Zhanghe River Research Articles

Meteorological–hydrological coupling flood forecast and error propagation characteristics based on radar data assimilation in small- to medium sized River Basin: a case study of Zhanghe River Basin in China

Meteorological–hydrological coupling flood forecast and error propagation characteristics based on radar data assimilation in small- to medium sized River Basin: a case study of Zhanghe River Basin in China

Impacts of LULC changes on runoff from rivers through a coupled SWAT and BiLSTM model: A case study in Zhanghe River Basin, China

Changes in river runoff have a significant impact on the sustainable use of water resources in a watershed, and these changes are closely linked to variations in land use/land cover (LULC). This research explores an innovative approach in the Zhang River Basin (ZRB), China, by coupling a concept-based hydrological model, the Soil and Water Assessment Tool (SWAT), with a deep-learning model, the Bidirectional Long Short-Term Memory Network (Bi-LSTM), to improve the accuracy of river runoff simulations. By analyzing LULC changes in 2002, 2012, and 2022, this study developed three SWAT models and three coupled SWAT-BiLSTM models to quantitatively assess the impacts of these changes on river runoff through eight LULC scenarios. The findings revealed significant LULC changes from 2002 to 2022, with cropland and grassland areas decreasing while forest and urban land areas increased. The total area of grassland, forest, and cropland made up over 93 % of the basin, indicating active land type conversions. Calibration and validation results demonstrated that the SWAT-BiLSTM model outperformed the conventional SWAT model, yielding higher accuracy in runoff simulations. Specifically, the SWAT-BiLSTM model achieved R2 values of 0.89 and 0.90 during calibration and validation, compared to the SWAT model's R2 values of 0.76 and 0.79. Scenario analyses indicated that expansions in farmland, grassland, and urban areas were correlated with increased river runoff, while an expansion in forested areas led to reduced runoff. Notably, urban land changes had the most pronounced impact on runoff, emphasizing the need for careful runoff management and flood risk mitigation in urban planning. By combining SWAT and Bi-LSTM models, this study provides an innovative assessment of the impact of LULC changes on water resources in the ZRB. The results offer valuable insights for water resource management, LULC optimization, and flood risk management, highlighting the potential application of deep learning techniques in hydrological simulation. This research serves as a scientific basis for policy-making and sustainable land use planning in the ZRB and similar regions.

Regional water demand forecasting based on shared socio-economic pathways in the Zhanghe River Basin

Abstract Based on the framework of shared socio-economic pathways, this study predicts future population and economic conditions of the Zhanghe River Basin and combines with the water quota to predict future water demand. First, the localization parameter system is constructed. Furthermore, the water demand is calculated. The results show that (1) under regional competitive pathway, the population is the largest, while under uneven pathway, the population is the smallest. The largest economic forecast is obtained under fossil fuel development pathway, while the smallest economic forecast is obtained under regional competitive pathway. (2) The results for domestic and economic water use in the basin show that the annual water demand shows an increasing trend. Fossil fuel development pathway is the scenario with the highest socio-economic water demand, while regional competitive pathway is the scenario with the least. (3) The Zhanghe River Basin faces a high risk of water resource shortage in the future. Even under the situation of minimum socio-economic water demand, the total water demand is difficult to meet fully. The forecasting framework established in this paper has high application value and can provide a reference for water demand forecasting and prospective water demand management in river basins.

Occurrence, removal, emission and environment risk of 32 antibiotics and metabolites in wastewater treatment plants in Wuhu, China

