High‐resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset

A 69‐station, densely spaced rain gauge network was maintained over the period 1951–1958 in the Coweeta Hydrologic Laboratory, located in the southern Appalachians in western North Carolina, USA . This unique dataset was used to develop the first digital seasonal and annual precipitation maps for the Coweeta basin, using elevation regression functions and residual interpolation. It was found that a 10‐m elevation grid filtered to an approximately 7‐km effective wavelength explained the most variance in precipitation ( R 2 = 0.82–0.95). A ‘dump zone’ of locally high precipitation a short distance downwind from the mountain crest marking the southern border of the basin was the main feature that was not explained well by the precipitation–elevation relationship. These data and maps provided a rare ‘ground‐truth’ for estimating uncertainty in the national‐scale Parameter‐elevation Relationships on Independent Slopes Model ( PRISM ) precipitation grids for this location and time period. Differences between PRISM and ground‐truth were compared with uncertainty estimates produced by the PRISM model and cross‐validation errors. Potential sources of uncertainty in the national PRISM grids were evaluated, including the effects of coarse grid resolution, limited station data, and imprecise station locations. The PRISM national grids matched closely (within 5%) with the Coweeta dataset. The PRISM regression prediction interval, which includes the influence of stations in an area of tens of kilometres around a given location, overestimated the local error at Coweeta (12–20%). Offsetting biases and generally low error rates made it difficult to isolate major sources of uncertainty in the PRISM grids, but station density and selection, and mislocation of stations were identified as likely sources of error. The methods used in this study can be repeated in other areas where high‐density data exist to gain a more comprehensive picture of the uncertainties in national‐level datasets, and can be used in network optimization exercises. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Daly, Christopher [verfasserIn] Slater, Melissa E Roberti, Joshua A Laseter, Stephanie H Swift, Lloyd W

Format:	Artikel
Sprache:	Englisch

Erschienen:	2017

Rechteinformationen:	Nutzungsrecht: © 2017 The Authors. published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.

Schlagwörter:	Coweeta precipitation mapping precipitation measurement interpolation rain gauge uncertainty orography PRISM Rainfall Hydrologic data Maps Watersheds Regressions Mountains Variance analysis Digital mapping Hydrology Wavelength Error analysis Mapping Ground truth Atmospheric precipitations High resolution Rain gauges Resolution Stations Identification methods Precipitation Interpolation Uncertainty Datasets Elevation Annual precipitation Slopes (topography) Rain Ground stations Optimization Precipitation (meteorology)

Systematik:	RA 1000

Übergeordnetes Werk:	Enthalten in: International journal of climatology - Chichester [u.a.] : Wiley, 1989, 37(2017), S1, Seite 124-137
Übergeordnetes Werk:	volume:37 ; year:2017 ; number:S1 ; pages:124-137

Links:	Volltext Link aufrufen Link aufrufen

DOI / URN:	10.1002/joc.4986

Katalog-ID:	OLC1996088831

Internformat


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520			\|a A 69‐station, densely spaced rain gauge network was maintained over the period 1951–1958 in the Coweeta Hydrologic Laboratory, located in the southern Appalachians in western North Carolina, USA . This unique dataset was used to develop the first digital seasonal and annual precipitation maps for the Coweeta basin, using elevation regression functions and residual interpolation. It was found that a 10‐m elevation grid filtered to an approximately 7‐km effective wavelength explained the most variance in precipitation ( R 2 = 0.82–0.95). A ‘dump zone’ of locally high precipitation a short distance downwind from the mountain crest marking the southern border of the basin was the main feature that was not explained well by the precipitation–elevation relationship. These data and maps provided a rare ‘ground‐truth’ for estimating uncertainty in the national‐scale Parameter‐elevation Relationships on Independent Slopes Model ( PRISM ) precipitation grids for this location and time period. Differences between PRISM and ground‐truth were compared with uncertainty estimates produced by the PRISM model and cross‐validation errors. Potential sources of uncertainty in the national PRISM grids were evaluated, including the effects of coarse grid resolution, limited station data, and imprecise station locations. The PRISM national grids matched closely (within 5%) with the Coweeta dataset. The PRISM regression prediction interval, which includes the influence of stations in an area of tens of kilometres around a given location, overestimated the local error at Coweeta (12–20%). Offsetting biases and generally low error rates made it difficult to isolate major sources of uncertainty in the PRISM grids, but station density and selection, and mislocation of stations were identified as likely sources of error. The methods used in this study can be repeated in other areas where high‐density data exist to gain a more comprehensive picture of the uncertainties in national‐level datasets, and can be used in network optimization exercises.
