Spatial and Temporal Variability in Rainfall Erosivity Under Alpine Climate: A Slovenian Case Study Using Optical Disdrometer Data

Bezak, Nejc; Petan, Sašo; Mikoš, Matjaž

doi:10.3389/fenvs.2021.735492

Cited by 7 publications

(3 citation statements)

References 69 publications

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“…Wind effects were shown by Pang & Graßl [109] to result in heterogeneous rainfall over a few seconds or a few meters. Wind effects were also highlighted by Bezak et al [110] and Capozzi et al [111], and Chinchella et al [112] in a study of the Thies laser disdrometer. Evidently, although optical disdrometers can measure drop sizes reliably, if drop arrival is strictly vertical, they are prone to significant errors under windy conditions.…”

Section: Disdrometers-optical and Electro-mechanical Drop Sizing Coun...mentioning

confidence: 74%

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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Section: Disdrometers-optical and Electro-mechanical Drop Sizing Coun...mentioning

confidence: 74%

Recording Rainfall Intensity: Has an Optimum Method Been Found?

Dunkerley

2023

Water

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“…In the scope of the RUSLE model, soil erosion is described with six factors, one of which is the rainfall erosivity factor R (in MJ mm ha-1 h-1, Renard et al, 1997). Rainfall erosivity is often characterised by large spatial and temporal variability (e.g., Bezak et al, 2020Bezak et al, , 2021aBezak et al, , 2021b2021c;Panagos et al, 2016Panagos et al, , 2017Petek et al, 2018), which means that rainfall erosivity estimations are not a straightforward task and require adequate temporal and spatial data resolution.…”

Section: Introductionmentioning

confidence: 99%

Comparison of rainfall generators with regionalisation for the estimation of rainfall erosivity at ungauged sites

Pidoto

Bezak

Müller-Thomy

et al. 2022

Preprint

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Abstract. The assessment of rainfall erosivity is one of the main inputs in determining soil erosion. To calculate mean annual rainfall erosivity (R), long-term high-resolution observed rainfall time series are required, which are often not available. To overcome the issue of limited data availability in space, four methods are employed: the direct regionalisation of R, the regionalisation of 5 minute rainfall, the disaggregation of daily rainfall into 5 minute timesteps, and the use of a regionalised stochastic rainfall model. In addition the minimum time series length necessary to adequately estimate R is investigated. The impact of station density is considered for each of the methods. The study is carried out using 159 recording and 150 non-recording (daily) rainfall stations in the federal state of Lower Saxony, Germany. Results show that the direct regionalisation of mean annual erosivity leads to the best results in terms of relative bias and relative root mean square error (RMSE). This is followed by the regionalisation of the 5 minute rainfall data, which yields better results than the rainfall generation models, namely an alternating renewal model (ARM) and a multiplicative cascade model (Disagg). However, a key advantage of using regionalised rainfall models is the generation of rainfall time series that can be used for the estimation of the erosive event characteristics, which is not possible through the direct regionalisation of R. Using the stochastic ARM it can be shown that in most cases more than 60 years of data is needed in order to obtain a stable estimate of the annual rainfall erosivity. Estimation of soil erosion based on only 5 or 10 years of data can lead to uncertain values. Such short time series are often used when regionalisation is applied. Moreover, it was also found that resolution of measuring device has a significant effect on the rainfall erosivity and coarser data resolution can lead to high relative bias.

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“…Quantitative research on the degree of RE and change mechanisms has become popular in recent years (Diodato et al, 2013;Bezak et al, 2021). Chinese scholars (Zhang et al, 2008;Yin et al, 2022) have increasingly conducted studies on RE through various mathematical models.…”

Section: Introductionmentioning

confidence: 99%

Study on multidimensional changes of rainfall erosivity during 1970–2017 in the North–South Transition Zone, China

Zhu²,

Liu³

et al. 2022

Front. Environ. Sci.

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China is one of the world’s most seriously affected regions by water and soil erosion. Soil erosion is a major cause and an important component of land degradation, which has a negative impact on ecological protection and sustainable socioeconomic development. Rainfall erosivity (RE) is one of the key parameters to assess the degree of soil erosion. Quantifying the content of RE and the formation mechanism is important to accurately measure the degree of soil erosion and provide a theoretical basis for soil erosion management. Here, this study analyzed the spatial and temporal characteristics of RE and their driving mechanisms in the Qinba Mountains from 1970 to 2017 using a daily rainfall model. Furthermore, geographical detector methods were used to quantitatively identify the dominant factors affecting RE and the dominant factors affecting RE on different topographic reliefs. The results showed that the RE between 1970 and 2017 averaged 4,197.85 MJ mm hm−2 h−1 a−1, with a mutation coefficient of 0.16. The spatial distribution of RE is high in the southeast and low in the northwest, and the mean annual RE declines with the increase in latitude in longitude and increases with the reduction in longitude in latitude. In addition, precipitation and temperature are the main factors affecting the spatial distribution of RE. Among these, precipitation can explain about 97% of the RE and temperature can explain about 65% of the RE. These findings should be essential for managing soil and water loss in the North–South Transition Zone, China.

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Spatial and Temporal Variability in Rainfall Erosivity Under Alpine Climate: A Slovenian Case Study Using Optical Disdrometer Data

Cited by 7 publications

References 69 publications

Recording Rainfall Intensity: Has an Optimum Method Been Found?

Recording Rainfall Intensity: Has an Optimum Method Been Found?

Comparison of rainfall generators with regionalisation for the estimation of rainfall erosivity at ungauged sites

Study on multidimensional changes of rainfall erosivity during 1970–2017 in the North–South Transition Zone, China

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