Preprints
https://doi.org/10.5194/hess-2022-370
https://doi.org/10.5194/hess-2022-370
 
01 Dec 2022
01 Dec 2022
Status: this preprint is currently under review for the journal HESS.

Evaluation of precipitation measurement methods using data from precision lysimeter network

Tobias Schnepper1,2,3, Jannis Groh1,4,5, Horst H. Gerke4, Barbara Reichert2, and Thomas Pütz1 Tobias Schnepper et al.
  • 1Institute of Bio- and Geoscience IBG-3: Agrosphere, Forschungszentrum Jülich GmbH, Jülich, 52428, Germany
  • 2Institute for Geosciences, University of Bonn, Nussallee 8, Bonn, 53113, Germany
  • 3GFZ German Research Centre for Geosciences, Telegrafenberg, Potsdam, 14473, Germany
  • 4Leibniz Centre for Agricultural Landscape Research (ZALF), Research Area 1 “Landscape Functioning”, Working Group “Hydropedology”, Eberswalder Straße 84, Müncheberg, 15374, Germany
  • 5Institute of Crop Science and Resource Conservation - Soil Science and Soil Ecology, University of Bonn, Nussallee 13, Bonn, 53113, Germany

Abstract. Accurate precipitation data are essential for assessing the water balance of ecosystems. Methods for point precipitation determination are influenced by wind, precipitation type and intensity and/or technical issues. High-precision weighable lysimeters provide precipitation measurements at ground level that are less affected by wind disturbances and are assumed to be relatively close to “true” precipitation. The problem in previous studies was that the biases on precipitation data introduced by different precipitation measurement methods were not comprehensively compared and quantified with those obtained by lysimeters under different climatic conditions.

The aim was to quantify measurement errors of standard precipitation gauges as compared to the lysimeter reference and to analyse the effect of precipitation correction algorithms on the gauge data quality. Both correction methods rely on empirical constants to account for known external influences on the measurements, following a generic and a site-specific approach. Reference precipitation data were obtained from high-precision weighable lysimeters of the TERrestial Environmental Observatories (TERENO)-SOILCan lysimeter network. Gauge types included tipping bucket gauges (TBs), weighable gauges (WGs), acoustical sensors (ASs), and optical laser disdrometers (LDs). The data were collected from 2015–2018 at three sites in Germany and compared with a temporal resolution of 1 hour for precipitation above a threshold of 0.1 mm h-1.

The results show that all investigated measurement methods underestimated the precipitation amounts relative to the lysimeter references for long-term precipitation totals with catch ratios (CRs) between 33–92 %. Data from ASs had overall biases of 0.25 to -0.07 mm h-1, while data from WGs and LDs showed the lowest measuring biases (-0.14 to -0.06 mm h-1 and 0.01 to 0.02 mm h-1). Two TBs showed systematic deviations with biases of -0.69 to -0.61 mm h-1, while other TBs were in the previously reported range with biases of -0.2 mm h-1. The site-specific and generic correction schemes reduced the hourly measuring bias by 0.13 and 0.08 mm h-1 for the TBs and by 0.09 and 0.07 mm h-1 for the WGs and increased long-term CRs by 14 and 9 % and by 10 and 11 %, respectively.

It could be shown that the lysimeter reference operated with minor uncertainties in long-term measurements under different climatic conditions. The results indicate that even with well-maintained and professionally operated stations, considerable precipitation measurement errors can occur, which generally lead to a loss of recorded precipitation amounts. Data from standard precipitation gauges therefore still represent potentially significant uncertainty factors. The results suggest that the application of relatively simple correction schemes, manual or automated data quality checks, instrument calibrations and/or adequate choice of observation periods can help improve the data quality of gauge-based measurements for water balance calculations, ecosystem modelling, water management, assessment of agricultural irrigation needs or radar-based precipitation analyses.

Tobias Schnepper et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-370', Anonymous Referee #1, 25 Dec 2022 reply
    • AC1: 'Reply on RC1', Tobias Schnepper, 05 Feb 2023 reply

Tobias Schnepper et al.

Tobias Schnepper et al.

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Short summary
We compared hourly data from precipitation gauges with lysimeter reference data at three sites under different climatic conditions. Our results show that precipitation gauges recorded 33–96% of the reference precipitation data for the period under consideration (2015–2018). Correction algorithms increased the registered precipitation by 9–14 %. It follows that when using point precipitation data, regardless of the precipitation measurement method used, relevant uncertainties must be considered.