The influence of geology and land-use on inorganic stream water quality in the Oslo region, Norway

Reimann, Clemens; Finne, Tor Erik; Nordgulen, Øystein; Sæther, O. M.; Arnoldussen, Arnold; Banks, David

doi:10.1016/j.apgeochem.2009.06.007

Cited by 47 publications

(27 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They proposed utilizing a regionalization scheme based on the environmental characteristics most statistically related to individual water quality parameters. However, Reimann et al (2009) and Banks et al (2001) suggested that changes in water chemistry due to anthropogenic impacts in Norway were hard to detect against the larger scale changes in natural background, with lithology being a major factor in the variation of inland water chemistry. A regional survey approach combined with site specific surveys was proposed to capture both the natural variation of chemical element concentrations and anthropogenic additions occurring at the local scale (Reimann and Garret, 2005).…”

Section: Introductionmentioning

confidence: 99%

Developing Indicators for Regional Water Quality Assessment: An Example from British Columbia Community Watersheds

Brown

Lavkulich

Schreier

2011

Canadian Water Resources Journal / Revue canadienne des ressour

View full text Add to dashboard Cite

An increased understanding of regional surface water quality and the key factors which differentiate regional from local differences is necessary for monitoring impacts such as mountain pine beetle infestation and related land management practices. This study develops a framework to identify water quality indicators which differentiate parameters influenced by rock type, by relatively short term anthropogenic activities, and those resulting from longer term climatic variability. Rock type was an overriding factor related to stream water chemistry in this British Columbia case study; with differences between watersheds differentiated by Ca, EC, Al and Fe. Grouping watersheds by their dominant rock type permitted the investigation of water quality with other watershed characteristics. The % forest cover, % pine cover, and dominant runoff processes demonstrated significant relationships with soluble cations, metals, turbidity and total organic carbon. Turbidity levels showed low variability, and relationships with mountain pine beetle were not strong; suggesting the need for more detailed data sets, selective monitoring of storm events, and longer term monitoring to improve predictive capacity.Ré sumé : Une meilleure connaissance de la qualité des eaux de surface régionales et des facteurs qui la distingue des eaux de surface locales est nécessaire pour mieux discerner les impacts associés aux perturbations causées par l'épidemie de dendrocthone du pin. Cette étude a pour but d'identifier les différents indicateurs de la qualité de l'eau qui sont influencés par la géologie du terrain et les activités anthropogéniques de courte durée de ceux influencés par les variations du climat à long terme. Dans le cas étudié, la géologie des bassins versants s'est avéré être un facteur déterminant en ce qui a trait aux propriétés chimiques de l'eau de ruissellement, avec des variations entre les différents bassins versants pour Ca, EC, Al et Fe. Le groupment des bassins versants selon leur type de géologie a permis d'analyser l'effet des autres caractéristiques des bassins versants sur la qualité de l'eau. Le pourcentage du couvert forestier, le pourcentage du couvert en pin et les processus de ruissellement montrent une corrélation significative avec les cations solubles, les métaux, la turbidité et le carbone organique total.

show abstract

Section: Introductionmentioning

confidence: 99%

Developing Indicators for Regional Water Quality Assessment: An Example from British Columbia Community Watersheds

Brown

Lavkulich

Schreier

2011

Canadian Water Resources Journal / Revue canadienne des ressour

View full text Add to dashboard Cite

show abstract

“…The study by Yang and Jin (2010) showed the significant role of soil hydrological features in load movement in water resources. Only a few studies have considered soil or geological attributes of a catchment in modeling (Haidary et al 2013;Hartmann et al 2014;Pratt and Chang 2012;Reimann et al 2009;Ryu et al 2007;Yang and Jin 2010). Even fewer studies have explored both soil and geological attributes of catchments (Haidary et al 2014;Sangani et al 2015).…”

Section: Introductionmentioning

confidence: 97%

Modeling the Relationship between Catchment Attributes and In-stream Water Quality

