Investigating the dynamic changes in vegetation and their driving mechanisms within the Guanzhong Plain urban agglomeration (GPUA) is crucial for the sustainable development of Northwestern China. This study analyzed the Normalized Difference Vegetation Index (NDVI) changes in the Guanzhong Plain Urban Agglomeration from 2001 to 2020 using Theil-Sen trend analysis, Mann-Kendall test, coefficient of variation, Hurst index, and machine learning models (Categorical Boosting-SHapley Additive exPlanations (CatBoostSHAP)). The response mechanisms of NDVI to natural factors and human activities were also elucidated. The results indicate that: (1) From 2001 to 2020, the NDVI of the GPUA exhibited a significant increasing trend, with an average annual growth rate of 0.0075/year, rising from 0.397 to 0.541; (2) The coefficient of variation of NDVI in the GPUA was 0.093, indicating overall stability, although certain urban expansion areas and regions surrounding water bodies showed considerable fluctuations. Hurst index analysis revealed that approximately 50.76 % of the areas exhibited positive persistence, suggesting potential future vegetation improvement, whereas 49.24 % showed anti-persistence, indicating a possible decline in NDVI and the need for enhanced ecological protection and restoration; (3) Through CatBoost-SHAP analysis, population density, land use type, precipitation, and elevation were identified as the primary driving factors of NDVI. This study provides scientific evidence for the ecological protection and sustainable development of the GPUA and aids in formulating effective vegetation protection and restoration strategies. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Enhancing the coordination among the green development index (GDI), eco-environmental security (EES), and social well-being (SWB) in the Yangtze River Economic Belt (YREB) is essential for promoting high-quality regional development. Using panel data from 2008 to 2022, this study develops an integrated dynamic assessment framework for the GDI-EES-SWB (GES) system by combining entropy-CRITIC-based coupling coordination degree (CCD) evaluation, obstacle-factor diagnosis, and CNN-GRU-Attention-based scenario prediction. The results show that: (1) the regional mean CCD increased from 0.598 to 0.660 and further to 0.717, maintaining an "east high-west low" spatial gradient while regional disparities gradually converged, indicating an overall improvement in coordinated development; (2) constraints were mainly concentrated in GDI and EES, with the core obstacles including the number of patents granted, the proportion of ecological water use, and environmental regulation intensity. Specifically, the upper reaches were constrained by insufficient innovation output, the middle reaches faced dual constraints of innovation deficits and governance burdens, whereas the lower reaches mainly faced ecological security bottlenecks. (3) Scenario-based projections suggest a continued CCD rise through 2030, with considerable growth potential in Anhui and Hubei, particularly under the "innovation-driven and green-regulation synergy" scenario. Accordingly, this study proposes strengthening green innovation, optimizing resource allocation, and deepening environmental governance. These measures can help foster regional coordination and provide policy support for the YREB to achieve high-quality development and "dual-carbon" goals.

Urban Ecological Resilience (UER) is essential for sustainable development, especially within ecologically sensitive regions such as China's Yellow River Basin (YRB). Existing assessments of UER often encounter difficulties attributable to extensive regional boundaries and retrospective methodologies, thereby limiting their applicability in policymaking. To address these limitations, this study presents an innovative framework. Initially, 51 cities were classified into seven functional clusters based on ecological and industrial similarities. Subsequently, the UER for each cluster was quantified from 2010 to 2024 utilizing the Entropy Weight Method. Projections for UER from 2025 to 2027 were generated employing an XGBoost (eXtreme Gradient Boosting) model that integrates temporal features derived from historical data. The findings indicate a concerning decline in UER within the traditional heavy industry cluster, alongside fluctuating decreases in the Loess Plateau agriculture and conventional agriculture clusters. Model interpretations identify vulnerable cities and low-performing indicators, such as per capita water resources, environmental protection budgets, and industrial pollution, which are strongly correlated with these predicted declines. Conditional simulation demonstrates that targeted interventions aimed at these indicators have the potential to mitigate adverse trends. This comprehensive approach provides a quantitative, proactive tool for formulating specific strategies to enhance UER across diverse regions.

