HS7.1 | Precipitation variability from drop scale to catchment scale : measurement, processes and hydrological applications
EDI
Precipitation variability from drop scale to catchment scale : measurement, processes and hydrological applications
Co-organized by AS1/NP3
Convener: Auguste Gires | Co-conveners: Katharina Lengfeld, Alexis Berne, Taha Ouarda, Marc Schleiss
Orals
| Wed, 17 Apr, 08:30–10:15 (CEST)
 
Room 2.31
Posters on site
| Attendance Wed, 17 Apr, 16:15–18:00 (CEST) | Display Wed, 17 Apr, 14:00–18:00
 
Hall A
Orals |
Wed, 08:30
Wed, 16:15
Rainfall is a “collective” phenomenon emerging from numerous drops. Understanding the relation between the physics of individual drops and that of a population of drops remains an open challenge, both scientifically and at the level of practical implications. This remains true also for solid precipitation. Hence, it is much needed to better understand small scale spatio-temporal precipitation variability, which is a key driving force of the hydrological response, especially in highly heterogeneous areas (mountains, cities). This hydrological response at the catchment scale is the result of the interplay between the space-time variability of precipitation, the catchment geomorphological / pedological / ecological characteristics and antecedent hydrological conditions. Therefore, (1) accurate measurement and prediction of the spatial and temporal distribution of precipitation over a catchment and (2) the efficient and appropriate description of the catchment properties are important issues in hydrology.

This session will bring together scientists and practitioners who aim to measure and understand precipitation variability from drop scale to catchment scale as well as its hydrological consequences. Contributions addressing one or several of the following topics are especially targeted:
- Novel techniques for measuring liquid and solid precipitation variability at hydrologically relevant space and time scales (from drop to catchment scale), from in situ measurements to remote sensing techniques, and from ground-based devices to spaceborne platforms. Innovative comparison metrics are welcomed;
- Precipitation drop (or particle) size distribution and its small scale variability, including its consequences for precipitation rate retrieval algorithms for radars, commercial microwave links and other remote sensors;
- Novel modelling or characterization tools of precipitation variability from drop scale to catchment scale from various approaches (e.g. scaling, (multi-)fractal, statistic, deterministic, numerical modelling);
- Novel approaches to better identify, understand and simulate the dominant microphysical processes at work in liquid and solid precipitation.
- Applications of measured and/or modelled precipitation fields in catchment hydrological models for the purpose of process understanding or predicting hydrological response.

Orals: Wed, 17 Apr | Room 2.31

Chairpersons: Auguste Gires, Marc Schleiss
08:30–08:35
Oral presentations
08:35–08:45
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EGU24-8789
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HS7.1
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ECS
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On-site presentation
Nathalie Rombeek, Markus Hrachowitz, Davide Wüthrich, and Remko Uijlenhoet

Real-time flood forecasting and warning during extreme rainfall events remains challenging since accurate and real-time available data are critical. Nowcasting based on radar rainfall can be utilized for this, as it has a high spatial and temporal resolution (i.e. typically 1 km and 5 min). However, the quantitative precipitation estimates (QPE) from the radar, upon which radar rainfall nowcasting is based, often contains substantial uncertainty and bias. While the QPE are usually corrected with official rain-gauge networks, these networks are sparse, and not always available in (near) real-time.

Instead, personal weather stations (PWS) can be used, as they have a much higher density and are available in real time. While PWS are prone to several sources of error, quality control algorithms can be used to improve their accuracy. Previous research already showed that merging quality controlled PWS with radar rainfall estimates reduces the underestimation for 1-hour accumulated rainfall at the pan-European scale. However, this has not yet been investigated at the catchment scale. This research aims to investigate the potential of merging PWS data with radar rainfall estimates for different catchments in the Netherlands, by considering multiple rainfall events starting from 2018. The goal is to quantify the performance in relation to rainfall type, quality control algorithms and catchment properties, validated against the climatological gauge-adjusted radar dataset from the KNMI. The insights obtained from this research have the potential to be utilized for real-time radar rainfall nowcasting and consequently flood forecasting.

How to cite: Rombeek, N., Hrachowitz, M., Wüthrich, D., and Uijlenhoet, R.: Merging personal weather stations with real-time radar rainfall estimates at the catchment scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8789, https://doi.org/10.5194/egusphere-egu24-8789, 2024.

08:45–08:55
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EGU24-13111
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HS7.1
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ECS
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On-site presentation
Anna Špačková, Martin Fencl, and Vojtěch Bareš

Commercial microwave links (CML) have already demonstrated their promising potential in rainfall observation and sensing. The CMLs enable indirect monitoring of path-averaged rainfall intensity as the transmitted signal is attenuated along the link path mainly by raindrops. However, the signal is also attenuated during dry weather periods and is affected by both atmospheric and hardware conditions. Faulty separation of wet and dry periods can easily lead to incorrect rainfall estimates and remains challenging to estimate due to irregular fluctuations of the attenuated signal.

This study aims to use information theory approach to estimate wet and dry periods in the CML signal attenuation observation, which is achieved by evaluating individual predictors and combinations of predictors. The method enables any data to be used as predictors without the need for parameters to describe relations between different variables, as the discrete probability distributions are applied. The model that provides the strongest information content to the wet and dry classification is binarized using an optimized threshold and validated. Thiesen et al. (2019) recently applied this approach to identify rainfall-runoff events in discharge timeseries.

Data of non-winter periods between 2014 and 2016 are used with a temporal resolution of 1 minute. For one CML in the Prague network, wet and dry periods were defined manually as reference (target). Predictors included raw CML data (signal attenuation), as well as derived timeseries such as signal attenuation shifted in time, relative magnitude of attenuation, gradient of the signal attenuation and signal deviation. In addition, external predictors such as temperature deviation, rain gauge precipitation observations or synoptic types are used as additional predictors.

