Penman-Monteith Evapotranspiration Research Articles

Temperature-based Evapotranspiration in the WSVI Drought Index Implementation

Drought is a natural hazard that affects environmental and socio-economic development. In this study, the Water Surplus Variability Index (WSVI), which is based on both precipitation and Penman-Monteith (PM) evapotranspiration (ET) estimates, was used to quantify drought. The application of the WSVI is not possible in some locations because of the missing data for estimating ET. The key objectives of this research were to 1. compare the different temperature-based WSVIs for the 1-, 3-, 6- and 12-month timescales with the original PM-based WSVI; 2. improve the WSVI by replacing the full-set PM equation with the corresponding temperature-based ET equation, such as Hargreaves, adjusted Hargreaves, Thornthwaite, modified Thornthwaite or temperature-based PM; and 3. compare the best temperature-based WSVI with the Standardized Precipitation Index (SPI). The set of meteorological data was obtained from several stations from Pannonian Basin (Budapest, Debrecen, Novi Sad and Sombor). According to statistical indicators, the Hargreaves-based WSVI (WSVIH) was ranked first or second for each time period at all locations. The WSVIH was additionally tested using the SPI for each location and each timescale. The obtained results indicated the capability of the WSVIH to quantify drought frequency, duration and severity identified by the SPI. Finally, the WSVI can be recommended using the Hargreaves equation instead of the full-set PM equation in future WSVI implementations in the Pannonian Basin.

Agrivoltaic system design tools for managing trade-offs between energy production, crop productivity and water consumption

Agrivoltaic systems that locate crop production and photovoltaic energy generation on the same land have the potential to aid the transition to renewable energy by reducing the competition between food, habitat, and energy needs for land while reducing irrigation requirements. Experimental efforts to date have not adequately developed an understanding of the interaction among local climate, array design and crop selection sufficient to manage trade-offs in system design. This study simulates the energy production, crop productivity and water consumption impacts of agrivoltaic array design choices in arid and semi-arid environments in the Southwestern region of the United States. Using the Penman–Monteith evapotranspiration model, we predict agrivoltaics can reduce crop water consumption by 30%–40% of the array coverage level, depending on local climate. A crop model simulating productivity based on both light level and temperature identifies afternoon shading provided by agrivoltaic arrays as potentially beneficial for shade tolerant plants in hot, dry settings. At the locations considered, several designs and crop combinations exceed land equivalence ratio values of 2, indicating a doubling of the output per acre for the land resource. These results highlight key design axes for agrivoltaic systems and point to a decision support tool for their development.

Single and basal crop coefficients for estimation of water use of tree and vine woody crops with consideration of fraction of ground cover, height, and training system for Mediterranean and warm temperate fruit and leaf crops

AbstractThis paper reviews the research on the FAO56 single and basal crop coefficients of fruit trees and vines performed over the past twenty-five years and focus on Mediterranean and warm temperate trees and vines. Two companion papers (López-Urrea et al., (2023) Single and basal crop coefficients for estimation of water use of tree and vine woody crops with consideration of fraction of ground cover, height, and training system for temperate climate fruit crops. Irrig Sci (submitted); Paredes et al. (2023) Single and basal crop coefficients for estimation of water use of tree and vine woody crops with consideration of fraction of ground cover, height, and training system for tropical and subtropical fruit crops. Irrig Sci (submitted)) are dedicated, respectively, to Temperate and to Tropical and Subtropical trees and vines. The main objective of the paper is to update available information on single (Kc) and basal (Kcb) standard crop coefficients, and to provide for updating and completing the FAO56 tabulated Kc and Kcb. The Kc is the ratio between non-stressed crop evapotranspiration (ETc) and the grass reference evapotranspiration (ETo), while Kcb is the ratio between crop transpiration (Tc) and ETo. The selection and analysis of the literature were performed considering only studies that adhere to the FAO56 method, thus computing ETo with the FAO Penman–Monteith ETo equation, the ASCE grass ETo, or another equation that could be properly related with the former, and ETc, or Tc, was obtained using properly accurate field measurements on crops under pristine or eustress conditions. The crops considered refer to Mediterranean (grapes and olive) and warm temperate areas (avocado, citrus, persimmon, loquat, and tea) fruit and leaf crops. Papers satisfying the above conditions were selected to provide for standard Kc and Kcb data. Preferably, studies should report on the crop cultivar and rootstock, planting density or plant spacing, fraction of ground cover (fc), crop height (h), crop age and training systems. Additional information was collected on pruning and irrigation method and strategy. The ranges of reported Kc and Kcb values were grouped according to crop density in relation with fc, h, and the training system, namely vase, hedgerow, or trellis systems. Literature collected Kc or Kcb values were compared with previously tabulated Kc and Kcb values, namely in FAO56, to define the standard Kc and Kcb values for the referred selected crops. The tabulated values are, therefore, transferable to other locations and aimed for use in crop water requirement computations and modeling, mainly for irrigation planning and scheduling, and for supporting improved water use and saving in orchards and vineyards.

