Displays

We examine the impact of urbanization on precipitation and flooding caused by tropical cyclones under a dynamical modeling framework, using Hurricane Harvey (2017) and Tropical Storms Allison (2001) and Imelda (2019) as case studies. Hurricane Harvey poured more than a metre of rainfall across the heavily populated Houston area, leading to unprecedented flooding and damage. Although studies have focused on the contribution of anthropogenic climate change to this extreme rainfall event, limited attention has been paid to the potential effects of urbanization on the hydrometeorology associated with this hurricane. Here we find that urbanization exacerbated not only the flood response but also the storm total rainfall. Using the Weather Research and Forecast model—a numerical model for simulating weather and climate at regional scales—and statistical models, we quantify the contribution of urbanization to rainfall and flooding. We expand these analyses to examine the impacts of urbanization on Tropical Storms Allison and Imelda, two other storms that affected the Houston area causing widespread heavy rainfall and flooding.

How to cite: Villarini, G., Zhang, W., Vecchi, G., and Smith, J.: Influence of Urbanization on Precipitation and Flooding Caused by Landfalling Tropical Cyclones: The Case of Houston, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2468, https://doi.org/10.5194/egusphere-egu2020-2468, 2020.

In general terms, climate adaptation in cities is highly complicated by the very high required spatial and temporal resolution. The high resolution is needed to capture both the full variability of small-scale high-impact weather phenomena and the associated response from the mosaic of land uses and buildings in urban environments. Most commonly available climate model simulations and projections are too spatially coarse (≥10 km) for a proper assessment of many important urban climate impacts. 

In terms of water-related impacts, a key issue concerns the reproduction of local short-duration rainfall extremes (cloudbursts) that may cause pluvial flooding. An accurate reproduction of the convective generation of such extremes requires a spatial resolution of at least 5 km, preferably even higher, in convection-permitting regional climate models (CPRCM). Conceivably, estimates of future changes in cloudburst characteristics and associated statistics based on CPRCM simulations will be more reliable than today’s estimates based on non-CP RCMs. Because of the extreme computational demand, however, the number of CPRCM simulations made is still rather low and generally limited to small domains and/or short time slices.

But many efforts are currently being made in this direction and the main focus of this presentation will be a case study evaluation of hourly rainfall extremes from 3×3 km² convection-permitting simulations with the HARMONIE-climate model over the Nordic region. The case study will focus on the region around the Öresund strait, that connects southern Sweden and eastern Denmark. This region contains the cities Malmö and Copenhagen that were both hit by heavy cloudburst in the last decade, that caused severe flooding and substantial damage to infrastructure.

The presentation will include different aspects of the simulations and their applicability:

• Historical performance. Evaluation of reference period simulations, with both ERA-Interim and GCM boundaries, against high-resolution observations, focusing at the reproduction of short-duration (sub-daily) extremes but also e.g. diurnal cycle and spatial variability.
• Future changes. Assessment in terms of climate factors for different durations, return periods and future time horizons. A comparison is made with climate factors estimated from lower-resolution, non-convection permitting downscalings based on the same GCM projections.
• End-user practices. A discussion of what resolution that is needed in order to meet different stakeholders’ needs in the light of climate adaptation. The key question is how the output from CPRCM simulations can be processed and interpreted to provide an added value. 

Besides the above analyses, two additional related investigations will be presented:

• Lessons learnt from experiments of tailored “urban downscaling” of climate projections down to 1×1 km² and 15 min over selected European urban regions (Stockholm, Bologna, Amsterdam) performed in the Urban SIS project.
• An evaluation of hourly rainfall extremes over selected European countries in a 11×11 km² EURO-CORDEX ensemble, including spatial patterns and temperature scaling of the estimated future changes.

How to cite: Olsson, J., Sörensen, J., Du, Y., An, D., Berg, P., Toivonen, E., and Belusic, D.: Short-duration rainfall extremes in very high-resolution climate projections: historical evaluation and future projections, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7007, https://doi.org/10.5194/egusphere-egu2020-7007, 2020.

The University of Hamburg operates a single-polarized X-band weather radar to investigate small scale precipitation in Hamburg’s center since 2013. This weather radar provides a temporal resolution of 30 s, a range resolution of 60 m, and a sampling resolution of 1° within a 20 km radius. The X-band observations refine the coarse measurements of the German nationwide C-band radars. On the one hand, the resolution enables new capabilities in research and detection of extreme events, e.g. flash floods or tornadoes in rain events. On the other hand, with the single polarization and small wavelength, attenuation, noise, and non-meteorological echoes become a challenging issue. How can we derive products from disturbed weather radar observations?

We demonstrate new methods to process X-band weather radar observations effectively using synthetic and real data. Firstly, we present our python package for local weather radars. This package combines all steps of processing our measurements and includes well-established algorithms of image processing and radar meteorology. Secondly, we study machine learning as a new and potential method for our weather radar products. The developed neural network uses raw reflectivity measurements as input and results in data, which is free of noise and non-meteorological echoes. We outline assets and drawbacks of both methods and show possible connections.

Further X-band weather radar systems are planned for 2020 to monitor precipitation for the Hamburg metropolitan region in a networked environment. The high-quality and -resolution weather radar products will be provided for urban hydrology research within the Cluster of Excellence CLICCS - Climate, Climatic Change, and Society.

How to cite: Burgemeister, F., Finn, T. S., Machnitzki, T., Clemens, M., and Ament, F.: New pathways for high-resolution weather radar products in the Hamburg metropolitan region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9299, https://doi.org/10.5194/egusphere-egu2020-9299, 2020.

