Statistical Analysis of Seasonal Precipitation for the Lake Pontchartrain Basin and Associated Watersheds

To investigate seasonal patterns of precipitation, statistical analysis was performed on a dataset of daily rainfall observed at 63 south Louisiana stations from 1836 to 2002. Each station record was examined for data quality and continuity with special attention to time periods surrounding station relocation or equipment exchange. Mean Areal Precipitation (MAP) sheets were compiled for every month from 1836 to 2002 to document the daily rainfall across south Louisiana and neighboring portions of southern and coastal Mississippi. Using these MAP sheets, missing data was examined to see if a reasonable value could be substituted to extend the continuity of a station\u27s rainfall record. Once these data quality and continuity checks were completed, a series of statistical tests were conducted to determine an accurate scheme to form station groups. To group stations together, each station was required to have a normal distribution of monthly average rainfall, a statistically equivalent variance, and a statistically equivalent mean when compared with other stations in the group. As a result of the Shapiro-Wilk Test, the F-Test, and the Student T-test, eight station groups were formed. To define seasonal rainfall patterns across south Louisiana, statistical tests were conducted for a 12 month period and six and three month intervals. For the six month intervals, group rainfall averages and pooled variances were calculated for each interval beginning with January-July and ending with December-May. For the three month intervals, group rainfall averages and pooled variances were calculated for January-March and concluded with December-February. To test the hypothesis of a statistically significant difference in mean rainfall between the eight groups for a 12, six, and three month period, the Student T-test was conducted. For an annual basis, there is a statistically significant difference in average rainfall at a five percent level of significance between all of the groups except the Southshore (S.S) group when compared to the SW1 group. For six and three month intervals, statistically significant differences exist between the eight groups especially during winter and segments of the Hurricane season from June to November

The CI-FLOW Project: A System for Total Water Level Prediction from the Summit to the Sea

Kildow et al. (2009) reported that coastal states support 81% of the U.S. population and generate 83 percent [$11.4 trillion (U.S. dollars) in 2007] of U.S. gross domestic product. Population trends show that a majority of coastal communities have transitioned from a seasonal, predominantly weekend, tourist-based economy to a year-round, permanently based, business economy where industry expands along shorelines and the workforce commutes from inland locations. As a result of this transition, costs associated with damage to the civil infrastructure and disruptions to local and regional economies due to coastal flooding events are escalating, pushing requirements for a new generation of flood prediction technologies and hydrologic decision support tools

Statistical Analysis of Seasonal Precipitation for the Lake Pontchartrain Basin and Associated Watersheds

To investigate seasonal patterns of precipitation, statistical analysis was performed on a dataset of daily rainfall observed at 63 south Louisiana stations from 1836 to 2002. Each station record was examined for data quality and continuity with special attention to time periods surrounding station relocation or equipment exchange. Mean Areal Precipitation (MAP) sheets were compiled for every month from 1836 to 2002 to document the daily rainfall across south Louisiana and neighboring portions of southern and coastal Mississippi. Using these MAP sheets, missing data was examined to see if a reasonable value could be substituted to extend the continuity of a station\u27s rainfall record. Once these data quality and continuity checks were completed, a series of statistical tests were conducted to determine an accurate scheme to form station groups. To group stations together, each station was required to have a normal distribution of monthly average rainfall, a statistically equivalent variance, and a statistically equivalent mean when compared with other stations in the group. As a result of the Shapiro-Wilk Test, the F-Test, and the Student T-test, eight station groups were formed. To define seasonal rainfall patterns across south Louisiana, statistical tests were conducted for a 12 month period and six and three month intervals. For the six month intervals, group rainfall averages and pooled variances were calculated for each interval beginning with January-July and ending with December-May. For the three month intervals, group rainfall averages and pooled variances were calculated for January-March and concluded with December-February. To test the hypothesis of a statistically significant difference in mean rainfall between the eight groups for a 12, six, and three month period, the Student T-test was conducted. For an annual basis, there is a statistically significant difference in average rainfall at a five percent level of significance between all of the groups except the Southshore (S.S) group when compared to the SW1 group. For six and three month intervals, statistically significant differences exist between the eight groups especially during winter and segments of the Hurricane season from June to November

