Pedro H. A. Medeiros

Modelling spatio-temporal patterns of sediment yield and connectivity in a semi-arid catchment with the WASA-SED model

Abstract Rainfall–runoff induced soil erosion causes important environmental degradation by reducing soil fertility and impacting on water availability as a consequence of sediment deposition in surface reservoirs used for water supply, particularly in semi-arid areas. However, erosion models developed on experimental plots cannot be directly applied to estimate sediment yield at the catchment scale, since sediment redistribution is also controlled by the transport conditions along the landscape. In particular, representation of landscape connectivity relating to sediment transfer from upslope areas to the river network is required. In this study, the WASA-SED model is used to assess the spatial and temporal patterns of water and sediment connectivity for a semi-arid meso-scale catchment (933 km2) in Brazil. It is shown how spatial and temporal patterns of sediment connectivity within the catchment change as a function of landscape and event characteristics. This explains the nonlinear catchment response in terms of sediment yield at the outlet. Citation Medeiros, P. H. A., Güntner, A., Francke, T., Mamede, G. L. & de Araújo, J. C. (2010) Modelling spatio-temporal patterns of sediment yield and connectivity in a semi-arid catchment with the WASA-SED model. Hydrol. Sci. J. 55(4), 636–648.

Bathymetric survey of water reservoirs in north-eastern Brazil based on TanDEM-X satellite data.

Water scarcity in the dry season is a vital problem in dryland regions such as northeastern Brazil. Water supplies in these areas often come from numerous reservoirs of various sizes. However, inventory data for these reservoirs is often limited due to the expense and time required for their acquisition via field surveys, particularly in remote areas. Remote sensing techniques provide a valuable alternative to conventional reservoir bathymetric surveys for water resource management. In this study single pass TanDEM-X data acquired in bistatic mode were used to generate digital elevation models (DEMs) in the Madalena catchment, northeastern Brazil. Validation with differential global positioning system (DGPS) data from field measurements indicated an absolute elevation accuracy of approximately 1m for the TanDEM-X derived DEMs (TDX DEMs). The DEMs derived from TanDEM-X data acquired at low water levels show significant advantages over bathymetric maps derived from field survey, particularly with regard to coverage, evenly distributed measurements and replication of reservoir shape. Furthermore, by mapping the dry reservoir bottoms with TanDEM-X data, TDX DEMs are free of emergent and submerged macrophytes, independent of water depth (e.g. >10m), water quality and even weather conditions. Thus, the method is superior to other existing bathymetric mapping approaches, particularly for inland water bodies. The proposed approach relies on (nearly) dry reservoir conditions at times of image acquisition and is thus restricted to areas that show considerable water levels variations. However, comparisons between TDX DEM and the bathymetric map derived from field surveys show that the amount of water retained during the dry phase has only marginal impact on the total water volume derivation from TDX DEM. Overall, DEMs generated from bistatic TanDEM-X data acquired in low water periods constitute a useful and efficient data source for deriving reservoir bathymetry and show great potential in large scale application.

Monitoring Seasonal Changes in the Water Surface Areas of Reservoirs Using TerraSAR-X Time Series Data in Semiarid Northeastern Brazil

The 933 km2 Benguê catchment in northeastern Brazil is characterized by distinct rainy and dry seasons. Precipitation is stored in variously sized reservoirs, which is essential for the local population. In this study, we used TerraSAR-X SM (HH) data for an one-year monitoring of seasonal changes in the reservoir areas from July 2011 to July 2012. The monitoring was based on acquisitions in the ascending pass direction, complemented by occasional descending-pass images. To detect water surface areas, a histogram analysis followed by a global threshold classification was performed, and the results were validated using in situ GPS data. Distinguishing between small reservoirs and similar looking dark areas was difficult. Therefore, we tested several approaches for identifying misclassified areas. An analysis of the surface area dynamics of the reservoirs indicated high spatial and temporal heterogeneities and a large decrease in the total water surface area of the reservoirs in the catchment by approximately 30% within one year.

Uncertainties of the 137Cs technique for validation of soil redistribution modelling in a semiarid meso-scale watershed

Scarcity of long-term series of sediment-related variables has led watershed managers to apply mathematical models to simulate sediment fluxes. Due to the high efforts for installation and maintenance of sedimentological gauges, tracers have been pointed out as an alternative to validate soil redistribution modelling. In this study, the 137Cs technique was used to assess the WASA-SED model performance at the Bengue watershed (933 km²), in the Brazilian semiarid. Qualitatively, good agreement was found among the 137Cs technique and the WASA-SED model results. Nonetheless, quantitatively great differences, up to two orders of magnitude, were found between the two methods. Among the uncertainties inherent to the 137Cs technique, definition of the reference inventory seems to be a major source of imprecision. In addition, estimations of water and sediment fluxes with mathematical models usually also present high uncertainty, contributing to the quantitative differences of the soil redistribution estimates with the two methods.

