GeoEnv 2008 Geovariances Poster Sessions

GEOVARIANCES consultant Nicolas Jeannée presents two posters during the GeoEnv 2008 conference:

1/ Retrospective geostatistical mapping of snow water equivalent over Québec.

Authors names: Nicolas JEANNEE (GEOVARIANCES), Dominique TAPSOBA (IREQ - HYDROQUEBEC), Ross BROWN (ENVIRONNEMENT CANADA)

Keywords: snow water equivalent, SWE, Quebec, kriging with external drift.

Abstract:

Snow accumulation over Quebec and adjacent Labrador is significant at a continental scale with annual maximum snow accumulations averaging 200-300 mm of snow water equivalent (SWE). This resource is vital for the economy of Quebec where a large fraction of the energy demand is met through hydro-electricity generation. For example it is estimated that 1 mm of SWE in the headwaters of the Caniapiscau-La Grande hydro corridor is equivalent to $1M in hydro-electric power production. However, snow cover variability and change in this region of North America has not been thoroughly assessed due to spatial and temporal limitations in the available snow observing systems.

The main objective of the project presented in this paper is to generate gridded historical SWE information over Quebec at a high resolution (10km x 10 km) in order to provide a high quality dataset to investigate the spatial and temporal variability in SWE over Quebec and improve the monitoring of SWE anomalies over Quebec hydro-electrical generation basins.

Extensive data are available from about the mid-1960s but the observations tend to be concentrated over southern Quebec. The main data source for SWE observations was the historical snow course compilation for Canada prepared by the Meteorological Service of Canada in 2000 supplemented with the Quebec snow course database maintained by the Quebec government (MDDEP) which includes observations from Hydro-Quebec, Alcan, and the Churchill Falls Power Corporation. The snow course dataset is overwhelmingly dominated by bi-monthly observations made on-or-near the 1st and 15th of each month from December to June. Daily ruler measurements of the depth of snow on the ground are available from Canadian synoptic and climate stations and usefully supplement the snow course observations.

A spatially and temporally complete reconstructed SWE field is derived from the simplified melt-index model of Brown et al. (2003) with air temperatures input from the NCEP reanalysis and daily precipitation from the CANGRD model. Correlation analysis of neighbouring observed (snow course) and reconstructed SWE values revealed that a classical kriging with external drift (KED) approach was feasible for generating final SWE maps due to a strong relationship between topography and SWE. The method was automated to provide maps for SWE at a 10-km resolution for the 1st and 15th of every month from December to June for the period from 1970 to 2005.

The paper focuses on the selection and validation of the geostatistical methodology and concludes that the KED approach provides spatially realistic SWE fields over Quebec with a number of improvements over previous methods. The added value of the interpolated dataset is illustrated and discussed in the context of the operational implementation of the system by Hydro-Quebec for monitoring water inflows to hydropower reservoirs.

2/ Integrating prior knowledge and locally varying model parameters with M-Geostatistics: methodology and application to bathymetry mapping.

Authors names: Cédric MAGNERON (ESTIMAGES), Nicolas JEANNEE (GEOVARIANCES), Olivier LE MOINE (IFREMER / LER Pertuis Charentais), Jean-François BOURILLET (IFREMER / GM)

Keywords: M-GeoStatistics, stationarity, variogram, local anisotropies, varying scale structures, bathymetry mapping.

Abstract:

Today, most geostatistical methods rely on a global variogram model. The variogram allows to build effective estimation (kriging) and simulation operators by catching the mean spatial correlation inherent to a data set. These methods commonly assume second-order stationarity for the underlying random function. This assumption is too constraining in numerous applications, as soon as the target area becomes large or involves complex structural patterns. Applying stationary approaches in such cases, even locally with a moving neighbourhood, can lead to unsuitable estimates and non stationary approaches are preferable to some extent, provided that one is ready to accept to loose some control on the underlying structural model. Furthermore, even non stationary algorithms hardly handle prior knowledge nor reproduce precisely complex structures, such as local anisotropies, spatially varying small-scale structures or heterogeneity, etc.

The paper aims at presenting an innovative methodology, called M-GS (M-GeoStatistics), which is fully dedicated to the local optimization of parameters involved in variogram-based models. M-GS considers the structural and computational parameters as a set of dependant parameters to be spatially optimized. The optimization process, which may be guided by objective or subjective criteria, is carried out during a M-structural analysis phase that leads to a set of spatially variable structural and computational parameters.

M-GS ensures a better adequacy between the geostatistical model and the data. In consequence, spatial estimation and simulation results are more precise than those obtained with conventional variogram-based models. Moreover this technology opens the way to new geostatistical mapping (even simulating) practices by allowing the user to introduce his structural a priori knowledge about the data field directly into the spatial estimation model. In that way geostatistical mapping is no more a variogram guided process aiming at generating the most probable map, but a human process aiming at generating the most probable desired map.

The methodology is then applied for bathymetry mapping. The availability of accurate seafloor estimates is essential for numerous oceanographic projects, including hydrographic, oceanographic and biological models, sedimentary processes, etc. Seafloor usually presents strong non stationarity and complex structures, such as small channels with varying orientations, spatially varying measurements errors, local heterogeneities for coastal areas, or deep canyons within general gentle slope for continental margins. The adequacy of the M-GS methodology in this framework is illustrated and compared with classical estimates for the Marenne-Oléron coast (West of France). Moreover such methodology could be used to input different local structures into a general model in the aim of a regional synthesis.