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To reveal the influence of freeze–thaw cycles (FTCs) on soil carbon and nitrogen changes, six typical soils in Northeast China were selected as the research objects to conduct a FTC simulation test in an artificial climate chamber. Three soil volumetric water contents (10%, 20%, 30%) and eight FTCs (0, 2, 4, 6, 8, 10, 15, 20) were set. The results showed that the soil organic carbon (SOC) and microbial biomass carbon (MBC) contents of different soil types under the FTCs initially exhibited a downward and then an upward trend, while the dissolved organic carbon (DOC) content exhibited an upward and then a downward trend. Otherwise, the fourth and sixth FTCs were the key points of change. The SOC, MBC and DOC contents in paddy fields were higher than those in dry fields, showing upward and then downward trends spatially from northeast to southwest. The SOC and MBC contents in each soil type were the highest at the 20% water content, and the DOC content gradually increased with increasing water content. The ammonium nitrogen (NH4+‐N) content in different soil types at different water contents under the FTCs showed an upward trend first, then a downward trend and finally an upward trend. The NH4+‐N content in paddy fields was higher than that in dry fields. The nitrate nitrogen (NO3‒‐N) content showed a downward trend first, then an upward trend and finally a downward trend. The NO3‒‐N content in dry fields was higher than that in paddy fields. The NH4+‐N contents in the three soil types on the Sanjiang Plain were significantly higher than those on the Songnen Plain. The NH4+‐N and NO3‒‐N contents showed upward trends with increasing water content, but the differences were not significant. The results have implications for the study of different types of soils and provide references for research on the mechanism of soil carbon and nitrogen transformation in typical farming areas in Northeast China. [ABSTRACT FROM AUTHOR]

In the process of rapid advancement of agricultural production, the dynamic evolution characteristics of land and water resources in the main grain-producing areas and the influencing factors are less studied. This study takes Heilongjiang Province, the main grain-producing area in China, as an example, constructs an index system from three functions: production, life, and ecology, uses information entropy to determine the weights and importance of each index, uses the rate of change of the index to determine the basic data of the dynamic development of the system, combines the weights to determine the dynamic evolution characteristics, and compares and analyzes them with the static evolution characteristics determined by the actual data. The results showed that there were differences in the important indicators under different conditions, and the important indicators under static and dynamic conditions were the proportion of the tertiary industry to GDP(A7) and GDP per unit area(A8), with importance weights of 7.45% and 8.0%, respectively. The static evolution index of the land and water resource system increased slowly from 0.16 to 0.91, while the dynamic evolution index fluctuated and declined from 0.58 to 0.34, indicating that the ability of the land and water resource system in the study area to maximize comprehensive benefits is constantly weakening. Managers can pre-control the development speed of the important indicators under dynamic conditions and promote the sustainable development of the land and water resource system. [ABSTRACT FROM AUTHOR]

In spring, rivers at middle and high latitudes in the Northern Hemisphere are prone to ice jams, which threaten the safety of hydraulic structures in rivers. Heilongjiang Province is located on the highest latitude in China, starting at 43°26′ N and reaching 53°33′ N. Rivers in Heilongjiang Province freeze in winter and break up in spring. Forecasting the break-up date of river ice accurately can provide an important reference for the command, dispatch, and decision-making of ice flood preventing and shipping. Based on the observed break-up date series of river ice from seven representative hydrological stations in Heilongjiang Province, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was used to decompose the observed break-up date series of river ice into several subsequences, and the long-short term memory neural network (LSTM) was used to forecast the subsequences decomposed by CEEDMAN. Then, the forecast results of each subsequence were summed to obtain the forecasting value for the break-up date of river ice proceeded by CEEMDAN-LSTM. Compared with the LSTM, the forecast accuracy of CEEMDAN-LSTM for the break-up date of river ice had been significantly improved, with the mean absolute error reduced from 0.80–6.40 to 0.75–3.40, the qualification rate increased from 60–100% to 80–100%, the root-mean-square difference reduced from 1.37–5.97 to 0.95–1.69, the correlation coefficient increased from 0.51–0.97 to 0.97–0.98, and the Taylor skill score increased from 0.87–0.99 to 0.99. CEEMDAN-LSTM performed well in forecasting the break-up date of river ice in the Heilongjiang Province, which can provide important information for command, dispatch, and decision-making of ice flood control. [ABSTRACT FROM AUTHOR]

The evolution of the average freezing depth and maximum freezing depth of seasonal frozen soil and their correlations with the average winter half-year temperature in Heilongjiang Province in China are analyzed. Linear regression, the Mann–Kendall test, and kriging interpolation are applied to freezing depth data from 20 observation stations in Heilongjiang Province from 1972 to 2016 and daily average temperature data from 34 national meteorological stations collected in the winters of 1972–2020. The results show that the average freezing depth decreases at a rate of 4.8 cm (10 yr)−1 and that the maximum freezing depth decreases at a rate of 10.1 cm (10 yr)−1. The winter half-year average temperature generally shows a fluctuating upward trend in Heilongjiang Province, increasing at a rate of 0.3°C (10 yr)−1. The correlations between the average and maximum freezing depths and the winter half-year average temperature are −0.53 and −0.49, respectively. For every 1°C increase in the average temperature during the winter half of the year, the average freezing depth decreases by 3.85 cm and the maximum freezing depth decreases by 7.84 cm. The average freezing depth sequence mutated in 1987, and the maximum freezing depth sequence mutated in 1988. The average temperature in the winter half-year displayed multiple abrupt changes from 1972 to 2020. The spatial variations in the average and maximum freezing depths are basically consistent with those in the average winter half-year temperature. These research results provide a theoretical basis for the design and site selection of hydraulic structures in cold areas and for regional development and agricultural planning. Significance Statement: The freeze–thaw balance in the frozen soil environment has been disrupted in recent years, and various degrees of degradation have occurred in the frozen soil. The degradation of frozen soil will further aggravate the greenhouse effect, which in turn will affect the accumulation of water in the soil and will have a significant impact on local agricultural production. This article uses Heilongjiang Province in China as an example. The results show that 1) the temperature in the winter half-year has exhibited an upward trend in recent years, 2) the temperature in the winter half-year has a considerable impact on the frozen soil environment, and 3) the response of the spatial distribution of frozen soil to temperature changes in the winter half-year is revealed. [ABSTRACT FROM AUTHOR]

