Availability Of Climate Data Research Articles

Applicability of CHIRPS-based Pitman model for simulation of climate change flows

Hydrological modelling and continuous monitoring are important for the management of water resources, especially with intensified climate variability. Effective water management is reliant on the availability of climate data and simulations of future projections of the available water resources. The increased likelihood of climatic extremes has made effective water management challenging, especially for vulnerable data-scarce catchments that are highly reliant on natural water resources and have low adaptive capacities. However, advancements in remote sensing have availed alternative sources of climate data for hydrological studies of such catchments. This research evaluated the applicability of Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) in simulating climate change flows. Climate change data of 11 Global Circulatory Models (GCMs), enforced by Representative Concentration Pathways (RCP) 4.5 & 8.5 were downloaded and downscaled to represent near (2021–2060) and far (2061–2099) future scenarios. Climate change signals were derived by comparing future scenarios with the present-day period (1981–2019). Climate change signals were then used to adjust input climate data for the Pitman model. The Pitman model was calibrated and projected future flows were simulated for study catchments in drainage regions B, V and G of South Africa. Climate change simulation results demonstrated projected decreases of between 1.49% and 29.96% for the near future scenarios & between 0.45% and 40.31% for the far future scenarios in all the study catchments. Simulated flows for drainage region B recorded contrasting increasing projections of between 17.18% and 24.88% (RCP 4.5) for the near future scenarios while drainage region G also illustrated contrasting increasing projections of between 6.23% and 7.67% (RCP 4.5) for the far future scenarios. Generally, the results of simulated climate change flows based on observed rainfall and CHIRPS data demonstrated decreasing future flows for the South African case study catchments, corroborating with previous studies in projecting a drier future. This research hence recommends the application of CHIRPS estimates as a suitable substitute to dwindling observed rainfall data in the simulation of climate change flows for data-scarce catchments using the Pitman model.

A longitudinal perspective of climate adaptation from the UK water sector, 2013–2023

Yorkshire Water provides clean water and waste water treatment for over 5 million customers in the north of England, UK. Weather and climate determine water supply, and extreme weather, particularly flooding, can severely alter their operations and ability to process waste water. This paper provides a unique longitudinal (2013–2023) perspective of how an infrastructure owner and operator has responded to changing policy contexts and embedded climate adaptation within operational processes. The uptake in adaptation measures was driven by a combination of factors, including the Adaptation Reporting Power mandated by the Climate Change Act 2008 ; increased availability of climate data; the need to recover from extreme weather events, particularly flooding; and changes to water-management policies. The latter have instigated greater partnership working to reduce risk associated with flood events and placed more emphasis on managing water through landscape-led natural processes such as natural flood management, with actions delivered through partnership working. This paper describes Yorkshire Water’s leadership in the early days of adaptation within the UK, and discusses the changing policy frameworks, business needs, climate knowledge and societal context that have led to more holistic and sustainable water resource management.

Machine Learning and Conventional Methods for Reference Evapotranspiration Estimation Using Limited-Climatic-Data Scenarios

Reference evapotranspiration (ET0) is one important agrometeorological parameter for hydrological studies and climate risk zoning. ET0 calculation by the FAO Penman–Monteith method requires several input data. However, the availability of climate data has been a problem in many places around the world, so the study of scenarios with different combinations of climate data has become essential. The aim of this study was to evaluate the performance of artificial neural network (ANN), random forest (RF), support vector machine (SVM), and multiple linear regression (MLR) approaches to estimate monthly mean ET0 with different input data combinations and scenarios. Three scenarios were evaluated: at the state level, where all climatological stations were used (Scenario I–SI), and at the regional level, where the Minas Gerais state was divided according to the climatic classifications of Thornthwaite (Scenario II–SII) and Köppen (Scenario III–SIII). ANN and RF performed better in ET0 estimation among the models evaluated in the SI, SII, and SIII scenarios with the following data combinations: (i) latitude, longitude, altitude, month, mean, maximum and minimum temperature, and relative humidity and (ii) latitude, longitude, altitude, month, mean temperature, and relative humidity. SVM and MLR models are recommended for all scenarios in situations with limited climatic data where only air temperature and relative humidity data are available. The results and information presented in this study are important for the agricultural chain and water resources in Minas Gerais state.

