216
Views
0
CrossRef citations to date
0
Altmetric
Section 2. Water Information Systems

Introduction to section 2

&

References

  • Acharya, B. S., Bhandari, M., Bandini, F., Pizarro, A., Perks, M., Joshi, D. R., Wang, S., Dogwiler, T., Ray, R. L., Kharel, G., & Sharma, S. (2021). Unmanned aerial vehicles in hydrology and water management: Applications, challenges, and perspectives. Water Resources Research, 57(11), e2021WR029925. https://doi.org/10.1029/2021WR029925
  • Adewole Adedayo, O., & Eludoyin Adebayo, O. (2019). Remote sensing and River Basin management: An expository review with special reference to Southwest Nigeria. In G. K. Gopal & M. Sutapa (Eds.), Current Practice in Fluvial Geomorphology (pp. 4). IntechOpen.
  • Ahmed, A. A., Sayed, S., Abdoulhalik, A., Moutari, S., & Oyedele, L. (2024). Applications of machine learning to water resources management: A review of present status and future opportunities. Journal of Cleaner Production, 441, 140715. https://doi.org/10.1016/j.jclepro.2024.140715
  • Akbarian, M., Saghafian, B., & Golian, S. (2023). Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran. Journal of Hydrology, 620, 129480. https://doi.org/10.1016/j.jhydrol.2023.129480
  • Amari, S. I. (1972). Learning patterns and pattern sequences by self-organizing nets of threshold elements. IEEE Transactions on Computers, C-21(11), 1197–1206. https://doi.org/10.1109/T-C.1972.223477
  • Assumpção, T. H., Popescu, I., Jonoski, A., & Solomatine, D. P. (2018). Citizen observations contributing to flood modelling: Opportunities and challenges. Hydrology and Earth System Sciences, 22(2), 1473–1489. https://doi.org/10.5194/hess-22-1473-2018
  • Bandini, F., Jakobsen, J., Olesen, D., Reyna-Gutierrez, J. A., & Bauer-Gottwein, P. (2017). Measuring water level in rivers and lakes from lightweight unmanned aerial vehicles. Journal of Hydrology, 548, 237–250. https://doi.org/10.1016/j.jhydrol.2017.02.038
  • Basin Management Action Plans. (2024, January 17). https://floridadep.gov/dear/water-quality-restoration/content/basin-management-action-plans-bmaps
  • Benson, D., Fritsch, O., Cook, H., & Schmid, M. (2014). Evaluating participation in WFD river basin management in England and Wales: Processes, communities, outputs and outcomes. Land Use Policy, 38, 213–222. https://doi.org/10.1016/j.landusepol.2013.11.004
  • Biancamaria, S., Lettenmaier, D. P., & Pavelsky, T. M. J. S. I. G. (2016). The SWOT Mission and Its capabilities for land Hydrology. Remote Sensing and Water Resources, 37, 307–337. https://doi.org/10.1007/s10712-015-9346-y
  • Buytaert, W., Zulkafli, Z., Grainger, S., Acosta, L., Alemie, T. C., Bastiaensen, J., De Bièvre, B., Bhusal, J., Clark, J., Dewulf, A. Foggin, M., and Zhumanova, M. (2014). Citizen science in hydrology and water resources: Opportunities for knowledge generation, ecosystem service management, and sustainable development. Frontiers in Earth Science, 2. https://doi.org/10.3389/feart.2014.00026
  • Cai, X., Marston, L. T., & Ge, Y. (2014). Decision support for integrated river basin management—scientific research challenges. Science China Earth Sciences, 58, 16–24. https://doi.org/10.1007/s11430-014-5005-2
  • Carr, G. (2015). Stakeholder and public participation in river basin management—an introduction. WIREs Water, 2(4), 393–405. https://doi.org/10.1002/wat2.1086
  • Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79–97. https://doi.org/10.1016/j.isprsjprs.2014.02.013
