The stakes could hardly be higher. Kabul’s population has exploded from around one million in 2001 to more than 5.3 million in 2021, and over 70 percent of its residents live in unplanned or informal settlements, many of them perched on steep, deforested hillsides or crowded into low-lying districts with inadequate drainage. Between 2000 and 2020, built-up areas expanded by 40 percent while agricultural land shrank by 32 percent. Yet until now, hazard information for the city has been fragmented across consultancy reports and isolated single-hazard studies, leaving planners without a coherent map of where dangers converge. The new research was designed precisely to close that gap in a place where reliable environmental data is scarce and field campaigns are often impossible.

The team focused on five hydro-climatic and environmental hazards: flood, heat, drought, soil erosion and groundwater stress. Earthquakes and landslides were deliberately excluded, the authors explain, because they require fundamentally different assessment frameworks and mitigation approaches. For each of the five hazards, the researchers selected a set of conditioning parameters, nine in total across the analysis, including elevation, slope, soil type, drainage density, precipitation, land use and land cover, and groundwater depth, depletion and quality. These were rated and weighted using the Analytic Hierarchy Process, a structured multi-criteria decision analysis method developed by Thomas Saaty, implemented within a geographic information system. The heat hazard took a different route: the team retrieved land surface temperature from a Landsat 8 image acquired on 18 June 2020, the hottest day of the study period with the lowest cloud cover, and flagged every pixel exceeding 40 degrees Celsius as a high-hazard zone.

The data engineering behind the maps is a case study in making the most of thin evidence. Land use classifications were built by merging Esri Sentinel-2 and OSM Geofabrik datasets, achieving a Kappa coefficient of 0.85, a measure of classification agreement that indicates high reliability. Precipitation surfaces were interpolated from a decade of observations spanning 2013 to 2022, drawn from the CHIRPS v3.0 and PERSIANN-CCS satellite products and averaged using inverse distance weighting, a method chosen because it demands no assumptions about spatial autocorrelation in a region with sparse and unevenly distributed gauges. Groundwater nitrate measurements were spread across the city using Thiessen polygons, while groundwater depth and depletion trends were interpolated with kriging. Every raster layer was resampled to a common 30-metre resolution to keep the analysis spatially consistent.

The individual hazard maps tell striking stories on their own. Flood hazard dominates the picture: roughly 60 percent of the study area falls into the high category and another 10 percent into the very high category, concentrated in the central and eastern districts where gentle slopes, low elevations and outdated drainage allow surface water to accumulate. Very high flood zones cluster alarmingly in densely built districts such as D11 and D15, which likely host critical infrastructure. Heat hazard shows a different geography, with the very high class covering about 6 percent of the area, chiefly in southern and northeastern districts blanketed by impervious surfaces and starved of vegetation, the classic signature of the urban heat island effect. Drought hazard inverts the flood pattern, concentrating in the steeper, drier southern and southwestern districts, a spatial mutual exclusivity the authors attribute to climatic and geomorphological gradients.

Groundwater stress may be the quietest crisis of the five. Kabul depends almost entirely on two shallow Quaternary aquifers, the central Kabul and Paghman-Darulaman aquifers, which have been relentlessly overexploited. Water tables have dropped by up to 60 metres in some areas, and contamination from sewage infiltration and agricultural runoff is increasingly compromising quality. The assessment found 46 percent of the area in the high groundwater stress category and 17 percent in the very high category, with districts such as D11 and central D06 showing severe nitrate contamination alongside marked depletion. Soil erosion, by contrast, remains low across most of the city, though 4.3 percent of the area, particularly the sloping northern peripheries of districts D11 and D15, shows high susceptibility where bare, steep terrain accelerates soil detachment and runoff.

The real innovation lies in how these layers were combined. Each hazard map was reclassified into a binary layer, with moderate, high and very high zones coded as hazard presence and low zones as absence. Summing the binary values pixel by pixel produced a composite map showing how many hazards overlap at any location, while a second, more elegant technique assigned each hazard a unique power-of-two weight, from 1 for heat to 16 for drought, and multiplied the binary rasters together. Because five hazards yield 32 possible combinations, every pixel could be decoded to reveal its exact hazard cocktail. The result: single-hazard zones cover just 1.8 percent of the city, two-hazard zones 18.6 percent, three-hazard zones 44.3 percent, four-hazard zones 32.9 percent, and areas facing all five hazards 2.8 percent.

