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Article

An Evaluation of Future Climate Change Impacts on Key Elements of the Water–Carbon Cycle Using a Physics-Based Ecohydrological Model in Sanchuan River Basin, Loess Plateau

by
Yujie Yuan
1,
Xueping Zhu
1,*,
Xuerui Gao
2 and
Xuehua Zhao
1
1
College of Water Resources Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China
2
Institute of Soil and Water Conservation, Northwest A&F University, Xianyang 712100, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(19), 3581; https://doi.org/10.3390/rs16193581
Submission received: 23 July 2024 / Revised: 14 September 2024 / Accepted: 23 September 2024 / Published: 26 September 2024
(This article belongs to the Topic Climate Change and Environmental Sustainability, 3rd Volume)
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Abstract

:
The cycle of carbon and water in ecosystems is likely to be significantly impacted by future climate change, especially in semiarid regions. While a considerable number of investigations have scrutinized the repercussions of impending climatic transformations on either the carbon or water cycles, there is a scarcity of studies delving into the effects of future climate change on the coupled water–carbon process and its interrelationships. Based on this, the Sanchuan River Basin, an ecologically fragile region of the Loess Plateau, was chosen as the research area. General circulation model-projected climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) and an ecohydrological model were integrated to predict (2021–2100) changes in actual evapotranspiration (ET), surface runoff (Rs), net primary productivity (NPP), and soil organic carbon (SOC). The results indicated that under the impacts of future climatic warming and humidification, ET, Rs, and NPP will increase by 0.17–6.88%, 1.08–42.04%, and 2.18–10.14%, respectively, while SOC will decrease by 3.38–10.39% in the basin. A path analysis showed that precipitation and temperature had significant effects on ET and NPP, Rs was more sensitive to precipitation, and temperature had a significant impact on SOC. Furthermore, all climate scenarios had an average ET-NPP correlation coefficient greater than 0.6, showing that the basin’s water–carbon cycle was tightly coupled. However, under SSP5-8.5, the correlation coefficient of Rs-NPP decreased from −0.35 in the near-future period to −0.44 in the far-future period, which may indicate that the positive effect of increased precipitation on Rs-NPP would barely offset the negative effect of large future temperature increases. As a foundation for achieving sustainable water resource management and ecosystem preservation policies, this study can be utilized to build adaptation methods to manage climate change.

1. Introduction

The latest IPCC Assessment Report states that as global climate change continues to intensify, extreme weather events will become more frequent. These events will alter the intensity and occurrence patterns of regional droughts and precipitation, thus profoundly affecting terrestrial ecosystems [1]. As the foundation of mass cycling and energy exchange in Earth’s surface ecosystem, water and carbon cycling in terrestrial ecosystems are more deeply affected by climate change [2,3,4]. For example, increasing temperatures and changing precipitation patterns affect physiological processes in vegetation, resulting in changes in vegetation evapotranspiration and carbon sequestration rates that in turn affect water recharge in the soil and surface runoff and ultimately affect the basin water–carbon cycle [5]. Thus, it is crucial to accurately predict and assess the effects of climate change on water–carbon cycles, which are critical to ensuring the ecological security and water resources of the region.
As a key tool for forecasting future climate change, general circulation models (GCMs) can accurately reflect future climate change by coupling multiple Earth system components and simulating their interactions and feedback mechanisms [6]. In reviewing the implications of future climate change on basins’ water and carbon cycles, methods that integrate GCMs’ projected climate scenarios into hydrological or biogeochemical models have been widely used [7,8,9]. Several scholars have investigated future changes in water resources, simulating and predicting the future water cycle of the Huaihe River Basin [7], Haihe River Basin [8], and Yalong River Basin [10]. At the same time, other scholars are concerned about future changes in ecosystem carbon dynamics [9,11,12], especially in the ecologically fragile Loess Plateau (LP) [13,14]. Chen et al. [7] used GCM outputs based on the SWAT model to evaluate the effects of climate change on future surface water resources and flood risk in the watershed of eastern China. Their results indicated that the predicted monthly runoff from 2061 to 2100 will increase and future climate change will have a clear impact on hydrological processes. By combining multimodel modeling with climate data from a collection of GCMs, Li et al. [11] evaluated the potential effects of climate change on important ecosystem service functions, and their results indicated that in the mid-21st century, the NPP in Central Asia will vary between −15.26% and +17.69%. Obviously, most of the current studies tend to treat the water cycle and carbon cycle as separate research objects, focusing on the impact of future climate change on a particular element or a certain process, and there are still limited studies on the interrelationship and coupling mechanism between climate change and water and carbon. Therefore, the response of the water–carbon cycle to future climate change needs to be studied urgently.
Water and carbon cycles are closely related in ecosystems [15]. Water availability affects vegetation photosynthesis and evapotranspiration processes, which in turn affect carbon sequestration and circulation [16]. Also, carbon cycling exerts an influence on soil water retention capacity and hydrological processes through mechanisms such as plant growth and litter decomposition [17]. Due to the complex interaction of these two cycles at temporal and spatial scales, studying one cycle in isolation may overlook critical reciprocal effects. Therefore, a joint analysis of water and carbon cycles is imperative for a comprehensive understanding of ecosystem functioning and its response to climate change. At present, physics-based hydrological models can be coupled with photosynthesis models or empirical vegetation growth models to simulate water–carbon coupling [18]. However, these models focus more on the water balance and may neglect the interactions between vegetation dynamics and water circulation. Biogeochemical models mainly focus on the carbon balance in soils and simulate soil-centered water–carbon processes; however, they face challenges in capturing key hydrological processes under complex underlying surface conditions [19]. Due to the scarcity of modeling tools that link water and carbon dynamics, little research has been undertaken regarding the effects of future climate change on the coupled water–carbon cycle. Therefore, the regional hydrological ecological simulation system (RHESSys) model was selected to analyze the impacts of future climate change on the water–carbon cycle in order to compensate for the limitations of traditional hydrological or biogeochemical models that focus only on the water or carbon cycle separately. As one of the most mature ecohydrological models worldwide [20], the RHESSys model has a relatively complete physical mechanism for ecological processes, hydrological cycles, and their interactions [21]. It can effectively simulate the connectivity between hydrological response units and lateral hydrological flux, as well as the interactions among ecosystem carbon and nitrogen cycles, nutrient cycles, and hydrology processes [19,22,23]. Although the RHESSys model has the ability to simulate both water and carbon cycles simultaneously, it has rarely been applied to forecast how future climate change may affect basins’ water–carbon relationship. The innovation of this study is the application of the RHESSys model, which has a physical mechanism and links water–carbon dynamics, combined with multiple GCMs from the latest Coupled Model Intercomparison Program (CMIP6). This approach enables the prediction and analysis of the basin’s water–carbon cycle under future climate change, addressing the current research gap where the water cycle or carbon cycle is often studied in isolation, and provides guidance for future natural resource management.
The loess hilly and gully region has serious soil and water loss, complex topography, infertile soil, little precipitation, and sparse vegetation; thus, its ecological environment is fragile and vulnerable to climate change [24,25]. The uncertain changes in the future climate of the LP may have unpredictable effects on water–carbon cycles [4]. The Sanchuan River Basin (SRB), located in the eastern region of the LP, represents a typical loess hilly and gully region. Therefore, the SRB, the study region, and the climate scenarios predicted by CMIP6 are integrated into the RHESSys model to deeply analyze the changes in water–carbon cycle elements under future climate change. The study aims are as follows: (1) to analyze the future precipitation and temperature changes of the SRB under three future climate scenarios (SSP1-2.6: sustainability—low radiative forcing scenario; SSP2-4.5: middle of the road—intermediate radiative forcing scenario; and SSP5-8.5: fossil-fueled development—high radiative forcing scenario); (2) to construct an RHESSys model that can predict the changes in ecohydrological elements under these scenarios; and (3) to assess the SRB’s water–carbon relationship in light of climate change. This study can be used to develop climate change adaptation strategies for sustainable resource management.

