Dagu River Basin Research Articles

Monthly-scale hydro-climatic forecasting and climate change impact evaluation based on a novel DCNN-Transformer network

Climate change has emerged as one of the foremost global challenges confronting humanity today, leading to a heightened frequency and intensity of extreme weather phenomena, including droughts, floods, and erratic rainfall patterns. Accurately predicting changes in runoff patterns under future climate conditions holds significant importance for effective regional water resource planning and management. Recent research on runoff forecast has centered on optimizing hyperparameters of ELM, RNN, LSTM models using PSO, GWO, SSA, and other algorithms. Additionally, key features are extracted through input variable decomposition and preprocessing methods like EMD, EEMD, and VMD. However, these approaches have difficulties in extracting the long-term dependencies information of sequence units, parallel computing, and hyperparameter sensitivity. To address these shortcomings, this study proposes a novel end-to-end deep runoff prediction model based on deep convolutional neural network and Transformer (DCTN). The deep convolutional modules of DCTN employs the deep convolutional operation to extract local features of climate data while the Transformer of DCTN makes full use of self-attention to capture the long-term dependencies, which can achieve more accurate runoff predictions. Experiments on historical runoff forecasting at the Shanjiaodi hydrology station in the Dagu River Basin show that the proposed DCTN obtains a notable improvement of approximately 30.9% compared to traditional models. Based on the prediction results of three shared socioeconomic pathways, the potential impacts of climate change on runoff in Dagu River Basin were evaluated using the DCTN model. The results reveal that the likelihood of spring floods is substantially amplified in the mid-century and late-century, while the probability of extreme summer runoff diminishes. This study advances the understanding of runoff prediction and its implications under changing climate scenarios, paving the way for more informed decision-making and effective water resource management strategies.

Characterization of seawater intrusion based on machine learning and implications for offshore management under shared socioeconomic paths

With the increase of climate change and the risk of human activities, seawater intrusion has become an essential threat to human health and sustainable economic development in coastal areas. Therefore, identifying the critical features of seawater intrusion is significantly important, but the existing research on it is insufficient. Moreover, most of the work cannot reflect the direct impact of different shared socioeconomic paths on groundwater salinity in the future. To solve this problem, this study first established a numerical model of variable density flow for the Dagu River Basin based on SEAWAT to analyze the main aquifers' current flow field and concentration field distribution. Then two machine learning methods, random forest and mutual information methods, are introduced to identify critical features of seawater intrusion for the first time. Finally, taking the characteristics affected by climate are used as variables for selecting GCMs, the seawater intrusion in the study area is predicted by six GCMs under shared socioeconomic paths (ssp1-2.6 and ssp5-8.5) from 2020.12.30 to 2030.12.30. The findings indicate that regional precipitation and groundwater extraction exert significant influence on seawater intrusion, with the hydraulic conductivity of the primary aquifer also demonstrating crucial importance. Moreover, it is observed that the potential impact of different shared socioeconomic paths on future variations of seawater intrusion is negligible. This study verifies the effectiveness of machine learning in seawater intrusion analysis to a certain extent and has specific reference significance for the optimal management of water resources in coastal areas and the effective mitigation of seawater intrusion.

Hydrological status of the Dagu River Basin and management suggestions based on soil and water assessment tool multi-station calibration

Soil and water assessment tool (SWAT) model was used to calculate the runoff, nitrogen and phosphorus emissions in the Dagu River Basin from 2002 to 2012 and provided preliminary suggestions on environmental protection measures. Calibration on hydrological watershed parameters was obtained from data obtained from multiple representative stations. Cluster analysis was used to group all stations, and the parameters of different representative stations were applied to the entire geographical area to reflect the hydrological conditions of the basin. Additionally, the spatiotemporal changes and pollution source characteristics of nitrogen and phosphorus were analyzed. Anthropogenic sources—chemical fertilizer loss and livestock breeding—accounted for 49.9–29.6% of total nitrogen (TN) and 41.4% and 40.3% of total phosphorus (TP), respectively. Non-point sources accounted for 85.8% of the TN and 89.4% of the TP. Six landscape management practices were set up to evaluate the reduction effect on non-point source pollution loads, and comprehensive measures were proposed to reduce TN (TP) by ~ 70%. According to water quality model, the response relationship between river control and nutrient distribution in Jiaozhou Bay is calculated. Based on the findings, we propose an optimal control scheme for non-point source pollution in the Dagu River Basin and other similar basins.

