Modular ecosystem modeling

Voinov, Alexey; Fitz, Carl; Boumans, Roelof

doi:10.1016/s1364-8152(03)00154-3

Cited by 77 publications

(27 citation statements)

References 20 publications

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“…Argent 2004;Lam et al 2004;Voinov et al 2004;Poch et al 2005;Pereira et al 2006). From a technical point of view, one can opt to build a new model for each application (integrative approach) or to utilize existing models where possible (combinatory approach).…”

Section: Integrating and Combining Modelsmentioning

confidence: 99%

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

et al. 2007

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To facilitate decision support in freshwater ecosystem protection and restoration management, habitat suitability models can be very valuable. Data driven methods such as artificial neural networks (ANNs) are particularly useful in this context, seen their time-efficient development and relatively high reliability. However, specialized and technical literature on neural network modelling offers a variety of model development criteria to select model architecture, training procedure, etc. This may lead to confusion among ecosystem modellers and managers regarding the optimal training and validation methodology. This paper focuses on the analysis of ANN development and application for predicting macroinvertebrate communities, a species group commonly used in freshwater assessment worldwide. This review reflects on the different aspects regarding model development and application based on a selection of 26 papers reporting the use of ANN models for the prediction of macroinvertebrates. This analysis revealed that the applied model training and validation methodologies can often be improved and moreover crucial steps in the modelling process are often poorly documented. Therefore, suggestions to improve model development, assessment and application in ecological river management are presented. In particular, data pre-processing determines to a high extent the reliability of the induced models and their predictive relevance. This also counts for the validation criteria, that need to be better tuned to the practical simulation requirements. Moreover, the use of sensitivity methods can help to extract knowledge on the habitat preference of species and allow peer-review by ecological experts. The selection of relevant input variables remains a critical challenge as well. Model coupling is a missing crucial step to link human activities, hydrology, physical habitat conditions, water quality and ecosystem status. This last aspect is probably the most valuable aspect to enable decision support in water management based on ANN models.

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Section: Integrating and Combining Modelsmentioning

confidence: 99%

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

et al. 2007

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“…Because of the lack of sustained monitoring efforts, forecasting vegetation scenarios are difficult and prone to unacceptable levels of uncertainty (Finzi et al 2011). Although a variety of vegetation models have been developed (see review by Woodward and Lomas 2004) incorporating both correlative and mechanistic approaches (e.g., Voinov et al 2004), application of these are problematic for alpine ecosystems for a few key reasons. Because alpine communities can be climatically decoupled from those nearby (Körner 1999), parameterizing both correlative and mechanistic models may be difficult if the specific environmental variables at the plot level are not described accurately through space and time.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Alpine Vegetation Change Using Repeat Sampling and a Novel Modeling Approach

Johnson

Ebert-May

Webber

et al. 2011

AMBIO

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Global change affects alpine ecosystems by, among many effects, by altering plant distributions and community composition. However, forecasting alpine vegetation change is challenged by a scarcity of studies observing change in fixed plots spanning decadal-time scales. We present in this article a probabilistic modeling approach that forecasts vegetation change on Niwot Ridge, CO using plant abundance data collected from marked plots established in 1971 and resampled in 1991 and 2001. Assuming future change can be inferred from past change, we extrapolate change for 100 years from 1971 and correlate trends for each plant community with time series environmental data . Models predict a decreased extent of Snowbed vegetation and an increased extent of Shrub Tundra by 2071. Mean annual maximum temperature and nitrogen deposition were the primary a posteriori correlates of plant community change. This modeling effort is useful for generating hypotheses of future vegetation change that can be tested with future sampling efforts.

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“…Voinov et al (2004) suggested that infiltration rate of the top soil layer is equal to rainfall intensity before soil saturation is reached. In fact, some studies suggested that the rate of infiltrated water from a typical cover system is correlated to the degree of saturation of the soil, soil moisture retention characteristic, and climatic factors (e.g.…”

Section: Soil Storagementioning

confidence: 99%

A generic system dynamics model for simulating and evaluating the hydrological performance of reconstructed watersheds

Keshta

Elshorbagy²,

Carey³

2009

Hydrol. Earth Syst. Sci.

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Abstract.A generic system dynamics watershed (GSDW) model is developed and applied to five reconstructed watersheds located in the Athabasca mining basin, Alberta, Canada, and one natural watershed (boreal forest) located in Saskatchewan, Canada, to simulate various hydrological processes in reconstructed and natural watersheds. This paper uses the root mean square error (RMSE), the mean absolute relative error (MARE), and the correlation coefficient (R) as the main performance indicators, in addition to the visual comparison. For the South Bison Hills (SBH), South West Sand Storage (SWSS) and Old Aspen (OA) simulated soil moisture, the RMSE values ranges between 2.5-4.8 mm, and the MARE ranges from 7% to 18%, except for the D2-cover it was 26% for the validation year. The R statistics ranges from 0.3 to 0.77 during the validation period. The error between the measured and simulated cumulative actual evapotranspiration (AET) flux for the SWSS, SBH, and the OA sites were 2%, 5%, and 8%, respectively. The developed GSDW model enables the investigation of the utility of different soil cover designs and evaluation of their performance. The model is capable of capturing the dynamics of water balance components, and may used to conduct short-and longterm predictions under different climate scenarios.

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Modular ecosystem modeling

Cited by 77 publications

References 20 publications

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

Forecasting Alpine Vegetation Change Using Repeat Sampling and a Novel Modeling Approach

A generic system dynamics model for simulating and evaluating the hydrological performance of reconstructed watersheds

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