Land use and climate change as well as legal requirements (e.g. the EU Water Framework Directive) pose challenges for the assessment and sustainable management of surface water quality at the catchment scale. Sources and pathways of nutrients and other pollutants as well as nutrient interactions have to be characterized to understand and manage the impacts in river systems. Additionally, water quality assessment needs to cover the chemical and ecological status to link the hydrological view to aquatic ecology.
Models can help to optimize monitoring schemes and provide assessments of future change and management options. However, insufficient temporal and/or spatial resolution, a short duration of observations and the widespread use of different analytical methods restrict the data base for model application. Moreover, model-based water quality calculations are affected by errors in input data, model errors, inappropriate model complexity and insufficient process knowledge or implementation. Additionally, models should be capable of representing changing land use and climate conditions, which is a prerequisite to meet the increasing needs for decision making. The strong need for advances in water quality models remains.
This session aims to bring scientist together who work on experimental as well as on modelling studies to improve the prediction and management of water quality constituents (nutrients, organic matter, algae, or sediment) at the catchment scale. Contributions are welcome that cover the following issues:
- Experimental and modelling studies on the identification of sources, hot spots, pathways and interactions of nutrients and other, related pollutants at the catchment scale
- New approaches to develop efficient water quality monitoring schemes
- Innovative monitoring strategies that support both process investigation and model performance
- Advanced modelling tools integrating catchment as well as in-stream processes
- Observational and modelling studies at catchment scale that relate and quantify water quality changes to changes in land use and climate
- Measurements and modelling of abiotic and biotic interaction and feedback involved in the transport and fate of nutrients and other pollutants at the catchment scale
- Catchment management: pollution reduction measures, stakeholder involvement, scenario analysis for catchment management
vPICO presentations: Fri, 30 Apr
Intensive agricultural land use have introduced vast quantities of nutrients such as reactive nitrogen (N) to soils and subsequently to groundwater and surface waters. High nitrate concentrations are still a pressing issue for drinking water safety and aquatic ecosystem health e.g. in Europe, although fertilizer inputs have been significantly lowered in the last decades. This is partly due to a slow response of riverine nitrate concentrations to changes in nitrogen inputs attributed to N legacies in catchments. N can be stored organically bound as a biogeochemical legacy in soils or can be slowly transported as nitrate in groundwater forming a hydrologic legacy. Legacy can thus lead to a net retention of N in catchments and to substantial time lags in the response to input changes. Here, we systematically explore legacy effects over a wide range of catchment in the Western European countries France and Germany. We are making use of long observational time series of nitrate concentration in 238 catchments covering 40% of the total area of France and Germany. We apply a Weighted Regression on Time, Discharge, and Season (WRTDS) to derive continuous daily flow-normalized concentrations and loads. The temporal pattern of concentration and loads at the catchment outlet is compared to the N input time series evolving from agricultural N surplus, atmospheric deposition and biological fixation. We found that on long-term catchments retain on average 72% of the N input. Time lags between input and output were successfully explained by a lognormal transport time distribution. The modes of these distributions were found to be rather short with a median mode of 5.4 years across all catchments. Based on this data-driven assessment only the fate of N in the catchments is hard to assess as denitrification in soil and groundwater can lead to similar observations as the storage of N in legacies. Focusing on the mobile part of N that is exported by catchments, we estimate that a substantial amount of N is still stored in the subsurface that will be released in the coming years. We therefore analyzed how catchment nitrate export will evolve under the scenario of a total cut down, reduced or constant future N inputs. We report the expected timescale of reaction to implemented measures to help tackling this pressing water quality problem.
How to cite: Musolff, A., Ehrhardt, S., Dupas, R., Kumar, R., Ebeling, P., and Fleckenstein, J. H.: Assessing Nitrogen Legacies in Western Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14526, https://doi.org/10.5194/egusphere-egu21-14526, 2021.
StorAge Selection (SAS) functions describe how a catchment selectively removes water and solute of different ages via discharge, thus controlling transit time distributions (TTDs) and solute composition of discharge. Previous studies have successfully applied SAS functions in a spatially lumped approach to capture catchment-scale transport phenomena of (non-)conservative solutes. The lumped approach assumes that water and solutes within a water parcel of a specific age are well-mixed. While this assumption does not cause any changes in the age of water, the spatial heterogeneity of solute concentrations within this water parcel is lost. In addition, in large catchments, headwater sub-catchments and lowland sub-catchments could behave in different ways, e.g., the transit times (TTs) and reaction rates between headwater and lowland sub-catchment could be of different magnitudes. This, in turn, might not be sufficiently represented in a lumped approach of SAS functions.
In this study, we applied the mHM-SAS model (Nguyen et al., 2020) with a semi-distributed approach of SAS functions. The nested mesoscale catchment (Selke catchment, Germany) with heterogeneous land use management practices, TTs, and subsurface reactivity was used as a case study. In addition to spatial variability, a functional relationship between the parameters of the SAS functions and storage dynamics was introduced to capture temporal dynamics of the selection preference for discharge. High frequency instream nitrate data were used to validate the proposed approach. Results show that the proposed approach can well represent nitrate export at both sub-catchment and catchment levels. The model reveals that catchment nitrate export is controlled by (1) the headwater sub-catchment with fast TTs and a high denitrification rate, and (2) the lowland sub-catchment with longer TTs and a low denitrification rate. In general, the proposed approach serves as a promising tool for understanding the interplay of transport and reaction times between different sub-catchments, which controls nitrate export in a mesoscale heterogeneous catchment.
Nguyen, T. V., Kumar, R., Lutz, S. R., Musolff, A., Yang, J., & Fleckenstein, J. H. (2020). Modeling Nitrate Export from a Mesoscale Catchment Using StorAge Selection Functions. Water Resources Research, 56, e2020WR028490
How to cite: Nguyen, T., Sarrazin, F., Lutz, S. R., Kumar, R., Musolff, A., and Fleckenstein, J. H.: Towards Application of StorAge Selection Functions in Large-Scale Catchments with Heterogeneous Travel Times and Subsurface Reactivity, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7812, https://doi.org/10.5194/egusphere-egu21-7812, 2021.
The use of fertilizers and pesticides in agriculture activity is a worldwide extended practice since decades for improving crops performance, which can cause, however, with excessive dosage rates, aquifers’ pollution and water quality problems, like the study case hereby presented of Mar Menor sea-lake waterbody and “Campo de Cartagena” aquifer, in the southern coast of Spain.
Due the agricultural practices, the Campo de Cartagena aquifer presents in this moment high values of nitrate, around 150 mgNO3 / l, appearing also these high values of nitrogen in soil in this area. This situation produces a great contribution of nitrogen to the Mar Menor lake, by two mainly processes, firstly, continuously through groundwater returns to the waterbody’s surface and secondly, through the precipitation events when a large amount of nitrogen is washed from soil by the rainfall. Finally, the large amount of nitrogen incomes to the Mar Menor sea lake contributes to deteriorate the status of this waterbody and also promotes the eutrophication processes that have been taking place during last years.
A large watershed scale nitrates’ transport simulation model, Patrical Model (Perez-Martín et al., 2016), is used to estimate the measures to recovery the “Campo de Cartagena” aquifer. The model establishes, mathematically, the relationship between nitrogen application, nitrogen surplus (excess), and nitrate concentration in groundwater and surface waterbodies.
Model results show that it is necessary to reduce around 80% of the current nitrogen surplus in the “Campo de Cartagena” aquifer to recovery the good status in the aquifer. This reduction of nitrogen surplus can be obtained by reducing the fertilizers dosage and consequently the nitrates contribution, with a maximum dose of nitrogen applied by farmers of 170 kgN /ha. Applying this measure could reduce significantly the nitrogen retained in soil in 1-2 years, so the nitrogen contribution during rainfall events also could be reduced significantly. Nitrogen levels in groundwater will gradually decrease in the following years, reaching values around 50 mgNO3 / l in 7-9 years after the application of these measures.
How to cite: Gómez Martínez, G., Pérez-Martín, M. Á., and Estrela Segrelles, C. E.: “Application of Hydrological Simulation models to solve pollution impacts in the water management decision-making processes. Measures for the recovery of Mar Menor sea lake and “Campo de Cartagena” aquifer (Spain)”, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1605, https://doi.org/10.5194/egusphere-egu21-1605, 2021.