540			\|a Nutzungsrecht: © 2017 The Authors. published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.
650		4	\|a Coweeta
650		4	\|a precipitation mapping
650		4	\|a precipitation measurement
650		4	\|a interpolation
650		4	\|a rain gauge
650		4	\|a uncertainty
650		4	\|a orography
650		4	\|a PRISM
650		4	\|a Rainfall
650		4	\|a Hydrologic data
650		4	\|a Maps
650		4	\|a Watersheds
650		4	\|a Regressions
650		4	\|a Mountains
650		4	\|a Variance analysis
650		4	\|a Digital mapping
650		4	\|a Hydrology
650		4	\|a Wavelength
650		4	\|a Error analysis
650		4	\|a Mapping
650		4	\|a Ground truth
650		4	\|a Atmospheric precipitations
650		4	\|a High resolution
650		4	\|a Rain gauges
650		4	\|a Resolution
650		4	\|a Stations
650		4	\|a Identification methods
650		4	\|a Precipitation
650		4	\|a Interpolation
650		4	\|a Uncertainty
650		4	\|a Datasets
650		4	\|a Elevation
650		4	\|a Annual precipitation
650		4	\|a Slopes (topography)
650		4	\|a Rain
650		4	\|a Ground stations
650		4	\|a Optimization
650		4	\|a Precipitation (meteorology)
700	1		\|a Slater, Melissa E \|4 oth
700	1		\|a Roberti, Joshua A \|4 oth
700	1		\|a Laseter, Stephanie H \|4 oth
700	1		\|a Swift, Lloyd W \|4 oth
773	0	8	\|i Enthalten in \|t International journal of climatology \|d Chichester [u.a.] : Wiley, 1989 \|g 37(2017), S1, Seite 124-137 \|w (DE-627)130763128 \|w (DE-600)1000947-4 \|w (DE-576)023035773 \|x 0899-8418 \|7 nnns
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856	4	1	\|u http://dx.doi.org/10.1002/joc.4986 \|3 Volltext
856	4	2	\|u http://onlinelibrary.wiley.com/doi/10.1002/joc.4986/abstract
856	4	2	\|u https://search.proquest.com/docview/1929352969
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Indexfelder

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matchkey_str	article:08998418:2017----::iheouinrcpttompignmutiosaesegontuhoeautnucran
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publishDate	2017
allfields	10.1002/joc.4986 doi PQ20171228 (DE-627)OLC1996088831 (DE-599)GBVOLC1996088831 (PRQ)p1196-537e9d23e032cc12aff758577902f613f82653056e531caa4c0aa2632bc2dbb3 (KEY)0104704320170000037000000124highresolutionprecipitationmappinginamountainouswa DE-627 ger DE-627 rakwb eng 550 DE-600 RA 1000 AVZ rvk Daly, Christopher verfasserin aut High‐resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A 69‐station, densely spaced rain gauge network was maintained over the period 1951–1958 in the Coweeta Hydrologic Laboratory, located in the southern Appalachians in western North Carolina, USA . This unique dataset was used to develop the first digital seasonal and annual precipitation maps for the Coweeta basin, using elevation regression functions and residual interpolation. It was found that a 10‐m elevation grid filtered to an approximately 7‐km effective wavelength explained the most variance in precipitation ( R 2 = 0.82–0.95). A ‘dump zone’ of locally high precipitation a short distance downwind from the mountain crest marking the southern border of the basin was the main feature that was not explained well by the precipitation–elevation relationship. These data and maps provided a rare ‘ground‐truth’ for estimating uncertainty in the national‐scale Parameter‐elevation Relationships on Independent Slopes Model ( PRISM ) precipitation grids for this location and time period. Differences between PRISM and ground‐truth were compared with uncertainty estimates produced by the PRISM model and cross‐validation errors. Potential sources of uncertainty in the national PRISM grids were evaluated, including the effects of coarse grid resolution, limited station data, and imprecise station locations. The PRISM national grids matched closely (within 5%) with the Coweeta dataset. The PRISM regression prediction interval, which includes the influence of stations in an area of tens of kilometres around a given location, overestimated the local error at Coweeta (12–20%). Offsetting biases and generally low error rates made it difficult to isolate major sources of uncertainty in the PRISM grids, but station density and selection, and mislocation of stations were identified as likely sources of error. The methods used in this study can be repeated in other areas where high‐density data exist to gain a more comprehensive picture of the uncertainties in national‐level datasets, and can be used in network optimization exercises. Nutzungsrecht: © 2017 The Authors. published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. Coweeta precipitation mapping precipitation measurement interpolation rain gauge uncertainty orography PRISM Rainfall