To address those gaps, the study combined the historical observations with precipitation products from NASA’s POWER satellite dataset. The researchers first evaluated the augmentation method against the available ground records. The comparison produced a strong relationship, with an R² of 0.9454 and a regression slope of 0.951.

These results indicate that the satellite-based values closely reproduced the measured rainfall record wherever direct comparisons could be made. The study then used the combined series to construct annual total rainfall aggregates for extreme-value analysis. To estimate rainfall return periods, the researchers fitted both Gumbel and Generalized Extreme Value, or GEV, distributions to the annual totals.

Kolmogorov–Smirnov and Anderson–Darling statistical tests favored the GEV model. Its Anderson–Darling value was 0.354, compared with 0.381 for Gumbel. The authors associated the superior fit with the GEV distribution’s ability to capture the heavy-tailed behavior of the region’s erratic precipitation.

These are model comparisons based on the Kalar record; the reported results do not establish that the same distribution will provide the best fit for other districts or climatic settings. The GEV model estimated a 100-year return-period annual rainfall total of 778.6 mm for Kalar, with a 95% confidence interval extending from 690.0 mm to 895.0 mm. This is a statistical estimate of an unusually wet annual rainfall total, not a direct observation of an event that has already occurred.

The analysis also revealed a widening disparity between extremely wet and dry years. The authors described this pattern as consistent with broader climate-change projections for the Eastern Mediterranean, but the case study alone does not quantify how much of Kalar’s historical variability is attributable to climate change or determine how future rainfall totals will evolve. The study’s scope is explicitly local: it covers Kalar district in the Garmian region, and the authors note that the results remain specific to the area’s local topography.

The work demonstrates a validated framework for combining NASA/POWER precipitation estimates with incomplete station records and then applying extreme-value models to annual rainfall. The findings may inform water-resource management, reservoir design, and drought-resilience planning in similar data-scarce environments, but the reported estimates should not be treated as continent-wide values or automatically transferred to regions with different rainfall regimes and physical settings.

Subject of Research: Satellite-assisted extreme-rainfall analysis in Kalar district, Kurdistan Region

Article Title: Integrating satellite observations for annual rainfall frequency analysis in data-scarce semi-arid regions: a case study of Kalar district, Kurdistan Region

Article References: Hussein, S. S., & Abdulla, F. R. (2026). Integrating satellite observations for annual rainfall frequency analysis in data-scarce semi-arid regions: a case study of Kalar district, Kurdistan Region. Theoretical and Applied Climatology, 157(11), Article 734. https://doi.org/10.1007/s00704-026-06665-2

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06665-2

Keywords: rainfall extremes, satellite precipitation, NASA POWER, Kalar district, Kurdistan Region, semi-arid climate, extreme-value modeling, GEV distribution, water-resource planning, Garmian region, Integrating, satellite

The research, led by Malaguièhèwa Grâce Pekeyi of the Federal University of Technology Minna in Nigeria, together with Appollonia Aimiosino Okhimamhe, Tayo Iyanda Yahaya and Isaiah Sule, analysed 45 years of climate data spanning 1981 to 2025 across three agroecological zones of northern and north-central Togo. The team combined records from three synoptic weather stations with two independent gridded datasets: NASA POWER reanalysis data and the CHIRPS satellite-based precipitation archive. This multi-source approach matters in a region where ground-based weather networks are sparse and patchy, a limitation the authors document explicitly in their appendix on missing station data.

Methodologically, the study is a tour de force of classical climate diagnostics. The researchers applied the Hamed-Rao modified Mann-Kendall test, a non-parametric trend test corrected for autocorrelation, alongside Sen’s slope estimator to quantify the magnitude of change. To detect abrupt shifts in climate records, they deployed three independent change-point tests: the Pettitt test, the Buishand test and the Standard Normal Homogeneity Test. All analyses were run at annual, seasonal and monthly timescales, giving the team a finely resolved view of when and where the climate system has shifted.

The headline result is striking for what it does not show. No statistically significant precipitation trends were detected at any station, at any timescale. In a region often assumed to be sliding steadily into drought, total rainfall has remained remarkably stable over the 45-year window. This finding aligns with a broader pattern across the Sahel and Sudanian zones of West Africa, where the catastrophic rainfall declines of the 1970s and 1980s have partially recovered, even as rainfall variability and extreme events have intensified.

But temperature tells a different story. All stations showed significant warming of between +0.27 and +0.34 degrees Celsius per decade, a rate consistent with regional and global warming trends. In a rain-fed agricultural system, this warming is far from benign. Higher temperatures increase atmospheric evaporative demand, meaning that even unchanged rainfall delivers progressively less effective moisture to soils and crops. It is precisely this mechanism that the study’s drought indices were designed to capture.

The researchers computed three complementary drought metrics: the Standardised Precipitation Index (SPI), which reflects rainfall anomalies alone; the Standardised Precipitation-Evapotranspiration Index (SPEI), which subtracts the moisture demand imposed by temperature; and the Willmott-Feddema Moisture Index, a ratio-based measure of climatic water balance. The comparison proved revealing. While SPI and SPEI tracked each other closely in the decades before 2000, they diverged significantly after that point, with the divergence strongest at Sokodé, the station in the wettest of the three zones. Because SPEI accounts for warming-driven evapotranspiration while SPI does not, this post-2000 split is a statistical fingerprint of temperature stress emerging on top of stable rainfall.

Perhaps the most technically intriguing result concerns what that divergence does and does not mean. The team tested an independent Penman-Monteith formulation of potential evapotranspiration, the physically rigorous standard that accounts for radiation, wind and humidity as well as temperature. Intriguingly, Penman-Monteith PET itself showed no comparable warming-driven increase, yet the SPI-SPEI divergence persisted even under this alternative formulation. The authors interpret this carefully: the divergence reflects a broader shift in the surface energy and moisture balance rather than temperature alone, a conclusion that guards against the common criticism that SPEI simply exaggerates warming effects when computed with temperature-based evapotranspiration estimates.

Change-point analysis added a further layer of nuance. All three tests pointed to a common shift year at exactly 2003, a remarkable coincidence across independent statistical methods. Yet the authors report that this change point was not statistically clear-cut, meaning the evidence for an abrupt regime shift falls short of conventional significance thresholds. The honest reading is that the early 2000s marked a gradual transition rather than a sharp break, with the moisture balance drifting into a new regime over several years rather than snapping at a single moment.

The Willmott-Feddema Moisture Index delivered the study’s most sobering spatial message. All stations recorded predominantly negative MI values across the study period, indicating a persistent climatic moisture deficit: on average, atmospheric demand for water exceeds supply across the entire region. Yet when the team mapped trends across the gridded datasets, they found predominantly positive, wetting trends, with significant drying confined to fewer than 8 percent of grid cells. The region, in other words, is simultaneously water-limited on average and not systematically getting drier in space, a paradox that underscores why single-metric drought assessments so often mislead.

