Atmospheric rivers are long, narrow corridors of concentrated water vapor transport in the atmosphere, often carrying more moisture than the mightiest terrestrial rivers. When they encounter mountain ranges or frontal systems, that vapor is forced upward, cools, and condenses into intense precipitation. In California and western Europe, these systems are famous for delivering both life-giving rain and devastating floods. In the Middle East, they have received less systematic attention, despite events such as the record Middle East floods documented by earlier researchers. The new study set out to close that gap by quantifying, for the first time at national scale, the frequency, intensity, and precipitation contribution of atmospheric rivers affecting Iran over nearly four decades.

The technical backbone of the research is the ERA5 reanalysis dataset, a state-of-the-art product from the European Centre for Medium-Range Weather Forecasts that blends observations with numerical modeling to produce a physically consistent record of the global atmosphere. The team computed Vertically Integrated Vapor Transport, or IVT, a measure of the total flux of water vapor through a column of air, for the rainy months from November through May between 1980 and 2020. IVT is the standard diagnostic for identifying atmospheric rivers because it captures both the humidity and the winds that carry moisture toward land. High IVT values arranged in elongated, coherent structures are the fingerprint of an atmospheric river.

Detecting these structures over Iran posed a distinctive challenge. Many global detection algorithms rely on fixed IVT thresholds tuned to maritime environments, where moisture transport is relatively uniform. Over Iran’s complex topography, which includes the Zagros and Alborz mountain ranges, the high Iranian Plateau, and low-lying southern coastal plains, a single threshold can fragment atmospheric rivers or miss them entirely. To overcome this, the researchers employed a spatially varying IVT threshold designed to preserve the continuity of atmospheric river structures across mountainous terrain, combined with geometric filtering criteria that ensure identified features possess the elongated shape characteristic of true atmospheric rivers. This approach improves the reliability of the climatology in regions where orography strongly modulates moisture flow.

The results reveal that approximately fifteen atmospheric river events affect Iran in a typical year, with activity peaking in March and reaching its minimum in May. That seasonal rhythm reflects the southward retreat of the subtropical jet stream and the strengthening of Mediterranean and Red Sea moisture sources during the heart of the cool season. Perhaps more striking is the geography of their impact. The contribution of atmospheric rivers to precipitation generally decreases from southern to northern regions of the country, and from the windward western slopes toward the arid central interior. This gradient tells a story about orography: when a moisture-laden river of air slams into the Zagros Mountains, the forced ascent wrings out enormous quantities of rain and snow, while regions sheltered behind the ranges receive far less of the transported moisture as precipitation.

To probe how atmospheric rivers behave in climatically anomalous years, the team classified years as wet or dry using the Nietzsche classification and then compared atmospheric river activity between the two groups. The contrast was clear. Wet years averaged about sixteen atmospheric river events annually, while dry years saw only around eleven. During wet years, the influence of atmospheric rivers on precipitation intensified markedly in western and southern Iran, the regions where orographic enhancement is strongest. During dry years, however, something subtler happened: the storm tracks themselves shifted, steering atmospheric rivers toward southern and southeastern Iran, where their maximum influence was observed. In other words, drought years are not simply years with fewer atmospheric rivers; they are years in which the rivers flow along different paths.

The study also uncovered a striking regional asymmetry in how atmospheric rivers relate to climate anomalies. In western Iran, wet and dry years are strongly tied to atmospheric river activity, making these systems a reliable indicator of the region’s hydrological fortunes. Central Iran, by contrast, behaves almost independently of atmospheric rivers, its precipitation anomalies apparently governed by other factors. In the southern stations, the researchers found no significant difference in the contribution of atmospheric rivers to precipitation between wet and dry years, suggesting that in those locations the presence of an atmospheric river does not by itself determine whether a year will be anomalously wet. These nuances matter enormously for anyone attempting to forecast seasonal water availability or anticipate flood risk.

Underlying all of these patterns is the fundamental role of topography. The authors conclude that mountains play a decisive part in converting atmospheric river moisture transport into precipitation, but that this conversion depends on geographical location and local climate. A given atmospheric river may dump torrential rain on the western slopes of the Zagros while leaving the central plateau nearly untouched. The interaction between atmospheric rivers and topography influences wet and dry conditions at individual stations to varying degrees, which helps explain why Iran’s precipitation regime is so spatially heterogeneous. This interplay between large-scale moisture transport and local orographic forcing is precisely the kind of process that global climate models often struggle to capture, making regional climatologies like this one especially valuable.

