Penman-Monteith Method Research Articles

Assessment of Long Term Spatio-temporal Climatic Changes Over the Central India: A GIS Approach

The climatic data of 107 meteorological stations of Central India were collected and maps of various climatic parameters were generated by using ArcGIS 10.5 software. Potential evapotranspiration (PET), rainfall, water deficit and moisture index trends were analyzed for 30 years (1988-2018). PET was computed by using Modified Penman-Monteith method as recommended by FAO. It concludes that though the amount of mean annual rainfall is moreover same but the variation in duration and distribution exists. The analysis shows that number of rainy days has decreased in this long period, which led to increase the PET. High water deficit resulted in shifting of bio-climate towards arid condition. The results indicates that Maximum mean annual rainfall was observed in Mahabaleswar station of Maharashtra (6098 mm) and minimum mean annual rainfall also reported in Malegaon (545 mm) of Maharashtra, in Central India. It was observed that western districts of Madhya Pradesh (Barwani, Alirajpur) are shifting significantly towards drier side during the period of 30 years. Thus, information of long term spatial climatic changes can be effectively used for contingent crop planning, irrigation scheduling and diversification of crops.

Effects of meteorological variables and substrate moisture on evapotranspiration and thermal performance of a green roof in a subtropical climate

This study analyzed the correlations between the meteorological/substrate-moisture variables and the evapotranspiration (ET) to improve the understanding of the factors influencing the thermal performance of green roofs, including cooling the surface and interior temperature of the building, in a subtropical climate. Daily ET of the green roof had been estimated using the FAO Penman-Monteith method to increase the number of ET data and the Nash-Sutcliffe coefficient is 0.51–0.62 compared to the observed daily ET from data of green roof experiments in Taipei during December 2012 and September 2013. Based on correlation analysis, this study then suggests that ET may have a higher influence on Tdiff, i.e. the difference in surface temperatures of green and original roofs, than the meteorological variables or substrate water content (SWC), although the radiative heating is sometimes more influential to Tdiff than the cooling caused by ET of the green roof in thin substrate condition. This study also found that under wet and dry substrates, daily ET is highly correlated with weather-related variables and with SWC, respectively. Furthermore, because of the low insulation of wet substrate, more downward fluxes are sometimes found under high than low SWC conditions. On the contrary, under the dry substrate, increasing SWC helps reduce the downward fluxes. This study, therefore, indicates that an optimal range of substrate moisture appears to maximize the function of a green roof in reducing the surface temperature and the downward substrate-bottom flux. Considering that a counterbalance exists between SWC and ET as well as irrigation can be applied to control SWC, future studies could be aimed at the optimal range of SWC to execute informed irrigation concerning the thermal performance, water-saving irrigation, and storm-water-retention function of the green roofs either in dry period or before storm events.

Projections of Drought Characteristics Based on the CNRM-CM6 Model over Africa

In a warming climate, drought events are projected to increase in many regions across the world, which would have detrimental impacts on water resources for agriculture activity and human life. Thus, projecting drought changes, especially the frequency of future drought events, is very important for the African continent. This study investigates the future changes in drought events based on the France Centre National de Recherches Météorologiques (CNRM-CM6) model in the Coupled Model Intercomparison Project phase six (CMIP6) datasets for four shared socio-economic pathways (SSP): SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5; and three time slices: near future (2020–2039), mid-century (2050–2069), and end-of-century (2080–2099), relative to a historical baseline period (1995–2014). The interannual variability and trends of the self-calibrating Palmer Drought Severity Index (scPDSI) based on the Penman–Monteith methods for measuring potential evapotranspiration (PET) are used to estimate future droughts. The temporal analysis shows that the drought frequency, intensity, and affected area will increase throughout the 21st century. Among the scenarios, SSP3-7.0 and SSP5-8.5 project a larger upward trend in drought characteristics than SSP1-2.6 and SSP2-4.5. The spatial pattern shows drought frequency decreases in humid regions and increases in non-humid regions across Africa. For all SSP scenarios, the projected wetting trend per decade ranges from 0.05 to 0.25, while the drying trend per decade ranges from −0.05 to 0.25. A regional trend analysis revealed key differences in spatial pattern, with varied trend projections of wetter and drier conditions in humid and non-humid regions under all SSP scenarios. Drier conditions are expected to intensify in Southern Africa under all SSP scenarios but are projected to be more intense under either SSP3-7.0 and SSP5-8.5. In general, the projected wetter trends in humid areas may favor agricultural production and ecological conservation, and drier trends in non-humid regions may call for the possible adoption of tailor-made drought adaptation strategies and development programmes to minimize impacts.