For Togo, the practical implications are immediate. The country’s northern regions have faced acute drinking water shortages, prompting government responses as recently as late 2025, and the study’s authors explicitly frame their work as relevant to early-warning systems and water-resource management. Because the SPI-SPEI divergence shows that rainfall-only drought indices now systematically understate moisture stress, the researchers argue that temperature-sensitive indices should anchor drought monitoring in the region. For the rain-fed cereal systems that dominate northern Togo’s agriculture, where previous work has linked rainfall variability directly to yield outcomes, the message is that the enemy is no longer declining rainfall but rising evaporative demand, a quieter and harder-to-see threat that demands equally vigilant measurement.

Subject of Research: Hydroclimatic variability and drought characterisation in northern and north-central Togo from 1981 to 2025

Article Title: Hydroclimatic variability and drought characterisation in Northern and North-Central Togo, 1981–2025

Article References: Pekeyi, M. G., Okhimamhe, A. A., Yahaya, T. I., & Sule, I. (2026). Hydroclimatic variability and drought characterisation in Northern and North-Central Togo, 1981–2025. Theoretical and Applied Climatology, 157(11), Article 701. https://doi.org/10.1007/s00704-026-06621-0

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06621-0

Keywords: drought, Togo, West Africa, climate change, Standardised Precipitation Index, SPEI, evapotranspiration, rain-fed agriculture, CHIRPS, NASA POWER, trend analysis, water resources

The research team, led by Ruqaya Ahmed M. Amin of AL-Iraqia University in Baghdad, analysed data from three climatic grid cells straddling the transect, a corridor that stretches across some of the driest terrain in southwestern Iraq. Rather than relying on sparse ground stations, which are notoriously uneven in this part of the world, the researchers turned to NASA POWER, a publicly available reanalysis product that blends satellite observations with modelled atmospheric data. This choice reflects a growing recognition that in data-scarce dryland environments, satellite-based datasets may be the only practical way to build a continuous, long-term climatic record. It also places the study within a broader movement in Middle Eastern climate science, where reanalysis products have increasingly been used to fill observational gaps across the Arabian Peninsula, the Levant, and North Africa.

To quantify how rainfall varied from year to year, the team applied Z-score standardisation, a statistical technique that expresses each year’s precipitation as a deviation from the long-term mean, allowing unusually dry and unusually wet years to be identified on a common scale. The analysis singled out 2018, 2020, and 2023 as very wet anomalies, years in which rainfall climbed well above the historical average. Perhaps more revealing was the behaviour of what the authors call the Wet-Month Contribution Ratio, or WMCR, a threshold-based metric that measures how much of the annual rainfall total is delivered by months receiving at least 20 millimetres. In a hyper-arid setting, where most months pass with little or no rain, this ratio effectively captures how dependent the water budget is on a handful of productive storms.

The WMCR results were among the most striking findings in the study. The contribution of relatively wet months to annual rainfall varied strongly between years, but high values became noticeably more frequent after 2015, peaking at 0.91 in 2020. In practical terms, that means nearly all of the year’s rain in 2020 fell in just a few months, a pattern that hydrologists describe as increased precipitation concentration. Concentrated rainfall is a double-edged sword in drylands: the same total volume of water arriving in fewer, heavier events is harder for soils and vegetation to absorb, raises flash-flood risk, and reduces the slow recharge of soil moisture and shallow aquifers that sustained ecosystems and traditional agriculture. The finding echoes similar concentration trends documented in Spain, the Mediterranean, and other semi-arid regions, suggesting that the intensification of the hydrological cycle may be reshaping dryland rainfall regimes well beyond Iraq’s borders.

Trend detection relied on two of the most widely used tools in climatology: the Mann–Kendall test, a nonparametric method that identifies monotonic trends without assuming any particular data distribution, and Sen’s slope estimator, which provides a robust measure of the trend’s magnitude. Together they revealed a significant increasing trend in annual rainfall, with a Sen’s slope of +2.03 millimetres per year. The wet-month signal strengthened in parallel: the frequency of wet months increased at a rate of 0.050 months per year, and the rainfall accumulated within those wet months grew by 1.76 millimetres per year. In other words, the region is not simply getting marginally wetter overall; its wet episodes are becoming both more frequent and more productive, while the arid baseline remains essentially unchanged.

That baseline remains formidable. Mean annual reference evapotranspiration, a measure of the atmospheric demand for water driven by temperature, sunshine, and wind, reached 2519.92 millimetres per year, dwarfing the 62.87-millimetre mean annual rainfall by a factor of roughly forty. The resulting aridity index, the ratio of precipitation to potential evapotranspiration, stood at approximately 0.025, placing the region firmly in the hyper-arid class under standard climate classifications. This means that despite the encouraging upward rainfall trend, the fundamental water balance of the region remains overwhelmingly negative: the atmosphere demands vastly more water than the sky delivers. Any increase in rainfall, however welcome, is absorbed into an enormous evaporative deficit, which is why the authors emphasise that hydroclimatic conditions remained highly arid throughout the study period even as wet anomalies multiplied.

The second half of the study turned from diagnosis to prediction, pitting two very different forecasting approaches against each other. The first, SARIMA, or Seasonal AutoRegressive Integrated Moving Average, is a classical statistical model that explicitly encodes the seasonal rhythm and temporal dependence structure of a time series, making it a natural fit for rainfall data with strong winter-wet and summer-dry cycles. The second, XGBoost, is a machine learning algorithm based on gradient-boosted decision trees that has earned a reputation for outperforming traditional models across many hydrological prediction tasks. The authors evaluated both models on an independent testing period using three standard metrics: root mean square error, mean absolute error, and the coefficient of determination.

The verdict was sobering. XGBoost achieved an RMSE of 19.24 millimetres, an MAE of 9.13 millimetres, and an R² of just 0.01, while SARIMA posted an RMSE of 19.43 millimetres, an MAE of 9.55 millimetres, and an R² of −0.01. An R² near zero means the models explained essentially none of the variance in the test data, barely improving on a naive prediction. The machine learning approach was only marginally better than the statistical one, and neither demonstrated meaningful predictive skill. This result carries an important lesson for the increasingly fashionable field of AI-driven environmental forecasting: in hyper-arid environments, where rainfall is sparse, episodic, and dominated by rare synoptic events, the signal available to any model, however sophisticated, may simply be too weak and noisy to extract reliable month-to-month predictions. Data-hungry algorithms cannot conjure information that the climate itself does not provide.

For the exploratory projections covering 2026 to 2035, the team chose SARIMA, a decision grounded not in superior accuracy but in the model’s structural advantages: it explicitly represents seasonal temporal dependence and provides model-based prediction intervals, allowing uncertainty to be quantified and communicated. The authors are careful to note that this choice does not imply SARIMA outperformed XGBoost. The projections suggest that the region’s seasonal rainfall architecture will persist, with winter remaining the wettest season and summer the driest, while forecast uncertainty widens steadily with lead time, a familiar and honest feature of statistical extrapolation. Such exploratory outlooks are not crystal balls, but they offer planners a plausible envelope of future conditions against which drought contingencies and water-allocation strategies can be stress-tested.

