Climate Change Project Update

Forecasting India's Water Future

The NORINDIA project sheds light on how climate change could affect monsoons, droughts, and glaciers in northern India.

By , Vidyunmala Veldore, Lu Li, R. Krishnan, Yvan Orsolini, Retish Senan, M. V. S. Ramarao, and Ellen Viste

The Indian subcontinent is particularly vulnerable to climate change because of its diversified socioeconomic and climatic conditions. Changes in monsoon variability and glacier melt may lead to droughts over the Indian plains as well as extreme rains and abrupt floods in the neighboring Himalayas [Turner and Annamalai, 2012]. Through our work with the NORINDIA project, we found that there is a risk of 50% glacier melt in the Beas River basin, which covers northwest India and northeast Pakistan, by 2050.

Fig. 1. The Indus River basin, which contains the Indus River and its tributaries, including the Beas River. Credit: Kmhkmh, CC BY 3.0
Fig. 1. The Indus River basin, which contains the Indus River and its tributaries, including the Beas River. Credit: Kmhkmh, CC BY 3.0

Understanding the monsoon variability and the hydrological cycle in the Hindu-Kush-Himalayan region would improve our ability to forecast changes and make informed policy decisions. The objective of NORINDIA was to understand the hydrological impacts of climate change in India. The project ran from 2012 to 2015 and was funded by the Norwegian Research Council.

NORINDIA has provided a fresh hydrological assessment of water resources in India through the use of scenarios from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report and other modeling systems. The Beas River basin (Figure 1) forecast is one example of NORINDIA’s work.

A Five-Part Project

The NORINDIA project was divided into five work packages (WP) that dealt with large- and small-scale atmospheric processes:

  • WP1 studied the effect of climate change on the Indian monsoon.
  • WP2 quantified the role of snow and surface processes on the Indian summer monsoon.
  • WP3 quantified the role of snow and glacier melt on water resources.
  • WP4 developed dynamical downscaled model output using the Weather Research and Forecasting (WRF) model and the LMDZ atmospheric general circulation model (Laboratoire de Météorologie Dynamique with zooming capability, which allows grids to be refined around specific sites).
  • WP5 studied changes in the hydrological cycle in the present and future at the Beas River basin (up to Pandoh Dam) in the western Himalayas using WRF-Hydro [Gochis et al., 2014] and the Soil and Water Assessment Tool model.

Changes Ahead

All the packages worked together to integrate results across different scales of time and space to produce a unified understanding of how climate change may affect the monsoon and water availability in India. The predictions pointed to changes in summer rainfall, the onset dates for snow and monsoons, and glacier mass balance.

In WP1, we studied how precipitation changes under the relatively aggressive Representative Concentration Pathway (RCP) 8.5 climate change scenario, which represents a possible radiative forcing (change in atmospheric energy caused by greenhouse gas emissions) of 8.5 watts per square meter in the year 2100 relative to preindustrial levels [van Vuuren et al., 2011]. Our results indicate a precipitation increase of around 10% in summer rainfall during the period 2076–2096 compared with recent climate in the Indo-Pacific region. This figure agrees with previous work by Christensen et al. [2013].

