Search Results (20)

Vegetation phenology is a biological factor that directly or indirectly affects the dynamic equilibrium between water and carbon fluxes in ecosystems. Quantitative evaluations of the regulatory mechanisms of vegetation phenology on water–carbon coupling are of great significance for carbon neutrality and sustainable development. In this study, the interannual variation and partial correlation between vegetation phenology (the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS)) and ET (evapotranspiration), GPP (gross primary productivity), WUE (water use efficiency; water–carbon coupling index) in the Huang-Huai-Hai and Yangtze River Basins in China from 2001 to 2019 were systematically quantified. The response patterns of spring (autumn) and growing season WUE to SOS, EOS, and LOS, as well as the interpretation rate of interannual changes, were evaluated. Further analysis was conducted on the differences in vegetation phenology in response to WUE across different river basins. The results showed that during the vegetation growth season, ET and GPP were greatly influenced by phenology. Due to the different increases in ET and GPP caused by extending LOS, WUE showed differences in different basins. For example, an extended LOS in the Huang-Huai-Hai basins reduced WUE, while in the Yangtze River Basin, it increased WUE. After extending the growing season for 1 day, ET and GPP increased by 3.01–4.79 mm and 4.22–6.07 gC/m², respectively, while WUE decreased by 0.002–0.008 gC/kgH₂O. Further analysis of WUE response patterns indicates that compared to ET, early SOS (longer LOS) in the Yellow River and Hai River basins led to a greater increase in vegetation GPP, therefore weakening WUE. This suggests that phenological changes may increase ineffective water use in arid, semi-arid, and semi-humid areas and may further exacerbate drought. For the humid areas dominated by the Yangtze River Basin, changes in phenology improved local water use efficiency. Full article

To grasp the spatial distribution of soybean planting areas in time is the prerequisite for the work of growth monitoring, crop damage assessment and yield estimation. The research on remote sensing identification of soybean conducted in China mainly focuses on the major producing areas in Northeast China, while paying little attention to the Huang-Huai-Hai region and the Yangtze River Basin, where the complex planting structures and fragmented farmland landscape bring great challenges to soybean mapping in these areas. This study used Chinese GF-6 WFV imagery acquired during the pod-setting stage of soybean in the 2019 growing season, and two counties i.e., Guoyang situated in the northern plain of Anhui Province and Mingguang located in the Jianghuai hilly regionwere selected as the study areas. Three machine learning algorithms were employed to establish soybean identification models, and the distribution of soybean planting areas in the two study areas was separately extracted. This study adopted a stepwise hierarchical extraction strategy. First, a set of filtering rules was established to eliminate non-cropland objects, so the targets of subsequent work could thereby focus on field vegetation. The focal task of this study involved the selection of well-behaved features and classifier. In addition to the 8 spectral bands, a variety of texture features, color space components, and vegetation indices were employed, and the ReliefF algorithm was applied to evaluate the importance of each candidate feature. Then, a SFS (Sequential Forward Selection) method was applied to conduct feature selection, which was performed coupled with three candidate classifiers, i.e., SVM, RF and BPNN to screen out the features conductive to soybean mapping. The accuracy evaluation results showed that, the soybean identification model generated from SVM algorithm and corresponding feature subset outperformed RF and BPNN in both two study areas. The Kappa coefficients of the ground samples in Guoyang ranged from 0.69 to 0.80, while those in Mingguang fell within the range of 0.71 to 0.76. The near-infrared band (B4) and red edge bands (B5 and B6), the ‘Mean’ texture feature and the vegetation indices, i.e., EVI, SAVI and CI_green, demonstrated advantages in soybean identification. The feature selection operation achieved a balance between extraction accuracy and data volume, and the accuracy level could also meet practical requirements, showing a good application prospect. This method and findings of this study may serve as a reference for research on soybean identification in areas with similar planting structures, and the detailed soybean map can provide an objective and reliable basis for local agricultural departments to carry out agricultural production management and policy formulation. Full article