The broader significance of the study lies less in any single number than in its integrated approach. By combining anomaly detection, concentration analysis, trend testing, hydroclimatic classification, and comparative forecasting within a single framework, the researchers have assembled exactly the kind of evidence base that drought monitoring, rainfall-risk assessment, and adaptive water-resource planning require in regions where ground observations are thin and climate pressures are mounting. As competition for water intensifies across the Middle East, and as global assessments warn of deepening aridity in the Mediterranean-Middle East hotspot, studies of this kind demonstrate that even the driest landscapes can be monitored, understood, and, to a degree, anticipated, provided the analytical tools are matched honestly to the limits of the data. For southwestern Iraq, the message is nuanced: more rain may be falling, but it arrives in fewer, sharper bursts, on land where the atmosphere’s thirst remains nearly insatiable.

Subject of Research: Rainfall variability, concentration, and exploratory forecasting in a hyper-arid dryland region of southwestern Iraq

Article Title: Rainfall concentration variability and exploratory forecasting in a dryland region of Southwestern Iraq

Article References: Amin, R. A. M., Shinichel, B. S., Abbas, A. M., & Suhail, M. A. (2026). Rainfall concentration variability and exploratory forecasting in a dryland region of Southwestern Iraq. Theoretical and Applied Climatology, 157(11), Article 696. https://doi.org/10.1007/s00704-026-06624-x

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06624-x

Keywords: rainfall variability, drylands, hyper-arid climate, southwestern Iraq, NASA POWER, SARIMA, XGBoost, Mann-Kendall test, Sen's slope, aridity index, drought monitoring, precipitation concentration

The motivation is rooted in a stubborn global problem. Plant diseases and pest infestations remain among the most powerful brakes on agricultural productivity in developing regions, where early detection and timely intervention often determine whether a season ends in harvest or loss. Maize, a staple crop across much of sub-Saharan Africa, is particularly vulnerable. Two threats in the study’s focus—aphid infestation and maize streak virus—can devastate yields if they are not recognized and managed quickly. Yet in many farming communities, expert diagnosis is scarce, extension services are stretched thin, and a farmer may not know until it is too late that a field is under attack. An AI system that can compress the distance between symptom and remedy could therefore have outsized consequences for both crop yield and food security.

At the heart of the framework is a multimodal fusion approach, meaning the system does not rely on a single stream of evidence. The first stream is visual: a deep learning-based image classification model examines maize leaf photographs and learns to distinguish healthy plants from those showing signs of aphid damage or maize streak virus infection. The second stream is environmental: the system ingests weather variables including temperature, humidity, rainfall, and solar radiation drawn from the NASA POWER database. The intuition behind this pairing is that disease and pest dynamics are never purely visual phenomena. Aphid outbreaks and viral infections unfold within specific climatic envelopes, and the environmental context can either sharpen or blur the diagnostic picture. By fusing the two modalities, the model gains a richer representation of each field’s condition than either images or weather data could provide alone.

The experimental design reflects a careful attention to real-world grounding. Rather than relying on generic datasets, the researchers used maize leaf images from the YEESI Lab dataset, a collection rooted in East African growing conditions, and paired them with a 61-day environmental record from NASA POWER for the geographic coordinates of Morogoro. This combination anchors the system in the actual climate and cropping context of the region it is meant to serve. Three classes of crop condition were considered—healthy plants, aphid infestation, and maize streak virus infection—allowing the team to test whether the framework could separate not only disease from health but also pest damage from viral pathology, two conditions that can look superficially similar to an untrained eye.

The results reveal both the power and the limits of each data stream. On its own, the image-based model achieved an accuracy of 91 percent, a strong showing that confirms what earlier work on plant disease classification has suggested: leaves carry a legible signature of the threats attacking them. The weather-based model, by contrast, performed considerably worse. The reason is instructive. The environmental characteristics of the different disease classes overlapped substantially, meaning that the temperature, humidity, rainfall, and radiation profiles accompanying an aphid outbreak could look much like those accompanying a viral infection. Weather alone, in other words, cannot tell a farmer what is wrong with a plant, even though it shapes what is wrong. This finding is a useful corrective to any assumption that satellite-derived climate data can substitute for on-the-ground observation.

When the two streams were combined, however, the picture changed dramatically. The multimodal fusion model reached a classification accuracy of 94 percent, outperforming the image-only model by three percentage points. That gain may sound modest, but in agricultural decision-making, where a misdiagnosis can trigger the wrong treatment or a costly delay, even incremental improvements in accuracy carry practical weight. The result demonstrates the core thesis of the study: visual and environmental information are complementary, and integrating them produces a diagnostic signal more reliable than either modality in isolation. The fusion architecture effectively lets the weather context resolve ambiguities that the image alone cannot settle, while the image anchors the diagnosis in direct physical evidence.

Diagnosis, though, is only half the problem. A farmer who learns that a field has maize streak virus still needs to know what to do about it, and generic advice downloaded from the internet may be wrong for a specific crop, climate, and stage of infection. This is where the framework’s second major component comes in: a context-aware recommendation engine. The engine takes the predicted crop condition from the fusion model and combines two sources of knowledge. The first is rule-based agronomic knowledge, a structured layer of established agricultural expertise that constrains what the system can advise. The second is Qwen2.5:7B, a large language model that translates the fused diagnosis and environmental context into natural-language recommendations a farmer can actually read and follow. The rule-based layer acts as a guardrail, keeping the language model’s fluent but sometimes unpredictable outputs tethered to verified agronomic practice.

Evaluating the quality of generated advice is one of the trickiest challenges in applied AI, and the researchers adopted a notably rigorous approach. The recommendations produced by the system were assessed using Llama 3.1:8B, a separate large language model, in a blind evaluation framework in which the evaluator model scored the outputs without knowing how they were generated. The assessment covered five quality dimensions: Safety, Technical Accuracy, Relevance, Actionability, and Clarity. The recommendation engine earned an overall score of 4.80 out of 5.00 across these dimensions, indicating that the generated advice was judged to be agronomically consistent, sensitive to context, and genuinely actionable. A blind evaluation by an independent model does not eliminate every concern about machine-judged quality, but it does provide a structured, repeatable measure that is harder to game than self-assessment.

The broader significance of the work lies in its architecture rather than any single number. Most AI tools in agriculture address one link in the chain—a classifier that labels a photo, a dashboard that displays weather, a chatbot that answers questions. The Morogoro framework instead chains them together: images and climate data flow into a fusion model, the diagnosis flows into a recommendation engine grounded in agronomic rules, and the output flows to the farmer as readable guidance. This end-to-end design is what distinguishes a decision-support system from a collection of disconnected tools. It also reflects a pragmatic understanding of the user. Smallholder farmers do not need probability scores or model confidence intervals; they need to know whether to treat, remove, or monitor a crop, and they need that answer in language they can act on immediately.

