Faculty Publications

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Spatiotemporal Analysis of Compound Agrometeorological Drought and Hot Events in India Using a Standardized Index
(American Society of Civil Engineers (ASCE), 2021) Muthuvel, D.; Mahesha, A.
Meteorological droughts abetted by hot events could instigate an agricultural drought that eventually affects crop yield. Different types of droughts may coexist or occur in succession. A single index based on one particular variable may not be sufficient to quantify such compound drought events. Therefore, this study embedded drought indexes ofstandardized precipitation index (SPI), standardized soil-moisture index (SSI), and standardized temperature index (STI) with Gaussian copula functions to study compound agrometeorological drought and hot events in India from 1948 to 2014. By standardizing the joint probability of the SPI, SSI, and STI time series, the standardized compound drought and hot index (SCDHI) was developed. The SCDHI values in the monsoon months of different climatic zones have a strong correlation of about 0.95 with other well-established indexes such as the standardized compound event indicator (SCEI), which incorporates SPI and STI, and the multivariate standardized drought index (MSDI), which incorporates SPI and SSI. Based on the areal extent, 1965-1966, 1972, 1987, and 2002 were identified as significant compound drought years in India. The index also identified three successive compound events of the 2012-2014 northest monsoon in the southern peninsular region. A notable increase in the frequency of compound drought and hot events was found post-2000. The case studies of the major drought events and the dependent pattern of SCDHI on its constituent indexes indicate that SCDHI performs well as an indicator of compound agrometeorological and hot events across different climatic regions and in both southwest and northeast monsoons. © 2021 American Society of Civil Engineers.
Future global concurrent droughts and their effects on maize yield
(Elsevier B.V., 2023) Muthuvel, D.; Sivakumar, B.; Mahesha, A.
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. © 2022 Elsevier B.V.
A multivariate index-flood approach for flood frequency analysis of ungauged watersheds: a case study on state of Kerala in India
(Springer Science and Business Media Deutschland GmbH, 2025) HariKrishna, M.; Vinod, D.; Desai, S.; Mahesha, A.
The multivariate index-flood method (MIF) advances flood risk evaluation at ungauged watersheds by utilizing information from gauged sites within a uniform region to forecast flood attributes where direct data is absent. It aims to enhance flood frequency analysis at ungauged watersheds by considering the interdependence between multiple flood variables using copulas and multivariate quantile curves. The proposed methodology involves screening data for anomalies, delineating homogeneous regions based on physiographic and hydrological descriptors, and selecting appropriate regional marginal distributions and copulas. Regional Flood Frequency Analysis and the index-flood method, MIF, can produce dependable multivariate quantile approximations, enhancing the precision of flood projections and risk evaluations at ungauged watersheds. Nine watersheds in the Indian state of Kerala situated along rivers flowing westward have been subjected to the suggested multivariate technique, which focuses on the bivariate case. This implementation involves recorded data series on flood volume and peak flow. The dataset includes daily maximum discharge data from India-WRIS, gridded precipitation and temperature data from IMD, and a 30 × 30 m DEM from USGS SRTM. The data record span 31–39 years. Subsequently, given a specific return period, a set of occurrences where volume and peak fall within a bivariate quantile curve is established at a designated watershed. The quantile curves derived from the regional methodology are juxtaposed with those obtained through the local method to assess the efficacy of the MIF technique. The model performed well for Arangali, Kalampur, Pattazhy, Pudur, and Mankara stations, as the quantile curves generated by the regional and local approaches matched well at these watersheds. In contrast, the regional and local quantile curves differ considerably at Perumannu, Ramamangalam, Kidangoor, and Erinjipuzha watersheds, indicating the effect of small sample size, higher sensitivity to local factors, modeling approach, and uncertainty involved. This investigation significantly enhances flood risk assessment in river areas using the MIF method to generate regional quantile curves, identify homogeneous regions, and compare regional and local quantile estimates, improving predictive accuracy at ungauged watersheds. The study confirmed data homogeneity across nine Kerala watersheds, with multivariate discordancy measures ?Di?<2.6, and a homogeneity test H value of -0.76. The BB8 copula best modeled the joint distribution of mean flood volume (V) and peak flow (Q), achieving a Kendall’s tau of 0.711 at Arangali. Regional quantile curves aligned well with standardized data, with the Gaussian copula (?)=0.4427, p<(1.75E-27) selected for multivariate regional analysis. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2025.

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