To reveal the spatiotemporal evolution patterns and influencing factors of backwater effect of runoff in the lower reaches of the Ganjiang River,a hydrological runoff model suitable for plain river network areas was constructed and spatiotemporally coupled with a self-developed two-dimensional hydrodynamic model.The coupled hydrological-hydrodynamic model was adopted to simulate the backwater effect in the lower reaches of the Ganjiang River.Based on the simulation results,a water level difference prediction model was further established through multivariate regression analysis.The results show that the backwater effect in the lower reaches of the Ganjiang River is closely related to the discharge at Waizhou Station.During low-flow periods,the backwater in the north branch elevates the water level at Nanchang Station,while when the discharge increases,the spiral flow in the main branch propagates downstream,generating a backwater effect between Changyi and Nanchang stations.When the discharge exceeds the critical threshold of 513 m3/s,the water level of Poyang Lake is higher than that of the lower reaches of the Ganjiang River,initiating a backwater effect from the lake onto the river.When the discharge further increases to 3 211 m3/s,the backwater effect dissipates,and the flow diversion between the East River and West River tends to stabilize.The rainfall-induced runoff influences the evolutionary process of the generation and recession of the backwater effect,with a retardation effect ranging from 12%to 19%.After incorporating a correction term of rainfall-induced runoff,the precision of the water level difference prediction model is improved.
The main producing areas of Chinese baijiu are mostly distributed in artesian basins,and the groundwater recharge for brewing water exhibits distinct geological characteristics.The contents of minerals and rare earth elements in water body are the primary factors contributing to the regional differences in liquor taste.To investigate the key components and source of liquor-brewing water,multiple geochemical approaches were adopted to conduct hydrochemical analysis,water volume calculation,and hydrogen-oxygen isotopic analysis on surface water and groundwater in the Chishui River Basin.The results show that characteristic mineral components such as strontium,sulfur,and calcium in brewing water originate from water-rock reactions between groundwater and surrounding rock during groundwater migration.More than half of water volume in the Chishui River is supplied by exogenous water,which serves as the main recharge source during dry season;spring recharge area is derived from river seepage water in the Qinghai-Xizang Plateau.The midstream strata of Chishui River host carbonate rock formations containing celestite,barite,and gypsum,which provide sources of strontium,sulfur,calcium,and other substances to water body.It is inferred that seepage water from the Qinghai-Xizang Plateau dissolves minerals such as strontium,calcium,and sulfur from carbonate rocks during transport and converges into Chishui River.The river water is rich in unique minerals and rare earth elements required for brewing high-quality liquors such as Moutai,Xijiu,and Langjiu,endowing Chishui River with its distinctive status as the"river of fine liquors".
Regarding the issue of insufficient attention to artificial intelligence methods in current evaluation of water resources intensive and safe utilization,an evaluation index system for water resources intensive and safe utilization was established.The nutcracker optimization algorithm(NOA)was employed to optimize the parameters of the support vector machine(SVM)model,leading to the construction of an NOA-SVM model for the evaluation of intensive and safe utilization of water resources.By altering the proportions of training set and testing set,the evaluation result of the NOA-SVM model was compared with that of the standard SVM model,and the performance of the NOA-SVM model under different kernel functions was analyzed and compared.Furthermore,an empirical analysis was conducted on the 31 provinces(autonomous regions and municipalities)of China excluding Hongkong,Macao and Taiwan,to verify the effectiveness of the model.The results indicate that the NOA-SVM model achieves a higher evaluation accuracy than the conventional SVM model in the evaluation of the intensive and safe utilization of water resources.In particular,the radial basis function kernel maintains a stable goodness-of-fit under different proportions of the training set,and its corresponding error metrics including MSE,RMSE,and MAE are all desirable and superior to those of the linear kernel and polynomial kernel.The top seven provinces(autonomous regions and municipalities)in the evaluation results of intensive and safe utilization of water resources are Tianjin,Hebei,Beijing,Henan,Shandong,Shanxi,and Inner Mongolia,and their core advantages lie in controlled development intensity,improved water use efficiency,and well-adapted industrial structure.The bottom seven provinces(autonomous regions,municipalities)are Jiangxi,Jiangsu,Hubei,Shanghai,Hainan,Guangxi,and Xizang,and their core issues include excessive resource development,low water use efficiency,and insufficient ecological water use.