Wastewater treatment plants (WWTPs) are considered important sources of antibiotics and metabolites in aquatic environments and pose a serious threat to the safety of aquatic organisms. In this study, we investigated the seasonal occurrence, removal, emission, and environmental risk assessment (ERA) of 32 antibiotics and metabolites at four WWTPs located in Wuhu, China. The main findings of this study are as follows: Ofloxacin concentrations dominated all WWTPs, and large quantities of sulfachinoxalin were only detected in WWTP 2 treating mixed sewage. The average apparent removal of individual parent antibiotics or metabolites ranged from −94.7 to 100 %. There was a noticeable seasonal emission pattern (independent t-test, t = 9.89, p < 0.001), with lower emissions observed during summer. WWTPs discharged 85.2 ± 43.8 g of antibiotics and metabolites each day. Approximately 87 % of emissions were discharged into the mainstream of the Yangtze River, while the remainder were discharged into its tributary, the Zhanghe River. The total emissions of 21 parent antibiotics were approximately 18 % of the prescription data, indicating that a considerable and alarming amount of prototype drugs entered the receiving water body. Based on the risk quotient (RQ) of the ERA, the Zhanghe River has a moderate risk of ofloxacin (RQ = 0.111–0.583), a low or insignificant risk of sulfamethoxazole (RQ = 0.003–0.048), and an insignificant risk of other antibiotics or metabolites. However, the risk of antibiotics or metabolites in the mainstream of Yangtze River is insignificant. This study could help understand the seasonal emission patterns of antibiotics and metabolites, as well as more antibiotics sensitive of environmental risks in tributary than that in mainstream.

Analysis of Flood Carrying Capacity Change of Small and Medium-sized Rivers in the Upper Reaches of the Zhanghe River in Southern Shanxi

Using hydrological data and historical flood survey data of the design rivers, the cross-sections of different reaches are measured, and the hydraulic factors of each cross-section are calculated. HEC-RAS (5.0.7) software is used to analyze and calculate the flood water surface lines of different reaches. Taking the cross-sections measured furthest upstream as the starting points and flood peak discharges of 2% flood frequency and the corresponding water levels as the initial and boundary conditions, the design water surface lines of the design reaches are calculated, And recheck calculation of embankment top elevations is performed. And the results show that: (1) the main channels of the rivers shrink continuously due to the short flood discharge time and the influence of farming and other human activities; (2) the average longitudinal slopes of the Cuiguo Village and Du Village reaches of the Jimingshui River are 9.8 ‰ and 7.2 ‰ respectively, and the average longitudinal slope of the Xixiawang Village reach of the Qinjiagou River is 8.6 ‰; (3) the roughness values of different reaches are analyzed, and the calculated design flood water levels of the Cuiguo Village and Du Village reaches of the Jimingshui River and the Xixiawang Village reach of the Qinjiagou River when a great flood with P=2% occurs are 966.6 m, 938.05 m and 931.16 m, respectively, which are all lower than the embankment top elevations of the left and right banks by more than 2 meters, indicating that the flood in these reaches will not overflow and can pass safely.

Distribution, Risk Assessment, and Source Identification of Potentially Toxic Elements in the Sediments of the Upper Reaches of Zhanghe River, Haihe Basin

The Beijing–Tianjin–Hebei region is mostly located in the Haihe Basin. Studying the distribution and sources of potentially toxic elements (PTEs) and heavy metals in the upper reaches of Zhanghe River, the largest tributary of Haihe runoff, is of great significance to the water security of Beijing–Tianjin–Hebei region, a world-class urban agglomeration. In this study, 32 sediment samples were collected in the upper reaches of Zhanghe River, and the concentrations of eight PTEs were analyzed. The results show that the concentrations of PTEs in sediments ranked are as follows: Mn &gt; Cr &gt; Zn &gt; Cu &gt; Pb &gt; Co &gt; As &gt; Cd. Cr, Cu, Zn, and Cd show a random spatial variation trend; Mn, As, and Pb are mainly distributed in the headwaters of the Zhuozhang River. Ninety percent of samples of Cr, Cu, Zn, Cd, and Pb are under the threshold effect level, and 16% of the Cr samples are above the probable effect level. PTEs in the study area have low to moderate pollution as the enrichment factor and geo-accumulation index showed, and the contribution of anthropogenic sources to the enhancement of PTEs in sediment samples is still at a preliminary stage. Potential ecological risk results indicate that 96% of the upper reaches of Zhanghe River have a low risk level. The three evaluation methods all point out that Cr is the main pollutant in the upper reaches of Zhanghe River, and the Southern Headwater of Zhuozhang River is the main polluted area. Cr, Mn, Co, and Pb originate from mining activities and road dust, while Cu, Zn, As, and Cd originate from agricultural activities.

UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms—A Case Study of the Zhanghe River, China

Timely monitoring of inland water quality using unmanned aerial vehicle (UAV) remote sensing is critical for water environmental conservation and management. In this study, two UAV flights were conducted (one in February and the other in December 2021) to acquire images of the Zhanghe River (China), and a total of 45 water samples were collected concurrently with the image acquisition. Machine learning (ML) methods comprising Multiple Linear Regression, the Least Absolute Shrinkage and Selection Operator, a Backpropagation Neural Network (BP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were applied to retrieve four water quality parameters: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphors (TP), and permanganate index (CODMn). Then, ML models based on the stacking approach were developed. Results show that stacked ML models could achieve higher accuracy than a single ML model; the optimal methods for Chl-a, TN, TP, and CODMn were RF-XGB, BP-RF, RF, and BP-RF, respectively. For the testing dataset, the R2 values of the best inversion models for Chl-a, TN, TP, and CODMn were 0.504, 0.839, 0.432, and 0.272, the root mean square errors were 1.770 μg L−1, 0.189 mg L−1, 0.053 mg L−1, and 0.767 mg L−1, and the mean absolute errors were 1.272 μg L−1, 0.632 mg L−1, 0.045 mg L−1, and 0.674 mg L−1, respectively. This study demonstrated the great potential of combined UAV remote sensing and stacked ML algorithms for water quality monitoring.

Quantify Runoff Reduction in the Zhang River Due to Water Diversion for Irrigation

In order to systematically analyze the impacts of climate change and human activities on runoff, this paper takes the Zhanghe River Basin, which is greatly affected by human activities, as the research object, constructs an attribution analysis model of runoff changes based on historical data and the SWAT (Soil and Water Assessment Tool) model. The results show that the runoff of the watershed has significantly decreased in the past 60 years, in which the contribution rate of climate change is 36.2% and that of human activities is 63.8%. Among the climate change factors, precipitation is the main contributing factor and canal diversion is the main contributing factor among human activities. In addition, with the decrease in precipitation during the flood season and the increase in the crop planting area in the catchment, the distribution of canal water diversion has also changed, and the water consumption of summer crops has gradually become the main factor affecting canal water diversion.

Using a grey multivariate model to predict impacts on the water quality of the Zhanghe River in China.

In order to assess the social factors affecting the water quality of the Zhanghe River and predict the potential impact of growth in primary, secondary, tertiary industries and population on water quality of the Zhanghe River in the next few years, a deformation derivative cumulative grey multiple convolution model (DGMC(1,N)) was applied. In order to improve the accuracy of the model, the accumulation of deformation derivatives is introduced, and the particle swarm optimization algorithm is used to solve the optimal order. The DGMC(1,N) model was compared with GM(1,2) and GM(1,1) models. The results show that the DGMC(1,N) model has the highest prediction accuracy. Finally, DGMC(1,N) model is used to predict the potential impact of growth in primary, secondary, tertiary industries and population on water quality in the Zhanghe River (using chemical oxygen demand (COD) as the water quality indicator).

Quantifying the effect of environmental drivers on water conservation variation in the eastern Loess Plateau, China

