Samani Method Research Articles

Assessment of Empirical Methods for Estimating Reference Evapotranspiration in Different Climatic Zones of Bosnia and Herzegovina

The study evaluated nine empirical methods for estimating reference evapotranspiration (ETo) in Bosnia and Herzegovina (BiH) across different climatic zones. The methods compared were the Hargreaves–Samani method (HS), the modified Hargreaves–Samani method (HM), the calibrated Hargreaves–Samani method (HC), the Priestley–Taylor method (PT), the Copais method (COP), the Makkink method (MAK), the Penman–Monteith method based on air temperature and overall average windspeed (PMT2), the Penman–Monteith method based on air temperature and regional average windspeed (PMT1.3), and the Penman–Monteith method based on air temperature and site-specific windspeed (PMTlok). These methods were tested against the “Food Agricultural Organization-Penman Monteith approach” (FAO-PM). The evaluation was performed using data from 20 meteorological stations in BiH, considering a common irrigation season (April–October) for two periods (2000–2005 and 2018–2022). The stations represented three climatic zones: semi-arid (SA), dry sub-humid (DSH), and moist sub-humid (MSH). The performance and ranking of the ETo methods were analyzed using the TOPSIS method. The trend of ETo during the common irrigation season for the period from 2018 to 2022 was determined using the Mann–Kendall test. The results of the study indicated that the HC method showed the best performance across all three climatic zones. The average root mean square error (RMSE) was 0.67 mm day−1, 0.49 mm day−1, and 0.50 mm day−1 for the SA, DSH, and MSH zones, respectively. As an alternative to the HC method, the PT method is recommended for its favorable results in both periods and in all zones. On the other hand, the HS method exhibited the highest average overestimation, particularly in the MSH zone, where ETo values were 18% higher compared with those of the FAO-PM method. The COP method also showed high overestimation and was not recommended for use. Regarding the MAK method, it resulted in underestimation during the period from 2000 to 2005, ranging from 17% in the DSH zone to 11% in the MSH zone. However, its performance improved during the period from 2018 to 2022, for which it ranked second place in the MSH zone. Among the PMT methods, the PMTlok, which utilized local average windspeed, yielded the best results. Despite performing well in the neighboring country of Serbia, the HM method showed poor overall performance in BiH. The findings of this study can serve as a foundation for further research in BiH to enhance irrigation practices in response to climate changes.

Spatial statistics techniques for SPEI and NDVI drought indices: a case study of Khuzestan Province.

Drought is a major water resources management issue in Iran. Khuzestan Province is in a drought state due to water shortage. Therefore, identifying areas at high risk of drought and when drought occurs is essential for drought management. For this purpose, this study used precipitation and temperature data of 12 selected stations and MODIS sensor images from the United States Geological Survey database in 2000–2017. The Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Normalized Difference Vegetation Index (NDVI) were calculated using the Hargreaves–Samani method and ENVI software. Moreover, different spatial statistics techniques were used in the ArcGIS environment to analyze the results. Also, time series diagrams were drawn, and the trend was evaluated using the Mann–Kendall test. Finally, the distribution of NDVI values was investigated using EasyFit software, and the amount of drought damage was determined using NDVI. The investigation of the cluster maps of the Anselin Local Moran’s Index along with hot and cold spots formed for both SPEI and NDVI showed that drought severity was higher at the southern stations than at the semi-northern and northwestern ones in the province. Moreover, the survey results using the EasyFit software showed that the southern stations, including the Ahvaz, Mahshahr, and Omidiyeh-Aghajari stations, were more at risk of drought than the other stations due to the drought threshold. Furthermore, the total damage caused by drought for the Ahvaz and Abadan stations showed a damage rate of 50%.

