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ASM (Activated Sludge Models)

A method is presented to calibrate transfer function-noise (TFN) models, operating at the same frequency as the input (auxiliary) variables, to sparsely or irregularly observed time series of the output (target) variable. Once calibrated, the TFN models can be used to predict or simulate the output variable at the same frequency as the input variable. Consequently, the method provides a useful tool for filling in gaps of irregularly or sparsely observed hydrological time series. Although generic and suitable for any type of time series, the method is described through the modeling of a time series of groundwater head data with precipitation surplus (precipitation minus potential evapotranspiration) as input variable. First, the TFN model is written in vector notation, yielding the state equation of a linear discrete stochastic system. Subsequently, the state equation is embedded in a Kalman filter algorithm. The Kalman filter is then combined with a maximum likelihood criterion to obtain estimates of the parameters of the TFN model for small time steps (e.g., 1 day) while using sparsely (e.g., two times a month) or even irregularly observed time series of groundwater head data. The method is illustrated using (subsets of) time series of groundwater head data with varying regular and irregular observation intervals.

Modeling of integrated urban water systems (IUWS) has seen a rapid development in recent years. Models and software are available that describe the process dynamics in sewers, wastewater treatment plants (WWTPs), receiving water systems as well as at the interfaces between the submodels. Successful applications of integrated modeling are, however, relatively scarce. One of the reasons for this is the lack of high-quality monitoring data with the required spatial and temporal resolution and accuracy to calibrate and validate the integrated models, even though the state of the art of monitoring itself is no longer the limiting factor. This paper discusses the efforts to be able to meet the data requirements associated with integrated modeling and describes the methods applied to validate the monitoring data and to use submodels as software sensor to provide the necessary input for other submodels. The main conclusion of the paper is that state of the art monitoring is in principle sufficient to provide the data necessary to calibrate integrated models, but practical limitations resulting in incomplete data-sets hamper widespread application. In order to overcome these difficulties, redundancy of future monitoring networks should be increased and, at the same time, data handling (including data validation, mining and assimilation) should receive much more attention.

