Dry Heat, Not Moist Heat, Drives Irrigation’s Labor Capacity Benefits in Arizona’s Cities and Croplands

Dry Heat, Not Moist Heat, Drives Irrigation’s Labor Capacity Benefits in Arizona’s Cities and Croplands | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Dry Heat, Not Moist Heat, Drives Irrigation’s Labor Capacity Benefits in Arizona’s Cities and Croplands Matei Georgescu, Xiangwen Deng, Gisel Guzman, Jennifer Vanos This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6940300/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The impact of irrigation on outdoor labor capacity under heat stress has long been debated due to the competing environmental effects of cooling (i.e., benefits) and moistening (i.e., drawbacks). We quantitatively address this debate through proposing an interdisciplinary framework that couples a regional climate model with a human heat balance model. We apply this framework to assess the impact of irrigation on labor capacity in the arid/semi-arid environments of Arizona (USA) during an extremely hot-dry summer. Results reveal that irrigation-induced environmental changes primarily modify labor capacity through dry heat (sensible heat exchange between human and environment) rather than moist heat (evaporative heat loss from human to environment) exchange. Through reduction of daytime dry heat gain from environment to human, irrigation decreases the proportion of discouraged outdoor work hours by ~ 30% in the hottest urban (i.e., Phoenix-metro) and cropland areas. Nocturnally, despite reduced dry heat loss from human to environment, labor capacity changes occur only 2% of the summertime. Our results demonstrate complex interplays between humans and their ambient environment, underscoring the necessity of coupled meteorological, physiological, and human biophysical principles to properly assess outdoor labor capacity. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Climate sciences Scientific community and society/Geography Earth and environmental sciences/Natural hazards Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Human exposure to excessive warm weather has reached alarming levels, with 2023 recording 27.7% more hours of heat exposure at moderate or higher risk during outdoor physical activity compared to the 1990s average 1 . High heat exposure disproportionately affects labor capacity within agriculture and construction occupations 2 . By 2030, agricultural and construction workers are projected to face the highest loss of working hours due to heat, accounting for 60% and 19%, respectively 3 . Irrigation, which plays a critical role in sustaining agricultural productivity and has also become a widespread landscape management practice across cities, critically impacts on outdoor worker’s heat stress in both urban and agricultural areas 4 , 5 . Irrigation has competing pathways: on the one hand, it contributes to reduced heat stress resulting from enhanced evapotranspiration that lowers ambient temperatures, while on the other hand the associated atmospheric moistening (hereafter humidification) may impede evaporation of sweat from the human skin. Therefore, the influence of irrigation on outdoor workers’ heat stress and associated labor capacity remains an area of active research 6 – 8 . The trade-off of these competing pathways varies depending on the geographical characteristics of irrigated areas (e.g., climate types 8 , 9 , topography, and land use patterns). The subsequent conversion of heat stress related information (i.e., local environmental conditions plus clothing and an individual’s metabolic rate) into labor capacity assessment introduces additional layers of uncertainty 10 . Consequently, a systematic quantification framework is required to robustly examine how the trade-offs between competing environmental impacts (e.g., cooling vs. humidification) arising from the same land management practice (i.e., irrigation) affect human-environment heat exchange and subsequent labor capacity, thus providing evidence-based guidelines for safe, outdoor, working practices. Developing such a framework is especially urgent in light of projected increases in population-weighted heat exposure in urban areas throughout the 21st century 11 , as well as the critical role of local adaptation strategies in mitigating adverse heat-related impacts 12 . In an effort to address these socioenvironmental challenges, meteorological/climatological and physiologically-based models are powerful tools that integrate heat-related information across global, regional, and local scales. Driven by climate model simulations at global 8 , 13 and regional scales 5 , 9 , previous efforts have widely utilized direct and empirical heat indices––including Wet Bulb Globe Temperature (WBGT) 5 , 9 , Environmental Stress Index 8 , and Humidex 13 ––to investigate the impacts of land management practices on heat stress and associated labor capacity. The strength of these indices lies in a climate model’s ability to generate multiple scenarios that isolate and quantify the environmental impacts of land management practices. However, these simple heat indices rely on empirical parameterizations of trade-offs between different environmental factors, potentially limiting the generalizability of their findings. For example, WBGT’s fixed 0.7 weighting for wet-bulb temperature (T w ) does not adequately represent the enhanced role of evaporative cooling as temperature increase 14 . Moreover, these indices have limited integration of the individual characteristics that affect thermoregulation (e.g., body size, activity levels, clothing, and sweat rates) 15 . Furthermore, established empirical thresholds do not represent other regional or global populations due to differences in acclimatization 16 , 17 . Alternatively, the use of physiologically-based models offers an opportunity to address the limitations highlighted above. By integrating dynamic human-environment heat exchange and realistic thermoregulatory characteristics across different demographic segments, these models can not only describe the drivers of heat stress/strain, but also examine the limits for safe sustained activities for diverse population groups 17 , 18 . However, existing physiologically-based modeling studies have mainly relied on climate simulations from Global Climate Models (GCMs) 16 , 17 or meteorological station data 19 , 20 . These approaches are limited in two important ways. First, they do not consider regional environmental heterogeneity (e.g., an airport weather station is unlikely to be representative of any part of an urban area). Second, such approaches do not account for the fact that local to regional scale meteorological dynamics are modulated by unique geographical and topographical features (e.g., Brandi et al. 21 ), and by extension, omit land management practices. To advance the integration of regional climate with physiological and biophysical principles of human heat stress, several studies have leveraged meteorological outputs from regional climate models (e.g., the Weather Research and Forecasting (WRF) model) to calculate human energy balance-based heat indices (e.g., Physiological Subjective Temperature, Universal Thermal Climate Index) 22 – 25 , or to examine the limitations of heat compensability for acclimatized and non-acclimatized populations 26 . These studies examine the intraurban variability of heat stress by relating index magnitudes with corresponding heat stress levels, but do not directly quantify human-environment heat exchange, making it virtually impossible to examine impacts on labor capacity. To bridge this gap, a key scientific question, and the foundation of our work, emerges: How can labor capacity responses to land management practices be quantitatively assessed through coupling of meteorological, physiological, and human biophysical principles? This study introduces an interdisciplinary framework via coupling of a regional climate model (i.e., WRF) and a human heat balance model grounded in physiological and human biophysical principles, as described in the liveability model proposed by Vanos et al. 17 . Applying this framework, we directly address the ongoing scientific debate regarding irrigation impacts on human heat stress and labor capacity. First, by comparing an Irrigation scenario (i.e., a summertime simulation with realistic incorporation of irrigation within WRF) against the Control scenario (i.e., a summertime simulation with no irrigation), our analysis of results isolates the irrigation-induced diurnal variability in environmental factors over cities and croplands located in the arid/semi-arid state of Arizona (USA). Second, a human heat balance model is coupled to WRF and used to estimate changes in components of human-environment heat exchange (i.e., dry heat exchange(s) and evaporative heat loss), thereby characterizing dry and moist heat experienced by standard, healthy adults. Finally, variations in labor capacity levels are estimated based on maximum safe metabolic rates determined by human-environment heat interactions, across the diurnal scale. Study Area Arizona (109° 3' W – 114° 50' W, 31° 20' N – 37° N) is the sixth largest state in the United States, covering 295,254 km 2 , with a population of 7,431,344 as of 2023 27 . Arizona is distinguished by complex terrain, featuring steep mountains to the north and east and a southwest-dipping valley with reduced relief (Fig. 1 d). This complex terrain contributes to pronounced meteorological variability across the state 21 . The Köppen climate classification of Arizona is primarily BWh (subtropical desert) in the southwestern and central portions, BWk (mid-latitude desert) in the northwestern regions, and BSk (Mid-latitude steppe) in northeastern Arizona 28 . Given the generally dry climate of the state, irrigation accounted for 95% of consumptive water use based on 2000–2020 withdrawals 29 . The focus of this study is on the state’s four major urban areas: Phoenix, Tucson, Flagstaff, and Prescott metropolitan areas, which are home to 88% of the state's population, and all cropland regions (Fig. 1 b). Given the state’s considerable topographic variability, cropland areas are distinguished into three regions with longitudinal divisions at 113°W and 111°W: Western, Central, and Eastern croplands (Fig. 1 b). We simulate the summer of 2023 (June–August; JJA), one of the hottest and driest summers on record. During this summer, Arizona's two most populous metropolitan areas experienced record-breaking extreme heat: the Phoenix metropolitan area recorded 55 days over 110°F (43.3℃), and the Tucson metropolitan area reached 18 days over 110°F – both far exceeding their 1991–2020 climate normals (21 and 4 days, respectively) 30 , 31 . The counties containing these urban areas, Maricopa (Phoenix metropolitan area) and Pima (Tucson metropolitan area), recorded 643 and 173 heat-related deaths, respectively 32 . Meanwhile, the summer months also mark the peak harvest season in Arizona for multiple crops, including corn, alfalfa, eggplant, squash, and tomatoes. Arizona’s agricultural workforce, exceeding 29,000 33 , is highly vulnerable to prolonged outdoor exposure during fieldwork-related activities (e.g., crop harvesting and maintenance of irrigation systems). Results Irrigation-induced environmental changes The irrigation-induced environmental modifications are isolated by computing differences between the Irrigation and Control scenarios. We present results as summertime averaged diurnal cycle differences. The differences reveal that during periods of active evapotranspiration (07:00–18:00 local time), irrigation enhances atmospheric humidity (water mixing ratio) in lower-elevation regions; the average median humidity increases by ~ 0.25 g/kg in Western croplands, ~ 0.20 g/kg in Phoenix and Tucson, and ~ 0.10 g/kg in Central croplands (Figs. 2a1, a2). This difference is attributed to increased soil moisture (see Methods for irrigation parameterization) that promotes greater evapotranspiration rates and increased latent heat flux of 20–100 W/m 2 (Figs. S3 a1, a2). The Western croplands undergo the most substantial increase in water mixing ratio due to the lower-elevation valley topography that facilitates moisture accumulation from evapotranspiration. Conversely, higher-elevation regions (Flagstaff, Prescott, Eastern croplands) experience smaller increases in latent heat flux (< 20 W/m 2 ), resulting in minimal humidity fluctuations. During nighttime hours, when evapotranspiration diminishes, irrigation produces trivial changes in latent heat flux and humidity across all urban and cropland regions in Arizona. Irrigation leads to cooling effects in lower-elevation regions during 07:00–18:00 (local time), with averaged median air temperature reduction of ~ 0.7 ℃ in Western croplands, ~ 0.4 ℃ in Phoenix, ~ 0.3 ℃ in the Central croplands, and ~ 0.2 ℃ in Tucson (Figs. 2b1, b2). These cooling impacts are generally not considerable; however, we emphasize the clear gradient in air temperature reduction with elevation (greatest cooling for Phoenix and Tucson, and reduced cooling for Prescott and Flagstaff). This cooling results from the shift in surface energy partitioning: enhanced latent heat flux diminishes sensible heat flux by 10–70 W/m 2 (Figs. S3 b1, b2). Meanwhile, the conversion of soil-stored heat to latent heat reduces ground