2. Materials and Methods

2.1. Overall Framework

This study’s objective was to investigate the potential effects of future climatic changes on the water–carbon cycle within the basin, and the methodology flowchart is shown in Figure 1. The first step was to collect all of the data required for the RHESSys modeling and future climate change projection. In the second step, statistical downscaling and delta deviation correction methods were used to process the future meteorological data. At the same time, the RHESSys model was constructed to output the water–carbon cycle elements (actual evapotranspiration (ET), surface runoff (Rs), net primary productivity (NPP), and soil organic carbon (SOC)) under different future emission scenarios. Finally, based on the downscaling meteorological data results and model outputs, the spatiotemporal changes in temperature, precipitation, and water–carbon cycle elements under different climate scenarios were analyzed, while statistical methods were used to evaluate the relationship between elements of the water–carbon cycle under future climate scenarios.

2.2. Study Area

The SRB is situated in the eastern LP of China (37°03′–38°08′N and 110°37′–111°35′E) and has an area of 4161 km2, as shown in Figure 2. The dominant soil types are loess and gray cinnamon soil, which are soft and easily erode. Thereby, the SRB is among the regions with severe soil and water loss on the LP. The topography of the basin is tilted from northeast to southwest. The vegetation condition in the eastern part of the basin (upstream) is better, characterized by mild soil and rock erosion, resulting in a lower degree of soil erosion; the west (downstream) is poorly vegetated, with fragmented topography and severe soil erosion; and the middle region (midstream) is densely populated and the main agricultural production area.

2.3. RHESSys Model

2.3.1. Model Description

The RHESSys model is a comprehensive, process-based model used to simulate hydrological and ecological processes on a regional scale. The RHESSys model is adapted and integrated from several existing models, including the MTN-CLIM, Biome-BGC, and TOPMODEL. The MTN-CLIM model is used to derive spatial climate variables [26]; the Biome-BGC model is used to estimate the flux of carbon, water, and potential nitrogen for various canopy types [27]; and TOPMODEL is used to simulate runoff generation and soil moisture redistribution [28]. As the RHESSys model developed, the CENTURY model and the DHSVM model were integrated to further optimize the carbon and nitrogen cycle simulation processes and hydrological processes [29,30]. In addition, multi-layer nested spatial discrete units (basin, zone, hillslope, patch, and canopy strata) were used in the model [21], which can effectively simulate the basin’s ecohydrological processes at different scales.