By selecting different predictors, it is possible to compare effectiveness in estimating the reference wet and dry periods. Variation in the strength of the relations between the target and the predictors allows ranking the suitability of available predictors and their combinations for the task. Subsequently, having the best performing predictor, it is combined with others and their collective performance was iteratively evaluated to find the most accurate combination of three predictors described in a multidimensional discrete distribution model. The resulting predictor combination was then converted into binary form and validated. A method comparison is performed with separation of constant and moving average baseline attenuation for wet periods identification as well as wet/dry classification using a threshold for rolling standard deviation of the signal.

Having sufficient data amount for data-driven models enables utilizing the relationships within the dataset without being limited by parametric or operational assumptions, which are often embedded part of wet/dry in classification methods.

References
Thiesen, S., Darscheid, P., and Ehret, U.: Identifying rainfall-runoff events in discharge time series: a data-driven method based on information theory, Hydrol. Earth Syst. Sci., 23, 1015–1034, https://doi.org/10.5194/hess-23-1015-2019, 2019.

This work was supported by the Grant Agency of the Czech Technical University in Prague, grant no. SGS23/048/OHK1/1T/11.

How to cite: Špačková, A., Fencl, M., and Bareš, V.: Identification of wet and dry periods in commercial microwave link observations via information theory framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13111, https://doi.org/10.5194/egusphere-egu24-13111, 2024.

08:55–09:05
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EGU24-16024
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HS7.1
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ECS
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On-site presentation
Ingrid O. Bækkelund, Mari B. Steinslid, and Harald Sodemann

Intermittency of rainfall is an important property, for example in the context of urban flooding. There is currently a lack of information about the ability of numerical weather prediction models to represent precipitation intermittency for different weather situations, in particular at high resolution in space and time. Here we present a new way to quantify rainfall intermittency based on a near-continuous, high-resolution precipitation dataset from Bergen, Norway, one of the rainiest cities in Europe. 

We quantify precipitation intermittency from a precipitation dataset acquired at the Geophysical Institute, Bergen, spanning the period 2019-2022 at a 1 min time resolution. Precipitation rates were obtained from a Total Precipitation Sensor TPS-3100 (Yankee Environmental Systems Inc., USA) and a Parsivel2 disdrometer (OTT Hydromet GmbH, Germany). In addition, we use precipitation output at 1 min resolution from the regional high-resolution weather forecasts model HARMONIE-AROME for selected events. Precipitation intermittency is then identified for a range of minimum inter-event times (MIT) from 1 min to 24 h, and precipitation event durations from 1 min to 33 days. Next, the precipitation events for different intermittencies are related to average meteorological characteristics during the events with respect to air temperature, pressure, wind speed, rain rate and amount, and corresponding weather regimes.  

We compile the intermittency information into a 2-dimensional heat map that can be considered as a characteristic fingerprint for precipitation in Bergen. Particular frequency maxima and minima appear to be related to different precipitation processes and weather regimes. A scale gap between 30 min and 2 h event duration for MIT larger than 12 h indicates that separate factors control precipitation processes at these time scales. Weather regimes show a clear influence on the precipitation characteristics, with a markedly higher probability for long-duration rain events in the zonal flow regime for longer event durations at high MITs compared to the Scandinavian trough regime. A comparison between precipitation intermittency simulated by HARMONIE-AROME shows reasonable agreement with observed event characteristics for events lasting more than 1h, while events with durations of 30 min and less are poorly represented. 

How to cite: Bækkelund, I. O., Steinslid, M. B., and Sodemann, H.: Quantifying precipitation intermittency for Bergen, Norway, from measurements and models across a wide range of time scales, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16024, https://doi.org/10.5194/egusphere-egu24-16024, 2024.

09:05–09:15
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EGU24-8714
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HS7.1
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ECS
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On-site presentation
Mark Dutton and Domenico Balsamo

Precipitation measurements provide historic and near real-time data for Met Services and ground truth references for modelling and forecasting.  Current methods suffer from well-known under-catch problems1.  These are caused by wind effect2 on the gauge, out-splash, evaporation, and internal tipping bucket (‘counting’) errors.  Thereby causing water-balance errors for Hydrology scientists.  Good gauge design and correct siting can minimise these errors but not eliminate them.

Over 10 years of research, into the best aerodynamic shape for a precipitation gauge, was carried out to minimize out-splash and maximize catch3.  Comparison field work1 and Computational Fluid Dynamic4 (CFD) research was undertaken between standard straight-sided, ‘chimney’ shaped, aerodynamic shaped and pit-installed (out of the wind) gauges.  This research demonstrated that it may be possible to quantify under-catch using gauge rim-based wind data, drop-size and drop-type information.  Field comparison between the “new instrument” and pit gauge will be needed.  Once quantified at source, it can then be used to accurately correct live data.

This new instrument uses ultrasonic wind sensors and Doppler-Shift measuring techniques to obtain wind versus rainfall catch data.  Also using optical and/or impact sensing techniques we can measure the individual drop size and count the drops involved in a rain event.  By adding weighing technology to the tipping bucket design and improving calibration methods, we can improve resolution and detect evaporation losses.  Also power efficient and controlled heating to allow the inclusion of solid precipitation measurements.  Then finally use machine learning (ML) techniques to correct the errors.

Therefore, the aim of this project is to design a simple to use intelligent instrument to minimise and possibly eliminate under-catch measurement errors balancing out the water budget.  Allow installation of the instruments at ground and raised levels without increase in errors caused predominately by the wind.  Create near real-time and historic field precipitation data, both corrected and non-corrected to be use by Met Services and Hydrology modelling scientists.