heat flux by 5–40 W/m 2 (Figs. S3 c1, c2) and contributes to a decrease in surface temperature ranging from 0.3 to 4.0 ℃ (Figs. S3 d1, d2). The decreased surface temperature reduces outgoing longwave radiation, leading to averaged median mean radiant temperature (MRT) reduction of ~ 1.2℃ in Western croplands, ~ 0.9℃ in Phoenix and Tucson, and ~ 0.4℃ in Central croplands (Figs. 2c1, c2). Nocturnal vegetation evapotranspiration is negligible, resulting in minimal latent heat flux variation. However, the enhanced thermal conductivity and heat capacity of irrigated, moist soils facilitate more efficient daytime heat storage in deeper layers and subsequent nocturnal release. This process increases ground heat flux (5–30 W/m 2 ), thus raising surface temperature by 0.5–3.0℃ and outgoing longwave radiation. In turn, both air temperature and MRT are increased. The average median air temperature increases by 0.5℃ in Western croplands, and 0.2℃ in Phoenix, Tucson, and Central croplands. During nighttime hours, the median peak MRT increases by 1.0℃ in Western croplands, and 0.5℃ in Phoenix, Tucson, and Central croplands. Higher-elevation regions show minimal irrigation-induced diurnal variations in air temperature and MRT due to the smaller changes in the surface energy balance and surface temperature. Generally, croplands (with lower heat capacity and heat storage than urban areas dominated by concrete, asphalt, and buildings) exhibit larger diurnal variations in air temperature and MRT. Wind speed, another environmental factor, shows negligible response to irrigation across Arizona throughout the day (Figs. 2d1, d2). Overall, the impact of irrigation on meteorological factors shows a similar tendency across Arizona, with similar magnitude depending on background environmental conditions (topography and land use patterns). Changes in components of human-environment heat exchange We evaluate human-environment heat exchange through two components: dry (i.e., sensible) heat exchange and evaporative heat loss. The main dry heat exchanges includes radiative and convective heat transfer between the human skin surface and environment 18 . These exchanges are primarily determined by the temperature gradient between human skin and the environment. The environment temperature here refers to a composite metric integrating air temperature and MRT 34 . Dry heat exchanges are bidirectional: (1) dry heat loss from human to environment (dry heat exchanges > 0) occurs with a positive human skin-environment temperature gradient; (2) dry heat gain from environment to human (dry heat exchanges < 0) occurs with a negative human skin-environment temperature gradient. Under the Control scenario, Arizona’s outdoor workers typically experience dry heat loss (Figs. 3a1, a2). However, during 09:00–18:00 (local time), outdoor workers in lower-elevation areas generally experience dry heat gain, with median values between − 220 and − 60W/m 2 , resulting in enhanced heat stress. During 9:00–18:00 (local time), irrigation-induced environmental changes increase dry heat exchange for outdoor workers in lower-elevation areas, with median values ranging from 4–60W/m 2 in Phoenix, 4–50W/m 2 in Tucson, 10–70W/m 2 in Western croplands, and 8–68W/m 2 in Central croplands (Figs. 3b1, b2). Given that outdoor workers in lower-elevation areas experience dry heat gain under the Control scenario (Figs. 3a1, a2), this increase indicates reduced dry heat gain. This heat stress alleviation occurs because irrigation-induced environmental changes (i.e., decreasing air temperature and MRT) lessen the negative skin-environment temperature gradient. Smaller modifications occur at higher-elevation areas due to: (1) lower air temperature and MRT (smaller negative human skin-environment temperature gradient) under the Control scenario (Fig. S4); and (2) minimal irrigation-induced changes in air temperature and MRT (Fig. 2 ). During nighttime (19:00–8:00 local time), irrigation reduces dry heat exchange for outdoor workers across Arizona, with an average median reduction of 22W/m 2 in Phoenix, 29W/m 2 in Tucson, 52W/m 2 in Flagstaff, 43W/m 2 in Prescott, 40W/m 2 in Western croplands, 38W/m 2 in Central croplands, and 44W/m 2 in Eastern croplands. As outdoor workers experience nighttime dry heat loss under the Control scenario, this reduction indicates reduced dry heat loss and increased heat stress. Unlike daytime patterns, irrigation results in greater nighttime impacts on dry heat exchange in higher-elevation areas. This is attributed to a larger positive human skin-environment temperature gradient in here under the Control scenario, enabling irrigation-induced increases in air temperature and MRT (though relatively weak, the trend is evident) to more effectively decrease the positive human skin-environment temperature gradient. Evaporative heat loss occurs through evaporation at the skin surface and respiratory tract 18 . This mechanism is most effective in hot-dry environments, and its effectiveness declines with increasing humidity. Evaporative heat loss is limited not only by the environment, but also by additional clothing, maximum skin wettedness, and maximum sweat rate. Under the Control scenario, evaporative heat loss values are capped at 306W/m² for outdoor workers throughout the day (Figs. 3c1, c2). However, the median values of irrigation-induced changes in evaporative heat loss are maintained close to 0 W/m², with overall changes in magnitude generally below 7 W/m² (Figs. 3d1, d2). During the daytime, this trivial change results from the counteracting irrigation-induced environmental effects. In this hot-dry environment, sweating capacity primarily governs evaporative heat loss. While increased humidity sweating capacity, concurrently decreased air temperature and MRT enhance suppresses sweating capacity. Nocturnally, this trivial change is only attributed to irrigation's negligible impact on humidity. This shift in dominant control occurs because under wetter nighttime conditions, environmental humidity (rather than sweating capacity) limits evaporative heat dissipation. Consequently, we conclude that irrigation primarily affects heat stress by altering dry heat exchanges rather than evaporative heat loss for outdoor workers in this arid/semi-arid state. Note that the above irrigation-induced conclusions do not consider the impact of wind speed, as irrigation has a negligible effect on wind speed. Spatiotemporal changes in labor capacity under heat stress Aggregating the components of human heat exchange permits estimation of the maximum safe metabolic rate without a sustained rate of positive heat storage ( \(\:{M}_{max}\) ). To quantify labor capacity, we divide \(\:{M}_{max}\) values into four classes: 1) uncompensable heat stress (which causes a rise in core temperature, making outdoor work inadvisable), and maximum safe activity level limited to 2) light, 3) moderate, and 4) vigorous intensity. These four categories are used to analyze the summertime hourly frequency of safe sustained labor capacity for the Control scenario and the irrigation-induced modification (Fig. 4 ). Analysis of the Control scenario indicates that cropland workers generally have lower daytime labor capacity than urban outdoor workers with more frequent occurrences of uncompensable heat stress periods. This lower labor capacity is due to the overall greater daytime dry heat gain for cropland workers (Figs. 3a1, a2). At 15:00 local time (i.e., the peak heat stress period), uncompensable heat stress occurs during 38% of the summer season (~ 34 days) in the Western croplands, 30% of the summer season (~ 27 days) in the Central croplands, and 19% of the summer season (~ 18 days) in the urban area (Phoenix). The frequency of hours within uncompensable heat stress is less than 2% for the rest of the regions. While uncompensable heat stress peaks at 15:00 local time, such conditions extend throughout the afternoon hours. Unlike daytime hours, cropland workers demonstrate greater nighttime labor capacity than urban outdoor workers. Specifically, cropland rural workers across the three cropland regions can safely perform vigorous-intensity activities for over 97% of the summer nighttime hours, compared to urban outdoor workers in Phoenix (88%) and Tucson (95%). This higher labor capacity is owing to the greater evaporative and dry heat loss for cropland workers (Fig. 3 ). In Flagstaff and Prescott, which have the highest elevation, outdoor workers have the greatest labor capacity. Workers in these environments can safely conduct vigorous-intensity activities during 99% of the summer nighttime hours. The impact of irrigation on labor capacity exhibits pronounced spatiotemporal variations (Figs. 4 and S5). During the extreme heat stress hours (13:00–18:00), irrigation most effectively enhances labor capacity for outdoor workers in Western croplands (Fig. 4d1), reducing the frequency of hours under uncompensable heat stress by an average of 8% (~ 7 days), followed by Phoenix with an average reduction of 4% (~ 4.5 days). Spatially, these two regions exhibit approximately 30% of the relative reductions in cumulative uncompensable heat stress hours (Fig. S5 a2). However, other urban and cropland regions show negligible labor capacity modification for outdoor workers. This minimal effect occurs in Central croplands because the irrigation-induced modifications to components of human-environment heat exchange remain insufficient to alter this region's already lower baseline (Control) labor capacity. The modest impact in other regions (Tucson, Flagstaff, Prescott, Eastern croplands) is attributed to their higher heat stress baseline (Control) labor capacity and lower changes in components of human-environment heat exchange. During 19:00–6:00, labor capacity changes in Arizona are generally less than 2%. Within the same area, irrigation-induced changes may produce weak but bidirectional labor capacity outcomes (e.g., the maximum safe activity level shifts between vigorous and moderate intensity) throughout the day. This finding also applies to the spatial distribution of irrigation-induced changes in cumulative hours under different labor capacity levels, especially in higher-elevation regions (Fig. S5). Our results reveal uncertainty layers in how alterations in components of human-environment heat exchange impact labor capacity outcomes as identical environmental changes may differentially intersect physiological tolerance limits depending on the spatiotemporal context of the environment. While irrigation appears to improve summer labor capacity in low-altitude regions, our framework demonstrates its significant reduction of dry heat gain across all of Arizona—particularly during periods of uncompensable heat stress (Fig. S6). In Western croplands, Central croplands, and Phoenix, irrigation decreases dry heat gain by approximately 100 W/m², while other regions experience 50–75 W/m² reductions. However, even under uncompensable heat stress conditions, irrigation has a negligible influence on evaporative heat loss. Overall, our framework establishes a complete pathway from irrigation implementation to labor capacity outcomes, as summarized in Fig. 5 . We systematically trace the irrigation process: beginning with irrigation-induced environmental modifications simulated through WRF, progressing through impacts on the human-environment heat exchange, which ultimately establishes the resultant labor capacity changes across different regions and time scales. Discussion We propose a new interdisciplinary framework to assess regional labor capacity through the coupling of meteorological, physiological and human biophysical principles. This framework can be applied to any climatic condition and customized for any population groups to examine the trade-offs between any kind of land management practice, human-environment heat interactions, and labor capacity. Building upon this approach, we conclude that in arid/semi-arid regions, irrigation's beneficial impact on labor capacity under heat stress primarily operates through dry heat exchange (dry heat) mediated by air temperature and MRT, rather than through changes in evaporative cooling (moist heat) impacted by both humidity and dry heat. This biophysical pathway elucidates a previously unrecognized energy exchange mechanism contributing to irrigation-induced cooling for the benefit of outdoor workers. Furthermore, our results reveal spatiotemporal heterogeneity in labor capacity responses to irrigation-induced environment changes even under comparable climatic conditions within the same region. The conclusions reached in this study differ from previous studies that equated values of humidity-inclusive heat metrics directly with moist heat 13 , 35 , 36 . Overreliance on direct and empirical heat indices that aggregate all environmental factors into a single value does not allow for the separation of avenues of heat exchanges (i.e., dry, moist) that interact to create heat stress. These simple indices may further lead to contradictory conclusions on moist heat when different indices are employed within identical environmental conditions. For example, Mishra et al. 35 observed 95th percentile decreases in Heat Index (HI; used by the U.S. National Weather Service) alongside increases in 95th percentile wet-bulb temperature (T w ) during Indian summers of 