Ganjiang River Research Articles

Quantitative characterization, spatiotemporal evolution, and analysis of driving factors of daily dry-wet abrupt alternation: A case study of the Ganjiang River Basin

Study RegionThis study focuses on the Ganjiang River Basin, a major tributary of the Poyang Lake located in southern China. Study FocusWith the growing frequency of extreme weather events driven by climate change, there is increased attention on compound disasters such as Dry-Wet Abrupt Alternation (DWAA). This study aims to quantify and define DWAA in the Ganjiang River Basin by developing and applying a DWAA Index (DWAAI) using a percentile threshold method. The objective is to investigate the spatiotemporal characteristics and patterns of DWAA in the region. Precipitation data from 12 meteorological stations were analyzed to track these events from 1970 to 2019. New Hydrological Insights For the regionThe results of this study provide new insights into DWAA dynamics in the Ganjiang River Basin. Key findings include: (i) The DWAAI effectively captures the extremes of Dry-Wet Abrupt Alternations, especially at the 1st and 99th percentiles; (ii) The basin experienced 37–48 dry-to-wet events (DtWs) during the study period, with higher frequencies observed in the central-eastern, western, and northern mountainous areas, and lower frequencies in the southern regions; (iii) Wet-to-dry events (WtDs) were less common than DtWs and exhibited a distinct spatial and temporal shift from the southern mountains toward the central basin; (iv) Temperature was identified as the dominant factor influencing DWAAI changes, while large-scale atmospheric patterns such as AO, ENSO, PDO, and Sunspot activity showed insignificant correlations. These findings offer critical insights for improving water resource management and climate adaptation efforts in the region.

Coupling Coordination Measurement and Multi-dimensional Conflict Diagnosis among Territorial Space Functions

Exploring the relationship between urban, agricultural, and ecological functions is crucial for optimizing the territorial spatial patterns and achieving balanced territorial spatial development. Based on the classification system of "urban-agricultural-ecological" territorial spatial planning and with the help of multi-source data, including land use, population density, nighttime light, road network, precipitation, and county statistical data from 2010 to 2020, we constructed an evaluation index system of territorial space functions at the grid scale in the Poyang Lake Ecological Economic Zone. The coupling coordination levels among territorial space functions was measured using the coupling coordination degree model and spatial autocorrelation analysis. Moreover, the multi-dimensional function conflict identification model was used to diagnose the type and intensity of function conflicts. The results showed that： ① The urban function was mainly improved, accounting for 75.67%. The grids with high function evaluation index were mainly located in the south of the study area, whereas the intensity of urban function improvement in the north was higher than that in the south. Otherwise, the agricultural and ecological functions were mainly reduced, accounting for 77.44 % and 57.66%, respectively, and the majority of grids with a significant decrease were distributed in the south of Poyang Lake. ② From 2010 to 2020, the coupling coordination type was dominated by an imbalance recession, accounting for 53.87% and 49.89 %, respectively. However, the number of grids with crucial imbalance and primary coordination was reduced, and the coupling coordination types tended to change from both ends to the middle. The coupling coordination hot spots were mainly concentrated in the southern plain, whereas the cold spots were mainly distributed in the northern mountainous and hilly areas, both of which showed a reducing trend. However, in the north of Poyang Lake, a small number of hot spots, distributed along the lake area with relatively flat terrain, showed an expanding trend. ③ The intensity of four territorial space function conflicts was mainly moderate or severe. In addition to the slight positive trend of 'urban-agricultural' function conflict, the function conflicts of "urban-ecological", "agricultural-ecological", and "urban-agricultural-ecological" were all aggravated. The grids with aggravating conflicts were mainly distributed in the Ganjiang River and Fuhe River Basin. The major reason for the aggravation was that the urban function increased obviously, whereas the ecological and agricultural functions decreased significantly. In conclusion, in both the coupling coordination types and multi-dimensional conflicts among territorial space functions, significant spatial differences existed； thus, proposing optimization strategies according to local conditions is necessary. Additionally, the protection of ecological and agricultural spaces during each stage of urbanization development must be focused.

Response of Water Quality to Land Use and Landscape Pattern in the Ganjiang River Watershed.