The Seasonal Water Balance of Western-Juniper-Dominated and Big-Sagebrush-Dominated Watersheds

The combined impacts of woody plant encroachment and climate variability have the potential to alter the water balance in many sagebrush steppe ecosystems in the Western USA, leading to reduced water availability in these already water-scarce regions. This study compared the water-balance characteristics of two adjacent semiarid watersheds in central Oregon, USA: one dominated by big sagebrush and one dominated by western juniper. Precipitation, springflow, streamflow, shallow groundwater levels, and soil moisture were measured. The potential evapotranspiration was calculated using the Hargreaves–Samani method. Potential evapotranspiration and a water-balance approach were used to calculate seasonal actual evapotranspiration. The shallow aquifer recharge was calculated using the Water-Table-Fluctuation-Method. Evapotranspiration, followed by deep percolation, accounted for the largest portion (83% to 86% of annual precipitation) of water output for both watersheds. Springflow and streamflow rates were generally greater at the sagebrush-dominated watershed. Snow-dominated years showed greater amounts of groundwater recharge and deep percolation than years where a larger portion of precipitation fell as rain, even when total annual precipitation amounts were similar. This study’s results highlight the role of vegetation dynamics, such as juniper encroachment, and seasonal precipitation characteristics, on water availability in semiarid rangeland ecosystems.

Natural Groundwater Recharge Response to Climate Variability and Land Cover Change Perturbations in Basins with Contrasting Climate and Geology in Tanzania

The response of aquifers with contrasting climate and geology to climate and land cover change perturbations through natural groundwater recharge remains inadequately understood. In Tanzania and elsewhere in the world, studies have been conducted to assess the impact of climate change and variability, and land use/cover changes on stream flow using different models, but similar studies on groundwater dynamics are inadequate. This study, therefore, examined the influence of land use/cover and climate dynamics on natural groundwater recharge in basins with contrasting climate and geology in Tanzania, applying the modified soil moisture balance method, coupled with the curve number (CN). The method hinges on the balance between the incoming water from precipitation and the outflow of water by evapotranspiration. The different parameters in the soil moisture balance method were computed using the Thornthwaite Water Balance software. The potential evapotranspiration (PET) was calculated using the daily maximum and minimum temperatures, utilizing two-temperature-based PET methods, Penman–Monteith (PM) and Hargreaves–Samani (HS). The rainfall data were obtained from the gauging stations under the Tanzania Meteorological Agency and some additional data were acquired from climate observatories management by water basins. The results show that there has been a quasi-stable CN in the Singida semi-arid, fractured crystalline basement aquifer (74.2 in 1997, 73.64 in 2005, and 73.87 in 2018). In the Kimbiji, humid, Neogene sedimentary aquifer, the CN has been steadily increasing (66.69 in 1997, 69.08 in 2008, and 71.42 in 2016), indicating the rapid land cover changes in the Kimbiji aquifer as compared to the Singida aquifer. For the Kimbiji humid aquifer, the PET calculated using the Penman–Monteith (PM) method for the 1996/1997, 2007/2008, and 2015/2016 hydrological years were 1156.5, 1079.5, and 1143.9 mm/year, respectively, while for the Hargreaves–Samani (HS) method, the PET was found to be 1046.1, 1138.3, and 1204.4 mm/year for the 1996/1997, 2007/2008, and 2015/2016 hydrological years, respectively. For the Singida semi-arid aquifer, the PM PET method resulted in 2083.3, 2053.6, and 1875.4 mm/year for the 1996/1997, 2004/2005, and 2017/2018 hydrological years, respectively. The HS method produced relatively lower PET values for the semi-arid area (1839.4, 1814.7, and 1710.2 mm/year) for the 1996/1997, 2004/2005, and 2017/2018 hydrological years, respectively. It was equally revealed that the recharge and aridity indices correspond with the PET calculated using two temperature-dependent methods. The decline of certain land covers (forests) and increase in others (built-up areas) have contributed to the increase in surface runoff in each study area, possibly resulting in the decreasing trend of groundwater recharge. An overestimation of the PET using the HS method in the Kimbiji humid aquifer was observed, which was relatively smaller than the overestimation of the PET using the PM method in the Singida semi-arid aquifer. Despite the difference in climate and geology, the response of the two aquifers to rainfall is similar. The combined influence of climate and land cover changes on natural groundwater recharge was observed to be prominent in the Kimbiji aquifer, while only climate variability appreciably influences natural groundwater recharge in the Singida semi-arid aquifer. El Nino and the Southern Oscillation as part of the climate variability phenomenon dwarfed the time lags between rainfall and recharge in the two basins, regardless of their difference in climate and geology.