Evaluation of different methods to estimate monthly reference evapotranspiration at Raichur region, India

The potential evapotranspiration (PET) is an important parameter to address water needs for growing crops. Estimation of potential evapotranspiration (PET) requires knowledge of the values of many climatic variables, some of which require special equipment and careful observations. Although potential evapotranspiration is an important component of water balance, the data required for its accurate estimation are commonly available only at widely spaced measurement stations. Numerous potential evapotranspiration models have been developed and are being used, depending upon the availability of weather data. For the proposed study, the daily data of all weather parameters viz., maximum and minimum air temperature, maximum and minimum relative humidity, wind speed, actual sunshine hours and rainfall for estimating potential evapotranspiration for the duration of 31 years (1991-2021) were collected from the weather station at Main Agricultural Research Station (MARS), Raichur. A number of empirical formulae or approaches have been used for the determination of potential evapotranspiration (PET) from meteorological data. Availability of climatic data and accurately converting them in terms of water requirement are of great constraints. Judging the accuracy of different PET estimation models is a difficult task. The FAO 56-Penman Monteith model was considered as a standard reference for PET estimation in this investigation over the study location. The objective was to compare various PET models with standard reference model (FAO 56 Penman-Monteith model) and choose the best suitable model for estimating PET for the study area.

How Climate Extremes Influence Conceptual Rainfall-Runoff Model Performance and Uncertainty

Rainfall-runoff models are frequently used for assessing climate risks by predicting changes in streamflow and other hydrological processes due to anticipated anthropogenic climate change, climate variability, and land management. Historical observations are commonly used to calibrate empirically the performance of conceptual hydrological mechanisms. As a result, calibration procedures are limited when extrapolated to novel climate conditions under future scenarios. In this paper, rainfall-runoff model performance and the simulated catchment hydrological processes were explored using the JAMS/J2000 model for the Berg River catchment in South Africa to evaluate the model in the tails of the current distribution of climatic conditions. An evolutionary multi-objective search algorithm was used to develop sets of parameters which best simulate “wet” and “dry” periods, providing the upper and lower bounds for a temporal uncertainty analysis approach to identify variables which are affected by these climate extremes. Variables most affected included soil-water storage and timing of interflow and groundwater flow, emerging as the overall dampening of the simulated hydrograph. Previous modeling showed that the JAMS/J2000 model provided a “good” simulation for periods where the yearly long-term mean precipitation shortfall was <28%. Above this threshold, and where autumn precipitation was reduced by 50%, this paper shows that the use of a set of “dry” parameters is recommended to improve model performance. These “dry” parameters better account for the change in streamflow timing of concentration and reduced peak flows, which occur in drier winter years, improving the Nash-Sutcliffe Efficiency (NSE) from 0.26 to 0.60 for the validation period 2015–2018, although the availability of climate data was still a potential factor. As the model performance was “good” (NSE > 0.7) during “wet” periods using parameters from a long-term calibration, “wet” parameters were not recommended for the Berg River catchment, but could play a large role in tropical climates. The results of this study are likely transferrable to other conceptual rainfall/runoff models, but may differ for various climates. As greater climate variability drives hydrological changes around the world, future empirically-based hydrological projections need to evaluate assumptions regarding storage and the simulated hydrological processes, to enhanced climate risk management.

ENACTS: Advancing Climate Services Across Africa

Despite recent and mostly global efforts to promote climate services in developing countries, Africa still faces significant limitations in its institutional infrastructure and capacity to develop, access, and use decision-relevant climate data and information products at multiple levels of governance. The Enhancing National Climate Services (ENACTS) initiative, led by Columbia University's International Research Institute for Climate and Society (IRI), strives to overcome these challenges by co-developing tailored, actionable, and decision-relevant climate information with and for a wide variety of users at the local, regional, and national levels. This is accomplished through an approach emphasizing direct engagement with the National Meteorological and Hydrological Services (NMHS) and users of their products, and investments in both technological and human capacities for improving the availability, access, and use of quality climate data and information products at decision-relevant spatial and temporal scales. In doing so, the ENACTS approach has been shown to be an effective means of transforming decision-making surrounding vulnerabilities and risks at multiple scales, through implementation in over a dozen countries at national level as well as at the regional levels in both East and West Africa. Through the ENACTS approach, challenges to availability of climate data are alleviated by combining quality-controlled station observations with global proxies to generate spatially and temporally complete climate datasets. Access to climate information is enhanced by developing an online mapping service that provides a user-friendly interface for analyzing and visualizing climate information products. Use of the generated climate data and the derived information products is promoted through raising awareness in relevant communities, training users, and co-production processes.

