Dr. Natthachet Tangdamrongsub

1. 2007 – 2012: PhD, Civil Engineering, National Chiao Tung University, Taiwan
2. 2005 – 2007: MSE., Radio Astronomy and Space Sciences, Chalmers University of Technology, Sweden
3. 2000 – 2004: BEng, Electrical Engineering, Thammasat University, Thailand

His research interests are Remote sensing of environment, Hydrology and land surface modeling, Data assimilation, Artificial intelligence, Natural hazards, Flood and drought analysis, Climate change, and Statistical optimization

1. GIS Applications in Water Resources
2. AI and Big Data in Water

1. Tangdamrongsub, N., 2023. Comparative Analysis of Global Terrestrial Water Storage Simulations: Assessing CABLE, Noah-MP, PCR-GLOBWB, and GLDAS Performances during the GRACE and GRACE-FO Era. Water 15, 2456. https://doi.org/10.3390/w15132456
2. Dong, J., Crow, W.T., Chen, X., Tangdamrongsub, N., Gao, M., Sun, S., Qiu, J., Wei, L.,Gao, H., Duan, Z., 2022. Statistical uncertainty analysis-based precipitation merging (SUPER): A new framework for improved global precipitation estimation. Remote Sensing of Environment 283, 113299. https://doi.org/10.1016/j.rse.2022.113299
3. Tangdamrongsub, N., Dong, J., Shellito, P., 2022. Assessing Performances of Multivariate Data Assimilation Algorithms with SMOS, SMAP, and GRACE Observations for Improved Soil Moisture and Groundwater Analyses. Water 14, 621. https://doi.org/10.3390/w14040621
4. Yin, W., Fan, Z., Tangdamrongsub, N., Hu, L., Zhang, M., 2021. Comparison of physical and data-driven models to forecast groundwater level changes with the inclusion of GRACE – A case study over the state of Victoria, Australia. Journal of Hydrology 602, 126735. https://doi.org/10.1016/j.jhydrol.2021.126735
5. Tangdamrongsub, N., Jasinski, M.F., Shellito, P.J., 2021. Development and evaluation of 0.05° terrestrial water storage estimates using Community Atmosphere Biosphere Land Exchange (CABLE) land surface model and assimilation of GRACE data. Hydrology and Earth System Sciences 25,4185–4208. https://doi.org/10.5194/hess-25-4185-2021
6. Tangdamrongsub, N., Hwang, C., Borak, J. S., Prabnakorn, S., Han, J., 2021. Optimizing GRACE/GRACE-FO data and a priori hydrological knowledge for improved global terrestial water storage component estimates, Journal of Hydrology, 598, 126463, https://doi.org/10.1016/j.jhydrol.2021.126463
7. Tangdamrongsub, N., Forgotson, C., Gangodagamage, C., Forgotson, J., 2021. The analysis of using satellite soil moisture observations for flood detection, evaluating over the Thailand’s Great Flood of 2011. Natural Hazards 108, 2879–2904. https://doi.org/10.1007/s11069-021-04804-8
8. Tangdamrongsub, N., Šprlák, M., 2021. The Assessment of hydrologic- and flood-induced land deformation in data-sparse regions using GRACE/GRACE-FO data assimilation. Remote Sensing 13, 235. https://doi.org/10.3390/rs13020235
9. Tangdamrongsub, N., Han, S.-C., Yeo, I.-Y., Dong, J., Steele-Dunne, S.C., Willgoose, G., Walker, J.P., 2020. Multivariate data assimilation of GRACE, SMOS, SMAP measurements for improved regional soil moisture and groundwater storage estimates. Advances in Water Resources 135, 103477. https://doi.org/10.1016/j.advwatres.2019.103477
10. Yin, W., Han, S.-C., Zheng, W., Yeo, I.-Y., Hu, L., Tangdamrongsub, N., Ghobadi-Far, K., 2020. Improved water storage estimates within the North China Plain by assimilating GRACE data into the CABLE model. Journal of Hydrology 590, 125348. https://doi.org/10.1016/j.jhydrol.2020.125348
11. Tangdamrongsub, N., Han, S.-C., Jasinski, M.F., Šprlák, M., 2019. Quantifying water storage change and land subsidence induced by reservoir impoundment using GRACE, Landsat, and GPS data. Remote Sensing of Environment 233, 111385. https://doi.org/10.1016/j.rse.2019.111385
12. Li, B., Rodell, M., Kumar, S., Beaudoing, H.K., Getirana, A., Zaitchik, B.F., Goncalves, L.G. de, Cossetin, C.,Bhanja, S., Mukherjee, A., Tian, S., Tangdamrongsub, N., Long, D., Nanteza, J., Lee, J.,Policelli, F., Goni, I.B., Daira, D., Bila, M., Lannoy, G. de, Mocko, D., Steele‐Dunne, S.C., Save, H.,Bettadpur, S., 2019. Global GRACE data assimilation for groundwater and drought monitoring: advances and challenges. Water Resources Research 55, 7564–7586. https://doi.org/10.1029/2018WR024618
13. Jasinski, M.F., Borak, J.S., Kumar, S.V., Mocko, D.M., Peters-Lidard, C.D., Rodell, M., Rui, H., Beaudoing, H.K., Vollmer, B.E., Arsenault, K.R., Li, B., Bolten, J.D., Tangdamrongsub, N., 2019. NCA- LDAS: overview and analysis of hydrologic trends for the National Climate Assessment. J. Hydrometeor. 20,
    1595–1617.https://doi.org/10.1175/JHM-D-17-0234.1
14. Tangdamrongsub, N., Han, S.-C., Decker, M., Yeo, I.-Y., Kim, H., 2018. On the use of the GRACE normal equation of inter-satellite tracking data for estimation of soil moisture and groundwater in Australia. Hydrol. Earth Syst. Sci. 22, 1811–1829. https://doi.org/10.5194/hess-22-1811-2018 
15. Tangdamrongsub, N., Steele-Dunne, S.C., Gunter, B.C., Ditmar, P.G., Sutanudjaja, E.H., Sun, Y., Xia, T., Wang, Z., 2017. Improving estimates of water resources in a semi-arid region by assimilating GRACE data into the PCR-GLOBWB hydrological model. Hydrology and Earth System Sciences 21, 2053–2074. https://doi.org/10.5194/hess-21-2053-2017 

1. 2020: NASA GSFC HBG Scientific and Technical Support
2. 2022: NASA GSFC HBG Scientific Achievement

1. 2012 – 2013: National Chiao Tung University, Taiwan
2. 2013 – 2016: Delft University of Technology, The Netherlands
3. 2016 – 2018: University of Newcastle, Australia
4. 2018 – 2022: NASA Goddard Space Flight Center / University of Maryland, USA

1. Satellite remote sensing
2. Data assimilation
3. Artificial intelligence
4. Hydrology
5. Land surface model
6. Geodesy
7. Natural hazards
8. Climate change