internship report

Summer Internship Report

on

Catastrophe Modelling

At

RMS INDIA

Submitted by

Tushar Gupta-11113114

Kaustubh Milind Kulkarni-1113052

Karan Tyagi-11113048

B. Tech. 4th year

Department of Civil Engineering

Indian Institute of Technology Roorkee

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ACKNOWLEDGEMENT:This internship proved to be a fruitful endeavor and we heartily thank the entire Analytics and Exposure Development team and Spatial Modelling Group at Risk Management Solutions (RMS), India who provided us with this opportunity. A special acknowledgement is deserved by Farhat Rafique, Kunal Jain, Edida Rajesh, Alpana Das and Nikhil Sharma who, throughout the internship period, not only helped us in comprehending various problems but also in developing the skills required to tackle them. It was because of their help that we could complete this entire task in eight weeks.

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Table of Contents

ACKNOWLEDGEMENT:.........................................................................................................................................2

INTRODUCTION:...................................................................................................................................................3

Background.......................................................................................................................................................3

Catastrophe Modeling......................................................................................................................................3

Software and frameworks:...................................................................................................................................3

EPA-SWMM:.....................................................................................................................................................3

inp.PINS:...........................................................................................................................................................4

inpFLOOD:.....................................................................................................................................................5

HEC-Ras:............................................................................................................................................................6

SWAT:...............................................................................................................................................................7

ArcGIS:..............................................................................................................................................................7

Urban flood modeling using SWMM....................................................................................................................9

Urban Flooding.................................................................................................................................................9

Urban Flood Modeling....................................................................................................................................10

Urban Flood Modeling Process:......................................................................................................................10

STEPS:.........................................................................................................................................................10

Sewer Networks Data:....................................................................................................................................11

Acquisition of Data.....................................................................................................................................11

Availability of Data......................................................................................................................................12

Conclusion..................................................................................................................................................12

PANAMA CITY, FLORIDA:................................................................................................................................12

Storm Water Network Data........................................................................................................................13

Generating Outlets.....................................................................................................................................14

Cleaning of Data.........................................................................................................................................14

Delineation of Channels..............................................................................................................................15

Coorelation of sewer networks and delineated channels..........................................................................16

Corelation of sewer network and road network........................................................................................16

Assigning flow direction to road networks.................................................................................................17

Conclusion..................................................................................................................................................18

BELLINGHAM CITY, WASHINGTON:................................................................................................................18

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Sewer Network Data...................................................................................................................................19

SENSITIVITY ANALYSIS.................................................................................................................................20

IMPLEMENTATION......................................................................................................................................23

INPFLOOD INVESTIGATION.........................................................................................................................23

ALGORITHM................................................................................................................................................24

MANUAL INCREASE OF WATER DEPTH.......................................................................................................25

NO RAIN CONDITION..................................................................................................................................27

DERIVING INVERT ELVEVATIONS FROM DEM.............................................................................................27

NO RAIN CONDITION FOR MODIFIED INVERTS...........................................................................................28

CONCLUSION..............................................................................................................................................29

SWAT..................................................................................................................................................................29

Deliverables....................................................................................................................................................29

Working Concept............................................................................................................................................30

Overview Of SWAT.........................................................................................................................................30

Calculation of Sub basin Parameters..........................................................................................................34

HRU Analysis...............................................................................................................................................36

HRU Definition............................................................................................................................................40

Writing Input Tables...................................................................................................................................43

Edit SWAT Input..........................................................................................................................................45

Edit Point Discharge Inputs.........................................................................................................................46

Edit Inlet Dischargers Input........................................................................................................................47

Edit Sub basins Data...................................................................................................................................47

SWAT Simulation Setup..............................................................................................................................47

Output Analysis...........................................................................................................................................48

Utility Network Analyst Tool...............................................................................................................................49

Creating Flow Direction for sewer network....................................................................................................50

Manually Assigning Direction:........................................................................................................................50

Trace Options:................................................................................................................................................53

Exposure Development:.....................................................................................................................................55

Background.....................................................................................................................................................55

Theory.............................................................................................................................................................56

PARAMETERS..................................................................................................................................................57

Building Inventory.......................................................................................................................................57

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Size of building............................................................................................................................................57

Replacement cost value..............................................................................................................................57

EXPOSURE CALCULATION...............................................................................................................................57

EXPOSURE DOWNSCALING.............................................................................................................................57

OBJECTIVE...................................................................................................................................................58

METHODOLOGY..........................................................................................................................................58

Urban Atlas Classification...........................................................................................................................58

Land Use/ Land Cover Classification...........................................................................................................58

Soil Sealing data..........................................................................................................................................59

Open Street Maps(OSM) Data....................................................................................................................59

PROCESS.....................................................................................................................................................60

RESULT........................................................................................................................................................60

CONCLUSION..............................................................................................................................................61

K-MEANS CLUSTERING FOR IDENTIFYING VEGETATION IN IMAGES..............................................................61

INTRODUCTION...........................................................................................................................................61

OBJECTIVE...................................................................................................................................................62

PROCESS.....................................................................................................................................................62

AGRICULTURAL EXPOSURE DEVELOPMENT : BELGIUM.................................................................................66

Base data summary....................................................................................................................................66

BUILDING INVENTORY AND FLOOR AREA...................................................................................................66

Steps...........................................................................................................................................................66

Total Built-up Area Data:............................................................................................................................67

Construction costs......................................................................................................................................68

Trending and Currency conversion.............................................................................................................69

Exposure calculations.................................................................................................................................70

RESULT........................................................................................................................................................70

ArcGIS Work....................................................................................................................................................71

Key Takeaways:...........................................................................................................................................71

REFRENCES:........................................................................................................................................................72

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INTRODUCTION:

The following is an account of our work as interns for two months at the Noida office of RMS India.

We were working in the Spatial Modeling Group and Model Development Group with Farhat Rafique and Alpana Das as our mentor. We were working on developing urban flooding models for estimating the flood inundation extents and flooding models for river basins using SWAT.

Background

Risk Management Solutions is a provider of products and services for the quantification and management of catastrophe risks. It offers technology and services for the management of insurance catastrophe risk associated with natural perils such as earthquakes, hurricanes, and windstorms and risk modeling for man-made disasters associated with acts of terrorism by analyzing the impact of weapons of mass destruction on property. The company's objective is to help clients improve financial performance by using products and services to gain the most complete view of their risk portfolio. In a nutshell, the nature of work of RMS is classified as catastrophe modeling.

Catastrophe Modeling

Catastrophe modeling is the process of using computer-assisted calculations to estimate the losses that could be sustained due to a catastrophic event such as a hurricane, earthquake, storm, flood and so on. The calculated losses are used by the insurance industry to better analyze the risks associated with their portfolios and helps in the pricing of premiums depending upon the susceptibility of the property to any natural hazard. The models simulate natural phenomena by using the engineering equations associated with the disaster.

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Software and frameworks:

EPA-SWMM:

The EPA Storm Water Management Model (SWMM) is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas.

The runoff component of SWMM operates on a collection of sub catchment areas that receive precipitation and generate runoff and pollutant loads.

The routing portion of SWMM transports this runoff through a system of pipes, channels, storage/treatment devices, pumps, and regulators.

SWMM tracks the quantity and quality of runoff generated within each sub catchment, and the flow rate, flow depth, and quality of water in each pipe and channel during a simulation period comprised of multiple time steps.

