The DOI matches its Crossref record; 51 of the 52 references it cites were found in a registry or an index.
Mitigating Prediction Error of Deep Learning Streamflow Models in Large Data‐Sparse Regions With Ensemble Modeling and Soft Data
Abstract
Predicting discharge in contiguously data‐scarce or ungauged regions is needed for quantifying the global hydrologic cycle. We show that prediction in ungauged regions (PUR) has major, underrecognized uncertainty and is drastically more difficult than previous problems where basins can be represented by neighboring or similar basins (known as prediction in ungauged basins). While deep neural networks demonstrated stellar performance for streamflow predictions, performance nonetheless declined for PUR, benchmarked here with a new stringent region‐based holdout test on a US data set with 671 basins. We tested approaches to reduce such errors, leveraging deep network's flexibility to integrate “soft” data, such as satellite‐based soil moisture product, or daily flow distributions which improved low flow simulations. A novel input‐selection ensemble improved average performance and greatly reduced catastrophic failures. Despite challenges, deep networks showed stronger performance metrics for PUR than traditional hydrologic models. They appear competitive for geoscientific modeling even in data‐scarce settings.
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In the citing paper
“While the LSTM's performance declines from temporal to PUB tests, it obtains better results than established process-based models calibrated on the test basins (Feng et al., 2021;Kratzert et al., 2019a).”
From The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment (2023) · cites this paper as Feng et al., 2021
In the citing paper
“For PUR, the LSTM's performance further declines significantly (Feng et al., 2021).”
From The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment (2023) · cites this paper as Feng et al., 2021
In the citing paper
“For both kinds of tests, regionalized LSTM models hold the performance record on daily hydrograph metrics (Feng et al., 2021;Kratzert et al., 2019a).”
From The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment (2023) · cites this paper as Feng et al., 2021
In the citing paper
“best PUR results in region 1 (northeast) and region 7 (northwest), with NSE/KGE medians larger than or close to 0.6 (Fig. B2), which are consistent with our previous PUR study using LSTM (Feng et al., 2021).”
From The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment (2023) · cites this paper as Feng et al., 2021
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
River flow forecasting through conceptual models part I — A discussion of principles
Nash, J. E., & Sutcliffe, J. V. (1970) · Journal of Hydrology · doi:10.1016/0022-1694(70)90255-6 · Cited by 24,401
Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations
Moriasi, D. N., Arnold, J. G., Liew, M. W. V., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007) · Transactions of the ASABE · doi:10.13031/2013.23153 · Cited by 13,446
Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009) · Journal of Hydrology · doi:10.1016/j.jhydrol.2009.08.003 · Cited by 6,758
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y., & Ghahramani, Z. (2015) · arXiv · doi:10.48550/arxiv.1506.02142 · Cited by 6,471
The Soil Moisture Active Passive (SMAP) Mission
Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J. T., et al. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2010.2043918 · Cited by 3,793
Constraints on future changes in climate and the hydrologic cycle
Allen, M. R., & Ingram, W. J. (2002) · Nature · doi:10.1038/nature01092 · Cited by 3,009
Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks
(2018) · doi:10.5194/hess-22-6005-2018 · Cited by 2,114
Shen, C. (2018) · Water Resources Research · doi:10.1029/2018wr022643 · Cited by 1,522
Self-citationA decade of Predictions in Ungauged Basins (PUB)—a review
Hrachowitz, M., Savenije, H. H. G., Blöschl, G., McDonnell, J. J., Sivapalan, M., Pomeroy, J. W., Arheimer, B., Blume, T., Clark, M., Ehret, U., et al. (2013) · Hydrological Sciences Journal · doi:10.1080/02626667.2013.803183 · Cited by 1,321
Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning
Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., & Nearing, G. (2019) · Water Resources Research · doi:10.1029/2019wr026065 · Cited by 1,089
Multiscale parameter regionalization of a grid‐based hydrologic model at the mesoscale
Samaniego, L., Kumar, R., & Attinger, S. (2010) · Water Resources Research · doi:10.1029/2008wr007327 · Cited by 858
