Meteorological Subdivisions Research Articles

Trends and variability of meteorological drought over the districts of India using standardized precipitation index

Meteorological drought during the southwest monsoon season and for the northeast monsoon season over five meteorological subdivisions of India for the period 1901–2015 has been examined using district and all India standardized precipitation index (SPI). Whenever all India southwest monsoon rainfall was less than −10% or below normal, for those years all India SPI was found as −1 or less. Composite analysis of SPI for the below normal years, viz., less than −15% and −20% of normal rainfall years indicate that during those years more than 30% of country’s area was under drought condition, whenever all India southwest monsoon rainfall was –15% or less than normal. Trend analysis of monthly SPI for the monsoon months identified the districts experiencing significant increase in drought occurrences. Significant positive correlation has been found with the meteorological drought over most of the districts of central, northern and peninsular India, while negative correlation was seen over the districts of eastern India with NINO 3.4 SST. For the first time, meteorological drought analysis over districts and its association with equatorial pacific SST and probability analysis has been done for the northeast monsoon over the affected regions of south peninsular India. Temporal correlation of all India southwest monsoon SPI and south peninsular India northeast monsoon SPI has been done with the global SST to identify the teleconnection of drought in India with global parameters.

Perceptible changes in Indian summer monsoon rainfall in relation to Indian Monsoon Index

The changes in the summer monsoon rainfall over 30 meteorological subdivisions of India with respect to changes in circulation and the Indian Monsoon Index (IMI) have been studied for the period 1953–2012. The relationship between the IMIs in different months and whole season and the corresponding summer monsoon rainfall is studied and tested. The positive and negative extremes are evaluated basing on the normalized values of the deviations from the mean of the IMI. Composite rainfall distributions over India and the zonal wind distributions in the lower and upper troposphere of IMI's both positive and negative extremes are evaluated separately and discussed. In the recent three decades of global warming, the negative values of IMI in July and August lead to weakening of the monsoon system over India. It is observed that the rainfall variations in the Northeast India are different from the rest of India except Tamil Nadu in general.

Changes in Seasonality Index Over Sub-Divisions of India During 1951-2015

Background:The varied topographical features of the Indian region are responsible for variation in distribution of rainfall over different parts of the country. More than 80% of the country’s rainfall is received during the monsoon season. Researchers noted that there is change in distribution of this monsoon rainfall associated with climate change and global warming. This changing pattern in rainfall can be investigated by seasonality index (SI) of rainfall. Such studies are essential to identify the changes in runoff, infiltration, surface and groundwater management, agricultural planning,etc.Method:The variation in seasonality in rainfall over the Indian region is examined using monthly rainfall values for the period 1951 to 2015 of 34 meteorological sub-divisions excluding two Sea Islands. A seasonality index (SI) of a monthly rainfall is computed on monthly, seasonal (June to September) and annual scale. It is observed that seasonality index of rainfall of 34 sub-divisions for all months are in the range 0.37 (Jammu & Kashmir) to 1.56 (Saurashtra Kutch & Diu).Results:The results show that rainfall is markedly seasonal with a long dry season and most rainfall in less than three months. Most of the rainfall occurs in monsoon months. The seasonality index for monsoon season is computed and it varies from 0.19 (Nagaland, Manipur, Mizoram,Tripura) to 0.59 (Saurashtra Kutch & Diu) resulting in rainfall spread throughout the year, but with a definite wetter season.Conclusion:Trends of this index through the 65-year period are identified and indicate that seasonality is increasing in Uttaranchal, Himachal Pradesh, Gujarat Region-Dadra & Nagar Haveli; Saurashtra-Kutch & Diu, Konkan & Goa, Madhya Maharashtra, Marathwada, Chattisgarh, Tamilnadu & Pondicherry. The analysis clearly showed the climate change impact on northwest sub-divisions of the country showing increase in SI values leading to dryness during the monsoon season. The negative trend in SI values was observed in Sub- Himalayan West Bengal, Haryana-Delhi-Chandigarh, Punjab, Jammu & Kashmir, West and east Rajasthan, coastal Andhra Pradesh showing increasing wetness for an already wet months although rainfall occurs in a very short period of just a month or two.

