Effective Irrigation Area Research Articles

Exploring the Sustainable Use Strategy of Scarce Water Resources for Rural Revitalization in Yanchi County from Arid Region of Northwest China

Water scarcity limits the coordination between economic development and ecological protection of arid regions. This study presented the consumption pattern and future challenges for water resources and proposed sustainable use strategies for water security in Yanchi county from the arid region of northwest China. Our results showed that water withdrawals were close to the total available water resources. Agriculture consumed about 84.72% of the total water supply. Agricultural water use was influenced by breeding stock, rural per capita net income and effective irrigation area. Estimation of agricultural water demand was about 6582.20 × 104 m3 under the rural revitalization scenario. Limited water supply and increased water demand pose challenges and impediments for rural revitalization and water security in Yanchi county. Water sustainable utilization can be achieved by increasing water supply from unconventional water resources and improving water use efficiency with governmental management. These findings may help policymakers to develop sustainable water use strategies during rural revitalization in arid regions.

Spatiotemporal evaluation and analysis of cultivated land ecological security based on the DPSIR model in Enshi autonomous prefecture, China

Evaluating and analyzing cultivated land ecological security (ES) can reliably optimize ecosystem service supplies in cultivated lands. This study selected Enshi Autonomous Prefecture (Enshi Prefecture) as the research area, aiming to assess the spatiotemporal variability of cultivated land ES level and analyze its main obstacle factors, thus providing paths for its enhancement. Based on the driving force-pressure-state-impact-response (DPSIR) model framework, an index system for evaluating cultivated land ES was constructed. Then, the index weights were calculated by the entropy method, and a comprehensive index of ES was calculated by the composite index method to determine its security level. Finally, the main obstacles affecting cultivated land ES were analyzed by the obstacle degree model. The results showed that (1) From 2005 to 2018, the ES composite index rose from 0.402 to 0.685. The ES level went through a relatively insecure stage (2005–2012), generally secure stage (2013–2016) and relatively secure stage (2017–2018). (2) Regional differences in the cultivated land ES level are obvious, with lower ES in the eastern and western regions than in the central region in 2005 and lower ES in the eastern region than in the central and western regions in 2010 and 2018. (3) The main obstacles affecting cultivated land ES underwent significant temporal changes. From 2005 to 2009, they included the proportion of environmental investment in gross domestic product (GDP), effective irrigation area ratio, and per-capita GDP; in 2010–2018, they included the population density, fertilizer application per cultivated land area unit, and share of primary industry to GDP. Compared with the main obstacles of 2005, those of 2018 demonstrated a significantly larger magnitude. This study improves the understanding of cultivated land ES and its obstacle factors, which supports the sustainable use of cultivated land resources and the protection of the trinity of quantity-quality-ecology in Enshi Prefecture and similar regions.

Historical and projected impacts of climate change and technology on soybean yield in China

CONTEXTIncreasing soybean yield is essential for China. In order to secure the desire soybean yield gains, it is important to understand how changes in climate and technology impact soybean yield. OBJECTIVEFirst, we used historical climate and specific technology data assessed the impact of historical climate and technology on soybean yield in China. Second, combined with the historical yield linear trends, we projected future soybean yield under climate change and technology improvement. METHODSWe collected the historical climate data (daily maximum and minimum temperatures, precipitation, and solar radiation) and actual crop data (soybean yield and agricultural management practices: sowing date, total agricultural machinery, effective irrigation area, pesticide application amount, and rural labor force) of 1312 soybean-growing counties in China from 1990 to 2017. Besides, we calibrated and validated the vegetative and reproductive stages, and the yield formation process genetic parameters of soybean of different maturity groups in DSSAT-CROPGRO-Soybean model by a five-year cycle based on soybean field experiment data. We defined agricultural management practices and genetic parameters as technology, and used Bayesian methods to conduct an impact assessment of historical climate and technology changes on soybean yield. We also projected soybean yield from 2021 to 2100 under climate change and different cases of technology improvement. RESULTS AND CONCLUSIONSFrom 1990 to 2017, more than 90% of the study counties showed an increase in soybean yield, although the yield increase slowed down after 2010. Agricultural management practices and genetic parameters changes increased soybean yield by 16% (−24 to 50%) and 6% (−14 to 24%), respectively, while climate change decreased soybean yield by 1% (−14 to 18%). The positive impacts of agricultural management practices were mainly attributed to pesticide application amount and total agricultural machinery. With changes in agricultural management practices alone, soybean yield showed a plateau or even a decrease after 2010. If the technology remains at the level it was in 2010s, soybean yield would decrease in the future in 49% of the counties. SIGNIFICANCEBased on climate, soybean yield, specific agricultural management practices and genetic parameters data, we quantified the impacts of historical climate and technology on soybean yield in China. Besides, we projected future soybean yield under climate change and different cases of technology improvement, and discussed possible solutions for future increasing soybean yield. This work has practical significance for understanding climate and technology impacts on soybean yield, and implementing countermeasures to address future climate change and increase yield.

