The changing trends of drought in the Loess Plateau and Grey-Markov chain prediction model
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DOI:10.7606/j.issn.1000-7601.2008.03.01
Key Words: drought  trend  Mann-Kendall test  Grey prediction model  Markov chain  Loess Plateau
Author NameAffiliation
ZHANG Jianxing Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Education,Yangling,Shaanxi 712100,China 
MA Xiaoyi Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Education,Yangling,Shaanxi 712100,China 
ZHAO Wenju Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Education,Yangling,Shaanxi 712100,China 
HAO Jingjing Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Education,Yangling,Shaanxi 712100,China 
QU Jinna Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Education,Yangling,Shaanxi 712100,China 
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Abstract:
      By applying the Mann-Kendall test?the changing trends of annual and seasonal climatic drought index were analyzed based on the precipitation data of 21meteorological observation stations(1957~2001) in the Loess Plateau.The changing trend of hydrological drought and agricultural drought were also analyzed at the same time.The results showed that:There were developing trends of drought for summer autumn winter and the whole year and the drought developing trend of summer and autumn lead to the drought developing trend of the year;Spring has a wetness developing trend.Taking the Weihe River basin for example?it was revealed that the water resources of Loess Plateau were becoming less and less;By analyzing the relationships between climatic drought and hydrological drought we found climatic drought may be the primary reason leading to the hydrological drought;At the same time?the agricultural drought situation was becoming heavier than before by analyzing the areas influenced by climatic drought in the Loess Plateau?especially in1990s.The Grey-Markov chain model was established to predict the climatic drought index at last;and the average precision of the prediction model was 88.26%.