Relevance of feedbacks between water availability and crop systems using a coupled hydrological–crop growth model

Hydrol. Earth Syst. Sci., 29, 4219–4239, 2025 https://doi.org/10.5194/hess-29-4219-2025 © Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License. Relevance of feedbacks between water availability and crop systems using a coupled hydrological–crop growth model Sneha Chevuru1, Rens L. P. H. van Beek1, Michelle T. H. van Vliet1, Jerom P. M. Aerts2,3, and Marc F. P. Bierkens1,4 1Department of Physical Geography, Utrecht University, Utrecht, the Netherlands 2Water Resources Section, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands 3Department of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands 4Unit Subsurface & Groundwater Systems, Deltares, Utrecht, the Netherlands Correspondence: Sneha Chevuru (s.che[email protected]) Received: 23 February 2024 – Discussion started: 12 March 2024 Revised: 8 April 2025 – Accepted: 1 July 2025 – Published: 9 September 2025 Abstract. Individual hydrological and crop growth models often oversimplify underlying processes, reducing the accuracy of both simulated hydrology and crop growth dynamics. While crop models tend to generalize soil moisture processes, most hydrological models commonly use constant vegetation parameters and prescribed phenologies, neglecting the dynamic nature of crop growth. Despite some studies that have coupled hydrological and crop models, a limited understanding exists regarding the feedbacks between hydrology and crop growth. Our objective is to quantify the feedback between crop systems and hydrology on a fine-grained spatiotemporal level. To this end, the PCRGLOBWB 2 hydrological model was coupled with the WOFOST crop growth model to quantify both the oneway and two-way interactions between hydrology and crop growth on a daily time step and at 5 arcmin (∼10 km) resolution. Our study spans the contiguous United States (CONUS) region and covers the period from 1979 to 2019, allowing a comprehensive evaluation of the feedback between hydrology and crop growth dynamics. We compare individual (stand-alone) as well as one-way and two-way coupled WOFOST and PCR-GLOBWB 2 model runs and evaluate the average crop yield and its interannual variability for rainfed and irrigated crops as well as simulated irrigation water withdrawal for maize, wheat, and soybean. Our results reveal distinct patterns in the temporal and spatial variation of crop yield depending on the included interactions between hydrology and crop systems. Evaluating the model results against reported yield and water use data demonstrates the efficacy of the coupled framework in replicating observed irrigated and rainfed crop yields. Our results show that two-way coupling, with its dynamic feedback mechanisms, outperforms one-way coupling for rainfed crops. This improved performance stems from the feedback of WOFOST crop phenology to the crop parameters in the hydrological model. Our results suggest that when crop models are combined with hydrological models, a two-way coupling is needed to capture the impact of interannual climate variability on food production. 1 Introduction Global trends in population and economic growth are expected to increase the demand for water, food, and energy, threatening the sustainable and equitable use of natural resources (Sophocleous, 2004; Tompkins and Adger, 2004). Water as a resource plays a crucial role in crop growth, cooling of thermoelectric plants, hydropower generation, and the covering of domestic and industrial demand. Water, therefore, is an essential resource at the core of the Water-EnergyFood-Ecosystem (WEFE) nexus. Currently, 70 % of total global freshwater withdrawals are accounted for by agriculture, making it the largest water user among all sectors (Dubois, 2011). The Food and Agriculture Organization (FAO) of the United Nations estimated that the demand for water and food resources will likely increase by 50 % by Published by Copernicus Publications on behalf of the European Geosciences Union. 4220 S. Chevuru et al.: Feedbacks between water availability and crop systems using a coupled model 2050 compared to 2015 (IRENA, 2015; Corona-López et al., 2021). The increasing demand for water and food will likely have negative impacts on the environment and will inhibit socio-economic development if a gap opens between growing water demand and water availability. The critical interplay between hydrology and crop growth becomes evident during hydroclimatic extremes (e.g., droughts, heatwaves), as rising demands coincide with potential declines in both water resources and food production (crop yield) (Jackson et al., 2021). In addressing the complexities associated with these challenges, studies by Jägermeyr et al. (2017), utilizing a dynamic vegetation model (LPJmL), evaluated achievable irrigated crop production under sustainable water management. Their findings revealed that 41 % of global water use currently compromises environmental flow requirements crucial for river ecosystems, potentially leading to losses in irrigated croplands. Concurrently, research by Vörösmarty et al. (2000) and Leclère et al. (2014) projects the impacts of climate change on global agricultural systems, foreseeing an increase in irrigated areas in the future, underscoring the necessity for significant investments in irrigation, energy, and water resource management. These findings emphasize the urgent need for improved modeling approaches to assess the complex interaction between water availability, climate change, and crop yields. To address these challenges, biophysical process-based models have been widely used to study the interactions between hydrology and crop growth (Siad et al., 2019; Zhang et al., 2021). These models provide valuable insights into how meteorological events influence water availability for crops, as well as how changes in crop growth and senescence affect hydrological fluxes such as evapotranspiration and root water uptake. However, existing stand-alone crop models and hydrological models often simplify these processes. For instance, crop models usually incorporate a simplified soilwater balance (Zhang et al., 2021) that overlooks local hydrological processes and often do not account for water use for irrigation and non-agricultural sectors. Conversely, most hydrological models simplify or neglect the effects of land cover, phenology, and vegetation changes on hydrological fluxes and the state of available water