Many studies have found that damming a river can change downstream hydrology, sediment transport, channel morphology, and fish habitat. However, little is known about river dam effects on downstream riparian wetland dynamics and their quantitative relationship with hydrological alterations. In this study, hydrological time series and wetland distribution data spanning nearly 40 years (1978–2016) before and after the construction of a large dam in 2005 across the Nenjiang River in Northeast China were used to reveal the impact of dam on the downstream discharge regime and wetland degradation. Hydro-statistical and stepwise multiple regression analyses were performed to quantify the relationship of riparian wetland area with a metrics of 33 hydrological indicators. Dam construction caused decline in peak discharge, flood frequency, and magnitude. Moreover, 150 km riparian wetlands along the downstream of the dam was largely reduced. The count and duration of high flow pulses, 1-day maximum, and date of maximum discharge changed significantly after the dam construction. The hydrological changes have made a significant contribution to the 44% reduction in riparian wetlands following the dam construction. Our results indicated that hydrological alterations caused by dam regulation led to the area reduction of downstream riparian wetlands. The findings provide relevant information for developing best dam operation practices to protect and restore downstream wetland ecosystems.

The soil freeze–thaw phenomenon is one of the most outstanding characteristics of the soil in Heilongjiang Province. Quantitative analysis of the characteristics of changes in key variables of the soil freeze–thaw processes is of great scientific importance for understanding climate change, as well as ecological and hydrological processes. Based on the daily surface temperature and air temperature data in Heilongjiang Province for the past 50 years, the spatial–temporal distribution characteristics of key variables and their correlations with air temperature and latitude in the freeze–thaw process of soil were analyzed using linear regression, the Mann–Kendall test, the local thin disk smooth spline function interpolation method, and correlation analysis; additionally, the spatial–temporal distribution of key variables and the changes in the surface temperature during the freeze–thaw process are discussed under different vegetation types. The results show that there is a trend of delayed freezing and early melting of key variables of the soil freeze–thaw process from north to south. From 1971 to 2019 a, the freezing start date (FSD) was delayed at a rate of 1.66 d/10 a, the freezing end date (FED) advanced at a rate of 3.17 d/10 a, and the freezing days (FD) were shortened at a rate of 4.79 d/10 a; with each 1 °C increase in temperature, the FSD was delayed by about 1.6 d, the FED was advanced by about 3 d, and the FD was shortened by about 4.6 d; with each 1° increase in latitude, the FSD was delayed by about 2.6 d, the FED was advanced by about 2.8 d, and the FD was shortened by about 5.6 d. The spatial variation in key variables of the soil freeze–thaw process under the same vegetation cover was closely related to latitude and altitude, where the lower the latitude and altitude, the more obvious the variation trend; among them, the interannual variation trend of key variables of soil freeze–thaw under meadow cover was the most obvious, which varied by 9.65, 16.86, and 26.51 d, respectively. In addition, the trends of ground temperature under different vegetation types were generally consistent, with the longest period of unstable freeze–thaw and the shortest period of stable freeze in coniferous forests, compared to the shortest period of unstable freeze–thaw and the longest period of stable freeze in meadows. The results of the study are important for our understanding of soil freeze–thaw processes and changes in Heilongjiang Province, as well as the evolution of high-latitude permafrost; they also promote further exploration of the impact of soil freeze–thaw on agricultural production and climate change.

To precisely delineate the transformation dynamics across several temporal and spatial scales in the middle and lower portions of the Songhua River basin in Heilongjiang Province, China, characterized by numerous irrigation zones and frequent transitions from surface water to groundwater. The SWAT-MODLFOW model, developed on the QSWATMOD2 platform, was validated using river runoff data and groundwater level observations. Consequently, the delay of precipitation on groundwater levels and the correlation between surface water and groundwater dynamics in the middle and lower portions of the Songhua River basin were modeled. The findings indicate: (1) The coefficient of calibration and Nash coefficient for monthly runoff during the calibration and validation periods yielded R² ≥ 0.86, NSE ≥ 0.87, R² ≥ 0.76, and NSE ≥ 0.77, respectively. Furthermore, the discrepancy between the simulated and actual groundwater table values is within 0.6 m, with R² values of 0.97 and 0.98 for the periodic and verification phases, respectively. The simulation outcomes of this test model are satisfactory and fulfill the criteria for scientific research. (2) The groundwater level in the research area typically declines from west-northwest to northeast and from south to east. The groundwater level lag time influenced by precipitation in Jiamusi, Fujin, and Tongjiang cities is around 10.56 days, 10.58 days, and 3.15 days, respectively. (3) The river channels facilitating surface water recharge to groundwater constitute 41.75% of the entire length of the Jiamusi-Tongjiang stretch of the Songhua River, with the annual average recharge representing 50.84% of the total exchanged water. Seasonally, the peak recharge value for each river section occurred in August, while the lowest recharge value was recorded in April. The peak recharge happened in 2009, while the lowest recharge was in 2014. The contribution of groundwater to surface water exhibits significant fluctuations, with seasonal variance between − 52% and 55%, and inter-annual variation between − 35% and 52%.