Land use is constantly changing. For example, urban areas expand as a result of population growth, cropping patterns change to fulfil the demand for bioenergy and natural vegetation recovers in locations where farmers cease to farm. Understanding these changes is pivotal to explore future land-use scenarios and to design spatial policies. Land-use models are increasingly being used for these purposes. They function as virtual laboratories in which scientists or policy analysts can conduct experiments. In order to reliably apply models for these purposes, they need to be calibrated, where calibration is the adjustment of parameters to improve the model’s performance. Consequently, the value of modelled land-use scenarios and policy assessments depends on the quality of the calibration. Assessment of the quality of the calibration is termed validation, and is ideally performed independently in the sense that the data that is used for validation has not been used for calibration. The development of a land-use model can be described by four sequential phases: analysis and conceptual modelling, computer programming of the conceptual model, calibration of the computerized model, and experimentation with the calibrated model. The first three phases all have their own evaluation procedures: conceptual validation, code verification and operational validation, respectively. Operational validation provides insights into the strengths and weaknesses of a particular model application, and sometimes it can suggest directions for improvement. However, available assessment methods have limitations for their application in land-use modelling. Therefore, there is a demand to develop and apply more appropriate methods. The work presented in this thesis first considers the properties of land-use models that are important for their assessment, and subsequently presents and applies several methods that can be used for this assessment. Many land-use changes are directly or indirectly the result of human decisions. However, human decisions are inherently uncertain, and therefore land-use models cannot be expected to simulate these land-use changes exactly. This is acknowledged by many land-use models as they use a stochastic component to simulate land-use changes. Therefore, land-use models should not only be validated on their predictive accuracy, their capacity to accurately allocate land-use changes on the map, but also on their process accuracy, their capacity to realistically simulate land-use change processes. Moreover, many models start from an initial land-use map and simulate changes relative to this map. The amount of change for a simulation is typically small relative to the entire map, which means that a large part of the result is caused by persistence. For this reason, a benchmark, such as a naive predictor, is required to properly asses the accuracy of simulation results. This benchmark can be implicit to the assessment method itself, or explicit, i.e. another land-use model which serves as a reference model. Outperforming the benchmark can be considered a minimum threshold to pass; however, it cannot directly inform whether a model is acceptable as this depends on the purpose of the model, the requirements of the study and the application domain. The predictive accuracy of a land-use model is typically assessed by comparing a simulation result with the actual land-use map at the end of a simulation. A common method for this is the Kappa statistic, which expresses the agreement between two land-use maps corrected for the expected agreement from a random allocation given the distribution of class sizes. However, this is not an appropriate reference level to assess the predictive accuracy of land-use models, because it does not account for the amount of change. This thesis presents Kappa Simulation, a new map-comparison method that is identical in form to Kappa, but which instead applies a more appropriate reference model based on random allocation of class transitions relative to the initial map. This implicitly accounts for the amount of change, which truly allows gauging the predictive accuracy of changes in land-use models. However, Kappa Simulation cannot differentiate between near-hits and complete misses, while this distinction is often very relevant for land-use modellers. This thesis therefore presents Fuzzy Kappa Simulation. This statistic is an improvement of Kappa Simulation, as it applies a fuzzy interpretation of class transitions and their locations. This means that it can account for near-hits, which makes it arguably the most suitable map comparison method to assess the predictive accuracy of land-use models. Because of the intrinsic uncertainty underlying land-use change processes, a realistic land-use model does not necessarily generate a high predictive accuracy, which justifies a separate assessment of its process accuracy. Ideally, process accuracy is assessed directly from the values of model parameters. However, it is often impossible to observe real-world values for these parameters because drivers for land-use changes are correlated or they cannot be measured. Therefore, the process accuracy is typically assessed indirectly from the land-use patterns generated by the model. Two groups of methods exist to characterize land-use patterns: landscape metrics and fractal metrics. Landscape metrics are a collection of algorithms that have been applied in landscape ecology to characterize land-use patterns. In this thesis, landscape metrics have been used to compare the simulated land-use map with the observed land-use map instead. Fractal metrics, which have their origin in complexity science, are another type of measures to characterize regularities in (urban) land-use patterns. Examples are power-law distributions for urban clusters and fractal dimensions of patches of urban land. Moreover, fractal metrics can be interpreted in absolute terms since they represent general regularities in urban systems for which values are known from literature. Therefore, fractal metrics also allow evaluation of the process accuracy of a synthetic application for which no observed land-use pattern is available for comparison. Neighbourhood rules represent the influence of the existing land-use distribution on the location of land-use changes. This includes the persistence, conversion and attraction/repulsion of land uses in the neighbourhood of a location. Because neighbourhood rules cannot be estimated directly from data, they need to be set in a calibration procedure. The work in this thesis indicates that agents consider their neighbourhood at different spatial scales: the direct vicinity of a location has a strong influence on the allocation of new urban land, but neighbourhood rules over larger distance – typically the size of urban regions – also improve the model performance. This thesis also discusses a special type of neighbourhood rules: rules describing the influence of the existing activity distribution on the allocation of activity changes, where activities denote a quantity or density related to a land use, such as inhabitants for residential land use. This study shows that relatively simple rules can grow a realistic urban settlement structure, which confirms that neighbourhood rules improve the process accuracy of a land-use model. It should be noted that while the methods presented and applied in this thesis are objective, the selection of assessment methods remains subjective. Moreover, because no method is yet capable of describing land-use patterns satisfactorily, more subjective methods such as visual assessment of simulation results or interpretation of parameter values remain of added value in the calibration and validation of land-use models.