There are, of course, hurdles between a promising prototype and a system running in farmers’ hands across the developing world. The study’s evaluation rests on three crop conditions in one region, and scaling to a wider catalogue of diseases, pests, crops, and climates will demand more data and more validation. The reliance on large language models raises questions about computational access in low-connectivity environments, and blind LLM evaluation, however structured, is not a substitute for field trials with real agronomists and real farmers. Yet the framework’s 94 percent diagnostic accuracy and its 4.80 out of 5.00 recommendation score mark a meaningful step toward precision agriculture that does not require precision-agriculture budgets. If the approach generalizes, the humble act of photographing a sick leaf could soon connect a farmer in Morogoro—or anywhere else—to a diagnostic and advisory intelligence that sees the field, senses its climate, and answers in words that matter.

Subject of Research: A multimodal context-aware AI recommendation framework for maize disease diagnosis and farmer decision support in smart farming

Article Title: A multimodal context-aware AI recommender for smart farming

Article References: Mhagama, J. T., & Garg, K. (2026). A multimodal context-aware AI recommender for smart farming. PLOS One, 21(10), e0359596. https://doi.org/10.1371/journal.pone.0359596

Image Credits: AI Generated

DOI: 10.1371/journal.pone.0359596

Keywords: smart farming, multimodal AI, maize disease, plant pathology, deep learning, large language models, precision agriculture, NASA POWER, Tanzania, food security, aphid infestation, maize streak virus

The scale of the decline in some areas is striking. The team divided the district into four watershed groups using a 30-meter digital elevation model and the HydroBASINS Level-12 sub-basin topology, then summarized groundwater observations from wells measured before and after the monsoon in 2005, 2010, 2015, and 2018. In the first watershed group, the mean increase in groundwater depth over the 13-year period was 19.32 meters, and all seven monitored wells in that group showed the water table falling, with individual changes ranging from 5.25 to 31.26 meters. The 95 percent confidence interval around that group mean, from 9.11 to 29.54 meters, was the only one of the four groups that excluded zero, making Group 1 the clearest and most statistically robust hotspot of decline. Group 4 followed with a mean increase of 15.69 meters, but its wells told wildly different stories, ranging from a 29.50-meter shallowing to a 52.42-meter deepening, the widest spread of any group.

What makes these numbers genuinely puzzling is that the most urbanized watershed was not the one sinking fastest. Group 2, which had the highest urban fraction in every mapped year, rising from 20.54 percent of its area in 2010 to 27.32 percent in 2020, recorded a mean groundwater-depth increase of only 7.21 meters. Group 1, by contrast, saw its urban fraction climb from just 3.64 percent to 10.56 percent, yet it suffered the steepest decline. Group 3, with the lowest mean change at 5.76 meters, contained wells that both deepened by more than 40 meters and shallowed by more than 24 meters. The researchers deliberately refrained from running correlations or regressions on these four group-level values, arguing that with so few units, any statistical test would be misleading. Instead, they describe the patterns as non-monotonic: urbanization and groundwater decline co-occur in some places and diverge in others.

To probe whether changing land surfaces might still be shifting the hydrology, the team turned to the Curve Number method, a standard engineering framework that combines land-cover classes with Hydrologic Soil Groups to estimate how much rainfall becomes direct runoff in a storm. Soil data from SoilGrids at 250-meter resolution showed that Hydrologic Soil Group D, the category with the highest runoff potential and lowest infiltration capacity, dominated all four watershed groups. That is why mean Curve Number values were uniformly high, between 89.15 and 89.94 in 2010, creeping up by only 0.24 to 0.64 points by 2020. A scenario analysis using hypothetical rainfall events of 10, 25, 50, and 100 millimeters showed that these small Curve Number shifts would translate into only modest increases in modeled runoff, at most 1.56 millimeters for a 100-millimeter storm. In other words, the land surface became slightly more runoff-prone as cities grew, but not enough to constitute a major hydrological upheaval.

Climate, the other obvious suspect, also failed to provide a clean explanation. Drawing on NASA POWER gridded reanalysis data from 2003 to 2018, quality-controlled down to 204,540 complete daily records across 35 meteorological grid cells, the researchers found that mean annual precipitation varied only modestly among the watershed groups, from 166.4 to 195.2 millimeters, with a spatial coefficient of variation of just 7.7 percent. Group 1 was actually the wettest group and had the lowest estimated reference evapotranspiration, yet it experienced the deepest groundwater decline. Mann-Kendall trend tests found no statistically significant monotonic trends in annual precipitation, monsoon rainfall, mean temperature, or Hargreaves reference evapotranspiration over the 16-year record. The climatic water balance, precipitation minus reference evapotranspiration, was deeply negative in every year and every group, averaging minus 1,744.2 millimeters per year, a stark reminder of how far atmospheric demand exceeds rainfall in this hot desert climate.

The drought record added further nuance. The 12-month Standardized Precipitation Index, computed relative to the study period rather than a long-term baseline, flagged 2003 as moderately dry and 2018, the final monitoring year, as severely dry, with December values of minus 1.46 and minus 1.55 respectively, while 2015 was extremely wet at plus 2.76. Yet groundwater depth generally increased across the monitoring period despite these alternating wet and dry phases. Exploratory correlations between district-mean groundwater depth and antecedent 90-, 180-, and 365-day precipitation, as well as the previous month’s SPI-12, were all negative, hinting that wetter conditions coincided with shallower water tables, but none reached statistical significance. With only eight intermittent groundwater snapshots, the authors caution, such relationships cannot be pushed toward causal interpretation.

This is precisely where the study’s intellectual honesty becomes its most valuable feature. The authors are explicit that their analysis does not simulate groundwater flow, calibrate a water balance, or attribute the observed declines to any single mechanism. Pumping rates, canal seepage, irrigation return flows, aquifer transmissivity, well-screen depths, hydraulic gradients, and river-aquifer exchange were not consistently available across the district, and without them, the spatial associations remain descriptive. The Indus Basin Irrigation System is a labyrinth of conjunctive water use; the larger Lower Bari Doab system alone carries roughly 201 kilometers of main canals, 95 kilometers of link canals, 2,264 kilometers of distributaries, and more than 60,000 tubewells in freshwater areas. Canal leakage and recirculated irrigation water can contribute substantially to aquifer replenishment, meaning that recharge in this landscape is engineered as much as natural, and it can vary dramatically over short distances.

The satellite era has only sharpened the urgency of understanding these local dynamics. Gravity-recovery missions have identified significant terrestrial water-storage losses in the Indus Basin since the mid-2010s, and global assessments have documented rapid groundwater declines, with occasional recovery, in dry regions dominated by irrigated agriculture, from the North China Plain to the US High Plains and the Central Valley. A recent national-scale analysis in Pakistan found regionally heterogeneous groundwater responses to land-use transitions, reinforcing the message that similar surface transformations do not produce uniform subsurface consequences. The Multan study adds a district-level, well-by-well texture to that picture, showing that even within a single administrative area, the mismatch between land-surface indicators and aquifer response can be profound, and that Global Moran’s I tests found no statistically significant district-wide spatial clustering of well-level declines under a four-nearest-neighbor weighting scheme.

