📰 Playing catch up in flood forecasting technology
India needs a technically capable workforce that can master ensemble weather and flood forecast models
•Have you ever wondered how a local agency makes a decision if a flood forecast merely uses the words “Rising” or “Falling” above a water level at a river point? Especially when the time available to act is just 24 hours, there is no idea of the area of inundation, its depth, and when the accuracy of the forecast decreases at 24 hours and beyond?
•There are many times this happens in India during flood events, when the end users (district administration, municipalities and disaster management authorities) receive such forecasts and have to act quickly. These compelling scenarios are often experienced across most flood forecast river points, examples readers will be familiar with — in Assam, Bihar, Karnataka, Kerala or Tamil Nadu.
•Compare this with another form of flood forecast (known as the “Ensemble forecast”) that provides a lead time of 7-10 days ahead, with probabilities assigned to different scenarios of water levels and regions of inundation. An example of the probabilities ahead could be something like this: chances of the water level exceeding the danger level is 80%, with likely inundation of a village nearby at 20%. The “Ensemble flood forecast” certainly helps local administrations with better decision-making and in being better prepared than in a deterministic flood forecast.
•The United States, the European Union and Japan have already shifted towards “Ensemble flood forecasting” alongwith “Inundation modelling”. India has only recently shifted towards “Deterministic forecast” (i.e. “Rising” or “Falling” type forecast per model run).
•The shortcomings with Indian flood forecasting are glaring.
A case of multiple agencies
•The India Meteorological Department (IMD) issues meteorological or weather forecasts while the Central Water Commission (CWC) issues flood forecasts at various river points. The end-user agencies are disaster management authorities and local administrations.
•Therefore, the advancement of flood forecasting depends on how quickly rainfall is estimated and forecast by the IMD and how quickly the CWC integrates the rainfall forecast (also known as Quantitative Precipitation Forecast or QPF) with flood forecast. It also is linked to how fast the CWC disseminates this data to end user agencies.
•Thus, the length of time from issuance of the forecast and occurrence of a flood event termed as “lead time” is the most crucial aspect of any flood forecast to enable risk-based decision-making and undertake cost-effective rescue missions by end user agencies.
•Technology plays a part in increasing lead time. Reports suggest that the IMD has about 35 advanced Doppler weather radars to help it with weather forecasting. Compared to point scale rainfall data from rain gauges, Doppler weather radars can measure the likely rainfall directly (known as Quantitative Precipitation Estimation or QPE) from the cloud reflectivity over a large area; thus the lead time can be extended by up to three days.
•But the advantage of advanced technology becomes infructuous because most flood forecasts at several river points across India are based on outdated statistical methods (of the type gauge-to-gauge correlation and multiple coaxial correlations) that enable a lead time of less than 24 hours. This is contrary to the perception that India’s flood forecast is driven by Google’s most advanced Artificial Intelligence (AI) techniques!
•These statistical methods fail to capture the hydrological response of river basins between a base station and a forecast station. They cannot be coupled with QPF too.
•Google AI has adopted the hydrological data and forecast models derived for diverse river basins across the world for training AI to issue flood alerts in India. This bypasses the data deficiencies and shortcomings of forecasts based on statistical methods.
Not uniform across India
•A study by the National Institute of Technology, Warangal, Telangana shows that it is only recently that India has moved to using hydrological (or simply rainfall-runoff models) capable of being coupled with QPF. So, a lead time of three days is sporadic in India, and at select river points.
•Just as the CWC’s technological gap limits the IMD’s technological advancement, the technological limitations of the IMD can also render any advanced infrastructure deployed by CWC infructuous. Here is another example. The United States which is estimated to have a land area thrice that of India, has about 160 next generation S-band Doppler weather radars (NEXRAD) with a range of 250-300 km. India will need at least an 80-100 S-band dense radar network to cover its entire territory for accurate QPF. Else, the limitations of altitude, range, band, density of radars and its extensive maintenance enlarge the forecast error in QPF which would ultimately reflect in the CWC’s flood forecast. Conspicuously, the error margin is always away from the public gaze.
