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This study focuses on the reconstruction of incomplete and spatially sparse air temperature data for the purpose of estimating evaporation from Lake Most – a large artificial reservoir in the Czech Republic with no natural inflow. The primary objective is to generate daily spatial temperature fields using spatio-temporal kriging and subsequently compute evaporation using a calibrated Hargreaves-Samani (HS) model. We utilize daily data from the years 2020-2022, collected from six low-cost microstations installed around the lake and from a nearby professional meteorological station (Kopisty, operated by the Czech Hydrometeorological Institute). Due to frequent outages, data coverage from the microstations ranges from 5% to 38%. To fill in missing values and estimate temperature over the lake surface, we apply a Gneiting covariance model. All computations are carried out in MATLAB using a in-house implementation. The reconstructed temperature fields exhibit realistic spatial structure and seasonal variability. Based on the interpolated daily mean, maximum, and minimum air temperatures, we compute daily and cumulative evaporation from the lake surface. The results show that even a sparse and unreliable sensor network can yield physically consistent inputs for evaporation estimation when combined with statistical interpolation. The proposed method is readily applicable to other reservoirs under limited measurement conditions and may support hydrological modeling and water balance analysis.
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Tom
Strony
388--392
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
Twórcy
autor
- VSB – Technical University Ostrava Faculty of Civil Engineering Department of Mathematics Ludvíka Podéště 1875/17, 708 00 Ostrava-Poruba, Czech Republic
autor
- VSB – Technical University Ostrava Faculty of Civil Engineering Department of Mathematics Ludvíka Podéště 1875/17, 708 00 Ostrava-Poruba, Czech Republic
autor
- VSB – Technical University Ostrava Faculty of Civil Engineering Department of Mathematics Ludvíka Podéště 1875/17, 708 00 Ostrava-Poruba, Czech Republic
Bibliografia
- [1] Dlouhá, D., Dubovský, V., & Pospíšil, L. (2021). Optimal calibration of evaporation models against Penman-Monteith equation. Water, 13(11), 1484.
- [2] Dlouhá, D., Pospíšil, L., & Dubovský, V. (2023). Simple network-based measuring system for improving evaporation estimation. Production Engineering Archives, 29(3), pp. 328-336.
- [3] Allen, R.G., Pereira, L.S., Raes, D., & Smith, M. (1998). Crop evapotranspiration–guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper, 56.
- [4] Hargreaves, G.H. and Samani, Z.A. (1985). Reference crop evapotranspiration from temperature. Applied Engineer-ing in Agriculture, 1(2): pp. 96-99.
- [5] Hargreaves, G.H.(1975). Moisture availability and crop production. Transactions of the ASAE, 18(5): pp. 980-984.
- [6] Dlouhá, D., Dubovský, V., & Pospíšil, L. (2024). Non-stationary Hargreaves-Samani model based on wind conditions. In Proceedings of ICNAAM.
- [7] Cressie, N.A.C. Statistics for Spatial Data. (1993). Wiley, revised edition.
- [8] Wackernagel, H. (2003). Multivariate Geostatistics: An Introduction with Applications. Springer, 3rd edition.
- [9] Li, J. & Heap, A.D. (2011). A review of spatial interpolation methods for environmental scientists. Geoscience Australia Record, 2011/23.
- [10] Lawrence, D.M. & Sellers, P.J. (2007). Spatial interpolation of daily potential evapotranspiration for New Zealand. Journal of Hydrometeorology, 8(3): pp. 727-739.
- [11] Ghanim, M. & Shexo, A.H.M. (2023). Using kriging technique to interpolate and forecasting temperatures spatio-temporal data. European Journal of Pure and Applied Mathematics, 16(1): pp. 373-385.
- [12] Takeuchi, K. & Ichikawa, Y. (2014). Evaporation from lake Kasumigaura: Annual totals and variability in time and space. Journal of Japan Society of Hydrology and Water Resources, 27(1): pp. 7-17.
- [13] Jimenez Arellano, C. (2020). Thermal mapping and evaporation estimation of cochiti lake using landsat 8 imagery. Master’s Thesis, University of New Mexico.
- [14] Khan, A., Ali, S. & Hussain, M. (2022). Spatio-temporal interpolation of reference evapotranspiration from meteorological station data in Pakistan. The Scientific World Journal, 2022:5488725.
- [15] Li, X., Wu, Y. & Zhao, Z. (2022). Spatio-temporal patterns of evapotranspiration in the Xiangjiang river basin. Hydrological Research, 54: pp. 924-937.
- [16] Wiley, J. & col. (2022). Spatio-temporal interpolation of reference evapotranspiration using functional data kriging. Computational Water, Energy, and Environmental Engineering, 11(3): pp. 97-114.
- [17] Yildirim, D., Kücüktopcu, E., Karakaya, A. & Yildiz, O. (2023). Comparison of machine learning techniques and spatial distribution of daily reference evapotranspiration in Türkiye. Applied Water Science, 13:107.
- [18] Zhang, L., Chen, W. & Kumar, A. (2025). A novel hybrid machine learning framework for spatio-temporal reference evapotranspiration modelling. Journal of Environmental Modelling.
- [19] Clarotto, L., Allard, D., Romary, T. & Desassis, N. (2022). The spde approach for spatio-temporal datasets with advection and diffusion. arXiv preprint. arXiv:2208.14015.
- [20] Stalder, M., Ozdemir, F., Safin, A. & Sukys, J. (2021). Probabilistic modeling of lake surface water temperature using a Bayesian spatio-temporal graph convolutional neural network. arXiv preprint. arXiv:2109.13235.
- [21] Gneiting, T. (2002). Nonseparable, stationary covariance functions for space–time data. Journal of the American Statistical Association, 97(458), pp. 590-600.
- [22] Dubovský, V., Dlouhá, D., & Pospíšil, L. (2022). The cross-validated calibration of Hargreaves-Samani evaporation model on Lake Most. In Proceedings of ICNAAM.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-f5267ab5-1d4c-457a-8805-4cddd18c492b
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