Transforming Access to and Use of Climate Information Products Derived from Remote Sensing and In Situ Observations

Making climate-sensitive economic sectors resilient to climate trends and shocks, through adaptation to climate change and managing uncertainties associated with climate extremes, will require effective use of climate information to help practitioners make climate-informed decisions. The provision of weather and climate information will depend on the availability of climate data and its presentation in formats that are useful for decision making at different levels. However, in many places around the world, including most African countries, the collection of climate data has been seriously inadequate, and even when available, poorly accessible. On the other hand, the availability of climate data by itself may not lead to the uptake and use of such data. These data must be presented in user-friendly formats addressing specific climate information needs in order to be used for decision-making by governments, as well as the public and private sectors. The generated information should also be easily accessible. The Enhancing National Climate Services (ENACTS) initiative, led by Columbia University’s International Research Institute for Climate and Society (IRI), has been making efforts to overcome these challenges by supporting countries to improve the available climate data, as well as access to and use of climate information products at relevant spatial and temporal scales. Challenges to the availability of climate data are alleviated by combining data from the national weather observation network with remote sensing and other global proxies to generate spatially and temporally complete climate datasets. Access to climate information products is enhanced by developing an online mapping service that provides a user-friendly interface for analyzing and visualizing climate information products such as maps and graphs.

Completamiento de series temporales de niveles freáticos en un área deprimida de la llanura bonaerense

Se presenta como objetivo el completamiento de registros temporales de niveles freáticos someros en un sector deprimido perteneciente a la cuenca del Arroyo del Azul (provincia de Buenos Aires), en el marco de una modelación matemática de la hidrología subterránea. Los registros completados conforman la condición de contorno aguas arriba del dominio de modelación. Se contemplaron diferentes series temporales para correlacionarlas con los niveles freáticos: a) anomalías acumuladas de precipitación, b) anomalías gravimétricas (misión satelital GRACE) y c) anomalías acumuladas hidrológicas (diferencia entre la precipitación y la evapotranspiración potencial). De los seis pozos freatimétricos seleccionados para el análisis y para el período donde se presentan las series en forma completa (oct 2014- abr 2016) se deduce que las anomalías de precipitación (a) presentan los coeficientes de correlación r más bajos y fue descartada como serie análoga. Las anomalías gravimétricas mensuales (b), Gravity Recovery and Climate Experiment (GRACE) misión conjunta de la NASA y el Centro Aeroespacial Alemán (DLR) presentaron correlaciones buenas, con valores de r entre 0.624 y 0.798, aunque la serie suministrada se discontinúa desde 2017. Las anomalías hidrológicas acumuladas, que fueron calculadas a escala diaria y retrasadas respecto los niveles un lagsemanal para mejorar las regresiones, presentaron correlaciones muy buenas, con r entre 0.861 y 0.894, fueron las seleccionadas como serie análoga de completamiento. Considerando la mejor disponibilidad de datos climáticos frente a los satelitales y en virtud de los buenos resultados alcanzados se recomienda el uso de esta metodología en sectores de niveles freáticos someros donde el proceso de evaporación puede considerarse una componente hidrológica de importancia.

Performance of simple temperature-based evaporation methods compared with a time series of pan evaporation measures from a standard 20 m2 tank

AbstractEvaporation and evapotranspiration is crucial part of hydrological and water resource management studies e.g. water footprinting. Proper methods for estimating evaporation/potential evapotranspiration using limited climatic data are critical if the availability of climatic data is extremely limited. In a large scale studies are very often used generalized (modelled or gridded) input data. For a large scale water footprint studies is also important to find methods as simple as possible with quantifiable error. In our study, nine simple temperature-based empirical equations were compared with a long term time series of real evaporation data from a 20 m2 tank at Hlasivo station. In the first step, we used real temperature measured at Hlasivo station for validation of equations. In the second step, the gridded temperature data (interpolated datasets) derived from the meteorological stations were used. For both datasets, the differences between observed and predicted values were categorized into three groups of accuracy and the statistical indices of each equation were calculated. Very good results were achieved with the Hamon equation from 1961 and the Oudin equation for both datasets with index of agreement (d) higher than 0.9, cross-correlation coefficient (R2) around 0.7 and root mean square error (RMSE) around 0.5 mm∙(24 h)−1 The Kharrufa equation, which was developed for semi-arid or arid areas, also provides results with sufficient accuracy. Comparison of the results with similar studies showed a lower accuracy of very simple equations against more complex equations, which have RMSE lower than 0.25 mm∙(24 h)−1. But for some kind of studies, quantifiable errors with sufficient accuracy can be more important than the absolute accuracy.

