Analyzing the contribution of climate change to long-term variations in sediment nitrogen sources for reservoirs/lakes

Xia, Xinghui; Wu, Qiong; Zhu, Baotong; Zhao, Pujun; Zhang, Shangwei; Yang, Lichao

doi:10.1016/j.scitotenv.2015.03.140

Cited by 39 publications

(11 citation statements)

References 75 publications

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“…The result usually showed in a general way of the whole lake or reservoir within a year (Greene et al 2015). By using historical sediment P concentration data or dating method with 210 Pb or 137 Cs, sediment core record can be traced back to the last century (Ruiz-Fernández et al 2002;Xia et al 2015), then, historical internal P release amount was calculated. It was generally based on annual output data.…”

Section: Discussionmentioning

confidence: 99%

Estimating internal P loading in a deep water reservoir of northern China using three different methods

Qin

Zeng

Zhang

et al. 2016

Environ Sci Pollut Res

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Much attention had been paid to reducing external loading of nutrients to improve water quality, while internal loading from sediment, which has been largely neglected, is also an important source for water eutrophication. The internal load in deep lakes or reservoirs is not easy to be detected and be quantified. In this study, three different methods (mass balance method, Fick's law, and regression equation) were combined to calculate the gross or/and net P release from sediment using limited data. Our results indicated that (1) ); (2) Hot periods of sediment releasing were suggested to occur from March to April and from August to September, which correspond to periods of high risks of algae blooms. The remaining months of the year were shown as net nutrient retention; (3) for the whole region, Baihedam and Chaohekuqu were identified as zones with a higher possibility to release P from sediment. (4) P loading to the Miyun Reservoir was greater in the inflow than in the outflow, suggesting a portion of the inflow P load was retained in the water or sediment; hence, release of sediment P may continue to be a major source of phosphorus in the future.

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Section: Discussionmentioning

confidence: 99%

Estimating internal P loading in a deep water reservoir of northern China using three different methods

Qin

Zeng

Zhang

et al. 2016

Environ Sci Pollut Res

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“…Taking into account the chlorophyll a vector, another ecological gradient becomes evident, with the eutrophic or hypertrophic lakes located in QI (moist and warm climates) while the (ultra)oligotrophic lakes are located in QII or QIII (cooler climates). High trophic levels in our data set seem to be at least partially related with climatic conditions, to warmer and moister climates where chemical weathering is intense and favors faster mobilization of chemical elements, including nutrients (N, P, Si) from the basement rocks into the lakes (Lewis, 1996;Malmaeus et al, 2006;Xia et al, 2015). It is interesting to note that in the PC1 vs. PC2 graph the lakes from opposite sides of the TMVB lie at adjacent quadrants (QIII and QIV) as they share high electric conductivities and total dissolved solids, associated to relatively dry climatic conditions.…”

Section: Climatic and Environmental Gradientsmentioning

confidence: 95%

Basic limnology of 30 continental waterbodies of the Transmexican Volcanic Belt across climatic and environmental gradients

Sigala¹,

Caballero²,

Correa‐Metrio³

et al. 2017

BSGM

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RESUMEN Empleando metodologías estandarizadas se estudiaron 30 cuerpos de agua en la Faja Volcánica Transmexicana a lo largo de un gradiente altitudinal (737 a 4283 msnm) y climatológico. Los lagos se incluyen en tres categorías: tectónicos (4), volcánicos (11 cráteres o maars, 7 represas volcánicas) y presas (8).]. In thirteen lakes, nutrient levels could be limiting primary productivity, mostly P, but also N and Si. Half of the lakes (16) were eutrophic or hypertrophic and most of these were also shallow (< 8 m) and correlated with warm, moist conditions. Ninety-seven taxa of aquatic organisms (phytoplankton, and surface sediment diatoms, testate amoebae, cladoceran and ostracodes) were reported, which could potentially be used as bioindicators. Principal component analysis (PCA) showed that temperature and precipitation were the main environmental gradients related to the lakes' limnological characteristics: colder climates related with lowest TDS (< 100 mg/L); dry climates with subsaline and hyposaline lakes, and warm-moist climates with eutrophic and hypertrophic lakes. Through the Procrustes technique we obtained indications that general geographic variables were important for some of the lakes, whereas the local factors were significant for others. It is through the knowledge of the basic limnology and biodiversity of Mexican lakes that more complex or detailed studies can be proposed leading to a better understanding, management, and conservation of water resources.

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“…The water sampling was carried out monthly except for the frozen period (mainly from January to March) from 2006 to 2012 for non-urban area and from 2009 to 2012 for urban area. The detailed measurements of Secchi depth, water temperature, pH, dissolved oxygen (DO), COD, BOD, TN, and TP of the water samples can be found in our previous study (Wu et al, 2014a;Xia et al, 2015b). Monthly stream discharges were collected from the online Water Resource Yearbook of the Haihe River Basin (http://www.hwcc.gov.cn/hwcc/wwgj/xxgb/ szygb/).…”

Section: Water Quality Sampling and Analysismentioning

confidence: 99%

Response of Surface Water Quality in Urban and Non-urban Areas to Heavy Rainfall: Implications for the Impacts of Climate Change

Wu¹,

Xia²

2019

J ENVIRON INFORM LET

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Frequency and intensity of floods and droughts are increased across the world due to global climate change. However, the responses of water quality in urban and non-urban areas to extreme weather events are not well understood. The heavy and extensive precipitation during the summer of 2011 and 2012 caused two severe floods in Beijing of China. This work evaluated the impacts of these two floods on the water quality of 10 urban lakes, 3 non-urban reservoirs, and 8 non-urban rivers through comparing the pre-flood and post-flood water quality as well as those in reference period (2006 ~ 2010). Results indicated that the lagging response of water quality to flood is more significant in non-urban areas than the urban areas. In addition, comprehensive pollutant index (CPI) during the first flood period in most sites were much higher than the reference values with the average increasing rate of 46%. The values of chemical and biological oxygen demand were elevated in most sites; total nitrogen in the non-urban areas and total phosphorous in the urban areas were higher than those in the reference period. The different responses of nitrogen and phosphorus to the flood events of the two areas were mainly due to the high nitrogen contents in non-urban soils and high phosphorus contents in the urban soils. Whereas, most pollutant concentrations during the second flood period were lower than the first flood period, or even lower than the reference period. This can be explained by that the first flood, belonging to the post-drought flood, scoured the land surface and brought plenty of pollutants into the aquatic environments, and the non-point source pollution caused by the second flood was less effective and mainly reflected as dilution effect. This study suggests that the response of surface water quality to floods caused by climate change will differ between urban and non-urban areas, as well as among different flood events.

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Analyzing the contribution of climate change to long-term variations in sediment nitrogen sources for reservoirs/lakes

Cited by 39 publications

References 75 publications

Estimating internal P loading in a deep water reservoir of northern China using three different methods

Estimating internal P loading in a deep water reservoir of northern China using three different methods

Basic limnology of 30 continental waterbodies of the Transmexican Volcanic Belt across climatic and environmental gradients

Response of Surface Water Quality in Urban and Non-urban Areas to Heavy Rainfall: Implications for the Impacts of Climate Change

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