Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549442
Title: Rainfall-runoff modelling and numerical weather prediction for real-time flood forecasting
Author: Liu, Jia
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2011
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Abstract:
This thesis focuses on integrating rainfall-runoff modelling with a mesoscale numerical weather prediction (NWP) model to make real-time flood forecasts at the catchment scale. Studies carried out are based on catchments in Southwest England with a main focus on the Brue catchment of an area of 135 km2 and covered by a dense network of 49 rain gauges and a C-band weather radar. The studies are composed of three main parts: Firstly, two data mining issues are investigated to enable a better calibrated rainfall-runoff model for flood forecasting. The Probability Distributed Model (PDM) is chosen which is widely used in the UK. One of the issues is the selection of appropriate data for model calibration regarding the data length and duration. It is found that the information quality of the calibration data is more important than the data length in determining the model performance after calibration. An index named the Information Cost Function (ICF) developed on the discrete wavelet decomposition is found to be efficient in identifying the most appropriate calibration data scenario. Another issue is for the impact of the temporal resolution of the model input data when using the rainfall-runoff model for real-time forecasting. Through case studies and spectral analyses, the optimal choice of the data time interval is found to have a positive relation with the forecast lead time, i.e., the longer is the lead time, the larger should the time interval be. This positive relation is also found to be more obvious in the catchment with a longer concentration time. A hypothetical curve is finally concluded to describe the general impact of data time interval in real-time forecasting. The development of the NWP model together with the weather radar allows rainfall forecasts to be made in high resolutions of time and space. In the second part of studies, numerical experiments for improving the NWP rainfall forecasts are carried out based on the newest generation mesoscale NWP model, the Weather Research & Forecasting (WRF) model. The sensitivity of the WRF performance is firstly investigated for different domain configurations and various storm types regarding the evenness of rainfall distribution in time and space. Meanwhile a two-dimensional verification scheme is developed to quantitatively evaluate the WRF performance in the temporal and spatial dimensions. Following that the WRF model is run in the cycling mode in tandem with the three-dimensional variational assimilation technique for continuous assimilation of the radar reflectivity and traditional surface/ upperair observations. The WRF model has shown its best performance in producing both rainfall simulations and improved rainfall forecasts through data assimilation for the storm events with two dimensional evenness of rainfall distribution; while for highly convective storms with rainfall concentrated in a small area and a short time period, the results are not ideal and much work remains to be done in the future. Finally, the rainfall-runoff model PDM and the rainfall forecasting results from WRF are integrated together with a real-time updating scheme, the Auto-Regressive and Moving Average (ARMA) model to constitute a flood forecasting system. The system is tested to be reliable in the small catchment such as Brue and the use of the NWP rainfall products has shown its advantages for long lead-time forecasting beyond the catchment concentration time. Keywords: rainfall-runoff modelling, numerical weather prediction, flood forecasting, real-time updating, spectral analysis, data assimilation, weather radar.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.549442  DOI: Not available
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