References

1. Sevruk, B. Methods of correction for systematic error in point precipitation measurement for operational use, World Meteorological Organization - Operational Hydrology, Report No. 21, 1982.

2. Pollock, M. D., et al. Quantifying and mitigating wind induced undercatch in rainfall measurements, Water Resources Research, 54, 2018.

3. Strangeways, Ian. Improving precipitation measurement. International Journal of Climatology. 24. 1443 - 1460. 10.1002/joc.1075, 2004.

4. Colli, M., et al.  A Computational Fluid-Dynamics Assessment of the Improved Performance of Aerodynamic Rain Gauges. Water Resources Research. 54. 10.1002/2017WR020549, 2018.

How to cite: Dutton, M. and Balsamo, D.: Improvements in rain gauge design and measurements to minimise under-catch errors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8714, https://doi.org/10.5194/egusphere-egu24-8714, 2024.

09:15–09:25
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EGU24-17471
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HS7.1
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On-site presentation
Luca G. Lanza, Arianna Cauteruccio, and Enrico Chinchella

In windy conditions, the measurement of liquid and solid atmospheric precipitation is still a challenge even using the most advanced automatic instrumentation (Cauteruccio et al., 2021). The measurement accuracy is affected by various environmental sources of bias, including siting issues and exposure. These add to the instrumental bias, which can be minimized in case of accurate instrument calibration. Wind is however recognised as the most impactful source of environmental bias, outperforming by 3 to 50 times the total impact of all other environmental factors.

Computational Fluid Dynamics simulation with embedded liquid (raindrops) and solid (snowflakes) particle tracking is here used to quantify the wind-induced bias of catching-type precipitation gauges. Starting from the numerically calculated catch ratios, six common commercial gauges having different outer geometry are compared in terms of their expected performance under various precipitation intensity and wind speed conditions. Preliminary wind tunnel experiments allowed full validation of the simulated aerodynamic behaviour and its effect on water drop trajectories.

The overall collection efficiency is shown to depend on the precipitation intensity and its functional dependence is quantitatively derived as a measure of the instrument performance under a wind climatology characterised by a uniform probability density function. A less pronounced diversion of hydrometeor trajectories is shown – at any given size – by instruments with aerodynamic design than in case of more traditional geometry.

Chimney-shaped instruments rank low in case of liquid precipitation measurements, while a high performance is shown by inverted conical and Nipher shielded instruments and the investigated quasi-cylindrical gauges have intermediate behaviour, which depends on their specific aerodynamic features. All instruments rank low at light to moderate precipitation intensity for the measurement of solid precipitation, except the Nipher shielded gauge.

This work provides the basic information needed to apply adjustments to the measured data and supports manufacturers in upgrading instruments with an existing design by introducing on-board adjustments of the measured precipitation. These would only require contemporary measurement of the wind velocity (often included in typical meteorological stations). The full work and the numerically derived adjustments for the six investigated commercial gauges are published in Cauteruccio et al. (2024).

References

Cauteruccio, A., Colli, M., Stagnaro, M., Lanza, L.G. & Vuerich, E. (2021). In situ precipitation measurements. In T. Foken (Ed.), Handbook of Atmospheric Measurements (359-400). Switzerland, Springer Nature. ISBN 978-3-030-52170-7, https://doi.org/10.1007/978-3-030-52171-4_12.

Cauteruccio, A., Chinchella, E. and L.G. Lanza (2024). The overall collection efficiency of catching-type precipitation gauges in windy conditions. Water Resour. Res., in press. https://doi.org/10.1029/2023WR035098.

How to cite: Lanza, L. G., Cauteruccio, A., and Chinchella, E.: Wind-induced bias of catching-type precipitation gauges and their overall collection efficiency, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17471, https://doi.org/10.5194/egusphere-egu24-17471, 2024.

09:25–09:35
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EGU24-10819
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HS7.1
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On-site presentation
András Bárdossy

The space time behaviour of precipitation is very complex. The knowledge of the dependence structures in space and time is very important for the assessment of flood risks. In this contribution the dependence structures of normal and extreme events are compared. Both rain gauges with high temporal resolution and radar images are investigated. Spatial and temporal copulas are used for this investigation. Due to the large number of zero observations, especially for short temporal aggregations an indicator approach is used to detect structural differences. The results show, that the temporal dependence structure of rainfall gradually changes with increasing intensity. Similar behaviour can be detected for the spatial structure with the addition of advection related differences in both ranges and angles of anisotropy. The findings indicate that metagaussian approaches which only consider spatial and temporal correlations are not appropriate for the description and the simulation of rainfall extremes. Finally a new structural simulation method using non-Gaussian dependence is presented.

How to cite: Bárdossy, A.: Spatial and temporal structure of normal and extreme rainfall, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10819, https://doi.org/10.5194/egusphere-egu24-10819, 2024.

09:35–09:45
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EGU24-18921
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HS7.1
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On-site presentation
Hoyoung Cha, Jongjin Baik, Hyeon-Joon Kim, Jinwook Lee, Jongyun Byun, and Changhyun Jun

Abstract

This study analyzed geodetic distribution about temporal characteristics in rainstorm (> 1 hour) observed at approximately 600 rainfall stations across Republic of Korea. Utilizing minute-scale precipitation data observed by rainfall stations from 2000 to 2022, independent rainstorm events separated from rainfall data per unit time (i.e., 10, 20, 30, and 60 minutes) and Inter-Event Time Definition (IETD) (i.e., 2, 3, 4, and 6 hours). The significant variations in rainfall characteristics are defined as the number of independent rainstorm events, rainfall duration (hour), amount (mm), and intensity (mm/hour) for quantifying the temporal characteristics across rainfall stations. We quantified temporal characteristics among rainfall characteristics observed by rainfall stations based on latitude and longitude. The number of independent rainstorm events varies significantly depending on unit time and IETD, and the occurrence of events was frequently observed in areas characterized by island features. The rainfall amount for independent rainstorm events obscured significant characteristics, excluding Halla Mountain on Jeju Island. The geodetic distribution for the duration and intensity per rainstorm event varied depending on the characteristics of the region (i.e., island, mountain, etc.). Based on these results, it was confirmed that certain temporal characteristics vary according to regional features. In future research, we intend to utilize this information to cluster rainfall stations based on temporal characteristics.