resources (Tsarouchi et al., 2014). These simplifications arise due to computational expediency, disparities in process scales between hydrology at the river basin level and crop yield at the field level, or incomplete understanding of the other domain by model developers or because of epistemological uncertainty (Siad et al., 2019; McMillan et al., 2018; Shafiei et al., 2014). Recognizing the strengths of both crop models and hydrological models, a coupling allows for the exploration of dynamic crop growth’s influence on hydrology and water use. Additionally, a model coupling allows the incorporation of spatiotemporal variations in hydrological fluxes, including water use, in estimates of crop yield. This understanding becomes crucial when assessed at the regional to global scale, where local deficits can have cascading consequences for both water and food security at the basin scale. The rationale for coupling hydrological and crop growth models is twofold. First, coupling these models allows for the possibility to assess the impact of limited irrigation water availability on crop yield. Second, it enables a detailed analysis of how changes in crop type and growth stages influence groundwater and surface water availability, particularly through processes such as evapotranspiration and root water uptake. By combining a hydrological model with a crop growth model, this study aims to enhance our understanding of hydrological and crop growth interactions and their implications for agricultural productivity and water resource management on the continental scale. Previous studies have attempted to couple hydrological and crop models. Noteworthy efforts by Droppers et al. (2021) have successfully coupled hydrological and crop models, primarily focusing on achieving attainable crop production. However, these efforts were conducted at halfdegree (∼50 km) spatial resolution and focused on long-term average crop yield. They therefore fall short in exploring the aspects of fine-scale spatiotemporal variability in particular as a result of interannual climate variability. Other recent efforts to couple crop growth models and global hydrological models (Jägermeyr et al., 2017) predominantly focus on assessing yield under different scenarios or adaptation measures. However, limited work focused on delving into how two-way interactions and feedback mechanisms between crop growth and hydrological systems operate. In addition, integrated assessment models have been instrumental in studying the combined effects of climate change and socio-economic developments on crop yield and water resources at a large scale. Typically, these models operate on a macro-regional level (Easterling, 1997) and use annual (or 5to 10-yearly) time steps, neglecting the impacts of interand intra-annual variability and particularly short-term hydroclimatic extremes. Furthermore, integrated assessment models often adopt an optimization modeling approach, making them less suitable for studying the effects of hydroclimatic extremes (Ewert et al., 2015). Another class of efforts to link water to crop production is water–food nexus studies, that, however, tend to concentrate on local linkages or provide qualitative descriptions of existing connections (Momblanch et al., 2019). For instance, a recent review of water–food nexus studies focusing on the contiguous United States (CONUS), shows that such studies focus mainly on water security indicators (Veettil et al., 2022) or climate variability impacts on crop yields (Huang et al., 2021). However, knowledge gaps persist, as water and food resources are often evaluated separately (CoronaLópez et al., 2021), exploring allocations through an optimization model (Mortada et al., 2018) that lacks spatiotemporal variability considerations. Notably, there is a lack of effort to understand the interactions between hydrology and crop growth. Further research is needed to bridge these gaps Hydrol. Earth Syst. Sci., 29, 4219–4239, 2025 https://doi.org/10.5194/hess-29-4219-2025 S. Chevuru et al.: Feedbacks between water availability and crop systems using a coupled model 4221 and enhance our understanding of the dynamic and interlinked processes shaping the water–food nexus. To address this knowledge gap, this study aims to quantify the two-way interactions between crop growth and hydrology, hypothesizing that coupling a crop growth model with a hydrological model will improve both crop yield and hydrological predictions by incorporating dynamic feedbacks between water availability and crop processes. Specifically, we hypothesize (1) that a more realistic representation of soil moisture dynamics and water availability will lead to better estimates of water stress and yield and (2) that directly integrating crop growth information into hydrological models will enhance the accuracy of predictions regarding irrigation needs and water resource allocation. To test these hypotheses, we compare three modeling approaches: a stand-alone crop model, a one-way coupled model (where hydrological conditions influence crop growth but not vice versa), and a two-way coupled model (where interactions between hydrology and crop growth are fully represented). By evaluating these different approaches, we aim to determine whether dynamic hydrological–crop growth feedbacks improve the performance of crop yield and irrigation water use simulations. Although this study has a global scale in scope, we limit this analysis to the contiguous United States (CONUS) region to keep the analysis tractable and because CONUS has detailed information on yearly crop production and water use. CONUS is a major producer and contributor to the global production of three primary crops: maize, soybean, and wheat. These crops were selected due to their substantial impact on the agricultural landscape and their pivotal role in shaping global food production trends. The CONUS serves as an ideal study area owing to its extensive availability of relevant data, particularly on agricultural statistics and irrigation water withdrawals, which can provide a basis for analysis and model evaluation. Additionally, the CONUS region exhibits diverse climatic and geographic conditions, contributing to a better understanding of crop and water system dynamics and their responses to various environmental factors. To test the hypotheses coined above, the PCR-GLOBWB 2 hydrological model (Sutanudjaja et al., 2018) is coupled to the WOFOST crop model (de Wit et al., 2019) at a