Climate Change and Drought: a Perspective on Drought Indices

Droughts occur naturally, but climate change has generally accelerated the hydrological processes to make them set in quicker and become more intense, with many consequences, not the least of which is increased wildfire risk. There are different types of drought being studied, such as meteorological, agricultural, hydrological, and socioeconomic droughts; however, a lack of unanimous definition complicates drought study. Drought indices are used as proxies to track and quantify droughts; therefore, accurate formulation of robust drought indices is important to investigate drought characteristics under the warming climate. Because different drought indices show different degrees of sensitivity to the same level of continental warming, robustness of drought indices against change in temperature and other variables should be prioritized. A formulation of drought indices without considering the factors that govern the background state may lead to drought artifacts under a warming climate. Consideration of downscaling techniques, availability of climate data, estimation of potential evapotranspiration (PET), baseline period, non-stationary climate information, and anthropogenic forcing can be additional challenges for a reliable drought assessment under climate change. As one formulation of PET based on temperatures can lead to overestimation of future drying, estimation of PET based on the energy budget framework can be a better approach compared to only temperature-based equations. Although the performance of drought indicators can be improved by incorporating reliable soil moisture estimates, a challenge arises due to limited reliable observed data for verification. Moreover, the uncertainties associated with meteorological forcings in hydrological models can lead to unreliable soil moisture estimates under climate change scenarios.

A Study of Crop Water Needs and Land Suitability in the Monoculture System and Plant Intercropping in Arjasari

Arjasari is an area with dry land characteristics developed for the cultivation of palawija crops. The main problem with dry land is the limited availability of water. Hence, it is necessary to know how much water needs of the crops and the suitability of the crops of palawija crops developed in this region in order to make efficient use of water. The research method used is descriptive analysis method for assessing the availability of climate data to calculate the crop water requirement. The estimation of water needs of this plant uses Blaney Criddle methods with air temperature data measured directly in the field, while the determination of land suitability uses the method of arithmetic matching of land characteristics, to the land suitability class for the requirements of growing corn crops. The analysis showed that the amount of corn water requirement was 418.9 mm / season, soybean was equal to 349.6 mm / season, and sweet potato of 350.55 mm per season. The results of the land location analysis are very suitable for corn crops. The results of land suitability classification analysis for corn plant. The results of planting trials use a water demand scheme with land suitability in a predetermined monoculture system resulted in an average productivity of maize crops in the intercropping system of 3.84 tons.ha-1 and sweet potatoes of 2.1 tons.ha-1. In addition, the average yield of corn productivity in the soybean-corn intercropping system is 4.16 ton / ha and soybean is equal to 0.248 tons.ha-1. Provision of water according to the needs of plants is able to produce high productivity of crops with the more efficient use of water.

Climate regionalization for main production areas of Indonesia: Case study of West Java

Spatially, climate condition is vary within a region and considered as essential information for planning activities such as agro-climate zonation. An approach to understand the spatial climate variability is the utilization of climate regionalization that is applied to rainfall data to distinguish differences in the pattern and magnitude (characteristics) of spatial rainfall variability over a region. Unfortunately, the application of climate regionalization poses a challenging issue in Indonesia, considering the availability of climate data. Recent advances in satellite and reanalysis data measuring climate variability over a large area provided an opportunity for the application of climate regionalization in the country. Using the West Java, one of main crop production regions in Indonesia, climate regionalization techniques were applied to map spatial variability of climate types based on rainfall data recorded by climate stations (point based analysis) and estimated by modeled/reanalysis data and satellite observations (gridded data). The regionalization derived from gridded rainfall data have reasonably better in capturing the zonal pattern of differences in climate types within the study region than the regionalization applied to insufficient numbers of site-based rainfall observation. This indicates that the gridded data offers an alternative for climate regionalization, when site-based observations are unavailable or limited.

