Abstract:This study investigates 72 catchments across the federal state of Baden‐Wuerttemberg, Germany, for changes in water quality during low‐flow events. Data from the state's water quality monitoring network provided seven water quality parameters (water temperature, electrical conductivity, concentrations of chloride, sodium, sulfate, nitrate, and phosphate), which were statistically related to streamflow variability. Water temperature changes during low‐flow showed seasonal dependence. Nitrate concentrations reve… Show more
“…We therefore assessed correlations between drivers and the first derivative (δC diel ) of the diel concentration signal C diel . This corresponds to the way biochemical processes are implemented in some recent solute models (Hensley and Cohen, 2016;Grace et al, 2015). However, changes in water level may affect NO − 3 concentrations both indirectly, e.g., by influencing hyporheic exchange and biochemical processes therein (Trauth and Fleckenstein, 2017), and directly, since additional flow components may be enriched or depleted in NO − 3 compared to pre-event water.…”
Section: Characterization Of Clustersmentioning
confidence: 99%
“…As a result, the benthic footprint, i.e., the upstream area influencing concentrations at the measurement point, depends on flow velocity and solute turnover rate. While gaseous solutes like DO may quickly equilibrate with the atmosphere, upstream discontinuities in non-gaseous solutes like NO − 3 (e.g., tributary confluxes, lakes or reservoirs, wastewater inputs from point sources) may persist further downstream (Hensley and Cohen, 2016). In open systems with unknown input signals, it is therefore unclear whether diel concentration patterns are produced by conditions in the investigated stream or stem from upstream sources from which they are transported downstream (Pellerin et al, 2009).…”
Abstract. Diel variability in stream NO3- concentration
represents the sum of all processes affecting NO3- concentration
along the flow path. Being able to partition diel NO3- signals
into portions related to different biochemical processes would allow
calculation of daily rates of such processes that would be useful for water
quality predictions. In this study, we aimed to identify distinct diel
patterns in high-frequency NO3- monitoring data and investigated
the origin of these patterns. Monitoring was performed at three locations in
a 5.1 km long stream reach draining a 430 km2 catchment.
Monitoring resulted in 355 complete daily recordings on which we performed a
k-means cluster analysis. We compared travel time estimates to time lags
between monitoring sites to differentiate between in-stream and transport
control on diel NO3- patterns. We found that travel time failed to
explain the observed lags and concluded that in-stream processes prevailed
in the creation of diel variability. Results from the cluster analysis
showed that at least 70 % of all diel patterns reflected shapes typically
associated with photoautotrophic NO3- assimilation. The remaining
patterns suggested that other processes (e.g., nitrification, denitrification,
and heterotrophic assimilation) contributed to the formation of diel
NO3- patterns. Seasonal trends in diel patterns suggest that the
relative importance of the contributing processes varied throughout the
year. These findings highlight the potential in high-frequency water quality
monitoring data for a better understanding of the seasonality in biochemical
processes.
“…We therefore assessed correlations between drivers and the first derivative (δC diel ) of the diel concentration signal C diel . This corresponds to the way biochemical processes are implemented in some recent solute models (Hensley and Cohen, 2016;Grace et al, 2015). However, changes in water level may affect NO − 3 concentrations both indirectly, e.g., by influencing hyporheic exchange and biochemical processes therein (Trauth and Fleckenstein, 2017), and directly, since additional flow components may be enriched or depleted in NO − 3 compared to pre-event water.…”
Section: Characterization Of Clustersmentioning
confidence: 99%
“…As a result, the benthic footprint, i.e., the upstream area influencing concentrations at the measurement point, depends on flow velocity and solute turnover rate. While gaseous solutes like DO may quickly equilibrate with the atmosphere, upstream discontinuities in non-gaseous solutes like NO − 3 (e.g., tributary confluxes, lakes or reservoirs, wastewater inputs from point sources) may persist further downstream (Hensley and Cohen, 2016). In open systems with unknown input signals, it is therefore unclear whether diel concentration patterns are produced by conditions in the investigated stream or stem from upstream sources from which they are transported downstream (Pellerin et al, 2009).…”
Abstract. Diel variability in stream NO3- concentration
represents the sum of all processes affecting NO3- concentration
along the flow path. Being able to partition diel NO3- signals
into portions related to different biochemical processes would allow
calculation of daily rates of such processes that would be useful for water
quality predictions. In this study, we aimed to identify distinct diel
patterns in high-frequency NO3- monitoring data and investigated
the origin of these patterns. Monitoring was performed at three locations in
a 5.1 km long stream reach draining a 430 km2 catchment.
