A systematic review of performance assessment in canal irrigation systems: Integrating socio-technical, remote sensing, and AI-driven approaches for a climate-resilient future

Document Type : Research/Original/Regular Article

Authors

1 Ph.D. Scholar, Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru-575025, Karnataka, India

2 Scantiest ‘C’, National Institute of Hydrology, Hard Rock Regional Centre, Visvesvaraya Nagar, Belagavi – 590019, Karnataka, India

3 Professor, Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru-575025, Karnataka, India

Abstract

This systematic review investigates the evolution of performance assessment in canal irrigation systems globally, drawing evidence from Asia, Africa, and Latin America. Adhering to PRISMA guidelines, it synthesized 98 peer-reviewed studies and key organizational reports published between 1990 and 2025, primarily from Scopus and Web of Science. The analysis reveals a clear methodological progression from direct measurements to remote sensing (RS) and agro-hydrological modeling, with Artificial Intelligence (AI) now evidenced as an applied tool in some assessments, not merely a future prospect. A critical insight, however, is that despite these technical advancements, persistent underperformance is primarily rooted in deep-seated non-technical (financial, institutional, social) barriers. The current review highlights a significant gap: the absence of a unified framework systematically integrating these technical and socio-institutional dimensions with forward-looking climate resilience. Our primary contribution is a novel, integrated socio-technical assessment framework designed to bridge this divide. Distinct from previous reviews, the proposed framework explicitly combines the methodological triad, comprehensive socio-institutional analysis, quantifiable climate resilience metrics, and mechanisms to ensure social equity in AI-driven management. This adaptable, multi-scale diagnostic tool offers an actionable blueprint, applicable from local canal management to national policy levels, that accounts for diverse regional data limitations. By enabling more effective problem diagnosis and intervention design, proposed framework provides significant analytical value and actionable lessons for enhancing the productivity, equity, and climate resilience of canal irrigation systems, thereby directly advancing Sustainable Development Goals 2 and 6.

Keywords

Main Subjects


  1. References

    Abhilash, R., Basappa, V., Chakragiri, S. V., & Patankar, D. B. (2022). Geospatial Approach for Integrated Command Area Management. Journal of Irrigation and Drainage Engineering, 148(4), 1–11. doi: 10.1061/(asce)ir.1943-4774.0001659

    Alataway, A., Al-Ghobari, H., Mohammad, F., & Dewidar, A. (2019). Lysimeter-Based Water Use and Crop Coefficient of Drip-Irrigated Potato in an Arid Environment. Agronomy, 9(11), 756. doi: 10.3390/agronomy9110756

    Amarasinghe, U. A., Sikka, A., Mandave, V., Panda, R. K., Gorantiware, S., Chandrasekharana, K., & Ambast, S. K. (2021). A re-look at canal irrigation system performance: a pilot study of the Sina irrigation system in Maharashtra, India. Water Policy, 23, 114–129. doi: 10.2166/wp.2020.291

    Amsalu, Y., & Mulu, A. (2025). Performance evaluation of irrigation scheme at Dimama Angerf Abay, Awi Zone, Amhara region, Ethiopia. Discover Water, 5(1), 40. doi: 10.1007/s43832-025-00224-y

    Azari, M. D., & Rizi, A. P. (2021). Challenges of irrigation water distribution from the viewpoint of socio-hydraulic relations*. Irrigation and Drainage, 70(5), 1273–1286. doi: 10.1002/ird.2596

    Bantero, B., Ayana, M., & Awulachew, S. (2011). Assessment of Irrigation Performance along the Canal Reach of Community Managed Scheme in Southern Ethiopia. Ethiopian Journal of Development Research, 32(1). doi: 10.4314/ejdr.v32i1.68599

    Bastiaanssen, W. G. M., & Bos, M. G. (1999). Irrigation performance indicators based on remotely sensed data: A review of literature. Irrigation and Drainage Systems, 13(4), 291–311. doi: 10.1023/A:1006355315251

