Yiluo River Basin Research Articles

Spatial Differences in the Response of Runoff to Climate and Land Use Changes in the Yiluo River Basin

Spatial Differences in the Response of Runoff to Climate and Land Use Changes in the Yiluo River Basin

Identification of driving mechanisms of actual evapotranspiration in the Yiluo River Basin based on structural equation modeling

BackgroundActual evapotranspiration (ETa) is a crucial aspect of the hydrological cycle. It serves as a vital link between the soil–vegetation–atmosphere continuum. Quantifying the leading factors of regional ETa change and revealing the multi-factor compound driving mechanism of ETa evolution is necessary. Structural equation modeling (SEM) has been widely used to study the structural relationships between variables in large-scale areas. However, there is an urgent need for more in-depth exploration of these complex relationships at the grid scale. Therefore, the Yiluo River Basin, a representative area of soil and water conservation engineering demonstration in the Loess Plateau, was selected as the study area, and the SEM at the basin scale and grid-scale were constructed to carry out the research.ResultsThe data indicate that ETa decreased at 1.97 mm per year at the watershed scale from 1982 to 2020. Climate change had the greatest impact on the change of ETa in the watershed, with a total impact coefficient of over 0.9. The direct impact of climate change on ETa increased by 0.571 from 1982–1992 to 1993–2020. The direct impact coefficients of vegetation cover and soil moisture decreased by 0.402 and 0.102, respectively, while the impact coefficient of the water body factors increased by 0.096. At the scale of individual grid cells, the ETa in the watershed was affected by changes in watershed climate, vegetation, and soil moisture, with contributions ranging from − 0.31 to 0.22, − 1.09 to − 0.08, and 0.61 to 0.90, respectively. Spatially, vegetation and soil moisture had a stronger impact on ETa in the upstream area, while climate change had a negative effect, and the downstream region had the opposite effect. Furthermore, the regulatory impact of large reservoirs mitigated the response of water surface evaporation to climate change in the upstream region.ConclusionsThe application of SEM at different spatial and temporal scales has effectively quantified the driving mechanisms behind actual evapotranspiration in the Yiluo River Basin, while visually representing the spatial distribution characteristics of various influencing factors on ETa. This research provides a theoretical foundation for studying slope water consumption processes and circulation mechanisms.

Evaluating the Spatiotemporal Distributions of Water Conservation in the Yiluo River Basin under a Changing Environment

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.

A study of macroinvertebrate community structure and diversity in response to land use type in the Yiluo River Basin

Land use types have a significant impact on river ecosystems. The Yiluo River is the largest tributary below Xiaolangdi Reservoir in the middle reaches of the Yellow River, and is one of the important water conservation areas in the Yellow River Basin. Studying the ecological status of the Yiluo River under varied land use types in this basin is crucial for both ecological protection and the high-quality development of the Yellow River Basin. This study investigated the impacts of land use types on the macroinvertebrate community and functional structure in the Yiluo River Basin and introduced the concept of the land use health index (LUI). During the survey period, a total of 11,894 macroinvertebrates were collected, and 143 species were identified, belonging to 4 phyla, 7 orders, 22 families, and 75 families. The results showed that LUI had the most significant impact on macroinvertebrate community structure, with substrate type, dry plant weight, total phosphorus, turbidity, and attached algae biomass also playing significant roles in affecting macroinvertebrate communities. The species richness, the Shannon-Wiener index, and the Margalef richness index exhibited a nonlinear positive correlation with LUI of the sampling site, increasing as LUI enhancing and eventually reaching a plateau. Functional richness showed a linear and positive correlation with LUI, increasing with its enhancement, while functional evenness and functional divergence exhibited a nonlinear correlation with LUI. Functional evenness initially increased and then decreased with the enhancement of LUI, while functional divergence decreased with LUI enhancement. This study can provide a scientific reference for river ecological management under various land use scenarios.The Yiluo River is the largest tributary below Xiaolangdi Reservoir in the middle reaches of the Yellow River, and is one of the important water conservation areas in the Yellow River Basin. Studying the ecological status of the Yiluo River under varied land use types in this basin is crucial for both ecological protection and the high-quality development of the Yellow River Basin. This study investigated the impacts of land use types on the macroinvertebrate community and functional structure in the Yiluo River Basin and introduced the concept of the land use health index (LUI). During the survey period, a total of 11,894 macroinvertebrates were collected, and 143 species were identified, belonging to 4 phyla, 7 orders, 22 families, and 75 families. The results showed that LUI had the most significant impact on macroinvertebrate community structure, with substrate type, dry plant weight, total phosphorus, turbidity, and attached algae biomass also playing significant roles in affecting macroinvertebrate communities. The species richness, the Shannon-Wiener index, and the Margalef richness index exhibited a nonlinear positive correlation with LUI of the sampling site, increasing as LUI enhancing and eventually reaching a plateau. Functional richness showed a linear and positive correlation with LUI, increasing with its enhancement, while functional evenness and functional divergence exhibited a nonlinear correlation with LUI. Functional evenness initially increased and then decreased with the enhancement of LUI, while functional divergence decreased with LUI enhancement. This study can provide a scientific reference for river ecological management under various land use scenarios.

