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Results of risk assessment by the ITO3dE model
The results in the I dimension show that, overall, the distribution was high in the west and low in the northeast and the southeast in all three periods (Fig. 3, I, II, III; Table 2), and this tallies with the topography of Chongqing. The northwestern and central regions of Chongqing are mainly hilly and slightly mountainous, while the southeastern and northeastern regions represent the Dabashan Mountain system and the Daloushan Mountain system, respectively. Thus, farmland in Chongqing is mainly distributed in the western regions as well as in regions with extensive flat areas, such as Dianjiang and Liangping. Some regions in Dianjiang, Yongchuan, Dazu, Shapingba, Wansheng, and Jiangbei show relatively high risks, but the risk level is still medium. Hence, it can be concluded that the risk level in the I dimension during 2005–2015 is, overall, not high. Considering there are too many single-factor graphs, we omitted these graphs, but provide the following description: Among the three single factors, I1 has the highest value, and I1 and I2 both present a first increasing and then decreasing trend (the maximum values of I1 in 2005, 2010, and 2015 were 3.38, 4.08, and 2.78, respectively, and those of I2 in 2005, 2010, and 2015 were 2.71, 3.37, and 2.48, respectively). For the I1 results, the risk levels of the regions with higher levels in 2005, such as Yongchuan, Fuling, and Liangping, showed a certain decrease in 2015, but the risk levels of some regions such as Pengshui, Qianjiang, and Xiushan showed an increasing trend. The risk grade of I2 was relatively lower than that of I1, but overall, the spatiotemporal variation trend was consistent with that of I1, except for the increasing trend of the risk level of Qianjiang. Basically, the risk grade of I3 was zero; only the risk level of Bishan was in the medium risk status, while those of Hechuan and Fengdu were low.
Figure 3

Result distribution map of I, T, and O dimensions of Chongqing in 2005, 2010, 2015.

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Table 2 Statistical results of I, T, and O dimensions in 2005–2015.
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Spatially, the results in the T dimension presented, overall, an opposite distribution pattern when compared to the I dimension, that is, with low levels in the western regions and high levels in the northeastern and southeastern regions (Fig. 3, IV, V, VI; Table 2). The annual differences in the T dimension data are mainly determined by the variations in the factors I4 and I7, which showed relatively higher risk levels in all three periods. The values of I4 in the years 2005, 2010, and 2015 were 1.42–5.78, 0.84–6.12, and 0.14–6.93, respectively, while those of I7 were 0, 0–5.38, and 0–5.06, respectively. Because Chongqing is a typical mountainous city with purple soil33, high-risk and extremely high-risk regions, I5 and I6, are widely distributed across the city. In addition, due to the introduction of the factor I8, the water areas had a higher risk level, which is consistent with the actual situation of AGNPS.
The results in the O dimension showed a smaller interannual variation, with a low overall risk level (Fig. 3, VII, VIII, IX; Table 2). The O dimension levels were mainly affected by the spatial changes in the paddy field area. As mentioned above, during the 10 years, the area of paddy fields in Chongqing was nearly reduced by half, which led to the decrease in the spatial distribution of I12 and an increased risk in counties such as Kaizhou, Fengjie, Liangping, and Changshou. Spatially, Yongchuan, Shapingba, Bishan, Dianjiang, Changshou, and Kaizhou showed higher risk levels, and the risk levels of Kaizhou, Fengjie, Wanzhou, Liangping, and Changshou showed a significantly increasing trend. The high risk values of I9 were mainly distributed in Yongchuan, Shapingba, Jiangbei, Changshou, Dianjiang, and Liangping, with Shapingba showing the highest value of 3.75, while Chengkou, Wushan, Fengjie, Shizhu, and Xiushan had lower values. The high risk values of I10 were mainly distributed in the western regions and were below the medium risk levels. The risk values in 2010 were higher than those in 2005 or 2015, but did not surpass 3.0, and the high values were mainly distributed in the western regions as well as in Dianjiang, Wanzhou, and Liangping. The risk values of I11 were all below 3.0, and the highest value of 2.78 was found for Fengjie; higher values were mainly distributed in the northeastern and southeastern counties. The high risk values of I12 were mainly distributed in the northeastern and southeastern counties, which mostly have only small areas of paddy fields.
Figure 4 shows the data on AGNPS risks during 2005–2015 in Chongqing. The risk distribution trends in 2005, 2010, and 2015 were basically consistent and in the ranges of 0.40–2.28, 0.41–2.57, and 0.41–2.28, respectively. The maximum risk values were all below 3.0 for the three periods. Regions with medium levels were mostly distributed in the western regions of Chongqing (Dazu, Jiangjin, etc.) as well as in the counties Dianjiang, Liangping, Kaizhou, Wanzhou, and Zhongxian. Larger spatial differences were observed among different counties or different parts of a certain county; for example, the middle flatland part and the mountain systems at the two sides in Liangping or the northwestern and southeastern parts in Shizhu.
Figure 4

Spatiotemporal distribution graph of the evaluation results of agricultural NPSP risks in Chongqing during 2005–2015: (a) 2005; (b) 2010; (c) 2015.

