A Robust Light Use Efficiency Model Parameterization Method Based on Ecosystem Properties

Abstract

In a model simulating the dynamics of a system, parameters can represent system properties and unresolved processes, therefore affecting the model accuracy and uncertainties. For a light use efficiency (LUE) model, which is a typical tool to estimate gross primary productivity (GPP), the plant-functional-type (PFT)-dependent parameterization method was widely used to extrapolate parameters to larger spatial scales. However, the method cannot capture the spatial variability within PFT and introduces misclassification errors. To overcome the shortage, here we proposed an ecosystem-property-based parameterization method (mNN-GPP) for an LUE model. This method refers to predicting model parameters using the multi-output artificial neural network based on collected variables including PFT, climate types, bioclimatic variables, vegetation features, atmospheric deposition and soil properties at 196 FLLUXNET eddy covariance flux sites. The neural network was optimized according to GPP errors and constraints on sensitivity functions of the LUE model. We compared mNN-GPP with eleven other typical parameter extrapolating methods, including PFT-, climate-specific parameterization, global and PFT-based parameter optimization, site-similarity-based, and regression methods. These twelve methods were assessed using Nash-Sutcliffe model efficiency (NSE), determination coefficient and normalized root mean squared error of the simulated GPP. The simulated results were also contrasted with those of site-specific calibration based on full-time-series GPP estimated from observational net ecosystem exchange. The N-fold cross-validated results showed that mNN-GPP had the best performance across various temporal and spatial scales (e.g., NSE=0.62 at the daily scale). No extrapolated parameters reached the same performance as the calibrated parameters (NSE=0.82), but the ranges of predicted parameters were constrained. Furthermore, the Shapley values, layer-wise relevance and partial dependence of the input features showed that bioclimatic variables, PFT, and vegetation features are the key variables determining parameters. We recommend using the parameterization method considering both ecosystem properties and prediction errors to other GPP models and across spatio-temporal scales.