daily time step and at a 5 arcmin (∼10 km) spatial resolution applied to CONUS (Sect. 2.1). In examining the interaction between hydrology and crop growth, we consider both one-way and two-way interactions. First, a one-way coupling is established to evaluate the effect of the simulated water availability of PCR-GLOBWB 2 for rainfed and irrigated crop growth in WOFOST (Sect. 2.1 and 2.3.1). In addition, a twoway coupling is established in which, additional to passing water availability from PCR-GLOBWB 2 to WOFOST, the crop phenology of WOFOST in terms of actual evapotranspiration, leaf area index, and rooting depth is fed back into PCR-GLOBWB 2 (Sect. 2.1 and 2.3.2). The justification for this coupling approach, along with technical implementation details, is elaborated upon in Sect. 2.2. Our framework was tested by comparing individual WOFOST and coupled one-way and two-way model runs to evaluate the impact of feedbacks on crop yield and irrigation water use (Sect. 2.4). The results of these simulations are compared with and evaluated against reported yield statistics and reported annual irrigation withdrawals to assess their validity (Sects. 2.5 and 3). In the end, we elaborate on the uncertainties, strengths, and usability of our coupled model framework for studying the water–food nexus under global change (Sect. 4). 2 Methods A newly coupled hydrological–crop growth model framework (Fig. 1) is developed to include the feedback between crop growth and hydrology. Here, we chose WOFOST as the crop growth model because of its detailed crop phenology and development and PCR-GLOBWB 2 as the hydrological model because of its detailed hydrological process simulation and large-scale applicability. This framework includes both a one-way and two-way coupling between the PCRGLOBWB 2 global hydrological and water resources model (Sutanudjaja et al., 2018) and the WOFOST crop growth model (de Wit et al., 2019). The coupled framework was then used to quantify the impacts of included feedbacks between hydrology and crop growth on a daily time step and 5 arcmin resolution for CONUS. The following (sub)sections provide a description of the PCR-GLOBWB 2 and WOFOST models and modules used (Sect. 2.1), justification of coupling (Sect. 2.2), the model coupling setup (Sect. 2.3), model coupling simulation experiments and parametrization (Sect. 2.3), and validation of crop yield and of irrigation water use (Sect. 2.4). 2.1 Model descriptions 2.1.1 PCR-GLOBWB 2 The PCRaster Global Water Balance (PCR-GLOBWB 2) model (Sutanudjaja et al., 2018), developed at Utrecht University, is a global hydrological and water resource model that operates on a latitude–longitude grid. This model simulates the terrestrial hydrological cycle with daily resolution, incorporating anthropogenic impacts like human-made reservoirs, sectoral water demands, withdrawals, consumptive use, and return flows. PCR-GLOBWB 2 is applied and tested across local to global scales. PCR-GLOBWB 2 utilizes time-explicit schemes for all dynamic processes, running on daily time steps for hydrology and water use and sub-daily steps for hydrodynamic river routing. It simulates moisture storage in two upper soil layers and manages water exchange among the soil, atmosphere, and groundwater. Atmospheric interactions include https://doi.org/10.5194/hess-29-4219-2025 Hydrol. Earth Syst. Sci., 29, 4219–4239, 2025 4222 S. Chevuru et al.: Feedbacks between water availability and crop systems using a coupled model Figure 1. The coupled model framework of the PCR-GLOBWB 2 hydrological and water resource model and the WOFOST crop growth model along with their model structures. The blue arrow represents the one-way coupling from PCR-GLOBWB 2 to WOFOST and the variables that are exchanged; the green arrow is added in case the full two-way coupling is considered. At the start of the day, WOFOST computes evapotranspiration, leaf area index, and rooting depth that is used by PCR-GLOBWB 2 to compute soil moisture status. At the end of the day, soil moisture storage in the upper and lower layers from PCR-GLOBWB 2 is fed to WOFOST to compute crop growth for the next day. Hydrol. Earth Syst. Sci., 29, 4219–4239, 2025 https://doi.org/10.5194/hess-29-4219-2025 S. Chevuru et al.: Feedbacks between water availability and crop systems using a coupled model 4223 precipitation, evaporation, transpiration, and snow processes. The model considers sub-grid variability in land use, soils, and topography, influencing run-off, interflow, groundwater recharge, and capillary rise. Run-off is routed through river networks using methods ranging from simple accumulation to kinematic wave routing, supporting floodplain inundation and surface water temperature simulation. The model includes a reservoir operation scheme for over 6000 human-made reservoirs from the GRanD database, integrated according to their construction year. Human water use is comprehensively modeled, estimating sectoral water demands and converting them into withdrawals from groundwater, surface water, and desalination sources, while accounting for resource availability and groundwater pumping capacity. Consumptive use and return flows are calculated for each sector. PCR-GLOBWB 2’s flexible structure encompasses five main hydrological modules: meteorological forcing, land surface, groundwater, surface water, irrigation, and water use. The meteorological module uses gridded temperature and precipitation data. Reference potential evaporation is calculated using Hamon’s method and employed in the land surface module to determine crop-specific potential evaporation. The groundwater and surface water modules handle fluxes and stores for groundwater and surface water, respectively. The irrigation and water use module simulates water demand, withdrawals, consumption, and return flows, sourcing water from surface water (rivers and reservoirs), groundwater (both renewable and non-renewable), and desalinated water, depending on availability. Detailed descriptions of each module are provided by Sutanudjaja et al. (2018). 