Session HS7.6

[Programme]

HS7.6 | Precipitation and urban hydrology

Precipitation and urban hydrology

Convener: Hannes Müller-Thomy | Co-conveners: Nadav Peleg, Susana Ochoa Rodriguez, Li-Pen Wang, Lotte de Vos

Orals

| Fri, 19 Apr, 16:15–18:00 (CEST)

Room 2.15

Posters on site

| Attendance Fri, 19 Apr, 10:45–12:30 (CEST) | Display Fri, 19 Apr, 08:30–12:30

Hall A

Urban hydrological processes are characterized by high spatial variability and short response times resulting from a high degree of imperviousness. Therefore, urban catchments are especially sensitive to space-time variability of precipitation at small scales. High-resolution precipitation measurements in cities are crucial to properly describe and analyses urban hydrological responses. At the same time, urban landscapes pose specific challenges to obtaining representative precipitation and hydrological observations.

This session focuses on high-resolution precipitation and hydrological measurements in cities and on approaches to improve modeling of urban hydrological response, including:
- Novel techniques for high-resolution precipitation measurement in cities and for multi-sensor data merging to improve the representation of urban precipitation fields.
- Novel approaches to hydrological field measurements in cities, including data obtained from citizen observatories.
- Precipitation modeling for urban applications, including convective permitting models and stochastic rainfall generators.
- Novel approaches to modeling urban catchment properties and hydrological response, from physics-based, conceptual and data-driven models to stochastic and statistical conceptualization.
- Applications of measured precipitation fields to urban hydrological models to improve hydrological prediction at different time horizons to ultimately enable improved management of urban drainage systems (including catchment strategy development, flood forecasting and management, real-time control, and proactive protection strategies aimed at preventing flooding and pollution).
- Strategies to deal with upcoming challenges, including climate change and rapid urbanization.

Orals: Fri, 19 Apr | Room 2.15

Chairpersons: Hannes Müller-Thomy, Li-Pen Wang

16:15–16:20

5-minute convener introduction

16:20–16:40

EGU24-3794

ECS

solicited

On-site presentation

Implementing Nature-Based Solutions in the urban environment: benefits, limitations, and future challenges

Elena Cristiano

According to the United Nations projections, the global population is expected to continuously grow in the next years, reaching 9.7 billion by 2050, and more than 70% of it will live in cities, with a consequent intensification of urbanization. In this context, the increase of short but intense rainfall events, as foreseen by the Intergovernmental Panel on Climate Change for many geographical regions, will lead to an increment of pluvial floods in urban areas, due to a higher and faster runoff generation. Besides a more traditional approach, where the sewer system is simply updated with larger pipes, multiple nature-based solutions, also called blue-green solutions, have been proposed and implemented to mitigate the runoff generation from impervious surfaces. These innovative solutions aim to reintroduce natural elements in the urbanized areas, and benefit from the soil retention capacity to store water during intense rainfall events and release it in the environment via evaporation, evapotranspiration, and infiltration processes. With the installation of these solutions, the volume of water collected by the drainage system is limited and, consequently, the pluvial flood risk is reduced. Among the most common nature-based solutions, it is worth recalling green roofs, green walls, rain gardens and retention and detention basins, which guarantee multiple benefits for the urban environment. Besides the runoff reduction, these structures can, in fact, help lowering the average temperatures, limiting the generation of the urban heat island, they improve the air quality, facilitating the CO₂ sequestration, they increase the biodiversity, and they add aesthetic value to the cities, supporting the citizens’ physical and mental health. In this framework, this work presents a review of the most common and efficient nature-based solutions, installed in cities to mitigate pluvial floods and to ensure a sustainable development of the urban environment. Several nature-based solutions have proposed to mitigate pluvial floods and have shown high potential at local scale, while a large scale, involving the entire city, needs to be further investigated. Moreover, the integration of different blue-green solutions in the urban environment should receive further attentions to enhance the creation of smart and resilient cities.

How to cite: Cristiano, E.: Implementing Nature-Based Solutions in the urban environment: benefits, limitations, and future challenges , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3794, https://doi.org/10.5194/egusphere-egu24-3794, 2024.

16:40–16:50

EGU24-13420

On-site presentation

Attributing the Variability of Hershfield Rainfall Sampling Adjustment Factors at Sub-Hourly Durations

Claudio Meier, Patricio Muñoz-Proboste, Apeksha Marasini, Nischal Kafle, and Francesco Dell'Aira

Engineers and scientists need to describe extreme precipitation at a location. For any duration of interest, IDFs (or DDFs) represent the rainfall that can be expected to be equalled or exceeded with a certain frequency. In urban drainage, for durations ranging from a few minutes to a few hours, DDFs must be derived analyzing maxima obtained from “continuously measuring” raingauges. However, most rainfall records are not truly continuous, but are instead totalized. As we cannot know the actual maxima in continuous time for those shorter rainfall durations similar to gauge resolution, we introduce a negative bias. Only recently, within the last 10 to 15 years at most, meteorological agencies in developed countries have widely installed raingauges with 1-min resolution, which are basically continuous. This means that the DDFs that we presently use all came from totalized data. How were these biased, “fixed maxima” converted into values that are closer to the actual, unconstrained maxima?

The traditional solution has been to use so-called rainfall sampling adjustment factors (SAFs), also referred to as Hershfield factors. These multiplicative correction factors can be derived at raingauges with higher temporal resolution, so that maxima can be extracted using sliding time windows which are closer to continuous, allowing for comparison with maxima extracted from the same data, but totalized. Typically, such SAFs are assumed to be applicable at other locations, or even universally. The constrained maxima extracted from totalized data are simply multiplied by a SAF in order to obtain their corresponding unconstrained equivalents, which are considered to be the actual, continuous maxima, that are then used to determine DDFs.

