雅鲁藏布江(以下简称雅江)流域的气象水文模拟是当前全球变化研究的热点与难点.Noah-MP(the Noah land surface model with multi-parameterizations)陆面过程模式作为该区域气象水文双向耦合过程的重要数值模拟工具,鲜有研究针对其径流模拟能力进行过系统性评估,限制了模式在该区域的水文应用.本研究基于中国区域地面气象要素数据集CMFD(China Meteorological Forcing Dataset)驱动Noah-MP模式,对雅江流域2000～2018年的径流进行时空分辨率为3h/5km的数值模拟;选取与流域径流产生机制相关的10个主要物理过程,评估了16种参数化方案组合对于径流模拟的影响,并确定了最优参数化方案组合.结果表明:(1)采用默认参数化方案,Noah-MP在奴下站的月尺度模拟纳什效率系数NSE(Nash-Sutcliffe efficiency)为0.23、偏差百分比PBias为-35.79％,而采用基于临界温度的雨雪分离方案、改进的二流近似辐射传输方案以及基于BATS的产流方案后,PBias分别减少至-23.36％、3.85％、-17.19％,NSE分别提高至0.37、0.58、0.60,显著优于默认方案;(2)进一步基于优选方案进行组合,奴下、羊村、奴各沙的月尺度径流NSE分别提高至0.89、0.87、0.81,而最上游拉孜站NSE仅为-0.06,低于个别方案,这表明拉孜流域的产流机制可能不同于下游流域.研究结果表明,无参数率定的Noah-MP模式在雅江径流模拟中的表现较为优异,具有较高的应用潜力,未来可通过进一步改进雨雪分离、辐射传输、产流过程的参数化方案来提高模式在高寒区的径流模拟能力.
Assessment of runoff simulation in the Yarlung Zangbo River Basin based on the multi-physics Noah-MP land surface model
Yarlung Zangbo River Basin(YZRB)is one of the highest basins in the world.Runoff in YZRB plays an important role in hydropower production and fresh water supply for millions of people in Asia.Land surface models(LSMs)are important tools for coupled hydrometeorological simulation and forecasting in this region.However,accurate runoff simulation over YZRB presents huge challenges due to the uncertainties in meteorological forcing and the highly complex runoff generation mechanisms in the basin(e.g.,snowmelt,glacier melt,permafrost freeze/thaw,groundwater,and monsoon runoff).In this study,we systematically assess the runoff simulation skill of a physically-based land surface model named the multi-physics Noah land surface model(Noah-MP)over the YZRB.Runoff from 2000 to 2018 is simulated at a spatio-temporal resolution of 5 km and three hours forced by the Chinese Meteorological Forcing Dataset(CMFD).The simulated runoff is evaluated at four sub-basin outlets(i.e.,Lazi,Nugesha,Yangcun and Nuxia)with respect to the Nash-Sutcliffe efficiency(NSE)and percentage bias(PBias)skill metrics.Our study features the assessment of ten physical parameterization schemes that are the most influential to runoff generation in the YZRB;they include:(1)Precipitation-related parameterization scheme(PPS)that concerns the precipitation partitioning into rainfall and snowfall,(2)evapotranspiration-related parameterization schemes(EPS)that describe the canopy stomatal resistance,soil moisture factor controlling stomatal resistance,surface drag,radiative transfer processes,and(3)runoff-related parameterization schemes(RPS)that describe snow albedo,frozen soil permeability,supercooled liquid water in frozen soil,glacier,runoff processes.Our results showed that the default parameterization scheme by Noah-MP only achieves a monthly NSE and PBias of 0.23 and-35.79％at Nuxia.Turning to the third option of precipitation partitioning(PTP_3),the modified two-stream radiative transfer parameterization scheme(RAD_1),and the BATS runoff parameterization scheme(RUN_4),the model performance is satisfactorily improved with PBias reduced to-23.36％,3.85％,and-17.19％,and NSE increased to 0.37,0.58,and 0.60 for Nugesha,Yangcun,and Nuxia,respectively.Based on the above results,runoff simulation was conducted using the combination of these schemes,and the monthly NSE can be improved to 0.89,0.87,and 0.81 at Nuxia,Yangcun,and Nugesha,respectively,while Lazi showed a relatively low NSE(-0.06),which may be explained by the different runoff generation mechanisms there.Overall,our result demonstrated that optimizing the parameterization selection of Noah-MP can achieve satisfactory runoff simulation skills over YZRB without conducting model parameter calibration.Such good simulation skills in a calibration-free modeling experiment highlight the promising potential of a physically-based model such as Noah-MP.We anticipate this study to provide a useful reference to future Noah-MP users/developers in conducting coupled hydro-meteorological simulations in this region.
land surface modelmulti-physics parameterization schemesYarlung Zangbo River Basinrunoff simulationapplicability
万方数据
维普
