Tutorial Calibration - Revision history

Chr at 13:13, 22 July 2014

2014-07-22T13:13:08Z

Chr at 13:03, 22 July 2014

2014-07-22T13:03:41Z

← Older revision		Revision as of 13:03, 22 July 2014
Line 68:		Line 68:

	The treatment of the äquifinality is tied closely to the consideration of model insecurities. Hydrological models have often there usage in critical systems, like the prediction of natural disasters, the prediction of longtime inffluences of climatic change or the development of adaptionstrategies of the water-infrastructure. Therefore is the estimation, preparation and communication of model insecurities and the borders of model predictions very important from the point of our society (Montanari et al., 2009).		The treatment of the äquifinality is tied closely to the consideration of model insecurities. Hydrological models have often there usage in critical systems, like the prediction of natural disasters, the prediction of longtime inffluences of climatic change or the development of adaptionstrategies of the water-infrastructure. Therefore is the estimation, preparation and communication of model insecurities and the borders of model predictions very important from the point of our society (Montanari et al., 2009).
		+
		+	==Model Validation==
		+	The model validation serves as evidence for the suitability of a model for a certain intended purpose (Bratley et al. 1987). Model validation is an important tool for quality assurance. A model is useful for implementation if it proves to be a reliable representation of real processes (Power 1993). Different key figures and proofs may be required subject to the purpose and type of model in order to validate the model.
		+
		+	In general, there are different quality criteria which play a role for model validation:
		+	*Accuracy/precision: The accuracy of a model represents the deviation of simulated values in comparison to real (measurement) values. This is mostly quantified by the Nash-Sutcliffe efficiency, degree of determination or mean square error.
		+	*Uncertainty: The input values of an environmental model often contain errors and uncertainties. In addition, there is no complete knowledge regarding a natural system, i.e. the modeling has uncertainties. The impact of these values is quantified by the insecurity of the model, i.e. the uncertainty reveals the confidence interval of a model response.
		+	*Ruggedness: The ability of the model to maintain its function if there are (minor) changes in the natural system (e.g. change of land use in a catchment area). This is closely linked to
		+	*Sensitivity: which indicates the influence of (minor) changes in the input data and model parameters on the result
		+	*Validity: is the property of the model to provide correct results for the right reasons, i.e. the explanation for a system which is given by the model is correct.
		+
		+	The following methods for the validation of hydrological models were put forward by Klemes (1986).
		+
		+	===Split Sample Validation===
		+	The amount of validation data which is available (e.g. runoff time scales) is split into two sections. First, section 1 is used for calibration and section 2 for validation of the model. Then the calibration part and the validation part are swapped. For this reason both section should have an equal size and provide a for adequate calibration.
		+	The model can be classified as acceptable if both validation results are similar and have sufficient properties for model application.
		+	If the available amount of data is not large enough for a 50/50 division, the data should be split in such a way that one segmaent is large enough to carry out a reasonable calibration. The part remaining is used for the validation. In this case the validation is supposed to be carried out in two different ways. For example, (a) the first 70% of the data are used for calibration and the remaining 30% for validation, (b) then the last 70% of the data for calibration and the remaining first 30% for validation.
		+
		+	===Cross Validation===
		+	[[File:Crossvalidation.png\|right]]
		+	Cross validation is a further development of the split sample test. The amount of data is split up into <math>n</math> sections of equal size. Now <math>n-1</math> sections are used for the calibration and the remaining part is used for validation. The amount for the validation is swapped, so every set of data is validated once. This method requires much more computational effort than the split sample validation but it has major advantages if there is a small amount of data and it allows for a validation of every kind of data set.
		+	A special case of this validation is the Leave-One-Out Validation (LOO). It always uses exactly one data set for validation and the remaining part for validation. Due to the huge effort this kind of validation is mostly applied in special cases.
		+
		+	===Proxy Basin Test===
		+	This is a basic test concerning the spatial transferability of a model. If, for example, the runoff is supposed to be simulated for a non-measured area C, two measured areas A and B can be selected within a region. The model should be calibrated with data from area A and validated using data from area B. Then roles of area A and B should be exchanged. Only if both validation results are acceptable with regard to modeling application, it can basically be assumed that the model is able to simulate the runoff of area C.
		+	This test should also be applied if an amount of data is available for area C which is not sufficient for the split sample test. The data set in area C would not be used for mode development but only as an additional validation.
		+
		+	===Differential Split-Sample Test===
		+	This test should always be used if a model is supposed to be applied under new conditions in a measured area. The test may have several variants, subject to the way in which the conditions/properties of the area have changed. Those changes include, for example, climate changes, changes of land use, the construction of building, which influence the hydrology in the region etc.
		+
		+	For the simulation of effects of climate change the test should have the following form. Two time periods with different climate parameters should be identified in historical data sets; for example, a time period of high mean precipitation and another period of low precipitation. If the model is supposed to be used for runoff simulation of humid climate scenarios, the model should be calibrated with the arid data set and the validation should be carried out with the "humid" data set. If it is supposed to be used for the simulation of an arid scenario, the procedure should be reversed.
		+	In general, the validation is supposed to test the model under changed conditions.
		+	If no sections with significantly different climatic parameters can be identified in historical data, the model should be tested in another area where the differential split-sample test can be carried out. This is mostly the case if not climate changes but, for instance, changes of land use are considered. In this case a measured area has to be identified where similar changes of land use have taken place. The calibration should be carried out using original land use and validation using the new land use.
		+
		+	If alternative areas are used, at least two areas of such kind should be identified. The model is calibrated and validated for both areas. The validation can only be successful if results for both areas are similar. It has to be noted that the differential split-sample test, in contrast to the proxy - basin - test, is carried out for both areas independently.
		+
		+	===Proxy-basin Differential Split-Sample Test===
		+	As the name suggests, this test is a combination of proxy-basin and differential split-sample test and it should be used if a model should be transferable in space and is applied under changed conditions.

