Browsing by Author "Jose, D.M."

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Analysis of Land Use Land Cover Changes in the Netravati Basin, Karnataka, India
(Springer Science and Business Media Deutschland GmbH, 2023) Nayana, N.; Jose, D.M.; Dwarakish, G.S.
Analysis and mapping of land use land cover (LULC) are essential to improve our understanding of the human-nature interactions and their effects on land use changes. In this study, LULC maps of the Netravati river basin for the years 2000, 2010, and 2020 were obtained using maximum likelihood classifier on Landsat images. The classifier produced LULC maps of 2000, 2010, and 2020 with overall accuracy of 87.34%, 85.74%, and 86.3%, respectively. The results of this study showed that there is an increase in the spatial extends of the urban area (3.54â€“9.21%) and agriculture (18.2â€“21.09%) during the period 2000 to 2020. In contrast, forest (55.48â€“51.02%), bare soil (6.61â€“5.91%), water bodies (1.64â€“1.23%), and vegetation (14.53â€“11.54%) cover have decreased from the year 2000 to 2020. The results of this study can be used for proper LULC management in the basin. This study is a prerequisite for the prediction and management of future urban growth. Â© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Assessment of coastal water quality along south west coast of India using multile regression analysis on satellite data
(2018) Jose, D.M.; Mandla, V.R.; Subbarao, S.S.V.; Rao, N.S.; Moses, S.A.
The coastal waters being the ultimate receiver of all the wastes, shows a declining trend in its quality. It is of immense importance to know the extent of pollution for its monitoringandmanagemenlMeasurementofdissolvedoxygen (DO), biologicaloxygen demand (BOD), pH and fecal coliform (FC) are vital in water quality monitoring and assessment studies. Usually these parameters are determined by analysing water samples collected from various locations. Since this is tedious and expensive, it is limited to small scales. In this paper, an effort has been made to quickly assess the quality of coastal waters of Kerala directly from the satellite imagery by estimating National Sanitation Federation Water Quality Index (NSFWQI) along with DO, BOD, pH and FC. Multiple linear regression is used to develop statistically significant models using Sea Surface Temperature (SST) and Remote Sensing Reflectance (Rrs) from Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ data available on DO, BOD, pH and FC. The models when validated showed good correlation between in situ values and predicted values with r values ranging from 0.73 (p=0.001) for DO to 0.89 for NSFWQI (p=0.018). Spatial maps are generated showing the distribution of these parameters along the coast. The parameters in the study are checked to see if they are in compliance with the standards. The study gives models to estimate the daily distribution of these parameters along the coast using MODIS data. Thus, appropriate control measures could be adopted to limit the effect on susceptible rural population. 2019 JPR Solutions. All rights reserved.
Assessment of coastal water quality along south west coast of India using multile regression analysis on satellite data
(National Institute of Rural Development Rajendranagar Hyderabad 500 030, 2018) Jose, D.M.; Mandla, V.R.; Subbarao, S.S.V.; Rao, N.S.; Moses, S.A.
The coastal waters being the ultimate receiver of all the wastes, shows a declining trend in its quality. It is of immense importance to know the extent of pollution for its monitoringandmanagemenlMeasurementofdissolvedoxygen (DO), biologicaloxygen demand (BOD), pH and fecal coliform (FC) are vital in water quality monitoring and assessment studies. Usually these parameters are determined by analysing water samples collected from various locations. Since this is tedious and expensive, it is limited to small scales. In this paper, an effort has been made to quickly assess the quality of coastal waters of Kerala directly from the satellite imagery by estimating National Sanitation Federation Water Quality Index (NSFWQI) along with DO, BOD, pH and FC. Multiple linear regression is used to develop statistically significant models using Sea Surface Temperature (SST) and Remote Sensing Reflectance (Rrs) from Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ data available on DO, BOD, pH and FC. The models when validated showed good correlation between in situ values and predicted values with r values ranging from 0.73 (p=0.001) for DO to 0.89 for NSFWQI (p=0.018). Spatial maps are generated showing the distribution of these parameters along the coast. The parameters in the study are checked to see if they are in compliance with the standards. The study gives models to estimate the daily distribution of these parameters along the coast using MODIS data. Thus, appropriate control measures could be adopted to limit the effect on susceptible rural population. © 2019 JPR Solutions. All rights reserved.
Bias Correction and Trend Analysis of Temperature Data by a High-Resolution CMIP6 Model over a Tropical River Basin
(Korean Meteorological Society, 2022) Jose, D.M.; Dwarakish, G.S.