Studying the spatiotemporal variation and driving mechanisms of vegetation net primary productivity (NPP) in the Guanzhong Plain Urban Agglomeration (GPUA) of China is highly important for regional green and low-carbon development. This study used the Theil-Sen trend analysis, Mann-Kendall trend test, coefficient of variation, Hurst index, and machine learning method (eXtreme Gradient Boosting and SHapley Additive exPlanations (XGBoost-SHAP)) to analyze the spatiotemporal variation of NPP in the GPUA from 2001 to 2020 and reveal its response to climate change and human activities. The results found that during 2001–2020, the averageNPP in the GPUA showed a significant upward trend, with an annual growth rate of 10.84 g C/(m2•a). The multi-year average NPP in the GPUA was 484.83 g C/(m2•a), with higher values in the southwestern Qinling Mountains and lower values in the central and northeastern cropland and built-up areas. The average coefficient of variation of NPP in the GPUA was 0.14, indicating a relatively stable state overall, but 72.72

The soil erodibility factor (K) is the main data required for regional soil erosion investigation and mapping using soil erosion models. Fine mapping of K and the study of the applicability of different K estimation methods at the global scale are important to improve the accuracy of global soil erosion evaluation. In this study, the USLE-K, RUSLE2-K, EPIC-K and Dg-K algorithms were used to calculate and compare the global K, and a global measured K database was established by using literature backtracking method and retrieval tool method. Spatial pattern and applicability analysis were carried out on the results of the above four algorithms. The four algorithms were corrected according to the measured K database. The results showed that (1) the global K spatial patterns obtained by the four algorithms were similar but slightly different, with the result of RUSLE2-K being the closest to the measured K, followed by the USLE-K and the EPIC-K, and the result of Dg-K differing significantly from the measured K. (2) The global K distribution characteristics showed some regularity with soil properties, such as soil silt content and sand content, with silt content having the greatest influence on K. (3) The results calculated by the corrected RUSLE2-K and USLE-K algorithms could meet the model applicability conditions and coincided with the results of local K mapping. The results of K mapping in this study on a global scale and the results of the comparative analysis of the applicability of different algorithms provide the necessary scientific basis for the selection of K algorithms globally and quantitative evaluation of soil erosion.

Soil erosion reduces soil fertility and land productivity while enhancing desertification, which seriously threatens ecological security, carbon sequestration, and sustainable economic and social development. Therefore, it is fundamental to precisely assess the long-term dynamics of soil erosion and explore its drivers to control the risk of soil erosion. Currently, machine learning-based analyses of the factors driving soil erosion are lacking in the Beiluo River Basin (BRB), China. This paper aimed to investigate the spatiotemporal dynamics of soil erosion by the Chinese soil loss equation coupled with the gully erosion factor, and then reveal its driving factors using the Boosting Regression Tree method (BRT) and estimate the influence of drivers' interactions on soil erosion in the BRB between 1990 and 2017. The findings demonstrated that the expanded Chinese soil loss equation accurately explained the variations in soil erosion in the BRB. The range of average soil erosion rates was 2,852.83-800.35 t km(-2 )a(- 1). Steep gully farmland, particularly areas with relatively rough terrain, was more vulnerable to intense erosion. Soil erosion rates for different geomorphologies decreased as follows: hilly-gully areas > gully-plateau areas > alluvial river plains > Rocky Mountains. The major drivers affecting the soil erosion rate were the biomass control factor (B) and slope, followed by slope length (L) and slope steepness (S). B-soil erodibility (1990), B-LS (2000), LS-rainfall erosivity (2010), and slope-rainfall erosivity (2017) interactions had the greatest influence on soil erosion, with interaction intensities of 32.3%, 17.75%, 46.05%, and 35.69%, respec- tively. The average soil erosion rates according to land classification types decreased as follows: farmland > grassland > forest. The results indicate that water and soil conservation in the study area benefited greatly from implementation of the Grain for Green Program, with forests reducing erosion more effectively than grassland.

