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Aims Ecosystem services such as carbon sequestration and climate regulation of wetland ecosystems are very important. Accurately assessing the carbon storage of natural reserves in the Yellow River Basin is helpful for carbon neutrality research and regional ecological protection and high-quality development. MethodsBased on field sampling and laboratory analysis, combined with remote sensing data, this study assessed carbon storage in the aboveground plant biomass and the top 50 cm soils of typical natural vegetation in Shaanxi Yellow River Wetland Provincial Nature Reserve. The total target area for assessment is 13 086.52 hm 2 , accounting for 23.87% of the nature reserve. Important findingsThe results showed that the aboveground carbon storage of the tall-grass vegetation was significantly higher than that of the short-grass vegetation and shrubland, and their carbon densities were 496.73, 23.45 and 138.38 g•m -2 , respectively; the carbon density of the soil at 0-50 cm was 7.15-11.98 kg•m -2 , and the soil carbon storage in the tall-grass vegetation area (5.02 × 10 5 t) was significantly higher than that of the beach without vegetation (2.09 × 10 5 t), the short-grass vegetation area (3.40 × 10 5 t) and short-shrubland area (1.45 × 10 5 t); finally, combining the aboveground carbon storage in plant biomass and the soil carbon storage in the top 50 cm, the total carbon storage is estimated around 1.22 × 10 6 t for the natural vegetation area of Shaanxi Yellow River Wetland Provincial Nature Reserve, of which proportions of carbon storage were 17.13%, 27.95%, 12.13% and 42.79% for beaches, short-grass vegetation area, short-shrubland, and tall-grass vegetation area. These results can provide basic data for the protection and restoration of natural wetlands and the improvement of carbon sink function in the middle reaches of the Yellow River.

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With population growth, climate volatility, and economic expansion, the conjunctive management of surface–groundwater (SGW) faces great challenges. In this study, a hybrid factorial optimization programming (HFOP) method is developed through integrating factorial analysis, interval linear programming, flexible fuzzy programming, and two-stage stochastic programming into a general framework. HFOP can effectively reflect the multiple uncertainties and quantitatively identify the effects of multiple factors. Then, a HFOP-SGW model is formulated for the middle reaches of the Amu Darya River Basin, where 125 scenarios are analyzed. Some of the major findings are: (i) the improvement of surface-water transport efficiency and the proper use of groundwater can effectively alleviate regional water shortage; (ii) agricultural users have a high risk of water scarcity for all states, especially under a low-flow level; (iii) uncertainties of water-flow levels and risk-reverse attitudes of decision makers have significant impacts on the system’s benefits and water-allocation scheme; and (iv) the surface-water-transmission loss rate and risk perceptions of decision makers are the main factors affecting the system’s benefit’s and water-allocation scheme. These findings can help decision makers obtain desired water-allocation strategies to respond to the variations in water availability.