Potential of Deep Learning in drought assessment by extracting information from hydrometeorological precursors

Abstract

This study explores the potential of the Deep Learning (DL) approach to develop a model for basin-scale drought assessment using information from a set of primary hydrometeorological precursors, namely air temperature, surface pressure, wind speed, relative humidity, evaporation, soil moisture and geopotential height. The novelty of the study lies in extracting the information from the hydrometeorological precursors through the efficacy of the DL algorithm, based on a one-dimensional convolutional neural network. Drought-prone regions, from where our study basins are selected, often suffer from the vagaries of rainfall that leads to drought-like situations. It is established that the proposed DL-based model is able to capture the underlying complex relationship between rainfall and the set of aforementioned hydrometeorological variables and, subsequently, shows its promise for the basin-scale meteorological drought assessment as revealed through different performance metrics and skill scores. The accuracy of simulating the correct drought category, among the seven categories, is also high (>70%). Moreover, in general, the skill of any climate model is much higher for the primary meteorological variables as compared with other secondary or tertiary variables/phenomena, like droughts. Thus, the novelty of the proposed DL-based model also lies in the improved assessment of ensuing basin-scale meteorological droughts using the projected meteorological precursors and may lead to new research directions. HIGHLIGHTS This study explores the potential of Deep Learning (DL) approach to capture the hidden complex hydrometeorological association.; A DL-based model is developed for basin-scale drought assessment using the hidden complex relationship from a set of primary hydrometeorological precursors.; DL may be effective to use simulated primary hydrometeorological variables from climate models to assess more complex phenomena.;

Keywords

• one-dimensional convolutional neural network (conv1d)
• deep learning
• drought
• hydrometeorology
• machine learning
• standardized precipitation anomaly index (spai)