ANALYSIS OF ESTIMATED RAINFALL DATA USING SPATIAL INTERPOLATION.

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ANALYSIS OF ESTIMATED RAINFALL DATA USING SPATIAL
INTERPOLATION.

• Preethi Raj
• GEOG 5650
• (Environmental Applications of GIS)

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INTRODUCTION

• Current research in Hydrology emphasizes on
  ability to forecast hydrologic parameters.

Precipitation
Evapo-transipiration
Infiltration
Stream flow
Hydrologic Cycle
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PROBLEMS

• Precipitation plays an important role in
  Hydrologic cycle.
• Need for precipitation data to have a better
  understanding of Hydrologic cycle.
• Due to practical difficulties not possible to
  have rain gauges all over the world.
• Need for an alternative to estimate
  precipitation data.

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STUDY AREA - USA

• Total number of stations 6322

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STUDY AREA - USA

• Number of stations selected 1904

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PROCESSES

• SPATIAL INTERPOLATION
• Kriging Interpolation
• Inverse Distance Weighted interpolation

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KRIGING
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KRIGING

• PREDICTION STANDARD ERROR MAP

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INVERSE DISTANCE WEIGHTED
IDW POWER - 3
IDW POWER- 2
IDW POWER - 4
IDW POWER - 5
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ANALYSIS

• TENNESSEE
• ALABAMA
• SELECTED
• STATIONS 62
• UNSELECTED STATIONS 148

SELECTED STATION UNSELECTED STATION
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RESULTS
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RESULTS
Interpolation Method Root-Mean-Square
Kriging 0.3734
IDW- Power 2 0.3652
Optimize power value(2.5595) 0.3602
IDW- Power 3 0.3619
IDW- Power 4 0.3704
IDW- Power 5 0.3784
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CONCLUSIONS

• Values obtained using Kriging, IDW- Power 2 3
  gives similar values and closer to actual
  precipitation value.
• Difference in values obtained using IDW Power 5
  is high.

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THANK YOU

• ANY
  QUESTIONS ?