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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.
Chapter 1014 sections
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.
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.
A simple method where the average of surrounding stations' rainfall is calculated, applicable when rainfall is uniform.
Used when normal rainfall varies more than 10% from those of missing data stations; adjusts for climatic variability.
Estimation based on geographic proximity, where nearby stations' readings influence the missing value.
A tool for checking the consistency of rainfall data by plotting cumulative readings against neighboring stations.
30-year averages provided by the Indian Meteorological Department, used for consistency checks and as a reference for estimation.
Total Questions
2
Estimated Time
4 min
Passing Score
70%
1 more question available