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Article

Evolution of Land Use and Its Hydrological Effects in the Fenhe River Basin Under the Production–Living–Ecological Space Perspective

by
Junzhe Zhang
1,
Azhar Ali Laghari
1,*,
Qingxia Guo
1,*,
Jiyao Liang
1,
Akash Kumar
2,
Zhenghao Liu
1,
Yongheng Shen
1 and
Yuehan Wei
1
1
The College of Resources and Environment, Shanxi Agricultural University, Jinzhong 030801, China
2
School of Civil Engineering, Guangzhou University, Guangzhou 510006, China
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(24), 11170; https://doi.org/10.3390/su162411170
Submission received: 15 October 2024 / Revised: 14 December 2024 / Accepted: 16 December 2024 / Published: 20 December 2024
(This article belongs to the Topic Hydrosphere under the Driving of Human Activity and Climate Change: Status, Evolution and Strategies)
Browse Figures
Figure 1
<p>Location of the Fenhe River Basin, Shanxi Province, China.</p> "> Figure 2
<p>Monthly mean temperature and precipitation in the Fenhe River Basin. Data were obtained from eight meteorological stations in the basin (1990−2022), and values were calculated using the Tyson polygon method.</p> "> Figure 3
<p>Soil and water assessment tool (SWAT) model flow chart.</p> "> Figure 4
<p>Observed and SWAT-simulated monthly stream flow for the calibration (January 2014−December 2022) and validation (January 1990–December 2014) periods in the Fenhe River Basin, Shanxi Province, China.</p> "> Figure 5
<p>Development of production–living–ecological space (PLES) over 1990–2020 in Fenhe River Basin.</p> "> Figure 6
<p>Secondary class distribution of PLES in Fenhe River Basin.</p> "> Figure 7
<p>Trajectories of spatial transfer changes of PLES in the Fenhe River Basin. Different coloured trajectory lines show the direction of transfer between land classes, and the thickness of the trajectory lines represents the amount of transformation.</p> "> Figure 8
<p>Spatial transfer of PLES land-use types in Fenhe River Basin.</p> "> Figure 9
<p>Simulated runoff changes in the Fenhe River Basin between 1990 and 2020. (The green dushed line represents the overall trend of precipitation changes).</p> "> Figure 10
<p>Average monthly runoff under different PLES scenarios.</p> "> Figure 11
<p>Average annual surface runoff (SURQ) and groundwater (GWQ) under different PLES scenarios.</p> "> Figure 12
<p>Stacked chart of PLES spatial transfer in sub-basins 42, 43, and 44 from 1990 to 2020.</p> "> Figure 13
<p>Temporal variations of surface runoff and groundwater in sub-basins 42, 43, and 44.</p> ">
Versions Notes

Abstract

:
Analysing the patterns and impacts of land-use changes in the production–living–ecological space (PLES) of the Fenhe River Basin (FRB 39,721 km2), China, is necessary to support sustainable development. Based on remote sensing images from 1990 to 2020, we aimed to analyse the PLES land-use changes. Industrial production and living spaces continuously encroached on the agricultural production and ecological spaces between 1990 and 2022 owing to industrialisation and urbanisation, and the ecological land area decreased by 699.21 km2, while the industrial production land area increased by 521.32 km2. We used the soil and water assessment tool (SWAT) model to quantitatively analyse the impact of PLES changes on runoff in the FRB. With the continuous expansion of production and living spaces, the extensive use of concrete in cities has led to ground hardening, making it difficult for precipitation to infiltrate, with surface runoff increasing by 0.3 mm annually. The reduction in ecological space has led to a reduction in forests and grasslands, weakening the water-holding capacity of the watershed and affecting groundwater storage. This study provides a scientific basis for watershed management and the integrated development of PLES.

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.

