on a π-calculus based paradigm to enhance collaboration and interaction among individual monitoring stations. Based on these two enabling technologies, we applied our framework to water quality monitoring at two carefully chosen sites in China.     

A Methodology for Spatial Consistency Improvement of Geographic Databases

In any information system the reliability of any results of queries, analysis or reasoning, depends on data quality (positional accuracy, consistency and so on). In some cases, answers cannot be obtained due to a lack of information, whereas in other cases answers are wrong or not complete because of inconsistent data. In geographical information systems (GIS), data quality management has to handle the spatial features of objects, which brings specific problems. The goal of this paper is to describe a methodology for spatial consistency improvement of geographical data sets in vector format. It is based on errors survey and classification. Three kinds of errors are identified which lead to three kinds of consistency, namely structural consistency, geometric consistency and topo-semantic consistency. Each of them needs specific checking and correcting processes. All these processes are integrated in a general framework that is presented in this paper. An application of this framework to the Lyon Urban Community GIS (the SUR) is currently conducted; first results are presented.     

突发水污染事件中遥感瓦片大数据存储系统

突发水污染事件频发,利用GIS平台、遥感技术及水质模型对突发水污染事件进行模拟研究逐渐成为一种发展趋势.针对目前海量、异构、动态遥感数据难以快速处置和高效存储的问题,基于HBase设计和实现了突发水污染事件的遥感瓦片大数据存储系统.该系统以数字地球平台为支撑,结合遥感瓦片金字塔模型和MapReduce框架,考虑实时、动态观察事件发展态势引起的瓦片数据频繁加载特点,对遥感瓦片数据的线性四叉树编码索引进行了改进,将其同水污染数据一并存储到HBase数据库中,并通过增加缓存区来提升遥感瓦片数据的访问效率.将此系统应用于渭河陕西段突发水污染事件中,实验结果表明:改进后的索引可在200个毫秒时间内提取到水污染区域的遥感数据,引入的缓存机制使得水污染区域遥感数据的加载时间节省了近2/3.因此,该存储系统访问效率高,能够满足突发水污染事件快速应急模拟的需求.     

Urban runoff modelling uncertainty: Comparison among Bayesian and pseudo-Bayesian methods

Urban stormwater quality modelling plays a central role in evaluation of the quality of the receiving water body. However, the complexity of the physical processes that must be simulated and the limited amount of data available for calibration may lead to high uncertainty in the model results. This study was conducted to assess modelling uncertainty associated with catchment surface pollution evaluation. Eight models were compared based on the results of a case study in which there was limited data available for calibration. Uncertainty analysis was then conducted using three different methods: the Bayesian Monte Carlo method, the GLUE pseudo-Bayesian method and the GLUE method revised by means of a formal distribution of residuals between the model and measured data (GLUE_f). The uncertainty assessment of the models enabled evaluation of the advantages and limitations of the three methodologies adopted. The models were then tested using the quantity–quality data gathered for the Fossolo catchment in Bologna, Italy. The results revealed that all of the models evaluated here provided good calibration results, even if the model reliability (in terms of related uncertainty) varied, which suggests the adoption of a specific modelling approach with respect to the others. Additionally, a comparison of uncertainty analysis approaches showed that, regarding the models evaluated here, the classical Bayesian method is more effective at discriminating models according to their uncertainty, but the GLUE approach performs similarly when it is based on the same founding assumptions as the Bayesian method.     

Towards integrating GIS and catchment models

Modeling the impact of non-point source pollution in catchments is a complex problem, and one that has troubled natural resource managers for many years. The development of spatially distributed hydrologic models has led to improved model forecasting at the cost of requiring more detailed spatial information. In addition, the analysis is much more sensitive to errors in the data. Incorporation of catchment models into a Geographical Information System (GIS) has improved matters by streamlining data input and providing better interpretation of model outputs. This paper reviews different strategies for linking a catchment model with GIS. It examines data issues related to the performance of models and how well they match physical landscape conditions. Integration with GIS is shown to be necessary for the efficient and proper operation of models in resource management situations. The paper concludes that tighter integration between generic sub-models for physical landscape processes and GIS is still required.     

空间相似查询中MBR边界区域关系研究

相似查询是基于向量空间的一种重要查询方法。点、线段、区域是向量空间对空间对象的三种基本表达。本文在不改变结点MBR区域前提下,通过区域扫描对MBR区域重叠面积进行计算。利用R*树结点MBR允许重叠的特性,在不能消除区域重叠产生的死空间情况下,研究了更为精确的MBR边界的线段关系,并给出线段的最近邻查询算法和相似线段选取算法。实验结果表明该方法的CPU计算代价较低且显著提高了相似查询与更新的效率。     

A geospatially-enabled web tool for urban water demand forecasting and assessment of alternative urban water management strategies

This study develops and demonstrates the Integrated Urban Water Model (IUWM) for forecasting urban water demand with options to assess effects of water conservation and reuse. While water and energy balance drive hydrologic, storage and recycling simulations on a daily timestep, social and infrastructural processes are resolved by spatially distributed parameters. IUWM is deployed as an online tool with geographical information system (GIS) interfaces, enhancing its ease of use and applicability at building to municipal scales. The performance of the model at varying spatial scales was evaluated with extensive water metering data for the City of Fort Collins, Colorado. The calibrated model provided very good estimates of demands at individual block group as well as the municipal service area. The capacity of IUWM for the assessment of the spatiotemporal variability of water consumption and effects of water demand management strategies under climate and urban growth scenarios is discussed.     

