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1 EIONET water workshop 12 October 2005 Developments in EEA needs in water quantity data Philippe Crouzet AIR/SAG.

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Presentation on theme: "1 EIONET water workshop 12 October 2005 Developments in EEA needs in water quantity data Philippe Crouzet AIR/SAG."— Presentation transcript:

1 1 EIONET water workshop 12 October 2005 Developments in EEA needs in water quantity data Philippe Crouzet AIR/SAG

2 2 Reasons for developments: Starting point Current Eionet Water/resource New Requirements Lessons from the current Eionet, WFD and besides it, Assessment and SoE Data centre Emerging issues and techniques

3 3 Specifying the needs by the foreseen outcomes Intended outcomes: 1.Water accounts (Discharge patterns and Quality extrapolation / weighting), resource balance (e.g. GW refilling and land uses). 2.Riverine fluxes where needed to improve mass balances and develop new assessments (sediments budgets), 3.To identify and develop new statistics adapted to inland (eco)systems assessment and fragmentation issues assessment 4.Regional assessment of changes and trends (CC, scenarios, etc.) 5.As control variables in uses assessments (e.g. stratified analysis of quality trends) Which data under which spatial and temporal aggregation? Spatially distributed / harmonised rainfall data from international organisations, with exceptions (Data flow totally separated from the current Eionet) River discharge data: monthly averages and yearly classical statistics along with long-term classical statistics for a large set of representative gauging stations, completed by daily data for a selected subset, in order to compute fluxes and carry out non standard statistics (Substantial development) Groundwater table (liaising with GW and ecosystems {wetlands}) Example 1 Example 2 Example 3

4 4 Tentative road map (discharge only) 1.Collect and analyse gauging stations characteristics: Position, duration of records, quality of data, etc. Select a representative set of stations thanks to stratified sampling (should provide ~1 GS/ km2) Pre-select a subset of stations where detailed information is required (~15-25% of representative set), starting with volunteer countries 2.Collect statistics on the representative GS set: monthly averages and long-term statistics (low water, high water), for the complete chronicle of station 3.Collect daily data on the subset: Carry out non-standard calculations on daily data and ad hoc indicators (e.g. number of days below / above reference value during certain period, number of floods. Collate and if necessary adjust GS sets as in step 1

5 5 Discussion…

6 6 Example: Water accounts Water accounts (quality / rivers) require reference discharge at the yearly / monthly time lag. Water accounts (resource) require Rainfall / evapotranspiration at the yearly (seasonal) time lag, River discharge balance at the yearly (seasonal) time lag, Water uses at the yearly (seasonal) time lag In this case, data requirements are yearly averages (final weighting) and VCN30/5 on as many gauging stations as possible

7 7 Example: Rainfall and quality trends Quality trends are analysed by stratified sampling of catchment information and time dependent correlations Concentration is depending on discharge regime, spatially represented by the relative effective rainfall Data requirements are: effective rainfall aggregated at the elementary catchment (c.a. n*100km 2, month)

8 8 Example: land based ecosystems Terrestrial habitats are largely driven by water availability and temperature patterns, thus making it possible to analyse, for instance in wetlands, the stress related to extrema duration during certain periods Many new indicators and statistics are required: QCN xdays during fish migration / spawning periods (and changes with time), Q range during water birds nesting periods, etc. None are available in the classical hydrological statistics Two families of data are required: I.Spatially distributed data (e.g. rainfall, soil humidity, temperature, hydraulic productivity) both on the long-term average and monthly (10 days?) time lag II.Detailed daily (frequent) data (possibly modelled) on spots for computing specific statistics (e.g. QCN1/QCX1 between D0 and D1)

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