Cloudburst where to find

In addition to the many hackable items in the room, you'll now be able to use the Voice Synthesizer to lure thugs into ambushes - but the Knight will wise his men up after a few uses. When all the militia are taken care of, head back to where you left Stagg to pick up his trail, and track his fingerprints down to the first floor, and along the right corridor to a single floor grate. Interrogate him to learn about what fuels the Cloudburst, and take a generator to use for the Batmobile.

Now leave Stagg Airships and head back to Poison Ivy. Batman Arkham Knight Wiki Guide. Last Edited: 3 Nov pm. At this point, Alfred will alert you to the loss of contact with Lucius Fox , starting a new side quest in Gotham's Most Wanted. Was this guide helpful? YES NO. In This Wiki Guide. Release Date. Next, the network's nodes are sorted topologically and saved in a list, so when stepping through it, all nodes and edges are stored and retrieved in exactly the right hierarchical sequence in accordance with the topological setup described previously.

Thus, all so-called first order nodes and edges appear first in the list, then the second order ones, and so on. In that way, the network is ready to trace the hierarchical downstream spillovers.

The final analysis step is an extension of the previous steps in which you were investigating the rain event at which the bluespot starts spilling over. After calculating downstream accumulated spillover, you can look up the smallest rain event producing a spillover from a bluespot by reviewing the SpillOverOutxxxmm fields in the Nodes attribute table. If no spillover is observed if a bluespot has a huge capacity not being filled up at the most extreme rain event investigated , a field value of -1 is assigned.

The Filled Events tool requires the Nodes feature class as input. The values represent the rain event at which the bluespot starts spilling over. Notice that several bluespots are assigned a field value of These bluespots have a huge capacity and do not fill up, even at the at the most extreme rain events investigated.

In this module, you reviewed and explored a sample dataset and used the tools models and scripts to predict the flooding consequences from a series of simulated storm water events. In the next module, you will evaluate and interpret the results of the bluespot modeling. The additional fields populated by the Filled Events tool will form part of this interpretation. Next, you will assess the modeling outputs and gain a better understanding of storm water consequences at different storm scenarios.

In this module, you will inspect the results generated by the Identify Bluespot Features model and the additional tools you used to create nodes and edges and calculate downstream accumulated spillover from one bluespot to the next bluespot or bluespots downstream. You will evaluate model outcomes to explore the consequences of storm water within the study area.

A broader goal is to predict the overall consequences of storm water to the study area and consider where to avoid future site developments that will be impacted adversely by climate changes. To facilitate exploration of the model results, you will need to finalize data processing by attaching nodes feature attributes to the Bluespots, Streams and BluespotWatersheds features. This will be achieved by joining and building one-to-one relationships for features with matching IDs.

The illustration below describes the one-to-one relationships and the matching ID fields. Before you continue, you will add a new map to your project and copy only the necessary layers needed for interpretation. A new map named Map1 is added to the project. You will use this map to investigate and explore the consequences of storm water in the Charlottenlund study area.

The selected layers are duplicated in the Investigate Stormwater map. This will ensure point and line layers are displayed above polygon layers. Labels are now placed at the lower right of the node point and are easily readable. Notice that the selected stream starts at the bluespot with ID value and ends at the bluespot with ID value Since the BluespotID values are available and serve as a key field, you can transfer results in the Nodes layer to the Bluespots and Streams layers through joins.

Next, you will visualize the events at which a bluespot starts spilling over, by joining nodes layer attributes to the Bluespots layer. The Bluespots layer is duplicated in the Contents pane. Next, you will rename the copy of the layer. For Input Table , verify FilledEvents is selected. Notice the addition of the storm water event modeling attributes originally contained in the Nodes layer. This also means that the bluespots spilling over have reached their maximum filled horizontal extent.

All sinks symbolized in gray have such high capacities that they are not yet filled and will not spill over even at the biggest rain event modeled—in this case, the mm event. Next, you will use a chart to visualize filled events. Charts give you an opportunity to summarize and visualize filled events in a concise and simple manner.

For Category or Date , choose FilledEventmm. For Aggregation , verify Count is selected. The chart shows the number of bluespots that are filled at a specific rain event in mm and start spilling over. Notice that the number of bluespots versus the FilledEventmm decreases asymptotically with increasing rain events. On the map, notice the large bluespot located east in the study area will not spill over at a mm rain event, either.

But looking into its attributes, notice its ActualVolumemm being 44, m 3 , which is very close to its capacity 45, m 3. So, presumably, this bluespot will be filled entirely and start spilling over at a mm event. When clicking the 20 mm bar chart column, it shows that 2, bluespots are filled at that event.