Evaluation of seawater intrusion and water quality prediction in Dagu River of North China based on fuzzy analytic hierarchy process exponential smoothing method.

It is of great significance to evaluate the seawater intrusion degree and predict the change of water quality for coastal groundwater resources. This study takes Dagu River in Jiaodong Peninsula of North China as the target area and combines the relevant theoretical research results to build a seawater intrusion fuzzy analytic hierarchy process (AHP) evaluation model. Five sensitive indicators of water quality, such as Cl-, SO42-, NO3-, TH, and TDS, were selected to evaluate the seawater intrusion level of the long series monitoring data in Xilaiwan, Guanzhuang, and Ligezhuang of Dagu River Basin by using the basic fuzzy mathematics principles and the improved hierarchical analysis method. In this study, the cubic exponential smoothing method was applied to predict groundwater quality change in Dagu River Basin. In order to evaluate the change of seawater intrusion in detail and make timely prediction, this paper innovatively divided the classification standard of seawater intrusion degree based on relevant norms and scholars' research and predicted the evaluation level of seawater intrusion by using long series historical observation data combined with fuzzy analytic hierarchy process.The cubic exponential smoothing method which has the characteristics of simple and fast was introduced to fit the observation elements, and the historical data were used to verify the prediction of the future development trend. Compared with the evaluation results of seawater intrusion by traditional methods, this study can reflect the whole development trend of seawater intrusion in detail and has the characteristics of more reasonable, accurate, and practical. It also provides a certain reference for the future seawater intrusion prevention. In addition to this case, the method proposed in this study will be applicable to a wider range of coastal zones, providing a new idea for the rational management and control of coastal groundwater resources.

Analysis of urea nitrogen leaching under high-intensity rainfall using HYDRUS-1D

The leaching of nitrogen in farmland could lead to groundwater nitrogen pollution, while precipitation is the driving factor. Nevertheless, the influences of extreme precipitation on soil organic nitrogen leaching, such as urea, is not clear. In this study, we used soil column experimental data to construct a nitrogen migration and transformation model. The constructed model was applied to unsaturated zones in farmlands in the Dagu River Basin of China. Three rainfall scenarios, namely rainstorms, heavy rainstorms, and extreme rainstorms, were simulated to analyze the variation in urea nitrogen leaching to groundwater. As the rainfall intensity increased, the quantity of urea nitrogen leaching increased from 0.04% to 18.09%. The cumulative urea nitrogen leaching flux is related to the initial soil moisture content. The time interval between fertilization inversely influenced the urea nitrogen leaching flux. Applying urea-based fertilizers shortly before high-intensity rainfall significantly increases the soil-crop system urea leaching loss and decreases crop nitrogen utilization. These findings imply that urea leaching under extreme rainstorms should not be ignored, and based on weather forecasting, fertilizer application in farmland should be avoided before high-intensity rainfall.

Optimizing river damming and impounding strategies to mitigate seawater intrusion in the coastal aquifer of Dagu River Basin, China

Optimizing river damming and impounding strategies to mitigate seawater intrusion in the coastal aquifer of Dagu River Basin, China

Multi-objective optimization-based reactive nitrogen transport modeling for the water-environment-agriculture nexus in a basin-scale coastal aquifer

