Monitor Avançado De Enchentes (Maven): a Hydroclimatologic Computational Framework for Early Flood Alert Systems (PAP014354)

Código

PAP014354

Autores

EDILSON RAIMUNDO SILVA, Eduardo Omar Oficialdegui, José Almir Cirilo, ALFREDO RIBEIRO NETO, CARLOS EDUARDO DE OLIVEIRA DANTAS, Keyla Almeida dos Santos

Tema

Flood forecasting and early warning systems

Resumo

The phenomena associated with water management issues show a level of complexity that requires the knowledge of many experts in addition to the use of modern computational tools, both in what concerns the use of powerful equipment as in the development of integrated and flexible information systems. This work presents the MAVEN system (Monitor AVançado de ENchentes - Advanced Floods Monitor), a computational structure designed and built specifically to monitor hydro-climatological conditions in watersheds, inserting manually or automatically the data monitored in hydrological and hydrodynamics modeling procedures, with the aim of generating values for the water level variation in riparian towns. MAVEN is an application developed for the studied basins. Basically, the operating logic of the system uses a server computer that, running the MAVEN system continually, searches for hydrological current conditions and regional weather forecasting (on Web services and FTP databases), running both automated and manual simulation procedures, for rainfall-runoff transformation integrated with the spread of water level, corresponding to a flood wave along the river. MAVEN provides the results according to four user profiles, associated with their functions within the operating system strategy, these are: the system administrator; the watershed models team; the water administrators; and civil defense officers. The implementation of this system is the result of interagency cooperation (UFPE / SRHE / CPRM) on research in hydrological and hydrodynamics modeling, driven by the Water Resources Group of UFPE, after the 2010 floods in the southern coastal forest of the state of Pernambuco, Brazil. Computational infrastructure and grants were provided by CNPQ, through RedeClima project.