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

<p>Evapotranspiration (ET) is one of the most important factors for the water budget and physical processes in the tropical region. This variable affects the atmospheric water and it is important for its capacity to control precipitation, including its influence on absorption and reflection of solar and terrestrial radiation. In the tropical context ET is a relevant process, where the condensation of large amounts of water vapor leads to the release of latent heat energy. In order to understand ecohydrological and climatic synergies and interactions in the tropical basins, different models have tried to represent the hydrological processes in time and space. But most of these models depend on variables that should be measured in situ and are rarely available or limited in the tropical countries. This inevitably requires the model to be simple enough and the parameters can be estimated from climate and basin characteristics. In this regard, Zhang et al. (2008) developed a hydrological model Dynamic Water Balance (DWB). DWB is a semi-distributed model supported in the Budyko framework, which uses partition curves to distribute water to a number of components based on water availability and demand concepts. In general, the model assumes the control over the water balance is mostly dominated by the precipitation (P) and potential evapotranspiration. </p><p>The hydrologic structure of DWB consists of two tanks, soil moisture store and groundwater store, and adjust its mathematical relations through the optimization of four parameters. Due to its simplicity and strong concepts, DWB had been implemented successfully in several types of basins around the globe (Rodriguez et al., 2019).</p><p>This work presents DWBmodelUN, a hydrological R-package with the implementation of DWB in a regular mesh at a monthly time step. DWBmodelUN contains 12 functions related to data entry pre-processing, mathematical development of DWB, calibration algorithm Dynamical Dimension Search and an interactive graphical  module. In overall terms, DWBmodelUN requires: (i) basin geographic data (defines the spatial resolution of the modelling), (ii) hydro-meteorological entry data (P, Temperatute, Streamflow) in raster format, (iii) initial values for the model parameters and (iv) setup data such as warm up, calibration and validation periods. </p><p>In addition, this package includes a practical example of application in Sogamoso River Basin, located at the Oriental mountain range of Colombia.  Therefore, data sets with hydrological, meteorological and setup information were incorporated within the package.</p><p>This tool intents to spread  the DWB model and facilitate its implementation in more basins. In this context, to execute DWBmodelUN users do not need extensive programming skills and the R-package was thought for easily adaptability.</p><p><strong>References</strong></p><p>Rodríguez, E., Sánchez, I., Duque, N., Arboleda, P., Vega, C., Zamora, D., … Burke, S. (2019). Combined Use of Local and Global Hydro Meteorological Data with Hydrological Models for Water Resources Management in the Magdalena - Cauca Macro Basin – Colombia. Water Resources Management. </p><p>Zhang, L., Potter, N., Hickel, K., Zhang, Y., & Shao, Q. (2008). Water balance modeling over variable time scales based on the Budyko framework – Model development and testing. Journal of Hydrology, 360(1–4), 117–131. </p>

Hydrological ensembles have gained importance for prediction and forecasting in water cycle variables. In spite of this, the relevance of the individual models in the ensemble is not usually established, in terms of the ensemble structure (i.e. their members) and the performance this structure exhibits through different climatic conditions (intrannual variability, for example). This analysis accounts for the uncertainty in the structure of the models and their responses (e.g. outputs), in comparison to the observed data. In this regard, the research here described attempts to determine the incidence of the ensemble members built for each month of the year, in the prediction of daily flows, through the use of the Bayesian Model Averaging (BMA) method. Moreover, using BMA calibrated parameters as inputs, an uncertainty analysis is carried out for the calibration period, and in monthly average terms, obtaining finer uncertainty bounds. This analysis was implemented in the Sumapaz River basin, part of the Magdalena Cauca Macro-Basin (MCMB) in Colombia. Results showed differences in ensemble structures and performance according to its original performance criteria, and better results when using a monthly BMA for the uncertainty analysis.Engineering