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 15 modeling of an area of 17 000 km 2 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.) 20 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 25 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. 30

The Temporal Variability of Rainfall and Streamflow into Lake Nakuru, Kenya, Assessed Using SWAT and Hydrometeorological Indices

“…Hydrological models such as the hydrologic modeling system (HEC-HMS) [7,8], the Australian water balance model (AWBM) [9], the soil moisture accounting and routing (SMAR) model [10], the topography-based hydrological model (TOPMODEL) [11,12] and the soil and water assessment tool (SWAT) [13] are useful for simulating the runoff from ungauged catchment based on the data availability and complexity of the hydrological system. Many studies have demonstrated that SWAT is an effective and promising tool to use for simulating flows and sediments for large-scale watersheds and complex basins with different land uses and various soil types (Access et al [14], Brouziyne et al [15], Palani et al [16], Amatya et al [17], and Tri et al [18], among others).…”

Assessment of groundwater quality for human consumption and its health risks in the Middle Magdalena Valley, Colombia

“…During the Mesozoic, the central and eastern Andes Mountain ranges were elevated, resulting in the formation of the MMV basin. It consists of sedimentary strata from the Cretaceous Period over a Jurassic igneous metamorphic basement with an average thickness of 8500 m [50][51][52][53] . To the south, there are outcrops of the Real Group, a Neogene formation whose thickness varies from 450 to 3500 m. In the north, there are Quaternary deposits outcrops underlying Real Group.…”

A Generalized Multistep Dynamic (GMD) TOPMODEL

“…Dynamic‐TOPMODEL of Beven and Freer (2001) relaxed both of the above assumptions by allowing subsurface storage of individual HSUs to vary locally and independently of both the catchment average storage and TI, by incorporating a time‐dependent kinematic wave solution to the subsurface flow. However, since its introduction 20 years ago, and despite significantly improving catchment representation, the original steady‐state version has remained the preferred choice (albeit sometimes with modifications/improvements) (Arenas‐Bautista et al., 2018; Fu et al., 2018; Gil & Tobón, 2016; Jeziorska & Niedzielski, 2018; J. Wang et al., 2020; Lane & Milledge, 2013; Li et al., 2019; Mukae et al., 2018; Park et al., 2019; Rogelis et al., 2016; Xue et al., 2018; Zhang et al., 2016). Aside from the considerable additional complexity in numerical implementation of the dynamic versus the steady‐state version, the lack of momentum in transitioning is most likely due to the substantially slower runtimes, making the dynamic version much less attractive for calibration purposes.…”