Soil wetness as an indicator of stream salinity: a landscape position index approach

Dryland salinity is a major environmental issue in much of Australia’s agricultural lands and is expressed as salt affected land or degraded stream water quality. Maps showing areas at risk of land and stream degradation are needed by regional, state and national management and planning authorities, as well as farmers. Part of the management involves establishing end-of-valley targets for water quality. Developing maps of salinity risk is limited by the availability of appropriate spatial data. Elevation data at appropriate scales are available for all Australia.This paper explores the potential to develop catchment and regional scale soil wetness maps, based only on elevation data, as a surrogate for stream salinity risk. Soil wetness indices were derived through the Fuzzy Landscape Analysis GIS (FLAG) model. While FLAG avoids the direct use of process models it uses process understanding. It does this through an index-based approach that requires a training set of areas of wetness, salinity or other attribute associated with position in the landscape. We test whether only one of the FLAG landscape position indices (UPNESS), that has been shown to characterise depositional zones, is correlated to baseflow stream salinity.UPNESS is the uphill area monotonically above each point in the landscape, and is a specialised form of contributing area, a measure of surface and sub-surface water accumulation. This measure characterises catchments as the ‘connectedness’ of drainage or prevalence of runoff sinks. It distinguishes, for example, between freely drained catchments and those with more extensive depositional zones that allow the accumulation and storage of salt and formation of preferential pathways in the system.FLAG analysis was applied over an area of ~12 000 km² in southeastern Australia where salinity research was being conducted by state agencies. Stream electrical conductivity measurements were obtained to compare with the UPNESS index. The results suggest that the model is useful for targeting further investigations in regional scale salinity management planning and research. FLAG is suggested as a first step for obtaining a highly visual rapid assessment of potential wetness, discharge and salinisation at catchment scales.     

A national-scale GIS-based system for modelling impacts of land use on water quality

Management of freshwater quality requires modelling tools for rapid evaluation of land use and management scenarios. This paper presents the CLUES (Catchment Land Use and Environmental Sustainability) model to address this need. CLUES provides steady state, spatially distributed, integrated catchment models tightly coupled to GIS software to predict mean annual loads of total nitrogen, total phosphorus, sediments and E. coli, and concentration of nutrients throughout New Zealand (268,000 km²) with a subcatchment resolution of 0.5 km². CLUES also estimates potential nutrient concentrations for estuaries and provides key farm socio-economic indicators. The model includes a user interface for study area selection, scenario creation, data geo-visualisation, and export of results. It is pre-populated with spatial data and parameter values for New Zealand. Evaluation of the model and a summary of applications demonstrate the tractability and utility of national-scale rapid scenario assessment tools within a GIS framework.     

Decision support methodology using rule-based reasoning coupled to non-parametric measurement for industrial wastewater network management

EU water framework directive [Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 Establishing a Framework for Community Action in the Field of Water Policy, Official Journal L 327, 22/12/2000 p. 0001–0073] encourages industrialists to improve the management of wastewater networks and thus reduce both pollution and treatment costs. Due to the complexity of wastewater treatment plant, the use of decision support system (DSS) becomes a complementary tool to assist decision-markers.A decision support methodology (DSM) was created which can optimise both the placement and number of physical and chemical sensors in industrial wastewater networks at petroleum refineries. Ongoing research concerns a promising method of analysing this dataflow to check whether the wastewater network is malfunctioning in any observable way.This method consists in five steps. First is to model the network to know each unit, node, etc. Second, provisional sampling points are selected in order to measure wastewater quality. Third, data is collected. Fourth, non-parametric measurement is studied: variability calculation on UV spectra. Fifth, the final selection of sampling points according to the variability study is made.With all these data, algorithm for the monitoring was developed to spot a dysfunction in the network (study of the monitoring section).These five steps of the DSM were developed and applied on a refinery wastewater treatment plant.     

SD-GIS-based temporal-spatial simulation of water quality in sudden water pollution accidents

System dynamics (SD) is well suited for studying dynamic nonlinear complex systems. In this paper, SD is applied to a rapid-onset water pollution accident using a 1-D water quality model and a conceptual GIS-SD framework is constructed to simulate the temporal-spatial changes of pollutant concentration. Based on the component GIS and the SD model, a prototype system of water quality simulation in water pollution accidents is developed. The data collected on the spot in the Songhua River water pollution accident in November 2005 were used for model parameter calibration and model validation. The results showed that: (1) the constructed model could simulate the changes of nitrobenzene concentration with time in the Songhua River water pollution accident, especially during the peak concentration and at the arrival time of peak concentration, and that the simulated values and the on-the-spot monitored values corresponded with each other well; (2) the scenario simulation could be made by adjusting parameters u (longitudinal current velocity), E (longitudinal diffusion coefficient), and k (decay rate coefficient). Such a model can provide decision makers with quantitative information to optimize related emergency response measures.     

