Quantifying Physical Climate Risk for Indian Corporate Assets

For most of the past decade, Indian companies treated physical climate risk as a qualitative narrative, something described in sustainability reports with language about commitment to resilience and acknowledgment of changing weather patterns. That era is ending. Three regulatory forces are converging to require actual numbers.

First, SEBI's Business Responsibility and Sustainability Reporting (BRSR) Core framework, mandatory for the top 150 listed companies from FY 2023-24 and expanding to the top 1,000 from FY 2024-25, requires companies to disclose the financial impact of climate-related risks in quantitative terms where material. Section C of the BRSR Core specifically asks for climate-related financial disclosures aligned with TCFD. A company that describes flood risk at its Gujarat plant without attaching an estimated financial exposure is no longer meeting the disclosure standard.

Second, the Reserve Bank of India's 2024 draft framework on climate-related financial risks for regulated entities, covering banks, NBFCs, and insurers, requires lenders to assess physical climate risk in their credit portfolios. Corporate borrowers from regulated entities increasingly face questions about their own physical risk quantification during credit review, because a lender that cannot assess borrower climate exposure cannot meet its own RBI compliance obligations. The practical consequence: companies that arrive at a credit negotiation with a credible physical risk quantification are differentiated from those that do not.

Third, international trade and investment flows now carry climate risk disclosure expectations. Indian companies listing on overseas exchanges, accessing ECBs, or seeking investment from ESG-screened foreign institutional investors face TCFD-aligned disclosure requirements from their counterparties even where Indian regulation does not yet mandate the same level of detail.

Physical climate risk quantification is therefore simultaneously a regulatory compliance task, a credit-market signalling exercise, and a genuine risk management input. The challenge is that most Indian companies have not built the data infrastructure or internal capability to do it rigorously. This post maps a practical path from exposure identification through data sourcing to insurance programme consequences.

The Task Force on Climate-related Financial Disclosures, established by the Financial Stability Board in 2015 and whose recommendations were endorsed by SEBI and RBI in subsequent guidance, divides physical climate risks into two categories: acute and chronic.

Acute physical risks are event-driven. They include cyclones, floods, landslides, storm surge, extreme heat events, and droughts of sufficient severity to cause production disruption. For Indian companies, the acute risk category is well-populated. The Arabian Sea and Bay of Bengal are producing more intense cyclones at higher frequencies, with post-monsoon cyclone activity in the Arabian Sea increasing particularly sharply since 2018. Flash flooding events in urban and peri-urban industrial areas, from Chennai (2015, 2023) to Bangalore (2022) to Surat (2019, 2024), are occurring at return periods shorter than engineering assumptions embedded in factory design and insurance models.

Chronic physical risks accumulate gradually. They include rising mean temperatures reducing outdoor worker productivity, increasing water stress reducing process water availability, sea-level rise increasing inundation probability at coastal facilities, and shifting monsoon patterns changing agricultural raw material availability for food and agribusiness processors. Chronic risks are harder to model because they manifest over decades rather than event windows, but their financial impact can be as material as acute events for industries dependent on specific climate conditions.

Applying TCFD to Indian corporate assets requires mapping each asset or asset cluster against both acute and chronic risk categories under at least two climate scenarios: a high-emission scenario (aligned with approximately 4 degrees of warming, representing current policy trajectories) and a low-emission scenario (aligned with 1.5 to 2 degrees, representing aggressive mitigation). TCFD calls these transition scenarios, and while the terminology is sometimes associated with transition risk, the physical risk models also use scenario analysis to generate different hazard intensities under different warming trajectories.

In practical terms for a manufacturing company with plants in coastal Gujarat, a mid-Gangetic industrial corridor, and a Deccan plateau location: the Gujarat coastal plant requires acute cyclone and storm surge modelling under both scenarios; the Gangetic plant requires river flood and extreme heat modelling; and the Deccan plant may require water stress and heat wave modelling as the dominant chronic risks. Each location has a different hazard profile, and the quantification must be location-specific rather than generic.

India's physical geography creates highly concentrated hazard zones that do not map uniformly to administrative or industrial boundaries. Risk quantification begins with assigning each asset location to its applicable hazard zones across multiple peril categories.

