Search Results (22)

Our study focused on identifying the evolution of soil erosion and its key drivers under multiple future scenarios in the Yiluo River Basin. Integrating the Universal Soil Loss Equation (USLE), future land use and vegetation cover simulation methods, and the Geodetector model, we analyzed historical soil erosion trends (2000–2020), projected future soil erosion risks under multiple Shared Socioeconomic Pathways (SSPs), and quantified the interactive effects of key driving factors. The results showed that soil erosion within the basin exhibited moderate intensity. Over the past 20 years, soil erosion decreased by 28.78%, with 76.29% of the area experiencing reduced erosion intensity. Future projections indicated an overall declining trend in soil erosion, showing reductions of 4.93–35.95% compared to baseline levels. However, heterogeneous patterns emerged across various scenarios, with the highest risk observed under SSP585. Land use type was identified as the core driving factor behind soil erosion (explanatory capacity q-value > 5%). Under diverse future climate scenarios, interactions between land use type and precipitation and temperature exhibited high sensitivity, highlighting the critical regulatory role of climate change in regulating erosion processes. This research provides a scientific foundation for the precise prevention and adaptive management of soil erosion in the Loess Plateau region. Full article

As a major tributary of the Yellow River, the Yiluo River holds vital importance for regional water resource management and ecological sustainability. In this study, the SWAT (version 2012) and PLUS models were used in combination to simulate the hydrological responses of the basin and to analyze how land use changes have influenced runoff dynamics over time. During the calibration and validation periods, the Nash–Sutcliffe efficiency coefficient (NS) and coefficient of determination (R²) for the SWAT model both exceeded 0.8, while the Kappa coefficient for the PLUS model indicated an overall accuracy of 0.91, confirming the applicability of both models to the Yiluo River Basin. However, despite strong annual performance, potential monthly or seasonal simulation uncertainties should be acknowledged and warrant further analysis. From 2000 to 2020, the areas of forest land, water, urban land, and unused land in the Yiluo River Basin increased by 795.15 km², 29.33 km², 573.67 km², and 0.25 km², respectively, while cultivated land and grassland decreased by 814.50 km² and 583.89 km². The spatial distribution of the annual average runoff depth generally exhibited a pattern of “higher in the upstream and lower in the downstream”. An increase in the forestland and grassland areas was found to suppress runoff generation, whereas the expansion of urban land promoted runoff production. Implementing water-sensitive land use strategies—such as expanding forest cover and conserving grasslands—is crucial for reducing the negative hydrological impacts of urban land expansion. Such measures can improve runoff regulation, enhance groundwater recharge, and support the sustainable management of water resources within the basin. Assuming climate conditions remain constant, land use in the Yiluo River Basin in 2025 and 2030 is expected to remain dominated by cultivated land and forestland. Under this scenario, the annual average runoff is projected to increase by 0.42% and 0.51% compared to in 2020, respectively. Full article

The concentrations of dissolved N₂O in river systems at the basin scale exhibit significant spatial and temporal variability, particularly under diverse landscape conditions. This study focused on a temperate basin—the Yiluo River (YLR) basin in China—to investigate the variations in dissolved N₂O concentrations and the indirect emission factors (EF_5r) between the dry and wet seasons. The differences among tributaries were analyzed to assess the impact of land use types. The findings revealed that N₂O concentrations and saturation levels were lower during the wet season in both the main streams and tributaries. In the dry season, the N₂O concentrations were strongly correlated with NH₄⁺-N, NO₃⁻-N, and oxidation–reduction potential (ORP) (R² = 0.743, p < 0.001), while in the wet season, the N₂O concentrations were correlated with dissolved phosphorus (DP), water temperature (Tw), NH₄⁺-N, and DOC (R² = 0.640, p < 0.001). Impervious land was identified as the primary source of nitrogen in both seasons, rather than cropland. Natural land, particularly shrubland, demonstrated a notable mitigating effect on N₂O accumulation and played a significant role in reducing NO₃⁻-N levels. The YLR basin exhibited lower EF_5r values (0.005–0.052%) compared to the default value recommended by the IPCC, with a significant decrease observed during the wet season (p < 0.001). Data analysis indicated that nutrient dynamics, particularly NO₃⁻-N, the ratio of dissolved organic carbon to NO₃⁻-N (DOC/NO₃⁻-N), and the ratio of NO₃⁻-N to DP (NO₃⁻-N/DP), were significantly correlated with EF_5r. These results underscore the need to re-evaluate regional N₂O emission potentials and provide new insights into mitigating N₂O emissions through strategic land use management. Full article