2.3.2. Input and Setup

The model-building process is usually divided into three parts.
First, the database needs to be created. GRASS GIS software was used to preprocess digital elevation model (DEM), land-use, vegetation coverage (FVC), soil, and meteorological (temperature (T), precipitation (Prec)) data to generate basic spatial data (basin, hillslope, patches, etc.) and other types of data. Table 1 shows the data sources used in this study. The spatial data were unified to a resolution of 500 × 500 m and exported to a model running file directory in Ascii format, and the RHESSys preprocessor was used to generate the model’s baseline file (Worldfile: model hierarchy and association methods; Flowtable: the connections between patches). In this step, 31 hillslopes and 15,807 patches were generated.
The second step is spin-up, which is designed to bring the C and N of vegetation and soils into equilibrium (with an interannual fluctuation range of less than 5%). The timing of the spin-up is determined by the type of climate, land-use type, and soil properties that are being run, and typically runs for hundreds of years due to the slow development of the soil organic pool. In this study, the spin-up run time was 1000 years.
The last step involves model parameter calibration, validation, and outputting the results. Once the model’s carbon and nitrogen balance requirements were satisfied, the model was calibrated and validated. After verifying the reliability of the model, the following indicators were exported for subsequent analysis: (1) ET—reflects the process of water returned to the atmosphere from the surface and vegetation, and is the key factor of the water cycle; (2) Rs—an important index in the water cycle, reflecting the amount of available surface water resources in a region; (3) NPP—the main indicator of an ecosystem’s ability to fix carbon through photosynthesis; and (4) SOC—represents the capacity of soil in long-term carbon storage and is an important part of the global carbon pool.

2.3.3. Calibration and Verification

Six sensitive parameters of the RHESSys model need to be calibrated (Table 2). The observed runoff data from Houdacheng Station (the outlet hydrological station of the SRB) were used for model calibration (2008–2012) and validation (2013–2017). In addition, MODIS ET and NPP yearly products (500 m) from 2011 to 2020 were selected to verify the model simulation results. The specific data sources are shown in Table 1. The model’s performance was assessed using the Nash–Sutcliffe efficiency coefficient (NSE), correlation coefficient (R2), root mean square error (RMSE), and percentage deviation (PB) [19]. If the NSE and R2 values are greater than 0.6, the simulation results are credible [25].

2.4. Future Climate Scenarios

The latest International Coupled Model Intercomparison Project (CMIP6) combines typical recognized concentrated pathways and shared socioeconomic pathways (SSPs) so that emission and land-use scenarios can be considered in the context of societal development factors. It provides plausible scenarios for predicting future ecohydrological processes [32]. In comparison to the standard RCP scenario of CMIP5, CMIP6 demonstrates a significant enhancement in terms of spatial resolution, simulation range, simulation performance, and the rationality of results [33,34,35]. Therefore, daily-scale meteorological data under three emission scenarios were retrieved from CMIP6. To reduce the uncertainty of the data, five of the GCM outputs were selected. The specific GCM information is shown in Table 3. The spatial resolution was unified to 500 m × 500 m after spatial downscaling and delta method deviation correction. For the specific formulas, see Chen et al. [7].
Before these future meteorological data were entered into the model, R2 and RMSE were used as evaluation indicators to compare the multimodel ensemble means of the 5 GCMs from 1991 to 2020 with the observed meteorological data from the same historical period (Figure 3). The maximum and minimum temperature (Tmax and Tmin) outputs from the GCMs were in good agreement with the observed data, with R2 values above 0.95. Compared with temperature, although the GCM output overestimated the peak value of precipitation to some extent, it still met the requirements. In general, downscaling data can predict future climate change in the SRB to some extent.
The future meteorological scenarios selected for this study consisted of an ideal low-emission scenario (SSP1-2.6), a normal development scenario (SSP2-4.5), and an extreme high-emission scenario (SSP5-8.5). Then, meteorological data were divided into three periods: the historical period (H), 1981–2020; the near-future period (NF), 2021–2060; and the far-future period (FF), 2061–2100. Hence, according to the different emission scenarios of future climate data, a total of 7 climate data input scenarios were established. The specific meteorological scenario settings are shown in Table 4.

2.5. Statistical Analysis

Trend analysis: The Mann–Kendall (M-K) trend test was used to analyze the change trend of each ecohydrological element time series. The calculation method for the standardized test statistic (Z) is detailed in Mann [36]. At the 5% confidence level, a trend is considered significant if |Z| ≥ 1.96.
Path analysis: Path analysis was performed using the partial least squares method. A root mean square residual (RMR) < 0.05, root mean square error of approximation (RMSEA) < 0.10, comparative fit index (CFI), and goodness-of-fit index (GFI) close to 1 indicate a good simulation [37]. If the original model did not fit well, it was modified by deleting or changing the non-significant path until the model met the criteria.
Correlation analysis: Spearman’s rank correlation was chosen to describe the relationships between water (ET, Rs), carbon (NPP, SOC), and the key elements involved in the coupled water–carbon cycle [38].

3. Results

3.1. Calibration and Validation

The RHESSys model calibration and validation results are shown in Figure 4. During calibration, the R2 and NSE of the monthly runoff were 0.78 and 0.60. These values improved in the validation period, with R2 reaching 0.79 and NSE increasing to 0.63. Although the runoff was underestimated due to the influence of upstream reservoirs, R2 and NSE were greater than 0.6 according to the performance standards of Sun et al. [25], indicating that the results were acceptable. Moreover, ET and NPP simulated using the RHESSys model had a good correlation with published MODIS products, with R2 values above 0.8 and PB values within 5%. The results showed that the RHESSys model accurately simulated the SRB’s ecohydrological processes.