The quantification of trade-offs between social-economic and environmental effects is of great importance, especially in the semi-arid coastal areas with highly developed agriculture. The study presents an integrated multi-objective simulation-optimization (S-O) framework to evaluate the basin-scale water-environment-agriculture (WEA) nexus. First, the variable-density groundwater model (SEAWAT) is coupled to the reactive transport model (RT3D) for the first attempt to simulate the environmental effects subject to seawater intrusion (SWI) and nitrate pollution (NP). Then, the surrogate assisted multi-objective optimization algorithm is utilized to investigate the trade-offs between the net agricultural benefits and extents of SWI and NP while considering the water supply, food security, and land availability simultaneously. The S-O modeling methodology is applied to the Dagu River Basin (DRB), a typical SWI region with intensive agricultural irrigation in China. It is shown that the three-objective space based on Pareto-optimal front can be achieved by optimizing planting area in the irrigation districts, indicating the optimal evolution of the WEA nexus system. The Pareto-optimal solutions generated by multi-objective S-O model are more realistic and pragmatic, avoiding the decision bias that may often lead to cognitive myopia caused by the low-dimensional objectives. Although the net agricultural benefits in Pareto-optimal solutions are declined to some extent, the environmental objectives (the extents of SWI and NP) are improved compared to those in the pre-optimized scheme. Therefore, the proposed multi-objective S-O framework can be applied to the WEA nexus in the river basin with intensive agriculture development, which is significant to implement the integrated management of water, food, and environment, especially for the semi-arid coastal aquifers.

Using one-way clustering and co-clustering methods to reveal spatio-temporal patterns and controlling factors of groundwater geochemistry

Effectively and efficiently protecting groundwater resources requires a better understanding of spatial and temporal patterns of groundwater geochemistry and their controlling factors (natural and anthropogenic). In this study, to reveal the spatial and temporal patterns of groundwater geochemistry, we explored two clustering methods, i.e., the one-way clustering implemented by using the hierarchical cluster analysis (HCA) and the co-clustering implemented by using the Bregman block average co-clustering algorithm with I-divergence (BBAC_I). The one-way clustering identifies the spatial and temporal patterns separately, whereas the co-clustering identifies the spatial and temporal patterns simultaneously. The two clustering methods were applied to 9 geochemical parameters of 329 groundwater samples collected over the period of 2000 – 2017 from 19 monitoring wells located in the unconfined aquifer of the Dagu River Basin, China. As a first attempt of applying BBAC_I co-clustering to groundwater geochemical data, results show that it is feasible to use the BBAC_I co-clustering method for simultaneously identifying location and timestamp clusters. The clusters identified by the HCA one-way clustering and the BBAC_I co-clustering are similar. Based on the clustering results, two geochemical zones and three geochemical periods of groundwater geochemistry were delineated. The controlling factors of the two geochemical zones are residual seawater freshening, elevated NO3− concentrations, and silicate weathering. The temporal changes between the three geochemical periods are caused by a heavy rainfall in July 2011 after a one-year drought and by ceased flow of the Dagu River after 2013 due to drought conditions. The impacts of drought conditions and anthropogenic activities in the Dagu River Basin on groundwater geochemistry should be considered for managing groundwater resources of the basin.

Elucidating the Pollution Sources and Groundwater Evolution in Typical Seawater Intrusion Areas Using Hydrochemical and Environmental Stable Isotope Technique: A Case Study for Shandong Province, China

Abstract Seawater intrusion has a serious impact on industry, agriculture, and people’s daily life. Thus, the present study was designed to elucidate the pollution sources and groundwater evolution in typical intrusion areas of Shandong Province by hydrochemistry and environmental isotope techniques. The water samples were collected to analyze the groundwater evolution under different intrusion, and groundwater evolution in the south of Laizhou Bay from 2005 to 2019. The findings indicated that the groundwater level dropping funnel caused by overexploitation was the direct causation of seawater intrusion in the three typical intruded areas. The groundwater evolution paths demonstrated that the groundwater in the south of Laizhou Bay had the fastest evolution rate and the highest degree of evolution, followed by the Dagu River Basin. The groundwater evolution extent and fitting of mixing lines indicated that the groundwater in the south of Laizhou Bay, Longkou, and Dagu River Basin was dominated by palaeosaltwater intrusion, modern seawater intrusion, and sea-saltwater mixed intrusion, respectively. Palaeosaltwater mixing produces a more severe salinization effect compared to seawater mixing. Meanwhile, the isotopes are gradually enriched with the deepening of intrusion, while the decrease of isotopes is delayed compared with the saltwater retreat. This is caused by that the stable isotopes enriched in the aquiclude due to the chemical permeation effect will be released into the aquifer after the salinity attenuates in the aquifer. The palaeosaltwater intrusion caused by anthropogenic activities has promoted serious fluorine pollution in the south of Laizhou Bay, while the groundwater nitrate pollution in Longkou was the most serious, followed by the Dagu River Basin due to high-density agricultural and domestic activity.

