Implementation timeline for the Water Framework Directive

Transcription

1 Towards the implementation of the European Water Framework Directive? Lessons learned from nutrient simulations in watersheds with different conditions Martin Volk

2 Implementation timeline for the Water Framework Directive

3 A river basin or drainage area is the land that catches all of the water that falls within its sides and sends the resulting discharge to a river system (and thus feeds it) Processes: Land surface, Sub-surface Processes: channel, floodplain Source: Flügel (1995)

4 River basins the challenges Implementation of water framework directive (or CWA in the US) is focused on river basins (other instruments may focus to other units) Global Change (environment, climate, market policy, demographic change, etc.) Land use changes, water management, nature protection (urbanisation, agriculture, forestry, industrialisation, etc.) and consider different functions and goals (trade-offs) Consequences on different scales (water quality, water quantity, soil erosion, etc.)

5 Achievement of WFD objectives by 2015 and use of exemptions in Germany (source: Borchardt et al. 2010) Surface water bodies Ground water bodies Goal achieved Goal achievement planned in 2015 Exception according to Art. 4 in addition: establishing environmental quality standards 2010 (limit the quantity of certain chemical substances)

6 The scale problem Crucial! Calculations of the impact of land use on hydrology, water quality and sediment transport depend on the quality of methods and data! Problem! often lack of data (insufficient), methods (incl. Monitoring strategies) not adapted to scale!..which requires corresponding tests!

7 Definition of scales relevant for planning and management Scale Planning relevance Planning unit Forest Management Biodiversity~ Agriculture~ 1:100,000-1:500,000 Micro~ Macro~ 1:25,000 (1:10,000-1:50,000) 1:5,000-1:10,000 1:1,000 Meso ~ Nano~ (Top) Management Planning Unit Landscape Unit Landscape (Trans-) regional EU directives / regulations Strategic planning Planning / realization of measures Growth region Ecoregion Forest Enterprise, Forest Management unit Forest district / compartment Planning corridor Protected area (special area of conservation) Water~ Sub-basin, catchment River section Stand Habitat Field block Water body Parcel Parcel River Basin, areas suggested for agri-env. measures Large farm, catchment Farm Field River Basin Hydrotope Policy making, Reports, Identification of «risk» and «preference» zones, water and nutrient balance Development stategies, Quantification of processes Planning / realization of measures, efficiency control

8 Development of land use and management scenarios on three different scales in the Ems catchment, Germany Scale-specific models (and measures), knowledge base, visualisation Recent Scenario A Scenario B

9 Development of a potential land use scenario which meets the standards of the WFD concerning the chemical water quality. Simulation of water balance and runoff dynamics Reasonable predictions of the effects of management options Basis for cost-efficiency-calculations

10 Main problems Nitrogen concentrations in surface and groundwater bodies exceeding limit values Land use is far from sustainability Far from achieving water quality standards of the WFD Objective WFD: Good ecological and chemical status of water bodies Total N Nitrate-N Nitrite-N Ammonium-N Limit values [mg/l] <= 3.0 <= 2.5 <= 0.1 <= 0.3 Study area ~ 6-10 ~5.4 ~0.2 ~0.3

11 Development of land use scenarios Overall goal: Reduction of nutrient inputs into the ecosystem - Reduction of agricultural land - Introduction of environmentally friendly management practices Step-wise implementation of different measures Scenario development on the basis of policy instruments - Water Framework Directive (WFD) - Common Agricultural Policy of the EU (CAP) - Local landscape development program (KULAP)

12 Uncertainties of model(s) and scenarios?

13 Question Is the recent data availability adequate to simulate such scenarios? NO 3 -N [mg/l] Status Quo Final Scenario Limit Value

14 Results Drastic land use and management changes are necessary to achieve the objectives of the WFD in the region Urban Areas ~9% Pasture ~4% Forest ~10% Agricultural Land ~77% Implementation unrealistic Urban Areas ~9% Floodplains (no management) ~9% Pasture ~15% Forest ~21% (To take the management out of the floodplains would cost 500 Euro/ha ~ 30 Mio. Euro) Designation as heavily modified or artificial water body Agricult. Land (conventional) ~33% Final scenario Agricult. Land (conservation) ~13% mg / l N total 3.8

