ABSTRACT

Sugarcane irrigation is one of the main technologies to increase productivity and profitability in the sugar-energy sector. However, to improve the management of water resources in Brazil, growers need to obtain a water rights permitfrom regulatory agencies. The calculation of water rights is determined from data based on estimates of rainfall and reference evapotranspiration (ET_o) provided by the Agência Nacional de Águas e Saneamento Básico (ANA). The primary hypothesis of this study was that the method proposed by ANA to calculate water rights did not provide operational security since it lacked an adequate probabilistic character. As a corollary, we included a secondary hypothesis stating that determining flows through the simplified FAO method proposed in the CROPWAT 8.0 manual displayed vulnerability due to the criteria to choose the representative years. This research aimed to compare such methodologies with a more detailed case study (standard method), taking into account the temporal variability of these estimates, the irrigation criteria, and the probability of occurrence of ET_o and rainfall to determine water rights for sugarcane irrigation. During the period between Apr and Sept (dry period), the calculation method proposed by ANA met the needs of the project. However, when rainfall was concentrated (Oct to Mar), the calculation method tended to underestimate the monthly values of available flows. The simplified method proposed by FAO and the alternative method proposed here approached ideal conditions.

Saccharum spp; reference evapotranspiration; water balance; water use permit

Introduction

Productivity of sugarcane crops depends significantly on the interaction of three main factors: plants, production environment, and management practices. Water deficit is the most severe adverse factor in the production environment. Even in areas with a humid climate, irregular distribution of rainfall in some periods can limit plant growth. Sugarcane crops and mills worldwide are located in regions with a broad climate variability; thus, dealing with these factors is essential to ensure a profitable and sustainable production (Gilbert et al., 2006Gilbert RA, Shine Junior JM, Miller JD, Rice RW, Rainbolt CR. 2006. The effect genotype, environmental and time of harvest on sugarcane yields in Florida, USA. Field Crops Research 95: 156-170. https://doi.org/10.1016/j.fcr.2005.02.006
https://doi.org/10.1016/j.fcr.2005.02.00... ; Souza et al., 2020Souza CAA, Silva TGF, Souza LSB, Moura MSB, Silva PP, Marin FR. 2020. Straw management effects on sugarcane growth, nutrient cycling and water use in the Brazilian semiarid region. Bragantia 79:1-10. https://doi.org/10.1590/1678-4499.20200227
https://doi.org/10.1590/1678-4499.202002... ; Fattori Junior et al., 2022; Amorim et al., 2022 Amorim FR , Patino MTO , Santos DFL . 2022. Soil tillage and sugarcane planting: an assessment of cost and economic viability. Scientia Agricola 79: e20190317. https://doi.org/10.1590/1678-992X-2019-0317
https://doi.org/10.1590/1678-992X-2019-0... ; Althoff and Rodrigues, 2023Althoff D, Rodrigues LN. 2023. Improvement of reference crop evapotranspiration estimates using limited data for Brazilian Cerrado. Scientia Agricola 80: e20210229. https://doi.org/10.1590/1678-992X-2021-0229
https://doi.org/10.1590/1678-992X-2021-0... ).

In Brazil, rainfall contributes substantially to supplying the crop water needs in most traditional sugarcane-producing regions; nevertheless, water must be provided from irrigation to fully meet the crop needs in some specific regions. Thus, rights are required to use available surface and underground water resources. Estimates of monthly abstraction flows for water rights are currently calculated using data based on estimations of probable effective rainfall and reference evapotranspiration (ET_o) provided by the Agência Nacional das Águas e Saneamento Básico (ANA) for specific locations and parameters. However, detailed knowledge of the relationships among the components of the soil-water-plant-atmosphere system is necessary for a more accurate estimate in terms of variations in the soil and the production environment (Rocha and Sparovek, 2021Rocha GC, Sparovek G. 2021. Scientific and technical knowledge of sugarcane cover-management USLE/RUSLE factor. Scientia Agricola 78: e20200234. https://doi.org/10.1590/1678-992X-2020-0234
https://doi.org/10.1590/1678-992X-2020-0... ; Santiago et al., 2022 Santiago DB , Barbosa HA , Correia Filho WLF , Oliveira-Junior JF . 2022. Interactions of environmental variables and water use efficiency in the MATOPIBA region via multivariate analysis. Sustainability 14: 8758. http://doi.org/10.3390/su14148758
http://doi.org/10.3390/su14148758... ).