Farm ponds, which are sometimes numerous and widely distributed in agricultural regions, have faced widespread degradation in recent decades. Although relevant conservation strategies have gradually increased, detailed assessments on their roles in regional biogeochemistry and ecology are lacking. We concluded that farm ponds provided hydrologic, biogeochemical, and socioeconomic benefits to southern China for thousands of years, but they are facing contemporary threats and management challenges, including (1) inadequate planning in terms of construction and conservation regulations; (2) rural nonpoint source and mini-point source pollution; (3) climate change-induced abnormalities in the hydroperiod and disturbance to wildlife; (4) invasive species; and (5) inadequate social and political capacity to consider ecological conservation. As farm ponds function as wetland complexes that are embedded within or integral to larger ecosystems, we recommend multi-disciplinary efforts over scales ranging from within-pond to regional for their assessment and conservation.
Excessive nitrogen (N) discharge from agriculture is a major factor of widespread problems in aquatic ecosystems. Knowledge of spatiotemporal patterns and source attribution of N pollution in these small, scattered ponds is a critical first step for nutrient management and ecosystem health in low-order agricultural watersheds. We applied the process-based HSPF model for ponds, ditches, and downstream waters in a 4.8 km2 test watershed in southern China. The results exhibited distinctive spatial-seasonal variations with an overall seriousness rank for the three indicators: total nitrogen (TN) > nitrate/nitrite nitrogen (NOx--N) > ammonia nitrogen (NH3-N). TN pollution was severe for the entire watershed, while NOx--N pollution was significant for ponds and ditches far from the village, and the NH3-N concentrations were acceptable except for the ponds near the village in summer. Although food and cash crop production accounted for the largest source of N loads, we discovered that mini-point pollution sources, including animal feeding operations, rural residential sewage, and waste, together contributed as high as 47% of the TN and NH3-N loads in ponds and ditches.
Our synthetic analysis and process-based modeling studies focused on farm ponds in an agriculturally dominated developing country (China), but similar small, scattered wetlands and their degradation trends are observed worldwide (e.g., vernal pools and prairie potholes in North American, farm ponds in Western and Central Europe, and chain of natural pond system in Australia). Nature-based solutions are becoming increasingly recognized as important for addressing the complex challenges in hydrology, ecology, and biodiversity under anthropogenic and climatic pressures. Apart from proposed conservation policies, including public awareness building, top-down regulations and bottom-up engagement, and sustainable management and utilization, we are also trying techniques that involve interconnected smart sensors and integrated modeling methods to better understand pond hydrological processes. We believe that such solutions can provide a basis for the numerical assessments on their ecosystem services and associated conservation cost analyses.
How to cite: Chen, W., Nover, D., Thorslund, J., Jarsjö, J., Yen, H., Luo, P., and He, B.: Exploring farm pond dynamics in low-order agricultural watersheds: A synthetic analysis and process-based modeling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-215, https://doi.org/10.5194/egusphere-egu21-215, 2020.
Acidic deposition derived from human activities causes negative effects on nutrient cycling in forest ecosystems. However, nutrient cycling of forest ecosystems is expected to recover because the emission of pollutants is generally decreasing in recent years. However, the extent of recovery would be differed between forest ecosystems in different climatic conditions. The study investigated changes of stream water chemistry of forest ecosystems in Shimanto River Basin in southwestern Japan. The 92 samples of stream water were collected from forested watersheds in summer of 1999 and 2020 and chemistry of the samples was compared. The mean pH value of the stream water in 2020 (7.60) was higher than that in 1999 (7.29). The mean concentration of potassium ion increased by 2.1% whereas that of sodium, calcium, and magnesium ions decreased by 2.5%, 10.3%, and 8.6%, respectively. The mean concentration of chloride, nitrate and sulfate ions decreased by 24.8%, 9.4% and 22.5%, respectively whereas that of bicarbonate increased by 0.7%. The relationship between mean annual temperature and the ratio of ion concentration in 2020 to that in 1999 was analyzed. The ratio of calcium and manganese concentration was lower at warmer sites. The ratio of sulfate concentration was lower at warmer sites whereas the ratio of chloride concentration was not related with mean annual temperature. The results suggest that the runoff of sulfate and chloride from forest ecosystems in the Shimanto River Basin have decreased presumably due to the reduced input of these elements and that the residence time of sulfur in forest ecosystems is shorter in warmer sites as indicated by the greater reduction of sulfate concentration.
How to cite: Inagaki, Y., Inagaki, M., Shichi, K., Yoshinaga, S., Yamada, T., Miura, S., Shinomiya, Y., and Fujii, K.: Spatial variation of stream water chemistry in the Shimanto River Basin in southwestern Japan: A comparison of results in 1999 and 2020, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9149, https://doi.org/10.5194/egusphere-egu21-9149, 2021.
The prediction of water quality is an efficient way for managing water resources and protecting ecosystems by providing an early warning against water quality deterioration. So far, the classical approach is to predict water quality by the utilization of complex process-based water quality models. However, these models are not easy to set up and require comprehensive input data. The local characteristics, detailed process understandings and eventually data from land users such as farmers are needed, to build up a valid model structure. Such constraints can end up in wrong scientific conclusions ranging from false alarms to unpredicted environmental pollution in practical water monitoring application. Long short-term memory (LSTM) algorithms are known to be able to overcome some of the typical constraints in hydrological model applications. However, their performance in water quality prediction has rarely been explored. In this study, we investigate the ability of a LSTM model to predict the complex, nonlinear behavior of water quality parameters in the Schwingbach Environmental Observatory (SEO), Germany. We predict weekly nitrogen-nitrate concentrations, weekly stable isotopes of water concentrations (δ18O) and daily water temperature in six stream‑ and six groundwater sources with different landuse and hillslope conditions. We use meteorological forcing data and catchment attributes as input variables. To ensure an efficient model performance, we employ a Bayesian optimization approach to optimize the hyperparameters of the LSTM. The model performance is evaluated by the Root Mean Squared Error (RMSE). Our LSTM is robust in capturing the dynamics of the water quality parameters over time. The RMSE for the LSTM performance ranges from 0.27 to 3.38 mg/l, from 0.069 to 0.27 ‰ and from 1.3 to 2.1 °C for nitrogen‑nitrate, δ18O and water temperature, respectively. We compare the RMSE with statistical parameters of data. Results confirm that the LSTM is a promising tool for early risk assessment of water quality, particularly in view that only a minimal set of catchment information is needed to gain robust results.
How to cite: Sahraei, A., Breuer, L., Kraft, P., and Houska, T.: Deep learning for water quality prediction: the application of LSTM model to predict water quality in catchment scale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8756, https://doi.org/10.5194/egusphere-egu21-8756, 2021.
Nitrogen (N) is one of the major pollutants to aquatic ecosystems. One of the key steps for efficient N reduction management at watershed scale is accurate quantification of N load. High frequency monitoring of stream water N concentration has not been common, and this has largely been the limiting factor for accurate estimation of N loading worldwide. N loads have often been estimated from sparse measurements. The objective of this study was to investigate the performance of the physical-based SWAT (Soil and Water Assessment Tool) model and three commonly used regression methods, namely LI (linear interpolation), WRTDS (Weighted Regression on Time, Discharge, and Season), and the LOADEST (LOAD ESTimator) on estimating nitrate load from sparse measurements through a case study in an agricultural watershed in eastern Canada. The range of daily nitrate load of SWAT and LOADEST was 0.05-1.29 and 0.14 - 1.35 t day-1, compared with 0.13 - 13.08 t day-1 and 0.15 - 16.75 t day-1 for LI and WRTDS, respectively. Mean daily nitrate load estimated by the four methods followed the order: WRTDS > LI > LOADEST > SWAT. The large discrepancies were mainly occurred during the non-growing season during which there was observation data available. As regression methods use concentration data from dry seasons to estimate the concentrations of wet seasons, there is a strong likelihood of overestimation of nitrate load for wet seasons. The results of this study shed new light on nitrate load estimation under conditions of different data availability. Under situations of limited water quality measurement, policy makers or researchers are likely to benefit from using hydrological models such as SWAT for constituent load estimation. However, the selection of the most appropriate method for load estimation should be seen as a dynamic process, and case by case evaluation is required especially when only sparsely measured data is available. As agri-environmental water quality issues become more pressing, it is critical that data collection strategies that encompass seasonal variation in streamflow and nitrate concentration be employed in regions like Atlantic Canada in the future.