SAFER-ET based assessment of irrigation patterns and impacts on groundwater use in the central Punjab, Pakistan

The excessive pumping of groundwater during the dry season in Pakistan is identified as a significant concern for groundwater sustainability. Farmers encounter difficulties in efficiently managing irrigation, and adopting the outputs of Irrigation Advisory System (IAS) to improve irrigation practices. Upgrading the existing IAS can help to identify the regions with over-irrigation issues and optimize outreach efforts. To solve these issues, we define the new concept of integrating Gravity Recovery and Climate Experiment (GRACE) total water storage anomalies (TWSA) and Landsat 8 imageries, which can enhance the IAS efficiency by accurately identifying regions facing groundwater depletion by estimating actual crop water consumption through application of the Simple Algorithm For Evapotranspiration Retrieving (SAFER) on Landsat 8 data and Penman-Monteith (PM) method was used to calculate crop water demand using meteorological forcing data from Global Land Data Assimilation System (GLDAS) over the five selected districts of Punjab during the dry seasons of year 2013–2020. Comparing actual water consumption (SAFER evapotranspiration (ET)) with in-situ groundwater depth, strong correlations were found between them that are supporting the use of Landsat 8 data for irrigation monitoring. Further, compared SAFER ET was with PM ET to calculate the percentage of over/under irrigated regions, and the results revealed that most of the selected districts were over-irrigated, indicating the potential for excessive irrigation water savings. Our results show that the integration of GRACE TWSA and Landsat 8 data enhance the efficiency of operational IAS and can lead to potential savings of 81% (equivalent to 155 million m3) of groundwater during the dry season in the Central Punjab, Pakistan. Satellite data integration globally enhances IAS implementation, aiding regions with unsustainable dry season irrigation. This empowers farmers to optimize agriculture through precise IAS outputs.

Global Sensitivity of Penman–Monteith Reference Evapotranspiration to Climatic Variables in Mato Grosso, Brazil

Understanding how climatic variables impact the reference evapotranspiration (ETo) is essential for water resource management, especially considering potential fluctuations due to climate change. Therefore, we used the Sobol’ method to analyze the spatiotemporal variations of Penman–Monteith ETo sensitivity to the climatic variables: downward solar radiation, relative humidity, maximum and minimum air temperature, and wind speed. The Sobol’ indices variances were estimated by Monte Carlo integration, with sample limits set to the 2.5th and 97.5th percentiles of the daily data of 33 automatic weather stations located in the state of Mato Grosso, Brazil. The results of the Sobol’ analysis indicate considerable spatiotemporal variations in the sensitivity of ETo to climatic variables and their interactions. The dominant climatic variable responsible for ETo fluctuations in Mato Grosso is incident solar radiation (53% to 93% of annual total sensitivity—Stot), which has a more significant impact in humid environments (70% to 90% of Stot), as observed in the areas of the Amazon biome in the state. Air relative humidity and wind speed have higher sensitivity indices during the dry season in the Cerrado biome (savanna) areas in Mato Grosso (20% and 30% of the Stot, respectively). Our findings show that changes in solar radiation, relative humidity, and wind speed are the main driving forces that impact the reference evapotranspiration.

