Eco-hydrological assessment of German lowland catchments experiences and challenges- N., B. Schmalz, G. HörmannH Dep. Hydrology & Water Resources Management Ecology Centre, CAU Kiel 1
Landscape processes in lowlands Low hydraulic gradients Large water retention/storage potential caused by lakes and wetlands Saturation overland flow > infiltration excess overland flow (Horton) High spatial variability in soils due to ice-age Connected/unconnected potholes Alpine Low mountain Lowland 2
Anthropogenic Impacts Tile drains Ditches River regulations 3
Assessment of lowland processes: -the catchments- 4
Treene and Kielstau catchments Kielstau-EZG (obere Treene) 5 UBA
Sampling strategy: nested approach Treene catchment: 517 km², 60 km river length Kielstau catchment 50 km², 11 km river length drained section of Kielstau catchment 0.15 km², 200 m ditch length 6
Assessment of water quality 7
Potential nutrient entry pathways Hennebach (Kysweek) Moorau # # # # # # ## Kielstau 8
Continuous observation and sampling Parameter Phosphate (PO 4 _P) Ammonium (NH 4 _N) Total Phosphorus (TP) Method Spectrophotometry Spectrophotometry Spectrophotometry Nitrate (NO 3 ) Ion chromatography Dep. Hydrology Chloride & Water (Cl) Resources Management, Ion chromatography CAU Kiel Sulphate (SO 4 ) Ion chromatography 9
In situ measurement campaingns 10
Water quality transects sampling strategy - 16 sampling points - 9 tributaries 11
Ammonium-N mainly from point sources NH4-N [mg/l] 2,5 2 1,5 1 Moorau ( 1 WWTP & poultry farm) Ammonium Hennebach ( 3 WWTP!) 0,5 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Messpunkt oberhalb unterhalb/ep im Zulauf GK I GK I-II GK II GK II-III GK III 12
Nitrate-N- mainly non-point sources Nitrat 16 14 12 NO3-N [mg/l] 10 8 6 4 2 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Messpunkt oberhalb unterhalb/ep im Zulauf GK I GK I-II GK II GK II-III GK III 13
Water quality assessment - LAWA 1998 Nitrate nitrogen 0.0 LAWA class III-IV (great burden) NO3_N mg/l All the other sampling points reached at least LAWA class III (enhanced burden) 14 12 10 8 6 4 2 0 08-Apr-05 28-Apr-05 18-May-05 07-Jun-05 27-Jun-05 17-Jul-05 06-Aug-05 26-Aug-05 15-Sep-05 05-Oct-05 25-Oct-05 14-Nov-05 04-Dec-05 24-Dec-05 13-Jan-06 02-Feb-06 22-Feb-06 14-Mar-06 Moorau tributary Moorau tributary 10.0 20.0 30.0 40.0 50.0 60.0 precipitation mm precipitation NO3_N (mg/l) I: zero burden I-II: very low burden II: moderate burden II-III: significante burden III: enhanced burden III-IV: great burden 14
Eco-hydrological modelling 15
Maps Input data for the Kielstau catchment Digital elevation model (LVermA, 1995) Land use (DLR, 1999) Soil map (BGR, 1999) Time series data: -- climate data data (DWD) -- discharge values (LANU) 16
Simulated distribution Ergebnis of 1drainage density, GIS- based algorithm Modelled drainage areas ca. 38% of the catchment Golon, 2006 17
Validation of drainage algorithm Agricultural area 381 ha Drained areas (digitized) 141 ha 37% (d. LN) Drained ares (modelled) 184 ha 48% (d. LN) Exact overlap 98,5 ha 70% Absolute area difference 11% Golon, 2006 18
Stepwise model calibration of the Kielstau 1.2 0.8 0.4 0-0.4-0.8-1.2-1.6-2 -2.4-2.8-2.59 0.80 0.82 0.74 0.78 0.72 0.61 0.73 0.61 0.66 0.67 0.71 0.44 0.58 0.55 0.36 Model - initial parameterization Model - 200 GW_DELAY Model - 50 GW_DELAY, GWQMN 1250 Model - GW_REVAP 0.2 Model - REVAPMN 0.01 Model - ponds & wetlands Model - Tile drainage Model - CN2 adjustments Statistical criteria Nash-Sutcliffe Index Correlation coefficient Calibrated period: 01.01.1990-31.12.1999 19
5 4,5 4 3,5 3 2,5 2 1,5 1 0,5 0 Daily runoff Kielstau Nash-Sutcliffe Index: 0,71 Correlation: 0,82 MAE: 0,18 RSME: 0,36 r2: 0,66 measured modelled Jan. 91 Jul. 91 Jan. 92 Jul. 92 Jan. 93 Jul. 93 Jan. 94 Jul. 94 Jan. 95 Jul. 95 Jan. 96 Jul. 96 Jan. 97 Jul. 97 Jan. 98 Jul. 98 Jan. 99 Jul. 99 (Tavares 2006) 20 Jul. 90 Jan. 90 discharge [m³/s]
Nitrate loads per day simulated and measured 700 600 500 400 300 200 100 0 4/1/2005 5/1/2005 6/1/2005 7/1/2005 8/1/2005 9/1/2005 10/1/2005 11/1/2005 12/1/2005 1/1/2006 2/1/2006 3/1/2006 4/1/2006 5/1/2006 6/1/2006 7/1/2006 8/1/2006 9/1/2006 10/1/2006 Zeit Bieger, 2007 21 kg/day
Geese Farm, active from Oktober - December 22
Comparision of modelling performance in different landscapes 23
Model performance - hydrology ME, r 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Krofdorfer Forst Dietzhölze Dill Aar1 Low mountain Aar2 mountain range Haiger Weiherbach Treene Stör Lowlands Kielstau 24 ME r
future perspectives 25
Modelling the ecological potential of aquatic habitats Watershed Hydrology and erosion Discharge dynamics, sediment load In-stream Hydraulics Flow velocity, water depth, sediment transport Ecological functions for each parameter and each species River Morphology Substrate, plants, V depth Ecological potential Kiesel (in prep.), phd thesis 2007-2010 Jens Kiesel, 2007-2010 26
SWAT model for XiangXi river in 2000 600 simulation discharge Xingshan discharge [m3/s] 400 200 Köplin,, Cai Qinghua, Jiang Tong 2006 0 29.02.1980 19.04.1980 08.06.1980 28.07.1980 16.09.1980 05.11.1980 27
Thank you to my team Lake water quality (Anne Grudzinski) runoff & Water quality (Filipa Tavares, u.a.) Drainage (Jona Golon) Vielen Dank Water quality transects (Katrin Bieger) Treene-Model (Thorsten Dey) Kielstau-Models (Filipa Tavares, Yong Zhang, Katrin Bieger, u.a.) für Ihre Aufmerksamkeit! Event based nutrient entry (Katrin Bieger) Stör model (Nadine Dobslaff) Habitat model (Jens Kiesel) Interaction Groundwater- Surface water (Pina Springer) 28
Department Hydrology & Water Resources Management, Ecology Centre, Kiel University nfohrer@hydrology.uni-kiel.de 29