The Mann–Kendall (MK) test was widely used to detect significant trends in hydrologic and climate time series (HCTS), but it cannot deal with significant autocorrelations in HCTS. To solve this problem, the modified MK (MMK) test and the over-whitening (OW) operation were successively proposed. However, there are still limitations for these two methods, especially for the OW operation. When an HCTS has unknown interaction scenarios of trends and autocorrelations, it is obviously unclear which of these two methods will perform better in the trend test. Additionally, the trend test is always accompanied by an autocorrelations test. In the dual test, it is also unknown how the significance level affects the accuracy of the trend test. To address these issues, this study first proposes a strategy of adding an outer loop to modify the OW-operation-based trend test. Then, two simulation experiments are designed to evaluate the performances of MMK-test-based and OW-operation-based methods, and the influence of the significance level on the trend test is analyzed. Moreover, six HCTSs in the Huaihe River basin are taken as examples to examine the consistence and difference of trend test results of these two methods. Results show that: (1) previous OW operations still have the risk of misjudging trends in the presence of significant autocorrelations, and the proposed strategy is necessary and effective to modify the OW operation; (2) these two methods are similar in the accuracy of the trend test results, but they may also produce opposite results when determining whether a significant trend is a pseudo trend or not; and (3) at a given significance level α, the accuracy rates of two methods are always less than 1-α, and the accuracy rate of the trend test tends to decrease for short HCTSs and increase for long HCTSs as the significance level decreases. This study would provide a new perspective for the trend test of HCTS based on the MK test.

Ecological flows in rivers are critical to the health and stability of river ecosystems, especially for inland drylands where ecological conditions are rapidly deteriorating. Climate change and human activities lead to hydrological variation, which in turn alters the hydrological and ecological balance of local ecosystems. Therefore, it is important to study the ecological flow under hydrological variation. In this study, the second-largest inland river basin in China, the Hei River Basin, was selected as the case study. The heuristic segmentation method, monthly minimum average flow method, the Lyon method, the average flow in the driest month method, and the monthly frequency method were employed to calculate the minimum and suitable ecological flow considering hydrological variation. Then, the results of the minimum and suitable ecological flow were evaluated and compared by the Tennant method. Finally, the ecological flows were recommended for the Hei River Basin after comparison and evaluation. Results show that: (1) It is necessary and feasible to calculate ecological flow demand considering hydrological variation in the Hei River Basin. (2) The evaluation results of the minimum ecological flow are mostly at a good level or above, and those of the suitable ecological flows are mostly at the optimum range. (3) Three scenarios with different periods and frequencies were set up to obtain suitable ecological flow; and it shows that the suitable ecological flow of scenario 3 (50% frequency in all months) has the best ecological benefits, and scenario 2 (frequency is taken as 75% in spring and autumn, 50% in summer, and 80% in winter) has the best comprehensive benefits. This study can provide important reference for water resources development and utilization and ecological protection in the Hei River Basin.

The multi-reservoir flood control operation (MRFCO) problem is characterized by high dimensions and multiple constraints. These features pose significant challenges to algorithms aiming to solve the MRFCO problem, requiring them not only to handle high-dimensional variables effectively but also to manage constraints efficiently. The Horned Lizard Optimization Algorithm (HLOA) performs excellently in handling high-dimensional problems and effectively integrates with penalty functions to manage constraints. However, it still exhibits poor convergence when dealing with certain benchmark functions. Therefore, this paper proposes the Enhanced Horned Lizard Optimization Algorithm (EHLOA), which incorporates Circle initialization and two strategies for avoiding local optima, thereby enhancing HLOA’s convergence performance. Firstly, EHLOA was tested on benchmark functions, where it demonstrated strong robustness and scalability. Then, EHLOA was applied to the MRFCO problem at the upper section of Lanzhou of the Yellow River in China, showing excellent convergence capabilities and the ability to escape local optima. The reduction rates of flood peaks achieved by EHLOA for the two millennial floods and two decamillennial floods were 55.6%, 52.8%, 58.1%, and 56.4%, respectively. Additionally, the generated operation schemes showed that the reservoir volumes changes were reasonable, and the discharge processes were stable under EHLOA’s operation. Overall, EHLOA can be considered a reliable algorithm for addressing the MRFCO problem.

