With a height of over 8.5 thousand meters above sea level, the Kanchenjunga peak is the tallest mountain in India. It borders Nepal and India and has five peaks. This was followed by Nanda Devi at a height of around 7.8 thousand meters. Most of these were present in the northern region of the country and make up a part of the Great Himalayan Ranges.

Standing at an altitude of over 8.8 thousand feet above sea level, the Anamudi Peak was the highest mountain in Southern India in 2021. Famously known as "the Himalayas of South India," the peak is located in the South Indian state of Kerela. Other significant mountains include Doddabetta Peak and Kolaribetta Peak, both of which are located in Tamil Nadu.

The total forest cover in various altitude zones in India was the highest for lower elevation zones less than 500 meters covering an area of 381 thousand square kilometers in 2021. The area of forest cover increased with a decrease in elevation in the country.

These data are digital elevation models which describe landscape topography. The data were created to support analysis of landscape change following the 7th February 2021 avalanche-debris flow in Chamoli District, Uttarakhand, India. The data were used as standalone datasets to support this analysis, but also supported numerical modelling using CAESAR-Lisflood (see data collection). The DEMs were created from CNES/Airbus Pléiades-HR stereo satellite imagery captured in along-track mode. They are a geospatial dataset created in raster (.tif) format. They are most commonly imported into GIS software, where they can be analysed or support other forms of geospatial analysis. Full details about this dataset can be found at https://doi.org/10.5285/5a1eaef4-9211-4227-a017-d20b08be5784

A global 1-km resolution land surface digital elevation model (DEM) derived from U.S. Geological Survey (USGS) 30 arc-second SRTM30 gridded DEM data created from the NASA Shuttle Radar Topography Mission (SRTM). GTOPO30 data are used for high latitudes where SRTM data are not available. For a grayscale hillshade image layer of this dataset, see "world_srtm30plus_dem1km_hillshade" in the distribution links listed in the metadata.

Previous studies have shown contrasting glacier elevation and mass changes in the sub-regions of high-mountain Asia. However, the elevation changes on an individual catchment scale can be potentially influenced by supraglacial debris, ponds, lakes and ice cliffs besides regionally driven factors. Here, we present a detailed study on elevation changes of glaciers in the Lahaul-Spiti region derived from TanDEM-X and SRTM C-/X-band DEMs during 2000-2012 and 2012-2013. We observe three elevation change patterns during 2000-2012 among glaciers with different extent of supraglacial debris. The first pattern (< 10 % debris cover, type-1) indicates maximum thinning rates at the glacier terminus and is observed for glacier with no or very low debris cover. In the second pattern (> 10 % debris cover, type-2), maximum thinning is observed up-glacier instead of glacier terminus. This is interpreted as the insulating effect of a thick debris cover. A third pattern, high elevation change rates near the terminus despite high debris cover (> 10 % debris cover, type-3) is most likely associated with either thinner debris thickness or enhanced melting at supraglacial ponds and lakes as well as ice cliffs. We empirically determined the SRTM C- and X-band penetration differences for debris-covered ice, clean ice/firn/snow and correct for this bias in our elevation change measurements. We show that this penetration bias, if uncorrected, underestimates the region-wide elevation change and geodetic mass balance by 20 %. After correction, the region-wide elevation change (1712 sqkm) was estimated to be -0.65±0.43 m/yr during 2000-2012. Due to the short observation period, elevation change measurements from TanDEM-X for selected glaciers in the period 2012-2013 are subject to large uncertainties. However, similar spatial patterns were observed during 2000-2012 and 2012-2013, but at different magnitudes. This study reveals that the thinning patterns of debris-covered glaciers cannot be generalized and spatially detailed mapping of glacier elevation change is required to better understand the impact of different surface types under changing climatic conditions.

In 2021, the lower elevation zones less than 500 meters in India had the highest forest cover of open forests covering an area of 190 thousand square kilometers. Areas with a forest canopy density of between ten percent and 40 percent were categorized as open forests in India.

