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 research, conducted by Lindumusa Myeni and Nisa Ayob of North-West University, analysed daily climate records from ten weather stations spanning contrasting agroecological zones in the Free State, Gauteng, KwaZulu-Natal, Mpumalanga and North West provinces, together with district-level maize yield records spanning more than 30 growing seasons from 1993/94 to 2023/24. The five provinces together produce over 85 percent of South Africa’s maize. The authors computed rainfall- and temperature-based extreme climate indices following the Expert Team on Climate Change Detection and Indices framework, then applied Sen’s slope estimator and the Mann-Kendall test to detect trends, Pearson correlation to link extremes with yields, and stepwise multiple regression to identify the dominant climatic drivers of yield variability.

The headline finding is unambiguous warming. Mean seasonal air temperature increased from 0.02 degrees Celsius per annum at the wetter Lydenburg station to 0.06 degrees Celsius per annum at the semi-arid stations of Klerksdorp and Vryburg, while maximum daytime temperatures rose by 0.05 to 0.11 degrees Celsius per annum across all stations. More striking still, the frequency of very hot days increased by 0.19 to 0.50 percent per annum, with the largest increases recorded at Klerksdorp, Newcastle and Carolina. In mirror image, the frequency of extreme cold days declined by 0.10 to 0.29 percent per annum at most stations. Together these trends signal a clear shift toward hotter growing-season conditions, with peak heat intensifying faster than average temperatures, a pattern the authors argue underscores why extremes, not just means, must be monitored.

Rainfall told a subtler story. Total seasonal rainfall showed highly variable and statistically non-significant trends across all stations, ranging between minus 7.08 and plus 5.91 millimetres per annum, consistent with earlier national analyses that found no coherent long-term rainfall signal. But the texture of rainfall is changing. The number of rain days declined significantly at 40 percent of stations, including Bronkhorstspruit, Estcourt, Klerksdorp and Vereeniging, at rates of 0.55 to 1.16 days per annum, implying that rain is becoming less frequent but potentially more intense, with longer dry intervals between events. Consecutive dry days increased significantly only at Vereeniging, at roughly 19 additional days per decade, while heavy rainfall indices rose significantly only at Lydenburg, where more intense downpours raise risks of runoff, erosion and waterlogging on vulnerable soils.

Perhaps most consequential for crops is the trajectory of moisture balance. The Standardized Precipitation Evapotranspiration Index, or SPEI, which captures the competition between water supply and atmospheric demand, declined significantly at 30 percent of stations, including Estcourt, Klerksdorp and Newcastle, at rates of 0.03 to 0.05 per annum. This indicates that evapotranspiration, driven largely by rising air temperatures, is increasingly outpacing precipitation, deepening water stress and drought severity. For rainfed maize, which dominates production in these semi-arid environments, such trends translate directly into greater susceptibility to prolonged dry conditions, reduced soil moisture and unstable yields. The study also noted a significant decrease in minimum nighttime temperatures at Bloemfontein, raising frost risk in that district, a reminder that warming is not spatially uniform.

Maize yields themselves varied enormously across the study municipalities. Mean yields ranged from as low as 2.42 tonnes per hectare at Vereeniging and Bloemfontein to as high as 7.49 tonnes per hectare at Estcourt, with cooler, wetter districts such as Newcastle, Lydenburg and Carolina generally outperforming drier western areas like Vryburg, Klerksdorp and Bloemfontein. Yield stability differed just as sharply: coefficients of variation spanned from 29.24 percent at Vereeniging to 54.28 percent at Vryburg, with high values at Vryburg, Newcastle and Bloemfontein pointing to strong interannual fluctuations likely driven by erratic rainfall, dry spells and temperature extremes during critical growth stages.

The correlation analysis drew a clear line between specific extremes and yield outcomes. Maize yields correlated negatively with heat indices across all municipalities, with the strongest significant relationships in semi-arid regions: at Vryburg, mean seasonal temperature correlated at r equals minus 0.64 and very hot days at r equals minus 0.62, while at Klerksdorp the corresponding values were minus 0.52 and minus 0.48. At Bethlehem, very hot days correlated at minus 0.46. By contrast, cold-related indices showed weak or non-significant correlations at most stations, suggesting cold stress is far less influential than heat. On the moisture side, yields correlated positively with total rainfall, rain days and SPEI, with SPEI reaching r equals 0.76 at Klerksdorp and 0.68 at Vryburg, confirming moisture availability as the primary limiting factor in these water-scarce districts. Consecutive dry days correlated negatively with yields at Klerksdorp, reinforcing the damage inflicted by intra-seasonal drought.