	=Calibration=		=Calibration=
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	'''Refresh View'''		'''Refresh View'''
	Updates the view. Please do not use the update function of your browser (usually F5) since this leads to a re-execution of the last command (e.g. starting the model).		Updates the view. Please do not use the update function of your browser (usually F5) since this leads to a re-execution of the last command (e.g. starting the model).
−
−	~~==Model Validation==~~
−	The model validation serves as evidence for the suitability of a model for a certain intended purpose (Bratley et al. 1987). Model validation is an important tool for quality assurance. A model is useful for implementation if it proves to be a reliable representation of real processes (Power 1993). Different key figures and proofs may be required subject to the purpose and type of model in order to validate the model.
−
−	~~In general, there are different quality criteria which play a role for model validation:~~
−	*Accuracy/precision: The accuracy of a model represents the deviation of simulated values in comparison to real (measurement) values. This is mostly quantified by the Nash-Sutcliffe efficiency, degree of determination or mean square error.
−	*Uncertainty: The input values of an environmental model often contain errors and uncertainties. In addition, there is no complete knowledge regarding a natural system, i.e. the modeling has uncertainties. The impact of these values is quantified by the insecurity of the model, i.e. the uncertainty reveals the confidence interval of a model response.
−	*Ruggedness: The ability of the model to maintain its function if there are (minor) changes in the natural system (e.g. change of land use in a catchment area). This is closely linked to
−	*Sensitivity: which indicates the influence of (minor) changes in the input data and model parameters on the result
−	*Validity: is the property of the model to provide correct results for the right reasons, i.e. the explanation for a system which is given by the model is correct.
−
−	~~The following methods for the validation of hydrological models were put forward by Klemes (1986).~~
−
−	~~===Split Sample Validation===~~
−	The amount of validation data which is available (e.g. runoff time scales) is split into two sections. First, section 1 is used for calibration and section 2 for validation of the model. Then the calibration part and the validation part are swapped. For this reason both section should have an equal size and provide a for adequate calibration.
−	~~The model can be classified as acceptable if both validation results are similar and have sufficient properties for model application.~~
−	If the available amount of data is not large enough for a 50/50 division, the data should be split in such a way that one segmaent is large enough to carry out a reasonable calibration. The part remaining is used for the validation. In this case the validation is supposed to be carried out in two different ways. For example, (a) the first 70% of the data are used for calibration and the remaining 30% for validation, (b) then the last 70% of the data for calibration and the remaining first 30% for validation.
−
−	~~===Cross Validation===~~
−	~~[[File:Crossvalidation.png\|right]]~~
−	Cross validation is a further development of the split sample test. The amount of data is split up into <math>n</math> sections of equal size. Now <math>n-1</math> sections are used for the calibration and the remaining part is used for validation. The amount for the validation is swapped, so every set of data is validated once. This method requires much more computational effort than the split sample validation but it has major advantages if there is a small amount of data and it allows for a validation of every kind of data set.
−	A special case of this validation is the Leave-One-Out Validation (LOO). It always uses exactly one data set for validation and the remaining part for validation. Due to the huge effort this kind of validation is mostly applied in special cases.
−
−	~~===Proxy Basin Test===~~
−	This is a basic test concerning the spatial transferability of a model. If, for example, the runoff is supposed to be simulated for a non-measured area C, two measured areas A and B can be selected within a region. The model should be calibrated with data from area A and validated using data from area B. Then roles of area A and B should be exchanged. Only if both validation results are acceptable with regard to modeling application, it can basically be assumed that the model is able to simulate the runoff of area C.
−	This test should also be applied if an amount of data is available for area C which is not sufficient for the split sample test. The data set in area C would not be used for mode development but only as an additional validation.
−
−	~~===Differential Split-Sample Test===~~
−	This test should always be used if a model is supposed to be applied under new conditions in a measured area. The test may have several variants, subject to the way in which the conditions/properties of the area have changed. Those changes include, for example, climate changes, changes of land use, the construction of building, which influence the hydrology in the region etc.
−
−	For the simulation of effects of climate change the test should have the following form. Two time periods with different climate parameters should be identified in historical data sets; for example, a time period of high mean precipitation and another period of low precipitation. If the model is supposed to be used for runoff simulation of humid climate scenarios, the model should be calibrated with the arid data set and the validation should be carried out with the "humid" data set. If it is supposed to be used for the simulation of an arid scenario, the procedure should be reversed.
−	~~In general, the validation is supposed to test the model under changed conditions.~~
−	If no sections with significantly different climatic parameters can be identified in historical data, the model should be tested in another area where the differential split-sample test can be carried out. This is mostly the case if not climate changes but, for instance, changes of land use are considered. In this case a measured area has to be identified where similar changes of land use have taken place. The calibration should be carried out using original land use and validation using the new land use.
−
−	If alternative areas are used, at least two areas of such kind should be identified. The model is calibrated and validated for both areas. The validation can only be successful if results for both areas are similar. It has to be noted that the differential split-sample test, in contrast to the proxy - basin - test, is carried out for both areas independently.
−
−	~~===Proxy-basin Differential Split-Sample Test===~~
−	As the name suggests, this test is a combination of proxy-basin and differential split-sample test and it should be used if a model should be transferable in space and is applied under changed conditions.