Analysing the impact of landscape composition and structure on water quality at different scales is of great significance to water quality protection. The aim of this study was to determine scale-dependent impacts of land use/landscape patterns on water quality. The Ganjiang River, the largest water system in the Poyang Lake watershed, the largest freshwater lake in China. The response of water quality to land use and landscape patterns in the Ganjiang River watershed was explored based on land use and water quality data using redundancy and Spearman correlation analyses. Considering upstream monitoring of the entire Ganjiang River watershed; watersheds at the county level administrative region; and 1, 2, 5, 10, 15, 20, and 30 km-radius circular buffer zones, a total of nine scales of land use/landscape patterns that influence water quality in the Ganjiang River watershed were analysed. Results indicated that the county-level scale and the land use type of the 20 km-radius buffer zone upstream of the monitoring site were closely linked to water quality (96.28% and 93.23%, respectively). Among the land use types, construction land and cultivated land were the main output sources of pollutants. Regarding landscape pattern index, the greater the fragmentation of the landscape, the heavier was the water pollution load; the more the patches per unit area, the more stable was the ecosystem and the lower was the pollutant concentration. In addition, the eco-hydrological system of the Ganjiang River watershed was revealed to some extent through multi-angle analysis. These conclusions can serve as a reference for government departments to formulate land management and water quality protection measures.

Hydrological Evaluation of CRA40 and ERA5 Reanalysis Precipitation Products over Ganjiang River Basin in Humid Southeastern China

This study evaluates two reanalysis precipitation products (CRA40 and ERA5) over the Ganjiang River Basin with precipitation data from 37 ground rainfall gauges and surface-observed stream flow data from January 1998 to December 2008. Direct comparison with rain gauge observations shows that both CRA40 and ERA5 can capture the spatial and temporal characteristics of precipitation at the basin scale of the Ganjiang River and reflect most of the precipitation events, but there are pronounced differences in the quality of precipitation between them. ERA5 performs better on the daily scale, capturing precipitation changes more accurately over short periods of time, while CRA40 performs better on the monthly scale, providing more stable and long-term precipitation trends. The results of stream flow simulations using two reanalysis precipitation products driving the VIC hydrological model show that (1) CRA40 outperforms ERA5 with a better Nash–Sutcliffe Efficiency (NSE, 0.65 and 0.6) and higher CC (0.96 and 0.91) in daily and monthly scale stream flow simulations, and ERA5 has a good CC (0.86 and 0.93, respectively), but its NSE is poor (0.29 and 0.30, respectively); (2) both CRA40 and ERA5 generally overestimate basin stream flows, especially during the flood season (April–September), with ERA5’s overestimation being more pronounced. This study is expected to provide a basis for the selection of reliable reanalysis products for Ganjiang River Basin precipitation and hydrological simulation.

Combining the digital filtering method with the SWAT model to simulate spatiotemporal variations of baseflow in a mountainous river basin

Study regionGanjiang River Basin, a typical mountainous river basin which located on the south bank of the middle-lower Yangtze River. The Ganjiang River is the seventh largest tributary of the Yangtze River. Study focusBaseflow, a key recharge source for the river streamflow. This study combined the digital filtering method with the SWAT model to examine the temporal and spatial patterns of baseflow across the Ganjiang River Basin, and quantitatively assessed land use change impact on baseflow. New hydrological insights for the regionBaseflow in the Ganjiang River Basin shows a "single peak" intra-annual distribution. Monthly variations of streamflow and baseflow across the basin are different. The variation of baseflow index is generally opposite to that of streamflow. A positive correlation has been noted between the annual baseflow and streamflow, while a negative correlation was found between the annual baseflow index and precipitation. Due to the potential influence of basin topography, river flow direction and rock layer distribution, baseflow and baseflow modulus showed a spatially increasing trend from south to north, with the northwest region having extremely strong groundwater recharge. In comparison to the basic scenario, under extreme land use scenarios of forest, grassland, and cropland, baseflow may experience an increase of 14.7 % and 2.9 %, while witness a decrease of 13.9 %. All results improve the understanding of baseflow spatiotemporal variations in river basins.

