Faculty Publications
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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 Groundwater level modeling using Augmented Artificial Ecosystem Optimization(Elsevier B.V., 2023) Nguyen, N.; Deb Barma, S.D.; van Lam, T.; Kisi, O.; Mahesha, A.Nature-inspired optimization is an active area of research in the artificial intelligence (AI) field and has recently been adopted in hydrology for the calibration (training) of both process-based and statistical models. This study proposes an improved AI model, Augmented Artificial Ecosystem Optimization-based Multi-Layer Perceptron (AAEO-MLP), to build a monthly groundwater level (GWL) forecasting model. AAEO-MLP model is built on the novel Augmented version of Artificial Ecosystem Optimization and traditional MLP network. In AAEO, Levy-flight trajectory and Gaussian random are utilized in exploration and exploitation to improve the optimizing ability. The AAEO-MLP model is tested on two time-series (1989–2012) datasets collected at two wells in India. Various explanatory variables such as monthly cumulative precipitation, mean temperature, tidal height, and previous measurements of GWL were considered for predicting 1-month ahead of GWL. The performance of AAEO-MLP was benchmarked against 17 different models (original AEO, 3 different variants of AEO, and 13 well-known models) in terms of forecasting accuracy based on six metrics (e.g., mean absolute error, root mean square error, Kling–Gupta efficiency, normalized Nash–Sutcliffe efficiency, Pearson's correlation index, a20 index). Furthermore, convergence analysis and model stability are employed to indicate the effectiveness of AAEO-MLP. The compared results express that the AAEO-MLP is superior to other models in terms of prediction accuracy, convergence, and stability. Overall, the results depict that the AAEO is a promising approach for optimizing machine learning models (e.g., MLP) and should be explored for other hydrological forecasting applications (e.g., streamflow, rainfall) to further assess its strengths over existing methods. © 2022 Elsevier B.V.Item Downscaling algorithms for CMIP6 GCM daily rainfall over India(Springer, 2024) Raj, R.; Vinod, D.; Mahesha, A.The global climate models (GCMs) are sophisticated tools for determining how the climate system will respond. However, the output of GCMs has a coarse resolution, which is unsuitable for basin-level modelling. Global climate models need to be downscaled at a local/basin scale to determine the impacts of climate change on hydrological responses. The present study attempted to evaluate how effectively various large-scale predictors could reproduce local-scale rain in 35 different locations in India using artificial neural networks (ANN), change-factors (CF), K-nearest neighbour (KNN), and multiple linear regression (MLR). The selection of predictors is made based on the correlation value. As potential predictors, air temperature, geo-potential height, wind velocity component, and relative humidity at specific mean sea-level pressure are selected. The comparison of four different downscaling methods concerning the reproduction of various statistics such as mean, standard deviation at chosen locations, quantile–quantile plots, cumulative distribution function, and kernel density estimation of the PDFs of daily rainfall for selected stations is examined. The CF approach outperforms the other methods at almost all sites (R2 = 0.92–0.99, RMSE = 1.37–28.88 mm, and NSE = –16.55–0.99). This also closely resembles the probability distribution pattern of IMD data. © Indian Academy of Sciences 2024.Item Spatial Dependence of Extreme Rainfall and Development of Intensity-Duration-Frequency Curves Using Max-Stable Process Models(American Society of Civil Engineers (ASCE), 2025) Vinod, D.; Mahesha, A.The effective management of flood risk and urban drainage design hinges on a comprehensive understanding and accurate modeling of extreme rainfall variations, particularly in vulnerable areas. The study proposes to model spatial extreme rainfall across various durations in the Ganga River basin of India using max-stable processes (MSP). Incorporating geographical covariates like longitude, latitude, and elevation, 28 surface response models were constructed for location and scale parameters, with linear variations in marginal parameters while keeping the shape parameter constant across space. Various max-stable characterizations were evaluated using the Takeuchi information criterion (TIC) value and likelihood ratio test statistics, including Brown-Resnick, Smith, Extremal-t, Schlatter, and Geometric-Gaussian models with different correlation functions. The findings showed that the Brown-Resnick model consistently simulated well for shorter extreme rainfall for 3, 4, and 6-h and 36-h durations. The extremal coefficients revealed higher dependency between closer locations for most durations. In comparison with classical univariate extreme value theory (UEVT), the MSP exhibits a minimal overestimation in extreme rainfall intensity at New Delhi (by 13.6 mm/h) and Diamond Harbor (by 10.2 mm/h) stations for shorter durations, i.e., 2-h to 6-h range. Its estimations align within the uncertainty bounds of the identical and independent distribution (I.I.D) for longer durations. This suggests the importance of considering the strengths and limitations of M.S.P. and UEVT approaches for accurate rainfall intensity estimation, especially in flood risk management and urban drainage design. In data-sparse region/ungauged basins, where traditional methods like univariate UEVT may be limited due to the absence of observed rainfall data. The fitted max-stable processes MSP can serve as a valuable tool when relevant geographical covariates are known. © 2024 American Society of Civil Engineers.