We applied inverse probability weighting (IPW) causal modeling with three air pollutants (PM2.5, O3, Aand NO2), Ameteorological parameters, and potential lag effect of exposures to estimate the marginal effect of shortterm exposure to air pollution (AP) on cause-specific mortality risks in the Indo-Gangetic Plain (IGP). A causal linkage between short-term exposure to PM2.5 and O3 was found and all the four assessed causes of deaths (viz. neurological, respiratory, cardiovascular, and nephrological deaths), whereas the NO2 was found to have a linkage only with the cardiovascular mortality risks-for every 10-ppb increase in NO2 exposure, the mean risk increased by 0.84% (95% Confidence Interval (CI) = 0.58, 1.10). For every 10 mu g m-3 increase in short-term PM2.5 exposure, the increase in the risk of in-hospital all-cause deaths, neurological deaths, respiratory deaths, cardiovascular deaths, and nephrological deaths was 1.76% (1.57, 1.95), 0.28% (0.07, 0.50), 0.73% (0.47, 0.98), 0.72% (0.48, 0.96), and 0.11% (0.09, 0.14), respectively. Likewise, after controlling for PM2.5 and NO2, Athe causational linkage between acute exposure to O3 and respiratory mortality risk was found to be highest among all studied scenarios-for every 10-ppb increase in O3 there was a 2.24% increase in the respiratory death risk (2.02, 2.45). AThis set of results from IPW causal modeling could serve as the causational evidence documenting relative risk (RR) estimates of premature non-accidental cause-specific mortalities attributable to ambient AP in a subset of Indian population.

. Weather forecasting with high spatial resolution has become increasingly relevant for decision support in agriculture and water management. Present work is carried out for verification of India Meteorological Department-Weather Research and Forecasting Model's (IMD-WRF) rainfall forecast with three day lead time over Nalanda, Supaul and East Chamaparan districts in Bihar, India. The model's skills up to a lead time of three days are evaluated with panchayat level daily in situ observations for Monsoon 2020 and 2021. Results confirmed show good agreement of forecast and observation throughout the domain and particularly over Supaul district, where about 70% of rain and no-rain days are correctly predicted for all panchayat. Also, False Alarm Ratio is < 0.3 in 90 percent of the total 812 panchayat and Hanssen and Kuiper's score is also found > 0.25 in almost all places. This evaluation supports the use of the WRF model forecast in agriculture up to three days in advance. However, the quantitative verification suggests that model output is more reliable for moderate rainfall.

BACKGROUND:Heatwaves are expected to increase with climate change, posing a significant threat to population health. In India, with the world's largest population, heatwaves occur annually but have not been comprehensively studied. Accordingly, we evaluated the association between heatwaves and all-cause mortality and quantifying the attributable mortality fraction in India. METHODS:We obtained all-cause mortality counts for ten cities in India (2008-2019) and estimated daily mean temperatures from satellite data. Our main extreme heatwave was defined as two-consecutive days with an intensity above the 97th annual percentile. We estimated city-specific heatwave associations through generalised additive Poisson regression models, and meta-analysed the associations. We reported effects as the percentage change in daily mortality, with 95% confidence intervals (CI), comparing heatwave vs non-heatwave days. We further evaluated heatwaves using different percentiles (95th, 97th, 99th) for one, two, three and five-consecutive days. We also evaluated the influence of heatwave duration, intensity and timing in the summer season on heatwave mortality, and estimated the number of heatwave-related deaths. FINDINGS:Among ∼ 3.6 million deaths, we observed that temperatures above 97th percentile for 2-consecutive days was associated with a 14.7 % (95 %CI, 10.3; 19.3) increase in daily mortality. Alternative heatwave definitions with higher percentiles and longer duration resulted in stronger relative risks. Furthermore, we observed stronger associations between heatwaves and mortality with higher heatwave intensity. We estimated that around 1116 deaths annually (95 %CI, 861; 1361) were attributed to heatwaves. Shorter and less intense definitions of heatwaves resulted in a higher estimated burden of heatwave-related deaths. CONCLUSIONS:We found strong evidence of heatwave impacts on daily mortality. Longer and more intense heatwaves were linked to an increased mortality risk, however, resulted in a lower burden of heatwave-related deaths. Both definitions and the burden associated with each heatwave definition should be incorporated into planning and decision-making processes for policymakers.