Stripe rust is one of the most destructive wheat diseases in China, negatively affecting the production safety and causing yield losses of wheat. Thus, it is important to analyze the environmental suitability and dynamic changes of wheat stripe rust in China. The occurrence of stripe rust is affected by multiple factors. Therefore, this study combined data from various disciplinary fields such as remote sensing, meteorology, biology, and plant protection to evaluate the environmental suitability of stripe rust in China using species distribution models. The study also discusses the importance and effect of various variables. Results revealed that meteorological factors had the greatest impact on the occurrence of stripe rust, especially temperature and precipitation. Wheat growth factors have a greater impact from April to August. Elevation has a greater impact in summer. The ensemble model results were better than the single model, with TSS and AUC greater than 0.851 and 0.971, respectively. Overlapping analysis showed that the winter stripe rust suitable areas were mainly in the Sichuan Basin, Northwestern Hubei, Southern Shaanxi, and Southern Henan wheat areas. In spring, the suitable areas of stripe rust increased in Huang-Huai-Hai and the middle and lower reaches of the Yangtze River and Guanzhong Plain, and the development of northwestern wheat areas such as Xinjiang and Gansu slightly lagged behind. In summer, wheat threatened by stripe rust is mainly in late-ripening spring wheat areas in Gansu, Ningxia, Qinghai, and Xinjiang. This study can provide a scientific basis for optimizing and improving the comprehensive management strategy of stripe rust. Full article

China is the largest vegetable producer in the world, and vegetable production is more geographically concentrated in the Huang-Huai-Hai region and the Yangtze River Basin. There are significant challenges ahead for increasing the average yields of the vegetables in this region. The effects of a cultivation system, a mulched ridge with a double row (MRDR), were evaluated by using the 2ZBX-2A vegetable transplanter newly designed in this paper. The key parameters of the equipment were designed and optimized by using the human–computer interaction method and the discrete element method according to agronomy requirements. Compared with the traditional ridge (TR) system on two typical solanaceous vegetables (eggplant and capsicum), the uniformities of the plant spacing and the planting depth in the MRDR system were significantly improved. Finally, the fresh fruit yield in the MRDR system increased significantly (p < 0.05) by 40.8% and 35.3% compared with that in the TR system for eggplant and capsicum, respectively. In addition, the water use efficiency (WUE) was also 54.9~59.7% higher under the MRDR system than under the TR system. All the results indicate that the MRDR system has the potential to improve the yields and WUE of solanaceous vegetables in the Huang-Huai-Hai Plain of China. Full article

Leaf rust of wheat caused by Puccinia triticina (Pt) is one of the most common fungal diseases in the southwest and northwest of China, the middle and lower reaches of the Yangtze River, and the southern part of the Huang-Huai-Hai river basin. Using 13 simple sequence repeat (SSR) markers, we systematically revealed the genotypic diversities, population differentiation and reproduction of Pt isolates in 15 wheat-producing areas in China. A total of 622 isolates were divided into 3 predominant populations, including the eastern Pt populations, consisting of Pt samples from 8 eastern provinces of Beijing, Hebei, Shanxi, Shaanxi, Anhui, Shandong, Henan, and Heilongjiang; the 4 western Pt populations from Gansu, Qinghai, Sichuan, and Inner Mongolia provinces; and the bridge Pt populations including Jiangsu, Hubei, and Yunnan, which communicated the other 2 populations as a “bridge”. The pathogen transmission of eastern Pt populations was more frequent than western Pt populations. The linkage disequilibrium test indicated that the whole Pt population was in a state of linkage disequilibrium. However, populations of Beijing, Hebei, Shaanxi, Jiangsu, Henan, and Heilongjiang provinces showed obvious linkage equilibrium, while the five provinces of Qinghai, Hubei, Anhui, Shandong, and Inner Mongolia supported clonal modes of reproduction. Full article

As the most basic physical geographic elements, basin terrain and river networks have high spatial complexity and are closely related. However, there is little research on the correlation between terrain and river networks. In this paper, the Yellow River Basin was selected as the study area. Topographic factors of multiple dimensions were calculated. The influence of different topographic factors on the river network structure at different scales and their correlation from a multifractal perspective based on geographical detectors and a geographically weighted regression model were determined. The explanatory power of topography on the river network structure at different scales was: multifractal spectrum width > multifractal spectrum difference > slope > average elevation > elevation maximum > elevation minimum, which generally indicated that the topographic factor that has the greatest influence on the river network structure is the complexity and singularity of the terrain. The second-order clustering of regression coefficients from the results of the geographically weighted regression model revealed that the Yellow River basin was divided into three types of high-aggregation areas, which are dominated by the Qinghai-Tibet Plateau, the Loess Plateau, and the Huang-Huaihai Plain, respectively. The clustering results also revealed that the river network structure was affected by different key topographic factors in the different types of areas. This research studies and quantifies the relationship between basin topography and river network structure from a new perspective and provides a theoretical basis for unraveling the development of topography and river networks. Full article

