Resources Research

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Posts Tagged ‘remote sensing

Sizing up rural and urban settlements in Maharashtra

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rg_maharashtra_districts_builtup_201610The districts of Jalna, Osmanabad, Hingoli, Satara, Ratnagiri, Washim, Nandurbar, Gondiya, Gadchiroli and Sindhudurg in Maharashtra all enjoy a rural built-up to urban built-up ratio of more than 2 (where the built-up area of the district’s rural settlements are at least twice the area of its urban settlements).

In the chart, the light green bars show a district’s rural built-up area, the light maroon its urban built-up area. The number associated with the name of the district is the ratio between the two kinds of built-up area.

Such a comparison helps us understand the dependency of the two kinds of populations in a district, rural and urban, upon the natural resources (as classified by land types). The chart shows us that some districts (see Jalgaon, Sholapur, Satara and Ratnagiri) have total rural built-up areas of 150 square kilometres and above. But whereas the urban built-up areas of Jalgaon and Sholapur are more than 100 sq km each this is not so for the other two districts.

Districts may have similar ratios between rural and urban built-up areas – see Ahmednagar, Akola and Dhule – but whereas the built-up areas of both types are more than 100 sq km in Ahmednagar they are smaller in the other two districts. There are only three districts for which the total rural built-up area is less than 50 sq km: Parbhani, Hingoli ad Washim.

There are 15 districts in which there is at least 1.5 sq km of rural built-up area for 1 sq km of urban built-up and this indicates that in these districts the base of agricultural and allied activities is still strong and therefore needs continuous encouragement. There are 7 districts for which this ratio is between 1.5 and 1 and these therefore must be watched for signs of quickening urbanisation which will need to be curbed in the interests of sustainability and indeed of the provision of food.

I have taken the data from the land use and land change information for 2011-12 collected by the Resourcesat-2 satellite with land classification and calculation carried out by the National Remote Sensing Centre (NRSC), Indian Space Research Organisation (ISRO), Department of Space, under the Natural Resources Census Project of the National Natural Resources Repository Programme. It is available through Bhuvan, the geo-platform of ISRO.

Urban areas are non-linear built-up areas covered by impervious structures adjacent to or connected by streets. This class includes residential areas, mixed built-up, recreational places, public and private utilities, communications, commercial areas, reclaimed areas, vegetated areas within urban zones, transportation infrastructure, industrial areas and their dumps, and ash/cooling ponds. Rural built-up areas are the lands used for human settlement in which the majority of the population is involved in agriculture. These are built-up areas, small in size, mainly associated with agriculture and allied sectors and non-commercial activities. They can be seen in clusters both non-contiguous and scattered.

The last 4 districts – Nagpur, Nashik, Thane and Pune – have their urban built-up bars coloured differently to indicate that their scales are beyond, and very much above, the 150 sq km of the chart. Mumbai city and suburban is omitted entirely.

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A method for a post-carbon monsoon

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RG_Goan_monsoon_2015

The uses to which we have put available climatic observations no longer suit an India which is learning to identify the impacts of climate change. Until 2002, the monsoon season was June to September, there was an assessment in May of how well (or not) the monsoon could turn out, and short-term forecasts of one to three days were available only for the major metros and occasionally a state that was in the path of a cyclone. But 2002 saw the first of the four El Niño spells that have occurred since 2000, and the effects on our Indian summer monsoon began to be felt and understood.

The India Meteorology Department (which has become an everyday abbreviation of IMD for farmers and traders alike) has added computational and analytical resources furiously over the last decade. The new research and observational depth is complemented by the efforts of a Ministry of Earth Sciences which has channelled the copious output from our weather satellites, under the Indian Space Research Organisation (ISRO), and which is interpreted by the National Remote Sensing Centre (NRSC), to serve meteorological needs.

The IMD, with 559 surface observatories, 100 Insat satellite-based data collection platforms, an ‘integrated agro-advisory service of India’ which has provided district-level forecasts since 2008, a High Performance  Computing  System commissioned in 2010 (whose servers run at Pune, Kolkata, Chennai, Mumbai, Guwahati, Nagpur, Ahmedabad, Bengaluru, Chandigarh, Bhubaneswar, Hyderabad and New Delhi) ploughs through an astonishing amount of numerical data every hour. Over the last four years, more ‘products’ (as the IMD system calls them) based on this data and its interpretation have been released via the internet into the public domain. These are reliable, timely (some observation series have three-hour intervals), and valuable for citizen and administrator alike.