The urban environment has undergone rapid change as a result of urbanization and the increase in global temperature, which has affected the energy balance and heat fluxes. The urban heat island (UHI) phenomenon is brought on by these changes in geometry, building materials, land use, and land cover, among other factors, which raise the temperature in cities relative to their rural and suburban surroundings. The microclimate, ecology, air quality, and infrastructure are all significantly impacted by the UHI effect, which also has a significant impact on energy needs. Meteorological data cannot be used to measure the UHI effect due to large spatial heterogeneity and the lack of dense weather station networks. Remote sensing offers an effective way to track, measure, and manage the UHI effect in such circumstances. Thermal remote sensing technology has made it possible to estimate the UHI effect in high spatial and temporal resolution. The effectiveness of the technique in multicity analysis has been reported in several studies, prompting research to advance the techniques. Urban ecosystems can be transformed from unsustainable to sustainable one on a global scale with the development of multispectral to hyperspectral imaging.

With rising global temperatures, extreme weather events have become more frequent, intense and of longer duration. CMIP6 GCMs provide improved climate simulations that need robust evaluation for historical period for reliable future projections. The present study assesses the ability of bias corrected CMIP6 14 Global Climate Models (GCMs) in simulating heat wave over India for March–June during the historical period (1951–2014). Heat waves were identified using IITM criteria. Model biases were removed using variance scaling bias correction method that showed higher correlation (0.93) and lower root mean square error (2.15) and improvement in approximating the inter-annual variability as well as spatial patterns as observed maximum temperature after bias correction. Evaluation of model performance for 95th and 99th percentile maximum temperature and heatwaves showed that most of the models simulate these extremes similar to observation. Northwestern, Central and South-central regions recorded highest number of heatwaves with a frequency of 50 heatwave days/decade, which were captured by the most of the GCMs varying in decadal frequency over the region. Among the GCM, although all models were found competent, ACCESS-ESM1–5, MPI-ESM1–2HR and MRI-ESM2–0 models were found to be the best performing models for extreme indices and heat wave simulation over India. The study will aid to the current understanding of CMIP6-GCMs performances over the different meteorological subdivisions of India and pave way for future projection of heat waves as well as reduction in uncertainty among the models.

An exponential growth in groundwater extraction will greatly concern the Varuna River basin of Central Ganga alluvium. This complex issue requires proper systematic evaluations to advocate various opportunities for management strategies for groundwater sustainability. The present study aims to convey scientific assessments of the groundwater using different soft-computing techniques to determine its hydrogeochemistry and quality for averting future risk and envisaging suitable management policies. SOM (Self-Organizing Map) model involves characterizing the spatial clusters of regional hydrogeochemistry and reported a gradual increase in salinity from the northwestern to the southeastern part of the area. PCA (Principal Component Analysis) provides evidence of rock-water interactions and anthropogenic activities as responsible factors in determining hydrogeochemistry. Quality assurance is judged by various systematic chemometric indices. CCMEWQI (Canadian Council of Ministers of the Environment Water Quality Index) and IWQI (Irrigation Water Quality Index) reveal that the groundwater is suitable for drinking/domestic and irrigation use. Continuous fluxes of freshwater nourish Gangetic alluvium and generally provide excellent quality. In contrast, the analysis results notice the degradation of drinking quality from the excellent to good category (CCMEWQI) and moderate to low restriction (IWQI) for irrigation suitability. Further study of potential health risks represents the presence of carcinogenic and non-carcinogenic risks for the exposure of NO3−, F−, Pb, and Cr elements. However, the heavy metal pollution and ecological risks are low. Based on the study's findings, the estimates will support initiatives and activities at the local, regional, and national levels, as nations like India need basin-level studies. It is also recommended to consider depth-wise geochemical and temporal chemical variation for further improved studies towards risk assessment.

The rising global temperature is one of the primary concerns of the world as it impacts the economy, environment and healthcare of any country which are more pronounced a regional level. Assessment of regional impacts of climate change at a local level requires fine resolution of climate data for which a robust and fast downscaling method is needed. In this study, we use three deep learning-based methods, namely long short-term memory network (LSTM), deep neural network (DNN) and recurrent neural network (RNN), to downscale CMIP6 13 GCMS models data (1.25 degrees x 1.25 degrees resolution) global climate model (GCM) maximum temperature (Tmax) at a regional scale of 0.5 degrees x 0.5 degrees spatial resolution for the period 1991-2010 over the Indo-Gangetic Plain (IGP). In addition to the temperature prediction, heat wave events have been also analysed in the study. The study found that LSTM method performs better than DNN and RNN in downscaling of all GCM model datasets when evaluated against observed maximum temperature data from the India Meteorological Department (IMD) in terms of RMSE (0.9-3.5), average of all grid MAE value between (1.2 and 2.68), correlation (0.68-0.9) along with and spatiotemporal variability. LSTM also performed better in heat wave prediction over the region with similar temporal range (12-36 events) and spatial occurrence as compared to the observation (12-28 events). Overall, the study concludes that LSTM performs better than two methods for Indo-Gangetic Plain with best hyper parameter tuning. Hence, we propose to utilize a deep learning framework based on LSTM for downscaling GCM dataset at a finer resolution.