Commercial microwave links (CMLs) are point-to-point radio connections widely used as cellular backhaul and thus very well covering urbanized areas. They can provide path-integrated quantitative precipitation estimates (QPEs) as they operate at frequencies where radio wave attenuation caused by raindrops is almost proportional to rainfall intensity. Pastorek et al. (2019b) demonstrated the feasibility of using CML QPEs to predict rainfall-runoff in a small urban catchment. Unfortunately, runoff volumes were highly biased, mostly for QPEs from short CMLs, although the temporal runoff dynamics were predicted very well, especially during heavy rainfall events. It was also shown that, for the heavy rainfalls, reducing the bias by adjusting the CML QPEs to traditional rainfall measurements (Fencl et al., 2017) leads to less accurate reproduction of the runoff temporal dynamics.

Current understanding is that the bias in CML QPEs is often caused by imprecise estimation of wet antenna attenuation (WAA), which is a complex process influenced by many physical phenomena, including radome hardware or positioning of the outdoor unit. However, traditional WAA estimation methods are typically unable to take into account all the individual-level factors. We proposed (Pastorek et al., 2019a) to estimate WAA separately for each of the examined CMLs by using discharge measurements at the outlet of a small urban catchment and showed that this approach can reduce the bias in CML QPEs, leading to generally satisfying performance of rainfall-runoff models, mainly for heavy rainfalls.

In the presented study, we evaluate the effect of the method proposed in Pastorek et al. (2019a) (method i) on rainfall-runoff modelling in more detail and compare it to the method of Fencl et al. (2017) (method ii). For a case study in Prague-Letňany, Czech Rep., a calibrated rainfall-runoff model is used to predict discharges at the outlet of the small urban catchment (1.3 km²) using QPEs from 16 CMLs. First results confirm that minimizing the bias in CML QPEs using method i is convenient mainly for heavy rainfalls, as Nash-Sutcliffe efficiency is considerably higher in this case for all but one CML (on average 0.65; only 0.40 for method ii). Moreover, method i preserves the information about the rainfall temporal dynamics during heavy rainfalls better than method ii for most of the individual CMLs (correlation coefficient with observed runoffs on average 0.83 for method i and 0.78 for method ii). Next steps should include generalization for other case studies, including an exploratory analysis of the potential mismatches.

References

Fencl, M., Dohnal, M., Rieckermann, J., Bareš, V., 2017. Gauge-adjusted rainfall estimates from commercial microwave links. Hydrol. Earth Syst. Sci. 21, 617–634.

Pastorek, J., Fencl, M., Rieckermann, J. and Bareš, V., 2019b. Commercial microwave links for urban drainage modelling: The effect of link characteristics and their position on runoff simulations. Journal of environmental management 251, 109522.

Pastorek, J., Fencl, M., and Bareš, V., 2019a. Calibrating microwave link rainfall retrieval model using runoff observations. Geophysical Research Abstracts 21, EGU2019-10072.

This study was supported by the project no. 20-14151J of the Czech Science Foundation and by the project of the Czech Technical University in Prague no. SGS19/045/OHK1/1T/11.

How to cite: Pastorek, J., Fencl, M., Rieckermann, J., and Bareš, V.: Commercial microwave links in urban rainfall-runoff modelling: Two different approaches to removing the bias, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9384, https://doi.org/10.5194/egusphere-egu2020-9384, 2020.

Urban catchments are characterized by a high degree of imperviousness, as well as a highly modified landscape and interconnectedness. The hydrological response of such catchments is usually complex and fast and sensitive to precipitation variability at small scales. To properly model and understand urban hydrological responses, high-resolution precipitation measurements to capture spatiotemporal variability is crucial input.

In urban areas floods are among the most recurrent and costly disasters, as these areas are often densely populated and contain vital infrastructure. Runoff from impervious surfaces as a result of extreme rainfall leads to pluvial flooding if the system’s drainage capacity is exceeded. Due to the fast onset and localised nature of pluvial flooding, high-resolution models are needed to produce fast simulations of flood forecasts for early warning system development. Data-driven models for predictive modelling have been gaining popularity, due to the fact they require minimal inputs and have shorter processing time compared to other types of models.

Data-driven models to forecast peak flows in drainage channels of Brussels, Belgium are being developed at sub-catchment scale, as a proxy for pluvial flooding within the FloodCitiSense project. FloodCitiSense aims to develop an urban pluvial flood early warning service. The effectiveness of these models relies on the input data resolution among others. High-temporal resolution rainfall and runoff data from 13 rainfall and 13 flow gauging stations in Brussels for several years is collected (Open data from Flowbru.be) and the data-driven models for forecasting peak flows in drainage channels are build using the Random Forest classification model.

Optimal model inputs are determined to increase model performance, including rainfall and runoff information from the current time step, as well as additional information derived from previous time steps.

The additional inputs are determined by progressively including rainfall data from neighboring stations and runoff from previous time steps equivalent to the lag time equal to the forecasting horizon, in our case two hours. The data-driven model we develop has the form as shown in the following equation.

Q_t = f(Q_t-lag, ∑RF_i,j)  for i is the number of rainfall stations considered and j is the time  from t-lag to t

Where Q_t  is the flow at a flow station at time t, Q_t-lag is the lagged flow at the station and RF_i,j is the rainfall values for station i and time j.

For Brussels nine relevant sub-catchments were identified based on historical flood frequency for which we are building data-driven flood forecasting models. For each sub-catchment, RF models are being trained and tested. More than 200,000 data point were available for training and testing the models. For most of the flow stations the data-driven models perform well with R-squared values up to 0.84 for training and 0.6 for testing for a 2-hour forecast horizon. 

