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Krysanova, V., Zaherpour, J., Didovets, I., Gosling, S.N., Gerten, D., Hanasaki, N., Müller Schmied, H., Pokhrel, Y., Satoh, Y., Tang, Q. & Wada, Y. ORCID: https://orcid.org/0000-0003-4770-2539 (2020). How evaluation of global hydrological models can help to improve credibility of river discharge projections under climate change. Climatic Change 163, 1353-1377. 10.1007/s10584-020-02840-0.

Zaherpour, J., Mount, N., Gosling, S., Dankers, R., Eisner, S., Gerten, D., Liu, X., Masaki, Y., Müller, H., Tang, Q. & Wada, Y. ORCID: https://orcid.org/0000-0003-4770-2539 (2019). Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models. Environmental Modelling & Software 114, 112-128. 10.1016/j.envsoft.2019.01.003.

Zaherpour, J., Gosling, S.N., Mount, N., Schmied, H.M., Veldkamp, T.I.E., Dankers, R., Eisner, S., Gerten, D., Gudmundsson, L., Haddeland, I., Hanasaki, N., Kim, H., Leng, G., Liu, J., Masaki, Y., Oki, T., Pokhrel, Y., Satoh, Y., Schewe, J. & Wada, Y. ORCID: https://orcid.org/0000-0003-4770-2539 (2018). Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts. Environmental Research Letters 13 (6), e065015. 10.1088/1748-9326/aac547.

Veldkamp, T., Zhao, F., Ward, P.J., Moel, H. de, Aerts, J.C.J.H., Müller Schmied, H., Portmann, F.T., Masaki, Y., Pokhrel, Y.N., Liu, X., Satoh, Y., Gerten, D., Gosling, S.N., Zaherpour, J. & Wada, Y. ORCID: https://orcid.org/0000-0003-4770-2539 (2018). Human impact parameterizations in global hydrological models improves estimates of monthly discharges and hydrological extremes: a multi-model validation study. Environmental Research Letters 13 (5), e055008. 10.1088/1748-9326/aab96f.