Water Availability Constraints Research Articles

Wind, solar and biogas power generation in water-stressed areas of Morocco considering water and biomass availability constraints and carbon emission limits

The energy sector of Morocco relies mainly on imported fossil fuels. The expensive import bills associated with fossils, as well as the global drive for greenhouse gas (GHG) emission reduction, have compelled the country to consider the utilization of renewable energy resources such as hydro, wind, and solar for energy generation. Power generation from wind and solar is highly intermittent hence require storage systems to guarantee supply security. Considering the abundant nature of sheep rearing in Morocco, power generation from sheep dung through anaerobic digestion (AD) can serve as an alternative power generation source to alleviate the intermittent situations of wind and solar since biomass supply for AD is not affected by the weather. Therefore, this paper evaluates the capability of hybrid power generation, using AD of sheep dung, wind, and solar in two selected areas of the Fez-Meknes region in Morocco. A mixed-integer linear programming model was implemented in A Mathematical Programming Language (AMPL) using a linear solver of CPLEX. The results of the analysis indicated that only standalone wind generation and hybrid wind/PV/biogas generation are able to meet the total load demand of the two study locations. The hybrid generation resulted in a total carbon emission of 349.30 and 3871.60 g/year at a cost of 13,088.38 and 145,394.13 $/year for Tazouta and Fez respectively in supplying the whole load of the study locations, whiles the standalone wind generator resulted in a total carbon emission of 274 and 2603 g/year at a total cost of 23,352 and 239,500 $/year for satisfying the electric load of these two regions respectively. The results shows that, hybrid solar/wind/PV is the most ideal for supplying power to the two study locations in terms of cost and load satisfaction, while the wind is the most ideal in terms of load satisfaction and carbon emissions.

Sustainable Water Harvesting for Improving Food Security and Livelihoods of Smallholders under Different Climatic Conditions of India

In India, the per capita availability of water is projected to be 1465 m3 and 1235 m3 by the years 2025 and 2050, respectively, and hence, India would be a water-stressed country as per the United Nations’ standard of less than 1700 m3 per capita water availability. India is predominantly an agricultural-dominant country. Rainfed agriculture in the country contributes 40% of food grain production and supports half of the human population and two-thirds of the livestock population. The country has 15 different agro-climatic zones, and each agro-climatic region has its own constraints of water availability and management along with the potential for their optimum utilization. Such situations warrant the formulation of regional-level strategies. Efforts were made to integrate and evaluate the feasibility of water harvesting and its utilization at twelve different sites representing six different agro-climatic conditions spanning pan India. It was found that water harvesting through tanks/ponds is a feasible approach and can increase the crop production as well as diversification. The results reveal that the range of crop diversification index increased from 0.49–0.85 to 0.65–0.98; the crop productivity index increased from 0.28–0.66 to 0.66–0.90; the cultivated land utilization index increased from 0.05–0.69 to 0.34–0.84; and the crop water productivity index increased from 0.20–0.51 to 0.56–0.96, among other production and diversification indices, due to additional water availability through rainwater harvesting intervention. Moreover, the gross return increased from INR 43,768–704,356 to INR 220,840–1,469,108 ha−1, representing a 108 to 400% increase in the returns due to the availability of water. The findings of this study suggest that the water harvesting in small ponds/tanks is economical and feasible, requires less technological intervention, and increases crop diversification in all the studied agro-climatic conditions, and hence, the same needs to be encouraged in the rainfed areas of the country.

The Dominance-based Rough Set Approach for analysing patterns of flexibility allocation and design-cost criteria in large-scale irrigation systems