We found several important issues and research gaps with the way we determine and apply SAFs in current practice: (i) different authors have used varied procedures to compute them, without comparing or discussing, (ii) no one has looked at the variability of SAFs at a given location, and (iii) SAFs are determined as a mean or central tendency value, across multiple locations, without considering their variability and how it affects the resulting predictions of extreme rainfall.

We use 862 German stations and 147 ASOS US stations, with 1-min rainfall data, to perform a detailed analysis of rainfall SAFs. Our aims are to compare the different procedures that have been proposed, study SAF variability both at a station and in space, and propose a unified engineering methodology for dealing with totalization effects on extreme rainfall estimation. As the 1-min records are short (10 to 15 years), we use partial duration series to compute the rainfall quantiles, restricting our work to low and intermediate ARIs, avoiding estimation issues.

Our results suggest that: (i) there is a preferred procedure for computing SAFs that should be adhered to, (ii) at-a-station variability of SAFs is large enough to be relevant for engineering design considerations, (iii) SAFs display spatial structure, and (iv) all of these findings should be studied in different regions, and consistently incorporated into engineering practice.

How to cite: Meier, C., Muñoz-Proboste, P., Marasini, A., Kafle, N., and Dell'Aira, F.: Attributing the Variability of Hershfield Rainfall Sampling Adjustment Factors at Sub-Hourly Durations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13420, https://doi.org/10.5194/egusphere-egu24-13420, 2024.

16:50–17:00

EGU24-8681

ECS

On-site presentation

Modeling the effects of urbanization expansion on convective precipitation: Insights from Shanghai

Qi Zhuang, Marika Koukoula, Shuguang Liu, Zhengzheng Zhou, and Nadav Peleg

With the expansion of large metropolitans and the increasing global population, there is a pressing need to have a better understanding of how cities affect extreme weather, especially heavy rainfall events that can potentially trigger urban floods. With the complex interplay and feedback between land, sea, and atmosphere, our understanding of how urbanization expansion impacts precipitation in coastal areas is limited. Here we use a high-resolution Weather Research and Forecasting (WRF) convection-permitting model to simulate 24 summer convective storms over Shanghai, China. We simulated the storms for the present urban setting and considered additional 3 urban expansion scenarios. Our results show that diverse urban-induced precipitation anomalies occur over the Shanghai metropolis due to different urban-surroundings gradients of low-level temperature and water vapor. 37.5% of storms show a constant increase in precipitation accumulation in response to urban expansion, whereas 29% have the reverse trend. The findings provide the potential mechanisms of urban rainfall modification in areas where land and water interact, offering useful insights for urban planning and flood control strategies in Shanghai, as well as other rapidly urbanizing cities.

How to cite: Zhuang, Q., Koukoula, M., Liu, S., Zhou, Z., and Peleg, N.: Modeling the effects of urbanization expansion on convective precipitation: Insights from Shanghai, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8681, https://doi.org/10.5194/egusphere-egu24-8681, 2024.

17:00–17:10

EGU24-4909

ECS

On-site presentation

Unlocking deep insights into temperature-dependent rainfall simulation: are we approaching it optimally?

Pei-Chun Chen and Li-Pen Wang

Statistically-based rainfall simulation has been a useful tool to generate long rainfall time series while preserving observed rainfall properties, commonly employed for hydrological applications such as drainage design. However, these models, typically constructed using historical gauge records, may overlook climate dynamics, failing to capture variations in underlying climate or weather conditions. Recent research works have aimed to address this limitation (Willems and Vrac, 2010; Kaczmarska et al., 2015; Cross et al., 2020; Ebers et al., 2023). For example, Cross et al. (2020) introduced a regression method linking monthly temperature to the parameters of a rainfall generator, while Ebers et al. (2023) proposed a temperature-dependent micro-canonical cascade model to enhance rainfall disaggregation for future climates. Many of these approaches adopt a temperature-dependent strategy due to the temperature dependence of the atmospheric precipitable water saturation value. Additionally, many of these methods involve using temperature in an 'aggregated' manner, associating the temperature averaged over a specific time duration (e.g., monthly or daily) with model parameters over the same duration. In this study, we aim to examine the soundness of this common approach, addressing two key research questions:

Given the complex atmospheric processes governing precipitation, is relying solely on temperature as a covariate for statistical rainfall simulation adequate?
Is the current 'aggregated' approach the most optimal method for incorporating temperature as a covariate?

To address these two questions, we employed the deep-learning model AtmoDist, proposed by Hoffmann and Lessig (2022). This model effectively captures underlying climate dynamics by extracting relevant features from successive input atmospheric variables and deriving the time difference between them based on the extracted features. We trained the model using input atmospheric variables with two different temporal arrangements: aggregation and concatenation. Aggregation, similar to many existing temperature-dependent approaches, involves averaging (or summing) temperature over a given duration with no overlap. Concatenation, on the other hand, involves simply concatenating temperature into a sequence over a given duration, preserving the entire temperature profile.

After successful training, we examined derived features and traced model weights to quantify the importance of each input atmospheric variable and to assess the impact of different temporal arrangements. For this experiment, we utilised four atmospheric variables (temperature, geopotential, u and v components of wind) from ERA5 hourly data spanning from 1940 to 2008. Results indicate that in an 'aggregated' arrangement, the model assigns similar weights to temperature and u and v components of wind. In a 'concatenation' arrangement, temperature plays a dominant role in capturing climate dynamics. These findings suggest that the common approach of solely using temperature as a covariate and in an 'aggregation' manner may not be the most optimal. Instead, Including additional variables or using temperature as a covariate in a 'concatenation' manner is recommended.

How to cite: Chen, P.-C. and Wang, L.-P.: Unlocking deep insights into temperature-dependent rainfall simulation: are we approaching it optimally?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4909, https://doi.org/10.5194/egusphere-egu24-4909, 2024.

