Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets

By Frederik Kratzert, Daniel Klotz, Guy Shalev, Günter Klambauer, Sepp Hochreiter, Grey Nearing. Hydrology and Earth System Sciences, 2019

893 citations recorded by the source corpus.

Abstract. Regional rainfall–runoff modeling is an old but still mostly outstanding problem in the 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 dataset 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 catchment similarities as a feature layer in a deep learning model. We show that these learned catchment similarities correspond well to what we would expect from prior hydrological understanding.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/4ab1ba56cc19f07926e785c1fbdd865f4a23b880

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Daniel Klotz
2. Frederik Kratzert
3. Grey Nearing

In Global and Planetary Change

1. Grey Nearing

In Water Science and Technology

1. Alden Keefe Sampson
2. Sepp Hochreiter
3. Daniel Klotz
4. Frederik Kratzert
5. Grey Nearing

Citations in the corpus

• Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning (2019, background): “In a companion paper to this one, Kratzert et al. (2019) showed that regionally trained long short-term memory (LSTM) network outperforms basin-specific calibrations of several traditional hydrology models and demonstrated that LSTM-type models were able to extract information from observable…”