Water planners must provide end-users with reliable and high-quality access to fresh water while complying with financial, institutional, and water availability constraints. In the pursuit of these goals, an over-investment in design can result in stranded assets of significant value and often unwanted environmental implications. Under-investment can lead to supply restrictions affecting human health, the economy, and the environment. The present study uses the Dominance-based Rough Set Approach (DRSA) to develop a balancing strategy concerning complexities encountered in water resource planning for irrigation systems. The methodology relies on the Dominance-based Learning from Examples Module (DOMLEM) algorithm, which extracts minimal set of rules regarding relevant combinations between flexibility allocation and design-cost criteria. The algorithm delineates outcomes in the form of “if., then.” rules that translate decision possibilities facing water planners into: “if (the design is more flexible by this amount), then (we expect this range of cost increment”). Then, a confusion matrix is computed for each irrigation system in order to exclude the rules generating incorrect and ambiguous classification results. The outcome reveals that cost is more subject to elasticity at the hydrant (eh) increment than the network’s coefficient (r). Furthermore, the analysis reveals that the parameter P(q) has only a minor impact on the cost and, as a result, the final decision. Any elasticity (eh) less than 3 assigned to any given coefficient (r) becomes a low-cost increment. For any given value of (eh), the cost increases as the coefficient (r) decrease. Elasticity from 4 to 5 with a network's coefficient (r) equal to or greater than 18/24 becomes a medium-cost increment. Elasticity (eh) from 5 to 6 associated with an (r) equal or less than 16/24 becomes a very high-cost increment. Finally, rather than identifying one solution that seems better than others, this approach provides an interactive schematic that helps identify the appropriate range of flexibility justified by the expense criterion, which allows for debate and supports decision-making.

Pulse Root Ideotype for Water Stress in Temperate Cropping System.

Pulses are a key component of crop production systems in Southern Australia due to their rotational benefits and potential profit margins. However, cultivation in temperate cropping systems such as that of Southern Australia is limited by low soil water availability and subsoil constraints. This limitation of soil water is compounded by the irregular rainfall, resulting in the absence of plant available water at depth. An increase in the productivity of key pulses and expansion into environments and soil types traditionally considered marginal for their growth will require improved use of the limited soil water and adaptation to sub soil constrains. Roots serve as the interface between soil constraints and the whole plant. Changes in root system architecture (RSA) can be utilised as an adaptive strategy in achieving yield potential under limited rainfall, heterogenous distribution of resources and other soil-based constraints. The existing literature has identified a “‘Steep, Deep and Cheap” root ideotype as a preferred RSA. However, this idiotype is not efficient in a temperate system where plant available water is limited at depth. In addition, this root ideotype and other root architectural studies have focused on cereal crops, which have different structures and growth patterns to pulses due to their monocotyledonous nature and determinant growth habit. The paucity of pulse-specific root architectural studies warrants further investigations into pulse RSA, which should be combined with an examination of the existing variability of known genetic traits so as to develop strategies to alleviate production constraints through either tolerance or avoidance mechanisms. This review proposes a new model of root system architecture of “Wide, Shallow and Fine” roots based on pulse roots in temperate cropping systems. The proposed ideotype has, in addition to other root traits, a root density concentrated in the upper soil layers to capture in-season rainfall before it is lost due to evaporation. The review highlights the potential to achieve this in key pulse crops including chickpea, lentil, faba bean, field pea and lupin. Where possible, comparisons to determinate crops such as cereals have also been made. The review identifies the key root traits that have shown a degree of adaptation via tolerance or avoidance to water stress and documents the current known variability that exists in and amongst pulse crops setting priorities for future research.

Multi objective optimization of ethanol production based on regional resource availability

Production of lignocellulosic biofuels must respect the regional water availability constraints, while simultaneously balancing the social and economic objectives. This work has proposed a multi-objective optimization model to design regional biomass to energy system incorporating these factors. The goal is to design a bioenergy system that is constrained by district level land availability, regional water availability, and competing water demands. The allocation of agricultural land to selected crops is the key decision. Additionally, the number of biorefineries, their locations, and their feedstock procurement strategies are also optimised. Four different objectives, namely, minimization of ethanol cost, minimization of water footprint of ethanol, minimization of irrigation water, and maximization of farmers’ profit, are considered for meeting a specified monthly ethanol demand. The resulting multi-objective mixed-integer linear programming problem was applied to a case study of 33 districts in Maharashtra, India. The problem was solved using the ϵ-constraint method where all objectives, except one, were used as constraints. Results indicated a significant trade-off between ethanol cost and irrigation water requirement. Sorghum, wheat and cotton were the prominent crops recommended by the model at various regions in the Pareto front, while Jalna, Kolhapur and Pune were among the most frequently selected locations for setting up biorefineries. Ethanol cost varied from ₹47/l ($0.63/l) to ₹60/l ($0.80/l), while water footprint varied from 665 l/l to 301 l/l, irrigation water varied from 43m3/ha to 2768m3/ha, and farmers’ profit from ₹21059/ha ($281/ha) to ₹6825/ha ($91/ha).