To address the difficulty in runoff forecasting in alpine areas,a runoff forecasting method coupling the iRainSnowHydro model and weighted Markov chain was proposed.The iRainSnowHydro model considered the regulation and storage of snowmelt water by constructing a snow reservoir,precisely depicting the process of snowmelt water outflows from wet snow.The method also introduced a weighted Markov chain to capture the transmission and transfer rules of simulation errors,achieving real-time error correction.The forecasting method was applied to the Wunonglong Basin in the upper reaches of the Lancang River.The results show that the determination coefficient and Nash efficiency coefficient in the calibration and validation periods were both over 0.8,and the absolute value of the percentage bias was less than 10%,indicating that the iRainSnowHydro model has a good simulation effect,especially during the snowmelt period(March to May).The weighted Markov chain effectively corrects the systematic underestimation of summer runoff by the model,reducing the average relative error from 15.68%to 7.52%and significantly improving the forecasting accuracy.
To capture the dynamic variation laws of hydrological processes and improve runoff prediction accuracy,by combining three kinds of correlation analysis with covariance analysis,we systematically analyzed the correlation characteristics between different meteorological variables and runoff,dynamically extracted high-contribution features for runoff prediction and optimized their weights.Then,based on the deep temporal convolutional network(DeepTCN),a p-DeepTCN model for multi-feature runoff prediction was constructed to capture both the long-term dependencies and short-term fluctuations in runoff sequence.The measured data of the Atsuma River Basin in Hokkaido from 2015 to 2020 were used to verify the p-DeepTCN model,and the results show that the p-DeepTCN model achieves the optimal prediction performance compared with other deep learning models.Specifically,the Nash-Sutcliffe efficiency coefficient of the p-DeepTCN model is increased by 28.48%,and the root mean square error is reduced by 27.63%.Although its percent bias is 23.34%higher than that of the WPMixer model,it is 17.44%lower than that of the other deep learning models.Meanwhile,although its coefficient of determination is 0.011 lower than that of the original DeepTCN model,it is 10.17%higher than that of other deep learning models.In summary,the p-DeepTCN model significantly improves the accuracy of runoff prediction,and exhibits superior adaptability and robustness.
To reveal the impact of landscape pattern evolution in the hilly mountainous regions of Southern China on water quality,this study analyzed the spatiotemporal changes in landscape element characteristics and landscape heterogeneity,as well as their response relationships with water quality indicators,using landscape pattern indices and the InVEST model based on water quality monitoring data from the Jiuqu Creek Basin in Wuyi Mountain.The spatiotemporal differentiation of water source conservation and water purification services were also be assessed.The results indicate that the vegetation in the study area is primarily composed of mixed coniferous and broad-leaved forests,evergreen broad-leaved forests,and bamboo forests,with relatively low landscape diversity.The water quality exhibits a pattern of superior conditions in the upstream and deteriorating conditions in the mid-to-lower reaches.From 2010 to 2020,the area of tea gardens expanded,landscape fragmentation increased,and water quality indicators deteriorated.Landscape fragmentation was significantly negatively correlated with fecal coliforms and suspended solids.The simulation results of the InVEST model show that the upstream unit area water conservation capacity increases,and the middle reaches are the core area of nitrogen and phosphorus pollution.It is suggested that ecological monitoring and digital management throughout the entire watershed to protect natural vegetation should be strengthened.Differentiated protection should be implemented for the upstream,midstream,and downstream,with strict ecological red lines in the upstream,control of agricultural non-point source pollution in the midstream,and improvement of urban stormwater management systems in the downstream.