    Bastiaanssen, W., Mobin-ud-Din Ahmad, & Zubair Tahir. (2003). Upscaling water productivity in irrigated agriculture using remote-sensing and GIS technologies. In W. Kijne, R. Barker, & D. Molden (Eds.), Water productivity in agriculture: limits and opportunities for improvement (pp. 289–300). CAB International. doi: 10.1079/9780851996691.0289

    Basukala, A. K., Eschenbach, A., & Rasche, L. (2024). Effect of irrigation canal conveyance efficiency enhancement on crop productivity under climate change in Nepal. Environmental Monitoring and Assessment, 196(12). doi: 10.1007/s10661-024-13405-4

    Belarbi, Z., & El Younoussi, Y. (2025). A Review on Optimizing Water Management in Agriculture through Smart Irrigation Systems and Machine Learning. In B. Benhala, A. Lachhab, A. Raihani, M. Qbadou, & A. Sallem (Eds.), ICEGC’2024. E3S Web of Conferences. doi: 10.1051/e3sconf/202560100078

    Blatchford, M., M. Mannaerts, C., Zeng, Y., Nouri, H., & Karimi, P. (2020). Influence of Spatial Resolution on Remote Sensing-Based Irrigation Performance Assessment Using WaPOR Data. Remote Sensing, 12(18), 2949. doi: 10.3390/rs12182949

    Bos, M. G., & Nugteren, J. (1990). On Irrigation Efficiencies. https://www.researchgate.net/profile/Mg-Bos/publication/44399513_On_irrigation_efficiencies_M_G_Bos_J_Nugteren/links/548ec86e0cf225bf66a633ce/On-irrigation-efficiencies-M-G-Bos-J-Nugteren.pdf

    Bos, M. G., Burton, M. A., & Molden, D. J. (2005). Irrigation and Drainage Performance Assessment Practical Guidelines. https://www.icid.org/Guidelines_Molden2005.pdf

    Bos, M. G., Wolters, W., Drovandi, A., & Morabito, J. A. (1991). The Viejo Retamo secondary canal - Performance evaluation case study: Mendoza, Argentina. Irrigation and Drainage Systems, 5(1), 77–88. doi: 10.1007/BF01102778

    Burt, C. M., & Styles, S. W. (1998). Modern Water Control and Management Practices in Irrigation: Impact on Performance. In Proceedings of the Expert Consultation on Modernization of Irrigation Schemes: Past Experiences and Future Options (Issue October). http://www.itrc.org/papers/modernwatercontrol.htm

    Cerjak, M., Medici, M., Faletar, I., Sundeep, J. V., & Canavari, M. (2025). Adoption of mobile-based agricultural extension services: evidence from South India. Journal of Rural Studies, 120, 103851. doi: 10.1016/j.jrurstud.2025.103851

    Chen, H.-Y., Sharma, K., Sharma, C., & Sharma, S. (2023). Integrating explainable artificial intelligence and blockchain to smart agriculture: Research prospects for decision making and improved security. Smart Agricultural Technology, 6, 100350. doi: 10.1016/j.atech.2023.100350

    Chukalla, A. D., Mul, M. L., van der Zaag, P., van Halsema, G., Mubaya, E., Muchanga, E., den Besten, N., & Karimi, P. (2022). A framework for irrigation performance assessment using WaPOR data: the case of a sugarcane estate in Mozambique. Hydrology and Earth System Sciences, 26(10), 2759–2778. doi: 10.5194/hess-26-2759-2022

    CWC. (2002). Guidelines for Performance Evaluation of Irrigation System. https://cwc.gov.in/sites/default/files/guidelines-performance-evaluation-irrigation-system-2002compressed.pdf

