This project contains a baseline conceptualization of ecosystem services (ES) supply and demand for the ARIES platform. The logical statements, data and models provided here are used in ARIES to answer queries for commonly recognized ES value metrics when not enough information is available to build detailed, dynamic flow models. A published article in Science of The Total Environment describes in detail the philosophy behind the models, their scope and their limitations.
The models built by ARIES using this knowledge have, in general, similar resolution and conceptual detail as those available in other ES assessment toolkits such as InVEST or ESTIMAP. Some of the methods implemented in this project are inspired by or derived from others; when so, sources are credited in the documentation built during each ARIES session.
The latest version of the content of this project is served by the ARIES semantic network and is thus available to any user of the ARIES platform, i.e. anyone who is using the k.LAB Explorer or Modeler software (locally or, when available, online) and has obtained a k.LAB certificate that includes ARIES. No further downloads or installations are required. Details on how to obtain a certificate and the software can be found at the ARIES or the Integrated Modelling Partnership web sites.
The models can run everywhere on the globe without user input. Customization of data, models and scenarios is possible using the k.LAB Modeler at the time of this writing. Drag-and-drop input of user data, as well as scenario analysis, will be made possible through the k.LAB Explorer in 2019.
As released, the project contains fully specified model content concerning the following ES problem areas:
- Riverine flood regulation
- Carbon storage
- Outdoor recreation
- Pollination
- Sediment retention
Areas in development for a forthcoming release in the short term include:
- Water availability (based on hydrological calculations)
- Biodiversity value (based on machine learning of expert opinion)
- Mariculture suitability
In addition, this project provides a bridge to Multiple Criteria Analysis models that allow automated trade-off analysis between diverse ES metrics, combined with user-specified weights that encode priorities according to the point of view of one or more stakeholders.
As ARIES operates on conceptual queries, the model content provided here does not implement monolithic "models" of ES, but rather provides the logical and computational underpinnings to resolve ES-related user queries in a user-selected spatial and temporal context. These can be entered in the ARIES Explorer interface as English sentences, as exemplified below, or chosen from a customizable "palette" of concepts of interest.
Some example queries per each ES category whose value is available globally at regional to country scales and with seasonal to annual temporal scale:
- Flood regulation:
- probability of Flood
- Potential value of FloodRegulation
- Demanded value of FloodRegulation
- Net value of FloodRegulation (surplus/deficit)
- Outdoors recreation:
- Potential value of Outdoor Recreation (Recreation Opportunity Spectrum)
- Theoretical value of Outdoor Recreation
- Demanded value of Outdoor Recreation
- value of Outdoor Recreation (production function combining supply and demand)
- Net value of Outdoor Recreation (surplus/deficit)
- Pollination:
- Occurrence of Pollinator Insect caused by Weather
- Occurrence of Pollinator Insect caused by Landscape
- Occurrence of Pollinator Insect
- Net value of Pollination (surplus/deficit)
- Sediment retention:
- Potential Removed Soil Mass
- Retained Soil Mass caused by Vegetation
- Carbon storage:
- Organic Carbon Mass [total soil and vegetation storage]
- Vegetation Carbon Mass [total vegetation storage]
- Soil Organic Carbon Mass [total soil storage]
- Mangrove Carbon Mass [mangrove above and below-ground storage]
For all of these queries, ARIES will build a spatially explicit observation, in most situations as a raster GIS coverage of user-selected resolution. The results will reflect the contents of the ARIES semantic web at the time of query; nearly all supporting data are currently available at spatial resolution ranging between 1km and 90m.
This project is written in the k.IM semantic modeling language and requires the k.LAB software stack version 0.10.0 or higher. Like in all k.LAB applications, each model in this project represents a complete strategy to observe one concept, and can be run in isolation or as a dependency of other models. Each is expressed in the terms of the concepts contained in the IM worldview and must be run within the k.LAB network in order to resolve every logical dependency to data or models appropriate for the context and scale of interest.
Models written in k.IM are close to the English language and aim to be naturally readable. A look at the k.IM files in the src/ directory should provide enough detail as is. Documentation templates for each model are also included in this project, formatted for the k.LAB documentation engine, so that each run can self-document into a detailed human-readable report.
More details on the models and their derivation are available in Martinez-López et al. (2018).
These models, like the entirety of the k.LAB infrastructure that runs them, are open source, released under the terms of the Affero General Public License 3.0 or any higher version.
The intellectual property of this content belongs to the Integrated Modelling Partnership and all individually listed authors.
Inquiries about ARIES, k.LAB or any of the models included in this project should be directed to [email protected].
- Kenneth J. Bagstad ([email protected])
- Stefano Balbi ([email protected])
- Ainhoa Magrach ([email protected])
- Javier Martínez-Lopez ([email protected])
- Ferdinando Villa ([email protected])
- Brian Voigt ([email protected])
Javier Martínez-López, Kenneth J. Bagstad, Stefano Balbi, Ainhoa Magrach, Brian Voigt, Ioannis Athanasiadis, Marta Pascual, Simon Willcock, Ferdinando Villa. Towards globally customizable ecosystem service models, Science of The Total Environment. Volume 650, Part 2, (2019), 2325-2336 DOI https://doi.org/10.1016/j.scitotenv.2018.09.371.