Comparisons of Different Machine Learning-Based Rainfall–Runoff Simulations under Changing Environments

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.

Deciphering the influence of climate change and human activities on the drought propagation

Study regionThe Yiluo River Basin (YLRB), China Study focusUnderstanding the alterations in drought propagation under evolving environmental conditions is crucial for the efficient management of water resources. In this study, the 'simulation-observation' comparison method was employed to analyze the drought propagation in distinct periods: baseline, simulated, and disturbed periods. This facilitated the quantification of the impact of climate change and human activities on drought propagation characteristics, such as the response time of the Standardized Streamflow Index (SSI) to the Standardized Precipitation Index (SPI) and the propagation threshold. New hydrological insights for the regionThe response times of the SSI to the SPI has been prolonged at monthly and seasonal scales due to climate change, while it has been reduced at the annual scale. The impact of human activities on the lengthening of response time is only evident at longer temporal scales. Human activities have contributed to higher thresholds for severe and extreme droughts on monthly and annual scales, increasing the probability of extreme droughts. Meanwhile, climate change has partially offset the negative impacts of human activities. However, at the seasonal scale, climate change is the main cause of the increasing propagation thresholds. The conclusions drawn from this research provide valuable insights for the development of policies aimed at managing drought in a changing environment.

Study on the Spatial–Temporal Variations and Driving Factors of Water Yield in the Yiluo River Basin

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.

Quantification and variation characteristics of baseflow nonpoint source pollution in Yiluo River Basin, China

Baseflow is an important component of runoff that is one of the essential mediums for groundwater pollution to migrate to runoff. Effective water-quality management requires full understanding of the nonpoint source (NPS) pollutants pathways. However, the contributions of baseflow and baseflow NPS pollutants at multiple scales are still challenging. In this study, the baseflow was quantified and compared by the nonlinear reservoir method, the digital filtering method and the Soil and water Assessment Tool (SWAT) in the Yiluo River, and SWAT model was used to simulate and quantify the NPS and baseflow NPS pollution. Then the scaling effect of baseflow NPS pollution was studied. The results indicated that the average annual baseflow of the basin outlet section and baseflow index of the whole basin was 38.07 m3/s and 0.58 during the studied years (2001–2017). The multiyear average baseflow pollution load for TN and TP was 4216.18 t/a and 293.33 t/a. The spatial distribution of the NPS pollution and baseflow NPS pollution showed higher in the northeast and lower in the southwest, and the baseflow NPS pollution contribution rate in most basins was more than 50 %. The spatial scale effects of NPS pollution load became more obvious with the increase of basin area, and the contribution of baseflow NPS pollution increased. The precipitation and temperature played important roles in baseflow pollution, especially baseflow TN load.

Water security assessment and driving mechanism in the ecosystem service flow condition.

