In this study, the future snowmelt runoff in the chilly northeast region’s Tangwang River Basin was simulated and predicted using the SWAT model, which was built and used based on the NEX-GDDP-CMIP6 climate model. This study conducted a detailed analysis of the spatial and temporal distribution characteristics of snowmelt runoff using high-resolution DEM, land use, and soil data, along with data from historical and future climatic scenarios. Using box plots and the Bflow digital filtering approach, this study first determined the snowmelt runoff period before precisely defining the snowmelt periods. Sensitivity analysis and parameter rate determination ensured the simulation accuracy of the SWAT model, and the correlation coefficients of the total runoff validation period and rate period were 0.75 and 0.76, with Nashiness coefficients of 0.75 for both. The correlation coefficients of the snowmelt runoff were 0.73 and 0.74, with Nashiness coefficients of 0.7 and 0.68 for both, and the model was in good agreement with the measured data. It was discovered that while temperatures indicate an increasing tendency across all future climate scenarios, precipitation is predicted to increase under the SSP2-4.5 scenario. The SSP2-4.5 scenario predicted a decreasing trend regarding runoff, while the SSP1-2.6 and SSP5-8.5 scenarios showed an increasing trend with little overall change and the SSP5-8.5 scenario even showed a decrease of 6.35%. These differences were evident in the monthly runoff simulation projections. Overall, the findings point to the possibility that, despite future climate change having a negligible effect on the hydrological cycle of the Tangwang River Basin, it may intensify and increase the frequency of extreme weather events, creating difficulties for the management of water resources and the issuing of flood warnings. For the purpose of planning water resources and studying hydrological change in this basin and other basins in cold regions, this study offers a crucial scientific foundation. An in-depth study of snowmelt runoff is of great practical significance for optimizing water resource management, rational planning of water use, spring flood prevention, and disaster mitigation and prevention, and provides valuable data support for future research on snowmelt runoff.

Human activities have significantly altered the hydrological processes of rivers. In recent years, the increased focus on global water resource exploitation and land use changes has heightened the significance of related ecological and environmental issues. To investigate the land use changes in Hulan River Basin between 1980 and 2020, and the corresponding flow under various ecological standards, a quantitative assessment of land use changes in Hulan River Basin was conducted by analyzing the Land Use Dynamic Degree (LUD) index and the land use change matrix. Two types of models, namely natural runoff models and status quo runoff models, were developed to evaluate alterations in basin runoff. Various hydrological techniques were utilized to calculate the ecological water deficit in Hulan River Basin. The results suggest the following: (1) human consumption comprises approximately 40% of surface water resources, with Hulan River Basin exhibiting a moderate consumption level; (2) when determining the minimum ecological flow, the Distribution Flow Method (DFM) method yielded slightly higher outcomes compared to alternative methodologies; both the variable Q₉₀ method and DFM (Q₂) method satisfy 10% of the natural river flow, however, in terms of capturing the hydrological pattern, DFM exhibits a slightly lower fitting degree compared to the variable Q₉₀ (monthly average flow with 90% guarantee rate) method; (3) DFM is identified as scientifically reasonable for determining the most suitable ecological flow in comparison to other hydrological methods; (4) despite the widespread water scarcity in Hulan River Basin, the variance between most periods and the ideal ecological flow remains minimal, indicating that severe water shortages are uncommon.

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.

Analyzing the development trend of non-conventional water resources and identifying the main influencing factors is the initial step toward rapidly increasing the utilization and allocation of these resources in a rational and scientific manner. This will help relieve pressure on water resources and improve the ecological environment. This study introduces the concept of comparison testing and employs advanced Dematel and Random Forest models to identify two sets of optimal indicators from a pool of nine. Based on the two best indicator sets, three prediction models—BP neural network, Particle Swarm Optimization-optimized BP neural network, and Genetic neural network—were used to forecast the future potential of non-conventional water resource use in Heilongjiang Province. The findings reveal that economic indicators are the most significant factors influencing Heilongjiang Province’s utilization of non-conventional water resources. The findings of this study help us understand the extent of development in utilizing non-conventional water resources.

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.

In the context of global warming and the intensification of human activities, the change in runoff is also increasing. It is very important to determine the change in runoff for the rational utilization of water resources. In order to determine the influencing factors of runoff change in Muling River, the SWAT model was used in this study to separate different coupling factors and calculate the contribution rate of a single factor to runoff change at the annual scale and quarterly scale, respectively. In the process of calibration, different single rate times were used to analyze the influence of different rate times on the calibration results. The results show that the runoff in the Muling River basin shows a downward trend, the quarterly temperature factor has the greatest influence on the runoff change, which is 50–60%, the annual precipitation has the greatest influence on the runoff change, which is 68%, and the maximum change in the runoff from the reservoir is 42.5% under the change in human activities. In the SWAT-CUP software, the optimal number of calibration for this basin is 500. This research provides a scientific basis for the flow analysis of the Muling River basin.