2.1.2 WOFOST WOFOST (WOrld FOod STudies) is a crop simulation model developed at Wageningen “School of De Wit”, in the Netherlands, designed to quantitatively analyze the crop growth and potential production of annual field crops at the field scale (Supit et al., 1994). WOFOST employs a fixed time step of 1 d to simulate crop growth based on eco-physiological processes such as phenological development and growth (de Wit et al., 2019). WOFOST has found extensive application in assessing the impacts of climate change and management strategies on crop growth and yield at local to global scales (Droppers et al., 2021). The WOFOST crop model comprises four modules: meteorological, crop, astronomical, and soil (Fig. 1). The WOFOST modules simulate a range of processes, including phenological development, CO2assimilation, leaf development, light interception, transpiration, respiration, root growth, assimilated partitioning to the various organs, and the formation of dry matter. The model’s output includes simulated crop biomass total, crop yield, and variables such as leaf area and crop water use. Temperature effects on crop development within WOFOST are modeled using temperature sums, which accumulate daily temperatures above a specified threshold. These sums influence germination and phenological stages, thereby affecting CO2assimilation. Additionally, the model accounts for the direct and indirect effects of suboptimal daytime temperatures on crop growth and development, which are critical to overall plant performance. Daily photosynthesis in the crop growth model is simulated by considering absorbed radiation and water stress. After accounting for the assimilates used in maintenance respiration, the remaining resources are allocated among the plant’s leaves, stems, roots, and storage organs. A key internal driver of this process is the leaf area index (LAI), which results from leaf area dynamics governed by photosynthesis, biomass allocation, leaf age, and developmental stage. LAI, in turn, influences the daily rates of photosynthesis. WOFOST has been finely tuned to account for diverse climate and soil conditions, particularly for commonly studied crops such as maize, soybean, and wheat, thereby reducing the need for further recalibration. This pre-tuning ensures that simulations reliably capture the growth and yield responses of these crops under varying environmental conditions. For more detailed information on the fine tuning of crop variables, see de Wit and Boogaard (2021). WOFOST employs a classic water balance approach designed for freely draining soils where groundwater is too deep to affect soil moisture content in the rooting zone. This approach divides the soil profile into two compartments: the rooted zone and the lower zone extending from the actual rooting depth to the maximum rooting depth. The subsoil below this maximum rooting depth is not considered. As roots extend deeper towards the maximum rooting depth, the lower zone gradually merges with the rooted zone. This approach is suitable for regional applications with limited soil property information. Soil moisture in the root zone serves as a primary link between the WOFOST model and the underlying soil module. For a detailed description of the WOFOST crop growth model, we refer to de Wit and Boogaard (2021) and Supit et al. (1994). 2.2 Justification of model coupling The integration of the hydrological model PCR-GLOBWB 2 (Sutanudjaja et al., 2018) with the crop growth model WOFOST (Supit et al., 1994) is crucial for accurately simulating the complex interactions between water availability and crop development. The hydrological model PCRGLOBWB 2 is designed to simulate hydrological processes such as river discharge, groundwater flow, and water storage dynamics. It provides detailed representation and insights into the state and dynamics of water resources over large spatial scales and long temporal scales. On the other hand, the crop growth model WOFOST is focused on simulating crop phenology, including the stages of crop development, https://doi.org/10.5194/hess-29-4219-2025 Hydrol. Earth Syst. Sci., 29, 4219–4239, 2025 4224 S. Chevuru et al.: Feedbacks between water availability and crop systems using a coupled model growth, and yield formation under varying environmental conditions. Despite the strengths of each model, they individually have limitations that can affect the accuracy of simulations. PCR-GLOBWB 2 relies on static vegetation parameters, such as fixed leaf area index (LAI) and root depth, which can limit its ability to reflect the dynamic nature of crop growth. On the other hand, WOFOST offers a detailed and dynamic representation of crop phenology and development, adjusting parameters like LAI and root depth based on actual growth stages. However, WOFOST employs a simplified water balance model, which may not adequately capture complex hydrological interactions. To address these limitations, it is important to combine the strengths of both models to enhance hydrological and crop modeling performance. By integrating WOFOST’s detailed crop growth simulation capabilities with the robust hydrological process simulations of PCR-GLOBWB 2, we can better understand and represent the soil–plant–atmosphere interactions. Therefore, this study integrates PCR-GLOBWB 2 and WOFOST by passing soil moisture data from PCRGLOBWB 2 to WOFOST and feeding vegetative fluxes from WOFOST back into PCR-GLOBWB 2 on a daily basis. Additionally, to understand the intricate dynamics between hydrology and crop model, PCR-GLOBWB 2 is coupled to WOFOST in one-way and two-way interactions. In evaluating various coupling methods for integrating hydrological and crop models, we identified several approaches, including one where the hydrological model directly provides detailed irrigation schedules and percolation rates to the crop model. While this method offers highly detailed hydrological inputs, it often leads to inconsistencies due to the separate handling of soil moisture dynamics between the models, resulting in errors in soil moisture management and water balance. Commonly used coupling procedures, such as those described by Li et al. (2014) and Tsarouchi et al. (2014), calculate potential evapotranspiration and vegetation water uptake within the hydrological model, which is then passed to the crop model to simulate crop growth. The crop model then calculates state variables like leaf area index, root depth, and canopy height, which are subsequently fed back into the hydrological model. However, these methods can introduce system errors, particularly in the transpiration module, if there is a discrepancy between evapotranspiration calculated by the crop and hydrological model, as highlighted by Wang et al. (2012). Our chosen coupling method, where soil moisture is calculated by PCRGLOBWB 2 and passed to WOFOST and vegetative dynamics and evapotranspiration fluxes are then fed back