中国亚热带土壤可蚀性K值预测的不确定性研究

UNCERTAINTY IN PREDICTION OF SOIL ERODIBILITY K-FACTOR IN SUBTROPICAL CHINA

土壤可蚀性K值是土壤侵蚀模型(如USLE和RUSLE)的必要参数,直接套用经验模型估算土壤可蚀性K值会给土壤侵蚀预报带来不可估计的误差。本文以我国亚热带7种典型土壤可蚀性K值的观测值为依据,选用平均绝对误差(MAE)、平均相对误差(MRE)、均方根误差(RMSE)和精度因子(Af)四种数学统计项为指标,评价了诺谟图模型、修正诺谟图模型、EPIC模型、几何平均粒径模型和Torri模型等5种土壤可蚀性K值预测模型的不确定性。结果表明,5种模型的不确定性从小到大的顺序为:Torri模型<修正诺谟图模型和诺谟图模型<EPIC模型<几何平均粒径模型;Torri模型的MRE为0.291,不确定性依然很大。但经优化的Torri模型,可将土壤可蚀性K值预测的不确定性降至最低,其K的预测值与观测值的线性回归系数b=1.028(R2=0.921,p<0.01),MRE仅为0.120,可用于预测我国亚热带地区某些土壤可蚀性K值。

Soil erodibility K-factor is an indispensable parameter in soil erosion prediction models, such as USLE and RUSLE. Immeasurable error might result from direct indiscriminate use of these empirical models in estimating K-factor. Based on observed K factors of seven typical soils in subtropical China, uncertainties of five K-factor prediction models （i. e. , nomogragh model, modified nomogragh model, EPIC model, Geometric mean particle model, and Torri model） are evaluated by means of statistics such as mean absolute error（MAE）, mean relative error（MRE）, root mean squared error（RMSE） and accuracy factor （Af）. Results show that the five models could be lined in the order of Torri model 〈 nomogragh model and modified nomogragh model 〈 EPIC model 〈 Geometric mean particle model in terms of uncertainty. Though MRE for the Torri model is only 0. 291, it is still high in uncertainty. However, an optimized Torri model can minimize the uncertainty. Its linear regression coefficient between observed K factors and predicted K factors is b = 1. 028 （R^2 =0. 921, p 〈0. 01 ）, with an MRE being 0. 120. It is therefore applicable to prediction of soil erodibility K-factor of certain soils in subtropical China.