Synergetic management of energy-water nexus system under uncertainty: An interval bi-level joint-probabilistic programming method

Synergic management of energy-water nexus (EWN) system is essential for coping with the dilemma of joint shortage of energy and water and supporting socio-economic sustainable development. The system is full of multiple uncertainties, making deterministic analysis methods infeasible. In this study, an interval bi-level joint-probabilistic programming (IBJP) approach is first developed through incorporating bi-level programming (BP) and interval joint-probabilistic programming (IJP) within a framework. IBJP has advantages in balancing the tradeoff between two-level decision makers under uncertainty, tackling uncertainties expressed as joint probabilities and interval values, and examining the risk of violating joint-probabilistic constraints. Then, the developed method is applied to planning China’s EWN system over a long-term planning horizon (2021–2050). Multiple scenarios related to different groups of constraint-violation levels for violating electricity demand and/or water availability constraints are examined. Results reveal that uncertainties associated with joint and individual probabilities have effects on the synergic management of EWN system. Results also disclose that limited water resource can promote electricity generation structure toward a low water-intensity, clean and sustainable pattern, in which the share of clean energy would increase to 66.25% by 2050 and the corresponding water withdrawal would save 41.20%.

Assessing the Effect of Irrigation Water Management Strategies on Napier Productivity—A Review

Napier, a fast growing and perennial grass has a dry matter (DM) yield potential of 78 tons/ha/yr. However, under water availability constraints Napier’s yield potential reduces to 62 tons/ha/yr. In an effort to attain Napier’s yield potential, irrigation management strategies have been integrated into its production to provide the highest productivity. This review assesses the effect of irrigation water management strategies on Napier productivity and also looks at future perspectives. Application of these strategies i.e., precision irrigation, deficit irrigation, and application of biophysical models, can increase Napier’s yield potential to 112 tons/ha/yr. Review findings revealed that there is a need to close the knowledge gap on response of Napier productivity to different irrigation water management strategies. The future perspective explores the potential of the FAO AquaCrop model in provision of pre-season decision-making on irrigation strategies due to its relatively low cost and simplifications required in parameterization.

An Empirical Study of Environmental Data Prediction in the United States Energy-Water Nexus

Electricity generation systems are dependent on water availability and planning for future water scarcity is currently hindered by limited data and predictive models. The Energy-Water-Emissions Dashboard (EWED) is a novel environmental data management system that integrates multiple heterogeneous data sources and provides information for nearly 10,000 individual power plants across the United States. This article describes our empirical research of using machine learning models for electricity prediction and water usage in the context of water availability constraints. We evaluate the use of linear regression, decision tree regression, random forest regression, eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN). Based on the performance evaluation of each model, we use ANN for generation and water consumption and XGBoost for water withdrawal prediction in the production environment. Model performance evaluation is based on statistical measures including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2), Willmott’s Index of Agreement (WIA), RMSE-observations to Standard deviation Ratio (RSR), Nash–Sutcliffe model Efficiency Coefficient (NSEC), and Percent Bias (PBIAS). This article presents performance improvements of our machine learning approach compared to the conventional coefficient method used by EWED, for example, RMSE decreased 8.1% in generation, 59% in water consumption, and 53% in water withdrawal prediction. The significance of this research is that it covers a wide variety of power plant types, it uses consistent methods across energy and water systems, and provides predictions at multiple management scales across the United States to assist with future planning at the energy-water nexus.

Quantifying the impact of water availability on China's energy system under uncertainties: A perceptive of energy-water nexus

Energy and water are interdependent and interwoven, forming energy-water nexus. The chronic of water shortage in China can make a negative impact on energy system and further hinder the socioeconomic sustainable development. The coal-dominated energy structure consumes a large amount of water, which further intensities the dilemma between increasing energy demand and shrinking water resources. In this study, a China's non-deterministic energy-water nexus (CNEWN) model is established through incorporating techniques of Monte Carlo simulation (MCS) and interval-parameter programming (IPP). The CNEWN model is capable of simulating probability distributions of water availability, reflecting uncertainties derived from economic development and technology choice, and providing optimal scheme for China's energy system management over a long-term (2021–2050) horizon. Multiple scenarios are set up to quantify the impact of water availability on the national-scale energy system and sensitivity analysis is performed to assess the influence of uncertain parameters on modeling outputs. Results indicate that a variety of uncertainties existed in the energy system's parameters can affect the modeling outputs and the making decisions. Strict water availability constraints can stimulate the development of renewable energies and promote the transition of energy structure to clean and low-carbon pattern. Results also reveal that water scarcity can have a significant influence on the national energy system assocaited with water resources utilization, pollutant and carbon dioxide emissions, as well as system cost. The results provide a solid scientific basis for coordinately optimizing energy and water as well as effectively supporting the sustainable development of national energy system.

