Ammonium Nitrogen Streamflow Transport Modelling and Spatial Analysis in Two Chinese Basins

Ammonium nitrogen (NH4+-N), which naturally arises from the decomposition of organic substances through ammonification, has a tremendous influence on local water quality. Therefore, it is vital for water quality protection to assess the amount, sources, and streamflow transport of NH4+-N. SPAtially Referenced Regressions on Watershed attributes (SPARROW), which is a hybrid empirical and mechanistic modeling technique based on a regression approach, can be used to conduct studies of different spatial scales on nutrient streamflow transport. In this paper, the load and delivery of NH4+-N in Poyang Lake Basin (PLB) and Haihe River Basin (HRB) were estimated using SPARROW. In PLB, NH4+-N load streamflow transport originating from point sources and farmland accounted for 41.83% and 32.84%, respectively. In HRB, NH4+-N load streamflow transport originating from residential land and farmland accounted for 40.16% and 36.75%, respectively. Hence, the following measures should be taken: In PLB, it is important to enhance the management of the point sources, such as municipal and industrial wastewater. In HRB, feasible measures include controlling the domestic pollution and reducing the usage of chemical fertilizers. In addition, increasing the vegetation coverage of both basins may be beneficial to their nutrient management. The SPARROW models built for PLB and HRB can serve as references for future uses for different basins with various conditions, extending this model’s scope and adaptability.

Climate, CO2, and Anthropogenic Drivers of Accelerated Vegetation Greening in the Haihe River Basin

Vegetation regulates the exchange of terrestrial carbon and water fluxes and connects the biosphere, hydrosphere, and atmosphere. Over the last four decades, vegetation greening has been observed worldwide using satellite technology. China has also experienced a notably widespread greening trend. However, the responsiveness of vegetation dynamics to elevated CO2 concentration, climate change, and human activities remains unclear. In this study, we attempted to explore the impact of natural (precipitation, air temperature), biogeochemical (CO2), and anthropogenic drivers (nighttime light, afforestation area) on changes in vegetation greenness in the Haihe River Basin (HRB) during 2002–2018 at the county-level. We further determined the major factors affecting the variation in satellite-derived normalized difference vegetation index (NDVI) from moderate resolution imaging spectroradiometer (MODIS) for each county. The results indicated that over 85% of the counties had a significantly increased NDVI trend, and the average linear trend of annual NDVI across the study region was 0.0037 per year. The largest contributor to the NDVI trend was CO2 (mean contribution 45%), followed by human activities (mean contribution of 27%). Additionally, afforestation was a pronounced driving force for NDVI changes in mountainous areas, resulting from ecosystem restoration efforts. Our findings emphasize the crucial role of CO2 fertilization in vegetation cover change, while considering CO2 concentration, climate change, and human activities, and shed light on the significant influences of afforestation programs on water resources, especially in mountainous areas.

Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing

The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH4-N), nitrate-nitrogen (NO3-N), and pH) were modeled and verified. The results show that the performance R2 of the training model is above 80%, and the performance R2 of the verification model is above 70%. In the training model, the highest fitting degree is TN (R2 = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R2 = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters.

Evaluation of Six Satellite-Based Terrestrial Latent Heat Flux Products in the Vegetation Dominated Haihe River Basin of North China

In this study, six satellite-based terrestrial latent heat flux (LE) products were evaluated in the vegetation dominated Haihe River basin of North China. These LE products include Global Land Surface Satellite (GLASS) LE product, FLUXCOM LE product, Penman-Monteith-Leuning V2 (PML_V2) LE product, Global Land Evaporation Amsterdam Model datasets (GLEAM) LE product, Breathing Earth System Simulator (BESS) LE product, and Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD16) LE product. Eddy covariance (EC) data collected from six flux tower sites and water balance method derived evapotranspiration (WBET) were used to evaluate these LE products at site and basin scales. The results indicated that all six LE products were able to capture the seasonal cycle of LE in comparison to EC observations. At site scale, GLASS LE product showed the highest coefficients of determination (R2) (0.58, p < 0.01) and lowest root mean square error (RMSE) (28.2 W/m2), followed by FLUXCOM and PML products. At basin scale, the LE estimates from GLASS product provided comparable performance (R2 = 0.79, RMSE = 18.8 mm) against WBET, compared with other LE products. Additionally, there was similar spatiotemporal variability of estimated LE from the six LE products. This study provides a vital basis for choosing LE datasets to assess regional water budget.

