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作者投稿 专家审稿 编辑办公 编委办公 主编办公

冰川冻土 ›› 2021, Vol. 43 ›› Issue (4): 1144-1156.doi: 10.7522/j.issn.1000-0240.2021.0056

• 冰冻圈水文与水资源 • 上一篇    下一篇


黄克威1,2(), 王根绪1(), 宋春林3, 俞祁浩4   

  1. 1.中国科学院、水利部 成都山地灾害与环境研究所,四川 成都 610041
    2.中国科学院大学,北京 100049
    3.四川大学 水力学与山区河流开发保护国家重点实验室,四川 成都 610065
    4.中国科学院 西北生态环境资源研究院 冻土工程国家重点实验室,甘肃 兰州 730000
  • 收稿日期:2021-01-08 修回日期:2021-03-28 出版日期:2021-08-31 发布日期:2021-09-09
  • 通讯作者: 王根绪 E-mail:huangkw@imde.ac.cn;wanggx@imde.ac.cn
  • 作者简介:黄克威,博士研究生,主要从事寒区水文模拟研究. E-mail: huangkw@imde.ac.cn
  • 基金资助:

Runoff simulation and prediction of a typical small watershed in permafrost region of the Qinghai-Tibet Plateau based on LSTM

Kewei HUANG1,2(), Genxu WANG1(), Chunlin SONG3, Qihao YU4   

  1. 1.Institute of Mountain Hazards and Environment,Chinese Academy of Sciences,Chengdu 610041,China
    2.University of Chinese Academy of Sciences,Beijing 100049,China
    3.State Key Laboratory of Hydraulics and Mountain River Engineering,Sichuan University,Chengdu 610065,China
    4.State Key Laboratory of Frozen Soil Engineering,Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou 730000,China
  • Received:2021-01-08 Revised:2021-03-28 Online:2021-08-31 Published:2021-09-09
  • Contact: Genxu WANG E-mail:huangkw@imde.ac.cn;wanggx@imde.ac.cn


冻土覆盖率高的小流域的径流形成受温度因素控制明显,普通水文模型不适用,而常规冻土水文模型因需要较多的气象观测要素而难以应用。考虑冻土流域产流机制,利用青藏高原腹地风火山小流域2017—2018年逐日降水、气温、径流观测数据,以降水、气温为输入,径流为输出,基于长短期记忆神经网络(LSTM)建立了适用于小流域尺度的冻土水文模型,并利用2019年观测数据进行验证。模型得益于LSTM特殊的细胞状态和门结构能够学习、反映活动层冻融过程和土壤含水量变化,具有一定的冻土水文学意义,能很好地模拟冻土区径流过程。模型训练期R2、NSE均为0.93,RMSE为0.63 m3·s-1,验证期R2、NSE分别为0.81、0.77,RMSE为0.69 m3·s-1。同时,为了验证模型可靠性,将模型应用于邻近的沱沱河流域,模型训练期(1990—2009年)R2、NSE均为0.73,验证期(2010—2019年)R2、NSE分别为0.66、0.64,模拟结果较好。考虑到未来气候变化,通过模型对风火山流域径流进行了预测:降水每增加10%,年径流增加约12%;气温每升高0.5 ℃,年径流增加约1%;春季融化期、秋季冻结期径流增幅明显,而由于蒸发加剧、活动层加深,径流在8月出现了减少。模型经训练后依靠降水、气温作为输入能较好地模拟、预测青藏高原冻土区小流域径流,为缺少土壤温度、水分等观测数据的冻土小流域径流研究提供了一种简单有效并具有一定物理意义的方法。

关键词: 多年冻土, 径流模拟, 冻融过程, LSTM


The Qinghai-Tibet Plateau, known as the Third Pole, with 42.4% permafrost coverage, is sensitive to climate change. Runoff generation of small-scale watershed with high permafrost coverage is significantly controlled by temperature factor, which makes ordinary hydrological model unsuitable for this area, while lack of measured data such as soil temperature and moisture makes common permafrost hydrological model difficult to be applied. Moreover, increase in air temperature will result in permafrost degradation, which fundamentally changes the hydrogeological conditions in permafrost regions and finally changes the runoff process in permafrost watershed. Thus, the air temperature is a key factor in permafrost runoff modeling. LSTM (long short-term memory) is a special recurrent neural network with a more detailed internal processing unit, which contains cell state and gate structures, helping it effectively use long-distance time series information in hydrology. In this study, we developed a permafrost hydrological model at small-scale watershed based on LSTM neural networks with the consideration of runoff generation mechanism in permafrost. And it was applied in Fenghuoshan watershed, a tributary of the source region of the Yangtze River with 100% permafrost coverage, located at the central of Qinghai-Tibet Plateau. In the LSTM permafrost hydrological model, the precipitation and air temperature are employed as model inputs, while the runoff is regarded as the output. The daily precipitation, air temperature, and runoff observation data from year 2017 to 2018 were employed to train the model, and the dataset of year 2019 was used for model validation. Benefiting from the special cell state and gate structures of LSTM, the model is capable of learning and reflecting freeze-thaw processes and soil moisture seasonal variation in the active layer, with cell state evolution of some LSTM neurons consistent with these processes. It gives the model a certain permafrost hydrological significance and the high performance of permafrost runoff simulation. The values of R2, NSE and RMSE were 0.93, 0.93, 0.63 m3·s-1 during training period, 0.81, 0.77, 0.69 m3·s-1 during validation period, respectively. Besides, the model performed well in all periods within the year, including spring flood period, summer recession period, summer flood period, autumn recession period and winter freezing period. The model was also applied in the Tuotuohe watershed, which is close to Fenghuoshan watershed. The values of R2, NSE were 0.73, 0.73 during training period, 0.66, 0.64 during validation period, respectively. The model result was comparable to the results of CRHM model and WEB-DHM-SF model, which demonstrates it was reasonable and reliable. And the model was employed to predict runoff changes of Fenghuoshan watershed under 10 different climate change scenarios, those were 10% or 20% increase in precipitation with 0 ℃, 1.0 ℃, 2.0 ℃ increase in air temperature and 0.5 ℃, 1.0 ℃, 1.5 ℃, 2.0 ℃ increase in air temperature with precipitation unchanged. It shows that every 10% increase in the precipitation will result in approximately 12% increase in the annual runoff, while every 0.5 ℃ increase in the air temperature will result in approximately 1% increase. The thaw of underground ice induced by increase in air temperature contributes little to the runoff increase. However, it significantly changes the runoff process through altering the freeze-thaw processes in the active layer, which has a different influence on the runoff during different periods. Increase in the air temperature will result in significant increase in the runoff during spring thaw and autumn freeze period, while the runoff decreases in August due to the increased evaporation and deepened active layer caused by the increase in the air temperature. Meanwhile, increase in the air temperature prolongs the thaw period and shortens the freeze period, which will change the runoff compositions. This illustrates the temperature-induced variable source area runoff generation process, namely the runoff generation in permafrost region not solely determined by soil moisture but controlled by temperature conditions. The results show that the trained model can be employed to simulate and predict runoff of small permafrost watershed with only precipitation and air temperature as inputs, which are easier available in permafrost areas. It provides a simple and effective method, with a certain physical meaning, for permafrost watershed lacking observation data such as soil temperature and moisture.

Key words: permafrost, runoff simulation, freeze-thaw processes, LSTM


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