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Urban flooding is a frequent problem affecting cities all over the world. The problem is more significant now that the climate is changing and urbanization trends are increasing. Various, physical hydrological models such as the Environmental Protection Agency Storm Water Management Model (EPA SWMM), MIKE URBAN-II and others, have been developed to simulate flooding events in cities. However, they require high accuracy mapping and a simulation of the underground storm drainage system. Sadly, this capability is usually not available for older or larger so-called megacities. Other hydrological model types are classified in the semi-physical category, like Cellular Automata (CA), require the incorporation of very fine resolution data. These types of data, in turn, demand massive computer power and time for analysis. Furthermore, available forecasting systems provide a way to determine total rainfall during extreme events, but they do not tell us what areas will be flooded. This work introduces an urban flooding tool that couples a rainfall-runoff model with a flood map database to expedite the alert process and estimate flooded areas. A 0.30-m Lidar Digital Elevation Model (DEM) of the study area (in this case Manhattan, New York City) is divided into 140 sub-basins. Several flood maps for each sub-basin are generated and organized into a database. For any forecasted extreme rainfall event, the rainfall-runoff model predicts the expected runoff volume at different times during the storm interval. The system rapidly searches for the corresponding flood map that delineates the expected flood area. The sensitivity analysis of parameters in the model show that the effect of storm inlet flow head is approximately linear while the effects of the threshold infiltration rate, the number of storm inlets, and the storm inlet flow reduction factor are non-linear. The reduction factor variation is found to exhibit a high non-linearity variation, hence requiring further detailed investigation.
Rafea Al-Suhili; Cheila Cullen; Reza Khanbilvardi. An Urban Flash Flood Alert Tool for Megacities—Application for Manhattan, New York City, USA. Hydrology 2019, 6, 56 .
AMA StyleRafea Al-Suhili, Cheila Cullen, Reza Khanbilvardi. An Urban Flash Flood Alert Tool for Megacities—Application for Manhattan, New York City, USA. Hydrology. 2019; 6 (2):56.
Chicago/Turabian StyleRafea Al-Suhili; Cheila Cullen; Reza Khanbilvardi. 2019. "An Urban Flash Flood Alert Tool for Megacities—Application for Manhattan, New York City, USA." Hydrology 6, no. 2: 56.
Spruce beetle-induced (Dendroctonus rufipennis (Kirby)) mortality on the Kenai Peninsula has been hypothesized by local ecologists to result in the conversion of forest to grassland and subsequent increased fire danger. This hypothesis stands in contrast to empirical studies in the continental US which suggested that beetle mortality has only a negligible effect on fire danger. In response, we conducted a study using Landsat data and modeling techniques to map land cover change in the Kenai Peninsula and to integrate change maps with other geospatial data to predictively map fire danger for the same region. We collected Landsat imagery to map land cover change at roughly five-year intervals following a severe, mid-1990s beetle infestation to the present. Land cover classification was performed at each time step and used to quantify grassland encroachment patterns over time. The maps of land cover change along with digital elevation models (DEMs), temperature, and historical fire data were used to map and assess wildfire danger across the study area. Results indicate the highest wildfire danger tended to occur in herbaceous and black spruce land cover types, suggesting that the relationship between spruce beetle damage and wildfire danger in costal Alaskan forested ecosystems differs from the relationship between the two in the forests of the coterminous United States. These change detection analyses and fire danger predictions provide the Kenai National Wildlife Refuge (KENWR) ecologists and other forest managers a better understanding of the extent and magnitude of grassland conversion and subsequent change in fire danger following the 1990s spruce beetle outbreak.
Katherine A. Hess; Cheila Cullen; Jeanette Cobian-Iñiguez; Jacob S. Ramthun; Victor Lenske; Dawn R. Magness; John D. Bolten; Adrianna C. Foster; Joseph Spruce. Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska. Remote Sensing 2019, 11, 283 .
AMA StyleKatherine A. Hess, Cheila Cullen, Jeanette Cobian-Iñiguez, Jacob S. Ramthun, Victor Lenske, Dawn R. Magness, John D. Bolten, Adrianna C. Foster, Joseph Spruce. Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska. Remote Sensing. 2019; 11 (3):283.
Chicago/Turabian StyleKatherine A. Hess; Cheila Cullen; Jeanette Cobian-Iñiguez; Jacob S. Ramthun; Victor Lenske; Dawn R. Magness; John D. Bolten; Adrianna C. Foster; Joseph Spruce. 2019. "Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska." Remote Sensing 11, no. 3: 283.
