Das Hydrologische Modellsystem Wasim
Comparing the eco-hydrological model SWIM to conceptually different hydrological models for climate change impact assessments on low flows
International SWAT Conference 2014 01. August 2014
Anne Gädeke1, Ina Pohle1, Hagen Koch2 und Uwe Grünewald1
1) Department of Hydrology and Water Resources Management, Brandenburg University of Technology 2) Potsdam Institute for Climate Impact Research, RD Climate Impacts and Vulnerability Background of the study
Climate change impact assessments are nowadays a prerequisite - for a successful integrated river basin planning and management - for the development of suitable climate change adaptation strategies
especially relevant for highly anthropogenically impacted catchments which are prone to low flows already under current climate conditions
Development of post mining lake
Schwarze Elster, 2006
SWAT 2014 (01.08.2014) Anne Gädeke 2 Problem statement
Climate change impact assessments on regional water resources are highly uncertain even opposing results are reported in the scientific literature (Teutschbein et al. 2011, Gädeke et al. 2014, Huang et al. 2013) uncertainty increases for extreme events, such as low flows uncertainty increases the smaller the scale of interest –> adaptation strategies are generally implemented on a smaller scale BUT regional stakeholder want to have “robust” projections due to economic relevance!!
SWAT 2014 (01.08.2014) Anne Gädeke 3 Aim of the study
. Evaluation of future low flow conditions using different climate downscaling approaches (statistical and dynamical)
. Evaluation of uncertainties related to conceptually different hydrological models
Prerequisites: - Good data base - Calibrated and validated hydrological models - Consistent output from climate downscaling approaches
SWAT 2014 (01.08.2014) Anne Gädeke 4 Study Area
SWAT 2014 (01.08.2014) Anne Gädeke 5 Study area Study catchments Spree (≈ 10,000 km²) Germany Europe
Schwarze Elster (≈ 5,500 km²)
Spree river catchment Schwarze Elster river catchment Gauges River State borders Reservoirs Study catchments Active lignite mines Post mining lakes/ lakes UNESCO biosphere reserve Spreewald
SWAT 2014 (01.08.2014) Anne Gädeke 6 Characteristics of the study catchments Location in a transition zone between Spree maritime and continental climate Low natural water availability (1961-1990)
Spree Germany
Precipitation [mm/a] 587 789
Temperature [°C] 8.7 8.2 Schwarze Elster Natural rainfall-runoff process strongly
b) impacted anthropogenically (especially by lignite mining activities) → Calibration on the measured discharge is not possible
Subcatchments chosen where anthropogenic impact on discharge is relatively low: a) a) Pulsnitz river catchment (≈ 245 km²)
c) b) Dahme river catchment (≈ 300 km²) c) Weißer Schöps river catchment (≈ 135 km²)
Anne Gädeke 7 Materials and Methods
SWAT 2014 (01.08.2014) Anne Gädeke 8 Study approach Climate change impact assessment
emission scenario (A1B) Downscaling approaches: dynamical global circulation model (ECHAM 5) REMO (1 realisation) CLM (2 realisations) climate downscaling approach statistical (STAR, WettReg2010, CLM, REMO) STAR (100 realisations) WettReg (10 realisations) reference period scenario period (01/04/1966-31/03/1990) (01/04/2031-31/03/2055) Raster-based dynamical DAs were interpolated onto the station based statistical Das hydrological models (WaSiM, SWIM, HBV-light) AM(7): annual minimum 7-day flow Q95: ninety-five percentile flow change in AM(7) and Q95
Daily simulation time step (low flow year (April-March))
SWAT 2014 (01.08.2014) Anne Gädeke 9 Hydrological models Decreasing level of complexity +++ ++ + WaSiM SWIM HBV-light (Water Balance Simulation (Soil and Water Integrated (Hydrologiska Byråns Model) Model) Vattenbalansavdelning) Conceptual basis physically-based process-based conceptual Spatial distribution fully-distributed semi-distributed (HRU) lumped
