Modeling microbial impact on macrophyte debris decomposition in macrophyte-dominated eutrophic lakes.

The decomposition of macrophytes plays a crucial role in the nutrient cycles of macrophyte-dominated eutrophication lakes. While research on plant decomposition mechanisms and microbial influences has rapid developed, it is curious that plant decomposition models have remained stagnant at the single-stage model from 50 years ago, without endeavor to consider any important factors. Our research conducted in-situ experiments and identified the optimal metrics for decomposition-related microbes, thereby establishing models for microbial impacts on decomposition rates (k_RDR). Using backward elimination in stepwise regression, we found that the optimal subset of independent variables-specifically Gammaproteobacteria-Q-L, Actinobacteriota-Q-L, and Ascomycota-Q-L-increased the adjusted R-squared (Ra2) to 0.93, providing the best modeling for decomposition rate (p = 0.002). Additionally, k_RDR can be modeled by synergic parameters of ACHB-Q-L, LDB-Q-L, and AB-Q-L for bacteria, and SFQ for fungi, albeit with a slightly lower Ra2 of 0.7-0.9 (p < 0.01). The primary contribution of our research lies in two key aspects. Firstly, we introduced optimal metrics for modeling microbes, opting for debris surface microbes over sediment microbes, and prioritizing absolute abundance over relative abundance. Secondly, our model represents a noteworthy advancement in debris modeling. Alongside elucidating the focus and innovative aspects of our work, we also addressed existing limitations and proposed directions for future research. SYNOPSIS: This study explores optimum metrics for decomposition-related microbes, offering precise microbial models for enhanced lake nutrient cycle simulation.

Phosphorus accumulation during the ice-on season in macrophyte-dominated eutrophic lakes and its implications.

Macrophyte overgrowth in eutrophic lakes can hasten the decline of shallow water bodies, yet the impact of macrophyte deposition on sediment phosphorus (P) accumulation in the ice-on season remains unclear. Comparative analyses of P variations among 13 semi-connected sub-lakes in Wuliangsu Lake in China, a typical MDE lake, considered external flow and macrophyte decomposition as driving forces. Sediment P fractions and water total phosphorus (TP) were analyzed at 35 sampling points across three ice-on season stages, along with macrophyte TP content to assess debris contributions. Our findings reveal that phosphorus accumulation occurs during the ice-on season in the MDE lake, with an average TP content increase of 16 mg/kg. However, we observed a surprisingly small sediment nutrient accumulation ratio (ΔTP/ΔTN=0.006) compared to macrophyte nutrient levels before decomposition. Further analysis of the dominant species, Potamogeton pectinatus, indicates that a significant portion (55%) of macrophyte phosphorus is released before the ice-on season. This highlights the critical importance of timing macrophyte harvesting to precede the phosphorus leaching process, which has implications for lake management and ecosystem restoration efforts. Additionally, our research demonstrates similar transformations among different sediment fractions as previously reported. Macrophyte debris decomposition likely serves as the primary source of Residual P (Res-P) or TP accumulation. In addition, Ca-bound P (Ca-P) generally showed a decrease, which mainly caused by its transformation to Fe/Al-bound P (Fe/Al-P), Exchange-P (Ex-P), and sometimes to Res-P. However, we emphasize the significant impacts of flow dynamics on Ca-P transport and transformations. Its hydrodynamic action increases water dissolved oxygen, which accelerates the transformation of Ca-P to more easily released Fe/Al-P and Ex-P. Furthermore, hydrodynamic transport also leads to upstream Ca-P transport to downstream. This underscores the necessity of considering flow dynamics when estimating phosphorus variations and formulating phosphorus restoration strategies.

A copula-based security risk evaluation and probability calculation for water-energy-food nexus.

Water-energy-food (WEF) are essential for human survival and development and they interact complexly. The research on Water-energy-food-coupling security risk (WEF-CSR) is crucial to promoting multi resource collaborative management. In this study, a comprehensive index system was constructed using three dimensions: reliability (Ra), coordination (C), and resilience (Rs). We selected the middle and upper reaches of the Yellow River Basin in China to study WEF-CSR conflicts, coupled with the Copula method of risk probability calculation. The results showed that: 1) from 2005 to 2021, the WEF-CSR index in the study area showed a progressively increasing trend, with a mean value of 0.49 in a critical safety state and lower values in the upper reaches of Ningxia and Gansu provinces. 2) The Ra-indexes in the criterion layer is not clearly affected by resource endowment conditions between annual periods; C-indexes are growing rapidly, with their annual growth rate four times as fast as the Ra-indexes, and Rs-indexes growing at about 2.7 times faster. 3) "Energy consumption per capita, carbon emissions, and water-related indicators" are the six main limiting factors, with a combined impediment degree of over 35 %, water resources are major constraint. 4) The Ra-C, C-Rs, and Ra-C-Rs indexes are well fitted by Clayton copula, and the C-Rs indexes are well fitted by Frank copula. The three-dimensional joint distribution risk probability, Ra-C-Rs (Ra ≤ 0.4, C ≤ 0.4, Rs ≤ 0.4) is 0.29. When the Ra or C indexes increases, the probability of unsafe Rs rises to 40 %-50 %, meaning future development must emphasize synergy, especially in Rs. In future, the five provinces in the middle and upper reaches of the Yellow River should focus on reducing energy pressure, improving water shortage conditions, and enhancing the overall resilience to avoid single-factor adverse conditions affecting WEF's coordinated and sustainable development.