A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning
Xiang, Z., Yan, J., & Demir, İ. (2020) · Water Resources Research · doi:10.1029/2019wr025326 · Cited by 791
Proceedings of the 28th International Conference on Neural Information Processing Systems
Cortes, C., Lee, D., Sugiyama, M., & Garnett, R. (2015) · Cited by 747
Yılmaz, K. K., Gupta, H. V., & Wagener, T. (2008) · Water Resources Research · doi:10.1029/2007wr006716 · Cited by 704
Feng, D., Fang, K., & Shen, C. (2020) · Water Resources Research · doi:10.1029/2019wr026793 · Cited by 633
Self-citationGloFAS – global ensemble streamflow forecasting and flood early warning
Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J., & Pappenberger, F. (2013) · Hydrology and earth system sciences · doi:10.5194/hess-17-1161-2013 · Cited by 583
Streamflow Prediction in Ungauged Basins: Review of Regionalization Methods
Razavi, T., & Coulibaly, P. (2012) · Journal of Hydrologic Engineering · doi:10.1061/(asce)he.1943-5584.0000690 · Cited by 461
The SWOT Mission and Its Capabilities for Land Hydrology
Biancamaria, S., Lettenmaier, D. P., & Pavelsky, T. M. (2016) · Surveys in Geophysics · doi:10.1007/s10712-015-9346-y · Cited by 458
Fang, K., Shen, C., Kifer, D., & Yang, X. (2017) · Geophysical Research Letters · doi:10.1002/2017gl075619 · Cited by 380
Self-citationTowards objective probabalistic climate forecasting
Allen, M. R., & Stainforth, D. A. (2002) · Nature · doi:10.1038/nature01092a · Cited by 342
Guo, Y., Zhang, Y., Zhang, L., & Wang, Z. (2020) · Wiley Interdisciplinary Reviews Water · doi:10.1002/wat2.1487 · Cited by 336
Ma, K., Feng, D., Lawson, K., Tsai, W., Liang, C., Huang, X., Sharma, A., & Shen, C. (2021) · Water Resources Research · doi:10.1029/2020wr028600 · Cited by 278
Self-citationCalibration of hydrological models using flow-duration curves
Westerberg, I. K., Guerrero, J.-L., Younger, P. M., Beven, K. J., Seibert, J., Halldin, S., Freer, J. E., & Xu, C. (2011) · Hydrology and earth system sciences · doi:10.5194/hess-15-2205-2011 · Cited by 255
Trabucco, A., Zomer, R. J., Bossio, D., van Straaten, O., & Verchot, L. V. (2008) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2008.01.015 · Cited by 254
The proper care and feeding of CAMELS: How limited training data affects streamflow prediction
Gauch, M., Mai, J., & Lin, J. (2020) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2020.104926 · Cited by 251
Regional flow-duration curves: reliability for ungauged basins
Castellarin, A., Galeati, G., Brandimarte, L., Montanari, A., & Brath, A. (2004) · Advances in Water Resources · doi:10.1016/j.advwatres.2004.08.005 · Cited by 222
Neal, R. M. (1996) · Lecture notes in statistics · doi:10.1007/978-1-4612-0745-0_2 · Cited by 210
Mu, X., Zhang, L., McVicar, T. R., Chille, B., & Gau, P. (2007) · Hydrological Processes · doi:10.1002/hyp.6391 · Cited by 205
Fang, K., & Shen, C. (2020) · Journal of Hydrometeorology · doi:10.1175/jhm-d-19-0169.1 · Cited by 186
Self-citationBeck, H. E., Pan, M., Lin, P., Seibert, J., van Dijk, A. I. J. M., & Wood, E. F. (2020) · Journal of Geophysical Research Atmospheres · doi:10.1029/2019jd031485 · Cited by 165
Shafii, M., & Tolson, B. A. (2015) · Water Resources Research · doi:10.1002/2014wr016520 · Cited by 164
High temporal resolution rainfall–runoff modeling using long-short-term-memory (LSTM) networks
Li, W., Kiaghadi, A., & Dawson, C. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05010-6 · Cited by 160
Zhou, Y., Wu, X., Ju, W., Chen, J. M., Wang, S., Wang, H., Yuan, W., Black, T. A., Jassal, R. S., Ibrom, A., et al. (2015) · Journal of Geophysical Research Biogeosciences · doi:10.1002/2014jg002876 · Cited by 148
A new regionalization approach and its application to predict flow duration curve in ungauged basins
Li, M., Shao, Q., Zhang, L., & Chiew, F. H. S. (2010) · Journal of Hydrology · doi:10.1016/j.jhydrol.2010.05.039 · Cited by 134
Masih, I., Uhlenbrook, S., Maskey, S., & Ahmad, M. (2010) · Journal of Hydrology · doi:10.1016/j.jhydrol.2010.07.018 · Cited by 126
Action-based flood forecasting for triggering humanitarian action
de Perez, E. C., van den Hurk, B., van Aalst, M., Amuron, I., Bamanya, D., Hauser, T., Jongma, B., López, A. M., Mason, S. J., de Suarez, J. M., et al. (2016) · Hydrology and earth system sciences · doi:10.5194/hess-20-3549-2016 · Cited by 117
Ouyang, W., Lawson, K., Feng, D., Ye, L., Zhang, C., & Shen, C. (2021) · Journal of Hydrology · doi:10.1016/j.jhydrol.2021.126455 · Cited by 116
Self-citationRecent Changes in Land Water Storage and its Contribution to Sea Level Variations