into PCRGLOBWB 2, offers a balanced approach that ensures consistency and the necessary complexity and efficiency in the simulations. The selected coupling approach also addresses specific challenges associated with the models. PCR-GLOBWB 2 allows for flexible land cover classification and parametrization, which is essential for accurately representing diverse crop types and their interactions with water resources. For this study, we defined 12 land cover types (tall natural, short natural, pasture, irrigated maize, irrigated soybean, irrigated wheat, non-paddy irrigated crops (irrigated other crops), paddy irrigated crop, rainfed maize, rainfed soybean, rainfed wheat, and rainfed others). WOFOST’s role in this coupling is to pass the fluxes of irrigated and rainfed maize, soybean, and wheat to PCR-GLOBWB 2, ensuring a detailed simulation of crop water use. One of the key considerations in this coupling is accurately calculating the soil-water balance. Given its more advanced soil moisture accounting scheme, PCR-GLOBWB 2 handles this aspect, as WOFOST’s simpler single-layer leaky bucket approach could introduce complexities if soil moisture data were passed from WOFOST to the multi-layered soil model of PCR-GLOBWB 2. Therefore, the coupling approach we selected minimizes potential discrepancies while maximizing the strengths of each model. It is important to acknowledge that individual models come with inherent uncertainties, related to model structure, parameters, and data. When coupling these models, the level of uncertainty compounds further (Kanda et al., 2018). Additionally, the nature of coupling itself can introduce another layer of uncertainty. According to Antle et al. (2001), coupling models leads to further conceptualization and computational problems, elevating uncertainty levels. Therefore, an efficient coupling is essential to minimize these risks. There are three primary methods for coupling models (Vereecken et al., 2016): light/loose coupling, external/framework coupling using a central coupler, and full coupling. In light or loose coupling, the output of one model serves as the input for the other, which can lead to a straightforward but limited interaction. Framework coupling uses a central coupler for communication between models without requiring code modification, offering a balance between integration and flexibility. Full coupling involves both models sharing the same boundary conditions, drivers, and variables, which requires significant code modification. 2.2.1 Implementation of the (BMI) framework coupling Given the complexity of integrating the PCR-GLOBWB 2 and WOFOST models and the need for efficient simulations, we opted for framework coupling. This approach was chosen because WOFOST and PCR-GLOBWB 2 are written in different programming languages (C and PCRaster-Python, respectively). Framework coupling allows for seamless interaction between the models at each time step, facilitating dynamic exchanges while limiting I/O-related computation times. We employed the Basic Model Interface (BMI) for this purpose (Hutton et al., 2020; Peckham et al., 2013). The decision to use BMI over alternative techniques was driven by its non-interfering nature, ensuring no code entanglement Hydrol. Earth Syst. Sci., 29, 4219–4239, 2025 https://doi.org/10.5194/hess-29-4219-2025 S. Chevuru et al.: Feedbacks between water availability and crop systems using a coupled model 4225 and facilitating seamless connection between the two models. BMI functions act as a bridge, enabling direct variable exchange between WOFOST and PCR-GLOBWB 2 without modifying their source code. This non-invasive approach ensures a flexible and robust coupling framework, allowing continuous model development without interruptions. Integrating BMI functions into both models provides a set of functions for retrieving or altering model variables, thereby enhancing adaptability and efficiency. An additional wrapper was required to translate the modelspecific BMI functions into Python-compatible information to establish a Python-based coupling framework. The Babelizer wrapper (CSDMS, 2024) was utilized for this purpose with the WOFOST BMI. Conversely, no supplementary wrapper is needed in the PCR-GLOBWB 2 BMI, as the model is inherently Python-compatible due to its programming language. The Babelizer wrapper facilitates the integration of the WOFOST model by utilizing an input file that provides essential details, including the model library, entry point, packages, and author information. This input file guides the construction of the necessary dependencies to generate Python bindings. Once these Python bindings are created, Babelizer ensures the successful integration of the WOFOST BMI into Python by verifying that the bindings are correctly built and loaded. 2.2.2 Workflow of PCR-GLOBWB 2–WOFOST model framework In the PCR-GLOBWB 2–WOFOST coupling framework, the workflow after implementing BMI functions remains consistent for both one-way and two-way coupling, up until the initialization of the hydrological and crop models (Fig. 2). Before initiating the Python session, it is crucial to activate the BMI wrap environment, which includes all necessary libraries for both hydrological and crop models. After this setup, the PCR-GLOBWB 2 and WOFOST models, along with their configuration files that define the coupling settings, are loaded into the Python session. BMIwrap reads the configuration file, initializing the model-specific configuration settings before establishing both models as a coupled entity. Once the coupled models are initialized, a loop is initiated, commencing at the start time and concluding at the end time. During each iteration of this loop, variables are exchanged between the models based on the one-way or two-way coupling configuration. This iterative process ensures a continuous and seamless flow of information between the PCRGLOBWB 2 hydrological model and the WOFOST crop model throughout the simulation period. 2.3 Model coupling setup The developed PCR-GLOBWB 2–WOFOST coupled model framework integrates hydrological and crop models through both one-way and two-way couplings, as illustrated in Figs. 1 and 3. This model coupling aims to assess the intricate interactions between hydrology and crop growth under different agricultural conditions, specifically irrigated and rainfed settings. The one-way coupling examines the impact of water availability on crop growth, while the two-way coupling incorporates the exchange of soil moisture status and hydrological parameters and fluxes based on crop status. 