The Loess Plateau is famous for its water shortages and soil erosion, quantitatively assessing the water conservation function is of great significance to maintain regional ecological security. Zhanghe River Basin with severe water conflicts in eastern Loess Plateau, was selected to investigate the variation characteristics of water conservation. The results showed that water conservation decreased slightly from 1960 to 2016, with a rate of 1.1 × 108 m3/10a. The mean annual water conservation (MAWC) is 31.46 × 108 m3, and accounted for 32% of the precipitation. The spatial pattern of water conservation and land use type were significantly correlated: More water resources (>213 mm) were conserved in the north hilly region with high vegetation coverage, while water resources were less (<140 mm) conserved in the south cropland and construction land. Using the calibrated Soil and Water Assessment Tool (SWAT), the contribution rates of climate change, land use, and other human activities (e.g. engineering measures, water consumption) on water conservation were quantitatively estimated through setting several different environmental scenarios. The research period (1974–2016) was divided into the baseline period (P0: 1974–1994), dramatically-increased period (P1: 1995–2005) and weakly-increased period (P2: 2006–2016). The attribution results indicated that other human activities were the dominant factor, and exerted greater effects on the increase of water conservation in the recent decades, with an increasing contribution from 82.88% in P1 to 122.16% in P2. Climate change was the secondary factor, and it increased water conservation in P1 but reduced it in P2, with the contribution from 18.01% to −15.45%. The contributions of climate change, land use/cover change (LUCC) and other human activities to water conservation reduction during P1-P2 were 32.53%, 1.67%, and 65.80%, respectively. In the recent decades, other human activities have become the most important factors on water conservation reduction, in which human water consumption contributed to 60.1%

A system dynamics simulation model for water conflicts in the Zhanghe River Basin, China

ABSTRACT This article presents a model that simulates the dynamics of water demand, water supply and the instability of water allocation schemes at the national river basin scale during water scarcity. The Zhanghe River Basin in China is used as a case study to demonstrate the model. The optimum solution, minimizing water allocation instability, allocated most of the river’s water to the downstream sub-basin, with most of the water assigned for downstream use allocated to Anyang city. The results show that the socioeconomic–environmental dynamics of the stakeholders in a water-sharing problem should be taken into account when allocating water.

Optimization Assessment of Projection Methods of Climate Change for Discrepancies between North and South China

Downscaling methods have been widely used due to the coarse and biased outputs of general circulation models (GCMs), which cannot be applied directly in regional climate change projection. Hence, appropriate selection of GCMs and downscaling methods is important for assessing the impacts of climate change. To explicitly explore the influences of multi-GCMs and different downscaling methods on climate change projection in various climate zones, the Heihe River Basin (HRB) and the Zhanghe River Basin (ZRB) were selected in this study to represent the north arid region and the south humid region in China, respectively. We first evaluated the performance of multi-GCMs derived from Coupled Model Inter-comparison Project Phase 5 (CMIP5) in the two regions based on in-situ measurements and the 40 year European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data. Subsequently, to construct appropriate climate change projection techniques, comparative analysis using two statistical downscaling methods was performed with consideration of the significant north–south meteorological discrepancies. Consequently, specific projections of future climate change for 2021–2050 under three representative concentration pathway (RCP) scenarios (RCP2.6, RCP4.5, and RCP8.5) were completed for the HRB and ZRB, including daily precipitation, maximum air temperature, and minimum air temperature. The results demonstrated that the score-based method with multiple criteria for performance evaluation of multiple GCMs more accurately captured the spatio-temporal characteristics of the regional climate. The two statistical downscaling methods showed respective advantages in arid and humid regions. The statistical downscaling model (SDSM) showed more accurate prediction capacities for air temperature in the arid-climate HRB, whereas model output statistics (MOS) better captured the probability distribution of precipitation in the ZRB, which is characterized by a humid climate. According to the results obtained in this study, the selection of appropriate GCMs and downscaling methods for specific climate zones with different meteorological features significantly impact regional climate change projection. The statistical downscaling models developed and recommended for the north and south of China in this study provide scientific reference for sustainable water resource management subject to climate change.

An Inexact Inventory Theory-Based Water Resources Distribution Model for Yuecheng Reservoir, China

In this study, an inexact inventory theory-based water resources distribution (IIWRD) method is advanced and applied for solving the problem of water resources distribution from Yuecheng Reservoir to agricultural activities, in the Zhanghe River Basin, China. In the IIWRD model, the techniques of inventory model, inexact two-stage stochastic programming, and interval-fuzzy mathematics programming are integrated. The water diversion problem of Yuecheng Reservoir is handled under multiple uncertainties. Decision alternatives for water resources allocation under different inflow levels with a maximized system benefit and satisfaction degree are provided for water resources management in Yuecheng Reservoir. The results show that the IIWRD model can afford an effective scheme for solving water distribution problems and facilitate specific water diversion of a reservoir for managers under multiple uncertainties and a series of policy scenarios.

