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	<title>evaporation control in Brazil &#8211; Science</title>
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	<title>evaporation control in Brazil &#8211; Science</title>
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		<title>Sun and Wind, Not Temperature, Drive Evaporation Across Brazil&#8217;s Climates</title>
		<link>https://scienmag.com/sun-and-wind-not-temperature-drive-evaporation-across-brazils-climates/</link>
		
		<dc:creator><![CDATA[Violet Maxwell]]></dc:creator>
		<pubDate>Mon, 05 Oct 2026 19:40:39 +0000</pubDate>
				<category><![CDATA[Earth Science]]></category>
		<category><![CDATA[atmospheric forces influencing evaporation]]></category>
		<category><![CDATA[Brazil]]></category>
		<category><![CDATA[climate change effects on evaporation drivers]]></category>
		<category><![CDATA[climate typologies]]></category>
		<category><![CDATA[climate variability across Brazilian regions]]></category>
		<category><![CDATA[drought risk assessment in Brazil]]></category>
		<category><![CDATA[evaporation control in Brazil]]></category>
		<category><![CDATA[FAO-56]]></category>
		<category><![CDATA[hydrological modeling of evaporation]]></category>
		<category><![CDATA[hydrology]]></category>
		<category><![CDATA[impact of solar radiation on evaporation]]></category>
		<category><![CDATA[influence of temperature versus wind and radiation]]></category>
		<category><![CDATA[irrigation management]]></category>
		<category><![CDATA[path analysis]]></category>
		<category><![CDATA[Penman-Monteith]]></category>
		<category><![CDATA[Penman-Monteith equation for evapotranspiration]]></category>
		<category><![CDATA[reference evapotranspiration]]></category>
		<category><![CDATA[reference evapotranspiration in agriculture]]></category>
		<category><![CDATA[relative humidity]]></category>
		<category><![CDATA[role of wind speed in water cycle]]></category>
		<category><![CDATA[solar radiation]]></category>
		<category><![CDATA[Vapor Pressure Deficit]]></category>
		<category><![CDATA[water management in tropical and semi-arid climates]]></category>
		<category><![CDATA[wind speed]]></category>
		<guid isPermaLink="false">https://scienmag.com/?p=239156</guid>

					<description><![CDATA[A thirty-year analysis of six Brazilian cities shows that solar radiation and wind speed, not air temperature, govern reference evapotranspiration, with the dominant control shifting by climate zone and season.]]></description>
										<content:encoded><![CDATA[<p>Evaporation is the quiet engine of the water cycle, and knowing how fast water leaves the land and returns to the sky determines how farmers irrigate, how reservoirs are managed, and how drought risk is assessed. A new study published in Theoretical and Applied Climatology has now mapped, in unusual detail, which atmospheric forces actually control this evaporative demand across Brazil, and the answer is a surprise to anyone who assumes temperature is king. Across six cities spanning equatorial rainforest to semi-arid scrub, the researchers found that solar radiation and wind speed, not air temperature, dominate the variability of reference evapotranspiration, the standard measure of how thirsty the atmosphere is.</p>
<p>Reference evapotranspiration, abbreviated ETo, represents the water loss from a standardized grass surface under given weather conditions. Because directly measuring evapotranspiration with lysimeters or micrometeorological towers is expensive and technically demanding, hydrologists and agronomists rely on ETo as a proxy for atmospheric demand. The internationally accepted way to compute it is the Penman–Monteith equation as standardized by FAO-56, a physically based formula that combines an energy term, driven by net radiation, with an aerodynamic term, driven by wind speed and the vapor pressure deficit between the surface and the air. The equation&#8217;s multivariate structure is its strength, but it also raises a critical question: which of these meteorological ingredients matters most, and does the answer change from place to place and season to season?</p>
<p>That question motivated a team led by Lucas da Costa Santos of the Federal University of the Jequitinhonha and Mucuri Valleys. The researchers assembled thirty years of daily meteorological records, from 1990 to 2019, for six Brazilian municipalities chosen to represent sharply contrasting climate typologies under the Köppen–Geiger classification: Belém in the Amazon with an equatorial monsoon climate, Salvador with a tropical rainforest regime, Petrolina in the hot semi-arid northeast, Pirenópolis with a tropical savanna climate, and Franca and São Luiz Gonzaga in the humid subtropical south. The sites span a remarkable range, from mean annual temperatures of about 21.4 degrees Celsius in São Luiz Gonzaga to 27.7 degrees in Belém, and from mean solar radiation of 16.69 megajoules per square meter per day in the subtropical south to 23.19 in Petrolina.</p>
<p>To disentangle the influences, the team turned to path analysis, a statistical technique that decomposes simple correlations between weather variables and ETo into direct and indirect effects. Direct effects capture the independent contribution of each variable, while indirect effects reveal how variables interact with one another through their mutual correlations. The researchers first checked for multicollinearity among predictors using variance inflation factors, retaining mean air temperature as the thermal variable because maximum and minimum temperatures introduced redundancy that destabilized the regression models. They also ran Mann–Kendall trend tests to check whether the predictor variables themselves were changing over the study period, finding significant long-term trends in at least one variable at five of the six locations.</p>
<p>The results were striking in their consistency and their variability at the same time. The path models explained between 97.7 and 99.5 percent of the annual variability in ETo across all six locations, an extraordinarily high proportion. Yet the identity of the dominant driver shifted from site to site. In São Luiz Gonzaga, Franca, Salvador, and Belém, solar radiation carried the largest direct effect, with standardized coefficients reaching 0.812 in Franca, indicating that in these places evaporative demand is primarily limited by the energy available at the surface. In Pirenópolis and Petrolina, by contrast, wind speed took the lead, with a coefficient of 0.711 in the semi-arid Petrolina, pointing to a regime in which the aerodynamic removal of vapor, rather than energy supply, sets the pace of evaporation.</p>
