Volume 22,Issue 5,2024 Table of Contents
2024, 22(5):833-844.
Abstract:
Under the background of globalization and urbanization, urban agglomeration has become one of the important engines to promote economic growth and social development. As one of China's important urban agglomerations and a spatial carrier for promoting ecological civilization construction, the Yellow River basin plays a prominent role in China's overall economic development, social stability and opening-up. However, compared with other regions, the resources, environment and economic and social development conditions of the urban agglomeration along the Yellow River are more unbalanced, and the spatial layout of urban construction is unreasonable, which leads to the higher vulnerability of the urban agglomeration and the lower comprehensive carrying capacity.
Under the background of globalization and urbanization, urban agglomeration has become one of the important engines to promote economic growth and social development. As one of China's important urban agglomerations and a spatial carrier for promoting ecological civilization construction, the Yellow River basin plays a prominent role in China's overall economic development, social stability and opening-up. However, compared with other regions, the resources, environment and economic and social development conditions of the urban agglomeration along the Yellow River are more unbalanced, and the spatial layout of urban construction is unreasonable, which leads to the higher vulnerability of the urban agglomeration and the lower comprehensive carrying capacity.
2024, 22(5):845-855.
Abstract:
The source area of the Yellow River is an important component of the “Chinese Water Tower”, which has the functions of water conservation and recharge, regulating the local regional climate, and plays a positive role in maintaining the water resources and ecological environment of the Yellow River basin. Meanwhile, as a "sensitive area" of climate change, the hydrological situation of the Yellow River source area on the Tibetan Plateau has attracted much attention. Currently, most of the studies on the response of the Yellow River source area to climate change are based on large-scale watersheds and single meteorological and hydrological elements, and there are relatively limited studies on the mechanisms between hydrological elements in typical watersheds in the source area, while the changes in hydrological processes at the sub-basin scale are crucial for future water resources planning and management. Based on the meteorological and hydrological data collected and collated from 1981 to 2020 in the Bai River basin of the Yellow River headwaters area, the hydrological sequence analysis method was used to analyze the interannual change process of hydrological elements; the parameters of the RCCC-WBM model were rate-set, and the runoff of the Bai River basin was simulated in phases for the years from 1981 to 2000 and from 2001 to 2020, respectively. The parameters of the RCCC-WBM model were calibrated to simulate the changes of runoff, actual evapotranspiration (ET) and soil water volume in from 1981 to 2000 and from 2001 to 2020 in the Bai River basin in stages; finally, the sensitivity of runoff, actual ET and soil water volume to climate change in the Bai River basin was analyzed by moderating the changes of precipitation and temperature on the basis of the available data. From 1981 to 2020, the average annual temperature in the Bai River basin showed a significant increasing trend, with an average temperature increase rate of 0.45 ℃ per decade, consistent with the background of global warming. Annual precipitation was relatively low from 1997 to 2010, with a slight rebound trend after 2010, although it was not significant. The annual potential evapotranspiration trend was similar to the annual average temperature, but the overall increasing trend was not significant. The interannual variation of annual runoff depth showed a non-significant decreasing trend, with a period of drought from 2001 to 2008. Precipitation in the Bai River basin peaked in June and September, and runoff, like precipitation, also exhibited a bimodal pattern, albeit with a time lag. The RCCC-WBM model, using meteorological and hydrological data, effectively simulated the runoff process in the Bai River basin, with Nash-Sutcliffe model efficiency coefficients (ENS) of 0.69 during the calibration period and 0.62 during the validation period. Under assumed climate change scenarios, with a 40% increase in precipitation and temperature change ranging from ?4 ℃ to 4 ℃, the corresponding runoff change ranged from 20.2% to 84.4%, and actual evapotranspiration change ranged from 1.4% to 51.0%. This indicated that with increased precipitation, the impact of temperature and evapotranspiration changes on runoff became more significant. With constant temperature, when precipitation changed from 0 to 20%, the change in soil moisture was 13.3%, while with precipitation changes of 20% to 40%, the change in soil moisture was 12.1%. This showed that as precipitation increased, soil moisture also increased, but the change in soil moisture per unit of precipitation became smaller. The RCCC-WBM model simulated the annual distribution of hydrological elements in the Bai River basin for the periods from 1981 to 2000 and from 2001 to 2020. In July, approximately 90% of evaporation and runoff were derived from precipitation. In December, only about 20% of evaporation and runoff came from precipitation, with the remainder supplemented by groundwater runoff and snowmelt runoff. Compared to the earlier period, after 2001, there was an increasing trend in hydrological elements primarily in April and May, while July and December showed a decreasing trend.
The source area of the Yellow River is an important component of the “Chinese Water Tower”, which has the functions of water conservation and recharge, regulating the local regional climate, and plays a positive role in maintaining the water resources and ecological environment of the Yellow River basin. Meanwhile, as a "sensitive area" of climate change, the hydrological situation of the Yellow River source area on the Tibetan Plateau has attracted much attention. Currently, most of the studies on the response of the Yellow River source area to climate change are based on large-scale watersheds and single meteorological and hydrological elements, and there are relatively limited studies on the mechanisms between hydrological elements in typical watersheds in the source area, while the changes in hydrological processes at the sub-basin scale are crucial for future water resources planning and management. Based on the meteorological and hydrological data collected and collated from 1981 to 2020 in the Bai River basin of the Yellow River headwaters area, the hydrological sequence analysis method was used to analyze the interannual change process of hydrological elements; the parameters of the RCCC-WBM model were rate-set, and the runoff of the Bai River basin was simulated in phases for the years from 1981 to 2000 and from 2001 to 2020, respectively. The parameters of the RCCC-WBM model were calibrated to simulate the changes of runoff, actual evapotranspiration (ET) and soil water volume in from 1981 to 2000 and from 2001 to 2020 in the Bai River basin in stages; finally, the sensitivity of runoff, actual ET and soil water volume to climate change in the Bai River basin was analyzed by moderating the changes of precipitation and temperature on the basis of the available data. From 1981 to 2020, the average annual temperature in the Bai River basin showed a significant increasing trend, with an average temperature increase rate of 0.45 ℃ per decade, consistent with the background of global warming. Annual precipitation was relatively low from 1997 to 2010, with a slight rebound trend after 2010, although it was not significant. The annual potential evapotranspiration trend was similar to the annual average temperature, but the overall increasing trend was not significant. The interannual variation of annual runoff depth showed a non-significant decreasing trend, with a period of drought from 2001 to 2008. Precipitation in the Bai River basin peaked in June and September, and runoff, like precipitation, also exhibited a bimodal pattern, albeit with a time lag. The RCCC-WBM model, using meteorological and hydrological data, effectively simulated the runoff process in the Bai River basin, with Nash-Sutcliffe model efficiency coefficients (ENS) of 0.69 during the calibration period and 0.62 during the validation period. Under assumed climate change scenarios, with a 40% increase in precipitation and temperature change ranging from ?4 ℃ to 4 ℃, the corresponding runoff change ranged from 20.2% to 84.4%, and actual evapotranspiration change ranged from 1.4% to 51.0%. This indicated that with increased precipitation, the impact of temperature and evapotranspiration changes on runoff became more significant. With constant temperature, when precipitation changed from 0 to 20%, the change in soil moisture was 13.3%, while with precipitation changes of 20% to 40%, the change in soil moisture was 12.1%. This showed that as precipitation increased, soil moisture also increased, but the change in soil moisture per unit of precipitation became smaller. The RCCC-WBM model simulated the annual distribution of hydrological elements in the Bai River basin for the periods from 1981 to 2000 and from 2001 to 2020. In July, approximately 90% of evaporation and runoff were derived from precipitation. In December, only about 20% of evaporation and runoff came from precipitation, with the remainder supplemented by groundwater runoff and snowmelt runoff. Compared to the earlier period, after 2001, there was an increasing trend in hydrological elements primarily in April and May, while July and December showed a decreasing trend.
2024, 22(5):856-864,908.
Abstract:
In the past, China's energy consumption continued to rise, making the stability and security of energy supply a top priority. Water resources were a limiting factor in energy production. Throughout the entire energy production cycle, such as in mining, processing, and conversion, water was essential. The total coal resources in the upper and middle reaches of the Yellow River accounted for 52.2% of the country's total, making it an important energy production region. However, the self-produced water quantity of the Yellow River was insufficient and decreasing, leading to tight competition for water resources among agriculture, energy, and ecology. The contradiction between water supply and demand was becoming increasingly prominent, and energy production faced a water crisis. Based on historical energy statistical data, the changing characteristics of energy production and structure in the upper and middle reaches of the Yellow River were analyzed. The blue water footprint of energy production was estimated using the water footprint theory, including the blue water footprint of energy extraction and energy processing. Additionally, the impact of energy production on water resource utilization in the river basin was analyzed. From 1990 to 2020, there was an overall upward trend in the total primary energy production in the upper and middle reaches of the Yellow River. It increased from 150 million tons of standard coal to 1.71 billion tons of standard coal. The proportion of national production increased from 13.7% to 41%, and secondary energy production also showed an upward trend. Energy production concentration has continuously strengthened, with a shift in focus towards upstream regions from the midstream. In the past, the energy structure has also transformed. From 1990 to 2020, the proportion of raw coal in the energy production structure of the upper and middle reaches of the Yellow River has shown a downward trend, while the proportion of natural gas production in primary energy output has significantly increased. There was no significant increase in the proportion of clean energy and crude oil in primary energy output. The spatial distribution of energy production structure in the upper and middle reaches of the Yellow River showed a pattern of "fossil energy in the midstream, clean energy in the upstream," with a widening range of clean energy distribution. The number of cities that primarily relied on raw coal had decreased, and the proportion of clean energy in their energy production structures had increased. The blue water footprint of energy production had increased significantly. It had increased from 464 million m3 in 1990 to 2518 million m3 in 2010. However, due to improvements in water use efficiency, the blue water footprint per unit of fossil energy production had decreased. At the same time, the increase in clean energy's share had also reduced the water footprint of energy production. Therefore, despite increasing production, the blue water footprint of energy production had slightly decreased to 2 424 million m3 in 2020. Except for crude oil and hydropower, all other forms of energy production in the upper and middle reaches of the Yellow River had maintained an upward trend. The growth rate of the blue water footprint of energy production was slower than that of energy output. Compared to the blue water footprint of energy extraction, that of energy processing was the main component of the blue water footprint during energy production. Thermal power generation was the most water-intensive form of energy processing. The high-value areas of the blue water footprint of energy production were concentrated in the "Ji-shaped bend region" in the past, where water scarcity issues were also severe, such as Erdos, Baotou, Yinchuan, and Taiyuan city.
In the past, China's energy consumption continued to rise, making the stability and security of energy supply a top priority. Water resources were a limiting factor in energy production. Throughout the entire energy production cycle, such as in mining, processing, and conversion, water was essential. The total coal resources in the upper and middle reaches of the Yellow River accounted for 52.2% of the country's total, making it an important energy production region. However, the self-produced water quantity of the Yellow River was insufficient and decreasing, leading to tight competition for water resources among agriculture, energy, and ecology. The contradiction between water supply and demand was becoming increasingly prominent, and energy production faced a water crisis. Based on historical energy statistical data, the changing characteristics of energy production and structure in the upper and middle reaches of the Yellow River were analyzed. The blue water footprint of energy production was estimated using the water footprint theory, including the blue water footprint of energy extraction and energy processing. Additionally, the impact of energy production on water resource utilization in the river basin was analyzed. From 1990 to 2020, there was an overall upward trend in the total primary energy production in the upper and middle reaches of the Yellow River. It increased from 150 million tons of standard coal to 1.71 billion tons of standard coal. The proportion of national production increased from 13.7% to 41%, and secondary energy production also showed an upward trend. Energy production concentration has continuously strengthened, with a shift in focus towards upstream regions from the midstream. In the past, the energy structure has also transformed. From 1990 to 2020, the proportion of raw coal in the energy production structure of the upper and middle reaches of the Yellow River has shown a downward trend, while the proportion of natural gas production in primary energy output has significantly increased. There was no significant increase in the proportion of clean energy and crude oil in primary energy output. The spatial distribution of energy production structure in the upper and middle reaches of the Yellow River showed a pattern of "fossil energy in the midstream, clean energy in the upstream," with a widening range of clean energy distribution. The number of cities that primarily relied on raw coal had decreased, and the proportion of clean energy in their energy production structures had increased. The blue water footprint of energy production had increased significantly. It had increased from 464 million m3 in 1990 to 2518 million m3 in 2010. However, due to improvements in water use efficiency, the blue water footprint per unit of fossil energy production had decreased. At the same time, the increase in clean energy's share had also reduced the water footprint of energy production. Therefore, despite increasing production, the blue water footprint of energy production had slightly decreased to 2 424 million m3 in 2020. Except for crude oil and hydropower, all other forms of energy production in the upper and middle reaches of the Yellow River had maintained an upward trend. The growth rate of the blue water footprint of energy production was slower than that of energy output. Compared to the blue water footprint of energy extraction, that of energy processing was the main component of the blue water footprint during energy production. Thermal power generation was the most water-intensive form of energy processing. The high-value areas of the blue water footprint of energy production were concentrated in the "Ji-shaped bend region" in the past, where water scarcity issues were also severe, such as Erdos, Baotou, Yinchuan, and Taiyuan city.