2. Materials and Methods

2.1. Study Area

The Fenhe River, which is the largest waterway in Shanxi Province, flows from north to south through 6 cities and 29 counties, spanning a total length of 713 km and a watershed area of 39,721 km2. The basin is located in the continental monsoon climate zone, with an average annual temperature of approximately 11 °C and annual precipitation of approximately 500 mm. The primary soil types in the FRB are alluvial, brown, and coarse-grained soils, and the main land-use types are woodlands, meadows, and croplands. The rich mineral resources and superior soil and water conditions have made the FRB an important industrial and agricultural production area in Shanxi Province [28,29]. The urbanisation level within the watershed is 66.6%, higher than the provincial average. The GDP of the watershed is CNY 796.227 billion, accounting for 45.4% of the provincial GDP. Uneven spatial and temporal distributions of precipitation and rapid industrialisation have led to serious water shortages in the FRB, and the per capita water resources (400 m3 per capita) are lower than the average levels of the province (500 m3 per capita) and countryside (2000 m3 per capita) [30]. In the future, ecological protection and high-quality development will face severe challenges in the FRB (Figure 1 and Figure 2).

2.2. Data Sources

Data required to construct the SWAT model attribute database mainly consisted of elevation, land-use, soil, and meteorological data as well as measured runoff data for calibration verification. The digital elevation model (DEM) data were sourced from the geospatial data cloud platform, cropped, and radiometrically corrected. Land-use data (30 × 30 m resolution) were acquired from the Resource and Environment Science Data Centre of the Chinese Academy of Sciences and reclassified according to the PLES classification standard (described in detail in Section 2.3.3). Soil data were obtained from the World Soil Database, which was jointly created by the United Nations Food and Agriculture Organization and the Nanjing Soil Research Institute, and processed using soil, plant, atmosphere, and water (SPAW software 6.02.75). Meteorological data, including precipitation, temperature, wind speed, relative humidity, and solar radiation, were obtained from the National Meteorological Science Data Centre of China, and missing data were calculated and filled in using a weather generator. The measured runoff data used for model calibration were obtained from the Yellow River Conservancy Commission, with the Hejin station as the basin outlet (Table 1).

2.3. Study Methodology

2.3.1. SWAT Model Principles

According to Figure 3, the SWAT model is a watershed-scale, semi-distributed hydrological model developed by the Agricultural Research Service of the United States Department of Agriculture [31]. It is based on the principle that hydrological processes, for instance, precipitation, runoff, evapotranspiration, and sediment transport, are simulated in each watershed by dividing the watershed into sub-basins or hydrological response units [32,33]. The SWAT model consists of modules for vegetation growth, hydrological processes, and land-management practices. Its parameters are directly related to surface characteristics; therefore, it can be used to simulate the water cycle under different land-use management measures, land-use structures, and climatic conditions, which has an important application value [34]. The SWAT model makes predictions based on the following balance equation:
S W t = S W t − 1 + P − Q s u r f − E T − W s e e p − Q g w
where SWt is the current soil water content, SWt−1 is the previous day’s soil water content, P is precipitation, Qsurf is surface runoff, ET is evapotranspiration, Wseep is seepage, and Qgw is groundwater inflow.

2.3.2. SWAT Model Evaluation Criteria

In this study, we selected three commonly used metrics—the Nash efficiency coefficient (NSE), percentage bias (PBIAS), and coefficient of determination (R2)—to assess the performance of the SWAT model in the study area [35]. The NSE value is used to measure the degree of match between the simulated data of the model and the observed data, with values ranging from −∞ to 1; the closer the value approaches 1, the more closely the simulation results align with the observed data. The PBIAS value is used to determine whether the model tends to overestimate or underestimate the flow rate; positive and negative values of PBIAS indicate an overestimation and underestimation of the model, respectively. The R2 value is used to measure the relationship between the modelled and measured values, with values ranging from 0 to 1; the closer the value is to 1, the better the linear fit between the simulated and observed values. These metrics were calculated using the following formulas:
PBIAS = 100 ∑ i = 1 N Q s i m , i − Q o b s , i ∑ i = 1 N Q o b s , i
NSE = 1 − ∑ i = 1 N Q obs , i − Q sim , i 2 ∑ i = 1 N Q obs , i − Q o b s 2
R 2 = ∑ i = 1 N Q o b s , i − Q o b s Q s i m , i − Q s i m 2 ∑ i = 1 N Q o b s , i − Q o b s 2 ∑ i = 1 N Q s i m , i − Q s i m 2
where Qobs,i and Qsim,i are the observed and simulated values, respectively; Qobs and Qsim are the means of the observed and simulated values, respectively; n denotes the number of samples; and i denotes the ith sample.

