Enteric viruses in a mangrove lagoon, survival and shellfish incidence

Mangrove oysters (Crassostrea rhizophorae) were screened for enteric viruses. For 18 months oysters were collected from Cano Boqueron, a tropical mangrove lagoon on the southwest coast of Puerto Rico. This popular tourist resort has two primary sewage treatment plants which service 158 single family cabanas. In spite of the heavy seasonal input of sewage to Cano Boqueron and high densities of fecal coliform bacteria, enteric viruses were not detected in shellfish meat. Because no viruses were detected in the oysters, a virus survival study was performed. Poliovirus type 1 was placed in diffusion chambers in situ at two sites in Cano Boqueron. More than 95% of the poliovirus inactivation occurred within 24 h. Virus inactivation was significantly different by site, indicating different inactivation rates within the lagoon. Chamber studies done simultaneously with Escherichia coli did not reveal differences between sites. It is suggested that the sewage effluent had an antiviral effect in the absence of an antibacterial effect. This study demonstrates the importance for establishing microbial contamination standards for shellfish growing waters in the tropics based upon in situ studies with tropical species, e.g. mangrove oyster

Rapid response tools and datasets for post-fire modeling: linking Earth Observations and process-based hydrological models to support post-fire remediation

Preparation is key to utilizing Earth Observations and process-based models to support post-wildfire mitigation. Post-fire flooding and erosion can pose a serious threat to life, property and municipal water supplies. Increased runoff and sediment delivery due to the loss of surface cover and fire-induced changes in soil properties are of great concern. Remediation plans and treatments must be developed and implemented before the first major storms in order to be effective. One of the primary sources of information for making remediation decisions is a soil burn severity map derived from Earth Observation data (typically Landsat) that reflects fire induced changes in vegetation and soil properties. Slope, soils, land cover and climate are also important parameters that need to be considered. Spatially-explicit process-based models can account for these parameters, but they are currently under-utilized relative to simpler, lumped models because they are difficult to set up and require spatially-explicit inputs (digital elevation models, soils, and land cover). Our goal is to make process-based models more accessible by preparing spatial inputs before a fire, so that datasets can be rapidly combined with soil burn severity maps and formatted for model use. We are building an online database (http://geodjango.mtri.org/geowepp /) for the continental United States that will allow users to upload soil burn severity maps. The soil burn severity map is combined with land cover and soil datasets to generate the spatial model inputs needed for hydrological modeling of burn scars. Datasets will be created to support hydrological models, post-fire debris flow models and a dry ravel model. Our overall vision for this project is that advanced GIS surface erosion and mass failure prediction tools will be readily available for post-fire analysis using spatial information from a single online site

Consensus statement on abusive head trauma in infants and young children

Abusive head trauma (AHT) is the leading cause of fatal head injuries in children younger than 2 years. A multidisciplinary team bases this diagnosis on history, physical examination, imaging and laboratory findings. Because the etiology of the injury is multifactorial (shaking, shaking and impact, impact, etc.) the current best and inclusive term is AHT. There is no controversy concerning the medical validity of the existence of AHT, with multiple components including subdural hematoma, intracranial and spinal changes, complex retinal hemorrhages, and rib and other fractures that are inconsistent with the provided mechanism of trauma. The workup must exclude medical diseases that can mimic AHT. However, the courtroom has become a forum for speculative theories that cannot be reconciled with generally accepted medical literature. There is no reliable medical evidence that the following processes are causative in the constellation of injuries of AHT: cerebral sinovenous thrombosis, hypoxic-ischemic injury, lumbar puncture or dysphagic choking/vomiting. There is no substantiation, at a time remote from birth, that an asymptomatic birth-related subdural hemorrhage can result in rebleeding and sudden collapse. Further, a diagnosis of AHT is a medical conclusion, not a legal determination of the intent of the perpetrator or a diagnosis of murder. We hope that this consensus document reduces confusion by recommending to judges and jurors the tools necessary to distinguish genuine evidence-based opinions of the relevant medical community from legal arguments or etiological speculations that are unwarranted by the clinical findings, medical evidence and evidence-based literature

