As a Chinese Lake Shrinks, Its Wetlands Are Quietly Leaking Carbon

On the central Inner Mongolia Plateau, in a semi-arid transition zone where grassland gives way to desert, sits Daihai Lake — a closed basin with no outlet to the sea. For decades the lake has been shrinking, and with it the wetlands that ring its shores. A new study published in Environmental Monitoring and Assessment has now traced four decades of that ecological unraveling, and its findings carry a warning that extends far beyond one basin in northern China: as the lake retreats, the region is losing its capacity to store carbon in the soil, releasing a quiet but measurable pulse of greenhouse gas potential into the global balance sheet.

The research team, led by Mengqing Jiao and Rong Ma of the Institute of Hydrogeology and Environmental Geology at the Chinese Academy of Geological Sciences, reconstructed the evolution of the Daihai wetlands from 1984 to 2023. Their central conclusion is stark. Over the full study period, the wetlands followed what the authors describe as an expansion-then-rapid-contraction trajectory, ending in a net loss of 9.12 square kilometers of wetland area. More significantly, the soil organic carbon held in those wetlands declined by approximately 1.63 times ten to the fifth tonnes — roughly 163,000 tonnes of carbon that had been locked away in waterlogged soils and is now effectively lost from regional storage.

What makes the study technically interesting is not simply the accounting of loss, but the way the team disentangled the forces driving it. Wetland change in semi-arid basins is rarely the product of a single cause. Climate warming increases evaporation, drought reduces inflows, irrigation diverts water, grazing and agriculture reshape the land surface, and the underlying topography determines how sensitive any given shoreline is to a falling water table. To separate these threads, the researchers needed methods capable of handling both spatial complexity and temporal change — and they deployed an unusually sophisticated combination of them.

The first challenge was simply defining where the wetlands were, and how their outlines changed. Rather than relying on a single satellite index, which can confuse wetlands with adjacent irrigated fields or salt flats, the team used multi-source remote sensing indices drawn from Landsat imagery processed on the Google Earth Engine platform. Landsat’s archive, stretching back to the early 1980s, provides the long temporal baseline that shorter-record sensors cannot match. But mapping wetland boundaries over forty years introduces a subtle problem: how do you verify that a boundary drawn by an algorithm in 1984 is comparable to one drawn in 2023, when the entire landscape has shifted?

The team’s answer was to borrow a tool from computer vision. The shape context algorithm, originally developed for object recognition in images, describes the geometry of a shape by measuring the distribution of points relative to one another. By applying shape context matching — combined with the Hungarian method for optimal assignment, a classic algorithm from operations research — the researchers could quantitatively compare wetland outlines across different epochs, validating that their delineations tracked genuine boundary evolution rather than mapping artifacts. It is a striking example of methodological cross-pollination: a technique designed to help machines recognize handwritten digits and objects in photographs now helps hydrogeologists audit the retreat of a lake’s ecological margin.

With the wetland boundaries established, the team turned to attribution. They employed an optimized version of Geodetector, a statistical framework widely used in Chinese geoscience for measuring how well a candidate explanatory variable accounts for spatial variation in an outcome. Geodetector’s strength is that it makes no assumption of linearity; it detects whether the spatial stratification of one variable — say, population density or slope — matches the spatial stratification of another, such as wetland extent. The optimization allows the model to search over parameter choices rather than fixing them in advance, reducing the risk that a true driver is missed simply because the analysis window was poorly specified.

The results reveal a clear regime shift in what has been controlling the wetlands’ fate. In the earliest phase of the record, topographic factors dominated: the shape of the lake basin itself, with its gentle or steep gradients, determined where wetlands could expand or had to retreat as water levels changed. During the middle period, human activities intensified and became the leading driver, consistent with the broader literature on closed lakes in China’s semi-arid agro-pastoral regions, where expanding irrigation has been shown to accelerate the shrinkage of terminal lakes. By the final stage, no single factor prevailed; instead, the study found multi-factor coupling, with climate, terrain, and anthropogenic pressures acting in combination. This progression — from geology to humanity to interaction — is itself a finding, suggesting that management responses calibrated to a single era’s drivers will quickly become obsolete.

The carbon story adds a second, deeper layer. Wetland soils are among the most carbon-dense on Earth, because waterlogging slows the decomposition of plant matter, allowing organic carbon to accumulate over centuries. To estimate how much carbon the Daihai wetlands held and lost, the team conducted in-situ soil sampling and built a soil organic carbon density-depth model, a standard approach that combines measured carbon concentrations with bulk density and depth intervals to compute stocks per unit area. Applying this model across the mapped wetland area through time revealed a non-linear, two-stage pattern mediated by wetland area itself. Notably, lake area and wetland soil organic carbon stock diverged before 2001 — the lake and its carbon reservoir were not moving in lockstep — before the sustained contraction of the lake drove both downward together.

That non-linearity matters for how we project the future. If carbon losses scaled simply with area, the remedy would be straightforward: preserve area, preserve carbon. But the Daihai record shows the relationship is mediated — the carbon storage capacity of the remaining wetlands weakens as the lake shrinks, meaning each additional hectare lost may carry a disproportionate carbon cost. This finding resonates with recent global research showing that while wetland restoration is effective, it is often insufficient to fully compensate for the soil organic carbon lost during degradation. Carbon accumulated over centuries cannot be rebuilt on management timescales, which makes every avoided loss more valuable than an equivalent restored gain.

Daihai is not an isolated case. Inner Mongolia’s lakes have been shrinking broadly over recent decades, with studies attributing the trend to combined climatic warming and human water use, and global assessments have documented rapid groundwater declines that threaten closed-basin lakes worldwide. What the Daihai study contributes is a full causal and carbon accounting for a single, well-instrumented basin — a template for how other shrinking lake systems, from the Aral region to the closed basins of the American West, might be assessed. The practical implications are pointed. Protecting the remaining wetland margin around Daihai requires addressing the drivers of the current stage, which the study identifies as coupled rather than singular — meaning water management, land use policy, and climate adaptation must be coordinated rather than pursued in isolation. And for the carbon accountants compiling national inventories, the message is that shrinking lakes are not merely a water story; they are a slowly unfolding carbon story, written in the soils left behind as the water withdraws.

Subject of Research: Driving mechanisms of wetland evolution and soil organic carbon loss under the shrinkage of Daihai Lake, an endorheic lake on China's Inner Mongolia Plateau

Article Title: An investigation into the driving mechanisms of wetland evolution under the influence of Daihai Lake’s shrinkage

Article References: Jiao, M., Ma, R., Jiang, R., Dong, W., & Zhao, L. (2026). An investigation into the driving mechanisms of wetland evolution under the influence of Daihai Lake’s shrinkage. Environmental Monitoring and Assessment, 198(11), Article 1178. https://doi.org/10.1007/s10661-026-16016-3

Image Credits: AI Generated

DOI: 10.1007/s10661-026-16016-3

Keywords: Daihai Lake, wetland evolution, soil organic carbon, lake shrinkage, remote sensing, Geodetector, endorheic lake, Inner Mongolia, carbon storage, semi-arid climate, land use change, Environmental Monitoring and Assessment