Agent-Based Models of groundwater systems - Database
| Authors: | |
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| Owners: | Marcos Canales |
| Type: | Resource |
| Storage: | The size of this resource is 108.8 KB |
| Created: | Sep 25, 2023 at 12:32 a.m. |
| Last updated: | Feb 21, 2024 at 2:08 p.m. (Metadata update) |
| Published date: | Feb 21, 2024 at 2:07 p.m. |
| DOI: | 10.4211/hs.5e6cc59ec95b4aa2bcc8f0be8f8832fb |
| Citation: | See how to cite this resource |
| Content types: | Single File Content |
| Sharing Status: | Published |
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| Views: | 628 |
| Downloads: | 35 |
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Abstract
Understanding how society can address and mitigate threats to groundwater sustainability remains a pressing challenge in the Anthropocene era. This article presents the first comprehensive and critical review of coupling Groundwater Models and Agent-Based Models (GW-ABMs) to address four key challenges: (1) adequately representing human behaviour, (2) capturing spatial and temporal variations, (3) integrating two-way feedback loops between social and physical systems, and (4) incorporating water governance structures. Our findings indicate a growing effort to model bounded rationality in human behaviour (Challenge 1 or C1) and a dominant focus on policy applications (C4). Future research should address data scarcity issues through Epstein’s Backward approach (C2), capture feedbacks via tele-coupled GW-ABMs, and explore other modelling techniques like Analytic Elements Groundwater Models (C3). We conclude with recommendations to thrust future GW-ABMs to the highest standards, aiming to enhance their acceptance and impact in decision-making and policy formulation for sustainable groundwater management. This resource represents the literature review database for the article: Marcos Canales, Juan Castilla-Rho, Rodrigo Rojas, Sebastian Vicuna, James Ball, Agent-based models of groundwater systems: A review of an emerging approach to simulate the interactions between groundwater and society, Environmental Modelling and Software (2024), https://doi.org/10.1016/j.envsoft.2024.105980
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| Start Date: | 01/01/2003 |
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| End Date: | 09/25/2023 |
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| This resource is referenced by | https://doi.org/10.1016/j.envsoft.2024.105980 |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| Australian Research Council | Discovery Project | DP190101584 |
| Australian Research Council | Linkage Project | LP220200350 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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