Chr: /* Identification of modellparameters */

2014-07-22T13:01:45Z

Identification of modellparameters

← Older revision		Revision as of 13:01, 22 July 2014
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	- Parsimonie: Äquifinality or parameter-insecurity can be an hind, that the chosen modelstructur is to complex in terms of data. The source of this philosophie is the observation, that easy, less data intensiv models provide often better predictions then complex, physical based models (Loague and Freeze, 1985;Reed et al., 2004). Thr principle of parsimonia tells, that always should be the easyiest explanation used for an phänomenon. Even Aristoteles represented this principle and it was postulated by William of Occam at the beginning of the 14 century. Therfore is the term "Occam's Razor" common. Following this rule, should there be the structure of hydrological model's be as easy as possible and only be added with complexity when the precision of the model doesn't fit the intended cause. Multiple papers show, that the parameter insecurity can be lowered by the reduction of modelcomplexity (Jakeman and Hornberger, 1993; Weater and Jakeman, 1993; Young et al., 1996; Wagener et al., 2002,2003b).		- Parsimonie: Äquifinality or parameter-insecurity can be an hind, that the chosen modelstructur is to complex in terms of data. The source of this philosophie is the observation, that easy, less data intensiv models provide often better predictions then complex, physical based models (Loague and Freeze, 1985;Reed et al., 2004). Thr principle of parsimonia tells, that always should be the easyiest explanation used for an phänomenon. Even Aristoteles represented this principle and it was postulated by William of Occam at the beginning of the 14 century. Therfore is the term "Occam's Razor" common. Following this rule, should there be the structure of hydrological model's be as easy as possible and only be added with complexity when the precision of the model doesn't fit the intended cause. Multiple papers show, that the parameter insecurity can be lowered by the reduction of modelcomplexity (Jakeman and Hornberger, 1993; Weater and Jakeman, 1993; Young et al., 1996; Wagener et al., 2002,2003b).