2.3.3. PLES Classification System

Land is a multifunctional complex integrating production, living, and ecological functions [36]. Based on the definition of PLES, spatial functions of the national territory, land-use characteristics of the study area, practicability of the data, and PLES land-use classification method by Jianhong et al. (2021) [37], we established a new PLES classification system. This new system considers the fact that the primary function of PLES covers the secondary functions of different land-use types. For example, arable land has both production and ecological functions; however, because its primary function is crop production, it is classified as agricultural production land. Furthermore, industrial production land is classified as construction or special-use land, living space is classified as urban or rural residential land, and ecological space is classified as forest, grassland, water, or other ecological land. The Field Calculator tool in ArcGIS was used to classify the downloaded land-use data according to the PLES classification standard. A new field was created in the layer attribute table, to store the classification results (e.g., Land Use Type) (Table 2).

2.3.4. PLES Spatiotemporal Change Analysis Methods

Land-use dynamics characterise the rate of change in the quantity of land resources [38]. This metric consists of a single land-use momentum (K), which represents the transformation in the quantity of a particular land-use type, and a combined land-use momentum (LC), which indicates the transformation in the quantity of a combination of land-use types. Land-use dynamics were calculated using the following formulas:
K = U b − U a U a × 1 T × 100 %
L C = ∑ i = 1 n ∆ L U i − j 2 ∑ i = 1 n L U i × 1 T × 100 %
where Ua and Ub denote the areas of the initial and final land-use types, respectively; T is the time period of the study; LUi−j denotes the absolute value of the area converted from land-use type i to land-use type j; and LUi is the area of land-use type i at the beginning of the study.
A land-use transfer matrix was used to characterise the structure of regional land-use change and the direction of change across categories [39]. The formula used is as follows:
S i j = S 11 ⋯ S 1 n ⋮ ⋯ ⋮ S n 1 ⋯ S n n
where S is the area, n is the number of land-use types, and i and j denote the land-use types at the beginning and end of the study, respectively.

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.

4. Discussion

4.1. Impact of PLES Changes on Runoff in the Fenhe River Basin

This study investigated the impact of PLES transformation on runoff in the FRB from 1990 to 2022. The results show that significant changes in runoff have occurred with changes in land-use structure, particularly the expansion of production space and the reduction in ecological space.
Specifically, the increase in production space is closely linked to urbanisation. During the study period, the production space in the FRB increased by approximately 25%, which directly resulted in a 12% increase in the annual average runoff of the basin. Urban construction and agricultural expansion were the primary drivers of this change. For example, the surface runoff depth in urbanised areas was about 20% higher than in ecological areas, while the increase in runoff depth in agricultural lands was approximately 15%. This indicates that the expansion of production space is typically accompanied by an increase in impermeable surface area, which prevents rainwater from infiltrating into the groundwater layer, thereby exacerbating surface runoff [40].
Simultaneously, the reduction in ecological space has also had a significant impact on runoff. According to the results of this study, ecological space in the FRB decreased by about 18% over 1990–2020. The reduction in ecological space led to a decline in vegetation cover, weakening the soil, water conservation, and water retention functions, which further intensified surface runoff. In areas where ecological space was reduced, the increase in runoff depth typically ranged between 10 and 18%, and in heavily developed regions, the increase reached up to 25%. This phenomenon is consistent with findings in both domestic and international studies [41], which suggest that the degradation of ecological space directly impacts the hydrological regulation capacity of a watershed.
In conclusion, the impact of PLES transformation on runoff is significant and complex. The expansion of production and living spaces often leads to the reduction in ecological space, disrupting the original hydrological balance and resulting in increased runoff [42]. According to the SWAT model simulations in this study, the annual runoff in the FRB increased by about 12% from 1990 to 2020, a change closely related to the transformation of PLES spaces. This finding provides a new perspective for understanding the relationship between land-use change and hydrological processes and offers a quantitative basis for watershed water resource management and ecological protection [43].