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Spatiotemporal change results of risk by transition matrix analysis
By assigning no risk, low risk, and medium risk levels with 1, 2, and 3, respectively, in GIS, we can obtain the spatiotemporal transition matrix according to the formula of the transition matrix. Figure 5 shows the spatiotemporal transition situation of the AGNPS risk evaluation in Chongqing. Basically, high levels show no changes, and the proportions of ‘no-risk no-change’, ‘low-risk no-change’, and ‘medium-risk no-change’ situations were 10.86%, 33.42%, and 17.25%, respectively, accounting for 61.53% of the total area of Chongqing. Among these, the ‘no-risk no-change’ situation was mainly distributed in Rongchang, the east of Nanchuan, Shizhu, Pengshui, and Qianjiang; the ‘low-risk no-change’ situation was widely distributed in Wulong, the southeast of Fengdu, the south of Nanchuan, and the northeastern counties of Chongqing, while the ‘medium-risk no-change’ situation was mainly distributed in Shapingba, Yongchuan, Dianjiang, the north of Nanchuan, and Kaizhou.
Figure 5

Spatiotemporal transition situation of agricultural NPSP risks in Chongqing during 2005–2015.

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During 2005–2015, the proportions of risk increase, risk decline, and risk fluctuation were 13.45%, 17.66%, and 7.36%, respectively. Risk increases mainly occurred in central Jiangjin, central Fengdu, Pengshui, Qianjiang, the midwest of Yunyang, central Liangping, Wuxi, Wushan, and Chengkou, while risk declines were mainly observed for the main urban area of Chongqing, northern Tongliang, Dazu, Youyang, and Xiushan. Risk fluctuation was concentrated in Jiangjin, Bishan, Fuling, and Youyang.
Results of risk concentration degree by Kernel density analysis
Figure 6 shows the kernel density analysis results of the medium-risk regions. As seen in the figures, the peak values of the kernel density at these three periods were all around 1,110, suggesting that the maximum gathering degree of medium-risk pattern spots basically showed no changes. The spatial distribution of kernel density at these three periods showed a consistent trend, but the distribution differences at different periods were significant. In 2005, medium-risk regions were mainly concentrated in Shapingba, southern Dazu, central Yongchuan, eastern Beibei, Dianjiang, central Kaizhou, northwestern Shizhu, northern Nanchuan, central Wanzhou, southwestern Zhongxian, and southeastern Xiushan, while in 2010, such regions mainly occurred in Shapingba, eastern Jiangjin, southeastern Beibei, northern Nanchuan, northeastern Changshou, Dianjiang, northern Fuling, northern Fengdu, northeastern Shizhu, northeastern Liangping, central Kaizhou, Wanzhou, northeastern Pengshui, and eastern Xiushan. In 2015, medium-risk regions were mainly concentrated in Shapingba, Yongchuan, central Jiangjin, northwestern Nanchuan, northeastern Beibei, Dianjiang, Liangping, the junction of Fuling and Fengdu, central Kaizhou, northern Yunyang, eastern Pengshui, southeastern Qianjiang, and central Xiushan.
Figure 6

Kernel density graphs of medium-risk areas in Chongqing during 2005–2015: (a) 2005; (b) 2010; (c) 2015.

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To further explore the distribution of regions with the high-risk gathering zones (Table 3), we conducted a separate analysis on the regions with kernel density values higher than 1,000 (the kernel density values of these regions were divided into 10 grades with equal intervals, and the 10th grade had values from 1,000 to 1,110).
Table 3 Distribution of regions with high-risk gathering zones.
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Results of hot and cold spots by Getis-Ord Gi* analysis
Applying Getis-Ord Gi* analysis is helpful to clearly identify high-value hot spots (Hot Spot-99% Confidence) and low-value cold spots (Cold Spot-99% Confidence). Figure 7 shows the Getis-Ord Gi* analysis results; the overall variation trends of high-value hot spots and low-value cold spots were consistent in all periods, with significant distribution differences. The regions located in the high-value hot spot zones in all three periods were Yongchuan, Shapingba, Dianjiang, Liangping, northwestern Fengdu, and Zhongxian, while those located in the low-value cold spot zones were Chengkou, Wuxi, Wushan, Pengshui, and Rongchang. Throughout the 10 years, the high-value hot spot zones showed significant diffusion in Fengjie, Yunyang, Kaizhou, central Qianjiang, and northern Nanchuan, while the low-value cold spot zones showed significant diffusion in some parts of the midwestern counties such as central Fuling and southern Yubei. These high-value hot spots or low-value cold spots were mainly distributed in the above-mentioned regions and their surrounding areas and showed significant “gathering trends”. The spatiotemporal variation trend of the distribution of these high-value hot spots or low-value cold spots can reflect the variation tendencies of hot spots or cold spots in different regions. Over time, the high-value hot spot zones gradually migrated towards the northeastern counties of Chongqing, while the low-value cold spot zones in the midwestern counties presented an obvious diffusion trend. The low-value cold spot zones in the northeastern regions gradually decreased, while those in the southeastern regions tended to become more fragmented. These results indicate that the high-value hot spot zones gradually dominated the northeastern regions, while the low-value cold spot zones gradually dominated the midwestern regions.
Figure 7

Getis-Ord Gi analysis results in Chongqing during 2005–2015.

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