Preprints
https://doi.org/10.5194/hess-2023-273
https://doi.org/10.5194/hess-2023-273
20 Dec 2023
 | 20 Dec 2023
Status: this preprint is currently under review for the journal HESS.

Drivers of global irrigation expansion: the role of discrete global grid choice

Sophie Wagner, Fabian Stenzel, Tobias Krüger, and Jana de Wiljes

Abstract. Global statistical irrigation modeling relies on geospatial data and traditionally adopts a discrete global grid based on longitude-latitude reference. However, this system introduces area distortion, which may lead to biased results. We propose using the ISEA3H geodesic grid based on hexagonal cells, enabling efficient and distortion-free representation of spherical data. To understand the impact of discrete global grid choice, we employ a non-parametric statistical framework, utilizing random forest methods, to identify main drivers of historical global irrigation expansion amongst others, also using outputs from the global dynamic vegetation model LPJmL.

Irrigation is critical for food security amidst growing population, changing consumption patterns, and climate change. It significantly boosts crop yields but also alters the natural water cycle and global water resources. Understanding past irrigation expansion and its drivers is vital for global change research, resource assessment, and predicting future trends.

We compare the predictive accuracy, the simulated irrigation patterns and identification of irrigation drivers between the two grid choices. Results demonstrate that using the ISEA3H geodesic grid increases the predictive accuracy by 29 % compared to the longitude-latitude grid. The model identifies population density, potential productivity increase, evaporation, precipitation, and water discharge as key drivers of historical global irrigation expansion. GDP per capita also shows minimal influence.

We conclude that the geodesic discrete global grid significantly affects predicted irrigation patterns and identification of drivers, and thus has the potential to enhance statistical modeling, which warrants further exploration in future research across related fields. This analysis lays the foundation for comprehending historical global irrigation expansion.

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.
Sophie Wagner, Fabian Stenzel, Tobias Krüger, and Jana de Wiljes

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-273', Anonymous Referee #1, 20 Dec 2023
    • AC1: 'Reply on RC1', Sophie Wagner, 17 Jan 2024
  • CC1: 'Comment on hess-2023-273', Marko Kallio, 05 Jan 2024
    • AC2: 'Reply on CC1', Sophie Wagner, 17 Jan 2024
  • RC2: 'Comment on hess-2023-273', Anonymous Referee #2, 24 Apr 2024
    • AC3: 'Reply on RC2', Sophie Wagner, 10 May 2024
Sophie Wagner, Fabian Stenzel, Tobias Krüger, and Jana de Wiljes

Data sets

Code files Sophie Wagner https://github.com/SophieWag/isea3h_irrigation

Model code and software

Data and code files Sophie Wagner https://doi.org/10.5281/zenodo.10012830

Sophie Wagner, Fabian Stenzel, Tobias Krüger, and Jana de Wiljes

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Short summary
Statistical models that explain global irrigation rely on location-referenced data. Traditionally, a system based on longitude and latitude lines is chosen. However, this introduces bias to the analysis due to the Earth’s curvature. We propose using a system based on hexagonal grid cells that allows for distortion-free representation of the data. We show that this increases the model’s accuracy by 29 % and identify biophysical and socioeconomic drivers of historical global irrigation expansion.