Search Results (32)

The upper Xin’an River basin serves as a critical ecological barrier and water-conservation area for the Yangtze River Delta. However, with rapid economic development, nitrogen pollution in the surface waters of this region has become increasingly pronounced. This study analyzed river water samples collected on four occasions from the upper Xin’an River basin for ammonium (NH₄⁺–N), nitrate-nitrogen (NO₃⁻–N), total nitrogen (TN), and nitrate isotopic (δ¹⁵N–NO₃⁻ and δ¹⁸O–NO₃⁻). The sources of nitrate (NO₃⁻) were apportioned using the MixSIAR stable-isotope mixing model, and the spatial distribution of these sources across the basin was characterized. Across the four sampling rounds, the mean TN concentration exceeded 1.3 mg/L, with NO₃⁻–N accounting for over 45% of TN, indicating that nitrate was the dominant inorganic nitrogen species. The δ¹⁵N–NO₃⁻ values ranged from 2.17‰ to 13.0‰, with mean values following the order summer > winter > autumn > spring. The δ¹⁸O–NO₃⁻ values varied from −5.20‰ to −3.48‰, and the average value showed a completely opposite seasonal variation pattern to that of δ¹⁵N–NO₃⁻. Process-based analysis of nitrogen transformations revealed that nitrification predominates during nitrate transport and transformation, whereas denitrification is comparatively weak. MixSIAR-based estimates indicate marked seasonal differences in the source composition of nitrate pollution in the upper Xin’an River basin; NO₃⁻ derives primarily from soil nitrogen (SN) and livestock/sewage manure nitrogen (LSN). LSN was the dominant contributor in spring and summer (49.2% and 59.9%, respectively). SN dominated in autumn (49.2%) and winter (54.1%). Fertilizer nitrogen (FN) contributed more during summer and autumn, when fertilization is concentrated and rainfall is higher. Atmospheric deposition (AN) contributed approximately 1% across all seasons and was thus considered negligible. These findings provide a scientific basis for source control of nitrogen pollution and water-quality management in the upper Xin’an River. Full article

After the non-point source pollutants are generated at the source position and migrate to the target water body, they will have different degrees of loss under the action of precipitation, adsorption, or absorption by plants, resulting in differences in pollution output load and generation amount. Taking the Xin’an River Basin as an example, this study analyzes the spatial distribution characteristics of non-point source pollution generation and output in the process of river migration and explores the influence of river migration on non-point source pollution based on the soil and water assessment tool (SWAT) model and mathematical statistical methods. The results showed that the spatial distribution intensity of total nitrogen and total phosphorus in different sub-basins of Xin’an River Basin is between 3.88 and 29.16 kg/ha and 0.11–1.18 kg/ha, respectively. The high intensity areas of non-point source pollution generation and output are mainly concentrated in the hydrologically sensitive areas in the southern part of the basin and the erosion-sensitive area in the southeastern part of the basin, and the critical source areas of non-point source pollution are a result of comprehensive effects of crop fertilizer input, soil nitrogen, and phosphorus storage as well as hydrology and soil erosion. There are differences in the spatial distribution of non-point source pollution generation and output in the process of river migration. Some sub-basins have significant changes in their generation and output, and the sub-basin output coefficients of total nitrogen and total phosphorus are between 0.856 and 1.014 and 0.998–1.061, respectively. The change intensity of pollutants after river migration is affected by the combined effects of migration time, runoff intensity, material adsorption, and desorption, etc. The research findings will provide scientific support for zonal management and targeted measures of non-point source pollution in the Xin’an River Basin. Full article

Predicting future hydrological drought characteristics can assist relevant departments in taking proactive measures to mitigate drought losses. Based on the SWAT model and the Sixth International Coupled Model Comparison Program, this study employs an improved Mann–Kendall test, cumulative anomaly method, and continuous wavelet transform to investigate future runoff and hydrological drought characteristics in the upper reaches of the Xin’an River under different Shared Socioeconomic Pathways (SSPs). The SSPs scenario consists of three typical paths. SSP126 represents the sustainable development path (low carbon emissions, ecological protection first), SSP245 is the intermediate balance path (equal emphasis on economic growth and environmental protection), and SSP585 is the fossil fuel-intensive path (high emissions, high development intensity). The results indicate that from 2000 to 2020, under the influence of ecological compensation policies, the upper reaches of the Xin’an River transitioned from hydrological drought to hydrological wetness in 2012. Under the three future scenarios, runoff volumes increased by 41.72%, 40.74%, and 40.72% compared to the historical period, respectively, with peak runoff occurring in May, June, and July, alleviating hydrological drought conditions. Under the SSP245 and SSP585 scenarios, drought characteristics were more pronounced, with the number of drought-free months increasing by 21 and 30 months, respectively, compared to the SSP126 scenario, and the number of extremely dry months increased by 9 months and 17 months, respectively. The standard runoff index in the SSP126 scenario exhibits two oscillation cycles of 400 months and 359 months, respectively, while SSP245 and SSP585 both exhibit an oscillation cycle of 835 months. After discussion, it was concluded that ecological compensation policies can improve hydrological drought conditions. Drought characteristics become increasingly pronounced as carbon emissions intensify. This research can provide theoretical references for water allocation and drought prevention in river basins. Full article