Characteristics of precipitating monsoon clouds over rain-shadow and drought-hit regions of India using radar

C-band radars were installed at Baramati and Shegaon as a part of operational cloud seeding program of Maharashtra State in the monsoon season (June–September) 2004. These provided first time a unique opportunity to study (1) characteristics of precipitating monsoon clouds (2) convection and (3) number of seedable clouds over Indian meteorological subdivisions of Madhya Maharashtra (rain-shadow) and Vidarbha (drought-hit). The monsoon season is divided into active and break periods. The cloud characteristics studied are: diurnal variation, cloud top heights and durations. Diurnal variation of cloud frequency shows maximum in the afternoon hours (10–11 UTC) and minimum in the early morning hours (3–4 UTC) in both the periods. Cloud tops show trimodal distributions with modes at 2–3, 8–9 and above 9 km. Mean cloud duration is 55 min. Congestus has been found prominent cloud type (65%) with mean top height of 6.76 km. Frequency of cumulonimbus clouds is found higher in the break periods. Cloud scale is taken as a metric for characterization of convection. Maximum frequency of cloud scale is found at C scale (mesoscale: area 100–1000 km2). Mesoscale Convective System has been found dominating convection type. The convection over the area has been shown to be hybrid type, consisting of basic oceanic type modulated by land convection. Convective clouds having maximum reflectivities between 25 and 35 dBZ, suitable for hygroscopic and glaciogenic seeding, are found in a large number. Understanding of characteristics of clouds and convection is useful for the diagnostic and precipitation enhancement studies over the rain-shadow/drought-hit regions.

Probability of occurrence of monthly and seasonal winter precipitation over Northwest India based on antecedent-monthly precipitation

Winter (December, January, and February (DJF)) precipitation over northwest India (NWI) is mainly associated with the eastward moving mid-latitude synoptic systems, western disturbances (WDs), embedded within the subtropical westerly jet (SWJ), and is crucial for Rabi (DJF) crops. In this study, the role of winter precipitation at seasonal and monthly scale over NWI and its nine meteorological subdivisions has been analyzed. High-resolution (0.25° × 0.25°) gridded precipitation data set of India Meteorological Department (IMD) for the period of 1901–2013 is used. Results indicated that the seasonal precipitation over NWI is below (above) the long-term mean in most of the years, when precipitation in any of the month (December/January/February) is in deficit (excess). The contribution of December precipitation (15–20%) to the seasonal (DJF) precipitation is lesser than January (35–40%) and February (35–50%) over all the subdivisions. December (0.60), January (0.57), and February (0.69) precipitation is in-phase (correlation) with the corresponding winter season precipitation. However, January precipitation is not in-phase with the corresponding December (0.083) and February (−0.03) precipitation, while December is in-phase with the February (0.21). When monthly precipitation (December or January or December–January or February) at subdivision level over NWI is excess (deficit); then, the probability of occurrence of seasonal excess (deficit) precipitation is high (almost nil). When antecedent-monthly precipitation is a deficit or excess, the probability of monthly (January or February or January + February) precipitation to be a normal category is >60% over all the subdivisions. This study concludes that the December precipitation is a good indicator to estimate the performance of January, February, January–February, and the seasonal (DJF) precipitation.

An intercomparison of observational precipitation data sets over Northwest India during winter