Evaluation of Drought Vulnerability of Maize and Influencing Factors in Songliao Plain Based on the SE-DEA-Tobit Model

Rain-fed agriculture is easily affected by meteorological disasters, especially drought. As an important factor of risk formation, actively carrying out agricultural drought vulnerability assessments is conducive to improving food security and reducing economic losses. In this study, an SE-DEA model with regional exposure and drought risk as input factors and the maize yield reduction rate and drought-affected area as output factors is established. The aim is to evaluate and zone the drought vulnerability of the maize belt in the Songliao Plain. The results show the following: (1) From 2000 to 2019, the drought vulnerability of maize showed a fluctuating increasing trend. The vulnerability in Harbin and central Jilin Province is high, which is extremely unfavorable for maize production. (2) Comparing the historical disaster data with the drought vulnerability map generated using the SE-DEA model, it could be found that the results obtained using the SE-DEA model are reliable. (3) The Tobit model shows that the proportion of the effective irrigated area is more important to alleviate vulnerability. For drought vulnerability zoning using a cluster analysis, we suggest that regulated deficit irrigation should be actively developed in high-vulnerability areas to ensure maize yield while improving water efficiency. The results of this study can provide a basis for the development of drought mitigation and loss reduction strategies, and they provide new ideas for future research.

Spatial Distribution and Driving Forces of the Vegetable Industry in China

Based on the ArcGIS geostatistical analysis method, this study offers a visualization of the spatial distribution pattern and spatial trend of vegetable production in China. The research also examines the degree of spatial agglomeration patterns of vegetable production by using the standard deviation ellipse technique and exploratory spatial data analysis method. In addition, we employ the spatial regression model partial differential method to explore the driving factors leading to the changing layout of vegetable production. The findings unveil that vegetable production in China exhibit strong spatial non-equilibrium characteristics, with “high-high” and “low-low” types as the main agglomeration patterns. Furthermore, the location distribution shows a northeast–southwest orientation with the center of gravity of distribution gradually directed toward the southwest. Regarding driving factors, the results show that the effective irrigated area of natural factors had a facilitating effect on the layout of vegetable production, while the affected area had an inhibiting effect on it. Climate indicators such as temperature, precipitation and light show different degrees of influence on the layout of vegetable production. The level of urbanization and transportation conditions have a negative impact on the layout of production in the region. Market demand has a positive spillover effect on the layout of local vegetable production, while it has a negative spillover effect on other regions. Technological progress shows positive spillover effects on the layout of vegetable production in the region and other regions. Financial support policy also shows positive effects from an overall perspective.

Quantitative Evaluation and Obstacle Factor Diagnosis of Agricultural Drought Disaster Risk Using Connection Number and Information Entropy.