Water resources are the foundation of human survival and development. Thus, it is essential to quantify the relationship between water supply and demand and assess water security to ensure sustainable use of water resources. In this study, we quantified the relationship between water supply and demand in the Yiluo River Basin (YRB) using the InVEST model, constructed a spatial flow model of water provision service at the sub-watershed scale, and analyzed the water security index (WSI) under static and dynamic conditions. Subsequently, we used geographic detectors to identify the explanatory power of the main influencing factors of the WSI. The results showed that (1) from 2005 to 2018, the water supply decreased continuously and the water demand first increased and then decreased in the YRB; (2) the spatial pattern of WSI showed that it was high in the southwest and low in the northeast, wherein the highest and lowest WSI values occurred in 2005 and 2018, respectively. From 2005 to 2018, the mean values of the static and dynamic WSI decreased from 0.77 to 0.60 and 1.56 to 1.20, respectively. (3) The precipitation factor from the supply dimension and the gross domestic product (GDP) factor from the demand dimension had the strongest explanatory power for the static WSI. For the dynamic WSI, the flow quantity of the water provision service flow had the strongest explanatory power. This study provided a reference for governments to formulate sustainable water resource management.

Calibrating a hydrodynamic model using water surface elevation determined from ICESat-2 derived cross-section and Sentinel-2 retrieved sub-pixel river width

Accurate observations of river water surface elevation (WSE) are critical for hydrodynamic (HD) model calibration in poorly gauged regions. This study presents a new river WSE retrieval approach that combines ICESat-2 derived cross-sections and Sentinel-2 observed sub-pixel river widths to achieve an effective HD model calibration. The unknown channel Manning coefficient, river bed elevation and cross-sectional shape in the HD model are calibrated simultaneously through a Bayesian optimization algorithm. The complete workflow is implemented in a flood-prone reach in Yiluo River basin. Results show that the WSE retrieved by the new approach has a higher spatiotemporal resolution in narrower rivers than that of Sentinel-3 altimetry derived WSE, and the root-mean-square error (RMSE) varies from 0.25 to 0.59 m against in-situ WSE. The HD model calibrated by WSE retrieved by the new approach simulates the WSE with RMSE of 0.36 m and the flood extent with critical success index (CSI) of 0.60 in validation, outperforming the results of the HD model calibrated by Sentinel-3 altimetry derived WSE, with corresponding results of 0.49 m and 0.58, respectively. Additional case studies in the Maqu and Lingkou reaches further demonstrate the applicability of the WSE retrieval approach proposed in this study for HD model calibration in different types of rivers. This study highlights the potential of the proposed WSE retrieval approach in HD model calibration to improve flood forecasting in poorly gauged regions.

Attribution identification of natural runoff variation in the Yiluo River Basin

Study regionThe Yiluo River Basin between the Sanmenxia Dam and the Huayuankou Station of the Yellow River basin. Study focusThe current research is designed to spatially identify different driving factors' contributions to natural runoff variation. The Water and Energy Transfer Process model was used to simulate the runoff of 2044 sub-basins. Based on this, trends of runoff variation were analyzed, and then, the mutation year of runoff variation in each sub-basin was detected. Moreover, the contribution rate of climatic variation and anthropogenic factors on runoff change in each sub-basin was quantified. New hydrological insights for the regionThe annual natural runoff in 90.66% of sub-basins and the flood-season (May to October) natural runoff in 45.99% of sub-basins showed declining trends. And the mutation year of runoff change mostly occurred in the 1970 s and 1980 s. The natural runoff change was primarily affected by climatic variation. The contribution rate of climate to the annual and flood-season natural runoff was 69.32% and 71.51%, respectively. While the contribution of man-made activities to the annual and the flood-season natural runoff was 30.68% and 28.49%, respectively, and the high-impact areas were largely distributed downstream. The research provides scientific support for developing and regulating water and land resources in the Yiluo River Basin.