Frozen soil infiltration widely occurs in hydrological processes such as seasonal soil freezing and thawing, snowmelt infiltration, and runoff. Accurate measurement and simulation of parameters related to frozen soil infiltration processes are highly important for agricultural water management, environmental issues, and engineering problems in cold regions. Temperature changes cause soil pore size distribution variations and consequently dynamic infiltration capacity changes during different freeze–thaw periods. To better understand these complex processes and to reveal the freeze–thaw action effects on soil pore distribution and infiltration capacity, black soils, meadow soils, and chernozem were selected as test subjects. These soil types account for the largest arable land area in Heilongjiang Province, China. Laboratory tests of soils at different temperatures were conducted using a tension infiltrometer and ethylene glycol aqueous solution. The stable infiltration rate and hydraulic conductivity were measured, and the soil pore distribution was calculated. The results indicated that for the different soil types, macropores, which constituted approximately 0.1 % to 0.2 % of the soil volume under unfrozen conditions, contributed approximately 50 % of the saturated flow, and after soil freezing, the soil macropore proportion decreased to 0.05 % to 0.1 %, while the saturated flow proportion decreased to approximately 30 %. Soil moisture froze into ice crystals inside relatively large pores, resulting in numerous smaller-sized pores, which reduced the number of macropores but increased the number of smaller-sized mesopores, so that the frozen soil infiltration capacity was no longer solely dependent on the macropores. After the ice crystals had melted, more pores were formed within the soil, enhancing the soil permeability. [ABSTRACT FROM AUTHOR]

Sample entropy can be used to investigate the complexity of precipitation series. However, the randomness of similar tolerance selection may lead to inaccurate results. To solve this problem, the distinction degree theory was introduced to optimize the similar tolerance of sample entropy, and a specific reference frame for the optimization process was provided. The optimized sample entropy was used to study the spatial differences in precipitation complexity and the possible causes from the perspective of the monthly precipitation (MP) and extreme daily precipitation (EDP) in Heilongjiang Province, China. The results demonstrated that the MP and EDP in Heilongjiang Province both had unsteady complex fluctuation characteristics and the optimal similar tolerances of the sample entropy were 0.24 and 0.13 times the standard deviation, respectively. The complexity of the EDP was higher than that of the MP, and their fluctuation ranges were 1.641–2.268 and 0.433–0.870, respectively. The complexity of the MP in the study area could be spatially represented as a basic pattern, which gradually decreased from east to west. The spatial distribution pattern of the EDP complexity indicated that the northern part was higher than the southern part. Altitude was the common influencing factor of the MP and EDP complexities, and it had a greater impact on the spatial pattern of the EDP complexity. Potential factors influencing the spatial difference in MP complexity also included changes in precipitation intensity, water area and residential and industrial land areas. Except for altitude, industrial economic development level may also affect the complexity of the EDP. The results may improve the application of sample entropy in climate system complexity measurements and could provide guiding significance for revealing the spatial variation of precipitation complexity. [ABSTRACT FROM AUTHOR]

Effective drought prediction methods are essential for the mitigation of adverse effects of severe drought events. This study utilizes the Reconnaissance Drought Index, Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index to assess the occurrence of future drought events in the study area of the Heilongjiang province of China over a period of 2016-2099. The drought indices were computed from the meteorological data (temperature, precipitation) generated by the global climate model (HadCM3A2). Moreover, Mann-Kendall trend test was applied for the assessment of future climatic trends and detecting probable differences in the behaviour of various drought indices. Drought forecasting periods has been divided into three categories: the early phase (1916-2030), middle phase (2031-2060) and late phase (2061-2099). The occurrence of future droughts is also ranked according to their intensity (mild, moderate, severe and extreme drought). Based on the drought results, more number of drought events are expected to occur during 12-month drought analysis are, RDI during 2084-2098 (DD = 14, DS = −1.38), SPEI during 2084-2098 (DD = 14, DS = −1.33) and SPI during 2084-2095 (DD = 12, DS = −1.19). The 1st and 2nd months of the years studied predicted a warming trend, while the 7th, 8th, and 9th months predicted a wetter trend. Finally, it was observed that RDI is more sensitive to drought and indicated a high percentage of years under severe and extreme drought conditions during the drought frequency analysis. Conclusively, this study provides a strategies for water resources management and monitoring of droughts, in which drought indices like RDI can play a central role. [ABSTRACT FROM AUTHOR]

Variability in precipitation is critical for the management of water resources. In this study, the research entropy base concept was applied to investigate spatial and temporal variability of the precipitation during 1964-2013 in the Songhua River basin of Heilongjiang Province in China. Sample entropy was applied on precipitation data on a monthly, seasonally, annually, decade scale and the number of rainy days for each selected station. Intensity entropy and apportionment entropy were used to calculate the variability over individual year and decade, respectively. Subsequently, Spearman's Rho and Mann-Kendall tests were applied to observe for trends in the precipitation time series. The statistics of sample disorder index showed that the precipitation during February (mean 1.09, max. 1.26 and min. 0.80), April (mean 1.12, max. 1.29 and min. 0.99) and July (mean 1.10, max. 1.20 and min. 0.98) contributed significantly higher than those of other months. Overall, the contribution of the winter season was considerably high with a standard deviation of 0.10. The precipitation variability on decade basis was observed to increase from decade 1964-1973 and 1994-2003 with a mean value of decadal apportionment disorder index 0.023 and 0.053, respectively. In addition, the Mann-Kendall test value (1.90) showed a significant positive trend only at the Shangzhi station. [ABSTRACT FROM AUTHOR]

