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  • 1.
    Grimvall, Anders
    et al.
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Wahlin, Karl
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Hussian, Mohamed
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    von Brömssen, C.
    Unit of Applied Statistics and Mathematics, Swedish University of Agricultural Sciences, Box 7013, SE-750 07 Uppsala, Sweden.
    Semiparametric smoothers for trend assessment of multiple time series of environmental quality data2008In: Environmetrics, ISSN 1180-4009, E-ISSN 1099-095XArticle in journal (Other academic)
    Abstract [en]

    Multiple time series of environmental quality data with similar, but not necessarily identical, trends call for multivariate methods for trend detection and adjustment for covariates. Here, we show how an additive model in which the multivariate trend function is specified in a nonparametric fashion (and the adjustment for covariates is based on a parametric expression) can be used to estimate how the human impact on an ecosystem varies with time and across components of the observed vector time series. More specifically, we demonstrate how a roughness penalty approach can be utilized to impose different types of smoothness on the function surface that describes trends in environmental quality as a function of time and vector component. Compared to other tools used for this purpose, such as Gaussian smoothers and thin plate splines, an advantage of our approach is that the smoothing pattern can easily be tailored to different types of relationships between the vector components. We give explicit roughness penalty expressions for data collected over several seasons or representing several classes on a linear or circular scale. In addition, we define a general separable smoothing method. A new resampling technique that preserves statistical dependencies over time and across vector components enables realistic calculations of confidence and prediction intervals.

  • 2.
    Norberg, Pernilla
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Persson, H Lennart
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Respiratory Medicine.
    Schmekel, Birgitta
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Alm Carlsson, Gudrun
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
    Wahlin, Karl
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Sandborg, Michael
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Gustafsson, Agnetha
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Does quantitative lung SPECT detect lung abnormalities earlier than lung function tests?: Results of a pilot study2014In: EJNMMI Research, ISSN 2191-219X, E-ISSN 2191-219X, Vol. 4, no 39, p. 1-12Article in journal (Refereed)
    Abstract [en]

    Background: Heterogeneous ventilation in lungs of allergic individuals, cigarette smokers, asthmatics and chronic obstructive pulmonary disease (COPD) patients has been demonstrated using imaging modalities such as PET, MR and SPECT. These individuals suffer from narrow and/or closed airways to various extents. By calculating regional heterogeneity in lung ventilation SPECT images as the coefficient of variation (CV) in small elements of the lung, heterogeneity maps and CV-frequency curves can be generated and used to quantitatively measure heterogeneity. This work explores the potential to use such measurements to detect mild ventilation heterogeneities in lung healthy subjects.

    Method: Fourteen healthy subjects without documented lung disease or respiratory symptoms, and two patients with documented airway disease, inhaled on average approximately 90 MBq 99mTc-Technegas immediately prior to the 20 min SPECT acquisition. Variation in activity uptake between subjects was compensated for in resulting CV values. The area under the compensated CV frequency curve (AUC), for CV values greater than a threshold value CVT, AUC(CV> CVT), was used as the measure of ventilation heterogeneity.

    Results: Patients with lung function abnormalities, according to lung function tests, generated higher AUC(CV>20%) values compared to healthy subjects (p=0.006). Strong linear correlations with the AUC(CV>20%) values were found for age (p=0.006) and height (p=0.001). These demonstrated that ventilation heterogeneities increased with age and that they depend on lung size. Strong linear correlations were found for the lung function value related to indices of airway closure/air trapping, RV/TLC (p=0.009), and DLCOc (p=0.009), a value partly related to supposed ventilation/perfusion mismatch. These findings support the association between conventional lung function tests and the AUC(CV>20%) value.

    Conclusions: Among the healthy subjects there is a group with increased AUC(CV>20%) values, but with normal lung function tests, which implies that it might be possible to differentiate ventilation heterogeneities earlier in a disease process than by lung function tests.

