Past and future trends of hydroclimatic intensity over the Indian monsoon region

Abstract

The hydroclimatic intensity index (HY‐INT) is a single index that quantitatively combines measures of precipitation intensity and dry spell length, thus providing an integrated response of the hydrological cycle to global warming. The HY‐INT index is a product of the precipitation intensity (PINT, intensity during wet days) and dry spell length (DSL). Using the observed gridded rainfall data sets of 1951–2010 period, the changes in HY‐INT, PINT, and DSL over the Indian monsoon region have been examined in addition to changes in maximum consecutive dry days (MCD). We have also considered 10 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models for examining the changes in these indices during the present‐day and future climate change scenarios. For climate change projections, the Representative Concentration Pathway (RCP) 4.5 scenario was considered. The analysis of observational data during the period 1951–2010 suggested an increase in DSL and MCD over most of central India. Further, statistically significant (95% level) increase in HY‐INT is also noted during the period of 1951–2010, which is mainly caused due to significant increase in precipitation intensity. The CMIP5 model projections of future climate also suggest a statistically significant increase in HY‐INT over the Indian region. Out of the 10 models considered, seven models suggest a consistent increase in HY‐INT during the period of 2010–2100 under the RCP4.5 scenario. However, the projected increase in HY‐INT is mainly due to increase in the precipitation intensity, while dry spell length (DSL) showed little changes in the future climate.

Southward shift of precipitation extremes over south Asia: Evidences from CORDEX data

“…Changes in the characteristics of extreme precipitation events due to changing climate in the climate "hot-spot" like south Asia 1 may have far-seeing socio-economical impacts. South Asian region, including the Indian sub-continent, receives most of the precipitation due to monsoon, which is strongly sensitive to climate change [2][3][4][5][6] . The characteristics of Indian Summer Monsoon Rainfall (ISMR) are changing in terms of its trend in extreme precipitation events [7][8][9][10][11][12][13][14][15] .…”

A threefold rise in widespread extreme rain events over central India

“…The variability of the monsoon makes the heavily populated South Asian subcontinent one of the most vulnerable regions around the world to the impacts of natural disasters such as droughts and floods. The monsoon variability has amplified in the recent decades 2 – 8 , with a gradual decline in the monsoon circulation and rainfall 9 – 13 and at the same time, a phenomenal rise in extreme rainfall events 2 , 5 , 14 – 20 . Observational evidence indicates a decrease in the northern summer (June–September) mean rainfall over South Asia since the 1950s, with a significant decline of 10–20% over the central Indian region where agriculture is still largely rain-fed (Fig.…”

Increased Occurrence and Intensity of Consecutive Rainfall Events in the China's Three Gorges Reservoir Area Under Global Warming

“…For CN05.1, because of the rain intensity diffusion and extended rainy days caused by interpolation, one elevated threshold, 1 mm per day, is used to define rainy days. One similar threshold was used in previous studies (Giorgi et al, 2011; Mohan & Rajeevan, 2017; Salinger & Griffiths, 2001). Here one CRE is defined to begin if any one of the following four cases: (1) It has at least 5 consecutive rainy days; (2) it has 6 or 7 rainy days within 7 or 8 consecutive days, despite no 5 consecutive rainy days; (3) it has 7 or 8 rainy days within 9 to 10 consecutive days but has at least 1 rainy day within any 2 consecutive days, although it does not meet (1) or (2) above; (4) it has more than 9 rainy days but has at least 1 rainy day within any 2 consecutive days, although it does not meet (1)–(3) above (Sun et al, 2016; Zheng et al, 2018).…”

“…Due to nonnormal characteristics of probability distributions, trends of daily precipitation amount and subsequently CREs cannot be estimated by the least squared fitting. As Santos and Fragoso (2013) and Mohan and Rajeevan (2017), here the trends are estimated by the Kendall's tau-based slop estimator (Sen's slope Q; Sen, 1968) as follows:…”