Corrigendum to “Streamflow forecasting in ungauged basins with CNN-LSTM and radar-based precipitation” [J. Hydro-environ. Res. 60–61 (2025) 100666] (Journal of Hydro-environment Research (2025) 60–61, (S157064432500019X), (10.1016/j.jher.2025.100666))

The authors regret the following errors in the above article: Incorrect formatting in the reference list where publisher names were listed instead of journal names, and incorrect publication years for several references which resulted in corresponding errors in the in-text citations. • Section 1 (Introduction): “Han and Morrison (2021)” should be “Han and Morrison (2022)”• Section 2.2.1.1 (Watershed characteristic data): “(Li et al., 2017)” should be “(Liu et al., 2018)”• Section 2.3.1.1 (Model Structure – (a) Feature Extraction Layer): “(He et al., 2015)” should be “(He et al., 2016)”.Complete corrected references list is provided below. Arsenault, R., Martel, J. L., Brunet, F., Brissette, F., & Mai, J. (2023). Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models. Hydrology and Earth System Sciences, 27(1), 139–157. https://doi.org/10.5194/hess-27-139-2023. Campling, P., Gobin, A., Beven, K., & Feyen, J. (2002). Rainfall‐runoff modelling of a humid tropical catchment: the TOPMODEL approach. Hydrological processes, 16(2), 231–253. https://doi.org/10.1002/hyp.341. Chen, C., Jiang, J., Liao, Z., Zhou, Y., Wang, H., & Pei, Q. (2022). A short-term flood prediction based on spatial deep learning network: A case study for Xi County, China. Journal of Hydrology, 607, 127535. https://doi.org/10.1016/j.jhydrol.2022.127535. Chen, Z., Lin, H., & Shen, G. (2023). TreeLSTM: A spatiotemporal machine learning model for rainfall-runoff estimation. Journal of Hydrology: Regional Studies, 48, 101474. https://doi.org/10.1016/j.ejrh.2023.101474. Chiang, S., Chang, C. H., & Chen, W. B. (2022). Comparison of rainfall-runoff simulation between support vector regression and HEC-HMS for a rural watershed in Taiwan. Water, 14(2), 191. https://doi.org/10.3390/w14020191. Choi, J., Lee, J., & Kim, S. (2022). Utilization of the Long Short-Term Memory network for predicting streamflow in ungauged basins in Korea. Ecological Engineering, 182, 106699. https://doi.org/10.1016/j.ecoleng.2022.106699. Douglas, S. C., & Yu, J. (2018). Why RELU units sometimes die: Analysis of single-unit error backpropagation in neural networks. In 2018 52nd Asilomar conference on signals, systems, and computers (pp. 864–868). IEEE. https://doi.org/10.1109/acssc.2018.8645556. Han, H., & Morrison, R. R. (2022). Data-driven approaches for runoff prediction using distributed data. Stochastic Environmental Research and Risk Assessment, 36(8), 2153–2171. https://doi.org/10.1007/s00477-021-01993-3. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778). https://doi.org/10.1109/CVPR.2016.90. Hendrycks, D., & Gimpel, K. (2016). Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415. https://doi.org/10.48550/arxiv.1606.08415. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780. https://dl.acm.org/doi/10.1162/neco.1997.9.8.1735. Ivanov, V. Y., Vivoni, E. R., Bras, R. L., & Entekhabi, D. (2004). Preserving high-resolution surface and rainfall data in operational-scale basin hydrology: A fully-distributed physically-based approach. Journal of Hydrology, 298(1–4), 80–111. https://doi.org/10.1016/j.jhydrol.2004.03.041. Jiang, S., Zheng, Y., Wang, C., & Babovic, V. (2022). Uncovering flooding mechanisms across the contiguous United States through interpretive deep learning on representative catchments. Water Resources Research, 58(1), e2021WR030185. https://doi.org/10.1029/2021wr030185. Kim, D., Lee, Y. O., Jun, C., & Kang, S. (2023). Understanding the way machines simulate hydrological processes—A case study of predicting fine-scale watershed response on a distributed framework. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–18. https://doi.org/10.1109/tgrs.2023.3285540. Kim, J., Warnock, A., Ivanov, V. Y., & Katopodes, N. D. (2012). Coupled modeling of hydrologic and hydrodynamic processes including overland and channel flow. Advances in Water Resources, 37, 104–126. https://doi.org/10.1016/j.advwatres.2011.11.009. Kim, Y., Kim, D., Park, J., & Jun, C. (2024). An effective algorithm of outlier correction in space–time radar rainfall data based on the iterative localized analysis. IEEE Transactions on Geoscience and Remote Sensing, 62, 1–16. https://doi.org/10.1109/tgrs.2024.3366400. Kratzert, F., Herrnegger, M., Klotz, D., Hochreiter, S., & Klambauer, G. (2019a). NeuralHydrology–interpreting LSTMs in hydrology. Explainable AI: Interpreting, explaining and visualizing deep learning, 347–362. https://doi.org/10.1007/978-3-030-28954-6_19. Kratzert, F., Klotz, D., Brenner, C., Schulz, K., & Herrnegger, M. (2018). Rainfall–runoff modelling using long short-term memory (LSTM) networks. Hydrology and Earth System Sciences, 22(11), 6005–6022. https://doi.org/10.5194/hess-22-6005-2018. Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., & Nearing, G. S. (2019b). Toward improved predictions in ungauged basins: Exploiting the power of machine learning. Water Resources Research, 55(12), 11344–11354. https://doi.org/10.1029/2019wr026065. Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., & Nearing, G. (2019c). Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets. Hydrology and Earth System Sciences, 23(12), 5089–5110. https://doi.org/10.5194/hess-23-5089-2019. Liu, J., Gao, G., Wang, S., Jiao, L., Wu, X., & Fu, B. (2018). The effects of vegetation on runoff and soil loss: Multidimensional structure analysis and scale characteristics. Journal of Geographical Sciences, 28, 59–78. https://doi.org/10.1007/s11442-018-1459-z. Li, P., Zhang, J., & Krebs, P. (2022). Prediction of flow based on a CNN-LSTM combined deep learning approach. Water, 14(6), 993. https://doi.org/10.3390/w14060993. Liu, H. Q., & Huete, A. (1995). A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE transactions on geoscience and remote sensing, 33(2), 457–465. https://doi.org/10.1109/tgrs.1995.8746027 Loshchilov, I., & Hutter, F. (2017). Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101. https://doi.org/10.48550/arxiv.1711.05101. Micikevicius, P., Narang, S., Alben, J., Diamos, G., Elsen, E., Garcia, D., & Wu, H. (2017). Mixed precision training. arXiv preprint arXiv:1710.03740. https://doi.org/10.48550/arxiv.1710.03740. Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885–900. https://doi.org/10.13031/2013.23153. Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I—A discussion of principles. Journal of hydrology, 10(3), 282–290. https://doi.org/10.1016/0022-1694(70)90255-6. [dataset] NASA JPL, (2020). NASADEM Merged DEM Global 1 arc second V001. NASA EOSDIS Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MEaSUREs/NASADEM/NASADEM_HGT.001. Paszke, A. (2019). Pytorch: An imperative style, high-performance deep learning library. arXiv preprint arXiv:1912.01703. https://doi.org/10.48550/arxiv.1912.01703. Rouse, J. W., Jr., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA. Goddard Space Flight Center 3d ERTS-1 Symp., 1(A), PAPER-A20. https://ntrs.nasa.gov/api/citations/19740022614/downloads/19740022614.pdf. Sagawa, S., Koh, P. W., Hashimoto, T. B., & Liang, P. (2019). Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. arXiv preprint arXiv:1911.08731. https://doi.org/10.48550/arxiv.1911.08731. Sinclair, S., & Pegram, G. (2005). Combining radar and rain gauge rainfall estimates using conditional merging. Atmospheric Science Letters, 6(1), 19-22. https://doi.org/10.1002/asl.85. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), 1929–1958. https://jmlr.org/papers/v15/srivastava14a.html. Thorndahl, S., Einfalt, T., Willems, P., Nielsen, J. E., Ten Veldhuis, M. C., Arnbjerg-Nielsen, K., & Molnar, P. (2017). Weather radar rainfall data in urban hydrology. Hydrology and Earth System Sciences, 21(3), 1359–1380. https://doi.org/10.5194/hess-21-1359-2017. Vega-Bayo, M., Pérez-Aracil, J., Prieto-Godino, L., & Salcedo-Sanz, S. (2024). Improving the prediction of extreme wind speed events with generative data augmentation techniques. Renewable Energy, 221, 119769. https://doi.org/10.1016/j.renene.2023.119769. Vivoni, E. R., Ivanov, V. Y., Bras, R. L., & Entekhabi, D. (2005). On the effects of triangulated terrain resolution on distributed hydrologic model response. Hydrological Processes: An International Journal, 19(11), 2101–2122. https://doi.org/10.1002/hyp.5671. Vivoni, E. R., Mascaro, G., Mniszewski, S., Fasel, P., Springer, E. P., Ivanov, V. Y., & Bras, R. L. (2011). Real-world hydrologic assessment of a fully-distributed hydrological model in a parallel computing environment. Journal of Hydrology, 409(1–2), 483-496. https://doi.org/10.1016/j.jhydrol.2011.08.053. Wang, Y., & Karimi, H. A. (2022). Impact of Spatial Distribution Information of Rainfall in Runoff Simulation Using Deep-Learning Methods. Hydrology and Earth System Sciences, 26(9), 2387–2403. https://doi.org/10.5194/hess-26-2387-2022. Wijayarathne, D., & Coulibaly, P. (2020). Application of weather radar for operational hydrology in Canada–A review. Canadian Water Resources Journal/Revue canadienne des ressources hydriques, 46(1–2), 17-37. https://doi.org/10.1080/07011784.2020.1854119. Xiang, Z., & Demir, I. (2020). Distributed long-term hourly streamflow predictions using deep learning–A case study for State of Iowa. Environmental Modelling & Software, 131, 104761. https://doi.org/10.1016/j.envsoft.2020.104761. Xiang, Z., Yan, J., & Demir, I. (2020). A rainfall‐runoff model with LSTM‐based sequence‐to‐sequence learning. Water Resources Research, 56(1), e2019WR025326. https://doi.org/10.1029/2019wr025326. Yilmaz, K. K., Gupta, H. V., & Wagener, T. (2008). A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model. Water Resources Research, 44(9). https://doi.org/10.1029/2007WR006716. Yu, Q., Tolson, B. A., Shen, H., Han, M., Mai, J., & Lin, J. (2024). Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach. Hydrology and Earth System Sciences, 28(9), 2107–2122. https://doi.org/10.5194/hess-28-2107-2024. Zhang, Y., Ragettli, S., Molnar, P., Fink, O., & Peleg, N. (2022). Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments. Journal of Hydrology, 614, 128577. https://doi.org/10.1016/j.jhydrol.2022.128577. The authors would like to apologise for any inconvenience caused.