This document summarizes a GIS project analyzing flooding in Allegheny County, Pennsylvania from a storm on June 16-17, 2008. Key steps included: collecting soil, land cover, and precipitation data; calculating runoff and flow accumulation; predicting flood severity levels; and tabulating impacts on population and roads by census block group. Maps and tables show northern areas of the county experienced the most severe flooding effects. The analysis provides a model to predict future flood patterns in Allegheny County.
Presentation at the conference Greenmetrics 2016 of the paper "Geographical Load Balancing across Green Datacenters: a Mean Field Analysis" (authors G. Neglia, M. Sereno, G. Bianchi)
Presentation at the conference Greenmetrics 2016 of the paper "Geographical Load Balancing across Green Datacenters: a Mean Field Analysis" (authors G. Neglia, M. Sereno, G. Bianchi)
Presentation given by Darius Bazazi, GeoPlace, as part of the EDINA Geoforum 2014 event on Thursday 19th June 2014 at the Informatics Forum, University of Edinburgh.
Presentation given by Peter Gibbs, Met Office and BBC broadcast meteorologist, as part of the EDINA Geoforum 2014 event on Thursday 19th June 2014 at the Informatics Forum, University of Edinburgh.
Presentation of Four Centennial-long Global Gridded Datasets of the Standardi...Agriculture Journal IJOEAR
Abstract— In this article four global gridded datasets of the Standardized Precipitation Index (SPI) are presented. They are computed from four different data sources: UDEL/GEOG/CCR v3.02, GPCC/ v7.0, NOAA-CIRES 20CR v2c and ECMWF ERA-20C each covering more than a century-long period. The SPI is calculated for the most frequently used time windows of 1, 3, 6, and 12 months. UDEL/GEOG/CCR v3.02 and GPCC/ v7.0 are used in the highest native resolution of 0.5×0.5° whilst NOAA-CIRES 20CR v2c and ECMWF ERA-20C are interpolated at 1.5×1.5° and 0.5×0.5° correspondingly. In contrast to some other indices, for example the popular Palmer Drought Severity Index (PDSI), SPI has significant advantages such as simplicity, suitability on variable time scales and robustness rooted in a solid theoretical development. SPI has been selected by the World Meteorological Organization (WMO) as a key indicator for monitoring drought ('Lincoln declaration'). As a result, drought monitoring centres worldwide are effectively exploiting this index and the National Meteorological and Hydrological Services (NMHSs) are encouraged to use it for monitoring meteorological droughts. These facts and the strong conviction of the authors that the free exchange of data and software services are а basis of effective scientific collaboration, are the main motivators to provide these datasets free of charge at ftp://xeo.cfd.meteo.bg/SPI/. The paper briefly presents some possible applications of the SPI data, revealing its suitability for various objective long-term drought studies at any geographical location.
El 29 de febrero y el 1 de marzo de 2016, la Fundación Ramón Areces analizó la relación entre 'Big Data y el cambio climático' en unas jornadas. ¿Puede el Big Data ayudar a reducir el cambio climático? ¿Cómo contribuirá ese análisis masivo de datos a prevenir y gestionar catástrofes naturales? Son solo algunas de las preguntas a las que intentarán responder los ponentes. Las ciencias vinculadas al clima tienen en el Big Data una herramienta muy prometedora para afrontar diferentes fenómenos asociados al cambio climático.
Landslide Susceptibility Assessment Using Modified Frequency Ratio Model in K...Dr. Amarjeet Singh
Landslides are the most common natural hazards in Nepal especially in the mountainous terrain. The existing topographical scenario, complex geological settings followed by the heavy rainfall in monsoon has contributed to a large number of landslide events in the Kaski district. In this study, landslide susceptibility was modeled with the consideration of twelve conditioning factors to landslides like slope, aspect, elevation, Curvature, geology, land-use, soil type, precipitation, road proximity, drainage proximity, and thrust proximity. A Google-earth-based landslide inventory map of 637 landslide locations was prepared using data from Disinventar, reports, and satellite image interpretation and was randomly subdivided into a training set (70%) with 446 Points and a test set with 191 points (30%). The relationship among the landslides and the conditioning factors were statistically evaluated through the use of Modified Frequency ratio analysis. The results from the analysis gave the highest Prediction rate (PR) of 6.77 for elevation followed by PR of 66.45 for geology and PR of 6.38 for the landcover. The analysis was then validated by calculating the Area Under a Curve (AUC) and the prediction rate was found to be 68.87%. The developed landslide susceptibility map is helpful for the locals and authorities in planning and applying different intervention measures in the Kaski District.