Generating High-Resolution and Long-Term SPEI Dataset over Southwest China through Downscaling EEAD Product by Machine Learning

Drought is an event of shortages in the water supply, whether atmospheric, surface water or ground water. Prolonged droughts have negative impacts on ecosystems, agriculture, society, and the economy. Although existing drought index products are widely utilized in drought monitoring, the coarse spatial resolution greatly limits their applications on regional or local scales. Machine learning driven by remote sensing observations offers an opportunity to monitor regional scale droughts. However, the limited time range of remote sensing observations such as vegetation index (VI) resulted in a substantial gap in generating high resolution drought index products before 2000. This study generated spatiotemporally continuous Standardized Precipitation Evapotranspiration Index (SPEI) data spanning from 1901–2018 in southwestern China by machine learning. It indicated that four Classification and Regression Tree (CART) approaches, decision trees (DT), random forest (RF), gradient boosted regression trees (GBRT) and extra trees (ET), can provide valid local drought information by downscaling the Estación Experimental de Aula Dei (EEAD) data. The in-situ SPEI dataset produced by the Penman–Monteith method was used as a benchmark to evaluate the temporal and spatial performance of the downscaled SPEI. In addition, the necessity of VI in SPEI downscaling was also assessed. The results showed that: (1) the ET-based product has the best performance (R2 = 0.889, MAE = 0.232, RMSE = 0.432); (2) the VI provides no significant improvement for SPEI re-construction; (3) topography exerts an obvious influence on the downscaling process, and (4) the downscaled SPEI shows more consistency with the in-situ SPEI compared with EEAD SPEI. The proposed method can be easily extended to other areas without in-situ data and enhance the ability of long-term drought monitoring.

Clean Water Supply in Tasikmalaya Municipality, Opportunities and Challenges

Currently, there are three sub-districts in Tasikmalaya City that are still vulnerable to clean water, namely Kawalu, Tamansari, and Cibeureum sub-districts. PDAM Tirta Sukapura, owned by the Tasikmalaya Regency Government, has not been able to meet clean water needs, so the Tasikmalaya Municipality Government plans to build a new PDAM as an alternative. The Ciwulan river in Cibeuti Village is a source of water that will be used as a collection point. Rain data was taken from 2 stations, namely Gunung Satria and Cikunten II stations, for ten years. The evapotranspiration value was calculated using the Penman-Monteith method. The calculation of the discharge in the intake area, namely Ciwulan-Cibeuti with a watershed area of ​​405 km2, used the NRECA method using parameters taken from the calibrated Ciwulan-Sukaraja station. The calibration parameters are PSUB = 0.86; GWF = 0.22, reduction coefficient = 0.80; and NSE = 0.764. The determination of the dependable flow is calculated using the Weibull method. The magnitude of the Q90's dependable flow is 4.3 m3/s. The projected population for the next 15 years is estimated at 307,857 people, so the amount of water needed is around 0.535 m3/s. Opportunities for business entities to participate in building PDAM are wide open, with the certainty of return plus profits or independent management by business entities within a certain period of time. The challenge for the government and business entities is to provide reasonable prices to customers and new networks.

Why the Effect of CO2 on Potential Evapotranspiration Estimation Should Be Considered in Future Climate

