scholarly journals Geospatial applications to landslide riskscape development: a modeling approach to quantify landslide riskscapes in the Colorado Front Range, USA

2021 ◽  
Author(s):  
Heather Brainerd Hicks ◽  
Melinda Laituri
Keyword(s):  
Landslide Susceptibility ◽  
Human Factor ◽  
Colorado Front Range ◽  
Landslide Risk ◽  
Front Range ◽  
Susceptibility Modeling ◽  
Novel Approach ◽  
Increased Risk ◽  
Social Components ◽  
The Impact

Abstract Riskscapes are interdisciplinary concepts that integrate multiple facets of physical, environmental, and social components in a spatial and temporal context. While the notion of risk is well documented for landslides, riskscapes are a novel approach in the natural hazard and spatial assessment studies. This term, ‘riskscape’, is described in terms of parameters required and quantification methodological approaches. Geographic Information Systems (GIS) or geospatial methods are an appropriate tool to define the development of these riskscape quantification methods. A weighted sum overlay model for a riskscape is developed with three weighted approaches using GIS to measure the strength of spatial relationships across a regional landscape in Colorado, focused on landslide susceptibility modeling in the riskscape context. Binary riskscapes resulted in a limited understanding of the impact of features related to landslide riskscapes, but both ranked and human-factor weighted riskscape models provided more details to inform policy and plan for response to landslide events. Clustering measures using spatial-autocorrelation tools revealed that riskscape outputs are clustered and can further be used to identify areas of increased risk due to landslides in emerging population-growth areas. In conclusion, ranked and human-factor riskscape models are developed and can support decision-making and prioritization for response deployment based on landslide susceptibility criteria to focus resources on areas of interaction between landslide risk and social factors.

scholarly journals Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3312
Author(s):  
Jiaying Li ◽  
Weidong Wang ◽  
Yange Li ◽  
Zheng Han ◽  
Guangqi Chen
Keyword(s):  
Landslide Susceptibility ◽  
Heavy Rainfall ◽  
Characteristic Curve ◽  
Landslide Susceptibility Mapping ◽  
Economic Losses ◽  
Support Vector ◽  
Landslide Risk ◽  
Chi Square ◽  
Area Index ◽  
The Impact

Landslide represents an increasing menace causing huge casualties and economic losses, and rainfall is a predominant factor inducing landslides. Landslide susceptibility assessment (LSA) is a commonly used and effective method to prevent landslide risk, however, the LSA does not analyze the impact of the rainfall on landslides which is significant and non-negligible. Therefore, the spatiotemporal LSA considering the inducing effect of rainfall is proposed to improve accuracy and applicability. In this study, the influencing factors are selected using the chi-square test, out-of-bag error and multicollinearity test. The spatial LSA are thus obtained using the random forest (RF) model, deep belief networks model and support vector machine, and compared using receiver operating characteristic curve and seed cell area index to determine the optimal assessment result. According to the heavy rainfall characteristics in the study area, the rainfall period is divided into four stages, and the effective rainfall model is employed to generate the rainfall impact (RI) maps of the four stages. The spatiotemporal LSAs are obtained by coupling the optimal spatial LSA and various RI maps and verified using the landslide warning map. The results demonstrate that the optimal spatiotemporal LSA is obtained using the spatial LSA of the RF model and temporal LSA of the rainfall data in the peak stage. It can predict the area where rainfall-induced landslides are likely to occur and prevent landslide risk.

scholarly journals Parameters Derived from and/or Used with Digital Elevation Models (DEMs) for Landslide Susceptibility Mapping and Landslide Risk Assessment: A Review

2019 ◽  
Vol 8 (12) ◽  
pp. 545 ◽  
Author(s):  
Nayyer Saleem ◽  
Md. Enamul Huq ◽  
Nana Yaw Danquah Twumasi ◽  
Akib Javed ◽  
Asif Sajjad
Keyword(s):  
Risk Assessment ◽  
Landslide Susceptibility ◽  
3D Visualization ◽  
Digital Elevation Models ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Landslide Risk ◽  
Conditioning Factors ◽  
Digital Elevation ◽  
The Impact