Taking Chaiwopu Lake,a terminal lake in an arid region,as the research object,an integrated monitoring system was implemented covering lake water,inflowing surface runoff,groundwater,soil,and sediment.Ordinary Kriging interpolation and spatial autocorrelation analysis were combined to identify pollutant hotspots.Based on the distance attenuation effect of pollutants,attenuation function relationships were established separately between water environmental quality indicators and two distance variables:radial distance from the lake shore and path distance from pollution sources.The pollutant halfattenuation distance were further calculated.The results show that water salinization is the dominant characteristic of Chaiwopu Lake,with localized organic overload and eutrophication.The highest chloride ion concentrations occur in deeper water areas and the centralwestern lake zone.The overstandard rates of total nitrogen and total phosphorus at sampling sites reach 73%and 55%,respectively.Chemical oxygen demand and biochemical oxygen demand exceed the standard at most sampling sites,mainly concentrated in the lakeside zone and lake center.Heavy metal contents exhibit obvious spatial heterogeneity among sites.Along the radial gradient,attenuation rates differ significantly among pollutants:salinity attenuates the slowest,nutrients at a moderate rate,and microorganisms the fastest,with corresponding average groundwater halfattenuation distances of approximately 10.34、5.56、3.31 km.Total phosphorus in sediment attenuates rapidly within 0-3 km offshore,with an average halfattenuation distance of about 5.6 km,whereas heavy metals show slow attenuation.Different pollution sources exert distinct impacts on the lake environment.Industrial sources are the dominant contributors to salinity and certain heavy metal pollution,with a typical influence radius of 8-10 km.Agricultural and domestic sources are the main drivers of nutrient,organic,and microbial pollution,with short to mediumrange influence radii of 2-6 km and 3-5 km,respectively.A spatially optimized governance pattern is proposed,consisting of three concentric zones(0-<3,3-<6,6-10km)plus priority corridor zones and equivalent outward displacement.Pollutant halfattenuation distances are translated into thresholds for equivalent relocation of projects and load reduction,forming a performanceoriented,benchmarked,and assessable closedloop management framework for zonal access and calibrated regulation based on regional needs.
Based on the vortex correlation system and evapotranspiration observation data in the Liulin watershed of Hebei Province,the correction effect of the Bowen ratio method on energy closure was evaluated.Particle swarm optimization algorithm and machine learning model were used to optimize the key parameters in the Penman-Monteith-Leuning(PML)model,and the long-term evaporation of the study area was simulated and its spatiotemporal variation was analyzed.The results showed that after energy closure correction,the determination coefficient(R2)of the fitting results between the observed values of the vortex related system and the lysimeters in the study area increased by 0.04.The use of machine learning models to optimize the dynamic soil evaporation coefficient improved the simulation performance of the PML model.The simulation results showed an R2 of 0.79 and a Nash efficiency coefficient of 0.76,with a relative error of-5.06%compared to the evapotranspiration simulated based on the watershed water balance method.The average annual evapotranspiration in the Liulin Watershed from 2001 to 2022 was 477.8 mm,with evapotranspiration concentrated from May to August.It had a spatial distribution characteristic of being higher in the northwest and lower in the southeast.Forest land had the highest average annual evapotranspiration,followed by grassland and farmland,and building land had the smallest.
In response to the problems of low water resources allocation efficiency and mismatching between water resources utilization and industrial structure in the Dawen River Basin,based on the principle of spatial balance,an analytical framework coupling the input-output model with the system dynamics model was constructed from two dimensions,i.e.,the matching between water resources and industrial sectors and the balance of spatiotemporal distribution.The intersectoral virtual water flow patterns were quantitatively identified,and the spatial balance level under multiple objectives was comprehensively evaluated.Through multi-scenario simulation,the water resources spatial balanced development path was revealed.The research shows that from 2012 to 2017,the direct water consumption coefficient and complete water consumption coefficient of all industries in the Dawen River Basin of Jinan City exhibited downward trends.There is room for improvement in the direct and indirect water use efficiencies of industries,including agriculture,forestry,animal husbandry and fishery,mining,and food and tobacco industries.The industrial structure of the basin has shifted from being dominated by agriculture and traditional industries to being led by the tertiary industry.Except for 2014 and 2015 when the water resources balance level was at a generally unbalanced state,the basin as a whole was in a relatively balanced state.The development scheme for new water sources can significantly reduce the water load coefficient and improve the water-soil matching coefficient,and the water-saving level improvement scheme can effectively improve the water use efficiency.The Dawen River Basin should prioritize the reduction of high-water-consuming and low-value-added industries,increase the proportion of services and high-tech industries,jointly achieving ecological protection and green development at the basin scale.
