Selected methods were applied to a\u00a0test dataset comprising water quality data for 2010\u20132019, supplemented by hydrological characteristics derived from long-term flow time series. For this study, long-term water quality monitoring profiles were selected for which reasonably accurate flow data at the\u00a0time of\u00a0sampling, as well as general catchment characteristics, were available.<\/span><\/p>\n Measured concentrations of\u00a0the\u00a0monitored determinants were compared with hydrological conditions, quantified using the\u00a0flow percentile on the\u00a0sampling date based on the\u00a0long-term flow time series. Concentration-discharge (C\u2013Q) relationships for selected substances can\u00a0be divided into three categories: chemostasis, where concentrations remain\u00a0constant or vary independently of\u00a0discharge; enrichment, where concentrations increase with increasing flow; and dilution, where concentrations decrease with increasing discharge. Which of\u00a0these relationships prevails, or whether several relationships occur in\u00a0combination, depends on both the\u00a0properties of\u00a0the\u00a0substances and catchment characteristics. In\u00a0some cases, the\u00a0observed relationship can\u00a0be used to identify the\u00a0predominant source of\u00a0pollution in\u00a0a\u00a0catchment.<\/span><\/p>\n The\u00a0aim of\u00a0this article is to highlight the\u00a0potential of\u00a0statistical methods for optimizing monitoring campaigns and identifying the\u00a0key factors and catchment characteristics that influence C\u2013Q relationships.<\/span><\/p>\n Climate change in\u00a0recent decades has affected the\u00a0hydrological characteristics of\u00a0watercourses, as demonstrated by long-term monitoring in\u00a0the\u00a0Czech Republic [1]. In\u00a0addition to declining mean\u00a0flows, drought episodes are becoming more frequent and prolonged, while extreme runoff events are also occurring more frequently. These changes have consequences for water quality, potentially causing serious problems given that many water bodies already fail to meet the\u00a0requirements for good chemical and ecological status. Despite numerous measures, improvement is slow or non-existent and, in\u00a0some cases, conditions are deteriorating [2].<\/span><\/p>\n A\u00a0number of\u00a0studies have examined on the\u00a0relationships between the\u00a0quantitative and qualitative characteristics of\u00a0watercourses in\u00a0different types of\u00a0catchments and under different hydrological conditions. Most studies focus on natural catchment chemistry [3, 4] and nutrients [5, 6], with less attention paid to other substances such as pharmaceuticals or pesticides. The\u00a0relationship between concentration and flow (C\u2013Q) is used as a\u00a0metric for this purpose and is strongly influenced by both catchment characteristics [7] and predominant pollution sources, including legacy pollution [8]. The\u00a0relationship manifests itself differently in\u00a0longer time series based on less frequent monitoring than\u00a0in\u00a0high-resolution time series, in\u00a0which relationships typical of\u00a0rainfall\u2013runoff events can\u00a0be identified [9]. The\u00a0factors determining the\u00a0behaviour of\u00a0substances in\u00a0a\u00a0catchment may also act in\u00a0combination, as demonstrated by the\u00a0model for nitrates developed by Minaudo et al. [10], which incorporates both seasonal patterns and individual events with largely opposing effects.<\/span><\/p>\n Understanding these relationships can\u00a0help identify pollution sources, model responses to climate change or, conversely, to proposed mitigation measures, and improve estimates of\u00a0constituent loads exported from catchments. Data preparation methods were therefore tested on a\u00a0selected dataset, and selected results are illustrated in\u00a0a\u00a0series of\u00a0graphs.