This horizontal bar chart displays urban population living in areas where elevation is below 5 meters (% of total population) by region using the aggregation sum and is filtered where the country is India. The data is about countries per year.

Goal 13: Take urgent action to combat climate change and its impactsThe greenhouse gas emissions from human activities are driving climate change and continue to rise. They are now at their highest levels in history. Global emissions of carbon dioxide have increased by almost 50% since 1990.The atmospheric concentrations of carbon dioxide, methane, and nitrous oxide have increased to levels unprecedented in at least the last 800,000 years. Carbon dioxide concentrations have increased by 40% since pre-industrial times, primarily from fossil fuel emissions and secondarily from net land use change emissions. The ocean has absorbed about 30% of the emitted anthropogenic carbon dioxide, causing ocean acidification.Each of the last three decades has been successively warmer at the Earth’s surface than any preceding decade since 1850. In the Northern Hemisphere, 1983-2012 was likely the warmest 30-year period of the last 1,400 years.From 1880 to 2012, average global temperature increased by 0.85°C. Without action, the world’s average surface temperature is projected to rise over the 21st century and is likely to surpass 3 degrees Celsius this century – with some areas of the world, including in the tropics and subtropics, expected to warm even more. The poorest and most vulnerable people are being affected the most.The rate of sea level rise since the mid-19th century has been larger than the mean rate during the previous two millennia. Over the period 1901 to 2010, global mean sea level rose by 0.19 [0.17 to 0.21] meters.From 1901 to 2010, the global average sea level rose by 19 cm as oceans expanded due to warming and melted ice. The Arctic’s sea ice extent has shrunk in every successive decade since 1979, with 1.07 million km² of ice loss every decade.It is still possible, using an array of technological measures and changes in behaviour, to limit the increase in global mean temperature to two degrees Celsius above pre-industrial levels.There are multiple mitigation pathways to achieve the substantial emissions reductions over the next few decades necessary to limit, with a greater than 66% chance, the warming to 2ºC – the goal set by governments. However, delaying additional mitigation to 2030 will substantially increase the technological, economic, social and institutional challenges associated with limiting the warming over the 21 century to below 2 ºC relative to pre-industrial levels.India has committed to reduce the emissions intensity of its GDP by 20 to 25% by 2020.This map layer is offered by Esri India, for ArcGIS Online subscribers, If you have any questions or comments, please let us know via content@esri.in.

Nitrous oxide (N2O) emissions response curves for crops grown outside temperate regions have been rare and have thus far arrived at conflicting conclusions. Most studies reporting N2O emissions from tropical cropping systems have examined only one or two nitrogen fertilizer application rate(s) which precludes the possibility of discovering nonlinear changes in emission factors (EF, % of added N converted to N2O-N) with increasing fertilizer-N rates. To examine the relationship between N rates and N2O fluxes in a tropical region, we compared farming practices with three or four N rates for their yield-scaled impacts from three crops in peninsular India. We measured N2O fluxes during nine seasons between 2012 and 2015, with N application rates ranging between 0 and 70, 0 and 90, and 0 and 480 kg-N ha-1 for foxtail-millet (Setaria italica L., locally called korra), groundnut (Arachis hypogaea L., also called peanut) and finger-millet (Eleusine coracana L., locally called ragi), respectively. In two cases, the highest N application rate greatly exceeded crop-N needs. Potential climate smart farming agricultural practices (with low/optimized N rates) led to a 50-150% reduction in N2O emissions intensity (per unit yield) along with a reduction of 0.2-0.75 tCO2e ha-1 season-1 as compared to high N conventional applications. We found a non-linear increase in N2O flux in response to increasing applied N for both N-fixing and non N-fixing crops and the extent of super-linearity for non N-fixing crops was much higher than what has been reported earlier. If a linear fit is imposed on our datasets, the emission factors (EFs) for finger-millet and groundnut were ~3.5% and ~1.8%, respectively. Our data shows that for low-N tropical cropping systems, even when they have low soil carbon content, increase in N use to levels just above crop needs to enhance productivity might lead to relatively small increase in N2O emissions as compared to the impact of equivalent changes in fertilizer-N use in systems fertilized far beyond crop N needs. Methods

