Displays

In the last few decades, the world has witnessed rapid urbanization. One of the many complex problems which have come up with increased urbanization is that of rapid drainage of stormwater from the inhabited areas. Roorkee, a sub-tropical urban town in India, has shown rapid unplanned growth in the past. The three-year wet weather flow data has been collected for this research. In order to study of pollutants in runoff emanating from different urban source areas, simple sampling devices were fabricated to contain polyethylene and glass bottles and installed in various source areas to collect runoff samples. The collected samples were analyzed for pH, solids, nutrients, organics and metals. From the study the rainwater ions concentration was observed to follow the pattern Ca²⁺> HCO3^- > Cl^-> NO3^- > Na⁺ >Mg²⁺>SO4^2- >K⁺. Stormwater Runoff results indicated a significant enhancement in the concentration of most measured constituents over their rainfall levels. The values of the runoff coefficient varied between 0.05 and 0.62, with the high values displayed by the paved areas. This paper presents the findings of a study of characteristics of rainwater and runoff emanating from different sources areas and the stormwater flows in the drains.

How to cite: Vishwakarma, R. K., Joshi, H., and Goonetilleke, A.: Stormwater Quality Assessment through different sources in a sub-tropical town of India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21302, https://doi.org/10.5194/egusphere-egu2020-21302, 2020.

Acid mine drainage (AMD) is considered as one of the most prevalent environmental problems worldwide and remediation of AMD-affected streams remains a major challenge due to the large affected areas, large volume of polluted water, poor accessibility, and lack of financial supports. Advanced oxidation processes (AOPs) have been widely investigated as potential remedial options for contaminated water bodies of variety of settings, such as groundwater and waste discharges. This study presents a novel cost-effective approach for utilizing AOPs on improving quality of AMD-affected streams. Slow-release cylinders and pellets were created using polymeric binder and reagent salts that release strong oxidant and alkalinity upon dissolution in water. Results of column tests demonstrated that release durations were over 29 days and up to 100% iron removal was achieved within 20 minutes. Field-scale slow-release forms were manufactured and applied to an AMD site in southeast Ohio, USA for a 29-day demonstration study. Narrow channels were constructed for installation of slow-release forms and characterization of quality and flow of mine seeps and AMD stream during low subsurface flow periods. Results of field investigations suggest that the slow-release forms can be used to rapidly remove metals from AMD, as well as improve water parameters such as pH and minimize ecological impacts of remediation within the system in cost-effective manner.

How to cite: Lee, E. S. and Wolbert, R.: Efficacy of slow-release system for improving the quality of water in streams affected by mining activities, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11103, https://doi.org/10.5194/egusphere-egu2020-11103, 2020.

Sampling-based water quality monitoring networks are inherently spatially sparse. In locations or times where no in-situ water quality data are available, satellite imagery is an essential source of information. Satellite remote sensing can provide high spatial or temporal resolution imagery and has provided a breakthrough for oceanography, but so far, applications for coastal and inland water were limited by data resolution. Recently established satellite systems provide significant advances: Sentinel-2 delivers imagery with 20 m resolution, suitable for viewing even small rivers and ponds. Sentinel-3 delivers daily imagery with 300 m pixel size, which for lakes and coastal seas allows tracking water quality processes at the speed they happen. Information on suspended sediment and chlorophyll concentrations in water can be derived from optical images using simple calculations. The accuracy of these operations will vary across locations and can only be assessed through calibration and validation with in situ data. In absence of such data for all lakes globally, UWQV is based on a small set of algorithms that have been verified on several optically complex water systems to have a close to linear correlation with chlorophyll or suspended sediment concentration. Suspended sediment visualization is based on radiances observed in the 620 or 700 nm spectral bands, while chlorophyll visualization uses fluorescence-based indicators: Fluorescence Line Height, Reflectance Line Height and Maximum Chlorophyll Index. Since remote sensing based chlorophyll retrieval in sediment-laden waters with low transparency is hardly possible, for such cases chlorophyll concentrations are not visualized. The viewer runs as a Custom Script in the Sentinel-Hub EO Browser, which is a global, near real-time satellite data viewing and algorithm testing framework. The Javascript code is open source and enables users to easily tune visualization parameters and select different algorithms for cloud and water masking and chlorophyll and suspended sediment visualization.
Wherever in-situ water quality measurements are available, UWQV contributes significant added value by complementing water sample or instrument-based data, providing a map view or even a timelapse of maps; by providing an early warning system for water quality deterioration; by supporting optimization of sampling times and locations based on spatially and temporally explicit information, and  enabling cross-validating water quality information from different sources to reduce uncertainty or identify implausible measurements. Additionally, data-driven spatially explicit models can be verified and tuned based on similarity of their output to situations observed on satellite imagery.
UWQV is has all the advantages and drawbacks of a global solution: it will never be more accurate than a locally tuned water quality remote sensing algorithm; however, we hope that it will encourage water quality authorities and stakeholders to initiate the development of locally optimized satellite-based monitoring. By providing easy to read visualizations in a framework accessible to the general public, UWQV can democratize water quality information and raise public awareness of water quality processes and problems.

