RETRACTION: Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data

Starting from the main eco-environmental problems faced by water environment, taking Yanhe River Basin as an example, this paper discusses the theoretical connotation and evaluation calculation method of eco-environmental water consumption. In order to study the eco-environmental water consumption of Yanhe River Basin, a runoff driving factor mining method based on big data analysis is established in this paper. Aiming at the problem that the statistical law and genetic law of runoff change frequently in changing environment, the mining technology method of runoff key driving factors is proposed by combining traditional methods with big data analysis. The characteristic factors that have no significant impact on runoff change are removed, the implicit characteristic factors affecting runoff change are extracted, the driving relationship of hydrological, meteorological, and vegetation characteristic factors on ecological water consumption change is identified, and the key driving factors of ecological water consumption change are extracted, which lays a data foundation for ecological water consumption prediction based on machine learning. The economic water consumption based on eco-environmental water consumption in Yanhe River Basin in the future is predicted (including water demand in three aspects of industry, agriculture, and life); that is, the prediction is to meet the economic water demand on the basis of ensuring that the water consumption of ecological environment will not be occupied, which can effectively ensure the improvement of ecological environment function in Yanhe River Basin and is conducive to the sustainable utilization of water resources in Yanhe River Basin. The research is only based on a small watershed such as Yanhe River Basin, and the purpose of the research is to provide a reference for ecological environment protection and sustainable utilization of water resources in the Loess Plateau, even in the arid, semiarid, and semihumid areas of North China.

The current landscape indicates that sustainability is gaining traction as one of the core business strategies. The use of data analytics to monitor and improve sustainability measures in organizations has remained one of the most effective approaches. Thus, this study examines the impact of Big Data Analytics (BDA) capabilities on process eco-innovation and sustainability performance across industries. We focus on four core capabilities—information technology, personnel expertise, management, and BDA—and their role in achieving sustainability goals. Our results reveal that predictive analytics can significantly reduce carbon emissions by 15% over five years, with emissions projected to drop from 100 metric tons in 2024 to 65 metric tons by 2030. Additionally, energy consumption accounts for 33% of overall resource usage, followed by carbon emissions (33%), water usage (24%), and waste generation (10%). Comparative metrics indicate a 30-40% reduction in carbon emissions, water consumption, and waste generation after adopting sustainability practices, underscoring the importance of data-driven innovation. Our findings highlight the varying needs across industries: the financial sector demands real-time decision- making, healthcare focuses on cost optimization, and retail prioritizes customer satisfaction and operational efficiency. Furthermore, regulatory compliance and resource heterogeneity shape BDA adoption, influencing organizational performance. This study offers practical insights into how industries can align analytics with eco-innovation, driving sustainable growth and operational excellence. These results emphasize the transformative potential of predictive analytics in enhancing sustainability, making BDA a critical component of future industrial strategies. The current landscape indicates that sustainability is gaining traction as one of the core business strategies. The use of data analytics to monitor and improve sustainability measures in organizations has remained one of the most effective approaches. Thus, this study examines the impact of Big Data Analytics (BDA) capabilities on process eco-innovation and sustainability performance across industries. We focus on four core capabilities—information technology, personnel expertise, management, and BDA—and their role in achieving sustainability goals. Our results reveal that predictive analytics can significantly reduce carbon emissions by 15% over five years, with emissions projected to drop from 100 metric tons in 2024 to 65 metric tons by 2030. Additionally, energy consumption accounts for 33% of overall resource usage, followed by carbon emissions (33%), water usage (24%), and waste generation (10%). Comparative metrics indicate a 30-40% reduction in carbon emissions, water consumption, and waste generation after adopting sustainability practices, underscoring the importance of data-driven innovation. Our findings highlight the varying needs across industries: the financial sector demands real-time decision- making, healthcare focuses on cost optimization, and retail prioritizes customer satisfaction and operational efficiency. Furthermore, regulatory compliance and resource heterogeneity shape BDA adoption, influencing organizational performance. This study offers practical insights into how industries can align analytics with eco-innovation, driving sustainable growth and operational excellence. These results emphasize the transformative potential of predictive analytics in enhancing sustainability, making BDA a critical component of future industrial strategies.

The increasing world population and rapid industrial development is driving the need for sustainable management solutions and preservation of the natural resources and the ecosystem. The Internet of Things (IoT) and big data (BD) analytics are expected to play key role toward sustainability in areas such as water, agriculture, energy, transportation and smart city. In this chapter, the focus is sustainability in water. To address the issues of sustainability of clean accessible water for human use and other living things, a water ecosystem is presented. The water ecosystem consists of five major elements which are water source, treatment, reservoir, consumption and wastage. There are several issues faced in sustainable water supply such as decreasing fresh water resources, loss of revenue due to water loss, complexity in managing the increasing demands. Existing water-management models are not efficient in addressing these issues. In addition, due to seasonal variations, changes in environmental laws, varying plant-operating conditions and other factors, there is a need for effective and efficient monitoring. The application of IoT and BD analytics technology is promising and can provide sustainable management solution in water. The practical deployment of IoT and BD analytics consists of four major components which are IoT devices, communication technology, internet and BD. The emerging low power wide area (LPWA) communication technology is expected to enhance massive connectivity required for water monitoring, data acquisition and promote sustainability in water. Hence, in this chapter, the features of the water ecosystem, the system architecture of IoT and BD and the application in water sustainability, challenges such as cyber security, policy and regulations, accuracy of the data and technology interoperability are discussed.