2000–2018. This discrepancy stems from the fact that different indices empirically parameterize environmental factor combinations. In India's highly humid climate, HI is less sensitive to humidity than T w . Indeed, T w –– by definition –– remains stable or increases with soil moisture regardless of background humidity 6 . To our knowledge, no studies have examined the irrigation-induced modification in moist and dry heat at regional scales using human heat balance models. However, some simple heat indices such as the simplified WBGT have been used to address this question. For example, Chakraborty et al. 9 and Parajuli et al. 5 found that irrigation reduced daytime but increased nighttime WBGT during summers in arid zones of North America. Though these studies find lower heat stress in the daytime (agreeing with our M max findings), their conclusions may overestimate the true irrigation impacts on the heat load to humans, as the expected heat strain for equivalent WBGT levels are consistently less stressful in hot-dry versus hot-humid environments 37 . Meanwhile, their higher nighttime heat stress due to irrigation contrasts with our results showing negligible (2%) irrigation-induced changes in outdoor labor capacity. Labor capacity under heat stress is typically estimated by employing empirical formulas/functions linking heat indices to physical labor capacity 10 , 38 , 39 . Previous studies using this method are often derived from human experimental trials in laboratory settings under varying environmental conditions, lending them credibility. However, broad application to diverse populations (differing from those used in a lab) introduces uncertainty. For example, it is difficult to equate the work capacity of young, fit male soldiers wearing olive green uniforms during military activities (the reference population for WBGT 40 ) to participants wearing light clothing walking on a treadmill at a fixed heart rate of 130 beats/min 10 . Yet these newer studies must continue to improve original models and find new ways to apply physiological-based empirical tests to real world environments 41 . Our modeling framework offers flexibility to account for physiological traits, body size, sweat rate, etc., which is increasingly critical for heat safety and productivity. Further, quantifying metabolic output as a labor capacity can support research examining the impact of heat on the economy, which causes serious economic strains globally 3 . Overall, our results reveal that the relationships between meteorological, physiological, and human biophysical factors of labor capacity are highly complex and inherently connected to geographical context and population characteristics, making simplified heat indices inadequate as standalone measures to understand environmental changes to the human-environment heat exchange and subsequent labor capacity. We present summer-specific conclusions for Arizona. Future research can apply this framework to examine irrigation effects in differing regions, across longer time periods, and during different seasons (e.g., using projections of future, warmer, summers). While we model standard healthy adults to derive conclusions applicable to outdoor workers across Arizona, future research could customize simulations for specific outdoor populations in targeted geographical areas and age groups to provide work guidance, or for vulnerable populations with comorbidities or thermoregulatory disorders to deliver more accurate behavioral recommendations. Meanwhile, although dry environments often support unlimited evaporation 17 , our assumption that people continuously cool themselves through sweating across all environmental conditions does not fully reflect physiological reality. Future research could employ more sophisticated models, such as the joint system thermoregulation model 42 to test physiological heat strain within specific times of exposure. Furthermore, following our framework, impact pathways similar to those in Fig. 5 could be developed to characterize the effects of other land management practices (e.g., increased green spaces) on labor capacity, providing a clear visual, and ultimately quantitative, representation of mechanisms. Methods WRF Model parameterization and configuration The Weather Research and Forecasting (WRF) model with the advanced dynamic solver version 4.3.3 is used to conduct high-resolution sensitivity experiments for irrigation in Arizona. We deploy a one-way, three-nested model domain using a Lambert conformal conic projection, with horizontal grid spacings of 18, 6, and 2 km from outermost to innermost domain, respectively (Fig. 1 a). The innermost domain covers nearly all major urban areas and croplands in Arizona, with an area of 462 km × 492 km. The vertical coordinate includes 45 terrain-following eta levels from the surface to 50 hPa. The initial and lateral boundary conditions for WRF are derived from the ERA5 reanalysis data produced by the European Centre for Medium-Range Weather Forecast ( https://cds.climate.copernicus.eu/ ), having a spatial resolution of 0.25° and a temporal resolution of 1 h. The main physical parameterizations used in this study are illustrated in Table S1 . For non-urban areas (rural areas and vegetated portions of urban areas), the Noah Land Surface Model (LSM) is selected to simulate the biophysical and radiative interactions between the soil, vegetation, and atmosphere 43 . These non-urban areas are represented by the MODIS land cover data. We represent urban processes using the Building Effect Parameterization and Building Energy Model (BEP-BEM), which is coupled to the Noah LSM 44 , 45 . BEP-BEM explicitly simulates the dynamic interaction between ambient and indoor thermal environments, including the anthropogenic heating from air conditioning systems—a key feature of summer climates in hot desert urban areas in Arizona 21 . Urban areas are characterized by urban sub-category classifications and impervious fraction derived from the National Land Cover Database 2021 (Figs. 1 b and c). The building parameters in BEP-BEM are obtained from Salamanca et al. 46 . Irrigation parameterization and experimental design The irrigation scheme is implemented within the Noah LSM to account for the addition of water across urban and non-urban areas. The Noah LSM has been widely used to investigate irrigation impacts on regional climate over Arizona and worldwide 47 , 48 . Following parks annual irrigation schedules from city of Phoenix, our irrigation scheme maintains all four soil layers of the Noah LSM—corresponding to vegetated portions of urban areas and croplands—at reference levels (i.e., threshold below which transpiration begins to stress, but above wilting point) each morning before sunrise (04:00–05:00 MST). This approach prevents excessive irrigation in vegetated zones and avoids water application to bare soil, which is aligned with the sustainable irrigation practices in arid/semi-arid regions. We conduct two parallel simulations: a Control scenario with the irrigation scheme turned off and an irrigated scenario with the scheme on. Both scenarios share identical large-scale meteorological forcing, physical parameterizations, and land surface characteristics to isolate the irrigation signal. The simulations span from 00:00 UTC on May 1, 2023, to 00:00 UTC on September 1, 2023. The initial month of WRF output is regarded as spin-up and discarded. The analysis focuses on hourly outputs during the summer months (June, July, and August) in the innermost domain. The WRF model's performance is evaluated against observational data from multiple meteorological networks, as detailed in Method S1, ensuring the reliability of the simulated environmental conditions used in subsequent analyses. Human Heat Balance Model We apply a human heat balance model 49 grounded in physiological and human biophysical principles to simulate heat transfer between the human body and the surrounding environment, and estimate the safe levels for sustained activity. With assumptions of null internal body heat storage, negligible mechanical work, and negligible conductive heat exchange, the heat balance equation is expressed as: \(\:{E}_{req}=M-{R}_{skin}-{C}_{skin}-{C}_{res}-{E}_{res}\) (W) (1) where \(\:{E}_{req}\) is the rate of evaporative heat loss from the skin surface required for heat balance; \(\:M\) is internal metabolic heat production, equaling metabolic rate since mechanical work is negligible; \(\:{R}_{skin}\) and \(\:{C}_{skin}\) are and the rate of dry heat exchange through the skin by radiation and convection combined; \(\:{C}_{res}\) and \(\:{E}_{res}\) are the convective and evaporative heat exchange in the respiratory tract. However, \(\:{E}_{req}\) cannot always be achieved due to limitations on the maximum evaporative heat loss capacity \(\:\:\) caused by the environment, clothing, and sweating capacity of human body ( \(\:{E}_{{max}_{constrain}}\) ). Therefore, \(\:{E}_{{max}_{constrain}}\) represents two distinct but related constraints on evaporative loss. In high-humidity environments, \(\:{E}_{{max}_{constrain}}\) is mainly limited by the thermal environment, clothing, and maximum skin wettedness, referred to as \(\:{E}_{{max}_{wet}}\) ; in dry environments, where the potential to evaporate is high, \(\:{E}_{{max}_{constrain}}\) is constrained mainly by maximum sweat rate, denoted as \(\:{E}_{{max}_{sweat}}\) , This constraint occurs when people are not able to produce the sweat required to achieve the needed evaporative heat loss. Therefore, \(\:{E}_{{max}_{constrain}}\) is defined as the minimum of these two values. \(\:{{E}_{{max}_{constrain}}=\text{m}\text{i}\text{n}(E}_{{max}_{wet}}{,\:E}_{{max}_{sweat}})\) (W) (2) In this context, we further adopt the concept of physiological compensability, which refers to the ability of the human body to maintain energy balance under prevailing heat stress conditions 49 . Specifically, heat stress is compensable when the metabolic heat and the heat exchanged with the environment \(\:\:\) ( \(\:{E}_{req}\) ) is fully counterbalanced by the maximum capacity of evaporative heat loss ( \(\:{E}_{{max}_{constrain}}\) ). In other terms, heat stress is compensable when \(\:{E}_{req}<\:{E}_{{max}_{constrain}}\) . Under this condition, the maximum safe metabolic rate ( \(\:{M}_{max}\) ) to maintain compensability can be estimated according to the following: \(\:{M}_{max}{=E}_{{max}_{constrain}}-{E}_{req}\) (W) (3) Specifically, \(\:{M}_{max}\) represents the maximum safe metabolic rate, or internal heat production that can be generated without a sustained rate of positive heat storage. Based on Equations (1) and (2), and taking \(\:M\) as \(\:{M}_{max}\) , Eq. (3) can be transformed into: \(\:{M}_{max}={(E}_{{max}_{constrain}}+{E}_{res})+{(R}_{skin}+{C}_{skin}+{C}_{res})\) (W) (4) Here, \(\:{(E}_{{max}_{constrain}}+{E}_{res})\) represents the contribution of evaporative heat loss, while \(\:\:{(R}_{skin}+{C}_{skin}+{C}_{res})\) denotes the contribution of dry heat exchange. Evaporative heat loss is always a positive value, representing heat dissipation from the human body to the environment through skin and respiratory evaporation. Dry heat exchange is typically positive (dry heat loss), indicating heat dissipation from the human body to the environment through radiative and convective heat exchanges at skin surface and convective heat exchange in the respiratory tract. However, when environment temperature exceeds skin temperature or radiative sources transfer heat to human body, it becomes negative (dry heat gain), signifying heat absorption from the environment. Detailed computational formulas for these variables are derived from liveability model in Vanos et al. 17 and provided in Method S2. To account for individual body size variations, all components of human-environment heat exchange are normalized by dividing by the body surface area of the simulated individual converting W to W/m². For \(\:{M}_{max}\) , we further convert it into the unit of MET (Metabolic Equivalent of Task). Based on the \(\:{M}_{max}\) value, we further defined outdoor labor capacity according to Herrmann et al. 50 : (1) maximum safe activity level limited to light intensity (1.5–2.9 METs); (2) maximum safe activity level limited to moderate intensity (3.0–5.9 METs); (3) maximum safe activity level limited to vigorous intensity (≥ 6.0 METs); (4) when \(\:{M}_{max}\) <1.5 METs, conditions are deemed physiologically “uncompensable”. Outdoor labor is not recommended during this period. A standardized healthy adult profile (19–59 years, 65 kg, 1.7 m) is adopted as input basis of the human heat balance model. The complete set of input parameters characterizing this simulated individual is provided in Table S3. The human heat balance model also requires a set of environmental inputs, which include dry-bulb temperature (℃), relative humidity (%), wind speed (m/s), and mean radiant temperature (MRT; ℃). Dry-bulb temperature is the WRF-simulated 2-m temperature. Relative humidity is calculated using WRF-simulated 2-m temperature, 2-m water vapor mixing ratio (g/kg), and surface pressure (Pa). Wind speed at 1.5 m height is calculated from the WRF-simulated 10-m wind speed (m/s) using the logarithmic wind profile with surface roughness length. MRT is estimated using the methodology of Salamanca et al. 26 , based on WRF-simulated land surface temperature (K), downward shortwave radiation (W/m 2 ), downward longwave radiation (W/m 2 ), surface albedo, and surface emissivity. Declarations Data availability The datasets and models used in this study include: Version 4.3.3 of the WRF-ARW regional climate model (https://github.com/wrf-model/WRF/releases), the human heat balance model derived from the liveability model (https://zenodo.org/records/10020137), ERA5 reanalysis data from NCAR (https://rda.ucar.edu/datasets/ds633-0/), and WRF static data from UCAR (https://www2.mmm.ucar.edu/wrf/users/download/get_sources_wps_geog.html), along with observational meteorology data from the Automated Surface Observing Systems (https://mesonet.agron.iastate.edu/request/download.phtml?network=AZ_ASOS), the Flood Control District of Maricopa County ALERT System (https://alert.fcd.maricopa.gov/showrpts_mc.html), and the Arizona Meteorological Network (http://ag.arizona.edu/azmet). References Romanello, M. et al. The 2024 report of the Lancet Countdown on health and climate change: facing record-breaking threats from delayed action. The Lancet 404 , 1847–1896 (2024). Humphrys, E. Inertia in transformed times: Work health and safety amid climate change. J. Ind. Relat. 