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CALIBRATION Derek Karssenberg Utrecht University, the Netherlands.

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1 CALIBRATION Derek Karssenberg Utrecht University, the Netherlands

2 Example model: rainfall-runoff model of a hillslope (France) DEM 100 m

3 Rainfall-runoff model of a hillslope (France) measured rainfall 3 hours rain, m/10 s

4 Rainfall-runoff model of a hillslope (France) measured discharge at outflow point 3 hours discarge, m 3 / s

5 Model structure Rainfall (timeseries) Infiltration constant infiltration capacity parameter: KSat (mm/h) Runoff Manning equation (kinematic wave) parameter: n timestep: 10 seconds, cellsize 10 m

6 dynamic # rain per timestep (m/timestep) Pr=timeinputscalar(RainTSS,Clone); # flow out off the cell (m/timestep) QR=(Q*T)/CA; # flow into the cell, from non channel cells (m/timestep) QRNCh=upstream(Ldd,QR); SurW=Pr+QRNCh; # infiltration SurW=SurW-I; # lateral inflow (m3/s) QIn=((SurW-QRNCh)*CA)/T; # per distance along stream ((m3/s)/m)) q=QIn/DCL; Q = max(0.0001,Q);...

7 # discharge (m3/s) Q=kinematic(Ldd,Q,q,Alpha,Beta,T,DCL); # discharge (m3/s) Q=kinematic(Ldd,Q,q,Alpha,Beta,T,DCL); # water depth (m) H=(Alpha*(Q**Beta))/Bw; # wetted perimeter (m) P=Bw+2*H; # Alpha Alpha=AlpTerm*(P**AlpPow);

8 Model run with measured (KSat) and tabulated value (n) KSat = 30 mm/h, n = 0.038 3 hours discarge, m 3 / s measured modelled

9 Model run with measured (KSat) and tabulated value (n) adjusted KSat = 15 mm/h, n = 0.038 3 hours discarge, m 3 / s measured modelled measured modelled measured

10 Model run with measured (KSat) and tabulated value (n) adjusted KSat = 20 mm/h, n = 0.038 3 hours discarge, m 3 / s modelled measured

11 Model run with measured (KSat) and tabulated value (n) adjusted KSat = 20 mm/h, adjusted n = 0.047 3 hours modelled, previous guess measured modelled, adjusted n

12 Calibration Finding inputs or parameters by minimizing the difference between model outputs and measurements of these outputs z 1..m 1..m state variables i 1..n 1..n inputs f 1..m 1..m functionals pparameters ooutputs i.e. a set of state variables in which the interest lies

13 manual adjustment Calibration, manual adjustment of parameters Manual adjustment of parameters / input visual comparison between measured and modelled outputs manual adjustment of parameters (trial and error) Disadvantages: subjective takes a lot of time it is difficult to find the ‘best’ values no information on the ‘uncertainty’ of the estimated parameters

14 introduction Calibration, automatic adjustment Approach: -define a goal function -optimize the parameters resulting in the lowest (highest) value of the goal function -optimization is done with a computer algorithm

15 goal function Goal function Provides an objective measure of the goodness of fit between (the) model output(s) and measured values of the corresponding variables Example: mean square error (MSE) modelled variable at t z t measured variable at t nnumber of timesteps

16 goal function MSE in our example modelled discharge (m 3 /s) at t z t measured discharge (m 3 /s) at t nnumber of timesteps, 691 (each time step is 5 s)

17 goal function... # discharge (m3/s) Q=kinematic(Ldd,Q,q,Alpha,Beta,T,DCL); report QTSS =timeoutput(Out,Q); # water depth (m) H=(Alpha*(Q**Beta))/Bw; # wetted perimeter (m) P=Bw+2*H; # Alpha Alpha=AlpTerm*(P**AlpPow); # CALIBRATION # measured discharge (m3/s) QMeas=timeinputscalar(QMeasTSS,1); Error=Q-QMeas; ErrorSquared=sqr(Error); SumErrorSquared=SumErrorSquared+ErrorSquared; RMSE=SumErrorSquared/self.currentTimeStep();

