Pan Wu

Groundwater quality and availability in coastal aquifers have become a serious concern in recent times due to increased abstraction for domestic, agricultural and industrial purposes. (1) Background: Zhuhai city is selected as a representative coastal aquifer in Southern China to comprehensively evaluate the hydrochemical characteristics, spatial distribution and controlling mechanisms of groundwater. (2) Methods: A detailed study utilizing statistical analyses, a Piper diagram, Gibbs plots, and ion ratios was conducted on 114 surface water samples and 211 groundwater samples. (3) Results: The findings indicate that the pH of most groundwater is from 6.06 to 6.52, indicating a weakly acidic environment. The pH of surface water ranges from 5.35 to 9.86, with most values being weakly alkaline. The acidity in the groundwater may be related to the acidic atmospheric precipitation, an acidic unsaturated zone, oxidation of sulphide minerals and tidal action. The groundwater chemical types are predominantly mixed, followed by Ca-Mg-HCO₃ type. Surface water samples are predominantly Na-Cl-SO₄ type. The NO₃⁻ concentration in groundwater is relatively high, with a mean value of 17.46 mg/L. The NO₂⁻ and NH₄⁺ concentrations in groundwater are relatively low, with mean values of 0.46 mg/L and 7.58 mg/L. (4) Conclusions: The spatial distribution of the principal chemical constituents in the groundwater is related to the landform. The chemical characteristics of groundwater in the study area are mainly controlled by the weathering and dissolution of silicate and sulfate minerals, evaporation, seawater mixing and cation exchange. Nitrate in clastic fissure groundwater, granite fissure groundwater and unconfined pore groundwater primarily originates from atmospheric precipitation, agricultural activities of slope farmland and forest land. Nitrate in confined pore groundwater and karst groundwater primarily originates from domestic sewage and mariculture wastewater. Our findings elucidate the processes characterizing the hydrogeology and surface water interactions in Zhuhai City’s coastal system, which are relevant to other catchments with similar geological characteristics.

Climate change has effects on hydrological change in multiple aspects, particularly in the headwaters of the Yellow River (HWYR), which is widely covered by climate-sensitive frozen ground. In this study, the annual runoff was partitioned into four runoff compositions: winter baseflow, snowmelt runoff, rainy season runoff, and recession flow. In addition, the effects of global warming, precipitation change, and frozen ground degradation were considered in long-term variation analyses of the runoff compositions. The moving t-test was employed to detect change points of the hydrometeorological data series from 1961 to 2013, and flow duration curves were used to analyze daily runoff regime change in different periods. It was found that the abrupt change points of cold season runoff, such as recession flow, winter baseflow, and snowmelt runoff, are different from that of the rainy season runoff. The increase in winter baseflow and decrease in snowmelt runoff at the end of 1990s was closely related to global warming. In the 21st century, winter baseflow presented a larger relative increase compared to rainy season runoff. The correlation analyses indicate that winter baseflow and snowmelt runoff are mainly controlled by water-resource-related factors, such as rainy season runoff and the accumulated precipitation in cold season. To analyze the global warming impacts, two runoff coefficients—winter baseflow discharge rate (R_w) and direct snowmelt runoff coefficients (R_s)—were proposed, and their correlation with freezing–thawing indices were analyzed. The increase of R_w is related to the increase in the air temperature thawing index (DDT), but R_s is mainly controlled by the air temperature freezing index (DDF). Meanwhile, the direct snowmelt runoff coefficient (R_s) is significantly and positively correlated to DDF and has decreased at a rate of 0.0011/year since 1980. Under global warming, the direct snowmelt runoff (runoff increment between March to May) of the HWYR could decrease continuously in the future due to the decrease of accumulative snow in cold season and frozen ground degradation. This study provides a better understanding of the long-term runoff characteristic changes in the HWYR.

Understanding the driving forces for alpine vegetation variations at different permafrost degrading stages is important when the Tibetan Plateau is experiencing climate warming. We applied the modified Frost Number model to simulate frozen ground distributions in the Tibetan Plateau and calculated the maximum thawing depth by the Stefan approach. We classified the simulated frozen ground into three subzones: seasonal frozen ground zone, changing zone, and permafrost zone. We evaluated the effects of precipitation, air temperature, and maximum thawing depth on Normalized Difference Vegetation Index (NDVI) in the subzones across five different stages from 1982 to 2012. The results show that permafrost zone, changing zone, and seasonal frozen ground zone account for about 30.6 percent, 23.3 percent, and 46.1 percent of the study area, respectively. Over the five stages, permafrost areas decreased at fast, slow, fastest, and then slowest rate from stage1 to stage 5, and the large continuous permafrost area has been degraded into pieces. Precipitation is strongly correlated with NDVI and contributes most `to the changes of NDVI. Maximum thawing depth and particularly air temperature show a much smaller correlation and contribute less to the variation rate of NDVI. The findings will have broad applications in investigating the impact of climate and environment changes on alpine vegetation variations in the Tibetan Plateau.

Hydrological responses to climate change are a widely concerning question, particularly for the source region of the Yellow River (SRYR), which is sensitive to climate change and is widely underlain by frozen ground. In considering climate change impacts on catchment properties, the traditional separation approach based on the Budyko framework was modified to identify and quantify the climatic causes of discharge changes. On the basis of the decomposition method, the traditional separation method and the modified separation method were used to analyse the discharge change in the SRYR. Using the observed annual maximum frozen depth (MFD) to indicate the frozen ground level, the impacts of frozen-ground degradation on the discharge change were further considered using the modified separation method. Our results show that the traditional separation approach underestimated climate-induced discharge change; over the past half-century, the discharge change in the SRYR has been primarily controlled by climate change. Increasing air temperature is generally a negative force on discharge generation; however, it also causes frozen ground to degrade—a positive factor for discharge generation. Such conflicting effects enhance the uncertainty in assessments of hydrological responses to climate change in the sub-basins with widely distributed permafrost.

Though extensive researches were conducted in the source region of the Yellow River (SRYR) to analyse climate change influence on streamflow, however, few researches concentrate on streamflow of the sub-basin above the Huangheyan station in the SRYR (HSRYR) where a water retaining dam was built in the outlet in 1999. To improve the reservoir regulation strategies, this study analysed streamflow change of the HSRYR in a mesoscale. A tank model (TM) was proposed and calibrated with monthly observation streamflow from 1991 to 1998. In the validation period, though there is a simulation deviation during the water storage and power generation period, simulated streamflow agrees favourably with observation data from 2008 to 2013. The model was further validated by two inside lakes area obtained from Landsat 5, 7, 8 datasets from 2000 to 2014, and significant correlations were found between the simulated lake outlet runoff and respective lake area. Then 21 Global Climate Models (GCM) ensembled data of three emission scenarios (SRA2, SRA1B and SRB1) were downscaled and used as input to the TM to simulate the runoff change of three benchmark periods 2011–2030 (2020s), 2046–2065 (2050s), 2080–2099 (2090s), respectively. Though temperature increase dramatically, these projected results similarly indicated that streamflow shows an increase trend in the long term. Runoff increase is mainly caused by increasing precipitation and decreasing evaporation. Water resources distribution is projected to change from summer-autumn dominant to autumn winter dominant. Annual lowest runoff will occur in May caused by earlier snow melting and increasing evaporation in March. According to the obtained results, winter runoff should be artificially stored by reservoir regulation in the future to prevent zero-flow occurrent in May. This research is helpful for water resources management and provides a better understand of streamflow change caused by climate change in the future.