The present scholars in the study of the evolution of a regional groundwater resource system tend to ignore the complexity of the system itself, making it difficult to truly realize the scientific management of groundwater resources. Thus, the quantization characteristics of the complexity of the regional groundwater resource system have become a hot issue concerned in the field of hydrology. In this paper, taking Jiansanjiang Administration of Agricultural Reclamation, Heilongjiang, China for example, a wavelet entropy method is used for diagnosing each of its monthly groundwater depth sequence complexity to determine the order of the complexity, thus calculating the averaged wavelet entropy of the monthly groundwater depth sequence for each zone and revealing the local monthly groundwater depth sequence complexity has an obvious regional characteristic gradually decreased from north to south. Through comparative analysis of three kinds of complexity measure algorithms, including wavelet entropy, multi-scale semi-square difference dimension, and sample entropy, we find that the diagnostic result of the complexity of the wavelet entropy algorithm has sufficient visibility and high operational efficiency, so that it is an effective way to measure the hydrological sequence complexity. The results of the analysis of the cause of the local monthly groundwater depth sequence complexity show that: changes in precipitation and agricultural production activities are the critical driving factors for the dynamic changes in the local groundwater depth sequence. Research result reveals the rules of the spatial variation on the local groundwater depth complexity and provides a research model for the research of the process complexity of regional hydrology as well as a scientific basis on zone management and sustainable use of regional groundwater resources. [ABSTRACT FROM AUTHOR]

• SFTS changed significantly in 1960 – 2015 and an abrupt change occurred in 1988. • Future trends will be consistent with the past. • First thawing date advanced by 6 d and thawing period shortened by 21 d. • Vegetation and snow cover can reduce the effect of global warming on SFTS. With global warming, the soil freeze-thaw state has greatly changed, which has profoundly impacted the balance between water resources, ecological security and regional agriculture and forestry production in frozen regions. Based on daily soil freezing/thawing depth data in Heilongjiang Province over the last 60 years, Mann-Kendall trend analysis, Sen's slope and Hurst exponent were performed to analyze the spatiotemporal characteristics of the soil freeze-thaw state, and the effects of nonclimatic factors "land use types" and snow cover on the change in the soil freeze-thaw state were discussed. The results indicated that (1) the soil freeze-thaw state significantly changed over the past 60 years. The maximum seasonally frozen depth decreased approximately by 48 cm, the first freezing date was delayed by approximately 10 d, the first thawing date and last date advanced approximately by 6 and 27 d, respectively, the freezing and thawing periods decreased by approximately 17 and 21 d, respectively, and the duration and actual number of freezing days decreased by 38 and 34 d, respectively. (2) The soil freeze-thaw state changed abruptly in 1988 and exhibited a significant trend after the 1990s. The Hurst exponent of the soil freeze-thaw state variables at approximately 2/3 of the stations reached the strong persistence level. (3) In forestland and grassland areas, vegetation coverage reduced the climate warming impact on the soil freeze-thaw state. In addition, the maximum snow depth was significantly correlated with the soil freezing depth and first thawing date, and the change in snow depth also offset the effects of temperature rise on soil freezing depth and first thawing date to a certain extent. Changes in the soil freeze-thaw state have both advantages and disadvantages on agricultural production. Therefore, it is necessary to strengthen agricultural monitoring and use territorial knowledge to reasonably regulate agricultural production. [ABSTRACT FROM AUTHOR]

• An improved standard precipitation evapotranspiration index (CASPEI) is proposed. • The SPEI is a special case when the attenuation parameter is 0 in the CASPEI. • Case study shows that CASPEI is better than SPEI in some cases. The drought index is an important indicator that reflects the degree of regional drought and plays an important role in drought disaster monitoring, prediction and evaluation. According to the process definition of the standard precipitation evapotranspiration index (SPEI), the influence of cumulative effect attenuation was considered and aggregated at different time scales k, the linear attenuation coefficient was defined, and attenuation parameters were used to adjust the degree of attenuation. A value closer to the current month was assigned a larger weight, and a value further from the current month was assigned a smaller weight. Thus, the cumulative water surplus or deficit was recalculated, and the standard precipitation evapotranspiration index based on the cumulative effect attenuation (CASPEI) was defined and applied to the drought analysis of Heilongjiang Province, which has the highest grain output in China. The comparative analysis showed that the CASPEI dataset was more consistent with the log-logistic distribution, and its sensitivity in identifying regional drought events was greater than that of the SPEI, which effectively corrected the original index's errors in identifying the drought levels of some stations and was more consistent with regional historical drought events. The temporal and spatial variation analysis revealed that drought occurrence is seasonal: summer is the season with the highest frequency of severe drought and extreme drought, and the area experiencing extreme drought is greater than that during other seasons. The areas with moderate drought in spring and autumn were relatively large. With the increase in time scale, the area of extreme drought decreased, and the area of severe drought increased. The area of moderate drought increased, while the area of light drought decreased. The CASPEI can accurately judge the extent to which drought occurs to respond to drought and effectively reduce or prevent large losses in people and the economy caused by drought. [ABSTRACT FROM AUTHOR]

Research on water pricing is important to effectively address the water resource crisis. The agricultural industry has the greatest water-saving potential. Using Heilongjiang Province, the main grain-producing region in China, as an example, an agricultural irrigation water price was determined based on macro and micro scales, and affordable water prices for peasants were calculated. The results show a macro-agricultural irrigation water price of 1.023 yuan/m3, indicating that the current agricultural irrigation water price does not reflect the water source value. The micro-agricultural irrigation water prices for surface water and groundwater range from 0.993 to 1.008 and from 2.343 to 2.358 yuan/m3, respectively, indicating differences in cost recovery and value. The surface water prices for maize, rice, and soybeans ranged from 0.286 to 0.476, from 0.101 to 0.179, and from 0.180 to 0.307 yuan/m3, respectively, while the appropriate groundwater prices ranged from 0.317 to 0.507, from 0.131 to 0.210, and from 0.211 to 0.337 yuan/m3, respectively. The government could formulate different subsidy policies according to the actual situation and raise agricultural irrigation water prices to the full-cost level to enable the implementation of law of value. [ABSTRACT FROM AUTHOR]