<\/span><\/p>\n The\u00a0purpose of\u00a0this study was to compile a\u00a0sufficiently robust dataset for the\u00a0Czech Republic combining hydrological and water quality characteristics of\u00a0watercourses, while testing which data and methods could be used for this purpose and identifying their limitations. A\u00a0further aim was to analyse the\u00a0data to determine whether findings reported in\u00a0the\u00a0literature also apply under conditions in\u00a0the\u00a0Czech Republic and to establish the\u00a0extent to which methods used for general chemical parameters can\u00a0be applied to other, less frequently monitored substances. A\u00a0series of\u00a0simple graphs generated using a\u00a0consistent procedure is intended to illustrate the\u00a0methods and general patterns and help identify the\u00a0parameters, relationships and types of\u00a0watercourses that warrant more detailed investigation.<\/span><\/p>\n The\u00a0relationship between water quality and quantity is most commonly determined using paired measurements of\u00a0concentration and discharge at the\u00a0time of\u00a0sampling. Both simple statistical approaches and advanced modelling methods are used to analyse these relationships, including linear and nonlinear regression, correlation analysis and multivariate statistical methods [3, 7].<\/span><\/p>\n Linear regression is used when an\u00a0approximately linear relationship between variables is assumed, although its application is constrained by the\u00a0requirement for normally distributed residuals. This is particularly problematic for hydrological data, as concentrations and flows typically have strongly skewed distributions. For this reason, one or both variables are often log-transformed, which stabilizes variability in\u00a0the\u00a0data and allows the\u00a0linear relationship to be better approximated in\u00a0the\u00a0transformed space [4, 7].<\/span><\/p>\n Correlation coefficients, particularly Pearson\u2019s\u00a0and Spearman\u2019s, are also used to assess the\u00a0relationship between concentration and flow. Spearman\u2019s\u00a0rank correlation coefficient is preferred when the\u00a0data do not meet the\u00a0assumption of\u00a0normality or when the\u00a0relationship is nonlinear or monotonic [9].<\/span><\/p>\n More advanced approaches include regression and semiparametric models, such as generalized linear models (GLMs), generalized additive models (GAMs), and mixed-effects models with random effects. Specialized approaches for estimating constituent transport are also frequently used in\u00a0hydrology, including WRTDS (Weighted Regressions on Time, Discharge, and Season), which can\u00a0better capture seasonality and long-term trends in\u00a0the\u00a0data.<\/span><\/p>\n When several explanatory variables are available, multivariate statistical methods such as factor analysis, cluster analysis, or principal component analysis can\u00a0be used to help identify the\u00a0dominant processes affecting the\u00a0export of\u00a0constituents from catchments [6].<\/span><\/p>\n Relating concentrations to flow percentiles is useful for identifying system behaviour under extreme conditions, such as high or low flows. This approach is often used to interpret concentration\u2013discharge (C\u2013Q) relationships [3,\u00a07]. The\u00a0advantages of\u00a0this method are its simplicity and interpretability, and the\u00a0fact that it can\u00a0be applied even when data are limited or include values below the\u00a0detection limit.<\/span><\/p>\n For this study, mean\u00a0concentrations were compared across flow percentiles, primarily because of\u00a0the\u00a0method\u2019s\u00a0simplicity, clarity, and ability to illustrate the\u00a0basic principles underlying the\u00a0relationship between hydrological conditions and constituent transport. However, it should be emphasized that any analysis of\u00a0this type is highly dependent on appropriate data preprocessing, which is essential for reliable interpretation of\u00a0the\u00a0results.