analysis of estimated rainfall data using spatial interpolation. preethi raj geog 5650...

ANALYSIS OF ESTIMATED RAINFALL DATA USING SPATIAL INTERPOLATION.

Preethi RajGEOG 5650(Environmental Applications of GIS)

Environmental Application of GIS Spring 06

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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 ?

analysis of estimated rainfall data using spatial interpolation. preethi raj geog 5650...

Documents

unselected stations

precipitation data

hydrologic parameters

study area usanumber

preethi rajgeog

important role

introductioncurrent

practical difficulties