Potential evapotranspiration (PET) is an important factor that needs to be considered in regional water management and allocation; thus, the reasonable estimation of PET is an important topic in hydrometeorology and other related fields. There is evidence that increased CO2 concentration alters the physiological properties of vegetation and thus affects PET. In this study, changes in PET with and without the CO2 effect over China is investigated using seven CMIP6-GCMs outputs under seven shared socioeconomic pathways (SSPs) based scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5), as well as the contribution rate of CO2 on PET in different climatic regions. Changes in estimated PET based on modified Penman–Monteith (PM) method that considers the CO2 effect is compared with the traditional PM method to examine how PET quantity varies (differences) between these two approaches. The results show that the PET values estimated by the two methods explored opposite trends in 1961–2014 over entire China; it decreases with consideration of CO2 but increases without consideration of CO2. In the future, overall PET is projected to increase under all scenarios during 2015–2100 for China and its three sub-regions. PET generally tends to grow slower when CO2 is taken into account (modified PM approach), than when it is not (traditional PM method). In terms of differences in the estimated PET by the two methods, the difference between the two adopted methods increased in China and its sub-regions for the 1961–2014 period. In the future, the difference in estimated PET is anticipated to continuously increase under SSP3-7.0 and SSP5-8.5. Spatially, a much greater extent of difference is found in the arid region. Across the arid region, the PET difference is projected to be the highest at 138% in the mid-term (2041–2060) with respect to the 1995–2014 period, whereas it tends to increase slower in the long-term period (2081–2100). Importantly, CO2 is found to be the most dominant factor (−154.2% contribution) to have a great effect on PET changes across the arid region. Our findings suggest that ignorance of CO2 concentration in PET estimation will result in significant overestimation of PET in the arid region. However, consideration of CO2 in PET estimation will be beneficial for formulating strategies on future water resource management and sustainable development at the local scale.

Quantification of Precipitation and Evapotranspiration Uncertainty in Rainfall-Runoff Modeling

Mountainous watersheds have always been a challenge for modelers due to large variability and insufficient ground observations, which cause forcing data, model structure, and parameter uncertainty. This study employed Differential Evolution Adaptive Metropolis (DREAM) algorithm which utilizes Markov Chain Monte Carlo (MCMC) approach to account for forcing data uncertainty. A conceptual degree day snowmelt model, MIKE 11 NAM (Nedbor Afstromnings Model), was used to simulate snowmelt runoff from Ilgaz basin, with an area of 28.4 km2 area, located in the northern part of Turkey. The mean elevation is around 1700 m and the basin is covered with broadleaf forest and has mainly brown soil with a high water holding capacity. Precipitation and evapotranspiration (ET) values were optimized in combination with model parameters conditioned on observed discharges and corrected values of input data were utilized for calibration and validation. Results showed that the observed precipitation was over-estimated by almost 10%, while evapotranspiration calculated by Penman–Monteith method was underestimated. The mean values of storm and ET multipliers were obtained as 1.14 and 0.84, respectively. When only parameter uncertainty was considered, calibration did not yield Nash–Sutcliffe Efficiency (NSE) greater than 0.64. However, when forcing data uncertainty was incorporated in the DREAM approach, an improved value of NSE (0.84) was obtained. After calibration and treatment of forcing data errors, the model yielded reasonable prediction uncertainty bounds and well-defined posterior distributions of NAM model parameters. Main objectives of the study are to assess the applicability of MIKE 11 NAM model to the selected catchment. In addition, the importance of errors in the input forcing variables to the model is demonstrated.

Environmental Assessment of Future Potential Evaporation for Al-Najaf Governorate, Iraq through Evaluating Various Estimation Methods Using Statistical Downscaling Model

The hydrological cycle in nature is affected by climate changes, which are reflected in the water resources on the Earth (surface and underground) as evapotranspiration, thereby causing water scarcity. Increasing global temperature rates have exacerbated evaporation rates (PET). To estimate PET, scientists invented many mathematical equations, the most prominent of which is the Penman-Monteith equation. Most of the innovative methods require the provision of many parameters to be implemented; therefore, feasibly worthless for application in data mingy regions. The performance of the four PET estimation methods has been investigated depending either on temperature or radiation observed at Al-Najaf Governorate, Iraq. The accuracy and efficiency of each method have been evaluated with the outputs of the reference Penman-Monteith method. Through assessment, Hargreaves Samani temperature-based method reveals high acceptability of the estimated PET than the Jensen-Haise method and the other radiation-based methods (Penman and Makkink). The Statistical Downscaling Model/Tool (SDSM version 5.2) is applied to predict temperature values for the years 2050 and 2100. Estimated PET using Hargreaves Samani formula for 2050 and 2100 shows that Al-Najaf Governorate will encounter severe water scarcity due to the expected future increase in evaporation rates. Overall, regions classified as dry with high temperatures are compatible with methods of estimating PET values that depend on temperatures more than those that depend on radiation.