Prevalence and virulence gene profiling of enteroaggregative Escherichia coli in malnourished and nourished Brazilian children

The impact of enteroaggregative E. coli (EAEC) infection on childhood malnutrition and inflammation has been suggested, regardless of diarrhea. We investigated whether EAEC and its virulence-related genes (VRGs) are associated with malnutrition in a case–control study. Children aged 6–24 months from Brazil were enrolled as malnourished if weight-for-age Z-score (WAZ) ≤ −2 and nourished if WAZ > −1. Stools were cultured and examined for E. coli. DNA was extracted from fecal isolates and tested for EAEC by polymerase chain reaction (PCR). Positive samples were analyzed by 5 multiplex PCRs to identify 20 EAEC VRGs. Biomarkers of intestinal barrier function and inflammation were measured. The prevalence of EAEC was 39.94%. Samples that presented both aaiC and aatA genes were associated with malnutrition (P = 0.045). A high prevalence of VRGs was observed and the aafC gene was significantly associated with malnourished (P = 0.0101). Strains lacking aar and pic genes were associated with malnutrition (P = 0.018), while the concomitant presence of aar, pic, agg4A, and capU genes was associated with nourished (P = 0.031). These data reinforce the EAEC impact on malnutrition, the importance of aar as negative regulator and the great contribution of AAF/II fimbria for the pathobiology of EAEC.

Comparison of approaches for water surface area segmentation using high resolution TerraSAR-X data for reservoir monitoring in a large semi-arid catchment in northeastern Brazil

The semi-arid Northeast of Brazil is characterized by distinct rainy and dry seasons. The water supply for the local population is based on surface reservoirs in which precipitation is collected. There are more than 150 reservoirs in the 933 km2 Benguê catchment, however, little is known about the temporal dynamics of the water storage in the reservoirs. In this study, we use TerraSAR-X imagery for a year-long monitoring of reservoir surface areas and their seasonal changes. The precise extraction of the reservoir surface areas forms the basis of the monitoring. Therefore, we evaluated the results of a pixel-based threshold classification and a feature-based segmentation (mean shift). The evaluation was based on in-situ GPS measurements and manual digitization. The results of the manual digitization and threshold classification were similar as both tended to underestimate the water surface area in comparison to GPS in-situ data. The mean shift segmentation, however, tended to spread over the shorelines into the surrounding areas. We used the threshold classification for the analysis of 47 TerraSAR-X images. The viewing direction of the TerraSAR-X sensor was also important for the distinction of the entire area of the reservoirs, since geometric effects at the shorelines shaded parts of the water surface area. For the monitoring of the reservoir area with only one viewing direction we derived an empirical geometry correction factor.

A novel mouse model of Campylobacter jejuni enteropathy and diarrhea

Campylobacter infections are among the leading bacterial causes of diarrhea and of ‘environmental enteropathy’ (EE) and growth failure worldwide. However, the lack of an inexpensive small animal model of enteric disease with Campylobacter has been a major limitation for understanding its pathogenesis, interventions or vaccine development. We describe a robust standard mouse model that can exhibit reproducible bloody diarrhea or growth failure, depending on the zinc or protein deficient diet and on antibiotic alteration of normal microbiota prior to infection. Zinc deficiency and the use of antibiotics create a niche for Campylobacter infection to establish by narrowing the metabolic flexibility of these mice for pathogen clearance and by promoting intestinal and systemic inflammation. Several biomarkers and intestinal pathology in this model also mimic those seen in human disease. This model provides a novel tool to test specific hypotheses regarding disease pathogenesis as well as vaccine development that is currently in progress.

Effective water surface mapping in macrophyte-covered reservoirs in NE Brazil based on TerraSAR-X time series

Abstract Water supplies in northeastern Brazil strongly depend on the numerous surface water reservoirs of various sizes there. However, the seasonal and long-term water surface dynamics of these reservoirs, particularly the large number of small ones, remain inadequately known. Remote sensing techniques have shown great potentials in water bodies mapping. Yet, the widespread presence of macrophytes in most of the reservoirs often impedes the delineation of the effective water surfaces. Knowledge of the dynamics of the effective water surfaces in the reservoirs is essential for understanding, managing, and modelling the local and regional water resources. In this study, a two-year time series of TerraSAR-X (TSX) satellite data was used to monitor the effective water surface areas in nine reservoirs in NE Brazil. Calm open water surfaces were obtained by segmenting the backscattering coefficients of TSX images with minimum error thresholding. Linear unmixing was implemented on the distributions of gray-level co-occurrence matrix (GLCM) variance in the reservoirs to quantify the proportions of sub-populations dominated by different types of scattering along the TSX time series. By referring to the statistics and the seasonal proportions of the GLCM variance sub-populations the GLCM variance was segmented to map the vegetated water surfaces. The effective water surface areas that include the vegetation-covered waters as well as calm open water in the reservoirs were mapped with accuracies >77%. The temporal and spatial change patterns of water surfaces in the nine reservoirs over a period of two consecutive dry and wet seasons were derived. Precipitation-related soil moisture changes, topography and the dense macrophyte canopies are the main sources of errors in the such-derived effective water surfaces. Independent from in-situ data, the approach employed in this study shows great potential in monitoring water surfaces of different complexity and macrophyte coverage. The effective water surface areas obtained for the reservoirs can provide valuable input for efficient water management and improve the hydrological modelling in this region.