How to cite: Liang, K., Jiang, Y., and Meng, F.-R.: Large discrepancies on nitrate loading estimates from sparse measurements by SWAT and statistical models at catchment scale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10458, https://doi.org/10.5194/egusphere-egu21-10458, 2021.
Understanding the impacts of land use changes (LUCC) on the dynamics of water quantity and quality is necessary to identify suitable mitigation measures that are needed for sustainable watershed management. Lowland catchments are characterized by a strong interaction of streamflow and near-surface groundwater that intensifies the risk of nutrient pollution. In this study, a hydrologic model (Soil and Water Assessment Tool, SWAT) and partial least squares regression (PLSR) were used to quantify the impacts of different land use types on the variations in actual evapotranspiration (ET), surface runoff (SQ), base flow (BF), and water yield (WYLD) as well as on sediment (SED), total phosphorus (TP), and total nitrogen (TN). To this end, the model was calibrated and validated with daily streamflow data (30 years) and daily sediment and nutrient data from measurement campaigns (3 years in total). Three model runs over thirty years were performed using the different land use maps of 1987, 2010, and 2019, respectively. Land use changes between those years were used to explain the modelled changes in water quantity and quality on the subbasin scale applying PLSR. SWAT achieved a good performance for streamflow (calibration: NSE=0.8, PBIAS=5.5%; validation: NSE=0.78, PBIAS=5.1%) and for TN (calibration: NSE=0.65, PBIAS= -11.3%; validation: NSE=0.87, PBIAS=2.7%) and an acceptable performance for sediment and TP (calibration: NSE=0.49-0.53, PBIAS=25.8% -29.7%; validation: 0.51-0.7, PBIAS= -23.9% - -8.7%) in Stör catchment. The variations in ET, SQ, BF, WYLD, SED, TP, and TN could be explained to an extent of 67%-88% by changes in the area, shape, dominance, and aggregation of individual land use types. They were largely correlated with the major LUCC in the study area i.e. a decrease of arable land, and a respective increase of pasture and settlement. The change in the percentage of arable land affected the dynamics of SED, TP, TN and BF, indicated by a Variable Influence on Projection (VIP) > 1.2 and largest absolute regression coefficients (RCs: 0.45-0.72 for SED, TP, TN; -0.84 for BF). The change in pasture area affected ET, SED, TP, and TN, as indicated by VIPs >1. The change in settlement percentage had VIP up to 1.62 for SQ and was positively and significantly influenced it (RC: 1.28). PLSR helped to identify the key contributions from individual land use changes on water quantity and quality dynamics. These provide a quantitative basis for targeting most influential land use changes to mitigate impacts on water quality in the future.
How to cite: Lei, C., Wagner, P., and Fohrer, N.: Influences of land use changes on the dynamics of water quantity and quality in the German lowland catchment of the Stör, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6777, https://doi.org/10.5194/egusphere-egu21-6777, 2021.
Freshwater ecosystems provide many benefits to a variety of species but, unfortunately, human-caused environmental issues are undermining their ability to provide key functions and services. Changes in climate and land use, for instance, impact the habitat suitability for freshwater organisms by affecting water quantity and quality. Nutrients, pesticides, heavy metals and other contaminants which are released to the environment as a result of anthropogenic activities have the potential to degrade the environment and damage freshwater communities. Hence, the present research activity aims to investigate aquatic ecosystem responses to environmental deterioration using a case study of Clariano River, Spain. The Soil and Water Assessment Tool (SWAT) is used as an eco-hydrological tool to model discharge, sediment and nutrients, and to predict the biological status in Clariano River under different scenarios. As the diversity and presence of species represent the quality of ecosystem, this study focuses on macroinvertebrates as biological indicators of stream health. The SUFI-2 algorithm in the SWAT-CUP program is used for the calibration, validation, sensitivity and uncertainty analysis of the SWAT model. The results from the calibrated model are then coupled to regression equations between measured nutrient concentrations and values of several macrobenthic metrics in six sampling sites along the Clariano River. The coupling of these regression equations with concentrations simulated with SWAT for different scenarios allows to improve the understanding of the relations between environmental changes in watersheds, nutrient concentrations, and the biologic status of stream water.
Keywords: water quality, macroinvertebrates, environmental degradation, eco-hydrological modelling, Clariano River
How to cite: Vagheei, H., Vezza, P., Palau-Salvador, G., and Boano, F.: Predicting Responses of Chemical and Biological Water Quality to Human-induced Environmental Changes: The Case Study of Clariano River, Spain, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4215, https://doi.org/10.5194/egusphere-egu21-4215, 2021.
Northern Ireland has been somewhat overlooked in terms of water quality modelling in the past. Many of its catchments have consistently failed to meet Water Framework Directive targets especially due to high levels of dissolved nutrients and poor ecological status. A catchment based modelling study to address this issue has not been undertaken here previously and the approach described here uses two water quality models to achieve this aim. The objectives of the modelling were firstly to identify the total load reductions (in terms of Phosphorus (P)) required to reduce in-stream loadings sufficiently for concentrations of soluble reactive P (SRP) to be reduced to achieve the WFD “Good” status levels, and secondly to split these loadings into diffuse and point components. The third objective was to identify the most likely flow pathways for the transport of the diffuse component of P to the watercourses particularly for the agricultural (mostly intensive grassland farming) land use which dominates in almost all NI catchments.
The first model applied is the Source Load Apportionment Model (SLAM) developed by the Irish EPA. This model provides a large-scale assessment of the point and diffuse load components across catchments where multiple pressures are occurring. The second model us the Catchment Runoff Flux Assessment Tool (CRAFT) which is able to back-calculate nutrient loads associated with three major flow pathways. SLAM is a static model which uses averaged loadings from diffuse agriculture and non-agricultural land uses, and point sources (where information can be obtained from various sources) to calculate N and P exports. For P, the agricultural diffuse load component uses an enhanced version of the export coefficient approach based on combining the sources of P from applied nutrients (slurry and fertiliser) and soil P. A modelling tool allows the user to evaluate load reduction scenarios where one or several components of P (both point and diffuse) are adjusted downwards to achieve the catchment’s required load reduction. The CRAFT model works on a dynamic (daily) modelling scale and has simulated sub-catchments where the SLAM model has identified the need for significant load reductions. It identifies the different reductions (P export) that are required for each flow pathway, which will then inform on the type of additional measures (e.g. sediment traps, riparian buffer strips and wetlands) that may also be required.
The initial aim of this study is to complete a pilot application to the trans-border (UK and ROI) Blackwater catchment (1360 km2). Through a review of alternative modelling options for the whole area of NI, an assessment of whether this approach is suitable for application to the entire territory can be made.
How to cite: Adams, R. and Doody, D.: Reducing Nutrient Loads in Northern Irish Catchments: A Modelling Approach Based on Load Apportionment and Flow Pathway Identification, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15216, https://doi.org/10.5194/egusphere-egu21-15216, 2021.
The third largest European river Dnipro covers 48% of Ukraine’s territory. An analysis of the main anthropogenic pressures in the Dnipro Basin was first performed according to the requirements of EU WFD.
Surface water pollution by organic substances and nutrients is principally attributed with point sources, among which the municipal wastewaters play the dominant role. The main load by organic substances and nutrients is caused by the wastewater discharges of big cities with Population Equivalent >100 000; 89% of such cities are located within the sub-basins of Middle Dnipro and Lower Dnipro.
Point sources form 33% of nitrogen and 61% of phosphorus loads in the Dnipro Basin. Diffuse sources related to agricultural production cause incoming of 29% of nitrogen and 36% of phosphorus. Phosphorus is transported to the water bodies mainly with erosion particles.
