Dr. Tume-Zapata, Pedro

Para una adecuada gestión hídrica resulta necesario conocer tanto los caudales simulados por un modelo como la incertidumbre asociada con éstos. El presente estudio busca cuantificar la incertidumbre en los caudales simulados por un modelo hidrológico junto con la propagación de ésta hacia aguas abajo, producto de incertidumbre en las precipitaciones, para así definir potenciales mejoras en los resultados de un modelo hidrológico. Se calibró un modelo conceptual semidistribuido y se determinó la incertidumbre asociada con la estructura y parámetros, para luego cuantificar la incertidumbre relacionada con una variación porcentual de las precipitaciones en diferentes periodos del año. Como resultado se obtuvo que el efecto de propagación de la incertidumbre hacia aguas abajo es despreciable debido al aumento de la magnitud de los caudales simulados, y que la incertidumbre en las salidas del modelo depende de la incertidumbre en las precipitaciones sólo en invierno.

Three models for estimating the daily reference evapotranspiration ETo are evaluated in the agricultural zone of Chillán, Chile: Penman-Monteith FAO PMF, Prietsley-Taylor PT and Hargreaves-Samani HS. The daily ETo values estimated through these methodologies are compared with the daily values of ETo estimated from pan evaporation ETB data series obtainedfrom meteorological stations of the Universidad de Concepción Campus Chillán and INIA Quilamapu, both located within Chillán ’s city limits. The comparison and analysis were performed with data covering a span of 13 years (1996 - 2008). Results indicate that the HS method systemically underestimates ETo values, particularly during dry periods. Moreover, differences between the three methods quantified with Root Mean Squared Error RMSE and Relative Differences RD computed for different time periods (1, 3, 7 and 30 days) suggest that the PT method fits better the observations, being more adequate for the agricultural area of Chillán - weekly averages yield RMSE of 1.01 mm/day and RD of 32.8%.

As hydrological models become more prevalent in water resources planning and management, increasing levels of detail and precision are needed. Currently, reliable models that simulate the hydrological behavior of a basin are indispensable; however, it is also necessary to know the limits of the predictability and reliability of the model outputs. The present study evaluates the influence of uncertainty in the main input variable of the model, rainfall, on the output uncertainty of a hydrological model. Using concepts of identifiability and sensitivity, the uncertainty in the model structure and parameters was estimated. Then, the output uncertainty caused by uncertainties in i) the rainfall amounts and ii) the periods of the rainfall was determined. The main conclusion is that uncertainty in rainfall estimation during rainy periods produces greater output uncertainty. However, in non-rainy periods, the output uncertainty is not very sensitive to the uncertainty in rainfall. Finally, uncertainties in rainfall during the basin filling and emptying periods (Apr. – Jun. and Sep. – Nov., respectively) alter the uncertainty in subsequent periods. Therefore, uncertainties in these periods could result in limited ranges of model predictability.

Different sets of parameters and conceptualizations of a basin can give equally good results in terms of predefined objective functions. Therefore, a need exists to tackle equifinality and quantify the uncertainty bands of a model. In this paper we use the concepts of equifinality, identifiability and uncertainty to propose a simple method aimed at constraining the equifinal parameters and reducing the uncertainty bands of model outputs, and obtaining physically possible and reasonable models. Additionally, the uncertainty of equifinal solutions is quantified to estimate the amount by which output uncertainty can be reduced by knowing how to discard most of the equifinal solutions of a model. As a study case, a conceptual model of the Chillán basin in Chile is carried out. From the study it is concluded that using identifiability analysis makes it possible to constrain equifinal solutions with reduced uncertainty and realistic models, resulting in a framework that can be recommended to practitioners, especially due to the simplicity of the method.