@article{deFigueiredodeAraujoMedeirosetal.2016,
  author    = {de Figueiredo, Jose Vidal and de Araujo, Jose Carlos and Medeiros, Pedro Henrique Augusto and Costa, Alexandre C.},
  title     = {Runoff initiation in a preserved semiarid Caatinga small watershed, Northeastern Brazil},
  series = {Hydrological processes},
  volume    = {30},
  journal   = {Hydrological processes},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0885-6087},
  doi       = {10.1002/hyp.10801},
  pages     = {2390 -- 2400},
  year      = {2016},
  abstract  = {This study analyses some hydrological driving forces and their interrelation with surface-flow initiation in a semiarid Caatinga basin (12km(2)), Northeastern Brazil. During the analysis period (2005 - 2014), 118 events with precipitation higher than 10mm were monitored, providing 45 events with runoff, 25 with negligible runoff and 49 without runoff. To verify the dominant processes, 179 on-site measurements of saturated hydraulic conductivity (Ksat) were conducted. The results showed that annual runoff coefficient lay below 0.5\% and discharge at the outlet has only occurred four days per annum on average, providing an insight to the surface-water scarcity of the Caatinga biome. The most relevant variables to explain runoff initiation were total precipitation and maximum 60-min rainfall intensity (I-60). Runoff always occurred when rainfall surpassed 31mm, but it never occurred for rainfall below 14mm or for I-60 below 12mmh(-1). The fact that the duration of the critical intensity is similar to the basin concentration time (65min) and that the infiltration threshold value approaches the river-bank saturated hydraulic conductivity support the assumption that Hortonian runoff prevails. However, none of the analysed variables (total or precedent precipitation, soil moisture content, rainfall intensities or rainfall duration) has been able to explain the runoff initiation in all monitored events: the best criteria, e.g. failed to explain 27\% of the events. It is possible that surface-flow initiation in the Caatinga biome is strongly influenced by the root-system dynamics, which changes macro-porosity status and, therefore, initial abstraction. Copyright (c) 2016 John Wiley \& Sons, Ltd.},
  language  = {en}
}
@article{PereiraMedeirosFranckeetal.2019,
  author    = {Pereira, Bruno and Medeiros, Pedro Henrique Augusto and Francke, Till and Ramalho, Geraldo and F{\"o}rster, Saskia and De Araujo, Jose Carlos},
  title     = {Assessment of the geometry and volumes of small surface water reservoirs by remote sensing in a semi-arid region with high reservoir density},
  series = {Hydrological sciences journal = Journal des sciences hydrologiques},
  volume    = {64},
  journal   = {Hydrological sciences journal = Journal des sciences hydrologiques},
  number    = {1},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {0262-6667},
  doi       = {10.1080/02626667.2019.1566727},
  pages     = {66 -- 79},
  year      = {2019},
  abstract  = {Water fluxes in highly impounded regions are heavily dependent on reservoir properties. However, for large and remote areas, this information is often unavailable. In this study, the geometry and volume of small surface reservoirs in the semi-arid region of Brazil were estimated using terrain and shape attributes extracted by remote sensing. Regression models and data classification were used to predict the volumes, at different water stages, of 312 reservoirs for which topographic information is available. The power function used to describe the reservoir shapes tends to overestimate the volumes; therefore, a modified shape equation was proposed. Among the methods tested, four were recommended based on performance and simplicity, for which the mean absolute percentage errors varied from 24 to 39\%, in contrast to the 94\% error achieved with the traditional method. Despite the challenge of precisely deriving the flooded areas of reservoirs, water management in highly reservoir-dense environments should benefit from volume prediction based on remote sensing.},
  language  = {en}
}