17:10–17:20

EGU24-12090

On-site presentation

Analysis of the resolution of precipitation data required to obtain robust results from a hydrodynamic sewer network model

Ehsan Rabiei, Holger Hoppe, and Henning Lebrenz

The need for precipitation data for calibrating hydrodynamic sewer network models is often compromised by using the nearest available rain gauges to study area. Due to the scarcity and irregular locations of the rain gauges, this way of satisfying the need for precipitation data can lead to incorrect conclusions with respect to the temporal and spatial patterns of precipitation, depending on the location of the rain gauges in the study area. Recent developments in the field of precipitation measurement by means of weather radar data open up new possibilities for the use of such data sources in hydrodynamic sewer network models. Even though weather radar provides precipitation information with a high temporal and spatial resolution, the raw radar data contains several sources of error and is inaccurate. The radar data are therefore often corrected and merged with ground measurements. The main objective of this study is to investigate the resolution of precipitation data required to obtain robust results in a hydrodynamic channel network model. The study area is a small catchment close to Munich in Bavaria, Germany. Data from the Isen weather radar station of the German Weather Service (DWD), which is located around 33 km from the study area, was used. Following the objectives of this study, various weather radar data products were processed in order to be used as input for a hydrodynamic sewer network model. The data with a temporal resolution of 5 minutes to 1h and a spatial resolution of 250 m x 250 m up to 1.000 m x 1.000 m form the basis for creation of datasets to be investigated. It has been observed that the use of high-resolution precipitation data leads to better model results, especially when the data is merged with rain gauges. However, it should be noted that the quality of the model results does not decrease linearly when the resolution of the precipitation data is reduced.

How to cite: Rabiei, E., Hoppe, H., and Lebrenz, H.: Analysis of the resolution of precipitation data required to obtain robust results from a hydrodynamic sewer network model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12090, https://doi.org/10.5194/egusphere-egu24-12090, 2024.

17:20–17:30

EGU24-14536

Virtual presentation

A new Dataset for Belowground Urban Stormwater Networks over the U.S.

Hongyi Li, Seife Eriget, Taher Chegini, Gautam Bisht, Darren Engwirda, Dongyu Feng, Chang Liao, Zeli Tan, Donghui Xu, Tian Zhou, and Ruby Leugn

Belowground urban stormwater network (BUSN) data are usually not available to the public at the regional or national scales, hindering predictive understanding of BUSN’s impacts on urban flooding under extreme climates. We derived a national BUSN dataset over the contiguous United States by leveraging a newly developed algorithm based on graph theory and extensively available information such as street networks, landuse, topography, etc. For the convenience of hydrologic modeling, the generated BUSN national dataset is available in a vector format and organized to the 12-digit Hydrologic Unit (HUC12) watersheds, the smallest hydrologic unit defined by the U.S. Geologic Survey. There are two categories of data included in this dataset: 1) network-level information, such as the spatial typology and connectivity between the stormwater pipes; 2) pipe-level information, such as pipe diameter, length, slope, and roughness. Data quality control is also performed to ensure the completeness of BUSN within each HUC12 watershed. We will also discuss the possible causes for the biases in the estimated BUSN, and briefly demonstrate using this dataset in an integrated urban-hydrologic modeling framework over the U.S. We suggest this dataset can be valuable for understanding and modeling urban flooding processes at regional and larger scales.

How to cite: Li, H., Eriget, S., Chegini, T., Bisht, G., Engwirda, D., Feng, D., Liao, C., Tan, Z., Xu, D., Zhou, T., and Leugn, R.: A new Dataset for Belowground Urban Stormwater Networks over the U.S., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14536, https://doi.org/10.5194/egusphere-egu24-14536, 2024.

17:30–17:40

EGU24-10727

On-site presentation

A flood prediction framework: integrating seamless predictions into urban hydrological modeling

Ricardo Reinoso-Rondinel, Daan Buekenhout, Michiel van Ginderachter, Ruben Imhoff, Lesley De Cruz, and Patrick Willems

In recent times, the escalating occurrences of intense precipitation and flooding have exposed substantial socio-economic repercussions, with projections indicating a further rise in their impact due to climate change. Addressing this issue necessitates timely warnings for actions like neighborhood evacuations. However, issuing such warnings poses a dual challenge. On the one hand, it demands accurate forecasts, a difficult task given the heterogeneous nature of rainfall. On the other hand, modeling hydrological processes tied to flood prediction in urban and valley settings proves arduous due to their nonlinear characteristics. Additionally, the accuracy and lead time of forecasted precipitation significantly influence hydrological models, making it challenging for a warning system to generate reliable predictions of flooding events.

This study introduces a comprehensive flood prediction framework that combines: 1) a probabilistic seamless prediction model spanning up to 12 hours, achieved by blending 48 ensemble members from radar-based nowcasting and numerical weather prediction (NWP) ALARO/AROME models, and 2) a distributed hydrodynamic model tailored for urban flood prediction. The primary objective is to evaluate the framework's efficacy in predicting catchment responses, accounting for inherent uncertainties within the models.

For illustrative purposes, rainfall rate estimates are derived from the rain-gauge adjusted radar product managed by the Royal Meteorological Institute of Belgium (RMI). The blended forecast product is sourced from the open-source pysteps community, customized by the RMI for operational use. The hydrodynamic model for flood prediction is implemented through the InfoWorks ICM software, configured to simulate flooding at street level in the city of Antwerp, Belgium. Case studies involve impactful events that led to flooding in major cities within the Flanders area.

Initial findings indicate that, for a rapidly evolving convective storm, precipitation forecasts remained reliable up to 180 minutes in advance, while the flood forecast model predicted flooding levels 2 hours in advance. This analysis is anticipated to underscore the advantages and limitations of an integrated probabilistic approach to flood prediction at urban scales, emphasizing the necessary compatibilities among rainfall products and their representation of uncertainties. The insights gained from this study will contribute to the development of data-driven urban flood prediction models in Belgium for real-time hydrological forecasting.

