The crux of the problem is spatial heterogeneity. Conventional evapotranspiration models were developed for agricultural fields and large-scale hydrological budgets, where vegetation is comparatively uniform. A bioretention basin is nothing of the sort. Within a few meters, plant height can jump from bare ground to four-meter shrubs, buildings and highway walls cast shifting shadows, and soil moisture swings dramatically between the flooded basin floor and its drier flanks. Because evapotranspiration, or ET, is governed by leaf area, solar radiation, vapor pressure deficit, and soil water availability, this patchiness propagates directly into uncertainty. Reported estimates of how much stormwater ET removes from green infrastructure range from nearly zero to almost 90 percent, a spread so wide it borders on useless for design purposes.

To pin down what is actually happening, the team spent a growing season measuring ET directly at eleven fixed plots within the Philadelphia basin using the closed dynamic chamber method. Transparent chambers were sealed over collars set into the soil, and a laser-based gas analyzer recorded water vapor accumulation once per second, allowing the researchers to calculate fluxes from the rate of buildup. Measurements were taken five to six times per day across seven dates from June through October, deliberately spanning dry spells and post-storm conditions. After discarding about 17 percent of records for quality reasons, 302 usable flux measurements remained, each tied to a specific combination of plant stature, topographic position, and microclimate.

The spatial context came from an unusual pairing of technologies. Terrestrial LiDAR scanning produced a bare-earth digital elevation model of the basin, while drone-based photogrammetry yielded a digital surface model of everything above it; subtracting the two mapped vegetation height across the site. To capture shading from the highway wall, nearby buildings, and a billboard, the researchers used a regional aerial LiDAR dataset and the Area Solar Radiation calculator in ArcGIS to compute shade coefficients for every pixel on every day of the study. These coefficients, typically between 0.75 and 0.95, scaled the measured solar radiation to reflect how much light actually reached each patch of ground. The basin was then segmented into 967 roughly plant-sized polygons using an image-clustering algorithm, and all environmental variables were aggregated to that scale.

With these ingredients, the team built a statistical model of ET that explicitly incorporated plant height, topographic position, shade, vapor pressure deficit, solar radiation, and soil moisture, complete with interaction terms and random effects for plot and date. The model explained most of the observed variation, with fixed effects alone accounting for 65 percent of variance. Its coefficients told a coherent physiological story: ET rose strongly with solar radiation and vapor pressure deficit, was lower on the drier upper flanks of the basin, and increased with plant height, reflecting greater leaf area per unit ground. Interestingly, the effect of radiation weakened in wetter soils, likely because near-saturated conditions restrict both evaporation and plant transpiration, while the effect of moisture on ET differed between the basin floor and its slopes.

Scaling the model across the whole basin revealed daily ET ranging from zero to about 6 millimeters per day, peaking in early and midsummer and declining to roughly 1 millimeter per day by mid-autumn. The clearest spatial pattern was elevated ET beneath the tallest vegetation, particularly a cluster of shrubs hugging the highway wall. Just as telling was what the sensitivity analysis showed. Setting all plant heights to zero reduced season-wide ET by 41 percent, while assigning every segment a uniform 100-centimeter height inflated it by 60 percent; even using the basin’s mean height of 36 centimeters everywhere depressed the total by 13 percent. Shade mattered too: removing all structural shading boosted seasonal ET by 11 percent, while assuming only a quarter of incoming radiation reached the basin cut it nearly in half. Topographic position, by contrast, had little effect on the basin-wide total.

How did the conventional models fare? The team ran six widely used equations, including the two Penman-Monteith variants endorsed by the American Society of Civil Engineers and the FAO, Priestley-Taylor, Hargreaves-Samani, Matt-Shuttleworth, and Granger-Gray. Most overpredicted ET on average. The Penman-Monteith, Priestley-Taylor, and Hargreaves-Samani models consistently overestimated relative to the empirical estimates, while Matt-Shuttleworth and Granger-Gray, which were less responsive to atmospheric conditions, overpredicted at the low end of the range and underpredicted at the high end. Digging into the discrepancies, the researchers found that differences between Penman-Monteith and empirical estimates grew systematically with wind speed, explaining up to 39 percent of the bias for the ASCE variant, whereas accounting for soil-water limitation made little difference; water stress was infrequent during the study period.

