Articles | Volume 387
https://doi.org/10.5194/piahs-387-17-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/piahs-387-17-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A hybrid approach to enhance streamflow simulation in data-constrained Himalayan basins: combining the Glacio-hydrological Degree-day Model and recurrent neural networks
Dinesh Joshi
CORRESPONDING AUTHOR
Department of Environmental Science and Engineering, School of Science, Kathmandu University, Dhulikhel, 45210, Nepal
Rijan Bhakta Kayastha
Department of Environmental Science and Engineering, School of Science, Kathmandu University, Dhulikhel, 45210, Nepal
Kundan Lal Shrestha
Department of Chemical Science and Engineering, School of Engineering, Kathmandu University, Dhulikhel, 45210, Nepal
Rakesh Kayastha
Department of Environmental Science and Engineering, School of Science, Kathmandu University, Dhulikhel, 45210, Nepal
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Kundan Lal Shrestha, Rijan Bhakta Kayastha, and Rakesh Kayastha
Proc. IAHS, 387, 25–31, https://doi.org/10.5194/piahs-387-25-2024, https://doi.org/10.5194/piahs-387-25-2024, 2024
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The Himalayan river basins have complex terrain and lack detailed hydrological and meteorological information. This has motivated us to develop a fast and distributed model named PyGDM to simulate the hydrology of this region, which is home to both glaciers and snow. PyGDM is good at simulating glacier and snow melt. Hence, the model is suitable for studying different aspects of the Himalayan region, such as the impact of climate change and hydropower scenarios.
Rakesh Kayastha, Rijan Bhakta Kayastha, Kundan Lal Shrestha, and Smriti Gurung
Proc. IAHS, 387, 53–58, https://doi.org/10.5194/piahs-387-53-2024, https://doi.org/10.5194/piahs-387-53-2024, 2024
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We have estimated hydropower potential in the two glacierized river basins of the Nepalese Himalayas. The Glacio-hydrological Degree-Day Model (GDM) was used with different geospatial criteria. In order to force the model simulation and to assess potential future hydrological regimes, a variety of climate variables were combined and used. The sensitivity of climate variables and their impact on hydropower potential were investigated with a combination of different climate variables.
Rijan Bhakta Kayastha and Sunwi Maskey
Proc. IAHS, 387, 59–63, https://doi.org/10.5194/piahs-387-59-2024, https://doi.org/10.5194/piahs-387-59-2024, 2024
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Glacial lake outburst flood (GLOF) modeling of Tsho Rolpa showed that, even if the lake breaches by 20 m in 40 years (from 2021), there will be a sufficient lead time of more than 7 h for early warning and human evacuations in the downstream areas. However, precautionary measures such as community-based GLOF early-warning systems and mechanisms allowing close observation in the case of GLOF events should be established in GLOF-prone regions.
Rijan Bhakta Kayastha, Hari Krishna Shrestha, and Dhiraj Pradhananga
Proc. IAHS, 387, 1–2, https://doi.org/10.5194/piahs-387-1-2024, https://doi.org/10.5194/piahs-387-1-2024, 2024
S. Sapkota, N. R. Bhatt, D. P. Bhatt, A. Bist, S. Thapa, and R. B. Kayastha
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5-W2, 83–88, https://doi.org/10.5194/isprs-annals-IV-5-W2-83-2019, https://doi.org/10.5194/isprs-annals-IV-5-W2-83-2019, 2019
Simone Brunamonti, Teresa Jorge, Peter Oelsner, Sreeharsha Hanumanthu, Bhupendra B. Singh, K. Ravi Kumar, Sunil Sonbawne, Susanne Meier, Deepak Singh, Frank G. Wienhold, Bei Ping Luo, Maxi Boettcher, Yann Poltera, Hannu Jauhiainen, Rijan Kayastha, Jagadishwor Karmacharya, Ruud Dirksen, Manish Naja, Markus Rex, Suvarna Fadnavis, and Thomas Peter
Atmos. Chem. Phys., 18, 15937–15957, https://doi.org/10.5194/acp-18-15937-2018, https://doi.org/10.5194/acp-18-15937-2018, 2018
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Based on balloon-borne measurements performed in India and Nepal in 2016–2017, we infer the vertical distributions of water vapor, ozone and aerosols in the atmosphere, from the surface to 30 km altitude. Our measurements show that the atmospheric dynamics of the Asian summer monsoon system over the polluted Indian subcontinent lead to increased concentrations of water vapor and aerosols in the high atmosphere (approximately 14–20 km altitude), which can have an important effect on climate.