To promote the application of entropy concepts in uncertainty analysis of water resources complex system, a quantitative evaluation and obstacle factor diagnosis model of agricultural drought disaster risk was proposed using connection number and information entropy. The results applied to Suzhou City showed that the agricultural drought disaster risks in Suzhou during 2007–2017 were all in middle-risk status, while it presented a decreasing trend from 2010. The information entropy values of the difference degree item bI were markedly lower than those of the difference degree b, indicating that bI provided more information in the evaluation process. Furthermore, the status of drought damage sensitivity and drought hazard were improved significantly. Nevertheless, high exposure to drought and weak drought resistance capacity seriously impeded the reduction of risk. Thus, the key to decreasing risk was to maintain the level of damage sensitivity, while the difficulties were to reduce exposure and enhance resistance. In addition, the percentage of the agricultural population, population density, and percentage of effective irrigation area were the main obstacle factors of risk and also the key points of risk control in Suzhou. In short, the results suggest that the evaluation and diagnosis method is effective and conducive to regional drought disaster risk management.

Quantifying the Contributions of Climate Change and Human Activities to Maize Yield Dynamics at Multiple Timescales

Under a changing environment, the effect of climate change and human activities on maize yield is vital for ensuring food security and efficient socio-economic development. The time series of maize yield is generally non-stationary and contains different frequency components, such as long- and short-term oscillations. Nevertheless, there is no adequate understanding of the relative importance of climate change. In addition, human activities on maize yield at multiple timescales remain unclear, which help in further improving maize yield prediction. Based on the ensemble empirical mode decomposition method (EEMD), the method of dependent variable variance decomposition (DVVD) and the Sen-slope method, the effect of climate change including growing-season precipitation and temperature (i.e., GSP, GEP, CDD, GST, GSMAT, and GSMT) and human activities including effective irrigation area (EIA) and the consumption of chemical fertilizers (CCF) on maize yield were explored at multiple timescales during 1979–2015. The Heilongjiang Province, a highly important maize production area in China, was selected as a case study. The results of this work indicate the following: (1) The original maize yield series was divided into 3.1-, 7.4-, 18.5-, and 37-year timescale oscillations and a residual series with an increasing trend, where the 3.1-year timescale (IMF1), the 18.5-year timescale (IMF3), and the increasing trend (R) were dominant; (2) the original sequence was mainly affected by human activities; (3) climate change and human activities had different effects on maize yield at different timescales: The short-term oscillation (IMF1) of maize yield was primarily affected by climate change. However, human activities dominated the mid- and long-term oscillations (IMF3 and R) of maize yield. This study sheds new insight into multiple timescale analysis of the role of climate and human activities on maize yield dynamics.

Patterns and driving forces of the agricultural water footprint of Chinese cities

The patterns and determinants of different types of agricultural water footprints in China are poorly understood at the prefecture-city level. In this paper, we evaluate Chinese agricultural water footprints from 2000 to 2017 and analyzed their spatio-temporal characteristics. Our estimation results show that the annual average agricultural water footprint in China was 5.038 × 109 m3, and the proportions of green water, blue water, and gray water were 70%, 9%, and 21%, respectively. In addition, high agricultural water-footprint cities with obvious urban agglomeration effects are mainly located in the Northeast, the Huanghuai River, the Yangtze River Basin, and Northwestern of Xinjiang, while low agricultural water-footprint cities are concentrated in high coastal urbanization-level areas or less developed agricultural areas of the west. We also investigate their determinants using a spatio-temporal fixed-effect model and find that GDP per capita, total investment in fixed assets, the income level of rural residents, the proportion of food grown, spray and drip irrigation technology, low-pressure pipe irrigation technology and seepage control irrigation technology have significant positive impacts on the agricultural water footprint. In contrast, the proportion of secondary and tertiary industries, social retail consumption, urbanization, technology expenditure, and the effective irrigation area proportion have a significant inhibitory effect. The primary determinants of the agricultural water footprint also vary substantially across water footprint categories (green, blue, and gray water footprints) and regions. Our findings imply that the agricultural water footprint should be incorporated into city water resource management and monitoring system.

Study on the Impact of Agricultural Digitization on the Output of Agricultural Products in China

With the popularization of the Internet and the promotion of Rural Revitalization Strategy, agricultural digitization has attracted the attention of many scholars. The study of agricultural digitization has important theoretical and practical significance for the development of agriculture. This paper selects the time series data from 2012 to 2020 and takes the agricultural output value as the explanatory variable. The number of agricultural broadband access users, Internet penetration and optical cable line length are selected as the statistical indicators of agricultural digitization. The entropy method is used to weight the three statistical indicators, and finally a weighted index is obtained as the main explanatory variable. At the same time, the total power of agricultural machinery, effective irrigation area and net amount of chemical fertilizer application were selected as explanatory variables. This paper analyzes the impact of agricultural digitization on the output of agricultural products in China by using row linear regression method. Finally, it summarizes the impact of agricultural digitization on agricultural gross output value, and puts forward corresponding policy suggestions.