Land use is constantly changing. For example, urban areas expand as a result of population growth, cropping patterns change to fulfil the demand for bioenergy and natural vegetation recovers in locations where farmers cease to farm. Understanding these changes is pivotal to explore future land-use scenarios and to design spatial policies. Land-use models are increasingly being used for these purposes. They function as virtual laboratories in which scientists or policy analysts can conduct experiments. In order to reliably apply models for these purposes, they need to be calibrated, where calibration is the adjustment of parameters to improve the model’s performance. Consequently, the value of modelled land-use scenarios and policy assessments depends on the quality of the calibration. Assessment of the quality of the calibration is termed validation, and is ideally performed independently in the sense that the data that is used for validation has not been used for calibration. The development of a land-use model can be described by four sequential phases: analysis and conceptual modelling, computer programming of the conceptual model, calibration of the computerized model, and experimentation with the calibrated model. The first three phases all have their own evaluation procedures: conceptual validation, code verification and operational validation, respectively. Operational validation provides insights into the strengths and weaknesses of a particular model application, and sometimes it can suggest directions for improvement. However, available assessment methods have limitations for their application in land-use modelling. Therefore, there is a demand to develop and apply more appropriate methods. The work presented in this thesis first considers the properties of land-use models that are important for their assessment, and subsequently presents and applies several methods that can be used for this assessment. Many land-use changes are directly or indirectly the result of human decisions. However, human decisions are inherently uncertain, and therefore land-use models cannot be expected to simulate these land-use changes exactly. This is acknowledged by many land-use models as they use a stochastic component to simulate land-use changes. Therefore, land-use models should not only be validated on their predictive accuracy, their capacity to accurately allocate land-use changes on the map, but also on their process accuracy, their capacity to realistically simulate land-use change processes. Moreover, many models start from an initial land-use map and simulate changes relative to this map. The amount of change for a simulation is typically small relative to the entire map, which means that a large part of the result is caused by persistence. For this reason, a benchmark, such as a naive predictor, is required to properly asses the accuracy of simulation results. This benchmark can be implicit to the assessment method itself, or explicit, i.e. another land-use model which serves as a reference model. Outperforming the benchmark can be considered a minimum threshold to pass; however, it cannot directly inform whether a model is acceptable as this depends on the purpose of the model, the requirements of the study and the application domain. The predictive accuracy of a land-use model is typically assessed by comparing a simulation result with the actual land-use map at the end of a simulation. A common method for this is the Kappa statistic, which expresses the agreement between two land-use maps corrected for the expected agreement from a random allocation given the distribution of class sizes. However, this is not an appropriate reference level to assess the predictive accuracy of land-use models, because it does not account for the amount of change. This thesis presents Kappa Simulation, a new map-comparison method that is identical in form to Kappa, but which instead applies a more appropriate reference model based on random allocation of class transitions relative to the initial map. This implicitly accounts for the amount of change, which truly allows gauging the predictive accuracy of changes in land-use models. However, Kappa Simulation cannot differentiate between near-hits and complete misses, while this distinction is often very relevant for land-use modellers. This thesis therefore presents Fuzzy Kappa Simulation. This statistic is an improvement of Kappa Simulation, as it applies a fuzzy interpretation of class transitions and their locations. This means that it can account for near-hits, which makes it arguably the most suitable map comparison method to assess the predictive accuracy of land-use models. Because of the intrinsic uncertainty underlying land-use change processes, a realistic land-use model does not necessarily generate a high predictive accuracy, which justifies a separate assessment of its process accuracy. Ideally, process accuracy is assessed directly from the values of model parameters. However, it is often impossible to observe real-world values for these parameters because drivers for land-use changes are correlated or they cannot be measured. Therefore, the process accuracy is typically assessed indirectly from the land-use patterns generated by the model. Two groups of methods exist to characterize land-use patterns: landscape metrics and fractal metrics. Landscape metrics are a collection of algorithms that have been applied in landscape ecology to characterize land-use patterns. In this thesis, landscape metrics have been used to compare the simulated land-use map with the observed land-use map instead. Fractal metrics, which have their origin in complexity science, are another type of measures to characterize regularities in (urban) land-use patterns. Examples are power-law distributions for urban clusters and fractal dimensions of patches of urban land. Moreover, fractal metrics can be interpreted in absolute terms since they represent general regularities in urban systems for which values are known from literature. Therefore, fractal metrics also allow evaluation of the process accuracy of a synthetic application for which no observed land-use pattern is available for comparison. Neighbourhood rules represent the influence of the existing land-use distribution on the location of land-use changes. This includes the persistence, conversion and attraction/repulsion of land uses in the neighbourhood of a location. Because neighbourhood rules cannot be estimated directly from data, they need to be set in a calibration procedure. The work in this thesis indicates that agents consider their neighbourhood at different spatial scales: the direct vicinity of a location has a strong influence on the allocation of new urban land, but neighbourhood rules over larger distance – typically the size of urban regions – also improve the model performance. This thesis also discusses a special type of neighbourhood rules: rules describing the influence of the existing activity distribution on the allocation of activity changes, where activities denote a quantity or density related to a land use, such as inhabitants for residential land use. This study shows that relatively simple rules can grow a realistic urban settlement structure, which confirms that neighbourhood rules improve the process accuracy of a land-use model. It should be noted that while the methods presented and applied in this thesis are objective, the selection of assessment methods remains subjective. Moreover, because no method is yet capable of describing land-use patterns satisfactorily, more subjective methods such as visual assessment of simulation results or interpretation of parameter values remain of added value in the calibration and validation of land-use models.