The Indo-Gangetic Plain (IGP), which is the site of India's Green Revolution, covers almost 15% of the country's landmass and is among the most extensively fertile lands across the world. The densely populated IGP region bears great importance for the socioeconomic facets of India and contributes to a major share of the GDP of the country. The present study demonstrates the regional-specific assessment of summer monsoon precipitation and associated extremes with dynamical and thermodynamical aspects over the IGP region using high-resolution regional climate models (RCMs) under the CORDEX-CORE framework. The analysis reveals that the eastern parts of the IGP receive low-to-moderate precipitation with a higher tail than the western parts, which is due to the direction of the monsoon low-level flow. The observed mean precipitation characteristics are well represented by the RCMs. Further, the research identifies extreme precipitation events over the IGP and conducts comprehensive analysis to understand their underlying mechanisms. It has been observed that extreme precipitation events are linked with the moisture transport associated with trough activity and instability, and RCMs are capable in representing the observed precipitation extremes and underlying mechanisms at localized scales. Overall, this study represents a significant step forward in understanding the evolution of spatio-temporal variability of precipitation over the IGP region, where agriculture is a major economic activity and millions of people depend on rainfed agriculture.

Precipitation over a region plays vital role in determining availability of water resource whereas study of spatiotemporal distribution of rainfall helps in managing precipitation associated risks like drought, flood etc. . The present study is carried out in the state of Bihar, India using District-wise rainfall data of Bihar for period 1900-2022. AMajor findings of study indicate that CV and SD of monsoon season and annual rainfall are in a moderate range except for districts Begusarai and Jahanabad; however, CVs for pre-monsoon, winter and post-monsoon seasons were high for all the districts. Innovative trend analysis of the rainfall indicates either no trend or a negative trend except in few districts, and in recent years the negative trend is observed in almost all the districts. To understand homogeneity in rainfall, the precipitation concentration index (PCI) and its trend analysis performed through Mann-Kendall test. PCI analysis indicates irregularly distributed annual and uniformly distributed monsoon rainfall. High inhomogeneity in annual rainfall is due to the post- monsoon PCI contribution. PCI trend was found positive in the southern, extreme northern, and central parts of Bihar, whereas extreme west districts of Bihar like Aurangabad, Bhojpur, Buxar, Rohtas and extreme eastern districts like Purnia, Katihar, Araria and Supaul had negative trend.

The Indo-Gangetic Plain (IGP), the source of grains for around 40% Indian population, is known as the breadbasket of India. The Indian Summer Monsoon Rainfall (ISMR) plays a vital role in the agricultural activities in this region. The rapid urbanization, land use and land cover change have significantly impacted the region's agriculture, water resources, and socioeconomic facets. The present study has investigated the observed and regional modeling aspects of ISMR characteristics, associated extremes over the IGP, and future perspectives under the high-emission RCP8.5-scenario. Future projections suggest a 10-20% massive decrease during pre-monsoon (March-May) and earlier ISM season months (i.e., June and July). A significant 40-70% decline in mean monsoon rainfall during the June-July months in the near future (NF; 2041-2060) has been projected compared to the historical period (1986-2005). An abrupt increase of 80-170% in mean monsoon rainfall during the post-monsoon (October-December) in the far future (FF; 2080-2099) is also projected. The distribution of projected extreme rainfall events shows a decline in moderate or rather heavy events (5 or more) in NF and FF. Further, an increase in higher rainfall category events such as very heavy (5-10) and extremely heavy rainfall (5 or more) events in NF and FF under the warmer climate is found. However, the changes are less prominent during FF compared to the NF. The mean thresholds for extremely heavy rainfall may increase by 1.9-4.9% during NF and FF. Further, the evolution patterns of various quantities, such as tropospheric temperature gradient (TG), specific humidity, and mean sea level pressure, have been analyzed to understand the physical processes associated with rainfall extremes. The strengthening in TG and enhanced atmospheric moisture content in NF and FF support the intensification in projected rainfall extremes over IGP.