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inp.PINS:

However, EPA SWMM does not have support for shp files and supports only inp files which is a workspace for EPA SWMM. inp.PINS is an open source plug in for Map Windows which converts data from shp files to inp files.

inpFLOOD:

inp.PINS has another module called inpFLOOD which uses the inp file for a particular project containing geometric and other data about different elements, the rpt file containing data about the results obtained during the run and the DEM of the region in order to give the flood map for the region as a shp file.

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HEC-Ras:

HEC-RAS is a program that models the hydraulics of water flow through natural rivers and other channels. It considers only the one dimensional aspects of flow. Although not the exact purpose for which it is meant, HEC-RAS can model the flood extents by using the flows from junctions. It requires:

• Centerline

• Banks

• Cross sections

• Flows

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HEC-GEORAS

Hec-GeoRAS is the plug-in for ArcGIS of HEC-RAS. The necessary layers for HEC-GeoRAS can be taken as:

• Stream center lines – Conduits

• Stream banks – A buffer functon on stream centre lines

• Either a DEM or a TIN model

• Stream cross sections.

Other layers are easily available but automated cross sections are difficult to obtain from the DEM.

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SWAT:

SWAT is a complex integrated river basin scale model which operates either on daily or hourly time step. It quantifies the impact of land management practices in hydrology, erosion and non-point source pollution.

The basic concept of SWAT involves water balance while accounting for weather, surface runoff, return flow, percolation, evapotranspiration, transmission losses, storage, crops, irrigation, groundwater flow, etc. For the calibration purposes, SWAT uses LH-OAT which stands for Latin Hypercube One factor At a Time analysis. It combines LH sampling and OAT design for simulation. It ensures parameter reduction by filtering out the less influential ones at calibration stage. Thus SWAT allows to calculate discharge over basins and simulate a model and compare it with the original data.

ArcGIS:ArcGIS is a geographic information system for working with maps and geographic information. It is used for creating and using maps, compiling geographic data, analyzing mapped information, sharing and discovering geographic information, using maps and geographic information in a range of applications, and managing geographic information in a database.

The system provides an infrastructure for making maps and geographic information available

throughout an organization, across a community, and openly on the Web.

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ArcGIS includes the following Windows desktop software:

Arc Reader - It allows one to view and query maps created with the other ArcGIS products;

ArcGIS for Desktop, which is licensed under three functionality levels:[6]

ArcGIS for Desktop Basic (formerly known as ArcView)- It allows one to view spatial data, create layered maps, and perform basic spatial analysis;

ArcGIS for Desktop Standard (formerly known as ArcEditor) - in addition to the functionality of ArcView, includes more advanced tools for manipulation of shapefiles and geodatabases.

ArcGIS for Desktop Advanced (formerly known as ArcInfo) - It includes capabilities for data manipulation, editing, and analysis.

ArcGIS is built around the geodatabase, which uses an object-relational database approach for storing spatial data. A geodatabase is a "container" for holding datasets, tying together the spatial features with attributes. The geodatabase can also contain topology information, and can model behaviour of features, such as road intersections, with rules on how features relate to one another. When working with geodatabases, it is important to understand about feature classes which are a set of features, represented with points, lines, or polygons. With shapefiles, each file can only handle one type of feature. A geodatabase can store multiple feature classes or type of features within one file.

Python Module for GIS:

ArcPy

ArcPy is a Python module for ArcGIS that can be used for scripting specific scripts for processes that cannot be performed by the available tools. It provides a much greater degree of customization as compared to the default tools or even the available plug ins on the internet developed by developers.

Python scripts can be run in either a shell or in the field calculator for evaluation of tables.

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http://en.wikipedia.org/wiki/Topology

http://en.wikipedia.org/wiki/Object-relational_database

http://en.wikipedia.org/wiki/Geodatabase

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Urban flood modeling using SWMM

Urban Flooding

Urban flooding is the inundation of land or property in a built environment, particularly in more densely populated areas, caused by rainfall overwhelming the capacity of drainage systems, such as storm sewers. Urban flooding is a condition, characterized by its impact on communities regardless of whether or not they are located within formally designated floodplains or near any body of water. There are several ways in which storm water enters properties: backup through sewer pipes, toilets and sinks into buildings; seepage through building walls and floors; the accumulation of water on property and in public rights-of-way; and the overflow from water bodies such as rivers and lakes.

Urban flooding is specific in the fact that the cause is a lack of drainage in an urban area. As there is little open soil that can be used for water storage nearly all the precipitation needs to be transport to surface water or the sewage system. High intensity rainfall can cause flooding when the city sewage system and draining canals do not have the necessary capacity to drain away the amounts of rain that are falling. Water may even enter the sewage system in one place and then get deposited somewhere else in the city on the streets.

Urban Flood Modeling

The simulation of urban floods primarily involves the knowledge of phenomena associated with hydrology and hydraulics and the associated engineering equations. Different frameworks are available which use available sewer network data to generate detailed simulations of water flow inside the sewers. The man-holes in the sewer network from which water will overflow are highlighted and the volume of water is then routed to the surrounding areas using the digital elevation model (DEM) of the region. The height of water in the region is displayed as the flood inundation map.

The primary assigned deliverables for the internship were:

1. Ideas and algorithms for the automatic cleansing of the data layer for major conduits and their implementation.

2. Running the model to achieve the ponding at intersections.

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Urban Flood Modeling Process:

The objective is to compare results obtained from simulations with the actual sewer networks data for cities for which it is available and from channels delineated with either DEM data or from any other utilities layer. The assignment of parameters should be done so as to achieve good correlation between both results.

STEPS:

• Review and understand

1. Review the study area city online.

• Analyze storm network data

1. Check for flow pattern

2. Check available information

3. Check if cleaning by information is possible

4. Check if spatial cleaning is possible

• Elevation data

1. Check 10 m DEM

2. Check availability of any other HD DEM

3. Interpolate 10 m DEM to 5m/2m

• Delineation

1. Delineate channels from DEM

2. Compare delineated channels and storm network data

3. Derive correlation between both networks

4. Generate catchments for delineated channels

5. Clean catchments for pipes if available or else generate them

• Attributization

1. Assign attributes required by urban flood model

• Simulation

1. Run the model using both channels and pipes.

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2. Derive the ponding with channels and pipes

• Correlation of ponding

1. Compare the ponding of channels and pipes

2. Find out solution if no correlation

Sewer Networks Data:

Acquisition of Data

Data for various quantities is readily available in the United States as compared to India. Information about utilities in urban areas is available in the form of shape files or GIS layers. I was particularly interested in the sewer network data for regions in the form of separate shape files for

• Junctions

• Conduits

• Outfalls

Because SWMM uses these layers and corresponding information for simulation. These layers are a prerequisite and simulations cannot be run in their absence.

Very detailed sewer networks for certain cities such as Los Angeles were available in the form of shp files with all associated data.

The data for the city of Spokane in Washington was available as a kmz file for Google Earth

New York however had limited data available only in the form of of sewer outfalls. The vital sewer data for conduits and nodes wasn’t available.

Availability of Data

The data for sewer networks, if available, is usually present on the website of the city or region. For certain regions, although the sewer network data was not available, related information such as

• Minnesota - Sewer sheds and limited sewer interceptors.

• Spokane - Wastewater data as a kml file.

• Salem, Oregon - Entire sewer network metadata as html files

• Austin, Texas – Water and wastewater fee boundary.

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So, all in all detailed sewer network data is not very readily available.

