• Oliveira, S., Cunha, J., Nóbrega, R. L., Gash, J. H. & Valente, F. Enhancing global rainfall interception loss estimation through vegetation structure modeling. J. Hydrol. 631, 130672 (2024).

    Article 

    Google Scholar
     

  • Zhang, Q. et al. Species and spatial differences in vegetation rainfall interception capacity: A synthesis and meta-analysis in China. CATENA 213, 106223 (2022).

    Article 

    Google Scholar
     

  • Aashna, M., Lisa, S. & Zoran, K. A review of serious games for urban water management decisions: Current gaps and future research directions. Water Res. 215, 118217 (2022).

    Article 

    Google Scholar
     

  • Gomes, S. et al. Capacity building for water management in peri-urban communities, Bangladesh: A simulation-gaming approach. Water 10–11, 1704 (2018).

    Article 

    Google Scholar
     

  • Liu, Y. et al. The divergence between potential and actual evapotranspiration: An insight from climate, water, and vegetation change. Sci. Total Environ. 807, 150648 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Gowda, P. et al. Deriving hourly evapotranspiration rates with SEBS: A lysimetric evaluation. Vadose Zone J. 12, 1–11 (2013).

    Article 

    Google Scholar
     

  • Lagos, L., Lillo-Saavedra, M., Fonseca, D. & Gonzalo, C. Evapotranspiration of partially vegetated surfaces from remote sensing. in Towards Horizon 2020 (eds Lasaponara, R., Masini, N. & Biscione, M.) The Proceedings of the 33rd EARSeL Symposium 613–624 (2013).

  • Moorhead, J. et al. Use of crop-specific drought indices for determining irrigation demand in the Texas high plains. Appl. Eng. Agr. 26, 905–916 (2013).


    Google Scholar
     

  • Yee, M., Beringer, J., Pauwels, V., Daly, E., Walker, J. & Rudiger, C. Measuring evapotranspiration: comparison of eddy covariance, scintillometers and enclosed chambers. in Geophysical Research Abstracts 16, 4590 (2014).

  • Madugundu, R., Al-Gaadi, K., Tola, E., Patil, V. & Biradar, C. Quantification of agricultural water productivity at field scale and its implication in on-farm water management. J. Indian Soc. Remote Sens. 45, 643–656 (2017).

    Article 

    Google Scholar
     

  • Madugundu, R., Al-Gaadi, K. A., Tola, E., Hassaballa, A. A. & Patil, V. C. Performance of the METRIC model in estimating evapotranspiration fluxes over an irrigated field in Saudi Arabia using Landsat-8 images. Hydrol. Earth Syst. Sci. 21(12), 6135–6151 (2017).

    Article 
    ADS 

    Google Scholar
     

  • Rangjian, Q., Chunwei, L., Zhenchang, W., Zaiqiang, Y. & Yuanshu, J. Efects of irrigation water salinity on evapotranspiration modifed by leaching fractions in hot pepper plants. Nature 7, 7231 (2017).


    Google Scholar
     

  • Ruiz-Ortega, F. J., Clemente, E., Martínez-Rebollar, A. & Flores-Prieto, J. J. An evolutionary parsimonious approach to estimate daily reference evapotranspiration. Sci. Rep. 14(1), 6736 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, K. et al. A global dataset of terrestrial evapotranspiration and soil moisture dynamics from 1982 to 2020. Sci. Data 11(1), 445 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • De-Ville, S. et al. Effect of vegetation treatment and water stress on evapotranspiration in bioretention systems. Water Res. 252, 121182 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Elnesr, M., Alazba, A. & Abu-Zreig, M. Spatio-temporal variability of evapotranspiration over the kingdom of Saudi Arabia. Appl. Eng. Agric. 26–5, 833–842 (2010).

    Article 

    Google Scholar
     

  • Bahrawi, J. A. Spatial distribution maps of evapotranspiration rate over Saudi Arabia. J. King Abdulaziz Univ. 25(2), 69 (2014).


    Google Scholar
     

  • Yang, X. S. & Gandomi, A. H. Bat algorithm: A novel approach for global engineering optimization. Eng. Comput. 29, 464–483 (2012).

    Article 

    Google Scholar
     

  • Bora, T. C., Coelho, L. D. S. & Lebensztajn, L. Bat-inspired optimization approach for the brushless DC wheel motor problem. IEEE Trans. Magn. 48, 947–950 (2012).

    Article 
    ADS 

    Google Scholar
     

  • Sambariya, D. K. & Prasad, R. Robust tuning of power system stabilizer for small signal stability enhancement using metaheuristic bat algorithm. Int. J. Electr. Power Energy Syst. 61, 229–238 (2014).