This information allows to verify the levels of water demand that these environments are subject to and the real irrigation needs for optimal crop productivity (Pereira et al., 2021Pereira RAA, Viana MS, Nassif DSP, Carvalho KS, Marin FR. 2021. Global sensitivity and uncertainty analysis of a sugarcane model considering the trash blanket effect. European Journal of Agronomy 130: 126371. https://doi.org/10.1016/j.eja.2021.126371
https://doi.org/10.1016/j.eja.2021.12637... ). This detailed procedure is used in the methods proposed by FAO (CROPWAT 8.0), working with a set of total climatological data (standard method) or with just a few chosen years (simplified method). However, this latter option is susceptible to problems due to the uncertainty of choosing the representative years (dry, medium, or wet years), limiting the water rights, or raising flows in years of extreme climate events, which cause an increase in demand.

This research aimed to propose and evaluate an alternative method to determine water rights for sugarcane irrigation, which takes considers the temporal variability in climate variables, and to compare the results with the methods practiced by ANA and with the two methods proposed by CROPWAT 8.0.

Materials and Methods

Study site

The study was conducted at the Rio Claro Unit of Odebrecht Agroindustrial, located in the municipality of Caçú, Goiás State, Brazil (18°33’19” S, 51°08’06” W, altitude 486 m). This unit has a crushing capacity of 4.5 million Mg of sugarcane per year, production of 420,000 m³ of ethanol per year, and generation of 480 GWh of bioelectricity with the excess of plant biomass.

In order to estimate the flow at irrigation catchment, an average annual area of 30,000 ha of sugarcane cultivation was considered for this unit. This area, which can be irrigated, was divided into three sections at the beginning of the crop cycle, corresponding to the beginning, middle, and end of the growth period (Marin et al., 2021Marin FR, Edreira JIR, Andrade JF, Grassini P. 2021. Sugarcane yield and yield components affected by harvest time. Sugar Tech 23: 819-826. https://doi.org/10.1007/s12355-020-00945-5
https://doi.org/10.1007/s12355-020-00945... ). Three soil types were considered in each season, with an available water capacity (AWC) of 50, 100, and 150 mm m¹ (Table 1).

For the generation of a 31-year series of climate parameters necessary to calculate daily evapotranspiration by the FAO Penman-Montheith method, the readings from three neighboring Instituto Nacional de Meteorologia (INMET) stations (Table 2) were obtained and averages were calculated. We used values obtained at the Itarumã rainfall station, 38 km away from the site, for daily rainfall. ET_o values were adjusted to a normal distribution and associated to the probability of non-overcoming (P %, probability of occurrence less than or equal to a certain value). Three typical years (wet, medium, and dry) were chosen for the frequency analysis for total rainfall.

Method adopted by ANA

To request a permit for the right to collect water for irrigation from a federal river, it is required to fill out a worksheet at Cadastro Nacional de Usuários de Recursos Hídricos (CNARH). This worksheet is available on ANA website and is used to estimate the monthly demands for water abstraction and use in irrigation. Estimates of water demand consider the needs at different stages of crop development through a simplified local water balance, irrigation efficiency, irrigated area, and irrigation calendar to determine the monthly water abstraction requirements for irrigation. Soil water storage parameters are not used in this balance. The meteorological data and agronomic parameters needed to complete the worksheet are provided by ANA upon request to the technician responsible to grant the request. For these data, ANA uses the FAOCLIM meteorological database, which contains data from 1,503 stations with rainfall and 798 with ET_o (monthly averages) in the Brazilian territory. The New_LocClim software was used to interpolate these data for the specific location of the project.

