Assessing the Impact of Parameter Uncertainty on Modeling Grass Biomass Using a Hybrid Carbon Allocation Strategy

Reyes, Julian; Tague, Christina; Evans, R. D.; Adam, J. C.

doi:10.1002/2017ms001022

Cited by 10 publications

(7 citation statements)

References 158 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Franklin et al () have reviewed several approaches for modeling NPP allocation in plants. However, allometric relationship and functional balance models are the only major types of dynamic model that have been widely incorporated into global DGVMs (Krinner et al, ; Moorcroft et al, ; Reyes et al, ; Sitch et al, ; Spahni et al, ; Wang et al, ; Xia et al, ).…”

Section: Introductionmentioning

confidence: 99%

Global Patterns in Net Primary Production Allocation Regulated by Environmental Conditions and Forest Stand Age: A Model‐Data Comparison

et al. 2019

View full text Add to dashboard Cite

The allocation of net primary production (NPP) to different plant structures, such as leaves, wood, and fine roots, plays an important role in the terrestrial carbon cycle. However, the biogeographical patterns of NPP allocation are not well understood. We constructed a global database of forest NPP to investigate the observed spatial patterns of forest NPP allocation, as influenced by environmental drivers and forest stand age. We then examined whether dynamic global vegetation models (DGVMs) could capture these allocation patterns. The NPP allocation response to variations in temperature or precipitation was often opposite in leaves and fine roots, a finding consistent with the functional balance theory for allocation. The observed allocation to fine roots decreased with increasing temperature and precipitation. The observed allocation to wood and leaves decreased with forest stand age. The simulated allocation with five DGVMs was compared with the observations. The five models captured the spatial gradient of lower allocation to fine roots with increasing temperature and precipitation but did not capture coincident gradients in allocation to wood and leaves. None of the five models adequately represented the changes in allocation with forest stand age. Specifically, the models did not reproduce the decrease in allocation to wood and leaves and the increase in allocation to fine roots with increasing forest stand age. An accurate simulation of NPP allocation requires more realistic representation of multiple processes that are closely related to allocation. The NPP allocation database can be used to develop DGVMs.

show abstract

Section: Introductionmentioning

confidence: 99%

Global Patterns in Net Primary Production Allocation Regulated by Environmental Conditions and Forest Stand Age: A Model‐Data Comparison

et al. 2019

View full text Add to dashboard Cite

show abstract

“…We aggregate parameters that are related by constraints, resulting in 237 unique EEs for each sensitivity metric. Previous studies that implemented sensitivity analyses of RHESSys generally adjusted a subset of the multipliers by limiting the analysis to process-specific parameters that are known or expected to affect outputs of interest (e.g., streamflow in Kim et al (2007), nitrogen export in Lin et al (2015) and Chen et al (2020), carbon allocation in Garcia et al (2016) and Reyes et al (2017), and evapotranspiration and streamflow in Shields and Tague (2012)). Most of these studies used local one-at-a-time sensitivity analysis near a best estimate of parameter values from calibration or prior information, with some exceptions that employed global sensitivity analyses (Lin et al, 2015;Reyes et al, 2017).…”

Section: Hydrologic Model Description: Rhessysmentioning

confidence: 99%

Guidance on evaluating parametric model uncertainty at decision-relevant scales

Smith

Lin

Quinn

et al. 2021

Preprint

View full text Add to dashboard Cite

Abstract. Spatially distributed hydrologic models are commonly employed to optimize the locations of engineering control measures across a watershed. Yet, parameter screening exercises that aim to reduce the dimensionality of the calibration search space are typically completed only for gauged locations, like the watershed outlet, and use screening metrics that are relevant to calibration instead of explicitly describing decision objectives. Identifying parameters that control physical processes in ungauged locations that affect decision objectives should lead to a better understanding of control measure effectiveness. This paper provides guidance on evaluating model parameter uncertainty at the spatial scales and flow magnitudes of interest for such decision-making problems. We use global sensitivity analysis to screen parameters for model calibration, and to subsequently evaluate the appropriateness of using parameter multipliers to further reduce dimensionality. We evaluate six sensitivity metrics that align with four decision objectives; two metrics consider model residual error that would be considered in spatial optimizations of engineering designs. We compare the resulting parameter selection for the basin outlet and each hillslope. We also compare basin outlet results to those obtained by four calibration-relevant metrics. These methods were applied to a RHESSys ecohydrological model of an exurban forested watershed near Baltimore, MD, USA. Results show that 1) the set of parameters selected by calibration-relevant metrics does not include parameters that control decision-relevant high and low streamflows, 2) evaluating sensitivity metrics at only the basin outlet does not capture many parameters that control streamflows in hillslopes, and 3) for some parameter multipliers, calibration of just one of the parameters being adjusted may be the preferred approach for reducing dimensionality. Thus, we recommend that parameter screening exercises use decision-relevant metrics that are evaluated at the spatial scales appropriate to decision making. While including more parameters in calibration will exacerbate equifinality, the resulting parametric uncertainty should be important to consider in discovering control measures that are robust to it.

show abstract

“…The net primary productivity (NPP) C, which remains after accounting for C losses via autotrophic respiration, is allocated to root, stem and leaf tissues. In DALEC-Grass, the above-to below-ground C allocation balance is dynamic and is calculated on a daily basis using the architecture-dependent strategy presented in Reyes et al (2017). According to this approach, the C that is transferred to the fine root C pool is linked to above-ground biomass and increases after the plant has grown a sufficient quantity of leaves.…”

Section: Dalec-grassmentioning

confidence: 99%