Modeling Spatio-Temporal Changes in Evaporation using Class a Pan Evaporation Data: Presenting a Novel Approach for Use in Dynamic and Distributed Models of Rainfall-Runoff

Parsamehr, Amir Hossein; Salajegheh, Ali; Khalighi Sigaroodi, Shahram; Ahmadaali, Khaled

doi:10.22092/wmrj.2023.362586.1544

	Modeling Spatio-Temporal Changes in Evaporation using Class a Pan Evaporation Data: Presenting a Novel Approach for Use in Dynamic and Distributed Models of Rainfall-Runoff
پژوهش های آبخیزداری
Article 7, Volume 37, Issue 3 - Serial Number 144, September 2024, Pages 94-110 PDF (2.71 M)
Document Type: Research
DOI: 10.22092/wmrj.2023.362586.1544
Authors
Amir Hossein Parsamehr¹; Ali Salajegheh^* ²; Shahram Khalighi Sigaroodi³; Khaled Ahmadaali⁴
¹Ph.D., Student of Watershed Science and engineering, Department of Reclamation of Arid and Mountainous Region, Faculty of Natural Resources, University of Tehran, Tehran, Iran
²Professor, Department of Reclamation of Arid and Mountainous Region, Faculty of Natural Resources, University of Tehran, Tehran, Iran
³Associate Professor, Department of Reclamation of Arid and Mountainous Region, Faculty of Natural Resources, University of Tehran, Tehran, Iran
⁴Assistant Professor, Department of Reclamation of Arid and Mountainous Region, Faculty of Natural Resources, University of Tehran, Tehran, Iran
Abstract
Introduction and Goal Evaporation is one of the important parameters in hydrology that plays a significant role in the water cycle. This parameter, in addition to various spatial distributions, with having altitude distribution causes the complexity of evaporation modeling The aim of this research is to present a new approach for spatio-temporal modeling of evaporation changes, which can be used in rain-runoff models. Materials and Methods In order to carry out this research, monthly evaporation data over a 20-years period (2002-2021) were used from the Maroun evaporation monitoring station located in the Paskouhak catchment, 27 km west of Shiraz, as well as three stations surrounding Paskouhak catchment including Shiraz, Ghalat, and Dasht Arjan stations. Initially, by using regression modeling and determining the relationship between evaporation and elevation above sea level for each month, monthly evaporation raster maps were drawn for the study area. Then, using the proposed approach of using the ratio equations method, the initial spatio-temporal model of evaporation changes was prepared. Due to the dynamic nature and sensitivity of the evaporation parameter, the impact of various factors on the intensity of evaporation was simulated and the initial raster maps were corrected to a large extent. For this purpose, correction coefficients obtained in the form of raster maps or numerical coefficients were used. These coefficients included the correction coefficient of evaporation intensity due to the ratio of water depth at the target surface to the water depth in the evaporation pan, the effect of different days of the year on the conversion coefficient of the evaporation pan, and the correction coefficient based on changes in elevation from the ground surface. All stages of the research were performed in the SNAP and MATLAB software. Finally, the final result was obtained in the ArcGIS software. Results and Discussion The results showed that using the linear regression model and elevation parameters above sea level, it is possible to obtain the spatial distribution of evaporation with high accuracy (R²=0.81 in December and R²=0.99 in March and October) in the form of a regular pixel grid (in this study 100 m²). In addition, the final spatio-temporal distribution model of evaporation showed that there is a noticeable difference between the results of the initial and the final evaporation models in some areas of the study region (pixels). This highlights the need for more corrective coefficients. Conclusion and Suggestions In this study, using the proposed approach, it is possible to model the spatiotemporal distribution of evaporation in the study area at time steps corresponding to the time series of data available at the evaporation monitoring stations. It is recommended to apply this model under various climatic and topographic conditions and to evaluate its results.
Keywords
Class A Pan Evaporation; Correction Coefficient; Evaporation modelling; Paskouhak catchment

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Modeling Spatio-Temporal Changes in Evaporation using Class a Pan Evaporation Data: Presenting a Novel Approach for Use in Dynamic and Distributed Models of Rainfall-Runoff

Abd‐Elhamid HF, Ahmed A, Zeleňáková M, Vranayová Z, Fathy I. 2021. Reservoir management by reducing evaporation using floating photovoltaic system: A case study of Lake Nasser. Egypt. Water, 13(6): 769-787. https://doi.org/10.3390/w13060769

Ahmadi H, Fallahghalhary Q, Shaemi A. 2016. Estimating and evaluating the trends of annual reference evapotranspiration based on influential climatic parameters in the North East of Iran. Water and Soil Science, 26(3-2): 257-269.

Alizadeh A. 2014. Water, soil and plant, relationship. 14th publication, Imam Reza Publication, Mashhad, Iran, 470 p. (In Persian).

