Many economic, social and ecological problems can be attributed to the scarcity and mismanagement of water and land resources. In this study, a multi-objective fuzzy–robust programming (MOFRP) method was developed for supporting the optimal use of land and water resources in agriculture. MOFRP improved existing methods through taking ecological services of crop cultivation into account. It was also capable of reflecting fuzziness in preferences, priorities and parameters that were largely neglected in previous agricultural decision making. This method was applied to address a case in arid northwestern China. Optimal plans of crop cultivation reconfiguration were generated for sustaining local development under economic, ecological and social objectives as well as physical restraints in water and land resources. Compared to the status quo, the optimized plan would increase economic and ecological benefits by 12.2% and 18.8%, respectively. The efficiency of irrigation water could also be enhanced with the economic and ecological benefits per unit water being raised and the water consumption per unit land being reduced. The comparisons of the MOFRP model to four alternatives validated that it was capable of achieving satisfactory benefits and reducing system-violation risks without neglecting valuable uncertain information and ecological services of crops. The proposed method was also applicable to other multi-objective management problems under uncertainty without loss of generality.

The grey water footprint theory was introduced into a fractional programming model to alleviate non-point source pollution and increase water-use efficiency through the adjustment of crop planting structure. The interval programming method was also incorporated within the developed framework to handle parametric uncertainties. The objective function of the model was the ratio of economic benefits to grey water footprints from crop production, and the constraints contained water availability constraints, food security constraints, planting area constraints, grey water footprint constraints and non-negative constraints. The model was applied to the Hetao Irrigation District of China. It was found that, based on the data in the year of 2016, the optimal planting plans generated from the developed model would reduce 34,400 m³ of grey water footprints for every 100 million Yuan gained from crops. Under the optimal planting structure, the total grey water footprints would be reduced by 21.9 million m³, the total economic benefits from crops would be increased by 1.138 billion Yuan, and the irrigation water would be saved by 44 million m³. The optimal results could provide decision-makers with agricultural water use plans with reduced negative impacts on the environment and enhanced economic benefits from crops.

Water scarcity and water pollution are essential factors limiting coordinated regional development, especially in water-deprived regions. Industrial restructuring is an effective water management solution to alleviate water scarcity and mitigate water pollution. However, due to widely existing inexact parameter information in the water resource management system, it is challenging to allocate water resources among industrial sectors. To address these problems, an export coefficient coupled with a two-stage stochastic robust programming method (EC-TSRP) was developed through integrating an export coefficient model (ECM), two-stage stochastic programming (TSP) and robust optimization. The proposed EC-TSRP model could effectively deal with the multiple uncertainties expressed as stochastic and the intervals with fluctuation ranges, and enhance the robustness of optimal plans for supporting water resource allocation among industrial sectors under complex uncertainties. It was then applied to Bayan Nur City, in arid north-west China. The optimization alternatives indicate that wheat, sheep and services would be the most sensitive sectors among all industrial sectors, when non-point source (NPS) pollution exports are restricted. In addition, comparing the EC-TSRP results with the deterministic model, the reliability of the system could be improved significantly, while the value of the objective function would be decreased slightly. The simulation results were also compared with the historical data from 2012 to 2016. Although the total revenue of Bayan Nur City would decrease by 1.52%, the pollutant loads of total nitrogen, total phosphorus and chemical oxygen demand (TN, TP and COD) would decrease by 14.5%, 7.75% and 2.07%, respectively, and total water allocation also would decrease from 4.6 billion m³ to 4.23 billion m³.

A healthy water environment is fundamental to maintaining ecosystem functions and supporting regional sustainable development. Water environmental health (WEH) management in estuarine and bay regions is challenged by interannual indicator variability, system imbalances, and management complexity. This study developed an integrated Water Environmental Health Assessment–Diagnosis–Optimization (WEH-ADO) framework combining interval information entropy (IIE), coupling coordination analysis, and indicator-adjustment optimization within pressure–state–response (PSR) structure. The framework was applied to the Zhejiang portion of the Hangzhou Bay region. The regional WEH index, constructed by aggregating the normalized pressure, state, and response indicators using IIE-derived weights, showed three distinct periods of change: an early decline, a mid-period improvement, and a later period of adjustment. Compared with the conventional entropy weight method, the IIE reduced the influence of individual indicators with high data dispersion. The coupling coordination analysis indicated high coupling among the PSR subsystems, with regional coordination classified as primarily or moderately coordinated. Using 2023 as the baseline year, a genetic algorithm was used to determine adjustment levels for eight indicators while holding the weights and non-adjustable indicators constant. The best-found scenario increased the mean WEH index from 0.498 to 0.528 and the mean coupling coordination degree from 0.657 to 0.681. These changes represent improvements in the calculated indices rather than simulated water-quality responses. A key finding was that the lowest-scoring indicators were not necessarily assigned the highest adjustment priorities by the optimization model. At the city level, Hangzhou, Ningbo, and Shaoxing together accounted for 82.3% of the total increase in the WEH index; at the indicator-adjustment level, ecological water replenishment (29.2%), soil erosion control (19.6%), wastewater discharge reduction (17.1%), and chemical oxygen demand discharge reduction (16.7%) jointly accounted for 82.6% of this increase. Overall, the WEH-ADO framework incorporates observed interannual variability into indicator weighting and links assessment and subsystem diagnosis to city- and indicator-specific adjustment scenarios.

Rainfall-adapted irrigation (RAI), the application of controlled-release nitrogen fertilizer (CRNF), and deep placement of nitrogen fertilizer can contribute to the improvement of resource utilization efficiency. Nevertheless, the interactive effects of these factors on nitrogen loss via runoff and leaching from paddy fields remain ambiguous. Consequently, a two-year field experiment was conducted to evaluate the interactive effects of four nitrogen management strategies on nitrogen losses through runoff and leaching from paddy fields and rice yield under RAI when compared to conventional flooding irrigation (CI). Compared to CI, RAI significantly reduced total nitrogen loss via runoff (−49.8%) and leaching (−35.9%) by lowering volume of runoff and leaching. Compared to conventional nitrogen application (surface application of common urea with 240 kg N ha⁻¹), deep placement of CRNF with 192 kg N ha⁻¹ decreased floodwater nitrogen concentration, reducing total nitrogen loss by 46.8% via runoff and 50.9% via leaching. Importantly, RAI combined with deep placement of CRNF with 192 kg N ha⁻¹ minimized nitrogen losses through leaching and runoff from paddy fields and maximized grain yield (8251 kg ha⁻¹) by improving nitrogen accumulation in rice. Collectively, RAI combined with deep-placed CRNF with an 80% nitrogen rate could reduce non-point source pollution from paddy fields.