Development, sensitivity and uncertainty analysis of LASH model

• Autor(a) principal: Beskow, Samuel
• Data de Publicação: 2011
• Outros Autores: Mello, Carlos Rogério de, Norton, Lloyd Darrell
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFLA
• Texto Completo: http://repositorio.ufla.br/jspui/handle/1/15624
• topic: Hydrology – Mathematical models – Modeling; Hydrology – Statistical methods; Monte Carlo simulation; Morris method; Hidrologia – Modelos matemáticos – Modelagem; Hidrologia – Métodos estatísticos; Simulação de Monte Carlo; Método de Morris; Lavras Simulation of Hydrology (LASH); Generalized Likelihood Uncertainty Equation (GLUE)

Many hydrologic models have been developed to help manage natural resources all over the world. Nevertheless, most models have presented a high complexity regarding data base requirements, as well as, many calibration parameters. This has brought serious difficulties for applying them in watersheds where there is scarcity of data. The development of the Lavras Simulation of Hydrology (LASH) in a GIS framework is described in this study, which focuses on its main components, parameters, and capabilities. Coupled with LASH, sensitivity analysis, parameter range reduction, and uncertainty analysis were performed prior to the calibration effort by using specific techniques (Morris method, Monte Carlo simulation and a Generalized Likelihood Uncertainty Estimation -GLUE) with a data base from a Brazilian Tropical Experimental Watershed (32 km2), in order to predict streamflow on a daily basis. LASH is a simple deterministic and spatially distributed model using long-term data sets, and a few maps to predict streamflow at a watershed outlet. We were able to identify the most sensitive parameters which are associated with the base flow and surface runoff components, using a reference watershed. Using a conservative threshold, two parameters had their range of values reduced, thus resulting in outputs closer to measured values and facilitating automatic calibration of the model with less required iterations. GLUE was found to be an efficient method to analyze uncertainties related to the prediction of mean daily streamflow in the watershed.