Long-term water quality monitoring in agricultural catchments in Sweden: Impact of climatic drivers on diffuse nutrient loads

Combined effects of flood, drought and land use dominate water quality and nutrient exports in Jialing River basin, SW China

Climate change and the associated increase in hydroclimatic extremes necessitate a deeper understanding of the resulting water quality responses. This study investigates the combined impacts of hydroclimatic extremes and land uses on water quality of the Chinese Jialing River, of which the middle and downstream areas experienced a flood in 2021 and a severe drought in 2022. Water Quality Index (WQI) and nutrient loads were assessed using daily data from 22 monitoring stations across the Jialing River and its two tributaries, the Qujiang River and Fujiang River, over 2021-2022. The results indicate a slight upward trend in water quality, as reflected by the WQI, for the tributaries from 2021 to 2022, while a declining trend was observed in the mainstream. Floods had a more pronounced impact on water quality than droughts, particularly on nutrient concentrations, and both dilution and flushing effects were observed as discharge increased in the Jialing River and its tributaries. Notably, water quality deterioration was most pronounced in the downstream areas with land uses dominated by cropland and built-up area, where intensified rainfall distributed and exacerbated nutrient losses in the rainy seasons. Nutrient fluxes, including Chemical Oxygen Demand (COD), Total Phosphorus (TP), and Total Nitrogen (TN), were closely linked to discharge, with hydroclimatic extremes therefore significantly affecting nutrient exports. This study elucidates the complex interactions between land use, extreme weather and water quality in the Jialing River Basin. Our findings underscore the need to strengthen the management of non-point source pollutants in the downstream areas of the Jialing River to address the challenges posed by anticipated increases in extreme rainfall in the near future.

Far-future hydrology will differentially change the phosphorus transfer continuum

Climate change is likely to exacerbate land to water phosphorus (P) transfers, causing a degradation of water quality in freshwater bodies in Northwestern Europe. Planning for mitigation measures requires an understanding of P loss processes under such conditions. This study assesses how climate induced changes to hydrology will likely influence the P transfer continuum in six contrasting river catchments using Irish national observatories as exemplars. Changes or stability of total P (TP) and total reactive P (TRP) transfer processes were estimated using far-future scenarios (RCP4.5 and RCP8.5) of modelled river discharge under climate change and observed links between hydrological regimes (baseflow and flashiness indices) and transfer processes (mobilisation and delivery indices). While there were no differences in P mobilisation between RCP4.5 and RCP8.5, both mobilisation and delivery were higher for TP. Comparing data from 2080 (2070-2099) with 2020 (2010-2039), suggests that P mobilisation is expected to be relatively stable for the different catchments. While P delivery is highest in hydrologically flashy catchments, the largest increases were in groundwater-fed catchments in RCP8.5 (+ 22% for TRP and + 24% for TP). The inter-annual variability of P delivery in the groundwater-fed catchments is also expected to increase. Since the magnitude of a P source may not fully define its mobility, and hydrological connections of mobilisation areas are expected to increase, we recommend identifying critical mobilisation areas to target future mitigation strategies. These are hydrologically connected areas where controls such as soil/bedrock chemistry, biological activity and hydrological processes are favourable for P mobilisation.

Spatio-temporal evaluation of metals and metalloids in the water of high Andean livestock micro-watersheds, Amazonas, Peru

Cattle ranching is a fundamental economic activity in northern Peru, where proper management of water resources is crucial. This study, a pioneer in the region, evaluated water quality and its suitability for human consumption, vegetable irrigation, and livestock production. It is also the first study to document the presence of metals and metalloids in vulnerable areas because they are located at the headwaters of river watersheds. The spatiotemporal evaluation of physicochemical parameters, metals, and metalloids was performed in five micro-watersheds (Cabildo, Timbambo, Pomacochas, Atuen, and Ventilla) from water samples collected in the dry season (October 2017) and wet season (March 2018). The parameters were analyzed using microwave plasma atomic emission spectrometry. The results were contrasted with international and Peruvian quality standards related to dairy cow production. The highest values of pH, total dissolved solids, and electrical conductivity were reported during the dry season, and the highest turbidity during the wet season. Of the metals evaluated, arsenic (As) was omnipresent in all the micro-watersheds, followed by lead (Pb). In contrast to World Health Organization regulations, concentrations of As, cadmium (Cd), Pb, and iron represent a risk; according to Peruvian regulations, As and Pb exceed the concentrations established for use in animal drinking water and vegetable irrigation, and according to water guidelines for dairy cattle, concentrations of As, Pb, Cd, and Al exceed the permitted limits. The high concentrations of these metals in the study area are attributable to a synergy between natural factors, such as Andean geology and livestock activity. The data reported will allow for proper water resource management, pollution prevention, and the design and adoption of mitigation measures.