CI-FLOW: Evaluating and Testing New Technologies for Accurate and Timely Identification of Inland and Coastal Floods in the Tar-Pamlico and Neuse River Basins of Coastal North Carolina

CI-FLOW is a new technology being utilized to identify flood hazards. CI-FLOW stands for the Coastal and Inland Flooding Observation and Warning project. CI-FLOW was implemented ten years ago by the directors of Sea Grant and NOAA, along with other North Carolina partners and state agencies. The National Weather Service has one forecast point in Louisburg, NC, on the Tar River. The addition of additional USGS gauges is important for effective identification of flood hazards. The accurate and timely identification of flood hazards is important given the growing and seasonally fluctuating population of Dare County. Approximately one-half of the housing in Dare County is seasonal, and emergency mangers must account for this. For instance, it is estimated that in 2030, if a category three hurricane hit during a time of peak tourism, emergency managers would need to clear 30,000 vehicles in 31 hours. Given the importance flood identification, an integrated approach is needed for accurate and timely identification. The utilization of storm surge models and observations of weather and river levels, is key in flood identification. Three-dimensional images linked with real-time data can help assess the dangers of a flood and aid in emergency management decisions. CI-FLOW is being used to time crests and discharges along with a coupled model which links water and storm surge discharge. In conclusion, the CI-FLOW project is a multi-agency evaluation of new technologies to better identify floods in the Tar-Pamlico and Neuse river basins

CI-FLOW: Evaluating and Testing New Technologies for Accurate and Timely Identification of Inland and Coastal Floods in the Tar-Pamlico and Neuse River Basins of Coastal North Carolina

CI-FLOW is a new technology being utilized to identify flood hazards. CI-FLOW stands for the Coastal and Inland Flooding Observation and Warning project. CI-FLOW was implemented ten years ago by the directors of Sea Grant and NOAA along with other North Carolina partners and state agencies. The National Weather Service has one forecast point in Louisburg NC on the Tar River. The addition of additional USGS gauges is important for effective identification of flood hazards. The accurate and timely identification of flood hazards is important given the growing and seasonally fluctuating population of Dare County. Approximately one-half of the housing in Dare County is seasonal and emergency mangers must account for this. For instance it is estimated that in 2030 if a category three hurricane hit during a time of peak tourism emergency managers would need to clear 30 000 vehicles in 31 hours. Given the importance flood identification an integrated approach is needed for accurate and timely identification. The utilization of storm surge models and observations of weather and river levels is key in flood identification. Three-dimensional images linked with real-time data can help assess the dangers of a flood and aid in emergency management decisions. CI-FLOW is being used to time crests and discharges along with a coupled model which links water and storm surge discharge. In conclusion the CI-FLOW project is a multi-agency evaluation of new technologies to better identify floods in the Tar-Pamlico and Neuse river basins

Prototyping a Hurricane-Flood-Landslide-Continuum Prediction System: A CI-FLOW Contribution to North Carolina and Broader Coastal Regions

A partnership between NASA and the US Agency for International Development (USAID) is examining prototyping a hurricane-flood-landslide continuum as part of CI-FLOW (Coastal and Inland Flooding Observation and Warning project). Using high resolution satellites, hydrological data can be used to monitor global flooding. The Terra and Aqua satellites use a key instrument called MODIS (Moderate Resolution Imaging Spectroradiometer) to map flood inundation. Aster flood maps are used to calibrate hydrologic modeling. Additionally, inland river flow and storm surge modeling is being done as a part of CI-FLOW. Part of the problem with the models is determining how to calibrate them effectively. Researchers examined a full spectrum of data calibration techniques. The guided DREAM technique performed the best in terms of minimizing bias. They also examined Hurricane Floyd, and, using a discharge simulation, the guided DREAM preformed well. Landslides can accompany floods, and LIDAR (Light Detection and Ranging) data can be useful in examining slope stability and rainfall. In order to effectively manage hazards, an integrated approach is necessary, using hydrological data and flood prediction systems. Recently, the prediction system has been implemented successfully