To improve the reliability of the data-driven models, as next step, we are including radar rainfall data input, which has the ability to capture temporal and spatial variability of rainfall from localized convective storms to large scale moving storms.

KEYWORDS

Data driven models, FloodCitiSense, Flood Early Warning System, Urban pluvial flooding

How to cite: Seyoum, S., Verbeiren, B., and Willems, P.: Urban Drainage Systems modelling for Early Warning Service Using Data-Driven Modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10916, https://doi.org/10.5194/egusphere-egu2020-10916, 2020.

Unmanned Aerial Vehicles (UAVs) are a very effective means to map river beds and flood extent accurately across a wide area, even while the flood is happening or shortly thereafter. Flood mapping information is also very valuable in a long-term context, for drainage infrastructure planning and management. Here, we will present three applications: UAV-based information for hydrologic modelling of the urban drainage system, for flood extent mapping and for identification of bottlenecks in the system that can cause urban flooding.

UAV flights were conducted in Kumasi, the second biggest and fastest growing city in Ghana, where urban flooding has become more frequent due to changes in the climate and have a more negative impact due to rapid urbanization and population growth. Not only are the natural flood plains increasingly being used for anthropogenic purposes, the increased population growth also brings along more solid (plastic) waste on the streets and into the riverbeds and riverways. This creates blockages in drains and riverways, which reduces its drainage capacity and adds to the flooding problems. UAVs were used to collect elevation information (DEM), river bed dimensions and land-use. This information was used to construct a hydrological model to predict river flows and flooding. In addition, using thermal imagery from UAV flights over partially flooded agricultural fields near the town of Kianjai, Kenya, we will demonstrate that UAV imagery can identify flooded areas even when cross-cut by vegetation or other obstacles.

We will present the three applications and discuss the promises and challenges of deploying UAVs for the purpose of urban hydrological modelling and flood mapping.

How to cite: Nolet, C., ten Veldhuis, M.-C., van Til, J., and de Klerk, M.: Urban hydrology and flood mapping using UAV imagery, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21668, https://doi.org/10.5194/egusphere-egu2020-21668, 2020.

Green infrastructure plays a key role in contemporary concepts to mitigate flooding in urban environments. Concepts like water sensitive cities, sponge cities, and water sensitive urban design aim to mimic features of the natural water cycle even in highly urbanized districts. For instance, green roofs – as a key element of green infrastructure – reduce runoff due to their storage capacity. Hence, evapotranspiration is also increased at the expense of runoff, which better matches the characteristics of the natural water cycle. In this presentation, we demonstrate the added value of green roofs for stormwater mitigation. First, a green roof test plot with a slope of zero degrees and dimensions of 20 m in length and 1 m in width is built under laboratory conditions. The vertical extent is 0.08 m filled with a homogeneous substrate layer with a 300 g m^-2 drainage mat below. The runoff leaving the green roof at one of the 1 m edges is collected in tanks, which allows to continuously monitor the outflow. The water level in the green roof is observed using cameras. In this physical experiment, a sprinkler system is set up in order to generate an artificial rainfall event that mimics a design storm with a rainfall volume of 27 l m^-2 in total falling within 15 minutes. This corresponds to a return period of 100 years at the experimental site in Hanover, Germany. A numerical model utilizing the open source Catchment Modelling Framework (CMF) is developed to represent the green roof in a physically based model representation, which solves the Darcy flow along a 1D numerical grid with a grid spacing of 0.2 m. The model captures the dynamics of the green roof’s hydrological response very well. The comparison of observed and modelled runoff time series, each with a temporal resolution of 1 minute, suggest a Nash-Sutcliffe model efficiency of 0.64. The root mean square error (RMSE) of modelled water levels in the green roof amounts to 1.2 cm. Both the physical experiment and the model suggest a runoff coefficient of 9% after 15 minutes. At present, we also focus on analyzing other configurations of green roofs with altered dimensions and slope (50 experiments in total with up to three repetitions each). These results highlight that (i) CMF represents the hydrology of the green roof with high accuracy, and (ii) green roofs are a very efficient measure of green infrastructure that helps to reduce runoff even for design storms well beyond return periods usually considered in urban drainage planning. This is especially relevant in the process of transforming grey to green infrastructure in the light of climate change adaptation.

How to cite: Förster, K., Westerholt, D., Bargel, L., Kraft, P., and Lösken, G.: Set storage to the rain – Experimental and model-based evidence in mitigating extreme rainfall excess with green roofs, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1608, https://doi.org/10.5194/egusphere-egu2020-1608, 2020.

The Intensity-Duration-Frequency (IDF) relationships are commonly used in urban hydrologic designs. A major source of uncertainty arises due to small samples of data and use of multiple GCMs, in developing the IDF for future periods. A major objective of this study is to address uncertainties in the IDF relationships for future periods, under climate change. The study proposes a Bayesian method for addressing the parameter uncertainty in the Generalized Extreme Value (GEV) distribution for the Annual Maximum Series (AMS). Uncertainties due to the use of multiple GCMs are addressed through the Reliable Ensemble Averaging (REA) method. The posterior distributions of the three parameters of GEV distribution are obtained using Markov Chain Monte Carlo (MCMC) method. Twenty-three CMIP5 GCMs with four RCPs are considered for studying the effect of climate change on the IDF relationship for the case study of Bangalore, India. Change Factor Method (CFM) is used for spatially downscaling the projected time series of precipitation and scale-invariance theory is used for temporal disaggregation. Results are compared across different CFM schemes considering multiple bin sizes. Uncertainties in design intensities are quantified through probabilistic IDF relationships.

How to cite: Tripathy, K. P. and Mujumdar, P.: Addressing Uncertainties in Projected IDF Relationships under Climate Change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2966, https://doi.org/10.5194/egusphere-egu2020-2966, 2020.