Synergistic use of SMAP and OCO-2 data in assessing the responses of ecosystem productivity to the 2018 U.S. drought

Soil moisture and gross primary productivity (GPP) estimates from the Soil Moisture Active Passive (SMAP) and solar-induced chlorophyll fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) provide new opportunities for understanding the relationship between soil moisture and terrestrial photosynthesis over large regions. Here we explored the potential of the synergistic use of SMAP and OCO-2 based data for monitoring the responses of ecosystem productivity to drought. We used complementary observational information on root-zone soil moisture and GPP (9 km) from SMAP and fine-resolution SIF (0.05°; GOSIF) derived from OCO-2 SIF soundings. We compared the spatial pattern and temporal evolution of anomalies of these variables over the conterminous U.S. during the 2018 drought, and examined to what extent they could characterize the drought-induced variations of flux tower GPP and crop yield data. Our results showed that SMAP GPP and GOSIF, both freely available online, could well capture the spatial extent and dynamics of the impacts of drought indicated by the U.S. Drought Monitor maps and the SMAP root-zone soil moisture deficit. Over the U.S. Southwest, monthly anomalies of soil moisture showed significant positive correlations with those of SMAP GPP (R2 = 0.44, p < 0.001) and GOSIF (R2 = 0.76, p < 0.001), demonstrating strong water availability constraints on plant productivity across dryland ecosystems. We further found that SMAP GPP and GOSIF captured the impact of drought on tower GPP and crop yield. Our results suggest that synergistic use of SMAP and OCO-2 data products can reveal the drought evolution and its impact on ecosystem productivity and carbon uptake at multiple spatial and temporal scales, and demonstrate the value of SMAP and OCO-2 for studying ecosystem function, carbon cycling, and climate change.

A modelling approach to assess wastewater reuse potential for Delhi city

Abstract Water availability is facing crisis throughout the world because of various factors viz., population growth, climate change, and rapid urbanization, leading to the requirement of treated wastewater as an additional source of water supply. However, the actual amount of wastewater that may be reused depends on many factors such as water demand, availability, cost and social acceptability, etc. In this study, a linear programming model has been developed to identify the amount of treated wastewater that may be used for various applications subject to water availability and demand constraints, taking Delhi city as a case study. The results suggest that wastewater reuse has the maximum potential in agriculture and landscape irrigation use followed by domestic and industrial applications. The framework developed in the study provides useful information for integrated planning and management of the reuse of wastewater in order to augment the existing water supply. It may be modified and used for the estimation of wastewater reuse potential in other areas with similar conditions.

ELECTRICITY INDUSTRIAL ORGANIZATION: WHAT ABOUT THE STRATEGIC BEHAVIOR OF HYDRO AND THERMAL OPERATORS?

In this paper, we develop a two-period model where we analyze and compare a hydro/thermal electrical system under different industrial organization: monopoly, Cournot competition and collusion; under storage constraint, water availability constraint and thermal turbine capacity constraint. First, we prove that the technological complementarity has an important role in satisfying electricity demand in the different industrial organizations. Second, we show by the analytical resolution, that intertemporal private monopoly water transfer from off-peak season to peak season is not as high as the same transfer under a public monopoly and therefore this increases the market price. Under Cournot competition, an increase in the peak season demand implies a water transfer strategy from off-peak to peak season. The results of collusion show that the electricity price is less dependent on the hydropower capacity. Keywords : Electricity market; Monopoly; Competition; Collusion; Hydro; Thermal. JEL Classifications : L20; Q40; Q25. DOI: https://doi.org/10.32479/ijeep.8775

Optimization of irrigation management: A multi-objective approach based on crop yield, growth, evapotranspiration, water use efficiency and soil salinity