Cyclone and storm surge exposure is highest on the east coast districts of Odisha, Andhra Pradesh, Tamil Nadu, and the West Bengal-Bangladesh borderlands. Odisha has been struck by severe to very severe cyclones (IMD categories 3 and 4) in 1999, 2013, 2014, 2019, 2020, and 2021, a frequency that now allows actuarial modelling rather than scenario speculation. Gujarat's coastal districts face increasing Arabian Sea cyclone risk, with Cyclone Tauktae in 2021 demonstrating that the Gujarat coast is no longer a low-frequency cyclone zone. Industrial concentrations in Kachchh, Jamnagar, and the Saurashtra coast face genuine cyclone exposure that insurance programmes and bank credit risk models historically discounted.

River flood exposure concentrates along the Brahmaputra-Barak system in Assam and the northeast, the Ganga-Yamuna doab across Uttar Pradesh and Bihar, the Godavari and Krishna deltas in Andhra Pradesh and Telangana, and the Mahanadi basin in Odisha and Chhattisgarh. Industrial and logistics corridors in Patna, Muzaffarpur, Varanasi, and the NH-30 freight route are highly exposed to river inundation in above-normal monsoon years.

Urban and flash flood exposure requires separate treatment from river flood exposure because the triggering mechanism is different: intense short-duration rainfall exceeding drainage capacity rather than river overtopping. Chennai's vulnerability was demonstrated in 2015 and again in 2023; Bangalore's IT corridor flooding in 2022 is the most-studied recent example of high-value asset loss in an urban flash flood setting. The URDPFI guidelines and NDMA guidelines both identify high urban flood risk in Ahmedabad, Surat, Vadodara, Mumbai, Pune, Hyderabad, and Chennai.

Landslide and mass movement exposure concentrates in the Western Ghats (particularly the Kodagu-Wayanad belt, as evidenced by the 2019 Kodagu and 2024 Wayanad disasters), the Uttarakhand Himalayan belt (Kedarnath 2013, Joshimath 2023), and the Darjeeling-Sikkim terrain. Manufacturing is less concentrated in these zones, but logistics routes, hydropower assets, and tourism infrastructure have substantial exposure.

Extreme heat exposure for industrial operations, specifically wet-bulb globe temperature exceedances that constrain outdoor labour, concentrates in Rajasthan, Vidarbha, interior Andhra Pradesh, and Telangana from April to June. The 2022 India heat wave, which saw wet-bulb temperatures in Nagpur and Churu exceed the physiological survival threshold for brief periods, is the reference event for occupational heat risk quantification.

Physical risk quantification requires hazard data, exposure data, and vulnerability models. For Indian assets, the data landscape is richer than many risk managers assume, though it requires integrating multiple sources.

The India Meteorological Department (IMD) provides the authoritative historical hazard dataset for India. IMD's gridded rainfall datasets (available at 0.25-degree resolution for 1901 to present through the National Data Centre, Pune), cyclone track databases, temperature records, and extreme event bulletins are the foundation for any statistically credible frequency-severity analysis. The IMD Best Track Archive for cyclones, which covers 1877 to present, provides 140-plus years of tropical cyclone track data and is the basis for Indian cyclone return period models. Access to IMD data varies by dataset: most summary statistics are publicly available; historical gridded data requires a formal request and modest fee through IMD's data portal.

Swiss Re's CatNet platform provides a global hazard mapping tool that covers Indian perils at district-level resolution for flood, cyclone, earthquake, and storm surge. CatNet is freely accessible to policyholders through their insurers or directly via subscription. It produces a hazard score for any geographic coordinate and provides return period curves for key perils. For a manufacturing company with 15 plant locations, CatNet can generate a hazard profile for each site within hours, giving a starting exposure map before engaging specialist modellers.

AIR Worldwide (now Verisk) operates the most widely used commercial probabilistic catastrophe model for Indian perils. The AIR India Typhoon model and the AIR India Flood model are used by reinsurers and insurers globally to price Indian catastrophe risk. These models produce exceedance probability curves and average annual loss estimates for portfolios of Indian assets. Accessing AIR model output directly requires a commercial licence, but Indian insurers and their reinsurers run your portfolio through these models as part of the underwriting process for large accounts, and requesting the output from your insurer as part of an insurance review is a reasonable ask.