Financial Analysis for Improving River Water Quality through Introduction of Organic Agriculture

With scientific awareness of the main source of water pollution from conventional agriculture along the Dagu River Basin, Qingdao, China, this study aims to identify economic efficiency in introducing an environmentally friendly agricultural policy instrument. For more exact financial analysis, considered were benefits obtained by residents and farmers from water quality improvement and conversion cost from conventional to organic agriculture. A bivariate probit model was used to estimate the mean willingness to pay (WTP) for improving the water quality of the river and determinants affecting residents’ WTP were identified. Empirical results show that mean WTPs are CNY 5.71 (USD 0.83) and CNY 26.47 (USD 3.84) per month per household, respectively. The opportunity cost of volunteering activity hours is CNY 222.77 (USD 32.39) per month per household. Application to the residents of Qingdao would obtain aggregate total benefits from improving the river water quality of CNY 7.54 billion (USD 1.09 billion)- CNY 8.23 billion (USD 1.19 billion) per year. Farmers’ profit obtained by value transfer and price index adjustment is CNY 46.61 billion (USD 6.75 billion) from organic agriculture, which is more profitable. After the implementation of organic agriculture policy, therefore, the total benefits of farmers and residents are much higher than the total cost of conversion to organic agriculture. The study results could provide a preponderant economic indicator to policy decision makers for water quality improvement projects.

Using hydrochemical signatures to characterize the long-period evolution of groundwater information in the Dagu River Basin, China

The evolution of hydrochemical compositions influenced by long-period interactions between groundwater and the geo-environment is a fundamental issue for exploring groundwater quality and vulnerability. This study systematically investigated the hydrochemical processes and anthropogenic interference occurring in the river basin by bivariate plots, Gibbs diagrams, saturation index, and the major ions ratios. Apparent changes in groundwater hydrochemistry have been observed in the study area, illustrating the origins of major ions are affected by various internal and external factors. Results highlighted that TDS varied from freshwater to brackish water, ranging between 187.90 and 2294.81 mg/L. Ca2+ and HCO 3 − are the dominant ions in the studied samples. The results gained by Gibbs diagrams, bivariate plots, saturation index, and the major ions ratios demonstrated that minerals dissolution/precipitation, cation exchange, and human inputs play crucial roles in the unconfined aquifers. Moreover, the overuse of nitrogen fertilizer, livestock manure, and industrial/domestic sewage led to nitrate and nitrite contamination and brought significant challenges to the surrounding hydrogeo-environment. The present study could make an unambiguous identification of natural processes and anthropogenic interventions influencing groundwater hydrochemistry’s long-period evolution and create a preliminary strategy for groundwater resources management.

Existence of microplastics in soil and groundwater in Jiaodong Peninsula

Microplastic pollution has become a major global problem demanding prompt solution. So far, most research has focused on marine environment, but there is a lack of information about microplastic distribution, and persistence uptake in coastal soil environment. In this study, the Huangshui River Basin in Laizhou Bay and Dagu River Basin in Jiaozhou Bay of Shandong Province of China was taken as the research area. A total of 3,352 microplastic items were collected from 12 samples from the four sites in this study. The results showed that local soil was already affected by microplastics, of which 92.9% were particles, 6.1% were fibers, and only 1% were films. The existence of film PE indicated that the microplastic pollution in soil probably came from the plastic film. The degree of pollution was correlated with the size, and the size of microplastics was negatively correlated with abundance. Only a few labelled fibers were detected in groundwater samples, which may come from the external environment. This study contributes to a deeper understanding of environmental microplastic pollution in the coastal region.

