The objective of this study was to evaluate, based on a data-scarce basin in southern Brazil, the potential of the Lavras Simulation of Hydrology (LASH) model for estimating daily streamflows, annual streamflow indicators and the flow-duration curve. It was also used to simulate the different runoff components and their consistency with the basin physiographical characteristics. The statistical measures indicated that LASH can be considered suitable according to widely used classifications and when compared with other studies involving hydrological models. LASH also showed satisfactory results for annual indicators, especially for maximum and average annual streamflows, as well as for the flowduration curve. It was found that the model was consistent with the basin characteristics when simulating runoff components. The results obtained in this study allowed us to conclude that the LASH model has the potential to aid practitioners in water resources management of basins with scarce data and similar soil and land-use conditions.

SUMMARYThe estimation of non available soil variables through the knowledge of other related measured variables can be achieved through pedotransfer functions (PTF) mainly saving time and reducing cost. Great differences among soils, however, can yield non desirable results when applying this method. This study discusses the application of developed PTFs by several authors using a variety of soils of different characteristics, to evaluate soil water contents of two Brazilian lowland soils. Comparisons are made between PTF evaluated data and field measured data, using statistical and geostatistical tools, like mean error, root mean square error, semivariogram, cross-validation, and regression coefficient. The eight tested PTFs to evaluate gravimetric soil water contents (Ug) at the tensions of 33 kPa and 1,500 kPa presented a tendency to overestimate Ug 33 kPa and underestimate Ug 1,500 kPa . The PTFs were ranked according to their performance and also with respect to their potential in describing the structure of the spatial variability of the set of measured values. Although none of the PTFs have changed the distribution pattern of the data, all resulted in mean and variance statistically

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