Uncertainty assessment of a process-based integrated catchment model of phosphorus |
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| Authors: | Sarah Dean Jim Freer Keith Beven Andrew J Wade Dan Butterfield |
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| Institution: | (1) Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK;(2) Aquatic Environments Research Centre, Department of Geography, University of Reading, Reading, RG6 6AB, UK;(3) Present address: School of Geographical Sciences, University of Bristol, University Road, Bristol, BS8 1SS, UK; |
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| 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’t sufficient data to support the many parameters. |
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