Case study on emergency disposal of aniline pollution in Zhanghe River

In view of the frequent occurrence of secondary environmental pollution caused by safety production accidents in China, a typical case of Zhanghe aniline pollution emergencies is selected and a comprehensive and systematic introduction to the emergency disposal technical scheme of this event is introduced, including the emergency target and general idea, situation prediction, response measures and summary of emergency disposal, which can be used for reference for the same type of emergency disposal technology for environmental emergencies in the future.

Application of Adapted Benthic Index of Biotic Integrity (B-IBI) for River Ecosystem Health Assessment in Zhanghe River Watershed, China

In order to evaluate the ecological health condition of Zhanghe River Watershed, an adapted Benthic Index of Biotic Integrity (B-IBI) was developed. Macro-invertebrates were sampled at 12 monitoring stations which were grouped into two condition categories (reference and impaired stations) according to the level of degradation. A total of 47 benthic macroinvertebrates taxa were identified, in which aquatic insects 33 taxa, Mollusca 8, Annalida 3 and Crustacea 3 taxa. Based on macro-invertebrate assemblages characters of this area, 18 candidate biological metrics in four categories, including taxonomic richness, community composition, pollution tolerance, trophic guild, and value distribution, were chosen. In which, four metrics were excluded because of low values or narrow distribution range. Discriminatory power between reference and impaired stations was analysed using box-plots, and six metrics were excluded because the medians of the box-plot inside the inter quartile range. Of all the rest eight metrics, four were not suitable for B-IBI index system because of their high Pearson correlation (| r | 0.75). Finally, total taxa, percentage of Crustacea and Mollusca, percentage of tolerant taxa and percentage of predators were screened out to form a B-IBI index system. Ratio scoring method for B-IBI index was used to get a uniform score. Evaluation criterion was established based on the 25 percentiles value of reference stations. Assessment results using B-IBI showed 5 of sampling stations were in ‘healthy’ and ‘sub-healthy’ state, 3 were in ‘fair’ state, and 4 were in ‘poor’ or ‘very poor’ state of the whole watershed.

Water allocation analysis of the Zhanghe River basin using the Graph Model for Conflict Resolution with incomplete fuzzy preferences

An incomplete fuzzy preference framework for the Graph Model for Conflict Resolution (GMCR) is proposed to handle both complete and incomplete fuzzy preference information. Usually, decision makers’ (DMs’) fuzzy preferences are assumed to be complete fuzzy preference relations (FPRs). However, in real-life situations, due to lack of information or limited expertise in the problem domain, any DM’s preference may be an incomplete fuzzy preference relation (IFPR). An inherent advantage of the proposed framework for GMCR is that it can complete the IFPRs based on additive consistency, which is a special form of transitivity, a common property of preferences. After introducing the concepts of FPR, IFPR, and transitivity, we propose an algorithm to supplement IFPR, that is, to find an FPR that is a good approximation. To illustrate the usefulness of the incomplete fuzzy preference framework for GMCR, we demonstrate it using to a real-world conflict over water allocation that took place in the Zhanghe River basin of China.

Fuzzy levels of preference strength in a graph model with multiple decision makers

A new hybrid preference structure combining multiple-level strength of preference and fuzzy preference is proposed to facilitate the modeling and analysis of strategic conflicts involving multiple decision makers (DMs) with complex preferences using the Graph Model for Conflict Resolution (GMCR). The new preference structure, named fuzzy strength of preference, provides a more comprehensive and flexible representation of DMs' relative preferences among states. A key contribution of this paper is to redefine four graph model stability definitions for fuzzy preferences, fuzzy Nash stability (FNash), fuzzy general metarationality (FGMR), fuzzy symmetric metarationality (FSMR), and fuzzy sequential stability (FSEQ), as general stabilities, strong stabilities, and weak stabilities at each level, permitting deeper analysis of graph models with the new preference structure. The resulting methodology can be utilized to model and analyze complex multiple-DM conflicts, thereby enhancing the capability of the graph model to provide strategic insights. A graph model of the Zhanghe River water allocation conflict in China demonstrates that the method can find the evolution path of a conflict and give new strategic insights for both practitioners and researchers. The fuzzy strength of preference framework makes GMCR more capable for addressing a wider range of practical conflicts.