Simultaneously, the matching bluespots are highlighted on the map. Typically, such bluespots are shallow, have small volumes, and are found in gardens and along infrastructures. The map and chart identify bluespots. Notice its capacity of , m 3. When reviewing its attributes, notice that for the mm event, the field named ActualVolumemm shows that the actual stored water volume is 32, m 3.

For Aggregation , choose Sum. In the chart, notice the bluespots not being filled up at a mm event still represented by a value of -1 make up the largest summed volume. On the map, note that two large bluespots dominate the 70 mm category. To estimate how many buildings might be affected during a rainstorm, a selection can be set up for features in the BuildingsNoGaps layer that intersects the filled bluespots at the 70 mm event.

For Input Rows , choose FilledEvents. For Operator , choose is less than or equal to. For Value , choose Next, use the Select By Location tool to identify the BuildingsNoGaps features that overlap the selected filled bluespots. For Relationship , choose Intersect. For Selecting Features , choose FilledEvents.

As a result, 3, buildings are selected. This means that 3, buildings are affected by bluespots, some more than others depending on their intersecting locations. What can we learn about bluespots and buildings? In this case, the flood damage may be more severe than for similarly located buildings without basements.

Another consideration is that buildings located completely within filled bluespots may be at higher risk than others. However, when selecting by location to identify buildings located completely within bluespots, no matches are returned, even if a visual inspection identifies such occurrences. Why is that? First, consider that all building footprint polygons have been converted from a vector to a raster format and are burned onto the DTM enforcing runoff around them.

Logically, this means that we will never identify bluespots inside buildings. Second, the derived bluespot areas were originally a raster representation, converted to vector polygons without feature simplification applied. In combination, this means that when Select By Location is used on the Bluespots or FilledEvents layer using the Intersect spatial relationship, the vectorized bluespots are matched with the BuidlingsNoGaps vector footprints. If that is the case, no matches are found.

In addition, if Select By Location is used on buildings located Completely Within bluespots, as mentioned before, no matches are identified. No matches identified is a limitation related to the data representations, and what was prioritized for the analysis: In this example, the location of buildings and the volumes that they occupy were prioritized over the ability to identify buildings located completely within filled bluespots.

If you did want to select buildings located completely within bluespots, everything must be remodeled using a DTM without buildings incorporated. This would cause bluespot volumes to be inaccurate, as building volumes are not accounted for affecting bluespot capacity and spillover estimations.

An additional consideration is that if you try to examine which buildings intersect bluespots at various filled events, some large buildings may intersect bluespots having different filled events. The example below shows a building affecting , and mm events. In this situation, when deriving statistics to count the number of buildings located next to bluespots filled up at various events, the same building may be counted more than once. In these cases, the building should only be counted with the lowest filled event value, which in the example illustrated is 20 mm.

You also need to consider flood risks to buildings located in bluespots at rain events only causing a partial fill-up. In this way, water depth can be estimated at a given volume. This analysis requires an additional set of tools that are not currently included in the model but are in consideration for future enhancements to the workflow. The spillover volumes in m 3 from a bluespot at a specific rain event indicate how serious the water flow is within it. The FilledEvents layer is already joined with the Nodes layer; copying the layer will preserve the join.

What did we learn about 60mm bluespot spillovers? It seems like the volumes in m 3 going downstream reach quite high values, especially in the lower central part of the study area next to or overlapping some vital infrastructure. This is, indeed, critical along the roads toward the southwest that serve a large regional hospital Gentofte Sygehus. In the next step, you will investigate how these spillovers may be assigned from bluespots to the flow paths leaving them.

For Input Table , verify StreamVolumes is selected. For Join Table , choose Nodes. Notice that the class intervals for the StreamVolumes layer are not the same as for the SpillOver60mm layer, although both layers are symbolized using the same SpillOverOut60mm attribute values.

In the StreamVolumes layer, pseudo nodes located where streams merge are included in the join of nodes to streams, so, as streams merge at pseudo nodes, the spillover values increase. However, pseudo nodes located where streams merge are not included in the join of node attributes to bluespots and thus the spillover volumes do not increase. For , type Do not change Where SpillOverOut60mm is equal to 0. The map updates and streams are symbolized based on the spillover volume at the 60 mm event.

Next, you can illustrate spillovers along the streams by using graduated symbols. To apply graduated symbology, complete the following steps:. To map the actual widths and depths of the water corridors, kinematic wave equations may be considered, however, this is not currently addressed in the model.

Also, notice how streams in many places are identified along the roads' curbsides, as shown below. When using a DTM of high precision, a road's centerline is sometimes visible as a drainage divide found along it, forcing the runoff onto the curbsides. In the layer, all minor bluespots not passing the combined depth and volume criteria were omitted. In this section, you will review the bluespot depths.