Keywords: Independent Rainstorm Events, Temporal Characteristics, Geodetic Distribution, Regional Features, Republic of Korea

Acknowledgment

This research was supported by Korea Environment Industry & Technology Institute (KEITI) funded by Korea Ministry of Environment (RS-2022-KE002032 and 2022003640001) and was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1A4A3032838 and No. RS-2023-00250239).

How to cite: Cha, H., Baik, J., Kim, H.-J., Lee, J., Byun, J., and Jun, C.: Unveiling the Geodetic Distribution of Temporal Characteristics in Rainstorm Events across Republic of Korea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18921, https://doi.org/10.5194/egusphere-egu24-18921, 2024.

09:45–09:55
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EGU24-6387
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HS7.1
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ECS
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On-site presentation
Megumi Okazaki, Kosei Yamaguchi, Tomoro Yanase, and Eiichi Nakakita

Precipitation droplets are influenced by environmental fields and transform in time and space, following cloud microphysical processes. Accordingly, a raindrop size distribution (DSD) changes shape in a various form. However, DSDs cannot be calculated directly in radar or bulk models and are expressed using an approximate function. Exponential and gamma distribution are well-known as approximation functions, but there are DSDs of shapes that cannot be represented by these functions. One of them is a bimodal DSD with two peaks. Previous modeling studies have indicated that the bimodal DSD is formed when the collision-breakup process reaches equilibrium. On the other hand, recent observation-based studies have discussed the influence of convective activity within the precipitation system on forming the bimodal DSD. However, observations have not been able to quantitatively study the microphysical changes of individual particles and have yet to reveal the formation mechanisms within the precipitation system. In this study, we investigated quantitatively the process of the formation of the bimodal DSD by two-dimensional simulation of multicellular convection with the bin method. The simulation results showed that the bimodal DSD was formed during the updraft and downdraft in the mature stage of the multicell. Additionally, the bimodal DSD was formed at lower altitudes where there was inflow into the precipitation system. Particles that constituted the maximum of the bimodal DSD were found to have been advected by the inflow. Particles that constituted the local maximum dropped against the updraft. In contrast to these, particles that constituted the local minimum were less affected by the inflow and had difficulty dropping against the updraft. These results suggested that the bimodal DSD was formed by horizontal and vertical size sorting because of inflow and updrafts in the mature multicellular convection. In the future, it is necessary to simulate the reproduction of observed cases and compare them with observations.

How to cite: Okazaki, M., Yamaguchi, K., Yanase, T., and Nakakita, E.: Spatiotemporal structure of raindrop size distribution due to flow field in a convective precipitation system simulated by bin cloud microphysics model., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6387, https://doi.org/10.5194/egusphere-egu24-6387, 2024.

09:55–10:05
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EGU24-12007
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HS7.1
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Virtual presentation
Michael Larsen, Andrei Vakhtin, and Anthony Gomez

The fall velocities of rain and drizzle drops are often assumed to be a deterministic function of their size. These diameter-fall speed relationships are intrinsically assumed in the retrievals provided by some commercial rain measurement instruments (e.g. the Joss-Waldvogel Disdrometer (Distromet), Micro Rain Radar (METEK), and 1-Dimensional Video Disdrometer (Joanneum Research)).

Some disdrometers are capable of independently measuring droplet size and fall-speed and provide evidence that not all drops adhere to the assumed size/fall-speed relationship. The ubiquity and magnitude of these deviations are still an area of some debate; clear identification of drizzle and rain drops falling at speeds different than their expected terminal fall velocities is muddied by conservative estimates of disdrometer resolution and performance. For a long time the bulk of observed non-terminal drop fall speeds were assumed to be instrumental artifacts and, even now, most investigators conclude drops falling at non-terminal speeds do not have a large impact on rain measurement science.

To date, uncertainties in disdrometer-derived drop sizes and fall speeds have usually been derived from the manufacturer estimates. Here, we improve on these estimates by using a field calibration source (the new ``Large Drop Generator'' from Mesa Photonics) that permits user-selectable generation of droplets with known sizes and fall speeds. From these data, empirical estimates of disdrometer sizing and fall velocity bias and uncertainty can be determined. This, then, allows for a more reliable estimate of the fraction of non-terminal drops in natural rain and a more reliable assessment of the impact of non-terminal drizzle and rain drops in data derived from instruments that assume a specific drop size/fall-speed relationship.

How to cite: Larsen, M., Vakhtin, A., and Gomez, A.: Revisiting nonterminal hydrometeors: Refining instrument uncertainty, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12007, https://doi.org/10.5194/egusphere-egu24-12007, 2024.

10:05–10:15

Posters on site: Wed, 17 Apr, 16:15–18:00 | Hall A

Display time: Wed, 17 Apr 14:00–Wed, 17 Apr 18:00
Chairpersons: Auguste Gires, Alexis Berne
A.82
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EGU24-651
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HS7.1
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ECS
Marcio Matheus Santos de Souza, Auguste Gires, and Jerry Jose

A disdrometer is an instrument designed to assess both the size and velocity of descending hydrometeors. The applications of rainfall measurements retrieved with the help of disdrometers are diverse, spanning areas such as traffic control, scientific research, airport observation systems, and hydrology. Modern disdrometers leverage microwave or laser technologies that have increased the accuracy of the measurements with each iteration. Still, the quality of measurements fluctuates depending on factors such as raindrop size, wind velocity, and rain rate. A comprehension of these variations is needed to better understand the level of reliability of each device depending on the specific rain conditions.

