HS7.2

Precipitation variability from drop scale to catchment scale : measurement, processes and hydrological applications

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.

Co-organized by AS5/NP3
Convener: Auguste Gires | Co-conveners: Alexis Berne, Katharina Lengfeld, Taha Ouarda, Remko Uijlenhoet
Presentations
| Wed, 25 May, 08:30–11:50 (CEST)
 
Room 2.44

Presentations: Wed, 25 May | Room 2.44

Chairpersons: Auguste Gires, Remko Uijlenhoet
08:30–08:32
Opportunistic sensors
08:32–08:39
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EGU22-7482
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ECS
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On-site presentation
Anna Špačková, Martin Fencl, and Vojtěch Bareš

One of the pivotal variables in the hydrological system processes is precipitation. In this context, many hydrological applications require a reliably captured structure and temporal development of rainfalls. Therefore, the crucial challenge is to monitor rainfall in high spatial and temporal resolution. The opportunistic sensors for rainfall measurements have a great potential since they can complete standard observation networks with high number of alternative measuring sensors. Nowadays, one of the most prominent opportunistic source of rainfall information are telecommunication networks composed of commercial microwave links (CMLs). CMLs can supply dense path-averaged rainfall information derived from power-law relationship of the microwave signal attenuation and the rainfall intensity.

However, the actual implementation and employment requires a careful consideration of the errors and uncertainties of the measurements. In this study, the influence of different state of the rainfall is excluded using the set of pairs of collocated independent CMLs with paths in the immediate vicinity. Therefore, each pair of collocated CMLs can be assumed as identically influenced by the same rainfall conditions, while their characteristics (e.g., lengths, frequencies, polarizations) vary. The dataset consists of 33 rainfall periods within the years 2014 – 2016 monitored by 13 groups of collocated CMLs.

High correlation (around 0.95) was found for collocated CMLs. Compared to conventional rainfall sensors, for example, Peleg et al. (2013) demonstrated a correlation of 0.92 for collocated tipping bucket rain gauges. The CMLs are also compared with the adjusted weather radar rainfall information which is used as a reference. The dispersion of the data within five intensity ranges was used to set the boundaries (5 % and 95 % quantile). Subsequently, the fit of the CML measurements into the boundaries was examined. CMLs with 0.2 dB/mm/h sensitivity had the highest fit ratio, almost 80 %. Contrastingly, sensors with sensitivity 1.5 dB/mm/h just exceeded the fit ratio of 60 %. Observed differences describe the uncertainties which are not directly driven by the propagation of the signal. The uncertainties of CML need to be further studied to maximize the knowledge-based use of the favourable spatial and temporal resolution of this opportunistic sensing network.

References
Peleg, N., Ben-Asher, M., and Morin, E. (2013) Radar subpixel-scale rainfall variability and uncertainty: lessons learned from observations of a dense rain-gauge network, Hydrol. Earth Syst. Sci., 17, 2195–2208, https://doi.org/10.5194/hess-17-2195-2013.


This study is supported by the project SpraiLINK (20-14151J) of the Czech Science Foundation and by the grant of Czech Technical University in Prague no. SGS21/052/OHK1/1T/11.

How to cite: Špačková, A., Fencl, M., and Bareš, V.: Comparison of rainfall retrieval from collocated commercial microwave links with adjusted radar reference, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7482, https://doi.org/10.5194/egusphere-egu22-7482, 2022.

08:39–08:46
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EGU22-9515
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ECS
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On-site presentation
Martin Fencl, Anna Spackova, and Vojtech Bares

Commercial microwave links (CMLs), point-to-point radio connections forming the backbone of cellular networks, can be used as opportunistic rainfall sensors and provide rain rate at high temporal resolution. The CML rainfall retrieval methods have been mostly developed for devices operating between 13 – 40 GHz where attenuation-rainfall relation is relatively insensitive to drop size distribution. New deployments have, however, an extensive share of E-band CMLs operating at 71 – 81 GHz frequency where drop size distribution (DSD) represents a major source of errors (Fencl et al., 2020). This study investigates for the first time the joint use of 13-40 GHz and 71-86 GHz CMLs with focus on evaluating different sources of errors.

Rainfall retrieved from 250 CMLs located in the city of Prague and its vicinity are compared to the quantitative precipitation estimates from C-band weather radar adjusted to the local network of 23 municipal rain gauges. Diverse path-lengths and frequencies of CMLs enable us to distinguish between different sources of errors. Shorter CMLs operated at lower frequencies are dominantly disturbed by errors related to antenna wetting whereas E-band CMLs are significantly more affected by DSD variability and non-uniform distribution of rain rates along the CML path. Moreover, longer E-band CMLs suffer from outages during heavy rainfalls. In general, E-band CMLs are more sensitive to low rain rates and thus suitable for retrieving light rainfalls whereas CMLs operating at lower frequencies are more accurate during heavy rainfalls.

Diverse characteristics of CMLs typically occurring in real-world cellular networks pose a challenge as each CML is affected by the instrumental errors in a different manner. On the other hand, the diversity in CML characteristics can be also exploited to quantify and possibly reduce these errors, especially in cities, where CML networks are usually dense and thus often provide collocated (redundant) rain rate measurements.

References:

Fencl, M., Dohnal, M., Valtr, P., Grabner, M., and Bareš, V.: Atmospheric observations with E-band microwave links – challenges and opportunities, 13, 6559–6578, https://doi.org/10.5194/amt-13-6559-2020, 2020.

Acknowledgements: This study was conducted within SpraiLINK project (20-14151J) and supported by Czech Science Foundation.

How to cite: Fencl, M., Spackova, A., and Bares, V.: Effect of diverse microwave link characteristics on rainfall retrieval errors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9515, https://doi.org/10.5194/egusphere-egu22-9515, 2022.

08:46–08:53
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EGU22-11125
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ECS
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Presentation form not yet defined
Christian Chwala, Julius Polz, Maximilian Graf, and Harald Kunstmann

Attenuation data from commercial microwave links (CMLs) has proven to provide useful rainfall information. With their high density in urban areas, CMLs offer a great potential to estimate and study rainfall variability on small scales. Since the transmission power of CML hardware is limited, heavy rainfall can, however, lead to a complete loss of signal at the receiving end. As a consequence, very high rain rates can be missing in CML-derived rainfall information. The rain rate for which a specific CML experiences complete loss of signal depends on its length and frequency as well as on its dynamic range which is defined by transmit power, receiver noise level and antenna gain.

