Climatic Change - Implications for India‚Äôs Water Resources

Lal, M.

doi:10.1201/9780203020777.ch7

Cited by 39 publications

(53 citation statements)

References 22 publications

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“…An increase in intense rainfall events leads to more severe floods and landslides. The number of cyclones originating from the Bay of Bengal and the Arabian Sea has decreased since 1970, but their intensity has increased (Lal, 2001). Moreover, the damage caused by intense cyclones has risen significantly in India.…”

Section: Signatures Of Climate Change Over Indiamentioning

confidence: 99%

“…Some past studies relating to changes in rainfall over India have concluded that there is no clear trend of increase or decrease in average annual rainfall over the country (Mooley & Parthasarathy, 1984;Sarker & Thapliyal, 1988;Thapliyal & Kulshrestha, 1991;Lal, 2001). Though no trend in the monsoon rainfall in India is found over a long period of time, particularly on the all-India scale, pockets of significant long-term rainfall changes have been identified (Koteswaram & Alvi, 1969;Jagannathan & Parthasarathy, 1973;Raghavendra, 1974;Chaudhary & Abhyankar, 1979;Kumar et al, 2005;Dash et al, 2007;Kumar & Jain, 2009).…”

Section: Signatures Of Climate Change Over Indiamentioning

confidence: 99%

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Analysis of long-term rainfall trends in India

Kumar

Jain

Singh

2010

Hydrological Sciences Journal

550

220

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The study of precipitation trends is critically important for a country like India whose food security and economy are dependent on the timely availability of water. In this work, monthly, seasonal and annual trends of rainfall have been studied using monthly data series of 135 years for 30 sub-divisions (sub-regions) in India. Half of the sub-divisions showed an increasing trend in annual rainfall, but for only three (Haryana, Punjab and Coastal Karnataka), this trend was statistically significant. Similarly, only one sub-division (Chattisgarh) indicated a significant decreasing trend out of the 15 sub-divisions showing decreasing trend in annual rainfall. In India, the monsoon months of June to September account for more than 80% of the annual rainfall. During June and July, the number of sub-divisions showing increasing rainfall is almost equal to those showing decreasing rainfall. In August, the number of sub-divisions showing an increasing trend exceeds those showing a decreasing trend, whereas in September, the situation is the opposite. The majority of sub-divisions showed very little change in rainfall in non-monsoon months. The five main regions of India showed no significant trend in annual, seasonal and monthly rainfall in most of the months. For the whole of India, no significant trend was detected for annual, seasonal, or monthly rainfall. Annual and monsoon rainfall decreased, while pre-monsoon, post-monsoon and winter rainfall increased at the national scale. Rainfall in June, July and September decreased, whereas in August it increased, at the national scale.Key words climate change; precipitation; rainfall trend; sub-division; non-parametric test; Mann-Kendall test; India Analyse des tendances pluviométriques de long terme en Inde Résumé L'étude des tendances de précipitation est très importante pour un pays comme l'Inde dont la sécurité alimentaire et l'économie dépendent de la disponibilité temporelle de l'eau. Dans ce travail, les tendances des précipitations mensuelles, saisonnières et annuelle ont été étudiées à partir des séries de données mensuelles de 135 années (1871-2005) de 30 sous-divisions (sous-régions) de l'Inde. La moitié de ces sous-divisions présente une tendance croissante de la précipitation annuelle, mais cette tendance n'est statistiquement significative que pour trois d'entre elles (Haryana, Punjab et Karnataka littoral). De même, une seule sous-division (Chattisgarh) présente une tendance décroissante significative parmi les 15 sous-régions qui présentent une tendance décroissante de la précipitation annuelle. En Inde, les mois de mousson de Juin à Septembre contribuent à plus de 80% de la précipitation annuelle. Pendant Juin et Juillet, les sous-divisions sont presque aussi nombreuses à présenter une précipitation croissante ou décroissante. En Août, les sous-divisions présentant une croissance sont plus nombreuses, contrairement à Septembre. La majorité des sous-divisions présente très peu de changement de précipitation pour les mois hors mousson. Cinq régions...

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Section: Signatures Of Climate Change Over Indiamentioning

confidence: 99%

Section: Signatures Of Climate Change Over Indiamentioning

confidence: 99%

Analysis of long-term rainfall trends in India

Kumar

Jain

Singh

2010

Hydrological Sciences Journal

550

220

View full text Add to dashboard Cite

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“…The different methods, scales, institutions and legal frameworks by which societies around the world manage water (Gleick and Palaniappan, 2010;Röckstrom et al, 2009) generate tremendous specificity in water systems, while variation in norms and cultural practices leads to a diversity of water management behavior. Access to water ranges from largely uncontrolled (Lal, 2005(Lal, , 2011Srinivasan et al, 2010a, b); to controlled but decentralized (Tanaka et al, 2006), to centralized and highly controlled (Coulibaly et al, 2001).…”

Section: Challenge 4: Social Factors Underlying Coupled Human-water Smentioning

confidence: 99%

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

Thompson

Sivapalan

Harman

et al. 2013

Hydrol. Earth Syst. Sci.