•Therefore, outdated technologies and a lack of technological parity between multiple agencies and their poor water governance decrease crucial lead time. Forecasting errors increase and the burden of interpretation shifts to hapless end user agencies. The outcome is an increase in flood risk and disaster.
Ensemble technology
•Global weather phenomenon is chaotic. For instance theoretically, “the flap of a butterfly’s wings in Chennai can create a Tornado in Tokyo” according to MIT’s Edward Lorenz. In simple terms and scientifically, any small change in the initial conditions of a weather model results in an output that is completely unexpected. Therefore, beyond a lead time of three days, a deterministic forecast becomes less accurate.
•The developed world has shifted from deterministic forecasting towards ensemble weather models that measure uncertainty by causing perturbations in initial conditions, reflecting the different states of the chaotic atmosphere. Probabilities are then computed for different flood events, with a lead time beyond 10 days.
•India has a long way to go before mastering ensemble model-based flood forecasting.
•Although, the IMD has begun testing and using ensemble models for weather forecast through its 6.8 peta flops supercomputers (“Pratyush” and “Mihir”), the forecasting agency has still to catch up with advanced technology and achieve technological parity with the IMD in order to couple ensemble forecasts to its hydrological models. It has to modernise not only the telemetry infrastructure but also raise technological compatibility with river basin-specific hydrological, hydrodynamic and inundation modelling. To meet that objective, it needs a technically capable workforce that is well versed with ensemble models and capable of coupling the same with flood forecast models. It is only then that India can look forward to probabilistic-based flood forecasts with a lead time of more than seven to 10 days and which will place it on par with the developed world.
•With integration between multiple flood forecasting agencies, end user agencies can receive probabilistic forecasts that will give them ample time to decide, react, prepare and undertake risk-based analysis and cost-effective rescue missions, reducing flood hazard across the length and breadth of India.
Whatever the benefits of Quad, India should not be led by the U.S. on regional initiatives
•With the second meeting of the Australia-India-Japan-United States Quadrilateral Strategic Dialogue of Foreign Ministers in Tokyo on Tuesday, the Quad has entered a decisive phase. The Ministers, who had last met at the UN General Assembly, made a considered push to hold the meeting, despite the COVID-19 pandemic. In a departure from the earlier secrecy, they made public a large part of their deliberations, including the decision to make the FM meeting an annual event, to cooperate on combating the pandemic, and on building infrastructure, connectivity and a supply chain initiative in the region. As the host, Japan’s Prime Minister Yoshihide Suga dispelled any notion that he might not be as proactive as his predecessor, Shinzō Abe, who originally conceived the idea in 2007. Australia’s Foreign Minister Marise Payne attended despite the two-week quarantine that she faces on return, and India’s External Affairs Minister S. Jaishankar undertook the journey despite the government’s preoccupation with the LAC stand-off. But it is probably the U.S. that displayed the most eagerness to hold the meeting, just weeks before the Presidential election. Mr. Trump’s COVID-19 illness and sudden hospitalisation prompted U.S. Secretary of State Mike Pompeo to cancel other scheduled stops, in South Korea and Mongolia. But in Tokyo, he made it clear that his mission was to direct the Quad towards building a coalition to counter Beijing’s aggression in the region, saying that their partnership was not “multilateralism for the sake of it”. He called on the entire Quad to “collaborate to protect” the region from what he called the “CCP’s exploitation, corruption, and coercion”, pointing to the LAC stand-off, as well as Chinese aggression in the South and East China Seas. What he seemed to propose was not just a coalition of democracies committed to a free and open Indo-Pacific, as the Quad’s informal charter has thus far stated. Instead, the U.S. seems keen on turning the Quadrilateral into a full-fledged military alliance of countries facing tensions with China.
