How to Perform Hydrology Analysis and Flood Risk Mapping in ArcGIS? A Complete Tutorial.

all right so in this video tutorial we're going to learn how to create blood risk map in gis environment and also we would like to become familiar with hydrology extension in arcgis environments so let's assume that based on the opinion of experts five factors should be considered for generating flood risk map in the previous lab we obtained the administrative boundaries as well as land cover digital elevation model and also we drive the slope from digital elevation model in this study area which was kyogre county ohio so you can find uh that video in the description and you can also notice where you can locate this kind of information according to the experts so these are the five factors and also these are the weights or importance of the factors for generating a flood risk map as you can see precipitation has the highest importance and among the factors elevation is the least important factor in generating a flood risk map so in arcgis environment let's add cuyahoga county gis blood mapping okay so we have county of the kyoga county uh let me make this hollow and also i'm gonna add i use a red color for the boundary of the city area so that's the kyogre county administrative boundary uh the next one was land cover type that we talked about that each if you load this each pixel value shows a specific type of land cover i think the resolution was this 30 meter spatial resolution another factor was a digital elevation model or dem and you add that each pixel uh shows uh elevation uh for that that specific location and again the spatial resolution was 30 meter and the elevation varies from 168 meters to 391 meters in the city area and also we drive slope uh from the digitalization model and as you can see the slope uh changes from 0 to 41 to agree that we already talked about that in the previous lab that how we can drive the slope from the digital elevation model the next factor is precipitation but first let's um from customize menu go to the extension and then check a spatial analyst on and after that and also you have to limit the process we want to limit all of the calculations to this study area so from geoprocessing and then environments so if you go to the processing extent we're gonna set extend as same as a county layer and also the cell size as digital elevation model so same as slope or degeneration model and also we want to use the county as the mask okay so click okay so we don't want to do any calculation outside of the study area okay so regarding uh precipitation we can get the precipitation information from uh word climb website if you go to the worklime.org and then click on downloads so wordclime provides relatively high spatial resolution global weather and climate data okay and it is highly it is extensively used in spatial modeling so we can get historical information of the climate or weather condition so if you click on historical and after that this website provides the monthly climate data for minimum mean maximum temperature precipitation solar radiation and so on okay so here we have the variables and also here we have the resolution so the best resolution is 30 second which is one square kilometer so i'm gonna uh download the precipitation uh with 30 second or one kilometer spatial resolution which uh provides the data and as you can see it's the values are between 1 to 12 so it's a monthly information so let me click on this one and download it so there's going to be 12 raster grid and we're going to focus on march because uh most of the floods occur normally occur during march and because of the after snow melts and because of the rainfall so i'm going to focus on only for the march months and consider it as the precipitation so if you uh open up this file so i already have unzipped this file so you'll notice that if you click on readme so it shows that they represent average monthly climate data from this period of time okay so uh we have the value from months one two three uh four and so on so we're going to focus on months three which is march and the data are for the entire world okay so we have to clip it with our study area so let me uh click on add data and then connect to folder of the uh downloads and then here i'm gonna select the month three and then add so you can see it's for the entire world if you i click on full extent it's for the entire world but i'm gonna just focus on cuyahoga county so let me zoom to this layer i have to clip this big raster with the kaiola county okay so if i uh type clip here and clip data management so input raster is going to be your precipitation for march and the output extent is going to be for county and uh after that we have to we want to use input feature for clipping uh geometry okay so if it's checked the geometry of the selected feature is used to clip so it's gonna be uh for the kyoga county so we don't need to convert this to no data to this value and i'm gonna uh save this as prc precipitation so uh that's the precipitation for kyogre county and maintain clip extent and then click okay so as you can see here we have the precipitation but the resolution is not perfect uh that's one of the limitation of this uh data that when you updated it for the entire world the spatial resolution is one kilometer is not 30 meter like the other uh variables that we have but uh so that's going to be our uh precipitation layer so i'm going to remove this one and only consider this as precipitation so uh before extracting before focusing on the the next variable which was uh proximity to extremes or channels let's look at the hill shade of the study area so what is hill shade so let me turn all the air off so the hill shade provides a more clear picture of topography yeah because it mimics the sun effects on hills and canyons okay so if you here if you type hill shade and hill shade a spatial analyst so your raster input is going