Monitoring resulted in 355 complete daily recordings on which we performed a
k-means cluster analysis. We compared travel time estimates to time lags
between monitoring sites to differentiate between in-stream and transport
control on diel NO3- patterns. We found that travel time failed to
explain the observed lags and concluded that in-stream processes prevailed
in the creation of diel variability. Results from the cluster analysis
showed that at least 70 % of all diel patterns reflected shapes typically
associated with photoautotrophic NO3- assimilation. The remaining
patterns suggested that other processes (e.g., nitrification, denitrification,
and heterotrophic assimilation) contributed to the formation of diel
NO3- patterns. Seasonal trends in diel patterns suggest that the
relative importance of the contributing processes varied throughout the
year. These findings highlight the potential in high-frequency water quality
monitoring data for a better understanding of the seasonality in biochemical
processes.
“…This complexity results in the loss of billions of dollars in the United States and around the world (Below, Grover‐Kopec, & Dilley, ; Irannezhad, Ahmadi, Kløve, & Moradkhani, ; Van Loon, ). Droughts have also degraded riverine natural habitats as well as flow regime and water quality (Hellwig, Stahl, & Lange, ; Lake, ; Mosley, ). Climate change is a consequence of increased greenhouse gas emissions and global warming (Intergovernmental Panel on Climate Change, ; Zahn, ; Zeng, Haifeng, Munoz, & Iacono, ).…”
Section: Introductionmentioning
confidence: 99%
“…Numerous studies analyse drought impacts on water quality (Barros, Mendo, & Negrão, 1995;García-Prieto, Cachaza, Pérez-Galende, & Roig, 2012;Hellwig et al, 2017;Muchmore & Dziegielewski, 1983;Van Vliet & Zwolsman, 2008). Fresh water quality is a function of streamflow, biogeochemical, and anthropogenic influences.…”
Climate extremes, in particular droughts, are significant driving forces towards riverine ecosystem disturbance. Drought impacts on stream ecosystems include losses that can be either direct (e.g., destruction of habitat for aquatic species) or indirect (e.g., deterioration of water quality, soil quality, and increased chance of wildfires). This paper combines hydrologic drought and water quality changes during droughts and represents a multistage framework to detect and characterize hydrological droughts while considering water quality parameters. This method is applied to 52 streamflow stations in the state of California, USA, over the study period of 1950–2010. The framework is assessed and validated based on two drought events declared by the state in 2002 and 2008. Results show that there are two opposite drought propagation patterns in northern and southern California. In general, northern California indicates more frequent droughts with shorter time to recover. Chronology of drought shows that stations located in southern California have not followed a specific pattern but they experienced longer drought episodes with prolonged drought recovery. When considering water quality, results show that droughts either deteriorate or enhance water systems, depending on the parameter of interest. Undesirable changes (e.g., increased temperature and decreased dissolved oxygen) are observed during droughts. In contrast, decreased turbidity is detected in rivers during drought episodes, which is desirable in water systems. Nevertheless, water quality deteriorates during drought recovery, even after drought termination. Depending on climatic and streamflow characteristics of the watersheds, it was found that it would take nearly 2 months on average for water quality to recover after drought termination.
“…However, there are relatively few studies explicitly dealing with this subject on the basis of monitoring campaigns in European rivers (e.g. zwolSman and borkHoven 2007; vliet and zwolSman 2008; worrall and burt 2008;zielinSki et al 2009;HanSlik et al 2016;HellwiG et al 2017). Results are diverse and show a need for further research.…”
Section: General Introduction and Aim Of The Studymentioning
numerous unpublished historical data. The month with the most pronounced low flow was usually August, except in 1934, when it was July. Oxygen content, permanganate index, chloride concentration, and water hardness of the Elbe during the low flow months were compared with the annual range. The annual maximum of these water quality parameters (or in the case of oxygen, the annual minimum) was often observed in the low flow month. Water quality during low flow corresponded to the general pollution level, most elevated in 1952 and 1964. During low flows in 2003 and 2015, the reduced input of easily oxidisable organic matter resulted in a stable oxygen regime. Chloride concentration and hardness of the Elbe were mostly determined by the tributary Saale and in 2003 and 2015 still considerably elevated against the natural background. The transferable method of a systematic comparison of several low flow events of a river over a long period of time facilitates the differentiation between event-specific influences (e.g. proportion of tributaries on the total discharge) and common influences (e.g. accumulation of substances due to a lack of dilution). At the same time, basics for characterisation and classification of former and present low flow events are provided.
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