Effects of water quality, flooding episode and management variables on the fish yield from self-stocked ponds in lower Rufiji floodplain, Tanzania

Considering the large temporal variability in surface water quality is essential for adequate water quality policy and management. Neglecting these dynamics may easily lead to decreased effectiveness of measures to improve water quality and to inefficient water quality monitoring. The objective of this paper is to summarise our understanding of temporal variability in surface water and upper groundwater quality and to discuss the consequences and opportunities for regional water quality monitoring. In regional monitoring networks, measurement frequencies are typically too low to capture the short-term temporal variations in solute concentrations. This causes large uncertainty in the assessment of (trends in) average concentrations and contaminant loads. The most important driver for short-term variations in water quality in most catchments is the variability in meteorological conditions, which induces changes in the relative discharge contributions of water from different flow routes and different chemical compositions. Various options exist for dealing with the transient behavior of water quality in regional water quality monitoring. Estimates of average concentrations and loads from low-frequency concentration data can be improved by using the explanatory strength of commonly available measurements of quantitative hydrological data like precipitation, discharge, and groundwater levels. This paper provides examples of the relationship between water quality and explanatory variables in conceptual, statistical, or process-based models. Another strategy for dealing with short-term variability in water quality monitoring is to measure long-term average solute concentrations using passive samplers. Similarly, on-site auto analyzers and ion specific electrodes provide opportunities for continuous water quality measurements.

Long-term trends in water quality and hydrological variables of natural systems reveal information about physical, chemical and biological changes and variations due to manmade and seasonal interventions. The objective of this study was to develop suitable stochastic models for predicting river water quality and hydrological variables through the establishment of dynamic relationship among the variables using transfer function modeling approaches. Autoregressive integrated moving average (ARIMA) model containing autoregressive (AR), integrated (I) and moving average (MA) was used for this purpose. The water quality variables, namely pH, color (TCU), turbidity (ppm), Al3+ (ppm), Fe2+ (ppm), NH4 + (ppm) and Mn2+ (ppm), and hydrological variables, namely rainfall and river discharge for Johor River, Malaysia, recorded for the period 2004–2007 were used in the study. Results showed that except Al3+, Fe2+, NH4 + and rainfall, all other variables are stationary. The non-stationary time series can be fitted with ARIMA (p, 1, q), while the stationary time series can be fitted with AR model with 1–5 time lags. The autocorrelations of all the samples were found within the 95% confidence bounds and the model residuals were found to follow normal probability distribution, which indicate the suitability of the models in forecasting water quality and hydrological variables. It is expected that the modeling approach developed in this paper can be replicated in other river basins for reliable prediction of river water quality due to changes in rainfall–runoff processes.

Water is a fundamental resource and, as such, the object of multiple environmental policies requiring systematic monitoring of its quality as a main management component. Automatic sensors, allowing for continuous monitoring of various water quality variables at high temporal resolution, offer new opportunities for enhancement of essential water quality data. This study investigates the potential of sensor-measured data to improve understanding and management of water quality at watershed level. Self-organizing data maps, non-linear canonical correlation analysis, and linear regressions are used to assess the relationships between multiple water quality and hydroclimatic variables for the case study of Lake Mälaren in Sweden, and its total catchment and various watersheds. The results indicate water discharge from dominant watersheds into a lake, and lake water temperature as possible proxies for some key water quality variables in the lake, such as blue-green algae; the latter is, in turn, identified as a potential good proxy for lake concentration of total nitrogen. The relationships between water discharges into the lake and lake water quality dynamics identify the dominant contributing watersheds for different water quality variables. Seasonality also plays an important role in determining some possible proxy relationships and their usefulness for different parts of the year.

Understanding the factors that influence temporal variability in water quality is critical for designing water quality management strategies. In this study, we explore the key factors that affect temporal variability in stream water quality across multiple catchments using a Bayesian hierarchical model. We apply this model to a case study data set consisting of monthly water quality measurements obtained over a 20‐year period from 102 water quality monitoring sites in the state of Victoria (Southeast Australia). We investigate six water quality constituents: total suspended solids, total phosphorus, filterable reactive phosphorus, total Kjeldahl nitrogen, nitrate‐nitrite (NOx), and electrical conductivity. We find that same‐day streamflow has the greatest effect on water quality variability for all constituents. Additional important predictors include soil moisture, antecedent streamflow, vegetation cover, and water temperature. Overall, the models do not explain a large proportion of temporal variation in water quality, with Nash‐Sutcliffe coefficients lower than 0.49. However, when considering performance on a site‐by‐site basis, we see high model performance in some locations, with Nash‐Sutcliffe coefficients of up to 0.8 for NOx and electrical conductivity. The effect of the temporal predictors on water quality varies between sites, which should be explored further for potential spatial patterns in future studies. There is also potential for further extension of these temporal variability models into a predictive spatiotemporal model of riverine constituent concentrations, which will be a useful tool to inform decision making for catchment water quality management.