to be your digital elevation model the output is going to be your hill shade and uh so these are these two at the mood and altitude are for lightening uh sources and optional uh information so i'm gonna accept the default uh values for the hill shade and uh after that i'm going to click all right so now here is the hill shade if you zoom in you can see the details of landscape so uh it's like almost 3d image okay so here we can see that no variations of the landscape uh but if you want to see better better the variations in the hill shade if you right click on hill shade properties go to the symbology tab and stretch it based on standard deviation and then apply okay you can have a more clear picture of the topography okay so here probably we have some depression we have some channels so you can see better see the uh the topography changes of this study area so let me add the length cover uh to on top of this layer so we had land cover here i'm gonna move it on top of the hill shade here and then uh what i'm gonna do is i'm going to in increase the transparency of the land cover okay so right click on that properties go to display and then for transparency i'm gonna increase it to 40 percent and then apply and okay so here we can so you can see land cover 3d land cover map kinda so this map is called shaded relief map is a 3d almost 3d form of the land land cover so this is how the landscape look like so we don't need this hill shade uh for our analysis but we just wanted to have a representation of the land uh landscape so we can remove it for from our analysis so let me remove the uh hill shades uh okay so the next step so we have the elevation slope land use precipitation the last one is proximity to extremes right so uh we want to extract streams from digital elevation model but there are multiple steps that we need to take uh to extract the streams or channels from digital elevation mode okay so let me remove all layers we can add them later so let me select all layers and then remove it okay so i'm gonna just keep the digital elevation model okay so uh in your digital elevation model uh the first step is that we have to fill the desalination model and what does it mean so in your digital elevation model there might be some sinks or pits that are pig cells that are lower than surrounding cells okay so here if you look at this one so if you look at so if this is the generation model and each cell represents elevation so some pixels are significantly lower than surrounding cells okay so the problem of zinc is water will trap in these cells okay and these spurious things are found in most digitization model products because of the small random errors in data collection and the field function as you can see uh helps us to modify the elevation value to eliminate these problems by feeling uh the artificial things in digital innovation model okay so we need to fill the digital elevation model and uh to do that if you go to the arc toolbox here and then from our toolbox if you scroll down and find spatial analyst and then the hydrology extension expand the hydrology and then here we're going to focus on this hydrology extension okay so the first operation is fill and the field the input is going to be your degeneration model and the output is going to be fill okay i'm just going to call this fill and z limits what is that limit uh because uh in order to differentiate natural and artificial sync okay sometimes you have natural you have something but sometimes you have since because of the errors it's an artificial thing so we need to set up a threshold value for errors under for example if i put the value of 10 the values that are under 10 is going to be fields okay and above that is considered a natural sink and should not be filled and here also explains that if the difference in z values between a sink and its power point is greater than the z limit this thing will not be filled okay and the value for z limit must be greater than zero we don't have any natural uh think so i'm gonna click on ok and have this convert this digital elevation model to fill it okay so as you can see uh so the values for this elevation model start from 168 to 391 but here after removing the sink and now it starts from 173 so in summary fill function corrects your digital elevation model okay so sometimes feel is called depression less digital elevation model okay so it's it's a corrected version of digitalization model and there is subtle difference between the digital elevation model and feel but we're gonna use field for the following steps so the next so the next step is that we need to create flow direction raster and flow direction raster means if rainfall falls on the surface how would it flow okay and we assume that water flows following the slope direction in other words the direction of steepest descent for instance in this image there are these are the pixel values of digital elevation so these are the pixel values of this elevation model okay so from 78 so which pixel or the neighbor pixel has the lowest value so it's obviously 67. and you can see that the water flows in this direction okay from the second one for the second one 72 which pixel value causes the steepest uh slope and definitely 56 right so it's going to be in this direction so that's why you can see uh flow direction is in description and for 69 it's going to be 449 is the cause's deepest value for for example 58 this one has the lowest value so it's in the downward direction okay so and so this is our flow direction but how we can computer cannot understand the direction can understand only the numbers right so in order to recode it we have to use this value so if it's in this direction we record it with two so two two two if it's in the southern direction we record it with four if it's in this direction we record it with eight and so on okay so this guy this is going to be a so flow direction it creates a matrix of numbers but