Geosci. Model Dev., 18, 3799–3817, 2025 https://doi.org/10.5194/gmd-18-3799-2025 © Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License. D evelopm entand technicalpaper Process-based modeling framework for sustainable irrigation management at the regional scale: integrating rice production, water use, and greenhouse gas emissions Yan Bo1,�, Hao Liang2,�, Tao Li3, and Feng Zhou1,2,4 1Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China 2Jiangsu Key Laboratory of Soil and Water Processes in Watershed, College of Geography and Remote Sensing, Hohai University, Nanjing, China 3International Rice Research Institute (IRRI), Los Baños, Philippines 4Southwest United Graduate School, Kunming, China �These authors contributed equally to this work. Correspondence: Feng Zhou (zhouf@pku.edu.cn) Received: 18 November 2024 – Discussion started: 19 December 2024 Revised: 12 March 2025 – Accepted: 2 April 2025 – Published: 27 June 2025 Abstract. Rice cultivation faces multiple challenges from rising food demand as well as increasing water scarcity and greenhouse gas emissions, intensifying the tension of the food–water–climate nexus. Process-based modeling is pivotal for developing effective measures to balance these challenges. However, current models struggle to simulate their complex relationships under different water manage- ment schemes, primarily due to inadequate representation of critical physiological effects and a lack of efficient spa- tially explicit modeling strategies. Here, we propose an ad- vancing framework that addresses these problems by inte- grating a process-based soil–crop model with vital physi- ological effects, a novel method for model upscaling, and the non-dominated sorting genetic algorithm II (NSGA-II) multi-objective optimization algorithm at a parallel comput- ing platform. Applying the framework accounted for 52 %, 60 %, 37 %, and 94 % of the experimentally observed varia- tions in rice yield, irrigation water use, methane, and nitrous oxide emissions in response to irrigation schemes. Compared with the original model using traditional parameter upscal- ing methods, the advancing framework significantly reduced simulation errors by 35 %–85 %. Moreover, it well repro- duced the multi-variable synergies and tradeoffs observed in China’s rice fields and identified an additional 18 % areas feasible for irrigation optimization, along with an additional 11 % and 14 % reduction potentials of water use and methane emissions, without compromising production. Over 90 % of the potentials could be realized at the cost of 4 % less yield increase and 25 % higher nitrous oxide emissions under mul- tiple objectives. Overall, this study provides a valuable tool for multi-objective optimization of rice irrigation schemes at a large scale. The advancing framework also has implications for other process-based modeling improvement efforts. Key points. – This study significantly improved rice yield simulations under various irrigation schemes by incorporating critical physiolog- ical processes into a process-based model. – This study developed a novel upscaling method of model pa- rameterization that well reproduced observed synergies and tradeoffs among multiple objectives (i.e., rice yield, irrigation water use, methane emissions, and nitrous oxide emissions). – This study provides a practical tool for the multi-objective op- timization of water management to deliver co-benefits of en- suring food production, saving water, and reducing greenhouse gas emissions of rice fields. Published by Copernicus Publications on behalf of the European Geosciences Union. 3800 Y. Bo et al.: Process-based modeling framework for sustainable irrigation management 1 Introduction Rice is the staple food for more than half of the world’s pop- ulation and is also the most water-intensive cereal crop with a significant contribution to greenhouse gas emissions (GHGs) (Lampayan et al., 2015; Carlson et al., 2017). Rice cultiva- tion currently accounts for 40 % of global irrigation water use (IRR), 30 % of methane (CH4), and 11 % of nitrous ox- ide (N2O) emissions in agriculture (Yuan et al., 2021). To meet the demand of the growing population, a 50 %–60 % increase in global rice production along with a 15 % increase in water use is required by 2050, potentially leading to higher greenhouse gas emissions and intensifying the food–water– climate tensions of rice fields (Flörke et al., 2018; World Bank, 2017). Therefore, ensuring food security while con- serving water resources and reducing GHGs in rice cultiva- tion is essential for achieving multiple United Nations Sus- tainable Development Goals. Optimizing water management is promising to address the multiple challenges. However, different water manage- ment schemes can lead to a wide range of outcomes in rice yield (−16.9 % to 21.9 %), IRR (−68.0 % to −0.3 %), CH4 (−85.5 % to −0.1 %), and N2O (0 % to 364 %) across climatic zones, reflecting complex interactions between en- vironmental factors and management strategies (Bo et al., 2022). Process-based models are powerful tools for predict- ing and managing the complicated interactions in responses to water management, given their strength in simulating crop growth, water dynamics, and soil biogeochemical processes under diverse genotype× environment×management con- ditions (Tian et al., 2021; Chen et al., 2022; Yan et al., 2024). Despite several relevant studies at site scales, extrapolation of optimized water management schemes from limited sites to the broader rice growing regions is hindered by the di- verse climate, soil, crop variety, field management, etc. (Yan et al., 2024; Liang et al., 2021). Region-specific simulations of the food–water–climate nexus are thus urgently needed to identify tailored solutions. Nevertheless, current models face challenges in accurately predicting yield responses to various water management practices and adequately reproducing the spatial heterogeneity of these responses. Despite extensive experimental research to understand critical physiological effects underlying yield responses, these processes have not been fully represented in models, especially the compensation mechanisms. Compared to con- tinuous flooding, imposing moderate water deficit and then rewatering the field could increase both effective leaf area and net photosynthetic rate upon re-irrigation to enhance photosynthesis for biomass production (Yang and Zhang, 2010). In addition, harvest index could increase due to en- hanced remobilization of assimilates and accelerated grain filling rate (Zhang et al., 2008). However, prevailing models (for example, ORYZA, DSSAT, APSIM, and WHCNS) pri- marily focus on the negative impacts of water deficit (i.e., re- duced photosynthesis or leaf rolling), while neglecting or in- directly simulating crop adaptation processes (e.g., enhanced root growth and water uptake in deeper soil layers) (Bouman et al., 2001; Li et al., 2017; Liang et al., 2021; Tsuji et al., 1998). As a consequence, yield sensitivities to water manage- ment could be overestimated, as evidenced by evaluations of the ORYZA (v3) model (Xu et al., 2018). Moreover, phys- iological processes respond differently to water availability at different growth stages, while crop models generally use a constant water effect coefficient throughout the rice growing season (Ishfaq et al., 2020). These imply model deficiencies in predicting yield response to water management, although no assessment across large scales exists. Accurate model parameters are essential for reproducing spatial heterogeneity of yield, IRR, and GHGs. Previous studies usually used either the same parameters at differ- ent pixels, calibrated against all observations, or the spatial proximity principle to extrapolate model parameters for re- gional simulations, as a result of a lack of sufficient obser- vations (Zhang et al., 2024, 2016). However, critical model parameters varied considerably when calibrated under dif- ferent environmental and management conditions, reflecting important impacts of these factors on underlying physiolog- ical and biogeochemical processes (Tan et al., 2021). As a consequence, traditional model parameterization approaches are unlikely to capture variability of yield, IRR, and GHGs due to their neglect of the environmental and management- related impacts (Song et al., 2023; Zhang et al., 2023). Be- sides, previous studies only evaluated simplified irrigation protocols (i.e., once