Using GRACE gravity satellite data combined with GLDAS data inversion can obtain the change of groundwater reserves, which can monitor the change of groundwater reserves in the study area on a large scale, but the spatial resolution of the obtained data is only 0.25°, and it is difficult to be applied in the small scale. Based on the spatial relationship between GRACE terrestrial water storage and GLDAS shallow surface water storage and precipitation, NDVI, a downscaling method of groundwater storage change based on geographically weighted regression model (GWR model) was proposed, and the spatial resolution of groundwater storage was downscaled to 1 km. The results show that the GWR model downscaling method is successfully applied to the Haihe River Basin. The correlation coefficients between the groundwater storage data after downscaling and the measured groundwater level data at 46 verification points are all greater than 0.6, and the simulation results are reasonable and reliable. Furthermore, the temporal and spatial variation characteristics of groundwater storage in Haihe River Basin and Beijing Plain were analyzed by using the downscaling results. The results is highly consistent with the existing data, indicating that the downscaling method based on GWR model can effectively improve the spatial resolution of groundwater storage change data.

The study of groundwater chemical varieties has a positive indicator effect on the regulation and improvement of regional groundwater quality caused by infiltration in the Yellow River Irrigation Area. The field in-situ infiltration test was carried out to monitor and analyze the recharge water-groundwater during the recharge process of paddy field and dry field, and the groundwater chemical evolution law was revealed by AquaChem hydrochemical analysis and SPSS mathematical statistics. Under the condition of infiltration, the characteristics of groundwater chemical change show four evolution processes: "rapid change and active zone in the early stage, gradual transition zone in the middle stage, gentle and stable zone in the late stage, and potential recovery zone after stopping irrigation". It can be seen from the experiment that the groundwater is mainly affected by the physical mixing of recharge water, accompanied by the water-rock physical adsorption ion exchange, chemical dissolution and leaching, etc. especially, the amount of recharge water in paddy field was large, and the water quality of recharge determined the direction of groundwater chemical evolution. At the same time, the concentrations of Na⁺、Cl^- of groundwater chemical components showed a good linear change, and the correlation coefficient was above 0.93, and the concentrations of Ca²⁺、Mg²⁺ showed a certain linear correlation with SO₄^2-、HCO₃^-. After stopping irrigation, the evaporation and concentration of groundwater was strong, and the ions of groundwater showed an increasing trend. Especially for Na⁺、Cl^-, the drainage and precipitation are timely needed after the irrigation. The research shows that the water supply of groundwater not only enriched groundwater sources, but also improved groundwater quality.

Maintaining ecological flow in a river is an important initiative to coordinate water resources development and utilization with river ecological protection. Most of the existing ecological flow calculation methods are limited by insufficient ecological data, difficult ecological modeling. In this paper, a Bayesian hierarchical model based on phytoplankton biomass was established to investigate the hydrological-ecological response relationship. The relationship between phytoplankton biomass and flow and water temperature in pre-flood, flood and post-flood periods was analyzed by taking the Shaying River as a case study. The results show that the Bayesian hierarchical model can identify the complex hydrological-ecological response relationship, and the advantage of hierarchical analysis improves the availability of short series data; The probability of biomass-flow covariate parameter β greater than 0 in pre-flood is 0.683, and the probability of less than 0 in flood is 0.577, and the guarantee of flow in the river channel in pre-flood and the control of discharge in flood have positive effects on phytoplankton biomass; Under the ecological flow conditions that meet the design requirements, the predicted maximum and minimum biomass improvements occur in river section 7 (+32.05% before flood, + 871.80% during flood, and +81.79% after flood) and river section 3 (+3.91% before flood, +1.16% during flood, and +2.89% after flood), respectively.

Factors such as climate change have increased the spatial-temporal distribution heterogeneity of rainfall, it is thus important to study the response of hydrograph to the influence of the spatial heterogeneity of rainfall, which is of great importance to provide guidance to the hydrologic simulation of the flooding under conditions that the rainfall of the mainstream and tributary streams have different occurrence frequencies. The watershed above Linyi station of the Yihe River in Shandong Province was selected as an example. The watershed hydrological model was established using the Xinanjiang model. Hourly rainfall data from June to September from 1980 to 2020 were used for frequency analysis to obtain the design storms with different return periods in the Yihe river basin with a duration of 72 h. The flood process at Linyi station was analyzed by considering combined design scenarios of design storms with different return periods. Simulations were run using the established hydrologic model to obtain the response of flood process to the spatial heterogeneity of rainfall. The results show that the spatial distribution of rainfall has a significant impact on peak flows, with rainfall in areas close to the basin outlet being the determining factor for peak flood flows, and that smaller amounts of rainfall can produce larger peak flows in scenarios with high spatial heterogeneity of rainfall. The results can provide reference for the flood forecasting of downstream cross-sections under conditions that the mainstream and tributary floods have different occurrence frequencies due to spatial heterogeneity of rainfall.