    Derardja, B., Khadra, R., Abdelmoneim, A. A. A., El-Shirbeny, M. A., Valsamidis, T., De Pasquale, V., Deflorio, A. M., & Volden, E. (2024). Advancements in Remote Sensing for Evapotranspiration Estimation: A Comprehensive Review of Temperature-Based Models. Remote Sensing, 16(11), 1927. doi: 10.3390/rs16111927

    El Hachimi, J., El Harti, A., Lhissou, R., Ouzemou, J.-E., Chakouri, M., & Jellouli, A. (2022). Combination of Sentinel-2 Satellite Images and Meteorological Data for Crop Water Requirements Estimation in Intensive Agriculture. Agriculture, 12(8), 1168. doi: 10.3390/agriculture12081168

    El-Agha, D. E., Molden, D. J., & Ghanem, A. M. (2011). Performance assessment of irrigation water management in old lands of the Nile delta of Egypt. Irrigation and Drainage Systems, 25(4), 215–236. doi: 10.1007/s10795-011-9116-z

    Elshaikh, A. E., Jiao, X., & Yang, S. hong. (2018). Performance evaluation of irrigation projects: Theories, methods, and techniques. Agricultural Water Management, 203, 87–96. doi: 10.1016/j.agwat.2018.02.034

    Er-Rami, M., D’Urso, G., Lamaddalena, N., D’Agostino, D., & Belfiore, O. R. (2021). Analysis of irrigation system performance based on an integrated approach with Sentinel-2 satellite images. Journal of Agricultural Engineering, 52(2). doi: 10.4081/jae.2021.1170

    FAO, & DWFI. (2015). Yield gap analysis of field crops – Methods and case studies. https://openknowledge.fao.org/server/api/core/bitstreams/bd44e093-8f41-4b99-875a-1387a1b1dd8d/content

    FAO. (2013). Climate-smart Agriculture Sourcebook Book. https://www.fao.org/3/i3325e/i3325e.pdf

    FAO. (2020). The State of Food and Agriculture 2020. Overcoming water challenges in agriculture. doi: 10.4060/cb1447en

    Farig, M., Shimizu, K., & Hassan, W. H. A. El. (2025). Artificial Intelligence in Agricultural Water Management Research: Literature Review and Research Agenda. International Journal of Advanced Engineering, Management and Science, 11(1), 126–134. doi: 10.22161/ijaems.111.9

    Gamage, A., Gangahagedara, R., Subasinghe, S., Gamage, J., Guruge, C., Senaratne, S., Randika, T., Rathnayake, C., Hameed, Z., Madhujith, T., & Merah, O. (2024). Advancing sustainability: The impact of emerging technologies in agriculture. Current Plant Biology, 40, 100420. doi: 10.1016/j.cpb.2024.100420

    Gowing, J. (1998). Effective Monitoring of Canal Irrigation with Minimum or No Flow Measurement. In Water and the Environment (1st ed.). CRC Press. https://www.taylorfrancis.com/chapters/mono/10.1201/9781482272086-40/effective-monitoring-canal-irrigation-minimum-flow-measurement-john-gowing?context=ubx

    Han, C., Zhang, B., & Chen, H. (2019). Spatially distributed crop model based on remote sensing. Agricultural Water Management, 218(March), 165–173. doi: 10.1016/j.agwat.2019.03.035

    Hussain, I., Khan, M. Z., Rafiq, N., & Bashir, S. (2025). Promoting the Adaptation of Climate-Smart Agriculture Practices Among Farming Communities. In Climate Smart Agriculture for Future Food Security (pp. 223–255). Springer Nature Singapore. doi: 10.1007/978-981-96-4499-5_9

    IFPRI. (2019). 2019 Global food policy report. doi: 10.2499/9780896293502

    Isidoro, D., Quílez, D., & Aragüés, R. (2004). Water balance and irrigation performance analysis: La Violada irrigation district (Spain) as a case study. Agricultural Water Management, 64(2), 123–142. doi: 10.1016/S0378-3774(03)00196-3

