HS7.6

Disasters associated with heavy rainfall such as urban floods, riverine floods, and landslides often simultaneously occur while each of them sensitively reacts to rainfall variabilities at distinct ranges of time scales. Therefore, a stochastic rainfall model suitable for modeling compounding of disasters must be good at reproducing the rainfall variability across all timescales relevant to all types of disasters. This study proposes a point stochastic rainfall generator that can reproduce various rainfall characteristics at timescales between 5 minutes and one decade. The model generates the fine-scale rainfall time series using a randomized Bartlett-Lewis Rectangular Pulse (RBLRP) model. Then the rainstorms are shuffled such that the correlation structure between the consecutive storms is preserved. Finally, the time series is rearranged again at the monthly timescale based on the result of the separate coarse-scale monthly rainfall model. The method was tested using the 69 years of 5-minute rainfall data recorded at Bochum, Germany. The mean, variance, covariance, skewness, and rainfall intermittency were well reproduced at the timescales from 5 minutes to a decade without any systematic bias. The extreme values were also well reproduced at timescales from 5 minutes to 3 days. The past-7-day rainfall before an extreme rainfall event, which is highly associated with the extreme riverine flow and landslide was reproduced well too. Then, the model was extended to integrate the influence of climate change. For this, the model was re-parameterised in terms of parameters representing average magnitude and temporal structure of the rainfall time series. Then, the relationship between these new parameters and the covariates (e.g. monthly, weekly, daily temperature) were investigated. Lastly, the derived regression relationships were applied to adjust the duration and the magnitude of rain storms and cells that were generated by the stationary RBLRP model.

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2021R1A2C2003471).

How to cite: Kim, D., Onof, C., Park, J., and Wang, L.: A Stochastic Rainfall Generator Suitable for Modeling Future Compound Disasters Associated with Heavy Rainfall, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2595, https://doi.org/10.5194/egusphere-egu22-2595, 2022.

The representation of extreme precipitation at small spatio-temporal scales is of major importance in urban hydrology. The present study compares observations from tipping bucket gauges and a C-band radar to two sets of re-analysis climate model output data for a historic period of 14 years where there is full spatial and temporal overlap between datasets. The reanalysis data are based on models with different parametrizations and spatio-temporal resolutions, one being a “convective-permitting” model while the other uses a convective parametrization scheme to account for convective rainfall. The study focuses on an area of approximately 100 by 150 km.

The datasets are compared with respect to seasonality of occurrence, intensity levels and spatial structure of the extreme events. All datasets have similar seasonal distributions, and comparable intensity levels. There are, however, clear differences in the spatial correlation structure of the extremes. Seemingly, the radar data is best representation of a “real” spatial structure for extreme precipitation, even though challenges appear in data when moving far from the physical radar. The spatial correlation in point observations is a valid representation of the spatial structure of extreme precipitation. The convective-permitting climate model seem to represent the spatial structure of extreme precipitation much more realistically, compared to the coarser convective parameterized model. However, improvement could be made for the shortest durations and smallest spatial scales.

How to cite: Arnbjerg-Nielsen, K., Thomassen, E. D., Thorndahl, S. L., Andersen, C. B., Gregersen, I. B., and Sørup, H. J. D.: Comparing extreme precipitation between data from rain gauges, weather radar and high-resolution climate models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2624, https://doi.org/10.5194/egusphere-egu22-2624, 2022.

The Computational Hydrometeorology Lab in National Taiwan University (NTU CompHydroMet Lab) recently launched a rainfall monitoring network, with a special focus on observing extreme storm events, such as typhoons and thunderstorms, over the south area of Taipei. Due to the topographic effect and the constant humidity brought by the sea breeze, together with the high temperature, south Taipei is a hotspot for the occurrence of thunderstorms in summer. The monitoring network constitutes a collocated pair of an OTT Pluvio S and an OTT Pluvio L weighing rain gauges, as well as two ‘unconventional’ rain sensors – an OTT Parsivel2 disdrometer and a Lufft WS100 radar precipitation sensor. These rain sensors are co-located within a 10 x 10 m2 area, providing rainfall estimates at high temporal resolutions, ranging from 10 seconds to 1 minute.