We analyze the occurrence and effect of such complete losses of signal, which we term “blackouts”, using two different datasets. First, a CML dataset with one minute temporal resolution consisting of 4000 CMLs in Germany is used to investigate the blackouts in real CML attenuation data over a period of three years. Second, the gauge-adjusted radar climatology RADKLIM-YW from the German Meteorological Service is used to derive synthetic rain induced attenuation data for each CMLs path with 5-minute temporal resolution for a period of 20 years.

For the real CML observations we introduce and apply a new algorithm to detect rain induced blackout gaps. This allows us to quantify the number and length of the blackout gaps stemming from heavy rainfall. Using the path-averaged RADKLIM-YW data as reference, we then quantify the rain rates and rainfall amount that is missed due to the CML blackout gaps. We find that longer CMLs are more likely to be affected by blackout gaps. This effect occurs even though the CMLs in our dataset are configured so that longer CMLs have a larger dynamic range to account for the increasing attenuation with increasing length. Using the dynamic range of each CML, we derive the long-term statistics of potential blackout occurrence from the synthetic attenuation data based on RADKLIM-YW. We find a pattern similar to the one in the real CML attenuation data, albeit with a smaller fraction of time steps affected by blackouts for all CMLs.

Our results provide a reliable basis for researchers to judge the capability of their CML dataset to capture rainfall extremes. Furthermore, it can serve as an improved basis for planning the layout and configuration and thus the dynamic range of individual CMLs.

How to cite: Chwala, C., Polz, J., Graf, M., and Kunstmann, H.: Missing extremes in CML rainfall estimates due to total loss of signal, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11125, https://doi.org/10.5194/egusphere-egu22-11125, 2022.

08:53–09:00
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EGU22-9843
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ECS
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On-site presentation
Luuk van der Valk, Miriam Coenders-Gerrits, Rolf Hut, Hidde Leijnse, Aart Overeem, Bas Walraven, and Remko Uijlenhoet

Single-frequency microwave links can be used to monitor path-averaged precipitation by determining the rain-induced attenuation along the link path, as for example is done with commercial microwave links (CMLs) from cellular telecommunication networks. However, using these networks to estimate precipitation, the temporal resolution of these estimates is bound to the temporal sampling strategy employed by the network operator, which solely uses the information on the link signal to assure the functioning of the network. Moreover, not all operators store the same variables describing the link signal. Most commonly, a temporal resolution of 15 minutes with a recording of the minimum and maximum values during this interval is applied. For research purposes, often higher temporal resolutions in combination with averaged values are preferred. Yet, it is uncertain how these sampling strategies affect the computed amount and intensity of rainfall. To address this uncertainty, we investigate the influence of various temporal sampling strategies regarding the link signal on the estimated amounts and intensities of rainfall events from a single microwave link. For the analysis, we resample microwave link data to multiple intervals and variables characterizing the measured signal. The original data consist of three collocated microwave links sampled at 20 Hz, all operational for more than a year, and covering a 2.2 km path over the city Wageningen in the Netherlands. Additionally, the resulting rainfall estimates for the intervals and variables are compared to measurements of five disdrometers deployed along the link path. Overall, the results of this study can help to quantify the uncertainties associated with rainfall estimates from microwave links.

How to cite: van der Valk, L., Coenders-Gerrits, M., Hut, R., Leijnse, H., Overeem, A., Walraven, B., and Uijlenhoet, R.: Measuring rainfall with microwave links: the influence of temporal sampling strategies, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9843, https://doi.org/10.5194/egusphere-egu22-9843, 2022.

09:00–09:07
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EGU22-12993
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ECS
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On-site presentation
Erlend Øydvin, Vegard Nilsen, Nils-Otto Kitterød, Mareile Astrid Wolff, and Christoffer Artturi Elo

Using Commercial Microwave Links (CMLs) for measuring precipitation have gained more and more attention the past 10 years as it seems like a promising supplement to weather radar and rain gauge observations. It works by relating rainfall to signal attenuation along the CMLs path. As the signal level also can change due to other meteorological conditions such as air temperature and water vapor content, this opportunistic sensing method requires sophisticated data processing in order to relate signal attenuation to rain rate. One of the processing steps involves detecting wet and dry periods. 

For this presentation, we classified wet and dry periods using a weather radar and a rain gauge in Ås, Norway. We use data like equivalent reflectivity and phase shift between horizontal and vertical polarization and compare it to ground truth measurements. The resulting wet dry classifications are then compared with a single CML link in the same area.

How to cite: Øydvin, E., Nilsen, V., Kitterød, N.-O., Wolff, M. A., and Artturi Elo, C.: Using weather radar to classify wet and dry periods for Commercial Microwave Links, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12993, https://doi.org/10.5194/egusphere-egu22-12993, 2022.

09:07–09:14
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EGU22-6968
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ECS
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Virtual presentation
Micha Eisele, András Bárdossy, Christian Chwala, Norbert Demuth, Abbas El Hachem, Maximilian Graf, Harald Kunstmann, and Jochen Seidel

Abstract

Precipitation is highly variable in space and time. Ground-based precipitation gauging networks such as those from national weather services are often not able to capture this variability. Weather radars have the potential to capture the spatio-temporal characteristics of rainfall fields but they also suffer from specific errors such as attenuation. The increasing number and availability of opportunistic sensors (OS), such as commercial microwave links (CML) and personal weather stations (PWS), provides new opportunities to improve rainfall estimates based on ground observations.

We have developed a geostatistical interpolation method that allows a combination of different opportunistic sensors and their specific features and geometric properties, e.g., point and line information. In addition, the uncertainty of the different data sets can be considered [1].

The flood event in the western provinces of Germany in July 2021 showed that both, the precipitation interpolations based on rain gauge data from the German National Weather Service and radar-based precipitation products, underestimated precipitation. We show that the additional information of OS data can improve precipitation estimates in terms of areal precipitation amounts and spatial distribution.  

 

References
[1] Graf, M., El Hachem, A., Eisele, M., Seidel, J., Chwala, C., Kunstmann, H. and Bárdossy, A.: Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales, https://doi.org/10.1016/j.ejrh.2021.100883

How to cite: Eisele, M., Bárdossy, A., Chwala, C., Demuth, N., El Hachem, A., Graf, M., Kunstmann, H., and Seidel, J.: Improvement of rainfall estimates using opportunistic sensors - the example of the flood in Rhineland-Palatinate in July 2021, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6968, https://doi.org/10.5194/egusphere-egu22-6968, 2022.

