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
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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