  • Costa, F. S., & Lopes, H. S. (2024). The Portuguese dams of the international Douro, climate change and adaptation strategies: Perspectives within the framework of the Albufeira Convention and the Water Framework Directive. Water International, 49(3–4), 417–428. https://doi.org/10.1080/02508060.2024.2321820
  • Coveney, S., & Roberts, K. (2017). Lightweight UAV digital elevation models and orthoimagery for environmental applications: Data accuracy evaluation and potential for river flood risk modelling. International Journal of Remote Sensing, 38(8–10), 3159–3180. https://doi.org/10.1080/01431161.2017.1292074
  • Darnswadi, R., Ratanachai, P. C., Teeraku, B., Janchum, K., & Keawthong, T. (2015). Stakeholder engagement and analysis for water governance of Songkhla Lake Basin, Thailand. Prince of Songkla University. http://kb.psu.ac.th/psukb/handle/2016/11908
  • De Groeve, T. (2010). Flood monitoring and mapping using passive microwave remote sensing in Namibia. Geomatics, Natural Hazards and Risk, 1(1), 19–35. https://doi.org/10.1080/19475701003648085
  • Dehghani, A., Moazam, H. M. Z. H., Mortazavizadeh, F., Ranjbar, V., Mirzaei, M., Mortezavi, S., Ng, J. L. Dehghani, A. (2023). Comparative evaluation of LSTM, CNN, and ConvLSTM for hourly short-term streamflow forecasting using deep learning approaches. Ecological Informatics, 75, 102119. https://doi.org/10.1016/j.ecoinf.2023.102119
  • Den Haan, R. J., Fliervoet, J. M., van der Voort, M. C., Cortes Arevalo, V. J., & Hulscher, S. J. M. H. (2019). Understanding actor perspectives regarding challenges for integrated river basin management. International Journal of River Basin Management, 17(2), 229–242. https://doi.org/10.1080/15715124.2018.1503186
  • Duester, L., Livrozet, N., Poturalski, S., Stoetter, T., Gerloff, A.-L., & Heintz, M. D. (2024). The ICPR measuring programme chemistry and its monitoring approach – a look back and a glimpse of the future. Water International, 49(3–4), 446–454. https://doi.org/10.1080/02508060.2024.2321691
  • Escobar Villanueva, J. R., Iglesias Martínez, L., & Pérez Montiel, J. I. (2019). DEM generation from fixed-wing UAV imaging and LiDAR-derived ground control points for flood estimations. Sensors, 19(14), 3205. https://doi.org/10.3390/s19143205
  • Galesic, M., Bruine de Bruin, W., Dalege, J., Feld, S. L., Kreuter, F., Olsson, H., van der Does, T. (2021). Human social sensing is an untapped resource for computational social science. Nature, 595(7866), 214–222. https://doi.org/10.1038/s41586-021-03649-2
  • Giakoumis, T., & Voulvoulis, N. (2018). The transition of EU water policy towards the water framework directive’s integrated River Basin management paradigm. Environmental Management, 62(5), 819–831. https://doi.org/10.1007/s00267-018-1080-z
  • Hartwig, K., Thurston, P., Smith, H., Carver, M., Utzig, G., MacDonald, R., Jollymore, A., & Trigg, N. (2024). Water security through community-directed monitoring in the Canadian Columbia Basin: democratizing watershed data. Water International, 49(3–4), 429–438. https://doi.org/10.1080/02508060.2024.2321823
  • Hung, P., Le, T. V., Vo, P. L., Duong, H. C., & Rahman, M. M. (2022). Vulnerability assessment of water resources using GIS, remote sensing and SWAT model – A case study: The upper part of Dong Nai river basin, Vietnam. International Journal of River Basin Management, 20(4), 517–532. https://doi.org/10.1080/15715124.2021.1901729
  • Ibrahim, I., Brown, R., & Truby, J. (2022). Big data analytics and its impact on Basin Water agreements and international water law: A study of the Ramotswa aquifer. Brill.