Uncertainties in evapotranspiration projections associated with estimation methods and CMIP6 GCMs for South Korea

This study compared the performance capabilities of three potential evapotranspiration (PET) methods, Thornthwaite (TW), Hargreaves and Samani (HS), and Penman-Monteith (PM), to simulate historical and future daily PET levels in South Korea using climate variables from Coupled Model Intercomparison Project 6 (CMIP6) Global Climate Models (GCMs). Five evaluation metrics were used to quantify the reproducibility of the climate variables and PETs at ten stations in South Korea for the historical period used here (1985–2014). The changes and uncertainty associated with the changes in PET in the near (2031–2060) and far (2071–2100) futures were calculated for two shared socioeconomic pathways (SSPs) of 2–4.5 and 5–8.5. As a result, PETs estimated using the three methods for the historical period showed high performance in terms of five evaluation metrics. Overall, PETs showed an increase for both the future periods and the SSPs. The PET estimated using the PM method showed the greatest increase, while that estimated using HS showed the most modest increase in the future. The PM method also showed the highest reliability and lowest uncertainty in the PET estimations, while the opposite was true for HS. This study contributes to our understanding of rational PET methods by which to calculate hydrological factors such as drought indexes for future periods via GCM simulations.

Effect of climate change and elevated CO2 on reference evapotranspiration in Varanasi, India - A case study

Evapotranspiration (ET) is the major component of hydrological cycle, and will affect the crop water requirement and future planning and management of water resources under changing climate scenarios. In the present study, an attempt was made to study the sensitivity of reference evapotranspiration (ETo) to different climatic variables, and effect of temperature and elevated CO2 on ETo using meteorological data (1973-2010) of the Banaras Hindu University (BHU), Varanasi. The FAO-56 Penman–Monteith method was used to estimate ETo, and sensitivity of ETo was studied in terms of changes in temperature, solar radiation, wind speed and vapour pressure deficit. The combined effect of temperature and elevated CO2 levels was studied by varying the temperature from 1°C to 5°C, and the CO2 level from 330 ppm to 660 ppm. The simulation results showed that the mean temperature (Tmean) influenced the annual ETo the most followed by solar radiation (Rs), vapour pressure deficit (VPD) and wind speed (U2). There are about 18.20, 13.80, 7.90 and 5.40% increase in annual ETo with 25% increase in Tmean, Rs, VPD, and U2, respectively. Simulating the combined effect of temperature and elevated CO2 indicated that the effect of 2.5 °C rise in temperature is offset by doubling of CO2 concentration. Thus, the effect of rising temperature is moderated by the increasing CO2 concentrations, and the crop water demand may not rise significantly under the climate change scenarios.

RASPOTION—A New Global PET Dataset by Means of Remote Monthly Temperature Data and Parametric Modelling

Regional estimations of Potential Evapotranspiration (PET) are of key interest for a number of geosciences, particularly those that are water-related (hydrology, agrometeorology). Therefore, several models have been developed for the consistent quantification of different time scales (hourly, daily, monthly, annual). During the last few decades, remote sensing techniques have continued to grow rapidly with the simultaneous development of new local and regional evapotranspiration datasets. Here, we develop a novel set T maps over the globe, namely RASPOTION, for the period 2003 to 2016, by integrating: (a) mean climatic data at 4088 stations, extracted by the FAO-CLIMWAT database; (b) mean monthly PET estimates by the Penman–Monteith method, at the aforementioned locations; (c) mean monthly PET estimates by a recently proposed parametric model, calibrated against local Penman–Monteith data; (d) spatially interpolated parameters of the Parametric PET model over the globe, using the Inverse Distance Weighting technique; and (e) remote sensing mean monthly air temperature data. The RASPOTION dataset was validated with in situ samples (USA, Germany, Spain, Ireland, Greece, Australia, China) and by using a spatial Penman–Monteith estimates in England. The results in both cases are satisfactory. The main objective is to demonstrate the practical usefulness of these PET map products across different research disciplines and spatiotemporal scales, towards assisting decision making for both short- and long-term hydro-climatic policy actions.

