Monthly pan coefficients for estimation of reference evapotranspiration in a semi-arid environment

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The daily data of maximum and minimum temperature, relative humidity, wind speed and sunshine hours of forty years (1984–2023) were used to calculate reference evapotranspiration (ETo). The monthly K pan were calculated by dividing ETo with class A pan evaporation. A comparison was made among monthly ETo obtained with standard PM method and six other empirical models viz. Cuenca (CU), Allen and Pruitt (AP), Snyder (SN), Orang (OR), Wahed and Snyder (WS) and Modified Snyder (MS) along with annual fix K pan value of 0.75 (FIX). The model performance was evaluated using mean absolute error, root mean square error, correlation coefficient, Nash–Sutcliffe model efficiency and ratio of performance to deviation. The results shows that no single model performed best among all performance criteria but considering average of their rankings in different statistical parameters, the performance of the models for estimation of ETo was found of the order as PM > OR > MS > AP > FIX > SN > WS > CU. In absence of meteorological parameters essential for calculation of PM ETo, Wahed and Snyder or Modified Snyder model can be used for estimation of ETo for similar type of climate. Monthly pan coefficient reference evapotranspiration semi-arid Penman Monteith Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 HIGHLIGHTS Monthly pan coefficients were calculated using PM model at Hisar, Haryana. These were compared against six empirical pan coefficient models viz. Cuenca, Allen and Pruitt, Snyder, Orang, Modified Snyder and Wahed & Snyder and a standard annual K pan value of 0.75. In multiple statistical comparison, different model performed at different levels. Performance of Wahed & Snyder and Modified Snyder were found close to Penman-Monteith for estimation of ETo in the semi-arid climate. 1. INTRODUCTION The crop water requirements (CWR) is the essential input for irrigation scheduling, which is the product of evaporative power of atmosphere (reference ET) and crop coefficient (Kc). Crop coefficients which is a measure of crop aggressiveness against the standard reference crop have been used to estimate evapotranspiration for specific crop by measuring potential or reference evapotranspiration (ETo). It must be derived empirically for each crop based on local climatic conditions. The Food and Agriculture Organization (FAO) of the United Nations recommended the FAO-56 Penman-Monteith (PM) equation as the standard for estimation of ETo (Allen et al. 1998 ) which is used as a reference in the field of agronomy, irrigation water management and related fields for research purposes (Alexandris et al. 2006 ). However, one of the primary challenges in using the PM equation is the requirement of comprehensive weather data, which might not be readily available in many meteorological stations (Chiew et al. 1995 ). Direct method of calculating ETo is multiplying crop coefficient by pan evaporation data which is readily available from class A pan evaporimeter. Which can be further multiplied with crop coefficient of a particular crop to get evaporative demand of that particular crop. The common practice is to use standard value of pan coefficient such as 0.75 for US Class A pan and 0.8 for ISI Standard pan. The single annual value of pan coefficient does not account well for the seasonal variability of pan coefficient. Many researchers have worked to find seasonal or monthly values of pan coefficient for different types of climates. Some empirical models which were widely exercised were Cuenca ( 1989 ), Allen and Pruitt ( 1991 ), Snyder ( 1992 ), Pereira (1995), Orang ( 1998 ), Modified Snyder (Grismer et al. 2002 ) and Wahed & Snyder ( 2008 ). There accuracy is usually compared against standard PM method in many studies (Jensen et al. 1990 ; Itenfisu et al. 2000 ; Walter et al. 2005 ) but it was found that different model behaved differently in different climates. Irmak et al. ( 2002 ) reported that Snyder Kpan model was found closest to PM in humid climate of north-central Florida. In a similar study by Gundekar et al. ( 2008 ), they found that Snyder K pan model is best suited for the semi-arid region of India, whereas the Pereira, Cuenca and Orang Kpan models perform poor. Sabziparvar et al. ( 2010 ) evaluated seven different K pan modes at two different climates of Iran, and found that Orang, Raghuwanshi and Wallender performed best for cold semi-arid climate conditions, whereas Snyder and Orang models performed better for warm and arid climatic conditions. Similarly, Rahimikhoob ( 2009 ), results depict that Orang model gave better results while Cuenca and Snyder models significantly overestimate ETo compared with the PM method in a subtropical climate of north of Iran. A study by Aschonitis et al. ( 2012 ) found that Cuenca model performing best in a semi-arid Mediterranean environment of Greece. Singh et al. ( 2014 ) observed that ETo calculated from Modified Snyder and Orang model agreed most with the PM method for daily, monthly and annual estimates in semi-arid of India. Khobragade et al. ( 2019 ), reported that most of the popular K pan models were found unsuitable in a humid tropical monsoon region of Chandigarh, India and the pan coefficients varied significantly by month and season. In another study by Pradhan et al. ( 2013 ) in a semi-arid region in India, the Snyder model was found nearest with the PM method, followed by the Cuenca, Orang, and Allen & Pruitt model. The same model in a dry sub-humid region in India performed best while the Pereira model performed worst (Kar et al. 2017 ). Snyder model also outperformed other K pan models at northern Iran in a mild, humid climate whereas the Pereira model underestimates ETo (Tabari et al. 2013 ). Similarly, Heydari and Heydari ( 2014 ) analyzed 21 years of climate data from an arid region of Iran, finding that the Cuenca model performed best in comparison with PM. Meshram and Gorantiwar ( 2015 ) found Synder method best among five methods (Cuenca, Allen and Pruitt, Snyder, Pereira and Orang) for estimating Kpan using data of 1983 to 2012 at Solapur station, Maharashtra (India). In another study, Shweta and Krishna (2015) compared four methods for estimating ETo and found that the Turc method closely matched Penman-derived values, making it a suitable alternative in areas with limited data. Dar et al. ( 2017 ) compared seven evapotranspiration models using 21 years of data from Ludhiana, Punjab, and concluded that although FAO-56 PM is the most accurate method, but empirical models such as Jensen-Haise and Hargreaves could offer reliable estimates where data is scarce. Vishwakarma et al. ( 2022 ) reported that FAO Penman-Monteith and Turc models demonstrated the best performance in humid and subtropical climate conditions of Indian Punjab. They further found that radiation-based and temperature-based models performance better in comparison to mass transfer-based models in the study area. Koç ( 2022 ) evaluated eight Kp models for ETo estimation in Turkey's Mediterranean climate using 22 years of data, finding that the Wahed & Snyder model performed best, while the Orang model showed the poorest results. Usta ( 2024 ) also revealing that the Wahed & Snyder model delivered the most accurate estimates, closely matching the FAO-56 PM values, whereas the Snyder model showed the poorest performance. From the past research, it can be seen that models behave differently in different climatic conditions and across locations with similar climates. Therefore, regional assessment must be made for all the available models for their local performance and suitability to the region, especially in the area having limited essential data for the calculation of pan coefficient from the standard PM model. Further, determining water demands accurately for irrigated plants is a crucial aspect of effective water management, particularly in regions where water resources are limited, such as arid and semi-arid areas of Haryana, India (Naresh et al. 2023 ). Therefore, the aim of present study to calculate monthly pan coefficients from different models and then assess the performances of various Kpan models using the 40-year daily climate data in the semi-arid climate of Hisar, Haryana. 2. METHODOLOY The following methodology was adopted for the estimation of Kpan in the present study: The 40-year daily reference evapotranspiration (ETo) was calculated using the Penman-Monteith technique, by employing maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours as input parameters for the study area. The above obtained daily data were converted into monthly data by averaging the daily ETo for that particular month. After that, monthly PM based ET was divided by class A pan evaporation (pan ET) to calculate monthly pan coefficients (Kpan). Additionally, Kpan was also calculated from others empirical methods such as Cuenca (CU), Allen and Pruitt (AP), Snyder (SN), Orang (OR), Modified Snyder (MS), and Wahed & Snyder (WS) using the required input parameters in a particular method. The obtained Kpan from various empirical methods was multiplied by pan ET to obtain ETo from various approaches. Further, comparison was made between monthly ETo obtained from PM method (taken as a standard) and six additional empirical methods to identify the best method for the semi-arid region if class A pan evaporation and large meteorological dataset (required for PM method) is unavailable. The performance of various approaches to estimating ETo was evaluated using mean absolute error, root mean square error, correlation coefficient, Nash-Sutcliffe model efficiency and performance-to-deviation ratio. The flowchart of mythology adopted is given in Fig. 1 . 2.1 Study area and data used Forty years (1984–2023) daily weather data of maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (RH), sunshine hours (SH), wind speed (WS) at height of 2.0 m and pan evaporation (Epan), were obtained from agro-meteorological observatory of CCS Haryana Agricultural University, Hisar, Haryana. The study area is located at a latitude of 21°15' N, and longitude of 75°68' E with an altitudes of 215.2 m msl (Fig. 2 ). The climate of the Hisar region sounds pretty intense, with distinct seasons and extreme temperature ranges. The summer months bring scorching heat, reaching up to 45°C or even higher, while winters can get quite cold, dropping to around 1 to 2°C and occasionally below freezing in December and January. This geographical location is at the outer edges of the monsoon region, around 1600 km away from the ocean which influences the climate by impacting the intensity and consistency of rainfall. Despite receiving around 470 mm of annual precipitation, the arid nature of the region is evident, especially with the majority of the rainfall concentrated during the monsoon season. 