Synergistic Impacts of Climate Change and Human Activities on Spatiotemporal Organic Nitrogen Burial Variation in a Plateau Lake in Southwest China

The concentration and sources of organic nitrogen (ON) in lake sediment significantly affect the lake nitrogen cycle. However, the influencing factors and contributors to the ON accumulation rate (ONAR) are unclear. In this study, tree sediment cores from northern, eastern, and southern Dianchi Lake (DC-N, DC-E, and DC-S, respectively), sampled in July 2014, were used to study the effects of autochthonous and allochthonous sources on ON. The results showed that ON and the ONAR increased 2.4–5.1 and 2.6–4.8 times, respectively, from1900 to2000, especially since the 1980s, at which point algal blooms occurred more frequently. The ON contents decreased in the order: DC-S > DC-N > DC-E, whereas the ONAR values followed the order: DC-N > DC-S > DC-E, suggesting that the ONAR was influenced by ON content as well as depositional environmental conditions. The total concentrations of n-alkanes (n-C12 to n-C34) ranged from 4719.4 ng g− 1 to 61,959.6 ng g− 1 in the three sediment cores, each of which exhibited different n-alkanes characteristic variation with vertical depth. The sources of ON were mainly allochthonous (soil erosion and terrestrial plants) and autochthonous (algal and aquatic plants) in DC-S and DC-N, respectively, whereas they were primarily mixed planktonic and terrestrial sources in DC-E. Using the stochastic impacts by regression on population, affluence, and technology model to further examine the ONAR values revealed that 1% increase in temperature and nitrogen fertilizer can increase the ONAR by 73.8–86.2% and 73.2–151.3% in all sediments, especially in DC-S and DC-E. However, a 1% increase in construction area could reduce the ONAR by 2.4–14.2%, especially in DC-N. Overall, climate change and human activities determine the spatial and temporal ONAR variation in Dianchi Lake.

Water clarity annual dynamics (1984–2018) dataset across China derived from Landsat images in Google Earth Engine

Water clarity provides a sensitive tool to examine spatial pattern and historical trend in lakes trophic status. Yet, this metric has insufficiently been explored despite the availability of remotely-sensed data. We used three Secchi disk depth (SDD) datasets for model calibration and validation from different field campaigns mainly conducted during 2004–2018. The red/blue band ratio algorithm was applied to map SDD for lakes (> 1 ha) based on the first SDD dataset, where R2 = 0.79, RMSE = 100.3 cm, rRMSE = 61.9 %, MAE = 57.7 cm. The other two datasets were used to validate the SDD estimation model, which were indicated the model had a stable performance of temporal transferability. The annual mean SDD of lakes were retrieved across China using Landsat top of air reflectance products in GEE from 1984 to 2018. The spatiotemporal dynamics of SDD were analysed at the five lake regions and individual lake scales, and the average, changing trend, lake number and area, and spatial distribution of lake SDDs across China were presented. In 2018, we found that the lakes with SDDs < 2 m accounted for the largest proportion (80.93 %) of the total lakes, but the total area of lakes with SDD between 0–0.5 m and > 4 m were the largest, accounting for 48.28 % of the total lakes. During 1984–2018, lakes in the Tibetan-Qinghai Plateau lake region (TQR) had the clearest water with an average value of 3.32 ± 0.38 m, while that in the Northeastern lake region (NLR) exhibited the lowest SDD (mean: 0.60 ± 0.09 m). Among the 10,814 lakes with SDD results more than 10 years, 55.42 % and 3.49 % of lakes experienced significant increasing and decreasing trends, respectively. At the five lake regions, except for the Inner Mongolia-Xinjiang lake region (MXR), more than half of the total lakes in every other lake region exhibited significant increasing trends. In the Eastern lake region (ELR), NLR and Yungui Plateau lake region (YGR), almost more than 50 % of the lakes that displayed an increase or decrease in SDD were mainly distributed in an area of 0.01–1 km2, whereas that in the TQR and MXR were primarily concentrated in large lakes (> 10 km2). Spatially, lakes located in the plateau regions generally exhibited higher SDD than those situated in the flat plain regions. The dataset can now be accessed through the website of the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn): DOI: 10.11888/Hydro.tpdc.271571.