Irrigated agriculture has undergone rapid developments in China, which has greatly increased food production but overexploited water resources as well. Spatial information on irrigated cropland is critical to balance irrigation yield gains against the negative impact on water resources. However, remote-sensing-based maps on irrigated areas with short temporal coverage often suffer from undermined accuracy in humid areas and inconsistency with statistics, which limit their applications in food policy and water management. The following study integrates existing irrigation maps, observed data on irrigated cropping system, and statistics by a synergy approach to map irrigated areas in China from 2000 to 2019. We also incorporate past information on actual irrigation to avoid divergence between observations and statistics from its fluctuation. Afterwards, 614 reference samples across mainland China have been used to validate resultant maps, which show that outperformance was above overall accuracy and Kappa coefficients. Moreover, our maps share a similar spatial pattern with Irrimap-Syn maps rather than remote-sensing-based maps (CCI-LC). Irrigated areas have grown rapidly from 55.42 Mha in 2000 to 71.33 Mha in 2019 but with different growth trends in different regions. Simultaneous large-scale expansion and abandonment occur in the Huang-Huai-Hai Plain and Yangtze River Basin, while the Northwest Inland Region and the Northeast Plain are the two largest net area gains. Rainfed croplands are dominant sources of expansion, followed by pastures, respectively, with over 70% and 20% contributions in total gains. This not only is a shift from rainfed to irrigated systems but also indicates an intensification of agriculture, which might contribute to agricultural drought reductions in the north and wide soil suitability. Other efforts on agricultural sustainability also have been detected, such as geographical shifts from vulnerable to relatively suitable areas, grain for green, cropland protection, and cropland protection in the competition of urbanization. Full article

This paper attempts to reveal the impact and mechanisms of digital inclusive finance (DIF) on agricultural carbon emission performance (ACEP). Specifically, based on the provincial panel data in China from 2011 to 2020, a super slacks-based measure (Super SBM) model is applied to measure ACEP. The panel regression model and spatial regression model are used to empirically analyze the impact of DIF on ACEP and its mechanism. The results show that: (1) during the study period, China’s ACEP exhibited a continuous growth trend, and began to accelerate after 2017. The high-value agglomeration areas of ACEP shifted from the Huang-Huai-Hai plain and the Pearl River Delta to the coastal regions and the Yellow River basin, the provincial differences displayed an increasing trend from 2011 to 2020. (2) DIF was found to have a significant positive impact on ACEP. The main manifestation is that the development of the coverage breadth and depth of use of DIF helps to improve the ACEP. (3) The positive impact of DIF on ACEP had a significant spatial spillover effect, that is, it had a positive effect on the improvement of ACEP in the surrounding provinces. These empirical results can help policymakers better understand the contribution of DIF to low-carbon agriculture, and provide them with valuable information for the formulation of supportive policies. Full article

The accurate estimation of the soil organic carbon (SOC) sequestration rate is very important for studying farmland soil fertility and environmental effects. In this research, a typical fluvor-aquic soil area, Fengqiu county, located in the Yellow River basin of the Huang-Huai-Hai Plain of China, was chosen as a study area. The physicochemical properties of 70 soil samples collected from the surface layer (at a depth of 0–20 cm) in 2011 were analyzed, and related data about the sampling sites were also collected from the Second State Soil Survey of China (SSSSC), conducted in 1981. The results revealed that the SOC density (SOCD) in Fengqiu county increased greatly on a spatio-temporal scale. The average SOCD increased from 15.66 to 26.09 Mg ha⁻¹, and the SOCD sequestration rate was more than 0.20 Mg C ha⁻¹ year⁻¹ in most regions. Few areas showed lost carbon in the past 30 years (1981–2011). In addition, the study suggested that all the areas present strong carbon sequestration potential in the coming decades from 2011, and the carbon sequestration potential was mainly between 32–40 Mg ha⁻¹. Finally, the SOCD sequestration rate was not only affected by natural factors, such as soil type and pH, but also positively correlated with artificial soil management measures, such as fertilization and straw returning. Therefore, we concluded that the farmland in Fengqiu county showed significant carbon sequestration characteristics in the past 30 years (1981–2011). Considering that soil has a great potential for carbon sequestration in the future, the trend of carbon sequestration in farmland soil might continue for a period of time. Furthermore, the results of this study emphasized that strengthening soil scientific management may play a positive role in improving soil carbon sequestration. Full article