2.2 Methods used Reference evapotranspiration (ETo) was estimated using the reference FAO Penman-Monteith equation (Allen et al. 1998 ) expressed as: where ETo = Reference evapotranspiration (mm day − 1 ), Rn = Net radiation (MJ m − 2 day − 1 ), G = Soil heat flux density (MJ m − 2 day − 1 ), Ta = Mean daily air temperature at 2 m height (°C), u 2 = Wind speed at 2 m height (m s − 1 ), e s = Saturation vapour pressure (kPa), e a = Actual vapour pressure (kPa), Δ = Slope of the vapour pressure curve (kPa °C − 1 ), \(\:{\gamma\:}\:\) = Psychrometric constant (kPa °C − 1 ). The detailed description of estimation of different parameters used in PM methods may be found in previous research works (Darshana et al. 2013; Jhajharia et al. 2012 ). 2.3 Estimation of monthly pan coefficients The monthly pan coefficient (K pan ) from the PM method were estimated using the following equation: Similarly, monthly pan coefficients from other six empirical pan evaporation models (Cuenca, Allen and Pruitt, Snyder, Orang, Modified Snyder and Wahed & Snyder methods) were calculated using the procedure presented in Table 1 . Table 1. Estimation of month pan coefficients using different empirical pan evaporation models Kpan= pan coefficient; U= wind velocity at 2 m above ground surface (m/s); RH=relative humidity (%); F=fatch distance (m) 2.4 Model performance indicators used in the present study The root mean square error (RMSE), correlation coefficient (CC), Nash–Sutcliffe efficiency (NSE) and ratio of performance to deviation (RPD) were used for testing the capabilities of different models for the estimation of K pan and procedures is explained in Table 2. The method which produce lowest RMSE and highest CC, NSE and RPD is considered as the best method which gives the closest value of ET to ET pan . Table 2. Performance indicators used in the present study Note: ETo, obs = observed ETo; ETo,cal= modeled ETo data; N = number of observations; ET̅o,obs = mean value of observed ETo; ET̅o,Cal = mean value of modeled ETo; 3. RESULTS AND DISCUSSION 3.1 Preliminary analysis of meteorological parameters The average monthly weather parameters and pan evaporation in the study area during 1984 to 2023 is shown in Figs. 3 and 4 . The monthly maximum and minimum temperature varies from 18.19 o C to 40.6 o C and 5.49 o C to 26.7 o C during the study period. The RH varied from 40.65–79.39% while WS lies between 2.33 km/h to 8.23 km/h. The monthly sunshine variation is from 4.48 hr to 8.66 hr. The pan evaporation ranged from 1.64 mm/day to 11.32 mm/day. 3.2 PM model based reference evapotranspiration Figure 5 shows the variation in PM based ETo during last 40 years (1984–2023) on monthly and annual basis. ETo varied from 1.79 mm/day in the month of January to 7.75 mm/day in the month of May. 3.3 Monthly pan coefficients obtained from different models The monthly pan coefficients obtained from the standard PM and six empirical models are presented in Table 3 . Table 3 Monthly pan coefficients obtained from the standard PM and six empirical models Month PM Cuenca Allen& Pruitt Snyder Orang Modified Snyder Wahed& Snyder Jan 0.99 0.43 0.89 1.02 0.89 0.88 0.72 Feb 0.94 0.44 0.89 1.01 0.88 0.88 0.71 Mar 0.87 0.45 0.88 0.99 0.87 0.87 0.70 Apr 0.73 0.51 0.84 0.88 0.81 0.81 0.65 May 0.70 0.52 0.82 0.85 0.79 0.79 0.63 Jun 0.71 0.50 0.84 0.89 0.82 0.81 0.65 Jul 0.81 0.47 0.87 0.96 0.85 0.85 0.68 Aug 0.88 0.45 0.88 0.98 0.86 0.86 0.69 Sep 0.85 0.45 0.88 0.98 0.87 0.86 0.70 Oct 0.78 0.46 0.88 0.97 0.86 0.86 0.69 Nov 0.78 0.45 0.89 0.99 0.87 0.87 0.70 Dec 0.88 0.43 0.89 1.01 0.88 0.88 0.71 3.4 Performance of Pan Coefficient models To compare the performance of different Kpan models, it is not possible to compare Kpan of different model directly. So, daily ETo of different models were plotted against daily ETo obtained from standard PM model. Figure 6 shows the scatter plots between ETo obtained from PM method taken as a reference and different methods (Fixed (0.75), Cuenca, Allen and Pruitt, Snyder, Orang, Modified Snyder and Wahed & Snyder) used in the present study. The first sub figure presents the results of a fixed value of pan coefficient (0.75) throughout the year, second sub figure depicts results of monthly Kpan obtained by PM model and rest of six subplots are corresponding to the six empirical models. The performance of these models was also analyzed using various statistical parameters and has been summarized in Table 4 . Radar chart (Moses, 2024 ) were plotted to visualize the performance of the models in predicting daily ETo (Fig. 7 ). The MAE values (in mm) for different models varied from 0.78 (PM) to 1.74 (CU). Similarly, the RMSE, R 2 , NSE and RPD values varied from 1.16 (WS) to 1.96 (CU), from 0.54 (CU) to 0.88 (PM), from 0.54 (CU) to 0.77 (PM) and from 5.73 (SY) to 14.37 (PM), respectively. It can be observed that Kpan obtained from PM gave best estimation of reference evapotranspiration. Among the six empirical models, the performance sequence was found as Wahed and Snyder > Modified Snyder > Allen and Pruitt > Orang > Snyder > Cuenca. Similar higher performance of Wahed and Snyder was observed by (Koc DL., 2022; Usta, 2024 ) however it performed not so well in humid tropical monsoon climate region (Khobragade et al. 2019 ) and cold Serbia (Milanovic et al. 2017) which further emphasize the need for local evaluation of various pan evaporation models. The local environments where evaporation pans are placed significantly impact the interpretation of evaporation pan data. Factors like vegetation, wind speed and humidity play a crucial role in this process. Table 4 Statistical analysis for the comparison between measured and estimated daily reference evapotranspiration K pan Model MAE RMSE R 2 NSE RPD FIX 1.13 1.38 0.80 0.61 8.60 PM 0.78 1.12 0.88 0.77 14.37 CU 1.74 1.96 0.54 0.54 4.71 AP 1.00 1.47 0.77 0.62 8.32 SY 1.24 1.77 0.65 0.49 5.73 OR 1.00 1.42 0.79 0.63 8.90 MS 0.93 1.36 0.81 0.67 9.77 WS 0.88 1.16 0.87 0.77 13.40 4. CONCLUSIONS Considering the difficulty of applying the PM, many other less aggressive approaches of estimation of pan coefficient which requires fewer input parameters provided an alternative approach that is more feasible for areas with limited meteorological data. These models use lesser meteorological inputs, primarily focusing on temperature and potentially some humidity measurements might be suitable option in cases where the necessary weather data for the PM equation might be lacking. The study focuses on better estimation of reference evapotranspiration using monthly pan coefficients instead of single standard annual value. Weather based irrigation scheduling is easy to employ and can cover special variation of input data easily with advancement of remotely sensed and cloud based availability of input data. The most accurate standard method of FAO based Penman Monteith calls for minimum five type of data viz. maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours. All these five input parameters are not readily available around especially in the current study area where weather based scheduling has not gained momentum yet. A handy instrument is pan whose evaporation can be converted to evapotranspiration by multiplying it with pan coefficient. This pan coefficient value have large temporal and special variations but current for study area all literate present only one single annual value of pan coefficient which need further refinement and precision. So this study was conducted to obtain monthly pan coefficient suitable for this study area which is characterized with semi-arid environment. Therefore, the last four decades data was used to calculate the monthly pan coefficients. The obtained monthly k pan predicts ETo better than single annual Kpan value. Many empirical k pan models are also available which perform quite differently at different locations and climates. Six k pan models viz. Cuenca (CU), Allen and Pruitt (AP), Snyder (SN), Orang (OR), Modified Snyder (MS) and Wahed & Snyder (WS) were also compared for their performance in estimating ETo here. Different models exhibits different performances in different criteria's so multiple statistical tools were used to get the overall picture.No single model performed best among all performance criteria but considering average of their rankings in different statistical analysis, the performance of the models for estimation of ETo was found of the order as PM > OR > MS > AP > FX > SN > WS > CU. In absence of meteorological parameters essential for calculation of PM ETo, Orang or Modified Snyder model can be used for estimation of ETo for similar type of climate. Declarations CONFLICT OF INTEREST There is no conflict. Author Contribution Ram Naresh: Conceptualization, data collection and methodology, writing- original draftMukesh Kumar: writing-review and editingAmandeep Singh: writing-review and editingDarshana Duhan: Conceptualization, methodology, writing- review and editingNarender Kumar: writing-review and editingBaljeet Singh Gaat: Formal analysis, writing- review and editing Acknowledgement The authors would like to acknowledge the Department of Agricultural Meteorology, CCS HAU Hisar for providing the extensive meteorological data for this study. The authors would like to thank the officials at the College of Agricultural Engineering and Technology, CCS HAU, Hisar, Haryana, India for providing permission, space and guidance to perform the research. DATA AVAILABILITY STATEMENT All relevant data are included in the paper. References Alexandris S, Kerkides P, Liakatas A (2006) Daily reference evapotranspiration estimates by the “Copais” approach. 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PeerJ 12: e17685. https://doi.org/10.7717/peerj.17685 Vishwakarma DK, Pandey K, KaurA, Kushwaha NL, Kumar R, Ali R, Elbeltagi A, Kuriqi A (2022) Methods to estimate evapotranspiration in humid and subtropical climate conditions. Agric Water Manage, 261: 107378. Wahed AMH, Snyder RL (2008) Simple equation to estimate reference evapotranspiration from evaporation pans surrounded by fallow soil. J Irrig and Drainage Engg 134(4): 425-429 Walter IA, Allen RG, Elliott R, Itenfisu D, Brown P, Jensen ME, Mecham B, Howell TA, Snyder R, Eching S, Spofford T (2005) Task committee on standardization of reference evapotranspiration. ASCE, Reston, VA, USA. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7284841","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500000529,"identity":"049af4b6-e8d6-4635-92aa-ed8376605687","order_by":0,"name":"Ram Naresh","email":"","orcid":"","institution":"CCS Haryana Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Ram","middleName":"","lastName":"Naresh","suffix":""},{"id":500000530,"identity":"a953b3ad-1c5b-4cb8-8ccc-5daa84c14c7d","order_by":1,"name":"Mukesh Kumar","email":"","orcid":"","institution":"CCS Haryana Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Mukesh","middleName":"","lastName":"Kumar","suffix":""},{"id":500000531,"identity":"4e2a11a3-acf7-432a-99f9-b2f245dc621f","order_by":2,"name":"Amandeep Singh","email":"","orcid":"","institution":"CCS Haryana Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Amandeep","middleName":"","lastName":"Singh","suffix":""},{"id":500000532,"identity":"df2fcdee-26d7-4304-9849-51e430b6b7a8","order_by":3,"name":"Darshana Duhan","email":"","orcid":"","institution":"CCS Haryana Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Darshana","middleName":"","lastName":"Duhan","suffix":""},{"id":500000533,"identity":"70f2ba7f-4add-447a-8927-1b0b7722938b","order_by":4,"name":"Narender Kumar","email":"","orcid":"","institution":"CCS Haryana Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Narender","middleName":"","lastName":"Kumar","suffix":""},{"id":500000534,"identity":"2ee3ab97-5f1a-44c3-9b95-7acd3da645bc","order_by":5,"name":"Baljeet Singh Gaat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIie2RvWrDMBCAJQTtZDJ3SV9BxkPIkjxIlwrPgo4e3HCTsxi6aijJK6SLZwmDuhi8Grok+AUCXTykpec0GW2thepD3HHiPk4/hHg8fxBOGPBroY8JRsbAodBe6S1GjarO2a2Qi8LKIOv3HMrs1sDTMTktXta11sHm+WGyRqVLikFlngsIVcVjVcVE3xXvUpUUaF59DB9MC4iCjGM/rrCwElBhNBtR6gNEX988vq9bosWrlVun0uAUfLQFb3CKgVTuXMpcHSDMbfQYNi03YLV8Q8WM3WU2iS3v0ulyWov285Su5KYuzb5LhhXkpv8UAb9FeY56rB9hewzLS7FyNHs8Hs9/5AeCUWctohCiLAAAAABJRU5ErkJggg==","orcid":"","institution":"CCS Haryana Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Baljeet","middleName":"Singh","lastName":"Gaat","suffix":""}],"badges":[],"createdAt":"2025-08-03 17:38:15","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7284841/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7284841/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89381870,"identity":"81573b02-f75c-4895-af96-2d814b78c66d","added_by":"auto","created_at":"2025-08-19 12:02:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":404086,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of mythology adopted in the study area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7284841/v1/9d9ee318ca1f08735a1adb58.png"},{"id":89382827,"identity":"fd6ed7ea-af23-479a-af0e-197e2103290c","added_by":"auto","created_at":"2025-08-19 12:10:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":484621,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area map of the meteorological observatory at CCS HAU, Hisar\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7284841/v1/4b0094629ecd915f6b67602f.png"},{"id":89381873,"identity":"e32c263c-8346-4704-8d69-4d8c9f41aef0","added_by":"auto","created_at":"2025-08-19 12:02:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":66366,"visible":true,"origin":"","legend":"\u003cp\u003eInput weather parameters and pan evaporation for the study period\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7284841/v1/357a6b28df662e2ba8178e8a.png"},{"id":89381874,"identity":"c2df793c-f749-41f7-b4ff-d9e65c87e7d6","added_by":"auto","created_at":"2025-08-19 12:02:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":61135,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMonth wise ETo calculated using PM equation\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7284841/v1/e0e4b4c466431888904b271a.png"},{"id":89382830,"identity":"33856829-d459-4136-a797-eb49f8331e27","added_by":"auto","created_at":"2025-08-19 12:10:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":263023,"visible":true,"origin":"","legend":"\u003cp\u003eCalculated monthly pan coefficients from PM method for period 1984-2023\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7284841/v1/8a1c2c8722dc8b8b0582ec3e.png"},{"id":89384563,"identity":"412aa9af-abba-45d2-86fc-8fbe72ce0f43","added_by":"auto","created_at":"2025-08-19 12:26:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1136036,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots of performance of FIX, PM and Kpan models\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7284841/v1/a4d0f3c1e02e80ec4fd7f560.png"},{"id":89381879,"identity":"a45738d8-effb-4c26-8931-7f4067c71142","added_by":"auto","created_at":"2025-08-19 12:02:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":218809,"visible":true,"origin":"","legend":"\u003cp\u003eRadar chart depicting performance of various models\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7284841/v1/23ad0fd5495adb04bfa019f1.png"},{"id":95705969,"identity":"1d12eb98-fd47-444b-b7d3-b6635c344602","added_by":"auto","created_at":"2025-11-12 06:39:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3437086,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7284841/v1/65eac160-d5ea-46de-97ce-6a73e52cbf8c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Monthly pan coefficients for estimation of reference evapotranspiration in a semi-arid environment","fulltext":[{"header":"HIGHLIGHTS ","content":"\u003cul\u003e\n \u003cli\u003eMonthly pan coefficients were calculated using PM model at Hisar, Haryana.\u003c/li\u003e\n \u003cli\u003eThese were compared against six empirical pan coefficient models viz. Cuenca, Allen and Pruitt, Snyder, Orang, Modified Snyder and Wahed \u0026amp; Snyder and a standard annual K\u003csub\u003epan\u003c/sub\u003e value of 0.75.\u003c/li\u003e\n \u003cli\u003eIn multiple statistical comparison, different model performed at different levels.\u003c/li\u003e\n \u003cli\u003ePerformance of Wahed \u0026amp; Snyder and Modified Snyder were found close to Penman-Monteith for estimation of ETo in the semi-arid climate.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe crop water requirements (CWR) is the essential input for irrigation scheduling, which is the product of evaporative power of atmosphere (reference ET) and crop coefficient (Kc). Crop coefficients which is a measure of crop aggressiveness against the standard reference crop have been used to estimate evapotranspiration for specific crop by measuring potential or reference evapotranspiration (ETo). It must be derived empirically for each crop based on local climatic conditions. The Food and Agriculture Organization (FAO) of the United Nations recommended the FAO-56 Penman-Monteith (PM) equation as the standard for estimation of ETo (Allen et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) which is used as a reference in the field of agronomy, irrigation water management and related fields for research purposes (Alexandris et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, one of the primary challenges in using the PM equation is the requirement of comprehensive weather data, which might not be readily available in many meteorological stations (Chiew et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Direct method of calculating ETo is multiplying crop coefficient by pan evaporation data which is readily available from class A pan evaporimeter. Which can be further multiplied with crop coefficient of a particular crop to get evaporative demand of that particular crop. The common practice is to use standard value of pan coefficient such as 0.75 for US Class A pan and 0.8 for ISI Standard pan.\u003c/p\u003e\u003cp\u003eThe single annual value of pan coefficient does not account well for the seasonal variability of pan coefficient. Many researchers have worked to find seasonal or monthly values of pan coefficient for different types of climates. Some empirical models which were widely exercised were Cuenca (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), Allen and Pruitt (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), Snyder (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), Pereira (1995), Orang (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), Modified Snyder (Grismer et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and Wahed \u0026amp; Snyder (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). There accuracy is usually compared against standard PM method in many studies (Jensen et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Itenfisu et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Walter et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) but it was found that different model behaved differently in different climates.\u003c/p\u003e\u003cp\u003eIrmak et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) reported that Snyder Kpan model was found closest to PM in humid climate of north-central Florida. In a similar study by Gundekar et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), they found that Snyder K\u003csub\u003epan\u003c/sub\u003e model is best suited for the semi-arid region of India, whereas the Pereira, Cuenca and Orang Kpan models perform poor. Sabziparvar et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) evaluated seven different K\u003csub\u003epan\u003c/sub\u003e modes at two different climates of Iran, and found that Orang, Raghuwanshi and Wallender performed best for cold semi-arid climate conditions, whereas Snyder and Orang models performed better for warm and arid climatic conditions. Similarly, Rahimikhoob (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), results depict that Orang model gave better results while Cuenca and Snyder models significantly overestimate ETo compared with the PM method in a subtropical climate of north of Iran. A study by Aschonitis et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) found that Cuenca model performing best in a semi-arid Mediterranean environment of Greece. Singh et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) observed that ETo calculated from Modified Snyder and Orang model agreed most with the PM method for daily, monthly and annual estimates in semi-arid of India. Khobragade et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), reported that most of the popular K\u003csub\u003epan\u003c/sub\u003e models were found unsuitable in a humid tropical monsoon region of Chandigarh, India and the pan coefficients varied significantly by month and season. In another study by Pradhan et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) in a semi-arid region in India, the Snyder model was found nearest with the PM method, followed by the Cuenca, Orang, and Allen \u0026amp; Pruitt model. The same model in a dry sub-humid region in India performed best while the Pereira model performed worst (Kar et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Snyder model also outperformed other K\u003csub\u003epan\u003c/sub\u003e models at northern Iran in a mild, humid climate whereas the Pereira model underestimates ETo (Tabari et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Similarly, Heydari and Heydari (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) analyzed 21 years of climate data from an arid region of Iran, finding that the Cuenca model performed best in comparison with PM. Meshram and Gorantiwar (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found Synder method best among five methods (Cuenca, Allen and Pruitt, Snyder, Pereira and Orang) for estimating Kpan using data of 1983 to 2012 at Solapur station, Maharashtra (India). In another study, Shweta and Krishna (2015) compared four methods for estimating ETo and found that the Turc method closely matched Penman-derived values, making it a suitable alternative in areas with limited data. Dar et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) compared seven evapotranspiration models using 21 years of data from Ludhiana, Punjab, and concluded that although FAO-56 PM is the most accurate method, but empirical models such as Jensen-Haise and Hargreaves could offer reliable estimates where data is scarce. Vishwakarma et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported that FAO Penman-Monteith and Turc models demonstrated the best performance in humid and subtropical climate conditions of Indian Punjab. They further found that radiation-based and temperature-based models performance better in comparison to mass transfer-based models in the study area. Ko\u0026ccedil; (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) evaluated eight Kp models for ETo estimation in Turkey's Mediterranean climate using 22 years of data, finding that the Wahed \u0026amp; Snyder model performed best, while the Orang model showed the poorest results. Usta (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) also revealing that the Wahed \u0026amp; Snyder model delivered the most accurate estimates, closely matching the FAO-56 PM values, whereas the Snyder model showed the poorest performance.