The future holds challenges for urban areas when it comes to handling pluvial floodings, occurring when the rainfall intensity exceeds both the man-made and natural infiltration and drainage capacity. To gain understanding of the effects and needed measures, tools for modelling the urban response to events such as cloudbursts are needed. The aim of this project was to build a model using the Spatially Distributed Travel Time (SDTT) approach to model the rainfall-runoff response of an urban watershed. The model was developed in ArcGIS Pro using a built-in module ArcPy allowing for the use of a Python script to ensure fast calculations and simulations on grid cell basis. In total six smaller watersheds within the larger catchment were modelled with a variety in size and degree of urbanisation. Unlike fully distributed models solving for both the continuity equation and momentum equation, the models save time by applying kinematic wave approximation solving the steady state, uniform continuity equation and the Manning’s equation. The study uses only one calibration parameter representing the upstream area contributing to runoff, used for adjusting the travel times to ensure they are not too slow which could generate a delay and underestimation of the peak discharge. The model was parameterized for a cloudburst event that occurred in the city of Gävle, in the year of 2021, and was validated against a fully distributed model (MIKE 21) simulating the same event. The generated response from the SDTT model successfully returns similar hydrographs to that of a fully distributed model in most cases. It performed very well in high urbanised areas with an even spatial distribution of the two land cover classes used, impervious and pervious surfaces, and small volumes of depressions. In areas with lesser degree of urbanisation and larger depression volumes collecting runoff, the simplified model struggled to capture the draining dynamics of these. However, the model managed to match the time to peak reasonably well in the struggling areas as well. To increase the applicability of the model the upstream area contributing to runoff should be based on physical characteristics and not calibration. Further, the model should be applied to other areas preferably using other rainfall event data or design storms, as well as investigate the performance using more than two land cover classes. Finally, a sensitivity analysis could be performed for parameters that were now set to fixed values, done so to reduce the calibration. / Framtiden kommer bjuda på utmaningar för urbana områden när det kommer till hanteringen av pluviala översvämningar, vilka inträffar när nederbördsintensiteten överstiger både den konstgjorda och naturliga infiltrations- och dräneringskapaciteten. För att få ökad förståelse av effekterna samt besluta om nödvändiga åtgärder behövs nya verktyg för att modellera den urbana responsen till följd av extrem nederbörd så som skyfall. Syftet med detta projekt vara att med hjälp av spatialt fördelade rinntider och kinematiska vågmodellen modellera nederbörden och avrinningen i ett urbant avrinningsområde. Modellen utvecklades i ArcGIS Pro med hjälp av den inbyggda modulen ArcPy vilken tillåter användningen av ett Python-skript som ger snabba beräkningar och korta simuleringar applicerade på cellnivå. Totalt modellerades sex mindre avrinningsområden inom det större området, alla med olika storlek och urbaniseringsgrad. Till skillnad från fullt distribuerade modeller som löser både kontinuitetsekvationen och rörelsemängdsekvationen, sparar modellen tid genom att tillämpa kinematisk vågteori, stationära kontinuitetsekvationen samt Mannings ekvation. Studien använder endast en kalibreringsparameter vilken representerar området uppströms om varje cell som bidrar till avrinning nedströms. Denna används för att justera rinntiderna för att säkerställa att modellen inte returnerar för långsamma tider vilket kan generera en fördröjning av responsen och underskattning av maxflödet. Modellen parametriserades för ett skyfall som inträffade i Gävle år 2021, och validerades mot en fullt distribuerad modell (MIKE 21) som simulerade samma händelse. Den modellerade responsen från avrinningsmodellen byggd på spatialt fördelade rinntider kunde framgångsrikt leverera liknande hydrografer som den fullt distribuerade modellen. Modellen presterade framför allt bra i områden med hög urbaniseringsgrad innehavande en jämn spatial fördelning av de två marktäckningsklasserna som användes, nämligen hårdgjorda och icke hårdgjorda ytor, samt små volymer av sänkor i området. I områden med en lägre urbaniseringsgrad och större volymer sänkor vilka fördröjer avrinningen hade modellen svårare att producera liknande hydrografer troligen då den förenklade modellen ej kan fånga dynamiken av att fylla och tömma dessa sänkor. Däremot lyckades den fortfarande att matcha tiden för maxflödet även för dessa områden. För att öka modellens tillämpbarhet bör området uppströms som bidrar till avrinning nedströms baseras på fysikaliska egenskaper och inte kalibrering. Vidare bör modellen tillämpas på andra områden, helst med hjälp av andra nederbördsdata eller designregn, samt undersöka prestandan om mer än två marktäckningsklasser används. Slutligen kan en känslighetsanalys utföras för parametrar som nu satts till fasta värden, detta för att minska kalibreringen.

