Uncertainty Estimation Due to the Use of Gridded Climate Datasets in Crop Modeling

Subject Areas : Article frome a thesis

Yaghoub Radmanesh ¹ , Mahdi Sarai Tabrizi ^{2 *} , Hadi Ramezani-Etedali ³ , Asghar Azizian ⁴ , Hossein Babazadeh ⁵

1 - Ph.D. student of Water Resources, Department of Water Engineering and Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Associate Professor, Department of Water Engineering and Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - Professor, Department of Water Sciences and Engineering, Imam Khomeini International University, Qazvin, Iran
4 - Associate Professor, Department of Water Sciences and Engineering, Imam Khomeini International University, Qazvin, Iran
5 - Professor, Department of Water Engineering and Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran

Received: 2024-10-16 Accepted : 2025-01-06 Published : 2025-11-29

Keywords: AquaCrop model, uncertainty analysis, climate data, Bootstrap method, gridded climate datasets,

Abstract :

Introduction: Accurate climatic data are essential for crop modeling and yield prediction, particularly under climate change. In developing countries, access to reliable data is constrained by the low density of meteorological stations and limited historical records. Gridded climate datasets can mitigate this limitation; however, as they are not directly measured, uncertainty analysis is necessary.

Methods: This study evaluated uncertainty from gridded climate datasets—including CPC Global, CRU TS, ERA-Interim, ERA5, and MERRA-2—in simulating wheat and maize using the AquaCrop model across diverse Iranian climates over 30 years (1989–2019). AquaCrop, developed for water management and agricultural productivity, requires inputs such as precipitation and temperature. Bootstrap method quantified uncertainty in simulated biomass, evapotranspiration, crop water requirement, and yield. P-factor and d-factor indices were also calculated to assess dataset accuracy.

Findings: ERA5 consistently showed the lowest uncertainty in AquaCrop simulations. For maize biomass at Ahvaz, p-factor and d-factor were 23.33% and 0.5, respectively. Overall, ERA5 and ERA-Interim exhibited minimal uncertainty across most climates, whereas MERRA-2, with the highest uncertainty, performed worst.

Conclusion: Selecting appropriate high-resolution datasets and applying rigorous uncertainty analyses can directly improve AquaCrop prediction accuracy. ERA5 emerged as the most reliable option. These practices enhance modeling precision and support informed decision-making in agricultural and water resource management.

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