Seasonal and event-scale variations in solute chemistry for four Sierra Nevada catchments

Holloway, JoAnn M.; Dahlgren, Randy A.

doi:10.1016/s0022-1694(01)00424-3

Cited by 71 publications

(70 citation statements)

References 23 publications

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“…The stormflow season can be further divided into flushing and dilution periods. This seasonal chemical pattern, noted in the Cosumnes, has also been witnessed in tributary studies in nearby watersheds (Holloway and Dahlgren, 2001;Lewis et al, 2000), but has never been explicitly reported. Each of the water quality seasons exhibits a unique and predictable chemistry, thus differentiation among them for scientific and management purposes becomes important.…”

Section: Discussionsupporting

confidence: 56%

“…Asynchrony within nutrient cycles in California's Mediterranean climate causes marked nitrate spiking during the flushing season (Holloway and Dahlgren, 2001). Instead of a continuous nitrogen feedback among senescing plants, their soils, and new growth, nitrogen is mineralized and accumulates in soils during the dry summer and fall months (Hart et al, 1993).…”

Section: Discussionmentioning

confidence: 99%

“…Instead of a continuous nitrogen feedback among senescing plants, their soils, and new growth, nitrogen is mineralized and accumulates in soils during the dry summer and fall months (Hart et al, 1993). With the onset of winter rains, water begins to flow through the upper soil horizons, mobilizing the accumulated nitrate (Holloway and Dahlgren, 2001) and transporting it to the stream channel. Each storm progressively flushes this nitrogen pool so that by March there is little if any nitrogen found in stormflows (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Findings such as these have lead to the widespread use of end-member mixing models to identify sources of streamwater in maritime (Christophersen and Neal, 1990;Creed et al, 1996) and temperate climates (Mulholland et al, 1990). Small catchment studies in the Mokelumne Watershed (adjacent to the Cosumnes) have used end-member sourcing and solute accumulation in the upper soil horizons to explain extraordinary nutrient spiking with the onset of the first winter rains (Holloway and Dahlgren, 2001;Holloway et al, 1998). In these instances, where studies have focused on the seasonal variability in stream chemistry, hydrologic flowpath may be equal in importance to discharge in regulating stream water chemistry (Creed and Band, 1998).…”

Section: Introductionmentioning

confidence: 99%

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Temporal dynamics of stream water chemistry in the last free-flowing river draining the western Sierra Nevada, California

Ahearn

Sheibley

Dahlgren

et al. 2004

Journal of Hydrology

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Temporal patterns of stream water chemistry were analyzed across the Cosumnes River Watershed (1989 km 2 ) for water years 1999-2002 to quantify hydrobiogeochemical dynamics in the last free-flowing watershed draining the western Sierra Nevada, California. The Mediterranean climate of California produces a distinct annual hydrologic pattern with three seasons: baseflow, stormflow, and meltflow. The baseflow season (July -October) is dominated by groundwater chemistry that primarily originates from high elevations, and thus does not vary much across the basin. During the baseflow season discharge is negatively correlated to ionic concentration, and sediment and nutrients are generally below detection levels. The stormflow season (November -March) is separated into a flushing period (where discharge is positively correlated to river water conductivity) and a dilution period (where discharge is negatively correlated to conductivity). During average flow years, virtually the entire annual load of nutrients and sediment moves through the watershed during the stormflow season. Because stormflow hydrologically links the land with local waterways, the stormflow season shows the greatest variance among sites across the diverse landscape of the Cosumnes Watershed. Chemistry of the meltflow season (April -June) is dominated by dilute upland snowmelt, and there is little chemical variation across the watershed. Storm-scale analysis in water year 2002 revealed that progressive flushing occurs with each storm event and that source area dynamics play an important role in chemograph response. With 19 of the 20 major rivers in the western Sierra Nevada having dams, these data provide scientists and regulators with a valuable reference to address how impoundment affects water quality. q

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Section: Discussionsupporting

confidence: 56%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Temporal dynamics of stream water chemistry in the last free-flowing river draining the western Sierra Nevada, California

