Radar rainfall forecasting for sewer flood modelling to support decision-making in sewer network operations

Radar quantitative precipitation estimates (QPEs) and forecasts (QPFs) are useful in urban hydrology because they can provide real time or forecasted rainfall information for flood forecasting/warning systems. Sewer flooding is a disruptive problem in England and Wales. Wastewater companies have reported that more than 4,700 customers are at risk of internal sewer flooding. Currently in the UK, mitigating sewer flooding before it occurs is difficult to achieve operationally because of the lack of accurate and specific data. As radar rainfall data is available from the UK Met Office, particularly radar QPFs with a maximum lead time of 6 hours, these datasets could be used to predict sewer flooding up to this maximum lead time. This research investigates the uses of radar Quantitative Precipitation Forecasts and Quantitative Precipitation Estimates to support short term decisions of sewer network operation in reducing the risk of sewer flooding. It is achieved by increasing the accuracy of deterministic radar quantitative precipitation forecasts, developing on probabilistic radar quantitative precipitation forecasts, and using spatial variability of radar quantitative precipitation estimates to estimate flood extents in sewer catchments from the North East of England. Radar rainfall data used in the case study is also sourced from this region of size 184 km x 140 km. The temporal and spatial resolutions of rainfall forecasts are important to producing accurate hydrological output. Hence, increasing these resolutions is identified to improving deterministic radar quantitative precipitation forecasts for hydrological applications. An interpolation method involving temporal interpolation by optical flow and spatial interpolation by Universal Kriging is proposed to increase the resolution of radar QPF from a native resolution of 15 mins and 2-km to 5 mins and 1-km. Key results are that the interpolation method proposed outperforms traditional interpolation approaches including simple linear temporal interpolation and spatial interpolation by inverse distance weighting. Probabilistic radar quantitative precipitation forecasts provide information of the uncertainty of the radar deterministic forecasts. However, probabilistic approaches have limitations in that they may not accurately depict the uncertainty range for different rainfall types. Hence, postprocessing probabilistic quantitative precipitation forecasts are required. A Bayesian postprocessing approach is introduced to postprocess probability distributions produced from an existing stochastic method using the latest radar QPE. Furthermore, non-normal distributions in the stochastic model are developed using gamma based generalised linear models. Key successes of this approach are that the postprocessed probabilistic QPFs are more accurate than the pre-processed QPFs in both cool and warm seasons of a year. Furthermore, the postprocessed QPFs of all the verification events better correlate with their QPE, thus improving the temporal structure. Spatial variability of radar QPE/QPF data influences flood dynamics in a sewer catchment. Moreover, combination of different percentiles of probabilistic QPFs, per radar grid, over a sewer catchment would produce different spatial distributions of rainfall over the area. Furthermore, simulating many probabilistic QPFs concurrently is computationally demanding. Therefore, generalised linear models have been used to estimate model flood variables using a spatial analysis of radar QPE. Spatial analysis involves using indexes representing specific information of the spatial distribution of rainfall. The novelty of this estimation method includes faster estimations of flood extents. The main points of success of this approach are that more detailed spatial analysis of large sewer catchments produce more accurate flood estimations that could be used without running hydraulic simulations. This makes the approach suitable for probabilistic sewer flood forecasting in real-time applications. A business case is proposed to use the outputs of this research for commercial applications. Probabilistic sewer flood forecasting is evaluated and recommended for industry application using a financial appraisal approach for Northumbrian Water Limited. The business case shows that the methods could be adopted by the wastewater company to mitigate sewer flooding before it occurs. This would support decision making and save costs with better intervention management

Data Types as Quotients of Polynomial Functors

A broad class of data types, including arbitrary nestings of inductive types, coinductive types, and quotients, can be represented as quotients of polynomial functors. This provides perspicuous ways of constructing them and reasoning about them in an interactive theorem prover

Examination of the diurnal cycle of rainfall and ensemble prediction strategies in WRF model simulations

Two studies were conducted addressing issues relevant to warm season precipitation forecasts in the United States. First, the representation of the diurnal cycle of rainfall in a 5 km grid-spacing mesoscale model explicitly representing convection was compared to a 22 km grid-spacing model implicitly representing convection. Improvements were expected in the 5 km grid-spacing model diurnal cycle of rainfall because of its lack of a cumulus parameterization scheme (CPS). Previous works show that problems with CPSs adversely affect the timing of precipitation and the ability of the model to represent the mesoscale dynamics that lead to propagation. Results revealed that the 5 km model did have a significantly better representation of the diurnal cycle of rainfall than the 22 km model. The timing, location, and representation of both propagating and non-propagating rainfall areas were superior in the 5 km model. Second, a comparison of forecast skill and spread was made between a mixed-physics (MP) and perturbed initial and lateral boundary conditions (PI) ensemble. Forecast skill was compared using deterministic forecasts derived from each ensemble and using probabilistic forecasts from each ensemble. Results revealed that the MP and PI ensembles had similar skill when the deterministic forecasts were evaluated using equitable threat scores (ETSs). However, when the area under the relative operating characteristic curve (ROC score) was used to evaluate the probabilistic forecasts, the MP ensemble had higher skill at the beginning of the forecast while the PI ensemble had higher skill at the end of the forecast. This behavior was directly related to the spread. In the MP ensemble, because the initial and lateral boundary conditions were the same for each ensemble member, the spread stopped increasing after about 24 hours, and shortly after this time the PI ensemble ROC scores surpassed the MP ensemble ROC scores. However, during the first 24 hours of the forecast, greater spread in the MP ensemble forecasts was accompanied by higher ROC scores than in the PI ensemble. This demonstrates the importance of perturbed lateral boundary conditions in ensembles using limited area models if forecasts are desired beyond 24-36 hours

Probabilistic Real-Time Urban Flood Forecasting Based on Data of Varying Degree of Quality and Quantity

This thesis provides a basic framework for probabilistic real-time urban flood forecasting based on data of varying degree of quality and quantity. The framework was developed based on precipitation data from two case study areas:Aarhus Denmark and Castries St. Lucia. Many practitioners have acknowledged that a combination of structural and non-structural measures are required to reduce the effects of flooding on urban environments, but the general dearth of the desired data and models makes the development of a flood forecasting system seem unattainable. Needless to say, high resolution data and models are not always achievable and it may be necessary to override accuracy in order to reduce flood risk in urban areas and focus on estimating and communicating the uncertainty in the available resource. Thus, in order to develop a pertinent framework, both primary and secondary data sources were used to discover the current practices and to identify relevant data sources. Results from an online survey revealed that we currently have the resources to make a flood forecast and also pointed to potential open source quantitative precipitation forecast (QPF) which is the single most important component in order to make a flood forecast. The design of a flood forecasting system entails the consideration of several factors, thus the framework provides an overview of the considerations and provides a description of the proposed methods that apply specifically to each component. In particular, this thesis focuses extensively on the verification of QPF and QPE from NWP weather radar and highlights a method for estimating the uncertainty in the QPF from NWP models based on a retrospective comparison of observed and forecasted rainfall in the form of probability distributions. The results from the application of the uncertainty model suggest that the rainfall forecasts has a large contribution to the uncertainty in the flood forecast and applying a method which bias corrects and estimates confidence levels in the forecast looks promising for real-time flood forecasting. This work also describes a method used to generate rainfall ensembles based on a catalogue of observed rain events at suitable temporal scales. Results from model calibration and validation highlights the invaluable potential in using images extracted from social network sites for model calibration and validation. This framework provides innovative possibilities for real-time urban flood forecasting