1. Introduction
Land-use and -cover change (LUCC) refers to changes in surface cover caused by changes in human land use and management practices (e.g., urbanisation and deforestation) [1,2], which affect ecosystems and the water cycle at regional, local, and global scales [3,4]. Ecohydrological effects induced by human activities are a focal point in the field of regional and global change research. By observing and modelling changes in runoff, an important indicator of the terrestrial water cycle, regional land-use patterns can be assessed and adjusted to increase surface production and prevent or mitigate sinking and flooding, thereby supporting the sustainable development of watersheds [5].
Since its reform and opening up in 1978, China’s urbanisation rate (proportion of the urban population to the total population in a country or region, used to measure the level and pace of urbanisation) has more than tripled [6], It increased from 17.9% in 1978 to 65.2% in 2022, with the resultant challenges of scarce land resources, increasing environmental pollution, and ecological degradation [7,8]. In 2012, the 18th Chinese Communist Party (CPC) National Congress proposed the construction of production–living–ecological space (PLES), which encompasses intensive and efficient production space, space for habitable living, and attractive ecological space, providing a direction for land optimisation [9]. However, defining PLES remains a challenge. Previous studies have determined that a dominant function typically emerges among the multiple functions of different land-use types [10]. Thus, the transformation of land use manifests as the transformation of the dominant function of land use. The change in dominant function reflects the socio-economic transformation and development, including production, living, or ecological space. Past studies have mainly focused on the ecological and environmental changes caused by land-use changes [11,12]. However, few studies have investigated the ecological and environmental problems caused by the transformation of the dominant functions of land use, especially the ecohydrological effects of changes to the structure of PLES at the watershed scale.
The Fenhe River is the second-largest tributary of the Yellow River and is located in Shanxi Province [13]. The Fenhe River Basin, known as the “granary of Shanxi”, is relatively rich in water resources, has fertile land, and displays favourable light and heat conditions [14]. However, the land-use structure of the basin has changed considerably in recent decades due to mining, urbanisation, and agricultural production expansion [15,16]. These changes have led to problems such as increased surface runoff, over-exploitation of groundwater, and water pollution, which have seriously constrained the sustainable socioeconomic development of the basin [17,18,19]. Since 2019, the Chinese government has prioritised the ecological protection and high-quality sustainable development of the Yellow River Basin [20]. Therefore, exploring the ecohydrological effects of PLES land-use transformation in the Fenhe River Basin (FRB) will highlight the systematic, holistic, and synergistic nature of basin governance and promote the construction of an integrated ecological and environmental governance system upstream and downstream to ensure the safety of the Fenhe and Yellow rivers [21,22].
The semi-distributed soil and water assessment tool (SWAT) is a hydrological model that predicts river runoff, sediment, and pollutants by simulating hydrological processes in a watershed [23,24]. This model is mainly used to assess the impact of land-use changes and management measures on the hydrological cycle [25]. In China, many scholars have used the SWAT model to study the impact of land-use change on runoff. Chen (2002) [26] simulated different land-cover scenarios in the upper reaches of the Yangtze River using the SWAT model and found that improvement in land cover reduced the depth of runoff and increased evapotranspiration. The Sino-German Cooperation Project of the Chinese Academy of Sciences (2004) [27] applied the SWAT model to the middle and lower reaches of the Yangtze River Basin to explore the impact of land-use change on hydrological processes and flooding mechanisms, providing a scientific basis for sustainable land use and ecological management in the Yangtze River Basin.
In this study, we aimed to analyse the patterns of land-use change in the FRB with a PLES perspective and investigate the hydrological effects of PLES changes in the FRB from 1990 to 2022. To this end, we used a transfer matrix, land-use dynamics, and the SWAT model. Spatial and temporal changes in runoff were used as important visual indicators to quantitatively reveal the impacts of the changes in PLES on runoff in the FRB. The results of this study serve as an important reference for improving the quality of the ecological environment and the security of water resources in the basin.