    Jägermeyr, J., Gerten, D., Heinke, J., Schaphoff, S., Kummu, M., & Lucht, W. (2015). Water savings potentials of irrigation systems: global simulation of processes and linkages. Hydrology and Earth System Sciences, 19(7), 3073–3091. doi: 10.5194/hess-19-3073-2015

    Jiang, Y., Xu, X., Huang, Q., Huo, Z., & Huang, G. (2015). Assessment of irrigation performance and water productivity in irrigated areas of the middle Heihe River basin using a distributed agro-hydrological model. Agricultural Water Management, 147, 67–81. doi: 10.1016/j.agwat.2014.08.003

    Kaini, S., Harrison, M. T., Gardner, T., & Sharma, A. K. (2024). Comprehensive Assessment of Climate Change Impacts on River Water Availability for Irrigation, Wheat Crop Area Coverage, and Irrigation Canal Hydraulic Capacity of Large-Scale Irrigation Scheme in Nepal. Water (Switzerland), 16(18). doi: 10.3390/w16182595

    Kazem Shahverdi. (2025). Supervised learning to manage irrigation canals’ operation. Journal of Water and Irrigation Management, 14(4). https://jwim.ut.ac.ir/article_99940_154e0944fad77aa4ef8c5aeac8f79eb5.pdf?lang=en

    Kharrou, M. H., Le Page, M., Chehbouni, A., Simonneaux, V., Er-Raki, S., Jarlan, L., Ouzine, L., Khabba, S., & Chehbouni, G. (2013). Assessment of Equity and Adequacy of Water Delivery in Irrigation Systems Using Remote Sensing-Based Indicators in Semi-Arid Region, Morocco. Water Resources Management, 27, 4697–4714. doi: 10.1007/s11269-013-0438-5

    Khaspuria, G., Khandelwal, A., Agarwal, M., Bafna, M., Yadav, R., & Yadav, A. (2024). Adoption of Precision Agriculture Technologies among Farmers: A Comprehensive Review. Journal of Scientific Research and Reports, 30(7), 671–686. doi: 10.9734/jsrr/2024/v30i72180

    Kori, P., & Umesh, K. B. (2020). Impact of Irrigation Water Shortage on Yield, Income and Employment of Farm Households in Tungabhadra Command Area of Karnataka. Asian Journal of Agricultural Extension, Economics & Sociology, 102–110. doi: 10.9734/ajaees/2020/v38i130302

    Korkmaz, N., Avci, M., Unal, H. B., Asik, S., & Gunduz, M. (2009). Evaluation of the Water Delivery Performance of the Menemen Left Bank Irrigation System Using Variables Measured On-Site. Journal of Irrigation and Drainage Engineering, 135(5), 633–642. doi: 10.1061/(asce)ir.1943-4774.0000053

    Krishan, R., Nikam, B. R., Pingale, S. M., Chandrakar, A., & Khare, D. (2018). Analysis of trends in rainfall and dry/wet years over a century in the Eastern Ganga Canal command. Meteorological Applications, 25, 561–574. doi: 10.1002/met.1721

    Kulkarni, T. (2020). The lucid dream of achieving equitable water distribution in India: A critique. International Journal of Multidisciplinary Research and Growth Evaluation, 1(4), 44–49. doi: 10.54660/.IJMRGE.2020.1.4.44-49

    Kumar, K. A., Reddy, M. D., Uma Devi, M., Narender, N., Neelima, T. L., Ramulu, V., Rao, V. P., & Raghavaiah, R. (2014). Irrigation Performance Assessment of Left Bank Canal, Nagarjuna Sagar Project, India During Rabi Using Remote Sensing and GIS. Agrotechnology, 03(01). doi: 10.4172/2168-9881.1000122