Winter (DJF) precipitation over Northwest India (NWI) is very important for the cultivation of Rabi crops. Thus, an accurate estimation of high-resolution observations, evaluation of high-resolution numerical models, and understanding the local variability trends are essential. The objective of this study is to verify the quality of a new high spatial resolution (0.25° × 0.25°) gridded daily precipitation data set of India Meteorological Department (IMD1) over NWI during winter. An intercomparison with four existing precipitation data sets at 0.5° × 0.5° of IMD (IMD2), 1° × 1° of IMD (IMD3), 0.25° × 0.25° of APHRODITE (APRD1), and 0.5° × 0.5° of APHRODITE (APRD1) resolution during a common period of 1971–2003 is done. The evaluation of data quality of these five data sets against available 26 station observations is carried out, and the results clearly indicate that all the five data sets reasonably agreed with the station observation. However, the errors are relatively more in all the five data sets over Jammu and Kashmir-related four stations (Srinagar, Drass, Banihal top, and Dawar), while these errors are less in the other stations. It may be due to the lack of station observations over the region. The quality of IMD1 data set over NWI for winter precipitation is reasonably well than the other data sets. The intercomparison analysis suggests that the climatological mean, interannual variability, and coefficient of variation from IMD1 are similar with other data sets. Further, the analysis extended to the India meteorological subdivisions over the region. This analysis indicates overestimation in IMD3 and underestimation in APRD1 and APRD2 over Jammu and Kashmir, Himachal Pradesh, and NWI as a whole, whereas IMD2 is closer to IMD1. Moreover, all the five data sets are highly correlated (>0.5) among them at 99.9% confidence level for all subdivisions. It is remarkably noticed that multicategorical (light precipitation, moderate precipitation, heavy precipitation, and very heavy precipitation) skill score of accuracy (>0.8) for the four data sets against IMD1 is good for all the subdivisions as well as NWI and is more in IMD2. IMD1 performs well in capturing the relationships of winter precipitation with climate indices such as Nino 3.4 region sea surface temperature, Southern Oscillation Index, Arctic Oscillation, and North Atlantic Oscillation. The results conclude that IMD1 is useful to understand the variability trends at the local climate scale and its global teleconnections.

Modelling surface run-off and trends analysis over India

The present study is mainly concerned with detecting the trend of run-off over the mainland of India, during a time period of 35 years, from 1971–2005 (May–October). Rainfall, soil texture, land cover types, slope, etc., were processed and run-off modelling was done using the Natural Resources Conservation Service (NRCS) model with modifications and cell size of 5×5 km. The slope and antecedent moisture corrections were incorporated in the existing model. Trend analysis of estimated run-off was done by taking into account different analysis windows such as cell, medium and major river basins, meteorological sub-divisions and elevation zones across India. It was estimated that out of the average 1012.5 mm of rainfall over India (considering the study period of 35 years), 33.8% got converted to surface run-off. An exponential model was developed between the rainfall and the run-off that predicted the run-off with an R 2 of 0.97 and RMSE of 8.31 mm. The run-off trend analysed using the Mann–Kendall test revealed that a significant pattern exists in 22 medium, two major river basins and three meteorological sub-divisions, while there was no evidence of a statistically significant trend in the elevation zones. Among the medium river basins, the highest positive rate of change in the run-off was observed in the Kameng basin (13.6 mm/yr), while the highest negative trend was observed in the Tista upstream basin (−21.4 mm/yr). Changes in run-off provide valuable information for understanding the region’s sensitivity to climatic variability.

Assessing crop water stress during late kharif season using Normalized Diurnal Difference Vegetation Water Content (nddVWC) of Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E)

AMSR-E derived vegetation water content (VWC) at predawn (01:30 LST) and afternoon (13:30 LST) were found to have differences relating to rainfall patterns (wet spell/dry spell) over the rainfed region during late kharif season (August–October). A new index called Normalized Diurnal Difference Vegetation Water Content (nddVWC), i.e., diurnal difference of VWC per unit leaf area index (LAI) was conceptualized to assess the spatiotemporal variations of crop water stress. The nddVWC was found to be significantly (p = 0.05) correlated with rainfall particularly at 3 days time lag. It is also found to have significant correlation with Drought Severity Index (DSI). Irrigated rice crop was used as a benchmark to characterize nddVWC to represent different intensity of crop water stress (no stress ≤0.4, moderate stress >0.4 to ≤0.8, high stress >0.8). Inter-year comparative analysis of temporal variations of nddVWC was performed to assess its sensitivity in characterizing the normal and sub-normal years in term of the intensity and the persistence of crop water stress. Persistent high/moderate stress condition was observed over Vidarbha meteorological subdivision during 2009 (drought year), whereas no stress condition was prevailing during 2005 (normal year), and the year 2008 showed an intermediate condition. The spatiotemporal patterns of nddVWC could capture the progression of crop water stress condition at regional level. The proposed index has potential to monitor crop water stress in the areas otherwise considered to be too densely vegetated for conventional soil moisture retrieval.

