Climate factors affect N2O emissions by influencing the migration and transformation of nonpoint source nitrogen in an agricultural watershed

Can the best management practices resist the combined effects of climate and land-use changes on non-point source pollution control?

Climate and land-use changes have an overlying impact on non-point source (NPS) pollution in river basins. However, the control effect of Best Management Practices (BMPs) for NPS pollution is not yet clear under future scenarios. The Soil and Water Assessment Tool (SWAT) model was coupled with the entropy-weighted method, global climate patterns and land-use data to explore the dynamic variations in total nitrogen (TN) and total phosphorus (TP) loads in the Jing River Basin during the baseline (2000-2020) and future periods (2021-2065), evaluate the pollution reduction effectiveness of individual and combined BMPs, and propose practical BMP configurations. Results indicate that a future trend of urban land expansion, particularly in the economic scenario (LU_SSP585), leads to weakened environmental ecosystems, while the sustainable scenario (LU_SSP126) exhibits more balanced land development. The MIROC-ES2L model demonstrates higher Taylor skill scores, forecasted significant increases in precipitation, maximum, and minimum temperatures under the SSP585 scenario. Spatial heterogeneity in TN and TP loads is notable, showing an upward trajectory in the future. The interaction between land-use and climate change has complex effects on TN and TP loads, with land-use-induced TN changes being relatively small (4.6 %) and TP changes substantial (24.3 %). The spatial distribution, under overlying effects, leans towards the influence of climate change, emphasizing its dominant role in TN and TP load variations. Distinct differences exist in the reduction of NPS pollution loads among different BMPs, with combined BMPs demonstrating superior effectiveness. The environmental-cost effectiveness trends of BMPs remain consistent across various future scenarios. RG (Return agricultural land to grass), RG + TT (Terracing), and RG + FR10 (Fertilizer reduction: 10 %) + GW (Grassed waterway) + FS (Filter strip) + TT emerge as the most effective single, double, and multiple BMP combinations, respectively. The results offer valuable insights for preventing and mitigating future NPS pollution risks, optimizing land-use layouts, and enhancing watershed management decisions.

Identification and prediction of climate factors based on factor analysis and a grey prediction model in China

Identifying and predicting the impacts of climate change are crucial for various purposes, such as maintaining biodiversity, agricultural production, ecological security, and environmental conservation in different regions. In this paper, we used the surface pressure (SP), surface temperature (ST), 2-m air temperature (AT), 2-m dewpoint temperature (DT), 10-m wind speed (WS), precipitation (PRE), relative humidity (RH), actual evapotranspiration (ET_a), potential evapotranspiration (ET_P), total solar radiation (TRs), net solar radiation (NRs), UV intensity (UVI), sunshine duration (SD), convective available potential energy (CAPE) as factors in our climate modeling. The spatiotemporal distribution characteristics of the climate factors were analyzed and identified based on historical data for China from 1950 to 2020 using factor analysis and a grey model (GM (1,1)), and their future change characteristics were predicted. The results show that there is a strong correlation between climate factors. ST, AT, DT, PRE, RH, and ET_a are the main factors that have the potential to cause heavy rain, thunderstorms, and other severe weather. Meanwhile, PRE, RH, TR_s, NR_s, UVI, and SD are among the major factors linked to climate change. Specifically, SP, ST, AT, and WS are among the minor factors in most areas. The top ten provinces in terms of combined factor scores are Heilongjiang, Neimenggu, Qinghai, Beijing, Shandong, Xizang, Shanxi, Tianjin, Guangdong, and Henan. The trend of climate factors in China is expected to remain relatively stable over the next 30 years, with a noteworthy decrease observed in CAPE compared to the past 71 years. Our findings can help to better mitigate the risks associated with climate change and enhance resilience; they also provide a scientific basis for environmental, ecological, and agricultural systems to cope with climate change.