Interpolation of monthly precipitation amounts in mountainous catchments with sparse precipitation networks

Most studies dealing with the interpolation of precipitation gauge data have focused in areas where the meteorological network is relatively dense, implying that it is still unknown what interpolation methods are more appropriate in the case of mountain catchments with scarce gauge data. This study...

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Autores principales:	Jacquin,Alexandra P, Soto-Sandoval,Jaime C
Lenguaje:	English
Publicado:	Instituto de Investigaciones Agropecuarias, INIA 2013
Materias:	Kriging with External Drift Optimal Interpolation Method Thiessen Polygons
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392013000400012
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spelling	oai:scielo:S0718-583920130004000122018-10-02Interpolation of monthly precipitation amounts in mountainous catchments with sparse precipitation networksJacquin,Alexandra PSoto-Sandoval,Jaime C Kriging with External Drift Optimal Interpolation Method Thiessen Polygons Most studies dealing with the interpolation of precipitation gauge data have focused in areas where the meteorological network is relatively dense, implying that it is still unknown what interpolation methods are more appropriate in the case of mountain catchments with scarce gauge data. This study evaluates the applicability of Kriging with External Drift (KED) and the Optimal Interpolation Method (OIM) for interpolation of monthly precipitation in these situations. Thiessen Polygons (TP) are used as benchmark. The study area corresponds to the upper subcatchment of Aconcagua River, Central Chile. Cross-validation experiments revealed that all these methods show similar performance in the lower zone of the study area, but OIM outperforms TP and KED at high elevations. Optimal Interpolation Method generally produces the smallest bias in the Andean zone of the study area, with mean errors whose absolute values are smaller than 9% of mean monthly precipitation. From April to September, the root mean squared errors of OIM are between 14% and 33% smaller than those of TP and KED in this zone. Although KED achieves a good agreement to mean monthly values at high elevations (mean errors smaller than 19% in absolute value), its performance is comparable to that of TP in terms of root mean squared errors. Long-term water balances did not provide evidence against the applicability of KED and OIM. Nevertheless, the results of the cross-validation experiments indicate that OIM is a better alternative than KED for the interpolation of monthly precipitation in the study area.info:eu-repo/semantics/openAccessInstituto de Investigaciones Agropecuarias, INIAChilean journal of agricultural research v.73 n.4 20132013-12-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392013000400012en10.4067/S0718-58392013000400012
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Kriging with External Drift Optimal Interpolation Method Thiessen Polygons
spellingShingle	Kriging with External Drift Optimal Interpolation Method Thiessen Polygons Jacquin,Alexandra P Soto-Sandoval,Jaime C Interpolation of monthly precipitation amounts in mountainous catchments with sparse precipitation networks
description	Most studies dealing with the interpolation of precipitation gauge data have focused in areas where the meteorological network is relatively dense, implying that it is still unknown what interpolation methods are more appropriate in the case of mountain catchments with scarce gauge data. This study evaluates the applicability of Kriging with External Drift (KED) and the Optimal Interpolation Method (OIM) for interpolation of monthly precipitation in these situations. Thiessen Polygons (TP) are used as benchmark. The study area corresponds to the upper subcatchment of Aconcagua River, Central Chile. Cross-validation experiments revealed that all these methods show similar performance in the lower zone of the study area, but OIM outperforms TP and KED at high elevations. Optimal Interpolation Method generally produces the smallest bias in the Andean zone of the study area, with mean errors whose absolute values are smaller than 9% of mean monthly precipitation. From April to September, the root mean squared errors of OIM are between 14% and 33% smaller than those of TP and KED in this zone. Although KED achieves a good agreement to mean monthly values at high elevations (mean errors smaller than 19% in absolute value), its performance is comparable to that of TP in terms of root mean squared errors. Long-term water balances did not provide evidence against the applicability of KED and OIM. Nevertheless, the results of the cross-validation experiments indicate that OIM is a better alternative than KED for the interpolation of monthly precipitation in the study area.
author	Jacquin,Alexandra P Soto-Sandoval,Jaime C
author_facet	Jacquin,Alexandra P Soto-Sandoval,Jaime C
author_sort	Jacquin,Alexandra P
title	Interpolation of monthly precipitation amounts in mountainous catchments with sparse precipitation networks
title_short	Interpolation of monthly precipitation amounts in mountainous catchments with sparse precipitation networks
title_full	Interpolation of monthly precipitation amounts in mountainous catchments with sparse precipitation networks
title_fullStr	Interpolation of monthly precipitation amounts in mountainous catchments with sparse precipitation networks
title_full_unstemmed	Interpolation of monthly precipitation amounts in mountainous catchments with sparse precipitation networks
title_sort	interpolation of monthly precipitation amounts in mountainous catchments with sparse precipitation networks
publisher	Instituto de Investigaciones Agropecuarias, INIA
publishDate	2013
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392013000400012
work_keys_str_mv	AT jacquinalexandrap interpolationofmonthlyprecipitationamountsinmountainouscatchmentswithsparseprecipitationnetworks AT sotosandovaljaimec interpolationofmonthlyprecipitationamountsinmountainouscatchmentswithsparseprecipitationnetworks
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Interpolation of monthly precipitation amounts in mountainous catchments with sparse precipitation networks

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