CMIP5 Projected Changes in the Annual Cycle of Indian Monsoon Rainfall

The annual cycle of Indian monsoon rainfall plays a critical role in the agricultural as well as the industrial sector. Thus, it is necessary to evaluate the behaviour of the monsoon annual cycle in a warming climate. There are several studies on the variability and uncertainty of the Indian monsoon. This study, examines the impact of climate change on the annual cycle of monsoon rainfall in India from 1871–2100 by applying 20 model simulations designed by the World Climate Research Programme (WCRP) coupled with the model inter-comparison Project 5 (CMIP5). It is found that the models MPI-ESM-LR, INM-CM4 and MRI-CGCM3 best capture the spatial patterns of the monsoon rainfall peak month (MRPM) of the winter monsoon compared to observations, whereas HadGEM2-AO and MIROC-ESM-CHEM best capture the MRPM of the summer monsoon. The MIROC, MIROC-ESM, and MIROC-ESM-CHEM models best capture the average rainfall intensity as well as the MRPM of all-India rainfall. This paper examines the future spatial distribution of the MRPM for meteorological sub-divisions of India, that can have crucial implications for water resources and management. Although the future projections as per the CMIP5 models indicate no changes in the MRPM of the all-India rainfall, a reduction in average intensity can be expected. The projections indicate a shift in the MRPM in some meteorological sub-divisions, particularly with regard to the summer monsoon but no significant change has been projected for the winter monsoon. For example, the summer monsoon MRPM is projected to move from July to August in northern and central India.

Role of distinct flavours of IOD events on Indian summer monsoon

The summer monsoon contributes to about 75 % of mean annual rainfall over the various meteorological subdivisions of India. The role of ocean–atmosphere phenomena such as Indian Ocean Dipole (IOD) and El Nino–Southern Oscillation (ENSO) on the Indian monsoon activity is intriguing. The impacts of ENSO, IOD and Equatorial Indian Ocean Oscillation on monsoon are distinct. The ENSO (IOD) in general affects the monsoon negatively (positively). The present study aims to understand the role of different types of IOD such as early IOD (EIOD), normal IOD and prolonged IOD (PIOD) on Indian Summer Monsoon Rainfall (ISMR). We find that an EIOD, which peaks in the mid-monsoon months (July and August), plays a significant role like PIOD in enhancing ISMR even though it has a medium Dipole Mode Index amplitude value compared to other IODs. During an EIOD, the combined effect of excess evaporation from Arabian Sea and the stronger cross-equatorial flow leads to the enhanced monsoon activity. In addition, there is a substantial decrease in the number of break spells during EIOD years.

Finer Scale Rainfall Projections for Kerala Meteorological Subdivision, India Based on Multivariate Empirical Mode Decomposition

Finer Scale Rainfall Projections for Kerala Meteorological Subdivision, India Based on Multivariate Empirical Mode Decomposition

District-wise trend analysis of rainfall pattern in last century (1901-2000) over Gangetic region in West Bengal, India

The aim of the present study was to estimate long-term trend in the amount of rainfall for Gangetic West Bengal (GWB) meteorological sub-division of India and each of the 13 districts under GWB separately. Monthly rainfall time series data of 100 years (1901-2000) were analyzed to measure monotonous trend of rainfall employing Sen’s slope estimator. Statistical significance of the trend was determined using non-parametric Mann-Kendall test. An important result derived from the analysis was that the GWB sub-division and South 24 Parganas (S24P) district showed significant increasing trend (mm/year) of annual rainfall measuring 2.025 and 4.99 respectively. An inclining trend of monsoon precipitation, which was significant, found in four districts viz. Bankura, North 24 Parganas (N24P), S24P and West Midnapore along with GWB itself. A major finding of the study revealed that six districts and GWB had significant increasing trend in September rainfall with a maximum value of 1.324 mm/year in S24P district. Contribution of rainfall in October and post-monsoon season as well increased considerably in Kolkata and S24P districts while in December, similar trend was observed for Birbhum and Howrah districts. Murshidabad, S24P and East Midnapore districts experienced significant rising trend of precipitation in July, August and November respectively. On the contrary, Burdwan and Nadia districts, in the month of May and pre-monsoon season, had considerable declining trend of rainfall. Significant decreasing trend (mm/year) of precipitation, a concern for Nadia district, with magnitude of 0.127 and 0.293, was observed in the months of March and April respectively.

