An Open Source Approach for Watershed's Delimitation to Support Flood Monitoring

An Open Source Approach for Watershed's Delimitation to Support Flood Monitoring

Tiago Carvalho, Leonardo B. L. Santos, Eduardo F. P. Luz, Rogério Ishibashi, Aurelienne A. S. Jorge, Luciana R. Londe
Copyright: © 2016 |Pages: 12
DOI: 10.4018/IJDST.2016100105
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Abstract

In the last years, natural disasters have become more frequent and their impacts have reached a large number of people. When these events happen in unprepared regions, they can cause human and material losses. The consequences can be even more catastrophic when the responsible entities are not properly prepared. To minimize the impacts of extreme weather events, especially extreme rain which can trigger severe floods, this paper proposes a methodological approach to monitor floods through open source software, based on watershed's delineation. The suggested approach can be a tool to support disaster risk reduction planning.
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Introduction

Reports from the Intergovernmental Panel on Climate Change point to an increase in frequency and intensity of natural phenomena and punctual extreme events in some regions (IPCC, 2012, 2014). When triggering factors, such as heavy precipitation events, reach inhabited places, natural (or other) disasters may happen. According to the United Nations International Strategy for Disaster Reduction - UNISDR, a disaster is “a serious disruption of the functioning of a community or a society involving widespread human, material, economic or environmental losses and impacts, which exceeds the ability of the affected community or society to cope using its own resources” (UNISDR, 2009).

Disasters in Brazil are frequently connected to floods, which are responsible for high numbers of affected people and deaths in the country (EM-DAT, 2014, Tominaga et al., 2009, Londe et al., 2014). As floods frequently reach large areas in Brazil, there are significant economic losses and many disturbances for daily life (Brasil, 2007). In the Brazilian state of Acre, for instance, water from floods of Madeira River and Solimões River in February-2015 reached more than 4 meters above the overflow value. Those floods affected more than 120 thousand people and closed the traffic in many parts of BR-364 road, causing huge economic losses (Estadão, 2015).

After floods, either a disruption in water supply and sanitation systems or deficient hygiene conditions in shelters may characterize a secondary disaster. Diseases outbreaks associated to inappropriate sanitation conditions, especially concerning leptospirosis, are common in a post-flood situation.

The need to either avoid or minimize the impacts of floods through preventive actions is indicated by the GAR15’s report, which states that “the focus of disaster risk reduction needs to move from managing disasters to managing risks” (UNISDR, 2015). The UNISDR (2009) defines disaster risk management as “the systematic process of using administrative directives, organizations, and operational skills and capacities to implement strategies, policies and improved coping capacities in order to lessen the adverse impacts of hazards and the possibility of disaster.”

Monitoring performs a key role in the disaster risks management. The monitoring process in Brazil, for example, is not automatic, depending upon experts’ analysis. Experts, although very specialized in key issues such as hydrology and geology, need some tools to support the delivery of both fast and effective flood warnings (Lima et al., 2016).

Concerning the need of efficient tools to help experts on their task, this work proposes a methodological approach to delimitate watersheds using open source software. It is based upon the idea that the Structure Watershed Area (WSA) is a fundamental tool to support flood monitoring systems (Carvalho et al., 2015). Our goal is to provide a detailed methodological approach, exclusively based on open-data and open software, for watershed delimitation and characterization in the context of flood monitoring. We present some study areas as examples of the method’s application addressed to Brazilian Center for monitoring and early warnings of Natural Disasters.

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