−	-efficient information usage: Traditionally will be only one objective criteria used for the calibration. By this one-criteria optimization will be the simulated and real drainage compared and reduced to only one numeric data, out of thounds of data sets. This information-loss is for Gupta et al. (1998) an reason for appearnce of Äquifinality . There are two ways of reducing the information-loss: first, agrumentated Gupta et al. (1998), that the search for optimal parametric data sets an multicriteria probleme by itself is.	+	-efficient information usage: Traditionally will be only one objective criteria used for the calibration. By this one-criteria optimization will be the simulated and real drainage compared and reduced to only one numeric data, out of thounds of data sets. This information-loss is for Gupta et al. (1998) an reason for appearnce of Äquifinality . There are two ways of reducing the information-loss: first, agrumentated Gupta et al. (1998), that the search for optimal parametric data sets an multicriteria probleme by itself is. The multi-criteria optimization considers at the calibration multiple objective criteria simultaneously. As an result is therean muliple amount of information that counters äquifinality. That is why this way has been very popular in the last ten years. The second way is to extract the information in the data by sub dividing the amount of data. Methods, that can be used are the Kalman filter and its extansions (Kitanidis and Bras, 1980), PIMLI (Vrugt et al., 2002), BARE (Thiemann et al., 2001) and DYNIA (Wagener et al., 2003a).
		+
		+	-acceptance of äquifinality: Äquifinality can be regared as an inherent propertie of the problem. The logical consequenzes are to to keep all potential solutions of the callibration problem and involve them into modelation, as long as they can not be separeted because of other reasons. This ensemble of solutions can be used, p.e. for analyzis of parameter insecurity with the Generalized Likelihood Uncertainty Estimation Method (GLUE, Beven and Freer 2001) or DREAM (Vrugt et al., 2008).
		+
		+	The treatment of the äquifinality is tied closely to the consideration of model insecurities. Hydrological models have often there usage in critical systems, like the prediction of natural disasters, the prediction of longtime inffluences of climatic change or the development of adaptionstrategies of the water-infrastructure. Therefore is the estimation, preparation and communication of model insecurities and the borders of model predictions very important from the point of our society (Montanari et al., 2009).

	=Calibration=		=Calibration=

Chr: /* Identification of modellparameters */

2014-07-22T12:42:41Z

Identification of modellparameters

← Older revision		Revision as of 12:42, 22 July 2014
Line 46:		Line 46:

	=Identification of modellparameters=		=Identification of modellparameters=
		+
		+	The search for fitting parameterdata can be done manuell or automaticaly. The manuell possibility can lead to excellent results, but it is hard and connected with a lot of worktime, personal, costs and is tied to subjectiv processdecisions (Boyle et al., 2000). An alternativ is the automatic callibration with its mathematical optimizationprocesses, that can solve quick and objectiv complex problems.
		+
		+	In the past was the automatic callibration done with local optimizationprocesses, like the gradientdescent. It was fast recognized that on this way it is not possible to discover optimal parameterdata reliabely. Because of a lot unfitting properties with this method (Duan et al., 1992). Therefore developed Duan et al. (1992) the Shuffled Complex Evolution (SCE) method, which proved itself in the last 20 years as very robust, efficient and effectiv. Thats why it is used until today in terms of hydrological themes (p.e. Sorooshian et al. 1993, Eckhardt and Arnold 2001, Liong and Atiquzzaman 2004).
		+
		+	General shouldnt be the calibration understood as an method to finde parameterdata, that will be used to adapt the modell to the surveied data (Vidal et al., 2007); rather it is an continuous process of the parameteridentification. Therefore is the initial insecurity reduced step by step; by reducing the parameterarea to the parameterdata, that leads to the modellbehaviour, which is qualitativ and quantitativ consistent in terms of the systembehaviour. There cant be an hundred percantage conformance of the modell and reality. Because every modellstructur and parametrization is an approximation of the real systemstructur and every modellprediction is affected with an insecurity. Gupta et al. (2005) characterizes an calibration of an hydrological modell as:
		+
		+	1. The input/output of the model is consistend with the observed drainagearea behaviour.
		+	2. The modelpredictions are accurate (that means they have an insignificiant system error) and exact (that means the insecurity of the predictions are low) and tough. That means, little changes of parameters result in little model changes.
		+	3. The model structure and model behaviour are consistent with the actual hydrological behaviour of the real system.
		+
		+	Äquifinality
		+
		+	The identification of modelparameters from an impuls/anwser-behaviour is known under the term: inverse problem or identificationproblem (Cobelli and DiStefano III., 1980) and is not only a term of hydrologcal context. Inverse problems are often bad posed (Kabanikhin, 2008), that means they have neither a solution or infinit solutions or the the systems behaves chaoticaly. Bellman and Astrom (1970) could demonstrate formal, that inverse problems aren't resolvable in very easy systems either, if there aren't every intern conditions of the system observable. That means for hydrological modeling that the systembehaviour can be explained by different models with different structures and parameterization. For this phenomenon has Beven (1993) defined the term of Äquifinality, which has its origin from Ludwig von Bertalanffy (Von Bertalanffy et al., 1950). Äquifinality is the term, that the provided information doesn't last to descripe the system completly. Wagner and Gupta (2005) differentiate in three philosophies about the handling of Äquifinality:
		+
		+	- Parsimonie: Äquifinality or parameter-insecurity can be an hind, that the chosen modelstructur is to complex in terms of data. The source of this philosophie is the observation, that easy, less data intensiv models provide often better predictions then complex, physical based models (Loague and Freeze, 1985;Reed et al., 2004). Thr principle of parsimonia tells, that always should be the easyiest explanation used for an phänomenon. Even Aristoteles represented this principle and it was postulated by William of Occam at the beginning of the 14 century. Therfore is the term "Occam's Razor" common. Following this rule, should there be the structure of hydrological model's be as easy as possible and only be added with complexity when the precision of the model doesn't fit the intended cause. Multiple papers show, that the parameter insecurity can be lowered by the reduction of modelcomplexity (Jakeman and Hornberger, 1993; Weater and Jakeman, 1993; Young et al., 1996; Wagener et al., 2002,2003b).
		+
		+	-efficient information usage: Traditionally will be only one objective criteria used for the calibration. By this one-criteria optimization will be the simulated and real drainage compared and reduced to only one numeric data, out of thounds of data sets. This information-loss is for Gupta et al. (1998) an reason for appearnce of Äquifinality . There are two ways of reducing the information-loss: first, agrumentated Gupta et al. (1998), that the search for optimal parametric data sets an multicriteria probleme by itself is.

	=Calibration=		=Calibration=

Chr at 11:40, 22 July 2014

2014-07-22T11:40:29Z

← Older revision		Revision as of 11:40, 22 July 2014
Line 43:		Line 43:
	Krause et al. (2005) get to the conclussion by the comparisson of multiple errorfunctions, that none of the ratios shows all relevant aspects of an hydrograph. They get to the conclussion that every errorfunction has its own specific pro and cons. Although there where a lot of errorfunction proposed that counter the deficits, there hasn't been an anwser for the universal ratio. An overview about the different errorratios and there characteristica		Krause et al. (2005) get to the conclussion by the comparisson of multiple errorfunctions, that none of the ratios shows all relevant aspects of an hydrograph. They get to the conclussion that every errorfunction has its own specific pro and cons. Although there where a lot of errorfunction proposed that counter the deficits, there hasn't been an anwser for the universal ratio. An overview about the different errorratios and there characteristica
	can be found here.		can be found here.
		+
		+
		+	=Identification of modellparameters=

