Comparing an exponential respiration model to alternative models for soil respiration components in a Canadian wildfire chronosequence (FireResp v1.0)

Forest fires modify soil organic carbon and suppress soil respiration for many decades after the initial disturbance. The associated changes in soil autotrophic and heterotrophic respiration from the time of the forest fire, however, are less well characterized. The FireResp model predicts soil autotrophic and heterotrophic respiration parameterized with a novel dataset across a fire chronosequence in the Yukon and Northwest Territories of Canada. The dataset consisted of soil incubation experiments and field measurements of soil respiration and soil carbon stocks. The FireResp model contains submodels that consider a Q(10) (exponential) model of respiration compared to models of heterotrophic respiration using Michaelis-Menten kinetics parameterized with soil microbial carbon. For model evaluation we applied the Akaike information criterion and compared predicted patterns in components of soil respiration across the chronosequence. Parameters estimated with data from the 5 cm soil depth had better model-data comparisons than parameters estimated with data from the 10 cm soil depth. The model-data fit was improved by including parameters estimated from soil incubation experiments. Models that incorporated microbial carbon with Michaelis-Menten kinetics reproduced patterns in autotrophic and heterotrophic soil respiration components across the chronosequence. Autotrophic respiration was associated with aboveground tree biomass at more recently burned sites, but this association was less robust at older sites in the chronosequence. Our results provide support for more structured soil respiration models than standard Q(10) exponential models.Peer reviewe

Efficient hyper-parameter determination for regularised linear BRDF parameter retrieval

Linear kernel driven models of the surface Bidirectional Reflectance Distribution Function (BRDF) are valuable tools for exploiting Earth observation data acquired at different sun–sensor geometries. Here we present a method that efficiently determines linear BRDF model weights using Tikhonov smoothing where the smoothing parameter λ is determined via a Generalized Singular Value Decomposition with the root mean square error prescribed depending on the MODIS band. We applied this method to twenty-six different deciduous broadleaf sites across an entire year using the MODIS Terra and Aqua reflectance data products. Kernel weights and white sky albedo derived from this GSVD method were generally consistent with those provided by the MCD43 data products. The GSVD derived results had less sample variability compared to the MCD43 data products, attributable to the assumed smoothness between kernel weights in the Tikhonov smoothing method. The GSVD technique consistently outperforms MCD43 in the reconstruction of observed MODIS reflectance data, of which retrievals from this method will do a better job of estimating albedo and normalizing data to specified geometries

The role of terrestrial productivity and hydrology in regulating aquatic dissolved organic carbon concentrations in boreal catchments

Abstract The past decades have witnessed an increase in dissolved organic carbon (DOC) concentrations in the catchments of the Northern Hemisphere. Increasing terrestrial productivity and changing hydrology may be reasons for the increases in DOC concentration. The aim of this study is to investigate the impacts of increased terrestrial productivity and changed hydrology following climate change on DOC concentrations. We tested and quantified the effects of gross primary production (GPP), ecosystem respiration (RE) and discharge on DOC concentrations in boreal catchments over three years. As catchment characteristics can regulate the extent of rising DOC concentrations caused by the regional or global environmental changes, we selected four catchments with different sizes (small, medium and large) and landscapes (forest, mire and forest-mire mixed). We applied multiple models: Wavelet coherence analysis detected the delay-effects of terrestrial productivity and discharge on aquatic DOC variations of boreal catchments; thereafter, the distributed-lag linear models (DLMs) quantified the contributions of each factor on DOC variations. Our results showed that the combined impacts of terrestrial productivity and discharge explained 62% of aquatic DOC variations on average across all sites, whereas discharge, GPP and RE accounted for 26%, 22% and 3%, respectively. The impact of gross primary production (GPP) and discharge on DOC changes was directly related to catchment size: GPP dominated DOC fluctuations in small catchments (1 km2). The direction of the relation between GPP and discharge on DOC varied. Increasing RE always made a positive contribution to DOC concentration. This study reveals that climate change-induced terrestrial greening and shifting hydrology change the DOC export from terrestrial to aquatic ecosystems. The work improves our mechanistic understanding of surface water DOC regulation in boreal catchments and confirms the importance of DOC fluxes in regulating ecosystem C budgets.Peer reviewe