  • 3.
    Nordgaard, Anders
    et al.
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Wahlin, Karl
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Grimvall, Anders
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Uncertainty in water quality data and its implications for trend detection2007In: Bulletin of the ISI, Vol. LXII: Proceedings, Lisboa, Portugal, 22-29 AUgust 2007 / [ed] Gomes M.I., Pinto Martins J.A., Silva J.A., 2007, p. 2597-2600Conference paper (Other academic)
  • 4.
    Rendek, Zlatica
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Region Östergötland, Local Health Care Services in Central Östergötland, Primary Health Care in Central County. Linköping University, Faculty of Medicine and Health Sciences.
    Falk, Magnus
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Local Health Care Services in West Östergötland, Research & Development Unit in Local Health Care.
    Grodzinsky, Ewa
    Linköping University, Department of Medical and Health Sciences, Division of Drug Research. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Local Health Care Services in West Östergötland, Research & Development Unit in Local Health Care. National Board Forens Med, Linkoping, Sweden.
    Wahlin, Karl
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Kechagias, Stergios
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Svernlöv, Rikard
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Medicine and Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Gastroentorology.
    Hjortswang, Henrik
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Gastroentorology.
    Effect of oral diclofenac intake on faecal calprotectin2016In: Scandinavian Journal of Gastroenterology, ISSN 0036-5521, E-ISSN 1502-7708, Vol. 51, no 1, p. 28-32Article in journal (Refereed)
    Abstract [en]

    Background. NSAIDs are a known source of increased faecal calprotectin (FC) levels. Currently, there is a lack of knowledge about how long it takes for an increased FC level to return to normal after NSAID intake. Objective. The aim was to investigate how oral diclofenac intake affects FC levels and assess how long it takes for an increased FC level to return to normal after oral diclofenac intake. Material and methods. Thirty healthy volunteers received diclofenac 50 mg three times daily for 14 days. Participants provided a stool sample on Days 0, 2, 4, 7, 14 during intake and Days 17, 21, 28 after discontinuation. FC levels were then followed at 7-day intervals until normalization. Results. During diclofenac intake, eight participants (27%) had FC levels exceeding the upper limit of normal (median, 76 mu g/g; range, 60-958 mu g/g), corresponding to 8.3% of measurements. FC was not constantly increased and became normal in most participants during diclofenac intake. FC levels were on average significantly higher during intake (M = 9.5, interquartile range (IQR) = 13.4) than on baseline (M = 7.5, IQR = 0.0), p = 0.003. After discontinuation, two participants had increased FC on Days 17 and 21, respectively. No significant differences in FC levels were found between baseline and measurements after discontinuation. Two weeks after discontinuation, all participants had normal FC levels. Conclusions. Short-term oral diclofenac intake is associated with increased FC levels. However, the likelihood of an increased test result is low. Our results suggest that 2 weeks of diclofenac withdrawal is sufficient to get an uninfluenced FC test result.

  • 5.
    Thorell, Lars-Håkan
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Neuroscience. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Local Health Care Services in Central Östergötland, Department of Psychiatry.
    Wolfersdorf, M.
    State Hospital, Bayreuth, Germany .
    Straub, R.
    University Hospital Ulm, Germany .
    Steyer, J.
    University Hospital Ulm, Germany .
    Hodgkinson, S.
    University Hospital Ulm, Germany .
    Kaschka, W.P.
    University Hospital Ulm, Germany .
    Jandl, M.
    University Hospital Ulm, Germany .
    Wahlin, Karl
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    A paradox in suicide statistics in estimating specificity of tests for suicide - reply to Mushquash and co-workers and Culver2014In: Journal of Psychiatric Research, ISSN 0022-3956, E-ISSN 1879-1379, Vol. 54, p. 142-143Article in journal (Other academic)
  • 6.
    Wahlin, Karl
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Roadmap for trend detection and assessment of data quality2008Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Regular measurements of the state of the environment constitute a cornerstone of environmental management. Without the support of long time series of reliable data, we would know much less about changes that occur in the environment and their causes. The present research aimed to explore how improved techniques for data analysis can help reveal flawed data and extract more information from environmental monitoring programmes. Based on our results, we propose that the organization of such monitoring should be transformed from a system for measuring and collecting data to an information system where resources have been reallocated to data analysis. More specifically, this thesis reports improved methods for joint analysis of trends in multiple time series and detection of artificial level shifts in the presence of smooth trends. Furthermore, special consideration is given to methods that automatically detect and adapt to the interdependence of the collected data. The current work resulted in a roadmap describing the process of proceeding from a set of observed concentrations to arrive at conclusions about the quality of the data and existence of trends therein. Improvements in existing software accompanied the development of new statistical procedures.