2024, 22(5):865-874.
Abstract:
The catastrophic flood eventb "23·7" super large flood in the Haihe River basin resulted from a combination of meteorological and geographical factors. The residual circulation from Typhoon Doksuri, combined with subtropical high pressure and moisture transport from Typhoon Khanun, interacted with the local topography to create conditions that led to extreme rainfall. This intense rainfall event, which took place from July 28 to August 1, 2023, was the most severe since 1963, with a total accumulated area rainfall amounting to 155.3 mm. This situation led to unprecedented flooding across the region, with 22 rivers exceeding their warning levels and eight rivers experiencing the most significant floods recorded in their history. As a response to the emergency conditions, eight flood detention areas within the basin were activated, resulting in significant sediment deposition. This, in turn, severely impacted both agricultural production and daily life in these regions. In order to investigate and evaluate the sediment deposition characteristics within the flood detention areas following the event, a comprehensive study was conducted. This study was distinguished by its use of advanced technologies, including satellite remote sensing, drone imagery, and ground surveys, which were carried out on August 29, 2023. Remote sensing images acquired from ZY-1F and GF-2 satellites were meticulously interpreted to identify the extent and characteristics of sediment deposition within the eight flood detention areas. Additionally, field surveys were conducted to obtain precise measurements of sediment thickness, volume, and mass within these areas. The maximum inundation depth was determined using flood mark measurements, and from these measurements, the average inundation depth, flood storage capacity, utilization rate, and sediment concentration were calculated. The sediment thickness and concentration calculations' reliability and accuracy were assessed using the Root Mean Square Error (ERMS) and Nash-Sutcliffe Efficiency (ENS) metrics. The integration of field data and remote sensing technology facilitated a detailed analysis of the spatial distribution of sediment deposition and the dynamics of the flood across the affected regions. The study's results revealed that the spatial interpolation of sediment thickness achieved a ERMS of 1.32 and a ENS of 0.78, demonstrating both the feasibility and reliability of the calculation methods employed. The flood storage calculation yielded an ERMS of 0.75 and an ENS of 0.92, further confirming the accuracy and validity of obtained results. Collectively, the eight flood detention areas stored a total flood volume of 2.463 billion m3, achieving a utilization rate of approximately 36.90%. The average sediment deposition thickness across areas was found to be 2.60 cm, resulting in a total sediment mass of 7.67×107t. During flood event, the average sediment concentration was calculated to be 31.14 kg/m3?in the Haihe River basin. Notably, in the Daqing River and Yongding River systems, sediment concentrations reached levels that were 20 to 40 times higher than the average recorded over the past 20 years. The study also exhibited that the spatial distribution of sediment deposition was significantly influenced by both the timing and sequence of flood detention area activation and the upstream-downstream relationship within the basin. Sediment deposition was more severe in the western and northern mountainous regions of the Haihe River basin, where topography played a crucial role in the observed spatial variations. The detention areas located in the upper reaches, which were activated earlier during the flood event, exhibited higher sediment deposition compared to other areas. This comprehensive assessment provides valuable data and insights for evaluating the impact of the "23?7" flood event. The stduy underscores the critical importance of enhancing the management and utilization of flood detention areas to mitigate potential flood risks in the future. It emphasize the integration of remote sensing and field methods to enable rapid and accurate post-disaster assessments of sediment deposition and flood impacts, thus providing essential support for disaster response and management efforts.
The catastrophic flood eventb "23·7" super large flood in the Haihe River basin resulted from a combination of meteorological and geographical factors. The residual circulation from Typhoon Doksuri, combined with subtropical high pressure and moisture transport from Typhoon Khanun, interacted with the local topography to create conditions that led to extreme rainfall. This intense rainfall event, which took place from July 28 to August 1, 2023, was the most severe since 1963, with a total accumulated area rainfall amounting to 155.3 mm. This situation led to unprecedented flooding across the region, with 22 rivers exceeding their warning levels and eight rivers experiencing the most significant floods recorded in their history. As a response to the emergency conditions, eight flood detention areas within the basin were activated, resulting in significant sediment deposition. This, in turn, severely impacted both agricultural production and daily life in these regions. In order to investigate and evaluate the sediment deposition characteristics within the flood detention areas following the event, a comprehensive study was conducted. This study was distinguished by its use of advanced technologies, including satellite remote sensing, drone imagery, and ground surveys, which were carried out on August 29, 2023. Remote sensing images acquired from ZY-1F and GF-2 satellites were meticulously interpreted to identify the extent and characteristics of sediment deposition within the eight flood detention areas. Additionally, field surveys were conducted to obtain precise measurements of sediment thickness, volume, and mass within these areas. The maximum inundation depth was determined using flood mark measurements, and from these measurements, the average inundation depth, flood storage capacity, utilization rate, and sediment concentration were calculated. The sediment thickness and concentration calculations' reliability and accuracy were assessed using the Root Mean Square Error (ERMS) and Nash-Sutcliffe Efficiency (ENS) metrics. The integration of field data and remote sensing technology facilitated a detailed analysis of the spatial distribution of sediment deposition and the dynamics of the flood across the affected regions. The study's results revealed that the spatial interpolation of sediment thickness achieved a ERMS of 1.32 and a ENS of 0.78, demonstrating both the feasibility and reliability of the calculation methods employed. The flood storage calculation yielded an ERMS of 0.75 and an ENS of 0.92, further confirming the accuracy and validity of obtained results. Collectively, the eight flood detention areas stored a total flood volume of 2.463 billion m3, achieving a utilization rate of approximately 36.90%. The average sediment deposition thickness across areas was found to be 2.60 cm, resulting in a total sediment mass of 7.67×107t. During flood event, the average sediment concentration was calculated to be 31.14 kg/m3?in the Haihe River basin. Notably, in the Daqing River and Yongding River systems, sediment concentrations reached levels that were 20 to 40 times higher than the average recorded over the past 20 years. The study also exhibited that the spatial distribution of sediment deposition was significantly influenced by both the timing and sequence of flood detention area activation and the upstream-downstream relationship within the basin. Sediment deposition was more severe in the western and northern mountainous regions of the Haihe River basin, where topography played a crucial role in the observed spatial variations. The detention areas located in the upper reaches, which were activated earlier during the flood event, exhibited higher sediment deposition compared to other areas. This comprehensive assessment provides valuable data and insights for evaluating the impact of the "23?7" flood event. The stduy underscores the critical importance of enhancing the management and utilization of flood detention areas to mitigate potential flood risks in the future. It emphasize the integration of remote sensing and field methods to enable rapid and accurate post-disaster assessments of sediment deposition and flood impacts, thus providing essential support for disaster response and management efforts.
5 Flood scenario in the Yongding River flood detention area with impacts of rain belt northward-moving
2024, 22(5):875-883.
Abstract:
As one of the main sub-catchments of Haihe River basin, the Yongding River covers Beijing, Tianjin and Hebei, Inner Mongolia and Shanxi province, with control area more than 47,000 km2. Since the beginning of 20th century, the Yongding river has frequently suffered super large flood disasters which often caused serious economic damages and casualties. Under this condition, many big reservoirs at the upstream and one detention area at the downstream of the catchment had been set up for intercepting the flood flow and reducing the peak to the cities in this area. Nowadays, the Yongding detention area with 522.65 km2 is the most important construction measure of flood defense and prevention which demands scientific management according to different flood scenarios. In order to have comprehensive understanding of the flood evolution process in the detention area for supporting the decision-making process, many hydrodynamic studies and models were set up in this area for assessing the effects of applying different regions of the detention area under various flood conditions. However, most of previous studies were either based on the modeling representation of past flood events or according to the simulation of designed scenarios. The modelling analysis of the future flood condition under the influence of rain belt northward-moving affected by the climate change was still missing in this area. By the Integrated Flood Model System (IFMS) developed by China Institute of Water Resources and Hydropower Research (IWHR), the flood progress of “23?7” super large flood in Yongding detention area was simulated and validated with the real-time monitoring data collected by satellite remote sensing during the flood period. The model was set up with 76,349 unstructured mesh based on 2 m resolution DEM which could well represent the topography variation in the modelling area. The difference between simulated and observed maximum flooded area was only 8.8%. And the accumulated flood storage calculated by IFMS model was 0.23 billion m3 which is almost as same as observation (0.24 billion m3). Therefore, the model applied for the Yongding detention area had been approved to be able to represent the flood progress and to be further used as one of operational tools for estimating future conditions. With the increasing ratio calculated with RegCM4.4 mode, the future summer heavy rainfall in 2025, 2035 and 2050 were estimated based on the “23?7” super large flood rainfall records. Integrated with the designed flood of 10, 20 and 50 year return periods at the upstream boundary, the modelling simulation shows that under the estimated rainfall in 2025, the maximum flooded area caused by 20 year return period designed flood would be similar as 50 year return period without taking into account the impacts of rain belt northward-moving. The modelling results of this study had indicated the amplification effect of rain belt northward-moving on the flood disaster. The potential flood risk of the Yongding detention area will show an increasing trend in the future. Therefore, for the future flood defense, it is important to the decision-makers to take into account the enlargement ofdisaster caused by the rain belt northward-moving impacts. Moreover, the model applied in this study showed higherpotentiality to be applied as one of the main reference tools for the future flood management in other river basins.
As one of the main sub-catchments of Haihe River basin, the Yongding River covers Beijing, Tianjin and Hebei, Inner Mongolia and Shanxi province, with control area more than 47,000 km2. Since the beginning of 20th century, the Yongding river has frequently suffered super large flood disasters which often caused serious economic damages and casualties. Under this condition, many big reservoirs at the upstream and one detention area at the downstream of the catchment had been set up for intercepting the flood flow and reducing the peak to the cities in this area. Nowadays, the Yongding detention area with 522.65 km2 is the most important construction measure of flood defense and prevention which demands scientific management according to different flood scenarios. In order to have comprehensive understanding of the flood evolution process in the detention area for supporting the decision-making process, many hydrodynamic studies and models were set up in this area for assessing the effects of applying different regions of the detention area under various flood conditions. However, most of previous studies were either based on the modeling representation of past flood events or according to the simulation of designed scenarios. The modelling analysis of the future flood condition under the influence of rain belt northward-moving affected by the climate change was still missing in this area. By the Integrated Flood Model System (IFMS) developed by China Institute of Water Resources and Hydropower Research (IWHR), the flood progress of “23?7” super large flood in Yongding detention area was simulated and validated with the real-time monitoring data collected by satellite remote sensing during the flood period. The model was set up with 76,349 unstructured mesh based on 2 m resolution DEM which could well represent the topography variation in the modelling area. The difference between simulated and observed maximum flooded area was only 8.8%. And the accumulated flood storage calculated by IFMS model was 0.23 billion m3 which is almost as same as observation (0.24 billion m3). Therefore, the model applied for the Yongding detention area had been approved to be able to represent the flood progress and to be further used as one of operational tools for estimating future conditions. With the increasing ratio calculated with RegCM4.4 mode, the future summer heavy rainfall in 2025, 2035 and 2050 were estimated based on the “23?7” super large flood rainfall records. Integrated with the designed flood of 10, 20 and 50 year return periods at the upstream boundary, the modelling simulation shows that under the estimated rainfall in 2025, the maximum flooded area caused by 20 year return period designed flood would be similar as 50 year return period without taking into account the impacts of rain belt northward-moving. The modelling results of this study had indicated the amplification effect of rain belt northward-moving on the flood disaster. The potential flood risk of the Yongding detention area will show an increasing trend in the future. Therefore, for the future flood defense, it is important to the decision-makers to take into account the enlargement ofdisaster caused by the rain belt northward-moving impacts. Moreover, the model applied in this study showed higherpotentiality to be applied as one of the main reference tools for the future flood management in other river basins.
2024, 22(5):884-895.