Determination of the Water use Efficiency of a Pecan Nut Orchard based on the Irrigation Scheduling and the Evapotranspiration Rate in Northern Mexico

Background: Adult pecan trees require a significant water supply for high yields and nut quality. Pecan nut orchards are established in semi-arid regions where water is the primary limiting resource for agriculture. Therefore, in these regions, improving water use efficiency is essential. Objective: Evaluate the water use efficiency of a pecan tree orchard based on the comparison of irrigation scheduling with the evapotranspiration rate data. Methods: The study was conducted in a pecan nut (Carya illinoinensis) orchard of 7.2 ha with 7-year-old Western and Wichita cv tress. The volume of water applied to each tree (drip irrigation) was converted to a daily irrigation depth and compared against the daily rate of actual (measured with an Eddy covariance system) and the FAO Penman-Monteith evapotranspiration. Results: Comparing the monthly water depth applied to each tree against the monthly FAO-Penman Monteith ET, surplus or deficit irrigation episodes were detected. Because the canopy trees only covered 18.7% of the orchard area, the daily rate of actual evapotranspiration during the months of the trees growing was very small (1 to 2 mm) compared with the orchards of mature pecan trees. The corresponding monthly crop coefficients (Kc) were also small (0.315 on average). Conclusion: This study showed that by comparing the rate of ETref against the irrigation depth applied to the trees in the irrigation scheduling, it is possible to reveal cases of surplus or deficit of water supplied to a pecan nut orchard.

Alternative net longwave radiation equation for the FAO Penman–Monteith evapotranspiration equation and the Penman evaporation equation

Alternative net longwave radiation equation for the FAO Penman–Monteith evapotranspiration equation and the Penman evaporation equation

ANN-based PCA to predict evapotranspiration: a case study in India

Abstract The Penman–Monteith evapotranspiration (ET) model has superior predictive ability to other methods, but it is challenging to apply in several Indian stations, owing to the need for a large number of climatic variables. The study investigated an artificial neural network (ANN) model for calculating ET for various agro-climatic regions of India. Sensitivity analysis showed that the overall average changes in ET0 values for 25% change in the climatic variables were 18, 16, 14, 7, 5, and 4%, respectively, for Tmax, RHmean, Rn, wind speed, Tmin, and sunshine hours. The dominant climatic variables were identified from the principal component analysis (PCA) and ET0 was computed using an ANN with dominant climatic variables. The ANN architecture with backpropagation technique had one hidden layer and neurons ranging from 10 to 30 for all climatic variables and from 5 to 10 for PCA variables. The new ET models were statistically compared with Penman–Monteith ET estimate, and found reliable. PCA variables guaranteed an estimate of ET0 accounting for 98% of the variability. The average values of coefficient of determination, standard error of estimate, and percentage efficiency were observed as 0.96, 0.24, and 94%, respectively.

Evaluation of the evapotranspiration rate of lacustrine wetland macrophytes in Lake Tana, Ethiopia

This study quantified the total evapotranspiration (ET) flux of three major types of macrophytes that cover lacustrine fringe wetlands in Lake Tana, Ethiopia during the dry season, April and May, 2022. This study was conducted to understand lacustrine wetland macrophytes' ET rates and provide an important guideline for designing their conservation and restoration plans. To assess ET rates during the peak dry season, three macrophytes of lacustrine wetlands in Lake Tana were chosen: Eichhornia crassipes (water hyacinth), Cyperus papyrus, and Echinochloa stagnina. Three replicate lysimeters for each of the three treatment types (macrophytes) and one control type (open water) were applied to measure ET. To calculate reference evapotranspiration (ETo), FAO's ETo Calculator Version 3.2 was applied. All statistical analyses were carried out using R software. The results of ET rates from wetland macrophytes (9.05mm/day) were considered to be significantly higher than open water evaporation (5.90mm/day). There was a significant difference in mean ET among the macrophytes [F (3, 488) = 194.6, p < 2e-16 ***]. In this study, crop coefficient (Kc1) (1.99 ± 0.65) referenced to FAO Penman–Monteith ETo is greater than Kc2 (1.58 ± 0.59) referenced to open water evaporation (Eow). In terms of wetland macrophyte type, E. crassipes had the maximum mean Kc values (Kc1 = 2.51±0.70 and Kc2 = 2.02±0.67). E. stagnina had the minimum mean Kc values (Kc1 = 1.52±0.32 and Kc2 = 1.19±0.24). Except for open water which strongly correlated with sunshine hours (r = 0.61) and solar radiation (r = 0.60), the correlation among daily ET values and meteorological variables was found to be weak. The result of our research indicated that the presence of floating macrophytes of E. crassipes will increase the ET flux during the dry season more than emergent macrophytes and open water surfaces. Our findings could provide an important guideline for designing conservation and restoration plans for C. papyrus and E. stagnina to reverse the spread of E. crassipes and maintaining lacustrine fringe wetlands for fish breeding ground and sediment control.

