Search

The impacts of climate change and human activity, combined with streamflow reduction in the Yellow River Basin, have presented significant challenges to water resource management strategies. Here, the trends and change points of streamflow were determined for the period 1956–2017 via five statistical methods. A runoff-sensitive coefficients method (the Budyko hypothesis) and a conceptual rainfall–runoff model (the TUW model) were applied to assess the streamflow variation. The following conclusions were ascertained: (1) 1989, 1986, and 1990 were the change points for streamflow in the upstream Tang-Nai-Hai and Lan-Zhou stations and the downstream Hua-Yuan-Kou hydrological station; (2) the streamflow showed statistically significant decreasing trends with spatiotemporal variations in the Yellow River Basin; (3) the relationship between runoff and precipitation showed a downward trend over time; (4) comparisons of the Budyko and TUW models show that human activity is responsible for more than 65% of streamflow reduction, while climate change contributes to less than 35% of the reduction. Therefore, human activity is the main reason for streamflow reduction in the Yellow River Basin. This finding is of critical importance for water resources management under changing environment.

The groundwater-dependent ecosystem in the Gnangara region is confronted with great threats due to the decline in groundwater level since the 1970s. The aim of this study is to apply multiple trend analysis methods at 351 monitoring bores to detect the trends in groundwater level using spatial, temporal and Hydrograph Analysis: Rainfall and Time Trend models, which were applied to evaluate the impacts of rainfall on the groundwater level in the Gnangara region, Western Australia. In the period of 1977–2017, the groundwater level decreased from the Gnangara’s edge to the central-north area, with a maximum trend magnitude of −0.28 m/year. The groundwater level in 1998–2017 exhibited an increasing trend in December–March and a decreasing trend in April–November with the exception of September when compared to 1978–1997. The rainfall + time model based on the cumulative annual residual rainfall technique with a one-month lag during 1990–2017 was determined as the best model. Rainfall had great impacts on the groundwater level in central Gnangara, with the highest impact coefficient being 0.00473, and the impacts reduced gradually from the central area to the boundary region. Other factors such as pine plantation, the topography and landforms, the Tamala Limestone formation, and aquifer groundwater abstraction also had important influences on the groundwater level.

As a crucial agricultural and economic development zone since the Qin Dynasty (221 to 206 BC), the Guanzhong section of the Weihe River basin is facing serious water resource shortages due to population growth and regional development. Its water resource amount per capita is only 361 m3 , about 1/6 of the average in China and less than 1/20 of the average in the world. Surface water and groundwater (SW-GW) interaction, having a significant influence on the spatiotemporal distribution of water resources, was qualitatively and quantitatively investigated during a wet year based on stable isotopes and hydrochemistry. The results show that the recharge pattern in the north part varies with season, that is, 40% of the surface water recharge comes from groundwater in the dry season, but 93% of the groundwater recharge comes from surface water in the rainy season. In the south part, groundwater is always recharged by surface water, with contributions of 47% and 61% in the rainy and dry seasons, respectively. For the main stream, the recharge pattern is complicated and varies with season and site. This study will provide useful information about SW-GW interaction at basin scale. Integrated management of groundwater and surface water could improve the efficiency of regional water resources utilization and promote accurate and sustainable water management in the semi-arid basin.

Streamflow from forested mountain watersheds is critical to aquatic ecosystems and social development in watersheds. However, understanding the intra-annual variability of streamflow is limited by the lack of observation of terrestrial water storage (TWS) in large-scale watersheds. This study developed a monthly Budyko framework incorporating TWS from the Gravity Recovery and Climate Experiment (GRACE). The extended Budyko framework was applied using four classic Budyko equations in the Qinba Mountains. The results showed that the extended Budyko framework could competently represent the relationship between monthly water supply and demand, with better performance than the original Budyko framework. Based on the extended Budyko framework, this study further quantified the contributors of streamflow variability using the variance decomposition method. The dominant contributor to intra-annual streamflow variability was precipitation (50%), followed by TWS (11%) and their covariance (-21%) in this region. Specifically, precipitation played a dominant role on streamflow variability in summer and autumn, while evapotranspiration and TWS significantly impacted streamflow in spring and winter, respectively. Furthermore, the hydrologic effects of rainfall intensity and vegetation were investigated to explain streamflow variability. As the rainfall intensity decreases, more precipitation is partitioned into evapotranspiration and TWS, while the increase of rainfall intensity leads to the partitioning of precipitation into streamflow. Similarly, monthly vegetation promotes the partitioning of precipitation into TWS, while inhibiting the partitioning of precipitation into streamflow. The opposite effect of vegetation on streamflow and TWS is weakened due to the neglect of TWS at an annual timescale, which may lead to an overestimation of the effect of annual vegetation on streamflow. The results have implications for improving the performance of the Budyko framework to reveal the relationship between monthly water supply and demand and understanding streamflow variability at an intra-annual timescale.