Vegetation information is a critical factor in regional environment management under climate change. In this study, a typical arid and semi-arid watershed on the Loess Plateau, the Zu Li River Basin (ZRB), was selected to study the long-term changes in vegetation cover and its drivers and impacts. Unlike existing normalized vegetation index (NDVI) products, which have coarse spatial resolution and short time horizons, this study used the 30 m Landsat dataset analyzed in the Google Earth Engine (GEE) to generate high-resolution and long-term NDVI data, which are the most ideal for monitoring vegetation dynamics using long-time-series data products. The results showed that the annual mean maximum NDVI (normalized vegetation index) in the ZRB increased during 1987–2021, with a significant (p < 0.05) increasing trend in most areas. Upstream vegetation cover increased more than midstream and downstream, but the increase was smaller. Precipitation in the ZRB area was significantly (p < 0.05) correlated with the NDVI series, except for the upstream pass area, where human activities played an important role. NDVI was significantly (p < 0.05) negatively correlated with runoff coefficient and sand content, indicating that vegetation cover was an important reason for the decrease in runoff coefficient and sand content.

Climate change is a significant force influencing catchment hydrological processes, such as baseflow, i.e., the contribution of delayed pathways to streamflow in drought periods and is associated with catchment drought propagation. The Weihe River Basin is a typical arid and semi-arid catchment on the Loess Plateau in northwest China. Baseflow plays a fundamental role in the provision of water and environmental functions at the catchment scale. However, the baseflow variability in the projected climate change is not well understood. In this study, forcing meteorological data were derived from two climate scenarios (RCP4.5 and RCP8.5) of three representative general circulation models (CSIRO-Mk3-6-0, MIROC5, and FGOALSg2) in CMIP5 and then were used as inputs in the Soil and Water Assessment Tool (SWAT) hydrological model to simulate future streamflow. Finally, a well-revised baseflow separation method was implemented to estimate the baseflow to investigate long-term (historical (1960–2012) and future (2010–2054) periods) baseflow variability patterns. We found (1) that baseflow showed a decreasing trend in some simulations of future climatic conditions but not in all scenarios (p < 0.05), (2) that the contribution of baseflow to streamflow (i.e., baseflow index) amounted to approximately 45%, with a slightly increasing trend (p ≤ 0.001), and (3) an increased frequency of severe hydrological drought events in the future (2041–2053) due to baseflows much lower than current annual averages. This study benefits the scientific management of water resources in regional development and provides references for the semi-arid or water-limited catchments.

Soil erosion is serious in China-the soil in plateau and mountain areas contain a large of rock fragments, and their content and distribution have an important influence on soil erosion. However, there are still no complete results for calculating soil erodibility factor (K) that have corrected rock fragments in China. In this paper, the data available on rock fragments in the soil profile (RFP); rock fragments on the surface of the soil (RFS); and environmental factors such as elevation, terrain relief, slope, vegetation coverage (characterised by normalised difference vegetation index, NDVI), land use, precipitation, temperature, and soil type were used to explore the effects of content of soil rock fragments on calculating of K in China. The correlation analysis, typical sampling area analysis, and redundancy analysis were applied to analyse the effects of content of soil rock fragments on calculating of K and its relationship with environment factors. The results showed that (1) The rock fragments in the soil profile (RFP) increased K. The rock fragments on the surface (RFS) of the soil reduced K. The effect of both RFP and RFS reduced K. (2) The effect of rock fragments on K was most affected by elevation, followed by terrain relief, NDVI, slope, soil type, temperature, and precipitation, but had little correlation with land use. (3) The result of redundancy analysis showed elevation to be the main predominant factor of the effect of rock fragments on K. This study fully considered the effect of rock fragments on calculating of K and carried out a quantitative analysis of the factors affecting the effect of rock fragments on K, so as to provide necessary scientific basis for estimating K and evaluating soil erosion status in China more accurately.

