# Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasets

arXiv: https://arxiv.org/abs/1907.08456 · HTML: https://mlsanity.com/paper/1907.08456/

## Key facts

- Cited by 69 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2019.

## Built on by (most cited first, 30 of 69)

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- [Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology](https://mlsanity.com/paper/2310.05227/) (2023)
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- [Probing the limit of hydrologic predictability with the Transformer network](https://mlsanity.com/paper/2306.12384/) (2023)
- [Adaptive Process-Guided Learning: An Application in Predicting Lake DO Concentrations](https://mlsanity.com/paper/2411.12973/) (2024)
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- [Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources](https://mlsanity.com/paper/2308.09766/) (2023)
- [Towards Interpretable Physical-Conceptual Catchment-Scale Hydrological Modeling using the Mass-Conserving-Perceptron](https://mlsanity.com/paper/2401.14521/) (2024)
- [Continental-scale streamflow modeling of basins with reservoirs: towards a coherent deep-learning-based strategy](https://mlsanity.com/paper/2101.04423/) (2021)
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- [Temporal Fusion Transformers for Streamflow Prediction: Value of Combining Attention with Recurrence](https://mlsanity.com/paper/2305.12335/) (2023)
- [AI Increases Global Access to Reliable Flood Forecasts](https://mlsanity.com/paper/2307.16104/) (2023)
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- [Earthquake Nowcasting with Deep Learning](https://mlsanity.com/paper/2201.01869/) (2021)
- [Task Aware Modulation using Representation Learning: An Approach for Few Shot Learning in Environmental Systems](https://mlsanity.com/paper/2310.04727/) (2023)
- [Evolution-based Feature Selection for Predicting Dissolved Oxygen Concentrations in Lakes](https://mlsanity.com/paper/2403.18923/) (2024)
- [A Deep State Space Model for Rainfall-Runoff Simulations](https://mlsanity.com/paper/2501.14980/) (2025)
- [Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings](https://mlsanity.com/paper/2601.01558/) (2026)
- [X-MethaneWet: A Cross-scale Global Wetland Methane Emission Benchmark Dataset for Advancing Science Discovery with AI](https://mlsanity.com/paper/2505.18355/) (2025)
- [RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting](https://mlsanity.com/paper/2505.22535/) (2025)

## Abstract (arXiv)

Regional rainfall-runoff modeling is an old but still mostly out-standing problem in Hydrological Sciences. The problem currently is that traditional hydrological models degrade significantly in performance when calibrated for multiple basins together instead of for a single basin alone. In this paper, we propose a novel, data-driven approach using Long Short-Term Memory networks (LSTMs), and demonstrate that under a 'big data' paradigm, this is not necessarily the case. By training a single LSTM model on 531 basins from the CAMELS data set using meteorological time series data and static catchment attributes, we were able to significantly improve performance compared to a set of several different hydrological benchmark models. Our proposed approach not only significantly outperforms hydrological models that were calibrated regionally but also achieves better performance than hydrological models that were calibrated for each basin individually. Furthermore, we propose an adaption to the standard LSTM architecture, which we call an Entity-Aware-LSTM (EA-LSTM), that allows for learning, and embedding as a feature layer in a deep learning model, catchment similarities. We show that this learned catchment similarity corresponds well with what we would expect from prior hydrological understanding.