About the Journal
Journal Title: Water Resources and Hydropower Engineering
Publication Cycle: Monthly(Published on the 20th of each month)
Governing Body: Ministry of Water Resources of the People's Republic of China
Sponsor: Development Research Center of the Ministry of Water Resources
Tel: 010-63205981
E-mail： 13941816@qq.com
China Standard Serial Number (CN): 10-1746/TV
International Standard Serial Number (ISSN): 1000-0860
News
Spatiotemporal variations of compound extreme temperature and precipitation events in Pearl River Basin from 1961 to 2020
ZHOU Biao;WEN Shanshan;JIANG Fushuang;WANG Chenyu;[Objective]The Pearl River Basin is an important economic and densely populated area in China, frequently affected by extreme temperature and precipitation events. Studying the spatiotemporal variations of compound extreme events is vital for regional climate adaptation management.[Methods]Based on meteorological observation data from 1961 to 2020, this study employs the relative threshold method and the Standardized Precipitation Evapotranspiration Index(SPEI) to identify four types of compound extreme events: hot-dry, hot-wet, cold-dry, and cold-wet. Trend analysis and spatial distribution studies were conducted to reveal the spatiotemporal distribution characteristics and trends of these compound events.[Results]The result indicate a significant negative correlation between temperature and SPEI in the Pearl River Basin from 1961 to 2020, particularly prominent in the western and southern regions during summer and winter. Notably, there has been a substantial increase in hot-wet events and a significant decrease in cold-dry events, with the most pronounced changes occurring in the early 21st century. Hot-wet events were mainly concentrated in the southeastern and southern coastal areas of the basin, and hot-dry events frequently occur in the western and southern regions. Cold-wet events are common in the northwest, and cold-dry events are most frequent in the northeast. In terms of intensity, hot-dry events are mainly classified as strong or medium, with their duration far exceeding that of hot-wet, cold-wet, and cold-dry events. From 1991 to 2020, the occurrence frequencies of hot-wet and hot-dry events increased by 24% and 99%, respectively, while those of cold-wet and cold-dry events decreased by 9% and 41%. Regionally, hot-dry events are most frequent in plains and hilly areas, particularly in Hainan Island and the South China Sea Islands, whereas cold-wet events are more prominent in mid-altitude and high-altitude areas. The changes reveal a significant transformation in the regional climate.[Conclusion]The findings indicate a notable increase in the frequency of hot-dry and hot-wet events in the Pearl River Basin, especially in the southern and western regions, reflecting high-risk exposure in these areas under future climate warming. Conversely, the decreasing trend of cold-dry and cold-wet events suggests a shift toward a warmer and wetter climate. Through an in-depth analysis of the spatiotemporal characteristics and evolution of compound extreme events, this study provides important scientific evidence and references for climate prediction, response strategies to compound disaster events, and enhancing disaster prevention and mitigation capabilities in the Pearl River Basin.
Flood prediction model based on decision trees
ZUO You;[Objective]Floods are natural disasters triggered by factors such as heavy rainfall, rapid snow and ice melt, and storm surges, often resulting in significant economic losses and severe disruption to daily life. Conventional flood prediction primarily relies on traditional hydrological method and experience-based statistical models. However, in areas lacking long-term and continuous hydrological monitoring data, alternative data-driven method for flood prediction are essential.[Methods]Machine learning algorithms based on decision trees, including Random Forest, XGBoost, and LightGBM, demonstrated excellent performance in classification and regression tasks due to their interpretability and strong functions, making them suitable for flood prediction. A dataset containing 50 000 records and 21 variables was used to evaluate the flood prediction performance of these three algorithms, namely Random Forest, XGBoost, and LightGBM. Their performance was assessed based on prediction accuracy and key variable identification, with the ROC-AUC curve used for comparative analysis.[Results]The result showed that all three models achieved high prediction accuracy. Among them, the XGBoost model exhibited the lowest mean squared error(0.000 186 2) and the highest coefficient of determination(0.925 2). Moreover, the LightGBM model achieved the highest AUC value(0.99) in the ROC-AUC curve. The Random Forest model underperformed the other two across all indicators.[Conclusion]The findings indicate that XGBoost delivers optimal performance for flood probability prediction with lowest prediction errors, while LightGBM is the optimal choice for binary classification tasks, such as predicting flood occurrence.
Analysis of carbon stock variation trends and driving factors in Dongting Lake area from 2003 to 2022
LI Wenjie;HUANG Cao;XIA Dan;ZHOU Hui;CHEN Weijing;[Objective]Dongting Lake, a crucial ecological barrier in China, not only plays a key role in regulating regional climate and maintaining biodiversity, but also has significant carbon sequestration capacity. Carbon stock and its variation trends in Dongting Lake area are analyzed in this study, and the effect of different hydrometeorological factors on the variation of carbon stock is investigated, aiming to provide a scientific basis for regional carbon management and ecological protection.[Methods]Based on the remote sensing data from 2003 to 2022 and the modified carbon density data, combined with the InVEST model, the dynamic variations of carbon stock in Dongting Lake area were quantitatively analyzed, and the spatiotemporal distribution characteristics of carbon stock and the influencing factors of carbon stock variations were discussed.[Results](1) From 2003 to 2022, the average carbon stock in Dongting Lake was 7.605 6×108 t, with the lowest value in 2007 at 7.368 4×108 t and the highest in 2005 at 7.904 7×108 t. Overall, the regional carbon stock showed a declining trend, with an average annual decrease of 0.12%.(2) Carbon stock was significantly affected by land use. The average values of carbon stock per unit area in hilly regions(forest land), plain regions(grassland and cultivated land), and water areas(water bodies and floodplains) were 27.05 kg/m2, 14.27 kg/m2, and 1.99 kg/m2, respectively.(3) Variations in carbon stock were affected by hydrometeorological factors such as temperature and precipitation. Average annual temperature showed a negative correlation with carbon stock variations, while precipitation in winter(October to December) exhibited a positive correlation with carbon stock.[Conclusion]From 2003 to 2022, carbon stock in Dongting Lake area shows an overall declining trend, indicating a weakening of its carbon sink function. Significant differences are observed in carbon stock across different areas. The hilly regions contribute the most to carbon sink capacity due to their high forest coverage, while the water areas contribute the least. Land use changes, climate fluctuations, and hydrological conditions have significant effects on carbon stock. Rising temperatures inhibit carbon stock, while winter precipitation enhances carbon sequestration. Addressing climate change effectively and optimizing land use structure are key strategies for improving the carbon sink function of Dongting Lake area.
