Lake Depth Research Articles

Simple model of morphometric constraint on carbon burial in boreal lakes

A geometric theory was developed to explain the empirical relationship between carbon burial and lake shape in boreal lakes. The key feature of this model is an attenuation length scale, analogous to models of marine organic carbon fluxes. This length scale is the ratio of how fast carbon is displaced vertically versus how fast it is respired and engenders a simple model with a single easily constrained free parameter. Lake depths are modeled based on fractal area–volume relationships that reflect the approximate scale invariance of Earth’s topography on idealized lake geometries. Carbon burial is estimated by applying the attenuation length scale to these depths. Using this model, we demonstrate the relationship between the dynamic ratio—a metric of lake morphometry calculated by dividing the square root of surface area by the mean depth—and carbon burial. We use scaling relationships to predict how dynamic ratio, and by extension carbon burial, varies across the lake size spectrum. Our model also provides a basis for generalizing empirical studies to the biome scale. By applying our model to a boreal lake census, we estimate boreal lake carbon burial to be 1.8 ± 0.5 g C/m2/yr or 1.1 ± 0.3 Tg C/yr among all boreal lakes.

Lake volume and potential hazards of moraine-dammed glacial lakes – a case study of Bienong Co, southeastern Tibetan Plateau

Abstract. The existence of glacial lakes in the southeastern Tibetan Plateau (SETP) is a potential hazard to downstream regions, as the outburst of such lakes has the potential to result in disastrous glacial lake outburst floods (GLOFs). In the present study, we conducted a comprehensive investigation of Bienong Co, a moraine-dammed glacial lake in the SETP. First, the lake basin morphology was determined, and the lake volume was estimated, showing that the maximum lake depth is ∼181 m and the lake volume is ∼102.3×106 m3. These scenarios included the possibility of GLOFs being triggered by ice avalanches (Scenarios A1–3) from the mother glacier or by landslides from the lateral moraines (Scenarios B1–3 and C1–3). Avalanche volumes of the nine trigger scenarios were obtained from the Rapid Mass Movement Simulation (RAMMS) modeling results. Next, the Basic Simulation Environment for Computation of Environmental Flow and Natural Hazard Simulation (BASEMENT) model was used to simulate the generation and propagation of the avalanche-induced displacement waves in the lake. With the model, the overtopping flows and erosion on the moraine dam and the subsequent downstream floods were also simulated. The results indicate that the ice avalanche scenario may cause the largest mass volume entering the lake, resulting in a displacement wave up to 25.2 m in amplitude (Scenario A3) near the moraine dam. Landslide scenarios with smaller volumes entering the lake result in smaller displacement waves. Scenarios A1, A2, and A3 result in released water volumes from the lake of 24.1×106, 25.3×106, and 26.4×106 m3, respectively. Corresponding peak discharges at the moraine dam are 4996, 7817, and 13 078 m3 s−1, respectively. These high discharges cause erosion of the moraine dam, resulting in breach widths of 295, 339, and 368 m, respectively, with the generally similar breach depth of approximately 19 m. In landslide scenarios, only overtopping flows generated by Scenarios B3 and C3 cause erosion on the moraine dam, with breach depths of 6.5 and 7.9 m and breach widths of 153 and 169 m, respectively. According to our simulations, GLOFs generated by Scenarios A1–3 all flow through 18 settlements downstream in 20 h, threatening more than half of them. Both Scenarios B3 and C3 produce GLOFs that flow through the first eight settlements downstream in 20 h and have a relatively small impact on them. Comparisons of the area, depth, and volume of glacial lakes for which the bathymetry data are available show that Bienong Co is the deepest known glacial lake with the same surface area on the Tibetan Plateau. This study could provide a new insight into moraine-dammed glacial lakes in the SETP and be a valuable reference for GLOF disaster prevention for the local government.