<\/span><\/p>\n To make the\u00a0statistical analysis as robust as possible, the\u00a0dataset needs to be as complete as possible. The\u00a0limiting factors in\u00a0selecting monitoring profiles were temporal coverage, the\u00a0availability of\u00a0data for the\u00a0broadest possible range of\u00a0parameters, and the\u00a0possibility of\u00a0obtaining flow data for the\u00a0time of\u00a0sampling. For this study, profiles were selected where water quality had been monitored monthly for ten years, measurements were available not only for general parameters relating to oxygen conditions and nutrients but also for other substances, and a\u00a0suitable hydrological gauging station could be assigned. To\u00a0obtain\u00a0hydrological data, each water quality monitoring profile was matched with the\u00a0nearest hydrological gauging station upstream or downstream. Gauged flows were adjusted according to the\u00a0ratio of\u00a0catchment areas. Profiles with a\u00a0catchment area ratio greater than\u00a04 or less than\u00a00.2 were excluded.<\/span><\/p>\n Parameter selection was based primarily on data availability. Particular emphasis was placed on parameters responsible for failure to achieve good status under the\u00a0Water Framework Directive [11], and preference was given to monitoring profiles for which a\u00a0broader range of\u00a0parameters was available. Operational monitoring data from 2010\u20132019 were used, and parameters for which insufficient measurement data were available were excluded; the\u00a0parameters included in\u00a0the\u00a0analysis and the\u00a0abbreviations used in\u00a0the\u00a0graphs are listed in\u00a0Tab.\u00a01<\/span><\/em>.<\/span><\/p>\n Daily flows from continuously monitored hydrological gauging stations were used to assess hydrological conditions. Flow percentiles were determined for each profile based on long-term daily flow series for 1980\u20132019; in\u00a0a\u00a0few cases, only shorter series were available, but these covered at least the\u00a0period 2002\u20132019. For each water quality monitoring profile, the\u00a0catchment area and the\u00a0proportions of\u00a0arable land and urban\u00a0development within\u00a0the\u00a0catchment were determined. The\u00a0catchments were then classified as small (< 500 km\u00b2), medium-sized (500\u20132,000 km\u00b2), or large; as having a\u00a0high (> 60%), medium (20\u201360%), or low proportion of\u00a0arable land; and as having a\u00a0high (> 5%), medium (1\u20135%), or low proportion of\u00a0urban\u00a0development (\u2264 1%). The\u00a0purpose of\u00a0this classification was to determine which factors influence the\u00a0relationships between water quality and hydrology \u2013 for example, whether small watercourses are more sensitive to periods of\u00a0drought or, conversely, to extreme rainfall\u2013runoff events, and whether the\u00a0likely dominant influence of\u00a0different types of\u00a0pollution can\u00a0be distinguished on the\u00a0basis of\u00a0the\u00a0type of\u00a0relationship. The\u00a0number of\u00a0profiles in\u00a0each group is shown in\u00a0Tab.\u00a02<\/span><\/em>. To allow conditions in\u00a0large and small catchments to be compared, profiles in\u00a0the\u00a0lower reaches of\u00a0watercourses were retained in\u00a0the\u00a0dataset even where their catchments overlapped with those of\u00a0upstream profiles.<\/span><\/p>\n This resulted in\u00a0138 partially overlapping catchments varying in\u00a0their characteristics (Fig.\u00a01<\/span><\/em>).<\/span><\/p>\n Although the\u00a0selected catchments cannot be considered representative for assessing hydrological conditions across the\u00a0Czech Republic, they nevertheless show a\u00a0slight downward trend in\u00a0mean\u00a0flows since the\u00a01980s, as well as the\u00a0dry period in\u00a0the\u00a0second half of\u00a0the\u00a02010s. Fig.\u00a02<\/span><\/em> shows the\u00a0mean\u00a0flow across all monitored catchments. The\u00a0fitted linear trend is downward, although this may be distorted by several dry years at the\u00a0end of\u00a0the\u00a0study period.<\/span><\/p>\n The\u00a0water quality data cannot be considered representative either; nevertheless, trends at the\u00a0selected profiles are consistent with those previously reported. Fig.