Spatial and temporal changes of primary production in a deep peri-alpine lake

Soomets, Tuuli; Kutser, Tiit; Wüest, Alfred; Bouffard, Damien

doi:10.1080/20442041.2018.1530529

Cited by 17 publications

(17 citation statements)

References 45 publications

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“…In terms of gyres, those structures are repeatedly observed in Lake Geneva (Bouffard et al, 2018;Kiefer et al, 2015). Because of the Coriolis force, subsequent strong up-/down-lifts of the thermocline occur, which structure the lateral dispersion of primary productivity (Soomets et al, 2019).…”

Section: Discussionmentioning

confidence: 90%

Data assimilation of in-situ and satellite remote sensing data to 3D hydrodynamic lake models

Baracchini

Chu

Šukys

et al. 2019

Preprint

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Abstract. The understanding of lakes physical dynamics is crucial to provide scientifically credible information for ecosystem management. We show how the combination of in-situ data, remote sensing observations and three-dimensional hydrodynamic numerical simulations is capable of delivering various spatio-temporal scales involved in lakes dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we present a flexible framework for DA into lakes three-dimensional hydrodynamic models. Using an Ensemble Kalman Filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in-situ and satellite remote sensing temperature data into a three-dimensional hydrodynamic model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatio-temporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed for the constraints of operational systems and near real-time operations (e.g. integration into http://meteolakes.ch).

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Section: Discussionmentioning

confidence: 90%

Data assimilation of in-situ and satellite remote sensing data to 3D hydrodynamic lake models

Baracchini

Chu

Šukys

et al. 2019

Preprint

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“…A similar model was applied to remote sensing data by Bergamino et al [76] to study Lake Tanganyika and Deng et al [23] to study Lake Taihu. Lake Geneva has been also investigated with remote sensing data using the same model as in the current study [42].…”

Section: Discussionmentioning

confidence: 99%

“…The PP model, originally developed by Arst et al [20,52] and further adapted for remote sensing data by Kauer et al [41] and Soomets et al [42] was used in this study. The main principle of this model is that the PP is a function of photosynthetically absorbed radiation and the quantum yield of carbon fixation [63]:…”

Section: The Primary Production (Pp) Modelmentioning

confidence: 99%

“…The PP model has three input parameters: (1) Chl a in mg m −3 , (2) K d,PAR in m −1 , and (3) q PAR in mol photons m −2 h −1 . In this study, we followed the calculation scheme of the PP model modified for remote sensing data by Soomets et al [42], and we used the integrated values of primary production (PP int , mg C m −2 h −1 ) by integrating PP(z) over the mean depth of the lake for all valid pixels.…”

Section: The Primary Production (Pp) Modelmentioning

confidence: 99%

“…Thus, combining bio-optical modeling and remote sensing would be a great solution for estimating productivity in lakes worldwide. Earth observation methods give a lake-wide perspective, which is essential for large and variable water-bodies [33], where single-point contact measurements give only a glimpse of the actual spatial variation present in a lake [41,42].…”

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Variability of Phytoplankton Primary Production in Baltic Lakes Using Sentinel-3 OLCI Data

et al. 2020

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Phytoplankton primary production (PP) in lakes play an important role in the global carbon cycle. However, monitoring the PP in lakes with traditional complicated and costly in situ sampling methods are impossible due to the large number of lakes worldwide (estimated to be 117 million lakes). In this study, bio-optical modelling and remote sensing data (Sentinel-3 Ocean and Land Colour Instrument) was combined to investigate the spatial and temporal variation of PP in four Baltic lakes during 2018. The model used has three input parameters: concentration of chlorophyll-a, the diffuse attenuation coefficient, and incident downwelling irradiance. The largest of our studied lakes, Võrtsjärv (270 km2), had the highest total yearly estimated production (61 Gg C y−1) compared to the smaller lakes Lubans (18 Gg C y−1) and Razna (7 Gg C y−1). However, the most productive was the smallest studied, Lake Burtnieks (40.2 km2); although the total yearly production was 13 Gg C y−1, the daily average areal production was 910 mg C m−2 d−1 in 2018. Even if lake size plays a significant role in the total PP of the lake, the abundance of small and medium-sized lakes would sum up to a significant contribution of carbon fixation. Our method is applicable to larger regions to monitor the spatial and temporal variability of lake PP.

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Hypolimnetic oxygen depletion rates in deep lakes: Effects of trophic state and organic matter accumulation

Steinsberger

Schwefel

Wüest

et al. 2020

Limnology & Oceanography

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This study investigated the consumption of oxygen (O 2) in 11 European lakes ranging from 48 m to 372 m deep. In lakes less than~100 m deep, the main pathways for O 2 consumption were organic matter (OM) mineralization at the sediment surface and oxidation of reduced compounds diffusing up from the sediment. In deeper lakes, mineralization of OM transported through the water column to the sediment represented a greater proportion of O 2 consumption. This process predominated in the most productive lakes but declined with decreasing total phosphorous (TP) concentrations and hence primary production, when TP concentrations fell below a threshold value of~10 mg P m −3. Oxygen uptake by the sediment and the flux of reduced compounds from the sediment in these deep lakes were 7.9-10.6 and 0.6-3.6 mmol m −2 d −1 , respectively. These parameters did not depend on the lake's trophic state but did depend on sedimentation rates for the primarily allochthonous or already degraded OM. These results indicate that in lakes deeper than~100 m, mineralization of autochthonous OM is mostly complete by the time of sedimentary burial. This explains why hypolimnetic O 2 concentrations improve more rapidly following TP load reduction in deeper lakes relative to shallower lakes, where larger sediment-based O 2 consumption by settled OM and release of reduced substances may inhibit the restoration of hypolimnetic O 2 concentrations.

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Spatial and temporal changes of primary production in a deep peri-alpine lake

Cited by 17 publications

References 45 publications

Data assimilation of in-situ and satellite remote sensing data to 3D hydrodynamic lake models

Data assimilation of in-situ and satellite remote sensing data to 3D hydrodynamic lake models

Spatio-Temporal Variability of Phytoplankton Primary Production in Baltic Lakes Using Sentinel-3 OLCI Data

Hypolimnetic oxygen depletion rates in deep lakes: Effects of trophic state and organic matter accumulation

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