Ahearn

Sheibley

Dahlgren

et al. 2004

Journal of Hydrology

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“…McFarland and Hauck (1999), in their study, found that higher nitrogen levels were detected in agricultural waters, where fertilizers, manure, and pesticide have been applied. Loadings on EC, Cl − , Ca, K, and Na are probably due to the mineral component of the river water (Barnes et al 1981;Dahlgren and Singer 1994;Holloway and Dahlgren 2001). This assumption is reasonable, as the water quality in this region is good and land use activities are mostly limited to agriculture and forest areas.…”

Section: Lpsmentioning

confidence: 99%

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

Juahir

Zain

Yusoff

et al. 2010

Environ Monit Assess

285

196

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This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderatepollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data.

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Reply to comment on ‘Kane et al. 2008. Precipitation control over inorganic nitrogen import‐export budgets across watersheds: a synthesis of long‐term ecological research. Ecohydrology 1: 105–117’

et al. 2010

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Our work described in Kane et al. (2008) was a first attempt to examine the effect of precipitation patterns and seasonality on net dissolved inorganic nitrogen (DIN) retention in watersheds across the Long Term Ecological Research (LTER) network and related sites. The principal result of this study was that net DIN retention, quantified as atmospheric DIN import minus riverine DIN export, decreased with increasing variation in precipitation across eight diverse sites in North America and Puerto Rico. The comment by Lajtha (2009) raises important points mainly associated with interpreting a large body of watershed biogeochemical data from diverse sites when spatial autocorrelation and statistical independence are issues of concern.Lajtha notes that the spatial pattern of N deposition coincided with the spatial pattern of seasonality of precipitation, leading to a spurious correlation between seasonal precipitation variability and wet N deposition. This is an important point to consider, as wet N deposition rates varied by over an order of magnitude across the regions we investigated (table III in Kane et al., 2008). Thus, even if DIN exports across sites were similar, 'pristine' areas with markedly low rates of N deposition (e.g. BNZ, 0Ð3 kg N ha 1 year 1 ) could appear to have very low retention capacity compared to areas of high N deposition. As such, a spatial correlation between N deposition and precipitation seasonality could mislead interpretation (figure 6 in Kane et al., 2008). Although spatial autocorrelation may have influenced the exact * Correspondence to: E. S. Kane, Michigan Technological University, School of Forest Resources and Environmental Science, Houghton, MI 49931, USA. E-mail: eskane@mtu.edu nature of the relationship between DIN retention and precipitation seasonality among study sites, there are two reasons why we believe the overall message of our paper remains valid. First, when we consider only the relationship between variation in seasonal precipitation and seasonal DIN retention for sites exhibiting similarly high N deposition rates, the relationship remains significant (Figure 1). Second, besides net DIN retention we also reported the relationship between variation in (seasonal) precipitation and net DIN retention efficiency (net DIN retention/DIN import). This analysis also showed a negative trend between DIN retention and precipitation variability across sites. Taken together, the data and findings discussed herein reinforce precipitation variability as a control over DIN retention, while also substantiating the need for caution in interpreting input-output budgets at multiple scales.To asses the validity of this relationship in a more quantitative way, one needs to determine the degree of spatial correlation among explanatory variables in our data set. Quantification of spatial autocorrelation could be done with Moran's I (Moran, 1950) and Geary's C (Geary, 1954) statistical indices. As pointed out by Lajtha, we did not assess the spatial inferences of each study site on the stat...

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Seasonal and event-scale variations in solute chemistry for four Sierra Nevada catchments

Cited by 71 publications

References 23 publications

Temporal dynamics of stream water chemistry in the last free-flowing river draining the western Sierra Nevada, California

Temporal dynamics of stream water chemistry in the last free-flowing river draining the western Sierra Nevada, California

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

Reply to comment on ‘Kane et al. 2008. Precipitation control over inorganic nitrogen import‐export budgets across watersheds: a synthesis of long‐term ecological research. Ecohydrology 1: 105–117’

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