\u00a03<\/span><\/em> shows trends in\u00a0the\u00a0average annual concentrations of\u00a0selected indicators at the\u00a0monitored profiles. To allow comparison between indicators, changes in\u00a0concentration are expressed not in\u00a0the\u00a0original units but as a\u00a0percentage of\u00a0concentrations at the\u00a0beginning of\u00a0monitoring. Most indicators show a\u00a0very slight decline or remain\u00a0stable, while phosphate phosphorus shows an\u00a0increase.<\/span><\/p>\n An important factor that needs to be taken into account is seasonal variation in both flow and water quality. Low flows are generally more frequent during the summer months, while high flows regularly occur in March and April. This is also reflected in the monthly distribution of samples among flow percentiles.<\/p>\n Fig. 4<\/em> shows the number of samples collected at different flow percentiles. Figs. <\/em>5<\/em>\u00a0 and 6<\/em> show the distribution of samples among flow per-centiles by month and year, respectively. Samples collected during the summer months were clearly more likely to coincide with the lowest flows.<\/p>\n Seasonal variation also affects the monitored water quality parameters. Higher nitrogen concentrations in winter and phosphorus concen-trations in summer were confirmed. The occurrence of pesticides and some of their metabolites is also seasonal, as clearly demonstrated in Figs. 7\u20139<\/em>. These figures show the occurrence of metabolites of alachlor (banned in 2008) and metolachlor (authorized during the data collection period). Although these substances were analysed regularly, measurable concentrations were detected in only some samples, making it necessary to include results below the limit of quantification (LOQ). Comparison of the two parameters shows the seasonal occur-rence of the substance still in use, in contrast to the constant occurrence of metabolites of the banned substance, both in terms of detection frequency and measured concentrations.<\/p>\n Fig. 7. Alachlor \u2013 ESA \u2013 numbers of records below and above the limit of quantification in each month<\/p>\n Using the C\u2013Q slope method, relationships between concentration and discharge can be divided into three basic types:<\/p>\n Chemostatic behaviour of\u00a0substances can\u00a0be attributed to stable long-term stores within\u00a0the\u00a0catchment and the\u00a0significant influence of\u00a0groundwater.<\/span><\/p>\n Arsenic provides an\u00a0example of\u00a0chemostasis in\u00a0the\u00a0test dataset: its concentration varies little with discharge, with only a\u00a0slight increase during extreme drought events, and even this increase is not particularly pronounced (Fig.\u00a010<\/span><\/em>).<\/span><\/p>\n Enrichment is a\u00a0relationship in\u00a0which concentration increases with increasing discharge. It is typical of\u00a0substances bound to sediment and those strongly influenced by surface runoff. In\u00a0the\u00a0test dataset, iron provides a\u00a0clear example of\u00a0enrichment (Fig.\u00a011<\/em>): concentrations remain\u00a0stable at below-average and average flows, increase slightly at higher flows, and then rise severalfold under extreme flow conditions.<\/p>\n A\u00a0similar pattern can\u00a0be observed for some polycyclic aromatic hydrocarbons (PAHs) (Fig.\u00a012<\/em>), with concentrations remaining stable under normal conditions but in\u00a0some cases increasing sharply at higher discharges.<\/p>\n A\u00a0somewhat different enrichment pattern is seen for nitrate nitrogen (Fig.\u00a013<\/em>): concentrations increase progressively across the\u00a0flow range, while the\u00a0additional increase under extreme flow conditions is only slight.<\/p>\n A\u00a0relationship in\u00a0which concentration decreases with increasing discharge may occur for substances entering surface waters primarily from groundwater; their decline may indicate a\u00a0decreasing contribution of\u00a0baseflow relative to surface runoff (e.g. calcium, Fig.