3.2. Changes in Precipitation and Temperature in the Future

Downscaled meteorological data were used to estimate the spatiotemporal variations in Tmax, Tmin, and Prec in the SRB during NF and FF under three future climate emission scenarios.
As shown in Figure 5, under the influence of the Lüliang Mountains in the east, the Tmax and Tmin in the SRB showed the characteristics of lower upstream and higher downstream in the historical period. Under SSP1-2.6, the basin’s multi-year average Tmax and Tmin were 16.34 °C and 3.86 °C, respectively, which were closest to the historical period. With the changes in emission scenarios, the temperature in the central and western parts of the basin gradually increased, and the temperature increase was the largest in the SSP5-8.5 scenario. The main reason for higher Prec in the upper reaches during the historic period is that upstream of the SRB are mountain ranges, where water vapor condenses more readily into precipitation. Future precipitation varied in the same way as temperature. With the enhancement of the radiative forcing scenario, the basin’s multi-year average Prec gradually increased to more than 600 mm.
As shown in Figure 6, during the NF period, Tmax and Tmin under the SSP2-4.5 scenario decrease by 0.05 and 0.20 °C compared with those in the H period. In the other scenarios, the temperature increases, but the overall change is within 1 °C. During the FF period, the temperature increases in all climate scenarios compared to those in the base period, especially under SSP5-8.5, where Tmax increases by 4.08 °C and Tmin increases by 4.37 °C. Regarding precipitation, future projections show an upward trend. The annual precipitation in the NF period increases the most (32.15%) under the SSP5-8.5 scenario and the least (20.89%) under SSP1-2.6 among the three future climate scenarios. Similarly, the precipitation in the FF period increases by 14.15%, 25.58%, and 34.74% compared with that in the base period. The future climate of the SRB shows a trend of wetting and warming. Fan et al. [39] show that the spatial distribution of temperatures in the northern and eastern LP exhibits a more significant increase in the future. Numerous studies also indicate a significantly higher temperature increase in the far future compared to the near future in the 21st century [39,40,41]. In terms of future precipitation, similar to our results, Li et al. [42] found that in the Yellow River basin, the annual precipitation growth rate of the SSP5-8.5 scenario is higher than that of the SSP1-2.6 scenario. Therefore, based on these results, it is shown that the results of this study are reliable.

3.3. Changes in Ecohydrological Elements under Climate Scenarios

The spatiotemporal variations in the future water–carbon cycle elements, as output by the RHESSys model, are shown in Figure 7. Under the SSP1-2.6 scenario, ET increases at a rate of 0.087 mm a−1, but the trend is not significant (Z = 0.98). However, under SSP2-4.5 and SSP5-8.5, ET increases significantly at rates of 0.458 mm a−1 and 0.264 mm a−1. The spatial distribution of ET under the different climate scenarios is high in the east and low in the west, and the area with high ET coincides with the area with better vegetation conditions. The scenario with the highest ET value is the far-future scenario (FFm) under SSP2-4.5, which is 6.88% higher than that under the baseline scenario. The scenario with the lowest ET value is the far-future scenario (FFl) under SSP1-2.6, with an average value of 414.94 mm. This may indicate that when precipitation and temperature tend to increase, the actual evapotranspiration tends to increase in the basin [43]. The FFh scenario has greater precipitation and temperature than the FFm scenario, but the FFh scenario has lower ET than the FFm scenario. The reason for this phenomenon may be that excessive temperature can adversely affect the stomatal conductance of vegetation and inhibit transpiration and photosynthesis [44]. In addition, the increase in CO2 concentration implies a decrease in the stomatal conductance of the vegetation, leading to a decrease in transpiration [8,45]. This finding indicates that the effect of temperature and precipitation on ET is greater than that of CO2 concentration on ET.
Compared with the historical scenario, Rs also increases when precipitation increases under various future climate scenarios, particularly in the central and western regions. In SSP1-2.6, there is no notable change in Rs, but in SSP2-4.5, Rs increases significantly at a rate of 0.103 mm a−1. The SSP5-8.5, which has a greater increase in precipitation, has a greater increase in Rs, with an increase rate of 0.199 mm a−1. Precipitation affects surface runoff by altering hydrological processes and water energy distribution [38]. In the SRB, Rs may increase with increasing precipitation. In general, future climate change will increase the hydrological elements in the basin, which presents new challenges and opportunities for water resource management.
NPP is an important indicator of the productive capacity of vegetation under natural environmental conditions. The NPP exhibits a noteworthy upward trend across all climate scenarios, with the greatest increase rate of 1.063 g C m−2 a−1 under SSP2-4.5. Moreover, the upstream areas with better vegetation conditions have higher NPP values, while the downstream areas with more human activities have lower NPP values. Compared with that in the base period, the relative change range of NPP is 4.99–10.14% in the NF period and decreases to 2.18–8.87% in the FF period. Sun et al. [45] showed that higher CO2 levels directly promote plant growth and productivity by promoting chloroplastic CO2 diffusion and photosynthesis. Even under extreme high-temperature conditions, greater CO2 concentrations can maintain net carbon uptake by ecosystems [4]. Therefore, NPP is higher in future scenarios with elevated CO2 concentrations compared to the baseline scenario. In general, suitable warming and a sufficient water supply can increase the photosynthetic rate of vegetation, thus increasing NPP [46,47]. However, in semiarid regions with limited water resources, excessive temperature can adversely affect vegetation stomatal conductance and inhibit photosynthesis [48]. Moreover, in this study, the adverse effects of high temperature values are not offset by the positive effects of increasing CO2 concentration and water content, which explains why the mean NPP of the FF period with higher temperature is lower than that of the NF period.
In terms of temporal trends, SOC shows a significant decreasing trend under the different climate scenarios. In SSP5-8.5, the rate of decrease in SOC is the greatest, at 0.0065 kg C m−2 a−1. The basin’s upper reaches have relatively high SOC values, which are influenced by forestland-dominated vegetation types and high FVC in the upper reaches. Unlike NPP, the SOC is lower than in the historical scenarios under future climate scenarios. In comparison to the H period, the SOC decreases by less than 5% in the NF period. However, the reduction is more drastic in the FF period, especially in the FFl period, when the SOC decreases by 10.39% compared to that in the historical scenario. Temperature directly affects microbial activity and biochemical reaction rates in soil [49]. A warming climate will activate the growth of microorganisms, thus promoting the decomposition of organic matter, potentially resulting in a decrease in SOC [50]. Moreover, changes in precipitation patterns also affect the decomposition of SOC. The probability of extreme rainfall in SSP5-8.5 is significantly greater than in the other scenarios, which may result in soil erosion and the loss of SOC [51]. This situation is also the cause of the largest decrease in SOC in the FFl scenario in comparison to the other scenarios.