The practical payoff came from testing three correction strategies. Applying shade coefficients to the solar radiation inputs improved agreement for the Penman-Monteith and Priestley-Taylor models. Multiplicative landscape coefficients, analogous to crop coefficients in agriculture and calculated here at roughly 0.5 to 1.0, also helped those same models. But the strongest results came from additive corrections: simply subtracting a regression-derived offset from Penman-Monteith and Priestley-Taylor estimates raised concordance correlation coefficients to about 0.88 or 0.89, a dramatic improvement. The caveat is that additive correction can produce negative daily ET values when uncorrected estimates are small, so it must be used with care. None of the corrections improved the already-decent Matt-Shuttleworth and Granger-Gray estimates, and the Hargreaves-Samani model remained the poorest performer regardless of adjustment, a notable finding given that some reviews have recommended it for its minimal data requirements.

The implications ripple outward from one Philadelphia basin. For designers of similar systems, the message is that Penman-Monteith and Priestley-Taylor, once corrected, can credibly represent ET in water balances used for regulatory compliance and volume-reduction credit, rather than being ignored or trusted blindly. The study also suggests a design lever: because plant height and shade dominate basin-scale ET, landscape architects can deliberately position larger plants in unshaded locations to boost atmospheric water removal, provided the coarse, fast-draining soil media can supply enough moisture between storms. Deep-rooted shrubs and trees may be particularly valuable here, drawing water from a broader depth range during dry spells.

The authors are candid about limitations. Their empirical model was calibrated on a single basin, and ET from the tallest plants, which covered only about 10 percent of the area but exerted outsized influence, required extrapolation beyond the height of the flux chambers. Microclimatic variables other than solar radiation were assumed uniform across the basin, and lacking inflow data, the team could not compute ET’s share of total stormwater removal. Still, the basin contains many features common to bioretention design everywhere, and the growing availability of high-resolution LiDAR, drone imagery, and weather data makes locally calibrated corrections increasingly feasible. As cities pour billions into green stormwater infrastructure to combat sewer overflows and climate-driven downpours, this study delivers a refreshingly actionable insight: the atmosphere’s share of the water budget is measurable, modelable, and, with a few well-chosen coefficients, finally predictable.

Subject of Research: Spatial heterogeneity in evapotranspiration estimates for urban bioretention basins

Article Title: Incorporating spatial heterogeneity into evapotranspiration estimates for bioretention basins

Article References: Caplan, J. S., Bouda, M., Salisbury, A. B., Alonzo, M., Nyquist, J. E., Toran, L., & Eisenman, S. W. (2026). Incorporating spatial heterogeneity into evapotranspiration estimates for bioretention basins. Hydrology and Earth System Sciences, 30(18), 6075-6093. https://doi.org/10.5194/hess-30-6075-2026

Image Credits: AI Generated

DOI: 10.5194/hess-30-6075-2026

Keywords: evapotranspiration, bioretention basins, green stormwater infrastructure, Penman-Monteith, spatial heterogeneity, urban hydrology, flux chambers, LiDAR, shade coefficients, landscape coefficients, Philadelphia, stormwater management

Land surface models are the bridge between the atmosphere and the ground in climate simulations. They calculate how sunlight, heat, and water move between the soil, the vegetation, and the air, and they feed those exchanges upward into the atmospheric component of a climate model. Historically, the models built for global circulation ran on grids tens to hundreds of kilometers wide, where a city was too small to matter. But computing power has changed the game. Global models are now pushing toward kilometer-scale grids, and regional models are running climate-length simulations over huge domains. At those resolutions, cities are no longer invisible specks; they are resolved features whose energy and water budgets can distort temperature, rainfall, and runoff if they are represented badly.

ORCHIDEE is a heavyweight in this world. It simulates coupled water, energy, and carbon cycles and serves as the land component of the IPSL climate model. Its architecture divides each grid cell into a mosaic of plant functional types, with separate soil columns for bare ground, high vegetation, and low vegetation. Until now, however, anything urban was simply assigned to the bare-soil category. That meant no reflective rooftops, no tall buildings stirring the wind, no concrete storing the afternoon sun, and no sealed asphalt blocking rain from soaking into the earth. The model could simulate a forest or a wheat field with sophistication, but a metropolis looked like a patch of exposed dirt.

