Water assets accounts implementing at the EU level

1. Water assets accounts implementing at the EU level Natural data collection and processing. Towards in-deep revision of processes Philippe Crouzet EEA

2. Water accounts: importance and status of data • Environmental accounting differs from modelling: • Modelling simulates a causal process and creates a category of data from another; • Accounting puts side to side different data sets and in principle does not apply any modelling between categories • Hence data is of paramount importance and one of the targets of accounting is testing data matching between categories • Contrasting with economic book-keeping, some data are more important, being the cornerstone of the balance of accounts: • Physical types (defines the method) • Relative importance (relative weight) of data categories /strata • Substitutability category (drives collection efforts) • All data being spatial, appropriate addressing is important

3. Data collection: at the verge of changing paradigm • Current data collection scheme based on “topic data collection”, separating spatial entities from time-series; • Lessons from WA strongly suggest deeply integrating data collection • Conceptual developments realised for the WA can be improved, • Analysis of the gaps and insufficiencies recorded during the process • Making regular production of Environmental accounts require fundamental revision of data collection schemes

4. Essential: cannot be modelled stratified stratified stratified Linear Replaceable: can be modelled from other variable Point Data categorisation & hierarchy All relate to spatial reference systems Surfacic

5. Data issues: reference systems • Making accounts is defining statistical units, aggregated as territory of references • Some minor errors in Ecrins river topology • Sub-basins still not 100% satisfactory (lack of GIS sub-units) and needed improvement • Still many point items lacking snapping • Major gap: groundwater systems could not be developed in time (and are still provisional)

6. Lakes and reservoirs seasonal storage) • Monthly accounts make it necessary considering seasonal storage • Lakes and reservoirs known as water mirrors fully connected to catchment and rivers (Ecrins: ~80000 lakes) • Volume and depth unknown in most cases, except if resulting from dam (Eldred2 DB fuelled by Icold data) • Comprehensive exploiting of Wikipedia!

7. OPEN FOR DISCUSSION Reference systems

8. Meteorological data • Modelled daily with Penman-Monteith equation • Data source: Ensembles (already mentioned) • CLC 2006 (coverage); soil characteristics from Soil DC • Aggregated per FEC from monthly aggregates • Considerable work to make data usable, however now mostly automated, but ma ny progresses required: • Appropriate density of station (reanalysis process to develop) • Solving local gaps in relation with details in computation

9. Understanding the gaps: Meteo data

10. River discharge data sets • They are very exemplary and drove the redesigning of data collection concepts: • Time series (daily-> specific QA) • Attached to stations (data set contextual QA) • Snapped to rivers / catchment (contextual productivity QA) • Only observed data are fully relevant • Highly reliable time gaps reconstruction • Quite erratic when reconstructing spatial gaps • Aiming at full documentation of stations and daily data

11. River discharge data information yield • Over ~9000 stations with daily data, ~2000 could not be used because spatial information insufficient to snap! • How mitigating the poor yield? • EEA / Eionet data collection? • EU centralised data collection (GRDC)? • Certainly a compromise process demanding stronger cooperation of countries (the current situation ranges between all data for all stations, fully free to no data at all in the EU!) • This is the central rub for the years to come

12. River data: some figures • Data sets are rather large: 68 million daily values require handling in SQL Server • Even such historically processed information is still problematic: • Ill-documented changes in units in source data, • Poorly documented gauging stations data (erroneous controlled catchment, no river name, etc.)

