Metrics on usage

Byrne, A, Lomeo, D, Owoko, W et al. (2024). LAQUA: a LAndsat water QUality retrieval tool for east African lakes. Remote Sensing 16(16). doi: https://doi.org/10.3390/rs16162903

Fendrich, AN, Ciais, P, Panagos, P et al. (2024). Including land management in a European carbon model with lateral transfer to the oceans. Environmental Research 245, 118014. doi: https://doi.org/10.1016/j.envres.2023.118014

Fleischmann, J, Birkel, C, Blechinger P et al. (2024). Guiding the data collection for integrated Water-Energy-Food-Environment systems using a pilot smallholder farm in Costa Rica. Energy Nexus 13, 100259. doi: https://doi.org/10.1016/j.nexus.2023.100259

Graham, DJ, Bierkens, MFP & van Vliet MTH (2024). Impacts of droughts and heatwaves on river water quality worldwide. Journal of Hydrology 629, 130590. doi: https://doi.org/10.1016/j.jhydrol.2023.130590

Jiang, P, Sun, S, Goh, SG et al. (2024). A Rapid Approach with Machine Learning for Quantifying the Relative Burden of Antimicrobial Resistance in Natural Aquatic Environments. Water Research, 122079. doi: https://doi.org/10.1016/j.watres.2024.122079

Jones, ER, Graham, DJ, van Griensven, A et al. (2024). Blind spots in global water quality monitoring. Environmental Research Letters 9, 091001. doi: https://doi.org/10.1088/1748-9326/ad6919

Kelly, MG, Teixeira, H, Lyche Solheim, A et al. (2024). Physico-chemical criteria to support Good Ecological Status in Europe. European Commission, Joint Research Centre, JRC136407. Publications Office of the European Union, Luxembourg. doi: https://data.europa.eu/doi/10.2760/355815

Kumar, S, Imen, S, Sridharan, VK et al. (2024). Perceived barriers and advances in integrating earth observations with water resources modeling. Remote Sensing Applications: Society and Environment 33, 101119. doi: https://doi.org/10.1016/j.rsase.2023.101119

Lausch, A, Bannehr, L, Berger, SA et al. (2024). Monitoring Water Diversity and Water Quality with Remote Sensing and Traits. Remote Sensing 16(13), 2425. doi: https://doi.org/10.3390/rs16132425

Liu, S, Hu, K, Xie, Z et al. (2024). Spatial-temporal change of river thermal environment and anthropogenic impact in China. Science of The Total Environment 929, 172697. doi: https://doi.org/10.1016/j.scitotenv.2024.172697

Manfreda, S, Miglino, D, Saddi, KC et al. (2024). Advancing river monitoring using image-based techniques: challenges and opportunities. Hydrological Sciences Journal 69(6), 657-677. doi: https://doi.org/10.1080/02626667.2024.2333846

Micella, I, Kroeze, C, Bak, MP et al. (2024). Causes of coastal waters pollution with nutrients, chemicals and plastics worldwide. Marine Pollution Bulletin 198, 115902. doi: https://doi.org/10.1016/j.marpolbul.2023.115902

Mintram, KS, Brown, AR, Maynard, SK et al. (2024). Predicting population-level impacts of projected climate heating on a temperate freshwater fish. Journal of Fish Biology, 1-9. doi: https://doi.org/10.1111/jfb.15889

Mukuyu, P, Warner, S, Chapman, DV et al. (2024). Innovations in water quality monitoring and management in Africa: towards developing an African Water Quality Program (AWaQ). International Water Management Institute, IWMI Working Paper 208. Published by the International Water Management Institute in Colombo, Sri Lanka. doi: https://doi.org/10.5337/2023.217

Naderian, D, Noori, R, Heggy, E et al. (2024). A water quality database for global lakes. Resources, Conservation and Recycling 202, 107401. doi: https://doi.org/10.1016/j.resconrec.2023.107401

Nkwasa, A, Chawanda, CJ, Nakkazi, MT et al. (2024). One Third of African Rivers Fail to Meet the ‘Good Ambient Water Quality’ Nutrient Targets. Available at Social Science Research Network (SSRN). doi: http://dx.doi.org/10.2139/ssrn.4829742

Pimentel, L (2024). O sistema de gerenciamento de recursos hídricos brasileiro como modelo na prevenção de conflitos. PhD thesis at Departamento de Geografia, Universidade de Brasília, Brazil. http://repositorio2.unb.br/jspui/handle/10482/48252

Ring, SJ, Henehan, MJ, Blukis, R et al. (2024). Adsorption pathways of boron on clay and their implications for boron cycling on land and in the ocean. Geochimica et Cosmochimica Acta. doi: https://doi.org/10.1016/j.gca.2024.08.014

Rydgård, M, Jensen, LS, Kroeze, C et al. (2024). Regionalised modelling of recycled fertiliser P in agricultural fields: Development of the life cycle inventory model PLCI 2.0. Journal of Cleaner Production 443, 141088. doi: https://doi.org/10.1016/j.jclepro.2024.141088

Strokal, M, Wang, M, Micella, I et al. (2024). Building trust in large-scale water quality models: 13 alternative strategies beyond validation. Discover Water 4(1), 82. doi: https://doi.org/10.1007/s43832-024-00149-y

Tu, Z, Zhang, Y, Shi, K et al. (2024). Landsat data reveal lake deoxygenation worldwide. Water Research 267, 122525. doi: https://doi.org/10.1016/j.watres.2024.122525

Wang, Q, Liu, G, Song, K et al. (2024). Comparison of Machine Learning Algorithms for Estimating Global Lake Clarity With Landsat TOA Data. IEEE Transactions on Geoscience and Remote Sensing 62, 1-14. doi: https://doi.org/10.1109/TGRS.2024.3400221

Wen, Z, Wang, Q, Ma, Y et al. (2024). Remote estimates of suspended particulate matter in global lakes using machine learning models. International Soil and Water Conservation Research 12(1), 200-216. doi: https://doi.org/10.1016/j.iswcr.2023.07.002

Zhi, W, Appling, AP, Golden, HE et al. (2024). Deep learning for water quality. Nature Water 2(3), 228-241. doi:
https://doi.org/10.1038/s44221-024-00202-z

Zhou, C, Zhou, M, Peng, Y et al. (2024). Unexpected increase of sulfate concentrations and potential impact on CH4 budgets in freshwater lakes. Water Research 261, 122018. doi: https://doi.org/10.1016/j.watres.2024.122018