\u00a014<\/span><\/em>). In\u00a0some cases, however, dilution can\u00a0also indicate pollution from point sources, which is independent of\u00a0hydrological conditions.<\/span><\/p>\n The\u00a0resulting pattern may reflect a\u00a0combination of\u00a0several factors, with extremes occurring at both ends of\u00a0the\u00a0flow range. Fig.\u00a015<\/span><\/em> shows a\u00a0classic dilution pattern for phosphate phosphorus. Total phosphorus follows a\u00a0broadly similar pattern at lower flows, but concentrations increase again\u00a0at high flows.<\/span><\/p>\n Catchment characteristics have a\u00a0major influence on the\u00a0relationship between hydrology and water quality. The\u00a0basic characteristics examined \u2013 catchment size and the\u00a0proportions of\u00a0arable and urban\u00a0land \u2013 also proved important in\u00a0the\u00a0test dataset. The\u00a0following figures illustrate these effects using nutrients as examples.<\/p>\n Figs. 16<\/em> and 17<\/em> compare nitrate nitrogen and total phosphorus concentrations in\u00a0small (< 500 km\u00b2) and large (> 2,000 km\u00b2) catchments. At average and high flows, nutrient concentrations are almost identical in\u00a0small and large catchments, whereas differences emerge at low flows, with higher concentrations particularly evident in\u00a0small catchments. For total phosphorus, this pattern occurs in\u00a0both large and small catchments but is considerably more pronounced in\u00a0small catchments. For nitrate nitrogen, concentrations in\u00a0large catchments continue to decline at low flows, whereas those in\u00a0small catchments remain\u00a0similar to concentrations at intermediate flows.<\/p>\n The\u00a0differences are even more pronounced between catchments with high and low proportions of\u00a0arable land (Figs. 18 <\/span><\/em>and 19<\/span><\/em>). For total phosphorus, the\u00a0overall pattern is broadly similar, but concentrations are considerably higher in\u00a0catchments with a\u00a0high proportion of\u00a0arable land. For nitrate nitrogen, however, the\u00a0patterns are completely opposite. In\u00a0catchments dominated by arable land, nitrate concentrations increase with increasing discharge, whereas in\u00a0catchments with a\u00a0low proportion of\u00a0arable land they remain\u00a0constant or\u00a0even decrease.<\/span><\/p>\n \u00a0<\/span><\/p>\n In\u00a0the\u00a0example shown, the\u00a0proportion of\u00a0urban\u00a0land is reflected more in\u00a0an\u00a0overall increase in\u00a0concentrations than\u00a0in\u00a0a\u00a0change in\u00a0the\u00a0pattern (Figs.\u00a020 <\/span><\/em>and 21<\/span><\/em>).<\/span><\/p>\n The\u00a0simple method used here clearly demonstrates the\u00a0wide range of\u00a0questions that can\u00a0be asked and the\u00a0various aspects of\u00a0the\u00a0relationship between hydrological and water quality parameters that can\u00a0be examined. However, detailed evaluation and the\u00a0drawing of\u00a0conclusions require many factors to be taken into account, which must be addressed according to the\u00a0specific purpose of\u00a0the\u00a0analysis.<\/span><\/p>\n For comparative purposes, independent (non-overlapping) catchments should be selected. Analysis of\u00a0catchment characteristics requires a\u00a0more thorough examination of\u00a0correlations among the\u00a0characteristics themselves, for example between population density and agricultural activity. In\u00a0this analysis, settlement area was represented only by its proportion of\u00a0land use; population and the\u00a0structure of\u00a0wastewater treatment would be more appropriate predictors. The\u00a0use of\u00a0mean\u00a0daily flows does not capture the\u00a0dynamics of\u00a0individual extreme events, such as the\u00a0first flush during extreme flow events [13], or differences in\u00a0the\u00a0effects of\u00a0drought depending on its duration.