Since the launch of the monitoring network in March 2021, the monitoring network has collected rainfall data for two typhoons and a number of thunderstorms, with the highest peak intensity at 245.6 mm/h. The measurements are generally consistent between four sensors; in particular, those from two weighing gauges are of the highest consistency. In addition, a  preliminary comparison shows that the high-intensity rainfall measured by weighing gauges and disdrometer are in high agreement. This suggests that weighing gauges –which were widely used as a verification gauge for the tipping bucket gauges in the operational context–  can provide reliable rainfall measurements with high accuracy, including capturing extreme rainfall. 
 
As compared to other sensors, WS100 tends to underestimate rainfall at high intensities. However, it is more sensitive to low-intensity rainfall than others; and, similarly to the disdrometer, it provides reflectivity data and requires less maintenance. The cause of underestimation is currently under investigation, which could potentially be improved through the calibration of the current algorithm with weighing gauges’ measurements. 

At the next stage of the work, these ground measurements will be compared with the coincidental three-dimensional radar data product from the Central Weather Bureau (CWB), Taiwan. The radar data product from CWB is available at approximately 1.2 km spatial resolution and 10-min intervals. The comparison result will be presented, and the potential of using the monitoring network to support the correction of radar data will be discussed.

How to cite: Chou, C.-C. and Wang, L.-P.: Observing Extreme Rainfall Events at Fine Timescales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11015, https://doi.org/10.5194/egusphere-egu22-11015, 2022.

The distribution functions for large rain events X_c in current climate is denoted F(x) = P{X_c ≤ x} and for large rain events X_f in a future climate G(x) = P{X_f ≤ x}. A climate factor k is introduced, and it is assumed that P{X_c ≤ x} = P{X_f ≤ k x} corresponding to G(k x) = F(x). If we further assume that the distribution functions F and G have exponential tails, the following simple transformation of the return period in current climate T_c to the corresponding return period in future climate T_f can be deduced

T_f = T_c^1/k

Applying a first order analysis on this equation with k as independent variable leads to a relation between the uncertainties of k and T_f. In terms of the coefficient of variations we get

CV{T_f} ≈ 1/ lnT_c CV{k}

This equation reveals that even with moderate uncertainty in k, the uncertainty in T_f is notably increased.

How to cite: Rosbjerg, D.: Return periods in current and future climate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1730, https://doi.org/10.5194/egusphere-egu22-1730, 2022.

In response to recent major flood events in Ireland, the authorities have prioritised the development of a national flood forecasting model for use as a tool in flood risk management. Accurate flood predictions by this model require high resolution spatiotemporal rainfall data. One source for this type of data is the remote sensing estimated precipitation provided by the Global Precipitation Measurement (GPM) satellite. The GPM has ability to detect and estimate all forms of precipitation using a range of advanced instruments, including Microwave and Radar technologies. This study evaluates the accuracy of detecting the large rainfall events which occurred in Ireland during the period 2014-2021 by three Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation products (i) early run ; (ii) late run; and (iii) final run. The satellite estimates of these events have been assessed using five statistical indices applied to various temporal scales; hourly, daily, and monthly. The results showed that, for satellite detection, all of the three IMERG products had an acceptable detection accuracy of the large rainfall events. In particular, the calibrated product – final run product – outperformed the other near-real-time products in terms of estimation error and bias. Overall, the results indicate that IMERG satellite precipitation products can be used with confidence to detect large events over high latitude areas such as Ireland. Besides, they have a high potential for coupling with in-situ data to improve the accuracy of the integrated flood forecasting model.

How to cite: Mohammed, S., Nasr, A., and Mahmoud, M.: Evaluation of the spatiotemporal representation of the GPM satellite precipitation products over diverse climatic regions in Ireland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8942, https://doi.org/10.5194/egusphere-egu22-8942, 2022.

