EGU2020-8292
https://doi.org/10.5194/egusphere-egu2020-8292
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Lessons learnt from quality-checking observed and simulated river flow data worldwide

Berit Arheimer1, Louise Crochemore1, Rafael Pineda2, Kristina Isberg1, Luis Pineda3, Abdulghani Hasan4, and Jafet Andersson1
Berit Arheimer et al.
  • 1SMHI, Hydrological Research, Norrköping, Sweden (berit.arheimer@smhi.se)
  • 2University of Cordoba, Edf. Leonardo Da Vinci, Campus de Rabanales, 14071, Córdoba, Spain
  • 3Yachay Tech University, Hacienda San José, Urcuquí, Ecuador
  • 4Lund University Box 117, SE-221 00, Lund, Sweden

Advances in open data science serve large-scale model developments and, subsequently, hydroclimate services. Local river flow observations are key in hydrology but data sharing remains limited due to unclear quality, or to political, economic or infrastructure reasons. This presentation provides methods for quality checking openly accessible river-flow time series. Availability, outliers, homogeneity and trends were assessed in 21 586 time series from 13 data providers worldwide. We found a decrease in data availability since the 1980s, scarce open information in southern Asia, the Middle East and North and Central Africa, and significant river-flow trends in Africa, Australia, southwest Europe and Southeast Asia. We distinguish numerical outliers from high-flow peaks, and integrate all investigated quality characteristics in a composite indicator.

Some 5338 gauges from these river flow time series (> 10 years) were used in the evaluation of the Worldwide-HYPE (WWH) hydrological model at the global scale (half for calibration and half for independent validation), resulting in a median monthly KGE of 0.4. However, WWH performance varies widely spatially and with the target flow signature. The model performs best (KGE > 0.6) in Eastern USA, Europe, South-East Asia, and Japan, as well as in parts of Russia, Canada, and South America. It also shows overall good potential to capture flow signatures of monthly high flows, spatial variability of high flows, duration of low flows, and constancy of daily flow. Nevertheless, there remains large potential for model improvements and we suggest both redoing the parameter estimation and reconsidering parts of the model structure for the next WWH version.

References:

Crochemore, L., Isberg, K., Pimentel, R., Pineda, L., Hasan, A., Arheimer, B., (2019). Lessons learnt from checking the quality of openly accessible river flow data worldwide. Hydrological Sciences Journal. https://doi.org/10.1080/02626667.2019.1659509

Arheimer, B., Pimentel, R., Isberg, K., Crochemore, L., Andersson, J. C. M., Hasan, A., Pineda, L., (accepted). Global catchment modelling using World-Wide HYPE (WWH), open data and stepwise parameter estimation. Hydrology and Earth System Sciences Discussions. In press. https://doi.org/10.5194/hess-2019-111

How to cite: Arheimer, B., Crochemore, L., Pineda, R., Isberg, K., Pineda, L., Hasan, A., and Andersson, J.: Lessons learnt from quality-checking observed and simulated river flow data worldwide, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8292, https://doi.org/10.5194/egusphere-egu2020-8292, 2020

How to cite: Arheimer, B., Crochemore, L., Pineda, R., Isberg, K., Pineda, L., Hasan, A., and Andersson, J.: Lessons learnt from quality-checking observed and simulated river flow data worldwide, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8292, https://doi.org/10.5194/egusphere-egu2020-8292, 2020

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