drainage at midseason or alternative wet- ting and drying with a constant threshold across the growing season) or only set bi-objectives as optimization targets (Tian et al., 2021; Chen et al., 2022), which likely underestimated the regulation potentials. Therefore, an integrated framework composed of a reliable modeling platform, broader water management schemes, and multi-objective optimization tar- gets is required for sustainable water management optimiza- tion. To address these challenges, this study proposed an ad- vancing framework that integrated a process-based soil– crop model (Soil Water Heat Carbon Nitrogen Simulator, WHCNS) with key physiological effects, a novel model up- scaling method, and a multi-objective optimization algorithm (non-dominated sorting genetic algorithm II, NSGA-II) at a parallel computing platform (see Fig. 1 for workflow). This study focused on rice yield (yield), irrigation water use (IRR), methane (CH4), and nitrous oxide emissions (N2O) of irrigated rice fields. First, three physiological effects were quantified and embedded into WHCNS to enhance the pre- diction of yield responses. Regionalized model parameters were then derived by developing parameter transfer functions for regional simulations. The model’s ability to reproduce the variations in the food–water–climate nexus was exten- sively validated against field observations. Multi-objective optimization was conducted using NSGA-II to investigate tradeoffs within the food–water–climate nexus and assess Geosci. Model Dev., 18, 3799–3817, 2025 https://doi.org/10.5194/gmd-18-3799-2025 Y. Bo et al.: Process-based modeling framework for sustainable irrigation management 3801 the regulation potentials of water management optimization. This framework was applied to China’s rice cropping system as an example, considering its position as the world’s largest rice producer and the ongoing conflicts between production demand, water scarcity, and greenhouse gas emissions. This study aims to provide a valuable framework for predicting and regulating rice’s food–water–climate nexus towards sus- tainable water management. 2 Data and methods 2.1 WHCNS model and input data The soil Water Heat Carbon Nitrogen Simulator (WHCNS) model was improved and incorporated into the advancing framework in this study to simulate rice yield, irrigation water use (IRR), methane (CH4), and nitrous oxide (N2O) emissions of irrigated rice fields at each pixel. The WHCNS model is a process-based agroecosystem model that runs at a daily time step and comprises six major components: sur- face ponding water dynamics, soil water movements, soil heat transfer, soil N transformation and transport, soil or- ganic turnover, and crop growth. Detailed model descriptions can be found in Liang et al. (2022, 2023, 2021). This model was chosen for several considerations: (i) The model directly outputs all four target variables simultaneously. This avoids biogeochemical models relying on crop models for detailed physiological parameters to simulate yield and calculating IRR externally to obtain all four targets as previously done (Tian et al., 2021; Yan et al., 2024). (ii) The model has been proven to simulate the effects of frequent dry–wet cycles ef- fect reasonably well in Chinese rice fields, due to simulat- ing water and nitrogen dynamics in the surface ponding wa- ter layer that is specific to rice fields (Liang et al., 2021). (iii) The model is executable at both site and regional scales with high efficiency and performs well in capturing spatial variation in key processes (Liang et al., 2023). (iv) The model has a very flexible irrigation setup, which allows for the pre- cise control of paddy field water surface levels by setting the minimum and maximum irrigation thresholds. It also en- ables calculating water usage for paddy field irrigation un- der various water management scenarios (Jiang et al., 2021). The model is particularly suitable for simulating the regional food–water–climate nexus of rice fields. This study ran the model at both site and regional scales (0.5 degree spatial resolution). Model input data include daily meteorological variables, soil properties by depth, and management variables related to planting, fertilization, and irrigation (Table S1). For site-scale simulations, these vari- ables were obtained from experimental studies; if unreported, they were extracted from spatial datasets according to geo- graphical locations. All spatial datasets were resampled to 0.5° spatial resolution for regional simulations. (1) Meteoro- logical variables, including daily mean, maximum, and min- imum air temperature; wind speed; precipitation; humidity; and downward solar radiation, were obtained from the fifth- generation ECMWF reanalysis (ERA5) at 0.25° resolution (Hersbach et al., 2023). (2) Soil data, including bulk den- sity, clay contents, and soil hydraulic properties (i.e., satu- rated water content, field water capacity, wilting point, and saturated hydraulic conductivity) at soil depths of 5, 15, 30, 60, 100, and 200 cm, were obtained from SoilGrids (10 km) (Han et al., 2015). (3) The planting and harvest dates were obtained from the crop calendar data of the Global Gridded Crop Model Intercomparison (GGCMI) phase 3 (Jägermeyr et al., 2021). (4) Fertilization practices were conducted by the auto-fertilization component of the WHCNS model, assum- ing no nitrogen stress (Liang et al., 2023). (5) Irrigation prac- tices are defined by three variables at a daily step, including upper threshold (UIRR), lower threshold (LIRR, with a posi- tive value representing field water level and a negative value representing soil water potential at 15 cm below the soil sur- face), and maximum allowable field water level after rainfall (Hp, also referred to as bund height). Since there is no spa- tially explicit information about realistic water management schemes, daily irrigation thresholds were set following Chen et al. (2022) for regional simulations. The model simulates field water level of surface ponding layer and soil water po- tential of stratified layers at a daily step. Irrigation would be triggered whenever field water level (LIRR > 0) or soil water potential at 15 cm below the soil surface (LIRR < 0) reaches the predetermined LIRR. Irrigation demand is then calculated as the differences between LIRR and UIRR. 2.2 Compilation of experimental observations Extensive literature reviews were conducted to collect exper- imental observations for model improvement and parameter calibration. Relevant studies should meet the following cri- teria: (1) Only field experiments covering an entire growing season were included, while pot and laboratory experiments under controlled environmental conditions were excluded. (2) The control and treatments only differed with respect to water management, with continuous flooding (CF) as the control and non-continuous flooding irrigation (NCF) as the treatment but not with respect to other agronomic practices (e.g., cropping intensity, fertilizer management, and tillage). This was to isolate water management effects while avoiding confounding effects of other factors. (3) Upper and lower irri- gation thresholds were explicitly reported, and lower thresh- olds were indicated by soil water potential measured at the soil depth of 15–20 cm. Observations based on soil water po- tential at other soil depths or other soil-water indicators (e.g., soil water contents) were excluded. (4) At least one of the target variables was observed, including rice yield (yield), irrigation water use (IRR), methane emissions (CH4), ni- trous oxide emissions (N2O), leaf area index (LAI), net pho- tosynthetic rate (Pn), and harvest index (HI). For LAI and Pn, the growth stages of observations (i.e., tillering, booting, https://doi.org/10.5194/gmd-18-3799-2025 Geosci. Model Dev., 18, 3799–3817, 2025 3802 Y. Bo et al.: Process-based modeling framework for sustainable irrigation management Figure 1. Research framework of this study. The framework mainly combines data compilation, model improvement, parameter regional- ization, scenario simulations, and multi-objective optimization. The framework can be flexibly adapted with alternative irrigation scenarios, optimization objectives, and optimization algorithms in other modeling studies. LAI, Pn, and HI represent leaf area index, net photosynthetic rate, and harvest index. AMIN, MPmax, fN2O_d,P LAI, P Pn, and P HI are model parameters calibrated and