Conclusion

Detailed data for sewer networks is rarely available and even if it’s available, it’s very rarely in a format suited to our purposes. So, instead of relying on sewer network data for obtaining urban flood maps, the focus should be on using delineated channels from DEM data or easily available networks like road maps by TeleAtlas to act as substitute for conduits. The nodes and the outfalls can be generated from the road network and the elevations can be assigned from the DEM. The flooding from such an obtained network, and from the actual network for cities for which data is available, should be compared. The parameters for the derived network should be calibrated in order to get accurate results so that generating urban flooding scenarios can be automated for the whole of the US.

PANAMA CITY, FLORIDA:

Panama City is a city in Bay County, Florida, United States. Panama City is located at 30°10 28″N ′85°39 52″W within the Florida Panhandle and along the Emerald Coast. Panama City has a humid ′subtropical climate, with short, mild winters and long, hot and humid summers.

The relatively flat terrain of Bay County and the large areas of wetlands and areas with a high water-table combine to present unique challenges for managing the frequent and sometimes intense rainfall events. Certain areas of Panama City are low-lying and subject to flooding from rising water

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Storm Water Network Data

The data for the sewer network for Panama City on the SMG server was pretty detailed with layers for

Sewer – gravity mains, force mains, manholes, lift stations, valves, meters

Water – fittings, distribution lines, fittings, hydrants, sampling sites, valves, meters

Storm water – pipes.

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Investigation revealed that water layers indicated the water distribution network, and the sewer and the storm water network were separate.

So the only layer useful for flood delineation was the storm water pipes. I found the missing storm water inlet data on the official site of Bay County which had an interactive GIS map.

Generating OutletsOutlets are mandatory for running simulations with SWMM. The data for Panama City did not have outlet data available.

I used a third party extension for ArcGIS called X Tools Pro for the generation of outlets.

‘From nodes’ and ‘to nodes’ were generated and then the removal of ‘from nodes’ from the ‘to nodes’ left the outlets. Default parameters were assigned to the outlets.

Cleaning of Data

The storm water pipes are much cluttered and such networks are not necessary for giving accurate flooding results. Rather, they make the computations for simulations extremely intensive. Hence cleaning the pipe network for removing obsolete pipes is necessary.

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Spatial Cleaning:

The pipes and nodes are represented as a link node data structure and the lower order pipes can be removed from the network.

For example, if, in an individual tree, the highest order pipe is 5, then all pipes with order lesser than 2 can be removed and higher order pipes can be left to get spatially cleaned networks.

Cleaning by attributes:

The information available about the attributes of the pipes can be used for removing the smaller pipes at the beginning of the network. Attributes like type, diameter, and material can be used.

Else a combination of spatial and attribute cleaning can be used to clean the network and get good results.

Delineation of ChannelsDelineated channels for a threshold limit of 1000 cells for the 3 m DEM.

Similarly channels were delineated for a threshold limit of 2000 and 4000 cells also.

Delineated channels for a threshold limit of 1000 cells for the 10 m DEM.

Similarly channels were delineated for a threshold limit of 5000, 2000 and 4000 cells also.

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Correlation of sewer networks and delineated channelsVisual analysis indicated the best correlation between the channels delineated for the 3m DEM at a threshold limit of 4000 or for the 10 m DEM at a threshold limit of 500 cells.

The network for the 3m DEM was obviously considerably denser as compared to the 10 m DEM for the same threshold value.

Possibly the 3 m DEM is giving better results because it can distinguish man made artefacts like roads and sewer networks very often coincide with major roads.

Correlation of sewer network and road networkVery detailed road networks are available for the US and major roads very frequently coincide with main sewer lines, which is what we are concerned with.

I took all the roads with Functional Road Class(FRC) up to 5 and found that they coincided well with major sewers. Roads with FRC 6 make the network very cluttered and will be very difficult to analyze.

I tried cleaning the road network using various other fields like type, length, class but the best results were seen only with the condition FRC <= 5.

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Assigning flow direction to road networksThe assigning of flow directions to road derived networks is essential.

I tried using various methods to assign directions.

Using DEM:

Taking points along a polyline and checking the flow directions of the polyline by assigning flow from higher to lower elevations. Used Hawths Tools for the sampling of data.

Using slope map:

Assigning flow directions using slope map of the region.

Using FDR:

Taking the clipped FDR map of the region as per the road network and assigning flow directions to polylines.

Various checks will also have to be applied to the assigned flow directions of the road network derived pipes. I was unable to get a concrete result but the matter can be investigated further. On the other hand, using the DEM derived channels ought to be a less cumbersome process.

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ConclusionI could not get flooding maps for Panama City because SWMM requires sub catchment objects from which flow has to be assigned to a particular node. I had to perform delineation of sub catchments. InfoSWMM Subcatchment Manager is a tool that automates the process of delineation of sub catchments, their characterization and the assignments of sub catchments to the appropriate nodes and rain gages. I had yet to try out the tool

Back when I did was doing my internship after my first year in MWH Global, they were working on developing flood maps for Mumbai. The delineation of sub catchments and the assignment of nodes was done manually over there. However an automated process would go a long way in deriving urban flood maps for the whole of US and other countries.

BELLINGHAM CITY, WASHINGTON:

Bellingham is the largest city in, and the county seat of, Whatcom County in the State of Washington.

The city is located at 48°45 N 122°29 W and it is situated on Bellingham Bay.′ ′

Flooding is an issue in the city with warnings for floods given on the official website of Bellingham city.

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Sewer Network DataDetailed sewer network data is available for Bellingham City with separate layers for junctions, conduits, outfalls and sub catchments.

The parameters needed for running simulations are mostly available or else, default values are used.

SENSITIVITY ANALYSIS

COMPARTMENTS

EPA SWMM consists of different compartments which are used to model the hydrological and hydraulic processes.

1. Atmosphere compartment

2. Surface compartment

3. Groundwater compartment

4. Transport Compartment

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ATMOSPHERE COMPARTMENTThe atmosphere compartment consists of rain gauge objects which can be used to assign precipitation characteristics. The rain gages can be linked to sub catchments to model precipitation characteristics for that area. The data can be entered in the form of a time series or a data file containing precipitation data for the station.

RAIN GAGE OBJECTS

The rain fall attributes are assigned to a rain gage object. The volume and intensity of the rainfall can be varied and different inundation patterns are expected for different storm events.

High intensity high volume events are expected to lead to worse flooding as compared to low intensity high volume events.

Low volume events are obviously expected to lead to lesser flooding as compared to high volume rainfall events.

SURFACE COMPARTMENTThe surface compartment consists of catchments and sub catchments with various parameters that are used to distribute the incoming precipitation into surface runoff and losses, which include evaporation and infiltration.

SUBCATCHMENTS

A large number of parameters are assigned to subcatchments which affect the surface routing of rain water and hence also affect flooding. The different parameters are :

1. Area

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2. Width

3. Assigned rain gage

4. Slope

5. Percent imperviousness

6. Manning's coefficient for impervious areas

7. Manning’s coefficient for pervious areas

8. Surface storage for impervious areas

9. Surface storage for pervious areas

GROUNDWATER COMPARTMENTThe ground water compartments simulates the storage of water below the surface of the ground using aquifer objects.

INFILTRATION

Different formulae are used to estimate infiltration of precipitation to aquifers.

• Horton

• Modified Horton

• Green Ampt

• Curve Number

TRANSPORT COMPARTMENTThe transport compartment models the flow of water over the surface of the land and through pipes.