the numbers have a meaning okay so for example 128 means that the direction is where the north is direction of the the flow direction is in the northeast for for this specific cell so how we can create flow direction so we're going to create flow direction based on the field digital elevation model okay so again from hydrology flow direction your surface raster is going to be your field and the output raster is gonna be flow so i'm gonna call it load dir low direction and we don't want to force the edge cell to flow outward and if because in the edges the errors are very high and we don't want to involve them in our calculation okay which is the default value if it's unchecked so and if it's checked uh all cells at the edge of the surface raster will flow outward from the surface raster and we don't want that right and uh the the technique for blood direction type is b8 is that the thing that i just explained it okay assign a flow direction based on the met flow method and this method assigns flow direction to the steepest down slope neighbor and which is the default value so i'm gonna select d8 and click on ok so it creates the flow direction and the values as you can see one two four 8 16 32 and so on which i explain what does it mean so after having the flow direction from the uh pill raptor the next step is to find the flow accumulation flow accumulation counts uh the amount of water or upstream cells that flow into a pig cell or down a sloped cell so in other words what is the gain of each pixel okay so uh from a smaller streams to larger streams and from larger stream to reverse okay so to create flow uh accumulation we have to use flow direction okay so the input for that to create the flow accumulation is flow direction and uh for example for this one how many units of water flow into this cell so definitely one unit is flowing in this cell right that's why it is one so how many uh how many units of water flow in this cell zero okay but how many uh how many units of water flow in this cell so this is going to be for this cell it's going to be one two and three right so that's why it is three so why this pixel gets the value of seven one two three pixels uh or unit of water flow in this cell right and another thing is that they already have the values they already have the value of one three and zero so it's going to be one plus two one plus one plus one is three plus one four plus three seven plus zero is seven that's why this cell gets the value of seven so keep in mind that each contributing cell has a gain from others and also from rainfall so that's how the flow accumulation can be calculated from the flow direction okay so again from uh arcmap if you click on blue accumulation so the input is going to be your flow direction raster so it's going to be below direction raster the output is going to be flow acc flow accumulation so uh we don't want to apply weight uh to each cell and the result is based only on the number of cells that flow into that specific cell okay so uh we accept the other so for example for the direction type is d8 and so on so we accept the other default settings and click ok all right so here is the result of blow accumulation so the pixel values if you use identified button here so the pixel values for example here is zero here's 29 here 0 38 the pixel value shows the number of cell upstream that flow in that cell that we just discussed okay so the cells with high flow accumulations should be in lower elevations such as in valleys or drainage channels okay so this image is kind of very dark so uh but you may see the streams right you can see the strings but let me change the symbology of this layer so if i go to the properties of the flow accumulation symbology and here stretch it based on the standard deviation and then apply an okay so here you can better see the channel stream of uh streams or channels based on the digital elevation model okay so as you can see some streams are main which have the higher uh values of the fellow accumulation and some of them are not main channels okay so we're gonna focus on most on the main channels or main streams another thing is that you can compare these streams with the hill shade that we just created so if you look at the hill shade and add it here so you can see that so it's it's kinda uh so this flow accumulation is kind of identified the hill shade right so uh the channels or streams that are visible from the hill shade so let me remove this hill shade and we're going to focus on this flow accumulation raster treat we would like to extract the extreme channel from this raster grid and if this dell value is greater than a threshold we're going to assign it to to a value of 1 which because it shows the main stream and if the cell value is not greater than the threshold you have to assign it to no data okay so it's going to be one or just a stream or no data and a rule of sum for a stream threshold is that the threshold is one percent of maximum flow accumulation okay so the maximum flow accumulation is 40 uh 438 so the threshold that i'm gonna set is like 4400 okay so it's one percent of the maximum flow accumulation so i'm going to reclassify their stressor so if i type reclassify uh into two classes so for the flow accumulation i'm gonna reclassify it into two classes the value is gonna be 4400 and anything after that is going to be 1 so this is going to be no data and here make sure that d is capital and anything above the threshold is going to be 1 because it shows the main streams so these are the flow accumulation and the output is going to be our streams okay so this is how we can extract the streams from the digital elevation model and change missing values to no date so let's click ok turn on layer up and here you can see that so these are the streams that we detected that from the digital elevation model okay so i just wanted to show you the application of hydrology extension so you can also delineate