Abstract:
The study was undertaken to gain a profound comprehension and optimize the patterns of regional water resource utilization, with a strong emphasis on efficiency, focusing particularly on Xuzhou, Suqian, and Huaian, the pivotal beneficiaries of the Jiangsu segment of the Eastern Route of the South-to-North Water Transfers Project. Recognizing the indispensable role of water resources in fueling socioeconomic development and safeguarding ecological integrity, a comprehensive analysis was conducted to explore the intricate interplay between these facets. The objective was to ensure sustainable utilization of water resources while promoting balanced socioeconomic growth and protecting the delicate ecological balance in these regions. A multifaceted analytical framework was devised to examine the coupling and coordinated development among the water resource utilization, socioeconomic development, and ecological environment protection systems. This model encompassed a broad range of 23 carefully selected indicators, distributed across three core dimensions. The methodology entailed the application of the entropy weight method to objectively and precisely determine the weights of these indicators, thereby enhancing the robustness of the analysis. Subsequently, the constructed model was benchmarked against a matching degree model, and an obstacle degree model was employed to meticulously identify and analyze the factors that potentially hindered the desired levels of coupling and coordination. This rigorous approach aimed to uncover insights into the complex dynamics of the integrated system and to pinpoint areas requiring targeted interventions. The study revealed marked advancements in the overall development levels of the integrated systems in Xuzhou, Suqian, and Huaian, showcasing a discernible transition from nascent to advanced stages. Specifically, their coupling coordination degrees underwent notable transformations: Xuzhou progressed from a state of barely coordinated to well-coordinated, Suqian experienced a leap from primary coordination to well-coordination, while Huaian progressed from primary coordination to a moderately coordinated state. Notably, in the later stages of the analysis, the coupling coordination degrees of all three cities surpassed their respective matching degrees, indicative of an optimal state of harmonious development. However, the investigation also surfaced key impediments, namely, the untapped potential of floodwater resource utilization, limited forest coverage, and inadequate volumes of floodwater resources. Furthermore, distinct dominant factors influencing the coupling coordination degree of each city's composite system were elucidated: water resource utilization emerged as the pivotal factor in Xuzhou, socioeconomic factors predominated in Suqian, and the ecological environment held sway in Huaian. To address the identified challenges and harness the full potential of water resources, it is imperative to implement effective measures aimed at enhancing the peak-clipping and storage capabilities of reservoirs and flood detention areas. These initiatives will not only mitigate the frequency and severity of floods but also minimize associated economic and ecological losses. Furthermore, ensuring the efficient utilization of water resources for both socioeconomic growth and ecological preservation is critical for the sustainable development of Xuzhou, Suqian, and Huaian. By adopting these strategies, a solid foundation will be laid for the long-term prosperity of these cities and the preservation of their fragile ecosystems, fostering a harmonious balance between human activities and the natural environment.
The study was undertaken to gain a profound comprehension and optimize the patterns of regional water resource utilization, with a strong emphasis on efficiency, focusing particularly on Xuzhou, Suqian, and Huaian, the pivotal beneficiaries of the Jiangsu segment of the Eastern Route of the South-to-North Water Transfers Project. Recognizing the indispensable role of water resources in fueling socioeconomic development and safeguarding ecological integrity, a comprehensive analysis was conducted to explore the intricate interplay between these facets. The objective was to ensure sustainable utilization of water resources while promoting balanced socioeconomic growth and protecting the delicate ecological balance in these regions. A multifaceted analytical framework was devised to examine the coupling and coordinated development among the water resource utilization, socioeconomic development, and ecological environment protection systems. This model encompassed a broad range of 23 carefully selected indicators, distributed across three core dimensions. The methodology entailed the application of the entropy weight method to objectively and precisely determine the weights of these indicators, thereby enhancing the robustness of the analysis. Subsequently, the constructed model was benchmarked against a matching degree model, and an obstacle degree model was employed to meticulously identify and analyze the factors that potentially hindered the desired levels of coupling and coordination. This rigorous approach aimed to uncover insights into the complex dynamics of the integrated system and to pinpoint areas requiring targeted interventions. The study revealed marked advancements in the overall development levels of the integrated systems in Xuzhou, Suqian, and Huaian, showcasing a discernible transition from nascent to advanced stages. Specifically, their coupling coordination degrees underwent notable transformations: Xuzhou progressed from a state of barely coordinated to well-coordinated, Suqian experienced a leap from primary coordination to well-coordination, while Huaian progressed from primary coordination to a moderately coordinated state. Notably, in the later stages of the analysis, the coupling coordination degrees of all three cities surpassed their respective matching degrees, indicative of an optimal state of harmonious development. However, the investigation also surfaced key impediments, namely, the untapped potential of floodwater resource utilization, limited forest coverage, and inadequate volumes of floodwater resources. Furthermore, distinct dominant factors influencing the coupling coordination degree of each city's composite system were elucidated: water resource utilization emerged as the pivotal factor in Xuzhou, socioeconomic factors predominated in Suqian, and the ecological environment held sway in Huaian. To address the identified challenges and harness the full potential of water resources, it is imperative to implement effective measures aimed at enhancing the peak-clipping and storage capabilities of reservoirs and flood detention areas. These initiatives will not only mitigate the frequency and severity of floods but also minimize associated economic and ecological losses. Furthermore, ensuring the efficient utilization of water resources for both socioeconomic growth and ecological preservation is critical for the sustainable development of Xuzhou, Suqian, and Huaian. By adopting these strategies, a solid foundation will be laid for the long-term prosperity of these cities and the preservation of their fragile ecosystems, fostering a harmonious balance between human activities and the natural environment.
2024, 22(5):896-908.
Abstract:
Jiangsu Province's Water Diversion from the Yangtze River to the North, which integrates the Yangtze River, Hongze Lake, and Luoma Lake as its main water sources, constitutes a water network system in Jiangsu Province along with the Eastern Route of South-to-North Water Transfers Project Phase I. Jiangsu Province's Water Diversion from the Yangtze River to the North has made great contributions to the economic and social development of northern Jiangsu Province; however, there are shortcomings in the current operation status of the project, such as high operating costs and large lake abandoned water volume. Therefore, the use of this study is intended as a model to construct the annual water dispatching of Jiangsu Province's Water Diversion from the Yangtze River to the North, starting from the Yangtze River and using Hongze Lake and Luoma Lake as the main control points to form a dual-line water conveyance topology structure of canal lines and west-to-east lines, and generalizes the water use units. Taking the water delivery capacity of each pump station, lake water levels, and reservoir capacity as constraints, and the water supply deficit, water inflow and outflow to Luoma Lake, and water inflow to Hongze Lake as decision variables, the objective function is to minimize the water supply deficit, optimize the ecological water level of the lake, and minimize the operating cost of the pump station. Using rule-based scheduling and stepwise optimization algorithms, as well as the method of target priority, the multi-objective problem is transformed into a single-objective problem. Starting from the end of the project hub (Linjiaba pump station and Taierzhuang pump station), it is solved in reverse order to the starting point hub (Baoying pump station and Jiangdu pump station) of the project, the results of the water dispatching scheme are obtained. For three typical years of high, normal, and low waterflow, a joint dispatching scheme for the gate pump-lake system was formulated to verify the rationality of the model. Further analysis of the lake regulation and storage process, engineering benefits, and engineering characteristics of each scheme was conducted, and scenarios of high water levels with low water use were analyzed. The following conclusions were drawn: First, the storage capacity of Luoma Lake is small, and the water level fluctuates frequently during the water transfer operation period. The Zhongyun Canal pumping station also frequently opens the lake for replenishment, and it is easy to reach the upper limit of water transfer capacity. Secondly, appropriately reducing the water level of Hongze Lake can fully utilize the surplus water in the lake and reduce the cost of water transfer. Thirdly, completely prohibiting the use of water from the high cascade of Luoma Lake for the low cascade will increase the operational burden of water supply tasks for the cascade lines below Hongze Lake, while also increasing the amount of abandoned water in Luoma Lake. Allowing high water for low use can appropriately reduce costs and pumping tasks for pump stations.However, due to the limitations of the rules, significant water abandonment in Luoma Lake results from insufficient joint dispatching between Hongze Lake and Luoma Lake. Optimizing and breaking the rules to find the optimal water supplement mechanism, optimizing collaborative dispatching between upstream and downstream lakes, maintaining stable water levels, and making engineering operations safer and more reliable are areas that require further research in the field of water resource dispatching for the Jiangsu Provincial Water Transfer Project. In summary, the research results can provide certain reference value for the scheduling and operation of the Water Diversion from the Yangtze River to the North.
Jiangsu Province's Water Diversion from the Yangtze River to the North, which integrates the Yangtze River, Hongze Lake, and Luoma Lake as its main water sources, constitutes a water network system in Jiangsu Province along with the Eastern Route of South-to-North Water Transfers Project Phase I. Jiangsu Province's Water Diversion from the Yangtze River to the North has made great contributions to the economic and social development of northern Jiangsu Province; however, there are shortcomings in the current operation status of the project, such as high operating costs and large lake abandoned water volume. Therefore, the use of this study is intended as a model to construct the annual water dispatching of Jiangsu Province's Water Diversion from the Yangtze River to the North, starting from the Yangtze River and using Hongze Lake and Luoma Lake as the main control points to form a dual-line water conveyance topology structure of canal lines and west-to-east lines, and generalizes the water use units. Taking the water delivery capacity of each pump station, lake water levels, and reservoir capacity as constraints, and the water supply deficit, water inflow and outflow to Luoma Lake, and water inflow to Hongze Lake as decision variables, the objective function is to minimize the water supply deficit, optimize the ecological water level of the lake, and minimize the operating cost of the pump station. Using rule-based scheduling and stepwise optimization algorithms, as well as the method of target priority, the multi-objective problem is transformed into a single-objective problem. Starting from the end of the project hub (Linjiaba pump station and Taierzhuang pump station), it is solved in reverse order to the starting point hub (Baoying pump station and Jiangdu pump station) of the project, the results of the water dispatching scheme are obtained. For three typical years of high, normal, and low waterflow, a joint dispatching scheme for the gate pump-lake system was formulated to verify the rationality of the model. Further analysis of the lake regulation and storage process, engineering benefits, and engineering characteristics of each scheme was conducted, and scenarios of high water levels with low water use were analyzed. The following conclusions were drawn: First, the storage capacity of Luoma Lake is small, and the water level fluctuates frequently during the water transfer operation period. The Zhongyun Canal pumping station also frequently opens the lake for replenishment, and it is easy to reach the upper limit of water transfer capacity. Secondly, appropriately reducing the water level of Hongze Lake can fully utilize the surplus water in the lake and reduce the cost of water transfer. Thirdly, completely prohibiting the use of water from the high cascade of Luoma Lake for the low cascade will increase the operational burden of water supply tasks for the cascade lines below Hongze Lake, while also increasing the amount of abandoned water in Luoma Lake. Allowing high water for low use can appropriately reduce costs and pumping tasks for pump stations.However, due to the limitations of the rules, significant water abandonment in Luoma Lake results from insufficient joint dispatching between Hongze Lake and Luoma Lake. Optimizing and breaking the rules to find the optimal water supplement mechanism, optimizing collaborative dispatching between upstream and downstream lakes, maintaining stable water levels, and making engineering operations safer and more reliable are areas that require further research in the field of water resource dispatching for the Jiangsu Provincial Water Transfer Project. In summary, the research results can provide certain reference value for the scheduling and operation of the Water Diversion from the Yangtze River to the North.
2024, 22(5):909-919.
Abstract:
In order to accurately simulate the one-dimensional hydrodynamic process, a coupled improved Kostiakov formula is proposed based on the hydrodynamic model of open channel unsteady flow. Considering that the river leakage loss is affected by the river flow and the underlying surface conditions of the river, the real flow change process under the river is simulated. By coupling the loss model and the hydrodynamic model considering the actual river water loss, the calculated flow of the hydrodynamic model is used as the flow boundary condition of the loss model at the same time step. The loss of the time step is calculated by the loss empirical parameters a and b, and the hydrodynamic river loss parameters are corrected in real time. The accuracy of the model was tested by four measured water replenishment data from 2022 to 2024 in the Yongding River basin, and the characteristics of ecological changes in the Yongding River before and after the "23·7" flood were analyzed. The model has good simulation accuracy in the lower reaches of the Yongding River, and has good applicability in different seasons and different basins. The minimum absolute value of the relative error is 0.004, and the maximum is 0.218, which is within the allowable range of error. The "23·7" flood has obvious scouring effect on the Yongding River channel, and the shape of the river channel has changed significantly. The roughness of the river channel and the infiltration loss of the river channel are reduced compared with those before the flood scouring. The lower reaches of the Yongding River can realize the water supply of the whole basin with small flow before the flood scouring, which can provide a reference for the ecological water transfer in the lower reaches of the Yongding River.
In order to accurately simulate the one-dimensional hydrodynamic process, a coupled improved Kostiakov formula is proposed based on the hydrodynamic model of open channel unsteady flow. Considering that the river leakage loss is affected by the river flow and the underlying surface conditions of the river, the real flow change process under the river is simulated. By coupling the loss model and the hydrodynamic model considering the actual river water loss, the calculated flow of the hydrodynamic model is used as the flow boundary condition of the loss model at the same time step. The loss of the time step is calculated by the loss empirical parameters a and b, and the hydrodynamic river loss parameters are corrected in real time. The accuracy of the model was tested by four measured water replenishment data from 2022 to 2024 in the Yongding River basin, and the characteristics of ecological changes in the Yongding River before and after the "23·7" flood were analyzed. The model has good simulation accuracy in the lower reaches of the Yongding River, and has good applicability in different seasons and different basins. The minimum absolute value of the relative error is 0.004, and the maximum is 0.218, which is within the allowable range of error. The "23·7" flood has obvious scouring effect on the Yongding River channel, and the shape of the river channel has changed significantly. The roughness of the river channel and the infiltration loss of the river channel are reduced compared with those before the flood scouring. The lower reaches of the Yongding River can realize the water supply of the whole basin with small flow before the flood scouring, which can provide a reference for the ecological water transfer in the lower reaches of the Yongding River.
2024, 22(5):920-926.
