Thermistor Chain Data Assimilation to Improve Hydrodynamic Modeling Skill in Stratified Lakes and Reservoirs

2008 ◽  
Vol 134 (8) ◽  
pp. 1123-1135 ◽  
Author(s):  
Peter S. Yeates ◽  
Jörg Imberger ◽  
C. Dallimore
2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Beat Müller ◽  
Thomas Steinsberger ◽  
Robert Schwefel ◽  
René Gächter ◽  
Michael Sturm ◽  
...  

AbstractAreal oxygen (O2) consumption in deeper layers of stratified lakes and reservoirs depends on the amount of settling organic matter. As phosphorus (P) limits primary production in most lakes, protective and remediation efforts often seek to reduce P input. However, lower P concentrations do not always lead to lower O2 consumption rates. This study used a large hydrochemical dataset to show that hypolimnetic O2 consumption rates in seasonally stratified European lakes remain consistently elevated within a narrow range (1.06 ± 0.08 g O2 m−2 d−1) as long as areal P supply (APS) exceeded 0.54 ± 0.06 g P m−2 during the productive season. APS consists of the sum of total P present in the productive top 15 m of the water column after winter mixing plus the load of total dissolved P imported during the stratified season, normalized to the lake area. Only when APS sank below this threshold, the areal hypolimnetic mineralization rate (AHM) decreased in proportion to APS. Sediment trap material showed increasing carbon:phosphorus (C:P) ratios in settling particulate matter when APS declined. This suggests that a decreasing P load results in lower P concentration but not necessarily in lower AHM rates because the phytoplankton community is able to maintain maximum biomass production by counteracting the decreasing P supply by a more efficient P utilization. In other words, in-lake organic matter production depends only on APS if the latter falls below the threshold of 0.54 g P m−2 and correspondingly, the atomic C:P ratio of the settling material exceeds ~155.


2019 ◽  
Author(s):  
Magdalena J. Mayr ◽  
Matthias Zimmermann ◽  
Jason Dey ◽  
Andreas Brand ◽  
Bernhard Wehrli ◽  
...  

AbstractLakes and reservoirs contribute substantially to atmospheric concentrations of the potent greenhouse gas methane. Lacustrine sediments produce large amounts of methane, which accumulate in oxygen-depleted hypolimnia of stratified lakes. Due to climate change and progressing eutrophication, the number of lakes with hypolimnetic methane storage may increase in the future. However, whether stored methane eventually reaches the atmosphere during lake overturn is a matter of controversy and depends critically on the response of the methanotroph assemblage. We show that the methanotroph assemblage in a mixing lake underwent both a substantial bloom and ecological succession. As a result, the methane oxidation capacity of the mixed layer kept pace with the methane supplied from the hypolimnion and most of the stored methane was oxidized. This previously unknown aspect of freshwater methanotroph ecology represents an effective mechanism limiting methane transfer from lakes to the atmosphere.


Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 829 ◽  
Author(s):  
Revel ◽  
Ikeshima ◽  
Yamazaki ◽  
Kanae

Water resource management has faced challenges in recent decades due to limited in situ observations and the limitations of hydrodynamic modeling. Data assimilation techniques have been proposed to improve hydrodynamic model outputs of local rivers (river length ≤ 1500 km) using synthetic observations of the future Surface Water and Ocean Topography (SWOT) satellite mission to overcome limited in situ observations and the limitations of hydrodynamic modeling. However, large-scale data assimilation schemes require computationally efficient filtering techniques, such as the Local Ensemble Transformation Kalman Filter (LETKF). Expansion of the assimilation domain to maximize observations is limited by error covariance caused by limited ensemble size in complex river networks, such as the Congo River. Therefore, we tested the LETKF algorithm in a continental-scale river (river length > 1500 km) using a physically based empirical localization method to maximize the observations available while filtering error covariance areas. Physically based empirical local patches were derived separately for each river pixel, considering spatial auto-correlations. An observing system simulation experiment (OSSE) was performed using empirical localization parameters to evaluate the potential of our method for estimating discharge. We found our method could improve discharge estimates considerably without affected from error covariance while fully using the available observations. We compared this experiment using empirical localization parameters with conventional fixed-shape local patches of different sizes. The empirical local patch OSSE showed the lowest normalized root mean square error of discharge for the entire Congo basin. Extending the conventional local patch without considering spatial auto-correlation results in very large errors in LETKF assimilation due to error covariance between small tributaries. The empirical local patch method has the potential to overcome the limitations of conventional local patches for continental-scale rivers using SWOT observations.


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