Identification of the long-term variations of groundwater and their governing factors based on hydrochemical and isotopic data in a river basin

A clear identification of the hydrochemical processes in groundwater systems is critical for the management and protection of alternative water resources. The Dagu River Basin (DRB) has become one of the most concerning areas due to the typical geographical conditions and intensive human disturbance. In this study, the spatio-temporal variations of groundwater hydrochemical characteristics were discriminated based on the self-organizing maps (SOM) clustering results from 2001 to 2017. Simultaneously, their main governing factors were also identified according to the composite properties of hydrogen and oxygen isotopic evidence, hydrochemical facies evolution diagram (HFE-D), chloro-alkaline indices (CAI) values, and the enrichment of nitrogen. Analysis results indicated that the long-term evolution of hydrochemical signatures basically conformed to the trends of rising in the dry season and falling in the rainy season. Total dissolved solids (TDS) fluctuated from 187.90 mg/L to 2294.81 mg/L, with an average of 881.77 mg/L. Moreover, the nitrate concentrations in the river basin varied significantly from 0.70 mg/L to 605.32 mg/L. The unconfined aquifer beneath DRB was experiencing freshening gradually, while some other samples were still subjected to the interference of environmental pollution. Spatio-temporal variations in the respective clusters demonstrated that natural factors including precipitation infiltration, evaporation, and water–rock interactions played vital roles in the samples of Clusters I-III, V, and VIII, while the samples of Cluster VII were influenced by seawater intrusion; and those of Clusters IV and VI were mainly affected by nitrate and nitrite contamination. This study suggests that the SOM connected with stable isotope analysis, HFE-Diagram, CAI values, and correlation analysis could be successfully used to interpret nonlinear and high-dimensional multivariate systems and provide supplementary information about the dominant mechanisms controlling the regional groundwater evolution.

Assessment of the hydrodynamics role for groundwater quality using an integration of GIS, water quality index and multivariate statistical techniques

To explore the impact of groundwater hydrodynamics on water quality, a cost-effective geospatial model was developed using geographic information system (GIS) technology and the Dupuit assumption. Meanwhile, the groundwater quality in the Dagu River Basin was evaluated based on the water quality index (WQI) and multivariate statistical analyses. In April (dry season) and September (rainy season) 2017, the groundwater level was automatically monitored from 115 wells, and the water quality including 21 hydrochemical parameters was sampled from 37 wells. Results reveal that the WQI values varied from 35.01 to 64.74, with mean values of 51.89 and 47.87 in the rainy and dry seasons. Approximately 80% of the samples exhibited moderate water quality, with no significant difference between the rainy and dry seasons. Nitrate pollution and the integrated water quality in the central and northern regions were generally worse than that in the southern region. The Darcy velocity in the central and northern regions was relatively high with a maximum rate of 0.56 m/d, compared with the southern region. This correlation illustrates the effect of groundwater hydrodynamics on quality. The sowing of greater chemical fertilizers combined with faster groundwater movement is likely responsible for the large–scale nitrate pollution in the central and northern regions. Results also proved the accuracy of the geospatial model with a valid uncertainty. The geospatial model provides a valuable alternative for the spatial analysis of the effect of groundwater hydrodynamics on water quality.

Global sensitivity analysis on a numerical model of seawater intrusion and its implications for coastal aquifer management: a case study in Dagu River Basin, Jiaozhou Bay, China

Seawater intrusion (SWI) has triggered an accelerating process of freshwater contamination and significantly affected the soil fertility and local groundwater supply in the coastal area of Jiaozhou Bay, Shandong Province, China. This study establishes a three-dimensional numerical model based on SEAWAT code to simulate transient regional SWI to coastal aquifers in Dagu River Basin (DRB) adjacent to Jiaozhou Bay. The hydrogeological parameters in the SEAWAT model are calibrated and validated with the observed data of groundwater level and chloride (Cl−) concentration from 1 January 2010 to 1 June 2018. Also, global sensitivity analysis is used to evaluate the impacts of hydrogeological parameters (aquifer hydraulic conductivity and specific yield), along with sources/sinks consisting of recharge from precipitation infiltration and groundwater abstraction, on the SWI model. The sensitivity analysis results indicate that the progression of SWI is sensitive to the groundwater recharge from precipitation infiltration and the groundwater abstraction in the study area, while the hydraulic conductivity is of secondary importance. Furthermore, the baseline SWI model is applied to predict the extent of SWI under different scenarios considering the possible future precipitation and groundwater abstraction. It is shown that increased recharge and reduced groundwater abstraction could effectively lessen the extent of future SWI. As a case study, the research efforts on the regional SWI model are of critical importance for investigating the occurrence of SWI, identifying the factors most influential on the SWI process, and providing useful predictive information for SWI management in the DRB aquifer of Jiaozhou Bay.