In this study, we compare the performance of two optical disdrometers : 3D Stereo disdrometer (manufactured by Thies Clima) and Parsivel2 (manufactured by OTT). Both devices provide size resolved measurement of rainfall along with velocity of falling drops. Parsivel is set to record data every 30 seconds over a sampling area of 54 cm² and arranges the information in 32 x 32 classes of drop size and velocity. Unlike the Parsivel, 3D Stereo does not discretize measurements, and directly provides the diameter and velocity of each falling drop in a sampling area of 100 cm² with a measuring resolution of 0.08 mm and 0.2 m/s respectively, and a temporal resolution of 1 millisecond. This finer resolution data enables us to study rainfall variability at very small scales which are not usually available.

Here, we used continuously and simultaneously measured data since 21/08/2023, from TARANIS observatory of ENPC (https://hmco.enpc.fr/portfolio-archive/taranis-observatory/). The initial comparison of the data was done using a time series of rain-rate for rainfall events in between a dry period of at least 15 minutes and total depth >0.7 mm. This revealed an unexpected disparity in the water volume collected between the devices. Parsivel collected more than 3D Stereo on every instance, and the disparity got bigger as the rain rate increased. With the purpose of studying the source of this disparity, the sampling area of the 3D Stereo was divided into 8 sections and compared with each other. This showed that the estimate of rainfall parameters such mean diameter, mean velocity of the drops (which were expected to be uniform over long periods regardless of the section where drops are measured) were not the same for the sections studied, and exhibited clear trends. To understand this discrepancy in a scale invariant way, and to evaluate the performance of devices across scales and not only at a single scale, the widely used framework for studying variability of geophysical fields – Universal Multifractals (UM) was employed for assessing the scaling behavior of fields. Rainfall from both devices showed previously reported average scaling behavior from 30 s to 30 min. The difference between rain events and also the behavior at finer scales, which can be accessed from 3D stereo disdrometer were also studied using the UM framework and will be discussed.

Authors acknowledge the Ra2DW project (supported by the French National Research Agency - ANR-23-CE01-0019), for partial financial support.

Keywords: rainfall; disdrometer; multifractals;

How to cite: Santos de Souza, M. M., Gires, A., and Jose, J.: Multi-scale comparison of rainfall measurement in Paris area between two optical disdrometers of different working principles, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-651, https://doi.org/10.5194/egusphere-egu24-651, 2024.

A.83
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EGU24-2655
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HS7.1
Nick van de Giesen, Frank Annor, Sylvester Ayambila, Richard Dogbey, Vincent Hoogelander, Gordana Kranjac-Berisavljevic, Kingsley Kwabena, Rob Mackenzie, Marc Schleiss, and Remko Uijlenhoet

Convective rainfall in West Africa is poorly monitored and understood. There are large gaps between remote sensing rainfall products and what is observed on the ground. There are several reasons for these gaps. First, satellites and rain gauges measure at very different scales so one would expect that remote sensing products contain more events at lower intensities than small gauges. Second, a lot happens between the clouds observed by satellites and the ground. Rainfall may evaporate and move with the wind, causing further disconnects between space and ground observations. There are also indications that clouds in West Africa contain many small drops due to the presence of many aerosols, thereby possibly “misleading” satellite products. Finally, it is likely that there are further factors that are not yet accounted for.

In order to tackle this disconnect between ground and space observations, we plan to build the TUD - UDS, or TUDS, rainfall observatory near Tamale and Nyankpala in northern Ghana. The following are initial ideas that we would like to discuss at the EGU. It will be a multi-scale observatory, starting at a grid of nine gauges on a 500m grid (1km x 1km total). This small grid should capture the inherent spatial variability of convective rainfall events with convective cells of 2km or less. The largest grid would also contain nine gauges and have an extent of 10km x 10km, or larger. This outer grid would capture the movement of convective cells, including those contained within so-called line squalls. An intermediate grid may complete this picture. The structure will look, more or less, like the one in the picture below.

Different instruments will be at our disposal, from simple totalling rain gauges to disdrometers. There will be five Thies disdrometers, one Ott Parsivel, and several TAHMO stations and/or tipping bucket rain gauges. Also experimental intervalometers will be placed in the grid to better understand rainfall structure over time and space. Several instruments will be co-located to examine strengths and weaknesses of the different methods.

We explicitly invite comments and contributions.  

 

TEMBO Africa: The work leading to these results has received funding from the European Horizon Europe Programme (2021-2027) under grant agreement n° 101086209. The opinions expressed in the document are of the authors only and no way reflect the European Commission’s opinions. The European Union is not liable for any use that may be made of the information.

How to cite: van de Giesen, N., Annor, F., Ayambila, S., Dogbey, R., Hoogelander, V., Kranjac-Berisavljevic, G., Kwabena, K., Mackenzie, R., Schleiss, M., and Uijlenhoet, R.: Designing the TUDS rainfall observatory in northern Ghana, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2655, https://doi.org/10.5194/egusphere-egu24-2655, 2024.

A.84
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EGU24-6767
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HS7.1
Svitlana Krakovska, Liudmyla Palamarchuk, and Anastasiia Chyhareva

Precipitation detailed characteristics, namely spectrum of particles by their sizes, phase and precipitation intensity with high-resolution timestep, still need to be investigated due to the complexity of their direct instrumental measurements but necessity for improving forecast for different applications including hydrological and emergency service. Our study is focused on the stratiform precipitation associated with cloud system (Ns-As) of warm front during prolonged and intense precipitation event on the 25th October 2023 in Kyiv, Ukraine. This warm front cloud system was connected with an occluded low over Poland which developed on the East periphery of a huge depression (970 hPa) over the Northern Atlantic.