Attribution of hydrological droughts in large river-connected lakes: Insights from an explainable machine learning model

Large lakes play an important role in water resource supply, regional climate regulation, and ecosystem support, but they face threats from frequent extreme drought events, necessitating an understanding of the mechanisms behind these events. In this study, we developed an explainable machine learning (ML) model that combines the Bayesian optimized (BO) long short-term memory (LSTM) model and the integrated gradients (IG) interpretation method to simulate and explain lake water level variations. In addition, the hydrological drought trends and extreme drought events in Poyang Lake from 1960 to 2022 were identified using the standardized water level index (SWI) and run theory. The analysis revealed that the frequency of hydrological droughts in Poyang Lake increased from 1960 to 2022, especially in the autumn after 2003. By selecting the flows of the catchment and the Yangtze River as the input features, the BO-LSTM model accurately predicted the water level of Poyang Lake. The IG method was then used to interpret the prediction results from three aspects: the importance ranking of the input features, their roles in the seasonal drought trends, and their roles in extreme drought events. The results indicate that (1) the most influential factor affecting the water level of Poyang Lake was the inflow of the Ganjiang River in the catchment. (2) The increase in the lake outflow caused by the Yangtze River's draining effect was the reason for the intensification of the autumn drought in Poyang Lake. (3) The extreme hydrological drought events were primarily caused by low catchment inflows. Overall, this research provides a new approach that balances prediction accuracy with interpretability for predicting and understanding the hydrological processes in large river-connected lakes. Moreover, this method was also applied to the attribution analysis of hydrological drought in Poyang Lake, providing theoretical support for regional water resource management.

A multi-variable calibration framework at the grid scale for integrating streamflow with evapotranspiration data to improve the simulation of distributed hydrological model

Study regionThe Ganjiang River Basin, China Study focusParameter calibration is crucial for the accurate and reliable operation of hydrological models. Traditional methods face challenges in calibrating spatially heterogeneous parameters of distributed hydrological models, and existing multi-variable calibration strategies often fall short in comprehensively improving model performance, particularly in streamflow simulations. To address these challenges, this study proposes a multi-objective calibration framework that integrates observed streamflow data and satellite-based evapotranspiration (ET) data. The spatiotemporal information of the merged ET is utilized to calibrate six hydrological parameters of the Variable Infiltration Capacity (VIC) model at the grid scale, enhancing hydrological simulations for the Ganjiang River basin. New hydrological insightsCompared to the benchmark scheme based solely on streamflow, the proposed calibration framework improves simulations of area-average ET at the sub-basin scale and soil moisture content in the Ganjiang River basin, without compromising the accuracy of daily streamflow simulations. Additionally, notable enhancements are observed in monthly streamflow simulations. This study provides a promising and comprehensive calibration framework using satellite-based data to constrain parameters and enhance the performance of distributed hydrological models.

A new approach to estimate total nitrogen concentration in a seasonal lake based on multi-source data methodology

Nitrogen, a key limiter in lake eutrophication, presents serious threats to both human health and ecological balance. Despite its non-optically active nature, this study introduces an advanced retrieval approach for total nitrogen, utilizing a synthesis of multi-source data and sophisticated machine learning algorithms to markedly boost estimation precision. This innovative method integrates environmental variables, such as water temperature, depth, and flow rate with spectral reflectance, significantly enhancing the predictive accuracy of our machine learning models with high stability. The models tested, including Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB), Multilayer Perceptron (MLP), and Convolutional Neural Networks (CNN), with XGB outperforming others by achieving robust metrics: an R2 of 0.78, a Mean Absolute Error (MAE) of 0.21 mg/L, and a Mean Absolute Percentage Error (MAPE) of 16.04 %. Applying the optimized XGB model, we documented fluctuations in nitrogen concentrations within Poyang Lake across different hydrological phases in 2021, revealing the lowest nitrogen levels during the flood season and the highest in low water periods, with high concentrations at the inlets of the North Branch of the Ganjiang River and the Raohe River estuaries. Monte Carlo simulations reveal that the model is not much sensitive to input feature errors, validating its stability. The approach proposed in this study may help more precise total nitrogen retrieval in other similar lake waters.

