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Predictability study of heavy rainfall events in the Serra do Mar

Grant number: 04/09649-0
Support type:Research Projects - Thematic Grants
Duration: September 01, 2005 - August 31, 2009
Field of knowledge:Physical Sciences and Mathematics - Geosciences
Principal Investigator:Chou Sin Chan
Grantee:Chou Sin Chan
Home Institution: Instituto Nacional de Pesquisas Espaciais (INPE). Ministério da Ciência, Tecnologia, Inovações e Comunicações (Brasil). São José dos Campos , SP, Brazil
Co-Principal Investigators:Carlos Afonso Nobre

Abstract

The Serra do Mar is a region of strategic importance to the State of São Paulo, both, for the sustainable development, due the remaining portions of the Mata Atlântica (the native forest), and for the economic development due to the railways, highways, fuel pipelines and the industrial and parI installations. However, this region suffer from frequent landslides which cause several material and life losses. These events have hydrometeorological causes in addition to the strong slope of the hills and the antropic actions. This project proposes the development of a monitoring and prediction system of risks in the Serra do Mar. The project is composed of 6 subprojects entitled: (1) "High resolution atmospheric modelling of extreme events in the Serra do Mar", (2) "Coupling an atmospheric model to a hydrological model", (3) "Characteristics of convective systems that cause extreme events in the Serra do Mar", (4) "Large scale characteristics associated with extreme events in the Serra do Mar", (5) "Development of a semi-automatic prediction and hydrometeorological information system in support of environmental disaster and risk managements", and (6) "the impact of geotechnical and hydrometeorological information from automatic surface stations on the forecasts over Serra do Mar region". The large scale atmospheric environment which characterizes a risky situation will be determined from calculations of atmospheric parameters and cluster analysis. The monitoring of the development, the trajectory and temporal evolution of the deep convective systems will be based on satellite imagery techniques for deep cloud morphology detection. This technique will provide Nowcasting of these weather systems. Wind forecasts from the mesoscale atmospheric model will be tested as input to this remote detection system in order to extend the forecast time range. Selected cases of extreme events will be simulated by the high resolution atmospheric and distributed hydrological models, Eta and Topog, respectively. Higher vertical and horizontal resolutions will be tested, the atmospheric non-hydrostatic effects will be evaluated. the sensitivity of the model to different convective and microphysics schemes and to land cover/use will be experimented to achieve better simulations. these numerical experimentations will be carried out in order to obtain an adjusted configuration of the models to produce 72 h forecasts. The Topog model will using observed and predicted information from the Eta modeI to provide river discharge forecast and indicate risks for landslides or floodings. The Topog will be coupled to the Eta model aiming at improving the rain and river discharge forecasts. Ensemble forecasts will be carried out to provide the information of probability of event occurrence. The proposed meteorological and hydrological mesonet- will provide real time information to capture the local circulation, heavy rains, the level of criticaI rivers and indication of regions of landslide risks. The observations are necessary not only for monitoring the rains, but also for better adjustment of the models and increase our understanding of the related phenomena. A database with socio-economic and geomorpholocial information will be implemented and combined with real time observations and numerical forecasts into a georreferenced information system. This database will be made available through the internet, so that the regions and the types of risk can be imediately identified. The outcome of this project is a prototype of monitoring and forecasting system developed for the risks due to heavy rains in mountainous regions. (AU)

Scientific publications (5)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
KRUK, NADIANE SMAHA; VENDRAME, IRIA FERNANDES; CHOU, SIN CHAN. Coupling a Mesoscale Atmospheric Model with a Distributed Hydrological Model Applied to a Watershed in Southeast Brazil. JOURNAL OF HYDROLOGIC ENGINEERING, v. 18, n. 1, p. 58-65, JAN 2013. Web of Science Citations: 3.
SELUCHI, MARCELO E.; CHOU, SIN CHAN; GRAMANI, MARCELO. A case study of a winter heavy rainfall event over the Serra do Mar in Brazil. GEOFISICA INTERNACIONAL, v. 50, n. 1, p. 41-56, JAN-MAR 2011. Web of Science Citations: 5.
GOMES, JORGE LUIS; CHOU, SIN CHAN. Dependence of partitioning of model implicit and explicit precipitation on horizontal resolution. METEOROLOGY AND ATMOSPHERIC PHYSICS, v. 106, n. 1-2, p. 1-18, FEB 2010. Web of Science Citations: 12.
FERNANDA CERQUEIRA VASCONCELLOS; IRACEMA FONSECA DE ALBUQUERQUE CAVALCANTI. Uma avaliação das previsões do modelo regional eta em alta resolução para dois casos de chuva intensa ocorridos na região da Serra do Mar. Revista Brasileira de Meteorologia, v. 25, n. 4, p. 501-512, Dez. 2010.
BUSTAMANTE, JOSIANE F.; CHOU, SIN CHAN. Impact of including moisture perturbations on short-range ensemble forecasts. JOURNAL OF GEOPHYSICAL RESEARCH, v. 114, OCT 28 2009. Web of Science Citations: 2.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.
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