Jon Olav Skøien

eHabitat, a multi-purpose Web Processing Service for ecological modeling

The number of interoperable research infrastructures has increased significantly with the growing awareness of the efforts made by the Global Earth Observation System of Systems (GEOSS). One of the societal benefit areas that is benefiting most from GEOSS is biodiversity, given the costs of monitoring the environment and managing complex information, from space observations to species records including their genetic characteristics. But GEOSS goes beyond simple data sharing to encourage the publishing and combination of models, an approach which can ease the handling of complex multi-disciplinary questions. It is the purpose of this paper to illustrate these concepts by presenting eHabitat, a basic Web Processing Service (WPS) for computing the likelihood of finding ecosystems with equal properties to those specified by a user. Despite the availability of the agreed WPS standard for Web-based geospatial modeling, few practical implementations exist, making eHabitat a significant addition to the field. On the other hand, the wide uptake of Web access standards for geospatial data has led to a wealth of data sources within GEOSS which can be effectively combined using eHabitat. When chained with other services providing data on climate change, eHabitat can be used for ecological forecasting and becomes a useful tool for decision-makers assessing different strategies when selecting new areas to protect. eHabitat can use virtually any kind of thematic data that can be considered as useful when defining ecosystems and their future persistence under different climatic or development scenarios. The paper will present the architecture and illustrate the concepts through case studies which forecast the impact of climate change on protected areas or on the ecological niche of an African bird.

rtop: An R package for interpolation of data with a variable spatial support, with an example from river networks

Abstract Geostatistical methods have been applied only to a limited extent for spatial interpolation in applications where the observations have an irregular support, such as runoff characteristics along a river network and population health data. Several studies have shown the potential of such methods, but these developments have so far not led to easily accessible, versatile, easy to apply and open source software. Based on the top-kriging approach suggested by Skoien et al. (2006) , we will here present the package rtop , which has been implemented in the statistical environment R ( R Core Team, 2013 ). Taking advantage of the existing methods in R for analysis of spatial objects ( Bivand et al., 2013 ), and the extensive possibilities for visualizing the results, rtop makes it easy to apply geostatistical interpolation methods when observations have a non-point spatial support. The package also offers integration with the intamap package for automatic interpolation and the possibility to run rtop through a Web Service.

The role of station density for predicting daily runoff by top-kriging interpolation in Austria

Abstract Direct interpolation of daily runoff observations to ungauged sites is an alternative to hydrological model regionalisation. Such estimation is particularly important in small headwater basins characterized by sparse hydrological and climate observations, but often large spatial variability. The main objective of this study is to evaluate predictive accuracy of top-kriging interpolation driven by different number of stations (i.e. station densities) in an input dataset. The idea is to interpolate daily runoff for different station densities in Austria and to evaluate the minimum number of stations needed for accurate runoff predictions. Top-kriging efficiency is tested for ten different random samples in ten different stations densities. The predictive accuracy is evaluated by ordinary cross-validation and full-sample crossvalidations. The methodology is tested by using 555 gauges with daily observations in the period 1987-1997. The results of the cross-validation indicate that, in Austria, top-kriging interpolation is superior to hydrological model regionalisation if station density exceeds approximately 2 stations per 1000 km2 (175 stations in Austria). The average median of Nash-Sutcliffe cross-validation efficiency is larger than 0.7 for densities above 2.4 stations/1000 km2. For such densities, the variability of runoff efficiency is very small over ten random samples. Lower runoff efficiency is found for low station densities (less than 1 station/1000 km2) and in some smaller headwater basins.

The eStation, an Earth Observation processing service in support to ecological monitoring

Abstract The eStation is a collecting and processing system designed to automatically deal with the reception, processing, analysis and dissemination of key environmental parameters derived from remotely sensed data. Developed mainly at the Joint Research Centre of the European Commission, the eStation has been distributed to 47 sub-Saharan countries in the frame of the AMESD (Africa n Monitoring of Environment for Sustainable Development) project to provide local institutions with the capacity to easily access a large range of remote sensing products on vegetation, precipitation, fires and oceans. These products, derived from the processing of images coming from various instruments including SPOT-Vegetation, MSG-SEVIRI and MODIS are developed to allow end-users to make local and regional assessments of the state of marine and terrestrial ecosystems. The products, dispatched to the users through the EUMETSAT data broadcasting system (EUMETCast) or provided by other Earth Observation (EO) data agencies (e.g. NASA), are further processed by the eStation to allow end-users to generate their own environmental, whether terrestrial or marine, assessments and reports. Initially designed as a stand-alone system using an open source development framework, the eStation has recently been further developed as a web processing service to allow a broader range of end-users to access the data and services over the Internet. It is the purpose of this paper to introduce the readers to the eStation and its products, to share the lessons learnt in deploying these services as well as to discuss its more recent use in chained environmental web based modeling services.

