Affiliation:
1. Civil Engineering Programme Area, Faculty of Civil Engineering, Universiti Teknologi Brunei, Jalan Tungku Link, Gadong, Bandar Seri Begawan BE 1410, Brunei
Abstract
Monitoring future irrigation water demand as a part of agricultural interventions is crucial to ensure food security. In this study, the impact of climate change on paddy cultivation in Brunei is investigated, focusing on the Wasan rice scheme. This research aims to project irrigation water requirement (IWR) and crop water requirement (CWR) or the main and off season using the WEAP-MABIA model. Historical data analysis over the past 30 years and future projections up to 2100 are employed to assess potential impacts. An ensemble of statistically downscaled climate models, based on seven CMIP6 GCMs under shared socioeconomic pathways (SSPs) (SSP245, SSP370, and SSP585), was utilised to project the IWR and CWR. Using downscaled CMIP6 data, three future periods were bias-corrected using quantile delta mapping (QDM) for 2020–2046 (near future), 2047–2073 (mid future), and 2074–2100 (far future). The WEAP-MABIA model utilises a dual crop coefficient approach to evaluate crop evapotranspiration (ETc), a critical factor in computing IWR. Results indicate that changes in future temperatures will lead to higher average ETc. Consequently, this results in elevated demands in irrigation water during the off season, and it is especially prominent in high-emission scenarios (SSP370 and SSP585). While the main season experiences a relatively stable or slightly increasing IWR trend, the off season consistently shows a decreasing trend in IWR. Moreover, the off season benefits from substantial rainfall increases, effectively reducing IWR despite the rise in both maximum and minimum temperatures. This study also highlights some recommendations for implementing possible improvements in irrigation management to address the effects of climate change on rice cultivation in the region. Future investigation should focus on enhancing crop yield predictions under climate change by integrating a dynamic crop growth model that adjusts for changing crop coefficient (Kc) values.
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