Evaluation Of Regional Climate Simulations For Agroenvironmental Applications In Northeastern Algeria

Abstract

Climate variability and climate change have important implications for agroclimatology, agroecosystems, agricultural vulnerability, environmental sustainability, and water-resource management, particularly in semi-arid regions of North Africa. This study evaluates the performance of two regional climate model configurations, CCMA-CanESM2/RCA4 from Africa-CORDEX and ICHEC-EC-EARTH/RCA4 from Euro-CORDEX, in simulating daily precipitation and air temperature at Souk Ahras station in northeastern Algeria during the period 1982–2005. The effectiveness of statistical bias correction using the Distribution Mapping (DM) method is assessed by comparing raw and corrected simulations with observational data. The objective is to identify reliable climate datasets that can support future agroclimatic and environmental assessments.

The results indicate that air temperature was generally better reproduced than precipitation in the raw simulations, with initial biases of −0.09 °C for CCMA-CanESM2/RCA4 and −0.17 °C for ICHEC-EC-EARTH/RCA4. After DM bias correction, temperature biases decreased to approximately 0.00 °C for both configurations, demonstrating the effectiveness of the method in reducing systematic errors. However, precipitation exhibited a more variable response to correction. The corrected ICHEC-EC-EARTH/RCA4 configuration achieved a residual bias of approximately −1% to −5%, whereas the corrected CCMA-CanESM2/RCA4 configuration retained a bias exceeding 30%. These findings demonstrate that the performance of regional climate models and bias correction methods depends on the climatic variable and model configuration, highlighting the importance of local validation and complementary statistical evaluation.

Reliable climate data are essential for agroclimatology and for assessing agricultural vulnerability to climate variability and climate change. They also provide a scientific basis for investigating the effects of climatic conditions on agroecosystems, supporting environmental sustainability, and improving water-resource management. Although this study does not directly measure agricultural productivity or ecosystem responses, its findings can support future research on climate-related risks to agricultural systems and the sustainable management of natural resources.

Overall, this study highlights the importance of evaluating regional climate models and applying model-specific bias correction to produce reliable climate datasets for agroclimatology and environmental assessment in northeastern Algeria. The results provide a foundation for future studies addressing agricultural vulnerability, agroecosystem resilience, environmental sustainability, and water-resource management under climate change in North Africa.