Comparison of evaporation estimation methods for water surface under floating coverage in arid areas

The main purpose of this study is to evaluate evaporation estimation methods for water surfaces under floating coverage in arid areas. The natural water surface evaporation methods (namely, the mass transfer method (MT), Bowen ratio energy balance (BREB) method, Penman method, the Penman-Monteith method (PM), and the Priestley-Taylor method (PT)) are corrected to establish the covered water surface evaporation methods, which are based on the micrometeorology near the covered water surface, energy balance components, and Bowen ratio. The radiation term of the natural water surface evaporation model is corrected by introducing the Bowen ratio of the entire surface, which includes the uncovered water surface and the floating cover surface. The mass transfer coefficient and aerodynamic impedance in the aerodynamic terms of the natural water surface evaporation model were corrected. Taking the evaporation of the water surface covered by spheres (coverage rate of spheres is 76.4%) as an example, based on the rose change characteristics of the above parameters, the effects of these modified evaporation models are comprehensively evaluated from three different time scales (even months, odd months and the complete test period). The evaluation methods include the root mean square error (RMSE), index of agreement (D), mean bias error (MBE), and coefficient of determination (R2). The results show that under three different time scales, the Penman-Monteith model is the most promising and superior in estimating water surface evaporation under floating coverage (for the complete test period, RMSE=0.11 mm·d−1, D=0.99, MBE= 0.07 mm·d−1, and R2 =0.99); the Priestley-Taylor model is slightly less effective in estimating evaporation than the Penman-Monteith model, and the mass transfer model and the Penman model are less effective in estimating evaporation. Furthermore, the analysis of the Penman-Monteith model shows that the evaporation of the radiation term (69.5%) is higher than that of the aerodynamic term (30.5%) during the evaporation of the water surface under floating coverage, while the evaporation of the radiation term is dominant. Moreover, during the complete test period, the weights of the radiation evaporation and aerodynamic evaporation changed significantly with time. The proportion of radiation evaporation varies from 54.8% to 81.7%, while that of aerodynamic evaporation varies from 18.3% to 45.2%.

The Performance of Thirty-Eight Evapotraspiation Methods against the Penman Montieth Method

Evapotranspiration is an important parameter to know in the management of agricultural water resources. There are several methods for estimating evapotranspiration (ETo). The FAO Penman-Monteith method is considered the universal standard for estimating ETo, however, the climate parameter data needed to calculate the ETo of the FAO Penman-Monteith method are not always available, so it is necessary to evaluate performance using several ETo equations as a comparison in estimating evapotranspiration. This study aims to find the performance of the ETo 38 empirical method against the FAO Penman Monteith method and the climatological data parameters that most influence the accuracy of the calculations. The data used is in the form of climatological data for Bora Station which represents land and settlement areas, Boladangko Station representing mountainous areas and Singkoyo Station representing coastal areas with an observation period of 32 years (1986-2017). The performance of the 38 empirical methods was evaluated using the PE, RMSE, MAE, MR and slope methods. The results of this study indicate that: the Makkink method, Abtew1, Irmak et al., Tabari & Talaee1, Valiantzas1 and Abtew3 showed excellent performance, while the WMO and Mahringer methods performed poorly in the three research locations; the number of climatological parameters used had no significant effect on the ETo results obtained. The use of climatological parameters in the form of temperature and solar radiation in the ETo calculation has the greatest effect on the accuracy of the ETo value.

Comparison of Crop Evapotranspiration and Water Productivity of Typical Delta Irrigation Areas in Aral Sea Basin