Het Dove-project (Diffuse belasting van het Oppervlaktewater in de Veehouderij) heeft tot doel om de bijdrage van de melkveehouderij aan de diffuse nutriëntenbelasting (stikstof en fosfor) van het oppervlaktewater te kwantificeren in een veenweidegebied, een zandgebied en een kleigebied. Daarbij is ondermeer gebruik gemaakt van modellen die het transport van water en stikstof- en fosforverbindingen kunnen beschrijven. Deze procesmodellen maken het mogelijk om uitspraken te doen over de nutriëntenbelasting op tijdstippen waarop en voor omstandigheden waarvoor geen meetgegevens beschikbaar zijn (bijvoorbeeld in scenario- analyses). In dit informatieblad is beschreven hoe de procesmodellen zijn gekalibreerd en gevalideerd voor een proefperceel in het veenweidegebied. Hierbij zijn nieuwe methoden gehanteerd die tot voor kort niet gebruikelijk waren. Een toepassing van de gekalibreerde en gevalideerde modellen voor het analyseren van het hydrologische systeem en voor het doorrekenen van scenario's wordt beschreven in respectievelijk B0-05-infoblad 03 en B0-05-infoblad 04.

Het Dove-project (Diffuse belasting van het Oppervlaktewater in de Veehouderij) heeft tot doel om de bijdrage van de melkveehouderij aan de diffuse nutriëntenbelasting (stikstof en fosfor) van het oppervlaktewater te kwantificeren in een veenweidegebied, een zandgebied en een kleigebied. Daarbij is ondermeer gebruik gemaakt van modellen die het transport van water en stikstof- en fosforverbindingen kunnen beschrijven. Deze procesmodellen maken het mogelijk om uitspraken te doen over de nutriëntenbelasting op tijdstippen waarop en voor omstandigheden waarvoor geen meetgegevens beschikbaar zijn (bijvoorbeeld in scenario- analyses). In dit informatieblad is beschreven hoe de procesmodellen zijn gekalibreerd en gevalideerd voor een proefperceel in het veenweidegebied. Hierbij zijn nieuwe methoden gehanteerd die tot voor kort niet gebruikelijk waren. Een toepassing van de gekalibreerde en gevalideerde modellen voor het analyseren van het hydrologische systeem en voor het doorrekenen van scenario's wordt beschreven in respectievelijk B0-05-infoblad 03 en B0-05-infoblad 04.