Under the background of global climate change and rapid urbanization, the low-lying coastal cities are vulnerable to urban waterlogging events, which seriously interrupt the sustainable development of society and economy. Urban waterlogging is a stagnant water disaster, which process affected by natural conditions and human activities. Previous studies had explored the effect of land-use type on waterlogging in relatively small watersheds. Few, however, have comprehensively revealed the relative contributions of the natural and anthropogenic factors to urban waterlogging concerning analysis scale variations. What is less known, are the dominant factors and the best analysis scale. The natural and anthropogenic factors such as topography, land cover characteristics (composition and spatial configuration), drainage density, and urban morphology are not comprehensively considered, which leads to some biases. To overcome this limitation, this study aims to investigate the complex mechanism of urban waterlogging by identifying the relative contribution of each influencing factor and the stability linking waterlogging to influencing factors at multiple analysis scales (i.e. 1 km, 2 km, 3 km, 4 km, and 5 km). We consider waterlogging events in the central urban districts of Guangzhou (PR China) from 2009 to 2015 as a case study. A novel method that integrates the stepwise regression model with hierarchical partitioning analysis is presented to quantify the complex relationship between urban waterlogging and influencing factors. Results show that the spatial distribution of waterlogging events in the central urban area presents a strong agglomeration pattern. The waterlogging hotspots are mostly concentrated in the historical area of Guangzhou (Liwan district, Yuexiu district, the northern part of Haizhu district and western part of Tianhe district). Under all analysis scales, urban waterlogging is confirmed to mainly affect by both land cover characteristics (the percent cover of urban green spaces and residential area) and urban topography (slope.std). However, the dominant factor of waterlogging varied noticeably among different analysis scales, which presents a strong scale effect. At a small analysis scale (1km), the urban topography factors (slope.std and relative elevation) are the dominant conditioning factors of urban waterlogging events; however, with the increase of analysis scale, the contribution of topographic factors gradually declines, while the relative contributions of land cover composition (greenspace, residence area, grassland) and land cover spatial configuration (LPI, AI, Cohesion index) are much higher than other factors. These results also reveal that both of the land cover composition and spatial configuration can significantly affect the magnitude of waterlogging, which indicates that even if the proportion of land cover remains constant, changing the spatial distribution pattern of land cover will also affect the magnitude of waterlogging. This finding improves our understanding that urban waterlogging can be alleviated by balancing the composition of land cover as well as by optimizing the land cover spatial pattern. This study extended our scientific understanding of the complex mechanisms of waterlogging in the highly urbanized coastal city with respect to a multi-scale analysis perspective, providing useful support for the prevention and management of urban waterlogging.

How to cite: Zhang, Q., Wu, Z., Zhang, H., Dalla Fontana, G., and Tarolli, P.: Characterizing the dominant conditioning factors of urban waterlogging in highly urbanized coastal cities, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3682, https://doi.org/10.5194/egusphere-egu2020-3682, 2020.

Accurate information on extreme rainfall frequency at sub-hourly timescales is useful for many hydrological applications, such as urban drainage design and stormwater management. However, the availability of sub-hourly rainfall records with sufficient length and quality is generally limited in most countries. With these short datasets, the conventional rainfall frequency analysis methods (e.g. annual maxima (AM) series) are prone to systematic biases and large uncertainties. In this work, we take advantage of long sub-hourly rainfall archives to explore the potential of alternative methods that exploit a larger fraction of the available data (or features), thus promising accurate estimates from relatively short data records.

The first method is based upon the Metastatistical Extreme Value (MEV) framework, which relaxes the asymptotic assumption of traditional AM methods. MEV considers, year by year, the full distribution of the underlying ordinary events and their number of occurrences. The second method, the Simplified MEV (SMEV, a variant of MEV), in which inter-annual variability is neglected in favour of simpler parametrisation and more robust parameter estimation, is also tested. So far, these two methods were shown to outperform traditional methods for daily amounts, but were never used on sub-hourly data.

The third method is based upon point process theory, which represents the temporal rainfall process in a realistic yet simple way, such that the hierarchical structure of rainfall is explicitly incorporated, and several parameters have a physical interpretation. Models based upon point process theory were known to be incapable of preserving extreme rainfall statistics at hourly and sub‑hourly timescales. Nonetheless, a recent breakthrough has overcome this deficiency (Onof and Wang, 2019). In this work, a revised randomised Bartlett-Lewis rectangular pulse model (RBL) is employed.

Five-minute rainfall data from 5 long recording rain gauges in Germany – Bochum (69 years), Aplerbeck, Kruckel, Marten and Nettebach (49 years) – are used. The comparison is conducted by resembling the scenarios where sub-hourly rainfall time series data are available with various short lengths (i.e. 5/10/15/20 years). SMEV and RBL generally outperform the MEV and AM in preserving sub-hourly rainfall extremes and are both much less sensitive to the use of short data records. SMEV outperforms RBL in preserving rainfall extremes at short return periods (< 10-year return periods), while they perform similarly at long return periods. RBL however has the advantage of preserving rainfall extremes across multiple timescales (i.e. from sub-hourly, hourly to 1-day) at the same time. The unsatisfactory performance of MEV is related to the influence of the low-intensity tail of yearly distributions.

How to cite: Wang, L.-P., Marra, F., and Onof, C.: Modelling sub-hourly rainfall extremes with short records - a comparison of MEV, Simplified MEV and point process methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6061, https://doi.org/10.5194/egusphere-egu2020-6061, 2020.

