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Abstract To determine the risk state distribution, risk level, and risk evolution situation of agricultural non-point source pollution (AGNPS), we built an ‘Input-Translate-Output’ three-dimensional evaluation (ITO3dE) model that involved 12 factors under the support of GIS and analyzed the spatiotemporal evolution characteristics of AGNPS risks from 2005 to 2015 in Chongqing by using GIS space matrix, kernel density analysis, and Getis-Ord Gi* analysis. Land use changes during the 10 years had a certain influence on the AGNPS risk. The risk values in 2005, 2010, and 2015 were in the ranges of 0.40–2.28, 0.41–2.57, and 0.41–2.28, respectively, with the main distribution regions being the western regions of Chongqing (Dazu, Jiangjin, etc.) and other counties such as Dianjiang, Liangping, Kaizhou, Wanzhou, and Zhongxian. The spatiotemporal transition matrix could well exhibit the risk transition situation, and the risks generally showed no changes over time. The proportions of ‘no-risk no-change’, ‘low-risk no-change’, and ‘medium-risk no-change’ were 10.86%, 33.42%, and 17.25%, respectively, accounting for 61.53% of the coverage area of Chongqing. The proportions of risk increase, risk decline, and risk fluctuation were 13.45%, 17.66%, and 7.36%, respectively. Kernel density analysis was suitable to explore high-risk gathering areas. The peak values of kernel density in the three periods were around 1110, suggesting that the maximum gathering degree of medium-risk pattern spots basically showed no changes, but the spatial positions of high-risk gathering areas somehow changed. Getis-Ord Gi* analysis was suitable to explore the relationships between hot and cold spots. Counties with high pollution risks were Yongchuan, Shapingba, Dianjiang, Liangping, northwestern Fengdu, and Zhongxian, while counties with low risks were Chengkou, Wuxi, Wushan, Pengshui, and Rongchang. High-value hot spot zones gradually dominated in the northeast of Chongqing, while low-value cold spot zones gradually dominated in the Midwest. Our results provide a scientific base for the development of strategies to prevent and control AGNPS in Chongqing.

The Fuling District is located in central Chongqing, China, and characterized by a high ecological status, a high ecological risk, a high agricultural proportion in economy, and a high agricultural non-point source pollution (AGNPS) risk, and represents the ecological security barrier of the Yangtze River and the Three Gorges Reservoir area. To analyze the output risk response of AGNPS under different land use scenarios in the future, we combined the advantages of various models and techniques such as the CLUE-S model, the Markov model, the SWAT model, and GIS technology and selected 12 driving factors for land use changes as well as one limiting factor of the ecological protection redline based on land use data of the Fuling District in 2009 and 2017, with the aim to perform an output risk probability evolution analysis of regional AGNPS. The three scenarios were natural development (ND), ecological priority (EP), and agricultural development (AD). The results were as follows: (1) from 2009 to 2017, land use change mainly consisted of the conversion from paddy field and dry land into construction land, accounting for 41.25% of the total area increase in construction land in 2017; (2) the simulation results of land use changes in Fuling District by combination of the CLUE-S model and the Markov model showed a high consistency; (3) from 2009 to 2017, the numbers of sub-basins where the TN risk level declined, remained unchanged, and increased were 36, 425, and 9, respectively, while in terms of the TP risk levels, the numbers were 16, 443, and 11, respectively; (4) under the three development scenarios of ND, EP, and AD, paddy field, dry land, forest land, and construction land were the main types of land use conversion; (5) the output risk levels of total nitrogen (TN) and total phosphorus (TP) both presented a declining trend at present and in the future, and the number of sub-basins where the risk level declined was highest under the EP scenario; (6) under the ND scenario, adjustments of ±5% or ±10% on the output coefficients of TN and TP could lead to an obvious response of the output risk probability level of sub-basins. Therefore, the sub-basins that were the most sensitive to changes in land use or output coefficients deserve considerable attention. Our results also indicate that the output risk levels of sub-basins and regional TN and TP could be reduced through land use optimization or fertilizer control, thereby minimizing regional AGNPS.

Agricultural non-point source pollution (AGNPS) risk prevention and control is more conducive to reducing costs than post-treatment. Therefore, how to effectively identify risks is a key problem to be solved. The study area is located in the NANTUO small watershed of the Three Gorges Reservoir Area in China, on the side of the Yangtze River, where agriculture is developed with many kinds of plantings. There is a water level fluctuation zone (WLFZ) with seasonal elevation difference up to 30 m due to the water level regulation of the Three Gorges reservoir, which is a hot area of AGNPS research. On the basis of daily water level observation in NANTUO small watershed, based on the theory of “source” and “sink”, this study explored the land use change in different seasons with the help of high-resolution remote sensing image (According to the change of water level, it can be divided into two periods: non-submergence period (low water level period from the beginning of June to the end of September) and submergence period (high water level period at other time)). With the help of “source” identification (Types of land use that produce pollutants) and resistance surface calculation index system (Minimum cumulative resistance model, MCR model), the risk level and risk transmission path (The easiest path for pollutants to move to water) of AGNPS in WLFZ were analyzed. Combined with the idea of ecological corridor construction and pollution control, the risk change of regional AGNPS under the optimization of land use mode (The cultivated land types within 50 m and 100 m (Landscape optimization zone) around the water area are adjusted to forest land respectively in two scenarios (Q1 and Q2)) was analyzed as well. Based on our results, 1) the difference in the water areas between the non-submergence and submergence periods was 3.79 km2, and the areas of grassland, farmland, and forest land in the non-submergence period increased by 0.62, 0.85, and 0.35 km2, respectively. The “source” land in this region was mainly sloping farmland with slope above 6°. 2) The calculation results of the minimum cumulative resistance base surface for each source land showed the characteristics of “high values in the west and dispersedly distributed, low values in the east and continuously distributed”. 3) The distribution of the resistance surface during the non-submergence and submergence periods was basically the same; the high-risk zones were widely distributed throughout the region, while the low-risk zones were mainly distributed in the eastern mountains. 4) The number of risk transmission paths followed the order of a4 (farmland with slopes between 6° and 15°) > a5 (farmland with slopes between 2° and 6°) > a1 (rural settlements) > a3 (farmland with slopes between 15° and 25°) > a6 (farmland with slopes ≤ 2°) > a2 (farmland with slopes above 25°), and about 90% of the risk transmission paths were distributed in the lower levels (i.e., level 1, level 2, level 3). The proportion of risk transmission paths in the lower levels during the submergence period was higher than that during the non-submergence period. 5) The proportions of high risk, relatively high risk, medium risk, low risk, and no risk in the study area varied respectively following the rules of 72.79%→70.07%→66.56%, 16.85%→18.16%→18.3%, 6.24%→7.21%→9.3%, 2.25%→2.36%→3.32%, and 1.87%→2.2%→2.52% under the intact status of non-submergence period (Q0), Q1 scenario, and Q2 scenario. 6)The maximum values of pollution transmission path resistance of “source” land under the intact status of non-submergence period (Q0), Q1 scenario, and Q2 scenario followed the order of Q0