3.4. Interrelationships among the Elements

In this study, to analyze the factors affecting the water–carbon cycle, Spearman’s rank correlation was employed to test the spatial relationships between the water–carbon cycle elements and key control factors under all scenarios, as shown in Figure 8.
The results showed that FVC, EL, and Pv are positively correlated with water–carbon cycle elements. In general, a higher FVC means that more plant surface area is available for transpiration: with sufficient precipitation and greater FVC, the actual evapotranspiration increases more readily [52]. Moreover, a higher FVC is conducive to reducing rainfall kinetic energy, increasing the infiltration capacity of rainfall and effectively reducing soil erosion and maintaining soil organic carbon [53]. Elevation affects the elements of the water–carbon cycle by influencing vegetation composition and the climatic conditions of the basin. With rising elevation, Prec may increase, which is more favorable for vegetation growth and may lead to higher FVC and NPP [54].
According to the results of the correlation analysis, a path analysis was conducted to quantify the impact of key control factors on ecohydrological elements in space. Figure 9 shows that the direct effects of T, Prec, and FVC on ET were −0.44, 0.39, and 0.67, respectively. Although EL had no direct effect on ET, EL had indirect effects on ET by affecting T, Prec, and FVC. Rs is only directly affected by Prec (0.83). Similarly to ET, T (−0.26), Prec (0.36), FVC (0.53), and EL (0.72) affected the distribution of NPP. For the SOC, temperature had a negative impact (−0.39). With the increase in Pv, SOC also increased, indicating that land-use type significantly affected SOC distribution. In addition, EL indirectly affected SOC by affecting Pv.
The water–carbon relationship in the basin during different periods was assessed (Figure 10). The average correlation coefficients of ET-NPP and ET-SOC exceed 0.6 in all climate scenarios. Unlike the correlations for ET, the correlation coefficients between Rs and NPP, as well as between Rs and SOC, are both negative. The average correlation coefficient of Rs-SOC was relatively stable, while the Rs-NPP decreased gradually from the NF to FF periods, especially under SSP5-8.5, where the coefficient of Rs-NPP decreased from −0.35 in the NF to −0.44 in the FF. Obtaining similar results, Zhao et al. [55] used the SWAT model and GCMs to predict the future water–carbon relationship in the Jinghe River Basin and showed that the correlation coefficient of ET-NPP under RCP8.5 was generally lower than that under RCP4.5 and RCP2.6. In addition, a negative relationship of Rs-NPP has been reported in many regions of the LP [19,55,56].

4. Discussion

4.1. Relationship between Water and Carbon Cycles

Water–carbon cycles in ecosystems are intricately connected, with stomatal regulation playing a pivotal role in managing the flow of both water and carbon between ecosystems and the atmosphere. Water–carbon cycles in ecosystems are connected as the stomatal regulation of the flow of water and carbon between ecosystems and the atmosphere via photosynthesis and transpiration [57,58]. The strong correlation between ET and NPP proves the tight coupling of local water and carbon cycles. In terrestrial ecosystems, vegetation growth rates are closely related to transpiration [59]. Higher transpiration means that vegetation releases water more actively, has a higher efficiency of solar radiation utilization, and has a higher photosynthesis rate, resulting in greater vegetation productivity. This finding also highlights the importance of water in terrestrial ecosystems [4,60]. However, this coupling is not immune to disruption, especially under changing climate conditions. In the FFh period, the correlation between ET and NPP decreases, likely due to the negative effects of excessive temperatures on vegetation growth. High temperatures can inhibit photosynthesis by causing stomata to close earlier to prevent water loss, thereby reducing CO2 assimilation [61,62]. In SSP5-8.5, the coefficient of Rs-NPP decreased from −0.35 in the NF to −0.44 in the FF. The reason for this phenomenon may be that the positive influence of increased precipitation is insufficient to counteract the negative impact of the substantial temperature increase, which adversely affects NPP. At the same time, the greatly increased precipitation significantly increased Rs, resulting in a stronger negative correlation between Rs and NPP. Therefore, it is important to take certain measures to alleviate the negative impacts of climate change.