Diatom endemism and taxonomic turnover: Assessment in high-altitude alpine lakes covering a large geographical range

Diatoms are widely used as ecological indicators and show various degrees of endemism. Many studies that support the idea of endemic species integrate several climate zones, a variety of ecosystem types, and often focus on a global scale. Here, we investigated whether endemism could be detected when considering a homogeneous type of ecosystem in a single climate zone.We sampled stone biofilms at 40–50 cm depth in high-altitude lakes in the Alpine climate zone. A total of 149 samples were obtained from the French and Georgian mountains, two areas separated by ∼3000 km. Using Amplicon Sequence Variants derived from DNA metabarcoding, we assessed taxonomic turnover and Zeta-diversity (a measure of endemism). We ran haplotype networks and phylogenetic tests to measure geographical signal in the phylogenies of dominant taxa.The French and Georgian communities shared 51 % of species. Species that were not shared across both regions were mostly rare, and often not characteristic of lakes but of neighboring habitats instead. In contrast, at the sub-species level, 87 % of the genotypes showed restricted distributions. Whereas endemism was the rule at sub-species level, most species were shared across both French and Georgian lakes, suggesting that geographic barriers strongly limited dispersal at the sub-species level but not species level. Dominant species hosted higher levels of sub-specific diversity than rare species. In contrast to global-scale studies, we did not find any significant geographical structuring in the phylogeny of the investigated species. This could indicate ongoing dispersal at a frequency fast enough to prevent allopatric divergence, yet slow enough to prevent sharing most haplotypes between France and Georgia. These results have implications for biomonitoring: depending on the taxonomic level chosen, robust generic tools (species level) or tools dedicated to a region able to discriminate fine pressures differences (sub-species level) may be developed.

Optimization of buoy-type energy harvesting device in lake using firefly algorithm

Abstract It is of utmost importance to prioritize the exploration of alternative green energy sources, reducing our dependence on coal and oil, in order to mitigate the detrimental effects of greenhouse gases. This paper introduces an innovative energy harvester that utilizes a single magnet driven by a buoy attached to a pole within a lake. We delve into the correlation between the induced electricity and various factors such as wind speed, lake length and depth, as well as the design parameters of the energy harvester. To enhance the performance of the energy harvester, we employ the firefly algorithm, gradually fine-tuning its control parameters to acquire the most optimal design data. Our analysis reveals that, under specific conditions including a fetch of 4 km, a lake depth of 10 m and a wind speed of 4.5 m/s, the one-magnet energy harvester exhibits a commendable electrical power output of 0.1 Watt. This research not only emphasizes the potential of hydraulic energy generation as a promising and sustainable green energy source but also provides valuable insights into optimizing energy harvesters to achieve maximum electricity production.

Remote sensing inversion of the Zabuye Salt Lake in Tibet, China using LightGBM algorithm

The Zabuye Salt Lake in Tibet, China is the only salt lake in the world that contains natural crystalline lithium carbonate. The grade and spatial distribution of mineral resources are of great importance to the development and utilization of salt lake mineral resources. The use of remote sensing technology for salt lakes observations can overcome the disadvantages of traditional station observations, such as spatial discontinuity, high time consumption, and high labor costs. In addition, machine learning algorithms can efficiently analyze the information from remote sensing data. In this study, Landsat-8 remote sensing image data and the Light Gradient Boosting Machine (LightGBM) algorithm were used to perform inversions of the depth, salinity, and lithium concentration of the Zabuye Salt Lake. Moreover, the water volume, total salinity, and total lithium content of Zabuye Salt Lake in 2000 and 2017 were estimated, and the distribution of mineral resources and changes during the study period were analyzed. The results show that the water depth and volume of the entire lake increased sharply in 2017, resulting in a decrease in salinity and lithium concentration in the lake. Due to the inflow from the surrounding dry salterns, the South Lake experienced a relatively small change. Furthermore, the amount of lithium resources in North Lake decreased significantly in 2017 compared to 2000, possibly due to higher temperatures during the month of observation, which led to precipitation of lithium carbonate. Our study proves the feasibility and accuracy of the LightGBM machine learning algorithm for rapid inversion of salt lakes, which provides technical insight into remote sensing inversion of other mineral resources in salt lakes. Thus, the development of remote sensing technology in recent years can provide increasingly detailed assessments of salt lake resources in the future.