Using historical data, long-term variations in pollutant sources and water quality in China’s coastal waters over the last three decades are reviewed. The results show that the total area of non-clean water, which reflects state of total water quality, increased rapidly before 2000, but then underwent two stages of decline, with a modest decline by one-quarter between 2001 and 2015, followed by a sharp decline of more than half of that in 2015 since then. Consequently, water quality at present is better than it was at the beginning of the 1990s. The total area of polluted water fluctuated without any trend from the end of the 1990s until 2015, but has declined sharply by nearly two-thirds since 2015, indicating that the water quality in China’s coastal seas has improved substantially. Geographically, the Bohai Sea was the first to see a turning point in water quality, followed by the Yellow Sea and East China Sea, while the South China Sea was the last. The main pollutants that govern the water quality grade and area are dissolved inorganic nitrogen and phosphate as well as petroleum hydrocarbons. As a response to variations in water quality, changes in both the frequency and total area affected by harmful algal blooms were similar to those of water quality over the last three decades, albeit with a slight lag. Analysis showed that variations in water quality were closely related to the land- and sea-sourced pollutant inputs. The combination of shift in the mode of economic growth from high-speed growth to high-quality development and the enforcement of the new, strictest ever Environmental Protection Law resulted in a significant decline of pollutant emissions, inducing a turning point in the water quality in the coastal seas of China in the mid-2010s.

<div> <div> <div> <div>Our current capacity to model stream water quality is limited particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatio-temporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years, across 102 catchments, which span over 130,000 km<sup>2</sup>. The modelling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate-nitrite (NO<sub>x</sub>), and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which had been identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explainable (19.9%), the model explains 38.2% (NO<sub>x</sub>) to 88.6% (EC) of total spatio-temporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability; temporal variability remains largely unexplained across all catchments, while long-term trends are well captured. The model is best used to predict proportional changes in water quality in a Box-Cox transformed scale, but can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot-spots and hot moments for waterway pollution; (2) predicting effects of catchment changes on water quality e.g. urbanization or forestation; and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on: (1) alternative statistical model structures to improve fitting for truncated data, for constituents where a large amount of data below the detection-limit; and (2) better representation of non-conservative constituents (e.g. FRP) by accounting for important biogeochemical processes.</div> </div> </div> </div>

The hydrological conditions of river-connected lakes are complex primarily owing to their considerable water-level fluctuations (WLFs). Water quality in such lakes varies with hydrodynamic variations; however, their relationship is not clear. To identify the unique relationship between water level and water quality in river-connected lakes, we used the comprehensive pollution index (CPI) and regression analysis to analyze the spatiotemporal variation in water quality in Dongting Lake from 2015 to 2018 and the effects of water level on water quality. Four water quality parameters were selected: total nitrogen (TN), total phosphorus (TP), permanganate index (CODMn), and chlorophyll a (Chl-a). The results showed significant spatial variation in the lake water quality, with relatively high concentrations of TN, TP, CODMn, and Chl-a in East Dongting Lake. TN and TP decreased by 12.15% and 37.61%, respectively, from 2015 to 2018, whereas CODMn increased from 1.781 to 2.009 mg/L. Seasonally, TN and TP concentrations were low in the summer and autumn, with high concentrations in the winter and spring. In contrast, CODMn and Chl-a concentrations exhibited opposite trends. The pollution level in Dongting Lake ranged between slightly and moderately polluted, with a CPI ranging from 0.76 to 1.32 across all sampling sites during 2015-2018. The water level in Dongting Lake initially increased and, then, decreased in a year, with marked WLFs owing to seasonal shifts in precipitation and human activities. The water level had significant negative relationships with TN and TP concentrations and a significant positive relationship with CODMn concentration (p < 0.05). Based on the results, strict control of excessive external nutrient loading should be actively implemented in Dongting Lake, in addition to hydrological regulation for effective lake water quality management.

Abstract. Our current capacity to model stream water quality is limited – particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatiotemporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years and across 102 catchments (which span over 130 000 km2). The modeling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate–nitrite (NOx) and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which were identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explained (19.9 %), the model explains 38.2 % (NOx) to 88.6 % (EC) of the total spatiotemporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability; the temporal variability remains largely unexplained across all catchments, although long-term trends are well captured. The model is best used to predict proportional changes in water quality on a Box–Cox-transformed scale, but it can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot spots and hot moments for waterway pollution; (2) predicting the effects of catchment changes on water quality, e.g., urbanization or forestation; and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on the following: (1) alternative statistical model structures to improve fitting for truncated data (for constituents where a large amount of data fall below the detection limit); and (2) better representation of nonconservative constituents (e.g., FRP) by accounting for important biogeochemical processes.