mapped in this study (Sect. 2.4). CF and NCF represent continuous flooding and non-continuous flooding irrigation. SWP and UFR represent soil water potential and the ratio of unflooded days to total rice growing days, indicating different irrigation schemes. (See Table A1 for detailed descriptions of parameters and variables.) heading, and ripening stage) were recorded to account for growth stage-dependent effects. As a result, we collected ob- servations of 119 experiments from 37 studies covering 28 sites in six countries (i.e., China, India, Philippines, Japan, Bangladesh, and Peru) (Fig. S1). These observations were split into two datasets according to target variables. The first dataset including yield, IRR, CH4, or N2O observations was used for calibration of model parameters. The second dataset of LAI, Pn, or HI observations was used to quantify water management effects on physiological processes for model improvement (Sect. 2.3). For each paired observation under the control and treat- ment, the effects of non-continuous flooding irrigation were calculated as the ratio of observations under treatment to those under control (Eq. 1). This yielded 251 records for Ryield, 235 for RIRR, 37 for RCH4 ,14 for RN2O, 561 for RLAI (including 61 from tillering stage, 159 from booting stage, 202 from heading stage, and 139 from ripening stage), 84 for RPn (including 42 between the tillering and filling stages), and 351 for RHI, calculated as follows: RX =XNCF/XCF , (1) where RX represents non-continuous flooding effects (NCF) on target variables X (including yield, IRR, CH4, N2O, LAI, Pn, and HI), and XNCF and XCF represent variable values un- der non-continuous flooding (NCF) and continuous-flooding irrigation (CF), respectively. Relative changes in target vari- ables were calculated as (RX −1)× 100 for interpretation and representation (e.g., 1yield, 1IRR, 1CH4, 1N2O). For each paired observation, four categories of informa- tion were also collected. First, climatic variables included mean daily air temperature (T ), precipitation (P ), and crop evapotranspiration (ETc) during the growing season. The dif- ference between P and ETc was further calculated to indi- Geosci. Model Dev., 18, 3799–3817, 2025 https://doi.org/10.5194/gmd-18-3799-2025 Y. Bo et al.: Process-based modeling framework for sustainable irrigation management 3803 cate climatological water availability (CWA). Second, soil variables included sand content, bulk density (BD), soil or- ganic carbon (SOC), pH, and soil hydrological properties (e.g., saturated water content (SAT), field water capacity (FWC)). Third, management-related variables included ni- trogen application rate, timing, and lower (LAWD) and up- per (UAWD) irrigation thresholds. Fourth, experimental pa- rameters included geographical location (latitude, longitude), dates of seeding (also transplanting date in transplanted sys- tems), anthesis, and harvest. These variables were used for running WHCNS (Sect. 2.1) and conducting correlation anal- yses (Sect. 3.1). 2.3 Model improvement 2.3.1 Incorporation of physiological effects In the original WHCNS model, water management effects on crop growth were simulated by calculating the water stress factor based on the Feddes reduction function (Feddes and Zaradny, 1978). Specifically, the water stress factor is cal- culated at a daily step as a function of soil water potential to reduce root water uptake, assuming 70 and 1500 kPa as thresholds for when root water uptake starts to decrease and approaches 0 (Eqs. 2–3). The calculated water stress factor was used to reduce the simulated actual biomass production rate, which further indirectly impacted the produced biomass allocated for leaf growth and yield formation, as shown in the following equations (Eqs. 4–6): Ta = ∫ LR S(h,h8,z)dz= Tp ∫ LR aw(h,z)as(h8,z)b(z)dz, (2) cf(w)= Ta Tp =  ∫ LR aw(h,z)as(h8,z)b(z)dz ω = ω ω = 1 ω > ωc∫ LR aw(h,z)as(h8,z)b(z)dz ωc = ω ωc < 1 ω ≤ ωc , (3) Fgc= DL× AMAX Ke × ln [ AMAX+CC AMAX+CC× (−LAI×Ke) ] , (4) Fgass= Fgc× 30 44 × cf(w)× cf(N), (5) GAA(org)= Fgass× fr(org), (6) where Ta and Tp are actual and potential root water up- take (cm d−1). LR indicates root length (cm). aw(h,z) and as(h8,z) are water and salt stress functions. b(z) is the root distribution function. wc is the critical threshold of volumet- ric soil water content w above which root water uptake is reduced in water-limited layers of the root zone, but the plant compensates by uptaking more water from other layers that have sufficient available water. Fgc is the daily potential dry matter production accounting for the light interception, ra- diation use efficiency, and the CO2 effects (kg hm−2 d−1). AMAX is the maximum assimilation rate accounting for temperature effect (kg hm−2 h−1). DL, Ke, and CC indicate day length (h d−1), extinction coefficient (–) and actual radi- ation use (kg hm−2 h−1). Fgass is the daily actual dry matter production (kg hm−2 d−1) accounting for water (cf(w)) and nitrogen stress (cf(N)). GAA indicates the produced biomass allocated to organs (leaf or grains) (kg hm−2 d−1) with the fraction fr(org). To modify the WHCNS, NCF effects on leaf expansion, photosynthesis rate, and assimilate partition were quantified based on experimental observations and incorporated into WHCNS (Fig. S2). To do so, mean values of observed ef- fects were first calculated by experimental gradient of soil water potential (SWP, negative values) and rice development stages (RDS, 0–1) (Tables S2–S4). The RDS corresponds to planting, tillering, booting, heading, and filling, and maturity stages were quantified as 0, 0.20, 0.40, 0.55, 0.75, and 1. Ef- fects at other levels of SWP and RDS were then estimated by bilinear interpolation (i.e., F LAI(SWP, RDS), F Pn(SWP, RDS), F HI(SWP)). Three functions were thus developed in- volving three new genetic parameters to account for differ- ences in cultivar sensitivities (P LAI, P Pn, P HI, Eqs. 7–9). The three functions were added to the original crop growth module to modify simulations of leaf area index, net photo- synthesis rate, and biomass allocated into grains (Eqs. 10–12, Fig. 2a). RLAI(SWP,RDS)= 1+ [( F LAI(SWP,RDS) ) − 1 ] ×P LAI, (7) RPn(SWP,RDS)= 1+ [( F Pn(SWP,RDS) ) − 1 ] ×P Pn, (8) RHI(SWP)= 1+ [ F HI(SWP)− 1 ] ×P HI, (9) LAI′ = GAA(leaf)×SLA×RLAI, (10) AMAX′ = AMAX×RPn, (11) GAA(grains)′ = Fgass× fr(grains)×RHI, (12) where RLAI, RPn, and RHI represent NCF effects on leaf area index, net photosynthetic rate, and harvest index, re- spectively. SWP represents soil water potential at 15–20 cm soil depth. RDS represents relative development stages (0– 1). P LAI, P Pn, and P HI are genetic parameters indicating cultivar sensitivities to irrigation regulation that were cali- brated based on observations (Sect. 2.4). LAI and SLA are leaf area index (m2 m−2) and specific leaf area (m2 kg−1). LAI’, AMAX’, and GAA(grains)’ denote simulations of the modified model. It is worth noting that the three functions can be flexibly coupled to other process-based crop models to modify the simulation of leaf area growth, biomass produc- tion, and allocation processes. The genetic parameters need to be recalibrated against observed yield responses, consid- ering different model structures. https://doi.org/10.5194/gmd-18-3799-2025 Geosci. Model Dev., 18, 3799–3817, 2025 3804 Y. Bo et al.: Process-based modeling framework for sustainable irrigation management Figure 2. Model improvements by incorporating water effects on physiological processes. (a) Schematic of critical physiological effects in re- sponse to different irrigation schemes and their representation in the WHCNS model. (b, c) Model performance for simulating 1yield (b) and 1IRR (c) based on the origin (blue) and modified (orange) WHCNS model. Darker colored dots indicate lower soil water potential (unit: kPa). (d, e) Sensitivity of 1yield and 1IRR to lower irrigation threshold of soil water potential. Black, blue, and orange colors show the results of observations and simulations based on the origin and modified WHCNS model, respectively. The circles are mean values; error bars show the 25 %–75 % interquartile range. The lines are the linear regression lines, with dashed lines indicating non-significant relationships based on a two-sided t test (P > 0.05). The shaded areas around each line represent the 95 % confidence interval. 