Suspension array platform based on aptamer for high-throughput detection of five environmental hormones

Background As a common small molecule substance, environmental hormones widely exist in nature, especially water sources, which have a profound effects in humans. Highly efficient and sensitive method for estrogens in the environment are essential. Results In this paper, a novel high-throughput platform was established based on five small hormones molecules specificity aptamer and magnetic beads (MBs). The results showed that the sensitivity of the proposed method are greatly improved. The limit of detection(LOD) of this method for atrazine(Atz), profenofos, bisphenolA(BPA), estradiol(E2), and polychlorinated biphenyls(PCBs) were 9.46, 20.75, 23.81, 8.97, 6.27 pg/mL, respectively. The Recovery rate of the diluted environmental hormones spiked in the samples of Haihe river were in the range of 87.5-111.02% with relative standard deviations (RSDs) lower than 28.44%. Conclusion This platform based on new complementary strand fragments can simultaneously rapid detection five environmental hormones. The whole procedure completed within 1.5h including sample treatment, incubation and detection, greatly improving the detection efficiency.

Impact of Assimilating Advanced Himawari Imager Channel 16 Data on Precipitation Prediction over the Haihe River Basin

Assimilation of high-resolution geostationary satellite data is of great value for precise precipitation prediction in regional basins. The operational geostationary satellite imager carried by the Himawari-8 satellite, Advanced Himawari Imager (AHI), has two additional water vapor channels and four other channels compared with its predecessor, MTSAT-2. However, due to the uncertainty in surface parameters, AHI surface-sensitive channels are usually not assimilated over land, except for the three water vapor channels. Previous research showed that the brightness temperature of AHI channel 16 is much more sensitive to the lower-tropospheric temperature than to surface emissivity, which is similar to the three water vapor channels 8–10. As a follow-up work, this paper evaluates the effectiveness of assimilating brightness temperature observations over land from both the three AHI water vapor channels and channel 16 to improve watershed precipitation forecasting through both case analysis (in the Haihe River basin, China) and batch tests. It is found that assimilating AHI channel 16 can improve the upstream near-surface atmospheric temperature forecast, which in turn affects the development of downstream weather systems. The precipitation forecasting test results indicate that adding the terrestrial observations of channel 16 to the assimilation of AHI data can improve short-term precipitation forecasting in the basin.

Spatial and temporal effects of climate change and land use change on runoff, sediment, nitrogen and phosphorus loss in the Haihe River Basin over the past 40 years

Investigating the impacts of climate and land use changes on basin’s hydrological cycle and environment is important to provide scientific evidence to manage the trade-off and synergies among water resource, agricultural production and environment protection. In this study, we quantified the contributions of climate and land-use changes to runoff, sediment, nitrogen and phosphorus losses in the Haihe River basin since the 1980s. The results showed that (1) climate and land-use changes significantly increased evapotranspiration (ET), transport loss (TL), sediment input (SI) and output (SO), and organic nitrogen (ON) and phosphorus production (OP), with ET, SI, and ON affected most. (2) The runoff, sediment and ammonia nitrogen were affected most by climate and land use changes in the Daqing River Basin (217.3 mm), Nanyun River Basin (3917.3 ton) and Chaobai River Basin (87.6 kg/ha), respectively. (3) The impacts of climate and land-use changes had explicit spatial-temporal patterns. In the Daqing River, Yongding River and Nanyun River, the contribution of climate change to runoff and sediment kept increasing and reached 88.6%~98.2% and 63%~77.2%, respectively. In the Ziya River and Chaobai River Basin, the contribution of land use was larger, reaching 88.6%~92.8% and 59.8%~92.7%, respectively. In the Yongding River Basin, Chaobai River Basin, Ziya River Basin and Daqing River Basin, the contribution of land use to nitrogen and phosphorus loss showed an increasing trend in the past 40 years (maximum: 89.7%). By contrast, in Nanyun River and Luanhe River, the contribution of climate change to nitrogen and phosphorus loss increased more obviously (maximum: 92.1%). We quantitatively evaluated the spatial and temporal impacts of climate and land-use changes on runoff, sediment, and nitrogen and phosphorus loss, which are useful to support the optimizations of land and water resources in the River Basin.