Documenting the impacts of increasing salinity in freshwater and coastal ecosystems: Introduction to the special issue

Documenting the impacts of increasing salinity in freshwater and coastal ecosystems: Introduction to the special issue

Using 222Rn temporal and spatial distributions to estimate the groundwater discharge rate and associated nutrient fluxes into high salinity lakes in Badain Jaran Desert, Northwest China.

Groundwater is the main source of water and salt recharge for to lakes in arid regions. Quantifying the groundwater discharge and its nutrient input is important in the evolution of lake environments in the Badain Jaran Desert (BJD), Northwest China. In this study, ten BJD lakes were sampled for 222Rn in April and September 2021, and the 222Rn mass balance model was used to quantify the groundwater discharge rates and derived nutrient fluxes to these lakes. The results showed that the 222Rn activity and the groundwater recharge rate of lake water both present a positively correlated with lake water depth. The hot points of high 222Rn activity in the lake water were consistent with the locations of groundwater discharge areas. According to the 222Rn temporal and spatial distributions, the mean groundwater recharge rates for the ten lakes in April and September were 5.4 ± 0.6 and 7.7 ± 1 mm/d, respectively, and the annual mean groundwater discharge rates varied between 1.1 ± 0.2 and 14.6 ± 1.6 mm/d, with a mean of 7 ± 0.9 mm/d. Given that all the perennial lakes in the BJD have the same groundwater recharge rate as the mean recharge rate of the ten studied lakes, the annual mean groundwater recharge amount received by the lakes in the entire BJD is (5.6 ± 0.7) × 107 m3/a. According to the groundwater recharge amount, the inputs of dissolved inorganic nitrogen, dissolved inorganic phosphorus, dissolved inorganic silicon, total nitrogen, and total phosphorus to the BJD lakes from groundwater were (4.7 ± 0.6) × 105, (3.8 ± 0.5) × 104, (7.9 ± 1) × 105, (7.2 ± 0.9) × 105, (2.5 ± 0.3) × 104 kg/a, respectively. This study provides a reference for quantifying of groundwater discharge rates into salt lakes in other arid regions.

Versatile Medium for the Isolation of Sulfur-Reducing Bacteria from Natural Ecotypes

Sulfate-reducing bacteria (SRB) are a diverse group of microorganisms that are commonly isolated from anoxygenic environments (lake depths, soil, or swamps) and are also found in the intestines of humans and animals. They are well known for outcompeting methanogens for common substrates thereby helping in the decrease of methane emissions and also playing a major role in sulfur cycling. Therefore, determining the cultivable methods for their isolation is important. Various media has been used for the cultivation and purification of SRB from natural wetlands. The luxuriant growth of SRB was observed in all the media tested, out of which the best suitable media to recover a maximum of colonies from the natural soil/sediment samples was discovered as a modified postage medium. The enumeration of the isolated SRB was done by the most probable number (MPN) technique. A total of twenty pure isolates of SRB were isolated from different ecotypes.

Measuring the thickness and Young’s modulus of the ice pack with DAS, a test case on a frozen mountain lake

SUMMARY We explored the potential of fibre optics coupled with distributed acoustic sensing (DAS) to measure the thickness and Young’s modulus of an ice layer, using the properties of guided seismic waves. During two winter seasons (2020 and 2021), an optical fibre was deployed over one of the frozen Roberts Mountain lakes (at 2400 m a.s.l) near Grenoble (France) and we measured both the continuous ambient seismic noise as well as signals generated by active sources (hammer), with a DAS interrogator. Following a Bayesian scheme, we inverted the dispersion curves of longitudinal and flexural guided waves retrieved from the analysis of active shot gathers and obtained Young’s modulus E = 3.4 ± 0.1 GPa and ice thickness h = 47 ± 1 cm from the second-year data. The ice thickness was consistent with field measurements. Field observations of porous and/or fracture ice may explain the relatively low effective Young’s modulus (relative to pure ice), which may also be affected by a snow layer not included in the model. The drastic improvements in the inversion results between the two years are related to better coupling conditions (drone deployment before early freezing), more appropriate acquisition parameters (2 m gauge length), and the upper snow layers (less thick and less heterogeneous in the second year). Moreover, we were able to use the non-dispersive low-frequency noise associated with gravity waves to estimate the lake depth H = 5 m which is compatible with independent observations. The use of DAS to record guided seismic waves could then appear as a relevant tool for monitoring environments like floating ice shelves and sea ice.