Improving near-real-time satellite precipitation products through multistage modified schemes

Near-real-time (NRT) satellite precipitation products (SPPs) exhibit great application potential in global precipitation estimation for their convenient acquisition and access. A universal framework was developed to modify precipitation occurrence and intensity in stages to correct and integrate multiple NRT SPPs with open-access gridded observational data. Based on this framework, both machine learning- and statistical-based correction methods were utilized to construct multi-stage modified schemes and three schemes, namely the double machine learning (DML), statistical error correction (SEC) and hybrid (ML-SEC) schemes, were stepwise established. The improvements of NRT SPPs by these three schemes were comparatively analyzed based on a dense network of rain gauges located in the Yiluo River Basin, China, which does not depend on reference data for modification and has derived a reliable evaluation result at grid-point scale. The results indicated that the DML scheme attains the highest accuracy with a grid-averaged Kling-Gupta efficiency (KGE) value of 0.70 and a critical success index (CSI) value of 0.60, followed by the ML-SEC and SEC schemes. The KGE and CSI values of NRT SPPs have been improved by 22.20% ∼ 535.48% and 30.30% ∼ 100.24%, respectively, through the DML scheme. The DML-modified result also outperformed the mainstream post-processed SPPs with a 1.95% ∼ 222.25% and 9.22% ∼ 102.00% higher KGE and CSI values, respectively. The main error sources of the SPPs due to false and hit events were reduced by the multistage schemes, and the DML scheme achieved a balanced efficiency at both occurrence and intensity modification stages. Moreover, the DML scheme was more advantageous than spatial interpolation under the scenario with fewer gauges, particularly in estimating precipitation occurrence. The multistage modified schemes in this study, especially the DML scheme, exhibit potential in near-real time and sub-daily scale applications. Multistage schemes for NRT SPP modification are helpful for obtaining high-quality precipitation estimates.

Tracking Riverine Nitrate Sources and Transformations in the Yiluo River Basin by Nitrogen and Oxygen Isotopes

The impacts of natural processes and anthropogenic input on riverine nitrate (NO-3) could be identified by NO-3 concentrations and nitrogen and oxygen isotope ratios (δ15N-NO-3 and δ18O-NO-3); however, the effects of variable land use on riverine NO-3 sources and transformations remain unclear. In particular, the human impacts on riverine NO-3 in mountain areas are still unknown. The Yihe River and Luohe River were used to elucidate this question due to their spatially heterogeneous land use. Hydrochemical compositions, water isotope ratios (δD-H2O and δ18O-H2O), and δ15N-NO-3 and δ18O-NO-3 values were utilized to constrain the NO-3 sources and transformations affected by different land use types. The results indicated that ① the mean nitrate concentrations in the Yihe River and Luohe River waters were 6.57 and 9.29 mg·L-1, the mean values of δ15N-NO-3 were 9.6‰ and 10.4‰, and the average δ18O-NO-3 values were -2.2‰ and -2.7‰, respectively. Based on the analysis of δ15N-NO-3 and δ18O-NO-3 values, the NO-3 in the Yihe and Luohe Rivers were derived from multiple sources, and nitrogen removal existed in the Luohe River, but the biological removal in the Yihe River was weak. ② The contributions of different nitrate sources were calculated using a Bayesian isotope mixing model (BIMM) based on δ15N-NO-3 and δ18O-NO-3 values of river water in the mainstream and tributaries with spatial locations. The results revealed that sewage and manure had major impacts on riverine nitrate in the upper reaches of both the Luohe River and Yihe River, where forest vegetation was widely distributed. However, the contributions from soil organic nitrogen and chemical fertilizer were higher in the upper reaches than in downstream ones. The contributions of sewage and manure still increased in the downstream reaches. Our results confirmed the primary impacts of point sources, e.g., sewage and manure, on riverine nitrate in the studied area, and the contributions of nonpoint sources, e.g., chemical fertilizer, had not increased as the agricultural activities elevated the downstream. Therefore, more attention should be paid to point source pollution treatment, and the high-quality development of ecological civilization in the Yellow River Basin should be maintained.

Study on the Contribution of Land Use and Climate Change to Available Water Resources in Basins Based on Vector Autoregression (VAR) Model

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.