The first version of the algorithm is available in the Sentinel-Hub Custom Script Repository under the following link: https://github.com/sentinel-hub/custom-scripts/tree/master/sentinel-2/ulyssys_water_quality_viewer

An interactive test example of the visualization can be accessed here: tinyurl.com/UWQV-example

How to cite: Zlinszky, A. and Padányi-Gulyás, G.: Ulyssys Water Quality Viewer: a satellite-based global near real time water quality visualization, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18256, https://doi.org/10.5194/egusphere-egu2020-18256, 2020.

The spatiotemporal dynamics of salinity in hypersaline lakes is strongly dependent on the rate of water flow feeding the lake, evaporation rate, and the phenomena of precipitation and dissolution. Although in-situ observations are most reliable in quantifying water quality variables, the spatiotemporal distribution of such data are typically limited or cannot be readily extrapolated for long-term projections. Alternatively, remotely-sensed imagery has facilitated less expensive and stronger ability to estimate water quality over a wide range of spatiotemporal resolutions. This study introduces a machine learning model that leverages in-situ measurements and high-resolution satellite imagery to estimate the salinity concentration in water bodies. To this end, 123 points were sampled in April and July of 2019 across the Lake Urmia surface covering the wide range of salinity fluctuations. Among the artificial neural networks, ANFIS, and linear regression tools examined to determine the relationship between salinity and surface reflectance, artificial neural networks yielded the best accuracy evidenced by R² = 0.94 and RMSE = 6.8%. The results show that the seasonal change of salinity is linearly correlated with the volume of water feeding the lake, witnessing that dilution imposes a stronger control on the salinity than bed salt dissolution. The impact of disturbance in the lake circulation due to the causeway is also evident from the sharp changes of salinity around the bridge piers near spring when the mixing of fresh and hypersaline water from the southern and northern parts, respectively, takes place. The results of this study prove the promising potential of machine learning tools fed multi-spectral satellite information to map other water quality metrics than salinity as well.

How to cite: Bayati, M. and Danesh-Yazdi, M.: Coupling machine learning with high resolution satellite imagery to estimate spatiotemporal changes of salinity in water bodies, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4156, https://doi.org/10.5194/egusphere-egu2020-4156, 2020.