The selected layer is added to the Investigate Stormwater map. Turn off all other layers. For 0. For 1. For 2. For 4. Do not change 7. The map updates and displays bluespot depths in meters. You may use Advanced Symbology Options to apply Data Exclusion and change to manual classification with break values for the first four intervals set at 0.

According to the FilledEvent layer, this very large bluespot is filled up at a 70 mm event, and since many buildings are located in the bottom of the bluespot at depths around 2 meters, this area is likely to experience severe flooding problems, even at lower rain events.

Among emergency planners, such a map is popular and named a 'rubber boot index map,' as it tells the teams where to use rubber boots, waders, or sail around in dinghies when rescuing citizens. Within the study area, classic flooding locations during rainstorms are located at underpasses where roads dip underneath railroad bridges.

For an example, review the location at the bookmark named Bernstorffsvej Station. When filled to its pour point level at a filled event of just 30 mm, this bluespot has a maximum depth of a staggering 3. Many nonlocal drivers are stuck there during rainstorms when they underestimate its depth. If you checked the original Streams layer, you will notice that several streams contribute to this major bluespot. When the spillover is this high, it may be dangerous to even walk or drive here because of possible strong currents.

As you can see, the large spillover continues toward the east into a nearby bluespot and then farther eastward until the water moves toward the north along the infrastructure. During the analysis and exploration of results for the study area, a number of critical locations were identified and discussed.

But as a challenge, the author of these lessons would like to encourage you to investigate and try to identify additional locations susceptible to flooding. As you have seen, many overpass and underpass locations are particularly susceptible to flooding and at various other local geographic features, overlapping bluespots of various depths may be identified beyond the example already discussed. You may also want to consider remediation measures such as how to reduce the downstream spillover by establishing retention or detention basins in existing parks.

This can be achieved by studying spillovers at various rain events from bluespots located in upstream parts of the hydrologic network. In this way, it would be possible to detect a decrease in downstream spillovers and the reduction in the number of affected buildings at specific rain events.

During your exploration, you may realize how the bluespot modeling tools might be efficient in the planning of detention basins near future site developments and where to avoid new constructions. For now, you have completed an exploration of the Charlottenlund study area.

In the next module, you will focus on how to prepare data from your own study area for the bluespot modeling. You can think of this module as a road map to investigating data requirements, data preparation steps, and associated tools used to create bluespots for predicting flood risk assessment due to storm water incidents in your own study area.

Steps and suggestions in this module will assist you in using the associated modeling tools to screen a digital terrain model for landscape depressions within a drainage basin, locate their hierarchical downstream locations, and predict how they will be filled up and spill over during simulated uniform rainstorms.

In preparation for performing your own bluespot analysis on local data for your study area, you need to be aware of several data considerations and preparation steps as outlined next. For the modeling process, you will need a terrain model constructed from ground points on bare earth.

If building footprints are available, these may be incorporated to improve the modeling results. For an overview of the hydrologic modeling tools in the ArcGIS Spatial Analyst extension, it is recommend that you review the Hydrologic analysis sample applications topic.

In bluespot modeling, a strong recommendation is to aim at using a digital terrain model DTM constructed from ground points on bare earth and optionally with building elevations added , and not a digital surface model DSM that includes combinations of vegetation heights and buildings.

See Exploring digital elevation models for more information. During modeling, it is noticeable that when using a DSM, many false bluespots are discovered in between tree canopies or in between tree canopies and buildings. When detected, huge nonexistent bluespot volumes and false runoff flow paths may be created. Unfortunately, a high-resolution DTM is far more expensive to produce than a DSM, so very few are publicly available as open geospatial data.

As a result, you may consider using a DTM of lower resolution, for example, 10 or 30 meters if available. Although imprecise and coarse-grained and possibly not free from artifacts, it may still provide interesting overviews. Some countries, such as Denmark, openly share lidar-based national elevation models with spatial resolutions of 0. For data, you may visit the following download site and select data for use in this lesson.

With the processed DTM, it should be straightforward to use it in bluespot modeling. Keep in mind that the DTM should cover an entire drainage basin—or at least a subset that doesn't have any upstream contributions. It is important to note that the most trustworthy storm water scenarios are produced from using a hydro-conditioned DTM with buildings added to divert runoff around them.

See An overview of the Hydrology toolset for more information on tools that may be used for this purpose. For more information on hydro-conditioning, it is recommended that you explore the ArcHydro extension for ArcGIS Pro to learn more about how you may produce a hydro-conditioned DTM yourself. In the next section, you will create and set up a project to perform your own bluespot analysis. ArcGIS Pro organizes all necessary data, maps, and tools in a project folder, keeping all required components together and thereby facilitating management and sharing.

