10. Missing Rainfall Data – Estimation
Estimation of missing rainfall data is crucial in hydrology for designing effective water resources projects. The chapter outlines various estimation methods, criteria for selecting appropriate techniques, and emphasizes the importance of consistency checks using tools like the Double Mass Curve. Additionally, it highlights the role of the Indian Meteorological Department in providing normals for effective data estimation.
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What we have learnt
- Missing rainfall data can arise from instrumental malfunction, human error, natural calamities, and operational constraints.
- Various methods such as Arithmetic Mean, Normal Ratio, IDW, and Multiple Regression can be utilized to estimate missing data.
- The choice of estimation technique should consider the length of the missing record, number of neighboring stations, and data consistency.
Key Concepts
- -- Arithmetic Mean Method
- A simple method where the average of surrounding stations' rainfall is calculated, applicable when rainfall is uniform.
- -- Normal Ratio Method
- Used when normal rainfall varies more than 10% from those of missing data stations; adjusts for climatic variability.
- -- Inverse Distance Weighting (IDW)
- Estimation based on geographic proximity, where nearby stations' readings influence the missing value.
- -- Double Mass Curve
- A tool for checking the consistency of rainfall data by plotting cumulative readings against neighboring stations.
- -- IMD Normals
- 30-year averages provided by the Indian Meteorological Department, used for consistency checks and as a reference for estimation.
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