Heilongjiang Province is under the pressure of a water shortage due to climate change, population growth and economic development. To effectively manage regional land and water resources, this paper describes a system dynamics model that was built to simulate the interaction between land and water resources and socioeconomic factors, as well as the evolution of regional land and water resources in different climates in Heilongjiang Province. The results show that the declining trend of unused land area and the water supply–demand ratio will not stop, even under the most optimistic (e.g., humid climate) climate conditions, if the current land use patterns continue. Therefore, measures should be taken to manage the unreasonable usage patterns of land and water resources in this region. This study simulated the evolution of regional land and water resources for five scenarios under an arid climate by changing the net irrigation quota for paddy fields, the water quota for industrial use, forestland area, annual change rate of farmland area, and the growth rate of the gross industrial output value. Further, a combined scenario that can maximally reduce the regional land and water resource sustainable risk was identified. The simulation of the combined scenario showed that it can effectively increase the degree of regional land and water resource use in the region, as well as reduce the risks that threaten these resources. This study provides theoretical support for the efficient use of land and water resources in the future. [ABSTRACT FROM AUTHOR]

To determine the influence of agricultural meteorological disasters on agriculture in Heilongjiang Province, the disaster areas associated with different types of disasters and their variation characteristics were analyzed based on the statistical data of agricultural disasters from 1983 to 2013 in Heilongjiang Province, China. The moving average and the Mann-Kendall test were applied to identify the variation trends of drought, flooding, hailstorms and freezing (based on the disaster ratio and the disaster intensity index). Then, the Morlet wavelet analysis method was used to identify the periodicity of these four kinds of agricultural meteorological disasters. Finally, a fuzzy comprehensive evaluation method was adopted to analyze the degrees of agricultural loss induced by these disasters. The following results were obtained: 1) The disaster ratio and disaster intensity index for drought exhibited increasing trends; the disaster ratio and disaster intensity index for flooding exhibited decreasing trends; for hailstorms, the disaster ratio exhibited no obvious trend of change, whereas the disaster intensity index exhibited an increasing trend; and for freezing, the disaster ratio also exhibited no obvious trend of change, whereas the disaster intensity index exhibited a decreasing trend. 2) Mutation points were observed in the disaster ratio series for drought, flooding and hailstorms, whereas no mutation point was evident in the disaster ratio series for freezing. 3) Multiple time-scale characteristics were observed in the disaster ratio series for all four types of agricultural meteorological disasters. Furthermore, the disaster ratio series for the different types of disasters had different main periodicities. 4) From the perspective of the degree of agricultural loss induced by each type of disaster, drought was identified as the most severe type of agricultural meteorological disaster, followed by flooding, freezing, and hailstorms. The degree of agricultural loss caused by each type of disaster was different during different periods. Finally, based on the results, several strategies were identified for mitigating the effect of agricultural meteorological disasters in Heilongjiang Province. [ABSTRACT FROM AUTHOR]

Plantation, forestry, and livestock are inseparably intertwined and constitute the main sectors of the agricultural system. An integrated analysis of energy flow and material conversion processes of the agro-forestry-livestock system is beneficial for achieving sustainable and coordinated agricultural development. The availability of water resources is sensitive to climatic conditions and human activities, and uncoordinated water allocation affects the combined efficiency of various agricultural sectors. Therefore, an integrated modeling framework, based on the water-energy-food nexus under water supply uncertainty, is developed to maximize the performance of agro-forestry-livestock system. The advantages of the model include (1) systematic analysis of energy flow and material conversion processes in farmland, forest land and livestock; (2) consideration of trade-offs between economic benefits, multiple energy use efficiency (biomass, light energy), environmental impacts, and ecological benefits in the system; and (3) rational allocation of farmland, forest land, and livestock resources under the uncertainty of water supply. Empirical frequency analysis was used to obtain different frequencies of water supply. The model framework is based on a multi-objective programming model, which is solved by an affiliation function approach. The model constructs several objective functions, such as economic benefits, bioenergy productivity, light energy use efficiency, ecological economic benefits, and greenhouse gas emissions of the agro-forestry-livestock system. The concordance index was applied to evaluate the integrated performance of the model. The method was applied to Heilongjiang Province in northeastern China, where plantation, forestry, and livestock are predominant. Results show that livestock manure and urine have the highest energy potential, followed by wood, and straw has the lowest energy production. The main contribution to greenhouse gas emissions from the plantation is N 2 O, and the main contribution from livestock is CH 4. The model had a higher concordance index for the larger water supply, but lower crop yield and photosynthetically active radiation. The proposed modeling framework contributes to the generation of stable and coordinated optimization solutions and provides decision makers methods and perspectives for sustainable agriculture. • Intrinsic relationships of water-food-energy nexus in plantation, forestry, and livestock. • An integrated resource management approach under water supply uncertainty. • Synergistically optimization of economic, energy, environmental and ecological. • Quantification and trade-off for the usage of light and biomass. • Uncertain water supply impacts the productivity of agro-forestry-livestock system. [ABSTRACT FROM AUTHOR]

• Constructing a three-layer large system for irrigation water allocation. • Balancing water supply and demand between farmland, irrigation areas and regions. • Farmer income, allocation equity, engineering and regional economy coordination. • Optimal water allocations for crops, canals and large irrigation areas. • Fluctuations in water supply affect water distribution of different scales. Reasonable and optimal allocation of irrigation water is of great significance to improve agricultural water use efficiency for large-area agricultural irrigation systems. To solve the problems of large agricultural production scale, unreasonable water distribution structure, many rivers in the region and large changes in runoff volume, this paper takes the Sanjiang Plain in Heilongjiang Province, China, as the research object to develop an efficient and accurate water optimal allocation method. First, the runoff volume of many rivers in the region was obtained through the ARIMA-SVM-BP combined runoff prediction model, based on which a 'regional-irrigation district-farmland' multilayer multiobjective water resource cooperative deployment system model was established to achieve the synergistic enhancement of farmers' interests and the economic-social and regional economic-engineering volume of the irrigation district. The joint optimization method, fuzzy mathematical planning, and large system decomposition-coordination coupling were used to solve the multilayer multiobjective regulation system. The results show that the optimal agricultural water allocation volumes for the current year (2020) and the forecast year (2025) of 37 irrigation districts in the Sanjiang Plain are 3.61 × 109 m3 and 3.89 × 109 m3, saving 17% of water and resulting in a 24% decrease in the agricultural water use ratio compared with the actual situation, respectively. In the process of repeated coordination of the large system, the water distribution error is controlled within 1%. The fairness of water distribution in the multilayer system was improved by 34.7% while obtaining high economic benefits. The method proposed in this study helps the coordinated regulation and planning of multiple water sources in large agricultural irrigation areas, improves the ability of water distribution in irrigation areas to cope with different levels of incoming water, and promotes the sustainable development of regional irrigated agriculture. [ABSTRACT FROM AUTHOR]

