• Ph.D. in Water Management & Hydrological Science, Texas A&M University (2023)
• M.S. in Biological Systems Engineering, Virginia Tech (2017)
• B.E. in Civil Engineering, Birla Institute of Technology, Mesra, India (2013)

Personal Webpage:

https://soil-hydrology.com/

• Soil physics/ Soil hydrology
• Scaling issues in hydrology
• Satellite remote sensing
• Terrestrial water-energy-carbon dynamics
• Hydrologic modeling
• Drought monitoring and impact assessment

• Dissertation Fellowship, Graduate & Professional School, Texas A&M University (2022)
• Data Science Ambassador Scholarship, Texas A&M Institute of Data Science (2022)
• Charles & Frances Fleming Academic Excellence Scholarship, Texas A&M University (2022)
• Class of 2017 Endowed Aggie Ring Scholarship, Texas A&M University (2022)
• Kirkham Conference Travel Award, Soil Science Society of America (2022)
• Valeen Silvy Fellowship, Water Management and Hydrologic Science Program, Texas A&M University (2021)
• Robert E. Stewart Graduate Excellence Award, Dept. of Bio & Ag. Eng., Texas A&M University (2021)
• Outstanding Contribution in Reviewing award by Journal of Applied Soft Computing (2018) & Journal of Hydrology (2017), Elsevier

• RPubs - Data Visualization with R (Updated 2021)
• RPubs - Large-scale geospatial analysis in R (Updated 2021)
• Parameters of Global Surface Soil Moisture Drydown using SMAP

• AGRO 4092-02: R for Spatial Analysis & Visualization (2 CR, as Special topics in Soil Science)
  This course focuses on hands-on knowledge of accessing, analyzing, and visualizing open-source satellite remote-sensing and geospatial datasets for hydrological, agricultural, and climatological studies within the R environment. The objective of this course is to learn R tools for i) analyzing geospatial datasets (raster and vector) ii) performing statistical analysis for each feature/ layer, and iii) mapping and visualizing spatial datasets. The course includes the latest R tools for working with global earth observational datasets, such as from NASA’s MODIS and SMAP satellites. Basic operations of geospatial analysis such as (re)projection, (re)sampling, summary statistics, merge/join, and (re)shape are covered. The students are introduced to structured/layered spatial datasets such as NetCDF and HDF formats used in climate modeling. Special emphasis is placed on the application of available out-of-the-box parallel computing techniques for geospatial analysis available in R. Course notes and material is available on GitHub
• AGRO 4077: Environmental Soil Physics (3 CR)
  The physical soil system; the soil components and their physical interactions; processes involving water flow in saturated and unsaturated soils, air, and heat; fate and transport of applied chemicals in the soil profile and processes governing the mobility of contaminants.