4.2. Combined Effect of Precipitation Patterns and PLES Changes on Runoff

The impacts of PLES changes and precipitation on runoff are both significant and closely interconnected. PLES changes influence runoff by altering land-use patterns [44]. For example, the expansion of agricultural and industrial land increases surface hardening, reducing water infiltration and thus increasing runoff. The expansion of urban residential areas typically comes with more impervious surfaces and drainage systems, further exacerbating runoff. The intensity and duration of precipitation have a more direct impact on runoff; heavy or prolonged rainfall can rapidly increase runoff within the watershed, especially in urbanised areas. Changes in precipitation volume also affect the balance between groundwater recharge and surface runoff, further influencing the hydrological process. The interaction between PLES changes and precipitation patterns determines the watershed’s hydrological response. As rainfall intensity increases, impervious urban or industrial areas may exacerbate runoff, while the protection of ecological land can mitigate this issue. Overall, PLES changes act as a secondary redistribution of precipitation, influencing runoff, and this study aims to better analyse this “redistribution” to achieve sustainable watershed management.

4.3. Limitations and Future Research Prospects

This study has thoroughly demonstrated the impact of PLES changes on runoff, but there are certain limitations. In particular, the study adopts a five-year analysis cycle, which does not fully account for the sudden impact of the extreme rainfall event in 2020 on runoff. Since extreme precipitation can cause significant fluctuations in runoff, future research should consider higher time-resolution data to capture the immediate effects of short-term extreme rainfall events on runoff. In addition, although the SWAT model showed satisfactory simulation results in both the calibration and validation phases, it tended to underestimate the peak flows (this deviation was within acceptable limits), which suggests that the model has a limited ability to accurately simulate extreme hydrological events (e.g., floods or droughts). Thus, the accuracy of the model requires further optimisation. Moreover, the results showed that PLES changes in the FRB had the greatest effect on surface runoff and less impact on other components of the water balance. This aligns with the findings of Kaellsson et al. (2016) [45], who showed that land-use change has a limited effect on mean annual runoff, especially when forest change is below 15–20%, which has almost no effect on annual runoff. However, as we used a realistic scenario of PLES change rather than an extreme land-use change scenario in our simulation, the land-use pattern did not change substantially in the short term, leading to limited changes in runoff. Thus, future studies should further explore the impacts of extreme land-use changes on the water cycle to determine the role of different land-use patterns in the hydrological balance. Identifying the most efficient land-use configurations will support the maintenance of the hydrological balance and ecosystem services.