• A model-based analysis framework is developed for economic-environmental nexus in grain trade. • Virtual water, footprint, LMDI and Tapio decoupling models are coupled. • Effects of grain trade on agricultural sustainability and driven factors are assessed and captured. • A real case study provides decoupling relationship, plantation tendency and control measures for sustainable agriculture. With the growth in the agricultural economy, the problem of agricultural sustainability has become increasingly prominent. This study proposes a decoupling analysis framework for economic development and multidimensional sustainability indicators in grain exporting by integrating the Tapio decoupling model with virtual water (VW) theory, the Logarithmic Mean Divisia Index (LMDI) model and the footprint method. This framework is advantageous for coordinating research on the problems of virtual water flow (VWF) and ecological consumption for different grains, decoupling economic development and multidimensional sustainability indicators, and identifying key drivers that promote decoupling and curb VW consumption. This framework is applied to a real-world case study in Heilongjiang Province, China. The results show that (1) the final water consumption coefficient of agriculture is the major positive influencing factor of VWF in grain exporting; (2) increasing the export volume of rice and corn instead of soybeans is beneficial for improving agricultural sustainability; and (3) promoting the coordination of multidimensional sustainability indicators effectively improves decoupling levels and relies on the contradiction between agricultural economic growth and environmental sustainability relief in the grain trade rather than on a single index.. [ABSTRACT FROM AUTHOR]

The evaluation of the coordination of regional water and soil resources and ecological environment systems from the perspective of speed characteristics is more conducive to decision-makers to understand the development status of water and soil resources and ecosystems in a timely manner. In this paper, taking Heilongjiang Province, the main grain producing area of China, as an example, 25 main indices are screened based on agricultural production factors and the weight of each index is determined by the information entropy, the speed characteristics of each index is characterized by the average annual growth rate. The coupling-coordination index of system development is determined by the vector angle and projection, and compare and analyze the coupling-coordination index between the result and the original data. The results show that the water and soil resources ecological environment system was in a relatively weak coupling stage of barely coordination, the coordinated development level of the system is low during the study period. In comparison with the original data results, the coupling and coordination of the system are better reflected by the calculation method using speed characteristics, and the calculation results of speed characteristics are ahead of time, which is more conducive to the timely assessment of the development of the water and soil resources ecological-environment system. • To conduct an evaluation of the coordination of environment from the perspective of development rate characteristics. • To use annual growth rate to characterize the development rate characteristics of various indicators. • To use vector included angle and projection to determine the coupling and coordination indices of the system's development. [ABSTRACT FROM AUTHOR]

• The research background is China's agricultural water price reform policy. • The economic means including agricultural water price reform and water price affordability constraint are used. • We put forward the water-saving technology subsidy. To improve the performance of agricultural water resources and food production against a background of China's agricultural water price reform, an interval two-stage stochastic programming model is established and applied to account for impacts of agricultural water price reform and a water-saving technology subsidy in Heilongjiang Province, China. A water price affordability constraint and a water-saving technology subsidy to support uptake of water-saving irrigation technology are both introduced. Results of model runs show that when the current agricultural water price is increased to reflect national water price reform without a technology subsidy, the total income of irrigation farming is reduced by 2%-8%, with potential negative impacts on food production. However when an irrigation water-saving technology subsidy is introduced to accompany the increased water price, farm income can be protected and overall food production can be maintained. The two part mechanism of increased water price and water-saving technology subsidy can be used to encourage farmer to reduce water application rates as well as reduce land in production of high-water-consuming crops. Such a two part program offers considerable hope for protecting unsustainable use of water in water scarce regions while protecting farm income and food security in the face of unknown future water supply fluctuations. [ABSTRACT FROM AUTHOR]

In the past decade, the application of biochar in agricultural soils has attracted wide attention. However, few studies have carefully explored the effects of biochar modification on plant leaf nutrients and the physiological process of plant leaves. To provide a better growing environment for crops and explore the best regulation mode of biochar in the farmland soil environment in the typical black loam area of Heilongjiang Province, through field experiments, we selected soybeans as the test crop and applied biochar in the soil. The agronomic characteristics and soil conditions of soybean plants were monitored by stage. The effects of different application methods and biochar gradients on the water and heat changes in soil tillage layers during different growth stages of crops were discussed, and the subtle differences of agronomic characteristics in different growth stages of crops were compared. The results showed that all kinds of biochar application modes could not change the general trend of water and heat change in soil tillage layer affected by environmental factors, and the effect of biochar application on soil liquid moisture content at 20 cm soil layer was not obvious. Biochar application can increase plant height and reduce stem diameter, but the effect is non-linear. The leaf nitrogen content (Leaf N-content) and leaf chlorophyll relative content (SPAD) were vertically distributed in the canopy, but they did not change significantly with the change of biochar application rate and mode. The application of biochar in autumn may bring crops into maturity earlier. Under the biochar application rate of 9 kg m−2, the mixed application in spring and autumn can bring the best biochar application effect. • Biochar content and freeze-thaw contribute to soil moisture changes in the field. • Biochar retains water only when the soil reaches a certain moisture condition. • Biochar application has no effect on plant nitrogen content. • The application period affects the water retention capacity of biochar. • Biochar application at a 9 kg. m−2 rate is the most beneficial to soybean growth. [ABSTRACT FROM AUTHOR]