    List of papers
    1. Semiparametric smoothers for trend assessment of multiple time series of environmental quality data
    Open this publication in new window or tab >>Semiparametric smoothers for trend assessment of multiple time series of environmental quality data
    2008 (English)In: Environmetrics, ISSN 1180-4009, E-ISSN 1099-095XArticle in journal (Other academic) Submitted
    Abstract [en]

    Multiple time series of environmental quality data with similar, but not necessarily identical, trends call for multivariate methods for trend detection and adjustment for covariates. Here, we show how an additive model in which the multivariate trend function is specified in a nonparametric fashion (and the adjustment for covariates is based on a parametric expression) can be used to estimate how the human impact on an ecosystem varies with time and across components of the observed vector time series. More specifically, we demonstrate how a roughness penalty approach can be utilized to impose different types of smoothness on the function surface that describes trends in environmental quality as a function of time and vector component. Compared to other tools used for this purpose, such as Gaussian smoothers and thin plate splines, an advantage of our approach is that the smoothing pattern can easily be tailored to different types of relationships between the vector components. We give explicit roughness penalty expressions for data collected over several seasons or representing several classes on a linear or circular scale. In addition, we define a general separable smoothing method. A new resampling technique that preserves statistical dependencies over time and across vector components enables realistic calculations of confidence and prediction intervals.

    National Category
    Computer and Information Sciences
    Identifiers
    urn:nbn:se:liu:diva-52243 (URN)
    Available from: 2009-12-11 Created: 2009-12-11 Last updated: 2018-01-12Bibliographically approved
    2. Uncertainty in water quality data and its implications for trend detection: lessons from Swedish environmental data
    Open this publication in new window or tab >>Uncertainty in water quality data and its implications for trend detection: lessons from Swedish environmental data
    2008 (English)In: Environmental Science and Policy, ISSN 1462-9011, E-ISSN 1873-6416, Vol. 11, no 2, p. 115-124Article in journal (Refereed) Published
    Abstract [en]

    The demands on monitoring systems have gradually increased, and interpretation of the data is often a matter of controversy. As an example of this, we investigated water quality monitoring and the eutrophication issue in Sweden. Our results demonstrate that powerful statistical tools for trend analysis can reveal flaws in the data and lead to new and revised interpretations of environmental data. In particular, we found strong evidence that long-term trends in measured nutrient concentrations can be more extensively influenced by changes in sampling and laboratory practices than by actual changes in the state of the environment. On a more general level, our findings raise important questions regarding the need for new paradigms for environmental monitoring and assessment. Introduction of a system in which conventional quality assurance is complemented with thorough statistical follow-up of reported values would represent a first step towards recognizing that environmental monitoring and assessment should be transformed from being a system for sampling and laboratory analyses into a system for interpreting information to support policy development.

    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:liu:diva-44473 (URN)10.1016/j.envsci.2007.12.001 (DOI)76781 (Local ID)76781 (Archive number)76781 (OAI)
    Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2018-01-12Bibliographically approved
    3. Roadmap for assessing regional trends in groundwater quality
    Open this publication in new window or tab >>Roadmap for assessing regional trends in groundwater quality
    2010 (English)In: Environmental Monitoring and Assessment, ISSN 0167-6369, Vol. 165, no 1-4, p. 217-231Article in journal (Refereed) Published
    Abstract [en]

    Assessing regional trends in groundwater quality can be a difficult task. Data are often scattered in space and time, and the inertia of groundwater systems can create natural, seemingly persistent changes in concentration that are difficult to separate from anthropogenic trends. Here, we show how statistical methods and software for joint analysis of multiple time series can be integrated into a roadmap for trend analysis and critical examination of data quality. Ordinary and partial Mann-Kendall (MK) tests for monotonic trends and semiparametric smoothers for multiple time series constitute the cornerstones of our procedure. The MK tests include a simple and easily implemented method to correct for serial dependence, and the associated software is designed to enable convenient handling of numerous data series and to accommodate covariates and nondetects. The semiparametric smoothers are intended to facilitate detection of synchronous changes in a network of stations. A study of Swedish groundwater quality data revealed true upward trends in acid-neutralizing capacity (ANC) and downward trends in sulphate, but also a misleading shift in alkalinity level that would have been difficult to detect if the time series had been analysed separately.