Natural conditions in the River Basin are one of the reasons of nitrogen load significant share (33%). Humus compounds and nitrogen compounds enter into water bodies due to the high bogginess of the Dnipro Basin upper part, especially the Prypiaty Basin. This leads to winter and summer anoxia in the rivers and upper reservoirs and creates prerequisites for eutrophication of the Dnipro cascade reservoirs. Rivers of the Prypiaty sub-basin, Upper Dnipro, and Desna sub-basins are extremely vulnerable to anthropogenic pollution by nutrients and organic substances that generates the increased background of organic compounds and nitrogen in the Dnipro reservoirs cascade.
The load of the Dnipro Basin surface water by hazardous substances (especially synthetic) still remains insufficiently studied. Currently, information is only available regarding load by heavy metals included to the list of priority substances and some other ones. Water pollution by metals is noted mostly in the Lower Dnipro sub-basin where the most of the metallurgical enterprises are located.
The high application of pesticides (> 3 kg/ha) in 4 administrative Rayons leads to the appearance of risk conditions for pollution of xenobiotics in 50 surface water bodies (SWBs).
The Dnipro reservoirs cascade serves as a powerful geochemical barrier causing heavy metals and pesticides deposition in bottom sediments. The highest pollution by metals is noted in the sediments of the Dnipro reservoirs that receive the metallurgy enterprises wastewaters. Probability of significant secondary remobilization is foremost noted for Cadmium. Organochloride pesticides content in the bottom sediments is 2 to 5 times lower than maximal allowable concentration in soil.
Water abstraction volume is around 22% of the annual flow of 95% probability. The natural flow of the Dnipro is regulated by 6 large reservoirs. Besides, there are 1072 dams and other cross-sectional artificial installations. Natural morphology changes are observed in a large number of rivers within the Dnipro Basin.
It was found that 56% of the Dnipro Basin SWBs are at risk of failing the “good” ecological status.
Hydromorphological alterations cause the main anthropogenic pressure in the Dnipro Basin (concerning 45% of the SWBs). Risks from diffuse sources and point sources are observed in 23% and 5% of SWBs, respectively.
How to cite: Osadcha, N., Nabyvanets, Y., Osadchyi, V., Ukhan, O., Osypov, V., Luzovitska, Y., Klebanov, D., and Biletska, S.: Pressures and impact analysis in the Dnipro river basin within Ukraine, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6493, https://doi.org/10.5194/egusphere-egu21-6493, 2021.
The Moskva River catchment is a complex system consisting of a network of rivers affected by a wide variety of land- and water-use factors that create unique spatial and temporal patterns of their water quality. Major sources of anthropogenic impact on the Moskva River and its tributaries include multiple flow regulation structures on streams, direct pollution from municipal sewage and industrial wastewaters of Moscow megacity and smaller towns, runoff generated in agricultural areas and within multiple landfills located on the watershed, and many more. Only a short upstream section of the Moskva River remains relatively unchanged in terms of water runoff and geochemistry.
In 2019, we began a pioneering study focusing on collecting detailed field data on geochemistry of water, suspended matter and bottom sediments of the Moskva River and its major tributaries, including concentrations of nutrients, potentially toxic elements (PTEs), polyaromatic hydrocarbons and total petroleum hydrocarbons (TPH). The main purpose of this project is to obtain a holistic picture of material fluxes within the river system combined with an inventory of natural and anthropogenic factors controlling them.
Our results indicate gradual increase of total dissolved solids, and content of nutrients and some PTEs (i.e., Cu) in water along the course of Moskva River. It can be linked to non-point pollution, as well as drastic changes occurring downstream Moscow and other urban areas caused by direct pollution. Massive increase of chloride, sulfate, sodium, mineral phosphorus, nitrogen, Mo and Sr concentrations in water is observed downstream outlets of Moscow wastewater treatment plants, which is characteristic of insufficiently treated urban sewage. Concentrations of nutrients and PTEs only slightly decrease downstream the city, remaining at levels often exceeding environmental guidelines up to the river’s mouth, whereas increased concentrations of other pollutants, such as TPH, are more closely limited to urban areas and fade more quickly with distance from the source.
Nutrient pollution of the Moskva River, as well as concentrations of some PTEs (i.e., Sb, Pb), steadily increased during summer low-flow period, when low dilution capacity limits biochemical self-purification. On the other hand, Mn, Co and Zn reached maximum concentrations during the spring flood due to their accumulation in city road dust and subsequent concentrated inflow with snowmelt runoff.
The Moskva River tributaries that flow within close proximity to the metropolitan area were revealed to have significantly higher pollution levels than the Moskva River itself, indicating stronger anthropogenic stress.
Balance calculations performed on our database showed that during the flood the Mozhaysk Reservoir – the single large reservoir on the Moskva River – retains huge volumes of major elements and PTE, at times even greater than their subsequent input from urban areas downstream from the dam. It proves crucial role of the reservoir’s retention capacity in the Moskva River’s geochemical balance formation.
Authors acknowledge Russian Geographical Society (project 28/2019-I), Russian Science Foundation (project 19-77-30004) and Russian Foundation for Basic Research (project 19-05-50109) for financial support.
How to cite: Shinkareva, G., Erina, O., Tereshina, M., Sokolov, D., and Lychagin, M.: Anthropogenic impact on behavior of nutrients and potentially toxic elements in the Moskva River water, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12864, https://doi.org/10.5194/egusphere-egu21-12864, 2021.
The ecohydrological models AnnAGNPS and ZIN-AgriTra are compared regarding their performance in a small watershed. Both models are presently applied for the transport simulation of plant protection products (PPP) from an agricultural area to a small stream to quantify the impact of reduction measures as part of a comprehensive study.
The spatial discretization of AnnAGNPS is based on hydrologic response units with homogeneous characteristics (land use, slope and soil type). For the continuous simulations daily time steps are used, only soil moisture is simulated using hourly time steps. The underlying equations are physically based, mostly simple calculation methods are used.
ZIN-AgriTra operates on grid cells, which allows a more accurate representation of the flow paths. The model is physically based, e. g. for the unsaturated soil zone the Richards equation is used. This requires detailed soil properties for its parameterization and leads to small computational time steps (minutes to hours) to fulfil the mass balance requirements. The detailed spatial and temporal scales, as well as the complex equations, result in a long computation time in comparison to AnnAGNPS.
AnnAGNPS and ZIN-AgriTra are compared regarding their accuracy in the water balance and the mass balance simulation. For the mass balance different constituents as e. g. sediment, phosphorus and selected pesticides are simulated.
The study area is located in southern Lower Saxony, Germany. The catchment area has a size of 5 km2. The investigated stream (Lahbach) flows along agriculturally cultivated land. The relatively high slopes and the fine soil texture lead to a high fraction of generated discharge (as surface runoff, erosion and rapid interflow) from precipitation events. In the ongoing study the catchment was intensively monitored regarding meteorological and hydrological data. In addition, an event-based monitoring campaign was performed to quantify the reaction of the Lahbach during precipitation events, particularly the change in constituent concentrations. Due to the close cooperation with a local farmer, management measures are known very precisely.
The different temporal resolution of the input data and the time step of output parameters lead to differences in the agreement between measured and simulated time series among the two models. Overall, ZIN-AgriTra led to a more accurate reproduction of the rainfall-runoff events.
How to cite: Schwenkel, J., Zeunert, S., Le, H., Müller-Thomy, H., Schöniger, M., and Meon, G.: Comparison of the ecohydrological models AnnAGNPS and ZIN-AgriTra for a small agricultural catchment , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12233, https://doi.org/10.5194/egusphere-egu21-12233, 2021.