The practical implications extend to water resource management and flood risk assessment in arid and semi-arid regions worldwide. Iran faces chronic water stress, with declining groundwater reserves, recurrent droughts, and occasional catastrophic floods. Knowing that roughly fifteen atmospheric rivers arrive each year, that they cluster in the late winter and early spring, and that their tracks shift southeastward in dry years gives water managers and forecasters a framework for anticipating when and where heavy precipitation is most likely. As the climate warms, atmospheric rivers are expected to intensify in many regions, raising the stakes for understanding their behavior in the Middle East. This 39-year climatology provides the baseline against which future changes can be measured, and a reminder that even in the driest landscapes, the atmosphere occasionally delivers its water in torrents.

Subject of Research: Climatological analysis of atmospheric rivers and their contribution to precipitation across Iran

Article Title: Climatological analysis of atmospheric rivers and their contribution to precipitation across Iran (1982–2020)

Article References: Pazhouhesh, F., Esfahani, M. A. N., & Ghasemi, A. R. (2026). Climatological analysis of atmospheric rivers and their contribution to precipitation across Iran (1982–2020). Theoretical and Applied Climatology, 157(10), Article 663. https://doi.org/10.1007/s00704-026-06570-8

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06570-8

Keywords: atmospheric rivers, Iran, precipitation, ERA5 reanalysis, integrated vapor transport, arid climate, drought, flooding, topography, water resources, climatology, Middle East meteorology

The research, led by Kazım Kaba of Atatürk University together with colleagues at Manisa Celal Bayar University, DAG, and Çukurova University, addresses a stubborn problem in light pollution science. Satellite instruments such as the Suomi National Polar-orbiting Partnership’s Visible Infrared Imaging Radiometer Suite, or VIIRS, provide high-resolution measurements of upward radiance from cities and towns, but they do not directly measure the glow that scattered light produces in the sky above an observatory. Ground-based sky quality meters, meanwhile, record actual sky brightness at specific points, but only at those points. Bridging the gap between these two views, one broad but indirect, the other direct but sparse, has long been a methodological challenge, particularly in mountainous regions where light propagation is shaped by complex topography.

To close that gap, the team built and rigorously compared several mapping approaches. Traditional geostatistical techniques, including Ordinary and Universal Kriging, were tested alongside modern machine learning regressors such as support vector regression and a hybrid framework that couples random forest regression with Kriging of its residuals, often called regression Kriging. The inputs combined VIIRS nighttime radiance with extensive in-situ sky quality meter measurements collected around the observatories. The comparison produced a cautionary tale about blindly trusting statistical scores. Ordinary and Universal Kriging, the workhorses of classical spatial interpolation, suffered from smoothing effects and overfitting when confronted with the complex terrain surrounding the sites, washing out real spatial structure.

Support vector regression told a different but equally instructive story. The algorithm achieved impressively high statistical validation scores, yet when asked to extrapolate into data-sparse regions it produced physically implausible predictions, in some cases implying sky brightness values that violate natural limits on how bright an unpolluted sky can actually be. For a field where the difference between a pristine site and a degraded one is measured in magnitudes per square arcsecond, such artifacts are not mere curiosities; they could mislead decisions about where to site future telescopes or how to protect existing ones. The lesson, the authors argue, is that physical consistency must be treated as a hard constraint, not an afterthought.

The random forest regression Kriging model emerged as the clear winner. It achieved robust validation accuracy, with coefficients of determination of 0.74 and 0.80 for the two study regions, while remaining strictly consistent with the natural brightness limits of the night sky. By letting the random forest capture nonlinear relationships between satellite-observed radiance, terrain, and measured sky glow, and then using Kriging to model the spatially structured residual, the hybrid approach preserved fine-grained detail that pure geostatistics smoothed away and avoided the runaway extrapolations of pure machine learning. The resulting maps offer observatory managers a realistic, high-resolution view of where light pollution originates and how it spreads across the landscape.

Beyond the methodology, the temporal analysis uncovered two strikingly different pollution regimes. Around the high-altitude Erzurum plateau, home to DAG, sky brightness follows a pronounced seasonal cycle driven by what researchers call snowglow. When snow blankets the ground, it acts as a vast white reflector, bouncing upward the light emitted by nearby settlements and amplifying human-induced skyglow by more than 1.5 times compared with snow-free conditions. The effect, which has been documented in suburban Europe, is particularly consequential for a mountain observatory, because the very winters that bring stable observing conditions also bring the reflective snowpack that magnifies whatever light leaks from the region’s towns and roads.