Urbanization, one of the extreme cases of land-use change, plays an important role in modifying precipitation and urban hydrology. In this study, urbanization effect on cloud and precipitation in the Yangtze River Delta of China is simulated using Weather Research and Forecasting (WRF) model coupled with a single-layer Urban Canopy Model(SLUCM). Based on the 4-summer simulation results from 2011 to 2014, we find that the influence of cities on clouds and precipitation is obviously affected by wind field. During the day, more cloud on higher level and precipitation occurs in urban area and downwind region of urban, induced by more unstable urban air transported downstream, which enhances vertical mixing and updraft moisture transport. At night, the urban dry island become the dominant factor, resulting in the decrease of cloud occurrence in the urban and downstream areas. The downstream effects of urbanization on cloud and precipitation turn out to be strongly related to the moisture and convective conditions.

How to cite: Han, X. and Sun, J.: Simulation of Downstream Effects of Urbanization on Cloud and Precipitation with WRF Model in Yangtze River Delta, China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6888, https://doi.org/10.5194/egusphere-egu2020-6888, 2020.

For planning of urban drainage systems using hydrological models, long, continuous precipi-tation series with high temporal resolution are needed. Since observed time series are often too short or not available everywhere, the use of synthetic precipitation is a common alternative.

This contribution discusses the results of a research project, providing 5-minutes continuous stochastic point rainfall data for entire Germany for urban hydrological applications. Two different stochastic rainfall models are employed: a parametric stochastic model based on Alternating-Renewal processes and a non-parametric approach based on Resampling. Using rainfall observations from about 800 stations in Germany, the parameters of the models are regionalized. Rainfall and discharge characteristics are utilised for the evaluation of the model performance using a subset of 45 stations.

The results show, that stochastic rainfall from either of the models is better suited for urban hydrologic design, compared to the common practice scenario, where data from the nearest precipitation station is used. Notably, it could be shown that a mixture of generated rainfall from both models leads to a compensation of errors and further improves results, contrasted with using only data from one single model.

How to cite: Haberlandt, U., Bárdossy, A., Birkholz, P., Eisele, M., Fangmann, A., Fuchs, L., Herrmann, O.-C., Kuchenbecker, A., Maßmann, S., Morales, B., Müller, T., Seidel, J., and Sympher, K.: Synthetic rainfall for Germany based on simulations from two stochastic models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7566, https://doi.org/10.5194/egusphere-egu2020-7566, 2020.

Precipitation plays a critical role in determining the influent volumes of wastewater for many urban wastewater treatment plants (WWTPs). Urban stormwater runoff, resulting from impervious surfaces and infiltration, can significantly increase WWTP influent volumes above normal dry weather flows. Other factors such as demographics and changing landuse landcover can also impact influent volumes. In the context of climate change, projected changes in precipitation events could, in particular, cause significant challenges to existing collection networks. However, there has been limited research to date on the direct impacts of various precipitation variables on combined collection systems. This research aims to assess the impacts of precipitation on influent wastewater volumes using an urban area case-study in Ireland. In Ireland, most collection networks in urban areas are combined foul and storm water systems. Thus, these networks, and their connected wastewater treatment plants, can be impacted significantly by storm water (both in terms of volume and wastewater characteristics).  

Daily data of influent volume and precipitation for a relatively large municipal wastewater treatment plant in Ireland for the period of 2011-2018 was used for this study. The precipitation intensity was categorised based on the percentile values to obtain clarity on its effects on influent volume. This study investigated the relationship between influent wastewater volume and precipitation, number of wet days (wet day characterised by rainfall greater than or equal to 1 mm) and the number of zero rainfall days. It was observed that on a monthly basis, the relationship between average daily values of influent volume and precipitation showed significant linear trends with R² values as high as 0.86 for all the years. Average daily influent volume estimated per month showed strong relationships and significant trends for all years when analysed with the number of wet days and separately with the number of zero rainfall days in that month. Impacts of rainfall events were generally seen on the same day with residuals over the following days, meaning any time lag could not be detected. The dry weather flow was estimated by averaging the flow of consecutive zero rainfall days excluding the flow values of the first two dry days of such an event to eliminate the effects of any preceding rainy days. This analysis gave insight to the impacts of other factors such as demographic changes due to tourism or seasons on influent wastewater volumes. Factors which were also considered in this study included the impacts of tides on the sewer network. This work is informing the ongoing analysis of a further 16 wastewater treatment plants which will enable improved planning and adaptation of wastewater infrastructure to climate change.

How to cite: Saikia, S., Clifford, E., and Ryan, P.: Modelling the Precipitation Impacts on Wastewater Influent Volumes in Galway, Ireland, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8694, https://doi.org/10.5194/egusphere-egu2020-8694, 2020.

The short term forecast of rainfall intensities for fine temporal and spatial resolutions, has always been challenging due to the unpredictable nature of rainfall. Commonly at such scales, radar data are employed to track and extrapolate rainfall storms in the future. For very short lead times, the Lagrange persistence can produce reliable results up to 20 min whilst for longer lead times hybrid models are necessary, in order to account for the birth, death and non-linear transformations of storms that might increase the predictability of rainfall. Recently, data driven techniques, are gaining popularity due to their high learning skills, although their performance is highly dependent on the size of the training dataset and don’t include any physical background. Thus the aim of this study is to investigate the use of data driven techniques in increasing the predictability of rainfall forecast at very fine scales.