In situ measurements
09:14–09:21
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EGU22-7339
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ECS
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On-site presentation
Enrico Chinchella, Mattia Stagnaro, Arianna Cauteruccio, and Luca G. Lanza

The need for high-resolution and low maintenance weather stations is the major factor behind the increasing adoption of Non-Catching Gauges (NCGs) by national weather services and research institutions. Data from such instruments are used for several applications and in numerous research fields, where instrumental biases can have a strong impact. For NCGs, rigorous testing and calibration are more challenging than for catching gauges. Hydrometeor characteristics like particle size, shape, fall velocity and density must be carefully reproduced to provide the reference precipitation, instead of the equivalent water flow used for the calibration of catching gauges. Instrument calibration is usually declared by the manufacturers, using internal procedures developed for the specific technology employed. No standard calibration methodology exists, that encompass all or at least most of the available NCGs (Lanza et al. 2021). The EURAMET project 18NRM03 ‘INCIPIT’ on the ‘Calibration and accuracy of non-catching instruments to measure liquid/solid atmospheric precipitation’, was initiated in 2019 to address such issues.

A calibration device was developed to achieve individual drop generation on demand and in-flight measurement of the released drops. Water drops in the range from 0.5 to 6 mm in diameter are generated to mimic natural raindrops. A high-precision syringe pump is used to form the drop of the desired volume at the tip of a calibrated nozzle. A high-voltage power supply is used to apply a large potential difference between the nozzle and a metallic ring, and the resulting electric field triggers the release of the drop. A precision motorized gantry moves the generator across the horizontal plane, to cover different releasing positions within the instrument sensing area. By either varying the release height or accelerating the drop using compressed air, different fractions of the terminal velocity can be achieved, depending on the drop size. A second gantry, just above the gauge under test, aligns the plane of focus of a high-resolution camera with the fall trajectory of the drop. Three images of the same drop are captured in a single picture, using speedlights triggered at fixed time intervals. Photogrammetric techniques and a photodiode to measure the time between flashes provide the shape, size, speed, and acceleration of the drop. This characterizes each released drop before it reaches the instrument sensing area and, by comparison with the gauge measurement, the instrumental bias is obtained. Laboratory tests are presented to assess the performance of the calibration device.

This work is funded as part of the activities of the EURAMET project 18NRM03 “INCIPIT Calibration and Accuracy of Non-Catching Instruments to measure liquid/solid atmospheric precipitation”. The project INCIPIT has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.

References:

Lanza L.G. and co-authors, 2021: Calibration of non-catching precipitation measurement instruments: a review. J. Meteorological Applications, 28.3(2021):e2002.

How to cite: Chinchella, E., Stagnaro, M., Cauteruccio, A., and Lanza, L. G.: A precision raindrop generator to calibrate non-catching rain gauges, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7339, https://doi.org/10.5194/egusphere-egu22-7339, 2022.

09:21–09:28
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EGU22-7871
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On-site presentation
Luca G. Lanza and Arianna Cauteruccio

Adjustments of the wind-induced bias of conventional catching type rain gauges derive from collection efficiency (CE) curves that can be obtained either from field experiments or from numerical simulation (Lanza and Cauteruccio, 2021). The use of numerical simulation allows to overcome the limitations of the experimental installations and monitoring campaigns (e.g., the many influencing variables involved and the variability of the rainfall process) to cover a wide range of wind speed and rainfall intensity (RI) conditions. Also, the accuracy of the measurements taken as a reference is still an issue in field experiments.

A Lagrangian particle tracking (LPT) model, suitably validated in the wind tunnel (see Cauteruccio et al., 2021), is applied to the results of computational fluid dynamic (CFD) simulations of the airflow field surrounding a rain gauge to derive a simple formulation of the collection efficiency curves as a function of wind speed (Cauteruccio and Lanza, 2020). A new parameterization is proposed to highlight the influence of rainfall intensity, based on the typical form of the drop size distribution (DSD) of rainfall events (data from the Italian territory). The methodology is applied to a cylindrical gauge, which has the typical outer shape of most tipping-bucket rain gauges, as a representative specimen of operational measurement instruments.

Using rainfall intensity as a controlling factor for the collection efficiency has solid physical bases in the relationship between RI and the DSD (Colli et al., 2020), and the role of RI can only be quantified using numerical simulations of both the airflow field (using CFD) and the particle motion (via the LPT).

A simple formulation of the adjustment curves is obtained, which can be easily applied in an operational context, since wind velocity is the only ancillary variable required to perform the adjustment. Wind is often measured by operational weather stations together with the precipitation intensity, so the correction adds no relevant burden to the cost of meteo-hydrological networks.

References

Cauteruccio, A. and L.G. Lanza (2020). Parameterization of the collection efficiency of a cylindrical catching-type rain gauge based on rainfall intensity. Water, 12(12), 3431. https://doi.org/10.3390/w12123431.

Cauteruccio, A., Brambilla, E., Stagnaro, M., Lanza, L.G. and D. Rocchi (2021). Wind tunnel validation of a particle tracking model to evaluate the wind-induced bias of precipitation measurements. Water Resour. Res., 57(7), e2020WR028766. https://doi.org/10.1029/2020WR028766.

Colli, M., Stagnaro, M., Lanza, L.G., R. Rasmussen and J.M. Thériault (2020). Adjustments for Wind-Induced Undercatch in Snowfall Measurements Based on Precipitation Intensity, J. Hydrometerol., 21, 1039-1050, https://doi.org/10.1175/JHM-D-19-0222.1.

Lanza, L.G and A. Cauteruccio (2021). Accuracy assessment and intercomparison of precipitation measurement instruments. Chapter 1, p. 3 – 35. In: Michaelides, S. (ed.), Precipitation Science. Elsevier, Amsterdam, Netherlands. ISBN: 978-0-12-822973-6, pp. 833. https://doi.org/10.1016/B978-0-12-822973-6.00007-X.

How to cite: Lanza, L. G. and Cauteruccio, A.: Influence of the drop size distribution on the collection efficiency of catching gauges as a function of rainfall intensity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7871, https://doi.org/10.5194/egusphere-egu22-7871, 2022.

