| 摘要: 利用土壤理化性质数据建立转换函数是间接获得土壤水力参数的重要手段之一。基于测定的土壤理化性质和土壤水分常数数据, 本文采用回归分析、BP神经网络和基于BP神经网络的Rosetta模型3种方式分别建立了青海三江源地区土壤饱和含水量、毛管持水量和田间持水量的转换函数, 并对其预测精度进行了比较。结果表明: (1)回归分析方法总体预测效果比较理想, 特别是田间持水量的平均误差(ME)和均方根误差(RMSE)都在3.397%以下, 决定系数(R2)高达0.868; (2)BP神经网络方法的预测效果非常理想, 各土壤水分常数平均误差和均方根误差都在4.685%以下, 并且决定系数均在0.857以上; (3)Rosetta模型的预测效果相对较差, 特别是饱和含水量和毛管持水量, 平均误差(ME)和均方根误差(RMSE)相对较大, 决定系数(R2)相对较小。3种方式中, BP神经网络方法所建立的毛管持水量和饱和含水量转换函数均为最佳, 回归方法所建立的田间持水量的转换函数要好于BP神经网络方法和Rosetta模型, Rosetta模型对土壤水分常数的预测效果不如其他两种方式。研究可为青海三江源地区土壤水力特性参数研究以及区域尺度上土壤水分估算提供科学依据。 |
| 关键词: 土壤水分常数 转换函数 回归分析 BP神经网络 Rosseta模型 |
| 中图分类号: |
| 基金项目:国家科技支撑计划项目(2009BAC61B01)资助 |
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| Establishment and comparison of pedotransfer functions of soil moisture constant in the Three-River Headwaters Region of Qinghai Province |
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YI Xiang-Sheng1, LI Guo-Sheng2, YIN Yan-Yu3
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1. Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China;2.Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;3. School of Geography, Beijing Normal University, Beijing 100875, China 4. Laboratory of Regional Geography, Beijing Normal University, Beijing 100875, China
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| Abstract: Soil moisture constants such as saturated water content and field water capacity are important hydraulic parameters in soil science. However, they are frequently lacking in routine soil surveys because direct measurements are time-consuming and labor intensive. As such, it was necessary to find indirect, cheap and rapid ways of acquiring soil moisture constants from other more easily available soil physical and chemical properties. In this study, pedotransfer functions were established by using the methods of regression, BP neural network and Rosetta model to predict saturated soil water content, capillary water-holding capacity and field water capacity from soil organic matter, texture and bulk density in the Three-River Headwaters Region of Qinghai Province. The general performance of pedotransfer functions was evaluated based on mean error, root mean squared error and determination coefficient between observed and predicted values. Vital results obtained were grouped mainly into three aspects as follows: (1) Mean error and root mean squared error of pedotransfer functions derived from regression method for the three soil moisture constants were small with big determination coefficients, showing good prediction. Specifically, mean error and root mean squared error of field water capacity derived from regression method were below 3.397%, with determination coefficient above 0.868. (2) Mean error and root mean squared error of pedotransfer functions for the three soil moisture constants obtained by BP neural network method were below 4.685% with determination coefficients above 0.857. Moreover, observed and predicted values of the three soil moisture constants were close to the 1︰1 line. It therefore generally suggested that the estimation of the three soil moisture constants by the BP neural network method was good. (3) As for pedotransfer functions derived from Rosetta model, mean error and root mean squared error of the three soil moisture constants were larger and with lower determination coefficients than the other two methods. This showed that Rosetta model performed relatively poor in estimating soil moisture constants in the study area. In general, pedotransfer functions of saturated soil water content and capillary water-holding capacity established by BP neural network method were the best. Furthermore, regression method was best for estimating field water capacity and Rosetta model was worse than the other two methods. This research not only provided the scientific basis for studying soil hydraulic parameters in the Three-River Headwaters Region in Qinghai Province, but was also critical for estimating soil water at region scales. |
| Keyword: Soil moisture constant, Pedotransfer function, Regression analysis, BP neural network, Rosetta model |