Rapid response tools and datasets for post-fire modeling: linking Earth Observations and process-based hydrological models to support post-fire remediation

Preparation is key to utilizing Earth Observations and process-based models to support post-wildfire mitigation. Post-fire flooding and erosion can pose a serious threat to life, property and municipal water supplies. Increased runoff and sediment delivery due to the loss of surface cover and fire-induced changes in soil properties are of great concern. Remediation plans and treatments must be developed and implemented before the first major storms in order to be effective. One of the primary sources of information for making remediation decisions is a soil burn severity map derived from Earth Observation data (typically Landsat) that reflects fire induced changes in vegetation and soil properties. Slope, soils, land cover and climate are also important parameters that need to be considered. Spatially-explicit process-based models can account for these parameters, but they are currently under-utilized relative to simpler, lumped models because they are difficult to set up and require spatially-explicit inputs (digital elevation models, soils, and land cover). Our goal is to make process-based models more accessible by preparing spatial inputs before a fire, so that datasets can be rapidly combined with soil burn severity maps and formatted for model use. We are building an online database (http://geodjango.mtri.org/geowepp /) for the continental United States that will allow users to upload soil burn severity maps. The soil burn severity map is combined with land cover and soil datasets to generate the spatial model inputs needed for hydrological modeling of burn scars. Datasets will be created to support hydrological models, post-fire debris flow models and a dry ravel model. Our overall vision for this project is that advanced GIS surface erosion and mass failure prediction tools will be readily available for post-fire analysis using spatial information from a single online site

Rapid response tools and datasets for post-fire modeling in Boreal and Arctic Environments

Preparation is a key component to utilizing Earth Observations and process-based models in order to support post-wildfire mitigation. Post-fire flooding and erosion can pose a serious threat to life, property and municipal water supplies. Increased runoff and sediment delivery due to the loss of surface cover and fire-induced changes in soil properties are of great concern to resource managers. Remediation plans and treatments must be developed and implemented before the first major storms in order to be effective. One of the primary sources of information for making remediation decisions is a soil burn severity map derived from Earth Observation data (typically Landsat) that reflects fire induced changes in vegetation and soil properties. Slope, soils, land cover and climate are also important parameters that need to be considered. Spatially-explicit process-based models can account for these parameters, but they are under-utilized relative to simpler, lumped models because they are both difficult to set up and require spatially-explicit inputs (digital elevation models, soils, and land cover). Our goal is to make process-based models more accessible by preparing spatial inputs before a wild fire, so that datasets can be rapidly combined with soil burn severity maps and formatted for model use. We have built an open source online database (http://geodjango.mtri.org/geowepp /) for the continental United States that allows users to upload soil burn severity maps into the database. The soil burn severity map is then rapidly combined with land cover and soil datasets in order to generate the spatial model inputs needed for hydrological modelling of burn scars. We believe our database could be expanded internationally to support other countries that face post-fire hazards. This summer we worked with the University of Alberta to model potential erosion from the Fort McMurray fire. We utilized Lidar based DEM, Canadian weather data, Canadian Soil Landscape data, pre and post-fire Landsat imagery, and the Alberta Biodiversity Monitoring Institute land cover map in our modeling. We were able to demonstrate that process based models could be rapidly applied for modeling post-fire effects in Canada. The datasets and modeling developed for the Fort McMurray fire will be refined and utilized under a new NASA SMAP program to help improve Canadian Forest Fire Danger Rating System predictions with SMAP soil moisture data. Data fusion techniques will be used to combine modeled predictions of soil moisture with SMAP observations with the goal of improving the spatial resolution of SMAP

Rapid response tools and datasets for post-fire hydrological modeling applied to the High Park Fire