Hydrologic data Maps Watersheds Regressions Mountains Variance analysis Digital mapping Hydrology Wavelength Error analysis Mapping Ground truth Atmospheric precipitations High resolution Rain gauges Resolution Stations Identification methods Precipitation Interpolation Uncertainty Datasets Elevation Annual precipitation Slopes (topography) Rain Ground stations Optimization Precipitation (meteorology) Slater, Melissa E oth Roberti, Joshua A oth Laseter, Stephanie H oth Swift, Lloyd W oth Enthalten in International journal of climatology Chichester [u.a.] : Wiley, 1989 37(2017), S1, Seite 124-137 (DE-627)130763128 (DE-600)1000947-4 (DE-576)023035773 0899-8418 nnns volume:37 year:2017 number:S1 pages:124-137 http://dx.doi.org/10.1002/joc.4986 Volltext http://onlinelibrary.wiley.com/doi/10.1002/joc.4986/abstract https://search.proquest.com/docview/1929352969 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_4311 RA 1000 AR 37 2017 S1 124-137
spelling	10.1002/joc.4986 doi PQ20171228 (DE-627)OLC1996088831 (DE-599)GBVOLC1996088831 (PRQ)p1196-537e9d23e032cc12aff758577902f613f82653056e531caa4c0aa2632bc2dbb3 (KEY)0104704320170000037000000124highresolutionprecipitationmappinginamountainouswa DE-627 ger DE-627 rakwb eng 550 DE-600 RA 1000 AVZ rvk Daly, Christopher verfasserin aut High‐resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A 69‐station, densely spaced rain gauge network was maintained over the period 1951–1958 in the Coweeta Hydrologic Laboratory, located in the southern Appalachians in western North Carolina, USA . This unique dataset was used to develop the first digital seasonal and annual precipitation maps for the Coweeta basin, using elevation regression functions and residual interpolation. It was found that a 10‐m elevation grid filtered to an approximately 7‐km effective wavelength explained the most variance in precipitation ( R 2 = 0.82–0.95). A ‘dump zone’ of locally high precipitation a short distance downwind from the mountain crest marking the southern border of the basin was the main feature that was not explained well by the precipitation–elevation relationship. These data and maps provided a rare ‘ground‐truth’ for estimating uncertainty in the national‐scale Parameter‐elevation Relationships on Independent Slopes Model ( PRISM ) precipitation grids for this location and time period. Differences between PRISM and ground‐truth were compared with uncertainty estimates produced by the PRISM model and cross‐validation errors. Potential sources of uncertainty in the national PRISM grids were evaluated, including the effects of coarse grid resolution, limited station data, and imprecise station locations. The PRISM national grids matched closely (within 5%) with the Coweeta dataset. The PRISM regression prediction interval, which includes the influence of stations in an area of tens of kilometres around a given location, overestimated the local error at Coweeta (12–20%). Offsetting biases and generally low error rates made it difficult to isolate major sources of uncertainty in the PRISM grids, but station density and selection, and mislocation of stations were identified as likely sources of error. The methods used in this study can be repeated in other areas where high‐density data exist to gain a more comprehensive picture of the uncertainties in national‐level datasets, and can be used in network optimization exercises. Nutzungsrecht: © 2017 The Authors. published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. Coweeta precipitation mapping precipitation measurement interpolation rain gauge uncertainty orography PRISM Rainfall Hydrologic data Maps Watersheds Regressions Mountains Variance analysis Digital mapping Hydrology Wavelength Error analysis Mapping Ground truth Atmospheric precipitations High resolution Rain gauges Resolution Stations Identification methods Precipitation Interpolation Uncertainty Datasets Elevation Annual precipitation Slopes (topography) Rain Ground stations Optimization Precipitation (meteorology) Slater, Melissa E oth Roberti, Joshua A oth Laseter, Stephanie H oth Swift, Lloyd W oth Enthalten in International journal of climatology Chichester [u.a.] : Wiley, 1989 37(2017), S1, Seite 124-137 (DE-627)130763128 (DE-600)1000947-4 (DE-576)023035773 0899-8418 nnns volume:37 year:2017 number:S1 pages:124-137 http://dx.doi.org/10.1002/joc.4986 Volltext http://onlinelibrary.wiley.com/doi/10.1002/joc.4986/abstract https://search.proquest.com/docview/1929352969 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_4311 RA 1000 AR 37 2017 S1 124-137