66 , 685–702 (2024). ILO, I. Working on a Warmer Planet: The Impact of Heat Stress on Labour Productivity and Decent Work . (Geneva: International Labour Organization., 2019). Gao, K. & Santamouris, M. The use of water irrigation to mitigate ambient overheating in the built environment: Recent progress. Build. Environ. 164 , 106346 (2019). Parajuli, S. P. et al. Impact of irrigation on farmworkers’ heat stress in California differs by season and during the day and night. Commun. Earth Environ. 5 , 1–16 (2024). Simpson, C. H., Brousse, O., Ebi, K. L. & Heaviside, C. Commonly used indices disagree about the effect of moisture on heat stress. Npj Clim. Atmospheric Sci. 6 , 1–7 (2023). Wouters, H. et al. Soil drought can mitigate deadly heat stress thanks to a reduction of air humidity. Sci. Adv. 8 , eabe6653 (2022). Orlov, A. et al. Changes in Land Cover and Management Affect Heat Stress and Labor Capacity. Earths Future 11 , e2022EF002909 (2023). Chakraborty, T. C., Qian, Y., Li, J., Leung, L. R. & Sarangi, C. Daytime urban heat stress in North America reduced by irrigation. Nat. Geosci. 1–8 (2025) doi:10.1038/s41561-024-01613-z. Havenith, G., Smallcombe, J. W., Hodder, S., Jay, O. & Foster, J. Comparing the efficacy of different climate indices for prediction of labor loss, body temperatures, and thermal perception in a wide variety of warm and hot climates. J. Appl. Physiol. 137 , 312–328 (2024). Broadbent, A. M., Krayenhoff, E. S. & Georgescu, M. The motley drivers of heat and cold exposure in 21st century US cities. Proc. Natl. Acad. Sci. 117 , 21108–21117 (2020). Georgescu, M., Broadbent, A. M. & Krayenhoff, E. S. Quantifying the decrease in heat exposure through adaptation and mitigation in twenty-first-century US cities. Nat. Cities 1 , 42–50 (2024). Yao, Y. et al. Impacts of irrigation expansion on moist-heat stress based on IRRMIP results. Nat. Commun. 16 , 1045 (2025). Budd, G. M. Wet-bulb globe temperature (WBGT)—its history and its limitations. J. Sci. Med. Sport 11 , 20–32 (2008). Grundstein, A. & Vanos, J. There is no ‘Swiss Army Knife’ of thermal indices: the importance of considering ‘why?’ and ‘for whom?’ when modelling heat stress in sport. (2021) doi:10.1136/bjsports-2020-102920. Fan, Y. & McColl, K. A. Widespread outdoor exposure to uncompensable heat stress with warming. Commun. Earth Environ. 5 , 1–13 (2024). Vanos, J. et al. A physiological approach for assessing human survivability and liveability to heat in a changing climate. Nat. Commun. 14 , 7653 (2023). Guzman-Echavarria, G., Middel, A., Vecellio, D. J. & Vanos, J. The development of an adaptive heat stress compensability classification applied to the United States. Int. J. Biometeorol. (2024) doi:10.1007/s00484-024-02766-7. Guzman-Echavarria, G., Middel, A. & Vanos, J. Beyond heat exposure — new methods to quantify and link personal heat exposure, stress, and strain in diverse populations and climates: The journal Temperature toolbox. Temperature 10 , 358–378 (2023). Vanos, J. K., Joshi, A., Guzman-Echavarria, G., Rykaczewski, K. & Hosokawa, Y. Impact of Reflective Roadways on Simulated Heat Strain at the Tokyo, Paris and Los Angeles Olympics. J. Sci. Sport Exerc. 6 , 288–302 (2024). Brandi, A., Martilli, A., Salamanca, F. & Georgescu, M. Urban boundary-layer flows in complex terrain: Dynamic interactions during a hot and dry summer season in Phoenix, Arizona. Q. J. R. Meteorol. Soc. 3099 , (2024). Deng, X. et al. Characterizing urban densification and quantifying its effects on urban thermal environments and human thermal comfort. Landsc. Urban Plan. 237 , 104803 (2023). Huang, X., Chang, J. M.-H., Shi, D., Chan, P. W. & Song, J. WRF-HEATS coupling: Incorporating human behaviors and city topography into urban heat stress evaluation. Build. Environ. 267 , 112191 (2025). Hwang, M.-K., Bang, J.-H., Kim, S., Kim, Y.-K. & Oh, I. Estimation of thermal comfort felt by human exposed to extreme heat wave in a complex urban area using a WRF-MENEX model. Int. J. Biometeorol. 63 , 927–938 (2019). Martilli, A. et al. WRF-Comfort: simulating microscale variability in outdoor heat stress at the city scale with a mesoscale model. Geosci. Model Dev. 17 , 5023–5039 (2024). Salamanca-Palou, F. et al. Effects of Urbanization and Climate Change on Heat Stress Under Relatively Dry and Wet Warm Conditions in a Semi-Arid Urban Environment. Earths Future 13 , e2024EF004983 (2025). United States Census Bureau. 2023 American Community Survey 1-Year Estimates. (2023). Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 11 , 1633–1644 (2007). Scanlon, B. R. et al. Multidecadal drought impacts on the Lower Colorado Basin with implications for future management. Commun. Earth Environ. 6 , 1–13 (2025). NWS Phoenix, N. 2023 Climate Year in Review for Phoenix, Yuma, and El Centro. https://www.weather.gov/psr/yearinreview2023 (2023). NWS Tucson, N. 110°+ occurrences information for Tucson. https://www.weather.gov/twc/Tucson110syearly (2023). Arizona Department of Health Services. Heat-Caused & Heat-Related Deaths from Exposure to Excessive Natural Heat in Arizona (2012-2023) . https://www.azdhs.gov/documents/preparedness/epidemiology-disease-control/extreme-weather/pubs/heat-related-mortality-year-2012-2023.pdf (2023). United States Department of Agriculture. 2022 United States Census of Agriculture. https://www.nass.usda.gov/Publications/AgCensus/2022/index.php (2024). Winslow, C.-E. A., Herrington, L. P. & Gagge, A. P. Physiological reactions of the human body to varying environmental temperatures. Am. J. Physiol.-Leg. Content 120 , 1–22 (1937). Mishra, V. et al. Moist heat stress extremes in India enhanced by irrigation. Nat. Geosci. 13 , 722–728 (2020). Jha, R., Mondal, A., Devanand, A., Roxy, M. K. & Ghosh, S. Limited influence of irrigation on pre-monsoon heat stress in the Indo-Gangetic Plain. Nat. Commun. 13 , 4275 (2022). Vanos, J. K. & Grundstein, A. J. Variations in Athlete Heat-Loss Potential Between Hot-Dry and Warm-Humid Environments at Equivalent Wet-Bulb Globe Temperature Thresholds. J. Athl. Train. 55 , 1190–1198 (2020). Kjellstrom, T., Freyberg, C., Lemke, B., Otto, M. & Briggs, D. Estimating population heat exposure and impacts on working people in conjunction with climate change. Int. J. Biometeorol. 62 , 291–306 (2018). Nelson, G. C. et al. Global reductions in manual agricultural work capacity due to climate change. Glob. Change Biol. 30 , e17142 (2024). Yaglou, C. P. & Minard, D. Control of heat casualties at military training centers. AMA Arch. Ind. Health 16 , 302–316 (1957). Vecellio, D. J. & Vanos, J. K. Aligning thermal physiology and biometeorological research for heat adaptation and resilience in a changing climate. J. Appl. Physiol. 136 , 1322–1328 (2024). Takahashi, Y. et al. Thermoregulation model JOS-3 with new open source code. Energy Build. 231 , 110575 (2021). Chen, F. & Dudhia, J. Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity. Mon. Weather Rev. 129 , 569–585 (2001). Martilli, A., Clappier, A. & Rotach, M. W. An Urban Surface Exchange Parameterisation for Mesoscale Models. Bound.-Layer Meteorol. 104 , 261–304 (2002). Salamanca, F., Krpo, A., Martilli, A. & Clappier, A. A new building energy model coupled with an urban canopy parameterization for urban climate simulations—part I. formulation, verification, and sensitivity analysis of the model. Theor. Appl. Climatol. 99 , 331–344 (2010). Salamanca, F. et al. Evaluation of the WRF-Urban Modeling System Coupled to Noah and Noah-MP Land Surface Models Over a Semiarid Urban Environment. J. Geophys. Res. Atmospheres 123 , 2387–2408 (2018). Georgescu, M., Moustaoui, M., Mahalov, A. & Dudhia, J. An alternative explanation of the semiarid urban area “oasis effect”. J. Geophys. Res. Atmospheres 116 , (2011). Li, P., Wang, Z.-H. & Wang, C. The potential of urban irrigation for counteracting carbon-climate feedback. Nat. Commun. 15 , 2437 (2024). Cramer, M. N., Gagnon, D., Laitano, O. & Crandall, C. G. Human temperature regulation under heat stress in health, disease, and injury. Physiol. Rev. 102 , 1907–1989 (2022). Herrmann, S. D. et al. 2024 Adult Compendium of Physical Activities: A third update of the energy costs of human activities. J. Sport Health Sci. 13 , 6–12 (2024). Additional Declarations There is NO Competing Interest. Supplementary Files Supplementary.docx Supplementary for Dry Heat, Not Moist Heat, Drives Irrigation’s Labor Capacity Benefits in Arizona’s Cities and Croplands Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6940300","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":477077553,"identity":"f2a8bdc7-6412-4ef9-a471-8afe12fcf5d4","order_by":0,"name":"Matei Georgescu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIie2QP2rDMBSHnxHYi0LWFIN9hRcMgRJIryIjiBYHCoXQIVBPmUKzprdobqAgsBcR082jQy7Qbs5QqE1ab7Y7FqpveH9+vA+EAAyGPwjKqjCYNYF3bXa7citJrfAmCHqVu1oBkE0Qxn3KJM0ORQHSG7rp26lcKbHdaoT3pWpXNCfIQAY3z9FDQBO12OURWrtjhyK5PWKQhq+azl0rVos4p0gG63YFs7NTMlBPlSIul1gJP9NIPruUnNvVjymG2klgEFeDjJBYnco5GDHk45cNJS5NxHifz+8Pm6PoeFh4+igfZ/6QOtWwmvpepvZFuZy2Kt9iXSg2u+y5/8EpfnloMBgM/40va1FbvvO+64sAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Matei","middleName":"","lastName":"Georgescu","suffix":""},{"id":477077554,"identity":"670d5c6b-7d70-4604-afea-c6f5d9ae1467","order_by":1,"name":"Xiangwen Deng","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"prefix":"","firstName":"Xiangwen","middleName":"","lastName":"Deng","suffix":""},{"id":477077555,"identity":"6940d32e-79fa-4dfa-9512-0573e6e8d438","order_by":2,"name":"Gisel Guzman","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"prefix":"","firstName":"Gisel","middleName":"","lastName":"Guzman","suffix":""},{"id":477077556,"identity":"08789480-3913-4343-9d95-2df8b353b2d7","order_by":3,"name":"Jennifer Vanos","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Vanos","suffix":""}],"badges":[],"createdAt":"2025-06-20 15:50:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6940300/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6940300/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85778609,"identity":"30b3b21a-802a-4a5c-8db4-f61caa959c79","added_by":"auto","created_at":"2025-07-01 14:46:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1324461,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConfiguration of WRF simulation domains.\u003c/strong\u003e (a) three nested WRF domains; high-resolution characterization of the innermost domain (D03) showing (b) land use/cover classification, (c) urban fraction distribution, and (d) topographic variation.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6940300/v1/495576a78b53e7615ad6ab2e.png"},{"id":85778610,"identity":"c46aa672-3183-44ec-ab6d-d9b0c50758c7","added_by":"auto","created_at":"2025-07-01 14:46:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1909606,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSimulated diurnal cycles of irrigation effects on environmental factors.\u003c/strong\u003e Box-and-whisker plots illustrate the distribution of diurnal changes in air temperature (a1, a2), water vapour mixing ratio (b1, b2), wind speed (c1, c2), and mean radiant temperature (d1, d2), contrasting urban areas (left panels) with croplands (right panels).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6940300/v1/de17f6eea14619b5130431f8.png"},{"id":85779581,"identity":"ec80bfc1-5e1a-42c6-b639-dba37c141471","added_by":"auto","created_at":"2025-07-01 14:54:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1709141,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSimulated diurnal cycles of human heat balance components for the Control scenario and the irrigation-induced modifications.\u003c/strong\u003e Box-and-whisker plots depict the baseline diurnal cycles of dry heat exchange (a1, a2) and evaporative heat loss (c1, c2) represented by the Control scenario, alongside irrigation-induced changes in dry heat exchange (b1, b2), and irrigation-induced changes in evaporative heat loss (d1, d2), contrasting urban areas (left panels) with croplands (right panels).