The Antalya coast surrounding TUG tells a darker story in a different sense. There, sky brightness has deteriorated monotonically, independent of season, in step with rapid tourism-oriented urbanization along the Mediterranean shoreline. Hotels, resorts, and expanding infrastructure emit light year-round, and the coastal topography channels that glow toward the observatory site. Unlike the seasonal snowglow signal at Erzurum, which at least offers a predictable annual rhythm, the Antalya trend points steadily in one direction, raising concerns about the long-term optical viability of one of Türkiye’s historic observing hubs if lighting practices remain unchanged.

The findings arrive amid a broader global reckoning with light pollution. Previous studies, including the world atlases of artificial night sky brightness and surveys of light pollution indicators at all major astronomical observatories, have shown that ground-based astronomy faces a growing threat from expanding artificial lighting. What the Turkish study adds is a transferable toolkit: a validated, physically constrained machine learning pipeline that fuses free satellite data with inexpensive ground sensors to produce actionable brightness maps. The VIIRS data used in the work are publicly distributed through NASA’s LAADS DAAC service, and the authors note that their sky quality meter measurements can be requested for further research, lowering the barrier for other observatory communities to replicate the approach.

The work was supported by the Scientific and Technological Research Council of Türkiye, TÜBİTAK, through its 3501 Career Development Program under project number 124F297, and the authors declare no competing interests. For DAG, which is moving toward first light with its large optical telescope, and for TUG, which has served Turkish astronomy for decades, the message of the study is double-edged. The new maps provide exactly the quantitative evidence needed to argue for dark-sky protections, shielding ordinances, and lighting curfews in surrounding communities. But they also confirm that the pressures of snow-reflected glow in the east and relentless coastal development in the west are real, measurable, and growing. Whether Türkiye’s flagship observatories keep their dark skies may now depend less on the mountains they sit on than on how the valleys below choose to light the night.

Subject of Research: Machine learning-based mapping of night sky brightness and light pollution dynamics at Türkiye's major astronomical observatories

Article Title: Long-term dynamics and machine learning-based mapping of night sky brightness: snowglow, urbanization, and topography effects at major astronomical observatories in Türkiye

Article References: Long-term dynamics and machine learning-based mapping of night sky brightness: snowglow, urbanization, and topography effects at major astronomical observatories in Türkiye. (n.d.). https://doi.org/10.1007/s10686-026-10076-6

Image Credits: AI Generated

DOI: 10.1007/s10686-026-10076-6

Keywords: light pollution, night sky brightness, snowglow, machine learning, regression Kriging, Suomi NPP VIIRS, sky quality meter, DAG observatory, TÜBİTAK National Observatory, urbanization, topography, Türkiye

The research team, led by Alisha Keprate with D.R. Bhardwaj, Prashant Sharma, and Rushal Dogra, sampled soils across four contrasting land-use systems at two topographic positions — sheltered valley floors and cooler mountain slopes — and at three depths reaching down to 60 centimeters. Their study area, spanning roughly 1,543 to 2,396 meters above sea level in the Lesser Himalayan range, receives about 850 millimeters of annual precipitation, mostly from the southwest monsoon, and its Brown Podzolic soils sit on slopes of 15 to 35 percent. This combination of high rainfall, rugged relief, and intensive smallholder farming makes the region a natural laboratory for studying how land management reshapes one of the planet’s largest terrestrial carbon pools.

Soil organic carbon is not a single substance but a family of fractions that differ sharply in how quickly they turn over. The researchers used a modified Walkley-Black oxidation procedure, applying sulfuric acid at increasing concentrations to separate very labile, labile, less labile, and non-labile carbon pools. The two most easily oxidized fractions together form the active pool, which fuels rapid nutrient cycling, while the more resistant fractions make up the passive pool, the long-term reservoir that locks carbon away for decades or centuries. Alongside these fractions, the team computed the lability index, carbon pool index, and carbon management index — composite metrics that translate raw chemistry into practical measures of soil health and management performance.

The results were striking. Agroforestry plots, in which ten-to-fifteen-year-old apple trees grow alongside annual crops such as peas, beans, rajmash, potatoes, and barley, recorded the highest total organic carbon at 19.83 milligrams per gram of soil — roughly 50 percent more than adjacent agricultural plots growing potatoes, peas, and barley, and more than double the 9.72 milligrams per gram found on barren reference land. Apple monoculture orchards fell in between at 17.71 milligrams per gram. The same hierarchy held for nearly every carbon fraction: the active carbon pool reached 11.26 milligrams per gram under agroforestry compared with just 4.38 on barren land, while the passive pool peaked at 8.57 milligrams per gram in the tree-based system. Soil carbon density followed suit, climbing to 48.59 megagrams per hectare under agroforestry against a meager 25.59 on barren ground.

