Search Results (17)

Reference evapotranspiration (ETo) is crucial for agriculture and is traditionally estimated using the Penman–Monteith (PM) method, which relies on multiple climatic variables. This study assessed machine learning (ML) techniques to estimate ETo at the Yauri meteorological station in Peru. Two ML models—K-nearest neighbors (KNN) and artificial neural networks (ANN)—were tested and compared against both the PM and the Hargreaves–Samani (HS) methods. Their accuracy was measured using metrics such as mean absolute error (MAE), anomaly correlation coefficient (ACC), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), and spectral angle (SA). The results indicate that ML techniques can effectively estimate ETo, providing robust alternatives in areas with limited meteorological data, thus enhancing water resource management. Full article

The amount of irrigation needed can be determined using reference evapotranspiration (ETo), the crop coefficient (Kc), and the water deficit index. Reference evapotranspiration is typically calculated utilizing the Penman–Monteith (PM) model, which necessitates various meteorological parameters, including temperature, humidity, net radiation, and wind speed. In regions where meteorological stations are absent, alternative methods must be employed to estimate these parameters. This study employs a combination of estimated meteorological parameters derived from different methodologies to calculate both reference evapotranspiration and irrigation rates, subsequently evaluating the results through wheat irrigation experiments. The daily irrigation rate for the T1 treatment was computed using real-time meteorological data, resulting in the highest grain yield of 561.73 g/m² and an irrigation water use efficiency of 7.61 kg/m³. The irrigation rate for the T2 treatment was determined based on real-time net radiation alongside monthly average values of temperature, humidity, and wind speed. In comparison to T1, the irrigation amount, yield, and irrigation water use efficiency for T2 decreased by 1.59%, 2.96%, and 1.42%, respectively. For the T3 treatment, the irrigation amount was calculated using monthly average values of temperature, humidity, and wind speed, with net radiation derived from daily light duration. The yield for T3 decreased by 19.4% relative to T1, the irrigation amount decreased by 12.95% relative to T1, and the irrigation water use efficiency decreased by 7.45% relative to T1. In the case of the T4 treatment, monthly average values of temperature, humidity, and wind speed were utilized, while net radiation was calculated using the Hargreaves–Samani (HS) model in conjunction with real-time temperature data. The yield for T4 decreased by 8.75% relative to T1, the irrigation amount decreased by 5.58% relative to T1, and the irrigation water use efficiency decreased by 3.39% relative to T1. For the T5 treatment, similar monthly average values were employed, and net radiation was calculated using HS methodology combined with monthly average temperature data. The yield for T5 decreased by 11.96% relative to T1, the irrigation amount decreased by 6.07% relative to T1, and the irrigation water use efficiency decreased by 6.3% relative to T1. Furthermore, the yield for the CK treatment under conventional irrigation decreased by 20.89% compared to T1, while the irrigation amount increased by 1.57% compared to T1 and the irrigation water use coefficient decreased by 22.14% compared to T1. Above all, this article posits that in areas lacking meteorological stations, monthly mean meteorological data should be utilized for parameters such as temperature, humidity, and wind speed, while the HS model is recommended for calculating net radiation. Full article

The absence of accurate measurement or calculation techniques for crop water requirements in greenhouses frequently results in over- or under-irrigation. In order to find a better method, this study analyzed the accuracy, data consistency and practicability of the Penman–Monteith (PM), Hargreaves–Samani (HS), Pan Evaporation (PAN), and Artificial Neural Network (ANN) models. Model-calculated crop evapotranspiration (ET_C) was compared with lysimeter-measured crop evapotranspiration (ET_C) in the National Precision Agriculture Demonstration Station in Beijing, China. The results showed that the actual ET_C over the entire experimental period was 176.67 mm. The ET_C calculated with the PM, HS, PAN, and ANN model were 146.07 mm, 189.45 mm, 197.03 mm, and 174.7 mm, respectively, which were different from the actual value by −17.32%, 7.23%, 11.52%, and −1.12%, respectively. The order of the calculation accuracy for the four models is as follows: ANN model > PAN model > PM model > HS model. By comprehensively evaluating the statistical indicators of each model, the ANN model was found to have a significantly higher calculation accuracy compared to the other three models. Therefore, the ANN model is recommended for estimating ET_C under greenhouse conditions. The PM and PAN models can also be used after improvement. Full article