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6940300/v1/14fa951d1fcaf97c4229e5f9.png"},{"id":85778618,"identity":"9a9e1b4c-54cc-436b-a4e8-73d1b9086e42","added_by":"auto","created_at":"2025-07-01 14:46:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2550346,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummertime hourly frequency of labor capacity categories for the Control scenario and the irrigation-induced modifications.\u003c/strong\u003e Labor capacity during summer 2023 in four urban areas (a1-a4) and three cropland regions (c1-c3) for the Control scenario, with corresponding irrigation impacts on the frequency in urban areas (b1-b4) and cropland regions (d1-d3). In panels b and d, positive (negative) values indicate percentage increases (decreases) in time occurrence for each labor capacity category.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6940300/v1/f6ee1e729875870b81b6696a.png"},{"id":85778616,"identity":"ff9e4a84-e2b7-4a26-999e-905bd6eef6df","added_by":"auto","created_at":"2025-07-01 14:46:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":837681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic diagram of our framework, through explicit analysis of irrigation-induced meteorological and human heat balance changes to labor capacity. \u003c/strong\u003eEmphasis is placed on cascading changes in meteorological and human physiological and biophysical drivers of labor capacity in regional climate and human heat balance models, showing how irrigation-enhanced soil moisture influences labor capacity during the daytime and nighttime periods. Color filled boxes represent the coupled variables between the regional climate model and human heat balance model, where meteorological outputs serve as inputs in human heat balance modeling.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6940300/v1/74f36725bad91cf41f56b95f.png"},{"id":96363059,"identity":"dc4009cc-b9bf-4696-88dd-3859a6160d53","added_by":"auto","created_at":"2025-11-20 10:04:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9184566,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6940300/v1/303a5ffe-8874-48c4-849e-39a93ff4fe64.pdf"},{"id":85780945,"identity":"19d5d428-5d64-47d4-9d0f-4c7a9c83a7ea","added_by":"auto","created_at":"2025-07-01 15:10:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4742307,"visible":true,"origin":"","legend":"Supplementary for Dry Heat, Not Moist Heat, Drives Irrigation\u0026#x2019;s Labor Capacity Benefits in Arizona\u0026#x2019;s Cities and Croplands","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6940300/v1/a5528dcdecb76e22e53c6965.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Dry Heat, Not Moist Heat, Drives Irrigation’s Labor Capacity Benefits in Arizona’s Cities and Croplands","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHuman exposure to excessive warm weather has reached alarming levels, with 2023 recording 27.7% more hours of heat exposure at moderate or higher risk during outdoor physical activity compared to the 1990s average \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. High heat exposure disproportionately affects labor capacity within agriculture and construction occupations \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. By 2030, agricultural and construction workers are projected to face the highest loss of working hours due to heat, accounting for 60% and 19%, respectively \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Irrigation, which plays a critical role in sustaining agricultural productivity and has also become a widespread landscape management practice across cities, critically impacts on outdoor worker\u0026rsquo;s heat stress in both urban and agricultural areas \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Irrigation has competing pathways: on the one hand, it contributes to reduced heat stress resulting from enhanced evapotranspiration that lowers ambient temperatures, while on the other hand the associated atmospheric moistening (hereafter humidification) may impede evaporation of sweat from the human skin. Therefore, the influence of irrigation on outdoor workers\u0026rsquo; heat stress and associated labor capacity remains an area of active research \u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe trade-off of these competing pathways varies depending on the geographical characteristics of irrigated areas (e.g., climate types \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, topography, and land use patterns). The subsequent conversion of heat stress related information (i.e., local environmental conditions plus clothing and an individual\u0026rsquo;s metabolic rate) into labor capacity assessment introduces additional layers of uncertainty \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Consequently, a systematic quantification framework is required to robustly examine how the trade-offs between competing environmental impacts (e.g., cooling vs. humidification) arising from the same land management practice (i.e., irrigation) affect human-environment heat exchange and subsequent labor capacity, thus providing evidence-based guidelines for safe, outdoor, working practices. Developing such a framework is especially urgent in light of projected increases in population-weighted heat exposure in urban areas throughout the 21st century \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, as well as the critical role of local adaptation strategies in mitigating adverse heat-related impacts \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn an effort to address these socioenvironmental challenges, meteorological/climatological and physiologically-based models are powerful tools that integrate heat-related information across global, regional, and local scales. Driven by climate model simulations at global \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and regional scales \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, previous efforts have widely utilized direct and empirical heat indices\u0026ndash;\u0026ndash;including Wet Bulb Globe Temperature (WBGT) \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, Environmental Stress Index \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, and Humidex \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u0026ndash;\u0026ndash;to investigate the impacts of land management practices on heat stress and associated labor capacity. The strength of these indices lies in a climate model\u0026rsquo;s ability to generate multiple scenarios that isolate and quantify the environmental impacts of land management practices. However, these simple heat indices rely on empirical parameterizations of trade-offs between different environmental factors, potentially limiting the generalizability of their findings. For example, WBGT\u0026rsquo;s fixed 0.7 weighting for wet-bulb temperature (T\u003csub\u003ew\u003c/sub\u003e) does not adequately represent the enhanced role of evaporative cooling as temperature increase \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Moreover, these indices have limited integration of the individual characteristics that affect thermoregulation (e.g., body size, activity levels, clothing, and sweat rates) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Furthermore, established empirical thresholds do not represent other regional or global populations due to differences in acclimatization \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlternatively, the use of physiologically-based models offers an opportunity to address the limitations highlighted above. By integrating dynamic human-environment heat exchange and realistic thermoregulatory characteristics across different demographic segments, these models can not only describe the drivers of heat stress/strain, but also examine the limits for safe sustained activities for diverse population groups \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, existing physiologically-based modeling studies have mainly relied on climate simulations from Global Climate Models (GCMs) \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e or meteorological station data \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. These approaches are limited in two important ways. First, they do not consider regional environmental heterogeneity (e.g., an airport weather station is unlikely to be representative of any part of an urban area). Second, such approaches do not account for the fact that local to regional scale meteorological dynamics are modulated by unique geographical and topographical features (e.g., Brandi et al. \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e), and by extension, omit land management practices. To advance the integration of regional climate with physiological and biophysical principles of human heat stress, several studies have leveraged meteorological outputs from regional climate models (e.g., the Weather Research and Forecasting (WRF) model) to calculate human energy balance-based heat indices (e.g., Physiological Subjective Temperature, Universal Thermal Climate Index) \u003csup\u003e\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, or to examine the limitations of heat compensability for acclimatized and non-acclimatized populations \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. These studies examine the intraurban variability of heat stress by relating index magnitudes with corresponding heat stress levels, but do not directly quantify human-environment heat exchange, making it virtually impossible to examine impacts on labor capacity.\u003c/p\u003e \u003cp\u003eTo bridge this gap, a key scientific question, and the foundation of our work, emerges: How can labor capacity responses to land management practices be quantitatively assessed through coupling of meteorological, physiological, and human biophysical principles? This study introduces an interdisciplinary framework via coupling of a regional climate model (i.e., WRF) and a human heat balance model grounded in physiological and human biophysical principles, as described in the liveability model proposed by Vanos et al. \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Applying this framework, we directly address the ongoing scientific debate regarding irrigation impacts on human heat stress and labor capacity. First, by comparing an Irrigation scenario (i.e., a summertime simulation with realistic incorporation of irrigation within WRF) against the Control scenario (i.e., a summertime simulation with no irrigation), our analysis of results isolates the irrigation-induced diurnal variability in environmental factors over cities and croplands located in the arid/semi-arid state of Arizona (USA). Second, a human heat balance model is coupled to WRF and used to estimate changes in components of human-environment heat exchange (i.e., dry heat exchange(s) and evaporative heat loss), thereby characterizing dry and moist heat experienced by standard, healthy adults. Finally, variations in labor capacity levels are estimated based on maximum safe metabolic rates determined by human-environment heat interactions, across the diurnal scale.\u003c/p\u003e\n\u003ch3\u003eStudy Area\u003c/h3\u003e\n\u003cp\u003eArizona (109\u0026deg; 3' W \u0026ndash; 114\u0026deg; 50' W, 31\u0026deg; 20' N \u0026ndash; 37\u0026deg; N) is the sixth largest state in the United States, covering 295,254 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, with a population of 7,431,344 as of 2023 \u003csup\u003e27\u003c/sup\u003e. Arizona is distinguished by complex terrain, featuring steep mountains to the north and east and a southwest-dipping valley with reduced relief (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). This complex terrain contributes to pronounced meteorological variability across the state \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The K\u0026ouml;ppen climate classification of Arizona is primarily BWh (subtropical desert) in the southwestern and central portions, BWk (mid-latitude desert) in the northwestern regions, and BSk (Mid-latitude steppe) in northeastern Arizona \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Given the generally dry climate of the state, irrigation accounted for 95% of consumptive water use based on 2000\u0026ndash;2020 withdrawals \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The focus of this study is on the state\u0026rsquo;s four major urban areas: Phoenix, Tucson, Flagstaff, and Prescott metropolitan areas, which are home to 88% of the state's population, and all cropland regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Given the state\u0026rsquo;s considerable topographic variability, cropland areas are distinguished into three regions with longitudinal divisions at 113\u0026deg;W and 111\u0026deg;W: Western, Central, and Eastern croplands (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe simulate the summer of 2023 (June\u0026ndash;August; JJA), one of the hottest and driest summers on record. During this summer, Arizona's two most populous metropolitan areas experienced record-breaking extreme heat: the Phoenix metropolitan area recorded 55 days over 110\u0026deg;F (43.3℃), and the Tucson metropolitan area reached 18 days over 110\u0026deg;F \u0026ndash; both far exceeding their 1991\u0026ndash;2020 climate normals (21 and 4 days, respectively) \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The counties containing these urban areas, Maricopa (Phoenix metropolitan area) and Pima (Tucson metropolitan area), recorded 643 and 173 heat-related deaths, respectively \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Meanwhile, the summer months also mark the peak harvest season in Arizona for multiple crops, including corn, alfalfa, eggplant, squash, and tomatoes. Arizona\u0026rsquo;s agricultural workforce, exceeding 29,000 \u003csup\u003e33\u003c/sup\u003e, is highly vulnerable to prolonged outdoor exposure during fieldwork-related activities (e.g., crop harvesting and maintenance of irrigation systems).