The new scheme is deliberately parsimonious. Rather than adding an elaborate three-dimensional canyon geometry with separate energy budgets for roofs, walls, and roads, the team introduced a single urban tile, or PFT, into the existing mosaic. This tile carries urban-specific values for albedo, building height, thermal conductivity, and heat capacity, all of which can be prescribed from global or local datasets. The approach follows a lesson from the Urban-PLUMBER intercomparison project, which evaluated thirty urban land surface models and found that simpler slab schemes often outperform more complex ones, provided that vegetation, water availability, and imperviousness are represented realistically. Geometry, in other words, is not everything; what the surface is made of matters just as much.

The genuine novelty lies in how the scheme handles imperviousness. Most urban models treat sealed ground as a surface phenomenon: rain hits the pavement and runs off. Lalonde and colleagues went deeper, literally. Instead of splitting the urban tile into pervious and impervious sub-tiles, they modify the saturated hydraulic conductivity of the entire soil column beneath the city. Two options were tested. The first, called Urban1, slashes conductivity to ten percent of its reference value throughout the column, representing a heavily sealed city. The second, Urban2, scales the reduction linearly with the impervious fraction, so a city that is fifty-five percent sealed sees its conductivity cut proportionally. This single parameter change ripples through the whole hydrology: it suppresses infiltration, boosts surface runoff, reduces soil water storage, and limits the water available for evaporation, all through the model’s existing Richards-equation soil physics rather than any bolted-on machinery.

The thermal side of the scheme is equally consequential. Urban materials conduct and store heat very differently from soil. In grid cells where urban cover exceeds fifty percent, the model now prescribes a thermal conductivity of 3.24 watts per meter per kelvin and a heat capacity of 1890 kilojoules per cubic meter per kelvin, values borrowed from the Noah-MP land surface model. Compare that with the median thermal conductivity of natural soils in the study region, around 1.20 watts per meter per kelvin, and the physical implication is clear: cities soak up far more energy during the day and release it slowly through the night. That stored heat is the engine of the urban heat island, and the model now captures it.

To test the scheme, the team ran standalone simulations at twenty urban flux tower sites drawn from the harmonized Urban-PLUMBER dataset, spanning thirteen countries and climates from tropical Singapore to dry Phoenix and continental Europe. Each simulation was spun up for forty years until soil moisture and temperature reached equilibrium, then run over twelve to sixteen years of observations, with fluxes computed every thirty minutes. The benchmark was the old bare-soil treatment of cities. The verdict, measured by mean absolute error against tower observations, was unambiguous: the new Urban1 configuration reduced the average error in latent heat flux from 30.3 to 27.3 watts per square meter and in sensible heat flux from 41.9 to 36.9 watts per square meter across all sites. At sites with more than fifty percent impervious cover, the gains were larger still, with sensible heat error falling from 46.3 to 38.2 watts per square meter.

The detailed physics behind those numbers is telling. At the reference site of AU-Preston in Melbourne, the urban configurations reproduced the observed summer sensible heat flux far better than the old scheme, both during the day and at night. The improved daytime heat storage, driven by the higher thermal conductivity, left less energy to be dumped into the atmosphere as sensible heat, cutting a summer overestimation that reached a median bias of 77 watts per square meter in the bare-soil runs down to roughly 14 to 18 watts per square meter in the urban runs. Nocturnal sensible heat, which cities keep elevated by releasing stored warmth, also improved in both seasons. Not everything was fixed: daytime sensible heat remained underestimated in winter, and latent heat stayed generally underestimated in summer, likely because the scheme lacks active urban vegetation, irrigation, and anthropogenic heat from traffic and buildings, which can reach nearly 93 watts per square meter at highly urbanized sites like Seoul’s Jungnang district.