NODES

The transport compartment uses nodes and links to simulate the routing of water over the surface of the land and through the sewer systems. The outfalls and the junctions are represented as nodes,

Junctions:

• Inflow

• Invert elevation

• Maximum depth

• Initial depth of water

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• Surcharge depth

• Ponded area

Outfalls:

• Inflow

• Invert elevation

• Type

LINKS

Conduits:

• Inlet node

• Outlet node

• Shape

• Maximum depth

• Length

• Roughness

• Initial flow

• Entry, average and exit losses

• Inlet and outlet offsets

IMPLEMENTATIONThe plan was to change the parameters and see the corresponding change in the flooding maps. The individual parameters for different objects were changed in SWMM and the simulation run. Using the results obtained from the rpt file and the data from the inp file and the DEM of the area, inp.PINS delineated the flood extents for the flood.

The actual results were totally awry from what I had expected and pretty much similar results were obtained for nearly each and every simulation with weird changes not corresponding to the expected change as per the change in parameters. And the results from SWMM were pretty good, so, most probably the problem lied somewhere with inpFLOOD.

So I began to check as to how inp.PINS derived the flood extents from the data.

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INPFLOOD INVESTIGATIONThe main idea of the tool is to create flood plains, by analyzing max Node Depth on the rpt file combined with transect data, if it exists and DEM grid. In case the transect data is not available, inpFLOOD creates the transects from the DEM.

The transect data for the city of Bellingham was not available and hence inpFLOOD could only perform DEM analysis.

ALGORITHM• The distance between the conduits is divided into units of distance "di", called as the minimum

breaking link distance.

• The water depth in each section is calculated by linearly interpolating the hydraulic grade line(HGL) from the upstream to the downstream node.

• Then the conduit transect for irregular conduit shapes or DEM grid at that section is analyzed.

• The transect is placed along the conduit for each "di“distance and its elevation is changed linearly by the conduit upstream and downstream invert level.

• If these two options are selected inpFlood will give the smallest flood plain between transect and DEM result.

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• Conduits are analyzed only at “di” distances and DEM analysis will take place to define flood plains for non irregular shape conduit.

• The max DEM transverse distance is the distance is the perpendicular distance from the conduit for which the DEM is analyzed for flooding.

• Inlet OR Outlet Node HGL >= Inv. level + Max Depth - inpFLOOD restricts building of flood plains only to conduit results that follow this condition. Flood extents are seen to decrease considerably when this condition is applied.

• Obviously decreasing the distance and increasing the transverse distance increases the complexity of the problem and greater time is required for simulation

MANUAL INCREASE OF WATER DEPTH• I wanted to check whether the inpFLOOD actually follows the algorithm mentioned and hence I

manually changed the water depths in conduits.

• I found the flood map for a particular region using a particular rain condition.

• Then I manually modified the water depths in each node by using Notepad++ and Excel and added 2 meters to each depth to see how the flooding map would be influenced.

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• The results were as expected and the map showed considerably more flood extents as compared to the original map.

ORIGINAL

EDITED

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NO RAIN CONDITIONIn order to check whether inpFLOOD gave sensible results for Bellingham city, I used the storm water network to run a simulation for a no rain condition and then checked the results for flooding.

Funnily, a large part of the city was shown to be flooded!

It turns out that in the event of zero depth of water for a particular node inpFLOOD takes the invert level of the node as the hydraulic grade line.

• On checking by using the field calculator, the invert level of the many nodes was actually found to be higher than the elevation at that point derived from the DEM.

• This discrepancy is possibly because the data for the DEM and the sewer networks and the DEM comes from different sources.

• In such a scenario using the invert elevations from the sewer networks data makes no sense as the results are bound to be incorrect.

• So, I instead decided to assign the invert elevations from the DEM data.

DERIVING INVERT ELVEVATIONS FROM DEM• I found out the value of the DEM rasters at each particular node by using the Hawths tools for

sampling of DEM

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• Then taking the difference between the sewer network invert and rim elevations, the average difference was found to be 2.92 m

• So I subtracted 3 m from DEM elevations for all the nodes and assigned them as the invert elevations for the nodes.

• Using this data, I again found out the flood extents.

NO RAIN CONDITION FOR MODIFIED INVERTSAgain running a no rain simulation for the modified network with the DEM derived network, a similar result was obtained with flooding extents.

For example, between the junctions

J485 & J1220, the value of the DEM is about 1.20 m at a point between the two junctions.

But the invert elevations and hence the HGL for J1220 is 1.88 m and HGL for J485 is 1.68. Interpolation will give a water depth above 1.2 m, hence incorrectly showing flooding in the region.

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CONCLUSION

• Changing the invert elevations by deriving them from the DEM, changing the minimum breaking link distance and the transverse distance for transects and modifying the various parameters associated with various elements in SWMM still could not give sensible results with inpFLOOD.

• Possibly the algorithm for inpFLOOD is way to simplistic to work in a widely varying terrain such as Bellingham City with the elevations ranging from a minimum of 1 m to a maximum of 260 m.

• inpFLOOD is certainly worth a try in flatter regions but on most cities it is unlikely it will give the desired results.

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SWAT

Deliverables Calculate Discharge over basin on South Atlantic Gulf region of US. Calibration with historical record/gauge and its result. Inundation maps and validation with historical maps.

SWAT is the acronym for Soil Water and Assessment Tool, a river basin, or watershed, scale model developed by Dr. Jeff Arnold for the USDA Agricultural Research Service (ARS). SWAT was developed to predict the impact of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use and management conditions over long periods of time.

The SWAT model is:

Is physically based. Rather than incorporating regression equations to describe the relationship between input and output variables, SWAT requires specific information about weather, soil properties, and topography, vegetation, and land management practices occurring in the watershed. The physical processes associated with water movement, sediment movement, crop growth, nutrient cycling, etc. are directly modeled by SWAT using this input data.

Uses readily available inputs. While SWAT can be used to study more specialized processes such as bacteria transport, the minimum data required to make a run are commonly available from government agencies.

Is computationally efficient. Simulation of very large basins or a variety of management strategies can be performed without excessive investment of time or money.

Enables users to study long-term impacts. Many of the problems currently addressed by users involve the gradual buildup of pollutants and the impact on downstream water bodies. To study these types of problems, results are needed from runs with output spanning several decades.

Thus SWAT is a complex integrated river basin scale model which operates either on daily or hourly time step and quantifies the impact of land management practices in hydrology, erosion and non-point source pollution.

Working Concept

Hydrologic cycle is simulated by SWAT model, which is based on the following balance equation:

SWt = SWo + (Rday – Qsurf - wseep – Ea – Qgw)

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where SWt is the humidity of the soil (mm H2O), SWo is the base humidity of the soil (mm H2O), t is time (days), Rday is rainfall volume (mm H2O), Qsurf is the value of surface runoff (mm H2O), Ea is the value of evapotranspiration (mm H2O), wseep is the value of seepage of water from soil into deeper layers (mm H2O) and Qgw is the value of underground runoff (mm H2O). SWAT model uses the following climate and hydrologic inputs: rainfall, air temperature and solar radiation, wind speed, relative air humidity, snow pack, snowmelt, elevation zones, water volume on plants, infiltration, water seepage into deeper soil layers, evapotranspiration, subsurface flow, surface flow, lakes, river network, underground flow and other inputs related to vegetation growth and development, erosion on the catchment area, nutrients, pesticides and land use.

Overview of SWAT

SWAT allows a number of different physical processes to be simulated in a watershed. For modeling purposes, a watershed may be partitioned into a number of sub watersheds or sub basins. The use of sub basins in a simulation is particularly beneficial when different areas of the watershed are dominated by land uses or soils dissimilar enough in properties to impact hydrology. By partitioning the watershed into sub basins, the user is able to reference different areas of the watershed to one another spatially.