  • International Atomic Energy, A. (2002). Managing water resources using isotope hydrology. International Atomic Energy Agency (IAEA). http://inis.iaea.org/search/search.aspx?orig_q=RN:33045334
  • Ireri, B. K., Makenzi, P. M., Makindi, S. M., Minang, P. A., & Mironga, J. M. (2024). Provisioning of water ecosystem services in the Kapingazi River Basin in Kenya: Can prospects of willingness to pay improve water quality and quantity? Water International, 49(3–4), 410–416. https://doi.org/10.1080/02508060.2024.2321819
  • Irvine, K., Weigelhofer, G., Popescu, I., Pfeiffer, E., Păun, A., Drobot, R., Habersack, H. (2016). Educating for action: Aligning skills with policies for sustainable development in the Danube river basin. Science of the Total Environment, 543, 765–777. https://doi.org/10.1016/j.scitotenv.2015.09.072
  • Jiang, X., Liu, Y., & Zhao, R. (2019). A framework for ecological compensation assessment: A case study in the upper Hun River Basin, Northeast China. Sustainability, 11(4). https://doi.org/10.3390/su11041205
  • Jiménez López, J., & Mulero-Pázmány, M. (2019). Drones for conservation in protected areas: Present and future. Drones, 3(1), 10. https://doi.org/10.3390/drones3010010
  • Khoshnoodmotlagh, S., Verrelst, J., Daneshi, A., Mirzaei, M., Azadi, H., Haghighi, M., Marofi, S. (2020). Transboundary basins need more attention: Anthropogenic impacts on land cover changes in aras river basin, monitoring and prediction. Remote Sensing, 12(20), 3329. https://doi.org/10.3390/rs12203329
  • Kumar, V., Kedam, N., Sharma, K. V., Mehta, D. J., & Caloiero, T. (2023). Advanced machine learning techniques to improve hydrological prediction: A comparative analysis of streamflow prediction models. Water, 15(14), 2572. https://doi.org/10.3390/w15142572
  • Lakshmikantha, V., Hiriyannagowda, A., Manjunath, A., Patted, A., Basavaiah, J., & Anthony, A. A. (2021). IoT based smart water quality monitoring system. Global Transitions Proceedings, 2(2), 181–186. https://doi.org/10.1016/j.gltp.2021.08.062
  • Langhammer, J., & Vacková, T. (2018). Detection and mapping of the geomorphic effects of flooding using uav photogrammetry. Pure and Applied Geophysics, 175(9), 3223–3245. https://doi.org/10.1007/s00024-018-1874-1
  • Li, Z., Li, L., Chu, Y., Wang, X., & Yang, D. (2023). GIS-based risk assessment of flood disaster in the Lijiang River Basin. Scientific Reports, 13(1), 6160. https://doi.org/10.1038/s41598-023-32829-5
  • Lim, C. H., Wong, H. L., Elfithri, R., & Teo, F. Y. (2022). A review of stakeholder engagement in integrated River Basin Management. Water, 14(19), 2973. https://doi.org/10.3390/w14192973
  • Marques, G. G., Formiga-Johnsson, R. M., Laigneau, P., Dalcin, A. P., Goldenstein, S., Bonilha, I., & Possantti, I. (2024). Integrating water charges policies and watershed plans for improved investment and financial sustainability in water resources management Water International, 49(3–4), 392–409. https://doi.org/10.1080/02508060.2024.2321818
  • Marshall, A. C., & Duram, L. A. (2017). Factors influencing local stakeholders’ perceptions of Tisza River Basin management: The role of employment sector and education. Environmental Science & Policy, 77, 69–76. https://doi.org/10.1016/j.envsci.2017.07.009
  • Meier, F., Fenner, D., Grassmann, T., Otto, M., & Scherer, D. (2017). Crowdsourcing air temperature from citizen weather stations for urban climate research. Urban Climate, 19, 170–191. https://doi.org/10.1016/j.uclim.2017.01.006
  • Mendoza-Cano, O., Aquino-Santos, R., López-de la Cruz, J., Edwards, R. M., Khouakhi, A., Pattison, I., Rangel-Licea, V., Castellanos-Berjan, E., Martinez-Preciado, M.A., Rincón-Avalos, P., Lepper, P., J. M., Uribe-Ramos, J. Ibarreche, & I. Perez, (2021). Experiments of an IoT-based wireless sensor network for flood monitoring in Colima, Mexico. Journal of Hydroinformatics, 23(3), 385–401. https://doi.org/10.2166/hydro.2021.126
  • Mody, J. (2004). Achieving accountability through decentralization: Lessons for integrated River Basin Management. The World Bank.
  • Mukta, M., Islam, S., Barman, S. D., Reza, A. W., & Khan, M. S. H. (2019, 23–25 February). Iot based smart water quality monitoring system. Paper presented at the 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), Singapore. Institute of Electrical and Electronics Engineers (IEEE).