Estimation of Reference Evapotranspiration during the Irrigation Season Using Nine Temperature-Based Methods in a Hot-Summer Mediterranean Climate

The FAO-56 Penman–Monteith (PM) equation is regarded as the most accurate equation to estimate reference evapotranspiration (ETo). However, it requires a broad range of data that may not be available or of reasonable quality. In this study, nine temperature-based methods were assessed for ETo estimation during the irrigation at fourteen locations distributed through a hot-summer Mediterranean climate region of Alentejo, Southern Portugal. Additionally, for each location, the Hargreaves–Samani radiation adjustment coefficient (kRs) was calibrated and validated to evaluate the appropriateness of using the standard value, creating a locally adjusted Hargreaves–Samani (HS) equation. The accuracy of each method was evaluated by statistically comparing their results with those obtained by PM. Results show that the calibration of the kRs, a locally adjusted HS method can be used to estimate daily ETo acceptably well, with RMSE lower than 0.88 mm day−1, an estimation error lower than 4% and a R2 higher than 0.69, proving to be the most accurate model for 8 (out of 14) locations. A modified Hargreaves–Samani method also performed acceptably for 4 locations, with a RMSE of 0.72–0.84 mm day−1, a slope varying from 0.95 to 1.01 and a R2 higher than 0.78. One can conclude that, when weather data is missing, a calibrated HS equation is adequate to estimate ETo during the irrigation season.

A comparative study of remote sensing and gene expression programming for estimation of evapotranspiration in four distinctive climates

An accurate estimation of Evapotranspiration (ET) is an important issue in hydrology, water resources management and irrigation scheduling. There are a wide range of methods for estimation of ET, among which, machine learning techniques and remote sensing-based approaches demonstrated more reasonable results. Accordingly, this study attempts to compare the capability of two developed models of Gene-Expression Programming (GEP) and Surface Energy Balance Algorithm for Land (SEBAL) in estimation of ET, at four different climate types of Temperate-Warm (T-W), Wet-Warm (W-W), Arid-Cold (A-C), and Arid-Warm (A-W). In this way, a-two year of daily records as weather variables (i.e., maximum and minimum temperature, dew-point temperature, vapor pressure, saturated vapor pressure, relative humidity, 24-h rainfall, sunshine hours, and wind speed) were considered as input variables, whereas ET values were computed as output variable (observed ET) by using FAO Penman–Monteith-56 method. After development of two predictive models, the statistical results were compared with well-known Hargreaves–Samani method. The results showed that while Hargreaves–Samani equation could not yield remarkable results in any of the climates, GEP and SEBAL demonstrated accurate predictions. In this way, GEP was the superior model in T-W (R2 = 0.902 and RMSE = 0.713 mm/day) and A-W (R2 = 0.951 and RMSE = 0.634 mm/day) climates but it dropped a bit in two other climates. However, SEBAL not only had the best performance in both climates of W-W (R2 = 0.967 and RMSE = 0.515 mm/day) and A-C (R2 = 0.990 and RMSE = 0.720 mm/day), but also demonstrated good predictions in T-W and A-W climates. Therefore, SEBAL is recommended as the best model for estimation of ET in all climate types.

Climatic Characterization and Temporal Analysis of Rainfall in the Municipality of Cruzeiro do Sul - AC, Brazil

Abstract The Climatological Water Budget (CWB) determined by the Thornthwaite and Matter (1955) allows the temporal visualization of the variables that compose the climate, thus allowing the climatic characterization of a region. Therefore, this work aimed at identifying and quantifying the months of water surplus and deficit through CWB to characterize the climate for the municipality of Cruzeiro do Sul - AC, Brazil, and analyze the temporal distribution of rainfall for the municipality. We used daily precipitation and temperature values and procedures to fill in the database. We calculated the potential evapotranspiration (ETP) using the Hargreaves Samani method (1985). Subsequently, we determined the periods of soil water storage (SWS), water deficiency (DEF), and water surplus (SUR) on a monthly scale. The climate of Cruzeiro do Sul, located in the northern region of Brazil, was characterized according to the Thornthwaite (1955) as B1rA'a' for a AWC of 140 mm, that is, a humid climate with annual rainfall average of 2227 mm and real evapotranspiration of 1660 mm, with small water deficiency in the months from June to October, with no thermal deficiency, and summer concentrated in three months with an average air temperature of 25 °C ranging between 18 °C and 32 °C.

