A near real-time algorithm for flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management and flood forecasting. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, and in urban areas with reasonable accuracy.

Evaluation of satellite-based and model re-analysis rainfall estimates for Uganda

The dependence of much of Africa on rain fed agriculture leads to a high vulnerability to fluctuations in rainfall amount. Hence, accurate monitoring of near-real time rainfall is particularly useful, for example in forewarning possible crop shortfalls in drought-prone areas. Unfortunately, ground based observations are often inadequate. Rainfall estimates from satellite-based algorithms and numerical model outputs can fill this data gap, however rigorous assessment of such estimates is required. In this case, three satellite based products (NOAA-RFE 2.0, GPCP-1DD and TAMSAT) and two numerical model outputs (ERA-40 and ERA-Interim) have been evaluated for Uganda in East Africa using a network of 27 rain gauges. The study focuses on the years 2001 to 2005 and considers the main rainy season (February to June). All data sets were converted to the same temporal and spatial scales. Kriging was used for the spatial interpolation of the gauge data. All three satellite products showed similar characteristics and had a high level of skill that exceeded both model outputs. ERA-Interim had a tendency to overestimate whilst ERA-40 consistently underestimated the Ugandan rainfall.

Near real-time flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, classifying 89% of flooded pixels correctly, with an associated false positive rate of 6%. Of the urban water pixels visible to TerraSAR-X, 75% were correctly detected, with a false positive rate of 24%. If all urban water pixels were considered, including those in shadow and layover regions, these figures fell to 57% and 18% respectively.

Realism of rainfall in a very high-resolution regional climate model

The realistic representation of rainfall on the local scale in climate models remains a key challenge. Realism encompasses the full spatial and temporal structure of rainfall, and is a key indicator of model skill in representing the underlying processes. In particular, if rainfall is more realistic in a climate model, there is greater confidence in its projections of future change. In this study, the realism of rainfall in a very high-resolution (1.5 km) regional climate model (RCM) is compared to a coarser-resolution 12-km RCM. This is the first time a convection-permitting model has been run for an extended period (1989–2008) over a region of the United Kingdom, allowing the characteristics of rainfall to be evaluated in a climatological sense. In particular, the duration and spatial extent of hourly rainfall across the southern United Kingdom is examined, with a key focus on heavy rainfall. Rainfall in the 1.5-km RCM is found to be much more realistic than in the 12-km RCM. In the 12-km RCM, heavy rain events are not heavy enough, and tend to be too persistent and widespread. While the 1.5-km model does have a tendency for heavy rain to be too intense, it still gives a much better representation of its duration and spatial extent. Long-standing problems in climate models, such as the tendency for too much persistent light rain and errors in the diurnal cycle, are also considerably reduced in the 1.5-km RCM. Biases in the 12-km RCM appear to be linked to deficiencies in the representation of convection.

Detection of flooded urban areas in high resolution Synthetic Aperture Radar images using double scattering

Flooding is a particular hazard in urban areas worldwide due to the increased risks to life and property in these regions. Synthetic Aperture Radar (SAR) sensors are often used to image flooding because of their all-weather day-night capability, and now possess sufficient resolution to image urban flooding. The flood extents extracted from the images may be used for flood relief management and improved urban flood inundation modelling. A difficulty with using SAR for urban flood detection is that, due to its side-looking nature, substantial areas of urban ground surface may not be visible to the SAR due to radar layover and shadow caused by buildings and taller vegetation. This paper investigates whether urban flooding can be detected in layover regions (where flooding may not normally be apparent) using double scattering between the (possibly flooded) ground surface and the walls of adjacent buildings. The method estimates double scattering strengths using a SAR image in conjunction with a high resolution LiDAR (Light Detection and Ranging) height map of the urban area. A SAR simulator is applied to the LiDAR data to generate maps of layover and shadow, and estimate the positions of double scattering curves in the SAR image. Observations of double scattering strengths were compared to the predictions from an electromagnetic scattering model, for both the case of a single image containing flooding, and a change detection case in which the flooded image was compared to an un-flooded image of the same area acquired with the same radar parameters. The method proved successful in detecting double scattering due to flooding in the single-image case, for which flooded double scattering curves were detected with 100% classification accuracy (albeit using a small sample set) and un-flooded curves with 91% classification accuracy. The same measures of success were achieved using change detection between flooded and un-flooded images. Depending on the particular flooding situation, the method could lead to improved detection of flooding in urban areas.

