Interpolation of Missing Precipitation Data Using Kernel Estimations for Hydrologic Modeling
Precipitation is the main factor that drives hydrologic modeling; therefore, missing precipitation data can cause malfunctions in hydrologic modeling. Although interpolation of missing precipitation data is recognized as an important research topic, only a few methods follow a regression approach. I...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2015/935868 |
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| author | Hyojin Lee Kwangmin Kang |
| author_facet | Hyojin Lee Kwangmin Kang |
| author_sort | Hyojin Lee |
| collection | DOAJ |
| description | Precipitation is the main factor that drives hydrologic modeling; therefore, missing precipitation data can cause malfunctions in hydrologic modeling. Although interpolation of missing precipitation data is recognized as an important research topic, only a few methods follow a regression approach. In this study, daily precipitation data were interpolated using five different kernel functions, namely, Epanechnikov, Quartic, Triweight, Tricube, and Cosine, to estimate missing precipitation data. This study also presents an assessment that compares estimation of missing precipitation data through Kth nearest neighborhood (KNN) regression to the five different kernel estimations and their performance in simulating streamflow using the Soil Water Assessment Tool (SWAT) hydrologic model. The results show that the kernel approaches provide higher quality interpolation of precipitation data compared with the KNN regression approach, in terms of both statistical data assessment and hydrologic modeling performance. |
| format | Article |
| id | doaj-art-3b7b786ae5fb44d48b4b38dcbfae9d18 |
| 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-3b7b786ae5fb44d48b4b38dcbfae9d182025-02-03T01:12:10ZengWileyAdvances in Meteorology1687-93091687-93172015-01-01201510.1155/2015/935868935868Interpolation of Missing Precipitation Data Using Kernel Estimations for Hydrologic ModelingHyojin Lee0Kwangmin Kang1APEC Climate Center, 12 Centum 7-ro, Haeundae-gu, Busan 612-020, Republic of KoreaSchool of Agriculture and Natural Science, University of Maryland, College Park, MD 20742, USAPrecipitation is the main factor that drives hydrologic modeling; therefore, missing precipitation data can cause malfunctions in hydrologic modeling. Although interpolation of missing precipitation data is recognized as an important research topic, only a few methods follow a regression approach. In this study, daily precipitation data were interpolated using five different kernel functions, namely, Epanechnikov, Quartic, Triweight, Tricube, and Cosine, to estimate missing precipitation data. This study also presents an assessment that compares estimation of missing precipitation data through Kth nearest neighborhood (KNN) regression to the five different kernel estimations and their performance in simulating streamflow using the Soil Water Assessment Tool (SWAT) hydrologic model. The results show that the kernel approaches provide higher quality interpolation of precipitation data compared with the KNN regression approach, in terms of both statistical data assessment and hydrologic modeling performance.http://dx.doi.org/10.1155/2015/935868 |
| spellingShingle | Hyojin Lee Kwangmin Kang Interpolation of Missing Precipitation Data Using Kernel Estimations for Hydrologic Modeling Advances in Meteorology |
| title | Interpolation of Missing Precipitation Data Using Kernel Estimations for Hydrologic Modeling |
| title_full | Interpolation of Missing Precipitation Data Using Kernel Estimations for Hydrologic Modeling |
| title_fullStr | Interpolation of Missing Precipitation Data Using Kernel Estimations for Hydrologic Modeling |
| title_full_unstemmed | Interpolation of Missing Precipitation Data Using Kernel Estimations for Hydrologic Modeling |
| title_short | Interpolation of Missing Precipitation Data Using Kernel Estimations for Hydrologic Modeling |
| title_sort | interpolation of missing precipitation data using kernel estimations for hydrologic modeling |
| url | http://dx.doi.org/10.1155/2015/935868 |
| work_keys_str_mv | AT hyojinlee interpolationofmissingprecipitationdatausingkernelestimationsforhydrologicmodeling AT kwangminkang interpolationofmissingprecipitationdatausingkernelestimationsforhydrologicmodeling |