Arias-Rodriguez, LF, Tüzün, UF, Duan, Z et al. (2023). Global Water Quality of Inland Waters with Harmonized Landsat-8 and Sentinel-2 Using Cloud-Computed Machine Learning. Remote Sensing 15(5), 1390. doi: https://doi.org/10.3390/rs15051390

Brendel, C, Capell, R & Bartosova, A (2023). To tame a land: Limiting factors in model performance for the multi-objective calibration of a pan-European, semi-distributed hydrological model for discharge and sediments. Journal of Hydrology: Regional Studies 50, 101544. doi: https://doi.org/10.1016/j.ejrh.2023.101544

Cacciatori, C, Mariani, G, Carollo, AM et al. (2023). The Gems of Water – How to become a gem of water? JRC133408, EUR 31486 EN, Publications Office of the European Union, Luxembourg. doi: 10.2760/334925

Chen, T, Liu, T, Wu, Z et al. (2023). Virus–pathogen interactions improve water quality along the Middle Route of the South-to-North Water Diversion Canal. The ISME Journal 17(10), 1719-1732. doi: https://doi.org/10.1038/s41396-023-01481-2

Chilton, J & Foster, S (2023). Long datasets for improved understanding, management and protection of groundwater. Hydrogeol J. 32, 347-352. doi: https://doi.org/10.1007/s10040-023-02759-7

de Waal, J, Miller, J & van Niekerk, A (2023). The impact of agricultural transformation on water quality in a data-scarce, dryland landscape—a case study in the Bot River, South Africa. Environ Monit Assess 195, 177(2023). doi: https://doi.org/10.1007/s10661-022-10776-4

Drechsel, P, Marjani Zadeh, S & Pedrero, F (2023). Water quality in agriculture: Risks and risk mitigation. Rome, FAO & IWMI. doi: https://doi.org/10.4060/cc7340en

Graham, DJ, Bierkens, MFP & van Vliet, MTH (2023). Impacts of droughts and heatwaves on river water quality worldwide. Journal of Hydrology 629, 130590. doi: https://doi.org/10.1016/j.jhydrol.2023.130590

Ibanez, C, Caiola, N, Barquin, J et al. (2023). Ecosystem-level effects of re-oligotrophication and N:P imbalances in rivers and estuaries on a global scale. Global Change Biology 29(5), 1248-1266. doi: https://doi.org/10.1111/gcb.16520

Jones, ER, Bierkens, MFP, van Puijenbroek, PJTM et al. (2023). Sub-Saharan Africa will increasingly become the dominant hotspot of surface water pollution. Nature Water, 1(7), 602-613. doi: 10.1038/s44221-023-00105-5

Jones, ER, Bierkens, MFP, Wanders, N et al. (2023). DynQual v1.0: a high-resolution global surface water quality model. Geosci. Model Dev., 16(15), 4481-4500. doi: 10.5194/gmd-16-4481-2023

Kaushal, SS, Likens, GE, Mayer, PM et al. (2023). The anthropogenic salt cycle. Nature Reviews Earth & Environment 4, 770-784(2023). doi: https://doi.org/10.1038/s43017-023-00485-y

Kelly, MG, Free, G, Kolada, A et al. (2023). Warding off freshwater salinization: Do current criteria measure up? WIREs Water, e1694. doi: https://doi.org/10.1002/wat2.1694

Khodamoradi Vatan, N, Mazaheri, M, Vali Samani, JM et al. (2023). Comparative evaluation and comparison of quality monitoring network of Iranʼs rivers with selected countries. Iranian Journal of Soil and Water Research 54(5), 737-751. doi: https://doi.org/10.22059/ijswr.2023.356969.669474

Li, C, Smith, P, Bai, X et al. (2023). Effects of carbonate minerals and exogenous acids on carbon flux from the chemical weathering of granite and basalt. Global and Planetary Change 221, 104053. doi: https://doi.org/10.1016/j.gloplacha.2023.104053

Maciel, DA, Pahlevan, N, Barbosa, CCF et al. (2023). Towards global long-term water transparency products from the Landsat archive. Remote Sensing of Environment 299, 113889. doi: https://doi.org/10.1016/j.rse.2023.113889

Misstear, B, Ruz Vargas, C, Lapworth, D et al. (2023). A global perspective on assessing groundwater quality. Hydrogeol J 31, 11–14 (2023). doi: https://doi.org/10.1007/s10040-022-02461-0

Murphy, J & Chanat, J (2023). Leveraging machine learning to automate regression model evaluations for large multi-site water-quality trend studies. Environmental Modelling & Software 170, 105864. doi: https://doi.org/10.1016/j.envsoft.2023.105864

Peker, İB & Gülbaz, S (2023). Examining the open-source datasets for water quantity and quality using the soil and water assessment tool (SWAT). Water Science and Technology, wst2023297. doi: 10.2166/wst.2023.297

Plisnier, PD, Kayada, R, MacIntyre, S et al. (2023). Need for harmonized long-term multi-lake monitoring of African Great Lakes. Journal of Great Lakes Research 49(6), 101988. doi: https://doi.org/10.1016/j.jglr.2022.01.016

Polcher, J, Schrapffer, A, Dupont, E et al. (2023). Hydrological modelling on atmospheric grids: using graphs of sub-grid elements to transport energy and water. Geoscientific Model Development 16, 2583-2606. doi: https://doi.org/10.5194/gmd-16-2583-2023

Rose, JB, Hofstra, N, Hollmann, E et al. (2023). Global microbial water quality data and predictive analytics: Key to health and meeting SDG 6. PLOS Water 2(8), e0000166. doi: https://doi.org/10.1371/journal.pwat.0000166

Roudbari, MV, Dehnavi, A, Jamshidi, S et al. (2023). A multi-pollutant pilot study to evaluate the grey water footprint of irrigated paddy rice. Agricultural Water Management 282, 108291. doi: https://doi.org/10.1016/j.agwat.2023.108291

Rupp, JM (2023). Trade Liberalization and Environmental Outcomes. MSc thesis at the Department of Social Science, New York University Abu Dhabi

Scanlon, BA, Fakhreddine, S, Rateb, A et al. (2023). Global water resources and the role of groundwater in a resilient water future. Nature Reviews Earth & Environment 4, 87–101 (2023). doi: https://doi.org/10.1038/s43017-022-00378-6

Schmidt, C, Bärlund, I, Batool, M et al. (2023). Improving global water quality information by combining in-situ data, remote sensing and modeling. EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13215. doi: https://doi.org/10.5194/egusphere-egu23-13215.