Spatial–Temporal Pattern and Influencing Factors of Drought Impacts on Agriculture in China

Agricultural drought disaster is a major natural disaster affecting economic and social development. It is of significance to investigate the spatial–temporal pattern and the dominant influence of natural and human factors on agricultural drought disasters for drought hazard relief. In this study, Mann–Kendall test was adopted to explore the evolution of agricultural drought disasters. Random forest algorithm, which integrates feature importance and accumulated local effects plot, was applied to quantify the effect of principal influencing factors on agricultural drought disasters. Results show that over the period from 1950 to 2019, agricultural drought disasters in China have undergone significant fluctuations. The spatial pattern of agricultural drought disaster tends to decrease in severity from north to south. The total sown area of crops, precipitation, effective irrigation area, domestic patent application authorization, and regional GDP are the top 5 dominant factors influencing agricultural drought disasters. It also found that agricultural drought disaster negatively correlates with precipitation, domestic patent application authorization, and regional GDP, and the nonlinear response of agricultural drought disaster to total sown area of crops and effective irrigation area can be basically divided into two stages. In the first stage, with the increase of feature value, agricultural drought disaster is also increasing. In the second stage, with the increase of feature value, agricultural drought disaster is growing slow or just decreasing. The results can deepen the understanding of agricultural drought disasters and provide scientific basis for drought event monitoring, evaluation, and early warning.

Quantitative Evaluation and Diagnosis of Water Resources Carrying Capacity (WRCC) Based on Dynamic Difference Degree Coefficient in the Yellow River Irrigation District

In order to effectively deal with the uncertainty between evaluation samples and evaluation criteria, and quantitatively identify the water resources carrying capacity (WRCC) and its obstacle factors in the Yellow River irrigation district, a calculation method of dynamic difference degree coefficient varying with evaluation sample was proposed, and then an evaluation and diagnosis model of WRCC was established. The results applied to the Dagong irrigation district showed that the overall WRCC of five counties in the irrigation district were improved from 2010 to 2017, especially since 2013. The improvement magnitudes of Changyuan County, Fengqiu County, and Hua County were significantly higher than those of Xun County and Neihuang County. In 2017, Fengqiu County, Changyuan County, Hua County, Xun County, and Neihuang County were in water resources critical overloaded status, and the connection number values were 0.231, 0.163, 0.120, −0.293, and −0.331, respectively, which is consistent with the fact that their distances become farther from the main stream of the Yellow River. In addition, the utilization ratio of water resources, available water resources amount per capita, GDP per capita, and water deficient ratio in each county belonged to the middle or strong obstacle index over a long period of time. They were the crucial obstacle factors of WRCC in the Dagong irrigation district, as well as the core and difficult points of water resources management. In some counties, the effective irrigation area ratio, effective utilization coefficient of irrigation water, and water consumption ratio of the ecological environment gradually developed from strong obstacle to weak or strong promotion index. These were important reasons for the improvement of their carrying situation, reflecting their control of agricultural and ecological water consumption. In short, the results of the case study suggest that the model established in this study is conducive to the identification of water resources’ carrying status and its key obstacle factors in the Yellow River irrigation district, and can be applied to the evaluation and regulation of resources and environment carrying capacity.