Abstract:
Long-term precipitation prediction refers to forecasting precipitation over a period of more than one month. This is a crucial aspect of integrated water resources management. The accuracy of long-term precipitation predictions is low due to various uncertainties. Traditional long-term precipitation prediction methods are mainly divided into dynamical numerical methods and mathematical statistical methods. Dynamical numerical methods simulate future weather conditions using sea-land thermodynamic models for precipitation prediction. This approach has a clear physical mechanism, but the model calculations are complex. Data-driven mathematical-statistical methods simulate the correlation between precipitation and predictors from a statistical perspective to establish a long-term prediction model. However, research on precipitation prediction based on mathematical statistical methods mainly focuses on improving the model, with relatively little emphasis on how to select the predictors. In fact, the predictors affect the accuracy of model predictions. Therefore, the focus and challenge of precipitation prediction lie in selecting the necessary predictors for modeling from the relevant factors. Random forest, as a flexible, efficient, and easy-to-use machine learning algorithm, has been widely used in hydrological prediction. The random forest method calculates the importance scores of various related factors and then selects predictors for the model based on empirical experience. This process can result in a certain error rate issue with the selected predictors. To address the issue of false discovery rate in the random forest algorithm when selecting key predictors, this study employs the false discovery rate control method in multiple hypothesis testing to ensure quality control in predictor selection. This transformation shifts variable selection from being experience-dependent to becoming data-dependent. Finally, the random forest algorithm is used to construct a long-term precipitation prediction model by integrating the selected precipitation predictors. Taking the upper basin of the Parana River in Brazil as the study area, the precipitation from 54 measured rainfall stations and 130 climate system indices was analyzed. The predictors influencing precipitation in the corresponding months of the following year were obtained using the "Model-X Knockoff" method. A monthly precipitation prediction model is established based on the predictors that influence the precipitation for the corresponding month of the following year. The top 5 predictors with the highest importance scores are directly selected for random forest modeling using the traditional random forest method. The validity of the proposed method is subsequently verified using 10-fold cross-validation and a test of the monthly precipitation prediction results from 2018 to 2020. The effect of 10-fold cross-validation for 54 rainfall stations shows that the model prediction pass rate of the method introduced is higher than that of the traditional random forest method from January to December, with the highest pass rate of 77% in June. The results of precipitation prediction from 2018 to 2020 indicate that our method achieved an average pass rate of 66% from January to December, outperforming the traditional random forest method, which scored 64%. In summary, our research combines multiple hypothesis testing with predictor selection and quality control to establish a long-term precipitation prediction model, which differs from the traditional random forest method. This model exhibits a higher prediction pass rate and improved stability, suggesting that this approach can serve as an effective tool for long-term precipitation prediction in a basin.
Long-term precipitation prediction refers to forecasting precipitation over a period of more than one month. This is a crucial aspect of integrated water resources management. The accuracy of long-term precipitation predictions is low due to various uncertainties. Traditional long-term precipitation prediction methods are mainly divided into dynamical numerical methods and mathematical statistical methods. Dynamical numerical methods simulate future weather conditions using sea-land thermodynamic models for precipitation prediction. This approach has a clear physical mechanism, but the model calculations are complex. Data-driven mathematical-statistical methods simulate the correlation between precipitation and predictors from a statistical perspective to establish a long-term prediction model. However, research on precipitation prediction based on mathematical statistical methods mainly focuses on improving the model, with relatively little emphasis on how to select the predictors. In fact, the predictors affect the accuracy of model predictions. Therefore, the focus and challenge of precipitation prediction lie in selecting the necessary predictors for modeling from the relevant factors. Random forest, as a flexible, efficient, and easy-to-use machine learning algorithm, has been widely used in hydrological prediction. The random forest method calculates the importance scores of various related factors and then selects predictors for the model based on empirical experience. This process can result in a certain error rate issue with the selected predictors. To address the issue of false discovery rate in the random forest algorithm when selecting key predictors, this study employs the false discovery rate control method in multiple hypothesis testing to ensure quality control in predictor selection. This transformation shifts variable selection from being experience-dependent to becoming data-dependent. Finally, the random forest algorithm is used to construct a long-term precipitation prediction model by integrating the selected precipitation predictors. Taking the upper basin of the Parana River in Brazil as the study area, the precipitation from 54 measured rainfall stations and 130 climate system indices was analyzed. The predictors influencing precipitation in the corresponding months of the following year were obtained using the "Model-X Knockoff" method. A monthly precipitation prediction model is established based on the predictors that influence the precipitation for the corresponding month of the following year. The top 5 predictors with the highest importance scores are directly selected for random forest modeling using the traditional random forest method. The validity of the proposed method is subsequently verified using 10-fold cross-validation and a test of the monthly precipitation prediction results from 2018 to 2020. The effect of 10-fold cross-validation for 54 rainfall stations shows that the model prediction pass rate of the method introduced is higher than that of the traditional random forest method from January to December, with the highest pass rate of 77% in June. The results of precipitation prediction from 2018 to 2020 indicate that our method achieved an average pass rate of 66% from January to December, outperforming the traditional random forest method, which scored 64%. In summary, our research combines multiple hypothesis testing with predictor selection and quality control to establish a long-term precipitation prediction model, which differs from the traditional random forest method. This model exhibits a higher prediction pass rate and improved stability, suggesting that this approach can serve as an effective tool for long-term precipitation prediction in a basin.
2024, 22(5):927-936.
Abstract:
The value of river ecosystem services was a crucial factor in determining the necessary ecological flow of river courses and ensuring the success of ecological water supplement projects. The primary course of the Fenhe River served as a case study to improve the traditional method of calculating river ecosystem service values by incorporating the Manning formula. Six hydrological stations, including Fenhe Reservoir Station, Yitang Station, and Hejin Station, were analyzed for their complex cross-sectional configurations and hydraulic parameters such as longitudinal gradient ratio and river roughness. This comprehensive approach was essential in understanding the diverse and variable nature of river ecosystem service values. A detailed analysis was conducted to explore the variability of river ecosystem service values. The results indicated a clear increase in river ecosystem service values with rising flow levels, although this increase exhibited diminishing marginal returns. The ecological and economic value of a single cubic meter of river water showed a gradual decrease as flow levels increased, indicating a progressively slower rate of decline. This phenomenon underscored the importance of optimizing flow levels to maximize both ecological and economic benefits without overburdening the river system. The relationship between flow and river water ecological value, combined with the actual situation of the study area, suggested that there was a range of flow rates that could both maintain a healthy river ecosystem and maximize socio-economic development. Significant insights were revealed when the Fenhe River introduced six hundred million cubic meters of Yellow River water, ensuring an ecological water supplement of at least fifteen cubic meters per second during the dry season. Under these conditions, the ecological economic value of a single cubic meter of water in the main stream of the Fenhe River was determined to be 8.74 yuan. Consequently, the annual ecological value of the river was calculated to be 4.131 billion yuan. These figures highlighted the substantial economic contributions of maintaining adequate ecological flows in river systems. The shape of the river channel cross-section affected the relationship between flow and water surface width, thus altering the relationship between flow and river water surface area. The total value of river ecological services increased with flow, but the rate of increase slowed down, consistent with the law of diminishing marginal returns. Several conclusions were derived from this study: First, based on the relationship between flow rates and river water ecological value found in this study, and considering the actual situation of the study area, it was determined that there is a range of flow rates that can maintain a healthy river ecosystem while maximizing socio-economic development. The shape of the river channel cross-section affected the relationship between flow and water surface width, thus altering the relationship between flow and river water surface area. Second, the total value of river ecological services increased with flow, but the rate of increase slowed down, consistent with the law of diminishing marginal returns. The relationship between river water ecological value and flow followed the theory of diminishing marginal returns, showing a negative correlation. As flow increased, the ecological value per cubic meter of river water decreased exponentially. Whatever, when six hundred million cubic meters of Yellow River water were introduced into the Fenhe River, with an ecological water supplement of no less than fifteen cubic meters per second during the dry season, an annual ecological value of 52.44 billion yuan was generated. Over sixteen years, the total ecological value reached 839.04 billion yuan, approximately eight times the investment of 10.41 billion yuan for the Fenhe ecological water replenishment and supply project. This has significant implications for maintaining the ecological flow of the Fenhe River. Finally, constructing a dynamic model of river ecosystem service value that accounts for spatiotemporal variations remains a key focus for future research. This approach will help provide a more accurate assessment and management strategy for river ecosystems, ensuring their sustainability and the long-term benefits they provide to both the environment and society.
The value of river ecosystem services was a crucial factor in determining the necessary ecological flow of river courses and ensuring the success of ecological water supplement projects. The primary course of the Fenhe River served as a case study to improve the traditional method of calculating river ecosystem service values by incorporating the Manning formula. Six hydrological stations, including Fenhe Reservoir Station, Yitang Station, and Hejin Station, were analyzed for their complex cross-sectional configurations and hydraulic parameters such as longitudinal gradient ratio and river roughness. This comprehensive approach was essential in understanding the diverse and variable nature of river ecosystem service values. A detailed analysis was conducted to explore the variability of river ecosystem service values. The results indicated a clear increase in river ecosystem service values with rising flow levels, although this increase exhibited diminishing marginal returns. The ecological and economic value of a single cubic meter of river water showed a gradual decrease as flow levels increased, indicating a progressively slower rate of decline. This phenomenon underscored the importance of optimizing flow levels to maximize both ecological and economic benefits without overburdening the river system. The relationship between flow and river water ecological value, combined with the actual situation of the study area, suggested that there was a range of flow rates that could both maintain a healthy river ecosystem and maximize socio-economic development. Significant insights were revealed when the Fenhe River introduced six hundred million cubic meters of Yellow River water, ensuring an ecological water supplement of at least fifteen cubic meters per second during the dry season. Under these conditions, the ecological economic value of a single cubic meter of water in the main stream of the Fenhe River was determined to be 8.74 yuan. Consequently, the annual ecological value of the river was calculated to be 4.131 billion yuan. These figures highlighted the substantial economic contributions of maintaining adequate ecological flows in river systems. The shape of the river channel cross-section affected the relationship between flow and water surface width, thus altering the relationship between flow and river water surface area. The total value of river ecological services increased with flow, but the rate of increase slowed down, consistent with the law of diminishing marginal returns. Several conclusions were derived from this study: First, based on the relationship between flow rates and river water ecological value found in this study, and considering the actual situation of the study area, it was determined that there is a range of flow rates that can maintain a healthy river ecosystem while maximizing socio-economic development. The shape of the river channel cross-section affected the relationship between flow and water surface width, thus altering the relationship between flow and river water surface area. Second, the total value of river ecological services increased with flow, but the rate of increase slowed down, consistent with the law of diminishing marginal returns. The relationship between river water ecological value and flow followed the theory of diminishing marginal returns, showing a negative correlation. As flow increased, the ecological value per cubic meter of river water decreased exponentially. Whatever, when six hundred million cubic meters of Yellow River water were introduced into the Fenhe River, with an ecological water supplement of no less than fifteen cubic meters per second during the dry season, an annual ecological value of 52.44 billion yuan was generated. Over sixteen years, the total ecological value reached 839.04 billion yuan, approximately eight times the investment of 10.41 billion yuan for the Fenhe ecological water replenishment and supply project. This has significant implications for maintaining the ecological flow of the Fenhe River. Finally, constructing a dynamic model of river ecosystem service value that accounts for spatiotemporal variations remains a key focus for future research. This approach will help provide a more accurate assessment and management strategy for river ecosystems, ensuring their sustainability and the long-term benefits they provide to both the environment and society.
2024, 22(5):937-945,958.
Abstract:
As integral components of China’s national water network project, water distribution systems play a pivotal role in ensuring the safe utilization of water across various urban industries. However, the substantial energy consumption associated with operating these systems poses a significant challenge, hindering both the efficient utilization of water resources and the implementation of carbon reduction policies Consequently, there is an urgent need to address this issue and pursue avenues for improvement. Optimal operation research has been identified as a key strategy for promoting energy conservation in water distribution systems. Traditional strategies on optimal operation in water distribution systems typically adopted time-table schedules by the fixed time step as explicit decision-making for pumps. This strategy is easy-to-use, but it lacks of flexibility for pump switch when there are storage infrastructures in water distribution systems. Another issue is that the fixed time step constrains the attainment of maximal economic benefits through pump scheduling. This paper introduced a novel strategy that integrates time-variable trigger levels in storage tanks as an implicit decision-making rule for pump operation, with the aim of investigating potential energy-saving opportunities. More specifically, distinct pairs of trigger levels in tanks were assigned to regulate corresponding pumps during different electricity tariff periods. A multi-objective operational optimization model for water distribution systems was established to minimize electricity costs while minimizing the redundancy level of water pressure. This model was compared with two traditional strategies: time-table schedule and operation rules based on fixed trigger levels in tanks. These three models were then evaluated in a network case involving multiple pumps and tanks, with the objective of exploring the operational characteristics of joint pump and tank operations in water distribution systems. Results demonstrated that the strategy using time-variable trigger levels was capable of yielding superior Pareto-optimal solutions across both objectives compared with the two other strategies. Maintaining equal redundancy level of hydraulic pressure, this strategy facilitated the identification of greater economic benefits, resulting in a minimum of 4.93% reduction in electricity consumption costs. At the same computational budget, solving the optimization model using trigger levels for optimization was proved to be significantly more efficient compared to the time-table schedule. This efficiency stemmed from the smaller search space available when trigger levels were employed. Through a comprehensive comparative analysis encompassing variations in trigger levels, pump statuses, and their response to varying electricity tariff periods, it became apparent that the dynamic adjustment of trigger levels in tanks enabled flexible pump scheduling and optimized storage capacity utilization. Contrasted with fixed trigger levels, the lower trigger-off level effectively minimized stored water volumes during peak tariff periods, harnessing the storage capacity of water tanks at a lower budgetary cost while mitigating redundancy in nodal pressure. In contrast to explicit time-table schedules for pumps, the inclusion of water trajectory considerations in tanks promoted proactive pump switching to prevent tank backwater effects, indicative of reduced nodal pressure redundancy. The operational rule utilizing time-variable trigger levels allowed for pump switching at variable time steps, enhancing flexibility in pump operation scheduling. This adaptive feature enhances the possibility of identifying optimal solutions aligned with predefined objectives. In multi-pump, upstream-downstream joint water supply systems, the scheduling with time-varying water level control can achieve more significant economic benefits.