The Huanghuaihai River Basin (HRB) is one of the most prominent areas of water resource contradiction in China. It is of great significance to explore the relationship between water balance in this area for a deep understanding of the response of the water cycle to climate change. In this study, machine learning methods are used to prolong the actual evapotranspiration (ET) of the basin on the time scale and explore water balances calculated from various sources. The following conclusions are obtained: (1) it is found that the simulation accuracy of Global Land Evaporation Amsterdam Model (GLEAM) products in HRB is good. The annual average ET spatial distribution tends to increase from northwest to southeast; (2) three machine learning algorithms are used to construct the ET calculation model. The correlation coefficients of the three methods are all above 0.9 and the mean relative error values of random forest (RF) are all less than 30%. The RF has the best effect; (3) the relative errors of water balance in HRB from 1956–1979, 1980–2002 and 2003–2018 are less than ±5%, which indicates that the calculation of each element of the water cycle in the study area can well reflect the water balance relationship of the basin. Full article

Water resources are essential for human beings. It is of significance to project future water resources carrying capacity for water resources planning and management. In this study, the Huang-Huai-Hai River Basin (HHHRB), where the contradiction between humans and water is prominent in China, is selected as the study area. The fuzzy comprehensive evaluation model of regional water resources carrying capacity is constructed, the variation characteristics of water resources affected by climate change are analyzed based on the Budyko-Fu model, and considering the influence of transit water resources and water diversion projects, the future water resources carrying capacity in HHHRB under four future climate scenarios in CMIP6 is projected. The results indicate that: (1) On the whole, the carrying capacity of water resources in HHHRB is weak, and the spatial difference is great. (2) Under the background of climate change in the future, precipitation, temperature, and water resources in HHHRB all show increasing trends with changes of 0.90–12.59%, 1.22–1.80 °C, and 13.12–34.29%. (3) Under the background of global change, the water resources carrying capacity of most prefecture-level cities in HHHRB will be greatly improved in the future, and the spatial distributions of change rates among different climate scenarios are relatively consistent. (4) The construction of water diversion projects such as the South-to-North Water Diversion Project has played an obvious role in improving the carrying capacity of water resources. The research results can provide important scientific and technological support for the rational allocation of water resources in the basin under the background of global change. Full article

In addition to agriculture, the water–land nexus (WLN) also profoundly affects the sustainable development of industry and residents’ lives. However, little research has been designed to assess the water–land nexus from the perspective of industry development and people’s quality of life. In the current paper, Wi, a regional industrial water–land nexus matching index, and Wd, a matching index of the domestic water–land nexus, were proposed for evaluating the water–land nexus from the industry development and quality of life perspectives separately in the current paper. Furthermore, climate change and human activities have significant impacts on the water–land nexus. The WLNs were assessed spatially and temporally for the first time based on these two indexes in 128 municipalities in the Huang-Huai-Hai River Basin of China from 1951 to 2017 to analyze the impacts of the changing environment on them. The impact of changing environment was explored based on changes of some climate factors and land use. The value of Wi are higher in the eastern and southern cities than the western and northern cities, while Zhenjiang city in Jiangsu Province has the highest Wi. For Wd, there are two low Wd zones across the basin, while the minimum values occurred in Linxia Hui Autonomous Region (Wd = 35.34 mm). Wi and Wd in most cities in the basin showed a significant downward trend, and some cities in the southwest of the basin have the fastest-decreasing of Wd. Wt and Wa were also calculated to assess the total and agricultural water–land nexus separately based on existing research. The Wt for the Huang-Huai-Hai River Basin gradually increases from northwest to southeast, and its spatial distribution characteristics are similar to precipitation in the river basin. In addition, the government should simultaneously implement water transfer plans to reduce the agricultural water pressure in Ningxia and Gansu provinces. Dynamic driving factors of change of the four assessment indexes (Wt, Wa, Wi, Wd) are briefly analyzed in the end of the paper. Full article