2.3.1 One-way coupling In the one-way coupling, information on soil moisture status is passed from PCR-GLOBWB 2 to WOFOST (Fig. 3b). Here, PCR-GLOBWB 2 simulates soil moisture content for every day, and the soil water storage is simulated separately for each land cover type. Consequently, WOFOST receives the soil moisture content from PCR-GLOBWB 2 as input, with generally higher values of soil moisture for irrigated crops than of nearby rainfed crops. WOFOST then simulates the crop yield based on the simulated soil moisture content and the same meteorological inputs as PCR-GLOBWB 2 uses. The combined model framework captures the impact of hydroclimatic conditions by assessing water stress and heat stress. Water stress, influenced by soil moisture levels derived from PCR-GLOBWB 2, affects various processes in WOFOST such as a reduction in the leaf area, a decrease in the assimilation of biomass (growth), changes in the partitioning of biomass, and an increase in various plant organs of senescence (aging processes). Elevated temperatures have varying effects across different stages of crop development. They can accelerate crop growth by promoting faster accumulation of growing degree days, which are essential for determining crop maturity. However, prolonged exposure to high temperatures can also induce heat stress, adversely impacting crop health and potentially shortening the overall duration of the crop’s growth cycle. Insufficient water availability that limits the evapotranspiration also reduces the amount of assimilation and the corresponding yield. 2.3.2 Two-way coupling –In addition to one-way coupling, the two-way coupling approach involves iterating data exchange between WOFOST and PCR-GLOBWB 2 twice per day. WOFOST calculates the vegetation states (such as leaf area index (LAI), biomass, and root depth) and fluxes (e.g., evapotranspiration) for irrigated and rainfed maize, soybean, and wheat crops, while other vegetation and non-vegetation fluxes for other crops are simulated within PCR-GLOBWB 2. To be more specific, for https://doi.org/10.5194/hess-29-4219-2025 Hydrol. Earth Syst. Sci., 29, 4219–4239, 2025 4226 S. Chevuru et al.: Feedbacks between water availability and crop systems using a coupled model Figure 2. Schematization of the workflow of the coupled PCR-GLOBWB 2–WOFOST model framework. the fraction of land cover that is different from maize, wheat, and soybean, the vegetation states and fluxes are calculated within the PCR-GLOBWB 2. For these land cover types, vegetation phenology in the form of crop factors is approximated by a yearly climatology. In the two-way coupling, data are exchanged between PCRGLOBWB 2 and WOFOST as follows (Fig. 3c): at the start of the day, PCR-GLOBWB 2 passes the previous day’s soil moisture to the WOFOST, assuming no root development has occurred overnight. WOFOST then computes the potential evapotranspiration based on the meteorological variables at the current time step and the pertinent vegetation states from the previous time step (leaf area index (LAI), rooting depth, and crop height). It also calculates the actual bare soil evaporation, actual transpiration (actual evapotranspiration), potential evaporation, and open water evaporation. –The calculated fluxes are passed to PCR-GLOBWB 2, together with the root depth. The root depth is used to partition the actual transpiration from the single root zone of WOFOST over the two soil layers of PCRGLOBWB 2, dependent on the root content. For both irrigated and rainfed crops, the actual evapotranspiration from WOFOST is forced to PCR-GLOBWB 2 and used to update the soil moisture content of the two soil layers in PCR-GLOBWB 2 for the current daily time step. –In the case of irrigated crops, the stages of vegetated development are used to compute the amount of irrigation in PCR-GLOBWB 2. Potential evaporation is used to calculate the irrigation water demand for paddy crops (not considered here), whereas the irrigation water requirement for non-paddy crops is computed based on the soil moisture status according to the FAO guidelines (Allen et al., 1998). The irrigation water requirement is withdrawn from the available water resources in PCRGLOBWB 2, and the available irrigation water supply is applied to the crops in addition to any natural precipitation. –At the end of the day, the resulting soil moisture from the two soil layers from PCR-GLOBWB 2 is aggregated to provide a total for the root zone of each crop, which is then passed back to WOFOST. –Using the updated soil moisture from PCRGLOBWB 2, WOFOST computes the actual transpiration and updates crop growth and the crop status. The new fluxes and crop parameters are then passed to PCR-GLOBWB 2 again on the next day (Figs. 1 and 3c). In this two-way coupling, the crop phenology from WOFOST determines evapotranspiration and thus the soil hydrology of PCR-GLOBWB 2, particularly during dry spells. Compared to the predefined phenology of PCRGLOBWB 2, the LAI, rooting depth, and evapotranspiraHydrol. Earth Syst. Sci., 29, 4219–4239, 2025 https://doi.org/10.5194/hess-29-4219-2025 S. Chevuru et al.: Feedbacks between water availability and crop systems using a coupled model 4227 Figure 3. Schematic view of the coupled model framework: panel (a) shows the calculated phenology from WOFOST and PCR-GLOBWB 2 over time along with the associated fluxes. Panel (b) displays a detailed representation of the one-way coupling approach, where soil moisture is transferred from PCR-GLOBWB 2 to WOFOST, and panel (c) illustrates the two-way coupling approach, where variables are exchanged in both directions between PCR-GLOBWB 2 and WOFOST. tion as simulated by WOFOST will lag during dry spells and less water may be lost from PCR-GLOBWB 2. However, the thinner rooting depth will also lead to an earlier drying out of the soil and reduced capillary rise. This subsequently leads to reduced soil moisture (compared to PCR-GLOBWB 2 standalone), which in turn feeds back to a reduced simulated yield in WOFOST, in particular for rainfed crops. For irrigated crops, the extra water supplied will largely offset these feedbacks and result in near-optimum growth. 