Water quality prediction is an important technical means for preventing and controlling water pollution and is crucial in the formulation of reasonable water pollution prevention and control measures. The time series structure of natural water quality is complex and heteroscedastic, so it is difficult for the traditional prediction model to reflect the actual situation well. Hence, Markov-switching (MS) theory is applied to a water quality autoregression (AR) prediction model (MSAR) in this paper. Further, MSAR is improved by introducing the crow search algorithm to obtain model parameters (CSA-MSAR). Then existing water quality time series for COD_Mn was selected as the data for the CSA-MSAR model after a normality test and the Box–Cox normality transformation. The results show that the CSA-MSAR model for COD_Mn with (s, p) values of (3, 5) has the best performance. The improvement degree for selection criteria compared with AR model is as follows: Akaike information criterion for MSAR is 32.020% and 31.611% for CSA-MSAR; Bayesian information criterion for MSAR is 10.632% and 13.464% for CSA-MSAR; likelihood value for MSAR is 40.016% and 40.801% for CSA-MSAR; C for MSAR is 63.559% and 64.968% for CSA-MSAR. Moreover, the results show that the average prediction precision of the first- to fifth-order prediction is raised by 89.016% for MSAR and 89.340% for CSA-MSAR compared with AR, indicating that the introduction of MS makes the CSA-MSAR and MSAR models conform to the smoothness of the mean and variance in each state. The results also indicate that the introduction of CSA into the maximum likelihood estimation to obtain the parameters raise the model prediction precision (the average prediction precision of CSA-MSAR is higher than MSAR by 5.231% excluding the fifth-order prediction) and the CSA-MSAR model is scientifically valid and reasonable for water quality prediction.

How to cite: Liu, C., Pan, C., Chang, Y., and Luo, M.: Water Quality Autoregression Prediction Model Based on Markov-Switching Theory Using Crow Search Algorithm, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5758, https://doi.org/10.5194/egusphere-egu2020-5758, 2020.

Antimicrobial resistant organisms and genes are now recognised as emerging water contaminants with significant potential adverse human and ecological health impacts. For example, the World Health Organisation have recently estimated that the global human health burden associated with antimicrobial infection will likely outstrip cancer mortalities by the year 2050, with >10 million deaths predicted due to these infections. The presence of antimicrobial resistant bacteria in drinking water supplies represents a particular concern due to daily associated exposures, with untreated groundwater consumption posing a substantial risk due to its unregulated nature. In the Republic of Ireland, high reliance on unregulated, private water wells and on-site domestic wastewater treatment in concurrence with a unique agricultural profile and diverse (hydro)geological settings, has resulted in the ‘perfect storm’ with respect to the relative ubiquity of sources and hydrological pathways for microbiological contamination, including antimicrobial resistant bacteria. 
The current study sought to identify the occurrence of antimicrobial resistant bacteria in the Irish subsurface environment, and apply spatiotemporal statistical modelling to identify environmental (e.g., hydrogeological, climatic, etc.) and anthropogenic (e.g., antimicrobial use, bacterial sources, etc.) drivers, and thus hydrological mechanisms associated with this occurrence. Private (unregulated) wells (n = 132) in 21/26 administrative counties were assessed and temporally sampled relative to periods of varying (human and veterinary) antimicrobial usage. Samples were analysed for bacterial (Escherichia coli and Pseudomonas aeruginosa) presence and, where isolated, antimicrobial resistance profiles were quantified. Data pertaining to well location, design and condition, adjacent (< 100 m) contamination sources, groundwater vulnerability and antecedent meteorology were geospatially collated to identify resistance hotspots and associated climatic, hydrogeological and anthropogenic risk factors. Preliminary results highlight the prevalence of bacterial contamination in Irish groundwater wells; 35 (26.5%) and 8 (6%) of the 132 supplies analysed during the first round of sampling (Autumn 2019) tested positive for E. coli and P. aeruginosa, respectively. While P. aeruginosa (an opportunistic soil/water resident) were only found in supplies from high (H=6.5%) and extreme (E=6.5%; X=14.3%) groundwater vulnerability categories, E. coli (faecal indicator) were found across all vulnerability categories. Differences in antimicrobial resistance levels across and within these two bacterial species will be used to provide insights into hydrological contamination pathways (i.e. ‘traditional’ recharge, direct ingress/preferential flow, or a combination depending on hydrogeological setting). Findings will provide evidence of the extent of antimicrobial resistance in domestic groundwater supplies, and valuable insights into the hydrogeological and microbiological mechanisms governing the potential public health risks associated with untreated groundwater consumption.

