Faculty Publications
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Item A Non-stationary Hydrologic Drought Index Using Large-Scale Climate Indices as Covariates(Springer Science and Business Media Deutschland GmbH, 2023) Sajeev, A.; Kundapura, S.The dry and wet periods can be analyzed based on different drought indices. Most existing drought studies are based on stationary assumptions, and environmental changes are not considered. This study proposes a non-stationary streamflow-based drought index, incorporating large-scale climate indices to study hydrological drought for 45 years. Climate indices are used as covariates for building the non-stationary model fitted to streamflow. Correlation analysis is carried out to determine the best covariates for the streamflow in the Netravati River basin in India. The Southern Oscillation Index (SOI) exhibited a significant influence on streamflow at all time scales. The non-stationary model is compared with the stationary model, and the best model is chosen based on the Akaike information criterion (AIC). Under statistical measures, non-stationary models performed better than stationary ones at all time scales. The generalized additive model for location, scale, and shape (GAMLSS) is used for non-stationary modeling. The models are developed for short-term (3 and 6 months) and long-term (12 and 24 months) droughts. The influence of climate variables on drought classes is analyzed, and more severe drought is observed under the non-stationary scenario. The deficiency in streamflow was more than 60% in the basin in 1987 and 2002. The non-stationary drought index detected more severe drought events than the stationary index under short-term scales. Hydrological drought properties such as drought severity, duration, and peak are calculated under stationary and non-stationary scenarios, and a noticeable difference is observed. Compared to stationary models, the non-stationary model yields more logical and satisfactory findings because it effectively takes into account non-stationarities in the streamflow caused by climate change. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Spatio-temporal Assessment and Monitoring of Agricultural Drought in Karnataka, India(Springer Science and Business Media Deutschland GmbH, 2025) Chandankumar, N.M.; Saicharan, V.; Shwetha, H.R.Agricultural drought monitoring is crucial as it affects food production and fodder, especially in countries like India; consequently, it affects the country’s economy, where nearly 70% of the population depends on agriculture for livelihood. Conventional drought indices consider the mean monthly rainfall values to assess the drought conditions by ignoring the intra-monthly rainfall variations. Due to climate change and erratic rainfall patterns, monthly mean values are not a suitable representation of the rainfall that occurred in the corresponding month. To address this challenge, the current study employed a methodology for calculating agricultural drought using a standardized net-precipitation evapotranspiration index (SNEPI) from 2000 to 2022 by accounting for rainfall variations at an intra-monthly scale. This study employed daily gridded rainfall data and monthly evapotranspiration obtained from the India Meteorological Department (IMD) and NASA’s global land data assimilation system (GLDAS), respectively, at 0.25° × 0.25° spatial resolution for the calculation of SNEPI. Intra-monthly variation of rainfall pattern is addressed by deriving the uniformity coefficient and multiplying it with mean monthly rainfall values. The results were compared with the widely used drought index, the standardized precipitation evapotranspiration index (SPEI). The spatial (agroclimatic zones and whole Karnataka level) and temporal (annual and monthly scale) analyses of SNEPI and SPEI were performed. According to the yearly reports of the Karnataka State Natural Disaster Management Centre (KSNDMC), the highest negative rainfall departure occurred in 2003 and 2016, both termed as deficiency periods. The results showed that in 2006, the drought was observed; however, the annual rainfall was near normal magnitude. Therefore, this study presented the detailed results of 2003, 2006, and 2016. A higher magnitude (0.98) of the correlation coefficient was observed for October, the monsoon season’s termination month. Also, the decreased correlations of 0.88, 0.88, and 0.84 were observed for the months of July, August, and September, respectively. This can be interpreted as increased intra-monthly variations, which SNEPI successfully captures, whereas SPEI ignores the variation. SNEPI succeeds in the early detection of drought events due to its ability to detect short-term dry and wet spells, which correlates well with SPEI at all considered months, inferred that it can be used for drought identification. The results suggest that this index is better for understanding the agricultural drought patterns spatially and temporally across Karnataka’s agro-climatic zones, which incorporates the intra-monthly rainfall variations and helps the agricultural community and