2.4 Model coupling simulation experiments and parametrization Hydrological simulations were conducted with a daily time step at a 5 arcmin grid resolution, where for each grid cell WOFOST was used to simulate crop growth for irrigated and rainfed maize, soybean, and wheat. To assess the impact of hydrology on crop growth and understand the interactions between hydrology and crop growth, three sets of simulations were carried out for both irrigated and rainfed crops: (a) stand-alone simulations using the WOFOST crop model solely, (b) one-way coupled, and (c) two-way coupled PCR-GLOBWB 2–WOFOST simulations. Note that for the stand-alone simulations with WOFOST under irrigation the potential crop yield is simulated, which is potential yield without water (and nutrient) stress except for temperature effects. When coupled to PCR-GLOBWB 2, water stress can occur even for irrigated crops in case there is not enough water available (in PCR-GLOBWB 2) to fully satisfy the crop water demand. For rainfed crops, growth is influenced by available soil moisture for all simulations and is thus sensitive to water stress and temperature. Green water from natural rainfall is the primary water supply in rainfed analysis, while irrigated crops get water from both green and blue water (from surface water and renewable groundwater) and nonrenewable groundwater, leading to groundwater depletion. Daily time step simulations covered the period from 1979 and 2019, using weather variables (minimum and maximum air temperature, shortwave radiation, precipitation, vapor pressure, wind speed, and humidity) from the W5E5 forcing data (Lange et al., 2021) as input to PCR-GLOBWB 2 https://doi.org/10.5194/hess-29-4219-2025 Hydrol. Earth Syst. Sci., 29, 4219–4239, 2025 4234 S. Chevuru et al.: Feedbacks between water availability and crop systems using a coupled model Figure 6. Mean irrigated crop yields for maize, soybean, and wheat within CONUS as obtained from stand-alone, one-way and two-way coupled simulations and differences between one-way and two-way coupled simulations for 1979–2019. Legend in percentage of values shown on the yaxes. Figure 7. Mean rainfed crop yields for maize, soybean, and wheat within CONUS as obtained from stand-alone, one-way and two-way coupled simulations and differences between one-way and two-way coupled simulation for 1979–2019. Legend in percentage of values shown on the yaxes. Hydrol. Earth Syst. Sci., 29, 4219–4239, 2025 https://doi.org/10.5194/hess-29-4219-2025 S. Chevuru et al.: Feedbacks between water availability and crop systems using a coupled model 4235 Figure 8. Coefficient of variation (CV) over 1979–2019 of irrigated crop yields for maize, soybean, and wheat within CONUS as obtained under stand-alone, one-way, and two-way coupling and the difference between one-way and two-way coupling. Figure 9. Coefficient of variation (CV) over 1979–2019 of rainfed crop yields for maize, soybean, and wheat within CONUS as obtained under stand-alone, one-way, and two-way coupling and the difference between one-way and two-way coupling. 4 Discussion and conclusion In this study, we developed a coupled hydrological–crop growth model framework to investigate the intricate feedbacks between water availability and crop growth within the CONUS region focusing on maize, soybean, and wheat. This discussion delves into the implications of the findings, emphasizing their significance and addressing both methodological considerations and inherent uncertainties. We hypothesized that a more realistic representation of soil moisture dynamics and water availability will lead to better estimates of water stress and yield outcomes. Validahttps://doi.org/10.5194/hess-29-4219-2025 Hydrol. Earth Syst. Sci., 29, 4219–4239, 2025 4236 S. Chevuru et al.: Feedbacks between water availability and crop systems using a coupled model Figure 10. Spatial variation of one-way and two-way irrigation water withdrawal compared with USGS-reported water withdrawal data per state for all crops across the CONUS region with a logarithmic scale. Figure 11. Temporal variation of one-way and two-way irrigation water withdrawal compared with USGS water withdrawal data of 5-year intervals across the CONUS region with a logarithmic scale. tion against reported yields however did not show a notable improvement compared to the stand-alone WOFOST, both for rainfed and for irrigated agriculture. Thus, if the focus is on yield only, coupling with a hydrological model such as PCR-GLOBWB 2 seems not needed. However, although not picked up by the validation exercise, the coupling still allows the inclusion of the impact of limited irrigation water availability as well as the impact of crop development on the hydrological system. Our study also shows that if the focus is on these impacts, it is necessary to use a two-way coupling to make sure that crop developments feed back on evaporation and soil moisture. Another hypothesis we tested is whether integrating realtime crop growth information into hydrological models will enhance the accuracy of predictions regarding irrigation needs and water resource allocation. Although it can be expected that feeding back crop information to PCRGLOBWB 2 in the two-way coupling would improve estimates of irrigation water withdrawal, this could not be substantiated by comparison with reported water withdrawal statistics. One possible explanation is the use of constant crop area data across all years, which introduces uncertainties and limits the model’s responsiveness to actual land-use dynamics. The spatiotemporal analysis of hydrological impacts on crop growth confirms the results shown from the comparison with reported values. Notably, for rainfed crops, the estimated yield is mostly higher for one-way coupled simulations compared to two-way and stand-alone simulations. Also, the inter-annual variation of yield, that is, the sensitivity to drier and wetter years, is notably higher for the twoway coupled and stand-alone simulations than the one-way coupled simulations. This suggests that for a correct sensitivity to drought, a two-way coupling that includes the feedback of crop status to the hydrological system is needed. Our studies adds to previous work by Droppers et al. (2021), which investigated worldwide water constraints and sustainable irrigation by coupling the Variable Infiltration Capacity (VIC) hydrological model with WOFOST, and Zhang et al. (2021), who focused on refining the coupled VIC hydrological model with a crop growth model EPIC by incorporating the evapotranspiration module at a regional scale. In comparison, our research extends the analysis to a finer spatial scale and places a stronger emphasis on the comprehensive integration of feedback