Although the studies on model prediction of daily ET_o based on public weather forecasts have been widely used, these studies lack the comparative evaluation of different types of models and do not evaluate the seasonal variation in model prediction of daily ET_o performance; this may result in the selected model not being the best model. In this study, to select the best daily ET_o forecast model for the irrigation season at three stations (Yinchuan, Tongxin, and Guyuan) in different climatic regions in Ningxia, China, the daily ET_os of the three sites calculated using FAO Penman–Monteith equations were used as the reference values. Three empirical equations (temperature Penman–Monteith (PMT) equation, Penman–Monteith forecast (PMF) equation, and Hargreaves–Samani (HS) equation) were calibrated and validated, and four machine learning models (multilayer perceptron (MLP), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and gradient boosting with categorical features support (CatBoost)) were trained and validated against daily observed meteorological data (1995–2015 and 2016–2019). Based on public weather forecasts and daily observed meteorological data (2020–2021), the three empirical equations (PMT, PMF, and HS) and four machine learning models (MLP, XGBoost, LightGBM, and CatBoost) were compared in terms of their daily ET_o prediction performance. The results showed that the daily ET_o performance of the seven models in the irrigation season with a lead time of 1–7 days predicted by the three research sites decreased in the order of spring, autumn, and summer. PMT was the best model for the irrigation seasons (spring, summer, and autumn) at station YC; PMT and CatBoost with C3 (T_max, T_min, and Wspd) as the inputs were the best models for the spring, autumn irrigation seasons, and summer irrigation seasons at station TX, respectively. PMF, CatBoost with C4 (T_max, T_min) as input, and PMT are the best models for the spring irrigation season, summer irrigation season, and autumn irrigation season at the GY station, respectively. In addition, wind speed (converted from the wind level of the public weather forecast) and sunshine hours (converted from the weather type of the public weather forecast) from the public weather forecast were the main sources of error in predicting the daily ET_o by the models at stations YC and TX(GY), respectively. Empirical equations and machine learning models were used for the prediction of daily ET_o in different climatic zones and evaluated according to the irrigation season to obtain the best ET_o prediction model for the irrigation season at the study stations. This provides a new idea and theoretical basis for realizing water-saving irrigation during crop fertility in other arid and water-scarce climatic zones in China. Full article

The FAO 56 Penman–Monteith equation (PM) is considered the most accurate method for estimating reference evapotranspiration (ETo). However, PM requires a large amount of data that is not always available. Thus, the objective of this study is to improve the Hargreaves–Samani (HS) reference evapotranspiration estimates in the Peruvian Altiplano (PA) by calibrating the radiation coefficient K_RS. The results show modified HS (HSM) ETo estimates at validation after K_RS calibration, revealing evident improvements in accuracy with Nash–Sutcliffe efficiency (NSE) between 0.58 and 0.93, percentage bias (PBIAS) between −0.58 and 1.34%, mean absolute error (MAE) between −0.02 and 0.05 mm/d, and root mean square error (RMSE) between 0.14 and 0.25 mm/d. Consequently, the multiple linear regression (MLR) model was used to regionalize the K_RS for the PA. It is concluded that, in the absence of meteorological data, the HSM equation can be used with the new values of K_RS instead of HS for the PA. Full article

Rubber (Hevea brasiliensis) plantations have high water consumption through evapotranspiration, which can contribute to water scarcity. In addition, there is a lack of spatial observation data and estimation methods for evapotranspiration (ET) for rubber plantations. To alleviate the water stress of expanding rubber plantations caused by seasonal drought in Xishuangbanna, Southwest China, an up to 7 days crop evapotranspiration (ET_c) forecast method, “K_c-ET₀” for rubber plantations with limited meteorological data, was proposed by using rubber crop coefficient K_c and public weather forecasts. The results showed that the average absolute error (MAE) of forecasted ET_c was 0.68 mm d⁻¹, the root mean square error (RMSE) was 0.85 mm d⁻¹, and the average correlation coefficient (R) was 0.69 during the rainy season, while during the dry season these metrics were 0.52 mm d⁻¹, 0.68 mm d⁻¹, and 0.85, respectively. The accuracy of ET_c forecast in the dry season was higher. Additionally, K_c was the main factor influencing the accuracy of rubber plantations ET_c forecast, while the accuracy of the temperature forecast and the chosen Hargreaves-Samani (HS) model were also considerable. Our results suggested that the “K_c-ET₀” short-term rubber plantation ET_c forecasting method shows good performance and acceptable accuracy, especially in the dry season. The study provides an important basis for creating ET-based irrigation scheduling for improving regional-scale water management in high water consumption rubber plantations. Full article