Shapiro, JS (2023). INSTITUTIONS, COMPARATIVE ADVANTAGE, AND THE ENVIRONMENT. NBER WORKING PAPER SERIES, Working Paper 31768. doi: 10.3386/w31768

Sheikholeslami, R & Hall, JW (2023). Global patterns and key drivers of stream nitrogen concentration: A machine learning approach. Science of The Total Environment 868: 161623. doi: https://doi.org/10.1016/j.scitotenv.2023.161623

Shimony, T, Teitelbaum, Y, Saavedra Cifuentes, E et al. (2023). Kaolinite Deposition Dynamics and Streambed Clogging During Bedform Migration Under Losing and Gaining Flow Conditions. Water Resources Research 59(9), e2023WR034792. doi: https://doi.org/10.1029/2023WR034792

Souza, AP, Oliveira, BA, Andrade, ML et al. (2023). Integrating remote sensing and machine learning to detect turbidity anomalies in hydroelectric reservoirs. Science of The Total Environment, 902, 165964. doi: https://doi.org/10.1016/j.scitotenv.2023.165964

Ural-Janssen, A, Kroeze, C, Lesschen, JP et al. (2023). HOTSPOTS OF NUTRIENT LOSSES TO AIR AND WATER: AN INTEGRATED MODELING APPROACH FOR EUROPEAN RIVER BASINS. Front. Agr. Sci. Eng. 10(4), 579–592. doi: https://doi.org/10.15302/J-FASE-2023526

Vlah, MJ, Bernhardt, ES, Rhea, S et al. (2023). Another Step Toward “Big” Catchment Science. Limnology and Oceanography Bulletin 32(4), 147-148. doi: https://doi.org/10.1002/lob.10590

Wang, J, Liu, X, Beusen, AHW et al. (2023). Surface-Water Nitrate Exposure to World Populations Has Expanded and Intensified during 1970–2010. Environ. Sci. Technol. 2023 57(48), 19395–19406. doi: https://doi.org/10.1021/acs.est.3c06150

Wen, Z, Wang, Q, Ma, Y et. al (2023). Remote estimates of suspended particulate matter in global lakes using machine learning models. International Soil and Water Conservation Research. doi: https://doi.org/10.1016/j.iswcr.2023.07.002

Xu, A, Hathorne, E, Laukert, G et al. (2023). Overlooked riverine contributions of dissolved neodymium and hafnium to the Amazon estuary and oceans. Nature Communications 14, 4156 (2023). doi: https://doi.org/10.1038/s41467-023-39922-3

Zhou, J, Mogollon, JM, van Bodegom, PM et al. (2023). Effects of Nitrogen Emissions on Fish Species Richness across the World’s Freshwater Ecoregions. Environ. Sci. Technol. 57(22), 8347–8354. doi: https://doi.org/10.1021/acs.est.2c09333

Amora, DJA, Apan, JV, Layosa, DNC, Sarmiento, RC, Tugado, JJ, et al. (2022): Soft Sensing Measurement of Dissolved Ammonia Nitrogen in Tank-Based Eel Aquaculture Systems Utilizing Deep Learning. 2022 IET International Conference on Engineering Technologies and Applications (IET-ICETA), Changhua, Taiwan, https://doi.org/10.1109/IET-ICETA56553.2022.9971667

Arora, NK, Mishra, I (2022):  Sustainable development goal 6: Global Water Security. Environmental Sustainability 5(3): 271-275, https://doi.org/10.1007/s42398-022-00246-5

Arsenault, J, Talbot, J, Brown, LE, Holden, J, Martinez-Cruz, K, et al. (2022): Biogeochemical Distinctiveness of Peatland Ponds, Thermokarst Waterbodies, and Lakes. Geophysical Research Letters 49(11), https://doi.org/10.1029/2021gl097492

Chapman, DV, Sullivan, T (2022): The role of water quality monitoring in the sustainable use of ambient waters. One Earth 5(2): 132-137, https://doi.org/10.1016/j.oneear.2022.01.008

Colohan, P, Onda, K (2022): Water data for water science and management: Advancing an Internet of Water (IoW). PLOS Water 1(3): e0000017, https://doi.org/10.1371/journal.pwat.0000017

Desbureaux, S, Mortier, F, Zaveri, E, van Vliet, MTH, Russ, J, et al. (2022): Mapping global hotspots and trends of water quality (1992–2010): a data driven approach. Environmental Research Letters 17: 114048, https://doi.org/10.1088/1748-9326/ac9cf6

Dzodzomenyo, M, Asamoah, M, Li, C, Kichana, E, Wright, J (2022): Impact of flooding on microbiological contamination of domestic water sources: a longitudinal study in northern Ghana. Applied Water Science 12: 235, https://doi.org/10.1007/s13201-022-01757-6

Ebeling, P, Kumar, R, Lutz, SR, Nguyen, T, Sarrazin, F, et al. (2022): QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany. Earth System Science Data 14: 3715-3741, https://doi.org/10.5194/essd-14-3715-2022

Fendrich, AN, Ciais, P, Lugato, E, Carozzi, M, Guenet, B, et al. (2022): Matrix representation of lateral soil movements: scaling and calibrating CE-DYNAM (v2) at a continental level. Geoscientific Model Development 15: 7835-7857, https://doi.org/10.5194/gmd-15-7835-2022

Garcia Usaga, JM (2022): Simulacion de la evolucion de los parametros fısico – quimicos del agua en las cuencas del departamento del Quindio, basada en redes complejas y ecuaciones en derivadas parciales. PhD thesis at the Universidad Nacional de Colombia.

Hall, CA, Saia, SM, Popp, AL, Dogulu, N, Schymanski, SJ, et al. (2022): A hydrologist’s guide to open science. Hydrology and Earth System Sciences 26: 647-664, https://doi.org/10.5194/hess-26-647-2022

Jones, ER, Bierkens, MFP, Wanders, N, Sutanudjaja, EH, van Beek, LPH, et al. (2022): Current wastewater treatment targets are insufficient to protect surface water quality. Communications Earth & Environment 3: 1-8, https://doi.org/10.1038/s43247-022-00554-y

Kross, A, Kaur, G, Jaeger, JAG (2022): A geospatial framework for the assessment and monitoring of environmental impacts of agriculture. Environmental Impact Assessment Review 97: 106851, https://doi.org/10.1016/j.eiar.2022.106851

Liu, S, Maavara, T, Brinkerhoff, CB, Raymond, PA (2022): Global Controls on DOC Reaction Versus Export in Watersheds: A Damköhler Number Analysis. Global Biogeochemical Cycles 36: e2021GB007278, https://doi.org/10.1029/2021GB007278

Lytvynenko, V, Yeremeyev, I, Dychko, A (2022): Risk-Oriented Approach to Assessment of Hexamethylenediamine Pollution of Aquatic Ecosystems. Ecological Engineering & Environmental Technology 23: 179-188, https://doi.org/10.12912/27197050/150237

Onda, K (2022): Water Data Infrastructure for Low- and Middle-Income Countries. Water Data Dialogues, Stanford Woods Institute for the Environment.