Abstract An innovative approach was developed to optimize plant water requirement, growth and yield parameters in irrigated agriculture considering the constraints of water availability and quality in two different cultivation environments. Eggplant total evapotranspiration and growth parameters were measured at the end of growing seasons in two successive years. In the first year, optimum irrigation conditions, namely, irrigation interval, water salinity, and the cultivation environment (three factors) were determined regarding the measured crop parameters (twelve responses). Optimization was carried out by modeling the parameters using response surface methodology and determining optimum desirability based on a central composite design. The optimum desirability was then used to determine an optimum region by overlaying the parameters. The accuracy of models developed to estimate crop yield, evapotranspiration, water use efficiency, soil salinity, shoot dry weight and fruit diameter was impressive with remarkably high R2, adjusted R2, predicted R2 and adequate precision (higher than 0.81, 0.72, 0.57 and 11.38, respectively). The optimum irrigation interval, water salinity and cultivation environment were 4.56 days, 1.47 ds/m and outdoor environment, respectively, resulting in optimum crop yield (2490.7 g/plant), evapotranspiration (604.76 mm), water use efficiency (3.32 g/(plant-mm)) and soil salinity (5.27 ds/m). In the second year, the experiments were repeated according to the optimum region by focusing on the optimum amounts and the results were quite satisfying. This confirmed that with optimization based on multiple modeling, not only the influence of each factor on a single response could be elucidated but also the optimum amounts were more commonplace for multiple responses combined.

The landscape-scale drivers of herbivore assemblage distribution on the central basalt plains of Kruger National Park

AbstractThe distribution and abundance of herbivores in African savannas are constrained by interactions between abiotic and biotic factors. At the species-level, herbivores face trade-offs among foraging requirements, vegetation structure and the availability of surface water that change over spatial and temporal scales. Characterizing herbivore requirements is necessary for the management of the environment in which they occur, as conservation management interventions such as fencing and artificial water provision consequently have effects on how herbivores address these trade-offs. We tested the effects of environmental attributes on the probability of presence of herbivore functional types at different distances to water in the Satara section of Kruger National Park over the period of a year. Hypotheses about species’ relative distribution and abundance were developed through a literature review of forage and water availability constraints on feeding preference and body size of herbivore. We expected strong seasonal relationships between vegetation biomass and quality, and biomass of water-dependent herbivores with increasing distance to water. Our analyses of herbivore distribution across the region confirmed broad-scale descriptions of interactions between forage requirements and water availability across a set of species which differ in functional traits.

Agricultural Water Management Model Based on Grey Water Footprints under Uncertainty and its Application

The grey water footprint theory was introduced into a fractional programming model to alleviate non-point source pollution and increase water-use efficiency through the adjustment of crop planting structure. The interval programming method was also incorporated within the developed framework to handle parametric uncertainties. The objective function of the model was the ratio of economic benefits to grey water footprints from crop production, and the constraints contained water availability constraints, food security constraints, planting area constraints, grey water footprint constraints and non-negative constraints. The model was applied to the Hetao Irrigation District of China. It was found that, based on the data in the year of 2016, the optimal planting plans generated from the developed model would reduce 34,400 m3 of grey water footprints for every 100 million Yuan gained from crops. Under the optimal planting structure, the total grey water footprints would be reduced by 21.9 million m3, the total economic benefits from crops would be increased by 1.138 billion Yuan, and the irrigation water would be saved by 44 million m3. The optimal results could provide decision-makers with agricultural water use plans with reduced negative impacts on the environment and enhanced economic benefits from crops.

Mathematical analyses of ecological and economic tradeoffs in irrigated agriculture based on inexact optimization principles and hierarchical crop projections

An entropy-based multi-objective interval stochastic programming (EMISP) approach based on interval hierarchical projection, interval information entropy, interval analysis and chance-constrained programming was developed for supporting agricultural water management. EMISP improved upon existing methods through measuring and balancing ecological and economic benefits of irrigated croplands under uncertainty. To demonstrate its applicability, the developed method has been applied to an arid Chinese watershed. The results suggested that grain crops would produce higher ecological benefits than economic crops. A series of benefit- and risk-explicit plans for agricultural water use were generated, which indicated that the relaxation of water availability constraints would generate higher comprehensive benefits, but increase the risks of system infeasibility. The results from the EMISP model were also compared to those from four potential alternative models. These comparisons revealed that EMISP could achieve higher water productivity and comprehensive benefits with controllable water-shortage risks.