How to cite: Reinoso-Rondinel, R., Buekenhout, D., van Ginderachter, M., Imhoff, R., De Cruz, L., and Willems, P.: A flood prediction framework: integrating seamless predictions into urban hydrological modeling , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10727, https://doi.org/10.5194/egusphere-egu24-10727, 2024.

17:40–17:50

EGU24-17392

ECS

On-site presentation

Hydraulic modelling of permeable pavements to mitigate pluvial flooding in the city of Genoa, Italy

Arianna Cauteruccio, Enrico Chinchella, Giorgio Boni, and Luca G. Lanza

Rapidly evolving pluvial floodings are typically experienced in cities due to the inefficiency of the urban drainage system in terms of the hydraulic failure of the storm water pipes and/or the insufficient capacity of the storm drain inlets. This is mainly due to the limited extension of the urban catchment areas, with a high building density and largely impervious surfaces. In addition, the rainfall regime in the Mediterranean region is characterized by short-duration and high-intensity events, which typically have a rather limited spatial extension and a very rapid evolution. The case study investigated in the present work is located within the metropolitan area of Genoa (Italy), which has recently experienced pluvial flooding, although associated with a rainfall event characterised by a low return period (between 1.5 and 3 years). The studied urban catchment is characterised by a flat area of about 1 km², bordered to the north by hills and to the south by the seaport.

With the aim of partially restoring the natural retention and detention capacity of the catchment area, the conversion of selected impervious pavements around buildings into permeable pavements is tested by means of hydraulic simulation. The hydrological behaviour of the applied solution has been experimentally derived in the “E. Marchi” hydraulic laboratory of the Department of Civil, Chemical and Environmental Engineering (DICCA) of the University of Genoa (Italy). A special in situ survey was preliminarily carried out to determine the number, type and degree of clogging of the rainwater inlets located in the study area.

Hydraulic modelling is carried out using the HEC-RAS 2D software code (v. 6.3.1). The stormwater drainage inlets are simulated as pumping stations with a customised stage-discharge relationship based on the available literature studies, while the hydrological response of the permeable pavements is set in terms of flow hydrographs along linear boundary layers enclosing the converted areas. Results are presented in the form of flood hazard maps and flooded water volumes within the study area for different return periods of the forcing rainfall event. Various extensions of the permeable pavement are tested to quantify the mitigation effect associated with the investigated sustainable urban drainage solution.

This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005).

How to cite: Cauteruccio, A., Chinchella, E., Boni, G., and Lanza, L. G.: Hydraulic modelling of permeable pavements to mitigate pluvial flooding in the city of Genoa, Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17392, https://doi.org/10.5194/egusphere-egu24-17392, 2024.

17:50–18:00

EGU24-13555

ECS

On-site presentation

Comparative Urban Pluvial Flood Risk Assessment: A Globally Applicable Workflow for Data-Scarce Environments

Jovan Blagojevic, Athanasios Paschalis, Joao Leitao, Nadav Peleg, and Peter Molnar

In this study we propose a new methodology for pluvial flood risk estimation, combining stochastic rainfall modelling, climate projection based adaptations of the rainfall frequency-intensity relations and DEM data sets, along with hydrodynamic modelling.

New global precipitation datasets, such as CMORPH, GSMaP or MERRA2 offer an affordable and accessible solution for water resource and water-hazard risk management in data-scarce regions and enable comprehensive global comparative studies. However, these datasets, often derived from satellite observations and coarse-scale climate modelling, consistently underestimate short-duration, high-intensity rainfall events, particularly those lasting one hour or less, that belong to the tails of the distributions (i.e., return levels higher than 30-year). This underestimation goes beyond spatial scale considerations, commonly addressed by areal reduction factors. Consequently, utilizing these global datasets for pluvial flood risk analysis results in conservative flood risk estimates.

The availability of global terrain models and mapped man-made structures like buildings, channels, and roads enables the generation of wide-coverage digital surface models. These can be used for flood inundation modelling in combination with corrected extremes of the global precipitation data sets, allowing near-global rough flood risk estimates.

In this study, we introduce a methodology for estimating pluvial flood risk using openly available global datasets. To achieve this, we derive hourly-scale Intensity-Duration-Frequency (IDF) curves suitable for pluvial flood inundation modeling in ungauged areas using global precipitation datasets. The first step uses high temporal resolution satellite remote sensing rainfall data (GSMaP) to train a stochastic rainfall generator model - the point process Bartlet-Lewis model. Subsequently, the weather generator is used to disaggregate daily global precipitation data (GPCC) through stochastic ensemble simulation. The resulting disaggregated ensemble data is then utilized to generate more accurate IDF curves including uncertainty, forming the basis for pluvial flooding risk assessments.

Our approach integrates the openly available FabDEM terrain model with OpenStreetMap to generate digital surface models for flood risk modeling analysis. Discrepancies in flood inundation risk estimates in urban environments, attributable to underestimated rainfall intensity, are demonstrated using CADDIES, a 2-dimensional hydrodynamic model. The workflow allows the IDF curves for the current climate to be adapted based on climate model projections of temperatures using the Clausius–Clapeyron relation, and to study their impact on future flood risk. A comparative risk analysis is presented for several tropical coastal cities, including future pluvial risk projections. All analytical steps adhere to FAIR principles, utilizing publicly available datasets. The proposed workflow provides globally applicable first order estimates of pluvial flood risk, especially in data-poor areas, with better quality than existing global IDF studies or IDF curves derived directly from global precipitation datasets.

How to cite: Blagojevic, J., Paschalis, A., Leitao, J., Peleg, N., and Molnar, P.: Comparative Urban Pluvial Flood Risk Assessment: A Globally Applicable Workflow for Data-Scarce Environments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13555, https://doi.org/10.5194/egusphere-egu24-13555, 2024.

