Meteorological drought grade prediction using three-dimensional log-linear models

被引:0
|
作者
Feng, Ping [1 ]
Hu, Rong [1 ]
Li, Jian-Zhu [1 ]
机构
[1] State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China
来源
关键词
Based on the monthly precipitation data from 21 rain gauges during the period of 1958-2009 in the Panjiakou Reservoir catchment of the Luanhe River Basin; drought class time series were derived from Standardized Precipitation Index (SPI) time series computed in a 12-month time scale. Log-linear models for three-dimensional contingency tables were fitted to the observed frequencies of drought class transitions during the period of 1958-2008. Odds and respective confidence intervals for drought class transitions were also calculated to estimate the drought class transition probabilities. Thus the drought classes with 1 and 2 months lead were predicted and the short term meteorological drought class prediction was achieved in the studied area. The validation of the predictions was performed for the 2009 drought. The results show that the contingency tables of drought class transitions present a strong diagonal tendency and results for all sites show a similar agreement between observed and expected frequencies with all the models presenting a test p-value exceeding the chosen significance level of α=0.05. And results of three-dimensional log-linear modeling present good results when comparing predicted and observed drought classes for those 21 stations. It could be concluded that log-linear prediction of drought class transitions is useful for short term drought warning in the Panjiakou Reservoir catchment;
D O I
10.13243/j.cnki.slxb.2014.05.001
中图分类号
学科分类号
摘要
引用
收藏
页码:505 / 512
相关论文
共 50 条
  • [1] Analysis of drought transitions using log-linear models in Iran
    Bahrami, Mehdi
    Zarei, Abdol Rassoul
    Chakav, Safie
    [J]. International Journal of Water, 2017, 11 (03) : 266 - 278
  • [2] SPI drought class prediction using log-linear models applied to wet and dry seasons
    Moreira, Elsa E.
    [J]. PHYSICS AND CHEMISTRY OF THE EARTH, 2016, 94 : 136 - 145
  • [3] DROUGHT LEVEL PREDICTION BASED ON LOG-LINEAR MODEL
    Huang, Chunyan
    [J]. ENVIRONMENTAL ENGINEERING AND MANAGEMENT JOURNAL, 2020, 19 (09): : 1467 - 1474
  • [4] Linear and log-linear models
    Christensen, R
    [J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2000, 95 (452) : 1290 - 1293
  • [5] Monthly Prediction of Drought Classes Using Log-Linear Models under the Influence of NAO for Early-Warning of Drought and Water Management
    Moreira, Elsa
    Russo, Ana
    Trigo, Ricardo M.
    [J]. WATER, 2018, 10 (01)
  • [6] Log-linear modeling using conditional log-linear structures
    P. Vellaisamy
    V. Vijay
    [J]. Annals of the Institute of Statistical Mathematics, 2009, 61 : 309 - 329
  • [7] NONSTANDARD LOG-LINEAR MODELS
    LANGEHEINE, R
    [J]. ZEITSCHRIFT FUR SOZIALPSYCHOLOGIE, 1983, 14 (04): : 312 - 321
  • [8] DIAGNOSTICS IN LOG-LINEAR MODELS
    Guria, Sibnarayan
    Sen Roy, Sugata
    [J]. ADVANCES AND APPLICATIONS IN STATISTICS, 2015, 44 (02) : 163 - 176
  • [9] AGGREGATION WITH LOG-LINEAR MODELS
    LEWBEL, A
    [J]. REVIEW OF ECONOMIC STUDIES, 1992, 59 (03): : 635 - 642
  • [10] LOG-LINEAR MODELS IN GEOGRAPHY
    UPTON, GJG
    FINGLETON, B
    [J]. TRANSACTIONS OF THE INSTITUTE OF BRITISH GEOGRAPHERS, 1979, 4 (01) : 103 - 115