Preprints
https://doi.org/10.5194/gmd-2022-123
https://doi.org/10.5194/gmd-2022-123
Submitted as: development and technical paper
 | 
02 Jun 2022
Submitted as: development and technical paper |  | 02 Jun 2022
Status: this preprint was under review for the journal GMD but the revision was not accepted.

CLUMondo v2.0: Improved model by adaptive determination of conversion orders for simulating land system changes with many-to-many demand-supply relationships

Peichao Gao, Yifan Gao, Xiaodan Zhang, Sijing Ye, and Changqing Song

Abstract. Land resources are fundamentally important to human society, and their transition from one macroscopic state to another is a vital driving force of environment and climate change locally and globally. Thus, many efforts have been devoted to the simulations of land changes. Among all spatially explicit simulation models, CLUMondo is the only one that simulates land changes by incorporating the multifunctionality of a land system and allows the establishment of many-to-many demand-supply relationships. Its central mechanism is complex and has not been fully revealed or clearly explained, thus preventing further improvement. In this study, we first investigated the source code of CLUMondo, providing for the first time the complete, detailed mechanism of this model. More importantly, we found that the featured function of CLUMondo – balancing demands and supplies in a many-to-many mode – relies on a parameter called conversion order. Still, the setting of this parameter should be improved because it is a manual process according to the characteristics of each study area and based on expert knowledge, which is not feasible for users without an understanding of the whole, detailed mechanism. Therefore, the second contribution of this study is the development of an automatic method for adaptively determining conversion orders. We revised the source code of CLUMondo to incorporate the proposed automated method, resulting CLUMondo Version 2.0. Comparative experiments demonstrated the proposed automated method’s validity, high effectiveness, and universal applicability. They showed that the new version of CLUMondo is more effective and easier to use than the existing version. A case study showed that the simulation performance has improved as high as 103.36 %. This study facilitates future improvement on CLUMondo and deep coupling with other earth system models, clearly describing its mechanism. It also helps to exploit the full potential of CLUMondo with a new version.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Peichao Gao, Yifan Gao, Xiaodan Zhang, Sijing Ye, and Changqing Song

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-123', Peter H. Verburg, 03 Jun 2022
    • AC2: 'Reply on RC1', Changqing Song, 05 Sep 2022
  • RC2: 'Comment on gmd-2022-123', Anonymous Referee #2, 07 Jun 2022
    • AC1: 'Reply on RC2', Changqing Song, 05 Sep 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-123', Peter H. Verburg, 03 Jun 2022
    • AC2: 'Reply on RC1', Changqing Song, 05 Sep 2022
  • RC2: 'Comment on gmd-2022-123', Anonymous Referee #2, 07 Jun 2022
    • AC1: 'Reply on RC2', Changqing Song, 05 Sep 2022
Peichao Gao, Yifan Gao, Xiaodan Zhang, Sijing Ye, and Changqing Song
Peichao Gao, Yifan Gao, Xiaodan Zhang, Sijing Ye, and Changqing Song

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Short summary
We found that the featured function of CLUMondo – balancing demands and supplies in a many-to-many mode – relies on a parameter called conversion order, but the setting of this parameter should be improved. This parameter should be set manually according to the characteristics of each study area and based on expert knowledge, which is not feasible for users without understanding the whole, detailed mechanism. This problem has been addressed in this study with CLUMondo Version 2.0.