The aim of this study is to promote appropriate land development policies and to improve operations of flood risk in urban areas. This study first illustrated a multi-parameter flood hazard index (FHI) model for assessing potential flood risk areas in the Guanzhong Urban Area (GUA), a large-scale urban area in northwestern China. The FHI model consisted of the following seven parameters: rainfall intensity, flow accumulation, distance from the river network, elevation, land use, surface slope, and geology. The parameter weights were assigned using an analytical hierarchy process and the sum weight of the first three parameters accounted for 71.21% of the total weight and had significant influence on flooding. By combining with population factor, the FHI model was modified to estimate the flood control area in the GUA. The spatial distribution of the flood risk was obviously different in the flood hazard area and flood control area. The very low risk and medium risk area in the flood control area increased by 11.19% and reduced by 9.03% compared to flood hazard area, but there were no obvious differences in other levels of risk areas. The flood control assessment indicated that very high flood risk areas were principally concentrated along river banks (the Weihe River and its tributaries) and in the middle of the Guanzhong Plain. Land use and population distribution are related to flooding. Especially, forestland was located in 84.48% of the very low risk area, while low risk areas were mainly located in 91.49% of high population dispersion area.

The micro-topography feature of a riverine system is a controlling attribute to induce the change of patterns and magnitudes of hyporheic water exchange. The study aims to determine how hyporheic water exchange is affected by the bank curvatures of test points at meter scale. A one-dimensional heat steady-state transport model was applied to determine patterns and magnitudes of vertical hyporheic water exchange in January and July 2015. The bank curvatures were calculated based on the curvature formula. The results demonstrate that vertical water exchange patterns of all test points were upwards during the two test periods, and the higher vertical fluxes mostly occurred in January 2015. Large curvatures for either sides of convex banks in the two periods resulted in higher vertical water exchange fluxes, and the significantly higher vertical fluxes occurred near the apex of bends. Additionally, a flow pattern from river bank discharging into stream was derived during the campaign in July 2015, and significantly higher fluxes were obtained along the straight bank where more riparian vegetation was adjacent to the bank/water interface. It can be suggested that the bank curvatures and riparian vegetation are considered the crucial attributes influencing hyporheic water exchange.

Nitrate contamination in rivers has raised widespread concern in the world, particularly in arid/semi-arid river basins lacking qualified water. Understanding the nitrate pollution levels and sources is critical to control the nitrogen input and promote a more sustainable water management in those basins. Water samples were collected from a typical semi-arid river basin, the Weihe River watershed, China, in October 2014. Hydrochemical assessment and nitrogen isotopic measurement were used to determine the level of nitrogen compounds and identify the sources of nitrate contamination. Approximately 32.4% of the water samples exceeded the World Health Organization (WHO) drinking water standard for NO3−-N. Nitrate pollution in the main stream of the Weihe River was obviously much more serious than in the tributaries. The δ15N-NO3− of water samples ranged from +8.3‰ to +27.0‰. No significant effect of denitrification on the shift in nitrogen isotopic values in surface water was observed by high dissolved oxygen (DO) values and linear relationship diagram between NO3−-N and δ15N-NO3−, except in the Weihe River in Huayin County and Shitou River. Analyses of hydrochemistry and isotopic compositions indicate that domestic sewage and agricultural activities are the main sources of nitrate in the river.