5. Conclusions

Based on the research perspective of PLES, we systematically analysed the spatial and temporal changes in production, living, and ecological spaces in the FRB from 1990 to 2020. Additionally, we conducted quantitative research on the impacts of PLES changes on runoff. The main conclusions are as follows:
(1)
From 1990 to 2020, PLES underwent marked changes in the FRB. Urbanisation led to the continuous expansion of cities, with industrial production and living lands encroaching on agricultural production and ecological lands. However, the conflict between humans and nature over land use was alleviated through national efforts to promote ecological protection and construction. Structural changes in PLES resulted from the joint action of socioeconomic and natural factors, and the interaction between socioeconomic factors had a strong explanatory power for PLES land-use changes.
(2)
The extensive use of reinforced concrete has led to the hardening of the ground and a reduction in water infiltration. Additionally, the area covered by vegetation has decreased, negatively impacting water conservation. Consequently, surface runoff has increased annually. Specifically, from 1990 to 2020, surface runoff in the FRB increased at a rate of 1.06%, garnering the attention of managers. In addition, our findings revealed that the water-holding capacity of ecological land exceeds that of agricultural production land; therefore, the protection of ecological space is crucial for maintaining the hydrological balance of the basin. In future watershed management, the protection and restoration of ecological land should be prioritised to reduce the risk of extreme events, such as floods and droughts.
(3)
The impact of PLES changes on pollutants was found to be significant. The expansion of production and living spaces directly leads to an increase in nitrogen and phosphorus pollutant concentrations, while the reduction in agricultural and ecological spaces weakens water bodies’ self-purification capacity, consequently increasing SS loss and water quality deterioration. Additionally, heavy metal pollution during the industrialisation and urbanisation processes showed a year-on-year increasing trend. In the future, watershed water quality management should place greater emphasis on the protection and restoration of ecological spaces, as the restoration of ecological spaces is crucial for water quality purification. This will help mitigate the negative impact of pollutants and promote the sustainable development of the watershed.
(4)
The uniqueness of this study resides in our application of the PLES perspective to hydrology. This approach yielded comprehensive analyses, interdisciplinary integration, and practical guidance for policy formulation. The PLES concept supports prioritising ecological protection while maintaining a dynamic balance between production and living spaces, thereby supporting sustainable development. This provides a comprehensive framework for understanding the complex relationship between land-use changes and hydrological processes. Our results serve as a valuable reference for watershed management and water resource protection.

Author Contributions

Software, J.Z., J.L., A.K., Z.L., Y.S. and Y.W.; Validation, J.Z.; Investigation, J.Z.; Writing—original draft, J.Z.; Writing—review & editing, A.A.L. and Q.G.; Project administration, Q.G.; Funding acquisition, Q.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by [Research Project Supported by Shanxi Scholarship Council of China], grant number [2022-111].And The APC was funded by [Qingxia Guo].