For this purpose, a deep convolutional artificial neural network (CNN) is employed to predict rainfall intensities at 5min and 1km² resolutions for the Hannover radar range area at lead times from 5min to 3 hours. The deep CNN is trained for each lead time based on a past window of 15 minutes. The training dataset consist of 93 events (convective, stratiform and mixed events) from the period 2003-2012 and the validating dataset of 17 convective events from the period 2013-2018. The performance is assessed by computing the correlation and the root mean square error of the forecasted fields from the observed radar field, and is compared against the performance of an existing Lagrange-based nowcast method; the Lucas-Kanade optical flow. Special attention is given to the quality of the radar input by using a merged product between radar and gauge data (100 recording stations are used) instead of the raw radar one.  

The results of this study reveal that the deep CNN is able to learn complex relationship and improve the nowcast for short lead times. However there is a limit that a CNN cannot pass; for those lead times a blending of the radar based nowcast with NWP might be more desirable. Moreover, since most urban models are validated on gauge observations, forecasting on merged data yields more reliable results for urban flood forecasting as the forecast agrees better with the gauge observation.

How to cite: Shehu, B., Gerchow, M., and Haberlandt, U.: A convolutional neural network for nowcasting rainfall intensities at fine temporal and spatial scales, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9108, https://doi.org/10.5194/egusphere-egu2020-9108, 2020.

Internet-of-Things (IoT) technology is evolving rapidly and within the frame of the FloodCitiSense.eu project we are exploring the potential of low-cost citizen observatories for the monitoring of intense rainfall and pluvial flooding in three pilot cities: Brussels, Rotterdam and Birmingham. In this presentation we focus on the Brussels pilot in which we evaluate the added value of low-cost rainfall sensors (developed by Disdrometrics, Delft – The Netherlands) to complement the existing network with 16 professional rain gauge (Flowbru.be – Open data). The main objective is to obtain a higher density of rainfall measurements enabling to capture, in near real-time, intense rainfall events. Due to the high degree of imperviousness of the city landscape intense rainfall is often the trigger for a fast hydrological response, sometimes causing pluvial flooding in Brussels. The low-cost rainfall sensors are disdrometers, counting the number and estimating the size of raindrops. The low-cost sensors make use of LoRa technology to send their data in near real-time to the central database. In Brussels 20 low-cost sensors were installed with help of citizens, mainly aiming at filling the “gaps” of the existing rain gauge network. To enable direct evaluation some of the low-cost sensors where installed next to professional rain gauges. We evaluate the performance of the low-cost sensor by (1) direct comparison (intensity and volumes) with the professional rain gauges of the Flowbru.be network, (2) comparing the spatial pattern of measured rainfall intensities, with and without low-cost sensors, to radar rainfall maps and (3) the reliability of the low-cost measurements. In this contribution we will focus on the first results from the test phase (October 2019 – January 2020). Next we also elaborate on the challenges involved in the deployment of a network of low-cost sensors. The FloodCitiSense.eu project is a close collaboration with TU Delft, Imperial College London, IIASA, Disdrometrics, VUB SMIT-imec, LGiU, EGEB and is funded within the ERA-NET Smart Urban Future programme (Urban Europe ENSUF).

How to cite: Verbeiren, B., Tondeur, K., Seyoum, S., and Pireaux, D.: Complementing urban rainfall/flood monitoring using low-cost citizen observatories: first result and challenges, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10884, https://doi.org/10.5194/egusphere-egu2020-10884, 2020.

With the increase in the global population observed since the 20th century, urban centers are becoming more prominent, and its dynamic is now far from the natural. The impact of urbanization on the rainfall has been noticed since 1921 when Horton observed that cities with more than 100,000 inhabitants created favorable conditions for convective precipitation. Later, Huff and Changnon (1972) estimated an increase of 6 to 15% on average rainfall during the summer in these regions. Several other studies confirmed the trend and pointed out that on a small and medium scale, precipitation change is usually justified by the effect of heat islands. To understand these changes, high-resolution precipitation data is needed; however, due to the lack of monitored data, especially on the largest cities in the developing countries, new sources of information should be used. MSWEP is a three hourly gridded precipitation dataset, with 0.1º spatial resolution that combines data from gauges, satellite, and reanalysis-based data to provide precipitation estimates over the entire globe (Beck, 2019). In this study, MSWEP precipitation was used in order to observe the variability of intense precipitation over the Metropolitan Area of Porto Alegre in Southern Brazil, where some previous studies indicated urban effects on precipitation. Statistical analysis was performed to evaluate changes in the intense precipitation throughout the decades. The results show that the spatial distribution patterns of intense precipitation are maintained; however, in all statistics, it was possible to observe an increase in intense precipitation over the decades, that follows the increase of the urbanized area over time.

How to cite: Allasia, D., Petry, I., Basso, R., Tassi, R., and Minetto, B.: Variability and trends of intense precipitation in a metropolitan area in the southern Brazil , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12716, https://doi.org/10.5194/egusphere-egu2020-12716, 2020.

The emergence of high-resolution observational tools, information and communication technologies, cloud computing and big data are disrupting the water sector in an unprecedented way. In the field of hydrological sciences, research calls aimed at observing, backcasting and forecasting terrestrial water resources at finer space-time resolutions have been made over the past years by the scientific community. Here, we introduce the Hydrological Observatory of Mexico City (OH-IIUNAM), an academic initiative consisting of a dense network of 55 state-of-the-art precipitation sensors (optical disdrometers and weighing rain gauges) located within Mexico City, one of the largest urban centers of the world. The objective of OH-IIUNAM, given its open-data philosophy, is to enable scientific research within urban environments by providing a real-time hydro-meteorological observational platform at the hyper-resolution (dt=1 minute). Potential niches of opportunity ranging from atmospheric processes to hydrological modeling and design in urban areas are envisaged and discussed. Future expansion phases of OH-IIUNAM are expected to incorporate streamflow, groundwater and water quality.