\u003c/p\u003e\u003cp\u003eFrom the past research, it can be seen that models behave differently in different climatic conditions and across locations with similar climates. Therefore, regional assessment must be made for all the available models for their local performance and suitability to the region, especially in the area having limited essential data for the calculation of pan coefficient from the standard PM model. Further, determining water demands accurately for irrigated plants is a crucial aspect of effective water management, particularly in regions where water resources are limited, such as arid and semi-arid areas of Haryana, India (Naresh et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, the aim of present study to calculate monthly pan coefficients from different models and then assess the performances of various Kpan models using the 40-year daily climate data in the semi-arid climate of Hisar, Haryana.\u003c/p\u003e"},{"header":"2. METHODOLOY","content":"\u003cp\u003eThe following methodology was adopted for the estimation of Kpan in the present study: The 40-year daily reference evapotranspiration (ETo) was calculated using the Penman-Monteith technique, by employing maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours as input parameters for the study area. The above obtained daily data were converted into monthly data by averaging the daily ETo for that particular month. After that, monthly PM based ET was divided by class A pan evaporation (pan ET) to calculate monthly pan coefficients (Kpan). Additionally, Kpan was also calculated from others empirical methods such as Cuenca (CU), Allen and Pruitt (AP), Snyder (SN), Orang (OR), Modified Snyder (MS), and Wahed \u0026amp; Snyder (WS) using the required input parameters in a particular method. The obtained Kpan from various empirical methods was multiplied by pan ET to obtain ETo from various approaches. Further, comparison was made between monthly ETo obtained from PM method (taken as a standard) and six additional empirical methods to identify the best method for the semi-arid region if class A pan evaporation and large meteorological dataset (required for PM method) is unavailable. The performance of various approaches to estimating ETo was evaluated using mean absolute error, root mean square error, correlation coefficient, Nash-Sutcliffe model efficiency and performance-to-deviation ratio. The flowchart of mythology adopted is given in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study area and data used\u003c/h2\u003e\u003cp\u003eForty years (1984\u0026ndash;2023) daily weather data of maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (RH), sunshine hours (SH), wind speed (WS) at height of 2.0 m and pan evaporation (Epan), were obtained from agro-meteorological observatory of CCS Haryana Agricultural University, Hisar, Haryana. The study area is located at a latitude of 21\u0026deg;15' N, and longitude of 75\u0026deg;68' E with an altitudes of 215.2 m msl (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The climate of the Hisar region sounds pretty intense, with distinct seasons and extreme temperature ranges. The summer months bring scorching heat, reaching up to 45\u0026deg;C or even higher, while winters can get quite cold, dropping to around 1 to 2\u0026deg;C and occasionally below freezing in December and January. This geographical location is at the outer edges of the monsoon region, around 1600 km away from the ocean which influences the climate by impacting the intensity and consistency of rainfall. Despite receiving around 470 mm of annual precipitation, the arid nature of the region is evident, especially with the majority of the rainfall concentrated during the monsoon season.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Methods used\u003c/h2\u003e\u003cp\u003eReference evapotranspiration (ETo) was estimated using the reference FAO Penman-Monteith equation (Allen et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) expressed as:\u003c/p\u003e\u003cp\u003e\u003cimg 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vu71PUzWJEOmUP79M4zCAi+/ABydSjh3IZzzcXdTcUtNQfpKvnx1CP379OHhl35ix+nCWvt1ietYMv1R+vQZzpzfTxNlGuBvVm4iEb9/yjuz5zJ/B3R59n1mPdGNNld6f1p4EPzwq7wx1puS9W/x7KhRPDQvjIoes/lk1kDaO1SPFVe1f5ynnxlNz4qNfDB+FKMefZ+fC4bx6twZPNrG6i6vjYjh+NJl7E+wp820yQxrYoaV8vIY+PuZXIXSshXtB41iQlNB1II/2Xkpv26+eAf+7jaU/BPUVeSkp1Ch14CZOcp6AiXIcfVogpuHDegNoL/FDDBjF4fXHWF7Zm+eeWUErT3MEDoNshvkvIacbPLysgEVckuzekuuZpb2ePk0RmVmgVGnQ2W8XBxWNsGvy8O8+LALFuVb+PazjWyMuNasBxXEnfyJBVvSSXN8mJmvPs7QZiazUUF15hoTwbF8Ha79utPVQo7SqimdegbT3BdCQ8M5GVVg+qarKvJzSE1KAJWMAG9vLC3rfsP1yZDJqkvb1+RSmHyGjUdl6LW9mDSuHU2dbuUz73KVpWTERZNv1IG7L24u9tf6YtwJBXg08sPG3oqczAzi02oGyH+XLiuCC39+yvo9UVxKSeHEzu/4Y+9xjuZfSVBBdtgOwi9dJMWmAzi6c7nj962z98BvwHiefGYqrz4zhUmD/Qh2Ul7LeGVyFE6d6fnsW3y15Hu+X7yYhQs+5blHRtPF2RLVlUtN5Y5Dr4nMXvQNy5Ys5pvF3/Dmyy8xPMTzrp9RUB26noOKVhi6j2JyD3usTBPc1+QordvTqWs3ApTf8+vOC8Sl3+7TYF3Xz80l/xyDgcqqCjAaQAiMRlFvG6yllQNKc3PIzkKoNVw/bJnKJHbbb6w6Wk6Th57lyQBLZEClznjDqkehUaPVXC5RG4zUV1OtUJljYWWPQaeHnDxKroY7M8x8+jLm3a9Y8PZgHI5+x6LXn+Chh8fz+OOPM/6Ricz8bBMZXjN45bOPeaaLPV713cl5x9h3spgsl06MGhd8uYTuRNMevWnr70XlmSOkRWVzJa81pdcUkxCrx0zRhKY+dljdVGFZIJChVFlja1UjczUWk3PkF75fvpZQz7EMnT2bsW2c7qupQvUVpaSlxCPTa8DGBnNzZcNMCCJTomrsh5+9MyIunoryisttt/82QUVWPpEHHHjwhR/57btPeDEwjYy4k+y6cLmZSmtBRlw4yfn5eHceRPfO/rjcbk5paYdLcCd69upFv3Z+uNZ77ciRuTaj84ABDBgwgAEDehDoUk9ChRN+na+kGUDPFo1MmrjuRgmcXbmblIqmtBjSEff76Nn35imw8POnf3c7Iv46RmhOeYN1wLzdy1JyJ2Qy5AoFMtmNr2ajQV/dXl2ciy0KCq8fe2vRRK5l9bE8kvwe4MXH/a9WiYrL3yeTy6jviUHI5MjrbbuuQRgxGo0YdDrIKcDNu0YUlpth6+yGj0JZ/WBS/VPRarUEejtQkVxCRWkVJYXFlNbfe43iM+vYGF6EZ6fRjLraA96IRXBLWrr4oCwMoyQrnoh6u3UK7Oysyc8DcMfeTobqb35ONRUWqMmMX813b8/h/blzWbhwIXNmv8Wi9eFEuA/hpU9m8cpLHQi5z8aA67VaigtLMBj0YASjsZ4L47bIwMYeVzNzKMjFysz8Oh0Wb4HCCXsrFXK5Cks7p9uc5S0fh/YdUATP5qmpz/D4c9N557kH8DDkEn4xEyOg10YRdbKAjDI/xo3sQ88m9QTT+4HIJC10G0uWLGHJr3+yITyPUh1AARnHt/PTkiUsWbGO309lUHk7EUdoMabsZ/tpDVVaG0L8b/BomH2add8uqT6WJUtY8sdeohuk5+NtKIlm9/Iax/LjBsJy7vC+sHEmoNuD9C44TXZOFRm3cz7rcVPZm6SBCYHRaKwe3nwDcrkCmQCsHCjTa7mZ+TBESRhH/trDXl0XBkx/ht41GjTtrc0xU8jQ6cDSwRIooygjlPjcciqMIJcZEVeqxK9HJkMul6NQKsHBlpz0K+UqHdrc4xz5dBYfLthDUffneOGzZaxZvYpVq1bxwcdvMe/p/rin/MjnL7/GFzuTSSw0vYrLiT0RTr7CDe9mfjU65sjAJgAPRzmBFmWciz7B4Yj6W5IqKipQKgH0GAz112zUpUCJntKCSE7v3sWO7dtZteIHNv26hs82JuHW/2nGDepI19uLGLdGX0JJYihbN29iw4YNt7ntJyKtjMt9mm9IoVJia2+NXKEAGcjl9VxkIo/Uk4fYvWsXBw4c4MCBA+zdf5Dt57Mo197gDBsN6IUAlKjV6nqaS0wIHYbcSI4f2FnPb9rAxvWr2Houm/KKLCL3/MGqetJs2LCNExcyyL1ui5MOrU5L4Ihu+DsDWOM8dBy9vLxRJOdXTw1bGMa+0xqqAqbQpZU7/v+Lv/u/QZRSGLOHXZ+/xfw5H7NgWxK5aoBKihMOcmzh+8x5ZyEzV0dRcqO/83VpEPEJnC3zwODjhWd9NW6UUxi5g80rvufnLX+wbd8+9u7ewDdLvuD7X49yJlPzN6NOGlIVFSknOPjHd/yw/hc27d3Lvn3b+PnnRSz9fgsHYiu47ZFvMntUgV60s4jk4qELJDbUw4npwHDJ/0DJJXH2s96iuZe1wOMJsfhkqahvJsSiba+JV/rZCQLHiYFfR9SbpiajoVAk/PCoGNMiRHSd+pP4NTxGxMbGiqioKBEVdVGc+GaiGNPaQtB8nHh19QVx5tAX4sMXg8QzKy6KqCohxPlF4stHvQT4iTbTNotz5XVn6jBmhYptrwYID3dnwQNfibWR5dU79JdE7KpnRUcLR2FhP1UsPpsvLk8Mee29JSli/+xmopOXXCh7zRHv7c6r8ZsMwlC6WSwZ1EEMn7RYfBdZ99fG/jRGTGmjEDR7RDywNKqe82EU6vCl4jEXRKOAx8S84wWiyDSJKc1RsfuDkcIWW9Giz0KxLr9MVFRWiorscHHw1YmitbWZsO00Wjz+S7wouqPZdG5SZayI+eMF0aF1yLUpXW95GyHmbb4kkkw/uz5558WBd9oIbycrgdez4vvTZeLqbLNCCCGMwqg/IdY//7Do0shROFmrhMrSVlgH9xMd398rEovqXiPVdEKk/yG+GNVaQFcxfWXK3/8tDKVCfXiBeGp0r3p+U6AIDPAV3o6WQqm0FA6N/GpPe3t16ySmLtghjppefFdVXzWGWhdPtDg272sx89GVIk5kieifRgk/rybi4e8uiujrfs79wZB/RER8MED09mktes4/IWJLhBDCKAyaMyL+i4fFII9Ooslru0VWed277W8ZikXl9tfE0NbPiOeWRYss0/1CCG3GavHj1P6ibe/HxNuHL4k8IYRRc1FsmdFOhDh0FE8uDReXbvG+05ZkiKzkqMv53o22GBEblyOKNAZhFELoSw+Lbe+PFh3aDhX/tzFMxBmNQog8cXzBUNHTOUAMe22nCNWYftvN0orStD/Ep52aib5TV4j1N3Vz/j2pBP5vsLDEvZEPFipzMOgRov6SYm52IiVF5WBujlJZX4evmowI7QVO7k4k/FIyFzfOY+74EQwfPpxRo0YxatQoJn24jX1xGkjaw4rXx/Lo01/wyy4N5VoVKgXg4oGTiztgQBjqHxutqSwhJyMFo0GHzNwCi8slNpGZSHL4SSLMrJAN6knHRg51OqvI7Fzo3msQbq726MNOU1mpvdYuqi8jfds6tqU1pVHP3gxqVvfX+nfqQ2CQPyTEIgpLyTRNAGg1ussLnhhu1NxfhxE5SqUNto42WFlaYuXelj7PjuC5ia0pC9vC0V9+ZnVc1T/fjmvug8+wN/hz9To2bdp0m9sipvb2xtP0s+sjl6NUqar7RuRkkJ9fSu0BizJkitb0f+tTPnm1F2Oa6dC5tMN98Ay+f6ozXrY3aFIQAoMQIFOg0+v/5voF5Nao2j3Je1/+XM9v2sSmjd/y4cMhuLm0ZPiby1i7eXPdNJt+590nu9O2vuENUF2bA9SuaPDExaMKB9dMws/Gc/xoGPpm43mgpy+Bly9ig8FAZWXlfbUByJ09sWrsjQJQXF6cBmTIzbyw9GqE0mhAebtRwqAnJzsDdWUVquvkX8m7V7M3RUbjIc/wWFtvHACZqjG9B/XE3yuJCzFRROaavuvGsg8t5IfZ1XnejbfxPP70r+zKKMcIFB86yr6ITBTdn2Zy7xCayGSAA2369qFDq3JiE05yOsH0226WEqXKHc/mKixsDRgbqFpBWo3sX1FEyeGFPPnMj2xIbsmc9b/y0nDvOvOdR38/hg8WbeRg41d5+u25zOt9o4GTAmHIJeV0IsnllRiobnu+cqOCkfKwX1i+/gCnrIbx4JhHeK6nG2CBW1B7AjwtsdQeYe8XnzHq7aP4DFzAV+ufYqCtqlY7S1X8YZbP7MP8k/Z4/99WlrzYnY5OcirDl/Pze88w+6wfTZ/9lTUzutPctLe2KMN46GNGv7mcLaEuvPbrGl54IhAfwFh2gf3vPsEzEQN4Yc5sXu3rWPeG15xiz0ef8di8bQQ+sYRZXzzFA06124FKQr/jixef58f8roz+bDPvjXDF4wZNb2iPseezTxn7zkH8+i1gwZapDLVWXR4VkE/WjkVMGP0Bp5y70PyFhWx5oStNbOoc2b2r8hLRy15hyHsHSMv34aUVa3jhsRD8TdMBhC7go3kLmJc6hJFvLmb1JA/TFDVoIHwxM2YuZUlyT579aD5LHvG5ww5y2ZyZN5GHf5bz0Peb+HSotWmC22Qk+c932VeQRbSsDeK7r0ietJp5L3WixeUAvnPnTu63ZSPmzp3LkCGdiFrxBtPeDYOp3/PjC10JtAPIIWnVx/zfi4e5NOVjjr03EA/rW7zuNXnE/fosUz8zp9XcD3jnsQBqXzH5hC2YxIxFp0l1CiLASWBlbYvRaESdG01yRi5NHv+J6S9NZqJvrTfeUEVqGPFJqSRcr6frVUrMLJvSsncQPjZ60v+YzYy3fuKIMoRgLzOszCxQKBRU5F8iPycLZY9XmPLmh8xsb/o5N0eTd5St705jmcPrPPH0ZMYFmqa4dbf7bCW5IxaY+/kQKFMh0yWSV1hKkWkS1MiNkJkGPk2CCGl+o+DN5ZKSO75dutH3ci/VYcOGMW7cuMvbw0wZ0pLmbkpUnq3pOPLRqz1eW3lZYikDzNzwaeKLM8Vo8lPJLpXV6S2pMi+nMh9ElRvdurTAzbH6EtKq9VSW68FopLxKA9fpDHVtmJYKlZUF5gCUU3VhJ6tOZ2LZsh1BfjZ1gzeAeQh+Pk708FZzKfY4h86X1elGYGHlgE9TO6r0hSSm5lF5Rz2nXHAPasOkkf5ociOJW72B42lVd94Z625i4Yxfr350UpihJJnwG2R8BmEBSjMUCoGtCqquN6Ef1e3fpenJZJfkY+bbGFdHxzsM3tXTAper9RiNejQVpQ1YGyLHpakXPm5GTpxJIcvhIR4f0xa/Gm3fXl5eDBs27L7aPD09ATXU2+9FXOd1EBXxxIf9yZzZbzBx4kSef/55Zsz9nvVputojZZQqXNw9sbCwuVzHZSqRgnRBWo4tTdp0pc+gIXTv3p2ePXvy8NNvMGvuQt54uDNdL/fjEbnn2f3di8yYMePy9hLv/xDGgfTa2YB1k0607XMl37vRNooRD7TCz0aFnCzKi8pJSLfErVl7ug8YRK9evejevTvjHpvBC7M/471nRjL0SseccjWVB//gtxeuHMsMZn2xlx3JNQ6kFgN6QwG5CXoqS0TDRV7TOnXJ/4JBaEtPirXj2wgfayvR5qW1YnV67ZZHUXZArHytp7C1DBCD3z8gok3aXoqidoj9K78R3/y+Q2w4n2/Splcfgyja8pJ4squ1sB/8hph3qL4VJkpE7s6PxJNNFcKq6UjxyMo0UVzrsAqF4fxC0b+JlVAFPieWnsoXV5YjqYpbI7a9GiBQOguzrgvFviyT3yOEEEIt8v8YK4YEmQlZ06nigwNl1UtSlieL8E+6imadh4tZe0pEfUd2VcKP4pcp3kLp0Vn0+fCsyNSa/PCC4+LA3J7Cq1GQ8Jm5S8Tk1XccNeiOi30fjRa22IvW/b8TOyq1tduA1ckib9+7ItgMgTJQjP7qnIiudYDlouTSfvHX4sXim2++Ed9siRDnLqWL3DOrxW9ffVD92sZjIrzIKG67+ewfZRDqgiNi7bhmwstWIcw7viS+PKSpf7nY8EXi83HOwqL1WDH5z4zrL2ZyWez3g8WwABsR9NTP4q9k0723QR8nDs7uJXy8+4gX12Y27FKlJQfF8R8niRY9HhKT5l8QKVV391IpDSdaXPh5sujt11YM+PR0jYV6CkXGxjfEA+4dReDre4Xuaju0VhQe/kCsX/S8CBrxknj4kUfE9EnDRP/2Q0W/l9eKPy9VXFtkxlgsDLveEIPbTBSTvjsn6l4Cp8TWqT2ELR3EC6uLTXfWoi+8IM7+8poYP+VhMeKpaWLWrDfEs0Pai8nTvhWzj4n6r9dbcklEfP6oaExjMXx+1LWFpephKM8XGevni9+++lzMe/N98dl774npj/QTXdo9KoZ+flHEV9R3NJWiMGqRmBncWAyYvk5szzTdf3sa6jlAckvkqKza0O+5fgzqbM75Nb+wdkd4jafXInL+/JWvVkVj8+BLDBjchiCTAZ85x5aw9tMZzPhwGR/vSat3zHZtchSGcmQGPTq9jCpjfX96O1xbd+OZF3si8g+x45efOJRUcW130hF+/WgJ+8vbEvzEBIY0c+TKFMwW3m0I6PgAbV1kaE9uZcPhGBJqFZF0FMbu4ruVx0gocmPgc4/T0dcGBVoq0qLZcziGCrk7yqxQTh6s7ulsuh08eJCD5/JJ0zqgz04kK/ws0SX62r/dyZ92fYfQwlhKbngcWeq/aWySCYxCjx4dAh0GWe0mA8x9cGjeg9kPN8NGH8/GLxawO7KqxmxKGtT5kYSvXcyHs15jxvyvWXUqmTMnzxJ2cC1/fPsJcxcs4csDacRX3KjI+m+RY27bhh5TH2ZwW0vMT69m5+rV7M6qrF25IcpIiU7hXFIFhjqT3dRDn0HyhXRSchrRrWs32t/dc32CnQfyEjNaVJrR6sEWOFvcp0PH6jDH0cURY3EZ+TmlVFVXiUHhJRLPRpOn04KNnLKr1ScGdBpLUvKdefbtz1m9ahXfLP2SVwOyiFr1M8fOFte4H1UIZ3u8LTNQZ+SSXefy98W/QyPaOOdwdNsmjtTcrykg/kIUF+JyUQOKihh+Wfwz28U4pn+7hAULPuH96SMZHFBMq7rTWN4GT1yDPenhrSFi90b21BymKspIi43hYnwuJVpAqyE+NYHtxuYEvfYer7//Pt+8OZoeqp1cCgslrbK+3LgCY3oeCXpXAjuG4H2jdZRvgbQe+L9FrsTSzwFRkkFyaBilVRWU2njjqi8k9/gm/lx8iFP61kx8ZRpPD/LEziTHLDjzJ6fOxBOma4lXu5483d3DpGPOFQKDrpTClBQOb1jEwbOZRGuaoLJzpk9zNyytzWqs6QzYOmDpAqUXTpMXe4ZUhSuOjhboimKIWrWZ7zZk4NbxGd6e9yBdnM2uBTuVMzaOPrgWXiChKI6z59TorH2wlOdRWZRPSc4Bdq5aypIDFag9JvDqqxMY3cwMGRnknPidj78+xcXEs5zY+hfLly1j2bJlLF++vM72+7oDHL6Yh1GosZQ7YtexF+0bW2N59UCskYk8MvdtIPS8ikbD+9LWywaLOudGYCjPJefsTnbuPcbOc6lYOXjj2DQAH1d7bMwUVxfrkNvZ4WqjJuHAMeIzI0kUTVCYOdHYwRxzK1tsGwXRsZUg53gMZ7LTMGvak659pvLc82MZ2rySpM27WZ9kR7cObWjpfhdOvaEwx6ZpN/z05ygtTOJseCIZOgcUKgXyinzy8/NJC9vHru1n2Hw6CbvGzfBoN4SRLe2uM85eT1X2ZtZ+vpvtld0YM2MCDzaxuPNaQ1FIyoHNbIyQETT0IQaG2NY7W+DtKacgRk1uWlN6zWhHU6Xszo/3niBDW5DA+bWnOF9siVeQG45Uknf+ACdWbeZIRjHakCB8fXzwtbfGTKHCyrcTnXr3pUeTy1HdXEXu+VVkhgzFP6Qz/f0vdxyQKZG5yCjZvo5UVQtc2rajlV3N77bG2VZNQd5FDsbHITybY6kvoyo/l4TQ3azbGsnFQkvadWqCIfUkcYeOcDbTieDg5jR1tsG9VX9ad+tBK5eG+FupsLExIiovsSfqHBX2XqhUBowF+WSeP8S2neGczlTiG+yLm6MNvl2HM7aLD03UReTm5JIYdZZzUeEk2g3hgYHB+NmYNBhpcsg8uJZvTrei92P9GRxge7n58M5IAfxf5YFvi47081GRv20d29as47c1K1m5/RjZAY/z2sJZTOnVGLdaEbZa8cUtHA+N5bwhiOZd+/JYZ/frBHAdVfn7+eO52Xy6I5qLBQJjcSoFUfFckjenU5fGONf6fAusHEPo2N6PJrnxRK7fyO/r1vLXyvXsiDOj8/OfMfvtQfR0t6qTeSptXQjs3IN2vpZYZMWzaeX3rFn9G6tWrmTjphOUyLrR7ZGXmDfzUfoFXc78qzLJPLWDhTvjURuqH8H1l/+9HoVCiUIhQ24pR+Xfj4EtPXCoEReVFmY4qCPZevosidZd6NzKn8Z1+jsZKT38JQu/Xcm32+PR6qGqJIGk82GUeA2kdaAD9lc7WFtjaW2NPmktEek6EiNOcmZvBJV2rWjW2RMnuQWWtjnErT7CKauBjHvsESZ1d8TS3BZbKzXZR7ZwMMWRHiOH0snT9KzdJWQq3IL70b5tMH4qNYc3rGPNmmWsXb2SlStXEhZRgW33ofT0UeMhM2AIGc6QEFvM68s51dlE/zGfhXuTcJ/0PpNHtsS/zvm/DaKA9ENb2HBeTvCD4xkQZF3nGrxtuliiIjMI1/kzcKgfrqY3kxAIowG9wYDRWD2Z0ZVNr9ej0+kQQmA0gkwup945moQRg1GATFb//n+FORZKWyyKorl0bAcbd25g/ZEE1M1707aZA2Vn9xF6JoqzmR6079YCPzslsloTPpVTGbOZ339JQ9V2LMPHNqfJ1ae66n45Im0jxzV2aMxa0D/IulbtjcwxkOAAWxwJZ83PP7P6p1WsXrWOCLUn3cY+xdCevjS2NcPc0hw3dRwxew6xbtMhTidX0qhLR1ytTBdcugM2jfELaISXxQV2rVnG6u/XsGblSo6lK2k18hkG9AyguYsZSrkMyEGbcIp13/zE/E8X8cP6g5xOlGHWbBBjh4XgY107gOsz49m1ZhF7vR9i6qCOBNc3095tkHqh3w30+aSFnie+REupRoO1tTWWXu3oFOJy3ae0yqwLpKblkmFww9Hbj3aNr9PxCwP6qkzij0aSapSjrJ7lBJ1OYN6oJR1aeWBXXyaMAU1yFDEJuaSUl6NQKrGw9CCgUwd8/m7pQnUheanpXEjLo7KqCqPRiLW1NbaOAfg088G9Zn88QzllmQmEJRQiDPVVPV2f3NwCa88WtGjsgHWt+0GHPmUd702ewaKLQ5i/7Tue62JrMqxNoM06z4WEPIo1106A0WjEIbAbIY1tsDYZIVWSeJiYTB3lGqiqErj7t6N5sDN2GNEWb+L74e/wgXwC737yEjO6VRc1qrL28OeLzzI7qj+frPuZJ4Nqf+bdR4c2N5PzkZco0hsoK6uuS7RzaIxPi+a4GbMpK66izDmYZm7mtWtvADCiSd/Al8OnMy+vCx9uXcWzbSywqfcau0WGWELn/x8TfpEz9tsNzH/Q7jZnY6tH/K98+1sYGwzjWTqvDwGmPe7yznFywxLmr01CbRC1ZlHU6XTodDosLS2RKQbw/KJpDA12oNYzi6iCyNXM2anCp/dQnuzi1AClxgai16POvsSluAyqJxxT4RzSDn/rKvIuXiCpEoSqCS06NqWRVY2bQuQTe/BH1u8sxirgYXoOCaJFE5s6CxRVZa1l1YLfOVg2hIc/eZ4H3UwSUEZF3iXCIgrRGgxUVFRg3ziEFq2a4341A9RBfgIXdxwm9MA2th6JIt1/MAPGPcdzU1s34GpsWrRFcZyNzKFcIygvL8fS1ZcWbdviaV3d90xU5HB62VP8vtcfi0HDaN7YgE30CTZuXE54wCd8+9k4+rrXPAt5ZG5fzdz3QvFc+D7TOvriWrdK8LZIAVxyXzLqc0nd9QnvTNnAxR6v8+630xh7UwOjb4cBdeFGvh/xLh/KxjHzo5eZ2bu6Ya4yfQcrXpzGO/GDmLv8K6a1Nx0df5/RnWfPu6/z8idqOn67mLcmt6V5Q/1kUUzGqQPsiwW/noPp5m995z3bL6vc8y7/N3cb8b0WsvyDPjQzHd6etIWdv37Bk9u8eXrqIHwUqRxb+h1hpQFY9hjACP8CMsMPsuZYb97d+wVPtjHDoeb7jWWIA/MYv9ab1hOnMbvXlaGK1+iKTnM+PJLfN5wmr7AIBwcHZEEDGDF+LD3dwBrQZJzk2ObfWXvBSFFREUajEVtbWywsLGjcehR9Jw6isw1UpUUQeSyUVXtOkK/XY2vriEPviQwf2pkudlfGe9+JTJKPbWTjzkrM2/amb5/OBF93GTs1Ofu+YPvaPPbaDeG1ecNof72Syc24dJzDu35l6Q/7SfR4gkc+folX2tnVOZ//jDLyTv7BZy/P4pDbfJ5d8CRPt7CBpMMsnfsY3yo/ZfGCMfR1vfIDC8g88wdrduWRbPYQz09vQaClsgHOf7W75iFQImlIcqUbvg++wCvzx9OpZBur53/GTxsvkHYz84veMhkyueJy1agC6xoVCTIUKJAhUyrqn6b0fmEsJevoZpZ98CVfHTXQ4t23mTapTcMFbwCZA15dxzB5yhh6NWDwRp9Eamo2h8/kYGOmrL9DqLYKS3d/PAY8z+T/m8LUqU8xqZMfgU06M/r1Obz33iI+encaT3f1wEmmrrEK3xUyUFmhVVdQWVrfskRlpOxaSlh4DDuTZOj1WiyK0zj1y+8s+fkoYfnVnTHLL0Uhyz5Hks6MKqMRBwcHqnLCyExIYcuRbACMVcnEblrEoZgSzuWDWl2MojSRLSt/Zum6i0RXT3h++4yZpB/axeq9BjTtJvL0uM4Eu+gpiIkg4mJKPQsNWeA+4Cl6DAlitF08h3ftYUVoDoU3PS+pmpKU8xxfeYR4NRDQnd7T32DGA27Ics4Tm1ZnNOk/SI+ri5asLGdcevdlYAsboIzUi7sIja5El16J0FQHb0PSAUL3/k5Yjhfp9m14eFobmjVg8OZO2sC1Wec5f+EiMZdSSE5OJjk5mcTERC5cuEB0dDQxMTEkJSWRnp5OcnIyqRlZVCidsbVRodLnk5scT+j56Kvvi4yMJDk5mbS0tKufl5CQwIULF0hLSyM1NZXk5GSySjRUKe1xrlmVI5HUywHPTr3o6JbCoeW/kVamoEBbhq1HAA6W8qud1O6cDKUmknN/7Gev8KfXQwPo6lHdMmfUXeL09i3sS2pC/wkj6eRxH163pQmcObafPXvCWLouEu/HX+K12SPoYt5gJ/ifpT7D0dVb+PGEEY+eDzCxn0+dTqOisoByoxxZ0x70aWKLlSKZhF37OZZpj3O3AQzwVWJh54pjSSmOQZ44u9jVrkKXmSErOMm6cxZ4tu9BP1/TNtAScs+e5WxZAKNeeZd5zz3CkOYOVG5dzE9hetr07U9nLzO0RVrUFt60fWo2s6c8zIgRIxgXJPBs2hsR1IPH29ohKpOJOXaMCy6DmPn2q8yY/CiDGpcQvvEr1sQ0YciAdgQ4mn7/zaqg+MwfrFy4jS+PKgnpa0ZVXDTRMRvZsyGWfKdWNAp0oG7HcFucg4IIds0j/tw5zumb0dHfCbubasAuxCI3jD/eWMvWcgOFeXEkXzhKbLyMCpf2DB/bjxZODVGrcDOUFBelU3EqgrgiGVllZRhyC5BXpRF2+ATn46xQeQXTKsQRm9RdnIwqIcFuGI+P7Eabv5vK43aYjiu7WUV73hezx7cRbo5WQqUyF9a27sLLP0AEBATUmpu4WVMP4WKpErYePmLCT3HV89rmbhKr3hosVCqVMFc5CY/GASIgMFAEBvqKJm4OwkqlElZ2rsLDt/r1gAA/0djZRpirVMJ74FTx4qb8Bhj3J/nv0IvSpHPi8LfPibdfnSw+OVLZgPOaG4RekyFSN84Uc0K8hHnwMPHikSSRUW4UQlcs0iPmiemDXYWFrJd4cUWUiCvX1TOH+z0uepn4+KPZYvQHm8Tuc1lC+zdD7+86lXvEttf6C+/AMWLw0ihhOrWAEEIIo0EYjQahv7rvrNgxbYAY1mO6mLW/xoTpBoMwGo3Vc2tX5IuM+MvzbkeeFhd/myb6j3lZPPvtYRF5eT7u2EuJIqtUL/TCKAw6jdDpapy83Ajx51M+ovsbv4rV4ZfXHDAaq7cr9Ini6FdzxKIvV4t9VyZlEAah19acdcAgik+uEAufby9avrNJxGTfycWfLC4uf1uM9vUVPj7+IrB5M9G8eXMRGOgv/Do8Kz7dnf034/Orj02tq55//OYUiKrENeKL4SNEsP+VWNFDjJ+xTmw8ZTp3//9AZaGI/2WOmNWnswgI6CIGT10ntm3cJjbP7iZ8/FqIwIe+FXvT1UIYtEKn04qaf9KGdtsBXF+WLTKPfSk+mtJagL0I6vaBWHomQkRGR9eaMD7h5ELx4VCVcPfwFGN+TBGxOiFEzlqx7u2eApmN8Al6Wyxcc05ciIoSUVFbxYY3xwg/EAG9XhZzd0ddfv2w2P7+CNFeJhOOvZ8VU9fmSQFccssM5dkiNzNF5JTXzIjvlFoUJfwlPurYTPhaWwgzKzvhOv4D8fnhKiFywsXal7xFs0YWwkxlI5zbTheLjxZem+jifqEuFDm5OSK1SH1v3peVB8WaF/uI/gNniI9uerWKM/UH8Kt0ouTUT2Jm7ysLsfiLAG8nYW3nLBw9/a4WdDoPGC3e2VEg8kxjqjFLJO/6VDzfbYx49Y8IEauuLwroRMHm18Trn/0o5qy+VO8ESHpNoShP+1N8Pn2AeOqFD8XyjJK/nYDnxjSiqjBXpCYni+TkZJGYmChiYmJEQkKCSE7OFoVVDXZj1aAXQl8hirMyRFJSkoiJiRGXLiWItFy1qPe0/A8YKopFYWqyiI2NE5fSSkR5hVpoitNFcnKySErJF2X1PgU2vDvrxGY4zp4FHzLy3eMEDVrIwt1P0Nc0Dblk7v6AOTN2o3lpC7OeC6RF4UZWfPAeL+5x5qFXVvLJM244yQEyubjoXR56+ReUD3zMW8vf5NErHSMy/mLRjAV8k9KaAXO+4pvRLg3XBiaR3DYj2rJUYk9dIvdKz2QLN3wDQvC3ryDt4hniiq+0qtrRuFUbAtxqjJ+X/PsMReRcSiBH7YBr8wAamXajrlc4O6fP5OvzQbSZ/ykL+pk29hvR5l0iIuwCKVWAUCOiN/HpARWNu/VnQnsH5ICZtQPerXoS3MgciysXRVUMZ/b9xfZjltj4D+HBMS1p6qwyye8EwnierZPmc6zDdLo92YdRzrWbZ8qPLeLblbs4EpfJxaRK2j/+Na+8OJTuDTSJiOTfd9tt4AAY00k+doLV+5Ow9+lD74fa4m+mMMmcrLHy8MfHsznBHVoS0MgKy5IzRB3JJcJiOJPf7kvnq2NBMkk6dID1eyPAryedB/ehhaOsunehnTN+4hIxqVoqg4YwppX1/6jXoURyIzIU5g64N21K0ytbE3ecbOSgNMfe0+/a6029cLauf1Umyb9IbomNiyfuHk7Y3nSpIJfEHXs4m+eOR79B9K/Tpi1DYe2MZ2AIISEhhIS0JsQhkT3JPrQd8wzPD21JSEgIzQOa0shOeW04ni6Sc7t2cCTSHdv2PRk1uj1N7UzzVEBfTuHp7/gq3I/ANt0Z1cWlzvAtM6WGSqM9BpvmdO3iQmXiEY7vNWDfzg9vOzMp/7wP1LkubonRiMFY/7KTNSmsm9Hu0Qn0a+OMkwywak7jbg8z+cEet/A06IF1i8GMeagvI4Ktkf/dl0okEsk/RodRZiC+oJRylWnorIexHFFSebUXer1TFWniObNhN1svemDXYwLPj2xPE8tSkk+FEZWWT8nVhAJRlczZH8KR9xxA816Naw1Z01VlELf3d44XtGbgozN47903eWPmELxT17Fu2zHUDv9EbyrJv+HOAvgN6VFnxZFwOpLkEpNu/o4d6TlpPK8/fSsD8OXYtH6I0ZOnMrGtdQP2IJZIJJKbJRDGXFJ37+JYTAZRyec5f+406+KK0N+wUCFAr0ZlZoG5da35RC8rJGvHd/y0aD+rTmRSUr6fzZvWsvavj1j60z5iKm2urr5mrMgi6+JmNia1op2LG22a1B5UrVLmUrjvZ9Z9+RXzvt7Exo0bObLuFCqPngyc8iAO5ZqGm71M8q9qkAAukCGXm2FuWXNygkwurJ7P3JcW8PtF7u4lGI1aNEUZxMfFEh0dfZtbAlnF9Y3/lEgk9xNhuETYurPsS9Di41JEyubv+eNQOpobrpsjBxsPHGzNsTTWWCDoqlQuhuZxMf0iZZE/sPTV13jjzXd4a8561iaq0emNuF9OaSjI4di2bZzr0oEubTxpZPJJBp055o2C0Zz9iw2LZ/LGG28wbc5ucj2m8PKU/nTyuJNZVCR3kzvrxKY9xu5PPmHcnIP49JnHvLVPMcRahUwIDNnb+PGLT/ghogWPfLSct3tx3WlBr4nl1CcfMHnW7ygGv8/r383hMT/ZP/+0qEkmceNXPP75AfJKbneV4aZM+Gghjz8UTAOs0y6RSO5SQlRQklFIydWpfxWY2Tjj4Wx5g/4NAtQl5FXIUFnZ4GBp2gJdRXlOMYVqLQIwGAzodDrMzMyQK+1x8XC6Ol2w0GooKcqixNwdH4d6JpMVOnTlZRQXllGi0SBkMszMzLC0dcPZyVJq+76PNEAA/4rH52yg3K4x3s1ccVDIkQFCnUtKWgYW7Scydf6vzOzKTQTifymAG8opS73I4cgs1Np6W6dugi1+HToT4OtAfRVkEolEIpE0pAYI4F/w6JztqPwGMuDR9gSaVfeyNRSdZ/+BQyQ7Def/PlrOG93u4gAukUgkEsk9pgEC+MeMmXOE5oO/ZtGuyfS6uvMM+z59jw+2uNBl/jLm9r2Lq9D/h6KiolizZo3pyxKJRCL5D+nRowcDBw40ffmWNEAA/4Sxcw7RtN/HfLx1KkOtlJd7xhWQtPFnNu1W4zRtDhNaSiVwgP379zNr1izTlyUSiUTyHzJ58mRmzJhh+vItacAAvoAFW6Yy1LpGT3RhxCiosfj73/mXArhRi6Ykj9S8cvS3uCb1NWY4NPLC2cHihsdrMBjQaO7qPvkSiUQi+YepVCpUKtMJgG7NPxvAaxAGHUaZErlcdoOemv9SAJd6oUskEonkHvM/CeDq7OMc+OVnDng8xeOjetCq7lpzl/1LAVzqhS6RSCSSe8ydBXD9CfZ98gmj3zmA33UDeBVcWsmcZz7nQM/FzHu+P/28aiWoIZGwz+cxeeZyFEM/ZNaPbzHeG+qtZCiOY/+anaw/H3f1JZemExg8oifdpSKwRCKRSO5zN9s4fR1GdAY1WhQoVTbY1wneAMmcWvcTW2LLEAoVTrVWuTdhKIDyEiqBcnUOaYX1BW81mozDbPl2KZ9vT6RA6YSLiyPyigQOb17E3G+Wsy2+0vRNEsl/mibtJMc2fsPyA9EkXptUW/IfpYnfxdYta1h+LIN8afrIe9Ztr0amzTrP+d2rWL/7FMdiS7GybYTKvoySS3HExsQQExNDbGwsRfH7WL70d46UWNN+/GyGtLXF2XTFH2FAnRHO2S2/sn37KfYmFFAp05GeJ8PB1g5XdwdsVNXPGvqyi8Su/Zxn54SR/+A05r03lScG92dId3M0l1azfMVRzmva0LN7Y1zMqyeVkUj+s4y5RO9Yz/aNW4m4FMt5EURIgB9e0noW/2ma2C1s2H+CiCwNSbFJKBya4OkkLXN7r7ntKvTivXP5ZNlOVh/PxmAUyGQKFKray94JIUAYMOiNmDt6MODNP5g5xh8f0wHhhipyd77PB7/sZ9vZPAwCkMmQyRxx6DmdhZ88Rt9G1W8qP7+cpTNf5bvcF3h7w2tM9rO9XEovJH33Qp4b9RnnPV/ine2zmNLckXomGpT812hLKSstpVBrgaWdA242