Journal
HYDROLOGY AND EARTH SYSTEM SCIENCES
Volume 23, Issue 12, Pages 5089-5110Publisher
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/hess-23-5089-2019
Keywords
-
Categories
Funding
- FWF [P 28660-N31]
- GPU [LIT-2017-3-YOU-003]
- Bosch
- ZF
- NVIDIA Corporation
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available