Minlake: A dynamic lake water quality simulation model

Riley, Michael; Stefan, Heinz G.

doi:10.1016/0304-3800(88)90002-6

Cited by 149 publications

(74 citation statements)

References 23 publications

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“…There are also more complex limnologic models such as Minlake [Riley and Stefan, 1988], the wetland model by Kadlec and Hammer [1988] and eutrophication models (Arhonditsis and Brett [2005]) that couple nutrient processes in the water column to the sediment zone, but these models have the added complexity of hydrodynamics, spatial dimensions, or ecological feedbacks that are beyond the scope of what can be parameterized from a data synthesis.…”

Section: Methods Of Modelling Nutrient Retention In Lentic Systemsmentioning

confidence: 99%

Biogeochemical hotspots: Role of small water bodies in landscape nutrient processing

Cheng

Basu

2017

Water Resources Research

185

157

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Increased loading of nitrogen (N) and phosphorus (P) from agricultural and urban intensification has led to severe degradation of inland and coastal waters. Lakes, reservoirs, and wetlands (lentic systems) retain these nutrients, thus regulating their delivery to downstream waters. While the processes controlling N and P retention are relatively well-known, there is a lack of quantitative understanding of how these processes manifest across spatial scales. We synthesized data from 600 lentic systems around the world to gain insight into the relationship between hydrologic and biogeochemical controls on nutrient retention. Our results indicate that the first-order reaction rate constant, k [T 21 ], is inversely proportional to the hydraulic residence time, s [T], across 6 orders of magnitude in residence time for total N, total P, nitrate, and phosphate. We hypothesized that the consistency of the relationship points to a strong hydrologic control on biogeochemical processing, and validated our hypothesis using a sediment-water model that links major nutrient removal processes with system size. Finally, the k-s relationships were upscaled to the landscape scale using a wetland size-frequency distribution. Results suggest that small wetlands play a disproportionately large role in landscape-scale nutrient processing-50% of nitrogen removal occurs in wetlands smaller than 10 2.5 m 2 in our example. Thus, given the same loss in wetland area, the nutrient retention potential lost is greater when smaller wetlands are preferentially lost from the landscape. Our study highlights the need for a stronger focus on small lentic systems as major nutrient sinks in the landscape. Plain Language Summary Excess nutrient pollution from intensive fertilizer use and farming operations poses an increasing threat to water quality worldwide. Lakes, streams, and wetlands restrict the movement of nutrients, and thus protect downstream waters. We have a limited understanding, however, of how removal processes are affected by the size and type of the water body. Based on a synthesis of data from lakes, reservoirs, and wetlands worldwide, we found that smaller water bodies tend to have higher nutrient removal rates. We applied our findings to the landscape scale and found that for the same wetland area lost, the loss of small wetlands corresponds to a greater loss in wetland nutrient removal potential. Such findings are significant to wetland protection and restoration efforts, which have historically focused on maximizing total wetland area rather than on preserving a distribution of different wetlands sizes within a landscape.

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Section: Methods Of Modelling Nutrient Retention In Lentic Systemsmentioning

confidence: 99%

Biogeochemical hotspots: Role of small water bodies in landscape nutrient processing

Cheng

Basu

2017

Water Resources Research

185

157

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“…Models are often related to each other. The MyLake model (Saloranta and Andersen 2007), for example, has characteristics that are also found in other lake models such as DYRESM-CAEDYM (Hamilton and Schladow 1997), MINLAKE (Riley and Stefan 1988), PROBE (Blenckner et al 2002) and BELAMO (Omlin et al 2001;Mieleitner and Reichert 2006). We aim for a more objective and in-depth analysis of a list of AEMs using a similarity index (for details on the analysis, see Online Resource 4 and for the data Online Resource 1, Datasheet 4).…”

Section: Documenting Diversity In Aemsmentioning

confidence: 99%

Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective

et al. 2015

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Here, we present a community perspective on how to explore, exploit and evolve the diversity in aquatic ecosystem models. These models play an important role in understanding the functioning of aquatic ecosystems, filling in observation gaps and developing effective strategies for water quality management. In this spirit, numerous models have been developed since the 1970s. We set off to explore model diversity by making an inventory among 42 aquatic ecosystem modellers, by categorizing the resulting set of models and by analysing them for diversity. We then focus on how to exploit model diversity by comparing and combining different aspects of existing models. Finally, we discuss how model diversity came about in the past and could evolve in the future. Throughout our study, we use Handling Editor: Piet Spaak.Electronic supplementary material The online version of this article (doi:10.1007/s10452-015-9544-1) contains supplementary material, which is available to authorized users. 123Aquat ) 49:513-548 DOI 10.1007 analogies from biodiversity research to analyse and interpret model diversity. We recommend to make models publicly available through open-source policies, to standardize documentation and technical implementation of models, and to compare models through ensemble modelling and interdisciplinary approaches. We end with our perspective on how the field of aquatic ecosystem modelling might develop in the next 5-10 years. To strive for clarity and to improve readability for non-modellers, we include a glossary.

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“…The modelling principle is mostly based on MINLAKE (Riley and Stefan 1988) with some adjustments and additions. In particular, incorporation of ice and snow dynamics based on physical processes (Leppäranta 1993;Saloranta 2000;Salonen et al 2009) gives the model code additional utility for boreal lakes.…”

Section: Mylakementioning

confidence: 99%

Challenges and opportunities for integrating lake ecosystem modelling approaches

et al. 2010

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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.

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Minlake: A dynamic lake water quality simulation model

Cited by 149 publications

References 23 publications

Biogeochemical hotspots: Role of small water bodies in landscape nutrient processing

Biogeochemical hotspots: Role of small water bodies in landscape nutrient processing

Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective

Challenges and opportunities for integrating lake ecosystem modelling approaches

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