This study aimed to explore the long-term vegetation cover change and its driving factors in the typical watershed of the Yellow River Basin. This research was based on the Google Earth Engine (GEE), a remote sensing cloud platform, and used the Landsat surface reflectance datasets and the Pearson correlation method to analyze the vegetation conditions in the areas above Xianyang on the Wei River and above Zhangjiashan on the Jing River. Random forest and decision tree models were used to analyze the effects of various climatic factors (precipitation, temperature, soil moisture, evapotranspiration, and drought index) on NDVI (normalized difference vegetation index). Then, based on the residual analysis method, the effects of human activities on NDVI were explored. The results showed that: (1) From 1987 to 2018, the NDVI of the two watersheds showed an increasing trend; in particular, after 2008, the average increase rate of NDVI in the growing season (April to September) increased from 0.0032/a and 0.003/a in the base period (1987–2008) to 0.0172/a and 0.01/a in the measurement period (2008–2018), for the Wei and Jing basins, respectively. In addition, the NDVI significantly increased from 21.78% and 31.32% in the baseline period (1987–2008) to 83.76% and 92.40% in the measurement period (2008–2018), respectively. (2) The random forest and classification and regression tree model (CART) can assess the contribution and sensitivity of various climate factors to NDVI. Precipitation, soil moisture, and temperature were found to be the three main factors that affect the NDVI of the study area, and their contributions were 37.05%, 26.42%, and 15.72%, respectively. The changes in precipitation and soil moisture in the entire Jing River Basin and the upper and middle reaches of the Wei River above Xianyang caused significant changes in NDVI. Furthermore, changes in precipitation and temperature led to significant changes in NDVI in the lower reaches of the Wei River. (3) The impact of human activities in the Wei and Jing basins on NDVI has gradually changed from negative to positive, which is mainly due to the implementation of soil and water conservation measures. The proportions of areas with positive effects of human activities were 80.88% and 81.95%, of which the proportions of areas with significant positive effects were 11.63% and 7.76%, respectively. These are mainly distributed in the upper reaches of the Wei River and the western and eastern regions of the Jing River. These areas are the key areas where soil and water conservation measures have been implemented in recent years, and the corresponding land use has transformed from cultivated land to forest and grassland. The negative effects accounted for 1.66% and 0.10% of the area, respectively, and were mainly caused by urban expansion and coal mining.

Soil erosion is one of the global ecological and environmental problems, which is an important factor leading to land degradation. To scientifically and effectively control soil erosion, it’s necessary to improve soil erosion evaluation methods that can obtain the actual rates of soil erosion, rather than potential erosion. For this, about 300 sampling units deployed in the Loess Plateau used as the basic data in our study, combining the seven soil erosion factors (rainfall-runoff erosivity factor, soil erodibility factor, slope length and steepness factor, biological-control factor, engineering-control factor, tillage practices factor) involved in the CSLE model and 50 soil erosion covariates related to climate, soil, topography, vegetation, human activities, etc. Using machine learning methods to establish an optimal model, and spatially predict the soil erosion rate and make a soil erosion mapof the entire study area. The prediction results show that the explanation degree of the random forest spatial prediction model is 73%. Among the selected optimal characteristic parameters, terrain and vegetation-related variables are the most important factors affecting soil erosion, from high to low, the order is LS > B > NDVI (May to September). Compared to previous studies with USLE/RUSLE/CSLE and GIS integrated mapping methods, or sampling survey based interpolation method, improvements in this paper can be concluded to : (1) the use of machine learning instead of simple multiply by soil erosion factors (linear regression), (2) higher resolution interpretation results supported by the project of “Pan-Third Pole Project”, which provide soil erosion that closed to the actual rates of soil erosion. (3) considerate additional related covariates such as population density, precipitation, soil conservation measures and so on. Further development of soil erosion prediction could provide a more accurate soil erosion evaluation method. This method can not only monitor and evaluate soil erosion in real time, and provide the possibility for the dynamic change analysis? of soil erosion in the future, but also help decision makers take effective measures in the process of mitigating soil erosion risk.

