Calibration of the tipping-bucket raingage

Marsalek, Jiri

doi:10.1016/0022-1694(81)90010-x

Cited by 90 publications

(79 citation statements)

References 1 publication

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“…Indeed, this was an intensive rainfall event (13.5 mm in 12 min) that could be miscalculated by the automatic tipping bucket rainfall gauge. This type of gauge usually underestimates the high rainfall by not considering the loss of water during the bucket rotation (Marsalek, 1981;Vasvári, 2005). Moreover, this stormy event may generate spatially high, variable rainfall distribution.…”

Section: Pedoelectrical Relationshipsmentioning

confidence: 99%

Three-dimensional monitoring of soil water content in a maize field using Electrical Resistivity Tomography

Beff

Günther

Vandoorne

et al. 2013

Hydrol. Earth Syst. Sci.

133

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Abstract.A good understanding of the soil water content (SWC) distribution at the field scale is essential to improve the management of water, soil and crops. Recent studies proved that Electrical Resistivity Tomography (ERT) opens interesting perspectives in the determination of the SWC distribution in 3 dimensions (3-D). This study was conducted (i) to check and validate how ERT is able to monitor SWC distribution in a maize field during the late growing season; and (ii) to investigate how maize plants and rainfall affect the dynamics of SWC distribution. Time Domain Reflectometry (TDR) measurements were used to validate ERT-inverted SWC values. Evolution of water mass balance was also calculated to check whether ERT was capable of giving a reliable estimate of soil water stock evolution. It is observed that ERT was able to give the same average SWC as TDR (R 2 = 0.98). In addition, ERT gives better estimates of the water stock than TDR thanks to its higher spatial resolution. The high resolution of ERT measurements also allows for the discrimination of SWC heterogeneities. The SWC distribution showed that alternation of maize rows and inter-rows was the main influencing factor of the SWC distribution. The drying patterns were linked to the root profiles, with drier zones under the maize rows. During short periods, with negligible rainfall, the SWC decrease took place mainly in the two upper soil horizons and in the inter-row area. In contrast, rainfall increased the SWC mostly under the maize rows and in the upper soil layer. Nevertheless, the total amount of rainfall during the growing season was not sufficient to modify the SWC patterns induced by the maize rows. During the experimental time, there was hardly any SWC redistribution from maize rows to inter-rows. Yet, lateral redistribution from inter-rows to maize rows induced by potential gradient generates SWC decrease in the inter-row area and in the deeper soil horizons.

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Section: Pedoelectrical Relationshipsmentioning

confidence: 99%

Three-dimensional monitoring of soil water content in a maize field using Electrical Resistivity Tomography

Beff

Günther

Vandoorne

et al. 2013

Hydrol. Earth Syst. Sci.

133

View full text Add to dashboard Cite

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“…The records obtained with the gutters were converted to areal averages every time the manual gauges were emptied, using a weighting procedure based on the relative magnitude of the surface areas of the respective gauge types. This correction also included any changes in the calibration of the tipping buckets (Marsalek, 1981). Stemflow was not measured in this study.…”

Section: Instrumentsmentioning

confidence: 99%

Modelling rainfall interception by a lowland tropical rain forest in northeastern Puerto Rico

Schellekens

Scatena

Bruijnzeel

et al. 1999

Journal of Hydrology

160

166

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Recent surveys of tropical forest water use suggest that rainfall interception by the canopy is largest in wet maritime locations. To investigate the underlying processes at one such location-the Luquillo Experimental Forest in eastern Puerto Rico-66 days of detailed throughfall and above-canopy climatic data were collected in 1996 and analysed using the Rutter and Gash models of rainfall interception. Throughfall occurred on 80% of the days distributed over 80 rainfall events. Measured interception loss was 50% of gross precipitation. When Penman-Monteith based estimates for the wet canopy evaporation rate (0.11 mm h Ϫ1 on average) and a canopy storage of 1.15 mm were used, both models severely underestimated measured interception loss. A detailed analysis of four storms using the Rutter model showed that optimizing the model for the wet canopy evaporation component yielded much better results than increasing the canopy storage capacity. However, the Rutter model failed to properly estimate throughfall amounts during an exceptionally large event. The analytical model, on the other hand, was capable of representing interception during the extreme event, but once again optimizing wet canopy evaporation rates produced a much better fit than optimizing the canopy storage capacity. As such, the present results support the idea that it is primarily a high rate of evaporation from a wet canopy that is responsible for the observed high interception losses. ᭧ 1999 Elsevier Science B.V. All rights reserved.

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“…While rain gauges measure rain accurately and continuously at a point, they offer little information on rainfall between gauges. Rain gauges themselves are not fully accurate and are influenced by factors such as calibration accuracy, wind effects and sampling uncertainty, which also limits the accuracy for sampling intervals smaller than 10 min (Humphrey et al, 1997;Calder and Kidd, 1978;Marsalek, 1981;Habib et al, 2001;Ciach, 2003;Sieck et al, 2007). Frozen precipitation like snow and hail also offers a problem as these hydro-meteors do not melt immediately and therefore will result in a lower precipitation rate estimate over a longer period than actually occurred.…”

Section: Introductionmentioning

confidence: 99%

Climatology of daily rainfall semi-variance in The Netherlands

Beek

Leijnse

Torfs

et al. 2011

Hydrol. Earth Syst. Sci.

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Abstract. Rain gauges can offer high quality rainfall measurements at their locations. Networks of rain gauges can offer better insight into the space-time variability of rainfall, but they tend to be too widely spaced for accurate estimates between points. While remote sensing systems, such as radars and networks of microwave links, can offer good insight in the spatial variability of rainfall they tend to have more problems in identifying the correct rain amounts at the ground. A way to estimate the variability of rainfall between gauge points is to interpolate between them using fitted variograms. If a dense rain gauge network is lacking it is difficult to estimate variograms accurately. In this paper a 30-year dataset of daily rain accumulations gathered at 29 automatic weather stations operated by KNMI (Royal Netherlands Meteorological Institute) and a one-year dataset of 10 gauges in a network with a radius of 5 km around CESAR (Cabauw Experimental Site for Atmospheric Research) are employed to estimate variograms. Fitted variogram parameters are shown to vary according to season, following simple cosine functions. Semi-variances at short ranges during winter and spring tend to be underestimated, but semi-variances during summer and autumn are well predicted.

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Calibration of the tipping-bucket raingage

Cited by 90 publications

References 1 publication

Three-dimensional monitoring of soil water content in a maize field using Electrical Resistivity Tomography

Three-dimensional monitoring of soil water content in a maize field using Electrical Resistivity Tomography

Modelling rainfall interception by a lowland tropical rain forest in northeastern Puerto Rico

Climatology of daily rainfall semi-variance in The Netherlands

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