We analyzed the OTT Parsivel² - Laser Weather Sensor measurement data with 10sec time steps. Parsivel² was installed nearby regular meteorological station, which is a part of the WMO network, and its measurements were used for verification. Precipitation intensity and raindrop distributions had wavy character, where we can distinguish a few waves of precipitation enhancement. The average intensity of the minimum wave was 0.02mm/min that corresponds to 30 raindrops with size varying from 0.5 to 1.5mm and maximum falling speed 4m/s for the largest raindrops. The average intensity of maximum precipitation enhancement wave was 0.15mm/min with around 100 raindrops per 10sec with sizes mainly from 0.5 to 2.5mm (with some raindrop sizes up to 3.5mm) and average falling speed 5-6m/s. Total amount of 26-hour precipitation event was 24.2mm according to OTT Parsivel² measurements and 26mm according to SYNOP data from Kyiv WMO station (ID 33345). We should note that in modern climate condition in Kyiv such prolonged frontal precipitation even in autumn is rather rare event in respect to previous decades.  

Gained results were compared with previous studies based on 20-year measurement by pluviograph at the same Kyiv WMO station. For stratiform precipitation, average maximum precipitation intensity within precipitation enhancement waves was around 0.11mm/min. Duration of main precipitation enhancement waves was around 21 minutes. Characteristics of precipitation enhancements waves are key for assessment of surface runoff value. The significant fraction of water on the ground that forms surface runoff goes mainly from such precipitation enhancement waves, when around 60 up to 90% of the maximum surface runoff can be formed.

In conclusion, OTT Parsivel² Laser Weather Sensor was used in Ukraine for the first time and demonstrated good performance versus the city station accumulation measurements and historical pluviograph data at the station. At the moment this instrument is under way to the Ukrainian Antarctic station Akademik Vernadsky where further exploitation will allow to test and obtain measurement data for different phase of precipitation, mostly mixed and solid and compare with data from Micro Rain Radar Pro. Obtained and future results will extend our understanding of precipitation formation, their microphysics and dynamics, interconnections between precipitation intensity and size/fall speed of raindrops and solid particles. Future studies could help to evaluate the transformation of cloud and precipitation formation processes under the climate change for better parameterization in numerical models, to study the microphysical structure and composition of precipitation.

How to cite: Krakovska, S., Palamarchuk, L., and Chyhareva, A.: Microphysical properties of the stratiform precipitation in Kyiv city based on OTT Parsivel2 and pluviograph data , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6767, https://doi.org/10.5194/egusphere-egu24-6767, 2024.

A.85
|
EGU24-7802
|
HS7.1
Guillaume Drouen, Daniel Schertzer, Auguste Gires, and Ioulia Tchiguirinskaia

The aim of the Fresnel platform of École des Ponts ParisTech is to foster research and innovation in multiscale urban resilience. Studying the hydrological response of such complex urban areas accounting also for small scale spatio-temporal precipitation variability requires adapted tools. For these reasons, RadX provides a user-friendly graphical interface to run simulations using a fully distributed and physically based model: Multi-Hydro. RadX is designed as a Software as a Service (SaaS) platform, allowing users to work with data across a wide range of space-time scales and the appropriate tools for analyzing and simulating this data.

The hydrological model, developed at École des Ponts ParisTech, integrates four open-source software applications previously used and validated independently by the scientific community as well as practitionners. Its modular structure includes a surface flow module, sewer flow module, a ground flow module and a precipitation module. It is able to simulate the quantity of runoff and rainwater infiltrated into unsaturated soil layers from any space-time varying rainfall event at any location of the studied peri-urban watersheds, as well as depth and flow in all the pipes and nodes of the sewer network.

Users can launch hydrological simulations using the Multi-Hydro model directly from their web browser, while they are run on dedicated servers. They can adjust two key input parameters: the land use of the studied catchment and the rainfall data. Dedicated tools have been developed to enable users to modify the land use of the catchment with the same ease as using a raster graphic editor. Users can either choose real rainfall events captured by the X-band weather radar located at École des Ponts ParisTech or utilize user-defined synthetic rainfall as input. Data from other radar can also easily be integrated. 

For the simulation output, the interface provides users with different tools to study in detail the impact of the chosen input parameters. For instance, by simply selecting two sewer junctions on an interactive map, users can generate a sewer path between these two points and display an interactive representation of the water level heights in sewer conduits and junctions along the user-defined sewer network path.

Additional components can be integrated into RadX to meet specific requirements using visual tools and forecasting systems, including those from third parties. Developments are still in progress, with a constant loop of requests and feedback from the scientific and professional world.

How to cite: Drouen, G., Schertzer, D., Gires, A., and Tchiguirinskaia, I.: Cloud based tool to enhance urban resilience with the Fresnel Platform using the Multi-Hydro Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7802, https://doi.org/10.5194/egusphere-egu24-7802, 2024.

A.86
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EGU24-12054
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HS7.1
Raquel Evaristo, Ju-yu Chen, Alexander Ryzhkov, and Silke Trömel

The RY precipitation product of the German Weather Service (DWD) is severely affected by the presence of the low melting layer and frequently shows circular features of enhanced precipitation around the radar sites during the winter time. 
The radars tend to be installed at relatively high terrain and to scan at elevations at a minimum of 0.5° in order to avoid beam blockage and ground clutter. In doing so two problems arise:

1) the difference between the ground and the radar beams becomes a problem especially at large distances from the radar, and consequently precipitation processes in the lowest layers are not observed.
2) the radar beam often reaches the melting layer and may even cross it where it is sampling the snow above.As a result problems arise when deriving surface QPE from the radar: regions of enhanced QPE in ring shapes around the radar sites, and underestimation of the precipitation beyond the melting layer.