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIrrigation-induced environmental changes\u003c/h2\u003e \u003cp\u003eThe irrigation-induced environmental modifications are isolated by computing differences between the Irrigation and Control scenarios. We present results as summertime averaged diurnal cycle differences. The differences reveal that during periods of active evapotranspiration (07:00\u0026ndash;18:00 local time), irrigation enhances atmospheric humidity (water mixing ratio) in lower-elevation regions; the average median humidity increases by ~\u0026thinsp;0.25 g/kg in Western croplands, ~\u0026thinsp;0.20 g/kg in Phoenix and Tucson, and ~\u0026thinsp;0.10 g/kg in Central croplands (Figs.\u0026nbsp;2a1, a2). This difference is attributed to increased soil moisture (see Methods for irrigation parameterization) that promotes greater evapotranspiration rates and increased latent heat flux of 20\u0026ndash;100 W/m\u003csup\u003e2\u003c/sup\u003e (Figs. S3 a1, a2). The Western croplands undergo the most substantial increase in water mixing ratio due to the lower-elevation valley topography that facilitates moisture accumulation from evapotranspiration. Conversely, higher-elevation regions (Flagstaff, Prescott, Eastern croplands) experience smaller increases in latent heat flux (\u0026lt;\u0026thinsp;20 W/m\u003csup\u003e2\u003c/sup\u003e), resulting in minimal humidity fluctuations. During nighttime hours, when evapotranspiration diminishes, irrigation produces trivial changes in latent heat flux and humidity across all urban and cropland regions in Arizona.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIrrigation leads to cooling effects in lower-elevation regions during 07:00\u0026ndash;18:00 (local time), with averaged median air temperature reduction of ~\u0026thinsp;0.7 ℃ in Western croplands, ~\u0026thinsp;0.4 ℃ in Phoenix, ~\u0026thinsp;0.3 ℃ in the Central croplands, and ~\u0026thinsp;0.2 ℃ in Tucson (Figs.\u0026nbsp;2b1, b2). These cooling impacts are generally not considerable; however, we emphasize the clear gradient in air temperature reduction with elevation (greatest cooling for Phoenix and Tucson, and reduced cooling for Prescott and Flagstaff). This cooling results from the shift in surface energy partitioning: enhanced latent heat flux diminishes sensible heat flux by 10\u0026ndash;70 W/m\u003csup\u003e2\u003c/sup\u003e (Figs. S3 b1, b2). Meanwhile, the conversion of soil-stored heat to latent heat reduces ground heat flux by 5\u0026ndash;40 W/m\u003csup\u003e2\u003c/sup\u003e (Figs. S3 c1, c2) and contributes to a decrease in surface temperature ranging from 0.3 to 4.0 ℃ (Figs. S3 d1, d2). The decreased surface temperature reduces outgoing longwave radiation, leading to averaged median mean radiant temperature (MRT) reduction of ~\u0026thinsp;1.2℃ in Western croplands, ~\u0026thinsp;0.9℃ in Phoenix and Tucson, and ~\u0026thinsp;0.4℃ in Central croplands (Figs.\u0026nbsp;2c1, c2).\u003c/p\u003e \u003cp\u003eNocturnal vegetation evapotranspiration is negligible, resulting in minimal latent heat flux variation. However, the enhanced thermal conductivity and heat capacity of irrigated, moist soils facilitate more efficient daytime heat storage in deeper layers and subsequent nocturnal release. This process increases ground heat flux (5\u0026ndash;30 W/m\u003csup\u003e2\u003c/sup\u003e), thus raising surface temperature by 0.5\u0026ndash;3.0℃ and outgoing longwave radiation. In turn, both air temperature and MRT are increased. The average median air temperature increases by 0.5℃ in Western croplands, and 0.2℃ in Phoenix, Tucson, and Central croplands. During nighttime hours, the median peak MRT increases by 1.0℃ in Western croplands, and 0.5℃ in Phoenix, Tucson, and Central croplands. Higher-elevation regions show minimal irrigation-induced diurnal variations in air temperature and MRT due to the smaller changes in the surface energy balance and surface temperature. Generally, croplands (with lower heat capacity and heat storage than urban areas dominated by concrete, asphalt, and buildings) exhibit larger diurnal variations in air temperature and MRT.\u003c/p\u003e \u003cp\u003eWind speed, another environmental factor, shows negligible response to irrigation across Arizona throughout the day (Figs.\u0026nbsp;2d1, d2). Overall, the impact of irrigation on meteorological factors shows a similar tendency across Arizona, with similar magnitude depending on background environmental conditions (topography and land use patterns).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eChanges in components of human-environment heat exchange\u003c/h3\u003e\n\u003cp\u003eWe evaluate human-environment heat exchange through two components: dry (i.e., sensible) heat exchange and evaporative heat loss. The main dry heat exchanges includes radiative and convective heat transfer between the human skin surface and environment \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. These exchanges are primarily determined by the temperature gradient between human skin and the environment. The environment temperature here refers to a composite metric integrating air temperature and MRT \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Dry heat exchanges are bidirectional: (1) dry heat loss from human to environment (dry heat exchanges\u0026thinsp;\u0026gt;\u0026thinsp;0) occurs with a positive human skin-environment temperature gradient; (2) dry heat gain from environment to human (dry heat exchanges \u0026lt; 0) occurs with a negative human skin-environment temperature gradient. Under the Control scenario, Arizona\u0026rsquo;s outdoor workers typically experience dry heat loss (Figs.\u0026nbsp;3a1, a2). However, during 09:00\u0026ndash;18:00 (local time), outdoor workers in lower-elevation areas generally experience dry heat gain, with median values between \u0026minus;\u0026thinsp;220 and \u0026minus;\u0026thinsp;60W/m\u003csup\u003e2\u003c/sup\u003e, resulting in enhanced heat stress.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring 9:00\u0026ndash;18:00 (local time), irrigation-induced environmental changes increase dry heat exchange for outdoor workers in lower-elevation areas, with median values ranging from 4\u0026ndash;60W/m\u003csup\u003e2\u003c/sup\u003e in Phoenix, 4\u0026ndash;50W/m\u003csup\u003e2\u003c/sup\u003e in Tucson, 10\u0026ndash;70W/m\u003csup\u003e2\u003c/sup\u003e in Western croplands, and 8\u0026ndash;68W/m\u003csup\u003e2\u003c/sup\u003e in Central croplands (Figs.\u0026nbsp;3b1, b2). Given that outdoor workers in lower-elevation areas experience dry heat gain under the Control scenario (Figs.\u0026nbsp;3a1, a2), this increase indicates reduced dry heat gain. This heat stress alleviation occurs because irrigation-induced environmental changes (i.e., decreasing air temperature and MRT) lessen the negative skin-environment temperature gradient. Smaller modifications occur at higher-elevation areas due to: (1) lower air temperature and MRT (smaller negative human skin-environment temperature gradient) under the Control scenario (Fig. S4); and (2) minimal irrigation-induced changes in air temperature and MRT (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring nighttime (19:00\u0026ndash;8:00 local time), irrigation reduces dry heat exchange for outdoor workers across Arizona, with an average median reduction of 22W/m\u003csup\u003e2\u003c/sup\u003e in Phoenix, 29W/m\u003csup\u003e2\u003c/sup\u003e in Tucson, 52W/m\u003csup\u003e2\u003c/sup\u003e in Flagstaff, 43W/m\u003csup\u003e2\u003c/sup\u003e in Prescott, 40W/m\u003csup\u003e2\u003c/sup\u003e in Western croplands, 38W/m\u003csup\u003e2\u003c/sup\u003e in Central croplands, and 44W/m\u003csup\u003e2\u003c/sup\u003e in Eastern croplands. As outdoor workers experience nighttime dry heat loss under the Control scenario, this reduction indicates reduced dry heat loss and increased heat stress. Unlike daytime patterns, irrigation results in greater nighttime impacts on dry heat exchange in higher-elevation areas. This is attributed to a larger positive human skin-environment temperature gradient in here under the Control scenario, enabling irrigation-induced increases in air temperature and MRT (though relatively weak, the trend is evident) to more effectively decrease the positive human skin-environment temperature gradient.\u003c/p\u003e \u003cp\u003eEvaporative heat loss occurs through evaporation at the skin surface and respiratory tract \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This mechanism is most effective in hot-dry environments, and its effectiveness declines with increasing humidity. Evaporative heat loss is limited not only by the environment, but also by additional clothing, maximum skin wettedness, and maximum sweat rate. Under the Control scenario, evaporative heat loss values are capped at 306W/m\u0026sup2; for outdoor workers throughout the day (Figs.\u0026nbsp;3c1, c2). However, the median values of irrigation-induced changes in evaporative heat loss are maintained close to 0 W/m\u0026sup2;, with overall changes in magnitude generally below 7 W/m\u0026sup2; (Figs.\u0026nbsp;3d1, d2). During the daytime, this trivial change results from the counteracting irrigation-induced environmental effects. In this hot-dry environment, sweating capacity primarily governs evaporative heat loss. While increased humidity sweating capacity, concurrently decreased air temperature and MRT enhance suppresses sweating capacity. Nocturnally, this trivial change is only attributed to irrigation's negligible impact on humidity. This shift in dominant control occurs because under wetter nighttime conditions, environmental humidity (rather than sweating capacity) limits evaporative heat dissipation. Consequently, we conclude that irrigation primarily affects heat stress by altering dry heat exchanges rather than evaporative heat loss for outdoor workers in this arid/semi-arid state. Note that the above irrigation-induced conclusions do not consider the impact of wind speed, as irrigation has a negligible effect on wind speed.\u003c/p\u003e\n\u003ch3\u003eSpatiotemporal changes in labor capacity under heat stress\u003c/h3\u003e\n\u003cp\u003eAggregating the components of human heat exchange permits estimation of the maximum safe metabolic rate without a sustained rate of positive heat storage (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{max}\\)\u003c/span\u003e\u003c/span\u003e). To quantify labor capacity, we divide \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{max}\\)\u003c/span\u003e\u003c/span\u003e values into four classes: 1) uncompensable heat stress (which causes a rise in core temperature, making outdoor work inadvisable), and maximum safe activity level limited to 2) light, 3) moderate, and 4) vigorous intensity. These four categories are used to analyze the summertime hourly frequency of safe sustained labor capacity for the Control scenario and the irrigation-induced modification (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Analysis of the Control scenario indicates that cropland workers generally have lower daytime labor capacity than urban outdoor workers with more frequent occurrences of uncompensable heat stress periods. This lower labor capacity is due to the overall greater daytime dry heat gain for cropland workers (Figs.\u0026nbsp;3a1, a2). At 15:00 local time (i.e., the peak heat stress period), uncompensable heat stress occurs during 38% of the summer season (~\u0026thinsp;34 days) in the Western croplands, 30% of the summer season (~\u0026thinsp;27 days) in the Central croplands, and 19% of the summer season (~\u0026thinsp;18 days) in the urban area (Phoenix). The frequency of hours within uncompensable heat stress is less than 2% for the rest of the regions. While uncompensable heat stress peaks at 15:00 local time, such conditions extend throughout the afternoon hours.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnlike daytime hours, cropland workers demonstrate greater nighttime labor capacity than urban outdoor workers. Specifically, cropland rural workers across the three cropland regions can safely perform vigorous-intensity activities for over 97% of the summer nighttime hours, compared to urban outdoor workers in Phoenix (88%) and Tucson (95%). This higher labor capacity is owing to the greater evaporative and dry heat loss for cropland workers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In Flagstaff and Prescott, which have the highest elevation, outdoor workers have the greatest labor capacity. Workers in these environments can safely conduct vigorous-intensity activities during 99% of the summer nighttime hours.\u003c/p\u003e \u003cp\u003eThe impact of irrigation on labor capacity exhibits pronounced spatiotemporal variations (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and S5). During the extreme heat stress hours (13:00\u0026ndash;18:00), irrigation most effectively enhances labor capacity for outdoor workers in Western croplands (Fig.