Søren Liedtke Thorndahl

Comparison Of Short Term Rainfall Forecasts For Model Based Flow Prediction In Urban Drainage Systems

Forecast-based flow prediction in drainage systems can be used to implement real-time control of drainage systems. This study compares two different types of rainfall forecast - a radar rainfall extrapolation-based nowcast model and a numerical weather prediction model. The models are applied as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real-time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 h. The best performance of the system is found using the radar nowcast for the short lead times and the weather model for larger lead times.

Stochastic long term modelling of a drainage system with estimation of return period uncertainty

Long term prediction of maximum water levels and combined sewer overflow (CSO) in drainage systems are associated with large uncertainties. Especially on rainfall inputs, parameters, and assessment of return periods. This paper proposes a Monte Carlo based methodology for stochastic prediction of both maximum water levels as well as CSO volumes based on operations of the urban drainage model MOUSE in a single catchment case study. Results show quite a wide confidence interval of the model predictions especially on the large return periods. Traditionally, return periods of drainage system predictions are based on ranking, but this paper proposes a new methodology for the assessment of return periods. Based on statistics of characteristic rainfall parameters and correlation with drainage system predictions, it is possible to predict return periods more reliably, and with smaller confidence bands compared to the traditional methodology.

On hydraulic and pollution effects of converting combined sewer catchments to separate sewer catchments

The overall objective of this paper is to contribute to the standing debate concerning the advantages of separate sewer systems compared to traditional combined sewer systems. By a case study this investigation reveals that transformation of part of a town from being serviced with combined sewer systems to become serviced with separate sewer systems decreases the volumes of storm water and pollutants diverted to the waste water treatment plant and discharged as combined sewer overflow. This happens at the expense of an increase in volumes of storm water and pollutant loads diverted to local receiving waters when detention ponds are not built-in the new separate sewer systems. It is concluded that consequences can be fatal for receiving waters, if no retention of pollutants is integrated into the system.

Estimating Subcatchment Runoff Coefficients using Weather Radar and a Downstream Runoff Sensor

This paper presents a method for estimating runoff coefficients of urban drainage subcatchments based on a combination of high resolution weather radar data and flow measurements from a downstream runoff sensor. By utilising the spatial variability of the precipitation it is possible to estimate the runoff coefficients of the separate subcatchments. The method is demonstrated through a case study of an urban drainage catchment (678 ha) located in the city of Aarhus, Denmark. The study has proven that it is possible to use corresponding measurements of the relative rainfall distribution over the catchment and downstream runoff measurements to identify the runoff coefficients at subcatchment level.

Probabilistic modelling of combined sewer overflow using the First Order Reliability Method

This paper presents a new and alternative method (in the context of urban drainage) for probabilistic hydrodynamical analysis of drainage systems in general and especially prediction of combined sewer overflow. Using a probabilistic shell it is possible to implement both input and parameter uncertainties on an application of the commercial urban drainage model MOUSE combined with the probabilistic First Order Reliability Method (FORM). Applying statistical characteristics on several years of rainfall, it is possible to derive a parameterization of the rainfall input and the failure probability and return period of combined sewer overflow to receiving waters can be found.

GLUE based marine X-band weather radar data calibration and uncertainty estimation

The Generalized Likelihood Uncertainty Estimation methodology (GLUE) is investigated for radar rainfall calibration and uncertainty assessment. The method is used to calibrate radar data collected by a Local Area Weather Radar (LAWR). In contrast to other LAWR data calibrations, the method combines calibration with uncertainty estimation. Instead of searching for a single set of calibration parameters, the method uses the observations to construct distributions of the calibration parameters. These parameter sets provide valuable knowledge of parameter sensitivity and the uncertainty. Two approaches are analyzed; the static calibration approach, where the LAWR is calibrated once for a long period and the dynamic approach, where the estimate is continuously adjusted based on ground observations. The analysis illustrates that the static calibration performs insufficiently, whereas the dynamic adjustment improves the performance significantly. It is found that even if the dynamic adjustment method is used the uncertainty of rainfall estimates can still be significant.

Sensitivity Analysis of an Integrated Urban Flood Model

In this paper, the model parameter sensitivity of an integrated urban flood model, including groundwater system, overland flow, sewer network, and river network, is investigated. The sensitivity analysis quantifies how influential the individual and correlated model parameters are to the simulated flood depth and area for an urban catchment in Aalborg, Denmark. The key parametric contributors to output sensitivity are analyzed by: (i) identify the most sensitive model parameters using a Morris screening; (ii) using the generalized Sobol’ method to investigate the sensitivity of the individual input parameters and the interaction between each parameter. The results indicate that the groundwater system, overland flow, and river network contribute most to the flood depth and as such, they need to be considered in urban flood modelling.

Evaluating catchment response to artificial rainfall from four weather generators for present and future climate

The technical lifetime of urban water infrastructure has a duration where climate change has to be considered when alterations to the system are planned. Also, models for urban water management are reaching a very high complexity level with, for example, decentralized stormwater control measures being included. These systems have to be evaluated under as close-to-real conditions as possible. Long term statistics (LTS) modelling with observational data is the most close-to-real solution for present climate conditions, but for future climate conditions artificial rainfall time series from weather generators (WGs) have to be used. In this study, we ran LTS simulations with four different WG products for both present and future conditions on two different catchments. For the present conditions, all WG products result in realistic catchment responses when it comes to the number of full flowing pipes and the number and volume of combined sewer overflows (CSOs). For future conditions, the differences in the WGs representation of the expectations to climate change is evident. Nonetheless, all future results indicate that the catchments will have to handle more events that utilize the full capacity of the drainage systems. Generally, WG products are relevant to use in planning of future changes to sewer systems.

Investigation of Impacts of Spatial Variability and Motion of Rainfall in Urban Drainage Modelling

The topic of this paper is to investigate which knowledge can be acquired from weather radar systems on the case of spatial variability of storm events. Furthermore, it is investigated how this variation affects the results of an urban drainage model. The main focus of the study is to determine if the motion of a storm event has any impact on the runoff response in the drainage system. To do this a novel method is developed to rotate and relocate existing radar images and use the result as input to a selected model. At the moment the results show that the dominant direction of the storm has little to none effect on the outcome, but further research is being conducted to confirm this.

Influence of Flood Water Contribution from Multiple Sources in Extreme Event Statistics of Urban Flooding

For pluvial flood risk assessment in urban areas it is important to be able to calculate how often a specific area is at risk of flooding. This is especially evident in urban areas subject to contribution from multiple sources, e.g. surcharging drainage system, surface runoff, overflowing rivers, etc. In this study extreme event statistics are assessed by simulation of rainfall impact and consecutive statistics of flood response in order to estimate return periods of flooding. The model applied is an integrated hydraulic model which includes relevant hydrological processes that contribute to urban flooding. The setup is analysed based on a small urban catchment in Aalborg Denmark. Results show that it is possible to estimate return periods of flood volume, flood extent and local water levels based on simulation and that rainfall and hydrological conditions critical to flooding can be identified.