Cropping pattern classification using artificial neural networks and evapotranspiration estimation in the Eastern Mediterranean region of Turkey

Determination of cropping pattern is a very important factor in quantifying irrigation water requirements at a catchment scale. In this regard, remote sensing is a robust tool for generating spatial-temporal variation of crops. This study focuses on crop classification by using remotely sensed data coupled with ground truth data. Therefore, this study aimed at both classifying each crop type and calculating crop evapotranspiration (ETc) based on reference evapotranspiration (ETo) by using the Penman-Monteith evapotranspiration model and crop coefficient (Kc). ETo was estimated by using data from two meteorological stations located in the study area. To this end, this study was conducted in Akarsu Irrigation District (≈95 km2), a sub-catchment in the Lower Seyhan Plain (LSP), in the 2021 hydrological year. Ground truth data were collected in the two growing seasons. The ENVI program was used to classify crop types from Sentinel 2A-2B satellite images with 10-m by 10-m spatial resolution. Image analysis results demonstrated that bare soil and citrus made up more than half of the area in the winter season, while corn and citrus were preponderant in summer. In addition, the total reference evapotranspiration and crop evapotranspiration were about 1308 mm and 890 mm, respectively in the 2021 water year. ETc values for second crop soybean, first crop corn, wheat, and citrus showed agreement with previous studies of direct methods of evapotranspiration in the Cukurova region. Furthermore, research findings led us to conclude that using remotely sensed satellite data in cropping pattern determination is promising in identifying the crops grown in large agricultural lands. Moreover, remote sensing images can be used to classify accurately crops in the winter and summer seasons, and this study has expanded the application value of remotely sensed data in large-scale irrigation schemes.

Analysis of Spatio-Temporal Evolution Characteristics of Drought and Its Driving Factors in Yangtze River Basin Based on SPEI

Using a dataset of 114 meteorological stations in the Yangtze River Basin from 1980–2019, the standardized precipitation evapotranspiration index (SPEI) was calculated based on the Penman-Monteith evapotranspiration model for multiple time scales, and the spatial and temporal evolution characteristics and driving factors of drought in the Yangtze River Basin were analyzed by combining spatial and temporal analysis methods as well as geodetector. The main results obtained are as follows: (1) The climate of the Yangtze River Basin is an overall wet trend, and the trend of summer drought is more similar to the annual scale trend. (2) Most areas in the Yangtze River Basin showed mild drought or no drought, and there is little difference in drought condition among the Yangtze River Basin regions. The areas with drought conditions are mainly distributed in the southwest and east of the Yangtze River Basin. (3) There are significant seasonal differences in drought conditions in all regions, and the drought condition is more different in autumn compared to spring, summer and winter. (4) The average annual precipitation and elevation factors are the dominant driving factors of drought in the Yangtze River Basin, and the double-factor interaction has a greater influence on the drought variation in the Yangtze River Basin than the single-factor effect, indicating that the difference of drought condition in the Yangtze River Basin is the result of the combination of multiple factors.

