Performance of the WRF model with different physical parameterizations in the precipitation simulation of the state of Puebla

Uribe, Indalecio Mendoza; Lugo-Morin, Diosey Ramón

doi:10.20937/atm.52640

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…This good correlation is verified through the Pearson correlation coefficient, which obtained a value of 0.92 at the Goiatins station and 0.84 at the Itacajá station. These values, considering other works presented in the literature that evaluated the performance of precipitation simulations using the WRF model [10,12,13], are considered very good.…”

Section: International Symposium On Innovation and Technology (Siintec)supporting

confidence: 61%

“…One Planet, one Ocean and one Health -2021 The analysis between the observed and simulated data was done graphically and with some statistical indices, named: the correlation coefficient R, the root mean square error (RMSE), and the BIAS estimator, which are widely used to evaluate forecast models [ 10].…”

Section: International Symposium On Innovation and Technology (Siintec)mentioning

confidence: 99%

“…The lower the RMSE, the better the model's performance. Finally, BIAS measures the average tendency of simulated values to be larger or smaller than the measured data [10,11].…”

Section: International Symposium On Innovation and Technology (Siintec)mentioning

confidence: 99%

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Precipitation Simulation Using the WRF-Hydro Model in the Matopiba Region

Silva¹,

Ramos²,

Souza³

et al. 2021

Blucher Engineering Proceedings

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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.

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Section: International Symposium On Innovation and Technology (Siintec)supporting

confidence: 61%

Section: International Symposium On Innovation and Technology (Siintec)mentioning

confidence: 99%

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Precipitation Simulation Using the WRF-Hydro Model in the Matopiba Region

Silva¹,

Ramos²,

Souza³

et al. 2021

Blucher Engineering Proceedings

View full text Add to dashboard Cite

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“…[17] used the output from the WRF model (over the upper-Jordan River basin) as forcing to run the Hydrological Model for Karst Environment (HYMKE). Similar experiments were set in different areas [20][21][22][23], and the authors showed that the precipitation estimates with WRF are able to reproduce the spatial distribution of precipitation, but may underestimate the magnitude of the heavy precipitation events when compared to rain gauges or global observation datasets.…”

Section: Introductionmentioning

confidence: 99%

Potential of the Coupled WRF/WRF-Hydro Modeling System for Flood Forecasting in the Ouémé River (West Africa)

Quenum

Arnault

Klutse

et al. 2022

Water

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Since the beginning of the 2000s, most of the West-African countries, particularly Benin, have experienced an increased frequency of extreme flood events. In this study, we focus on the case of the Ouémé river basin in Benin. To investigate flood events in this basin for early warning, the coupled atmosphere–hydrology model system WRF-Hydro is used, and analyzed for the period 2008–2010. Such a coupled model allows exploration of the contribution of atmospheric components into the flood event, and its ability to simulate and predict accurate streamflow. The potential of WRF-Hydro to correctly simulate streamflow in the Ouémé river basin is assessed by forcing the model with operational analysis datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF). Atmospheric and land surface processes are resolved at a spatial resolution of 5 km. The additional surface and subsurface water flow routing are computed at a resolution of 500 m. Key parameters of the hydrological module of WRF-Hydro are calibrated offline and tested online with the coupled WRF-Hydro. The uncertainty of atmospheric modeling on coupled results is assessed with the stochastic kinetic energy backscatter scheme (SKEBS). WRF-Hydro is able to simulate the discharge in the Ouémé river in offline and fully coupled modes with a Kling–Gupta efficiency (KGE) around 0.70 and 0.76, respectively. In the fully coupled mode, the model captures the flood event that occurred in 2010. A stochastic perturbation ensemble of ten members for three rain seasons shows that the coupled model performance in terms of KGE ranges from 0.14 to 0.79. Additionally, an assessment of the soil moisture has been developed. This ability to realistically reproduce observed discharge in the Ouémé river basin demonstrates the potential of the coupled WRF-Hydro modeling system for future flood forecasting applications.

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