How to cite: Andrade, L., Hynds, P., Weatherill, J., and O'Dwyer, J.: Modelling the mechanistic determinants of antimicrobial resistant Escherichia coli and Pseudomonas aeruginosa within private groundwater systems in the Republic of Ireland, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5108, https://doi.org/10.5194/egusphere-egu2020-5108, 2020.

Water resources management in coastal where the freshwater availability is limited has often led to rising concerns about the capability of local resources to cover the increased water needs. This condition is especially amplified in areas whereextra stress is added to the water sources from overexploitation and/or quality degradation.

The present study is located in the island of Skiathos, which is one of the Greek islands that are most popular to tourists. Throughout the long touristic period the population of the island is steeply increased resulting to an increase in water demand compared to the remaining months.The island is dealing with serious water supply issues since groundwaterquality is deteriorated due to aquifer salinization and Hg contamination, making the tap water not safe for drinking and other household uses for more than a decade.

Mercury concentration in water for domestic usage is monitored systematically, with values up to 6 μg/L (maximum permitted European limit for total mercury in the drinking water is 1 μg/L).The local water utility company, in order to cover the increased water demand, intensifies the pumping from the main well resulting to sea intrusion in the aquifer. Mercury is present in the rocks of Skiathos with the form of the mineral cinnabar (HgS) and although it is practically insoluble, the increase of chloride concentration due to aquifer salinization, shifts the reaction equilibrium towards mercury solubilization by complexation with chloride. Thus, mercury is released from the rocks to the groundwater entering later on the water supply network.

In order to face the twofold problem of groundwater salinization and mercury contamination the present study aims at defining a threshold for thevolume that is abstracted from the aquifer on a daily basis. The investigation involves the development of a groundwater flow model covering an area of approximately 13.3 km² that is exploited for supplying the town of Skiathos with tap water. Information about the general hydrogeological conceptual model is derived from previous investigations in the area. The groundwater model achieves an optimizationof the groundwater pumping rates that prevent seawater from entering the aquifer and deteriorating the groundwater quality. Results show that a reduction of 27.2 % in the pumping rates, in combination with the already planned upgrade in the distribution networks, would prevent seawater from entering the aquifer and affecting groundwater reserves, while, at the same time, the need for tap water in the town of Skiathos is met.

How to cite: Pouliaris, C., Spyropoulou, A., Sarris, I., Laspidou, C., and Kallioras, A.: Assessment of hydrogeochemical processes and geogenic mercury pollution in coastal karstic aquifers in semi-arid environments , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13242, https://doi.org/10.5194/egusphere-egu2020-13242, 2020.

Central Asia (CA) is considered a hot-spot for climate-change impact on the water-cycle because of a high density of glaciated, montane catchments. Of particular concern are catchments in the Tien Shan and the Pamir Mountains in the areas, where glacial-fed rivers flow past major urban centres, and in the west of Central Asia near the Caspian and Aral Seas. Climate-change studies, which focus on Central Asia, consider mainly long-term changes in air temperature and precipitation, shrinking glaciers and physical hydrology, complex transboundary water management and policy issues. While, water quality (WQ) has received much less attention yet is noted as a potential issue primarily due to exposure of heavy metals and trace elements due to glacial retreat, release of aerosols deposited in snow and ice, and the dilution of pollutants from urban and farmed areas further downstream. To fill this knowledge gap the current project ‘Solutions to secure clean water in the glacier-fed catchments of Central Asia – what happens after the ice?’ established WQ monitoring program in four CA countries. The project aims to characterise and model, in a consistent and comparable way, the impacts of climate change and diminishing cryosphere on water availability and quality in the selected glacier-fed catchments informing environmental policies and adaptation strategies and building research capacity in the region. To this end WQ sampling and measurements were established in four glacier-fed alpine catchments on rivers passing major cities: Kishi and Ulken Almaty rivers (Kazakhstan, Almaty city), Ala-Archa River (Kyrgyzstan, Bishkek city), Chirchik River (Uzbekistan, Tashkent city), Varzob (Tajikistan, Dushanbe city). The WQ monitoring programme is based on bi-weekly sampling  along river elevation profile from upstream (closer to glacierized  area) to downstream (up to a reservoir or inflow to a major river). Groundwater (urban, artesian, springs), streamwater, reservoirs have been sampled and measured for temperature, electrical conductivity (EC), total dissolved solids (TDS), pH, nitrates, phosphates in situ and in the labs by local teams. These measurements are complemented by extended analysis for cations and anions during peak of steam flow (glacier and snow melt period) and low flow season in autumn (baseflow dominated). The preliminary results show that these catchments relatively clean with potentially toxic elements not exceeding WHO drinking water values in all monitored components. The dilution effect of glacier and snow melt on streamwater in summer is reflected in EC seasonal pattern. Primary concerns are elevated nitrate concentrations in urban groundwater in three studied catchments (Kyrgyzstan, Uzbekistan, and Tajikistan) with median values exceeding 10 mg/L of nitrate-N (a WHO’s drinking water guidelines threshold). The intermittent spikes of high phosphates in streamwater and groundwater in Uzbekistan in the autumn, in some cases reaching more than 4 mg/L (phosphate-P) are possibly linked to fertilizers wash-out by rainfall.