09:28–09:35
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EGU22-6420
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ECS
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Virtual presentation
Brianna G. Brunson and Michael L. Larsen

Historically, disdrometer data records have been divided into disjoint, equal-time intervals (often of 1- or 5-minute durations). Previous research of drop-resolving disdrometer data taken by the two-dimensional video disdrometer (Joanneum Research, Graz, Austria) has noted evidence of statistical structures on sub-minute timescales, which could lead to underestimations of rainfall variability when 1- or 5-minute partitionings are used. Here, we introduce and explore alternatives to the standard fixed-duration partitioning of disdrometer data. We compare the distributions of standard bulk rain measurements (rainfall rate and mass weighted mean diameter) under each partitioning method to demonstrate the utility of these alternative partitioning methods.

How to cite: Brunson, B. G. and Larsen, M. L.: An Examination of Alternate Partitioning Methods for Disdrometer Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6420, https://doi.org/10.5194/egusphere-egu22-6420, 2022.

09:35–09:42
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EGU22-9945
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ECS
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Virtual presentation
Chi-Ling Wei and Li-Pen Wang

Raindrop size distribution (DSD) is the key factor to derive reliable rainfall estimates. It is highly related to a number of integral rainfall parameters, including rain intensity (R), rain water content (W) and radar echo (Z). Disdrometers are the sensors commonly used to measure DSD based upon microwave or laser technologies; for example, JWD (Joss-Waldvogel Disdrometer), Parsivel and 2DVD (Two-Dimensional Video Disdrometer). These sensors may have their own strengths and weakness, but they are all relatively expensive. This hinders the possibility to have a high-density network for observing DSD at large scales. In this work, the ultimate goal is to develop a lightweight and low-cost disdrometer with descent accuracy.

We started with establishing a model that can well simulate the signal response of a single drop falling on a cantilever piezo film. A series of experiments were conducted to test the reaction of drops at different sizes (i.e. diameters ranging from 2 - 4 mm) and as drops fall onto various locations of the film. We then modelled the collision by assuming the piezo film to be a damped cantilever beam and drop force to be a step force. The drop force can be derived based upon the measurement of the deflection of beam end, which can be further used to calibrate the damp ratio. Preliminary results suggest that the signal response of a single drop hits can be well simulated based upon the proposed model under current experimental setting. We then developed an algorithm to optimize the simulation of signal responses with four four variables; these include drop’s weight, film thickness, film damping ratio and drop force. The result shows that the simulated drop force constitutes a strong linear relationship with the real drop’s weight.

We are now experimenting on the capacity of the developed model to work with a more complex yet realistic setting. For this purpose, we have created a more realistic rainfall condition by employing a micro pump. This pump can help control the size and timing of drops, so we can generate continuous single drops of consistent quality. In addition, we utilise a simple 1-D laser device to simultaneously measure the size of drops by analyzing the fluctuation in the laser signal. This would enable better understanding the actual size distribution of drops.  We expect that the outcome of the experiments  will provide useful insights on developing low-cost disdrometers with a cantilever piezo film.

How to cite: Wei, C.-L. and Wang, L.-P.: Toward a low-cost disdrometer: Measuring drop size with a cantilever piezo film, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9945, https://doi.org/10.5194/egusphere-egu22-9945, 2022.

09:42–09:49
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EGU22-2887
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Virtual presentation
Christopher K. Blouin, Carson A. Barber, and Michael L. Larson

Simultaneous measurements of the rain drop size distribution were made by a 2-dimensional video disdrometer (2DVD, Joanneum Research, Graz, Austria) and a MicroRain Radar-Pro (MRR-Pro, Metek, Elmshorn, Germany) deployed near Charleston, South Carolina, USA and horizontally separated by approximately 20 meters. The 2DVD data was post-processed to correct for spurious drop detection and incorrect assignment of effective sensor area, and the MRR-Pro spectral data was corrected to incorporate a height-dependent estimate of the ambient vertical wind. Surface 2DVD drop measurements were utilized to reconstruct an approximation of the drop size distribution aloft at different heights and times to compare to the inferred MRR-Pro drop spectrum and bulk rain parameters. Despite fundamentally different measurement principles and different sets of assumptions associated with the reconstruction of drop size distributions aloft, the agreement between the 2DVD and MRR-Pro data showed promise. The two data sets are further investigated in order to reveal possible features of boundary layer rain vertical variability, estimates of drop-drop collision rates, and near-surface rain microphysical phenomena.

How to cite: Blouin, C. K., Barber, C. A., and Larson, M. L.: Intercomparison between 2DVD and MRR Datasets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2887, https://doi.org/10.5194/egusphere-egu22-2887, 2022.

09:49–09:56
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EGU22-6386
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Virtual presentation
Christopher Williams

Vertically pointing radars (VPRs) provide detailed observations of precipitating cloud systems as they pass directly over the radar site. Two VPRs operating side-by-side and at different millimeter wavelengths (mm-wave) will observe the same raindrops but will have different return signals due to wavelength dependent raindrop backscattering and attenuation characteristics. These differences enable the retrieval of raindrop size distributions and vertical air motions. Yet, as the rain rate increases, the attenuation increases. Eventually, at some combination of path length [km] and rain specific attenuation [dB/km], the attenuation [dB] will extinguish high frequency VPR return signals; limiting high frequency VPRs to studying rain processes close to the ground. 

In order to estimate how far VPRs can measure into rain shafts, this study simulated constant rain rate precipitation columns and then estimated the path length needed to produced enough attenuation to drop the VPR signal-to-noise ratio below the VPR’s detection limit. This study used surface disdrometer observations and publically available T-Matrix scattering code to produce realistic VPR measurements at frequencies from 3 to 200 GHz.

These simulations found that in order to observe raindrops above a 3.5 km rain shaft, the constant rain rate needed to be less than 138, 67, 26, 14, and 4 mm/h for VPRs operating in the X-, Ku-, K-, Ka-, and W-bands, respectively (i.e., 9, 13.6, 24, 35.6, and 94 GHz). Additionally, due solely to atmospheric gas attenuation, the G-band (200 GHz) VPR return signal will be completely extinguished by 3.5 km. Preventing a G-band VPR from detecting raindrops above 3.5 km.

How to cite: Williams, C.: How far into a rain shaft can mm-wave vertically pointing radars detect raindrops?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6386, https://doi.org/10.5194/egusphere-egu22-6386, 2022.

09:56–10:00
Coffee break
Chairpersons: Auguste Gires, Remko Uijlenhoet
10:20–10:22
Precipitation measurement and nowcasting with weather radars
10:22–10:29
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EGU22-2840
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Virtual presentation
John R. Wallbank, Steven J. Cole, Robert J. Moore, David Dufton, Ryan R. Neely III, and Lindsay Bennett

Observing, in a quantitative and robust way, the dynamic space-time pattern of precipitation in mountainous terrain presents a major challenge of great practical importance. The difficulties of this task are further exacerbated in mid to high latitudes where the typical melting layer for precipitation (i.e. the 0°C isotherm) is often close to the surface during winter months. One way to address this challenge is by improving observations made using networks of weather radars. Quantitative Precipitation Estimates (QPEs) derived from these instruments have many applications, for example as input to a hydrological model to simulate river flow for flood forecasting purposes. 