Evaluation of water-land resources regulation potential in the Yiluo River Basin, China

Our study was designed to spatially assess the regulation potential of watershed water-land resources, and determine its regulation objectives for subsequent optimal allocation. We centred around the Yiluo River Basin, being a soil conservation demonstration plot in the middle course of the Yellow River Basin. In accordance with the criteria of the green development, the high efficiency and the safety, the evaluation system of water-land resources regulation potential was established. Then the fuzzy comprehensive evaluation method was applied to assess the watershed water-land resources during 2001–2020 and 2030–2050, and the evaluation results were represented by the eigenvalue T. The research findings pointed out that the assessment grade of watershed water-land resources was mainly moderate and better state, and presented significant spatial agglomeration features in the historical and forecast years. In the context of dual influences of climatic variation and water-land resources exploitation, the assessment eigenvalue T of watershed water-land resources increased by 6% in the historical periods, and decreased by 4.57 ∼ 7.74% in the future periods. Based on the assessment results, it was recommended to restrict the water-land resources exploitation in the upstream, control the soil erosion in the midstream, and coordinate the relationship between the exploitation and protection of water-land resources in the lower reaches. Our study offers scientific evidence for the elaborate regulation of watershed water-land resources.

Assessment of changes in water conservation capacity under land degradation neutrality effects in a typical watershed of Yellow River Basin, China

Large-scale implementation of the Grain for Green Project since 1999, which took the Yellow River Basin (YRB) as the core ecological restoration area, has had an important impact on the hydrological processes and ecological environment. Whether such forest restoration construction is conducive to achieving land degradation neutrality as well as improving water conservation capacity has become a hot research topic. The Yiluo River basin (YLRB), one of the most important water conservation areas in the YRB, was selected as the study area. Firstly, the transfer matrix was used to analyze the changes in the two land use maps in 1990 and 2015 and to determine the transformation relationship between different land use types. Furthermore, land degradation was also investigated for further exploration of the correlation between land degradation neutrality and water conservation. The effects of land use and cover changes (LUCC) on the water conservation capacity across the YLRB were investigated by combining the Soil and Water Assessment Tool (SWAT) model, water conservation assessment method and land use change scenarios method. The results showed that the land degradation area was mainly located in the residential area and agricultural land in the downstream of the YLRB, and the execution of the Grain for Green Project is beneficial in promoting land degradation neutrality and increasing the water conservation capacity. The water conservation capacity across the YLRB approximately increased by 19.30 mm yr−1 as every 10% of agricultural land converted to forest, showing a greater increase in the upstream than that in the downstream. Rainfall, Normalized Difference Vegetation Index (NDVI), and temperature were the primary factors affecting water conservation in the YLRB, while geological lithology also affects the spatial distribution of water conservation. The findings of this study could provide a theoretical basis for water resources and ecological environment protection.

Spatiotemporal variations and driving factors of reference evapotranspiration in the Yiluo river basin

The variations in the reference evapotranspiration (ET0) are closely related to meteorological factors. The purpose of this study is to explore the relationships between the meteorological factors and the ET0. Based on meteorological data from 26 meteorological stations in the Yiluo River Basin (YLRB) and its surrounding areas from 1958 to 2020, in this study, the temporal and spatial variations and driving factors of the ET0 in the YLRB are investigated. The results are as follows. Spatially, the annual ET0 decreases from the northeast to the southwest in the YLRB. Temporally, the annual ET0 exhibits a fluctuating decreasing trend rather than a monotonic decreasing trend during the entire period. The trend of the ET0 contains two mutation points, in 1972 and 1994. Thus, the research period can be divided into three periods. It is concluded that the variations in the ET0 are the most sensitive to the relative humidity, but the driving factor that contributes the most to the variations in the ET0 is the wind speed. The driving factors are closely related to the rates of relative change of the meteorological factors.

