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Precipitation estimation primarily relies on calculating average rainfall data. This approach is foundational in hydrology and meteorology as it allows for the integration of long-term weather data to assess the expected rainfall over a specific period. By analyzing historical precipitation patterns and averages, engineers and meteorologists can make informed predictions about future rainfall events.

This principle is crucial for resource management in agriculture, as understanding average precipitation directly influences irrigation practices, crop selection, and sustainability planning. Utilizing average data helps establish baseline conditions and aids in addressing variability caused by climate change or seasonal shifts.

The other options reflect various methods of rainfall assessment, but they do not serve as the primary principle underpinning precipitation estimation. For instance, measuring current rainfall only captures a moment in time without providing context over time, while using satellite images can help visualize storm events but does not inherently provide an average or historical context essential for estimation. Similarly, although determining historical rainfall differences can give insights into climate trends, it lacks the systematic approach that average calculations offer for direct precipitation estimation.