Taking double-discharge building in branching stream of Baiyang Lake as an example, the relationship between water level and discharge capacity of only spillway gate and overflow weir was determined by model test, and then the mutual influence between them was determined by numerical simulation. The method of combined model test and numerical simulation to determine the diversion ratio and discharge capacity were explored. Through this method, the diversion ratio of the spillway gate and the overflow weir and the water level under the gate and weir were obtained. Compared with the design value, the difference is very small within 6%. The results show that the discharge capacity of the spillway gate and overflow weir mainly meet the requirements.

Concrete faced rockfill dam is widely used and rapidly developed in water conservancy and hydropower construction in China. This paper optimized the design method of concrete face dam based on BIM technology, realizing the dynamic comparison and selection of toe slab alignment by visual comparison and selection, achieving dynamic calculation of structural stress by structural parametric design and dynamic decision of dam excavation and filling through module-drawing-chart correlation measurement. As the result of dynamic design method, the model was applied to engineering digitization. Then the method was applied to the design of Gaopingqiao Reservoir face dam. Through the development of BIM plug-in, visual programming and customized intelligent cross section, it has played a role in improving the quality and efficiency of toe slab alignment, BIM-FEM joint analysis and zoning quantity calculation. In addition, the lightweight delivery of the model results supports the "intelligent application of Gaopingqiao digital reservoir" and gives full play to the value of the whole life cycle of BIM.

In order to understand the mechanism of crack formation in the RCC gravity dam body during operation, this paper takes the Shimantan reservoir dam as the research object, adopts a method of using the ESEL command in APDL language and the rotation of local coordinate system to complete the random aggregate delivery, and constructs a three-dimensional macro-micro finite element model of the No. 9 non-overflow dam section of the Shimantan dam. The simulation results of aggregate placement show that the method can quickly generate three-dimensional spherical and polyhedral aggregates, greatly improving the efficiency of aggregate generation. The results calculated by the macro-micro model are more consistent with the actual crack distribution of the dam, and the impact of the rapid rise in summer temperature and rapid drop in winter temperature on the cracking of concrete dams during operation cannot be ignored.

Accurate inversion of dam mechanical parameters based on dam monitoring data is crucial to ensure the safe and stable operation of the dam. This paper presented an arch dam parameter inversion model based on radial basis function (RBF) network and Artificial Gorilla Troops Optimizer (GTO). Firstly, the RBF surrogate model was used to replace the finite element model to discuss the relationship between the material parameters and the displacement response of the monitoring point. The sampling data of the RBF surrogate model was generated by the efficient Latin hypercube sampling technology. Secondly, the GTO intelligent optimization algorithm was adopted to minimize the objective function of material parameter identification. The analysis results of engineering examples show that the RBF-GTO model can achieve high-precision parameter inversion analysis of concrete super-high arch dams while reducing the calculation cost.

The arch dam deformation monitoring model is the most commonly used method for arch dam health monitoring. Aiming at the deformation monitoring problem of extra-high arch dams, this paper proposes an intelligent optimization support vector machine deformation monitoring model for concrete extra-high arch dams. Particle swarm optimization (PSO) was used to optimize the penalty factor, kernel function parameters of the support vector machine (SVM), and tolerate bias. The deformation monitoring model of concrete extra-high arch dam based on PSO-SVM was established, and the influence of aging factors on the model performance was analyzed. Engineering examples show that the PSO-SVM deformation monitoring model of concrete extra-high arch dam has good prediction accuracy and generalization ability, which is suitable for deformation monitoring of extra-high arch dam.

The noise and nonlinear characteristics in the deformation sequence of concrete dam seriously affect the accuracy of dam deformation prediction. In this paper, ensemble empirical modal decomposition (EEMD) was used to decompose the horizontal displacement signal of the dam to mine the effective deformation information. The singular spectrum analysis (SSA) was used to extract features from the high-frequency eigenmodal components (IMF) obtained from the decomposition to reduce the loss of effective information. Considering the complex stochastic and non-linear mapping relationship between effector and environmental variables, extreme gradient boosting (XGBoost) was used to model the prediction of the noise-reduced data. Considering the significant influence of XGBoost hyperparameters on the prediction performance of the model, the Northern Goshawk algorithm (NGO) with better global search capability was introduced to perform parameter search, and an NGO-XGBoost-based dam displacement prediction model was constructed. The calculation results show that the EEMD-SSA can effectively remove the noise from the dam displacement monitoring information, and the dam deformation prediction model based on NGO-XGBoost can significantly improve the prediction accuracy.