通过制作土壤侵蚀图，分析土壤侵蚀主控因子，为巴基斯坦水土流失与保护提供合理的科学依据及治理参考。以土壤侵蚀抽样调查单元数据和土壤侵蚀因子数据为数据源，基于CSLE模型分别以空间插值法和地图代数法定量计算巴基斯坦水蚀区土壤侵蚀图，以空间插值结果为参照对地图代数计算结果做直方图匹配得到巴基斯坦水蚀速率图；采用水利部SL 190—2007标准对巴基斯坦风蚀强度进行了定性评价；使用分类决策树分析土壤侵蚀的主控因子。结果表明，空间插值法制图具有空间预测的准确性，地图代数法制图可以表现良好的局地变异特征；直方图匹配土壤侵蚀图兼具这2种方法的优点，土壤水蚀速率平均值为972.9 t/（km2·a），水蚀区土壤侵蚀比较严重，风蚀区以剧烈风蚀和极强烈风蚀为主，大部分地区生物措施因子是影响土壤侵蚀的主控因子，耕作区和山区的主控因子分别是R因子和LS因子。

为探讨珠江流域土壤侵蚀状况,分析研究区土壤侵蚀的空间格局及主控因子,本文基于中国土壤侵蚀模型(Chinese Soil Loss Equation,CSLE),使用地图代数和空间插值2种方法进行土壤侵蚀制图,并应用地理探测器方法分析土壤侵蚀的主控因子.结果表明:1)研究区土壤侵蚀主要位于研究区内贵州省及云南省、广西中部和广东省沿海区,其中强烈和极强烈侵蚀分布范围较小且位于较零散的坡耕地上;2)地图代数法的制图结果对局地变异表达较好,而空间插值法则对侵蚀速率的宏观格局表达更好,因此空间插值法可作为区域土壤侵蚀制图的首选方法;3)土地利用类型是影响土壤侵蚀的主控因子,其次为植被的影响,其他因素(降雨、地形和土壤等)总体上未表现出控制性影响.因此,调整土地利用结构、优化植被水保功能是今后的主要治理方向,该研究结果可为该区水土流失治理和生态监视提供科学依据.

依据渭河和泾河流域1956—2016年实测水文资料、水利水保统计数据、TerraClimate年平均温度和Landsat地表反射率数据集,分析了流域水文要素、气温及植被覆盖度的历年变化规律,采用双累积值曲线法、累积距平法、有序聚类法、Lee-Heghinan法、秩和检验法等数理统计方法,确定了流域年径流量和年输沙量变化的突变年份,分析了降水和人类活动的减水减沙效应.结果表明:(1)渭河和泾河流域历年降水量、径流量、输沙量、含沙量均呈显著减少趋势,渭河流域的降水和径流比泾河流域减少较多,泾河流域的泥沙比渭河流域减少较多.(2)人类活动对2个流域径流泥沙量的影响均大于降水量对其的影响,且泾河流域受人类活动影响较渭河流域明显.(3)2个流域的水沙特征差异性较大,渭河的年径流量、年径流深、径流系数是泾河流域的2.0～2.4倍,渭河的年输沙量、年输沙模数、年均含沙量仅是泾河流域的1/2～1/5.2个相邻流域水沙特征差异性较大的主要原因是,流域气温、降水等气候条件不同,植被覆盖度等下垫面条件存在差异,水利水保措施、水资源开发利用程度等人类活动的影响所致.