allfields_unstemmed	10.1002/joc.4986 doi PQ20171228 (DE-627)OLC1996088831 (DE-599)GBVOLC1996088831 (PRQ)p1196-537e9d23e032cc12aff758577902f613f82653056e531caa4c0aa2632bc2dbb3 (KEY)0104704320170000037000000124highresolutionprecipitationmappinginamountainouswa DE-627 ger DE-627 rakwb eng 550 DE-600 RA 1000 AVZ rvk Daly, Christopher verfasserin aut High‐resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A 69‐station, densely spaced rain gauge network was maintained over the period 1951–1958 in the Coweeta Hydrologic Laboratory, located in the southern Appalachians in western North Carolina, USA . This unique dataset was used to develop the first digital seasonal and annual precipitation maps for the Coweeta basin, using elevation regression functions and residual interpolation. It was found that a 10‐m elevation grid filtered to an approximately 7‐km effective wavelength explained the most variance in precipitation ( R 2 = 0.82–0.95). A ‘dump zone’ of locally high precipitation a short distance downwind from the mountain crest marking the southern border of the basin was the main feature that was not explained well by the precipitation–elevation relationship. These data and maps provided a rare ‘ground‐truth’ for estimating uncertainty in the national‐scale Parameter‐elevation Relationships on Independent Slopes Model ( PRISM ) precipitation grids for this location and time period. Differences between PRISM and ground‐truth were compared with uncertainty estimates produced by the PRISM model and cross‐validation errors. Potential sources of uncertainty in the national PRISM grids were evaluated, including the effects of coarse grid resolution, limited station data, and imprecise station locations. The PRISM national grids matched closely (within 5%) with the Coweeta dataset. The PRISM regression prediction interval, which includes the influence of stations in an area of tens of kilometres around a given location, overestimated the local error at Coweeta (12–20%). Offsetting biases and generally low error rates made it difficult to isolate major sources of uncertainty in the PRISM grids, but station density and selection, and mislocation of stations were identified as likely sources of error. The methods used in this study can be repeated in other areas where high‐density data exist to gain a more comprehensive picture of the uncertainties in national‐level datasets, and can be used in network optimization exercises. Nutzungsrecht: © 2017 The Authors. published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. Coweeta precipitation mapping precipitation measurement interpolation rain gauge uncertainty orography PRISM Rainfall Hydrologic data Maps Watersheds Regressions Mountains Variance analysis Digital mapping Hydrology Wavelength Error analysis Mapping Ground truth Atmospheric precipitations High resolution Rain gauges Resolution Stations Identification methods Precipitation Interpolation Uncertainty Datasets Elevation Annual precipitation Slopes (topography) Rain Ground stations Optimization Precipitation (meteorology) Slater, Melissa E oth Roberti, Joshua A oth Laseter, Stephanie H oth Swift, Lloyd W oth Enthalten in International journal of climatology Chichester [u.a.] : Wiley, 1989 37(2017), S1, Seite 124-137 (DE-627)130763128 (DE-600)1000947-4 (DE-576)023035773 0899-8418 nnns volume:37 year:2017 number:S1 pages:124-137 http://dx.doi.org/10.1002/joc.4986 Volltext http://onlinelibrary.wiley.com/doi/10.1002/joc.4986/abstract https://search.proquest.com/docview/1929352969 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_4311 RA 1000 AR 37 2017 S1 124-137
allfieldsGer	10.1002/joc.4986 doi PQ20171228 (DE-627)OLC1996088831 (DE-599)GBVOLC1996088831 (PRQ)p1196-537e9d23e032cc12aff758577902f613f82653056e531caa4c0aa2632bc2dbb3 (KEY)0104704320170000037000000124highresolutionprecipitationmappinginamountainouswa DE-627 ger DE-627 rakwb eng 550 DE-600 RA 1000 AVZ rvk Daly, Christopher verfasserin aut High‐resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A 69‐station, densely spaced rain gauge network was maintained over the period 1951–1958 in the Coweeta Hydrologic Laboratory, located in the southern Appalachians in western North Carolina, USA . This unique dataset was used to develop the first digital seasonal and annual precipitation maps for the Coweeta basin, using elevation regression functions and residual interpolation. It was found that a 10‐m elevation grid filtered to an approximately 7‐km effective wavelength explained the most variance in precipitation ( R 2 = 0.82–0.95). A ‘dump zone’ of locally high precipitation a short distance downwind