Statistical Selection of the Optimum Models in the CMIP5 Dataset for Climate Change Projections of Indian Monsoon Rainfall

Monsoons are the life and soul of India’s financial aspects, especially that of agribusiness in deciding cropping patterns. Around 80% of the yearly precipitation occurs from June to September amid monsoon season across India. Thus, its seasonal mean precipitation is crucial for agriculture and the national water supply. From the start of the 19th century, several studies have been conducted on the possible increments in Indian summer monsoon precipitation in the future. Unfortunately, none of them has endeavoured to discover the models whose yield give the best fit to the observed data. Here some statistical tests are performed to quantify the models of Coupled Model Inter-comparison Project 5 (CMIP5). Then, after, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to select optimum models. It shows that four models, CCSM4, CESM1-CAM5, GFDL-CM3, and GFDL-ESM2G, best capture the pattern in Indian summer monsoon rainfall over the historical period (1871–2005). Further, Student’s t-test is utilized to estimate the significant changes in meteorological subdivisions of selected optimum models. Also, our results reveal the Indian meteorological subdivisions which are liable to encounter significant changes in mean at confidence levels that differ from 80% to 99%.

Time–frequency characterization of sub-divisional scale seasonal rainfall in India using the Hilbert–Huang transform

Time–frequency characterization is useful in understanding the nonlinear and non-stationary signals of the hydro-climatic time series. The traditional Fourier transform, and wavelet transform approaches have certain limitations in analyzing non-linear and non-stationary hydro-climatic series. This paper presents an effective approach based on the Hilbert–Huang transform to investigate time–frequency characteristics, and the changing patterns of sub-divisional rainfall series in India, and explored the possible association of monsoon seasonal rainfall with different global climate oscillations. The proposed approach integrates the complete ensemble empirical mode decomposition with adaptive noise algorithm and normalized Hilbert transform method for analyzing the spectral characteristics of two principal seasonal rainfall series over four meteorological subdivisions namely Assam-Meghalaya, Kerala, Orissa and Telangana subdivisions in India. The Hilbert spectral analysis revealed the dynamic nature of dominant time scales for two principal seasonal rainfall time series. From the trend analysis of instantaneous amplitudes of multiscale components called intrinsic mode functions (IMFs), it is found that both intra and inter decadal modes are responsible for the changes in seasonal rainfall series of different subdivisions and significant changes are noticed in the amplitudes of inter decadal modes of two seasonal rainfalls in the four subdivisions since 1970s. Further, the study investigated the links between monsoon rainfall with the global climate oscillations such as Quasi Bienniel Oscillation (QBO), El Nino Southern Oscillation (ENSO), Sunspot Number (SN), Atlantic Multidecadal Oscillation (AMO) etc. The study noticed that the multiscale components of rainfall series IMF1, IMF2, IMF3, IMF4 and IMF5 have similar periodic structure of QBO, ENSO, SN, tidal forcing and AMO respectively. As per the seasonal rainfall patterns is concerned, the results of the study indicated that for Assam-Meghalaya subdivision, there is a likelihood of extreme rare events at ~0.2 cycles per year, and both monsoon and pre-monsoon rainfall series have decreasing trends; for Kerala subdivision, extreme events can be expected during monsoon season with shorter periodicity (~2.5 years), and monsoon rainfall has statistically significant decreasing trend and post-monsoon rainfall has a statistically significant increasing trend; and for Orissa subdivision, there are chances of extremes rainfall events in monsoon season and a relatively stable rainfall pattern during post-monsoon period, but both monsoon and post-monsoon rainfall series showed an overall decreasing trend; for Telangana subdivision, there is a likelihood of extreme events during monsoon season with a periodicity of ~4 years, but both monsoon and post-monsoon rainfall series showed increasing trends. The results of correlation analysis of IMF components of monsoon rainfall and five climate indices indicated that the association is expressed well only for low frequency modes with similar evolution of trend components.