For inter-basin water transfer (IBWT) projects, the conflict between social, economic, and ecological objectives makes water allocation processes more complex. Specific to the problem of water resource conflict in IBWT projects, we established an optimal allocation model of generalized (conventional) water resources (G (C) model) to demonstrate the advantages of the G model. The improved multi-objective cuckoo optimization algorithm (IMOCS) was applied to search the Pareto frontiers of the two models under normal, dry, and extremely dry conditions. The optimal allocation scheme set of generalized (conventional) water resources (G (C) scheme set) consists of ten Pareto optimal solutions with the minimum water shortage selected from the Pareto optimal solutions of the G (C) model. The analytic hierarchy process (AHP) combined with criteria importance using the inter-criteria correlation (CRITIC) method was used to assign weights of evaluation indexes in the evaluation index system. The non-negative matrix method was employed to evaluate the G (C) scheme set to determine the best G (C) scheme for the Jiangsu section of the South-to-North Water Transfer (J-SNWT) Project. The results show that (1) the Pareto frontier of the G model is better than that of the C model, and (2) the best G scheme shows better index values compared to the best C scheme. The total water shortages are reduced by 254.2 million m³ and 827.9 million m³ under the dry condition, respectively, and the water losses are reduced by 145.1 million m³ and 141.1 million m³ under the extremely dry condition, respectively. These findings could not only provide J-SNWT Project managers with guidelines for water allocation under normal, dry, and extremely dry conditions but also demonstrate that the G model could achieve better water-allocation benefits than the C model for inter-basin water transfer projects.

The perennial storage water level (PL), the water level at the end of wet season (WL), and the water level at the end of dry season (DL) are three critical water levels for multiyear regulating storage (MRS) reservoirs. Nevertheless, the three critical water levels have not been paid enough attention, and there is no general method that calculates them in light of developing regulating rules for MRS reservoirs. In order to address the issue, three-parameter regulation (TPR) rules based on the coordination between the intra- and interannual regulation effects of MRS reservoirs are presented. Specifically, a long-term optimal scheduling (LTOS) model is built for maximizing the multiyear average hydropower output (MAHO) of a multireservoir system. The TPR rules are a linear form of rule with three regulation parameters (annual, storage, and release regulation parameters), and use the cuckoo search (CS) algorithm to solve the LTOS model with three regulation parameters as the decision variables. The approach of utilizing the CS algorithm to solve the LTOS model with the WL and DL as the decision variables is abbreviated as the OPT approach. Moreover, the multiple linear regression (MLR) rules and the artificial neural network (ANN) rules are derived from the OPT approach-based water-level processes. The multireservoir system at the upstream of Yellow River (UYR) with two MRS reservoirs, Longyangxia (Long) and Liujiaxia (Liu) reservoirs, is taken as a case study, where the TPR rules are compared with the OPT approach, the MLR rules, and the ANN rules. The results show that for the UYR multireservoir system, (1) the TPR rules-based MAHO is about 0.3% (0.93 × 10⁸ kW∙h) more than the OPT approach-based MAHO under the historical inflow condition, and the elapsed time of the TPR rules is only half of that of the OPT approach; (2) the TPR rules-based MAHO is about 0.79 × 10⁸ kW∙h more than the MLR/ANN rules-based MAHO under the historical inflow condition, and the TPR rules can realize 0.1–0.4% MAHO more than the MLR and ANN rules when the reservoir inflow increases or reduces by 10%. According to the annual regulation parameter, the PLs of Long and Liu reservoirs are 2572.3 m and 1695.2 m, respectively. Therefore, the TPR rules are an easy-to-obtain and adaptable LTOS rule, which could reasonably and efficiently to determine the three critical water levels for MRS reservoirs.