The hydrological results reveal both the promise and the limits of the new imperviousness parameterization. Under the strong Urban1 hypothesis, annual surface runoff roughly doubled at most stations, and the balance flipped so that surface runoff exceeded subsurface runoff at fifteen of the twenty sites, exactly the signature urbanization leaves on real catchments. But the moderate Urban2 option produced smaller and sometimes non-monotonic responses, and the simulated reduction in subsurface runoff sits awkwardly with observational studies showing that urban groundwater recharge can actually increase, thanks to leaking pipes and irrigation. The authors are candid that without runoff or soil moisture observations at the tower sites, the water-budget analysis remains a sensitivity assessment rather than a validation.

What comes next is where the story gets ambitious. The team plans convection-permitting simulations over Paris at resolutions below three kilometers, where cities are explicitly resolved and an inaccurate urban energy balance would feed wrong signals straight into the atmosphere. They also intend to add anthropogenic heat fluxes, refine the thermal parameters against measured data, embed active vegetation with seasonal phenology inside the urban tile, and exploit datasets like WUDAPT and the Copernicus Urban Atlas to make albedo, building height, and imperviousness vary across real cityscapes. Notably, most Earth system models contributing to CMIP still ignore cities altogether, a gap that becomes untenable as climate risk assessments increasingly focus on the built environment. A computationally cheap, physically grounded urban tile that works from the neighborhood scale to the globe is exactly the kind of tool that can close it, and ORCHIDEE now has one.

Subject of Research: Development and benchmarking of a new urban land surface scheme with imperviousness-dependent soil hydraulics in the ORCHIDEE 2.2 land surface model

Article Title: Benchmarking a new urban scheme in the ORCHIDEE 2.2 land surface model

Article References: Lalonde, M., Bastin, S., Oudin, L., Arboleda-Obando, P. F., & Ducharne, A. (2026). Benchmarking a new urban scheme in the ORCHIDEE 2.2 land surface model. Geoscientific Model Development, 19(19), 9463-9488. https://doi.org/10.5194/gmd-19-9463-2026

Image Credits: AI Generated

DOI: 10.5194/gmd-19-9463-2026

Keywords: ORCHIDEE, land surface model, urban climate, imperviousness, saturated hydraulic conductivity, surface energy balance, Urban-PLUMBER, sensible heat flux, latent heat flux, urban hydrology, convection-permitting simulations, Geoscientific Model Development

The research, led by Rabin Chakrabortty of the American University of Sharjah together with colleagues across several departments, applied the InVEST Urban Stormwater Retention model, an open-source tool developed by the Natural Capital Project at Stanford University. Unlike conventional event-based hydrologic models such as SWMM or HEC-HMS, which simulate the response of a drainage network to a single storm, InVEST operates on an annual climatological timescale. This makes it particularly suited to strategic planning questions: how much water does a city’s landscape soak up over a full year, how much pollution does that absorption prevent from reaching the sea, and what would it cost to reproduce those services artificially?

To answer those questions, the team assembled a remarkably detailed digital portrait of the emirate. A land use and land cover map was derived from Sentinel-2 satellite imagery at 10-meter resolution and classified into seven categories, ranging from high-intensity developed zones to cultivated land, grassland, scrub and shrub, bare land, and open water. Annual rainfall came from the CHIRPS gridded climate dataset, resampled to match the satellite grid. Hydrologic soil groups were compiled from the FAO Soil Map, ISRIC SoilGrids, and the UAE Soil Survey, then reclassified into the standard USDA categories A through D according to texture and permeability. Road centerlines from OpenStreetMap were rasterized to capture the hydrologic connectivity of impervious surfaces, and all layers were clipped to the Sharjah Municipality boundary and projected into UTM Zone 40N.

The model’s core logic is elegantly simple. For every 10-meter pixel, annual precipitation is partitioned according to a runoff coefficient and a percolation ratio assigned to each land cover class. The retention ratio is the implicit complement of these fluxes, representing the fraction of rainfall captured through interception, infiltration into soil, and eventual return to the atmosphere via evapotranspiration. Crucially, the model also accounts for urban drainage connectivity: pixels flagged as directly connected impervious areas, such as high-intensity development and surfaces within 50 meters of roads, were assigned high runoff coefficients and negligible retention, because stormwater there is rapidly whisked into storm drains rather than absorbed locally. This 50-meter retention radius was chosen as representative of parcel-scale drainage distances in Sharjah’s urban fabric.