How to cite: Yapiyev, V., Wade, A., Saidaliyeva, Z., Shahgedanova, M., Kapitsa, V., Kasatkin, N., Severskiy, I., Ismukhanova, L., Kulbekova, R., Sultanbekova, B., Madibekov, A., Esenaman, M., Kalashnikova, O., Usubaliev, R., Akbarov, F., Umirzakov, G., Petrov, M., Rakhimov, I., Kayumova, D., and Kayumov, A.: Water quality in urbanized alpine catchments of Central Asia - what happens after the ice?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-802, https://doi.org/10.5194/egusphere-egu2020-802, 2020.

MEDSAL is a research project (www.medsal.net) focusing on groundwater salinization in the Mediterranean area, funded by the PRIMA Program (Partnership for Research and Innovation in the Mediterranean Area), and running for 36 months starting from September 2019. MEDSAL constitutes a joint Euro-Mediterranean cooperation network of organizations from Mediterranean countries and associated states of the EU contributing national funds. The partnership involves eight academic partners from seven countries (plus an external collaborator – private firm), covering a wide range of academic experts in various scientific fields (e.g. hydrogeology, hydrogeochemistry, environmental isotopes, modeling, hydro-informatics, geostatistics, machine learning).

MEDSAL aims at developing innovative methods to identify various sources and processes of salinization and at providing an integrated set of modeling tools that capture the dynamics and risks of salinization. Thereby, it aims to secure the availability and quality of groundwater reserves in Mediterranean coastal areas, which are amongst the most vulnerable regions in the world to water scarcity and quality degradation. MEDSAL encompasses six (6) test sites located in five (5) countries: Rhodope, Greece, (ii) Samos Island, Greece, (iii) Salento, Italy, (iv) Tarsus, Turkey, (v) Boufichia, Tunisia, and (vi) Bouteldja, Algeria.

MEDSAL’s principal objectives are the following: a) Deliver new tools for the identification of complex salinization sources and processes, b) Exploit the potential of Artificial intelligence and Deep Learning methods to improve detection of patterns in multi-dimensional hydrogeochemical and isotope data, c) Elaborate tailor-made risk assessment and development of management plans by coupling salinization forecasts with climate change impacts and future scenarios, and d) Develop a public domain web-GIS Observatory for monitoring, alerting, decision support and management of coastal groundwater reserves around the Mediterranean.

MEDSAL is expected to have a significant impact on water resources availability and quality by improving the identification and development of adequate strategies and measures for the protection and management of salinization in coastal aquifers. In this context, MEDSAL will provide innovative classification and detection methods of groundwater salinization types for Mediterranean coasts, also in complex karstic and data-scarce environments. These outcomes will be reached by better integration of hydrogeochemical and environmental isotope data with physical-based groundwater flow and transport models and advanced geostatistics. Artificial intelligence and deep learning methods will be also used to improve the detection of patterns in multi-dimensional hydrogeochemical and isotope data.