  • Muller, C. L., Chapman, L., Johnston, S., Kidd, C., Illingworth, S., Foody, G., Leigh, R. R. (2015). Crowdsourcing for climate and atmospheric sciences: Current status and future potential. International Journal of Climatology, 35(11), 3185–3203. https://doi.org/10.1002/joc.4210
  • Okolie, C. J., & Smit, J. L. (2022). A systematic review and meta-analysis of Digital elevation model (DEM) fusion: Pre-processing, methods and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 188, 1–29. https://doi.org/10.1016/j.isprsjprs.2022.03.016
  • Pansera, J.-N. (2024). International Meuse Commission: 20 years of cooperation. Water International, 49(3–4), 455–465. https://doi.org/10.1080/02508060.2024.2321689
  • Prasad, A. N., Mamun, K. A., Islam, F. R., & Haqva, H. (2015 , 2–4 December). Smart water quality monitoring system. Paper presented at the 2015 2nd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/APWCCSE.2015.7476234
  • Richards, C. E., Tzachor, A., Avin, S., & Fenner, R. (2023). Rewards, risks and responsible deployment of artificial intelligence in water systems. Nature Water, 1(5), 422–432. https://doi.org/10.1038/s44221-023-00069-6
  • Sahu, A., Kumar, N., Pal Singh, C., & Singh, M. (2023). Environmental DNA (eDNA): Powerful technique for biodiversity conservation. Journal for Nature Conservation, 71, 126325. https://doi.org/10.1016/j.jnc.2022.126325
  • Salmoral, G., Rivas Casado, M., Muthusamy, M., Butler, D., Menon, P. P., & Leinster, P. (2020). Guidelines for the use of unmanned aerial systems in flood emergency response. Water, 12(2), 521. https://doi.org/10.3390/w12020521
  • Satellite Data and Digital Twin Models to support River Basin Management. (2023). https://sdgs.un.org/partnerships/satellite-data-and-digital-twin-models-support-river-basin-management
  • Şerban, G., Rus, I., Vele, D., Breţcan, P., Alexe, M., & Petrea, D. (2016). Flood-prone area delimitation using UAV technology, in the areas hard-to-reach for classic aircrafts: Case study in the north-east of Apuseni Mountains, Transylvania. Natural Hazards, 82(3), 1817–1832. https://doi.org/10.1007/s11069-016-2266-4
  • Sheffield, J., Wood, E. F., Pan, M., Beck, H., Coccia, G., Serrat-Capdevila, A., & Verbist, K. J. W. R. R. (2018). Satellite remote sensing for water resources management: Potential for supporting sustainable development in data‐poor regions. Water Resources Research, 54, 9724–9758. https://doi.org/10.1029/2017WR022437
  • Singh, S., Rai, S., Singh, P., & Mishra, V. K. (2022). Real-time water quality monitoring of River Ganga (India) using internet of things. Ecological Informatics, 71, 101770. https://doi.org/10.1016/j.ecoinf.2022.101770
  • Sithole, B. (2001). Participation and stakeholder dynamics in the water reform process in zimbabwe: the case of the mazoe pilot catchment board. African Studies Quarterly, 5(3), 19–40. https://asq.africa.ufl.edu/wp-content/uploads/sites/168/Sithole-Vol-5-Issue-3.pdf
  • Smart Water Management Platform. (2022, August 24). https://cordis.europa.eu/project/id/777112
  • Stötter, T., Livrozet, N., & Vietoris, F. (2024). Reduction of micropollutants in the Rhine catchment area – Monitoring and assessment system. Water International, 49(3–4), 439–445. https://doi.org/10.1080/02508060.2024.2321803
  • Syahputra, B., Fajar, B., & Sudarno. (2022). Community participation in river basin management. In R. L. Ray, D. G. Panagoulia, & N. S. Abeysingha (Eds.), River Basin Management (pp. 265-278). IntechOpen.