Occurrence, distribution, and correlation of antibiotics in the aquatic ecosystem of Poyang Lake Basin, China

The widespread existence and persistence of antibiotics in the aquatic environment, and their extensive ecological risks, have attracted considerable attention. The objective of this study was to evaluate the occurrence and distribution of 25 antibiotics in environmental and biological samples from Poyang Lake Basin in China. SPE–HPLC–MS/MS was used to quantify the concentrations in different matrices. The total concentrations ranged from 144 to 933 ng/L in the water and 346 to 1154 ng/g in the sediment. In the spatial distribution analysis of this basin, the concentrations in the Ganjiang River were generally higher than those in Poyang Lake. The seasonal distribution in the wet and dry seasons showed comparatively higher concentrations during the dry season than the wet season. Additionally, antibiotics were found in various hydrophytes and animals, and the bioconcentration factor values followed the order: emergent plants > floating plants > submerged plants and benthic organisms > ducks > fish. Moreover, correlations among different matrices showed that antibiotics in viviparid snails were significantly positively correlated with those in ducks, and negatively correlated with those in carps, indicating the transmission relationship through the food chain. The results showed the trophic transfer of antibiotics in the food web and their potential environmental impacts on Poyang Lake Basin need constant attention.

Development of a comprehensive framework for quantifying the collective and individual influence of climate change and human activities on hydrological regimes

Previous research rarely identified the synergistic effects of climate change and human activities on hydrological regimes, thus leading to an incomplete attribution of hydrological regime changes. To address this issue, this paper presents a complete approach for accurately quantifying these impacts. The framework integrates various methodologies, including the runoff abrupt change point judgment method for distinguishing pre- and post-impact periods, The Soil and Water Assessment Tool (SWAT) is used to isolate runoff processes influenced solely by climate change, the linear separation method for identifying runoff processes affected solely by human activities, and the Indicator of Hydrologic Alteration (IHA) for quantifying changes in hydrological regimes. Application of this framework to the Ganjiang River Basin (GRB) unveils significant alterations in hydrological regimes. This has resulted in a significant decline in aquatic organism species compared to the prior-impact period levels. Notably, climate change and human activities exert opposing effects on minimum flow indicators and flow pulse indicators in the GRB. Moreover, these factors exhibit a substantial synergistic effect on numerous indicators, resulting in hydrological regimes combined influenced by climate change and human activities being considerably different with the cumulative impact on hydrological regimes solely influenced by climate change and those solely influenced by human activities. Consequently, in future water management, it is crucial to recognize the positive role of prudent human activities in mitigating the adverse impacts of climate change.

Novel framework for quantifying dam construction, climate change, and land-use change impacts on flow regime: A case study in Ganjiang River Basin, China

Study regionThis study was conducted in the Ganjiang River Basin, situated in eastern China. Study focusPrevious studies often quantified the impacts of climate change, land use, and dam construction on flow regime by only focusing on two of these factors at one time. In addition, the previous method used to quantify the deviation in the timing of annual extreme water conditions has a flaw. Therefore, this study improved the formula for calculating the deviation in the timing of annual extreme water conditions. Moreover, we proposed a novel framework for quantifying the impacts of dam construction, climate change, and land-use change on flow regime. New hydrological insights for the regionThe study observed significant alterations in the flow regime of the Ganjiang River Basin. These modifications were predominantly influenced by dam construction and climate change. Dam construction led to a notable increase in dry season flow and minimum flow indicators, while decreasing maximum flow indicators. Conversely, climate change resulted in a reduction of minimum flow indicators and an increase in maximum flow indicators. Climate change has also led to an increase in the frequency and duration of flow pulses. Intriguingly, dam construction and climate change played offsetting roles for 77 % of the indicators. Considering the global climate change context, judicious dam operation could to mitigate the impact of climate change on the flow regime.