SWAT comes with the option of ‘Automatic Watershed Delineation’ which allows delineating stream networks for a Digital Elevation Model data. Instead of performing different operations individually as done in ArcGIS, it groups all the options in a single task.

The tool‘s functions are divided into five sections, namely: DEM setup, Stream Definition, Outlet and Inlet Definition, Watershed Outlet(s) Selection and Definition. This tool is used to create watershed delineations using a combination of DEM, digitized network and other user inputs.

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This is how the dialog box for Automatic Watershed Dileation looks like.

First of all we need to upload DEM on which the analysis has to be carried out.

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After uploading the DEM, the Flow Direction and Flow Accumulation Raster are created which gives the total area in acres by counting the number of cells and the resolution of each cell. The projection system of the DEM also needs to be defined.

The DEM used by me had a resolution of 30m and the projection system used was NAD_1983_ALBERS.The other projection systems used are UTM

After creation of the FDR and FAC rasters, the next step is to create streams and outlets. Streams and outlets are created based on the FAC rasters.

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Here blue lines represent the stream network.

By specifying the threshold limit for Flow Accumulation rasters, the stream networks can be selected based on the need of user.I was supposed to work on South Atlantic Gulf Basin area and so the delineated network for the area looked like this:

Catchment boundaries along with longest reach path and nodes are created.

After the creation of stream networks, inlets and outlets can be added manually in the DEM based on the need of the user.

After the inlet and outlet definition, all the nodes are selected and based on those nodes different watersheds are created.

Total 19 watersheds were created.

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Calculation of Sub basin Parameters

Now the sub basin parameters are calculated.

It gives the length and the area of each watershed:

It also gives the reach and length of each watersheds and also the respective inlets and outlets and also the X and Y co-ordinates of each node:

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Similarly other data such as shape index, etc. is also calculated.

Now the watershed delineation is complete.

At the time of calculating sub basin parameters, we can also check whether longest flow path calculation and geometry check are to be performed. It saves the compilation time depending on the need of the user.

HRU Analysis

HRU stands for Hydrologic Response Units. It is the basic modeling unit-defined as the network of elementary hydrologic areas with the selected discretization (grid cell, representative hill slope, sub watershed), measure of which is dependent upon the desired accuracy, as well as upon data accuracy.

Discretization techniques are classified as: Sub Watershed: It divides the watershed into subbasins based on topographic features of the

watershed. It preserves the natural flow path, boundaries, and channels required for realistic routing of water, sediment and chemicals.

Grid Cell: The routing methodology is same as that of sub watershed with the difference being average size of the subbasin and method used to define subbasin boundaries. It allows user to capture high level of heterogeneity or variability in the simulation.

Representative hill slope: It allows overland flow from one subbasin to flow onto the land area of another subbasin. It allows SWAT to model hillslope processes.

HRUs are lumped land areas within the subbasin that are comprised of unique land cover, soil, and management combinations.

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HRU Definition requires assigning Land Use Cover, Soil Cover and Slope Cover data for the concerned area.

The number of HRUs created are generally much more than the number of sub basins.

Land Use, Soil and Slope DefinitionThe Land Use, Soil and Slope Definition option in the HRU Analysis menu allows the user to specify the land use, soil and slope themes that will be used for modeling using SWAT. These themes are then used to determine the hydrologic response unit (HRU) distribution in each sub-watershed.SWAT require land use data to determine the area of each land category to be simulated within each subbasin. In addition to land use information, SWAT relieson soil data to determine the range of hydrologic characteristics found within each subbasin. Land Use, Soil and Slope Definition option guides the user through the process of specifying the data to be used in the simulation and of ensuring that those data are in the appropriate format. In particular, the option allows the user to select land use or soil data that are in either shape or grid format. Shapefiles are automatically converted to grid, the format required by ArcGIS to calculate land use and soil distributions within the sub basins of interest.

Sources Of Data Sets:

USGS-US Geological Survey: classifies as 4 major levels, namely regions (21), sub regions (221), accounting units and cataloging units.

NRCS-National Resource Conservation System

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NLCD-National Land Cover Database: 16 class land cover classification NOAA- National Oceanic and Atmospheric Administration

These are some of the agencies which provide datasets required for HRU Definition.

Application Of Different Datasets

LULC Map:

The Land Use and Land Cover (LULC) data files describe the vegetation, water, natural surface, and cultural features on the land surface. The United States Geological Survey (USGS) provides these data sets and associated maps as a part of its National Mapping Program. The LULC mapping program is designed so that standard topographic maps of a scale of 1:250,000 can be used for compilation and organization of the land use and land cover data. In some cases, such as Hawaii, 1:100,000 scale maps are also used.

Slope Map:

Slope Map cannot be applied directly, rather slope classification map needs to be created separately and the slope classes for HRU Definition are to determined accordingly.SWAT allows to classify slopes in 1 to 5 groups with the highest limit for the last class being 9999.

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Soil Map:

Soil Map is inbuilt in SWAT from US STASGO and can be used directly from there or it can be uploaded

manually.

This data set by STATSGO is a digital general soil association map developed by the National Cooperative Soil Survey and distributed by the Natural Resources Conservation Service of the U.S. Department of Agriculture. It consists of a broad based inventory of soils and nonsoil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. The soil maps for STATSGO are compiled by generalizing more detailed soil survey maps. Where more detailed soil survey maps are not available, data on geology, topography, vegetation, and climate are assembled, together with Land Remote Sensing Satellite (LANDSAT) images.

Map unit composition for a STATSGO map is determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the whole map unit.

Inbuilt Soil map

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Manual Assignment of Soil Class:

Final Land Use, Soils, Slope Distribution Reports

HRU Definition

Before executing SWAT, the distribution of hydrologic response units (HRUs) within the watershed must be determined based on the land use, soil and slope layers specified in the previous step. The interface allows the user to specify criteria to be used in determining the HRU distribution. One or more unique land use/soil/slope combination or HRUs can be created for each sub basin. Subdividing the watershed

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into areas having unique land use, soil and slope combinations enables the model to reflect differences in evapotranspiration for various crops and soils. Runoff is predicted separately for each HRU and routed to obtain the total runoff for the watershed. This increases accuracy and gives a much better physical description of the water balance.

HRU Thresholds:

This option allows to set threshold limits for Land Use, Soil and Slope Class percentages. Based on the threshold limit, the classes with lower percentages can be grouped together with a class having higher percentage.

The Dominant Land Use and Soil option will you allow to create only one HRU for each sub basin defining the dominant landuse and soil class.

The Multiple Hydrologic Response Units option will allow you to create multiple HRUs within each sub basin.

1. Land Use Percentage2. Slope Class Percentage3. Soil Class Percentage

For the analysis purposes I have considered a threshold limit of 10% for Land use, 15% for Soil class and 10% again for slope class.

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Land Use Refinement

Land Use Refinement tab is used to specify more detailed criteria.1. Split one land use type into two or several sub land use types.2. Set one land use type exempt.

Final HRU Distribution Report

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Writing Input Tables

This menu contains functions to build database files that include information needed to generate default input for the SWAT model.

The commands on the menu need to be implemented only once for a project. However, if the user modifies the HRU distribution after building the input database files, these commands must be reprocessed again.

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This dialog will allow the user to define the input data for rainfall, temperature and other weather data. For weather data, you have the option of simulating the data in the model or to read from data tables. If no observed weather data is available, then information can be simulated using a weather generator. The weather generator data must be defined before you can continue to define the other data, like precipitation and temperature.

