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We have developed a hybrid model that integrates chaos theory and an extreme learning machine with optimal parameters selected using an improved particle swarm optimization (ELM-IPSO) for monthly runoff analysis and prediction. Monthly streamflow data covering a period of 55 years from Daiying hydrological station in the Chaohe River basin in northern China were used for the study. The Lyapunov exponent, the correlation dimension method, and the nonlinear prediction method were used to characterize the streamflow data. With the time series of the reconstructed phase space matrix as input variables, an improved particle swarm optimization was used to improve the performance of the extreme learning machine. Finally, the optimal chaotic ensemble learning model for monthly streamflow prediction was obtained. The accuracy of the predictions of the streamflow series (linear correlation coefficient of about 0.89 and efficiency coefficient of about 0.78) indicate the validity of our approach for predicting streamflow dynamics. The developed method had a higher prediction accuracy compared with an auto-regression method, an artificial neural network, an extreme learning machine with genetic algorithm and with PSO algorithm, suggesting that ELM-IPSO is an efficient method for monthly streamflow prediction.

The rapid growth of urbanization and industrialization, along with dramatic climate change, has strongly influenced hydrochemical characteristics in recent decades in China and thus could cause the variation of pH and general total hardness of a river. To explore such variations and their potential influencing factors in a river of the monsoon climate region, we analyzed a long-term monitoring dataset of pH, SO4 (2-), NOx, general total hardness (GH), Mg(2+), Ca(2+), and Cl(-) in surface water and groundwater in the Luan River basin from 1985 to 2009. The nonparametric Seasonal Kendall trend test was used to test the long-term trends of pH and GH. Relationship between the affecting factors, pH and GH were discussed. Results showed that pH showed a decreasing trend and that GH had an increasing trend in the long-term. Seasonal variation of pH and GH was mainly due to the typical monsoon climate. Results of correlation analysis showed that the unit area usage amounts of chemical fertilizer, NO3 (-), and SO4 (2-) were negatively correlated with pH in groundwater. In addition, mining activity affected GH spatial variation. Acid deposition, drought, and increasing the use of chemical fertilizers would contribute to the acidification trend, and mining activities would affect the spatial variation of GH. Variations of precipitation and runoff in semi-arid monsoon climate areas had significant influences on the pH and GH. Our findings implied that human activities played a critical role in river acidification in the semi-arid monsoon climate region of northern China.

Modeling pollutant processes in soil and water is helpful to better understand a pollutant’s potential adverse impacts on an environmental system. There are many watershed pollution load models; however, their effectivity is generally poor in semiarid and semihumid areas because of the complexity of hydrological processes and pollutant transport processes. In this paper, a pollution load module was combined with the well-established HIMS (Hydro-Informatic Modelling System) model for the purpose of simulating the processes of pollution generation and transport and quantifying the pollution load in semiarid and semihumid areas. The model has the advantages of a simple structure and low data requirement, and can simulate dissolved and solid-phase nitrogen and phosphorus. As a demonstration, the HIMS-pollution load (HIMS-PL) model was respectively tested for three semiarid and semihumid basins in China—the Liuhe and the Yixunhe River basins, two sub-basins of the Luanhe River basin, and the Chaohe River basin. Model simulations of streamflow and pollution loads at monthly time step accurately matched the observed streamflow (with NSEs 0.77–0.95 and REs 0.53–11.24%) and pollution load (with NSEs 0.54–0.83 and REs 2.45–12.72% for total nitrogen loads and with NSEs 0.51–0.79 and REs 2.22–19.70% for total phosphorus loads). In comparison with HSPF and GWLF models, the HIMS-PL model performed better than the GWLF model in all of the above-mentioned basins and better than the HSPF model in some of the basins. Overall, the proposed model is generally suitable for simulating streamflow and pollution load in semiarid and semihumid regions.