pk+Q9ydRWUDsjq95/82fSPcdzyPTJjNxaCucLBXIr/tkq0NTVEK5zgwLZzus61ap3Z20ZRTnFpJbqUF2eepQa3t3HB0s6qkVvFl6KvPzyS0up0qnQy6XY25ujszSElsnd5zqVg/emKaQgqJyKhUOONjbYfuPd+65PmHQUpoaSeS+n/h6bRiJjk/wzjuPMKSFa51lSSV3r9sO4IbyHHKLKtAar90eQgg0Gg1GY/VQrJrd5GVyJVaObthbq1DVyTwE+tJscorV6MW1RwCj0YhOYY9noyslcEH5ya94Yco8jrZZxE/fTaCP07W7oPLCn3w9/TGWpoxjyu/f8UovZ5yu7pX8V2lOLeKbb39hcUIHOjz+Kn9ObYl5nWvwPiPiOfPVF8xacgGzsa8y8/letG/kht2NcmejntLk9az/ciuntX0Y9tHTDHXhDgLg/4imlNSN37Ni+TZWxmegBszldvQZ9gyjn51CnxCrW+sIK/QYDGlUXIpi7bJdrNwbSnJpYfUuYYCQ1vR97HVmDOxOa8cbjaqpwVhBxoZZvP39HiL8XuaFaVN5ss2/fWaNGMtySDz9B9++s4xDmrH837JXmNRSKvjcK267Cl1uZoOtvSMODg61NhcXF1xdXXF1dcXJyenq6/b2dliZKa6zDKgMubktdiaf5+joiLO9JWZX31REVfwJvl9ymtLWwxn3QBCNLa+VprR5mUQf+53j+QEEDBpGz0BbrK7ulfw3lZO+52dWrDjAsQQNMnkIPceF4HHD4Yz3uhJSNn7G3Pe3k91iKv/3wWMMb2KL+Y0qHooj2PXLPN798Ed2nUpC17g3zQe0p6XNHXeU+UeJ0gyOfzuZj368gFm3Jxj48HBGD+vGAx4qDi1byZ7IYmjdlkB3i5uYCRKglKKIlSyevZCvV4ZRHPwgbQb2ZcID/Rk4sC+jWrhRsHUfR8JCuaD3xsXLH/+ai3HXS4shbQc/fvA9v59IQBs4lJZtO9LD+98+szJk5rY4+fnj75jGkaUbOVDgSmC31gTY/tvHJrkZ99hfyRwzKyvMdQVoEyJJKjCiq7lXmU9WjiUGjxA6B7sjNfP91wnQxxMXl8++JB1UxKLIPcbxS7IGX/ZVaNK4sHIBb733PdtiKrndwYgNIvMAmzftYKMhhO5PPM4Yr+tFbh3kh7Pjp4+Y/swXbD50ioMXEghPq0BvaUcj+4bNIPQ5oWz6bBZvL97D+YKad+5t0ueTd+oXlmw4wV6rXgSPGsf0x8cxbtxkHnhiJA+2L+Pg9h/4YflJIgtudoKmKhwV2YRuP8yB0iA8Ovdj2pTRjBs3jnHjxjP06Wd4Y2xzzJOOcHDZH+w9nkqe6UeYqkok4s/F/LgrmaxSA+ZoMf+3C9+1uBMwZDJPjjKQtPMTdkYUkiLNNXVPaMj783/AGqVbYx4Z7U3qua18uzuVjCu7jGlkndvDvnx3/IaMo4WTQWrL+c+TURi6iq0xRuwDg2nrpyM57yw7j6aha4D4UZNRm0XBqXUs/2MPF0qtuNlw0eBEARm7VvPXrhI6PvkCg3o4XLfkWZl8jsrIE8QWKqj060LIoFGMbt8EK6wwM7PH3pIGraVQkkn4xmUs3x5DnvJWG5DrEkWRnNuyjq0J/rR9+FEGdrK/trNJa0a+Oo7OtmWEfv89F7OMlNd883XZUKIIZsSLLzPnnWd5uoc7jjV3WzamzZOD6NO6KeWRhynJLSK35n5T2nxKYg/z+ZYKXFu40cgRtJp/9fGufuYh9Hz+QUZ45LNm4e8cS7/h4uaSu8Q9FsBB1agDfR99mL6dzMk8+icbd51k48aNbFi6iJ/3paJvN5KJw9sRYHfnGYTkHifyuLB+KyfK/Rn+7HP837AmlGfmcubAKVIqGziDkikpU+sxGrRUlRaZ7v0fMWIojmTf3giiSkPo2iqYbo1M01yjKSnnUHg2Vm1HMP+zaUyfOp4xTZtghwadMDT4Q4imuBy13ohRV0lZkdp09y2qQpMZwy9/ncfc0JQebdywq1XR4IBzq94MclZhUx7K7mPnib+pr7TGPng0T7z7Pq+PaoZPfY3B5pbYymWAJQorS2xN91+lRxu/k98W/UFC+9k8/0Rvgt2gUn0XBnClFa7dJjCgqStVhzZxMa6IUtM0krvOPRfAsfDD98HZfP3aeJ622MbyVybzxhtv8Oay80T5Tefdt2Yztb1C6oTxn2dAk3+A0BNyVE4tGDlhLD3adce3JAVx/Bgn8yu53Ulzr+dKb9Db7Bdag4aSvFRSU1LIKlCjq/WsIRD6coqyMkhJSSGnuIqrcUlUUB53lKPRZVR26ktjb/sbduJ0aNWHwS/M4elBIXgDoMbCxvbq72hoQojLny3u/BwZk0iMOM3JfCuEVVOaeJiZ3PMqtFpbvIMMWCkyOXU2hvjkhngkKaXgVDhhGYXYeHWgka8DrqZJrtBdJGzPJv48ZU6/fu1p6WKDUifDWF+1hr6UwoxUUlJSSElJJSunmPKaf3dtCUXZV/ankZ5ZTLne2IB/KxkKRWuCu9vSwjmWM6ciOF9hmkZyt7n3Ajggs3Si1bBneenjdWzcsYetW7eybeVPLJw+kmHtPFDWd4NI/ls0eSRvXsu27GD8e3agracXzk1a0ttdg65qD0fOlFHY0A3hDaKCgpTf+eD/+tC/Tx8mvLaCw5GlNTLqCiqifmfB5HH06fMQH62/SMKVnVUlpIUeIqKoCJemPng4Xb9sCCCTy1EolTU6lhoQsnvk5inOIzs1iTSsoHkTGlmq6vQ0V5m74+6jwt4R0tOzySq4wzKlqKI0bQ/LvjrEhXRbhrzwFIN7uVynsKCj+OhW1h4uQIyayfN9nfEy06C0qK9BwwBFR9j86asM79ePPn0GMuW1X9ieaeRqU3T2QbZ9N5MB/fvSp88wHpr4C7uzK2r1AbpjcnO8fANwcirl3IWzhMc2cC2VpMHdkwEcAJUNNm4++Pj40KxZMwIDfPCyr2+ImuS/SFuWSPSpi0Q3aUb39s1wlMtx8WlBv77uVIlCdh4+R07R3ZdBaZJPEP7nl6zbn0xCSgrHty1mxcFznL0ye1pVEemntnI6+RIpDp2wcHLB8/IuQ5WBtEsXKdWU0cSzEa6Ot35733HJ+H+lII/crAyMyJFZmaGS1R1VoDC3x7OxL5ZWVhh1elTGO6i6Lkph37dz+XD61+yx68lTny/jrad60s7iOuc4cSPbV58nVDuUZ1/oRxMnJej1yBT1pZeDbRt6ThzJjIEBGFISyMyvqj1DmnM7uo0axuujAqhISSE9o5IqlUXDNnPIBW6N/LC1tyY3M4P4tALTFJK7TH1Xk0RybzMWYkg4w+rzdoQM7k3XkOrlZVSNA2k3oBsepTryd5wgvkTN3RXCjZSl5HPxiBuPvr2cFYvf52mvGGKjz3Ao5nIXrCpIjQ0no6wAnx5D6d6+MY6XI5dOk0pmWhVlRS74NXbDpVbvq/uMVoNGc7kRxFC9bLEpmUKGlbVDdTaXlkEV8lsMeEmE//IpL48Zx+xZ7zJrwXd8uuUYxQEdGPxoP9q71i31AyASObP6V7ZesiX44Uk87KkEBGqDgHofkGRg4U1Al0cYO6QVvnIjOq0as5pPJNZN8G83knGD2+GLAWGoQltVWeeh5Y7IFKga++Jr74yIu0R5aRnSpNR3NymAS+47xuI0TmxZxlFNGx4Z0Z9mV0qiVu4EdulL86pSVIk7uVQkyL2DQllNChtH7CyUyBVmWNk71TOH/83IwaVzPxRBbzJ92mQemz6Nd58ejEVpCmejcwBQV0QTebyYYkMIk0b3pL37tfFIFnYKKivMqFQ7Y2djgVW90eXfY+Foi6VSjlxljZ1T/RXPN00uRy7/m7FYQmAwGhBGI6Rn4ebueFvD+2SO9iSWGegzfjijhgSQFraVXz9czvqIKnLqPBHoKT3xG7+F6ihvNYznxnpVd3IzGBCXo61cLrvWYaIWGQYzBdbyGh0qahFoMcO+OuTXn+SOyMDGHldzcyjMxVplca1/heSuJAVwyX1Hm5fC2dMJWDZvTxM7wbX1c6zRyB0I8tBjrjzD5l1nuZR/k2VwowZt5jkO7tnOhg0b6mzr/lrP7ugCKstSObt9BWvrSbNhww7CYnO5/pBkLVqlnOCRXfG0AmSueI0YRw97F0R6dc92dVYoB84rES2epVsLexrXaFKtKilFJjMix4DBaLzF0mYD0BVSEH+ynt9dva1ec5CIvCoqM0PZs3JVnf0bNmxg8+YjRGbdRAdDYbw64+N1yWTI5XJkMjm4OJGXW3yLGZ4f7Z56g4W//MKqP//g88/fY9Yzgwgq3c+qz1/mo4Xb2R1VVWtOAX3+AXasOEiY0zAefPYhOl3phqCwwc7KDKUcNAY5Nk4KIJ/89NNcytegNlI9M5rhRn+36t98Ow8hN81oQC8EoEStUTdsCV/S4G7tepZI7naGPHKSwoiKCySgY2v8HWuOLbLE2qk53YfaYOVgTuiJc6Rk3+SMFcZyKi6sZ/Hn83jzzTfrbLPf/ZplJ7MoK4pi30/vM7ueNG+++QF/7I8n+bo5cGPMVK7071tjai/fdvQ0t8AnK5t0Uok7tJ+zLt4MeXIUrVxql0DNzc0AGQIZsnrahP9xlckkH15ez++u3t7+fCUH0yoou7SDFR+/XWf/m2++ydvvfs/26CKKTT/blEKJ8spY8uvNqmc0oNVUgUyAXxOE0XCH08IG0nXEPL57exQDgwycWzGPfTvCibvck0yo07jwy5d8t7sIKydvnIwxxMXFER0dTXTkGc5eyqeo0khJWgyZ8TGc2bmQb774PxYeLiBXS4OUqbXl+WSkJhEbG0tiYiIpKVkU3+QlDpcfemQywIhSoWyAI5L8k6QALrm/ZIVxfm84oTZDGDGiGT4m0/EpnRrRodcArFVWEBVN1U0WwFHYY9d9Gp8v/Y1NmzbV2jZv3syGVV/yzlBfHN06M37uH6w3SVO9LWPmw20Jvt7EaJdvx9rByAt3rxKsHXIIC4vkRFgkqlaTeLCbC94mVeRqtQYwIsNYY8jW/5BNEMGj36rnd1dva5a+wthAexxbT2bWD2vZvHlz3TSrP+SJTm64mH62KUcXXN0bXR5WV/9wKoNWQ3ZGIlptJTILC8yvv4LLzTN3IXDsJEb5+2NjiCA25QIR6QAGtOXnOLotmZj0BM6seJNZY0cyfPhwRo0axagx43lq4WHOpGrh4jqWTB/FxOk/s+ow6AwqzOpcEzJuZUCAEJUYik6z8cdZzHhpMCNHD2fIkEH0f+gZ5q46TXxxfWeoHkJgEAJkCnQGff0PRpK7hhTAJfcVbdYljl1Io6hTB/p52WNpkgPJLFxw7zGAPioLVBknSMkpJqV2kvrJlChsPfDzb0ZwcHCtLSgoiBYdWtPc3RqVuSPeIZ1oZZKmegvEy8Uaq1vKFZ2wsM/DxTeSYwfCSYxxovsDj9DFx7pOaVKpcsPJzYC9QyY5eaUU39zUYw1HYYWVc+N6fnf11rZNAJ42Sszsm9C8fVuCgoLqpAlq7oObrerv+xA4u+Lm3QR31HApi1ytrs6QKmE0Q6fRg1HQOqQ5fj7VnRnvmE0wQc0dcQVKy0qp1ALIUdm2Z+gHP/Dn9k2s/f0nFn/5FQsWLKjePpzL7EfaEOShwr7tCIY99wFLf/iDb75cyiu9HeossSyT3VwAv5Kk9Pxatn7+Gj/vuYSx84u88+HHfPz+GMb7V7Lmy4/44tvjxJi8ty4j5GWTUl4Ofn5gppCmo77LSQFccv+oiiPhwll251nQtlcnfCxMQxyANSr3LvRubcDbPIrdR8O5kG2a5tYZysuo0BoQRh3q8pI6weT2KfBs1hhP+0oOn86h0msijwzxwaue5dRU5p54N3bDwkZDUno2+bc9IVx1u3FDZw7qsko0BoFRr6ai9G9buW9M4Yi3fyCNKYO8RLKLDdfGTF8m5AVoiqGy0I6O7VvSuFGdYu5tEcYyykp1aAFhvDJjnQy5uReBvXrRb8AABgwYwAMPPHB5DvVxjHtkEpP6B9DESYm5b2d6jnuYAQMGMKBPZ4LdrzywCCwtbTAaQK1JIydPVC+tfJUSpVJZ78ONOjGWiMQ0TpgN4aUpL/D42HGMm/QOk/q64Zm8geMnI4gs5Abt69VNDmXpyWSX5GPm2xhXp9vtjCn5X2noe1Qi+ddoL+1n55FwKoIm8uJobyyuk/uYOXRk+MROBPiYEbX3OJeSGi7c/hPsW7XHPLuIkoxiVMMm0cNPXu/wJZmVHJ/WXfC0cCItI5vcoludqcYMc4USJeXodJU337zwr3DG3N6bfs3NqRIxbD+aRl6tLtOVqEoj2JEgSHUYgK+LGTXjt1AXk3LkV1b+uoQlm85wJuPKkLQyii/u4K8/fmNHXCX1TUYmM2SQnlZKOs40a9WR4JsZrqcvp7jSgM4A6KqoKqmvSltFpboSV3fQGgup0ChqT0plKKQ0N4N45BhRoKqxtKN1s150mfQ27708lf5XJgbAAdcmfvTyt8dcUd/DrAm5BdlZ8VSWGPDz8adVswaqsZD8Y6QALrk/iBKSz0ZwLLoCa5U5RecOcGD/fg4cOFB327+N8Cw7ygyWVJ45RHZG9t+vKPVvcm6EIk9BW5kNwUP8cbze8Ckzezw69KWbpz2VFy6QlVd24xJXHWVka7VoKKOgOIn4BqiZ+OdYYevbiSlvDsTFPIIDv3zPnouF135v3nm2fLiQVVGutJg2nUEtXWtNeWoozyJ8xQt8/PYMZnx7kK1Xxtnr8yg+toTZM19m9lcbOZxoUlOgzyFlzW72xGXg0noKvft2podb7ST1Uloj15eB0YBGD9p668fNsLRS0bK1BUXZJaSmF3M1zusySY08yrawGIqsjAiFFgPWVx/kbFoOZfDwp3mtb83eA0a0hbkkF7kjc25MiNPfZPiGLNIuZpCc3YhuXbvTwbe+Y5TcTW7495RI7hmlJ4i8mMqB0EQi/nyJKUOGMGDgQPr3719nGzBwBA+8uZrQ+EIwxhJz/jyhBfWViO4SMkssHXrSwrsnnZpTp+37GgtUnp3o2dGFRpcOERWXSeINS9E6tGU5JMfEEB19keNr17EhLoUCIDPxOJvXHuP8xWiiY5JJL9ReHup0F3EKwGvwIzw1yIfmOavYvO5P1p6OJjo6lFOrNvP7NkFQyCRendSeNp51q2OEuPyDZLJrM9DJzbBu1JQmri5krvycjd+tYdvRiOqe5NEXOL16NT98fJJYWSsmvDaFYZ3t/iYTFRg0ReREhrH/2BkKijWUpCdw4ugJ0vKr6jS1OHq3onFQIM5lyRz/83eWbYwkMjGRuPM7OJqex8UqG7o5VGHURBJ2+Axnc8uoMP2Qy0TFUU4cSiXVqS+t+jUjxDRBLQaq8vYRerKUFPPONAvxJECK33e9G197Esm9oiCX1JJSypRKFDKB0Xj9BTOEEBiRoVAqUanKiI8P52K8aQvqrRIo5LLqYTgNPfxGnU2+uZyyAB+86vRSr01uFUDvBwfQvXUBBw/uZU/kjaJuPjmnfmX2w2MZNWoMj73yGztPpKBUqSi9uJf9nzzOuNEjGTv2TRZszSH+DqflUioVyGWArHoO9jtnhYPXw7zwwWIWTumF3ZpPeW/CKEaNepQpS49j9sRnfLnyRSY1c6y7tLBcgbm1E3KlChU6rMwuPxYpXbDuOpHXp/4fY1s5kbXqQ96bPK66J/mo8Uycc4CTzacyZ9FvvD+uFc1NP7cONWVpu/jxmeeYu76QyAIVqpg17Fv4EfPXxNZZilQWMIL+z7zFzHF2OB7+gC9fHsuY6dN565QVXt0e5KmuzbEoVlGaf4RV7zzBO7+c5Fy+yYcA6BM4sfhzvjypoPXUF5kz2tc0RW2aPBLW/8nW7GJaTplI52b2NMRfSPLPkonr5XISyb2kIoOklAwSs8pM9/wtM+fGuHs3pZnL7WdZ+rIwDrz9HFO2+TJt+Ure6GlWbzv17TBe+JpPVyRwyvFxls7uyA1WCK1WFc72+TN4YmEZAz9cxWevBuNlmgaASioyLxEWmkChUWBtbY2yRmA1GAyUl5eDzIlGrToR5GtNrWH1typtDe+On8EvtrP4ccUrPHAzVc83SeTEEx2ZSlJFBUaZDCsrJzxb9yD4OkuFCV0lBUlnSMjRUm7jj7+vF76OV0rpBnQlZWSnp5GXnUuuWk2FWo1SqcTa2h2bJi3p0MzmJjt4GdCWphB9JIoclRmKy23RGo0c+6ZtaBfkhJXpW6iiNPUC8fFlFAlBudGIQ9O2dApww7owkfPhSVyN2W7BtPT3xL3mh+gSObV4FrM+jsZ5xufMfGkIXWoslV6XQJO5gUXDp/N+Zgfmb1vDc+0ssZGKd3c9KYBLJA3AqMsj5fh+DqbYENJvAJ0aWzRQ9ZaWgvWv8PSn4VSMXsyyWR2uE4xrMlAWtZwfXl7M94ltGffD58zr73yTAeefoy+KJXTfMWJVbejdrw3+tRfwljSIZNI2zOf/nj+Jasg8Xv54LH0b3ajOBtBFsG/u67z0YQXtv1nMW0+0J+ja9IWSu5gUwCWSu5nmIqcWvceod8Pp+ekmFr7UksamaepVTsHhdfz0/QYOl/rQ9oGhjH1wAB2aNFS9gOSuU5lJ3tEFvP32KmICv2T2e48x7EZ1/MYysk4cYNeedazdk4pl/5m89uYwutr8TcCX3DUappAgkUj+GRXxREWnkyOcURu09VS3Xo8Nzr2n8OaHT9LLeJSINSvZtXUtR+KLKL5OpyfJPcxYRm7YX8z74DdOe8zk+QU1grc6n7LsiyQU1RgHXppI+KGtbNh+lK9WJWAzdjqvzpeC971GCuASyV1NYCYH9+YhWLk64Gy6++/4juK1P7Yw5xk/lBd/Ykd4OunSElP3GYEh8xCH1qxgZVYgTfr1wqciltjY6u3gL5+x7rdvWBnLtV7vWcfYF3qB3ZY9+OSvNax45SG63bB7pORuJFWhSyR3s6Jt/PzyXP4qGcqo+XN4odVtthtriikqKkZt4Y6DrSWW1x+LJrnn6Cg6vIhZz83h1xQzHNw9cbaoHoVhNBrRlVUSMmg6D8+bzWO+VPcu1xSRV6pDrbTH09H8BkMTJXczKYBLJHczXT4ZsYkUytxxC/DBvcbyoRJJNSPavEQuXkihUFQPkywrK7u63Kq5uRUOXq1o3tIb13+7J6OkQUkBXCKRSCSSe5DUBi6RSCQSyT1ICuASiUQikdyDpAAukUgkEsk9SArgEolEIpHcg6QALpFIJBLJPUgK4BKJRCKR3IOkAC6RSCQSyT1ICuASiUQikdyDpAAukUgkEsk9SArgEolEIpHcg6QALpFIJBLJPej/ASiT4hvBEZ8hAAAAAElFTkSuQmCC\" style=\"width: 322px; height: 66.8669px;\" width=\"322\" height=\"66.8669\"\u003e\u003c/p\u003e\u003cp\u003ewhere ETo\u0026thinsp;=\u0026thinsp;Reference evapotranspiration (mm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Rn\u0026thinsp;=\u0026thinsp;Net radiation (MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), G\u0026thinsp;=\u0026thinsp;Soil heat flux density (MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Ta\u0026thinsp;=\u0026thinsp;Mean daily air temperature at 2 m height (\u0026deg;C), u\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Wind speed at 2 m height (m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), e\u003csub\u003es\u003c/sub\u003e = Saturation vapour pressure (kPa), e\u003csub\u003ea\u003c/sub\u003e = Actual vapour pressure (kPa), Δ\u0026thinsp;=\u0026thinsp;Slope of the vapour pressure curve (kPa \u0026deg;C\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}\\:\\)\u003c/span\u003e\u003c/span\u003e= Psychrometric constant (kPa \u0026deg;C\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The detailed description of estimation of different parameters used in PM methods may be found in previous research works (Darshana et al. 2013; Jhajharia et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Estimation of monthly pan coefficients\u003c/h2\u003e\u003cp\u003eThe monthly pan coefficient (K\u003csub\u003epan\u003c/sub\u003e) from the PM method were estimated using the following equation:\u003c/p\u003e\u003cp\u003e\u003cimg 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\" style=\"width: 250px; height: 