    Article 

    Google Scholar
     

  • Cao, Y., Cui, Z., Li, F., Dai, C. & Chen, W. Improved low energy adaptive clustering hierarchy protocol based on local centroid bat algorithm. Sens. Lett. 12, 1372–1377 (2014).

    Article 

    Google Scholar
     

  • Sathya, M. R. & Ansari, M. M. T. Load frequency control using Bat inspired algorithm based dual mode gain scheduling of PI controllers for interconnected power system. Int. J. Electr. Power Energy Syst. 64, 365–374 (2015).

    Article 

    Google Scholar
     

  • Sun, S. & Xu, B. Node localization of wireless sensor networks based on hybrid bat-quasi-Newton algorithm. J. Comput. Appl 11, 38–42 (2015).


    Google Scholar
     

  • Alsalibi, B., Venkat, I. & Al-Betar, M. A. Amembrane-inspired bat algorithm to recognize face sinunconstrained scenarios. Eng. Appl. Artif. Intell 64, 242–260 (2017).

    Article 

    Google Scholar
     

  • Almufti, S. Using swarm intelligence for solving NPHard problems. Acad. J. Nawroz Univ. 6–3, 46–50 (2017).

    Article 

    Google Scholar
     

  • Hamidzadeh, J., Sadeghi, R. & Namaei, N. Weighted support vector data description based on chaotic bat algorithm. Appl. Soft Comput. 60, 540–551 (2017).

    Article 

    Google Scholar
     

  • Cui, Z., Cao, Y., Cai, X., Cai, J. & Chen, J. Optimal LEACH protocol with modified bat algorithm for big data sensing systems in Internet of Things. J. Parallel Distrib. Comput 132, 217–229 (2017).

    Article 

    Google Scholar
     

  • Cui, Z. et al. Detectin of malicious code variants based on deep learning. IEEE Trans. Ind. Inform 14, 3187–3196 (2018).

    Article 

    Google Scholar
     

  • Cui, Z. et al. A pigeon-inspired optimization algorithm for many-objective optimization problems. Sci. China Inf. Sci 62, 70212 (2019).

    Article 

    Google Scholar
     

  • OuladNaoui, N., Cherif, E. & Djehiche, A. Modeling of meteorological data optimization to study hydrological behavior of watersheds: Case study-MZAB basin, southeast of Algeria. Desalin. Water Treat. 81, 95–104 (2017).

    Article 

    Google Scholar
     

  • OuladNaoui, N., Cherif, E., Djehiche, A. & Sekkoum, M. Modelling of climatic change effects on runoff values using bird swarm algorithm: Case study Seine basin (France). Int. J. Hydrol. Sci. Technol. 14–4, 359–374 (2022).


    Google Scholar
     

  • OuladNaoui, N., Sekkoum, M., Cherif, E. & Djehiche, A. Newton method-GR1 coupling to model rainfall-runoff relationship: Case study-Boumessaoud basin (NO of Algeria) and Seine basin (NO of France). Model. Earth Syst. Environ. 8, 5023–5029 (2022).

    Article 

    Google Scholar
     

  • Sekkoum, M., Noureddine, O. N. & El Amine, C. Hydrological modeling using chicken swarm optimization algorithm-case study Oued El Melah (NE of Algeria). Ecol. Eng. Environ. Technol. 25(8), 109–119 (2024).

    Article 

    Google Scholar
     

  • Al-Areeq, A. M., Al-Zahrani, M. A. & Sharif, H. O. The performance of physically based and conceptual hydrologic models: A case study for Makkah watershed, Saudi Arabia. Water 13, 1098 (2021).

    Article 

    Google Scholar
     

  • Soulis, K. X. Chapter 2: Hydrological data sources and analysis for the determination of environmental water requirements in mountainous areas. in Environmental Water Requirements in Mountainous Areas 51–98 (2021).

  • Jaiswal, R. K., Ali, S. & Bharti, B. Comparative evaluation of conceptual and physical rainfall–runoff models. Appl. Water Sci. 10–48, 1–14 (2020).


    Google Scholar
     

  • Mudesir, N. A review of model selection for hydrological studies. Arab. J. Geosci. 16, 102 (2023).

    Article 

    Google Scholar
     

  • Kumar, A. & Saharia, M. Exploratory analysis of hydrological data. in Python for Water and Environment. Innovations in Sustainable Technologies and Computing 23–41 (Springer, 2024).