Fig. 2. High spring snow over the Himalaya-Tibet Plateau is associated with anomalously warm and dry conditions over the Indian peninsula. Contour lines indicate composite differences (in °C) in forecasted June mean temperatures at 2 meters above the surface between high and low April snow depth over the Himalaya-Tibet Plateau (27°N-40°N, 70°E-100°E) taken over the period 1981 to 2010. Color shading shows the corresponding composite difference for precipitation in millimeters per day. The 15-member ensemble seasonal hindcasts with the European Centre for Medium-Range Weather Forecasts Seasonal Forecasting System 4 were started on 1 April. The snow depths are taken from ERA-Interim/Land, a global land surface reanalysis dataset. Also shown is the composite of 1-15 June averaged precipitation (millimeters per day, as colored stippling) based on gridded station rain gauge data from the Indian Meteorological Department. Data are shown only at the 95% significance level. Credit: Yvan Orsolini and Retish Senan, adapted from Senan et al. [2016].
Fig. 2. High spring snow over the Himalaya-Tibet Plateau is associated with anomalously warm and dry conditions over the Indian peninsula. Contour lines indicate composite differences (in °C) in forecasted June mean temperatures at 2 meters above the surface between high and low April snow depth over the Himalaya-Tibet Plateau (27°N–40°N, 70°E–100°E) taken over the period 1981 to 2010. Color shading shows the corresponding composite difference for precipitation in millimeters per day. The 15-member ensemble seasonal hindcasts with the European Centre for Medium-Range Weather Forecasts Seasonal Forecasting System 4 were started on 1 April. The snow depths are taken from ERA-Interim/Land, a global land surface reanalysis data set. Also shown is the composite of 1–15 June averaged precipitation (millimeters per day, as colored stippling) based on gridded station rain gauge data from the Indian Meteorological Department. Data are shown only at the 95% significance level. Credit: Yvan Orsolini and Retish Senan, adapted from Senan et al. [2016]
As part of WP2, Senan et al. [2016] assessed the impact of springtime snow over the Himalaya-Tibetan Plateau (HTP) on the onset of the Indian summer monsoon (ISM), using land reanalyses to initialize the coupled ensemble seasonal forecasts. They showed that deep snow over the HTP in spring influenced the meridional (north–south) tropospheric temperature gradient reversal that marks the ISM onset. Composite differences based on a normalized HTP snow index reveal that in high-snow years, the monsoon onset is delayed by about 8 days, and negative precipitation anomalies, as well as persisting dry and warm surface conditions, prevail over India (Figure 2). Half of this delay can be attributed to the initialization of snow over the HTP, highlighting the importance of improving the realism of land reanalyses over that region.

In WP3, Viste and Sorteberg [2015] showed that increasing temperatures may reduce annual snowfall in the Himalaya–Hindu Kush–Karakoram region, in spite of a likely increase in precipitation. With the RCP8.5 scenario, we estimated a reduction in annual snowfall between 30% and 50% in the Indus basin, 50% and 60% in the Ganges basin, and 50% and 70% in the Brahmaputra basin (Figure 3). These results are qualitatively in line with those of Wiltshire [2014].

Fig. 3. Projected changes in temperature, precipitation, and snowfall in subbasins of the Indus, Ganges, and Brahmaputra basins. Data are Coupled Model Intercomparison Project Phase 5 (CMIP5) multimodel mean changes from 1971–2000 to 2071–2100, with the Representative Concentration Pathway (RCP) 8.5 scenario; methods were described by Viste and Sorteberg [2015]. Credit: Ellen Viste, CC BY 3.0
Fig. 3. Projected changes in temperature, precipitation, and snowfall in subbasins of the Indus, Ganges, and Brahmaputra basins. Data are Coupled Model Intercomparison Project Phase 5 (CMIP5) multimodel mean changes from 1971–2000 to 2071–2100, with the Representative Concentration Pathway (RCP) 8.5 scenario; methods were described by Viste and Sorteberg [2015]. Credit: Ellen Viste, CC BY 3.0
In WP4, we showed that for the less aggressive RCP4.5 scenario (radiative forcing of 4.5 watts per square meter), there is a robust increase in surface temperature compared with the present climate. For the Beas basin in northeastern India and the Brahmaputra basin covering Bangladesh, Bhutan, and southern China, we calculated the increase to be on the order of 1.8°C in 2039–2080 and 3°C in 2079–2100. For the Indus basin, between the Himalayan Mountains and the Arabian Sea, the data suggest a significant increase in surface temperature (~3°C) in 2079–2100 but not in earlier decades.

Using long-term climate observations and high-resolution model experiments, we further noted a weakening trend in the monsoon circulation and a precipitation decline over South Asia during recent decades (1951–2005). This downward trend is largely attributable to anthropogenic forcing from aerosols, land use and land cover changes, and rapid warming of the equatorial Indian Ocean [Krishnan et al., 2015; Ramarao et al., 2015].

In addition, the experiments show that the surface-warming trend over the Indian region is accompanied by a decline in precipitation and soil moisture starting from the mid-1950s and continuing into the 21st century. Again, this result agrees with recent findings from observations [Panda and Wahr, 2016].

Finally, in WP5, our hydrological modeling shows that at present, the runoff (including rainfall runoff and ice and snow melt) from glacier-covered areas accounts for 28% of the total runoff measured at the Thalout station in the Beas River basin. The annual glacier imbalance accounts for about 13% of the total runoff in this area.

