Evaluation of net anthropogenic nitrogen inputs in the Three Gorges Reservoir Area

Abstract

Excessive nitrogen input caused by human activities is an important factor threatening regional water security and economic development. In this study, net anthropogenic nitrogen input has offered an advantage in evaluating nitrogen input’s temporal and spatial variation in the Three Gorges Reservoir Area in 2006–2019. Component characteristics like sensitivity and proportional contribution of net anthropogenic nitrogen input in the time trend were assessed using a mathematical differential equation multivariate function principle for the first time. Then, redundancy analysis quantitatively analysed the distribution and interpretation of districts’ socio-economic and environmental impact factors on net anthropogenic nitrogen input. The results showed high net anthropogenic nitrogen input levels in the Three Gorges Reservoir Area in 2006–2019 with significant temporal and spatial variations (one-way ANOVA, p < 0.01), the mean value of net anthropogenic nitrogen input in the Three Gorges Reservoir Area was 12399.35 kg·km−2·a−1. Net anthropogenic nitrogen input in Xingshan (4907.45 kg·km−2·a−1) was the smallest, and that in Fuling (24005.10 kg·km−2·a−1) was the largest. The spatial distribution characteristics were high at two ends, they were low in the middle of the Three Gorges Reservoir Area. Even though the proportion of nitrogen fertiliser with the largest input source of net anthropogenic nitrogen input, accounted for 48%–53%, the main methods to reduce net anthropogenic nitrogen input also reduced the nitrogen fertilisers’ input in the Three Gorges Reservoir Area. Subsequently, since the atmospheric nitrogen deposition proportion was generally increasing, we focused on controlling the emission of the atmospheric nitrogen in the future. Districts had different net anthropogenic nitrogen input compositions, revealing the analysis of districts was necessary. Additionally, the sensitive coefficients of the nitrogen fixation and nitrogen of crop production were the largest, accounting for 1.09 and 0.98, which indicated that biological nitrogen fixation is the most sensitive factor in adjusting the agricultural structure that can quickly affect net anthropogenic nitrogen input in the Three Gorges Reservoir Area. Similarly, results showed that the sensitive coefficient and proportional contributions changed with districts and presented different characteristics. Therefore, the transformation from the reservoir to the region can serve as a more accurate management strategy. Moreover, the distribution of net anthropogenic nitrogen input in the districts was highly and significantly correlated with sowing area (p < 0.01), making it significantly and negatively correlated with the forest coverage rate. This result indicated that while the agricultural structure was the most sensitive factor affecting net anthropogenic nitrogen input, accurate nitrogen input management could be realised on a district scale. Therefore, we consider analyses of district component characteristics and impact factors important in providing decision support for the accurate and effective regional control of nitrogen input management.

Shuhui Wang

PhD Student

My study interests are 1) Machine Learning and Data Mining in Geosciences & remote sensing (e.g., simulation-based optimization framework, fuzzy programming, surrogate modeling); 2) Development of robust watershed management strategies and decision support; 3）Hydrological responses to LULC, human activities and climate change; 4) Hydrological/ waters quality (HWQ) models