Maohua Ding

This study presents an empirical model for atmospheric temperature (T) estimation based on a Geographic Grid Neural Network (GGNN) framework. As a critical parameter in GNSS tropospheric tomography, T facilitates the conversion of wet refractivity (Nw) to water vapor density (ρv). The proposed GGNNTemp model integrates grid-specific neural networks across a 1°×1° geographic grid, where each node employs a multilayer perceptron with month and altitude as inputs to predict temperature. Comparative evaluations against traditional neural networks and the GPT2w model demonstrate the superior performance of the GGNNTemp model, achieving a BIAS value of 0.01 K and an RMSE of 1.60 K—significantly outperforming GPT2w’s 12.37 K RMSE. The model exhibits consistent accuracy across varying latitudes, altitudes (up to 10 km), and seasons, with ρv conversion errors maintained below 5‰. This work highlights the efficacy of GGNN technology in modeling geophysical parameters and advances its potential for broader applications in GNSS meteorology.

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.

Ecological compensation has recently gained significant attention as an economic incentive promoting natural resource management. However, there remain several challenges to its application. A key issue is the lack of a method clearly define the standard of ecological compensation. This study established an accounting methodology for the ecological compensation standard for the water-receiving area in an inter-basin water diversion in China. Beijing, a major water-receiving area of the South-to-North Water Diversion Project, was used as an example of the application of this approach. First, the evaluation index of ecosystem service value of the water-receiving area was selected, then, emergy theory was used to calculate the increment of ecological service value based on the characteristics of each indicator. The ecological service value due to the project was calculated to be 3.898 billion RMB, while the willingness-to-pay by the public was estimated at 915 million RMB. Therefore, the increment of ecological service value was the highest standard of compensation, and the public’s willingness-to-pay was the lower limit of ecological compensation. The final compensation standard can be determined through negotiation between suppliers and beneficiaries under the leadership of relevant governments and water-transfer authorities. Thus, this study aimed to provide a scientific basis for the construction of diversified ecological compensation mechanisms and promote sustainable development of the region.

We correct some errors and give some more research for our paper published in Survey Review, 2016, 48(351), 385–391. This note is a correction owing to the use of the questionable surface meteorological measurements of the International GNSS Service (IGS) and also gives more sufficient studies to validate our method due to our inadequate study. Numerical weather model values of surface meteorological data were used to check the IGS surface meteorological measurements and long-time zenith tropospheric delay values at 76 radiosonde stations in Russia and near Russia were added as test data. Revised, updated and additional results were shown and analysed, which provide more evidence to show that the new proposed ISAAS model has a better accuracy than the Saastamoinen in Russia.