At this point you have the option to generate all the input data files using the WRITE ALL option under the INPUT menu or generate each input file separately. The input files needed are:

• Watershed Configuration file (.fig)• Soil data (.sol)• Weather generator data (.wgn)• General HRU data (.hru)• Soil chemical input (.chm)• Stream water quality input (.swq)• Pond input (.pnd)• Management Input (.mgt)• Main channel data (.rte)• Ground water data (.gw)• Water use data (.wus)

For sub basin and main channel input files a prompt asks whether to change the default Manning’s n value. Click No to use 0.014. For the management input file generation a message prompt verifies if the US weather database is sufficient to estimate the Plant Heat Units. Click Yes if the study area is within US.

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Edit SWAT Input

The commands listed under the Edit SWAT Input menu bring up dialog boxes that allow you to alter default SWAT input data.

The Edit SWAT Input menu can be used to make input modifications during the model calibration process.

By using a procedure similar to editing soils database, the Database option under the Edit Input menu you can edit or add information to the weather, land cover/plant growth, fertilizer, pesticide, tillage, and urban area databases.

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Edit Point Discharge Inputs

The dialog box allows the input of point source data in one of four formats: constant daily loadings, average annual loadings, average monthly loadings, and daily loadings.

Edit Inlet Dischargers InputThe dialog box allows the input of inlet discharge data in one of four formats: constant daily loadings, average annual loadings, average monthly loadings, and daily loadings.

Edit Sub basins Data

This dialog box contains the list of sub basins, land uses, soil types and slope levels within each sub basin and the input files corresponding to each sub basin/land use/soil/slope combination. To select an input file, select the sub basin, land use, soil type and slope that you would like to edit. When you select a sub basin, the combo box of land uses, soil types, and slope levels will be activated in sequence. Specify the sub basin/land use/soil combination of interest by selecting each category in the combo box.

Similarly user can edit:

Soil physical data(.sol)-edit SNAME(soil name), NLAYERS(no. of layers), HYDGRP(1-4 where 1 is well drained and 4 is poor drained), ANION_EXCL(fraction of porosity from which anions are excluded),etc.

Weather generator data(.wgn)-WLATITUDE and WLONGITUDE( latitude and longitude of weather station used to create statistical parameters, etc.

General HRU data(.hru)-HRU_FR(fraction of subbasin area contained in HRU), SLSUBBSN-avg. slope length, HRU_SLP(Avg. slope steepness), etc.

Main channel input file(.rte)- CH_W(Avg. width of channel at top of tank), CH_D(depth of main channel from top of tank to bottom), ICANAL(code for irrigation canal), etc.

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Similarly other input parameter files can be edited.

SWAT Simulation Setup

SWAT simulation menu contains commands that setup and run SWAT simulation.

The above dialog box lets you specify duration of simulation, type of rainfall whether skewed normal or mixed exponential, whether the results should be printed daily, weekly or monthly. It also lets you specify NYSKIP which is the warm up period, that is the time taken for simulation to get adjusted to surroundings and the results are printed after that particular warm up period.

Output Analysis

After setting up the SWAT simulation, the simulation is saved and the result files can be imported to the folder Scenarios. The output files can be found inside TxtInOut folder.

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2000 2000 2001 2002 2002 2003 2004 2004 2005 2006 2006 2007 2008 2008 2009 20100

5000100001500020000250003000035000

Months

Mo

nth

ly d

is-ch

arge

(cu

bic

m

/sec

)

The above graph shows the relation between the observed and simulated discharge over a period of 10 years. As we can see that there is considerable difference between the two results and hence the simulation needs calibration of different parameters to match the results.

Utility Network Analyst Tool

As a part of my internship project, I was supposed to find a tool or extension in ArcGIS using which the direction of stream or road (basically line) network can be manually assigned as per the need of the user. To perform analysis of line networks, Networlk Analyst extension and Utility Network Analyst tool were available but for flow network data, Utility Network Analyst tool seemed better and more reasonable choice. Here is an overview of Utility Network Analyst tool:

The Utility Network Analyst toolbar provides the interface for performing analyses on geometric networks. The Utility Network Analyst toolbar in ArcMap allows you to choose a geometric network, set and view flow direction, change analysis settings, add flags and barriers to analyses, and perform various trace tasks.

Two types of networks

Geometric datasets: River networks and utility networks—like electrical, gas, sewer, and water lines—allow travel on edges in only one direction at a time. The agent in the network—for instance, the oil flowing in a pipeline—can't choose which direction to travel; rather, the path it takes is determined by external forces: gravity, electromagnetism, water pressure, and so on. An engineer can control the flow of the agent by controlling how external forces act on the agent.

Network datasets (transportation networks): Transportation networks—like street, pedestrian, and railroad networks—can allow travel on edges in both directions. The agent on the network—for instance, a truck driver traveling on roads—is generally free to decide the direction of traversal as well as the destination.

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Geometric networks comprise two main elements: edges and junctions.

Edges — An edge is a feature that has a length through which some commodity flows. Edges are created from line feature classes in a feature dataset and correspond to edge elements in a logical network. Examples of edges: Water mains, electrical transmission lines, gas pipelines, and telephone lines.

Junctions — A junction is a feature that allows two or more edges to connect and facilitates the transfer of flow between edges. Junctions are created from point feature classes in a feature dataset and correspond to junction elements in the logical network. Examples of junctions: Fuses, switches, service taps, and valves.

Edges and junctions in a network are topologically connected to each other—edges must connect to other edges at junctions; the flow from edges in the network is transferred to other edges through junctions.

Creating Flow Direction for sewer network

Create Flow Direction is used to assign flow direction to a geometric network.

Geometric network along with flow option is taken as input. Flow Option indicates method by which flow direction will be established; there is no default value.

There are four Flow Options namely:

WITH_DIGITIZED_DIRECTION —Establish flow direction along the digitized direction of edges. AGAINST_DIGITIZED_DIRECTION —Establish flow direction against the digitized direction of

edges. WITH_SOURCES_SINKS —Establish flow direction using sources and sinks. RESET_FLOW_DIRECTION —Reset flow direction on all edges to be uninitialized.

Using Utility Network Analyst tool we can trace network elements upstream or downstream from a point which can be used to determine which valves to shut off when a pipe bursts.

Manually Assigning Direction:

To manually assign direction to a particular network Start Editing the layer which contains source/sink features using Editor Tool. Select a particular junction point according to which you want to modify your network. Click on the attributes table button and go to Ancillary tab and from the drop down menu

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select source/sink as per your needs. After that click on Set Flow Direction icon in Utility Network Analyst toolbar. The flow direction for the entire network gets modified according to that particular junction point. Save the edits.

The highlighted blue point serves as a sink for this flow direction.

Here the highlighted blue point serves as a source. Notice the change in flow direction.

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Types of flow directions: Depending upon the flow options, there are three types of flow directions, namely:

Determinate flow direction: If the flow direction of an edge can be uniquely determined from the connectivity of the network, the locations of sources and sinks, and the enabled or disabled states of features, the feature is said to have determinate flow. Determinate flow for an edge is specified as either with or against the direction in which the feature was digitized.

Indeterminate flow direction: Indeterminate flow in a network occurs when the flow direction cannot be uniquely determined from the topology of the network, the locations of sources and sinks, or the enabled or disabled states of the features. Indeterminate flow commonly occurs for edges that form part of a loop, or closed circuit. It can also occur for an edge whose flow is determined by multiple sources and sinks, where one source or sink is driving the flow in one direction through the edge, but another source or sink is driving it in the opposite direction.