Penman-Monteith/ reduction Calculation of ETP not included, Turc-Ivanov/ ETP/ETA to ETA depending on matrix ETA is calculated based on soil Ritchie Concept potential water storage
Interception LAI dependent bucket approach not included not included Green-Ampt approach modified Infiltration SCS curve number method not included by Peschke [1987] Richards equation Unsaturated zone parameterized based on van similar to SWAT based on a linear storage approach Genuchten [1980] integrated 2D groundwater linear storage approach Saturated zone linear storage approach model (shallow and deep) kinematic wave approach/flow runoff transformation by triangular Routing Muskingum velocity after Manning-Strickler weighting function
SWAT 2014 (01.08.2014) Anne Gädeke 10 Hydrological models
Decreasing level of complexity +++ ++ + WaSiM SWIM HBV-light (Water Balance Simulation (Soil and Water Integrated (Hydrologiska Byråns Model) Model) Vattenbalansavdelning)
Two modelers: Anne Ina Anne
SWAT 2014 (01.08.2014) Anne Gädeke 11 Hydrological model set up and parameterization
WaSiM-ETH SWIM HBV-light
Anne Ina Anne
Precipitation correction
temperature, ETP, Interpolation of Interpolation of precipitation meteorological meteorological input data input data
Concentration only on structural differences between the models
Manual model parameterization Manual model parameterization No manual model (landuse, soil, groundwater) (landuse, soil) parameterization
SWAT 2014 (01.08.2014) Anne Gädeke 12 Hydrological model calibration
WaSiM-ETH SWIM HBV-light
automated: automated Approach local approach manual global approach (gradient-based (PEST)) (genetic algorithm)
Objective function min (퐐퐬퐢퐦 퐭 − 퐐퐨퐛퐬(퐭))ퟐ NSE, LNSE, r², MBE LNSE NSE: Nash Sutcliffe Efficiency, LNSE: Nash Sutcliffe Efficiency using logarithmic discharges, r²: coefficient of determination, MBE: Mass Balance Error
After automated calibration, a multi-criteria evaluation was performed (as for SWIM)
Calibration: 1999-2002 Validation: 2002-2006
SWAT 2014 (01.08.2014) Anne Gädeke 13 Results
SWAT 2014 (01.08.2014) Anne Gädeke 14 Model Calibration and Validation
SWAT 2014 (01.08.2014) Anne Gädeke 15 Results – Calibration (daily time step)
Catchment: Weißer Schöps, Gauge Särichen
r² NSE LNSE MBE WaSiM 0.81 0.81 0.82 -3.5 SWIM 0.76 0.74 0.68 -7.5 HBV-light 0.85 0.85 0.80 -0.5 r² = coefficient of determination, NSE = Nash-Sutcliffe Efficiency, LNSE = Nash Sutcliffe Efficiency using logarithmic discharges, MBE = Mass Balance Error SWAT 2014 (01.08.2014) Anne Gädeke 16 Results – Validation (daily time step)
Catchment: Weißer Schöps, Gauge Särichen
r² NSE LNSE MBE WaSiM 0.79 0.77 0.65 5.4 SWIM 0.55 0.53 0.54 -3.1 HBV-light 0.72 0.71 0.70 -0.8 r² = coefficient of determination, NSE = Nash-Sutcliffe Efficiency, LNSE = Nash Sutcliffe Efficiency using logarithmic discharges, MBE = Mass Balance Error SWAT 2014 (01.08.2014) Anne Gädeke 17 Performance outside calibration and validation (mean monthly flow, 1966-1990) 2.0 measured Catchment: Weißer Schöps, Gauge Särichen WaSiM 1.6 SWIM HBV 1.2
0.8
0.4 Mean runoff [m³/month] runoff Mean 0.0 1 2 3 4 5 6 7 8 9 10 11 12
WaSiM SWIM HBV-light r² 0.98 0.96 0.77 WaSiM-ETH performs better outside of the calibration and validation period NSE 0.96 0.31 0.77 LNSE 0.91 0.56 0.75 MBE [%] -2.1 -33 -3.2 NSE: Nash Sutcliffe Efficiency LNSE: Nash Sutcliffe Efficiency using logarithmic discharges MARE: Mean absolute relative error MBE: Mass Balance Error SWAT 2014 (01.08.2014) Anne Gädeke 18 Performance outside calibration and validation Flow duration curve (Q95, 1966-1990) Reference period: 1966-1990
Q95
0.27 0.21 0.11 0.1
Logaritmic scale!!
