Effects of changes in land use structure on nitrogen input in the Pingzhai Reservoir watershed, a karst mountain region.

Optimizing land use composition to control nitrogen input into water bodies is one way to address surface source pollution in karst mountain regions. In this study, changes in land use, N sources, and spatial and temporal changes of N migration in the Pingzhai Reservoir watershed were evaluated from 2015 to 2021, and the relationship between land use composition and N input was elucidated. N was the main pollution in the water of the watershed; NO3- was the dominant form of N, and it did not react during migration. N came from soil, livestock manure or domestic sewage, and atmospheric deposition. Isolating the fractionation effects of source nitrogen is crucial to improve the accuracy of nitrogen and oxygen isotope traceability in the Pingzhai Reservoir. From 2015 to 2021, the grassland area in the Pingzhai Reservoir increased by 5.52%, the woodland area increased by 2.01%, the water area increased by 1.44%, the cropland decreased by 5.8%, unused land decreased by 3.18%, and construction land remained unchanged. Policies and reservoir construction were the main drivers of changes in land-use type in the catchment. Changes in land use structure affected nitrogen input patterns, with unused land having a highly significant positive correlation with inputs of NH3-N, NO2-, and TN, and construction land having a significant positive correlation with the input of NO2-. The inhibitory effect of forest and grassland on nitrogen input in the basin was offset by the promoting effect of cropland and construction land on nitrogen input, with unused land becoming a new focus area for nitrogen emissions due to a lack of environmental management. Modifying the area of different land use types in the watershed can effectively control nitrogen input to the watershed.

Seasonal variations of cave dripwater hydrogeochemical parameters and δ13CDIC in the subtropical monsoon region and links to regional hydroclimate.

Stalagmites are considered natural archives of climate proxies. However, under the combined effects of atmospheric circulation patterns, precipitation, and karst environments, drip hydrogeochemical processes can be coupled and linked to each other to control cave sediment record information. Therefore, the evolution of chemistry and factors controlling the isotopic composition of the dripwater during regional precipitation migration from the surface to caves need to be evaluated. In this study, hydrogeochemical characteristics and the isotopic composition of the dripwater in the Mahuang Cave in Guizhou Province, Southwest China, including stable isotope (δ13CDIC) and trace element ratios, were monitored from August 2018 to December 2020. The results showed seasonal variations in the δ13CDIC, Mg/Ca, and Sr/Ca values of the dripwater in dry and wet seasons under the control of water-gas-rock reactions, such as soil CO2 concentrations and carbonate rock dissolution. In addition, the five monitored dripwater points in the Mahuang Cave showed fast and slow seepage due to the complex cave fractures and stratigraphy, reflecting the effects of precipitation variations to different degrees. Indeed, the δ13CDIC were more sensitive to the recharge changes from extreme precipitation and drought events. Therefore, dripwater δ13CDIC is a reliable indicator of the recorded hydrological signal in the southwest monsoon region.

Determination of nitrate sources in a karst plateau reservoir based on nitrogen and oxygen isotopes.

Investigating the sources, migration and proportional contribution of nitrate is essential to effectively protect water quality. δ15N-NO3-, δ18O-NO3- and Stable Isotope Analysis in R (SIAR) were used to qualitatively and quantitatively analyse nitrate sources in the Pingzhai Reservoir water body. The values of δ15N-NO3- and δ18O-NO3- in water vary with season. Soil organic nitrogen and chemical fertilisers are the main sources of nitrate in autumn, while domestic sewage and livestock manure are the primary sources of nitrate in winter and spring. The SIAR results showed that chemical fertilisers, livestock manure, sewage, and soil organic nitrogen had the highest proportional contribution. In autumn, the proportional contribution of chemical fertilisers to river and reservoir were 47 and 51 %. During winter, the proportional contributions of livestock manure and sewage to river and reservoir were 53 and 68 %, respectively, and in spring 49 and 68 %, respectively. Considering the fragility of karst ecosystems, strict measures should be formulated for the use of chemical fertilisers and standards for sewage discharge should be raised. Control nitrogen input from agricultural activities and prevent water quality deterioration.

Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data.

Accurate crop classification is the basis of agricultural research, and remote sensing is the only effective measuring technique to classify crops over large areas. Optical remote sensing is effective in regions with good illumination; however, it usually fails to meet requirements for highly accurate crop classification in cloud-covered areas and rainy regions. Synthetic aperture radar (SAR) can achieve active data acquisition by transmitting signals; thus, it has strong resistance to cloud and rain interference. In this study, we designed an improved crop planting structure mapping framework for cloudy and rainy regions by combining optical data and SAR data, and we revealed the synchronous-response relationship of these two data types. First, we extracted geo-parcels from optical images with high spatial resolution. Second, we built a recurrent neural network (RNN)-based classifier suitable for remote sensing images on the geo-parcel scale. Third, we classified crops based on the two datasets and established the network. Fourth, we analyzed the synchronous response relationships of crops based on the results of the two classification schemes. This work is the basis for the application of remote sensing data for the fine mapping and growth monitoring of crop planting structures in cloudy and rainy areas in the future.