Post-fire flooding and erosion can pose a serious threat to life, property and natural resources. Time is critical in post-fire remediation as plans and treatments must be developed and deployed before the first major storms to be effective. We have developed an interactive database for the continental United States to facilitate rapid assessment of runoff and erosion risks from burned watersheds. This interactive database allows modelers to upload earth observations of soil burn severity and quickly download spatially-explicit model inputs at either 10- or 30-m resolution for process-based models, particularly WEPP based models (http://geodjango.mtri.org/geowepp/). Other modeling applications include agriculture, construction, or mining. The online database has allowed post-fire remediation teams in the western U.S. to rapidly predict post-fire erosion and effects of mulching treatments for over a dozen fires ranging in size from 4-540 km2. There is an urgent need for field data to validate these modeling efforts. One of the first fires to utilize this database was the High Park Fire, which burned approximately 330 km2 in northcentral Colorado in June 2012. Beginning in late summer of 2012 rainfall, site characteristics, and sediment production were measured for 21 unmulched and 8 mulched hillslopes. In 2013 sediment yields from unmulched and mulched hillslopes averaged 7.9 and 3.0 Mg ha-1, respectively, and these values dropped by more than an order of magnitude in 2014. Initial comparisons of measured and predicted hillslope sediment yields were poorly correlated, although they are of a similar order of magnitude. The causes of the poor correlation are being evaluated, and include a discrepancy between the small scale of the measured hillslopes and the much larger scale of the modeled hillslopes, problems with some sediment fences overtopping, an exceptionally large and long duration storm in September 2013, and the lack of suspended sediment data. Additional modeling is being conducted to obtain a better match between the predicted and measured hillslopes; and to model specific subsets of the data to minimize problems with the measured sediment yields and the effect of the September storm. These results will be used to both validate and recalibrate post-fire soil parameters for WEPP based models

Rapid response tools and datasets for post-fire erosion modeling: An online database to support post-fire erosion modeling

Once the danger posed by an active wildfire has passed, land managers must rapidly assess risks posed by post-fire runoff and erosion due to fire-induced changes in soil properties and the loss of surface cover. Post-fire assessments and proposals to mitigate risks to downstream areas due to flooding, erosion, and sedimentation are typically undertaken by interdisciplinary Burned Area Emergency Response (BAER) teams. One of the first and most important priorities of a BAER team is the development of a burn severity map that reflects the fire-induced changes in both vegetative cover and soils. Currently these maps are known as BARC (Burned Area Reflectance Classification) maps and they are generated from multi-spectral remote sensing data. BAER teams also have access to many erosion modeling tools and datasets, but process-based, spatially explicit models are currently under-utilized relative to simpler, lumped models because they are more difficult to set up and they require the preparation of spatially-explicit data layers such as digital elevation models (DEM), soils, and land cover. We are working to make spatially-explicit modeling easier by preparing large-scale spatial data sets that can be rapidly combined with burn severity maps and then used to quickly run more accurate, process-based models for spatially explicit predictions of post-fire erosion and runoff. A prototype database consisting of 30-m DEM, soil, land cover, and Monitoring Trends in Burn Severity (MTBS) maps for Colorado has been created for use in GeoWEPP (Geo-spatial interface for the Water Erosion Prediction Project) with Disturbed WEPP parameters developed for post-fire conditions. Additional soil data layers have been gathered to support a spatial empirical debris flow model that also utilizes BARC maps. Future plans include developing the dataset to support other models commonly used by BAER teams. The importance of preparing spatial data ahead of time can be illustrated with two contrasting modeling exercises from recent fires. The 2012 High Park Fire that burned near Fort Collins, Colorado and a small portion of the 2011 Rock House Fire (Hospital Canyon) that burned in western Texas. A lack of preparatory work meant useful products could not be produced in a timely manner for the Rock House Fire. In contrast, an earlier project meant that baseline soil and land cover data were readily available for the 2012 High Park Fire, which burned 330 km2 and threatened the drinking water for Fort Collins, Greeley, and other downstream communities. These datasets were combined with the burn severity map and used to model post-fire erosion and run-off in GeoWEPP using a two hour storm event with a total rainfall of 2.2 inches. Predictions of post-fire erosion rates ranged from 0 to 10.4 Mg/ha and the maps were used by the BAER team to assess relative erosion risks, and develop the associated proposals for post-fire mitigation efforts