With accelerating climate change, droughts have increased in frequency and exerted a substantial influence on socioeconomic factors. Under conditions of insufficient precipitation and high temperatures, meteorological droughts have the potential to develop into more intense hydrological droughts, and the independent impact of temperature factors on drought propagation has not been considered separately. This study constructed a Standardized Temperature Index (STI) and, combined with time-series datasets of standardized indices of precipitation and runoff (SPI and SRI), based on Bayesian network principles, analyzed the probabilistic characteristics of drought propagation from meteorology to hydrology due to the influence of single or dual factors in the Yiluo River Basin (1961–2020). It also explored the transmission mechanisms of temperature and precipitation that drive and affect meteorological and hydrological drought. The results showed that propagation of meteorological to hydrological droughts increased with rising temperatures, and the propagation probability to severe and extreme hydrological drought increased by approximately 5%. Under the most adverse circumstances (high temperature and precipitation shortage scenarios), the likelihood of meteorological droughts progressing into intense hydrological drought events rose to 80%. Increasing temperature is expected to lead to more severe hydrological droughts. This study offers a theoretical foundation for drought prevention and mitigation. Full article

This study focuses on the spatiotemporal dynamic changes in water retention capacity and the nonlinear research of its influencing factors. By using the InVEST model, the changing trends of water retention capacity in different regions and at various time scales were analyzed. Based on this, the results were further examined using the CatBoost model with SHAP (SHapley Additive exPlanations) analysis and PDP (Partial Dependence Plot) analysis. The results show the following: (1) From 2003 to 2023, the water conservation capacity first increased and then decreased, and spatially, the water conservation capacity of the mountainous area in the west of the Yiluo River Basin and Xionger Mountain in the middle part of the basin increased as a whole. At the same time, the forest land in the basin contributed more than 60% of the water conservation capacity. (2) Precipitation is the most significant driving factor for water conservation in the basin, and plant water content, soil type, and temperature are also the main driving factors for water conservation in the Yiluo River Basin. (3) The interaction between temperature and other influencing factors can significantly improve water conservation. This research not only provides scientific evidence for understanding the driving mechanisms of water conservation but also offers references for water resource management and ecological protection planning. Full article

Rapid human development has altered land use types, significantly impacting carbon stock, and poor land use will lead to an increase in carbon emissions and exacerbate climate change. Understanding the relationship between land use changes and carbon storage is critical for developing sustainable land management strategies that support carbon sequestration and climate change mitigation. In this study, we analyzed and processed the land use transition changes from 1990 to 2020 and calculated the corresponding carbon storage. Based on the patterns of change and influencing factors (elevation, slope, soil type, GDP, population density, etc.), we predicted the future changes in land use and carbon storage in the Yiluo River Basin under different social development scenarios. It was found that due to the severe impact of natural factors, from 1990 to 2020, the area of cultivated land and grassland decreased by 1150.04 km² and 936.66 km², respectively, and the area of forested land and built-up area expanded by 1087.84 km² and 969.26 km², respectively. Carbon stocks in the region decreased between 1990 and 2010, followed by a modest recovery from 2010 to 2020, resulting in a total reduction of approximately 2.188 × 10⁶ t. Spatially, carbon stocks diminished in the eastern part but increased in the western part. To assess the long-term sustainability implications, the study simulated four future development scenarios for human society: natural development, urban development, ecological protection, and water conservation. The results showed that in the urban expansion scenario, the proportion of construction land increased significantly, while the ecological protection scenario led to a substantial expansion of forested areas. Notably, carbon stocks showed a significant increase only under the ecological protection scenario, whereas they exhibited a declining trend in all other scenarios. Full article