Here, a set of QPEs - obtained from an observation campaign using the National Centre for Atmospheric Science’s mobile X-band dual-polarisation Doppler weather radar (NXPol) in a mountainous area of Northern Scotland - are assessed with reference to observed river flows. Each form of QPE is used as an input to Grid-to-Grid (G2G), a distributed hydrological model used for flood forecasting across Great Britain, and the simulated river flows compared to observations. The location of the radar was specially chosen to infill an area of reduced coverage in the existing C-band radar network for the British Isles.

Assessments of radar QPE often only examine a final precipitation “best estimate” product and typically with reference to raingauges at specific locations. Here, we exploit the processing capabilities of NXPol and the hydrological modelling framework to investigate the benefits of ten separate processing methods that increase with complexity and make differing use of dual-polarisation variables. The role of the radar beam elevation and distance from the radar is investigated, and NXPol QPEs are compared to that provided by the radar network. Additionally, a preliminary investigation is carried out into the role of the drop-size distribution on the relationship between radar-reflectivity and rain-rate using disdrometer data.

The hydrological assessment reported on here has the benefit of integrating the precipitation over space and time which serves to complement and extend a previous meteorological assessment using raingauge data alone. The assessment proves to be insensitive to issues affecting both raingauges (e.g. representativity, wind-induced under-catch) and local artefacts in the space-time radar-rainfall field. It facilitates a direct assessment of whether potential benefits in the new QPEs are carried forward to an end-use such as flood forecasting, providing fresh insights for the development of new dual-polarisation radar QPE methods.

How to cite: Wallbank, J. R., Cole, S. J., Moore, R. J., Dufton, D., Neely III, R. R., and Bennett, L.: Dual-polarisation X-band radar estimates of precipitation assessed using a distributed hydrological model for mountainous catchments in Scotland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2840, https://doi.org/10.5194/egusphere-egu22-2840, 2022.

10:29–10:36
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EGU22-3256
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ECS
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Virtual presentation
Tiantian Yu, Chandra V.Chandrasekar, Hui Xiao, Ling Yang, and Li Luo

The microphysical parameters of snowfall directly impact the hydrological and atmospheric models. Dual-frequency radar retrievals of particle size distribution (PSD) parameters are developed and evaluated over complex terrain during the International Collaborative Experiment held during the Pyeongchang 2018 Olympics and Paralympic winter games (ICE-POP 2018). The observations used to develop retrievals were included the NASA Dual-frequency Dualpolarized Doppler Radar (D3R) and a collection of second-generation Particle Size and Velocity (PARSIVEL2) disdrometer. Conventional look-up table method (LUT) and random forest method are applied to the disdrometer data to develop retrievals for volume-weighted mean diameter Dm, the shape factor mu, snowfall rate S, and ice water content IWC. Evaluations are performed between D3R radar and disdrometer observations using these two methods. The results show that the random forest method performs better in retrieving microphysical parameters because the mean errors of the retrievals relative to disdrometer observations are small compared with the LUT method.

How to cite: Yu, T., V.Chandrasekar, C., Xiao, H., Yang, L., and Luo, L.: Dual-frequency radar retrievals of snowfall using Random Forest, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3256, https://doi.org/10.5194/egusphere-egu22-3256, 2022.

10:36–10:43
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EGU22-4756
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ECS
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Virtual presentation
Bing-Xue Zhuang, Kao-Shen Chung, and Chih-Chien Tsai

The purpose of this study is to investigate the impact of assimilating dual-polarimetric parameters, i.e. differential reflectivity (ZDR) and specific differential phase (KDP), in addition to reflectivity (ZH) and radial wind (Vr) in a severe weather system. A squall line case forced by the synoptic southwesterly wind is selected to conduct the assimilation experiments. Besides, different microphysics parameterization schemes, including GCE, MOR, WSM6 and WDM6, are examined in the experiments. The results of the analysis field show that assimilating additional ZDR with single moment schemes (GCE and WSM6) can capture better mean raindrop size, yet it deteriorates the intensity of simulated ZH and KDP. Differ from GCE and WSM6, assimilating additional ZDR with double moment schemes (MOR and WDM6) would not lead to significant deterioration in the simulated ZH and KDP since the prognostic hydrometeor variables in double moment schemes include both mixing ratio and total number concentration. There will be more flexibility in adjusting microphysical states with two independent prognostic hydrometeor variables. The results of the short-term quantitative precipitation forecast (QPF) show that assimilating additional dual-polarimetric parameters with either single or double moment schemes increases the maximum of accumulated rainfall and the probability of heavy rainfall. In conclusion, double moment schemes can make better use of the extra information from dual-polarimetric parameters; furthermore, assimilating additional dual-polarimetric parameters, even with single moment schemes, can improve the performance of QPF, especially heavy rainfall events.

How to cite: Zhuang, B.-X., Chung, K.-S., and Tsai, C.-C.: Impact of Additional Assimilation of Dual-Polarimetric Parameters: Analysis and Forecasts in a Real Case, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4756, https://doi.org/10.5194/egusphere-egu22-4756, 2022.

10:43–10:50
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EGU22-5361
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ECS
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Presentation form not yet defined
Daniele Trappolini, Luca Scofano, Alessio Sampieri, Francesco Messina, Fabio Galasso, Saverio Di Fabio, and Frank Silvio Marzano

Forecasting weather systems are capable to model atmospheric phenomena at various space-time scales. At very short space-time nowcasting techniques are still relying on measured data processing from ground-based microwave radars and satellite-based geostationary spectrometers. In this respect, precipitation field nowcasting from a few minutes up to a few hours is one of the most challenging goals to provide rapid and accurate updated features for civil prevention and protection decision-makers (e.g., from emergency services, marine services, sport, and cultural events, air traffic control, emergency management, agricultural sector and moreover flood early-warning system). Deep learning precipitation nowcasting models, based on weather radar network reflectivity measurements, have recently exceeded the overall performance of traditional extrapolation models, becoming one of the hottest topics in this field. This work proposes a novel network architecture to increase the performance of deep learning mesoscale precipitation prediction. Since precipitation nowcasting can be viewed as a video prediction problem, we present an architecture based on Graph Convolutional Neural Network (GCNN) for video frame prediction. Our solution exploits, as a cornerstone, the topology of Space-Time-Separable Graph-Convolutional- Network (STS-GCN), originally used for posing forecasting. We have applied our model on the TAASRAD19 radar data set with the aim of comparing our performance with other models, namely the Stacked Generalization (SG) Trajectory Gated Recurrent Unit (TrajGRU) and S-PROG Spectral Lagrangian extrapolation program (S-PROG).