Since Hinton et al. introduced Deep Learning (DL) in 2006 [1], DL methods have led to breakthroughs in various scientific fields, such as speech recognition, medical, materials, and many more. Various early attempts to apply DL to short-term rainfall forecasting (nowcasting) were also reported. However, these early models did not lead to significant improvements as compared to non-AI nowcasting models such as STEPS [2]. It was TrajGRU model [3] which first demonstrated the potential gains that may be achieved with DL-based nowcasting models. Since then, a variety of DL models have been proposed or applied to tackle rainfall nowcasting, with the most iconic ones including U-Net, MetNet and DGMR [4-6]. Similarly to the Trajectory GRU (TrajGRU) model, the U-Net and MetNet models show clear improvements in predicting the occurrence of rainfall at high spatial and temporal resolutions and with a longer lead time, as compared to non-AI models. However, the predicted rainfall images from these three models (and their variants) become overly smooth rather quickly (at lead times of 15-20 minutes); this is a common ‘feature’ of many other DL models [7]. This means that significant amount of spatial rainfall details is lost, which is undesirable for certain hydrological applications, such urban flow and flood forecasting where small-scale rainfall variability -in particular localised peaks- may have tangible impacts [8]. In 2016, DeepMind [6] proposed a new type of DL-based nowcasting model called the Deep Generative Model of Radar (DGMR), which is based upon a Generative Adversarial Network (GAN) framework. The DGMR successfully improves the aforementioned smoothing drawback of other DL-models by incorporating noise into the rainfall forecast generator such that small-scale rainfall details can be preserved and, consequently, localised peak intensities can be better predicted. DGMR thus shows great potential for hydrological applications.

In spite of the success, the model structure of DGMR is complex and hard to digest by someone without proper training in DL. Therefore, even though the model structure has been published, it remains a mystery for most hydrologists, thus hindering its application.

In this work, we explore the success of DGMR with an in-depth analysis of its model structure. More specifically, through the process of re-constructing the DGMR model, we have developed a short tutorial on the different model components, in plain language and with example images and intermediate analyses. This will enable better understanding of the features and behaviour of the DGMR model and of the implications for hydrological applications. Additionally, a better understanding of the DGMR model components may instigate further improvements.

References:

[1] Hinton, G.E., et al., Neural Comput., 18 (7), 1527-1554, 2006.

[2] Bowler, N.E., et al., Q. J. Roy. Meteor. Soc., 132(620), 2127-2155, 2006.

[3] Shi, X., arXiv preprint arXiv:1706.03458, 2017.

[4] Agrawal, S., et al., arXiv preprint arXiv:1912.12132, 2019.

[5] Sønderby, C.K., et al., arXiv preprint arXiv:2003.12140, 2020.

[6] Ravuri, S., et al., Nature, 597, 672-677, 2021.

[7] Ayzel, G., Geosci. Model Dev., 13(6), 2631-2644, 2020.

[8] Ochoa-Rodriguez, S., et al., J. Hydrol., 531, 389-407, 2015.

How to cite: Heh, Y.-T. and Wang, L.-P.: Unraveling the mystery of DeepMind’s rainfall nowcasting: a step-by-step tutorial for hydrologists, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9054, https://doi.org/10.5194/egusphere-egu22-9054, 2022.

Accurate rainfall estimates in urban areas are vital for water management, pollution transport, and flood forecasts. To cover the high spatial (and temporal) variability of rainfall, uniformly distributed observation networks are required.

In many urban areas dedicated rainfall observations are limited because of low available budgets or unsuitable technology. Therefore, this study compared and assessed the accuracy of three “non-traditional” rainfall datasets in the Odaw (Accra, Ghana) river basin to help future modellers to decide which dataset is the best fit, for instance to predict floods. The Odaw river basin is one of the main drainage systems in Accra with a total catchment area of about 270 km². Over the past three decades, the Odaw basin has been challenged with floods, but due to the lack of a good representation of rainfall measurements, the ability to accurately simulate or forecast floods in the basin is limited.