Technological advancements like increase in computational power have led to high-resolution simulations of climate variables by Global Climate Models (GCMs). However, significant biases exist in GCM outputs when considered at a regional scale. Hence, bias correction has to be done before using GCM outputs for impact studies at a local/regional scale. Six bias correction methods, namely, delta change (DC) method, linear scaling (LS), empirical quantile mapping (EQM), adjusted quantile mapping (AQM), Gamma-Pareto quantile mapping (GPQM) and quantile delta mapping (QDM) were used to bias correct the high-resolution daily maximum and minimum temperature simulations by Meteorological Research Institute-Atmospheric General Circulation Model Version 3.2 (MRI-AGCM3–2-S) model which is part of Coupled Model Intercomparison Project Phase 6 (CMIP6), of Netravati basin, a tropical river basin on the south-west coast of India. The quantile-quantile (Q–Q) plots and Taylor diagrams along with performance indicators like Nash–Sutcliffe efficiency (NSE), the Root-Mean Square Error (RMSE) or Root-Mean Square Deviation (RMSD), the Mean Absolute Error (MAE), the Percentage BIAS (PBIAS) and the correlation coefficient (r) were used for the evaluation of the performance of each bias correction method in the validation period. Considerable reduction in the bias was observed for all the bias correction methods employed except for the LS method. The results of QDM method, which is a trend preserving bias correction method, was used for analysing the trend of future temperature data. The trend of historical and future temperature data revealed an increasing trend in the annual temperature. An increase of 0.051 °C and 0.046 °C is expected for maximum and minimum temperature annually during the period 2015 to 2050 as per RCP 8.5 scenario. This study demonstrates that the application of a suitable bias correction is needed before using GCM projections for climate change studies. © 2021, Korean Meteorological Society and Springer Nature B.V.
Development of Satellite Data-Based Multiple Regression Equations for the Estimation of Total Coliform and Petroleum Hydrocarbons Along South West Coast of India
(Springer Science and Business Media Deutschland GmbH, 2021) Jose, D.M.; Mandla, V.R.; Neerukattu, S.R.; Saladi, S.V.S.
Coastal waters are showing deteriorating trend in its quality. This leads to the damage of marine ecosystems and interferes in its normal use. In order to tackle this issue, it is important to know about the extent of pollution. Conventional method of water quality estimation includes analysis of water samples from various locations. This is a tiresome and costly process limiting its application to small scales and accessible sampling sites. In this paper, an attempt has been made to quickly estimate the concentration of Petroleum Hydrocarbons (PHC) and counts of Total Coliform (TC) which are important water quality parameters, along the south west coast of India. This study formulated satellite data-based multiple regression equations for determining the count of total coliform bacteria and concentration of petroleum hydrocarbons. The sea surface temperature and remote sensing reflectance values of different bands of MODIS sensor along with field values were used in the process. The developed algorithm is validated for future use. Maps are created using these equations, showing the distribution of these parameters along the coast using Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) data. Hence, the feasibility of VIIRS for determination of these parameters with the same algorithm is examined. Thus, based on the results, areas of high PHC and TC could be identified and necessary control measures could be adopted. Â© 2021, Springer Nature Singapore Pte Ltd.
Enhanced Third-Order Nonlinear Optical Response in Cross-Conjugated Pyrrole-Based ortho-Halo Arylhydrazono-?-Diketones
(American Chemical Society, 2025) Jose, D.M.; Jayaraman, K.; Chidambaram, S.G.; Alnajjar, R.; Gandhiraj, V.; Lakshmi, V.
The growing demand for high-performance nonlinear optical (NLO) materials has driven the development of novel organic systems with enhanced third-order NLO responses. Herein, we report the design and synthesis of a new series of ortho-halogen-substituted arylhydrazono dipyrrolyldiketones 1–4, featuring a cross-conjugated ?-system tailored to optimize the ?-electron delocalization, molecular planarity, and intramolecular charge transfer. Pyrrole substitution on the ?-diketone core was employed to redshift the electronic transitions and boost the NLO activity. Combined experimental and theoretical analyses revealed that both halogen and pyrrole modifications significantly altered the electronic polarization and third-order NLO responses. Open-aperture Z-scan measurements, performed using a 532 nm nanosecond pulsed Nd: YAG laser, revealed clear reverse saturable absorption (RSA) behavior in all four compounds. Among the series, the iodo-substituted compound 4 exhibited the highest nonlinear absorption coefficient (? = 4.51 × 10–11 m/W) and the lowest optical limiting (OL) threshold (4.85 × 1012 W/m2), confirming its superior RSA and OL performance. To the best of our knowledge, this is the first study showing that halogen engineering and pyrrole incorporation synergistically enhance RSA and OL performance in hydrazono ?-diketones, offering structure–property insights and design guidelines for advanced photonic materials. © 2025 American Chemical Society
Frequency-intensity-distribution bias correction and trend analysis of high-resolution CMIP6 precipitation data over a tropical river basin
(Springer, 2022) Jose, D.M.; Dwarakish, G.S.
