Farmland-atmosphere feedbacks amplify decreases in diffuse nitrogen pollution in a freeze-thaw agricultural area under climate warming conditions

Optimizing the spatial pattern of land use in a prominent grain-producing area: A sustainable development perspective

Spatial patterns are essential for examining the sustainability derived from land systems. Constructing spatial patterns for sustainable land development is now high on the global agenda to guarantee human welfare. However, there is as yet no consensus on the comprehensive framework for optimizing the spatial pattern of land development (SPLD) contrapose a prominent grain-producing area (PGPA). To narrow this gap, we propose a synthetic framework to shape a more reasonable SPLD for a sustainable development strategy by measuring the equilibrium between the production-living-ecological space (PLES) functions and the resource and environment carrying capacity (RECC). Taking a prominent grain-producing area (PGPA) as the object, a case study involving the Jianghan Plain (JHP) in China is conducted, leading to the following novel insights. (i) The quality of PLES and RECC in a PGPA is affected by multiple dimensions: agriculture, ecology, environment, and society. The indices of the PLES function and the RECC have significant spatial heterogeneity. SPLD in regions with fragile ecological environments and strong development is often under overload pressure. (ii) Based on the spatial zoning results of SPLD, the five partitions were taken as the optimized objects, including zones of the eco-economic, model-agricultural, core-living, eco-conservation, and coordinated-development. The land function definition of these five types of zoning covers the production-living-ecological function orientation in a PGPA. (iii) The SPLD optimization framework proposed above has strong universality because it comprehensively considers the multi-dimensional spatial functional needs of PGPA. In this study, an optimization decision framework of SPLD based on measurement and zoning was established for a PGPA. Significantly, the introduced framework is applicable and practical for optimizing SPLD from a sustainable equilibrium perspective, and the findings have considerable implications for sustainable development in prominent grain-producing areas.

Rapid soil rewetting promotes limited N2O emissions and suppresses NH3 volatilization under urea addition

The alternation of dry and wet is an important environmental factor affecting the emission of nitrous oxide from soil. However, the consistent or opposite effects on NH₃ and N₂O emissions caused by adding exogenous urea in this process have not been fully considered. Here, we controlled the initial (slow drying) and final (adding water) water-filled pore space (WFPS) at 70%, 60%, or 50% through microculture experiment to simulate a process of slow drying-fertilization and rapid wetting of the soil from rice harvest to dryland crop fertilization. Through measuring soil chemical properties and the abundance and composition of related microbial communities during drying process, we studied the pathways of influence of drying and rewetting on the emission of N₂O and NH₃ after urea application. During the progressive drying process (WFPS decreasing from 70% to 60% and 50%), soil N₂O and NH₃ emissions decreased by 49.77%-72.13% and 17.89%-42.19%, respectively. After rapid rewetting (WFPS increasing from 60% to 70%, 50%-60% and 70%), N₂O emissions showed a slight increase, while NH₃ volatilization continued to decrease. Soil NH₄⁺-N and DOC contents both decreased during progressive drying, while the soil NO₃^--N content was enhanced. The drying process changed the community structure of ureC and amoA-b and reduced their abundance but had no effect on amoA-a, nirK or nirS. Correlation analysis indicated that the reductions in NH₄⁺-N content and the abundances of ureC and amoA-b were the main factors suppressing N₂O and NH₃ emissions. We believe that drying process limits the related microbial activity and substrate supply during ammonia oxidation process in terms of N₂O emissions, while in terms of NH₃ volatilization, it reduces the related microbial activity of urea hydrolysis process and increases the ammonium adsorption to the soil.