A total of 162 groundwater samples were collected during November and December 2022 in the Xin’an River Basin during the dry season. In this research, the concentrations of various indicators in most samples did not exceed the prescribed standards. The indicators with the largest number of exceedances were iodine and manganese, with 22 and 23 samples, respectively. Overall, the groundwater quality in the Xin’an River Basin was generally good, with only 7 samples with the EWQI values greater than 150, which exhibited poor groundwater quality. The primary factors influencing groundwater quality were the concentrations of I, Mn, and Al, which were predominantly affected by water–rock interactions. Groundwater quality in the Xin’an River Basin was mainly influenced by natural factors rather than anthropogenic activities. Both the carcinogenic and non-carcinogenic health risks posed by groundwater in the Xin’an River Basin were higher for children than for adults. The long-term chronic cumulative effect was the most important factor contributing to both carcinogenic and non-carcinogenic health risks. Iodine presented the highest non-carcinogenic health risks for both adults and children. In regions where high-iodine groundwater was distributed, it is recommended to enhance the monitoring of iodine concentrations in the groundwater. Full article

Based on a comprehensive review of the current research status of ecological compensation both domestically and internationally, combined with field survey data, this study delves into the issue of multi-stakeholder participation in the ecological compensation mechanisms of the Xin’an River Basin. This research reveals that the joint participation of multiple stakeholders is crucial to achieving the goals of ecological compensation in river basins. The government plays a significant role in macro-guidance, financial support, policy guarantees, supervision, and management. It promotes the comprehensive implementation of ecological environmental protection by formulating relevant laws and regulations, guiding the public to participate in ecological conservation, and supervising and punishing pollution behaviors. The public, serving as the main force, forms strong awareness and behavioral habits of ecological protection through active participation in environmental protection, monitoring, and feedback. As participants, enterprises contribute to industrial transformation and green development by improving resource utilization efficiency, reducing pollution emissions, promoting green industries, and participating in ecological restoration projects. Scientific research institutions, as technology enablers, have effectively enhanced governance efficiency through technological research and innovation, ecosystem value accounting to provide decision-making support, and public education. Social organizations, as facilitators, have injected vitality and innovation into watershed governance by extensively mobilizing social forces and building multi-party collaboration platforms. Communities, as supporters, have transformed ecological value into economic benefits by developing characteristic industries such as eco-agriculture and eco-tourism. Based on the above findings, further recommendations are proposed to mobilize the enthusiasm of upstream communities and encourage their participation in ecological compensation, promote the market-oriented operation of ecological compensation mechanisms, strengthen cross-regional cooperation to establish joint mechanisms, enhance supervision and evaluation, and establish a sound benefit-sharing mechanism. These recommendations provide theoretical support and practical references for ecological compensation worldwide. Full article

This study explores flood forecasting in the Pa River basin, a major tributary of the Beijiang River in South China, by integrating the Xin’anjiang hydrological model with the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm for parameter calibration. Fifteen observed flood events from April to August 2024 were employed in this study, with twelve events used for model calibration and the remaining three for validation. Additionally, to assess model performance under extreme conditions, a 50-year return period flood event from June 2020 was incorporated as a supplementary validation case. The calibrated model reproduced flood hydrographs with high accuracy, achieving Nash–Sutcliffe Efficiency (NSE) values of up to 0.98, relative peak discharge errors generally within ±10%, and peak timing deviations under 3 h. The validation results demonstrated consistent performance across both typical and extreme events, indicating that the proposed framework provides a feasible and physically interpretable approach for flood forecasting in data-limited catchments. Full article