The proposed model, named STSU-GCN (Space-Time-Separable Unet3d Graph Convolutional Network), has a structure composed of an encoder, decoder, and forecaster. The role of the encoder and decoder are accomplished by a Unet3d a structure borrowed with the specific purpose of modifying the spatial component, but not the temporal component. In the bottleneck of this Unet3D network, we use a graph-based forecaster. The performance of the STSU-GCN has been quantified using conventional metrics, such as the Critical Success Index (CSI), widely used in the meteorological community for the nowcasting task. Using TAASRAD19 radar data set and literature data, these CSI metrics have been applied to 4 different classes of rain rate, that is 5, 10, 20, 30 mm/h. Our STSU-GCN model has overperformed both TrajGRU and S-PROG in the classes 10 mm/h and 20 mm/h obtaining a CSI respectively of 0.148 and 0.097. On the other hand, STSU-GCN is underperforming in class 5mm per hour getting a CSI respectively of 0.099. Our STSU-GCN model is aligned with the results of the S-PROG benchmark, for the class 30 mm/h confirming a model skillful for classes with a high rain rate. In this work, we will also illustrate the results of the proposed STSU-GCN algorithm using case studies in the area of interest of the Italian Central Apennines during the summer of 2021. Statistical performances, potential developments, and critical issues of the STSU-GCN algorithm will be also discussed.

How to cite: Trappolini, D., Scofano, L., Sampieri, A., Messina, F., Galasso, F., Di Fabio, S., and Marzano, F. S.: Mesoscale precipitation nowcasting from weather radar data using space-time-separable graph convolutional networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5361, https://doi.org/10.5194/egusphere-egu22-5361, 2022.

10:50–10:57
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EGU22-9096
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Virtual presentation
Guillaume Drouen, Daniel Schertzer, Auguste Gires, and Ioulia Tchiguirinskaia
Urban areas are at stake under the threat of climate change. To overcome this challenge it is necessary to deepen our understanding of heavier and particularly local rainfall to avoid flooding and build resilient cites that can become sustainable. The main difficulty is that geophysics and urban dynamics are strongly nonlinear with associated extreme variability over a wide range of space-time scales.

To better connect theoretical and experimental research on these topics, an advanced urban hydro-meteorological observatory with associated SaaS (Software as a Service) developments, the Fresnel platform of the Co-Innovation Lab of the École des Ponts ParisTech, has been purposely set-up. The mission of the Fresnel platform is to facilitate synergies between research and innovation in the pursuit of upstream research and the development of innovative downstream applications. With profiled access for specialized services, it provides the concerned communities with the necessary high resolution measurements in real time and in replay form, that easily yield Big Data.

The Fresnel platform unites several components. One of them, the RadX SaaS platform, provides online tools to study rainfall data over the greater Paris area (i.e., about 50 km radius and more). It provides an easy access to various products based on precipitation measurements performed by the ENPC polarimetric X-band radar at the pixel scale of 125 m. It broadcasts these measurements in free access and in real-time (https://radx.enpc.fr) together with a point measured environmental parameters provided by another component of Fresnel, namely the exTreme and multi-scAle RAiNdrop parIS observatory (Taranis) observatory, containing several, a 3D sonic anemometer and a meteorological station.

The RadX platform was developed in participatory co-creation, and in scientific collaboration with the world industrial leader in water management. As the need for data accessibility, fast and reliable infrastructure were major challenges, the platform was constructed as a cloud-based solution. The components that make up this platform are designed to be configurable for specific case studies using an adjustable visual interface. Depending on a case study, specific components can be integrated to meet particular needs using maps, other visual tools and forecasting systems, eventually 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.: The Fresnel Platform for increasing the Greater Paris resilience to spatio-temporal variability of local rainfall, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9096, https://doi.org/10.5194/egusphere-egu22-9096, 2022.

Product comparison and hydrological applications
10:57–11:04
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EGU22-961
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ECS
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On-site presentation
wiam salih, Abdelghani chehbouni, and Terence Epule Epule

The Tensift basin in Morocco is prominent for its ecological and hydrological diversity. This diversity is marked by rivers flowing into areas such as Ourika. In addition to agriculture, the basin is a hub of variable land use systems. It is important to have a better understanding of the relationship between simulated and observed precipitation measurements in this region to be able to better understand the role of precipitation in the variability of the climate and water resources in the basin. This study aims at evaluating the performance of multi-source satellite products against weather stations precipitation in the basin. In this work, the satellite product based data were first culled for seven satellite products namely PERSIANN, PERSIANN CDR, TRMM3B42, ARC2, RFE2, CHIRPS, and ERA5 (simulated precipitation) from, CHRS iRain, RainSphere, NASA, EUMETSAT, NOAA, FEWS NET, ECMWF respectively. Precipitation observations data from six weather stations, located at Tachedert (2343 m), Imskerbour (1404 m), Asni (1170 m), Grawa (550 m), Agdal (489 m), and Agafay (487 m) at different altitudes, latitudes and temporal scales (1D, 1M, 1Y), over the period 13/05/2007 and 31/09/2019, at Tensift basin were used. The data were compared and analyzed through inferential statistics such as Nash-Sutcliffe Efficiency Coefficient, Bias, Root Mean Square Error (RMSE), Root Mean Square Deviation (RMSD), the standard deviation, the Correlation Coefficient (R) and the Coefficient of Determination (R²) and visualized through taylor diagrams and scatter plots to have a visual idea of the closeness between the seven satellite products and the observed precipitation data. A second analysis was carried out on the monthly precipitation resulting from the six weather stations based on standardized precipitation index (SPI) in order to  determine the onset, duration, and magnitude of the meteorological drought. The results show that PERSIANN CDR performs best and is more reliable with regrad to its ability to estimate precipitation rates over a wide spatial and temporal scale over the basin. The precipitation of Persiann CDR  has significant rates for the different statistics (Bias: -0.05 (Daily asni), RMSE: 2.86 (Daily Agdal), R: 0.83, R²:0.687 (Monthly Agdal)). However, most of the time, this product records low or negative Nash values (-6.06 (Annual Grawa)), due to the insufficient weather station data in the study area (Tensift). It  was observed that TRMM overestimates precipitation during heavy precipitation and underestimates during low precipitation. This makes it important for the latter observations to be viewed with caution due to the quality of annual comparison results and underscores the need to develop more efficient precipitation comparison approaches. Also, the performance of the satellite products is better at low altitudes and during wet years. Finally, it was concluded from the SPI that Tensift Region has experienced 13 drought periods over the study period, with the longest event of 12 months was from Marsh 2015 to February 2016 and  the most intense event with the highest drought severity (19.6) and the lowest SPI value (-2.66) was in 2019.

