Carlos Ruberto Fragoso

Challenges and Opportunities for Integrating Lake Ecosystem Modelling Approaches

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: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and trait-based 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.

A community-based framework for aquatic ecosystem models

Here, we communicate a point of departure in the development of aquatic ecosystem models, namely a new community-based framework, which supports an enhanced and transparent union between the collective expertise that exists in the communities of traditional ecologists and model developers. Through a literature survey, we document the growing importance of numerical aquatic ecosystem models while also noting the difficulties, up until now, of the aquatic scientific community to make significant advances in these models during the past two decades. Through a common forum for aquatic ecosystem modellers we aim to (i) advance collaboration within the aquatic ecosystem modelling community, (ii) enable increased use of models for research, policy and ecosystem-based management, (iii) facilitate a collective framework using common (standardised) code to ensure that model development is incremental, (iv) increase the transparency of model structure, assumptions and techniques, (v) achieve a greater understanding of aquatic ecosystem functioning, (vi) increase the reliability of predictions by aquatic ecosystem models, (vii) stimulate model inter-comparisons including differing model approaches, and (viii) avoid ‘re-inventing the wheel’, thus accelerating improvements to aquatic ecosystem models. We intend to achieve this as a community that fosters interactions amongst ecologists and model developers. Further, we outline scientific topics recently articulated by the scientific community, which lend themselves well to being addressed by integrative modelling approaches and serve to motivate the progress and implementation of an open source model framework.

Potential effects of climate change and eutrophication on a large subtropical shallow lake

In many aquatic ecosystems, increased nutrient loading has caused eutrophication, which is reflected in the trophic structure of the ecosystem. In Lake Mangueira, a large shallow subtropical lake in Brazil, nutrient loading has also increased, but it is still unclear what the effects of this increase will be and how this relates to climate change. To evaluate the effects of increased nutrient loadings in such large lake one would need to integrate hydrological and ecological processes into one model, an approach that has rarely been used before. Here, we apply different versions of a complex 3D ecological model, called IPH-TRIM3D-PCLake, which describes the integrated hydrodynamic, water-quality, and biological processes in the lake. First, the nutrient loadings from the watershed were estimated using a separate hydrological water quality model of the watershed based on field data. Second, we calibrated the 3D ecological model for a 6-year monitoring period in the lake using a simplified non-spatial version of the model. Finally, the calibrated ecological model was applied to evaluate the spatial explicit effects of different scenarios of land use, water pumping for irrigation, and climate change. On short term (1.5 year), the system seemed to be rather resilient, probably because of the lake size related to its high inertia. Our simulations indicated warming can increase water transparency in Lake Mangueira which may be related to two factors: (a) the current meso-oligotrophic state of the lake which may easily lead to nutrient limitation; and (b) submerged macrophytes grow during the whole season. The combined effect of climate change and increased nutrient loading, less strong than increased nutrient loading alone. The model can only be used for qualitative predictions of the effect of management scenarios, such as maintenance of water levels in the dry season, and water-pumping rules for irrigation in order to maintain the ecosystem structure and functions in the future under additional stress caused by increased use or climate change.

Hydrodynamic Control of Plankton Spatial and Temporal Heterogeneity in Subtropical Shallow Lakes

During the last 200 years, many lakes have suffered from eutrophication, implying an increase of both nutrient loading and organic matter (Wetzel, 1996). An aspect that has often been neglected in freshwater systems is the fact that phytoplankton is often not evenly distributed horizontally in space in shallow lakes. Although the occurrence of phytoplankton patchiness in marine systems has been known for a long time (e.g., Platt et al., 1970; Steele, 1978; Steele & Henderson, 1992), phytoplankton in shallow lakes is often assumed to be homogeneously distributed. However, there are various mechanisms that may cause horizontal heterogeneity in shallow lakes. For example, grazing by aggregated zooplankton and other organisms may cause spatial heterogeneity in phytoplankton (Scheffer & De Boer, 1995). Submerged macrophyte beds may be another mechanism, through reduction of resuspension by wave action and allopathic effects on the algal community (Van den Berg et al., 1998). For large shallow lakes, wind can be a dominant factor leading to both spatial and temporal heterogeneity of phytoplankton (Carrick et al., 1993), either indirectly by affecting the local nutrient concentrations due to resuspended particles, or directly by resuspending algae from the sediment (Scheffer, 1998). In the management of large lakes, prediction of the phytoplankton distribution can assist the manager to decide on an optimal course of action, such as biomanipulation and regulation of the use of the lake for recreation activities or potable water supply (Reynolds, 1999). However, it is difficult to measure the spatial distribution of phytoplankton. Mathematical modeling of a phytoplankton can be an important alternative methodology in improving our knowledge regarding the physical, chemical and biological processes related to phytoplankton ecology (Scheffer, 1998; Edwards & Brindley, 1999; Mukhopadhyay & Bhattacharyya, 2006). Over the past decade there has been a concerted effort to increase the realism of ecosystem models that describe plankton production as a biological indicator of eutrophication. Most

Understanding drought dynamics during dry season in Eastern Northeast Brazil

Eastern Northeast Brazil (ENEB) generally experiences a high variability in precipitation in the dry season, with amplitudes that can overcome 500 mm. The understanding of this variability can help in mitigating the socio-economic issues related to the planning and management of water resources this region, which is highly vulnerable to drought. This work aims to assess spatio-temporal variability of precipitation during the dry season and investigate the relationships between climate phenomena and drought events in the ENEB, using univariate (Spearman correlation) and multivariate statistical techniques, such as Principal Component Analysis, Cluster Analysis and Maximum Covariance Analysis. The results indicate that the variability of precipitation in the dry season can be explained mainly (62%) by local physical conditions and climate conditions have a secondary contribution. Further analysis of the larger anomalous events suggests that the state of Atlantic and Pacific oceans can govern the occurrence of those events, and the conditions of Atlantic Ocean can be considered a potential modulator of anomalous phenomena of precipitation in ENEB.

Long-term relationships between climate oscillation and basin-scale hydrological variability during rainy season in eastern Northeast Brazil

ABSTRACT The high variability in the hydrological regime of the Eastern Hydrological Region (EHR) of Northeast Brazil often results in floods and droughts, leading to serious socio-economic issues. Therefore, this work aimed to investigate connections between spatiotemporal hydrological variability of the EHR and large-scale climate phenomena. Multivariate statistical techniques were applied to relate climate indices with hydrological variables within two representative river basins in the EHR. The results indicated a multi-annual relationship between the state of the sea surface temperature of the Atlantic and Pacific oceans and anomalous hydrological variability in the basins. In addition, the northern Tropical Atlantic conditions were shown to play an important role in modulating the long-term variability of the hydrological response of the basins, whilst only extreme ENSO anomalies seemed to affect the rainy season. This knowledge is an important step towards long-term prediction of hydrological conditions and contributes to the improvement of water resources planning and management in the EHR.