SWAT 2014 (01.08.2014) Anne Gädeke 19 Performance outside calibration and validation mean of AM(7) during 1966-1990
Dynamic is similar between the hydrological models In absolute difference, SWIM performs best
Measured WaSiM SWIM HBV-light mean of AM(7) during 1966-1990 11.2 20.4 11.4 26.3
SWAT 2014 (01.08.2014) Anne Gädeke 20 Climate Change Impact Assessment
SWAT 2014 (01.08.2014) Anne Gädeke 21 Same hydrological model (SWIM), different climate downscaling approaches
SWAT 2014 (01.08.2014) Anne Gädeke 22 Low flows under different climate change scenarios based on SWIM (2031-2055 compared to 1966-1990) 60 42 %
Q95 40 27 % deviations up to 120 %
20 CLM WettReg in % in 0 REMO STAR -20 -13 % -40 Change Change -60 -80 -100 -82 %
REMO CLM STAR WettReg 0 deviations up to 71 %
AM(7) -20 -22 %
in % in -40 -40 % -42 %
-60 Change Change -80
-100 -93 %
SWAT 2014 (01.08.2014) Anne Gädeke 23 Different hydrologicals, same climate downscaling approach (STAR)
SWAT 2014 (01.08.2014) Anne Gädeke 24 Difference between hydrological models Results based on STAR (2031-2055)
0.22
0.17 36% 0.14
SWAT 2014 (01.08.2014) Anne Gädeke 25 One reason of poor agreement of WaSiM……
SWAT 2014 (01.08.2014) Anne Gädeke 26 0 0 Groundwater levels Ebersbach Kodersdorf -1 -1 -2 WaSiM-ETH -2 -3 -3 Weißer Schöps -4 -4 -5
-5 -6
Depth to groundwater table groundwater to Depth depth to groundwater table groundwater to depth -6 -7
0 0
Girbigsdorf Mückenhain -1 -2 -2 -4 -3 measured -6 -4 simulated
-5 -8
depth to groundwater table groundwater to depth table groundwater to depth -6 -10
0
0
-1 Holtendorf Königshain -2 -1 -3 -2 -4 -5 -3
Poor agreement between -6
depth to groundwater table groundwater to depth depth to groundwater table groundwater to depth measured and simulated -7 -4 groundwater levels Summary
. WaSiM and HBV-light outperformed SWIM during model calibration and validation based on daily time step . Outside of the calibration and validation period, WaSiM outperformed the more conceptual models for mean monthly flow (1966-1990) . Concerning the low flow indicators AM(7) and Q95, SWIM outperformed WaSiM and HBV-light – even though WaSiM uses a 2D groundwater approach . For the climate change impact assessment, the choice of the climate downscaling approach adds the largest share of uncertainty to the final results (even opposing trends for Q95) . The analysis of measured and simulated groundwater levels based on WaSiM reveals that the internal processes are not simulated reliably yet
SWAT 2014 (01.08.2014) Anne Gädeke 28 Conclusion
. During model calibration, the modeller has to make sure the model functions well for the purpose that the model will be used later on . Setting up a model which works well for all flow conditions (high, mean, low flows) is difficult to achieve in most cases . Weaknesses of statistical performance criteria need to be considered during model calibration and validation . For climate change impact assessments focussing on low flows, the choice of the hydrological adds less uncertainty to the final results than the climate downscaling approach
SWAT 2014 (01.08.2014) Anne Gädeke 29 Thank You! Question?
References: Gädeke, A., H. Hölzel, H. Koch, I. Pohle, and U. Grünewald (2014), Analysis of uncertainties in the hydrological response of a model-based climate change impact assessment in a subcatchment of the Spree River, Germany, Hydrological Processes, 28(12), 3978–3998. Huang, S., V. Krysanova, and F. Hattermann (2013), Projection of low flow conditions in Germany under climate change by combining three RCMs and a regional hydrological model, Acta Geophysica, 61(1), 151-193. Teutschbein, C., F. Wetterhall, and J. Seibert (2011), Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale, Climate Dynamics, 1-19.
SWAT 2014 (01.08.2014) Anne Gädeke 30