Station observation is a good data source to monitor the potential evapotranspiration (PET) changes of a specific site particularly for the purpose of crop irrigation activities; however it represents only the site geographic characteristics and provides real-time/historical records. Hence, there was an urgent need to find a promising tool and a simple empirical to predict/project the PET in locations where station observation is not feasible. The Hargreaves–Samani method (HS) is recommended after the Penman−Monteith equation. To address this issue, the Regional Climate Modeling version 4 (RegCM4) with spatial resolution 25 km was used to compute the PET using the HS for the period 1979–2017. Era-Interim reanalysis of 1.5 degrees (EIN15) and NCEP/NCAR reanalysis version 2 of 2.5 degrees (NNRP2) were used to examine the influence of the lateral boundary condition on the simulated PET. The two simulations were designated as EIN15-RegCM4 and NNRP2-RegCM4, respectively. To examine the possible influences on the simulated PET, a comparison was conducted between EIN15-RegCM4 and NNRP2-RegCM4. After that, a comparison was conducted between the original HS formula (HS) and its calibrated version (HSnew) with respect to the 0.1−degree ERA5-land derived reanalysis product (hereafter ERA5) using EIN15-RegCM4 (as an example). Results showed that switching between EIN15 and NNRP2 did not show a notable influence on the simulated PET. Further, calibrating the HS coefficients indicates a considerable improvement in estimating the PET (relative to the original equation) when it is compared with ERA5. Such improvement is confirmed by a significant low mean bias. Over the majority of locations, the RegCM4 shows a good performance using the calibrated HS equation. In conclusion, the RegCM4 can be used to estimate the PET using the calibrated HS either for making a daily forecast or for projecting the future PET under different global warming scenarios. Full article

Accurate short-term forecasts of daily reference evapotranspiration (ET₀) are essential for real-time irrigation scheduling. Many models rely on current and historical temperature data to estimate daily ET₀. However, easily accessible temperature forecasts are relatively less reported in short-term ET₀ forecasting. Furthermore, the accuracy of ET₀ forecasting from different models varies locally and also across regions. We used five temperature-dependent models to forecast daily ET₀ for a 7-day horizon in the North China Plain (NCP): the McCloud (MC), Hargreaves-Samani (HS), Blaney-Criddle (BC), Thornthwaite (TH), and reduced-set Penman–Monteith (RPM) models. Daily meteorological data collected between 1 January 2000 and 31 December 2014 at 17 weather stations in NCP to calibrate and validate the five ET₀ models against the ASCE Penman–Monteith (ASCE-PM). Forecast temperatures for up to 7 d ahead for 1 January 2015–19 June 2021 were input to the five calibrated models to forecast ET₀. The performance of the five models improved for forecasts at all stations after calibration. The calibrated RPM is the preferred choice for forecasting ET₀ in NCP. In descending order of preference, the remaining models were ranked as HS, TH, BC, and MC. Sensitivity analysis showed that a change in maximum temperature influenced the accuracy of ET₀ forecasting by the five models, especially RPM, HS, and TH, more than other variables. Meanwhile, the calibrated RPM and HS equations were better than the other models, and thus, these two equations were recommended for short-term ET₀ forecasting in NCP. Full article

The most important biotic stress factor impacting tomato crop biophysical, biochemical, physiological, and morphological features is water stress. A pot experiment was undertaken in a greenhouse to study the drought responsiveness of tomato (Solanum lycopersicum) yield and quality indices in sandy loam and silty loam soils. For both sandy loam and silty loam soils, the water supply levels were 70–100% FC, 60–70% FC, 50–60% FC, and 40–50% FC of ET_o (crop evapotranspiration) from the vegetative stage to the fruit ripening stage, calculated using the Hargreaves–Samani (HS) model compared to the time-domain reflectometer (TDR) values calibrated using volumetric water content (VWC). The experiment was conducted as a 2 × 4 factorial experiment, arranged in a completely randomized block design, with four treatments replicated four times. In this study, we examined how sandy loam and silty loam soils at different % FC affect the total marketable yield and quality components of tomatoes, concentrating on total soluble solids (Brix), fruit firmness, dry fruit mass, pH, titratable acid (TA), ascorbic acid (Vit. C), and carotenoid composition. Lycopene and β-Carotene were estimated using the UV spectroscopy method, with absorption spectra bands centered at 451 nm, 472 nm, 485 nm, and 502 nm. The results revealed that even though there were some limitations, TDR-based soil moisture content values had a strong positive correlation with HS-based evapotranspiration, with R² = 0.8, indicating an improvement whereby TDR can solely be used to estimate soil water content. Tomato plants subjected to 40–50% FC (ET_o) water stress in both sandy loam and silty loam soils recorded the highest total soluble solids, titratable acidity, ascorbic acid content, and β-carotene content at an absorption peak of 482 nm, and lycopene content at an absorption peak of 472 nm, with lower fruit firmness, fruit juice content, and fruit juice pH, and a reduced marketable yield. Similarly, tomato plants subjected to 60–70% FC throughout the growing season achieved good fruit firmness, percent juice content, total soluble solids, titratable acidity, ascorbic acid content, and chlorophyll content (SPAD), with minimum fruit juice pH and high marketable yield in both soil textural types. It is concluded that subjecting tomato plants to 60–70% FC (ET_o) has a constructive impact on the marketable yield quality indices of tomatoes. Full article

