Location and density of rain gauges for the estimation of spatial varying precipitation

Accurate estimation of precipitation and its spatial variability is crucial for reliable discharge simulations. Although radar and satellite based techniques are becoming increasingly widespread, quantitative precipitation estimates based on point rain gauge measurement interpolation are, and will continue to be in the foreseeable future, widely used. However, the ability to infer spatially distributed data from point measurements is strongly dependent on the number, location and reliability of measurement stations. In this study we quantitatively investigated the effect of rain gauge network configurations on the spatial interpolation by using the operational hydrometeorological sensor network in the Thur river basin in north-eastern Switzerland as a test case. Spatial precipitation based on a combination of radar and rain gauge data provided by MeteoSwiss was assumed to represent the true precipitation values against which the precipitation interpolation from the sensor network was evaluated. The performance using scenarios with both increased and decreased station density were explored. The catchment-average interpolation error indices significantly improve up to a density of 24 rain gauges per 1000 km2, beyond which improvements were negligible. However, a reduced rain gauge density in the higher parts of the catchment resulted in a noticeable decline of the performance indices. An evaluation based on precipitation intensity thresholds indicated a decreasing performance for higher precipitation intensities. The results of this study emphasise the benefits of dense and adequately distributed rain gauge networks. DOI: https://doi.org/10.1111/geoa.12094 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-112590 Published Version Originally published at: Girons Lopez, Marc; Wennerström, Hjalmar; Nordén, Lars-Åke; Seibert, Jan (2015). Location and density of rain gauges for the estimation of spatial varying precipitation. Geografiska Annaler: Series A, Physical Geography, 97(1):167-179. DOI: https://doi.org/10.1111/geoa.12094 LOCATION AND DENSITY OF RAIN GAUGES FOR THE ESTIMATION OF SPATIAL VARYING PRECIPITATION MARC GIRONS LOPEZ, HJALMAR WENNERSTRÖM, LARS-ÅKE NORDÉN and JAN SEIBERT Department of Earth Sciences, Uppsala University, Uppsala, Sweden Department of Information Technology, Uppsala University, Uppsala, Sweden Department of Geography, University of Zurich, Zurich, Switzerland Girons Lopez, M., Wennerström, H., Nordén, L.-Å. and Seibert, J., 2015. Location and density of rain gauges for the estimation of spatial varying precipitation. Geografiska Annaler: Series A, Physical Geography, 97, 167–179. doi:10.1111/geoa.12094 ABSTRACT. Accurate estimation of precipitation and its spatial variability is crucial for reliable discharge simulations. Although radar and satellite based techniques are becoming increasingly widespread, quantitative precipitation estimates based on point rain gauge measurement interpolation are, and will continue to be in the foreseeable future, widely used. However, the ability to infer spatially distributed data from point measurements is strongly dependent on the number, location and reliability of measurement stations. In this study we quantitatively investigated the effect of rain gauge network configurations on the spatial interpolation by using the operational hydrometeorological sensor network in the Thur river basin in north-eastern Switzerland as a test case. Spatial precipitation based on a combination of radar and rain gauge data provided by MeteoSwiss was assumed to represent the true precipitation values against which the precipitation interpolation from the sensor network was evaluated. The performance using scenarios with both increased and decreased station density were explored. The catchment-average interpolation error indices significantly improve up to a density of 24 rain gauges per 1000 km, beyond which improvements were negligible. However, a reduced rain gauge density in the higher parts of the catchment resulted in a noticeable decline of the performance indices. An evaluation based on precipitation intensity thresholds indicated a decreasing performance for higher precipitation intensities. The results of this study emphasise the benefits of dense and adequately distributed rain gauge networks. Accurate estimation of precipitation and its spatial variability is crucial for reliable discharge simulations. Although radar and satellite based techniques are becoming increasingly widespread, quantitative precipitation estimates based on point rain gauge measurement interpolation are, and will continue to be in the foreseeable future, widely used. However, the ability to infer spatially distributed data from point measurements is strongly dependent on the number, location and reliability of measurement stations. In this study we quantitatively investigated the effect of rain gauge network configurations on the spatial interpolation by using the operational hydrometeorological sensor network in the Thur river basin in north-eastern Switzerland as a test case. Spatial precipitation based on a combination of radar and rain gauge data provided by MeteoSwiss was assumed to represent the true precipitation values against which the precipitation interpolation from the sensor network was evaluated. The performance using scenarios with both increased and decreased station density were explored. The catchment-average interpolation error indices significantly improve up to a density of 24 rain gauges per 1000 km, beyond which improvements were negligible. However, a reduced rain gauge density in the higher parts of the catchment resulted in a noticeable decline of the performance indices. An evaluation based on precipitation intensity thresholds indicated a decreasing performance for higher precipitation intensities. The results of this study emphasise the benefits of dense and adequately distributed rain gauge networks.