In veel stikstofmodellen wordt denitrificatie vaak gemodelleerd met behulp van eenvoudige rekenregels. Actuele denitrificatie is gelijk aan de potentiële denitrificatie gereduceerd door het actuele nitraatgehalte, de actuele water verzadigingsgraad en de actuele bodemtemperatuur. Deze reductie vindt plaats aan de hand van een drietal reductiefuncties, welke gezamenlijk vier onbekende parameters bevatten. In dit rapport wordt beschreven hoe deze vier parameters zijn bepaald voor een zestal locaties in Nederland door denitrificatie te meten aan grondmonsters die onderling verschillen in nitraatgehalte, verzadigingsgraad en temperatuur. Uit deze studie wordt geconcludeerd dat de parameters locatiespecifiek zijn, en dat er geen relatie duidelijk wordt tussen de waarden van deze parameters en de hoofdgrondsoorten zand, veen en klei

In veel stikstofmodellen wordt denitrificatie vaak gemodelleerd met behulp van eenvoudige rekenregels. Actuele denitrificatie is gelijk aan de potentiële denitrificatie gereduceerd door het actuele nitraatgehalte, de actuele water verzadigingsgraad en de actuele bodemtemperatuur. Deze reductie vindt plaats aan de hand van een drietal reductiefuncties, welke gezamenlijk vier onbekende parameters bevatten. In dit rapport wordt beschreven hoe deze vier parameters zijn bepaald voor een zestal locaties in Nederland door denitrificatie te meten aan grondmonsters die onderling verschillen in nitraatgehalte, verzadigingsgraad en temperatuur. Uit deze studie wordt geconcludeerd dat de parameters locatiespecifiek zijn, en dat er geen relatie duidelijk wordt tussen de waarden van deze parameters en de hoofdgrondsoorten zand, veen en klei

In dit onderzoek is de freatische grondwaterstand landelijk gekarakteriseerd zodat deze gegevens gebruikt kunnen worden voor kalibratie van hydrologische modellen. Met tijdreeksanalyse is de klimaatsrepresentatieve GHG en GLG bepaald voor peilbuislocaties. Daarnaast zijn de HG3 en de LG3 voor het kalenderjaar 1995 berekend. Op basis van stambuisregressie zijn veldmetingen van grondwaterstanden die gedurende de winter en de zomer op een groot aantal gerichte opnamelocaties zijn verzameld, omgezet naar gemiddelde grondwaterstandskarakteristieken. Er is een databestand opgezet dat bestaat uit ca. 3100 buislocaties en ca. 9400 gerichte opnamelocaties waarvoor de HG3-95, LG3-95, GHG en GLG alsmede de onzekerheid is gekwantificeerd.

In dit onderzoek is de freatische grondwaterstand landelijk gekarakteriseerd zodat deze gegevens gebruikt kunnen worden voor kalibratie van hydrologische modellen. Met tijdreeksanalyse is de klimaatsrepresentatieve GHG en GLG bepaald voor peilbuislocaties. Daarnaast zijn de HG3 en de LG3 voor het kalenderjaar 1995 berekend. Op basis van stambuisregressie zijn veldmetingen van grondwaterstanden die gedurende de winter en de zomer op een groot aantal gerichte opnamelocaties zijn verzameld, omgezet naar gemiddelde grondwaterstandskarakteristieken. Er is een databestand opgezet dat bestaat uit ca. 3100 buislocaties en ca. 9400 gerichte opnamelocaties waarvoor de HG3-95, LG3-95, GHG en GLG alsmede de onzekerheid is gekwantificeerd.