Pahlevan, N, Greb, S, Decker, A (2022): Earth Observation in Support of SDG 6.3.2/6.6.1: Reporting Surface Water Quality. In: Kavvada, A, Cripe, D, Friedl, L (eds.) Earth Observation Applications and Global Policy Frameworks, Geophysical Monograph 274. John Wiley & Sons, Inc., https://doi.org/10.1002/9781119536789.ch04

Pandit DN, Kumari, R, Shitanshu, SK (2022): A comparative assessment of the status of Surajkund and Rani Pond, Aurangabad, Bihar, India using overall Index of Pollution and Water Quality Index. Acta Ecologica Sinica 42: 149-155, https://doi.org/10.1016/j.chnaes.2020.11.009

Peker, IB, Gülbaz, S (2022): Examining the Use of GEMStat Global Data Source for a Water Quality Model. IWA DIPCON 4th Regional Conference on Diffuse Pollution & Eutrophication, Istanbul, Turkey.

Pizani, FMC, Maillard, P (2022): The determination of water quality parameters by remote sensing: 2000-2020. Universidade Federal de Minas Gerais, https://doi.org/10.13140/RG.2.2.32203.87849

Plisnier, PD, Kayanda, R, MacIntyre, S, Obiero, K, Okello, W, et al. (2022): Need for harmonized long-term multi-lake monitoring of African Great Lakes. Article in Press, https://doi.org/10.1016/j.jglr.2022.01.016

Polcher, J, Schrapffer, A, Dupont, E, Rinchiuso, L, Zhou, X, et al. (2022): Hydrological modelling on atmospheric grids; using graphsof sub-grid elements to transport energy and water. EGUsphere Discussion, https://doi.org/10.5194/egusphere-2022-690

Rajesh, M, Rehana, S (2022): Impact of climate change on river water temperature and dissolved oxygen: Indian riverine thermal regimes. Scientific Reports 12: 9222, https://doi.org/10.1038/s41598-022-12996-7

Rigon, R, Formetta, G, Bancheri, M, Tubini, N, D’Amato, C, et al. (2022): Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists. Hydrology and Earth System Sciences 26: 4773-4800, https://doi.org/10.5194/hess-26-4773-2022

Rotteveel, L, Sterling, SM (2022): The Surface Water Chemistry (SWatCh) database: A standardized global database of water chemistry to facilitate large-sample hydrological research. Earth System Science Data 14: 4667–4680, https://doi.org/10.5194/essd-14-4667-2022

Ruppen, D (2022): Effective Monitoring Strategies for Mining-Related Water Pollution. Doctoral thesis at ETH Zürich, https://doi.org/10.3929/ethz-b-000574276

Russ, J, Zaveri, E, Desbureaux, S, Damania, R, Rodella AS (2022): The impact of water quality of GDP growth: Evidence from around the world. Water Security 17: 100130, https://doi.org/10.1016/j.wasec.2022.100130

Simpson, G, Jewitt, GPW, Becker, W, Badenhorst, J, Neves, A, et al. (2022): The Water-Energy-Food Nexus Index: A Tool for Integrated Resource Management and Sustainable Development. Frontiers in Water 4: 825854, https://doi.org/10.3389/frwa.2022.825854

Song, K, Wang, Q, Liu, G, Jacinthe, PA, Li, S, et al. (2022): A unified model for high resolution mapping of global lake (>1 ha) clarity using Landsat imagery data. Science of the Total Environment 810: 151188, https://doi.org/10.1016/j.scitotenv.2021.151188

Wang, L, Lv, A (2022): Identification and Diagnosis of Transboundary River Basin Water Management in China and Neighboring Countries. Sustainability 14: 12360, https://doi.org/10.3390/su141912360

Wang, X, Wen, Z, Liu, G, Tao, H, Song, K, et al. (2022): Remote estimates of total suspended matter in China’s main estuaries using Landsat images and a weight random forest model. ISPRS Journal of Photogrammetry and Remote Sensing 183: 94-110, https://doi.org/10.1016/j.isprsjprs.2021.11.001.

Wen, Z, Wang, Q, Liu, G, Jacinthe, PA, Wang, X, et al. (2022): Remote sensing of total suspended matter concentration in lakes across China using Landsat images and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing 187: 61-78, https://doi.org/10.1016/j.isprsjprs.2022.02.018

Zhang, Y, Shi, K, Sun, X, Zhang, Y, Li, N, et al. (2022): Improving remote sensing estimation of Secchi disk depth for global lakes and reservoirs using machine learning methods. GIScience & Remote Sensing 59: 1367-1383, https://doi.org/10.1080/15481603.2022.2116102

Arias-Rodriguez, LF, Duan, Z, Diaz-Torres, JJ, Basilio Hazas, M, Huang, J, et al. (2021): Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine. Sensors 21: 4118, https://doi.org/10.3390/s21124118

Gemmel, A, Sterckx, A, Ruz Vargas, C, Abrahams, E, Flügel, T, et al. (2021): Big Data Analytics and Transboundary Water Collaboration: Consolidation of Data and Application of Big Data Tools to Enhance National and Transboundary Data Sets in Southern Africa that Support Decision-Making for Security of Water Resources. WRC Report K5/2880. Water Research Commission, Pretoria, South Africa.

Heal KV, Bartosova A, Hipsey MR, et al. (2021): Water quality: the missing dimension of water in the water–energy–food nexus. Hydrological Sciences Journal: 1-14, https://doi.org/10.1080/02626667.2020.1859114.

Hori, M (2021): Near-daily monitoring of surface temperature and channel width of the six largest Arctic rivers from space using GCOM-C/SGLI. Remote Sensing of Environment 263: 112538, https://doi.org/10.1016/j.rse.2021.112538.

Jankowski KJ, Mejia FH, Blaszczak JR, et al. (2021): Aquatic ecosystem metabolism as a tool in environmental management. WIREs Water: e1521, https://doi.org/10.1002/wat2.1521.