In this project the group members will play with daily rainfall data collected in Gulf coast (535stations in total) from 1949 to 2017. The purposes of this exercise are to:
1) to give students an idea of a typical example of a climate data set (spatio-temporal data) and someassociated scientific questions (e.g. how rainfall extremes vary in space and time and how that mightbe affected by other things like greenhouse gases or temperatures).
2) to get students familiar with data analysis using R including data manipulation, data visualization, and data summary.
3) to introduce some statistical methods (e.g. time series analysis, spatial statistics, extreme value analysis) to analyze this kind of data to "answer" (perform statistical inference) the questions of interest.
Group members: Lin Ge, Jianan Jang, Jessica Robinson, Erin Song, Seth Temple, Adam Wu
Livingo, presente in 4 Paesi europei (IT, FR, DE, ES), si è affermato con successo sul mercato come portale di shopping, leader nel settore dei mobili e dell’arredamento d’interni.
Livingo ha già oltre 100 negozi online partner e un catalogo di oltre 5 milioni di prodotti.
Specializzandoci nel settore dell’arredamento e dei mobili, conduciamo traffico di alta qualità ai nostri clienti sulla base di una tariffa CPC chiara e trasparente.
In breve questi sono i vantaggi che possiamo offrirvi:
• Nessuna spesa di installazione.
• Nessun canone mensile.
• Impostazione flessibile del budget giornaliero.
• Contratto di durata flessibile.
• Admin Partner privato con le statistiche dei click.
• CPC trasparente.
This report provides a comparison and overview of the third generation (3G, 3.5G, 3.75 and 3.9G) mobile services, rates and offerings in the Arab World. By October 2015, forty four cellular operators in seventeen Arab countries offered 3G/3.5G/3.75/3.9G services. Countries in which these services were offered are Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Mauritania, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syria, Tunisia and the UAE. The report provides a full listing of the available 3G/3.5G/3.75/3.9G mobile services in each of the covered countries and was conducted during the period October through mid-November 2015.
Presentation given by Darius Bazazi, GeoPlace, as part of the EDINA Geoforum 2014 event on Thursday 19th June 2014 at the Informatics Forum, University of Edinburgh.
Presentation given by Peter Gibbs, Met Office and BBC broadcast meteorologist, as part of the EDINA Geoforum 2014 event on Thursday 19th June 2014 at the Informatics Forum, University of Edinburgh.
Presentation of Four Centennial-long Global Gridded Datasets of the Standardi...Agriculture Journal IJOEAR
Abstract— In this article four global gridded datasets of the Standardized Precipitation Index (SPI) are presented. They are computed from four different data sources: UDEL/GEOG/CCR v3.02, GPCC/ v7.0, NOAA-CIRES 20CR v2c and ECMWF ERA-20C each covering more than a century-long period. The SPI is calculated for the most frequently used time windows of 1, 3, 6, and 12 months. UDEL/GEOG/CCR v3.02 and GPCC/ v7.0 are used in the highest native resolution of 0.5×0.5° whilst NOAA-CIRES 20CR v2c and ECMWF ERA-20C are interpolated at 1.5×1.5° and 0.5×0.5° correspondingly. In contrast to some other indices, for example the popular Palmer Drought Severity Index (PDSI), SPI has significant advantages such as simplicity, suitability on variable time scales and robustness rooted in a solid theoretical development. SPI has been selected by the World Meteorological Organization (WMO) as a key indicator for monitoring drought ('Lincoln declaration'). As a result, drought monitoring centres worldwide are effectively exploiting this index and the National Meteorological and Hydrological Services (NMHSs) are encouraged to use it for monitoring meteorological droughts. These facts and the strong conviction of the authors that the free exchange of data and software services are а basis of effective scientific collaboration, are the main motivators to provide these datasets free of charge at ftp://xeo.cfd.meteo.bg/SPI/. The paper briefly presents some possible applications of the SPI data, revealing its suitability for various objective long-term drought studies at any geographical location.