The role of terrestrial productivity and hydrology in regulating aquatic dissolved organic carbon concentrations in boreal catchments

The past decades have witnessed an increase in dissolved organic carbon (DOC) concentrations in the catchments of the Northern Hemisphere. Increasing terrestrial productivity and changing hydrology may be reasons for the increases in DOC concentration. The aim of this study is to investigate the impacts of increased terrestrial productivity and changed hydrology following climate change on DOC concentrations. We tested and quantified the effects of gross primary production (GPP), ecosystem respiration (RE) and discharge on DOC concentrations in boreal catchments over 3 years. As catchment characteristics can regulate the extent of rising DOC concentrations caused by the regional or global environmental changes, we selected four catchments with different sizes (small, medium and large) and landscapes (forest, mire and forest-mire mixed). We applied multiple models: Wavelet coherence analysis detected the delay-effects of terrestrial productivity and discharge on aquatic DOC variations of boreal catchments; thereafter, the distributed-lag linear models quantified the contributions of each factor on DOC variations. Our results showed that the combined impacts of terrestrial productivity and discharge explained 62% of aquatic DOC variations on average across all sites, whereas discharge, gross primary production (GPP) and RE accounted for 26%, 22% and 3%, respectively. The impact of GPP and discharge on DOC changes was directly related to catchment size: GPP dominated DOC fluctuations in small catchments (\u3c1 km2), whereas discharge controlled DOC variations in big catchments (\u3e1 km2). The direction of the relation between GPP and discharge on DOC varied. Increasing RE always made a positive contribution to DOC concentration. This study reveals that climate change-induced terrestrial greening and shifting hydrology change the DOC export from terrestrial to aquatic ecosystems. The work improves our mechanistic understanding of surface water DOC regulation in boreal catchments and confirms the importance of DOC fluxes in regulating ecosystem C budgets

The role of terrestrial productivity and hydrology in regulating aquatic dissolved organic carbon concentrations in boreal catchments

Abstract The past decades have witnessed an increase in dissolved organic carbon (DOC) concentrations in the catchments of the Northern Hemisphere. Increasing terrestrial productivity and changing hydrology may be reasons for the increases in DOC concentration. The aim of this study is to investigate the impacts of increased terrestrial productivity and changed hydrology following climate change on DOC concentrations. We tested and quantified the effects of gross primary production (GPP), ecosystem respiration (RE) and discharge on DOC concentrations in boreal catchments over three years. As catchment characteristics can regulate the extent of rising DOC concentrations caused by the regional or global environmental changes, we selected four catchments with different sizes (small, medium and large) and landscapes (forest, mire and forest-mire mixed). We applied multiple models: Wavelet coherence analysis detected the delay-effects of terrestrial productivity and discharge on aquatic DOC variations of boreal catchments; thereafter, the distributed-lag linear models (DLMs) quantified the contributions of each factor on DOC variations. Our results showed that the combined impacts of terrestrial productivity and discharge explained 62% of aquatic DOC variations on average across all sites, whereas discharge, GPP and RE accounted for 26%, 22% and 3%, respectively. The impact of gross primary production (GPP) and discharge on DOC changes was directly related to catchment size: GPP dominated DOC fluctuations in small catchments (1 km2). The direction of the relation between GPP and discharge on DOC varied. Increasing RE always made a positive contribution to DOC concentration. This study reveals that climate change-induced terrestrial greening and shifting hydrology change the DOC export from terrestrial to aquatic ecosystems. The work improves our mechanistic understanding of surface water DOC regulation in boreal catchments and confirms the importance of DOC fluxes in regulating ecosystem C budgets.Peer reviewe

Understanding the effect of disturbance from selective felling on the carbon dynamics of a managed woodland by combining observations with model predictions

