Effects of land cover, topography, and built structure on seasonal water quality at multiple spatial scales

2012 ◽  
Vol 209-210 ◽  
pp. 48-58 ◽  
Author(s):  
Bethany Pratt ◽  
Heejun Chang
CATENA ◽  
2017 ◽  
Vol 151 ◽  
pp. 182-190 ◽  
Author(s):  
Peng Shi ◽  
Yan Zhang ◽  
Zhanbin Li ◽  
Peng Li ◽  
Guoce Xu

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Cory B Goff ◽  
Susan C Walls ◽  
David Rodriguez ◽  
Caitlin R Gabor

Abstract Environmental change associated with anthropogenic disturbance can lower habitat quality, especially for sensitive species such as many amphibians. Variation in environmental quality may affect an organism’s physiological health and, ultimately, survival and fitness. Using multiple health measures can aid in identifying populations at increased risk of declines. Our objective was to measure environmental variables at multiple spatial scales and their effect on three indicators of health in ornate chorus frog (Pseudacris ornata) tadpoles to identify potential correlates of population declines. To accomplish this, we measured a glucocorticoid hormone (corticosterone; CORT) profile associated with the stress response, as well as the skin mucosal immune function (combined function of skin secretions and skin bacterial community) and bacterial communities of tadpoles from multiple ponds. We found that water quality characteristics associated with environmental variation, including higher water temperature, conductivity and total dissolved solids, as well as percent developed land nearby, were associated with elevated CORT release rates. However, mucosal immune function, although highly variable, was not significantly associated with water quality or environmental factors. Finally, we examined skin bacterial diversity as it aids in immunity and is affected by environmental variation. We found that skin bacterial diversity differed between ponds and was affected by land cover type, canopy cover and pond proximity. Our results indicate that both local water quality and land cover characteristics are important determinants of population health for ornate chorus frogs. Moreover, using these proactive measures of health over time may aid in early identification of at-risk populations that could prevent further declines and aid in management decisions.


2015 ◽  
Vol 48 ◽  
pp. 417-427 ◽  
Author(s):  
Zhenyao Shen ◽  
Xiaoshu Hou ◽  
Wen Li ◽  
Guzhanuer Aini ◽  
Lei Chen ◽  
...  

2013 ◽  
Vol 28 (20) ◽  
pp. 5259-5272 ◽  
Author(s):  
Guoqiang Wang ◽  
Yinglan A ◽  
Zongxue Xu ◽  
Shurong Zhang

2009 ◽  
Vol 38 (4) ◽  
pp. 1473-1482 ◽  
Author(s):  
William C. Floyd ◽  
Stephen H. Schoenholtz ◽  
Stephen M. Griffith ◽  
Parker J. Wigington ◽  
Jeffrey J. Steiner

2021 ◽  
Author(s):  
Xiao Shu ◽  
Weibo Wang ◽  
Mingyong Zhu ◽  
Jilei Xu ◽  
Xiang Tan ◽  
...  

Abstract The coupling between land use/landscape pattern and water quality in river system varies across different spatial and temporal scales. It is important to understand the association between water quality and land use/landscape pattern across different spatial and temporal scales for the protection of water resources. Here, we measured seasonal water quality at 12 sub-basins in the upper reaches of the Han River (UHR) between 2010 and 2018. We conducted factor analysis and redundancy analysis to determine the links between land use and water quality at multiple spatial scales and to identify the main factors influencing water quality. We found that the concentration of nutrients, including total nitrogen, total phosphorus, nitrate-N, and ammonium-N were higher during the wet season than the dry season. Total nitrogen was identified as the main driver of nutrient pollution of UHR, whereas total phosphorus was identified as another potential nutrient pollutant. We also found that water quality parameters had a stronger related to land use types over the wet season than the dry season. Croplands and urban lands increased phosphorus concentrations of river water, whereas forest and grass lands decreased the nitrogen concentrations of river water at the sub-basins scale. Land use at riparian zone scales better explained variations in water quality than land use at sub-basin scales. The explained variations in landscape metrics were generally higher over the dry season compared to that over the wet season. The largest patch index and Shannon's diversity index were the main predictors of river water quality in UHR.


2019 ◽  
Vol 72 ◽  
pp. 1-17 ◽  
Author(s):  
Britta Schmalz ◽  
Marion Kruse

Knowledge of the interactions of hydrological processes with the landscape are important to understand variations in basic hydrological data for the comprehensive management of basins. Land cover and land use is one essential factor in the assessment of such management problems. In this study in a representative German basin, available land cover and land use data is analysed in correspondence with available hydrological measuring data.The aim of this study is to analyse the relationships between hydrological data and land use and to obtain a monitoring strategy which allows a valuable support to a comprehensive management of river basins.Two spatial scales, the basin Gersprenz and its subbasin Fischbach, are described in detail regarding the variations in electrical conductivity (EC) as a parameter of water quality with high resolution field data from the state-wide monitoring network (12 stations) as well as from own research monitoring (12 stations). The results show that water quality, using EC as an indicator, can be related to land use pattern. From stream source to mouth, there is an increase in anthropogenic impacts and the EC values show an increasing tendency in downstream direction. This anthropogenic impact is due to agricultural use, settlements, commerce and industry areas, and discharges of waste water. The hydrological monitoring will be continued in the future to give the possibility to assess long-term variations on different spatial and temporal scales.


Ecohydrology ◽  
2021 ◽  
Author(s):  
Xiao Shu ◽  
Weibo Wang ◽  
Mingyong Zhu ◽  
Jilei Xu ◽  
Xiang Tan ◽  
...  

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