El 29 de febrero y el 1 de marzo de 2016, la Fundación Ramón Areces analizó la relación entre 'Big Data y el cambio climático' en unas jornadas. ¿Puede el Big Data ayudar a reducir el cambio climático? ¿Cómo contribuirá ese análisis masivo de datos a prevenir y gestionar catástrofes naturales? Son solo algunas de las preguntas a las que intentarán responder los ponentes. Las ciencias vinculadas al clima tienen en el Big Data una herramienta muy prometedora para afrontar diferentes fenómenos asociados al cambio climático.
Landslide Susceptibility Assessment Using Modified Frequency Ratio Model in K...Dr. Amarjeet Singh
Landslides are the most common natural hazards in Nepal especially in the mountainous terrain. The existing topographical scenario, complex geological settings followed by the heavy rainfall in monsoon has contributed to a large number of landslide events in the Kaski district. In this study, landslide susceptibility was modeled with the consideration of twelve conditioning factors to landslides like slope, aspect, elevation, Curvature, geology, land-use, soil type, precipitation, road proximity, drainage proximity, and thrust proximity. A Google-earth-based landslide inventory map of 637 landslide locations was prepared using data from Disinventar, reports, and satellite image interpretation and was randomly subdivided into a training set (70%) with 446 Points and a test set with 191 points (30%). The relationship among the landslides and the conditioning factors were statistically evaluated through the use of Modified Frequency ratio analysis. The results from the analysis gave the highest Prediction rate (PR) of 6.77 for elevation followed by PR of 66.45 for geology and PR of 6.38 for the landcover. The analysis was then validated by calculating the Area Under a Curve (AUC) and the prediction rate was found to be 68.87%. The developed landslide susceptibility map is helpful for the locals and authorities in planning and applying different intervention measures in the Kaski District.
In this project the group members will play with daily rainfall data collected in Gulf coast (535stations in total) from 1949 to 2017. The purposes of this exercise are to:
1) to give students an idea of a typical example of a climate data set (spatio-temporal data) and someassociated scientific questions (e.g. how rainfall extremes vary in space and time and how that mightbe affected by other things like greenhouse gases or temperatures).
2) to get students familiar with data analysis using R including data manipulation, data visualization, and data summary.
3) to introduce some statistical methods (e.g. time series analysis, spatial statistics, extreme value analysis) to analyze this kind of data to "answer" (perform statistical inference) the questions of interest.
Group members: Lin Ge, Jianan Jang, Jessica Robinson, Erin Song, Seth Temple, Adam Wu
Livingo, presente in 4 Paesi europei (IT, FR, DE, ES), si è affermato con successo sul mercato come portale di shopping, leader nel settore dei mobili e dell’arredamento d’interni.
Livingo ha già oltre 100 negozi online partner e un catalogo di oltre 5 milioni di prodotti.
Specializzandoci nel settore dell’arredamento e dei mobili, conduciamo traffico di alta qualità ai nostri clienti sulla base di una tariffa CPC chiara e trasparente.
In breve questi sono i vantaggi che possiamo offrirvi:
• Nessuna spesa di installazione.
• Nessun canone mensile.
• Impostazione flessibile del budget giornaliero.
• Contratto di durata flessibile.
• Admin Partner privato con le statistiche dei click.
• CPC trasparente.