Nutrients and not temperature are the key drivers for cyanobacterial biomass in the Americas

Cyanobacterial blooms imperil the use of freshwater around the globe and present challenges for water management. Studies have suggested that blooms are trigged by high temperatures and nutrient concentrations. While the roles of nitrogen and phosphorus have long been debated, cyanobacterial dominance in phytoplankton has widely been associated with climate warming. However, studies at large geographical scales, covering diverse climate regions and lake depths, are still needed to clarify the drivers of cyanobacterial success. Here, we analyzed data from 464 lakes covering a 14,000 km north-south gradient in the Americas and three lake depth categories. We show that there were no clear trends in cyanobacterial biomass (as biovolume) along latitude or climate gradients, with the exception of lower biomass in polar climates. Phosphorus was the primary resource explaining cyanobacterial biomass in the Americas, while nitrogen was also significant but particularly relevant in very shallow lakes (< 3 m depth). Despite the assessed climatic gradient water temperature was only weakly related to cyanobacterial biomass, suggesting it is overemphasized in current discussions. Depth was critical for predicting cyanobacterial biomass, and shallow lakes proved more vulnerable to eutrophication. Among other variables analyzed, only pH was significantly related to cyanobacteria biomass, likely due to a biologically mediated positive feedback under high nutrient conditions. Solutions toward managing harmful cyanobacteria should thus consider lake morphometric characteristics and emphasize nutrient control, independently of temperature gradients, since local factors are more critical – and more amenable to controls – than global external forces.

Glacial Isostatic Adjustment Shapes Proglacial Lakes Over Glacial Cycles

AbstractAs ice sheets load Earth's surface, they produce ice‐marginal depressions which, when filled with meltwater, become proglacial lakes. We include self‐consistently evolving proglacial lakes in a glacial isostatic adjustment (GIA) model and apply it to the Laurentide ice sheet over the last glacial cycle. We find that the locations of modeled lakes and the timing of their disappearance is consistent with the geological record. Lake loads can deflect topography by &gt;10 m, and volumes collectively approach 30–45 cm global mean sea‐level equivalent. GIA increases deglaciation‐phase lake volume up to five‐fold and average along‐ice‐margin depth ≤90 m compared to glaciation‐phase ice volume analogs—differences driven by changes in the position and size of the peripheral bulge. Since ice‐marginal lake depth affects grounding‐line outflow, GIA‐modulated proglacial lake depths could affect ice‐sheet mass loss. Indeed, we find that Laurentide ice‐margin retreat rate sometimes correlates with proglacial lake presence, indicating that proglacial lakes aid glacial collapse.

Can we estimate the lake mean depth and volume from the deepest record and auxiliary geospatial parameters?

The accurate quantification of lake volume is essential for regional water resources management and ecosystem health. Because of the high cost of traditional full-lake depth measurements, the bathymetric and volume information for most lakes globally is inaccessible. Whether the limited field measurements over the lake can be used to estimate lake volume is worth investigating. This study aims to propose an effective method for estimating lake mean depth/volume based on the lake deepest record. We first constructed the empirical model that relies on the linear relationship between lake maximum depth and lake mean depth/volume. The different machine learning (ML) methods were then developed and tested based on the available lake deepest record and multi-type geospatial parameters. Although the linear model shows good performance for estimating lake mean depth (R2 = 0.83), it is difficult to predict lake volume (R2 = 0.23). Most ML models perform better (R2 ≥ 0.85) than linear models. However, the support vector machines (SVM) model (SVM-3: R2 = 0.54, MAPE = 134.93 %) and deep neural network (DNN) model (DNN-3: R2 = 0.83, MAPE = 82.56 %) constructed with low influential input parameters performed poorly. In contrast, extremely gradient boosting tree (XGBoost) and random forest (RF) methods have high stability and accuracy both in predicting lake mean depth (XGBoost-1: R2 = 0.87, MAPE = 23.35 %; RF-1: R2 = 0.90, MAPE = 22.75 %) and volume (XGBoost-3: R2 = 0.99, MAPE = 31.03 %; RF-3: R2 = 0.98, MAPE = 32.63 %). The RF and XGBoost models constructed with a small amount of measured lake depth data in a different region also had a good performance. Generally, the results suggest that the XGBoost and RF methods have great potential in lake volume estimation. This research is expected to provide a feasible approach to predict lake volume and benefit lake water resources management.