Accurately identifying the impact of different factors on water conservation is influenced by the spatial grid scale. However, existing studies on water conservation often overlook the Modifiable Areal Unit Problem (MAUP). MAUP is one of the key factors contributing to the uncertainty in spatial analysis results. The Qinling Mountains are a critical water conservation area, with the Yiluo River Basin (YLRB) as a key sub-basin. This study uses the Optimized Parameter GeoDetector (OPGD) model to analyze water conservation changes and influencing factors in the YLRB from 1990 to 2020. By optimizing spatial scale (2 km grid) and driving factor discretization, the OPGD model addresses spatial heterogeneity and the MAUP, enhancing analysis accuracy. Results show a fluctuating upward trend in water conservation depth, averaging 0.94 mm yearly, with a spatial decline from southwest to northeast. High–high and low–low clusters dominate the region, with some areas consistently showing high or low values. Key conservation zones expanded by 2748 km², reflecting significant enhancement. Natural factors, particularly precipitation, predominantly influence water conservation, outweighing human activities. The interaction between precipitation and temperature notably affects dynamic changes, while human impacts, such as land use, play a secondary role. The findings suggest water management should prioritize climatic factors and integrate land-use policies to enhance conservation. The OPGD model’s application improves factor identification and supports targeted ecological and water management strategies. Full article

Water conservation is a crucial indicator that measures the available water resources needed for maintaining regional ecological services and socioeconomic development. The Yiluo River Basin plays an essential role in water conservation in the Yellow River Basin, which is one of the most important river basins with vulnerable ecological conditions and a large population in China. However, previous studies have a limited understanding of the distribution of water conservation in the Yiluo River Basin. To address this knowledge gap, we developed a SWAT model to evaluate water conservation in the Yiluo River Basin with high spatial and temporal details on a monthly scale. From a monthly perspective, water conservation accumulation primarily took place in July (54.6 mm), August (23.5 mm), and September (33.2 mm), which are in the flood season. From 1966 to 2018, we found a significant 47% reduction in basin-wide water conservation, and the reduction was primarily influenced by meteorological conditions and underlying surface dynamics. The results of the temporal correlation analysis identified precipitation as the most significant factor influencing water conservation, while the spatial correlation analysis revealed that potential evapotranspiration, vegetation, and elevation had the highest spatial correlation with water conservation. By combining SWAT outputs on the HRU (hydrological response unit) scale with the spatial distribution of HRUs, the study achieved the visualization of the spatial distribution of water conservation, identifying Luonan County, Luanchuan County, and Luoning County as the key regions that experienced water conservation decline over the past decades. These findings advance our understanding of the distributions of water conservation and their key driving factors in the study area and provide valuable policy implications to support ecological protection and water resource management in the Yellow River Basin. Full article

Accurately estimating nutrient loads is crucial for effective management and monitoring of aquatic ecosystems. This study evaluated the uncertainty in different sampling frequencies and calculation methods for estimating total nitrogen (TN) and total phosphorus (TP) loads in the Yiluo River watershed, a tributary of the Yellow River in China. Using daily TN and TP concentration data from 2019 to 2020, we conducted a bootstrapping analysis to evaluate the accuracy of nine different load estimation methods at different sampling frequencies. Our results showed that Method 3 (M_3, constant concentration interpolation) and Method 7 (M_7, flow-weighted concentration method), when used with a biweekly sampling frequency, had the lowest Standard Deviation of the Percentage errors (STD) (7.70% and 8.60% for TN, 12.0% and 18.8% for TP, respectively) and Mean Relative Error (MRE) values (0.078% and −1.60% for TN, 0.305% and 2.33% for TP, respectively) on an annual scale. For monthly TN and TP load estimates, M_7 can control the MRE within ±20% at a biweekly sampling frequency. Furthermore, the uncertainty in TN and TP load estimates was generally larger during the summer months (June–September), emphasizing the important role of storm events in nutrient export. Extreme events (<10% of the time) contributed approximately 50% of the annual nutrient loads. The findings of this study provide a scientific basis for optimizing water quality monitoring schemes and management strategies in agricultural watersheds. Full article