Considering atmospheric N2O dynamic in SWAT model avoids the overestimation of N2O emissions in river networks

Modeling studies have focused on N₂O emissions in temperate rivers under static atmospheric N₂O (N₂O_airc), with cold temperate river networks under dynamic N₂O_airc receiving less attention. To address this knowledge and methodological gap, the dissolved N₂O concentration (N₂O_disc) and N₂O_airc algorithms were integrated with an air-water gas exchange model (F_N2O) into the SWAT (Soil and Water Assessment Tool). This new model (SWAT-F_N2O) allows users to simulate daily riverine N₂O emissions under dynamic atmospheric N₂O. The spatiotemporal fluctuations in the riverine N₂O emissions was simulated and its response to the static and dynamic atmospheric N₂O were analyzed in a middle-high latitude agricultural watershed in northeastern China. The results show that the SWAT-F_N2O model is a useful method for capturing the hotspots in riverine N₂O emissions. The model showed strong riverine N₂O absorption and weak N₂O emissions from September to February, which acted as a sink for atmospheric N₂O in this cold temperate area. High N₂O emissions occurred from April to July, which accounted for 83.34% of the yearly emissions. Spatial analysis indicated that the main stream and its tributary could contribute 302.3-1043.7 and 41.5-163.4 μg N₂O/(m²·d) to the total riverine N₂O emissions (15.02 t/a), respectively. The riverine N₂O emissions rates in the subbasins dominated by forests and paddy fields were lower than those in the subbasins dominated by arable and residential land. Riverine N₂O emissions can be overestimated under the static atmospheric N₂O rather than under the increasing atmospheric N₂O. This overestimation has increased from 1.52% to 23.97% from 1990 to 2016 under the static atmospheric N₂O. The results of this study are valuable for water quality and future climate change assessments that aim to protect aquatic and atmospheric environments.

Vertical difference of climate change impacts on vegetation at temporal-spatial scales in the upper stream of the Mekong River Basin

As the upper section of the Mekong River Basin, the vegetation quality of the Lancang River Basin (LRB) and the related ecological functions are critical for the whole basin. With time-series Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2015 and local daily climatic data since 1976, their vertical interaction differences were identified. The results showed that the spatial variation in Normalized difference vegetation index (NDVI) of grassland and forest were sensitive to elevation. The NDVI value in the southern area at elevations less than 3000 m was more than 0.80 and decreased to 0.30-0.60 with elevations higher than 4500 m. The general vegetation quality showed a positive trend under climate change over 16 years. The M-K test of daily precipitation and temperature from 12 local weather stations showed that the basin temperature varied more significantly than precipitation. The temporal correlation between NDVI with precipitation as well as temperature at each pixel indicated that temperature was the dominant factor affecting grassland and forest dynamics in the LRB. The interaction between vegetation and climate was more sensitive at elevations lower than 3000 m. Based on the RCP4.5 scenario, the future temperature distribution was predicted, and its impact on NDVI was simulated at the pixel scale. Under future drier and warmer climate conditions, the responded NDVI in the upper stream with higher elevation may increase soil erosion and decrease streamflow. The NDVI in the downstream area will be improved and be able to adapt to the related climate impacts. Because of the large amount of water and biomass in this basin, higher temperatures will accelerate the decomposition of forest foliar litter. Thus, more organic carbon and forest diffuse pollution will be discharged into the water, potentially affecting the water quality of the whole basin.

Watershed diffuse pollution dynamics and response to land development assessment with riverine sediments

Sediment cores can reflect diffuse pollution history due to the accumulation of pollutants over time, therefore, the quantitative relationship between the sedimentation flux of pollutants and diffuse loads can identify the historical change. Sediment cores were collected from two river reaches in a small agricultural watershed (143 km²), and the total nitrogen (TN) and total phosphorus (TP) concentrations were determined. The sediments were dated using ²¹⁰Pb isotope radioactivity and the TN and TP sedimentation flux was calculated with Constant Rate of Supply (CRS) and Constant Initial Concentration (CIC) models. Watershed loss loads were simulated using the Soil and Water Assessment Tool (SWAT) in the same temporal period. As the similar natural condition in the post-depositional period of sediments, a linear regression model was used to analyze the relationship between TN and TP sedimentation flux and the hindcast model data. The TP sedimentation flux showed a clear positive relationship with its simulated load (R² = 0.600 and 0.664) using the CRS model, and better reflected long-term diffuse pollution loss dynamics than nitrogen. The impacts of land use change on diffuse pollution loading were identified with the combination of sedimentation flux from different reaches and watershed modeling. During the expansion of paddy land in the lower reach, the difference of TP sedimentation flux between upper and lower reaches narrowed, while gap of TN sedimentation flux increased. Base on the lateral correlations of two sections, the sediment concentration of TP was more reliable for the long term diffuse pollution assessment under land development.