<p>The decomposition of indirect effects added a layer of nuance that simple correlation studies miss entirely. In Franca, the seemingly dominant direct effects of radiation and wind were substantially offset by negative indirect pathways, cutting their total effects to a fraction of their direct coefficients. In humid Belém, the positive direct effect of wind speed was overwhelmed by a strong negative indirect effect mediated through solar radiation, so that wind&#8217;s net association with ETo actually turned negative. In Petrolina, the opposite occurred: positive indirect effects reinforced the direct dominance of wind, pushing its total effect to 0.837. In other words, the same meteorological variable can be amplified, muted, or even reversed in its apparent influence depending on the interaction structure of the local atmosphere.</p>
<p>Seasonality compounded the picture. When the team repeated the analysis for each meteorological season of the Southern Hemisphere, the hierarchy of controls reconfigured systematically through the year. Solar radiation ranked among the top drivers during the high-insolation summer and spring months, while wind speed gained prominence during autumn and especially winter, when energy availability declines but atmospheric ventilation remains strong, particularly at inland and semi-arid sites. Relative humidity, meanwhile, displayed negative direct effects at every location and in every season, a physically coherent signal of the vapor pressure deficit that governs the aerodynamic term of the Penman–Monteith equation. Drier air pulls water from the surface more aggressively, and the humidity coefficients, reaching −0.469 in São Luiz Gonzaga and −0.374 in Petrolina, quantify that pull.</p>
<p>Perhaps the most consequential finding concerns what did not dominate: temperature. Mean air temperature ranked among the three strongest drivers at only half of the locations at the annual scale, and even there its coefficients were systematically smaller than those of radiation or wind. This challenges the widespread reliance on temperature-based empirical methods such as Hargreaves–Samani, which implicitly assume that thermal proxies can capture evaporative demand. The authors argue that temperature functions mainly as a thermodynamic state variable, signaling the energy content of the atmosphere, while the actual fluxes of vapor depend on radiation balance, humidity gradients, and wind. In regions with strong ventilation or high radiative variability, temperature-only models risk systematic underestimation of water demand, with tangible consequences for irrigation scheduling and water resource planning.</p>
<p>The practical implications extend into a warming future. As global temperatures rise, the atmosphere&#8217;s water-holding capacity increases, and vapor pressure deficit tends to grow disproportionately, intensifying evaporative demand in ways that temperature records alone do not reveal. The study&#8217;s authors are careful to note the limitations of their approach: ETo is not an independently observed quantity but a deterministic product of the same variables used as predictors, so the path coefficients should be read as indicators of relative contributions within the FAO-56 framework rather than as evidence of independent causation. Solar radiation was estimated from sunshine duration rather than measured directly, each municipality was represented by a single weather station, and the significant long-term trends in several predictors mean the reported hierarchies describe dominant tendencies over the study period rather than fixed relationships.</p>
<p>Even with those caveats, the diagnosis is clear and actionable. There is no universal driver of evaporative demand; the balance between radiative and aerodynamic control is a property of each climate and each season. For water managers in aerodynamically dominated environments like Petrolina, monitoring wind speed and humidity is essential, and neglecting them can mean underestimating irrigation requirements during dry periods. In radiatively controlled regions such as Belém, Salvador, Franca, and São Luiz Gonzaga, cloud cover and seasonal insolation set the agenda, and models that ignore radiation do so at their peril. The study makes a strong case that the era of one-size-fits-all, temperature-based evaporation estimates should give way to multivariate, physically consistent approaches capable of tracking how the atmosphere&#8217;s thirst is truly regulated across a changing planet.</p>
<p><strong>Subject of Research:</strong> Meteorological controls on reference evapotranspiration across contrasting Brazilian climate typologies</p>
<p><strong>Article Title:</strong> Energy versus aerodynamic controls of reference evapotranspiration: a multi-regional assessment across Brazilian climate typologies</p>
<p><strong>Article References:</strong> da Costa Santos, L., Capuchinho, F. F., do Patrocínio Figueiró, L. S., José, J. V., dos Reis, E. F., Bonfá, C. S., Araujo, J. E., &amp; Nhongo, E. J. S. (2026). Energy versus aerodynamic controls of reference evapotranspiration: a multi-regional assessment across Brazilian climate typologies. <em>Theoretical and Applied Climatology, 157</em>(10), Article 646. <a href="https://doi.org/10.1007/s00704-026-06580-6" rel="noopener noreferrer">https://doi.org/10.1007/s00704-026-06580-6</a></p>
<p><strong>Image Credits:</strong> AI Generated</p>
<p><strong>DOI:</strong> <a href="https://doi.org/10.1007/s00704-026-06580-6" rel="noopener noreferrer">10.1007/s00704-026-06580-6</a></p>
<p><strong>Keywords:</strong> reference evapotranspiration, Penman–Monteith, solar radiation, wind speed, relative humidity, vapor pressure deficit, path analysis, Brazil, climate typologies, irrigation management, FAO-56, hydrology</p>
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