    Kumar, S. V., M. Mocko, D., Wang, S., Peters-Lidard, C. D., & Borak, J. (2019). Assimilation of Remotely Sensed Leaf Area Index into the Noah-MP Land Surface Model: Impacts on Water and Carbon Fluxes and States over the Continental United States. Journal of Hydrometeorology, 20(7), 1359–1377. doi: 10.1175/JHM-D-18-0237.1

    Li, M., Wang, P., Tansey, K., Guo, F., & Zhou, J. (2025). Improved leaf area index reconstruction in heavily cloudy areas: A novel deep learning approach for SAR-Optical fusion integrating spatiotemporal features. International Journal of Applied Earth Observation and Geoinformation, 142, 104745. doi: 10.1016/j.jag.2025.104745

    Li, P., & Ren, L. (2019). Evaluating the effects of limited irrigation on crop water productivity and reducing deep groundwater exploitation in the North China Plain using an agro-hydrological model: II. Scenario simulation and analysis. Journal of Hydrology, 574(2), 715–732. doi: 10.1016/j.jhydrol.2019.03.034

    Liu, L., Guo, Z., & Huang, G. (2018). Evaluation of water productivity under climate change in irrigated areas of the arid Northwest China using an assemble statistical downscaling method and an agro-hydrological model. Proceedings of the International Association of Hydrological Sciences, 379, 393–402. doi: 10.5194/piahs-379-393-2018

    Maggo, G. (2025). The Ethics of AI in Water Management. Pani Ki Kahani. https://panikikahani.org/research/The-Ethics-of-AI-in-Water-Management/33863/

    Makin, V. I. W. (2023). ICID Guidelines on Modernization. International Symposium on Pathways and Technologies for Modern Irrigation Services. https://icid-ciid.org/icid_data_web/25Cong-International-Symposium.pdf

    Mao, Y., Van Niel, T. G., & McVicar, T. R. (2023). Reconstructing cloud-contaminated NDVI images with SAR-Optical fusion using spatio-temporal partitioning and multiple linear regression. ISPRS Journal of Photogrammetry and Remote Sensing, 198, 115–139. doi: 10.1016/j.isprsjprs.2023.03.003

    Maselli, F., Battista, P., Chiesi, M., Rapi, B., Angeli, L., Fibbi, L., Magno, R., & Gozzini, B. (2020). Use of Sentinel-2 MSI data to monitor crop irrigation in Mediterranean areas. International Journal of Applied Earth Observation and Geoinformation, 93, 102216. doi: 10.1016/j.jag.2020.102216

    Mdemu, M., Kissoly, L., Kimaro, E., Bjornlund, H., Ramshaw, P., Pittock, J., Wellington, M., & Bongole, S. (2025). Climate change adaptation benefits from rejuvenated irrigation systems at Kiwere and Magozi schemes in Tanzania. International Journal of Water Resources Development, 41(2), 325–349. doi: 10.1080/07900627.2024.2397400

    Mekonnen, Y. G., Alamirew, T., Tadesse, K. B., & Chukalla, A. D. (2024). Monitoring small-scale irrigation performance using remote sensing in the Upper Blue Nile Basin, Ethiopia. Agricultural Water Management, 300. doi: 10.1016/j.agwat.2024.108928

    Mishra, V., Denis, D., Mishra, H., & Kumar, M. (2023). Assessing Irrigation Performance of a Canal Command Area Using Space and Ground Observation. A Case Study of Belan Canal, Prayagraj. In A. Ramdane-Cherif, T. P. Singh, R. Tomar, T. Choudhury, & J. Um (Eds.), Machine Intelligence and Data Science Applications (MIDAS 2022) (pp. 491–505). Springer. doi: 10.1007/978-981-99-1620-7_38

    Mkhwenkwana, A., Matongera, T. N., Blaauw, C., & Mutanga, O. (2025). A critical review on the applications of Sentinel satellite datasets for soil moisture assessment in crop production. International Journal of Applied Earth Observation and Geoinformation, 141, 104647. doi: 10.1016/j.jag.2025.104647