\u0026nbsp;4d1), reducing the frequency of hours under uncompensable heat stress by an average of 8% (~\u0026thinsp;7 days), followed by Phoenix with an average reduction of 4% (~\u0026thinsp;4.5 days). Spatially, these two regions exhibit approximately 30% of the relative reductions in cumulative uncompensable heat stress hours (Fig. S5 a2). However, other urban and cropland regions show negligible labor capacity modification for outdoor workers. This minimal effect occurs in Central croplands because the irrigation-induced modifications to components of human-environment heat exchange remain insufficient to alter this region's already lower baseline (Control) labor capacity. The modest impact in other regions (Tucson, Flagstaff, Prescott, Eastern croplands) is attributed to their higher heat stress baseline (Control) labor capacity and lower changes in components of human-environment heat exchange. During 19:00\u0026ndash;6:00, labor capacity changes in Arizona are generally less than 2%. Within the same area, irrigation-induced changes may produce weak but bidirectional labor capacity outcomes (e.g., the maximum safe activity level shifts between vigorous and moderate intensity) throughout the day. This finding also applies to the spatial distribution of irrigation-induced changes in cumulative hours under different labor capacity levels, especially in higher-elevation regions (Fig. S5). Our results reveal uncertainty layers in how alterations in components of human-environment heat exchange impact labor capacity outcomes as identical environmental changes may differentially intersect physiological tolerance limits depending on the spatiotemporal context of the environment.\u003c/p\u003e \u003cp\u003eWhile irrigation appears to improve summer labor capacity in low-altitude regions, our framework demonstrates its significant reduction of dry heat gain across all of Arizona\u0026mdash;particularly during periods of uncompensable heat stress (Fig. S6). In Western croplands, Central croplands, and Phoenix, irrigation decreases dry heat gain by approximately 100 W/m\u0026sup2;, while other regions experience 50\u0026ndash;75 W/m\u0026sup2; reductions. However, even under uncompensable heat stress conditions, irrigation has a negligible influence on evaporative heat loss. Overall, our framework establishes a complete pathway from irrigation implementation to labor capacity outcomes, as summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. We systematically trace the irrigation process: beginning with irrigation-induced environmental modifications simulated through WRF, progressing through impacts on the human-environment heat exchange, which ultimately establishes the resultant labor capacity changes across different regions and time scales.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe propose a new interdisciplinary framework to assess regional labor capacity through the coupling of meteorological, physiological and human biophysical principles. This framework can be applied to any climatic condition and customized for any population groups to examine the trade-offs between any kind of land management practice, human-environment heat interactions, and labor capacity. Building upon this approach, we conclude that in arid/semi-arid regions, irrigation's beneficial impact on labor capacity under heat stress primarily operates through dry heat exchange (dry heat) mediated by air temperature and MRT, rather than through changes in evaporative cooling (moist heat) impacted by both humidity and dry heat. This biophysical pathway elucidates a previously unrecognized energy exchange mechanism contributing to irrigation-induced cooling for the benefit of outdoor workers. Furthermore, our results reveal spatiotemporal heterogeneity in labor capacity responses to irrigation-induced environment changes even under comparable climatic conditions within the same region.\u003c/p\u003e \u003cp\u003eThe conclusions reached in this study differ from previous studies that equated values of humidity-inclusive heat metrics directly with moist heat \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Overreliance on direct and empirical heat indices that aggregate all environmental factors into a single value does not allow for the separation of avenues of heat exchanges (i.e., dry, moist) that interact to create heat stress. These simple indices may further lead to contradictory conclusions on moist heat when different indices are employed within identical environmental conditions. For example, Mishra et al. \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e observed 95th percentile \u003cem\u003edecreases\u003c/em\u003e in Heat Index (HI; used by the U.S. National Weather Service) alongside \u003cem\u003eincreases\u003c/em\u003e in 95th percentile wet-bulb temperature (T\u003csub\u003ew\u003c/sub\u003e) during Indian summers of 2000–2018. This discrepancy stems from the fact that different indices empirically parameterize environmental factor combinations. In India's highly humid climate, HI is less sensitive to humidity than T\u003csub\u003ew\u003c/sub\u003e. Indeed, T\u003csub\u003ew\u003c/sub\u003e –– by definition –– remains stable or increases with soil moisture regardless of background humidity \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo our knowledge, no studies have examined the irrigation-induced modification in moist and dry heat at regional scales using human heat balance models. However, some simple heat indices such as the simplified WBGT have been used to address this question. For example, Chakraborty et al. \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and Parajuli et al. \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e found that irrigation reduced daytime but increased nighttime WBGT during summers in arid zones of North America. Though these studies find lower heat stress in the daytime (agreeing with our M\u003csub\u003emax\u003c/sub\u003e findings), their conclusions may overestimate the true irrigation impacts on the heat load to humans, as the expected heat strain for equivalent WBGT levels are consistently less stressful in hot-dry versus hot-humid environments \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Meanwhile, their higher nighttime heat stress due to irrigation contrasts with our results showing negligible (2%) irrigation-induced changes in outdoor labor capacity.\u003c/p\u003e \u003cp\u003eLabor capacity under heat stress is typically estimated by employing empirical formulas/functions linking heat indices to physical labor capacity \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Previous studies using this method are often derived from human experimental trials in laboratory settings under varying environmental conditions, lending them credibility. However, broad application to diverse populations (differing from those used in a lab) introduces uncertainty. For example, it is difficult to equate the work capacity of young, fit male soldiers wearing olive green uniforms during military activities (the reference population for WBGT \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e) to participants wearing light clothing walking on a treadmill at a fixed heart rate of 130 beats/min \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Yet these newer studies must continue to improve original models and find new ways to apply physiological-based empirical tests to real world environments \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Our modeling framework offers flexibility to account for physiological traits, body size, sweat rate, etc., which is increasingly critical for heat safety and productivity. Further, quantifying metabolic output as a labor capacity can support research examining the impact of heat on the economy, which causes serious economic strains globally \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOverall, our results reveal that the relationships between meteorological, physiological, and human biophysical factors of labor capacity are highly complex and inherently connected to geographical context and population characteristics, making simplified heat indices inadequate as standalone measures to understand environmental changes to the human-environment heat exchange and subsequent labor capacity. We present summer-specific conclusions for Arizona. Future research can apply this framework to examine irrigation effects in differing regions, across longer time periods, and during different seasons (e.g., using projections of future, warmer, summers). While we model standard healthy adults to derive conclusions applicable to outdoor workers across Arizona, future research could customize simulations for specific outdoor populations in targeted geographical areas and age groups to provide work guidance, or for vulnerable populations with comorbidities or thermoregulatory disorders to deliver more accurate behavioral recommendations. Meanwhile, although dry environments often support unlimited evaporation \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, our assumption that people continuously cool themselves through sweating across all environmental conditions does not fully reflect physiological reality. Future research could employ more sophisticated models, such as the joint system thermoregulation model \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e to test physiological heat strain within specific times of exposure. Furthermore, following our framework, impact pathways similar to those in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e could be developed to characterize the effects of other land management practices (e.g., increased green spaces) on labor capacity, providing a clear visual, and ultimately quantitative, representation of mechanisms.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003e\u003c/h3\u003e\n "},{"header":"Methods","content":"\u003ch2\u003eWRF Model parameterization and configuration\u003c/h2\u003e\u003cp\u003eThe Weather Research and Forecasting (WRF) model with the advanced dynamic solver version 4.3.3 is used to conduct high-resolution sensitivity experiments for irrigation in Arizona. We deploy a one-way, three-nested model domain using a Lambert conformal conic projection, with horizontal grid spacings of 18, 6, and 2 km from outermost to innermost domain, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The innermost domain covers nearly all major urban areas and croplands in Arizona, with an area of 462 km × 492 km. The vertical coordinate includes 45 terrain-following eta levels from the surface to 50 hPa. The initial and lateral boundary conditions for WRF are derived from the ERA5 reanalysis data produced by the European Centre for Medium-Range Weather Forecast (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu/\u003c/span\u003e\u003cspan address=\"https://cds.climate.copernicus.eu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), having a spatial resolution of 0.25° and a temporal resolution of 1 h.\u003c/p\u003e\u003cp\u003eThe main physical parameterizations used in this study are illustrated in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. For non-urban areas (rural areas and vegetated portions of urban areas), the Noah Land Surface Model (LSM) is selected to simulate the biophysical and radiative interactions between the soil, vegetation, and atmosphere \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. These non-urban areas are represented by the MODIS land cover data. We represent urban processes using the Building Effect Parameterization and Building Energy Model (BEP-BEM), which is coupled to the Noah LSM \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. BEP-BEM explicitly simulates the dynamic interaction between ambient and indoor thermal environments, including the anthropogenic heating from air conditioning systems—a key feature of summer climates in hot desert urban areas in Arizona \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Urban areas are characterized by urban sub-category classifications and impervious fraction derived from the National Land Cover Database 2021 (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb and c). The building parameters in BEP-BEM are obtained from Salamanca et al. \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cem\u003eIrrigation parameterization and experimental design\u003c/em\u003e\u003cp\u003eThe irrigation scheme is implemented within the Noah LSM to account for the addition of water across urban and non-urban areas. The Noah LSM has been widely used to investigate irrigation impacts on regional climate over Arizona and worldwide \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Following parks annual irrigation schedules from city of Phoenix, our irrigation scheme maintains all four soil layers of the Noah LSM—corresponding to vegetated portions of urban areas and croplands—at reference levels (i.e., threshold below which transpiration begins to stress, but above wilting point) each morning before sunrise (04:00–05:00 MST). This approach prevents excessive irrigation in vegetated zones and avoids water application to bare soil, which is aligned with the sustainable irrigation practices in arid/semi-arid regions. We conduct two parallel simulations: a Control scenario with the irrigation scheme turned off and an irrigated scenario with the scheme on. Both scenarios share identical large-scale meteorological forcing, physical parameterizations, and land surface characteristics to isolate the irrigation signal. The simulations span from 00:00 UTC on May 1, 2023, to 00:00 UTC on September 1, 2023. The initial month of WRF output is regarded as spin-up and discarded. The analysis focuses on hourly outputs during the summer months (June, July, and August) in the innermost domain. The WRF model's performance is evaluated against observational data from multiple meteorological networks, as detailed in Method S1, ensuring the reliability of the simulated environmental conditions used in subsequent analyses.