Digital elevation models (DEMs) are considered an imperative tool for many 3D visualization applications; however, for applications related to topography, they are exploited mostly as a basic source of information. In the study of landslide susceptibility mapping, parameters or landslide conditioning factors are deduced from the information related to DEMs, especially elevation. In this paper conditioning factors related with topography are analyzed and the impact of resolution and accuracy of DEMs on these factors is discussed. Previously conducted research on landslide susceptibility mapping using these factors or parameters through exploiting different methods or models in the last two decades is reviewed, and modern trends in this field are presented in a tabulated form. Two factors or parameters are proposed for inclusion in landslide inventory list as a conditioning factor and a risk assessment parameter for future studies.

scholarly journals The Representation of Cumulus Convection in High-Resolution Simulations of the 2013 Colorado Front Range Flood

2016 ◽  
Vol 144 (11) ◽  
pp. 4265-4278 ◽  
Author(s):  
Kelly M. Mahoney
Keyword(s):  
Extreme Rainfall ◽  
Heavy Rain ◽  
Moisture Flux ◽  
Heavy Precipitation ◽  
Colorado Front Range ◽  
Convective Precipitation ◽  
Gray Zone ◽  
Front Range ◽  
Model Errors ◽  
The Impact

Abstract Model simulations of the 2013 Colorado Front Range floods are performed using 4-km horizontal grid spacing to evaluate the impact of using explicit convection (EC) versus parameterized convection (CP) in the model convective physics “gray zone.” Significant differences in heavy precipitation forecasts are found across multiple regions in which heavy rain and high-impact flooding occurred. The relative contribution of CP-generated precipitation to total precipitation suggests that greater CP scheme activity in areas upstream of the Front Range flooding may have led to significant downstream model error. Heavy convective precipitation simulated by the Kain–Fritsch CP scheme in particular led to an alteration of the low-level moisture flux and moisture transport fields that ultimately prevented the generation of heavy precipitation in downstream areas as observed. An updated, scale-aware version of the Kain–Fritsch scheme is also tested, and decreased model errors both up- and downstream suggest that scale-aware updates yield improvements in the simulation of this event. Comparisons among multiple CP schemes demonstrate that there are model convective physics gray zone considerations that significantly impact the simulation of extreme rainfall in this event.

scholarly journals Inhibition of TLR4 Signaling Impedes Tumor Growth in Colitis-Associated Colon Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Eva Pastille ◽  
Tabea Faßnacht ◽  
Alexandra Adamczyk ◽  
Nhi Ngo Thi Phuong ◽  
Jan Buer ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Tumor Growth ◽  
Pathogenic Bacteria ◽  
Intestinal Inflammation ◽  
Specific Inhibitor ◽  
Body Weight Loss ◽  
Tlr4 Signaling ◽  
Novel Approach ◽  
Increased Risk ◽  
The Impact

Patients suffering from ulcerative colitis are at increased risk of developing colorectal cancer. Although the exact underlying mechanisms of inflammation-associated carcinogenesis remain unknown, the intestinal microbiota as well as pathogenic bacteria are discussed as contributors to inflammation and colitis-associated colon cancer (CAC). In the present study, we analyzed the impact of TLR4, the receptor for Gram-negative bacteria derived lipopolysaccharides, on intestinal inflammation and tumorigenesis in a murine model of CAC. During the inflammatory phases of CAC development, we observed a strong upregulation of Tlr4 expression in colonic tissues. Blocking of TLR4 signaling by a small-molecule-specific inhibitor during the inflammatory phases of CAC strongly diminished the development and progression of colonic tumors, which was accompanied by decreased numbers of infiltrating macrophages and reduced colonic pro-inflammatory cytokine levels compared to CAC control mice. Interestingly, inhibiting bacterial signaling by antibiotic treatment during the inflammatory phases of CAC also protected mice from severe intestinal inflammation and almost completely prevented tumor growth. Nevertheless, application of antibiotics involved rapid and severe body weight loss and might have unwanted side effects. Our results indicate that bacterial activation of TLR4 on innate immune cells in the colon triggers inflammation and promotes tumor growth. Thus, the inhibition of the TLR4 signaling during intestinal inflammation might be a novel approach to impede CAC development.

