Evaluation and modeling of factors influencing the depth of mixing layer in which soil solute releasing from soil to surface runoff

The depth of mixing layer is one of the important parameters which cannot be assigned a constant value affected by many factors in the slope runoff. The objective of this study was to investigate the effect of slope length and underground biomass on slope runoff, solute transport processes, as well as mixing layer depth. In this study, the experimental plots with the four slope lengths (5, 10, 15, and 20 m) and a width of 2 m were built on the slope with the gradient of 20°. In addition, the plots with the millet or wheat planting were built on the slope. The change of runoff and solute transport was analyzed through simulated rainfall experiments and then to estimate mixing layer depth. The results showed that the runoff rate decreased and more runoff seeped into the slope soil with increasing slope length. Increasing underground biomass also promoted greater rainfall infiltration into the soil. The increase in slope length increased the concentration of solute in runoff, but more underground biomass reduced the nutrients transported with runoff. The effective mixing depth increased with an increase in slope length, but effective mixing depth decreased with increased underground biomass. The modified expression of the equivalent mixing model under different slope lengths and underground biomass could accurately describe the solute transfer process in runoff when compared with complete mixing model and incomplete mixing model based on exponential functions. This research provided a reference for improving the application of mixing layer models in the slope management.

Using new observations and Machine Learning to improve organic sinking processes in the PlankTOM global ocean biogeochemical model

<p>A lot of effort has been put in the representation of surface ecosystem processes in global carbon cycle models, in particular through the grouping of organisms into Plankton Functional Types (PFTs) which have specific influences on the carbon cycle. In contrast, the transfer of ecosystem dynamics into carbon export to the deep ocean has received much less attention, so that changes in the representation of the PFTs do not necessarily translate into changes in sinking of particulate matter. Models constrain the air-sea CO<sub>2</sub> flux by drawing down carbon into the ocean interior. This export flux is five times as large as the CO<sub>2</sub> emitted to the atmosphere by human activities. When carbon is transported from the surface to intermediate and deep ocean, more CO<sub>2 </sub>can be absorbed at the surface. Therefore, even small variability in sinking organic carbon fluxes can have a large impact on air-sea CO<sub>2</sub> fluxes, and on the amount of CO<sub>2</sub> emissions that remain in the atmosphere.</p><p>In this work we focus on the representation of organic matter sinking in global biogeochemical models, using the PlankTOM model in its latest version representing 12 PFTs. We develop and test a methodology that will enable the systematic use of new observations to constrain sinking processes in the model. The approach is based on a Neural Network (NN) and is applied to the PlankTOM model output to test its ability to reconstruction small and large particulate organic carbon with a limited number of observations. We test the information content of geographical variables (location, depth, time of year), physical conditions (temperature, mixing depth, nutrients), and ecosystem information (CHL a, PFTs). These predictors are used in the NN to test their influence on the model-generation of organic particles and the robustness of the results. We show preliminary results using the NN approach with real plankton and particle size distribution observations from the Underwater Vision Profiler (UVP) and plankton diversity data from Tara Oceans expeditions and discuss limitations.</p>

Extreme convection in the Lofoten Basin of the Norwegian Sea

<p>One of the factors affecting the variability of the global climate is strong oceanic convection. Current research declares the results of the investigation on the extreme convection in the Lofoten Basin (LB) using the Argo profilers data. The most common parameter reflecting the convection intensity is Mixed Layer Depth (MLD). In the frames of the understudied period, MLD exceeds 1000 m in March-April and December 2010 in the Lofoten Basin Eddy (LBE), whereas the average MLD is about 200 m and rarely exceeds 400 m in the basin. Water volume formed at mid-depth of the central LB, between 1000 m depth and the isosteric surface s07 is connected with the extreme convection events. We analytically assess the final mixing depth that corresponds well to measured values of the MLD. Such a correspondence indicates the variations in the buoyancy flux and stratification as the main reasons for MLD variability in the LB. We easily explain this variability due to heat release in the basin. Atmospheric patterns during the extreme convection are described. It occurs that northerly winds are as common as dominating south-westerly winds during the months with extreme convection. 32 cases of extreme convective events with MLD exceeding 350 m were analyzed and we reveal that correspondent composite maps of Sea Level Pressure (SLP) and surface heat flux match well NAO-/EAP- atmospheric pattern in the Northern Atlantic, while negative NAO pattern prevails in climate during winter-spring. We define the heat release as the major trigger of strong convection. Heat release associated with extreme convection events in the LB is twice stronger than usual.</p>

Winter limnology: how do hydrodynamics and biogeochemistry shape ecosystems under ice?