Climate change and human activities have a great impact on the environment and have challenged the assumption of the stability of the hydrological time series and the consistency of the observed data. In order to investigate the applicability of machine learning (ML)-based rainfall–runoff (RR) simulation methods under a changing environment scenario, several ML-based RR simulation models implemented in novel continuous and non-real-time correction manners were constructed. The proposed models incorporated categorical boosting (CatBoost), a multi-hidden-layer BP neural network (MBP), and a long short-term memory neural network (LSTM) as the input–output simulators. This study focused on the Dongwan catchment of the Yiluo River Basin to carry out daily RR simulations for the purpose of verifying the model’s applicability. Model performances were evaluated based on statistical indicators such as the deterministic coefficient, peak flow error, and runoff depth error. The research findings indicated that (1) ML-based RR simulation by using a consistency-disrupted dataset exhibited significant bias. During the validation phase for the three models, the R² index decreased to around 0.6, and the peak flow error increased to over 20%. (2) Identifying data consistency transition points through data analysis and conducting staged RR simulations before and after the transition point can improve simulation accuracy. The R² values for all three models during both the baseline and change periods were above 0.85, with peak flow and runoff depth errors of less than 20%. Among them, the CatBoost model demonstrated superior phased simulation accuracy and smoother simulation processes and closely matched the measured runoff processes across high, medium, and low water levels, with daily runoff simulation results surpassing those of the BP neural network and LSTM models. (3) When simulating the entire dataset without staged treatment, it is impossible to achieve good simulation results by adopting uniform extraction of the training samples. Under this scenario, the MBP exhibited the strongest generalization capability, highest prediction accuracy, better algorithm stability, and superior simulation accuracy compared to the CatBoost and LSTM simulators. This study offers new ideas and methods for enhancing the runoff simulation capabilities of machine learning models in changing environments. Full article

Water yield (WY) is an significant characteristic that reflects ecosystem services. In order to realize high-quality development, it is vital to explore the spatial and temporal (ST) distribution of WY and its driving factors in the Yiluo River Basin (YLRB) to uphold ecological stability and advance long-term sustainable growth. This paper quantifies WY in the YLRB from 2010 to 2020 using the WY model in the InVEST toolkit. Exploring ST characteristics and driving factors at both the raster and sub-watershed levels, results indicate that the overall WY (average water depth) of the YLRB in 2010, 2015, and 2020 was 26.93 × 10⁸ m³ (136.50 mm), 22.86 × 10⁸ m³ (113.38 mm), and 26.81 × 10⁸ m³ (137.61 mm), respectively. The spatial pattern of watershed WY remains consistent across various periods, illustrating spatial variation in the depth of low WY in the central and western regions and high WY depth in the eastern region. At the sub-watershed level, the Luo River (LR) Basin has the highest contribution (69%) to the WY of the entire basin and served as the principal WY region of the YLRB. Conversely, the Yiluo River section, formed after the confluence of the Yi River (YR) and the LR, has the lowest WY contribution (7%) in the entire watershed. Distinct variations exist in the WY capacity among various land use (LU) types. Construction land (CSL) and unused land (UL) exhibited the highest WY capacity (315.16 mm and 241.47 mm), whereas water area (WA) had the lowest (0.01 mm). WY was significantly positively correlated with slope, precipitation, actual evapotranspiration, percentage of cultivated land, and NDVI. It showed a significant negative correlation with altitude, WA, and population density. This study helps promote the research and development of watershed ecosystem services. It also provides scientific support resolving conflicts between watershed protection and economic development and promoting harmony in the YLRB. Full article

Land-use change has a great impact on regional ecosystem balance and carbon storage, so it is of great significance to study future land-use types and carbon storage in a region to optimize the regional land-use structure. Based on the existing land-use data and the different scenarios of the shared socioeconomic pathway and the representative concentration pathway (SSP-RCP) provided by CMIP6, this study used the PLUS model to predict future land use and the InVEST model to predict the carbon storage in the study area in the historical period and under different scenarios in the future. The results show the following: (1) The change in land use will lead to a change in carbon storage. From 2000 to 2020, the conversion of cultivated land to construction land was the main transfer type, which was also an important reason for the decrease in regional carbon storage. (2) Under the three scenarios, the SSP126 scenario has the smallest share of arable land area, while this scenario has the largest share of woodland and grassland land area, and none of the three scenarios shows a significant decrease in woodland area. (3) From 2020 to 2050, the carbon stocks in the study area under the three scenarios, SSP126, SSP245, and SSP585, all show different degrees of decline, decreasing to 36,405.0204 × 10⁴ t, 36,251.4402 × 10⁴ t, and 36,190.4066 × 10⁴ t, respectively. Restricting the conversion of land with a high carbon storage capacity to land with a low carbon storage capacity is conducive to the benign development of regional carbon storage. This study can provide a reference for the adjustment and management of future land-use structures in the region. Full article