3. Results
3.1. SWAT Model Suitability Evaluation
Following the input of the DEM data, the SWAT model automatically extracts river networks and generates relevant watershed information. Based on the attributes of soil, land use, and slope (all with a threshold setting of 10%), the FRB was divided into 45 sub-watersheds and 585 hydrological response units. Subsequently, the 24 parameters of the catchment were ranked for sensitivity, and the optimum values were determined in the SWAT-CUP using the SUFI-2 algorithm (see Table 3). The parameters in the hydrological model, VCH_K2.rte, R__SOL_AWC(‥).sol, and V__GW_REVAP.gw, which have a large impact on runoff, control processes such as water flow, evaporation, infiltration, and recharge, indicating that groundwater infiltration and flow had a great impact on hydrological processes in the watershed.
Figure 4 shows a time-series comparison of the observed and simulated values based on the SWAT model for the calibration and validation periods. The results indicate that the SWAT model accurately reproduced the dynamics of flow in the basin, although some deviations were observed in the simulation of peak flows (e.g., some of the peaks were not fully captured throughout the calibration period). The deviation in simulating peak flows is primarily due to the model’s temporal resolution, as SWAT is calibrated using daily data, which may not fully capture the rapid changes associated with extreme, short-duration rainfall events. The NSE values exceeded 0.7, and R2 was between 0.7 and 0.8 during the calibration and validation periods, indicating that the model is effective for the analysis and prediction of hydrological processes in the FRB and that the results are credible.
3.2. Analysis of PLES Temporal and Spatial Changes
3.2.1. Spatial and Temporal Distribution of and Changes in PLES
Figure 5 and Figure 6 show the distribution of PLES in the FRB in 1990, 2000, 2010, and 2020, as visualised using ArcGIS 10.5 (Figure 6 is specific to secondary land classes). The primary land-use types in the basin were ecological and production spaces, accounting for 52.8% and 40.9% of the basin area, respectively. The ecological space was distributed in an enclosed figure eight shape, with a ring in the north and a semi-ring in the south. From 1990 to 2020, the ecological space gradually decreased, with a total decrease of 632.52 km2. In particular, the forest area first decreased and then increased, which was related to the national policy of returning farmland to forest that was implemented in 2000. However, the grassland area gradually decreased from 10,135 km2 in 1990 to 9408 km2 in 2020, and this condition mainly occurred at the edge of agricultural production land. Meanwhile, production land includes agricultural and industrial production lands, and was mainly concentrated in Taiyuan Basin in the north and Linfen Basin in the south, both of which are dense urban areas. The agricultural production land area was larger, and its proportion gradually decreased from 41.86% to 39.38% from 1990 to 2020. In contrast, industrial production land increased from 91 km2 in 1990 to 610 km2 in 2020, representing an increase of approximately seven times. Living space consists of urban and rural residential lands. As shown in Figure 5, urban living space was block-shaped and concentrated around the provincial capital of Taiyuan City and the counties, whereas rural living space was more dispersed and distributed in a point-like pattern, with both areas showing an upward trend.
3.2.2. Changes in PLES Land-Use Dynamics
As shown in Table 4, agricultural production land in the FRB experienced negative growth between 1990 and 2020, and the rate of decline slowed after 2010. Industrial production land showed the greatest change, reaching its highest value between 2000 and 2010. Both urban and rural residential lands increased; however, urban residential land increased faster than rural residential land owing to urbanisation. Changes in ecological space were irregular and broadly aligned with the implementation of national or local ecological policies. The combined land-use dynamics of the PLES secondary classification increased from 1990 to 2020, with an average value of 0.46%, and changes between different land-use categories were generally frequent and dramatic. From 1990 to 2000, the transformation between PLES land-use types was relatively slow (0.07%), and the human–land relationship remained fairly consistent. From 2000 to 2010, change in land use was significant (1.73%), the human–land relationship was unstable, and the spatial pattern of PLES was unstable. From 2010 to 2020, the change in land use declined to 0.69%, the changes in PLES land-use types were consistent, human–land relationship became more stable, and development became more balanced.
