Potential Evapotranspiration Rates Research Articles

Evapotranspiration fusion and attribution analysis in the upper and middle reaches of the Yellow River Basin

Study regionThe Upper and Middle Reaches of the Yellow River Basin Study focusTaking the upper and middle reaches of the Yellow River as the research area, 15 types of models developed by the fusion of four multi-source products of GLDAS-Noah V2.1, ERA5-Land, GLEAM v3.6a and GLEAM v3.6b were assessed. Based on the fusion results of actual evapotranspiration(ET), the key factors that drive the spatial variation in actual evapotranspiration was explored, the land surface parameters of Fu formula in different watersheds were estimated, the relationship between land surface parameters and ecological construction measures was established, and the contribution rate of precipitation, potential evapotranspiration, NDVI and check-dam control area to ET change was quantified. New hydrological insights for the regionThe multi-year average actual evapotranspiration from 2003 to 2018 in the watersheds controlled by Tangnaihai, Lanzhou, Toudaoguai, Longmen and Tongguan is 378, 368, 346, 368 and 410 mm/year, respectively. The optimal fusion of the actual evapotranspiration is employing the Extreme Learning Machine to fuse the GLDAS-Noah V2.1 and GLEAM v3.6b products. The long-term actual evapotranspiration estimated from the Fu formula incorporating the ecological construction measures have high agreement with the fusion results. The impact of soil moisture on the spatial variation of actual evapotranspiration is significant. The contribution rate of precipitation to ET change is the highest, followed by NDVI, potential evapotranspiration and check-dam control area.

Growth And Yield Of Tomato As Influenced By Water Stress At Different Phenological Stages

The study investigates the effect of water stress at different phenological stages on tomato growth and yield. Tomato is a vital vegetable crop in Nigeria, and water scarcity poses challenges to its productivity. The study was carried out in a screen house, water was applied daily with 100% potential evapotranspiration rate except during the ten days water stress applied at every phenological stage. The parameters assessed include number of leaves, plant height (cm), stem diameter (mm), and root and shoot dry weights (g), in addition, the water use efficiency. The results were subjected to Analysis of Variance and the means were compared using Least Significant Difference at 5% level of significance. The results show that there was no significant difference (p≥0.05) in the water use efficiency of the tomato plants subjected to water stress at the phenological stages. Notably, the study highlights that the vegetative stage is particularly sensitive to water stress, leading to reduced shoot dry weight and compromised overall biomass. Similarly, water stress during the flowering stage diminishes root dry weight. However, the fruiting stage exhibits relatively better yields under water stress than other stages. The findings emphasize the importance of effective irrigation management, particularly during the vegetative phase, to promote optimal plant development. Moreover, the research underscores the significance of providing adequate water during reproductive phases to enhance fruit production and overall plant performance.

Study on the relationship between regional soil desertification and salinization and groundwater based on remote sensing inversion: A case study of the windy beach area in Northern Shaanxi

Soil desertification and salinization are important environmental concerns in arid regions, and their relationship with groundwater change must be further clarified. However, the relationships among soil desertification, salinization, and groundwater are difficult to investigate on a large spatiotemporal scale using traditional ground surveys. In the windy beach area in Northern Shaanxi (WBANS), desertification and salinization problems coexist; therefore, this area was selected as the study area. The feasibility of implementing large-scale remote sensing inversions to identify the degree of desertification and salinization was verified based on measured data, and the degree of influence of groundwater burial depth (GBD) on desertification and salinization was quantified using the geodetector and residual trend analysis methods. The results showed that the GBD in the WBANS presented an increasing trend and the degree of salinization showed a decreasing trend. Moreover, the joint influence of the unique natural environment and anthropogenic activities has led to increases in fractional vegetation cover and considerable improvements in the ecological environment. The intensity of desertification explained by GBD in the WBANS increased significantly (p < 0.01) at a rate of change of 0.0190/year, with high q-values above 0.66 for both Yuyang and Shenmu. The contribution rate of potential evapotranspiration and precipitation to salinization in Yuyang and Shenmu was >97 %, and the contribution rate of GBD to salinization in Dingbian, Jingbian, and Hengshan was 34.78 %, 31.15 %, and 29.41 %, respectively. Overall, the suitable GBD in the WBANS is 2–4 m. The study results provide a reference for research on the inversion, monitoring, and prevention of desertification and salinization dynamics on a large spatiotemporal scale and offer a scientific basis for rationally determining GBD.