This report provides a comparison and overview of the third generation (3G, 3.5G, 3.75 and 3.9G) mobile services, rates and offerings in the Arab World. By October 2015, forty four cellular operators in seventeen Arab countries offered 3G/3.5G/3.75/3.9G services. Countries in which these services were offered are Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Mauritania, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syria, Tunisia and the UAE. The report provides a full listing of the available 3G/3.5G/3.75/3.9G mobile services in each of the covered countries and was conducted during the period October through mid-November 2015.
Material didatico do documentário a caminho da escolaSandra Figueiredo
Algumas crianças percorrem um longo caminho para ir para a escola... Jackson (Quénia): 15 km em duas horas. Samuel (Índia): mais de uma hora de cadeira de rodas. Sahira (Marrocos): 22 km em quatro horas. Carlos (Argentina): uma hora e meia em seu cavalo... Com direção de Pascal Plisson, A Caminho da Escola foi reconhecido como o melhor documentário nos prémios César de 2014.
AS Level Biology - 10/11) Infectious Diseases and ImmunityArm Punyathorn
Finally, to end the AS level syllabus - learn about the diseases that pose threats not only to ourselves but to the community as a whole for being contagious. Also learn about how our body organizes a military section to protect us - discover how the army can be come turncoat and how espionage and information collection can be helpful in secondary responses.
Cuento ilustrado idóneo para 4º de Primaria ESTA HISTORIA ESTÁ INSPIRADA EN LOS TALLERES QUE LA FUNDACIÓN ART AND LIFE ORGANIZA EN DISTINTOS PAÍSES EN APOYO DE LOS NIÑOS Y LAS NIÑAS DE TODO EL MUNDO, ESPECIALMENTE DE PAÍSES EN VÍAS DE DESARROLLO
Determination of homogenous regions in the Tensift basin (Morocco).IJERA Editor
The aim of this study is to determine homogenous region in the Tensift basin within which the hydrological behavior is similar. In order to do this we used two methods: The Principal components analysis on the monthly precipitation registered at the 23 rainfall stations. This resulted in setting apart 4 groups of stations. The second method is analysis of land use map, geological map, pedagogical map, vegetation map and slope map of the studied area. This method allowed us to delineate 4 homogenous areas. The two methods yielded complementary results and the superposition of groups and regions obtained allowed us to retain 4 homogenous regions corresponding to 3 groups of stations.
A study confined to the lower tapi basin in Gujarat, India to find out the primary causes for 2006 floods in Surat city. The study involves collection of topographical data from the local geological survey organization, rainfall data from meteorological department of india and the application of HEC-HMS software from US Army corps of engineers to identify the primary cause of the runoff.
An Introduction to the Environment Agency extreme offshore wave, water level ...Stephen Flood
An Introduction to the Environment Agency extreme offshore wave, water level and wind conditions data sets, transformed to nearshore for events covering up to the 10000 year extreme coastal event, available to all for use in local studies.
Presented at the DHI UK Symposium 2018.
Vulnerability Assessment and Rapid Warning System Enhancements inKeith G. Tidball
This presentation represents initial efforts to down scale a global flood vulnerability model developed in a cloud based computing tool Google Earth Engine for the noncoastal “upstate areas” of the State of New York. This customized New York application of the model is the result of collaboration with colleagues at Yale University. The model analyzes social and physical vulnerability to riverine flooding based on multiple data inputs, outputs the high risk areas for flooding, and runs statistics on the population living in the flooded zone. Initial results examine the ability for the model to predict risk for a specific storm area, county, or watershed in 1-30 seconds. Future work requires further testing and validation of the model, a more advanced algorithm, and dynamic user-friendly interface for public risk communication of both underlying vulnerability and an early warning system.
Sea level rise and storm surge tools and datasets supporting Municipal Resili...GrowSmart Maine
Why plan for growth and change, when it seems so much easier to simply react?
When there is a distinct and shared vision for your community - when residents, businesses and local government anticipate a sustainable town with cohesive and thriving neighborhoods - you have the power to conserve your beautiful natural spaces, enhance your existing downtown or Main Street, enable rural areas to be productive and prosperous, and save money through efficient use of existing infrastructure.
