Surface water is the main source of irrigation and drinking water for rural communities by the Hulan River basin, an important grain-producing region in northeastern China. Understanding the spatial and temporal distribution of water quality and its driving forces is critical for sustainable development and the protection of water resources in the basin. Following sample collection and testing, the spatial distribution and driving forces of water quality were investigated using cluster analysis, hydrochemical feature partitioning, and Gibbs diagrams. The results demonstrated that the surface waters of the Hulan River Basin tend to be medium–weakly alkaline with a low degree of mineralization and water-rock interaction. Changes in topography and land use, confluence, application of pesticides and fertilizers, and the development of tourism were found to be important driving forces affecting the water quality of the basin. Non-point source pollution load fluxes of nitrogen (N) and phosphorus (P) were simulated using the Soil Water and Assessment Tool. The simulation demonstrated that the non-point source pollution loading is low upstream and increases downstream. The distributions of N and P loading varied throughout the basin. The findings of this study provide information regarding the spatial distribution of water quality in the region and present a scientific basis for future pollution control.

The SWAT model has been widely used to simulate snowmelt runoff in cold regions thanks to its ability of representing the effects of snowmelt and permafrost on runoff generation and confluence. However, a core method used in the SWAT model, the temperature index method, assumes both the dates for maximum and minimum snowmelt factors and the snowmelt temperature threshold, which leads to inaccuracies in simulating snowmelt runoff in seasonal snowmelt regions. In this paper, we present the development and application of an improved temperature index method for SWAT (SWAT+) in simulating the daily snowmelt runoff in a seasonal snowmelt area of Northeast China. The improvements include the introduction of total radiation to the temperature index method, modification of the snowmelt factor seasonal variation formula, and changing the snowmelt temperature threshold according to the snow depth derived from passive microwave remote sensing data and temperature in the seasonal snowmelt area. Further, the SWAT+ model is applied to study climate change impact on future snowmelt runoff (2025–2054) under the climate change scenarios including SSP2.6, SSP4.5, and SSP8.5. Much improved snowmelt runoff simulation is obtained as a result, supported by several metrics, such as MAE, RE, RMSE, R², and NSE for both the calibration and validation. Compared with the baseline period (1980–2019), the March–April ensemble average snowmelt runoff is shown to decrease under the SSP2.6, SSP4.5, and SSP8.5 scenario during 2025–2054. This study provides a valuable insight into the efficient development and utilization of spring water resources in seasonal snowmelt areas.

Drought monitoring is one of the significant issues of water resources assessment. Multiple drought indices (DIs), including Percent of Normal (PN), Standardized Precipitation Index (SPI), statistical Z-Score, and Effective Drought Index (EDI) at 18 different timesteps were employed to evaluate the drought condition in Wuyuer River Basin (WRB), Northeast China. Daily precipitation data of 50 years (1960–2010) from three meteorological stations were used in this study. We found DIs with intermediate time steps (7 to 18 months) to have the highest predictive values for identifying droughts. And DIs exhibited a better similarity in the 12-month timestep. Among all the DIs, EDI exhibited the best correlation with other DIs for various timesteps. When further comparing with historical droughts, Z-Score, SPI, and EDI were found more sensitive to multi-monthly cumulative precipitation changes (r² > 0.55) with respect to monthly precipitation changes (r² ≤ 0.10), while EDI was more preferable when only monthly precipitation data were available. These results indicated that various indices for different timesteps should be investigated in drought monitoring in WRB, especially the intermediate timesteps should be considered.

The objectives of this study were to assess land use changes and their hydrological impacts in the Nenjiang River Basin (NRB). The Soil and Water Assessment Tool (SWAT) model was employed to evaluate the impacts of land use changes. The Cellular Automata-Markov model was used to predict a land use map in 2038. Streamflow under each land use state was simulated by the SWAT model. The results showed that there was a significant expansion of agriculture area at the expense of large areas of grassland, wetland, and forest during 1975–2000. The land use changes during the period of 1975 to 2000 had decreased the water yield (3.5%), surface runoff (1.7%), and baseflow (19%) while they increased the annual evapotranspiration (2.1%). For impacts of individual land use type, the forest proved to have reduced streamflow in the flood season (10%–28%) and increased surface runoff in the drought season (20%–38%). Conversely, grassland, dry land, and paddy land scenarios resulted in increase of streamflow during summer months by 7%–37% and a decrease of streamflow in the cold seasons by 11.7%–59.7%. When the entire basin was changed to wetland, streamflow reduced over the whole year, with the largest reduction during January to March. The 2038 land use condition is expected to increase the annual water yield, surface runoff and wet season flow, and reduce evapotranspiration and baseflow. These results could help to improve sustainable land use management and water utilization in the NRB.

The calculation of hydrologic frequency is an important basic step in the planning and design stage of any water conservancy project. The purpose of the frequency analysis is to deduce the hydrologic variables under different guarantee rates, and to provide hydrologic information for water conservancy project planning and design. The calculation of hydrologic frequency requires that the sample size is large enough, as only then can the statistical characteristics of samples take the place of the total statistical eigenvalues. This means that the samples can reveal the statistical characteristics of hydrologic variables and identify the randomness rule of hydrologic phenomena. Many countries in the East Asian monsoon climate zone (China, Japan and South Korea) have stipulated a sample size of 30 years for hydrologic frequency analysis. In this paper the rationality of the 30-year sample size is proved by analyzing the periodic and random rules of hydrologic phenomenon and the influencing mechanism of solar activity, and by adopting the general conclusion of the sampling theorem. Then, using the wavelet analysis method to examine annual precipitation data in a long series generated from representative precipitation observation stations in China, the strong-weak cycle of solar activity is proved to be 10 years, which is consistent with the wet-dry cycle of the representative precipitation stations (10–12 years). Finally, adopting numerical modeling to analyze the normal distribution of randomly generated samples and long-range annual precipitation data collected from representative stations, hypothesis testing (u, F and t) is used to prove that a 30-year sample size is reasonable. This research provides a reference as to how to prove the necessary sample size for relevant statistical analyses (for example, how large the sample should be for analyzing hydrologic factors trend evolution, hydrologic data consistency and ergodicity of statistical samples), thus ensuring the reliability of the analytical results.

Low Impact Development (LID) is one of the sustainable approaches to urban stormwater management in areas with rapid urbanization. Although LID has been shown to have a positive effect in flood reduction, the hydrological balance regulation effect of LID under a variety of rainfall events is not fully understood. In this study, we assessed the hydrological efficiency of LID at two residential–commercial mixed sites in Korea to investigate the main function of LID in terms of diverse rainfall characteristics. Storm Water Management Model (SWMM) was constructed to simulate the hydrological process numerical simulations in the pre-development, post-development and LID design scenarios, respectively. The model was calibrated and validated by using five observed rainfall–runoff events. Then, four single and four multiple LID practices (LIDs) were used to estimate their effectiveness under seven different designed rainfall events. The results indicate that LIDs substantially influence the hydrology cycle system, while the regulating effect varies with rainfall amounts. The efficiency of LIDs in flood reduction is proved to be more effective during lower storm events. However, LIDs should be designed to primarily prioritize the restoration of hydrological balance when the rainfall return period is longer.