The method used to estimate ET_o and crop evapotranspiration (ET_c) is proposed by Allen et al. (1998)Allen RG, Pereira LS, Raes D, Smith M. eds. 1998. Crop Evaporation Guidelines for Computing Crop Water Requirements. FAO, Rome, Italy., employing the equation ET_c = ET_o K_c. An adjustment coefficient, kaj, equal to 0.9, is also proposed for deficit irrigation. Precipitation probability (Pp %) is estimated by a historical series that ensures 80 % of reliability. Effective precipitation, which is the amount available to plants, is determined by using an empirical formula, while the probable effective monthly precipitation combines the two previous definitions. The system application efficiency must be compatible with ANA Resolution 707 of 2004, which presents minimum indicators for the rational use of water.

The default CROPWAT 8.0 template (FAO)

The CROPWAT 8.0 model is a computer program that calculates the water needs of a crop and its irrigation needs, using inputs of climate data (meteorological elements), soil parameters, and crop parameters. The program allows to plan and schedule irrigation for different management criteria and approaches. FAO developed the CROPWAT 8.0 software to estimate water demand worldwide in medium to large irrigated perimeters . All calculation procedures in CROPWAT 8.0 are based on FAO recommendations set out in publication No. 56 (Allen et al., 1998Allen RG, Pereira LS, Raes D, Smith M. eds. 1998. Crop Evaporation Guidelines for Computing Crop Water Requirements. FAO, Rome, Italy.) and No. 33 (Doorenbos and Kassam, 1979Doorenbos J, Kassam AH. 1979. Yield Response to Water. FAO, Rome, Italy.).

The main features and functionality of CROPWAT 8.0 include:

• Entry of meteorological data on a monthly, ten-day, or daily time scale to calculate probability of effective precipitation and ET_o.

• Estimates of crop water and irrigation needs on a ten-day or daily time scale based on updated algorithms (models), including the possibility of adjusting the crop coefficient (K_c) value.

• Planning, programming, and creating a calendar for irrigation management according to the criteria and approaches adopted in the simulations.

• Data output in tabular format with the crop water balance on a daily time scale.

The CROPWAT 8.0 model has been widely used as a tool in the planning and management of water in agricultural systems, including public irrigation perimeters financed by the World Bank, through estimates of crop water requirements and irrigation needs, as well as for estimating relative productivity reductions under water deficit conditions. CROPWAT comprises eight input and output modules: climate/ET_o, rain, crop, soil, crop pattern, CWR (10-day irrigation need), schedule and scheme. Data entry in the climate/ET_o module of CROPWAT 8.0 was carried out in two ways to allow year-by-year simulations of the entire 31-year historical series and a service level of 50 % or 80 % in atmospheric demand.

Data entry in the rain module of CROPWAT 8.0 was performed to allow year-by-year simulations of the entire 31-year historical series (standard method). The model has a routine that calculates the precipitation available for the plants (effective precipitation).

Only the ratoon cane cycles over 365 days were considered when entering data in the crop module. The values used worldwide by FAO were proposed by Doorenbos and Kassam (1979)Doorenbos J, Kassam AH. 1979. Yield Response to Water. FAO, Rome, Italy. and Allen et al. (1998)Allen RG, Pereira LS, Raes D, Smith M. eds. 1998. Crop Evaporation Guidelines for Computing Crop Water Requirements. FAO, Rome, Italy., as shown in Table 3. In the simulations carried out year-by-year, three times related to the beginning of the growth cycle were adopted (sprouting and beginning of stumps): 01 Apr, 01 July, and 01 Oct.

When entering data for the soil module, three types of soil were selected, namely: AWC50 (50 mm m¹), AWC100 (100 mm m¹), and AWC150 (150 mm m¹), all allowing an effective root system depth of 1 m. The remaining soil-related input parameters were fixed for all three soil types. The value of 30 mm d¹ was adopted for the maximum rain infiltration rate. The maximum rooting depth was fixed at a standard value of 900 cm and the initial soil moisture depletion, which represents the relative deficit of water storage in the soil at the beginning of the crop growth period, was considered null (0 %), which meant that the soil profile was at field capacity at the beginning of the simulations. Thus, the initial available soil moisture was considered equal to the value of the AWC, for each soil type studied.