    Mohammedshum, A. A., Mannaerts, C. M., Maathuis, B. H. P., & Teka, D. (2023). Integrating Socioeconomic Biophysical and Institutional Factors for Evaluating Small-Scale Irrigation Schemes in Northern Ethiopia. Sustainability, 15(2). doi: 10.3390/su15021704

    Molden, D. (Ed.). (2013). Water for Food, Water for Life: A Comprehensive Assessment of Water Management in Agriculture. Earthscan, and Columbo: International Water Management Institute. doi: 10.4324/9781849773799

    Molden, D. J., & Gates, T. K. (1990). Performance Measures for Evaluation of Irrigation-Water-Delivery Systems. Journal of Irrigation and Drainage Engineering, 116(6), 804–823. doi: 10.1061/(ASCE)0733-9437(1990)116:6(804)

    Molden, D., Sakthivadivel, R., Perry, C. J., Fraiture, C. de, & Kloezen, W. H. (1998). Indicators for Comparing Performance of Irrigated Agricultural Systems. Colombo, Sri Lanka: International Water Management Institute (IWMI). https://hdl.handle.net/10568/39803

    Murray-Rust, H., & Snellen, W. Bart. (1993). Irrigation system performance assessment and diagnosis. Colombo, Sri Lanka. International Irrigation Management Institute. https://cgspace.cgiar.org/server/api/core/bitstreams/e0aca2cf-0cf2-47e1-8a99-353ff0f69023/content

    Muturi, J. W., Ndehedehe, C. E., & Kennard, M. J. (2025). A review of the use of remote sensing techniques in assessing irrigation water use. Agricultural Water Management, 319, 109759. doi: 10.1016/j.agwat.2025.109759

    Mwadzingeni, L., Mugandani, R., & Mafongoya, P. L. (2022). Socio-demographic, institutional and governance factors influencing adaptive capacity of smallholder irrigators in Zimbabwe. PLOS ONE, 17(8), e0273648. doi: 10.1371/journal.pone.0273648

    Namara, R. E., Hanjra, M. A., Castillo, G. E., Ravnborg, H. M., Smith, L., & Van Koppen, B. (2010). Agricultural water management and poverty linkages. Agricultural Water Management, 97(4), 520–527. doi: 10.1016/j.agwat.2009.05.007

    Nigam, J., Raju, T. B., & Pannala, R. K. P. K. (2023a). Performance Evaluation of Irrigation Canals Using Data Envelopment Analysis for Efficient and Sustainable Irrigation Management in Jharkhand State, India. Energies, 16(14). doi: 10.3390/en16145490

    Nigam, J., Totakura, B. R., & Kumar, R. (2023b). Assessment of Barriers to Canal Irrigation Efficiency for Sustainable Harnessing of Irrigation Potential. Water, 15(14), 2558. doi: 10.3390/w15142558

    Nikam, B. R., Garg, V., Thakur, P. K., & Aggarwal, S. P. (2020). Application of Remote Sensing and GIS in Performance Evaluation of Irrigation Project at Disaggregated Level. Journal of the Indian Society of Remote Sensing, 48(7), 979–997. doi: 10.1007/s12524-020-01128-1

    Niu, J., Liu, Q., Kang, S., & Zhang, X. (2018). The response of crop water productivity to climatic variation in the upper-middle reaches of the Heihe River basin, Northwest China. Journal of Hydrology, 563, 909–926. doi: 10.1016/j.jhydrol.2018.06.062

    Obaideen, K., Yousef, B. A. A., AlMallahi, M. N., Tan, Y. C., Mahmoud, M., Jaber, H., & Ramadan, M. (2022). An overview of smart irrigation systems using IoT. Energy Nexus, 7, 100124. doi: 10.1016/j.nexus.2022.100124