watershed and also use it in utility network in the future all right so we found the streams from digital elevation model based on multiple steps that we took but we need to find the proximity to these streams or channels okay and the function that you know is euclidean this sense okay so if you type euclidean and euclidean distance spatial analyst so we're going to find the distance to this streams okay so your input is going to be a streams and uh because we don't need to convert this to the feature or polyline because it also can get the raster okay and uh also the output so this is a raster or feature data that identifies cell or location to wish to you clearly distance is calculated the output is going to be our rims okay so what is the output the output is going to be for each cell in the output the euclidean distance uh to the closest cell uh will be calculated okay so it's gonna be the distance this stands to this stream okay and uh what else the cell size is like the digital elevation model everything is good and then click ok all right so as you can see so these are the distance to the streams or the channels okay so when you are closer to the stream then you are closer to the stream the distance is lower when you are far from the stream so this sense is going to be higher so that's the proximity to the streams or channels all right so we have all the information right we have elevation slope land use land cover precipitation and then proximity to streams so what is the next step let's only keep the stream distance to a stream layer or proximity to a stream so we don't need the flow accumulation we don't need flow direction we don't need fill we just want to have detailation model we have proximity to the streams we need a slope we need county and then land cover and precipitation so this is where the uh factors that we needed right so for the counties we just need to have the uh hollow and red color for the borders okay so this is these are the variables that we're going to focus on so similar to the suitability analysis lab we need to reclassify all layers which makes them all unitless and ready for overlay analysis okay so we have to reclassify them into a common scale that zero means not suitable two means very low four means low six means medium eight means high and ten means very high suitability okay so the first one is digital elevation model okay so we have to reclassify a digital elevation mod so if you select reclassify a spatial analysis and focus on digital elevation model click on classify so we're gonna have five classes based on the natural break classification technique and then click okay so uh it is obvious that the flat areas are more at risk of flooding compared to the regions with a higher elevation right so for the for the first one so these are the flat areas so it it is the high risk area okay so we're going to create the map of flood risk okay so here it's going to get the value of 10 which means that high very high suitability for our flight okay and then eight high six four and two very low suitability for risk and we're gonna also change missing values to no data and the output is going to be reclass of digital elevation model anything else and click ok so let me turn on layer off so here you can see that so when you are so here when you are closer to the lowland areas closer to the lake we have the highest suitability for flood which kind of makes sense and in these areas with that are very high elevation we have the lowest suitability for flood all right so the next one is a slope and similar to juve and similar to a digital elevation model flat areas or lower slopes are more flood prone because uh low slopes increases the quantity of water soaked into soil right so lower uh elevation and lower slopes are more susceptible to flux so again for from reclassify if you select spatial analyst and then here based on this slope i'm gonna reclassify it into click on classify i'm gonna classify it into five categories based on natural break but i'm not going to use the natural brick i'm going to use manual classification i'm going to say less than five or sorry less than two is the very high uh you know flood prone areas right so i'm gonna select two the next one is between two and five is uh high between five and ten is going to be medium between 10 and 20 is going to be low and above 20 is going to be very low okay so click ok so between 0 and 2 flat areas are at the highest very high risk right so this one gets the value of 10 then 8 6 4 and 2. and the output is going to be reclass underscore slope and we want to change missing values to note okay click ok so in terms of a slope most part of the study area is like 10 gets the value of 10 which means that they are at very high risk of flood because it's kind of flat so less than 2 degrees slow so the next one is precipitation right so again if you go to the reclassify spatial analyst and then precipitation prc so we're going to classify into based on natural break five classes and uh the output so it is evident that the heavier precipitation intensity increases the chance of floods right so it is a no-brainer so here it's going to be very low uh importance right so it gets the value of 2 and then gets the value of 4 6 8 and this area with heavy precipitation gets the highest chance of flood right so or gets the highest score for flood and then here i'm gonna call it the output as the class prc precipitation which includes rainfall melting uh snows and also ice and so on so change missing values to no data and then okay all right so the next one is land cover and for land cover it is obvious that urbanized areas are less vulnerable than vets land areas or rivers right so first of all wetland areas if you look at the legend of the land cover so let me turn all the air off for the land cover type so the legend the value that is 21 22 23 24 are developed areas so they are at the lowest risk of watch right so they get the value of two which means that very low uh risk of flooding so the way that it works is uh if you click on reclassify and then for the lens cover so for the lens cover we have to click on unique value because we're going to get all of the unique value so 11 was open water and gets the highest value which was uh 10 and 21 22 23 24 according to the legends are developed urbanized areas right so high end is high intensity low intensity medium and open space so this value this categories get the value of two very low uh chance for flux 24. 