How to cite: salih, W., chehbouni, A., and Epule, T. E.: Evaluation of the Performance of Multi-Source Satellite Products in Simulating Precipitation over the Tensift Basin in Morocco, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-961, https://doi.org/10.5194/egusphere-egu22-961, 2022.

11:04–11:11
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EGU22-970
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ECS
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Virtual presentation
Hadir Abdelmoneim and Hisham Eldardiry

Data availability and accuracy is predominantly an issue for building hydrological applications, particularly in data-scare regions, like Africa. This is further one of the challenges that hinders understanding the climate variability and its subsequent extreme flood and drought events. Forcing data from different sources, e.g., satellite sensors, in-situ observations, or reanalysis products, are required to derive hydrological models. Reanalysis products have recently become an alternative tool of meteorological data given their long record at various temporal and spatial scales. The overarching goal of this study is to evaluate the primary forcing data for hydrological models; precipitation, as produced by six different reanalysis data (JRA55, 20CRv3, ERA5, ERA-20C, MERRA, NCEP/NCAR). We here focused our evaluation on the major river basins in Africa during a 15-year period spanning from 2001 to 2015. The five major river basins include the Nile River, Congo River, Zambezi River, Orange River, and Niger River basins. Our evaluation method is summarized as follows: Firstly, precipitation data is compared with the gridded gauged data, e.g., CHIRPS for precipitation. Secondly, statistical indices, including categorical and continuous statistical metrics, will be used to assess the accuracy of reanalysis products over each of the major basins. Finally, we present the intercomparison of reanalysis products for extreme events including floods and droughts. The results from our evaluation will pinpoint the skill of reanalysis products and thus benefit the future development of hydrological modeling over the river basins in Africa.

How to cite: Abdelmoneim, H. and Eldardiry, H.: Intercomparison of reanalysis products during extreme flood and drought events: evaluation over the major river basins of Africa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-970, https://doi.org/10.5194/egusphere-egu22-970, 2022.

11:11–11:18
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EGU22-8119
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ECS
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Virtual presentation
Jessica Sienel, Lennart Schönfelder, and Jochen Seidel

Gathering accurate precipitation data is an important task for setting up hydrological models. In Norway, the gauge network density is higher in the southern parts and decreases in the north. Furthermore, the amount of high evaluated precipitation gauges is rather scarce. Radar data is available but lacks an accurate reflectivity-precipitation relation and errors in precipitation estimation are caused for example by beam blockage.

For modelling purposes, this study aims to evaluate whether the application of radar derived data gives any benefit, especially when modelling in a higher temporal resolution. The results of this study can give decision support for modellers having difficulties choosing the precipitation product. For that cause, spatial interpolated precipitation products were evaluated and compared in terms of performance in hydrological models. The Meteorological Institute Norway publishes gridded hourly datasets covering the Norwegian mainland: seNorge2, where gauge data is interpolated using an optimal interpolation, and the numerical weather prediction product (NWP), a combination of gauge data, radar data and a numerical weather model. Five different catchments were simulated in the numerical precipitation-runoff model HYPE with both datasets for comparison. The catchments vary in area, hydrological regime and availability of nearby gauges. The simulation was done in an hourly time step in order to compare precipitation variability on a small time scale.

In this study, a calibration method was developed that generates comparable and stable performance results in terms of the Kling–Gupta efficiency (KGE) for each catchment and dataset. The resulting discharges and water balances of the catchments were analysed and compared. Additionally, selected precipitation events, where the precipitation products were not able to describe atmospheric processes appropriately, were analysed. The datasets were further compared by spatially accumulating annual precipitation sums over the catchments, by using a private weather station to evaluate the fit of the data and by comparing the runoff and precipitation volume of the basins.

Preliminary results show the significant differences in water volume and spatial distribution of precipitation between these products. Furthermore, when comparing a private gauge with the precipitation products at an ungauged area, daily precipitation data tends to be more accurate than hourly data.

How to cite: Sienel, J., Schönfelder, L., and Seidel, J.: Using radar-derived precipitation data for hydrological modelling in selected study sides in Norway, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8119, https://doi.org/10.5194/egusphere-egu22-8119, 2022.

11:18–11:25
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EGU22-2494
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ECS
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Virtual presentation
Shahin Khosh Bin Ghomash, Daniel Bachmann, Daniel Caviedes-Voullième, and Christoph Hinz

Rainfall is a complex, spatial and temporally variated process and one of the core inputs for hydrological and hydrodynamic modelling. Most rainfalls are known to be moving storms with varying directions and velocities. Storm movement is known to be an important influence on runoff generation, both affecting peak discharge and the shape of hydrographs. Therefore, exploring the extent rainfall dynamics affect runoff generation and consequently flooded areas, can be an asset in effective flood risk management.

In this work, we study how storm movement (e.g. characterized by velocity and direction) can affect surface flow generation, water levels and flooded areas within a catchment. Moreover, the influence of rainfall temporal variability in correlation with storm movement is taken into account. This is achieved by means of numerical-based, spatially explicit surface flow simulations using the tool ProMaIDes (2021), a free software for risk-based evaluation of flood risk mitigation measures. The storm events are generated using a microcanonical random cascade model and further on trajected across the catchment area.

The study area is the Kan river catchment located in the province of Tehran (Iran) with a total area of 836 km², which has experienced multiple flooding events in recent years. Due to its semi-arid climate, steep topography with narrow valleys, this area has high potential for flash flood occurrence as a result of high intensity precipitation.

The results of this study show a range of possible magnitudes of influence of rainfall movement on the catchment´s runoff response. The resulting flood maps highlight the importance of rainfall velocity and most importantly the direction of the movement in the estimation of flood events as well as their likelihood in catchment area. Moreover, its shown that the magnitude of influence of storm velocity and direction on discharge  strongly depends on the location within the river network which it is measured.