Posters on site: Fri, 19 Apr, 10:45–12:30 | Hall A

Display time: Fri, 19 Apr, 08:30–Fri, 19 Apr, 12:30

Chairpersons: Hannes Müller-Thomy, Li-Pen Wang

A.39

EGU24-18097

ECS

Model Tree and Regularization Approaches for Estimation of Missing precipitation Records

Thu Nguyen, Anika Azad, and Ramesh Teegavarapu

Missing precipitation records occur for several reasons, and their estimation is a significant challenge due to the spatial-temporal variability of precipitation. In this study, model tree (MT), regression tree (RT) approaches, and different variations of optimization formulations combined with three regularization schemes (i.e., ridge regression and Elastic net) are proposed and used to estimate missing precipitation data. Concepts of objective selection of sites for estimating missing data using correlations and distributional similarity are also used. The MT and RT models based on optimization and regularization approaches were developed and tested to estimate missing daily precipitation data from 1971 to 2016 at twenty-two rain gauges in Kentucky, U.S.A. The models were analyzed and evaluated using multiple performance and error measures. Results indicate that MT-based and regularization models provided the best estimates considering the performance measures. Regularization models provided better estimates of missing data than the optimization models while reducing the complexity of the model and improving performance. Objective selection of the sites for estimation also improved missing data estimation.

How to cite: Nguyen, T., Azad, A., and Teegavarapu, R.: Model Tree and Regularization Approaches for Estimation of Missing precipitation Records, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18097, https://doi.org/10.5194/egusphere-egu24-18097, 2024.

A.40

EGU24-5437

Using personal weather station data for improving precipitation estimates and gauge adjustment of radar data

Jochen Seidel, Thomas Einfalt, Markus Jessen, András Bárdossy, Abbas El Hachem, and Adrian Treis

Personal Weather Stations (PWS) are simple, low cost meteorological instruments that can be set up by private persons or companies. In Central Europe, the number of PWS has increased significantly over the last years, in the meantime clearly outnumbering the number of rain gauges operated by national weather services and other authorities. However, the data from PWS suffer from many drawbacks since these stations are not set up and maintained according to professional standards. Apart from this, there are additional sources of errors and uncertainty originating from data transmission errors and incorrect information about the location of a PWS. Hence, the precipitation data from PWS has to be filtered and corrected before this information can be used e.g. for improving precipitation interpolation. Such algorithms have been developed e.g. by de Vos et al. (2019) and Bárdossy et al. (2021).

In the area of the water boards Emschergenossenschaft and Lippeverband (EGLV), investigations were carried out to determine whether data from private weather stations (PWS) can improve the interpolation of rainfall fields and if PWS can be used for the gauge-based adjustment of radar data. The area of the EGLV is located in a densely populated area in the federal state of North Rhine-Westphalia, where there is also a large number of PWS available. Furthermore, the EGLV operates a dense rain gauge network which is required for the quality control (QC) algorithm by Bárdossy et al. (2021) which was used in this study.

The results show that the additional information from PWS can capture the spatial structures of precipitation better than a standard measurement network alone. However, the spatial resolution and the maxima of the radar data are not achieved, especially in areas with low PWS density. Another aspect that was investigated is the question whether individual PWS can be used for the gauge adjustment of radar data. In principal, individual quality controlled PWS can be used for this purpose, there are however issues due to data gaps and the underestimation of hourly precipitation maxima, which currently limits the use of PWS for commonly used gauge-adjustment procedures.

References

Bárdossy, A., Seidel, J., and El Hachem, A. (2021), The use of personal weather station observations to improve precipitation estimation and interpolation. Hydrol. Earth Syst. Sci., 25, 583–601.

de Vos, L., Leijnse, H.,Overeem, A., and Uijlenhoet, R. (2019), Quality control for crowdsourced personal weather stations to enable operational rainfall monitoring. Geophysical Research Letters,46,8820–8829.

How to cite: Seidel, J., Einfalt, T., Jessen, M., Bárdossy, A., El Hachem, A., and Treis, A.: Using personal weather station data for improving precipitation estimates and gauge adjustment of radar data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5437, https://doi.org/10.5194/egusphere-egu24-5437, 2024.

A.41

EGU24-4612

ECS

Urbanization and climate change impacts on future heavy summer rainfall in Milan, Italy

Marika Koukoula, Herminia Torelló-Sentelles, and Nadav Peleg

Over the past fifty years, urbanization has undergone a swift surge, with over 50% of the global population now residing in cities. A further rise in urban population in the forthcoming decades is expected based on future projections. This surge in urbanization, along with associated alterations in land use/land cover, has the potential to modify the temporal and spatial characteristics of precipitation. Additionally, the anticipated escalation in global warming is likely to amplify both the magnitude and frequency of short-duration (convective) heavy rainfall. These two factors separately have the potential to lead to an increased risk of urban flooding. Consequently, it is imperative to comprehend how urbanization and climate change together may impact short-duration heavy rainfall events - a crucial aspect for effective flood risk assessments and planning sustainable urban drainage systems. To this end, we explore the influence of climate change and urbanization on the spatiotemporal properties of rainfall. Our investigation involves the simulation of current and future scenarios of urban development and warming over Milan, utilizing the convection-permitting Weather Research and Forecasting (WRF) physically-based atmospheric model. The findings of this study underscore that future urbanization will influence the distribution of rainfall in terms of both time and space. Furthermore, the combined effects of urbanization and climate change can significantly reshape the structure of short-duration heavy precipitation events.

How to cite: Koukoula, M., Torelló-Sentelles, H., and Peleg, N.: Urbanization and climate change impacts on future heavy summer rainfall in Milan, Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4612, https://doi.org/10.5194/egusphere-egu24-4612, 2024.

