The parameters of hydrological models are usually calibrated to achieve good performance, owing to the highly non-linear problem of hydrology process modelling. However, parameter calibration efficiency has a direct relation with parameter range. Furthermore, parameter range selection is affected by probability distribution of parameter values, parameter sensitivity, and correlation. A newly proposed method is employed to determine the optimal combination of multi-parameter ranges for improving the calibration of hydrological models. At first, the probability distribution was specified for each parameter of the model based on genetic algorithm (GA) calibration. Then, several ranges were selected for each parameter according to the corresponding probability distribution, and subsequently the optimal range was determined by comparing the model results calibrated with the different selected ranges. Next, parameter correlation and sensibility were evaluated by quantifying two indexes, R_{C Y,  X} and S_E, which can be used to coordinate with the negatively correlated parameters to specify the optimal combination of ranges of all parameters for calibrating models. It is shown from the investigation that the probability distribution of calibrated values of any particular parameter in a Xinanjiang model approaches a normal or exponential distribution. The multi-parameter optimal range selection method is superior to the single-parameter one for calibrating hydrological models with multiple parameters. The combination of optimal ranges of all parameters is not the optimum inasmuch as some parameters have negative effects on other parameters. The application of the proposed methodology gives rise to an increase of 0.01 in minimum Nash–Sutcliffe efficiency (E_NS) compared with that of the pure GA method. The rising of minimum E_NS with little change of the maximum may shrink the range of the possible solutions, which can effectively reduce uncertainty of the model performance.

Shanghai is the economic center of China, but is vulnerable to threats from natural hazards. Along with climate change and fast urbanization, flood is the inevitable deterrent to the city’s development. Flood hazard mitigation is considered as a critical work for the city government to ensure safety, maintain sustainable development, and reduce damages and losses. A comprehensive overview of flood management systems and considerations are of great value. So in this paper, the major causes of flooding in Shanghai are investigated and their characteristics are analyzed. Based on the new strategy of flood management aiming at reducing future impacts of flood disaster, the authors’ considerations about the current flood control measurements in Shanghai are demonstrated, especially at the aspects of flood design standard and the potential threats existing in the engineering system and nonengineering system. Though it is clear that Shanghai has made remarkable achievements in flood management, flood management should be conducted from a strategic perspective of disaster prevention.

The Precipitation-Runoff Modeling System (PRMS) has been applied to simulate rainfall runoff in Zamask-Yingluoxia subbasin of the Heihe River Basin in this study. By using observed data in the subbasin, the model has been calibrated by comparing model simulations of daily stream flow to observed data at Yinglouxia station for the period of summer in 2004. Then model verification was conducted by keeping the same model parameters for the simulation of the period from 1 January 2003 to 31 December 2006. Results from model verification indicate that the model is able to provide good accuracy of simulations of daily rainfall runoff and river flow at Yinglouxia station, with a Nash-Sutcliffe Efficiency coefficient of 0.90 and the root-mean-square error of 15.7 m³/s. The error of maximum peak flow is 6.9 m³/s (1.8%) and the error of mean flow is 1.4 m³/s (2.5%). Comparing to previous studies, results indicate the improvement of model accuracy in simulations of daily rainfall runoff. The calibrated and verified hydrological model can be used to support flood hazard mitigations and water resource management in the Zamask-Yingluoxia subbasin.

Many irrigation districts along the Yellow River have been suffering shallow groundwater depression and agriculture-use water shortage. For comprehending response relationships of shallow groundwater level and various factors under different conditions of groundwater buried depth, the hydro-meteorological time series and the agricultural production data in Puyang area of Henan Province, China during 2006–2018 were collected for performing wavelet analysis of the relationship between the groundwater level and the four different factors, such as precipitation, air temperature, water stage of the Yellow River, and well irrigation water amount. It is shown that when the burial depth of groundwater varied from 0–10 m to over 10 m, the groundwater level was related with both the precipitation and air temperature from moderately to weakly and the delayed response times of the groundwater level to them extended from 2–4 months to more than 5 months. The groundwater level maintained a medium correlation with the well irrigation water amount as the burial depth increased, but the lag response time of groundwater level to well irrigation dramatically decreased when the burial depth exceeded 10 m. The dynamic response relationship between the groundwater and the water stage of the Yellow River was mainly affected by the distance away from the Yellow River rather than the burial depth and the influence of the river stage on the groundwater level was limited within the distance approximate to 20 km away from the Yellow River. The findings are expected to provide the reference for groundwater level prediction and groundwater resources protection.