How to cite: Tziritis, E., Aschonitis, V., Balacco, G., Daras, P., Doulgeris, C., Fidelibus, M. D., Gaubi, E., Gueddari, M., Güler, C., Hamzaoui, F., Külls, C., Kurt, M. A., Kyriakidis, P., Liodakis, S., Mazmancı, B., Menani, R. M., Nikolaidou, K., Ouertani, N., Panagopoulos, A., Pisinaras, V., Thakur, J. K., Yıldırım, Ü., and Zammouri, M.: MEDSAL Project - Salinization of critical groundwater reserves in coastal Mediterranean areas: Identification, risk assessment and sustainable management with the use of integrated modelling and smart ICT tools, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2326, https://doi.org/10.5194/egusphere-egu2020-2326, 2020.

Excessive nitrate inputs into groundwater have been recognized as a main reason for failing drinking water standards since decades. Agricultural N-emissions originating from mineral or organic fertilizers are regarded as the most relevant source of nitrate in groundwater worldwide. Accordingly, strategies to cope with the nitrate pollution of groundwater are focused on controlling the agricultural sources of nitrate. In Europe this is reflected in the water legislation on EU level, i.e. the EU Water Framework Directive (EU-WFD), the EU Marine Strategy Framework Directive and the EU Nitrates Directive, obliging the polluter to implement measures to reduce the nitrogen impact on groundwater.

With an average population density of 525 inhabitants/km² the Federal State of North Rhine-Westphalia represents an example for a densely populated region in Germany. Consequently, the assessment of water bodies showed that a number of groundwater and surface water bodies are not in good status due to high nitrogen loads resulting e.g. in high nitrate concentrations in groundwater. There is a debate in North Rhine-Westphalia to what extent agricultural and non-agricultural N-emissions contribute to high nitrate concentrations.

The German Working Group on water issues of the Federal States and the Federal Government, require that the nitrate concentration in the leachate should not exceed 50 mg NO₃/l. Against this background it is obvious that the nitrate concentration in the leachate represents a decisive parameter for both, the assessment on the nitrate pollution of groundwater and as starting point to determine the N reduction requirements.

We used an interdisciplinary model network consisting of a nutrient balance model, a nutrient balancing model (RAUMIS, Henrichsmeyer et al., 1996), a water balance model (mGROWA, Hermann et al., 2015), a reactive nitrate transport model in soil (DENUZ, Wendland et al., 2009) and a reactive nitrate transport model in groundwater (WEKU, Kunkel & Wendland, 1997) to predict the nitrogen intakes and the nitrogen losses to groundwater and surface waters from different input sources and pathways.

The nitrogen flux was modelled using nitrogen input data from the time period 2014-2016 and hydrological data for the time period 1981-2010. The nitrate concentrations in the leachate were calculated separately for agricultural and non agricultural N-sources involved, to enable the identification of the main polluter in a certain region, i.e. the one who has to implement measures to to reduce the nitrogen impact on groundwater.

From the model analysis it becomes evident that non-agricultural sources do only locally cause nitrate concentrations in the leachate above 50 mg NO₃/l in spite of the high population density (525 inhabitants / km²). It could be confirmed that agricultural sources (N-balance surpluses from agriculture and atmospheric NH₄ deposition) are exclusively responsible for extended areas of nitrate concentrations above 50 mg NO₃/l. Especially in the northern (Münsterland) and western (Lower Rhine basin) parts of the Federal State the implementation of measures to reduce agricultural N-emissions in the context of the WFD program of measures is necessary. These results will not only support the right dimensioning of agricultural N-reduction measures, but also affect the selection and implementation of regionally adapted N-reduction measures.

How to cite: Kunkel, R., Bergmann, S., Eisele, M., Gömann, H., Herrmann, F., Kreins, P., and Wendland, F.: Agricultural nitrogen reduction requirement to reach groundwater and surface water quality targets in North Rhine-Westphalia, Germany , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18026, https://doi.org/10.5194/egusphere-egu2020-18026, 2020.