Assessment of smallholders’ vulnerability to drought based on household-scale planting strategies and adaptability: A survey study of Xinghe County

The increase of the frequency, scope and intensity of drought has a great impact on agricultural production under the climate change. Smallholders are the most direct victims of drought. This paper focused on smallholders' planting strategies and family-scale indicators from the micro perspective and constructed a framework for smallholders' drought vulnerability assessment in semiarid rain-fed agricultural areas. The results show that: (1) There are significant spatial variations in smallholders' drought adaptability and the affected by drought, which may be related to the fragmentation of smallholders' cultivated land in different regions. (2) The drought vulnerability of smallholders also has obvious differences between north and south in Xinghe County. There are several obvious vulnerability hot spots in the south, especially in Datong Yao and Dianzi. The vulnerability index of smallholders in these spots may more than 0.7. (3) The effective irrigation area may be the best factor to explain the spatial variations of smallholders' drought vulnerability. Its interaction with slope can explain more than 20% of the vulnerability difference, which is significant at 0.05 level. This study is expected to enrich the evaluation dimensions of smallholders’ drought vulnerability assessment in arid and semiarid areas, reduce the risk of drought losses of smallholders, and improve the level of regional risk governance.

Assessment and management of composite risk in irrigated agriculture under water-food-energy nexus and uncertainty

Growing demands for water, energy, and food put many systems at a composite risk of resource shortages. There was a lack of approaches capable of effectively assessing tridimensional composite risk and describing non-linear correlations among subsystem risks within a Water-Energy-Food (WEF) nexus system. In this study, an integrated approach was developed to assess the composite risk of WEF nexus systems and generate risk-based plans. Specifically, a composite risk assessment model that could capture the interdependence among the risk for water, energy, and food shortage was proposed. Furthermore, a generalized Copula-based chance-constrained programming model and its solving algorithm were developed. The proposed approach has been applied to an agricultural WEF nexus system in northern China, where the shortage of water, energy and / or land affected agricultural outputs. Results show that the composite risk of the entire system would be higher than the maximum value among subsystem risks, but less than their sum. Higher composite risks could bring higher benefits. Under a certain composite risk, the overall system benefit would vary with different combinations of subsystem risks; and it could be promoted through coordinating resources supplied by different subsystems. A risk - benefit frontier consisting of optimal solutions corresponding to different combinations of composite and subsystem risks was identified through Monte Carlo simulation. The scheme that could generate 12.76 billion Yuan under a moderate composite risk level of 0.22 was recommended for the study problem. Accordingly, net irrigation water, energy for agriculture, and effective irrigation area should be no less than 1.66 × 109 m3, 81.08 × 103 tce, and 509.12 × 103 hm2, respectively.

Study on agricultural cultivation development layout based on the matching characteristic of water and land resources in North China Plain

The North China Plain is the main producing area of winter wheat in China. Due to the relatively poor condition of water resources, the competition of water between industry and agriculture is fierce. The increasing shortage of water resources has led to serious overexploitation of groundwater in some areas. Agricultural development is faced with severe challenges. In order to ensure regional food security and sustainable winter wheat planting, 52 cities of North China Plain (NCP), in the following of the paper, use the shorted name NCP，were taken as research units to analyze the matching characteristics of agricultural water and land resources, so as to reveal the direction of irrigated agriculture development and winter wheat suitable planting layout considering their balance in future. The paper firstly analyzed the cultivated area determined by water resource and irrigation development by agricultural available water, and then pointed out the direction of water-suited planting layout of winter wheat. The results showed that: 1) the Gini coefficient showed an increasing trend, which indicated that the area of cultivated land and irrigation area supported by water resources have nearly reached the upper limit. The large-scale extension of both cultivated and irrigation area is impossible under the restriction of water. 2) The amount of agricultural available water resources per unit of effective irrigation area and the effective precipitation increased gradually from north to south in the NCP, but the spatial distribution of water requirement of winter wheat in its growth period changed in the opposite way. This indicated the poor spatiotemporal match between winter wheat planting and water resources, leading to the cost of spending more water to maintain wheat production. 3), The further development of irrigation and the water-suited planting of winter wheat should pay attention to the southern part of the plain with rich water (such as southern Henan Province, Jiangsu Province, Anhui Province, etc.) to ensure the regional agricultural development. That is of great significance to the regional sustainable development, water shortage alleviation and national grain safety.