from the mountain crest marking the southern border of the basin was the main feature that was not explained well by the precipitation–elevation relationship. These data and maps provided a rare ‘ground‐truth’ for estimating uncertainty in the national‐scale Parameter‐elevation Relationships on Independent Slopes Model ( PRISM ) precipitation grids for this location and time period. Differences between PRISM and ground‐truth were compared with uncertainty estimates produced by the PRISM model and cross‐validation errors. Potential sources of uncertainty in the national PRISM grids were evaluated, including the effects of coarse grid resolution, limited station data, and imprecise station locations. The PRISM national grids matched closely (within 5%) with the Coweeta dataset. The PRISM regression prediction interval, which includes the influence of stations in an area of tens of kilometres around a given location, overestimated the local error at Coweeta (12–20%). Offsetting biases and generally low error rates made it difficult to isolate major sources of uncertainty in the PRISM grids, but station density and selection, and mislocation of stations were identified as likely sources of error. The methods used in this study can be repeated in other areas where high‐density data exist to gain a more comprehensive picture of the uncertainties in national‐level datasets, and can be used in network optimization exercises. Nutzungsrecht: © 2017 The Authors. published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. Coweeta precipitation mapping precipitation measurement interpolation rain gauge uncertainty orography PRISM Rainfall Hydrologic data Maps Watersheds Regressions Mountains Variance analysis Digital mapping Hydrology Wavelength Error analysis Mapping Ground truth Atmospheric precipitations High resolution Rain gauges Resolution Stations Identification methods Precipitation Interpolation Uncertainty Datasets Elevation Annual precipitation Slopes (topography) Rain Ground stations Optimization Precipitation (meteorology) Slater, Melissa E oth Roberti, Joshua A oth Laseter, Stephanie H oth Swift, Lloyd W oth Enthalten in International journal of climatology Chichester [u.a.] : Wiley, 1989 37(2017), S1, Seite 124-137 (DE-627)130763128 (DE-600)1000947-4 (DE-576)023035773 0899-8418 nnns volume:37 year:2017 number:S1 pages:124-137 http://dx.doi.org/10.1002/joc.4986 Volltext http://onlinelibrary.wiley.com/doi/10.1002/joc.4986/abstract https://search.proquest.com/docview/1929352969 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_4311 RA 1000 AR 37 2017 S1 124-137
allfieldsSound	10.1002/joc.4986 doi PQ20171228 (DE-627)OLC1996088831 (DE-599)GBVOLC1996088831 (PRQ)p1196-537e9d23e032cc12aff758577902f613f82653056e531caa4c0aa2632bc2dbb3 (KEY)0104704320170000037000000124highresolutionprecipitationmappinginamountainouswa DE-627 ger DE-627 rakwb eng 550 DE-600 RA 1000 AVZ rvk Daly, Christopher verfasserin aut High‐resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A 69‐station, densely spaced rain gauge network was maintained over the period 1951–1958 in the Coweeta Hydrologic Laboratory, located in the southern Appalachians in western North Carolina, USA . This unique dataset was used to develop the first digital seasonal and annual precipitation maps for the Coweeta basin, using elevation regression functions and residual interpolation. It was found that a 10‐m elevation grid filtered to an approximately 7‐km effective wavelength explained the most variance in precipitation ( R 2 = 0.82–0.95). A ‘dump zone’ of locally high precipitation a short distance downwind from the mountain crest marking the southern border of the basin was the main feature that was not explained well by the precipitation–elevation relationship. These data and maps provided a rare ‘ground‐truth’ for estimating uncertainty in the national‐scale Parameter‐elevation Relationships on Independent Slopes Model ( PRISM ) precipitation grids for this location and time period. Differences between PRISM and ground‐truth were compared with uncertainty estimates produced by the PRISM model and cross‐validation errors. Potential sources of uncertainty in the national PRISM grids were evaluated, including the effects of coarse grid resolution, limited station data, and imprecise station locations. The PRISM national grids matched closely (within 5%) with the Coweeta dataset. The PRISM regression prediction interval, which includes the influence of stations in an area of tens of kilometres around a given location, overestimated the local error at Coweeta (12–20%). Offsetting