Subsurface contamination due to diffuse agrochemical pollutants such as pesticides, herbicides, excess nutrients (N, P, K) is a widespread problem in a cultivated areas across Europe. Large-scale spatio-temporal patterns emerge from interplay of heterogeneous and dynamic hydrologic and biogeochemical processes in the near-surface critical zone (top one-meter of root-zone soil layer) which contribute to landscape filtering of stochastic hydro-climatic forcing. Such outcomes are of interest in characterizing the transient behavior of transport-reaction dynamics operating in the root-zone soil compartment which drive recharge and solute loads to sub-surface compartments (shallow groundwater and eventually to river networks).  Here, using novel state-of-the art daily-scale hydrologic simulations (mHM; around 5x5 km grid) driven by observed hydro-climatic forcing, we demonstrate the strong spatio-temporal heterogeneity of hydrologic transport at the continental scale – reflected in time-varying travel time distributions (TTDs) – primarily controlled by the prevailing hydro-climatic gradient of aridity index across Europe. We link the space-time dynamics of TTDs – representing the intrinsic vulnerability of hydrologic system - to spatial heterogeneity and temporal fluctuations of biogeochemical turnover time-scales to provide a parsimonious  biogeochemical model for identifying the extent of subsurface contamination due to diffuse (agrochemical) pollutants. Our assessment results show a large increase in the extent of vulnerable areas that are prone to subsurface nitrate leaching across Europe, compared to current (static) indices based approaches. We highlight the implications of improved vulnerability maps to better support agricultural subsidies and nitrate management across Europe. 

How to cite: Kumar, R., Hesse, F., Rao, S., Musolff, A., Jawitz, J., Sarrazin, F., Samaniego, L., Fleckenstein, J., Rakovec, O., Thober, S., and Attinger, S.: Towards large-scale characterization of subsurface vulnerability due to agrochemical pollutants across Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12011, https://doi.org/10.5194/egusphere-egu2020-12011, 2020.

Salinization of freshwater resources is a growing water quality issue, which poses challenges for different sectoral water uses. While it is generally recognized that salinity may constrain irrigation water use, our ability to evaluate the severity and extent of the problem has been hampered by a lack of assessments at the global scale. The aim of this study is to (i) quantify spatial and temporal trends in salinization of surface- and groundwaters in irrigated regions globally, and (ii) evaluate its implications for irrigation water use, by considering global salinity guidelines.

To address these aims, we collected and harmonized electrical conductivity (EC) monitoring data between 1980-2018 at both ground- and surface water locations in irrigated areas around the world. We used a suit of data sources including local, regional and global online water quality databases, and data provided by governmental organizations, river basin management commissions and scientific literature. Estimates of irrigated regions and associated groundwater and surface water withdrawal rates for irrigation water use was estimated using global grid-based hydrological outputs of the PCR-GLOBWB model.

Our results show that 23 % and 73 % of all surface water and groundwater stations, respectively, have long-term annual average EC values that exceed FAO guidelines of irrigation water use restrictions (700 µS/cm). Regionally, dryland areas, such as central and western parts of the US, eastern parts of Australia, South Africa and Southern Europe are particularly affected, but also coastal areas of Bangladesh, Florida and Vietnam show elevated EC levels. Regarding temporal variability, groundwater stations generally have low absolute EC variability, but with a majority of stations exceeding irrigation water use guidelines at more than 50 % of their total measurements for all continents except Europe and South America. For surface waters stations, more variability in terms of both exceedance levels and absolute EC was observed across continents, but with increasing EC during low flow periods, suggesting discharge (and seasonality) to be a strong control on surface water salinity. These results are a first step in assessing global impacts of salinity on irrigation water use constraints. Further assessments on salinity trends and its large-scale drivers will be provide necessary information for sustainable irrigation water use and management today and in the future.

How to cite: Thorslund, J. and van Vliet, M. T. H.: Spatial and temporal patterns of freshwater salinity and impacts on irrigation water use constraints globally, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8623, https://doi.org/10.5194/egusphere-egu2020-8623, 2020.