The management of water resources, today, is still a global challenge. Current climate models are not yet capable of satisfactorily representing interannual climate variations caused by atmospheric oscillations. Thus, the better representation of water resources issues requires data related to the variables involved with better quality, as well as the establishment of methodologies that demonstrate how these data relate to observed and expected climatic variations. In this context, the WRF-Hydro system represents the state of the art concerning water resources, and the objective of this study through exploratory research is to analyze the viability of a tool capable of monitoring and generating a predictive analysis of water bodies in the MATOPIBA region.(Maranhão-Tocantins-Piauí-Bahia). The region was geoprocessing by ArcGIS and WRF-Hydro was tested with the simulation of some hydrometeorological data for ten days. Although the WRF-Hydro system demonstrates efficiency in the region's hydrometeorological simulation, it has a range of subprocesses that make the learning curve complex. Therefore, due to the possibility of integrating the tools, it is proposed to build a web tool for greater software usability.
Hydrological modeling is an important technique for monitoring a region's water resources. The WRF-Hydro model is a powerful system for this type of study and it is attracting more and more attention from the academic community. Studying the hydrology of a region involves several physical components sensitive to the occurrence of precipitation. Thus, to verify whether a hydrological model effectively simulates parameters such as evapotranspiration, surface runoff, or river flow, it must be effective in estimating precipitation. Therefore, this work evaluates the performance of the precipitation simulation of the WRF-Hydro system in the Brazilian region named MATOPIBA, located in the North and north east of Brazil. The simulation presented good correlation with the observed data, showing itself as promising.
The current scenario of a global pandemic caused by the virus SARS-CoV-2 (COVID19), highlights the importance of water studies in sewage systems. In Brazil, about 35 million Brazilians still do not have treated water and more than 100 million do not have basic sanitation. These people, already exposed to a range of diseases, are among the most vulnerable to COVID-19. According to studies, places that have poor sanitation allow the proliferation of the coronavirus, been observed a greater number of infected people being found in these regions. This social problem is strongly related to the lack of effective management of water resources, since they are the sources for the population's water supply and the recipients of effluents stemming from sanitation services (household effluents, urban drainage and solid waste). In this context, studies are needed to develop technologies and methodologies to improve the management of water resources. The application of tools such as artificial intelligence and hydrometeorological models are emerging as a promising alternative to meet the world's needs in water resources planning, assessment of environmental impacts on a region's hydrology, risk prediction and mitigation. The main model of this type, WRF-Hydro Weather Research and Forecasting Model), represents the state of the art regarding water resources, as well as being the object of study of small and medium-sized river basins that tend to have less water availability. hydrometeorological data and analysis. Thus, this article aims to analyze the feasibility of a web tool for greater software usability and computational cost use, making it possible to use the WRF-Hydro model integrated with Artificial Intelligence tools for short and medium term, optimizing the time of simulations with reduced computational cost, so that it is able to monitor and generate a predictive analysis of water bodies in the MATOPIBA region (Maranhão-Tocantins-Piauí-Bahia), constituting an instrument for water resources management. The results obtained show that the WRF-Hydro model proves to be an efficient computational tool in hydrometeorological simulation, with great potential for operational, research and technological development purposes, being considered viable to implement the web tool for analysis and management of water resources and consequently, assist in monitoring and mitigating the number of cases related to the current COVID-19 pandemic. This research are in development and represents a preliminary results with future perspectives.
Hydrological modeling is a technique for monitoring a region’s water resources. The WRF-Hydro model is a powerful system for this type of study and it is attracting more and more attention from the academic community. Studying the hydrology of a region involves several physical components sensitive to the occurrence of precipitation. Thus, to verify whether a hydrological model effectively simulates parameters such as evapotranspiration, surface runoff, or river flow, it has to be effective in estimating precipitation. Therefore, this work evaluates the performance of the precipitation simulation of the WRF-Hydro system in the Brazilian region named MATOPIBA, located in North and northeast Brazil. The simulation presented a good correlation with the observed data, showing itself as promising.
In the Amazon, the frequency of extreme events has been increasing notably in recent decades. In April, May, and June 2021, the city of Manaus faced the greatest flood in 119 years, and the Rio Negro reached at a level of 29.98 m. Because of this, the present work aims to evaluate the performance of the WRF-Hydro model in simulating precipitation during an extreme flood event in the Amazon Basin. The simulations were performed with 1 km spatial resolution and a 250 m channel network from April 30 to June 5, 2021. Such applications were evaluated using comparisons of the variability of accumulated precipitation with observed data from National Water Agency rainfall stations. The results showed a tendency for the model to underestimate the accumulated precipitation, slightly reproducing some observed precipitation patterns. We concluded that the tool presents a capacity for precipitation estimation, with potential for operational purposes.
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