The model at the heart of the study, called GLOBGM v1.1, is a two-layer, transient groundwater flow model built on a parallelized prototype of the widely used MODFLOW6 code. To make a global simulation at 30-arcsecond resolution computationally feasible, the researchers split the planet into four independent but coupled continental-scale models covering Afro-Eurasia, the Americas, Australia, and the remaining islands. Each of these is further partitioned into non-overlapping sub-models that are solved together within the MODFLOW linear solver. The two-layer design allows the model to represent unconfined, confined, and semi-confined aquifer systems: where a confining layer exists, the upper layer represents that barrier and the lower layer the pressurized aquifer beneath it. Forcing data, including runoff, recharge, and groundwater abstraction, come from the global hydrological model PCR-GLOBWB2, resampled from its native 5-arcminute grid down to the finer GLOBGM grid.

Two technical innovations distinguish this version from its predecessor. The first is a dynamic drainage elevation routine, in which the elevation at which water drains from the groundwater system adjusts according to soil saturation. As soils become wetter, the drainage level rises toward the surface; as they dry, it drops. This coupling, based on the improved ARNO scheme for identifying saturated areas, keeps simulated water levels closer to the surface in wetland regions and suppresses unrealistic head fluctuations and intermittent discharge events. The second is a statistical downscaling of groundwater recharge. Because PCR-GLOBWB2 is known to underestimate recharge, particularly in arid and semi-arid regions where recharge is low and episodic, the team used a Generalized Additive Model trained on observed recharge data to produce a kilometer-scale recharge field, then applied a multiplicative correction factor to the model’s monthly recharge inputs, capped at local precipitation to preserve numerical stability.

Calibration was a major undertaking. The team tested 162 permutations of prefactors applied to hydraulic conductivity, anisotropy, and entrance resistance, varying the conductivity adjustments by lithological class drawn from the global GLiM dataset. Candidate settings were evaluated against long-term average water table depths from 34,800 observation wells in IGRAC’s Global Groundwater Monitoring Network, deliberately excluding known groundwater abstraction hotspots so that depletion would not be confused with model error. The best-performing configuration reduced the mean bias in water table depth from minus 4.8 meters in the uncalibrated predecessor to 3.6 meters, with the depth-weighted bias falling from 34.2 to 32.5 meters. The improvements were most pronounced for shallow water tables, which matter most for human use and for sustaining ecosystems through baseflow.

Validation against independent observations showed that the model produces skillful predictions, measured by a non-parametric Kling-Gupta Efficiency skill score, in approximately 83 percent of shallow to intermediate wells with water tables between zero and 60 meters deep when compared at the monthly scale, and in about 60 percent of deep wells exceeding 60 meters. At the annual scale, 71 percent of shallow wells and 66 percent of deep wells were skillful. The depth dependence is physically sensible: deeper aquifers respond slowly to recharge through thick unsaturated zones that the model does not explicitly resolve. To check performance at scales where point observations are absent, the team compared simulated total water storage anomalies with satellite gravimetry from the GRACE and GRACE-FO missions, finding agreement in trend direction over 62.3 percent of the global land surface and agreement in both direction and magnitude over 45.7 percent, consistent with other state-of-the-art global hydrological models.

The researchers also added a machine learning bias correction. A two-stage LightGBM ensemble, one classifier predicting whether the model overestimates or underestimates groundwater heads and one regressor predicting the magnitude of the error, was trained on nearly 97,000 observation wells using model parameters, Google’s AlphaEarth geospatial embeddings, and absolute latitude as a proxy for aridity. Training and testing were separated spatially using K-means clustering to prevent autocorrelation from inflating performance. The correction correctly predicted the direction of bias in 80 percent of holdout cases and shifted median bias toward zero across all depth classes, with the largest gains in deep wells. An asymmetric ramp-factor scheme prevents the correction from pushing simulated water tables unrealistically close to the land surface, preserving the model’s physics where it matters most.

The historical simulation from 1960 to 2019, driven by the ISIMIP3a observational forcing, reproduced the world’s best-documented groundwater depletion zones with striking fidelity: the southern and central High Plains of the United States, Iran, the Arabian Peninsula, and the Indo-Gangetic Plain all show the deepening water tables that independent observations have recorded. The model also identified rising water tables in regions known to be gaining storage, including the Guarani aquifer in South America, the northern Great Plains of North America, and the Karoo Basin in South Africa. Intriguingly, it flagged rising water tables across northern latitudes and the Arctic, potentially linked to climate-driven increases in recharge, though the authors caution that these areas overlap with permafrost zones where the model is known to be less reliable.

