摘要
淠河流域汛期旱涝灾害频发,严重影响当地的经济发展和居民生命财产安全。研究汛期旱涝灾害演变规律及趋势对指导区域内科学防灾、减灾、救灾具有重要的现实意义。本文将墨西哥帽小波和叠加马尔科夫链分析相结合探讨淠河流域汛期旱涝的演变规律,并对未来年份进行预测。结果表明,在不同时间尺度上流域汛期具有不同的旱涝演变情景,2003年以来在偏涝的背景下发生洪涝灾害的风险大。近50年来淠河流域汛期旱涝演变的第一主周期为2年,第二主周期为9年。叠加马氏链可以准确预测汛期旱涝等级状态,淠河流域汛期涝年各等级出现的概率均大于相应的旱年等级,重现期短于后者。小波分析为马尔科夫链方法提供趋势和背景,马氏链可以实现中短期汛期年景预测,两者互为补充,互为验证,可以提高预测精度和可靠性。
Droughts and floods occurr frequently in the rainy season in the Pi River Valley, and they seriously affect the life and properties of local inhabitants as well as the sustainable development of economy in this region. Therefore, analysis on the changing patterns and prediction of floods and droughts are of great importance for taking precautions against and fight natural adversities in the Pi River Valley. Wavelet transforms in Mexican Hat Function and overlay Markov chain analysis are used in the paper to explore the changing patterns of floods and droughts, so as to predict the conditions during the rainy seasons in future years in Pi River Valley. The research reveals the periodic changes of floods and droughts in Pi River Valley in recent 50 years on different time scales. Since 1980s, the frequency and intensity of floods and waterlogs have become greater. The quasi-fluctuations of 2a is most noticeable from mid-1960s to the end of 1970s and the quasi-fluctuations of 3-4a show predominance after 1993. The two major periods of floods and droughts variations in the rainy seasons of Pi River Valley in the past five decades are 2a and 9a respectively. The risks of floods and waterlogs will be high in the 10 to 12 years after 2003. Overlay Markov chain can reliably forecast the grade of floods and droughts in future years. The results show that precipitation in the rainy seasons in 2009 and 2010 should be normal. The probability of a year with normal precipitation during the rainy season is the largest and the multiyear return period is only 2.02 years. The probability of each flood grade is greater than it of the corresponding drought grade, and the multiyear return periods of each flood grade is shorter than it of the corresponding drought grade. Wavelet transforms offer trend and background for Markov chain analysis, and Overlay Markov chain can forecast grades of floods and droughts in medium short-term correctly. The combination of wavelet transforms and Markov chain analysis can improve the correctness and reliability of prediction of floods and droughts since they are complementary to each other.
出处
《资源科学》
CSSCI
CSCD
北大核心
2009年第6期1046-1050,共5页
Resources Science
基金
国家自然科学基金委创新群体计划支持项目(编号:40421001)
安徽省高等学校青年教师科研资助计划项目(编号:2008jq1157)
安徽省人文地理学重点学科资助
关键词
旱涝
汛期
小波变换
马尔科夫链
淠河流域
Floods and droughts
Rainy season
Wavelet transforms
Markov chain
Pi River Valley
