Modeling the Impacts of Nitrogen Dynamics on Regional Terrestrial Carbon and Water Cycles over China with Noah-MP-CN

As an important part of biogeochemical cycling,the nitrogen cycle modulates terrestrial ecosystem carbon storage,water consumption,and environmental quality.Modeling the complex interactions between nitrogen,carbon and water at a regional scale remains challenging.Using China as a testbed,this study presents the first application of the nitrogenaugmented community Noah land surface model with multi-parameterization options(Noah-MP-CN)at the regional scale.Noah-MP-CN parameterizes the constraints of nitrogen availability on photosynthesis based on the Fixation and Uptake of Nitrogen plant nitrogen model and the Soil and Water Assessment Tool soil nitrogen model.The impacts of nitrogen dynamics on the terrestrial carbon and water cycles are investigated by comparing the simulations with those from the original Noah-MP.The results show that incorporating nitrogen dynamics improves the carbon cycle simulations.NoahMP-CN outperforms Noah-MP in reproducing leaf area index(LAI)and gross primary productivity(GPP)for most of China,especially in the southern warm and humid regions,while the hydrological simulations only exhibit slight improvements in soil moisture and evapotranspiration.The impacts of fertilizer application over cropland on carbon fixation,water consumption and nitrogen leaching are investigated through a trade-off analysis.Compared to halved fertilizer use,the actual quantity of application increases GPP and water consumption by only 1.97%and 0.43%,respectively;however,the nitrogen leaching is increased by 5.35%.This indicates that the current level of fertilizer use is a potential concern for degrading the environment.

Systematic Hydrological Evaluation of the Noah-MP Land Surface Model over China

We evaluate water budget components-namely,soil moisture,runoff,evapotranspiration,and terrestrial water storage (TWS)-simulated by the Noah land surface model with multi-parameterization options (Noah-MP) in China,a large geographic domain challenging for hydrological modeling due to poor observational data and a lack of one single parameterization that can fit for complex hydrological processes.By comparing the model simulations with multi-source reference data,we show that Noah-MP can generally reproduce the overall spatiotemporal patterns of runoff and evapotranspiration over six major river basins,with the annual correlation coefficients generally greater than 0.8 and the Nash-Sutcliffe model efficiency coefficient exceeding 0.5.Among the six basins evaluated,the best model performance is seen over the Huaihe River basin.The temporal trend of the modeled TWS anomalies agrees well with GRACE (Gravity Recovery and Climate Experiment) observations,capturing major flood and drought events in different basins.Experiments with 12 selected physical parameterization options show that the runoff parameterization has a stronger impact on the simulated soil moisture-runoff-evapotranspiration relationships than the soil moisture factor for stomatal resistance schemes,a result consistent with previous studies.Overall,Noah-MP driven by GLDAS forcing simulates the hydrological variables well,except for the Songliao basin in northeastern China,likely because this is a transitional region with extensive freeze-thaw activity,while representations of human activities may also help improve the model performance.

NoahMP-RAPID在高海拔山地流域的模拟检验与误差分析

高海拔山地流域水能资源丰富、山洪易发;但产汇流机制复杂、降水数据不确定性大,准确的山地水文过程模拟面临较大困难.本研究采用中国区域地面气象要素数据集CMFD(China Meteorological Forcing Dataset)驱动NoahMP陆面过程-RAPID河网汇流耦合模式,模拟青藏高原东侧大渡河干流逐日流量,并根据铜街子、龙头石流量站观测数据率定RAPID中的波速参数,检验了NoahMP中SIMGM和NOAH两种不同的产流过程参数化方案的模拟能力,评估了CMFD降水驱动数据的系统性偏差.研究发现,基于Philip入渗模型的NOAH方案优于基于TOPMODEL的SIMGM方案,能较为准确地模拟大渡河干流流量逐日变化,相关系数大于0.85、纳什系数约为0.3;对纳什系数的数学分解发现,纳什系数较低主要是由于模拟流量显著偏低造成的,若剔除系统性偏差,NOAH方案的纳什系数可提高至约0.7.模拟流量的系统性偏差主要来自于降水驱动数据;与雨量站观测和反演数据相比,CMFD显著低估了大渡河流域平均降水量,且其系统性偏差与高程有关,在低海拔地区高估,高海拔地区低估.本研究表明,使用NOAH产流方案的NoahMP-RAPID耦合模式对大渡河流域水文过程有较好的模拟能力,可进一步应用于水库调度优化;而提高雨量站密度、降低降水数据产品的系统性偏差是进一步改进高海拔山地流域水文过程模拟的关键.

Machine learning-assisted systematical polymerization planning:case studies on reversible-deactivation radical polymerization

The combined influence of chemical composition,molecular weight(MW)and molecular weight distribution(D)on the functions and performances of polymeric materials necessitates simultaneous satisfaction of multidimensional requirements during polymer synthesis.However,the complexity of polymerization reactions often dissuades chemists when precisely accessing diversified polymer targets.Herein,we developed a machine learning(ML)-assisted systematical polymerization planning(SPP)platform for addressing this challenge.With ML model providing integrated navigation of the reaction space,this approach can conduct multivariate analysis to uncover complex interactions between the polymerization result and conditions,prescribing optimal reaction conditions to achieve discretionary polymer targets concerning three dimensions including chemical composition,MWandD values.Given the increasing importance of polymerization in advanced material engineering,this ML-assisted SPP platform provides a universal strategy to access tailored polymers with on-demand prediction of polymerization parameters.