Cross‐Scale Interactions Dictate Regional Lake Carbon Flux and Productivity Response to Future Climate

Zwart, Jacob A.; Hanson, Z.; Read, Jordan S.; Fienen, Michael N.; Hamlet, Alan F.; Bolster, Diogo; Jones, Stuart

doi:10.1029/2019gl083478

Cited by 15 publications

(6 citation statements)

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“…Through this synthesis, T water is shown to be a major predictor of lake and reservoir NEE, which is consistent with past work of Zwart et al (2019) and Eugster et al (2020). There is a high degree of spatiotemporal variability between these two variables.…”

Section: Forcing Differences By Typesupporting

confidence: 89%

Diel, seasonal, and inter-annual variation in carbon dioxide effluxes from lakes and reservoirs

Gołub

Koupaei‐Abyazani

Vesala

et al. 2023

Environ. Res. Lett.

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Accounting for temporal changes in carbon dioxide (CO2) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO2 based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO2 flux at all sites. By accounting for diel variation, only 11% of site-months were net daily sinks of CO2. Annual CO2 emissions had an average of 25% (range 3-58%) interannual variation. Similar to studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO2 flux variability were delineated through mutual information analysis. Sample analysis of CO2 fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions.

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Section: Forcing Differences By Typesupporting

confidence: 89%

Diel, seasonal, and inter-annual variation in carbon dioxide effluxes from lakes and reservoirs

Gołub

Koupaei‐Abyazani

Vesala

et al. 2023

Environ. Res. Lett.

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“…With variable future climate projections (warmer conditions, wet winters combined with wet or dry summers), the regional lake population responded in the simulations with a wide range of hydrologic changes; however, the magnitude and type of individual lake responses was largely dependent on the lake characteristics and position within the landscape. Thus, quantitative metrics such as FHEE (based on observations or simulations) can be used to characterize the likely effects of climate change on the water availability and water quality (Zwart et al 2019a) of different lakes by identifying those historically with predominantly drainage or seepage lake characteristics.…”

Section: Discussionmentioning

confidence: 99%

Projected changes of regional lake hydrologic characteristics in response to 21st century climate change

et al. 2021

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Inland lakes are socially and ecologically important components of many regional landscapes. Exploring lake responses to plausible future climate scenarios can provide important information needed to inform stakeholders of likely effects of hydrologic changes on these waterbodies in coming decades. To assess potential climate effects on lake hydrology, we combined a previously published spatially explicit, processed-based hydrologic modeling framework implemented over the lake-rich landscape of the Northern Highlands Lake District within the United States with an ensemble of climate change scenarios for the 2050s (2041-2070) and 2080s (2071-2100). Model results quantify the effects of climate change on water budgets and lake stage elevations for 3692 lakes and highlight the importance of landscape and hydrologic setting for the response of specific lake types to climate change. All future climate projections resulted in loss of ice cover and snowpack as well as increased evaporation, but variability in climate projections (warmer conditions, wet winters combined with wet or dry summers) interacted with lake characteristics and landscape position to produce variable lake hydrologic changes. Water levels for drainage lakes (lakes with substantial surface water inflows and outflows) showed nearly no change, whereas minimum water levels for seepage lakes (minimal surface water fluxes) decreased by an average of up to 2.64 m by the end of the 21st century. Our physically based modeling approach is parsimonious and computationally efficient and can be applied to other lake-rich regions to investigate interregional variability in lake hydrologic response to future climate scenarios.

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“…In particular, a lesson we learned, that demographic stochasticity is not the dominant uncertainty on forest gap models, likely extends more broadly, suggesting that the current reliance on specific uncertainties or stochasticities in other ecosystem modeling fields may be misleading ecologists about the dominant drivers of uncertainty. Similarly, many ecological projections have focused on uncertainty in parameters and meteorological drivers (Kremer, 1983; Eberhardt, 1987; Regan et al, 2002; Grimm et al, 2005; Zwart et al, 2019). While it is clear that these uncertainties do contribute to ecological modeling in general, it remains unclear what the relative contributions of parameter and meteorological uncertainty are to total forecast uncertainty across different spatial and temporal scales.…”

Section: Discussionmentioning

confidence: 99%

Towards understanding predictability in ecology: A forest gap model case study

Raiho

Dietze

Dawson

et al. 2020

Preprint

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11Predictions from ecological models necessarily include five different uncertainties: demographic stochas-12 ticity, initial conditions, external forcing (i.e., drivers/covariates), parameters, and modeled processes. 13 However, most predictions from process-based ecological models only account for a subset of these un-14 certainties (e.g. only demographic stochasticity). This underestimation of uncertainty runs the risk of 15 producing precise, but inaccurate predictions. To address these limitations, we created a new generaliz-16 able ensemble state data assimilation algorithm that accommodates two common features of ecological 17 data, zero-truncation and zero-inflation, and allows the estimation of process error and its covariance 18 among multiple ecological variables. We then demonstrate the use of this novel algorithm by assimilating 19 50 years of tree-ring-estimated species-level biomass at Harvard Forest into a process-based forest gap 20 model. Finally, we partitioned the variance in this hindcast to test long-standing assumptions in the 21 ecological modeling community about which uncertainties dominate our ability to forecast forest com-22 munity and carbon dynamics. Contrary to >40 years of research relying on stochastic forest gap models, 23 we found that demographic stochasticity alone massively underestimated forecast uncertainty (0.09% 24 of the total) and resulted in overconfident and biased model predictions. Similarly, despite decades of 25 reliance on unconstrained "spin ups" to initialize models, constraining initial conditions with data led to 26 the largest increases in prediction accuracy. Counter to conventional wisdom from modeling other Earth 27 1 system process, initial condition uncertainty declined very little over the forecast time period. Process 28 variance, which heretofore had been difficult to estimate in mechanistic ecosystem model projections, 29 dominated the prediction uncertainty over the forecast time period (49.1%), followed by meteorological 30 uncertainty (32.5%). Parameter uncertainty, which has recently been the focus of the modeling commu-31 nity, contributed a modest 18.3%. These findings call into question much of our conventional wisdom 32 about how to improve forest community and carbon cycle projections on multi-decadal to centennial time 33 scales. This foundation can be used to test long standing modeling assumptions across fields in global 34 change biology and suggests a fairly significant reorientation of the modeling community toward better 35 initialization of models with current observations and efforts to better quantify, propagate, and reduce 36 process error. These approaches have the potential to improve both the accuracy of ecological forecasts 37 and our understanding of the predictability of ecological processes. 38 Running Head: Drivers of multi-decadal forecast uncertainty 39 nity ecology, Tobit Wishart ensemble filter (TWEnF) 41 1 Introduction 42 Understanding how different component uncertainties contribute to the overall uncertai...

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Cross‐Scale Interactions Dictate Regional Lake Carbon Flux and Productivity Response to Future Climate

Cited by 15 publications

References 82 publications

Diel, seasonal, and inter-annual variation in carbon dioxide effluxes from lakes and reservoirs

Diel, seasonal, and inter-annual variation in carbon dioxide effluxes from lakes and reservoirs

Projected changes of regional lake hydrologic characteristics in response to 21st century climate change

Towards understanding predictability in ecology: A forest gap model case study

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