Use of raster-based data layers to model spatial variation of seismotectonic data in probabilistic seismic hazard assessment

Recent achievements in computer and information technology have provided the necessary tools to extend the application of probabilistic seismic hazard mapping from its traditional engineering use to many other applications. Examples for such applications are risk mitigation, disaster management, post disaster recovery planning and catastrophe loss estimation and risk management. Due to the lack of proper knowledge with regard to factors controlling seismic hazards, there are always uncertainties associated with all steps involved in developing and using seismic hazard models. While some of these uncertainties can be controlled by more accurate and reliable input data, the majority of the data and assumptions used in seismic hazard studies remain with high uncertainties that contribute to the uncertainty of the final results. In this paper a new methodology for the assessment of seismic hazard is described. The proposed approach provides practical facility for better capture of spatial variations of seismological and tectonic characteristics, which allows better treatment of their uncertainties. In the proposed approach, GIS raster-based data models are used in order to model geographical features in a cell-based system. The cell-based source model proposed in this paper provides a framework for implementing many geographically referenced seismotectonic factors into seismic hazard modelling. Examples for such components are seismic source boundaries, rupture geometry, seismic activity rate, focal depth and the choice of attenuation functions. The proposed methodology provides improvements in several aspects of the standard analytical tools currently being used for assessment and mapping of regional seismic hazard. The proposed methodology makes the best use of the recent advancements in computer technology in both software and hardware. The proposed approach is well structured to be implemented using conventional GIS tools.     

Non-point source critical area analysis in the Gisselö watershed using GIS

In the southeast in Norrköping, Sweden, is a small fjord-like bay called Slätbaken. The water quality in Slätbaken—with its narrow outlet to the Baltic Sea—depends highly on the water quality of the streams that flow in it. While point pollution sources can be identified easily in general, the non-point sources are harder to find. The most important sources for non-point pollution are agricultural areas, and the pollutants are mostly nutrients like phosphorus, which come from the fertilisation of the fields. One important catchment area for Slätbaken is the 57.7 km² Gisselö river basin (part of the topographic map 8GNO), which contains large agricultural areas.The transport of water pollutants is based on the same hydrological processes as erosion and sediment transport. The implementation of such a model in a GIS allows the analysis of a large area with a high resolution. When choosing the model, special attention was paid to the possibility of using a verified model that is easy to implement in a commercial GIS without the need of too much expert knowledge. This may allow its widespread use in many administrative applications that need non-point source information. A feasibility test for an enhanced GIS USLE model was done in the Gisselö drainage basin before it was implemented for all river basins in the whole administrative area of Norrköpings kommun.It is possible to use the suggested simplified USLE model to estimate the load of both pollutants and sediments, and it is able to show the areas that are critical for the water quality at the outlet of the water basin. The model has been evaluated in three studies [Int. J. Geogr. Inf. Syst. 2 (4) (1988) 365; A GIS to target critical areas for non point source management, in: Proceedings of the International Non Point Source Management Symposium, Austin, TX, November 7, 1989; Vatten 48 (1992) 117]. Then, implemented in a very simple GIS that allowed only rough estimates of terrain models and distances, the model was able to estimate the total suspended solids (TSS) and total phosphorus (TP) loads in the Svartå river basin of 1539 km² in the same region as Gisselö and the Bornsjö river basin outside Stockholm. Besides an estimated R² of 0.91–0.98 (verified by a more than one year measurement from manual and automated sampling stations in the whole river basin), the benefit with the GIS implemented USLE was the possibility to identify the risk areas with high spatial accuracy. During the last decade, both available databases and software have changed the possibilities to identify areas at risk of nutrient leakage. Schein [GIS Methods for Monitoring Sustainable Development by Analysis of Land-use and Land Cover Changes in the Surroundings of Linköping (Sweden), Institut für Photogrammetrie und Fernerkundung, Technische Universität Dresden, Germany] and Schein and Sivertun [Method and models for sustainable development monitoring and analyses in GIS, in: Proceedings of the International Workshop on Geo-Spatial Knowledge Processing for Natural Resource Management, University of Insubria, Varese, Italy, June 28–29, 2001] show that the enhanced land use data available through the European Union agricultural support program can be used together with remote sensing data to fine tune the modified GIS USLE model. The problems with the new 50×50 m digital elevation data now available are also pointed out here. Obvious errors in the data and possibilities to enhance the model by introducing a better terrain model were two important suggestions in these works. In this article, two modifications to the original model are suggested. One is enhancement of the digital terrain model by using height curves from the digital 1:50 000 scale topographic map, and the other is a smooth distance function that better reflects the impact of nutrients on water bodies.Because of its easy implementation on standard low cost systems, the GIS USLE model is suitable for analysing huge areas for critical places. The results can lead to more detailed studies in the risk areas thus identified or to investigations on the effect of land use changes, or can be used simply for taking care in the use of fertilisers and other chemicals in the critical agricultural areas.