Integration of Water and Energy Planning to Promote Sustainability

This paper examines the inter-linkages between the water and energy sectors and their planning processes, by describing a comprehensive analytical tool developed to evaluate water energy nexus operational cost trends and planning to assist decision makers in exploring and evaluating alternative courses of action. Brazil has been chosen as a case study, because its electricity production is highly dependent on water to keep affordable tariffs, which in turn also serves as input to other important sectors, such as water services and sanitation, and raises disputes especially in basins marked by water scarcity, such as the Sao Francisco basin. In light of hydrological factors (e.g., droughts) and non-hydrological factors (e.g., chronic delays in delivery of new plants) there has been water availability constraints for electricity generation and energy prices have risen, while water quantity and quality have decreased for multiple users. Both of which impact negatively on water services and sanitation providers, because electricity figures as their fastest growing costs in times when they need more energy to source water from longer distances, or deeper levels because of water quantity and quality issues. Energy and water are characterized as common pool resources with planning processes along silos in Brazil that do not serve well the purpose of sustainable development. Better integrated water-energy plans at basin level is the alternative proposed under this paper to advance sustainability and mitigate the risks related to water scarcity that have resulted in negative impacts on both electricity and water sectors.

Electricity systems capacity expansion under cooling water availability constraints

Large and reliable volumes of water are required to cool thermal power plants. Yet across the world growing demands from society, environmental regulation and climate change impacts are reducing the availability of reliable water supplies. This in turn constrains the capacity and locations of thermal power plants that can be developed. The authors present an integrated and spatially explicit energy systems model that explores optimal capacity expansion planning strategies, taking into account electricity and gas transmission infrastructure and cooling water constraints under climate change. In Great Britain, given the current availability of freshwater, it is estimated that around 32 GW of combined cycle gas turbine capacity can be sustainably and reliably supported by freshwater. However, to maintain the same reliability under a medium climate change scenario, this is halved to 16 GW. The authors also reveal that the current benefit of available freshwater to the power sector is ∼£50 billion between 2010 and 2050. Adapting to expected climate change impacts on the reduced reliability of freshwater resources could add an additional £18–19 billion in system costs to the low-carbon energy transition over the time horizon, as more expensive cooling technologies and locations are required.

Implications of water constraints on electricity capacity expansion in the United States

The development of new electricity generation capacity is constrained by water resource availability. However, the sufficiency of available water resources is rarely incorporated into the planning of electricity capacity expansion in the United States. Previous studies on the implications of water constraints on US electricity generation are limited in terms of scale and robustness. Here, we extend previous studies by including physical water constraints within a state-level model of the US energy system embedded within a global integrated assessment model (GCAM-USA) that integrates both supply and demand effects under a consistent framework. We show that water constraints have two general effects across the United States: (1) to increase the cost of electricity generation, which results in slightly reduced electrification of end-use sectors, and (2) to incentivize early retirement of water-intensive technologies before the end of their designed lifetimes, while simultaneously boosting investment in less water-dependent technologies. Our results suggest that water availability constraints may cause substantial capital stock turnover and result in non-negligible economic costs for the western United States, whereas fewer impacts may be anticipated in the eastern United States. Our work emphasizes the need to integrate water availability constraints into electricity capacity planning and highlights the state-level challenges to facilitate regional strategic resource planning. Water constraints can affect plans to expand electricity capacity. This study shows that in the United States such constraints can increase the cost of electricity generation with slightly reduced electrification of end-use sectors, and can incentivize early retirement of water-intensive technologies.

Spatial variations in crop growing seasons pivotal to reproduce global fluctuations in maize and wheat yields.

Testing our understanding of crop yield responses to weather fluctuations at global scale is notoriously hampered by limited information about underlying management conditions, such as cultivar selection or fertilizer application. Here, we demonstrate that accounting for observed spatial variations in growing seasons increases the variance in reported national maize and wheat yield anomalies that can be explained by process-based model simulations from 34 to 58% and 47 to 54% across the 10 most weather-sensitive main producers, respectively. For maize, the increase in explanatory power is similar to the increase achieved by accounting for water stress, as compared to simulations assuming perfect water supply in both rainfed and irrigated agriculture. Representing water availability constraints in irrigation is of second-order importance. We improve the model's explanatory power by better representing crops' exposure to observed weather conditions, without modifying the weather response itself. This growing season adjustment now allows for a close reproduction of heat wave and drought impacts on crop yields.