By: Eric Lo, Nikunj Beria

Background

On the night of 28 July 2025, a series of cloudbursts drove a debris-laden flash flood through Mandi, Himachal Pradesh. Three people died, dozens of vehicles were buried, ground floors along the market streets filled with mud, and road links in and out of the district fractured. The geography of Mandi makes such events brutally fast: steep gullies plunge from the Lesser Himalaya into a tight urban basin of Mandi on the Beas river, so runoff concentrates in minutes rather than hours. In a basin this steep, the decisive variable isn’t hard infrastructure; it’s operational lead time for the few minutes that separate life-saving evacuations from life-threatening exposure.

Understanding why the flood struck with such intensity requires looking at how Mandi’s landscape and drainage patterns have changed over the past half-century. In the 1970s (Figure 1b), the built footprint was compact and buffered by orchards, reserve forests, and open channel margins that functioned as informal flood storage. Over the decades (Figure 1a), improvements to roads and bridges and the expansion of services and tourism pulled construction down onto the valley floor and along drainage lines: seasonal washlands were infilled, side channels were partially culverted or lined, and shops, hostels, workshops, and housing filled the interstices.

Figures 1a and 1b: Maps of Mandi. Blue circles represent analytic “evaluation zones” around places where flood concentrated.  Credit: Abhijit Ekbote & ESRI Satellite ArcGIS World Imagery, Web-map by Abhijit Ekbote.

Figure 1a (2025 satellite) and Figure 1b (1970 topographic map) makes the shift to urbanization clear. Figure 1a shows a tight street grid and a high concentration of rooftops within a short walking distance of the Beas and its tributaries; Figure 1b shows wider river margins, fewer crossings near channels, and more contiguous forest reserve wedges pressing into the town. The four blue circles overlaid on both figures (geo-referenced to the same locations) mark the places where this encroachment is most consequential: confluence of tributaries, culvert outlets, and narrow floodplain pinch points where flow naturally concentrates. When analyzed together, the maps tell a simple story: the hazard pathways have always been there; it is the exposure of people, buildings, vehicles, inventories that has migrated into them.

In the sections below, we try to assess the expected loss and damage to assets in Mandi by using a consistent set of assumptions and calculations as explained in the Methodology:

Tracing Legacy Drainage

Figures 2a and 2b: Satellite image of Mandi (left) with traces of legacy khads from 1970 topography map (right).  Credit: Abhijit Ekbote & ESRI Satellite ArcGIS World Imagery, Web-map by Abhijit Ekbote.

In Figure 2b, 1970 topographic sheet notes small khads or natural channels stepping down into Mandi. We digitized those channels and overlaid them on Figure 2a, the 2025 satellite image. The red drainage lines cut directly through the flood areas. These channels did not disappear; they were culverted, narrowed, or built over. When cloudbursts exceed the capacity of these channels, water and debris reoccupy the historic paths—and they do so quickly.

Estimating Loss in the Concentrated Areas

Area 1: Suspension Bridge

*Credit: Abhijit Ekbote & ESRI Satellite ArcGIS World Imagery*

At the bend of the Beas River, the suspension bridge stitches the old market to the civic heart of Mandi. On the night of the cloudbursts, the bridge entrances disappeared under fast, brown water; shopfronts snapped shut, and lanes that usually carry foot traffic began carrying flow. This is the tightest pinch point in town – a place where legacy drains mapped in the 1970 map converge with today’s street grid. Within this compact area, we estimated ~200 buildings, and tallied ~35 vehicles caught at curbside as damage concentrated on ground floors and inventories. Our conservative direct asset loss estimated using our methodology shows a loss of ₹12.04 crore (≈$USD 1.37M).

Area 2: Stadium/ Parking Flank

Downstream of the bridge, the stadium precinct looks safe upon first glance: broad pavement and little distance from the main channel. However in cloudburst conditions it behaves like a shallow bowl. We counted 30 buildings (6 small businesses, 24 homes), but the dominant exposure is vehicular: in a time-slice of the lot and adjoining road, we counted ≈85 vehicles on site. As the bowl filled, cars were pinned, and shop thresholds took mud. Our conservative direct asset loss estimated using our methodology shows a loss of ₹4.46 crore (≈$USD 0.51M).

