Filters: Tags: Variability (X) > partyWithName: Conservation Biology Institute (X)
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Coefficient of variation (in percent) of modeled monthly discharge for the period 1993-2002 for major rivers. This dataset is based on a reconstruction of terrestrial discharge for the last century using a macro-scale hydrological model forced with observed global climate data.
For each pixel, the geodesic distance and the vertical distance to the closest weather station for precip and for temp were calculated. These three dimensions were combined into a single one for precip and one for temp based on the Euclidean distance. The less distance there is these 3 dimensions (x,y,z), the less uncertainty there is. Higher values mean more uncertainty.
This dataset is based on the time series data for the NCDC and the FAOCLIM2 combined datasets stations measuring at least 30 years or more. For each month the coefficient of variation for precipitation was calculated. For each month the standard deviation for temperature was calculated. These values were interpolated using spline for each month, a total of 24 layers. The 12 coefficient of variation interpolated layers derived from precip, were combined using the standard Euclidean distance. The 12 standard deviation interpolated layers derived from temp, were combined using the standard euclidean distance. Higher values indicate more variability (higher uncertainty).
This dataset is based on the time series data for the NCDC and the FAOCLIM2 combined datasets stations measuring at least 30 years or more. For each month the coefficient of variation for precipitation was calculated. For each month the standard deviation for temperature was calculated. These values were interpolated using spline for each month, a total of 24 layers. The 12 coefficient of variation interpolated layers derived from precip, were combined using the standard Euclidean distance. The 12 standard deviation interpolated layers derived from temp, were combined using the standard euclidean distance. Higher values indicate more variability (higher uncertainty).
For each pixel, the geodesic distance and the vertical distance to the closest weather station for precip and for temp were calculated. These three dimensions were combined into a single one for precip and one for temp based on the Euclidean distance. The less distance there is these 3 dimensions (x,y,z), the less uncertainty there is. Higher values mean more uncertainty.
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