Evaluation of Simplified Surface Energy Balance Index (S-SEBI) Method for Estimating Actual Evapotranspiration in Kangsabati Reservoir Command Using Landsat 8 Imagery

Evapotranspiration (ET) is an important hydrological variable for better irrigation management, water budgeting, and runoff estimation which should be estimated as precisely as possible both in space and time. However, most of the available crop coefficient-based ET computation methods provide point-scale estimates which need upscaling to apply at the catchment or command area scale. This study evaluates the applicability of the simplified surface energy balance index (S-SEBI) method to compute the spatially distributed daily ET in the Kangsabati reservoir command in eastern India considering the crop coefficient-based coupled Hargreaves–Samani (ETc_HG) method as the benchmark. The study is based on two major crops of paddy and potato in the Rabi season of 2015 at 100 surveyed ground truth locations in the selected command area having different crop growth stages and using the site-specific Landsat 8 images on three cloud-free dates. The S-SEBI method shows improved ET estimates during the crop development stage characterized by higher canopy cover than that during the initial crop development stage with lesser canopy cover that traps less radiation. Results revealed that S-SEBI-based ET estimates correlated well with ETc_HG with r and RMSE value of 0.06 and 1.13 mm/day (initial stage), 0.71 and 0.52 mm/day (development stage) and 0.77 and 0.52 (maturity stage) for paddy. The r and RMSE value for potato is found to be better during the development stage (0.43, 0.69 mm/day) than the initial stage (0.17, 0.64 mm/day) in a similar trend with paddy. Therefore, the crop coefficient-based method could be advantageous at point-scale with adequate data availability conditions, whereas the S-SEBI method could be used in data-scarce areas to estimate the spatially distributed ET values.

Using Statistical and Dynamical Downscaling to Assess Climate Change Impacts on Mine Reclamation Cover Water Balances

The oil sands industry in Canada uses soil–vegetation–atmosphere-transfer (SVAT) water balance models, calibrated against short-term (<≈ 10 years) field monitoring data, to evaluate long-term (≈60 years) reclamation cover design performance. These evaluations use long-term historical climate data; however, the effects of climate change should also be incorporated in these analyses. Although statistical downscaling of global climate change projections is commonly used to obtain local, site-specific climate, high resolution dynamical downscaling can also be used. The value of this latter approach to obtain local site-specific projections for mine reclamation covers has not been evaluated previously. This study explored the differences in key water balance components of three reclamation covers and three natural sites in northern Alberta, Canada, under future, site-specific, statistical, and dynamical climate change projections. Historical meteorological records were used to establish baseline periods. Temperature datasets were used to calculate potential evapotranspiration (PET) using the Hargreaves–Samani method. Statistical downscaling uses the Long Ashton Research Station Weather Generator (LARS-WG) and global circulation model (GCM) projections of temperature and precipitation. Dynamical climate change projections were generated on a 4 km grid using the weather research and forecasting (WRF) model. These climate projections were applied to a physically-based water balance model (i.e. Hydrus-1D) to simulate actual evapotranspiration (AET) and net percolation (NP) for the baseline and future periods. The key findings were: (a) LARS-WG outperformed WRF in simulating baseline temperatures and precipitation; (b) both downscaling methods showed similar directional shifts in the future temperatures and precipitation; (c) this, in turn, created similar directional shifts in future growing season median AET and NP, although the increase in future NP for LARS-WG was higher than that for WRF. The relative increases in future NP were much higher than the relative increases in future AET, particularly for the reclamation covers.

Modeling phreatic levels in a rural watershed in Lontras, High Itajaí Valley, SC-Brazil

This study analyzed the behavior of phreatic levels in a rural watershed located the municipality of Lontras, High Itajaí Valley, Santa Catarina, Southern Brazil. Phreatic levels are monitored by 3 wells (PZMC located in riparian forests and PZ2127 and PZ3 in pasture areas). Rainfall and fluviometric level are also monitored. Fluviometric station is located near PZ2127. Daily evapotranspiration was calculated using the Samani method. Monitoring was performed from January 1, 2012 to July 15, 2019, combining 7.5 years of data. An exploratory data analysis and a trend analysis of groundwater levels was performed. Simulation was performed with the PIRFICT model (from December 31, 2014 to January 19, 2016). Trend analysis identified significant decay trends for PZMC and PZ3, and elevation trends for PZ2127. The PIRFICT model presented acceptable adjustments. The errors were low and it was possible to characterize the dynamic impulse response relationship between the explanatory variables and the observed levels. The information obtained from the phreatic levels monitoring and modeling proved to be necessary for the local groundwater resources management, providing support for planning and decision making.