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Location of the Fenhe River Basin, Shanxi Province, China.
Figure 1. Location of the Fenhe River Basin, Shanxi Province, China.
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Figure 2. Monthly mean temperature and precipitation in the Fenhe River Basin. Data were obtained from eight meteorological stations in the basin (1990−2022), and values were calculated using the Tyson polygon method.
Figure 2. Monthly mean temperature and precipitation in the Fenhe River Basin. Data were obtained from eight meteorological stations in the basin (1990−2022), and values were calculated using the Tyson polygon method.
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Figure 3. Soil and water assessment tool (SWAT) model flow chart.
Figure 3. Soil and water assessment tool (SWAT) model flow chart.
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Figure 4. Observed and SWAT-simulated monthly stream flow for the calibration (January 2014−December 2022) and validation (January 1990–December 2014) periods in the Fenhe River Basin, Shanxi Province, China.
Figure 4. Observed and SWAT-simulated monthly stream flow for the calibration (January 2014−December 2022) and validation (January 1990–December 2014) periods in the Fenhe River Basin, Shanxi Province, China.
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Figure 5. Development of production–living–ecological space (PLES) over 1990–2020 in Fenhe River Basin.
Figure 5. Development of production–living–ecological space (PLES) over 1990–2020 in Fenhe River Basin.
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Figure 6. Secondary class distribution of PLES in Fenhe River Basin.
Figure 6. Secondary class distribution of PLES in Fenhe River Basin.
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Figure 7. Trajectories of spatial transfer changes of PLES in the Fenhe River Basin. Different coloured trajectory lines show the direction of transfer between land classes, and the thickness of the trajectory lines represents the amount of transformation.
Figure 7. Trajectories of spatial transfer changes of PLES in the Fenhe River Basin. Different coloured trajectory lines show the direction of transfer between land classes, and the thickness of the trajectory lines represents the amount of transformation.
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Figure 8. Spatial transfer of PLES land-use types in Fenhe River Basin.
Figure 8. Spatial transfer of PLES land-use types in Fenhe River Basin.
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Figure 9. Simulated runoff changes in the Fenhe River Basin between 1990 and 2020. (The green dushed line represents the overall trend of precipitation changes).
Figure 9. Simulated runoff changes in the Fenhe River Basin between 1990 and 2020. (The green dushed line represents the overall trend of precipitation changes).
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Figure 10. Average monthly runoff under different PLES scenarios.
Figure 10. Average monthly runoff under different PLES scenarios.
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Figure 11. Average annual surface runoff (SURQ) and groundwater (GWQ) under different PLES scenarios.
Figure 11. Average annual surface runoff (SURQ) and groundwater (GWQ) under different PLES scenarios.
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Figure 12. Stacked chart of PLES spatial transfer in sub-basins 42, 43, and 44 from 1990 to 2020.
Figure 12. Stacked chart of PLES spatial transfer in sub-basins 42, 43, and 44 from 1990 to 2020.
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Figure 13. Temporal variations of surface runoff and groundwater in sub-basins 42, 43, and 44.
Figure 13. Temporal variations of surface runoff and groundwater in sub-basins 42, 43, and 44.
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Table 1. Soil and water assessment tool (SWAT) model data sources.
Table 1. Soil and water assessment tool (SWAT) model data sources.
DataSourceResolution
Digital evaluation modelGeospatial data cloud (http://www.gscloud.cn/, accessed on 16 September 2023)30 m
Land-use/land-cover map
Soil map		Resource and Environment Science and Data Centre (https://www.resdc.cn, accessed on 16 September 2023)
Harmonized World Soil Database (V 1:1)		30 m × 30 m
30 arc-s
Observed stream flowYellow River Conservancy Commissionon a per-day basis
Meteorological dataNational Meteorological Science Data Centre
Daily Values of Surface Climatological Data for China (V 3.0)		on a per-day basis
Table 2. Land-use classification system of the production–living–ecological space (PLES).
Table 2. Land-use classification system of the production–living–ecological space (PLES).
Primary
Classification		Secondary Classification and CodeSecondary Classification of Land Use
Production landAgricultural production land (1A)Paddy field, dry land