Based on one-dimensional hydrodynamic-water quality coupled models and water environmental capacity models,this study analyzes the spatiotemporal distribution characteristics of ammonia nitrogen overflow pollution and water environmental capacity in urban rivers at a daily scale.Using the entropy weight method and fuzzy comprehensive evaluation method,a comprehensive regulation and control scheme for overflow pollution control is proposed,taking into account both environmental and economic benefits.The results show that at the daily scale,the peak mass concentration of ammonia nitrogen at river sections and the valley value of water environmental capacity exhibit an obvious lag effect in response to rainfall and pollutant discharge.Additionally,the water quality recovery time prolongs with increasing rainfall intensity.A spatial mismatch exists between overflow pollution distribution and water environmental capacity in rain-fed urban rivers:the midstream urban area serves as both the critical load zone for overflow pollution and the bottleneck zone for water environmental capacity,while the downstream area demonstrates strong pollution buffering and carrying capacity.Terminal storage measures can effectively reduce the peak mass concentration of ammonia nitrogen at river sections under light and moderate rainfall scenarios and maintain adequate water environmental capacity.For heavy rainfall scenarios,a combination of terminal storage and ecological water replenishment is required,and pollution load reduction near assessment sections yields the most direct improvement in water quality.Construction cost is a key factor restricting the implementation of overflow pollution control schemes.Under light and moderate rainfall,priority should be given to pollution load reduction,whereas under heavy rainfall,the pollution load reduction rate and ecological water replenishment volume need to be coordinately regulated to maximize environmental benefits under cost constraints.
To improve the accuracy and efficiency of flood inundation mapping,a flood monitoring framework based on a lightweight deep learning model and multi-source remote sensing data fusion was proposed.Taking the 2022"22·6"catastrophic flood in the Beijiang River Basin as a case study,based on Sentinel-2 multispectral optical images and GF-3 synthetic aperture radar images,the flood inundation range was extracted using threshold segmentation method and M-UNet deep learning model,and the disaster situation was analyzed in combination with land use types.The results indicate that the overall accuracy of water extraction using the threshold segmentation method exceeded 95%.The overall accuracy of the M-UNet model is 98.20%,with an average intersection to union ratio of 88.60%and a Kappa coefficient of 92.45%.Compared with the UNet model and DeeplabV3+model,it exhibits excellent flood remote sensing recognition ability.The flood inundation range extracted by integrating multi-source remote sensing data is basically consistent with the post disaster field survey results,among which Yingde,Qingcheng,Qingxin,and Fogang in Qingyuan are the most severely affected,and cultivated land is the main type of land use affected by the disaster.
This study applies a non-stationary bias correction method,integrating techniques of quantile delta mapping and rank resampling for distributions and dependencies,to evaluate and enhance precipitation simulations from 23 CMIP6 models over the Huang-Huai-Hai and Jiang-Huai plains using daily gridded precipitation datasets,examining model performance in reproducing both climatological mean precipitation patterns and extreme precipitation characteristics before and after bias correction.The results show that,prior to correction,the CMIP6 multi-model ensemble demonstrates systematic overestimation of mean annual precipitation across both plains,characterized by underestimation in summer and overestimation in other seasons,while regarding extreme precipitation events,the uncorrected ensemble exhibits overestimation of precipitation frequency and maximum consecutive wet periods alongside underestimation of precipitation intensity and maximum consecutive dry periods.The implementation of non-stationary bias correction method yields substantial improvements in CMIP6 precipitation simulation capabilities,with post-correction results demonstrating climatological mean precipitation biases constrained within±10%and average TS scores for eight extreme precipitation indicators enhanced by approximately 10%.