Assessment of Kalpana-1 Rainfall Product over Indian Meteorological Sub-Divisions During the Summer Monsoon Season

In this paper, Kalpana-1 derived INSAT Multispectral Rainfall Algorithm (IMSRA) rainfall estimates are compared with two multisatellite rainfall products namely, TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 and Global Satellite Mapping of Precipitation (GSMaP), and India Meteorological Department (IMD) surface rain gauge (SRG)-based rainfall at meteorological sub-divisional scale over India. The performance of the summer monsoon rainfall of 2013 over Indian meteorological sub-divisions is assessed at different temporal scales. Comparison of daily accumulated rainfall over India from IMSRA shows a linear correlation of 0.72 with TMPA-3B42 and 0.70 with GSMaP estimates. IMSRA is capable to pick up daily rainfall variability over the monsoon trough region as compared to TMPA-3B42 and GSMaP products, but underestimates moderate to heavy rainfall events. Satellite-derived rainfall maps at meteorological sub-divisional scales are in reasonably good agreement with IMD-SRG based rainfall maps with some exceptions. However, IMSRA performs better than GSMaP product at meteorological sub-divisional scale and comparable with TMPA data. All the satellite-derived rainfall products underestimate orographic rainfall along the west coast, the Himalayan foothills and over the northeast India and overestimate rainfall over the southeast peninsular India. Overall results suggest that IMSRA estimates have potential for monsoon rainfall monitoring over the Indian meteorological sub-divisions and can be used for various hydro-meteorological applications.

Heavy rainfall in the Kedarnath valley of Uttarakhand during the advancing monsoon phase in June 2013.

During the monsoon season of 2013, the advance of monsoon over northwest (NW) Indian region showed large abnormality as arrival of rainfall over Punjab, Himachal Pradesh (HP), Uttarakhand, Haryana and Delhi occurred between 13 and 16 June 2013, nearly twice the standard deviation earlier than normal. Such an early arrival by mid-June has been exceptional. The event was marked by unprecedented very heavy rainfall between 14 and 18 June 2013 over different meteorological sub-divisions of NW India. The event also led to human tragedy in Uttarakhand, in which many local people and pilgrims lost their lives. This heavy rainfall in fact moved from Punjab and HP during 14 and 15 June 2013 to Uttarakhand. The rainfall between 14 and 18 June 2013 over NW India was highly organized and it was continuously sustained with mesoscale enhanced intensity over Uttarakhand, which dispels the opinion about cloudburst. The present communication is aimed to study the observational aspects of the vigorous and rapid advance of monsoon rainfall over NW India and its intensification during 15-17 June 2013 over Uttarakhand.

An assessment of Kalpana-1 rainfall product for drought monitoring over India at meteorological sub-division scale

In this study, the potential of Kalpana-1 derived rainfall product is assessed for drought monitoring over India at the meteorological sub-division scale. A preliminary analysis is done for the anomalous south-west monsoon season of 2009 using two drought indices: percent of normal and Standardized Precipitation Index. A considerable difference between these two indices is observed even from the same rainfall data. The severe drought condition over most parts of India during June is not well captured by Kalpana-1 derived rainfall data. Additionally, due to underestimation of orographic rainfall by Kalpana-1 derived product, the meteorological sub-divisions located over these regions showed extreme drought condition throughout the season.

Variability of summer monsoon rainfall over Indo-Gangetic plains in relation to El-Nino/La-Nina