Performance of potential evapotranspiration models across different climatic stations in New South Wales, Australia

Study regionNew South Wales, southeast Australia Study focusEstimating potential evapotranspiration (ETp) rates, detecting its temporal trends and analysing its interannual oscillation are critical for long-term assessment of water availability and regional drought. This study aimed to evaluate the comprehensive performance of 12 simplified models in characterising ETp against the benchmark Penman model across different climate sites in southeast Australia. This study used Taylor skill score (S), normalised root mean square error (nRMSE) and relative mean bias error (rMBE) to estimate models’ capability in estimating ETp rates. Then, this study adopted Mann-Kendall test and continuous wavelet transform (CWT) to test temporal trends and periodicity of ETp estimated by all models. New hydrology insights for the regionJensen-Haise model was capable to produce fair (nRMSE ≤ 30%) estimates of daily ETp across all stations. Models except Mak1 were generally able to produce reasonable estimates of ETp at larger time scale. In addition, we found that the 12 alternative ETp models generally agreed with the Penman model on the primary (9.6–12.4-year) and quasi (2.6–3.9-year) periods of ETp, but they did not show matchable ability in detecting ETp temporal trends. The comprehensive investigation on models’ performance will shed light on models’ selection in estimation of drought and hydrological cycle.

Comparative analysis of the influence of temperature and precipitation on drought assessment in the Marmara region of Turkey: an examination of SPI and SPEI indices

Abstract Drought is one of the most serious problems for human societies and ecosystems. Climate change has begun to affect the whole world, but regional climate change can have different effects. Precipitation is frequently used to determine drought; besides, temperature has started to be used in recent years. The effect of temperature on the occurrence of drought was investigated for the Marmara region, located in the northeast of Turkey. The spatiotemporal variability of drought in the Marmara region was analyzed by using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) for 3-, 6-, and 12-month series. Results show that SPEI droughts occur with longer duration and increased magnitude, which can be attributed to the rise in potential evapotranspiration rates with an increase in temperature. The correlation between SPI and SPEI is strong for the same time series, and the number of extreme drought values for SPI is higher than SPEI, while the number of severe and moderate drought values is lower compared to SPEI. The years 1989, 1990, 2001, 2007, and 2014 were characterized as the highest drought years throughout the region. The study concludes that temperature should be considered in future studies.

Paleoclimate quantitative reconstruction and characteristics of continental red beds: a case study of the lower fourth sub-member of Shahejie Formation in the Bonan Sag

Varied origins have the ability to construct the continental red beds, such as paleoclimate, provenance, drainage status, etc. Reconstructing paleoclimate is the key to investigate the origin. Thus, this paper outlines the normal distribution constrained method (NDCM) to reconstruct paleoclimate quantitatively and accurately during the lower fourth sub-member of Shahejie (Es4x) in the Bonan Sag, which is in low requirement of data and environmental conditions. Based on the NDCM, the paleoclimate is still in a long-term arid background as the potential evapotranspiration rates (PERs) are larger than 6 in the transgressive system tract (TST) and regressive system tract (RST) during the period of Es4x. The decreasing PERs that range from 6.28 to 6.04, decreasing atmospheric temperature range from 14.37 to 13.95 °C and increasing mean precipitation of the wettest month and mean precipitation of the driest month indicate the paleoclimate is breaking away from the hot and arid background from TST to RST. Meanwhile, the paleoclimate fluctuation develops decreasing frequency and increasing amplitude which can be inferred from the increasing standard deviation (std), Th/K and Th/U curves. The hot and arid background is still the main origin of red color during the Es4x. Moreover, the other origin may also develop since there is no clear law between red coloration and the developing location of sediments.

Impact of shading on evapotranspiration and water stress of urban trees

AbstractEvapotranspiration of urban street trees is essential in mitigating urban heat islands due to its cooling effect. However, current shifts in rainfall and temperature regimes towards drier and hotter periods in Central Europe have caused substantial water stress for street trees. Quantifying and subsequently managing these changing dynamics as well as estimating evapotranspiration and water availability is necessary but at the same time extremely challenging in urban environments. Both dynamics are influenced by soil sealing and complex shading patterns of the surrounding street canyon, which vary on a small spatial scale as a function of the canyon layout and orientation. In the present study, the diurnal patterns of six typical urban shading types for street trees were derived by considering a large set of street orientations, widths and tree positions within the street canyon. A shading model was integrated into a hydrological urban tree model to assess the impact of those shading types on diurnal patterns of radiation and evapotranspiration rates calculated using the Penman–Monteith approach and the resulting soil moisture conditions for several vegetation seasons and water‐supply scenarios. The modelling results showed that the six shading patterns significantly influenced the simulated hourly, daily and seasonal potential and actual evapotranspiration rates and water availability. Shaded trees have a substantially reduced, simulated water stress period, regardless of initial water supply, and are able to provide a longer‐lasting cooling function during dry periods due to higher evapotranspiration rates later in the summer season.