<\/span><\/p>\n The\u00a0test data also demonstrate, among other things, the\u00a0influence of\u00a0arable land on nitrate concentrations. This influence is sufficiently strong to reverse the\u00a0pattern of\u00a0the\u00a0relationship: enrichment predominates in\u00a0catchments with a\u00a0high proportion of\u00a0arable land, whereas dilution predominates in\u00a0catchments with no arable land. This pattern suggests a\u00a0likely combination of\u00a0sources that could be captured by more complex models, such as that described in\u00a0[10], which distinguishes between enrichment, predominating in\u00a0agricultural catchments in\u00a0winter, and dilution, predominating in\u00a0all catchment types in\u00a0summer. A\u00a0similar model appears likely to be applicable under conditions in\u00a0the\u00a0Czech Republic. The\u00a0results also show that enrichment does occur at extremely high flows, but is not pronounced. Small catchments are more vulnerable to drought.<\/span><\/p>\n A\u00a0number of\u00a0authors have investigated phosphorus in\u00a0the\u00a0Czech environment, e.g. [13, 14]. The\u00a0test data confirm that dilution is the\u00a0predominant relationship for phosphorus; enrichment occurs only for total phosphorus under extreme flow conditions. Further research should examine whether enrichment at high flows occurs in\u00a0all types of\u00a0catchment or only in\u00a0those with a\u00a0higher proportion of\u00a0arable land. Small catchments with dense development and intensive agriculture are the\u00a0most vulnerable in\u00a0terms of\u00a0phosphorus, with dry periods potentially leading to severalfold increases in\u00a0concentrations. A\u00a0more detailed analysis would be possible with more comprehensive data on population and wastewater management.<\/span><\/p>\n Similar statistical methods can\u00a0be applied to any parameters for which sufficient data are available. Substances other than\u00a0nutrients are much less well represented in\u00a0the\u00a0literature. The\u00a0test data clearly show a\u00a0positive relationship between the\u00a0concentrations of\u00a0most PAHs and discharge \u2013 i.e. enrichment \u2013 which becomes more pronounced as flows become more extreme. This relationship mirrors that observed for suspended solids and, for example, iron, indicating an\u00a0association with the\u00a0proportion of\u00a0surface runoff. Further investigation could usefully compare the\u00a0influence of\u00a0impervious surfaces with that of\u00a0other land-cover types, such as erosion-prone arable land. As this relationship is relatively consistent, it could potentially be used to model concentrations based on correlations with discharge or suspended solids. Fieldwork could then focus on validation measurements, or the\u00a0relationship could be used to improve estimates of\u00a0emissions, as required for priority substances under the\u00a0Water Framework Directive.<\/span><\/p>\n Analysis of\u00a0the\u00a0pesticide results yields interesting findings, although these are severely limited by data availability. Given the\u00a0large number of\u00a0results below the\u00a0limit of\u00a0quantification and the\u00a0inconsistent limits used for different measurements, one option is to include these data and focus on the\u00a0probability of\u00a0exceeding the\u00a0limit of\u00a0quantification.<\/span><\/p>\n Overall, the\u00a0influence of\u00a0hydrological conditions can\u00a0be so strong that it obscures long-term improvements in\u00a0pollution levels. Many substances responsible for failure to achieve good status are sensitive to both extreme rainfall events and, in\u00a0particular, episodes of\u00a0drought.<\/span><\/p>\n C\u2013Q relationship models can\u00a0be used, with a\u00a0certain\u00a0degree of\u00a0uncertainty, to compensate for limited data availability, for example to improve estimates of\u00a0constituent loads.<\/span><\/p>\n Many statistical methods are available for working with large datasets and, with appropriately formulated questions and carefully selected data, they can\u00a0yield valuable insights. Such analyses inevitably involve a\u00a0degree of\u00a0simplification and cannot replace detailed analysis of\u00a0the\u00a0catchment under study. On the\u00a0other hand, statistical analyses of\u00a0existing data are quick and inexpensive and can\u00a0provide valuable information, for example for analysing pollution sources within\u00a0a\u00a0catchment, modelling responses to proposed measures, or\u00a0planning monitoring campaigns to obtain\u00a0as much information as possible at the\u00a0lowest cost.