policymakers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Item Artificial intelligence application in drought assessment, monitoring and forecasting: a review(Springer Science and Business Media Deutschland GmbH, 2022) Kikon, A.; Deka, P.C.Drought is a natural hazard creating havoc on economic, social and environmental aspects. As a result of its slow and creeping nature, it is problematic to establish the onset as well as the termination of drought. Irrespective of its spatial and temporal variability, drought occurs in almost all regions. A wide range of drought studies has been conducted by many researchers over a long period of time. The damage caused by drought has a huge impact on the social, economic and agricultural sectors. Researchers have defined drought in different ways depending upon the parameters and its characteristics, and universally there is no proper definition for drought because of its complexity in nature. This review is focused mainly on various Artificial Intelligence techniques used in drought assessment, monitoring, management and forecasting. The findings from the study shows that drought prediction has become significance in the field of hydrology, Water Resources Management, sustainable agriculture, etc. by using the various AI techniques. In recent studies, AI has been used widely in analysing drought in different regions. The applications of AI techniques in the domain of drought assessing, monitoring, forecasting, etc., shows a rapid growth and that the impact of these will be increasing in future. For understanding the different concepts of drought study, it is needed to establish different system of drought management in order to monitor the different factors affecting drought and then take proper measures to mitigate the damage. Literature studies have been done to analyze the onset and other measures of drought management. Future research may be oriented towards Modeling and probabilistic analysis of climatic data for refining the drought vulnerability mapping, analysis of onset and termination, warning system and drought declaration process depending on the conditions of the region. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Item Hybrid wavelet packet machine learning approaches for drought modeling(Springer, 2020) Das, P.; Naganna, S.R.; Deka, P.C.; Pushparaj, J.Among all the natural disasters, drought has the most catastrophic encroachment on the surrounding and environment. Gulbarga, one of the semi-arid districts of Karnataka state, India receives about 700 mm of average annual rainfall and is drought inclined. In this study, the forecasting of drought for the district has been carried out for a lead time of 1 month and 6 months. The multi-temporal Standardized Precipitation Index (SPI) has been used as the drought quantifying parameter due to the fact that it is calculated on the basis of one simplest parameter, i.e., rainfall and additionally due to its ease of use. The fine resolution daily gridded precipitation data (0.25º × 0.25º) procured from Indian Meteorological Department (IMD) of 21 grid locations within the study area have been used for the analysis. Forecasting of drought plays a significant role in drought preparedness and mitigation plans. With the advent of machine learning (ML) techniques over the past few decades, forecasting of any hydrologic event has become easier and more accurate. However, the use of these techniques for drought forecasting is still obscure. In this study, Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques have been employed to examine their accuracy in drought forecasting over shorter and longer lead times. Furthermore, two hybrid approaches have been formulated by coupling a data transformation method with each of the aforementioned ML approaches. At the outset, pre-processing of input data (i.e., SPI) has been carried out using Wavelet Packet Transform (WPT) and then used as inputs to ANN and SVR models to induce hybrid WP-ANN and WP-SVR models. The performance of the hybrid models has been evaluated based on the statistical indices such as R2 (co-efficient of determination), RMSE (Root Mean Square Error), and MAE (Mean Absolute Error). The results showed that the hybrid techniques have better forecast performance than the standalone machine learning approaches. Hybrid WP-ANN model performed relatively better than WP-SVR model for most of the grid locations. Also, the forecasting results deteriorated as the lead time increased from 1 to 6 months. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.Item Bivariate Drought Characterization of Two Contrasting Climatic Regions in India Using Copula(American Society of Civil Engineers (ASCE), 2021) Sajeev, A.; Deb Barma, S.; Mahesha, A.; Shiau, J.