As integral components of China’s national water network project, water distribution systems play a pivotal role in ensuring the safe utilization of water across various urban industries. However, the substantial energy consumption associated with operating these systems poses a significant challenge, hindering both the efficient utilization of water resources and the implementation of carbon reduction policies Consequently, there is an urgent need to address this issue and pursue avenues for improvement. Optimal operation research has been identified as a key strategy for promoting energy conservation in water distribution systems. Traditional strategies on optimal operation in water distribution systems typically adopted time-table schedules by the fixed time step as explicit decision-making for pumps. This strategy is easy-to-use, but it lacks of flexibility for pump switch when there are storage infrastructures in water distribution systems. Another issue is that the fixed time step constrains the attainment of maximal economic benefits through pump scheduling. This paper introduced a novel strategy that integrates time-variable trigger levels in storage tanks as an implicit decision-making rule for pump operation, with the aim of investigating potential energy-saving opportunities. More specifically, distinct pairs of trigger levels in tanks were assigned to regulate corresponding pumps during different electricity tariff periods. A multi-objective operational optimization model for water distribution systems was established to minimize electricity costs while minimizing the redundancy level of water pressure. This model was compared with two traditional strategies: time-table schedule and operation rules based on fixed trigger levels in tanks. These three models were then evaluated in a network case involving multiple pumps and tanks, with the objective of exploring the operational characteristics of joint pump and tank operations in water distribution systems. Results demonstrated that the strategy using time-variable trigger levels was capable of yielding superior Pareto-optimal solutions across both objectives compared with the two other strategies. Maintaining equal redundancy level of hydraulic pressure, this strategy facilitated the identification of greater economic benefits, resulting in a minimum of 4.93% reduction in electricity consumption costs. At the same computational budget, solving the optimization model using trigger levels for optimization was proved to be significantly more efficient compared to the time-table schedule. This efficiency stemmed from the smaller search space available when trigger levels were employed. Through a comprehensive comparative analysis encompassing variations in trigger levels, pump statuses, and their response to varying electricity tariff periods, it became apparent that the dynamic adjustment of trigger levels in tanks enabled flexible pump scheduling and optimized storage capacity utilization. Contrasted with fixed trigger levels, the lower trigger-off level effectively minimized stored water volumes during peak tariff periods, harnessing the storage capacity of water tanks at a lower budgetary cost while mitigating redundancy in nodal pressure. In contrast to explicit time-table schedules for pumps, the inclusion of water trajectory considerations in tanks promoted proactive pump switching to prevent tank backwater effects, indicative of reduced nodal pressure redundancy. The operational rule utilizing time-variable trigger levels allowed for pump switching at variable time steps, enhancing flexibility in pump operation scheduling. This adaptive feature enhances the possibility of identifying optimal solutions aligned with predefined objectives. In multi-pump, upstream-downstream joint water supply systems, the scheduling with time-varying water level control can achieve more significant economic benefits.
2024, 22(5):946-958.
Abstract:
For inter-basin water transfer projects, the water conflict between society, economy, ecology, and other sectors makes the water allocation process more complicated. The optimal allocation of water resources was one of the most effective ways to alleviate the conflict between water supply and demand. It was necessary to implement an effective water resources optimization allocation concept in inter-basin water transfer projects to ensure the efficient implementation of inter-basin water transfer projects. This study presents an improved multi-objective cuckoo algorithm to effectively solve the multi-objective optimal allocation problem of inter-basin water resources. To address the shortcomings of multi-objective cuckoo algorithm such as slow convergence speed and easy to fall into local optimal solutions, chaos theory and variation mechanism were introduced, and adaptive discovery probability and step size were used to improve multi-objective cuckoo algorithm and enhance the overall performance of improved multi-objective cuckoo algorithm. A water resources optimal allocation model of the Jiangsu section of the South-to-North Water Transfers Project was established. Improved multi-objective cuckoo algorithm and multi-objective cuckoo algorithm were adopted to solve the water resources optimal allocation model, respectively. A non-negative matrix factorization method based on combined weighting was used for scheme evaluation. The results show that: improved multi-objective cuckoo algorithm was better than multi-objective cuckoo algorithm in terms of convergence, distribution, and overall performance, and was capable of yielding higher-quality Pareto solution sets; Compared with the optimal allocation solution solved by multi-objective cuckoo algorithm under the 50%, 75%, and 95% incoming water conditions, the total water shortage of the optimal allocation solution solved by improved multi-objective cuckoo algorithm was reduced by 21 million m3, 51 million m3 and 7 million m3, and the water loss was reduced by 13 million m3, 15 million m3 and 11 million m3, respectively. Therefore, improved multi-objective cuckoo algorithm could provide an effective algorithmic reference for the calculation of multi-objective optimal allocation of inter-basin water resources.
For inter-basin water transfer projects, the water conflict between society, economy, ecology, and other sectors makes the water allocation process more complicated. The optimal allocation of water resources was one of the most effective ways to alleviate the conflict between water supply and demand. It was necessary to implement an effective water resources optimization allocation concept in inter-basin water transfer projects to ensure the efficient implementation of inter-basin water transfer projects. This study presents an improved multi-objective cuckoo algorithm to effectively solve the multi-objective optimal allocation problem of inter-basin water resources. To address the shortcomings of multi-objective cuckoo algorithm such as slow convergence speed and easy to fall into local optimal solutions, chaos theory and variation mechanism were introduced, and adaptive discovery probability and step size were used to improve multi-objective cuckoo algorithm and enhance the overall performance of improved multi-objective cuckoo algorithm. A water resources optimal allocation model of the Jiangsu section of the South-to-North Water Transfers Project was established. Improved multi-objective cuckoo algorithm and multi-objective cuckoo algorithm were adopted to solve the water resources optimal allocation model, respectively. A non-negative matrix factorization method based on combined weighting was used for scheme evaluation. The results show that: improved multi-objective cuckoo algorithm was better than multi-objective cuckoo algorithm in terms of convergence, distribution, and overall performance, and was capable of yielding higher-quality Pareto solution sets; Compared with the optimal allocation solution solved by multi-objective cuckoo algorithm under the 50%, 75%, and 95% incoming water conditions, the total water shortage of the optimal allocation solution solved by improved multi-objective cuckoo algorithm was reduced by 21 million m3, 51 million m3 and 7 million m3, and the water loss was reduced by 13 million m3, 15 million m3 and 11 million m3, respectively. Therefore, improved multi-objective cuckoo algorithm could provide an effective algorithmic reference for the calculation of multi-objective optimal allocation of inter-basin water resources.
2024, 22(5):959-966,977.
Abstract:
The safe and stable operation of the pumping station system is of great significance for ensuring supply for domestic water agricultural irrigation, and industrial water. Therefore, real-time monitoring of pump station operating parameters and establishing predictive models for fault diagnosis and intelligent alarm of unit equipment have significant application value. The data-driven method for fault diagnosis is currently a hot topic in the research of pump station equipment status monitoring. However, there are problems such as insufficient data samples, difficulty in feature extraction, and insufficient generalization ability in practical application. Addressing the challenge of predicting the trends in operating parameters of water pump units under complex working conditions, a prediction model for operating parameters of water pump units was proposed based on multi-task learning method and attention mechanism. Firstly, the historical working condition data was fully utilized, and a multi-task learning model was established to find the common characteristics of the historical working condition data on the basis of traditional principal component analysis methods. Secondly, an attention mechanism was introduced to dynamically allocate weight coefficients for common feature mapping when predicting the trend of parameter changes under new operating conditions, highlighting key common features and improving the accuracy of the prediction. Based on the actual operating data of a pumping station hub unit, the performance of the model was tested. By monitoring the statistical parameters T2 and Q, which reflecting the stability and accurately of the model, results showed that the prediction model proposed has good stability and prediction accuracy under 98% and 95% control thresholds. On this basis, a multi-level equipment operation monitoring and alarm model was also preliminarily established. The alarm level is divided into three levels: yellow, orange, and red. Management personnel can take different disposal measures based on the alarm level, such as paying attention to observation, strengthening monitoring and inspection frequency, adjusting equipment operation, and even shutting down for maintenance, to avoid accidents and ensure the safety of equipment and personnel. By comparing the predictive performance of the proposed method with single task learning and the model without attention mechanism, it can be concluded that: Compared with traditional static PCA model prediction methods, the multi-task learning model can fully utilize the common features of historical data to predict changes in unit parameters, fully consider the correlation between different tasks, and improve the robustness of the prediction model. The introduction of attention mechanism enables the model to dynamically adjust the mapping weights based on the characteristics of unit operating parameters in new time periods, further improving the stability of the model and the accuracy of prediction. The results have important application value for safety monitoring and intelligent warning of pump unit operation in pumping stations.
The safe and stable operation of the pumping station system is of great significance for ensuring supply for domestic water agricultural irrigation, and industrial water. Therefore, real-time monitoring of pump station operating parameters and establishing predictive models for fault diagnosis and intelligent alarm of unit equipment have significant application value. The data-driven method for fault diagnosis is currently a hot topic in the research of pump station equipment status monitoring. However, there are problems such as insufficient data samples, difficulty in feature extraction, and insufficient generalization ability in practical application. Addressing the challenge of predicting the trends in operating parameters of water pump units under complex working conditions, a prediction model for operating parameters of water pump units was proposed based on multi-task learning method and attention mechanism. Firstly, the historical working condition data was fully utilized, and a multi-task learning model was established to find the common characteristics of the historical working condition data on the basis of traditional principal component analysis methods. Secondly, an attention mechanism was introduced to dynamically allocate weight coefficients for common feature mapping when predicting the trend of parameter changes under new operating conditions, highlighting key common features and improving the accuracy of the prediction. Based on the actual operating data of a pumping station hub unit, the performance of the model was tested. By monitoring the statistical parameters T2 and Q, which reflecting the stability and accurately of the model, results showed that the prediction model proposed has good stability and prediction accuracy under 98% and 95% control thresholds. On this basis, a multi-level equipment operation monitoring and alarm model was also preliminarily established. The alarm level is divided into three levels: yellow, orange, and red. Management personnel can take different disposal measures based on the alarm level, such as paying attention to observation, strengthening monitoring and inspection frequency, adjusting equipment operation, and even shutting down for maintenance, to avoid accidents and ensure the safety of equipment and personnel. By comparing the predictive performance of the proposed method with single task learning and the model without attention mechanism, it can be concluded that: Compared with traditional static PCA model prediction methods, the multi-task learning model can fully utilize the common features of historical data to predict changes in unit parameters, fully consider the correlation between different tasks, and improve the robustness of the prediction model. The introduction of attention mechanism enables the model to dynamically adjust the mapping weights based on the characteristics of unit operating parameters in new time periods, further improving the stability of the model and the accuracy of prediction. The results have important application value for safety monitoring and intelligent warning of pump unit operation in pumping stations.
2024, 22(5):967-977.
Abstract:
The frigid regions were characterized by low annual average temperatures and significant diurnal temperature variations, which made it prone for dams to develop temperature cracks. The conventional high concrete arch dams at that time utilized a mix of "moderate heat Portland cement with 35% Class I fly ash". Despite achieving the then-current advanced level of temperature control measures, the safety factor for concrete crack resistance remained at approximately 1.8. Consequently, it was imperative to conduct research on temperature control and crack prevention, starting from the very source of the materials. With the concrete double-curvature arch dam of a hydropower station in Xizang as its research backdrop, low-heat cement concrete was chosen as the dam-building material. By reducing the temperature rise from the source of the material, it aimed to further mitigate the risk of cracking. Drawing upon the theories of unstable temperature and stress field calculations, a comparative analysis of the temperature and stress fields between moderate-heat and low-heat cement concrete arch dams was conducted, highlighting the advantages of using low-heat cement concrete for dam construction. Following this, it optimized and compared various temperature control measures for low-heat cement concrete arch dams, ultimately formulating a tailored temperature control and crack prevention strategy suitable for frigid regions. The results indicated that the maximum temperature of the low-heat cement concrete arch dam was approximately 4.0℃ lower than that of the medium-heat cement concrete dam, and the maximum stress was reduced by about 0.7 MPa. Moreover, the safety factor was elevated from 2.48 to 3.65. For low-heat cement concrete arch dams, the spacing of water cooling pipes in the constrained zone could be relaxed to 1.5 m×1.5 m, and the thickness of the pouring layer in the strongly constrained zone could be increased from 1.5 m to 3.0 m. The water flow measures on the dam surface could be eliminated, and the pouring temperature in summer could be relaxed to 16.0 ℃, allowing for normal temperature pouring in winter. Outside the constrained zone, during the high-temperature season from May to September, the pouring temperature could be appropriately increased to 18.0 ℃. The thickness of the layer had a minor impact on the maximum temperature and stress of the dam, permitting an extension to 6 m. The dam adopted permanent thermal insulation throughout the year, with a equivalent heat release coefficient of β≤ 3.05 kJ/(m2?h?℃). These research outcomes validated the superiority of low-heat cement concrete arch dams in crack resistance and provided optimized temperature control measures for frigid regions. They facilitated rapid dam construction while further reducing temperature control and construction costs, offering technical guidance for the application of low-heat cement in frigid regions.