2.3.2 Contribution analysis Scenario simulations were conducted to isolate the contri- butions of the three physiological effects on yield changes (1yield) (Table S5). Four scenarios were simulated by con- sidering all the three effects (Table S5) and omitting one of the three effects at a time (Table S5). For each scenario, the model was run under CF and NCF conditions, respec- tively, to calculate 1yield. The differences in the simulated 1yield between S1 and S2–S4 represent yield changes in- duced by changes in leaf expansion, photosynthesis rate, and assimilate partition, respectively (i.e., 1yieldLAI, 1yieldPn, 1yieldHI). The relative contribution of each process was cal- culated as the ratio of the absolute yield change induced by Geosci. Model Dev., 18, 3799–3817, 2025 https://doi.org/10.5194/gmd-18-3799-2025 Y. Bo et al.: Process-based modeling framework for sustainable irrigation management 3805 the process to the sum of the absolute yield changes induced by the three processes (Eq. 13). CONp = ∣∣1yieldp ∣∣∑3 p=1 ∣∣1yieldp ∣∣ × 100, (13) where p represents the three new physiological processes (i.e., p = 1, 2, 3), CONp indicates the relative contribution of the process p to 1yield, and 1yieldp is the yield change induced by the process p. 2.4 Parameters regionalization Spatially explicit model parameters are critical for reason- ably reproducing spatial variability of target variables. In this study, seven key model parameters were selected and mapped at 0.5° spatial resolution due to their high influ- ence on target variables, including accumulated temperature for crop maturity (Cumtemp), minimum assimilation rates (AMIN), the maximum CH4 production rate per soil weight at 30 °C (MPmax), the maximum portion of denitrification to N2O production (fN2O_d), and the three new genetic param- eters (P LAI, P Pn, P HI). These parameters were first finely calibrated at site scales (Sect. 2.4.1) and then upscaled to re- gional scales (Sect. 2.4.2). To capture spatial variability of NCF effects, different parameters were used under CF and NCF conditions, except for the genetic parameters. This was consistent with a previous modeling study, aiming to indi- cate different potentials of methane production and denitri- fication under different water management regimes (Song et al., 2023). 2.4.1 Calibration of site-scale parameters Under CF conditions, the parameter Cumtemp was first de- termined by cultivar as the minimum cumulative daily tem- perature higher than 10 °C (base temperature for rice growth) across all experiments using the cultivar. Then AMIN, MP- max, and fN2O_d were calibrated to achieve the best fit of predicted target variables with observations under continuous flooding conditions (i.e., experimental control). Under NCF conditions, Cumtemp and AMIN were the same as those cal- ibrated from CF conditions. The other parameters (MPmax, fN2O_d,P LAI, P Pn, and P HI) were then calibrated by min- imizing the sum of simulated squared residuals under non- continuous flooding conditions (Table S6). To obtain more accurate parameter estimates, the advanced parameter esti- mation algorithm (PEST) was used (Doherty, 2010). As a result, 51 groups of genetic parameters (Cumtemp, AMIN, P LAI, P Pn, and P HI), 56 parameter values of MPmax (19 for control and 37 for treatment), and 24 parameter values of fN2O_d (10 for control and 14 for treatment) were calibrated. 2.4.2 Parameter upscaling To upscale genetic parameters (AMIN, Cumtemp, P LAI, P Pn, P HI) calibrated at site scales to regional scales, the rice cultivar for each grid was first determined. Then, the cali- brated genetic parameters of the cultivar were used to cre- ate the grid. Since the spatial distribution of rice cultivars is unknown, the cultivar of each grid cell was determined as follows. First, cultivars with Cumtemp lower than the effec- tive accumulative temperature requirement of the grid were identified. This ensures that the cultivar could reach matu- rity under the grid cell’s temperature conditions. The grid’s temperature requirement was calculated as Cumtemp during rice growing periods specified by the crop calendar data of GGCMI phase 3 (Jägermeyr et al., 2021). Subsequently, cul- tivars with AMIN that closely match the baseline AMIN of the grid cells were selected. The baseline AMIN was esti- mated using PEST to achieve the best fit of the yield simula- tion with the records in the county-scale statistical yearbooks of China (downscaled to 0.5° spatial resolution). These pro- cedures were designed to ensure that yield simulations were aligned with the cultivar’s genetic potential and were spa- tially consistent with observations. To upscale parameters MPmax and fN2O_d, two parame- ter transfer functions (PTFs) were developed. The dependent variables were the ratio of site-calibrated parameters under treatment to those under control (i.e., RMPmax and RfN2O_d ) (Eqs. 14–15). The independent variables were determined as field water capacity (FWC) for RMPmax and bulk density (BD) for RfN2O_d , due to their higher correlations with the dependent variables. The function forms were determined as the form with the highest R2. As a result, the relationship between field water capacity and RMPmax was best fitted by an exponential function (R2 = 0.62, p < 0.001), and the re- lationship between bulk density and RfN2O_d was best fitted by a quadratic function (R2 = 0.91, p < 0.001) (Fig. S5). The importance of soil properties in regulating spatial het- erogeneity of denitrification potentials aligns with previous studies (Tang et al., 2024). Parameters of the PTFs were cal- ibrated using the least squares method (Eqs. 14–15). With the calibrated PTFs, the ratio of parameters under NCF rela- tive to CF (RMPmax and RfN2O_d ) for each grid could be pre- dicted by combining a spatial dataset of FWC and BD. Then, gridded MPmax and fN2O_d for CF conditions (MPCF max and f CF N2O_d) were estimated using PEST, targeting CH4 from the EDYGA v8.0 dataset (Crippa et al., 2024), and N2O emis- sions estimated by Cui et al. (2024) (Fig. S4). These param- eters were estimated for 2013 and 2015 and subsequently validated for 2014 and 2016 to assess their ability to repro- duce the spatial variability of target variables (Fig. S3). Fi- nally, MPmax and fN2O_d for NCF conditions were calculated by multiplying MPCF max and f CF N2O_d with the predicted ratio (RMPmax and RfN2O_d ). https://doi.org/10.5194/gmd-18-3799-2025 Geosci. Model Dev., 18, 3799–3817, 2025 3806 Y. Bo et al.: Process-based modeling framework for sustainable irrigation management RMPmax =MPNCF max /MPCF max = 986× e−26×FWC, (14) RfN2O_d = f NCF N2O_d/f CF N2O_d = 268 ×BD2 + 789×BD+ 581, (15) where RMPmax and RfN2O_d represent the ratio of the pa- rameter MPmax and fN2O_d calibrated under non-continuous flooding (treatment) to that under continuous flooding (control). FWC and BD represent field water capacity (cm3 cm−3) and soil bulk density (g cm−3) obtained from SoilGrids (10 km) (Han et al., 2015). To prove the efficacy of the PTFs, two other parameter up- scaling approaches were also used for comparison, includ- ing the mean parameters approach and the spatial proxim- ity approach. These approaches were widely used in pre- vious modeling studies to derive regional parameters and conduct regional simulations (Zhang et al., 2024). To adopt the mean parameter approach, the mean value of the site- calibrated MPmax and fN2O_d (Sect. 2.4.1) was calculated, respectively, for CF and NCF conditions, and then the two constant mean parameters were used in regional simulations. To adopt the spatial proximity approach, the nearest site of a site was first identified according to geographical coordi- nates. Then, both MPmax and fN2O_d calibrated from the nearest site were used for simulation of this site. The three approaches were compared in their performance to reproduce the observed variations in 1CH4 and 1N2O (Fig. 3). 