Two rainfall datasets are derived from satellite observations and one from crowdsourced rain gauges. The three estimates were all available for the study period (2020) and were analysed and compared at a thirty-minute time-interval. The first space-based product is the most recent version (V06B) of IMERG, the gridded multisatellite precipitation product of the Global Precipitation Measurement (GPM) mission; the second space-based product is the MSG-SEVIRI infrared satellite imagery, an innovative rainfall dataset based on geostationary data available day and night. The ground-based data was retrieved from ten TAHMO rain gauges. Because the satellite products consists of pixels while the TAHMO observations are point measurements, the stations were assigned to the pixels of the satellite products.

Results show that all three rainfall datasets revealed a systematic spatial variation, with on average more rainfall observed upstream than downstream. Although all datasets reproduced a similar annual accumulation, the rainfall intensity observed by the TAHMO stations (point measurements) were much higher, sometimes even more than twice as high. Days with high rainfall amounts (when the daily average TAHMO rain rate exceeded 15 mm/hr) were used as case studies, as these days were hypothesised to be related to flooding. During these days space-borne radar overpasses were used to get some impression about the spatial characteristics of the rainfall events. With this presentation we aim to demonstrate the applicability of freely available data to estimate rainfall at various temporal and spatial scales in (formerly) ungauged urbanized river basins.

How to cite: Bogerd, L., Pinto, R. B., van Emmerik, T., and Uijlenhoet, R.: Gauging the ungauged: Estimating rainfall in urbanized river basins using ground-based and spaceborne sensors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11572, https://doi.org/10.5194/egusphere-egu22-11572, 2022.

In this study, we investigated the chemical characteristics of rainwater and evaluated the correlation among rainwater quality factors for seven precipitation events from June 2020 to August 2020. Rainwater samples (n = 84) were collected every 50 mL at Pukyong National University, Busan, South Korea. Values of pH and electrical conductivity (EC) were measured in the field, and concentrations of water-soluble cations (Na⁺, Mg²⁺, K⁺, Ca²⁺, and NH₄⁺) and anions (Cl^-, NO₃^-, and SO₄^2-) were determined using ion chromatography. For all rainwater samples, the pH ranged from 3.63 to 5.59, with mean pH = 4.78, and the measured mean EC was 30.54 µS/cm, indicating that the precipitation was acidified in Busan, South Korea. A strong negative correlation (r = -0.83) was found between the pH and EC values. The major ionic components of rainwater were SO₄^2- > NH₄⁺ > NO₃^-, which are predominantly attributed to anthropogenic forces in the study area, such as emissions from vessels and fossil fuels. Anion concentrations of rainwater samples were SO₄^2- (average concentration: 2.15 mg/L) > NO₃^- (1.43 mg/L) > Cl^- (1.04 mg/L) and showed a strong positive correlation with EC values (r = 0.81) and a negative correlation with pH values (r = -0.72) of rainwater samples. The average concentrations of cations (NH₄⁺ (1.56 mg/L) > Ca²⁺ (1.31 mg/L) > Na⁺ (0.63 mg/L) > K⁺ (0.57 mg/L) > Mg²⁺(0.29 mg/L)) were relatively lower than those of anions. Cation concentrations showed no significant correlation with the values of EC (r = 0.29) and pH (r = -0.21). The result of this study indicates that acidic precipitation occurs even in summer with relatively low concentrations of air pollution and strong rainfall intensity.

How to cite: Park, H., Kim, T., and Yang, M.: Chemical characteristics of summer rainwater at an urban site in South Korea, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1587, https://doi.org/10.5194/egusphere-egu22-1587, 2022.

Urban drainage is subject to a variety of external influencing factors that can have a negative impact on hydraulic system performance. These include changing precipitation characteristics due to climate change [1], an increase in sealed surfaces due to advancing urbanisation [2], but also further failures and malfunctions [3] in the technical grey and green infrastructure. With an increasing share of decentralised urban stormwater measures and uncertainties regarding responsibility, care and maintenance of these facilities, an increase in malfunctions can be assumed [4]. In this work, we are investigating common malfunctions in urban drainage systems with 1D/2D urban flood model in a virtual urban study site. The goal is to highlight differences between the failures and malfunctions in both grey and blue-green infrastructures for different design rainfall events (Type Euler II) and compare them to other possible scenarios like climate change and urbanization.

