Due to the complicated terrain conditions in montane catchments, runoff formation is fast and complicated, making accurate simulation and forecasting a significant hydrological challenge. In this study, the spatiotemporal variable source mixed runoff generation module (SVSMRG) was integrated with the long short-term memory (LSTM) method, to develop a semi-distributed model (SVSMRG)-based surface flow and baseflow segmentation (SVSMRG-SBS). Herein, the baseflow was treated as a black box and forecasted using LSTM, while the surface flow was simulated using the SVSMRG module based on hydrological response units (HRUs) constructed using eco-geomorphological units. In the case study, four typical montane catchments with different climatic conditions and high vegetation coverage, located in the topographically varying mountains of the eastern Tibetan Plateau, were selected for runoff and flood process simulations using the proposed SVSMRG-SBS model. The results showed that this model had good performance in hourly runoff and flood process simulations for montane catchments. Regarding runoff simulations, the Nash–Sutcliffe efficiency coefficient ($NSE$) and correlation coefficient ($R^2$) reached 0.8241 and 0.9097, respectively. Meanwhile, for the flood simulations, the $NSE$ ranged from 0.5923 to 0.7467, and $R^2$ ranged from 0.6669 to 0.8092. For the 1-, 3-, and 5-h baseflow forecasting with the LSTM method, it was found that model performances declined when simulating the runoff processes, wherein the $NSE$ and $R^2$ between the measured and modeled runoff decreased from 0.8216 to 0.8087 and from 0.9095 to 0.8871, respectively. Similar results were found in the flood simulations, the $NSE$ and $R^2$ values declined from 0.7414–0.5885 to 0.7429–0.5716 and from 0.8042–0.6547 to 0.7936–0.6067, respectively. This means that this new model achieved perfect performance in montane catchment runoff and flood simulation and forecasting with 1-, 3-, 5-h steps. Therefore, as it considers vegetation regulation, the SVSMRG-SBS model is expected to improve runoff and flood simulation accuracy in montane high-vegetation-covered catchments.

中文翻译：

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Montane植被覆盖集水区的径流从快速地表流分段延迟的改进中的径流模拟和预测

由于山区流域的地形条件复杂，径流的形成既快速又复杂，因此进行精确的模拟和预测是一个重大的水文挑战。在这项研究中，时空可变源混合径流生成模块（SVSMRG）与长短期记忆（LSTM）方法集成在一起，以开发基于半分布式模型（SVSMRG）的地表流和基流分割（SVSMRG-SBS） ）。在此，基流被视为黑匣子，并使用LSTM进行了预测，而地表流是使用SVSMRG模块模拟的，该模块基于使用生态地貌单元构造的水文响应单元（HRU）。在案例研究中，四个典型的山地流域具有不同的气候条件和高植被覆盖率，使用拟议的SVSMRG-SBS模型，选择位于青藏高原东部地形变化多变的山区的山坡进行径流和洪水过程模拟。结果表明，该模型在山区流域的小时径流和洪水过程模拟中具有良好的性能。关于径流模拟，纳什-萨特克利夫效率系数（$ñ\mathrm{小号}Ë$）和相关系数（${\mathrm{[R}}^2$）分别达到0.8241和0.9097。同时，对于洪水模拟，$ñ\mathrm{小号}Ë$ 范围从0.5923到0.7467，以及 ${\mathrm{[R}}^2$范围从0.6669到0.8092。对于使用LSTM方法进行的1小时，3小时和5小时基流预测，发现在模拟径流过程时模型的性能下降了，其中$ñ\mathrm{小号}Ë$ 和 ${\mathrm{[R}}^2$实测径流和模拟径流之间的差值分别从0.8216降至0.8087和从0.9095降至0.8871。在洪水模拟中发现了类似的结果，$ñ\mathrm{小号}Ë$ 和 ${\mathrm{[R}}^2$值分别从0.7414-0.5885降至0.7429-0.5716和从0.8042-0.6547降至0.7936-0.6067。这意味着该新模型在山区流域径流和洪水模拟以及以1、3、5小时为步长的预报中取得了完美的性能。因此，考虑到植被调节，SVSMRG-SBS模型有望提高山区高植被覆盖集水区的径流和洪水模拟精度。