Prediction model of ecological environmental water demand based on big data analysis

Predicting land change trends and water consumption in typical arid regions using multi-models and multiple perspectives

The challenge of a depleting Mississippi River Valley Alluvial Aquifer (MRVAA) requires reducing groundwater withdrawal for irrigation, increasing aquifer recharge, and protecting water quality for sustainable water use. To meet the challenge, the National Center for Alluvial Aquifer Research (NCAAR) is oriented towards producing scientific work aimed at improving irrigation methods and scheduling, employing alternative water sources, and improving crop management and field practices to increase water use efficiency across the region. Big data is key for NCAAR success. Its scientists use big data for research in the form of various soil, weather, geospatial, and water monitoring and management devices to collect agronomic or hydrogeologic data. They also produce, process, and analyze big data which are converted to scientific publications and farm management recommendations via technology transfer. Similarly, decision tools that would help producers leverage the wealth of data they generate from their operations will also be developed and made available to them. This article outlines some of the many ways big data is intertwined with NCAAR’s mission.

Digitalization and hydroinformatics

The Beijing–Tianjin–Hebei region in China is experiencing a serious ecological water scarcity problem in the context of climate warming and drying. There is an urgent need for practical adaptation measures to cope with the adverse impacts of climate change and provide a scientific basis for urban water supply planning, water resource management, and policy formulation. Urban ecological water can maintain the structure and function of urban ecosystems, both as an environmental element and as a resource. Current research lacks quantitative analysis of the impact of regional meteorological factors on ecological water use at the small and medium scales. Based on the meteorological data and statistical data of water resources in the Beijing–Tianjin–Hebei (BTH) region, this paper analyzed the trend of climate change and established an ecological climatic water model using gray correlation analysis, polynomial simulation, and singular spectrum analysis to predict the ecological water consumption. And, we assessed the climatic sensitivity of ecological water use and estimated the future ecological climatic water use in the BTH region based on four climate scenarios’ data. The results showed that the average multi-year temperature was 13.2 °C with a clear upward trend from 1991 to 2020 in the BTH region. The multi-year average precipitation was 517.1 mm, with a clear shift in the period of abundance and desiccation. Ecological climatic water modeling showed that a 1 °C increase in temperature will increase ecological water use by 0.73 × 108 m3~1.09 × 108 m3 in the BTH region; for a 100 mm increase in precipitation, ecological water use will decrease by 0.49 × 108 m3~0.88 × 108 m3; under the four climate scenarios of SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5, the regional ecological climatic water use will be 5.14 × 108 m3, 6.64 × 108 m3, 7.82 × 108 m3, and 9.06 × 108 m3 in 2035, respectively; and in 2050, the ecological climatic water use will be 8.16 × 108 m3, 9.75 × 108 m3, 10.71 × 108 m3, and 12.41 × 108 m3, respectively. The methodology and results of this study will support the quantification of climate change impacts on ecological water use in the BTH region and serve as a theoretical basis for future research on ecological water use adaptation to climate change. This study can provide a basis for the development of the overall planning of urban ecological water supply, and at the same time, it can lay a foundation for the study of measures to adapt to climate change by ecological water use.

The state and development of vegetation cover is an important criterion for the improvement of the ecological environment of arid regions; therefore, the study of the ecological water use of vegetation has become a pressing problem in ecology and hydrology. This study covered eight counties in the central and southern parts of Ningxia Hui Autonomous Region, which are located from north to south in an arid region in northwestern China. The purpose of the study was to assess the potential evaporation and environmental water consumption of local vegetation based on meteorological data, vegetation-distribution data, data on the state of water resources, etc. This study can help us to understand and assimilate patterns in the spatiotemporal distribution of ecological water consumption and can provide a basis for the planning and cultivation of forest–meadow vegetation in a region. First, the models of Thornthwaite and Penman-Monteith were used to calculate ecological water consumption. Comparison of the calculation results showed that the data obtained from the Penman-Monteith model were more acceptable, since the model uses a number of meteorological variables and geographic location factors. At the same time, the Jensen formula and the regional soil characteristics curve were used in the calculation of the ecological water consumption to determine the factor for soil moisture correction. Second, the potential evaporation and ecological water consumption of local vegetation was estimated month by month based on precipitation. The spatial and temporal variability of potential values was analyzed on this basis. The results showed that th epotential evaporation tends to increase from month to month from January to July and decrease from August to December. Regarding the spatial distribution, the potential evaporation gradually increases from south to north. The spatial variability of the balance between precipitation and the ecological water consumption of vegetation was analyzed; the results showed that the ecological reserves of water in the central region are more substantial than in the southern region, and the largest reserves of water were found in Yanchi, the northernmost district of the central arid region. Conversely, the ecological water consumption in forests was excessive throughout the growing season in the southernmost district of Jingyuan. In addition, the spatiotemporal variability of the relationship between precipitation-dependent ecological water consumption and water resources is discussed. The results showed that there is still enough space for the regional distribution of vegetation in Yanchi, Tongxin, and Haiyuan in the central arid region and relatively dry districts, such as Yuanzhou, Siji and Pengyang in the southern highlands. More land for an increase in vegetation was observed in the Longde districts and Jingyuan, located in the south of a highland where there is a relatively high amount of precipitation.