Global and regional trends of the Aerosol Optical Depth (AOD) from Coupled Model Intercomparison Project (CMIP) Phase 6 simulations for the study period 1971-2014 were compared against the satellite retrievals and the intermodel variations were analysed. The AOD from multimodel mean (MMM) of eight general circulation models (GCMs) has been evaluated against the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectro Radiometer (MISR) AOD for the 2001-2014 period. Angstrom exponents (AE and its first derivative) that represent the size distribution of aerosols are estimated globally from the perturbed initial condition ensemble of MRI-ESM2-0 and MPI-ESM-1-2-HAM models to report the aerosol variations through their size distribution. We found that the global AOD obtained from the MMM8 showed an insignificant decreasing trend, while this trend is significantly positive over the northern tropical region. The MMM8 has overestimated the MODIS AOD over North Africa, India, China, and Australia while this overestimation is confined to North Africa and eastern China when compared against MISR AOD. The absolute percent bias of MMM8 is 28.1% and 24.1% over the globe when compared against MODIS and MISR AOD, respectively. The spatial pattern of AE showed the dominance of fine- and coarse-mode particles during the boreal/austral winter and summer seasons, respectively, that replicate the seasonality of aerosols. The AE derived from MPI-ESM-1-2-HR demonstrated better agreement with AATSR SU's (Advanced Along Track Scanning Radiometer instrument series, with the algorithm developed by Swansea University) AE (550-870 nm). On the other hand, MRI-ESM2-0 consistently underestimated AE across different regions and wavelength ranges, suggesting an over representation of larger aerosol particles in the model's portrayal of aerosol size distribution compared to satellite observations.

Background The evidence for acute effects of air pollution on mortality in India is scarce, despite the extreme concentrations of air pollution observed. This is the first multi-city study in India that examines the association between short-term exposure to PM (25) and daily mortality using causal methods that highlight the importance of locally generated air pollution. Methods We applied a time-series analysis to ten cities in India between 2008 and 2019. We assessed city-wide daily PM (25) concentrations using a novel hybrid nationwide spatiotemporal model and estimated city-specific effects of PM (25) using a generalised additive Poisson regression model. City-specific results were then meta-analysed. We applied an instrumental variable causal approach (including planetary boundary layer height, wind speed, and atmospheric pressure) to evaluate the causal effect of locally generated air pollution on mortality. We obtained an integrated exposure-response curve through a multivariate meta-regression of the city-specific exposure-response curve and calculated the fraction of deaths attributable to air pollution concentrations exceeding the current WHO 24 h ambient PM (25) guideline of 15 pg/m (3) . To explore the shape of the exposure-response curve at lower exposures, we further limited the analyses to days with concentrations lower than the current Indian standard (60 pg/m( 3) ). Findings We observed that a 10 pg/m (3) increase in 2 & Oslash;y moving average of PM (25) was associated with 14% (95% CI 07-22) higher daily mortality. In our causal instrumental variable analyses representing the effect of locally generated air pollution, we observed a stronger association with daily mortality (36% [21-50]) than our overall estimate. Our integrated exposure-response curve suggested steeper slopes at lower levels of exposure and an attenuation of the slope at high exposure levels. We observed two times higher risk of death per 10 pg/m (3) increase when restricting our analyses to observations below the Indian air quality standard (27% [17-36]). Using the integrated exposure-response curve, we observed that 72% (42%-101%) of all daily deaths were attributed to PM (25) concentrations higher than the WHO guidelines. Interpretation Short-term PM (25) exposure was associated with a high risk of death in India, even at concentrations well below the current Indian PM (25) standard. These associations were stronger for locally generated air pollutants quantified through causal modelling methods than conventional time-series analysis, further supporting a plausible causal link. Funding Swedish Research Council for Sustainable Development. Copyright (c) 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.

