Global sensitivity analysis of DRAINMOD-FOREST, an integrated forest ecosystem model

Abstract

Global sensitivity analysis is a useful tool to understand process-based ecosystem models by identifying key parameters and processes controlling model predictions. This study reported a comprehensive global sensitivity analysis for DRAINMOD-FOREST, an integrated model for simulating water, carbon (C), and nitrogen (N) cycles and plant growth in lowland forests. The analysis was carried out for multiple long-term model predictions of hydrology, biogeochemistry, and plant growth. Results showed that long-term mean hydrological predictions were highly sensitive to several key plant physiological parameters. Long-term mean annual soil organic C content and mineralization rate were mainly controlled by temperature-related parameters for soil organic matter decomposition. Mean annual forest productivity and N uptake were found to be mainly dependent upon plant production-related parameters, including canopy quantum use efficiency and carbon use efficiency. Mean annual nitrate loss was highly sensitive to parameters controlling both hydrology and plant production, while mean annual dissolved organic nitrogen loss was controlled by parameters associated with its production and physical sorption. Parameters controlling forest production, C allocation, and specific leaf area highly affected long-term mean annual leaf area. Results of this study could help minimize the efforts needed for calibrating DRAINMOD-FOREST. Meanwhile, this study demonstrates the critical role of plants in regulating water, C, and N cycles in forest ecosystems and highlights the necessity of incorporating a dynamic plant growth model for comprehensively simulating hydrological and biogeochemical processes. Figure 4. Results of the two-step global sensitivity analysis for long-term annual mean soil organic matter (SOM) content. (A) The relative sensitivity index (RSI), (B) the first-order sensitivity index (S i ), and (C) the total order index (S TI ) of these identified top 20 parameters through screening procedure. Parameters associated with soil physical properties, plant, and soil carbon and nitrogen cycles are highlighted by grey-filled, blank, and dashed bars, respectively 4396 S. TIAN ET AL.Figure 11. Summary of the first-order sensitivity index (S i ) of E-FAST analysis for each of the nine model outputs. Parameters related to the DON module were excluded from this figure. Parameters associated with soil physical properties, plant, and soil carbon and nitrogen cycles are highlighted by solid, dashed, and dotted rectangles, respectively 4404 S. TIAN ET AL.

A Recommended Calibration and Validation Strategy for Hydrologic and Water Quality Models

“…Sensitivity analyses can be global, which attempts to assess all combinations of all parameter values, or local, which evaluates a specific set of parameters one at a time while the remaining parameters are fixed. Global and local methods may yield different results because of how they select and prioritize parameters (Arnold et al, 2012) and address interactions among parameters (Wang et al, 2005;Tian et al, 2014). It is essential to identify key parameters and define their precision for effective model calibration (Ma et al, 2000;DeJonge et al, 2012).…”

Impact of Nitrogen Fertilization on Forest Carbon Sequestration and Water Loss in a Chronosequence of Three Douglas-Fir Stands in the Pacific Northwest

“…A global sensitivity analysis for an integrated forest ecosystem model [35] also showed the intrinsic coupling between water and the C cycle through stomatal conductance, which affect both water and C fluxes between the biosphere and atmosphere. Nutrients may not only affect productivity and foliar biomass but are also associated with evapotranspiration (ET) in forests and other ecosystems [36].…”

Testing DRAINMOD-FOREST for predicting evapotranspiration in a mid-rotation pine plantation

“…The model has been successfully applied to simulate long-term hydrological and biogeochemical processes in artificially drained pine plantations (Tian et al, 2012a,b). A global sensitivity analysis demonstrated the critical role of ET simulation in affecting the predictions of other hydrological and water quality variables (Tian et al, 2014). However, like many other forest hydrological models, DRAINMOD-FOREST and its predecessors have not yet been explicitly tested for simulating ET dynamics because of the limited data available for previous applications.…”