Articles | Volume 28, issue 20
https://doi.org/10.5194/hess-28-4715-2024
© Author(s) 2024. 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-28-4715-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
29 Oct 2024
Technical note |
| 29 Oct 2024
| 29 Oct 2024
Technical note: A guide to using three open-source quality control algorithms for rainfall data from personal weather stations
Abbas El Hachem
Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, 70569 Stuttgart, Germany
Federal Waterways Engineering and Research Institute (BAW), Karlsruhe, Germany
Jochen Seidel
CORRESPONDING AUTHOR
Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, 70569 Stuttgart, Germany
Tess O'Hara
School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Roberto Villalobos Herrera
School of Civil Engineering, Universidad de Costa Rica, Ciudad Universitaria Rodrigo Facio, San José, Costa Rica
Aart Overeem
Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Remko Uijlenhoet
Department of Water Management, Delft University of Technology, Delft, the Netherlands
András Bárdossy
Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, 70569 Stuttgart, Germany
Lotte de Vos
Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
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Simone Gelsinari, Valentijn R. N. Pauwels, Edoardo Daly, Jos van Dam, Remko Uijlenhoet, Nicholas Fewster-Young, and Rebecca Doble
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Estimates of recharge to groundwater are often driven by biophysical processes occurring in the soil column and, particularly in remote areas, are also always affected by uncertainty. Using data assimilation techniques to merge remotely sensed observations with outputs of numerical models is one way to reduce this uncertainty. Here, we show the benefits of using such a technique with satellite evapotranspiration rates and coupled hydrogeological models applied to a semi-arid site in Australia.
András Bárdossy, Jochen Seidel, and Abbas El Hachem
Hydrol. Earth Syst. Sci., 25, 583–601, https://doi.org/10.5194/hess-25-583-2021, https://doi.org/10.5194/hess-25-583-2021, 2021
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Jolijn van Engelenburg, Erik van Slobbe, Adriaan J. Teuling, Remko Uijlenhoet, and Petra Hellegers
Drink. Water Eng. Sci., 14, 1–43, https://doi.org/10.5194/dwes-14-1-2021, https://doi.org/10.5194/dwes-14-1-2021, 2021
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This study analysed the impact of extreme weather events, water quality deterioration, and a growing drinking water demand on the sustainability of drinking water supply in the Netherlands. The results of the case studies were compared to sustainability issues for drinking water supply that are experienced worldwide. This resulted in a set of sustainability characteristics describing drinking water supply on a local scale in terms of hydrological, technical, and socio-economic characteristics.
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Short summary
This study presents an overview of open-source quality control (QC) algorithms for rainfall data from personal weather stations (PWSs). The methodology and usability along technical and operational guidelines for using every QC algorithm are presented. All three QC algorithms are available for users to explore in the OpenSense sandbox. They were applied in a case study using PWS data from the Amsterdam region in the Netherlands. The results highlight the necessity for data quality control.
This study presents an overview of open-source quality control (QC) algorithms for rainfall data...