Climate change (precipitation and temperature) has significantly affected the hydrological regimes and future climate projection. Integration of climate model with physical based model is crucial for quantitative measurement of changes in surface water regime. For accurate estimation, modelling framework need finer scale resolution of climate model output. In this study, we examined the bias corrected, statistically downscale models drawn from the NASA, Earth Exchange Global Daily Downscaled Projections–Coupled Model Intercomparison Project Phase 5 (NEX-GDDP-CMIP5) over the study region. The rainfall and temperature projection output from the INMCM-4, MRI-CGCM3 and their ensemble mean performed well over the Mahi River basin (MRB), India. In this study, the climate data integrated with the SWAT model to analyse the potential impact of climate change on the discharge of MRB. The finding indicates that in the near future (2011–2040) projection of annual average streamflow increases by 76.74% based on the INMCM-4 outputs, 25% based on the MRI-CGCM3 outputs, and 24.53% based on the ensemble mean in comparison to the baseline period (1981–2010). Further, the modelling results of mean monthly streamflow in rainy season indicated that the lowest and highest streamflow changes will be ranging from about 631.07–2718.42 m3/s as observed by INMCM-4, 491.71–2938 m3/s observed by MRI-CGCM3, 513.02–2270.18 m3/s observed by ensemble mean, in the near future. Similarly, in the summer season, the lowest level of stream flow is found to be 158.27 m3/s observed by MRI-CGCM3, 193.38 m3/s (ensemble mean) and 258.53 m3/s (INMCM-4), respectively. Additionally, the streamflow trend was assessed by Mann–Kendall and Sen’s slope method at the monthly, seasonal and annual scales. The future streamflow projection represented the ascending trend observed in south west and winter monsoon, while the descending trend was observed in pre-monsoon and post-monsoon under the INMCM-4, MRI-CGCM3, and ensemble mean. Results on projected precipitation, temperature and streamflow accretion would help to develop effective adaptation measures for reducing the impacts of climate change and to work out long-term water resource management plans in the river basin.

Future changes in heat wave characteristics over India have been analyzed using Coordinated Regional Climate Downscaling Experiments (CORDEX) for South Asia (SA) regional climate model simulations for mid-term (2041-2060) and long-term (2081-2099) future under the representative concentration pathway (RCP) 4.5 and RCP 8.5 emission scenarios, respectively. SMHI_CSIRO-MK3.6 was found to be the best model in simulating heat wave trend over India for historical period. Future projections show a four-to-seven-fold increase in heat wave frequency for mid-term and long-term future under RCP 4.5 scenario, and five-to-ten-fold increase under RCP 8.5 scenario with increase in frequency dominating intensity in both the scenarios. Northwestern, Central, and South-central India emerged as future heat wave hotspots with largest increase in the south-central region. This high-resolution regional future projection of heat wave occurrence will serve as a baseline for developing transformational heat-resilient policies and adaptation measures to reduce potential impact on human health, agriculture, and infrastructure.

A modeling effort is considered to understand the effect of dust aerosol storms in the Indian summer monsoon (ISM) onset propagation over the Indian subcontinent. The study found how the dust aerosol loading during the pre-monsoon period over the Indian subcontinent positively impacts the advancing ISM onset. The heavy loading of dust aerosol from middle east Asia to the northwest (NW) Indian region boosts the temperature gradient over the mid-troposphere. This sudden rise in the mid-tropospheric atmospheric temperature triggers the low-pressure belt over the monsoon trough. It amplifies the Elevated Heat Pump mechanism and loads enough dust for the dust storm over north India. The formation of the monsoon trough over the Indo-Gangetic Plain (IGP) pulls the moisture-loaded wind towards the Indian subcontinent and the IGP, followed by the onset of monsoon over Kerala within a week. Due to this storm, the intense heat gradient in the troposphere also induces the monsoon propagation and the monsoon advancement from Kerala to BoB within a short time. However, it makes up for the naturally delayed monsoon on time. Plain language summary: Study shows how the pre-monsoon dust storm modulates the Indian summer monsoon (ISM) propagation over the Indian region. The onset of the monsoon was delayed in a particular year, and the pre-monsoon dust storm over northwest India changed the regular monsoon mechanism and generated an entirely different mechanism. The elevated heat pump theory applies that pushes the monsoon advancement faster than usual. A dust storm over north India causes a sudden rise in the mid-tropospheric temperature gradient and more rapid monsoon advancement than usual.