Combined use of satellite estimates and rain gauge observations to generate high-quality historical rainfall time series over Ethiopia

Climate data are used in a number of applications including climate risk management and adaptation to climate change. However, the availability of climate data, particularly throughout rural Africa, is very limited. Available weather stations are unevenly distributed and mainly located along main roads in cities and towns. This imposes severe limitations to the availability of climate information and services for the rural community where, arguably, these services are needed most. Weather station data also suffer from gaps in the time series. Satellite proxies, particularly satellite rainfall estimate, have been used as alternatives because of their availability even over remote parts of the world. However, satellite rainfall estimates also suffer from a number of critical shortcomings that include heterogeneous time series, short time period of observation, and poor accuracy particularly at higher temporal and spatial resolutions. An attempt is made here to alleviate these problems by combining station measurements with the complete spatial coverage of satellite rainfall estimates. Rain gauge observations are merged with a locally calibrated version of the TAMSAT satellite rainfall estimates to produce over 30-years (1983-todate) of rainfall estimates over Ethiopia at a spatial resolution of 10 km and a ten-daily time scale. This involves quality control of rain gauge data, generating locally calibrated version of the TAMSAT rainfall estimates, and combining these with rain gauge observations from national station network. The infrared-only satellite rainfall estimates produced using a relatively simple TAMSAT algorithm performed as good as or even better than other satellite rainfall products that use passive microwave inputs and more sophisticated algorithms. There is no substantial difference between the gridded-gauge and combined gauge-satellite products over the test area in Ethiopia having a dense station network; however, the combined product exhibits better quality over parts of the country where stations are sparsely distributed.

High-precision measurements of the co-polar correlation coefficient: non-Gaussian errors and retrieval of the dispersion parameter µ in rainfall

The co-polar correlation coefficient (ρhv) has many applications, including hydrometeor classification, ground clutter and melting layer identification, interpretation of ice microphysics and the retrieval of rain drop size distributions (DSDs). However, we currently lack the quantitative error estimates that are necessary if these applications are to be fully exploited. Previous error estimates of ρhv rely on knowledge of the unknown "true" ρhv and implicitly assume a Gaussian probability distribution function of ρhv samples. We show that frequency distributions of ρhv estimates are in fact highly negatively skewed. A new variable: L = -log10(1 - ρhv) is defined, which does have Gaussian error statistics, and a standard deviation depending only on the number of independent radar pulses. This is verified using observations of spherical drizzle drops, allowing, for the first time, the construction of rigorous confidence intervals in estimates of ρhv. In addition, we demonstrate how the imperfect co-location of the horizontal and vertical polarisation sample volumes may be accounted for. The possibility of using L to estimate the dispersion parameter (µ) in the gamma drop size distribution is investigated. We find that including drop oscillations is essential for this application, otherwise there could be biases in retrieved µ of up to ~8. Preliminary results in rainfall are presented. In a convective rain case study, our estimates show µ to be substantially larger than 0 (an exponential DSD). In this particular rain event, rain rate would be overestimated by up to 50% if a simple exponential DSD is assumed.

Modeling Of District Metered Areas With Relatively High Leakage Rate. The Case Study Of Kalipoli’s DMA

The article reviews the modelling of District Metered Areas (DMAs) with relatively high leakage rate. As a generally recognised approach in modelling of leakage does not exist, modelling of leakage by enginners and other researchers usually takes place by dividing the whole leakage rate evenly to all available nodes of the model. In this article, a new methodology is proposed to determine the nodal leakage by using a hydraulic model. The proposed methodology takes into consideration the IWA water balance methodology, the Minimum Night Flow (MNF) analysis, the number of connections related to each node and the marerial of pipes. In addition, the model is illustrated by a real case study, as it was applied in Kalipoli’s DMA. Results show that the proposed model gives reliable results.