A.42

EGU24-4921

ECS

Quantifying the impact of urbanization on regional climate based on a partial differential method: a case study of vapor pressure deficit

Siqi Gong, Jun Xia, and Dunxian She

The rapid development of urbanization has significant impacts on regional climate, and thereby affects the hydrological characteristics of urban areas. Urban hydrological models have been mainly focused on the changes in hydrological response caused by complex urban underlying surfaces and urban pipe network construction in previous studies, while there is a need to strengthen research on the climate change patterns caused by urbanization. Vapor pressure deficit (VPD) is a key indicator for studying water cycle in climate system, and it has a close relationship with hydrological processes such as precipitation, evapotranspiration, and surface water transport. However, as a meteorological indicator affected by multiple factors, a deep understanding of the quantitative analysis method for the contribution of different factors to VPD changes is still lacking. This study uses a urban-rural station pairing method to analyze the impact of urbanization and proposes a method based on partial differential equations to quantitatively explore the contribution of different factors to urban-rural VPD difference. Taking daily-scale data of urban-rural paired stations in mainland China as an example, the study finds that urbanization significantly increases VPD in the core urban areas, and the urban-rural VPD difference gradually expands over time, showing significant seasonal and geographical variations. The method based on partial differential equations can effectively capture the trend of the urban-rural VPD difference, thereby confirming the validity of the derived method for evaluating the contributions. Relative humidity is the main factor contributing to the urban-rural differences in VPD in most regions, but shows a different pattern in some plateau continental climate regions. This study establishes a framework for analyzing the impact of urbanization on specific meteorological indicators, especially providing a way to quantify the contribution of factors causing urban climate change, which is of reference value for further considering the uniqueness of urban climate in the construction of urban hydrological models.

How to cite: Gong, S., Xia, J., and She, D.: Quantifying the impact of urbanization on regional climate based on a partial differential method: a case study of vapor pressure deficit, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4921, https://doi.org/10.5194/egusphere-egu24-4921, 2024.

A.43

EGU24-10830

ECS

Morphing 2-D rainfall fields based on temperature shifts as a basis for future urban flood assessment

Wenyue Zou, Daniel B. Wright, and Nadav Peleg

2-D rainfall fields play a critical role in assessing urban flood impacts and planning drainage systems. High-resolution rainfall fields, obtained from remote sensing devices such as weather radar and satellites, are not largely available and are even more limited for rainfall and flood frequency applications. One method that can be used to estimate extreme rainfall frequency—even with limited data—is Stochastic Storm Transposition (SST), which transposes observed rainfall fields within a region. In the context of climate change, there is a need to alter the observed rainfall fields to account for nonstationary changes in storm intensity and structure. Here, we suggest using Spatial Quantile Mapping (SQM) to modify the intensities and structures of rainfall fields with temperature as a covariate to generate an archive of plausible rainfall fields, which can then be used within SST as an input to assess changes in rainfall and floods. We take Beijing city as a case study, employing 22 years of 1 km hourly downscaled rainfall from CMORPH and near-surface air temperature data from ERA5, to demonstrate the effectiveness of this approach. Initially, SST is run under the current climate and validated for the 2- to 100-year rainfall return levels compared with those of 21 stations within Beijing city. Subsequently, according to the observed relationships between hourly rainfall and temperature, the rainfall fields are modified by the SQM method to fit future temperature conditions. Ultimately, the future extreme rainfall intensities, ranging from 2- to 100-year return levels, are obtained through the integration of the SST and SQM methods. The results indicate that the combined SST-SQM approach can be efficiently used to estimate future rainfall extremes in a changing climate.

How to cite: Zou, W., B. Wright, D., and Peleg, N.: Morphing 2-D rainfall fields based on temperature shifts as a basis for future urban flood assessment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10830, https://doi.org/10.5194/egusphere-egu24-10830, 2024.

A.44

EGU24-5081

ECS

pyBL: an open source Python package for fine-scale rainfall modelling with short records based upon a Bartlett Lewis model

Li-Pen Wang, Chi-Ling Wei, Pei-Chun Chen, Chien-Yu Tseng, Ting-Yu Dai, Yun-Ting Ho, Ching-Chun Chou, and Christian Onof

Bartlett-Lewis (BL) model is a stochastic model that represents rainfall based upon the theory of Poisson cluster point process. It had been used for daily and hourly stochastic rainfall time series modelling for over 30 years. It was however known to underestimate sub-hourly rainfall extremes until some recent advances, where this shortcoming has been overcome. It could therefore serve as an alternative to the existing rainfall frequency analysis methods based upon, for example, annual maxima time series.

The implementation of the BL model is however a non-trivial task. The formulation of the BL model is of high complexity, and the calibration of the model parameters constitutes a nonlinear optimisation process with high numerical instability. This hinders the widespread use of the BL model. Another computational challenge of BL modelling lies in sampling. In particular, when using this type of rainfall generators, it often requires sampling a large number of long-term realisations.

In this work, with the purpose of promoting BL model and of demonstrating its capacity in modelling sub-hourly rainfall (both standard and extreme statistics), we have initiated an open source Python library for the BL model: pyBL, where a set of data structures and algorithms are designed specifically for the BL model, making the fitting and sampling processes more efficient and lightweight in terms of memory. In particular, one of our designs is a lossless time series compression method that perfectly suits BL model and a set of algorithms and can calculate statistical properties without any decompression. Additionally, we have implemented user interfaces and packaging at various levels, making experimental adjustments and optimisation methods more flexible and concise.

Finally, two scientific experiments resembling real-world scenarios were conducted here to demonstrate pyBL's capacity of modelling sub-hourly rainfall extremes with short records, as well as flexibility of utilising records at various resolutions and with various data lengths. We show that, with the help from the BL model, we can well model hourly and sub-hourly rainfall extremes with merely half data length required by the widely-used Annual Maxima method.