Prior to using hydraulic and spatially distributed modelling softwares, the theory of the unit hydrograph was a commonly used tool for modelling of surface and runoff water. While distributed models often provide detailed results from extensive calculation durations, the unit hydrograph have been questioned for simplifying the physical characteristics of the watershed modelled. Typically, the unit hydrograph theory does not explicitly take the flow paths of the watershed in consideration during calculation. With the rise of geographical information systems, methods of deriving spatially distributed unit hydrographs have been developed. The aim of these have commonly been to find a spatially varied form of hydrological modelling, while still keeping the computation times low. The method is commonly built by calculating the travel time to the watershed outlet along the flow path. In this study, spatially distributed unit hydrographs are derived separately for the watershed’s pervious and impervious surfaces in a Python script using map algebra and the Esri’s Python wrapper module Arcpy. The travel times are generated from a velocity field calculated using Maidment and Olivera’s velocity equation. The velocity equation contains three unknown parameters; one for an average velocity and two calibration parameters. The excess precipitation is calculated of a 100 year return period Chicago Design Storm hyetograph using the SCS-CN method. The direct runoff hydrographs are calculated over three semi-urban watersheds in Smedby in southern Sweden, and the results are compared to MIKE 21 hydrograph data of each corresponding watershed and rain input. The result obtained showed to replicate the hydrograph response quite well, but only if the unknown parameters in the velocity equation were calibrated to match the MIKE 21 data. The unknown parameters of the velocity equations produces uncertainties of using the method without calibration data, which implies that the script is not well adapted to use for modelling predictions. It may be of interest to calculate the travel times of the locations within the watershed using a different formula. The script tool could be tested using different design storms as input, and areas of different characteristics compared to Smedby could be tested. / Innan det blev vanligt att använda hydrauliska och rumsliga modellerings- mjukvaror användes ofta teorin bakom enhetshydrografen för modellering av avrinning. Medan de rumsliga mjukvarorna ofta erbjuder detaljerade resultat till priset av långa beräkningstider, har enhetshydrografen ifrågasatts för att förenkla den fysiska karaktären av avrinningsområdet. Typiskt sett tar inte enhetshydrografen avrinningsområdets flödesvägar direkt i hänseende vid beräkning. Utveckling och ökad tillgänglighet av geografiska informations- system förenklade möjligheterna att utveckla beräkning av enhetshydrografer som tar hänsyn till avrinningsområdets karaktär, typiskt sett genom att beräkna rinntiden från varje läge i avrinningsområdet, längs rinnvägarna och till utloppet. I den här studien beräknas spatiala enhetshydrografer separat för avrinningsområdets hårdgjorda och icke hårdgjorda ytor, genom att utveckla ett Python skript med hjälp av karalgebra och Esri’s wrapper modul ArcPy. Rinntiderna från olika lägen i avrinningsområdet beräknas med Maidments och Oliveras formel för hastighet, vilken innehåller okända parametrar för en uppskattad medelhastighet samt två kalibreringsparametrar. Effektivt regn från ett Chicago Design Storm regn med en återkomsttid på 100 år beräknas med hjälp av SCS-CN metoden. Hydrograferna för direkt avrinning faltas för tre semi-urbana avrinningsområden i Smedby i södra Sverige för att sedan jämföras mot MIKE 21 genererad hydrograf data för respektive motsvarade avrinningsområde. Hydrografdata producerat av MIKE 21 har tagits fram med lika CDS-regn data som input. Resultatet visar att hydrografer snarlika MIKE 21 hydrograferna kan tas fram med Maidments spatialt fördelade enhetshydrograf, om de okända parametrarna i Maidments formel kalibrerades mot MIKE 21 data. Utan kalibreringsdata för att bestämma de okända parametrarna kan resultatet anses vara mycket osäkert, vilket antyder att Python skriptet ej bör användas för använda metoden för att förutspå responser av regnevent. Andra beräkningar än Maidments ekvation kan vara av intresse att implementera. Olika typer av regninput samt spatial data över andra platser än Smedby kan vara av intresse att testa Python skriptet för.