A Model Web approach to modelling climate change in biomes of Important Bird Areas

Abstract Protected Areas (PA) are designated to conserve species and habitats and protect against anthropogenic pressures. Park boundaries, however, offer no protection against climatic change and where boundaries are actual constructions, they may also act as physical barriers to species movements to new suitable habitats. The means for assessing the consequences of climate change on ecosystems and for identifying gaps in PA connectivity are therefore a conservation priority. The complexity of the scientific questions raised requires a multi-disciplinary approach given the variety of the information required. This includes species observations and their theoretical distributions, as well as ecosystem assessments and climate change models. Such complex questions can be more easily dealt with if there is appropriate access to data and models, a strategy endorsed by GEO-BON, the Group on Earth Observations Biodiversity Observation Network. In this paper, we show how data and models recently made available on the World Wide Web can be coupled through interoperable services and used for climate change forecasting in the context of Important Bird Areas (IBAs) and how, for any bird species described in the databases, areas can be identified where the species may find a more suitable environment in the future. As presented, this is an example of the Model Web.

A geostatistical approach to data harmonization - Application to radioactivity exposure data

Environmental issues such as air, groundwater pollution and climate change are frequently studied at spatial scales that cross boundaries between political and administrative regions. It is common for different administrations to employ different data collection methods. If these differences are not taken into account in spatial interpolation procedures then biases may appear and cause unrealistic results. The resulting maps may show misleading patterns and lead to wrong interpretations. Also, errors will propagate when these maps are used as input to environmental process models. In this paper we present and apply a geostatistical model that generalizes the universal kriging model such that it can handle heterogeneous data sources. The associated best linear unbiased estimation and prediction (BLUE and BLUP) equations are presented and it is shown that these lead to harmonized maps from which estimated biases are removed. The methodology is illustrated with an example of country bias removal in a radioactivity exposure assessment for four European countries. The application also addresses multicollinearity problems in data harmonization, which arise when both artificial bias factors and natural drifts are present and cannot easily be distinguished. Solutions for handling multicollinearity are suggested and directions for further investigations proposed.

Comparing Geostatistical Models for River Networks

Geostatistical methods have become popular in various fields of hydrology, and typical applications include the prediction of precipitation events, the simulation of aquifer properties and the estimation of groundwater levels and quality. Until recently, surprisingly little effort has been undertaken to apply geostatistics to stream flow variables. This is most likely because of the tree-like structure of river networks, which poses specific challenges for geostatistical regionalization. Notably, the shape of catchments (irregular block support), the nestedness of catchments along the river network (overlapping support), and the definition of a relevant distance measure between catchments pose specific challenges. This paper attempts an annotated survey of models proposed in the literature, stating contributions and pinpointing merits and shortcomings. Two conceptual viewpoints are distinguished: one-dimensional models which use covariances along a river network based on stream distance, and two-dimensional models where stream flow is conceptualized as the integral of the spatially continuous local runoff process over the catchment area. Both geostatistical concepts are evaluated relative to geostatistical standard methods based on Euclidean distances. It is shown how the methods perform in various examples including spatial prediction of environmental variables, stream flows and stream temperatures.

Topological Kriging of Runoff

In this paper we spatially interpolate hourly runoff data by a Top-kriging (Skoien 2006) approach and compare the results with ordinary kriging and a deterministic rainfall-runoff model. Cross-validation indicates that the Top-kriging approach performs better than both ordinary kriging and the deterministic model for a large number of catchments in Austria. We suggest that the Top-kriging approach can be used for filling in temporal gaps in observed runoff time series and for real time spatial mapping of the flow situation.