ProMaIDes (2021): Protection Measures against Inundation Decision support. https://promaides.h2.de

How to cite: Khosh Bin Ghomash, S., Bachmann, D., Caviedes-Voullième, D., and Hinz, C.: Storm movement effects on the flash flood response of the Kan catchment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2494, https://doi.org/10.5194/egusphere-egu22-2494, 2022.

11:25–11:32
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EGU22-526
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ECS
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Virtual presentation
Matteo Verdone, Marco Borga, Andrea Dani, Federico Preti, Paolo Trucchi, Giulia Zuecco, Ilja van Meerveld, Christian Massari, and Daniele Penna

Understanding the role of forest on rainfall interception is fundamental for a correct analysis and modelling of runoff generation and catchment hydrological response. Despite many studies were carried out at the stand and hillslope scale, very little is known about the role of hillslope topography and the associated tree population characteristics on throughfall spatio-temporal variability. Therefore, this work aimed at better understanding the dominant factors on throughfall variability and on the temporal persistence of throughfall spatial patterns along a transect on a steep hillslope characterized by trees with different size and density.

The experimental activities were carried out in the upper part of the densely-forested Re della Pietra catchment, Tuscany Apennines, Central Italy. The hillslope is roughly 110 m long and 60 m wide, has a mean slope of 30°, and is dominantly covered by beech trees and by sparce individuals of oak trees. A grid of 126 throughfall collectors was installed in July 2020 and divided in three sub-plots: two plots of 144 m2 with 2-m spaced 49 collectors at the bottom and the top of the hillslope, and a transect of 28 1-m spaced collectors from the bottom to the top of the hillslope. A survey was conducted to measure the diameter and basal area of the stand. Throughfall was manually measured from the collectors approximately monthly from June 2020 to November 2021, and compared with gross precipitation measured by a rain gauge placed outside the vegetation cover. Moreover, five automatic gauges connected to 1.5 m-long gutter to increase the collection area were installed in November 2021 along the hillslope to measure throughfall at high temporal resolution.

Preliminary results from 25 manual measurements over the experimental grid highlighted a large temporal variability of interception (mean: 17%, standard deviation: ±31%), reflecting the variable seasonal precipitation pattern of Mediterranean areas and the phenological stage of trees (leaves/no leaves). Overall, the spatial variability in throughfall increased with increasing gross precipitation. Particularly, the bottom plot, characterized by lower tree density and larger tree size compared to the top plot, showed a lower spatial variability with respect to the top plot, while the longitudinal transect exhibited an intermediate variability. Analogously, the temporal stability analysis revealed that the most temporally-stable and representative measurement points laid on the transect that, overall, captured the different tree characteristics along the hillslope.

Future work will make use of the high-resolution measurements of the five gauges to assess their representativeness compared to the manual grid and to test and validate an interception model at the hillslope scale to be possibly upscaled to the entire catchment.

How to cite: Verdone, M., Borga, M., Dani, A., Preti, F., Trucchi, P., Zuecco, G., van Meerveld, I., Massari, C., and Penna, D.: Throughfall variability at the hillslope scale: the role of topography and tree characteristics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-526, https://doi.org/10.5194/egusphere-egu22-526, 2022.

11:32–11:39
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EGU22-4319
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Virtual presentation
Ileana Mares, Constantin Mares, Venera Dobrica, and Crisan Demetrescu

The aim of the study is to reduce the uncertainty of the influence of Palmer-type drought indices in estimating seasonal discharge in the lower Danube basin. For this, four indices were considered: Palmer Drought Severity Index (PDSI), Palmer Hydrological Drought Index (PHDI), Weighted PDSI (WPLM) and Palmer Z-index (ZIND). These indices were quantified by PC1 of EOF decomposition, obtained from 15 stations located along the Danube basin.

The influences of these indices on the Danube discharge were tested, both simultaneously and with certain lags, by linear and nonlinear methods applying the elements of information theory. Nonstationarity was tested by wavelet analysis. The results differ depending on the season and the Palmer index.

The linear connections are generally obtained for synchronous links, and the nonlinear and nonstationary ones for the predictors considered with certain lags (in advance) compared to the discharge predictand. This result is useful for estimating the discharge, as Palmer indices can be estimated from the simulated data by the General Circulation Models or Regional Climate Models.

 

How to cite: Mares, I., Mares, C., Dobrica, V., and Demetrescu, C.: Testing nonlinearity and nonstationarity of the connection between Palmer drought indices and Danube discharge in the lower basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4319, https://doi.org/10.5194/egusphere-egu22-4319, 2022.

11:39–11:46
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EGU22-8106
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ECS
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Virtual presentation
Bushra Amin and András Bárdossy

This research addresses a strong need to precisely improve the statewide seasonal precipitation intensity duration frequency (IDF) estimates at ungauged locations. In order to obtain IDFs at unvisited sites, IDFs observation locations are interpolated. Therefore, different deterministic and geostatistical approaches include Inverse Distance Weighted (IDW), Ordinary kriging (OK), Regression Kriging (RK), Co-Kriging (CoK), Kriging with External Drift (KED), and Functional Kriging (FK) have been taken into account for comparison. Apart from visual assessment, a cross-validation approach is used to compare these methods to judge their prediction accuracy.

Annual or intra-annual IDF calculations across the state is not well correlated with other variables except elevation, thus directionally smoothed altitude is only considered as a covariate that offered a significant reduction in bias.  All results indicate that IDW interpolation is incapable of improving the regional point IDF approximations provided by kriging algorithms except in the case of annual IDF predictions at shorter scales where its performance is more or less similar to OK.  Whereas summer IDF observations are well predicted by KED that also exhibits good behavior for longer duration extremes of all seasons. Moreover, the shorter duration winter IDF guesstimates are best achieved with CoK. From now, it can be noticed that the accuracy of the interpolator changes according to the hydrological seasons and storm durations.

Overall, this study ensures to design of a well-planned map in advance for the entire state of Baden Wurttemberg on the basis of accurate forecasting of seasonal IDF estimates of precipitation extremes at unsampled sites. Hence, this crucial step will surely help us to tackle the natural disasters due to climate change before time.

How to cite: Amin, B. and Bárdossy, A.: Evaluation of various regionalization techniques for the seasonal precipitation IDF estimates of Baden Württemberg, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8106, https://doi.org/10.5194/egusphere-egu22-8106, 2022.

11:46–11:50