Additional index words: nutrient cycling, soil modelling, uncertainty analysis, calibration, scenario analysis, model errorIn this thesis different nutrient cycling and soil acidification models, developed for use at different scales, are presented and evaluated. The models considered are Nucsam (NUtrient Cycling and Soil Acidification Model), Resam (REgional Soil Acidification Model) and Smart2 (an extended version of Simulation Model for Acidification's Regional Trends). These are mechanistic dynamic models, which simulate biogeochemical processes in semi-natural terrestrial ecosystems at a variety of scales. The research tool Nucsam, which is specifically developed for application on a local scale, includes simulation of the biogeochemical processes in various soil layers and on a daily time-scale. Resam and Smart2, tools to support policy makers, were specifically developed to evaluate long-term soil responses to deposition scenarios on a regional scale (national to continental, respectively). For that reason, the models Resam and Smart2 are relative simple models and operate on a yearly time-scale. These models were developed in view of the following research hypotheses:Adequate simulation of temporal responses in soil solution chemistry on a daily basis at various depth requires a detailed multi-layer biogeochemical model (Nucsam);Annual average responses in soil solution chemistry at the bottom of the root zone can be adequately simulated with a simple, one-layer biogeochemical model (Smart2);Simulation of soil solution chemistry on a regional scale requires a simplified model;Adequate simulation of soil solution chemistry on a regional scale requires parameterisation, calibration, validation and uncertainty analysis on that scale.Therefore, this thesis primarily aims at testing these hypotheses by (i) validation and calibration, (ii) uncertainty analysis, and (iii) model comparison. More specifically, the models Nucsam (site scale), Resam (site scale/regional scale) and Smart2 (regional scale) will be evaluated with respect to the optimal balance between model complexity, data availability and model aim.The detailed model Nucsam reproduced the magnitude and trends of measured quantities, such as soil water contents and soil solution chemistry, fairly well. However, the application on a site scale hampers from the lack of sufficiently good quality data. A model, such as Nucsam, can not be applied at a large spatial scale because of the lack of data availability. The simplified model Smart2 is capable to simulate the observed flux-weighted annual averaged concentrations. Ignoring seasonal variations of weather conditions, ignoring of different soil layers and simplifying process description simplification does not need to greatly affect the modelled long-term annual average responses to acid deposition. A simplified model, such as Smart2, is an acceptable tool for making long-term evaluation of environmental abatement strategies. Model performance is seriously improved and the prediction uncertainties strongly decreased by model calibration at the scale required for the ultimate output. Further improvement through calibration is hampered from the lack of good quality data on a national scale.

Voor landelijke milieuverkenningen is het nutriëntenuitspoelingsmodel STONE ontwikkeld. Hierbij wordt Nederland opgedeeld in een groot aantal plots die een combinatie zijn van unieke combinaties van de hydrologie (UC) en andere indelingscriteria. Per UC wordt met een 1-dimensionaal model SWAP de hydrologie van het topsysteem berekend. In dit model is de interactie tussen grond- en oppervlaktewater binnen de plot en het oppervlaktewatersysteem integraal opgenomen

Voor landelijke milieuverkenningen is het nutriëntenuitspoelingsmodel STONE ontwikkeld. Hierbij wordt Nederland opgedeeld in een groot aantal plots die een combinatie zijn van unieke combinaties van de hydrologie (UC) en andere indelingscriteria. Per UC wordt met een 1-dimensionaal model SWAP de hydrologie van het topsysteem berekend. In dit model is de interactie tussen grond- en oppervlaktewater binnen de plot en het oppervlaktewatersysteem integraal opgenomen