This is the dollars and sense of smart growth.
Success is clearly visible in Maine, from the creation of a community-built senior housing complex and health center in Fort Fairfield to conservation easements creating Forever Farms to Rockland's revitalized downtown. Communities have options. We have the power to manage our own responses to growth and change.
After all, “Planning is a process of choosing among those many options. If we do not choose to plan, then we choose to have others plan for us.” - Richard I. Winwood
And in the end, this means that our children and their children will choose to make Maine home and our economy will provide the opportunities to do so.
The Summit offers you a wonderful opportunity to be a part of the transformative change in Maine that we’ve seen these gatherings produce. We encourage you to consider the value of being actively involved in growing Maine’s economy and protecting the reasons we choose to live here.
SIMULATION OF DRAINAGE IN VIT BY SWMM SOFTWAREvivatechijri
During monsoon ,we face water logging and Drainage Overflow every year. This happens because of many factors such as, Topography of land because of which the Storm water gets accumulated in a particular area and Drainage system not being designed for that amount of Storm water ,Overflows. And thus,creating a havoc for the people passing by that area. SWMM is one of the software which can be used to simulate the storm water in the given dimension of drainage system considering every type of catchment areas. We can find out that for particular amount of storm water, what dimension of drainage system would be suitable. Which will help to reduce accumulation of water, hence, there would not be any kind of havoc.
Evaluating Satellite Precipitation Error Propagation in Runoff Simulations of...Yiwen Mei
This study investigates the error characteristics of six quasi-global satellite precipitation products and associated error propagation in flow simulations for 16 mountainous basin scales (areas ranging from 255 to 6967 km2) and two different periods (May-Aug & Sep-Nov) in northeast Italy. The satellite products used in this study are 3B42-CCA, 3B42-V7, CMORPH and PERSIANN with their respect gauge-adjusted products. To evaluate the error propagation in flood simulations satellite precipitation datasets were used to force a gauge-calibrated hydrologic model to simulate runoff for the 16 basins, and comparing them to the gauge-driven simulated hydrographs for a range of moderate to high flood events spanning a nine-year period (2002 to 2009). Statistics describing the systematic and random error, the temporal similarity and error ratios between precipitation and runoff are presented.
Getting the Most From Weather Data - Daniel Pearson, Mark Lenz, Nelun Fernand...TWCA
TWCA Fall Conference 2019 - (helpful links below)
USGS Links:
Water Alert - https://maps.waterdata.usgs.gov/mapper/wateralert/
National Water Information System: Web Interface - https://waterdata.usgs.gov/tx/nwis/current?type=flow
Water Services - https://waterservices.usgs.gov/
Texas Water Dashboard - https://txpub.usgs.gov/txwaterdashboard
NWS Austin/San Antonio - weather.gov/sanantonio
TWDB Links:
Water Data for Texas – https://waterdatafortexas.org/
Flood viewer - https://map.texasflood.org/#/
TexMesonet - https://www.texmesonet.org/
LCRA Hyrdromet - hydromet.lcra.org
2018 GIS in Government: Pits and False Hills and Spikes, Oh My Fixing Blunder...GIS in the Rockies
The U.S. Geological Survey (USGS) National Geospatial Technical Operations Center (NGTOC) maintains the USGS Seamless 1/3 Arc-Second (approximately 10-meter resolution) Digital Elevation Model (DEM). This national dataset provides foundational elevation information for earth science studies and mapping applications over the conterminous United States, Hawaii, Puerto Rico, other territorial islands, and parts of Alaska. Through the 3D Elevation Program, the Seamless DEM is continually updated with new lidar and interferometric synthetic aperture radar (ifSAR) collections (IfSAR in Alaska only). Although eventually all of the 1/3 Arc-Second Seamless DEM will be derived from lidar or ifSAR, currently portions of the dataset, especially in the western United States, are still sourced from legacy data created from digitized 1:24,000 scale topographic map contour lines. This legacy data contains some blunders resulting from errors in data capture, processing, or in the original source map sheet. The purpose of this presentation will be to discuss the types of blunders that are present in a small fraction of our legacy data, how those blunders came to be, and what steps USGS is taking to fix these issues to better support our customers.
