Creating Mean Precipitation Isohyets in ArcGIS
Isohyet maps are the standard way to represent spatial precipitation distribution when you have point data from rain gauges. The process isn't as simple as running one tool and calling it done. You need to understand what's happening at each step, especially if your gauge network is uneven or sparse. Start with your gauge data. Make sure it's in a point feature class with fields for station ID, elevation, and mean annual or monthly precipitation. If you're working with daily data, aggregate it first. I usually calculate Thiessen polygons early on to check my gauge density before doing anything else. Here's the practical workflow. Create a point feature class from your tabular data if needed. Then use the Topo to Raster tool or the Kriging tool from the Geostatistical Wizard. Topo to Raster is generally better for precipitation because it incorporates elevation as a covariate, which matters since precipitation correlates strongly with altitude in most regions. Set your elevation raster as a factor field if your area has significant topography.
After interpolation, convert the raster to contour lines using the Contour tool. These contours are your isohyets. Label them with their precipitation values, symbolize them appropriately, and add your gauge points on top so reviewers can see where the actual measurements sit relative to the interpolated lines. I ran into a problem once with a network in the Andean foothills where the interpolation consistently overestimated precipitation in valleys. The issue was that my gauge distribution was skewed toward ridge positions. I ended up using co-kriging with a DEM-derived flow accumulation raster as a secondary variable, which forced the interpolation to respect the drainage patterns. It took about three hours to set up properly but the resulting isohyets matched observed patterns much better than plain ordinary kriging.
Important Technical Details Most Guides Skip
Search radius matters more than people realize. If you set it too large, your isohyets will smooth out real precipitation gradients. If it's too small, you get chaotic localized bumps that don't reflect anything real. I typically start with a search neighborhood of 5 to 8 neighbors forOrdinary Kriging and adjust based on variogram results. The semivariogram model selection is where most people just accept the default. Don't. Spherical, exponential, and Gaussian models produce noticeably different interpolation surfaces even with the same data. The spherical model tends to produce smoother surfaces while exponential can create more localized variation. Run cross-validation after each model choice and compare the RMSE values. Sparse networks are a real limitation. Below one gauge per thousand square kilometers, isohyetal maps become speculative rather than descriptive. There's no tool that fixes bad data. If your region has this problem, consider combining gauge data with satellite estimates like CHIRPS or TMPA before interpolation. The merging process itself deserves its own workflow but the basic idea is to use gauge data to bias-correct the satellite product, then interpolate the combined dataset.
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Raster resolution should match your gauge density. A common mistake is generating a 30-meter DEM-resolution precipitation raster from five gauges. The output will look detailed but it's meaningless detail. Match your cell size to roughly one-fifth of your average gauge spacing. This gives you enough resolution to draw reasonable contours without implying false precision. The Topo to Raster tool requires an extension license in some ArcGIS versions. If you're working with ArcMap instead of ArcGIS Pro, the procedure is slightly different. You'd use the Kriging tool from the Spatial Analyst extension, export the raster, then run the Contour tool. The principle stays the same but the interface and some parameter names differ between versions.