Additional index words: nutrient cycling, soil modelling, uncertainty analysis, calibration, scenario analysis, model errorIn this thesis different nutrient cycling and soil acidification models, developed for use at different scales, are presented and evaluated. The models considered are Nucsam (NUtrient Cycling and Soil Acidification Model), Resam (REgional Soil Acidification Model) and Smart2 (an extended version of Simulation Model for Acidification's Regional Trends). These are mechanistic dynamic models, which simulate biogeochemical processes in semi-natural terrestrial ecosystems at a variety of scales. The research tool Nucsam, which is specifically developed for application on a local scale, includes simulation of the biogeochemical processes in various soil layers and on a daily time-scale. Resam and Smart2, tools to support policy makers, were specifically developed to evaluate long-term soil responses to deposition scenarios on a regional scale (national to continental, respectively). For that reason, the models Resam and Smart2 are relative simple models and operate on a yearly time-scale. These models were developed in view of the following research hypotheses:Adequate simulation of temporal responses in soil solution chemistry on a daily basis at various depth requires a detailed multi-layer biogeochemical model (Nucsam);Annual average responses in soil solution chemistry at the bottom of the root zone can be adequately simulated with a simple, one-layer biogeochemical model (Smart2);Simulation of soil solution chemistry on a regional scale requires a simplified model;Adequate simulation of soil solution chemistry on a regional scale requires parameterisation, calibration, validation and uncertainty analysis on that scale.Therefore, this thesis primarily aims at testing these hypotheses by (i) validation and calibration, (ii) uncertainty analysis, and (iii) model comparison. More specifically, the models Nucsam (site scale), Resam (site scale/regional scale) and Smart2 (regional scale) will be evaluated with respect to the optimal balance between model complexity, data availability and model aim.The detailed model Nucsam reproduced the magnitude and trends of measured quantities, such as soil water contents and soil solution chemistry, fairly well. However, the application on a site scale hampers from the lack of sufficiently good quality data. A model, such as Nucsam, can not be applied at a large spatial scale because of the lack of data availability. The simplified model Smart2 is capable to simulate the observed flux-weighted annual averaged concentrations. Ignoring seasonal variations of weather conditions, ignoring of different soil layers and simplifying process description simplification does not need to greatly affect the modelled long-term annual average responses to acid deposition. A simplified model, such as Smart2, is an acceptable tool for making long-term evaluation of environmental abatement strategies. Model performance is seriously improved and the prediction uncertainties strongly decreased by model calibration at the scale required for the ultimate output. Further improvement through calibration is hampered from the lack of good quality data on a national scale.

When applied to large simulation models, the process of parameter estimation is also called calibration. Calibration of complex non-linear systems, such as activated sludge plants, is often not an easy task. On the one hand, manual calibration of such complex systems is usually time-consuming, and its results are often not reproducible. On the other hand, conventional automatic calibration methods are not always straightforward and often hampered by local minima problems. In this paper a new straightforward and automatic procedure, which is based on the response surface method (RSM) for selecting the best identifiable parameters, is proposed. In RSM, the process response (output) is related to the levels of the input variables in terms of a first- or second-order regression model. Usually, RSM is used to relate measured process output quantities to process conditions. However, in this paper RSM is used for selecting the dominant parameters, by evaluating parameters sensitivity in a predefined region. Good results obtained in calibration of ASM No.1 for N-removal in a full-scale oxidation ditch proved that the proposed procedure is successful and reliable.

This paper discusses the sensitivity of calibration of hydrological model parameters to different objective functions. Several functions are defined with weights depending upon the hydrological background. These are compared with an objective function based upon kriging. Calibration is applied to piezometric readings from the Isle of Goeree in the Netherlands. For a study on the permeability of the first aquifer, the kriging predictor yields weights that differ from using prior knowledge, and emphasizes more strongly spatially isolated points than commonly applied objective functions. It reduces the range of differences between measurements and model simulations, but the mean absolute error increases. For a study on the resistance of the top layer and of the aquitard, use of prior information in the objective functions leads to a reduction in standard deviations of the differences between measured and calculated values by 40-80%

The paper presents a concise overview of respirometric experiments for the calibration of ASM1. First, the popularity of respirometry is explained by its historical impact and its sensitivity and robustness. The body of the text consists of a systematic overview of existing methods for assessment of nearly all ASM1: (i) component concentrations in sludge and waste water; and (ii) biokinetic and stoichiometric parameters. Real-life examples illustrate the methods. A difference is made between direct methods that use explicit calculations and optimisation methods that require numerical optimisation algorithms. It is stressed that the latter approach is especially useful to extract multiple parameters and component concentrations from single respirometric experiments. Finally, the importance of reflecting on the translation of lab-scale respirometric results to a full-scale model is stressed.