The frigid regions were characterized by low annual average temperatures and significant diurnal temperature variations, which made it prone for dams to develop temperature cracks. The conventional high concrete arch dams at that time utilized a mix of "moderate heat Portland cement with 35% Class I fly ash". Despite achieving the then-current advanced level of temperature control measures, the safety factor for concrete crack resistance remained at approximately 1.8. Consequently, it was imperative to conduct research on temperature control and crack prevention, starting from the very source of the materials. With the concrete double-curvature arch dam of a hydropower station in Xizang as its research backdrop, low-heat cement concrete was chosen as the dam-building material. By reducing the temperature rise from the source of the material, it aimed to further mitigate the risk of cracking. Drawing upon the theories of unstable temperature and stress field calculations, a comparative analysis of the temperature and stress fields between moderate-heat and low-heat cement concrete arch dams was conducted, highlighting the advantages of using low-heat cement concrete for dam construction. Following this, it optimized and compared various temperature control measures for low-heat cement concrete arch dams, ultimately formulating a tailored temperature control and crack prevention strategy suitable for frigid regions. The results indicated that the maximum temperature of the low-heat cement concrete arch dam was approximately 4.0℃ lower than that of the medium-heat cement concrete dam, and the maximum stress was reduced by about 0.7 MPa. Moreover, the safety factor was elevated from 2.48 to 3.65. For low-heat cement concrete arch dams, the spacing of water cooling pipes in the constrained zone could be relaxed to 1.5 m×1.5 m, and the thickness of the pouring layer in the strongly constrained zone could be increased from 1.5 m to 3.0 m. The water flow measures on the dam surface could be eliminated, and the pouring temperature in summer could be relaxed to 16.0 ℃, allowing for normal temperature pouring in winter. Outside the constrained zone, during the high-temperature season from May to September, the pouring temperature could be appropriately increased to 18.0 ℃. The thickness of the layer had a minor impact on the maximum temperature and stress of the dam, permitting an extension to 6 m. The dam adopted permanent thermal insulation throughout the year, with a equivalent heat release coefficient of β≤ 3.05 kJ/(m2?h?℃). These research outcomes validated the superiority of low-heat cement concrete arch dams in crack resistance and provided optimized temperature control measures for frigid regions. They facilitated rapid dam construction while further reducing temperature control and construction costs, offering technical guidance for the application of low-heat cement in frigid regions.
2024, 22(5):978-986.
Abstract:
With the continuous increase of the application scale of pumping station engineering and the improvement of the complexity of operation and management, it has become an important research field to carry out the optimal scheduling of pumping station units and realize economic operation. Many scholars in China explore and study the construction and solution methods of pumping station optimization model. The traditional pumping station optimization scheduling methods mainly include nonlinear programming, dynamic programming, experimental optimization and large-scale system optimization methods. Among them, the dynamic programming method is widely used in the optimal scheduling of pumping stations. In the solution process, the decision variables are usually discretized with a certain step size, and the size of the discrete step size has a certain degree of influence on the accuracy of the optimal solution of the model target. Therefore, this paper attempts to find a particle swarm optimization algorithm that decision variables are randomly generated in the feasible region and can be continuously updated to further discuss the influence of different value methods of decision variables on the optimization results. According to the shortcomings of particle swarm optimization algorithm, which is easy to fall into local optimal solution and low precision, a combined improvement method of multi-strategy fusion of "Sobol sequence optimization initial population & real-time adjustment of inertia weight & sine and cosine substitution learning factor" was proposed. (1) Sobol sequence was applied to initialize the population, which made the initial population distribution more uniform and laid a good foundation for the global search of the algorithm. (2) A real-time nonlinear decreasing adjustment of inertia weight with number of iterations was adopted to improve the search ability of the algorithm at different stages. (3) The sine-cosine factor in the position update formula of SCA (sine-cosine algorithm) was introduced to replace the learning factor. Each particle could search and move between the best position of the individual and the best position of the population, so that it could carry out multi-directional search and enhance its search ability. At the same time, combined with the real-time nonlinear decreasing adjustment of inertia weight, the collaborative improvement of search ability of particle swarm optimization algorithm was realized in different stages. Through the performance test of four benchmark functions, it was verified that the improved particle swarm optimization algorithm had a significant improvement in search ability and accuracy compared with the basic particle swarm algorithm. On this basis, the improved particle swarm optimization algorithm was applied to the solution of the single unit variable speed optimization model with the minimum power consumption cost as objective function in a large-scale pump station. The optimal decision-making scheme and the corresponding optimal objective value were obtained, then compared with the calculation results of the dynamic programming method and basic PSO algorithm. The results showed that: (1) Compared with the basic PSO algorithm, the multi-strategy fusion improvement effect of the improved PSO algorithm is more significant (the water-pumping cost is reduced by 11.4 % at 60 % load operation, and the unit water-pumping cost is reduced by 11.9 %). (2) The optimal decision-making process of the two methods (improved PSO algorithm and dynamic programming method) was basically consistent, that was, in the process of variable speed operation of single unit, the unit was generally shutdown when the electricity price and lift head were relatively higher. Even if the unit was turned on, the speed was generally smaller, the amount of water was relatively less; However, when the electricity price and lift head were relatively lower, the unit was turned on, and the speed was generally larger, and the amount of water was relatively more. (3) The accuracy of the optimal objective values of two methods (improved PSO algorithm and dynamic programming method) was comparable. Under three different operating load scenarios, the unit cost of water-pumping of two methods were very close, and the absolute value of the deviation rate was not more than 0.1%. It can be seen that the combination improvement strategy of particle swarm optimization proposed was feasible and effective, and the solution result was satisfactory. Therefore, the combined improved particle swarm optimization algorithm can be used as an effective method to solve the unit variable speed optimization model of pumping station.
With the continuous increase of the application scale of pumping station engineering and the improvement of the complexity of operation and management, it has become an important research field to carry out the optimal scheduling of pumping station units and realize economic operation. Many scholars in China explore and study the construction and solution methods of pumping station optimization model. The traditional pumping station optimization scheduling methods mainly include nonlinear programming, dynamic programming, experimental optimization and large-scale system optimization methods. Among them, the dynamic programming method is widely used in the optimal scheduling of pumping stations. In the solution process, the decision variables are usually discretized with a certain step size, and the size of the discrete step size has a certain degree of influence on the accuracy of the optimal solution of the model target. Therefore, this paper attempts to find a particle swarm optimization algorithm that decision variables are randomly generated in the feasible region and can be continuously updated to further discuss the influence of different value methods of decision variables on the optimization results. According to the shortcomings of particle swarm optimization algorithm, which is easy to fall into local optimal solution and low precision, a combined improvement method of multi-strategy fusion of "Sobol sequence optimization initial population & real-time adjustment of inertia weight & sine and cosine substitution learning factor" was proposed. (1) Sobol sequence was applied to initialize the population, which made the initial population distribution more uniform and laid a good foundation for the global search of the algorithm. (2) A real-time nonlinear decreasing adjustment of inertia weight with number of iterations was adopted to improve the search ability of the algorithm at different stages. (3) The sine-cosine factor in the position update formula of SCA (sine-cosine algorithm) was introduced to replace the learning factor. Each particle could search and move between the best position of the individual and the best position of the population, so that it could carry out multi-directional search and enhance its search ability. At the same time, combined with the real-time nonlinear decreasing adjustment of inertia weight, the collaborative improvement of search ability of particle swarm optimization algorithm was realized in different stages. Through the performance test of four benchmark functions, it was verified that the improved particle swarm optimization algorithm had a significant improvement in search ability and accuracy compared with the basic particle swarm algorithm. On this basis, the improved particle swarm optimization algorithm was applied to the solution of the single unit variable speed optimization model with the minimum power consumption cost as objective function in a large-scale pump station. The optimal decision-making scheme and the corresponding optimal objective value were obtained, then compared with the calculation results of the dynamic programming method and basic PSO algorithm. The results showed that: (1) Compared with the basic PSO algorithm, the multi-strategy fusion improvement effect of the improved PSO algorithm is more significant (the water-pumping cost is reduced by 11.4 % at 60 % load operation, and the unit water-pumping cost is reduced by 11.9 %). (2) The optimal decision-making process of the two methods (improved PSO algorithm and dynamic programming method) was basically consistent, that was, in the process of variable speed operation of single unit, the unit was generally shutdown when the electricity price and lift head were relatively higher. Even if the unit was turned on, the speed was generally smaller, the amount of water was relatively less; However, when the electricity price and lift head were relatively lower, the unit was turned on, and the speed was generally larger, and the amount of water was relatively more. (3) The accuracy of the optimal objective values of two methods (improved PSO algorithm and dynamic programming method) was comparable. Under three different operating load scenarios, the unit cost of water-pumping of two methods were very close, and the absolute value of the deviation rate was not more than 0.1%. It can be seen that the combination improvement strategy of particle swarm optimization proposed was feasible and effective, and the solution result was satisfactory. Therefore, the combined improved particle swarm optimization algorithm can be used as an effective method to solve the unit variable speed optimization model of pumping station.
2024, 22(5):987-996.
Abstract:
Roller compacted concrete has the characteristics of low hydration heat, zero slump, high application speed, and low cost, making it suitable for constructing large concrete structures. The roller compacted concrete dam is constructed layer by layer using the roller compacted construction method with dry and hard concrete. However, layered construction often forms many layers, and the interlayer joints of rolled concrete are the weak link in engineering. Under continuous loads, cracks, holes and other defects will inevitably appear, endangering the anti-skid stability of the layer and posing a threat to the safety and durability of the dam. Layer cracks are a significant sign of structural failure. Therefore, it is necessary to conduct research on the crack propagation law of roller compacted concrete under load. Three different types of mortars (cement mortar, nano SiO2 mortar, and expansion agent mortar) were designed to treat the layer of roller compacted concrete specimens, and a universal testing machine was used to apply splitting load for splitting tensile test. The splitting tensile strength and surface morphology of different specimens were recorded. During the splitting tensile test, the DIC analysis system was used to collect the strain of the specimen during the splitting process, in order to obtain a strain cloud map of the entire process of crack formation and propagation in the roller compacted concrete layer. The variation patterns of crack width and propagation rate were calculated and analyzed through system software. The cutting machine was used to extract the layer of roller compacted concrete, and the microhardness of different mortar treatments was tested using a microhardness tester. The microhardness has a high linear relationship with crack propagation rate and splitting tensile strength. The experimental results indicated that: (1) The mortar treatment could better embed aggregates and provide mechanical interlocking force for layer bonding. Therefore, the splitting tensile strength of the compacted concrete layer was improved after mortar treatment. Nano SiO2 and expansion agents could promote cement hydration and fill pores, and the improvement effect of the three types of mortar was based on the following: nano SiO2 cement mortar. (2) Under the action of splitting load, strain concentration zones gradually appeared on the layer of roller compacted concrete. Based on the change of strain color gradient, the crack development process was divided into initial stage, initiation stage, expansion stage, and penetration stage. Mortar treatment could improve the stability of the layer structure and delay the development of layer cracks. The load displacement curve shown that the peak loads of the specimen layer treated with cement mortar, nano SiO2 mortar, and expansion agent mortar were 44.43 kN, 52.90 kN, 62.48 kN, and 59.81 kN, respectively. (3) The width and propagation rate of cracks were basically 0 in the initial stage, and then began to rise, with a sharp increase in the expansion stage. The crack propagation rate of the layer treated with nano SiO2 mortar were the smallest. (4) The microhardness of layer was lower than that of the upper and lower layers of rolled compacted concrete, and the determination coefficients between microhardness, crack propagation rate, and splitting tensile strength are 0.941 and 0.960, respectively. When carrying out roller compacted concrete construction, the layer as a weak structure, the occurrence of cracking needs to be considered. The expansion law of cracks on the layer of roller compacted concrete was studied through DIC technology, which could provide reference value for improving the bonding quality of roller compacted concrete dam layer and reducing the occurrence of cracks.
