Chinese Journal of Agrometeorology ›› 2021, Vol. 42 ›› Issue (09): 775-787.doi: 10.3969/j.issn.1000-6362.2021.09.005

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A Comprehensive Drought Evaluation Model in Beijing-Tianjin-Hebei Region Based on Deep Learning Algorithm

HU Xiao-feng, WANG Dong-li, ZHAO An-zhou, LIU Xian-feng, WANG Jin-jie   

  1. 1. School of Mining and Geomatics, Hebei University of Engineering, Handan 056038, China; 2. Key Laboratory of Natural Resources and Spatial Information, Handan 056038; 3. Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101; 4. School of Geography and Tourism, Shaanxi Normal University, Xi,an 710119
  • Received:2021-01-07 Online:2021-09-20 Published:2021-09-11

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Abstract:

Drought is the most common natural hazard in the Beijing-Tianjin-Hebei region. Timely and accurate drought evaluation is crucial for socio-economic development and agricultural production. Unilateral factors such as vegetation or precipitation are usually only considered in current drought assessment, which have some limitations in actual drought evaluation. In this study, multiple drought-causing factors such as precipitation, temperature, soil and terrain were considered comprehensively. Land surface temperature (LST), normalized difference vegetation index (NDVI), precipitation and other multi-source data from 2007 to 2017 were used to construct a comprehensive drought evaluation model with standardized precipitation evapotranspiration index (SPEI) as the target value under Tensorflow frame in the Beijing-Tianjin-Hebei region, Chinese main grain production base. Determination coefficient (R2) and root mean square error (RMSE) were used to evaluate model accuracy. The station standardized precipitation index (SPI), soil relative moisture data and meteorological disaster data for the Beijing-Tianjin-Hebei region in 2016 were used to validate the accuracy of model in time and space. The results showed that model in training and test sets had different performance in various months, with R2 both greater than 0.5 and RMSE less than 0.55. Comprehensive drought evaluation model had the best performance in November. Comprehensive drought index (CDI) output from the model was close to SPI and SPEI at Miyun station, and the change trend was basically consistent. The correlation coefficients between the CDI of the model and SPI, relative soil moisture at a 10cm depth were greater than 0.7 and 0.4 respectively. Both of them passed 0.01 significance level test. Spatially, compared with SPEI, the results of drought events in Beijing-Tianjin-Hebei region from March to July in 2016 calculated by CDI were more consistent with actual situation, which indicated that the comprehensive evaluation model was applicable for drought monitoring in Beijing-Tianjin-Hebei region. 

Key words: Comprehensive drought evaluation model, Deep learning, Multi-source data, Beijing-Tianjin-Hebei region, Comprehensive drought index (CDI)