Sunshine, Not Just Heat, Drives Iran’s Water Loss Over 58 Years

In a country where every drop of water is contested, the question of what actually drives evaporation from fields and reservoirs is not academic. A new study published in Theoretical and Applied Climatology has taken one of the longest looks yet at this problem, analyzing 58 years of meteorological records from 34 synoptic stations across Iran to determine which climatic variables exert the greatest control over reference evapotranspiration, the standard measure of how much water the atmosphere demands from a well-watered reference crop. The findings, from Abdol Rassoul Zarei of Fasa University’s College of Agriculture and statistician Mohammad Reza Mahmoudi of Fasa University’s College of Science, carry a surprise: when the statistical accounting is done properly, sunshine hours, not maximum temperature, emerge as the dominant driver for the overwhelming majority of the study period.

Reference evapotranspiration, abbreviated ETo, is the backbone of irrigation planning worldwide. It represents the rate at which water would be lost from an idealized, uniformly green surface with ample moisture, and it is the quantity that farmers, water managers, and hydrologists multiply by crop coefficients to estimate real irrigation demand. The internationally accepted method for computing it is the FAO Penman–Monteith equation, a physically based formula that combines temperature, humidity, wind, and solar radiation into a single estimate. Because the equation draws on so many inputs simultaneously, a long-standing puzzle has been which of those inputs matters most, and whether the answer shifts from place to place and decade to decade. That question has become urgent as climate change alters the individual variables at different rates.

Zarei and Mahmoudi assembled a dataset spanning 1967 to 2024, one of the longest windows ever applied to this problem in the Middle East. From the records of 34 synoptic stations, they extracted nine candidate drivers: maximum, minimum, and mean temperature; maximum, minimum, and mean relative humidity; wind speed; sunshine hours; and precipitation. For thirteen representative years distributed across the nearly six decades, they computed ETo at every station using the FAO Penman–Monteith equation and then interpolated the results into continuous spatial maps using the Inverse Distance Weighting method, a technique that estimates values at unsampled locations by weighting nearby observations according to their distance.

To move beyond station-by-station analysis, the researchers overlaid 2,000 randomly distributed points across Iran’s territory and extracted both climatic data and interpolated ETo values at each one. This created a vast statistical sample capturing the country’s extraordinary climatic diversity, from the humid Caspian coast to the arid central deserts and the mountainous northwest. The random-point approach meant that the subsequent modeling reflected the spatial character of the whole country rather than the arbitrary placement of weather stations, which tend to cluster in populated areas.

The heart of the study lies in its dual statistical framework, and this is where the methodological story becomes genuinely interesting for anyone who follows climate science. The first tool, the Corrplot approach, examines simple linear correlations between each climatic variable and ETo, identifying which variable shows the strongest individual association in each year. The second, the Lindeman, Merenda, and Gold method, usually abbreviated LMG, belongs to a family of techniques called variance decomposition. Rather than looking at each variable in isolation, LMG averages the contribution of each predictor across all possible orderings in which it could enter a regression model, thereby partitioning the explained variance fairly among predictors that are correlated with one another.

Why does that distinction matter so much? Climatic variables are notoriously interdependent. Maximum temperature and sunshine hours rise and fall together; humidity tracks inversely with temperature; precipitation suppresses both sunshine and temperature. In statistical language, this is multicollinearity, and it is the bane of attribution studies. A naive correlation analysis can assign importance to a variable that is merely riding on the coattails of another. The LMG method, by decomposing R-squared across all orderings of predictors, corrects for this shared variance and reveals which variable carries genuine, independent explanatory power. The contrast between the two methods in this study is a textbook demonstration of why the correction matters.

According to the Corrplot results, maximum temperature was the most frequently dominant variable, leading in 46.16 percent of the years examined, followed by sunshine hours at 23.08 percent, with precipitation and wind speed each dominant in 15.38 percent of years. That picture is broadly consistent with intuition in a warming country: hotter days, more evaporative demand. But when the LMG variance decomposition was applied, the hierarchy flipped dramatically. Sunshine hours emerged as the most frequently dominant factor in 92.31 percent of the study period, with maximum temperature leading in only 15.38 percent of cases. In other words, much of the apparent importance of temperature in the simple correlation analysis appears to reflect its correlation with sunshine duration rather than an independent physical influence of that magnitude.

The authors validated both the spatial interpolations and the statistical models rigorously. The quality of the IDW-generated maps was assessed using the Nash–Sutcliffe Efficiency coefficient, a standard hydrological goodness-of-fit measure, and Willmott’s Index of Agreement, which evaluates how closely predicted values track observed ones. Both metrics confirmed the reliability of the spatial products at the 1 percent significance level, and the LMG model’s performance was likewise statistically verified at the 1 percent level for every year investigated. These are demanding thresholds, and their satisfaction across nearly six decades of data lends considerable weight to the central finding.

The practical implications for Iran are substantial. The country is already one of the most water-stressed in the world, with shrinking aquifers, recurrent drought, and intense competition between agriculture and urban demand. If sunshine hours, a proxy for the solar radiation that supplies the energy for evaporation, are the true dominant lever on ETo, then projections of future water demand need to track cloud cover, aerosol loading, and radiation trends as closely as they track temperature. It also suggests that the well-documented global phenomenon of dimming and brightening, in which the solar radiation reaching the surface has changed over decades due to aerosols and cloudiness, may have played an underappreciated role in Iran’s evolving water balance. Irrigation scheduling tools, drought indices, and crop water models calibrated around temperature sensitivity alone could be systematically misattributing the drivers of demand.

The study also offers a methodological lesson that extends well beyond Iran. Sensitivity analyses of the Penman–Monteith equation have been published for regions around the world, from the Yangtze basin to Australia’s climatic zones to tropical savannas, and their conclusions have often varied. Some of that variation is surely real, reflecting genuinely different climates. But this study demonstrates that part of the disagreement may be an artifact of method: analyses relying on simple correlations can rank drivers differently than analyses that properly account for multicollinearity. As climate change reshapes the individual variables at unequal speeds, knowing which lever actually moves evapotranspiration will determine whether water managers in arid lands anticipate the future accurately or are surprised by it. For Iran, at least, the answer now points upward, toward the sun itself.

Subject of Research: Spatiotemporal drivers of reference evapotranspiration in Iran over 58 years

Article Title: A multi-decadal spatiotemporal evaluation of reference evapotranspiration drivers in Iran: Leveraging an integrated LMG and Corrplot statistical framework

Article References: Zarei, A. R., & Mahmoudi, M. R. (2026). A multi-decadal spatiotemporal evaluation of reference evapotranspiration drivers in Iran: Leveraging an integrated LMG and Corrplot statistical framework. Theoretical and Applied Climatology, 157(11), Article 703. https://doi.org/10.1007/s00704-026-06635-8

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06635-8

Keywords: reference evapotranspiration, Iran, FAO Penman-Monteith, sunshine hours, LMG variance decomposition, multicollinearity, Corrplot, Inverse Distance Weighting, climate variables, water resources, irrigation planning, Theoretical and Applied Climatology