Time-Varying Evaluation of Compound Drought and Hot Extremes in Machine Learning–Predicted Ensemble CMIP5 Future Climate: A Multivariate Multi-Index Approach
Publication: Journal of Hydrologic Engineering
Volume 29, Issue 2
Abstract
Compound extremes can be expressed as the joint distribution or dynamic interaction of multiple variables and the interdependence of several extremes that have major effects on the agricultural sector. Analysis of these compound extremes in space-time varying domains is a challenging task for climate experts. It is even difficult to forecast such compound extremes in future climate when several widely accepted global climate models (GCMs) and regional climate models (RCMs) are available. Thus, an attempt is made to develop a compound extreme index considering precipitation, temperature, and runoff, with a specific focus on promoting sustainable water resource management, particularly for the benefit of the agricultural sector. This work presents a new outlook on the modeling framework for compound drought and hot extremes (CDHE) by developing the Standardized Compound Extreme Event Index (SCEEI), and highlights their time-varying evaluation (trend and prediction) along with severity assessments. The SCEEI is derived using three individual indices: Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), and Standardized Temperature Index (STI), which are obtained from three physical variables (precipitation, runoff, and temperature) followed by multivariate Gaussian distribution. The applicability of the SCEEI is explained with a case study on the Brahmani and Baitarani rivers of eastern India, aiming at the modeling of dry and hot extremes at a regional scale. Further, the time-varying evaluation of CDHE events—the projection of the extremes—is studied in machine learning (ML)-based ensemble future. Random forest (RF) and support vector regression (SVR) MLs are employed to derive ensemble future from seven RCM models of the Coordinated Regional Downscaling Experiment (CORDEX). The linear scaling approach (LSA)-based bias-corrected data were used as input for the ensembling process. The RF-predicted ensembles performed statistically better for the study area. The extreme events and their severities are further assessed in RF-predicted future ensembles. The two-tail-based Mann–Kendall test, followed by Sen’s slope estimator, is used to investigate the trend-based time-varying evaluation of CDHE events. The notable conclusion drawn from this study is that the severity and frequency of CDHE events are increasing in the ensemble future climate, particularly in representative concentration pathway (RCP) 8.5. The developed multivariate framework might allow new prospects for suitable statistical analysis techniques for dependent compound extremes in climate-related studies and hydrology, which is further necessary for applying an adaptation strategy to boost resilience and adapt society for future climatic extremes.
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Data Availability Statement
The discharge data used in this study are available from the corresponding author upon reasonable request. The meteorological data are available from India Meteorological Department (IMD), Pune (http://www.imdpune.gov.in). The future climate model data are available in CORDEX database (https://esgf-node.llnl.gov/projects/esgf-llnl/).
Acknowledgments
The first author would like to thank the Ministry of Human Resources Development (MHRD) of Government of India for funding the research fellowship. The authors appreciate the India Meteorological Department (IMD), Pune and Central Water Commission (CWC), Bhubaneswar, for providing the necessary hydrometeorological data sets to carry out this research. These data can be accessed from these agencies after fulfilling the data sharing policy.
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Journal of Hydrologic Engineering
Volume 29 • Issue 2 • April 2024
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© 2024 American Society of Civil Engineers.
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Received: Feb 24, 2023
Accepted: Oct 11, 2023
Published online: Jan 11, 2024
Published in print: Apr 1, 2024
Discussion open until: Jun 11, 2024
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