Barely Significant

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0.44 ha; during this period, the grassland and desert in the study area showed a continuous decline; the construction land showed a continuous increase, with a larger increase in the later part of the study period (2014–2019), with a net increase of 6957.05 ha; the area of wetland in the study area showed a trend of first decrease and then increase, with the largest decrease in the pre-study period (2009–2014), when the area decreased by 3858.88 ha, and a net increase of 950.42 ha during the decade; the area of waters in the study area showed an increasing trend followed by a decreasing trend, with a net increase of 2784.15 ha; the mining land in the study area also showed a continuous decreasing trend, with a net decrease of 717.95 ha over the study period.
Results Analysis of land use status and transition Analysis of the current land use situation As shown in Table 4 , the farmland area in the study area showed a continuous decreasing trend from 2009 to 2019, with a net decrease of 10,421.42 ha during the decade; the study area had the highest proportion of woodland, which showed a decreasing trend followed by an increasing trend, with a net increase of 3940.44 ha; during this period, the grassland and desert in the study area showed a continuous decline; the construction land showed a continuous increase, with a larger increase in the later part of the study period (2014–2019), with a net in
Results Analysis of land use status and transition Analysis of the current land use situation As shown in Table 4 , the farmland area in the study area showed a continuous decreasing trend from 2009 to 2019, with a net decrease of 10,421.42 ha during the decade; the study area had the highest proportion of woodland, which showed a decreasing trend followed by an increasing trend, with a net increase of 3940.44 ha; during this period, the grassland and desert in the study area showed a continuous decline; the construction land showed a continuous increase, with a larger increase in the later part of the study period (2014–2019), with a net increase of 6957.05 ha; the area o
nimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E_{{{\text{b}} - {\text{j}}}}^{{{\text{III}}}}$$\end{document} E b - j III shows a decreasing trend with the increase of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t_{1}^{{{\text{III}}}}$$\end{document} t 1 III .
The differences between the G ( d ) and E ( d ) values for 2008, 2014, and 2020 are 0.0233, 0.0238, and 0.0292, respectively; this difference gradually increases, indicating an increasing trend of spatial clustering in the synergy development level between 2008 and 2020 in the Chengdu–Chongqing urban agglomeration.
The improvement in the total head is stable and follows an increasing trend from the low-flow-rate region near 0.86 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varphi }_{d}$$\end{document} φ d to the high-flow-rate region, with an increase of at least 2.89% compared to the total head of the reference model.