    Orkodjo, T. P., Kranjac-Berisavijevic, G., & Abagale, F. K. (2022). Impact of climate change on future availability of water for irrigation and hydropower generation in the Omo-Gibe Basin of Ethiopia. Journal of Hydrology: Regional Studies, 44, 101254. doi: 10.1016/j.ejrh.2022.101254

    Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. doi: 10.1136/bmj.n71

    Pereira, L. S., Cordery, I., & Iacovides, I. (2012). Improved indicators of water use performance and productivity for sustainable water conservation and saving. Agricultural Water Management, 108, 39–51. doi: 10.1016/j.agwat.2011.08.022

    Rasul, G. (2016). Managing the food, water, and energy nexus for achieving the Sustainable Development Goals in South Asia. Environmental Development, 18, 14–25. doi: 10.1016/j.envdev.2015.12.001

    Rosa, L., & Sangiorgio, M. (2025). Global water gaps under future warming levels. Nature Communications, 16(1), 1192. doi: 10.1038/s41467-025-56517-2

    Rudraswamy, G. K., & Umamahesh, N. V. (2024). Investigating the impact of climate change on irrigation and crop water requirements of Bhadra and Tungabhadra command area: A CMIP-6 GCMs and CROPWAT 8.0 approach. Water Supply, 24(2), 625–642. doi: 10.2166/ws.2024.022

    Sakthivadivel, R., Thiruvengadachari, S., Amerasinghe, U., Bastiaanssen, W. G. M., & Molden, D. (1999). Performance evaluation of the Bhakra Irrigation System, India, using remote sensing and GIS techniques. Colombo, Sri Lanka: International Water Management Institute. https://hdl.handle.net/10568/39814

    Schultz, B., Thatte, C. D., & Labhsetwar, V. K. (2005). Irrigation and drainage. Main contributors to global food production. Irrigation and Drainage, 54, 263–278. doi: 10.1002/ird.170

    Small, L. E., & Svendsen, M. (1990). A framework for assessing irrigation performance. Irrigation and Drainage Systems, 4, 283–312. doi: 10.1007/BF01103710

    Somda, W., Tischbein, B., & Bogardi, J. J. (2020). Water use inside inland valleys agro-systems in the Dano basin, Burkina Faso. Water Cycle, 1, 88–97. doi: 10.1016/j.watcyc.2020.06.003

    Tahir, Z., & Habib, Z. (2000). Land and Water Productivity: Trends across Punjab Canal Commands. Colombo, Sri Lanka: International Water Management Institute. https://hdl.handle.net/10568/39235

    Tiruye, A., Ditthakit, P., Pham, Q. B., Wipulanusat, W., Weesakul, U., & Thongkao, S. (2023). Assessing Water Consumption Pattern and Delivery Irrigation Performance Indicators Using the Wapor Portal Under Data-Limited Conditions, Ethiopia. Engineered Science. doi: 10.30919/es1046

    Uday, G., Purse, B. V., Kelley, D. I., Vanak, A., Samrat, A., Chaudhary, A., Rahman, M., & Gerard, F. F. (2025). Radar versus optical: The impact of cloud cover when mapping seasonal surface water for health applications in monsoon-affected India. PLOS ONE, 20(1), e0314033. doi: 10.1371/journal.pone.0314033

    1. (2022). The sustainable development goals report 2022. In United Nations publication issued by the Department of Economic and Social Affairs. https://unstats.un.org/sdgs/report/2022/The-Sustainable-Development-Goals-Report-2022.pdf

    UNESCO. (2021). The United Nations World Water Development Report 2021: Valuing Water. https://unesdoc.unesco.org/ark:/48223/pf0000375724

    Uniyal, B., & Dietrich, J. (2021). Simulation of Irrigation Demand and Control in Catchments – A Review of Methods and Case Studies. Water Resources Research, 57(7), 1–21. doi: 10.1029/2020WR029263

