数据同化谱写陆面过程研究中理论模型与观测数据的和弦

陆面数据同化通过融合理论模型和观测数据,为科学家们提供了一个强大的工具,以更准确地模拟和预测土壤湿度、积雪、蒸散发、径流、地下水、农业灌溉、湍流通量、陆表温度、植被动态、作物产量等地表过程关键参量[1~4].数据同化如同一把钥匙,开启了对地球系统科学深入理解的大门.

Regional and Global Land Data Assimilation Systems: Innovations,Challenges, and Prospects

Since the North American and Global Land Data Assimilation Systems(NLDAS and GLDAS) were established in2004, significant progress has been made in development of regional and global LDASs. National, regional, projectbased, and global LDASs are widely developed across the world. This paper summarizes and overviews the development, current status, applications, challenges, and future prospects of these LDASs. We first introduce various regional and global LDASs including their development history and innovations, and then discuss the evaluation, validation, and applications(from numerical model prediction to water resources management) of these LDASs. More importantly, we document in detail some specific challenges that the LDASs are facing: quality of the in-situ observations, satellite retrievals, reanalysis data, surface meteorological forcing data, and soil and vegetation databases; land surface model physical process treatment and parameter calibration; land data assimilation difficulties; and spatial scale incompatibility problems. Finally, some prospects such as the use of land information system software, the unified global LDAS system with nesting concept and hyper-resolution, and uncertainty estimates for model structure,parameters, and forcing are discussed.

Assessment and improvement of Noah-MP for simulating water and heat exchange over alpine grassland in growing season

Alpine grassland is the main ecosystem of the Tibetan Plateau(TP),thus accurate simulation of water and heat exchange in the grassland will significantly enhance the understanding of the land-atmosphere interaction process on the TP.In this study,we assessed and improved the ensemble numerical simulations of the community Noah land surface model with multiparameterization options(Noah-MP)by using observations collected from four alpine grassland observation sites.The four observation sites belong to the upper Heihe River Basin Integrated Observatory Network located in the northeastern part of the TP.First,an ensemble of 1008 numerical simulation experiments,based on multiparameterization options of seven physical processes/variables in the Noah-MP,was carried out for the vegetation growing season.The Taylor skill score was then used to assess the model performance and select the optimal combination of parameterization options for a more exact simulation of the water and heat exchange in alpine grassland.The accuracy of Noah-MP simulation was further improved by introducing new parameterizations of thermal roughness length,soil hydraulic properties,and vertical root distribution.It was found that:(1)Simulation of water and heat exchange over alpine grassland in the growing season was mainly affected by the parameterizations of dynamic vegetation,canopy stomatal resistance,runoff and groundwater dynamics,and surface exchange coefficient for heat transfer.Selection of different parameterization options for these four physical processes/variables led to large differences in the simulation of water and heat fluxes.(2)The optimal combination of parameterization options selected in the current Noah-MP framework suffered from significant overestimation of sensible heat flux(H)and underestimation of soil moisture(θ)at all observation sites.(3)The overestimation of H was significantly improved by introducing a new parameterization of thermal roughness length.Furthermore,the underestimation ofθwas resolved by introducing a new parameterization of soil hydraulic properties that considered the organic matter effect and a new vertical distribution function for the vegetation root system.The results of this study provide an important reference for further improving the simulation of water and heat exchange by using the land surface model in alpine grassland.