The combination analysis is where the findings become genuinely actionable. The most widespread pairing is groundwater stress with heat, covering 17.79 percent of the area, followed by the triple combination of soil erosion, groundwater stress and heat at 18.81 percent. Flood paired with groundwater stress covers 8.47 percent, soil erosion with heat 8.54 percent, and drought with groundwater stress and heat 8.16 percent. These clusters, the authors argue, are exactly the kind of spatial intelligence needed to target nature-based solutions, interventions such as floodplain restoration, vegetated slope stabilization, urban green corridors and recharge area protection that can address multiple hazards at once while delivering co-benefits for biodiversity, climate resilience and public well-being. A flood-prone corridor might call for water retention landscapes and river management, while drought-exposed southern districts need soil moisture enhancement and drought-resilient vegetation.

The researchers are candid about the limits of their work. The maps represent relative hazard susceptibility, not probabilistic forecasts: they do not quantify hazard magnitude, recurrence intervals or expected losses, and they exclude population exposure and socio-economic vulnerability. Validation relied on qualitative comparison with previous studies and official reports rather than field verification, which was infeasible under prevailing conditions in Afghanistan. Temporal inconsistency is another caveat, with heat derived from a single 2020 satellite image while precipitation data span a decade. Interpolation of sparse groundwater measurements and the binary simplification of hazard zones introduce further uncertainty. Still, the framework is transparent, replicable and explicitly designed for data-scarce cities of the Global South, and the authors sketch a research agenda that includes machine-learning comparisons, climate scenario analysis and dynamic hazard modelling.

For Kabul’s planners and for cities across the rapidly urbanizing Global South, the study marks a foundational shift from asking which hazard threatens a neighbourhood to asking which combination does. With more than 90 percent of the city exposed to at least one hazard and vast swaths facing three or more, single-hazard thinking is no longer defensible. What this map offers is a starting point for risk-sensitive land-use planning and ecosystem-based resilience strategies in one of the world’s most challenging urban environments, a city where, as the data now make unmistakably clear, the crises are not waiting their turn.

Subject of Research: Integrated geospatial multi-hazard assessment of flood, heat, drought, soil erosion and groundwater stress in Kabul, Afghanistan

Article Title: Unveiling Multi-Hazard Hotspots in Kabul: An Integrated Geospatial Assessment in a Data-Scarce Urban Environment

Article References: Rafiee, M., Ansari, M. R., Kempa, D., Kuhn, T., Esmail, B. A., Biber-Freudenberger, L., & Albert, C. (2026). Unveiling Multi-Hazard Hotspots in Kabul: An Integrated Geospatial Assessment in a Data-Scarce Urban Environment. Environmental Management, 76(9), Article 300. https://doi.org/10.1007/s00267-026-02588-w

Image Credits: AI Generated

DOI: 10.1007/s00267-026-02588-w

Keywords: Kabul, multi-hazard assessment, GIS, flood hazard, urban heat island, groundwater stress, drought, soil erosion, nature-based solutions, Analytic Hierarchy Process, remote sensing, urban planning

The research team, comprising Badriya Al Mawali, Issam Ali and Alaa Ibrahim from the Physics Department at Sultan Qaboos University, together with Martin Köhler and Maike Ahlgrimm of Deutscher Wetterdienst, the German national weather service, and Sultan Al Yahyai of Code Academy in Muscat, built their investigation around controlled sensitivity experiments. Al Mawali also holds an affiliation with Oman’s Directorate General of Meteorology, a connection that underscores the practical motivation behind the work: understanding how regional weather models respond to environmental conditions is directly relevant to forecasting and hazard assessment in a country that sits at the edge of the cyclone-prone Arabian Sea.

At the heart of the study is the ICON numerical weather prediction model, a state-of-the-art forecasting system run here as a regional ensemble at a spatial resolution of 6.5 kilometers. Ensemble simulations are a cornerstone of modern cyclone research because they generate many slightly perturbed versions of the same forecast, allowing scientists to distinguish robust signals from noise. Rather than trying to predict Shaheen as it happened, the team ran a control simulation of the storm and then systematically modified individual ingredients of the environment: the temperature of the sea surface, the height of Oman’s terrain, and the roughness of the ocean surface as felt by the wind.

The most dramatic response came from the ocean. When the researchers raised sea surface temperature by 2 degrees Celsius, the simulated cyclone intensified and rainfall along the coast increased. This result follows directly from the physics of tropical cyclones, which are heat engines fueled by the flux of energy and moisture from warm water into the atmosphere. A warmer sea surface raises the enthalpy available to the storm, encouraging stronger convection, lower surface pressure and higher wind speeds, and in this case the model translated that extra energy into heavier precipitation concentrated along the coastal zone where the storm interacted with land.