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How to cite: Pedrozo-Acuña, A., Breña-Naranjo, J. A., Soriano-Monzalvo, J. C., Blanco-Figueroa, J., Magos-Hernández, J., and Sánchez-Peralta, J. A.: The Hydrological Observatory of Mexico City (OH-IIUNAM): a unique setup for hydrological research within large urban environments, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12742, https://doi.org/10.5194/egusphere-egu2020-12742, 2020.

Natural catchments have formed efficient river networks for a long time. similarly, urban drainage networks have been developed with the purpose of efficiently draining rainfall from catchments to flood mitigation. In this study, we analyzes and compares the characteristics between the naturally formed river networks for a long time and the artificially formed drainage networks using Gibbs’ Model. Gibbs’ Model is a stochastic stream network model, which can generate multiple realizations of stochastic networks based on a single parameter value of . Gibbs‘ Model was applied to a total number of 239 urban catchments in Seoul, South Korea and 70 natural catchments in the Midwestern areas of US. Topographic characteristics of catchments are analyzed along with the efficiency of drainage networks, which are presented by for both natural and urban catchments. The result of this study demonstrates the difference between natural and artificial drainage network characteristics and suggests a new alternative measures to mitigate flood risks in urban catchments facing extreme hydrologic events with climate change.

How to cite: Kim, K. and Seo, Y.: Comparison of natural and urban drainage network characteristics based on Gibbs’ Model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13675, https://doi.org/10.5194/egusphere-egu2020-13675, 2020.

Due to the climate change caused uncertainty, the urban areas face new challenges. In addition to mitigating the negative effects, it is important the developments need to implemented in a sustainable manner. The problem of urban areas is substantial on account of their growing spatial size and population, furthermore the inadequate infrastructure. Urban districts with inadequate infrastructure can be a major source of water pollution, but also have a significant impact on the well-being of the citizens. In modern urban planning the sustainable urban water management based on the usage of green infrastructure. Green infrastructure is an important tool to make urban water cycle sustainable by linking artificial, engineered elements (gray infrastructure) with the services provided by vegetation. Green infrastructure can help to make the urban water cycle sustainable in many ways. Its primary role is the mitigating effect, such as reducing and retaining surface runoff with the process of interception and evaporation. Due to the complex structure of vegetation, it can also play an important role in infiltration (by root system), thus also reducing surface runoff.

Providing adequate data on the role of green infrastructure – even on a city-wide scale – can help decision makers. To accomplish this, hydrological models can play an important role. If these models (i-Tree Hydro) based on appropriate meteorological and land cover data, they can help to estimate the runoff and infiltration of study areas and the reducing effect of vegetation (interception, evaporation). In our study, we attempted to compare two significantly different urban district based on these aspects and to analyze the differences. Analyzes in the two study areas of Szeged (Hungary) all suggest the vegetation can significantly contribute to the reduction of surface runoff. Differences between these urban districts can be quantified so these data can serve as a basis for decision making in urban planning processes.

As another element of our research, we analyzed the relationship between surface runoff and infiltration in modeling study (SWMM) of rainwater harvesting systems in public institutions (kindergartens). In this part of the research, besides the efficiency of the rainwater harvesting systems, we got data about the extent of surface runoff, evaporation and infiltration on yard of kindergartens.

How to cite: Gulyás, Á. and Csete, Á.: Assessment of the role of green infrastructure in sustainable urban water management, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17994, https://doi.org/10.5194/egusphere-egu2020-17994, 2020.

Traditionally in Denmark, rainwater detention basins are designed based on an input box rain with a given return period and a constant outflow from the basin. The water authorities specify the outflow rate in order to avoid erosion in the recipient. Intensity-Duration-Frequency (IDF) curves are used as a prerequisite for the method. Given the design return period, and varying the duration of the box rain, the basin volume that prevents spill from the basin can be determined. By analysing for a series of outflow rates, a basin volume curve for the selected return period can be developed. The current practice is revisited, and a new analytical solution for the duration of the design rain is found.

A constant outflow rate for the basin is, however, not always a realistic assumption, and thus there is a risk for underestimation. An alternative design method has been analysed, assuming that the outflow from the basin takes place corresponding to at linear reservoir with maximum outflow rate equal to the one specified by the water authorities. The method is described in detail, and the results compared with those of the current guidelines.

How to cite: Rosbjerg, D.: Design of rainwater detention basins, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20104, https://doi.org/10.5194/egusphere-egu2020-20104, 2020.

The efficiency of urban drainage networks are very important within the framework of flood mitigation planning. This study suggests a methodology to evaluate the efficiency of urban drainage networks. Gibbs’s model was applied to 237 catchments in Seoul. If the parameter β is less than 10⁰, it is regards as an inefficient network. Otherwise, it is an efficient network. The results show number of catchments with lower β is greater than with higher β. This is contradictory to common sense that urban drainage networks are efficient. Identifying the efficiency of an urban drainage networks suggest potential flood reduction by an alternative method, which is related to a layout of the networks.

How to cite: Hwang, J., Seo, Y., and Choi, H. I.: Efficiency of Urban Drainage Networks: A Case Study in Seoul, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21263, https://doi.org/10.5194/egusphere-egu2020-21263, 2020.

The benefit of urban roadside trees to provide ecosystem services and wellbeing of human in expanding and compacted cities gets more and more attention. For northern Germany it is predicted that climate change rises summer temperatures and that precipitation patterns shift to drier vegetation periods. In cities, those impacts will intensify water (soil sealing) and heat problems (urban heat island) even more. Furthermore, roadside trees have to deal with several specific site limitations like extreme soil compaction and soil sealing, low water infiltration rates, sandy and anthropogenic deposited substrates, and soil volume restrictions. The consequences for the trees are drought stress combined with reduced vitality and life expectancy.