Application of Sentinel-2A/B satellites to retrieve turbidity in the Guadalquivir estuary (Southern Spain)
Due to climate change, contamination, and diverse anthropogenic effects, water quality monitoring is intensifying its importance nowadays. Remote sensing techniques are becoming an important tool, in parallel with fieldwork, for supporting the cost-effective accomplishment of water quality mapping and management. In the recent years, Sentinel-2A/B twin satellites of the European Commission Earth Observation Copernicus programme emerged as a promising way to monitor complex coastal waters with higher spatial, spectral and temporal resolution. However, atmospheric and sunglint correction for the Sentinel-2 data over the coastal and inland waters is one of the major challenges in terms of accurate water quality retrieval. This study aimed at evaluating the ACOLITE atmospheric correction processor in order to develop a regional turbidity model for the Guadalquivir estuary (southern Spain) and its adjacent coastal region using Sentinel-2 imagery at a 10 m spatial resolution. Two settings for the atmospheric correction algorithm within the ACOLITE software were applied: the standard dark spectrum fitting (DSF) and the DSF with an additional option for sunglint correction. Turbidity field data were collected for calibration/validation purposes from the monthly Guadalquivir Estuary-LTER programme by Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA) using a YSI-EXO2 multiparametric sonde for the period 2017-2020 at 2 fixed stations (Bonanza and Tarfia) sampling 4 different water masses along the estuary salinity gradient. Several regional models were evaluated using the red band (665 nm) and the red-edge bands (i.e. 704, 740, 783 nm) of the Sentinel-2 satellites. The results revealed that DSF with glint correction performs better than without glint correction, especially for this region where sunglint is a major concern during summer, affecting most of the satellite scenes. This study demonstrates the invaluable potential of the Sentinel-2A/B mission to monitor complex coastal waters even though they were not designed for aquatic remote sensing applications. This improved knowledge will be a helpful guideline and tool for the coastal managers, policy-makers, stakeholders and the scientific community for ensuring sustainable ecosystem-based coastal resource management under a global climate change scenario.
How to cite: Chowdhury, M., Vilas, C., VanBergeijk, S., Navarro, G., Laiz, I., and Caballero, I.: Application of Sentinel-2A/B satellites to retrieve turbidity in the Guadalquivir estuary (Southern Spain), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15445, https://doi.org/10.5194/egusphere-egu21-15445, 2021.
The multispectral mission of Sentinel-2 enables reliable, affordable and continuous environmental monitoring systems in fields like agriculture, biodiversity, environmental hazards and surface water. Several studies have proven that main water quality parameters like total suspended solids (TSS) and chlorophyll (Chl-a) can be estimated from multispectral data using different methods and algorithms. However, independently of the specific approach, these algorithms are selected and optimized to work primarily for one of the main water types i.e. open water, coastal water or inland water. This is also shown by the fact that there is not a single universal algorithm, which can be applied to all water types with consistent and reliable performance at the same time.
Ca Mau peninsula is a spacious area located in the southern part of the Mekong Delta, with an area of around 1.6 million hectares. This area has high growth rates of agricultural and aquaculture production, hence diverse water demands and water use types. In this study we use Sentinel-2 remote sensing data to monitor surface water quality using adaptive ML models to account for the different surface water types which occur in this area. Through using remote sensing data, we can provide a synoptic and sufficient view in spatial aspects about water quality parameters in the Ca Mau peninsula. Adapting the ML model will address the bio-optical model for a mixed water scenario.
The study is based on Sentinel-2 satellite images acquired in 2019 and 2020, supplemented by field data, i.e. hyperspectral measurements using close range observations, in-situ measurements and water samples, with the aim to collect a comprehensive reference data set as biophysical parameters are closely connected with spectral parameters at close range as well as at high spectral resolution. Therefore, surface hyperspectral measurement has been used to simulate Sentinel 2 multispectral image data at the respective bands.
We automatically assign the water type classes to observed surface water by integrating GIS data and remote sensing as the pre-processing step. For each class, the ML models are trained based on the experimental measurements with the multispectral and the simulated multispectral images on the respective water types. We devote special attention to water type boundaries to provide a smooth transition of estimated parameters.
The outputs of this model are surface water quality distribution maps with turbidity, TSS, and Chl-a parameters for all areas in Ca Mau peninsula, independent of the actual water type. Through the acceptable accuracy of model testing, the consolidation model will contribute water quality parameters that are crucial and meaningful to the planning and use of water for domestic use and production, besides, it also supports the decision-making of sustainable water use.
How to cite: Vu Huu, L., Schenk, A., and Hinz, S.: Adaptive ML-Models for analysis of TSS, Chlorophyll-a, in mixed water type scenarios in Ca Mau peninsula, Vietnam, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15059, https://doi.org/10.5194/egusphere-egu21-15059, 2021.
It is probably hard to overestimate the significance of the River Ganges for its spiritual, cultural and religious importance. As the worlds’ most populated river basin and a major water resource for the 400 million people inhabiting its catchment, the Ganges represents one of the most complex and stressed river systems globally. This makes the understanding and management of its water quality an act of humanitarian and geopolitical relevance. Water quality along the Ganges is critically impacted by multiple stressors, including agricultural, industrial and domestic pollution inputs, a lack and failure of water and sanitation infrastructure, increasing water demands in areas of intense population growth and migration, as well as the severe implications of land use and climate change. Some aspects of water pollution are readily visualised as the river network evolves, whilst others contribute to an invisible water crisis (Worldbank, 2019) that affects the life and health of hundreds of millions of people.
We report the findings of a large collaborative study to monitor the evolution of water pollution along the 2500 km length of the Ganges river and its major tributaries that was carried out over a six-week period in Nov/Dec 2019 by three teams of more than 30 international researchers from 10 institutions. Surface water and sediment were sampled from more than 80 locations along the river and analysed for organic contaminants, nutrients, metals, pathogen indicators, microbial activity and diversity as well as microplastics, integrating in-situ fluorescence and UV absorbance optical sensor technologies with laboratory sample preparation and analyses. Water and sediment samples were analysed to identify the co-existence of pollution hotspots, quantify their spatial footprint and identify potential source areas, dilution, connectivity and thus, derive understanding of the interactions between proximal and distal of sources solute and particulate pollutants.
Our results reveal the co-existence of distinct pollution hotspots for several contaminants that can be linked to population density and land use in the proximity of sampling sites as well as the contributing catchment area. While some pollution hotspots were characterised by increased concentrations of most contaminant groups, several hotspots of specific pollutants (e.g., microplastics) were identified that could be linked to specific cultural and religious activities. Interestingly, the downstream footprint of specific pollution hotspots from contamination sources along the main stem of the Ganges or through major tributaries varied between contaminants, with generally no significant downstream accumulation emerging in water pollution levels, bearing significant implications for the spatial reach and legacy of pollution hotspots. Furthermore, the comparison of the downstream evolution of multi-pollution profiles between surface water and sediment samples support interpretations of the role of in-stream fate and transport processes in comparison to patterns of pollution source zone activations across the channel. In reporting the development of this multi-dimensional pollution dataset, we intend to stimulate a discussion on the usefulness of large river network surveys to better understand the relative contributions, footprints and impacts of variable pollution sources and how this information can be used for integrated approaches in water resources and pollution management.
How to cite: Krause, S. and the Team SAPTANADI: Taking the pulse of Mother Ganga - Revealing the visible and invisible water pollution crisis along the Ganges River, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10801, https://doi.org/10.5194/egusphere-egu21-10801, 2021.
In a basin-wide survey of the River Ganga and key tributaries, from the Himalayan source to the Bay of Bengal in India, we aim to improve the conceptual understanding of downstream water quality trends along > 2000 km. Here we explore the spatial distribution of a suite of inorganic and organic chemicals, nutrients and wastewater indicators to determine the dominant geochemical process controls across the basin. Sampling was undertaken at 81 sites in the post-monsoon period of 2019. We use chemical signatures to identify likely sources, characterise potential higher-pollution zones and to determine the relative importance of regional versus localized controls on the observed water quality parameters, including in relation to contaminant type. The influence from key tributaries is determined. We seek to unravel the relative importance of mechanisms such as dilution, evaporation, water-rock interactions and anthropogenic inputs in controlling contaminant distribution. We assess the representativeness of river bank sampling in comparison to cross-river transects in select locations. We compare our data to historical records across previous annual cycles, noting differences in extent of agreement according to contaminant type. This coordinated, catchment-wide survey presents a much broader and more comprehensive dataset than typically reported, hence leading to substantially improved process understanding of dominant controls on contaminant distribution across the catchment. This work may have implications on informing future monitoring efforts and in identifying future remediation priorities.
Acknowledgements This research was supported by the NERC-DST Indo-UK Water Quality Programme (NE/R003386/1 and DST/TM/INDO-UK/2K17/55(C) & 55(G) to DP et al; NE/R003106/1 and DST/TM/INDO-UK/2K17/30 to DR et al.), NE/R000131/1 to Jenkins et al. and a Dame Kathleen Ollerenshaw Fellowship (LR).