For the research, a model of a small virtual urban study site (1.5ha) is developed with the commercial software PCSWMM2D [5], which represents a small part of an urban catchment. It includes the following sub-structures and assets: i) combined sewer system, ii) urban stream, iii) urban structures including buildings, marketplaces, streets, bridges, pathways, and underbridges and iv) four sustainable urban drainage system (SUDS) structures (green roofs, permeable pavements, swales and bioretention cell). Connected to the combined sewer system are three border areas (30ha, 10ha, 10ha), representing inflows from outside. The urban drainage infrastructures and SUDS were designed based on a design rainfall (Euler Typ II) event with a 5-year return period and 1-hour event duration.

In total 12 different scenarios were designed for the virtual urban study site i) the SUDS-base-scenario which includes four different green infrastructure assets, ii) three reference scenarios with climate change, urbanisation and wet preconditions and iii) the malfunction-scenarios with seven single malfunction scenarios and one worst case which is all of the single scenarios combined. Each scenario was run with design rainfall events with an Euler II distribution and interval lengths between 15 minutes and 24 hours as well as return periods between 1 and 100 years.

To compare the different scenarios and assess their severity for the urban area we used 3 different objective values. i) The maximum water depth in the vulnerable infrastructure (underbridge), ii) the flooded area with water depths > 10cm and iii) the total combined sewer overflow emissions released into the urban stream.

Results show a clear difference between the different malfunction scenarios, with a higher influence of malfunctions in grey than in green infrastructures. In most cases, the reference scenarios climate change, urbanisation and wet preconditions show higher values than the malfunctions scenario. All scenarios are highly dependent on the rainfall event characteristics, with no differences in the objective values compared to the base case for low return periods and rising differences for medium to high return periods.

How to cite: Funke, F., Reinstaller, S., and Kleidorfer, M.: Impact of malfunctions on urban drainage for different design rainfall events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6652, https://doi.org/10.5194/egusphere-egu22-6652, 2022.

In the UK, decision-makers use hydraulic model outputs to inform funding, connection consent, adoption of new
drainage networks and planning application decisions. Current practice requires the application of design storms to
calculate sewer catchment performance metrics such as flood volume, discharge rate and flood count. With flooding
incidents occurring more frequently than their designs specify, hydraulic modelling outputs required by practice are
questionable. The main focus of this paper is the peakedness factor (ratio of maximum to average rainfall intensity)
of design storms, adjudging that this is a key contributor to model bias. Hydraulic models of two UK sewer
catchments were simulated under historical storms, design storms and design storms with modified peakedness to
test bias in modelling outputs and the effectiveness of peakedness modification in reducing bias. Sustainable
drainage systems (SuDS) were implemented at catchment scale and the betterment achieved in the modelling outputs
was tested. The proposed design storm modification reduced the bias that occurs when driving hydraulic models
using design storms in comparison with historical storms. It is concluded that SuDS benefits are underestimated when
using design rainfall because the synthetic rainfall shape prevents infiltration. Thus, SuDS interventions cannot
accurately be evaluated by design storms, modified or otherwise.

How to cite: Muhandes, S., Dobson, B., and Mijic, A.: A method for adjusting design stormpeakedness to reduce biasin hydraulic simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5356, https://doi.org/10.5194/egusphere-egu22-5356, 2022.

Weather radars have become a valuable tool for urban hydrological studies because they capture the rainfall intensities at a high spatial and temporal resolution. However, radar products are affected by objects or phenomena not of meteorological interest, making it necessary to apply various algorithms to correct and improve their rainfall estimation. In addition, multiple methods for merging rain-gauge and radar data are presented in the literature, which combines the advantages of high spatial resolution of radar products with the measurement accuracy of rain gauge stations. While merging methods are commonly validated on rain-gauge measurements, little has been discussed in the literature about the influence of such techniques on urban hydrological models. Therefore, this study investigates the use and selection of gauge-radar merging methods as input for urban hydrological modeling.