High frequency of Paracoccidioides brasiliensis infection in armadillos (Dasypus novemcinctus): an ecological study

The fungus Paracoccidioides brasiliensis has been isolated from nine-banded armadillos (Dasypus novemcinctus) in different regions where paracoccidiodomycosis (PCM) is endemic. The link between PCM and these animals has provided the first valuable clue in the effort to elucidate the ecological niche of P. brasiliensis. The present study was aimed at correlating P. brasiliensis infection in armadillos with local ecological features and, if possible, the presence of the fungus in the soil in the Botucatu hyperendemic area of PCM. In this region the mean temperature ranges from 14.8 to 25.8degreesC and the annual average precipitation is 1520 mm. The sites where 10 infected animals (positive group) were collected were studied and compared with the sites where five uninfected animals were found. The occurrence of the fungus in soil samples collected from the positive armadillos' burrows and foraging sites was investigated by the indirect method of animal inoculation. Environmental data from the sites of animal capture, such as temperature, rainfall, altitude, vegetation, soil composition, presence of water and proximity of urban areas, were recorded. All 37 soil samples collected from the sites had negative fungal cultures. Positive animals were found much more frequently in sites with disturbed vegetation, such as riparian forests and artificial Eucalyptus Or Pinus forests, in altitudes below 800 m, near water sources. The soil type of the sites of positive animals was mainly sandy, with medium to low concentrations of organic matter. The pH was mainly acidic at all the sites, although the concentrations of aluminum cations (H+Al) were lower at the sites where positive animals were found. Positive armadillos were also captured in sites very close to urban areas. Our data and previous studies indicate that P. brasiliensis occurs preferentially in humid and shady disturbed forests in a strong association with armadillos.

Dissolved organic carbon in rainwater from areas heavily impacted by sugar cane burning

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Potassium leaching from millet straw as affected by rainfall and potassium rates

Pearl millet (Penisetum glaucum) is an interesting species to be used as cover crop in tropical areas, showing a high ability in potassium uptake. Potassium (K) is not linked to organic compounds in the plant, and can easily be released from decaying straw becoming available for subsequent crops. This experiment evaluated K leaching from millet straw grown under potassium rates (0, 100, 200, and 300 mg dm-3), and submitted to five levels of simulated rain (5, 10, 20, 40, and 80 mm). Plants were grown in soil filled pots in a greenhouse. On the 50th day after emergence, the plants were desiccated with glyphosate. Artificial rain was applied over the straw. Potassium deficiency speeds up millet dehydration after herbicide application and increases lightly rain water retention in the straw. The amount of K leached right after plant desiccation is correlated with the residue nutrient content and can be as high as 64 kg ha-1 considering a mulch of 8 t ha -1. Although well-nourished millet plants release considerable amounts of K with the first rains, a large percentage of the nutrient is still retained in the straw. Copyright © Taylor & Francis, Inc.

Urban and suburban malaria in Rondônia (Brazilian Western Amazon) II. Perennial transmissions with high anopheline densities are associated with human environmental changes

Longitudinal entomological surveys were performed in Vila Candelária and adjacent rural locality of Bate Estaca concomitantly with a clinical epidemiologic malaria survey. Vila Candelária is a riverside periurban neighborhood of Porto Velho, capital of the state of Rondônia in the Brazilian Amazon. High anopheline densities were found accompanying the peak of rainfall, as reported in rural areas of the region. Moreover, several minor peaks of anophelines were recorded between the end of the dry season and the beginning of the next rainy season. These secondary peaks were related to permanent anopheline breeding sites resulting from human activities. Malaria transmission is, therefore, observed all over the year. In Vila Candelária, the risk of malaria infection both indoors and outdoors was calculated as being 2 and 10/infecting bites per year per inhabitant respectively. Urban malaria in riverside areas was associated with two factors: (1) high prevalence of asymptomatic carriers in a stable human population and (2) high anopheline densities related to human environmental changes. This association is probably found in other Amazonian urban and suburban communities. The implementation of control measures should include environmental sanitation and better characterization of the role of asymptomatic carriers in malaria transmission.