A data assimilation-based method for optimizing parameterization schemes in a land surface process model

Optimizing the parameters of a land surface process model(LSPM) through data assimilation(DA) can not only improve and perfect the parameterization schemes in the LSPM through the physical mechanism, but also increase its regional adaptability and simulation capability. This has practical importance for improving simulation results and the climate-prediction capability of general circulation models(GCMs) and regional climate models(RCMs). This paper presents a DA-based method for optimizing the parameterization schemes in LSPMs. We optimize the unsaturated-soil water flow(Un SWF) model as an example by developing a soil-moisture assimilation scheme based on the Un SWF model and the extended Kalman filter(EKF) algorithm, and then combining them with the Variable Infiltration Capacity(VIC) model. Using a month as the assimilation window, we used the Shuffled Complex Evolution–University of Arizona(SCE-UA) algorithm to minimize the objective function through simulated and assimilated soil moisture, achieved the best fit with the given objective function measurement, and optimized the parameters of the Un SWF model, including the saturated-soil hydraulic conductivity, moisture content, matrix potential, and the Clapp and Hornberger constant. The optimal values of the model parameters were obtained during the DA period(the year 1986), and then the optimized parameters were used to improve the Un SWF model. Finally, numerical simulation experiments were carried out from 1986 to 1993 to evaluate the simulation capability of the improved model and to explore and realize the DA-based method for optimizing the soil water parameterization scheme in LSPMs. The experimental results indicated that the optimized model parameters improved and perfected the model based on the physical mechanism, and increased its simulation capability; the optimized model parameters had good temporal portability and their adaptability was stronger, achieving the aim of improving the model. Therefore, this method is reasonable and feasible. This paper provides a good reference for DA-based optimization of the parameterization schemes in LSPMs.

A SIMPLE LAND SURFACE PROCESS MODEL FOR USE IN CLIMATE STUDY

A quantitative description of the processes taking place among the atmosphere, vegetation and soil is needed for studying air-land interaction and interrelation between the geosphere and the biosphere. In this paper, a simple land surface process model is proposed. Through transfers and exchanges of heat and water, the therrnal and moisture states of the atmosphere, vegetation and soil are linked in a coupled system, in which vegetation is considered as a horizontally uniform layer, soil is divided into three layers and the horizontal differences of variables in the system are neglected. The preliminary results of the experiment indicate that the model is capable of predicting the thermal and moisture conditions of the land surface and suitable to climate study.

Plant drought tolerance trait is the key parameter in improving the modeling of terrestrial transpiration in arid and semi-arid regions

The prediction of precipitation depends on accurate modeling of terrestrial transpiration.In recent decades,the trait-based plant hydraulic stress scheme has been developed in land surface models,in order to better predict the hydraulic constraint on terrestrial transpiration.However,the role that each plant functional trait plays in the modeling of transpiration remains unknown.The importance of different plant functional traits for modeled transpiration needs to be addressed.Here,the Morris sensitivity analysis method was implemented in the Common Land Model with the plant hydraulic stress scheme(CoLM-P_(50)HS).Traits related to drought tolerance(P_(50);),stomata,and photosynthesis were screened as the most critical from all 17 plant traits.Among 12 FLUXNET sites,the importance of P_(50);,measured by normalized sensitivity scores,increased towards lower precipitation,whereas the importance of stomatal traits and photosynthetic traits decreased towards drier climate conditions.P_(50);was more important than stomatal traits and photosynthetic traits in arid or semi-arid sites,which implies that hydraulic safety strategies are more crucial than plant growth strategies when plants frequently experience drought.Large variation in drought tolerance traits further proved the coexistence of multiple plant strategies of hydraulic safety.Ignoring the variation in drought tolerance traits may potentially bias the modeling of transpiration.More measurements of drought tolerance traits are therefore necessary to help better represent the diversity of plant hydraulic functions.

Influences of agricultural phenology dynamic on land surface biophysical process and climate feedback

Response and feedback of land surface research priorities in the field of geoscience. The process to climate change is one of the current study paid more attention to the impacts of global change on land surface process, but the feedback of land surface process to climate change has been poorly understood. It is becoming more and more meaningful under the framework of Earth system science to understand systematically the relationships between agricultural phenology dynamic and biophysical process, as well as the feedback on climate. In this paper, we summarized the research progress in this field, including the fact of agricultural phenology change, parameterization of phenology dynamic in land surface progress model, the influence of agricultural phenology dynamic on biophysical process, as well as its feedback on climate. The results showed that the agriculture phenophase, represented by the key phenological phases such as sowing, flowering and maturity, had shifted significantly due to the impacts of climate change and agronomic management. The digital expressions of land surface dynamic process, as well as the biophysical process and atmospheric process, were improved by coupling phenology dynamic in land surface model. The agricultural phenology dynamic had influenced net radiation, latent heat, sensible heat, albedo, temperature, precipitation, circulation, playing an important role in the surface energy partitioning and climate feedback. Considering the importance of agricultural phenology dynamic in land surface biophysical process and climate feedback, the following research priorities should be stressed： （1） the interactions between climate change and land surface phenology dynamic; （2） the relations between agricultural phenology dynamic and land surface reflectivity at different spectrums; （3） the contributions of crop physiology characteristic changes to land surface biophysical process; （4） the regional differences of climate feedbacks from phenology dynamic in different climate zones. This review is helpful to accelerate understanding of the role of agricultural phenology dynamic in land surface process and climate feedback.

Soil moisture drought detection and multi-temporal variability across China

Soil moisture droughts can trigger abnormal changes of material and energy cycles in the soil-vegetation-atmosphere system,leading to important effects on local ecosystem,weather,and climate.Drought detection and understanding benefit disaster alleviation,as well as weather and climate predictions based on the understanding the land-atmosphere interactions.We thus simulated soil moisture using land surface model CLM3.5 driven with observed climate in China,and corrected wet bias in soil moisture simulations via introducing soil porosity parameter into soil water parameterization scheme.Then we defined soil moisture drought to quantify spatiotemporal variability of droughts.Over the period from 1951 to 2008,40%of months(to the sum of 12×58)underwent droughts,with the average area of 54.6%of total land area of China's Mainland.The annual monthly drought numbers presented a significant decrease in arid regions,but a significant increase in semi-arid and semi-humid regions,a decrease in humid regions but not significant.The Mainland as a whole experienced an increasing drought trend,with77.3%of areal ratio of decrease to increase.The monthly droughts in winter were the strongest but the weakest in summer,impacting 54.3%and 8.4%total area of the Mainland,respectively.The drought lasting three months or more occurred mainly in the semi-arid and semi-humid regions,with probability>51.7%,even>77.6%,whereas those lasting 6 and 12 months or more impacted mainly across arid and semi-arid regions.