Sexual growth dimorphism is common among animals, growth rate differing between the genders. Growth dimorphism is common also in fish, but the regulatory mechanisms remain unclear. Variations in feeding rate may lead to sexual growth dimorphism in fish, the growth rate of females decreasing steeper than the growth rate of males when feeding rate decreases. Because water quality strongly affects the prey detection by fish, variations in water quality could affect sexual growth dimorphism. Additionally, variations in fish density could affect dimorphism through food competition. We studied experimentally with perch Perca fluvialitis , whether the effects of decreasing water transparency and increasing fish density on the feeding rate of planktivorous fish are gender‐dependent. We expected that the feeding efficiency of females decrease steeper with increasing water colour and increasing fish density than the feeding rate of males. Additionally, we collected field data and studied the effects of water colour on the growth rate of male and female perch. The results showed that the effect of water colour on the feeding rate of perch was gender‐dependent, while perch density had no effect on the feeding rate difference between males and females. In highly humic water, the feeding rate of male and female perch did not differ, but in clear water females showed a significantly higher feeding rate than males. The results suggested that due to their high energy demand, female perch were feeding at high rate in both water colours, while the feeding rate of males in the clear water experiments was much lower than their possible maximum rate. This was probably due to the decreased feeding activity of males to reduce predation risk. The results were supported by field data, which revealed a significant effect of water colour on the gender growth difference in planktivorous 3‐year‐old perch. The results suggested that variations in water quality may be a factor behind the population‐dependency of dimorphism in fish.

Surface water samples of baseflow were collected from 20 headwater sub-watersheds which were classified into three types of watersheds (natural, urban and agricultural) in the flood, dry and transition seasons during three consecutive years (2010–2012) within a coastal watershed of Southeast China. Integrating spatial statistics with multivariate statistical techniques, river water quality variations and their interactions with natural and anthropogenic controls were examined to identify the causal factors and underlying mechanisms governing spatiotemporal patterns of water quality. Anthropogenic input related to industrial effluents and domestic wastewater, agricultural activities associated with the precipitation-induced surface runoff, and natural weathering process were identified as the potential important factors to drive the seasonal variations in stream water quality for the transition, flood and dry seasons, respectively. All water quality indicators except SRP had the highest mean concentrations in the dry and transition seasons. Anthropogenic activities and watershed characteristics led to the spatial variations in stream water quality in three types of watersheds. Concentrations of NH4 +-N, SRP, K+, CODMn, and Cl− were generally highest in urban watersheds. NO3 –N Concentration was generally highest in agricultural watersheds. Mg2+ concentration in natural watersheds was significantly higher than that in agricultural watersheds. Spatial autocorrelations analysis showed similar levels of water pollution between the neighboring sub-watersheds exhibited in the dry and transition seasons while non-point source pollution contributed to the significant variations in water quality between neighboring sub-watersheds. Spatial regression analysis showed anthropogenic controls played critical roles in variations of water quality in the JRW. Management implications were further discussed for water resource management. This research demonstrates that the coupled effects of natural and anthropogenic controls involved in watershed processes, contribute to the seasonal and spatial variation of headwater stream water quality in a coastal watershed with high spatial variability and intensive anthropogenic activities.

Spatio-temporal changes in surface water quality and sediment phosphorus content of a large reservoir in Turkey

Spatial and temporal variations in surface water quality of the dam reservoirs in the Tigris River basin, Turkey

Impact of spatial variations in water quality and hydrological factors on the food-web structure in urban aquatic environments

Karst aquifers in subtropical regions are characterized by high variability of water availability and quality due to changes associated with rainy and dry seasons. An additional challenge for water management is the combination of surface-water and karst groundwater systems since high spatiotemporal dynamics cause high variability of water quality. In these cases, adapted protection strategies are required. In this study, a protection approach for the catchment of a river-water diversion point in a rural area in northern Vietnam is developed. The variability of water quality was evaluated by rainy and dry season synoptic surveys of suspended particles and microbial contamination at 49 sites and time series at three sets of paired sites under constant hydraulic conditions. The anthropogenic land-use activities in the catchment were mapped to identify potential contamination sources and to highlight the challenging combination of surface-water and karst groundwater management. The analyzed data indicate differences in water quality between the dry and rainy seasons and a higher influence on water quality from land use than from hydrologic conditions. Furthermore, the results suggest a high risk of contamination resulting from residential areas, agriculture, and livestock farming, and reveal the necessity of implementation of appropriate measures such as restricted farming and the hook-up of buildings to municipal sewage disposal. Finally, the data show that water quality can be improved by adjusting water withdrawals by the time of day. The applied methods can be transferred to other surface-water and karst groundwater systems in similar subtropical environments.

Chemical treatment response to variations in non-point pollution water quality: Results of a factorial design experiment