Agro-hydrological water management frameworks help to integrate expected planned management and expedite regulation of water allocation for agricultural production. Low production is not only due to the variability of available water during crop growing seasons, but also poor water management decisions. The Tanjung Karang Rice Irrigation Scheme in Malaysia has yet to model agro-hydrological systems for effective water distribution under climate change impacts. A climate-smart agro-hydrological model was developed using Excel-based Visual Basic for Applications (VBA) for adaptive irrigation and wise water resource management towards water security under new climate change realities. Daily climate variables for baseline (1976–2005) and future (2010–2099) periods were extracted from 10 global climate models (GCMs) under three Representative Concentration Pathway scenarios (RCP4.5, RCP6.0, and RCP8.5). The projected available water for supply to the scheme would noticeably decrease during the dry season. The water demand in the scheme will differ greatly during the months in future dry seasons, and the increase in effective rainfall during the wet season will compensate for the high dry season water demand. No irrigation will therefore be needed in the months of May and June. In order to improve water distribution, simulated flows from the model could be incorporated with appropriate cropping patterns.

中文翻译：

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马来西亚大规模水稻灌溉计划的气候智能农业水文模型

农业水文水管理框架有助于整合预期的计划管理和加快农业生产用水分配的监管。产量低不仅是由于作物生长季节中可用水的可变性，而且还归因于水资源管理决策不力。马来西亚的丹绒卡朗水稻灌溉计划尚未建立农业水文学系统的模型，以在气候变化影响下有效分配水。使用基于Excel的Visual Basic应用程序（VBA）开发了一种气候智能的农业水文模型，用于在新的气候变化现实下进行自适应灌溉和明智的水资源管理，以实现水资源安全。在三种代表性浓度路径情景（RCP4.5，RCP6.0和RCP8.5）下，从10个全球气候模型（GCM）中提取了基准（1976-2005）和未来（2010-2099）时期的每日气候变量。预计该计划的可用水量将在干旱季节明显减少。该计划中的需水量在未来的旱季的几个月中将有很大的不同，而在雨季有效降雨的增加将弥补旱季的高用水需求。因此在五月和六月的几个月中将不需要灌溉。为了改善水分配，可以将来自模型的模拟流量与适当的种植模式结合在一起。预计该计划的可用水量将在干旱季节明显减少。该计划中的需水量在未来的旱季的几个月中将有很大的不同，而在雨季有效降雨的增加将弥补旱季的高用水需求。因此在五月和六月的几个月中将不需要灌溉。为了改善水分配，可以将来自模型的模拟流量与适当的种植模式结合在一起。预计该计划的可用水量将在干旱季节明显减少。该计划中的需水量在未来的旱季的几个月中将有很大的不同，而在雨季有效降雨的增加将弥补旱季的高用水需求。因此在五月和六月的几个月中将不需要灌溉。为了改善水分配，可以将来自模型的模拟流量与适当的种植模式结合在一起。