Paleochannel-controlled earth fissures in Daming, North China Plain and their implication for underground paleogeomorphology

Earth fissures are a geologic phenomenon that mainly affects the medium with shallow surface soil and mainly developed in the Quaternary sediment cover. In the Yellow River-Qinghe River - Zhanghe River shallow buried paleochannel Band (HQZ-Qh2-II Band) on the North China Plain, 93 earth fissures are developed. Among them, 29 fissures are evolved in the Daming zone in the upper reaches of the paleochannel band. Thus, the zone belongs to a densely grouped fissure area. This study, through a series of field trenching and surveying, revealed that these earth fissures have unique plane distribution rules and profile structure features of short length (several meters to several hundred meters), shallow depth (smaller than 10 m) and good correspondence to Daming paleochannels. These basic characteristics reveal that unlike other tectonic earth fissures, Daming earth fissures are a new and unknown type of fissures, i.e., paleochannel-controlled earth fissures. Theoretical analysis of their features showed that they were formed due to the actions of gravity, primary joint factors and groundwater pumping activities and controlled by ancient river factors including paleochannels, sediments and groundwater. Among them, the process of Mode I can be summed up as “self-gravity action - tensile cracks - surface water scouring - earth fissures”. The process of Mode II is “primary join - surface water infiltration - piping - cave in - earth fissures”. The process of Mode III is “groundwater extraction - aquifer compression - inhomogeneous ground subsidence - earth fissures”. These formation mechanisms of paleochannel-induced earth fissures can be used to predict the fine distribution of shallow buried paleochannels and the overall migration process of the rivers. Thus, this kind of earth fissures is of great significance for understanding the underground paleogeomorphology (buried paleochannel) of the North China Plain and other alluvial plains of the world.

Nonstationary Multivariate Hydrological Frequency Analysis in the Upper Zhanghe River Basin, China

Design annual runoff is a classical issue in traditional univariate hydrological frequency analysis (HFA). We developed a multivariate HFA approach for designs for a study region covering the confluence of two streams. HFA relies on the assumption that probability distribution is consistent in the past, present, and future; however, it has been asserted as incorrect under an uncertain and changing environment. A change-point was detected in our study and adopted to divide runoff into two periods, with no significant trends in all subseries. The post-change design annual runoff witnessed dramatic mean value decline by about half at four frequencies (50%, 75%, 90% and 95%), which were selected in the bivariate analysis. Probability distribution models were constructed with univariate p-III distributions through Clayton, Frank, and Gumbel copulas and independence. Frank copula showed a generally better match with observations than others. The traditional approach, adding up the same-frequency results from both tributaries independently, was disproved by the systematically smaller design values in independence model than copulas and the 40% asynchronous encounter probability. The 25.6% worst synchronous dry-dry encounter situation may be a concern for water resource managers. Consequently, multivariate HFA should prevail as design approach in terms of water resources security.

Simulating Water Resource Disputes of Transboundary River: A Case Study of the Zhanghe River Basin, China

Water resource disputes within transboundary river basin has been hindering the sustainable use of water resources and efficient management of environment. The problem is characterized by a complex information feedback loop that involves socio-economic and environmental systems. This paper presents a system dynamics based model that can simulate the dynamics of water demand, water supply, water adequacy and water allocation instability within a river basin. It was used for a case study in the Zhanghe River basin of China. The base scenario has been investigated for the time period between 2000 and 2050. The result shows that the Chinese national government should change the water allocation scheme of downstream Zhanghe River established in 1989, more water need to be allocated to the downstream cities and the actual allocation should be adjusted to reflect the need associated with the socio-economic and environmental changes within the region, and system dynamics improves the understanding of concepts and system interactions by offering a comprehensive and integrated view of the physical, social, economic, environmental, and political systems.