Terrain, by contrast, acted less as an energy source and more as a steering and organizing influence. When the team reduced the height of Oman’s mountains, the simulated cyclone’s track shifted, and the terrain-enhanced rainfall that typically develops when moist winds are forced up over topography weakened over the mountainous interior. The changes rippled through the storm’s vertical structure as well: the low-level flow, the convergence of moisture flux that feeds convective bands, the mid-level ascent that drives cloud formation, and the upper-level divergence that vents air out of the storm’s core were all reorganized when the mountains were lowered. In other words, topography did not simply block the storm; it reshaped the three-dimensional circulation that determines how a cyclone behaves as it approaches land.

The third variable, sea-surface roughness, produced a comparatively muted response. Roughness describes the frictional drag that the ocean surface exerts on the wind, and physically based changes to this parameter altered the simulated cyclone’s intensity and precipitation structure only modestly. This hierarchy of effects is one of the study’s clearest takeaways: of the surface properties tested, temperature dominated the storm’s energy budget and the magnitude of coastal rainfall, while terrain height dominated the storm’s path, its vertical circulation and the geographical distribution of where rain fell. Roughness, though physically important, sat in the background of this particular case.

For Oman and the surrounding region, the distinction between these mechanisms carries real weight. Coastal rainfall magnitude and storm intensity are the quantities that matter most for wind damage and flash flooding along the shore, and those responded most strongly to sea surface temperature. Track and rainfall distribution, which determine which valleys, cities and wadis are actually hit, responded most strongly to terrain. A forecasting system that misrepresents either ingredient could therefore err in different directions: too-warm sea surfaces in a model might inflate the amount of rain a storm delivers, while poorly resolved mountains might misplace the heaviest precipitation or shift the landfall zone. The study suggests that evaluating both of these surface conditions is essential for trustworthy regional cyclone simulation.

The work also speaks to a broader question that looms over the Arabian Sea: what happens as ocean waters warm? The researchers are careful on this point. The 2-degree increase was used as a controlled test of the model’s sensitivity, not as a prediction of how future cyclones will behave. Because the experiments involve a single storm and idealized modifications of the environment, the results should be read as a diagnostic of how the ICON model responds to changed conditions, not as a forecast of a warmer future. That caveat matters, because the relationship between sea surface temperature and cyclone behavior in the real climate involves many competing factors, from atmospheric stability to wind shear, that a single-storm sensitivity study cannot capture.

Even so, the methodology offers a template for future work. By isolating one environmental variable at a time within an ensemble framework, the researchers could attribute specific changes in the simulated storm to specific causes, something that is nearly impossible to do with observations alone. The approach can be extended to other cyclones, other regions and other model configurations, building a picture of which surface conditions regional prediction systems handle well and where they need improvement. The authors suggest the findings may support further evaluation and refinement of regional cyclone simulations and contribute to assessments of heavy-rainfall and flood hazards, capabilities that are increasingly urgent as coastal populations in the Middle East grow and extreme weather draws closer scrutiny.

Tropical Cyclone Shaheen itself remains a benchmark event for the region. Its direct impact on northern Oman in October 2021 was unusual enough that it provides a valuable, well-observed case for testing how models represent cyclones in a basin where such storms are historically infrequent but potentially devastating. By dissecting that storm in the laboratory of a 6.5-kilometer ensemble model, the Sultan Qaboos University team and their collaborators have shown that the ocean’s warmth and the mountains’ height are not interchangeable pieces of the forecast puzzle. One sets the storm’s power and the volume of rain it can deliver to the coast; the other sets its course and the map of where that rain will fall. For forecasters, modelers and hazard planners alike, keeping both in sharp focus may be the key to anticipating the next time warm Arabian Sea waters send a cyclone toward Oman’s shores.

Subject of Research: Sensitivity of regional ensemble simulations of Tropical Cyclone Shaheen to sea surface temperature and terrain-height changes in Oman

Article Title: Could warmer waters bring heavier coastal rain? Insights from Cyclone Shaheen simulations

Article References: Could warmer waters bring heavier coastal rain? Insights from Cyclone Shaheen simulations. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Tropical Cyclone Shaheen, Oman, sea surface temperature, terrain height, ICON model, numerical weather prediction, ensemble simulation, coastal rainfall, Arabian Sea, Scientific Reports, Sultan Qaboos University, flood hazard