|Da Silva, Antonio|
Submitted to: Scientia Agricola
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/31/2010
Publication Date: 5/31/2011
Citation: Beskow, S., Mello, C.R., Norton, L.D., Da Silva, A.M. 2011. Development, sensitivity and uncertainty analysis of LASH model. Scientia Agricola. 68(3):265-274. Interpretive Summary: Predicting water delivery from watersheds is needed for many practical reasons. The only way to predict the effects of changing land use or climate change scenarios on flow out of a watershed is to use simulation models. Most existing models are complex and require large amounts of input data to make an estimate of watershed flow or sediment delivery. In many cases sufficient data to drive these models does not exist especially in areas in developing countries. This paper reports on a new simple watershed hydrology model that was developed to easily estimate the water flow leaving a watershed with minimal data. It also reports on which input parameters are sensitive for the predictions and where potential for errors in the predictions may occur. It was tested for a medium sized watershed in the Brazilian tropical area where minimal data was available and a comparison was made between predicted and measured flows. The model was found to be very simple to use and accurately prediced watershed flow. The impact of the develoment and validation of this model is that it can reliable be applied to other watersheds with limited data to evaluate the effects of future land-use or climatic changes on stream flow to drinking water supplies, hydro-electric facilities, irrigation systems and streams.
Technical Abstract: 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.