冰川冻土,2021,43(4):1144-1156doi:10.7522/j.issn.1000-0240.2021.0056
冰冻圈水文与水资源
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
2.UniversityofChineseAcademyofSciences，Beijing100049，China
3.StateKeyLaboratoryofHydraulicsandMountainRiverEngineering，SichuanUniversity，Chengdu610065，China
4.StateKeyLaboratoryofFrozenSoilEngineering，NorthwestInstituteofEco-EnvironmentandResources，ChineseAcademyofSciences，Lanzhou730000，China
编委:武俊杰
收稿日期:2021-01-08修回日期:2021-03-28
Received:2021-01-08Revised:2021-03-28
作者简介Aboutauthors
关键词：多年冻土;径流模拟;冻融过程;LSTM
Keywords：permafrost;runoffsimulation;freeze-thawprocesses;LSTM
本文引用格式
黄克威,王根绪,宋春林,俞祁浩.基于LSTM的青藏高原冻土区典型小流域径流模拟及预测[J].冰川冻土,2021,43(4):1144-1156doi:10.7522/j.issn.1000-0240.2021.0056
HUANGKewei,WANGGenxu,SONGChunlin,YUQihao.RunoffsimulationandpredictionofatypicalsmallwatershedinpermafrostregionoftheQinghai-TibetPlateaubasedonLSTM[J].JournalofGlaciologyandGeocryology,2021,43(4):1144-1156doi:10.7522/j.issn.1000-0240.2021.0056
图1研究区域位置
Fig.1Locationofthestudyarea
图2基于LSTM的冻土水文模型结构
Fig.2StructureofpermafrosthydrologymodelbasedonLSTM
①将t时刻的降水Pt、气温Tt标准化后作为模型输入。
②通过遗忘门（Ft）移除t-1时刻细胞状态的信息。
③通过输入门（INt）确定用以更新细胞状态的信息Ct。
④通过输出门（Ot）计算细胞状态的输出及生成的隐藏层状态变量Ht。
⑤将模型输出去标准化后，得到模拟的冻土区径流RMt。
采用决定系数（R2）、纳什效率系数（NSE）及均方根误差（RMSE）指标来评价模型的模拟效果，具体计算公式为
表1模型主要超参数
Table1Mainhyperparametersofthemodel
图3模型训练期模拟结果
Fig.3Simulationresultsduringmodeltraining：comparisonbetweensimulatedrunoffandmeasuredrunoffduringmodeltraining（a），andsimulationresultsofrunoffprocessduringmodeltraining（b）
图4模型验证期模拟结果
Fig.4Simulationresultsduringmodelvalidation：comparisonbetweensimulatedrunoffandmeasuredrunoffduringmodelvalidation（a），andsimulationresultsofrunoffprocessduringmodelvalidation（b）
图5沱沱河流域模拟结果
Fig.5SimulationresultsofTuotuohewatershed
表2不同气候变化情景下风火山流域径流变化幅度
Table2RangeofrunoffchangeinFenghuoshanwatershedunderdifferentclimatechangescenarios
注：正值表示增加幅度，负值表示减少幅度。
图6模型验证期LSTM神经元细胞状态值变化过程
Fig.6CellstatechangeprocessesofLSTMneuronsduringmodelvalidation：measuredprecipitation，airtemperatureandrunoff（a），cellstatechangeprocessesofNeuron1（b），andcellstatechangeprocessesofNeuron2（c）
图7不同气候变化情景下的模拟结果
Fig.7Simulationresultsunderdifferentclimatechangescenarios
本文依据多年冻土区产流机制，以降水、气温作为输入，建立了基于LSTM的冻土水文模型，探究了其冻土水文学意义，并分析了气温升高对冻土区径流过程的影响，得到以下结论：
（1）LSTM模型的细胞状态能够存储冻土水文过程中的长期记忆信息，如活动层冻融状态、土壤含水量等，而遗忘门、输入门和输出门则可以类比理解为活动层中的能量、土壤含水量等状态变量的消耗、增长和出流。因此，通过降水、气温和径流来训练模型时，其能够学习活动层冻融过程、土壤含水量变化规律，并且在验证时能通过细胞状态值反映出这些冻土水文关键变量随气温、降水的变化，从而使模型具有了一定的冻土水文学意义，并在风火山流域表现出了良好的适用性。
综上所述，基于LSTM的冻土水文模型具有一定的冻土水文学意义，在缺少土壤温度、水分观测资料的条件下，能够依靠有限的降水、气温、径流资料模拟青藏高原上的多年冻土区受活动层冻融过程影响的径流过程，为冻土水文模拟研究提供了一种简单有效、具有一定物理意义的方法。
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