Uncertainty assessment of a process-based integrated catchment model of phosphorus

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 decisionmaking 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 km 2 ), 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.

Impacts of climate change, land-use change and phosphorus reduction on phytoplankton in the River Thames (UK)

“…The uncertainty of the INCA model has been assessed in several papers, such as for example Raat et al (2004), who pointed out the problem of equifinality and suggested a multi-objective calibration approach, as well as the use of frequent measurements (fortnightly frequency) as reference values for calibration. Dean et al (2009) applied a generalised likelihood uncertainty estimation (GLUE) framework to the INCA-P model, and concluded that the uncertainty due to the model structure and parameterisation was similar to the uncertainty of the measured values of total phosphorus in the river. Rankinen et al (2006) also applied a GLUE approach to evaluate the uncertainty of the INCA-N model results, integrating "soft data", or experimental knowledge of the processes, into the calibration procedure.…”

“…However, it can be assessed qualitatively. This modelling combination involves around 20-25 influential parameters, based on previous uncertainty assessments on the models used in this study (Dean et al, 2009;Futter et al, 2014;Raat et al, 2004;Rankinen et al, 2006;Whitehead et al, 2015b). However, as stated for example by Skeffington et al (2007), translating input uncertainties into uncertainty in the outputs is typically less than the summed uncertainty in the input parameters.…”

Dominant mechanisms for the delivery of fine sediment and phosphorus to fluvial networks draining grassland dominated headwater catchments

“…Many well established factors act to define the CSAs of fine sediment and P, however, our understanding of how and when these areas are connected to the fluvial networks is limited by the heterogeneity of factors governing process rates (Dean et al, 2009). These factors include antecedent moisture conditions, runoff mechanisms, spatial variation of rainfall intensity, and land management operations.…”

Prediction of storm transfers and annual loads with data-based mechanistic models using high-frequency data

“…Clearly, where the factors contributing to catchment response have obviously changed (such as if all septic tanks were upgraded or if farm budgeting reduced the additions of P), then simple transfer function models would not be able to predict the changes over time, whereas, in theory, processbased models might be able to account for such changes, albeit with much uncertainty (e.g. Dean et al, 2009;Yang et al, 2008). However, for rainfall-dominated responses, or responses to changes in rainfall patterns, simple transfer function models can provide valuable understanding of the dominant modes of a catchment, which, in turn, can be used to target management interventions.…”

“…Some of the less complex models for diffuse pollution include export coefficient models (Johnes, 1996) and the phosphorus indicators tool (PIT) (Heathwaite et al, 2003;Liu et al, 2005). The most complex water quality models are idealised, process-based representations of our best understanding of reality, with a highly complex, fixed structure and many parameters, for which there is often little or no site-specific data (Dean et al, 2009). These models often include a component for sediment-bound P, where the sediment transfer is based on a form of the universal soil loss equation (USLE), which is a semi-empirical model known to perform poorly (Evans and Boardman, 2016).…”