Future freshwater security relies on hydroclimatic (HC) shifts and regimes for sustainable development. The approximation of the HC system faces major uncertainties and complexities due to the incorporation of heavy datasets, characteristics, and constraints. The proposed study focused on the parallel computing of emulator modeling-based spatial optimization to enhance the HC systems with the perspective of future freshwater security in the Upper Chattahoochee River basin (UCR). Here, the framework compiles both physical and machine learning concepts with adaptive technology for the replication of real-world scenarios. Besides, it contains 2Emulator Model Fitting, Spatial Optimization, Parallel Computing, and Initial and Adaptive sampling to upgrade model efficiency, while UCR has inadequate groundwater and the assessment of freshwater security in UCR is more necessary for varying future climatic conditions. The results displayed that the proposed spatial optimization algorithm proved to be an effective and efficient approach in the approximation of HC models. The assessment of water security in UCR was showed in terms of scarcity and vulnerability indicators for median and low-level conditions, respectively. Moreover, this study provides the potential framework for the enhancement of physical model predictions with the incorporation of hybrid concepts for problem-solving technology which can provide significant information on HC issues.

未来的淡水安全依赖于水文气候 (HC) 的变化和可持续发展制度。由于大量数据集、特征和约束的结合，HC 系统的近似面临主要的不确定性和复杂性。拟议的研究侧重于基于模拟器建模的空间优化的并行计算，以从查塔胡奇河流域 (UCR) 未来淡水安全的角度来增强 HC 系统。在这里，该框架使用自适应技术编译物理和机器学习概念，以复制现实世界的场景。此外，它还包含 2Emulator 模型拟合、空间优化、并行计算以及初始和自适应采样以提升模型效率，而 UCR 的地下水不足，并且对 UCR 的淡水安全评估对于未来不同的气候条件更为必要。结果表明，所提出的空间优化算法被证明是一种逼近 HC 模型的有效方法。UCR 的水安全评估分别以中值和低水平条件的稀缺性和脆弱性指标显示。此外，这项研究为增强物理模型预测提供了潜在框架，将混合概念纳入解决问题的技术，可以提供有关 HC 问题的重要信息。结果表明，所提出的空间优化算法被证明是一种逼近 HC 模型的有效方法。UCR 的水安全评估分别以中值和低水平条件的稀缺性和脆弱性指标显示。此外，这项研究为增强物理模型预测提供了潜在框架，将混合概念纳入解决问题的技术，可以提供有关 HC 问题的重要信息。结果表明，所提出的空间优化算法被证明是一种逼近 HC 模型的有效方法。UCR 的水安全评估分别以中值和低水平条件的稀缺性和脆弱性指标显示。此外，这项研究为增强物理模型预测提供了潜在框架，将混合概念纳入解决问题的技术，可以提供有关 HC 问题的重要信息。