The carbon management index told an even more compelling story. Relative to the barren reference, the index rose by 137 percent under agroforestry, 114 percent under horticulture, and 44 percent under agriculture. Because this index integrates both the size of the carbon pool and the lability of its constituent fractions, the authors argue it captures a genuine improvement in the quantity and quality of soil carbon under tree-based management. The mechanism, they suggest, is a steady supply of organic matter — leaf litter, pruning residues, root biomass, and root exudates — that feeds microbial communities, promotes stable soil aggregates, and builds organo-mineral associations. Apple leaf litter, rich in phenolic compounds, may further slow decomposition and favor gradual nutrient release, tipping the balance toward carbon stabilization rather than loss.

Topography proved to be a second, independent sculptor of soil carbon. Valley soils, warmer and biologically livelier, held more of the fast-cycling active fractions: very labile carbon reached 5.14 milligrams per gram there, against 3.99 on mountain slopes, and the active pool overall measured 9.08 versus 6.73 milligrams per gram. Mountain soils, by contrast, accumulated more of the recalcitrant material — the passive pool reached 9.04 milligrams per gram on slopes compared with 5.39 in valleys, and non-labile carbon rose to 3.65 milligrams per gram. The researchers attribute this split to cooler slope temperatures that suppress microbial mineralization, strengthen the adsorption of organic matter onto clay minerals, and favor fungal communities that convert labile substrates into stable, microbial-derived organic matter. In effect, valleys recycle carbon quickly while mountains bank it.

Depth added a third dimension to the pattern. Total organic carbon declined steadily from 17.64 milligrams per gram in the surface 0-to-20-centimeter layer to 12.86 at 40-to-60 centimeters, and the active fractions fell in parallel, reflecting the concentration of litter and root inputs near the surface. Yet the non-labile fraction moved in the opposite direction, rising from 2.10 to 3.88 milligrams per gram with depth — a signature of older, more persistent carbon that has escaped rapid decomposition in the subsoil. A principal component analysis confirmed the split, with the first two axes explaining 56.4 percent of total variance and cleanly separating surface, labile, nitrogen-rich soils from deeper horizons dominated by passive fractions.

Nitrogen dynamics mirrored the carbon story. Total nitrogen was highest under agroforestry at 0.092 percent, and soil nitrogen density peaked at 2.27 megagrams per hectare in the tree-crop system, compared with 1.93 under agriculture and 1.94 on barren land. Valley soils again outpaced mountain soils, and all nitrogen measures declined with depth. Ammoniacal nitrogen reached 66.11 milligrams per kilogram under agroforestry, while nitrate nitrogen was highest in horticultural orchards at 61.44 milligrams per kilogram — a difference the authors link to heavy fertilizer inputs and the absence of intercropped plants competing for nitrate uptake in monoculture orchards. Correlation analysis reinforced the coupling of the two nutrient cycles: total organic carbon and total nitrogen were strongly positively correlated, and the carbon management index tracked closely with both carbon lability and nitrogen availability.

Taken together, the findings carry a clear message for the fragile mountains of the Himalayas and beyond: converting conventional annual agriculture to apple-based agroforestry could simultaneously enlarge the soil’s carbon bank, improve its nitrogen capital, and boost the overall carbon management index — a trifecta for climate-resilient mountain farming. The authors caution that their study reflects a single sampling period and that the underlying biological and physicochemical mechanisms were inferred rather than directly measured. Even so, in a region where steep slopes, erodible soils, and shifting land use threaten both livelihoods and carbon stocks, the evidence that mixing trees with crops builds richer, more stable ground adds scientific weight to a strategy many Himalayan farmers have practiced for generations — and gives policymakers a quantified reason to encourage it.

Subject of Research: Effects of land-use type, topographic position, and soil depth on soil organic carbon fractions and nitrogen dynamics in the wet temperate northwestern Himalayas

Article Title: Land-use and topographic effects on soil carbon and nitrogen fractions across contrasting land-use systems in the wet temperate northwestern Himalayas

Article References: Keprate, A., Bhardwaj, D., Sharma, P., & Dogra, R. (2026). Land-use and topographic effects on soil carbon and nitrogen fractions across contrasting land-use systems in the wet temperate northwestern Himalayas. Journal of Agriculture and Food Research, 31, Article 103262. https://doi.org/10.1016/j.jafr.2026.103262

Image Credits: AI Generated

DOI: 10.1016/j.jafr.2026.103262

Keywords: soil organic carbon, agroforestry, carbon fractions, carbon management index, soil nitrogen, northwestern Himalayas, land use change, topography, apple orchards, carbon sequestration, soil depth, climate-resilient agriculture