R E S U M OAs mudanças no uso e na ocupação do solo, provocadas pelas ações antrópicas, têm gerado grandes impactos nas paisagens. Esses impactos podem ser mitigados através do monitoramento do uso e da cobertura do solo utilizando-se informações espaço-temporais das modificações ocorridas na paisagem. Este trabalho apresenta uma análise espaço-temporal detalhada da dinâmica do uso e ocupação do solo da bacia hidrográfica do riacho São Paulo, localizada na região semiárida do Estado de Pernambuco, entre os anos de 1991 e 2010. Partindo da classificação de três imagens de satélite dos anos de 1991, 2000 e 2010, foram utilizadas matrizes de transição, associadas à álgebra de mapas e métricas da paisagem. Os resultados mostraram que durante o período estudado as classes de uso e ocupação do solo passaram por uma grande transição, com a substituição progressiva das áreas de vegetação de caatinga aberta por áreas com pastagem/agricultura. Junto com o rápido crescimento da classe pastagem/agricultura, a paisagem predominante de vegetação nativa foi sendo substituída por uma paisagem cada vez mais devastada, heterogênea e fragmentada, como mostraram os índices das métricas da paisagem.Dynamic of land use/cover change processes in a Brazilian semiarid watershed A B S T R A C TThe changes in the use and occupation of land, caused by human actions, have created major impacts on the landscapes. These impacts can be mitigated by monitoring the use and land cover, using spatial and temporal information of the changes occurring in the landscape. This paper presents a detailed analysis of spatial and temporal dynamics of land use and occupation of the stream São Paulo watershed, located in the semiarid region of Pernambuco State, during the period from 1991 to 2010. Based on the classification of three satellite images for the years 1991, 2000 and 2010, transition matrices were used associated with the algebra of maps, and landscape metrics. The results showed that during the studied period, the classes of land use and occupation went through a major transition, with the gradual replacement of sparse area of 'caatinga' vegetation by grazing areas/agriculture. Along with the rapid growth of the grazing areas/agriculture class, the predominant landscape of native vegetation has been replaced by a landscape increasingly devastated, fragmented and heterogeneous, as shown by the index of landscape metrics.

The objective of this work is to assess the impacts of IPCC AR5 climate change scenarios on water resources and hydrological processes across the entire Brazilian territory. Hydrological simulations are carried out in total drainage area of about 11,535,645 km 2 and average stream flow of about 272,460 m 3 /s. The study area consists of different climates and land covers such as the Amazon Forest, Northeast Semiarid, Brazilian Savannah, Pantanal wetlands and temperate climate in the South. The atmospheric forcing to drive the large-scale hydrological model MGB-IPH is derived from the downscaling of two global climate models, HadGEM2-ES and MIROC5, by the Eta Regional Climate Model, at 20 km resolution. The Eta model provided the downscaling of the baseline and three time-slices (2011-2040, 2041-2070 and 2071-2099). These projections adopted two emission scenarios, the RCP 4.5 and RCP 8.5. The change in the average and extremes of precipitation, evapotranspiration, rates of river discharge and soil moisture were assessed. The simulations showed the response of the hydrographic regions due to change of precipitation and potential evapotranspiration in the scenarios. Water availability decreases in almost the entire study area (exception for the South) and the major basins for hydroelectric power generation are affected. The Northwest, Amazon and a small area along the Northeast Atlantic coast exhibited intensification of the extremes discharges, where the anomaly is positive for high-flow (Q 10 ) and negative for low-flow (Q 95 ). The results highlight the most climatic sensitive regions in Brazil in terms of hydrological variables and water resources.

Abstract:The goal of this study was to validate soil moisture data from Soil Moisture Ocean Salinity (SMOS) using two in situ databases for Pernambuco State, located in Northeast Brazil. The validation process involved two approaches, pixel-station comparison and areal average, for three regions in Pernambuco with different climatic characteristics. After validation, the SMOS data were used for drought assessment by calculating soil moisture anomalies for the available period of data. Four statistical criteria were used to verify the quality of the satellite data: Pearson correlation coefficient, Willmott index of agreement, BIAS, and root mean squared difference (RMSD). The average RMSD calculated from the daily time series in the pixel and the areal assessment were 0.071 m 3 ·m −3 and 0.04 m 3 ·m −3 , respectively. Those values are near to the expected 0.04 m 3 ·m −3 accuracy of the SMOS mission. The analysis of soil moisture anomalies enabled the assessment of the dry period between 2012 and 2017 and the identification of regions most impacted by the drought. The driest year for all regions was 2012, when the anomaly values achieved −50% in some regions. The use of SMOS data provided additional information that was used in conjunction with the precipitation data to assess drought periods. This may be particularly relevant for planning in agriculture and supporting decision makers and farmers.

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In this work, we aim to evaluate the feasibility and operational limitations of using Sentinel-1 synthetic aperture radar (SAR) data to monitor water levels in the Poço da Cruz reservoir from September 2016–September 2020, in the semi-arid region of northeast Brazil. To segment water/non-water features, SAR backscattering thresholding was carried out via the graphical interpretation of backscatter coefficient histograms. In addition, surrounding environmental effects on SAR polarization thresholds were investigated by applying wavelet analysis, and the Landsat-8 and Sentinel-2 normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) were used to compare and discuss the SAR results. The assessment of the observed and estimated water levels showed that (i) SAR accuracy was equivalent to that of NDWI/Landsat-8; (ii) optical image accuracy outperformed SAR image accuracy in inlet branches, where the complexity of water features is higher; and (iii) VV polarization outperformed VH polarization. The results confirm that SAR images can be suitable for operational reservoir monitoring, offering a similar accuracy to that of multispectral indices. SAR threshold variations were strongly correlated to the normalized difference vegetation index (NDVI), the soil moisture variations in the reservoir depletion zone, and the prior precipitation quantities, which can be used as a proxy to predict cross-polarization (VH) and co-polarization (VV) thresholds. Our findings may improve the accuracy of the algorithms designed to automate the extraction of water levels using SAR data, either in isolation or combined with multispectral images.