Qinling Mountains is the north–south boundary of China’s geography; the vegetation changes are of great significance to the survival of wildlife and the protection of species habitats. Based on Landsat products in the Google Earth Engine (GEE) platform, Pearson’s correlation coefficient method, and classification and regression models, this study analyzed the changes in NDVI (Normalized Difference Vegetation Index) in the Qinling Mountains in the past 38 years and the sensitivity of its driving factors. Finally, residual analysis method and accumulate slope change rate are used to identify the impact of human activities and climate change on NDVI. The research results show the following: (1) The NDVI value in most areas of Qinling Mountains is at a medium-to-high level, and 99.76% of the areas correspond to an increasing trend of NDVI, and the significantly increased area accounts for more than 20%. (2) From 1981 to 2019, the NDVI of the Qinling Mountains increased from 0.63 to 0.78, showing an overall upward trend, and it increased significantly after 2006. (3) Sensitivity analysis results show that the western high-altitude area of Qinling Mountain area dominated by grassland is mainly affected by precipitation. The central and southeastern parts of the Qinling Mountains are significantly affected by temperature, and they are mainly distributed in areas dominated by forest. (4) The contribution rates of climate change and human activities to NDVI are 36.04% and 63.96%, respectively. Among them, the positive impact of human activities on the NDVI of the Qinling Mountains accounted for 99.85% of the area. The area with significant positive effect accounted for 36.49%. The significant negative effect area accounts for only 0.006%, mainly distributed in urban areas and coal mining areas.

应用祖厉河流域1956～2016年实测水文资料,采用水文统计法、差积曲线法、突变检验法等方法,分析了流域水沙时空分布规律及水沙关系.结果表明:祖厉河流域年降水量、年径流深从上游向下游减小;支流关川河巉口以上年输沙模数相对较小,中游会宁-郭城驿最大.4个代表站历年降水量、径流量、输沙量均呈显著减少趋势,降水量年内分配主要集中在5～9月,径流量和输沙量主要集中在6～9月;水沙变化的突变年份为1999年,基准期1956～1999到措施期2000～2016年多年平均年径流量和输沙量分别减少了39.6％、72.9％.流域降水量年均减少0.53～1.23mm,气温年均升高0.029～0.042℃/a,植被覆盖度年均增加0.85％/a,降水减少、水土保持措施面积增加和植被覆盖度大幅提高是流域水沙持续减少的主要原因.

The Qin Mountains region is one of the most important climatic boundaries that divide the North and South of China. This study investigates vegetation covers changes across the Qin Mountains region over the past three decades based on the Landsat-derived Normalized Difference Vegetation Index (NDVI), which were extracted from the Google Earth Engine (GEE). Our results show that the NDVI across the Qin Mountains have increased from 0.624 to 0.776 with annual change rates of 0.0053/a over the past 32 years. Besides, its abrupt point occurred in 2006 and the change rates after this point increased by 0.0094/a (R2 = 0.8159, p < 0.01) (2006–2018), which is higher than that in 1987–1999 and 1999–2006. The mean NDVI have changed in different elevation ranges. The NDVI in the areas below 3300 m increased, such increased is especially most obviously in the cropland. Most of the forest and grassland locate above 3300 m with higher increased rate. Before 2006, the temperature and reference evapotranspiration (PET) were the important driven factors of NDVI change below 3300 m. After afforestation, human activities become important factors that influenced NDVI changes in the low elevation area, but hydro-climatic factors still play an important role in NDVI increase in the higher elevations area.

