Hydrology & Water Resources Engineering - Vol 1 | 10. Missing Rainfall Data – Estimation by Abraham | Learn Smarter
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Knowledge
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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Sections

  • 10

    Missing Rainfall Data – Estimation

    This section discusses the significance and methods of estimating missing rainfall data essential for hydrological projects.

    Learning Practice

  • 10.1

    Causes Of Missing Rainfall Data

    This section outlines the various causes leading to missing rainfall data, which are crucial for effective hydrological analysis.

    Learning Practice

  • 10.2

    Importance Of Estimating Missing Rainfall Data

    Estimating missing rainfall data is vital for ensuring the integrity of long-term hydrological records and is essential for effective water resource management.

    Learning Practice

  • 10.3

    Criteria For Estimation Method Selection

    The selection of an estimation method for missing rainfall data depends on various criteria that ensure the method's appropriateness and reliability.

    Learning Practice

  • 10.4

    Estimation Techniques

    This section outlines various techniques for estimating missing rainfall data, essential for reliable hydrological analysis.

    Learning Practice

  • 10.4.1

    Arithmetic Mean Method

    The Arithmetic Mean Method is a simple technique used to estimate missing rainfall data when surrounding stations report relatively uniform rainfall.

    Learning Practice

  • 10.4.2

    Normal Ratio Method

    The Normal Ratio Method estimates missing rainfall data by comparing observed rainfall at neighboring stations to the normal rainfall at those stations.

    Learning Practice

  • 10.4.3

    Inverse Distance Weighting Method (Idw)

    The Inverse Distance Weighting Method (IDW) is an estimation technique used for interpolating missing rainfall data based on the proximity of neighboring stations.

    Learning Practice

  • 10.4.4

    Multiple Regression Method

    The Multiple Regression Method is used to estimate missing rainfall data by establishing linear relationships among rainfall at different stations.

    Learning Practice

  • 10.5

    Checking The Consistency Of Rainfall Data

    This section discusses the importance of verifying rainfall data consistency using the Double Mass Curve method before estimating any missing data.

    Learning Practice

  • 10.6

    Homogeneity And Stationarity Of Rainfall Data

    This section discusses the importance of homogeneity and stationarity in rainfall data for reliable estimation of missing values.

    Learning Practice

  • 10.7

    Role Of Imd Normals

    IMD normals provide standardized rainfall data essential for estimating missing rainfall data.

    Learning Practice

  • 10.8

    Use Of Gis And Software Tools

    This section discusses the application of GIS and software tools in estimating missing rainfall data, emphasizing their role in enhancing accuracy and efficiency.

    Learning Practice

  • 10.9

    Practical Guidelines

    This section provides practical guidelines for estimating missing rainfall data, emphasizing the selection of neighboring stations and ensuring the reliability of the estimations.

    Learning Practice

References

Chapter_10_Missi.pdf

Class Notes

Memorization

What we have learnt

  • Missing rainfall data can a...
  • Various methods such as Ari...
  • The choice of estimation te...

Final Test

Revision Tests