Whether there is an obvious "hiatus" of warming at the global or regional scale is an outstanding issue, which has attracted worldwide attention. To investigate the response of temperature variation to global warming in Northeast China, it is necessary to reanalyze whether the regional warming trend has slowed down. Heilongjiang Province, located in the cold region in Northeast China, was selected as the study area. Based on the meteorological data recorded at 26 meteorological stations from 1960 to 2015, the spatiotemporal characteristics of temperature in this region were analyzed using the Mann-Kendall trend analysis, Sen's slope method and inverse distance weighting (IDW) method, and then compared with the temperature variations of the globe and mainland China. Furthermore, the cross wavelet method was applied to explore the teleconnection between temperature and large-scale climate patterns to investigate possible causes of temperature variations of this region. The results demonstrated that: 1) the spatial distribution of temperature showed latitudinal variations, and annual and seasonal temperatures showed warming trends, among which the warming trend in autumn was the most significant and the variation range in winter was the greatest. 2) In term of net temperature, global warming might have greater impacts over the central, western and northwestern parts of Heilongjiang Province. 3) Compared to the temperature variations of the globe and mainland China, hiatus phenomenon was detected in Northeast China, for there was an obvious cooling trend in spring. 4) Arctic oscillation (AO) was a key factor affecting the temperature change in this cold region and Pacific decadal oscillation (PDO) could be another closely-related climatic factor contributed to this hiatus period. • Under the influence of global warming, the minimum temperature in the cold regions is more likely to be affected. • The hiatus phenomenon was more evident in Northeast China. • The cooling trend in Northeast China since 2000 was closely related to large-scale climate patterns. [ABSTRACT FROM AUTHOR]

• An evaluation system was constructed more in line with the water resource carrying capacity in the eastern region of the Heilongjiang Province. • The improved TOPSIS model adopts a new method of assigning weights. • It avoids the deviation caused by subjective factors in the traditional analytic hierarchy process. • It avoids the emergence of extreme weights using the anti-entropy weighting method. Assessments and analyses of the water resource carrying capacity provide practical guidance for regional water resource planning and water resource management. The water resource carrying capacity index system and the corresponding level criteria are constructed from the perspective of the carrying capacity subsystem. Additionally, a water resource carrying capacity model is constructed using the improved TOPSIS method. Finally, a comprehensive evaluation and analysis of the water resource carrying capacity in the study area from 2009 to 2018 is conducted, with the eastern part of Heilongjiang as a case study. The results show that the combination of the improved AHP and the anti-entropy weighting method can avoid the bias caused by subjective factors in the traditional AHP and the bias caused by the randomness of data in the entropy weighting method, yielding accurate estimates of the water resource carrying capacity. A new method of assigning weights in TOPSIS models is developed. In addition, the water resource carrying capacity of the studied cities is generally in state III (Moderate) in the eastern region of Heilongjiang, and the multiyear average value obtained from the comprehensive evaluation is 0.4581. The Qitaihe and Hegang areas have higher water resource carrying capacities than other areas. Overall, water resource and societal factors have an obvious influence on the water resource carrying capacity in the eastern region of Heilongjiang, and these factors vary in different areas. The water supply modulus and proportion of water used for agricultural irrigation have the greatest effects on the water resource carrying capacity in the study area. [ABSTRACT FROM AUTHOR]

With the development of the social economy, a reasonable evaluation of the sustainable utilization capacity of water resources (WR) is of great significance to alleviate the pressures of water shortages and pollution faced by cities. This study first proposed a water footprint sustainability (WFS) system using system dynamics to evaluate the influence of WR on the environment, economy, and society of Heilongjiang province. The WFS assessment used a comprehensive evaluation method, and the coupling coordination degree model was applied to analyze the coupling coordination of the WFS system. The results showed a fluctuating increase tendency in the total water footprint (WF), blue WF, green WF, agricultural WF, and ecological WF from 2000 to 2019. However, the gray WF, domestic WF, and industrial WF presented a fluctuating decrease. The prediction for the next 30 years indicated that the green WF, agricultural WF, and domestic WF would be stable while the other indicators would increase with time. The blue water shortage degree was greater than 1; however, the green water scarcity index and water pollution degree were less than 1. The economic sustainability of the total WF, blue WF, and gray WF increased year by year; however, the social sustainability of the total WF gradually weakened. In addition, the water supply and demand ratio was below 1. Overall, the regional WFS worsened, and the coupling degree and coupling coordination degree of the WFS system varied in the range of 0.85–0.91 and 0.28–0.31, respectively. This study was of great significance in realizing the sustainable management of regional WR. • A water footprint sustainability (WFS) system using system dynamics was proposed. • Blue water shortage degree was greater than 1. • Green water scarcity index and water pollution degree were less than 1. • Water supply and demand ratio was below 1. • Coupling degree and coupling coordination degree of the WFS system vary in the range of 0.85–0.91 and 0.28–0.31, respectively. [ABSTRACT FROM AUTHOR]

Resilience is a significant attribute used to measure the sustainable development of the environment, and research on optimal measurement models is very important. This study took 15 farms in the Jiansanjiang Administration of Heilongjiang Province in China as the research object and constructed a resilience evaluation indicator system containing 31 indicators for the regional agricultural soil-water resource composite system (ASWRS). The combined weight (BFCM-CRITIC) and entropy weight (EW) combined with the variable fuzzy assessment (VFA) model and the improved technique for order preference by similarity to an ideal solution (TOPSIS) model were used to calculate the resilience exponent and to analyse the characteristics of space-time variation. The stability and reliability of the two models under different weights were verified by the Spearman correlation coefficient and discrimination theory to determine the optimal resilience exponent diagnosis method. The results show that according to the BFCM-CRITIC-VFA model, the levels of resilience were the highest at the Nongjiang, Hongwei and Erdaohe farms, and the resilience levels were strong and scattered. The resilience of the Jiansanjiang Administration has been increasing over time, and the spatial distribution has generally decreased from north to south and from the Heilong River and Wusuli River basins inland. A comparison of the reliability and stability of the two models for different weights indicates that the VFA model optimized based on combined weights was superior to the other methods in terms of stability and reliability, which verifies that the BFCM-CRITIC-VFA model is the most suitable for measuring the resilience exponent. • The resilience of the ASWRS in the main grain-producing areas in China is evaluated. • The optimal measurement model for evaluating the resilience is determined. • A diagnostic model of resilience based on multimethod optimization is established. • The temporal and spatial variation characteristics of resilience are analysed. • The results provide scientific and technological support for the ASWRS optimization. [ABSTRACT FROM AUTHOR]