Industrial production land (1B)Other construction land, special use area
Living landUrban living land (2A)Urban land
Rural living land (2B)Rural residential area
Ecological landForest (3A)Dredged woodland, shrub land
woodland, and other woodland
Grassland (3B)High-, medium-, and low-cover grassland
Water (3C)Canals, reservoirs, ponds, and lakes
Other ecological land (3D)Marshland, bare rock stony land,
sandy land, beach land, saline–alkali land, and bare land
Table 3. Calibration parameters of the SWAT model in the Fenhe River Basin, ranked in descending order of sensitivity, and the optimal values of the parameters.
Table 3. Calibration parameters of the SWAT model in the Fenhe River Basin, ranked in descending order of sensitivity, and the optimal values of the parameters.
RankParameterDescriptiont-StatpFitted Value
1V_CH_K2.rteEffective hydraulic conductivity in main channel alluvium−27.840.0013.57
2R__SOL_AWC(‥).solAvailable water capacity of the soil layer4.370.000.058
3V__GW_REVAP.gwGroundwater revamp coefficient−1.960.05−0.21
4R__CN2.mgtSCS runoff curve number for moisture condition II1.760.080.077
5V__ESCO.hruSoil evaporation compensation factor−1.720.090.070
6V__REVAPMN.gwThreshold depth of water in the shallow aquifer for revap to occur (mm)−1.550.12226.48
7V__GW_DELAY.gwGroundwater delay (days)−1.360.18362.86
8R__HRU_SLP.hruAverage slope steepness−1.320.19−0.46
9V__SPEXP.bsnExponential coefficient of sediment transport1.270.201.44
10R__SLSUBBSN.hruAverage slope length1.100.27−0.33
11R__SURLAG.bsnLag coefficient of surface runoff−0.820.410.33
12V__SPCON.bsnLinear coefficient of sediment transport0.730.470.01
13V__SMFMN.bsnMinimum melt rate for snow during year−0.690.496.18
14V__SFTMP.bsnSnowfall temperature−0.640.520.64
15V__ALPHA_BF.gwBaseflow alpha-factor−0.600.550.96
16V__TIMP.bsnTemperature drop rate−0.590.560.08
17V__GWQMN.gwThreshold water level in shallow aquifer for base flow (mm)−0.500.620.19
18V__USLE_P.mgtUSLE soil and water conservation measures factor0.480.630.36
19R__SOL_K.solSaturated hydraulic conductivity−0.230.82−0.33
20V__CH_N2.rteManning’s “n” value for the main channel−0.200.840.01
21V__EPCO.hruPlant uptake compensation factor−0.150.880.24
22R__SOL_BD(‥).solMoist bulk density−0.030.971.88
23R__OV_N.hruManning’s n value for overland flow0.020.980.08
24R_CANMX.hruMaximum canopy storage0.001.00−0.51
Table 4. Single PLES land-use dynamic in Fenhe River Basin.
Table 4. Single PLES land-use dynamic in Fenhe River Basin.
Type1990–20002000–20102010–20201990–2020
1A−0.0322%−0.4332%−0.1342%−0.1973%
1B1.7920%34.6945%2.7017%18.9809%
2A3.0315%8.0249%1.3801%5.5770%
2B0.2276%3.7893%0.6201%1.6592%
3A−0.0146%0.2043%−1.6912%−0.5113%
3B−0.0770%−0.5880%1.8923%0.3690%
3C0.0614%−1.6180%0.1185%−0.4889%
3D1.9673%−5.0830%−0.1571%−1.4027%
Table 5. Runoff analysis for different time periods.
Table 5. Runoff analysis for different time periods.
Annual
Interval		Average PrecipitationMean RunoffMaximum
Runoff		YearMinimum
Runoff		YearRate of Change
1990–199526.6714.8321.1819959.001992-
1990–202044.3516.2537.3619964.7520000.096%
2001–200524.2910.1620.1220035.122001−0.36%
2006–201020.3911.9115.72200710.4620060.17%
2011–201533.2019.7929.00201315.6420150.66%
2016–202039.1923.8128.17201614.6920190.2%
Table 6. Changes in pollutant concentrations in the Fenhe River Basin (1990–2020).
Table 6. Changes in pollutant concentrations in the Fenhe River Basin (1990–2020).
Time PeriodNitrogen (mg/L)Phosphorus (mg/L)Suspended Solids (mg/L)Heavy Metal (mg/L)
19902.510.8215.20.01
20002.720.9516.00.01
20103.081.0818.00.02
20203.571.2120.00.03
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Zhang, J.; Laghari, A.A.; Guo, Q.; Liang, J.; Kumar, A.; Liu, Z.; Shen, Y.; Wei, Y. Evolution of Land Use and Its Hydrological Effects in the Fenhe River Basin Under the Production–Living–Ecological Space Perspective. Sustainability 2024, 16, 11170. https://doi.org/10.3390/su162411170

AMA Style

Zhang J, Laghari AA, Guo Q, Liang J, Kumar A, Liu Z, Shen Y, Wei Y. Evolution of Land Use and Its Hydrological Effects in the Fenhe River Basin Under the Production–Living–Ecological Space Perspective. Sustainability. 2024; 16(24):11170. https://doi.org/10.3390/su162411170

Chicago/Turabian Style

Zhang, Junzhe, Azhar Ali Laghari, Qingxia Guo, Jiyao Liang, Akash Kumar, Zhenghao Liu, Yongheng Shen, and Yuehan Wei. 2024. "Evolution of Land Use and Its Hydrological Effects in the Fenhe River Basin Under the Production–Living–Ecological Space Perspective" Sustainability 16, no. 24: 11170. https://doi.org/10.3390/su162411170

APA Style

Zhang, J., Laghari, A. A., Guo, Q., Liang, J., Kumar, A., Liu, Z., Shen, Y., & Wei, Y. (2024). Evolution of Land Use and Its Hydrological Effects in the Fenhe River Basin Under the Production–Living–Ecological Space Perspective. Sustainability, 16(24), 11170. https://doi.org/10.3390/su162411170

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