loops between hydrology and crop growth. Particularly, we demonstrate the importance of two-way coupling in capturing realistic yield outcomes, which is particularly evident for rainfed crops. This is mainly because the two-way coupled system addresses the influence of crop status on evapotranspiration and rooting depth, thereby impacting soil moisture content, which in turn feed backs on crop growth. The two-way coupling approach provides a more realistic depiction of water availability for crops, which results in larger inter-annual variability and lower mean crop yields when inter-annual climate variability is significant. Including this two-way interaction is particularly important under drier conditions (see Sect. 3.2) or if the coupled framework is used to assess reduced surface water availability under climate change or the impact of environmental constraints on groundwater and surface water use. While the results of this study offer valuable insights into the coupled hydrological–crop growth model framework, it is essential to recognize and address the uncertainties associated with the structure and parametrization, as well as inherent limitations in the research. A significant limitation is that the study does not account for potential advanceHydrol. Earth Syst. Sci., 29, 4219–4239, 2025 https://doi.org/10.5194/hess-29-4219-2025 S. Chevuru et al.: Feedbacks between water availability and crop systems using a coupled model 4237 ments in agricultural technology and evolving farming practices, which could impact crop yields. This becomes evident when comparing yield estimates with observations over time (Sect. 3.1; Fig. 4). Furthermore, uncertainties linked to input datasets (Porwollik et al., 2017; Roux et al., 2014) such as crop calendars, cultivars, and land-use changes introduce potential limitations and implications for the study results. Accurate representations of crop growth dynamics hinge on accurate crop calendar definitions (Wang et al., 2022), encompassing planting, maturation, and harvesting periods. Variations in these timelines due to climate change or evolving agricultural practices potentially introduce uncertainties in yield predictions. Additionally, the assumption of static cultivars neglects potential shifts in agricultural practices or the introduction of new varieties, influencing crop growth responses to environmental stressors over time. Land-use changes further contribute to uncertainties (Prestele et al., 2016; Eckhardt et al., 2003; Dendoncker et al., 2008) as dynamic shifts in agricultural practices alter water demand, evapotranspiration patterns, and overall hydrological dynamics. Ignoring these potential shifts limits the model’s ability to capture the complex interactions between water and crop systems, and this should be considered in future development steps. Hence, future work should also consider representing the dynamic nature of crop areas, including both irrigated and rainfed crop harvest areas, as well as the total crop area. The assumption of constant areas, as made in prior studies (Müller et al., 2017; Ai and Hanasaki, 2023; Jägermeyr et al., 2021), was based on data availability constraints but acknowledging the potential variability in these factors over time. Addressing this aspect is crucial for enhancing the accuracy of yield calculations and, consequently, advancing the overall understanding of hydrological–crop growth interactions. The integration of such variability into modeling frameworks is essential not only for improving the accuracy of assessments but also for contributing to an enhanced understanding of the broader water–food nexus. In conclusion, the development and application of the twoway coupled hydrological–crop growth model framework presented in this study represent a significant advancement in our ability to understand the cascading mechanisms and feedbacks between water and crop systems. Although it does not show an improvement of yield estimates per se, the coupling framework enhances our understanding of the interplay between hydrology and crop growth. Also, through the sectoral water use modules of PCR-GLOBWB 2, it contains the necessary components to evaluate large-scale water use management strategies and simulate the large-scale impacts of informed decision-making under change, particularly when dealing with hydroclimatic extremes. Code and data availability. The developed coupled PCRGLOBWB 2-WOFOST model framework is available at https://doi.org/10.5281/zenodo.10681452 (Chevuru, 2024). The datasets used in the coupled model framework are available at https://opendap.4tu.nl/thredds/catalog/data2/pcrglobwb/version_ 2019_11_beta/pcrglobwb2_input/catalog.html (last access: 30 August 2025). Supplement. The supplement related to this article is available online at https://doi.org/10.5194/hess-29-4219-2025-supplement. Author contributions. SC designed the study and performed the analyses, validation, and visualization of the results under the supervision of LPHvB, MTHvV, and MFPB. SC developed the coupled framework in close collaboration with LPHvB. JA contributed to the conceptualization of software. SC wrote the original draft manuscript, and all co-authors reviewed and edited the manuscript. 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, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Acknowledgements. The authors acknowledge Bram Droppers (Utrecht University) and Iwan Supit (Wageningen University) for their valuable advice on the WOFOST crop model. Financial support. This research has been funded by the European Union Horizon program GoNexus project (grant agreement number 101003722). Michelle T. H. van Vliet was financially supported by the Netherlands Scientific Organisation (NWO) by a VIDI grant (VI.Vidi.193.019) and the European Research Council (ERC) under the European Union’s Horizon Europe Research and Innovation program (grant agreement 101039426 B-WEX). Review statement. This paper was edited by Shraddhanand Shukla and reviewed by four anonymous referees. https://doi.org/10.5194/hess-29-4219-2025 Hydrol. Earth Syst. Sci., 29, 4219–4239, 2025 4238 S. Chevuru et al.: Feedbacks between water availability and crop systems using a coupled model References Ai, Z. and Hanasaki, N.: Simulation of crop yield using the global hydrological model H08 (crp.v1), Geosci. Model Dev., 16, 3275– 3290, https://doi.org/10.5194/gmd-16-3275-2023, 2023. Allen, R. G., Pereira, L. 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