LSU Soil Hydrology & Remote Sensing Lab

BY TOPIC

Multiscale Soil Physics

Stochastic Hydrology

Drought Impact Assessment

COMPLETE LIST OF PEER-REVIEWED PUBLICATIONS
  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. 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
  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. 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
  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.
  20. Huang, J., Sehgal, V., Alvarez, L. V., Brocca, L., Cai, S., Cheng, R., et al. (2025). Remotely sensed high-resolution soil moisture and evapotranspiration: Bridging the gap between science and society. Water Resources Research, 61, e2024WR037929. https://doi.org/10.1029/2024WR037929
  21. Mishra, D., Sehgal, V., & Mohanty, B. P. (2025). An optimal transport framework for water-energy coupling in soil-vegetation-atmosphere continuum. Water Resources Research, 61, e2025WR041794. https://doi.org/10.1029/2025WR041794