Assessing the snow cover dynamics and its relationship with different hydro-climatic characteristics in Upper Ganges river basin and its sub-basins

Land Surface Snow Phenology Based on an Improved Downscaling Method in the Southern Gansu Plateau, China

Snow is involved in and influences water–energy processes at multiple scales. Studies on land surface snow phenology are an important part of cryosphere science and are a hot spot in the hydrological community. In this study, we improved a statistical downscaling method by introducing a spatial probability distribution function to obtain regional snow depth data with higher spatial resolution. Based on this, the southern Gansu Plateau (SGP), an important water source region in the upper reaches of the Yellow River, was taken as a study area to quantify regional land surface snow phenology variation, together with a discussion of their responses to land surface terrain and local climate, during the period from 2003 to 2018. The results revealed that the improved downscaling method was satisfactory for snow depth data reprocessing according to comparisons with gauge-based data. The downscaled snow depth data were used to conduct spatial analysis and it was found that snow depth was on average larger and maintained longer in areas with higher altitudes, varying and decreasing with a shortened persistence time. Snow was also found more on steeper terrain, although it was indistinguishable among various aspects. The former is mostly located at high altitudes in the SGP, where lower temperatures and higher precipitation provide favorable conditions for snow accumulation. Climatically, factors such as precipitation, solar radiation, and air temperature had significantly singular effectiveness on land surface snow phenology. Precipitation was positively correlated with snow accumulation and maintenance, while solar radiation and air temperature functioned negatively. Comparatively, the quantity of snow was more sensitive to solar radiation, while its persistence was more sensitive to air temperature, especially extremely low temperatures. This study presents an example of data and methods to analyze regional land surface snow phenology dynamics, and the results may provide references for better understanding water formation, distribution, and evolution in alpine water source areas.

Water Quality Evaluation and Variation Trend Analysis of Rivers Upstream of the Dahuofang Reservoir in China

The Dahuofang Reservoir is one of the most important water sources in Liaoning Province, China, so it is critical to identify the status and evolution characteristics of its water quality. Six monitoring indicators were selected to analyze water quality differences and variation trends of each indicator in three inlet sections of the reservoir during different hydrological periods from 2003 to 2021, and an improved comprehensive pollution index method was proposed to study the pollution variation trends. The results showed three findings. (1) The water quality of the three rivers is better in high water periods than that in low water periods. (2) In terms of the spatial state of the water environment, water quality of the Hun River is the worst, the Suzi River is poor, and the She River is better. The worst indicator of the three rivers, TN (total nitrogen), has exceeded the standard for many years (Grade IV–Inferior Grade V). TP (total phosphorus) in the Hun River, which has deteriorated severely since 2013 and is positively correlated with rainfall; it is mainly influenced by pollution from agricultural activities. (3) The P value obtained by the improved method is lower than that of original method, which is mainly because TN is relatively stable, and the exceeding standard of TP is not as serious as TN. The improved method takes into account the interactions and fluctuations of indicators, so that it can reflect the pollution situation more scientifically. These results are helpful to evaluate the pollution status of surface water. It is suggested that water be transferred appropriately to improve water quality and take necessary management measures to reduce TN and TP in the Hun River.