Area 3: Jail Road

Jail Road is a dense ribbon of one-room shops and small homes where the city’s everyday economy lives – tuition centers, grocers, and repair shops. We estimated ~95 buildings within the circle. Water and debris arrived through undersized culverts and backed into the lanes; the mud line sits between a motorbike’s seat and a car door handle, pointing to ~0.6–1.0 m on the street. We counted ~25 vehicles damaged at high severity as grit and trash jammed against parked wheels. Our conservative direct asset loss estimated using our methodology shows a loss of ₹5.54 crore (≈$0.63M).

Area 4: Hospital and Hillside Gully

Above the core, a steep khad steps down past the city hospital, whose two long blocks sit amid a larger cluster where we measured an additional 170 buildings and ~25 vehicles. Sediment spread across roadway approaches and parking bays, potentially complicating emergency access. Our conservative direct asset loss estimated using our methodology shows a loss of ₹11.38 crore (≈$USD 1.3 M).

Short and Long-Term Resilience Solutions

While an early-warning system (EWS) can’t stop structural damage; it buys minutes to move people and movable assets out of harm’s way. In practice, upstream rain-rate gauges and automatic level recorders trigger town-wide sirens and phone alerts over secondary, offline channels; residents and shopkeepers use that five-minute window to relocate vehicles to pre-identified high ground, shut power at the mains, lift documents and cash, and get to safety. Applied to our four focus areas, those actions would avoid ≈₹5.95 crore (≈$0.68 million)—~18% of the ₹33.42 crore (≈$3.81 million) direct asset loss we estimate—almost entirely from vehicles and contents, not the structures. That ratio is by design: we count only conservative, physical asset categories and do not monetize the larger benefit—lives saved and injuries reduced—nor the downstream savings in emergency response, cleanup, and disruption.

A long-term path for Mandi is not higher ad-hoc walls but exposure reduction and resilience by design. That begins with mapped hazard lines in the town plan, mandatory plinth elevations and freeboard for any redevelopment in risk zones, and protection of drainage corridors and culvert inlets from encroachment. Equally important is building with the hills rather than against them. Himachal’s Kath-Kuni tradition—timber-laced stone masonry on raised plinths with continuous bands and permeable perimeters—embeds drainage and replaceable sacrificial elements. The legacy stream channels should be open as no-build strips and overflow corridors. Where culverts remain, increase conveyance, add debris racks, and provide controlled surface bypass when flows exceed design.

Mandi’s tragedy represents a lesson with wide relevance across the Himalaya: when storms are short-fused, a town can buy its most valuable asset, time, by installing an early warning system. An early-warning system provides safety at a modest cost. When combined with risk-aware planning and building practices, it can transform a town into a place of lasting safety.

Methodology

What we measured. We estimate direct physical asset losses (structures, contents, vehicles). Land value, public works, cleanup, business interruption, and mortality are out of scope, so results are conservative.

Building values. Structure replacement cost = floor area × CPWD 2023 Plinth Area Rate. We use the residential PAR as a central proxy (₹25,000/m²), consistent with CPWD guidance for preliminary estimates. (Source: Indian Railways)

Floor area assumptions. State-typical urban dwelling size for Himachal Pradesh = 34.2 m², taken from the NIUA Handbook of Urban Statistics (2022), Table 2.6 from NSS 76 (2018); small shops are parameterized at 45 m² as a modest uplift for frontage units; hospital blocks are modeled explicitly by footprint. (Source: NIUA)

Contents valuation (by use). Contents are valued as a share of structure value: homes 50%, small shops 80%, hospitals 120% (equipment-intensive). These shares are standard in rapid post-flood accounting and align with ranges in depth–damage literature and practice; we implement them deterministically here and expose them as inputs in the workbook.

