Moisture wrecks a farmer's life. Since February this year, lakhs of farmers across 14 states were left with damaged crops. Unseasonal rains destroyed crops on 11 million hectares spread over Rajasthan, Uttar Pradesh, Haryana, Madhya Pradesh, Maharashtra and Punjab. It can lead to up to five per cent loss in this year’s wheat harvest. After a deficit monsoon last year and low prices for key crops such as rice and wheat, the unseasonal rains and hailstorms now have put rural incomes under severe stress.

All because of moisture, or to be accurate, too much of it.

Moisture is the fuel that sustains seasonal weather-creating atmospheric systems called western disturbances. Originating from the Mediterranean, the disturbances travel across geographies before moving into north-west India. They stick to their usual frequency of rains once or twice (or even an odd third) a week. But this year, they packed more power as the level of moisture was above normal. In the bargain, there were at least two full-blown low-pressure areas (intensified weather) spinning up over north-west India, sparking off snow in the hills and thunderstorms/rains in the plains. In Jammu and Kashmir, there was a near-repeat of the flood that wrecked the valley six months ago.

A ‘gamble’

Indian agriculture’s tryst with monsoon, or the South-West monsoon to be precise, is an age-old one. It is also unique. There is hardly any other climatic event across the globe that can match the Asian monsoon in its grandiose sweep and bearing on the economy. The monsoon that hits India is the largest in the world because of the extent of area covered, which is practically the whole subcontinent. “Industry in India depends greatly on the monsoon,” says Laxman Singh Rathore, Director General, India Meteorological Department (IMD). “It is believed that only the agriculture sector is affected by monsoon. Despite its contribution to the GDP declining to 15 per cent, it remains a vital sector for rural India where 65 per cent of our population resides. But all other sectors, particularly power, are equally dependent on the season,” adds Rathore.

A century ago, Viceroy Lord George Curzon had said that the Indian economy is a ‘gamble on the monsoon.’ Rathore agrees that it continues to be so. Weather patterns impact farm and industrial output, labour productivity, energy demands and health. India, which is the world’s second-biggest grower of rice and wheat, depends on the June-September rains to water its farms because about 60 per cent of arable land isn’t irrigated. Farmers rely on the timing of the monsoon to decide which crops to grow. The season typically starts on the first day of June. Every few years, parts of the country are impacted due to insufficient rains. This drives up food prices and hits electricity output. This causes inflation, the bugbear of policymakers, to flare up.

Reserve Bank of India’s Governor Raghuram Rajan acknowledged the role of monsoon in taming inflation. “The upside risks to inflation stem from the unlikely possibility of significant fiscal slippage, uncertainty on the spatial and temporal distribution of the monsoon during 2015 as also the low probability but highly influential risks of reversal of international crude prices due to geo-political events,” he said while presenting the credit policy earlier this year. “Food prices will be affected by the seasonal upturn that typically occurs ahead of the South-West monsoon and, therefore, steps the government takes on food management will be critical in determining the inflation outlook,” he added.

A bad monsoon crimps rural incomes and slashes demand, forcing the government to offer more support to farmers. In recent years, the worst monsoon was in 2002, when GDP growth fell to 4 per cent for the fiscal year 2003, from 6 per cent a year earlier. During years of poor monsoons, either tax payers and consumers, or farmers – sometimes all three – get hurt, according to a recent Credit Suisse note. Farming makes up 10.6 per cent of China’s economy and 13.5 per cent of Indonesia’s. In India, while the share has shrunk to around 15 per cent from nearly 30 percent in the early 1990s, it still employs 60 per cent of the country’s massive population.

Riding the luck

The country rode the crest of luck (see tables) with a series of successive good monsoons since 1988. But it has become increasingly unpredictable ever since, giving a torrid time to the India Meteorological Department. And this has exposed the country’s nagging reliance on the seasonal rains despite its rapid growth and modernisation in recent times. But, according to investment firm Morgan Stanley, its importance to investors will slowly decline as agriculture's share of the economy falls. The rural economy has also been bolstered by remittances from migrant workers and farm-friendly policies, including a job guarantee scheme for landless labourers and subsidised fertiliser. “From a client's perspective, a commercial investor's perspective, this could be gone in say, maybe, three, four years. But from a human impact perspective, it will remain an issue,” Morgan Stanley adds in a recent note.

At present, though, monsoon-watch continues to be an annual financial and political event. And therein is the problem.

Searching for perfection

Monsoon is generated from a seasonal reversal of trade winds in the southern hemisphere, dictated by the northward movement of the sun. Being a global phenomenon, it is influenced by what happens in large water bodies such as the Pacific Ocean and the Indian Ocean (El Nino-La Nina and the Indian Ocean Dipole, for instance).

There are other minute factors that work at the core of the system, any disorder of which can impact monsoon performance – something unfailingly brought home year after year. Evolving weather in India's backyard also has a major say on how a prospective monsoon may perform. The extent to which the land gets heated up during the summer and the length of the winter in the Himalayas are among them. The unpredictability of the monsoon is synonymous with the indifferent success that the IMD has had with various models it has used to base its forecasts. In 2012, the then minister Jaipal Reddy admitted in the Lok Sabha that the IMD’s statistical model has limitations and the monsoon forecasts have often gone wrong. From 1998 to 2002, the IMD used the 16-parameter statistical model, which worked well until it floundered on the 2002 drought. The IMD later realised that some parameters in the model showed increasingly weakening correlations. Since then, the new eight- and 10-parameter models using newer parameters have been at work.

The IMD is now working on the National Monsoon Mission (NMM). Under the mission, the IMD will collaborate with weather research organisations nationally as well as internationally to improve monsoon forecasting. “The NMM aims to improve models for short (12-72 hours), medium (72-240 hours), extended (10-30 days) and seasonal prediction,” says D Sivanand Pai, Head, Long Range Forecasting Division, IMD, Pune. “For example, since the introduction of the Statistical Ensemble Forecast model for long period average, the average absolute error for the 2003-2014 period has come down by 2 per cent in comparison to the previous 12 years. We are also working on a hybrid model using both statistical and dynamic forecasting, as well as trying for multi-decadal forecast,” he adds.

NMM”s focus is on developing a dynamic model (dynamic because it is not based on set parameters) for monsoon prediction and ₹400 crore has been earmarked for it over five years. Dynamic models simulate sea and atmospheric conditions and are acknowledged the most dependable across the world. But they are still in the experimental stage when it comes to the Indian monsoon and need a lot more investigating and research. “It is still in research mode. The Indian Institute for Tropical Meteorology is the main coordinator and is interacting with US agency NOAA (National Oceanic and Atmospheric Administration). We are trying to improve forecast across the seasonal model and make it suitable for Indian conditions,” says Pai.

With inputs from Alka Kshirsagar

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