水科学进展
水科學進展
수과학진전
Advances in Water Science
2015年
5期
649-659
,共11页
段良霞%黄明斌%张洛丹%索立柱%张永坤
段良霞%黃明斌%張洛丹%索立柱%張永坤
단량하%황명빈%장락단%색립주%장영곤
坡面尺度%土壤水分%线性回归%状态空间模拟%黄土高原
坡麵呎度%土壤水分%線性迴歸%狀態空間模擬%黃土高原
파면척도%토양수분%선성회귀%상태공간모의%황토고원
slope scale%soil moisture%linear regression%state-space model%Loess Plateau
为掌握黄土高原沟壑区坡地土壤水分的空间分布特征及其影响因素, 采用状态空间模型和经典线性回归方法对该区不同土层深度土壤含水率的分布进行模拟. 结果表明, 不同土层深度的土壤含水率呈中等程度变异, 并与海拔高度、 黏粒、 粉粒、 砂粒含量和分形维数具有显著的空间自相关和交互相关关系, 可用于状态空间模拟分析. 不同因素组合下的状态空间模拟效果均要优于线性回归方程, 其中采用海拔高度、 砂粒含量和分形维数的三因素状态空间方程模拟精度最高( R2=0. 992). 状态空间模拟方法可用于黄土高原坡面尺度不同土层深度土壤含水率的预测.
為掌握黃土高原溝壑區坡地土壤水分的空間分佈特徵及其影響因素, 採用狀態空間模型和經典線性迴歸方法對該區不同土層深度土壤含水率的分佈進行模擬. 結果錶明, 不同土層深度的土壤含水率呈中等程度變異, 併與海拔高度、 黏粒、 粉粒、 砂粒含量和分形維數具有顯著的空間自相關和交互相關關繫, 可用于狀態空間模擬分析. 不同因素組閤下的狀態空間模擬效果均要優于線性迴歸方程, 其中採用海拔高度、 砂粒含量和分形維數的三因素狀態空間方程模擬精度最高( R2=0. 992). 狀態空間模擬方法可用于黃土高原坡麵呎度不同土層深度土壤含水率的預測.
위장악황토고원구학구파지토양수분적공간분포특정급기영향인소, 채용상태공간모형화경전선성회귀방법대해구불동토층심도토양함수솔적분포진행모의. 결과표명, 불동토층심도적토양함수솔정중등정도변이, 병여해발고도、 점립、 분립、 사립함량화분형유수구유현저적공간자상관화교호상관관계, 가용우상태공간모의분석. 불동인소조합하적상태공간모의효과균요우우선성회귀방정, 기중채용해발고도、 사립함량화분형유수적삼인소상태공간방정모의정도최고( R2=0. 992). 상태공간모의방법가용우황토고원파면척도불동토층심도토양함수솔적예측.
Soil water content is one of the key factors affecting plant growth and eco-environment reconstruction on the Loess Plateau of China. To assess the spatial heterogeneity of soil water content and its potential influencing factors on a hillslope in the gully region of the Loess Plateau, the state-space approach and a classical linear regression approach were applied in order to identify and quantify the significant relationships between soil water content and elevation, contents of clay, silt, and sand, median soil grain size, and fractal dimension. The results showed that the soil water contents in different soil layers exhibited moderate variation, and were significantly influenced by the elevation, the contents of clay, silt, and sand, and by the fractal dimension. Autocorrelation for the six potential influencing factors were conducted, and cross-correlation functions indicated strong spatial dependences between the soil water content and the elevation, the contents of clay, silt, and sand, and the fractal dimension. The state-space approach simulated the soil water content much better than any equivalent linear regression method. The best state-space model included the elevation, the sand content, and the fractal dimension, which could explain 99% of the variation in the soil water contents;the model accurately predicted the soil water contents along two transects. Consequently, the state-space a-nalysis was verified to be an effective tool for estimating soil water contents in different soil layers on a hillslope on the Loess Plateau.