An analysis of the spatial evolution and influencing factors of rural settlements along the Shandong section of the Grand Canal of China

AbstractSince 1980s, the weakening of the transportation function of the Grand Canal and rapid urbanisation have generated significant changes in the spatial pattern of rural settlements along the Grand Canal. Analysing the changing characteristics of this spatial pattern, and exploring the natural, social, and economic factors influencing these changes are key to understanding the regional spatial structure and development law, and grasping the degree of influence of these factors. This study selected approximately 13,000 rural settlements in 27 county‐level units in the Shandong section of the Grand Canal, from 1980 to 2018, as the research object. The Grand Canal's distance buffer zone is divided into canal‐side, near‐canal, far‐canal, and further‐canal settlements. The correlation model with the canal is constructed through the controlled experiment. The distribution, scale, and form of the settlement are analysed quantitatively by applying the change of gravity centre model (CGC), average nearest neighbour analysis (ANN), landscape metrics (LM), and other methods. The quantitative analysis of geographic detectors in spatial pattern differentiation factors shows the relative importance and interaction within them. This study indicates that the spatial distribution shows ‘large dispersion and small concentration’ and ‘small‐scale agglomeration’ characteristics. The larger the scale of the settlement expands, the more the number of settlements decreases. The closer the settlement nears the river, the more complex the shape clusters. The total output value of agriculture, forestry, animal husbandry, fisheries, the total power of agricultural machinery, and the effective irrigation area are the main influencing factors. The grain area and the per capita disposable income of rural residents are the main auxiliary influencing factors.

New indices system for quantifying the nexus between economic-social development, natural resources consumption, and environmental pollution in China during 1978–2018

It is meaningful to study how China can maintain the sustainable utilization of natural resources and the continuous improvement of environmental conditions while ensuring the stable development of the economy and society. In this study, a new indices system was proposed for the analysis of nexus among social-economic-natural resource-environment complex systems following the DPSIR (Driving Force - Pressure - State - Impact - Respond) framework, CCD (Coupling Coordination Degree) analysis and VAR (Vector Auto-Regressive) model were applied for quantifying the synergy and trade-off of China in the nexus framework. Results showed that: (1) Although China's rapid development has caused big consumption of natural resources and increasing pollutants discharges during 1978–2018, China has not got into trouble of extreme resource depletion and ecosystem collapse. On the contrary, China guaranteed food supply, stopped forest degradation, and avoided pollution-induced healthy crises & life-shortening. (2) Adjustment of water pollution industries and the increase of wastewater treatment investment contributed 39% and 37% to the reduction of water pollutant discharge, respectively. The contribution of energy structure adjustment to acid rain control was 26%. The pollutants discharged in no less than 70% of the provinces are strictly controlled below the environmental capacity. The increase of fertilizer application and effective irrigated area contributed 32% to China's grain increase, and China's grain self-sufficiency rate has been maintained above 110%. The improvement of the water-saving irrigation rate contributed 28% to the reduction of water consumption. The reduction of comprehensive efficiency contributed 23.8% to the decrease in energy consumptions per GDP. The CCD assessment showed that China has entered a phase of pre-eminently coordinated development since 2013.

Life cycle water footprint analysis of crop production in China

Global water resources are under tremendous pressure influenced by population growth and economic development. Considering that the agricultural sector accounts for the largest consumption and that China is one of the major agricultural countries, this study conducted an impact-oriented water footprint analysis to quantify the environmental burdens of crop production in China from a life cycle perspective. The temporal variation of potential environmental impacts during 2008–2017 was evaluated, and 334 cities in China were used to investigate the spatial characteristics of environmental burdens. Results showed that the environmental burdens were mainly ascribed to direct consumption, fertilizer production, and diesel production. The overall burdens uninterruptedly increased from 2008 to 2015 and then declined in 2016. The decomposition results of temporal variation demonstrated that agricultural economic scale and crop productivity were the largest positive and negative contributors to burden growth, respectively. The burdens exhibited an evident spatial autocorrelation, and high–high agglomerations were mostly located in the North China Plain and Yangtze River Delta due to the superior natural resource endowments and dense population in these regions. Such factors as natural resource endowments (i.e., cultivated land and precipitation), agricultural input intensity (i.e., fertilizer and diesel), and effective irrigation area proportion significantly influenced the spatial variation of environmental impacts at the city level. Therefore, adopting water-saving technologies, strengthening fertilization management, promoting large-scale and mechanized agriculture, and choosing clean alternative fuels are highly recommended to achieve sustainable agricultural development. Furthermore, in consideration of the dominance of high–high clusters in crop production in China, advanced and eco-friendly production technologies should be vigorously incentivized in these regions to enhance their agglomeration and radiation force.