<p>The ice-covered period in lakes is increasingly recognized for its unique hydrodynamic and biogeochemical phenomena and ecological relevance yet it remains poorly studied compared to the ice-free season. Knowledge gaps exist where research areas – hydrodynamics, biogeochemistry and biology – intersect. For example, density-driven circulation under ice coincides with an expansion of the anoxic zone, but abiotic and biotic controls on oxygen depletion have not been disentangled. While heterotrophic microorganisms and migrating phytoplankton often thrive at the oxycline, the extent to which physical processes induce fluxes of heat and substrates that further support under-ice food webs is uncertain. Similarly, radiatively-driven convection under ice in spring can promote growth of motile phytoplankton or diatoms depending on flow velocity, water clarity and mixing depth, but links between functional trait selection, trophic transfer to zooplankton and fish and the prevalence of microbial versus classical food webs in seasonally ice-covered lakes remain unclear. Under-ice processes cascade into and from the ice-free season, and are relevant to annual cycling of energy and carbon through aquatic food webs. Understanding the coupling between state transitions and the reorganization of trophic hierarchies is essential for predicting complex ecosystem responses to climate change. In this presentation, we briefly review existing knowledge regarding physical processes in lakes in winter and the parallel developments in under-ice biogeochemistry and ecology. We then illustrate interactions between these processes, identify extant knowledge gaps whose solution requires interdisciplinary approaches, and present (novel) methods to address outstanding questions.</p>

Modeling the Effects of Hydrodynamics on Thermal Stratification and Algal Blooms in the Xiangxi Bay of Three Gorges Reservoir

Algal blooms have been reported in some tributary bays since the initial impoundment of Three Gorges Reservoir, which has seriously affected the water ecology and drinking water safety. Hydrodynamics plays a crucial role in algae growth. The recent numerical models of hydrodynamics and water quality are effective to identify the effects of hydrodynamics on phytoplankton and find potential strategies for controlling algal blooms. In this study, the CE-QUAL-W2 model was applied to simulate the hydrodynamics and algal blooms in the Xiangxi Bay (XXB) of the Three Gorges Reservoir. The model performed well in simulating flow patterns, water temperature profile, and algal blooms. The results indicated that the hydrodynamics showed the spatial and temporal differences in the XXB. In the mouth area, the intensity and plunge depth of density currents were dynamic and characterized by a typical seasonal pattern. The transformation of density currents from interflow to overflow will provide more opportunities for vertical mixing, resulting in un-stratification and reducing of algal blooms. However, in the middle and upper areas, strong stratification and low velocity at upstream provide enough favorable conditions for the growth of algae and increase algal blooms. The simulation results revealed that the variation of mixing depth explains the spatial and temporal differences of Chl.a. It played a vital role in seasonal stratification and the dynamics of phytoplankton succession in XXB.

Water quality and spatial and seasonal dynamics in the largest water supply reservoir in Brazil and implications for diatom assemblages

Aim in this paper we investigated how spatial factors and seasonal dynamics influenced the diatom community in a tropical deep environment of low productivity waters in Brazil. Methods we used physical and chemical characteristics of the water and planktonic diatoms from 9 sampling stations during dry (austral winter) and wet (austral summer) seasons (N = 18) as the outline to identify water quality, spatial and seasonal patterns. To evaluate spatially and temporally integrated events from the recent past (approximately the last 5 years before sampling), and the species from diverse habitats of the system, we used diatoms from the surface sediment (top 2 cm, N = 9). Since we used the top 2 cm of surface sediment containing the dead diatoms that were deposited over recent past of the reservoir, seasonal sampling of the sediment was not needed. Results during the dry season heavily silicified long colonial planktonic diatom species associated mainly with higher mixing depth, pH, and transparency dominated the plankton, whereas in the wet season the reservoir became stratified, favoring planktonic solitary diatoms with high surface volume ratios. For the sediment, a general pattern emerged where planktonic species dominated in the deep sections of the reservoir, and the abundance of benthic species in shallow areas near the tributaries increased. Conclusions the diatom assemblages was mainly influenced by seasonal variations and mixing regime. Surface sediment samples provided longer-term information, and revealed habitat differentiation shaping diatom assemblages. Overall, the small centric planktonic Aulacoseira tenella (Nygaard) Simonsen stood out as the most abundant species in the entire reservoir in both, the plankton and the sediment, indicating that size and shape serve as adaptive strategies for buoyancy and nutrient uptake stand as a competitive advantage in deep low productivity environments.