New Estimation of Antibiotic Resistance Genes in Sediment Along the Haihe River and Bohai Bay in China: A Comparison Between Single and Successive DNA Extraction Methods

Sediment is thought to be a vital reservoir for antibiotic resistance genes (ARGs). Often, studies describing and comparing ARGs and their potential hosts in sediment are based on single DNA extractions. To date, however, no study has been conducted to assess the influence of DNA extraction efficiency on ARGs in sediment. To determine whether the abundance of ARGs is underestimated, we performed five successive extraction cycles with a widely used commercial kit in 10 sediment samples collected from the Haihe River and Bohai Bay. Our results showed that accumulated DNA yields after five extractions were 1.8–3.1 times higher than that by single DNA extractions. High-throughput sequencing showed that insufficient DNA extraction could generate PCR bias and skew community structure characterization in sediment. The relative abundances of some pathogenic bacteria, such as Enterobacteriales, Lactobacillales, and Streptomycetales, were significantly different between single and successive DNA extraction samples. In addition, real-time fluorescent quantitative PCR (qPCR) showed that ARGs, intI1, and 16S rRNA gene abundance strongly increased with increasing extraction cycles. Among the measured ARGs, sulfonamide resistance genes and multidrug resistance genes were dominant subtypes in the study region. Nevertheless, different subtypes of ARGs did not respond equally to the additional extraction cycles; some continued to have linear growth trends, and some tended to level off. Additionally, more correlations between ARGs and bacterial communities were observed in the successive DNA extraction samples than in the single DNA extraction samples. It is suggested that 3–4 additional extraction cycles are required in future studies when extracting DNA from sediment samples. Taken together, our results highlight that performing successive DNA extractions on sediment samples optimizes the extractable DNA yield and can lead to a better picture of the abundance of ARGs and their potential hosts in sediments.

Improving the Accuracy of Water Storage Anomaly Trends Based on a New Statistical Correction Hydrological Model Weighting Method

The Gravity Recovery and Climate Experiment (GRACE) satellite solutions have been considerably applied to assess the reliability of hydrological models on a global scale. However, no single hydrological model can be suitable for all regions. Here, a New Statistical Correction Hydrological Model Weighting (NSCHMW) method is developed based on the root mean square error and correlation coefficient between hydrological models and GRACE mass concentration (mascon) data. The NSCHMW method can highlight the advantages of good models compared with the previous average method. Additionally, to verify the effect of the NSCHMW method, taking the Haihe River Basin (HRB) as an example, the spatiotemporal patterns of Terrestrial Water Storage Anomalies (TWSA) in HRB are analyzed through a comprehensive comparison of decadal trends (2003–2014) from GRACE and different hydrological models (Noah from GLDAS-2.1, VIC from GLDAS-2.1, CLSM from GLDAS-2.1, CLSM from GLDAS-2.0, WGHM, PCR-GLOBWB, and CLM-4.5). Besides, the NSCHMW method is applied to estimate TWSA trends in the HRB. Results demonstrate that (1) the NSCHMW method can improve the accuracy of TWSA estimation by hydrological models; (2) the TWSA trends continue to decrease through the study period at a rate of 15.7 mm/year; (3) the WGHM and PCR-GLOBWB have positive reliability with respect to GRACE with r > 0.9, while all the other models underestimate TWSA trends; (4) the NSCHMW method can effectively improve RMSE, NES, and r with 3–96%, 35–282%, 1–255%, respectively, by weighting the WGHM and PCR-GLOBWB. Indeed, groundwater depletion in HRB also proves the necessity of the South-North Water Diversion Project, which has already contributed to groundwater recovery.