Understanding pollutant transport process and source apportionment is critical to urban stormwater pollution mitigation. Previous studies have investigated transport and sources of road deposited sediments (RDS) and sewer sediments individually, and most of these studies focused on stormwater pollution in combined sewer systems. However, studies about pollutant transport and source apportionment of the entire urban non-point source pollution process in separate sewer systems are lacking. This study analyzed particle size distribution and chemical pollutants in five media during the entire pollutant process including RDS, roof runoff, road runoff, sewer sediments, and sewer runoff. The outcomes found that mass percentage of fine particles became greater during pollutant transport in stormwater runoff. According to transport characteristics, particles were grouped into three types: particles20 μm, 20-105 μm, and105 μm. Particles20 μm had the highest mobility capacity and particles105 μm had the lowest mobility capacity, while mobility capacity of particles 20-105 μm was uncertain. Pollutant concentrations in road runoff were significantly influenced by rainfall intensity and pollutant concentrations in sewer runoff could become lower during rainy seasons ignoring rainfall intensity. RDS was the main contributor of heavy metals while organic matter and nutrients were primarily contributed by sewer sediments. Roof runoff, road runoff and sewer sediments contributed 5.35%, 69.24% and 25.41% particles to urban receiving water, respectively. Based on the outcomes, several suggestions were given for stormwater management.

Identification of the interactive responses of water quantity and quality to changes in nature and human stressors is important for the effective management of water resources. Many studies have been conducted to determine the influence of these stressors on river discharge and water quality. However, there is little information about whether sewage treatment plants can improve water quality in a region where river streamflow has decreased sharply. In this study, a seasonal trend decomposition method was used to analyze long-term (1996–2015) and seasonal trends in the streamflow and water quality of the Guanting Reservoir Basin, which is located in a semi-arid region of China. The results showed that the streamflow in the Guanting Reservoir Basin decreased sharply from 1996–2000 due to precipitation change and human activities (human use and reservoir regulation), while the streamflow decline over the longer period of time (1996–2015) could be attributed to human activities. During the same time, the river water quality improved significantly, having a positive relationship with the capacity of wastewater treatment facilities. The water quality in the Guanting Reservoir showed a deferred response to the reduced external loading, due to internal loading from sediments. These results implied that for rivers in which streamflow has declined sharply, the water quality could be improved significantly by actions to control water pollution control. This study not only provides useful information for water resource management in the Guanting Reservoir Basin, but also supports the implementation of water pollution control measures in other rivers with a sharp decline in streamflow.

The hydrologic simulation program-FORTRAN (HSPF) model is widely used to develop management strategies for water resources, but its effectiveness is limited by predictive uncertainties associated with model input data. This study evaluated the effect of rainfall data resolution on the model performance when predicting runoff. We examined hourly, 3-hourly, 12-hourly, and 24-hourly temporal resolutions, and spatial resolutions from seven to one rain gauges. We used a statistical sensitivity analysis to test the effect of resolution on model accuracy, and a dynamic sensitivity analysis to test the effect on model parameters. Our results indicate that the model performance reduces when using a coarser rainfall resolution. The model used the corresponding parameters to absorb the effect of various resolution changes and reduced their impact on the runoff simulations. We used the paired-samples t-test to examine the significance of the rainfall data resolution to the model parameters, which revealed that the model accuracy was more sensitive to the temporal resolution. Our statistical analysis of the dispersion examined the parameter values. It showed that one parameter was sensitive to temporal resolution and three parameters were sensitive to spatial resolution. This study provided useful information for determining HSPF model parameters using rainfall data at different resolutions.

Nutrient discharge during storm events is a critical pathway for nutrient export in semi-arid catchments. We investigated nutrient dynamics during three summer storms characterized by different rainfall magnitude in 2012 in a semi-arid catchment of northern China. The results showed that, in response to storm events, nutrient dynamics displayed big variation in temporal trends of nutrient concentration and in nutrient concentration-flow discharge relationships. Nutrient concentrations had broader fluctuations during an extreme storm than during lesser storms, whereas the concentration ranges of the a moderate storm were no broader than those of a smaller one. The different concentration fluctuations were caused by storm magnitude and intensity coupled with the antecedent rainfall amount and cumulative nutrients. Correlation coefficients between nutrient concentrations and flow discharge varied from positive to negative for the three different events. There were no consistent hysteresis effects for the three different events, and no hysteresis effects were observed for any of the variables during the moderate storm (E2). Our findings provide useful information for better understanding nutrient loss mechanisms during storm events in semi-arid areas of a monsoon climate region.

An in-depth understanding of the road-deposited sediments (RDS) wash-off process is essential to estimation of urban surface runoff pollution load and to designing methods to minimize the adverse impacts on the receiving waters. There are two debatable RDS wash-off views: source limited and transport limited. The RDS build-up and wash-off process was characterized to explore what determines the wash-off process to be source limited or transport limited based on twelve RDS sampling activities on an urban road in Beijing. The results showed that two natural rain events (2.0mm and 23.2mm) reduced the total RDS mass by 30%-40%, and that finer particles (