In the schedule module, where irrigation management is planned, two types of management were adopted: supplementary irrigation, in which the crop demand is fully met with effective precipitation plus irrigation, and deficit irrigation, which aims to meet the crop demand partially. In the latter case, there is a reduction in ET_c, known as real evapotranspiration (ET_r), calculated through the adoption of a water stress coefficient (K_s). The necessary flows for supplementary (potential) and deficit irrigation are also calculated.

CROPWAT 8.0 model applied in a simplified way

Given the lengthy procedure of the year-to-year water balance, the simplified method proposed by FAO suggests working with only three years (wet, medium, and dry) based on total annual precipitation (TAP) with an attainable probability of 20, 50, and 80 %: (i) TAP-20 % (wet year with theoretical probability achievable by the irrigation system of 20 %); (ii) TAP-50 % (average year with attainable theoretical probability of 50 %); and (iii) TAP-80 % (dry years with attainable theoretical probability of 80 %). In the case of rainfall, instead of entering data for all 31 years, three typical years were chosen as examples of wet, medium, and dry years in terms of total annual rainfall, for which the frequency analysis was carried out.

Proposed alternative method

The fourth method proposed requires that the data entry of the three years into the CROPWAT 8.0 model (wet, medium, and dry) for the frequency analysis include only the accumulated precipitation in the dry period of each year (from Apr to Sept), instead of using the total annual period, as used in the simplified FAO CROPWAT 8.0 method. This procedure identifies the dry periods that reduce crop productivity more accurately.

Results

The ET_o values followed the variation of global solar radiation in the region and the averages ranged from 3.5 to 5.0 mm d¹. Solar radiation is an essential input variable to estimate the water demand of crops in the field, and variation in solar radiation affects the water and energy balance, reflected in differences in ET_o (Pereira et al., 2015Pereira LS, Allen RG, Smith M, Raes D. 2015. Crop evapotranspiration estimation with FAO56: Past and future. Agricultural Water Management 147: 4-20. https://doi.org/10.1016/j.agwat.2014.07.031
https://doi.org/10.1016/j.agwat.2014.07.... ; Paredes and Pereira, 2019Paredes P, Pereira LS. 2019. Computing FAO56 reference grass evapotranspiration PM-ETo from temperature with focus on solar radiation. Agricultural Water Management 215: 86-102. https://doi.org/10.1016/j.agwat.2018.12.014
https://doi.org/10.1016/j.agwat.2018.12.... ), as observed in this research (Figure 1).

It was observed that for 80 % probability of not exceeding (less than or equal to) the limit, the maximum values were around 5.6 mm d¹. Considering an application efficiency of 80 % and an average K_c of 1.0 (average value considering three planting times), and excluding water storage in the soil, an approximate water demand of 0.81 L s¹ ha¹ was obtained, which needs to be supplied by rain and irrigation in Sept and Oct (Figure 2).

The analysis of the average behavior and sample standard deviations of the total effective annual precipitation, for a ten-day period, as well as the monthly average accumulated as a function of the total historical series available, showed that the rainfall contribution for the months of greater evapotranspiration (Sept and Oct) was around 65 mm month¹ or 0.25 L s¹ ha¹ (Figure 3). This means that supplementary irrigation must provide about 0.56 L s¹ ha¹ to reach the approximate water requirement of 0.81 L s¹ ha¹ calculated for that period, or about two-thirds of the water requirement. Thus, although it is estimated for the region that irrigation only contributes to 400 to 500 mm yr¹for a total annual sugarcane demand of 1,800 mm yr¹, for the time of greater water need, the contribution of irrigation is crucial (Gasparotto et al., 2022Gasparotto LG, Rosa JM, Grassini P, Marin FR. 2022. Developing and operating framework to diagnose yield gaps in commercial sugarcane mills. Field Crops Research 278: 108433. https://doi.org/10.1016/j.fcr.2022.108433
https://doi.org/10.1016/j.fcr.2022.10843... ; Gonçalves et al., 2022Gonçalves IZ, Costa LG, Marin FR. 2022. Simulating sugarcane yield response do ETc replacements and green cane trash blanket maintenance in Brazil. Revista Brasileira de Engenharia Agrícola e Ambiental 26: 583-596. https://doi.org/10.1590/1807-1929/agriambi.v26n8p586-593
https://doi.org/10.1590/1807-1929/agriam... ).

