Browsing by Author "Palacios Garate, Karina Fernanda"

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A hybrid neural network based technique to improve the flow forecasting of physical and data driven models: methodology and case studies in andean watersheds
(2020) Farfan Duran, Juan Fernando; Ulloa, Jacinto; Avilés Añazco, Alex Manuel; Palacios Garate, Karina Fernanda
The present study was conducted in the Machángara Alto and Chulco rivers, which belong to the Paute basin in the provinces of Azuay and Cañar in southern Ecuador. Study focus: Andean watersheds are important providers of water supply for human consumption, food supply, energy generation, industrial water use, and ecosystem services and functions for many cities in Ecuador and in the rest of South America. In these regions, accurate quantification and prediction of water flow is challenging, mainly due to significant climatic variability and sparse monitoring networks. In the context of flow forecasting, this work evaluates the accuracy of two physical models (WEAP and GR2M) and two models based on artificial neural networks (ANN) that use meteorological data as input variables. Then, a hybrid technique is proposed, using the time series generated by the individual models as inputs of a new ANN. This approach aims to increase the accuracy of the simulated flow by combining and exploiting the information provided by physical and data-driven models. To assess the performance of the proposed methodology, statistical analyses are conducted for two case studies in the Andean region, where comparative analyses are performed for the individual models and the hybrid technique. New hydrological insights: The results indicate that the proposed technique is able to improve the individual performance of physical and ANN-based models, yielding good results in the calibration and validation stages for the two case studies. Specifically, increases in NSE were observed from 0.64 to 0.99 in the MachÁngara Alto river, and from 0.88 to 0.99 in the Chulco river. Higher accuracy of the hybrid technique was observed for all evaluation criteria considered in the analyses. The performance of the hybrid technique was also reflected in terms of water supply and demand, suggesting possible applications for the regional management of water resources, where accurate flow predictions are of utmost importance.
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.