<\/span><\/p>\n Despite the\u00a0numerous challenges encountered, the\u00a0dataset and analytical approach proved effective.<\/span><\/p>\n The\u00a0data and methods used show that hydrological conditions have a\u00a0major influence on surface water quality. Summer drought episodes significantly increase phosphorus concentrations, with a\u00a0greater effect in\u00a0small streams than\u00a0in\u00a0large rivers with extensive catchments. Slight to moderate increases in\u00a0discharge are associated with higher nitrate concentrations, particularly in\u00a0winter, but only in\u00a0catchments with a\u00a0substantial proportion of\u00a0arable land; concentrations remain\u00a0constant in\u00a0catchments without arable land. Concentrations of\u00a0PAHs increase very sharply at high to extreme flows. Pesticides warrant a\u00a0separate, more detailed investigation.<\/span><\/p>\n The\u00a0simple visualizations demonstrate clear relationships for both nutrients and other parameters. Future research should focus on additional or more precise catchment characteristics (e.g. population and connection to public sewerage systems rather than\u00a0the\u00a0crude proportion of\u00a0built-up land) and investigate other parameters in\u00a0greater detail \u2013 particularly pesticides and pharmaceuticals \u2013 where sufficient measurements are available under a\u00a0range of\u00a0conditions.<\/span><\/p>\n This article was produced as part of\u00a0Technology Agency of\u00a0the\u00a0Czech Republic project No. SS02030040, Prediction, Assessment and Research into the\u00a0Sensitivity of\u00a0Selected Systems and the\u00a0Effects of\u00a0Drought and Climate Change in\u00a0Czechia (https:\/\/www.perun-klima.cz\/). We thank the\u00a0River Basin\u00a0Authorities and the\u00a0Czech Hydrometeorological Institute for providing the\u00a0data.<\/span><\/span><\/em><\/p>\n The\u00a0Czech version of\u00a0this article was peer-reviewed, the\u00a0English version was translated from the\u00a0Czech original by Environmental Translation Ltd.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":" Changing climatic and hydrological conditions also affect the quality of water in streams and reservoirs. A number of studies have been published on this topic, mostly focused on specific areas and situations. The Czech Republic has sufficiently long and detailed series of monitoring of both flows and quality to enable detailed data analysis to trace the prevailing types of relationships depending on the characteristics of the catchment area and sources of pollution.<\/p>\n","protected":false},"author":8,"featured_media":39464,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","_members_access_role":[],"_members_access_error":""},"categories":[94,86],"tags":[4200,4201,316,301,4199],"coauthors":[759],"class_list":["post-39297","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-current-issue","category-hydraulics-hydrology-and-hydrogeology","tag-concentration-discharge-relationship","tag-statistical-methods","tag-surface-water","tag-water-quality","tag-water-quantity"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts\/39297","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/comments?post=39297"}],"version-history":[{"count":3,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts\/39297\/revisions"}],"predecessor-version":[{"id":39661,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts\/39297\/revisions\/39661"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/media\/39464"}],"wp:attachment":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/media?parent=39297"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/categories?post=39297"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/tags?post=39297"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/coauthors?post=39297"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}INTRODUCTION<\/h2>\n