4.2. Contrast with Prior Research

Based on the results, the climate of the basin will be wetter and warmer under all future scenarios, resulting in increases in ET, Rs, and NPP, while SOC will decrease under all scenarios. These results aligned with findings from prior research. Lei et al. [63] used the Hydrological Model of the École de Technologie Supérieure (HMETS) and the Xinanjiang model to simulate projected hydrological changes in future periods. The results showed that the streamflow in China will increase from 2071 to 2100, with a significant increase in precipitation as the dominant factor. Zhang et al. [64] examined hydro-climatological processes via GCMs and hydrological models, showing that higher future downscaled temperature values would lead to enhanced evapotranspiration in the Xin River Basin. Crowther et al. [9] quantified the impacts of global soil C loss due to climate warming and suggested that increasing temperature leads to soil carbon loss into the atmosphere, potentially exacerbating global warming through climate feedback from soil carbon.
However, different CMIP climate data may lead to different results. Zhao et al. [58] employed the SWAT-DayCent model to predict changes in ecohydrological factors under future climate scenarios. They concluded that during the NF period, ET would remain almost constant (<1%), annual streamflow would decrease by 8.1–20.1%, and NPP values would be lower than those in historical scenarios. The reason for the inconsistency with our results may be the difference in the CMIP. Several studies have shown that CMIP6 models outperform CMIP5 models in predicting future climate [33,34,35]. Li et al. [8] used the modified SWAT model driven by GCMs homologous to CMIP5 and CMIP6 and showed that the increase in average annual precipitation under the CMIP6 SSP scenario was greater than that under the CMIP5 RCP scenario, resulting in greater simulated surface runoff for the SSP scenario than for the RCP scenario. Precipitation is a decisive variable of ecohydrological components, and different data sources and different interpolation methods will inevitably lead to uncertainty in the spatial distribution of precipitation. Moreover, different models may produce different results due to differences in the structure, parametrization, and process representation of the model, which also accounts for the differences in results.

4.3. Limitations and Future Improvements

There were ecohydrological model and GCM uncertainties in the process of modeling the future ecohydrological cycle. The RHESSys model has many parameters, the estimations of which may affect the precision of the simulation results [19]. To reduce the uncertainty, MODIS data were used to verify the ET and NPP outputs of the RHESSys model. There was strong concordance between the simulated and MODIS data, but the uncertainty of the validated data may affect the ecohydrological element simulations. Therefore, to improve the reliability of model simulations, using multiple variables to calibrate the model or seeking more efficient validation methods will be important to future research. Different GCMs are understood to produce different climate projections, and the results produced by a single model may potentially be inaccurate. Multimodel averaging performs better than a single model [65]. To reduce the uncertainty in the simulation of a single model, we used the multimodel ensemble mean of five GCMs to drive the model output. Although there is a slight difference between the multimodel ensemble mean and the observed value from the meteorological stations, the GCM downscaling method and the selection of baseline periods may generate additional uncertainty [66], hindering accurate predictions of future climate change. Furthermore, we primarily examined the effects of future climate change on water–carbon coupling in the basin, without accounting for land-use changes and anthropogenic activities. These aspects should be assessed in future research to enhance our understanding of water–carbon relationships.

4.4. Countermeasures

Amid global climate change, alterations in drought and precipitation patterns can significantly influence ecohydrological processes in semi-arid regions. Restoring vegetation helps alleviate the effects of climate change on watershed ecosystems. Therefore, based on the projected effects of climate change on the water–carbon relationship in the SRB, we propose the following recommendations. In terms of tree species selection for vegetation construction, tree species with strong adaptability to water change should be given priority in the SRB. Considering the frequency of future extreme weather under SSP5-8.5, tree species such as Pinus tabulaeformis, Salix cheilophila, and Robinia pseudoacacia should be prioritized to enhance vegetation survival rates. Simultaneously, vegetation diversity should be enhanced. The planting of shrubs and herbs with strong adaptability, such as Hippophae rhamnoides and Medicago sativa, should be considered. This could enhance ecosystem diversity and stability, enabling better adaptation to extreme climate events. For vegetation construction, restoration efforts should be intensified in the mid- and lower basin regions, while vegetation conditions in the upper reaches should be maintained. At present, the upper reaches are dominated by forest and shrub, with favorable vegetation conditions, slight soil erosion, and abundant carbon sink resources. The mid- and lower basin regions are dominated by cropland with worse vegetation conditions and serious soil erosion. Therefore, vegetation restoration intensity in the mid- and lower basin regions should be strengthened to control soil and water loss, increase vegetation carbon sequestration, and enhance soil carbon storage.