-T.This study aims to construct the multiple time-scale joint distributions of drought duration and severity using two-dimensional copulas and compare the drought characteristics in India's two contrasting climate regions: the arid Rajasthan and humid, tropical Kerala. The drought occurrences were defined by the standardized precipitation index (SPI) with a threshold below -0.8 at time scales of 3, 6, 12, and 24 months for 1900-2016. Significant correlations were noted between the drought severity and drought duration in both regions. The Clayton copula gave a better fit than other copulas for modeling the dependence among the observed drought duration and severity. The results indicate that the probability of short-term droughts (SPI-3 and SPI-6) is more significant than those of long-term droughts (SPI-12 and SPI-24) for an identical drought event in both regions. Also, the probability of severe drought events with greater duration and severity for long-term droughts (SPI-12 and SPI-24) is higher in Kerala than that in western Rajasthan. For all the time-scale SPIs, the conditional probability of drought severity for a given duration exceeding a threshold showed an increasing trend in both regions. Furthermore, the conditional probability of the drought duration given the severity for short-term droughts is greater than that of the long-term droughts for the same drought event. For short-term droughts, the conditional return period of an identical drought event is lower in Kerala than in western Rajasthan. In contrast, the conditional return period of long-term droughts is lower in western Rajasthan. Additionally, copula-based nonexceedance conditional distributions for the major crops were established based on rainfall. © 2020 American Society of Civil Engineers.Item Long-Term Climate Variability and Drought Characteristics in Tropical Region of India(American Society of Civil Engineers (ASCE), 2021) Vijay, A.; Sivan, S.D.; Mudbhatkal, A.; Mahesha, A.This work reports climate change signals and long-Term trend analysis of climate variables, meteorological drought, and extreme climate indexes over the tropical state of Kerala in India. The trend analysis reveals statistically significant decrease of annual and southwest monsoon rainfall (as much as 63 mm and 55 mm per decade, respectively). A decrease in number of annual rainy days (up to 2.8 days/decade) is also reported. Temperature trend analysis indicates an increasing trend with as high as 1.3°C/decade. The spatio-Temporal variation of extreme climate indexes across Kerala shows a decreasing trend of extreme precipitation indexes and an increasing trend of extreme temperature indexes. R95 and R95p decreased in northern and southern Kerala whereas R5 index increased in central and southern regions. Warm days have significantly increased whereas cold days exhibit a decreasing trend across the state. The increase in warmer nights is statistically significant whereas colder nights are decreasing in central and southern regions. Meteorological drought using Standardized Precipitation Index (SPI) reveals increasing occurrence of droughts in Kerala with higher frequencies over southern and central Kerala. © 2021 American Society of Civil Engineers.Item 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.Item Connection between Meteorological and Groundwater Drought with Copula-Based Bivariate Frequency Analysis(American Society of Civil Engineers (ASCE), 2021) Pathak, A.A.; Dodamani, B.M.Groundwater is a major resource of freshwater that provides additional resilience to agricultural drought during rainfall deficit and also helps in understanding the nature of the hydrological drought risk of an area. This study investigated the response of groundwater drought to meteorological drought and local aquifer properties by considering monthly groundwater levels of a tropical river basin in India. Further, bivariate frequency analysis was carried out for groundwater drought to develop severity-duration-frequency curves by considering the copula function. Long-term monthly groundwater levels were procured, and cluster analysis was performed on groundwater observations to classify the wells. Standardized Groundwater level Index (SGI) was used to evaluate groundwater drought for each cluster, and the same was compared with the meteorological drought of different association periods. The cluster analysis conveyed that wells can be grouped into three clusters optimally. Based on the comparison of groundwater drought with meteorological drought, it was inferred that SGI is well harmonized with the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) in humid and semiarid regions, respectively. Analysis of hydraulic diffusivity with the autocorrelation structure of SGI emphasizes the crucial role of aquifer characteristics in local groundwater droughts. The results of joint and conditional return periods obtained from bivariate frequency analysis conveyed that high severity and high-duration droughts were more frequent in the well of Clusters 1 as well as Cluster 3 and comparatively less for the well of Cluster 2. The outcome of the study will be helpful to design proactive drought mitigation and preparedness strategies by considering conjunctive use of surface and groundwater. It also provides a framework to evaluate groundwater drought risk in other parts of the world. © 2021 American Society of Civil Engineers.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.