The practical payoff is a screening framework rather than a verdict. Group 1 emerges as the unambiguous management priority, with every monitored well declining and a confidence interval that excludes zero, while Group 4 demands attention for its high mean decline and extreme well-to-well variability, and Groups 2 and 3 require finer-scale investigation before uniform measures are imposed. The authors recommend denser monitoring networks with continuous water-level loggers, measurements referenced to a common elevation datum, pumping inventories, canal-delivery and seepage records, aquifer tests, and eventually calibrated groundwater-flow or integrated surface-water-groundwater models. Their broader warning deserves to travel well beyond Punjab: urban fraction and Curve Number alone should never be used to infer groundwater decline. In data-scarce semi-arid plains around the world, organizing whatever observations exist within common hydrological units is a sensible first step, but causal stories about dying aquifers must wait for the pumping, recharge, and subsurface measurements that reveal what is really happening underground.

Subject of Research: Spatial associations between urbanization, runoff potential, and groundwater-depth change in the semi-arid alluvial plain of Multan, Pakistan

Article Title: Spatial associations between urbanization, watershed runoff potential, and groundwater-depth change in a Semi-Arid Alluvial Plain, Multan, Pakistan

Article References: Ullah, A., Wang, Y., Wang, H., liu, J., Sajjad, M. M., Hussain, I., Abbas, H., & Nazim, H. (2026). Spatial associations between urbanization, watershed runoff potential, and groundwater-depth change in a Semi-Arid Alluvial Plain, Multan, Pakistan. Environmental Earth Sciences, 85(16), Article 419. https://doi.org/10.1007/s12665-026-13164-0

Image Credits: AI Generated

DOI: 10.1007/s12665-026-13164-0

Keywords: groundwater, urbanization, Multan, Indus Basin, Curve Number, Hydrologic Soil Groups, NASA POWER, watershed delineation, semi-arid climate, land-use change, water-table decline, hydrogeology

The research, published in Discover Geoscience, analyzed 42 years of meteorological data from 1981 to 2022 at eight locations spanning Nigeria’s major eco-climatic zones, from the humid Mangrove swamps of Ikot Akpaden and the Freshwater zone of Yenagoa in the south, through the transitional Rainforest and Guinea Savannah sites of Ibadan, Abuja, and Jalingo, to the semi-arid Sudan and Sahel Savannah stations of Sokoto, Gombe, and Kukawa in the north. Using temperature, relative humidity, atmospheric pressure, and precipitation records drawn from NASA’s POWER data repository, the team calculated refractivity using the standard formula recommended by the International Telecommunication Union, which combines a dry component driven by pressure and temperature with a wet component governed by water vapour pressure.

The results reveal a striking north-south divide. Refractivity reached its highest values, around 380 N-units, at the coastal locations of Yenagoa and Ikot Akpaden, where relative humidity hovers near 90 percent, while the arid northern stations of Sokoto and Kukawa recorded the lowest values, around 310 N-units. This gradient matters because high refractivity in humid coastal air creates conditions favorable to atmospheric ducting, a phenomenon in which radio waves become trapped in layers of the atmosphere and travel in unexpected ways. In practical terms, network operators in the humid south may need to increase transmission power to maintain reliable connectivity, while northern sites face different propagation challenges altogether.

To detect long-term trends, the researchers deployed two complementary statistical tools. The Mann-Kendall trend test, a non-parametric method widely used in climate science, identified statistically significant monotonic changes in the time series. Five of the eight locations, including Yenagoa, Ikot Akpaden, Ibadan, Sokoto, and Kukawa, showed significant increasing trends in refractivity, with the coastal stations exhibiting Sen’s slope values of roughly 0.16 N-units per year. The second tool, Şen’s Innovative Trend Analysis, or ITA, goes further by splitting each record into two halves and plotting them against each other, allowing researchers to see whether trends are confined to low, medium, or high values rather than spread uniformly across the distribution.

The ITA approach proved crucial for uncovering behavior the Mann-Kendall test missed. At Kukawa in the Sahel, for example, the overall trend was increasing, but ITA showed the rise was concentrated only in the high-value range of the data. At Jalingo and Gombe, refractivity actually declined in the lower and medium value ranges even as other sites rose steadily. This range-specific sensitivity matters because localized climate effects can push different portions of a distribution in different directions, and a single summary statistic can obscure exactly the kind of variability that matters most for engineers designing radio links in a specific place.

The seasonal analysis added another layer of insight. Rather than treating the year as simply wet or dry, the team divided it into four phases: onset, peak rainy, cessation, and dry periods, defined by the movement of the Inter-Tropical Discontinuity, the boundary where moist maritime air from the Atlantic meets dry continental air from the Sahara. Positive refractivity trends were largest and most consistent during the wet and cessation periods, particularly over humid and coastal areas, reaching 0.13 to 0.15 N-units per year. In contrast, the dry and onset periods in the Sahel and Sudan Savannah showed weaker or negative trends, ranging from -0.236 to 0.224 N-units per year. During the onset months in the north, the sporadic northward migration of the Inter-Tropical Discontinuity produces alternating pulses of moist and dry air that suppress any persistent trend.

Correlation analysis confirmed what the physics predicts: relative humidity is the dominant driver of refractivity. At the northern stations the relationship was almost deterministic, with correlation coefficients exceeding 0.90 at Kukawa, Sokoto, and Gombe, peaking at 0.98 in Kukawa. Temperature showed a moderately positive relationship with refractivity but a strongly negative one with humidity, reaching -0.93 at Abuja, reflecting the well-known inverse coupling between heat and moisture in tropical environments. Atmospheric pressure, by contrast, correlated only weakly with refractivity, indicating that its role, while embedded in the governing equation, is secondary in practice.

To quantify these relationships, the team built a multilinear regression-based sensitivity framework, standardizing all variables so that coefficients could be compared fairly across locations. The models explained between 82 and 99 percent of refractivity variability, which is unsurprising given that refractivity is physically defined by the same meteorological inputs. The percentage contributions, however, told a fascinating story about climate. Relative humidity accounted for 65 to 77 percent of refractivity variation in the arid Sudan and Sahel zones, but only about 52 percent in the humid coastal south, where temperature played a proportionally larger role. The explanation lies in the saturation physics of the Clausius-Clapeyron relation: in dry climates with high saturation vapour pressure, even small changes in humidity produce large swings in the water vapour term of the refractivity equation, amplifying its influence.