5. Conclusions

The outputs of the GCMs from CMIP6 and the RHESSys model, which incorporates physical mechanisms, were utilized to evaluate the spatiotemporal changes in ecohydrological elements under future climate scenarios and to quantify the impacts of impending climate variations on the water–carbon interactions in the SRB. The climate projections indicate that the basin’s future climate will be both wetter and warmer. In response to these climate changes, ET, Rs, and NPP are projected to increase, while SOC is expected to decline. In analyzing the influencing factors of the water–carbon cycle elements, ET and NPP—both directly linked to vegetation—are influenced by changes in both precipitation and temperature, whereas Rs shows a stronger response to precipitation and temperature significantly impacts SOC. In addition, FVC, EL, and Pv are positively correlated with water–carbon cycle elements. Higher FVC results in more plant surface area available for transpiration, reducing rainfall kinetic energy and thus decreasing soil erosion and preserving soil organic carbon. Elevation affects the elements of the water–carbon cycle by influencing vegetation composition and the climatic conditions of the basin. With rising elevation, precipitation may increase, which is more favorable for vegetation growth and may lead to higher FVC and NPP.
Regarding the water–carbon relationship, all climate scenarios exhibit an average ET-NPP correlation coefficient exceeding 0.6, indicating a strong coupling of the water–carbon cycle in the basin. However, in the FFh scenario, unlike ET and NPP, which have higher values than the baseline scenario, the correlation coefficient of ET-NPP is lower than the baseline scenario. The reason for this phenomenon may be that in the extreme climate scenario (SSP5-8.5), excessive temperature may lead to decoupling of the water–carbon cycle. High temperatures inhibit photosynthesis by causing stomata to close earlier to prevent water loss, thus reducing CO2 assimilation. Moreover, the positive effects of increased precipitation are not sufficient to offset the negative effects of large increases in temperature, which are also responsible for the decrease in the average correlation coefficient of ET-NPP for the FFh scenario. These findings underscore the need for future climate adaptation strategies to address the complex interactions within the water–carbon cycle to ensure ecosystem health and stability.

Author Contributions

Conceptualization, Y.Y. and X.Z. (Xueping Zhu); methodology, Y.Y. and X.Z. (Xueping Zhu); software, Y.Y.; validation, Y.Y.; writing—original draft preparation, Y.Y.; writing—review and editing, X.Z. (Xueping Zhu) and X.Z. (Xuehua Zhao); visualization, X.G.; supervision, X.Z. (Xuehua Zhao); project administration, Y.Y.; funding acquisition, X.G. and X.Z. (Xueping Zhu). All authors have read and agreed to the published version of the manuscript.

Funding

The researchers thank the National Natural Science Foundation of China (52379018, U22A20613, 42377346), the Central Government Guides Local Science and Technology Development Fund Projects, China (YDZJSX2024D024), the Special Funds for Scientific and Technological Innovation Teams of Shanxi Province, China (202204051002027), and the Natural Science Foundation Program of Shanxi Province, China (20210302123120).