The dispersions of flows for water rights, simulated year-by-year for 31 years by the CROPWAT 8.0 model, assuming supplementary irrigation of the total area (30,000 ha), and the flows for water rights are represented in terms of % of service can be seen in Figures 4 and 5, respectively. For a service percentage of 80 % with a return period of five years, the peak occurs in Sept, with a total flow of 18.1 m³ s¹, which represents a specific demanded flow of 0.603 L s¹ ha¹, a value that satisfactorily approaches (Marin et al., 2020Marin FR, Inman-Bamber G, Silva TGF, Viana MS, Nassif DSP, Carvalho KS. 2020. Sugarcane evapotranspiration and irrigation requirements in tropical climates. Theoretical and Applied Climatology 140: 1349-1357. https://doi.org/10.1007/s00704-020-03161-z
https://doi.org/10.1007/s00704-020-03161... ) the 0.56 L s¹ ha¹ approximation previously obtained. The minimum flow occurs in the month of Jan with a specific value of 0.25 L s¹ ha¹.

The flows of water rights obtained by CROPWAT 8.0 applied in a simplified way for the dry, medium, and wet years (Yr_dry, Yr_medium and Yr_wet) considering two ET_o service levels (50 and 80 %), with these three years, were selected depending on the total annual period or just the dry period (P_total or P_dry) (Figure 6). For 80 % Yr_dry P_total, the maximum demand occurs in the month of Sept, with a flow of 15.6 m³ s¹ for the total area, or 0.52 L s¹ ha¹ an underestimation occurs in relation to the year-on-year. For 80 % Yr_dry P_dry (method proposed in this work), the maximum demand occurs in the month of Jan, with a flow of 17.1 m³ s¹ for the total area or 0.57 L s¹ ha¹ and the minimum occurs in March, with a value of 0.33 L s¹ ha¹.

Regarding the flows for water rights in the total area according to ANA spreadsheet, it was found that the maximum demanded flow occurs in Aug and Sept, reaching 16.2 m³ s¹ for the total area, or 0.54 L s¹ ha¹. The minimum occurs in Dec, with a value of only 0.16 L s¹ ha¹, which represents an underestimation of the minimum of 36 % in relation to the year-on-year method with CROPWAT 8.0 (Figure 7).

The flows for water rights calculated by the CROPWAT 8.0 program year-by-year (standard method) for the total series of 31 years for the case of deficit irrigation can be seen in Figure 8. The monthly flows calculated by the CROPWAT 8.0 program are presented according to the % of attendance. For 80 % of service, it appears that the highest demand occurs in Sept, with a total flow of 12.73 m³ s¹, representing a specific demand of 0.42 L s¹ ha¹. The minimum flow assumes a value of 0.18 L s¹ ha¹, occurring in the month of Jan. Compared to supplementary irrigation, deficit irrigation decreased 29.7 % for the maximum flow and 28 % for the minimum flow (Figure 9).

The flows for water rights obtained by CROPWAT 8.0 applied using the simplified method for the dry, medium, and wet years (Yr_dry, Yr_medium and Yr_wet), considering two levels of ET_o service (50 and 80 %) can be seen in Figure 10. These three years were selected according to the total annual period or only the dry period (P_total or P_dry); the P_dry method was used in this work. For 80 % Yr_dry P_total, the maximum demand occurred in Sept, with a flow of 11.88 m³s¹ for the total area or 0.40 L s¹ ha¹ (underestimation of the maximum of 6.7 % in relation to the standard). As for 80 % Yr_dry P_dry (proposed method), the maximum demand occurred in Aug, with a flow of 12.96 m³ s¹. The minimum, for 80 % Yr_dry P_dry, is 0.27 L s¹ ha¹.

