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Temporal sampling strategies and uncertainty in calibrating a conceptual hydrological model for a small boreal catchment
KTH, School of Architecture and the Built Environment (ABE), Land and Water Resources Engineering.
Department of Geography, University of Zürich, Switzerland, Department of Physical Geography and Quaternary Geology, Stockholm University, Sweden.
Artesia Groundwater Consulting, Täby, Sweden.
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.

Place, publisher, year, edition, pages
2009. Vol. 23, no 21, 3093-3109 p.
National Category
Agricultural Science Other Environmental Engineering
Identifiers
URN: urn:nbn:se:kth:diva-32463DOI: 10.1002/hyp.7421ISI: 000270935400010Scopus ID: 2-s2.0-70350128539OAI: oai:DiVA.org:kth-32463DiVA: diva2:410744
Note
QC 20110414Available from: 2011-04-14 Created: 2011-04-14 Last updated: 2017-12-11Bibliographically approved
In thesis
1. Water and Carbon Balance Modeling: Methods of Uncertainty Analysis
Open this publication in new window or tab >>Water and Carbon Balance Modeling: Methods of Uncertainty Analysis
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.
Series
Trita-LWR. LIC, ISSN 1650-8629 ; 2048
Keyword
Modeling, uncertainty analysis, water balance, carbon balance, GLUE
National Category
Other Environmental Engineering
Identifiers
urn:nbn:se:kth:diva-12160 (URN)978-91-7415-564-8 (ISBN)
Presentation
2010-03-24, V32, Teknikringen 72, KTH, 13:15 (English)
Opponent
Supervisors
Note
QC 20110414Available from: 2010-03-22 Created: 2010-03-17 Last updated: 2011-04-15Bibliographically approved
2. Environmental Modelling: Learning from Uncertainty
Open this publication in new window or tab >>Environmental Modelling: Learning from Uncertainty
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Environmental models are important tools; however uncertainty is pervasive in the modeling process.   Current research has shown that under­standing and representing these uncertainties is critical when decisions are expected to be made from the modeling results.  One critical question has become: how focused should uncertainty intervals be with consideration of characteristics of uncertain input data, model equation representations, and output observations?   This thesis delves into this issue with applied research in four independent studies.  These studies developed a diverse array of simply-structured process models (catchment hydrology, soil carbon dynamics, wetland P cycling, stream rating); employed field data observations with wide ranging characteristics (e.g., spatial variability, suspected systematic error); and explored several variations of probabilistic and non-probabilistic uncertainty schemes for model calibrations.  A key focus has been on how the design of various schemes impacted the resulting uncertainty intervals, and more importantly the ability to justify conclusions.  In general, some uncertainty in uncertainty (u2) resulted in all studies, in various degrees.  Subjectivity was intrinsic in the non-probabilistic results.  One study illustrated that such subjectivity could be partly mitigated using a “limits of acceptability” scheme with posterior validation of errors.  u2 was also a factor from probabilistic calibration algorithms, as residual errors were not wholly stochastic.  Overall however, u2 was not a deterrent to drawing conclusions from each study. One insight on the value of data for modeling was that there can be substantial redundant information in some hydrological time series.  Several process insights resulted: there can be substantial fractions of relatively inert soil carbon in agricultural systems; the lowest achievable outflow phosphorus concentration in an engineered wetland seemed partly controlled by rapid turnover and decomposition of the specific vegetation in that system.  Additionally, consideration of uncertainties in a stage-discharge rating model enabled more confident detection of change in long-term river flow patterns.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2012. xii, 36 p.
Series
Trita-LWR. PHD, ISSN 1650-8602 ; 1068
Keyword
Models, data, error, uncertainty, hydrology, soil carbon, wetlands, phosphorus
National Category
Environmental Engineering
Identifiers
urn:nbn:se:kth:diva-104336 (URN)978-91-7501-538-5 (ISBN)
Public defence
2012-11-23, V2, Teknikringen 76 2tr, KTH, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20121105

Available from: 2012-11-05 Created: 2012-11-01 Last updated: 2012-11-05Bibliographically approved

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