Browsing by Author "Jimenez Yucta, Stalin Daniel"

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Sensitivity exploration of water balance in scenarios of future changes: a case study in an andean regulated river basin
(2020) Avilés Añazco, Alex Manuel; Palacios Garate, Karina Fernanda; Pacheco Nivelo, Jheimy Lorena; Jimenez Yucta, Stalin Daniel; Zhiña Villa, Dario Xavier; Delgado Inga, Victor Omar
Effects of climate change on water resources availability have been studied extensively; however, few studies have explored the sensitivity of water to several factors of change. This study aimed to explore the sensitive of water balance in water resources systems due to future changes of climate, land use and water use. Dynamical and statistical downscaling were applied to four global climate models for the projections of precipitation and temperature of two climate scenarios RCP 4.5 and RCP 8.5. Land use projections were carried out through a combination of Markov chains and cellular automata methods. These projections were introduced in a hydrologic model for future water supply evaluation, and its interactions with water use projections derived from a statistical analysis which served to assessment deficits and surplus in water to 2050. This approach was applied in the Machángara river basin located in the Ecuadorian southern Andes. Results showed that the water supply exceeds the water demand in most scenarios; however, taking into account the seasonality, there were months like August and January that would have significant water deficit in joint scenarios in the future. These results could be useful for planners formulating actions to achieve water security for future generations.
Support vector regression to downscaling climate big data: an application for precipitation and temperature future projection assessment
(Springer Nature Switzerland AG 2020, 2020) Jimenez Yucta, Stalin Daniel; Avilés Añazco, Alex Manuel; Galan Montero, Luciano Agustin; Flores Maza, Washington Andrés; Matovelle Bustos, Carlos Marcelo; Vintimilla Ulloa, Cristian Arturo
The techniques for downscaling climatic variables are essential to support tools for water resources planning and management in a climate change context in the entire world. Support vector machines (SVM) through regression approach (SVR), constitute an artificial intelligence method to downscaling climatic variables. Since that statistical downscaling based on regression methodologies is susceptible to the predictor variables, the aim of this study was exploring a big database of predictor variables to achieve the best performance of a statistical downscaling model using SVR to predict precipitation and temperature future projections. Data from regional climate models of Ecuador and information of three meteorological stations was used to apply this approach in the Tomebamba river sub-basin, located in southern Ecuadorian Andean region. The results show that the downscaling model has a better performance with the climatic averages. The precipitation extremes do not estimate in a good manner, but the model achieves an effective behavior with the temperature extremes values. These results could serve to improve water balance projections in the future for formulating suitable measures for climate change decision-making.