Impact of Land Use Change on Water-Related Ecosystem Services under Multiple Ecological Restoration Scenarios in the Ganjiang River Basin, China

Ecological restoration programs (ERPs) can lead to dramatic land use change, thereby affecting ecosystem services and their interaction. Determining the optimal ERPs is a crucial issue for ecological restoration in ecologically fragile regions. This study analyzed the impacts of land use change on four water-related ecosystem services (WESs), namely water yield, soil retention, water purification, and food production in the Ganjiang River basin, China during the past two decades. Then, trade-off and synergy between WESs were detected based on correlation analysis. Finally, to quantify the effect of ERPs on WESs, we comprehensively considered the types and intensity of ERPs and designed four categories of scenarios: returning farmland to forest (RFF) scenarios; planting forest (PF) scenarios; riparian forestland buffer (RFB) scenarios; and riparian grassland buffer (RGB) scenarios. Each category contains five scenarios of different intensities. The results showed that water yield, soil retention, and food production increased while water purification decreased from 2000 to 2020. The deterioration of water quality was mainly due to transitions from forestland to farmland and built-up land. Trade-offs only occurred between regulating services and provisioning services. Among all ecological restoration scenarios, only the RFF scenarios can significantly improve soil retention and water purification at the same time, although food production will decrease. Considering food security, returning farmland with a slope greater than 10 degrees to forestland was the optimal scenario in the study area. This study highlighted that both the type and intensity of ERPs should be considered in ecological restoration. This study can contribute to ecological restoration in the Ganjiang River basin and other subtropical mountainous regions.

Unravelling the spatiotemporal variation in the water levels of Poyang Lake with the variational mode decomposition model

AbstractPoyang Lake is a dynamic floodplain lake system that exhibits complex water level fluctuations and experiences significant regime changes over space and time, which remains to be further explored. This study used the variational mode decomposition (VMD) model to decompose the Poyang Lake's water levels from 1960 to 2022 at four key stations into six intrinsic mode functions (IMFs), namely IMF1–IMF6, representing variations on different time scales. The results present significant spatiotemporal heterogeneity. The multi‐year variation (IMF1) accounts for 5.6%–12.4% of the total variation and displays a northward decreasing trend, reflecting the lake's river‐like characteristics. The spectrum of IFM1 also reveals a significant 3.6‐year fluctuation mainly attributed to the tributary inflow, especially the Ganjiang River. The IMF1 differences between stations show abrupt decreases since the 2000s, indicating the impact of concentrated sand mining activities on the northern and central regions. The annual variation (IMF2) is the most prominent, contributing 76.1%–88.4% of the total variation, and shows a southward attenuation trend, likely due to the weakening influence of the Yangtze River flow. The intra‐annual scale (IMF3–IMF6) represents 6.0%–11.5% of the total variation and exhibits less spatial difference compared to the multi‐year and annual variations. The VMD model effectively separates the water level signals into different frequency bands, providing insights into the complex interactions between the lake, tributaries, and Yangtze River, as well as the impacts of human activities like sand mining, enhancing understanding of floodplain lake dynamics. The results also imply the importance of coping with the water level decline of Poyang Lake.

Multi-Model Comparison in the Attribution of Runoff Variation across a Humid Region of Southern China

The natural hydrological cycle of basins has been significantly altered by climate change and human activities, leading to considerable uncertainties in attributing runoff. In this study, the impact of climate change and human activities on runoff of the Ganjiang River Basin was analyzed, and a variety of models with different spatio-temporal scales and complexities were used to evaluate the influence of model choice on runoff attribution and to reduce the uncertainties. The results show the following: (1) The potential evapotranspiration in the Ganjiang River Basin showed a significant downward trend, precipitation showed a significant upward trend, runoff showed a nonsignificant upward trend, and an abrupt change was detected in 1968; (2) The three hydrological models used with different temporal scales and complexity, GR1A, ABCD, DTVGM, can simulate the natural distribution of water resources in the Ganjiang River Basin; and (3) The impact of climate change on runoff change ranges from 60.07% to 82.88%, while human activities account for approximately 17.12% to 39.93%. The results show that climate change is the main driving factor leading to runoff variation in the Ganjiang River Basin.