This work studies the influence of different precipitation products (rain-gauge stations, radar, and radar-gauge merged products) on flow rates simulated with a hydrodynamic model in two cities: Hildesheim and Osnabrück, Germany. Sewer pipe measurements at least every 2 minutes for several discharge events within 2020-2021 are available and used to evaluate different rainfall products. The techniques to be assessed are temporal and spatial smoothing and radar merging methods such as external drift kriging, quantile mapping, and conditional merging. This study will allow identifying if, in general, there is a single product that presents the best results for urban flow simulations or if, on the contrary, it depends on the type of rainfall event. Additionally, since the study areas are located at different distances from the Hannover radar station, it will be possible to analyze the influence of the attenuation correction on the improvement of the radar product.

How to cite: Echeverri-Salazar, E., Shehu, B., Verworn, A., and Wallner, M.: Effect of spatially distributed radar-gauge rainfall products on simulated urban flows, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10291, https://doi.org/10.5194/egusphere-egu22-10291, 2022.

Rainfall data with high spatiotemporal resolutions are of great value in many research fields, such as meteorology, hydrology, global warming, and urban disaster monitoring. Current rainfall observation systems include ground-based rain gauges, remote sensing-based radar and satellites. However, there is an increasing demand of the spatiotemporal rainfall data with high resolution. Thanks to the advocacy from many research institutions and international organizations, several innovative crowdsourcing ideas including opportunistic sensing and citizen science initiatives have been followed in recent years. Commercial cellular communication networks, windshield wipers or optical sensors in moving vehicles, smart phones, social medias, and surveillance cameras/videos have been identified as alternative rain gauges. In particular environmental audio recordings are a rich and underexploited source to identified and even characterize rainfall events.
Widespread surveillance cameras can continuously record rainfall information, which even provides a basis for the possibility of rainfall monitoring. Comparing the aforementioned methods, surveillance audio-based rainfall estimation has been discussed in existing studies with advantages of high-spatiotemporal-resolution, low cost and all-weather. Therefore, this study focuses on mining the rainfall information from urban surveillance audio for quantitative inversion on precipitation. Rain sound is generated by the collision of rain particles with other underlying objects in the process of falling. In real applications, the complex subsurface structure and random background noises from human activity in urban areas make surveillance rainfall sound vulnerable and surveillance audio-based rainfall estimation more challenging. In our study, the rainfall acoustic indicators were selected for rainfall sound representation. Deep learning-based rainfall observation systems were built based on urban surveillance audio data. Experimental results show the efficiency of our system in rainfall estimation. Our research is a new attempt to develop crowdsourcing-based rainfall observations, which can also provide a beneficent supplement to the current rainfall observation networks.

How to cite: Wang, X., Liu, X., Glade, T., and Wang, M.: Surveillance audio-based rainfall observation: a crowdsourcing approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2799, https://doi.org/10.5194/egusphere-egu22-2799, 2022.

In recent years, scientists have shown that the increasing trend in precipitation and flash floods during the monsoon season, combined with rapid land-use change, is leading to an increase in river discharge and flood inundation in the Kathmandu valley. The Kathmandu valley is a mid to low elevation mountain region (mean ~ 1250 m), surrounded by hills, particularly to the north, south and west, and has a population of over 3 million.  In this study, statistical analysis of 30 to 50 years of historical rainfall and river discharge data indicate a strong spatial variability in daily rainfall over the catchment during the monsoon season. Hilly regions which surround the Kathmandu basin receive significantly more rainfall than the valley, and rainfall intensity can vary greatly between the northern and southern hills, in particular. Combining our statistical analysis with physical-based numerical modelling of a range of historical flood events we demonstrate that the spatial variability in rainfall can lead to large differences in flood inundation patterns across the valley. Traditional flood early warning systems in the Kathmandu valley do not consider the effect of spatial variability of rainfall on flooding in the basin, which can lead to over or under predictions of flood extent in certain regions for a given event. We demonstrate that flood extent is the centre of Kathmandu and to the west of the city will be significantly higher if heavy rainfall occurs in the northern region of the valley.

How to cite: Creed, M. and Muthusamy, M.: Modelling the impact of spatial variability of precipitation on flood hazards in the Kathmandu Valley, Nepal, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12829, https://doi.org/10.5194/egusphere-egu22-12829, 2022.