The future projections, run with five global climate models under three combined socioeconomic and climate scenarios from ISIMIP3b, paint a nuanced picture. Globally, more regions are projected to see rising than falling water tables through 2100, consistent with an intensifying hydrological cycle, and the pace of change increases from the low-emission SSP1-RCP2.6 scenario to the high-emission SSP5-RCP8.5. Water tables are projected to rise across much of the Sahel, the Horn of Africa, North America’s Pacific Coast, the Tibetan Plateau, and the Arabian Peninsula, while declines are projected for the Mediterranean, southeastern Australia, and the African and South American tropics. Europe stands out as the sole continent where water tables are expected to deepen markedly, particularly under high emissions. Known depletion hotspots are expected to persist, and the model projects continued declines over southern Europe, the Amazon, California’s Central Valley, and the High Plains, diverging from earlier studies that omitted groundwater abstraction, a difference the authors attribute to the inclusion of human water demand.

The team is transparent about the model’s limits. Simulations are flagged as less reliable in karst aquifers, where Darcian flow assumptions break down; in permafrost regions, where frozen ground complicates recharge; and in steep mountainous terrain, where the schematization is too coarse to resolve hillslope hydrology. Quality assurance maps accompany the dataset so users can interpret results appropriately. All simulations, along with the model code, are openly accessible, and because the outputs follow the ISIMIP framework, they can be combined with other Earth system datasets to study how changing groundwater affects ecosystems, food production, and society. In a world where the United Nations has warned of an era of global water bankruptcy, a kilometer-scale, globally consistent view of where water tables are falling and rising may prove one of the most consequential datasets yet produced for planning a sustainable water future.

Subject of Research: Global hyper-resolution modeling of historical and future groundwater dynamics at approximately 1 km resolution

Article Title: Global hyper-resolution modeling of historical and future groundwater dynamics

Article References: van Jaarsveld, B., Wanders, N., Otoo, N. G., Sutanudjaja, E. H., Verkaik, J., Zamrsky, D., & Bierkens, M. F. P. (2026). Global hyper-resolution modeling of historical and future groundwater dynamics. Earth System Dynamics, 17(5), 1201-1236. https://doi.org/10.5194/esd-17-1201-2026

Image Credits: AI Generated

DOI: 10.5194/esd-17-1201-2026

Keywords: groundwater, hyper-resolution modeling, GLOBGM, water table depth, climate change, groundwater depletion, MODFLOW, ISIMIP, machine learning bias correction, GRACE satellite, SSP-RCP scenarios, water resources management

The study, published in the journal Environmental Challenges, tackled a deceptively simple question: is a given grassland on peat soil shallow-drained or deep-drained? The answer matters enormously for climate accounting. Under guidelines from the Intergovernmental Panel on Climate Change, drainage status is defined by the mean annual water table depth. If the water table sits, on average, less than 30 centimetres below the surface, the site is classed as shallow-drained; at 30 centimetres or deeper, it is deep-drained. Each class carries a different emission factor, so national greenhouse gas inventories depend on knowing which category applies where. Ireland’s inventory currently estimates around 339,130 hectares of grassland peat soils, of which roughly 141,000 hectares are believed to be deeply drained, but the spatial detail behind those figures remains coarse.

Traditional monitoring relies on dipwells, perforated pipes sunk into the soil and fitted with pressure loggers that record water levels every 15 minutes. These instruments are accurate but expensive to install and maintain, and they only describe the ground immediately around each sensor. Extrapolating from a handful of points across an entire farm, let alone a whole country, invites error. Remote sensing promised a way out, yet satellite imagery is frequently blocked by Ireland’s stubborn cloud cover and its resolution, typically around 10 metres, is too coarse to resolve the drainage ditches and microtopography that govern water movement in peat.

The researchers, led by Charmaine Cruz of Dublin City University together with colleagues from Teagasc and other Irish institutions, turned to unoccupied aerial vehicles flying below the clouds. Their platform was a DJI Matrice 350 RTK carrying two sophisticated instruments: a MicaSense Altum-PT sensor capturing five multispectral bands plus thermal imagery sensitive to temperature differences of just 0.05 degrees Celsius, and a DJI Zenmuse L2 LiDAR system capable of building centimetre-scale three-dimensional models of the ground surface. Flights at altitudes of 100 to 120 metres, with 80 percent overlap between images, were conducted twice at each site, once in summer and once in winter, to capture the seasonal extremes between which the mean annual water table lies.