Spring algal blooms in mid–high-latitude lakes are facing serious challenges such as earlier outbreaks, longer duration, and increasing frequency under the dual pressure of climate warming and human activities, which threaten the health of freshwater ecosystems and water security. At present, the freeze-thaw processes is the key to distinguishing spring algal blooms in mid- to high-latitude lakes from low-latitude lakes. Based on the visualization and an analysis of the literature in the WOS database during 2007–2023, we clarified the driving mechanism of the freeze-thaw process (freeze-thaw, freeze-up, and thawing) on spring algal bloom in lakes by describing the evolution of the freeze-thaw processes on the nutrient migration and transformation, water temperature, lake transparency and dissolved oxygen, and physiological characteristics of algae between shallow lakes and deep lakes. We found that the complex phosphorus transformation process during the frozen period can better explain the spring-algal-bloom phenomenon compared to nitrogen. The dominant species of lake algae also undergo transformation during the freeze-thaw process. On this basis, the response mechanism of spring algal blooms in lakes to future climate change has been sorted out. The general framework of “principles analysis, model construction, simulation and prediction, assessment and management” and the prevention strategy for dealing with spring algal bloom in lakes have been proposed, for which we would like to provide scientific support and reference for the comprehensive prevention and control of spring algal bloom in lakes under the freezing and thawing processes.

The shortage of water resources is a long-standing constraint on the development of the Chinese economy and society. In this paper, the climate change occurring in Hulan River Basin is analyzed using the data collected at Wangkui Meteorological Station from 1960 to 2020. The overall temperature in the basin shows an upward trend, with a cumulative increase of 1.6 °C, as does the precipitation, which reaches 566.2 mm. In contrast, there is a downward trend shown by wind speed, with a cumulative decrease of 1.313 m/s. GIS remote sensing technology is applied to build a SWAT distributed hydrological model for the purpose of conducting runoff simulation in Hulan River Basin, and SWAT-CUP software is used to correct and analyze the simulation results. The parameters of snow melt are set to improve the accuracy of the model. The runoff data collected from Lanxi Hydrological Station from 2008 to 2020 are used to verify the model. The results show that the efficiency coefficient (NES) and correlation coefficient (R²) are 0.75 and 0.84, respectively, in the validation period from 2010 to 2013, while they are 0.77 and 0.93, respectively, in the correction period from 2014 to 2016, meeting the criteria of model evaluation. It can be seen from results noted above that SWAT is applicable in Hulan River Basin, providing a certain reference for the management of hydrological and water resources available in this region and for the construction of a distributed hydrological model of rivers in those high-latitude cold regions.

Human activities have substantially altered present-day flow regimes. The Headwater Area of the Yellow River (HAYR, above Huanghe’yan Hydrological Station, with a catchment area of 21,000 km² and an areal extent of alpine permafrost at ~86%) on the northeastern Qinghai-Tibet Plateau, Southwest China has been undergoing extensive changes in streamflow regimes and groundwater dynamics, permafrost degradation, and ecological deterioration under a warming climate. In general, hydrological gauges provide reliable flow records over many decades and these data are extremely valuable for assessment of changing rates and trends of streamflow. In 1998–2003, the damming of the Yellow River by the First Hydropower Station of the HAYR complicated the examination of the relations between hydroclimatic variables and streamflow dynamics. In this study, the monthly streamflow rate of the Yellow River at Huanghe’yan is reconstructed for the period of 1955–2019 using the double mass curve method, and then the streamflow at Huagnhe’yan is forecasted for the next 20 years (2020–2040) using the Elman neural network time-series method. The dam construction (1998–2000) has caused a reduction of annual streamflow by 53.5–68.4%, and a more substantial reduction of 71.8–94.4% in the drier years (2003–2005), in the HAYR. The recent removal of the First Hydropower Station of the HAYR dam (September 2018) has boosted annual streamflow by 123–210% (2018–2019). Post-correction trends of annual maximum (Q_Max) and minimum (Q_Min) streamflow rates and the ratio of the Q_Max/Q_Min of the Yellow River in the HAYR (0.18 and 0.03 m³·s⁻¹·yr⁻¹ and −0.04 yr⁻¹, respectively), in comparison with those of precorrection values (−0.11 and −0.004 m³·s⁻¹·yr⁻¹ and 0.001 yr⁻¹, respectively), have more truthfully revealed a relatively large hydrological impact of degrading permafrost. Based on the Elman neural network model predictions, over the next 20 years, the increasing trend of flow in the HAYR would generally accelerate at a rate of 0.42 m³·s⁻¹·yr⁻¹. Rising rates of spring (0.57 m³·s⁻¹·yr⁻¹) and autumn (0.18 m³·s⁻¹·yr⁻¹) discharge would see the benefits from an earlier snow-melt season and delayed arrival of winter conditions. This suggests a longer growing season, which indicates ameliorating phonology, soil nutrient availability, and hydrothermal environments for vegetation in the HAYR. These trends for hydrological and ecological changes in the HAYR may potentially improve ecological safety and water supplies security in the HAYR and downstream Yellow River basins.