How to cite: Wang, L.-P., Wei, C.-L., Chen, P.-C., Tseng, C.-Y., Dai, T.-Y., Ho, Y.-T., Chou, C.-C., and Onof, C.: pyBL: an open source Python package for fine-scale rainfall modelling with short records based upon a Bartlett Lewis model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5081, https://doi.org/10.5194/egusphere-egu24-5081, 2024.

A.45

EGU24-7318

ECS

Incorporating cell evolution into object-based convective storm nowcasting

Yun-Man Hsu and Li-Pen Wang

Radar-based nowcasting plays a crucial role in meeting the urgent demand for short-term, high-intensity convective rainfall predictions. Given the dynamic and clustering nature of convective storms, object-based nowcasting has emerged as an effective approach, characterised by its ability to identify, track, and extrapolate their motion. These models excel in identifying rainfall objects in radar images and constructing their temporal associations. However, a critical limitation in many of existing methods lies in their lack of mechanism to incorporate the evolution of rain cell intensity into the nowcasting process.

A recent study by Cheng et al. (2023) demonstrated the effectiveness of utilising convective core altitude – a property retrieved from three-dimensional radar data– to improve the prediction of the evolution of single-core convective cell lifecycle. Their results suggest that, compared to persistence nowcasts, the prediction errors in rainfall intensity can be reduced by 50% at 15-min forecast lead time. However, this model focused on predicting ‘mean’ cell properties, while promising, still falls short for operational forecasting.

This research aims to enhance object-based nowcasting by developing methods to integrate the cell evolution model proposed by Cheng et al. (2023) with an operational positional forecasting model. A recent development of a Kalman filter based object-based convective storm nowcasting model, co-developed by researchers from several international sectors and the UK Met Office (Wang et al., 2022), is employed here for positional prediction of convective cells. The key challenge of the integration lies in producing spatial-distributed convective cell nowcasts and the associated evolution prediction uncertainty that can be incorporated with the positional prediction uncertainty under a Kalman filter framework.

To tackle this challenge, we test on two approaches. Inspired by Shehu and Haberlandt (2022), the first approach employs a cell analog method to identify historical cells with similar mean properties predicted by the cell evolution model. Those cell analogs with high similarity are then used to empirically constitute prediction uncertainty. For the second approach, we generate spatially-distributed cells via fitting bivariate Gaussian or Exponential shape (Willems, 2001; Féral et al., 2003) models using predicted mean properties, which can further cell samples with similar mean properties. Two approaches will then be integrated with the positional nowcasting model, respectively. Probabilistic nowcasting will be undertaken to generate ensemble nowcasts that account for both positional and evolution variations. Ensemble members from each approach at each forecasting time step can constitute a convective storm ‘hazard’ map. We will indirectly evaluate the performance of two proposed approaches via assessing these hazard maps with radar observations.

How to cite: Hsu, Y.-M. and Wang, L.-P.: Incorporating cell evolution into object-based convective storm nowcasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7318, https://doi.org/10.5194/egusphere-egu24-7318, 2024.

A.46

EGU24-3073

Estimation of runoff coefficient and curve number based on observed rainfall-runoff events from contrasting catchments in the urban environment

Mark Bryan Alivio, Matej Radinja, Nejc Bezak, and Zoltán Gribovszki

In most hydrological analyses, it is common practice to select values for the runoff coefficient (RC) and curve number (CN) from standard lookup tables available in literature-based handbooks or engineering manuals. However, these generic values may not adequately account for the distinct characteristics of a given catchment, particularly in urban environments where the heterogeneity of land use/land cover creates a diverse range of hydrological responses. This study focuses on estimating and analyzing the RC and CN based on observed rainfall-runoff events in two contrasting catchments in the city of Ljubljana, Slovenia, namely the urban mixed forest and highly impervious urban area. The analysis of 86 rainfall events that occurred between August 2021 and August 2023 revealed that the two studied catchments demonstrated contrasting runoff responses to rainfall, which could be attributed to their distinct land use/land cover patterns. The urban mixed forest generated an order of magnitude less runoff per unit of rainfall than the urban area. A mean RC of 0.1 was observed in the urban mixed forest, approximately 5 times less than those in the urban area (0.6). These computed mean RC values are lower compared to the tabulated RC values from the American Society of Civil Engineers (ASCE) manual for the given soil type and slope of the specific land use being compared. Similarly, the mean and median CN values in the urban mixed forest are 82.7 and 83.9, respectively, which are lower than the values recorded in the urban area (mean = 95.5, median = 96.8). Additionally, a standard behavior response with asymptotic CN_∞ of 71.7 and 90.7 was observed in the urban mixed forest and urban area, respectively. Thus, the CN values based on the central tendency method appear to be higher than the CN_∞ estimated from the standard asymptotic fit and the tabulated CN values of the Natural Resources Conservation Service National Engineering Handbook (NRCS-NEH). Furthermore, we observed an absence of statistically significant seasonal differences in RC and CN between the growing and dormant seasons in both catchments. However, a bi-monthly analysis revealed a temporal variation in both parameters, with RC peaking in autumn and CN being highest in winter. High-intensity storms in summer and long-duration heavy rainfall events in autumn may have potentially overwhelmed the dry antecedent soil conditions. Hence, examining specific rainfall-runoff events in the urban mixed forest revealed that the initial soil moisture and antecedent rainfall have a contributing role in the observed variations in RC and CN.

Acknowledgments: Results are part of the ongoing research entitled “Microscale influence on runoff” supported by the Slovenian Research and Innovation Agency (N2-0313) and National Research, Development, and Innovation Office (OTKA project grant number SNN143972). The study was also carried out within the scope of the CELSA project entitled “Interception experimentation and modeling for enhanced impact analysis of nature-based solutions”.