Taking China's 31 provincial-level administrative regions,excluding Hongkong,Macao and Taiwan as research objects,this paper systematically evaluates the phased characteristics,regional disparities and dynamic evolution mechanisms of the regional-scale decoupling relationship in China from 2003 to 2023 by employing methods including remapping of decoupling index,kernel density estimation,Dagum Gini coefficient decomposition and spatial Markov chain.The results indicate that China's overall decoupling level has improved steadily,among which eastern region has maintained a high-level decoupling state for a long time,and central and western regions present a pattern of coexisting phased improvement and structural fluctuation.Decoupling index shows an overall upward trend in each period with continuously enhanced regional coordination,but a certain degree of reversal has occurred from 2021 to 2023.Interregional disparity is dominant contributor to unbalanced decoupling development,and unbalanced regional development is core issue.Although the internal coordination of each region has gradually improved over time,polarization trend has intensified in the later period.Since 2021,provincial decoupling index has exhibited significant spatial agglomeration characteristics:eastern region has formed a high-high agglomeration area with high decoupling level,while some western provinces have formed a low-low agglomeration area with low decoupling level.The regional decoupling state presents obvious spatial dependence and neighborhood spillover effects.The high decoupling state of neighboring regions can significantly increase the probability of local upward transition,while the adverse state also poses the risk of spatial diffusion.
Based on the total primary productivity and evapotranspiration data of vegetation in Southwest China from 2000 to 2020,this study used methods such as trend analysis,partial correlation analysis,and residual trend analysis to explore the spatiotemporal evolution laws and driving mechanisms of vegetation water use efficiency in Southwest China.The results showed that from 2000 to 2020,the vegetation water use efficiency in Southwest China showed a non-significant downward trend over time,with a decrease rate of 0.0024 g/(m2·mm·a)(in terms of carbon content).The overall spatial distribution pattern is high in the south and low in the north,and the spatial distribution is dominated by the total primary productivity of vegetation.The vegetation water use efficiency is positively correlated with relative humidity,sunshine hours,and temperature,and negatively correlated with precipitation and wind speed.The relative contribution of climate change to changes in vegetation water use efficiency is 52.6%,while the relative contribution of human activities is 47.4%.Climate change and human activities have a negative impact on vegetation water use efficiency in Guangxi hills,the Yunnan-Guizhou Plateau and Sichuan Basin,while the positive and negative impacts in Hengduan Mountains and Ruoergai Plateau account for the same proportion.
Considering the impact of the uncertainty of the initial water level caused by the dynamic control of the operating water level during the flood season on subsequent flood control scheduling,a multi-objective flood control risk analysis method for reservoir groups based on the dynamic control of the operating water level during the flood season was proposed.And Monte Carlo method was used to quantitatively analyze the synergistic effect of hydrological and meteorological forecasting errors,systematically characterizing the uncertainty of the starting water level.A multi-objective flood control optimization scheduling model for reservoir groups considering upstream and downstream flood control target conflicts was constructed by incorporating the uncertainty of the starting water level into flood control risk sources.A multi-level risk index system of"reservoir-point-surface"was proposed,revealing the risk feedback mechanism in stochastic environments.The results of case study in the Dongpi River Basin indicate that the uncertainty of the starting water level cannot be ignored in the short-term flood control process.There is a clear competitive relationship between upstream and downstream flood control targets,and the system risk rate shows marginal benefit characteristics with the selection of non inferior solutions.The coupling effect of uncertainty between flood forecasting and water level adjustment reduces the risk rate of the reservoir group system by 8%,indicating a certain synergistic offsetting mechanism among heterogeneous risk sources.
The virtual water flow scale and structure of grain in Northeast China were analyzed using multi-source data,and their impact on water resources pressure was assessed.The results show that from 1990 to 2022,the total amount of grain virtual water exported from Northeast China is 1 652.003 billion m3,including 1 260.478 billion m3 of green water and 391.525 billion m3 of blue water.Climate warming and economic interests jointly drive the scale and structure of the grain virtual water flow,and the water resources stress index caused by the grain virtual water flow rises from 0.47 to 0.87,and the stress level changes from moderate to severe.Northeast China should adjust the planting structure and promote the construction of water network projects in the light of water resources conditions.The central government needs to promote the integrated management and control of virtual and physical water resources to ensure food security and the sustainable use of water resources.