As one of the main driving forces for the change in surface energy balance, land use and cover change affects the ecological climate through different levels of biogeochemical and physical processes. However, many studies on the surface energy balance are conducted from the perspective of biogeochemistry, ignoring biogeochemical processes. By using core methods such as the surface energy balance algorithm and Mann-Kendall trend test, we analyzed the surface energy balance mechanism and ecological climate effects of five land use types in the Huang-Huai-Hai Basin in China. The results showed that: (1) the net radiation and latent heat flux in the five land use types increased significantly, and their highest values were located in cropland areas and urban expansion areas, respectively. (2) The influence of net radiation on surface energy absorption was greater than latent heat flux. This relationship was more obvious in land use types that were greatly influenced by human activities. (3) The net surface energy intake in the Huang-Huai-Hai River Basin showed a decreasing trend and decreased with the increase in human influence intensity, indicating that human activities weakened the positive trend in net surface energy intake and increased the warming effect. This study reveals the difference in energy budgets of different land use types under the influence of human activities. It is helpful for understanding how to formulate sustainable land management strategies, and it also provides a theoretical basis for judging the climate change trends and urban heat island effects in the Huang-Huai-Hai River Basin from a biogeophysical perspective. Full article

Irrigation is an important factor affecting the change of terrestrial water storage (TWS), especially in grain-producing areas. The Northeast China Plain (NECP), the Huang-Huai-Hai Plain (HHH) and the middle and lower reaches of the Yangtze River Basin Plain (YRB) are major grain-producing regions of China, with particular climate conditions, crops and irrigation schemes. However, there are few papers focusing on the different variation pattern of water storage between NECP, HHH and YRB. In this paper, the characteristics of terrestrial water storage anomaly (TWSA) and groundwater storage in the three regions mentioned above from 2003 to 2014 were analyzed, and the main reasons for water storage variations in the three regions were also discussed. The result shows that although effective irrigated areas increased in all three regions, TWSA only decreased in HHH and TWSA in the other two regions have shown an increasing trend. Spatially, the water storage deficit was more serious in middle and south NECP and HHH. In the three regions, water storage variations were impacted by meteorological condition and anthropogenic stress (e.g., irrigation). However, irrigation water consumption has a greater impact on water storage deficit in HHH than the other two regions, and water storage variation in YRB was mainly impacted by meteorological conditions. In this case, we suggest that the structure of agricultural planting in HHH should be adjusted to reduce the water consumption for irrigation. Full article

The Huang-Huai-Hai River Basin plays an important strategic role in China’s economic development, but severe water resources problems restrict the development of the three basins. Most of the existing research is focused on the trends of single hydrological and meteorological indicators. However, there is a lack of research on the cause analysis and scenario prediction of water resources vulnerability (WRV) in the three basins, which is the very important foundation for the management of water resources. First of all, based on the analysis of the causes of water resources vulnerability, this article set up the evaluation index system of water resource vulnerability from three aspects: water quantity, water quality and disaster. Then, we use the Improved Blind Deletion Rough Set (IBDRS) method to reduce the dimension of the index system, and we reduce the original 24 indexes to 12 evaluation indexes. Third, by comparing the accuracy of random forest (RF) and artificial neural network (ANN) models, we use the RF model with high fitting accuracy as the evaluation and prediction model. Finally, we use 12 evaluation indexes and an RF model to analyze the trend and causes of water resources vulnerability in three basins during 2000–2015, and further predict the scenarios in 2020 and 2030. The results show that the vulnerability level of water resources in the three basins has been improved during 2000–2015, and the three river basins should follow the development of scenario 1 to ensure the safety of water resources. The research proved that the combination of IBDRS and an RF model is a very effective method to evaluate and forecast the vulnerability of water resources in the Huang-Huai-Hai River Basin. Full article