Climate change scenarios show that precipitation may increase about 11% by 2050 and 18% by the end of 2100. Forecasts for the year 2050 predict glacier area loss in the Beas River basin of about 47% for the RCP4.5 scenario and 49% for RCP8.5. Also, by the end of 2100, the glacier area loss in this basin is about 73% for the RCP4.5 scenario and 80% for RCP8.5. This would result in a reduction in runoff of 25% by about 2050 and 29.9% by the end of the century. These results contribute to the ongoing discussion on glacier mass balance in India [Moors et al., 2011].

What We Learned

The NORINDIA project has contributed to furthering the understanding of the hydrological impacts in India under different climate change scenarios. We presented our results to stakeholders at the Norwegian Programme for Research Cooperation with India (INDNOR) collaborative meeting in 2015. This meeting gathered three other India-related projects to discuss changes in climate and the consequent hydrological impact. We hope that NORINDIA will make a significant contribution to stakeholders and policy makers with respect to the future of water resources in India.

A continuing project called “C-ICE: Counteracting effect of future Antarctic sea-ice loss on projected increases of summer Monsoon rainfall” has been funded and is expected to start in May 2016. The program will investigate the sensitivity of ISM to future Antarctic sea ice loss, which may partially counteract the general tendency toward increased monsoon rainfall over India and may also contribute to increasing its subseasonal variability.

Acknowledgments

We would like to thank the Norwegian Research Council and Statkraft for providing funding to the NORINDIA project with grant 216576.

References

Christensen, J. H., et al. (2013), Monsoon systems, in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the IPCC AR5, edited by T. F. Stocker et al., sect. 14.2, pp. 1225–1234, Cambridge Univ. Press, Cambridge, U.K.

Gochis, D. J., W. Yu, and D. N. Yates (2014), The WRF-Hydro model technical description and user’s guide, version 1.0, NCAR technical document report, 120 pp., Natl. Cent. for Atmos. Res., Boulder, Colo.

Krishnan, R., et al. (2015), Deciphering the desiccation trend of the South Asian monsoon hydroclimate in a warming world, Clim. Dyn., doi:10.1007/s00382-015-2886-5.

Moors, E. J., et al. (2011), Adaptation to changing water resources in the Ganges basin, northern India, Environ. Sci. Policy, 14(7), 758–769.

Panda, D. K., and J. Wahr (2016), Spatiotemporal evolution of water storage changes in India from the updated GRACE-derived gravity records, Water Resour. Res, 52, 135–149, doi:10.1002/2015WR017797.

Ramarao, M. V. S., et al. (2015), Understanding land surface response to changing South Asian monsoon in a warming climate, Earth Syst. Dyn., 6(2), 569–582.

Senan, R., et al. (2016), Impact of springtime Himalayan-Tibetan Plateau snowpack on the onset of the Indian summer monsoon in coupled seasonal forecasts, Clim. Dyn., doi:10.1007/s00382-016-2993-y, in press.

Turner, A. G., and H. Annamalai (2012), Climate change and the South Asian summer monsoon, Nat. Clim. Change, 2(8), 587–595.

van Vuuren, D. P., et al. (2011), The representative concentration pathways: An overview, Clim. Change, 109(1–2), 5–31.

Viste, E., and A. Sorteberg (2015), Snowfall in the Himalayas: An uncertain future from a little-known past, Cryosphere, 9(3), 1147–1167.

Wiltshire, A. J. (2014), Climate change implications for the glaciers of the Hindu Kush, Karakoram and Himalayan region, Cryosphere, 8(3), 941–958.

Author Information

Michel d. S. Mesquita, Uni Research Climate, Bjerknes Centre for Climate Research, Bergen, Norway; email: [email protected]; Vidyunmala Veldore, The Energy and Resources Institute, New Delhi, India; Lu Li, Uni Research Climate, Bjerknes Centre for Climate Research, Bergen, Norway; R. Krishnan, Indian Institute of Tropical Meteorology, Pune, India; Yvan Orsolini, Norwegian Institute for Air Research, Kjeller, Norway, and Bjerknes Centre for Climate Research, Bergen, Norway; Retish Senan, University of Oslo, Norway; M. V. S. Ramarao, Indian Institute of Tropical Meteorology, Pune, India; and Ellen Viste, University of Bergen, Bergen, Norway

Citation: Mesquita, M. d. S., V. Veldore, L. Li, R. Krishnan, Y. Orsolini, R. Senan, M. V. S. Ramarao, and E. Viste (2016), Forecasting India’s water future, Eos, 97, doi:10.1029/2016EO049099. Published on 31 March 2016.

© 2016. The authors. CC BY-NC 3.0