Fatehi

Amiri

Alizadeh

et al. 2015

Water Resour Manage

View full text Add to dashboard Cite

The physical attributes of catchments have a significant influence on the chemistry and physical features of in-stream water quality. Consequently, modeling this relationship is important for informing management strategies aimed at improving regional water quality. This study used a machine learning approach (Artificial Neural Networks or ANNs) to model the relationship between land use/cover, associated with other physical attributes of the catchment such as geological permeability and hydrologic soil groups, and in-stream water quality parameters (e.g., K + , Na + , Mg 2+ , Ca 2+ , SO 4 2− , Cl − , HCO 3 − , SAR, pH, EC, TDS). Eighty-eight catchments in the southern basins of the Caspian Sea were explored. To enhance the architecture of ANNs, the study applied backward elimination-based multiple linear regression, through which the optimum input nodes of ANNs can be determined amongst the most relevant variables. A transformation approach was also applied to qualify the performance of ANNs in four quality classes, ranging from unsatisfactory to very good. According to the findings, ANN based TDS model performance improved from unsatisfactory to very good. However, the linear regression-based pH model resulted in a decrease in performance, from Bvery good^to satisfactory. Moreover, among all catchment attributes, urban areas had the greatest impact on K + , Na + , Mg 2+ , Cl − , SO 4 2− , EC and SAR concentration values. K + , TDS and EC were influenced by agricultural area (%). Bare land areas (%) had the largest impact on Na + , Ca 2+ and HCO 3 − . Assessing the performance of the ANN-based models Water Resour Manage developed in this study indicates that 10 out of 11 models had Bvery good^quality ratings and can be reliably used in practice.

show abstract

“…Some studies estimating the spatial variability of trace elements in Scandinavia, at spatial scales from 10-100 km 2 , have been published (Tarvainen et al, 1997;Edén et al, 1999;Andersson et al, 2006;Dahlqvist et al, 2007;Reimann et al, 2009;Lidman et al, 2012). The novel focus in this study is on headwaters, < 2 km 2 , in combination with larger streams in the channel network of complete rivers, including the lakes embedded in those networks.…”

Section: Introductionmentioning

confidence: 99%

“…Since increasing trace element use in the technosphere is increasing their spread in the environment, it is important to define current levels and patterns against which future changes can be judged Karlsson et al, 2006;Reimann et al, 2009). Documentation of trace elements can also help in defining controls on the heterogeneity of terrestrial exports to surface waters (Lofts and Tipping, 2000), lake processes (Johansson et al, 2001;Taillefert and Gaillard, 2002) and downstream transformations that can be of value in managing land use for water quality (Reimann et al, 2009). Quantifying landscape variation and potential drivers of water quality is relevant for management of forestry practices and nutrient leaching.…”

Section: Introductionmentioning

confidence: 99%

Spatial patterns of some trace elements in four Swedish stream networks

Temnerud

Düker²,

Karlsson

et al. 2013

Biogeosciences

View full text Add to dashboard Cite

Four river basins in southern Sweden, with catchment sizes from 0.3 to 127 km² (median 1.9), were sampled in October~2007. The 243 samples were analysed for 26 trace elements (Ag, As, Au, Ba, Be, Bi, Cd, Co, Cr, Cu, Ga, Ge, In, La, Li, Mo, Ni, Pb, Sb, Se, Sn, Tl, Ti, U, V and Zn) to identify spatial patterns within drainage networks. The range and median of each element were defined for different stream orders, and relationships to catchment characteristics, including deposition history, were explored. The sampling design made it possible to compare the differences along 40 stream reaches, above and below 53 stream junctions with 107 tributaries and between the 77 inlets and outlets of 36 lakes. The largest concentration differences (at reaches, junctions and lakes) were observed for lakes, with outlets usually having lower concentration compared to the inlets for As, Ba, Be, Bi, Cd, Co, Cr, Ga, Ge, Ni, Pb, Sn, Ti, Tl, U, V and Zn. Significantly lower concentrations were observed for Cd and Co when comparing headwaters with downstream sites in each catchment. Common factor analysis (FA) revealed that As, Bi, Cr, Ga, Ge, Tl and V co-vary positively with Al, Fe and total organic carbon (TOC) and negatively with La, Li and pH. The strong removal of a large number of trace elements when passing through lakes is evident though in the FA, where lake surface coverage plots opposite to many of those elements. Forest volume does not respond in a similar systematic fashion and, surprisingly, the amount of wetland does not relate strongly to either Fe or TOC at any of the rivers. A better understanding of the quantitative removal of organic carbon and iron will aid in understanding trace element fluxes from landscapes rich in organic matter and iron

show abstract

The influence of geology and land-use on inorganic stream water quality in the Oslo region, Norway

Cited by 47 publications

References 18 publications

Developing Indicators for Regional Water Quality Assessment: An Example from British Columbia Community Watersheds

Developing Indicators for Regional Water Quality Assessment: An Example from British Columbia Community Watersheds

Modeling the Relationship between Catchment Attributes and In-stream Water Quality

Spatial patterns of some trace elements in four Swedish stream networks

Contact Info

Product

Resources

About