Prototyping a Hurricane-Flood-Landslide-Continuum Prediction System: A CI-FLOW Contribution to North Carolina and Broader Coastal Regions

A partnership between NASA and the US Agency for International Development (USAID) is examining prototyping a hurricane-flood-landslide continuum as part of CI-FLOW (Coastal and Inland Flooding Observation and Warning project). Using high resolution satellites hydrological data can be used to monitor global flooding. The Terra and Aqua satellites use a key instrument called MODIS (Moderate Resolution Imaging Spectroradiometer) to map flood inundation. Aster flood maps are used to calibrate hydrologic modeling. Additionally inland river flow and storm surge modeling is being done as a part of CI-FLOW. Part of the problem with the models is determining how to calibrate them effectively. Researchers examined a full spectrum of data calibration techniques. The guided DREAM technique performed the best in terms of minimizing bias. They also examined Hurricane Floyd and using a discharge simulation the guided DREAM preformed well. Landslides can accompany floods and LIDAR (Light Detection and Ranging) data can be useful in examining slope stability and rainfall. In order to effectively manage hazards an integrated approach is necessary using hydrological data and flood prediction systems. Recently the prediction system has been implemented successfully

Quantitative proteomics analysis identifies MUC1 as an effect sensor of EGFR inhibition

Tumor responses to cancer therapeutics are generally monitored every 2-3 months based on changes in tumor size. Dynamic biomarkers that reflect effective engagement of targeted therapeutics to the targeted pathway, so-called "effect sensors", would fulfill a need for non-invasive, drug-specific indicators of early treatment effect. Using a proteomics approach to identify effect sensors, we demonstrated MUC1 upregulation in response to epidermal growth factor receptor (EGFR)targeting treatments in breast and lung cancer models. To achieve this, using semi-quantitative mass spectrometry, we found MUC1 to be significantly and durably upregulated in response to erlotinib, an EGFR-targeting treatment. MUC1 upregulation was regulated transcriptionally, involving PI3K-signaling and STAT3. We validated these results in erlotinib-sensitive human breast and non-small lung cancer cell lines. Importantly, erlotinib treatment of mice bearing SUM149 xenografts resulted in increased MUC1 shedding into plasma. Analysis of MUC1 using serial blood sampling may therefore be a new, relatively non-invasive tool to monitor early and drug-specific effects of EGFR-targeting therapeutics.</p

Quantitative proteomics analysis identifies MUC1 as an effect sensor of EGFR inhibition

Tumor responses to cancer therapeutics are generally monitored every 2-3 months based on changes in tumor size. Dynamic biomarkers that reflect effective engagement of targeted therapeutics to the targeted pathway, so-called "effect sensors", would fulfill a need for non-invasive, drug-specific indicators of early treatment effect. Using a proteomics approach to identify effect sensors, we demonstrated MUC1 upregulation in response to epidermal growth factor receptor (EGFR)targeting treatments in breast and lung cancer models. To achieve this, using semi-quantitative mass spectrometry, we found MUC1 to be significantly and durably upregulated in response to erlotinib, an EGFR-targeting treatment. MUC1 upregulation was regulated transcriptionally, involving PI3K-signaling and STAT3. We validated these results in erlotinib-sensitive human breast and non-small lung cancer cell lines. Importantly, erlotinib treatment of mice bearing SUM149 xenografts resulted in increased MUC1 shedding into plasma. Analysis of MUC1 using serial blood sampling may therefore be a new, relatively non-invasive tool to monitor early and drug-specific effects of EGFR-targeting therapeutics