Long-term manure application increased greenhouse gas emissions but had no effect on ammonia volatilization in a Northern China upland field

The impacts of manure application on soil ammonia (NH₃) volatilization and greenhouse gas (GHG) emissions are of interest for both agronomic and environmental reasons. However, how the swine manure addition affects greenhouse gas and N emissions in North China Plain wheat fields is still unknown. A long-term fertilization experiment was carried out on a maize-wheat rotation system in Northern China (Zea mays L-Triticum aestivum L.) from 1990 to 2017. The experiment included four treatments: (1) No fertilizer (CK), (2) single application of chemical fertilizers (NPK), (3) NPK plus 22.5t/ha swine manure (NPKM), (4) NPK plus 33.7t/ha swine manure (NPKM+). A short-term fertilization experiment was conducted from 2016 to 2017 using the same treatments in a field that had been abandoned for decades. The emissions of NH₃ and GHGs were measured during the wheat season from 2016 to 2017. Results showed that after long-term fertilization the wheat yields for NPKM treatment were 7105kg/ha, which were higher than NPK (3880kg/ha) and NPKM+ treatments (5518kg/ha). The wheat yields were similar after short-term fertilization (6098-6887kg/ha). The NH₃-N emission factors (EF_amm) for NPKM and NPKM+ treatments (1.1 and 1.1-1.4%, respectively) were lower than NPK treatment (2.2%) in both the long and short-term fertilization treatments. In the long- and short-term experiments the nitrous oxide (N₂O) emission factors (EF_nit) for NPKM+ treatment were 4.2% and 3.7%, respectively, which were higher than for the NPK treatment (3.5% and 2.5%, respectively) and the NPKM treatment (3.6% and 2.2%, respectively). In addition, under long and short-term fertilization, the greenhouse gas intensities for the NPKM+ treatment were 33.7 and 27.0kg CO₂-eq/kg yield, respectively, which were higher than for the NPKM treatment (22.8 and 21.1kg CO₂-eq/kg yield, respectively). These results imply that excessive swine manure application does not increase yield but increases GHG emissions.

Assessment of future climate change impacts on nonpoint source pollution in snowmelt period for a cold area using SWAT

The source area of Liao River is a typical cold region in northeastern China, which experiences serious problems with agricultural nonpoint source pollution (NPS), it is important to understand future climate change impacts on NPS in the watershed. This issue has been investigated by coupling semi distributed hydrological model (SWAT), statistical downscaling model (SDSM) and global circulation model (GCMs). The results show that annual average temperature would rise by 2.1 °C (1.3 °C) in the 2080 s under scenario RCP8.5 (RCP4.5), and annual precipitation would increase by 67 mm (33 mm). The change in winter temperature and precipitation is most significant with an increase by 0.23 °C/10a (0.17 °C/10a) and 1.94 mm/10a (2.78 mm/10a). The future streamflow, TN and TP loads would decrease by 19.05% (10.59%), 12.27% (8.81%) and 10.63% (6.11%), respectively. Monthly average streamflow, TN and TP loads would decrease from March to November, and increase from December to February. This is because the increased precipitation and temperature in winter, which made the spring snowpack melting earlier. These study indicate the trends of nonpoint source pollution during the snowmelt period under climate change conditions, accordingly adaptation measures will be necessary.