Two contrasting commercial farms in County Offaly served as the proving grounds, roughly 10 kilometres apart. The Clara site, about 97 hectares of intensively grazed dairy grassland, borders a natural raised bog. The Tumbeagh site, around 27 hectares of ungrazed, unimproved grassland, contains heterogeneous fen peat with cutover margins and borders a state-owned industrial bog previously used for peat extraction. Both farms carry a legacy of drainage ditches of varying depth and condition. Peat depth, probed on a 15 by 15 metre grid with fibreglass rods and verified with soil corers, ranged from zero to over five metres at both sites, revealing dramatic lateral transitions from mineral ground to deep organic soil.

On the ground, the team installed 12 dipwells at Tumbeagh and 16 at Clara, each fitted with a submersible pressure transducer logging water levels every 15 minutes, and supplemented these with 10 older dipwells per site. Tipping-bucket rain gauges recorded rainfall at the same temporal resolution. The summer surveys followed dry spells, with just 7.4 and 10.0 millimetres of rain in the preceding fortnight, while winter surveys followed 65.4 and 82.8 millimetres. As expected, water tables plunged in summer, at some mineral-soil dipwells reaching more than two metres down, and rose in winter, occasionally breaking the surface.

From the drone data the researchers generated 15 candidate explanatory variables: the five calibrated reflectance bands, vegetation indices such as NDVI, EVI2, NDWI and MSAVI2, a thermal index, a LiDAR-derived digital terrain model with slope and topographic wetness index, distance to mapped open drains, and interpolated peat depth. A random forest regression model, implemented in Python’s scikit-learn library, learned the relationship between these proxies and the dipwell measurements, then predicted water table depth across every pixel of each farm. Model performance, assessed through the out-of-bag score, ranged from 0.66 to 0.84, with the strongest performance at the Clara site in summer.

The variable importance rankings delivered the study’s most striking insight. In summer, peat depth dominated predictions at both sites, with importance scores of roughly 0.30 at Tumbeagh and 0.23 at Clara, far ahead of any other variable. Deeper peat consistently coincided with shallower summer water tables. In winter, however, the hierarchy shifted: topography took the lead at Tumbeagh while surface temperature became the top predictor at Clara, with peat depth slipping to second or third place. This seasonal flip means a model trained on one season’s imagery cannot simply be reused for another, underscoring the value of the two-survey approach the team adopted.

When the summer and winter predictions were averaged and classified against the IPCC threshold, the verdict was sobering. At Tumbeagh, every field fell into the deep-drained category, with 91 percent of the peat area showing mean annual water tables between 30 and 60 centimetres. At Clara, 94 percent was deep-drained, though a small 2-hectare zone near restored peatland and redundant drains qualified as shallow. The classification matched an independent year-round hydrological assessment of the same farms, suggesting that two well-timed drone flights can substitute for continuous monitoring where resources are limited. Notably, the model assigned drainage status even to fields without dipwells, extending coverage beyond what ground measurements alone could achieve.

The implications stretch from carbon farming schemes to European Union reporting obligations. Field-scale drainage maps could help regulators prioritise rewetting projects, guide farmers toward lower-intensity management on the wettest ground, and feed into future Tier 3 national inventory methods that demand spatially explicit emission estimates. The approach has limits: battery life, line-of-sight rules and altitude restrictions confine drone surveys to a few square kilometres, so national mapping would still require satellite imagery, likely with separate models for fens, raised bogs and blanket bogs whose distinct vegetation confounds transferable predictions. The authors also point toward hyperspectral sensors, ground-penetrating radar and deep learning as the next frontier. For now, the message is clear: the hidden plumbing of Ireland’s peat grasslands, long inferred from sparse points on a map, can finally be seen in full, one centimetre-scale pixel at a time.