The response of forests and terrestrial ecosystems to disturbance is an important process in the global carbon cycle in the context of a changing climate. blackThis study focuses on the effect of selective felling (thinning) at a managed forest site. Previous statistical analyses of eddy covariance data at the study site had found that disturbance from thinning resulted in no significant change to net ecosystem carbon uptake. In order to better understand the effect of thinning on carbon fluxes we use the mathematical technique of four-dimensional variational data assimilation. Data assimilation provides a compelling alternative to more common statistical analyses of flux data as it allows for the combination of many different sources of data, with the physical constraints of a dynamical model, to find an improved estimate blackof the state of a system. We develop blacknew observation operators to assimilate daytime and nighttime net ecosystem exchange observations with a daily time-step model, increasing blackobservations available by a factor of 4.25. Our results support previous analyses, with a predicted net ecosystem carbon uptake for the year 2015 of 426 ± 116g C m−2 for the unthinned forest and 420 ± 78g C m−2 for the thinned forest despite a model-predicted reduction in gross primary productivity of 337g C m−2. We show that this is likely due to reduced ecosystem respiration post-disturbance compensating for a reduction in gross primary productivity. This supports the theory of an upper limit of forest net carbon uptake due to the magnitude of ecosystem respiration scaling with gross primary productivity

Modeling Mayfly Nymph Length Distribution and Population Dynamics Across a Gradient of Stream Temperatures and Stream Types

We analyze a process-based temperature model for the length distribution and population over time of mayfly nymphs. Model parameters are estimated using a Markov Chain Monte Carlo parameter estimation method utilizing length distribution data at five different stream sites. Two different models (a standard exponential model and a modified Weibull model) of mayfly mortality are evaluated, where in both cases mayfly length growth is a function of stream temperature. Based on model-data comparisons to the modeled length distribution and the Bayesian Information Criterion, we found that approaches that length distribution data can reliably estimate 2–3 model parameters. Future model development could include additional factors include such as upstream environmental factors, abiotic conditions, inter- specific competition, predation, or stream salinity. Outputs of this model could be applied to predict mayfly emergence across a geographic domain or to forecast mayfly population responses to climate change

Investigating the role of prior and observation error correlations in improving a model forecast of forest carbon balance using Four Dimensional Variational data assimilation

Efforts to implement variational data assimilation routines with functional ecology models and land surface models have been limited, with sequential and Markov chain Monte Carlo data assimilation methods being prevalent. When data assimilation has been used with models of carbon balance, prior or “background” errors (in the initial state and parameter values) and observation errors have largely been treated as independent and uncorrelated. Correlations between background errors have long been known to be a key aspect of data assimilation in numerical weather prediction. More recently, it has been shown that accounting for correlated observation errors in the assimilation algorithm can considerably improve data assimilation results and forecasts. In this paper we implement a Four-Dimensional Variational data assimilation (4D-Var) scheme with a simple model of forest carbon balance, for joint parameter and state estimation and assimilate daily observations of Net Ecosystem CO2 Exchange (NEE) taken at the Alice Holt forest CO2 flux site in Hampshire, UK. We then investigate the effect of specifying correlations between parameter and state variables in background error statistics and the effect of specifying correlations in time between observation errors. The idea of including these correlations in time is new and has not been previously explored in carbon balance model data assimilation. In data assimilation, background and observation error statistics are often described by the background error covariance matrix and the observation error covariance matrix. We outline novel methods for creating correlated versions of these matrices, using a set of previously postulated dynamical constraints to include correlations in the background error statistics and a Gaussian correlation function to include time correlations in the observation error statistics. The methods used in this paper will allow the inclusion of time correlations between many different observation types in the assimilation algorithm, meaning that previously neglected information can be accounted for. In our experiments we assimilate a single year of NEE observations and then run a forecast for the next 14 years. We compare the results using our new correlated background and observation error covariance matrices and those using diagonal covariance matrices. We find that using the new correlated matrices reduces the root mean square error in the 14 year forecast of daily NEE by 44% decreasing from 4.22 gCm−2 day−1 to 2.38 gCm−2 day−