biases and generally low error rates made it difficult to isolate major sources of uncertainty in the PRISM grids, but station density and selection, and mislocation of stations were identified as likely sources of error. The methods used in this study can be repeated in other areas where high‐density data exist to gain a more comprehensive picture of the uncertainties in national‐level datasets, and can be used in network optimization exercises. Nutzungsrecht: © 2017 The Authors. published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. Coweeta precipitation mapping precipitation measurement interpolation rain gauge uncertainty orography PRISM Rainfall Hydrologic data Maps Watersheds Regressions Mountains Variance analysis Digital mapping Hydrology Wavelength Error analysis Mapping Ground truth Atmospheric precipitations High resolution Rain gauges Resolution Stations Identification methods Precipitation Interpolation Uncertainty Datasets Elevation Annual precipitation Slopes (topography) Rain Ground stations Optimization Precipitation (meteorology) Slater, Melissa E oth Roberti, Joshua A oth Laseter, Stephanie H oth Swift, Lloyd W oth Enthalten in International journal of climatology Chichester [u.a.] : Wiley, 1989 37(2017), S1, Seite 124-137 (DE-627)130763128 (DE-600)1000947-4 (DE-576)023035773 0899-8418 nnns volume:37 year:2017 number:S1 pages:124-137 http://dx.doi.org/10.1002/joc.4986 Volltext http://onlinelibrary.wiley.com/doi/10.1002/joc.4986/abstract https://search.proquest.com/docview/1929352969 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_22 GBV_ILN_4311 RA 1000 AR 37 2017 S1 124-137
language	English
source	Enthalten in International journal of climatology 37(2017), S1, Seite 124-137 volume:37 year:2017 number:S1 pages:124-137
sourceStr	Enthalten in International journal of climatology 37(2017), S1, Seite 124-137 volume:37 year:2017 number:S1 pages:124-137
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topic_facet	Coweeta precipitation mapping precipitation measurement interpolation rain gauge uncertainty orography PRISM Rainfall Hydrologic data Maps Watersheds Regressions Mountains Variance analysis Digital mapping Hydrology Wavelength Error analysis Mapping Ground truth Atmospheric precipitations High resolution Rain gauges Resolution Stations Identification methods Precipitation Interpolation Uncertainty Datasets Elevation Annual precipitation Slopes (topography) Rain Ground stations Optimization Precipitation (meteorology)
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container_title	International journal of climatology
authorswithroles_txt_mv	Daly, Christopher @@aut@@ Slater, Melissa E @@oth@@ Roberti, Joshua A @@oth@@ Laseter, Stephanie H @@oth@@ Swift, Lloyd W @@oth@@
publishDateDaySort_date	2017-01-01T00:00:00Z
hierarchy_top_id	130763128
dewey-sort	3550
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author	Daly, Christopher
spellingShingle	Daly, Christopher ddc 550 rvk RA 1000 misc Coweeta misc precipitation mapping misc precipitation measurement misc interpolation misc rain gauge misc uncertainty misc orography misc PRISM misc Rainfall misc Hydrologic data misc Maps misc Watersheds misc Regressions misc Mountains misc Variance analysis misc Digital mapping misc Hydrology misc Wavelength misc Error analysis misc Mapping misc Ground truth misc Atmospheric precipitations misc High resolution misc Rain gauges misc Resolution misc Stations misc Identification methods misc Precipitation misc Interpolation misc Uncertainty misc Datasets misc Elevation misc Annual precipitation misc Slopes (topography) misc Rain misc Ground stations misc Optimization misc Precipitation (meteorology) High‐resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset
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topic_title	550 DE-600 RA 1000 AVZ rvk High‐resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset Coweeta precipitation mapping precipitation measurement interpolation rain gauge uncertainty orography PRISM Rainfall Hydrologic data Maps Watersheds Regressions Mountains Variance analysis Digital mapping Hydrology Wavelength Error analysis Mapping Ground truth Atmospheric precipitations High resolution Rain gauges Resolution Stations Identification methods Precipitation Interpolation Uncertainty Datasets Elevation Annual precipitation Slopes (topography) Rain Ground stations Optimization Precipitation (meteorology)