The five study farms were in the Indian states of Karnataka and Andhra Pradesh. Emissions during cultivation of Groundnut (peanut), Foxtail- and Finger- millet were studied at two, one and two farms, respectively. The measurement of GHG emissions, yield and other agro-economic indicators was performed for a total of nine seasons at three regional laboratories established by a coalition of partners interested in promoting climate smart farming in agro-ecological regions 8.2 and 3.0 of the semi-arid peninsula of India. Soil characteristics and weather conditions Each of the five experimental sites was a farmer owned and managed small-holder plot and was located in peninsular India between 12.77-14.66 N (Latitude), 77.20-77.75 E (Longitude) and 350-790 m (elevation above sea level). The experimental sites had sandy-loam and loamy-sand texture (680-750 g kg-1 Sand, 120-170 g kg-1 Silt and 130-170 g kg-1 Clay) and soil organic matter concentration varying between 3.2 and 14.3 g kg-1 (i.e., between 1.9 and 8.3 g kg-1 soil C). Except in the case of foxtail millet (which was a newly cultivated site), the groundnut and finger millet plots were under continuous groundnut or finger-millet systems, respectively, for over a decade before establishment of our experiments. The soil characteristics of each site are given in S1 Table. The climate of all study locations was semi-arid with measured seasonal rainfall varying from 56-480 mm during the experimental period. The lowest and the highest temperatures observed at our sites varied from 10-21 and 33-40 °C, respectively (see S1 Table for details of each site). All experimental sites were between 0.1 and 0.42 ha in size and the experimental treatments were implemented by the farmer under supervision of a trained field and laboratory research team. There were three replicates for each treatment and each subplot received one treatment with stratified randomized block design. Nitrous oxide emissions were measured for both finger-millet and groundnut during four cropping seasons each, along with some fallow periods flanking these growing seasons between July 2012 and December 2015. Groundnut was sown between July 10-September 4 and harvested between November 3-December 25. Finger-millet was sown between August 3-August 25 and harvested between November 25-January 1. Due to severe drought and other complications, N2O emissions data from the foxtail-millet farm could be collected only for one season between October 12, 2014 and January 19, 2015. The data from two groundnut growing seasons (dry kharif and irrigated rabi in 2012) was published earlier (Kritee et al, 2015) and is presented here with new estimates of mineralized organic nitrogen which impacted the calculation of EFs. During the fallow periods, there were no inputs of water or fertilizer to the experimental sites, except to prepare for the upcoming cropping season. Treatments We compared N2O emissions from three or four broad categories of treatments: Very-high-N (VHN, conventional practices with N rates varying from 91 to 276 kg N ha-1), High-N (HN, conventional practices identified via our local farmer surveys with total N rate varying from 53 to 248 kg N ha-1; see S3 Table for farmer survey results), Low-N (LN, farm-specific potential climate-smart farming practices including completely organic practices for groundnut farms, total N varying from 17-78 kg N ha-1) and a zero N (control). We explored changes in N2O emissions with changing N fertilizer inputs under scenarios where water input was either below or above water requirements for groundnut (>280 mm) and finger-millet (>450 mm). The dry sites for groundnut had water input between 100-200 mm in the rainfed season (locally called kharif) whereas the wet site had a water input of 370 mm (irrigated winter season locally called rabi). The dry and wet rainfed sites for finger-millet had water inputs between 100-350 mm and ~480 mm, respectively. The Low-N treatment (Table 1 and S3-S4 Tables) represented farm-specific “alternate” practices that were investigated for their potential to deliver similar (or higher) yields and economic benefits to farmers as well as lower climate impacts. The potential climate-smart farming practices investigated for foxtail-millet and groundnut farms in agro-ecological region (AER) 3.0 involved completely organic (with no synthetic) inputs. Except in the case of finger-millet, the High-N treatment represents the conventional “business-as-usual” crop management practices as currently implemented by farmers with average to large land-holdings in this region. The conventional practices were identified via regional farmer surveys conducted during the study. The recommended inorganic N use for groundnut, finger- and foxtail- millet is 20-30, 50, and 30 kg N ha-1, respectively. Farmer surveys conducted during this study or by the Indian government indicated that farmers were using much higher fertilizer N application rates than the crop-specific recommendations by the state/district governments and/or academic institutions. Please see S3 Table for comparison of survey results with “High N” treatments. The Very-High-N treatments for finger-millet and groundnut included addition of nitrogen fertilizers much higher than the respective crop’s nitrogen needs. These treatments were included specifically to test the extent of super-linear response in N2O emissions when N inputs are very high. Overall, the N fertilization rates for groundnut, finger-millet and foxtail millet varied from 0 to 77, 0 to 470 and 0 to 49 kg N ha-1, respectively The rate and timing of all organic and inorganic fertilizer applications are provided in S2 Table and total N rate (including contribution from mineralized organic N) for each treatment is presented in Table 1.

