Application of Hydrological Modeling Related to the 2011 Disaster in the Mountainous Region of Rio De Janeiro, Brazil

Abstract

Natural disasters have been responsible for thousands of deaths in recent decades that, added to the environmental, social and economic impacts, require the implementation of prevention strategies. The largest share of disasters is of hydrological origin. In this context, hydrological models are potential alternatives for monitoring and preventing events of this nature. The objective of this study was to analyze the applicability of the semi-distributed model SWAT (Soil and Water Assessment Tool) and the concentrated model SMAP (soil moisture accounting procedure) in predicting the extreme flood event that occurred in Brazil in the mountainous region of Rio de Janeiro in 2011. The results showed that the mean relative error in calibration and validation was 12% and 53% for SMAP, and 18.46% and 88.73% for SWAT, respectively. The better performance of SMAP in validation integrated with its ease of data collection, simplicity of execution and semi-automatic calibration included in its routine, allows for the conclusion that this model proved to be more suitable for hydrological monitoring. In this study, for the first time, a model of SWAT’s complexity was applied to a watershed located in the mountainous region of the state of Rio de Janeiro, a region that, unfortunately, has accounted for thousands of deaths over the past decades associated with mass movements and floods. The SWAT model, besides being able to predict the level and flow of the main course of the river and its tributaries, also enables the calculation of sediment transport in extreme events. Looking from an operational point of view, the work clearly shows how poor hydro-meteorological monitoring, as is the case in this region, makes a good quality prediction for extreme events impossible. It was demonstrated that under these conditions, a simpler and concentrated modeling approach, such as the SMAP model, is able to obtain better results than SWAT.

Keywords

• natural disasters
• hydrological models
• environmental monitoring