How to cite: Richards, L. A. and the Team SAPTANADI: Water Quality across the River Ganga Basin in India: Trends, Dominant Geochemical Processes and Impacts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10642, https://doi.org/10.5194/egusphere-egu21-10642, 2021.
There is increasing interest in monitoring spatial variability in biogeochemical processes using field deployable sensors. Despite this, rigorous assessments of accuracy and optimal sensor configurations remain limited for such applications. We undertook a comprehensive field study, between November and December 2019 (post-monsoon), across diverse monitoring locations on the River Ganges and its tributaries in Northern India. At 81 sites, from the foothills of the Himalayas to the tidal limit at Kolkata, the following suite of routine sensor measurements were taken; dissolved oxygen (DO), electrical conductivity (EC), pH and turbidity. In addition “new” optical parameters were also measured; absorbance (190 – 360 nm) and tryptophan-like fluorescence (TLF). Parallel water samples were collected for laboratory determination of dissolved organic carbon (DOC), nitrogen species (NO3 and NH4), phosphorus fractions (SRP, TP, TDP), absorbance and fluorescence excitation emission matrices (EEMs). A series of predictive models for each laboratory derived nutrient parameter were developed based on partial least squares regression, lasso regression, and stepwise regression approaches. The predictive power of the best models (i.e. combination of sensors and model approach) were assessed using 10-fold cross validation. Residual patterns were inspected to help infer the environmental conditions under which in-situ sensors could be used reliably. The highest predictive power was apparent for NO3, DOC and SRP. This was apparent when considering models based on the routinely measured parameters (R2cv = 0.45 – 0.6; EC explained most variance) or when new optical parameters were included (R2cv = 0.6 - 0.8; absorbance <280 nm and TLF explained most variance). No suitable surrogate model could be derived for ammonium (R2cv = 0.3) or TDP/TP (R2cv both <0.4). For DOC, changes in DOM composition from upstream – downstream influenced model fit while the nitrate model appeared robust with no spatial pattern in the residuals identified. These findings highlight clear potential for optical sensors to improve our understanding of spatial variability in nutrient concentrations and inform future development of multi-parameter sensing sondes for rapid assessment of nutrient concentration. Further research is required to assess the transferability of field calibrations across seasonal and inter-annual timescales.
How to cite: Khamis, K. and the Team SAPTANADI: Using in-situ sensors to quantify spatial variability in nutrient concentrations across the Ganges river basin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11071, https://doi.org/10.5194/egusphere-egu21-11071, 2021.
High industrial discharge, excessive agricultural activities, untreated sewage disposal make the Kanpur region one of the most contaminated stretches of the Ganga river. This study analyses water quality for the combined future climate change and land use land cover scenarios for mid-century for a 238km long Kanpur stretch of Ganga river. Climate change projections from 21 General Circulation Models for the scenarios of RCP 4.5 and RCP 8.5 are considered and Land use Land Cover (LULC) projections are made with QGIS software. Streamflow and water temperature are modelled using the HEC-HMS model and a Water-Air temperature regression model, respectively. Water quality analysis is simulated using the QUAL2K model in terms of nine water quality parameters, dissolved oxygen, biochemical oxygen demand (BOD), ammonia nitrogen, nitrate nitrogen, total nitrogen, organic phosphorus, inorganic phosphorus, total phosphorus and faecal coliform. Climate change impact alone is projected to result in degraded water quality in the future. Combined climate change and LULC change may further degrade water quality, especially at the study area's critical locations. Our study will provide guidance to policymakers to safeguard the Ganga river from further pollution.
How to cite: Santy, S., Mujumdar, P., and Bala, G.: Combined impact of Climate change and Land use on water quality in the mid-21st century: A modelling study for a highly industrialized stretch of Ganga River, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-795, https://doi.org/10.5194/egusphere-egu21-795, 2021.
High-resolution water quality data obtained with in situ sensors and analysers coupled to flow discharge records can reveal critical information on hydrochemical and biogeochemical functioning of aquatic ecosystems. In this study we explore a rich high-resolution hydrochemical dataset to synthesise the impact of hydrological flushing and biogeochemical cycling on water quality in a 3rd order groundwater-fed stream draining an agricultural catchment dominated by grassland.Our results show that despite large storm to storm diversity in hydrochemical responses, storm event magnitude and timing have a critical role in controlling the type of mobilisation, flushing and cycling behaviour. These results can be used to evaluate pollution risks in streams and their effects on freshwater quality.
How to cite: Bieroza, M. and Heathwaite, A. L.: The interplay between hydrological flushing and biogeochemical cycling in a 3rd order stream, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8314, https://doi.org/10.5194/egusphere-egu21-8314, 2021.
Water quality in the rivers and tributaries of the Brantas catchment (about 12.000 km2; East Java, Indonesia), which is deteriorating due to various reasons, is measured by different agencies involved in water resource development and management. We discuss how different time series of water quality data from three local agencies in the Brantas basin (differing in specific parameters and measurement frequency) have been used to provide recommendations on the improvement of (using) the different measurement strategies (in policy recommendations). In general, monthly to quarterly data were available from 2009 until 2019 at 104 locations. Data were analyzed with Principal Component Analysis (PCA) to show which parameters vary significantly across the catchment. Preliminary results suggested how parameters were related, based on series of box plots of the PCA scores. This provided insights on the first order processes that control the physical-chemical status of the Brantas River, of each agency and for all the data sets combined. Applying Python and QGIS to separate the parameters and map the hot spots in terms of eigen functions allowed relating water levels with hot spots to estimate the fluctuations in the concentrations of different parameters in time and space. These data elaborations allow improving the different measurement campaigns, and to address specific policy questions related to water quality monitoring more efficiently.
How to cite: Willard, T., Pramana, R., Pande, S., van Breukelen, B., and Ertsen, M.: Analysis of water quality time series for improving the measurement strategy in the Brantas basin, Indonesia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13254, https://doi.org/10.5194/egusphere-egu21-13254, 2021.
Current understanding is fragmented of the environmental, economic, and social processes involved in water quality issues. The fragmentation is particularly evident for coastal water quality, impacted both by local land catchment and larger-scale marine pressures and impacts. Research and policy so far has primarily addressed coastal water quality issues from either a land-based or a sea-based perspective, which does not support integrated management of the coupled land-coast-sea systems affecting coastal waters. For example, mitigation measures for improving the severe Baltic Sea eutrophication have mostly focused on land-based drivers, and not yet managed to sufficiently improve coastal or marine water quality. The strong human dimension involved in these water quality issues also highlights a need for participatory approaches to facilitate knowledge integration and drive synergistic strategic planning for sustainable management of coastal water quality. Considering the Swedish water management district of Northern Baltic Proper, including its main Norrström drainage basin and surrounding coastal catchment areas and waters, this study has used a participatory approach to evaluate various land-sea water quality interactions and associated management measures. A causal loop diagram has been co-created with different stakeholder groups, following a problem-oriented system thinking approach. This has been further used in fuzzy-cognitive scenario analysis to assess integrated land-coast-sea system behavior under changing human pressures and hydro-climatic conditions. Results show that synergy of several catchment measures is needed to improve coastal water quality locally, while cross-system/sector cooperation is also needed among all contributing national catchments to mitigate coastal eutrophication at the scale of the whole Baltic Sea. Furthermore, large-scale hydro-climatic changes and long-lived nutrient legacy sources also need to be accounted for in water quality management strategies and measures. System dynamics modelling, based on co-created causal loop diagrams and fuzzy-cognitive scenario analysis like those developed in this study, can support further quantification and analysis of the impacts of various mitigation strategies and measures on regional water quality problems and their possible sustainable solutions.
How to cite: Seifollahi-Aghmiuni, S., Kalantari, Z., and Destouni, G.: Use of co-created causal loop diagrams and fuzzy-cognitive scenario analysis for water quality management, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5210, https://doi.org/10.5194/egusphere-egu21-5210, 2021.
The recognition of natural environment current functioning is possible throughout the determination of the energy and material balance (mainly water and dissolved substances) in various catchments. Dissolved matter circulation in the river catchment reflects natural hydrometeorological and hydrochemical processes as well as anthropogenic activity, which appears primarily as the supply of pollutants.