2.5 Regional scenario simulations and driver identification Scenario simulations were conducted to test whether the pro- posed framework could reasonably predict the response sen- sitivity of target variables and their relations under different irrigation schemes. To do so, the well-calibrated WHCNS model was run under the baseline and a series of non- continuous irrigation scenarios using the parallel computing framework (Liang et al., 2023). For the baseline condition, ir- rigation thresholds were set according to Chen et al. (2022). For non-continuous flooding irrigation scenarios, a range of the lowest irrigation threshold levels were set based on obser- vations (−5, −10, −15, −20, −30, −40, and −50 kPa). The upper irrigation thresholds were kept the same as the baseline for consistency with experiments. NCF effects were then cal- culated from model simulations and compared with observed effects. Observed effects were obtained from two datasets. The first is the one compiled for this study (Sect. 2.2) us- ing soil water potential to distinguish irrigation schemes. The second was obtained from Bo et al. (2022), who used the ra- tio of days with no surface water to total growing days (UFR) to differentiate irrigation schemes. To facilitate comparison, the UFR of each irrigation scenario was also calculated and output by WHCNS (Fig. S9). To identify the dominant factor driving spatial patterns of NCF effects, correlation analyses between simulated NCF effects and variables were performed following Cui et al. (2021). Climatic, soil, and management-related factors were selected as independent variables, including T, P, ET, Clay, BD, SOC, and fertilizer rate. The analyses were con- ducted, respectively, for 1yield, 1IRR, 1CH4, and 1N2O using 3.5° by 3.5° moving windows. The data resolution was 0.5° by 0.5°, meaning the surrounding 49 pixels were used for each grid. The correlation coefficient and its significance in each grid were first calculated and the dominant driver was then defined as the factor with the largest absolute correlation coefficient. To assess the robustness of the results, similar analyses were done with moving windows at higher spatial resolutions (e.g., 2.5° by 2.5°). 2.6 Single-objective and multi-objective optimizations Based on scenario simulations, four single-objective and a multiple-objective were designed to identify optimal ir- rigation schemes. The four single-objective targets are (1) maxYield, which maximizes rice yield; (2) minIRR, which minimizes irrigation water use; (3) minCH4, which minimizes CH4 emissions; and (4) minN2O, which mini- mizes N2O emissions. Under all targets, yield reduction com- pared to CF conditions was avoided. With the optimal solu- tion under the four single-objective scenarios, the largest reg- ulation potentials to increase yield and reduce IRR, CH4, and N2O emissions were assessed. For comparison, the scenario simulations and optimization were also conducted using the original WHCNS model (Fig. 5). The multi-objective optimization was conducted by com- bining the improved WHCNS model and the NSGA-II (Deb et al., 2002). First, a set of 100 parental populations was initialized with random solutions. Each population includes 1993 individuals, corresponding to 1993 grid cells of irri- gated rice areas. Second, the objective functions were com- puted with each solution by executing the WHCNS model (Eq. 16). Third, the performance of each population was eval- uated by ranking the fitness of its objective functions. Fitness is a measure of how well a solution performs and is calcu- lated based on the non-dominated sorting rank. Then, a new generation was generated through selection, crossover, and mutation based on fitness. Finally, Pareto fronts were gener- ated after 100 generations had been evaluated (that is, 10 000 populations). Geosci. Model Dev., 18, 3799–3817, 2025 https://doi.org/10.5194/gmd-18-3799-2025 Y. Bo et al.: Process-based modeling framework for sustainable irrigation management 3807 Figure 3. Comparison of model parameter upscaling approaches. Model performance in simulating methane and nitrous oxide emissions changes based on parameters derived from (a, b) parameter transfer functions (PTFs), (c, d) mean site-calibrated parameters, and (e, f) spa- tially nearest parameters. The color of the dots indicates lower irrigation thresholds of soil water potential under non-continuous flooding irrigation (unit: kPa). The solid lines are regression lines, with dashed lines indicating non-significant relationships (P > 0.05). Blue shading around each line represents the 95 % confidence interval. fobjective =  fmax{ N∑ n=1 WHCNS(yield)} fmin{ N∑ n=1 WHCNS(IRR)} fmin{ N∑ n=1 WHCNS(GWP)}  , (16) WHCNS(GWP)= 27.2×WHCNS(CH4) + 273×WHCNS(N2O), (17) where fobjective (yield, IRR, and GWP) denotes the collection of objective functions, fmax denotes the objective that needs to be maximized (e.g., rice yield), and fmin denotes the objec- tive that needs to be minimized (e.g., IRR and GWP). GWP is the integrated global warming potential of combined emis- sions of CH4 and N2O and is calculated based on WHCNS simulations (Eq. 17) (IPCC, 2021). It should be noted that this study set equal weight for each target variable to eval- uate the fitness of each solution. Decision-makers can sim- ply set the weight values of different objectives according to their preferences to adopt advanced multi-objective decision- making methods such as the efficiency coefficient method (Guo et al., 2021). The regulation potentials of multiple- objective optimization were calculated as the averaged NCF effects (1yield, 1IRR, 1CH4, 1N2O, 1GWP) of all non- dominated solutions. The potentials were further compared with those from single-objective optimizations to investigate tradeoffs between target variables (Fig. 6). https://doi.org/10.5194/gmd-18-3799-2025 Geosci. Model Dev., 18, 3799–3817, 2025 3808 Y. Bo et al.: Process-based modeling framework for sustainable irrigation management 3 Results and discussion 3.1 Performance of model improvement The origin WHCNS model was first evaluated in reproduc- ing variabilities of rice yield and irrigation water use un- der various irrigation schemes. For rice yield, model perfor- mance is satisfying when mixing observations under contin- uous flooding (CF, experimental control) and non-continuous flooding (NCF, experimental treatments) irrigation schemes together (R2 = 0.41, normalized root mean square error, nRMSE= 11 %) (Fig. S6). In particular, with fine-tuned crop genetic parameters (i.e., Cumtemp and AMIN), the ori- gin model performed well under CF conditions (R2 = 0.74, nRMSE= 13 %), while it had worse performance under NCF conditions (R2 = 0.22, nRMSE= 13 %) (Fig. S6). As a consequence, the origin model failed to reproduce variations in observed yield changes (1yield) (R2 = 0.03, nRMSE= 17 %) (Fig. 2b). More importantly, the simulations could not reproduce 1yield sensitivities to soil water poten- tials presented in field experiments (Fig. 2d). In contrast to yield, model performance in simulating irrigation water use responses (1IRR) variability and its sensitivities to soil water potentials was acceptable (Fig. 2c and e). These results high- light the primary modeling deficiency in simulating 1yield. Given the satisfying model performance in simulating yield under CF and 1IRR, the underperformance is likely due to a lack of critical physiological processes responsible for yield responses to NCF rather than uncertainties of crop parame- ters. After incorporating the three functions of NCF effects and fine calibration of genetic parameters (Sect. 2.3, Fig. 2a), the model performance was substantially improved. The ex- plained variabilities of 1yield increased from 3 % to 52 % and nRMSE decreased from 17 % to 11 % (Fig. 2b). The ob- served 1yield sensitivities to soil water potential (9 % kPa−1, P < 0.001) could be reasonably reproduced by the modified model (13 % kPa−1, P < 0.001) rather than the origin model (P > 0.05) (Fig. 2d). The cultivar differences of yield re- sponses could also be simulated (R = 0.67) (Fig. S7). Across the three processes, leaf area growth (1yieldLAI) was primar- ily responsible for yield losses, while net photosynthetic rate (1yieldPn) and biomass translocation (1yieldHI) contributed to yield increases (Sect. 2.3.2, Fig. S8). The positive con- tributions are larger in warmer and more humid areas and in acidic soils with larger field water holding capacity and higher SOC. These findings conform with empirical relation- ships between 1yield and environmental factors reported by previous meta-analysis (Carrijo et al., 2017). These results prove the efficacy of the modified model to predict and reg- ulate 1yield under diverse irrigation schemes and environ- mental conditions. Besides being coupled to WHCNS as an integrated sys- tem, the new functions also contribute to advancing related modeling studies by directly involving positive physiologi- cal effects and considering stage-dependent response sensi- tivities (Li et al., 2017). By contrast, most prevailing crop models only account for negative effects of soil drying and reduced transpiration, while they do not incorporate direct compensation effects (such as increased photosynthesis rate upon rewatering). Moreover, constant stress sensitivity pa- rameters were generally used for all growth stages (such as ORYZA and DSSAT) (Bouman et al., 2001; Tsuji et al., 1998). These models could flexibly incorporate the three new functions and recalibrate the genetic parameters (i.e., P LAI, P Pn, and P HI) following the procedures of this study to im- prove their performance in predicting yield responses. 