Runoff Prediction in Different Forecast Periods via a Hybrid Machine Learning Model for Ganjiang River Basin, China

Accurate forecasting of monthly runoff is essential for efficient management, allocation, and utilization of water resources. To improve the prediction accuracy of monthly runoff, the long and short memory neural networks (LSTM) coupled with variational mode decomposition (VMD) and principal component analysis (PCA), namely VMD-PCA-LSTM, was developed and applied at the Waizhou station in the Ganjiang River Basin. The process begins with identifying the main forecasting factors from 130 atmospheric circulation indexes using the PCA method and extracting the stationary components from the original monthly runoff series using the VMD method. Then, the correlation coefficient method is used to determine the lag of the above factors. Lastly, the monthly runoff is simulated by combining the stationary components and key forecasting factors via the LSTM model. Results show that the VMD-PCA-LSTM model effectively addresses the issue of low prediction accuracy at high flows caused by a limited number of samples. Compared to the single LSTM and VMD-LSTM models, this comprehensive approach significantly enhances the model’s predictive accuracy, particularly during the flood season.

Impact of the comprehensive remediation project on hydrological conditions in the lower reaches of the Ganjiang River.

This study undertakes a systematic analysis of the hydrological changes before and after the implementation of the Comprehensive Remediation Project in the lower reaches of the Ganjiang River. It focuses on changes in downstream inflow, ratios of flow distribution, and water levels, as well as water velocity near the gates. The results indicate a significant improvement in the spatial distribution of water resources in the lower reaches of the Ganjiang River. The project enhances the inflow from the northern and southern branches, positively influencing downstream water usage and the ecological environment. Building upon these findings, the study proposes operational recommendations tailored to different hydrological years, such as timely adjustments to the southern branch's water inflow and optimizing flow distribution ratios. This research provides a scientific basis for the implementation and dispatch of comprehensive remediation projects and offers insights into water resource management in similar regions.

Quantifying the diffusion history of Yangmingism

AbstractYangmingism, a crucial component of Chinese Confucianism, has profoundly impacted the development of Eastern civilization throughout history and gained new significance within modern contexts. This study employs social network and GIS spatial analyses to investigate the spatio-temporal evolution and patterns of Yangmingism during the Ming and Qing dynasties. Conclusions drawn include: (1) Yangmingism spread during the Ming and Qing dynasties followed five phases (rise-peak-decline-revival-trough), consistent with general cultural diffusion processes. (2) The spatial structure of regions accepting Yangmingism changed from polycentric to localized ribbon-polycentric, then to monocentric, polycentric, and ultimately, fragmented distribution. Diffusion began with a point-axis structure along traffic routes, transitioned to a core-periphery spatial structure, and returned to a point-axis structure due to reduced diffusion potential energy. (3) Yangmingism diffusion exhibited distance decay characteristics, with the most extensive diffusion in the Han cultural region, primarily located within the middle and lower reaches of the Ganjiang River basin and the Yangtze River Delta. (4) Yangmingism diffusion reflected influences from local administrative hierarchies and cultural development. During the rise-to-peak phase, Yangmingism reached areas with higher administrative and cultural levels, while during the peak-to-decline phase, it disseminated from higher to lower hierarchy regions. (5) The spatial diffusion of Yangmingism is mainly relocation diffusion, followed by expansion diffusion. In the process of relocation diffusion, Yangmingism may reverse the diffusion from the “target regions” to the “source regions” due to the influence of the radiation of the high-ranking regions and the cultural innovation of the “target regions”.

An Ecology-Oriented Single–Multi-Objective Optimal Operation Modeling and Decision-Making Method in the Case of the Ganjiang River