Almedeij J. 2012. Modeling pan evaporation for Kuwait by multiple linear regressions. The Scientific World Journal, pp. 1–9. doi: 10.1100/2012/574742

Chu CR, Li MH, Chang YF, Liu TC, Chen YY. 2012. Wind-induced splash in class A evaporation pan. Journal of Geophysical Research, 117(D11): 1–7. https://doi.org/10.1029/2011JD016848

Chu CR, Li MH, Chen YY, Kuo YH. 2010. A wind tunnel experiment on the evaporation rate of class A evaporation pan. Journal of Hydrology, 381(3–4): 221–224. https://doi.org/10.1016/j.jhydrol.2009.11.044

Daryaei A, Sohrabi H, Atzberger C, Immitzer M. 2021. Mapping vegetation in riparian areas using pixel-ased and object-based classification of Sentinel-2 multi-temporal imagery. Iranian Journal of Remote Sensing and GIS, 13(3): 19-32.

Doi: 10.52547/GISJ.13.3.19

Hu Z, Wang G, Sun X, Zhu M, Song C, Huang K, Chen X. 2018. Spatial‐temporal patterns of evapotranspiration along an elevation gradient on Mount Gongga, Southwest China. Water Resources Research. 54(6):4180-4192.

Irmak S, Haman DZ, Jones JW. 2002. Evaluation of class A pancoefficients for estimating reference evapotranspiration in humid location. Journal of Irrigation Drainage Engineering, 128(3): 153–159.

https://doi.org/10.1061/(ASCE)0733-9437(2002)128:3(153)

Jafari M, Dinpashoh Y. 2019. Derivation of regression models for pan evaporation estimation, Environmental Resources Research, 7(1): 29–42. (In Persian).

Khoshhal Jahromi F, Zand Parsa Sh. 2015. Monthly evaporation zooning of pan evaporation in Fars Province using inverse distance weighting method (Idw) and regression based on digital elevation model (Dem). 3th National Conference on Agriculture and Sustainable Natural Resources, 14 June, Tehran.

Klaassen W, 2001. Evaporation from rain-wetted forest in relation to canopy wetness, canopy cover, and net radiation. Water Resources Research, 37(12):3227-3236. https://doi.org/10.1029/2001WR000480

Lawrimore JH, Peterson TC. 2000. Pan evaporation trends in dry and humid regions of the United States. Journal of Hydrometeorology, 1(6): 543–546. DOI: https://doi.org/10.1175/1525-7541(2000)001<0543:PETIDA>2.0.CO;2

Lu X, Ju Y, Wu L, Fan J, Zhang F, Li Z. Daily pan evaporation modeling from local and cross-station data using three tree-based machine learning models. Journal of Hydrology. 2018 Nov 1;566:668-84. https://doi.org/10.1016/j.jhydrol.2018.09.055

Magliano PN, Whitworth-Hulse JI, Cid FD, Leporati JL, Van Stan JT, Jobbágy EG. 2022. Global rainfall partitioning by dry land vegetation: Developing general empirical models. Journal of Hydrology, 607, 1-8. https://doi.org/10.1016/j.jhydrol.2022.127540

Malik A, Kumar A. 2015. Pan evaporation simulation based on daily meteorological data using soft computing techniques and multiple linear regression. Water Resources Management, 29: 1859–1872. https://doi.org/10.1007/s11269-015-0915-0

MiKlanek P. 1996. Estimation of mean evaporation patterns with respect to elevation. International Conference on Ecohydrology of High Mountain Area, pp. 285–290.

Patle GT, Chettri M, Jhajharia D. 2020. Monthly pan evaporation modeling using multiple linear regression and artificial neural network techniques. Water Supply, 20 (3): 800–808. doi: 10.2166/ws.2019.189

Poormohammadi S, Malekinezhad H, Rahimian M. 2010. Investigating the role of physiographical factors on temperature-related parameters affecting evapotranspiration (Case study: Yazd Province). Journal of Arid Biome, 1(2): 9–19. (In Persian).

Rafahi HGh. 2001. Wind erosion and conservation. 2th Publication, Tehran University Publication, Tehran, Iran, 320 p. (In Persian).

Sabzevari T, Rezaeian MR. 2012. Final report on: Estimation of ungagged watershed flood by using geomorphologic instaneous unit hydrograph method. Fars Regional Water Authority, pp. 1-92. (In Persian).

Sattari MT. Ahmadifar V, Delirhasannia R, Apaydín H. 2021. Estimation of the pan evaporation coefficient in cold and dry climate conditions via the M5 regression tree model. Atmósfera, 34(3): 289-300. https://doi.org/10.20937/atm.52777

Alizadeh A. 2014. Water, soil and plant, relationship. 14^th publication, Imam Reza Publication, Mashhad, Iran, 470 p. (In Persian).

Khoshhal Jahromi F, Zand Parsa Sh. 2015. Monthly evaporation zooning of pan evaporation in Fars Province using inverse distance weighting method (Idw) and regression based on digital elevation model (Dem). 3^th National Conference on Agriculture and Sustainable Natural Resources, 14 June, Tehran.

Rafahi HGh. 2001. Wind erosion and conservation. 2^th Publication, Tehran University Publication, Tehran, Iran, 320 p. (In Persian).