# Prediction in ungauged regions with sparse flow duration curves and input-selection ensemble modeling

arXiv: https://arxiv.org/abs/2011.13380 · HTML: https://mlsanity.com/paper/2011.13380/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 9 of 9)

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- [Differentiable modeling to unify machine learning and physical models and advance Geosciences](https://mlsanity.com/paper/2301.04027/) (2023)
- [Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings](https://mlsanity.com/paper/2601.01558/) (2026)
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- [Toward Routing River Water in Land Surface Models with Recurrent Neural Networks](https://mlsanity.com/paper/2404.14212/) (2024)
- [A Differentiable Framework for Global Circulation Model Precipitation Bias Correction](https://mlsanity.com/paper/2604.23045/) (2026)

## Abstract (arXiv)

While long short-term memory (LSTM) models have demonstrated stellar performance with streamflow predictions, there are major risks in applying these models in contiguous regions with no gauges, or predictions in ungauged regions (PUR) problems. However, softer data such as the flow duration curve (FDC) may be already available from nearby stations, or may become available. Here we demonstrate that sparse FDC data can be migrated and assimilated by an LSTM-based network, via an encoder. A stringent region-based holdout test showed a median Kling-Gupta efficiency (KGE) of 0.62 for a US dataset, substantially higher than previous state-of-the-art global-scale ungauged basin tests. The baseline model without FDC was already competitive (median KGE 0.56), but integrating FDCs had substantial value. Because of the inaccurate representation of inputs, the baseline models might sometimes produce catastrophic results. However, model generalizability was further meaningfully improved by compiling an ensemble based on models with different input selections.