The results reveal a striking spatial divide. Across the emirate, the mean retention ratio was 0.81, translating into a total retention volume of 122.77 million cubic meters per year. Runoff, by contrast, averaged a ratio of 0.19, corresponding to 31.29 million cubic meters annually, concentrated in densely built-up districts and along major transport corridors. A separate percolation calculation estimated potential groundwater recharge at just over 5 percent of annual precipitation, or 7.97 million cubic meters per year, a modest fraction that nonetheless carries real significance in a region where aquifers are stressed by over-extraction and saline intrusion from the Arabian Gulf. The authors emphasize that this recharge represents a component of the retained water rather than an additional fraction of the water balance.

Perhaps the most consequential finding concerns which land covers deliver these services. Cultivated land contributed approximately 35 percent of total citywide retention, grassland about 25 percent, and scrub and shrub a further 20 percent, meaning that vegetated and semi-natural classes together supplied roughly 80 percent of the retained volume, far out of proportion to their share of the map. Bare land accounted for around 10 percent, while high-intensity developed areas contributed a mere 5 percent, and open water was essentially negligible. In other words, the emirate’s remaining green and agricultural patches are doing the overwhelming majority of the hydrologic work, a pattern consistent with prior studies in Doha, Qatar, where researchers documented a 422 percent increase in stormwater runoff between 1984 and 2020 as urban expansion replaced natural land with impervious surfaces.

The water quality implications follow directly from the hydrology. Because no local stormwater monitoring data were available, the team adopted screening-level event mean concentrations from peer-reviewed arid-region studies: 2.0 milligrams per liter for total nitrogen and 0.4 milligrams per liter for total phosphorus. Applying these values to the modeled volumes, the researchers estimated that annual runoff exports roughly 62,580 kilograms of nitrogen and 12,516 kilograms of phosphorus toward receiving waters, while the retention process avoids nearly four times as much, approximately 245,536 kilograms of nitrogen and 49,107 kilograms of phosphorus per year. In a coastal emirate where stormwater is often discharged untreated into the Arabian Gulf, these avoided loads represent a meaningful, if screening-level, indication of how green infrastructure could reduce nutrient enrichment and eutrophication risk in sensitive marine waters.

To translate these biophysical services into economic terms, the study applied a replacement cost method, multiplying the modeled retention volume by an assumed benchmark of 1,800 UAE dirhams per cubic meter of engineered stormwater storage. The authors are careful to stress that the resulting figure is an illustrative, order-of-magnitude capital-equivalent benchmark rather than an annual benefit estimate or a formal benefit-cost analysis, and that it is sensitive to the assumed unit cost. Even so, the calculation makes the central point vivid: reproducing the retention capacity that Sharjah’s landscape currently provides for free, using detention basins, underground storage tanks, and pumping facilities alone, would require capital investment on a scale that dwarfs typical municipal stormwater budgets. The implicit message for planners is that every hectare of farmland or green space paved over is infrastructure quietly decommissioned.

The study is candid about its limitations. The annual time step of the InVEST model cannot capture the flash floods and first-flush pollutant pulses that dominate flood risk in hyper-arid climates, where rainfall arrives in short, violent winter storms between December and March; complementary event-based modeling would be needed for detailed flood design. The nutrient estimates rest on assumed uniform concentrations rather than locally calibrated measurements, and the soil group reclassification reflects generalized infiltration potential rather than site-specific hydraulic testing. The economic valuation, moreover, was aggregated rather than spatially explicit, limiting its use in pinpointing conservation hotspots. The authors call for local soil infiltration measurements, stormwater quality monitoring campaigns, sensitivity analyses, and scenario-based studies of land-use change and climate variability to sharpen the picture.

