New Tool Release: Missing Rainfall Data Estimator & Time-Series Patcher
I am thrilled to announce the newest addition to the CivilSheets Hydrology Suite: the Missing Rainfall Data Estimator! 🌧️🔧
In continuous hydrologic modeling, a single broken rain gauge or a missing week of data can completely halt your simulation. You cannot run a continuous routing model with gaps in your time-series. So, how do you legally and mathematically "fill in the blanks"?
This new web-based worksheet automates the patching process. It uses nearby "Index Stations" to synthetically reconstruct missing storm data at your target location. It instantly calculates the spatial distances, evaluates historical micro-climates, selects the statistically proven best-fit method, and exports the patched time-series data—all in your browser.
The "Broken Gauge" Problem
In hydrologic analysis, historical rainfall data is everything. But what do you do when you are running a continuous 10-year simulation and discover that your primary rain gauge broke during the most critical hurricane of 2018?
You can't leave it blank, and you can't just copy the data from the gauge one town over. Why? Because rainfall is heavily influenced by micro-climates and topography (orographic lift). If your broken gauge is located in a high-elevation mountain pass, and the nearest working gauge is down in the dry valley, simply copying the valley data will massively under-predict your flood risk.
We need mathematical methods to scale and patch that data correctly.
How the Tool Solves It
This worksheet automatically processes your time-series data using three distinctly different spatial estimation methods simultaneously:
1. The Arithmetic Mean Method
The simplest approach. It assumes the region is perfectly flat and meteorologically uniform. It simply averages the rainfall from the working gauges and applies it to the missing gauge.
2. The Normal Ratio Method (The Gold Standard)
This method solves the "Mountain Problem." It uses Normal Annual Precipitation ($N$)—the long-term 30-year historical average rainfall of a location—as a baseline memory of the micro-climate.
If your broken mountain gauge historically gets exactly twice as much rain as the working valley gauge every year, the Normal Ratio Method mathematically doubles the valley gauge's current storm data before patching it into your model.
3. Inverse Distance Weighting (IDW)
A purely spatial interpolation method. If you don't have decades of historical data to calculate Normal averages ($N$), IDW relies strictly on geographic proximity. It assumes that gauges physically closer to the broken station have a higher influence ($1/d^2$) than gauges further away.
How to Use the Tool
I built this worksheet to act like a dynamic, instant-feedback dashboard. Here is a quick step-by-step guide to patching your first dataset:
Set Your Target Station(s)
Start by defining the location that is missing data. Input the gauge's Normal Annual Rain ($N_x$), Latitude, and Longitude.
Batch Mode: Do you have multiple broken gauges? Click the + Add Target button to add as many as you need. The tool will calculate the matrices and patch the storms for all of them simultaneously!
Configure Index Stations (Map)
Next, add your surrounding "working" gauges. You can type in their coordinates directly, or for a faster approach, use the interactive map.
Simply drag their blue markers around on the map to automatically update their latitude and longitude inputs. The Inverse Distance Weighting ($\frac{1}{d^2}$) matrix will instantly recalculate in real-time as you drag!
Paste Your Time-Series Data
Copy your chronological storm data straight from Excel and paste it into the yellow input box.
Important: Ensure the data columns exactly match the vertical order of your Index Stations. Column 1 is Time, Column 2 is Station 1, Column 3 is Station 2, etc.
Analyze Output & Export
Review the Method Selection Evidence panel to see whether the tool mathematically proved the Arithmetic Mean or the Normal Ratio method is best for your topography.
Check the reconstructed Hyetograph Chart, verify the totals, and then click Export Estimation Report in the toolbar to download your cleanly patched CSV!
Pro Tips for Modelers
How do you know whether to use Arithmetic Mean or Normal Ratio? The standard hydrologic rule of thumb (Chow, 1988) states that if the Normal Annual Precipitation ($N$) of any index station differs from the target by more than 10%, you must use the Normal Ratio.
You don't need to check this yourself. The tool has a built-in "Method Selection Evidence" panel that automatically calculates the variance, highlights the offending station, and stamps a green "Recommended Fit" badge on the correct mathematical approach.
Have multiple broken gauges? Click the "+ Add Target" button. The tool will calculate the matrices and patch the time-series for all of them simultaneously in the background. You can select "All Targets Summary" from the Batch Viewer dropdown to instantly compare the totals across your entire watershed.
Export and Model
The tool features full Google Earth KML and CSV file upload support. You can drag your station markers around on the interactive Leaflet map to automatically update the Latitude/Longitude and recalculate the IDW spatial distances in real-time.
Once you are satisfied with the patched data, click the Export Estimation Report button. You'll get a detailed CSV report containing the static weight matrix, the recommendation logic, and the complete time-series patching results ready to be imported into HEC-HMS or EPA SWMM.
Head over to the tool page and try loading some sample data. Let me know in the comments if you run into any specific edge cases with your datasets!
Happy Modeling!
- CivilSheets
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