3.2 Performance of regionalized parameters To simulate regional NCF effects, the model was first run for CF (baseline) and NCF conditions, respectively, using the parallel computing framework at a spatial resolution of 0.5°. NCF effects were then calculated using model simulations following Eq. (1) (Fig. 1 and Sect. 2.4). Using the PEST- calibrated gridded model parameters for CF (Sect. 2.4.1), the nRMSE between model simulations and their spatial datasets was 20 % to 29 % for yield, ∼ 7 % for IRR, ∼ 4 % for CH4, and 4 % to 6 % for N2O during the validation period (year 2014 and 2016) (Fig. S2). It was noted that the nRMSE of rice yield was relatively larger than that of other target vari- ables, despite being within an acceptable range (< 30 % for the validation periods). This could be caused by interannual cultivar changes, which were difficult to consider in large- scale simulations due to the lack of spatial distribution of rice cultivars. Overall, these results reveal a satisfying model cal- ibration to simulate baseline values and spatial variabilities of target variables. To reproduce observed variabilities of NCF effects on tar- get variables, NCF effects on key model parameters (MPmax and fN2O_d) were incorporated for constraining model sim- ulations. To do so, NCF effects on model parameters were first quantified from site-scale calibrations and extrapolated to regional scale (Sect. 2.4). Three approaches of parame- ter extrapolation were tested and compared, including devel- oping parameter transfer functions (PTFs), using mean site- calibrated parameters (mean), and using spatially nearest cal- ibrated parameters (spatial) (Sect. 2.4.3). Results showed that developing PTFs performed the best to reproduce observed variabilities of 1CH4 and 1N2O (Fig. 3). Model simula- tions using parameters estimated by PTFs explained 37 % and 94 % of variations in 1CH4 and 1N2O, with nRMSE be- ing 25 % for 1CH4 and 10 % for 1N2O (Fig. 3a–b). By con- trast, simulations based on the other two approaches could hardly reproduce observed variabilities of 1CH4 and 1N2O, with nRMSE achieving 66 % to 72 % for 1CH4 and 29 % to 73 % for 1N2O (Fig. 3c–f). These results prove the efficacy of the developed PTFs and suggest soil variables as good pre- dictors for spatial extrapolation of site-calibrated parameters to simulate CH4 and N2O. The PTFs could also be referred Geosci. Model Dev., 18, 3799–3817, 2025 https://doi.org/10.5194/gmd-18-3799-2025 Y. Bo et al.: Process-based modeling framework for sustainable irrigation management 3809 by other biogeochemical models for regional simulations of CH4 and N2O (such as the Denitrification–Decomposition model and the Dynamic Land Ecosystem Model) (Zhang et al., 2016). Considering scarce observations of NCF effects across space, it was impractical to directly evaluate the regional- ized parameters in reproducing spatial variability of NCF ef- fects. Therefore, the proposed framework was evaluated in terms of the response sensitivity of target variables and their relationships under different irrigation schemes (Sect. 2.5). Scenario simulations broadly conformed with observations regarding the magnitude of NCF effects and response sen- sitivity across soil water potential gradients (Fig. S9). With decreased soil water potential threshold, 1yield decreased quasi-linearly, 1CH4 and 1IRR decreased at a decelerating rate, while 1N2O showed slight variabilities (Fig. S9a). The decelerating decrease in 1CH4 was also observed in experi- ments, suggesting the model’s ability to simulate maximum potentials of CH4 mitigation (Balaine et al., 2019). The re- sponse sensitivity was further validated using an alternative observation dataset (Fig. S9b). Besides, the observed synergy or tradeoffs of the yield–IRR–GHGs nexus were broadly covered by scenario simulations using the modified model rather than by using the original model (Fig. S9c). Such bias could further impact assessment of regulation potentials of the food–water–climate nexus. 3.3 Assessment of regional regulation potentials Scenario simulations revealed large spatial variabilities of NCF effects on all target variables (Fig. 4). Applying the same irrigation scheme (e.g., lower irrigation threshold of −15 or −30 kPa) could induce larger yield increase in the southwestern single-rice region (XNS: 2.4 % to 3.4 %), while larger yield losses in northern regions (HHH: −3.2 %) (Fig. 4a and b). The HHH region also showed larger yield sensitivity with decreased lower irrigation threshold (−0.24 % kPa−1) (Fig. 4c). For IRR, relatively larger water saving benefits occurred in the southern regions, whereas response sensitivity was larger in the northeastern regions (−1.7 % kPa−1). For CH4, the northern rice growing regions showed relatively higher reductions (NES: 64 % to 82 %, HHH: 77 % to 88 %) and higher response sensitivity to de- creased soil water potential threshold. The findings about larger water saving benefits in South China and larger CH4 mitigation in North China were consistent with previous assessments (Tian et al., 2021). However, N2O emissions showed widespread increase regardless of lower irrigation threshold, except for northeastern regions, indicating low op- portunities to reduce N2O by only optimizing water manage- ment. To further understand the drivers shaping the spatial varia- tions in NCF effects, correlation analyses were conducted for each target variable across varying lower irrigation thresh- olds. Overall, climatic and edaphic variables were the most important drivers, while management-related variables were less important (Fig. 5). Exceptions occurred in the southern double rice region (HND) for 1yield and the southwestern single rice region (XNS) for 1N2O, where a higher fertilizer application rate was associated with a larger yield increase but decreased N2O reduction potentials (Figs. S10 and S11). For both 1yield and 1IRR, clay content was the most im- portant driver at higher irrigation thresholds, while climate factors showed increasing importance with decreased irriga- tion thresholds (Fig. 5a and b). By contrast, reduction poten- tials for CH4 and N2O emissions were dominated by edaphic factors regardless of irrigation threshold (i.e., clay for CH4 and bulk density for N2O) (Fig. 5c and d). These findings highlight the complex interplay of factors influencing regu- lation potentials of rice production, irrigation water use, and greenhouse gas emissions through NCF adoption. To identify the largest regulation potentials from NCF adoption, four single objective targets were designed, in- cluding maximizing rice yield, minimizing IRR, CH4 emis- sions, or N2O emissions (denoted as maxYield, minIRR, minCH4, and min N2O, in Sect. 2.6). Results indicated that the largest regulation potentials of 1yield, 1IRR, 1CH4 and 1N2O were 4.6 %, −61.0 %, −64.2 % and −10.9 %, respec- tively (Fig. 6a). These potentials could be achieved respec- tively over 91 %, 91 %, 88 % and 26 % of national rice areas (Fig. 6b). Spatially, larger yield increase potential occurred in the south (HND: 7.7 %) and southwestern regions (XNS: 6.8 %) (Fig. S12a). The reduction potential of IRR and CH4 showed relatively slight spatial variabilities. In contrast, re- duction potential of N2O primarily concentrated in northern regions (NES: −30 %) due to increased N2O in southern re- gions (Figs. 5a and S12a). N2O increase in southern regions is associated with higher nitrogen application rates, provid- ing substrate for nitrification and denitrification processes to facilitate N2O emissions (Jiang et al., 2019). The results conform to previous studies in that irrigation and nitrogen should be co-regulated for these areas to avoid unintended N2O emissions from water management (Jiang et al., 2019; Kritee et al., 2018). The largest regulation potentials of 1yield, 1IRR, 1CH4, and 1N2O are not likely to be achieved at the same time, as evidenced by different