Water environment evaluation is the basis of water resource planning and sustainable utilization. As a successful case of the coordinated progress of ecological protection and economic development, the Xin’an River Basin is a model for exploring the green development model. However, there are still some problems in the synergistic cooperation between the two provinces. Exploring the differences within the basin is a key entry point for solving the dilemma of synergistic governance in the Xin’an River Basin, optimizing the allocation of resources, and improving the overall effectiveness of governance. Based on the DPSIR model, 21 water environment–related indicators were selected, and the entropy weight–TOPSIS method and gray correlation model were used to evaluate the temporal and spatial status of water resources in each county of the Xin’an River Basin. The results show that (1) The relative proximity of the water environment in Xin’an River Basin fluctuated in “M” shape during the ten years of the study period, and the relative proximity reached the optimal solution of 0.576 in 2020. (2) From the five subsystems, the state layer and the corresponding layer are the most important factors influencing the overall water environment of the Xin’an River Basin. In the future, it is intended to improve the departmental collaboration mechanism. (3) The mean values of relative proximity in Qimen County, Jiande City, and Chun’an County during the study period were 0.448, 0.445, and 0.439, respectively, and the three areas reached a moderate level. The water environment in Huizhou District and Jixi County, on the other hand, is relatively poor, and the mean values of proximity are 0.337 and 0.371, respectively, at the alert level. The poor effect of synergistic development requires a multi–factor exploration of reasonable ecological compensation standards. We give relevant suggestions for this situation. Full article

Traditional villages are crucial for the sustainable development of both urban and rural areas, and identifying their spatial patterns is key to guiding village construction and promoting urban–rural integration. This research selected 274 traditional Huizhou villages located in the upper basin of the Xin’an River. It examined how the four main factors—construction period, geography, ecology, and social and economic development—shape and influence each other. By incorporating an optimal parameters-based geographical detector model, this study further explored the driving mechanisms behind spatial differentiation. The villages exhibit a “one belt, two cores, and multiple dispersion” pattern, with Shexian and Yixian counties as hot gathering areas of traditional villages. Population migration, internal growth, and external cultural and commercial exchanges drove village formation in three stages. Spatial distribution favors locations with gentle slopes, sunny aspects, proximity to water, suitable climates, convenient transportation, and distance from crowded areas. Topography, water systems, and external communication are key drivers, while the synergy between water systems and transportation is particularly significant. This study concludes that water systems have the greatest influence on village spatial patterns, recommending watersheds as regional boundaries and advocating a clustering development model for planning and protection efforts. Full article

To establish a sound ecological compensation (EC) mechanism in the Xin’an River Basin, this study suggested utilizing ecosystem service valuation to determine the compensation amount. In this study, the first step was to establish a reasonable watershed EC model using the ecological compensation supply coefficient (ECSC) based on the value spillover theory (VST) of the ecosystem services and the ecological compensation demand coefficient (ECDC). The second step was to classify the ecosystem services of the Xin’an River Basin into three categories, including supply service, regulating service, and cultural service, with 14 specific functions to determine the ecological compensation standard accounting scope in these services. Then, a case study on the Xin’an River Basin for EC standards was presented. The total ecosystem service value (ESV) in the Xin’an River Basin was estimated to be CNY 70.271 billion, with supply service accounting for 22.7%, regulating service accounting for 24.6%, and cultural service accounting for 52.7%. Based on the compensation scope, the ecosystem service values for the upper and lower limits of the EC were calculated as CNY 57.779 billion and CNY 17.292 billion. Combined with the results of the ECSC and ECDC, the upper and lower limits of the EC standard in the Xin’an River Basin were computed to be CNY 4.085 billion and CNY 1.438 billion, respectively. Therefore, the ESV-based EC model for the Xin’an River Basin can effectively address the challenge of inadequate EC in the watershed. It also facilitates balanced regional development and serves as a theoretical foundation and empirical evidence for the government to establish a unified national policy on cross-border river basin ecological compensation. Full article

Due to the complexity of terrain and climate in the mountain–plain transition zone, it is difficult to simulate and forecast the flow discharge of river basins accurately. The poor regularity of the river thus leads to uncertain flood control scheduling. Meanwhile, reservoirs and flood detention areas are constructed to store and divert water when severe floods threaten the safety of the basin. In order to improve the accuracy of flood forecasts and the effectiveness of flood control, a hydrological and 1D/2D hydrodynamic coupling model was developed to enable the joint computation of multiple objects, including mountainous streams, plains river networks, hydraulic control structures, and flood detention areas. For the hydrological component, the Xin’anjiang model with the Muskingum module is employed to simulate mountainous flow discharge. For the hydrodynamic component, the Saint–Venant equations and shallow water equations are applied to estimate flood processes in rivers and on land surfaces, respectively. The Dongtiaoxi River Basin in Zhejiang Province, China, serves as the case study, where river flow is influenced by both upstream mountainous floods and downstream backwater effects. Using the integrated model, flood routing and scheduling are simulated and visualized. Both the Xin’anjiang model and the 1D hydrodynamic model demonstrate over 80% acceptability in calibration and validation, confirming their robustness and reliability. Meanwhile, inundation in flood detention areas can be effectively estimated by coupling the 1D and 2D hydrodynamic models with a flood diversion scheduling model. The coupled model proves capable of simulating flood routing in complex river basins that include mountains, plains, and hydraulic control structures, accounting for the interactions between hydrological elements. These findings provide a new perspective on flood simulation in other similarly complex river basins. Full article