Stepwise multiple regression then quantified how much of the yield variability climate extremes can actually explain. The explanatory power of the models ranged from weak at Lydenburg, where the coefficient of determination was just 0.12, to strong at Klerksdorp and Vryburg, where it reached 0.69, meaning climate variability accounted for up to 69 percent of interannual yield fluctuations in these water-limited regions. SPEI carried large, highly significant positive coefficients at Klerksdorp and Vryburg, leading the authors to propose the drought index as a practical early-warning indicator for maize production forecasting and risk assessment. At Estcourt and Newcastle, with coefficients of determination of 0.49 and 0.67 respectively, both temperature extremes and rainfall characteristics, including amount, frequency and intensity, shaped yields in more complex ways. Where model explanatory power was low, non-climatic factors such as soils, management and technology likely dominated.

The authors stress that these relationships are scale-dependent and that coarser provincial or national analyses can obscure localised impacts, which is why district-level assessment matters for crafting adaptation. Their recommendations diverge by zone: in hot, semi-arid areas, drought- and heat-tolerant seed varieties, adjusted planting dates, conservation tillage, mulching, residue retention, cover cropping, rainwater harvesting and supplementary irrigation offer the most promise, while in wetter regions the priority is managing rainfall distribution variability, mitigating heat stress and optimising planting calendars. Climate information services, seasonal forecasts and agrometeorological advisories, they argue, can help farmers anticipate risks and act proactively rather than reactively.

The study acknowledges limitations, including the coarse resolution of station data, seasonal indices that may miss extremes during critical phenological windows, and district-level yield records that mask local variation in soils, cultivars and management. Future work, the authors suggest, should integrate high-resolution climate projections, finer-scale yield and management data, and machine learning approaches capable of capturing nonlinear climate-yield relationships. With heat extremes intensifying and moisture availability declining across the maize belt, the message for policymakers is that uniform adaptation policies will fall short; resilience must be built district by district, informed by the specific climatic constraints each farming community faces.

Subject of Research: Long-term trends in extreme climate indices and their impacts on district-level maize yields in South Africa's summer rainfall region

Article Title: Long-term changes in the climate extremes and their impacts on maize yields in the summer rainfall region of South Africa

Article References: Long-term changes in the climate extremes and their impacts on maize yields in the summer rainfall region of South Africa. (n.d.). https://doi.org/10.1007/s00704-026-06550-y

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06550-y

Keywords: climate extremes, maize yields, South Africa, heat stress, drought, SPEI, rainfed agriculture, Mann-Kendall trend analysis, semi-arid regions, food security, adaptation strategies, agroclimatology

The four sites examined in the study are far more than arbitrary sampling points. Each receives effluent from a distinct urban and industrial catchment, and each has developed its own chemical fingerprint over the quarter-century of observation. Oued Moghogha has been consistently enriched in chromium, manganese, and nickel, a pattern the researchers link to industrial discharges typical of metal-processing and tanning activities in its watershed. Oued Negro stands out for copper, while Oued Souani is dominated by lead. The Rejet Fnideq site, near the coastal town of Fnideq, shows a signature enriched in cadmium and zinc, metals commonly associated with urban runoff, galvanized surfaces, and mixed domestic and commercial waste streams. The persistence of these distinct signatures across twenty-five years of sampling demonstrates how tightly sediment chemistry mirrors the character of the land use feeding each outfall.

Methodologically, the team layered several complementary analytical techniques. Descriptive statistics established baseline concentrations and variability for each metal at each site. Non-parametric trend analysis using the Mann–Kendall test, paired with Theil–Sen slope estimation, allowed the researchers to detect monotonic increases or decreases in contamination over time without requiring the data to follow a normal distribution—a crucial consideration for environmental monitoring data, which are frequently skewed and censored. Inter-metal correlation analysis, principal component analysis, and hierarchical cluster analysis then served as multivariate lenses, grouping metals that behave together and clustering years and sites that share similar contamination profiles. Finally, five forecasting models—a naive drift method, a linear trend model, the autoregressive integrated moving average approach known as ARIMA, and two machine learning algorithms, random forest and XGBoost—were trained on the historical records to project concentrations through 2030.

One of the most striking findings is the identification of a regime shift around 2016–2017. Before this transition, several metals, most notably lead and zinc, followed one trajectory; after it, they settled into a distinctly different pattern. A regime shift of this kind typically signals a structural change in the sources or transport of contaminants—perhaps altered wastewater treatment, changes in industrial activity, or shifts in sediment transport driven by rainfall and coastal dynamics. In the Tangier–Tetouan corridor, the shift affected lead and zinc most strongly, while chromium and manganese told the opposite story: they declined consistently across the monitoring period, suggesting sustained reductions in the inputs driving their enrichment.