\u003c/p\u003e\u003ch2\u003eHuman Heat Balance Model\u003c/h2\u003e\u003cp\u003eWe apply a human heat balance model \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e grounded in physiological and human biophysical principles to simulate heat transfer between the human body and the surrounding environment, and estimate the safe levels for sustained activity. With assumptions of null internal body heat storage, negligible mechanical work, and negligible conductive heat exchange, the heat balance equation is expressed as:\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{req}=M-{R}_{skin}-{C}_{skin}-{C}_{res}-{E}_{res}\\)\u003c/span\u003e \u003c/span\u003e (W) (1)\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{req}\\)\u003c/span\u003e\u003c/span\u003e is the rate of evaporative heat loss from the skin surface required for heat balance; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:M\\)\u003c/span\u003e\u003c/span\u003e is internal metabolic heat production, equaling metabolic rate since mechanical work is negligible; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{skin}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{skin}\\)\u003c/span\u003e\u003c/span\u003eare and the rate of dry heat exchange through the skin by radiation and convection combined; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{res}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{res}\\)\u003c/span\u003e\u003c/span\u003e are the convective and evaporative heat exchange in the respiratory tract.\u003c/p\u003e\u003cp\u003eHowever, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{req}\\)\u003c/span\u003e\u003c/span\u003e cannot always be achieved due to limitations on the maximum evaporative heat loss capacity\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\)\u003c/span\u003e\u003c/span\u003ecaused by the environment, clothing, and sweating capacity of human body (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{{max}_{constrain}}\\)\u003c/span\u003e\u003c/span\u003e). Therefore, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{{max}_{constrain}}\\)\u003c/span\u003e\u003c/span\u003e represents two distinct but related constraints on evaporative loss. In high-humidity environments, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{{max}_{constrain}}\\)\u003c/span\u003e\u003c/span\u003e is mainly limited by the thermal environment, clothing, and maximum skin wettedness, referred to as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{{max}_{wet}}\\)\u003c/span\u003e\u003c/span\u003e; in dry environments, where the potential to evaporate is high, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{{max}_{constrain}}\\)\u003c/span\u003e\u003c/span\u003e is constrained mainly by maximum sweat rate, denoted as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{{max}_{sweat}}\\)\u003c/span\u003e\u003c/span\u003e, This constraint occurs when people are not able to produce the sweat required to achieve the needed evaporative heat loss. Therefore, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{{max}_{constrain}}\\)\u003c/span\u003e\u003c/span\u003e is defined as the minimum of these two values.\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{{E}_{{max}_{constrain}}=\\text{m}\\text{i}\\text{n}(E}_{{max}_{wet}}{,\\:E}_{{max}_{sweat}})\\)\u003c/span\u003e \u003c/span\u003e (W) (2)\u003c/p\u003e\u003cp\u003eIn this context, we further adopt the concept of physiological compensability, which refers to the ability of the human body to maintain energy balance under prevailing heat stress conditions \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Specifically, heat stress is compensable when the metabolic heat and the heat exchanged with the environment\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\)\u003c/span\u003e\u003c/span\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{req}\\)\u003c/span\u003e\u003c/span\u003e) is fully counterbalanced by the maximum capacity of evaporative heat loss (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{{max}_{constrain}}\\)\u003c/span\u003e\u003c/span\u003e). In other terms, heat stress is compensable when \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{req}\u0026lt;\\:{E}_{{max}_{constrain}}\\)\u003c/span\u003e\u003c/span\u003e. Under this condition, the maximum safe metabolic rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{max}\\)\u003c/span\u003e\u003c/span\u003e) to maintain compensability can be estimated according to the following:\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{max}{=E}_{{max}_{constrain}}-{E}_{req}\\)\u003c/span\u003e \u003c/span\u003e (W) (3)\u003c/p\u003e\u003cp\u003eSpecifically, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{max}\\)\u003c/span\u003e\u003c/span\u003e represents the maximum safe metabolic rate, or internal heat production that can be generated without a sustained rate of positive heat storage. Based on Equations (1) and (2), and taking \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:M\\)\u003c/span\u003e\u003c/span\u003e as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{max}\\)\u003c/span\u003e\u003c/span\u003e, Eq.\u0026nbsp;(3) can be transformed into:\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{max}={(E}_{{max}_{constrain}}+{E}_{res})+{(R}_{skin}+{C}_{skin}+{C}_{res})\\)\u003c/span\u003e \u003c/span\u003e (W) (4)\u003c/p\u003e\u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{(E}_{{max}_{constrain}}+{E}_{res})\\)\u003c/span\u003e\u003c/span\u003e represents the contribution of evaporative heat loss, while\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{(R}_{skin}+{C}_{skin}+{C}_{res})\\)\u003c/span\u003e\u003c/span\u003e denotes the contribution of dry heat exchange. Evaporative heat loss is always a positive value, representing heat dissipation from the human body to the environment through skin and respiratory evaporation. Dry heat exchange is typically positive (dry heat loss), indicating heat dissipation from the human body to the environment through radiative and convective heat exchanges at skin surface and convective heat exchange in the respiratory tract. However, when environment temperature exceeds skin temperature or radiative sources transfer heat to human body, it becomes negative (dry heat gain), signifying heat absorption from the environment. Detailed computational formulas for these variables are derived from liveability model in Vanos et al. \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and provided in Method S2.\u003c/p\u003e\u003cp\u003eTo account for individual body size variations, all components of human-environment heat exchange are normalized by dividing by the body surface area of the simulated individual converting W to W/m². For \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{max}\\)\u003c/span\u003e\u003c/span\u003e, we further convert it into the unit of MET (Metabolic Equivalent of Task). Based on the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{max}\\)\u003c/span\u003e\u003c/span\u003e value, we further defined outdoor labor capacity according to Herrmann et al. \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e: (1) maximum safe activity level limited to light intensity (1.5–2.9 METs); (2) maximum safe activity level limited to moderate intensity (3.0–5.9 METs); (3) maximum safe activity level limited to vigorous intensity (≥ 6.0 METs); (4) when \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{max}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;1.5 METs, conditions are deemed physiologically “uncompensable”. Outdoor labor is not recommended during this period.\u003c/p\u003e\u003cp\u003eA standardized healthy adult profile (19–59 years, 65 kg, 1.7 m) is adopted as input basis of the human heat balance model. The complete set of input parameters characterizing this simulated individual is provided in Table S3. The human heat balance model also requires a set of environmental inputs, which include dry-bulb temperature (℃), relative humidity (%), wind speed (m/s), and mean radiant temperature (MRT; ℃). Dry-bulb temperature is the WRF-simulated 2-m temperature. Relative humidity is calculated using WRF-simulated 2-m temperature, 2-m water vapor mixing ratio (g/kg), and surface pressure (Pa). Wind speed at 1.5 m height is calculated from the WRF-simulated 10-m wind speed (m/s) using the logarithmic wind profile with surface roughness length. MRT is estimated using the methodology of Salamanca et al. \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, based on WRF-simulated land surface temperature (K), downward shortwave radiation (W/m\u003csup\u003e2\u003c/sup\u003e), downward longwave radiation (W/m\u003csup\u003e2\u003c/sup\u003e), surface albedo, and surface emissivity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch1\u003eData availability\u003c/h1\u003e\n\u003cp\u003eThe datasets and models used in this study include: Version 4.3.3 of the WRF-ARW regional climate model (https://github.com/wrf-model/WRF/releases), the human heat balance model derived from the liveability model (https://zenodo.org/records/10020137), ERA5 reanalysis data from NCAR (https://rda.ucar.edu/datasets/ds633-0/), and WRF static data from UCAR (https://www2.mmm.ucar.edu/wrf/users/download/get_sources_wps_geog.html), along with observational meteorology data from the Automated Surface Observing Systems (https://mesonet.agron.iastate.edu/request/download.phtml?network=AZ_ASOS), the Flood Control District of Maricopa County ALERT System (https://alert.fcd.maricopa.gov/showrpts_mc.html), and the Arizona Meteorological Network (http://ag.arizona.edu/azmet).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRomanello, M. \u003cem\u003eet al.\u003c/em\u003e The 2024 report of the Lancet Countdown on health and climate change: facing record-breaking threats from delayed action. \u003cem\u003eThe Lancet\u003c/em\u003e \u003cstrong\u003e404\u003c/strong\u003e, 1847\u0026ndash;1896 (2024).\u003c/li\u003e\n\u003cli\u003eHumphrys, E. 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Sport Health Sci.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 6\u0026ndash;12 (2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6940300/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6940300/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe impact of irrigation on outdoor labor capacity under heat stress has long been debated due to the competing environmental effects of cooling (i.e., benefits) and moistening (i.e., drawbacks). We quantitatively address this debate through proposing an interdisciplinary framework that couples a regional climate model with a human heat balance model. We apply this framework to assess the impact of irrigation on labor capacity in the arid/semi-arid environments of Arizona (USA) during an extremely hot-dry summer. Results reveal that irrigation-induced environmental changes primarily modify labor capacity through dry heat (sensible heat exchange between human and environment) rather than moist heat (evaporative heat loss from human to environment) exchange. Through reduction of daytime dry heat gain from environment to human, irrigation decreases the proportion of discouraged outdoor work hours by ~\u0026thinsp;30% in the hottest urban (i.e., Phoenix-metro) and cropland areas. Nocturnally, despite reduced dry heat loss from human to environment, labor capacity changes occur only 2% of the summertime. Our results demonstrate complex interplays between humans and their ambient environment, underscoring the necessity of coupled meteorological, physiological, and human biophysical principles to properly assess outdoor labor capacity.\u003c/p\u003e","manuscriptTitle":"Dry Heat, Not Moist Heat, Drives Irrigation’s Labor Capacity Benefits in Arizona’s Cities and Croplands","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 14:46:34","doi":"10.21203/rs.3.rs-6940300/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fb3e7d68-cf3d-42c2-9808-a4d97ea5777d","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50661318,"name":"Earth and environmental sciences/Environmental sciences"},{"id":50661319,"name":"Earth and environmental sciences/Climate sciences"},{"id":50661320,"name":"Scientific community and society/Geography"},{"id":50661321,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2025-11-15T00:50:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-01 14:46:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6940300","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6940300","identity":"rs-6940300","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}