You will download a project containing the bluespot modeling tools and then add your own data and set up the tools to locate and use your data layers. The project opens to display a map and located in the Catalog pane the necessary geodatabases and toolboxes.

All project data is stored in file geodatabases, and input data is separated from the output data. In addition, there are several folders containing project layer files, Python scripts, and model tools.

In the Change settings for the current project pane, review the current Home folder , Default geodatabase , and Default toolbox settings for the project. The Outputs geodatabase stores output data generated by the modeling tools. Before continuing, ensure that all your data the elevation model and optional building footprints is projected and that the planar unit and the elevation data's z-unit are in meters. If not, all area and volume calculations produced by the model tools will be inaccurate.

This must be meters; if not, use the project tool update the data source to a projection with linear units in meters. You may copy or import the feature classes into the Inputs geodatabase, or add a folder connection to an existing geodatabase containing your input data and DTM.

Copying a feature class may only be done between geodatabases; if your data is in a non-geodatabase format, you would need to import instead of copying. These feature classes represent your processed data to be used in the tools and model to predict the flooding consequences from storm water events. Next, you will review the data preparation steps and tools available to process your input data.

Now that you have set up your project and have collected and added input data to the necessary geodatabase, you need to initiate several data housekeeping steps to make the modeling efficient and useful. The first consideration is to ensure you include the extent of the entire drainage basin affecting your area of interest. In other words, it does not make sense to execute the model on the area of a city where the extent of the city does not include the entire drainage basin in which it is located.

Accurate hydrologic modeling depends on the location of the major drainage divide a study area falls within. The following example illustrates the drainage basins derived from a DTM covering the Charlottenlund case area.

The outcome shows several minor drainage basins presented in different colors around the Charlottenlund drainage basin presented in yellow. There is no reason to include additional drainage basins or parts thereof in the screening ahead than the basin or basins covering the area of interest, as the neighboring basins don't affect the area of interest's surface hydrology.

The following is the workflow you may follow to generate and extract the basin relevant to your study area. Review the model steps; when done, close and do not save changes.

After extracting the DTM for the basin applicable to your study area, you are ready to execute the Identify bluespot features model. However, you should consider incorporating buildings into your DTM to enable flows around structures and to eliminate the buildings' volumes if they are located within a bluespot. If you choose not to incorporate structures, you may continue by executing the model.

If you can provide a feature class of building footprints for your study area, it would greatly enhance the results of the model if you were able to 'stamp' them onto the DTM. However, you do need to prepare the building layer by eliminating tiny slivers in between adjacent buildings having widths lesser than or equal to the cell size for the DTM used, and you need to identify and fill gaps inside closed building complexes such as courtyards.

The illustration below shows a small subset of building features. The left shows the before situation with a small sliver between adjacent buildings and gaps inside closed building complexes.

The right shows the same buildings after execution of the Add Buildings to DTM model and illustrates how courtyards 1 and 2 were filled, but that courtyard 3 was not filled, as the gap between buildings did not meet the sliver elimination criteria slivers between adjacent buildings must have a width less than or equal to the cell size of the DTM used.

The Add Buildings to DTM model first calls and executes the Aggregate Buildings model to eliminate the tiny slivers in between adjacent buildings and identify and fill gaps inside closed building complexes courtyards using the Union tool.

During its execution, all building polygons are converted to a temporary raster dataset with the same cell size as the DTM. The Add Buildings to DTM model executes and the resulting DTM now incorporates building footprints that have slivers between adjacent buildings eliminated and gaps inside closed building complexes filled.

As you head to Bleake Island to Destroy the Cloudburst , Alfred will raise the bridges, leaving you trapped on the island with the Arkham Knight 's Cloudburst tank - as well as seven Cobra Tanks. This makes things difficult, as the Cloudburst is impervious to any attacks for the moment, has a constantly rotating degree view, and will patrol among the other tanks.

Patience will be the name of the game, as you will have to carefully find the few Cobra tanks that have strayed from the main groups and take them out one at a time.

The 60mm Cannon Lure is a great upgrade to have for this fight, letting you set up ambushes by causing tanks to investigate an area. Also make sure to navigate in Battle Mode for easier strafing and turning, and use the dodge boost to get a little speed navigating alleys.

If you get caught, hightail it out of there and keep turning down alleys until you loose your pursuers. When at last the Cobra Tanks are gone, you'll finally have some intel on the Cloudburst. Like the Cobra Tanks, you'll have to get close to lock on to weak spot.

Unfortunately there are four such weak spots, located on all sides of the tank. Since the tank is constantly rotating regardless of the direction it's moving - you will need to stay in the blind spots and take a shot, then quickly retreat at full speed.