Assessing the influence of lake and watershed attributes on snowmelt bypass at thermokarst lakes

Abstract. Snow represents the largest potential source of water for thermokarst lakes, but the runoff generated by snowmelt (freshet) can flow beneath lake ice and via the outlet without mixing with and replacing pre-snowmelt lake water. Although this phenomenon, called “snowmelt bypass”, is common in ice-covered lakes, it is unknown which lake and watershed properties cause variation in snowmelt bypass among lakes. Understanding the variability of snowmelt bypass is important because the amount of freshet that is mixed into a lake affects the hydrological and biogeochemical properties of the lake. To explore lake and watershed attributes that influence snowmelt bypass, we sampled 17 open-drainage thermokarst lakes for isotope analysis before and after snowmelt. Isotope data were used to estimate the amount of lake water replaced by freshet and to observe how the water sources of lakes changed in response to the freshet. Among the lakes, a median of 25.2 % of lake water was replaced by freshet, with values ranging widely from 5.2 % to 52.8 %. For every metre that lake depth increased, the portion of lake water replaced by freshet decreased by an average of 13 %, regardless of the size of the lake's watershed. The thickness of the freshet layer was not proportional to maximum lake depth, so that a relatively larger portion of pre-snowmelt lake water remained isolated in deeper lakes. We expect that a similar relationship between increasing lake depth and greater snowmelt bypass could be present at all ice-covered open-drainage lakes that are partially mixed during the freshet. The water source of freshet that was mixed into lakes was not exclusively snowmelt but a combination of snowmelt mixed with rain-sourced water that was released as the soil thawed after snowmelt. As climate warming increases rainfall and shrubification causes earlier snowmelt timing relative to lake ice melt, snowmelt bypass may become more prevalent, with the water remaining in thermokarst lakes post-freshet becoming increasingly rainfall sourced. However, if climate change causes lake levels to fall below the outlet level (i.e., lakes become closed-drainage), more freshet may be retained by thermokarst lakes as snowmelt bypass will not be able to occur until lakes reach their outlet level.

Sedimentary, littoral and planktic diatoms show different diversity patterns and assembly mechanisms in mountain lakes of the northern European Alps

This study analysed diversity patterns of sedimentary, littoral and planktic diatoms in 43 mountain lakes in the northern European Alps and identified processes that contribute to these patterns. Linear regression models showed a significant increase of sedimentary α-diversity with lake area and conductivity and a negative trend with increasing elevation, whilst the littoral diatom α-diversity increased significantly with conductivity and lake water temperature. Planktic diatom α-diversity significantly decreased with lake area and depth. August water temperature, total phosphorus, conductivity and lake depth explained a significant part of the variation and were significantly correlated with pairwise β-diversities in the data sets, but spatial and shared effects of space and environment were more important for planktic and littoral diatoms. A null model approach based on assemblages’ dissimilarities revealed that the structure of littoral and planktic assemblages was predominantly stochastic. In contrast, sedimentary diatoms were formed by both deterministic and stochastic processes. Abundant and widespread species contributed a large part to the assemblage β-diversity. The results point to a stronger role of niche assembly in sedimentary than for littoral and planktic diatoms. Dispersal limitation, in turn, is likely to contribute to the spatial patterns and stochastic assembly processes observed for littoral and planktic diatoms.