    Uniyal, B., Dietrich, J., Vu, N. Q., Jha, M. K., & Arumí, J. L. (2019). Simulation of regional irrigation requirement with SWAT in different agro-climatic zones driven by observed climate and two reanalysis datasets. Science of the Total Environment, 649, 846–865. doi: 10.1016/j.scitotenv.2018.08.248

    Van Dam, J. C., Singh, R., Bessembinder, J. J. E., Leffelaar, P. A., Bastiaanssen, W. G. M., Jhorar, R. K., Kroes, J. G., & Droogers, P. (2006). Assessing options to increase water productivity in irrigated river basins using remote sensing and modelling tools. International Journal of Water Resources Development, 22(1), 115–133. doi: 10.1080/07900620500405734

    Vandersypen, K., Bengaly, K., Keita, A. C. T., Sidibe, S., Raes, D., & Jamin, J. Y. (2006). Irrigation performance at tertiary level in the rice schemes of the Office du Niger (Mali): Adequate water delivery through over-supply. Agricultural Water Management, 83(1–2), 144–152. doi: 10.1016/j.agwat.2005.11.003

    Wakweya, R. B. (2023). Challenges and prospects of adopting climate-smart agricultural practices and technologies: Implications for food security. Journal of Agriculture and Food Research, 14, 100698. doi: 10.1016/j.jafr.2023.100698

    Waqas, M. M., Waseem, M., Ali, S., Kebede Leta, M., Noor Shah, A., Awan, U. K., Hamid Hussain Shah, S., Yang, T., & Ullah, S. (2021). Evaluating the spatio-temporal distribution of irrigation water components for water resources management using geo-informatics approach. Sustainability (Switzerland), 13. doi: 10.3390/su13158607

    Ward, C., Burt, C., Valieva, S., Shawky, A., Casanova, D., & Meerbach, D. (2024). Innovation and Modernization in Irrigation and Drainage: A Guide to Why, What, and How. World Bank, Washington, DC. https://documents1.worldbank.org/curated/en/099811001272528137/pdf/IDU-5d7c851f-f0c2-4af3-bad1-6a4717b6f888.pdf

    Woznicki, S. A., Nejadhashemi, A. P., & Parsinejad, M. (2015). Climate change and irrigation demand: Uncertainty and adaptation. Journal of Hydrology: Regional Studies, 3, 247–264. doi: 10.1016/j.ejrh.2014.12.003

    Xue, J., Anderson, M. C., Gao, F., Hain, C., Yang, Y., Knipper, K. R., Kustas, W. P., & Yang, Y. (2021). Mapping Daily Evapotranspiration at Field Scale Using the Harmonized Landsat and Sentinel-2 Dataset, with Sharpened VIIRS as a Sentinel-2 Thermal Proxy. Remote Sensing, 13(17), 3420. doi: 10.3390/rs13173420

    Yapa, L. G. D. S., Rainis, R., Abdullah, A. L., & Hemakumara, G.P.T.S. (2020). Head-tail disparity in irrigation management in Sri Lanka: A review of empirical evidence. Malaysian Journal of Society and Space, 16(4). doi: 10.17576/geo-2020-1604-04

    Younes, A., Elassad, Z. E. A., Meslouhi, O. El, Elassad, D. E. A., & Abdel Majid, E. (2024). The application of machine learning techniques for smart irrigation systems: A systematic literature review. Smart Agricultural Technology, 7, 100425. doi: 10.1016/j.atech.2024.100425

    Zafar, A., Prathapar, S., Bastiaanssen, W., Awan, W. K., Cai, X., & Manunta, P. (2021). Optimization of Canal Management Using Satellite Measurements (0 ed., ADB Briefs). Asian Development Bank. doi: 10.22617/BRF210022-2

    Zhu, M., Wang, J., Yang, X., Zhang, Y., Zhang, L., Ren, H., Wu, B., & Ye, L. (2022). A review of the application of machine learning in water quality evaluation. Eco-Environment & Health, 1(2), 107–116. doi: 10.1016/j.eehl.2022.06.001