15 Examples of scale-specfic environmental measures in the project Scale Area Measure Macroscale Upper Ems River (3740 km²) - Change arable land to pasture in areas with hydromorphic soils in floodplains - Limiting nitrogen surplus from fertilizers on arable land and pasture to a maximum of 50 kg/ha/year Mesoscale Microscale Subbasin Münstersche Aa - Reduction of surface sealing (160 to 350 km²) - Afforestation Floodplain between Telgte - Extensification, renaturation and Greven of floodplain areas (change (13.5 km²) arable land to rough grazing, floodplain forests, pasture to rough grazing, riparian buffer stripes)

16 Conclusions 1: The models seemed to be suitable to simulate trends of the impact of land use and management scenarios on water quality But: Quality of simulation results depends on data quality and availability - Existing water quality monitoring strategies are not adapted to the requirements of the WFD (Processes? Impact on models?) - Dynamics of nutrient fluxes can not be calibrated and validated in a sound way - Lack of transparency in management practices (e.g. amounts of nutrients applied by the farmers) Cause and effect delay between catchment response to implemented measures in the model and in reality (..regional specifics?..forests?) Sensitivity of the model(s) to measures (management practices, etc.)?)

17 Influence of the uncertainty of monitoring data on model calibration and evaluation

18 Load estimation Equations (1) and (2) are the first and second choice of the OSPAR (Oslo-Paris) Convention [Littlewood, 1995, OSPAR, 2004]. Interpolation methods extrapolation method with: L- load (t), F- factor to take account period of record, c- sample concentration (mg/l), Q- discharge at sample time (m³/s), N- number of samples, Q sd - mean discharge for sampling day (m³/s), Q sd - mean flow (m³/s), r Qc - correlation coefficient of Q and c, σ Q - standard deviation of Q, σ c - standard deviation of c, T- mean daily transport (t) SEITE 18

19 Nitrate-N load in tons Nitrate-N load in tons Range of all calculated load in tons Nitrate-N load in tons Monthly calculated nitrate-n load using different sampling strategies and different load estimation methods (Central Germany): a) range of all strategies and methods b) maximum, minimum and mean values of daily composite MAX load calculated MIN load calculated MEAN load calculated Nov 99 Jan 00 Mrz 00 Mai 00 Jul 00 Sep 00 Nov 00 Jan 01 Mrz 01 Mai 01 Jul 01 Sep 01 Nov 01 MAX load calculated MIN load calculated Jan 02 Mrz 02 Mai 02 Jul 02 Sep Nov 99 Jan 00 Mrz 00 Mai 00 Jul 00 Sep 00 Nov 00 Jan 01 Mrz 01 Mai 01 Jul 01 daily composite data set c) sub-monthly discrete and d) monthly discrete data sets MAX load calculated MIN load calculated Sep 01 Nov 01 Jan 02 Mrz 02 Mai 02 Jul 02 Sep MEAN load calculated 80 MEAN load calculated Page Nov 99 Jan 00 Mrz 00 Mai 00 Jul 00 Sep 00 Nov 00 Jan 01 sub-monthly data set Mrz 01 Mai 01 Jul 01 Sep 01 Nov 01 Jan 02 Mrz 02 Mai 02 Jul 02 Sep Nov 99 Jan 00 Mrz 00 Mai 00 Jul 00 Sep 00 Nov 00 Jan 01 Mrz 01 monthly data set Mai 01 Jul 01 Sep 01 Nov 01 Jan 02 Mrz 02 Mai 02 Jul 02 Sep 02

20 Comparison of simulation results based on composite data set compared against submonthly and monthly data set Mean estimation results of composite data set (calibration results) Sub-monthly data set* Monthly data set* R² NSE PBIAS RSR (* not separately calibrated) Page 20

21 Conclusions 2: For a satisfactory simulation of nutrient processes, a good calibration data base is required! We recommend: - implementation of optimised monitoring programs - the use of more than one load estimation method to describe the river water quality - the use of value ranges for model simulations Further work: Recommendation of monitoring strategies and model calibration on different catchment sizes