The implications extend well beyond Nigerian borders. As climate change continues to warm the planet and alter moisture distributions, the propagation environment for radio waves is shifting in ways that most network planning models never anticipated. Rising refractivity in humid regions heightens the likelihood of super-refraction and ducting, which can cause signal blackouts, multipath interference, and unpredictable communication ranges. Declining refractivity in arid zones during dry seasons points toward sub-refraction and reduced signal range. The authors recommend that telecommunications operators incorporate real-time meteorological data into network performance monitoring, that regulators consider atmospheric variability in spectrum management, and that meteorological agencies collaborate directly with telecom providers. For a country of more than 200 million people whose digital economy depends on reliable wireless connectivity, the message is clear: the weather is not just something that interrupts your signal during a storm. It is slowly, measurably, and predictably rewriting the rules of how radio waves travel, and the networks of the future will need to be designed with the climate in mind.

Subject of Research: Long-term trends in meteorological parameters and radio refractivity across Nigeria's eco-climatic zones

Article Title: Spatiotemporal trends and seasonal variations in meteorological parameters and radio refractivity across Nigeria’s eco-climatic zones

Article References: Agbo, E. P., Nathaniel, E. U., Thomas, J. E., Offorson, G. C., Nkajoe, U., & Ndoma, E. G. (2026). Spatiotemporal trends and seasonal variations in meteorological parameters and radio refractivity across Nigeria’s eco-climatic zones. Discover Geoscience, 4(1), Article 336. https://doi.org/10.1007/s44288-026-00708-x

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00708-x

Keywords: radio refractivity, Nigeria, climate change, telecommunications, radio wave propagation, relative humidity, Mann-Kendall test, innovative trend analysis, eco-climatic zones, atmospheric ducting, NASA POWER, seasonal variability

The research, published in Theoretical and Applied Climatology by Eskinder Gidey and Paidamwoyo Mhangara of the University of the Witwatersrand, stitched together two very different strands of climate evidence. For the historical record, spanning 1984 to 2023, the authors drew on NASA POWER data built on the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), a satellite-assimilating reanalysis product that reconstructs consistent daily climate variables across the globe. For the future, they turned to the Coupled Model Intercomparison Project Phase 6 (CMIP6), using the medium-emissions SSP2-4.5 scenario to project conditions in Gauteng from 2024 to 2050.

What makes the analysis distinctive is its statistical machinery. Rather than relying on simple averages, the researchers applied an outlier-resistant, median-based robust approach to tame erratic values in the datasets. They then ran the Modified Mann-Kendall test, a non-parametric trend detector that corrects for the autocorrelation that plagues climate time series, and estimated the magnitude of change with Sen’s slope estimator, which computes the median of all pairwise slopes and is therefore largely immune to extreme outliers. All of the processing was carried out in RStudio, and the combination allowed the team to distinguish genuine long-term signals from the year-to-year noise that dominates semi-arid climates.

The historical results are unambiguous on temperature. Gauteng warmed at a rate of about 0.02 degrees Celsius per year between 1984 and 2023, a statistically significant trend with a Kendall’s tau of 0.28 and a p-value of 0.01. That warming translated directly into more frequent heat extremes: heatwave events climbed by roughly 0.04 events per year, a highly significant increase with tau of 0.30 and p below 0.001. Intriguingly, cold waves also ticked upward slightly, by about 0.01 events per year, but that trend failed the significance test with a p-value of 0.26, suggesting it may be a statistical artefact of natural variability rather than a real climatic shift.

The water-side variables tell a more troubling story. Precipitation across the province declined by 3.63 millimetres per year over the four-decade observation window. Cloud cover fell by 0.33 percent per year, and both soil moisture and relative humidity showed declining trends as well. For a landlocked province perched on continental high ground, far from major rivers, these coupled declines matter enormously: less rain, drier soils, drier air and thinner cloud all conspire to reduce the water that reaches reservoirs, recharges groundwater and sustains the wetlands and streams of the Vaal and Crocodile river systems that Gauteng depends on.

The projections sharpen the warning. Two CMIP6 models, the Australian ACCESS-CM2 and the United Kingdom’s HadGEM3-GC31-LL, both foresee significant warming in Gauteng between 2024 and 2050. ACCESS-CM2 projects a temperature rise of 1.68 degrees Celsius, equivalent to a trend of 0.35 percent per year, while HadGEM3-GC31-LL projects 1.60 degrees Celsius, or 0.33 percent per year. On the precipitation side, the models diverge in magnitude but agree in direction: HadGEM3-GC31-LL projects rainfall declining by 7.26 millimetres per year, while ACCESS-CM2 projects a gentler decline of 1.95 millimetres per year. Either way, the province faces a hotter future with less water falling from the sky.

The implications ripple far beyond climatology. Gauteng is the engine of the South African economy, home to Johannesburg, Pretoria and a population that continues to grow through migration, all of it concentrated on a water supply already stretched by periodic drought. The province’s water security has long depended on inter-basin transfers, most famously the scheme that pipes water from the Lesotho highlands into the Vaal system. A simultaneous increase in evaporative demand, driven by rising temperatures and falling humidity, together with declining rainfall, means that reservoir inflows could shrink even as household and industrial demand climbs. Previous research on urban water demand in Gauteng has already flagged the compounding effect of climate change and population growth on the province’s supply systems.

The study’s authors frame their findings as a contribution to South Africa’s National Development Plan 2030, which calls for climate-resilient water management and robust early warning systems. By combining multi-decadal reanalysis observations with CMIP6 projections and rigorous trend-detection statistics, the work offers water managers a defensible quantitative baseline: not just the assertion that conditions are changing, but measured rates of change for temperature, heatwave frequency, rainfall, soil moisture, humidity and cloud cover, together with model-based expectations for the coming quarter century. That kind of evidence is what allows planners to size new infrastructure, set demand-management targets and design drought triggers with some confidence about the trajectory they are planning against.

There are also broader scientific lessons in the methodology. Reanalysis products like NASA POWER (MERRA-2) provide consistent, freely available daily climate data even in regions with sparse ground-based station networks, a chronic problem across much of Africa. Pairing such observations with the CMIP6 archive, and filtering both through robust, autocorrelation-aware statistics, offers a template that can be replicated for other provinces and catchments. The researchers acknowledge the inherent uncertainties of model projections, and the divergence between the two models’ rainfall estimates is a reminder that scenario-based futures are ranges rather than forecasts. But the direction of travel, warmer, drier and more extreme, is consistent across both the observed record and the projections.

For the residents of Gauteng, the message distilled from forty years of satellite-era data and twenty-six years of model projections is stark but actionable. The province has already lost rainfall at more than three and a half millimetres per year while gaining heatwaves at a statistically robust clip, and the coming decades promise roughly another degree and a half of warming on top of what has already occurred. Whether that future translates into crisis or into a managed transition will depend on how quickly the province’s water institutions, municipalities and households absorb what the data is telling them, and on whether the policies built for the climate of the twentieth century are retooled for the one now arriving.