Development of Evaporation Estimation Methods for a Reservoir in Gujarat, India

Evaporation is an important parameter for many projects related to hydrology and water resource systems, but measurement of evaporation is difficult and time‐consuming. Although there are numerous empirical formulas and approaches, availability of climatic data limits their application across all locations. An indirect method using meteorological data offers a potential alternative. Focusing on the study area of Dharoi Reservoir in northern Gujarat, India, the authors obtained empirical relations for estimating daily and monthly evaporation as functions of available meteorological data such as air temperature, relative humidity, wind velocity, and hours of sunshine. These data, reflecting a 10‐year period from 2001 to 2010, can be considered representative of the semiarid region of Gujarat. Various tools such as empirical equations, dimensional analysis, and regression analysis were used for assessing evaporation loss from open water surfaces. The method proposed here demonstrated reasonable agreement with pan evaporation data, as evidenced by its coefficient of determination.

Assessment of TRMM Rainfall Data (2001-2015) Over the Indian Region

Introduction: Now-a-days to overcome the difficulty of availability of climatic data, especially on a daily time scale, researchers prefer to use satellite data which has its wide applications in many fields viz. meteorology, hydrology, water resources models, climatic studies etc. However, it is essential to verify these data for more consistent with the observed data. In view of this, our study aimed to assess the TRMM data with that of observed rainfall data over the Indian region. Methods: Observed rainfall data and TRMM data for 2001-2014 monsoon season were used. Analysis of data was carried out by programming and Microsoft Excel functions. Results: The TRMM values are to the higher side in the years 2001, 2002, 2003, 2010, and 2015 whereas there is hardly any difference between the two in the years 2008 to 2014. The observed rainfall is more than TRMM rainfall in the years 2004 to 2007. Conclusions: Observed rainfall and TRMM rainfall values for Jun-Sept period are comparable. There is high consistency between the estimates of these two data sets during 2008 to 2015 than the earlier seven years period.

Hydrological Evaluation of TRMM Rainfall over the Upper Senegal River Basin

The availability of climatic data, especially on a daily time step, has become very rare in West Africa over the last few years due to the high costs of climate data monitoring. This scarcity of climatic data is a huge obstacle to conduct hydrological studies over some watersheds. In this context, our study aimed to evaluate the capacity of Tropical Rainfall Measuring Mission (TRMM) satellite data to simulate the observed runoffs over the Bafing (the main important tributary of the Senegal River) before their potential integration in hydrological studies. The conceptual hydrological model GR4J (modèle du Génie Rural (Agricultural Engineering Model) à 4 paramètres Journalier (4 parameters Daily)) has been used, calibrated and validated over the 1961–1997 period with rainfall and Potential Evapotranspiration (PET) as inputs. Then, the parameters that best reflect the rainfall-runoff relation, obtained during the cross-calibration-validation phase, were used to simulate runoff over the 1998–2004 period using observed and TRMM rainfalls. The findings of this study show that there is a high consistency between satellite-based estimates and ground-based observations of rainfall. Over the 1998–2004 simulation period, the two rainfall data series show quite satisfactorily results. The output hydrographs from satellite-based estimates and ground-based observations of rainfall coincide quite well with the shape of observed hydrographs with Nash-Sutcliffe Efficiency coefficient (NSE) of 0.88 and 0.80 for observed rainfalls and TRMM rainfalls, respectively.

Daily gridded meteorological variables in Brazil (1980–2013)

ABSTRACTBasic meteorological data are essential for evaluating impacts of spatiotemporal variability in climate forcing on hydrology and agroecosystems. The objective of this work was to develop high‐resolution grids (0.25∘ × 0.25∘) of daily precipitation, evapotranspiration, and the five climate variables generally required to estimate evapostranspiration for Brazil. These five variables are maximum and minimum temperature, solar radiation, relative humidity, and wind speed. We tested six different interpolation schemes to create the grids for these variables. The data were obtained from 3625 rain gauge and 735 weather stations for period of 1980–2013. We used a cross‐validation approach that compares point observed data to point interpolated estimates to select the best interpolation scheme for each climate variable. We also present the performance of the best interpolation for each climate variable at daily timescales and for river basins. The inverse distance weighting and angular distance weighting methods produced the best results. Performance of all methods was poorer prior to 1995 because of fewer stations and available data. The performance of the interpolation varies for different seasons for almost all variables. Forecasting capability was tested for precipitation only and performed adequately for the system state (wet or dry). Variations in the interpolation schemes across river basins are primarily attributed to differences in gauge or station network density. This freely available gridded meteorological data set significantly advances the availability of climate data in Brazil.