The multivariate analyses sharpened the picture considerably. Principal component analysis revealed three coherent metal associations—chromium–manganese–nickel, cadmium–zinc, and a lead-dominated grouping—that map cleanly onto the site-specific contamination signatures. These associations are more than statistical convenience. Metals that co-vary in sediments often share common sources or similar geochemical behavior, so identifying a chromium–manganese–nickel cluster at one site and a cadmium–zinc cluster at another provides circumstantial evidence about where the contamination originates and how it might respond to changes in land use. Hierarchical cluster analysis of the annual observations similarly grouped years by sector rather than scattering them randomly, confirming that each monitoring location has maintained its own contamination identity throughout the entire record.

The forecasting results carry a clear practical message. Projections for 2026 to 2030 suggest that the post-2017 regime will persist: chromium, copper, manganese, and nickel are expected to continue declining or to stabilize, while lead and zinc are forecast to remain at sustained elevated concentrations at the affected sites. For environmental managers in the Tangier–Tetouan region, this asymmetry matters. It implies that whatever processes reduced the first group of metals are working, while the sources behind lead and zinc contamination either remain active or reflect legacy pollution locked in the sediment that will not dissipate on its own. Forecasting, in this sense, converts a monitoring archive into an early-warning tool.

Equally instructive is the study’s candid assessment of its own models. Counterintuitively, the simplest method—a naive drift forecast that extrapolates recent rates of change—performed best for most of the metals, while random forest was the preferred model for manganese. Iron forecasts proved unreliable across the board, likely because iron behaves largely as a crustal, geogenic element whose concentrations reflect natural sediment mineralogy more than anthropogenic inputs, making its temporal behavior noisy and difficult to predict from short annual series. The lesson echoes a broader finding in the forecasting literature: with limited observations and relatively smooth temporal dynamics, sophisticated algorithms do not automatically outperform simple baselines. Comparing multiple models, as this study did, is essential to avoid placing unwarranted confidence in any single projection.

The broader significance of the work lies in its demonstration that long-term, multi-site monitoring combined with statistical and machine learning tools can disentangle the complex dynamics of coastal pollution. Heavy metals in sediments are persistent by nature: unlike organic pollutants, they do not degrade, and they accumulate in fine-grained particles where benthic organisms can ingest them, entering food webs that extend to fisheries consumed by millions of people along the Mediterranean. Regional reviews of heavy metal pollution in Mediterranean sediments have repeatedly flagged urbanized coastal corridors like Tangier–Tetouan as hotspots, and earlier studies of Tangier Bay and the Tetouan coast documented concerning enrichment decades ago. What the new study adds is temporal resolution—the ability to say not merely that contamination exists, but whether it is improving, worsening, or holding steady, and for which metals and where.

For Morocco, where rapid coastal urbanization, tourism development, and industrial expansion continue to place pressure on the Mediterranean littoral, the findings arrive at a consequential moment. The forecast of persistent lead and zinc elevation suggests that targeted interventions—improved wastewater treatment at the responsible outfalls, control of urban runoff, or remediation of contaminated hotspots—would yield the greatest benefit at the Oued Souani and Rejet Fnideq systems. Meanwhile, the consistent declines in chromium and manganese offer tentative evidence that past management measures or changes in industrial discharge have had measurable effects. Sustaining the annual monitoring program that made these conclusions possible is itself a policy recommendation embedded in the research: without a continuous, standardized record, neither the 2016–2017 regime shift nor the site-specific fingerprints would ever have been detectable, and the machine learning forecasts would have had no foundation on which to stand.

The study also contributes methodologically to a growing global movement that treats environmental monitoring data as a resource for predictive modeling. As climate change alters rainfall patterns and sediment delivery, and as coastal cities across the Mediterranean and beyond continue to grow, the ability to forecast contamination trajectories—even with simple, transparent models—will become an increasingly important component of adaptive environmental management. The Tangier–Tetouan record, spanning a quarter century and four contrasting catchments, stands as a template for how such forecasting can be built from long-term fieldwork, rigorous statistics, and a healthy skepticism about complexity for its own sake.

Subject of Research: Long-term spatiotemporal analysis and machine learning forecasting of heavy metal contamination in Mediterranean riverine sediments in the Tangier–Tetouan region of Morocco

Article Title: Spatiotemporal dynamics and machine learning forecasting of heavy metal contamination in Mediterranean riverine sediments: a case study of the Tangier–Tetouan region, Morocco

Article References: El Hir, S., El Macouti, N. E. H., Maanan, M., Lagrat, I., & Ait Errouhi, A. (2026). Spatiotemporal dynamics and machine learning forecasting of heavy metal contamination in Mediterranean riverine sediments: a case study of the Tangier–Tetouan region, Morocco. Environmental Monitoring and Assessment, 198(10), Article 1065. https://doi.org/10.1007/s10661-026-15913-x

Image Credits: AI Generated

DOI: 10.1007/s10661-026-15913-x

Keywords: heavy metals, sediment contamination, machine learning, forecasting, Mediterranean, Morocco, Tangier, Tetouan, Mann–Kendall trend analysis, random forest, XGBoost, environmental monitoring