Flood depth–damage functions. We translate observed severity (photos/video cues and local hydraulics such as pinch points, fans, culverts) into damage fractions for structure and contents. Functions and ranges reference the FEMA HAZUS Flood Model Technical Manual (v7.0, 2025), which documents residential/commercial depth–damage curves and their updates. (Source: FEMA)

Vehicles. Vehicle losses = count × average used-car value × loss ratio. For a central value we use an average selling price ≈ ₹5.66 lakh (ASP for used cars in India reached ~₹5.66 lakh in 2024 per Cars24’s market report), then apply loss ratios by setting (higher near debris-laden channels, lower in fringe basins). (Source: Cars24)

Currency. INR→USD conversions use the working rate applied in the model (shown in the workbook and summary). (Methodologically, totals are scale-free with respect to exchange rate.)

Early-warning (EWS) benefits. EWS savings are prospective (a yet-to-be-installed siren + drill program). We credit savings only to movables—a share of contents and share of vehicles that can be lifted or relocated within ~5 minutes of alert. This aligns with operational good practice for short-fuse floods; sirens/phone alerts triggered by upstream rain gauges and auto level recorders provide the lead time. (See Hazus and emergency-ops guidance for context on operational lead time.) (Source: FEMA)

Damage-fraction settings (central):
Area 1 (bridge pinch-point): 35% structure / 50% contents.
Area 2 (fringe basin/parking): 20% / 35%.
Area 3 (street-level inundation): 30% / 45%.
Area 4 (hillside gully/alluvial fan): 30% / 45%.
(These reflect observed water marks, debris burial, and setting)

EWS salvage shares (central):
Areas 1, 3, 4: contents 15%, vehicles 60%;
Area 2 (parking-heavy): contents 10%, vehicles 50%.

Provenance of building counts. All counts are manual tallies on geo-referenced imagery within the blue evaluation circles (2025 satellite and traced 1970 khads overlaid), with specific landmark checks (bridge approaches, stadium lot, hospital campus). Other establishments (schools, shops, hospitals) are not accounted for as we only include those identified by Google Maps. Any building that is not labeled by Google Maps we assume to be residential.

References

The Hindu. (2025, July 29). Flash flood in Himachal’s Mandi claims at least three lives. The Hindu. https://www.thehindu.com/news/national/himachal-pradesh/flash-flood-in-himachals-mandi-claims-at-least-three-lives/article69868098.ece

Brigadier J.A.F Dalal & Government of India. (1970). Himachal Pradesh (First) [Topographic]. Government of India.

CARS24, & Team-BHP. (2025, January 23). Gears of growth: The 2024 Indian used car market report. https://cdn.cars24.com/prod/auto-news24-cms/CARS24-Blog-Images/2025/01/23/c09aec15-acf9-4bc8-9d1f-ec5e3d8241f3-C24-gears-of-growth.pdf

Central Public Works Department. (2023). Plinth Area Rates 2023. Ministry of Housing and Urban Affairs, Government of India. https://indianrailways.gov.in/railwayboard/uploads/directorate/civil_engg/2025/PLINTH_AREA_RATES_2023.pdf

Chauhan, C. (2025, August 5). Himachal flash floods: How unplanned development led to disaster in Mandi, puts entire region at risk. Hindustan Times. https://www.hindustantimes.com/cities/delhi-news/himachal-unplanned-development-behind-mandi-disaster-101754337190373.html

CityResource.in: GIS consultancy, data collection, training and capacity-building workshops. Abhijit Ekbote https://cityresource.in/

CNN-News18. (2025, July 30th). Himachal Flash Floods: How Unplanned Development Led To Disaster In Mandi Region [Video]. YouTube. https://www.youtube.com/watch?v=vsO2AH-_LV8

Federal Emergency Management Agency. (2025). Hazus flood model technical manual: Hazus 7.0. https://www.fema.gov/sites/default/files/documents/fema_rsl_hazus-7-fltm_06272025_0.pdf

National Institute of Urban Affairs. (2022). Handbook of urban statistics 2022. https://niua.in/intranet/sites/default/files/2802.pdf

Mandi’s 2025 cloudburst: maps, losses, and why an early-warning siren is the first line of defense