Subject of Research: Historical and projected climate variability and trends in Gauteng, South Africa, and their implications for water resources

Article Title: Climate variability and its implications for water resources in Gauteng, South Africa (1984–2050) based on NASA POWER (MERRA-2) observations and CMIP6 SSP2-4.5 projections

Article References: Gidey, E., & Mhangara, P. (2026). Climate variability and its implications for water resources in Gauteng, South Africa (1984–2050) based on NASA POWER (MERRA-2) observations and CMIP6 SSP2-4.5 projections. Theoretical and Applied Climatology, 157(10), Article 676. https://doi.org/10.1007/s00704-026-06600-5

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06600-5

Keywords: Gauteng, climate variability, water resources, NASA POWER, MERRA-2, CMIP6, SSP2-4.5, heatwaves, precipitation trends, Sen's slope estimator, Modified Mann-Kendall test, South Africa

Atmospheric evaporative demand sits at the hinge of the terrestrial water balance. It governs the moisture exchange between land and air, determines how much of a given rainfall episode is lost back to the sky, and in water-limited regions it is tightly coupled to the onset and intensification of drought. When demand rises faster than supply, soils dry, reservoirs shrink and vegetation stress compounds even if precipitation itself does not collapse. Yet despite this central role, the multi-decadal behaviour of ET0 and its structural changes have remained poorly characterised, particularly in semi-arid regions where the question of whether atmospheric conditions are statistically associated with their impact on water balance variability has not been systematically answered. The Tamil Nadu study set out to close that gap by examining long-term hydroclimatological behaviour through four lenses: spatial heterogeneity, temporal non-stationarity, atmospheric control and impacts on the climatic water balance across contrasting hydro-climatic regimes.

The evidence base was drawn from the NASA POWER database, a publicly accessible satellite-derived and modelled meteorological record that provided temperature, relative humidity, solar radiation, wind speed and precipitation for the period 1985 to 2024. Rather than relying on a single station, the researchers selected representative stations spanning the diverse hydro-climatic regimes of Tamil Nadu, from the wetter coastal and western zones to the drier interior plains. This design matters because a state-level average can easily mask the fact that neighbouring districts may be drifting in opposite directions. By treating each regime on its own terms, the analysis could reveal whether the drivers of evaporative demand behave uniformly or whether their dominance changes with the local climate setting.

Methodologically, the study leaned on a battery of robust non-parametric techniques chosen for their resilience to outliers and to the non-normal distributions typical of hydro-climatic data. Trend detection used the Mann–Kendall test paired with Sen’s slope estimator, a combination that has become a standard for identifying monotonic change in environmental series. The pivotal innovation, however, was the Pettitt test for regime shift analysis, which searches for a single abrupt change point in a time series rather than assuming gradual change. Extreme event analysis characterised the behaviour of ET0 at the tails of the distribution, where agricultural stress concentrates, and a SARIMA model, a seasonal autoregressive integrated moving average framework, was used to forecast short-term ET0 dynamics. Finally, a driver dominance analysis quantified the relative contribution of individual atmospheric factors to ET0 variability, allowing the team to rank the controls rather than merely list correlations.

The headline finding is that ET0 in Tamil Nadu does not follow a simple, monotonic trajectory. Instead, the records show statistically significant regime changes, meaning that the series jumps between quasi-stable states with different mean levels and different atmospheric controls. The interaction between temperature, radiation and humidity emerges as the governing mechanism, and the balance of power among these three variables shifts from one hydro-climatic regime to another. In practical terms, this means that a warming trend does not translate into a uniform increase in atmospheric thirst everywhere. Where humidity is high, rising temperatures may be partially offset by the suppression of evaporation; where the air is already dry, the same warming can push demand sharply upward. The study’s driver dominance analysis makes this regime dependence explicit, showing that the atmospheric control on ET0 varies systematically under different hydro-climatic conditions.

Extreme ET0 behaviour and the response of the climatic water balance also diverged across regimes, a pattern the authors interpret as the spatial fingerprint of climatic water stress. In the drier interior, where rainfall is marginal and evaporative demand is chronically high, shifts in ET0 translate almost directly into deeper water deficits, because there is little buffer between supply and demand. In wetter zones, the same shifts may be absorbed by soil moisture and surface storage, at least temporarily. This asymmetry has a sobering implication: identical large-scale climate signals can produce radically different drought outcomes depending on where they land. Water planners who rely on state-wide or basin-wide averages risk misjudging both the severity and the geography of emerging stress.

Perhaps the most consequential conclusion of the paper is a methodological warning. Atmospheric evaporative demand, the authors argue, cannot be understood on the basis of monotonic trends alone, because its spatial heterogeneity, temporal non-stationarity and regime dependence violate the assumptions underlying simple trend analysis. A Mann–Kendall test applied to a series that has jumped between two regimes may report a significant trend that is really an artefact of a step change, or conversely may miss genuine change that is concentrated in a short transition. For predictability, this reframing is critical. Forecast systems and drought early-warning schemes that extrapolate a linear trend will systematically misjudge the future if the underlying process is one of regime dynamics. Recognising the regime structure, by contrast, opens the door to forecasts conditioned on the current state, which is precisely what the SARIMA component of the study begins to explore for short-term ET0 dynamics.

The findings arrive amid a broader scientific reassessment of global evaporative demand. Recent work has documented that climate change has increased evaporative demand across most of the planet, with South Asia standing out as a notable exception in some global assessments, and researchers have begun naming prolonged episodes of extreme atmospheric demand, such as so-called thirstwaves, as a distinct class of agricultural hazard. The Tamil Nadu results add regional texture to this global picture, showing that even within a single Indian state the trajectory of atmospheric thirst is not uniform. They also echo a long-standing puzzle in hydrology, the so-called evaporation paradox, in which observed pan evaporation can decline even as temperatures rise, a reminder that humidity, radiation and wind interact with temperature in ways that defy single-variable intuition.

For a state where agriculture consumes the bulk of freshwater and where monsoon failures routinely trigger drinking-water emergencies, the practical stakes are considerable. Knowing which atmospheric variable dominates ET0 in each regime tells irrigation authorities what to watch: humidity and radiation in some zones, temperature in others. Knowing that regimes shift abruptly rather than drift smoothly suggests that water budgets should be revised at detected change points rather than on fixed assumptions. The availability of the underlying NASA POWER data, covering 1985 to 2024, and of the processed analytical outputs on reasonable request from the corresponding author, means the framework can be replicated and extended to other semi-arid regions facing similar questions. The research was supported by the Tamil Nadu Chief Minister’s Research Grant, reflecting state-level interest in the predictability problem.

Ultimately, the study reframes atmospheric evaporative demand from a passive consequence of warming into an active, regime-governed component of the climate system with its own predictability structure. In semi-arid Tamil Nadu, the atmosphere’s thirst arrives in steps, not slopes, and each step rewrites the local water balance in a different way. Capturing that step-like behaviour, the authors contend, is the key to anticipating drought before it takes hold, and to managing a water future in which the demand side of the equation may change faster than the supply side ever will.

