Different storm responses of organic carbon transported to Lake Taihu by the eutrophic Tiaoxi River, China

Human and natural activities regulate organic matter transport in Chinese rivers

Rivers connect terrestrial and aquatic ecosystems and export approximately 55.47 % of the net terrestrial carbon fixation. However, due to unavailable high-frequency monitoring data, litter is known about diurnal variation in riverine carbon transport on a national scale. Based on daily measurements between March 2021 and February 2022 at 1491 stations across China, this study clarified the spatiotemporal variations in riverine organic matter indicated by chemical oxygen demand (COD). Spatially, COD content showed a spatial pattern with high values in the northwest (p < 0.05), and COD flux was determined by water discharge (84.01 %). Human activities explained 73.20 % of the spatial variations in riverine COD content; in particular, agricultural planting significantly elevated riverine COD (r = 0.73, p < 0.01). Seasonally, 95.53 % of stations showed significant seasonal variations in COD contents (p < 0.05); 69.72 % (25.81 %) were identified as Type II (III) typically had the maximum (minimum) COD in summer (autumn). Moreover, except for human activities (41.08 ± 22.94 %), natural factors also contributed 47.41 ± 24.04 % to the seasonal variations. In summer, high temperatures increased COD by promoting algal proliferation at Type II stations; however, heavy precipitation diluted COD contents at Type III stations. In these cases, seasonal measurements were essential for estimating riverine organic matter transport, especially the values measured in spring and winter. This study has significant implications for managing the aquatic environment, estimating riverine organic matter transport, and balancing the global carbon budget.

High frequency UV-Vis sensors estimate error in riverine dissolved organic carbon load estimates from grab sampling

High frequency ultraviolet - visible (UV-VIS) sensors offer a way of improving dissolved organic carbon (DOC) load estimates in rivers as they can be calibrated to DOC concentration. This is an improvement on periodic grab sampling, or the use of pumped sampling systems which store samples in-field before collection. We hypothesised that the move to high frequency measurements would increase the load estimate based on grab sampling due to systemic under-sampling of high flows. To test our hypotheses, we calibrated two sensors in contrasting catchments (Exe and Bow Brook, UK) against weekly grab sampled DOC measurements and then created an hourly time series of DOC for the two sites. Taking this measurement as a 'true' value of DOC load, we simulated 1,000 grab sampling campaigns at weekly, fortnightly and monthly frequency to understand the likely distribution of load and error estimates. We also performed an analysis of daily grab samples collected using a pumped storage sampling system with weekly collection. Our results show that: a) grab sampling systemically underestimates DOC loads and gives positively skewed distributions of results, b) this under-estimation and positive skew decreases with increasing sampling frequency, c) commonly used estimates of error in the load value are also systemically lowered by the oversampling of low, stable flows due to their dependence on the variance in the flow-weighted mean concentration, and d) that pumped storage systems may lead to under-estimation of DOC and over estimation of specific ultra-violet absorbance (SUVA), a proxy for aromaticity, due to biodegradation during storage.

Water Quality Chl-a Inversion Based on Spatio-Temporal Fusion and Convolutional Neural Network

The combination of remote sensing technology and traditional field sampling provides a convenient way to monitor inland water. However, limited by the resolution of remote sensing images and cloud contamination, the current water quality inversion products do not provide both high temporal resolution and high spatial resolution. By using the spatio-temporal fusion (STF) method, high spatial resolution and temporal fusion images were generated with Landsat, Sentinel-2, and GaoFen-2 data. Then, a Chl-a inversion model was designed based on a convolutional neural network (CNN) with the structure of 4-(136-236-340)-1-1. Finally, the results of the Chl-a concentrations were corrected using a pixel correction algorithm. The images generated from STF can maintain the spectral characteristics of the low-resolution images with the R2 between 0.7 and 0.9. The Chl-a inversion results based on the spatio-temporal fused images and CNN were verified with measured data (R2 = 0.803), and then the results were improved (R2 = 0.879) after further combining them with the pixel correction algorithm. The correlation R2 between the Chl-a results of GF2-like and Sentinel-2 were both greater than 0.8. The differences in the spatial distribution of Chl-a concentrations in the BYD lake gradually increased from July to August. Remote sensing water quality inversion based on STF and CNN can effectively achieve high frequency in time and fine resolution in space, which provide a stronger scientific basis for rapid diagnosis of eutrophication in inland lakes.