Atmospheric Data Collection, Processing and Database Management in India Meteorological Department

The availability of a proper meteorological database is a major prerequisite for studying the processes of climate by scientists and policy makers. The acquisition of pertinent meteorological data, timely access and database management are important components that make the climate information valuable. New technologies in the field of communication and great strides in the automation age have offered new ways of making data products available to the user community. Data collection and database management are the key components of the availability of climate data. Due to increased observation frequency, huge amounts of data are received at data centres. Fast and effective quality control for identification and flagging of suspicious observations is needed at the data centres to provide easy access to information and dissemination of quality assured observations to the users. India Meteorological Department (IMD) collects, processes, archives and disseminates a wide range of weather, climate and other environmental data generated by the department’s observational network.With the installation of ‘CliSys’ Climate Database Management System (CDMS) in the National Data Centre located at Pune, the data acquisition and processing operations have been automated. This system provides a set of tools and procedures that allow all data relevant to climate studies to be properly stored and managed. It serves the main objectives of climate data collection, storage, quality control, easy access, data protection, climate data analysis and customized product generation.

Combined use of satellite estimates and rain gauge observations to generate high‐quality historical rainfall time series over Ethiopia

ABSTRACTClimate data are used in a number of applications including climate risk management and adaptation to climate change. However, the availability of climate data, particularly throughout rural Africa, is very limited. Available weather stations are unevenly distributed and mainly located along main roads in cities and towns. This imposes severe limitations to the availability of climate information and services for the rural community where, arguably, these services are needed most. Weather station data also suffer from gaps in the time series. Satellite proxies, particularly satellite rainfall estimate, have been used as alternatives because of their availability even over remote parts of the world. However, satellite rainfall estimates also suffer from a number of critical shortcomings that include heterogeneous time series, short time period of observation, and poor accuracy particularly at higher temporal and spatial resolutions. An attempt is made here to alleviate these problems by combining station measurements with the complete spatial coverage of satellite rainfall estimates. Rain gauge observations are merged with a locally calibrated version of the TAMSAT satellite rainfall estimates to produce over 30‐years (1983‐todate) of rainfall estimates over Ethiopia at a spatial resolution of 10 km and a ten‐daily time scale. This involves quality control of rain gauge data, generating locally calibrated version of the TAMSAT rainfall estimates, and combining these with rain gauge observations from national station network. The infrared‐only satellite rainfall estimates produced using a relatively simple TAMSAT algorithm performed as good as or even better than other satellite rainfall products that use passive microwave inputs and more sophisticated algorithms. There is no substantial difference between the gridded‐gauge and combined gauge‐satellite products over the test area in Ethiopia having a dense station network; however, the combined product exhibits better quality over parts of the country where stations are sparsely distributed.

Comportamento do clima de Palotina/PR de 1973 a 2010

Located in western Parana, the city of Palotina had begun it colonization in the late 50's and the city has undergone many changes where land with dense forest was almost totally occupied by crops. The increased population of approximately 4,000 people in 1960 to 30,000 in 2010 and currently has much of the economy based on agriculture and agribusiness. With the availability of climate data since 1972 the aim of this work was to identify changes and trends in the series of daily data of precipitation, air relative humidity, maximum, minimum and mean temperature, thermal amplitude and insulation in the city from 1973 to 2010. Linear regression and Mann-Kendall seasonal methods are applied. For the whole period can be inferred trend of decrease in relative humidity, increased in maximum and mean temperatures, insolation and thermal amplitude. It is also possible to detect an increase in extreme values of maximum and minimum temperatures and increase dried in the last decade. It is also shown the evolution of deforestation in the period and the possible correlation between the change in use and land cover and climate change on a local scale.

Comparison of simplified methods for estimation of reference evapotranspiration (ETo) for producing brazilian regions of coffee

This work aimed to estimate and compare two simplified methods to estimate the reference evapotranspiration – Eto, Hargreaves-Samani (HS) and FAO-Blaney-Criddle, (BC) to the Penman-Monteith-FAO (PM) method. The results of the estimates were obtained on the basis of climatic data available at each location under study, using Irriplus software. The cities of interest for the study were selected according to the representativeness of the characteristics of the region between the main brazilian producing centers of coffee: Cerrado, East, South and Zona da Mata of Minas Gerais state, west and southwest of Bahia state, and the cities chosen were Araguari, Patrocinio, Caratinga, Lavras, Varginha, Vicosa, in Minas Gerais state, and Vitoria da Conquista and Barreiras, Bahia state. In all locations, HS equation best fit to the equation of PM, this being recommended for irrigation management in conditions of limited availability of climate data.