Enhancing water quality prediction for fluctuating missing data scenarios: A dynamic Bayesian network-based processing system to monitor cyanobacteria proliferation

Tackling the impact of missing data in water management is crucial to ensure the reliability of scientific research that informs decision-making processes in public health. The goal of this study is to ascertain the root causes associated with cyanobacteria proliferation under major missing data scenarios. For this purpose, a dynamic missing data management methodology is proposed using Bayesian Machine Learning for accurate surface water quality prediction of a river from Limia basin (Spain). The methodology used entails a sequence of analytical steps, starting with data pre-processing, followed by the selection of a reliable dynamic Bayesian missing value prediction system, leading finally to a supervised analysis of the behavioral patterns exhibited by cyanobacteria. For that, a total of 2,118,844 data points were used, with 205,316 (9.69 %) missing values identified. The machine learning testing showed the iterative structural expectation maximization (SEM) as the best performing algorithm, above the dynamic imputation (DI) and entropy-based dynamic imputation methods (EBDI), enhancing in some cases the accuracy of imputations by approximately 50 % in R2, RMSE, NRMSE, and logarithmic loss values. These findings can impact how data on water quality is being processed and studied, thus, opening the door for more reliable water management strategies that better inform public health decisions.

Pollutant transport model with large time-scale and estimation of land-use export coefficients at a watershed level

The pollutant transport equilibrium in a watershed can be analyzed on a large time scale, and land-use export coefficients can be calculated directly under certain hydrologic and transport conditions, by ignoring hydrologic and transport processes at small space and time scales on hydrologic response units. In this study, the water environment system of a watershed was deconstructed into three parts (source, source-sink, and runoff transport) to construct a pollutant transportation equilibrium model on a large time scale. A watershed with an annual source-sink accumulation of zero was defined as a completely transported watershed; therefore, we derived a completely transported equilibrium equation. The problem of seeking the land export coefficient was converted into a problem of seeking the optimal solution of linear programming, which can be estimated according to the variation in pollutant output processes. The feasibility of the solution can be analyzed using multi-year stochastic rainfall processes. The model was used to analyze the transport equilibrium of chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) upstream of the monitored cross-sections in a watershed, which covered 3145.66 km2. The land export coefficients were calculated according to the model. The model calculations indicated that the watershed was completely transported during perennial years. The calculated export coefficients of COD, TN, and TP for farmland, primary vegetation, and urban land were within the range of general empirical values. The calculated maximum accumulations of COD, TN, and TP were 0.19 × 107, 0.063 × 107, and 0.049 × 106 kg, respectively, for perennial rainfall. PRACTITIONER POINTS: A completely transported watershed was defined, and a model of pollutant transportation equilibrium with large time-scale was constructed. A problem of seeking the optimal solution of a linear programming was designed to estimate the land export coefficient of COD, TN, and TP. The runoff transport and accumulation processes of COD, TN, and TP in a watershed was analyzed.

Managing multi-functional peri-urban landscapes: Impacts of horse-keeping on water quality

Eutrophication assessments in water management to quantify nutrient loads and identify mitigating measures seldom include the contribution from horse facilities. This may be due to lack of appropriate methods, limited resources, or the belief that the impact from horses is insignificant. However, the recreational horse sector is growing, predominantly in multi-functional peri-urban landscapes. We applied an ecosystem management approach to quantify nutrient loads from horse facilities in the Stockholm Region, Sweden. We found that horses increased the total loads with 30-40% P and 20-45% N, with average area-specific loads of 1.2 kg P and 7.6 kg N ha-1 year-1. Identified local risk factors included manure management practices, trampling severity, soil condition and closeness to water. Comparisons of assessment methods showed that literature standard values of area-specific loads and water runoff may be sufficient at the catchment level, but in small and more complex catchments, measurements and local knowledge are needed.