Quantitative assessment of hydrological response to vegetation change in the upper reaches of Luanhe River with the modified Budyko framework

For assessing the influence of vegetation variation in upper reaches of Luanhe River (URLR) on streamflow, we first computed the equation between underlying surface parameter (ω) and the Normalized Difference Vegetation Index (NDVI). Then we introduced the vegetation information into the Budyko equation and established a modified Budyko equation. Finally, the impact of vegetation change on streamflow in URLR were estimated using the modified Budyko equation. Results showed that: (1) The NDVI in the URLR presented an increasing trend from 1982 to 2016, which is opposite to the runoff depth. The sudden change of NDVI occurred in 1998, and a simple linear model between ω and NDVI was obtained (p &amp;lt; 0.01). (2) The contribution rate of precipitation, potential evapotranspiration, NDVI, and anthropogenic factor on streamflow change during the changing period (1999–2016) are 44.99, 11.26, 29.45, and 17.30%, respectively. Although precipitation is still the main driver of runoff impact in the ULRB, with the increasing vegetation in the ULRB, vegetation has become the second driver of runoff impact followed by human activities and potential evapotranspiration. The mechanism of the effect of vegetation change on the water cycle needs to be further investigated. The results of this study can provide a theoretical basis for water use and conservation in the URLR.

Using Soil Water Balance to Estimate Recharge of Underground Water in Central Sulaimani Province

In this study, we estimated the recharge of underground water by using soil water balance in the Sulaimani Province. Based on recorded data in three metrological centres, including Sulaimani, Bakrajo, and Bazyan from 2009 -2021. We found an estimated rate of potential evapotranspiration by using the FAO-Penman -Monteith equation by CropWat Ver.8 Software Programs. Based on this estimate, we found the water deficit and water surplus months in the study area. Moreover, grounded on (SCS-CN) model in the mentioned area which would be divided into 8 different ( CN). Accordingly, an estimate was obtained for the rate of runoff water per month of a calendar year. Finally, we found the amount of rainwater that would take part in the recharge of underground water throughout each month and over years. In this study, we also used statistical analysis of quantitative mathematical results to find amounts of underground water in the study area. Besides, we used (ArcGis10.8) program to create maps for various purposes. In conclusion, we successfully discovered that it is possible to develop a scientific strategy to collect rainwater and replenish underground water sources and protect them by finding out amounts of underground water recharge in the Formation zone that would be 104.2 mm. Importantly, (CN-57) which is located in the district of Bazyan (Khormala -Sinjar Fn.) has the highest amount of underground water recharge that is equivalent to 210.2mm of the total amount of the precipitation equivalent to 32% of the annual sum of rain.

Effects of meteorological forcing uncertainty on high-resolution snow modeling and streamflow prediction in a mountainous karst watershed

In the mountainous Western U.S., a considerable portion of water supply originates as snowmelt passing through karst watersheds. Accurately simulating streamflow in snow-dominated, karst basins is important for water resources management. However, this has been challenging due to high spatiotemporal variability of meteorological and hydrogeological processes in these watersheds and scarcity of climate stations. To overcome these challenges, a physically based snow model is used to simulate snow processes at 100 m resolution, and the calculated snowmelt and potential evapotranspiration rates are fed into a deep learning model to simulate streamflow. The snow model was driven by meteorological variables from a regional scale Weather Research and Forecasting (WRF) model or from the North American Land Data Assimilation System (NLDAS-2). The two datasets were used both at the original resolution and downscaled to 100 m resolution based on orographic adjustments, leading to four sets of forcings. Snow model simulation results from the four sets of forcings showed large differences in simulated snow water equivalent (SWE) and snowmelt rate and timing. However, the differences were damped in simulated streamflow, as the deep learning model is partially immune to input bias and picked up different streamflow responses to snowmelt and rainfall when trained using snow model results. While the meteorological datasets considered yielded close streamflow simulation accuracy, averaging simulated streamflow from the four sets of forcings consistently achieved better performance, suggesting the value of including multiple meteorological datasets for modeling streamflow in mountainous watersheds.

