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Uncertainty assessment of a process-based integrated catchment model of phosphorus (INCA-P).

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Uncertainty assessment of a process-based integrated catchment model of phosphorus (INCA-P). / Dean, Sarah; Freer, Jim E.; Beven, Keith J. et al.
In: Stochastic Environmental Research and Risk Assessment, Vol. 23, No. 7, 10.2009, p. 991-1010.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Dean, S, Freer, JE, Beven, KJ, Wade, AJ & Butterfield, D 2009, 'Uncertainty assessment of a process-based integrated catchment model of phosphorus (INCA-P).', Stochastic Environmental Research and Risk Assessment, vol. 23, no. 7, pp. 991-1010. https://doi.org/10.1007/s00477-008-0273-z

APA

Dean, S., Freer, J. E., Beven, K. J., Wade, A. J., & Butterfield, D. (2009). Uncertainty assessment of a process-based integrated catchment model of phosphorus (INCA-P). Stochastic Environmental Research and Risk Assessment, 23(7), 991-1010. https://doi.org/10.1007/s00477-008-0273-z

Vancouver

Dean S, Freer JE, Beven KJ, Wade AJ, Butterfield D. Uncertainty assessment of a process-based integrated catchment model of phosphorus (INCA-P). Stochastic Environmental Research and Risk Assessment. 2009 Oct;23(7):991-1010. doi: 10.1007/s00477-008-0273-z

Author

Dean, Sarah ; Freer, Jim E. ; Beven, Keith J. et al. / Uncertainty assessment of a process-based integrated catchment model of phosphorus (INCA-P). In: Stochastic Environmental Research and Risk Assessment. 2009 ; Vol. 23, No. 7. pp. 991-1010.

Bibtex

@article{cf2340c9e1f2489381ba5075796c0810,
title = "Uncertainty assessment of a process-based integrated catchment model of phosphorus (INCA-P).",
abstract = "Despite the many models developed for phosphorus concentration prediction at differing spatial and temporal scales, there has been little effort to quantify uncertainty in their predictions. Model prediction uncertainty quantification is desirable, for informed decision-making in river-systems management. An uncertainty analysis of the process-based model, integrated catchment model of phosphorus (INCA-P), within the generalised likelihood uncertainty estimation (GLUE) framework is presented. The framework is applied to the Lugg catchment (1,077 km2), a River Wye tributary, on the England–Wales border. Daily discharge and monthly phosphorus (total reactive and total), for a limited number of reaches, are used to initially assess uncertainty and sensitivity of 44 model parameters, identified as being most important for discharge and phosphorus predictions. This study demonstrates that parameter homogeneity assumptions (spatial heterogeneity is treated as land use type fractional areas) can achieve higher model fits, than a previous expertly calibrated parameter set. The model is capable of reproducing the hydrology, but a threshold Nash-Sutcliffe co-efficient of determination (E or R 2) of 0.3 is not achieved when simulating observed total phosphorus (TP) data in the upland reaches or total reactive phosphorus (TRP) in any reach. Despite this, the model reproduces the general dynamics of TP and TRP, in point source dominated lower reaches. This paper discusses why this application of INCA-P fails to find any parameter sets, which simultaneously describe all observed data acceptably. The discussion focuses on uncertainty of readily available input data, and whether such process-based models should be used when there isn{\textquoteright}t sufficient data to support the many parameters.",
keywords = "INCA-P - GLUE - Uncertainty estimation - Phosphorus models - Diffuse agricultural pollution - Water quality modelling",
author = "Sarah Dean and Freer, {Jim E.} and Beven, {Keith J.} and Wade, {Andrew J.} and Dan Butterfield",
year = "2009",
month = oct,
doi = "10.1007/s00477-008-0273-z",
language = "English",
volume = "23",
pages = "991--1010",
journal = "Stochastic Environmental Research and Risk Assessment",
issn = "1436-3240",
publisher = "Springer New York",
number = "7",

}

RIS

TY - JOUR

T1 - Uncertainty assessment of a process-based integrated catchment model of phosphorus (INCA-P).

AU - Dean, Sarah

AU - Freer, Jim E.

AU - Beven, Keith J.

AU - Wade, Andrew J.

AU - Butterfield, Dan

PY - 2009/10

Y1 - 2009/10

N2 - Despite the many models developed for phosphorus concentration prediction at differing spatial and temporal scales, there has been little effort to quantify uncertainty in their predictions. Model prediction uncertainty quantification is desirable, for informed decision-making in river-systems management. An uncertainty analysis of the process-based model, integrated catchment model of phosphorus (INCA-P), within the generalised likelihood uncertainty estimation (GLUE) framework is presented. The framework is applied to the Lugg catchment (1,077 km2), a River Wye tributary, on the England–Wales border. Daily discharge and monthly phosphorus (total reactive and total), for a limited number of reaches, are used to initially assess uncertainty and sensitivity of 44 model parameters, identified as being most important for discharge and phosphorus predictions. This study demonstrates that parameter homogeneity assumptions (spatial heterogeneity is treated as land use type fractional areas) can achieve higher model fits, than a previous expertly calibrated parameter set. The model is capable of reproducing the hydrology, but a threshold Nash-Sutcliffe co-efficient of determination (E or R 2) of 0.3 is not achieved when simulating observed total phosphorus (TP) data in the upland reaches or total reactive phosphorus (TRP) in any reach. Despite this, the model reproduces the general dynamics of TP and TRP, in point source dominated lower reaches. This paper discusses why this application of INCA-P fails to find any parameter sets, which simultaneously describe all observed data acceptably. The discussion focuses on uncertainty of readily available input data, and whether such process-based models should be used when there isn’t sufficient data to support the many parameters.

AB - Despite the many models developed for phosphorus concentration prediction at differing spatial and temporal scales, there has been little effort to quantify uncertainty in their predictions. Model prediction uncertainty quantification is desirable, for informed decision-making in river-systems management. An uncertainty analysis of the process-based model, integrated catchment model of phosphorus (INCA-P), within the generalised likelihood uncertainty estimation (GLUE) framework is presented. The framework is applied to the Lugg catchment (1,077 km2), a River Wye tributary, on the England–Wales border. Daily discharge and monthly phosphorus (total reactive and total), for a limited number of reaches, are used to initially assess uncertainty and sensitivity of 44 model parameters, identified as being most important for discharge and phosphorus predictions. This study demonstrates that parameter homogeneity assumptions (spatial heterogeneity is treated as land use type fractional areas) can achieve higher model fits, than a previous expertly calibrated parameter set. The model is capable of reproducing the hydrology, but a threshold Nash-Sutcliffe co-efficient of determination (E or R 2) of 0.3 is not achieved when simulating observed total phosphorus (TP) data in the upland reaches or total reactive phosphorus (TRP) in any reach. Despite this, the model reproduces the general dynamics of TP and TRP, in point source dominated lower reaches. This paper discusses why this application of INCA-P fails to find any parameter sets, which simultaneously describe all observed data acceptably. The discussion focuses on uncertainty of readily available input data, and whether such process-based models should be used when there isn’t sufficient data to support the many parameters.

KW - INCA-P - GLUE - Uncertainty estimation - Phosphorus models - Diffuse agricultural pollution - Water quality modelling

U2 - 10.1007/s00477-008-0273-z

DO - 10.1007/s00477-008-0273-z

M3 - Journal article

VL - 23

SP - 991

EP - 1010

JO - Stochastic Environmental Research and Risk Assessment

JF - Stochastic Environmental Research and Risk Assessment

SN - 1436-3240

IS - 7

ER -