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# Tips for a Good Meshing Experience 📏
Meshes are very powerful and flexible tools for modeling 2D overland flows in complex urban environments with intricate geometries. However, working with intricate geometries can be extremely frustrating and time-consuming for modelers. 😣 This guide covers best practices and helpful tips to streamline the creation and setup of detailed, high-quality 2D models in InfoWorks ICM. 💻
While this guide focuses specifically on preliminary data cleanup using ArcGIS, where relevant, comparable tools available within InfoWorks ICM are also noted. 🗺️
## Key Steps for Efficient 2D Mesh Creation
### Identifying Areas Prone to Flooding 💧
When provided with an InfoWorks ICM model that contains a 1D pipe network with flooding issues, the specific locations vulnerable to flooding are typically unknown initially. 🤷♂️ As an initial step, create a large, coarse 2D mesh zone with large element sizes to broadly encompass the full modeled area. 🖌️ Then assign any nodes intended to connect with the 2D surface a "2D" flood type, using default flooding coefficient values. 💦 Execute a simulation using the largest design storm, and use the maximum flood depth results to identify and refine the 2D zone boundaries to only include areas with significant flooding depths. 📏 Including large areas in the 2D mesh that remain dry provides no modeling benefit. 🚫
### Simplifying and Correcting GIS Geometries 🗺️
Additional GIS datasets are often utilized to add further detail to 2D meshes, such as buildings, walls, land use polygons, etc. However, GIS data intended primarily for mapping visualization may contain inadequacies that lead to issues when used for hydraulic modeling and geoprocessing. ⚠️ All supplemental GIS data should be carefully examined and corrected prior to incorporation into the 2D mesh creation workflow. 👀
Specific recommendations include: 📝
- Check all geometry for errors like self-intersections, null geometries and vertex order inconsistencies using ArcGIS tools. Fix any identified issues before using data to build 2D mesh. 🛠️
- Simplify geometries to balance modeling needs with computational effort. Reduce number of vertices along lines and boundaries while retaining adequate shape representation. 🖌️
- Identify and correct polygon gaps, overlaps and slivers which can cause substantial meshing issues. 📏
- Dissolve or eliminate unnecessary adjacent polygons to limit model complexity. 🪄
- Clip polygon layers to 2D mesh zone extents to avoid intersections with irrelevant exterior polygons. ✂️
- Avoid multi-part polygon features where possible for compatibility and performance. 💨
By investing effort to simplify and improve supplemental GIS data quality upfront, 2D mesh creation and simulation runtime can be dramatically enhanced. ⚡️
### Innovative Modeling Approaches 💡
In some cases, thinking creatively about modeling objectives enables innovative analysis solutions. 🧠 For example, modeling distinct roughness zones based on land use polygons can require retaining extremely complex dissolved polygon geometries. Rather than directly modeling this complex shape, the polygon can be deleted entirely if the 2D zone "default" roughness reasonably reflects the paved areas previously covered by the complex polygon. 🚧 Pursuing such unconventional approaches can hugely simplify model formulations. 😊
### Elevation Data Considerations ⛰️
Another key factor in determining appropriate 2D mesh element sizes is the nature of the underlying terrain elevation data. Typical LiDAR density and vertical RMSE statistics provide insight into reasonable minimum mesh element areas. 📏 As a general rule of thumb, the minimum element area can be set to 1-3 times the LiDAR point spacing squared. 🤓 However, additional considerations around model sensitivity and objectives should factor into selecting appropriate sizes as well. 🧐 Steep terrain may warrant smaller elements to better represent surface storage while flat areas allow coarser resolutions. 🏔️
## Recommendations for Efficient Future Updates 🤖
Investing time to create streamlined ArcGIS tools or model workflows pays dividends for future model updates or enhancements. 📈 Parameterizing and automating key data preprocessing steps allows efficiently regenerating 2D data for alternative scenarios or new model versions without repetitive manual effort. 🤖
In summary, while intricate 2D mesh development requires significant upfront effort, following GIS preprocessing best practices, creatively considering alternative modeling approaches, understanding terrain data accuracy impacts, and automating workflows can help to cost-effectively build detailed InfoWorks ICM models for urban flood analysis. 👍 Let me know if you need any clarification or have additional questions!
