A computational ecologist is analyzing rainfall data from 120 monitoring stations. If 75% recorded data daily and 20% of those had missing data on a given day, how many stations provided complete data?

Tue, 05 May 2026 07:30:49 +0000

**A computational ecologist is analyzing rainfall data from 120 monitoring stations. If 75% recorded data daily and 20% of those had missing data on a given day, how many stations provided complete data?** In an era of heightened awareness around climate tracking and data reliability, the work of computational ecologists helps make sense of how weather systems are monitored across vast regions. For researchers and environmental planners, accurate rainfall data fuels decisions, from agriculture to disaster preparedness—especially when station performance may fluctuate. **The Raw Numbers Behind Rainfall Reliability**
analyzed 120 monitoring stations, with 75% providing daily data entries. That equals 90 stations reporting each day. Among these, 20% experienced missing data—likely due to sensor issues, transmission errors, or maintenance delays. Calculating the gap reveals 20% of 90, or 18 stations, had incomplete records. Subtracting from 90, a total of 72 stations delivered fully complete rainfall datasets. Complete data is vital for trustworthy ecological modeling and timely community responses. **Why Data Gaps Matter in Rainfall Analysis**
A computational ecologist cross-references 120 stations to spot trends in weather patterns, drought risks, and flood forecasting. Missed data introduces uncertainty—like a missing pixel in a map—affecting models that guide water resource management. Even small data losses, when widespread, distort regional analysis. Understanding station performance helps refine data validation methods, supporting more accurate long-term ecological insights across US climate studies.