Flooding is one of the most devastating natural
disasters in Nigeria. The impact of flooding on human activities
cannot be overemphasized. It can threaten human lives, their
property, environment and the economy. Different techniques
exist to manage and analyze the impact of flooding. Some of these
techniques have not been effective in management of flood
disaster. Remote sensing technique presents itself as an effective
and efficient means of managing flood disaster. In this study,
SPOT-10 image was used to perform land cover/ land use
classification of the study area. Advanced Space borne Thermal
Emission and Reflection Radiometer (ASTER) image of 2010 was
used to generate the Digital Elevation Model (DEM). The image
focal statistics were generated using the Spatial Analyst/
Neighborhood/Focal Statistics Tool in ArcMap. The contour map
was produced using the Spatial Analyst/ Surface/ Contour Tools.
The DEM generated from the focal statistics was reclassified into
different risk levels based on variation of elevation values. The
depression in the DEM was filled and used to create the flow
direction map. The flow accumulation map was produced using
the flow direction data as input image. The stream network and
watershed were equally generated and the stream vectorized. The
reclassified DEM, stream network and vectorized land cover
classes were integrated and used to analyze the impact of flood on
the classes. The result shows that 27.86% of the area studied will
be affected at very high risk flood level, 35.63% at high risk,
17.90% at moderate risk, 10.72% at low risk, and 7.89% at no
risk flood level. Built up area class will be mostly affected at very
high risk flood level while farmland will be affected at high risk
flood level. Oshoro, Imhekpeme, and Weppa communities will be
affected at very high risk flood inundation while Ivighe, Uneme,
Igoide and Iviari communities will be at risk at high risk flood
inundation level. It is recommended among others that buildings
that fall within the “Very High Risk” area should be identified
and occupants possibly relocated to other areas such as the “No
Risk” area.
1. Figure 1. Map of Pennsylvania with Allegheny County highlighted.
GIS Final Project
Allegheny County, Pennsylvania
Armando Drain, Juliet Fielding, Matthew Vaughan
Introduction
Our storm was located in Allegheny County, Pennsylvania on 17 June 2008. Allegheny County is
located in southwest Pennsylvania. The main geographic features of the county that are significant to our
project are the Allegheny Mountains and Allegheny, Monongahela, and Ohio Rivers. These three rivers all
flow through Pennsylvania into Pittsburgh, Allegheny County’s largest city. Other cities include
McKeesport, Wexford, Lexington, Penn Hills, and Mount Lebanon. Due to the amount of waterways
flowing through Allegheny County, there is a relatively high risk of flooding when storms with large
amounts of rainfall pass through the area. Our storm was centered on the northern part of the county.
For this reason it did not seem to have a significant impact on Pittsburgh or cause large amounts of
flooding. There was rainfall for about twelve hours and damage was limited.
Objectives
The objective of our project was to examine a storm with rainfall, and produce maps that show
what areas have the highest probability of flooding throughout the county by creating a GIS model. This
enabled us to predict the effects of rainfall on the population and roads.
Software and Data Sources
1. ESRI ArcGIS
2. Microsoft Excel
3. Microsoft Access
4. SSURGO soil Data
5. US Census Bureau population data
6. NOAA National Weather Service
7. TIGER Data
8. National Land Cover Dataset (NLCD)
9. National Climate Data Center NEXRAD
Methods
Preparing for the Final Project
Leading up to the final project, we picked a county that had potential for large amounts of
flooding based on its geography. Then we had to determine that our selected county had completed soil
survey data and a documented flash flood that was in the access database “flash floods,” which was
provided to us. We originally selected the Allegheny County storm with the greatest amount of damage;
however it did not have complete data. Therefore, we picked the second largest storm with flash flooding
2. reported and confirmed its soil survey data was compete.