Soil erosion is a serious environmental problem in the Loess Plateau, China. Therefore, it is important to understand and evaluate soil erosion process in a watershed. In this study, the Chinese Soil Loss Equation (CSLE) is developed to evaluate the soil loss and analyze the impact of land use and slope on soil erosion in Jiuyuangou (JYG) watershed located in the hilly-gullied loess region of China 1970–2015. The results show that the quantities of soil erosion decreased clearly from 1977 to 2015 in the study area, which from 2011 (t/km²·a) in 1977 to 164 (t/km²·a) in 2004 and increased slowly to 320 (t/km²·a) in 2015. No significant soil erosion (<300 t/km²·a) changed in JYG watershed, which increased dramatically from 8.93% to 69.34% during 1977–2015. The area of farmland in this study area has been reduced drastically. Noting that the annual average soil erosion modulus of grassland was also showing a dropped trend from 1977 to 2015. In addition, the study shows that the annual average soil erosion modulus varied with slope gradient and the severe soil erosion often existed in the slope zone above 25°, which accounted for 4657 (t/km²·a) in 1977 and 382.27 (t/km²·a) in 2015. Meanwhile, soil erosion of different land-use types presented the similar changing trend (declined noticeably and then increased slowly) with the change of slope gradient from 1977 to 2015. Combined the investigations of extreme rainfall on 26 July 2015 for JYG watershed, the study provides the scientific support for the implementation of soil and water conservation measures to reduce the soil erosion and simplify Yellow River management procedures.

Precipitation and human activities are two essential forcing dynamics that influence hydrological processes. Previous research has paid more attention to either climate and streamflow or vegetation cover and streamflow, but rarely do studies focus on the impact of climate and human activities on streamflow and sediment. To investigate those impacts, the Zuli River Basin (ZRB), a typical tributary basin of the Yellow River in China, was chosen to identify the impact of precipitation and human activities on runoff and sediment discharge. A double mass curve (DMC) analysis and test methods, including accumulated variance analysis, sequential cluster, Lee-Heghnian, and moving t-test methods, were utilized to determine the abrupt change points based on data from 1956 to 2015. Correlation formulas and multiple regression methods were used to calculate the runoff and sediment discharge reduction effects of soil conservation measures and to estimate the contribution rate of precipitation and soil conservation measures to runoff and sediment discharge. Our results show that the runoff reduction effect of soil conservation measures (45%) is greater than the sediment discharge reduction effect (32%). Soil conservation measures were the main factor controlling the 74.5% and 75.0% decrease in runoff and sediment discharge, respectively. Additionally, the contribution rate of vegetation measures was higher than that of engineering measures. This study provides scientific strategies for water resource management and soil conservation planning at catchment scale to face future hydrological variability.

In recent years, global reanalysis weather data has been widely used in hydrological modeling around the world, but the results of simulations vary greatly. To consider the applicability of Climate Forecast System Reanalysis (CFSR) data in the hydrologic simulation of watersheds, the Bahe River Basin was used as a case study. Two types of weather data (conventional weather data and CFSR weather data) were considered to establish a Soil and Water Assessment Tool (SWAT) model, which was used to simulate runoff from 2001 to 2012 in the basin at annual and monthly scales. The effect of both datasets on the simulation was assessed using regression analysis, Nash-Sutcliffe Efficiency (NSE), and Percent Bias (PBIAS). A CFSR weather data correction method was proposed. The main results were as follows. (1) The CFSR climate data was applicable for hydrologic simulation in the Bahe River Basin (R 2 of the simulated results above 0.50, NSE above 0.33, and |PBIAS| below 14.8. Although the quality of the CFSR weather data is not perfect, it achieved a satisfactory hydrological simulation after rainfall data correction. (2) The simulated streamflow using the CFSR data was higher than the observed streamflow, which was likely because the estimation of daily rainfall data by CFSR weather data resulted in more rainy days and stronger rainfall intensity than was actually observed. Therefore, the data simulated a higher base flow and flood peak discharge in terms of the water balance, except for some individual years. (3) The relation between the CFSR rainfall data (x) and the observed rainfall data (y) could be represented by a power exponent equation: y=1.4789x 0.8875 (R 2=0.98, P<0.001). There was a slight variation between the fitted equations for each station. The equation provides a theoretical basis for the correction of CFSR rainfall data.