Modeling Spatiotemporal Precipitation: Effects of Density, Interpolation, and Land Use Distribution
Characterization of precipitation is critical in quantifying distributed catchment-wide discharge. The gauge network is a key driver in hydrologic modeling to characterize discharge. The accuracy of precipitation is dependent on the location of stations, the density of the network, and the interpola...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
|
| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2015/174196 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832545750863577088 |
|---|---|
| author | Christopher L. Shope Ganga Ram Maharjan |
| author_facet | Christopher L. Shope Ganga Ram Maharjan |
| author_sort | Christopher L. Shope |
| collection | DOAJ |
| description | Characterization of precipitation is critical in quantifying distributed catchment-wide discharge. The gauge network is a key driver in hydrologic modeling to characterize discharge. The accuracy of precipitation is dependent on the location of stations, the density of the network, and the interpolation scheme. Our study examines 16 weather stations in a 64 km2 catchment. We develop a weighted, distributed approach for gap-filling the observed meteorological dataset. We analyze five interpolation methods (Thiessen, IDW, nearest neighbor, spline, and ordinary Kriging) at five gauge densities. We utilize precipitation in a SWAT model to estimate discharge in lumped parameter simulations and in a distributed approach at the multiple densities (1, 16, 50, 142, and 300 stations). Gauge density has a substantial impact on distributed discharge and the optimal gauge density is between 50 and 142 stations. Our results also indicate that the IDW interpolation scheme was optimum, although the Kriging and Thiessen polygon methods produced similar results. To further examine variability in discharge, we characterized the land use and soil distribution throughout each of the subbasins. The optimal rain gauge position and distribution of the gauges drastically influence catchment-wide runoff. We found that it is best to locate the gauges near less permeable locations. |
| format | Article |
| id | doaj-art-edd0102bb239414e83daf909e096a136 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-edd0102bb239414e83daf909e096a1362025-02-03T07:24:58ZengWileyAdvances in Meteorology1687-93091687-93172015-01-01201510.1155/2015/174196174196Modeling Spatiotemporal Precipitation: Effects of Density, Interpolation, and Land Use DistributionChristopher L. Shope0Ganga Ram Maharjan1U.S. Geological Survey, Utah Water Science Center, 2329 W. Orton Circle, Salt Lake City, UT 84119, USADepartment of Soil Physics, University of Bayreuth, 95447 Bayreuth, GermanyCharacterization of precipitation is critical in quantifying distributed catchment-wide discharge. The gauge network is a key driver in hydrologic modeling to characterize discharge. The accuracy of precipitation is dependent on the location of stations, the density of the network, and the interpolation scheme. Our study examines 16 weather stations in a 64 km2 catchment. We develop a weighted, distributed approach for gap-filling the observed meteorological dataset. We analyze five interpolation methods (Thiessen, IDW, nearest neighbor, spline, and ordinary Kriging) at five gauge densities. We utilize precipitation in a SWAT model to estimate discharge in lumped parameter simulations and in a distributed approach at the multiple densities (1, 16, 50, 142, and 300 stations). Gauge density has a substantial impact on distributed discharge and the optimal gauge density is between 50 and 142 stations. Our results also indicate that the IDW interpolation scheme was optimum, although the Kriging and Thiessen polygon methods produced similar results. To further examine variability in discharge, we characterized the land use and soil distribution throughout each of the subbasins. The optimal rain gauge position and distribution of the gauges drastically influence catchment-wide runoff. We found that it is best to locate the gauges near less permeable locations.http://dx.doi.org/10.1155/2015/174196 |
| spellingShingle | Christopher L. Shope Ganga Ram Maharjan Modeling Spatiotemporal Precipitation: Effects of Density, Interpolation, and Land Use Distribution Advances in Meteorology |
| title | Modeling Spatiotemporal Precipitation: Effects of Density, Interpolation, and Land Use Distribution |
| title_full | Modeling Spatiotemporal Precipitation: Effects of Density, Interpolation, and Land Use Distribution |
| title_fullStr | Modeling Spatiotemporal Precipitation: Effects of Density, Interpolation, and Land Use Distribution |
| title_full_unstemmed | Modeling Spatiotemporal Precipitation: Effects of Density, Interpolation, and Land Use Distribution |
| title_short | Modeling Spatiotemporal Precipitation: Effects of Density, Interpolation, and Land Use Distribution |
| title_sort | modeling spatiotemporal precipitation effects of density interpolation and land use distribution |
| url | http://dx.doi.org/10.1155/2015/174196 |
| work_keys_str_mv | AT christopherlshope modelingspatiotemporalprecipitationeffectsofdensityinterpolationandlandusedistribution AT gangarammaharjan modelingspatiotemporalprecipitationeffectsofdensityinterpolationandlandusedistribution |