Evaluating of eight evapotranspiration estimation methods in arid regions of Iran

Evapotranspiration (ET) plays an important role in efficient irrigation and water resources management. ET can be accurately estimated via the FAO–Penman–Monteith method (ETFPM), which is known as the standard reference method for the ET estimation. However, this model requires more detailed meteorological data than are generally available at most weather stations. Alternative methods for predicting daily ET should be found which are simpler to work with less input data without scarifying prediction accuracy. The main purpose of this study was to determine a reliable alternative ET models that requires fewer input data compared with the ETFPM as a reference method. Here, five radiation-based models including Makkink (ETMAK), Priestley and Taylor (ETPT), Abtew (ETABT), Jensen-Haise (ETJH), McGuinness and Bordne (ETMB), and three temperature-based models including Hargreaves and Samani (ETHS), Hamon (ETHAM), Linacre (ETLIN) were evaluated by comparing them with PETFPM on a daily and growing season scale, using long-term data from 13 meteorological stations in northeast Iran. The statistical analysis revealed that the ETHS, ETJH, ETHAM, and ETLIN were the best methods for predicting daily ET in 54%, 15%, 15% and 15% of areas, respectively. In general, the temperature-based methods outperformed the radiation-based-methods in the study area. The cumulative values of daily ET during the growth periods showed that the Jensen−Haise method performed the best for the warm growing season compared to other alternative methods, while the Hargreaves–Samani method was the best prediction method for the cool growing season. Nevertheless, divergence between estimations of the best alternative methods and the reference method showed that the best ET alternative methods might be unreliable in some regions, and could not be recommended for estimating crop water requirements. Accordingly, the spatiotemporal variability in predictability performance of ET models should be taken into account prior to use.

A monthly regression correction model for the Hargreaves–Samani method in Mainland China

AbstractReference crop evapotranspiration (ET0) is an important parameter for irrigation engineering. It can be calculated using the Penman–Monteith method (PM), which results in ET0‐PM. This study uses ET0‐PM to determine the availability of a monthly linear regression correction model for the Hargreaves–Samani method (HG). The data were obtained from 647 meteorological stations in mainland China, which are contained within the data set of monthly values of climate data from Chinese surface stations. For most stations, the correlation coefficient of ET0‐PM and ET0‐HG (determined by the HG method) ranges from 0.5 to 1.0. This provides the necessary conditions for the monthly linear regression correction of ET0‐HG. The mean relative change of the mean absolute error before and after the ET0‐HG correction for each year indicates that the scheme adopted in this study yields improved correction results during autumn, winter and spring in mainland China and poor results during summer. However, the monthly regression correction model for the HG is only available for part of the stations in each season. This study supplements a case for the larger‐scale ET0‐HG monthly regression correction. This can be a reference for the further refinement of the ET0‐HG spatio‐temporal correction © 2020 John Wiley &amp; Sons, Ltd.

Improvement of Hargreaves–Samani Reference Evapotranspiration Estimates with Local Calibration

Improving irrigation water management is an important asset when facing increased water shortages. The Hargreaves–Samani (HS) method is a simple method that can be used as an alternative to the Penman–Monteith (PM) method, which requires only temperature measurements for estimating reference evapotranspiration (ETo). However, the applicability of this method relies on its calibration to local meteorological specificities. The objective of this study was to investigate the effects of local calibration on the performance of the HS equation. The study was carried out for the middle portion of the São Francisco River Basin (MSFB), Brazil, and considered four calibration approaches: A1—single calibration for the entire MSFB; A2—separate calibration by clusters of months; A3—by clusters of stations; and A4—for all contexts resulting by combining A2 and A3. Months from the wet season showed larger improvements by the calibration of the HS model, since mean air temperature and its daily range showed stronger correlations to ETo. On the other hand, the months from the dry season and stations from the eastern region of MSFB performed poorly regardless of the calibration approach adopted. This occurred because, in those cases, ETo presented larger correlation to variables that are missing in the HS equation, and the use of the full PM equation seems unavoidable.