Causes of Variations in Sediment Yield in the Jinghe River Basin, China.

The Jinghe River remains the major sediment source of the Yellow River in China; however, sediment discharge in the Jinghe River has reduced significantly since the 1950s. The objective of this study is to identify the causes of sediment yield variations in the Jinghe River Basin based on soil and water conservation methods and rainfall analyses. The results revealed that soil and water conservation projects were responsible for half of the total sediment reduction; sediment retention due to reservoirs and water diversion projects was responsible for 1.3% of the total reduction. Moreover, the Jinghe River Basin has negligible opportunity to improve its vegetation cover (currently 55% of the basin is covered with lawns and trees), and silt-arrester dams play a smaller role in reducing sediment significantly before they are entirely full. Therefore, new large-scale sediment trapping projects must be implemented across the Jinghe River Basin, where heavy rainfall events are likely to substantially increase in the future, leading to higher sediment discharge.

Evaluation on Early Drought Warning System in the Jinghui Channel Irrigation Area.

With the economic growth, continuous global environment deterioration, and increasingly serious water resources shortage, droughts have become more and more serious and produced great impacts on both the regional ecology and sustainable economic development. This paper has established the "green, blue, yellow, orange, and red lights" as the early warning grades for agricultural droughts. By using the two influencing factors, namely precipitation and soil moisture, this paper has established the drought assessment index evaluation model using weighted coupling method. It has carried out the analogue simulation of the early drought warning based on the Jinghui Channel's 2013 water source situations. The soil moisture in January and February is relatively ideal, and the actual early drought warning is expressed by the "green light". The soil moisture deficit is comparatively serious in March, but the situation concerning water inflow is ideal with the "green light". Actually, the early warning signal is basically consistent with the soil moisture drought degree between April and August. The actual early warning is expressed by the "green light" as well, but the soil moisture is not so ideal, however, this is the seeding time of the winter wheat so the lack of soil moisture has no impact on the crops output. In November and December, the winter wheat is at the growth and development stage and does not need much moisture. At this stage, the soil moisture is relatively poor. By integrating the time effects into the early drought warning system, this paper provides administrators of irrigation areas with a scientific decision-making based on the drought control measures.

Sudden water pollution accidents and reservoir emergency operations: impact analysis at Danjiangkou Reservoir.

Danjiangkou Reservoir is the source reservoir of the Middle Route of the South-to-North Water Diversion Project (MRP). Any sudden water pollution accident in the reservoir would threaten the water supply of the MRP. We established a 3-D hydrodynamic and water quality model for the Danjiangkou Reservoir, and proposed scientific suggestions on the prevention and emergency management for sudden water pollution accidents based on simulated results. Simulations were performed on 20 hypothetical pollutant discharge locations and 3 assumed amounts, in order to model the effect of pollutant spreading under different reservoir operation types. The results showed that both the location and mass of pollution affected water quality; however, different reservoir operation types had little effect. Five joint regulation scenarios, which altered the hydrodynamic processes of water conveyance for the Danjiangkou and Taocha dams, were considered for controlling pollution dispersion. The results showed that the spread of a pollutant could be effectively controlled through the joint regulation of the two dams and that the collaborative operation of the Danjiangkou and Taocha dams is critical for ensuring the security of water quality along the MRP.

Hierarchical prediction of industrial water demand based on refined Laspeyres decomposition analysis.

A recent study decomposed the changes in industrial water use into three hierarchies (output, technology, and structure) using a refined Laspeyres decomposition model, and found monotonous and exclusive trends in the output and technology hierarchies. Based on that research, this study proposes a hierarchical prediction approach to forecast future industrial water demand. Three water demand scenarios (high, medium, and low) were then established based on potential future industrial structural adjustments, and used to predict water demand for the structural hierarchy. The predictive results of this approach were compared with results from a grey prediction model (GPM (1, 1)). The comparison shows that the results of the two approaches were basically identical, differing by less than 10%. Taking Tianjin, China, as a case, and using data from 2003-2012, this study predicts that industrial water demand will continuously increase, reaching 580 million m3, 776.4 million m3, and approximately 1.09 billion m3 by the years 2015, 2020 and 2025 respectively. It is concluded that Tianjin will soon face another water crisis if no immediate measures are taken. This study recommends that Tianjin adjust its industrial structure with water savings as the main objective, and actively seek new sources of water to increase its supply.

Using the high-level based program interface to facilitate the large scale scientific computing.

This paper is to make further research on facilitating the large-scale scientific computing on the grid and the desktop grid platform. The related issues include the programming method, the overhead of the high-level program interface based middleware, and the data anticipate migration. The block based Gauss Jordan algorithm as a real example of large-scale scientific computing is used to evaluate those issues presented above. The results show that the high-level based program interface makes the complex scientific applications on large-scale scientific platform easier, though a little overhead is unavoidable. Also, the data anticipation migration mechanism can improve the efficiency of the platform which needs to process big data based scientific applications.