Wada, Y., Reager, J. T., Chao, B. F., Wang, J., Lo, M., Song, C., Yuwen, L., & Gardner, A. (2016) · Surveys in Geophysics · doi:10.1007/s10712-016-9399-6 · Cited by 114
Regionalization of daily flow characteristics in part of the Eastern Cape, South Africa
Smakhtin, V., Hughes, D. A., & CREUSE-NAUDIN, E. (1997) · Hydrological Sciences Journal · doi:10.1080/02626669709492088 · Cited by 96
Water sustainability and watershed storage
McDonnell, J. J., Evaristo, J., Bladon, K. D., Buttle, J. M., Creed, I. F., Dymond, S. F., Grant, G. E., Andres Iroume, A., Jackson, C. R., Jones, J. A., et al. (2018) · Nature Sustainability · doi:10.1038/s41893-018-0099-8 · Cited by 91
Synthetic design hydrographs for ungauged catchments: a comparison of regionalization methods
Brunner, M. I., Furrer, R., Sikorska‐Senoner, A. E., Viviroli, D., Seibert, J., & Favre, A. (2018) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-018-1523-3 · Cited by 53
Nruthya, K., & Srinivas, V. V. (2015) · Aquatic Procedia · doi:10.1016/j.aqpro.2015.02.083 · Cited by 45
Sadegh, M., Vrugt, J. A., Gupta, H. V., & Xu, C. (2016) · Journal of Hydrology · doi:10.1016/j.jhydrol.2016.01.027 · Cited by 34
Synthetic regional flow duration curve for southern Taiwan
Yu, P., & Yang, T. (1996) · Hydrological Processes · doi:10.1002/(sici)1099-1085(199603)10:3 · Cited by 30
Post processing the U.S. National Water Model with a Long Short-Term Memory networkPreprint
Frame, J., Nearing, G., Kratzert, F., & Rahman, M. (2020) · doi:10.31223/osf.io/4xhac · Cited by 25
The index gage method to develop a flow duration curve from short-term streamflow records
Zhang, Z. (2017) · Journal of Hydrology · doi:10.1016/j.jhydrol.2017.07.045 · Cited by 13
Daily Flow Estimation at Ungauged Regions Based on Regional Flow Duration Curves
Huan, G., & Zhu, C. (2009) · Springer eBooks · doi:10.1007/978-3-540-89465-0_10 · Cited by 4
Analysis of the Regional Flow Duration Curve for Dongjiang Basin, South China
Jian, T., & Huan, G. (2009) · Springer eBooks · doi:10.1007/978-3-540-89465-0_9 · Cited by 2
Research works printed but not linked: 4
- Addor N., 2017, Catchment attributes for large-sample studies
- Global Runoff Data Center (GRDC), 2020, BfG‐River discharge data‐Station maps
- Newman A. J., 2014, A large‐sample watershed‐scale hydrometeorological dataset for the contiguous USA
- O’Neill P. E., 2020, SMAP Enhanced L3 Radiometer Global Daily 9 km EASE‐Grid Soil Moisture, Version 4 . April 2015 to March 2020
Cited by
Sorted by citations
Differentiable modelling to unify machine learning and physical models for geosciences
Shen, C., Appling, A., Gentine, P., Bandai, T., Gupta, H. V., Tartakovsky, A. M., Baity‐Jesi, M., Fenicia, F., Kifer, D., Li, L., et al. (2023) · Nature Reviews Earth & Environment · doi:10.1038/s43017-023-00450-9 · Cited by 594
Self-citationFeng, D., Liu, J., Lawson, K., & Shen, C. (2022) · Water Resources Research · doi:10.1029/2022wr032404 · Cited by 386
Self-citationHybrid forecasting: blending climate predictions with AI models
Slater, L. J., Arnal, L., Boucher, M.-A., Chang, A. Y.-Y., Moulds, S., Murphy, C., Nearing, G., Shalev, G., Shen, C., Speight, L., et al. (2023) · Hydrology and Earth System Sciences · doi:10.5194/hess-27-1865-2023 · Cited by 281
Self-citationKumar, V., Kedam, N., Sharma, K. V., Mehta, D., & Caloiero, T. (2023) · Water · doi:10.3390/w15142572 · Cited by 270
Feng, D., Beck, H., Lawson, K., & Shen, C. (2023) · Hydrology and Earth System Sciences · doi:10.5194/hess-27-2357-2023 · Cited by 197
Self-citation
Metadata
- Authors
- Feng, D., Lawson, K., & Shen, C.
- Affiliations
- Pennsylvania State University (US)
- Corresponding author
- Shen, Chaopeng
- Venue
- Geophysical Research Letters
- Publisher
- American Geophysical Union (AGU)
- Published
- 2021-07-19
- Volume
- 48
- Issue
- 14
- ISSN
- 0094-8276 (ISSN-L)
- 1944-8007
- OpenAlex
- W3174646361
- Cited by
- 162
- References
- 52
- Citations by year
- 2026: 20 · 2025: 27 · 2024: 38 · 2023: 33 · 2022: 43 · 2021: 1
- FWCI
- 9.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Geophysical Research Letters (publishedVersion)
- Funders
- National Science Foundation (EAR#1832294)
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
- Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Environmental Monitoring and Data Management
- Keywords
- streamflow forecasting, deep neural network, ensemble learning, feature selection, soft information, satellite soil moisture, low flow simulation, data-scarce basins, forecast uncertainty, ungauged basins
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