No abstract

The NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner.

The need for predictive process-oriented planktonic ecosystem models is widely recognized by the aquatic science community. We conducted a meta-analysis of recent mechanistic aquatic biogeochemical models (153 studies published from 1990 to 2002), to assess their ability to predict spatial and temporal patterns in the physical, chemical and biological dynamics of planktonic systems. The selected modeling studies covered a wide range of model complexity, ecosystem-types, spatio-temporal scales and purposes for model development. Despite the heterogeneous nature of this data set, we were able to identify model behavior trends and illuminate aspects of current modeling practice that need to be reevaluated. Temperature and dissolved oxygen had the highest coefficients of determination (respective median r 2 values were 0.93 and 0.70) and the lowest relative error (median RE < 10%), nutrients and phytoplankton had intermediate predictability (median r 2 values ranging from 0.40 to 0.60 and median RE ~ 40%), whereas bacteria (median r 2 = 0.06) and zooplankton (median RE = 70%) dynamics were poorly predicted. Longer simulation periods (i.e. months to decades) reduced model predictability, and increased model complexity did not improve fit. Aquatic biogeochemical modelers need to be more consistent in how they apply conventional methodological steps during model development (i.e. sensitivity analysis, validation), and the aquatic modeling community should adopt generally accepted standards of model performance. Recent advancements in data assimilation techniques, the combination of the present family of models with goal functions (derived from non-equilibrium thermodynamics) and the development of models with a stronger physiological basis are promising frameworks for obtaining more accurate simulations of planktonic processes.

A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: superindividual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.

Watershed models have been widely used for creating the scientific basis for management decisions regarding nonpoint source pollution. In this study, we evaluated the current state of watershed scale, spatially distributed, process-based, water quality modeling of nutrient pollution. Beginning from 1992, the year when Beven and Binley published their seminal paper on uncertainty analysis in hydrological modeling, and ending in 2010, we selected 257 scientific publications which (i) employed spatially distributed modeling approaches at a watershed scale; (ii) provided predictions of flow, nutrient/sediment concentrations or loads; and (iii) reported fit to measured data. Most "best practices" (optimization, validation, sensitivity, and uncertainty analysis) are not consistently employed during model development. There are no statistically significant differences in model performance among land uses. Studies which used more than one point in space to evaluate their distributed models had significantly lower median values of the Nash-Sutcliffe Efficiency (0.70 vs 0.56, p<0.005, nonparametric Mann-Whitney test), and r2 (p<0.005). This finding suggests that model calibration only to the basin outlet may mask compensation of positive and negative errors of source and transportation processes. We conclude by advocating a number of new directions for distributed watershed modeling, including in-depth uncertainty analysis and the use of additional information, not necessarily related to model end points, to constrain parameter estimation.