Processes affecting transfer of sediment and colloids, with associated phosphorus, from intensively farmed grasslands: a critical note on modelling of phosphorus transfers

Abstract

In this commentary we are concerned with mathematical models of phosphorus transfers from agricultural land, particularly intensive grassland, to receiving waters (Haygarth et al., 2006). We believe that the complexity of this problem requires an initially wide discussion of possible modelling concepts and the associated benefits or problems with these approaches. Hence we are reluctant to break the phosphorus-modelling problem down to smaller scales or detailed levels of complexity, e.g. sediments/colloids, which is the main concern of previous papers in this series of commentaries. However, many aspects of the following discussion could be applied equally to sub-elements of the phosphorus-modelling problem as well as wider environmental modelling applications.Two objectives justify the building of mathematical phosphorus transfer models: (1) they formalize our knowledge about the system under study and in this way they act as vehicles for testing our understanding of phosphorus transfers; and (2) they may allow quantitative predictions under changing and complex environmental conditions. In the light of international legislation such as the EU Water Framework Directive (2000/60/EC), it is increasingly important to predict the impacts of land management practises and measures for mitigating diffuse pollution. This, however, requires a confidence in the process formulation (be it empirically or physically-based) and a predictive capability of models, which may not be justified in many cases, especially where there are limited observational data available to develop, test and drive model assumptions.In the previous papers of this series of commentaries, we learned that our perceptual understanding of phosphorus, sediment and colloid transfers from intensive grasslands is far from perfect (Bilotta et al., 2007; Gimbert et al., 2007; Granger et al., 2007; Haygarth et al., 2006). However, this perceptual understanding is (and will always be) better than our translation of these perceptions into conceptual and, finally, procedural models. 1 Two main approaches to modelling the phosphorus transfer problem can be identified from the literature: (1) empirical phosphorus export models to calculate annual or seasonal loads (this includes empirical risk assessment techniques); and (2) process-orientated phosphorus models to simulate storm dynamics. This includes conceptual and physically-based models 2 (Table I). In the following sections, we highlight a few critical points regarding these approaches and, finally, 1 Here we refer to the three classic steps of model development: the 'perceptual model' is our qualitative understanding of system behaviour, the 'conceptual model' comprises the mathematical equations to describe this behaviour, and the 'procedural model' is the computer code to run the actual simulations. Note the term 'conceptual model' is also used with a different notion later in this paper, referring to a mathematical model class of intermediate complexity. 2 The classification of models by the degree of c...

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

“…Thus, there will be an inevitable compromise between improvements and uncertainty in applications to those majority of sites that will not be data rich (Beven 2000(Beven , 2002. Krueger et al (2007) have suggested that problems of this type would require new modelling strategies within an uncertainty learning framework for improving phosphorus model development and evaluation. Importantly, we need to further investigate what is the correct balance between the process complexity of the models we develop with the quality and quantity of observations we will ever have to drive and evaluate models used to inform WFD policy decisions at national scales of interest.…”

“…It is important, therefore, that the uncertainties in model predictions are well understood. Therefore, estimating prediction uncertainties in water quality modelling is becoming increasingly appreciated (Krueger et al 2007;Page et al 2004Page et al , 2005Radwan et al 2004;Rode et al 2007;Singh et al 2007;van Griensven and Meixner 2006).…”

Linkage Between In-Stream Total Phosphorus and Land Cover in Chugoku District, Japan: An Ann Approach

“…Therefore it is important to be the uncertainties in model predictions are well understood. Estimating prediction uncertainties in water quality modelling is becoming increasingly appreciated (Krueger et al, 2007;Page et al, 2004Page et al, , 2005Radwan et al, 2004;Rode et al, 2007;Singh et al, 2007;van Griensven and Meixner, 2006). While there are various methods available for quantifying the uncertainty in physical hydrologic models (Christiaens and Feyen, 2002;Beven and Binley, 1992), little discussion is found in literature regarding the uncertainty analysis of the ANN hydrologic models except a few (Kingston et al, 2005;Khan and Coulibali, 2006;Han et al, 2006;Srivastav et al, 2007).…”

A Case Study of Uncertainty: Applying GLUE to EUROSEM

“…A number of reasons for revisiting model evaluation are discussed in Chapter 4, not least that iterative improvements of both models and techniques to evaluate models are vital if we are to develop better predictions of soil erosion. There is also a growing understanding amongst many environmental scientists that developing more robust analyses of model predictions requires an evaluation of the uncertainties involved in the modelling process (Beven & Freer, 2001;Krueger et al, 2007).…”

“…Others have chosen to concentrate on the uncertainties in the data directly, for example using bootstrapping methods to assess rating curve and sediment load dynamics, showing considerable uncertainties in this information (Rustomji & Prosser, 2001). In a series of papers, Krueger et al (2007Krueger et al ( , 2009Krueger et al ( , 2010 demonstrate the importance of taking into account uncertainties in hydrological measurements as well as those associated with models. Hydrographs are not errorfree, and Krueger et al (2009) demonstrate that errors may be significant (see Section 5.4).…”