84.4687px;\" width=\"250\" height=\"84.4687\"\u003e\u003c/p\u003e\u003cp\u003eSimilarly, monthly pan coefficients from other six empirical pan evaporation models (Cuenca, Allen and Pruitt, Snyder, Orang, Modified Snyder and Wahed \u0026amp; Snyder methods) were calculated using the procedure presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eTable 1. Estimation of month pan coefficients using different empirical pan evaporation models\u003c/p\u003e\n\u003cp\u003e\u003cimg 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≤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\" style=\"width: 402px; height: 400.746px;\" width=\"402\" height=\"400.746\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eKpan= pan coefficient; U= wind velocity at 2 m above ground surface (m/s); RH=relative humidity (%); F=fatch distance (m)\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e2.4 Model performance\u0026nbsp;indicators used in the present study\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe root mean square error (RMSE), correlation coefficient (CC), Nash\u0026ndash;Sutcliffe efficiency (NSE) and ratio of performance to deviation (RPD) were used for testing the capabilities of different models for the estimation of K\u003csub\u003epan\u003c/sub\u003e and procedures is explained in Table 2. The method which produce lowest RMSE and highest CC, NSE and RPD\u0026nbsp;is considered as the best method which gives the closest value of ET to ET\u003csub\u003epan\u003c/sub\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Performance indicators used in the present study\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" style=\"width: 469px; height: 536.314px;\" width=\"469\" height=\"536.314\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003eNote:\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003e\u0026nbsp;ETo, obs = observed ETo; \u0026nbsp; ETo,cal= modeled ETo data; N = number of observations;\u003cem\u003e\u0026nbsp;\u003c/em\u003eET̅o,obs = mean value of observed ETo; ET̅o,Cal = mean value of modeled ETo;\u003c/sup\u003e\u003c/p\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Preliminary analysis of meteorological parameters\u003c/h2\u003e\u003cp\u003eThe average monthly weather parameters and pan evaporation in the study area during 1984 to 2023 is shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The monthly maximum and minimum temperature varies from 18.19\u003csup\u003eo\u003c/sup\u003eC to 40.6\u003csup\u003eo\u003c/sup\u003eC and 5.49\u003csup\u003eo\u003c/sup\u003eC to 26.7\u003csup\u003eo\u003c/sup\u003eC during the study period. The RH varied from 40.65\u0026ndash;79.39% while WS lies between 2.33 km/h to 8.23 km/h. The monthly sunshine variation is from 4.48 hr to 8.66 hr. The pan evaporation ranged from 1.64 mm/day to 11.32 mm/day.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 PM model based reference evapotranspiration\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the variation in PM based ETo during last 40 years (1984\u0026ndash;2023) on monthly and annual basis. ETo varied from 1.79 mm/day in the month of January to 7.75 mm/day in the month of May.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Monthly pan coefficients obtained from different models\u003c/h2\u003e\u003cp\u003eThe monthly pan coefficients obtained from the standard PM and six empirical models are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMonthly pan coefficients obtained from the standard PM and six empirical models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCuenca\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAllen\u0026amp; Pruitt\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSnyder\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOrang\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModified Snyder\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eWahed\u0026amp; Snyder\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJan\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFeb\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMar\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eApr\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMay\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJun\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJul\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAug\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSep\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOct\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNov\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDec\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Performance of Pan Coefficient models\u003c/h2\u003e\u003cp\u003eTo compare the performance of different Kpan models, it is not possible to compare Kpan of different model directly. So, daily ETo of different models were plotted against daily ETo obtained from standard PM model. Figure\u0026nbsp;6 shows the scatter plots between ETo obtained from PM method taken as a reference and different methods (Fixed (0.75), Cuenca, Allen and Pruitt, Snyder, Orang, Modified Snyder and Wahed \u0026amp; Snyder) used in the present study. The first sub figure presents the results of a fixed value of pan coefficient (0.75) throughout the year, second sub figure depicts results of monthly Kpan obtained by PM model and rest of six subplots are corresponding to the six empirical models. The performance of these models was also analyzed using various statistical parameters and has been summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Radar chart (Moses, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) were plotted to visualize the performance of the models in predicting daily ETo (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The MAE values (in mm) for different models varied from 0.78 (PM) to 1.74 (CU). Similarly, the RMSE, R\u003csup\u003e2\u003c/sup\u003e, NSE and RPD values varied from 1.16 (WS) to 1.96 (CU), from 0.54 (CU) to 0.88 (PM), from 0.54 (CU) to 0.77 (PM) and from 5.73 (SY) to 14.37 (PM), respectively. It can be observed that Kpan obtained from PM gave best estimation of reference evapotranspiration. Among the six empirical models, the performance sequence was found as Wahed and Snyder\u0026thinsp;\u0026gt;\u0026thinsp;Modified Snyder\u0026thinsp;\u0026gt;\u0026thinsp;Allen and Pruitt\u0026thinsp;\u0026gt;\u0026thinsp;Orang\u0026thinsp;\u0026gt;\u0026thinsp;Snyder\u0026thinsp;\u0026gt;\u0026thinsp;Cuenca. Similar higher performance of Wahed and Snyder was observed by (Koc DL., 2022; Usta, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) however it performed not so well in humid tropical monsoon climate region (Khobragade et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and cold Serbia (Milanovic et al. 2017) which further emphasize the need for local evaluation of various pan evaporation models. The local environments where evaporation pans are placed significantly impact the interpretation of evaporation pan data. Factors like vegetation, wind speed and humidity play a crucial role in this process.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStatistical analysis for the comparison between measured and estimated daily reference evapotranspiration\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK\u003csub\u003epan\u003c/sub\u003e Model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRMSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRPD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFIX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4. CONCLUSIONS","content":"\u003cp\u003eConsidering the difficulty of applying the PM, many other less aggressive approaches of estimation of pan coefficient which requires fewer input parameters provided an alternative approach that is more feasible for areas with limited meteorological data. These models use lesser meteorological inputs, primarily focusing on temperature and potentially some humidity measurements might be suitable option in cases where the necessary weather data for the PM equation might be lacking.\u003c/p\u003e\u003cp\u003eThe study focuses on better estimation of reference evapotranspiration using monthly pan coefficients instead of single standard annual value. Weather based irrigation scheduling is easy to employ and can cover special variation of input data easily with advancement of remotely sensed and cloud based availability of input data. The most accurate standard method of FAO based Penman Monteith calls for minimum five type of data viz. maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours. All these five input parameters are not readily available around especially in the current study area where weather based scheduling has not gained momentum yet. A handy instrument is pan whose evaporation can be converted to evapotranspiration by multiplying it with pan coefficient. This pan coefficient value have large temporal and special variations but current for study area all literate present only one single annual value of pan coefficient which need further refinement and precision. So this study was conducted to obtain monthly pan coefficient suitable for this study area which is characterized with semi-arid environment. Therefore, the last four decades data was used to calculate the monthly pan coefficients. The obtained monthly k\u003csub\u003epan\u003c/sub\u003e predicts ETo better than single annual Kpan value. Many empirical k\u003csub\u003epan\u003c/sub\u003e models are also available which perform quite differently at different locations and climates. Six k\u003csub\u003epan\u003c/sub\u003e models viz. Cuenca (CU), Allen and Pruitt (AP), Snyder (SN), Orang (OR), Modified Snyder (MS) and Wahed \u0026amp; Snyder (WS) were also compared for their performance in estimating ETo here. Different models exhibits different performances in different criteria's so multiple statistical tools were used to get the overall picture.No single model performed best among all performance criteria but considering average of their rankings in different statistical analysis, the performance of the models for estimation of ETo was found of the order as PM\u0026thinsp;\u0026gt;\u0026thinsp;OR\u0026thinsp;\u0026gt;\u0026thinsp;MS\u0026thinsp;\u0026gt;\u0026thinsp;AP\u0026thinsp;\u0026gt;\u0026thinsp;FX\u0026thinsp;\u0026gt;\u0026thinsp;SN\u0026thinsp;\u0026gt;\u0026thinsp;WS\u0026thinsp;\u0026gt;\u0026thinsp;CU. In absence of meteorological parameters essential for calculation of PM ETo, Orang or Modified Snyder model can be used for estimation of ETo for similar type of climate.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCONFLICT OF INTEREST\u003c/h2\u003e\u003cp\u003eThere is no conflict.