Comparison Between Rain Gage and Lysimeter Measurements

Morgan, David L.; Lourence, F. J.

doi:10.1029/wr005i003p00724

Cited by 9 publications

(5 citation statements)

References 4 publications

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“…,rguez-lturbe and Mej;a, 1974] (diagonal error covariance matrix is used).The measurement error variance and data collection costs associated with each discrete point are given inTable 1. The error variances were assigned so as to resemble variances obtained from data given byMorgan and Lourence [1969] for errors observed in readings of Fisher and Porter, Standard 8in., and USSR-GGI3000 rain gages, in comparison to lysimeter measurements. In doing these calculations to obtain the desired ball park figures it was assumed that the lysimeter readings represented the true mean value, that rainfall processes were stationary, and that rain gages behave similarly for different storms.…”

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confidence: 99%

Network design for the estimation of areal mean of rainfall events

Bras¹,

Rodrı́guez-Iturbe²

1976

Water Resources Research

131

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IGNACIO RODRIGUEZ-ITURBE Universidad Simdn Boh'var, Caracas, VenezuelaThis work recognizes rainfall as a multidimensional stochastic process. By using the knowledge of such processes and of multivariate estimation theory a procedure for designing an optimal network to obtain the areal mean precipitation of an event over a fixed area is developed. The methodology used in this problem allows consideration of the following aspects of network design: (1) spatial uncertainty and correlation of process, (2) errors in measurement techniques and their correlation, and (3) nonhomogeneous sampling costs. Optimal networks are given in terms of the number and location of stations together with the resulting cost and mean square error of rainfall estimation. INTRODUCTIONThe problem of rainfall data collection network design has been divided into various levels [Rodda eta!., 1969]. Levels 1 and 2 can be classified as problems in regional estimation; i.e., there is no clearly defined final goal or use for the collected data. The problem of rainfall monitoring for estimating the areal total precipitation average for a storm event and the problem of finding the long-term (time) areal mean precipitation belong to these two levels. Level 3 networks are those designed to collect data for a specific clearly defined objective which implies known net benefits and utility of the data. The problem of rainfall monitoring for use together with a flood forecasting system theoretically fits within this framework. This work deals with the level 1 problem of designing a network to obtain the areal average of an event. Another article by Bras and Roddguez-lturbe [1975, 1976b] deals with a problem similar to the latter example on level 3 design. The areal mean of an event has innumerable uses in hydrology. It is a traditional parameter in runoff and water yield studies. Given a set of rain gages and data points, the hydrologists have devised numerous techniques to obtain estimates of the mean of the process over a given area. The well known methods of Thiessen polygon and isohyetal analyses are among the available estimators of the areal mean of an event. These data analysis techniques acknowledge uncertainty, due to spatial variability, of the rainfall process. Rainfall is a random function in time and space. Total rainfall depth of an event is a random function in space. With no clearly defined utility functions the objectives in the network design are then accuracy and minimum cost. Both accuracy and cost are functions of the density of the network (number of observation stations in the area of interest), location of the observation sites, and instruments used in the observations (accurate instruments are usually related to higher costs). The accuracy of the network is also a function of the stochasticity of rainfall itself in terms of its spatial and temporal variations. Most existing network design methods consider cost or accuracy or both as a function of only density of observations. Recently, Rodrt'guez-lturbe and Mej;a [1974] used mean...

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confidence: 99%

Network design for the estimation of areal mean of rainfall events

Bras¹,

Rodrı́guez-Iturbe²

1976

Water Resources Research

131

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“…Morgan & Lourence [158] reported broad agreement between a grassed lysimeter 20 feet (~6.6 m) in diameter located in the Central Valley of California and several types of recording rain gauges located nearby. The resolution in rainfall depth was reported as 1/1000th inch (0.025 mm).…”

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confidence: 93%

Recording Rainfall Intensity: Has an Optimum Method Been Found?

Dunkerley

2023

Water

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Many design principles for rain gauges that have the capacity to record rainfall intensity have been proposed or developed. These are here grouped into 15 categories, and the abilities and limitations of each are discussed. No standard or optimum method has emerged, despite more than 80 years of effort in the last two centuries, together with prior work from the 17th C onwards. Indeed, new methods continue to be explored for both point-based and area-wide collections of intensity data. Examples include the use of signal attenuation by rain along the tower-to-tower links of cellular phone networks, monitoring the speed of vehicle windscreen wipers, and exploiting the sound or vision from security and traffic-monitoring cameras. Many of these approaches have the potential to provide vastly more observation sites than conventional meteorological stations equipped with rain gauges. Some of these contemporary approaches seek to harness the potential of crowdsourced or citizen-science data. It is hoped that the present overview of methods will provide a guide for those wishing to collect or analyses rainfall intensity data for application in areas such as soil erosion processes, ecohydrology, agrochemical washoff, or urban flash flooding. Because rainfall intensity is one of the key aspects of the hydrologic cycle likely to respond as climate change and variability proceed, the choice of appropriate data collection methods has additional contemporary importance for the monitoring of regional and global precipitation changes.

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“…The mean storm used to characterize the family of interest is shown in Figure 4 The measurement error variances given were set to be compatible with results on rain gage accuracy published by Morgan and Lourence [1969]. A wide range of values was purposely used to create a more heterogeneous system.…”

Section: The Second Step Consists Of Another Computer Program This Pmentioning

confidence: 99%

Rainfall network design for runoff prediction

Bras¹,

Rodrı́guez-Iturbe²

1976

Water Resources Research

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A multivariate state-space stochastic model of rainfall based on a multidimensional rainfall generator suggested by Bras and Rodriguez-lturbe (1976a) is used together with a runoff model to study the accuracy of discharge prediction as a function of the rainfall-sampling network. The runoff model used is a spatially distributed simulation based on a finite difference solution of the kinematic wave equations. Discharge prediction accuracy at any point in a basin can be obtained in terms of the mean square error as a function of the number of rain-sampling stations, their location, and the errors in sampling devices. The solution is also a function of the physics of the basin at hand which is incorporated in the'rainfall-runoff model. The mean square error of discharge estimation is obtained by using linear estimation theory for dynamic systems. Particularly, the technique used is the Kalman-Bucy filter, which permits filtering and extrapolation of noisy and incomplete observations.

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Comparison Between Rain Gage and Lysimeter Measurements

Cited by 9 publications

References 4 publications

Network design for the estimation of areal mean of rainfall events

Network design for the estimation of areal mean of rainfall events

Recording Rainfall Intensity: Has an Optimum Method Been Found?

Rainfall network design for runoff prediction

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