  • Tiyasha, Tung, T. M., & Yaseen, Z. M. (2020). A survey on river water quality modelling using artificial intelligence models: 2000–2020. Journal of Hydrology, 585, 124670. https://doi.org/10.1016/j.jhydrol.2020.124670
  • Torgersen, C. E., Faux, R. N., McIntosh, B. A., Poage, N. J., & Norton, D. J. (2001). Airborne thermal remote sensing for water temperature assessment in rivers and streams. Remote Sensing of Environment, 76(3), 386–398. https://doi.org/10.1016/S0034-4257(01)00186-9
  • UNESCO. (2023). Overview of eDNA sampling campaigns. https://www.unesco.org/en/edna-overview
  • Van Metre, P. C., Qi, S., Deacon, J., Dieter, C., Driscoll, J. M., Fienen, M., Kenney, T., Lambert, P., Lesmes, D., Mason, C. A., Mueller-Solger, A. Wolock, D. (2020). Prioritizing river basins for intensive monitoring and assessment by the US Geological Survey. Environmental Monitoring and Assessment, 192(7), 458. https://doi.org/10.1007/s10661-020-08403-1
  • Vasudevan, S. K., & Baskaran, B. (2021). An improved real-time water quality monitoring embedded system with IoT on unmanned surface vehicle. Ecological Informatics, 65, 101421. https://doi.org/10.1016/j.ecoinf.2021.101421
  • Verbrugge, L. N. H., Ganzevoort, W., Fliervoet, J. M., Panten, K., & van den Born, R. J. G. (2017). Implementing participatory monitoring in river management: The role of stakeholders’ perspectives and incentives. Journal of Environmental Management, 195, 62–69. https://doi.org/10.1016/j.jenvman.2016.11.035
  • Verhagen, B. T. (2003). Isotope hydrology and its impact in the developing world. Journal of Radioanalytical and Nuclear Chemistry, 257(1), 17–26. https://doi.org/10.1023/A:1024724705499
  • Waylen, K. A., Blackstock, K. L., van Hulst, F. J., Damian, C., Horváth, F., Johnson, R. K., Johnson, R. K, Kanka, R., Külvik, M. Macleod, C. J., Meissner, K., Oprina-Pavelescu, M. M., Van Uytvanck, J. (2019). Policy-driven monitoring and evaluation: Does it support adaptive management of socio-ecological systems? Science of the Total Environment, 662, 373–384. https://doi.org/10.1016/j.scitotenv.2018.12.462
  • Weeser, B., Gräf, J., Njue, N. K., Cerutti, P., Rufino, M. C., Breuer, L., & Jacobs, S. R. (2021). Crowdsourced water level monitoring in Kenya’s sondu-miriu basin—who is “the crowd”?. Frontiers in Earth Science, 8. https://doi.org/10.3389/feart.2020.602422
  • World Heritage Marine Programme. (2024, January 28). https://whc.unesco.org/en/marine-programme/
  • Wu, Q., Ke, L., Wang, J., Pavelsky, T. M., Allen, G. H., Sheng, Y., Duan, X, Zhu, Y, Wu, J, Wang, L, Liu, K Song, C. (2023). Satellites reveal hotspots of global river extent change. Nature Communications, 14(1), 1587. https://doi.org/10.1038/s41467-023-37061-3
  • Wunsch, A., Liesch, T., & Broda, S. (2022). Deep learning shows declining groundwater levels in Germany until 2100 due to climate change. Nature Communications, 13(1), 1221. https://doi.org/10.1038/s41467-022-28770-2
  • Yaykıran, S., Cuceloglu, G., & Ekdal, A. (2019). Estimation of water budget components of the Sakarya River Basin by using the WEAP-PGM model. Water, 11(2), 271. https://doi.org/10.3390/w11020271
  • Zeitoun, M., Goulden, M., & Tickner, D. (2013). Current and future challenges facing transboundary river basin management. WIREs Climate Change, 4(5), 331–349. https://doi.org/10.1002/wcc.228
  • Zheng, F., Tao, R., Maier, H. R., See, L., Savic, D., Zhang, T., Chen, Q, Assumpção, TH, Yang, P, Heidari, B, Rieckermann, J. Popescu, I. (2018). Crowdsourcing methods for data collection in geophysics: State of the art, issues, and future directions. Reviews of Geophysics, 56(4), 698–740. https://doi.org/10.1029/2018RG000616
  • Zhou, Y. (2020). Real-time probabilistic forecasting of river water quality under data missing situation: Deep learning plus post-processing techniques. Journal of Hydrology, 589, 125164. https://doi.org/10.1016/j.jhydrol.2020.125164

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.