Songhua River Basin, northeast China, has seen significant changes due to climate change and human activities from 1990 to 2000, when forests were largely reclaimed and agricultural land was taken up to change the terrestrial water cycle drastically. This paper investigates hydrological changes in three basins: the main stream basin of the Songhua River, the Second Songhua River Basin, and the Nenjiang River Basin. Machine learning and signal processing techniques have been applied to reconstruct historical river records with high accuracy, achieving determination coefficients exceeding 0.97. The physically based WEP model effectively simulates both natural hydrological patterns and human-induced hydrological processes in the northern Nenjiang region. Climate projections indicate clear temperature increases across all scenarios. The most significant warming is observed under the SSP5-8.5 scenario, where runoff increases by 8.52% to 12.02%t, with precipitation driving 62% to 78% of the changes. Summer runoff shows the most significant increase, while autumn runoff decreases, particularly in the Nenjiang Basin, where permafrost loss alters spring melt patterns. This change elevates flood risk in summer, with the rate of increase strongly dependent on the scenario. Water resources show strong scenario dependence, with the average growth rate of SSP5-8.5 being 4 times that of SSP1-2.6. A critical threshold is reached at a 2.5 °C increase in temperature, triggering system instability. These results emphasize the need for adaptation to spatial differences to address emerging water security challenges in rapidly changing northern regions, including nonlinear hydroclimatic responses, infrastructure resilience to flow changes, and cross-basin coordination.

Understanding the spatiotemporal dynamics of runoff and its drivers is essential for water resources management in mid–high latitude basins. This study investigates runoff changes in the Songhua River Basin, Northeast China, during 1980–2022 using the Budyko framework, combined with Mann–Kendall trend analysis, Pettitt tests, Hurst index, and wavelet analysis. Results indicate significant climatic shifts, with basin-wide warming, heterogeneous precipitation changes, and declining relative humidity, leading to intensified cold-season drying. Temperature and evapotranspiration showed strong persistence, while precipitation exhibited high variability and periodicities linked to ENSO and East Asian monsoon anomalies. Runoff increased significantly in the mainstream Songhua and Nenjiang basins, especially in autumn, with abrupt changes clustered between 2009 and 2015. The Second Songhua Basin displayed weaker variability, largely influenced by reservoir regulation and land-use change. Attribution analysis confirmed precipitation as a primary driver, with elasticity coefficients exceeding 3 in the Nenjiang Basin during some summers, indicating extreme sensitivity. Evapotranspiration suppressed runoff under high temperatures, while freeze–thaw processes and human interventions became critical in spring and autumn. The aridity index revealed persistent winter deficits and rising spring–autumn drying trends after 2000, posing risks for snowmelt runoff and baseflow sustainability. Overall, runoff evolution reflects a shift from gradual to threshold-triggered regime changes driven by both climate variability and human regulation. These findings provide a basis for adaptive, basin-specific water management and climate resilience strategies in Northeast China.

Under the influence of global climate change, water conservancy projects located in the high-latitude cold regions of the world are facing severe challenges. This study addresses the contradiction between water supply stability and ecological flow during the dry season in cold regions. Taking Linhai Reservoir as the core, it integrates the HEC-RAS hydrodynamic model with multi-source data such as basin topography, hydro-meteorological data, and water conservancy project parameters to construct a multi-scenario water supply scheduling model during the dry season. The aim is to provide scientific recommendations for different reservoir operation strategies in response to varying frequencies of upstream inflow, based on simulations conducted after the reservoir’s completion. Taking into account winter runoff reduction characteristics and engineering parameters, we simulated the relationships between water level and flow, ecological flow requirements, and urban water shortages. The results indicate that in both flood and normal years, dynamic coordination of storage and discharge can achieve a daily water supply of 120,000 cubic meters, with 100% compliance for the ecological flow rate. For mild and moderate drought years, additional water diversion becomes necessary to achieve 93.5% and 89% supply reliability, respectively. During severe and extreme droughts, significantly reduced reservoir inflows lower ecological compliance rates, necessitating emergency measures, such as utilizing dead storage capacity and exploring alternative water sources. The study proposes operational strategies tailored to different drought intensities: initiating storage adjustments in September for mild droughts and implementing peak-shifting measures by mid-October for extreme droughts. These approaches enhance storage efficiency and mitigate ice blockage risks. This research supports the water supply security and river ecological health of urban and rural areas in Mudanjiang City and Hailin City and provides a certain scientific reference basis for the multi-objective coordinated operation of reservoirs in the same type of high-latitude cold regions.

