A disposition of interpolation techniques

A large collection of interpolation techniques is available for application in environmental research. To help environmental scientists in choosing an appropriate technique a disposition is made, based on 1) applicability in space, time and space-time, 2) quantification of accuracy of interpolated values, 3) incorporation of ancillary information, and 4) incorporation of process knowledge. The described methods include inverse distance weighting, nearest neighbour methods, geostatistical interpolation methods, Kalman filter methods, Bayesian Maximum Entropy methods, etc. The applicability of methods in aggregation (upscaling) and disaggregation (downscaling) is discussed. Software for interpolation is described. The application of interpolation techniques is illustrated in two case studies: temporal interpolation of indicators for ecological water quality, and spatio-temporal interpolation and aggregation of pesticide concentrations in Dutch surface waters. A valuable next step will be to construct a decision tree or decision support system, that guides the environmental scientist to easy-to-use software implementations that are appropriate to solve their interpolation problem. Validation studies are needed to assess the quality of interpolated values, and the quality of information on uncertainty provided by the interpolation method

Evaluating the Potential of a Geospatial/Geostatistical Methodology for Locating Rain-Derived Infiltration and Inflow into Wastewater Treatment Systems in the Minneapolis/St. Paul Metropolitan Area, Minnesota, USA

A significant issue facing municipal wastewater treatment infrastructure (WWTI) is how to manage infiltration and inflow (I/I). I/I of rain and ground water permeate into WWTI after precipitation events, periods of groundwater table rise, and percolation from surrounding surface waters. This can create discharges above the infrastructure\u27s flow capacity, increase costs for processing the wastewater and add undesired stress to aging wastewater networks. In an attempt to assess this problem cost and time inefficient approaches have commonly been applied. This study utilizes a new and more radical methodology to try and make WWTI management more efficient. This study applies ArcGIS and Geostatistical Analysis to seven counties within the Metropolitan Council Environmental Services (MCES) network in the Minneapolis/St. Paul metro area. Data is collected from rain gauges and flow meters an average ten-year flow record is created from this data. The data is then analyzed in ArcGIS through Kriging to interpolate and predict where significant rates of I/I, due to high magnitude precipitation events, are located throughout the study area. I/I rates for high magnitude precipitation events are estimated through the comparison of the max flow rate data and the ten-year average flow rate. A percentage of increase flow is then calculated. Results reveal spatial patterns indicating variable I/I susceptibility across the MCES WWTI. By collaborating with MCES it is possible to determine how accurately this methodology can locate areas of high-risk I/I potential within the existing WWTI

Optimizing the location of weather monitoring stations using estimation uncertainty

In this article, we address the problem of planning a network of weather monitoring stations observing average air temperature (AAT). Assuming the network planning scenario as a location problem, an optimization model and an operative methodology are proposed. The model uses the geostatistical uncertainty of estimation and the indicator formalism to consider in the location process a variable demand surface, depending on the spatial arrangement of the stations. This surface is also used to express a spatial representativeness value for each element in the network. It is then possible to locate such a network using optimization techniques, such as the used methods of simulated annealing (SA) and construction heuristics. This new approach was applied in the optimization of the Portuguese network of weather stations monitoring the AAT variable. In this case study, scenarios of reduction in the number of stations were generated and analysed: the uncertainty of estimation was computed, interpreted and applied to model the varying demand surface that is used in the optimization process. Along with the determination of spatial representativeness value of individual stations, SA was used to detect redundancies on the existing network and establish the base for its expansion. Using a greedy algorithm, a new network for monitoring average temperature in the selected study area is proposed and its effectiveness is compared with the current distribution of stations. For this proposed network distribution maps of the uncertainty of estimation and the temperature distribution were created. Copyright (c) 2011 Royal Meteorological Societyinfo:eu-repo/semantics/publishedVersio

Spatial Statistical Data Fusion on Java-enabled Machines in Ubiquitous Sensor Networks

Wireless Sensor Networks (WSN) consist of small, cheap devices that have a combination of sensing, computing and communication capabilities. They must be able to communicate and process data efficiently using minimum amount of energy and cover an area of interest with the minimum number of sensors. This thesis proposes the use of techniques that were designed for Geostatistics and applies them to WSN field. Kriging and Cokriging interpolation that can be considered as Information Fusion algorithms were tested to prove the feasibility of the methods to increase coverage. To reduce energy consumption, a compression method that models correlations based on variograms was developed. A second challenge is to establish the communication to the external networks and to react to unexpected events. A demonstrator that uses commercial Java-enabled devices was implemented. It is able to perform remote monitoring, send SMS alarms and deploy remote updates

Using Kriging, Cokriging, and GIS to Visualize Fe and Mn in Groundwater

For aesthetic, economic, and health-related reasons, allowable concentrations of iron (Fe) and manganese (Mn) found present in drinking water are 0.3 mg/L and 0.05 mg/L, respectively. Water samples taken from private drinking wells in the rural communities within Buncombe County, North Carolina contain amounts of these metals in concentrations higher than the suggested limits. This study focused on bedrock geology, elevation, saprolite thickness, and well depth to determine factors affecting Fe and Mn. Using ArcGIS 10.2, spatial trends in Fe and Mn concentrations ranges were visualized, and estimates of the metal concentrations were interpolated to unmonitored areas. Results from this analysis were used to create a map that delineates the actual spatial distribution of Fe and Mn. The study also established a statistically significant correlation between Fe and Mn concentrations, which can be attributed to bedrock geology. Additionally, higher Fe in groundwater was concentrated in shallower wells and valley areas