Search Results (4)

The combined hydrochemical analysis, factor analysis, and isotopic signals of water and nitrate were applied to explore the hydrochemical origin and identify the sources and transformation of nitrate in river water and groundwater in the Huashan watershed. Additionally, a Bayesian isotope mixing model (SIAR) was employed for quantitative assessment of the nitrate sources. The results indicated that both river water and groundwater were dominated by HCO₃-Ca and HCO₃-Ca·Mg types; both originated from precipitation and were influenced by evaporation. The main constituent ions in the river water and groundwater primarily originated from carbonate and silicate dissolution, with the presence of cation exchange in the groundwater. The water chemistry of river water was greatly influenced by physicochemical factors, while that of groundwater was mainly controlled by water–rock interaction. NO₃⁻ in river water was mainly influenced by soil nitrogen (SN) and manure and septic wastes (MSWs), while NO₃⁻ in groundwater was jointly affected by ammonium fertilizers (AF), SN, and MSWs. With the exception of denitrification observed in the groundwater at the watershed outlet, denitrification was absent in both groundwater in the piedmont area and in river water. The SIAR model results demonstrated that the contribution rates of atmospheric precipitation (AP), AF, SN, and MSWs to river water were 12%, 21%, 25%, and 42%, respectively, while to groundwater, they were 16%, 27%, 10%, and 47%, respectively. Overall, MSWs were the main sources of nitrate in the river water and groundwater. It is necessary to prevent the leakage of MSWs when managing water resources. Full article

The Huashan Creek watershed is the largest water source and the main production area of honeydew in Pinghe County, whose extensive cultivation of honeydew has exacerbated soil and water pollution. However, the spatial application of remote sensing ecological index (RSEI) in this watershed and key driving factors are not clear considering the applicability of data quality and the diversity of methodological scales. To explore the RSEI and driving factors at distinct scales in Huashan Creek watershed, this study constructed the RSEI based on the environmental balance matrix at seven scales in 2020, revealed its spatial response characteristics at different scales, and analyzed the key drivers. The results show that the 240 m grid as well as rural and watershed scale convergence analyses satisfy the assessment of RSEI, whose Moran indexes are 0.558, 0.595, and 0.146, respectively. The RSEIs at different scales have significant spatial aggregation characteristics, but the overall status is moderate. The central town–riparian area with poor RSEI contrasts with the western mountainous area, which has comparatively better quality. Population has a major influence on RSEI at multiple scales (0.8), with elevation and patch index acting significantly at the village and grid scales, respectively. These findings help to identify the spatial distribution of quality and control mechanisms of RSEI in the Huashan Creek watershed and provide new insights into key scales and drivers of ecological restoration practices in the watershed. Full article

Quantifying the spatiotemporal variations of basin-scale surface water (SW)–groundwater (GW) interactions is vital for the conjunctive management of water resources in the basin. In this study, an integrated hydrological model (SWAT-MODFLOW) is used to simulate the SW–GW system in the Huashan Basin. The numerical model was calibrated and validated using the streamflow observations of the watershed outlet and the groundwater levels of the long-term monitoring wells from 2016 to 2020 in the study area. The model results show that the SWAT–MODFLOW can achieve a better fit for the streamflow discharge, compared with the results in the single SWAT model, with R² (coefficient of correlation) and NSE (Nash-Sutcliffe efficiency coefficient) of 0.85 and 0.83, respectively. The water table fitting results indicate that R² and RMSE can reach 0.95 and 0.88, respectively. The water budgets analysis demonstrates that the average rate (0.5281 m³/s) of GW abstraction to SW is larger than the rate (0.1289 m³/s) of SW recharge to GW. Moreover, the exchange rate of SW and GW gradually reaches a peak value from June to August, and the lowest value is shown in April, for each hydrological year. Based on the IPPC6 CanESM5 dataset supplied by the Canadian Climate Centre, the regional precipitation scenario subject to climate change was predicted by the ASD (Auto Statistical Downscaling Model) a statistical downscaling method, under the climate scenarios of SSP2_4.5 and SSP5_8.5. The SW–GW interaction pattern was modeled under the future scenarios in the study area. The current (2016–2020) average annual rate of the SW–GW interaction is considered as the base value. Subject to the SSP2_4.5 scenario, the average exchange rate of the SW recharge to GW is 0.1583 m³/s, which is an increase of 22.8%. The average exchange rate of the GW discharge to SW is 0.5189 m³/s which is a reduction of 0.017%. Subject to the SSP5_8.5 scenario, the average exchange rate of SW recharge to GW is 0.1469 m³/s, which is an increase of 14.7%. The average exchange rate of the GW discharge to SW is 0.5953 m³/s, which is an increases of 12.7%. The results can assist in water resource management in the basin, by identifying potential locations of nutrient transport from the aquifer to the river, as well as changes in spatial variability under future climatic conditions. Full article

A high concentration of nitrate (NO₃⁻) in surface water threatens aquatic systems and human health. Revealing nitrate characteristics and identifying its sources are fundamental to making effective water management strategies. However, nitrate sources in multi-tributaries and mix land use watersheds remain unclear. In this study, based on 20 surface water sampling sites for more than two years’ monitoring from April 2012 to December 2014, water chemical and dual isotopic approaches (δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻) were integrated for the first time to evaluate nitrate characteristics and sources in the Huashan watershed, Jianghuai hilly region, China. Nitrate-nitrogen concentrations (ranging from 0.02 to 8.57 mg/L) were spatially heterogeneous that were influenced by hydrogeological and land use conditions. Proportional contributions of five potential nitrate sources (i.e., precipitation; manure and sewage, M & S; soil nitrogen, NS; nitrate fertilizer; nitrate derived from ammonia fertilizer and rainfall) were estimated by using a Bayesian isotope mixing model. The results showed that nitrate sources contributions varied significantly among different rainfall conditions and land use types. As for the whole watershed, M & S (manure and sewage) and NS (soil nitrogen) were major nitrate sources in both wet and dry seasons (from 28% to 36% for manure and sewage and from 24% to 27% for soil nitrogen, respectively). Overall, combining a dual isotopes method with a Bayesian isotope mixing model offered a useful and practical way to qualitatively analyze nitrate sources and transformations as well as quantitatively estimate the contributions of potential nitrate sources in drinking water source watersheds, Jianghuai hilly region, eastern China. Full article