METHODOLOGY<\/h2>\n
Selection of\u00a0monitoring sites<\/h3>\n
Parameter selection<\/h3>\n
Tab. 1. Number of available values<\/h5>\n
![\"\"](\"https:\/\/www.vtei.cz\/wp-content\/uploads\/2026\/08\/Semeradova-tab-1-1.jpg\")
<\/a>\nMethod for assessing hydrological conditions<\/h3>\n
\nThe\u00a0adjusted flow at the\u00a0time of\u00a0water quality sampling was compared with the\u00a0corresponding adjusted percentile and assigned to the\u00a0appropriate group. This made it possible to determine whether flow at the\u00a0time of\u00a0sampling was significantly higher or lower than\u00a0usual.<\/span><\/p>\nCatchment characteristics<\/h3>\n
Tab.\u00a02. Number of\u00a0profiles<\/span><\/h5>\n
<\/a>\n<\/a>\nFig.\u00a01. Location and type of\u00a0profiles (dot size indicates small, medium and large catchments; green, yellow and red indicate a\u00a0small, medium and large share of\u00a0arable land, respectively)<\/span><\/h6>\n
RESULTS<\/h2>\n
Trends<\/h3>\n
<\/a>\nFig.\u00a02. Average flow rate of\u00a0the\u00a0test dataset (m\u00b3\/s)<\/h6>\n
<\/a><\/h6>\nFig.\u00a03. Development of\u00a0average annual concentrations of\u00a0selected indicators in\u00a0tested profiles (as a\u00a0percentage of\u00a02010\u00a0levels)<\/h6>\n
Seasonality<\/h3>\n
<\/a>\nFig. 4. Number of samples by flow percentile at the time of sampling<\/h6>\n
<\/a>\nFig. 5. Number of samples by flow percentile at the time of sampling in each month<\/h6>\n
<\/a>\nFig. 6. Number of samples by flow percentile at the time of sampling in each year<\/h6>\n
<\/a><\/h6>\n<\/a><\/h6>\nFig. 8. Metolachlor \u2013 numbers of records below and above the limit of quantification in each month<\/h6>\n
<\/a>\nFig. 9. Alachlor \u2013 ESA and Metolachlor \u2013 average measured concentrations in each month<\/h6>\n
C-Q relationships<\/h3>\n
\n\n
\n
\n<\/li>\n
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\n<\/li>\nChemostasis<\/h3>\n
<\/a><\/p>\nFig.\u00a010. Average arsenic concentrations (\u00b5g\/L) by flow percentile \u2013 example of\u00a0a\u00a0chemostatic relationship<\/h6>\n
Enrichment<\/h3>\n
<\/a>\nFig.\u00a011. Average iron concentrations (mg\/L) by flow percentile \u2013 example of\u00a0enrichment<\/h6>\n
<\/a><\/h6>\nFig.\u00a012. Average concentrations of\u00a0selected polycyclic aromatic hydrocarbons (\u00b5g\/L) by flow percentile \u2013 example of\u00a0enrichment<\/h6>\n
<\/a>\nFig.\u00a013. Average nitrate nitrogen concentrations (mg\/L) by flow percentile \u2013 example of\u00a0another type of\u00a0enrichment<\/h6>\n
Dilution<\/h3>\n
<\/a><\/p>\nFig.\u00a014. Average calcium concentrations (mg\/L) by flow percentile \u2013 example of\u00a0dilution<\/h6>\n
Combination<\/h3>\n
<\/a>\nFig.\u00a015. Average concentrations of\u00a0phosphate phosphorus (dilution) and total phosphorus (combination of\u00a0dilution and enrichment) (mg\/L) by flow percentile<\/h6>\n
Influence of\u00a0catchment characteristics<\/h3>\n
<\/a>\nFig.\u00a016. Differences in\u00a0the\u00a0C\u2013Q relationship for nitrate nitrogen (mg\/L) by catchment size<\/h6>\n
<\/a><\/h6>\nFig.\u00a017. Differences in\u00a0the\u00a0C-Q relationship for total phosphorus (mg\/L) by catchment size<\/h6>\n
<\/a><\/span><\/p>\nFig.\u00a018. Differences in\u00a0the\u00a0C-Q relationship for nitrate nitrogen (mg\/L) by proportion of\u00a0arable land in\u00a0the\u00a0catchment<\/h6>\n
<\/a><\/h6>\nFig.\u00a019. Differences in\u00a0the\u00a0C-Q relationship for total phosphorus (mg\/L) by proportion of\u00a0arable land in\u00a0the\u00a0catchment<\/h6>\n
<\/a><\/p>\nFig.\u00a020. Differences in\u00a0the\u00a0C-Q relationship for nitrate nitrogen (mg\/L) by proportion of\u00a0urban\u00a0development in\u00a0the\u00a0catchment<\/h6>\n
<\/a>\nFig.\u00a021. Differences in\u00a0the\u00a0C-Q relationship for total phosphorus (mg\/L) by proportion of\u00a0urban\u00a0development in\u00a0the\u00a0catchment<\/h6>\n
DISCUSSION<\/h2>\n
CONCLUSION<\/h2>\n
Acknowledgements<\/h3>\n