Kaushal SS, Likens GE, Pace ML, et al. (2021): Freshwater salinization syndrome: from emerging global problem to managing risks. Biogeochemistry, https://doi.org/10.1007/s10533-021-00784-w.

Kleinschroth F, Winton RS, Calamita E, et al. (2021): Living with floating vegetation invasions. Ambio 50: 125-137, https://doi.org/10.1007/s13280-020-01360-6.

Mamani Larico AJ, Rendón Dávila VO, Figueroa Tapia ÁM, et al. (2021): Bioenergetic and water quality modeling for eutrophication assessment of El Pañe Reservoir, Peru. Ecohydrology & Hydrobiology 21: 114-128, https://doi.org/10.1016/j.ecohyd.2020.08.004.

Molina, MO (2021): Los datos sobre el agua en México en las plataformas de los organismos internacionales: paradigmas, actores y agendas. Consejo Nacional de Ciencia y Tecnología, Mexico-City, Mexico.

Pickens, A (2021): Dynamics of Global Surface Water 1999-present. PhD thesis, University of Maryland, USA.

Pinheiro, JPS, Windsor, FM, Wilson, RW, Tyler, CR (2021): Global variation in freshwater physico-chemistry and its influence on chemical toxicity in aquatic wildlife. Biological Reviews 96: 1528-1546, https://doi.org/10.1111/brv.12711

Ridzuan, S (2021): Inequality and water pollution in India. Water Policy, 10, https://doi.org/10.2166/wp.2021.057.

Ruescas, A, Stelzer, K, Brockmann, K, Taylor, P, Dobel, A, et al. (2021): Lake Water Quality In-Situ Data – Requirements and Availability. Lake Water Quality Report EEA/DIS/R0/20/001 Lot 1, European Environment Agency.

Shukla T, Sen IS, Boral S, et al. (2021): A Time-Series Record during COVID-19 Lockdown Shows the High Resilience of Dissolved Heavy Metals in the Ganga River. Environmental Science & Technology Letters 8: 301-306, https://doi.org/10.1021/acs.estlett.0c00982.

Thorslund, J, Bierkens, MFP, Oude Essink, GHP, et al. (2021): Common irrigation drivers of freshwater salinisation in river basins worldwide. Nature Communications 12: 4232, https://doi.org/10.1038/s41467-021-24281-8.

Topp, S (2021): Multidecadal Remote Sensing of Inland Water Dynamics. PhD thesis, University of North Carolina, USA.

Vega-Rodríguez MA, Pérez CJ, Reder K, et al. (2021): A stage-based approach to allocating water quality monitoring stations based on the WorldQual model: The Jubba River as a case study. Science of The Total Environment 762: 144162, https://doi.org/10.1016/j.scitotenv.2020.144162.

Virro H, Amatulli G, Kmoch A, et al. (2021): GRQA: Global River Water Quality Archive. Earth System Science Data Discussions 2021: 1-30, https://doi.org/10.5194/essd-2021-51.

Wu, J, Xu, N, Wang, Y, Zhang, W, Borthwick, AGL, et al. (2021): Global syndromes induced by changes in solutes of the world’s large rivers. Nature Communications 12: 5940, https://doi.org/10.1038/s41467-021-26231-w

Yang, D, Shrestha, RR, Lung, JLY, et al. (2021): Heat flux, water temperature and discharge from 15 northern Canadian rivers draining to Arctic Ocean and Hudson Bay, Global and Planetary Change 204: 103577, https://doi.org/10.1016/j.gloplacha.2021.103577.

Alvarez-Risco A, Del-Aguila-Arcentales S, Rosen MA (2020): Management of Water. In: Alvarez-Risco A, Rosen M, Del-Aguila-Arcentales S,  Marinova D (eds.) Building Sustainable Cities: Social, Economic and Environmental Factors: 217-230, https://doi.org/10.1007/978-3-030-45533-0_16

Antman, FM (2021): For Want of a Cup: The Rise of Tea in England and the Impact of Water Quality on Mortality. IZA Discussion Papers 15016, http://dx.doi.org/10.2139/ssrn.4114546

Arreguin-Cortes FI, Saavedra-Horita JR, Rodriguez-Varela JM, et al. (2020): State level water security indices in Mexico. Sustainable Earth 3: 9, https://doi.org/10.1186/s42055-020-00031-4.

Barik SK, Kar BB, Dixit PR, et al. (2020): Water Quality Index as a critical tool for an assessment of biodiversity of inland water ecosystem. Journal of Water Engineering 1: 44-54.

Carena L, Vione D (2020): Mapping the Photochemistry of European Mid-Latitudes Rivers: An Assessment of Their Ability to Photodegrade Contaminants. Molecules 25: 424, https://doi.org/10.3390/molecules25020424.

De Filippis G, Piscitelli P, Castorini IF, et al. (2020): Water Quality Assessment: A Quali-Quantitative Method for Evaluation of Environmental Pressures Potentially Impacting on Groundwater, Developed under the M.I.N.O.Re. Project. International Journal of Environmental Research and Public Health 17: 1835, https://doi.org/10.3390/ijerph17061835.

Denisova I, Efimova L, Sharapova E, et al. (2020): Distribution of nutrient elements and organic carbon in the lower reaches of the Selenga River. Limnology and Freshwater Biology 4: 834-835, https://doi.org/10.31951/2658-3518-2020-A-4-834.

Dickens C, McCartney M, Tickner D, et al. (2020): Evaluating the Global State of Ecosystems and Natural Resources: Within and Beyond the SDGs. Sustainability 12: 7381, https://doi.org/10.3390/su12187381.

Elhag, M, Gitas, I, Othman, A, Bahrawi, J (2020): Effect of water surface area on the remotely sensed water quality parameters of Baysh Dam Lake, Saudi Arabia. Desalination and Water Treatment 194: 369-378, https://doi.org/10.5004/dwt.2020.25358

Fabre C, Sauvage S, Probst JL, et al. (2020): Global-scale daily riverine DOC fluxes from lands to the oceans with a generic model. Global and Planetary Change 194: 103294, https://doi.org/10.1016/j.gloplacha.2020.103294.

Hatono M, Yoshimura K (2020): Development of a global sediment dynamics model. Progress in Earth and Planetary Science 7: 59, https://doi.org/10.1186/s40645-020-00368-6.