The agricultural sector is one of the major users of water resources. Water is an important asset that needs to be preserved using the latest available technologies. Modern technologies and digital tools can transform the agricultural domain from being manual and static to intelligent and dynamic leading to higher production with lesser human supervision. This study describe the agronomic models that should be integrated with the intelligent system which schedule the irrigation and fertilization according to the plant needs, and monitors and maintains the desired soil moisture content via automatic watering. Solar fertigation is a fertigation support system based on photovoltaic solar power energy and an IoT system for precision irrigation purposes. The system monitors the temperature, radiation, humidity, soil moisture, and other physical parameters. An agronomic DSS platform based on the integration of soil, weather, and plant data and sensors was described. Furthermore, a three-year study on seven ETo models, such as three temperature-, three radiation-, and a combination-based models were tested to evaluate the sustainable ETo estimation and irrigation scheduling in a Mediterranean environment. Results showed that solar fertigation and Hargreaves–Samani (H-S) equation represented a nearby correlation to the standard FAO P–M and does offer a small increase in accuracy of ETo estimates. Furthermore, the hybrid agronomic DSS is suitable for smart fertigation scheduling. Full article

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. Full article

The present study develops ArcMap models to implement the following three methods: FAO-56 Penman–Monteith (FAO PM), Hargreaves–Samani (HS) and Hansen, with the former used as a reference. Moreover, three models implementing statistical indices (RMSD, MB, NMB) are also created. The purpose is threefold, as follows: to investigate the variability in the daily mean reference evapotranspiration (ETo) for the Decembers and Augusts during 2016–2019, over Peloponnese, Greece. Furthermore, to investigate the agreement between the methods’ ETo estimates, and examine the former along with MODIS ET (daily) averaged products. The study area is a complex Mediterranean area. Meteorological data from sixty-two stations under the National Observatory of Athens (NOA), and MODIS Terra LST products, have been employed. FAO PM is found sensitive to wind speed and depicts interactions among climate parameters (T, evaporative demand and water availability) in the frame of climate change. The years 2016–2019 are four of the warmest since the preindustrial era. Hargreaves–Samani’s estimations for the Decembers of 2016–2019 were almost identical to MODIS ET, despite their different physical meaning. However, for the Augusts there are considerable discrepancies between the methods’ and MODIS’s estimates, attributed to the higher evaporative demand in the summertime. The GIS models are accurate, reliable, time-saving, and adjustable to any study area. Full article

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 (k_Rs) 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 k_Rs, 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. Full article

The potential or reference evapotranspiration (ET₀) is considered as one of the fundamental variables for irrigation management, agricultural planning, and modeling different hydrological pr°Cesses, and therefore, its accurate prediction is highly essential. The study validates the feasibility of new temperature based heuristic models (i.e., group method of data handling neural network (GMDHNN), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree)) for estimating monthly ET₀. The outcomes of the newly developed models are compared with empirical formulations including Hargreaves-Samani (HS), calibrated HS, and Stephens-Stewart (SS) models based on mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency. Monthly maximum and minimum temperatures (T_max and T_min) observed at two stations in Turkey are utilized as inputs for model development. In the applications, three data division scenarios are utilized and the effect of periodicity component (PC) on models’ accuracies are also examined. By importing PC into the model inputs, the RMSE accuracy of GMDHNN, MARS, and M5Tree models increased by 1.4%, 8%, and 6% in one station, respectively. The GMDHNN model with periodic input provides a superior performance to the other alternatives in both stations. The recommended model reduced the average error of MARS, M5Tree, HS, CHS, and SS models with respect to RMSE by 3.7–6.4%, 10.7–3.9%, 76–75%, 10–35%, and 0.8–17% in estimating monthly ET₀, respectively. The HS model provides the worst accuracy while the calibrated version significantly improves its accuracy. The GMDHNN, MARS, M5Tree, SS, and CHS models are also compared in estimating monthly mean ET₀. The GMDHNN generally gave the best accuracy while the CHS provides considerably over/under-estimations. The study indicated that the only one data splitting scenario may mislead the modeler and for better validation of the heuristic methods, more data splitting scenarios should be applied. Full article

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. Full article