Future global concurrent droughts and their effects on maize yield

Dineshkumar Muthuvel; Bellie Sivakumar; Amai Mahesha

doi:10.1016/j.scitotenv.2022.158860

Future global concurrent droughts and their effects on maize yield

Sci Total Environ. 2023 Jan 10:855:158860. doi: 10.1016/j.scitotenv.2022.158860. Epub 2022 Sep 17.

Authors

Dineshkumar Muthuvel¹, Bellie Sivakumar², Amai Mahesha³

Affiliations

¹ Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra 400076, India.
² Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra 400076, India. Electronic address: b.sivakumar@iitb.ac.in.
³ Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka Surathkal, Mangaluru 575025, India.

PMID: 36126712
DOI: 10.1016/j.scitotenv.2022.158860

Abstract

Droughts are one of the most devastating natural disasters. Droughts can co-exist in different forms (e.g. meteorological, hydrological, and agricultural) as concurrent droughts. Such concurrent droughts can have far reaching implications for crop yield and global food security. The present study aims to assess global concurrent drought traits and their effects on maize yield under climate change. The standardized indices of precipitation, runoff, and soil moisture incorporated as multivariate standardized drought index (MSDI) using copula functions are used to quantify the concurrent droughts. The ensemble data of several General Circulation Models (GCMs) considering the high emission scenario of Coupled Model Intercomparison Project phase 6 (CMIP6) are utilized. Applying run theory on a time series (1950-2100) of MSDI values, the duration, severity, areal coverage, and average areal intensity of concurrent droughts are computed. The temporal evolution of drought duration and severity are compared among historical (1950-2014), near future (2021-2060), and far future (2061-2100) timeframes. The results indicate that the most vulnerable regions in the late 21st century are Central America, the Mediterranean, Southern Africa, and the Amazon basin. The indices and spatial extent of the individual droughts are used as predictor variables to predict the country-level crop index of the top seven producers of maize. The historical dynamics between maize yield and different drought forms are projected using XGBoost (Extreme Gradient Boosting) algorithms. The future temporal changes in drought-crop yield dynamics are tracked using probabilities of various drought forms under yield-loss conditions. The conditional concurrent drought probabilities are as high as 84 %, 64 %, and 37 % in France, Mexico, and Brazil, revealing that concurrent drought affects the maize yield tremendously in the far future. This approach of applying statistical and soft-computing techniques could aid in drought mitigation under changing climatic conditions.

Keywords: CMIP6; Climate change; Compound drought; Copula; Multivariate standardized drought index.

MeSH terms

Agriculture
Climate Change
Droughts*
Meteorology
Zea mays*