Abstract 2375: Oseltamivir Treatment Reduces the Risk of Stroke Following Influenza Infection

Circulation ◽  
2007 ◽  
Vol 116 (suppl_16) ◽  
Author(s):  
Mohammad Madjid ◽  
Suellen Curkendall ◽  
William A Blumentals
Keyword(s):  
Ischemic Stroke ◽  
Group Treatment ◽  
Influenza Infection ◽  
Antiviral Treatment ◽  
Antiviral Drug ◽  
Significant Risk ◽  
Control Group ◽  
Novel Approach ◽  
Increased Risk ◽  
The Impact

Background: Influenza infection is known to trigger the incidence of heart attack and ischemic stroke. Seasonal influenza epidemics are associated with an increased risk of cardiovascular diseases, including ischemic stroke. Influenza vaccine has been shown to reduce the risk of myocardial infarction and stroke. The antiviral drug oseltamivir has been shown to reduce the severity and duration of influenza symptoms and the risk of hospitalization in patients diagnosed with influenza. We conducted a retrospective cohort study to examine the impact of oseltamivir treatment on the risk of stroke and transient ischemic attack (TIA) following influenza in adults. Methods: Anonymous, patient-level medical and pharmaceutical claims data from May 2000 to September 2006 were obtained from a managed care database. We selected patients diagnosed with influenza who were prescribed oseltamivir within 1 day before or 2 days after influenza diagnosis (oseltamivir group) and compared them to patients who were not prescribed any antiviral treatment for influenza (control group). The incidence of stroke or TIA in the 6 months following influenza diagnosis in the two groups was compared using multivariate analyses adjusted for factors including age, gender, and history of risk factors for stroke, and differences expressed as hazard ratios (HR) with 95% confidence intervals (CI). Results: There were 151,930 eligible patients aged ≥18 years: 49,238 patients in the oseltamivir group, and 102,692 patients in the control group. Treatment with oseltamivir was associated with a significant 29% reduction in the risk of stroke or TIA at 6 months following an influenza diagnosis (HR=0.71; 95% CI 0.61– 0.81). Significant reductions in risk were also observed at 1 and 3 months. The effect of oseltamivir on the risk of stroke or TIA was more pronounced in patients aged ≥40 years: significant risk reductions were observed at months 1 (HR=0.50; 95% CI 0.37– 0.68), 3 (HR=0.62; 95% CI 0.51– 0.75), and 6 (HR=0.73; 95% CI 0.63– 0.84). In those aged <40, HR at 6 months was 0.56 (95% CI: 0.37– 0.86). Conclusions: Prescription of oseltamivir after influenza is associated with a reduced risk of stroke or TIA. Our results, once confirmed by randomized studies, offer a novel approach to prevent stroke and TIA.

scholarly journals The Impact of Quality of Digital Elevation Models on the Result of Landslide Susceptibility Modeling Using the Method of Weights of Evidence

Geosciences ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 488
Author(s):  
Mirosław Kamiński
Keyword(s):  
Digital Elevation Model ◽  
Roc Curve ◽  
Landslide Susceptibility ◽  
Weights Of Evidence ◽  
Identification System ◽  
Susceptibility Modeling ◽  
Digital Elevation ◽  
Elevation Model ◽  
The Impact