S. H. Ali, I. Bano, R. B. Kayastha, and A. Shrestha
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 1487–1494, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1487-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1487-2017, 2017
Koji Fujita, Hiroshi Inoue, Takeki Izumi, Satoru Yamaguchi, Ayako Sadakane, Sojiro Sunako, Kouichi Nishimura, Walter W. Immerzeel, Joseph M. Shea, Rijan B. Kayastha, Takanobu Sawagaki, David F. Breashears, Hiroshi Yagi, and Akiko Sakai
Nat. Hazards Earth Syst. Sci., 17, 749–764, https://doi.org/10.5194/nhess-17-749-2017, https://doi.org/10.5194/nhess-17-749-2017, 2017
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We create multiple DEMs from photographs taken by helicopter and UAV and reveal the deposit volumes over the Langtang village, which was destroyed by avalanches induced by the Gorkha earthquake. Estimated snow depth in the source area is consistent with anomalously large snow depths observed at a neighboring glacier. Comparing with a long-term observational data, we conclude that this anomalous winter snow amplified the disaster induced by the 2015 Gorkha earthquake in Nepal.
Sujan Shrestha, Siva Praveen Puppala, Bhupesh Adhikary, Kundan Lal Shrestha, and Arnico K. Panday
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-287, https://doi.org/10.5194/acp-2017-287, 2017
Revised manuscript not accepted
Cited articles
Barry, R. G.: Recent advances in mountain climate research, Theor. Appl. Climatol., 110, 549–553, https://doi.org/10.1007/s00704-012-0695-x, 2012.
Bengio, Y., Simard, P., and Frasconi, P.: Learning Long-Term Dependencies with Gradient Descent is Difficult, IEEE Trans. Neural Netw., 5, 157–166, https://doi.org/10.1109/72.279181, 1994.
Beven, K.: Towards an alternative blueprint for a physically based digitally simulated hydrologic response modelling system, Hydrol. Process., 16, 189–206, https://doi.org/10.1002/HYP.343, 2002.
Bocchiola, D., Diolaiuti, G., Soncini, A., Mihalcea, C., D'Agata, C., Mayer, C., Lambrecht, A., Rosso, R., and Smiraglia, C.: Prediction of future hydrological regimes in poorly gauged high altitude basins: the case study of the upper Indus, Pakistan, Hydrol. Earth Syst. Sci., 15, 2059–2075, https://doi.org/10.5194/hess-15-2059-2011, 2011.
Carrera, J., Alcolea, A., Medina, A. V., Hidalgo, J. J., and Slooten, L. J.: Inverse problem in hydrogeology, Hydrogeol. J., 13, 206–222, https://doi.org/10.1007/s10040-020-02176-0, 2005.
Cho, K. and Kim, Y.: Improving streamflow prediction in the WRF-Hydro model with LSTM networks, J. Hydrol., 605, 127297, https://doi.org/10.1016/j.jhydrol.2021.127297, 2022.
Cho, K., van Merriënboer, B., Bahdanau, D., and Bengio, Y.: On the Properties of Neural Machine Translation: Encoder-Decoder Approaches, Proceedings of SSST 2014 – 8th Workshop on Syntax, Semantics and Structure in Statistical Translation, 74, 103–111, https://doi.org/10.48550/arxiv.1409.1259, 2014.
Dahlstrom, D.: Calibration and Uncertainty Analysis for Complex Environmental Models, Groundwater, 53, 1234–1245, https://doi.org/10.1111/gwat.12360, 2015.
