Examinando por Materia "ENSO"

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    Ítem
    Estimation of potential groundwater recharge in a growing touristic neotropical dry forest area
    (Elsevier, 2024-11-15) Ballesteros-Buitrago Karen; Jaramillo Marcela; Vergara-Bechará Santiago; González-Jiménez Lauren; Universidad EAFIT
    La Tatacoa Desert, Colombia's second most arid area after La Guajira, is one of the country's main tropical dry forest ecosystems and most attractive natural tourist areas. However, due to its climatic and hydrological conditions, this region presents a worrying panorama on water resources since 90% of the streams crossing La Tatacoa dry up during summer, affecting the water supply for human consumption, agriculture, and livestock. Therefore, groundwater in the area is an invaluable resource that could help meet future demand, and identifying the primary source of recharge becomes an urgent matter. In this paper, we intend to approach the subject only from the analysis of direct recharge for the three main hydrologic conditions in the region: neutral, dry (el Niño), and humid (la Niña), considering the influence of the ENSO. For this purpose, potential recharge was estimated using the SWB (soil water balance) method suggested by the USGS (United States Geological Service). Our results showed that direct recharge for humid conditions is around 380 mm/yr. For neutral and dry conditions, it ranges between 115 mm/yr and 160 mm/yr, corresponding to a recharged precipitation of 10% and 15%, respectively. These values are similar to those reported for semiarid areas, even though rainfall in La Tatacoa ranges between 1000 and 1500 mm/yr. Such low values of direct recharge, compared with the reported use of groundwater in the area, might suggest that there is a complementary source of recharge, probably from the perennial rivers surrounding La Tatacoa (Magdalena or Cabrera), but this is something that is yet to be proven. This study enhances our understanding of groundwater recharge in arid regions, offering new insights for sustainable groundwater management. However, further studies are needed to assess the impact of climate change on direct recharge so that more sustainable water management can be implemented in La Tatacoa, especially concerning supply for the increasing touristic activities.
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    Ítem
    Predicción del precio de la energía eléctrica en Colombia mediante un enfoque de machine learning
    (Universidad EAFIT, 2023) Villarreal Marimon, Yeison José; Flores San Martín, Luis Armando; Almonacid Hurtado, Paula María
    In this research, numerous predictive models are developed, including regression models, VAR models, ARIMA models, ARIMAX models and SARIMAX models, which were further used to estimate and predict the electricity spot price, and therefore obtaining an approximate value for the sale of a kilowatt-hour, a critical input for calculating the revenues in the valuation models of electric power generations projects in Colombia. This was accomplished using the historical records from XM’s databases, analyzing the relationship between the historical spot price for electricity in the frame of time from January 2000 to July 2023, other input variables were also considered such as hydrological contributions, hydrological discharges and hydrological reserves expressed in terms of energy, as well as the potential effects of climatological phenomena like the El Niño Southern Oscillation (ENSO) that occurs in the country. The results of the research indicate that the prices of the kilowatt-hour are affected by the rainy season and specially by the occurrence of the El Niño phenomena, during which prices increase triggering the scarcity price of the system, which can be observed in the years 2015 and 2016. Finally, as a result, all models follow the price behavior trends. The models were subjected to different time horizon tests, finding that the model to be used depends on the time horizon that the investor needs to analyze: VAR models for the short-term, SARIMAX models for the medium-term and multiple regression models for the long-term.