Delayed response of net primary productivity with climate change in the Yiluo River basin

Climatic factors are the main drivers that change net primary productivity (NPP). We chose the Yiluo River Basin as the study area to investigate the response of NPP to climatic factors in the Yellow River’s middle reaches. The temporal and spatial distribution of net primary productivity and the trend change of slope in the basin from 2000 to 2020 are analyzed. In addition, we analyzed the correlation between the NPP and climatic factors (precipitation and temperature) and the time-lag effect. The results show that 1) the annual average NPP in the basin is 556.4 gC/m2/a, the maximum value that occurred in 2019. Most of the NPP’s mutation points in the basin’s forest appeared in 2004, while the NPP’s mutation points in arable land mostly appeared in 2008 and 2009. 2) The slope trend analysis result of NPP from 2000 to 2020 shows that the NPP in 67.88% of the basin shows a significant increasing trend, 3.8% of the basin shows a significant decrease, and the other area show no significant change. 3) The correlation analysis between NPP and climatic factors shows a positive correlation between precipitation and NPP in 62.3% of the basin and a positive correlation between air temperature and NPP in 62.5%. The response of NPP to precipitation and temperature in the basin has different degrees of a time-lag effect. The NPP has a time-lag effect on precipitation in 44.8% of the basin, and its mean lag time is 8 days. NPP has a time-lag effect on temperature in 55.7% of the basin, and its mean lag time is 1 day. This study is beneficial to understanding the influence of climatic factor change on vegetation activities in the Yiluo River Basin. Our study illustrates the significance of time lag in analyzing the relationship between NPP and climatic factors. Furthermore, it provides support for scientific vegetation regulation and allocation, as well as adaptation to climate change in areas similar to the Loess Plateau, based on the lag time distribution corresponding to different vegetation types in different regions of the Yiluo River Basin. After that, these areas’ water resources will be fully utilized by vegetation, and ecological restoration could be accelerated.

Delineation of traditional village boundaries: The case of Haishangqiao village in the Yiluo River Basin, China.

In the conservation and development of traditional villages, it is vital to delineate reasonable village boundaries. At present, China's traditional village boundaries are mainly defined through the boundaries of village construction land in its land policy. This approach ignores the "natural" state of traditional village boundaries and does not truly reflect villagers' use of their villages. Using the constituent line segments of traditional village boundaries as parameters, we delineate a series of traditional village boundaries and constructed an assessment system for traditional village boundaries in terms of the convenience of villagers' activities and their habitual use of village space. We used the delineation of the boundary of Haishangqiao village in the Yiluo River Basin in China as an example to test the effectiveness of the traditional village boundary delineation method and assessment system. The results show the following: (1) The virtual line segments connecting the buildings on the traditional village boundary are the main factors affecting the delineation of the traditional village boundary. (2) The amplitude of vibration and the global deviation of traditional village boundaries are the main influencing factors of complexity. (3) The complexity of traditional village boundaries is negatively correlated with the convenience of villagers' activities. (4) The complexity of traditional village boundaries is negatively correlated with the emptiness of the internal space of traditional villages. The results of this study can provide practical reference values for scholars of rural planning, rural construction, rural conservation, rural management, and rural research.

Evolution and attribution of the water yield coefficient in the Yiluo river basin

Our aim in this research was to detect historical and future water yield coefficient evolution and attribution. Based on the calibrated and validated water yield coefficient model in the Yiluo River Basin, the coefficient for the years 2000–2020 was simulated, along with the future projection for 2030–2050 under four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585). The spatio-temporal evolution of historical and future water yield coefficients was then analyzed. Moreover, the geographical detector model was used to detect the impacts of climate, land use, and terrain factors on the water yield coefficient. The results showed that the water yield coefficient increased by 8.53% from 2000 to 2020, with the coefficient of farmland increasing by 10.47% and that of forestland decreasing by 8.93%. The coefficient was highest under the SSP370 scenario and the lowest under the SSP585 scenario in projections for 2030–2050. Compared to 2000–2020, the coefficients of the two scenarios increased by 12.2% and 2.0%, respectively. Consequently, under the SSP370 and SSP585 scenarios, the coefficient of farmland increased by 13.2% and 2.7%, and that of the forestland decreased by 0.9% and 14.6%, respectively. Driving factors detection indicated that land use types had the strongest explanatory power affecting the water yield coefficient; the explanatory value reached 26.5% in 2000–2020 and will exceed 29.5% in 2030–2050. In addition, the interaction between any two factors was stronger than a single factor. This research provides scientific support for the precise management of watershed and water-land resources.