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The precise assessment of a water body's eutrophication status is essential for making informed decisions in water environment management. However, conventional approaches frequently fail to consider the randomness, fuzziness, and inherent hidden information of water quality indicators. These would result in an unreliable assessment. An enhanced method was proposed for the eutrophication assessment under uncertainty in this study. The multi-dimension gaussian cloud distribution was introduced to capture the randomness and fuzziness. The Shannon entropy based on various sample size and trophic levels was proposed to maximize valuable information hidden in the datasets. Twenty-seven significant lakes and reservoirs located in the Yangtze River Basin were selected to demonstrate the proposed method. The sensitivity and consistency were used to evaluate the accuracy of the proposed method. Results indicate that the proposed method has the capability to effectively assess the eutrophication status of lakes and reservoirs under uncertainty and that it has a better sensitivity since it can identify more than 33-50% trophic levels compared to the traditional methods. Further scenario experiments analysis revealed that the sample information richness, i.e., sample size and the number of trophic levels is of great significance to the accuracy/robustness of the method. Moreover, a sample size of 60 can offer the most favorable balance between accuracy/robustness and the monitoring expenses. These findings are crucial to optimizing the eutrophication assessment., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Demands for water, food, and energy are rising owing to increasing population and growing economy. Agriculture is the main source of food and is the largest consumer of water and has therefore the greatest impact on water security. It also greatly affects energy security and is also the main source of non-point source pollution. Changes in the availability of water, energy, and land are driven by uncertainties in nature and strongly affect food production, with severe implications for the security of water–food–energy nexus (WFEN). Thus, a coordinated and effective management of WFEN in the agricultural sector is needed. This paper therefore proposes an integrated modelling approach for the optimization of agricultural WFEN in a random environment. The approach quantifies the interactions and feedbacks within agricultural water, food and energy subsystems, making tradeoffs between agricultural benefit and environmental impacts, which will serve as sustainability indicators for agricultural systems, including crop farming and livestock farming. The incorporation of stochastic mathematical programming in the modeling framework aids in understanding how strategies and comprehensive benefits change under different scenarios. The approach was applied in a real-world case study in an irrigation district in northeast China. The development and implementation of such an integrated approach are anticipated to be applied in other agriculture-centered regions to guide policies of sustainable water, food, energy and land resources management. [ABSTRACT FROM AUTHOR]

Effective management of land, water, and fertilizer resources for rice production is critical for efficient and sustainable agriculture but is challenging due to the uncertainties associated the resources. The aim of this study is to propose a unified model for simultaneously optimizing crop planting area, irrigation water, and nitrogen fertilizer considering uncertainty. Water, carbon, energy, and ecological footprints are analyzed to jointly evaluate the re-allocation of resource. The proposed model has an advantage in addressing trade-offs of land, water, and fertilizer resources for different irrigation subareas in a cost-efficient, resources-efficient, and environmental-friendly manner, thus helping decision makers track resource utilization and where effort should be made from different perspectives, and also dealing with non-linearity and uncertainty. The model is applied to an irrigation district in northeast China. Results demonstrate that rice production is based on the simultaneous allocation of land, water, and fertilizer resources, but the objectives for the optimization model are conflicting. The model performs best when both water supply and precipitation are at their medium level. Compared with status quo, the value of irrigation water use efficiency increases by 1.6%, and the value of Global warming potential deceases by 7.45%. The allocation of 1.596 × 107 m3 of water can be regarded as a reference amount for water planning, which is obtained by considering possible scenarios of high-low flow level combinations of water supply and precipitation. Results of "footprint family" assessment suggest that improving the efficiency of nitrogen fertilizer utilization is particularly important to reduce greenhouse gas emissions and energy consumption, and indicate that land re-allocation helps improve the utilization of rice straw. The proposed model can help manage agricultural land, water, and fertilizer resources in a sustainable way in a changing environment, and is applicable for similar agriculture-centered regions. [ABSTRACT FROM AUTHOR]