Subject of Research: Mapping water table depth and drainage status of grassland peat soils using UAV remote sensing and machine learning

Article Title: Determination of drainage status at varied grassland peat soil sites using a combination of multi-sensor UAV technology and on-the-ground measurements

Article References: Cruz, C., Fenton, O., Bari, M. I., Broderick, E., Daly, E., Donoghue, M., Fealy, R. M., Green, S., McCarthy, E., O'Sullivan, L., Shnel, A., Tuohy, P., & Connolly, J. (2026). Determination of drainage status at varied grassland peat soil sites using a combination of multi-sensor UAV technology and on-the-ground measurements. Environmental Challenges, 25, Article 101667. https://doi.org/10.1016/j.envc.2026.101667

Image Credits: AI Generated

DOI: 10.1016/j.envc.2026.101667

Keywords: peat soils, water table depth, UAV remote sensing, LiDAR, random forest, greenhouse gas emissions, drainage status, Ireland, grassland, machine learning, carbon emissions, rewetting

The research team, led by Charuni Jayasekara of RMIT University together with colleagues at the University of Melbourne, Queensland University of Technology, the Queensland Herbarium and La Trobe University, conducted a systematic quantitative literature review following established PRISMA-style protocols. From an initial pool of 341 articles identified through databases including Web of Science, Scopus and Google Scholar, the authors applied strict inclusion criteria: only field-based studies reporting annual net ecosystem exchange values, meaning full-year measurements or repeated seasonal measurements extrapolated to a complete year, qualified for analysis. Plot-scale studies smaller than ten square meters, laboratory experiments and short-term snapshots were excluded to ensure that every data point represented the carbon balance of a functioning ecosystem at policy-relevant temporal scales. The final dataset of 58 articles and 120 sites spans both hemispheres, every continent except Antarctica, and measurement records ranging from a single year to an exceptional 17-year series.

The quantitative picture that emerges is one of dramatic divergence between land uses. Peatlands under their original, undrained land use were, on average, net carbon sinks absorbing about 30 grams of carbon per square meter per year, while agricultural peatlands emitted a mean of 490 grams of carbon per square meter per year, a statistically significant difference. Among agricultural categories, tropical plantations were the worst offenders, averaging 1,132 grams of carbon emitted per square meter annually, with the single highest recorded emission of 3,758 grams coming from an oil palm plantation established on drained tropical peat. Pastures and croplands each emitted roughly 420 grams per square meter per year. Peat extraction sites were also net sources, though less extreme, at around 137 grams. The underlying mechanism is well understood: drainage lowers the water table, exposing previously saturated peat to oxygen and accelerating aerobic microbial decomposition of organic matter that took centuries to accumulate, while the removal of natural vegetation simultaneously eliminates the photosynthetic uptake that once offset respiration.

Peatland condition told a parallel story. Degraded sites, about 55 percent of the dataset, averaged net emissions of 306 grams of carbon per square meter per year, significantly higher than intact sites, which averaged a net uptake of 124 grams. But the most consequential finding concerns restoration. Of the 18 restored sites in the analysis, the category as a whole remained a net carbon source, averaging 110 grams of emissions per square meter per year, and 39 percent of individual restored sites were still emitting carbon dioxide at the time of measurement. The authors attribute this to insufficient recovery time, incomplete hydrological recovery, legacy effects of severe degradation, and in some cases restoration methods that prioritize biodiversity or hydrological objectives over immediate carbon benefits. Severely degraded sites that have lost substantial peat may require many decades to regain sink function, and some may never return to it.

The single most powerful predictor of carbon exchange across all peatland types and climatic zones was water table depth. Across 98 sites with paired hydrological data, the regression analysis revealed that every centimeter of water table drawdown increases carbon dioxide emissions by approximately 7.2 grams of carbon per square meter per year. The relationship explained about 19 percent of the variance in net ecosystem exchange and, critically, identified a transition point: peatlands shift from net sink to net source when the water table drops to roughly 7.8 centimeters below the peat surface. This figure aligns closely with hydrological tipping points reported in other experimental studies, which place the threshold between about 14 and 24 centimeters depending on peatland type. The finding carries direct policy weight, because it converts an abstract ecological principle into a concrete management target: keep the water table within a few centimeters of the surface, and the peatland keeps functioning as a carbon bank; drain it, and the stored carbon begins flowing into the atmosphere.

Climatic geography added further nuance. Tropical peatlands were the strongest mean net sources, emitting on average 270 grams of carbon per square meter per year, and showed by far the greatest variability, a reflection of the intense land-use pressure they face, particularly in Southeast Asia where peat swamp forests are being rapidly converted to plantations. Alpine and sub-polar peatlands, by contrast, were the strongest sinks, absorbing roughly 34 to 37 grams per square meter per year. Temperate sites, which dominated the dataset at 77 percent of all locations, averaged as net sources overall, largely because so many European temperate peatlands have been drained for agriculture. Vegetation type mattered independently of land use: native shrub and grass systems and moss-dominated sites were mean sinks, while tree-dominated natural sites averaged as sources, and every agricultural vegetation category was a net emitter.

