Towards reduced uncertainty in catchment nitrogen modelling: quantifying the effect of field observation uncertainty on model calibration

Abstract

The value of nitrogen (N) field measurements for the calibration of parameters of the INCA nitrogen in catchment model is explored and quantified. A virtual catchment was designed by running INCA with a known set of parameters, and field measurements were selected from the model run output. Then, using these measurements and the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA), four of the INCA model parameters describing N transformations in the soil were optimised, while the measurement uncertainty was increased in subsequent steps. Considering measurement uncertainty typical for N field studies, none of the synthesised datasets contained sufficient information to identify the model parameters with a reasonable degree of confidence. Parameter equifinality occurred, leading to considerable uncertainty in model parameter values and in modelled N concentrations and fluxes. Fortunately, combining the datasets in a multi-objective calibration was found to be effective in dealing with these equifinality problems. With the right choice of calibration measurements, multi-objective calibrations resulted in lower parameter uncertainty. The methodology applied in this study, using a virtual catchment free of model errors, is proposed as a useful tool foregoing the application of a N model or the design of a N monitoring program. For an already gauged catchment, a virtual study can provide a point of reference for the minimum uncertainty associated with a model application. When setting up a monitoring program, it can help to decide what and when to measure. Numerical experiments indicate that for a forested, N-saturated catchment, a fortnightly sampling of NO 3 and NH 4 concentrations in stream water may be the most cost-effective monitoring strategy.

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

“…For example, Futter et al (2014) implemented a Monte Carlo-based approach for sensitivity and parameter uncertainty analysis of the PERSiST model for River Thames, finding that the model results were especially sensitive to evapotranspiration parameters and residence times. 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.…”

“…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.…”

Modeling nutrient in-stream processes at the watershed scale using Nutrient Spiralling metrics

“…On the other hand, the complexity of deterministic models often creates intensive data and calibration requirements, which generally limits their application in large watersheds. Deterministic models also lack robust measures of uncertainty in model coefficients and predictions, although recent developments for hydrological applications can be used in biogeochemical models as well (Raat et al, 2004). Nonetheless, deterministic models are abstractions of reality that can include unrealistic assumptions in their formulation.…”

Nutrient dynamics, transfer and retention along the aquatic continuum from land to ocean: towards integration of ecological and biogeochemical models

“…Mechanistic models often lack robust measures of uncertainty in model coefficients and predictions, although recent developments for hydrological applications can also be used in biogeochemical models (Raat et al, 2004). Uncertainty analysis of more simple approaches such as the nutrient spiraling concept is much less difficult (Marcé and Armengol, 2009).…”