  • Ayan, S. et al. Patterns and drivers of evapotranspiration in South American wetlands. Nat. Commun. 14, 6656 (2023).

    Article 

    Google Scholar
     

  • Pierre, P., Leydy, A. C. D., David, R. & Ioulia, T. Evapotranspiration evaluation using three different protocols on a large green roof in the greater Paris area. Earth Syst. Sci. Data 16, 2351–2366 (2024).

    Article 

    Google Scholar
     

  • Xu, J. Model calibration. In Advances in modeling and simulation. Simulation Foundations, methods and applications (eds Tolk, A. et al.) 27–46 (Springer, 2017).


    Google Scholar
     

  • Arsenault, R., Brissette, F. & Martel, J. L. The hazards of split-sample validation in hydrological model calibration. J. Hydrol. 566, 346–362 (2018).

    Article 
    ADS 

    Google Scholar
     

  • Bérubé, S., Brissette, F. & Arsenault, R. Optimal hydrological model calibration strategy for climate change impact studies. J. Hydrol. Eng. 27–3, 1–13 (2022).


    Google Scholar
     

  • Shen, H., Tolson, B. A. & Mai, J. Time to update the split-sample approach in hydrological model calibration. Water Resour. Res. 58, e2021WR031523 (2022).

    Article 
    ADS 

    Google Scholar
     

  • Hongren, S., Bryan, A. & Juliane, M. Time to update the split-sample approach in hydrological model calibration. Water Resour. Res. 58, 2021WR031523 (2022).

    Article 

    Google Scholar
     

  • Olivier, B. et al. Presentation and evaluation of the IPSL-CM6A-LR climate model. J. Adv. Model. Earth Syst. 12, e2019MS002010 (2020).

    Article 

    Google Scholar
     

  • Oluwafemi, E. A. et al. Minimizing uncertainties in climate projections and water budget reveals the vulnerability of freshwater to climate change. One Earth 7, 72–87 (2024).

    Article 

    Google Scholar
     

  • Cemek, B. et al. Machine learning techniques in estimation of eggplant crop evapotranspiration. Appl. Water Sci. 13, 136 (2013).

    Article 
    ADS 

    Google Scholar
     

  • Zhao, L. et al. Estimating maize evapotranspiration based on hybrid back-propagation neural network models and meteorological, soil, and crop data. Int. J. Biometeorol. 68, 511–525 (2024).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Pan, J., Bo, J., Xiaoxuan, J. & Shenglong, W. Analysis and application of grey wolf optimizer-long short-term memory. IEEE Access 8, 121460–121468 (2020).

    Article 

    Google Scholar
     

  • Tonglin, F. & Xinrong, L. Hybrid the long short-term, memory with whale optimization algorithm and variational mode decomposition for monthly evapotranspiration estimation. Nature 12, 20717 (2022).


    Google Scholar
     

  • Guodong, S., Mu, M., Qiyu, Z., Qiujie, R. & Qinglong, Y. Application of the CNOP-P ensemble prediction (CNOP-PEP) method in evapotranspiration forecasting over the Tibetan plateau to model parameter uncertainties. J. Adv. Model. Earth Syst. 15, e2022MS003110 (2023).

    Article 

    Google Scholar
     

  • Wang, L. et al. Ensemble learning based on remote sensing data for monitoring agricultural drought in major winter wheat-producing areas of China. Progress Phys. Geograph.: Earth Environ. 48–2, 171–190 (2024).

    Article 
    ADS 

    Google Scholar
     

  • Piri, J. & Kisi, O. Hybrid non-linear probabilistic model using Monte Carlo simulation and hybrid support vector regression for evaporation predictions. Hydrol. Sci. J. 69(15), 2249–2277 (2024).

    Article 

    Google Scholar
     

  • Shirazi, F., Zahiri, A., Piri, J. & Dehghani, A. A. Estimation of river high flow discharges using friction-slope method and hybrid models. Water Resour. Manage 38(3), 1099–1123 (2024).

    Article 

    Google Scholar
     

  • Muthee, S. W. et al. Using the SARIMA model to predict the trends of evapotranspiration and runoff in the Muringato river basin, Kenya. Stoch. Environ. Res. Risk Assess. 37, 4707–4718 (2023).

    Article 

    Google Scholar
     

  • Zhu, N. et al. Unveiling evapotranspiration patterns and energy balance in a subalpine forest of the Qinghai-Tibet Plateau: Observations and analysis from an eddy covariance system. J. For. Res 35, 53 (2024).

    Article 

    Google Scholar
     



  • Source link