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This final master work presents the results of the evaluation of the model Dynamic Water Balance (DWB) model at daily scale to simulate runoff in tropical watersheds. The present project used as input dataset information generated in the framework of the 2018 National Water Study developed by IDEAM. Therefore, 30 out of 497 watersheds area selected to be assessed in this study. The selection of the watersheds is made through the unsupervised k-means classification algorithm that considered morphometric, hydroclimatological, demographic, and geographic variables and their relationship with a variable that quantified the change in land cover. Thus, 8 clusters or groups of watersheds with similar behavior and heterogeneous characteristics are identified.The 30 selected watersheds were subjected to hydrological modeling with DWB following the hydrological modeling protocol. Then, the uncertainty given by the model parameters is evaluated.Given the results found in the literature review, a multi-objective function combining the KGE and RVE is adopted to improve the daily runoff performance, emphasizing the representation of the flow duration curve which was the major advantage identified by the model developers at the daily scale.The process of evaluating the results showed that the DWB model can reproduce the flow duration curves except for low flows. When evaluating the temporal representation through analysis of the objective function, the model yields very good to satisfactory results in more than 70% of the watersheds in clusters 1, 5, 7 and 8, these basins with good results are located mainly in the mountains.As a general result, this work led to the identification of opportunities for adapting the DWB model to a daily simulation of runoff. It is identified that the hydrologic routing process should be included and for this purpose two strategies are suggested: [1] the inclusion of a hydrologic routing based on the convolution of the unit hydrograph that has been implemented in other parsimonious hydrologic models and [2] the coupling with external algorithms that have been designed to perform this process.

Abstract:Evapotranspiration is an important component of hydrological cycle and a key input to hydrological models. Therefore, analysis of the spatiotemporal variation of potential evapotranspiration (PET) will help a better understanding of climate change and its effect on hydrological cycle and water resources. In this study, the Penman-Monteith method was used to estimate PET in the Wei River basin (WRB) in China based on daily data at 21 meteorological stations during 1959-2008. Spatial distribution and temporal trends of annual and seasonal PET were analysed by using the Spline interpolation method and the Mann-Kendall test method. Abrupt changes were detected by using the Pettitt test method. In order to explore the contribution of key meteorological variables to the variation of PET, the sensitivity coefficients method was employed in this study. The results showed that: (1) mean annual and seasonal PET in the WRB was generally decreasing from northeast to southwest. Summer and spring made the major contributions to the annual values; (2) annual and seasonal PET series in most part of the WRB exhibited increasing trends; (3) abrupt changes appeared in 1993 for annual and spring PET series for the entire basin, while summer value series was detected in the late 1970s. (4) Relative humidity was the most sensitive variable for PET in general for the WRB, followed by wind speed, air temperature and solar radiation. In the headwater and outlet of the WRB, relative humidity and air temperature were the most sensitive variables to PET, while relative humidity and wind speed were more influential in most part of the middle-lower region.