Earth data assimilation in hydrologic models: recent advances Saranya Jeyalakshmi, Vinod Chilkoti, Tirupati Bolisetti, Ram Balachandar International Journal of Environmental Studies, 2021 Hydrologic model forecasts have inherent uncertainties from input errors, lack of physical representation, and parameter equifinality. Accurate modelling results with reduced uncertainty are necessary for water resources management and decision-making, especially in a changing climate scenario. To this end, the hydrologic modelling community widely accepts the assimilation of satellite remote sensing data. This paper reviews the recent developments in hydrologic data assimilation (DA) focusing on progress in the role of satellite remote sensing data in reducing model uncertainty.
Watershed modeling with remotely sensed big data: Modis leaf area index improves hydrology and water quality predictions Adnan Rajib, I Luk Kim, Heather E. Golden, Charles R. Lane, Sujay V. Kumar, Zhiqiang Yu, Saranya Jeyalakshmi Remote Sensing, 2020 Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershed in the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’s LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.
RECENT SCHOLAR PUBLICATIONS
CLASSIFICATION OF TOMATO DISEASES USING ENSEMBLE LEARNING S Jeyalakshmi, R Radha ICTACT Journal on Soft Computing 11 (4) , 2021 2021 Citations: 5
An effective approach to feature extraction for classification of plant diseases using machine learning S Jeyalakshmi, R Radha Indian Journal of Science and Technology 13 (32), 3295-3314 , 2020 2020 Citations: 26
A novel approach to segment leaf region from plant leaf image using automatic enhanced grabcut algorithm S Jeyalakshmi, R Radha Compusoft: An International Journal of Advanced Computer Technology 8 (11) , 2019 2019 Citations: 7
A REVIEW ON DIAGNOSIS OF NUTRIENT DEFICIENCY SYMPTOMS IN PLANT LEAF IMAGE USING DIGITAL IMAGE PROCESSING. S Jeyalakshmi, R Radha ICTACT Journal on Image & Video Processing 7 (4) , 2017 2017 Citations: 66
An effective algorithm for edges and veins detection in leaf images R Radha, S Jeyalakshmi 2014 World congress on computing and communication technologies, 128-131 , 2014 2014 Citations: 29
MOST CITED SCHOLAR PUBLICATIONS
A REVIEW ON DIAGNOSIS OF NUTRIENT DEFICIENCY SYMPTOMS IN PLANT LEAF IMAGE USING DIGITAL IMAGE PROCESSING. S Jeyalakshmi, R Radha ICTACT Journal on Image & Video Processing 7 (4) , 2017 2017 Citations: 66
An effective algorithm for edges and veins detection in leaf images R Radha, S Jeyalakshmi 2014 World congress on computing and communication technologies, 128-131 , 2014 2014 Citations: 29
An effective approach to feature extraction for classification of plant diseases using machine learning S Jeyalakshmi, R Radha Indian Journal of Science and Technology 13 (32), 3295-3314 , 2020 2020 Citations: 26
A novel approach to segment leaf region from plant leaf image using automatic enhanced grabcut algorithm S Jeyalakshmi, R Radha Compusoft: An International Journal of Advanced Computer Technology 8 (11) , 2019 2019 Citations: 7
CLASSIFICATION OF TOMATO DISEASES USING ENSEMBLE LEARNING S Jeyalakshmi, R Radha ICTACT Journal on Soft Computing 11 (4) , 2021 2021 Citations: 5
