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Seasonally dry forests in tropical regions show over 300% inter-annual biomass variability that directly affects the runoff and erosion dynamics. However, biomass fluctuation is mostly overlooked in hydrosedimentological analysis, including in connectivity analysis. The aim of this paper is to understand how the dryland vegetation seasonality in Brazilian drylands affects the potential runoff and sediment connectivity using the Index of Connectivity (stream and outlet targets). Two main analytical steps were used to identify the influence of dry forest biomass fluctuation on connectivity: Creation of vegetation scenarios based on the relationship between rainfall patterns and NDVI fluctuations (Landsat images); Identification of the effect of the vegetation scenarios on Index of Connectivity. The method was applied to a 90 km 2 watershed in NE Brazil, creating a daily vegetation classification using five vegetation scenarios related to rainfall parameters, with average NDVI values from 0.18 during very dry scenarios (<20 mm of antecedent rainfall) to 0.62 in very wet scenario (>500 mm of antecedent rainfall). The primary connectivity behaviour is controlled by a continuous connectivity decrease, reaching 32%, related to increase of humidity and vegetation biomass. At the same time, due to rainfall irregularity, high magnitude rainfall events can occur even during very dry scenarios, when the watershed shows very high potential connectivity. It indicates that connectivity in runoff-dominated regions is temporally variable due to the highly seasonal vegetation and variable incidence of intense rainstorms.

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RESUMOAs primeiras utilizações de Modelos Digitais de Elevação (MDE) nas análises geomorfológicas e hidrológicas datam da década de 1960, já as primeiras extrações automáticas de informação datam da década de 1970, enquanto que os trabalhos focados na extração da drenagem e na delimitação de bacias foram iniciados a partir da década de 1980. Apesar de algumas diferenças de rotina/algoritmos a maioria das metodologias segue os passos de preenchimento de depressão, identificação da direção do fluxo, identificação do fluxo acumulado e definição do limiar de iniciação de canais. Visando testar as diferenças entre os dados de imagens de satélites ASTER GDEM, SRTM e TOPODATA, o presente trabalho comparou as drenagens extraídas para a área da bacia do Riacho do Saco, em Serra Talhada -PE, relacionando-as com a drenagem de referência a partir de informações morfométricas. Foi escolhido o limiar de 0,243Km , analisando as informações topográficas entre os três MDEs, a principal variação deu-se pela baixa amplitude da declividade do SRTM, resultado do tamanho maior do pixel. Em relação à drenagem apesar do SRTM apresentar números totais mais próximos da drenagem de referência o ASTER GDEM apresenta 50% de sobreposição com a drenagem real, enquanto que o SRTM apresenta apenas 11%. Assim, o ASTER GDEM mostrou-se mais adequado para a extração automática da drenagem, sendo que grande parte das diferenças morfométricas apresentadas podem ser corrigidas a partir da inclusão dos lagos existentes e da exclusão da drenagem presentes na área dos lagos.Palavras chave: Extração de drenagem. MDE. ASTER GDEM. SRTM. Morfometria. ABSTRACTThe first efforts to use DEM in geomorphological and hydrological analyzes were realized in the 1960's, but the automatic extractions were only developed in the 1970's. Following, the routines to automatic drainage extraction were defined in the 1980's, creating the basis to actual routines. The actual routines, that might have different algorithms, consist of to fill the depressions, to identify the flow direction, to identify the flow accumulation, and to define the flow accumulation threshold to channel beginning. The present research examined the divergences morphometric between the real drainage and the extract drainages to Riacho do Saco basin, in Serra Talhada -PE, from ASTER GDEM, SRTM and TOPODATA. The accumulation threshold was 0.243 Km . Firstly, the SRTM MDE shows a inferior slope angles because of the bigger pixel size. The SRTM morphometric drainage results are more similar to the reference drainage. Nevertheless the ASTER GDEM drainage shows a better matching with the reference drainage (50%), while the SRTM drainage matches only 11%. Consequently, the ASTER GDEM drainage is more adequate to automatic