Regarding the flows for water rights for the total area according to ANA spreadsheet for deficit irrigation, the maximum demanded flow occurred in Aug and Sept, reaching 14.56 m³ s¹ for the total area or 0.49 L s¹ ha¹ (Figure 7). The minimum occurred in Dec with only 0.09 L s¹ ha¹, which represents an underestimation of the minimum of 51.9 % in relation to the year-on-year method. Variations in demanded flows are due to significant changes in meteorological variables that directly affect the availability of natural water for crops throughout the year, mainly in tropical regions, with rainfall concentrated in spring and summer (Oct to Feb) and a well-defined dry season from June to Sept (Tukimat et al., 2017Tukimat NNA, Harun S, Shahid S. 2017. Modeling irrigation water demand in a tropical paddy cultivated area in the context of climate change. Journal of Water Resources Planning and Management 143: 05017003. https://doi.org/10.1061/ (ASCE)WR.1943-5452.0000753
https://doi.org/10.1061/ (ASCE)WR.1943-5... ; De Graaf et al., 2014De Graaf IEM, Van Beek LPH, Wada Y, Bierkens MFP. 2014. Dynamic attribution of global water demand to surface water and groundwater resources: Effects of abstractions and return flows on river discharges. Advances in Water Resources 64: 21-33. https://doi.org/10.1016/j.advwatres.2013.12.002
https://doi.org/10.1016/j.advwatres.2013... ; Shahdany et al., 2019 Shahdany SMH , Taghvaeian S , Maestre JM , Firoozfar AR . 2019. Developing a centralized automatic control system to increase flexibility of water delivery within predictable and unpredictable irrigation water demands. Computers and Electronics in Agriculture 163: 104862. https://doi.org/10.1016/j.compag.2019.104862
https://doi.org/10.1016/j.compag.2019.10... ).

Discussion

The method to calculate flows for water rights proposed by ANA underestimates the minimum irrigation demand (rainy season) from 36.0 to 51.9 % compared to the CROPWAT 8.0 method, performing a year-by-year water balance (standard method with 31-year series). The ANA method does not provide operational security to large enterprises, especially in the rainy season, since it needs an adequate probabilistic character that considers the annual variations in precipitation.

On a worldwide scale, water rights regulatory agencies and corresponding markets work to address water scarcity by establishing tradable, limited-access permits to water resources. The broader insights of this research suggest that when rights holders accurately assess water demand, optimal engagement strategies can be developed that add to farm-scale profitability and allow to determine whether existing rights ownership matches risk tolerance (Fachinelli and Pereira Junior, 2015; Delorit et al., 2019Delorit JD, Parker DP, Block PJ. 2019. An agro-economic approach to framing perennial farm-scale water resources demand management for water rights markets. Agricultural Water Management 218: 68-81. https://doi.org/10.1016/j.agwat.2019.03.029
https://doi.org/10.1016/j.agwat.2019.03.... ; Eshete et al., 2020Eshete DG, Sinshaw BG, Legese KG. 2020. Critical review on improving irrigation water use efficiency: Advances, challenges, and opportunities in the Ethiopia context. Water-Energy Nexus 3: 143-154. https://doi.org/10.1016/j.wen.2020.09.001
https://doi.org/10.1016/j.wen.2020.09.00... ; Portoghese et al., 2021Portoghese I, Giannoccaro G, Giordano R, Pagano A. 2021. Modeling the impacts of volumetric water pricing in irrigation districts with conjunctive use of surface and groundwater resources. Agricultural Water Management 244: 106561. https://doi.org/10.1016/j.agwat.2020.106561
https://doi.org/10.1016/j.agwat.2020.106... ).