    Place, publisher, year, edition, pages
    Springer Science Business Media, 2010
    Keywords
    Groundwater quality, Environmetrics, Multiple time series, Environmental monitoring, Mann-Kendall test
    National Category
    Mathematics
    Identifiers
    urn:nbn:se:liu:diva-52246 (URN)10.1007/s10661-009-0940-7 (DOI)000277371000019 ()
    Note
    The original publication is available at www.springerlink.com: Karl Wahlin and Anders Grimvall, Roadmap for assessing regional trends in groundwater quality, 2010, Environmental Monitoring and Assessment, (165), 1-4, 217-231. http://dx.doi.org/10.1007/s10661-009-0940-7 Copyright: Springer Science Business Media http://www.springerlink.com/ Available from: 2009-12-11 Created: 2009-12-11 Last updated: 2010-05-31Bibliographically approved
    4. Estimating artificial level shifts in the presence of smooth trends
    Open this publication in new window or tab >>Estimating artificial level shifts in the presence of smooth trends
    2008 (English)In: Environmental Monitoring and Assessment, ISSN 0167-6369Article in journal (Other academic) Submitted
    Abstract [en]

    Changes in observational data over time can be severely distorted by errors in measurements, sampling, or reporting. Here, we show how smooth trends in vector time series can be separated from one or two abrupt level shifts that occur simultaneously in all coordinates. Trends are modelled nonparametrically, whereas abrupt changes and the impact of covariates are modelled parametrically. The model is estimated using a backfitting algorithm in which estimation of smooth trends is alternated with estimation of regression coefficients for covariates and assessment of sudden level shifts. The proposed method is adaptive in the sense that the degree of smoothing over time and across coordinates is controlled by a roughness penalty and cross-validation procedure that automatically identifies the interdependence of the analysed data. Furthermore, it uses a resampling technique that can accommodate correlated error terms in the assessment of the uncertainty of both smooth trends and discontinuities. The method is applied to water quality data from Swedish national monitoring programmes to illustrate how known discontinuities can be quantified and how previously unrecognized discontinuities can be detected.

    National Category
    Social Sciences
    Identifiers
    urn:nbn:se:liu:diva-52245 (URN)
    Available from: 2009-12-11 Created: 2009-12-11 Last updated: 2011-05-20Bibliographically approved
    5. Reduced Models of the Retention of Nitrogen in Catchments
    Open this publication in new window or tab >>Reduced Models of the Retention of Nitrogen in Catchments
    Show others...
    2004 (English)In: Proceedings of the International Environmental Modelling and Software Society Conference (iEMSs), 14-17 June, Osnabrück, Germany, 2004, p. 1081-1086Conference paper, Published paper (Refereed)
    Abstract [en]

    Process-oriented models of the retention of nitrogen in catchments are by necessity rather complex. We introduced several types of ensemble runs that can provide informative summaries of meteorologically normalised model outputs and also clarify the extent to which such outputs are related to various model parameters. Thereafter we employed this technique to examine policy-relevant outputs of the catchment model INCA-N. In particular, we examined how long it will take for changes in the application of fertilisers on cultivated land to affect the predicted riverine loads of nitrogen. The results showed that the magnitude of the total intervention effect was influenced mainly by the parameters governing the turnover of nitrogen in soil, whereas the temporal distribution of the water quality response was determined primarily by the hydromechanical model parameters. This raises the question of whether the soil nitrogen processes included in the model are elaborate enough to correctly explain the widespread observations of slow water quality responses to changes in agricultural practices.

    Keywords
    Model reduction; Ensemble runs; Catchment; Nitrogen; Retention
    National Category
    Computer and Information Sciences
    Identifiers
    urn:nbn:se:liu:diva-17110 (URN)
    Available from: 2009-03-06 Created: 2009-03-06 Last updated: 2018-01-13Bibliographically approved
  • 7.
    Wahlin, Karl
    et al.
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Grimvall, Anders
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Roadmap for assessing regional trends in groundwater quality2010In: Environmental Monitoring and Assessment, ISSN 0167-6369, Vol. 165, no 1-4, p. 217-231Article in journal (Refereed)
    Abstract [en]

    Assessing regional trends in groundwater quality can be a difficult task. Data are often scattered in space and time, and the inertia of groundwater systems can create natural, seemingly persistent changes in concentration that are difficult to separate from anthropogenic trends. Here, we show how statistical methods and software for joint analysis of multiple time series can be integrated into a roadmap for trend analysis and critical examination of data quality. Ordinary and partial Mann-Kendall (MK) tests for monotonic trends and semiparametric smoothers for multiple time series constitute the cornerstones of our procedure. The MK tests include a simple and easily implemented method to correct for serial dependence, and the associated software is designed to enable convenient handling of numerous data series and to accommodate covariates and nondetects. The semiparametric smoothers are intended to facilitate detection of synchronous changes in a network of stations. A study of Swedish groundwater quality data revealed true upward trends in acid-neutralizing capacity (ANC) and downward trends in sulphate, but also a misleading shift in alkalinity level that would have been difficult to detect if the time series had been analysed separately.

