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.

The main objective of the present study is to evaluate the performance of different physical parameterizations available in the Weather Research and Forecasting (WRF-Hydro) mesoscale model, in order to identify which one best represents the behavior of precipitation in the city of Manaus during an extreme event flood, which occurred on May 30, 2021, with monthly accumulated rainfall of 127.2 mm. The simulations were performed with a spatial resolution of 1 km and a channel network of 100 m for the period from April 30 to June 5, 2021. To validate the performance of the WRF-Hydro, the simulated data is compared with CPTEC's MERGE product data. The results showed a tendency for the model to underestimate the daily accumulated precipitation, reasonably reproducing some observed precipitation patterns. It is concluded that the tool presents a capacity for precipitation estimation, with potential for operational purposes.

In the Amazon, the frequency of extreme events has been increasing notably in recent decades. In the months of April, May and June 2021, the city of Manaus faced the greatest flood in 119 years, reaching the Rio Negro a level of 29.98 m. In view 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 250 m channel network for the period from 04/30 to 06/05 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. It is concluded that the tool presents a capacity for precipitation estimation, with potential for operational purposes.

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.

Currently, the NCAR (U.S. National Center for Atmospheric Research), the institution responsible for the WRF-Hydro (Weather Research and Forecasting - Hydro) model initiative, highlights four major global challenges: floods, pollution, droughts, and biodiversity. Thus, given the current scenario of a global pandemic caused by the Sars-CoV-2 virus (Covid19), the importance of hydrological studies, their correlation with contamination levels, and incidence of COVID-19 cases are also in the spotlight. Among the challenges around water resources management, the lack of good and representative data, especially for small water bodies and developing countries, to perform inferences and to manage these natural resources is critical. This situation applies not only to observational data and but also to input data for hydrological models. In this context, the WRF-Hydro system represents the state of the art for hydrometeorological modeling. Thus, the model emerges as a computational tool that becomes possible to provide auxiliary data for patterns analysis in time series and computational prediction. Also, with the evolution of artificial intelligence (AI), it is possible to consider the integration of this modern approach with the WRF-Hydro model simulations. Therefore, the main of this study is to analyze the feasibility of a web tool that integrates these functionalities. The coupled WRF-Hydro with AI will support the management and generate a water predictability analysis in the MATOPIBA region (Maranhão-Tocantins-Piauí-Bahia), northeastern Brazil, the focus area of this study. Although the WRF-Hydro system demonstrates efficiency in the hydrometeorological simulation for the region, the model has a range of subprocesses which has a high computational cost, especially for long-term studies. Therefore, due to the possibility of integrating these computational tools, it is proposed to develop and analyze the construction of a web tool using the WRF-Hydro system for the short and medium-term with AI tools for the short term (a few hours to a few days), to optimize the computational cost. Thus, the combined application of the WRF-Hydro and AI system can improve the water bodies management and assist the identification of contamination levels by Sars-CoV-2, given the presence of the virus in water bodies and the correlation of the pandemic with hydrological variables.