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: Multi-decadal variability and regime dynamics of atmosphere evaporative demand and climatic water balance in Tamil Nadu, India: implications for predictability. (n.d.). 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