The Analysis Of Parameters' Sensitivity Of Land Surface Model In Grain Producing Areas Of Henan Province

Henan province plays an irreplaceable role in ensuring national food security. Considering the grain producing areas of Henan province in north central are often affected by drought, so shallow soil moisture has a great impact on the agricultural production in the areas. The Common Land Model is a widely used and more advanced land model in the world, many experiments have shown that the Co LM has a good simulation capacity in China. However, the accuracy of the crucial surface characteristic parameters has affected the model's simulations. Thus, screening the paramaters sensitive to shallow soil moisture by conditional nonlinear optimal parameter perturbation method(CNOP-P), optimizing them, and then determining sensitive parameters which can affect Henan's weather condition, improving model prediction ability has a lot of practical meaning to Henan agriculture and land surface parameterization, also is very profound to promote land surface model's application to the grain producing areas of Henan province.The National Centers for Environmental Prediction(NCEP) and the National Center for Atmospheric Research(NCAR) has accomplished about 60-yr(from 1948 to present) reanalysis for global meteorological observation, which becomes the longest reanalysis datasets used in climatic research. According to NCEP/NCAR reanalysis, this article uses CNOP-P to optimize two kinds of parameters: soil parameters and vegetation parameters, also design different parameter combinations to test sensitivity, to conduct off-line simulation experiments in Henan grain producing areas—Yanjin County in march-june 2009. The main conclusions show as follows:(1) the Co LM model is initialized using a spin-up process to derive state of equilibrium, and the correlation coefficients for shallow soil moisture simulated by Co LM before being parameters improved and NCEP/NCAR reanalysis data is 0.8897.(2) Comparison of shallow soil moisture's simulation results between single parameter optimizations and no optimization shows that: Co LM after single parameter optimizations get better simulated performances; the quality of shallow soil moisture' simulation is improved; single parameter optimizations strengthen model's predict capabilities. And shallow soil moisture is more sensitive to soil parameters, vegetation parameters mainly enhance the model's simulation after mid-April. In single parameter optimizations period, different parameter have different influence on shallow soil moisture, the sensitivity's sorting of shallow soil moisture is: P1>P4 >P2 >P3 >P5 >P8 >P6 >P10 >P9 >P7.(3) The optimizations of three parameters' combinations indicate their better improvement than single parameter optimizations in Co LM's simulation; shallow soil moisture is more sensitive to three soil parameters than three vegetation parameters; the sensitivity of combinations of one soil parameter plus two vegetation parameters is is stronger than one vegetation parameter plus two soil parameters; this experiments also show that nonlinear interaction between parameters.(4) 10 parameters simultaneously optimized model can reflect the diurnal variation's characteristics of the shallow soil moisture truly, make the model simulation effect get the biggest increase in all these experiments.