A new methodology (PVPR - Polarimetric Vertical Profile of Reflectivity) developed by Ryzhkov et al. 2022 is tested here for which the radar reflectivity (ZH) is reconstructed to correct for the effect of the melting layer and snow beyond. In this methodology the melting layer is detected independently for each azimuth based on the values of ZH and ρHV (cross-correlation coefficient between horizontal and vertically polarized radar waves). In particular the range bin at which the melting layer was reached is recorded (mlb_r). The strength of the melting layer (ML_S) is defined based on how much the value of ρHV  dropped within the melting layer. The values of ML_r and ML_S at a specific elevation are considered sufficient to characterize the melting layer, and are then compared with lookuptables which were generated by simulations of the melting layer effect on the radar beam. A correction factor is then applied based on the lookuptables to the ZH profile within and beyond the melting layer. Visually the result shows a smoother field of reflectivity without the obvious bright band and decreased values associated with snow at farther ranges.

In this study the PVPR methodology was used to correct ZH which in turn was used to calculate rain rates and rain accumulations in a few winter events in Germany.  The results show a strong improvement in the quality of the QPE when compared to rain gauges. The quality of the resulting QPE depends on the event and on the location of the radar. More specifically, the quality decreases when the melting layer is very low, at heights comparable to the radar height, and when the difference between the beam and the surface increases. These problems will be analyzed and potential solutions will be tested in order to improve the quality of the rainfall product.

Ryzhkov, Alexander, Pengfei Zhang, Petar Bukovčić, Jian Zhang, and Stephen Cocks. 2022. "Polarimetric Radar Quantitative Precipitation Estimation" Remote Sensing 14, no. 7: 1695. https://doi.org/10.3390/rs14071695 

How to cite: Evaristo, R., Chen, J., Ryzhkov, A., and Trömel, S.: Testing a new Radar QPE methodology for winter events with a low melting layer, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12054, https://doi.org/10.5194/egusphere-egu24-12054, 2024.

A.87
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EGU24-12374
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HS7.1
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ECS
Mauro Boreggio, Matteo Barbini, Martino Bernard, Matteo Berti, Massimiliano Schiavo, Alessandro Simoni, Sandivel Vesco Lopez, and Carlo Gregoretti

In a mountainous environment, high-intensity and short-duration precipitation can generate sudden and abundant runoff at the base of rocky cliffs. This runoff, upon impacting the debris deposits present there, can trigger debris-flow phenomena. In the province of Belluno, in the Boite River valley, a network of rain gauges has been set up to monitor precipitation in the Rovina di Cancia site, where 12 debris-flow events have occurred in the last 10 years. The rain gauges are strategically placed both upstream and downstream of the debris-flow initiation area. In most cases, the precipitation showed significant spatial variability in both planimetric and altimetric aspects. This variability is crucial when simulating the runoff that triggers stony debris flows. The simulation of the peak runoff that triggered the 12 occurred events using a single rain gauge presented a high scatter compared to the simulation performed with the spatially recorded rainfall, except when the chosen rain gauge was close to the rocky cliffs. Furthermore, modelling using radar estimates as rainfall input also displayed significant variability based on the rain gauge used to correct the radar data. Essentially, accurate real-time simulation of runoff triggering debris flows requires the presence of rain gauges upstream of the initiation area, particularly in close proximity to the rocky cliffs.

How to cite: Boreggio, M., Barbini, M., Bernard, M., Berti, M., Schiavo, M., Simoni, A., Vesco Lopez, S., and Gregoretti, C.: Implications of the rainfall spatial variability for the real-time modeling of runoff triggering stony debris flows, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12374, https://doi.org/10.5194/egusphere-egu24-12374, 2024.

A.88
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EGU24-14062
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HS7.1
|
Tristan Gilet and Loïc Tadrist

The interception of raindrops by plant leaves induces a redistribution of water, nutrients, and micro-organisms, from the surface of these leaves to their surroundings. It consequently shapes the plant ecosystem. For example, in wheat fields (as in most major crops), splashing raindrops are the main mechanism of spore dispersal for fungal diseases at the epidemic stage, with severe consequences on crop yield. Surprisingly, the observed dispersal is not only downward (wash off / dripping) or outward (splash), but also upward, which may considerably speed up the fungus propagation. Other nutrients and microorganisms might also benefit from such upward transport external to the plant.

In this work, we unravel an efficient and universal mechanism of upward transport: after a raindrop splashed on a plant leaf, the residual water on the leaf can be shot upward as the leaf springs back. We illustrate this phenomenon with several plant leaves. Then we present results obtained from systematic experiments with artificial leaves, thanks to which both the mechanics of rain-induced leaf motion and the fluid dynamics of leaf-induced droplet ejections are elucidated. We identify the range of mechanical properties of the leaf that makes upward shooting fully effective. Finally, we show that the efficiency of this upward transport increases more than proportionally with rain intensity. Its occurrence and role in shaping ecosystems will be largely amplified in the case of an increased frequency of extreme rain events.

How to cite: Gilet, T. and Tadrist, L.: Upward transport in a canopy assisted by raindrop impacts on plant leaves, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14062, https://doi.org/10.5194/egusphere-egu24-14062, 2024.