The intensity of agricultural activities and the characteristics of water consumption affect the hydrological processes of inland river basins in Central Asia. The crop water requirements and water productivity are different between the Amu Darya and Syr Darya river basins due to the different water resource development and utilization policies of Uzbekistan and Kazakhstan, which have resulted in more severe agricultural water consumption of the Amu Darya delta than the Syr Darya delta, and the differences in the surface runoff are injected into the Aral Sea. To reveal the difference in water resource dissipation, water productivity, and its influencing factors between the two basins, this study selected the irrigation areas of Amu Darya delta (IAAD) and Syr Darya delta (IASD) as typical examples; the actual evapotranspiration (ETa) was retrieved by using the modified surface energy balance algorithm for land model (SEBAL) based on high spatial resolution Landsat images from 2000 to 2020. Land use and cover change (LUCC) and streamflow data were obtained to analyze the reasons for the spatio-temporal heterogeneity of regional ETa. The water productivity of typical crops in two irrigation areas was compared and combined with statistical data. The results indicate that: (1) the ETa simulated by the SEBAL model matched the crop evapotranspiration (ETc) calculated by the Penman–Monteith method and ground-measured data well, with all the correlation coefficients higher than 0.7. (2) In IAAD, the average ETa was 1150 mm, and the ETa had shown a decreasing trend; for the IASD, the average ETa was 800 mm. The ETa showed an increasing trend with low stability due to a large amount of developable cultivated land. The change of cultivated land dominated the spatio-temporal characteristics of ETa in the two irrigation areas (3). Combined with high spatial resolution ETa inversion results, the water productivity of cotton and rice in IAAD was significantly lower than in IASD, and wheat was not significantly different, but all were far lower than the international average. This study can provide useful information for agricultural water management in the Aral Sea region.

Determination of Reference Evapotranspiration Using Penman-Monteith Method in Case of Missing Wind Speed Data under Subhumid Climatic Condition in Hungary

The reference evapotranspiration was calculated using Penman-Monteith method proposed. This method was evaluated on data measured by lysimeter in Szarvas experimental station in Hungary. The results of the two methods were in good agreement. However, this method requires an amount of data which is not available at all sites of meteorological measurement. Therefore it was necessary to investigate which elements influencing evapotranspiration are important and which elements are less important. With the help of investigation was indicated that radiation and vapor pressure deficit play important role in determination of reference evapotranspiration. Taking into account this there was two possibilities to calculate evapotranspiration. One of these is to use Penman-Monteith formula with constant wind speed as advised by Allen. Another one is to neglect wind speed data. Both methods were investigated and the method with constant wind speed was found better in a subhumid climatic condition of Hungary.

Performance of summer groundnut under different types of plastic mulch

Groundnut is the main kharif crop of the Saurashtra region of Gujarat state. It is also cultivated in summer season in this region. The productivity of summer groundnut is limited by low temperature at initial growth stage, limited availability of irrigation water and higher temperature during reproductive growth period. A field experiment was conducted for two years to evaluate the performance of summer groundnut under six mulch treatments i.e. silver black plastic, transparent LDPE, PP nonwoven, biodegradable, no mulch (raised bed) and flatbed (control). Experiment was laid out with Randomized Block Design (RBD) with four replications. Crop water requirement was calculated by FAO Penman-Monteith method. Irrigation water was applied through drip irrigation in all treatments except flatbed. Silver black plastic mulch resulted in highest plant height, number of pods/plant, weight of pods/plant, pod and haulm yield and minimum weed intensity. Maximum water saving (41.2%), water use efficiency (6.77 kg/ha-mm) and benefit cost ratio (2.85) were obtained from use of silver black plastic mulch.

Water use efficiency of sugarcane clones under rainfed condition

Crop water use study is necessary for crop management. The purpose of the research was to determine water use efficiency (WUE) of sugarcane clones under rainfed conditions. A field trial was carried out in rainfed at Kediri, East Java, from August 2018 to June 2020 in two seasons of plant cane (PC) and first ratoon cane (RC1). A Split-Plot Design was used for the field trial with three replications. Three planting arrangements were allocated to the main plot. Sub-plots consisted of five clones and one check variety. The WUE is the ratio of sugarcane yield to total crop evapotranspiration. Crop evapotranspiration was the reference evapotranspiration multiply by crop coefficients. Reference evapotranspiration needs meteorological data and relied on the FAO Penman-Monteith method. The average sugarcane crop evapotranspiration for two seasons was 1260.9 mm/year in PC and RC1, with effective rainfall of 817.6 mm and 797 mm, respectively. Average water use efficiency to produce sugarcane were 11.47 and 10.85 t/100 mm and sugar 1.085 and 0.884 t/100 mm, respectively, in PC and RC1. The WUE benefit as criteria for crop selection and to calculate the potential yield given the available water.