Identifying the potential pollution sources of surface water pollutants is essential for the management and protection of regional water environments in drinking water source areas. In this study, absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models were applied to assess water quality and identify the potential pollution sources affecting the surface water quality of Xin’an River Basin. For this purpose, a 10-year (2011–2020) dataset of eight water quality indicators (including pH, EC, DO, COD, NH₃-N, TN, TP, and FC) covering eight monitoring stations and 7248 monthly observations was used. The results indicated that Pukou section had the worst water quality among the eight monitoring stations, and TN was the most serious water quality index. Both the APCS-MLR and PMF models identified agricultural nonpoint source pollution, urban nonpoint source pollution and rural domestic pollution, and meteorological factors. The sum of these three sources was very close, accounting for 60% and 58%, respectively. The APCS-MLR results demonstrated that for EC, COD, and NH₃-N, the major pollution sources were urban nonpoint sources and rural domestic pollution. The major contamination source of TN was agricultural nonpoint source pollution (30.4%). Meanwhile, the major pollution sources of pH, DO, TP, and FC were unidentified factors. The PMF model identified five potential sources, and pH and DO were affected by meteorological factors. NH₃-N and TP were influenced mainly by agricultural nonpoint source pollution. Atmospheric deposition was the major pollution source (87.9%) of TN. FC was mostly derived from livestock and poultry breeding (88.3%). EC and COD were mostly affected by urban nonpoint sources and rural domestic pollution. Therefore, receptor models can help managers identify the major sources of pollution in watersheds, but the major factors affecting different pollutants need to be supplemented by other methods. Full article

Ensemble precipitation forecasts (EPFs) can help to extend lead times and provide reliable probabilistic forecasts, which have been widely applied for streamflow predictions by driving hydrological models. Nonetheless, inherent biases and under-dispersion in EPFs require post-processing for accurate application. It is imperative to explore the skillful lead time of post-processed EPFs for summer streamflow predictions, particularly in mountainous regions. In this study, four popular EPFs, i.e., the CMA, ECMWF, JMA, and NCEP, were post-processed by two state of art methods, i.e., the Bayesian model averaging (BMA) and generator-based post-processing (GPP) methods. These refined forecasts were subsequently integrated with the Xin’anjiang (XAJ) model for summer streamflow prediction. The performances of precipitation forecasts and streamflow predictions were comprehensively evaluated before and after post-processing. The results reveal that raw EPFs frequently deviate from ensemble mean forecasts, particularly underestimating torrential rain. There are also clear underestimations of uncertainty in their probabilistic forecasts. Among the four EPFs, the ECMWF outperforms its peers, delivering skillful precipitation forecasts for 1–7 lead days and streamflow predictions for 1–4 lead days. The effectiveness of post-processing methods varies, yet both GPP and BMA address the under-dispersion of EPFs effectively. The GPP method, recommended as the superior method, can effectively improve both deterministic and probabilistic forecasting accuracy. Moreover, the ECMWF post-processed by GPP extends the effective lead time to seven days and reduces the underestimation of peak flows. The findings of this study underscore the potential benefits of adeptly post-processed EPFs, providing a reference for streamflow prediction over mountain river basins. Full article

The Xin’anjiang hydrological model (XHM) is the practical tool for runoff simulation and flood forecasting in most regions in China, but it still presents some challenges when applied to Northwest China, where the river runoff mostly comes from high-temperature snowmelt, as the model lacks such a functional module. In this study, the improved XHM coupling snowmelt module is presented to complete the existing XHM for better suitability for flood simulation in areas dominated by snowmelt. The improved model includes four sub-models: evapotranspiration, runoff yield, runoff separation, and runoff routing, where the snowmelt runoff module is introduced in both the runoff yield and separation sub-models. The watershed is divided into two types, non-snow areas with lower altitudes and snow-covered areas with higher altitudes, to study the mechanism of runoff production and separation. The evaluation index, determination coefficients (R²), mean square error (MSE), and Nash efficiency coefficients (NSE) are used to assess the improved XHM’s effect by comparing it with the traditional model. Results show that the R² of the improved XHM coupled with snowmelt are around 0.7 and 0.8 at the Zamashk and Yingluoxia stations, respectively, while the MSE and NSE are also under 0.4 and above 0.6, respectively. The absolute value of error of both flood peaks in the Yingluoxia station simulated by improved XHM is only 10% and 6%, and that of traditional XHM is 32% and 40%, indicating that the peak flow and flood process can be well simulated and showing that the improved XHM coupled with snowmelt constructed in this paper can be applied to the flood forecasting of the Heihe River Basin. The critical temperature of snow melting and degree-day factor of snow are more sensitive compared with other parameters related to snow melting, and the increasing trend of peak flow caused by both decreased critical temperature and increased degree-day factor occurs only when the value of the model’s state (snow reserve) is higher. These results can expand the application scope in snow-dominated areas of the XHM, providing certain technical references for flood forecasting and early warning of other snowmelt-dominated river basins. Full article

