Advances in the identification and evaluation of complex environmental systems models

Abstract

Advances in our ability to model complex environmental systems are currently driven by at least four needs: (1) the need for the inclusion of uncertainty in monitoring, modelling and decisionmaking; (2) the need to provide environmental predictions everywhere; (3) the need to predict the impacts of environmental change; and (4) the need to adaptively evolve observation networks to better resolve environmental systems and embrace sensing innovations. Satisfying these needs will require improved theory, improved models and improved frameworks for making and evaluating predictions. All of these improvements should result in the long-term evolution and improvement of observation systems. In the context of this paper we discuss current bottlenecks and opportunities for advancing environmental modelling with and without local observations of system response. More realistic representations of real-world thresholds, nonlinearities and feedbacks motivates the use of more complex models as well as the consequent need for more rigorous evaluations of model performance. In the case of gauged systems, we find that global sensitivity analysis provides a widely underused tool for evaluating models' assumptions and estimating the information content of data. In the case of ungauged systems, including the modelling of environmental change impacts, we propose that the definition of constraints on the expected system response provides a promising way forward. Examples of our own work are included to support the conclusions of this discussion paper. Overall, we conclude that an important bottleneck currently limiting environmental predictions lies in how our model evaluation and identification approaches are extracting, using and evolving the information available for environmental systems at the watershed scale.

Process verification of a hydrological model using a temporal parameter sensitivity analysis

“…This approach is based on the fact that hydrological processes and discharge phases vary temporally and hence also the dominance of model components (Boyle et al, 2000Wagener et al, 2003Wagener et al, , 2009Garambois et al, 2013;Guse et al, 2014).…”

From maps to movies: high-resolution time-varying sensitivity analysis for spatially distributed watershed models

“…Sensitivity analysis is a foundational diagnostic approach in the hydrologic modeling literature, (e.g., Hornberger and Spear, 1981;Franchini et al, 1996;Freer et al, 1996;Wagener et al, 2001;Muleta and Nicklow, 2005;Sieber and Uhlenbrook, 2005;Bastidas et al, 2006;Demaria et al, 2007;Cloke et al, 2008;Van Werkhoven et al, 2008aWagener et al, 2009a;Herman et al, 2013a, b). However, very few studies…”

Flow Forecasting using Deterministic Updating of Water Levels in Distributed Hydrodynamic Urban Drainage Models

“…This means that the so far unknown variable in Equation (1), H n+1 , is now given by data taken from a measurement. A new unknown variable, Q correction , is then introduced on the right-hand side of Equation (1): Figure 1 illustrates the difference between Equations (1) and (2). In the figure to the left H n+1 is illustrated by the dashed circle, while in the figure to right it is illustrated by the dotted circle, which equals the measured water level.…”

“…Another driver for this development will be the need for online versions of models referred to as "models of everywhere" [2]: models even providing predictions for ungauged parts of the system. Recent examples of such models can be found in studies discussed by [3,4].…”

“…For instance, a physically based, distributed urban drainage model was recently developed for a part of the drainage system of Copenhagen (Denmark) that discharge wastewater from more than half a million people to the Lynetten wastewater treatment plant. The model covers an area of 76 km 2 and includes 5000 nodes [15]. Even though the model mainly includes the largest pipes and most important detention basins and overflow structures (corresponding to about 20% of the nodes in the real pipe system), it runs rather slowly -in close to real time on powerful desktop computers available by the year 2013 [16].…”