A novel multi-scale parameter estimation approach to the Hargreaves-Samani equation for estimation of Penman-Monteith reference evapotranspiration

The main focus of this study is to develop a multi-scale surrogate model for the FAO-56 Penman-Monteith (PM) evapotranspiration (ETo) using Hargreaves-Samani (HS) equation, which uses only temperature as a hydrometeorological variable to estimate ET. This feature is particularly useful for scarce data regions and climate change impact assessment studies, where the direct estimation of ETo from the PM equation can be problematic. As the parameters of the HS equation may vary across space, a Bayesian approach was adopted to estimate (or recalibrate) them rather than relying on the fixed values as suggested in the traditional model. The Bayesian approach allows a sound development of our model in a multi-scale temporal framework, where the ETo at daily, monthly and annual scales are jointly used to estimate the HS equation parameters. The proposed and reference models are applied and tested using meteorological data from 17 stations located across the Han river basin in South Korea. The results indicate that the traditional HS equation with fixed parameters and without recalibration tends to overestimate the reference ET for all stations. The locally recalibrated approach to the HS equation at a daily temporal scale can effectively reduce the systematic bias associated with the use of the traditional HS equation but fails to reproduce the underlying distribution of ETo at different temporal scales (e.g., monthly and annual). This leads to a large systematic bias in ETo at these scales. In contrast, the proposed multi-scale surrogate model offers a more precise estimation of the reference ET at a daily timescale as well as at the aggregated monthly and annual temporal scales. This is particularly useful to minimize the systematic bias often observed when using traditional surrogate models for the reference ET in hydrological studies such as rainfall-runoff modeling and assessment of climate change impact on water resources.

Analysis of the effect of seasonal changes on sensitive parameters of LAI-based Penman–Monteith evapotranspiration model based on particle swarm algorithm

Analysis of the effect of seasonal changes on sensitive parameters of LAI-based Penman–Monteith evapotranspiration model based on particle swarm algorithm

Evaluation of Reference Evapotranspiration Estimation Methods for the Assessment of Hydrological Impacts of Photovoltaic Power Plants in Mediterranean Climates

Large-scale photovoltaic (PV) power plants may affect the hydrological cycle in all its components. Among the various components, evapotranspiration is one of the most important. As a preliminary step for assessing the impacts of PV plants on evapotranspiration, in this study, we performed an evaluation study of methods for estimating reference evapotranspiration (ETo). FAO and ASCE recommend the Penman–Monteith (PM) method for the estimation of ETo when the data for all involved variables are available. However, this is often not the case, and different empirical methods to estimate ETo, requiring mainly temperature data, need to be used. This study aimed at assessing the performance of different temperature- and radiation-based empirical ETo estimation methods against the standardized PM ETo method in an experimental photovoltaic power plant in Piazza Armerina, Sicily, Italy, where a meteorological station and a set of sensors for soil moisture were installed. The meteorological data were obtained from the Lab from July 2019 to end of January 2022. By taking the ETo estimations from the PM method as a benchmark, the study assessed the performance of various empirical methods. In particular, the following methods were considered: Hargreaves and Samani (HS), Baier and Robertson (BR), Priestley and Taylor (PT), Makkink (MKK), Turc (TUR), Thornthwaite (THN), Blaney and Criddle (BG), Ritchie (RT), and Jensen and Haise (JH) methods, using several performance metrics. The result showed that the PT is the best method, with a Nash–Sutcliffe efficiency (NSE) of 0.91. The second method in order of performance is HS, which, however, performs significantly worse than PT (NSE = 0.51); nevertheless, this is the best among methods using only temperature data. BG, TUR, and THN underestimate ETo, while MKK, BG, RT, and JH showed overestimation of ETo against the PM ETo estimation method. The PT and HS methods are thus the most reliable in the studied site.