At present, big data is becoming the focus of all walks of life, and is even considered as an information revolution that will change the future. The Smart River Basin is a data-centric complex information system established in order to solve the practical problems of the river basin, and it is the higher stage of the Digital River Basin which is bound to be affected by Big Data. This paper analyzes the challenges brought by the Big Data to the information integration, data analysis, data security and energy consumption of the Smart River Basin construction according to the 3V characteristics of Big Data, and sketches the opportunities for the river basin management practice in the hope that it can provide certain reference for the construction of China Smart River Basin.

Water agencies, governmental organizations, and non-governmental organizations accountable for water use, development, and conservation are dealing with ways to address changes in water data collection, maintenance, storage, visualization, and communication. As demand for water resources and variability of water availability increases, water data are essential to monitoring changes and finding solutions. Coupled with other data efforts to enhance “big data” and serve critical environmental issues, water data reveal the complex data-scape that demands streamlined data standards across scientific communities where data processing systems are fragmented due to multiple sources and methodologies, limited data sharing, and incomplete data coverage. With a better understanding of some of the discrepancies in the science, practice, and policy of water data systems, we need to consider and implement innovative ways to foster stronger water data sharing arrangements. This special issue on Water Data explores the science, practice, and policy of water data systems, provides examples in which data integration has been successful or ineffective, and explores the technological frontier of water data systems.

Editorial and News

The story of the Twenty-four Solar Terms (24-STs) is one of the most popular elements in Chinese culture, which has a profound influence on agriculture production, health care, and even daily life in both ancient and modern China. This traditional calendric system was invented by the Chinese ancestors through combining fundamental astronomical knowledge with climatic and phenological conditions in the Yellow River Basin some 2000 years ago. Although the basic philosophy of the 24-STs remains valid for the country as a whole to date, their regional robustness has been increasingly challenged by accumulating observational data in terms of temporal shift and spatial inhomogeneity. To tackle these issues, we propose to recalibrate the medically related critical timings of Great Heat and Great Cold in the classic ST system by using big meteorological data, and adjust them by introducing geographically correlated analytical models. As a result, a novel calendric system, called the Twenty-four Medical Terms (24-MTs), has been developed as an upgraded version of the traditional 24-STs. The proposed 24-MTs are characterized by two striking features with respect to the 24-STs: A varying duration of each MT instead of a fixed one for the ST, and a geographically dependent timing for each MT instead of a unified one for the entire nation. As such, the updated 24-MTs are expected to provide a more realistic estimate of these critical timings around the year, and hence, a more precise guidance to agronomic planning and health care activity in China.

The hypsometric integral (HI) is a terrain analysis factor that reflects the landform erosion stage. As a macroscopic parameter, application of HI could reveal the quantitative characteristics of landform evolution at the catchment scale. A 1-arc-second resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer global digital elevation model was used as a basic information source to investigate the spatial variation of HI and the implications for local base levels in the Yanhe River Basin in the Loess Plateau of China. The hypsometric curve was found to be S-shaped, which implies that the Yanhe River is in the mature stage of landscape development. The mean HI value was 0.48, with a maximum of 0.61 and a minimum of 0.14, which indicated that the Yanhe River was in the sub-mature stage. The HI result was compared with longitudinal profiles of the Yanhe River and three local base levels were identified at elevation zones of approximately 1020, 880, and 780 m. The Yanhe River can therefore be defined as being in the sub-mature stage accompanied by intense erosion. This also reflects the erosional signature of the watershed based on the geological stage of development.

Quantifying the impact of climate change and human activities on hydrological processes is of great importance for regional water-resource management. In this study, trend analysis and analysis of the short-term variations in annual streamflow and sediment load in the Yanhe River Basin (YRB) during the period 1972–2011 were conducted using linear regression and the Pettitt test. The Soil and Water Assessment Tool (SWAT) was employed to simulate the hydrological processes. The results show that both annual mean streamflow and annual mean sediment load in the YRB significantly decreased (P < 0.05) during the study period. The relative contributions from climate change and human activities to YRB streamflow decline between 1996 and 2011 were estimated to be 55.8 and 44.2%, respectively. In contrast to the results for streamflow, the dominant cause of YRB sediment-load decline was human activity (which explained 64% of the decrease), rather than climate change. The study also demonstrates that topographical characteristics (watershed subdivision threshold value, digital elevation model spatial resolution) can cause uncertainties in the simulated streamflow and sediment load. The results presented in this paper will increase understanding of the mechanisms of soil loss and will enable more efficient management of water resources in the YRB.