Hydrological Modeling in Tropical Regions via TopModel. Study Case: Central Sector of the Middle Magdalena Valley - Colombia

Abstract

Hydrological modeling allows us to make a comprehensive assessment of the interaction between dynamics of the hydrological cycle, climate conditions, and land use.  These modeling results are relevant in water resources management field. We use TopModel (TOPography based hydrological MODEL for the hydrological modeling of an area of 17 000 km2 in the Middle Magdalena Valley (MMV), a tropical basin located in Colombia. This study is located in the intertropical convergence zone (ITCZ) which is characterized by special meteorological conditions and fast water fluxes over the year. This area has been subjected to significant land use changes, as a result of intense economic activities, e.g., agriculture, hydropower energy and oil & gas production (Avellaneda, 2003). The proposed model is based on a record of 12 years of: i.) daily precipitation data from observed gauges, ii.) daily evapotranspiration data from temperature data and iii.) daily streamflow data as observed data. A calibration process was performed using data from 2000 to 2008, and a validation was performed with data from 2009 to 2012. The Nash-Sutcliffe coefficient was used as an objective function to assess the quality of these processes (values of this metric are between 0.74 and 0.73 respectively, for model calibration and validation). The results show us an adequate performance of the model in areas of the tropical region and allow us to analyze the relationship between water storage capacity in the soils of the area with subsurface runoff. This conclusion is consistent with the characteristics of the region. The calibrated model provides an idea about the hydrological functioning of the basin and estimates an approximation of the groundwater recharge in the region. The estimation of the recharge is important to quantify the interaction of surface water and groundwater, especially during the dry season, due to its importance in the analysis of scenarios with climate variability.