  • 8.
    Wahlin, Karl
    et al.
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Grimvall, Anders
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Uncertainty in water quality data and its implications for trend detection: lessons from Swedish environmental data2008In: Environmental Science and Policy, ISSN 1462-9011, E-ISSN 1873-6416, Vol. 11, no 2, p. 115-124Article in journal (Refereed)
    Abstract [en]

    The demands on monitoring systems have gradually increased, and interpretation of the data is often a matter of controversy. As an example of this, we investigated water quality monitoring and the eutrophication issue in Sweden. Our results demonstrate that powerful statistical tools for trend analysis can reveal flaws in the data and lead to new and revised interpretations of environmental data. In particular, we found strong evidence that long-term trends in measured nutrient concentrations can be more extensively influenced by changes in sampling and laboratory practices than by actual changes in the state of the environment. On a more general level, our findings raise important questions regarding the need for new paradigms for environmental monitoring and assessment. Introduction of a system in which conventional quality assurance is complemented with thorough statistical follow-up of reported values would represent a first step towards recognizing that environmental monitoring and assessment should be transformed from being a system for sampling and laboratory analyses into a system for interpreting information to support policy development.

  • 9.
    Wahlin, Karl
    et al.
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Grimvall, Anders
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Sirisack, S.
    Department of Mathematics, National University of Laos, Vientiane, Laos.
    Estimating artificial level shifts in the presence of smooth trends2008In: Environmental Monitoring and Assessment, ISSN 0167-6369Article in journal (Other academic)
    Abstract [en]

    Changes in observational data over time can be severely distorted by errors in measurements, sampling, or reporting. Here, we show how smooth trends in vector time series can be separated from one or two abrupt level shifts that occur simultaneously in all coordinates. Trends are modelled nonparametrically, whereas abrupt changes and the impact of covariates are modelled parametrically. The model is estimated using a backfitting algorithm in which estimation of smooth trends is alternated with estimation of regression coefficients for covariates and assessment of sudden level shifts. The proposed method is adaptive in the sense that the degree of smoothing over time and across coordinates is controlled by a roughness penalty and cross-validation procedure that automatically identifies the interdependence of the analysed data. Furthermore, it uses a resampling technique that can accommodate correlated error terms in the assessment of the uncertainty of both smooth trends and discontinuities. The method is applied to water quality data from Swedish national monitoring programmes to illustrate how known discontinuities can be quantified and how previously unrecognized discontinuities can be detected.

  • 10.
    Wahlin, Karl
    et al.
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Shahsavani, Davood
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Grimvall, Anders
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Wade, A. J.
    Aquatic Environments Research Centre, School of Human and Environmental Sciences, The University of Reading, UK.
    Butterfield, D.
    Aquatic Environments Research Centre, School of Human and Environmental Sciences, The University of Reading, UK.
    Jarvie, H. P.
    Centre for Ecology and Hydrology, Wallingford, UK.
    Reduced Models of the Retention of Nitrogen in Catchments2004In: Proceedings of the International Environmental Modelling and Software Society Conference (iEMSs), 14-17 June, Osnabrück, Germany, 2004, p. 1081-1086Conference paper (Refereed)
    Abstract [en]

    Process-oriented models of the retention of nitrogen in catchments are by necessity rather complex. We introduced several types of ensemble runs that can provide informative summaries of meteorologically normalised model outputs and also clarify the extent to which such outputs are related to various model parameters. Thereafter we employed this technique to examine policy-relevant outputs of the catchment model INCA-N. In particular, we examined how long it will take for changes in the application of fertilisers on cultivated land to affect the predicted riverine loads of nitrogen. The results showed that the magnitude of the total intervention effect was influenced mainly by the parameters governing the turnover of nitrogen in soil, whereas the temporal distribution of the water quality response was determined primarily by the hydromechanical model parameters. This raises the question of whether the soil nitrogen processes included in the model are elaborate enough to correctly explain the widespread observations of slow water quality responses to changes in agricultural practices.

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