Machine Learning Based Prediction of Reference Evapotranspiration (ET0) Using IoT

Accurate estimation of Reference Evapotranspiration (ET₀) is important for efficient management and conservation of irrigation water. Existing methods of ET₀ rate determination are complex for application at the farmer level. Apart from standard methods of ET₀ determination, many data-driven soft computing approaches were also proposed to determine the ET₀ with limited data set. We proposed a temperature and humidity-based ML approach for ET₀ rate determination on directly sensed environmental conditions of the crop field. Crop field environmental conditions for (ET₀) rate determination are sensed by the proposed Internet of Things (IoT) architecture. Crop field environmental conditions from the year 2015 to 2021 in Pakistan are used for the training and testing of the proposed model. Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), and Artificial Neural Network (ANN) based models are compared for performance. Crop fields directly sensed temperature and humidity pass to the model to train and predict the ET₀-rate of crop fields. The 10-fold cross-validation technique is applied for the evaluation of the proposed approach. The accuracy of the proposed solution for the ET₀ rate is compared against the Food and Agriculture Organization (FAO) recommended Penman-Monteith method for ET₀ rate determination. As concerned of the ML-based models the KNN model is more accurate as compared to SVM,GNB and ANN models with 92% accuracy. The KNN model of ET₀ is more efficient in reducing the Root Mean Squared Errors (RMSE) by 16% and Mean Absolute Errors (MAE) by 3% against the state of the art approach.

Assessing the influence of the hydraulic boundary conditions on clay slope stability: The Fontana Monte case study

Recent studies have assessed that slope-vegetation-atmosphere, SLVA, interaction may trigger the activity of deep landslides in clayey slopes. In some cases, the presence of an underground aquifer, fed by an upstream hydraulic recharging area, may represent a predisposing factor of such activity, being co-responsible of deep piezometric heads, which can undergo seasonal fluctuations due to the SLVA interaction. In this perspective, the present paper illustrates the results of a scientific research, carried out in a pilot site of the Daunia Apennines, the Fontana Monte slope at Volturino (Foggia, Italy), considered as a prototype of the class of landslide mechanisms controlled by both the rainfall water infiltration and the presence of a water-bearing aquifer in the hillslope.Numerical simulations of the saturated/partially saturated transient seepage flow in the slope have been performed by means of uncoupled hydraulic finite element analyses, with the aim of investigating the sources of large piezometric heads, related to climatic, hydrogeological and hydraulic slope features. The rainfall and evapotranspiration fluxes, the latter defined through the FAO Penman-Monteith method, are both implemented as ground surface input, while seasonal variations of the upstream hydraulic boundary conditions are imposed to predict the seasonal piezometric excursion at shallow and deep monitoring points. The transient seepage results are then used as input for limit equilibrium analyses to assess the influence of the hydraulic settings on the stability of the considered landslide body. The work shows the impact of both climatic and hydraulic factors on the seepage processes, affecting the stability of the slope. Moreover, it is highlighted that the accurate implementation of the upstream hydraulic feeding is fundamental for a reliable prediction of the monitored piezometric regime, strictly related to the recharge of the water-bearing aquifer.

Optimal Selection of Empirical Reference Evapotranspiration Method in 36 Different Agricultural Zones of China

The Food and Agriculture Organization has proposed the current version of the Penman–Monteith method (FAO56-PM) as the standard for calculating reference evapotranspiration (ET0); however, high meteorological data requirements limit its application in many areas. There is thus an urgent need to identify the best alternative empirical method to accurately calculate ET0 in regions that lack sufficient meteorological data. In this study, three temperature-based methods and five radiation-based methods were evaluated using ET0 values generated using the FAO56-PM method in 36 agricultural zones in China based on meteorological data from 823 stations, measured between 2011 and 2020. The results showed that the optimal temperature-based method and radiation-based method differed for different agricultural zones, and no one temperature method or radiation method could be suitable for all agricultural zones. The eight empirical methods were regionally calibrated to improve the ET0 calculation accuracy in the different zones. The relationship between the optimal methods and climatic conditions showed that the most reliable empirical method could be selected according to the local annual mean temperature and aridity index. The results provide useful guidance for the selection of reliable empirical ET0 methods in agricultural zones outside China.