The research was conducted in 4 hydrological years (2016-2019) within the borders of a small urban catchment in the northern part of the city of Poznań (Poland), the main watercourse of which is the Różany Stream (Różany Strumień). The natural environment of the Różany Stream catchment is characterized by significant transformations due to human activity. The most important environmental problems include threats related to the pollution of surface waters and groundwater as a result of processes related to the functioning of an urban catchment.
The main aim of this work is to present the magnitude of pollution supply into the catchment and to determine the temporal variability of matter circulation in a small urban catchment in years with different pluvial conditions and therefore quantitatively changing atmospheric supply reaching the geoecosystem.
The magnitude of pollution supply to the catchment was determined on the basis of systematic, comprehensive measurements of the natural environment. The measurement system and the field research methodology refer to the methodological concept of the system functioning, as well as the assumptions of the European International Cooperative Programme on Integrated Monitoring of Air Pollution Effects on Ecosystems (ICP IM) and Integrated Monitoring of the Natural Environment in Poland (ZMSP) programs.
This work presents the results of measurements of several components of the natural environment, initially including meteorological conditions (mainly precipitation and air temperature). The next elements of the research concerned air pollution with sulphur dioxide and nitrogen dioxide as well as the chemical composition of precipitation, which is considered as an entry into the geoecosystem. Moreover, there are also presented the results of the physicochemical properties of surface waters (including levels, flows and chemical composition) and groundwater.
The quantitative and qualitative characteristics of the atmospheric supply to the geoecosystem, the water cycle in the catchment and the water runoff confirm the assumptions that the dissolved matter circulation is one of the most important indicators of changes in the natural environment in the moderate morphoclimatic zone.
How to cite: Major, M., Chudzińska, M., Majewski, M., and Stefaniak, M.: The magnitude of pollution supply in small urban catchment (Różany Stream) in Poznan, Poland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2977, https://doi.org/10.5194/egusphere-egu21-2977, 2021.
Efficient management of drinking water quality is critical for the water supply, so effective monitoring of supply and storage systems is a priority. This project aims to predict the presence of Taste and Odour (T&O) compounds in drinking water reservoirs, using molecular analyses and smart in-situ monitoring systems. The most common T&O compounds, Geosmin and 2-MIB, are secondary metabolites that can be produced in waterbodies by cyanobacteria and actinomycetes and impact drinking water taste and odour. Although there is no evidence of related health risks, they can be perceived by humans at very low concentrations (5-10 ng/L) and the treatment process to remove them from drinking water is costly. Early assessment of T&O risk is crucial, but currently requires time-consuming and costly sampling as well as laboratory analysis which prevents real-time monitoring and a timely management response.
Cyanobacterial species responsible for T&O production can be monitored with eDNA techniques and potentially provide an early warning of T&O episodes. Moreover, detection of the genes that are responsible for T&O production within the DNA of the freshwater community can help to speed up analysis. We show that qPCR methods can target the Geosmin synthase gene (geoA) and that this correlates significantly with Geosmin concentrations >15 ng/L. Alternatively, in-situ sensors that can be deployed remotely and transmit data, can provide real-time monitoring for early warning and potentially predictive capacity. Commercially available sensors do not currently exist for T&O compounds, but they do for many other water quality parameters. We consider the analytes that could be effective for T&O warning systems, using a Welsh reservoir as an exemplar case. Assessment of nutrient dynamics suggests N and P ratios are critical, hence we evaluate the sensors that are available for these compounds and associated environmental controls on their behaviour. We present recommendations for the design of an in-situ monitoring programme and introduce the planned tests that will evaluate it.
How to cite: Elfferich, I., Bagshaw, E., Perkins, R., Kille, P., Straiton, S., von Benzon, E., and Hooper, A. S.: In-situ techniques for monitoring drinking water quality, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12109, https://doi.org/10.5194/egusphere-egu21-12109, 2021.
Climate change is expected to have a significant impact on water resource systems, affecting both water quantity and quality. Among other probable impacts on raw water, the increase of sapid compounds such as geosmin and MIB (2-methylisoborneol) is one of the most challenging for urban water supply, as it alters both water taste and odour. Water managers and water utility companies need to anticipate events that increase the concentration of sapid compounds. Proper methods and tools are necessary to design adaptation strategies for future drinking water supply. In this research we analyse the drivers of MIB and geosmin growth, and study the consequence that an increasing occurrence and intensity of sapid compounds events will have on the required water treatments. The research has been developed for a Mediterranean reservoir used for water supply to the city of Valencia, the 3rd largest city in Spain.
The methodology applies a chain of models that integrates water quantity and quality processes in the same modelling framework. The modelling framework includes climate models, hydrological and water resource management models at the basin scale, and a reservoir management and quality models. Key environmental variables were selected using statistical analysis and expert criteria. Fuzzy logic systems were then applied to predict MIB and geosmin concentration under different time periods and climate change scenarios. Two representative concentration pathways (RCP 4.5 and 8.5) and two-time horizons (short term 2020-2040, and mid term 2041-2070) were considered.
Results show a significant increase of MIB and geosmin under climate change, especially during spring and summer. Concentrations of MIB would steadily rise until they double, reaching peaks of up to 0.50 µg/l by 2070 for all scenarios, while the World Health Organization maximum safe concentration is 0.01 µg/l. Geosmin concentrations also increase in all scenarios, reaching 0.05 µg/l by 2070. The microbiological data shows that benthic cyanobacteria Aphanocapsa delicatissima could be associated with MIB. Decreasing water storage, higher nitrate concentrations, and higher temperatures would stimulate MIB production, favoured by a likely increased of light penetration and resuspension of cyanobacteria present in the benthos of the reservoir. These environmental conditions appear mainly during drought events and force water treatment plants to change their processes to face the higher concentration of sapid compounds in raw water.
This study has been supported by the European Research Area for Climate Services programme (ER4CS) under the INNOVA project (Grant Agreement 690462) and the Agencia Estatal de Investigación (PCIN-2017-066), and by the ADAPTAMED project (RTI2018-101483-B-I00), funded by the Ministerio de Economia y Competitividad (MINECO) of Spain and with EU FEDER funds.
How to cite: Garcia-Prats, A., Llario, F., Macian-Sorribes, H., Rubio-Martin, A., Macian-Cervera, J., and Pulido-Velazquez, M.: A fuzzy logic approach for the prediction of sapid compounds concentration in a water supply system under climate change, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6370, https://doi.org/10.5194/egusphere-egu21-6370, 2021.
Estimation of dissolved organic carbon (DOC) runoff load in forested watershed is important for the assessment of the global carbon cycle as well as for the control of regional water environments. A few process-based models have been proposed to estimate the DOC load to water environments, which assume DOC source in topsoil and transport processes to the river, however, these models exhibited difficulties with the availability of input data and applicability to short time-scale rainfall-runoff processes in the Asian monsoon area. This study presents a new process-based model that consists of two separate systems for determining DOC load enforced by DOC Source Area (DSA) concept. For the runoff system, a semi-distributed hydrological modelling unit (‘modified-TOPMODEL’) was installed, by which surface and subsurface water flows, representing for DSA, were sequentially simulated. For the soil system, a wet-dry cycle was successfully simulated by an advection-diffusion and dissolution formulation as well as seasonal temperature effect. The model is first evaluated upstream (98ha) and downstream (1798ha) in the Mizugaki Watershed, Yamanashi, Japan and then applied for a Miuchi (203ha) watershed, Aichi, Japan during 2014 to 2018. The results of cumulative DOC load at baseflow and stormflow periods that the model performed well between the simulations and observations for both study sites. Considering the stormflow periods, from 25.2% to 32.0%, and 31.1% of high flows contributed to 50% of the total DOC load at Mizugaki and Miuchi watershed, respectively. Overall, the proposed model successfully simulated DOC load under different geochemical and hydrological condition by capturing the DSA variability.
How to cite: Ebata, K., Ichikawa, Y., Ishidaira, H., Matsumoto, Y., and Nishida, K.: Application of Dissolved Organic Carbon Runoff Model Considering Soil Infiltration and River Runoff Processes in Multiple Forested Watersheds, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6986, https://doi.org/10.5194/egusphere-egu21-6986, 2021.
