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).
Zhang, Q. et al. Species and spatial differences in vegetation rainfall interception capacity: A synthesis and meta-analysis in China. CATENA 213, 106223 (2022).
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).
Gomes, S. et al. Capacity building for water management in peri-urban communities, Bangladesh: A simulation-gaming approach. Water 10–11, 1704 (2018).
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).
Gowda, P. et al. Deriving hourly evapotranspiration rates with SEBS: A lysimetric evaluation. Vadose Zone J. 12, 1–11 (2013).
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).
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).
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).
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).
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).
Zhang, K. et al. A global dataset of terrestrial evapotranspiration and soil moisture dynamics from 1982 to 2020. Sci. Data 11(1), 445 (2024).
De-Ville, S. et al. Effect of vegetation treatment and water stress on evapotranspiration in bioretention systems. Water Res. 252, 121182 (2024).
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).
Bahrawi, J. A. Spatial distribution maps of evapotranspiration rate over Saudi Arabia. J. King Abdulaziz Univ. 25(2), 69 (2014).
Yang, X. S. & Gandomi, A. H. Bat algorithm: A novel approach for global engineering optimization. Eng. Comput. 29, 464–483 (2012).
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).
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).
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).
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).
Sun, S. & Xu, B. Node localization of wireless sensor networks based on hybrid bat-quasi-Newton algorithm. J. Comput. Appl 11, 38–42 (2015).
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).
Almufti, S. Using swarm intelligence for solving NPHard problems. Acad. J. Nawroz Univ. 6–3, 46–50 (2017).
Hamidzadeh, J., Sadeghi, R. & Namaei, N. Weighted support vector data description based on chaotic bat algorithm. Appl. Soft Comput. 60, 540–551 (2017).
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).
Cui, Z. et al. Detectin of malicious code variants based on deep learning. IEEE Trans. Ind. Inform 14, 3187–3196 (2018).
Cui, Z. et al. A pigeon-inspired optimization algorithm for many-objective optimization problems. Sci. China Inf. Sci 62, 70212 (2019).
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).
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).
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).
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).
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).
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).
Mudesir, N. A review of model selection for hydrological studies. Arab. J. Geosci. 16, 102 (2023).
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).
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).
Xu, J. Model calibration. In Advances in modeling and simulation. Simulation Foundations, methods and applications (eds Tolk, A. et al.) 27–46 (Springer, 2017).
Arsenault, R., Brissette, F. & Martel, J. L. The hazards of split-sample validation in hydrological model calibration. J. Hydrol. 566, 346–362 (2018).
Bérubé, S., Brissette, F. & Arsenault, R. Optimal hydrological model calibration strategy for climate change impact studies. J. Hydrol. Eng. 27–3, 1–13 (2022).
Shen, H., Tolson, B. A. & Mai, J. Time to update the split-sample approach in hydrological model calibration. Water Resour. Res. 58, e2021WR031523 (2022).
Hongren, S., Bryan, A. & Juliane, M. Time to update the split-sample approach in hydrological model calibration. Water Resour. Res. 58, 2021WR031523 (2022).
Olivier, B. et al. Presentation and evaluation of the IPSL-CM6A-LR climate model. J. Adv. Model. Earth Syst. 12, e2019MS002010 (2020).
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).
Cemek, B. et al. Machine learning techniques in estimation of eggplant crop evapotranspiration. Appl. Water Sci. 13, 136 (2013).
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).
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).
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).
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).
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).
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).
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).
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).
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).