Perhaps the most sobering pattern in the data concerns the reliability of simple classification schemes. Nineteen percent of sites classified as intact were actually net carbon sources, while 21 percent of degraded sites were net sinks. This means that categorical labels based on land-use history or visual assessment cannot reliably capture the functional carbon status of a peatland. The authors point to several explanations: natural interannual variability means one or two years of measurement may misrepresent long-term trajectories; cryptic degradation through nitrogen deposition, invasive species or subtle hydrological shifts can erode sink function without visible structural change; and in some counterintuitive cases, vegetation changes following disturbance, such as dense tree establishment on a drained bog, can increase photosynthetic uptake enough to maintain or even strengthen net carbon sequestration despite lowered water tables.

The review also exposed a glaring geographic bias in the science itself. Eighty-six percent of the 120 sites lie in the Northern Hemisphere, and 60 percent are in Europe, with Germany, Denmark, Ireland and Latvia alone hosting dozens of studies. Only 14 percent of sites are in the Southern Hemisphere. Africa and South America contributed just three sites each, and Oceania six, despite these regions containing substantial, carbon-dense and highly threatened peatlands. The authors attribute the gap to technical, financial and political constraints on establishing long-term flux monitoring infrastructure, seasonal logistics in cold climates, and the likelihood that research from underrepresented regions exists in non-English or gray literature inaccessible to global syntheses. This underrepresentation is particularly troubling for tropical peatlands, which hold the largest share of global peatland carbon, accumulate carbon faster than their boreal counterparts, at rates of 59 to 145 grams per square meter per year, and face the fastest rates of conversion for agriculture and forestry.

Methodological fragmentation compounds the problem. The review identified 22 different software programs used to process carbon flux data, 15 for eddy covariance measurements alone, alongside fundamentally different measurement approaches: eddy covariance systems integrate fluxes over footprints of 100 to 1,000 square meters continuously, while chamber methods sample patches of less than one square meter and require spatial upscaling and temporal interpolation to estimate annual budgets. Different gap-filling algorithms, quality control protocols and chamber placement strategies introduce inconsistencies that may partly explain the enormous variability observed in restored peatland fluxes, which ranged from a sink of 397 to emissions of 1,170 grams of carbon per square meter per year. The authors argue that methodological standardization, combined with longer monitoring periods, since 43 of the 120 sites reported only a single year of data, is essential if net ecosystem exchange is to serve as a dependable restoration monitoring tool.

The implications extend from national greenhouse gas inventories to the growing portfolio of nature-based climate solutions. With approximately 65 million hectares, 16 percent of the world’s peatlands, already drained, and degraded peatlands responsible for roughly three percent of anthropogenic greenhouse gas emissions in 2020, the evidence assembled here supports a clear hierarchy of action: protect intact peatlands before they are lost, rewet drained ones with water table targets anchored to reference conditions from functioning sink systems, and commit to long-term monitoring and adaptive management rather than expecting rapid carbon payback from restoration. The authors also caution that raising water tables promotes methane production, meaning that the net climate benefit of any restoration project should be judged against the full greenhouse gas balance, not carbon dioxide alone. What the synthesis makes unmistakable is that the fate of hundreds of gigatonnes of soil carbon now hinges on a hydrological variable measurable with a ruler: centimeters of water above or below the peat surface.

Subject of Research: Global patterns and drivers of net ecosystem exchange in peatlands across land use and environmental gradients

Article Title: Global Patterns of Net Ecosystem Exchange in peatlands: A Systematic Review and Meta-analysis of Drivers Across Land Use and Environmental Gradients

Article References: Jayasekara, C., Leigh, C., Shimeta, J., Silvester, E., & Grover, S. (2026). Global Patterns of Net Ecosystem Exchange in peatlands: A Systematic Review and Meta-analysis of Drivers Across Land Use and Environmental Gradients. Environmental Management, 76(9), Article 310. https://doi.org/10.1007/s00267-026-02621-y

Image Credits: AI Generated

DOI: 10.1007/s00267-026-02621-y

Keywords: peatlands, net ecosystem exchange, carbon cycle, water table depth, peatland restoration, land use change, CO2 emissions, carbon sink, drainage, tropical peatlands, meta-analysis, nature-based climate solutions