Rain gauge measurements

Lanza, Luca Giovanni; Cauteruccio, Arianna; Stagnaro, Mattia

doi:10.1016/b978-0-12-822544-8.00002-0

Cited by 11 publications

(57 citation statements)

References 61 publications

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“…Compared to remote sensing techniques, surveillance systems can provide more information. Common monitoring devices include inclinometers [37] pore water pressure sensors [38], rain gauges [39], etc. • Artificial signal reflection.…”

Section: Data Sourcesmentioning

confidence: 99%

Recent Advances of Deep Learning in Geological Hazard Forecasting

Wang¹,

Sun²,

Chen³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Geological hazard is an adverse geological condition that can cause loss of life and property. Accurate prediction and analysis of geological hazards is an important and challenging task. In the past decade, there has been a great expansion of geohazard detection data and advancement in data-driven simulation techniques. In particular, great efforts have been made in applying deep learning to predict geohazards. To understand the recent progress in this field, this paper provides an overview of the commonly used data sources and deep neural networks in the prediction of a variety of geological hazards. KEYWORDSGeological hazard; deep learning; neural networks; geohazard data sources; earthquake; volcanic 1 Introduction Geological hazards are caused by endogenous and exogenous geological processes or anthropogenic factors on earth [1,2], which encompass a broad range of abnormal stratigraphic activity or extreme changes in geological environments, such as landslides, avalanches, debris flows, earthquakes, etc. Geological hazards pose a serious threat to human life and property [3]. Only by accurately forecasting the occurrence of geohazards can emergency measures be devised to mitigate the damage caused by the impending disaster. Geological hazard forecasting refers to the use of logical reasoning, numerical simulation, and comprehensive analysis based on historical geological hazard activity patterns, formation conditions, occurrence mechanisms, and other factors to speculate and assess the development and changes of geological hazards and the possible degree of danger and damage in a certain period in the future. Geohazard forecasting has attracted tremendous attention nowadays, particularly considering natural resource scarcity, environmental degradation, population expansion, sustainable development, and world economic integration. Many efforts have been made in forecasting geological hazards in the past decades. The "3S technology", including Global Position System (GPS), Geographic Information System (GIS) [4], and Remote Sensing (RS), has been widely applied in the field of geological hazard forecasting [5]. The

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Section: Data Sourcesmentioning

confidence: 99%

Recent Advances of Deep Learning in Geological Hazard Forecasting

Wang¹,

Sun²,

Chen³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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“…TBRs include a receiver (a funnel) that collects rainwater into a bucket, which is one-half of a two-bucket receptacle, pivoted on a cylindrical axis, that tips once the upper bucket fills up to a specified water amount (tipping volume, Vt) and raises the lower bucket into a position under the waterfall line, where it will begin to fill at the same time as the other bucket empties. In this movement, a magnet located in the tipping bucket mechanism generates an output signal through a magnetic reed switch to a recorder, and each pulse is set as a nominal amount of rainfall according to a fixed Vt, which can be set by two calibration stop screws (one for each bucket; Figure 1) [2,63,64]. TBRs have become one of the most extensive rain gauges, due to their simple manufacturing structure, low cost of production, and energy saving capacity (a key element for the manufacturing of most weather instruments), being commonly used by National Meteorological Services, airports, industries, farmers, and private individuals to measure the rain depth and intensity [2,28,65].…”

Section: Tipping Bucket Rain Gauges and Their Measurement Uncertaintiesmentioning

confidence: 99%

“…In this movement, a magnet located in the tipping bucket mechanism generates an output signal through a magnetic reed switch to a recorder, and each pulse is set as a nominal amount of rainfall according to a fixed Vt, which can be set by two calibration stop screws (one for each bucket; Figure 1) [2,63,64]. TBRs have become one of the most extensive rain gauges, due to their simple manufacturing structure, low cost of production, and energy saving capacity (a key element for the manufacturing of most weather instruments), being commonly used by National Meteorological Services, airports, industries, farmers, and private individuals to measure the rain depth and intensity [2,28,65]. In hydrological research, they are the most common gauges used to measure precipitation, which is one of the main variables for runoff models, calibration of distributed precipitation measurements [3,4,66], downscaling of remote sensing precipitation models [6,67], radar calibration [7,40,68], basin water balance, flood models [23], structure design, and profitability in different projects (hydroelectrical generation, dams, irrigation, city planning, etc.)…”

Section: Tipping Bucket Rain Gauges and Their Measurement Uncertaintiesmentioning

confidence: 99%

“…Among all ofthese, the last one is the most relevant source of error in many precipitation monitoring sensors. According to [64], the effect of wind is caused by the interaction between the gauge body and the airflow. The rain gauge behaves as a "bluff-body" when impacted by the wind that produces airflow deformations around it, which results in significant acceleration and vertical velocity components above the collector that deviate the trajectories of the hydrometeors (Figure 3) and induces a significant reduction in their performance [28,69,79,86,88].…”

Section: External Sources Of Biasmentioning

confidence: 99%

See 1 more Smart Citation

Tipping Bucket Rain Gauges in Hydrological Research: Summary on Measurement Uncertainties, Calibration and Error Reduction Strategies

Segovia-Cardozo,

Bernal-Basurco,

Rodríguez-Sinobas

2023

Preprint

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Tipping bucket rain gauges (TBRs) have been, and apparently will continue to be one of the most widely used pieces of equipment for rainfall monitoring, being frequently used for the calibration, validation and downscaling of radar and remote sensing data, due to their major advantages–low cost, simplicity, and low energy consumption. Thus, many works have focused and continue to focus on their main disadvantage–measurement biases (mainly in wind and mechanical underestimations). However, despite arduous scientific effort, calibration methodologies are not frequently implemented by monitoring networks operators or data users, propagating bias in databases and in the different applications of such data, causing uncertainty in the modeling, management, and forecasting in hydrological research, mainly due to a lack of knowledge. Within this context, this work presents a review of the scientific advances in TBR measurement uncertainties, calibration, and error reduction strategies from a hydrological point of view, by describing different rainfall monitoring techniques in Section 2, summarizing TBR measurement uncertainties in Section 3, focusing on calibration, and error reduction strategies in Section 4, a discussion and perspectives in Section 5, and conclusions in Section 6, providing an overview of the of the state of the art and future perspectives of the technology.

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“…Though ranking high among the relevant environmental variables (due to the wellknown significant interactions with the everyday human life and economic activities), atmospheric precipitation is not yet measured operationally with neither the degree of accuracy that would meet the most demanding applications nor any rigorous standardization framework [1,2]. Precipitation records are indeed collected in various countries using different approaches to the treatment of the measurement accuracy and uncertainty issues.…”

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confidence: 99%