Articles | Volume 24, issue 2
https://doi.org/10.5194/hess-24-535-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-535-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global catchment modelling using World-Wide HYPE (WWH), open data, and stepwise parameter estimation
Hydrology Research, Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, 60176 Norrköping, Sweden
Rafael Pimentel
Hydrology Research, Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, 60176 Norrköping, Sweden
Edf. Leonardo Da Vinci, University of Cordoba, Campus de Rabanales, 14071, Córdoba, Spain
Kristina Isberg
Hydrology Research, Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, 60176 Norrköping, Sweden
Louise Crochemore
Hydrology Research, Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, 60176 Norrköping, Sweden
Jafet C. M. Andersson
Hydrology Research, Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, 60176 Norrköping, Sweden
Abdulghani Hasan
Hydrology Research, Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, 60176 Norrköping, Sweden
Department of Physical Geography and Ecosystem Science, Lund University Box 117, 221 00, Lund, Sweden
Luis Pineda
Hydrology Research, Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, 60176 Norrköping, Sweden
School of Earth Sciences, Energy and Environment, Yachay Tech University, Hacienda San José, Urcuquí, Ecuador
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8 citations as recorded by crossref.
- A Regularization Approach to Improve the Sequential Calibration of a Semidistributed Hydrological Model A. de Lavenne et al. 10.1029/2018WR024266
- Flash droughts present a new challenge for subseasonal-to-seasonal prediction A. Pendergrass et al. 10.1038/s41558-020-0709-0
- Global Fully Distributed Parameter Regionalization Based on Observed Streamflow From 4,229 Headwater Catchments H. Beck et al. 10.1029/2019JD031485
- Lessons learnt from checking the quality of openly accessible river flow data worldwide L. Crochemore et al. 10.1080/02626667.2019.1659509
- Evaluation of precipitation datasets against local observations in southwestern Iran A. Fallah et al. 10.1002/joc.6445
- A Flexible Framework for Simulating the Water Balance of Lakes and Reservoirs From Local to Global Scales: mizuRoute‐Lake S. Gharari et al. 10.1029/2022WR032400
- Advances in Quantifying Streamflow Variability Across Continental Scales: 1. Identifying Natural and Anthropogenic Controlling Factors in the USA Using a Spatially Explicit Modeling Method R. Alexander et al. 10.1029/2019WR025001
- What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts? I. Pechlivanidis et al. 10.1029/2019WR026987
Latest update: 20 Nov 2024
Short summary
How far can we reach in predicting river flow globally, using integrated catchment modelling and open global data? For the first time, a catchment model was applied world-wide, covering the entire globe with a relatively high resolution. The results show that stepwise calibration provided better performance than traditional modelling of the globe. The study highlights that open data and models are crucial to advance hydrological sciences by sharing knowledge and enabling transparent evaluation.
How far can we reach in predicting river flow globally, using integrated catchment modelling and...