Global Flood Awareness System (GloFAS) and the Copernicus Climate Change Service (C3S) provide global flood and climate projections that are accessible at no cost and cover India at resolutions useful for regional planning, though not plant-level precision. For chronic risk projections, the IPCC AR6 reports include India-specific projections for temperature, precipitation, and extreme events under multiple SSP scenarios.

For ground-level validation, state disaster management authority (SDMA) hazard atlases exist for most Indian states and are available through state government portals. Odisha's SDMA has one of the most detailed district-level hazard atlases in India; Gujarat's GSDMA is another high-quality source for the state's cyclone and earthquake hazard. These state-level atlases should be used alongside IMD and commercial model data to capture local drainage, topography, and infrastructure factors that national models may miss.

Physical risk quantification generates location-level hazard scores and probable maximum loss (PML) estimates. The insurance application is to compare these against the insurance programme limits and structure to identify gaps.

The central metric is the 1-in-100-year probable maximum loss (PML100) for each peril at each asset location. The PML100 represents the loss expected to be exceeded only once in a hundred years on average, the standard benchmark for property catastrophe coverage. If AIR models or CatNet scores indicate a PML100 of INR 85 crore for flood at a plant with a total insured value of INR 250 crore, and the current policy has a flood sub-limit of INR 15 crore, there is a coverage gap of INR 70 crore at the benchmark loss level. This is a concrete, actionable number that the CFO, the board risk committee, and the lender can all evaluate.

The same analysis applies to cyclone at coastal plants. If a Gujarat petrochemical facility has a PML100 for cyclone of INR 120 crore but the IAR policy cyclone limit is INR 60 crore (with wind and storm surge treated as separate perils, each with its own sub-limit), the effective coverage may be INR 60 crore on wind and INR 20 crore on storm surge, totalling INR 80 crore against a modelled need of INR 120 crore. The structural gap in how the policy treats single-event multi-peril losses, discussed in detail in related analysis on nat-cat claims aggregation, interacts directly with the physical risk quantification.

For business interruption, physical risk quantification informs indemnity period adequacy. If a cement plant in Odisha faces a plausible cyclone scenario causing INR 40 crore of physical damage with an 18-month reconstruction timeline, the BI policy's indemnity period of 12 months is structurally inadequate. Physical risk scenario modelling makes this visible before the loss rather than after.

RBI's climate risk guidance for regulated entities explicitly references the concept of physical risk-adjusted asset values. For corporate borrowers, this means that the sum insured on a property asset should reflect not just current replacement cost but the probability that the asset experiences insured losses that erode its value. A lender applying physical-risk-adjusted loan-to-value ratios may require higher insurance coverage than the company had previously maintained. The quantification work thus has direct implications for financing covenant compliance, not just for claims recovery.

The SEBI BRSR Core, which carries mandatory third-party assurance requirements for the top 150 listed companies, specifies nine high-assurance ESG metrics as of FY 2023-24. Physical climate risk disclosure is embedded in the climate-related financial disclosures section of the BRSR and is expected to align with TCFD's four pillars: governance, strategy, risk management, and metrics and targets.

Under TCFD alignment, companies are expected to describe the physical climate risks that could affect their operations, identify the most materially affected assets or business lines, and disclose the potential financial impact in monetary terms where the information is material and estimable. SEBI's guidance notes on BRSR clarify that companies should use established frameworks (TCFD, science-based targets) to structure their disclosures and that disclosure of the methodology used for risk quantification is expected.

For D&O liability, BRSR physical risk disclosure creates a directorial accountability mechanism. If a company discloses a specific physical risk as material in its BRSR filing, directors are asserting that the risk management programme addresses it. A disclosure gap, a stated flood risk at the Surat plant paired with a flood sub-limit of INR 5 crore on a INR 180 crore asset, creates directorial exposure if the gap materialises in a claim that triggers investor or regulatory scrutiny. SEBI's enforcement of BRSR is still developing, but the Securities and Exchange Board of India (Substantial Acquisition of Shares and Takeovers) Regulations and LODR provisions create a framework under which material misstatement in sustainability disclosures can attract regulatory action.

