Ecological models are used to quantify processes over large regions. When the model is nonlinear and input variables are spatially averaged, the regional mean will be in error. A formula for estimating the upscaling error can be derived from Taylor expansion of the model (Bresler and Dagan 1988). We test this for simple models under three different input distributions (Gaussian, exponential, lognormal). In several cases the formula is exact, in others it provides a reasonable approximation. We then study models for emissions of methane, ammonia, and nitrous oxide across the UK. We scale from 1 × 1 km to 32 × 32 km. The UK-average upscaling errors are −12%, −48% and −3%, well estimated using the formula. The formula is a useful tool for...
Global, spatially and temporally explicit estimates of carbon and water fluxes derived from empirica...
Inverse modelling of carbon sources and sinks requires an accurate quality estimate of the modelling...
In recent years, knowledge extraction from data has become increasingly popular, with many numerical...
The role of greenhouse gases (GHGs) in global climate change is now well recognized and there is a c...
Complex environmental model outputs used to inform decisions often have systematic errors and are of...
In the recently finished EU-funded project Carbo-Extreme, we developed a simple probabilistic method...
Many different process-based models of vegetations are in use today. The majority of these models ar...
One of the important sources of greenhouse gases is the emission of methane from rice fields. Methan...
One of the important sources of greenhouse gases is the emission of methane from rice fields. Methan...
The role of greenhouse gases (GHGs) in global climate change is now well recognized and there is a c...
Inverse modelling of carbon sources and sinks requires an accurate estimate of the quality of the ob...
Quantifying the current carbon cycle of terrestrial ecosystems requires that we translate spatially ...
In the recently finished EU-funded project Carbo-Extreme, we developed a simple probabilistic method...
Quantifying the current carbon cycle of terrestrial ecosystems requires that we translate spatially ...
A comparison was made between upscaled model results of nitrogen (N) fluxes to air and water from 45...
Global, spatially and temporally explicit estimates of carbon and water fluxes derived from empirica...
Inverse modelling of carbon sources and sinks requires an accurate quality estimate of the modelling...
In recent years, knowledge extraction from data has become increasingly popular, with many numerical...
The role of greenhouse gases (GHGs) in global climate change is now well recognized and there is a c...
Complex environmental model outputs used to inform decisions often have systematic errors and are of...
In the recently finished EU-funded project Carbo-Extreme, we developed a simple probabilistic method...
Many different process-based models of vegetations are in use today. The majority of these models ar...
One of the important sources of greenhouse gases is the emission of methane from rice fields. Methan...
One of the important sources of greenhouse gases is the emission of methane from rice fields. Methan...
The role of greenhouse gases (GHGs) in global climate change is now well recognized and there is a c...
Inverse modelling of carbon sources and sinks requires an accurate estimate of the quality of the ob...
Quantifying the current carbon cycle of terrestrial ecosystems requires that we translate spatially ...
In the recently finished EU-funded project Carbo-Extreme, we developed a simple probabilistic method...
Quantifying the current carbon cycle of terrestrial ecosystems requires that we translate spatially ...
A comparison was made between upscaled model results of nitrogen (N) fluxes to air and water from 45...
Global, spatially and temporally explicit estimates of carbon and water fluxes derived from empirica...
Inverse modelling of carbon sources and sinks requires an accurate quality estimate of the modelling...
In recent years, knowledge extraction from data has become increasingly popular, with many numerical...