topic	ddc 550 rvk RA 1000 misc Coweeta misc precipitation mapping misc precipitation measurement misc interpolation misc rain gauge misc uncertainty misc orography misc PRISM misc Rainfall misc Hydrologic data misc Maps misc Watersheds misc Regressions misc Mountains misc Variance analysis misc Digital mapping misc Hydrology misc Wavelength misc Error analysis misc Mapping misc Ground truth misc Atmospheric precipitations misc High resolution misc Rain gauges misc Resolution misc Stations misc Identification methods misc Precipitation misc Interpolation misc Uncertainty misc Datasets misc Elevation misc Annual precipitation misc Slopes (topography) misc Rain misc Ground stations misc Optimization misc Precipitation (meteorology)
topic_unstemmed	ddc 550 rvk RA 1000 misc Coweeta misc precipitation mapping misc precipitation measurement misc interpolation misc rain gauge misc uncertainty misc orography misc PRISM misc Rainfall misc Hydrologic data misc Maps misc Watersheds misc Regressions misc Mountains misc Variance analysis misc Digital mapping misc Hydrology misc Wavelength misc Error analysis misc Mapping misc Ground truth misc Atmospheric precipitations misc High resolution misc Rain gauges misc Resolution misc Stations misc Identification methods misc Precipitation misc Interpolation misc Uncertainty misc Datasets misc Elevation misc Annual precipitation misc Slopes (topography) misc Rain misc Ground stations misc Optimization misc Precipitation (meteorology)
topic_browse	ddc 550 rvk RA 1000 misc Coweeta misc precipitation mapping misc precipitation measurement misc interpolation misc rain gauge misc uncertainty misc orography misc PRISM misc Rainfall misc Hydrologic data misc Maps misc Watersheds misc Regressions misc Mountains misc Variance analysis misc Digital mapping misc Hydrology misc Wavelength misc Error analysis misc Mapping misc Ground truth misc Atmospheric precipitations misc High resolution misc Rain gauges misc Resolution misc Stations misc Identification methods misc Precipitation misc Interpolation misc Uncertainty misc Datasets misc Elevation misc Annual precipitation misc Slopes (topography) misc Rain misc Ground stations misc Optimization misc Precipitation (meteorology)
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title	High‐resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset
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title_full	High‐resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset
author_sort	Daly, Christopher
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title_sort	high‐resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset
title_auth	High‐resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset
abstract	A 69‐station, densely spaced rain gauge network was maintained over the period 1951–1958 in the Coweeta Hydrologic Laboratory, located in the southern Appalachians in western North Carolina, USA . This unique dataset was used to develop the first digital seasonal and annual precipitation maps for the Coweeta basin, using elevation regression functions and residual interpolation. It was found that a 10‐m elevation grid filtered to an approximately 7‐km effective wavelength explained the most variance in precipitation ( R 2 = 0.82–0.95). A ‘dump zone’ of locally high precipitation a short distance downwind from the mountain crest marking the southern border of the basin was the main feature that was not explained well by the precipitation–elevation relationship. These data and maps provided a rare ‘ground‐truth’ for estimating uncertainty in the national‐scale Parameter‐elevation Relationships on Independent Slopes Model ( PRISM ) precipitation grids for this location and time period. Differences between PRISM and ground‐truth were compared with uncertainty estimates produced by the PRISM model and cross‐validation errors. Potential sources of uncertainty in the national PRISM grids were evaluated, including the effects of coarse grid resolution, limited station data, and imprecise station locations. The PRISM national grids matched closely (within 5%) with the Coweeta dataset. The PRISM regression prediction interval, which includes the influence of stations in an area of tens of kilometres around a given location, overestimated the local error at Coweeta (12–20%). Offsetting biases and generally low error rates made it difficult to isolate major sources of uncertainty in the PRISM grids, but station density and selection, and mislocation of stations were identified as likely sources of error. The methods used in this study can be repeated in other areas where high‐density data exist to gain a more comprehensive picture of the uncertainties in national‐level datasets, and can be used in network optimization exercises.