3.2.3. PLES Land-Use Transfer Matrix
As shown in Figure 7, the biggest change in PLES was the decrease in agricultural production space. Approximately 14,272 km2 of agricultural land was transferred to other land-use types (almost half was transferred to living space), accounting for 36% of the total transfer area. From 1990 to 2000, increases in the agricultural production space mainly resulted from the transfer of ecological land, with a much smaller area of agricultural production land converted to forests and grasslands than vice versa, indicating that deforestation and land clearing were serious issues during this period. At the beginning of the 21st century, large areas of agricultural land unsuitable for farming were gradually restored to forests and grasslands, and ecological environmental protection was increasingly emphasised, resulting in a greater balance between inflow and outflow ecological land areas. The largest source of industrial production land was agricultural land, and from 2000 to 2010, agricultural land area converted into industrial land was 276 km2.
Figure 8 shows spatial changes in the transfer of PLES categories in the FRB. The greatest changes resulted from the transfers of production land to living land and ecological land to production land. The transfer of production land to living land was mainly concentrated in Taiyuan Basin in the north and gradually expanded to Linfen City and its neighbouring cities and counties in the south. This change was related to the national “Rise of Central China Strategy” and the strong support of Shanxi Province. Between 2010 and 2020, there was a significant shift from ecological land to production land; however, a large amount of production land was simultaneously converted to ecological land under national ecological civilisation construction measures.
3.3. Impact of PLES Changes on Runoff
3.3.1. Temporal Variation in Runoff
Runoff processes under different PLES scenarios for 1990, 2000, 2010, and 2020 were simulated using the calibrated SWAT model, and the results are shown in Figure 9. The annual runoff in the watershed decreased slightly and then increased substantially, with an overall upward trend, especially during 2011–2015, when the increase in runoff was most significant. Table 5 lists the average, maximum, and minimum runoff, as well as the rate of change in runoff during 1990–2020. Runoff increased by 8.98 mm from 1990 to 2020, with the highest runoff occurring in 1996 and the lowest in 2000.
According to Table 5, the Pearson correlation coefficient (p) between precipitation and runoff is 0.912, indicating a strong positive correlation between the two. This means that an increase in precipitation generally leads to an increase in runoff, and this relationship is statistically significant (with a p-value of 0.022, which is less than 0.05). This suggests that precipitation is an important driving factor behind changes in runoff. However, despite the strong correlation between precipitation and runoff, we should also consider other factors, such as land-use changes and surface cover, which may have significant impacts on runoff during certain periods. For example, short-term extreme precipitation events may lead to dramatic fluctuations in runoff, even if precipitation increases, as runoff may also be influenced by these additional factors.
To further explore the impacts of PLES changes on runoff, we classified the monthly average runoff by season during 1990–2020. As shown in Figure 10, runoff increased significantly in summer (June–August) and autumn (September–November), with peaks in July and August. This indicates that during the flood season (summer), runoff increased significantly, whereas during winter (December–February) and spring (March–May), runoff was lower. These results reflect the characteristics of the monsoon climate, which brings heavy rainfall in summer, leading to seasonal runoff patterns. Notably, the runoff volume in 2020 increased over several months compared to that in other years and was higher during the flood season. Since meteorological and soil data were controlled for consistency in the modelling, it is reasonable to assume that the increase in runoff was due to PLES changes. Rapid urbanisation, the expansion of industrial and domestic land use, the reduction in vegetation cover, and the decrease in land permeability likely resulted in more precipitation forming runoff rather than infiltrating into the ground.
3.3.2. Spatial Variation in Runoff
To further visualise the response of runoff to PLES changes, we performed a spatial visualisation of the annual average surface runoff and groundwater in the FRB using ArcGIS. As shown in Figure 11, during 1990–2020, the highest values of surface runoff occurred near sub-basin 16 in the north and sub-basins 43, 44, and 45 in the south, which are located in the vicinity of the provincial capital, Taiyuan, and several large cities in the south, consistent with the living and production spaces. Furthermore, the maximum value of surface runoff increased from 1990 to 2020, and the area of the high value range gradually expanded. For example, in sub-basin 16, the modelled surface runoff increased from 2.52 mm in 1990 to 9.91 mm in 2020, which was directly related to the encroachment of production and living spaces into the ecological space due to rapid economic and social development and the expansion of the city. Over 1990–2020, the proportion of ecological land has decreased from 42.8% to 37.2%, while urban living land has increased 2.13 times. The growth rate of rural residential land was approximately 134.28 km2 per year.