Deriving potential evapotranspiration from satellite-based reference evapotranspiration, Upper Tekeze Basin, Northern Ethiopia

Study regionZamra catchment, Upper Tekeze Basin, Northern Ethiopia. Study focusDirect field estimates of potential evapotranspiration (PET) remain a challenge, because they are expensive and have limited spatial representativeness. An alternative is to derive PET by remote sensing as a product of reference evapotranspiration (ETo) and land use-land cover (LULC) factor (Kc). The aims of this study were to: i) validate satellite-derived Daily Reference Evapotranspiration (DMETREF-ETos); ii) correct the advection-bias of DMETREF-ETos as compared to in-situ FAO Penman-Monteith ETo (FAO-ETog); and iii) convert the bias-corrected DMETREF-ETos into PET. Four ATMOS 41 weather stations were installed and 1-year daily data was used to validate the DMETREF-ETos. The wet season actual evapotranspiration from FAO Water Productivity Open-access portal (WaPOR) was used to validate PET. New hydrological insights for the regionEvaluation of DMETREF-ETos showed large underestimation biases when compared to FAO-ETog derived from in-situ weather stations, particularly in warm conditions, due to unaccounted advection-bias. The bias correction was performed as a function of near-surface air temperature obtained from ERA5-Land, merged with in-situ air temperature; the results showed substantial improvement of ETo. Finally, the daily PET was estimated by multiplying the corrected DMETREF-ETos by a spatio-temporally variable Kc, defined as linearly dependent on the NDVI obtained from Sentinel 2. This study emphasized substantial difference between ETo and PET, implying that the ETo cannot substitute PET, which in many hydrological studies is still a common practice.

Modern Techniques to Modeling Reference Evapotranspiration in a Semiarid Area Based on ANN and GEP Models

Evapotranspiration (ET) is a significant aspect of the hydrologic cycle, notably in irrigated agriculture. Direct approaches for estimating reference evapotranspiration (ET0) are either difficult or need a large number of inputs that are not always available from meteorological stations. Over a 6-year period (2006–2011), this study compares Feed Forward Neural Network (FFNN), Radial Basis Function Neural Network (RBFNN), and Gene Expression Programming (GEP) machine learning approaches for estimating daily ET0 in a meteorological station in the Lower Cheliff Plain, northwest Algeria. ET0 was estimated using the FAO-56 Penman–Monteith (FAO56PM) equation and observed meteorological data. The estimated ET0 using FAO56PM was then used as the target output for the machine learning models, while the observed meteorological data were used as the model inputs. Based on the coefficient of determination (R2), root mean square error (RMSE), and Nash–Sutcliffe efficiency (EF), the RBFNN and GEP models showed promising performance. However, the FFNN model performed the best during training (R2 = 0.9903, RMSE = 0.2332, and EF = 0.9902) and testing (R2 = 0.9921, RMSE = 0.2342, and EF = 0.9902) phases in forecasting the Penman–Monteith evapotranspiration.

Foam-based floatovoltaics: A potential solution to disappearing terminal natural lakes

Terminal lakes are disappearing worldwide because of direct and indirect human activities. Floating photovoltaics (FPV) are a synergistic system with increased energy output because of water cooling, while the FPV reduces water evaporation. This study explores how low-cost foam-based floatovoltaic systems can mitigate the disappearance of natural lakes. A case study is performed on 10%–50% FPV coverage of terminal and disappearing Walker Lake. Water conservation is investigated with a modified Penman-Monteith evapotranspiration method and energy generation is calculated with an operating temperature model experimentally determined from foam-based FPV. Results show FPV saves 52,000,000 m3/year of water and US$6,000,000 at 50% FPV coverage. The FPV generates 20 TWh/year of renewable energy, which is enough to offset all coal-fired power plants in Nevada thus reducing carbon-emission based climate forcing partially responsible for a greater rate of disappearance of the lake. The results of this study, which is the first of its kind, indicate foam-based FPV has potential to play a crucial role in mitigation efforts to prevent the disappearing of natural lakes worldwide.