A.89
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EGU24-19986
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HS7.1
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ECS
|
Subhadip Sarkar, Jayanarayanan Kuttippurath, and Vikas Kumar Patel

Precipitation efficiency (PE) is the relationship between cloud condensation and precipitation that links the atmospheric circulation to the hydrological cycle. Definition and thus, estimates vary greatly due to the underlying microphysical dependencies of PE. It also quantifies the fraction of condensed water in clouds that falls as surface precipitation. Consequently, there is a dearth of knowledge about the sensitivity of PE to the global warming and its subsequent implications for climate change. Therefore, here, we estimate the global PE based on the surface precipitation and cloud water path1 , and quantify its role in the variability and changes in precipitation in the climate change context using the Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Precipitation Measurement (GPM) observations for the period of 2001–2020. We find that the annul mean PE is very high in the tropics, including both the West Pacific and Indian Ocean Warm Pool. Other regions in the tropics, like Amazonia and Central Africa also show very high PE values. These regions also have very high surface precipitation. There is gradual decline in PE in the tropics in the last decade (2010–2020), which is consistent with the decreasing trend of precipitation there. Regions such as the warm pool, Northeast India and Amazonia show a decreasing pattern in both PE and precipitation in the past decade. This decline in both PE and precipitation in the tropics suggests that, a fraction of condensed water falls as precipitation decreases. On the other hand, there is notable rise in temperature in the tropics during the same period. However, the quantification of the sensitivity of PE to global warming on a regional scale is very critical.

How to cite: Sarkar, S., Kuttippurath, J., and Patel, V. K.: The changing relationship between precipitation efficiency and global precipitation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19986, https://doi.org/10.5194/egusphere-egu24-19986, 2024.

A.90
|
EGU24-20231
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HS7.1
Martin Fencl and Marc Schleiss

Commercial microwave links (CMLs) serve as point-to-point radio connections in cellular backhaul and offer a promising way to measure rainfall opportunistically. Raindrops along the CML path attenuate electromagnetic waves, allowing the conversion of this attenuation into path-averaged rain rates. Wide coverage of CML networks, high density in urban areas, and cost-effective operation present clear advantages over traditional rain gauges and radar networks. However, the integrated nature of CML data poses a challenge. When transforming this data into spatially representative rainfall estimates, such as 2D maps, path-integrated rain rates need to be converted into point data and interpolated to a regular two-dimensional Cartesian grid. The most direct method involves reducing each CML observation to a single-point measurement at the path's center, followed by interpolation using techniques like kriging or inverse distance weighted (IDW) interpolation. Yet, past studies indicate that for longer CMLs (several kilometers) and intense localized rain showers, this approach can introduce significant biases and unrealistic rainfall distributions due to the substantial spatial and temporal variability of rainfall.

In this contribution, we introduce a new disaggregation method employing random cascades. The method redistributes rainfall amounts along CML paths across progressively smaller scales using a discrete, conservative multiplicative random cascade. Inspired by the EVA (Equal-volume area) cascade developed by Schleiss (2020) for disaggregating spatially intermittent rainfall fields, our approach involves splitting each CML segment into two new segments with different path-lengths but identical path-integrated rainfall. We call this new method CLEAR (CML segments with equal amounts of rain). CLEAR is tested for CML network of 77 CMLs located in Prague, CZ. First, the disaggregation is evaluated using simulated CML observations and, second, CML rain rates derived from real attenuation data.

Our findings demonstrate that CLEAR surpasses reconstruction algorithms that reduce CML observations into a single point. It accurately replicates the highly diverse rainfall distributions observed along CMLs, including their intermittency. Moreover, the stochastic nature of the cascade enables the quantification of uncertainty associated with the spatial redistribution of rainfall rates along CMLs.

References

Schleiss, Marc. “A New Discrete Multiplicative Random Cascade Model for Downscaling Intermittent Rainfall Fields.” Hydrology and Earth System Sciences 24, no. 7 (July 23, 2020): 3699–3723. https://doi.org/10.5194/hess-24-3699-2020.

How to cite: Fencl, M. and Schleiss, M.: A new method for disaggregating path-averaged rain rates from commercial microwave links, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20231, https://doi.org/10.5194/egusphere-egu24-20231, 2024.

A.91
|
EGU24-20898
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HS7.1
Jürgen Komma, Borbala Szeles, Katarina Zabret, Mojca Šraj, and Juraj Parajka

In natural environments, rainfall causes soil erosion, which has a significant impact on the agricultural production and the ecological conditions of the streams. Due to different types of vegetation, their unique characteristics and seasonality, there are still a lot of open scientific questions about how rainfall interception process influences the rainfall erosivity and soil erosion. With the aim of improving knowledge about rainfall interception by different vegetation and its impact on the rainfall erosivity, an interdisciplinary and international research team (Faculty of Civil and Geodetic Engineering at the University of Ljubljana, Slovenian Forestry Institute and Technical University of Vienna) work together in the research project entitled “Evaluation of the impact of rainfall interception on soil erosion”. In the scope of the project, drop size distribution measurements above and below selected plants will be conducted in combination with classical measurements of rainfall partitioning. The measurements are ongoing in the small urban park in Ljubljana, Slovenia and in the experimental catchment with mainly agricultural land use in Lower Austria (The Hydrological Open Air Laboratory HOAL in Petzenkirchen). To evaluate the differences in rainfall characteristics for the two research plots, a comparative analysis on rainfall event properties such as rainfall amount, duration and intensity, size and velocity distribution of raindrops is performed. The aim of the presentation is to introduce the project and presents the first comparison of the rainfall characteristics at research plots in Austria and Slovenia.

Acknowledgments: This contribution is part of the ongoing research project entitled “Evaluation of the impact of rainfall interception on soil erosion” supported by the Slovenian Research and Innovation Agency (project J2-4489) and the Austrian Science Fund (FWF) I 6254-N.

How to cite: Komma, J., Szeles, B., Zabret, K., Šraj, M., and Parajka, J.: Comparative analysis of rainfall characteristics for two distinct research plots, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20898, https://doi.org/10.5194/egusphere-egu24-20898, 2024.