The trend in groundwater change was consistent with the allocation of ecological areas, especially in the northwestern and south-central parts of the country where groundwater storage is higher. Groundwater recharge in agricultural production land was approximately 10.36 mm, which was lower than that in ecological land (16.28 mm), suggesting that ecological spaces, such as forests and grasslands, play a key role in storing and replenishing water resources. In contrast, the contribution of agricultural production land to groundwater decreased due to high-intensity development.
3.3.3. Quantitative Analysis of the Impact of PLES Changes on Runoff
The trend in groundwater change was consistent with the distribution of ecological space, As illustrated in Figure 12, agricultural production land remains the largest land-use type, with a self-sustained area of about 198.29 km2. Significant portions were transferred to industrial production and urban living lands, reflecting industrialisation and urbanisation. Industrial land self-sustained area increased to 33.3 km2, encroaching on some agricultural land, while urban residential land expanded by 19.2 km2, mainly from agricultural land and grassland. Forest and grassland largely retained their areas but underwent some transformation to urban and industrial land. These changes align with economic development and urban expansion trends.
Figure 13 demonstrates the significant impact of PLES changes on runoff. From 1990 to 2020, as agricultural and ecological land was extensively converted to industrial and urban land, surface runoff in sub-basin 42 increased from approximately 1.5 mm to 4.0 mm, while groundwater recharge decreased from approximately 7.0 mm to 3.5 mm. Similarly, in sub-basin 43, surface runoff increased from approximately 1.0 mm to 3.0 mm, and groundwater recharge declined from approximately 6.0 mm to 3.0 mm. In sub-basin 44, surface runoff increased from approximately 0.5 mm to 2.5 mm, while groundwater recharge decreased from approximately 4.0 mm to 2.0 mm. This trend indicates that the expansion of industrial and urban land, which increases impervious surfaces, significantly reduces rainfall infiltration, leading to higher surface runoff and lower groundwater recharge. Sub-basin 42 experienced the most pronounced changes. The above results provide reliable data for our quantitative study of the relationship between PLES changes and runoff.
3.4. Impact of PLES Changes on Pollutants
In the Fenhe River Basin, as the PLES changes, especially with the expansion of production and living spaces and the reduction in ecological space, the changes in water quality pollutants are evident. This study used the SWAT model to simulate the trends in pollutants such as nitrogen (N), phosphorus (P), suspended solids (SS), and heavy metals under land-use changes. The results are outlined in Table 6.
Changes in nitrogen and phosphorus pollutants are due to the expansion of production and living spaces caused by rapid industrialisation and urbanisation, which has led directly led to the reduction in agricultural production spaces. This in turn has reduced the use of fertilisers and pesticides, thereby decreasing the leaching of nitrogen and phosphorus. However, with the increase in industrial production land, particularly the expansion of living space brought about by urbanisation, nitrogen and phosphorus concentrations increased by 40% and 50%, respectively, between 1990 and 2020. This change is closely related to the increased emissions from urban and industrial development, especially during the period from 2000 to 2010, when the pace of production space expansion accelerated, leading to intensified of nitrogen and phosphorus pollution.
The suspended solids concentration also showed an increasing trend during PLES change related to the expansion of urbanisation and industrialisation, especially construction activities and increased soil erosion. The reduction in agricultural land and the increase of industrial land could have led to soil exposure and erosion, which in turn increased SS loss. For instance, in the Fenhe River Basin, SS concentration increased by approximately 5% from 2010 to 2020. This change was primarily influenced by construction activities and land hardening during the urbanisation process.
Changes in heavy metal pollutants increased gradually with the expansion of industrial production, particularly during the 2000–2010 period. Heavy metal and wastewater emissions led to a steady rise in heavy metal concentrations in water bodies. According to model predictions, the concentration of heavy metals in 2020 increased by approximately 30% compared to 1990, especially in the industrial concentration areas along the Fenhe River.
In summary, the impact of PLES changes on pollutants is significant. The expansion of production and living spaces directly leads to an increase in the concentrations of nitrogen and phosphorus pollutants, while the reduction in agricultural and ecological spaces weakens the self-purification capacity of water bodies, increasing the loss of suspended solids and worsening water quality. Additionally, heavy metal pollution during the industrialisation and urbanisation processes shows a year-on-year increasing trend.
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