The paper discusses the impact that the quality of the digital elevation model (DEM) has on the final result of landslide susceptibility modeling (LSM). The landslide map was developed on the basis of the analysis of archival geological maps and the Light Detection and Ranging (LiDAR) digital elevation model. In addition, complementary field studies were conducted. In total, 92 landslides were inventoried and their degree of activity was assessed. An inventory of the landslides was prepared using a 1-m-LiDAR DEM and field research. Two digital photogrammetric elevation models with an elevation pixel resolution of 20 m were used for landslide susceptibility modeling. The first digital elevation model was obtained from a LiDAR point cloud (DEM–airborne laser scanning (ALS)), while the second model was developed based on archival digital stereo-pair aerial images (DEM–Land Parcel Identification System (LPIS)). Both models were subjected to filtration using a Gaussian low-pass filter to reduce errors in their elevation relief. Then, using ArcGIS software, a differential model was generated to illustrate the differences in morphology between the models. The maximum differences in topographic elevations between the DEM–ALS and DEM–LPIS models were calculated. The Weights-of-Evidence model is a geostatistical method used for the landslide susceptibility modeling. Six passive factors were employed in the process of susceptibility generation: elevation, slope gradient, exposure, topographic roughness index (TRI), distance from tectonic lines, and distance from streams. As a result, two landslide susceptibility maps (LSM) were obtained. The accuracy of the landslide susceptibility models was assessed based on the Receiver Operating Characteristic (ROC) curve index. The area under curve (AUC) values obtained from the ROC curve indicate that the accuracy of classification for the LSM–DEM–ALS model was 78%, and for the LSM–LPIS–DEM model was 73%.

scholarly journals The impact of aged wildfire smoke on atmospheric composition and ozone in the Colorado Front Range in summer 2015

2017 ◽  
Author(s):  
Jakob Lindaas ◽  
Delphine K. Farmer ◽  
Ilana B. Pollack ◽  
Andrew Abeleira ◽  
Frank Flocke ◽  
...  
Keyword(s):  
Pacific Northwest ◽  
Rocky Mountain ◽  
Colorado Front Range ◽  
Atmospheric Composition ◽  
Urban Site ◽  
Front Range ◽  
Wildfire Smoke ◽  
Mixing Ratios ◽  
Urban Rural ◽  
The Impact

Abstract. The relative importance of wildfire smoke for air quality over the western U.S. is expected to increase as the climate warms and anthropogenic emissions decline. We report on in situ measurements of ozone (O3), a suite of volatile organic compounds (VOCs), and reactive oxidized nitrogen species collected during summer 2015 at the Boulder Atmospheric Observatory (BAO) in Erie, CO. Aged wildfire smoke impacted BAO during two distinct time periods during summer 2015: 6–10 July and 16–30 August. The smoke was transported from the Pacific Northwest and Canada across much of the continental U.S. Carbon monoxide and particulate matter increased during the smoke-impacted periods, along with peroxyacyl nitrates and several VOCs that have atmospheric lifetimes longer than the transport timescale of the smoke. During the August smoke-impacted period, nitrogen dioxide was also elevated during the morning and evening compared to the smoke-free periods. There were six days during our study period where the maximum 8-hour average O3 at BAO was greater than 65 ppbv, and two of these days were smoke-impacted. We examined the relationship between O3 and temperature at BAO and found that for a given temperature, O3 mixing ratios were greater (~ 10 ppbv) during the smoke-impacted periods. Enhancements in O3 during the August smoke-impacted period were also observed at two long-term monitoring sites in Colorado: Rocky Mountain National Park and the Arapahoe National Wildlife Refuge near Walden, CO. Our data provide a new case study of how aged wildfire smoke can influence atmospheric composition at an urban site, and how smoke can contribute to increased O3 abundances across an urban-rural gradient.

scholarly journals Multi-temporal relative landslide risk analysis for sustainable development of rapidly growing cities

2021 ◽  
Author(s):  
Mariano Di Napoli ◽  
Pietro Miele ◽  
Luigi Guerriero ◽  
Mariagiulia Annibali Corona ◽  
Domenico Calcaterra ◽  
...  
Keyword(s):  
Relative Risk ◽  
Risk Analysis ◽  
Population Growth ◽  
Landslide Susceptibility ◽  
Temporal Analysis ◽  
Population Variation ◽  
Landslide Risk ◽  
Increased Risk ◽  
Multi Temporal ◽  
The City

