Effects of sample and grid size on the accuracy and stability of regression-based snow interpolation methods
This work analyses the responses of four regression-based interpolation methods for predicting snowpack distribution to changes in the number of data points (sample size) and resolution of the employed digital elevation model (DEM). For this purpose, we used data obtained from intensive and random s...
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Published in | Hydrological processes Vol. 24; no. 14; pp. 1914 - 1928 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
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Chichester, UK
John Wiley & Sons, Ltd
01.07.2010
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Abstract | This work analyses the responses of four regression-based interpolation methods for predicting snowpack distribution to changes in the number of data points (sample size) and resolution of the employed digital elevation model (DEM). For this purpose, we used data obtained from intensive and random sampling of snow depth (991 measurements) in a small catchment (6 km²) in the Pyrenees, Spain. Linear regression, classification trees, generalized additive models (GAMs), and a recent method based on a correction made by applying tree classification to GAM residuals were used to calculate snow-depth distribution based on terrain characteristics under different combinations of sample size and DEM spatial resolution (grid size).The application of a tree classification to GAM residuals yielded the highest accuracy scores and the most stable models. The other tested methods yielded scores with slightly lower accuracy and varying levels of robustness under different conditions of grid and sample size. The accuracy of the model predictions declined with decreasing resolution of DEMs and sample size; however, the sensitivities of the models to the number of data points showed threshold values, which has implications (when planning fieldwork) for optimizing the relation between the effort expended in gathering data and the quality of the results. Copyright © 2009 John Wiley & Sons, Ltd. |
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AbstractList | This work analyses the responses of four regression‐based interpolation methods for predicting snowpack distribution to changes in the number of data points (sample size) and resolution of the employed digital elevation model (DEM). For this purpose, we used data obtained from intensive and random sampling of snow depth (991 measurements) in a small catchment (6 km2) in the Pyrenees, Spain. Linear regression, classification trees, generalized additive models (GAMs), and a recent method based on a correction made by applying tree classification to GAM residuals were used to calculate snow‐depth distribution based on terrain characteristics under different combinations of sample size and DEM spatial resolution (grid size).
The application of a tree classification to GAM residuals yielded the highest accuracy scores and the most stable models. The other tested methods yielded scores with slightly lower accuracy and varying levels of robustness under different conditions of grid and sample size. The accuracy of the model predictions declined with decreasing resolution of DEMs and sample size; however, the sensitivities of the models to the number of data points showed threshold values, which has implications (when planning fieldwork) for optimizing the relation between the effort expended in gathering data and the quality of the results. Copyright © 2009 John Wiley & Sons, Ltd. Abstract This work analyses the responses of four regression‐based interpolation methods for predicting snowpack distribution to changes in the number of data points (sample size) and resolution of the employed digital elevation model (DEM). For this purpose, we used data obtained from intensive and random sampling of snow depth (991 measurements) in a small catchment (6 km 2 ) in the Pyrenees, Spain. Linear regression, classification trees, generalized additive models (GAMs), and a recent method based on a correction made by applying tree classification to GAM residuals were used to calculate snow‐depth distribution based on terrain characteristics under different combinations of sample size and DEM spatial resolution (grid size). The application of a tree classification to GAM residuals yielded the highest accuracy scores and the most stable models. The other tested methods yielded scores with slightly lower accuracy and varying levels of robustness under different conditions of grid and sample size. The accuracy of the model predictions declined with decreasing resolution of DEMs and sample size; however, the sensitivities of the models to the number of data points showed threshold values, which has implications (when planning fieldwork) for optimizing the relation between the effort expended in gathering data and the quality of the results. Copyright © 2009 John Wiley & Sons, Ltd. This work analyses the responses of four regression-based interpolation methods for predicting snowpack distribution to changes in the number of data points (sample size) and resolution of the employed digital elevation model (DEM). For this purpose, we used data obtained from intensive and random sampling of snow depth (991 measurements) in a small catchment (6 km²) in the Pyrenees, Spain. Linear regression, classification trees, generalized additive models (GAMs), and a recent method based on a correction made by applying tree classification to GAM residuals were used to calculate snow-depth distribution based on terrain characteristics under different combinations of sample size and DEM spatial resolution (grid size).The application of a tree classification to GAM residuals yielded the highest accuracy scores and the most stable models. The other tested methods yielded scores with slightly lower accuracy and varying levels of robustness under different conditions of grid and sample size. The accuracy of the model predictions declined with decreasing resolution of DEMs and sample size; however, the sensitivities of the models to the number of data points showed threshold values, which has implications (when planning fieldwork) for optimizing the relation between the effort expended in gathering data and the quality of the results. Copyright © 2009 John Wiley & Sons, Ltd. This work analyses the responses of four regression-based interpolation methods for predicting snowpack distribution to changes in the number of data points (sample size) and resolution of the employed digital elevation model (DEM). For this purpose, we used data obtained from intensive and random sampling of snow depth (991 measurements) in a small catchment (6 km super(2)) in the Pyrenees, Spain. Linear regression, classification trees, generalized additive models (GAMs), and a recent method based on a correction made by applying tree classification to GAM residuals were used to calculate snow-depth distribution based on terrain characteristics under different combinations of sample size and DEM spatial resolution (grid size). |
Author | Lehmann, A. Moreno, J. Ignacio López Latron, J. |
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Snippet | This work analyses the responses of four regression-based interpolation methods for predicting snowpack distribution to changes in the number of data points... This work analyses the responses of four regression‐based interpolation methods for predicting snowpack distribution to changes in the number of data points... Abstract This work analyses the responses of four regression‐based interpolation methods for predicting snowpack distribution to changes in the number of data... |
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SubjectTerms | Classification Data points DEM resolution Discrete element method Interpolation Mathematical models Regression analysis regression-based methods sample size Snow spatial interpolation Trees |
Title | Effects of sample and grid size on the accuracy and stability of regression-based snow interpolation methods |
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