Faculty Publications
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Item Assessment of meteorological drought return periods over a temporal rainfall change(Springer Science and Business Media Deutschland GmbH, 2021) Datta, R.; Pathak, A.A.; Dodamani, B.M.Investigation of the rainfall homogeneity along with bivariate frequency analysis of drought considering change points in long-term annual precipitation series has been carried out in this study. Nonparametric Pettitt’s test was applied for detecting change points of annual precipitation series at different grid locations over the Ghataprabha River Basin. Depending on the results of change point analysis, we divided the entire period of 1950–2013 into two subperiods: from 1950 to 1980 and 1981 to 2013. Characterization of meteorological drought is performed with the help of the Standardized Precipitation Index (SPI) at a time scale of three months for the period before the change point (1950–1980), after the change point (1981–2013) and for the entire period of 1950–2013. Three Archimedean copulas, namely Clayton, Gumbel–Houggard, and Frank, were tested for joint distribution modeling. The Akaike’s and Bayesian information criteria have been implemented for selecting the best copula; the Gumbel–Hougaard copula performed comparatively better for all three periods. Drought return periods were calculated using the joint distribution of drought characteristics. The study gives valuable insight into drought risk management on a regional scale. © Springer Nature Singapore Pte Ltd 2021.Item Multivariate analysis of concurrent droughts and their effects on Kharif crops—A copula-based approach(John Wiley and Sons Ltd, 2022) Muthuvel, D.; Mahesha, M.Apart from creating an ecological imbalance, drought events could affect an agrarian country's economy and food security by reducing crop yields. The antecedent meteorological droughts could prolong into hydrological and (or) agricultural droughts and may co-exist as concurrent droughts. The current study aims to comprehensively study Indian concurrent droughts, their effects on crop yield, and possible teleconnection with ENSO (El Niño–Southern Oscillation), adopting a copula-based multivariate approach. The copula functions can replicate the correlation among the variables and keep the dependence structure intact. The concurrent drought characteristics are computed using a multivariate standardized drought index that incorporates the three primary drought indices using the Gaussian copula. Some of the severe concurrent drought years such as 2002, 1987, 1972, and 1965 caused considerable yield losses in Kharif season crops of groundnut, millet, and rice. This prompts to construct quad-variate models involving the crop yield and the three drought indices using the vine copulas that perform better than the elliptical and symmetric Archimedean copula. Though the isolated forms of droughts could cause mild yield losses, the probability of concurrent droughts causing high to exceptional losses is more. Further, the ENSO teleconnection with the concurrent monsoon droughts is analysed and mapped. The above-normal warming of the Nino 3.4 region over the tropical Pacific during the months leading up to the monsoon could signal concurrent monsoon droughts in the areas under the Ganga-Brahmaputra basin at a probability of around 45%. These results could be helpful in drought mitigation measures and policymaking. © 2021 Royal Meteorological Society.Item 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.Item Temporal Assessment of Meteorological Drought Events Using Stationary and Nonstationary Drought Indices for Two Climate Regions in India(American Society of Civil Engineers (ASCE), 2023) Sajeev, A.; Kundapura, S.This study attempts to build nonstationary indices for assessing meteorological drought in two different climate zones in India: the arid Saurashtra and Kutch and humid-tropical Coastal Karnataka. Time and climate indices are considered as covariates to develop nonstationary models using the generalized additive model in location, scale, and shape (GAMLSS) for the period, 1951-2004. A comparative study has been conducted to assess the statistical performance of stationary and nonstationary models on various time scales (3, 6, 12, and 24 months). The best model is selected to conduct copula-based bivariate drought analysis. For this purpose, drought properties such as drought severity, duration, and peak are calculated. The annual and seasonal rainfall departures are also analyzed, and more rainfall-deficient years are detected in Saurashtra and Kutch regions than in Coastal Karnataka. The nonstationary index performed better in capturing drought properties in statistical analysis over both the study areas at all time scales. The nonstationary drought index shows better consistency with historical drought and flood events than the stationary index. Cooccurrence and joint return periods are calculated and compared with univariate return periods. A significant difference is observed between bivariate and univariate return periods, and more risk is detected in Saurashtra and Kutch than in Coastal Karnataka. The impacts of rainfall and drought on the yield of major crops in study areas are also analyzed. The yield loss rate of bajra significantly correlates with the nonstationary standardized precipitation index (NSPI) in Saurashtra and Kutch, whereas rice yield has no significant correlation with the index in Coastal Karnataka. This new aspect of drought analysis provides feasible results in both arid and humid regions in a changing environment. © 2023 American Society of Civil Engineers.Item 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.