The new 11-grade indicator for assessing weekly rainfall departures in districts. Same data, but dramatically more useful guidance.

The new 11-grade indicator for assessing weekly rainfall departures in districts. Same data, but dramatically more useful guidance.

Even so, the IMD’s framing of how its most popular measures are categorised is no longer capable of describing what rain – or the absence of rain – affects our districts. These popular measures are distributed every day, weekly and monthly in the form of ‘departures from normal’ tables, charts and maps. The rain adequacy categories are meant to guide alerts and advisories. There are four: ‘normal’ is rainfall up to +19% above a given period’s average and also down to -19% from that same average, ‘excess’ is +20% rain and more, ‘deficient’ is -20% to -59% and ‘scanty’ is -60% to -99%. These categories can mislead a great deal more than they inform, for the difference between an excess of +21% and an excess of +41% can be the difference between water enough to puddle rice fields and a river breaking its banks to ruin those fields.

In today’s concerns that have to do with the impacts of climate change, with the increasing variability of the monsoon season, and especially with the production of food crops, the IMD’s stock measurement ‘product’ is no longer viable. It ought to have been replaced at least a decade ago, for the IMD’s Hydromet Division maintains weekly data by meteorological sub-division and by district. This series of running records compares any given monsoon week’s rainfall, in a district, with the long period average (a 50-year period). Such fineness of detail must be matched by a measuring range-finder with appropriate  interpretive indicators. That is why the ‘no rain’, ‘scanty’, ‘deficient’, ‘normal’ or ‘excess’ group of legacy measures must now be discarded.

In its place an indicator of eleven grades translates the numeric density of IMD’s district-level rainfall data into a much more meaningful code. Using this code we can immediately see the following from the chart ‘Gauging ten weeks of rain in the districts’:

1. That districts which have experienced weeks of ‘-81% and less’ and ‘-61% to -80%’ rain – that is, very much less rain than they should have had – form the largest set of segments in the indicator bars.

2. That districts which have experienced weeks of ‘+81% and over’ rain – that is, very much more rain than they should have had – form the next largest set of segments in the indicator bars.

3. That the indicator bars for ‘+10% to -10%’, ‘-11% to -20%’ and ‘+11% to +20%’ are, even together, considerably smaller than the segments that show degrees of excess rain and degrees of deficient rain.

Far too many districts registering rainfall departures in the categories that collect extremes of readings. This is the detailed reading required to alert state administrations to drought, drought-like and potential flood conditions.

Far too many districts registering rainfall departures in the categories that collect extremes of readings. This is the detailed reading required to alert state administrations to drought, drought-like and potential flood conditions.

Each bar corresponds to a week of district rainfall readings, and that week of readings is split into eleven grades. In this way, the tendency for administrations, citizens, the media and all those who must manage natural resources (particularly our farmers), to think in terms of an overall ‘deficit’ or an overall ‘surplus’ is nullified. Demands for water are not cumulative – they are made several times a day, and become more or less intense according to a cropping calendar, which in turn is influenced by the characteristics of a river basin and of an agro-ecological zone.

The advantages of the modified approach (which adapts the Food and Agriculture Organisation’s ‘Global Information and Early Warning System’ categorisation, designed to alert country food and agriculture administrators to impending food insecurity conditions) can be seen by comparing the single-most significant finding of the IMD’s normal method, with the finding of the new method, for the same point during the monsoon season.

By 12 August 2015 the Hydromet Division’s weekly report card found that 15% of the districts had recorded cumulative rainfall of ‘normal’ and 16% has recorded cumulative rainfall of ‘deficient’. There are similar tallies concerning rainfall distribution – by region and temporally – for the meteorological sub-divisions and for states. In contrast the new eleven-grade measure showed that in seven out of 10 weeks, the ‘+81% and over’ category was the most frequent or next-most frequent, and that likewise, the ‘-81% and less’ category was also the most frequent or next-most frequent in seven out of 10 weeks. This finding alone demonstrates the ability of the new methodology to provide early warnings of climatic trauma in districts, which state administrations can respond to in a targeted manner.