State Departments of Transportation (DOTs) in the U.S. have an increasing amount of digital data from various sources. One such set of data is structured unit price data collected from bid lettings. Such data contain unit prices of thousands of bid items from hundreds of projects every year. While state DOTs have such data from over a decade-long period, utilizing such data has been challenging because of the lack of automated analytical and visualization methodologies and tools to generate meaningful and actionable insights. This study develops an automated methodology to quickly and accurately generate color-coded visualization maps representing unit price variation across a geographical region. It uses Inverse Distance Weighted (IDW) technique that is based on the Tobler’s First Law of Geography. The law states that points closer together in space are more likely to have a similar value than points that are farther away. The methodology is automated using ArcPy site package in ArcGIS. It imports unit price data from preformatted spreadsheets and boundary maps from existing ArcGIS shape files to generate unit price maps. The tool and the visualizations are expected to aid state DOTs in generating and communicating meaningful insights for making data-driven decisions. It can be used to investigate areas with higher unit prices for various items which can aid state DOTs in identifying potential causes of higher unit prices such as lack of competition and lack of sources of materials (e.g. quarry) in nearby locations.

In spatial analysis, many operations are performed considering the neighbouring locations of a feature. The standard definition of a neighbourhood is an area confined by geometrical length and direction with respect to its focus. When allocating a location for a service, the population distribution is often considered. Standard GIS software includes tools for computations with uniform neighbourhoods, usually equal sized circles. These tools can be used for distribution analysis. Many geographic studies used as basis for city planning decisions use distance as an evaluator. It is a frequent occurrence that the actual distance is approximated using factored straight-line distance. For great distances and large datasets, this is a sufficient means of evaluation, whilst for smaller distances for specific locations, it poses major drawbacks. For distribution analysis in a network space, the neighbourhood would need to be derived from the local set of network features, creating a unique neighbourhood for each location. The neighbourhood can then be used to overlay other datasets to perform analysis of features within the network space. This report describes the application of network distance based neighbourhoods to design a tool, Network Location Analysis, for calculating focal statistics for use as a city planning decision support. The tool has been implemented as a workflow of ArcGIS tools scripted as a Python toolbox. The input required by the tool is a population point layer and a vector network dataset. The output is a grid of points with population statistics as attributes and corresponding neighbourhoods generalized as polygons. The tool has been tested by comparing it to standard focal operations implemented in ArcGIS and by applying it to the dataset used when conducting a study on the location of a new metro station using conventional ArcGIS tools. The results have been analysed and visualized and compared to data used in the study. The result concludes that Network Location Analysis surpasses conventional ArcGIS tools when conducting analysis on features in a network. It derives an accurate set of sum and proximity statistics for all locations within the processing extent, enabling analysis on the population distribution throughout the area and for specific points. The output is intuitive, manageable and can be visualized as raster or by displaying the neighbourhoods as polygons and can be used to evaluate population distribution and network connectivity. The drawbacks of the tool are its lack of robustness, its rigidity to input and the inefficient implementation causing execution time to be unpractical.