Our research is based on soil water monitoring at 17 roadside plantation sites across the city of Hamburg.  We focus on the water availability of prepared planting soils and the development of the trees root systems. Sensors for soil water tension and soil temperature were installed in different soil areas of each site: topsoil, root ball, tree pit substrate, lateral space, and subsoil. The general goal of this study was to characterize the soil water availability at roadside planting pits during the first years after plantation (here: 2017, 2018 and 2019). Based on these results the long-term objective is to elaborate recommendations for the soil-related technology of future urban tree planting sites. The Creation of more suitable conditions in the planting site enhances roadside tree vitality and provides ecosystem services by the trees on a higher level.

The data analysis focused on two main aspects. First, the effect of weather conditions, especially  the extreme wet and dry vegetation periods, on the soil water availability in the tree pit. Second, the three-year temporal development of soil water distribution in the different soil areas within the planting pitafter plantation.

We found that soil water availability in the vegetation period (VP; April-October) at the investigated roadside plantation sites are highly correlated to weather conditions (air temperature (aT) and precipitation (P)). During a cold and wet VP (aT: 14,0 °C, P: 631 mm), soil tensions reached a critical value on average at 24 ± 18 days (11 ± 9 % of VP). In a hot and dry VP (aT: 16,0 °C, P: 222 mm), soil tensions reached a critical value on average at 115 ± 22 days (54 ± 10 % of VP).

Furthermore, the results showed that soil water scarcity in the first VP occurred mainly in the root ball, whereas during the second VP water scarcity developed in all soil areas within the planting site, except for the subsoil. Although the amount of precipitation during the last vegetation period was more than doubled compared to the second, the subsoil reached higher water tensions. This finding leads to the conclusion that root development after plantation took place from the root ball over the prepared planting soil into the surrounding soil within depths of up to 1 m.  

How to cite: Schütt, A., Gröngroeft, A., Schaaf-Titel, S., and Eschenbach, A.: A three year study on the soil water availability at roadside trees in Hamburg, Germany, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21403, https://doi.org/10.5194/egusphere-egu2020-21403, 2020.

Aim of this study is to evaluate the impact of Precipitation Measurement Biases (PMBs) of tipping-bucket rain gauges onto the hydraulic modelling of urban drainage networks.  As a case study, the monitored experimental suburban catchment of Parco d’Orleans located in the University Campus of Palermo, Italy and managed since 1987 by the Department of Engineering of the University of Palermo is considered. . Two tipping-bucket rain gauges provide a good spatial coverage of the catchment area and an acoustic level gauge is installed at the outlet of the drainage network for flow mesaurements. Contemporary high temporal resolution rainfall and runoff data series are available between 1993 to 1998, and are used for the calibration of the hydraulic model in terms of roughness of the urban surfaces. The total drainage area is 12.8 ha with 68% of impervious areas; the drainage network is composed of circular and egg-shaped concrete conduits. In the present work, the sensitivity of this rapid response system to the accuracy of the rainfall input is studied, with reference to drainage failures and urban flooding issues. In order to quantify the instrumental mechanical error of the two available Tipping Bucket Rain-gauges, these were calibrated at the rain gauge laboratory of the WMO Lead Centre on Precipitation Intensity “B. Castelli” following the procedure described in the recent EN 17277:2019 standard on precipitation measurements. For each gauge a calibration curve was provided in order to quantify the measurement bias and the associated calibration uncertainty.

For rainfall-runoff transformation in the urban drainage system, a conceptual model for urban catchment, which incorporates semi-distributed modelling concepts has been used. The urban basin is divided in external sub-catchments connected to the drainage network. Each external sub-catchment is modelled as two separate conceptual linear elements, a reservoir and a channel, one for the pervious part, the other for the impervious part of the investigated area. The drainage network is schematized as a cascade of non-linear cells and the flood routing is simplified in the form of kinematic wave and represented as a flux transfer between adjacent cells. The sensitivity of this rapid response system to the accuracy of the rainfall input has been studied with reference to drainage failures and urban flooding issues.

To examine the effects due to PMBs on the catchment response, a number of simulations were carried out using raw rainfall data and corrected data obtained after the application of the calibration curve for each rain gauge. Results, expressed in terms of comparisons between the hydrographs at catchment outlet, show a significant influence of the PMB on the peak flow and the total hydrograph volume.

How to cite: Candela, A., Francipane, A., Stagnaro, M., Cauteruccio, A., and Lanza, L. G.: Propagation of precipitation measurement biases into the hydraulic modelling of urban drainage systems – A case study of the Parco D’Orleans sub-urban catchment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21577, https://doi.org/10.5194/egusphere-egu2020-21577, 2020.

There has been a surge of interest in the field of urban flooding in recent years, due to the growth of cities and the increase in frequency and magnitude of extreme rainfall events. Hydrological modeling is a useful tool to understand urban floods and compare different stormwater management solutions. In this study, we use the Storm Water Management Model (SWMM) in an urban catchment, Grefsen in Norway, to analyze the effects of different Low Impact Development (LID) methods to reduce combined sewer overflow (CSO). Additionally, we examine the cost of these solutions and find an optimized solution in terms of maximum effects and minimum cost. The results are useful for decision-makers to achieve sustainable stormwater management.

Acknowledgement:

This research is funded by the Norwegian Research Council via the project New Water Ways.

How to cite: Xu, C.-Y. and Li, H.: Cost and benefit analysis of Low Impact Development (LID) for stormwater management in an urban catchment in Norway , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21881, https://doi.org/10.5194/egusphere-egu2020-21881, 2020.