The Indo-Gangetic plains (IGPs) incorporate seven meteorological subdivisions, viz. Punjab, West Uttar Pradesh and East Uttar Pradesh, Bihar, Jharkhand, and Gangetic West Bengal. IGPs are generally characterised by fertile soils, favourable climate and an abundant supply of water. They are considered as the ‘breadbasket’ for much of South Asia. The main source of rainfall is the southwest monsoon which is normally sufficient for agriculture. Based on observed available rainfall data, a time series of summer monsoon rainfall (SMR) (1871–2012) over the IGPs has been developed. Decadal and 30-year variability in SMR reveal alternate epochs of above and below normal rainfall. These epochs tend to last for a decade or two. Although small in magnitude, the long-term trend (1871–2012) in SMR shows a decreasing nature. On the other hand, the decadal variability in the SMR reveal a dominance of a positive phase in the past (during the period 1913–1922) and a negative phase during the recent decade (1993–2002). Recent climatology (1983–2012) shows a downward trend of the rainfall anomalies with the turning point around 1997 (associated with century’s strongest El-Nino, in 1997). The period before 1997 depicts subdued above normal rainfall, while the period after 1997 depicts below normal rainfall activity. Furthermore, the study reveals that SMR variability is enhanced during the El-Nino years and is suppressed during the La-Nina years, but the presence of El-Nino (La-Nina) does not guarantee droughts (floods) over the Gangetic plains. The spatiotemporal changes in rainfall activity are attributed to global warming and associated changes in the Indian summer monsoon circulations and the general atmospheric circulation.

Monsoonal Droughts In India – A Recent Assessment

AbstractDrought frequencies during the years 1901–2010 were investigated over three spatial units – All India, 5 Homogeneous Regions (HR) and 36 Meteorological subdivisions (MSs). The All-India rainfall trend is in fact indicative of no trend, while the Northeast HR (NER) shows a significant decrease. Furthermore, a significant decrease in rainfallis to be observed over Himachal Pradesh, Madhya Pradesh, Maharashtra and the Southern Peninsular region, and a significant increase over West Bengal, Punjab, Haryana, Coastal Karnataka, North Interior Karnataka and Rayalaseema.There have been 21 All-India drought years during the last century, of which 13 were linked to El Niño. When compared with HRs, the WCR is highly prone to El Niño while the NER is not affected by this global tele-connection. Western Uttar Pradesh, Eastern Rajasthan, Uttarakhand, Vidharbha and Telangana shared 11–10 drought occasions with El Niño.Maximum frequencies of droughts (21) were reported for East Madhya Pradesh within the WCR and Orissa within the Central Northeast Region (CNER), while Andaman, Nicobar and Rayalaseema experienced minimum drought episodes (12) over the last century.Sixty percent of the MSs in the West Central Region (WCR) and the Northwest Region (NWR) were coherent with All-India droughts. During the years 1918, 1972 and 2002, the majority of HRs (except NER) witnessed normal or below-normal rainfall. Western Madhya Pradesh within the WCR saw maximum drought events (13). The highest degree of simultaneous occurrence of drought years between the MSs and all-India concern Eastern Rajasthan, Western Madhya Pradesh, East and West Uttar Pradesh. The study also found that the MSs in HRs highly affected are East Rajasthan (NWR), West Madhya Pradesh (WCR), West and East Uttar Pradesh (CNER), NER and Coastal Karnataka (Peninsular Region). Western Uttar Pradesh, Eastern Rajasthan, Vidarbha and Telangana had 10–11 occasions when El Niño and Drought years occurred.

Forecasts of Rainfall (Departures from Normal) over India by Dynamical Model

Intra-seasonal lack(excess) of rainfall that lead to situations of meteorological drought (flood) in one or other part of country every year apart from inter-annual variability of rainfall(Krishnamurthy, 2000). India received 88% of long period average (normal) rainfall 886.9mm during southwest monsoon season(monsoon) in 2014. Northwest(NW), central, northeast (NE) and south peninsula of India received 79%, 90%, 88% and 93% of normal rain respectively. Twelve meteorological subdivisions i.e. about 30% area of country received deficient category(below 19% of normal) by the end of monsoon. Forecasts of Global Forecasting System(GFS/ T574L64) at ESSO-NCMRWF have been studied for all the thirty six meteorological subdivisions(subdivisions) of India during monsoon(June to September) 2014. Predicted values of weekly percentage departure of rainfall from normal are found in good agreement with observed kind of departures(-ve or less than normal) on more than about 80% of the cases at sub divisional spatial scales. Model forecasts for monsoon season were consistently found reasonably good for both the recent years 2009 and 2014 when India received 22% and 12% less rainfall than normal respectively. Scores o f models forecasted weekly rainfall over meteorological subdivisions for monsoon 2014 are found improved compared to those for monsoon in 2009.