De Filippis, G., Stevenazzi, S., Camera, C., Pedretti, D., and Masetti, M.: An agile and parsimonious approach to data management in groundwater science using open-source resources, Hydrogeol. J., 28, 1993–2008, https://doi.org/10.1007/s10040-020-02176-0, 2020.
Government of Nepal, Ministry of Energy, Water Resources and Irrigation, Department of Hydrology and Meteorology: Hydro-meteorological data, http://dhm.gov.np/, last access: 22 July 2024.
Heaton, J., Goodfellow, I., Bengio, Y., and Courville, A.: Deep learning, Genet. Program. Evolvable Mach., 19, 305–307, https://doi.org/10.1007/s10710-017-9314-z, 2018.
Hochreiter, S. and Schmidhuber, J.: Long Short-Term Memory, Neural Computat., 9, 1735–1780, https://doi.org/10.1162/NECO.1997.9.8.1735, 1997.
Immerzeel, W. W., Wanders, N., Lutz, A. F., Shea, J. M., and Bierkens, M. F. P.: Reconciling high-altitude precipitation in the upper Indus basin with glacier mass balances and runoff, Hydrol. Earth Syst. Sci., 19, 4673–4687, https://doi.org/10.5194/hess-19-4673-2015, 2015.
Ji, H., Chen, Y., Fang, G., Li, Z., Duan, W., and Zhang, Q.: Adaptability of machine learning methods and hydrological models to discharge simulations in data-sparse glaciated watersheds, J. Arid Land, 13, 549–567, https://doi.org/10.1007/s40333-021-0066-5, 2021.
Jia, X., Willard, J., Karpatne, A., Read, J. S., Zwart, J. A., Steinbach, M., and Kumar, V.: Process Guided Deep Learning for Modeling Physical Systems: An Application in Lake Temperature Modeling, International Geoscience and Remote Sensing Symposium (IGARSS), Online, 19–24 July 2020, Abstract number 3494–3496, https://doi.org/10.1109/IGARSS39084.2020.9323723, 2020.
Kayastha, R. B. and Kayastha, R.: Glacio-hydrological degree-day model (GDM) useful for the Himalayan river basins, in: Himalayan Weather and Climate and their Impact on the Environment, Springer, 379–398, https://doi.org/10.1007/978-3-030-29684-1_19, 2019.
Moriasi, D. N., Gitau, M. W., Pai, N., and Daggupati, P.: Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria, T. ASABE, 58, 1763–1785, https://doi.org/10.13031/trans.58.10715, 2015.
Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Klotz, D., Frame, J. M., Prieto, C., and Gupta, H. V.: What Role Does Hydrological Science Play in the Age of Machine Learning?, Water Resour. Res., 57, e2020WR028091, https://doi.org/10.1029/2020WR028091, 2020.
Refsgaard, J. C. and Knudsen, J.: Operational Validation and Intercomparison of Different Types of Hydrological Models, Water Resour. Res., 32, 2189–2202, https://doi.org/10.1029/96WR00896, 1996.
Réveillet, M., Six, D., Vincent, C., Rabatel, A., Dumont, M., Lafaysse, M., Morin, S., Vionnet, V., and Litt, M.: Relative performance of empirical and physical models in assessing the seasonal and annual glacier surface mass balance of Saint-Sorlin Glacier (French Alps), The Cryosphere, 12, 1367–1386, https://doi.org/10.5194/tc-12-1367-2018, 2018.
Solomatine, D. P.: Data-Driven Modeling and Computational Intelligence Methods in Hydrology, Encyclopedia of Hydrological Sciences, edited by: Singh, V. P., John Wiley & Sons, HSA021, https://doi.org/10.1002/0470848944.HSA021, 2006.
Short summary
This study explores the potential of integrating data science models to enhance the predictive capacity of a theory-guided glacier hydrological model for improved river discharge simulations in the Himalayan basins. By combining data science and physical process models, the study addresses the limitations inherent in each approach.
This study explores the potential of integrating data science models to enhance the predictive...