Depth and temperature drive patterns of spatial overlap among fish thermal guilds in lakes across Ontario, Canada

AbstractAimThe spatial distribution of ectotherms is strongly dependent on the temperature of their environments. In temperate lakes, fishes with different thermal optima can become spatially segregated during summer stratification. This habitat partitioning, or niche complementarity, may play a role in the coexistence of trophically similar species; however, the extent of partitioning is dependent on the resources available within each habitat. Although habitat partitioning of fish thermal guilds has been studied in individual lakes, broad‐scale patterns of spatial overlap and segregation are not yet understood. In this study, we explore the patterns and drivers of spatial overlap among thermal guilds (cold‐, cool‐, and warm‐water) at a broad scale.LocationOntario, Canada.MethodsWe built a multivariate regression tree to explore patterns and environmental drivers of spatial overlap in freshwater fishes across three thermal guilds from 438 lakes.ResultsWe identified five clusters of lakes exhibiting different patterns of spatial overlap among the three thermal guilds. Temperature (growing degree days) and maximum lake depth were strong drivers of the spatial overlap patterns.Main ConclusionsThese findings provide a better understanding of broad‐scale patterns of spatial overlap and allow us to predict how spatial overlap, and ultimately species interactions and competition, may change under a warming climate.

The Effect of Suspended Particulate Matter on the Supraglacial Lake Depth Retrieval from Optical Data

Supraglacial lakes (SGL) are a specific phenomenon of glaciers. They are important for ice dynamics, surface mass balance, and surface hydrology, especially during ongoing climate changes. The important characteristics of lakes are their water storage and drainage. Satellite-based remote sensing is commonly used not only to monitor the area but also to estimate the depth and volume of lakes, which is the basis for long-term spatiotemporal analysis of these phenomena. Lake depth retrieval from optical data using a physical model requires several basic assumptions such as, for instance, the water has little or no dissolved or suspended matter. Several authors using these assumptions state that they are also potential weaknesses, which remain unquantified in the literature. The objective of this study is to quantify the effect of maximum detectable lake depth for water with non-zero suspended particulate matter (SPM). We collected in-situ concurrent measurements of hyperspectral and lake depth observations to a depth of 8 m. Additionally, we collected water samples to measure the concentration of SPM. The results of empirical and physically based models proved that a good relationship still exists between the water spectra of SGL and the lake depth in the presence of 48 mg/L of SPM. The root mean squared error for the models ranged from 0.163 m (Partial Least Squares Regression—PLSR model) to 0.243 m (physically based model), which is consistent with the published literature. However, the SPM limited the maximum detectable depth to approximately 3 m. This maximum detectable depth was also confirmed by the theoretical concept of Philpot (1989). The maximum detectable depth decreases exponentially with an increase in the water attenuation coefficient g, which directly depends on the water properties.

Multiple kernel fusion: A novel approach for lake water depth modeling

Multiple kernel fusion (MKF) refers to the task of combining multiple sources of information in the Hilbert space for improved performance. Very often the combined kernel is formed as a linear composition of multiple base kernels where the combination weights are learned from the data. As the first application of an MKF approach in hydrological modeling, lake water depth as one of the pivot factors in the reservoir analysis is simulated by considering different hydro-meteorological variables. The role of each individual input parameter is initially investigated by applying a kernel regression approach. We then illustrate the utility of an MKF formalism which learns kernel combination of weights to yield an optimal composition for kernel regression. A set of 40-year data collected from 27 groundwater and streamflow stations and 7 meteorological stations for precipitation and evaporation parameters in the vicinity of Lake Urmia are utilized for model development. Both visual and quantitative statistical performance criteria illustrate a superior performance for the MKF approach compared to kernel ridge regression (KRR), the support vector regression (SVR), back propagation neural network (BPNN) and auto regressive (AR) models. More specifically, while each individual input parameter fails to provide an accurate prediction for lake water depth modeling, an optimal combination of all input parameters incorporating the groundwater level, streamflow, precipitation and evaporation via a multiple kernel learning approach enhances the predictive performance of the model accuracy in the multiple scenarios. The promising results (RMSE = 0.098 m; R2 = 0.987; NSE = 0.986) may motivate the application of a MKF approach towards solving alternative and complex hydrological problems.