1. Sehgal, V., Mohanty, B. P., & Reichle, R. H. (2024). Rootzone soil moisture dynamics using terrestrial water‐energy coupling. Geophysical Research Letters, 51, e2024GL110342. https://doi.org/10.1029/2024GL110342
2. Sehgal, V., Gaur, N., & Mohanty, B. P. (2021). Global flash drought monitoring using surface soil moisture. Water Resources Research, 57(9), e2021WR029901. https://doi.org/10.1029/2021WR029901
3. Sehgal, V. and Mohanty, B.P., (2023). Preferential hydrologic states and tipping characteristics of global surface soil moisture. Water Resources Research 60, no. 4 (2024): e2023WR034858. https://doi.org/10.1029/2023WR034858
4. Mohanty, B.P., Mbabazi, D., Miller, G., Moore, G., Everett, M., Rajan, N., Morgan, C., Gaur, N., Sehgal, V., Sedaghatdoost, A. and Hong, M., 2024. Texas Water Observatory: A Distributed Network for Monitoring Water, Energy, and Carbon Cycles Under Variable Climate and Land Use on Gulf Coast Plains. Journal of Hydrometeorology. https://doi.org/10.1175/JHM-D-23-0201.1
5. Sehgal, V., Gaur, N., & Mohanty, B. P. (2020). Global surface soil moisture drydown patterns. Water Resources Research, 57(1), e2020WR027588. https://doi.org/10.1029/2020WR027588
6. Sachindra, D., A., K., Rashid, M., Sehgal, V., Shahid, S., Perera, B., et al. (2019). Pros & cons of using wavelets in conjunction with genetic programming & generalised linear models in statistical downscaling of precipitation. Theoretical & Applied Climatology, 138(1), 617–638. https://doi.org/https://link.springer.com/article/10.1007/s00704-019-02848-2
7. Sehgal, V., & Sridhar, V. (2019). Watershed−scale retrospective drought analysis & seasonal forecasting using multi−layer, high−resolution simulated soil moisture for southeastern US. Weather & Climate Extremes, 23, 100191. https://doi.org/10.1016/j.wace.2018.100191
8. Sehgal, V., Lakhanpal, A., Maheswaran, R., Khosa, R., & Sridhar, V. (2018). Application of multi−scale wavelet entropy & multi−resolution Volterra models for climatic downscaling. Journal of Hydrology, 556, 1078–1095. https://doi.org/10.1016/j.jhydrol.2016.10.048
9. Sehgal, V., & Sridhar, V. (2018). Effect of hydroclimatological teleconnections on the watershed−scale drought predictability in the southeastern United States. Int’l Journal of Climatology, 38, e1139–e1157. https://doi.org/10.1002/joc.5439
10. Sehgal, V., Sridhar, V., Juran, L., & Ogejo, J. A. (2018). Integrating climate forecasts with the soil & water assessment tool (SWAT) for high−resolution hydrologic simulations & forecasts in the southeastern U.S. Sustainability, 10(9), 3079. https://doi.org/10.3390/su10093079
11. Lakhanpal, A., Sehgal, V., Maheswaran, R., Khosa, R., & Sridhar, V. (2017). A non−linear & non−stationary perspective for downscaling mean monthly temperature: A wavelet coupled second order Volterra model. Stochastic Environmental Research & Risk Assessment, 31(9), 2159–2181. https://doi.org/10.1007/s00477-017-1444-6
12. Sehgal, V., Sridhar, V., & Tyagi, A. (2017). Stratified drought analysis using a stochastic ensemble of simulated & in−situ soil moisture observations. Journal of Hydrology, 545, 226–250. https://doi.org/10.1016/j.jhydrol.2016.12.033
13. Agarwal, A., Maheswaran, R., Sehgal, V., Khosa, R., Sivakumar, B., & Bernhofer, C. (2016). Hydrologic regionalization using wavelet−based multiscale entropy method. Journal of Hydrology, 538, 22–32. https://doi.org/10.1016/j.jhydrol.2016.03.023
14. Sahay, R. R., & Sehgal, V. (2014). Wavelet−ANFIS models for forecasting monsoon flows: Case study for the Gandak river (India). Water resources, 41(5), 574–582. https://doi.org/10.1134/S0097807814050108
15. Sehgal, V., & Chatterjee, C. (2014). Auto updating wavelet based MLR models for monsoonal river discharge forecasting. Int. J. Civ. Eng. Res, 5, 401–406.
16. Sehgal, V., Sahay, R. R., & Chatterjee, C. (2014). Effect of utilization of discrete wavelet components on flood forecasting performance of wavelet based ANFIS models. Water resources management, 28(6), 1733–1749. https://doi.org/10.1007/s11269-014-0584-4
17. Sehgal, V., Tiwari, M. K., & Chatterjee, C. (2014). Wavelet bootstrap multiple linear regression−based hybrid modeling for daily river discharge forecasting. Water resources management, 28(10), 2793–2811. https://doi.org/10.1007/s11269-014-0638-7
18. Sahay, R. R. & Sehgal, V. (2013). Wavelet regression models for predicting flood stages in rivers: A case study in Eastern India. Journal of Flood Risk Management, 6(2), 146–155. https://doi.org/10.1111/j.1753-318X.2012.01163.x
19. Sharma, N. K., Mitra, S., Sehgal, V., & Mishra, S. (2012). An assessment of physical properties of coal combustion residues w.r.t. their utilization aspects. Int. J. Environ. Protection, 2(2), 31–38.