18 goal function Response surface, MSE value in the example MSE

19 goal function Response surface, MSE value in the example MSE modelled measured

20 introduction Response surface, MSE value in the example MSE modelled measured

21 goal function Response surface, MSE value in the example MSE modelled measured

22 goal function Response surface, MSE value in the example MSE modelled measured

23 goal function Response surface, MSE value in the example MSE modelled measured

24 goal function Response surface, MSE value in the example MSE modelled measured

25 goal function Examples of other shapes of a 2D response surfaces

26 goal function Examples of other shapes of a 2D response surfaces

27 goal function 1D respons surface with many local minima

28 goal function Higher-dimensional response surfaces When several parameters are unknown, e.g. - saturated conductivity of several soil layers - vegetation cover of several vegetation units - maximum interception store - surface storage of several soil units - manning’s - groundwater flow parameters - etc..

29 automatic adjustment, optimize Calibration, automatic adjustment Approach: -define a goal function -optimize the parameters resulting in the lowest (highest) value of the goal function i.e.: how do we find the set of parameter values resulting in the lowest (highest) value of the goal function or, in other words: how do we find the minimum (or maximum) of the response surface

30 automatic adjustment, optimize Calibration, automatic adjustment Approach: -define a goal function -optimize the parameters resulting in the lowest (highest) value of the goal function -optimization is done with a computer algorithm -brute force -hill-climbing techniques -genetic algorithms

31 automatic adjustment, optimize Choice of optimization algorithms Important is: -how close does the algorithm get to the real minimum value of the goal function (response surface)?

32 automatic adjustment, optimize Choice of optimization algorithms Important is: -how close does the algorithm get to the real minimum value of the goal function (response surface)? -is the global minimum found or just a local minimum?

33 automatic adjustment, optimize Choice of optimization algorithms Important is: -how close does the algorithm get to the real minimum value of the goal function (response surface)? -is the global minimum found or just a local minimum? -how many model runs are needed to find the minimum?

34 automatic adjustment, optimize Brute force approach 1.run the model for a large set of parameter value combinations 2.select the combination with the lowest value of the goal function

35 automatic adjustment, optimize Brute force approach advantages: -simple -‘whole’ respons surface is calculated -(large) local minima are found disadvantages: -local minima are missed when small -optimization is not done inbetween the steps for parameter values used -many model runs are needed

36 automatic adjustment, optimize Hill-climbing techniques Use the shape of the response surface to reach the minimum value MSE starting point

37 automatic adjustment, optimize Hill-climbing techniques Use the shape of the response surface to reach the minimum value MSE starting point

38 automatic adjustment, optimize Hill-climbing techniques, steps in algorithm: 1.choose for each parameter a starting value (= location on the response surface) 2.calculate the gradient of the response surface at that location (by running the model with slightly different parameter values) 3.go in the direction of this gradient over the response surface to a new location, if minimum is found, stop, or else continue at 2

39 automatic adjustment, optimize Hill-climbing techniques advantages: -small number of runs needed (compared to brute force) -location of minimum can be found with high precision

40 automatic adjustment, optimize Hill-climbing techniques advantages: -small number of runs needed (compared to brute force) -location of minimum can be found with high precision disadvantages -danger exists that only a local minimum is found (search is always downhill

41 automatic adjustment, optimize Can this be solved with hill climbing?

42 automatic adjustment, optimize Genetic algorithm Uses analogy with biological evolution: -individual: one parameter set coded as a binary string -population: several individuals (several parameter sets) -population evolves until individuals emerge that represent a very low (high) value of the objective function -evolution by selection, cross-over and mutation

43 automatic adjustment, optimize. model objective function

44 automatic adjustment, optimize Genetic algorithms advantages: -capable to search in many local minima -relatively small number of model runs (compared to brute force) disadvantages -not possible (or very difficult) to describe the value of the outcome by means of statistics

45 automatic adjustment, optimize Choice of the optimization algorithm simple problems: -brute force -hill climbing approach -standard software available (PEST) multiple local minima: -genetic algorithm -or combination of hill climbing and genetic algorithm

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