For example, consider a geometric network with the sources and sinks positioned like this:

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In this case, the flow direction for edges 1 and 2 is set; however, edge 3 has indeterminate flow. To understand why edge 3 has indeterminate flow, consider the case where only the source is present.

This results in a flow direction of edge 3 to the right.

Now, consider the case where only the sink is present.

This results in a flow direction of edge 3 to the left. Due to the opposite potential flow directions of edge 3, this results in a conflict.

For each edge, if the flow direction is in agreement between both the source-only and sink-only cases, the flow direction is set to that direction (as seen with edges 1 and 2). However, if there is a conflict, as there is with edge 3, the flow direction is set to indeterminate since there are two possible outcomes.

Another example resulting in indeterminate flow would be if an edge has a source at both of its ends.

Uninitialized flow direction

Uninitialized flow direction in a network occurs in edges that are isolated from the sources and sinks in the network. This can happen if the edge is not topologically connected through the network to the sources and sinks or if the edge is only connected to sources and sinks through disabled features.

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Trace Options:

1. All network elements that lie upstream of a given point in your network (traceupstream)

2. All network elements that lie downstream of a given point in your network(trace downstream)

3. The total cost of all network elements that lie upstream of a given point in your network (upstream accumulation)

4. An upstream path from a point in your network (find path upstream) 5. The common features that are upstream of a set of points in your network (find common

ancestors)

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6. All the features that are connected to a given point through your network (find connected)

7. All the features that are not connected to a given point through your network (find disconnected)

8. Loops that can result in multiple paths between points in a network (find loops)

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9. A path between two points in the network (The path found can be just one of a number of paths between these two points depending on whether or not your network contains loops)(find path)

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Exposure Development:

Background

Buildings or establishments are constantly under a risk of being razed down by some catastrophe or the other. This directly results in a monetary risk to the owners (either individual or a group) of that establishment. In order to reduce the burden on these owners, the insurance companies agree to share the risk in return of a comparatively small premium paid to the company. These companies rely heavily on probability skills in order to evaluate the gain they might have in the entire process. But, as history clearly demonstrates, there have been certain catastrophes which have rendered the insurance companies bankrupt.

In order to reduce the risk on the insurance companies, and indirectly help humanity, Risk Management Solutions (RMS) has been involved in developing various methods of evaluating the loss these companies might have to bear in case a catastrophe strikes a particular region insured by them. One of them is exposure development.

Theory

‘Exposure’ can be defined as the amount of rebuild cost of all the buildings in a region. The basic idea is to calculate the total amount of construction cost of all the buildings in a region. The rebuild cost, which is the cost required to rebuild an establishment once it is razed down in some calamity, is directly related to (some fraction of) this total construction cost.

This entire procedure is an arduous one. It starts with the search for the correct data sets which can be used in the development followed with formulation of a method for exposure calculations and culminates in an exposure value.

The basic data required in the exposure calculation is the number of buildings or establishments, floor area of each establishment and the construction cost per unit area of the establishment. Combining them using correct methodology gives us the exposure.

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PARAMETERS

Building Inventory

It is the total number of buildings or establishments in a geographically well-defined area.

Size of building

The average size of building, usually expressed in terms of the total floor area of the buildings.

Replacement cost value

The Construction cost, or the present day replacement cost of the building, expressed as construction cost per unit floor area.

EXPOSURE CALCULATION

If data that contains all the three parameters in the specified manner, then the exposure calculation is easy and goes about in the following manner

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* CONSTRUCTION COST

TOTAL FLOOR AREA

=EXPOSURE

Since, every country does not provide us the data in this form, hence each of them require a different procedure to finally yield the above mentioned data.

On developing the exposure for two countries, South Korea and Vietnam, a direct correlation was observed in between the exposure values and the economic parameters like GDP per capita, which is what the intuition says. Similarly, the more densely populated regions had more exposure values.

This exercise, at times, need elaborate calculation. For example, when the data for construction cost is comprehensive then the cost per unit area is calculated using the cost of items involved in construction (eg. Cement, steel bars, etc). This is quite similar to the task of building materials estimation done by civil engineers.

EXPOSURE DOWNSCALINGThe exposure calculated using the above mentioned steps, is often obtained at a very coarse resolution, most often at country or county level. This exposure model often needs to be downscaled to a finer resolution in order to obtain catastrophe loss at a fine and detailed resolution. Thus the overall aim of downscaling is to distribute the total building cost to a smaller resolution, such as municipality level or a 50m grid level.

OBJECTIVEThe objective was to downscale exposure in Belgium to a 100m grid level

METHODOLOGYIn order to downscale Belgium exposure, parameters were to be selected that could be used as predictors for assigning exposure at 100m grid level. By doing some online research, I could come up with 3 such parameters that could be used as tools for downscaling.

Urban Atlas ClassificationThe European Urban Atlas is part of the local component of the GMES/Copernicus land monitoring services. It provides reliable, inter-comparable, high-resolution land use maps for 305 Large Urban Zones and their surroundings (more than 100.000 inhabitants as defined by the Urban Audit) for the reference year 2006. The GIS data can be downloaded together with a map for each urban area covered and a report with the metadata.

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Land Use/ Land Cover ClassificationLand Use/Land Cover data refers to data that is a result of classifying raw satellite data into "land use and land cover" (lulc) categories based on the return value of the satellite image. There are not very many lulc datasets because a) satellite data acquisition is usually very expensive, and b)the classification process is very labor intensive. Most lulc data products are released several years after the satellite images were taken, and thus out of date to a certain extent when they are released. Nonetheless, lulc provides a very valuable method for determining the extents of various land uses and cover types, such as urban, forested, shrubland, agriculture, etc. Land use/land cover data are most commonly in a raster or grid data structure, with each cell having a value that corresponds to a certain classification.

Soil sealing dataSoil Sealing is the loss of soil resources due to the covering of land for housing, roads or other construction work. The covering of the soil surface with impervious materials as a result of urban development and infrastructure construction is known as soil sealing. The term is also used to describe a change in the nature of the soil leading to impermeability (e.g. compaction by agricultural machinery). Sealed areas are lost to uses such as agriculture or forestry while the ecological soil functions are severely impaired or even prevented (e.g. soil working as a buffer and filter system or as a carbon sink). In addition, surrounding soils may be influenced by change in water flow patterns or the fragmentation of habitats. Current studies suggest that soil sealing is nearly irreversible.

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Open Street Maps (OSM) DataOpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. Two major driving forces behind the establishment and growth of OSM have been restrictions on use or availability of map information across much of the world and the advent of inexpensive portable satellite navigation devices.

PROCESS First Step: Downloaded the Open Street Map data set for Hungary containing a whole inventory

of more than 120,000 buildings classified into various types by crowd-sourcing of data. Created a mapping between the OSM classes and the required Lines of business (Five in

number) to classify the buildings in OSM dataset into required LOBs. Overlayed the LULC, UA and SS raster layers on the map and spatially joined these classes to the

OSM buildings dataset, to obtain a database which had, for each building, all the three classes and the assigned LOB.

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Divided the Open Street Map dataset for Hungary into two parts, one for carrying out a logistic regression, and the other smaller dataset for checking the regression results.

Ran a logistic regression between assigned LOBs and the LULC, SS and UA classes to obtain probabilities of a building lying in each combination of the three classes to fall in a particular LOB.