• Research on Flood disaster resilience based on Bayesian belief network. • Using fuzzy mathematical theory to make decision from the manager's position. • Realizing the prediction of resilience level with missing indicators. • Using diagnostic and sensitivity analysis to explore the driving mechanism. Flood disaster resilience management and decision-making are comprehensive prevention and control strategies that integrate pre-disaster prevention, in-disaster scheduling and post-disaster reconstruction. In view of the many deficiencies in current flood disaster resilience research in terms of uncertainty measurement, dynamic regulation and management decision-making, a new method for the analysis of the driving mechanism of flood disaster resilience and management decision-making based on the fuzzy mathematical theory improved Bayesian belief network (BBN) is proposed. A case study was conducted in 15 state-owned farms under the jurisdiction of the Jiansanjiang Branch in Heilongjiang Province, China. The model predicts the resilience level with missing indicators. A diagnostic analysis of the correlation between indicators and resilience improvement and a sensitivity analysis of the dynamic evaluation of the importance of indicators are performed, and fuzzy decisions are made to quantify different decision-making schemes. The results show that when a single indicator is missing, the prediction accuracy exceeds 80%. However, the lack of economic indicators has a substantial impact on the results, and the accuracy is reduced to 63.3%, indicating that economic indicators are the key dimension affecting the evaluation of flood disaster resilience levels in the study area, which also provides a focus for previous data collection efforts. The diagnostic analysis constructs an index sequence to guide the improvement in resilience, in which the water surface rate and rainfall are the most direct driving forces affecting resilience. On this basis, the differences in the driving forces of each farm are further analyzed in detail. Sensitivity analysis shows that rainfall, per capita GDP, and the energy consumption of agricultural machinery are the indicators that occur most frequently among the top three sensitivity indicators of all farms. The 859 farm, Chuangye farm and Nongjiang farm are typical farms with a high sensitivity to rainfall and economic indicators. Fuzzy decision-making takes the comprehensive grade score of implementation motivation as a quantitative evaluation criterion, which provides guidance for decision-making based on the two dimensions of time and space. [ABSTRACT FROM AUTHOR]

Climate warming causes significant changes in regions with seasonally frozen ground and permafrost, and these changes have a profound impact on the balance of water resources, ecological security and engineering structures in cold regions. Based on daily soil freezing depth and temperature data from 31 meteorological stations in Heilongjiang Province, combined with 11 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), the detailed spatiotemporal evolution of the maximum freezing depth of seasonally frozen ground (SFD) and permafrost continuity in Heilongjiang Province during 1960–2014 and 2015–2100 were analyzed. The results show that (1) average maximum SFD in the early 21st century is approximately 30.78 cm lower than that in the 1960s, and the maximum reduction occurs in the high elevation central part of the study area; under the four scenarios, SSP126, SSP245, SSP370 and SSP585, the SFD will decrease by 7.68, 19.44, 38.34 and 43.06 cm, respectively, by 2100. (2) Permafrost regions with continuities of 70%–80% (P 70), 30%–70% (P 30) and 5%–30% (P 5) and seasonally frozen ground areas in the 1960s accounted for 8.43%, 7.44%, 21.89% and 62.24% of the total study area, respectively. In the early 21st century, they accounted for 3.08%, 11.39%, 17.50% and 68.04%, respectively. The permafrost region will decrease by 47.36% and 61.34% by 2100 under SSP126 and SSP245, respectively, and will disappear under SSP370 and SSP585. (3) The abrupt change in seasonally frozen ground is basically consistent with the change in temperature, but the abrupt change in permafrost exhibits a lag relative to the change in temperature. The maximum SFD and permafrost region in the study area will decrease by 21.62 cm and 45.98%, respectively, in response to a temperature increase of 1 °C. • In the early 21st century, average SFD decreased by 31 cm compared with the 1960s. • SFD will decrease by 7.68, 19.44, 38.34 and 43.06 cm under different scenarios. • In the past, P 70 and P 5 area decreased and P 30 and SFG area increased. • Permafrost areas disappear under the SSP370 and SSP585 scenarios. • Permafrost change has "hysteresis" and "nonlinearity" for temperature change. [ABSTRACT FROM AUTHOR]

Sustainability is the core concept of modern agricultural planning. Existing studies lack quantitative research on the dynamic effects of the optimal use of agricultural water and land resources (AWLR) on agricultural sustainability. This paper constructs a simulation-optimization-assessment-based modeling framework for the dynamic correlation between agricultural sustainability and the water-land nexus under uncertainty. The framework includes uncertainty simulation of the water supply and demand, development of an optimization model for synergistic regulation of AWLR, and construction of a two-layer dynamic response relationship function between integrated water and land resource use and agricultural sustainability. This framework provides the following advantages: (1) the synergistic allocation of AWLR can be optimized to achieve synergistic development of the five dimensions of resources, economy, society, ecology and environment; (2) the dynamic probability distribution of AWLR and the corresponding response to each dimensional objective and agricultural sustainability can be generated; and (3) the response of agricultural sustainability and AWLR use to changing environments can be obtained. The framework was quantified and analyzed via stochastic simulation, multi-objective nonlinear optimization and fuzzy C-means clustering and applied to the Heilongjiang area of the Songhua River Basin in China. The results indicate that when the irrigation water volume is reduced by 10% and the planted area is increased by 5% in the study area, the economic efficiency increases by 10.49%, the social and ecological effects increase by 10%, and the agricultural sustainability increases by 10.07%. The initial surface water and groundwater irrigation amounts should be regulated within the ranges of [600,800] 107 m3 and [400,600] 107 m3, respectively, and the total amount should not exceed 1150,107 m3. The proposed framework can help reveal the intrinsic linkages between agricultural sustainability and AWLR allocation considering different environmental change scenarios, which can provide authorities with AWLR management decisions in response to changing environments. [Display omitted] • A simulation-optimization-evaluation modeling framework for AWLR is developed. • Probability distributions of AWLR are optimized under changing environment. • Balance conflicts between resource, economy, society, ecology, and environment. • Bilayer dynamic correlations for sustainable use of AWLR are built. • The performance of the framework is evaluated. [ABSTRACT FROM AUTHOR]