Optimized Irrigation Model - Rebuild and optimize the food system

At present, the global food system is unstable, people are facing serious food shortages, security and environmental damage and other problems.By establishing multiple linear regression model and optimizing irrigation system model, this paper studied the most influential factor of grain yield, namely effective irrigation area, by using analytic hierarchy process, and then analyzed this factor to find the conditions required by crops when the yield is optimal. Thus, the problem of grain yield is optimized. a multiple linear regression model was established. The results showed that to improve wheat yield and profitability, it was necessary to increase the irrigation amount during winter jointing to increase wheat yield.

Baseflow dynamics and multivariate analysis using bivariate and multiple wavelet coherence in an alpine endorheic river basin (Northwest China)

Baseflow is a component of streamflow derived from shallow and deep subsurface flows that is concurrently controlled by multiple factors. Rational estimation of baseflow is critical for understanding its spatiotemporal dynamics and influencing factors within a river basin. To address this, different filtering parameters were applied to separate the baseflow of the Heihe River Basin (HRB) in Northwest China using digital filtering methods. Moreover, using bivariate and multivariate wavelet coherences, multivariate relationships between baseflow and meteorological factors/large-scale circulation indices were identified for several factors, which explained most of the variations. Results showed annual average baseflow was 10.3–91.1 mm and that the baseflow index (BFI) varied between 0.50 and 0.72 (average: 0.62). This indicates that 62% of long-term streamflow likely originates from groundwater discharge and other delayed sources. Positive/negative Spearman correlation coefficients between baseflow and extreme climate indices were more significant at upstream (Yingluoxia, Liyuanbao-and Wafangcheng) stations in comparison with midstream (Suyukou, Shunhua) and downstream (Yangyangchi) stations. Correlation for the BFI was relatively weaker than for baseflow. Furthermore, bivariate wavelet coherences revealed that precipitation (six stations) and the Atlantic Multidecadal Oscillation (four stations) were the individual factors that best explained baseflow variations. Multiple wavelet coherence demonstrated that all meteorological factors/large-scale circulation indices had the highest percentage of the numbers of power significant at the 95% significance level that could best explain baseflow variations. However, the average power of wavelet coherence was not increased. Differences likely attributable to consideration of additional variables were diminished by collinearity effects among factors. Furthermore, baseflow at the midstream Zhengyxia and downstream Yangyangchi stations had significant positive and negative correlation with population and effective irrigation area, respectively. The findings indicate that development of regional hydrometeorological models should primarily consider the impact of climate change in the upstream HRB, whereas the effects of both climate change and human activities should be considered in the midstream and downstream HRB.

A Fuzzy Comprehensive Evaluation Method of Area Resource Carrying Capacity

The evaluation of resource carrying capacity of Xiong'an New Area can clearly recognize the endowment characteristics, carrying capacity and carrying level of resources in Xiong'an New Area, which could provide certain theoretical guidance for the sustainable development of economy, society and ecology in the New Area. Based on the analysis of the current situation of resources in the New Area, following the main principles of scientific, dynamic, reflecting the coordination of human land relationship and sustainable development, from three aspects of land resources, water resources and mineral resources, 13 indexes of the per capita cultivated land area, per capita construction land area, per unit cultivated land productivity, per capita water resources, per unit effective irrigation area, utilization rate of water resources, water use efficiency, ore production per unit land use and geothermal field area are selected to build the evaluation index system of resource carrying capacity of the New Area. Based on the fuzzy comprehensive evaluation method, the resource carrying capacity of Xiong'an New Area from 2014 to 2018 is evaluated with the administrative areas of Xiong county, An’xin county and Rongcheng county (including Baiyangdian water area) as the main evaluation unit, which could provide policy guidance for the land, water and mineral resources management of the New Area.