Effects of phosphorus control on primary productivity and deep-water oxygenation: insights from Lake Lugano (Switzerland and Italy)

Attempts to restore Lake Lugano, Switzerland and Italy, from eutrophication have produced weak responses in the target variables (primary productivity and hypolimnetic oxygen concentrations), indicating shortcomings in the underlying eutrophication model. An analysis of monitoring data showed that the decrease in phosphorus concentration, although nearly compliant with restoration targets, produced only slight decreases in primary productivity and no change in hypolimnetic oxygen conditions. These target variables were equally or more sensitive to factors external to trophic state, including plankton structure, which influenced primary productivity, and the depth of mixing during turnovers, which influenced hypolimnetic oxygen. To improve the chance of success, the restoration approach should revise the phosphorus concentration target and explicitly account for the influence of external variation, especially mixing depth.

Development of an Algorithm for Chemically Dispersed Oil Spills

An algorithm utilizing four basic processes was described for chemical oil spill dispersion. Initial dispersion was calculated using a modified Delvigne equation adjusted to chemical dispersion, then the dispersion was distributed over the mixing depth, as predicted by the wave height. Then the droplets rise to the surface according to Stokes’ law. Oil on the surface, from the rising oil and that undispersed, is re-dispersed. The droplets in the water column are subject to coalescence as governed by the Smoluchowski equation. A loss is invoked to account for the production of small droplets that rise slowly and are not re-integrated with the main surface slick. The droplets become less dispersible as time proceeds because of increased viscosity through weathering, and by increased droplet size by coalescence. These droplets rise faster as time progresses because of the increased size. Closed form solutions were provided to allow practical limits of dispersibility given inputs of oil viscosity and wind speed. Discrete solutions were given to calculate the amount of oil in the water column at specified points of time. Regression equations were provided to estimate oil in the water column at a given time with the wind speed and oil viscosity. The models indicated that the most important factor related to the amount of dispersion, was the mixing depth of the sea as predicted from wind speed. The second most important factor was the viscosity of the starting oil. The algorithm predicted the maximum viscosity that would be dispersed given wind conditions. Simplified prediction equations were created using regression.

Characterizing urban pollution variability in Central Poland using radon-222

Four years of observations of radon, meteorology and atmospheric pollution was used to demonstrate the efficacy of combined diurnal and synoptic timescale radon-based stability classification schemes in relating atmospheric mixing state to urban air quality in Zgierz, Central Poland. Nocturnal radon measurements were used to identify and remove periods of non-stationary synoptic behaviour (13–18% of each season) and classify the remaining data into five mixing states, including persistent temperature inversion (PTI) conditions, and non-PTI conditions with nocturnal conditions ranging from well mixed to stable. Mixing state classifications were performed completely independently of site meteorological measurements. World Health Organization guideline values for daily PM2.5/PM10 were exceeded only under strong PTI conditions (3–15% of non-summer months) or often under non-PTI stable nocturnal conditions (14–20% of all months), when minimum nocturnal mean wind speeds were also recorded. In non-summer months, diurnal amplitudes of NO (CO) increased by the factors of 2–12 (3–7) from well-mixed nocturnal conditions to PTI conditions, with peak concentrations occurring in the morning/evening commuting periods. Analysis of observations within radon-derived atmospheric mixing ‘class types’ was carried out to substantially clarify relationships between meteorological and air quality parameters (e.g. wind speed vs. PM2.5 concentration, and atmospheric mixing depth vs. PM10 concentration).