Is the crop evapotranspiration rate a good surrogate for the recommended irrigation rate?

AbstractNumerous methodologies and technologies of varying levels of sophistication for irrigation rate determination have been developed over the years to address limited irrigation water availability and optimize water productivity. Crop evapotranspiration is currently evaluated mostly according to the FAO56 approach, which has been augmented in recent years with remote sensing of correlated plant indices. When no other information is available, the evapotranspiration rate does give an indication of the optimal irrigation rate in some circumstances. However, in many other circumstances discussed in this short communication, replenishing the soil with crop evapotranspiration either substantially overestimates or underestimates the optimal, physiological (agronomical/economical) irrigation rate. Consequently, this note raises doubts and opens a discussion about the extensive reliance on the evaluated crop (potential or actual) evapotranspiration rate for recommending an optimal irrigation rate (irrigation dose/irrigation amount).

Spatio-temporal Variation in Vegetation Cover and Its Driving Mechanism Exploration in Southwest China from 2000 to 2020

Using the MOD13A3 NDVI time series from 2000 to 2020, climate date from 1999 to 2020, and land use type data in 2000 and 2020, the spatio-temporal variation in vegetation cover and the driving mechanisms of climate change and human activities to vegetation variation were analyzed based on Theil-Sen Median analysis, the Mann-Kendall significance test, the multi-collinearity test, residual analysis, and relative analysis. The results showed that the vegetation cover exhibited a fluctuating and increasing trend with a magnitude of 0.0016 a-1 in southwest China from 2000 to 2020. The increasing trend of vegetation cover was mostly significant in the Guangxi Hills and Yunnan-Guizhou Plateau and slightly significant in the Tibet Plateau. The vegetation cover had increased in the context of climate change and human activities, with an increasing rate of 0.0010 a-1 and 0.0006 a-1, respectively. The vegetation improvement was mostly dominated by the combination effects of climate change and human activities. The vegetation improvement was dominated by climate change, and the relative role of climate change reached 61.86%. What is more, the vegetation degradation was dominated by human activities, and the relative role of human activities reached 58.39%. Vegetation cover was positively related to minimum temperature, precipitation, maximum temperature, potential evapotranspiration rate, and relative humidity and negatively related to mean temperature, atmosphere pressure, sunshine duration, warmth index, and humidity index. As a whole, the minimum temperature, sunshine duration, and precipitation were the dominant climate factors affecting the vegetation variation in southwest China. Furthermore, the land use and land cover change were significantly related to vegetation variation in southwest China. The implementation of ecological afforestation projects could be beneficial to regional vegetation improvement, whereas the vegetation degradation was mostly conducted by the built-up land expansion.

Evaluation of the Effect of Changes in Climatic Variables at Different Levels on the Rate of Potential Evapotranspiration in Arid and Semi-Arid Regions

Evaluation of the Effect of Changes in Climatic Variables at Different Levels on the Rate of Potential Evapotranspiration in Arid and Semi-Arid Regions

Spatiotemporal Analysis of Urban Heat Islands in Relation to Urban Development, in the Vicinity of the Atacama Desert

Near the Atacama Desert, Tacna city in Peru is among the largest arid cities with constant urban development, thus understanding of the urban surface thermal pattern is needed. We propose a comprehensive study of the urban heat island phenomenon, with the objective of (1) determining the spatial and temporal variations of the urban heat islands (UHIs), in the period 1985 to 2020; (2) analyzing the relationship between the UHI and influencing factors such as vegetation, urban area, and population, using indices calculated with satellite images. The Google Earth Engine repository was used to evaluate the corrected images from the years 1985 to 2020. The coincidence between the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) was good, negative between NDVI and the land surface temperature (LST), attributable to dense vegetation, and negative and very high (−0.81) between NDBI and NDVI, as massive urbanization leads to the reduction in the vegetal surface. The NDBI has a high impact on the LST; a coefficient of connections is recorded as 0.46. Tacna is a very arid region, and an increase in the time of the LST occurred with the increase in industrialization and urbanization. The land use/cover change (LUCC) evidences change in the climate in the city of Tacna; temperatures of 24.2 °C to 44.2 °C are observed in the built-up areas. In vegetated areas, the temperature remains below 24 °C, which is associated with a high rate of potential evapotranspiration. Thus, this study shows that variations in urban form and growth have produced the development of intraurban surface thermal patterns.