Proposal for the use of daily thermal amplitude for the calibration of the Hargreaves-Samani equation

Rationalized water use has become increasingly important in recent years, due to deficient rain distribution and increased water demands, such as in forestry and agricultural production. Evapotranspiration is an important variable of the hydrological cycle and one of the main components of the hydrological balance in the soil. The use of simplified equations is a potential alternative to estimate reference evapotranspiration (ET0) when meteorological data is limited. The objective of this study was to apply and test different methods to estimate reference evapotranspiration with limitations on meteorological data in the state of Espírito Santo, using the Hargreaves and Samani method under different meteorological conditions, adopting the Penman-Monteith FAO-56 as a reference. ET0 was calibrated using the Hargreaves and Samani equation with different adjustments by linear regression, considering all the weather stations used in this study (general adjustment), different climate types (adjustment by climate), by dry and rainy season (dry and rainy season adjustments), thermal amplitude class (adjustment by class) and climate type combined with thermal amplitude class (adjustment by climate and class). ET0 was also estimated for the aims of comparison, using the Hargreaves and Samani original (HS) and Penman-Monteith with limitation of climatic data (PML). In increasing order of efficiency, quantified by the average absolute error (MAE, mm day−1), the methods to estimate evapotranspiration with limitation of meteorological data were: PML (1.46); original HS equation (0.68); general adjustment of ET0 estimated by the HS equation (0.53) adjustment by thermal amplitude class (0.51); adjustment by climate type (0.51); adjustment for rainy or dry season (0.51); and adjustment by climate and class (0.49). The adjustment by climate and thermal amplitude class provided better ET0 estimates, especially on days with higher evaporative demand on air (greater evapotranspiration), when better evapotranspiration estimates for diverse applications were necessary.

Modifying Hargreaves-Samani Equation for Estimating Reference Evapotranspiration in Dryland Regions of Amudarya River Basin

Reference evapotranspiration (ETo) is a key factor in determining the amount of water needed for crops, which is crucial to correct irrigation planning. FAO Penman-Monteith (EToPM) is among the most popular method to estimate ETo. Apparently sometimes it is difficult to compute ETo using Penman-Monteith due to challenges on data availability. FAO Penman-Monteith method requires many parameters (solar radiation, air temperature, wind speed and humidity), while Hargreaves-Samani method calculates ETo based on air temperature. Because Central Asia is a data limited region with weather stations unable to provide all required parameters for the PM method, this study aimed to estimate ETo using the Hargreaves and Samani (HS) method in Karshi Steppe, in Kashkadarya province, in southern Uzbekistan, based on data from 2011 to 2017. Reference evapotranspiration calculated by non-modified HS method is underestimated during the summer months. The reason for this underestimation might be higher air temperature and wind speed during these months. Therefore, the HS method in its original form cannot be used in our study area to estimate ETo. Modification of the EToHS, through application of a bias correction factor, had better performance and allowed improving the accuracy of the ETo calculation for this region. The calculated ETo values can inform decision making and management practices regarding water allocation, irrigation scheduling and crop selection in dry land regions of Amudarya river basin and the greater Central Asia area.

Modeling daily reference evapotranspiration in middle south Saurashtra region of India for monsoon season using most dominant meteorological variables and the FAO-56 Penman-Monteith method

Many methods are available to estimate reference evapotranspiration (ETo) from standard meteorological observations. The FAO-56 Penman-Monteith method is considered to be the most physical and reliable method and is often used as a standard to verify other empirical methods. However, it needs a lot of different input parameters. Hence, in the present study, a model based on most dominant meteorological variables influencing ETo is proposed to estimate ETo in the Middle South Saurashtra region of Gujarat (India). The performance of five different alternative methods and proposed model is compared with the standard FAO-56 Penman-Monteith method. The five quantitative standard statistical performance evaluation measures, Nash-Sutcliffe efficiency coefficient (E), coefficient of determination (R2), refined Willmott’s index (dr), root mean square of errors-observations standard deviation ratio (RSR) and mean absolute error (MAE) are employed in evaluating the performance of the selected methods and proposed model. The results show that the developed model and Hargreaves and Samani (1985) method with recalibrated parameters provide the most reliable results in estimation of (ETo) and it can be recommended for estimating (ETo) in the study region.