Roller compacted concrete has the characteristics of low hydration heat, zero slump, high application speed, and low cost, making it suitable for constructing large concrete structures. The roller compacted concrete dam is constructed layer by layer using the roller compacted construction method with dry and hard concrete. However, layered construction often forms many layers, and the interlayer joints of rolled concrete are the weak link in engineering. Under continuous loads, cracks, holes and other defects will inevitably appear, endangering the anti-skid stability of the layer and posing a threat to the safety and durability of the dam. Layer cracks are a significant sign of structural failure. Therefore, it is necessary to conduct research on the crack propagation law of roller compacted concrete under load. Three different types of mortars (cement mortar, nano SiO2 mortar, and expansion agent mortar) were designed to treat the layer of roller compacted concrete specimens, and a universal testing machine was used to apply splitting load for splitting tensile test. The splitting tensile strength and surface morphology of different specimens were recorded. During the splitting tensile test, the DIC analysis system was used to collect the strain of the specimen during the splitting process, in order to obtain a strain cloud map of the entire process of crack formation and propagation in the roller compacted concrete layer. The variation patterns of crack width and propagation rate were calculated and analyzed through system software. The cutting machine was used to extract the layer of roller compacted concrete, and the microhardness of different mortar treatments was tested using a microhardness tester. The microhardness has a high linear relationship with crack propagation rate and splitting tensile strength. The experimental results indicated that: (1) The mortar treatment could better embed aggregates and provide mechanical interlocking force for layer bonding. Therefore, the splitting tensile strength of the compacted concrete layer was improved after mortar treatment. Nano SiO2 and expansion agents could promote cement hydration and fill pores, and the improvement effect of the three types of mortar was based on the following: nano SiO2 cement mortar. (2) Under the action of splitting load, strain concentration zones gradually appeared on the layer of roller compacted concrete. Based on the change of strain color gradient, the crack development process was divided into initial stage, initiation stage, expansion stage, and penetration stage. Mortar treatment could improve the stability of the layer structure and delay the development of layer cracks. The load displacement curve shown that the peak loads of the specimen layer treated with cement mortar, nano SiO2 mortar, and expansion agent mortar were 44.43 kN, 52.90 kN, 62.48 kN, and 59.81 kN, respectively. (3) The width and propagation rate of cracks were basically 0 in the initial stage, and then began to rise, with a sharp increase in the expansion stage. The crack propagation rate of the layer treated with nano SiO2 mortar were the smallest. (4) The microhardness of layer was lower than that of the upper and lower layers of rolled compacted concrete, and the determination coefficients between microhardness, crack propagation rate, and splitting tensile strength are 0.941 and 0.960, respectively. When carrying out roller compacted concrete construction, the layer as a weak structure, the occurrence of cracking needs to be considered. The expansion law of cracks on the layer of roller compacted concrete was studied through DIC technology, which could provide reference value for improving the bonding quality of roller compacted concrete dam layer and reducing the occurrence of cracks.
2024, 22(5):997-1006.
Abstract:
The safety assessment of hydraulic gates is crucial for the regulatory capacity of hydropower stations, as it is closely related to factors such as water levels upstream and downstream of the station, structural stress, gate vibration, and the status of opening and closing mechanisms. Many of the hydraulic gates in operation in China were built in the 1960s and 1970s, and due to the harsh working environment, there are numerous safety risks. Therefore, conducting safety evaluations of hydraulic gates is essential to prevent potential accidents. However, existing studies on gate safety assessments have two main shortcomings in terms of indicator selection and system construction. Firstly, many studies overlook the impact of correlation between indicators in the evaluation system on the assessment results. High correlation between indicators can lead to redundant and interfering information, potentially distorting the evaluation results. This is especially evident in hydraulic systems like hydraulic gates, which have numerous parameters and complex structures. Additionally, as the number of measurement points increases, the scale of the indicator system also grows exponentially, complicating the evaluation process. Secondly, existing studies generally lack the identification and selection process of indicators' influence on the comprehensive safety assessment results. The impact of indicators on assessment results mainly lies in two aspects: the different magnitudes of changes in each indicator and the varying responses in comprehensive assessment values caused by relative changes in each indicator, indicating varying levels of sensitivity. Therefore, in conducting safety evaluations of hydraulic gates, both aspects need to be considered to ensure the rationality of the indicator selection process, the effectiveness of selected indicators, and the simplification of the indicator system. Two approaches, overall transformation and gradual reduction of indicators were employed in this study, utilizing uniform manifold approximation and projection (UMAP) and convolutional neural networks (CNN) to reduce the dimensionality of the evaluation indicator system. Initially, an initial indicator system for the safety assessment of hydraulic gates was constructed. Based on the analysis of indicator correlations, uniform UMAP was used to reduce the dimensionality of the original indicator system. A CNN training sample generation method based on indicator value discretization was proposed, introducing two indicators, relative change magnitude, and sensitivity, to quantitatively evaluate the impact of indicators and their relative changes on the comprehensive safety assessment of gates, thereby selecting key evaluation indicators. By comparing and verifying the results before and after dimensionality reduction of the evaluation indicator system based on monitoring data from the middle hole gate of Shaping II hydropower station, the differences and respective advantages and disadvantages of the two dimensionality reduction methods from multiple perspectives were disscussed. The results indicate that the dimensionality reduction strategy based on UMAP demonstrates significant advantages in reducing inter-indicator correlations and improving computational efficiency, while the CNN-based dimensionality reduction strategy shows more pronounced superiority in maintaining the accuracy of the indicators' physical meanings. These proposed methods not only advance the theory and practice of reducing complex indicator systems but also enhance the rationality of the indicator selection process, the effectiveness of selected indicators, and the simplicity of the indicator system, providing professionals with a quantitative analytical tool. Furthermore, based on the findings of this study, future research will focus on how to more effectively coordinate the weight distribution between indicators for specific applications such as safety assessments of hydraulic gates, propose a more reasonable comprehensive safety assessment method for hydraulic gates, and further develop safety classification identification and warning technologies to offer more comprehensive and in-depth theoretical and practical support in this field.
The safety assessment of hydraulic gates is crucial for the regulatory capacity of hydropower stations, as it is closely related to factors such as water levels upstream and downstream of the station, structural stress, gate vibration, and the status of opening and closing mechanisms. Many of the hydraulic gates in operation in China were built in the 1960s and 1970s, and due to the harsh working environment, there are numerous safety risks. Therefore, conducting safety evaluations of hydraulic gates is essential to prevent potential accidents. However, existing studies on gate safety assessments have two main shortcomings in terms of indicator selection and system construction. Firstly, many studies overlook the impact of correlation between indicators in the evaluation system on the assessment results. High correlation between indicators can lead to redundant and interfering information, potentially distorting the evaluation results. This is especially evident in hydraulic systems like hydraulic gates, which have numerous parameters and complex structures. Additionally, as the number of measurement points increases, the scale of the indicator system also grows exponentially, complicating the evaluation process. Secondly, existing studies generally lack the identification and selection process of indicators' influence on the comprehensive safety assessment results. The impact of indicators on assessment results mainly lies in two aspects: the different magnitudes of changes in each indicator and the varying responses in comprehensive assessment values caused by relative changes in each indicator, indicating varying levels of sensitivity. Therefore, in conducting safety evaluations of hydraulic gates, both aspects need to be considered to ensure the rationality of the indicator selection process, the effectiveness of selected indicators, and the simplification of the indicator system. Two approaches, overall transformation and gradual reduction of indicators were employed in this study, utilizing uniform manifold approximation and projection (UMAP) and convolutional neural networks (CNN) to reduce the dimensionality of the evaluation indicator system. Initially, an initial indicator system for the safety assessment of hydraulic gates was constructed. Based on the analysis of indicator correlations, uniform UMAP was used to reduce the dimensionality of the original indicator system. A CNN training sample generation method based on indicator value discretization was proposed, introducing two indicators, relative change magnitude, and sensitivity, to quantitatively evaluate the impact of indicators and their relative changes on the comprehensive safety assessment of gates, thereby selecting key evaluation indicators. By comparing and verifying the results before and after dimensionality reduction of the evaluation indicator system based on monitoring data from the middle hole gate of Shaping II hydropower station, the differences and respective advantages and disadvantages of the two dimensionality reduction methods from multiple perspectives were disscussed. The results indicate that the dimensionality reduction strategy based on UMAP demonstrates significant advantages in reducing inter-indicator correlations and improving computational efficiency, while the CNN-based dimensionality reduction strategy shows more pronounced superiority in maintaining the accuracy of the indicators' physical meanings. These proposed methods not only advance the theory and practice of reducing complex indicator systems but also enhance the rationality of the indicator selection process, the effectiveness of selected indicators, and the simplicity of the indicator system, providing professionals with a quantitative analytical tool. Furthermore, based on the findings of this study, future research will focus on how to more effectively coordinate the weight distribution between indicators for specific applications such as safety assessments of hydraulic gates, propose a more reasonable comprehensive safety assessment method for hydraulic gates, and further develop safety classification identification and warning technologies to offer more comprehensive and in-depth theoretical and practical support in this field.
2024, 22(5):1007-1015.
Abstract:
Studying the improvement of large volumes of sandy soil is one of the important topics in the current road engineering field. In order to comprehensively consider the availability of materials and the requirements of engineering cost, loess with a low clay content was used to improve sandy soil. This choice is based on the fact that loess is widely available in many areas and is relatively cheap, and its soil properties can also meet the needs of engineering improvement. The main purpose of this study is to deeply explore the coupling effect of stress field and seepage field. It should be noted that the volume of silt sand that needs to be improved in the project is often large, and the amount of high-quality clay soil in the region is small. Whether it is possible to use soil with low viscosity to improve silt sand is of great research significance. The sand in Jinzhong area was taken as the research object, and uses a stress-strain controlled triaxial shear permeability tester to explore in detail the influence of loess on the stress-seepage coupling relationship of sand. During the test, by changing variables such as the water content, confining pressure and loess content of the sand, the changes in the strength and permeability of the sand under the conditions of multi-factor coupling were systematically analyzed. The test results show that during the shearing process, the strength of sand shows a decreasing trend as the moisture content increases. Specifically, as the moisture content increases, the friction between sand particles decreases, resulting in a decrease in its overall strength. At the same time, the strength of sand also decreases with the increase of confining pressure. This may be because as the confining pressure increases, the porosity between sand particles decreases, resulting in an increase in the binding force between particles, resulting in a decrease in the shear strength. In addition, the study also found that the strength of sand soil is most affected by the amount of loess. When the loess content increases, the strength of the sand soil increases significantly, which shows that the addition of loess has a positive effect on improving the mechanical properties of the sand soil. Test results on the permeability of sand show that it changes significantly with changes in stress. In particular, the permeability coefficient decreases with increasing axial stress. This phenomenon can be explained by the fact that the increase in axial stress further compresses the pores between sand particles, thereby reducing the channels for water flow, resulting in a decrease in permeability. More importantly, experimental data also show that the permeability coefficient of sand decreases by an order of magnitude as the loess content increases. This result shows that the addition of loess significantly improves the permeability properties of sand, making it better able to prevent leakage and water intrusion in practical engineering applications. By analyzing the data provided by the stress-seepage coupling test of sand, the effectiveness of adding local loess to improve the properties of sand was proved. Although there are certain differences in viscosity between local loess and traditionally used clay, the results show that loess can still achieve similar improvement effects in key indicators such as strength and permeability. This provides a solid theoretical basis and practical basis for using loess to improve sand in actual engineering. In summary, this study proved the feasibility and effectiveness of using local loess to improve sand through detailed experiments and data analysis. This not only provides a reliable technical solution for engineering practice in Jinzhong, but also provides a valuable reference for sand improvement in other similar areas. It is expected that the performance changes of loess-improved sand under different stress conditions and environmental factors will be further studied to provide more comprehensive technical support for applications in the field of civil engineering.
Studying the improvement of large volumes of sandy soil is one of the important topics in the current road engineering field. In order to comprehensively consider the availability of materials and the requirements of engineering cost, loess with a low clay content was used to improve sandy soil. This choice is based on the fact that loess is widely available in many areas and is relatively cheap, and its soil properties can also meet the needs of engineering improvement. The main purpose of this study is to deeply explore the coupling effect of stress field and seepage field. It should be noted that the volume of silt sand that needs to be improved in the project is often large, and the amount of high-quality clay soil in the region is small. Whether it is possible to use soil with low viscosity to improve silt sand is of great research significance. The sand in Jinzhong area was taken as the research object, and uses a stress-strain controlled triaxial shear permeability tester to explore in detail the influence of loess on the stress-seepage coupling relationship of sand. During the test, by changing variables such as the water content, confining pressure and loess content of the sand, the changes in the strength and permeability of the sand under the conditions of multi-factor coupling were systematically analyzed. The test results show that during the shearing process, the strength of sand shows a decreasing trend as the moisture content increases. Specifically, as the moisture content increases, the friction between sand particles decreases, resulting in a decrease in its overall strength. At the same time, the strength of sand also decreases with the increase of confining pressure. This may be because as the confining pressure increases, the porosity between sand particles decreases, resulting in an increase in the binding force between particles, resulting in a decrease in the shear strength. In addition, the study also found that the strength of sand soil is most affected by the amount of loess. When the loess content increases, the strength of the sand soil increases significantly, which shows that the addition of loess has a positive effect on improving the mechanical properties of the sand soil. Test results on the permeability of sand show that it changes significantly with changes in stress. In particular, the permeability coefficient decreases with increasing axial stress. This phenomenon can be explained by the fact that the increase in axial stress further compresses the pores between sand particles, thereby reducing the channels for water flow, resulting in a decrease in permeability. More importantly, experimental data also show that the permeability coefficient of sand decreases by an order of magnitude as the loess content increases. This result shows that the addition of loess significantly improves the permeability properties of sand, making it better able to prevent leakage and water intrusion in practical engineering applications. By analyzing the data provided by the stress-seepage coupling test of sand, the effectiveness of adding local loess to improve the properties of sand was proved. Although there are certain differences in viscosity between local loess and traditionally used clay, the results show that loess can still achieve similar improvement effects in key indicators such as strength and permeability. This provides a solid theoretical basis and practical basis for using loess to improve sand in actual engineering. In summary, this study proved the feasibility and effectiveness of using local loess to improve sand through detailed experiments and data analysis. This not only provides a reliable technical solution for engineering practice in Jinzhong, but also provides a valuable reference for sand improvement in other similar areas. It is expected that the performance changes of loess-improved sand under different stress conditions and environmental factors will be further studied to provide more comprehensive technical support for applications in the field of civil engineering.