	=Calibration=		=Calibration=

Chr: /* Requirements */

2014-07-22T11:36:10Z

Requirements

← Older revision		Revision as of 11:36, 22 July 2014
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	The task of the calibration is it to reduce the parameter uncertainty step by step to an minimum. For this optimization is it necessary to compare evaluations of the simulated hydrographs. Although it is the best method to examine the hydrograph bye an expert, for judging the performance of the modell (Krause et al., 2005), it is necessary to have an mathematical errorfunction for an objective quantifiable evaluation. Errorfunctions represent the part of measured data, that could not be reproduced by the modell. It is possible to classify in absolute and relative errorfunctions. Relative errorfunctions have the advantage of beeing dimensionless, that means they can be compared overall applications. Errorfunctions, that are often used in the context of hydrologie, are p.e. the Nash-Sutcliffe efficience (Nash and Sutcliffe, 1970) and there modifications, the determinationrate and the absolute - relative voluminaerror (Krause et al., 2005).		The task of the calibration is it to reduce the parameter uncertainty step by step to an minimum. For this optimization is it necessary to compare evaluations of the simulated hydrographs. Although it is the best method to examine the hydrograph bye an expert, for judging the performance of the modell (Krause et al., 2005), it is necessary to have an mathematical errorfunction for an objective quantifiable evaluation. Errorfunctions represent the part of measured data, that could not be reproduced by the modell. It is possible to classify in absolute and relative errorfunctions. Relative errorfunctions have the advantage of beeing dimensionless, that means they can be compared overall applications. Errorfunctions, that are often used in the context of hydrologie, are p.e. the Nash-Sutcliffe efficience (Nash and Sutcliffe, 1970) and there modifications, the determinationrate and the absolute - relative voluminaerror (Krause et al., 2005).

−	Krause et al. (2005) get to the conclussion	+	Krause et al. (2005) get to the conclussion by the comparisson of multiple errorfunctions, that none of the ratios shows all relevant aspects of an hydrograph. They get to the conclussion that every errorfunction has its own specific pro and cons. Although there where a lot of errorfunction proposed that counter the deficits, there hasn't been an anwser for the universal ratio. An overview about the different errorratios and there characteristica
		+	can be found here.

	=Calibration=		=Calibration=

Chr: /* Requirements */

2014-07-22T09:52:55Z

Requirements

← Older revision		Revision as of 09:52, 22 July 2014
Line 39:		Line 39:
	Specification of qualitycriteria and target functions		Specification of qualitycriteria and target functions

−	The task of the calibration is it to reduce the parameter uncertainty step by step to an minimum. For this optimization is it necessary to compare evaluations of the simulated hydrographs.	+	The task of the calibration is it to reduce the parameter uncertainty step by step to an minimum. For this optimization is it necessary to compare evaluations of the simulated hydrographs. Although it is the best method to examine the hydrograph bye an expert, for judging the performance of the modell (Krause et al., 2005), it is necessary to have an mathematical errorfunction for an objective quantifiable evaluation. Errorfunctions represent the part of measured data, that could not be reproduced by the modell. It is possible to classify in absolute and relative errorfunctions. Relative errorfunctions have the advantage of beeing dimensionless, that means they can be compared overall applications. Errorfunctions, that are often used in the context of hydrologie, are p.e. the Nash-Sutcliffe efficience (Nash and Sutcliffe, 1970) and there modifications, the determinationrate and the absolute - relative voluminaerror (Krause et al., 2005).
		+
		+	Krause et al. (2005) get to the conclussion

	=Calibration=		=Calibration=

Chr: /* Requirements */

2014-06-05T11:37:17Z

Requirements

← Older revision		Revision as of 11:37, 5 June 2014
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	The question for the selection of parameters to calibrate isnt trivial at all. It depends from the to calibrating model and his application. A lot of hydrological modells own more parameters than can be calibrated. Therfore should only be the parameteres calibrated that can't be directly determined (e.g. from the landscape attributs) and that have significient impact on the modellbehavior. The identification of those parameters can be done by an sensitivity analysis of the modell. This provides not only information about the quantitiy effect of the paramerters, as well as an big help for understanding the modell (Saltelli et al., 2008).		The question for the selection of parameters to calibrate isnt trivial at all. It depends from the to calibrating model and his application. A lot of hydrological modells own more parameters than can be calibrated. Therfore should only be the parameteres calibrated that can't be directly determined (e.g. from the landscape attributs) and that have significient impact on the modellbehavior. The identification of those parameters can be done by an sensitivity analysis of the modell. This provides not only information about the quantitiy effect of the paramerters, as well as an big help for understanding the modell (Saltelli et al., 2008).
		+
		+	Often is the definition of parameterborders not obvious either. Depending on the grade of physical connections can critical values and matching standard values of the parameters be: (i) evaluated directly through measuring (in labor conditions or real systems), (ii) determined from plausible-considerations or (iii) from experienced - literaturvalues transfered. Often provide modelldocumentations a choice of parameterborders (see e.g. DYRESM (Hamilton and Schladow, 1997), J2000(Krause, 2001), WaSim-ETH (Schulla and Jasper, 2000)). This borders have an large range , because they have to reflect the initial uncertainty of the parameters when knowledge about the modellusage is missing.
		+	An introduction of callibrationparamerers of the modells: J2000 and J2000g can be found here and here.
		+
		+	Specification of qualitycriteria and target functions
		+
		+	The task of the calibration is it to reduce the parameter uncertainty step by step to an minimum. For this optimization is it necessary to compare evaluations of the simulated hydrographs.

