Research review and perspective of drought forecasting
MA Miaomiao, ZHANG Xuejun, LYU Juan, SU Zhichen
1. China Institute of Water Resources and Hydropower Research, Beijing 100038; 2. Research Center on Flood and Drought Disaster Reduction of the Ministry of Water Resources, Beijing 100038
Abstract：The frequency and intensity of drought disasters increase due to the impact of global climate change and the aggravation of human activities， which seriously threat the food disaster prevention and water security of China. Accurate and timely drought forecasting is important for formulating scientific and effective drought mitigation strategies and reducing the corresponding losses. This study reviews the research progresses based on statistical forecasting methods and physical mechanism forecasting models. It reveals the existing problems of current forecasting technology， and puts forward targeted solutions. The future research should focus on improving the quality of drought monitoring data， breaking through some key technologies， and building a national drought forecasting operational system， so as to provide strong scientific and technological supports for drought mitigation and disaster reduction.
[1] 中华人民共和国国家质量监督检验检疫局, 中国国家标准化管理委员会. 区域旱情等级:GB/T 32135-2015[S].2015. [2] 屈艳萍, 吕娟, 苏志诚, 等. 抗旱减灾研究综述及展望[J]. 水利学报, 2018, 49(1):115-125. [3] 徐冬梅, 赵晓慎. 中长期水文预报方法研究综述[J]. 水利科技与经济, 2010(1):1-7. [4] Box G, Jenkens G.Time series analysis:forecasting and control[M]. San Francisco:Holden Day press, 1976. [5] Ooms M,Franses PH.A seasonal periodic long memory model for monthly river flows[J].Environmental Modelling & Software, 2001,16:559-569. [6] Huang W,Xu B, Chan H.Forecasting flows in Apalachicola River using neural networks[J].Hydrological Processes, 2010, 18(13):2545-2564. [7] 周淼, 田富强, 胡和平. 基于联合主成分分析的宜昌站径流中长期预报[J]. 水利发电学报, 2013, 32(1):37-43. [8] 王龙昌, 贾志宽, 王立祥. 灰色灾变理论在宁南山区干旱气候预测中的应用[J]. 干旱区资源与环境, 2003, 17(1):60-63. [9] 巍玉君, 慕运动, 徐传洲. 辉县市降水量灰色拓扑预测[J]. 河南林业科技, 2002, 22(2):13-15. [10]徐祝, 姚普林, 蔡久艳. 应用灰色系统理论GM (1, 1)模型预测朝阳旱灾发生年[J]. 干旱地区农业研究, 1996, 14(4):90-93. [11]杨建伟. 灰色理论在干旱预测中的应用[J]. 水文, 2009(2):50-51. [12]Paulo A A,Ferreira E, Coelho C, et al.Drought class transit on analysis through Markov and Loglinear models,an approach to early warning[J]. Agricultural Water Management, 2005, 77(1-3):59-81. [13]王彦集, 刘峻明, 王鹏新, 等. 基于加权马尔可夫模型的标准化降水指数干旱预测研究[J]. 干旱地区农业研究, 2007, 25(5):198-203. [14]金龙, 陈宁, 林振山. 基于人工神经网络的集成预报方法研究和比较[J]. 气象学报, 1999, 57(2):198-207. [15]李帆, 史岩, 孙凯, 等. 基于BP网络的土壤墒情预报模型研究[J]. 莱阳农学院学报(自然科学版), 2006, 3(2):136-140. [16]迟道才, 张兰芬, 李雪, 等. 基于遗传算法优化的支持向量机干旱预测模型[J]. 沈阳农业大学学报, 2013, 44(2):190-194. [17]樊高峰, 张勇, 柳苗, 等. 基于支持向量机的干旱预测研究[J]. 中国农业气象, 2011, 32(3):475-478. [18]Burges C J.A tutorial on support vector machines for pattern recognition[J].Data Mining and Knowledge Discovery, 1998(2):127-167. [19]李晓辉, 杨勇, 杨洪伟. 基于BP神经网络与灰色模型的干旱预测方法研究[J]. 沈阳农业大学学报, 2014, 45(2):253-256. [20]侯姗姗, 王鹏新, 田苗. 基于相空间重构与RBF神经网络的干旱预测模型[J]. 干旱地区农业研究, 2011, 29(1):224-230. [21]田苗, 王鹏新, 侯姗姗, 等. 基于相空间重构与RBF神经网络模型的面上干旱预测研究[J]. 干旱地区农业研究, 2013, 31(6):164-168. [22]DayGN.Extended streamflow forecasting using NWSRFS[J].Journal of Water Resources Planning and Management, 1985, 111:157-170. [23]李岩, 胡军, 王金星, 等. 河流集合预报方法(ESP)在水资源中长期预测中的应用研究[J]. 水文, 2008, 28(1):25-27. [24]Yang L, Tian F, Sun Y, et al. Attribution of hydrologic forecast uncertainty within scalable forecast windows[J].Hydrology and Earth System Sciences, 2014(18):775-786. [25]Hamlet A F,Lettenmaier D P. Columbia River streamflow forecasting based on ENSO and PDO climate signals[J].Journal of Water Resources Planning and Management, 1999, 125(6):333-341. [26]Werner K, Brandon D, Clark M,et al.Climate index weighting schemes for NWS ESP-based seasonal volume forecasts[J].Journal of Hydrometeorology, 2004(5):1076-1090. [27]Werner K,Brandon D,Clark M,et al. Incorporating medium-range numerical weather model output into the ensemble streamflow prediction system of the National Weather Service[J].Journal of Hydrometeorology, 2005(6):101-114. [28]Van Dijk,Pe~na-Arancibia J L,Wood E F, et al.Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide[J]. Water Resources Research, 2013, 49:2729-2746. [29]Wood A W,Lettenmaier D P. A test bed for new seasonal hydrologic forecasting approaches in the western United States[J].Bulletin of the American Meteorological Society, 2006, 87:1699-1712. [30]Webster PJ. Extended-range probabilistic forecast of Ganges and Brahmaputra floods in Bangladesh[J].Bulletin of the American Meteorological Society, 2010, 91:1493-1514. [31]Sheffied J, Wood E F, Chaney N, et al.A drought monitoring and forecasting system for sub-Sahara African water resources and food security[J].Bulletin of the American Meteorological Society, 2014, 95(6):861-882. [32]张学君. 大尺度陆面水文过程监测与季节预报研究[D]. 北京:中国科学院大学, 2016. [33]高冰. 长江流域的陆气耦合模拟及径流变化分析[D]. 北京:清华大学, 2012. [34]Yuan X,Wood E F,Roundy J K,et al. CFSv2-based seasonal hydroclimatic forecasts over the conterminous United States[J].Journal of Climate, 2013, 26:4828-4847.
2021, Vol. 31 