2024, 22(5):1016-1028.
Abstract:
Currently, owing to the significant impact of carbonate weathering processes, the chemical composition of groundwater in the Tianjin Plain exhibits a distinct horizontal zonation pattern, both from north to south and from north to east. This zonation is a result of complex geological and hydrogeological interactions that shape the unique characteristics of the groundwater in this region. The pH value of the groundwater typically ranges between 7.0 and 8.5, classifying it as neutral to slightly alkaline. This pH range is influenced by various factors, including the mineralogy of the aquifer materials and the presence of dissolved ions. One notable feature of the groundwater in the Tianjin Plain is its high total dissolved solids (TDS) content. This elevated TDS level is primarily attributed to the abundance of specific ions, notably sodium (Na+), chloride (Cl?), bicarbonate (HCO?3)，and calcium (Ca2+) ions. In order to study the hydrochemical characteristics of groundwater in Tianjin Plain and the spatial and temporal evolution characteristics of carbonate weathered carbon sinks, ArcGIS antidistance weight spatial evolution analysis, cluster analysis and SPSS data statistical analysis were conducted by the sampling data from 2020 to 2022 in the research area. It is found that in terms of spatial and temporal evolution characteristics, the mass concentration of Na+ and Cl? increased gradually from 2020 to 2022, and the content of salt water in the groundwater between 116°63′E-116°84′E、117°12′E-117°47′E, Ca2++ Mg2+to HCO?3+SO24? was less than 1. In the latitudinal spatial distribution of ions in Tianjin Plain, the mass concentration interval of cations Na+ and Ca2+ in groundwater is [104 mg/L,4 812.21 mg/L], Ca2+[38.3 mg/L,713.67 mg/L], and the mass concentration interval of anions Cl?and HCO?3 is [73.6 mg/L, 11 282.49 mg/L], [78 mg/L, 539.46 mg/L], respectively. In terms of the influence of salinity water intrusion, by comparing and analyzing the concentration ratio of Cl? and other ions (Na+, Ca2+) in groundwater, it is found that the salinity degree of groundwater is high near the coastal area, and further empirical analysis found that seawater intrusion has a significant impact on the chemical composition of groundwater (Δ [Na+]=4 708.21mg/L,Δ[Cl?]=11 208.89mg/L). In terms of characteristic analysis of carbonate weathered carbon sink, the weathered carbon sink of groundwater in the western area of Tianjin Plain decreased, while the eastern area increased, and the mass concentration of Ca2+ and Mg2+had a significant positive effect on carbonate weathered carbon sink (B [Ca2+]=0.910,p=0.03; B[Mg2+]=0.312,p=0.09). The results further verify the weathering of groundwater carbonate in Tianjin Plain and the relationship between the chemical characteristics of groundwater. In terms of spatial and temporal evolution characteristics, the mass concentration of Na+ and Cl? showed a trend of increasing year by year from 2020 to 2022, indicating that the salt water content in groundwater was gradually increasing. In terms of the impact of brine intrusion, the coastal area, namely the area with higher longitude, showed a higher salinity of groundwater, indicating that seawater intrusion had a significant influence on the chemical composition of groundwater. In terms of the characteristic analysis of carbonate weathered carbon sink, the carbonate weathered carbon sink of groundwater in the western area of Tianjin Plain decreased, while the amount of groundwater carbonate in the eastern region increased, and the mass concentration of Ca2+ and Mg 2+ had a significant positive effect on the carbonate weathered carbon sink. Compared with southwest karst region and the loess plateau region, it is found that although these areas of rock dissolution and carbon sink effect in common, but the Tianjin Plain rock type, dissolution rate and climate conditions, which leads to the carbonate weathering carbon sink characteristics and influencing factors, also has certain uniqueness.
Currently, owing to the significant impact of carbonate weathering processes, the chemical composition of groundwater in the Tianjin Plain exhibits a distinct horizontal zonation pattern, both from north to south and from north to east. This zonation is a result of complex geological and hydrogeological interactions that shape the unique characteristics of the groundwater in this region. The pH value of the groundwater typically ranges between 7.0 and 8.5, classifying it as neutral to slightly alkaline. This pH range is influenced by various factors, including the mineralogy of the aquifer materials and the presence of dissolved ions. One notable feature of the groundwater in the Tianjin Plain is its high total dissolved solids (TDS) content. This elevated TDS level is primarily attributed to the abundance of specific ions, notably sodium (Na+), chloride (Cl?), bicarbonate (HCO?3)，and calcium (Ca2+) ions. In order to study the hydrochemical characteristics of groundwater in Tianjin Plain and the spatial and temporal evolution characteristics of carbonate weathered carbon sinks, ArcGIS antidistance weight spatial evolution analysis, cluster analysis and SPSS data statistical analysis were conducted by the sampling data from 2020 to 2022 in the research area. It is found that in terms of spatial and temporal evolution characteristics, the mass concentration of Na+ and Cl? increased gradually from 2020 to 2022, and the content of salt water in the groundwater between 116°63′E-116°84′E、117°12′E-117°47′E, Ca2++ Mg2+to HCO?3+SO24? was less than 1. In the latitudinal spatial distribution of ions in Tianjin Plain, the mass concentration interval of cations Na+ and Ca2+ in groundwater is [104 mg/L,4 812.21 mg/L], Ca2+[38.3 mg/L,713.67 mg/L], and the mass concentration interval of anions Cl?and HCO?3 is [73.6 mg/L, 11 282.49 mg/L], [78 mg/L, 539.46 mg/L], respectively. In terms of the influence of salinity water intrusion, by comparing and analyzing the concentration ratio of Cl? and other ions (Na+, Ca2+) in groundwater, it is found that the salinity degree of groundwater is high near the coastal area, and further empirical analysis found that seawater intrusion has a significant impact on the chemical composition of groundwater (Δ [Na+]=4 708.21mg/L,Δ[Cl?]=11 208.89mg/L). In terms of characteristic analysis of carbonate weathered carbon sink, the weathered carbon sink of groundwater in the western area of Tianjin Plain decreased, while the eastern area increased, and the mass concentration of Ca2+ and Mg2+had a significant positive effect on carbonate weathered carbon sink (B [Ca2+]=0.910,p=0.03; B[Mg2+]=0.312,p=0.09). The results further verify the weathering of groundwater carbonate in Tianjin Plain and the relationship between the chemical characteristics of groundwater. In terms of spatial and temporal evolution characteristics, the mass concentration of Na+ and Cl? showed a trend of increasing year by year from 2020 to 2022, indicating that the salt water content in groundwater was gradually increasing. In terms of the impact of brine intrusion, the coastal area, namely the area with higher longitude, showed a higher salinity of groundwater, indicating that seawater intrusion had a significant influence on the chemical composition of groundwater. In terms of the characteristic analysis of carbonate weathered carbon sink, the carbonate weathered carbon sink of groundwater in the western area of Tianjin Plain decreased, while the amount of groundwater carbonate in the eastern region increased, and the mass concentration of Ca2+ and Mg 2+ had a significant positive effect on the carbonate weathered carbon sink. Compared with southwest karst region and the loess plateau region, it is found that although these areas of rock dissolution and carbon sink effect in common, but the Tianjin Plain rock type, dissolution rate and climate conditions, which leads to the carbonate weathering carbon sink characteristics and influencing factors, also has certain uniqueness.
2024, 22(5):1029-1040.
Abstract:
Water, wind, and solar energy are essential components of clean energy. Water energy resources are widely distributed, with mature development and utilization technology and low economic cost, and they have been widely applied and developed around the world. Wind and solar energy resources also have the characteristics of abundant reserves, wide distribution, and no pollution. They have developed rapidly in recent years and become essential directions for the development of renewable energy. Global climate change is one of the problems that has huge influence on the world. Water, wind, and solar energy are directly related to climate variables such as precipitation, temperature, wind speed, and radiation. At present, few studies comprehensively summarize and compare the impacts of climate change on water energy resources, wind energy resources, and solar energy resources in China. The commonly used indicators and evaluation methods for water, wind, and solar energy resources were summarized. It also summarizes the impacts of climate change on these resources in the historical period and their changes under different climate change scenarios in the future across China were also summarized. The main evaluation indicators of water energy resources include theoretical reserves, technical exploitability, economic exploitability, already developed and under development amount, hydropower generation, gross hydropower potential, developed hydropower potential and river runoff. The main evaluation indicators of wind energy resources include average wind speed, wind direction frequency, wind power density, effective hours, turbulence intensity, wind energy content, and technical exploitability. The main evaluation indicators of solar energy resources include abundance, stability, direct ratio, solar radiation, and sunshine duration. In summary, the evaluation of water energy resources, wind energy resources, and solar energy resources could adopt evaluation methods based on observation data, numerical model simulation results, satellite remote sensing products, and reanalysis data. Water energy, wind energy, and solar energy are significantly affected by climate change. In most of previous studies, the total amounts of water energy resources were projected to increase, and wind energy resources and solar energy resources were projected to decrease generally, with spatial-temporal heterogeneity, in the future. Climate change would affect the development and utilization of renewable energy, dominated by water, wind, and solar energy. To cope with the adverse impacts of climate change, the following three frontier topics are suggested to be focused on in the future. First, research on the evaluation indicators of water, wind, and solar energy resources should be further conducted to form a unified evaluation standard under the changing climate. Second, the primary database of clean energy resources evaluation and the universal software used for resources evaluation should be constructed to improve the digital intelligence level of the power system. Third, the accurate and comprehensive evaluation of water energy resources, wind energy resources, and solar energy resources under the changing climate in the future and the medium and long-term forecasting of hydropower, wind power, and solar power generation should be further studied, which could improve the accuracy of resources evaluation and medium and long-term forecasting.
Water, wind, and solar energy are essential components of clean energy. Water energy resources are widely distributed, with mature development and utilization technology and low economic cost, and they have been widely applied and developed around the world. Wind and solar energy resources also have the characteristics of abundant reserves, wide distribution, and no pollution. They have developed rapidly in recent years and become essential directions for the development of renewable energy. Global climate change is one of the problems that has huge influence on the world. Water, wind, and solar energy are directly related to climate variables such as precipitation, temperature, wind speed, and radiation. At present, few studies comprehensively summarize and compare the impacts of climate change on water energy resources, wind energy resources, and solar energy resources in China. The commonly used indicators and evaluation methods for water, wind, and solar energy resources were summarized. It also summarizes the impacts of climate change on these resources in the historical period and their changes under different climate change scenarios in the future across China were also summarized. The main evaluation indicators of water energy resources include theoretical reserves, technical exploitability, economic exploitability, already developed and under development amount, hydropower generation, gross hydropower potential, developed hydropower potential and river runoff. The main evaluation indicators of wind energy resources include average wind speed, wind direction frequency, wind power density, effective hours, turbulence intensity, wind energy content, and technical exploitability. The main evaluation indicators of solar energy resources include abundance, stability, direct ratio, solar radiation, and sunshine duration. In summary, the evaluation of water energy resources, wind energy resources, and solar energy resources could adopt evaluation methods based on observation data, numerical model simulation results, satellite remote sensing products, and reanalysis data. Water energy, wind energy, and solar energy are significantly affected by climate change. In most of previous studies, the total amounts of water energy resources were projected to increase, and wind energy resources and solar energy resources were projected to decrease generally, with spatial-temporal heterogeneity, in the future. Climate change would affect the development and utilization of renewable energy, dominated by water, wind, and solar energy. To cope with the adverse impacts of climate change, the following three frontier topics are suggested to be focused on in the future. First, research on the evaluation indicators of water, wind, and solar energy resources should be further conducted to form a unified evaluation standard under the changing climate. Second, the primary database of clean energy resources evaluation and the universal software used for resources evaluation should be constructed to improve the digital intelligence level of the power system. Third, the accurate and comprehensive evaluation of water energy resources, wind energy resources, and solar energy resources under the changing climate in the future and the medium and long-term forecasting of hydropower, wind power, and solar power generation should be further studied, which could improve the accuracy of resources evaluation and medium and long-term forecasting.
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