Next, we downloaded TIGER data for streams, roads, and
block groups from the Census Bureau web site, and added
to our base map in ArcGIS. The geodatabase was set in
UTM and included spatial reference, feature datasets, and
feature classes. All pre-collected data was brought into
ArcGIS and imported into the geodatabase.
The next step was to download NLCD land cover
and NED elevation raster datasets from the USGS National
Map Viewer. It was essential to isolate the data for
Allegheny County and clip all excess outside areas. The
new data once again had to be put into UTM for
compatibility with all preexisting layers. The land cover data was coded to show the various categories of
cover by color. County place names were gathered as well.
The first step of the final project was to collect SSURGO data about the various soil types within
the county by requesting the data from the US Department of Agriculture. The National Climatic Data
Center provided the data about our selected storm. The data was collected from the NEXRAD station
closest to our storm, which happened to be in our county.
Step 1: Raster Total Storm Precipitation Layer
Using the data collected from the National Climate Data Center NEXRAD we collected the total
storm precipitation of the chosen storm at its peak rainfall. After producing frames of the storm, we
exported the one where the storm was most intense as a vector shapefile.
Step 2: Rainfall Data Collection
The next step was to find the 1-year value for our storm. The 1-year value is the total amount of
rainfall over a specific period of time that is likely to occur once every year. To find our value, we
examined rainfall data by date on the NOAA’s National Weather Service website. Our storm’s rainfall
lasted for about twelve hours on 16-17 June 2008. Therefore, according to the rainfall probabilities table,
for Allegheny County, our 1-year value is 1.67. Once we had the rainfall value, we converted the soils
vector layer into raster, and using the combine tool combined it with the NLCD land cover raster layer.
With the combined soil and land cover raster, we created new fields as necessary and joined the curve
values (imported from Excel table) to the layer based on the unique code given to each soil and land cover
combination. This gave us a raster layer with curve values for every cell.
Step 3: SSURGO Soil Data
The raster SSURGO data was used to determine soil retention levels in each cell and assign them
numerical values indicating the amount of runoff they will likely produce. This data is important to our
Figure 2. Allegheny County, PA.
3. final product, because surfaces with lower retention
levels are more likely to produce higher amounts of
runoff.
S = (1000.0/CN) - 10.0
CN = curve numbers from USGS
(values 30-100), based on unique soil-
land cover code
Step 4: Hydrology Calculations
The calculated values were used to compute
the actual values of runoff in each cell based on the 1-
year storm value (1.67) to produce the Q layer.
However, because runoff does not start until the rainfall
total (P layer) is greater than .25, everything less was
reclassified to 0.
Q= (P-.2S)^2 / (P+0.8(1.67))
Step 5: Flow Accumulation
In order to determine the flow accumulation, we needed to complete three major steps. First,
we had to run the Fill tool on the NED elevation layer to assure that no pits existed to stop the calculation
of water flow downstream, and export the new layer. Second, we ran the Flow Direction tool to
determine the direction in which runoff would flow from each cell. Finally, we ran the Flow Accumulation
tool on the Flow Direction output to determine the total flow of runoff into each cell. The Q layer was
used as the weight when running the Flow Accumulation tool in order to simulate actual water amounts.
Step 6: Flow Accumulation Surface
To create a surface displaying the predicted flow accumulation, we plugged the provided
equations into the raster calculator with the actual rainfall totals from our raster storm precipitation layer
rather than the 1-year value (1.67). The same steps were taken to create the storm flow accumulation as
for the 1-year flow accumulation.
Step 7: Reclassify
The next step was to create a flow accumulation layer that predicts water accumulation in each
cell. To limit the actual flow accumulations to streams or flood-created streams, we reclassified all flow
accumulation values less than 1000 to 0, and all other values were reclassified to 1. We then multiplied
the flow accumulation raster by this multiplier layer using the Raster Calculator to remove the undesired
values. This was done for both the actual storm and one-year value layer.
Figure 3. Storm Rainfall 16-17 June 2008.
