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This paper presents a novel method for measuring the data for evaporation estimation as the key ingredient for the final decision of the reclamation form in the area of the Most Basin. The area has been intensively mined for many decades, resulting in significant landscape devastation, loss of natural habitats, and negative environmental impact. Currently, it is assumed that by 2050, three large-scale reclamation projects will be implemented in the area and it is necessary to decide which form of reclamation to choose. Whether to build lakes according to the currently valid rehabilitation and reclamation plan or to leave the area of the quarries in succession with the support of spontaneous inflow of water up to a naturally sustainable water level. Whether the first or second option is approved, or a combination of both, the prediction of evaporation from the free water surface will always be of great importance. To deal with this goal, the available meteorological data must be combined with a suitable calculation method. In our work, we suggest utilizing a measuring network of meteorological devices that describe the character of the weather in a given area of interest in a long-term time series. Together with the state-of-the-art calibration of models for calculating evaporation, the measurement network helps to provide more accurate evaporation data for a given area. Based on the analysis of research results, it will be possible to choose a specific right decision and thus contribute to the long-term sustainability of these reclamations.
Czasopismo
Rocznik
Tom
Strony
328--336
Opis fizyczny
Bibliogr. 24 poz., rys.
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. Allen, R.G., Pereira, L., Raes, D., Smith, M., 1998. Crop evapotranspiration-Guidelines for computing crop water requirements - FAO. Irrigation and drainage paper, 56, United Nation - Food and Agriculture organisation.
- 2. Cabrera, M., Anache, J., Youlton, C., Wendland, E., 2016. Performance of evaporation estimation methods compared with standard 20 m2 tank. Rev. Bras. Eng. AgríCola Ambient, 20, 874.
- 3. Djaman, K., Balde, A., Sow, A., Muller, B., Irmak, S., N’Diaye, M., Manneh, B., Moukoumbi, Y.D., Futakuchi, K., Saito, K., 2015. Evaluation of six-teen reference evapotranspiration methods under sahelian conditions in the Senegal River Valley. Journal of Hydrology: Regional Studies, 3, 139-159.
- 4. Dlouhá, D., Dubovský, V., Pospíšil, L., 2021. Optimal Calibration of Evaporation Models against Penman-Monteith Equation. Water, 13(11), DOI:10.3390/w13111484.
- 5. Dlouhá, D., Dubovský, V., Pospíšil, L., 2021. The Evaporation Estimation on Lake Most. MAPE, 4(1), 221-231, DOI:10.2478/mape-2021-0020.
- 6. Dlouhá, D., Dubovský, V., 2021. Specification of the Climate Character in the Study Area of Projected Hydric Reclamation. Inżynieria Mineralna, 47(1), 75-79, DOI: 10.29227/IM-2021-01-10.
- 7. Dubovský, V., Dlouhá, D., Jarošová, M., 2022. On the Influence of the Measurement Inaccuracy on the E_FAO Evaporation Estimates (in the Area of the Lake Most). AIP Conference Proceedings, DOI: 10.1063/5.0081483.
- 8. Gavilán, P., Berengena, J., Allen, R.G., 2007. Measuring versus estimating net radiation and soil heat flux: Impact on Penman-Monteith reference ET estimates in semiarid regions. Agricultural Water Management, 89, 275-286.
- 9. Gay, D.M., 1990. Usage summary for selected optimization routines. Computing Science Technical Report No. 153, 1-21.
- 10. Grebski, W., Grebski, M., 2022. Project of Micro-hydroelectric Power Generation, System - Case study. Production Engineering Archives, 28(2), 178-184, DOI:10.30657/pea.2022.28.21.
- 11. Hargreaves, G., 1975. Moisture Availability and Crop Production. Transactions of the ASAE, 18, 0980–0984. DOI:10.13031/2013.36722.
- 12. Hargreaves, G., Samani, Z., 1985. Reference Crop Evapotranspiration From Temperature. Applied Engineering in Agriculture, 1. DOI:10.13031/2013.26773.
- 13. Jabloun, M., Sahli, A., 2008. Evaluation of FAO-56 methodology for estimating reference evapotranspiration using limited climatic data: Application to Tunisia. Agricultural Water Management, 95, 707-715.
- 14. Kowol, D., Kurama, H., 2020. Recovery of Fine Coal Grains from Post-Mining Wastes with Use of Autogenous Suspending Bed Technology. Management Systems in Production Engineering, 28(4), 220-227, DOI:10.2478/mspe-2020-0032.
- 15. Lang, D., Jiangkun, Z., Shi, J., Liao, F., Ma, X., Wang, W., Chen, X., Zhang, M., 2017. A Comparative Study of Potential Evapotranspiration Estimation by Eight Methods with FAO Penman-Monteith Method in South-western China. Water, 9, 734.
- 16. Linacre, E.T., 1993. Data-sparse estimation of lake evaporation, using a simplified Penman equation. Agricultural and Forest Meteorology, 64, 237-256, DOI:10.1016/0168-1923(93)90031-C.
- 17. Lovelli, S., Pizza, S., Caponio, T., Rivelli, A., Perniola, M., 2005. Lysimetric determination of muskmelon crop coefficients cultivated under plastic mulches. Agricultural Water Management, 72, 147-159.
- 18. Mohawesh, O., 2011. Evaluation of evapotranspiration models for estimating daily reference evapotranspiration in arid and semiarid environments. Plant Soil Environ, 57, 145-152.
- 19. Moriasi, D., Arnold, J., Van Liew, M., Bingner, R., Harmel, R., Veith, T.L., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50, 885-900.
- 20. Mostafa, A., Benzaghta, M. Benzaghta, A., Mohammed, A., Ekhmaj, A., 2012. Prediction of Evaporation from Algardabiya Reservoir. Libyan Agriculture Research Center Journal International, 3, 120-128.
- 21. R Core Team. R., 2013. A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
- 22. Stone, M., 1974. Cross validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36, 111-147.
- 23. Trenberth, K.E., Fasullo, J.T., Mackaro, J., 2011. Atmospheric Moisture Transports from Ocean to Land and Global Energy Flows in Reanalyses. Journal of Climate, 24, 4907-4924, DOI:10.1175/2011JCLI4171.1.
- 24. Yao, H., 2009. Long-Term Study of Lake Evaporation and Evaluation of Seven Estimation Methods: Results from Dickie Lake, South-Central Ontario, Canada. Journal Water Resource and Protection, 2, 59-77, DOI:10.4236/jwarp.2009.12010.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-eaf7819d-ff49-4a52-9f3f-c3841d2a3210