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Rating curve uncertainty and change detection in discharge time series: Case study with 44-year historic data from the Nyangores River, Kenya
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Land and Water Resources Engineering. (Ekosystemteknik)
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Land and Water Resources Engineering. (Ekosystemteknik)
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Land and Water Resources Engineering. (Ekosystemteknik)
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.

Place, publisher, year, edition, pages
2014. Vol. 28, no 4, 2509-2523 p.
Keyword [en]
Discharge uncertainty, Flow duration curve, Hydrological change, Hypothesis testing, Rating curve
National Category
Oceanography, Hydrology, Water Resources
Identifiers
URN: urn:nbn:se:kth:diva-104342DOI: 10.1002/hyp.9786ISI: 000330743000077Scopus ID: 2-s2.0-84892443876OAI: oai:DiVA.org:kth-104342DiVA: diva2:564024
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
In thesis
1. 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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