Accuracy of reference evapotranspiration (ETo) estimates under data scarcity scenarios in the Iberian Peninsula

The standard approach for computing reference crop evapotranspiration (ETo) is the FAO-56 Penman-Monteith (FAO-PM) method, which requires data on air temperature, radiation, air humidity and wind speed. Unlike air temperature the other variables are less frequently available, hindering the application of FAO-PM. A lot of efforts exist to find the best method to estimate FAO-PM ETo when some variables are not available. The FAO-56 manual recommends to estimate the missing variables based on those currently observed (PM-R), or use the less demanding Hargreaves and Samani method (HS). Additionally, if the missing variables are measured at nearby stations, spatial interpolation can be used to estimate the missing data previous to applying FAO-PM (PM-IC). This paper focuses on the comparison, at the monthly time scale, of the performance of these methods to in the Iberian Peninsula. By using 53 weather stations with all data to calculate FAO-PM, data scarcity scenarios are simulated and the mentioned methods are tested (PM-R, HS, PM-IC)PM-IC yielded consistently the best results according to a number of tests. It yielded the lowest mean absolute error (MAE) at 7.56mm/month, while PM-R yielded values of 10.15mm/month and HS 9.36mm/month and biased results. PM-IC was also best at reproducing the long-term variability and trends in ETo. A good and unbiased estimation of monthly ETo time series are required for irrigation planning and crop design.

Evaluation of temperature-based Penman–Monteith (TPM) model under the humid environment

In this study, an attempt is made to check the suitability of TPM approach and present software to estimate reference evapotranspiration based on TPM and Hargreaves–Samani (HS) method by using Visual Basic Environment. Considering the FAO-56 Penman–Monteith (FAOPM) as the standard model, the comparative performances of selected methods were evaluated under the climatic condition of Umaim, Meghalaya, India. Using the Root Mean Square Error (RMSE)’ and ‘correlation coefficient (r)’ as statistical measures of ‘goodness-of-fit’ the regression analysis was carried out. Based on the regression analysis TPM (RMSE = 0.014 mm day−1, r = 0.92) was found better followed by HS (RMSE = 0.054 mm day−1, r = 0.87). The average overestimate (e.g. 1.97 %) by TPM were found to be in close agreement with the FAOPM. These results support the use of the TPM approach under limiting climatic data conditions for Umaim.

Calibration of the Hargreaves–Samani method for the calculation of reference evapotranspiration in different Köppen climate classes

The Penman–Monteith equation (FAO-56) is accepted as the standard model for estimating reference evapotranspiration (ETo). However, the major obstacle to using FAO-56 widely is that it requires numerous climatic data. The Hargreaves–Samani (HS) method is frequently used for the calculation of ETo since it is based on measurements of daily minimum and maximum air temperature alone. Those are commonly recorded at many meteorological stations throughout the world. It is the objective of this paper to evaluate the quality of HS and calibrate the coefficients of this method for different climates as represented by the Köppen classification. Estimated values are compared with Penman–Monteith ETo values in terms of the coefficient of efficiency Ceff as well as the root mean square error, the mean absolute error and the Bayes information criterion. The Penman–Monteith equation for ETo (FAO-56) is based on physics and known to provide best estimates of ETo. The results of our work show that the correlation between long-term monthly means of HS and FAO-56 can be improved significantly by introducing climate-class specific coefficients.

Estimation of Reference Evapotranspiration in Middle South Saurashtra Region of India Using Dominant Meteorological Variables

In this paper attempt is made to estimate reference evapotranspiration (ETo) from standard meteorological observations. The FAO-56 Penman-Monteith method is the most physical, reliable and mostly used as a standard to verify other empirical methods. However, it needs a lot of different input parameters. Hence, in the present study, a model based on most dominant meteorological variables influencing ETo is proposed to estimate ETo in the Middle South Saurashtra region of Gujarat (India). The performance of five different alternative methods and proposed model is compared keeping the FAO-56 Penman-Monteith method as reference. The models are evaluated by using Nash-Sutcliffe efficiency coefficient (E), (R2), (dr), (RSR) and (MAE) statistical criterions. The results show that the developed model and Hargreaves and Samani (1985) method provide the most reliable results in estimation of (ETo), and it can be recommended for estimating (ETo) in the study region.