analysis of estimated rainfall data using spatial interpolation. preethi raj geog 5650...
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ANALYSIS OF ESTIMATED RAINFALL DATA USING SPATIAL INTERPOLATION.
Preethi RajGEOG 5650(Environmental Applications of GIS)
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INTRODUCTION
Current research in Hydrology emphasizes on ability to forecast hydrologic parameters.
Precipitation
InfiltrationEvapo-transipiration
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 WEIGHTEDIDW POWER- 2 IDW POWER - 3
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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STNID STNNAME ELEV LON LAT ANN Avg_Ann KrigeValue Pow2_idw Pow3_idw Pow4_idw Pow5_idw
010008 ABBEVILLE 1 466 -085.2833 31.5833 56.44 4.70 4.50 4.50 4.45 4.42 5.39010178 ALICEVILLE 135 -088.1667 33.1333 54.93 4.58 4.53 4.57 4.53 4.51 5.31010184 ALICEVILLE L 164 -088.2833 33.2333 53.72 4.48 4.57 4.60 4.57 4.56 5.26010252 ANDALUSIA 3 249 -086.5333 31.3000 59.75 4.98 0.00 5.04 5.13 5.22 5.37010272 ANNISTON FAA 610 -085.8500 33.5833 52.88 4.41 4.78 4.71 4.72 4.73 5.37010369 ASHLAND 3 EN 991 -085.8000 33.2833 58.82 4.90 4.71 4.75 4.75 4.76 5.49010395 ATHENS 2 718 -086.9833 34.8000 57.64 4.80 4.61 4.68 4.68 4.68 5.10010430 AUBURN AGRON 653 -085.5000 32.6000 56.47 4.71 4.66 4.65 4.67 4.68 5.27010440 AUTAUGAVILLE 200 -086.6833 32.4667 53.17 4.43 4.81 4.73 4.74 4.75 5.58010505 BANKHEAD LOC 279 -087.3500 33.4500 58.17 4.85 4.68 4.74 4.72 4.70 5.42010616 BEATRICE 1 E 177 -087.2000 31.7333 55.61 4.63 4.96 4.85 4.85 4.84 5.38010655 BELLE MINA 2 600 -086.8833 34.7000 54.75 4.56 4.61 4.69 4.68 4.68 5.09010764 BESSEMER 3 W 446 -087.0000 33.4000 59.11 4.93 4.80 4.79 4.79 4.78 5.59010823 BILLINGSLEY 358 -086.7000 32.6667 56.56 4.71 4.84 4.75 4.77 4.80 5.68010831 BIRMINGHAM F 620 -086.7500 33.5667 54.58 4.55 4.76 4.78 4.79 4.79 5.62010957 BOAZ 1070 -086.1667 34.2167 55.99 4.67 4.66 4.66 4.66 4.65 5.21011288 CALERA 531 -086.7461 33.1106 57.05 4.75 4.92 4.83 4.88 4.92 5.95011301 CAMDEN 3 NW 236 -087.3167 32.0333 56.11 4.68 4.73 4.66 4.61 4.58 5.30013761 HEADLAND 371 -085.3333 31.3500 56.05 4.67 4.63 4.60 4.55 4.51 5.26013816 HIGHLAND HOM 594 -086.3167 31.9500 56.07 4.67 4.62 4.65 4.64 4.66 5.30014064 HUNTSVILLE W 623 -086.7667 34.6500 57.18 4.76 4.62 4.68 4.68 4.68 5.09014209 JACKSONVILLE 610 -085.7833 33.8167 53.78 4.48 4.76 4.70 4.72 4.73 5.35014226 JASPER 518 -087.2833 33.9000 57.61 4.80 4.76 4.80 4.82 4.82 5.52014306 JORDAN DAM 289 -086.2500 32.6167 55.05 4.59 4.80 4.74 4.77 4.80 5.59014502 LAFAYETTE 830 -085.4000 32.9000 57.56 4.80 4.66 4.70 4.74 4.77 5.48014603 LAY DAM 420 -086.5167 32.9667 53.53 4.46 4.92 4.79 4.82 4.83 5.69014619 LEEDS 636 -086.5500 33.5500 56.65 4.72 4.75 4.78 4.78 4.78 5.58
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 ?