22 Effects of variations of land management practices on the watershed level

23 nutrients Nährstoffe in kg/ha; in kg/ha; sediment Sediment in t/ha in t/ha Variation of tillage operations and tillage practices (conventional (CVT), conservation (CST), no-tillage (NOT)) (Central Germany) 0,2 0,15 Sediment sediment org. organic Stickstoff nitrogen org. organic Phosphor phosphorus Nitrat nitrate im in Oberflächenabfluss surface runoff Nitrat nitrate im leached Sickerwasser 0,1 0,05 0 CVT_1 CVT_1a CVT_2 CVT_2a CVT_3 CVT_3a CVT_4 CVT_4a CVT_5 CVT_6 CVT_6a CVT_6b CVT_6c CST_1 CST_1a CST_2 CST_3 CST_3a CST_4 CST_4a notillage conventional konventionell Management Systeme conservation management (tillage) scenario konservierend ohne Examples spring barley Impact on nutrient and sediment simulation NOT SEITE 23

24 Conclusion 3: Parametrisation and regionalisation of management practices Based on the results of our analysis the following sensitivity ranking can be concluded: 1) Duration of vegetation period and soil cover over the time with 1a) implementation of undersown crop, and 1b) dates of planting (winter/spring crop); 2) Soil cover characteristics of applied crops (e.g. grains/row crops); 3) Tillage intensity (means applied tillage practice; basic scenarios); 4) Dates of tillage operations.

25 Further work: - Recommendations on land management parameterisation on different catchment sizes - Regionalisation of land management practices on different catchment sizes (collaboration with M. Schönhart, BOKU) - Implementation of temporal variability of applied management practices

26 Low flow Biodiversity Trade-offs different functions and services of landscapes have to considered at the same time - What are the searching for? - How much do we gain in goal A if we decrease goal B? Functional relationships between different goals? Functional relationships between goals and policy instruments? Yield

27 ESS ESS ESS 2 ESS 3 From model results to management support? climate land use model management action Yield Low flow Water quality Yield ESS 1 Water quality Yield Low flow ESS 1 Water quality Low flow

28 ESS ESS 2 From model results to management support? climate land use management action optimization model Yield Low flow Water quality Yield Low flow ESS 1 Water quality

29 Trade-offs - for bioenergy/food production (example) optimization objective function crop rotation schemes existing land use distribution 5 perc. min discharge food arable land SWAT average NO 3 - conc bioenergy non arable land yield food yield bioenergy Lautenbach, Seppelt, Strauch, Volk, in prep

30 Considering policy constraints All Two-culture Rapeseed

31 Conclusions 4 Importance of spatial configuration Policy support needs functional trade-offs Optimization techniques are an important tool for that To dos: Correct interpretation of complex results needs time Additional crop rotation schemes Adaptation of management schemes depending on HRU properties Include contribution margin or /and protein content? UFZ image gallery

32 Final Remarks Integrated modelling: Methods and measures need to be adapted to different scales and regional specifics Monitoring and data uncertainty: Required: Improved and adapted monitoring strategies, solutions for data-poor regions Management and tillage: Required: Sensitivity analysis and regionalisation methods Spatial processes: Improve description of spatial distribution Forests: Consider functions in river basins Trade-offs: Functional trade-offs need to be included in river basin management How could models and DSS efficiently used? Model interaction, stakeholder involvement during development, ensure update and maintenance

33 Some of my related work to the topics: Integrated modelling and scales: Rode, M. et al (2008): Water SA (34(4): Volk, M. et al (2008): Ecological Economics 66: Volk, M. et al (2009): Land Use Policy 28(3): Monitoring and data uncertainty: Bende-Michl, U. et al (2011): Environmental Modelling & Software 26: Strauch, M. et al (2012): Journal of Hydrology : Ullrich, A., M. Volk (2010): Environmental Monitoring and Assessment 171: Management and tillage: Cerro, I. et al (2012): Journal of Environmental Quality (Online first: Ullrich, A., M. Volk (2009): Agricultural Water Management 96(8): Spatial processes: Arnold, J.G. et al (2010): Transactions of the ASABE 53(5): Bosch, D.D., et al (2010): Transactions of the ASABE 53(5): Bonuma, N. et al (2012): Journal of Environmental Quality (submitted) Forests in river basins: Lorz, C. et al (2007): Forest Ecology and Management 248: Trade-offs: Lautenbach, S. et al (2012): Agricultural Water Management and Environmental Modelling & Software (in prep) How could models and DSS efficiently used? McIntosh, B.S. et al (2011): Environmental Modelling & Software 26(12): Volk, M. et al. (2010): Environmental Management 46:

34 Thank you for your attention Questions?