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Water and Carbon Balance Modeling: Methods of Uncertainty Analysis
KTH, School of Architecture and the Built Environment (ABE), Land and Water Resources Engineering. (Environmental Physics)
2010 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

How do additional data of the same and/or different type contribute to reducing model parameter and predictive uncertainties? This was the question addressed with two models – the HBV hydrological water balance model and the ICBM soil carbon balance model – that were used to investigate the usefulness of the Generalized Likelihood Uncertainty Estimation (GLUE) method for calibrations and uncertainty analyses.  The GLUE method is based on threshold screening of Monte Carlo simulations using so-called informal likelihood measures and subjective acceptance criterion. This method is highly appropriate for model calibrations when errors are dominated by epistemic rather than stochastic uncertainties.  The informative value of data for model calibrations was investigated with numerous calibrations aimed at conditioning posterior parameter distributions and boundaries on model predictions.  The key results demonstrated examples of: 1) redundant information in daily time series of hydrological data; 2) diminishing returns in the value of continued time series data collections of the same type; 3) the potential value of additional data of a different type; 4) a means to effectively incorporate fuzzy information in model calibrations; and 5) the robustness of estimated parameter uncertainty for portability of a soil carbon model between and tropical climate zones.  The key to obtaining these insights lied in the methods of uncertainty analysis used to produce them.  A paradigm for selecting between formal and informal likelihood measures in uncertainty analysis is presented and discussed for future use within a context of climate related environmental modeling.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology , 2010. , 26 p.
Trita-LWR. LIC, ISSN 1650-8629 ; 2048
Keyword [en]
Modeling, uncertainty analysis, water balance, carbon balance, GLUE
National Category
Other Environmental Engineering
URN: urn:nbn:se:kth:diva-12160ISBN: 978-91-7415-564-8OAI: diva2:304204
2010-03-24, V32, Teknikringen 72, KTH, 13:15 (English)
QC 20110414Available from: 2010-03-22 Created: 2010-03-17 Last updated: 2011-04-15Bibliographically approved
List of papers
1. Temporal sampling strategies and uncertainty in calibrating a conceptual hydrological model for a small boreal catchment
Open this publication in new window or tab >>Temporal sampling strategies and uncertainty in calibrating a conceptual hydrological model for a small boreal catchment
2009 (English)In: Hydrological Processes, ISSN 0885-6087, E-ISSN 1099-1085, Vol. 23, no 21, 3093-3109 p.Article in journal (Refereed) Published
Abstract [en]

How much data is needed for calibration of a hydrological catchment model? In this paper we address this question by evaluating the information contained in different subsets of discharge and groundwater time series for multi-objective calibration of a conceptual hydrological model within the framework of an uncertainty analysis. The study site was a 5.6-km(2) catchment within the Forsmark research site in central Sweden along the Baltic coast. Daily time series data were available for discharge and several groundwater wells within the catchment for a continuous 1065-day period. The hydrological model was a site-specific modification of the Conceptual HBV model. The uncertainty analyses were based on a selective Monte Carlo procedure. Thirteen subsets of the complete time series data were investigated with the idea that these represent realistic intermittent sampling strategies. Data Subsets included split-samples and various combinations of weekly, monthly, and quarterly fixed interval subsets, as well as a 53-day 'informed observer' Subset that utilized once per month samples except during March and April-the months containing large and often dominant snow melt events-when sampling was once per week. Several of these subsets, including that of the informed observer, provided very similar constraints on model calibration and parameter identification as the full data record, ill terms of credibility bands on simulated time series, posterior parameter distributions, and performance indices calculated to the full dataset. This result Suggests that hydrological sampling designs can, at least in some cases, be optimized. Copyright (C) 2009 John Wiley & Sons, Ltd.

National Category
Agricultural Science Other Environmental Engineering
urn:nbn:se:kth:diva-32463 (URN)10.1002/hyp.7421 (DOI)000270935400010 ()2-s2.0-70350128539 (ScopusID)
QC 20110414Available from: 2011-04-14 Created: 2011-04-14 Last updated: 2012-11-05Bibliographically approved
2. Uncertainty analyses for calibrating a soil carbon balance model to agricultural field trial data in Sweden and Kenya
Open this publication in new window or tab >>Uncertainty analyses for calibrating a soil carbon balance model to agricultural field trial data in Sweden and Kenya
2010 (English)In: Ecological Modelling, ISSN 0304-3800, Vol. 221, no 16, 1880-1888 p.Article in journal (Refereed) Published
Abstract [en]

How do additional data of the same and/or different type contribute to reducing model parameter and predictive uncertainties? Most modeling applications of soil organic carbon (SOC) time series in agricultural field trial datasets have been conducted without accounting for model parameter uncertainty. There have been recent advances with Monte Carlo-based uncertainty analyses in the field of hydrological modeling that are applicable, relevant and potentially valuable in modeling the dynamics of SOC. Here we employed a Monte Carlo method with threshold screening known as Generalized Likelihood Uncertainty Estimation (GLUE) to calibrate the Introductory Carbon Balance Model (ICBM) to long-term field trail data from Ultuna, Sweden and Machang'a, Kenya. Calibration results are presented in terms of parameter distributions and credibility bands on time series simulations for a number of case studies. Using these methods, we demonstrate that widely uncertain model parameters, as well as strong covariance between inert pool size and rate constant parameters, exist when root mean square simulation errors were within uncertainties in input estimations and data observations. We show that even rough estimates of the inert pool (perhaps from chemical analysis) can be quite valuable to reduce uncertainties in model parameters. In fact, such estimates were more effective at reducing parameter and predictive uncertainty than an additional 16 years time series data at Ultuna. We also demonstrate an effective method to jointly, simultaneously and in principle more robustly calibrate model parameters to multiple datasets across different climatic regions within an uncertainty framework. These methods and approaches should have benefits for use with other SOC models and datasets as well.

Soil organic carbon, Soil carbon, Carbon budgets, Model, Modeling, Agriculture, Uncertainty analysis, GLUE, ICBM
National Category
Agricultural Science Ecology Other Environmental Engineering
urn:nbn:se:kth:diva-29493 (URN)10.1016/j.ecolmodel.2010.04.019 (DOI)000279889700005 ()2-s2.0-77954032674 (ScopusID)

QC 20110203

Available from: 2011-02-03 Created: 2011-02-02 Last updated: 2012-11-05Bibliographically approved

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