Hydro power has provided significant economic benefits to society due to its cleanliness and convenience. As the number of hydropower stations has increased, many serious ecological issues have also emerged. This study uses Wan’an Reservoir as its research object and investigates single–multi-objective optimal operation and decision-making regarding reservoirs for ecology-oriented operation, to meet ecological water demand and seek the optimal operation schemes for energy generation and ecological benefits. The full-process research is conducted based on the “objective-modeling constraint optimization scheme decision-making” framework. The Mann–Kendall test and ordered clustering method were used to diagnose the hydrological variation in the basin. Based on this, a hierarchical and phased ecological flow process was derived. The objectives were defined according to the flow process, and optimal operation models were constructed. The differential evolution algorithm (DE) and improved non-dominated sorting genetic algorithm-II (NSGA-II) were used to solve the models. A non-fitting curve method was used to determine the approximate inflection point of the Pareto front curve, and the curve was fitted linearly according to the approximate inflection point to obtain the conversion formula between the objectives. Based on the coefficient of variation and Mahalanobis distance, a new multi-attribute decision-making method for reservoir operation, CV-ITOPSIS, was constructed by improving the traditional TOPSIS. The results show that: (1) There is a piecewise linear contradiction between energy generation and ecological objectives, and the contradiction intensifies with an increase in incoming water frequency. (2) Before the approximate inflection point, the head significantly influences the conversion rate from the energy generation to ecology, while the discharge flow is the major influencing factor after the inflection point. The inflection point and the formula for the piecewise straight line can reveal the conversion law between the two objectives. (3) CV-ITOPSIS considers the degree of differentiation of index data and fully considers the correlation between indicators while retaining the good evaluation performance of the traditional method. It recommends the optimal benefit scheme for a multi-objective non-inferior solution set. The research results provide a theoretical foundation and decision support for the optimal ecological operation of the Ganjiang River Basin.

Study on the constraint handling method for high-dimensional optimization of cascade reservoirs

Multicomplex constraints often need to be considered in the optimal operation of cascade reservoirs with high-dimensional decision variables, making it difficult for traditional optimization methods and modern intelligent algorithms to solve such problems. Therefore, this study proposes a constraint handling method combining a penalty function nested DPSA-POA and an intelligent algorithm, to determine the optimal flood control operation of cascade reservoirs in the middle reaches of the Ganjiang River, a problem with decision variables of up to 2196 dimensions. The results indicate that the constraint handling method proposed in this paper can solve high-dimensional optimization problems in three modes: continuous nesting (Mode 1), optimization with intelligence after obtaining a feasible solution (Mode 2), and optimization with the DPSA-POA after obtaining a feasible solution (Mode 3). Of the three modes, Mode 2 has the highest accuracy, but its calculation time is approximately 10 h. Although the accuracy of Mode 3 is slightly worse, can only achieve 98%–99% of Mode 2, its calculation time is only approximately 1–3 h. The comprehensive performance of Mode 1 is poor, the convergence accuracy can only reach 97% of Mode 2, which corresponds to a calculation time of approximately 4–6 h. The existing superiority of feasibility (SF), stochastic ranking (SR), penalty function (PF), ε-constraint (EC) and adaptive ε-constraint (Adaptive EC) methods cannot converge to a feasible solution stably, and the accuracy of the results of these methods under the condition of obtaining a feasible solution is significantly lower than that of Mode 2 and Mode 3, reaching only 95%.

The Applicability of the Drought Index and Analysis of Spatiotemporal Evolution Mechanisms of Drought in the Poyang Lake Basin

Poyang Lake, the largest freshwater lake in China, is an important regional water resource and a landmark ecosystem. In recent years, it has experienced a period of prolonged drought. Using appropriate drought indices to describe the drought characteristics of the Poyang Lake Basin (PLB) is of great practical significance in the face of severe drought situations. This article explores the applicability of four drought indices (including the precipitation anomaly index (PJP), standardized precipitation index (SPI), China Z-index (CPZI), and standardized precipitation evapotranspiration index (SPEI)) based on historical facts. A systematic study was conducted on the spatiotemporal evolution patterns of meteorological drought in the PLB based on the optimal drought index. The results show that SPI is more suitable for the description of drought characteristics in the PLB. Meteorological droughts occur frequently in the summer and autumn in the PLB, with the frequency of mild drought being 17.29% and 16.88%, respectively. The impact range of severe drought or worse reached 22.19% and 28.33% of the entire basin, respectively. The probability of drought occurrence in the PLB shows an increasing trend in spring, while in most areas, it shows a decreasing trend in other seasons, with only a slight increase in the upper reaches of the Ganjiang River (UGR). One of the important factors influencing drought in the PLB is atmospheric circulation. The abnormal variation of the Western Pacific Subtropical High was one of the key factors contributing to the severe drought in the PLB in 2022. This study is based on a long-term series of meteorological data and selects the drought index for the PLB. It describes the spatiotemporal distribution characteristics and evolution patterns of drought and investigates the developmental path and influencing factors of drought in typical years. This study provides a reliable scientific basis for similar watershed water resource management.