Integrated water resource management methods and tools have been developed to reflect how different water resources managers and users perceive problems and predict possible long-term impacts. Criteria that do not take into account an analysis of temporal variability (year-by-year analysis) are no longer sufficient to face the challenges related to water management in the Brazilian agricultural system (Bronstert et al., 2000Bronstert A, Jaeger A, Guntner A, Hauschild M, Döll P, Krol M. 2000. Integrated modelling of water availability and water use in the semi-arid northeast of Brazil. Physics and Chemistry of the Earth. Part B: Hydrology, Oceans and Atmosphere: 25: 227-232. https://doi.org/10.1016/S1464-1909 (00)00008-3
https://doi.org/10.1016/S1464-1909 (00)0... ; Krol et al., 2001Krol MS, Jaeger A, Bronstert A, Krywkow J. 2001. The semi-arid integrated model (SIM), a regional integrated model assessing water availability, vulnerability of ecosystems and society in NE-Brazil. Physics and Chemistry of the Earth. Part B: Hydrology, Oceans and Atmosphere: 26: 529-533. https://doi.org/10.1016/S1464-1909 (01)00045-4
https://doi.org/10.1016/S1464-1909 (01)0... ; Fachinelli and Pereira Junior, 2015).

The simplified method proposed by FAO (CROPWAT 8.0), due to the criteria to choose the three representative years (dry, medium, and wet), had a good performance in the wet period. However, it resulted in underestimating the maximum demanded flow from 6.7 to 13.8 % in the dry period when compared to CROPWAT 8.0 applied year-on-year. The FAO simplified method was subject to vulnerabilities since it does not take into account the variability of the monthly rainfall distribution in the selection of the year taken as a basis.

Estimation of water requirements is vital to designing and operating an agricultural water resource system. Techniques, such as FAO simplified method, are used to estimate the high spatial and temporal hydrological variable of water demand. This is certainly an advance over methods that did not have an adequate probabilistic character; however, it is still important to interpret these estimates properly to ensure practical information to water resource managers (Fernandes et al., 2019Fernandes RDM, José JV, Wolff W, Costa JO, Folegatti MV. 2019. Probability distribution functions applied in the water requirement estimates in irrigation projects. Revista Caatinga 32: 189-199. https://doi.org/10.1590/1983-21252019v32n119rc
https://doi.org/10.1590/1983-21252019v32... ; Parsinejad et al., 2022Parsinejad M, Raja O, Chehrenegar B. 2022. Practical analysis of remote sensing estimations of water use for major crops throughout the Urmia Lake basin. Agricultural Water Management: 260: 107232. https://doi.org/10.1016/j.agwat.2021.107232
https://doi.org/10.1016/j.agwat.2021.107... ).

The alternative method to the year-by-year balance proposed here allows a more accurate estimation of the demanded flow for irrigation, mainly in the dry period of the year, providing a slight overestimation in the wet period (from 5.1 to 7.5 %) when compared to the FAO standard method. Notably, this period coincides with the highest supply of water bodies, which leads to the conclusion that the more detailed approach of the proposed method , considering monthly values, can optimize supplementary or deficit irrigation strategies in years where rainfall is below average. This ensures a better-balanced use of water in months when there is a surplus in the water courses, contributing to the stability and security of the agricultural farm. In the dry period, the method proposed also showed a satisfactory performance.

The comparison between supplementary irrigation and deficit irrigation, based on simulations carried out year-by-year on the FAO model (the standard method of the 31-year series), showed that irrigation with a deficit promotes a saving of about 30 % in the water resource demand for the region under study, regarding irrigation of the sugarcane crop.

Our results show that the alternative method to the year-by-year balance proposed in this investigation allows a more accurate estimation of the demanded flow for sugarcane irrigation in Brazil, when compared with the ANA method. The simplified method proposed by the FAO (CROPWAT 8.0) performs well but displays some vulnerability. We verified that the proposed method helped improve water resource management in Brazil and worldwide. This method could also be used as a benchmark to validate other methods that governments, regulatory agencies, research institutions, and others may suggest.

Acknowledgements

This experiment was supported by the Brazilian Research Agency “Fundação de Amparo à Pesquisa do Estado de São Paulo” - FAPESP 2012/50083-7 – “Biomass Water Productivity and Energy for Sugarcane Varieties at Different Irrigation Levels: Experimentation and Simulation”, approved at the “Research Program in Partnership for Technological Innovation (PITE)” in collaboration with the Universidade de São Paulo (USP) and ETH Bioenergy Company.

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