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Environmental Modelling: Learning from Uncertainty
KTH, School of Architecture and the Built Environment (ABE), Land and Water Resources Engineering. (Ekosystemteknik)
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 [en]
Models, data, error, uncertainty, hydrology, soil carbon, wetlands, phosphorus
National Category
Environmental Engineering
Identifiers
URN: urn:nbn:se:kth:diva-104336ISBN: 978-91-7501-538-5 (print)OAI: oai:DiVA.org:kth-104336DiVA: diva2:563992
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
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
Identifiers
urn:nbn:se:kth:diva-32463 (URN)10.1002/hyp.7421 (DOI)000270935400010 ()2-s2.0-70350128539 (Scopus ID)
Note
QC 20110414Available from: 2011-04-14 Created: 2011-04-14 Last updated: 2017-12-11Bibliographically 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, E-ISSN 1872-7026, 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.

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

QC 20110203

Available from: 2011-02-03 Created: 2011-02-02 Last updated: 2017-12-11Bibliographically approved
3. A model of phosphorus cycling to explore the role of biomass turnover in submerged aquatic vegetation wetlands for Everglades restoration
Open this publication in new window or tab >>A model of phosphorus cycling to explore the role of biomass turnover in submerged aquatic vegetation wetlands for Everglades restoration
2013 (English)In: Ecological Modelling, ISSN 0304-3800, E-ISSN 1872-7026, Vol. 251, 135-149 p.Article in journal (Refereed) Published
Abstract [en]

Engineered wetlands using submerged aquatic vegetation (SAV) are a cornerstone to the Stormwater Treatment Area (STA) project for stripping phosphorus from agricultural stormwater and lake water before entering protected Everglades marshes in south Florida, USA. However, recent efforts have suggested that the apparent lowest achievable outflow P (C*) in SAV systems (similar to 16 mu g/l) may not be low enough for proposed regulatory criteria. Thus, deepened predictive understanding on the functionality of these systems is of critical importance. Here, we develop a steady-state mass balance model of intermediate complexity to investigate C* in SAV systems. The model focuses on the role of SAV biomass turnover and P release to the water column, drawing upon established principles from shallow lake studies. This study introduces several large and unique datasets collected from a single study site (STA-2 Cell 3) over a 10-year period and demonstrates coherence in these data through the modeling approach. The datasets included inflow outflow values, P storage in accrued sediment at two intervals, annual surveys of SAV species composition, gradients of SAV tissue-P, and gradients of internal water column P concentration (previously published). The model was implemented and calibrated in an uncertainty framework with Monte Carlo methods, threshold screening, and multi-criteria limits of acceptability. Model calibration and validation appeared successful, resulting distributions of model parameters and accepted model simulations were relatively narrow, and results deepened perspectives on the previously identified C*. Rooted SAV species may be mining substantial P from underlying soils via root uptake and thus contributing internal loads. Steady turnover and decomposition of SAV biomass may be accounting for up to about a third of the background C*. These perspectives are relevant to STA optimization; our unique data, usage, and calibration strategy should be of interest to the aquatic ecosystem modeling community in general.

Keyword
Everglades, Phosphorus, Stormwater Treatment Areas, Submerged aquatic vegetation, SAV, Mass balance, Model, Uncertainty, GLUE, Limits of acceptability
National Category
Environmental Engineering
Identifiers
urn:nbn:se:kth:diva-104340 (URN)10.1016/j.ecolmodel.2012.12.001 (DOI)000317258100014 ()2-s2.0-84873256210 (Scopus ID)
Note

QC 20130520. Updated from manuscript to article in journal.

Available from: 2012-11-01 Created: 2012-11-01 Last updated: 2017-12-07Bibliographically approved
4. Rating curve uncertainty and change detection in discharge time series: Case study with 44-year historic data from the Nyangores River, Kenya
Open this publication in new window or tab >>Rating curve uncertainty and change detection in discharge time series: Case study with 44-year historic data from the Nyangores River, Kenya
2014 (English)In: Hydrological Processes, ISSN 0885-6087, E-ISSN 1099-1085, Vol. 28, no 4, 2509-2523 p.Article in journal (Refereed) Published
Abstract [en]

The intersection of the developing topic of rating curve and discharge series uncertainty with the topic of hydrological change detection (e.g., in response to land cover or climatic change) has not yet been well studied. The work herein explores this intersection, with consideration of a long-term discharge response (1964-2007) for a ~650-km2 headwater basin of the Mara River in west Kenya, starting with stream rating and daily gauge height data. A rating model was calibrated using Bayesian methods to quantify uncertainty intervals in model parameters and predictions. There was an unknown balance of random and systemic error in rating data scatter (a scenario not likely unique to this basin), which led to an unknown balance of noise and information in the calibrated statistical error model. This had implications on testing for hydrological change. Overall, indications were that shifts in basin's discharge response were rather subtle over the 44-year period. A null hypothesis for change using flow duration curves (FDCs) from four different 8-year data intervals could be either accepted or rejected over much of the net flow domain depending on different applications of the statistical error model (each with precedence in the literature). The only unambiguous indication of change in FDC comparisons appeared to be a reduction in lowest baseflow in recent years (flows with >98% exceedance probability). We defined a subjective uncertainty interval based on an intermediate balance of random and systematic error in the rating model that suggested a possibility of more prevalent impacts. These results have relevance to management in the Mara basin and to future studies that might establish linkages to historic land use and climatic factors. The concern about uncertain uncertainty intervals (uncertainty2) extends beyond the Mara and is relevant to testing change where non-random rating errors may be important and subtle responses are investigated.

Keyword
Discharge uncertainty, Flow duration curve, Hydrological change, Hypothesis testing, Rating curve
National Category
Oceanography, Hydrology, Water Resources
Identifiers
urn:nbn:se:kth:diva-104342 (URN)10.1002/hyp.9786 (DOI)000330743000077 ()2-s2.0-84892443876 (Scopus ID)
Funder
Sida - Swedish International Development Cooperation Agency
Note

QC 20140312. Updated from manuscript to article in journal.

Available from: 2012-11-01 Created: 2012-11-01 Last updated: 2017-12-07Bibliographically approved

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