Hunt, CF, Voulvoulis, N (2020): Chemical Pollution of the Aquatic Environment and Health. In: Harrison, RM (ed.), Environmental Pollutant Exposures and Public Health, Royal Society of Chemistry: 39-69, https://doi.org/10.1039/9781839160431-00039

Kaushal SS, Wood KL, Galella JG, et al. (2020): Making ‘chemical cocktails’ – Evolution of urban geochemical processes across the periodic table of elements. Applied Geochemistry 119: 104632, https://doi.org/10.1016/j.apgeochem.2020.104632.

McDowell RW, Noble A, Pletnyakov P, et al. (2020): Global mapping of freshwater nutrient enrichment and periphyton growth potential. Scientific Reports 10: 3568, https://doi.org/10.1038/s41598-020-60279-w.

McDowell RW, Noble A, Pletnyakov P, et al. (2020): Global database of diffuse riverine nitrogen and phosphorus loads and yields. Geoscience Data Journal 00: 1-12, https://doi.org/10.1002/gdj3.111.

Metcalfe CD, Collins P, Menone ML, et al. (2020): The Paraná River Basin: Managing water resources to sustain ecosystem services. Routledge, Taylor and Francis, London, UK.

Musonge PSL (2020): Ecological Assessment of Rivers and Streams in the Rwenzori Region, Uganda. PhD thesis, Ghent University, Belgium.

Núñez M, Finkbeiner M (2020): A Regionalised Life Cycle Assessment Model to Globally Assess the Environmental Implications of Soil Salinization in Irrigated Agriculture. Environmental Science & Technology 54: 3082-3090, https://doi.org/10.1021/acs.est.9b03334.

Pandit DN, Kumari R, Shitanshu SK (2020): A comparative assessment of the status of Surajkund and Rani Pond, Aurangabad, Bihar, India using overall Index of Pollution and Water Quality Index. Acta Ecologica Sinica, https://doi.org/10.1016/j.chnaes.2020.11.009.

Polyakova K, Garanis L (2020): Building local indicators for more evidence-based policy making in the water and sanitation sector in Bomet county, Kenya. MSc thesis, University of Geneva, Switzerland.

Rotteveel L (2020): Trends, Patterns, and Drivers of Freshwater Aluminium Concentrations: Revisiting the Conceptual Model of Freshwater Acidification. MSc thesis, Dalhousie University, Canada.

Russ JD, Zaveri ED, Damania R, et al. (2019): Salt of the Earth: Quantifying the Impact of Water Salinity on Global Agricultural Productivity. World Bank, Washington, D.C., https://openknowledge.worldbank.org/handle/10986/33070.

Topp SN, Pavelsky TM, Jensen D, et al. (2020): Research Trends in the Use of Remote Sensing for Inland Water Quality Science: Moving Towards Multidisciplinary Applications. Water 12: 169, https://doi.org/10.3390/w12010169.

Alcocer J, Bernal-Brooks FW (2019): Physical and Chemical Characterization of Inland Waters. In: Ibáñez AL (ed) Mexican Aquatic Environments: A General View from Hydrobiology to Fisheries: 1-41.

Ayana E (2019): Determinants of Declining Water Quality. World Bank, Washington, D.C., USA, https://openknowledge.worldbank.org/handle/10986/33224.

Chanapathi T, Thatikonda S (2019): Fuzzy-Based Regional Water Quality Index for Surface Water Quality Assessment. Journal of Hazardous, Toxic, and Radioactive Waste 23: 04019010, https://doi.org/10.1061/(ASCE)HZ.2153-5515.0000443.

Dadson, SJ, Garrick, DE, Penning-Rowsell, EC, Hall, JW, Hope, R, et al. (2019): Charting the World’s Water Future? In: Dadson, SJ, Garrick, DE, Penning-Rowsell, EC, Hall, JW, Hope, R, et al. (eds.), Water Science, Policy, Management: A Global Challenge, Wiley-Blackwell: 363-366, https://doi.org/10.1002/9781119520627.ch20

Dallosch MA (2019): Optimal algorithms for deriving estimates of phytoplankton biomass in lakes from LANDSAT satellite imagery. MSc thesis, The University of Western Ontario, Canada.

Damania R, Desbureaux S, Rodella AS, Russ, J, Zaveri, E (2019): Quality Unknown: The Invisible Water Crisis. World Bank, Washington, D.C., USA, https://openknowledge.worldbank.org/handle/10986/32245.

Desbureaux, S, Damania, R, Rodella, AS, Russ, J, Zaveri, E (2019): The Impact of Water Quality on GDP Growth – Evidence from Around the World. World Bank, Washington, D.C., USA, https://openknowledge.worldbank.org/handle/10986/33071

Evans AEV, Mateo-Sagasta J, Qadir M, Boelee, E, Ippolito, A (2019): Agricultural water pollution: key knowledge gaps and research needs. Current Opinion in Environmental Sustainability 36: 20-27, https://doi.org/10.1016/j.cosust.2018.10.003.

Haque SJ, Onodera S-i, Shimizu Y (2019): Surface Water Nitrogen Load Due to Food Production-Supply System in South Asian Megacities: A Model-based Estimation. In: Al-Naggar AMM (ed): Advances and Trends in Agricultural Sciences.

Hofstra N, Vermeulen LC, Derx J, Flörke, M, Mateo-Sagasta, J, et al. (2019): Priorities for developing a modelling and scenario analysis framework for waterborne pathogen concentrations in rivers worldwide and consequent burden of disease. Current Opinion in Environmental Sustainability 36: 28-38, https://doi.org/10.1016/j.cosust.2018.10.002.

Karim RA, Deschênes L, Bulle C (2019): Regionalized aquatic ecotoxicity characterization factor for zinc emitted to soil accounting for speciation and the transfer through groundwater. The International Journal of Life Cycle Assessment 24: 2008-2022, https://doi.org/10.1007/s11367-019-01633-7.

Li M, Peng C, Zhou X, Yang, Y, Guo, Y, et al. (2019): Modeling Global Riverine DOC Flux Dynamics From 1951 to 2015. Journal of Advances in Modeling Earth Systems 11: 514-530, https://doi.org/10.1029/2018MS001363.

Li X, Zhao N, Jin R, Liu, S, Sun, X, et al. (2019): Internet of Things to network smart devices for ecosystem monitoring. Science Bulletin 64: 1234-1245, https://doi.org/10.1016/j.scib.2019.07.004.

Pita Merino, L (2019): Análisis y Modelización de la Evolución de Patrones Globales y Regionales en los Conflictos Socio Ambientales. Aplicación a la Amazonía Ecuatoriana. PhD thesis at Universitat Politècnica de Catalunya, Barcelona, Spain.

