TY  - JOUR
A1  - Ayzel, Georgy
A1  - Heistermann, Maik
T1  - The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU
T2  - Computers & geosciences : an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets ; an official journal of the International Association for Mathematical Geology
N2  - We systematically explore the effect of calibration data length on the performance of a conceptual hydrological model, GR4H, in comparison to two Artificial Neural Network (ANN) architectures: Long Short-Term Memory Networks (LSTM) and Gated Recurrent Units (GRU), which have just recently been introduced to the field of hydrology. We implemented a case study for six river basins across the contiguous United States, with 25 years of meteorological and discharge data. Nine years were reserved for independent validation; two years were used as a warm-up period, one year for each of the calibration and validation periods, respectively; from the remaining 14 years, we sampled increasing amounts of data for model calibration, and found pronounced differences in model performance. While GR4H required less data to converge, LSTM and GRU caught up at a remarkable rate, considering their number of parameters. Also, LSTM and GRU exhibited the higher calibration instability in comparison to GR4H. These findings confirm the potential of modern deep-learning architectures in rainfall runoff modelling, but also highlight the noticeable differences between them in regard to the effect of calibration data length.
KW  - Artificial neural networks
KW  - Calibration
KW  - Deep learning
KW  - Rainfall-runoff
KW  - modelling
Y1  - 2021
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/57754
SN  - 0098-3004
SN  - 1873-7803
VL  - 149
PB  - Elsevier
CY  - Amsterdam
ER  -