Evolution analysis and environmental management of intruded aquifers of the Dagu River Basin of China

The seawater intrusion in the Dagu River Basin, China, has attracted intensive attention from the government and scholars. Increasing data have become available with the development of data acquisition technology. This situation brings unprecedented opportunities and challenges to the hydrochemical evolution analysis and improvement of seawater intrusion. The hydrochemical evolution process of groundwater is studied on the basis of our collected data in the Dagu River Basin by using mathematical statistics, end-element mixing, Durov, and Gibbs. The negative influencing factors of the groundwater environment are determined. Results show that the groundwater on the north side of the cutoff wall is mainly affected by residual saltwater, sulfuric acid leakage, and NO− 3 pollution. The groundwater on the south side of the cutoff wall is seriously affected by seawater invasion, followed by NO− 3 pollution. Meanwhile, the groundwater on the west bank of the Dagu River Basin is mainly affected by NO− 3 pollution. The groundwater on the north side of the cutoff wall must be discharged on a large scale for its improvement. Meanwhile, unpolluted water is recharged to renew the groundwater resources. Groundwater exploitation should be reduced, and the exploitation area should be dispersed to mitigate seawater intrusion and increase the recharge of the groundwater resources. NO− 3 pollution mainly comes from the sowing of chemical fertilizers and pesticides and domestic waste. Therefore, we should reduce the use of pesticides and fertilizers to control the generation, storage, and treatment of domestic pollutants strictly.

Study on Soil erosion Dynamic monitoring Based on “3S” technology

Dynamic remote-sensing monitoring based on the “3S” technology is conducted in areas suffering from severe water and soil loss in the northern mountainous region of the Dagu River Basin to quickly and accurately extract information factors about water and soil loss in this region. The research focuses on analyzing the correlation between water and soil loss of the northern mountainous region of the Dagu River Basin and influence factors such as land utilization, vegetation cover, landform features and human factor. The results of the dynamic monitoring are analyzed to figure out the causes of water and soil loss in this region so as to provide data support for following effective governance and regional planning.

Identifying key factors of the seawater intrusion model of Dagu river basin, Jiaozhou Bay

Seawater intrusion is a complex groundwater - seawater interaction process, and it is influenced by many factors from ground surface to underground, from groundwater to seawater. Generally, for seawater intrusion model, some model parameters and boundary conditions are always specified by model users’ personal experiences or literature's reference value. The defective model would damage the groundwater management for controlling and preventing seawater intrusion when making decisions are based on this model. In order to improve the reliability of seawater intrusion model, the influences of model inputs on output should be identified prior at optimizing model inputs.Dagu river basin, Jiaozhou Bay is one of the most serious areas of seawater intrusion in China, and it is chosen as the study area in this study. The seawater intrusion model of Dagu river basin is built based on a general program SEAWAT4. The key influence factors of model output are analyzed by two sensitivity analysis methods, i.e., stepwise regression and mutual entropy. The results demonstrated that the most important influence factors which have largest sensitivities to groundwater Cl- concentration are the precipitation rate and groundwater pumping in agriculture area. In addition, the hydraulic conductivity of zone 1 has a non-negligible influence on seawater intrusion process. Stepwise regression analysis is capable of identifying most important influence factor, and it can’t handle complicated nonlinear input-output relationship. Mutual entropy analysis is reliable for identifying the influence factors for complex seawater intrusion model.

Water Quality Variations in Temporal and Spatial of Dagu River Groundwater Reservoir

It has important significance for the economic and social development of Qingdao city of Dagu River Groundwater Reservoir. Introduced Dagu River groundwater reservoir area characteristics and hydrogeological conditions, analyzed temporal and spatial variation of groundwater quality in the study area. Result showed that pollution of groundwater in this area is serious and has worse trend, provide the basis for the Dagu River Basin of groundwater quality comprehensive treatment.