The objective of this study is to achieve the dynamic optimization of the flood limited water level (FLWL) in parallel reservoirs, using Luhun Reservoir and Guxian Reservoir as case studies. The innovation lies in establishing a dynamic control optimization model for the FLWL of parallel reservoirs, considering the uncertainty in the forecasting period of the flood forecast due to the varying locations of the rainstorm center from upstream to downstream. To commence, the Fisher optimal segmentation method is employed for flood season staging to determine the staged FLWL of each reservoir. Subsequently, considering the uncertainty in the foresight period, the upper range of the dynamic FLWL is determined through the improved pre-discharge capacity constraint method and Monte Carlo simulation. Finally, a multi-objective optimization model is established to determine the optimal dynamic FLWL control operation scheme for parallel reservoirs, utilizing the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This model takes into account both downstream flood control requirements and the water supply benefits of the parallel reservoirs. Through the optimization of the scheme, the water supply of the parallel reservoirs can be augmented by 15,347.6 m³ during the flood season. This optimization effectively achieves a harmonious balance between flood control and water supply, holding significant implications for mitigating drought risks amid changing conditions. Full article

Under the influence of global climate change and urbanization processes, the number of available water resources (AWRs) in basins has become significantly more uncertain, which has restricted the sustainable development of basins. Therefore, it is important for us to understand the relationship between land use (LU) patterns and climate change on AWRs in a basin for sustainable development. To this end, the vector autoregressive (VAR) method was adopted to construct a quantitative model for AWRs in the basin in this study. Taking the Yiluo River Basin (YRB) as an example, the dynamic relationship between the five elements of agricultural land (AD), woodland (WD), grassland (GD), construction land (CD), and annual precipitation (PREP) and AWRs in the basin was studied. The results show the following: (1) The constructed VAR model was stable, indicating that the use of the proposed VAR model to characterize the degree of the effect of LU pattern and PREP on AWRs in the YRB was reasonable and effective. (2) AWRs in the YRB showed a downward trend, and their responses to the change in LU and PREP were delayed. The changes in the AWRs in the YRB tended to occur the year after changes to the LU pattern and PREP occurred. (3) In the long run, the degree of the contribution of each influencing factor to changes to AWRs was 23.76% (AD), 6.09% (PREP), 4.56% (CD), 4.40% (WD), and 4.34% (GD), which meant that the impact of the LU pattern was more than 90%. This study provides new ideas for similar research, water resource allocation, and LU planning in other river basins from a macroscopic perspective. Full article

Runoff is an important component of water resources and also the basis for regional water resources development and utilization. In order to explore the new characteristics of the spatiotemporal variation of runoff in the whole Yellow River Basin, the spatiotemporal variation of runoff in the Yellow River Basin from 1982 to 2012 was studied based on the measured runoff data of 14 representative basins in the upper, middle, and lower reaches of the Yellow River Basin. The results showed that the runoff depth of the Yellow River Basin from 1982 to 2012 showed a decreasing trend, with a decrease rate of 0.3 mm/a. Among them, the discharge depth decreased significantly (p < 0.01) from 1982 to 1999, with a rate of 1.55 mm/a. Most of the area of the basin has a discharge depth of 0–10 mm, which is relatively dry. The area of higher runoff depth (40–100 mm) is decreasing and gradually concentrating in high-altitude steep-slope areas, while the area of lower runoff depth (0–10 mm) is increasing and spreading to low-altitude gentle-slope areas. After 1999, the discharge in the four sub-basins in the upper reaches decreased, and most of the sub-basins in the middle reaches also showed a decreasing trend, while the discharge in a few sub-basins, such as Qinhe River and Yiluo River, increased. The discharge depth of the sub-basins in the lower reaches increased, but the magnitude and rate of change of most of the sub-basins were consistent with the overall trend of the Yellow River Basin, which showed a decreasing trend. Full article