optimized irrigation strategies be- tween single-objective targets (Figs. 6 and S13). For ex- ample, the lower irrigation threshold should be higher than −20 kPa for most areas (84 %) under maxYield, while lower than −20 kPa over half areas under minIRR and minCH4. This suggests tradeoffs between yield increase and IRR/CH4 mitigation (Bo et al., 2022). To compare, using the origin model could overlook nearly 20 % feasible areas for apply- ing optimized irrigation schemes (Fig. 6). As a consequence, regulation potentials of 1yield, 1IRR, 1CH4, and 1N2O could be underestimated by 4 %, 11 %, 14 %, and 2 %, espe- cially for the southwestern regions (XNS) (Fig. 6a). More- over, optimal NCF strategies also differed from those identi- fied by the improved model, particularly under maxYield tar- https://doi.org/10.5194/gmd-18-3799-2025 Geosci. Model Dev., 18, 3799–3817, 2025 3810 Y. Bo et al.: Process-based modeling framework for sustainable irrigation management Figure 4. Spatial pattern of relative changes in target variables under different irrigation schemes. The four columns correspond to the four target variables 1yield, 1IRR, 1CH4, and 1N2O, respectively. (a) Relative changes in target variables under a lower irrigation potential of −15 kPa, (b) relative changes in target variables under a lower irrigation potential of −30 kPa, (c) differences between (b) and (a), and (d) the results for different rice growing regions. NES (northeast single rice), HHH (HuangHuaiHai single rice), CJS (Yangtze River single rice), CJD (Yangtze River double rice), HND (South China double rice), and XNS (Southwest China single rice) represent six major rice-growing areas in China, respectively. Publisher’s remark: please note that the above figure contains disputed territories. gets (Fig. 6b). These results showed important implications of the improved framework for prompting sustainable water management. 3.4 Tradeoffs between food, water, and greenhouse gas emissions The NSGA-II was conducted to investigate synergies or tradeoffs of the food–water–climate nexus (Fig. 7 and Sect. 2.6). There were evident tradeoffs between reducing CH4 (or IRR) and N2O (Fig. 7a). In contrast, synergies were noted between reducing IRR and CH4, and between inhibit- ing N2O emissions and increasing rice yield. The relation- ships between yield increase and CH4 (or IRR) reductions were more complicated due to the impacts of varying irriga- tion timing and no-flooded days (Yan et al., 2024). Adopting non-dominated solutions from multi-objective optimization could realize over 90 % of the largest reduction potentials of IRR and CH4, while at the cost of 4 % less yield increase (4.6 % versus 0.5 %) and 25 % higher nitrous dioxide emis- sions (−11 % versus 14 %). The N2O increase is because this study used integrated warming potentials of CH4 and N2O Geosci. Model Dev., 18, 3799–3817, 2025 https://doi.org/10.5194/gmd-18-3799-2025 Y. Bo et al.: Process-based modeling framework for sustainable irrigation management 3811 Figure 5. Drivers regulating spatial variations in relative changes in yield (a), IRR (b), CH4 (c), and N2O (d). The numbers and colors indicate correlation coefficients, with gray indicating non-significant correlations (N.S., P > 0.05). The pie plots represent the proportion of irrigated rice areas (%) for which variation in relative changes is regulated by the dominant drivers. The dominant driver is defined as the factor with the largest absolute correlation coefficient in each grid cell, identified based on 3.5° by 3.5° moving windows. The numbers in blue, orange, and green around the pie plots denote the area proportions dominated by climate (i.e., T +P +ET), soil (i.e., Clay+BD+SOC), and management-related (i.e., fertilizer rate) factors under corresponding lower irrigation threshold. Spatial distributions of dominant drivers are shown in Figs. S10 and S11. emissions (GWP) to indicate greenhouse gas emissions so that CH4 outweighed N2O due to large emission quantities (Sect. 2.6). Spatially, over 90 % of the reduction potentials for IRR and CH4 could be achieved across 53 % and 60 % of the na- tional rice areas, primarily in southern regions (Figs. 7 and S14). In these areas, N2O increase was inevitable, but yield increase could be expected. By contrast, stronger tradeoffs occurred in the northern regions, where the reduction po- tentials of IRR and CH4 were limited even with decreased yield and increased N2O emissions. Therefore, NCF adop- tion should be prioritized in southern regions (e.g, XND, CJD, and CJS) to achieve a national optimum balance among rice production, water use, and greenhouse gas emissions mitigation. Note that other objective functions could also be designed for multi-objective optimization, such as apply- ing other indicators (e.g., water productivity and yield-scaled GWP), setting distinguished weights for each indicator or grid cell. 3.5 Uncertainties and future direction This framework is subject to several uncertainties, mainly sourced from observational gaps and management-related in- put data. First, the absence of field observations for base- line CH4 and N2O emissions across regional scales forced us to use estimates from inventory or data-driven approaches as a proxy for deriving gridded model parameters in this study (Cui et al., 2021; Crippa et al., 2024). Despite un- certainties in predicting absolute values, these parameters could reasonably reproduce the spatial patterns and could be further refined given increased field observations. Sec- ond, the limited experimental observations of CH4 (n= 37) https://doi.org/10.5194/gmd-18-3799-2025 Geosci. Model Dev., 18, 3799–3817, 2025 3812 Y. Bo et al.: Process-based modeling framework for sustainable irrigation management Figure 6. Comparison of the origin and modified model between (a) regulation potentials and (b) optimized irrigation schemes under single- objective targets. The four columns show results under four single-objective targets: maximizing rice yield (maxYield), minimizing irrigation water use (minIRR), minimizing CH4 emissions (minCH4), and minimizing N2O emissions (maxN2O). (a) Area-weighted 1yield, 1IRR, 1CH4, and 1N2O for China and six rice growing regions. Blue and orange indicate results from the origin and modified model, respectively. (b) Proportions of rice areas with corresponding optimized lower irrigation thresholds (LIRR) to total irrigated rice areas under the four single objective targets. NES, HHH, CJS, CJD, HND, and XNS indicate six rice growing areas of China, namely, northeast single rice, HuangHuaiHai single rice, Yangtze River single rice, Yangtze River double Rice, South China double rice, and Southwest China single rice, respectively. and N2O (n= 14) under various irrigation schemes have con- tributed to uncertainties in developing and applying parame- ter transfer functions (PTFs). The values of PTF predictors (bulk density and field water capacity) in the observation dataset (1.34–1.48 g cm−3 and 0.25–0.30 cm3 cm−3) did not encompass the full range across national rice areas (1.24– 1.48 g cm−3 and 0.22–0.32 cm3 cm−3), indicating potential extrapolation in parameters regionalization (Fig. S1). Despite these uncertainties, the PTFs significantly improved over pre- vious approaches (constant parameters or spatial proximity approach). Lastly, current irrigation practices across large scales remain largely unknown, so that irrigation thresholds were set following previous recommendations. However, ac- tual farmer practices are influenced by various factors and may not align with these recommendations. This discrepancy could lead to an overestimation or underestimation of target variables and further introduce uncertainties to the assess- ment of regulation potentials. These uncertainties provide insights to enlighten future re- search efforts, including conducting extensive observations and experiments and developing high-resolution input data. On the one hand, intensive GHG monitoring networks are es- sential to reduce uncertainties associated with parameteriza- tion (Arenas-Calle et al., 2024). To better constrain the PTFs and reduce extrapolation uncertainty, field experiments com- bined with incubation experiments across a broader range of climate conditions (e.g., colder and more humid areas) and soil properties (e.g., areas with higher SOC or lower bulk density) should be conducted (Fig. S1). In addition, exten- sive field experiments with simultaneous measurements of yield, IRR, CH4, and N2O emissions across diverse environ- ments are required to validate the framework further. On the other hand, developing a high-resolution dataset of current irrigation schemes is crucial for more accurate model param- eter calibration and realistic assessment of regulation poten- tials. This could be achieved by integrating remote sensing technologies with extensive field investigations (Novick et al., 2022). 