Cyanobacteria, one of the most widespread photoautotrophic microorganisms on Earth, have evolved an inorganic CO₂-concentrating mechanism (CCM) to adapt to a variety of habitats, especially in CO₂-limited environments. Leptolyngbya boryana, a filamentous cyanobacterium, is widespread in a variety of environments and is well adapted to low-inorganic-carbon environments. However, little is currently known about the CCM of L. boryana, in particular its efficient carbon fixation module. In this study, we isolated and purified the cyanobacterium CZ1 from the Xin’anjiang River basin and identified it as L. boryana by 16S rRNA sequencing. Genome analysis revealed that L. boryana CZ1 contains β-carboxysome shell proteins and form 1B of Rubisco, which is classify it as belonging to the β-cyanobacteria. Further analysis revealed that L. boryana CZ1 employs a fine CCM involving two CO₂ uptake systems NDH-1₃ and NDH-1₄, three HCO₃⁻ transporters (SbtA, BicA, and BCT1), and two carboxysomal carbonic anhydrases. Notably, we found that NDH-1₃ and NDH-1₄ are located close to each other in the L. boryana CZ1 genome and are back-to-back with the ccm operon, which is a novel gene arrangement. In addition, L. boryana CZ1 encodes two high-affinity Na⁺/HCO₃⁻ symporters (SbtA1 and SbtA2), three low-affinity Na⁺-dependent HCO₃⁻ transporters (BicA1, BicA2, and BicA3), and a BCT1; it is rare for a single strain to encode all three bicarbonate transporters in such large numbers. Interestingly, L. boryana CZ1 also uniquely encodes two active carbonic anhydrases, CcaA1 and CcaA2, which are also rare. Taken together, all these results indicated that L. boryana CZ1 is more efficient at CO₂ fixation. Moreover, compared with the reported CCM gene arrangement of cyanobacteria, the CCM-related gene distribution pattern of L. boryana CZ1 was completely different, indicating a novel gene organization structure. These results can enrich our understanding of the CCM-related gene arrangement of cyanobacteria, and provide data support for the subsequent improvement and increase in biomass through cyanobacterial photosynthesis. Full article

Access to clean and equitable water is vital to human survival and an essential component of a sustainable society. Using 59 monitoring sections, the water quality of 32 rivers in 12 river systems within two river basins in one resource-depleted city (Jiaozuo) was examined in four seasons to better comprehend the extent of river pollution, and the distribution prediction of main indexes was conducted. In total, 92% of the monitoring sections met the national standards. Overall, 12.5%, 62.5%, and 25% of samples met water quality standards III, IV, and V, respectively. The concentrations of total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD) ranged from 0.527 to 7.078, 0.001 to 1.789, and 0.53 to 799.25 mg/L, respectively. The Yellow River Basin has higher annual mean concentrations of total carbon (TC), TN, and total organic carbon (TOC) than the Haihe River Basin. The highest and lowest concentrations of specific water quality indices varied across seasons and rivers. Dashilao and Rongyou Rivers have the best water quality, while Dasha, Xin, and Mang Rivers have the worst. TN, TP, and NH₄⁺-N concentrations in the Laomang River midstream were greater than those upstream and downstream. Statistically, significant positive associations were found between NH₄⁺-N and TC, TOC, and COD (p < 0.025), where NH₄⁺-N and COD influenced water quality the most. A significant positive relationship between COD and TP (p < 0.01) was observed. Overall, water quality values were highest in the summer and lowest in winter. The distribution prediction revealed TN, TP, NH₄⁺-N, and COD showed significant regional differences. Household sewage, industrial sewage discharge, and agricultural contamination were all the possible significant contributors to declining water quality. These findings could provide a scientific reference for river water resource management in resource-depleted cities. Full article