Understanding the spatiotemporal evolution of runoff and its driving mechanisms is of great significance for water resources development, utilization, and sustainable management in mid- to high-latitude river basins under climate change. However, runoff variability is jointly influenced by multiple meteorological factors, and a comprehensive understanding of its multi-scale response characteristics and the relative contributions of different drivers remains limited. In this study, runoff data from three hydrological stations in the Songhua River Basin during 1980–2022 were analyzed. A set of statistical and time-series methods, including the Mann–Kendall test, Pettitt change-point test, Hurst exponent, wavelet analysis, and wavelet coherence, was applied, and a random forest model was used to quantify the influence of key climatic factors such as precipitation, air temperature, and evapotranspiration. The results show that air temperature exhibits significant increasing trends in all four seasons, with the strongest warming occurring in spring (Sen’s slope ≈ 0.06 °C a⁻¹). Precipitation displays pronounced spatial heterogeneity and interannual variability, while evapotranspiration shows an overall increasing trend. Both runoff and major meteorological variables exhibit significant spatial heterogeneity across the basin. Hydro-meteorological variables also show distinct periodic variations among seasons, with temperature, precipitation, and evapotranspiration exhibiting stronger seasonal fluctuations during summer. Wavelet coherence analysis indicates that short-term runoff variability is mainly driven by temperature and precipitation. Temperature exhibits significant coherence with runoff across multiple time scales ranging from approximately 2 to 20 years, whereas precipitation shows stronger coherence at medium- to long-term scales (approximately 10–35 years), with evident seasonal differences. Random forest results indicate that evapotranspiration is the most important contributor to runoff variability at all three stations, accounting for 33.5%, 28.6%, and 26.2% of the total importance at Jiamusi, Fuyu, and Jiangqiao stations, respectively. Temperature and sunshine duration rank second, while precipitation and relative humidity contribute comparatively less. These findings indicate that evapotranspiration plays a key regulatory role in long-term water balance. In addition, runoff exhibits multi-scale variability and a transition from gradual changes to stage-like abrupt shifts. The findings provide a scientific basis for water resources management, flood mitigation, and climate change adaptation in the Songhua River Basin.

Hydrometeorological changes profoundly influence runoff generation and evolution in river basins. It is of great significance to carry out runoff prediction research to ensure water resources security and improve disaster prevention and mitigation capabilities. In this paper, the Ganhe River Basin in Northeast China was taken as the research object. Based on the hydrometeorological and runoff data from 1980 to 2022, a variety of statistical methods were used to systematically study the climate change, runoff evolution characteristics and driving mechanism of the basin. Combined with BP neural network model and CMIP6 climate scenario data, the future runoff changes were predicted. The results showed that the precipitation and relative humidity showed a downward trend, while the temperature, sunshine and evapotranspiration showed an upward trend during the study period. The runoff showed a non-significant upward trend, and an abrupt change occurred in 2009. After the abrupt change, the runoff increased by 38.7% compared with the baseline period. The change in land use was the most significant from 1990 to 2000, and the area of cultivated land increased significantly. Correlation analysis showed that precipitation was the dominant meteorological factor affecting runoff change, and the contribution rate of human activities was 88.51%, which was much higher than that of climate change. The BP neural network model demonstrated satisfactory simulation performance, and the training set and test set R2 reached 0.88 and 0.82, respectively. In the future, both temperature and precipitation will increase under different SSP scenarios. On this basis, the BP neural network prediction results show that the runoff of the basin is generally increasing, and the increase is the most significant under the high emission scenario, and the risk of extreme hydrological events may be further aggravated. These findings provide scientific support for water resources management and ecological conservation in the Ganhe River Basin.

Vegetation dynamics are key indicators of climate change and ecological risk in cold regions. This study investigated vegetation–climate interactions in Heilongjiang Province, China, using an integrated analytical framework that combined the Mann–Kendall test, Pettitt test, Hurst exponent, Continuous Wavelet Transform, and Wavelet Transform Coherence with the Normalized Difference Vegetation Index and meteorological data from 1990 to 2024. The dominant vegetation comprises cold–temperate coniferous forests, mixed broadleaf–conifer forests, meadow steppes, and croplands. Results showed that (1) the climate system experienced asynchronous abrupt changes, with potential evapotranspiration, precipitation, and temperature changing in 2008, 2011, and 2013, respectively. Accordingly, the Normalized Difference Vegetation Index reversed in 2011, with greening rates in the range of 0.009–0.022 yr⁻¹ in the northwestern and central regions and browning rates in the range of −0.010 to −0.025 yr⁻¹ in the southwestern and eastern regions. (2) The Hurst exponent ranged from 0.23 to 0.48 for temperature and potential evapotranspiration, indicating strong anti-persistence and high future ecological vulnerability. (3) Wavelet coherence analysis identified precipitation as the dominant climatic driver at 6–9-year scales, whereas temperature shifted from a short-term positive driver to a long-term stressor, and potential evapotranspiration mainly regulated vegetation at 5–8-year scales. These findings provide scientific support for ecological risk assessment and climate-resilient cold regions engineering.