关键词: 多年冻土, 径流模拟, 冻融过程, LSTM

Abstract:

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