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Perspectives in handling uncertainty in European data. Philippe Crouzet European Environment Agency (EEA). Contents. Importance of uncertainty Uncertainty in the assessment process Uncertainty in the Policy questions proper Two open questions, among others Gross nutrient balances
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Perspectives in handling uncertainty in European data Philippe CrouzetEuropean Environment Agency (EEA)
Contents • Importance of uncertainty • Uncertainty in the assessment process • Uncertainty in the Policy questions proper • Two open questions, among others • Gross nutrient balances • Fragmentation of rivers • Example Response in the EEA work • Catchment – water relationships
data Other factors Processing Assessing data Monitoring Decision Other factors Implementing Measures Uncertainty in the data process
River fragmentation • Dams fragment rivers: • Large volume and residence time settle sediments, • Large volume modifies water regime • Wall jeopardizes fauna migration • 6700 large dams in Europe represent ~90% of stored volume (~1450 km3) and less than (??%) of walls • Fragmentation vs. fish is more uncertain than sediment trapping and water regime changes, but: • Actual reservoir volume? Current discharge pattern? • True commissioning year • Has dam a lake? • Are all dams properly located • Data base accuracy is key issue
Gross nutrient balances • Conceptual • GNB = Input {N} –output {N}, can be >0 or <0 • Negative values do not compensate positive values • Calculation scale and conceptual model issues • Large calculation areas are smoothed: problematic areas are faded, thus distorting results
GNB Sensitivity NUTS3 + CLC Ref (NUTS4 + CLC) Comparing catchments (298 units) surpluses by regression yields : S(dpt + CLC) = 0.94 S(ref) (R2 = 0.86) S(dpt) = 0.86 S(ref) (R2 = 0.82) Total surplus is comparable in the first case. NUTS3 alone
Implicit uncertainty in policies and assessment • EEA provides support to policy effectiveness assessment: • Has a given policy delivered expected results on the environment? • Were results observed at enforced time target? • Difficult questions are raised: • Is result defined? How expressing “meeting of the target” in accurate terms? • Can time the target is met be defined in variable conditions within compliance constraints? • Any response being “bound uncertainty” “unbound uncertainty” • Making reliable assessment difficult and improvable
Analysis of the WFD specifications • Demands programme of measures: • to “prevent deterioration of the status” and achieve “good status... at the latest 15 years”, and “bring the bodies of water progressively to the required status”, • Following “coherent and comprehensive overview of water status”, and survey it • Through identification of “significant | main” pressures and • Assessment of status at the “water body type” level, minimizing the impact of seasonal variability ..... Reflect “...changes in the WB as a result of changes...” {pressures} • All terms pose practical and theoretical questions about their accuracy and uncertainty
Analysing trends • Stratified statistics were carried out considering: • Catchments as statistical population, (comprehensive) • Pressures through aggregates of land cover, population and livestock, defining strata in the catchments population, (significant pressures, WB types) • Nitrate, phosphate and ammonium developments with time (progressively) • On annual unbiased averages (...seasonal..) • To compute time trends and year of target meeting (achieve... at the latest...)
Sample results • Intra strata variability, • Inter-years variability • Source data uncertainty • Stratum uncertainty • Orphan strata assessment • Increasing uncertainty with reduction in size • Stationarity, linearity? • Reporting choices
Reporting uncertainties “Thickness of the line” Data dispersion frame Non compliant observations post target meeting
Towards conclusion... • Data related uncertainties now soundly addressed • Assessment related uncertainties are necessary for accurate policy assessment • They becomes considered at the assessment stage and addressed, but risk of shortage in data might make it inapplicable • Lack of understanding the uncertainty attached to complex systems of data, key to spatial assessment . Thanksfor your attention