To promote the efficient use of water resources and the sustainable development of the agricultural economy, the main evaluation index that links economic production and the effective use of water resources, irrigation water use efficiency (IWUE), is scientifically evaluated and analyzed. To obtain accurate evaluation results, an evaluation model for IWUE based on an extreme learning machine (ELM) model optimized by the spider monkey optimization (SMO) algorithm (i.e., the SMO-ELM model) was proposed. The model was applied to the evaluation and analysis of IWUE in 24 large and medium irrigation districts in Heilongjiang Province. On this basis, the main driving factors of IWUE differentiation in the irrigation districts were identified, and adaptive control strategies were explored. In addition, to thoroughly test the performance of the constructed SMO-ELM evaluation model, a comparative analysis was performed for a BP model, an ELM model, an ELM model with crow search algorithm optimization (CSA-ELM) and the SMO-ELM model. The results show that there are regional differences in the IWUE of the irrigation districts and that the IWUE of the districts in the eastern part of the study area is better than that of the districts in the central and western parts. The irrigation districts with relatively high evaluation grades (IV to V) are distributed mostly in the eastern part of the study area, and most of the irrigation districts with low evaluation grades (I to II) are located near the main branch of the Songhuajiang River. Differences in indexes, such as the soak field quota and proportion of rice sowing area, are the most important factors related to variations in IWUE grades among the irrigation districts, and targeted control strategies such as planting structure optimization are proposed. The rationality and superiority of the SMO-ELM model are verified by comparing the coefficient of determination (R 2), mean square error (MSE), mean absolute percentage error (MAPE), and accuracy of classification (accuracy) of the BP, ELM, CSA-ELM and SMO-ELM models. The R 2, MSE , MAPE , and accuracy of the SMO-ELM model are 0.9999, 4.33 × 10−10, 0.00039, and 99.8%, respectively, which are much better than those of the other models. The results of complexity analysis verify the stability of the IWUE evaluation results for the irrigation districts. This article provides a new model for the evaluation of IWUE and scientific and technological support for ensuring the safety of water resources and maintaining the sustainable green development of agriculture. • Propose an improved irrigation water use efficiency evaluation model (SMO-ELM). • The irrigation water use efficiency was better in the eastern part of the study area than in the western or central parts. • Differences in canal water utilization coefficient and proportion of rice sowing area caused differences in IWUE. • SMO-ELM model has superior generalization performance and excellent stability. [ABSTRACT FROM AUTHOR]

• Multiscale permutation entropy is used for regional precipitation complexity. • Calculation method of multiscale permutation entropy eigenvalue is unique and simple. • Multiscale permutation entropy eigenvalue was improved by CMFO-PPTTE-MSPE. • Compare and verify the performance and advantages of CMFO-PPTTE-MSPE algorithm. The influence of entropy values obtained under different multiscale permutation entropy scales on the complexity and unconstrained extreme value problem of traditional bionics algorithms was assessed. The multiscale permutation entropy (MSPE) eigenvalue calculation method based on the chaotic moth-flame optimization-based projection pursuit threat target evaluation (CMFO-PPTTE) model is proposed (CMFO-PPTTE-MSPE). The CMFO-PPTTE-MSPE not only solves the problems of rough calculation and information loss in traditional eigenvalue calculation but also improves the shortcomings of slow convergence and local optimality in the intelligent bionic algorithm. For this purpose, the monthly precipitation of 13 administrative regions in Heilongjiang Province under the Global Precipitation Climatology Centre dataset (GPCC) from 1967 to 2017 was evaluated to improve the precipitation complexity entropy accuracy. The main influencing factors were altitude (p < 0.05), water area (p < 0.05), urban construction area (p < 0.05) and forestland area (p < 0.01). Radial basis function (RBF) neural networks were used to forecast the precipitation in Heilongjiang Province over 36 months, and a better forecast was obtained. To verify the rationality of CMFO-PPTTE-MSPE under GPCC data, compared with the situ data, it is found that GPCC data can more accurately classify the complexity grade of Daxing'anling region. At the same time, the administrative discrimination of GPCC data (1.107) is significantly higher than the situ data (1.023). To verify the rationality of the CMFO-PPTTE-MSPE, the partial mean of MSPE (MSPE-PM), whale optimization algorithm PPTTE (WOA-PPTTE) and ω-particle swarm optimization PPTTE (ω-PSO-PPTTE) models were also used to calculate the MSPE eigenvalues under GPCC data. Comparisons and evaluations were performed after dividing the grade based on geographical discrimination (CMFO-PPTTE-MSPE (1.107) > WOA-PPTTE-MSPE (1.094) > W-PSO-PPTTE-MSPE (1.090) > MSPE-PM (1.063)) and administrative discrimination (CMFO-PPTTE-MSPE (1.146) > W-PSO-PPTTE-MSPE (1.002) > MSPE-PM (1.012) > WOA-PPTTE-MSPE (1.002)). The CMFO-PPTTE-MSPE had a significantly higher distinguishing capabilities than the other algorithms and thus could better distinguish the complexity of precipitation in different regions and geographic locations. In summary, the CMFO-PPTTE-MSPE is beneficial for analyzing precipitation complexity and represents a novel approach for mining the fine structural features of regional hydrological series. However, determining how to apply artificial intelligence algorithms to reasonably calibrate key parameters of the MSPE algorithm to further improve the accuracy of complexity diagnosis for hydrological series will be an important avenue of research. [ABSTRACT FROM AUTHOR]