Seismic modelling and observations of rainfall

Bakker, Maarten; Legoût, Cédric; Gimbert, Florent; Nord, Guillaume; Boudevillain, Brice; Freche, Guilhem

doi:10.1016/j.jhydrol.2022.127812

Cited by 15 publications

(31 citation statements)

References 103 publications

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“…As observed in Figure 5, the seismic power values during both events are similar, with peaks around -85 dB. This is consistent with the exponential relationship between seismic PSD and rainfall intensity pointed by 40 . The intervals with highest seismic power are coincident with the occurrence of raindrops with diameters above 3 mm.…”

Section: Discussion Conclusionsupporting

confidence: 86%

“…Although rainfall has been observed in multiple seismic studies, in particular those related to the so-called " uvial seismology" 37,38 , there are few contributions analyzing in detail the relationship between the rainfall physical properties and the seismic signals. Dean 39 concluded that seismic signals related to the impact of rain drops on the ground have frequencies above 80 Hz and can be detected at offsets up to 0.8 m. Recently, Bakker et al 40 have modeled the relation between seismic power at frequencies above 50 Hz and rainfall intensity and kinetic energy estimations derived from disdrometer data, showing that the relationships are exponential and that up to 90% of the seismic power is related to the small fraction of drops larger than 3 mm. The authors conclude that seismic data can be particularly useful in soil erosion studies.…”

Section: Discussion Conclusionmentioning

confidence: 99%

See 1 more Smart Citation

Monitoring storm evolution using high density seismic arrays

Díaz¹,

Ruiz²,

Udina

et al. 2022

Preprint

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Data acquired by a dense seismic network deployed in the Cerdanya basin (Eastern Pyrenees) is used to track the temporal and spatial evolution of meteorological events such as rainfall episodes or thunderstorms. Comparing seismic and meteorological data, we show that for frequencies above 40 Hz, the dominant source of seismic noise is rainfall and hence the amplitude of the seismic data can be used as a proxy of rainfall. The interstation distance of 1.5 km provides an unprecedented spatial resolution of the evolution of rainfall episodes along the basin. Two specific episodes, one dominated by stratiform rain and the second one dominated by convective rain are analyzed in detail, using high resolution disdrometer data from a meteorological site near one of the seismic instruments. Seismic amplitude variations follow a similar evolution to radar reflectivity values, but in some stratiform precipitation cases it differs from the radar-derived precipitation estimates in this region of abrupt topography where radar may suffer antenna beam blockage. Hence, we demonstrate the added value of seismic data to complement other sources of information such as rain-gauge or weather radar observations to describe the evolution of ground-level rainfall fields at high spatial and temporal resolution. The seismic power and the rainfall intensity have and exponential relationship and the periods with larger seismic power are coincident with raindrop diameters above 3 mm. The time periods with rain drops diameters exceeding 4 mm cannot be detected in the seismic data, suggesting that there is a threshold value above which seismic data are not sensitive any more to larger rain drop impacts. Thunderstorms can be identified by the recording of the sonic waves generated by thunders. We show that single thunders can be recorded to distances of a few tens of kilometers. As the propagation of these acoustic waves is expected to be strongly affected by parameters as air humidity, temperature variations or wind, the seismic data could provide an excellent tool to investigate atmospheric properties variations during thunderstorms.

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Section: Discussion Conclusionsupporting

confidence: 86%

Section: Discussion Conclusionmentioning

confidence: 99%

Monitoring storm evolution using high density seismic arrays

Díaz¹,

Ruiz²,

Udina

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Bakker et al. (2022) highlighted the importance of the contribution of the larger drops in the seismic signal. Their results also showed a power‐law scaling relations between the seismic noise and the rain intensity (or kinetic energy).…”

Section: Introductionmentioning

confidence: 99%

“…From our knowledge, the specific question of the rain seismic signature is poorly documented in the literature. During the last decade(s), several experiments deployed seismic stations in the vicinity of rivers to monitor their activity (e.g., Burtin et al., 2008; Gonzalez, 2019; Roth et al., 2016), but only few of them focused on the rain signature itself (e.g., Bakker et al., 2022; Dean, 2017). Despite the different environments of these studies, they may all have recorded signals emitted by falling raindrops (Dean, 2017) and wind activity (e.g., Wilcock et al., 1999), which generally occur simultaneously in time and space, and potentially in overlapping frequency bands.…”