Pradinaud C, Núñez M, Roux P, Junqua, G, Rosenbaum, RK (2019): The issue of considering water quality in life cycle assessment of water use. The International Journal of Life Cycle Assessment 24: 590-603, https://doi.org/10.1007/s11367-018-1473-5.

Sellers S, Ebi KL, Hess J (2019): Climate Change, Human Health, and Social Stability: Addressing Interlinkages. Environmental Health Perspectives 127: 045002, https://doi.org/10.1289/EHP4534.

Silva, IS (2019): Inteligencia Artificial Para Avaliacao Da Qualidade Da Agua. MSc thesis at Universidade Federal de Sergipe, São Cristóvão, Brazil.

Tang T, Strokal M, van Vliet MTH, Seuntjens, P, Burek, P, et al. (2019): Bridging global, basin and local-scale water quality modeling towards enhancing water quality management worldwide. Current Opinion in Environmental Sustainability 36: 39-48, https://doi.org/10.1016/j.cosust.2018.10.004.

Tritschler, F (2019): Beurteilung und Sammlung von Niederschlag zur Verwendung als aktiver und bezüglich der Mineralisierung inverser Grundwassertracer. Doctoral thesis at Technische Universität Dresden, Germany.

Wang, M, Tang, T, Burek, P, Havlik, P, Krisztin, T, et al. (2019): Increasing nitrogen export to sea: A scenario analysis for the Indus River. Science of the Total Environment 694: 133629, https://doi.org/10.1016/j.scitotenv.2019.133629

Whitehead P, Dolk M, Peters R, Leckie, H (2019): Water Quality Modelling, Monitoring, and Management. In: Dadson SJ, Garrick DE, Penning‐Rowsell EC, Hall JW, Hope R,  Hughes J (eds.): Water Science, Policy, and Management:  A Global Challenge: 55-73.

Ahdab YD, Thiel GP, Böhlke JK, et al. (2018): Minimum energy requirements for desalination of brackish groundwater in the United States with comparison to international datasets. Water Research 141: 387-404, https://doi.org/10.1016/j.watres.2018.04.015.

Chirgwin W, Maheshwari BL (2018): Evaluating the suitability of water quality indices for the health of urban waterways: a case study of the Parramatta River. Water e-journal 3, https://doi.org/10.21139/wej.2018.011.

Guppy L, Uyttendaele P, Villholth KG, et al. (2018): Groundwater and sustainable development goals: Analysis of interlinkagesUNU-INWEH Report Series, Issue 04, Hamilton, Canada.

IOCCG (2018): Earth Observations in Support of Global Water Quality Monitoring. In: Greb S, Dekker A,  Binding C (eds.): Reports of the International Ocean-Colour Coordinating Group, Dartmouth, Canada, pp. 125.

Khan AU, Jiang J, Wang P (2018): Decadal water quality variations at three typical basins of Mekong, Murray and Yukon. IOP Conference Series: Earth and Environmental Science 121: 032009, https://doi.org/10.1088/1755-1315/121/3/032009.

Khan AU, Wang P, Jiang J, et al. (2018): Long-term trends and probability distributions of river water quality variables and their relationships with climate elasticity characteristics. Environmental Monitoring and Assessment 190: 648, https://doi.org/10.1007/s10661-018-7044-1.

Lyon-Marion BA, Mittelman AM, Rayner J, et al. (2018): Impact of chlorination on silver elution from ceramic water filters. Water Research 142: 471-479, https://doi.org/10.1016/j.watres.2018.06.008.

Maheshwari, B, Chirgwin, W (2018): Evaluating the Suitability of Water Quality Indices for the Health of Urban Waterways. Water e-Journal 3: 1-21, https://doi.org/10.21139/wej.2018.011

Ortigara ARC, Kay M, Uhlenbrook S (2018): A Review of the SDG 6 Synthesis Report 2018 from an Education, Training, and Research Perspective. Water 10: 1353, https://doi.org/10.3390/w10101353.

Payen S, Basset-Mens C, Colin F, et al. (2018): Inventory of field water flows for agri-food LCA: critical review and recommendations of modelling options. The International Journal of Life Cycle Assessment 23: 1331-1350, https://doi.org/10.1007/s11367-017-1353-4.

Philipps RR, Xu X, Mills GL, et al. (2018): Impact of natural organic matter and increased water hardness on DGT prediction of copper bioaccumulation by yellow lampmussel (Lampsilis cariosa) and fathead minnow (Pimephales promelas). Environmental Pollution 241: 451-458, https://doi.org/10.1016/j.envpol.2018.05.059.

Adu-Manu KS, Tapparello C, Heinzelman W, et al. (2017): Water Quality Monitoring Using Wireless Sensor Networks: Current Trends and Future Research Directions. ACM Transactions on Sensor Networks 13: Article 4, https://doi.org/10.1145/3005719.

Fischer S, Pietroń J, Bring A, et al. (2017): Present to future sediment transport of the Brahmaputra River: reducing uncertainty in predictions and management. Regional Environmental Change 17: 515-526, https://doi.org/10.1007/s10113-016-1039-7.

Gruiz K (2017): Monitoring and early warning in environmental management. In: Gruiz K, Meggyes T,  Fenyvesi E (eds.): Engineering Tools for Environmental Risk Management: 3 Site Assessment and Monitoring Tools: 99.

Hassall G, van den Belt M (2017): Global sustainability: policy networks for the Sustainable Development Goals. Policy Quarterly 13, https://doi.org/10.26686/pq.v13i1.4641.

Hering JG (2017): Managing the ‘Monitoring Imperative’ in the Context of SDG Target 6.3 on Water Quality and Wastewater. Sustainability 9: 1572, https://doi.org/10.3390/su9091572.

Karthe D, Chalov S, Moreido V, et al. (2017): Assessment of runoff, water and sediment quality in the Selenga River basin aided by a web-based geoservice. Water Resources 44: 399-416, https://doi.org/10.1134/S0097807817030113.

Khan AU, Jiang J, Sharma A, et al. (2017): How Do Terrestrial Determinants Impact the Response of Water Quality to Climate Drivers?—An Elasticity Perspective on the Water–Land–Climate Nexus. Sustainability 9: 2118, https://doi.org/10.3390/su9112118.

Khan AU, Jiang J, Wang P, et al. (2017): Influence of watershed topographic and socio-economic attributes on the climate sensitivity of global river water quality. Environmental Research Letters 12: 104012, https://doi.org/10.1088/1748-9326/aa8a33.