Even with those caveats, the work stands as one of the first integrated, city-scale assessments of stormwater retention, nutrient load reduction, and economic valuation in a hyper-arid Gulf city, and its framework is deliberately replicable. Built entirely on openly available datasets, Sentinel-2 imagery, CHIRPS rainfall, OpenStreetMap roads, and public soil maps, and executed with a standardized open-source modeling platform, the approach could be transferred to other rapidly growing dryland cities across the Gulf, North Africa, and beyond. As climate change renders extreme precipitation more intense and erratic even in arid zones, and as recent flooding episodes in Sharjah and Dubai have exposed the limits of engineered drainage, the study offers a quantitative argument that the cheapest and most versatile stormwater infrastructure a desert city possesses may already be growing out of the ground. Protecting it, the authors conclude, is not merely an environmental nicety but an economically justified pillar of water security and climate resilience.

Subject of Research: Nature-based stormwater retention and water quality assessment using the InVEST model in the arid city of Sharjah, UAE

Article Title: Nature based stormwater management for annual retention and water quality in Sharjah, United Arab Emirates: an InVEST assessment

Article References: Chakrabortty, R., Ali, T., Abouleish, M., Atabay, S., Ahmad, N., Abu-Rukba, R., & Meraj, G. (2026). Nature based stormwater management for annual retention and water quality in Sharjah, United Arab Emirates: an InVEST assessment. Discover Geoscience, 4(1), Article 323. https://doi.org/10.1007/s44288-026-00620-4

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00620-4

Keywords: stormwater retention, nature-based solutions, InVEST model, ecosystem services, Sharjah, United Arab Emirates, water quality, groundwater recharge, urban hydrology, green infrastructure, arid cities, Sentinel-2

The evidence base for urban modification of rainfall is no longer anecdotal. It stretches back to the METROMEX field campaigns of the 1970s around St. Louis, and it has been dramatically sharpened by modern radar networks and dense gauge arrays. Recent studies have documented strong intensification of hourly rainfall extremes attributable to urbanization, including analyses over the Yangtze River Delta showing urbanization-enhanced summertime extreme hourly precipitation, and work identifying robust urban signatures in short-duration rainfall extremes. A global-scale assessment of urban precipitation anomalies published in 2024, along with a meta-analysis of urbanization impacts on rainfall modification, suggests the phenomenon is widespread rather than confined to a handful of well-instrumented American and Chinese cities.

The physical mechanisms fall into several interacting categories. The first is thermodynamic: the urban heat island, generated by waste heat from vehicles, industry and air conditioning combined with the reduced evapotranspiration of impervious surfaces, makes cities warmer than their rural surroundings. This warming enhances atmospheric instability and can intensify updrafts within convective storms. Numerical experiments have shown sensitivity of urban rainfall to anthropogenic heat flux, and modeling over the Seoul metropolitan area has implicated both anthropogenic heat and building height in modifying precipitation. A warmer, more buoyant boundary layer over a city is, in effect, a loaded gun for any storm that drifts overhead.

The second mechanism is aerodynamic. Tall, rough buildings increase surface friction and create mechanical turbulence that can slow approaching storms, split them, or force low-level convergence that triggers new convection. Classic case studies from Atlanta linked the urban heat island and roughness to summertime convective thunderstorms, and more recent work has examined how vertical wind shear determines whether squall lines are disrupted or amplified by city encounters. The geometry of the city matters too: studies of city shape in inland and coastal environments, and of urban development patterns and their influence on extreme rainfall occurrences, indicate that the spatial form of a metropolis—not just its size—leaves a fingerprint on where and when heavy rain falls.

The third mechanism is aerosol-driven, and it is the most contested. Cities emit vast quantities of particulate pollution that serve as cloud condensation nuclei, and the sign of the resulting effect depends on the storm environment. In moisture-rich settings, added nuclei can invigorate convection by delaying coalescence and releasing more latent heat aloft; in drier regimes, competition for water vapor can suppress rain. Idealized supercell simulations, studies of urban plume aerosols on mesoscale convective systems, and sensitivity studies such as one focused on Dallas–Fort Worth all illustrate this ambivalence. Work in the Pearl River Delta has even connected aerosols to modified lightning activity, underscoring how deeply urban emissions can penetrate storm electrification processes.

