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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

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Decision Support Modeling: Data Assimilation, Uncertainty Quantification, and Strategic Abstraction.

John Doherty, Catherine Moore¹

¹CSIRO Land and Water, Floreat, WA, Australia.

|December 6, 2019

Summary

This study introduces a framework for decision support modeling using scientific metrics. It emphasizes data assimilation and uncertainty quantification, crucial for accurate predictions in complex systems like groundwater modeling.

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Area of Science:

Environmental modeling
Decision support systems
Scientific methodology

Background:

Decision support modeling often lacks rigorous scientific underpinnings.
Integrating data assimilation and uncertainty quantification is challenging.
Groundwater modeling presents unique difficulties in data integration and prediction.

Purpose of the Study:

To present a framework for designing and deploying decision support models based on scientific metrics.
To highlight the importance of data assimilation and predictive uncertainty quantification.
To address challenges in applying these metrics, particularly in groundwater modeling.

Main Methods:

Framework development based on scientific method principles.
Identification of data/prediction contexts influencing implementation difficulty.
Strategic abstraction of model parameters and processes tailored to specific predictions.

Main Results:

Three distinct data/prediction contexts were identified, with groundwater modeling being the most challenging.
Appropriate model design can ameliorate implementation difficulties.
Model design should prioritize providing receptacles for decision-pertinent information over pure simulation.

Conclusions:

Simulation in decision support models should serve data assimilation, not vice versa.
Data assimilation is key to quantifying and reducing uncertainties in model predictions.
Effective decision support modeling necessitates robust data assimilation and uncertainty quantification.