Spatial analysis of temporal trend of rainfall and rainy days during the Indian Summer Monsoon season using daily gridded (0.5° × 0.5°) rainfall data for the period of 1971-2005

Das, Prabir Kumar; Chakraborty, Abhishek; Seshasai, M. V. R.

doi:10.1002/met.1361

Cited by 77 publications

(42 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the past few decades, increasing trends of extreme heavy precipitation events have been observed (Rajeevan et al, 2008;Stocker et al, 2013). Monsoon precipitation has been increasing especially in the Ganges Basin (Moors et al, 2011), but precipitation amounts before and after the monsoon have been declining (Ramesh and Goswami, 2007), with a declining number of rainy days (Das et al, 2013). As suggested by these studies, when precipitation increases over a shorter period of time with a reduced number of rainy days, flash flooding is expected regardless of the influence by the climate modes.…”

Section: Implications For Flooding and Droughtmentioning

confidence: 70%

Spatial and seasonal responses of precipitation in the Ganges and Brahmaputra river basins to ENSO and Indian Ocean dipole modes: implications for flooding and drought

Pervez

Henebry

2015

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. We evaluated the spatial and seasonal responses of precipitation in the Ganges and Brahmaputra basins as modulated by the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) modes using Global Precipitation Climatology Centre (GPCC) full data reanalysis of monthly global land-surface precipitation data from 1901 to 2010 with a spatial resolution of 0.5 • × 0.5 • . The GPCC monthly total precipitation climatology targeting the period 1951-2000 was used to compute gridded monthly anomalies for the entire time period. The gridded monthly anomalies were averaged for the years influenced by combinations of climate modes. Occurrences of El Niño alone significantly reduce (88 % of the long-term average (LTA)) precipitation during the monsoon months in the western and southeastern Ganges Basin. In contrast, occurrences of La Niña and co-occurrences of La Niña and negative IOD events significantly enhance (110 and 109 % of LTA in the Ganges and Brahmaputra Basin, respectively) precipitation across both basins. When El Niño co-occurs with positive IOD events, the impacts of El Niño on the basins' precipitation diminishes. When there is no active ENSO or IOD events (occurring in 41 out of 110 years), precipitation remains below average (95 % of LTA) in the agriculturally intensive areas of Haryana, Uttar Pradesh, Rajasthan, Madhya Pradesh, and Western Nepal in the Ganges Basin, whereas precipitation remains average to above average (104 % of LTA) across the Brahmaputra Basin. This pattern implies that a regular water deficit is likely, especially in the Ganges Basin, with implications for the agriculture sector due to its reliance on consistent rainfall for successful production. Historically, major droughts occurred during El Niño and co-occurrences of El Niño and positive IOD events, while major flooding occurred during La Niña and co-occurrences of La Niña and negative IOD events in the basins. This observational analysis will facilitate well-informed decision making in minimizing natural hazard risks and climate impacts on agriculture, and supports development of strategies ensuring optimized use of water resources in best management practice under a changing climate.

show abstract

Section: Implications For Flooding and Droughtmentioning

confidence: 70%

Spatial and seasonal responses of precipitation in the Ganges and Brahmaputra river basins to ENSO and Indian Ocean dipole modes: implications for flooding and drought

Pervez

Henebry

2015

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…IMD data are also validated against another observational data set namely APHRODITE (Yatagai et al ., ) and are found to be nearly identical (Rajeevan and Bhate, ). The IMD data set is used in numerous works to study various observed dynamical aspects of ISM precipitation (Roja Raman et al ., ; Panda et al ., ; Panda and Kumar, ; Prakash et al ., ; Rai et al ., ) as well as a reference in evaluation of climate models (Das et al ., ; Kumar et al ., ; Pattnayak et al ., ; ; Das et al ., ; Maharana and Dimri, ; ; Dash et al ., ). Furthermore, it is available at a resolution of 0.5° which matches with that of CORDEX‐SA RCMs that are considered for bias correction here.…”

Section: Methodsmentioning

confidence: 99%

On bias correction of summer monsoon precipitation over India from CORDEX‐SA simulations