4. Step 8: Accumulation Percentage
To compute the storm’s percentage of 1-year flow accumulation at all locations, we used the
raster calculator to create a new layer by dividing the storm flow accumulation layer by the 1-year flow
accumulation layer.
Step 9: Reclassify
Before proceeding, we reclassified all values below 100% as 0. These values represented
locations where flow was less than expected and there was no probability of flooding. Using the storm
percentage flow accumulation data, we then reclassified all values greater than 100% on a scale of 1-4
using equal intervals to indicate the probability of flooding within each cell. 1 represented the lowest
probability of flooding, and 4 the highest.
Step 10: Flooding Severity
In this step, we needed to account for flooding outside of the banks of rivers and streams. To
accomplish this we created a series of buffers using the Expand tool. The final buffer expressed the
varying levels of flooding with 2 expanded by 1 cell, 3 expanded by 2 cells, and 4 expanded by 3 cells. The
buffers were developed by using the Expand tool in the following series:
Flood Buffer 1: Expand, 2, 3, 4 by 1 (Green/little to no flooding)
Flood Buffer 2: Expand 3, 4 by 1 (Yellow/moderate flooding)
Flood Buffer 3 (Final): Expand 4 by 1 (Red/most severe flooding)
The streams were coded in red, orange, yellow, and green to indicate the severity of flooding in each cell.
As expected, the most severe flooding occurred in the northern region of the county.
Step 11: Tabulate Area Tool
The block groups UTM layer was used as the input into the Tabulate Area tool, with the flood
buffer as the feature class data input to calculate the area within block groups affected by flooding. The
block groups UTM table needed an area field in order to determine what area of each block group had
been affected by the storm. We joined the block groups UTM table to the Area Affected table to create
one table capable of computing data about flood damage within each block group. We then sorted the
new table to determine which block groups were affected and to what degree, and exported the new
data. The affected area in each block group then needed to be divided by the total area of the block group
to determine the percent that was affected. Based on their percent they were classified as none, low,
moderate, high, and very high flood percentage. To calculate which block groups fell into each category,
the Field Calculator was used. To display the extent to which the population was affected in each block
group, five additional fields were calculated with the same scale (none-very high). Once again all fields
were calculated using the Field Calculator, and subtracting the various levels of damage from the total
population, and placing all remaining citizens into the “no flood” field.
5. To determine if the roads in each block group were affected by flooding, a spatial join was run on
the “roadsUTM” layer to determine the kilometers of road within each block group. Next, the unique sum
values were added to the table. In newly created fields, we once again used the none-very high scale to
determine to what extent any roads were affected by flooding. They were calculated with the block group
flood percentage and new road length (within block groups) fields in the Field Calculator.
Conclusion
In conclusion, we were able to produce a series of tables and maps that clearly depict the effect
our storm had on Allegheny County. Our maps show where the storm took place, its varying levels of
severity, and which areas felt the greatest effects of flow accumulation/flash flooding. These tables and
maps could be used by Allegheny County to predict future flood patterns.
12. Storm Q Layer:
Runoff (in.)
NLCD Land
Cover
Storm Flow
Accumulation
1-Year Flow
Accumulation
Combined Soil and
Land Cover with
Curve Values
S Layer: Soil
Moisture
Retention
SSURGO Soil
Data
NCDC NEXRAD
Storm Rainfall (P)
US Census
Population
Data
TIGER Data
NED 1/3”
Elevation
USGS Curve
Values from
Table
Join
Raster Calc: S = (1000/CN) - 10
Combine
Raster Calc: Q = (P – 0.2S)2 / (P + 0.8S) NCDC NEXRAD 1-
Year Value (P)
1-Year Q Layer:
Runoff (in.)
Fill
Flow Direction: Q as Weight
Storm’s Percentage of
1-Year Flow
Accumulation
Reclassify
Raster Calc: Divide
Expand
Flood Buffer
Reclassify
Reclassify
Flooding by Block
Groups
Population and
Roads Affected
Flow Chart of Final Project Process
Join