	=Calibration=		=Calibration=

Chr: /* Requirements */

2014-06-02T10:23:54Z

Requirements

← Older revision		Revision as of 10:23, 2 June 2014
Line 28:		Line 28:


−	For an succesfull callibration is it necessary to check the the data in regard of consistency and homogenity (Grundmann, 2009). The calibration can be done with different observations. There are plenty of thesis of groundwater measurements (Blazkova et al., 2002; Madsen, 2003), the snowcoverage (Bales and Dozier, 1998),ground humidity (Merz and Bárdossy, 1998; Western and Grayson, 2000; Scheffler, 2008) and evapotranspiration (Immerzeel and Droogers, 2008). But in the most cases are the hydrological modells callibrated with the measuered drainflowlines (Seibert, 2000).	+	For an succesfull callibration is it necessary to check the the data in regard of consistency and homogenity (Grundmann, 2009). The calibration can be done with different observations. There are plenty of thesis of groundwater measurements (Blazkova et al., 2002; Madsen, 2003), the snowcoverage (Bales and Dozier, 1998),ground humidity (Merz and Bárdossy, 1998; Western and Grayson, 2000; Scheffler, 2008) and evapotranspiration (Immerzeel and Droogers, 2008). But in the most cases are the hydrological modells callibrated with the measuered drainflowlines (Seibert, 2000). The reasons therefore are: (i) a lot of waters have an longtime draintimeline, (ii) the drain is an aggregated signal for most of the relevant hydrological processes of the river-drainage-basin, with (iii) relatively small measuringerrors and (iv) an high repeat value. Therefore is the calibration often done with the drainage-course-line.
		+
		+	Choice of calibration parameters and definition of parameterarea
		+
		+	The question for the selection of parameters to calibrate isnt trivial at all. It depends from the to calibrating model and his application. A lot of hydrological modells own more parameters than can be calibrated. Therfore should only be the parameteres calibrated that can't be directly determined (e.g. from the landscape attributs) and that have significient impact on the modellbehavior. The identification of those parameters can be done by an sensitivity analysis of the modell. This provides not only information about the quantitiy effect of the paramerters, as well as an big help for understanding the modell (Saltelli et al., 2008).

	=Calibration=		=Calibration=

Chr: /* Requirements */

2014-05-14T09:06:54Z

Requirements

← Older revision		Revision as of 09:06, 14 May 2014
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	1. Selection of Data, that should be used		1. Selection of Data, that should be used
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	2. Selection of definitions and to callibrated parameters in the parameterspace		2. Selection of definitions and to callibrated parameters in the parameterspace
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	3. Specification of excellencecriteria and target function		3. Specification of excellencecriteria and target function
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	4. Selection of optimizing strategie		4. Selection of optimizing strategie
		+
	5. Selection of strategie for validation and insecurity analysis		5. Selection of strategie for validation and insecurity analysis

	Datasurvey		Datasurvey
		+
		+
	For an succesfull callibration is it necessary to check the the data in regard of consistency and homogenity (Grundmann, 2009). The calibration can be done with different observations. There are plenty of thesis of groundwater measurements (Blazkova et al., 2002; Madsen, 2003), the snowcoverage (Bales and Dozier, 1998),ground humidity (Merz and Bárdossy, 1998; Western and Grayson, 2000; Scheffler, 2008) and evapotranspiration (Immerzeel and Droogers, 2008). But in the most cases are the hydrological modells callibrated with the measuered drainflowlines (Seibert, 2000).		For an succesfull callibration is it necessary to check the the data in regard of consistency and homogenity (Grundmann, 2009). The calibration can be done with different observations. There are plenty of thesis of groundwater measurements (Blazkova et al., 2002; Madsen, 2003), the snowcoverage (Bales and Dozier, 1998),ground humidity (Merz and Bárdossy, 1998; Western and Grayson, 2000; Scheffler, 2008) and evapotranspiration (Immerzeel and Droogers, 2008). But in the most cases are the hydrological modells callibrated with the measuered drainflowlines (Seibert, 2000).