Precise Point Positioning with real-time kinematic (PPP-RTK) technology, which relies on Global Navigation Satellite Systems (GNSS), encounters difficulties in achieving high-precision and rapid convergence during ionospheric active conditions such as those occurring in thunderstorms. Most existing research on PPP-RTK has primarily focused on calm ionospheric conditions, with limited analysis of its performance under ionospheric anomalies. This study analyzes 13-day data collected from 305 Australian stations, encompassing both ionospheric anomalies (from 10 to 13 May 2024) and calm periods. We evaluated the residuals of uncalibrated phase delay (UPD), the accuracy of atmospheric modeling, as well as the positioning accuracy and convergence time of PPP-RTK. The results reveal that during ionospheric anomalies, compared to calm conditions, the accuracy of wide-lane and narrow-lane UPDs decreases by 2.4% and 1.4%, respectively. Meanwhile, the accuracy of estimated ionospheric and tropospheric delays deteriorates by 167.1% and 17.3%, respectively. In terms of PPP-RTK services, for the horizontal component, the convergence times increase by 25.0%, 44.4%, and 55.6% for the GPS-only, GPS + Galileo, and GPS + Galileo + BDS solutions, respectively. For the vertical component, the increases are 56.9%, 81.6%, and 87.2%, respectively. Regarding the positioning accuracies, for the horizontal component, they decline by 5.5%, 7.4%, and 10.4% for the GPS-only, GPS + Galileo, and GPS + Galileo + BDS solutions, respectively. For the vertical component, the declines are 11.8%, 13.0%, and 18.5%, respectively. This indicates that ionospheric anomalies significantly disrupt PPP-RTK services, mainly due to the degradation of ionospheric delay estimates, which directly affects positioning results. Although the ionosphere can lead to significant degradation in positioning performance, the positioning performance can still be substantially improved with an increase in the number of satellites. This study thus offers new insights into the performance of PPP-RTK during ionospheric active conditions.

Precise Point Positioning with real-time kinematic (PPP-RTK) technology, which relies on Global Navigation Satellite Systems (GNSS), encounters difficulties in achieving high-precision and rapid convergence during ionospheric scintillation events such as those occurring in thunder-storms. Most existing research on PPP-RTK has primarily focused on calm ionospheric conditions, with limited analysis of its performance under ionospheric anomalies. This study analyzes 13-day data collected from 305 Australian stations, encompassing both ionospheric anomalies (from May 10th to 13th, 2024) and calm periods. We evaluated the residuals of uncalibrated phase delay (UPD), the accuracy of atmospheric modeling, as well as the positioning accuracy and convergence time of PPP-RTK. The results reveal that during ionospheric anomalies, com-pared to calm conditions, the accuracy of wide-lane and narrow-lane UPDs decreases by 2.4% and 1.4%, respectively. Meanwhile, the accuracy of estimated ionospheric and tropospheric de-lays deteriorates by 167.1% and 17.3%, respectively. In terms of PPP-RTK services, for the hori-zontal component, the convergence times increase by 25.0%, 44.4%, and 55.6% for the GPS-only, GPS+Galileo, and GPS+Galileo+BDS solutions, respectively. For the vertical component, the in-creases are 56.9%, 81.6%, and 87.2%, respectively. Regarding the positioning accuracies, for the horizontal component, they decline by 5.5%, 7.4%, and 10.4% for the GPS-only, GPS+Galileo, and GPS+Galileo+BDS solutions, respectively. For the vertical component, the declines are 11.8%, 13.0%, and 18.5%, respectively. This indicates that ionospheric anomalies significantly disrupt PPP-RTK services, mainly due to the degradation of ionospheric delay estimates, which directly affects positioning results. Although the ionosphere can lead to significant degradation in posi-tioning performance, the positioning performance can still be substantially improved with an in-crease in the number of satellites. This study thus offers new insights into the performance of PPP-RTK during ionospheric disturbances.

This study investigates upper-reservoir dam-axis siting in pumped-storage hydropower projects, where cut–fill balance and construction cost are critical under complex terrain conditions. Existing approaches still rely heavily on manual interpretation or static GIS-based analysis and therefore do not adequately optimize dam-axis geometry or earthwork balance. To address this limitation, we propose an Improved Adaptive Variable Neighborhood Search (IAVNS) algorithm that integrates high-resolution digital elevation model (DEM) data within a two-layer adaptive framework. The inner layer performs staged planar and elevation adjustments through adaptive neighborhood operators, whereas the outer layer conducts fitness-guided subregion migration to strengthen global exploration. Experiments on the Qiannan pumped-storage project show that IAVNS obtains layouts with improved cut–fill balance. In the 30-run benchmark comparison, IAVNS achieved a mean CFR of 1.31, which is close to, although slightly above, the upper bound of the adopted earthwork-balance reference interval. In the separate 20-run case-study analysis, the average storage-volume deviation was 0.13%, with run-level deviations ranging from $-1.39\%$ to $1.16\%$. In benchmark comparisons, IAVNS improves solution quality by 22.8% relative to the Genetic Algorithm (GA) and by 16.5% relative to classical Variable Neighborhood Search (VNS), while reducing convergence time by 49.5% and 27.4%, respectively. Sensitivity analysis further suggests that the framework remains locally robust under practically reasonable parameter perturbations, and the module-level ablation study indicates that the observed performance gains arise mainly from the problem-tailored search mechanisms for dam-axis siting rather than from a generic combination of metaheuristic components. Taken together, the case-study results, repeated-run comparison, sensitivity analysis, and ablation study support the use of IAVNS as a geometry-oriented decision-support framework for preliminary dam-axis design in terrain-sensitive hydraulic engineering applications.