In-situ monitoring of the temporal variation of solutes’ (nutrients and metals) concentrations as tracers can enhance knowledge of the hydrological and biogeochemical behavior of catchments. UV-Visible spectrometry represents a relatively inexpensive and easily used tool to explore how those concentrations vary in time at high temporal frequency. However, it is not yet clear which are the best calibration methods and which solutes can be modeled with this approach. In this investigation we explored the relationship between solutes’ concentrations and wavelength absorbance in the UV-Visible range to find the best calibration method and to identify solutes that could be effectively predicted. To this end, we installed a UV–Visible spectrometer probe in a high-altitude and organic-rich tropical Andean (Páramo) stream to record the wavelength absorbance at a 5-min temporal resolution from December 2017 to March 2019. Simultaneously, we sampled stream water at 4-hour frequency for subsequent determination of solutes via ICP-MS in the laboratory. Our results show that multivariate statistical methods outperformed simpler calibration strategies to model the solutes’ concentrations that could be effectively predicted using calibration and validation datasets. Eleven out of 21 evaluated solutes (Al, DOC, Ca, Cu, K, Mg, N, Na, Rb, Si and Sr) were successfully calibrated (NSE > 0.50). This finding suggests the possibility of calibrating solutes (i.e., metals) that had not previously been calibrated through UV-Visible spectrometry in the field. Interestingly, the calibration was feasible for all solutes that presented a statistically significant correlation with dissolved organic carbon. The findings of this research provide insights into the value of in-situ operation of spectrometers to monitor water quality in organic-rich streams (e.g., peatlands). This research contributes to our understanding of aquatic ecosystems alongside assessing catchment hydrological functioning and also can enhance the protection of human water supplies.
How to cite: Pesántez, J., Birkel, C., Mosquera, G., Peña, P., Arizaga, V., Mora, E., McDowell, W., and Crespo, P.: In-situ UV-Visible spectrometry as an alternative to determine solute concentrations at high temporal frequency in organic-rich stream waters, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13875, https://doi.org/10.5194/egusphere-egu21-13875, 2021.
Understanding of chemical weathering process involved in ionic elution helps in distinguishing the CO2 sequestration rate at the different micro-climatic setup of Himalayan catchments. In the present study, we have selected three glaciated basins from two different climatic zones of Western Himalayas (Lato and Phutse from the cold-arid zone of Ladakh and Chhota Shigri from the monsoon-arid zone of Himachal Pradesh, India) for determining various solute sources, CO2 sequestration rate and its control over melt-water quality. Solute sourcing models used in this work shows major cations like Ca2+ and Mg2+ are from crustal rock-weathering while Na+ and K+ sourced out from the sea-salt origin. However, major anions like SO42- (> 85%) were derived from the crustal origin and HCO3- mostly derived from atmospheric sources (39% to 45 %) in all catchments except HCO3- contribution from carbonation dissolution and silicate weathering is ~29% and ~16% for Ladakh catchments compared to ~9 % and ~29% in Chhota Shigri respectively. The solute model also reveals that the contribution of sulphate oxidative mediated carbonate dissolution (SOCD) in HCO3- flux is relatively higher in Chhota Shigri (~16%) than others (~9%). It is also observed that catchment like Chhota Shigri having a combined network of channelized and distributed drainage patterns with lower specific discharge, more glacierized area, low pH, high pCO2, Low molar ratio [Ca2+ + Mg2+]/[ Na+ + K+], high SMF (~ 0.4), low CO2 carbonate/CO2 silicate ratio (~1.3) show relatively more sulphide oxidative and silicate weathered products than other catchments. Conversely, presence of excess non-glaciated areas in Stok and Phutse having well-channelized subsurface discharge with high CO2 carbonate/CO2 silicate ratio (~10 to ~5) show enhanced carbonation via atmospheric CO2 (CAC) and carbonate dissolution with high annual CO2 sequestration. Thus, varying subglacial drainage system, specific discharge pattern and reactive rock-types with distinct hydro-micro-climatic set up alters the chemical weathering mechanism in these catchments and control meltwater quality.
How to cite: biswal, K., kumar, N., soheb, M., and al, R.: Evolution of chemical weathering processes and CO2 sequestration in the glaciated basins of Western Himalayas, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15282, https://doi.org/10.5194/egusphere-egu21-15282, 2021.
This study provides an identification and evaluation of the Potentially Toxic Element (PTE) (Co, Cu, Cd, Pb, Zn, and Ni) sources, speciation, mobility, distribution patterns, enrichment, and relationships along the Varatic Creek and its tributaries in the Baiut Mining Area, Romania. ICP-OES trace element concentrations were measured in collected samples. The geochemical characterization of the Varatec Creek revealed that the water contains high dissolved metals, high sulfate concentrations, and low pH values, dominated by Ca+2 and SO₄2- cation and anion in streamwater.
The calculated median concentrations were much higher than the average surface water concentrations in Europe (FOREGS) and decreased in the order of Zn(126.2μg.l-1)>>Cu(3.4μg.l-1)>Ni(1.6μg.l-1)=Cd(1.6μg.l-1)>Co(0.5μg.l-1)>Pb(0.3μg.l-1). The relative variability (MAD/median) follow the order Cd(90%)>Co(80%)=Zn(80%)>Cu(60%)=Ni(60%)>Pb(50%). The regional enrichment factor calculated as the Median/FOREGS(European level) follow the order Cd(156)>>Zn(47)>>Cu(3.8)>Pb(3)=Co(3)>Ni(0.9).
Element distribution, geochemical behavior and source, aqueous speciation modeling, and correlation analysis were performed to estimate the metal sorption to Fe-oxyhydroxide, Mn-oxyhydroxide, and sulfates. Detailed data analysis, reaction modelling and geochemical interpretation revealed two distinct groups of PTEs in the studied mining-impacted streamwater: Ni and Co seem to be associated with the geochemical background, while Cd, Pb, Zn, and Cu are originating from mining activities.
How to cite: Santanna, D., Aryampa, A., Jordan, G., Gheorghe, D., and Szabo, C.: Geochemical characteristics and water pollution by Potentially Toxic Elements at the Varatec creek, Baiut mining area, Romania., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13995, https://doi.org/10.5194/egusphere-egu21-13995, 2021.
The Usumacinta River is the most extensive tropical fluvial system in North America and the principal river in Mexico and the tenth of North America. Diverse and growing anthropogenic activities (land-use change, agriculture, and urban development) modify water quality. However, to separate natural (e.g., geology) from anthropic factors responsible for this system characteristics, we looked to evaluate geological environment’s influence on the river’s water quality.
Water and sediment samples were collected along the mainstem and principal tributaries in the rainy and the dry seasons (2017-2018). We analyzed the major ionic composition in water and metals in sediments. We applied inverse and evaporation models (PHREEQC code) to reveal the physicochemical reactions taking place in the river.
The inverse models in the middle basin in both seasons showed the influence of ion-exchange between Ca and K, dissolution of dolomite, and precipitation of kaolinite and calcite, whereas in the lower basin in the rainy season suggested the chemical composition is controlled by ion-exchange among Ca, Na and K, dissolution of dolomite, halite, plagioclase, and feldspar and precipitation of calcite, gypsum, and kaolinite. In addition, the evaporation models in the dry season in the lower basin demonstrate the dominant process taking place is the precipitation of calcite, dolomite, gypsum, halite, and kaolinite.
We found that Cr and Ni are the most abundant metals in the sediments along the river. The geological environment in the basin suggests that the volcanic rocks with felsic minerals could be the source of Ni, whereas sedimentary rocks such as shales and clays could be the source of Cr.
The use of geochemical models in river systems is of great relevance to understanding the presence of major ions concentrations in water and their seasonal and spatial variations, as well the physicochemical processes (i.e., ion-exchange, dissolution, precipitation, redox reactions, and so on) that allow associating or discard the presence of metals.
How to cite: Olea-Olea, S., Alcocer, J., and Oseguera, L. A.: Physicochemical processes in the main river of Mexico using geochemical models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14358, https://doi.org/10.5194/egusphere-egu21-14358, 2021.
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