Articles | Volume 25, issue 8
Hydrol. Earth Syst. Sci., 25, 4373–4401, 2021
Hydrol. Earth Syst. Sci., 25, 4373–4401, 2021

Research article 11 Aug 2021

Research article | 11 Aug 2021

Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling

Herath Mudiyanselage Viraj Vidura Herath et al.

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
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Natthachet Tangdamrongsub, Michael F. Jasinski, and Peter J. Shellito
Hydrol. Earth Syst. Sci., 25, 4185–4208,,, 2021
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Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times
Seán Donegan, Conor Murphy, Shaun Harrigan, Ciaran Broderick, Dáire Foran Quinn, Saeed Golian, Jeff Knight, Tom Matthews, Christel Prudhomme, Adam A. Scaife, Nicky Stringer, and Robert L. Wilby
Hydrol. Earth Syst. Sci., 25, 4159–4183,,, 2021
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Paul C. Astagneau, Guillaume Thirel, Olivier Delaigue, Joseph H. A. Guillaume, Juraj Parajka, Claudia C. Brauer, Alberto Viglione, Wouter Buytaert, and Keith J. Beven
Hydrol. Earth Syst. Sci., 25, 3937–3973,,, 2021
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A new fractal-theory-based criterion for hydrological model calibration
Zhixu Bai, Yao Wu, Di Ma, and Yue-Ping Xu
Hydrol. Earth Syst. Sci., 25, 3675–3690,,, 2021
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The value of water isotope data on improving process understanding in a glacierized catchment on the Tibetan Plateau
Yi Nan, Lide Tian, Zhihua He, Fuqiang Tian, and Lili Shao
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Cited articles

Addor, N., and Melsen, L. A.: Legacy, rather than adequacy, drives the selection of hydrological models, Water Resour. Res., 55, 378–390,, 2019. 
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology: Artificial neural networks in hydrology. II: Hydrologic applications, J. Hydrol. Eng., 5, 124–137, 2000. 
Baartman, J. E., Melsen, L. A., Moore, D., and van der Ploeg, M. J.: On the complexity of model complexity: Viewpoints across the geosciences, Catena, 186, 104261,, 2019. 
Babovic, V.: Data mining in hydrology, Hydrol. Process., 19, 1511–1515, 2005. 
Babovic, V.: Introducing knowledge into learning based on genetic programming, J. Hydroinform., 11, 181–193, 2009. 
Short summary
Existing hydrological knowledge has been integrated with genetic programming based on a machine learning algorithm (MIKA-SHA) to induce readily interpretable distributed rainfall–runoff models. At present, the model building components of two flexible modelling frameworks (FUSE and SUPERFLEX) represent the elements of hydrological knowledge. The proposed toolkit captures spatial variabilities and automatically induces semi-distributed rainfall–runoff models without any explicit user selections.