13. OPEN FOR DISCUSSION Natural water resources

14. Surface to groundwater • Assuming that GW simplified aquifers are ready and related to surface water: • Soil water to GW computed with SUGAR transfer constants (partial exception breaching the rule of “no model between compartments”) • GW to surface water , envisaged from systematic computation of automated and simplified “depletion curves”

15. Observations and suggestions: natural systems • Reference systems • Ecrins Ok, but improvable (towards v2 on-going with RDA) • GW systems to complement and populate for the WA (and estimate proxy storable volumes from porosity) • Natural data • Most datasets have large gaps (no consistent EU and still lesser EEA coverage) • Meteo data uneven quality and density; • Snow and glaciers data sets collection to improve • River discharge should become compulsory provision; • Reservoirs changes in volume more and more “secret” • GW levels to systematise as stock change estimates

16. Water uses: major issue • Final target is policy relevant indicators: Are all “need”/”resource”: aggregated figure demands accurate ingredients

17. Spatial modelling Individual modelling Detailed data Specifying appropriate water uses DC principles • Water uses are asymmetrically distributed: • A few % of entities abstract / use majority of resource -> volumes AND location to be known accurately • Larger number weight less -> lumped density may suffice • Current data collection was backed by this principle, that could be applied since its prerequisite is having at disposal a statistical reference population.

18. Urban and domestic water uses • Constraints: • No detailed values available; • “cities” not defined (back on Urban Atlas, Urban Audit, UMZs and administrative layers) • Population figures comprehensive from US source only; • UWWTP directive incompletely reported and not directly related to administrative reference • Systematically documented approach based on “best available data”, completed by … Wikipedia for largest entities…, ready for better data.

19. Industrial waters: cooling and processing (generic) • Cooling water not specific to energy production • Large combustion plants (LCPs) in manufacturing as well • Major activities (coke, steel, oil, chemistry, ... ) require cooling • Manufacturing vary diverse needs / uses of water.

20. Industrial waters: cooling and processing (mitigating poor data) • Lack of reference population of industrial sites: • EPRTR is truncated and water vols. /activity are optional (=not filled); Some relevant positioning • Platts interesting information on energy production (positioning highly imprecise) • Professional institutes: CONCAWE, CEPI, etc. have accurate (albeit lumped) information on activities and /or volumes • None have information on the resource mobilised • Obtaining positioned volumes per resource and related to activity is complex process with uncertain results

21. Industrial waters: cooling and processing (populating databases) • General process aims at transparency and to the possibility of refutation • Compute most likely volumes per branch / class (as ISIC for the SEEAW), based on most likely information / activity • When possible use proxies from EPRTR (e.g. CO2 emissions for LCPs) to assess utilised volumes • When possible use EPRTR or Platts to apportion volumes per site and position the site • Otherwise apportion blindly (e.g. nb of sites) • Breakdown the resource according to • Proximity to river / sea • Type of production (surface / GW) • Details: report to be issued soon

22. Agriculture • Main volume is natural soil water, this is not recorded as such, albeit available as outcome of meteorological processes • Irrigation volumes self abstracted of taken from irrigation supply companies • Breakdown poorly documented in reporting • Total volumes need being taken from EU wide data sets (comprehensiveness) • EU wide data sets underestimate compared to national sources. • Themselves contradictory • Revision of data source and dataflows is paramount issue

23. Lessons and way forward • Water accounting (as part of Env. Accounting) is a powerful tool for: • Validating and making data seamless; • Making more relevant (frequency based) indicators • Support policies and SoER (contextual & harmonised information) • Asks for two major changes in data collection schemes: • Address asymmetric data under a stratified approach (partly implemented) • Address data in their spatial context (to explicit)

24. OPEN FOR DISCUSSION water uses by economical sectors

25. Spatial contextualisation of data Example: any time series “event” (e.g. reservoir volume) changes at short time (day, week), and depends on a superstructure (e.g. dammed lake) changing at medium time (decades to century, with abrupt changes), within a catchment landscape which rate of change is beyond centuries. However, each level deeply impact the others which analysis and data collection must be done under these constraints. Data chain must be continuous (driving organisation, contacts, responsibilities for update of each element of the chain and data QA).

26. Organisational issues • Data collection “matrix” approach: • Eionet is the legal reference instrument for data collection at the EA level; however Eionet often has not the competency to address needed data • Professional organisations have competencies but not legal permissions (e.g. Eureau close to urban abstractions and uses, but need permissions). • Could be discussed in the next strategy

27. Thanks for your attention eea.europa.eu