To quantitatively study the impact of the inflow of Dongting Lake and Poyang Lake on the erosion of the middle and lower reaches of the Yangtze River,based on the water and sediment data of the upstream main stream and the inflow of the two lakes,the average water flow erosion intensity over the past five years was used to characterize the water and sediment conditions.A formula for calculating the cumulative erosion of the reaches considering the inflow of lakes was established,and the contribution rate of the inflow of the two lakes to the cumulative erosion of the study reaches was calculated.The results show that after the application of the Three Gorges Project,the inflow of Dongting Lake and Poyang Lake accounts for 45%of the downstream runoff at Datong Station from 2002 to 2023,and the sediment content accounts for 22%.The cumulative erosion amounts of the Chenglingji-Hankou and Hukou-Datong reaches are 510 million m3 and 660 million m3,respectively.The calculated cumulative erosion amount of the research reaches fits well with the measured values,and the determination coefficients are both greater than 0.92.The contribution rate of Dongting Lake's inflow to the cumulative erosion of the Chenglingji-Hankou Reach is 3.3%to 87.4%,while the contribution rate of Poyang Lake's inflow to the cumulative erosion of the Hukou-Datong Reach is 6.6%to 19.7%.
To accurately evaluate the water resources system resilience in the central and southern regions of Hebei Province,based on the three stages of water resource system disturbance:before,during,and after,18 indicators covering key factors such as natural environment,population,socioeconomy,and technical management were selected to construct an evaluation index system for water resource system resilience in the central and southern regions of Hebei Province,reflecting resistance,recovery,and adaptability.To address the uncertainty and fuzziness in decision-making information,the Pythagorean fuzzy set(PFS)was introduced.By combining PFS entropy measurement and PFS divergence measurement,the indicator weights were determined.The evaluation model for water resource system resilience based on variable Pythagorean fuzzy and VIKOR coupling method was established.The model evaluation results indicated that the water resources system resilience in the central and southern regions of Hebei Province showed an overall upward trend from 2015 to 2023,but was affected by fluctuations in the adaptive subsystem,leading to variations during 2018-2019 and 2021-2022.Although the resilience values of water resources systems in various cities fluctuate,they are generally good,and the resilience values change in different years due to changes in specific indicators.
Taking the Shiyang River Basin as study area,this study integrated multi-source data including field surveys,unmanned aerial vehicle(UAV)imagery and remote sensing data,and adopted a fused supervised classification method to extract vegetation information.It quantitatively analyzed the spatiotemporal variation characteristics and influencing factors of azonal vegetation distribution before and after the inter-basin water transfer project.The results show that after water diversion,the average groundwater depth in Qingtu Lake increased by 1.1 m,and the water surface area increased by 26.7 km2.The annual runoff at the Caiqi Section increased from 95 million m3 in 2000 to 287 million m3 in 2020.Groundwater depth was identified as the key factor determining the spatial distribution of Nitraria tangutorum populations in the terminal lake area.Following groundwater recovery,both the community density and patch coverage of Nitraria tangutorum increased significantly,accompanied by intensified spatial heterogeneity.River runoff and distance from the river channel jointly determined the distribution pattern of riparian vegetation.With increasing distance from the river,the normalized difference vegetation index(NDVI)decreased significantly from 0.227 to 0.131,while groundwater depth increased notably from 5.1 m to 16 m.After water diversion,the NDVI growth rate in areas within 2 000 m from the riverbank was significantly higher than that in areas farther away,indicating that 2 000 m from the river represents a critical threshold for significant changes in groundwater recharge intensity from runoff and corresponding NDVI variations.This study reveals the quantitative impacts of inter-basin water transfer on the spatial distribution of azonal vegetation communities in arid regions.
To obtain high-precision soil moisture data in basins,three types of soil moisture data from SMAP,AMSR2,and CLDAS were integrated.Considering the effects of precipitation,normalized difference vegetation index,slope,and other factors on soil moisture,a convolutional neural network(CNN)downscaling model for soil moisture that can consider the spatial neighborhood relationship of auxiliary factors was established based on CNN,and the soil moisture data with a spatial resolution of 1 km were obtained.The model was applied to the basin from Pushi County to Wuqiangxi Dam site in Hunan Province,and the results show that the spatial distribution characteristics of soil moisture data before and after downscaling are consistent,and the soil moisture data after downscaling can correctly reflect the response of soil moisture to flood events and add more spatial distribution details.Compared with the measured soil moisture data at soil moisture stations,the mean bias,mean absolute error,and root mean square error of the downscaled soil moisture are-0.061,0.086,and 0.099 cm3/cm3.The CNN downscaling model also shows better stability in comparison with the results of the random forest downscaling model.