4 Conclusion This study introduced an advancing framework for process- based modeling of the complex food–water–climate nexus in rice fields under various water management schemes. By integrating the Soil Water Heat Carbon Nitrogen Simulator (WHCNS) with key physiological effects, a novel model up- scaling method, and the NSGA-II multi-objective optimiza- tion algorithm at a parallel computing platform, the frame- work provides a comprehensive approach to optimize irriga- tion strategies. Applying this framework to China’s rice crop- ping system, we assessed the largest regulation potentials of 1yield, 1IRR, 1CH4,, and 1N2O as 4.6 %, −61.0 %, −64.2 %, and −10.9 % from 91 %, 91 %, 88 %, and 26 % of national rice areas. However, these regulation poten- tials could not be simultaneously realized due to compli- cated tradeoffs among food–water–GHGs. Based on NSGA- II multi-objective optimization targeting food–water–GHG co-benefits, over 90 % of the reduction potentials in water use and methane emissions could be realized, while at the cost of 4 % less yield increase and 25 % higher nitrous diox- ide emissions. The proposed framework is a valuable tool Geosci. Model Dev., 18, 3799–3817, 2025 https://doi.org/10.5194/gmd-18-3799-2025 Y. Bo et al.: Process-based modeling framework for sustainable irrigation management 3813 Figure 7. Regulation potentials of 1yield, 1IRR, 1CH4,, and 1N2O under single-objective and multi-objective targets. (a) Synergies or tradeoffs between target variables with different solutions of multi-objective optimization. Dot colors indicate probability density distributions of variable changes from all non-dominated solutions (N = 10 000) of the NSGA-II optimization. The vertical and horizontal dashed lines show national regulation potentials of the target variable under single-objective targets, with corresponding spatial distributions presented in panel (b). Note that the results of 1N2O potentials (−11 %) were not shown in the third, fifth, and sixth subplots for a clearer view. (b) 1yield, 1IRR, 1CH4, and 1N2O under single-objective targets of maximizing rice yield (maxYield), minimizing irrigation water use (minIRR), minimizing CH4 emissions (minCH4), and minimizing N2O emissions (maxN2O). These results indicate the maximum benefits of each target variable from adopting non-continuous irrigation, which could not necessarily be realized simultaneously. (c) 1yield, 1IRR, 1CH4, and 1N2O under multi-objective optimization. These figures show mean benefits from all non-dominated solutions of the NSGA-II optimization (N = 10 000). Publisher’s remark: please note that the above figure contains disputed territories. for irrigation optimization in rice cultivation and also offers a transferable paradigm for incorporating other management effects into process-based models, thus supporting compre- hensive assessments of sustainable management measures. https://doi.org/10.5194/gmd-18-3799-2025 Geosci. Model Dev., 18, 3799–3817, 2025 3814 Y. Bo et al.: Process-based modeling framework for sustainable irrigation management Appendix A Table A1. Abbreviation table of parameters and variables. Type Abbreviation Description Target variables yield Rice yield (kg ha−1) IRR Irrigation water use (mm) CH4 Methane emissions (kg ha−1) N2O Nitrous oxide emissions (kg ha−1) GWP Integrated global warming potential of CH4 and N2O at 100-year scale, calculated as 27.2×CH4+ 273×N2O (kg ha−1) LAI Leaf area index (m2 m−2) Pn Net photosynthetic rate (kg ha−1) HI Harvest index (–) Effect sizes Ryield,RIRR,RCH4 , RN2O,RLAI,RPn,RHI Effect size of non-continuous flooding irrigation (NCF) on target variables, calculated as the ratio of observations under NCF to those under continuous flooding (CF) (–) Relative changes 1yield, 1IRR, 1CH4,1N2O Relative changes in target variables under NCF compared to CF, calculated as (R− 1)× 100 (%) Model parameters Cumtemp Accumulated temperature for crop maturity (°C) AMIN Minimum assimilation rates (kg hm−2 h−1) P LAI,P Pn,P HI Genetic parameters accounting for cultivar sensitivities to NCF effects on leaf area index, net photosynthetic rate, and harvest index MPmax Maximum CH4 production rate per soil weight at 30 °C (g C g−1 d−1) fN2O_d Maximum portion of denitrification to N2O production (–) Environmental variables T Mean daily air temperature during rice growing season (°C) P Total precipitation during rice growing season (mm) PETc Total crop evapotranspiration during rice growing season (mm) CWA Climatological water availability, calculated as the difference between P and PETc (P−PETc, mm) Soil variables BD Bulk density (g cm−3) Sand Sand content (%) Clay Clay content (%) SOC Soil organic carbon (%) SAT Saturated water content (cm3 cm−3) FWC Field water capacity (cm3 cm−3) Management variables LAWD Lower irrigation threshold, indicated by SWP (kPa) UAWD Upper irrigation threshold (cm) SWP Soil water potential (kPa) UFR Ratio of unflooded days to total growing days (%) Optimization objectives maxYield Maximizing rice yield minIRR Minimizing irrigation water use minCH4 Minimizing CH4 emission minN2O Minimizing N2O emissions Geosci. Model Dev., 18, 3799–3817, 2025 https://doi.org/10.5194/gmd-18-3799-2025 Y. Bo et al.: Process-based modeling framework for sustainable irrigation management 3815 Code and data availability. Origin code of the WHCNS model and required model input files are available at https://figshare.com/s/139f3ad8a70faa99724d (Bo, 2025). The spatial dataset on the harvested area of irrigated rice is available from https://doi.org/10.7910/DVN/KAGRFI (Frolking et al., 2020). Origin climate data are avail- able from https://doi.org/10.24381/cds.adbb2d47 (Hers- bach et al., 2023). Origin soil data are available from https://doi.org/10.7910/DVN/1PEEY0 (Han et al., 2015). The processed climate and soil data for running the model are available at https://figshare.com/s/139f3ad8a70faa99724d (Bo, 2025) (see Readme for detailed explanations of each file). Crop calendar data are available from https://doi.org/10.5281/zenodo.5062513 (Jägermeyr et al., 2021). All other data that support the findings of this study are available in the main text or in the Supplement. Supplement. The supplement related to this article is available on- line at https://doi.org/10.5194/gmd-18-3799-2025-supplement. Author contributions. FZ designed the study. YB and HL per- formed all computational analyses. YB, HL, and FZ prepared the paper. YB, HL, TL, and FZ reviewed and commented on the paper. Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, pub- lished maps, institutional affiliations, or any other geographical rep- resentation in this paper. While Copernicus Publications makes ev- ery effort to include appropriate place names, the final responsibility lies with the authors. Acknowledgements. 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Model Dev., 18, 3799–3817, 2025 https://doi.org/10.17957/IJAB/15.0471 https://doi.org/10.1016/j.agrformet.2023.109785 https://doi.org/10.1093/jxb/erq112 https://doi.org/10.1038/s41467-021-27424-z https://doi.org/10.1038/s41467-021-27424-z https://doi.org/10.1002/2016GB005381 https://doi.org/10.2134/agronj2007.0169 https://doi.org/10.1111/gcb.17199 https://doi.org/10.1016/j.agwat.2023.108265 Abstract Key points Introduction Data and methods WHCNS model and input data Compilation of experimental observations Model improvement Incorporation of physiological effects Contribution analysis Parameters regionalization Calibration of site-scale parameters Parameter upscaling Regional scenario simulations and driver identification Single-objective and multi-objective optimizations Results and discussion Performance of model improvement Performance of regionalized parameters Assessment of regional regulation potentials Tradeoffs between food, water, and greenhouse gas emissions Uncertainties and future direction Conclusion Appendix A Code and data availability Supplement Author contributions Competing interests Disclaimer Acknowledgements Financial support Review statement References