选取2015年6月—2018年8月玛多站观测资料作为驱动CLM5.0(Community Land Model)模式的强迫场数据,应用CLM5.0模式中不同土壤分层方案,对这一时段玛多站土壤温湿变化特征进行模拟,并检验了模拟效果。结果表明:(1)对于土壤温度,CLM5.0模式的4种土壤分层方案均能很好地模拟出一年中玛多站不同深度土壤温度的季节变化趋势,浅层土壤温度模拟值与观测值相关性更高,深层土壤温度模拟值的变化幅度相对较小且曲线较光滑。4种分层方案中,20层方案对土壤温度的模拟效果最好,平均相关系数为0.942。(2)对于土壤湿度,4种土壤分层方案均能较好地模拟出各层土壤湿度的季节变化和日变化趋势,但较观测值都有不同程度的偏差。20层方案对土壤湿度的模拟效果更好,平均相关系数为0.730。
The accuracy of the simulation of carbon and water processes largely relies on the selection of atmospheric forcing datasets when driving land surface models(LSM).Particularly in high-altitude regions,choosing appropriate atmospheric forcing datasets can effectively reduce uncertainties in the LSM simulations.Therefore,this study conducted four offline LSM simulations over the Tibetan Plateau(TP)using the Community Land Model version 4.5(CLM4.5)driven by four state-of-the-art atmospheric forcing datasets.The performances of CRUNCEP(CLM4.5 model default)and three other reanalysis-based atmospheric forcing datasets(i.e.ITPCAS,GSWP3 and WFDEI)in simulating the net primary productivity(NPP)and actual evapotranspiration(ET)were evaluated based on in situ and gridded reference datasets.Compared with in situ observations,simulated results exhibited determination coefficients(R2)ranging from 0.58 to 0.84 and 0.59 to 0.87 for observed NPP and ET,respectively,among which GSWP3 and ITPCAS showed superior performance.At the plateau level,CRUNCEP-based simulations displayed the largest bias compared with the reference NPP and ET.GSWP3-based simulations demonstrated the best performance when comprehensively considering both the magnitudes and change trends of TP-averaged NPP and ET.The simulated ET increase over the TP during 1982-2010 based on ITPCAS was significantly greater than in the other three simulations and reference ET,suggesting that ITPCAS may not be appropriate for studying long-term ET changes over the TP.These results suggest that GSWP3 is recommended for driving CLM4.5 in conducting long-term carbon and water processes simulations over the TP.This study contributes to enhancing the accuracy of LSM in water-carbon simulations over alpine regions.
Shan LinKewei HuangXiangyang SunChunlin SongJuying SunShouqin SunGenxu WangZhaoyong Hu
