Department of Irrigation and Reclamation Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, 3158777871, Iran.
Department of Geography, University of California, Santa Barbara, CA, 93106, USA.
Sci Rep. 2022 Feb 2;12(1):1813. doi: 10.1038/s41598-022-05643-8.
There is substantial evidence suggesting climate change is having an adverse impact on the world's water resources. One must remember, however, that climate change is beset by uncertainty. It is therefore meaningful for climate change impact assessments to be conducted with stochastic-based frameworks. The degree of uncertainty about the nature of a stochastic phenomenon may differ from one another. Deep uncertainty refers to a situation in which the parameters governing intervening probability distributions of the stochastic phenomenon are themselves subjected to some degree of uncertainty. In most climatic studies, however, the assessment of the role of deep-uncertain nature of climate change has been limited. This work contributes to fill this knowledge gap by developing a Markov Chain Monte Carlo (MCMC) analysis involving Bayes' theorem that merges the stochastic patterns of historical data (i.e., the prior distribution) and the regional climate models' (RCMs') generated climate scenarios (i.e., the likelihood function) to redefine the stochastic behavior of a non-conditional climatic variable under climate change conditions (i.e., the posterior distribution). This study accounts for the deep-uncertainty effect by evaluating the stochastic pattern of the central tendency measure of the posterior distributions through regenerating the MCMCs. The Karkheh River Basin, Iran, is chosen to evaluate the proposed method. The reason for selecting this case study was twofold. First, this basin has a central role in ensuring the region's water, food, and energy security. The other reason is the diverse topographic profile of the basin, which imposes predictive challenges for most RCMs. Our results indicate that, while in most seasons, with the notable exception of summer, one can expect a slight drop in the temperature in the near future, the average temperature would continue to rise until eventually surpassing the historically recorded values. The results also revealed that the 95% confidence interval of the central tendency measure of computed posterior probability distributions varies between 0.1 and 0.3 °C. The results suggest exercising caution when employing the RCMs' raw projections, especially in topographically diverse terrain.
有大量证据表明，气候变化正在对世界水资源产生不利影响。然而，人们必须记住，气候变化充满不确定性。因此，使用基于随机的框架进行气候变化影响评估具有重要意义。随机现象的本质的不确定性程度可能彼此不同。深度不确定性是指控制随机现象的介入概率分布的参数本身存在一定程度不确定性的情况。然而，在大多数气候研究中，对气候变化深度不确定性性质的作用评估受到限制。这项工作通过开发涉及贝叶斯定理的马尔可夫链蒙特卡罗（MCMC）分析来填补这一知识空白，该分析将历史数据的随机模式（即先验分布）与区域气候模型（RCMs）生成的气候情景（即似然函数）合并，以重新定义非条件气候变量在气候变化条件下的随机行为（即后验分布）。通过重新生成 MCMC 来评估后验分布的中心趋势度量的随机模式，本研究考虑了深度不确定性的影响。伊朗的卡伦河流域被选来评估所提出的方法。选择这个案例研究有两个原因。首先，该流域在确保该地区的水、粮食和能源安全方面发挥着核心作用。另一个原因是流域的地形轮廓多种多样，这给大多数 RCMs 带来了预测挑战。我们的结果表明，虽然在大多数季节，除了夏季，未来短期内气温会略有下降，但平均气温将继续上升，最终超过历史记录值。结果还表明，计算后验概率分布的中心趋势度量的 95%置信区间在 0.1 到 0.3°C 之间变化。结果表明，在使用 RCMs 的原始预测时要谨慎，特别是在地形多样的地区。
