Quantitative precipitation forecasting (QPF) for surface water flood forecasting

The understanding and reduction of the uncertainties associated with meteorological forecasting and hydrology is a critical part of any integrated, interactive drainage system. The development of a dynamic surface water management system, based on the concept of Sustainable Urban Drainage System (SUDS) and coupled with a real-time extreme rainfall forecasting system, is currently in the design phase in the North Glasgow Area. Critical to the design's success is the integration of extreme rainfall forecasting in a timely manner (design criteria is 24hrs warning) to allow the system's water level and storage control to operate effectively. Directly impacting on this topical field, this MRes research examines the current state and future likely development trends of Quantitative Precipitation Forecasting (QPF) in terms of accuracy, location and timing (Murphy, 1993) for application to the North Glasgow Area and wider use in urban hydrology assessments. Frontal systems producing moderate rainfall are accurately forecast with sufficient lead time (beyond 24 hours) however extreme convective cell rainfall events are still poorly predictable in terms of localization and rainfall volumes. Improving the understanding and modelling of convection is of paramount importance to overcome these limitations. Predicting the localization of the rainfall peak is a challenge and it has been found that the error in the first few hours is 20-30 km growing considerably with lead time. The interpretation of timing error is still controversial and methods to treat it in forecast verification and post-processing are being developed. The critical synthesis of this review has led to the development of a conceptual solution for the system design. Overall, the knowledge and technologies in QPF are already mature to properly feed dynamic water management systems however its reliability will be further enhanced by likely infrastructure developments, such as hardware upgrades and a finer observational network in the next decade.

Name dc:type.qualificationname

mres

Level dc:type.qualificationlevel

masters-pg

Grantor dc:publisher.institution

University of Strathclyde

Year dc:date.issued

2015

Author dc:creator

• Marinari, Mariano

DOI

10.48730/am0d-3v39

Identifier

T14182

Author Identifier

201389076

OAI identifier oai:identifier

oai:strathclyde:xw42n7934