In the last decades, developing countries have experienced an increase in impact of natural disasters due to both the ongoing climate change and the sustained expansion of urban areas. Intrinsic vulnerability of settlements due to poverty and poor governance, as well as the lack of tools for urban occupation planning and mitigation protocols, have made such impact particularly severe. Cuenca (Ecuador) is a significant example of a city that in the last decades has experienced considerable population growth and an associated increasing of loss due to landslide occurrence. Despite such effects, updated urban planning tools are absent, a condition that suggested an evaluation of multi-temporal relative landslide risk, here presented based on updated data depicting the spatial distribution of landslides and their predisposing factors, as well as population change between 2010 and 2020. In addition, a multi-temporal analysis accounting for risk change between 2010 and 2020 has been carried out. Due to the absence of spatially distributed data about the population, electricity supply contract data have been used as a proxy of the population. Results indicate that current higher relative risk is estimated for municipalities (parroquias) located at the southern sector of the study area (i.e. Turi, Valle, Santa Ana, Tarqui and Paccha). Moreover, the multi-temporal analysis indicates that most municipalities of the city located in the hilly areas that bound the center (i.e. Sayausi, San Joaquin, Tarqui, Valle, Sidcay, Banos, Sidcay, Ricaurte, Paccha and Chiquintad), experiencing sustained population growth, will be exposed to an increased risk with a consistently growing trend. This information is consistent with landslide susceptibility data derived by a machine learning-based analysis that indicate higher susceptibility to landslides in hilly areas surrounding the city center. The obtained relative risk maps can be considered as a useful tool for guiding land-planning, occupation restriction and early warning strategy adoption. The used methodological approach, accounting for landslide susceptibility and population variation through proxy data analysis, has the potential to be applied in a similar context of growing-population cities of low to mid-income countries, where data, usually needed for a comprehensive landslide risk analysis, are only partly available.

scholarly journals A decadal satellite analysis of the origins and impacts of smoke in Colorado

2013 ◽  
Vol 13 (15) ◽  
pp. 7429-7439 ◽  
Author(s):  
M. Val Martin ◽  
C. L. Heald ◽  
B. Ford ◽  
A. J. Prenni ◽  
C. Wiedinmyer
Keyword(s):  
Air Quality ◽  
Vertical Distribution ◽  
Ambient Air ◽  
Colorado Front Range ◽  
Orthogonal Polarization ◽  
Front Range ◽  
Aerosol Loading ◽  
Modis Aod ◽  
High Park Fire ◽  
The Impact

Abstract. We analyze the record of aerosol optical depth (AOD) measured by the MODerate resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite in combination with surface PM2.5 to investigate the impact of fires on aerosol loading and air quality over Colorado from 2000 to 2012, and to evaluate the contribution of local versus transported smoke. Fire smoke contributed significantly to the AOD levels observed over Colorado. During the worst fire seasons of 2002 and 2012, average MODIS AOD over the Colorado Front Range corridor were 20–50% larger than the other 11 yr studied. Surface PM2.5 was also unusually elevated during fire events and concentrations were in many occasions above the daily National Ambient Air Quality Standard (35 μg m−3) and even reached locally unhealthy levels (> 100 μg m−3) over populated areas during the 2012 High Park fire and the 2002 Hayman fire. Over the 13 yr examined, long-range transport of smoke from northwestern US and even California (> 1500 km distance) occurred often and affected AOD and surface PM2.5. During most of the transport events, MODIS AOD and surface PM2.5 were reasonable correlated (r2 = 0.2–0.9), indicating that smoke subsided into the Colorado boundary layer and reached surface levels. However, that is not always the case since at least one event of AOD enhancement was disconnected from the surface (r2<0.01 and low PM2.5 levels). Observed plume heights from the Multi-angle Imaging SpectroRadiometer (MISR) satellite instrument and vertical aerosol profiles measured by the space-based Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) showed a complex vertical distribution of smoke emitted by the High Park fire in 2012. Smoke was detected from a range of 1.5 to 7.5 km altitude at the fire origin and from ground levels to 12.3 km altitude far away from the source. The variability of smoke altitude as well as the local meteorology were key in determining the aerosol loading and air quality over the Colorado Front Range region. Our results underline the importance of accurate characterization of the vertical distribution of smoke for estimating the air quality degradation associated with fire activity and its link to human health.

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