Advancements in computational power have enabled general circulation models (GCMs) to simulate climate variables at a higher resolution. However, GCM outputs often deviate from the observed climatological data and therefore need bias correction (BC) before they are used for impact studies. While there are several BC methods, BCs considering frequency, intensity and distribution of rainfall are few. This study proposes a BC method which focuses on separately correcting the frequency, intensity and distribution of precipitation. This BC was performed on high-resolution daily precipitation simulations of Meteorological Research Institute-Atmospheric General Circulation Model Version 3.2 with a 20-km grid size (MRI-AGCM3-2-S) model which is part of Coupled Model Intercomparison Project Phase 6 (CMIP6) on Netravati basin, a tropical river basin in India. The historical rain gauge station data was considered for testing the effectiveness of the BC method applied. The quantile–quantile (Q–Q) plot, Taylor diagram, Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), percentage bias (PBIAS) and correlation coefficient (R) are employed for the evaluation of the BC method. Higher R and R2 and lower RMSE, MAE and PBIAS values were observed for the bias-corrected GCM data than raw simulation. The PBIAS reduced from 15.6 to 6% when BC was applied. The analysis suggested that the proposed method effectively corrects the bias in rainfall over the basin. Furthermore, an attempt has been made to analyse the trend of historical and future rainfall in the basin. The analysis revealed a declining trend of precipitation in monsoon months with the magnitude of 12.44 mm and 56.7 mm in the historical and future periods respectively. This study demonstrates that BC should be applied before the use of GCM simulated precipitation for any analysis or impact studies to improve the predictions. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.
Improving multiple model ensemble predictions of daily precipitation and temperature through machine learning techniques
(Nature Research, 2022) Jose, D.M.; Vincent, A.M.; Dwarakish, G.S.
Multi-Model Ensembles (MMEs) are used for improving the performance of GCM simulations. This study evaluates the performance of MMEs of precipitation, maximum temperature and minimum temperature over a tropical river basin in India developed by various techniques like arithmetic mean, Multiple Linear Regression (MLR), Support Vector Machine (SVM), Extra Tree Regressor (ETR), Random Forest (RF) and long short-term memory (LSTM). The 21 General Circulation Models (GCMs) from National Aeronautics Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset and 13 GCMs of Coupled Model Inter-comparison Project, Phase 6 (CMIP6) are used for this purpose. The results of the study reveal that the application of a LSTM model for ensembling performs significantly better than models in the case of precipitation with a coefficient of determination (R2) value of 0.9. In case of temperature, all the machine learning (ML) methods showed equally good performance, with RF and LSTM performing consistently well in all the cases of temperature with R2 value ranging from 0.82 to 0.93. Hence, based on this study RF and LSTM methods are recommended for creation of MMEs in the basin. In general, all ML approaches performed better than mean ensemble approach. © 2022, The Author(s).
Uncertainties in predicting impacts of climate change on hydrology in basin scale: a review
(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2020) Jose, D.M.; Dwarakish, G.S.
The sensitivity of the hydrological system to climate change and the role of hydrological systems in the environment have motivated researchers to study the impacts of climate change on hydrology. Modelling the hydrological impacts of climate change is generally done in various stages and has uncertainty associated with each of them. These include scenario uncertainty in climate scenario selection, model uncertainty in climate simulation by global climatic models (GCMs), uncertainties while downscaling GCMs, biases in downscaled data, erroneous input to the hydrological model, and uncertainty in the structure and parameterisation of the hydrological model. The present paper aims at reviewing the uncertainties involved at each stage of climate change impact assessment of hydrology. In the near future, climate scenario uncertainties would be smaller than those associated with the choice of GCMs. Multi-model ensemble approach takes better account of uncertainties involved with GCMs. Moreover, considering a range of possible climate scenarios is recommended than using a single best or average case climate scenario. GCMs shall be downscaled by statistical or dynamical methods (regional climatic models (RCMs)) before using them for regional studies. Bias correction methods can considerably improve the RCM simulations. Evaluation of model performance is recommended for regional-scale studies for the preparation of adaptation strategies. Taking into account the uncertainties associated with climate impact studies can help formulate effective adaptation strategies. © 2020, Saudi Society for Geosciences.