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRam Naresh: Conceptualization, data collection and methodology, writing- original draftMukesh Kumar: writing-review and editingAmandeep Singh: writing-review and editingDarshana Duhan: Conceptualization, methodology, writing- review and editingNarender Kumar: writing-review and editingBaljeet Singh Gaat: Formal analysis, writing- review and editing\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to acknowledge the Department of Agricultural Meteorology, CCS HAU Hisar for providing the extensive meteorological data for this study. The authors would like to thank the officials at the College of Agricultural Engineering and Technology, CCS HAU, Hisar, Haryana, India for providing permission, space and guidance to perform the research.\u003c/p\u003e\u003ch2\u003eDATA AVAILABILITY STATEMENT\u003c/h2\u003e\u003cp\u003eAll relevant data are included in the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlexandris S, Kerkides P, Liakatas A (2006) Daily reference evapotranspiration estimates by the \u0026ldquo;Copais\u0026rdquo; approach. Agric Water Manage 82: 371\u0026ndash;386\u003c/li\u003e\n\u003cli\u003eAllen RG, Pruitt WO (1991) FAO-24 Reference evapotranspiration factors. J Irrig and Drainage Engg 117(5): 758-773 \u003c/li\u003e\n\u003cli\u003eAllen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9): D05109\u003c/li\u003e\n\u003cli\u003eAschonitis VG, Antonopoulos VZ, Papamichail DM (2012) Evaluation of pan coefficient equations in a semi-arid Mediterranean environment using the ASCE-standardized Penman-Monteith method. Agric Sci 3: 1605-1614 \u003c/li\u003e\n\u003cli\u003eChiew FHS, Kamaladasa NN, Malano HM, McMahon TA (1995) Penman-Monteith, FAO-24 reference crop evapotranspiration and class-A pan data in Australia. Agric Water Manage 28(1): 9-21.\u003c/li\u003e\n\u003cli\u003eCuenca RH (1989) Irrigation system design: an engineering approach. Prentice Hall, Englewood Cliffs. 552. Evapotranspiration. Water Resources Bulletin 10(3): 486-498.\u003c/li\u003e\n\u003cli\u003eDar EA, Yousuf A, Brar AS (2017) Comparison and evaluation of selected evapotranspiration models for Ludhiana district of Punjab. J Agromet, 19(3): 274-276\u003c/li\u003e\n\u003cli\u003eDarshana, Pandey A, Pandey RP (2013) Analysing trends in reference evapotranspiration and weather variables in the Tons River Basin in Central India. Stoch Environ Res Risk Assess, 27: 1407\u0026ndash;1421 \u003c/li\u003e\n\u003cli\u003eGrismer ME, Orang M, Snyder R, Matyac R (2002) Pan evaporation to reference evapotranspiration conversion methods. J Irrig and Drainage Engg, 128(3): 180-184\u003c/li\u003e\n\u003cli\u003eGundekar HG, Khodke UM, Sarkar S, Rai RK (2008) Evaluation of pan coefficient for reference crop evapotranspiration for semi-arid region. Irrig Sci, 26: 169-175\u003c/li\u003e\n\u003cli\u003eHeydari MM, Heydari M (2014) Evaluation of pan coefficient equations for estimating reference crop evapotranspiration in the arid region. Arch Agron and Soil Sci, 60(5): 715-731 \u003c/li\u003e\n\u003cli\u003eIrmak S, Haman DZ and Jones JW (2002) Evaluation of class A pan coefficients for estimating reference evapotranspiration in humid location. J Irrig and Drainage Engg, 128(3): 153-159\u003c/li\u003e\n\u003cli\u003eItenfisu D, Elliot RL, Allen RG, Walter IA (2000) Comparison of reference evapotranspiration calculations across a range of climates, Proc., 4th National Irrigation Symposium, ASAE, Phoenix, 216\u0026ndash;227\u003c/li\u003e\n\u003cli\u003eJensen ME, Burman RD, Allen RG (1990) Evapotranspiration and irrigation water requirements, ASCE Manuals and Reports on Engineering Practices, 70: 332\u003c/li\u003e\n\u003cli\u003eJhajharia D, Dinpashoh Y, Kahya E, Singh VP, Fakheri‐Fard A (2012) Trends in reference evapotranspiration in the humid region of northeast India. Hydro Processes, 26(3): 421-435\u003c/li\u003e\n\u003cli\u003eKar SK, Kumar NA, Mishra CD, Sinha BL (2017) Evaluation of Class-A Pan coefficient models for estimation of reference crop evapotranspiration for dry sub-humid climates. Journal of Agricultural Engineering, 54(3): 67-74\u003c/li\u003e\n\u003cli\u003eKhobragade SD, Semwal P, Senthil AR and Nainwal HC (2019) Pan coefficients for estimating open-water surface evaporation for a humid tropical monsoon climate region in India. J Earth System Sci, 128: 1-14\u003c/li\u003e\n\u003cli\u003eKo\u0026ccedil; DL (2022) Assessment of pan coefficient models for the estimation of the reference evapotranspiration in a Mediterranean environment in Turkey. PeerJ, 10 e13554. \u003cu\u003ehttps://doi.org/10.7717/peerj.13554\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eMeshram DT, Gorantiwar SD (2015) Evaluation of pan coefficient for estimating reference crop evapotranspiration in Solapur station, Maharashtra. Mausam, 66(2): 205-210\u003c/li\u003e\n\u003cli\u003eMilanovic M Trajković S and Gocić M (2017) Comparative analysis of methods for the calculation of the pan coefficient.5\u003csup\u003eth\u003c/sup\u003e International Conference Contemporary Achievements in Civil Engineering, ,Subotica, Serbia. DOI:10.14415/konferencijaGFS2017.069.\u003c/li\u003e\n\u003cli\u003eMoses (2024) spider_plot,https://github.com/NewGuy012/spider_plot/releases/tag/20.6.\u003c/li\u003e\n\u003cli\u003eNaresh R, Kumar M, Kumar S, Singh K and Sharma P (2023) Estimation of reference evapotranspiration using artificial neural network models for semi-arid region of Haryana. J Agromet 25(1): 145-150\u003c/li\u003e\n\u003cli\u003eOrang M (1998) Potential accuracy of the popular non-linear regression equations for estimating pan coefficient values in the original and FAO-24 tables.Unpublished report, California Department of Water Resources Report. Sacramento, USA.\u003c/li\u003e\n\u003cli\u003ePereira AR, Nova NAV, Pereira AS, Barbieri V (1995) A model for the class A pan coefficient. Agric and Forest Meteo 76(2): 75-82\u003c/li\u003e\n\u003cli\u003ePradhan S, Sehgal VK, Das DK, Bandyopadhyay KK, Singh R (2013) Evaluation of pan coefficient methods for estimating FAO-56 reference crop evapotranspiration in a semi-arid environment. J Agromet 15: 90-93\u003c/li\u003e\n\u003cli\u003eRahimikhoob A (2009) An evaluation of common pan coefficient equations to estimate reference evapotranspiration in a subtropical climate (north of Iran). Irrig Sci 27: 289-296\u003c/li\u003e\n\u003cli\u003eSabziparvar AA, Tabari H, Aeini A, Ghafouri M (2010) Evaluation of class A pan coefficient models for estimation of reference crop evapotranspiration in cold semi-arid and warm arid climates. Water Resources Manage, 24: 909-920\u003c/li\u003e\n\u003cli\u003eShweta, Krishna AP (2015) Selection of the Best Method of ET o Estimation Other Than Penman\u0026ndash;Monteith and Their Application for the Humid Subtropical Region. Agric Res 4: 215-219\u003c/li\u003e\n\u003cli\u003eSingh PK, Patel SK, Jayswal P and Chinchorkar SS (2014) Usefulness of class A Pan coefficient models for computation of reference evapotranspiration for a semi-arid region. Mausam 65(4): 521-528\u003c/li\u003e\n\u003cli\u003eSnyder RL (1992) Equation for evaporation pan to evapotranspiration conversions. J Irriga and Drainage Engg 118(6): 977-980. https://doi.org/10.1061/(ASCE)0733-9437(1992)118:6(977)\u003c/li\u003e\n\u003cli\u003eTabari H, Grismer ME, Trajkovic S (2013) Comparative analysis of 31 reference evapotranspiration methods under humid conditions. Irrig Sci 31: 107-117\u003c/li\u003e\n\u003cli\u003eUsta S (2024) Estimation of reference evapotranspiration using some class-A pan evaporimeter pan coefficient estimation models in Mediterranean-Southeastern Anatolian transitional zone conditions of Turkey. PeerJ 12: e17685. https://doi.org/10.7717/peerj.17685\u003c/li\u003e\n\u003cli\u003eVishwakarma DK, Pandey K, KaurA, Kushwaha NL, Kumar R, Ali R, Elbeltagi A, Kuriqi A (2022) Methods to estimate evapotranspiration in humid and subtropical climate conditions. Agric Water Manage, 261: 107378.\u003c/li\u003e\n\u003cli\u003eWahed AMH, Snyder RL (2008) Simple equation to estimate reference evapotranspiration from evaporation pans surrounded by fallow soil. J Irrig and Drainage Engg 134(4): 425-429\u003c/li\u003e\n\u003cli\u003eWalter IA, Allen RG, Elliott R, Itenfisu D, Brown P, Jensen ME, Mecham B, Howell TA, Snyder R, Eching S, Spofford T (2005) Task committee on standardization of reference evapotranspiration. ASCE, Reston, VA, USA.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Monthly pan coefficient, reference evapotranspiration, semi-arid, Penman Monteith","lastPublishedDoi":"10.21203/rs.3.rs-7284841/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7284841/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the present study, monthly pan coefficients (K\u003csub\u003epan\u003c/sub\u003e) was estimated using standard FAO Penman-Monteith method (PM) and performance of different pan evaporation empirical models were evaluated for the semi-arid climate at Hisar, India. The daily data of maximum and minimum temperature, relative humidity, wind speed and sunshine hours of forty years (1984\u0026ndash;2023) were used to calculate reference evapotranspiration (ETo). The monthly K\u003csub\u003epan\u003c/sub\u003e were calculated by dividing ETo with class A pan evaporation. A comparison was made among monthly ETo obtained with standard PM method and six other empirical models viz. Cuenca (CU), Allen and Pruitt (AP), Snyder (SN), Orang (OR), Wahed and Snyder (WS) and Modified Snyder (MS) along with annual fix K\u003csub\u003epan\u003c/sub\u003e value of 0.75 (FIX). The model performance was evaluated using mean absolute error, root mean square error, correlation coefficient, Nash\u0026ndash;Sutcliffe model efficiency and ratio of performance to deviation. The results shows that no single model performed best among all performance criteria but considering average of their rankings in different statistical parameters, the performance of the models for estimation of ETo was found of the order as PM\u0026thinsp;\u0026gt;\u0026thinsp;OR\u0026thinsp;\u0026gt;\u0026thinsp;MS\u0026thinsp;\u0026gt;\u0026thinsp;AP\u0026thinsp;\u0026gt;\u0026thinsp;FIX\u0026thinsp;\u0026gt;\u0026thinsp;SN\u0026thinsp;\u0026gt;\u0026thinsp;WS\u0026thinsp;\u0026gt;\u0026thinsp;CU. In absence of meteorological parameters essential for calculation of PM ETo, Wahed and Snyder or Modified Snyder model can be used for estimation of ETo for similar type of climate.\u003c/p\u003e","manuscriptTitle":"Monthly pan coefficients for estimation of reference evapotranspiration in a semi-arid environment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 12:02:02","doi":"10.21203/rs.3.rs-7284841/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3e474819-19c2-4a1c-b112-821a9fd437b8","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-12T06:38:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 12:02:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7284841","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7284841","identity":"rs-7284841","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}