EGU23-11208, updated on 08 Jan 2024
EGU General Assembly 2023
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Future drought prediction using time-series of drought factors and the US drought monitor data based on deep learning over CONUS

Bokyung Son1, Jaese Lee1, Jungho Im1, and Sumin Park2
Bokyung Son et al.
  • 1Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
  • 2Satellite Application Division, Korea Aerospace Research Institute, Daejeon, South Korea

Predicting future drought conditions is crucial for preventing massive agricultural and/or hydrological resource damage caused by drought. This study predicts future (in this case, 3-month forecast lead time) drought conditions in the contiguous United States, especially focusing on five different dry and drought severity classes indicated by the United States Drought Monitor (USDM) during 2000-2020. A deep learning model was trained using the time-series of USDM and four different types of drought-related variables (i.e., hydro-meteorological variables) such as precipitation and temperature from Phase 2 of the North American Land Data Assimilation System. UNet, one of the image-to-image translation techniques, was used as a basic deep learning architecture to consider the spatial characteristics (extents of each drought severity class) of drought across the continent. As drought classes in USDM are ordinal, the loss function of the deep learning model was set to be able to consider ordinal problems utilizing the cross-entropy loss function. The results of the proposed model were compared to the existing seasonal drought outlooks provided by the National Oceanic and Atmospheric Administration Climate Prediction Center. The performance for the validation period (2 years) showed an overall accuracy of about 65%. When compared to the seasonal outlooks, it demonstrated about a 6% improvement in terms of overall accuracy for changing drought conditions. Future research will further discuss the performance of the proposed model with other comparable reference data and the impact of each input variable to predict future drought conditions.

How to cite: Son, B., Lee, J., Im, J., and Park, S.: Future drought prediction using time-series of drought factors and the US drought monitor data based on deep learning over CONUS, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11208,, 2023.