Choudhary

Dimri

2018

Intl Journal of Climatology

View full text Add to dashboard Cite

In the present study, four different bias correction methods are assessed in improving the precipitation output from a set of two regional climate simulations for 1971–2005 period. The simulations come from Coordinated Regional Climate Downscaling Experiment over South Asia (CORDEX‐SA). The bias correction methods applied here are linear scaling (Scaling), empirical quantile mapping (EQM), power transformation (PTR) and local intensity scaling (LOCI). All the bias correction techniques show skill in improving the model results, but there are clear differences in the degrees of success depending upon the characteristic of precipitation studies—like seasonal mean, seasonal extremes or inter‐annual variability. Most of the methods show equivalently high ability in correcting the seasonal mean precipitation values; however, there also exist residual biases which vary spatially over India. The largest reduction in bias was observed for the frequency of dry days and precipitation on very wet days, which are significantly altered after adjustment by EQM and PTR. It is found that distribution‐based EQM method is more skilful followed by PTR and LOCI in most of the cases including correction of frequency‐based indices (e.g., dry days number and inter‐annual variability).

show abstract

“…The negative values (magnitude) indicate an increased moisture anomaly condition, whereas the positive values represent a decreasing moisture anomaly condition. The negative trend was found over major parts of NE India, eastern part of NW India, northeastern parts of central India and southern parts of peninsular India (Subbaramayya and Naidu, ; Singh et al , ; Rajeevan et al , ; Pattanaik, ; Guhathakurta and Rajeevan, ) (Figures (a) and (c)), whereas the positive trends were spread over western coast, central parts of peninsular Indian and western parts of NW India (Parthasarathy and Dhar, ; Rupa Kumar et al , ; Rakhecha and Soman, ; Singh et al , ; Das et al , ). The results of field significance test at homogeneous level revealed that in NW and NE India the magnitude of moisture anomaly are significantly increasing, whereas the significant decreasing trends were observed in NW and peninsular India.…”

Section: Resultsmentioning

confidence: 99%

“…For the details of the techniques and advantages, Das et al . () may be referred. The grids with significant ( p = 0.05) moisture anomaly duration and magnitude trends were considered for further analysis.…”

Section: Methodsmentioning

confidence: 98%

Characterizing Indian meteorological moisture anomaly condition using long‐term (1901–2013) gridded data: a multivariate moisture anomaly index approach

Das

Midya

Das

et al. 2017

Intl Journal of Climatology

Self Cite

View full text Add to dashboard Cite

The long‐term (1901–2013) gridded rainfall and potential evapotranspiration (PET) data were utilized to develop a new index, multivariate moisture anomaly index (MMAI), for characterizing the meteorological moisture anomaly condition during monsoon season over Indian region. The 6‐month timescale standardized precipitation index (SPI) and standardized evapotranspiration index (SEI) were computed using time series rainfall and PET data using gamma and log‐logistic distribution, respectively. The long‐term seasonal SPI and SEI were converted into moisture anomaly magnitude and duration information, and their trends were analysed using Sen's slope and Mann–Kendall test, respectively. The standardized precipitation evapotranspiration index (SPEI) at 6‐month timescale was also analysed to find the impact of rainfall and PET on meteorological moisture anomaly. Both the trends and probability analysis showed that SPEI was mainly representing the trends and pattern of SPI only and was unable to capture the impact of PET. The MMAI was developed by fitting the time series SPI and SEI information into different joint probability distribution and by adapting the best model for each grid. The new index was able to consider the impact of both rainfall and PET. Based on the MMAI trend analysis, it was found that the overall moisture anomaly was decreasing in northwest (NW) India, whereas it was increasing in northeast (NE), central and peninsular India. The trends analysis of SPI and SEI depicted that in NW India the significant decrease in moisture anomaly may be due to both increase in rainfall and decrease in PET, whereas in NE India the significant increase in moisture anomaly may be due to both increase in PET and decrease in rainfall. In eastern and Gujarat coast the increase in moisture anomaly may be attributed to increase in PET whereas the significant increase in rainfall might lead to reduced moisture anomaly over western coast.

show abstract

Spatial analysis of temporal trend of rainfall and rainy days during the Indian Summer Monsoon season using daily gridded (0.5° × 0.5°) rainfall data for the period of 1971-2005

Cited by 77 publications

References 39 publications

Spatial and seasonal responses of precipitation in the Ganges and Brahmaputra river basins to ENSO and Indian Ocean dipole modes: implications for flooding and drought

Spatial and seasonal responses of precipitation in the Ganges and Brahmaputra river basins to ENSO and Indian Ocean dipole modes: implications for flooding and drought

On bias correction of summer monsoon precipitation over India from CORDEX‐SA simulations

Characterizing Indian meteorological moisture anomaly condition using long‐term (1901–2013) gridded data: a multivariate moisture anomaly index approach

Contact Info

Product

Resources

About