Subject of Research: Multi-decadal variability and regime shifts in atmospheric evaporative demand and climatic water balance in Tamil Nadu, India

Article Title: Multi-decadal variability and regime dynamics of atmosphere evaporative demand and climatic water balance in Tamil Nadu, India: implications for predictability

Article References: V, M., D, R., & K, G. (2026). Multi-decadal variability and regime dynamics of atmosphere evaporative demand and climatic water balance in Tamil Nadu, India: implications for predictability. Theoretical and Applied Climatology, 157(10), Article 671. https://doi.org/10.1007/s00704-026-06592-2

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06592-2

Keywords: evaporative demand, reference evapotranspiration, ET0, Tamil Nadu, drought, regime shift, Pettitt test, Mann-Kendall trend analysis, SARIMA forecasting, climatic water balance, semi-arid climate, NASA POWER

The study, conducted by Hüdaverdi Gürkan of the University of Florida, the Turkish State Meteorological Service, and Adana Alparslan Turkes Science and Technology University, together with Gerrit Hoogenboom of the University of Florida’s Global Food Systems Institute, set out to answer a deceptively simple question: when NASA POWER says it rained, or reports yesterday’s maximum temperature, how closely does that match what actually happened on the ground? The answer, the researchers found, depends heavily on which variable you care about and where the point of interest sits on the map. For temperature and solar radiation, the news is overwhelmingly positive. For humidity, wind, and precipitation, geography matters enormously, and the dataset’s reliability can degrade sharply along coastlines and in mountainous terrain.

The statistical evidence for temperature is striking. Maximum daily temperature records from NASA POWER explained between 87 and 98 percent of the variance in ground observations, with root mean square errors ranging from 1.50 to 6.89 degrees Celsius. Minimum temperatures performed nearly as well, with R-squared values between 0.78 and 0.97 and errors from 1.23 to 8.37 degrees Celsius. Solar radiation estimates tracked ground measurements with R-squared values from 0.76 to 0.97 and root mean square errors between 1.73 and 3.84 megajoules per square meter. Across all temperature and radiation variables, the study reports overall R-squared values between 0.76 and 0.98, a level of agreement that the authors say establishes NASA POWER as a dependable foundation for climate modeling and agricultural decision support in most settings.

The implications for crop modeling are significant. Crop simulation models, which are used worldwide to forecast yields, evaluate irrigation strategies, and assess climate change impacts, are notoriously sensitive to input weather data. Small biases in daily minimum temperature can alter calculations of growing degree days and chilling requirements, while errors in solar radiation propagate directly into photosynthesis and evapotranspiration estimates. Previous work by Hoogenboom and colleagues, including earlier evaluations of NASA satellite-derived temperature and radiation data over the continental United States, laid the groundwork for using such datasets in agricultural applications. The new study extends that legacy by testing the modern POWER product across a far broader range of topographic and climatic settings, from the humid subtropics of Florida to the complex mountain terrain of Türkiye.

That topographic diversity is precisely where the study’s cautionary findings emerge. The researchers found that the dataset’s performance for relative humidity, wind speed, and precipitation is strongly controlled by local geography. In coastal areas, land-sea breeze circulations and marine atmospheric layers introduce discrepancies that the gridded satellite and reanalysis data struggle to capture. NASA POWER produces values on a grid, essentially averaging conditions over cells of finite size, and these spatial averages smooth over the sharp gradients that occur where land meets ocean. A weather station a few hundred meters from the shoreline can experience humidity and wind regimes that differ substantially from the grid-cell average, producing errors that would surprise a user relying on the dataset without checking it first.

High-relief terrain presents a parallel problem that atmospheric scientists call topographic smoothing. When elevation changes dramatically within a single grid cell, as it does across the mountain ranges of Anatolia or the slopes of Washington State’s Cascades, a single gridded value cannot represent the true spread of conditions from valley floor to ridge crest. The dataset’s estimates regress toward the mean of the cell, underestimating the extremes that stations actually record. This phenomenon, well documented in studies of gridded precipitation in complex terrain, means that hydrologists and agronomists working in mountainous regions should treat NASA POWER outputs for precipitation, humidity, and wind with particular skepticism, or apply bias correction before feeding them into models.

The practical value of the study lies in the regional performance thresholds it establishes. Rather than issuing a blanket verdict on the dataset, the authors provide what amounts to a decision guide: researchers can look up their variable of interest and their terrain type, and determine in advance whether raw NASA POWER data is adequate or whether bias correction is necessary. This distinction matters because bias correction is not free. It requires access to local observations, adds processing steps, and introduces its own uncertainties. Knowing that temperature and radiation can typically be used as-is, while coastal precipitation may demand correction, saves both computational effort and scientific risk. In an era when open-access geospatial datasets are increasingly substituted for sparse station networks, such interoperability benchmarks become essential infrastructure for environmental monitoring.

The validation design itself reflects a growing awareness that dataset quality assessments must span decades, not just single seasons. By drawing on records of 15 to 43 years from networks including the Florida Automated Weather Network, the Georgia Automated Environmental Monitoring Network, Washington State University’s AgWeatherNet, and the Turkish State Meteorological Service, the study captures the full range of interannual variability, El Niño and La Niña cycles, heat waves, and drought years that any operational dataset must handle. A dataset that performs well only in average years is of limited use for the extreme events that matter most to farmers and water managers. The multi-decadal approach also guards against the possibility of agreement arising by coincidence in a short window of unusually stable weather.

The research also situates NASA POWER within a crowded field of gridded climate products, including ERA5, MERRA-2, CRU TS, and CHIRPS, each with its own strengths and known weaknesses. What distinguishes POWER is its accessibility: it is free, requires no registration, and delivers daily agroclimatic variables in formats that plug directly into widely used crop models. The authors’ benchmark gives the global community of agricultural modelers, many of whom work in data-sparse regions of Africa, Central Asia, and South America, a defensible basis for choosing POWER, and a clear-eyed understanding of where it will need help. As climate change intensifies the demand for reliable weather information in agriculture, studies like this one transform a free dataset from a convenient substitute into a rigorously characterized scientific instrument.

Subject of Research: Multi-decadal validation of the NASA POWER gridded meteorological dataset against ground weather stations in the United States and Türkiye for climatology and agricultural modeling applications

Article Title: Validating NASA POWER meteorological data for applied climatology and agricultural modeling: a multi-decadal assessment across diverse topographies in the US and Türkiye

Article References: Gürkan, H., & Hoogenboom, G. (2026). Validating NASA POWER meteorological data for applied climatology and agricultural modeling: a multi-decadal assessment across diverse topographies in the US and Türkiye. Theoretical and Applied Climatology, 157(10), Article 621. https://doi.org/10.1007/s00704-026-06532-0

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06532-0

Keywords: NASA POWER, gridded meteorological data, weather station validation, applied climatology, agricultural modeling, crop simulation, solar radiation, temperature data, precipitation accuracy, topographic effects, bias correction, Türkiye