Liu J, Yang H, Gosling SN, et al. (2017): Water scarcity assessments in the past, present, and future. Earth’s Future 5: 545-559, https://doi.org/10.1002/2016EF000518.

Metcalfe C, Guppy L, Qadir M (2017): Global barriers to improving water quality: a critical review. UNU-INWEH Report Series, Issue 02, Hamilton, Canada.

Ouedraogo I (2017): Mapping groundwater vulnerability at the pan-African scale. PhD thesis, UCL-Université Catholique de Louvain.

Pérez CJ, Vega-Rodríguez MA, Reder K, et al. (2017): A Multi-Objective Artificial Bee Colony-based optimization approach to design water quality monitoring networks in river basins. Journal of Cleaner Production 166: 579-589, https://doi.org/10.1016/j.jclepro.2017.08.060.

Thorslund J, Jarsjö J, Wällstedt T, et al. (2017): Speciation and hydrological transport of metals in non-acidic river systems of the Lake Baikal basin: Field data and model predictions. Regional Environmental Change 17: 2007-2021, https://doi.org/10.1007/s10113-016-0982-7.

Abbaspour S (2011): Water quality in developing countries, South Asia, South Africa, water quality management and activities that cause water pollution. 2011 International Conference on Environmental and Agriculture Engineering, Singapore, pp. 94-102.

Barker, S, Robarts, R, Yamashiki, Y et al. (2007). UNEP-GEMS/Water Programme—water quality data, GEMStat and open web services—and Japanese cooperation. Hydrological Processes, 21(9), 1132-1141. doi:https://doi.org/10.1002/hyp.6673

Boulay A-M, Motoshita M, Pfister S, et al. (2015): Analysis of water use impact assessment methods (part A): evaluation of modeling choices based on a quantitative comparison of scarcity and human health indicators. The International Journal of Life Cycle Assessment 20: 139-160, https://doi.org/10.1007/s11367-014-0814-2

Brainerd E, Menon N (2014): Seasonal effects of water quality: The hidden costs of the Green Revolution to infant and child health in India. Journal of Development Economics 107: 49-64, https://doi.org/10.1016/j.jdeveco.2013.11.004

Bring A, Destouni G (2009): Hydrological and hydrochemical observation status in the pan-Arctic drainage basin. Polar Research 28: 327-338, https://doi.org/10.1111/j.1751-8369.2009.00126.x

Davies-Colley RJ, Smith DG, Ward RC, et al. (2011): Twenty Years of New Zealand’s National Rivers Water Quality Network: Benefits of Careful Design and Consistent Operation. Journal of the American Water Resources Association 47: 750-771, https://doi.org/10.1111/j.1752-1688.2011.00554.x

Donnelly C, Arheimer B, Capell R, et al. (2013): Regional overview of nutrient load in Europe–challenges when using a large-scale model approach, E-HYPEH04, IAHS-IAPSO-IASPEI Assembly, Gothenburg, Sweden.

Erisman JW, van Grinsven H, Grizzetti B, et al. (2011): The European Nitrogen Assessment: Sources, Effects and Policy Perspectives. In: Sutton MA, Howard CM, Erisman JW, Billen G, Bleeker A, Grennfelt P, van Grinsven H,  Grizzetti B (eds.): Nitrogen in Europe: the present position: 9-31.

Gómez-Bombarelli R, González-Pérez M, Calle E, et al. (2011): Reactivity of mucohalic acids in water. Water Research 45: 714-720, https://doi.org/10.1016/j.watres.2010.08.040

Grosse J, Bombar D, Doan HN, et al. (2010): The Mekong River plume fuels nitrogen fixation and determines phytoplankton species distribution in the South China Sea during low and high discharge season. Limnology and Oceanography 55: 1668-1680, https://doi.org/10.4319/lo.2010.55.4.1668

Holley C, Sinclair D, L. E (2016): Regulation, Technology, and Water: Buy-In as a Precondition for Effective Real-Time Advanced Monitoring, Compliance, and Enforcement. George Washington Journal of Energy and Environmental Law 7: 52.

Lehmann A, Giuliani G, Ray N, et al. (2014): Reviewing innovative Earth observation solutions for filling science-policy gaps in hydrology. Journal of Hydrology 518: 267-277, https://doi.org/10.1016/j.jhydrol.2014.05.059

Li S, Lu XX, Bush RT (2014): Chemical weathering and CO2 consumption in the Lower Mekong River. Science of The Total Environment 472: 162-177, https://doi.org/10.1016/j.scitotenv.2013.11.027

Makapela L, Newby T, Gibson L, et al. (2015): Review of the use of Earth Observations Remote Sensing in Water Resource Management in South Africa. Report KV329/15, Pretoria.

McCrackin ML, Harrison JA, Compton JE (2014): Factors influencing export of dissolved inorganic nitrogen by major rivers: A new, seasonal, spatially explicit, global model. Global Biogeochemical Cycles 28: 269-285, https://doi.org/10.1002/2013GB004723

McDowell RW, Hill SJ (2015): Speciation and distribution of organic phosphorus in river sediments: a national survey. Journal of Soils and Sediments 15: 2369-2379, https://doi.org/10.1007/s11368-015-1125-3

Raptis CE, van Vliet MTH, Pfister S (2016): Global thermal pollution of rivers from thermoelectric power plants. Environmental Research Letters 11: 104011, https://doi.org/10.1088/1748-9326/11/10/104011

Rickwood, CJ, & Carr, GM (2009). Development and sensitivity analysis of a global drinking water quality index. Environmental Monitoring and Assessment, 156(1), 73-90. doi: https://doi.org/10.1007/s10661-008-0464-6

Sigman H (2014): Decentralization and Environmental Quality: An International Analysis of Water Pollution Levels and Variation. Land Economics 90: 114-130, https://doi.org/10.3368/le.90.1.114

Soomro M, Khokhar M, Hussain W, et al. (2011): Drinking water quality challenges in Pakistan: 17-28.

Srebotnjak T, Carr G, de Sherbinin A, et al. (2012): A global Water Quality Index and hot-deck imputation of missing data. Ecological Indicators 17: 108-119, https://doi.org/10.1016/j.ecolind.2011.04.023

Thorslund J, Jarsjö J, Chalov SR, et al. (2012): Gold mining impact on riverine heavy metal transport in a sparsely monitored region: the upper Lake Baikal Basin case. Journal of Environmental Monitoring 14: 2780-2792, https://doi.org/10.1039/C2EM30643C