abstractGer	A 69‐station, densely spaced rain gauge network was maintained over the period 1951–1958 in the Coweeta Hydrologic Laboratory, located in the southern Appalachians in western North Carolina, USA . This unique dataset was used to develop the first digital seasonal and annual precipitation maps for the Coweeta basin, using elevation regression functions and residual interpolation. It was found that a 10‐m elevation grid filtered to an approximately 7‐km effective wavelength explained the most variance in precipitation ( R 2 = 0.82–0.95). A ‘dump zone’ of locally high precipitation a short distance downwind from the mountain crest marking the southern border of the basin was the main feature that was not explained well by the precipitation–elevation relationship. These data and maps provided a rare ‘ground‐truth’ for estimating uncertainty in the national‐scale Parameter‐elevation Relationships on Independent Slopes Model ( PRISM ) precipitation grids for this location and time period. Differences between PRISM and ground‐truth were compared with uncertainty estimates produced by the PRISM model and cross‐validation errors. Potential sources of uncertainty in the national PRISM grids were evaluated, including the effects of coarse grid resolution, limited station data, and imprecise station locations. The PRISM national grids matched closely (within 5%) with the Coweeta dataset. The PRISM regression prediction interval, which includes the influence of stations in an area of tens of kilometres around a given location, overestimated the local error at Coweeta (12–20%). Offsetting biases and generally low error rates made it difficult to isolate major sources of uncertainty in the PRISM grids, but station density and selection, and mislocation of stations were identified as likely sources of error. The methods used in this study can be repeated in other areas where high‐density data exist to gain a more comprehensive picture of the uncertainties in national‐level datasets, and can be used in network optimization exercises.
abstract_unstemmed	A 69‐station, densely spaced rain gauge network was maintained over the period 1951–1958 in the Coweeta Hydrologic Laboratory, located in the southern Appalachians in western North Carolina, USA . This unique dataset was used to develop the first digital seasonal and annual precipitation maps for the Coweeta basin, using elevation regression functions and residual interpolation. It was found that a 10‐m elevation grid filtered to an approximately 7‐km effective wavelength explained the most variance in precipitation ( R 2 = 0.82–0.95). A ‘dump zone’ of locally high precipitation a short distance downwind from the mountain crest marking the southern border of the basin was the main feature that was not explained well by the precipitation–elevation relationship. These data and maps provided a rare ‘ground‐truth’ for estimating uncertainty in the national‐scale Parameter‐elevation Relationships on Independent Slopes Model ( PRISM ) precipitation grids for this location and time period. Differences between PRISM and ground‐truth were compared with uncertainty estimates produced by the PRISM model and cross‐validation errors. Potential sources of uncertainty in the national PRISM grids were evaluated, including the effects of coarse grid resolution, limited station data, and imprecise station locations. The PRISM national grids matched closely (within 5%) with the Coweeta dataset. The PRISM regression prediction interval, which includes the influence of stations in an area of tens of kilometres around a given location, overestimated the local error at Coweeta (12–20%). Offsetting biases and generally low error rates made it difficult to isolate major sources of uncertainty in the PRISM grids, but station density and selection, and mislocation of stations were identified as likely sources of error. The methods used in this study can be repeated in other areas where high‐density data exist to gain a more comprehensive picture of the uncertainties in national‐level datasets, and can be used in network optimization exercises.
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title_short	High‐resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset
url	http://dx.doi.org/10.1002/joc.4986 http://onlinelibrary.wiley.com/doi/10.1002/joc.4986/abstract https://search.proquest.com/docview/1929352969
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up_date	2024-07-03T23:46:51.193Z
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Potential sources of uncertainty in the national PRISM grids were evaluated, including the effects of coarse grid resolution, limited station data, and imprecise station locations. The PRISM national grids matched closely (within 5%) with the Coweeta dataset. The PRISM regression prediction interval, which includes the influence of stations in an area of tens of kilometres around a given location, overestimated the local error at Coweeta (12–20%). Offsetting biases and generally low error rates made it difficult to isolate major sources of uncertainty in the PRISM grids, but station density and selection, and mislocation of stations were identified as likely sources of error. 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High‐resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset

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