Coherence of global hydroclimate classification systems

Abstract. Climate classification systems are useful for investigating future climate scenarios, water availability, and even socioeconomic indicators as they relate to climate dynamics. There are several classification systems that apply water and energy variables to create zone boundaries, although there has yet to be a simultaneous comparison of the structure and function of multiple existing climate classification schemes. Moreover, there are presently no classification frameworks that include evapotranspiration (ET) rates as a governing principle. Here, we developed a new system based on precipitation and potential evapotranspiration rates as well as three systems based on ET rates, which were all compared against four previously established climate classification systems. The within-zone similarity, or coherence, of several long-term hydroclimate variables was evaluated for each system based on the premise that the interpretation and application of a classification framework should correspond to the variables that are most coherent. Additionally, the shape complexity of zone boundaries was assessed for each system, assuming zone boundaries should be drawn efficiently such that shape simplicity and hydroclimate coherence are balanced for meaningful boundary implementation. The most frequently used climate classification system, Köppen–Geiger, generally had high hydroclimate coherence but also had high shape complexity. When compared to the Köppen–Geiger framework, the Water-Energy Clustering classification system introduced here showed overall improved or equivalent coherence for hydroclimate variables, yielded lower spatial complexity, and required only 2, compared to 24, parameters for its construction.

Vegetation Resilience under Increasing Drought Conditions in Northern Tanzania

East Africa is comprised of many semi-arid lands that are characterized by insufficient rainfall and the frequent occurrence of droughts. Drought, overgrazing and other impacts due to human activity may cause a decline in vegetation cover, which may result in land degradation. This study aimed to assess drought occurrence, vegetation cover changes and vegetation resilience in the Monduli and Longido districts in northern Tanzania. Satellite-derived data of rainfall, temperature and vegetation cover were used. Monthly precipitation (CenTrends v1.0 extended with CHIRPS2.0) and monthly mean temperatures (CRU TS4.03) were collected for the period of 1940–2020. Eight-day maximum value composite data of the normalized difference vegetation index (NDVI) (NOAA CDR—AVHRR) were obtained for the period of 1981–2020. Based on the meteorological data, trends in rainfall, temperature and drought were determined. The NDVI data were used to determine changes in vegetation cover and vegetation resilience related to the occurrence of drought. Rainfall did not significantly change over the period of 1940–2020, but mean monthly temperatures increased by 1.06 °C. The higher temperatures resulted in more frequent and prolonged droughts due to higher potential evapotranspiration rates. Vegetation cover declined by 9.7% between 1981 and 2020, which is lower than reported in several other studies, and most likely caused by the enhanced droughts. Vegetation resilience on the other hand is still high, meaning that a dry season or year resulted in lower vegetation cover, but a quick recovery was observed during the next normal or above-normal rainy season. It is concluded that despite the overall decline in vegetation cover, the changes have not been as dramatic as earlier reported, and that vegetation resilience is good in the study area. However, climate change predictions for the area suggest the occurrence of more droughts, which might lead to further vegetation cover decline and possibly a shift in vegetation species to more drought-prone species.

Hydro-Meteorological Trends in an Austrian Low-Mountain Catchment

While ongoing climate change is well documented, the impacts exhibit a substantial variability, both in direction and magnitude, visible even at regional and local scales. However, the knowledge of regional impacts is crucial for the design of mitigation and adaptation measures, particularly when changes in the hydrological cycle are concerned. In this paper, we present hydro-meteorological trends based on observations from a hydrological research basin in Eastern Austria between 1979 and 2019. The analyzed variables include air temperature, precipitation, and catchment runoff. Additionally, the number of wet days, trends for catchment evapotranspiration, and computed potential evapotranspiration were derived. Long-term trends were computed using a non-parametric Mann–Kendall test. The analysis shows that while mean annual temperatures were decreasing and annual temperature minima remained constant, annual maxima were rising. Long-term trends indicate a shift of precipitation to the summer, with minor variations observed for the remaining seasons and at an annual scale. Observed precipitation intensities mainly increased in spring and summer between 1979 and 2019. Catchment actual evapotranspiration, computed based on catchment precipitation and outflow, showed no significant trend for the observed time period, while potential evapotranspiration rates based on remote sensing data increased between 1981 and 2019.

