Publications

Prior Works

Journal Articles

Dahal, K., Sharma, S., Shakya, A., Talchabhadel, R., Adhikari, S., Pokharel, A., Sheng, Z., Pradhan, A. M. S., & Kumar, S. (2023). Identification of groundwater potential zones in data-scarce mountainous region using explainable machine learning. Journal of Hydrology, 130417.  https://doi.org/10.1016/j.jhydrol.2023.130417   

Book Chapters

Kalas, M., Ommer, J., Shakya, A., Vraníc, S., Kolokol, D., & Sabattini, T. (2024). Challenges for a Better Use of Crowdsourcing Information in Climate Emergency Situational Awareness and Early Warning Systems. Responding to Extreme Weather Events, 141-162. https://doi.org/10.1002/9781119741374.ch7 

Conference Contributions

Karimi, S., Shakya, A., Rietbroek, R., Penning de Vries, M., and van der Tol, C.: Estimating terrestrial water storage trends by developing a joint inversion scheme using GRACE and GRACE-FO data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11918, https://doi.org/10.5194/egusphere-egu24-11918, 2024. 

Suryawahshi, M., Shakya, A., Chander S., Nikam B., Dasika N., and Vishwakarma B.: Statistical downscaling of GRACE total water storage changes over India, National Space Science Symposium (NSSS 2024), Goa, India, 2024.

Shakya, A., Rietbroek, R., van der Wal, D.: Do altimetric water level observations in an ungauged basin agree well with streamflow model discharge? A preliminary analysis on the Nile, Hydrospace 2020, Lisbon, Portugal, 27 Nov - 1 Dec 2023, https://www.hydrospace2023.org, 2023.

Rietbroek, R., Karimi, S., and Shakya, A.: Unravelling watershed fluxes to detect emerging changes of the water balance, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13524, https://doi.org/10.5194/egusphere-egu23-13524, 2023. 

Shakya, A., Giri, S., Iwasaki, T., Nabi, M., Bhattacharya, B., and Solomatine, D.: Towards developing a generic method for predicting Flow Resistance in Alluvial Rivers using Machine Learning, AGU Fall Meeting 2022, Chicago, U.S.A., 2022. https://ui.adsabs.harvard.edu/abs/2022AGUFM.H11J..05S  

Shakya, A., Kalas, M., Ommer, J., Leo, L., and Sabatino, S.: Geospatial Information Knowledge Platform (GeoIKP) for NatureBased Solutions: Recent Progress and a Way Forward, Water Security and Climate Change Conference 2022, Bangkok, Thailand, 2022.

J. Krstajic, Q. Ye, J. Steyaert, and A. Shakya: Climate Change and Karst Aquifers – Methodologies Review. International Symposium

on Drought and Climate Change, Hohai, China, 2022.

Nlend, B., mulokoshi, G., Otekpo, A., Agbaje, Y., Oketch, M., Anyanwu, I., Shakya, A., Khatri, S., Guo, Z., and Frem, M.: African Voices from the UNESCO Groundwater Youth Network: Developing modalities for co-creating knowledge, gender equilibrium and responses in the face of present and future Groundwater Challenges, 5th SADC Conference, Windhoek, Namibia, 2022.

Shakya, A., Giri, S., Iwasaki, T., Nabi, M., Bhattacharya, B., and Solomatine, D.: Towards capturing bedform transition: harnessing capabilities of CFD bedform models and machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2398, https://doi.org/10.5194/egusphere-egu22-2398, 2022. 

Shakya A, NR, JLFM (2022) Citizen science landscape in Nepal: A case for a national citizen science repository, KEC Conference 2022 

Shakya A, SG, TI, BB (2022) Geomorphological considerations for river studies in the Nepali context, KEC Conference 2022 

Shakya A,  SG,  DCF,  TI,  SY,  MN,  SN,  PM,  BB,  YS (2021) Approaches to predict bedform states, associated flow resistance and depth: Physics-based to machine learning. NCR DAYS 2021: Rivers in an uncertain future: 90-91

Giri, S., Shakya, A., Nabi, M., Naqshband, S., Iwasaki, T., Yamaguchi, S., Froehlich, D. C., Bhattacharya, B., and Shimizu, Y.: Recent advances on bedform research and application: Process-based to machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9017, https://doi.org/10.5194/egusphere-egu21-9017, 2021.