Analysis of water conservation and carbon reduction in high-water-consuming industry: A case study of Jiangsu Province
LI En;HOU Rui;HOU Fangling;LIU Han;HUANG Changshuo;WANG Yining;Yangtze Institute for Conservation and Development;[Objective]Industry is one of the sectors with the highest concentration of water and energy use and is also one of the most carbon-intensive. The rapid development of the industrial economy has intensified the exploitation of water and energy resources, leading to a sharp increase in greenhouse gases such as carbon dioxide. To explore the water-saving and carbon-reduction potential of high-water-consuming industry in Jiangsu Province and to promote the rational development and utilization of water resources and energy, as well as the healthy, green development of industry, [Methods]adopting a comprehensive “water intake—water production—water distribution—water use—water discharge” perspective in Jiangsu Province, the social water system is simplified into five subsystems: water intake, water production, water distribution, water use, and water discharge. Energy consumption analysis for each subsystem under different water resource behaviors is conducted, establishing a relationship between water saving, energy consumption, and carbon reduction. Using the SFA model, the water-saving potential of high-water-consuming industry in Jiangsu Province is calculated, and scenarios with baseline, moderate, and high levels are analyzed to assess water-saving and carbon-reduction potential.[Results]The result show that in three scenarios, water conservation reduced energy consumption by 4.46×108 kWh, 4.50×108 kWh, and 4.54×108 kWh, respectively, and decreased carbon emissions by 347,000 tons, 349,700 tons, and 352,500 tons.[Conclusion]The findings indicate that water conservation in high-water-consuming industry will play an increasingly important role in supporting energy savings and reducing carbon emissions. The social water system reveals the relationship between industrial water conservation and energy savings at various stages but does not establish a direct link to carbon reduction. The SFA model can comprehensively consider factors such as population, economy, and water usage to calculate the potential for water conservation in high-water-consuming industry, offering significant application value. The analysis of water conservation and carbon reduction in high-water-consuming industry in Jiangsu Province demonstrates the substantial impact of water conservation on carbon reduction and provides a reference for industrial water management and energy distribution in Jiangsu Province.
Spatiotemporal evolution and driving factors of vegetation NPP in Jialing River Basin
DAI Qiankun;YANG Haiqing;GUO Ziyu;HUANG Huiqin;LIU Haoyu;WANG Mingmin;LI Hang;[Objective]Vegetation net primary productivity(NPP) is a crucial component in the carbon cycle of terrestrial ecosystems. Investigating its spatiotemporal evolution and driving factors is of great significance for promoting regional ecological civilization development.[Methods]Taking the Jialing River Basin as the study area, the spatiotemporal evolution patterns and driving factors of vegetation NPP were analyzed based on the coefficient of variation, Theil-Median trend method, Mann-Kendall statistical test, R/S analysis, geodetector, and PLS-SEM model.[Results]NPP showed a fluctuating upward trend, with spatial variation increasing and then decreasing with elevation. The overall trend remained stable, with fluctuations characterized by “higher in the south and lower in the north”. Historically, extremely significant increases predominated, while future trends were divided into two types: continuous increase and shift to decrease. Spatial differentiation was mainly dominated by natural factors such as temperature(Temp), normalized difference vegetation index(NDVI), and digital elevation model(DEM), while anthropogenic factors such as GDP also had significant effects. In interaction detection, Temp∩Pre, NDVI∩GDP, and Temp∩NDVI demonstrated the strongest explanatory power. PLS-SEM revealed that topography indirectly promoted NPP the most by inhibiting climate deterioration and promoting vegetation growth. Climate directly inhibited NPP but indirectly exerted a positive regulatory effect through vegetation growth, while indirectly inhibiting NPP through human activities. Human activities had negative effects both directly and indirectly. Based on future risk zoning, vegetation and urban landscapes should be optimized in the Sichuan Basin. A synergistic artificial-natural restoration system should be constructed in the northwest alpine area. Ecological redlines and corridors should be strengthened in mountainous and hilly areas. Ecological compensation and soil and water conservation projects should be coordinated across the entire basin.[Conclusion]Vegetation NPP in the Jialing River Basin is mainly increasing, with spatial differentiation dominated by natural factors. Temp∩Pre, NDVI∩GDP, and Temp∩NDVI have the strongest explanatory power. Topography indirectly promotes NPP by inhibiting climate deterioration and promoting vegetation growth. Climate directly inhibits NPP but indirectly regulates it positively through vegetation growth. Human activities inhibit NPP both directly and indirectly. Future strategies should focus on zoned management.
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2022—2023 Journal Catalogue