New cognition on the response of reference evapotranspiration to climate change in China using an independent climatic driver system

Reference evapotranspiration (ETo) is a key parameter in hydrometeorological studies, but dependent climatic drivers can induce uncertainties when attributing changes of ETo to climate change, and many studies have neglected this issue. In this study, two sets of climatic drivers (the independent/dependent driver system) were used to attribute changes of Penman–Monteith ETo to climatic variables in China. Results show ETo presented an upward–downward–upward trend with the change points in 1978 and 1996. Using the dependent driver system, the sensitivity coefficient and the contribution of average air temperature (Tmean) were underestimated by at least 57% and 46% during three periods (1960–1978, 1979–1996 and 1997–2019). The largest relative changes of contribution of Tmean (C_Tmean) were found in southeast of China with high temperatures, while largest relative changes of C_Tmean peaked in autumn. Therefore, in order to promote the acquisition of relatively objective results in attributing changes of ETo, the independence of climatic drivers must be carefully addressed. Using the independent driver system, we found ETo was most sensitive to Tmean in spring, summer and winter. Tmean was always a dominant factor for the largest percentage (at least 41%) of grids across China, followed by WS (at most 30%) during three periods. However, the large changes of WS in northwest of China promote the changes of ETo during 1960–1978 and 1979–1996, leading to increased WS, decreased WS and increased Tmean responsible for the upward–downward–upward trend of ETo for entire China as a whole during three periods. This study emphasizes the effect of Tmean in changing ETo during the entire study area, and help improve our understanding of the evolution of ETo and providing a guideline for water resource management and water use planning for agriculture.

Integrating Daily CO2 Concentrations in SWAT-VSA to Examine Climate Change Impacts on Hydrology in a Karst Watershed

HighlightsWe used SWAT-VSA to assess the effects of climate change with rising CO2 on the water balance of a karst basin.For future climate, SWAT-VSA with rising CO2 yielded 7.1% less ET and 6.3% more runoff than standard SWAT-VSA.Rising CO2 also affected variable source areas, with greater ET declines and runoff increases in the wettest soils.Findings suggest CO2 effects on water balance should be included in future climate change studies with SWAT-VSA.Abstract. Characterizing the effects of climate change on hydrology is important to watershed management. In this study, we used SWAT-VSA to examine the effects of climate change and increasing atmospheric CO2 (CO2) on the water balance of Spring Creek watershed, a mixed land-use karst basin in the Upper Chesapeake Bay watershed. First, we modified the stomatal conductance and leaf area index (LAI) routines of SWAT-VSA’s Penman-Monteith evapotranspiration (ET) procedure and enabled the model to accept daily CO2 data. Using downscaled climate projections from nine global climate models (GCMs), we then compared water balance estimations from baseline SWAT-VSA against two modified versions of SWAT-VSA. One SWAT-VSA version integrated daily CO2 levels (SWAT-VSA_CO2), while another version added flexible stomatal conductance and LAI routines (SWAT-VSA_CO2+Plant) to the dynamic CO2 capacity. Under current climate (1985-2015), the three SWAT-VSA models produced generally similar water balance estimations, with 51% of precipitation lost to ET and the remainder converted to runoff (10%), lateral flow (9%), and percolate (30%). For future climate (2020-2065), water balance simulations diverged between baseline SWAT-VSA and the two modified SWAT-VSA models with CO2. Notably, variable stomatal conductance and LAI routines produced no detectable effects beyond that of CO2. For the 2020-2065 period, baseline SWAT-VSA projected ET increases of 0.7 mm year-1, while SWAT-VSA models with CO2 suggested that annual ET could decline by approximately -0.4 mm year-1 over the same period. As a result, the two CO2-based SWAT-VSA models predicted streamflow increases of almost 1.6 mm year-1 over the 2020-2065 period, which were roughly double the streamflow increases projected by baseline SWAT-VSA. In general, SWAT-VSA models with CO2 effects produced 22.4% more streamflow in 2045-2065 than the SWAT-VSA model without CO2. Results also showed that adding daily CO2 to SWAT-VSA reduced ET in wetter parts of Spring Creek watershed, leading to greater runoff losses from variable source areas compared to baseline SWAT-VSA. Findings from the study highlight the importance of considering increasing atmospheric CO2 concentrations in water balance simulations with SWAT-VSA in order to gain a fuller appreciation of the hydrologic uncertainties with climate change. Keywords: Carbon dioxide, Climate change, Hydrologic model, Water balance, Watershed.

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.