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The methodology flowchart.
Figure 1. The methodology flowchart.
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Figure 2. Location of the SRB.
Figure 2. Location of the SRB.
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Figure 3. Comparison of (a) Maximum temperature, (b) Minimum temperature, and (c) precipitation for the GCM with monthly observations from 1991 to 2020.
Figure 3. Comparison of (a) Maximum temperature, (b) Minimum temperature, and (c) precipitation for the GCM with monthly observations from 1991 to 2020.
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Figure 4. Comparison of (a) the simulated and observed monthly runoff in the calibration period (2008–2012) and (b) the validation period (2013–2017); (c) ET of the RHESSys model and MODIS products, and (d) NPP of the RHESSys model and MODIS products.
Figure 4. Comparison of (a) the simulated and observed monthly runoff in the calibration period (2008–2012) and (b) the validation period (2013–2017); (c) ET of the RHESSys model and MODIS products, and (d) NPP of the RHESSys model and MODIS products.
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Figure 5. Spatial distribution characteristics of multi-year average values of meteorological elements in historical (1981–2020) and future (2021–2100) periods. Among them, (a1–c1) represent the historical period, and (a2–c2): SSP1-2.6; (a3–c3): SSP2-4.5; (a4–c4): SSP5-8.5 represent the future period under different emission scenarios.
Figure 5. Spatial distribution characteristics of multi-year average values of meteorological elements in historical (1981–2020) and future (2021–2100) periods. Among them, (a1–c1) represent the historical period, and (a2–c2): SSP1-2.6; (a3–c3): SSP2-4.5; (a4–c4): SSP5-8.5 represent the future period under different emission scenarios.
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Figure 6. Temporal changes in meteorological elements in H, NF, and FF periods under different climate scenarios. The shaded area of each color represents the range of standard deviations of the annual mean. (a–c): the maximum temperature under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, respectively; (d–f): the minimum temperature under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, respectively; (g–i): the precipitation under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, respectively.
Figure 6. Temporal changes in meteorological elements in H, NF, and FF periods under different climate scenarios. The shaded area of each color represents the range of standard deviations of the annual mean. (a–c): the maximum temperature under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, respectively; (d–f): the minimum temperature under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, respectively; (g–i): the precipitation under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, respectively.
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Figure 7. (a) Temporal characteristics of ecohydrological elements (ET, Rs, NPP, and SOC) under different scenarios. The gray line is the trend line for each variable. (b) Spatial distribution characteristics of ecohydrological elements under different scenarios. The box plots represent spatial statistics at the hillslope scale. The solid line is the median value, and the circle is the mean value.
Figure 7. (a) Temporal characteristics of ecohydrological elements (ET, Rs, NPP, and SOC) under different scenarios. The gray line is the trend line for each variable. (b) Spatial distribution characteristics of ecohydrological elements under different scenarios. The box plots represent spatial statistics at the hillslope scale. The solid line is the median value, and the circle is the mean value.
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Figure 8. Correlation matrix between climatic variables, FVC, elevation (EL), percentage of forestry and grass coverage (Pv), and water–carbon cycle elements. The figure shows the average of multiple climate scenarios. The higher-right panel reflects the Spearman rank correlation coefficients, while the lower-left panel shows the matrix scatter plot, with each point represents a hillslope.
Figure 8. Correlation matrix between climatic variables, FVC, elevation (EL), percentage of forestry and grass coverage (Pv), and water–carbon cycle elements. The figure shows the average of multiple climate scenarios. The higher-right panel reflects the Spearman rank correlation coefficients, while the lower-left panel shows the matrix scatter plot, with each point represents a hillslope.
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Figure 9. Path analysis of (a) ET, (b) Rs, (c) NPP, (d) SOC, and key control factors (***: p ≤ 0.01).
Figure 9. Path analysis of (a) ET, (b) Rs, (c) NPP, (d) SOC, and key control factors (***: p ≤ 0.01).
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Figure 10. (a) Correlation between ET-NPP and ET-SOC and (b) correlation between Rs-NPP and Rs-SOC under the different scenarios. In the figure, each point represents a value on the hillslope and the curve represents the normal distribution of these points.
Figure 10. (a) Correlation between ET-NPP and ET-SOC and (b) correlation between Rs-NPP and Rs-SOC under the different scenarios. In the figure, each point represents a value on the hillslope and the curve represents the normal distribution of these points.
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Table 1. Datasets.
Table 1. Datasets.
DataYearResolutionSource
DEM/30 mhttps://search.earthdata.nasa.gov/ (accessed on 1 August 2023)
Land-use202030 mhttps://www.resdc.cn/ (accessed on 1 August 2023)
Soil/1 kmHarmonized World Soil Database (HWSD) [31]
FVC2020500 mhttp://www.glass.umd.edu (accessed on 1 August 2023)
Climate1981–2020Point scale;
Daily		https://www.resdc.cn/ (accessed on 1 August 2023)
1991–2100Dailyhttps://esgf-node.llnl.gov/search/cmip6/ (accessed on 1 August 2023)
Runoff2008–2017Monthlyhttp://www.geodata.cn/ (accessed on 1 August 2023)
ET2011–2020500 m;
yearly		https://lpdaac.usgs.gov/ (accessed on 1 August 2023)
NPP2011–2020
Table 2. RHESSys calibration parameters.
Table 2. RHESSys calibration parameters.
ParameterPhysical MeaningRangeFinal Value
mvVertical decay of hydraulic conductivity with depth0.01–200.20
mhHorizontal decay of hydraulic conductivity with depth0.01–200.19
Ksat_0_vVertical saturated hydraulic conductivity0–600362
Ksat_0_hHorizontal saturated hydraulic conductivity0–600243
g w 1 Percentage of infiltration volume into deep groundwater0.001–0.30.06
g w 2 Deep-groundwater drainage rate0.01–0.90.63
Table 3. The GCMs used in this study.
Table 3. The GCMs used in this study.
GCMResolutionCountryPeriod
BCC-CSM2-MR1.13° × 1.12°China2021–2100
GFDL-ESM41° × 1.25°USA2021–2100
INM-CM4-82° × 1.5°Russia2021–2100
MIROC61.41° × 1.40°Japan2021–2100
MRI-ESM2-01.13° × 1.12°Japan2021–2100
Table 4. Scenario design.
Table 4. Scenario design.
Model Climate
Input Scenario		Time PeriodEmission ScenarioAssumed Average CO2
Concentration (ppm)
H1981–2020/350
NFl2021–2060SSP1-2.6436
NFmSSP2-4.5497
NFhSSP5-8.5578
FFl2061–2100SSP1-2.6428
FFmSSP2-4.5533
FFhSSP5-8.5807
Note: Subscripts l, m, and h represent the low (SSP1-2.6), medium (SSP2-4.5), and high (SSP5-8.5)-emission scenarios, respectively.
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Yuan, Y.; Zhu, X.; Gao, X.; Zhao, X. An Evaluation of Future Climate Change Impacts on Key Elements of the Water–Carbon Cycle Using a Physics-Based Ecohydrological Model in Sanchuan River Basin, Loess Plateau. Remote Sens. 2024, 16, 3581. https://doi.org/10.3390/rs16193581

AMA Style

Yuan Y, Zhu X, Gao X, Zhao X. An Evaluation of Future Climate Change Impacts on Key Elements of the Water–Carbon Cycle Using a Physics-Based Ecohydrological Model in Sanchuan River Basin, Loess Plateau. Remote Sensing. 2024; 16(19):3581. https://doi.org/10.3390/rs16193581

Chicago/Turabian Style

Yuan, Yujie, Xueping Zhu, Xuerui Gao, and Xuehua Zhao. 2024. "An Evaluation of Future Climate Change Impacts on Key Elements of the Water–Carbon Cycle Using a Physics-Based Ecohydrological Model in Sanchuan River Basin, Loess Plateau" Remote Sensing 16, no. 19: 3581. https://doi.org/10.3390/rs16193581

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