Section: Introductionmentioning

confidence: 99%

Seismic Signature of Rain and Wind Inferred From Seismic Data

Rindraharisaona

Réchou

Fontaine

et al. 2022

Earth and Space Science

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Seismic stations are increasingly used to monitor river activity and to quantify sediment transport during flood events. In tropical regions, cyclone‐induced floods are often associated with heavy rain and strong wind episodes, generating complex seismic records involving the simultaneous signature of water, sediment, rainfall and wind. Hence, seismic characterization of rain and wind is then required to better decipher each process and improve our understanding of the river seismic signature. In this study, we investigate experimentally the seismic response of rain and wind using data recorded by geophones deployed in various soil types and at different burial depth (BD), co‐located with various meteorological instruments. Our results show that the power spectral density (PSD) of the seismic noise intensifies at a frequency between 60 and 500 Hz for rain and 5 and 500 Hz for wind, in the presence of rain precipitation as low as 0.025 mm/min and/or wind speed ≥3 m/s. PSD analysis indicates that the seismic signal associated with rain decreases with the BD with a value of ∼2–5 dB in a depth difference of 10 cm. We also observe that each soil type has its own seismic signature. The 4‐min root mean square correlation between the seismic signal amplitude and the rain precipitation suggests that they best correlate with Pearson coefficient >0.90 at BD of 30 cm. The transfer function between the precipitation rate (or kinetic energy) and the seismic signal amplitude shows that the signal recorded by the geophone can be used as a robust proxy for these parameters.

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“…However, few contributions analyze detailed the relationships between rainfall physical properties, as the drop size, and seismic amplitudes. Dean 50 concluded that seismic signals related to the impact of rain drops on the ground have frequencies above 80 Hz and can be detected at offsets up to 0.8 m. Recently, Bakker et al 51 have modeled the relation between seismic power at frequencies above 50 Hz and rainfall intensity and kinetic energy estimations derived from disdrometer data, showing that the relationships are exponential and that up to 90% of the seismic power is related to the small fraction of drops larger than 3 mm. The authors conclude that seismic data can be particularly useful in soil erosion studies.…”

Section: Discussionmentioning

confidence: 99%

Monitoring storm evolution using a high-density seismic network

Díaz¹,

Ruiz²,

Udina

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Data acquired by a dense seismic network deployed in the Cerdanya basin (Eastern Pyrenees) is used to track the temporal and spatial evolution of meteorological events such as rainfall episodes or thunderstorms. Comparing seismic and meteorological data, we show that for frequencies above 40 Hz, the dominant source of seismic noise is rainfall and hence the amplitude of the seismic data can be used as a proxy of rainfall. The interstation distance of 1.5 km provides an unprecedented spatial resolution of the evolution of rainfall episodes along the basin. Two specific episodes, one dominated by stratiform rain and the second one dominated by convective rain, are analyzed in detail, using high resolution disdrometer data from a meteorological site near one of the seismic instruments. Seismic amplitude variations follow a similar evolution to radar reflectivity values, but in some stratiform precipitation cases, it differs from the radar-derived precipitation estimates in this region of abrupt topography, where radar may suffer antenna beam blockage. Hence, we demonstrate the added value of seismic data to complement other sources of information such as rain-gauge or weather radar observations to describe the evolution of ground-level rainfall fields at high spatial and temporal resolution. The seismic power and the rainfall intensity have an exponential relationship and the periods with larger seismic power are coincident. The time intervals with rain drops diameters exceeding 3.5 mm do not result in increased seismic amplitudes, suggesting that there is a threshold value from which seismic data are no longer proportional to the size of the drops. Thunderstorms can be identified by the recording of the sonic waves generated by thunders, with. Single thunders detected to distances of a few tens of kilometers. As the propagation of these acoustic waves is expected to be strongly affected by parameters as air humidity, temperature variations or wind, the seismic data could provide an excellent tool to investigate atmospheric properties variations during thunderstorms.

show abstract

Seismic modelling and observations of rainfall

Cited by 15 publications

References 103 publications

Monitoring storm evolution using high density seismic arrays

Monitoring storm evolution using high density seismic arrays

Seismic Signature of Rain and Wind Inferred From Seismic Data

Monitoring storm evolution using a high-density seismic network

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