How to cite: Alivio, M. B., Radinja, M., Bezak, N., and Gribovszki, Z.: Estimation of runoff coefficient and curve number based on observed rainfall-runoff events from contrasting catchments in the urban environment , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3073, https://doi.org/10.5194/egusphere-egu24-3073, 2024.

A.47

EGU24-10466

ECS

Kinematic Hierarchical Filling-&-Spilling vs. fully 2D schemes for pluvial inundation

Kay Khaing Kyaw, Valerio Luzzi, Stefano Bagli, and Attilio Castellarin

Urban areas are especially vulnerable to the effects of pluvial flooding due to the high population density and concentration of valuable resources as well as the fact that heavy precipitation events are becoming more common and more intense because of climate change. The use of high-resolution hydrologic-hydraulic numerical models for pluvial flood risk assessments in large metropolitan areas is still very resource-intensive. Several studies have pointed out the potential of fast-processing DEM-based methods, like Hierarchical Filling-&-Spilling Algorithms (HFSAs), considering the increasing availability of LiDAR (Light Detection and Ranging) high-resolution DEMs (Digital Elevation Models). We have developed a fast-processing HFSA, as part of a web-based digital twin solution for flood risk intelligence (see https://saferplaces.co/), that enables building-by-building pluvial flooding hazard and risk modelling in large urban areas by accounting for spatially distributed rainfall input and infiltration processes through a pixel-based Green-Ampt model. In this study, we upgrade SaferPlaces’ HFSA based on kinematic wave approximation to depression points and analysing the impact of flow contributions from one cell to another on the formulation of travel time, as well as the backwater effect in depression-related watershed areas. We present the first applications of the kinematic HFSA, comparing it with two state-of-the-art fully 2D rain-on-grid inundation models (i.e. HEC-RAS and UnTRIM). We discuss the potential and limitations of Saferplaces' upgraded HFSA and identify future research possibilities.

Keywords: Hierarchical Filling-&-Spilling Algorithms, kinematic wave, pluvial flooding, rain on grid.

How to cite: Kyaw, K. K., Luzzi, V., Bagli, S., and Castellarin, A.: Kinematic Hierarchical Filling-&-Spilling vs. fully 2D schemes for pluvial inundation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10466, https://doi.org/10.5194/egusphere-egu24-10466, 2024.

A.48

EGU24-16934

ECS

Simulated response of urban drainage networks to heterogeneous precipitations

Elisa Costamagna, Fulvio Boano, and Luca Ridolfi

In urban areas, drainage networks are usually formed by small subnetworks, hence resulting in small-sized basins with fast times of response. For this reason, the prediction of pluvial flood hydrographs in urban basins is hampered by considerable uncertainty due to the spatial and temporal variability of rainfall intensity, which is difficult to characterize with the sparse observations from the limited amount of rain gauges that are typically available. To better understand and quantify this uncertainty, the drainage network of Turin is considered as a case study. The city of Turin has >800.000 residents and is located in Northwestern Italy on a relatively flat area bordering a hill on the East. The area is mostly urbanized, and it receives an average precipitation of around 800 mm per year. The drainage network was developed since the end of the 19th century as a separate network that receives only stormwater from a catchment area of around 100 km², for a total network length of around 1200 km. At present, the network experiences some criticalities due to infrastructure ageing and urban development, and occasional flooding episodes are observed at some points.

The sensitivity of flood hydrographs at the basin outlet to spatial and temporal patterns of rainfall intensity is analyzed using a SWMM hydraulic model of different parts of the drainage networks. Spatial and temporal variability of rainfall intensity over the area is quantified using the observations of a set of 20 rain gauges. Then, the analysis of the sensitivity of the flood hydrograph in a monitored subnetwork is performed based on two rain gauges (2 km apart) and one flow meter at the basin outlet. The results provide valuable insight into the response of urban drainage networks to heterogeneous spatial fields of precipitation.

How to cite: Costamagna, E., Boano, F., and Ridolfi, L.: Simulated response of urban drainage networks to heterogeneous precipitations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16934, https://doi.org/10.5194/egusphere-egu24-16934, 2024.

A.49

EGU24-15683

ECS

Urban surface runoff under simulated heavy rainfalls

Aleksandra Czuchaj, Mikołaj Majewski, and Marek Marciniak

In the face of ongoing climate change, changes in precipitation patterns are observed. More and more often, rainfall is characterized by high intensity and short duration. Such rainfall poses a threat to urban areas as it may generate urban flash floods. As a result of intense rainfall on impermeable surfaces, surface runoff accumulates and the capacity of the storm sewer system is exceeded. The aim of the presented research was to identify the dynamics of surface runoff depending on the rainfall intensity, type of land cover and soil moisture conditions. It was carried out as a series of field experiments with a rainfall simulator.

The experiment was conducted at the research station located in the Różany Strumień catchment (Poznań, Poland). The station consists of 4 plots (20 x 1 m each), with different land cover: black fallow, grass, concrete paver blocks and an impermeable testing plot. The program of experiments included seven types of precipitation, corresponding to the classification of Chomicz from A0 (strong rain, intensity 4 mm∙h^-1, duration 360 min) to B2 (torrential rain, intensity 60 mm∙h^-1, duration 70 min). Each rainfall was simulated twice in dry and wet ground conditions. The experiment was carried out in July 2022.

The result of field research is 56 surface runoff dynamics curves depending on: type of land cover, precipitation category according to Chomicz and soil moisture conditions. On the basis of curves obtained from the experiments, four new descriptors were determined, characterizing the variability of surface runoff in urbanized areas: volume of surface runoff, runoff coefficient, the moment of runoff initiation and runoff dynamics coefficient.

How to cite: Czuchaj, A., Majewski, M., and Marciniak, M.: Urban surface runoff under simulated heavy rainfalls, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15683, https://doi.org/10.5194/egusphere-egu24-15683, 2024.