Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2685-2021
https://doi.org/10.5194/hess-25-2685-2021
Research article
 | 
20 May 2021
Research article |  | 20 May 2021

A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling

Frederik Kratzert, Daniel Klotz, Sepp Hochreiter, and Grey S. Nearing

Related authors

A data-centric perspective on the information needed for hydrological uncertainty predictions
Andreas Auer, Martin Gauch, Frederik Kratzert, Grey Nearing, Sepp Hochreiter, and Daniel Klotz
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-64,https://doi.org/10.5194/hess-2024-64, 2024
Revised manuscript accepted for HESS
Short summary
Technical Note: The Divide and Measure Nonconformity
Daniel Klotz, Martin Gauch, Frederik Kratzert, Grey Nearing, and Jakob Zscheischler
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-59,https://doi.org/10.5194/hess-2024-59, 2024
Revised manuscript accepted for HESS
Short summary
HESS Opinions: Never train an LSTM on a single basin
Frederik Kratzert, Martin Gauch, Daniel Klotz, and Grey Nearing
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-275,https://doi.org/10.5194/hess-2023-275, 2024
Revised manuscript under review for HESS
Short summary
Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks
Grey S. Nearing, Daniel Klotz, Jonathan M. Frame, Martin Gauch, Oren Gilon, Frederik Kratzert, Alden Keefe Sampson, Guy Shalev, and Sella Nevo
Hydrol. Earth Syst. Sci., 26, 5493–5513, https://doi.org/10.5194/hess-26-5493-2022,https://doi.org/10.5194/hess-26-5493-2022, 2022
Short summary
Flood forecasting with machine learning models in an operational framework
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal, Asher Metzger, Chen Barshai, Dana Weitzner, Dafi Voloshin, Frederik Kratzert, Gal Elidan, Gideon Dror, Gregory Begelman, Grey Nearing, Guy Shalev, Hila Noga, Ira Shavitt, Liora Yuklea, Moriah Royz, Niv Giladi, Nofar Peled Levi, Ofir Reich, Oren Gilon, Ronnie Maor, Shahar Timnat, Tal Shechter, Vladimir Anisimov, Yotam Gigi, Yuval Levin, Zach Moshe, Zvika Ben-Haim, Avinatan Hassidim, and Yossi Matias
Hydrol. Earth Syst. Sci., 26, 4013–4032, https://doi.org/10.5194/hess-26-4013-2022,https://doi.org/10.5194/hess-26-4013-2022, 2022
Short summary

Related subject area

Subject: Global hydrology | Techniques and Approaches: Modelling approaches
Influence of irrigation on root zone storage capacity estimation
Fransje van Oorschot, Ruud J. van der Ent, Andrea Alessandri, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 2313–2328, https://doi.org/10.5194/hess-28-2313-2024,https://doi.org/10.5194/hess-28-2313-2024, 2024
Short summary
River flow in the near future: a global perspective in the context of a high-emission climate change scenario
Omar V. Müller, Patrick C. McGuire, Pier Luigi Vidale, and Ed Hawkins
Hydrol. Earth Syst. Sci., 28, 2179–2201, https://doi.org/10.5194/hess-28-2179-2024,https://doi.org/10.5194/hess-28-2179-2024, 2024
Short summary
A high-resolution perspective of extreme rainfall and river flow under extreme climate change in Southeast Asia
Mugni Hadi Hariadi, Gerard van der Schrier, Gert-Jan Steeneveld, Samuel J. Sutanto, Edwin Sutanudjaja, Dian Nur Ratri, Ardhasena Sopaheluwakan, and Albert Klein Tank
Hydrol. Earth Syst. Sci., 28, 1935–1956, https://doi.org/10.5194/hess-28-1935-2024,https://doi.org/10.5194/hess-28-1935-2024, 2024
Short summary
Unveiling hydrological dynamics in data-scarce regions: experiences from the Ethiopian Rift Valley Lakes Basin
Ayenew D. Ayalew, Paul D. Wagner, Dejene Sahlu, and Nicola Fohrer
Hydrol. Earth Syst. Sci., 28, 1853–1872, https://doi.org/10.5194/hess-28-1853-2024,https://doi.org/10.5194/hess-28-1853-2024, 2024
Short summary
Technical note: Comparing three different methods for allocating river points to coarse-resolution hydrological modelling grid cells
Juliette Godet, Eric Gaume, Pierre Javelle, Pierre Nicolle, and Olivier Payrastre
Hydrol. Earth Syst. Sci., 28, 1403–1413, https://doi.org/10.5194/hess-28-1403-2024,https://doi.org/10.5194/hess-28-1403-2024, 2024
Short summary

Cited articles

Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017a. a
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: Catchment attributes for large-sample studies, Boulder, CO, UCAR/NCAR, https://doi.org/10.5065/D6G73C3Q, 2017b. a
Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N., and Clark, M. P.: A Ranking of Hydrological Signatures Based on Their Predictability in Space, Water Resour. Res., 54, 8792–8812, https://doi.org/10.1029/2018WR022606, 2018. a, b
Alemohammad, S. H., McColl, K. A., Konings, A. G., Entekhabi, D., and Stoffelen, A.: Characterization of precipitation product errors across the United States using multiplicative triple collocation, Hydrol. Earth Syst. Sci., 19, 3489–3503, https://doi.org/10.5194/hess-19-3489-2015, 2015. a
Beck, H. E., Vergopolan, N., Pan, M., Levizzani, V., van Dijk, A. I. J. M., Weedon, G. P., Brocca, L., Pappenberger, F., Huffman, G. J., and Wood, E. F.: Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling, Hydrol. Earth Syst. Sci., 21, 6201–6217, https://doi.org/10.5194/hess-21-6201-2017, 2017. a
Download
Short summary
We investigate how deep learning models use different meteorological data sets in the task of (regional) rainfall–runoff modeling. We show that performance can be significantly improved when using different data products as input and further show how the model learns to combine those meteorological input differently across time and space. The results are carefully benchmarked against classical approaches, showing the supremacy of the presented approach.