In general, the soils in the two agro-ecological regions are not amenable to cultivation without ploughing. For groundnut and foxtail-millet, tillage was done once in each season about 25 days before sowing. For finger-millet, tillage was done 2-4 times between March and July soon after rainfall depending on soil hardness and manure (if any) was incorporated during the last 1-2 tillage events. Bullock cart ploughing tills soil to a depth of 12 cm and local tractors (used only when the soil is very hard) plough to the depth of up to 18 cm. There was no tillage done to control weeds and there was no use of herbicides and pesticides. During the rainfed south-west monsoon season (from July to December; locally called kharif), sowing was done manually at a seed rate 146 ± 27 kg ha-1 for groundnut (Kadiri 6 variety) at a 30 cm row spacing, 10 cm plant spacing and to a depth of 5 cm, 12 kg ha-1 for foxtail millet (local variety called Jadda Korra) at a 30 cm row spacing, 8-12 cm plant spacing and to a depth of 3-6 cm and 24.7 kg ha-1 for finger-millet (MR1 variety) at a 25 row spacing to a depth of 3-6 cm. Both millets are sown with a seed drill attached to a bullock and the plots are thinned/weeded 12-20 and 20-25 days after sowing of finger- and foxtail-millet, respectively. The seed rates used in a given crop and season were the same for all treatments. All of the aboveground biomass (as well as belowground biomass for groundnut) was harvested manually 110-130 days after sowing (see exact dates in S1 Table). N2O flux monitoring Manual closed chambers were used to collect air samples from each of the three replicate treatment plots and the air samples were analyzed by electron capture detector (ECD) in a gas chromatograph (Thermo Fisher Trace GC 600) to quantify N2O emissions rates based on methodology developed in our labs. Because most N2O emissions occur within 1-4 days following N addition and/or irrigation/rainfall, N2O flux measurements are more reliable when the sampling frequency is high and the sampling schedule captures spatio-temporal variability in

These data are input files for CAESAR-Lisflood (CL), a numerical hydrodynamic-landscape evolution model. These files were created to support coupled hydrodynamic-landscape evolution modelling to evaluate the geomorphological response of river channels affected by the 7th February 2021 ice-rock avalanche and debris flow in Chamoli District, Uttarakhand, India. They include 10 m digital elevation models (DEMs) of bed rock and land surface topography in a gridded (raster) format. They also include reanalysis-derived river discharge data generated by the GEOGloWS project at the following locations: Rontigad, Rishiganga, Dhauliganga, and Alaknanda. The configuration settings and parameters for CL modelling are also included.