In establishing adequate climate change policies regarding water resource development and management, the most essential step is performing a rainfall-runoff analysis. To this end, although several physical models have been developed and tested in many studies, they require a complex grid-based parameterization that uses climate, topography, land-use, and geology data to simulate spatiotemporal runoff. Furthermore, physical rainfall-runoff models also suffer from uncertainty originating from insufficient data quality and quantity, unreliable parameters, and imperfect model structures. As an alternative, this study proposes a rainfall-runoff analysis system for the Kratie station on the Mekong River mainstream using the long short-term memory (LSTM) model, a data-based black-box method. Future runoff variations were simulated by applying a climate change scenario. To assess the applicability of the LSTM model, its result was compared with a runoff analysis using the Soil and Water Assessment Tool (SWAT) model. The following steps (dataset periods in parentheses) were carried out within the SWAT approach: parameter correction (2000–2005), verification (2006–2007), and prediction (2008–2100), while the LSTM model went through the process of training (1980–2005), verification (2006–2007), and prediction (2008–2100). Globally available data were fed into the algorithms, with the exception of the observed discharge and temperature data, which could not be acquired. The bias-corrected Representative Concentration Pathways (RCPs) 4.5 and 8.5 climate change scenarios were used to predict future runoff. When the reproducibility at the Kratie station for the verification period of the two models (2006–2007) was evaluated, the SWAT model showed a Nash–Sutcliffe efficiency (NSE) value of 0.84, while the LSTM model showed a higher accuracy, NSE = 0.99. The trend analysis result of the runoff prediction for the Kratie station over the 2008–2100 period did not show a statistically significant trend for neither scenario nor model. However, both models found that the annual mean flow rate in the RCP 8.5 scenario showed greater variability than in the RCP 4.5 scenario. These findings confirm that the LSTM runoff prediction presents a higher reproducibility than that of the SWAT model in simulating runoff variation according to time-series changes. Therefore, the LSTM model, which derives relatively accurate results with a small amount of data, is an effective approach to large-scale hydrologic modeling when only runoff time-series are available.

中文翻译：

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使用深度学习进行气候变化情景下湄公河流域未来径流分析

在制定有关水资源开发和管理的适当气候变化政策时，最重要的步骤是进行降雨径流分析。为此，虽然在许多研究中已经开发和测试了多种物理模型，但它们需要一个基于网格的复杂参数化，该参数化使用气候、地形、土地利用和地质数据来模拟时空径流。此外，由于数据质量和数量不足、参数不可靠和模型结构不完善，物理降雨径流模型也存在不确定性。作为替代方案，本研究使用长短期记忆 (LSTM) 模型（一种基于数据的黑盒方法）为湄公河干流上的桔井站提出了一个降雨径流分析系统。通过应用气候变化情景来模拟未来径流变化。为了评估 LSTM 模型的适用性，将其结果与使用土壤和水评估工具 (SWAT) 模型的径流分析进行了比较。在 SWAT 方法中执行了以下步骤（括号中的数据集周期）：参数校正（2000-2005）、验证（2006-2007）和预测（2008-2100），而 LSTM 模型经历了训练过程(1980–2005)、验证 (2006–2007) 和预测 (2008–2100)。全球可用的数据被输入到算法中，观察到的排放和温度数据除外，这些数据无法获取。偏差校正的代表性浓度路径 (RCP) 4.5 和 8.5 气候变化情景用于预测未来径流。当评估两个模型验证期（2006-2007 年）在桔井站的再现性时，SWAT 模型显示的 Nash-Sutcliffe 效率（NSE）值为 0.84，而 LSTM 模型显示出更高的准确度，NSE = 0.99。桔井站 2008-2100 年径流预测的趋势分析结果，无论是情景还是模型，都没有显示出统计上的显着趋势。然而，两个模型都发现 RCP 8.5 情景中的年平均流量显示出比 RCP 4.5 情景中更大的可变性。这些发现证实，在根据时间序列变化模拟径流变化时，LSTM 径流预测比 SWAT 模型具有更高的可重复性。因此，用少量数据推导出相对准确结果的LSTM模型，