Horizontal and vertical distribution of microplastics in Gehu Lake, China

Abstract Microplastics (MPs) widely exist in the aquatic environment. In this study, the horizontal and vertical distribution of MPs in different depths of Gehu Lake was analyzed. The study found that MPs were detected in water bodies at different depths. The Gehu Lake Basin's MP abundance throughout the entire lake region ranged from 1.40 to 5.53 n/L. Microplastic abundance in surface water (3.13 ± 0.32 n/L) was highest, and the average abundance of MPs in the middle water (2.28 ± 0.24 n/L) and bottom water (1.03 ± 0.14 n/L) was appreciably reduced (p &amp;lt; 0.05). The microplastics observed in water samples were analyzed, and it was found that the particle size of most of the obtained microplastic particles was between 100 and 500 microns, the main shape was fibrous MP, and the MP content of transparent color was highest, and the four most common MPs was polyethylene (PE), polystyrene, polyester (PES), and polypropylene (PP). In this article, we emphasize the importance of comprehending the specific distribution characteristics of MPs in Gehu Lake. It provides a data foundation for future monitoring of MPs present in Gehu Lake, provides scientific and technological support for the assessment and prevention of microplastic pollution in this area, and offers some assistance for future lake water quality regulation.

Variability of carbon stored in inland freshwater wetland in Northeast India

Inland freshwater wetland ecosystems are among the largest sink of carbon (C) in the biosphere. However, improved scientific understanding of the C stability and sequestration potential is required to predict response of C pool under environmental change and to identify priorities for lacustrine C sink management. This study analyses the concentration of organic C fractions based on their stability and estimates C stock along with depth and eco-zones of the Rudrasagar lake in Northeast India. Sediment samples up to 100 cm depth were collected from littoral, sub-littoral and deep layers, and analysed for organic C concentrations. Results showed that C concentration decreases with depth in the littoral layer but increases with depth in sub-littoral and deep layers. Two-way analysis of variance showed that concentrations of soil organic C (SOC) fractions were significantly different among the eco-zones but not between the soil depth. Average SOC stock was significantly higher in the deep layer (334.9 Mg C ha−1) followed by sub-littoral (248.4 Mg C ha−1) and littoral layer (106.1 Mg C ha−1). Overall, we show that substantial spatial variability in SOC stock exists among the eco-zones and depth that may be driven by water inundation in deep layer and fluctuating hydrological conditions at the edges of the lacustrine ecosystem. Our study demonstrates that inland freshwater wetland is a major sink of organic C and if disturbed it can act as a carbon dioxide source.

Modeling Investigation of Thermal Circulations of a Large and Shallow Subtropical Lake

Lake Taihu, a typical shallow lake in eastern China, was selected for study in this paper. Methods: Considering that the accurate parameterization scheme is the key to a lake breeze simulation, then based on Lake Taihu’s characteristics, the setting of parameters of the lakes’ scheme was adjusted in the WRF (Weather Research and Forecasting Model) lake model. Results: By comparing the observed values of the mesoscale flux platform of Lake Taihu from June to August 2012, the results showed that the model significantly improved the simulation. The root-mean-square error of the simulated and observed comparison of the latent heat fluxes over the lake improved from 42.77 to 89.00. The adjusted WRF-Lake model better presents the characteristics of Lake Taihu’s lake wind. The different lake depth cases showed that the maximum difference in surface temperature between the shallow lake case and the deep lake case reached 9.9 °C, and the average was about 3 °C. Conclusion: Compared with the deep lake case, the shallow lake case stimulated less lake breeze circulation with a shorter time and smaller range in the horizontal and vertical directions. The lake breeze frequency, simulated by the shallow lake case, was 18.5%, while for the deep lake case, it reached 38%. These simulations of lake breeze contribute to understanding the influence of a lake’s depth on lake breeze characteristics and the accurate parameterization scheme of the inland shallow lakes.