Rainfall-induced shallow landslides are one of the most frequent hazards on slanted terrains. Intense storms with high-intensity and long-duration rainfall have high potential to trigger rapidly moving soil masses due to changes in pore water pressure and seepage forces. Nevertheless, regardless of the intensity and/or duration of the rainfall, shallow landslides are influenced by antecedent soil moisture conditions. As of this day, no system exists that dynamically interrelates these two factors on large scales. This work introduces a Shallow Landslide Index (SLI) as the first implementation of antecedent soil moisture conditions for the hazard analysis of shallow rainfall-induced landslides. The proposed mathematical algorithm is built using a logistic regression method that systematically learns from a comprehensive landslide inventory. Initially, root-soil moisture and rainfall measurements modeled from AMSR-E and TRMM respectively, are used as proxies to develop the index. The input dataset is randomly divided into training and verification sets using the Hold-Out method. Validation results indicate that the best-fit model predicts the highest number of cases correctly at 93.2% accuracy. Consecutively, as AMSR-E and TRMM stopped working in October 2011 and April 2015 respectively, root-soil moisture and rainfall measurements modeled by SMAP and GPM are used to develop models that calculate the SLI for 10, 7, and 3 days. The resulting models indicate a strong relationship (78.7%, 79.6%, and 76.8% respectively) between the predictors and the predicted value. The results also highlight important remaining challenges such as adequate information for algorithm functionality and satellite based data reliability. Nevertheless, the experimental system can potentially be used as a dynamic indicator of the total amount of antecedent moisture and rainfall (for a given duration of time) needed to trigger a shallow landslide in a susceptible area. It is indicated that the SLI algorithm can be re-built for other regions where deterministic studies are not feasible. This represents a significant step towards rainfall-induced shallow landslide hazard readiness.
Cheila Avalon Cullen; Rafea Al-Suhili; Reza Khanbilvardi. Guidance Index for Shallow Landslide Hazard Analysis. Remote Sensing 2016, 8, 866 .
AMA StyleCheila Avalon Cullen, Rafea Al-Suhili, Reza Khanbilvardi. Guidance Index for Shallow Landslide Hazard Analysis. Remote Sensing. 2016; 8 (10):866.
Chicago/Turabian StyleCheila Avalon Cullen; Rafea Al-Suhili; Reza Khanbilvardi. 2016. "Guidance Index for Shallow Landslide Hazard Analysis." Remote Sensing 8, no. 10: 866.
Rainfall induced landslides are one of the most frequent natural hazards on slanted terrains. They lead to significant economic losses and fatalities worldwide. Most factors inducing shallow landslides are local and can only be mapped with high levels of uncertainty at larger scales. This work presents an attempt to determine slope instability using buffer and threshold techniques to downscale large areas and minimize slope uncertainties at local scales, then in a second stage, logistic regression is used to determine susceptibility at large scales. ASTER GDEM V2 is used for topographical characterization of slope and buffer analysis. Four static parameters (slope angle, soil type, land cover and elevation) for 230 shallow rainfall-induced landslides listed in a comprehensive landslide inventory for the continental United States are examined. A delimiting buffer equivalent to 5, 25 or 50 km is created around each landslide event facilitating the statistical analysis of slope thresholds. Slope angle thresholds at the pixel points 50, 75, 95, 99 and maximum percentiles are compared to one another and tested for best fit in a logistic regression environment. It is determined that values lower than the 75-percentile threshold misrepresents susceptible slope angles by not including slopes higher than 35°. Best range of slope angles and regression fit can be achieved when utilizing the 99 percentile slope angle threshold. The resulting logistic regression model predicts the highest number of cases correctly with 97.2% accuracy. The logistic regression model is carried over to ArcGIS where all variables are processed based on their corresponding coefficients. A regional landslide probability map for the continental United States is created and analyzed against the available landslide records and their spatial distributions. It is expected that future inclusion of dynamic parameters like precipitation and other proxies like soil moisture into the model will further improve accuracy. Keywords: Shallow landslides; Slope instability; Threshold analysis; Logistic regression; Regional analysis; GIS; Remote sensing Introduction Rainfall induced landslides are one of the most frequent natural hazards on slanted terrains. They usually result in great economic losses and fatalities globally. Worldwide at least 32,322 deaths between 2004 and 2010 have been reported [1] and in the United States alone, landslides cause $1-2 billion in damages and more than 25 fatalities in average each year [2]. Understanding, mapping, modeling and preventing the aftermath of these devastating events represents an important scientific and operational endeavor [3]. The term “Landslide” describes the downward and outward movement of slope-forming materials that include rock, earth, and debris or a combination of these [4]. Although landslides are considered to be dependent on the complex interaction of several static and dynamic factors [5-7] slope angle has great influence on the susceptibility of a slope to sliding. Increased slope angle usually correlates to increased likelihood of failure even if the material distribution on the slope is uniform and isotropic [5]. Undeniably, many other parameters are essential to the analysis of landslide risk. For example, changes in land use and land cover such as deforestation, forest logging, road construction, cultivation and fire on steep slopes can have a significant effect on landslide activity [8]. In addition, forest vegetation
Cullen Ca; Kashuk S. A Multistage Technique to Minimize Overestimations of Slope Susceptibility at Large Spatial Scales. Journal of Remote Sensing & GIS 2016, 5, 1 .
AMA StyleCullen Ca, Kashuk S. A Multistage Technique to Minimize Overestimations of Slope Susceptibility at Large Spatial Scales. Journal of Remote Sensing & GIS. 2016; 5 (2):1.
Chicago/Turabian StyleCullen Ca; Kashuk S. 2016. "A Multistage Technique to Minimize Overestimations of Slope Susceptibility at Large Spatial Scales." Journal of Remote Sensing & GIS 5, no. 2: 1.