Citas bibligráficas
Segovia, K., (2016). Precipitación estimada y sequía meteorológica futura en la cuenca Río Grande mediante el modelo de circulación global MRI [Tesis, Universidad Nacional Agraria La Molina]. https://hdl.handle.net/20.500.12996/2489
Segovia, K., Precipitación estimada y sequía meteorológica futura en la cuenca Río Grande mediante el modelo de circulación global MRI [Tesis]. : Universidad Nacional Agraria La Molina; 2016. https://hdl.handle.net/20.500.12996/2489
@misc{renati/249238,
title = "Precipitación estimada y sequía meteorológica futura en la cuenca Río Grande mediante el modelo de circulación global MRI",
author = "Segovia Inga, Kerly Silvana",
publisher = "Universidad Nacional Agraria La Molina",
year = "2016"
}
At present, there have been various studies approached on the development of Global Circulation Models in order to obtain an estimation of rain at regional scale mainly in little or no instrumented basins. Thus, the Meterorological Research Institute (MRI) and the Japan Meterorological Agency (JMA) developed a model of high resolution MRI-AGCM 20 km contribute in simulations of climate and weather predictions (Mizuta et al., 2006). In this study was performed a statistical downscaling in order to compare and evaluate the estimated precipitacion MRI AGCM 20 km 3.1 S and 3.2.S with the rainfall recorded in the basin Río Grande (Ica) for the current period (1980-1999), using statistical tools to timely and spatial scale to determine its usefulness in the study area. Finally, the model was use to estimate a future scenario for the near future periods (2020-20139) and distant future (2080-2099): and thusm assess changes in the characteristics of meteorological drought through three drought indexes; Percentage of Normal Rainfall, Standadized Precipitacion Index and Z-score. The results indicate that the correlations between estimated rainfall from the model and reference rainfall are acceptable on a monthly scale. In winter (dry season) the monthly correlation is stronger with T3 (MRI 3. IS-CDF) and T4 (3.2S-cdf MR1) products, because the presence of rainless days (zero) favors a higher correlation, because the rains are concentrated more in the mountains area and that orographic effect is not well detected by the hydrostatic model. However, the rainfall of T3 and T4 are better adjusted than recorded. While T1 and T2 are overestimated with values that exceed 100 percent, but they manage to differentiate the marked seasonality.
Este ítem está sujeto a una licencia Creative Commons Licencia Creative Commons