Droughts are persistent, longer-than-normal dry spells that can be devastating. They can wipe out crops, cause water shortages, and impact millions of lives. Each monsoon season in India carries the hopes and fears of millions, as rain or the lack of it can spell the difference between bountiful harvests and arid fields. Add to this delicate balance the chaos of climate change, and droughts become unpredictable and devastating.
A recent study from the Indian Institute of Technology (IIT) Palakkad illuminates the unpredictability of droughts in India, especially as climate change continues to rattle our expectations. The research uncovers how different ways of measuring drought can lead to vastly different forecasts, painting a picture of uncertainty that has real-world impacts for everyone, from farmers to policymakers.
Their toolkit consisted of six major multivariate meteorological drought indices (MMDIs): Standardized Precipitation Evaporation Index (SPEI), Reconnaissance Drought Index (RDI), self-calibrated Palmer Drought Severity Index (scPDSI), Standardized Palmer Drought Index (SPDI), Standardized Moisture Anomaly Index (SZI) and Supply Demand Drought Index (SDDI). MMDIs are drought assessment tools that incorporate multiple meteorological variables, like precipitation and evapotranspiration, to provide a more comprehensive picture of drought conditions. Each of the six selected indices had slightly different ways of calculating drought severity.
Additionally, two methods were chosen to estimate evapotranspiration: Thornwaithe (TW) and the slightly more complex Penman-Monteith (PM). Historical climate data came from the European Centre for Medium-Range Weather Forecasts (ECMWF - ERA5). For projections, the team used CMIP6, a well-regarded climate model.
The research was thorough in its scope, encompassing the entire Indian subcontinent, which was divided into climate zones using an aridity index. This setup allowed the researchers to construct maps illustrating the agreement between different indices, using techniques like correlation and category difference analysis. Furthermore, a global sensitivity analysis (GSA) helped untangle how much each variable—like climate models and drought indices—contributed to the overall uncertainty in drought projections.
The researchers found interesting patterns under historical climate conditions. By comparing different methods to predict drought, they noticed that one index—the Standardized Palmer Drought Index, or scPDSI—tended to show a slightly higher number of droughts than other methods. Historically, all these different methods of drought forecasting mostly agreed with each other, which was good. However, when they looked into the future, under different climate change scenarios, the agreement started to fall apart. Some methods predicted that India would get drier, while others predicted it would get wetter.
This investigation also shows that as the projections moved to the end of the 21st century, the difference in outcomes based on the choice of index grew bigger, particularly in arid and semi-arid areas. In the realms of high-emission scenarios and drier zones, the consistency between various methods for predicting drought dropped significantly.
One significant contributor to this uncertainty is the combination of the selected drought index and the model used to simulate future climate conditions. They observed the highest uncertainty in projections when they used the Penman-Monteith method to estimate evapotranspiration. This method, while sophisticated, underscores how the selection of different modelling tools can drastically affect outcomes.
The researchers, however, acknowledge that their models primarily study meteorological impacts, leaving room for future research to explore how these indices react to specific challenges, like soil moisture or impacts on urban water supply. They also note that different seasons have varying drought characteristics—a summer dry spell is not quite the same as a winter one—suggesting another rich vein of research into seasonal drought indices.
The study stresses the importance of carefully weighing which drought index to use, as the outcome can differ significantly depending on the choice. It also highlights the need to probe the different variables further to accurately predict drought conditions accurately.
In a world where each snippet of information can shape critical decisions for billions, understanding drought uncertainties is critical. It can aid farmers in planning crop cycles, help city planners manage water resources, and provide policymakers with robust data to make informed decisions about food security. This study, however, highlights the complexities of predicting the future, especially that of climate and weather.
This research article was written with the help of generative AI and edited by an editor at Research Matters.