Dongting Lake wetland is a typical lake wetland in the Middle and Lower Yangtze River Plain in China. Due to the influence of natural and human activities, the landscape pattern has changed significantly. This study used 12 Landsat images from 1991 to 2022 and applied three common classification methods (support vector machine, maximum likelihood, and CART decision tree) to extract and classify the landscape information, with the latter having a superior annual accuracy of over 90%. Based on the CART decision tree classification results, the dynamic characteristics of wetland spatial patterns were analyzed through the landscape pattern index, dynamic degree model, and transition matrix model. Redundancy and grey correlation analysis were employed to investigate the driving factors. The results showed increased landscape fragmentation, reduced heterogeneity, and increased complexity from 1991 to 2022. The water and mudflat areas exhibited three distinct stages: gradual decline until 2001 (−3.06 km²/a); sharp decrease until 2014 (−19.44 km²/a); and steady increase (22.93 km²/a). Vegetation conversion, particularly between sedge and reed, dominated the change in landscape pattern. Reed area initially increased (18.88 km²/a), then decreased (−35.89 km²/a), while sedge showed the opposite trend. Woodland area fluctuated, peaking in 2016 and declined by 2022. The construction of the Three Gorges Dam significantly altered landscape dynamics through water level changes, reflected by a 4.03% comprehensive dynamic degree during 2001–2004. Potential evaporation also emerged as a significant natural factor, exhibiting a negative correlation with the landscape index. During 1991–2001 and 2004–2022, the comprehensive explanatory rates of temperature, precipitation, potential evaporation, and water level on landscape pattern dynamics were 88.56% and 52.44%, respectively. Other factors like policies and socio-economic factors played a crucial role in wetland change. These findings offer valuable insights into the dynamic evolution and driving mechanisms of Dongting Lake wetland.

Inorganic pollution is widespread in groundwater, and sulfate pollution is one of the important types, which has an important impact on the ecological environment and human health. Elevated concentrations of sulfate ion pollutants often come from the sewage discharge of chemical plants. This study takes a sulfate-contaminated site in Dongying City, Shandong Province, China, as the research object. Nine boreholes were arranged along the sewage discharge ditches in the site to collect and analyze soil samples and groundwater samples in layers. The concentration of pollutants and the change with depths were determined; the maximum concentration of sulfate ion was 10,330 mg L⁻¹ in groundwater, and the maximum pollution depth was no more than 8 m. A hydraulic barrier was carried out to cut off the pollution sources. Based on a comprehensive understanding of the hydrogeological conditions and pollution degree of the site, the hydraulic capture technique was used to control the water pumping and injection volume, so that the scope of the pollution plume was gradually reduced. The pumping wells were arranged in the polluted area, and the water injection wells were arranged at the outer edge of the pollution zone. According to the calculation of the single well water inflow and influence radius, 28 pumping wells and 66 water injection wells were needed to be arranged on the site. The treatment process was divided into four stages according to the following steps: water pumping, water injection, stoppage of water injection, stoppage of water pumping, collecting water samples after the water level recovered, and the completion stage of treatment. The above process was repeated twice. Chemical precipitation was employed in the last step. The results of the remediation showed that the sulfate concentrations in 54 percent of the samples decreased significantly to less than 100 mg L⁻¹, and the rest were between 100 and 200 mg L⁻¹ after the treatment process. Two pumping wells with serious pollution were selected as long-term monitoring wells, and two-year continuous monitoring results showed that sulfate concentrations in the monitoring wells ranged from 110 to 220 mg L⁻¹, indicating that integrated groundwater remediation techniques are more effective and more reliable than one single technique.