Carried out a logistic regression to assign probabilities of LOB occurrence for each combination of the three classes .

Applied the regression results on the test sample to check whether assigned LOBs and predicted LOBs match.

RESULT

CONCLUSIONAs can be seen from the above table, the mapping between the assigned and predicted LOBs can be seen, which suggests that only for SFD(Single family dwelling buildings), the results were very good Almost 94% of the buildings in SFD were predicted as SFD only. But in the case of other LOBs, the percentage of buildings mapped into the correct LOB was poor(less than 30%). After discussing with senior members of the team, we came to the conclusion that these classes(LULC, SS, UA) were not good enough to be used as predictors for exposure downscaling.

K-MEANS CLUSTERING FOR IDENTIFYING VEGETATION IN IMAGES

INTRODUCTIONK-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.

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Given a set of observations (x1, x2, …, xn), where each observation is a d-dimensional real vector, k-means clustering aims to partition the n observations into k sets (k ≤ n) S = {S1, S2, …, Sk} so as to minimize the within-cluster sum of squares (WCSS):

where μi is the mean of points in Si.

In this manner, K-means clustering aims to partition n observations into k clusters in which each observation belongs to a clusters whose mean has the smallest euclidian distance to the observation.

OBJECTIVEThe aim was to develop a machine learning algorithm that could be used to separate greenery from satellite real color images. This could be really helpful especially in exposure downscaling as there would be no buildings in green parts of an image. For this purpose, K-means clustering algorithm was adopted to filter green pixels from an image.

PROCESSA program was written which generates a sample of random points on a 2-d plane, then obtains k values of cluster means and then classifies the data points into clusters.

These points were divided into 7 clusters, and were color-coded according to their clusters, so that if the color seperation is good enough, it can be implied that the clustering algorithm was successful in classifying into the different clusters.

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Trying the similar clustering on the pixels of an Image containing greenery, and then showing the pixels that fell in the green cluster, the results were as follows:

Original Image

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Now trying this method on a map that contains greenery, the result was as follows.

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All the green pixels were apparently not separated, but this was surely a motivation to continue further work on this method, which due to time limits of my vacations I could not carry out.

AGRICULTURAL EXPOSURE DEVELOPMENT : BELGIUMObjective was to develop the exposure at country level for Belgium for Agricultural line of business. i.e. Total building cost of all the agricultural buildings in the whole of country. By doing some online research, I came to know that the buildings in agricultural LOB contains mostly farm buildings such as small to large warehouses.

Base data summary This is a summary of the data that was available from the websites of statistical offices of Belgium, and of other international organizations which collect building inventory data.

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BUILDING INVENTORY AND FLOOR AREASteps Overall aim was to obtain the total floor area at province level. For this the floor area had also to be trended to 2014 vintage using the number of farms growth rate data.

Total Built-up Area Data:

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Provin

ce d'Anve

rs

Provin

ce du Brab

ant fl

aman

d

Provin

ce de F

landre

occiden

tale

Provin

ce de F

landre

oriental

e

Provin

ce du Lim

bourg

Provin

ce du Brab

ant w

allon

Provin

ce du Hain

aut

Provin

ce de L

iège

Provin

ce du Lu

xembourg

Provin

ce de N

amur

0

20

40

60

80

Since the total floor area data available was of vintage 2013, it had to be trended to 2015 vintage. For this the data containing number of farms at country level was used, and the average annual growth rate was applied to trend the area to 2015 vintage.

Variation of number of farms in Belgium from 1997-2007

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 20070

100

200

300

400

500

600

700

800

900

Trended total floor area : Province level

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Provin

ce of W

alloon Brab

ant

Provin

ce of F

lemish

Braban

t

Provin

ce of N

amur

Provin

ce of L

iège

Provin

ce of L

uxembourg

Provin

ce of L

imburg

Provin

ce of H

ainau

t

Provin

ce of A

ntwerp

Provin

ce of E

ast Fl

anders

Provin

ce of W

est Fl

anders

0

100,000200,000300,000400,000500,000600,000700,000800,000900,000

Construction costs Steps

Given the steps, following is the summary of building cost per unit floor area that was available from various online sources.

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BKI RLB Construction Costs.eu0

200400600800

10001200

Construction costs : Belgium Agricultural LOB

As is apparent from the above graph, the construction cost are more or less similar from all the three sources. I used the BKI data from the above three because in BKI, type of buildings were specifically mentioned as agricultural buildings.

Sources : BKI 2013 , rlb.com, www.constructioncosts.eu

Trending and Currency conversion Construction Cost IndexConstruction Cost Index is an indicator of the average cost movement over time of a fixed basket of representative goods and services related to Construction Industry. CCI data is available for various countries from the Census websites.

2008 2009 2010 2011 2012 2013 20149698

100102104106108

CCI

CCI

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Since the construction costs that were obtained from BKI were of vintage 2011, they had to be trended to 2015, using CCI data available. Average growth rate of CCI was used to trend these costs to 2015 vintage.

Now since the exposure values were to be obtained in USD, the costs were converted from Euro to USD using the conversion rates in year 2011 available online.

January

Febru

aryMarc

hApril

MayJune

July

August

Septem

ber

October

November

December

0

0.4

0.8

1.2

1.6Euro to USD factors 2011

Euro to USD factor...

Using this data, the construction costs were converted to be used for 2015Q1.

Source : http://statbel.fgov.be/en/statistics/figures/economy/indicators/prix_prod_con/

Exposure calculations Now having obtained the sufficient data for the purpose, the exposure calculation was a simple step, just multiplying the total floor area and construction costs per unit floor area.

RESULT

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* CONSTRUCTION COSTTOTAL FLOOR AREA=EXPOSURE

Provin

ce of W

alloon Brab

ant

Provin

ce of F

lemish

Braban

t

Provin

ce of N

amur

Provin

ce of L

iège

Provin

ce of L

uxembourg

Provin

ce of L

imburg

Provin

ce of H

ainau

t

Provin

ce of A

ntwerp

Provin

ce of E

ast Fl

anders

Provin

ce of W

est Fl

anders

0

100000000002000000000030000000000400000000005000000000060000000000700000000008000000000090000000000

PROVINCE LEVEL EXPOSURE

PROVINCE LEVEL EXPOSURE

ArcGIS Work ● Calculation of distribution factors : Distribution factors means the fraction of a particular

building footprint that lies within a particular grid polygon, in my case it was 50m grid level. For this the union and spatial join functions were used.

● Finding no. of buildings at county level : By finding the centroid of the building footprint polygon, the number of buildings were assigned to different counties.

● Finding no. of buildings at metropolitan level : The method adopted was same as above, except it was for municipality level.

Key Takeaways: ● Exposure modelling concepts

● Statistical concepts

● Excel Skills

● ArcGIS Skills

● Coding in R

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REFRENCES:

Influences of Potential Evapotranspiration Estimation Methods on SWAT’s Hydrological Simulation In A Northwestern Minnesota Watershed by X. Wang, A. M. Melesse, W. Yang.

SWAT-Based Runoff Modeling in Complex Catchment Areas – Theoretical Background and Numerical Procedures by Z. Simić, N. Milivojević, D. Prodanović, V. Milivojević, N. Perović.

web.ics.purdue.edu/~vmerwade/education/arcswat.pdf- SWAT tutorial prepared by Venkatesh Merwade and Adnan Rajib ,School of Civil Engineering, Purdue University

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internship report

Documents

sewer networks data

sewer network50manually

model development group

exposure development

spatial modeling group

spatial modelling group

flood inundation extents

farhat rafique