130

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Abstract.Globally, many different kinds of water resources management issues call for policy-and infrastructure-based responses. Yet responsible decision-making about water resources management raises a fundamental challenge for hydrologists: making predictions about water resources on decadal-to century-long timescales. Obtaining insight into hydrologic futures over 100 yr timescales forces researchers to address internal and exogenous changes in the properties of hydrologic systems. To do this, new hydrologic research must identify, describe and model feedbacks between water and other changing, coupled environmental subsystems. These models must be constrained to yield useful insights, despite the many likely sources of uncertainty in their predictions. Chief among these uncertainties are the impacts of the increasing role of human intervention in the global water cycle -a defining challenge for hydrology in the Anthropocene. Here we present a research agenda that proposes a suite of strategies to address these challenges from the perspectives of hydrologic science research. The research agenda focuses on the development of co-evolutionary hydrologic modeling to explore coupling across systems, and to address the implications of this coupling on the long-time behavior of the coupled systems. Three research directions support the development of these models: hydrologic reconstruction, comparative hydrology and model-data learning. These strategies focus on understanding hydrologic processes and feedbacks over long timescales, across many locations, and through strategic coupling of observational and model data in specific systems. We highlight the value of use-inspired and team-based science that is motivated by real-world hydrologic problems but targets improvements in fundamental understanding to support decision-making and management. Fully realizing the potential of this approach will ultimately require detailed integration of social science and physical science understanding of water systems, and is a priority for the developing field of sociohydrology.

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“…IPCC (2001a) has indicated that the average global surface air temperature has increased by 0.6 ± 0.2 °C since the late 19th century and it is projected to increase by 1.4-5.8° C over the period 1990-2100. Lal and Singh (2001) and Lal (2001) have reported that the average annual mean surface temperature over Indian sub-continent is likely to increase by about 2.7 and 3.8 °C during the decades of 2050 and 2080 s, respectively. Dyurgerov and Meier (2005) and Prasad et al (2009) mentioned that in recent years, the glaciers and snowfields of the Himalaya-KarakoramHindu Kush (HKH) mountain belts and Tibetan plateau are found to be amongst the fastest receding glacial and snow covers in the world.…”

Section: Introductionmentioning

confidence: 99%

Change in snow cover area of Brahmaputra river basin and its sensitivity to temperature

Barman

Bhattacharjya

2015

Environ Syst Res

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Background:The discharge of the River Brahmaputra is highly affected by the melting of snow at the upper part of its catchment. Increase in discharge due to significant retreat of snow in turn affects the downstream flow characteristics of the river giving rise to severe catastrophic problems such as flood and erosion. Rising temperature is one of the major reasons of melting of snow at the upper Brahmaputra catchment. Keeping in mind such issue, in this paper, a study has been conducted to see how the snow cover area of the Brahmaputra river basin changes with respect to the change in temperature. MODIS image MOD09A1.5 (MODIS/Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid) of 500 m resolution consisting of seven bands has been taken to prepare the normalized difference snow index maps of the study area. The normalized difference snow index map is then used to obtain the areal extent of snow in the Brahmaputra catchment area. The normalized difference snow index maps were prepared starting from 2002 to 2012 for four different months, viz. January, April, July and October. For temperature data, HadCM3 data of spatial resolution 2.5° × 3.75° (latitude by longitude) has been used. Results:The evaluation of the results shows that the snow cover area of the basin shows decreasing trends with respect to the increasing trend of temperature except for the month of January. Further, this study also shows that MODIS data can efficiently be used for snow cover area variation study. Conclusions:Variation of snow cover area is an indication of climate change as the melting of snow clearly reflects the rise in temperature. The attenuation of river flow due to the melting of snow in the river Brahmaputra basin may affect the downstream discharge of the river giving rise to severe flood and erosion problem. It has also been observed that MODIS data can efficiently be used in mapping snow cover of large areas, because of its good spatial as well as high temporal resolution.

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Climatic Change - Implications for India‚Äôs Water Resources

Cited by 39 publications

References 22 publications

Analysis of long-term rainfall trends in India

Analysis of long-term rainfall trends in India

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

Change in snow cover area of Brahmaputra river basin and its sensitivity to temperature

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