Forests play an essential role towards net primary productivity, biological cycles and provide habitat to flora & fauna. To monitor key physiological activities in forest canopies such as photosynthesis, respiration, transpira-tion, spatially-explicit and precise information of the biochemical (biological) variables such as Leaf Chlorophyll Content (LCC) is required. While lookup-table (LUT)-based Radiative Transfer Model (RTM) inversion against optical remote sensing imagery is regarded as a physically sound and robust approach for retrieving biochemical and biophysical variables, regularization procedures are required to offset the problem of ill-posedness. To optimize the RTM inversion of LCC over a sub-tropical pine forest plantation, in the Western Himalaya, we investigated the role of: (1) cost functions (CFs), (2) added noise, and (3) multiple finest solutions in LUT inversion. Principal CFs were evaluated belonging to three categories: information measures, M-estimates, and minimal contrast approaches. The inversion approaches were applied to a LUT produced by the coupled leaf -canopy model known as PROSAIL RTM and tested in contrast field spectral data obtained from reflectance data derived from UAV (Unmanned Aerial Vehicle) images taken over the canopies of covered pine forests. The Bhattacharyya divergence, an information measure, outperformed all other CFs in LCC inversion, with R2 of 0.94, RMSE of 6.20 mu g/cm2 and NRMSE of 12.27% during the validation. The optimized inversion strategy was subsequently applied to a UAV-acquired multispectral image at an 8.2 cm pixel resolution for detailed landscape forest LCC mapping. The associated residuals as provided by the LUT-based inversion provided insights in the spatial consistency of the LCC map.

This study focuses on determining significant controlling factors of chemical consequences, inverse geochemical modeling, water quality, and human health risk in the Varuna River basin of India. The study interprets that according to pH, total dissolved solids, and total hardness, the maximum number of groundwater samples are alkaline, fresh, and have substantial hardness. The abundance of major ions follows a pattern: Na > Ca > Mg > K, and HCO3 > Cl > SO4 > NO3 > F. Piper diagram shows that Ca–Mg–HCO3 facies are predominant during both seasons. Na-normalized molar ratios of HCO3/Na, Mg/Na, and Ca/Na are 0.62, 0.95, and 1.82 (pre-monsoon) and 0.69, 0.91, and 1.71 (post-monsoon), respectively, elucidating the coupled silicate and carbonate weathering (dolomite dissolution) sources. The Na/Cl molar ratio is 5.3 (pre-monsoon) and 3.2 (post-monsoon), indicating silicate alteration as the primary process rather than halite dissolution. The chloro-alkaline indices confirm the presence of reverse ion- exchange. Geochemical modeling using PHREEQC identifies the formation of secondary kaolinite minerals. The inverse geochemical modeling categorizes the groundwaters along the flow path from recharge area waters (Group I: Na–HCO3–Cl), transitional area waters (Group II: Na–Ca–HCO3), and discharge area waters (Group III: Na–Mg–HCO3). The model demonstrates the prepotency of water–rock interactions in pre-monsoon justified by the precipitation of Chalcedony and Ca-montmorillonite. The mixing analysis shows that in the alluvial plains, groundwater mixing is a significant hydrogeochemical process that affects groundwater quality. The Entropy Water Quality Index ranks 45

Climate modes like ENSO (El Nino Southern Oscillation) and IOD (Indian Ocean Dipole) produce an impact on the monsoon rainfall over India. Monsoon rainfall is extremely important for the agriculture of our country. The impact of these climate modes on monsoon rainfall thus in turn affects the rain-fed crops (kharif). In this study, four kharif season crops namely rice (Oryza sativa), maize (Zea mays), pulses and sugarcane (Saccharum officinarum) are chosen over four arid/semi-arid agro-climatic zones of western India to study the effect of the climate modes on selected crops. The detailed analysis has been carried out to show the impact of rainfall in the El Nino/La Nina (phases of ENSO) and IOD years on the crop productions over the mentioned zones viz. (Central plateau and hills region; Western plateau and hills region; Gujarat hills and plains region; Western dry region) from 1966–2011. The results show that rice productions which require hot and humid conditions have been largely affected during drought years associated with El Nino which results in poor rainfall over all the zones. The production of pulses which does not require excess humid conditions shows marginal improvement during the neutral years or non-El Nino/non-La Nina years. Maize production seems to be better in La Nina years and worst in the El Nino years as La Nina years are responsible for good rainfall in all zones. El Nino years provide a minor impact on sugarcane productions in different zones. La Nina years are well suited for sugarcane production in all zones of our study as sugarcane requires a good amount of moisture. Positive IOD years are associated with poor crop productions as compared to negative IOD years mostly in all zones as most of the positive IOD years happen to be El Nino years. El Nino-rainfall relation being dominant than positive IOD-rainfall relation is, therefore, responsible for negative rainfall anomalies over the selected zones.