Comparing precision lysimeter rainfall measurements against rain gauges in a coastal dune belt, Spain

Kohfahl, Claus; Saaltink, Maarten W.

doi:10.1016/j.jhydrol.2020.125580

Cited by 11 publications

(13 citation statements)

References 17 publications

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“…Thus, an increased underestimation of the precipitation intensity can be recognized with increasing reference values. Kohfahl and Saaltink (2020) also found that TBs located at the same site differed in the amount of precipitation measured during six rain events compared to a high-precision weighable lysimeter for bare soil conditions. The authors assumed that the TB, which showed significant errors with minor amounts of rainfall, was affected by an individual technical problem.…”

Section: Evaluation Of Gauge Types For the Comparison Of Precipitatio...mentioning

confidence: 81%

“…The increased lysimeter data quality (i.e. precision and temporal resolution) has already led to a number of studies that 80 compared lysimeter measurements with those of other precipitation gauges (Gebler et al, 2015;Haselow et al, 2019;Hoffmann et al, 2016;Kohfahl and Saaltink, 2020;Schrader et al, 2013). Gebler et al (2015) found a 16 % underestimation https://doi.org/10.5194/hess-2022-370 Preprint.…”

Section: Introduction 40mentioning

confidence: 99%

“…They found an underestimation by the TB and an overestimation by the piezoelectric sensor compared to the lysimeter measurements. Kohfahl and Saaltink (2020) compared precipitation measurements of a precision lysimeter with two TBs and one weighable rain gauge. The authors found good agreement between the measured data from one TB rain gauge, the weighable rain gauge and the lysimeter, while recommending correction of the data from the other TB which 90 underestimates precipitation intensities compared to the lysimeter reference.…”

Section: Introduction 40mentioning

confidence: 99%

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Evaluation of precipitation measurement methods using data from precision lysimeter network

Schnepper

Groh

Gerke

et al. 2022

Preprint

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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.

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Section: Evaluation Of Gauge Types For the Comparison Of Precipitatio...mentioning

confidence: 81%

Section: Introduction 40mentioning

confidence: 99%

Section: Introduction 40mentioning

confidence: 99%

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Evaluation of precipitation measurement methods using data from precision lysimeter network

Schnepper

Groh

Gerke

et al. 2022

Preprint

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“…These lysimeters are able to observe non-rainfall precipitation, such as dew and water vapour adsorption (Zhang et al, 2019). A case in a dune belt in southern Spain (average annual precipitation was 550 mm) measured annual precipitation using a lysimeter, which exceeded the tipping bucket pluviometer by 13% due to water vapour adsorption (Kohfahl et al, 2019;Kohfahl & Saaltink, 2020). The structure of rain gauges easily makes summertime raindrops splash or evaporate and causes frost and snow to be lost before melting measurements in winter (Sevruk, 1996;Sevruk et al, 1989).…”

Section: Introductionmentioning

confidence: 99%

Responses of soil water dynamics to precipitation events in an alpine meadow ecosystem of the Qinghai Lake Basin based on high‐precision lysimeter measurements

Zuo

Deng

et al. 2023

Hydrological Processes

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In high-altitude cold areas, how precipitation and freeze-thaw processes affect soil water dynamics is not well understood due to a lack of high real-time resolution measurements. This study measured soil water balance components with a high-precision lysimeter in an alpine meadow ecosystem in the Qinghai Lake watershed from June 30, 2020, to June 30, 2021. The results showed that the total precipitation (TP), evapotranspiration (ET), soil water storage change (ΔS) and the vertical water flow (WF) of this ecosystem were 705.18, 633.21, 72.58 and À0.60 mm, respectively, dur-

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“…Several effective calibration methods have been proposed over the years: Calder and Kidd [30] suggested a method based on rain gauge parameters (bucket volume and the time between successive bucket tips); Niemczynowicz [29] proposed the equation I = aNb, where I is the measured rain gauge rate, N is the tipping rate, and a and b are fitting parameters. Marsalek [18] determined an analytical expression comparing the nominal rainfall depth increment per tip and the time required for filling the bucket; Humphrey et al [17] described an automated system performing dynamic calibration; Costello and Williams [31] proposed a method considering a laboratory-determined calibration curve; Luyckx and Berlamont [32] developed an analytical calibration taking into account the relationship between the rain gauge resolution and the slope of its regression line; Shedekar et al [33] developed a linear regression and two dynamic calibration techniques; Shimizu et al [34] developed a generalized correction equation for various TBRs; Sypka [35] developed an algorithm to implement a real-time dynamic volumetric correction based on a "tip interval" and nominal tip volume; Kohfahl and Saaltink [36] compared TBR measurements with a precision lysimeter and a weighing rain gauge. Alternatively, other authors have proposed different calibration methods [14,22,[37][38][39][40][41][42][43][44][45][46].…”

Section: Introductionmentioning

confidence: 99%

Understanding the Mechanical Biases of Tipping-Bucket Rain Gauges: A Semi-Analytical Calibration Approach

Segovia-Cardozo

Rodríguez-Sinobas

Díez‐Herrero

et al. 2021

Water

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Tipping bucket rain gauges (TBR) are widely used worldwide because they are simple, cheap, and have low-energy consumption. However, their main disadvantage lies in measurement errors, such as those caused by rainfall intensity (RI) variation, which results in data underestimation, especially during extreme rainfall events. This work aims to understand these types of errors, identifying some of their causes through an analysis of water behavior and its effect on the TBR mechanism when RI increases. The mechanical biases of TBR effects on data were studied using 13 years of data measured at 10 TBRs in a mountain basin, and two semi-analytical approaches based on the TBR mechanism response to RI have been proposed, validated in the laboratory, and contrasted with a simple linear regression dynamic calibration and a static calibration through a root-mean-square error analysis in two different TBR models. Two main sources of underestimation were identified: one due to the cumulative surplus during the tipping movement and the other due to the surplus water contributed by the critical drop. Moreover, a random variation, not related to RI, was also observed, and three regions in the calibration curve were identified. Proposed calibration methods have proved to be an efficient alternative for TBR calibration, reducing data error by more than 50% in contrast with traditional static calibration.

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Comparing precision lysimeter rainfall measurements against rain gauges in a coastal dune belt, Spain

Cited by 11 publications

References 17 publications

Evaluation of precipitation measurement methods using data from precision lysimeter network

Evaluation of precipitation measurement methods using data from precision lysimeter network

Responses of soil water dynamics to precipitation events in an alpine meadow ecosystem of the Qinghai Lake Basin based on high‐precision lysimeter measurements

Understanding the Mechanical Biases of Tipping-Bucket Rain Gauges: A Semi-Analytical Calibration Approach

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