•The government should not downplay the import of such openly stated intentions. While Japan and Australia are bound by alliance treaties to the U.S., New Delhi has thus far charted its course on strategic autonomy. Mr. Pompeo’s words could well be bluster borne of politics ahead of the U.S. elections, but they point to an interest in bringing India into bilateral tensions in the Indo-Pacific, while inviting the Quad to take a role in India-China tensions as well. The Modi government has rebuffed such suggestions, and any shift would be unwise now. India has much to gain strategically and in terms of capacity building from the Quadrilateral dialogue, but little from the impression it is being led by Washington on an important initiative for the region in which India is an equal and important stake-holder.
📰 Gig work and its skewed terms
The new labour codes do little to provide better payand definitive rights to platform workers
•The new Code on Social Security allows a platform worker to be defined by their vulnerability — not their labour, nor the vulnerabilities of platform work.
•Swiggy workers have been essential during the pandemic. Even so, they have faced a continuous dip in pay and no rewards for being essential workers. During the last six months, many platform workers have unionised under the All India Gig Workers Union and have protested day in and day out, deploring Swiggy for reducing their base pay from Rs. 35 to Rs. 10 per delivery order.
•It has been truly remarkable to see the ‘food delivery’ identity being developed through collective action, just as that of Uber and Ola taxi drivers has been taking shape for a few years now. Stable terms of earning have been a key demand of delivery-persons and drivers through years of protests.
•The three new labour codes passed by Parliament recently acknowledge platform and gig workers as new occupational categories in the making, in a bid to keep India’s young workforce secure as it embraces ‘new kinds of work’, like delivery, in the digital economy. But do the codes let Swiggy workers ask for the pay that they were promised? No. What a platform worker is allowed to claim as rights, responsibilities and working conditions that can be legally upheld is the key question in these codes, such as for factory workers, who have been an important industrial element in India and around the world. The specific issues of working in factories, the duration of time needed on a factory floor, and associated issues are recognised as the parameters for defining an ideal worker under most labour laws, and this has not shifted much.
Defining an ‘employee’
•The Code on Wages, 2019, tries to expand this idea by using ‘wages’ as the primary definition of who an ‘employee’ is. The wage relationship is an important relationship in the world of work, especially in the context of a large informal economy. Even so, the terms ‘gig worker’, ‘platform worker’ and ‘gig economy’ appear elsewhere in the Code on Social Security.
•Since the laws are prescriptive, what is written within them creates the limits to what rights can be demanded, and how these rights can be demanded. Hence, the categories and where they appear become key signs for understanding what kind of identity different workers can have under these new laws. Platform delivery people can claim benefits, but not labour rights. This distinction makes them beneficiaries of State programmes. This does not allow them to go to court to demand better and stable pay, or regulate the algorithms that assign the tasks. This also means that the government or courts cannot pull up platform companies for their choice of pay, or how long they ask people to work.
•The main role of the laws for a ‘platform worker’ is to make available benefits and safety nets from the government or platform companies. Even though platforms are part of the idea of how work will evolve in the future, the current laws do not see them as future industrial workers.
No guarantees
•In the Code on Social Security, 2020, platform workers are now eligible for benefits like maternity benefits, life and disability cover, old age protection, provident fund, employment injury benefits, and so on. However, eligibility does not mean that the benefits are guaranteed. None of these are secure benefits, which means that from time to time, the Central government can formulate welfare schemes that cover these aspects of personal and work security, but they are not guaranteed. Actualising these benefits will depend on the political will at the Central and State government-levels and how unions elicit political support. For some states like Karnataka, where a platform-focused social security scheme was in the making last year, this will possibly offer some financial assistance by the Centre. However, that is not assured. The language in the Code is open enough to imply that platform companies can be called upon to contribute either solely or with the government to some of these schemes. But it does not force the companies to contribute towards benefits or be responsible for workplace issues.
•The ‘platform worker’ identity has the potential to grow in power and scope, but it will be mediated by politicians, election years, rates of under-employment, and large, investment- heavy technology companies that are notorious for not complying with local laws. But there are no guarantees for better and more stable days for platform workers, even though they are meant to be ‘the future of work’.