Critically, the review emphasizes that none of these mechanisms operates in isolation from the background weather. A recurring finding across the literature is that urban effects are scale-dependent and regime-dependent: they tend to be most visible in weakly forced, summertime convective environments and harder to detect when strong synoptic systems—fronts, mesoscale convective complexes, landfalling tropical cyclones—dominate. Yet even hurricanes are not immune: research on Hurricane Harvey showed that urbanization exacerbated the rainfall and flooding in Houston, and studies over South China and Charlotte, North Carolina, have quantified urban contributions to tropical cyclone rainfall. A 2026 study documented divergent urban storm responses across convective, frontal and tropical systems, reinforcing the point that there is no single ‘urban rainfall effect’ but a family of context-dependent interactions.

This complexity cascades directly into flood risk. Short-duration rainfall extremes are precisely the events that overwhelm urban drainage, because impervious surfaces convert intense rain into runoff within minutes rather than hours. Analyses of the Charlotte metropolitan region’s flood frequency, studies of storm scale, position and movement in controlling urban flood response, and investigations of flash-flood-producing storms in small urban watersheds all show that the hydrological consequences depend on the fine-grained alignment between rainfall location, storm motion and drainage network structure. When a city also intensifies the storm itself, the feedback loop tightens: more extreme rain produces more runoff, and the design storms engineers rely on—typically estimated from historical records that may already embed urban modification—can silently underestimate present-day hazard, let alone future risk.

The implications extend beyond flooding into water resources and ecosystems. In arid and semi-arid regions, urban-induced precipitation variability has been documented as a real feature of the climate, with consequences for aquifer recharge—groundwater storage change in the Phoenix area being one documented example. Altered runoff quality, including the ‘first flush’ of pollutants from roads documented in Chongqing, degrades receiving waters. The concept of a ‘hydrological urban heat island’ describes how warm urban runoff stresses stream ecosystems. Even vegetation is affected: recent research attributes accelerating loss of resilience in suburban woodlands largely to changed urban precipitation patterns. Meanwhile, drinking-water systems face their own vulnerabilities, as the 2007 Lake Taihu crisis in China illustrated when climatic variability and lake management failures converged.

What makes the review’s framing genuinely new is its insistence on co-evolution. Cities grow in response to economic and demographic forces, and their growth alters the rainfall environment; that altered environment then feeds back into flood exposure, water security and land-use decisions, which in turn shape further development. The authors argue that this coupling must be built into attribution studies, climate projections and risk management, rather than treated as a nuisance term. Attribution of individual flood events, for instance, cannot cleanly separate ‘climate change’ from ‘urbanization’ when the two interact—though one 2025 analysis of tropical cyclone rainfall patterns concluded that climate change dominates over urbanization in that context, a useful reminder that the balance varies by hazard and region.

Meeting this challenge will require both better observations and better models. On the observing side, the review highlights the promise of phased-array radar networks now being deployed for hazard monitoring in urban environments, polarimetric radar estimates of extreme urban rainfall, opportunistic sensing through crowdsourced personal weather stations such as those quality-controlled in the Amsterdam metropolitan area, and machine-learning approaches to downscaling urban precipitation climatology. On the modeling side, convection-permitting and hectometric-scale simulations, improved urban canopy parameterizations evaluated in projects such as URBAN-PLUMBER, and city-descriptive input datasets are closing the gap between the scales at which urban processes act and the scales at which models represent them. The authors argue that integrating this urban–storm coupling into the next generation of attribution, projection and risk frameworks is not an academic refinement but a necessity: as cities expand into a warmer atmosphere, the storms they help intensify will increasingly be the storms their infrastructure must survive.

Subject of Research: Urban modification of extreme rainfall and its co-evolution with city development

Article Title: Co-evolution of cities and extreme rainfall

Article References: Yang, L., Smith, J., Uijlenhoet, R., Masson, V., Fowler, H. J., & Niyogi, D. (2026). Co-evolution of cities and extreme rainfall. Nature Cities. https://doi.org/10.1038/s44284-026-00530-z

Image Credits: AI Generated

DOI: 10.1038/s44284-026-00530-z

Keywords: urban rainfall effect, extreme precipitation, urban heat island, flood risk, aerosol-cloud interactions, urbanization, convection, storm dynamics, urban hydrology, climate attribution, Nature Cities, co-evolution