Beyond SEBI, listed companies with international investors face proxy adviser scrutiny. ISS and Glass Lewis have added climate risk governance metrics to their India proxy guidelines, including assessments of whether stated physical risk management programmes are backed by credible insurance coverage. The institutional investor community is treating the insurance programme as operational evidence of a company's actual climate risk management, not just its stated commitment.

Practical implication: the sustainability team that writes the BRSR climate risk section and the risk management function that buys the insurance programme must work from the same physical risk quantification. Disconnected processes produce the most common form of BRSR climate disclosure risk: aspirational text unsupported by operational substance.

Physical climate risk quantification generates a portfolio of outputs. The CFO's job is to translate those outputs into three categories of decision: insurance programme adjustments, balance sheet provisioning, and capital allocation.

For insurance programme adjustments, the quantification provides the basis for negotiating adequate sub-limits, selecting appropriate indemnity periods for BI, and evaluating whether parametric supplements to conventional indemnity cover are warranted. A CFO who can present a location-level PML100 matrix to an insurer is negotiating from a position of data rather than convention. Insurers who see this level of analysis from a buyer typically price the risk more accurately and are more willing to provide the sub-limit increases required, because the analytical work reduces their own uncertainty about what they are underwriting.

The immediate steps: commission a physical risk quantification study aligned with TCFD for all assets above INR 50 crore total insured value; use IMD historical data and CatNet as baseline sources; engage AIR Worldwide or RMS through the reinsurer for PML modelling on high-value sites; compare PML100 outputs against current sub-limits and indemnity periods; identify gaps; and present the gap analysis to the insurance broker before the next renewal cycle with a mandate to close the largest gaps.

For balance sheet provisioning, physical risk quantification supports the estimation of stranded or impaired asset values at locations where chronic risks (water stress, long-term temperature increases) may reduce the useful life or operating capacity of assets before their engineering or accounting life expires. A thermal power plant on a river whose low-flow season is extending by two weeks per decade due to climate change faces a chronic curtailment risk that affects revenue projections without necessarily triggering a physical damage event. This type of chronic risk does not fit insurance, but it does feed into impairment testing under Ind AS 36.

For capital allocation, physical risk scores can inform site selection for new investment. A company considering two sites for a new warehouse, one in a coastal Odisha SEZ and one in an inland Madhya Pradesh location, can attach modelled climate risk costs to each option. The Odisha site may offer tax incentives and port proximity, but its cyclone PML and flood risk add an expected insurance cost differential and an uninsured chronic disruption cost that can be quantified and compared against the incentive package. This is the kind of climate-informed capital allocation that RBI, SEBI, and institutional investors are asking companies to demonstrate.

Most Indian companies below the top 100 by market capitalisation have not yet built internal physical risk quantification capability. The practical path is to build it incrementally, starting with the tools and data already available.

Start with the free tier. CatNet provides hazard scores for any location at no cost through the Swiss Re portal or via your insurer. IMD's published district-level hazard atlases and SDMA documents cover most major industrial locations. These tools can produce an initial hazard map for all company assets within a week of effort from a single analyst. The output is not actuarially precise, but it identifies which assets have meaningful exposures and which do not, enabling prioritisation.

For high-value assets, engage a specialist. For any single asset above INR 100 crore total insured value, commissioning a site-specific flood or cyclone vulnerability assessment from a qualified risk engineering firm is cost-justified. The output, a site-level PML curve, feeds directly into insurance negotiation and BRSR disclosure. Typical cost for a single-site assessment is INR 2-5 lakh, a fraction of the premium on the asset.

Integrate with the insurance renewal process. The 90-day pre-renewal window is the right time to run the quantification analysis. By bringing location-level hazard data and gap analysis to the renewal meeting, the company moves from a passive buyer accepting whatever terms the market offers to an active participant shaping the coverage structure. Brokers who specialise in large commercial accounts in India increasingly have access to catastrophe modelling tools and can help translate raw hazard data into insurance programme recommendations.

For BRSR disclosure preparation, the quantification outputs should be reviewed by the company's statutory auditor or a specialist ESG assurance provider before inclusion in the annual report. The BRSR Core assurance standard requires the assurer to evaluate not just whether the disclosure was made but whether the methodology and data underlying it are credible. A physical risk quantification built on publicly available IMD data and CatNet with documented methodology will pass this test. An undocumented assertion that climate risk is managed will not.