31 uh 30 41 42 43 52 71 so these are uh deciduous forest evergreen mixed forest barren grassland so they are in very low uh not very low they are low uh chance for chance or risk for floods okay so they get the value of four so 31 is going to be 4 41 same for 41 42 43 and 52 and 70 oops 52 and 71 they get the value of four so 81 and 82 are planted or cultivated crops so they get the value of six and 90 and 95 are wetland areas and they are at high risk of what so they get the value of eight all right so this is how we can reclassify the raster and after that let me check the first one gets the value of 10 2 2 2 4 6 8 and 8. all right so this is going to be the class underscore lens cover and change the missing value to no data and then okay all right so the last one is proximity to streams or channels and again it is obvious that the closer distance to the strings the higher probability for flock right so if you go to the reclassify spatial analyst so proximity uh distance to the streams and here uh click on classify we're gonna classify it into five categories based on natural break and then click okay so the closer gets the highest value so it's gonna be 10 and then eight then six four and finally two and after that so this is going to be a class underscore uh stream or str okay and change missing values to know the time then click okay all right so now we have reclassified all layers which means all layers are unitless and they have been rated or escort so the next step is that we have to combine these reclassified layers based on the weights based on the given weights based on the opinion of the experts that we hear we mentioned elevation has 10 percent slope 15 10 percent for land cover precipitation is the most important one and proximity to the stream it's going to be 30 okay so how we can combine these layers so this is based on the reclassified factors after you prepare them all of the factors reclassify them which means that you rate them you make them unitless the next step is that we have to combine them based on the given weights according to the opinion of the experts so and again you can use a raster calculator that we did that for the last time or another way or more user-friendly way is to use weighted sum function so if you type weight at sum and here you can directly give the each raster the weight okay so for example we had free class for precipitation we had reclass for streams or distance to the streams then we had uh so let me remove this one reclass for slope and reclass for digital elevation model and finally the class for land cover right so for the precipitation was 35 so let me see the the totals has to be 100 35 30 65 75 85 90 and 100 so here is going to be 35 percent 0.35 and reclass for distance to the streams is 30 uh the class slope is 15 and the last two are ten percent right if you add them up the total is going to be 35 65 75 plus 8 5 is 80 and 90 and 100 and so this is going to be your final flood risk map okay flat underscore risk and then click option so this is let me uncheck uh whole layer turn all layer off and flood risk so here you can see that in the western or sorry the eastern part of the study area we have higher chance of flood because also we have here we had the higher precipitation if you remember so here you know the chance of precipitation because precipitation was the most important factor that's why we are in these areas compared to the western part that uh had the lower precipitation so it kind of makes sense because the precipitation was the most important factor and uh so that's how we can create a risk map for the flood however in the future it would be okay to consider some other factors like soil type like vegetation cover like for example normal difference vegetation index and dvi that we will obtain that from satellite images in the future maps so um you can also reclassify this flood risk map so let me reclassify it so i'm gonna reclassify this flood risk into five uh classes based on the natural break so the values are so as you can see the values are starts from three to 10 so which is like three percent of the chance of the flooding or 30 percent here is 96 percent compared to the other areas so click okay so here the value one means low chance of flooding two means so this one is very low low medium high and very high okay so here it's going to be your reclass underscore risk okay and then okay so here is going to be your uh the chance of what okay so let me change the symbology use a better color okay so in these areas you have higher chance of flood compared to the these areas because here we gave higher rates because here we had higher precipitation and precipitation was the most important factor according to the uh opinion of the experts compared to the other so depending on the rates that you give to the layer you will have different layer a different risk map and also it depends on the months of the precipitation final thing is that you can create so if you go to the layout view so here you can create a risk map you can add the so for example you can add the title you can add a legend uh what this color indicates you can add the north side or scale bar your name date and after that export it as a jpeg file

Channel: HealthGIS

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Id: 0DVVihogMQ0

Length: 42min 6sec (2526 seconds)

Published: Thu Jun 16 2022