张建兴,马孝义,赵文举,郝晶晶,屈金娜.黄土高原地区干旱长期变化趋势及预测[J].干旱地区农业研究,2008,(3):1~6
黄土高原地区干旱长期变化趋势及预测
The changing trends of drought in the Loess Plateau and Grey-Markov chain prediction model
  
DOI:10.7606/j.issn.1000-7601.2008.03.01
中文关键词:  干旱  趋势  预测  Mann-Kendall检验  灰色预测模型  Markov链  黄土高原
英文关键词:drought  trend  Mann-Kendall test  Grey prediction model  Markov chain  Loess Plateau
基金项目:国家自然科学基金项目(50479052); 国家科技支撑计划课题(2006BAD11B04); 西北农林科技大学青年学术骨干计划资助课题
作者单位
张建兴 西北农林科技大学旱区农业水土工程教育部重点实验室,陕西 杨凌 712100 
马孝义 西北农林科技大学旱区农业水土工程教育部重点实验室,陕西 杨凌 712100 
赵文举 西北农林科技大学旱区农业水土工程教育部重点实验室,陕西 杨凌 712100 
郝晶晶 西北农林科技大学旱区农业水土工程教育部重点实验室,陕西 杨凌 712100 
屈金娜 西北农林科技大学旱区农业水土工程教育部重点实验室,陕西 杨凌 712100 
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中文摘要:
      根据黄土高原地区21个分布较为均匀的气象站1957~2001年的降水资料,应用Mann-Kendall趋势检验方法对其年、季气象旱涝指标的变化趋势进行分析,同时分析该区水文干旱和农业干旱变化趋势,结果表明:黄土高原地区年、夏、秋和冬季有向干旱化发展趋势,春季有向湿润化发展趋势;夏、秋季的干旱化趋势造成了年干旱化趋势。以渭河流域为例,黄土高原地区地表水资源呈显著性减少趋势,气象干旱可能是造成水文干旱的主要原因。黄土高原各省份旱情呈逐年代加重态势,尤其是进入20世纪90年代后趋势更加明显。用灰色-Markov链模型对该区旱涝指标进行预测,模型平均精度为88.26%。
英文摘要:
      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%.
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