Comparison the ANN models of Penman-Monteith Potential Evapotranspiration with combination two input of climatological data in Surabaya

In Surabaya, as a part of the equatorial region, determine the rate of daily potential evapotranspiration (PET) turns into a requirement. During the scarcity season, the rate of PET significantly increases for certifying water availability. The PET model founded from several inputs of climatological data that are relative-humidity, wind speed, average daily temperature, and the duration of sun exposure. Modeling a PET through prolonged and complicated steps. To simplify the development of modeling PET, this research using Artificial Neural Network (ANN) based on data-driven modeling with fewer inputs. The PET-ANN model intends to match the PET estimated with Penman-Monteith (PM). This research purpose is learning what the best combination of two climatological data as input. The perform MSE and R on the validating process present how the different results come. The results show the best combination of two climatological data from the entire data set as an input. The conclusion is that the combination of relative humidity and wind speed as an input to the PET-ANN presents the best result than other combinations of climatological data. Besides, it approves that the relative humidity and wind speed as an undoubted input to the PET model even using ANN or not.

Effects of Improving Evapotranspiration Parameterization Scheme on WOFOST Model Performance in Simulating Maize Drought Stress Process

To solve the problem on poor performance of crop growth model in simulating crop growth process under water stress, three schemes including improving evapotranspiration parameterization scheme with Penman-Monteith method, building dynamic crop coefficient (Kc) and considering simultaneously two above-mentioned solutions which are respectively named as the PM, CC and PMCC schemes are used to improve WOFOST model. Their effects on model performance in simulating maize drought stress process are evaluated based on experiments of different sowing dates on 20 April, 30 April and 10 May conducted in Jinzhou in the year with normal precipitation (2012) and the dry year (2015 and 2018). The results show that compared with the default model, PM scheme plays a role in increasing potential evapotranspiration and transpiration rate in 2012, while CC scheme decreases (increases) transpiration rate as Kc is larger (smaller) than the model default value 1, respectively. Three schemes hardly affect simulation accuracies of soil moisture in rooted zone, total above ground production, leaf area index (ILA) and yield. In 2015, PM scheme makes ILA, total above ground production yield and soil moisture dramatically smaller than those with the default model after the jointing stage of maize. It also increases (decreases) transpiration rate before (after) the whorl stage of maize. After improving the model with CC scheme, ILA, total above ground production, yield and soil moisture are slightly larger than those simulated by the default model after the whorl stage, and the simulated transpiration rates are smaller (larger) than those simulated by the default model before (after) the whorl stage of maize. Nevertheless, the above-mentioned variables simulated by the improved model with PMCC scheme range between those simulated by the model with PM and CC schemes, and ILA, total above ground production and yield are obviously closer to the observations. Specifically, the mean increments of simulation accuracies for all growth periods in three sowing dates are 6%, 21% and 3% for ILA and 8%, 8% and 14% for total above ground production, respectively. The simulation accuracies of yield for three sowing dates increase by 66%, 63% and 66%, respectively. In 2018, the simulation accuracies of total above ground production and yield for the sowing dates on 20 April and 30 April are obviously improved by PMCC scheme, and increase by 5%, 1% in total above ground production as well as 32%, 5% in yield. Therefore, the model performance with PMCC scheme in simulating maize growth is significantly improved under water stress.

Estimating Potential Evapotranspiration in Maranhão State Using Artificial Neural Networks

Abstract The use of technology and planning in agricultural production is essential in Northeastern Brazil, which is the region of the country that most suffers from water shortage. For the best irrigation management, it is necessary to know the potential evapotranspiration rate for water control in order to increase productivity. There are several direct and indirect methods for estimating evapotranspiration, but the standard method recommended by the United Nations Agriculture Organization (FAO) is the Penman-Monteith (PETpm) method because it has higher accuracy than other methods. However, it is a difficult method to be used due to the need for a large number of meteorological elements. In this context, the objective of this study was to estimate potential evapotranspiration by the Penman-Monteith method in the micro-region of Baixo Parnaíba in Maranhão state using artificial neural networks. Agro-meteorological data were collected daily over 34 years, from 1984 to 2017, and these data were obtained from the NASA/POWER website. Subsequently, liquid radiation and potential evapotranspiration were calculated by the Penman-Monteith standard method (1998). To predict potential daily evapotranspiration, the Multi-Layer Perceptron (MLP) was chosen, which is a traditional Artificial Neural Network. The period that presented a higher evapotranspiration index was the same one that showed precipitation with a lower volume and higher temperatures. The artificial neural network model that best adapted to estimate PETpm was MLP 2-5-1. It is concluded that artificial neural networks estimate with accuracy and precision the Penman-Monteith daily potential evapotranspiration of the Lower Parnaiba in Maranhão, and potential evapotranspiration can be estimated by the Penman-Monteith method using neural networks with inputs of air temperatures.